Polymer-modified fertilizers (PMFs) with prolonged nutrient release present a promising solution to address the challenges associated with conventional fertilization practices, particularly for sensitive crops such as strawberries. This study investigates the effectiveness of biodegradable PMFs in maintaining nutrient availability at optimal levels while minimizing root burn and nutrient losses. A factorial field experiment was conducted to evaluate the impact of varying concentrations of polyvinyl alcohol (PVA) on the nutrient release rates from complex NPK fertilizer and monoammonium phosphate, as well as their subsequent effects on strawberry yield and soil properties. Results indicate that polymer modifications significantly slow down nutrient release, leading to prolonged nutrient retention in the soil at salt levels that are optimal for maximizing yield while remaining low enough to prevent the risk of root burn. Consequently, the application of PMFs not only enhances strawberry yield but also promotes healthier plant growth by ensuring a steady supply of nutrients throughout the growing season without inducing stress. The findings underscore the potential of biodegradable polymers to optimize fertilizer efficiency, reduce environmental impacts, and support sustainable agricultural practices. This research provides valuable insights into the development of more effective fertilization strategies for strawberry cultivation and other sensitive crops using PMFs.

Soft pneumatic actuators/robotics have received significant interest in the medical and health fields, due to their intrinsic elasticity and simple control strategies enabling desired interaction. However, current soft hand pneumatic exoskeletons often exhibit uniform deformation, mismatch the profile of the interacted objects, and seldom quantify the assistive effects during activities of daily life (ADL), such as extension angle and joint stiffness predicted. The lack of quantification poses challenges to the effective and sustainable advancement of rehabilitation technology. This paper introduces the design, modeling, and testing of pneumatic bioinspired Segmented Composite Proprioceptive Bending Actuators (SCPBAs) for hand rehabilitation in ADL tasks. The actuator’s soft-joint/rigid-bone segmented structure, inspired by the human finger anatomy, provides a superior fit compared to traditional continuous structures. A quasi-static model is established to predict the bending angles based on geometrical parameters. The SCPBAs also include a quantitative evaluation of joint stiffness and extension angle utilizing proprioceptive bending information. Additionally, a soft under-actuated hand exoskeleton equipped with SCPBAs demonstrates its potential in ADL rehabilitation scenarios.

Texas cotton production is facing challenges arising from increased temperatures and extended droughts. One potential solution to mitigate these environmental stresses lies in enhancing soil health through measures aimed at increasing soil organic matter and microbial biomass. Our field study investigated the effectiveness of adding residue on soil surface to increase soil organic matter content, microbial biomass, and cotton production under experimental warming with open-top chambers (OTCs). The OTCs raised the air temperature by 2 °C but did not affect soil temperature. OTCs also increased microbial biomass and soil respiration rate. Residue addition increased moisture content in non-irrigated (i.e., dryland) soils under experimental warming and concurrently reduced the daily temperature range in warmed plots. Furthermore, residue addition increased microbial biomass and soil respiration, particularly under OTC treatments in dryland soils. We also observed increased soil organic matter, microbial biomass, cotton biomass, and yield in irrigated fields compared to dryland. Our findings suggest that residue retention can mitigate the adverse effects of warming by stabilizing soil microclimate and enhancing soil organic matter and microbial biomass levels, especially in drylands. This, in turn, can help attenuate the potential impacts of future climate change on soil health and crop production in semiarid environments.

Berberis vulgaris L. (Berberidaceae) is a shrub that has been widely used in European folk medicine as an anti-inflammatory and antimicrobial agent. The purpose of our study was to elucidate the mechanisms of antiproliferative action of the plant’s methanolic root extract (BVR) against colon cancer cells. Cytotoxicity and pro-apoptotic activity were determined in human colon adenocarcinoma cell lines (LS180 and HT-29) and control colon epithelial CCD841 CoN cells. According to the MTT assay, after 48 h of cell exposure, the IC50 values were as follows: 4.3; 46.1 and 50.2 µg/mL for LS180, HT-29, and CCD841 CoN cells, respectively, showing the greater sensitivity of cancer cells to BVR. The Cell Death Detection ELISAPLUS kit demonstrated that BVR induced programmed cell death only against HT-29 cells. RT-qPCR with relative quantification method showed significant changes in the expression of genes related to apoptosis in both colon cancer cell lines. The genes BCL2L1 (126.84-366.66%), BCL2L2 (277.08-330.12%), CASP3 (180.36-261.36%) and CASP9 (158.08-213.46%) had significantly elevated expression, while BCL2 (23.08-52.06%) had reduced expression. Furthermore, in a panel of antioxidant tests, BVR showed positive effects (63.93±0.01, 122.92±0.01, and 220.29±0.02 mg Trolox equivalents (TE)/g in the DPPH•, ABTS•+ and ORAC assays, respectively). In the lipoxygenase (LOX) inhibition test, BVR revealed 62,60±0.87% of enzyme inhibition. The chemical composition of BVR was determined using UHPLC-UV-CAD-MS/MS analysis and confirmed the presence of several known alkaloids, including berberine, as well as other alkaloids and two derivatives of hydroxycinnamic acid (ferulic and sinapic acid hexosides). This is the first such extensive assessment of the biological activity of BVR. The results are very promising and encourage for the use of BVR as a comprehensive chemopreventive agent (anti-inflammatory, antioxidant, and pro-apoptotic) in colorectal cancer.

Transfer factor is the name given to material derived from activated lymphocytes that is probably composed of a complex of a peptide and a short segment of RNA and which has the reported ability to transfer specific T cell immunity to uncommitted lymphocytes. Many independent groups around the world reported isolating transfer factors between 1955 and 1990 and demonstrating their ability to transfer passive immunity from one animal or individual to another, often within 24 hours of inoculation. Such activity is potentially revolutionary both in making T cell vaccines readily manufacturable and also because the existence of transfer factors would undermine the basic assumptions of the clonal selection theory, which currently dominates immunological theory. Unfortunately, lack of the microanalytical and synthetic techniques required to properly identify transfer factors, combined with safety factors associated with it derivation from blood sources susceptible to HIV and prion infections, put an end to transfer factor research after 1990. This paper reviews the evidence supporting transfer factor activity and suggests that this potentially revolutionary concept be resurrected and subjected to renewed scrutiny in light of CRISPR-Cas mechanisms and because of its potential to make possible T cell vaccination and provide a novel basis for understanding immunological function.

Revealing the spatial distribution mechanism of the coupling effect between soil water and salt is essential for managing saline-alkali farmland. This study utilized the multifractal method to analyze soil water and salt under varying water content levels in arid saline-alkali farmland. Soil samples were collected on the second (S1), fifth (S2), eighth (S3), eleventh (S4), and fourteenth (S5) days after a rainfall event. The findings showed that a significant decrease in soil water content, contrasting with an increase in soil salt content throughout the soil layers post-rainfall. As water content decreased, the spatial variability of soil water initially increased from S1-3 and then decreased, while the spatial variability of soil salt decreased. The spatial distribution between soil water and salt exhibited a high correlation at S3-4 (with relative water content of soil ranging from 0.52 to 0.75) due to their coupling effect. However, soil salt was unevenly leached by rainfall at high water content levels (S1-2) and precipitated at low water content levels (S5), resulting in low spatial variability correlations between soil water and salt. This study elucidated the coupling process of soil water and salt, identifying their spatiotemporal distribution mechanism in dryland agricultural areas.

Current theories of autoimmunity are diverse, sometimes contradictory, and suffer from incompleteness. Although substantial evidence exists that adaptive and innate immunity, sex, genetic predisposition, and the microbiome all play essential roles in autoimmune disease etiologies and pathogenesis, and that antigen processing is altered during disease induction, no existing theory integrates all of these factors through a single, coherent mechanism. In an attempt to focus the field on the need to elucidate such an integrative mechanism, I propose one possibility here that, if nothing else, helps to identify the nature of the problems that need to be addressed. My theory is that autoimmune diseases are induced by normal immunological responses to unique pairs of complementary antigens, at least one of which is a molecular mimic of a host target. Each antigen in the complementary pair induces a complementary immune response (T or B cell); although each immune response is idiotypic in origin, the antigenic complementarity results in what appears to be an idiotype-anti-idiotype relationship between the responses. Additionally, because of the antigenic complementarity, each immune response mimics one of antigens, abrogating the distinction between self and non-self. If at least one of the antigens mimics a host antigen, then the resulting immunological civil war spreads to a host tissue. Complementary antigens also alter antigen processing so that antigens that would normally be proteolytically digested are presented by the major histocompatibility complex (MHC) to T and B cell receptors inducing a cross-reactive immune response. The resulting civil war is supported by the innate immune system due to the complementarity of the initiating antigens.. Complementary antigens stimulate synergistic toll-like receptors (TLR) and/or nucleotide-binding oligomerization receptors (NOD) resulting in up-regulation of cytokine production and further stimulation of the adaptive immune response. Because the immune responses (e.g., antibodies) mimic the initiating antigens, this synergistic activation of innate immunity becomes chronic. Additionally, TLR and NOD function are highly sensitive to sex hormones, some becoming up-regulated and some down-regulated in the presence of either testosterone or estrogens. This sensitivity explains how sex modifies susceptibility to autoimmune diseases. Genetic mutations in TLR, NOD and MHC further alter antigen presentation and the degree to which antigens stimulate an immune response explaining how genetics also modifies susceptibility. Finally, sex hormones also alter the host microbiome, which in turn modulates autoimmune disease risk by shaping the immunological nature of self and by mediating susceptibility to microbial infection. Moreover, it appears that the microbiome camouflages itself from the immune system by mimicking the host antigenic repertoire; the mimicry between the antigens of the microbiome and the host results in selective attacks on microbiome constituents concomitant with any autoimmune attack on host tissues. This antigenic complementarity theory thereby integrates all major elements known to affect, or be affected by, autoimmune diseases and provides a set of testable implications.

In this paper, a method is developed for presenting a novel virtual torque sensor based on precise model and position measurements avoids the need of traditional strain gauges and amplifiers. More specifically, the harmonic drive compliance model and the Gaussian process regression (GPR) technique are used together to achieve virtual torque sensor measurement. While the harmonic drive compliance model provides the analytic part, the Gaussian process regression method is used to reconstruct the unmolded part based on motor-side and link-side joint angles as well as motor current. After an automatic offline calibration, the method allows for a lean online implementation. The virtual torque sensor measurement is compared with measurements of a commercial torque sensor, and the results have attested the effectiveness of the proposed method.

The use of chemical pesticides in agriculture contributes to soil, water and air pollution, biodiversity loss and can harm non-target species. The European Commission has already established a Harmonized Risk Indicator to quantify the progress in reducing the risks linked to pesticides. Therefore, there is an increasing need to promote biopesticides or so-called low-risk pesticides (LRP). Extract from Melaleuca alternifolia – tea tree oil (TTO) is known for its antiseptic, antimicrobial, antiviral, antifungal, and anti-inflammatory properties. Tea tree oil has been extensively studied in pest management as well as in the pharmaceutical and cosmetic industry and there are already products based on its active substances on the market. This review focuses on the overall evaluation of tea tree oil in terms of effectiveness and safety as a biopesticide for the first time. The collected data can be an added value for further evaluation of tea tree oil in terms of the authorization extension as a fungicide in 2026.

The LTTD process for highly concentrated crude oil-contaminated soil was studied to evaluate the remediation efficiency. In addition, the environmental impact difference between HTTD and LTTD was studied. OpenLCA software obtained from PE International was used to perform the analyses. The total ADP environmental impact of the HTTD process was 1.63E-04 MJ, whereas the total GWP impact was 4.14E+02 kg CO2-eq. In contrast, the LTTD process exhibits total ADP and GWP environmental impacts of 1.29E-04 MJ and 2.78E+02 kg CO2 eq, respectively. Analysis of the overall environmental impact reduction revealed that replacing the HTTD process with the LTTD process resulted in a total ADP reduction of 3.40E-05 MJ (20.5%) and a GWP reduction of 1.36E+02 kg CO2 -eq (32.9%). It is inferred that the LTTD treatment of contaminated soil can significantly reduce the environmental impact compared with conventional incineration and HTTD processes. Additionally, the LTTD-treated soil with coke or carbonized residue could act like biochar as a soil amendment. Approximately 50% of organic hydrocarbon contaminants are sequestered into coke or carbonized residues on the soil surface. With additional analyses of various oil-contaminated soils, the potential utility of LTTD-treated soils is expected to be maximized.

Recent technological advancements in conservation genetics and genomics have resulted in diverse tools for aiding conservation of species. The precision and resolution of high throughput sequencing technologies provide valuable insights to aid conservation decisions, but these technologies are often financially unfeasible or unavailable in resource constrained countries. Inter-Simple Sequence Repeat (ISSR) markers, when combined with sensitive automated detection systems, provide a simple, cheap means to investigate genetic diversity and discriminate closely related species. Here we apply this technology to assess genetic diversity and taxonomic delimitation in the Encephalartos eugene-maraisii species complex, a highly threatened, taxonomically dubious group of cycads in South Africa. Our analyses support taxonomic singularity of E. dyerianus, E. dolomiticus and E. eugene-maraisii. Relationships between E. nubimontanus and E. cupidus remain uncertain. E. middelburgensis samples showed no clustering but had poor amplification success. This study demonstrates the suitability of automated ISSR fingerprinting as a method for plant conservation studies, especially in resource-constrained countries, and we make recommendations as to how this methodology can be effectively implemented.

Neonatal hypoxic ischemic encephalopathy (HIE) occurs in 1.5 per 1000 live births, leaving affected children with long-term motor and cognitive deficits. Few animal models of HIE incorporate maternal immune activation (MIA) despite the significant risk MIA poses to HIE incidence and diagnosis. Our non-invasive model of HIE pairs late gestation MIA with postnatal hypoxia. HIE pups exhibited a trend toward smaller overall brain size and delays in the ontogeny of several developmental milestones. In adulthood, HIE animals had reduced strength and gait deficits, but no difference in speed. Surprisingly, HIE animals performed better on the rotarod, an assessment of motor coordination. There was significant upregulation of inflammatory genes in microglia 24 hours after hypoxia. Single cell RNAseq revealed two microglia subclusters of interest following HIE. Pseudobulk analysis revealed increased microglia motility gene expression and upregulation of epigenetic machinery and neurodevelopmental genes in macrophages following HIE. No sex differences were found in any measures. These results support a two-hit noninvasive model pairing MIA and hypoxia as a model for HIE in humans. This model results in a milder phenotype compared to established HIE models; however, HIE is a clinically heterogeneous injury resulting in a variety of outcomes in humans. The pathways identified in our model of HIE may reveal novel targets for therapy for neonates with HIE.

This paper proposes a novel single-stage buck-boost three-Level neutral-point-clamped (NPC) inverter with two independent dc sources coupled for the grid-tied photovoltaic (PV) application, which can effectively solve the unbalanced operational conditions generally appeared between two coupled independent PV sources induced by the unequal irradiation and temperature distribution. The proposed control scheme can simultaneously guarantee the maximum power point (MPP) operation of both PV sources and maintain the output waveform quality. Compared to the traditional two-stage PV inverter, the proposed NPC inverter could reduce the PV array voltage requirement and dc-link capacitors’ voltage rating, meanwhile show the advantage in operational efficiency. MATLAB simulations and the captured experimental results are presented to show the performance of the proposed three-level inverter.

