To accurately and non-destructively monitor the growth of spring wheat in the Hetao Irrigation Area, UAV remote sensing was employed during various fertility stages. Digital image indices from diverse fertilization treatments were calculated and compared with physiological indices to identify the most sensitive digital image indices corresponding to these indices. The study underscored the critical importance of the flowering stage in the growth of spring wheat, thus highlighting the necessity of focusing on this stage. This finding reiterated that the flowering stage was pivotal for spring wheat development in the Hetao Irrigation Area. Several digital image indices, such as GLA, R, G, INT, g, GRVI, MGRVI, RGBVI, EXG, and GRRI, exhibited a high frequency of significant correlations with physiological indices during the four primary reproductive stages of wheat. Consequently, these sensitive digital image indices during the flowering stage can more effectively characterize the physiological indices of spring wheat.

Surface water distribution extracted from remote sensing data has been used in water resource assessment, coastal management, and environmental change studies. Traditional manual methods for extracting water bodies cannot satisfy the requirements for mass processing of remote sensing data; therefore, accurate automated extraction of such water bodies has remained a challenge. The histogram bimodal method (HBM) is a frequently used objective tool for threshold selection in image segmentation. The threshold is determined by seeking twin peaks, and the valley values between them; however, automatically calculating the threshold is difficult because complex surfaces and image noise which lead to not perfect twin peaks (single or multiple peaks). We developed an operational automated water extraction method, the modified histogram bimodal method (MHBM). The MHBM defines the threshold range of water extraction through mass static data; therefore, it does not require the identification of twin histogram peaks. It then seeks the minimum values in the threshold range to achieve automated threshold. We calibrated the MHBM for many lakes in China using Landsat 8 Operational Land Imager (OLI) images, for which the relative error (RE) and squared correlation coefficient (R2) for threshold accuracy were found to be 2.1% and 0.96, respectively. The RE and root-mean-square error (RMSE) for the area accuracy of MHBM were 0.59% and 7.4 km2. The results show that the MHBM could easily be applied to mass time-series remote sensing data to calculate water thresholds within water index images and successfully extract the spatial distribution of large water bodies automatically.

Pore structure characterization and fractal analysis have great significance for understanding and evaluating tight limestone reservoirs. In this work, the pore structure of tight limestone is characterized combined with low-temperature nitrogen adsorption (LTNA) and low-field nuclear magnetic resonance (NMR), and the fractal dimension of tight limestone pore structure is discussed based on LTNA and NMR data. The results indicate that the pores of tight limestone have H3 and H4 types, the pore size distribution (PSD) of H3 type is a wave distribution ranging from 2 to 10 nm, and the PSD of H4 type is a unimodal distribution ranging from 2 to 10 nm. The transverse relaxation time (T2) spectrum of tight limestone shows single peak (DF), double peak (SF) and three peak (TF), and the T2 spectrums for micropores, mesopores and macropores range from 0.1 to 10 ms, from 10 to 100 ms, and greater than 100 ms, respectively. The LTNA fractal dimension of tight limestone (DL) ranges between 2.4446 and 2.7688, with an average of 2.5729, and the NMR fractal dimensions of micropores (DNMR1), mesopores (DNMR2) and macropores (DNMR3) are distributed between 0.3744 to 1.1293, 2.4263 to 2.9395 and 2.6582 to 2.9989, respectively. Moreover, there is no correlation between fractal dimension and permeability and porosity, a negative correlation between DL and average pore radius, a positive correlation between DL and specific surface area, and a positive correlation between DNMR2 and DNMR3 and micropore content, while DNMR2 and DNMR3 are negatively correlated with the content of mesopores and macropores.

A Cu₂ZnSnS₄ (CZTS) film with a thickness of approximately 1.5 μm was fabricated on a Mo-coated glass substrate by annealing a CZTS precursor fabricated from nanoparticle ink. The chemical states of the elements in the CZTS thin film in the depth direction were studied to identify the presence of secondary phases, which are detrimental to the performance of solar cells containing CZTS. X-ray diffraction was unable to detect any secondary phases in CZTS because of their small relative amount. Instead, X-ray photoelectron spectroscopy (XPS), which is highly sensitive to chemical state, was conducted at different depths in the CZTS film to further check the presence of secondary phases. XPS analysis revealed peaks shift consistent with the presence of secondary phases. For the CZTS film annealed in a S atmosphere at 575 °C for 3 h, the film surface consisted of a secondary-phase layer composed of CuS, ZnS, and SnSx (x=1 or 2) originating from the decomposition of CZTS. At depths below 80 nm, the film was pure CZTS. Formation of MoS2 at the CZTS–Mo interface was confirmed by XPS analysis of Mo and S.

Breast cancer remains a global health problem requiring effective diagnostic methods for early detection in order to achieve WHO’s ultimate goal of breast self-examination (BSE). A literature review indicates the urgency of improving diagnostic methods and identifies thermography as a promising, cost-effective, and non-invasive and adjunctive and complementary detection method. This research explores the potential of using machine learning (ML) techniques, specifically Bayesian Networks (BN) combined with Convolutional Neural Networks (CNN) to improve breast cancer diagnosis at early stages. Explainable artificial intelligence (XAI) aims to clarify the reasoning behind any output of artificial neural networks based models. The proposed integration adds interpretability of the diagnosis, which is particularly significant for a medical diagnosis. We have constructed two diagnostic expert models. In research model A, combining thermal images after XAI process together with medical records, an accuracy of 84.07% has been achieved, while model B, that includes also CNN prediction, achieved an accuracy of 90.93%. These results demonstrate the potential of XAI to transform breast cancer diagnosis, increasing accuracy and reducing the risk of misdiagnosis.

.Bacground: Sepsis remains a major health challenge worldwide, characterized by a dysre-gulated host response to infection, leading to high mortality and morbidity in intensive care units (ICUs). The Fibrosis 4 (FIB-4) index, originally developed to assess liver fibrosis in hepatitis C patients, has recently been explored for its potential prognostic value in sepsis patients. Method: This study retrospectively analyzed 309 sepsis patients admitted to the Internal Me-dicine and Anaesthesia ICUs between December 12, 2021, and December 15, 2023, to inves-tigate the relationship between FIB-4 levels, Acute Physiology and Chronic Health Evaluation (APACHE), and Sequential Organ Failure Assessment (SOFA) and clinical outcomes. Results: The study found that higher FIB-4 measurements were statistically significantly asso-ciated with increased 28-day mortality, with a cut-off value of 4.9 providing a sensitivity of 54.92% and specificity of 74.25%. Logistic regression analysis indicated that elevated Conclusion:FIB-4 levels were a significant predictor of early mortality, suggesting that the FIB-4 index could serve as a valuable prognostic tool in assessing the severity and prognosis of sepsis patients.

The treatment of the bulky Rind-based dibromosilanes, (Rind)2SiBr2 (2) [Rind = 1,1,7,7-tetra-R1-3,3,5,5-tetra-R2-s-hydrindacen-4-yl: EMind (a: R1 = Et, R2 = Me) and Eind (b: R1 = R2 = Et)], with two equivalents of tBuLi in Et2O at low temperatures resulted in the formation of blue solutions derived from the diarylsilylenes, (Rind)2Si: (3). Upon warming the solutions above −20 °C, the blue color gradually faded, accompanying the decomposition of 3 and yielding the cyclic hydrosilanes (4) via intramolecular C−H bond insertion at the Si(II) center. The molecular structures of the bulky Eind-based 3b and 4b were confirmed by X-ray crystallography. Thus, at −20 °C, blue crystals were formed (Crystal-A), which were identified as mixed crystals of 3b and 4b. Additionally, colorless crystals of 4b as a singular component were isolated (Crystal-B), whose structure was also determined by an X-ray diffraction analysis. Although the isolation of 3 was difficult due to their thermally-labile nature, their structural characteristics and electronic properties were discussed based on the experimental findings complemented by computational results. We also examined the hydrolysis of 3b to afford the silanol, (Eind)2SiH(OH) (5b).

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.

Plant leaf area index (LAI) is a key characteristic affecting field canopy microclimate. In addition to traditional professional measuring instruments, smartphone camera sensors have been used to measure plant LAI. However, when smartphone methods were used to measure conifer forest LAI, very different performances were obtained depending on whether the smartphone was held at the zenith angle or at a 57.5° angle. To validate further the potential of smartphone sensors for measuring conifer LAI and to find the limits of this method, this paper reports the results of a comparison of two smartphone methods with an LAI-2000 instrument. It is shown that both methods can be used to reveal the conifer leaf-growing trajectory. However, the method with the phone oriented vertically upwards always produced better consistency in magnitude with LAI-2000. The bias of the LAI between the smartphone method and the LAI-2000 instrument was explained with regard to four aspects that can affect LAI: gap fraction, leaf projection ratio, sensor field of view (FOV), and viewing zenith angle (VZA). It was concluded that large FOV and large VZA cause the 57.5° method to overestimate the gap fraction and hence underestimate conifer LAI, especially when tree height is greater than 2.0 m. For the vertically upward method, the bias caused by the overestimated gap fraction is compensated for by an underestimated leaf projection ratio.

The present study was performed to examine that transforming growth factor beta (TGF-β) in root-surrounding tissues on deciduous teeth during the physiological root resorption regulates the differentiation induction into odontoclast. We prepared root-surrounding tissues with (R) or without (N) physiological root resorption scraped off at three regions (R1-R3 or N1-N3) from the cervical area to the apical area of the tooth and measured both TGF-β and the tartrate-resistant acid phosphatase (TRAP) activities. The TGF-β activity level was increased in N1-N3, whereas the TRAP activity was increased in R2 and R3. In vitro experiments for RANKL-mediated osteoclast differentiation revealed that TGF-β in N1-N3 and R1-R3 enhanced the TRAP activity in RAW264 cells. A genetic study indicated that the mRNA level of TGF-β1 in N1 and N2 was significantly increased, and corresponded with that of osteoprotegerin (OPG). In contrast, the expression level of receptor activator of NF-κB ligand (RANKL) was increased in R2 and R3. Our findings suggest that TGF-β is closely related to the regulation of OPG induction and RANKL-mediated odontoclast differentiation depending on the timing of RANKL and OPG mRNA expression in the root-surrounding tissues of deciduous teeth during physiological root resorption.

A simple protocol for preparation of O-acylated enol form - (R/S)-ethyl-2-acetoxy-4-phenyl-4H-chromene-3-carboxylate 5 was presented. The compound was characterized by 1H-, 13C-and DEPT135 NMR spectra, including {1H,1H} COSY, {1H,13C} HSQC, {1H,13C} HMBC and 2D-NOESY spectra. The preferred regioselectivity for O-acylation of 3,4-dihydrocoumarin 5 in the presence of substituent in 4-th position in the chroman ring and accounting the steric hindrance of the ester group in 3-th place was confirmed.

Stress is a natural yet potentially harmful aspect of human life, necessitating effective management, particularly during overwhelming experiences. This paper presents a scoping review of personalized stress detection models using wearable technology. Employing the PRISMA-ScR framework for rigorous methodological structuring, we systematically analyzed literature from key databases including Scopus, IEEE Xplore, and PubMed. Our focus was on biosignals, AI methodologies, datasets, wearable devices, and real-world implementation challenges. The review presents an overview of stress and its biological mechanisms, details the methodology for the literature search, and synthesizes the findings. It shows that biosignals, especially EDA and PPG, are frequently utilized for stress detection and demonstrate potential reliability in multimodal settings. Evidence for a trend towards deep learning models was found, although the limited comparison with traditional methods calls for further research. Concerns arise regarding the representativeness of datasets and practical challenges in deploying wearable technologies, which include issues related to data quality and privacy. Future research should aim to develop comprehensive datasets and explore AI techniques that are not only accurate but also computationally efficient and user-centric, thereby closing the gap between theoretical models and practical applications to improve the effectiveness of stress detection systems in real scenarios.

Pueraria mirifica (PM), a plant whose dried and powdered tuberous roots are now widely used in rejuvenating preparations to promote youthfulness in both men and women, may have major estrogenic influence. In this study, we investigated modifying effects of PM at various doses on mammary and endometrial carcinogenesis in female Donryu rats. Firstly, PM administered to ovariectomized animals at doses of 0.03%, 0.3%, and 3% in a phytoestrogen-low diet for 2 weeks caused significant increase in uterus weight. Secondly, a 4 week PM application to non-operated rats at a dose of 3% after 7,12-dimethylbenz[a]anthracene (DMBA) initiation resulted in significant elevation of cell proliferation in the mammary glands. In a third experiment, postpubertal administration of 0.3% (200 mg/kg body weight (b.w.)/day) PM to 5-week-old non-operated animals for 36 weeks following initiation of mammary and endometrial carcinogenesis with DMBA and N-ethyl-N′-nitro-N-nitrosoguanidine (ENNG), respectively, resulted in significant increase of mammary adenocarcinoma incidence. A significant increase of endometrial atypical hyperplasia multiplicity was also observed. Furthermore, PM at doses of 0.3% and, more pronouncedly, of 1% induced dilatation, hemorrhage and inflammation of the uterine wall. In conclusion, postpubertal long-term PM administration to Donryu rats exerts estrogenic effects in the mammary gland and uterus, and at a dose of 200 mg/kg b.w./day was found to promote mammary carcinogenesis initiated by DMBA.

The 5-hydroxymethylfurfural (HMF) is a six-carbon heterocyclic organic compound with two substituents an aldehyde group and an alcohol group. It is formed from saccharides as a result of two reactions: the Maillard reaction and the caramelization of saccharides. It has both a negative and a positive effect on the human body, which makes it, even more, an attractive research object. The objective of the study was to determine HMF content in honey samples available on the Polish market using three varied methods and compare the results obtained. The methodology used included: two spectrophotometric methods (by White and Winkler) and the HMF determination method based on the LC-MS/MS technique developed uniquely for this study. The determined HMF content in all tested honey samples did not exceed the applicable standard. All results are within the scope of the European Council Directive 2001/110/EC of 20 December 2001, i.e. they do not exceed HMF content in honey, which is 40 mg/kg.

