Citrus waste has been used as a source of bioplastic research in different ways. Because the juice industry produces significant amounts of residue each year, it would be advantageous to use the byproducts in the creation of new materials. For many years, researchers have been investigating an environmentally friendly way to take citrus and other organic waste and turn it into polymers that may be used, for instance, to produce bio-based and biodegradable films for food packaging. The goal of this study is to create biofilms from orange (OW) and ginger waste (GW) using an ultrafine grinder and study the films' properties. Since pectin has the ability to gel and because cellulosic fibres are strong, citrus waste has been studied for its potential to produce biofilms. After being washed, dried, and milled, orange and ginger waste were shaped into films using a casting process. Tensile testing was used to determine the mechanical properties of biofilms, while dynamic mechanical thermal analysis (DMTA), thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC) were used to determine their thermal properties. As the number of grinding cycles increased, the suspension's viscosity increased from 29 to 57 mPa.s for OW and from 217 to 376 mPa.s for GW while the particle size in the suspension significantly decreased. For OW and GW films, the highest tensile strength was 17 MPa and 15 MPa, respectively. The maximum strain obtained among all films was 4.8%. All the tested films were stable up to 150°C, and maximum degradation occurs after 300°C.

The definition of UTC in 1972 balanced the needs of the time and frequency community for a universal time scale that was traceable to the definition of frequency in the SI system of units with the needs of the astronomical community for a time scale that was closely linked to UT1, a time scale related to the rotation of Earth. The balance between these two considerations has changed very significantly since 1972, and there is widespread agreement that the method of relating UTC to UT1 should be changed by increasing the maximum tolerance between the two scales to more than the current limit of 0.9 s. I propose to realize this decision by means of an algorithmic adjustment process based on the variation in the length of the day over the previous centuries. I use these historical data to extrapolate the length of the day for a century into the future, and to compute a periodic rate adjustment to UTC based on this extrapolation. The adjustment process will not use time steps. The magnitude of the periodic rate adjustment will be re-considered every century, but the basic adjustment algorithm will not change. I suggest a maximum tolerance between UT1 and UTC that is large enough to absorb the irregular and unpredictable variations in the length of the day with respect to the long-term average increase in this value but plays no role in determining the parameters of the adjustment process that is proposed. I also propose that the adjustment be implemented at 12 UTC on 1 January since that is closest to a universal holiday in all time zones.

MIMO (multiple-input multiple-output) radar provides much more flexibility than the traditional radar for its ability to realize far more observation channels than the actual number of T/R (transmit and receive) elements. Designing the array of MIMO imaging radar, the commonly used virtual array theory generally assumes that all elements are placed on the same line. However, due to the physical size of the antennas and coupling effect between T/R elements, a certain height difference between T/R arrays is essential, resulting in the defocusing of edge points of the scene. On the other hand, the virtual array theory implies far-field approximation, leading to inevitable high grating lobes in the imaging result of near-field edge points of the scene observed by common MIMO array. To tackle these problems, this paper derives the relationship between target’s PSF (point spread function) and pattern of T/R arrays, by which the design criterion of near-field imaging MIMO array is presented. Firstly, the proper height between T/R arrays is designed to focus the near-field edge points well. Secondly, the far-field array is modified to suppress the grating lobes in the near-field area. Finally, the validity of the proposed methods is verified by simulations and an experiment.

This study aims to investigate the interfacial tension, emulsion stability, and the impact on oil displacement effectiveness of mixed systems containing nonionic surfactants and amphoteric surfactants at different ratios. To achieve this, three different emulsion stability formulations were prepared by mixing betaine and nonionic surfactants in ratios of 1:1, 3:2, and 3:1 respectively. By adjusting the proportions of betaine and nonionic surfactants, variations in interfacial tension and emulsion formation among the three mixed systems were studied to analyze the mechanism of interaction between betaine and nonionic surfactants and evaluate their effects on crude oil displacement and enhanced oil recovery in reservoirs. Subsequently, microscopic and macroscopic oil displacement experiments were conducted on the three mixed systems, along with experiments on emulsion flow resistance. The aim was to verify the oil displacement effectiveness of different mixed systems and further understand their potential applications in reservoirs. By comprehensively analyzing the experimental results, optimal ratios and conditions for the mixed systems were determined. The experimental results indicate that the emulsification system formed by mixing betaine and nonionic surfactants at a ratio of 3:1 achieves molecular synergies through electrostatic interactions and mixed adsorption with betaine, resulting in a compact arrangement of interface membranes and demonstrating a synergistic effect in reducing interfacial tension to an ultra-low value of 10 -4 mN/m. Additionally, as the proportion of betaine surfactant increases, the viscosity of the aqueous phase liquid film increases, the rate of thinning of the liquid film slows down, the rate of water release slows down, and the stability of the emulsion increases. Microscopic oil displacement experiments verified that the three mixed systems increase oil washing efficiency through low interfacial tension and emulsification stripping action, thereby increasing the oil displacement mechanism's coefficient. Emulsion flow resistance experiments demonstrated that as the proportion of betaine surfactant increases, the system exhibits better plugging ability. However, macroscopic oil displacement experiments revealed differences in compatibility between different emulsion stability solutions and rocks with different permeabilities. The mixed system of betaine and nonionic surfactants at a ratio of 3:1 exhibits the best emulsification plugging ability in rocks with a permeability of 109mD, while the 1:1 mixed system performs best in rocks with a permeability of 12mD. This holds significant theoretical and practical significance for reservoir development and enhanced production.