Intelligent fault diagnosis techniques have replaced the time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning model can improve the accuracy of intelligent fault diagnosis with the help of its multilayer nonlinear mapping ability. This paper has proposed a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in first convolutional layer for extracting feature and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform state of the art DNN model which is based on frequency features under different working load and noisy environment.

By introducing environmental mediator, for avoiding randomness in selecting indicators and subjectivity in deciding interaction path coefficients to reveal interaction mechanism among forest industry, ecology and environmental capacity, structural Pressure–State–Impact–Response (PSIR) and quantitative Structural Equation Modeling (SEM) are utilized with data on thirty–one provinces of China. We can find that: (i) forest industry has a negative influence on ecology, whereas ecology has a positive influence on industry; (ii) the destructive influence that industry has on ecology is much greater than the positive effect ecology has on industry; and (iii) environmental capacity plays a partial mediating role between industry and ecology. According to these results we can conclude that: (i) forest industry and ecology are not in a symbiotic relationship and have not reached a level of ecological security; (ii) the interaction between industry and ecology is in a period of transition from antagonistic to beneficial; and (iii) industry dominates ecology. However, we prospect the following advice: (i) methods of forest industry should be changed; (ii) new modes of industrial integration and circular economy should be developed; and (iii) the ability of the environment to sustain itself should be enhanced. The level of forest ecological security to discuss the symbiotic mechanism of industry and ecology will propose to be measured in future.

Two conundrums have puzzled COVID-19 investigators: 1) morbidity and mortality is rare among Infants and young children and 2) rates of morbidity and mortality exhibit very large variances across nations, locals and even within cities. These differences correlate with rates of Haemophilus influenzae type B (Hib) and pneumococcal vaccination, which are almost universal among infants and vary widely by geography among adults and the elderly. The higher the rate of vaccination, the lower the COVID-19 morbidity and mortality. Vaccination rates with other vaccines, including BCG and poliovirus, do not correlate with COVID-19 risks. Notably, both Hib and pneumoccoci are common co-infections with influenza and coronaviruses and are associated with more severe disease and risk of death. Whether the vaccines simply protect against COVID-19 complications, directly protect against COVID-19 infection by inducing cross-reactive immunity, or are markers for some other types of protection such as availability of better healthcare, is not yet known. What is known is that improving coverage rates of Hib and pneumococcal vaccination has significantly lowered severe morbidity and mortality in influenza epidemics and might have similar efficacy for mitigating coronavirus outbreaks. If infants and children are valid indicators, the beneficial effects might be very significant. The possibility that anti-viral proteins in milk (e.g., lactoferrin) protect against COVID-19 is also explored.

The article presents the results of studies aimed to identify the effect of foliar treatments of winter wheat, sugar beets and soybeans with mineral and bioorganic fertilizers. Their positive effect on increasing the concentration of mineral forms of nitrogen in soil was revealed. The crop types have a greater influence on the size of nitrous oxide emissions: in winter wheat crops - 679 µg/m3, soybeans - 637.2 µg/m3, sugar beets - 576.8 µg/m3. The amounts of nitrous oxide emissions were lower in the variants without the use of phosphorus-potassium fertilizers. The Ruter AA fertilizer showed the greatest efficiency on winter wheat crops - 42.7 c/ha. Soybean grain yield of 44.2 c/ha was also achieved by using Ruter AA, as well as Geo-humate together with phosphorus-potassium fertilizers. On the background of the actual supply of phosphorus and potassium, maximum yield was obtained when using N30 on the leaf - 39.3 c/ha. Foliar feeding ensured a yield of sugar beet root crops of up to 67.1-77.4 c/ha on the backdrop of the application of phosphorus-potassium fertilizers with the advantage of using Amino turbo. The largest yield of sugar beet roots without use of mineral phosphorus and potassium was ensured by N30 treatment of the leaf in the phases of 4-6 and 8 leaves - 63.2 t/ha. Of the organo-mineral fertilizers and biostimulants, the best result was shown by the use of Ruter A - 62.6 t/ha. We believe that foliar feeding of crops can be a sustainable farming method that can increase crop yields and reduce N2O emissions.

Various studies indicate that vaccination, especially with pneumococcal vaccines, protects against symptomatic cases of SARS-CoV-2 infection and death. This paper explores the possibility that pneumococcal vaccines in particular, but perhaps other vaccines as well, contain antigens that might be cross-reactive with SARS-CoV-2 antigens. Comparison of the glycosylation structures of SARS-CoV-2 with the polysaccharide structures of pneumococcal vaccines yielded no obvious similarities. However, while pneumococcal vaccines are primarily composed of capsular polysaccharides, some are conjugated to CRM197, a modified diphtheria toxin, and all contain about three percent protein contaminants, including the pneumococcal surface proteins PsaA, PspA and probably PspC. All of these proteins have very high degrees of similarity, using very stringent criteria, with several SARS-CoV-2 proteins including the spike protein, membrane protein and replicase 1a. CRM197 is also present in Hib and meningitis vaccines. Equivalent similarities were found at lower rates, or were completely absent, among the proteins in diphtheria, tetanus, pertussis, measles, mumps, rubella, and poliovirus vaccines. Notably, PspA and PspC are highly antigenic and new pneumococcal vaccines based on them are currently in human clinical trials so that their effectiveness against SARS-CoV-2 disease is easily testable.

Bare slope instability is a prevalent concern. The root system of, herbaceous vegetation enhances the shear strength of shallow–slope soil. Indoor experiments were conducted on rootless undisturbed soil (RUS) and undisturbed soil with a root system (USRS) using a triaxial compression apparatus to analyze the slope stability of composite soil with a Tagetes erecta root system. Significance tests and correlation analysis of the factors affecting shear performance were conducted using R software. The slope reinforcement, effect by the plant root system was simulated under 24 working conditions using the MIDAS finite element method. The results revealed the influence of the–root content (RC), moisture content (MC), and stress on the shear strength of USRS, the contribution degree, and the variables’ influences on slope stability. Both RUS and USRS exhibited strain hardening during shearing. The internal friction angle (φ) and cohesion (c) of USRS were negatively and positively correlated with the RC and MC (root burial depth), respectively, and a good fit was obtained for the relationship. The maximum deviatoric stress during shear failure was 1.29 times higher for USRS than for RUS. The RC (root depth) was positively correlated with the slope safety coefficient and the slope of the line under different working conditions, whereas the slope gradient was negatively correlated with the slope safety coefficient. The reinforcement effect by the root system resulted in a 13.2% increase in the safety coefficient and improved stability of slopes with a gradient larger than 1.5%. This article investigated the mechanism of the root–soil system and, the effects of different influencing factors on the shear strength of the soil, and slope stability. The findings provide new insights into shallow slope stability in practical slope protection projects.

Present study on land use land cover analysis and taluka wise combination of cropped area, carried out in Beed district of Maharashtra state in India and studied 11 talukas of the district. Secondary data on land use land cover and area in various crops has been collected, compiled and analyzed from various secondary sources. Simple descriptive statistics used to examine the agricultural land use land cover and J.C. Weaver’s crop combination technique was used for taluka wise analysis of crop combinations and on the basis of highest to smallest area engaged in particular crop, crops have been ranked. The study showed that, in the district as a whole, percentage of uncultivated land other than fallow was about 46 percent as compare to net sown area is only 35 percent in total geographical area (1063855ha) of Beed district. The study further revealed that about 42 percent area engaged in food crops and about 58 percent area occupied by nonfood crops, which includes cereals (25%), cotton (19%), pulses (13%), oilseed (12%), sugarcane (5%) and fruits and vegetables (3%). Results of Weaver’s crop combination technique showed that the Wadwani taluka demarcated as most diversified taluka by means of maximum number (sixteen) of crops combinations with the lowest variance value (14) found in the present investigation. Further, the study revealed that the high crop diversity found in Dharur taluka with eighteen crop combinations and the taluka has given first rank amongst others. To derive the crop diversity ranking, taluka wise maximum to minimum number of crop combinations derived in different talukas and given ranks accordingly. The study concluded that, agricultural land use analysis is essential for solving and optimizing the land use problem, maintaining socio-ecological balance, best utilization of scarce resources. The authors enlightened that the rational use of agricultural land has important for food security and biodiversity which increase the productivity and profitability of the agriculture sector and it indicates the decision making ability of farmers in study area.

: Greenhouse plants are usually grown in containers filled with growing media. Peat is the main component of the growing media used in greenhouses. However, peat substrate is made from peat from drained peatlands, which is a limited resource. Wood fiber can be an alternative to use as a growing media for controlled-climate crops. The chemical properties of the growing media interact and change continuously due to the small volume of the growing media, which is limited by the vegetative container. Therefore, this study aims to gain new knowledge on the impact of nutrient changes in the microbial degradation of carbon compounds in wood fiber and mixtures with peat substrate on the content and uptake of nutrients required by plants. Cucumbers (Cucumis sativus L.) were cultivated in growing media of peat substrate and wood fiber: (1) peat substrate (PS); (2) wood fiber (WF); (3) wood fiber and peat substrate 50/50 v/v (WF/PS 50/50); (4) wood fiber and peat substrate 25/75 v/v (WF/PS 25/75). The rates of fertilization were the following: (1) conventional fertilization (CF); (2) N13; (3) N23; (4) N30. Fertilization of cucumbers with different rates of nitrogen influenced the nutrient uptake. Plants grown in WF/PS 50/50 and WF/PS 25/75 growing media had the best Cu uptake when fertilized with N23. When the plants grown in the wood fiber media and the 50/50 media were fertilized with N13, N23, and N30, the Mn content in the growing media at the end of the growing season was significantly lower than the Mn content in the media with conventional fertilization. Thus, nitrogen improved the uptake of Mn by plants grown not only in the wood fiber but also in combinations with peat substrate. Growing plants in wood fiber and fertilizing them with N13 can result in optimum uptake of micronutrients. The number and mass of cucumber fruits per plant were influenced by the amount of wood fiber in the growing media and the application of nitrogen fertilizer. The highest number of fruits and mass of fruits per plant was obtained significantly higher when cucumbers were grown on WF/PS 50/50 growing media with additional N13 fertilization.

This paper presents a comprehensive comparative analysis of four different computational vision algorithms for implementing an unpalleting system in a factory environment, under typical edge computing constraints. The primary objective is to automate the process of detecting and locating specific variable-shaped objects on a pallet, enabling a robotic system to accurately unstack them. The four algorithms evaluated are Pattern Matching, Scale-Invariant Feature Transform, Oriented FAST and Rotated BRIEF, and Haar cascade classifiers. Each technique is described in detail in this paper, and their implementations are outlined. Experimental results are thoroughly analyzed, assessing the algorithms’ performance based on accuracy, robustness to variability, computational speed, detection sensitivity, and resource consumption. The findings reveal the strengths and limitations of each algorithm, providing valuable insights for selecting the most appropriate technique based on the specific requirements of the unpalleting system. This research contributes to the advancement of automated object detection and localization in industrial environments, paving the way for more efficient and reliable unpalleting systems.

Batch and transport experiments were used to investigate the remediation of a loamy sand soil contaminated with Cr(VI) using zero valent iron nanoparticles (nZVI) stabilized by the carboxy methyl cellulose (CMC-nZVI). The effect of pH, ionic strength (IS), and flow rate on the removal efficiency of Cr(VI) were investigated under equilibrium (uniform transport) and non-equilibrium (Two-site sorption) transport using the Hydrus-1D model. The overall removal efficiency ranged from 70 to over 90% based on the chemical characteristics of the CMC-nZVI suspension and the transport conditions. The concentration and pH of the CMC-nZVI suspension had the most significant effect on the removal efficiency and transport of Cr(VI) in the soil. The average removal efficiency of Cr(VI) was increased from 24.1 to 75.5% when the concentration of CMC-nZVI nanoparticles was increased from 10 to 250 mg L-1, mainly because of the increased total surface area at larger particle concentration. Batch experiments showed that removal efficiency of Cr(VI) was much larger under acidic conditions. The average removal efficiency of Cr(VI) reached 90.1 and 60.5 % at pH 5 and 7, respectively. The Two-site sorption model described quite well (r2 = 0.96-0.98) the transport of Cr(VI) in soil as compared to the uniform transport model (r2 = 0.81-0.98). The average retardation of Cr(VI) was 3.51 and 1.61 at pH 5 and 7, respectively, indicating earlier arrival for the breakthrough curves and shorter time to reach maximum relative concentration at lower pH. The Hydrus-1D model has proven to be an efficient tool for assessing the removal of Cr(VI) pollutants, offering innovative, cost-effective, and environmentally friendly remediation methodologies.

In this paper, the entire downhole fluid sucker rod pump system is replaced with a viscoelastic vibration model, namely a third-order differential equation with an inhomogeneous forcing term. Both Kelvin and Maxwell viscoelastic models can be implemented along with the dynamic behaviors of a mass point attached to the viscoelastic model. By employing the time-dependent polished rod force measured with a dynamometer as the input to the viscoelastic dynamic model, we have obtained the displacement responses, which match closely with the experimental measurements in actual operations, through an iterative process. The key discovery of this work is the feasibility of the so-called inverse optimization procedure which can be utilized to identify the equivalent scaling factor and viscoelastic system parameters. The proposed Newton-Raphson iterative method with some terms in the Jacobian matrix expressed with averaged rates of changes based on perturbations of up to two independent parameters provides a feasible tool for optimization issues related to complex engineering problems with mere information of input and output data from either experiments or comprehensive simulations. The same inverse optimization procedure is also implemented to model the entire fluid delivery system of a very viscous non-Newtonian polymer modeled as a first-order ordinary differential system similar to the transient entrance developing flow. The convergent parameter reproduces transient solutions which match very well with those from full-fledged computational fluid dynamics models with the required inlet volume flow rate and the outlet pressure conditions.

Taiwan’s auditors have suffered from processing excessive audit data, including drawing audit evidence. This study advances sampling techniques by integrating machine learning with sampling. This machine learning integration helps avoid sampling bias, keep randomness and variability, and target the risker samples. We first apply a Naive Bayes classifier to classify data into some classes. Next, a user-based, item-based, or hybrid approach is employed to draw audit evidence. The representativeness index is the primary metric for measuring the representativeness of audit evidence. The user-based approach denotes the selection of samples between two percentiles in a class as audit evidence. It may be equivalent to a combination of monetary and variable sampling methods. The item-based approach represents the choice of risky samples as audit evidence. It may be identical to a combination of non-statistical and monetary sampling methods. Auditors can hybridize those user-based and item-based approaches to balance representativeness and riskiness in selecting audit evidence. Three experiments show that sampling using machine learning integration has the benefits of drawing unbiased samples, handling complex patterns, correlations, and unstructured data, and improving efficiency in sampling big data. However, the limitations are the classification accuracy output by machine learning algorithms and the range of prior probabilities.