Nowadays, animal breeders look for effective innovations to care animal health. Use of probiotics, postbiotics, parabiotics and the other beneficial natural substances are dominated. Mundticin-like substance EM 41/3 is thermo-stable bacteriocin (postbiotic) with a broad antimicrobial spectrum. In this study its effect in broiler rabbits was assessed. Rabbits represent food-derived animal and they are also suitable animal model. The parameters tested were analyzed by validated methods. Administration of MLS EM 41/3 produced by non-autochthonous strain Enterococcus mundtii EM 41/3 lead to significant increase of parameter immunity-phagocytic activity, while it did not influence microbiota composition. It also did not influence blood biochemistry and no oxidative stress was noted. Moreover, higher growth parameters were noted and also stimulated hydrolytic activity. Finally, no negative influence on rabbit meat quality was noted. Postbiotic MLS EM 41/3 administration in feeding strategy seems to be contributing fact for rabbit health status maintaining.

Deflectometry is a key component in the precise measurement of specular (mirrored) surfaces, however traditional methods often lack an end-to-end approach that performs 3D reconstruction in a single shot with high accuracy and generalizes across different free-form surfaces. This paper introduces a novel deep neural network (DNN)-based approach for end-to-end 3D reconstruction of free-form specular surfaces using single-shot deflectometry. Our proposed network, VUDNet, innovatively combines discriminative and generative components to accurately interpret orthogonal fringe patterns and generate high-fidelity 3D surface reconstructions. By leveraging a hybrid architecture integrating a Variational Autoencoder (VAE) and a modified U-Net, VUDNet excels in both depth estimation and detail refinement, achieving superior performance in challenging environments. Extensive data simulation using Blender, leading to a dataset which we are making available, ensures robust training and enables the network to generalize across diverse scenarios. Experimental results demonstrate the strong performance of VUDNet, setting a new standard for 3D surface reconstruction. The implementation of VUDNet is available on GitHub at https://github.com/hadious/VUDNet.

Zwitterionic polymers emerge as very useful materials for applications in antifouling surfaces. The properties of these polymers can be tuned by variation of the chemical structure of the corresponding monomer. In this study, two zwitterionic ammonium sulfonate monomers bearing an hydroxyl function and a styrenic polymerizable groups were prepared in two steps. The two monomers were obtained using 4-vinylbenzyl chloride as the key precursor. The zwitterionic monomers were characterized by 1H NMR, 13C NMR, IR spectroscopy and high-resolution mass spectrometry (HRMS). These two monomers enable the preparation of novel zwitterionic polymers with enhanced hydrophilicity due to the presence of a hydroxyl group.

With the exponential growth of internet usage and mobile devices, the volume of user behavior data has surged, offering significant opportunities and challenges for businesses. This paper explores user behavior analysis in the context of big data, highlighting its pivotal role in driving innovation and strategic decision-making. We begin by discussing the importance of user behavior data and reviewing technological advancements such as machine learning, natural language processing, and data mining that enable deep insights into user behavior. Through a case study on linguistic analysis in livestreaming e-commerce, we demonstrate how text-mining techniques can correlate linguistic characteristics with sales performance, providing actionable marketing insights. We further explore the broader business applications, including personalized recommendations, user portraits, product design, and strategic decision-making. Technical and ethical challenges, such as data integration, privacy concerns, and algorithmic bias, are also addressed. The paper concludes with a discussion on future trends, such as AI and ML advancements, edge computing, IoT proliferation, and ethical AI frameworks, poised to shape user behavior analysis. Our findings underscore the transformative potential of user behavior data in enhancing business operations and user experiences, providing recommendations for future research focused on real-time analytics, privacy-preserving techniques, and ethical implications of data-driven decisions.

Climate change is increasing air temperatures and altering the precipitation and hydrological re-gime on a global scale. Challenges arise when assessing the impacts of climate change on the local scale for water resources management proposes, especially for low mountain headwater catch-ments that not only serve as important water towers for local communities, but also have distinct hydrological characteristics. Until now, no low flow or hydrological drought studies have been carried out on the Lauter River. This study is unique in that it compares the Lauter River, a transboundary Rhine tributary with a nearby station on the River Rhine just below its confluence at the French-German border. The Lauter catchment is a mostly natural, forested catchment, however, its water course has been influenced by past and present cultural activities. Climate change disturbances cascade through the hydrologic regime right down to the local scale. As we are expecting more low flow events, the decrease in water availability could cause conflicts be-tween different water user groups in the Lauter catchment. However, the choice of different methods for identifying low flow periods may cause confusion for local water resources manag-ers. Using flow rate time series of the Lauter River between 1956 and 2022 we compare for the first time three low flow identification methods: the variable threshold method (VT), the fixed threshold method (FT), and the Standardized Streamflow Index (SSI). Similar analyses are applied and compared to the adjacent Maxau station on the Rhine River for the same time period. This study aims at 1) interpreting the differences amongst the various low flow identification methods and 2) revealing the differences in low flow characteristics of the Lauter catchment compared to the Rhine River. It appears that the FT reacts faster to direct climate or anthropogenic impacts whereas VT is more sensitive to indirect factors such as decreasing subsurface flow which is typical for small headwater catchments, such as the Lauter where flow dynamics react faster to flow disturbances. Abnormally low flow during the early Spring in tributaries such as the Lauter can help predict low flow conditions of the Rhine River during the following half year and espe-cially for the summer. The results could facilitate Early Warning of hydrological droughts and drought management for water users in the Lauter catchment and further downstream along some of the Rhine.

Toxicity mechanisms of per- and polyfluoroalkyl substances (PFAS), a chemical class present in diverse ecosystems, are not well characterized in aquatic species. Perfluorooctanesulfonamide (PFOSA, C8H2F17NO2S) is a common precursor of perfluorooctane sulfonic acid (PFOS), a long-chain PFAS. Here, we assessed sub-lethal endpoints related to development, oxidative stress, locomotor activity, and gene expression in zebrafish embryos and larvae following continuous exposure to PFOSA beginning at 6 hours post fertilization (hpf). PFOSA decreased survival in fish treated with 1 µg/L PFOSA, however the effect was modest relative to the controls (difference of 10%). Exposure up to 10 µg/L PFOSA did not affect hatch rate, nor did it induce reactive oxygen species in 7-day-old larvae fish. However, locomotor activity of larval fish treated with 100 µg/L PFOSA was reduced relative to the solvent control. Transcripts related to oxidative stress response and apoptosis were measured and BCL2 associated X, apoptosis regulator (bax), cytochrome c, somatic (cycs), catalase (cat), superoxide dismutase 2 (sod2) were induced with high concentrations of PFOSA. Genes related to neurotoxicity were also measured and transcript levels of acetylcholinesterase (ache), elav-like RNA binding protein 3 (elavl3), growth associated protein 43 (gap43), synapsin II (syn2a), and tubulin 3 (tubb3) levels were all increased in larval fish with higher PFOSA exposure. This study contributes to our knowledge regarding the relative toxicity of PFOSA to fish and contributes data to future risk assessment strategies of these concerning, persistent environmental pollutants.

A mathematical procedure enabling the transformation of an arbitrary tessellation of a surface into a bi-colored complete graph is introduced. Polygons constituting the tessellation are represented by vertices of the graphs. Vertices of the graphs are connected by two kinds of links/edges, namely, by a green link, when polygons have the same number of sides, and by a red link, when the polygons have a different number of sides. This procedure gives rise to a semi-transitive, complete, bi-colored Ramsey graph. The Ramsey number was established as R_trans (3,3)=5. Shannon entropies of the tessellation and graphs are introduced. Ramsey graphs emerging from random Voronoi and Poisson Line tessellations were investigated. The limits ζ=lim┬(N→∞)⁡〖N_g/N_r 〗, where N is the total number of green and red seeds, N_g and N_r, were found ζ=0.272±0.001 (Voronoi) and ζ= 0.47±0.02 (Poisson Line). The Shannon Entropy for the random Voronoi tessellation was calculated as S= 1.690±0.001 and for the Poisson line tessellation as S =1.265±0.015.

Bridges enable communications and transportation of goods nationally and internationally, underpinning economic and social activities, thus they are pylons of our prosperity and mobility. Bridges worldwide are constantly subjected to physical wear, ageing, deterioration, hazards, environmental influences, and increased loading. Loss of performance and functionality of bridge structures would have a crucial impact on overall infrastructural resilience and would cause significant negative economic and social consequences. Monitoring their behaviour for different loading conditions relies on accurate estimations of the stress-strain state of various critical components and remaining capacities. These activities are of high importance for better planning and lifespan prolongation, that is the extension of their service life and prevention of unforeseen collapses, in line with sustainability principles of well-informed maintenance. In many cases, access to the structure is limited or even impossible, which causes the need for the deployment of remote and contactless methods. One such innovative technique, which has recently attracted attention in scientific and practical applications is the digital image correlation (DIC). DIC is a contactless approach applicable for obtaining the full field of strains and deformations of full-scale real structures. Although the DIC approach has been widely used in world engineering practice for monitoring bridges and has proved to be a reliable and accurate method, there is a lack of systematic integral review on previous practical applications, revealing limitations and perspectives. This is the main motivation and novelty of this study, which will describe selected case studies, in which DIC was used on real full-scale bridge structures and propose improvements for the method.

Exopolysaccharide (EPS)-producing bacteria were isolated from the intestines of freshwater fish as prebiotics. Among the isolates, Bacillus sp. P1 was the potent EPS producer with a high EPS production and was then identified as Bacillus subtilis P1 based on 16S rRNA and biochemical characteristics. The produced microbial EPS was characterized its functional groups by FTIR, showing a 90.20% correlation to inulin while the EPS molecular weight was approximately 105 Da with a high PDI (>1.5). Moreover, the EPS from B. subtilis P1 was assessed for prebiotic properties by growing probiotic bacteria and the significant cell growth occurred in sugar-free TSB with 0.5% EPS (p<0.05). EPS exhibited approximately 55.37% DPPH radical scavenging at 20 mg/mL and inhibited certain fish pathogens at 400 μg/mL (10-13 mm inhibition zone). EPS cytotoxicity was confirmed (<1% hemolysis) prior to use as immunobiotics in Nile Tilapia (Oreochromis niloticus) diet supplementation. Nile tilapia growth and immune response were monitored after feeding with control (C): Basal Diet (BD) and treatment (T): BD + EPS 5 g/kg. In the T-group, WG, %SGR, and ADG significantly increased compared to control (p<0.05) by week 4th, with no negative effects on blood chemistry parameters. Lysozyme activity and respiratory burst activity in T-groups were significantly higher than control (p<0.05), with a 50% RLP survival rate of Nile Tilapia challenged with Aeromonas veronii in the T-group within two weeks. On the other hand, for the proximate analysis revealed higher protein content in the T-group. These findings suggest that EPS from B. subtilis P1 in fish diets efficiently supports fish growth and stimulates innate immune responses in Nile Tilapia, thus showing potential as the immunobiotics for aquaculture.

Esterquats constitute a unique group of quaternary ammonium salts (QASs) that contain an ester bond in the structure of the cation. Despite the numerous advantages of this class of compounds, only two mini-reviews discuss the subject of esterquats: the first one (2007) briefly summarizes their types, synthesis, and structural elements required for a beneficial environmental profile and only briefly covers their applications whereas the second one only reviews the stability of selected betaine-type esterquats in aqueous solutions. The rationale for writing this review is to critically reevaluate the relevant literature and provide others with a “state-of-the-art” snapshot of choline-type esterquats and betaine-type esterquats. Hence, the first part of this survey thoroughly summarizes the most important scientific reports demonstrating effective synthesis routes leading to the formation of both types of esterquats. In the second section, the susceptibility of esterquats to hydrolysis is explained, and the influence of various factors, such as the pH, the degree of salinity or the temperature of the solution, was subjected to thorough analysis that includes quantitative components. The next two sections refer to various aspects associated with the ecotoxicity of esterquats. Consequently, their biodegradation and toxic effects on microorganisms are extensively analyzed as crucial factors that can affect their commercialization. Then, the reported applications of esterquats are briefly discussed, including functionalization of macromolecules, such as cotton fabric as well as their successful utilization on a commercial scale. The last section demonstrates the most essential conclusions and reported drawbacks that allow us to elucidate future recommendations regarding the development of these promising chemicals.

Healthcare professionals constantly face situations that reflect ecosystems’ degradation. These can negatively affect their mental health. Research suggests that hope and resilience can play an important role in this scenario, since they are related to/predict mental health in highly heterogeneous samples (considering geography, age, profession, health, etc.). In this context, the aims of the present study are: to characterize and explore the relationship between hope, resilient coping and mental health of Portuguese healthcare professionals. Using Google Forms, 276 healthcare professionals answered to the GHQ-28, the (adult) Trait Hope Scale, and the Brief Resilient Coping Scale (cross-sectional study). The minimum and maximum possible scores were reached, with the exception of the maximum score of GHQ-28-Total. Regarding Hope, 19.6% scored below the midpoint (M=43.46, SD=11.97); 29.3% revealed low resilience (M=14.93, SD=4.05); and the average of 4 of the 5 Mental Health scores (exception: Severe Depression) indicates the probability of a psychiatric case. Hope correlated with Social Dysfunction and GHQ-28-Total; resilient coping proved to be a (weak) predictor of 4 of the 5 GHQ-28 indicators (exception: Severe depression). The results support the need to promote the sample's mental health, hope and resilient coping. They also suggest that stimulating resilient coping may contribute to improving healthcare professionals’ mental health.

This study aims to propose a method for constructing basic digital twin data in South Korea by adhering to international standards and utilizing publicly available data. Specifically, the study focuses on designing and proposing a digital twin data model for buildings, which are the most applicable among the basic digital twin data. To achieve this, the first section provides essential background information, introduces concepts and requirements related to digital twin basic data, and offers a brief overview of CityGML. The second section explains the methodology and data used in this study. The third section presents the main findings: the selection of public data (buildings) for constructing digital twin basic data, the mapping process using CityGML, and the creation of UML diagrams. The fourth section discusses these findings. Finally, the conclusion and suggestions for future research are provided. This approach enhances the accuracy of building-related digital twin data and supports the use of digital twin services in both public and private sectors by enabling various spatial analyses.