The relaxation process of lithium batteries caused by load variation is considered. It is shown that such processes have strong dependence on internal physical and chemical processes and battery technical conditions. Theoretical expressions of the relaxation process caused by a step-like load variation have been obtained for 1^st and 2^nd order equivalent electrical circuits. The experimental investigations show that the obtained models fit the real relaxation processes and the behavior of the identified parameters could be explained by specific features of physical and chemical processes within the lithium battery. It should be noted that the obtained results can be generalized for a different type of electrochemical power source. The proposed approach makes it possible to provide means for electrochemical power source characterization and diagnostic, the main advantages of which are good time localization of measurement procedures and inexpensive apparatus implementation.

There are several forms of maintenance high-efficiency hemodialysis (HD) including hemodiafiltrations (HDF) in different technic modes and expanded HD using dialyzers with medium cut-off membranes. The aim of the study was to assess the intradialytic tolerance and length of dialysis recovery time (DRT) in these modalities. This is an exploratory, cross-over study in maintenance HD patients with low comorbidity and no clinical indications for the use of high-efficiency HD, who were exposed to five intermittent dialysis in random order: high flux hemodialysis (S-HD), expanded HD (HDx), pre-dilution HDF (PRE-HDF), mix-dilution HDF (MIX-HDF) and post-dilution HDF (POST-HDF). 24 dialysis sessions of each method were included in the analysis. Dialysis parameters including blood flow rate, dialysis fluid flow rate and temperature, and pharmacological treatment were constant. Average total convection volume for post-HDF, pre-HDF and mix-HDF were 25.6 (3.8), 61.5 (7.2) and 47.1 (11.4) l, respectively. During all therapies, patients had similar hydration status monitored using bioimpedance spectroscopy and similar variability over time in systemic blood pressure, cardiac output, and peripheral resistance monitored using impedance cardiography. The lowest frequency of all intradialytic adverse events were observed during HDx. Delayed DRT was the shortest during PRE-HDF. Patients were also more likely to report immediate recovery while receiving PRE-HDF. These differences did not reach statistical significance, however the study results suggest that, intradialytic tolerance and DRT may depend on the dialysis method used. This support the need of taking into account patient preferences and quality of life while individualizing high efficiency therapy in HD patients.

This study aims to spatially analyze the distribution characteristics of vacant homes in Asan-si, Chungcheongnam-do, and understand the differences in factors causing vacant homes by type. We use a variety of statistical methods to examine these factors and identify key influencing factors such as the number of vacant stores, changes in overall population, land use complexity, and physical characteristics of land and buildings. The findings reveal that the factors contributing to the occurrence of vacant homes vary significantly by region and property type, highlighting the need for region-specific policies. For instance, in old town areas, small lot ratios and land use complexity are significant, while in rural village, the average number of floors and land use complexity play a major role. This study underscores the diversity of factors influencing vacant homes, suggesting that policies should be tailored to the unique characteristics of different regions to effectively address this issue.

The temporal evolution of pandemics described by the susceptible-infectious-recovered (SIR)-compartment model is sensitively determined by the time dependence of the infection (a(t)) and recovery (u(t)) rates regulating the transitions from the susceptible to the infected and from the infected to the recovered compartment, respectively. Here approximated SIR-solutions for different time dependencies of the infection and recovery rates are derived which are based on the adiabatic approximation assuming time-dependent ratios k(t)=u(t)/a(t) varying slowly in comparison to the typical time characteristics of the pandemic wave. For such slow variations the available analytical approximations from the KSSIR-model, valid for a stationary value of the ratio k, are used to insert a-posteriori the adopted time-dependent ratio of the two rates. Instead of investigating endless different combinations of the time dependencies of the two rates a(t) and u(t) a suitably parameterized reduced time dependence of the ratio k(tau) is adopted. Together with the definition of the reduced time this parameterized ratio k(tau) allows us to cover a great variety of different time dependencies of the infection and recovery rates. The agreement between the solutions from the adiabatic approximation in its four different studied variants and the exact numerical solutions of the SIR-equations is remarkably good providing strong confidence in the accuracy of the proposed adiabatic approximation.

With the ongoing amendment of the EU legislation on the treatment of urban wastewater, stricter requirements for the removal of pollutants are expected, which calls for the need for innovative wastewater treatment technologies. Biological systems are still the first choice. The survey of typical bioreactors applied in wastewater treatment is presented. Wastewater treatment objective, bio-chemical environment and microbial growth are selected as a main criterion for classification of these bioreactors. Hydraulic and kinetic aspects are considered, advantages and drawbacks of these bioreactors regarding the selection of appropriate type of the reactor as well as regarding operation of reactors are mentioned. The aim of this paper is to provide operators and designers with a brief overview of selected traditional and advanced processes, reactors, and technologies for nutrient removal from municipal wastewater. Another goal is to discuss the possibilities and limitations to comply with more strict effluent standards. From the evaluation of the published papers, it follows that the currently applied traditional methods of nutrients removal have the potential to convey also with the expected stricter limits.