The Katerini-Kolindros aquifer system (KKas) is a critical freshwater resource, essential for the local and regional economy. To understand contamination sources, groundwater flow paths, and assess the aquifer's health, an extensive dataset of 751 samples from 113 wells collected between 2010 and 2020 was analyzed using multivariate statistical analysis (MVSA) and hydrogeochemical diagrams. The prevailing water types identified were Ca-HCO₃ and Mg-HCO₃, with mixed water types resulting from various factors affecting groundwater quality. The chemistry of groundwater in the KKas is shown to be affected by ion exchange processes and to a lesser extent by reverse ion exchange processes. No indications of sea-water intrusion have been identified in the water wells monitored. On the other hand, nitrate contamination has been detected in specific water wells spatially associated with urban-type land uses and activities. It is considered that nitrate contamination originates primarily from septic tanks and urban sewage networks or secondarily from fertilization of football pitches, cemeteries or urban waste management practices.

Crawling robots are the focus of intelligent inspection research, and the main feature of this type of robot is the flexibility of in-plane attitude adjustment. The crawling robot HIT_Spibot is a new type of steam generator heat transfer tube inspection robot with a unique mobility capability different from traditional quadrupedal robots. This paper introduces a hierachical motion planning approach for HIT_Spibot, aiming to achieve efficient and agile maneuverability. The proposed method integrates three distinct planners to handle complex motion tasks: a nonlinear optimization-based base motion planner, a TOPSIS-based base orientation planner, and a Mask-D3QN (MD3QN) algorithm-based gait motion planner. Initially, the robot's base and foot workspace are delineated through envelope analysis, followed by trajectory computation using Larangian methods. Subsequently, the TOPSIS algorithm is employed to establish an evaluation framework conducive to foundational turning planning. Finally, the MD3QN algorithm trains foot points to facilitate robot movement along predefined paths. Experimental results demonstrate the method's adaptability across diverse tube structures, showcasing robust performance even in environments with random obstacles. Compared to the D3QN algorithm, MD3QN achieves a 100% success rate, enhances average overall scores by 6.27%, reduces average stride lengths by 39.04%, and attains a stability rate of 58.02%. These results not only validate the effectiveness and practicality of the method but also showcase the significant potential of HIT_Spibot in the field of industrial inspection.

In this study the effects of three different main preparatory tillage operations [ploughing at 0.4 m (P40) and 0.20 m (P20) depth and minimum tillage at 0.20 m depth (MT) each of them carried out at two different soil water contents (WC) [low, 58% (LH) and high, 80% (HH) of field capacity] were investigated. The results obtained in this research show high values of soil strength in term of Penetration resistance (CI) and shear strength (SS) particularly in deeper soil layers at lower water content. Fossil-fuel energy requirements both for P40 LH and P20 LH were 25 and 35% higher with respect to the HH treatments and tractor slip were very high (P40 LH = 32.4%) with respect to the P40 HH treatment (16%). Therefore soil water content had significantly influenced tractor performance during soil ploughing, particularly at 0.40 m depth while MT was not influenced at all. A significant correlation between grain yield and soil penetration resistance was found highlighting how soil strength may be good indicator of its productivity. Obtained results during these field tests allowed considering MT and P20 treatments more suitable for this type of soil in climate change scenarios.

Drought decreases grain yield of sorghum [Sorghum bicolor (L.) Moench], and understanding the mechanism(s) related to drought tolerance is critical for sustaining sorghum production. Variation in root and shoot traits associated with drought tolerance were analyzed to provide an integrated view of factors that underlie the drought tolerance of sorghum. The plants were grown in the root column up to the five-leaf stage, then exposed to either 0.9 fraction of transpirable soil water (FTSW) or 0.4 FTSW for five days. In another experiment, at the five-leaf stage, stress was imposed for 14 days. Various root and shoot traits associated with drought tolerance were recorded. The seminal root angle of IS13540 was lower (24.4) than IS23143 (42.6). Drought stress increased the maximum root length (40%) and total root length (58%) of IS13540 than its irrigated control. In contrast, the maximum root length and total root length were decreased in IS23143. Similarly, across the lines, drought stress decreased stomatal conductance (37%), transpiration rate (42%), photosynthetic rate (40%), photosystem II quantum yield (20%), photochemical quenching (44%), and total dry matter production (34%) than irrigated control. An increased transpiration rate was observed in IS23143 than IS13540 under irrigated and drought stress. In IS23143, the reduction in photosynthetic rate under drought may be a combination of stomatal and non-stomatal factors. However, in IS13540, the reduction is especially by the stomatal factors. It is evident that IS13540 is a drought-tolerant line, and tolerance is related to a deep prolific root system and reduced tran-spiration rate.

Cobalt (Co) is a transition metal and is classified as a beneficial metal for plants, but its benefits for plants remain obscure. Cobalt has been reported to negatively affect plant physiochemical processes both at higher (toxic) and lower (deficient) applied levels. High concentrations of cobalt in plant organs cause irreversible changes to the plant cells, mainly via enhanced production of reactive radicals. Moreover, Co and its compound play an essential role in humans as they are the central atoms of cobalamin, a co-enzyme precursor whose absence causes anemia. Hence, the optimum Co supply to plants is critical for customary plant metabolic workings. Henceforth, monitoring Co behavior in the soil-plant-human system is highly imperative. This review highlighted the latest literature on (i) Co in soil and plants; (ii) its mobility and phyto-availability in soil; (iii) phytouptake and translocation towards shoot tissues; (iv) toxic and deficient effects of Co on plants; (v) plant detoxification mechanisms under increased Co levels inside plants; and (v) its role in the human body. For this purpose, 1026 plant observations from literature data were analyzed related to Co biogeochemical behavior in the soil-plant system. The data analysis revealed an overall increase in 567 observations and a decrease in 381 observations. However, these general trends in plant responses vary significantly for different types of plant species and physiological attributes. Overall, the current review delineates an updated and critical representation of the biogeochemical behavior of Co in the soil-plant-human system, supported by up-to-date 9 main tables, 14 supporting tables, and 3 figures. The authors believe that the literature presented here can be of great interest to scientists, researchers, policymakers, and graduate-level students.

P-band radar remote sensing applied during the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission has shown great potential for estimation of root zone soil moisture. When retrieving the soil moisture profile (SMP) from P-band radar, a mathematical function describing the vertical moisture distribution is required. Because only a limited number of observations are available, the number of free parameters of the mathematical model must not exceed the number of observed data. For example, a second order polynomial that contains 3 free parameters was presumed based on in-situ SMP data. The polynomial is currently parameterized based on 3 backscatter observations provided by AirMOSS (i.e. one frequency at three polarizations of HH, VV and HV). In this paper, a more realistic, physically-based SMP model containing 3 free parameters is derived based on a solution to Richards’ equation for unsaturated flow in soils. Evaluation of the new SMP model based on both numerical simulations and measured data revealed that it exhibits greater flexibility for fitting measured and simulated SMPs than the currently applied polynomial. It is also demonstrated that the new SMP model can be reduced to a second order polynomial at the expense of fitting accuracy.

Celiac disease is a chronic small intestinal immune-mediated enteropathy precipitated by exposure to dietary gluten, affecting approximately 1% of the global population and two million Americans. An increasing number of studies have identified a link between celiac disease and adverse maternal and fetal outcomes during pregnancy and after birth. Additionally, both celiac disease and pregnancy are associated with an increased risk for nutrient deficiencies, specifically vitamin B12 and folate. Deficiencies in these key nutrients are linked with several negative maternal and fetal health outcomes including preeclampsia, gestational diabetes, spontaneous abortion/miscarriage, preterm birth, NTDs, Intrauterine Growth Restriction, and low gestational age and birthweight. This review examines the current literature related to the folate trap and vitamin B12 deficiency in patients with celiac disease and pregnant women independently, and to provide rationale for future research to explore the correlation between the folate to B12 ratio in pregnant woman with celiac disease.

In the dynamic landscape of the telecommunications industry, understanding and mitigating customer churn is paramount for maintaining competitive advantage and sustaining revenue streams. This comprehensive literature review delves into the intricacies of customer churn prediction, a crucial aspect in fortifying the bond between telecom businesses and their customers. The review methodically analyzes 11 articles published between 2000 and March 2024, spotlighting the strategic role of Customer Relationship Management and the prevalence of high churn rates that necessitate a shift from customer acquisition to retention strategies. Furthermore, the review navigates through the challenges inherent in churn prediction, including the complexities of managing large, imbalanced, and noisy datasets and the intricacies involved in model selection and optimization. Despite these challenges, the paper highlights the superior capability of machine learning techniques in handling complex datasets to provide accurate predictions, albeit acknowledging their practical complexities. The main aim of the study is to provide an overview, serving as a knowledge base for researchers interested in machine learning techniques within the realm of telecommunications.

In view of the fact that the field monitoring information can more intuitively and accurately reflect the stability state of slopes, this paper takes the warning threshold of slope deformation rate monitoring as the slope stability evaluation standard, and puts forward a method for solving the safety coefficient of rocky slopes. The discrete element method 3DEC, which is suitable for rocky slopes, is selected as the numerical calculation tool, and the convergence criterion of its strength reduction method is modified to the slope deformation rate threshold, and the method is realized by the bifurcation method through its built-in FISH programming language. The results of the classical case show that, by selecting the slope deformation rate threshold in the appropriate interval, the results of this paper's method are very close to those of the finite unit stress method and the limit equilibrium method, which verifies the reliability of this paper's method. Further, the method of this paper is applied to an open-pit mine slope project, based on the slope deformation on-site monitoring data, through the time series prediction method to determine the slope deformation rate warning threshold, using the threshold as an evaluation criterion to solve the slope's coefficient of safety, the calculation results show that: the slope's coefficient of safety in natural working conditions is 1.086, basically stable, suffered from continuous rainfall, the slope body is gradually saturated, the slope's coefficient of safety is reduced to 0.987, the slope's safety is reduced to 0.987. After continuous rainfall and gradual saturation of the slope, the coefficient of safety decreased to 0.987, destabilizing and destroying, which is consistent with the site conditions.

This article presents a new input device for spastic patients and others with similar symptoms. The sensor consists of a disc used to determine positions and can replace standard joysticks in medical devices such as electric-powered wheelchairs. Using a standard joystick while operating a powered wheelchair can result in dangerous situations when spastic movements occur and extremities cramp making them uncontrollable. To avoid this, a disc was developed that can be controlled with any body part. By shifting weight (x- and y-axis) the disc can tilt in any direction creating a proportional output signal and can also be pushed down in the center (z-axis) to open a menu on a screen for example. When spasms occur it is impossible for users to get stuck on the input device because the disc is flat and can be mounted within a control panel. Body parts coming into contact with the disc would merely slide across the disc without triggering unintentional actions. The sensor presented here is also adaptive and can be adjusted to fit a user’s strength and range of motion. This proposal aims to develop an input device that enables spastic patients to operate sensitive systems safely.

Alternative fertilizations are essential to minimize the deteriorating effects of chemical fertilizers on soil and water quality/health. Accordingly, the present work investigated the effects of combined organic-inorganic fertilization (COIF) on wheat and rice yields, soil nutrients, and soil Cd accumulation. Hence, seven different treatments were set up: control (CK); conventional fertilization (CF); optimized fertilization (OF); organic fertilizer replacing 25% (T1) and 50% (T2) of OF; and organic nitrogen (N) replacing 25% (M1) and 50% (M2) of OF-N. Overall, significant increases occurred in the yields of COIF crops. Compared with the CF, the highest wheat and rice yields happened in M1 treatment (approximately by18.5%) (P < 0.05). COIF slightly alleviated soil acidification, and improved the cation exchange capacity (CEC) of the study soils. Besides, COIF treatments significantly increased the contents of total phosphorus, total potassium, available phosphorus, and available potassium by 6.35 to 16.9 %, 3.17 to 10.9 %, 5.53 to 28.7 %, and 2.6 to 12 %, respectively (P <0.05). Nevertheless, negligible increases took place in Cd content of COIF soils compared with the CK. Altogether, our results concluded on 25% replacement of OF-N by organic N (M1) to effectively improve the fertility/ecological sustainability of the study soils.

This study investigates how root environment acidification impacts tea seedlings and the role of magnesium (Mg) in mitigating these effects. We examine varying pH and Mg levels' influence on tea seedlings' resistance to abiotic stress, focusing on antioxidant capacity and nutritional content. In a hydroponic experiment, we varied root pH (3.5, 5.0, 6.5) and Mg concentrations (0.01, 0.4, 0.8 mM), assessing parameters like antioxidant capacity, peroxidative damage, and nutritional content at 1, 7, 15, and 30 days post-treatment. Root environment acidification and Mg deficiency worsened peroxidative damage in tea plant leaves and roots. Increased Mg supplementation enhanced antioxidant enzyme activity, reducing malondialdehyde and mitigating oxidative damage from root environment acid stress. Under acid stress, 0.8 mM Mg significantly increased tea leaf polyphenols, amino acids, and water-soluble extracts. Mg notably boosted chlorophyll content, surpassing lower Mg levels at pH 5. Additionally, Mg reversed root vitality inhibition induced by acid stress, leading to increased nitrogen, potassium, and Mg concentrations in leaves, promoting balanced nutrient absorption. Mg supplementation is crucial for enhancing tea plant antioxidant capacity, alleviating growth inhibition from root environment acid stress, and improving chlorophyll content and root vitality, highlighting Mg's significance in tea cultivation and broader agricultural practices.

This paper addresses the evolving landscape of electricity markets in Europe, with a focus on the integration of Renewable Energy Communities as introduced by the Renewable Energy Directive 2018/2001. Residents within a postal code area are highly incentivized to join a community, which enables them to exchange energy among themselves at lower procurement costs. Thereby, energy management systems optimize the operation of respective energy systems, with electrical load forecasting playing a key role. Given that prosumers may switch between communities on a daily basis, electricity demands within these groups will vary, leading to data that is non-stationary, discontinuous as well as non-identical and independently distributed. To encounter this issue, we propose a sophisticated forecasting model that applies federated learning, using informations from various distributed communities to learn domain-invariant features. To achieve this, we initially utilize synthetic electrical load time series at district level and aggregate them to profiles of Renewable Energy Communities with dynamic portfolios. Subsequently, we develop a forecasting model that accounts for the composition of residents of a Renewable Energy Community, adapt data pre-processing in accordance with the time series process, and detail a federated learning algorithm that incorporates weight averaging and data sharing. Following the training of various experimental setups, we ultimately evaluate their effectiveness. The findings suggest that our proposed framework is capable of effectively forecast non-stationary and discontinuous time series and that it can be applied to new, unseen data through the integration of knowledge from multiple sources.