Cybercrime Threat Intelligence (CTI) allows us to change our actions from reactive to proactive in the fight against threat actors, and to make more informed, data-driven security decisions. Thus this study proposes the practical implementation of CTI in the Arab world. This study focuses on strengthening CTI by integrating Indicators of Compromise (IoCs) and collecting realistic security alerts from honeypot systems and open source intelligence. The collected data is stored in the Malware Information Sharing Platform (MISP), an open source platform that allows users to create and share IoCs with other organizations while staying informed about new threats. It features an intuitive interface for data analysis and threat identification, facilitating sharing, storage, and correlation of IoCs. Therefore, we leveraged MISP to generate IoCs based on the collected data and analyzed the results to identify potential cyber threats. The implemented platform aims to provide organizations with actionable information to prevent, detect and respond to cybercrime. This study presents a practical approach to strengthening CTI in the Arab world and provides an opportunity to strengthen the region’s security posture.

Objective Image Quality Assessment (IQA) measures are playing an increasingly important role in the evaluation of digital image quality. New IQA indices are expected to be strongly correlated with subjective observer evaluations expressed by MOS/DMOS scores. One such recently proposed index is the SuperPixel-based SIMilarity (SPSIM) index, which uses superpixel patches instead of the rectangular pixel grid.The authors in this paper have been proposed three modifications of SPSIM index. For this purpose, the color space used by SPSIM was changed and the way SPSIM determines similarity maps was modified using methods derived from the algorithm for computing the MDSI index. The third modification was a combination of the first two. These three new quality indices were used in the assessment process. The experimental results obtained on many color images from five image databases demonstrated the advantages of the proposed SPSIM modifications.

Following the detection of highly pathogenic avian influenza (HPAI) virus in countries bordering Kenya to the west, we conducted surveillance among domestic and wild birds along the shores of Lake Victoria. In addition, between 2018 and 2020, we conducted surveillance among poultry and poultry workers in live bird markets and among wild migratory birds in various lakes that are resting sites during migration to assess introduction and circulation of avian influenza viruses in these populations. We tested 7,464 specimens (oropharyngeal and cloacal swabs) from poultry and 6,531 fresh fecal specimens from wild birds for influenza A viruses by real-time RT-PCR. Influenza was detected in 3.9% (n=292) of specimens collected from poultry and 0.2% (n=10) of fecal specimens from wild birds. On hemagglutinin subtyping, most of the influenza A positives from poultry (274/292, 93.8%) were H9. Of 34 H9 specimens randomly selected for further subtyping, all were H9N2. On phylogenetic analysis, these viruses were genetically similar to other H9 viruses detected in East Africa. Only two of the ten influenza A positive specimens from the wild bird fecal specimens were successfully subtyped; sequencing analysis of one specimen collected in 2018 was identified as low pathogenicity avian influenza H5N2 virus of the Eurasian lineage and the second specimen, collected in 2020, was subtyped as H11. A total of 18 OP/NS swabs from poultry workers with ARI (12%) were collected; none were positive for influenza A virus. We observed significant circulation of H9N2 influenza viruses in poultry in live bird markets in Kenya. During the same period, low pathogenic H5N2 virus was detected in a fecal specimen collected in a site hosting a variety of migratory and resident birds. Although HPAI H5N8 was not detected in this survey, these results highlight the potential for introduction and establishment of highly pathogenic avian influenza viruses in poultry populations and the associated risk of spillover to human populations.

The general desire of the world's population is to eat as healthily as possible, an aspect that has a direct impact on the technological complex aimed at animals raising (proper breeding systems, microclimate factors with implications for the welfare of animals, etc.), as well as on the quality of the finished products that will enter the food supply. Therefore, the authors set out to study how the growth system influences the health status of rainbow trout raised in different systems. The observed experimental factors did not undergo significant changes. However, differences were generated by the applied growth system. For the amount of glucose in the blood, no statistically significant differences were identified between the groups. In contrast, comparison of the amount of glycogen led to obtaining very distinctly significant statistical fluctuations (the mean for P-si was 2.314±0.638 and for P-i 1.980±0.822). Growth hormone varied between 0.504±0.46 in the case of P-si and 0.694±0.22 ng/ml for P-i. The values obtained for cortisol showed a significant influence of stress factors on the studied trout. The general conclusion of the present study is that various technological factors influence the health status of livestock.

Acute Respiratory Distress Syndrome (ARDS) is a severe lung condition with a high mortality rate for which there are no effective therapeutics. The failure of the alveolar-capillary barrier, composed of lung endothelial (EC) and alveolar epithelial (AEC) cells, is a critical factor leading to excessive inflammation and edema characteristics of acute lung injury (ALI). Phosphodiesterases (PDE) are enzymes well-recognized for their roles in regulating endothelial permeability and inflammation. Although PDE inhibitors are used as therapeutics for inflammatory diseases like COPD (chronic obstructive pulmonary disease), their efficacy in treating ARDS has not yet been established. In this study, we investigate the effects of ensifentrine, an FDA-approved novel dual PDE 3/4 inhibitor, on lung endothelial and epithelial dysfunction caused by methicillin-resistant S. aureus (MRSA), a pathogen involved in bacterial ARDS. Human primary lung endothelial cells and alveolar epithelial cell lines (A549 and immortalized AEC) were treated with heat-killed MRSA, and their responses were assessed in the presence or absence of ensifentrine. Ensifentrine given either pre- or post exposure attenuated MRSA-induced increased lung endothelial permeability. VE-cadherin junctions, which serve to stabilize the EC barrier, were disrupted by MRSA; however, ensifentrine effectively prevented this disruption. Pre-treatment with ensifentrine protected against MRSA-induced EC pro-inflammatory signaling by inhibiting the expression of VCAM-1, ICAM-1, and by reducing the IL-6 release. In AEC, MRSA caused upregulation of ICAM-1, activation of NF-kB and production of IL-8, all of which were inhibited by ensifentrine. These results indicate that dual inhibition of phosphodiesterases 3 and 4 by ensifentrine is barrier protective and attenuates MRSA-induced inflammation in both lung endothelial and epithelial cells. The PDE3/4 inhibitor ensifentrine may represent a promising novel strategy for the treatment of ALI.

Vilsmeier–Haack reaction of (E)-1-(1-(3,5-difluorophenyl)ethylidene)-2-phenylhydrazine (1) using dimethyl formamide in excess of phosphorous oxychloride by conventional method, resulted in the synthesis of title compound 3-(3,5-difluorophenyl)-1-phenyl-1H-pyrazole-4- carbaldehyde (2) in good yield and high purity. Structure characterization of the novel title compound was done by IR, ¹H NMR, ¹³C NMR and mass spectral analysis.

The idea of developing a robot controlled by iris movement to assist physically disabled individuals is indeed innovative and has the potential to significantly improve their quality of life. This technology can empower individuals with limited mobility and enhance their ability to interact with their environment. Disability of movement has huge impact on the life of physically disabled people. Therefore, there is need to develop a robot which can be controlled using iris movement. The main idea of this work revolves around iris recognition from an eye image, specifically identifying the centroid of the iris. The centroid's position is then utilized to issue commands to control the robot. This innovative approach leverages iris movement as a means of communication and control, offering a potential breakthrough in assisting individuals with physical disabilities. The proposed method aims to improve the precision and effectiveness of iris recognition by incorporating advanced segmentation techniques and fuzzy clustering methods. The fast gradient filters using a fuzzy inference system (FIS) is employed to separate the iris from its surroundings. Then, the bald eagle search (BES) algorithm is employed to locate and isolate the iris region. Subsequently, the Fuzzy KNN algorithm is applied for the matching process. This combined methodology aims to improve the overall performance of iris recognition systems by leveraging advanced segmentation, search, and classification techniques. The results of the proposed model are validated using True Success Rate (TSR) and compared to other existing models. The results highlight the effectiveness of the proposed method for the 400 tested images representing 40 people.

The rationalization of single molecule magnets’ (SMMs) magnetic properties by quantum mechanical approaches represents a major task in the field of the Molecular Magnetism. The fundamental interpretative key of molecular magnetism is the phenomenological Spin Hamiltonian and the understanding of the role of its different terms by electronic structure calculations is expected to steer the rational design of new and more performing SMMs. This paper deals with the ab initio calculation of isotropic and anisotropic exchange contributions in the Fe(III) dimer [Fe₂(OCH₃)₂(dbm)₄]. This system represents the building block of one of the most studied Single Molecule Magnets ([Fe₄RC(CH₂O)₃)₂(dpm)₆] where R can be an aliphatic chain or a phenyl group just to name the most common functionalization groups) and its relatively reduced size allows the use of a high computational level of theory. Calculations were performed using CASSCF and NEVPT2 approaches on the X-ray geometry as assessment of the computational protocol, which has then be used to evinced the importance of the outer coordination shell nature through organic ligand modelization. Magneto-structural correlations as function of internal degrees of freedom for isotropic and anisotropic exchange contributions are also presented, outlining for the first time the extremely rapidly changing nature of the anisotropic exchange coupling.

Lipid droplet (LD) accumulation in hepatocytes is one of the major symptoms associated with fatty liver disease. Mitochondria play a key role in catabolizing fatty acids for energy production through β-oxidation. The interplay between mitochondria and LD assumes a crucial role in lipid metabolism, while it is obscure how mitochondrial morphology affects systemic lipid metabolism in the liver. We previously reported that cilnidipine, an already existing anti-hypertensive drug, has potency to prevent pathological mitochondrial fission by inhibiting protein-protein interaction between dynamin-related protein 1 (Drp1) and filamin, an actin-binding protein. Here, we found that cilnidipine and its new dihydropyridine (DHP) derivative, 1,4-DHP, which lacks Ca2+ channel blocking action of cilnidipine, prevent the palmitic ac-id-induced Drp1-filamin interaction, LD accumulation and cytotoxicity of human hepatic HepG2 cells. Cilnidipine and 1,4-DHP also suppressed the LD accumulation accompanied by reducing mitochondrial contact with LD in obese model and high fat diet-fed mouse livers. These results propose that targeting Drp1-filimin interaction becomes a new strategy for the prevention or treatment of fatty liver disease.

(1) Background: The purpose of this study is to evaluate the full arch scan accuracy (precision and trueness) of nine digital intra-oral scanners and four lab scanners. Previous studies have compared the accuracy of some intra-oral scanners, but as this is a field of quickly developing technologies, a more up-to-date study was needed to assess the capabilities of currently available models.; (2) Methods: The present in vitro study compared nine different intraoral scanners (Omnicam 4.6; Omnicam 5.1; Primescan; CS 3600; Trios 3; Trios 4; Runyes; i500 and DL206) as well as four lab light scanners (Einscan SE; 300e; E2 and Ineos X5) to investigate the accuracy of each scanner by examining the overall trueness and precision. Ten aligned and cut scans from each of the intra-oral and lab scanners in the in vitro study were brought into CloudCompare. A comparison was made with the master STL using the CloudCompare 3D analysis best-fit algorithm. The results were recorded along with individual standard deviation and a colorimetric map of the deviation across the surface of the STL mesh; a comparison was made to the master STL, quantified at specific points. ; (3) Results: In the present study, the Primescan had the best overall trueness (17.3 ± 4.9). Followed by (in order of increasing deviation) the Trios 4 (20.8 ± 6.2), i500 (25.2 ± 7.3), CS3600 (26.9 ± 15.9), Trios 3 (27.7 ± 6.8), Runyes (47.2 ± 5.4), Omnicam 5.1 (55.1 ± 9.5), Omnicam 4.6 (57.5 ± 3.2) and Launca DL206 (58.5 ± 22.0). Regarding the lab light scanners, the Ineos X5 had the best overall trueness with (0.0 ± 1.9). Followed by (in order of increasing deviation) the 3Shape E2 (3.6 ± 2.2), Up3D 300E (12.8 ± 2.7), and Einscan SE (14.9 ± 9.5); (4) Conclusions: This study confirms that all current generations of intra-oral digital scanners can capture a reliable, reproducible full arch scan in dentate patients. Out of the intra-oral scanners tested, no scanner produced results significantly similar in trueness to the Ineos X5. However, the Primescan was the only one to be statistically of a similar level of trueness to the 3Shape E2 lab scanner. All scanners in the study had mean trueness of under 60-micron deviation. While this study can compare the scanning accuracy of this sample in a dentate arch, the scanning of a fully edentulous arch is more challenging. The accuracy of these scanners in edentulous cases should be examined in further studies.

Thresholding is considered as a statistical-decision making theory which can lessen the average error incurred in allocating pixels to two or more groups. The traditional Bayes decision rule can be applied with the prior knowledge of the Probability Density Function (PDF) of each class. It is surmised that a threshold resulting in the best class separation is the optimal one. In this paper, Otsu’s thresholding for image segmentation has been implemented. The well-known Otsu’s method is to learn a threshold that can maximize the between-class variance or equivalently make light of the within-class variance of the entire image. At first, a color image of a tree is taken. After that, the image is transformed into a grayscale image. Then in the first part, two-level thresholding is conducted, and later on, three-level thresholding is also applied. Again, two-level thresholding, as well as three level thresholding, are also applied to some other images. Finally, the comparison is made between two level thresholding and three level thresholding

A conjugate compound 5, (E)-6-hydroxy-2-oxo-2H-chromen-7-yl 3-(4-hydroxy-3-methoxyphenyl)acrylate, of 6,7-hydroxycoumarin (esculetin) 3 and (E)-3-(4-hydroxy-3-methoxyphenyl)- acrylic acid (ferulic acid) 1 was prepared in 61% yield by the esterification reaction of a protected ferulic acid 2a with esculetin 3 in the presence of triethylamine in dichloromethane for 3 h, followed by deprotection using 3M HCl. The structure of compound 5 was confirmed by 1H, 13C NMR spectroscopy, mass-spectrometry and elemental analysis.