In the last decades droughts, deforestation and wildfires have become recurring phenomena that have affected both human activities and natural ecosystems in Amazonia. The time an ecosystem requires to recover from carbon losses is a crucial metric to evaluate disturbance impacts on forests. However, the factors influencing and controlling the recovery time and its spatiotemporal patterns at the regional scale are still poorly understood. In this study, we combined forest growth model, remote sensing and field plots, to map Amazonia-wide (300-ha resolution) impact and recovery time of aboveground biomass (AGB) after drought, fire and a combination of logging and fire. Our simulated results indicate that AGB decreases by 4%, 19% and 46% in forests disturbed by drought, fire and logging + fire, respectively, with an average AGB recovery time of 27 years for drought, 44 years for burned and 63 years for logged + burned areas and with maximum values reaching 184 years in areas of high fire intensity. Our findings provide two major insights in the spatial and temporal patterns of drought and wildfire in the Amazon: 1) the recovery time of the forests takes longer in the southeastern part of the basin, and, 2) as droughts and wildfires become more frequent – since the intervals between the disturbances is getting shorter than forest regeneration – potentially causing a long-lasting damage in these fragile ecosystems and a permanent degradation.

The study describes the acute and delayed time course of recovery following the CrossFit® Benchmark Workout Karen. Eight trained men (28.4±6.4 years; 1RM back squat 139.1±26.0 kg) undertook the Karen protocol. The protocol consists of 150 Wall Balls, aiming to hit a target 3 meters high. Countermovement jump height (CMJ), creatine kinase (CK), and perceived recovery status scale (PRS) (general, lower and upper limbs) were assessed pre, post-0h, 24h, 48h and 72h after the session. The CK concentration 24h after was higher than pre-exercise (338.4 U/L vs. 143.3 U/L; effect size: 0.74; p≤0.05). At 48h and 72h following exercise, CK concentration had returned to baseline levels. The PRS general and of the lower limbs were lower in the 24-hours post-exercise compared to pre-exercise (PRS general: 4.7 ±1.5 and 7.9 ±1.7 mmol/L; and PRS of the lower limbs: 4.0 ±2.5 and 7.9 ±0.8, respectively). The PRS general, lower, and upper limbs were reduced at 48-post exercise compared to 72-hours post-exercise scores. Our findings provide insights into the fatigue profile and recovery in acute CrossFit® and can be useful to coaches effectively design the daily session.

Carbon dioxide (CO2)miscible fracturing huff and puff technology plays a pivotal role in enhancing the recovery rate of crude oil, particularly in reservoirs with challenging physical properties, strong water sensitivity, high injection pressure, and complex water injection dynamics. In this study, the oil-increasing mechanism and huff and puff effect of CO2 miscible fracturing fluid are investigated through a comprehensive experimental approach. Specifically, experiments on PVT gas injection expansion, minimum miscible pressure, and CO2 miscible fracturing huff and puff are conducted on the G fault block reservoir in J Oilfield. The findings demonstrate that injecting CO2 into reservoirs leads to oil volume expansion, viscosity reduction, and saturation pressure increase. Additionally, the inclusion of solubilizers and viscosity reducers further enhances the efficiency of crude oil extraction. Solubilizers not only improve oil recovery but also reduce the minimum miscible pressure required for effective CO2 dispersion. Furthermore, the shut-in time, permeability, and huff and puff method all have considerable impacts on huff and puff recovery rate. This study offers valuable technical insights, supporting the application of CO2 miscible fracturing huff and puff technology to enhance oil recovery in low-permeability reservoirs.

Time recordings of impulse-type oscillation responses are short and highly transient. These characteristics may complicate the usage of classical spectral signal processing techniques for a) describing the dynamics and b) deriving discriminative features from the data. However, common model identification and validation techniques mostly rely on steady-state recordings, characteristic spectral properties and non-transient behavior. In this work, a recent method, which allows reconstructing differential equations from time series data, is extended for higher degrees of automation. With special focus on short and strongly damped oscillations, an optimization procedure is proposed that fine-tunes the reconstructed dynamical models with respect to model simplicity and error reduction. This framework is analyzed with particular focus on the amount of information available to the reconstruction, noise contamination and non-linearities contained in the time series input. Using the example of a mechanical oscillator, we illustrate how the optimized reconstruction method can be used to identify a suitable model and to extract features from uni-variate and multivariate time series recordings in an engineering-compliant environment. Moreover, the determined minimal models allow for identifying the qualitative nature of the underlying dynamical systems as well as testing for the degree and strength of non-linearity. The reconstructed differential equations would then be potentially available for classical numerical studies, such as bifurcation analysis. These results represent a physically interpretable enhancement of data-driven modeling approaches in structural dynamics.