Abstract: High step-up voltage gain nonisolated DC-DC converter have attracted much attention in photovoltaic, fuel cells and other renewable energy system applications. In this paper, by combining input current ripple-free boost cell with coupled-inductor voltage-doubler cell, an input current ripple-free high voltage gain nonisolated converter is proposed. In addition, passive lossless clamp circuit is adopted to recycle the leakage inductor energy and to reduce the voltage spike across the power switch. By utilizing voltage-doubler cell consisting of diode and capacitor, the voltage stress of switch is further reduced and the resonance between the leakage inductor and the stray capacitor of the output diode is eliminated. A low switch-on-resistance low-voltage-rated MOSFET can therefore be employed to reduce the conduction loss and cost. The reverse recovery loss of output diode is reduced, and the efficiency of converter can be improved. Furthermore, the proposed converter can achieve nearly zero input current-ripple and make the design of electromagnetic interference (EMI) filter easy. Steady state analysis and operation mode of the converter is performed. Finally, experimental results are presented to verify the analysis results of the proposed converter.

:Peat substrate is made from peat from drained peatlands, which is a limited resource. A realistic estimate is that 50% of the world’s wetlands have been lost. Peat is used in horticulture, especially for the cultivation of vegetables in greenhouses. The consequences of peatland exploitation are an in-crease in the greenhouse effect and a decrease in carbon stocks. Wood fiber can be used as an al-ternative to peat. The chemical properties of the growing media interact and change continuously due to the small volume of the growing media, which is limited by the growing container. This study aims to gain new knowledge on the impact of nutrient changes in the microbial degradation of carbon compounds in wood fiber and mixtures with peat substrate on the content and uptake of nutrients required by plants. Cucumbers (Cucumis sativus L.) variety “Dirigent H” developed in the Netherlands, were cultivated in growing media of peat substrate and wood fiber: (1) peat substrate (PS); (2) wood fiber (WF); (3) wood fiber and peat substrate 50/50 v/v (WF/PS 50/50); (4) wood fiber and peat substrate 25/75 v/v (WF/PS 25/75). The rates of fertilization were the following: (1) con-ventional fertilization (CF); (2) N13; (3) N23; (4) N30. The experiment was carried out in three repli-cations. As the amount of wood fiber increased, the humidity and pH of the growing medium in-creased. Fertilization of cucumbers with different rates of nitrogen influenced the nutrient uptake. Plants grown in 50/50 and 25/75 growing media had the best Cu uptake when fertilized with N23. When the plants grown in the wood fiber media and the 50/50 media were fertilized with N13, N23, and N30, the Mn content in the growing media at the end of the growing season was significantly lower than the Mn content in the media with conventional fertilization. Thus, nitrogen improved the uptake of Mn by plants grown not only in the wood fiber but also in combinations with peat sub-strate. Growing plants in wood fiber and fertilizing them with N13 can result in optimum uptake of micronutrients. The number and mass of cucumber fruits per plant were influenced by the amount of wood fiber in the growing media and the application of nitrogen fertilizer. The highest number of fruits and mass of fruits per plant was obtained significantly higher when cucumbers were grown on WF/PS 50/50 growing media with additional N13 fertilization.

Utilization of wasted heat instead of fuel combustion is effective to reduce primary energy consumption for mitigating global warming problem. Because wasted heat sources are not necessarily located close to areas of heat demand, one of the difficulties is that wasted heat has to be transferred from heat source side to heat demand side, which may require heat transportation over long distance. From this point we proposed and have examined new idea of heat transportation using ammonia-water as the working fluid which system is named Solution Transportation Absorption chiller, in short STA. Our previous studies of STA were mainly the experimental investigation with STA facility which cooling power was 25RT (90kW). As a result, the COP of STA was found almost same value 0.65 with the conventional absorption chiller without depending on the transportation distances. The simulation using AspenHYSYS also examined with same experimental condition. The experimental data showed good agreement with the simulation calculation. In this study, we examined the large-scale cooling power STA on simulation. The examination cooling powers were from 90 kW(25RT) to 3517 kW(1000RT). All cooling power achieved around COP 0.64 including pump power consumptions. In addition, we performed the dynamic simulation. As the results, there was no effect of pipeline size on the cooling capacities and mass flow rates. Furthermore, the stability time of the cooling capacities and mass flow rates were almost same regardless of the pipeline size and cooling capacity. In other words, STA may be achieved the same COP even though having various complex conditions compared with the conventional absorption chiller.

Background: beta blockers of three generations are first-line drugs in the treatment of chronic heart failure (CHF). However, to date there is no clear understanding of the effect of beta blockers on myocardial energy metabolism disorders in CHF. The aim of the study: to conduct a study of beta-blockers of different generations on myocardial energy metabolism in the experimental CHF. Methods: CHF was modeled in white outbred rats by administering doxorubicin. The study drugs were administered intragastrically – new drug Hypertril (1-(β-phenylethyl)-4-amino-1,2,4-triazolium bromide)-3.5 mg/kg, metoprolol - 15 mg/kg, Nebivolol -10 mg/kg, Carvedilol 50 mg/kg, Bisoprolol, 10 mg/kg. In the myocardium, the main indices of energy metabolism were determined - ATP, ADP, AMP, malate, lactate, pyruvate, succinate dehydrogenase (SDH) activity, NAD-dependent malate dehydrogenase (NAD-MDH) activity. Results: traditional second-generation beta-blockers (Metoprolol and Bisoprolol) did not affect the studied indices of energy metabolism, and third-generation beta-blockers with additional properties - Carvedilol and, especially, Nebivalol and Hypertril improved myocardial energy metabolism. Conclusions: obtained results will help to expand our understanding of the effect of beta-blockers of various generations used to treat cardiovascular diseases on energy metabolism, and are also an experimental justification for the practical choice of these drugs in the complex therapy of CHF.

Hereditary hemochromatosis (HH) is the main genetic cause of iron overload and is regarded as metal-related human toxicosis. HH may result from HFE and non-HFE gene mutations causing hepcidin deficiency or hepcidin resistance. The disorder has a high biochemical penetrance, but a low clinical prevalence. Unfortunately, the majority of patients with HH remain undiagnosed at their disease-curable stage. The main aim of HH therapy is to prevent iron overload in the early-phase disease and elimination of excess iron from the body by phlebotomy in its late stage. Raising global awareness of HH among health professionals, their training to pay attention to early HH signs and symptoms, and strict patient monitoring are critical management strategies for preventing treatment delays, upgrading its efficacy, and improving patient prognosis.

The composition simplex (N, P, K, Ca, and Mg) of the leaf is the main scores used by different approaches like the Diagnosis and Recommendation Integrated System and Compositional Nutrient Diagnosis to study the nutrient interactions and balance in plant leaves. However, the application and validation of these concepts to grain composition remains unexplored. Contrary to foliar analysis's early intervention for nutrient deficiency detection and correction, applying this approach to seeds assesses diverse cultivars' potential, enabling anticipation of their adaptation to climate conditions and informed selection for future crops. In the present study, a collected database of more than 924 scores including the grain yield (kg ha-1) and the nutrient composition (mg kg-1) of different corn varieties is used to develop a novel nutrient-based diagnostic approach to identify reliable markers of nutrient imbalance. A 'nutrient signature' model is proposed based on the impact of the environ-mental conditions on the nutrient indices and composition (N, P, K, Ca, and Mg) of the corn grains. The yield threshold used to differentiate between low and high-yielding subpopulations is established at 12.000 kg ha-1and the global nutrient imbalance index (GNII) of 2.2 is determined using the chi-square distribution function and vali-dated by the Cate-Nelson partitioning method, which correlated yield data distribution with the GNII. Therefore, the nutrient compositions were classified into highly balanced (GNII ≤ 1.6), balanced (1.6 < GNII ≤ 2.2), and imbalanced (GNII > 2.2). In addition, we found that the Xgboost model's predictive accuracy for GNII is signif-icantly affected by soil pH, organic matter, and rainfall. These results pave the way for adapted agricultural practices by providing insights into the nutrient dynamics of corn grains under varying environmental conditions.

Pakistan relies mostly on fossil fuel for fulfilling its energy demand which is expensive as 11 well as not friendly for the environment. The need of the hour is to shift from fossil fuels to renew-12 able energy resources like geothermal, wind, solar etc. to cater for global warming issues. Pakistan 13 has a lot of potential geothermal sites because of several fault lines and hot springs. A sound 14 knowledge of ground temperature is essential to use geothermal energy, which is obtained by drill-15 ing boreholes and putting sensors making it an expensive procedure. Thus, to avoid huge cost for 16 drilling bore holes, particularly for ground temperature analysis, a numerical approach has been 17 considered for determining ground temperature. Furthermore, correlation charts between air and 18 ground temperatures have been developed as there were no proper studies on the ground temper-19 ature of Pakistan. Then, with the help of a boreholes drilled in National University of Sciences and 20 Technology Islamabad Pakistan, the actual ground and numerically calculated temperatures have 21 been compared which shows an error margin in the range between 0.27% for higher depths to 7.3% 22 near surface. Thus, it is shown that the proposed method is easy to implement and better than large 23 scale testing methods for the depths at which geothermal energy is extracted.

This paper aims to develop a hierarchical risk assessment model using the newly-developed evidential reasoning (ER) rule, which constitutes a generic conjunctive probabilistic reasoning process. In this paper, we first provide a brief introduction to the basics of the ER rule and emphasize the strengths for representing and aggregating uncertain information from multiple experts and sources. Further, we discuss the key steps of developing the hierarchical risk assessment framework systematically, including (1) formulation of risk assessment hierarchy, (2) representation of both qualitative and quantitative information, (3) elicitation of attribute weights and information reliabilities, (4) aggregation of assessment information using the ER rule and (5) quantification and ranking of risks using utility-based transformation. The proposed hierarchical risk assessment framework can potentially be implemented to various complex and uncertain systems. A case study on the fire/explosion risk assessment of marine vessels demonstrates the applicability of the proposed risk assessment model.

Cloud computing refers to applications delivered as services over the Internet. Cloud systems employ policies that are inherently dynamic in nature and that depend on temporal conditions defined in terms of external events, such as the measurement of bandwidth, use of hosts, intrusion detection or specific time events. In this paper, we investigate an optimized resource management scheme named v-Mapper. The basic premise of v-Mapper is to exploit application-awareness concepts using software-defined networking (SDN) features. This paper makes three key contributions to the field: (1) We propose a virtual machine (VM) placement scheme that can effectively mitigate the VM placement issues for data-intensive applications; (2) We propose a validation scheme that will ensure that a service is entertained only if there are sufficient resources available for its execution and (3) We present a scheduling policy that aims to eliminate network load constraints. An evaluation was carried out with various benchmarks and demonstrated that v-Mapper shows improved performance over other state-of-the-art approaches in terms of average task completion time, service delay time and bandwidth utilization. Given the growing importance of supporting large-scale data processing and analysis in datacentres, the v-Mapper system has the potential to make a positive impact in improving datacentre performance in the future.

The early 2-factor (E2F) family of transcription factors, including E2F1-8, plays a critical role in apoptosis, metabolism, proliferation, and angiogenesis within glioblastoma (GBM). However, the specific functions of E2F transcription factors (E2Fs) and their impact on the malignancy of Bevacizumab (BVZ)-responsive GBM subtypes remain unclear. This study used data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to explore the impact of eight E2F family members on the clinical characteristics of BVZ-responsive GBM subtypes and possible mechanisms of recurrence after BVZ treatment.To predict the possibility of GBM patient survival and progression, we classified and comparedthe expression of E2Fs according to BVZ-responsive subtypes and employed a machine learning method-TreeBagger, one random forest algorithm. Multiple bioinformatics analyses have shown that the significant increased E2F8 post BVZ treatment may enhance the function of angiogenesis and stem cell proliferation, indicating one of the mechanisms of GBM recurrence after treatment. In addition, BVZ treatment in unresponsive GBM patients may potentially worsen disease progression. Our findings suggest that E2F family members play important roles in GBM malignancy and BVZ treatment response, highlighting their potential as prognostic biomarkers and therapeutic targets and recommending precise BVZ treatment for GBM patients.

: Image guided superficial radiation therapy (IGSRT) combines superficial radiation therapy (SRT) with full dermal visualization (FDV) via high resolution dermal ultrasound (HRDUS) for the treatment of non-melanoma skin cancer (NMSC). The gold standard for IGSRT delivery includes a comprehensive cancer care model with support for the dermatologists by a multidisciplinary team. When delivered in this model, IGSRT can achieve cure rates of 99% for treatment of NMSC. This paper focuses on the benefits of IGSRT during therapy. Medical records from 7 dermatology clinics of 883 patients with 1,507 cases of NMSC treated with IGSRT be-tween 2017 and 2018 were retrospectively reviewed. 92% of the NMSC lesions showed daily depth fluctuations, 60.32% of lesions did not require changes during therapy, and nearly 40% of lesions required at least one compensatory change during therapy. 83% of NMSC lesions were labeled high-risk based on the 2024 NCCN guidelines. Increasing and decreasing tumor depth measurements during IGSRT inform dermatologists when adaptive changes in energy (kV), TDF and dose will result in more efficacy and less toxicity, respectively.

This study examines the Soil Organic Carbon (SOC) within Greek croplands, offering a comprehensive understanding of its dynamics. SOC, a cornerstone in soil health, nutrient cycling, and global carbon dynamics, assumes critical significance in sustainable agriculture and climate change mitigation. Drawing on diverse soil properties, including pH, soil texture as well as different drainage and slope categories, the research explores the nuanced relationships shaping SOC dynamics in the diverse agroecological landscape of Greece. The investigation transcends local boundaries, emphasizing SOC's global role in climate change mitigation by sequestering carbon dioxide. Two maps were used as data sources: 1) SOC stock baseline map (2010) by JRC, 2) SOC stock map (2021) by the Institute of Soil and Water Resources, Hellenic Agricultural Organization – DIMITRA in collaboration with FAO. Greek croplands emerge as a mosaic of agroecological diversity, where anthropogenic activities wield transformative influences on SOC stock, demanding a delicate balance between agricultural productivity and soil health. The study unveils the influence of Soil Order, weaving a tapestry of SOC variability. Microscopic factors, from soil texture to cation exchange capacity, further shape SOC dynamics, emphasizing the role of clayey soils and coarse materials in carbon retention. Although soil organic carbon decreased from 2010 to 2021 the degree of carbon loss varied. This scientific endeavor synthesizes existing knowledge and unveils novel insights, offering a narrative woven from diverse soil properties. The findings contribute valuable insights for sustainable land management practices and climate change mitigation strategies, underscoring the importance of region-specific approaches in addressing global challenges.

This article utilizes the Canny edge extraction algorithm based on contour curvature and the cross-correlation template matching algorithm to extensively study the impact of high-repetition-rate CO2 pulsed Laser on the target extraction and tracking performance of the infrared imaging detector. It establishes a quantified dazzling pattern for lasers on infrared imaging systems. By conducting laser dazzling and damage experiments, a detailed analysis of the normalized correlation between target and dazzling images is performed to quantitatively describe laser dazzling effects. Simultaneously, an evaluation system, including target distance and laser power evaluation factors, is established to determine the dazzling level and whether the target is recognizable. The research results reveal that laser power and target position are crucial factors affecting the detection performance of infrared imaging-guided weapon systems under laser dazzling. Different laser powers are required to successfully interfere with the recognition algorithm of infrared imaging-guided weapons at different distances. And laser dazzling will produce a lot of false edge information, which seriously affects the performance of pattern recognition algorithm. In laser damage experiments, the detector experienced functional damage, with a quarter of the image displaying as completely black. The energy density threshold required for functional damage to the detector is approximately 3 J/cm2. The dazzling assessment conclusions also apply to evaluating damage results. Finally, the proposed evaluation formula aligns with experimental results, objectively reflecting the actual impact of laser dazzling on the target extraction and tracking performance of infrared imaging systems. This study provides an in-depth and accurate analysis for understanding the influence of lasers on the performance of infrared imaging-guided weapons.