Affective computing has emerged as a prominent technology in recent years, driven by the increasing demand for machines capable of understanding and interpreting emotions. The COVID-19 pandemic has intensified this need by reducing physical interactions and increasing reliance on digital communication, which often involves complex emotional exchanges. To enhance social interactions by identifying and addressing negative emotions, an interactive emotion-perception system based on multi-sensing interfaces and affective computing has been proposed. Integration of gesture, voice, and brainwave sensors is employed to provide users with a rich sensory experience and immersive environment. Design principles and system development processes are derived from the affinity diagram method and prototype development approach. The proposed system was constructed, and experiments were conducted with 60 participants. Interpersonal interaction scenarios were explored to understand emotional transmission mechanisms, aiming to enhance user satisfaction and positive interpersonal experiences. Data were collected through indirect observation and questionnaires, followed by evaluation and statistical analysis. Findings include: (1) The emotion-perception system enhances positive interpersonal interactions by identifying 36 distinct emotions, with overall user emotions being positive but exhibiting notable individual variations; (2) The system effectively stimulates positive emotions and strengthens interpersonal interactions, with affective computing technology fostering enjoyable emotional resonance and high-quality relationships; (3) A positive correlation was observed between semantic analysis and brainwave metrics of relaxation and concentration, showing increased concentration and enhanced positive emotional interactions during message transitions; and (4) The multi-sensing and affective computing technology provides a pleasant and relaxing emotional journey, with interactions shifting from enthusiasm to comfort, demonstrating the system’s capacity for a satisfying and enjoyable experience.

Abstract Introduction: In the last thirty years, brain neuromodulation techniques have been used as an alternative to pharmacological treatment of neurological disorders. Parkinson's disease (PD) is a neurodegenerative disorder leading to bradykinesia, rest tremor, postural changes, and non-motor symptoms such as depression, anxiety, sleep disorders, pain, and cognitive decline that compromises executive functions (EFs), responsible for the orderly execution of behaviors and tasks of daily life and intentional and directed actions. To this date, a few studies with transcranial direct current stimulation (tDCS) have shown beneficial effects in PD patients concerning specific motor and non-motor symptoms, targeting the motor cortex and/or prefrontal regions. Objective: The main objective of this study was to evaluate the effects of left prefrontal tDCS across a broad spectrum of motor and non-motor symptoms of PD using established validated scales. Method: Single-blind randomized clinical trial with 18 volunteers with PD, aged between 45 and 80 years (66.1 ± 9.65), who met inclusion and exclusion criteria. Participants were submitted to assessments of motor and non-motor functions employing psychometric scales and tests to evaluate EFs and were randomly divided into two groups: control (sham stimulation) and experimental (active stimulation). All participants were involved in three separate tDCS sessions. The anode was positioned over the left dorsolateral prefrontal cortex and the cathode over the right supraorbital region, with a direct current intensity of 2mA, lasting 20 minutes. At the end of the three sessions, all participants were reassessed. Results: Significant effects of tDCS on non-motor functions were observed for cognition (verbal fluency of actions, clock copy test, appointment by visual confrontation, and verbal memory with immediate free recall) and subjective assessment of sleep quality (overall restlessness and discomfort in the arms and legs at night, leg and arm cramps at night and distressing dreams). There was also an improvement in the rate of errors and successes for congruent and incongruent stimuli of the Stroop Test. The beneficial effects on motor function were decreased rigidity, improved gait, and greater agility in the finger-tapping test. Conclusion: Three tDCS sessions showed positive results for participants with PD, producing significant improvements in various motor and non-motor functions, including sleep quality, cognition, and EFs. Additionally, the present results indicate that tDCS neuromodulation of the left dorsolateral prefrontal cortex region is feasible, safe, and provides significant objective benefits for PD patients.

In this work, a novel single crystal (3-(4-chlorophenyl)-4,5-dihydroisoxazol-5-yl)methyl benzenesulfonate has been synthetized via a one-pot sequential strategy under sonication. A single crystal X-ray diffraction analysis was reported. In the crystal, a layer structure is generated by C—H···O and C—H···N hydrogen bonds. A Hirshfeld surface analysis indicates that the most important contributions to the crystal packing are from H···H (28.9%), H···O/O···H (26.7%) and H···C/C···H (15.8%) interactions. The optimized structure calculated using density functional theory at the B3LYP/6–311 G+(d,p) level is compared with the experimentally determined structure in the solid state. The calculated highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy gap is 4.6548 eV.

The effectiveness of combining strength-specific training with endurance training to enhance the performance of endurance runners remains uncertain. This study aimed to analyze effect of practising maximum-strength (MAX), explosive strength (EXP) or both combined (COMB) on seven runners’ performance-indicators: vertical jump (VJ), one-repetition-maximum-squat (1RM), peak-velocity/peak-running-speed (PV), lactate-threshold (over incremental-test-protocols, LT), middle-distance-time-trial (TT), maximum-oxygen-consumption (VO2max), running-economy (RE). Systematic-review (Scopus, Wed of Science, Sports Discuss, PubMed) with meta-analysis was conducted following PRISMA standards. Inclusion-criteria (PICOS) were: recreational or well-trained athletes aged 18-45 performing concurrent-training ≥five-week. Used search-terms were related to different types of strength/endurance, participants’ age, sport-modality. 20 manuscripts were selected, quality-assessed with PEDro. MAX training is more effective than EXP, COMB in improving VJ, 1RM, PV, while COMB is more effective than MAX, EXP to enhance TT. MAX is more effective than EXP in improving LT. Concurrent-workouts do not provide additional benefits to VO2max. It is unknown which strength-modality (MAX, EXP or COMB) is more effective in improving RE. Concurrent-training is more effective than single-mode-endurance-training for enhancing specific performance-variables in adult-endurance-runners. Middle-, long-distance-runners may consider incorporating MAX-training to target specific goals, e.g., improving VJ, 1RM, LT, PV while utilizing COMB-training to enhance TT. Certain variables may benefit from EXP. New randomized-controlled-trials are required to confirm these findings.

The goal of the study presented in this article is to investigate interconnection issues of the Intelligent Electrical Networks (IENs) with the existing ones, as well as the deployment of the appropriate load forecasting demand techniques. The concept of IENs has recently emerged as a potential solution to the global energy problem, due to the more flexible dispersed production units that are involved. In the same context, additional issues, such as the reduction of production-transfer-distribution costs as well as power losses are analyzed as well, via the penetration of Renewable Energy Sources (RESs). To this end, it is necessary to use appropriate advanced infrastructure, where in combination with modern telecommunication networks will make possible the full exploitation of IENs. Moreover, to make the electricity system more efficient, to avoid voltage and frequency imbalance issues and to implement optimal production and consumption planning, the Electric Load Forecast (ELF) is a vital process and plays a key role in the planning as well as in the management of electricity systems. Therefore, recent state of the art approaches in ELF methods are discussed as well.

The chemical stability of tetramethylammonium (TMA) head groups, both with and without the presence of a choline chloride and ethylene glycol-based deep eutectic solvent (DES), was studied using Density Functional Theory (DFT) calculations and ab initio Molecular Dynamics (MD) simulations. DFT calculations of transition state energetics (ΔEreaction, ΔGreaction, ΔEactivation, and ΔGactivation) for key degradation mechanisms, ylide formation (YF) and nucleophilic substitution (SN2), suggested that the presence of DES enhances the stability of the TMA head groups compared to systems without DES. Ab initio MD simulations across hydration levels (HLs) 1 to 5 indicated that without DES, YF dominates at lower HLs, while SN2 does not occur. In contrast, both mechanisms are suppressed in the presence of DES. Temperature also plays a role: without DES, YF dominates at 298 K, while SN2 becomes prominent at 320 K and 350 K. With DES, both degradation mechanisms are inhibited. These findings suggest DES could improve the chemical stability of TMA head groups in anion exchange membranes.

This paper proposes a heuristic approach for managing complex construction projects. The tool incorporates with Primavera P6 EPPM and Synchro 4D, enabling proactively clash detection and resolution of spatial conflicts during concurrent tasks. Additionally, it performs resource verification for sufficient allocation before task initiation. This integrated approach facilitates the generation of conflict-free and feasible construction schedules. By adhering to project constraints and seamlessly integrating with existing industry tools, the proposed solution offers a comprehensive and robust approach for construction project management. This constitutes to our knowledge the first dynamic digital twin for the delivery of a complex project.

Gas and power networks are tightly coupled and interact with each other due to physically interconnected facilities. In an integrated gas and power network, a contingency observed in one system may cause iterative cascading failures, resulting in network wide disruptions. Therefore, understanding the impacts of the interactions in both systems is crucial for governments, system operators, regulators and operational planners, particularly, to ensure security of supply for the overall energy system. Although simulation has been widely used in the assessment of gas systems as well as power systems, there is a significant gap in simulation models that are able to address the coupling of both systems. In this paper, a simulation framework that models and simulates the gas and power network in an integrated manner is proposed. The framework consist of a transient model for the gas system and a steady state model for the power system based on AC-Optimal Power Flow. The gas and power system model are coupled through an interface which uses the coupling equations to establish the data exchange and coordination between the individual models. The bidirectional interlink between both systems considered in this studies are the fuel gas offtake of gas fired power plants for power generation and the power supply to LNG terminals and electric drivers installed in gas compressor stations and underground gas storage facilities. The simulation framework is implemented into an innovative simulation tool named SAInt (Scenario Analysis Interface for Energy Systems) and the capabilities of the tool are demonstrated by performing a contingency analysis for a real world example. Results indicate how a disruption triggered in one system propagates to the other system and affects the operation of critical facilities. In addition, the studies show the importance of using transient gas models for security of supply studies instead of successions of steady state models, where the time evolution of the line pack is not captured correctly.

Artificial intelligence provides a new research concept for digital image processing. However, at present, artificial intelligence is rarely introduced into the teaching of digital image processing in colleges and universities, and there are problems such as obsolete teaching content, single teaching methods, and simple course experiments, which affect the teaching effect and are not conducive to the cultivation of comprehensive and innovative talents. Digital image processing technology brings more possibilities to communication engineering and makes communication more convenient. For example, video calls and photo transmission make people's communication methods in daily life more and more diversified. Time and space limitations allow people to meet online, creating more communication possibilities. However, there are still many problems and methods worthy of in-depth exploration. Therefore, this paper has a comprehensive understanding and mastery of the traditional and deep learning methods of digital image processing to improve the relevant project practice and scientific research exploration ability and refer for similar research conclusions.

Synthesis of new series of 1-phenyl-3-(4-(pyridin-3-yl) phenyl) urea derivatives and its in vitro antiproliferative activities against NCI-60 human cancer cell lines of nine different cancer types are described. Fourteen compounds 5a-n have been synthesized with three different hydrogen bondable moieties (4-hydroxylmethylpiperidinyl and trimethoxyphenyloxy and 4-hydroxyethylpiperazine) attached to the core structure 1-phenyl-3-(4-(pyridin-3-yl) phenyl) urea. Different substituents with different π and σ values were added on the terminal phenyl group. Compounds with 4-hydroxymethylpiperidine moiety showed higher mean percentage inhibition values over the 60-cell line panel at 10-µM concentration. They showed broad-spectrum antiproliferative activity over many cell lines of different cancer types. For instance, compound 5a elicited some lethal rather than inhibition effects on SK-MEL-5 melanoma cell line, 786-0, A498, RXF 393 renal cancer cell lines, and MDA-MB-468 breast cancer cell line by 146.1, 108.7, 136.2, 134.8, 116.6 % at 10 µM, respectively. Compounds 5a-e exhibited superior antiproliferative activity than Paclitaxel and Gefitinib against the most sensitive cell lines. Two compounds, 5a and 5d showed promising mean growth inhibitions and thus were further tested at five-dose testing mode to determine their IC₅₀ values. The data revealed that 5a and 5d urea compounds are the most active derivatives with significant efficacies and superior potencies than Paclitaxel in 21 different cancer cell lines, belonging particularly to renal cancer and melanoma cell lines. Moreover, 5a and 5d had superior potencies than Gefitinib in 38 and 34 cancer cell lines, respectively; belonging particularly to colon cancer, breast cancer and melanoma cell lines.

Many dedicated designs for real-time operations provide functionality on fixed-sized operators, but where speed, scalability, and flexibility are required, extensive research is demanded. Dedicated designs can provide real-time processing for many applications. This paper presents an FPGA-based design of a general image processor. The proposed design is based on a fixed-point representation of binary numbers. The proposed design provides a mechanism to manage matrices on-chip along with matrix arithmetic. The matrices are represented with simple identifiers and microinstruction that assist in the computation of many operations which are useful for solving complex problems. The design was successfully implemented and tested using VHDL language. The proposed design is an efficient architecture as a standalone processor with all embedding computational resources necessary for an embedded image processing application.

Image fusion is a useful tool for producing a high-resolution multispectral image to be used for land use and land cover mapping. In this study, we use nine pansharpening algorithms namely Color Normalized (CN), Gram-Schmidt (GS), Hyperspherical Color Space (HCS), High Pass Filter (HPF), Nearest-Neighbor Diffusion (NND), Principal Component Analysis (PCA), Resolution Merge (RM), Stationary Wavelet Transform (SWT), and Wavelet Resolution Merge (WRM) to fusion Worldview-3 multispectral Bands and panchromatic band. In spectral and spatial fidelity, several image quality metrics are used to evaluate the performance of pansharpening algorithms. The SWT and PCA algorithms showed better results compared to other pansharpening algorithms while GS and CN algorithms showed the worst results for the original image fusion. The effect of fusion on each band was separately investigated and according to the calculations, we found that the CoastalBlue band and the Blue band showed the best result and the NIR-1 band and NIR-2 band show the worst result for the original image fusion. In the end, we conclude that the choice of fusion method depends on the requirement of remote sensing application.