The present study deals with the transplantation of mature Argan trees (Argania spinosa L. Skeels) and focuses on optimizing survival rates depending on the seasons, the type of shoot pruning applied to the trees and preconditioning treatments consisting of root pruningand watering for a few months before transplanting. So far, the transplantation of this species at the adult stage has not received enough attention in spite of its ecological and socio-economic importance for the regions where it thrives. The results obtained show that transplanting in summer, autumn and winter resulted in survival rates exceeding 50%. The Argan trees transplanted in spring hardly achieved 10% survival. However, the total cutback of the crown induced survival improvement that reached a rate greater than 70% for summer, autumn and winter, and 50% for spring. Watering alone or combined with root pruning for 6 months helped improve the recovery rate but only when carried out in autumn with transplantation in spring. On the other hand, when these pre-treatments were applied in the spring to transplant the trees in summer, the recovery rate was not improved.

Iron is an essential nutrient for humans and microbes, such as bacteria. Iron deficiency occurs commonly in critically ill patients, but iron replacement therapy is not considered during the acute phase of critical illness, since it increases iron availability for invading microbes and oxidative stress. However, persistent iron deficiency in the recovery phase is harmful with potential adverse outcomes such as cognitive dysfunction, fatigue and cardiopulmonary dysfunction. Therefore, it is important to treat iron deficiency timely and efficiently. This article reviews current knowledge about iron-related biomarkers in critically ill patients and provides possible criteria to guide decision-making for iron supplementation in the recovery phase of critical illness.

Nowadays, the identification and characterization of grapevine cultivars resilient to climate and water stress while preserving quality traits is crucial for the wine industry. Therefore, the objective of this work was to characterize according to their aromatic potential 9 white and 6 red minority cultivars recently recovered from Castilla-La Mancha region (Spain), subjected at two different water deficit regimes: rainfed, with subsistence irrigation, and irrigated. For this, the varietal aromatic potential index (IPAv) and the detailed aromatic composition were analyzed by HS-SBSE-GC/MS, in extracts of two different ph. For IPAv values, red varieties did not show a clear trend with respect to irrigation. However, in white minority varieties, higher values were obtained under irrigation conditions. Thus, a clear differentiation of the minority varieties in comparison to the references was observed, primarily attributed to the content of esters and acids, in both white and red varieties. A notable contrast was observed at different pH, indicating a greater extractability of certain compounds like linalool, under more acidic conditions. This suggests that some recovered minority cultivars could be promising for cultivation in semi-arid regions with limited water, contributing to the wine sector sustainability in the future.

The growing use of electronic devices in modern society has sparked concerns about managing e-waste on a global level. Coupled with ever-increasing copper demand, it poses both challenges and potential opportunities for sustainable resource management. The study reinspects the established process of recycling copper with aqua regia from waste PCB & also optimizes the extraction procedure of different aqua regia samples. This research also analyzes the impacts of temperature leaching duration and stirring speed. The results reveal that using a stirring speed of 500 rpm and a leaching time of one hour, the results show that a 1:3 ratio of HNO₃ to HCl in aqua regia produces the best copper extraction efficiency, reaching 72.2% at 180°C. Reduced extraction efficiencies were observed when compared with separate HCl and HNO₃ applications, essentially highlighting the joint effects of utilizing both acids encompassed in the aqua regia solution. Variations suggested by discrepancies found in the temperature range of 140°C to 150°C and overlapping error margins in data on extraction time call for further research. The study also raises concerns about the potential environmental impacts of traditional copper mining and refining processes & attempts to mitigate these effects by recycling copper more efficiently using optimum aqua regia solution. Aqua regia can be the economic & sustainable solution in countries where copper and other valuable metal recovery is rare due to the expensive and complex nature of existing technologies. Developing safer and more sustainable extraction technologies, optimizing process conditions, & researching the selective recovery of other important metals from WPCB should be emphasized in future studies. Addressing these problems will be critical for developing the practical applicability of copper extraction from electronic waste.

The impulse of the Fourth Industrial Revolution is triggering the demand for few specific metals. These include copper, silver, gold, and platinum group metals (PGMs), with important applications in renewable energies, green hydrogen, and electronic products. However, the continuous extraction of these metals is leading to a rapid decline in their ore grades and, consequently, increasing the environmental impact of extraction. Hence, obtaining metals from secondary sources, such as waste electrical and electronic equipment (WEEE), becomes imperative for both environmental sustainability and ensuring availability. This recovery entails few problems such as allocation due to the simultaneous production of several metals, the use of non-renewable resources, and the exergy destruction during the life cycle of the metals. Therefore, this work analyses the waste printed circuit boards (PCBs) recycling process by proposing different exergy-based cost allocations for the mentioned metals, disaggregating the exergy cost into non-renewable and renewable, and considering the complete life cycle of metals with the Circular Thermoeconomics methodology. The results show a significant saving of non-renewable energy by using renewable energies in primary extraction (67-87%), recycling (97.6-98.5%), and renewable energies in recycling (98.7%, 99.0%), compared to conventional primary extraction. However, when considering the entire life cycle, between 47% and 53% of the non-renewable exergy is destroyed during recycling. Therefore, delaying recycling as much as possible would be the most desirable option for maximizing the use of non-renewable resources, which nature cannot replace in a short time.