The nutrient absorption and utilization efficiency in the roots of seedlings are beneficial for increasing crop yield. Creating marker assisted selection for wheat root traits can assist wheat breeders in choosing robust roots to maximize nutrient uptake. Exploring and identifying the effect of different nitrogen applications on RSA is of great significance for breeding nitrogen efficient wheat varieties. In this study, A total of 243 wheat varieties native to the Yellow and Huai Valley regions of China were utilized for genome-wide association studies (GWAS). Furthermore, a recombinant inbred line (RIL) population of 123 lines derived from the cross between Avocet and Chilero was utilized for linkage examination and confirmation of the hereditary loci distinguished by GWAS. Five RSA traits include the relative number of root tips (RNRT); relative total root length (RTRL); relative total root surface area (RTRS); relative total root volume (RTRV) and relative average root diameter (RARD) where evaluated under two N conditions—normal N (NN) supply and low N (LN)—to characterize the NUE of each accession. GWAS and linkage analysis were performed by integrating data from the wheat 660k single nucleotide polymorphism (SNP) chip and diversity arrays technology (DArT) to identify genetic loci associated with RSA. The results showed that based on the ratio of RSA related traits under two N supply conditions, a total of 598 SNP markers, which are significantly associated with RSA related traits, were detected at 160 genetic loci by GWAS. A total of 10 QTL loci related to RSA were dis-covered and identified by linkage mapping. In which, three significant RSA related Quantitative Trait Locus (QTL) were identified, namely qRtrl-3D, qRnrt-3D, and qRtrl-7D. The study has the potential to enhance the effectiveness of selecting root traits in wheat breeding programs, offering valuable insights into the genetic underpinnings of nitro-gen use efficiency (NUE) in wheat. This could lead to the creation of NUE wheat varieties by implementing focused breeding strategies.

This paper investigates the effects of various process parameters, specifically the substrate temperature, on weld track geometry in Directed Energy Deposition (DED) processes. A specialized experimental setup integrated within a DED machine facilitates controlled thermal conditioning of sample sheets. Through Design of Experiments (DoE) methods, individual weld marks are generated and analysed to assess geometric characteristics. Regression models are constructed to predict machine parameters for desired weld geometry at different substrate temperatures. Validation experiments confirm the accuracy and reliability of the regression models. Results show a consistent trend towards target geometric features across a broad range of substrate temperatures. Despite deviations in measured values, successful fabrication is achieved, demonstrating robust bonding between weld and substrate. Contact angle predictions exhibit precision within a partial temperature range for proper deposition. The developed model offers insights for optimizing DED process parameters to achieve desired weld characteristics, advancing the capabilities and reliability of additive manufacturing technology. Future work aims to refine the regression model and explore additional mathematical relationships for enhanced accuracy. An implication of this work is the potential to vary the local mechanical properties of parts by controlling the temperature profile while maintaining consistent geometric characteristics. By manipulating the substrate temperature, it may be possible to tailor the microstructure and mechanical properties of fabricated components in the future to meet specific requirements in different regions of the part.

This study assessed the perception and use of digital applications for soil fertility management and conservation strategies among small-scale crop farmers in southwest Nigeria. A total of 376 farmers were randomly selected across the six southwest states. The data collected were analyzed using descriptive statistics and Logit regression. Majority of the farmers relied on perception and other non-conventional approaches such as the appearance of weeds and performance of crops in the previous season to assess soil fertility. Only 1.1% and 0.3% of the farmers assessed soil fertility through soil test and digital application respectively. Most farmers adopted bush fallowing and the use of inorganic fertilizer to improve soil fertility. Although, 4.8% of the farmers indicated that they had digital application on their mobile phones, only 2.9% reported to have used such. More than half (56.4%) of the famers stated that lack of awareness of the existence of digital application and internet enabled telephones were reasons they have not been able to use digital applications. Majority of the farmers (97.3%) indicated their willingness to embrace the use of new farm decision digital application which can provide more information especially on soil fertility if introduced. Logit model results revealed that literacy level, membership of farmers’ association and extension contact significantly increased the likelihood of willing to use soil digital application, but older farmers are less likely to use digital application.

This study focuses on predicting PM2.5 levels at the University of Petroșani by employing advanced machine learning techniques to analyze a dataset that encapsulates a wide array of air pollutants and meteorological factors. Utilizing data from IoT sensors and established environmental monitoring stations, the research leverages Random Forest, Gradient Boosting Machines, and Support Vector Regression models to forecast air quality, emphasizing the complex interplay between various pollutants. The models demonstrate varying degrees of accuracy, with the Random Forest model achieving the highest predictive power, indicated by an R2 score of 0.82764. Our findings highlight the significant impact of specific pollutants such as NO, NO2, and CO on PM2.5 levels, suggesting targeted mitigation strategies could enhance local air quality. Additionally, the study explores the role of temporal dynamics in pollution trends, employing time-series analysis to further refine the predictive accuracy. This research contributes to the field of environmental science by providing a nuanced understanding of air quality fluctuations in a university setting and offering a replicable model for similar environments seeking to reduce airborne pollutants and protect public health.

In the paper, the author presents explicit and unified expressions for a sequence of improper integrals in terms of the beta functions and the Wallis ratios. Hereafter, the author derives integral representations for the Catalan numbers originating from combinatorics.

The study of biologically active substances from natural sources shows the prospect of obtaining a new type of antimicrobial agents, highly effective against pathogenic microorganisms and non-toxic to humans. Our study reveals for the first time that Cornu aspersum mucus has the potential to act as a source of antibacterial agents against bacterial pathogens such as Bacillus cereus 1085, Propionibacterium acnes 1897, Salmonella enterica 8691, Enterococcus faecalis 3915, and Enterococcus faecium 8754. Both mucus fractions with MW<20 kDa and MW>20 kDa showed antimicrobial activity and antioxidant properties that’s why they can be turned into an alternative agent to expensive synthetic antibacterial compounds. Using de novo sequencing 16 novel peptides with potential antibacterial activity were identified by in a fraction with MW<20 kDa. The results of electrophoretic and proteomic analysis of mucus fraction with MW >20 kDa presented proteins with potential antibacterial activity highly homologous to hemocyanins, mucus protein Aspernin, lectins, L-amino acid oxidase-like protein, and mucins (mucin-5AC, mucin-5B, mucin-2 and mucin-17). Thanks to the synergistic action of the bioactive components, the mucus fraction with MW > 20 kDa in low concentrations per 8–2 µg/mL showed promising antimicrobial activity against the tested bacteria, comparable to Vancomycin and without unwanted cytotoxic effects against non-target cells. Additionally, C. aspersum mucus fractions were found to have a positive effect on eukaryotic organisms by reducing the levels of intracellular oxidative damage and increasing the antioxidant capacity of the cell. These findings could serve as a basis for the development of a potent non-cytotoxic antibacterial agent to replace conventional antibiotics and avoid the development of antibiotic resistance.

Tackling the intricacies of network dismantling in complex systems poses significant challenges. This task has relevance across various practical domains, yet traditional approaches focus primarily on singular metrics, such as the number of nodes in the Giant Connected Component (GCC) or average pairwise connectivity. In contrast, we propose a unique metric that concurrently targets nodes with the highest degree and reduces the GCC size. Given the NP-hard nature of optimizing this metric, we introduce MaxShot, an innovative end-to-end solution that leverages graph representation learning and reinforcement learning. Through comprehensive evaluations on both synthetic and real-world datasets, our method consistently outperforms leading benchmarks in accuracy and efficiency. These results highlight MaxShot’s potential as a superior approach to address the network dismantling problem effectively.

Our study aimed to assess sprinkler irrigation's impact on the activity of selected soil enzymes concerning nitrogen metabolism and oxidation-reduction processes in the soil with different doses of inorganic nitrogen fertilizers. The soil Alfisol was sampled from the experimental field of spring barley in the University Research Center in the central part of Poland in the moderate transitory climate during the growing seasons 2015–2017. The index resistance (RS) was derived to recognise the resistance enzymes of drought. In the maturity phase, nitrate reductase activity was at an 18% higher level in irrigated soils and the activity of other enzymes was higher in no-irrigated treatments by 25% in dehydrogenase, 22% in peroxydase 33% in catalase and 17% in urease case. The development stage of barley influenced nitrate reductase activity. Enzymatic activity has been changing in the examined years of the research, depending on the weather conditions. During the maturity stage, the soil's lower ammonium nitrogen content resulted from higher spring barley uptake due to drought stress. Irrigation probably contributes to increased leaching of nitrate in soil. The highest index of resilience was presented in the soil catalase activity.

The real systems are often complex, nonlinear, and noisy in various areas including mathematics, natural science, and social science. We present the symplectic entropy (SymEn) measure as well as an analysis method based on SymEn to estimate the nonlinearity of the complex system by analyzing the given time series. The SymEn estimation is a kind of entropy based on symplectic principal component analysis (SPCA) which represent organized but unpredictable behaviors of systems. The key to SPCA is to preserve the global submanifold geometrical properties of the systems through symplectic transform in the phase space, which is a kind of the measure-preserving transforms. The capability of preserving the global geometrical characteristics makes the SymEn a test statistic to detect the nonlinear characteristics in several typical chaotic time series and the stochastic characteristic in the Gaussian white noise. The results are in agreement with findings in the approximate entropy (ApEn), the sample entropy (SampEn) and the fuzzy entropy (FuzzyEn). Moreover, the SymEn method is also used to analyze the nonlinearities of the real signals (including the EEG signals for ASD and healthy subjects, and the sound and vibration signals for the mechanical systems). The results indicate that the SymEn estimation can be taken as a measure for describing the nonlinear characteristics in the data collected from the natural complex systems.

Despite various interventions in road safety work, fatal and severe road traffic accidents (RTAs) remain a significant challenge leading to human suffering and economic costs. Understanding the multicausal nature of RTAs, where multiple conditions and factors interact, is crucial for developing effective prevention measures in road safety work. This study investigates the multivariate statistical analysis of co-occurring conditions in RTAs, focusing on single-vehicle accidents with single occupancy and personal injury on Austrian roads outside built-up areas from 2012 to 2019. The aim is to detect recurring combinations of accident-related variables, referred to as blackpatterns (BPs), using the Austrian RTA database. The study proposes Fisher’s exact test to estimate the relationship between an accident-related variable and fatal and severe RTAs (severe casualties). In terms of pattern recognition, the study develops the maximum combination value (MCV) of accident-related variables, a procedure to search through all possible combinations of variables to find the one that has the highest frequency. The accident investigation proceeds with the application of pattern recognition methods, including binomial logistic regression and a newly developed method, the PATTERMAX-method, created to accurately detect and analyse variable-specific BPs in RTA data. Findings indicate significant BPs contributing to severe accidents. The combination of binomial logistic regression and the PATTERMAX-method appears to be a promising approach to investigate severe accidents, providing both insights into detailed variable combinations and their impact on accident severity.

Two desirable functions of healthy soil are nutrient cycling and pest suppression. We review recent literature on the contributions of soil microarthropods to soil health through their intersecting roles in decomposition and nutrient cycling and direct and indirect suppression of plant pests. Microarthropods can impact soil and plant health directly by feeding on pest organisms or serving as alternate prey for larger predatory arthropods, and indirectly, by mediating the ability of crop plants to resist or tolerate insect pests and diseases through interactions with the decomposition food web in support of plant nutrition. Soil fauna, including microarthropods, are key regulators of decomposition at local scales but their role at larger scales is unresolved. Future research priorities include the incorporation of multi-channel omnivory into food web modeling and understanding the vulnerability of our soil carbon to increased global temperatures.

Phosphite (Phi) has gained attention in agriculture due to its biostimulant effect on crops. This molecule has been found to benefit plant performance by providing protection against pathogens, improving yield and fruit quality, and improving nutrient and water use efficiency. It is still unclear how Phi enhances plant growth and protects against multiple stresses. It has been hypothesized that Phi acts by directly affecting the pathogens and interacting with the plant cellular components and molecular machinery to elicit defense responses. This study delves into elucidating the mechanisms underlying Phi’s beneficial effects on plants, revealing a complex interplay with fundamental signaling pathways. A RNA-seq study of Arabidopsis seedlings under optimal and limiting phosphate conditions helped us unveil Phi’s role in promoting plant growth by activating the expression of genes involved in the biosynthesis and signaling pathways associated with abscisic acid (ABA), salicylic acid (SA), and jasmonic acid (JA). Expression of ABA-related genes, known for their involvement in stress response and development regulation, is triggered by Phi treatment, contributing to enhanced resilience and growth. Simultaneously, the activation of the SA pathway, associated with defense responses, suggests Phi’s potential in bolstering plant immunity. Moreover, Phi influences JA biosynthesis and signaling, which are crucial for defense against herbivores and pathogens, thereby strengthening plants’ defenses. Our findings reveal a multifaceted mechanism through which Phi benefits Arabidopsis development. Understanding its intricate interplay with key signaling pathways opens avenues for leveraging Phi as a strategic tool to enhance plant resilience, immunity, and growth in agricultural and ecological contexts.

This review provides a comprehensive and updated information on chalcones, presenting their sources, methods of identification and properties putting emphasis on applications in agricultural industry. Vast exploitation of synthetic pesticides led to the weed and pest resistance issue, causing economic loss but more importantly health risk due to pesticide overuse. Because one of the aim of the European Green Deal 2030 is the Farm to Fork strategy which targets on reducing by 50% the use of chemical pesticides by 2030, and push towards natural pesticides which are more environmentally friendly and restore natural resources, chalcones and their derivatives seem to fit perfectly. They have herbicidal, fungicidal, bactericidal and antiviral properties. Moreover, chalcones are naturally occurring substances, known for their favorable health effect for years, working on many areas. This review is complementary to the previous ones describing chalcones use in agriculture, updates them but additionally shows methods of detection.

Peptides, both natural and synthetic, are well suited for a wide range of purposes and offer versa-tile applications in different fields such as biocatalysts, injectable hydrogels, tumor treatment, and drug delivery. The rising prominence of therapeutic peptides encompasses anticancer, antiviral, antimicrobial, and anti-neurodegenerative properties. The metals Na, K, Mg, Ca, Fe, Mn, Co, Cu, Zn, and Mo are ten of the twenty elements that are considered essential for life. Crucial for under-standing the biological role of metals is the exploration of metal-bound proteins and peptides. Aside from essential metals, there are other, non-essential metals that also interact biologically, exhibiting either therapeutic or toxic effects. Irregularities in metal binding contribute to diseases like Alzheimer’s, neurodegenerative disorders, Wilson's, and Menkes disease. Certain metal com-plexes have potential applications as radiopharmaceuticals. The examination of these complexes was done by preforming UV-Vis, IR, EPR, NMR spectroscopy, and X-ray analysis.