Childhood obesity has become a global epidemic, with significant increases in prevalence over recent decades. While excessive calorie consumption and physical inactivity are known factors, emerging research highlights the role of Endocrine-Disrupting Chemicals (EDCs), particularly obesogens, in obesity's pathogenesis. This review explores the historical context of the environmental obesogens hypothesis, their sources, mechanism of action, impact on prenatal and postnatal development and epigenetics. Additionally, it discusses the long-term consequences of childhood obesity and proposes prevention strategies that will mitigate the negative health effects. Obesogens were found to disrupt hormonal balance and metabolic processes through various mechanisms such as altering gene expression, hormonal interference, and inflammation. Especially significant, was exposure during critical windows of development which correlates with increased risk of obesity in childhood or adolescence. Long-term effects of childhood obesity include chronic health conditions and psychosocial issues. A comprehensive approach is necessary to address childhood obesity, encompassing genetic, environmental, and lifestyle factors. Prevention strategies should focus on reducing obesogens exposure, promoting healthy lifestyles, and implementing regulatory policies. Future research should investigate obesogens-diet interactions, microbiome impacts, and combined obesogens effects. Long-term human studies are also crucial for validating findings from animal models and allow for inform decision-making to combat the obesity pandemic.

This study explores novel techniques to improve the detection accuracy of skarn iron deposits using advanced image processing methodologies. Leveraging the capabilities of Aster image, Band Ratio (BR) images, and Principal Component Analysis (PCA), alongside the power of 3D Convolutional Neural Networks (3D-CNN), the research aims to enhance the precision and efficiency of ore detection in complex geological environments. The proposed method employs a specific 3D-CNN architecture, accepting input as a 7×7×C image patch, where C represents the combined number of selected Aster image bands, Principal Component (PC) bands, and computed BR images. To evaluate the accuracy of the proposed method, five distinct image bands combinations, including the proposed bands combination, were tested and evaluated based on the overall accuracy (OA), average accuracy (AA), and kappa coefficient. The results demonstrated that while the incorporation of BR images alongside Aster bands initially seemed promising, it introduced significant confusion in certain classifications, leading to unexpected mis-classification rates. Surprisingly, utilizing solely Aster bands as input parameters yielded higher accuracy rates (OA= 93.13%, AA = 91.96%, Kappa = 90.91%) compared to scenarios involving the integration with band ratios (OA = 87.02%, AA = 79.15, and Kappa = 82.60%) or the integration of BR images to PC bands (OA= 87.78%, AA = 82.39%, Kappa = 83.81%). However, the amalgamation of Aster bands with selected PC bands showed slight improvements in accuracy (OA= 94.65%, AA = 92.93%, Kappa = 93.45%), although challenges in accurately classifying certain features persisted. Ultimately, the proposed combination of Aster bands, PC bands, and BR images (proposed bands combination) presented the most visually appealing and statistically accurate results (OA = 96.95%, AA= 94.87%, and Kappa = 95.93%), effectively addressing misclassifications observed in the other combinations. These findings underscore the synergistic contributions of each of the Aster bands, PC bands, and BR images, with Aster bands proving pivotal for optimal skarn classification, PC bands enhancing intrusions classification accuracy, and BR images strengthening wall rock classification accuracy. In conclusion, the proposed combination of input image bands emerges as a robust and comprehensive methodology, demonstrating unparalleled accuracy in the remote sensing detection of skarn iron minerals.

As technology and multimedia production have advanced, there has been a significant rise in attacks on digital media, resulting in duplicated, fraudulent, and altered data and the infringement of copyright laws. This paper presents a robust and secure digital image watermarking technique that has been implemented in the spatial domain and exploits the erratic and chaotic behaviour of the powerful elementary cellular automata rule-30. The crucial characteristics of the watermarking system, i.e., imperceptibility, capacity, and robustness, have been perfectly balanced by the suggested blind watermarking technique. In this approach, prior to embedding, the grayscale watermark image is downsized to its two Most Significant Bits (MSBs). Then, the 2-MSBs watermark is encrypted using an ECA rule-30 so as to level up the security attribute of the system. Then, the host image is scrambled using ECA rule-30 to distribute the watermark pixels throughout the host image and thus achieve the highest robustness against geometrical attacks. Finally, the encrypted watermark data is embedded into the scrambled host image using the ECA rule-30-based embedding key. The proposed method performs better in terms of imperceptibility, capacity, and robustness when compared to several systems with similar competencies. The simulation's findings demonstrate strong imperceptibility as evaluated by the Peak Signal-to-Noise Ratio (PSNR), which has an average value of 58.3735 dB and a high payload. The experimental outcomes, observed across a diverse range of standardized attack scenarios, unequivocally establish the ascendancy of the proposed algorithm over competing methodologies in the realm of image watermarking.

2-(5,6-diphenyl-1,2,4-triazin-3-yl)pyridinium dichloroiodate (I) (1) was synthesized by reacting 3-(2-pyridyl)-5,6-diphenyl-1,2,4-triazine with ICl in dichloromethane solution. The structural characterization of 1 by SC-XRD analysis was accompanied by elemental analysis, FT-IR, and FT-Raman spectroscopy measurements.

Two-dimensional ultrasound (2D US) is commonly used in clinical settings for its cost-effectiveness and non-invasiveness, but it is limited by spatial orientation and operator dependency. Three-dimensional ultrasound (3D US) overcomes these limitations by adding a third dimension and enhancing integration with other imaging modalities. Advances in deep learning (DL) have further propelled the viability of freehand image-based 3D reconstruction, broadening clinical applications in intraoperative and point-of-care (POC) settings. This review evaluates state-of-the-art freehand 3D US reconstruction methods that eliminate the need for external tracking devices, focusing on experimental setups, data acquisition strategies, and reconstruction methodologies. PubMed, Scopus, and IEEE Xplore were searched for studies since 2014 following PRISMA guidelines, excluding those using additional imaging or tracking systems other than inertial measurement units (IMUs). Fourteen eligible studies were analyzed, showing a shift from traditional speckle decorrelation towards DL-based methods, particularly Convolutional Neural Networks (CNNs). Variability in datasets and evaluation methods hindered a comprehensive quantitative comparison, but notable accuracy improvements were observed with IMUs and integration of contextual and temporal information within CNNs. These advancements enhance freehand 3D US reconstruction feasibility, though variability limits definitive conclusions about the most effective methods. Future research should focus on improving precision in complex trajectories and adaptability across clinical scenarios.

This paper presents a study of the 2D roughness profiles on a flat surface generated on a steel work piece, by ball nose end milling, with linear equidistant tool paths (pick-intervals). The milled surface exploration with a surface roughness tester (on pick and feed directions) produces usually 2D roughness profiles having periodical evolutions. These evolutions can be seen as time-dependent signals, describable as a sum of sinusoidal components (the wavelength of each component being regarded as a period). In order to detect a well approximated description of these components, two appropriate signal processing techniques are used in this work: first technique provides a direct mathematical (analytical) description and is based on computer aided curve (signal) fitting (more accurate), the second technique (providing an indirect and incomplete description) is based on the spectrum generated by fast Fourier transform (less accurate). This study can be seen as a way for a better understanding the interaction between the tool and the work piece or to achieve a mathematical description of the machined surface microgeometry in terms of roughness (e. g. its description being a collection of closely spaced 2D roughness profiles).

The aim of this study was to examine and understand the physiological and biochemical components of the blood and vitreous humor of goldfish (Carassius auratus). Blood was drawn from the arterial (static) line and vitreous humour was extracted using a syringe from healthy goldfish. An automatic haematology analyzer was used to detect 17 physiological indicators, including white blood cells and red blood cells. At the same time, 20 biochemical indexes, including albumin, calcium, and glutamic pyruvic transaminase, were found in the automatic biochemical analyzer. Experiments were also conducted on the effect of blood and vitreous fluid collection on the subsequent survival rate of the celestial goldfish. The findings demonstrated that there were values for 17 physiological indicators in the blood of the goldfish, including red blood cells (2.19 × 1012), white blood cells (62.21 × 109), hemoglobin (138.25g/L), and no eosinophils or basophils. However, no data for 17 physiological indications of vitreous humor were discovered. The quantities of total protein, glutamic oxaloacetic transaminase, and potassium in the vitreous humor were substantially lower than those in the serum (P < 0.01) according to the results of 20 biochemical indexes in the serum and vitreous humor of goldfish. Alkaline phosphatase, chlorine, and creatinine between vitreous humor and serum did not differ significantly(P > 0.05). The levels of glutamic pyruvic transaminase and γ-glutamyl transpeptide in vitreous humor were significantly higher than those in serum (P < 0.01). This experiment provides basic datas for the healthy culture of goldfish and the perfection of hematology in goldfish, and the results will be convenient for the further study of development and formation of celestial eye traits.

We investigated the sleep-wake rhythm of nursery school children with the aim of supporting their health and balanced mental/physical development. We analyzed 4881 children from infant to 6 years of age, using 2-week sleep tables recorded by their guardians. The table contains sleep onset times, wake times, nighttime/daytime sleep duration, and the differences in these between weekdays and weekends. The total sleep decrement of children with age is due to a decrease in daytime sleep only, and nighttime sleep duration remains almost unchanged at about 10 hours, and is therefore called the nighttime basic sleep duration (NBSD). While sleep onset time stabilizes at around 9:30 pm by the age of 2, wake-up times to be before 7 am, resulted in weekdays sleep insufficiency. This lack of sleep is compensated by long naps on weekdays and by catching up sleep on weekends morning, which may be the background to future social-jet-lag. Guardians are encouraged to know their children's exact NBSD and set an ap-propriate bedtime to maintain it on weekdays, which prevent sleep dept and form a daily rhythm of waking up at the same time both on weekdays/weekends. These conditions are believed to maintain balance in mental/physical development and school-social-adaptation.

With the increasing concern of environment, the energy-efficiency scheduling of manufacturing industry is becoming urgent and popular. In turning processes, both spindle speed and processing time can affect the final energy consumption and thus the spindle speed and scheduling scheme need to be optimized simultaneously. Since the turning workshop can be regarded as the flexible flow shop, this paper formulates a mixed integer linear programming model for energy-efficient scheduling of flexible flow shop. Accordingly, a new decoding method is developed by considering of the optimization of spindle speed and scheduling scheme simultaneously, and an estimation of distribution algorithm adopting the new decoding method is proposed to solve large-size problems. The parameters of this algorithm are determined by statistical technique with a simplified practical case. In order to validate the proposed method, a case from practical factories is studied, in which the makespan can be shortened by 25.22%, and the consumed energy can be saved by 5.48%. These results demonstrate the effectiveness of the proposed mathematical model and algorithm.

Analytical instruments used for scientific research and education are often expensive. This limitation poses a challenge for schools and educators in low-income countries to provide practical science training to students. To address this problem, the improvisation of analytical instruments and instructional materials has emerged as a viable solution. By incorporating practical experiments into science education, students have the opportunity to engage directly with scientific instruments, conduct investigations, and collect data. This hands-on approach fosters a deeper understanding of scientific principles, enhances critical thinking skills, and cultivates scientific mindsets among students. Herein, we review and discuss, step-by-step, the approach of using a smartphone for quantitative analysis, referred to as smartphone digital image colorimetry (SDIC). SDIC, with its practicality and accessibility, utilizes smartphone digital cameras for image acquisition and the freely available image processing programs, such as ImageJ and smartphone Apps, for image quantification. Using SDIC can enable students in low-income countries to develop essential scientific skills, including critical thinking, data interpretation, and an evidence-based approach to scientific research. By leveraging the ubiquity and affordability of smartphone technology, SDIC offers an accessible and cost-effective approach to practical science education, bridging the gap between theory and practice in resource-constrained settings.

Chronic obstructive pulmonary disease (COPD) patients are particularly susceptible to respiratory infections like influenza, which exacerbate symptoms and increase healthcare utilization. While smoking cessation and influenza vaccination are recommended preventive measures, their combined impact on healthcare resource utilization is underexplored. The Charlson Comorbidity Index (CCI) assesses comorbidity burden in COPD patients and may influence healthcare outcomes. We conducted a retrospective analysis of 357 COPD patients, evaluating smoking cessation success over one year and influenza vaccination receipt, stratifying patients by CCI scores. Healthcare utilization outcomes included emergency room visits, hospitalizations, and medical expenses. Results showed that 51.82% of patients quit smoking and 59.66% received influenza vaccination, with higher comorbidity prevalence in advanced COPD stages (p=0.002). Both smoking cessation and influenza vaccination independently correlated with decreased emergency room visits, hospital admissions, days, and costs. Patients who both quit smoking and received influenza vaccination exhibited the lowest healthcare utilization rates. In conclusion, smoking cessation and influenza vaccination significantly reduce healthcare resource utilization in COPD patients, with the combination yielding synergistic benefits, particularly in those with lower CCI scores. Integrating these interventions and comorbidity management in COPD strategies is essential for optimizing patient outcomes and healthcare efficiency.

Objective: The article presents a methodology for the design and validation of two assessment instruments: one focused on competency development and the other on knowledge acquisition on sustainability, aimed at nutrition students from a Mexican university. Method: These instruments were adapted using the triangulation methodology and administered to a group of 45 students after receiving an intervention based on the systemic approach to nutrition. Following the intervention, an analysis of the results was carried out to validate the reliability and validity of the instruments, and to evaluate the impact of the intervention compared to a control group. Results: The findings indicated that the dimensions used in the instrument are suitable for evaluating competency development, additionally, it was found that both instruments are reliable tools for assessing competency development and knowledge in sustainability among nutrition students, concluding that they have the potential to be used for evaluating education in sustainability. Furthermore, the results reveal that the application of an intervention based on the systemic approach to nutrition has a positive impact on the development and acquisition of sustainable nutrition education. The students who received the intervention achieved significantly higher scores in all sustainability sub-competencies (W=100, p=0.019), as well as in the total percentage of sustainable knowledge (w=53, n=0.01). Conclusions: The results of this study pave the way for the operationalization of competency and knowledge dimensions in food sustainability, which can contribute to the evaluation of sustainability education and its impact on the training of nutrition students committed to sustainability.