This case report describes the successful repair of a radial bone fracture and skin defect in a dog using an ulnar bone graft and an advancement flap. A one-year-old spayed female Poodle dog weighing 1.7 kg was brought in with non-weight-bearing lameness, an open wound, and a frac-ture in the right forelimb. The dog had fallen off the bicycle being ridden by its owner, causing its right forelimb to be caught in the wheel. The treatment included surgical debridement of the necrotic tissue, skin reconstruction with an advancement flap, and radial bone reconstruction with an ulnar bone graft, bone morphogenetic protein (BMP), humeral cancellous bone and a ti-tanium T-shaped plate. Postoperative management included antibiotics, pain medication, and activity restriction. The dog made a good recovery and was able to bear weight on the affected limb four months after surgery.

In order to solve the problems of waveform distortion and signal delay by many physical and electrical systems with linear low-pass transfer characteristics with multiple complex poles, a general digital-signal-processing (DSP)-based method of real-time recovery of the original source waveform from the distorted output waveform is proposed. From the convolution kernel representation of a multiple-pole low-pass transfer function with an arbitrary denominator polynomial with real valued coefficients, it is shown that the source waveform can be accurately recovered in real time using a particular moving average algorithm with real-valued DSP computations only, even though some or all of the poles are complex. The proposed digital signal recovery method is DC-accurate and unaffected by initial conditions, transient signals, and resonant amplitude enhancement. This method can be applied to most sensors and amplifiers operating close to their frequency response limits or around their resonance frequencies to accurately deconvolute the multiple-pole characteristics and to improve the overall performances of data acquisition systems and digital feedback control systems.

Using coherent optical detection and digital signal processing, laser phase noise and equalization enhanced phase noise can be effectively mitigated using the feed-forward and feed-back carrier phase recovery approaches. In this paper, theoretical analyses of feed-back and feed-forward carrier phase recovery methods have been carried out in the long-haul high-speed n-level phase shift keying (n-PSK) optical fiber communication systems, involving a one-tap normalized least-mean-square (LMS) algorithm, a block-wise average algorithm, and a Viterbi-Viterbi algorithm. The analytical expressions for evaluating the estimated carrier phase and for predicting the bit-error-rate (BER) performance (such as the BER floors) have been presented and discussed in the n-PSK coherent optical transmission systems by considering both the laser phase noise and the equalization enhanced phase noise. The results indicate that the Viterbi-Viterbi carrier phase recovery algorithm outperforms the one-tap normalized LMS and the block-wise average algorithms for small phase noise variance (or effective phase noise variance), while the one-tap normalized LMS algorithm shows a better performance than the other two algorithms for large phase noise variance (or effective phase noise variance). In addition, the one-tap normalized LMS algorithm is more sensitive to the level of modulation formats.

Abscisic acid (ABA) plays a major role in promoting ripening in sweet cherry, a non-climateric fruit. Exogenous application of ABA has been performed to study fruit ripening and cracking, but this growth regulator is not used for commercial production. To determine the potential of this growth regulator to improve sweet cherry fruit quality, ABA canopy spraying was assayed in four cultivars. Canopy spraying of S-ABA significantly: 1) enhanced sweet cherry fruit color in ‘Glenred’, ‘Lapins’ and 'Bing' cultivars, but not in ‘Royal Rainier’ (a bi-colored cultivar), and 2) decreased fruit size and firmness in ‘Lapins’, ‘Bing’ and ‘Royal Rainier’. Seasonally reproducible effects were seen in ‘Lapins’ (mid/late-maturing) but not in ‘Glenred’ (early-maturing). Canopy spraying of nordihydroguaiaretic acid (NDGA) decreased color and increased fruit size in ‘Lapins’, but not in ‘Glenred’. Direct application of ABA on fruits attached to the tree, without application to the foliage, increased Lapins' fruit color without reducing size. These results suggest a localized fruit response to exogenous ABA application on fruit color development, but that a decrease in fruit size may be due to the effects of exogenous ABA on the tree canopy foliage.

We consider the financial planning problem of a retiree wishing to enter a retirement village at a future uncertain date. The date of entry is determined by the retiree's utility and bequest maximisation problem within the context of uncertain future health states. In addition, the retiree must choose optimal consumption, investment, bequest and purchase of insurance products prior to her full annuitisation on entry to the retirement village. A hyperbolic absolute risk-aversion (HARA) utility function is used to allow necessary consumption for basic living and medical costs. The retirement village will typically require an initial deposit upon entry. This threshold wealth requirement leads to exercising the replication of an American put option at the uncertain stopping time. From our numerical results, active insurance and annuity markets are shown to be a critical aspect in retirement planning.