Addressing escalating energy demands and greenhouse gas emissions in the oil and gas industry has driven extensive efforts in carbon capture and utilization (CCU), focusing on power plants and industrial facilities. Utilizing CO2 as a primary feedstock for industrial fuels and petrochemical materials offers a favorable alternative to sequestration, with byproducts substituting traditional chemical feedstocks. This comprehensive review explores the current landscape of CO2 capture technologies, examining opportunities, economic viability, and efficiency challenges. It provides a detailed analysis of various CO2 utilization methods and the combined process of capture and utilization. The review highlights primary obstacles in planning, production, and widespread implementation, emphasizing the need for strategic integration of materials research, process performance, and cost-effectiveness to advance new technologies.

Currently, there is a tenacious need to find new ways to purify water by eliminating bacterial biofilms, textile dyes, and toxic water pollutants. These contaminants pose significant risks to both human health and the environment. To address this issue, in this study, we have developed an eco-friendly approach that involves synthesizing Cobalt-doped Cerium Iron Oxide (CCIO) nanocomposite (NC) using an aqueous extract of Gossypium arboreum L. stalks. The resulting nanoparticles can be used to effectively purify water and tackle the challenges associated with these harmful pollutants. Nanoparticles excel in water pollutant removal by providing high surface area for efficient adsorption, versatile design for simultaneous removal of multiple contaminants, catalytic properties for organic pollutant degradation, and magnetic features for easy separation, offering cost-effective and sustainable water treatment solutions. CCIO nanocomposite has been synthesized by green co-precipitation method due to biomolecules and co-enzymes present in the aqueous extract of Gossypium arboreum L. stalk; a single-step procedure completed in 5 h reaction time. Further, the synthesis of nanocomposites was confirmed by various characterization techniques such as Fourier-transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD), Field emission scanning electron microscopy (FE-SEM), Transmission electron microscopy (TEM), Thermogravimetric analysis (TGA), Dynamic light scattering (DLS), and Energy Dispersive X-Ray (EDX). CCIO NCs were discovered to have a spherical shape and an average size of 40 nm. Based on DLS zeta potential analysis, CCIO NCs were found to be anionic. Also, CCIO NCs showed significant antimicrobial and antioxidant activity. Overall, considering the physical and chemical properties, the application of CCIO NCs in the adsorption of various dyes (~91 %) and water pollutants (chromium = ~ 60 %) has been considered since they exhibit great adsorption capacity owing to their microporous structure which can be a step forward in water purification.

In the United States, several land use and land cover (LULC) data sets are available based on Landsat. These data sets often fail to accurately represent the features on the ground despite having several advantages over other data. Detailed mapping of complex heterogeneous landscapes for informed decision-making is possible using high spatial resolution orthoimagery from the National Agricultural Imagery Program (NAIP). However, large-area mapping at this resolution remains challenging due to radiometric differences between scenes, low spectral depth of imagery, landscape heterogeneity, and computational limitations. Machine learning (ML) techniques have shown promise in improving LULC maps. The primary purposes of this study are to examine and evaluate bagging (Random Forest; RF), Boosting (Gradient Boosting Machines [GBM] and Extreme Gradient Boosting [XGB]), and stacking ensemble models on sentinel 2A fusion data on NAIP imagery. We also compared the accuracy based on random cross-validation (80% / 20% split) without accounting for spatial autocorrelation and target-oriented validation (10 clusters) accounting for spatial structures of the training data set. To make the LULC map of a portion of Tom Green and Irion counties in central Texas, we used a time series of sentinel data and NAIP orthoimagery. We created several spectral indices, structural variables, and geometry-based variables, reducing the dimensionality of features generated on Sentinel and NAIP data. The random and target-oriented validation results show that autocorrelation in the training data offers over-optimistic results. In our case, the overestimation ranged from 2 % to 3.5 %. The XGB-boosted stacking ensemble on-base learners (RF, XGB, and GBM) improved model performance over individual base-learner. The main contribution of this research is that meta-learners are just as sensitive to overfitting as base models, as these algorithms are not designed to account for spatial information. Finally, we show that the fusion of Sentinel 2A data with NAIP data improves land use land cover classification using Geographic Object-Based Image Analysis (GEOBIA).

Despite the growing adoption of Mobility as a Service (MaaS) in urban transportation systems, standard monitoring methods for evaluating its impact and effectiveness still need to be developed. This study proposes a quantitative state of MaaS analysis based on mobility market indicators and macroeconomic metrics to generate a MaaS Status Index (MSI). The intention is to introduce a standardised quantitative methodology for systematically assessing and comparing the state of MaaS in urban mobility systems. The MSI aims to quantitatively capture the economic, social, technological, and infrastructural conditions relevant to MaaS implementation. The methodology includes four steps: identifying relevant mobility markets, defining mobility market metrics, integrating macroeconomic metrics, and deriving the MSI formula. We apply the MSI methodology to the Austrian mobility market as a case study, demonstrating its practicality in assessing MaaS readiness and highlighting specific challenges and opportunities within the Austrian mobility system. The analysis covers the present (2017-2022) and the projected future (2023-2028). The findings indicate that the proposed MSI is an effective tool for evaluating MaaS implementation readiness.

In the Vietnamese Mekong delta, soil quality and crop yield are steadily declining under rice monocultures with three crops per year. The objective of this study was to evaluate the medium-term effects of rotating rice with upland crops and adding organic amendments on rice yield and relate it to soil quality. A field trial with split-plot design including two factors and three replicates was carried out from 2017 to 2020, over the course of nine consecutive cropping seasons. Crop rotations and organic amendments were applied as main-plot and subplot factors, respectively. The rotations were: 1) rice–rice–rice (R–R–R), 2) soybean–rice–rice (So–R–R), and 3) sesame–rice–rice (Se–R–R), while organic amendment treatments included: i) no amendment (NO-AM); ii) compost of rice straw and cow manure (RS+CM), and iii) sugarcane compost (SGC); the composts were applied at a rate of 2.0 t ha-1. The rotation cycle started with the so-called spring–summer (SS), followed by summer–autumn (SA) and ended with the winter–spring (WS) season. Rice yield significantly (P &lt; 0.05) increased by organic amendments after nine growing seasons (2019–2020 WS), with an increment of 5.1% for RS+CM (7.07 ton/ha) and 6.1% for SGC (7.14 ton/ha). Contrary to our expectation, rotations with upland crops did not significantly increase rice yield. Rice yield significantly and positively correlated with an integrated soil quality index SQI (r = 0.86) for the topsoil (0–15 cm), but not for the subsoil (15–30 cm). The increased availability of soil nutrients (Si and marginally also P) and improved soil physical properties probably induced by organic amendments, along with other soil properties under study, cumulatively attributed to enhanced rice yield. Use of a SQI involving several soil quality indicators enables to overall quantify the importance of soil fertility for rice yield versus other factors and it provides an effective means of quantifying the integrated effect of improved management. Moreover, integrating a wide range of soil quality indicators in a SQI ensures its applicability across diverse settings, including different crop rotations and various soil types.

Root rot diseases remain a major global threat to the productivity of agricultural crops. They are usually caused by more than one type of pathogen and are thus often referred to as a root rot complex. Fungal and oomycete species are the predominant participants in the complex, while bacteria and viruses are also known to cause root rot. Incorporating genetic resistance in cultivated crops is considered as the most efficient and sustainable solution to counter root rot; however, resistance is often quantitative in nature. Several genetics studies in various crops have identified quantitative trait loci associated with resistance. With access to whole genome sequences, the identity of the genes within the reported loci is becoming available. Several of the identified genes have been implicated in pathogen response. However, it is becoming apparent that at the molecular level, each pathogen engages a unique set of proteins to either infest the host successfully or be defeated or contained in doing so. In this review, a comprehensive summary of the genes and potential mechanisms underlying resistance or susceptibility against the most investigated root rots of important agricultural crops is presented.

The intricate relationship between Gray-Level Co-occurrence Matrix (GLCM) metrics and machine learning model performance underscores the need for rigorous dataset evaluation and selection protocols to ensure the reliability and generalizability of classification outcomes. This study involved a thorough examination of selected publicly available plant diseases datasets, with an emphasis on how well they performed as measured by GLCM metrics. After first classifying the datasets according to their GLCM metrics, dataset_2 (D2) and dataset_5 (D5) were found, respectively, to be the best-performing dataset in all GLCM analyses. The same datasets were then used to train deep learning models, and their classification performances were assessed. A noteworthy association was observed between the results of training deep learning models and the performance ratings derived from GLCM studies. More specifically, dataset_2 (D2) performed best in both GLCM analysis and deep learning model performance, indicating a strong correlation between the accuracy of classification and the textural qualities that GLCM captured. In the context of plant disease identification, in particular, these results highlight the significance of clearly defined dataset selection criteria in deep learning applications. Scholars can improve the accuracy and dependability of deep learning models for diagnosing plant diseases by giving preference to datasets with favorable GLCM metrics. The research also emphasizes the importance of texture features being taken into account in addition to conventional image features, highlighting the necessity of transparency and rigor in dataset selection procedures.

Abstract: This study investigates the transition to sustainable shipping industry within the framework of the IMO's 2050 carbon neutrality objective, focusing on socio-technical systems analysis. The research delves into the sustainable paradigm shift towards alternative marine fuels such as LNG, methanol, ammonia, and hydrogen from a socio-technical transition perspective, analyzing the multi-level per-spective challenges and opportunities. Specifically, it scrutinizes the implications of adopting these al-ternative fuels for global shipping, identifying technical uncertainties, financial constraints, and infra-structure needs. Through a comprehensive review of existing literature on ship emissions and alterna-tive fuels, coupled with socio-technical transition theory, the study addresses the multifaceted chal-lenges and opportunities in meeting the IMO's environmental objectives. It emphasizes the necessity of collaborative governance, innovative niche strategies, and a holistic approach to leverage the current window of opportunity in facilitating the maritime industry's transition to sustainable fuel options. Fi-nally, this research enhances our understanding of the intricate interplay between technological, social, industrial, and cultural dynamics in the greening of shipping and offers insights into the strategic adop-tion of environmentally friendly marine fuels for a sustainable future.

Demographic studies have revealed a strong association between exposure to high ambient temperatures during pregnancy and increased risks of preterm birth (PTB). The mechanism underlying this association is unclear, but it is plausible that altered placental function may contribute. In this study, we conducted differential gene expression analysis, gene set enrichment analysis (GSEA), and gene ontology (GO) analysis on bulk RNA-seq data from human placentas delivered at term and preterm during the warmer months compared to placentas delivered at term and preterm during the colder months in the UK. We detected 48 differentially expressed genes in preterm placentas delivered during the warmer months compared to preterm placentas delivered during the colder months, the majority of which were inflammatory cytokines and chemokines, including SERPINA1, IL1B, CCL3, CCL3L3, CCL4, CCL4L2, CCL20, and CXCL8. The GSEA positively enriched 17 signalling pathways, including the NF-κB, IL17, toll-like receptor, and chemokine signalling pathways, in warm-exposed preterm placentas, which were not observed in warm-exposed term placentas. The GO analysis revealed several biological processes, including neutrophil, granulocyte, monocyte, and lymphocyte chemotaxis, as well as inflammatory and humoral immune responses in warm-exposed preterm placentas, but not in warm-exposed term placentas. We conclude that maternal exposure to warm environmental temperatures during pregnancy alters the placental transcriptome towards inflammation and immune regulation, potentially leading to PTB.

Herein, we present the design technique of a resonant rectifier for piezoelectric (PE) energy harvesting. We propose two diode equivalents to reduce the voltage drop in the rectifier operation, a minuscule-drop-diode equivalent (MDDE) and a low-drop-diode equivalent (LDDE). The diode equivalents are embedded in resonant rectifier integrated circuits (ICs), which use symmetric bias-flip to reduce the power wasted for charging and discharging the internal capacitance of a PE transducer. The self-startup function is supported by synchronously generating control pulses for the bias-flip from the PE transducer. Two resonant rectifier ICs, using both MDDE and LDDE, are fabricated in a 0.18 μm CMOS process and their performances are characterized under external and self-power conditions. Under the external-power condition, the rectifier using LDDE delivers an output power POUT of 564 μW and a rectifier output voltage VRECT of 3.36 V with a power conversion efficiency (PCE) of 90.1%. Under self-power conditions, the rectifier using MDDE delivers a POUT of 288 μW and a VRECT of 2.4 V with a corresponding PCE of 74.6%. The result shows that the power extraction capability of the proposed rectifier is 5.9 and 3.0 times higher than that of a conventional full-bridge rectifier.

In terahertz communication systems, lens antennas used in transceivers are basically plano-convex dielectric lenses. As the aperture increases and the focal length decreases, the thickness and mass of the plano-convex lens increase rapidly. Through theory and simulation, we designed a Fresnel lens suitable for the terahertz band to meet the requirements of large aperture and short focal length, and simulated the performance, advantages and disadvantages of the terahertz Fresnel lens. A 300GHz terahertz wireless communication system was built to verify the gain effect of the Fresnel lens antenna. The experimental results show that the Fresnel lens can amplify the signal in the terahertz band. The theoretical gain of a 30 cm Fresnel lens is 48.83dB, while the actual measured gain is approximately 45dB.

Monitoring the integrity of aeronautical structures is fundamental for safety. Structural Health Monitoring Systems (SHMS) perform real-time monitoring functions, but their performances must be carefully assessed. In this work the damage detection performances of a strain-based SHMS, were evaluated on a composite helicopter rotor blade root. The SHMS monitored the bonding between the central core and the surrounding antitorsional layer. A damage detection algorithm was trained through Finite Element analyses performed on the component. The effects of the loads variability and of damages were decoupled by including a load recognition step in the algorithm. Load recognition was accomplished in different ways: version#1 adopted an Artificial Neural Network (ANN) trained on blades in pristine condition; version#2 was trained also on damaged blades; version#3 adopted a calibration matrix method. Anomaly detection, damage assessment, and localization were performed by using an ANN. Results showed a higher load identification and anomaly detection accuracy for version#1 and version#2 compared to version#3. Gaussian noise reduced anomaly detection and damage assessment performances, and reduced damage localization accuracy for bigger damages. The damage detection performance were obtained for a fibre optic based-SHMS, including the trade-off between probability of detection and false alarm rate for different damage sizes.