Chiral oxazolidine moiety is an important core in asymmetric synthesis due to its ability to participate in various stereoselective chemical reactions and because forms a part of more complex important active compounds. Therefore, in this letter we report a convenient diastereoselective and practical strategy for the synthesis of chiral methyl 2-((4R)-3-acryloyl-4-phenyloxazolidin-2-yl)acetates starting from (R)-(–)-2-phenylglycinol and methyl propiolate which were obtained via simple two chemical and stereoselective reactions.

Assessment of extreme events and climate change on reservoir inflow is important for water and power stressed countries. Projected climate is subject to uncertainties related to climate change scenarios and Global Circulation Models (GCMs’). Extreme climatic events will increase with the rise in temperature as mentioned in the AR5 of the IPCC. This paper discusses the consequences of climate change that include extreme events on discharge. Historical climatic and gauging data were collected from different stations within a watershed. The observed flow data was used for calibration and validation of SWAT model. Downscaling was performed on future GCMs’ temperature and precipitation data, and plausible extreme events were generated. Corrected climatic data was applied to project the influence of climate change. Results showed a large uncertainty in discharge using different GCMs’ and different emissions scenarios. The annual tendency of the GCMs’ is bi-vocal: six GCMs’ projected a rise in annual flow, while one GCM projected a decrease in flow. The change in average seasonal flow is more as compared to annual variations. Changes in winter and spring discharge are mostly positive, even with the decrease in precipitation. The changes in flows are generally negative for summer and autumn due to early snowmelt from an increase in temperature. The change in average seasonal flows under RCPs’ 4.5 and 8.5 are projected to vary from -29.1 to 130.7% and -49.4 to 171%, respectively. In the medium range (RCP 4.5) impact scenario, the uncertainty range of average runoff is relatively low. While in the high range (RCP 8.5) impact scenario, this range is significantly larger. RCP 8.5 covered a wide range of uncertainties, while RCP 4.5 covered a short range of possibilities. These outcomes suggest that it is important to consider the influence of climate change on water resources to frame appropriate guidelines for planning and management.

Despite the need for a sustainable energy resource, bioenergy gained its spotlight in the 2000s. Sugarcane is a significant crop in terms of sugar and energy capacity, and it can be an alternative energy source to mitigate the effects of climate change. Bioenergy production from sugarcane in Brazil is one of the most efficient. This production lends a centrality to biofuels' importance in confronting the effects of climate change. The present article reviews the Brazilian history of this crop as a biofuel source. We highlight the historical changes related to scientific, technological, industrial, and environmental advances since the beginning of the 20th century. We describe how creating governmental institutes and disseminating scientific knowledge strengthened public policies that led Brazil to occupy leadership positions in producing, distributing, and using bioenergy throughout the country. The compiled data shows the improvement and the new approaches needed in ethanol sugarcane. We developed a bibliometric analysis to evaluate Brazilian science's contribution to this process compared to other countries. Brazil's history of science and investments in sugarcane biofuel development for transportation may be divided into two phases: ethanol-only and flex-fuel cars. A third phase is starting, which is the ethanol-to-hydrogen era.

This study investigates the factors influencing consumers' intentions to purchase overseas travel packages via social media in Thailand, with a focus on consumer trust and perceived risks. A mixed-methods approach was employed, combining qualitative and quantitative research techniques. In the qualitative phase, the Fuzzy Set Delphi Method was applied to gather expert consensus from 19 participants. For the quantitative phase, a survey was conducted among 600 individuals who had experience purchasing overseas travel packages on social media in Thailand. First-order and second-order confirmatory factor analyses were used to examine the relationships between eight identified factors: social media influencer, e-WOM, trust, perceived risk, brand image, rating review, personal attitude, and destination image. The results indicate that trust and perceived risk are the most significant factors affecting purchase intention. Trust plays a crucial role in customers' decision-making processes, while perceived risk influences their tendency to avoid purchasing packages if the risk is perceived as high. The insights gained from this study contribute to the understanding of factors influencing purchase intentions for overseas travel packages through social media in Thailand, providing valuable information for travel agencies to develop effective marketing strategies.

This paper shows the results of applying high-resolution Unmanned Aircraft System (UAS) photogrammetric surveying on a large landslide. A real case study, where permanently installing GCPs could be complex, where natural shaped and formed land pose severe limitations in deploying ground targets with optimal geometric configuration. We analysed performances in terms of survey accuracy obtained by performing photogrammetric surveys through the Zenmuse P1 DJI optical camera and Phantom 4 Pro 2. In combination with DJI Matrice 300 UAS, the P1 camera allows direct georeferencing through GNSS observations in RTK mode. Photogrammetric surveys, performed through different georeferencing methods, have been compared. Several targets have been permanently installed on the ground over the maximum vegetation height to guarantee long-lasting reference over the years in the area, which is characterised by a diffuse short vegetation coverage. Multitemporal UAS surveys have been then compared using Digital Image Correlation (DIC) algorithms, and deformation maps have been produced. Afterwards, DIC results were compared with observations made by the GNSS ground-based permanent receivers resulting in a standard deviation of 0,077 m. Through results analysis, good accordance between ground-based GNSS observations and DIC analysis on the photogrammetric surveys have been identified for the same time span. To conclude, this type of landslide presents a moderate deformation speed; in such a case, effective deformations monitoring could be achieved using pseudo-direct georeferencing, which permitted a 0.24 m accuracy on the whole tested area.

This paper has two major parts. In the first part histogram equalization for the image enhancement was implemented without using the built-in function in MATLAB. Here, at first, a color image of a rat was chosen and the image was transformed into a grayscale image. After this conversion, histogram equalization was implemented on the grayscale image. Later on, in the same image for each RGB channel, histogram equalization was implemented to observe the effect of histogram equalization on each channel. In the end, the histogram equalization was implemented to this specific color image of a rat. In the second part, for the grayscale image in part 1, the desired histogram of another colored image of a rat was introduced and histogram specification was implemented on the original colored image.

This paper has two major parts. In the first part histogram equalization for the image enhancement was implemented without using the built-in function in MATLAB. Here, at first, a color image of a rat was chosen and the image was transformed into a grayscale image. After this conversion, histogram equalization was implemented on the grayscale image. Later on, in the same image for each RGB channel, histogram equalization was implemented to observe the effect of histogram equalization on each channel. In the end, the histogram equalization was implemented to this specific color image of a rat. In the second part, for the grayscale image in part 1, the desired histogram of another colored image of a rat was introduced and histogram specification was implemented on the original colored image.

Considering animal welfare, the free-range laying hen farming model is increasingly gaining attention. However, in some countries, large-scale farming still relies on the cage-rearing model, making the focus on the welfare of caged laying hens equally important. To evaluate the health status of caged laying hens, a dataset comprising visible light and thermal infrared images was established for analyses, including morphological, thermographic, comb, and behavioural as-sessments, enabling a comprehensive evaluation of the hens' health, behaviour, and population counts. To address the issue of insufficient data samples in the health detection process for indi-vidual and group hens, a dataset named BClayinghens was constructed containing 61,133 images of visible light and thermal infrared images. The BClayinghens dataset was completed using three types of devices: smartphones, visible light cameras, and infrared thermal cameras. All thermal infrared images correspond to visible light images and have achieved positional alignment through coordinate correction. Additionally, the visible light images were annotated with chicken head labels, obtaining 63,693 chicken head labels, which can be directly used for training deep learning models for chicken head object detection and combined with corresponding thermal infrared data to analyze the temperature of the chicken heads. To enable the constructed deep-learning object detection and recognition models to adapt to different breeding environ-ments, various data enhancement methods such as rotation, shearing, colour enhancement, and noise addition were used for image processing. The BClayinghens dataset is important for ap-plying visible light images and corresponding thermal infrared images in the health detection, behavioural analysis, and counting of caged laying hens under large-scale farming.

Background and Objectives: To investigate the relationship between the SII index and the micro-macro complications and death that develop in the first year and the following 3 years, following the calculation of the index in type 2 diabetic retinopathy patients. Materials and Methods: This study was performed retrospectively with 523 type 2 diabetic retinopathy patients admitted to the endocrinology outpatient clinic of our hospital between January 2019 and December 2019. The demographic, and clinical characteristics of the patients were evaluated using descriptive statistics. Shapiro-Wilk test was used for the normal distribution of quantitative data; Mann-Whitney U, Mc Nemar Chi-square, and Cochran's Q tests were used to analyze SII values between groups and complication rates by years; sensitivity and specificity were determined by ROC analysis and considered statistically significant with a significance level of P

Here we report the preparation and characterization of a novel biomimetic toothpaste containing morphogenetically active amorphous polyphosphate (polyP) microparticles enriched with retinyl acetate (“a-polyP/RA-MP”). The spherical microparticles (average size, 550±120 nm), prepared by co-precipitating sodium-polyP with calcium chloride and supplemented with retinyl acetate, were incorporated into a basis toothpaste at a final concentration of 1% or 10%. The paste containing “a-polyP/RA-MP” significantly increased the growth of human mesenchymal stem cells (MSC), compared to a commercial toothpaste which acts rather inhibitory and the paste without polyP and retinyl acetate. qRT-PCR experiments revealed that the retinoid causes an induction of the expression of the MSC marker genes for osteoblast differentiation encoding collagen type I and alkaline phosphatase. On the other hand, the polyP ingredient, supplied as Zn-polyP microparticles (“Zn-a-polyP-MP”) strongly inhibited the growth of the cariogenic bacterium Streptococcus mutans. We demonstrate that the amorphous polyP-containing toothpaste, enriched with retinyl acetate, efficiently repairs both cracks/fissures and carious lesions in the tooth enamel, and reseals dentinal tubules, already after a 5 d treatment (brushing) of teeth twice daily for 5 min as examined by SEM and quantitative EDX analysis. The stability of the occlusion of dentin cracks even turned out to resist against short high power sonication treatment. Our results demonstrate that the novel toothpaste prepared here, containing amorphous polyP and retinyl acetate, is particularly suitable for prevention/repair of (cariogenic) damages of tooth enamel/dentin and for treatment of dental hypersensitivity.

The optimal structural design is imperative to minimize material consumption and reduce the environmental impact of construction. Given the complexity in the formulation of structural design problems, the process of optimization is commonly performed using artificial intelligence (AI) global optimization, such as Genetic Algorithm (GA). However, the integration of AI-based optimization together with visual programming (VP) in building information modeling (BIM) projects warrants further investigation. This study proposes a workflow by combining structure analysis, VP, BIM, and GA to optimize trusses. The methodology encompasses several steps including: (i) generation of parametric trusses in Dynamo VP; (ii) performing the finite element modeling (FEM) using Robot Structural Analysis (RSA); (iii) retrieving and evaluating the FEM results interchangeably between Dynamo and RSA; (iv) finding the best solution using GA; and (v) importing the optimized model into Revit, enabling the user to perform simulations and engineering analysis, such as life cycle assessment (LCA) and quantity surveying. The feasibility of the proposed workflow was tested on benchmark problems and compared with open literature. The outcomes of this study offer insight into the opportunities and limitations of combining VP, GA, FEA, and BIM for structural optimization applications, particularly to enhance structural efficiency and sustainability in construction.

In this work, optimal consumption and investment strategies are derived for risk-averse investors under the 4/2 stochastic volatility class of models. We work under an expected utility (EUT) framework and consider a Constant Relative Risk Aversion (CRRA) investor, who might also be ambiguity-averse. The corresponding Hamilton-Jacobi-Bellman (HJB) and HJB-Isaacs (HJBI) equations are solved in closed-form for a subset of the parametric space and under some restrictions on the portfolio setting, for complete markets. Conditions for proper changes of measure and well defined solutions are provided. These are the first analytical solutions for the 4/2 stochastic volatility model and the embedded 3/2 model for the type of excess returns established in .

The reaction of 2-bromo-1-(5-methyl-1-(4-nitrophenyl)-1H-1,2,3-triazol-4-yl)ethan-1-one (1) with 4-amino-5-((2,4-dichlorophenoxy)methyl)-4H-1,2,4-triazole-3-thiol (2) in absolute ethanol in the presence of triethyl amine as catalyst gave 2-((4-amino-5-((2,4-dichlorophenoxy)methyl)-4H-1,2,4-triazol-3-yl)thio)-1-(5-methyl-1-(4-nitrophenyl)-1H-1,2,3-triazol-4-yl)ethan-1-one (3) in 73% yield. The structure of the title heterocycle (3) was confirmed by X-ray single crystal diffraction and spectral analyses (NMR and IR).

Introduction: The association of gene variants for interleukin 6(IL-6) (rs1800796), interleukin 10 (IL-10) (rs1800896) and tumor necrosis factorα (TNFα (rs1800629) with the occurrence of spontaneous preterm birth (PTB) was investigated to determine whether these genetic variants are a risk factor. Materials and methods: 199 blood samples from pregnant women who had given birth prematurely and 200 control blood samples were analyzed to determine single nucleotide polymor-phisms (SNPs) of genes for IL-6 (rs1800796), IL-10 (rs1800896) and TNFα (rs1800629). The control samples were samples from pregnant women with term delivery. Isolation of DNA was per-formed on mini-spin columns according to the manufacturer's protocol. The quality and purity of the isolated DNA were tested using a Qubit 3 fluorometer. Genotyping was performed with an ABI PRISM 7500 SDS using TaqMan SNP genotyping assays. The genotypes obtained were an-alyzed using the 7500 Software v2.3 package. Results: Carriers of the A/A genotype for the rs1800629 of the TNFα gene have 4.81 times greater chance of late-onset PTB compared to carriers of the G/G and A/G genotypes in the recessive inheritance model. The presence of the G/G genotype in the recessive inheritance model compared with the G/A and A/A genotypes for the rs1800896 of the IL-10 gene represents a potentially protective factor, with mothers in the term-birth group having an almost 2-fold lower odds of PTB in general and an almost 10-fold lower odds of early PTB. On the other hand, carriers of the A/G genotype of rs1800896 have a 1.54-fold higher chance of preterm birth in general and a 1.6-fold higher chance of late preterm birth in the super dominant inheritance model compared to the A/A and G/G genotypes in the group of mothers with PTB. In this study, no association was found between PTB and rs1800796 of the IL-6 gene. Conclusion: rs1800629 in mothers was associated with PTB. rs1800896 shows a potentially protective effect for the occurrence of PTB in this study. No association was found between PTB and rs1800796.