In modern times, technology has become more than just a tool; it is a vital component of our lives, driving society and humanity towards new horizons, better living standards, and solu-tions for complex, global challenges. This article explores the world of dependable systems, specifically focusing on system design, software solutions, and architectural decisions that fa-cilitate collaborative work. It aims to dive into the intricacies of designing robust and effective document collaboration software. Throughout this paper, readers will gain insights into the proposed design process, enabling them to understand, learn from, and critically evaluate the examined methodologies and outcomes. Like everything else in the software technology field, there are multiple solutions to any given problem, and this work is no exception to this rule. The objective of this study is to provide a detailed architectural overview of the microservices pattern in an orchestrated cloud environment based on independent containers. To examine the pros and cons of such a system, the paper will detail how a real-time document collaboration system can benefit from such an architecture regarding availability, elasticity, scaling, and disaster recovery. Moreover, the paper will analyze how cloud-native technologies can be used to create a scalable platform solution, capable of handling traffic, dynamic resource storage, asynchronous com-munication, and application scaling. The intricate nature of this system renders this paper a valuable resource for prospective investigations within the domain of dependable systems, cloud computing, and distributed systems. This paper not only showcases the design and implementation process but also sets a founda-tion for future research and innovation in dependable systems, collaborative technologies, sustainable solutions, and distributed system architecture.

Despite being stored at 113 K and atmospheric pressure, LNG cold potential is not exploited to reduce green ships’ energy needs. An innovative system based on three Organic Ranking Cycles integrated into the regasification equipment is proposed to produce additional power and recover cooling energy from condensers. A first-law analysis identified ethylene and ethane as suitable working fluids for the first and the second ORC making available freshwater and ice. Propane, ammonia and propylene could be arbitrarily employed in the third ORC for air-conditioning. An environmental analysis that combines exergy efficiency, ecological indices and hazard aspects for marine environment and ship’s passengers, indicated propylene as safer and more environmentally friendly. The exergy analysis confirms more than 20% of the LNG potential can be recovered from every cycle to produce a net clean power of 76 kW, whereas 270 kW can be saved by recovering condensers' cooling power to satisfy some ship needs. Assuming the sailing mode, a limitation of 162 kg in LNG consumptions was determined, avoiding the emission of 1584 kg of CO2 per day. Marine thermal pollution is reduced by 3.5 times by recovering the working fluids' condensation heat for the LNG pre-heating.

Aiming at the problems of inaccurate segmentation of long object and information loss of small object in real-time semantic segmentation algorithm, this paper proposes a lightweight multi-branch real-time semantic segmentation network based on BiseNetV2. The new auxiliary branch makes full use of spatial details and context information to cover the long object in the field of view. Meanwhile, in order to ensure the inference speed of the model, the asymmetric convolution is used in each stage of the auxiliary branch to design a structure with low computational complexity. In the multi-branch fusion stage, the alignment and fusion module is designed to provide guidance information for deep and shallow feature mapping, so as to make up for the problem of feature misalignment in the fusion of information at different scales, and thus reduce the loss of small target information. In order to further improve the model’s awareness of key information, a global context module is designed to capture the most important features in the input data. The proposed network uses NVIDIA GeForce RTX 3080 Laptop GPU experiment, on the road street view data set Cityscapes and CamVid average occurring simultaneously ratio reached 77.1% and 78.4% respectively, with running speed of 127 frames/s respectively and 112 frames/s. The experimental results show that the proposed algorithm can achieve real-time segmentation and improve the accuracy significantly, showing good semantic segmentation performance

Precision metagenomic approaches using Oxford Nanopore Technology (ONT) sequencing has been shown to allow recovery of complete genomes of Escherichia coli O157:H7 from overnight enrichments of agricultural waters. This study tests the applicability of a similar approach for Cronobacter genome recovery from powdered infant formula (PIF) overnight enrichments, where Cronobacter typically dominates the overall microbiome (>90%). In this study, we aimed to test whether using ONT sequencing of overnight PIF enrichments could recover a completely closed Cronobacter genome for further genomic characterization. Ten PIF samples, each inoculated with different Cronobacter strains, covering Cronobacter sakazakii, C. muytjensii, C. dublinensis, C. turicensis, and C. universalis, were processed according to the Bacteriological Analytical Manual (BAM) protocol. qPCR was used for initial screening (detection and quantification) of the overnight enrichments and confirmed that the spiked PIF samples after the overnight enrichment have high levels of Cronobacter (107 to 109 CFU/mL). DNA from overnight PIF enrichments was extracted from the enrichment broth and sequenced using ONT. Results showed that ONT sequencing could accurately identify, characterize, and close the genomes of Cronobacter strains from overnight PIF enrichments in 3 days, much faster than the nearly two weeks required by the current BAM method. Complete genome recovery and species differentiation were achieved. This suggests that combining qPCR with ONT sequencing provides a rapid, cost-effective alternative for detecting and characterizing Cronobacter in PIF, enabling timely corrective actions during outbreaks.