The study of underwater vehicle wake detection is of significant importance within the field of target detection, localisation and tracking of underwater vehicles. Given that propellers are the propellers of modern ships and underwater vehicles, the propeller wake field represents the principal target source for wake detection in underwater vehicles. The objective of this paper is to propose a method for measuring the wake of an underwater propeller based on a position-sensitive detector. A theoretical model of the relationship between the laser spot displacement and the change of refractive index of the wake field is established on the basis of the principle of laser beam deflection. A prototype experimental setup for underwater propeller wake measurement was constructed based on the aforementioned optical measurement method. Furthermore, the simulation of the propeller wake flow field with strong density stratification and linear density stratification was conducted based on the experimental setup. In the experiment, a three-bladed propeller with a diameter of 39mm was selected, the water tank size was 980mm×380mm×380mm, and the laser acted at a distance of 550 mm from the propeller. The experimental results indicate that the wake dissipation time of the propeller in a strong density-stratified water environment is approximately 800s. Following the stabilization of the wake field density, the laser spot position is observed to be stable at 0.341 mm, with a corresponding refractive index change of 2.99×10-6 RIU. The wake dissipation time of the propeller in a linear density-stratified water environment is approximately 750 s. Once the wake field density has reached a stable state, the laser spot position is found to be 0.441mm, corresponding to a refractive index change of 3.87×10-6 RIU. These experimental results are found to be in general agreement with the simulation results of the propeller wake field. A comparison of the experimental device-based wake measurements with the CTD-based wake measurements reveals a consistent trend.

In the past few years, cloud-based additive manufacturing has shown its application and potential in industries. For this reason, when the demand for cloud-based additive products increases, efficient scheduling of related tasks becomes more important to achieve optimized duration. This research proposes a Heuristic Scheduling Algorithm (HSA) for use in Cloud-Based Additive Manufacturing (CBAM) with an approach to Critical Chain Buffer Management (CC/BM). This research aims to schedule and optimize the time in cloud-based additive manufacturing, in addition to the parts production process, it also includes the steps of receiving online orders and informing the manufacturing unit and sending them to the customer. The HSA proposed in this research consists of three phases 1- Buffer sizing, 2- Additive manufacturing and logistics scheduling, and 3- Buffer controlling. To evaluate, the performance of the algorithm is simulated. The finding shows that the proposed HAS can effectively solve the CBAM scheduling problem.

Understanding potential uptake and biodistribution of engineered nanoparticles in soil-grown plants is imperative for toxicity and risk assessment considering the oral exposure of edibles by humans. Herein, we assessed potential influence of particle size (25, 50, and 250 nm) and concentration (0, 50, 100, 200, and 500 mg/kg-soil) of Copper oxide nanoparticles (CuONPs) on: (1) the root system architecture, and the physicochemical attributes of soil at the soil-root interface, (2) leading to Cu transport and accumulation in root, stem, leaf and seed in soybean (Glycine max cv Kowsar) grown for entire lifecycle of 120 days, and compared with soluble Cu²⁺ ions and water-only controls, and (3) performed a comparative assessment of total seed Cu levels in soybean with other valuable food sources for Cu intake and discussed its human health implications. Our findings showed particle size- and concentration-dependent influence of CuONPs on Cu uptake and tissue distribution in root, stem, leaf and seed in soybean. Alterations in root architecture (root dry weight, root length, root volume, and root area) were dependent on the Cu compound type, Cu concentrations, and their interactions (p<0.05), except for root density. Concentration-response relationships for all three sized CuONPs, and Cu²⁺ ions, were linear. CuONPs and Cu²⁺ ions had inhibitory effects on root growth and development. Overall, soybean responses to smallest size CuONPs-25 nm were higher for all parameters investigated compared to two larger sized CuONPs (50 nm, 250 nm) or Cu²⁺ ions. Cu uptake/bioaccumulation differed among soybean tissues in the order: root > leaf > stem > seed. Despite reduced root architecture and seed yield, our smallest size CuONPs-25 nm led to increased total seed Cu uptake compared to the larger sized CuONPs and Cu²⁺ ions tested. Our findings also suggest that soil amendment by CuONPs, more so by the smallest size CuONPs-25 nm, could significantly improve nutritional Cu value in soybean seed as reflected by % Daily Values (DV), and are rated “Good” to “Very Good” according to the “World’s Healthiest Foods” rating. However, until the potential toxicity and risk from consumption of soybean seed is characterized in humans, caution should be exercised when the Cu fortified seeds are used for daily human consumption when addressing Cu deficiency and associated illnesses, globally.

Successful fertilization treatments targeted to improve stand productivity while reducing operational complexities and cost depend on a clear understanding of soil nutrient availability under varying environmental conditions. Soil nutrient data collected from 154 forest sites throughout the Inland Northwest, USA were analyzed to examine soil nutrient characteristics on different geologic soil parent materials and to rank soil fertility. Results show that soil parent material explains significant differences in soil nutrient availability. Soils developed from volcanic rocks have the highest CEC and are relatively high in P, K, S, Mg, Cu, Ca, and B, but generally poor in N. Forest soils developed from plutonic rocks exhibit the lowest CEC and are low in N, S, K, Mg, Cu, and Ca, but higher in P. Some soils located on mixed glacial till are low only in K, Cu, Mg, and Ca, but many glacial soils are relatively rich in other nutrients, albeit the second lowest CEC. Soils developed from metasedimentary and sedimentary rocks are among those with lowest soil nutrient availability for P and B. Sulfur was found to have the highest concentrations in metasedimentary influenced soils and the least in sedimentary derived soils. Our results should be useful in designing site-specific fertilizer and nutrient management prescriptions for forest stands growing on soils developed from these major geologies within the Inland Northwest region of the United States.

The article presents several innovative systems developed through student laboratory projects, including two autonomous vehicles, a four-legged walking robot, an active ankle-foot orthosis, a ball on beam balancing mechanism, a ball on plate system, and a manipulator arm—all powered by pneumatic artificial muscles (PAMs). Fluidic muscles offer significant potential in various mechatronic systems, robots, and manipulators due to their flexible, lightweight nature and compliance. Their ability to produce smooth and adaptable motions is particularly beneficial in applications requiring natural and human-like movement, such as rehabilitation and assistive devices. Although PAMs powered control systems face challenges with nonlinear phenomena, their biologically inspired design holds promise for future applications across various industries. These may include innovative applicability in industrial, automotive, and aerospace sectors, as well as use in sports, medical aids, entertainment, and animatronics. Incorporating self-made laboratory systems actuated by PAMs into control systems education offers a comprehensive learning opportunity that combines theoretical concepts with hands-on experience. This approach develops the necessary skills of future engineers, expanding their understanding of technical principles and preparing them for future challenges in engineering practice.

Crop production in Afghanistan suffers from limited phosphorus (P) availability, which severely hinders national agriculture sustainability. This present study hypothesized that deep fertilizer placement could significantly enhance uptake of immobile P and thus tissue P accumulation and crop yield. A two-year pot experiment growing two maize (Zea mays) hybrid cultivars (Xida-789 and Xida-211) was therefore conducted to test these hypotheses under three contrasting fertilizer placement methods (broadcast, side band, and deep band). In doing so, P concentrations in both maize tissues and soils were compared at 45, 60, and 115 days after sowing (DAS) under nine combinations of nitrogen (N) and P fertilizer rates (kg ha-1: N112P45, N112P60, N112P75, N150P45, N150P60, N150P75, N187P45, N187P60, N187P75). Results have shown that deep band placement significantly increased P uptake efficiency, leading to greater P concentration and accumulation in maize tissues compared to other two fertilization methods. This improved P uptake was attributed to several factors associated with deep placement, including reduced P fixation, enhanced root access to P, and moisture availability for P uptake. Additionally, deep band placement combined with higher N application rates (N187 and N150) further enhanced plant P uptake by promoting P availability and utilization mechanisms. Deep band placement also resulted in significantly higher total soil P, Olsen-P, and P use efficiency than broadcast and side band methods, indicating a more efficient P fertilization strategy for maize that can improve growth and yield. This present study also found positive correlations between P concentration in plant organs and soil Olsen-P, highlighting the importance of adequate soil P levels for optimal plant growth. Overall, our results have showed that deep band fertilizer placement emerged as a superior strategy for enhancing P uptake efficiency, utilization, and maize productivity compared to broadcast and side band placement. This approach can potentially optimize P fertilizer utilization, improve maize growth and yield, while minimizing environmental P losses associated with broadcast applications.

The refractive index of seawater is one of the essential parameters in ocean observation, so it is necessary to achieve high-precision seawater refractive index measurements. A seawater refractive index measurement system based on position-sensitive detectors is an effective means to achieve high-precision measurement of seawater refractive index. However, in the actual seawater refractive index measurement process, the impact of the measurement error caused by the jitter signal obtained from the measurement on the measurement results cannot be ignored. In this paper, we theoretically analyse the causes of the long jitter signals during seawater refractive index measurement and quantify the influencing factors. Through the analysis, it can be seen that the angle between the two windows in the refractive index measurement area of seawater causes a great error on the measurement results, so the angle between the two windows should be controlled within 2.06° in order to effectively reduce the measurement error. At the same time, the factors affecting the measurement accuracy of the position-sensitive detector are analysed, and the factors leading to the reduction of the measurement accuracy of the position-sensitive detector are mainly the background light, the size of the photosensitive surface, and the change of the temperature of the working environment. For the above factors, we add a narrow-band filter with a bandwidth of 0.9nm in front of the photosensitive surface of the position-sensitive detector during the construction of the measurement system, so as to filter out stray light other than the signal light source. On the premise of ensuring the measuring range of the refractive index of seawater, a position-sensitive detector with a photosensitive surface size of 4 mm×4 mm was selected. In order to reduce the temperature variation of the working environment of the position-sensitive detector, the measurement system was partitioned. In order to validate the testing error range of the optimised test system, standard seawater samples under the same conditions were measured. It can be seen that the jitter signal of the measurement system has been reduced from 0.0022mm before the optimisation to 0.0011mm after the optimisation, and the deviation of the refractive index has been reduced by 1.09×10−8RIU. The experimental results show that the refractive index of seawater has been effectively reduced by adjusting the optical path and the structure of the seawater refractive index measurement system.

With the development of higher voltage power grid, the high and low voltage parallel loops are emerging, which lead to energy losses, even threaten the security and stability of power system. The multi-infeed HVDC configurations widely appearing in AC/DC interconnected power system make this situation even worse. Aimed at energy saving and system security, a decision optimization method for power grid operating condition with high and low voltage parallel loops is proposed in this paper. Firstly, considering hub substation distribution and power grid structure, parallel loop opening schemes are generated with GN algorithm. Then, candidate opening schemes are preliminarily selected from all these generated schemes based on a filtering index. Finally, with the influence on power system security, stability and operation economy in consideration, an evaluation model for candidate opening schemes is founded based on analytic hierarchy process (AHP). And a fuzzy evaluation algorithm is used to find the optimal scheme. Simulation results of New England 39-bus system and an actual power system validate the effectiveness and superiority of this proposed method.

Fusarium species are agriculturally important fungi with a broad host range and can be found as endophytic, pathogenic, or opportunistic parasites in many crop plants. This study aimed to identify Fusarium species in bare-rooted, dormant plants in Turkish grapevine nurseries using molecular identification methods and assess their pathogenicity. Asymptomatic dormant plants were sampled from grapevine nurseries (43) in different regions of the country, and fungi were isolated from plant roots and internal basal tissues. The Fusarium isolates were identified by performing gene sequencing (TEF1-α, RPB2) and phylogenetic analyses. Pathogenicity tests were carried out by inoculating mycelial agar pieces of isolates onto the stem or conidial suspensions into the rhizosphere of vines (1103 Paulsen rootstock). Laboratory tests revealed that Fusarium species were highly prevalent in Turkish grapevine nurseries (41 out of 43). Gene sequencing and phylogenetic analyses unraveled that 12 Fusarium species (F. annulatum, F. brachygibbosum, F. clavum, F. curvatum, F. falciforme, F. fredkrugeri, F. glycines, F. nanum, F. nematophilum, F. nirenbergiae, F. solani, and Fusarium spp.) existed in the ready-to-sale plants. Some of these species (F. annulatum, F. curvatum and F. nirenbergiae) consistently caused wood necrosis of seedling stems, rotting of the basal zone and roots, and reduced root biomass. Although the other nine species also caused some root rot and root reduction, their virulence was not as severe as the pathogenic ones, and they were considered opportunistic parasites or endophytic species. This study is the first detailed investigation of Fusarium species in grapevine nurseries in Türkiye and emphasizes the need to consider pathogenic species in producing healthy grapevine seedlings.

Maintaining sufficient levels of plant nutrients in the soil and controlling certain heavy metals, which can be toxic to the environment, are critical to ensure sustainable agricultural production. The study aimed to assess the linkage of crop-specific agricultural practices established by farmers in the Kvemo Kartli region (Georgia) with metal accumulation in soils of agricultural lands being subject to influence from polluted irrigation water in the past. In particular, we tried to identify the primary sources of micronutrients, including iron (Fe), copper (Cu), manganese (Mn), nickel (Ni), and zinc (Zn), and toxic elements such as cadmium (Cd) and lead (Pb), and share of the contaminated irrigation water and other factors related to agricultural practices under different land uses, such as intensive and extensive arable farming, vineyards, orchards, and permanent pastures having the least disturbed soil. Based on principal component analysis, five primary sources were identified and categorized according to farmers' interviews and previous studies conducted in the region. The results showed that increased concertation of plant-available Cu, Zn, Cd and Pb were mainly associated with irrigation water and intensive use of fungicides; Fe, Mn, and Ni were closely linked to several factors, such as the mineralogical composition of soils, mineral and organic fertilizers inputs, and atmospheric deposition from diffuse sources, where exhausts from transport are probably the primary source. During our study, we attempted to differentiate irrigation water inputs from fungicides using simulation based on irrigation pattern and irrigation water quality on the one hand and fungicide application rate and their metal content on the other. The simulation revealed that the intensive application of fungicides, especially in vineyards, is more significant in enriching soils with Cu and Zn than irrigation water. Identification of factorial dependences was supported by statistical analysis and application of a number of contamination assessment methods: Contamination Factor (CF), Pollution Load Index (PLI), single-factor Pollution Index (PI), Nemerow’s comprehensive pollution index (PIN), Enrichment Factor (EF), and Geo-Accumulation Index (Igeo). Applied environmental indices indicate that the soils under the former and existing vineyards are the most enriched with Cu and Zn, highlighting the significance of agricultural practices on heavy metals accumulation in soils of agricultural lands.

This study examined how Information Use Environments (IUEs) influence the information behavior of people living with neglected tropical diseases (PLWNTDs) and how this affects their participation in diagnosis and treatment programs. The author demonstrated that local information use environments (IUEs) significantly impacted the information behavior of people living with neglected tropical diseases (PLWNTDs'), which can be harnessed to improve their involvement in diagnosis and treatment programs. The author compared user-centered and program-centered approaches within common IUEs in remote and impoverished areas, finding that NTDs persist due to a disconnect between program organizers and the affected individuals’ information use environments (IUEs). Data was sourced from published papers, and OpenAI (ChatGPT) was used to generate list of local sources (IUEs) in affected remote communities. The findings indicated that understanding local IUEs can help program organizers enhance participation rates of individuals with NTDs in diagnosis and treatment initiatives.

Here we are concerning with two problems of a coupled system of random and stochastic nonlinear differential equations with two coupled systems of nonlinear nonlocal random and stochastic integral conditions. The existence of solutions will be studied. The sufficient condition for the uniqueness of the solution will be given. The continuous dependence of the unique solution on the nonlocal conditions will be proved.