The information about the BCoV receptors, co/receptors that might be involved in the BCoV entry to the host cells is inconceivable. In addition to that, the roles of other host cell proteases, such as Furin and TMPRSS2, have not yet been well studied during BCoV replication. The current study’s main objectives are to identify some novel BCoV receptors and host cell proteases that might fine-tune the BCoV replication, tissue tropism, and molecular pathogenesis. We used a combination of some in silico prediction, molecular docking, and gene expression profile tools to predict some new BCoV receptors out of some known other coronavirus receptors and some host cell enzymes that are potentially involved in BCoV replication. To achieve these goals, we applied the in-silico prediction tools to check the potential interactions between eh BCoV/S glycoprotein and some known coronaviruses receptors (ACE2, APN1, NPR1, CECAM-1, AXL, and DPP4). We also established some potential models for the interaction between the BCoV/S and some common host cell proteases proved to play important roles in other coronavirus replication cycle (Furin, TMPRRS2, and Cathepsin-L). To confirm our prediction models, we assessed the gene expression profiles of these potential receptors and proteases in some bovine cell lines infected with BCoV using the next generation sequencing and the qRT-PCR assay. Our results show that the NPR1, AXL1 and the ACE2 have the strongest and firm binding affinities to the BCoV-S protein. Our prediction models and gene expression profiles show the potential bindings of the Furin and TMPRSS2 to the BCoV-S polybasic cleavage site (RRSRR|A). Notably, high expression of the host cell Furin was observed in response to both BCoV/Ent and BCoV/Resp infection, whereas TMPRSS2 expression was upregulated in the case of the BCoV/Ent infection, positioning Furin as a common receptor for different BCoV isolates. Further research is needed to study the roles of these top-ranked receptors and host cell proteases in molecular pathogenesis, and tissue tropism.

The drought response index (DRI) is an indicator of drought tolerance after adjustment for variation in flowering date and potential yield under well-watered conditions. Using a temperate japonica mapping population of 97 recombinant inbred lines from a cross between Otomemochi (OTM) and Yumenohatamochi (YHM), we evaluated DRI during the reproductive stage under very severe drought in one year and under severe drought in the next year. DRI under very severe drought (−6.4 to 15.9) and severe drought (−3.9 to 8.3) positively correlated with grain dry weight under drought. Three QTLs for DRI were identified: RM3703–RM6911–RM6379 and RM6733–RM3850 both on chromosome 2 in both years combined;. and RM8120–RM2615–RM7023 on chromosome 6 in the second year. The latter collocated with putative genes for signaling and defense mechanisms (e.g., PIN1B, BZIP46) revealed by database analysis. Top-DRI lines retained root dry weight and had bigger steles. QTL-by-environment interaction had a greater relative contribution than the main effects of QTLs. Comparison with three previous studies revealed that the QTLs for DRI were unique to each experiment and/or population; most of them closely colocalized with reported drought-yield QTLs.

Many neurodegenerative diseases are directly related to the formation of toxic protein aggregates, such as Parkinson’s and Alzheimer’s diseases, which are associated with the aggregation of alpha-synuclein (α-syn) and beta-amyloid (Aβ ), respectively. In this context, protein fibrils are the hallmark of these neurodegenerative diseases. In this sense, planning or designing compounds that are capable of preventing or reducing the formation of protein aggregation in the brain can be of fundamental importance for the curative treatment of these diseases. This work presents an in silico and experimental study to investigate the inhibitory potential of the ligands Octovespin (bioinspired by the wasp venom Polybia Occidentalis) and Fraternine-10 (bioinspired by the wasp venom Parachartergus fraternus wasp) against the aggregation of fibrils of the peptides Aβ(17-42) and α-syn, respectively. Using molecular docking and molecular dynamics simulations with 100 ns, the disaggregation capacity of these ligands was studied. The results obtained indicate that Octovespin and Fraternine-10 interact with the Aβ and α-syn protein fibrils throughout the simulation time. The RMSD values and the interactions with amino acids responsible for fibril aggregation demonstrate that both Octovespin and Fraternine-10 have a significant disaggregation potential, which corroborates the observations in vitro and in vivo. Furthermore, experimental results of Fraternine-10 demonstrate an anti-aggregation effect, indicating that it can promote the disaggregation of fibrils and prevent them from aggregating again to form oligomers. These discoveries qualify these molecules as potential candidates to become medicines against Parkinson’s and Alzheimer’s diseases.

The central aim of this paper is to present the Green Web Meter software which is designed to support the measurement of digital sustainability performance of websites and web apps, as well as providing sound based analytic tools for improving their overall quality. We analyzed scientific literature and specifically selected three quality assessment models (E-S Qual, Sitequal, Webqual 4.0) which we used as a starting point for identifying the most crucial aspects for the monitoring activities. A consistent set of Key Performance Indicators (KPIs) suitable for automated tracking has been provided. We finally developed model-based software able to deliver solutions for real-time website performance analysis and reports. We structured the model to offer accurate references for digital sustainability reporting by aggregating the KPIs into an Environmental, Social, and Governance (ESG) framework and computing a comprehensive score for its pillars.

So that the levels of water stress are not harmful to the development of the crop and affect its productivity, its detection and monitoring are necessary, and it can occur in different ways. One of them is through the Crop Water Stress Index (CWSI). This index quantifies water stress through the normalization of leaf temperature between the maximum and minimum plant temperatures as a function of evaporation conditions. The responses of a low-cost infrared (IR) sensor were crossed with image processing through segmentation by the Excess Green model to develop a water stress detection system using CWSI. A soil/plant temperature map was generated through a point-to-point scan of the IR sensor. And when it overlaid with a segmented image of the experimental area, only points identified as plants had their temperature values maintained. The Non-Water-Stressed Baseline (NWSB) equation was parameterized for the same conditions of the experiment and external environmental. The experimental area was divided into three different treatments, maintained under stable water conditions throughout the experiment and the system was able to identify stably different stress values between treatments. Although the relationship between crop and environment affected the results, this work showed that using an irrigation system based on CWSI is possible.

In this paper, at first, a color image of a car is taken. Then the image is transformed into a grayscale image. After that, the motion blurring effect is applied to that image according to the image degradation model described in equation 3. The blurring effect can be controlled by a and b components of the model. Then random noise is added in the image via Matlab programming. Many methods can restore the noisy and motion blurred image; particularly in this paper Inverse filtering as well as Wiener filtering are implemented for the restoration purpose. Consequently, both motion blurred and noisy motion blurred images are restored via Inverse filtering as well as Wiener filtering techniques and the comparison is made among them.

The primary objective of this study was to demonstrate the potential of digital image analysis as a tool to identify microplastic (MP) particles in surface waters and to facilitate their characterisation in terms of 2D and 3D morphology. Digital image analysis preceded by microscopic analysis was used for an exhaustive quantitative and qualitative evaluation of MPs isolated from the Vistula river. Using image processing procedures, 2D and 3D shape descriptors were determined. The Principal Components Analysis (PCA) was used to interpret the relationships between the parameters studied, characterising MP particle geometry, type and colour. This multivariate analysis of the data allowed 3 or 4 main factors to be extracted, explaining approximately 90% of the variation in the data characterising MP morphology. It was found that the first principal component for granules, flakes and films was largely represented by strongly correlated with 2D shape descriptors (area, perimeter, equivalent area diameter) and 3D shape descriptors (CSF, Compactness, Dimensionality). Considering the scraps, PC1 was represented by only five of the above descriptors, and the Compactness variable had the largest contribution to PC2. In addition, for granules, flakes and films, a relationship between 2D shape and the colour of their particles could be observed. For the most numerous MP group identified of multicoloured scraps, no such association was found. The results of our study can be used for further multivariate analysis regarding the presence of microplastic floating on the river surface, with a particular focus on particles of secondary origin. This is of key importance for optimising future efforts in conducting small-scale and multidimensional monitoring of and reducing plastics in the aquatic environment.

This review provides an overview of eXplainable AI (XAI) methods for oncological ultrasound image analysis and compares their performance evaluations. A systematic search of Medline Embase and Scopus between March 25 and April 14 2024 identified 17 studies describing 14 XAI methods, including visualization, semantics, example-based, and hybrid functions. These methods primarily provided specific, local, and post-hoc explanations. Performance evaluations focused on AI model performance, with limited assessment of explainability impact. Standardized evaluations incorporating clinical end-users are generally lacking. Enhanced XAI transparency may facilitate AI integration into clinical workflows. Future research should develop real-time methodologies and standardized quantitative evaluative metrics.

Pain diagnosis remains a challenging task due to its subjective nature, the variability in pain expression among individuals, and the difficult assessment of the underlying biopsychosocial factors. In this complex scenario, artificial intelligence (AI) can offer the potential to enhance diagnostic accuracy, predict treatment outcomes, and personalize pain management strategies. This review aims at dissecting the current literature on computer-aided diagnosis methods. It also discusses how AI-driven diagnostic methods can be integrated into multimodal models that combine various data sources, such as facial expression analysis, neuroimaging, and physiological signals, with advanced AI techniques. Despite the significant advancements in AI technology, its widespread adoption in clinical settings faces crucial challenges. Ethical considerations related to patient privacy, biases, and lack of reliability and generalizability are the main issues. Furthermore, there is a need for high-quality real-word validation as well as the development of standardized protocols and policy rules to guide the implementation of these technologies in diverse clinical settings.

This research aims to deepen the understanding of the relationship between conductivity and morphology in polypyrrole (PPy) via a comparison of the bipolaron to polaron ratios with a focus on the C-H deformation area. PPy samples were synthesized with different surfactants: sodium dodecyl sulfate (SDS), cetyltrimethylammonium bromide (CTAB), and tween 80 (TW). The study revealed that SDS significantly altered the bipolaron and polaron in C-H deformation region and showed higher conductivity than other surfactants. Notably, the morphological shifts to a sheet-like structure when using ammonium sulfate (APS) contrasted with the particle-like form observed with ferric chloride (FeCl3). These results showed that if the oxidant changed, the bi-polaron and polaron ratios in C-H deformation were unrelated to PPy morphology. However, this work showed a consistent relationship between SDS use, the bipolaron and polaron ratios in the C-H deformation, and the conductivity properties. Moreover, the natural positive charge of PPy and negatively charged SDS molecules may lead to an electrostatic interaction between PPy and SDS. This work assumes that this interaction might cause the transformation of polaron to bipolaron in the C-H deformation region, resulting in improved conductivity of PPy. This work offers more support for the future investigation of PPy characteristics.

Understanding complex biological processes of cells in culture, particularly those related to metabolism, can be biased by culture conditions, since the choice of energy substrate impacts all main metabolic pathways. When glucose is replaced by galactose cells decrease their glycolytic flux, working as an in vitro model of limited nutrient availability. However, the effect of these changes on related physiological processes such as REDOX control is not well documented, particularly in endothelial cells where mitochondrial oxidation is considered to be low. We evaluated the differences in mitochondrial dynamics and function in endothelial cells exposed to galactose or glucose culture medium. We observed that cells maintained in galactose-containing medium show higher mitochondrial oxidative capacity, more fused mitochondrial network, and higher intercellular coupling. These factors are documented to impact the cellular response to oxidative stress. Therefore, we analyzed the levels of two main REDOX regulators and found that Bovine Aortic Endothelial Cells (BAEC) in galactose media had higher levels of FOXO3 and lower levels of Nrf2 than those with glucose in the media. Thus, culture of endothelial cells in galactose containing medium may provide a more suitable condition to study in vitro mitochondrial related processes than glucose media, and deeply influence REDOX signaling in these cells.

Hessenberg differential algebraic equations (Hessenberg-DAEs) with high index play a critical role in the modeling of mechanical systems and multibody dynamics. Motivated by the widely used Lie Group Differential Algebraic Equation (LGDAE) method which only handles index 2 systems, we propose a Modified Extended Lie Group Differential Algebraic Equation (MELGDAE) method for solving index 3 Hessenberg-DAEs, and provide theoretical analysis to deepen the foundation of the MELGDAE method.The performance of the MELGDAE method is compared with the standard methods RADAU and MEBDF on index 2 and 3 DAE systems, and it is demonstrated that the MELGDAE integrator exhibits competitive performance in terms of high accuracy and the preservation of algebraic constraints. In particular, all differential variables in index 3 Hessenberg DAEs achieve second-order convergence using the MELGDAE method, which suggests potential for extension to Hessenberg-DAEs with an index of 4 or higher.

The transforming growth factor-β (TGF-β), in which overexpression have been associated with various diseases, has become an attractive molecular target for the treatment of cancers. Three series of 3-substituted-4-(quinoxalin-6-yl) pyrazoles 14a–h, 15a–h, 16a–h, 22a, 22b, 22d, 23a, 23b, 23d, 24b, and 24d were synthesized and evaluated for their activin receptor-like kinase 5 (ALK5) and p38α mitogen activated protein (MAP) kinase inhibitory activity in an enzymatic assays. Among these compounds, the most active compound 16f inhibited ALK5 phosphorylation with an IC₅₀ value of 0.28 µM, with 98% inhibition at 10 µM. Compound 16f also had good selectivity index of >35 against p38α MAP kinase, with 9.0-fold more selective than clinical candidate, compound 3 (LY-2157299). Molecular docking study was performed to identify the mechanism of action of the synthesized compounds and their good binding interactions were observed. ADMET prediction of good active compounds showed that these ones possess good pharmacokinetics and drug-likeness behavior.