This study aimed to determine the COVID-19 recovery time and identify predictors among hospitalized patients in the Dhanusha District of Madhesh Province, Nepal. This hospital-based longitudinal study involved 507 COVID-19 patients admitted to three distinct medical facilities for therapeutic intervention between April to October 2021. Data were collected for patient demography, symptoms, vital signs, oxygen saturation levels, temperatures, heart rates, respiratory rates, and blood pressure measurements and other health-related conditions. Kaplan–Meier Survival Curve estimated the recovery time from, and Cox proportional hazard model was used to identify the predictors of recovery time. Of the total participants, mean age was 51.1 (SD=14.9) years, 68.0% were males, 49.5% recovered, and 16.8% died. The median for patient recovery was 26 days (95% CI: 25.1–26.7). Patients with severe or critical conditions were less likely to recover compared to those with milder conditions (Hazard Ratio (HR)=0.34, 95% CI: 0.15-0.79; p=0.012). In addition, an increase in oxygen saturation was associated with an elevated likelihood of recovery (HR=1.09, 95% CI=1.01-1.17, p=0.018). This study underscores the need for early admission in hospital and emphasizing the targeted interventions in severe cases. Additionally, the results highlight the importance of optimizing oxygen levels in COVID-19 patient care.

Counter-current spontaneous imbibition of water is a critical oil-recovery mechanism. In the laboratory, the Amott test is a widely used technique to assess efficacy of brine imbibition into oil-saturated core plugs. The classic Amott-cell methodology was designed to estimate ultimate oil recovery, but not the recovery dynamics that provides essential information about the imbibition mechanisms. Retention of oil droplets at the external core surface and delayed onset of oil production are the two key artifacts of the classic Amott experiment. This retention, referred to here as the ``external-surface oil holdup effect'', often results in stepwise recovery curves that obscure true dynamics of spontaneous imbibition. To address these holdup drawbacks of the classic Amott method, we modified the Amott cell and experimental procedure. For the first time, using water-wet Indiana limestone cores saturated with brine and mineral oil, we showed that our improvements of the Amott method enabled accurate and reproducible measurements of oil-recovery dynamics. We used the Generalized Extreme Value (GEV) statistics to describe oil-production histories from water-wet heterogeneous limestone cores with finite initial water saturations, for the first time. We demonstrated that our four-parameter GEV model accurately described the recovery dynamics, and that optimal GEV parameter values systematically reflected the key characteristics of the oil-rock system, such as oil viscosity and rock permeability. These findings gave us a more fundamental understanding of spontaneous, counter-current imbibition mechanisms and insights into what constitutes a predictive model of counter-current water imbibition into oil-saturated rocks with finite initial water saturation.

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.

The susceptible-infected-recovered/removed-vaccinated (SIRV) epidemics model is an important generalization of the SIR epidemics model as it accounts quantitatively for the effects of vaccination campaigns on the temporal evolution of epidemics outbreaks. Additional to the time-dependent infection ($a(t)$) and recovery ($\mu (t)$) rates, regulating the transitions between the compartments $S\to I$ and $I\to R$, respectively, the time-dependent vaccination rate $v(t)$ accounts for the transition between the compartments $S\to V$ of susceptible to vaccinated fractions. An accurate analytical approximation is derived for arbitrary and different temporal dependencies of the rates which is valid for all times after the start of the epidemics for which the cumulative fraction of new infections $J(t)\ll 1$. As vaccination campaigns automatically reduce the rate of new infections by transferring susceptible to vaccinated persons, the limit $J(t)\ll 1$ is even better fulfilled than in the SIR-epidemics model. The comparison of the analytical approximation for the temporal dependence of the rate of new infections $\jt(t)=a(t)S(t)I(t)$, the corresponding cumulative fraction $J(t)$, and $V(t)$, respectively, with the exact numerical solution of the SIRV-equations for different illustrative examples proves the accuracy of our approach. The considered illustrative examples include the cases of stationary ratios with a delayed start of vaccinations, and an oscillating ratio of recovery to infection rate with a delayed vaccination at constant rate. The proposed analytical approximation is self-regulating as the final analytical expression for the cumulative fraction $\jinf $ after infinite time allows us to check the validity of the original assumption $J(t)\le \jinf \ll 1$.

The dynamical equations of the susceptible-infected-recovered/removed (SIR) epidemics model play an important role to predict and/or analyze the temporal evolution of epidemics outbreaks. Crucial input quantities are the time-dependent infection ($a(t)$) and recovery ($\mu (t)$) rates regulating the transitions between the compartments $S\to I$ and $I\to R$, respectively. Accurate analytical approximations for the temporal dependence of the rate of new infections $\dot{J}(t)=a(t)S(t)I(t)$ and the corresponding cumulative fraction of new infections $J(t)=J(t_0)+\int_{t_0}^tdx\dot{J}(x)$ are available in the literature for either stationary infection and recovery rates and for a stationary value of the ratio $k(t)=\mu (t)/a(t)$. Here a new and original accurate analytical approximation is derived for general, arbitrary and different temporal dependencies of the infection and recovery rates which is valid for not too late times after the start of the infection when the cumulative fraction $J(t)\ll 1$ is much less than unity. The comparison of the analytical approximation with the exact numerical solution of the SIR-equations for different illustrative examples proves the accuracy of the analytical approach.