Estimating ground surface PM2.5 with fine spatiotemporal resolution is a critical technique for exposure assessments in epidemiological studies of its health risks. Previous studies have utilized monitoring, satellite remote sensing or air quality modeling data to evaluate the spatiotemporal variations of PM2.5 concentrations, but such studies rarely combined these data simultaneously. We develop a three-stage model to fuse PM2.5 monitoring data, satellite-derived aerosol optical depth (AOD) and community multi-scale air quality (CMAQ) simulations together and apply it to estimate daily PM2.5 at a spatial resolution of 0.1˚ over China. Performance of the three-stage model is evaluated using a cross-validation (CV) method step by step. CV results show that the finally fused estimator of PM2.5 is in good agreement with the observational data (RMSE = 23.00 μg/m^3 and R2 = 0.72) and outperforms either AOD-retrieved PM2.5 (R2 = 0.62) or CMAQ simulations (R2 = 0.51). According to step-specific CVs, in data fusion, AOD-retrieved PM2.5 plays a key role to reduce mean bias, whereas CMAQ provides all-spacetime-covered predictions, which avoids sampling bias caused by non-random incompleteness in satellite-derived AOD. Our fused products are more capable than either CMAQ simulations or AOD-based estimates in characterizing the polluting procedure during haze episodes and thus can support both chronic and acute exposure assessments of ambient PM2.5. Based on the products, averaged concentration of annual exposure to PM2.5 was 55.75 μg/m3, while averaged count of polluted days (PM2.5 > 75 μg/m3) was 81, across China during 2014. Fused estimates will be publicly available for future health-related studies.

Objective: To determine the effect of sandblasting before and after sintering on the surface roughness of zirconia and the micro-tensile bond strength of a pressable veneering ceramic to zirconia. Methods: Pre-sintered zirconia blocks (IPS e.max ZirCAD, Ivoclar Vivadent) were divided into a control group (CTR, no surface treatment) and four test groups of three specimens each: Pre-S-30, Pre-S-50, Pre-S-110 were sandblasted with 30µm SiO2, 50µm Al2O3 and 110µm Al2O3 particles respectively, before sintering, Post-S-30 was sandblasted with 30µm SiO2 after sintering. For each treatment, the surface roughness was measured (Ra, Perthometer M4P, Mahr Perthen). After sintering the zirconia blocks, a liner and a pressable ceramic (IPS e.max ZirPress, Ivoclar Vivadent) were fired. Sixteen micro-bars were obtained from each block and submitted to the micro-tensile bond strength (µTBS) test. Data were analyzed with one-way ANOVA. Any correlation between Ra and µTBS was evaluated (Sperman test). Results: Sandblasting before sintering with 110µm Al2O3 (Ra=3.44±0.44µm), 50µm Al2O3 (Ra=2.32±0.46µm), and 30µm SiO2 (Ra=1.22±0.22µm) resulted in significantly higher roughness than sandblasting after sintering with 30µm SiO2 (Ra=0.46±0.11µm). The highest µTBS was measured when the sintered zirconia was sandblasted with 30μm SiO2 (26.79±14.80 MPa), which was significantly different from that of specimens that were sandblasted before sintering (Pre-S-30=20.90±11.70; Pre-S-50=21.27±15.19; Pre-S-110=23.99±16.83) or were not treated (CTR=17.44±14.03). Conclusions: Sandblasting zirconia before sintering enhances the surface roughness proportionally to the particle size of the sand used. Sandblasting with 30µm SiO2 after sintering appeared to improve bonding between the veneering ceramic and zirconia. Clinical Significance: Sandblasting with 30µm SiO2 after sintering may improve bonding between veneering ceramics and zirconia, thus reducing ceramic fractures/chippings.

Sarcopenia is associated with clinical complications, which increase mortality. Effective sarcopenia risk screening in the elderly should be grounded in a comprehensive assessment, encompassing both nutritional status and medication use. Current screening tools, while emphasizing muscular per-formance, exhibit limited sensitivity in identifying elderly individuals at risk of sarcopenia. This cross-sectional study aimed to develop a sarcopenia risk screening tool, named Sarc-Global, with enhanced sensitivity, incorporating factors pertinent to the overall health of the elderly. Utilizing the criteria established by EWGSOP2 for diagnosing sarcopenia, we evaluated 395 communi-ty-dwelling elderly individuals. This evaluation served as a reference for assessing the efficacy of two validated questionnaires, Sarc-F and Sarc-CalF, and our newly developed Sarc-Global. Con-structed through multiple logistic regression, Sarc-Global integrates variables such as sex, age, medication use, body mass index, arm circumference, and handgrip strength – all significantly associated with sarcopenia (p

This study aims to improve the shaft with hexagon joints to be a type not requiring welding or bolts in the static load test . In order to evaluate the bearing capacity of helical piles, two sites were selected to conduct pile installation for the field test and the pile load test. For the pile load test, the static pile load test and the dynamic pile load test were carried out, and torque was measured during pile installation for the field test to compare and analyze expected bearing capacity and thus assess the feasibility of the method for estimating the bearing capacity. The field pile load test revealed the bearing capacity of the gravity grout pile was the same or greater than 600kN in the static pile load test in accordance with AC 358 Code. The non-grout pile showed the bearing capacity the same or smaller than 600kN, suggesting gravity grouting is required. Moreover, the field pile load test was used to establish the bearing capacity equation considering the torque in pile installation, and a small number of samples were used to establish the equation which can be used as a basic data.

Antimicrobial peptides represent a promising alternative to traditional drugs in relation to cost, toxicity and, primarily, the growing problem of drug resistance. Here we report on the activity against HSV-1 and HSV-2 of a previously described wide-spectrum synthetic decapeptide, Killer Peptide (KP). As determined by plaque reduction assays, treatment with KP at 100 μg/mL resulted in a reduction in the viral yield titer of 3.5 Logs for HSV-1 and 4.1 Logs for HSV-2. Further evaluation of KP antiviral activity focused on the early stages of virus replicative cycle, including the determination of the residual infectivity of viral suspensions treated with KP. A direct effect of the peptide on viral particles, impairing virus absorption and penetration, was shown. The toxicity profile proved to be extremely good with a selectivity index of 29.6 for HSV-1 and 156 for HSV-2. KP was also active against an acyclovir (ACV)-resistant HSV-1 strain, while HSV-1 subcultures in the presence of sub-inhibitory doses of KP did not lead to the emergence of resistant strains. Finally, the antiviral action of KP proved to be synergistic with that of ACV. Overall, these results demonstrate that KP could represent an interesting addition/alternative to acyclovir for antiviral treatment.

At present, aeroponic control is mainly regulated according to the environmental factors of aeroponics, such as the temperature, humidity, light intensity, carbon dioxide concentration, pH, and electrical conductivity (EC) of the nutrient solution. There is a lack of knowledge available in the literature for an aeroponic control system for plant growth information as the primary feedback signal. Therefore, a fuzzy logic-controlled aeroponics system was developed for the growth of lettuce as a feedback signal. After designing, the effectiveness test of system was performed with experiment having three treatments T1 (180C), T2 (220C) and without control (CK). The results show an increasing trend in shoot length, root length, and shoot area over time in all treatments. In terms of shoot area, the maximum of 345 cm² was observed in the T1 treatment, while the CK treatment had a minimum of 241.8 cm². Statistical analysis indicated that the T1 and T2 treatments were not significantly different, but both were significantly different from the CK treatment. The study demonstrates the effectiveness of the fuzzy logic control system and an 18°C nutrient solution temperature, resulting in improved lettuce growth and reduced root-to-shoot ratio. The findings suggest the potential of using fuzzy logic control in aeroponics for optimized cultivation outcomes.

This study investigates the long-term stability of the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) moderate-resolution Thermal Emissive Bands (M TEBs; M12 – M16) covering a period from February 2012 to August 2020. It also assesses inter-sensor consistency of the VIIRS M TEBs among three satellites-S-NPP, NOAA-20, and NOAA-21 over eight months spanning from March 18 to November 30, 2023. The field of interesting is limited to the ocean surface between 60°S and 60°N, specifically under clear-sky conditions. Taking radiative transfer modeling (RTM) as the transfer reference, we employed the Community Radiative Transfer Model (CRTM) to simulate VIIRS TEB brightness temperature (BTs), incorporating European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data as inputs. Our results reveal two key findings. Firstly, the reprocessed S-NPP VIIRS TEBs exhibit a robust long-term stability, as demonstrated through analyses of the observation minus background BT differences (O-B ∆BTs) between VIIRS measurements (O) and CRTM simulations (B). The drifts of the O-B BT differences are consistently less than 0.105 K/Decade across all S-NPP VIIRS M TEB bands. Notably, observations from VIIRS M14 and M16 stand out with drifts well within 0.04 K/Decade, reinforcing their exceptional reliability for climate change studies. Secondly, excellent inter-sensor consistency among these three VIIRS instruments is confirmed through the double-difference analysis method (O-O). This method relies on the O-B BT differences obtained from daily data. The mean inter-VIIRS O-O BT differences remain within 0.08 K for all M TEBs, except for M13. Even in the case of M13, the O-O BT differences between NOAA-21 and NOAA-20/S-NPP have values of 0.312 K and 0.234 K, respectively, which are comparable to the 0.2 K difference between some TEBs among VIIRS and MODIS. These disparities are primarily attributed to the significant differences in the Spectral Response Function (SRF) of NOAA-21 compared to NOAA-20 and S-NPP. Our study confirms the RTM-based TEB quality evaluation method’s versatility and effectiveness in assessing long-term sensor stability and inter-sensor consistency. The double-difference approach effectively mitigates uncertainties and biases inherent to CRTM simulations, establishing itself a robust mechanism for assessing inter-sensor consistency. In addition, we’ve observed that, except for M13, M12 always exhibits larger spatial variations of O-O BT differences with greater uncertainties compared to the other M TEBs. The influence of solar contribution through sea surface reflection on the Top of Atmosphere (TOA) radiance measurements during daytime in M12 should not be underestimated.

The success of deep learning (DL) in offline brain-computer interfaces (BCIs) has not yet translated into efficient online applications. This is due to three primary challenges. First, most DL solutions are designed for offline decoding, making the transition to online decoding complex and unclear. Second, the use of sliding windows in online decoding increases the computational complexity of DL training. Third, DL models typically require large amounts of training data, which are often scarce in BCI applications. To address these issues and enable real-time decoding, even across different subjects without calibration data, we first introduce a novel approach called real-time adaptive pooling (RAP). RAP modifies the pooling layers of existing offline DL models towards the online decoding requirements. Additionally, it significantly reduces the computational demand during training by jointly decoding consecutive windows. To reduce the amount of training data required, our approach leverages different levels of domain adaptation. We show how different settings enable different adaptation solutions. The results demonstrate that our approach is both powerful and can be calibration-free, providing a robust and practical solution for real-time BCI applications. These findings pave the way for the development of co-adaptive and highly efficient DL-based online BCI systems.

Additives for anaerobic digestion (AD) can play a significant role in optimising the process by increasing biogas production, stabilising the system and improving digestate quality. The role of additives largely boils down to: (i) enhancing direct interspecies electron transfer (DIET) between microbial communities, resulting in improved syntrophic interactions and methane production rates (e.g. biochar, magnetite and carbon nanotubes), (ii) adsorption of toxic substances that may inhibit microbial activity (e.g. activated carbon, zeolites), (iii) improving microbial activity and increasing process stability (e.g. cobalt, nickel, iron, selenium), (iv) maintaining optimal pH levels for microbial activity (e.g. magnesium oxide), (v) reducing inhibition (the aforementioned adsorbents and conductive substances), (vi) accelerating the decomposition of complex organic materials into simpler compounds that are more easily digested by microorganisms, thereby increasing the rate of hydrolysis (enzymes, including cellulases, proteases and lipases). Through the aforementioned action, additives can significantly affect AD performance. The function of these materials varies, from enhancing microbial activity to maintaining optimal conditions and protecting the system from inhibitors. The choice of additives should be carefully tailored to the specific needs and conditions of the digester to maximise benefits and ensure sustainability. In light of these considerations, this paper characterizes the most commonly used additives and their combinations based on a comprehensive review of recent scientific publications, including a report on the results of conducted studies. The publication features chapters that describe: carbon-based conductive materials, metal oxide nanomaterials, trace metal and biological additives, including enzymes and microorganisms. It concludes with a chapter summarising reports on various additives and discussing their indications for functional systems with determined properties. A notable advantage of this work is the updated literature data, clear summaries, and a substantive description of the performance of the additives discussed.

The evolution of human societal dynamics over the course of decades and centuries has led to an ever-increasingly complex digital modern life, one of the hallmark of which is the need for multi-tasking, inclusive of that with media. Is there an empirical mapping between this macroscopic (digital) evolution of the human societal dynamics (in terms of multi-tasking) and the microscopic evolution of the functional molecular units within cells (i.e., proteins) and their manifold cross-talks? Multi-tasking requires a certain degree of intrinsic or adapted flexibility which is as true at the bio-molecular level. Evolutionary diversification of structure-function relationship in proteins emphasizes the functional importance of intrinsically disordered entities (IDPs/IDRs) which are highly dynamic biological soft matters. Multi-functionality is favorably supported by their fluid-like shapes and other new malleable members of the protein family. The new wealth of knowledge coming from recent research on protein intrinsic disorder, fold-switching, moonlighting, hub proteins etc. has given us new insights into protein structure – functional relationship. This has led to a paradigm shift in protein science, clearly diverging from the traditional 'one structure – one function' model applicable to enzyme classes to a more complex understanding of protein functionality. This paradigm shift has caused scientists to delve deeper into the subject, exploring various evolutionary toys and tools to fit the cumulative multi-functional demands in higher organisms. This commentary covers the different evolutionary arsenals to achieve the growing multi-functionality and argues in favor of protein intrinsic disorder as probably the sharpest weapon of all.

Chronic stress, culminating in burnout, evolves gradually and is notoriously difficult to detect due to vague symptoms and individuals' hesitance to acknowledge their struggles. To expedite the identification and recognition of this condition, enabling timely psychological intervention, the integration of Artificial Intelligence (AI) is increasingly considered. This research does not assert the feasibility of an AI system managing all aspects of chronic stress's impact. However, it highlights the capability of current technology to detect stress indicators based on quantifiable data like Heart Rate (HR) and organizational climate dimensions. Although other physiological markers such as oximetry, skin galvanic response, and EKG have been explored, they have not shown reliable differentiation between stress and joy. Focused on the unique context of school principals in the Bedouin regions of Israel, this study investigates the interplay between HR, organizational climate, and stress levels. It introduces a novel application of a fuzzy logic tool that combines HR and organizational climate metrics to aid in stress diagnosis. This tool incorporates the psychologist's expertise to provide real-time data crucial for developing effective coping strategies. While the AI-supported fuzzy system does not replace professional psychological intervention, it significantly enhances the speed of condition identification and intervention planning, thus shortening the response time to stress-related issues in educational leadership within culturally specific settings. The application of such AI-driven tools is pivotal for sustaining the well-being and effectiveness of educational leaders, thereby supporting the broader goal of educational sustainability.