Objective: To report time-loss match injuries in Portugal’s “Divisão de Honra” (Portuguese First Tier) 2022/23 season focusing on injury incidence, injury burden, anatomical region and type of injury. Methods: A cohort prospective observational study was conducted. Data was collected through an online form sent for every team medical department categorizing player position, injury type, location and severity. Injury incidence and burden were calculated per 1000-player-match-hours. Results: The overall injury incidence was 54.4 injuries per 1000 player-match-hours (CI95% 30.3-96.2). Forwards had a higher incidence comparing to backs. Lower limbs were the most affected location (52% of the injuries). Sprain/ligaments injuries were the most common type of injury followed by muscle injuries. Discussion: The injury incidence was higher than what is reported in amateur competitions and lower than professional ones. Injury severity was similar to other studies. Our study suggests a lower injury burden compared to other semi-professional and elite leagues. Further research with broader participation and training injury data is needed.

Timely crop yield forecasts at national level are substantial to support food policies, to assess agricultural production and to subsidize regions affected by food shortage. This study presents an operational crop yield forecasting system for Poland that employs freely available satellite and agro-meteorological products provided by the Copernicus programme. The crop yield predictors consist of: (1) vegetation condition indicators provided daily by Sentinel-3 OLCI (optical) and SLSTR (thermal) imagery, (2) a backward extension of Sentinel-3 data (before 2018) derived from cross-calibrated MODIS data, (3) air temperature, total precipitation, surface radiation, and soil moisture derived from ERA-5 climate reanalysis generated by the European Centre for Medium-Range Weather Forecasts. The crop yield forecasting algorithm is based on thermal time (growing degree days derived from ERA-5 data) to better follow the crop development stage. The recursive feature elimination is used to derive an optimal set of predictors for each administrative unit, which are ultimately employed by the Extreme Gradient Boosting regressor to forecast yields using official yield statistics as a reference. According to intensive leave-one-year-out cross validation for 2000–2019 period, the relative RMSE for NUTS-2 units are: 8% for winter wheat, and 13% for winter rapeseed and maize. Respectively, for the LAU units it equals 14% for winter wheat, 19% for winter rapeseed, and 27% for maize. The system is designed to be easily applicable in other regions and to be easily adaptable to cloud computing environments (such as DIAS or Amazon AWS), where data sets from the Copernicus programme are directly accessible.

The increasing focus on sustainability and the adoption of green practices across industries underscore the necessity of equipping future professionals with competencies in implementing sustainable solutions. This study explores the perceptions of former students from two departments within the technological sector towards sustainability practices. A cross-sectional online survey was carried out among alumni from two programs at the University of Thessaly, a university recognized for its commitment to sustainability and green initiatives as a member of the INVEST European University alliance. The survey questionnaire included background inquiries and 16 statements designed to gauge alumni perspectives towards sustainability practices. Specifically, it aims to assess their views through self-assessment and compare differences between the two alumni groups. Responses were collected on a 5-point Likert scale ranging from strongly disagree to strongly agree. A total of 300 graduates from the Computer Science and Telecommunications as well as Electrical Engineering undergraduate programs were encouraged to take part in the survey, achieving a response rate of 69%. Of those who responded, 61% (n = 126) represented the Computer Science and Telecommunications group, while 39% (n = 81) were from the Electrical Engineering program. Our findings indicate a generally positive attitude towards sustainability, with statistically significant differences in perspectives on the implementation of sustainable technologies and stakeholder engagement. These results underscore the necessity of integrating sustainability education deeply within the curricula of technological programs to cultivate a workforce capable of addressing future environmental challenges. This study illuminates the critical role of educational frameworks in shaping the sustainability competencies of technology professionals.

High temperature (HT) significantly affects the crop physiological traits and reduces the 12 productivity in plants. To increase yields as well as survival of crops under HT, developing heat13 tolerant plants is one of the main targets in crop breeding programs. The present study attempted 14 to investigate the linkage of the heat tolerance between the seedling and the reproductive growth 15 stages of tomato cultivars ’Dafnis‘ and ’Minichal’. This research was undertaken to evaluate heat 16 tolerance under two experimental designs such as screening at seedling stage and screening from 17 reproductive traits in greenhouses. Survival rate and physiological responses in seedlings of 18 tomatoes with 4-5 true leaf were estimated under HT (40 °C, RH 70%, day/night, respectively) and 19 under two control and HT greenhouse conditions (day time 28 °C and 40 °C, respectively). Heat 20 stress significantly affected physiological-chemical (photosynthesis, electrolyte conductivity, 21 proline) and vegetative parameters (plant height, shoot fresh weight, root fresh weight) in all 22 tomatoes seedlings. The finding revealed that regardless of tomato cultivars the photosynthesis, 23 chlorophyll, total proline and electrical conductivity parameters were varied in seedlings during the 24 heat stress period. The heat tolerance rate of tomatoes in the seedling stage might not be associated 25 always with reproductive parameters. HT reduced the fruit parameters likeas fruit weight (31.9%), 26 fruit length (14.1%), fruit diameter (19.1%) and fruit hardness (9.1%) in compared to NT under HT 27 in heat susceptible tomato cultivar ‘Dafnis’, while in heat tolerant cultivar ‘Minichal’ fruit length 28 (7.1%) and fruit diameter (12.1%) was decreased by the affect of HT but on the contrary fruit weight 29 (3.6%) and fruit hardness (8.3%) were increased. In conclusion, screening and selection for tomatoes 30 should be evaluated at the vegetative and reproductive stages with consideration of reproductive 31 parameters.

Background/Objectives: The aim of the project is a longitudinal assessment of body image and mental health in scoliosis females pre-, postoperatively and in a follow-up, and implementation (one group-CBT scoliosis sample, CBTSS) or not (second group-control scoliosis sample, CSS) of CBT interventions. A healthy female sample (HFS) was also selected for comparative purposes. Methods. 36 in total AIS patients participated in the 1st and 2nd study phases, whereas 23 were eligible in the follow-up. Regarding HFS, 18 participants were included in the study. The CBT intervention took place pre- and postoperatively, during the patients' stay in hospital. Participants completed the Spinal Appearance Questionnaire (SAQ), Body Esteem Scale (BES) and Strengths and Difficulties Questionnaire-25 (SDQ-25). Body image was also assessed using a virtual reality-based application “Avatar Scoliosis 3D”. Results: The difference between the desired and objective, actual body shape is significant preoperatively both in the CBTSS and CSS at p

Studies show Human Milk Oligosaccharides (HMOs) influence on infant growth were conflicted. This study aims to determine the relationship between HMO profiles and weight growth indicators of infants ages 0-4 months in Indonesia. A prospective longitudinal study was conducted among 120 mother-infant pairs aged 0 – 4 months from the hospital and Public Health Center in Jakarta between August 2021 – May 2022. The gene analysis for the Secretor and Lewis status of the mother were performed by using targeted Next Generation Sequencing (NGS) and Sanger at baseline. The HMO profiles (2'FL, LNFP I, LNT, LNnT, 3'SL, and 6'SL), breast milk calories, and fat were examined at 0, 2, and 4 months. Weight for age z-score, weight for length z-score, infection episode, and mother's body mass index were examined every month. Data were analyzed using Pearson or Spearman correlation test (correlation coefficient), general linear model for repeated measures ANOVA test and Friedman test (mean difference between and within groups, respectively) and logistic regression unadjusted OR (association between dependent and independent variables), with a p-value <0.05 considered statistically significant. Infants' weight velocities were higher among high LNT and 3'SL levels in the second month, and in high 2'FL and LNFP I mothers in the fourth month. HMO profile (2’FL, LNFP I, and LNT) of lactating mothers have a positive relationship with weight growth indicators, and innovative interventions to enhance HMO concentration among mothers should be considered as one of the ways to improve infant growth.

The popularity of handheld devices for point-of-care ultrasound (POCUS) has increased in recent years due to their portability and cost-effectiveness. However, POCUS has the drawback of lower imaging quality compared to conventional ultrasound, because of hardware limitations. Improving the quality of POCUS through post-image processing would therefore be beneficial, with deep learning approaches showing promise in this regard. This review investigates the state-of-the-art progress of image enhancement using deep learning suitable for POCUS applications. A systematic search was conducted from January 2024 to February 2024 on PubMed and Scopus. From the 457 articles that were found, the full text was retrieved for 69 articles. From this selection, 15 articles were identified addressing multiple quality enhancement aspects. A disparity in the baseline performance of the low-quality input images was seen across these studies, ranging between 8.65–29.24 dB for the Peak Signal-to-Noise Ratio (PSNR) and 0.03-0.71 for the Structural Similarity Index Measure (SSIM). In six studies, where both PSNR and the SSIM metrics were reported for the baseline and the generated images a mean difference of 6.60 (SD ± 2.99) and 0.28 (SD ± 0.15) was observed for the PSNR and SSIM, respectively. The reported performances demonstrate the potential of deep-learning-based image enhancement for POCUS. However, variability in the extent of performance gain across datasets and articles was notable, and the heterogeneity across articles makes quantifying the exact improvements challenging.

Psoriasis is a chronic immune-mediated skin disease that involves interaction of both immune and skin cells, and is characterized by cytokine-driven epidermal hyperplasia, deviant differentiation, inflammation and angiogenesis. Because available treatments for psoriasis have significant limitations, dietary products are potential natural sources of therapeutic molecules, which can rescind molecular defects associated with psoriasis and could be developed for its management. Fisetin (3,7,3′,4′- tetrahydroxyflavone), a phytochemical naturally found in pigmented fruits and vegetables has demonstrated pro-apoptotic and antioxidant effects in several malignancies. This study utilized biochemical, cellular, pharmacological and tissue-engineering tools to characterize the effects of fisetin on normal human epidermal keratinocytes (NHEKs), peripheral blood mononuclear cells (PBMC) and CD4+ T lymphocytes in 2D and 3D psoriasis-like disease models. Fisetin treatment of NHEKs dose and time-dependently induced differentiation and inhibited interleukin-22-induced proliferation, as well as activation of the PI3K/Akt/mTOR pathway. Fisetin treatment of TNF-α-stimulated NHEKs significantly inhibited the activation of p38 and JNK, but had no effect on ERK1/2. In addition, fisetin treatment significantly decreased the secretion of Th1/Th-17 pro-inflammatory cytokines, particularly IFNγ and IL-17A by 12-O- tetradecanolylphorbol 13-acetate (TPA)-stimulated NHEKs and anti-CD3/CD28-activated human PBMCs. Furthermore, we established the in-vivo relevance of fisetin functions, using a 3D full-thickness human skin model of psoriasis (FTRHSP) that closely mimics in-vivo human psoriatic skin-lesions. Herein, fisetin significantly ameliorated psoriasis-like disease features, and decreased the production of IL-17 by CD4+ T lymphocytes co-cultured with FTRHSP. Collectively, our data identify pro-differentiative, anti-proliferative and anti-inflammatory effects of fisetin, via modulation of PI3K-Akt-mTOR and p38/JNK pathways and the production of cytokines in 2D and 3D human skin model of psoriasis. These results suggest that fisetin has a great potential to be developed as an effective and inexpensive agent for the treatment of psoriasis and other related inflammatory skin disorders.

Dark pigmentation can be observed in various parts of the plant, ranging from foliage to petals to berries. The molecular basis of black pigmentation appears to vary among species, with anthocyanins playing a significant role, although specific anthocyanin types and their mechanisms differ. These findings suggest that the development of phenotypes is species-specific or varies between larger taxonomic groups; this is further supported by the polyphyletic nature of dark pigmentation. Additionally, several different regulatory mechanisms have been described for the occurrence of dark pigmentation. Firstly, the repression or knockout of the competing flavone biosynthesis has been shown to lead to darker pigmentation while another mechanism is based on the activation and upregulation of the anthocyanin biosynthesis genes in the presence of MYB transcription factors. Potential ecological functions of dark pigmentation were identified as the protection of the photosynthesis apparatus, camouflage against herbivores, and the attraction of pollinators. Promising industrial applications include microbial factories for the production of natural food colourants, induction of novel phenotypes for the ornamental plant industry and lastly the increase of anthocyanins within agriculturally relevant crops.

Exoskeleton technology has been shown to be useful for individuals with spinal cord injury (SCI), potentially facilitating their recovery of motor function and social reintegration. However, studies on the safety and usability of exoskeletons are limited. This study aimed to design a powered exoskeleton for SCI and evaluate its safety and usability. Ten healthy adults participated in this study. The prototype exoskeleton featured an adjustable frame, computer-controlled hip and knee joint actuators, and a gait-trigger sensor. The experimental actions included walking, standing, and sitting, with and without the prototype. Safety outcomes included adverse events, vital signs, numerical rating scale (NRS) scores for pain, discomfort, and fatigue, and prototype-human body clearance. Usability outcomes included donning and doffing times, subjective ratings, gait speed, stride length, and kinematic parameters of hip and knee joint angles. Safety outcomes showed no serious adverse events, stable vital signs, minimal NRS scores, and acceptable clearance. Usability outcomes showed high efficiency and received positive ratings. Kinematic parameters showed significant positive correlations between normal walking and walking with the prototype for both hip and knee joint angles, confirming its effectiveness in assisting walking. However, further device improvements and gait parameter tuning are necessary for future clinical application.

Digital Image Correlation (DIC) is an optical technique used to measure surface displacements and strains in materials and structures. This technique has demonstrated significant utility in structural examination and monitoring. This manuscript offers a comprehensive review of contemporary research and applications that have leveraged the DIC technique in laboratory-based structural tests. The reviewed works encompass a broad spectrum of structural components, such as concrete beams, columns, pillars, masonry walls, infills, composite materials, structural joints, steel beams, slabs, and other structural elements. These investigations have underscored the efficacy of DIC as a metrological instrument for precise quantification of surface deformation and strain in these structural components. Moreover, the constraints of the DIC technique have been highlighted, especially in scenarios involving extensive or complex test configurations. Notwithstanding these constraints, the DIC methodology has validated its effectiveness as a strain measurement instrument, offering numerous benefits such as non-invasive operation, full-field measurement capability, high precision, real-time surveillance, and compatibility with integration into other measurement instruments and methodologies.