The architectural configuration of the space plays a crucial role in the acoustics of Neonatal Intensive Care Units (NICUs). The design of the environment, the materials used in construction, and the organisation of noise sources within the room significantly influence the noise levels present in these critical areas. For this research, noise levels found in two hospitals, with different architectural configurations and sizes but similar construction materials, have been analyzed. Data were recorded at 1-second intervals over a period of more than 24 hours. The data collected in these hospitals confirm the magnitude of the noise problem in NICUs, highlighting the urgent need to address it effectively to ensure an appropriate and safe environment for the recovery of neonates. Good architectural planning can help mitigate noise, while poor configuration can exacerbate acoustic problems, negatively affecting both patients and healthcare staff. It is crucial to involve architects, acoustic engineers, and healthcare staff in the design and renovation of NICUs to create spaces that not only meet medical needs but also consider the auditory well-being of neonates.

Relationships of the three bryophyte lineages (liverworts, mosses, and hornworts) to vascular plants is a pivotal question in the study of origin and evolution of land plants. In recent decades, this question has been subject to intense phylogenetic analyses using morphological characters, multigene DNA sequences, and genome structural characters. A tentative consensus reached ten years ago suggested that bryophytes are a paraphyletic group, with liverworts being sister to all other land plants and hornworts being sister to vascular plants. However, several more recent nuclear phylogenomic studies have concluded that bryophytes represent a monophyletic group that is sister to vascular plants. A discussion is presented here on strengths and weaknesses of different types of characters (morphological traits, nucleotide sequences, and genome structural arrangements), and their suitability for resolving deep phylogenetic relationships. Moreover, several criteria for credible phylogenetic reconstruction are proposed. Strong statistical support for reconstructed relationships should be derived from high quality, independent characters selected for suitability to the particular question being addressed. The relationships inferred in a study should be congruent with those from as many other lines of phylogenetic evidence as possible. Any incongruities should be explicable by well-understood biological mechanisms. It is concluded that the relationships of the three bryophyte lineages to vascular plants should currently be viewed as unresolved. This is a difficult phylogenetic problem; the land plants underwent a rapid radiation a long time ago. Yet, further exploration of analytical methods and careful choice of characters should lead to eventual elucidation of diversification patterns among early land plants.

The digital transformation in Industry 4.0 has revolutionized quality control paradigms, integrating advanced technologies such as augmented reality, deep learning, and computer vision systems into a new era called "Quality 4.0". This study systematically reviews how these technologies are shaping new practices in continuous quality supervision and improvement, adapting to increasingly automated and connected production environments. Through a comprehensive analysis of the literature, practical applications in various industrial sectors, from manufacturing and agriculture to the production of sweets and snacks, are highlighted. Additionally, the conceptual and detailed design of a quality control system using artificial intelligence tools is presented, focusing on the inspection of dimensions, color, weight, and labeling of MDF boxes. The proposed system demonstrates an effective integration of sensors, high-definition cameras, and deep learning algorithms, validated through tests with boxes of different sizes. The results suggest that the adoption of these technologies not only improves the accuracy and efficiency of inspections but is also essential to maintaining high-quality standards in a dynamic and competitive industrial environment.

Sharding shows great potential for extending the blockchain. The current challenge facing sharded blockchain technology lies in addressing the extended transaction confirmation times caused by isolated states between shards and unbalanced transaction allocation strategies. These factors contribute to an increase in cross-shard transactions and disproportionate shard workload, ultimately resulting in indefinite confirmation delays for cross-shard transactions. A critical priority for sharded blockchain systems is to conduct a comprehensive qualitative analysis to better understand and mitigate the prolonged transaction confirmation times. We introduce a Product-Form Queue Network (PFQN) model to address the transaction confirmation time problem in sharded blockchains and incorporate a new confirmation queue to more accurately simulate the actual transaction confirmation process in the blockchain. In addition, we provide a detailed quantitative analysis of the relationship between network load, consensus efficiency, and security in sharded blockchains, laying a theoretical foundation for achieving robustness and efficiency in sharded blockchains. This research not only contributes to addressing the scalability issues in sharded blockchains but also offers a new perspective for future research directions.

In this paper, we consider the Cauchy problem of a time fractional nonlinear diffusion equation. According to the Kaplan’s first eigenvalue method, we first prove the blow-up of the solutions in finite time for some sufficient conditions. We next give sufficient conditions for the existence of global solutions by using the result of Zhang and Sun. In conclusions, we find the second critical exponent for the existence of global and non-global solutions via the decay rates of the initial data at spatial infinity.

In electromagnetic ultrasonic testing, it is difficult to recognize small-size bottom cracks by time of flight (ToF), and the lift-off fluctuation of the probe affects the accuracy and consistency of the inspection results. In order to overcome the difficulty, a novel EMAT (electromagnetic acoustic transducer)-PEC (pulse eddy current) composite sensor is designed. We use amplitude of bottom echo recorded by EMAT to identify the tiny bottom crack as well as the amplitude of PEC signals picked up by the integrated symmetric coils to measure the average lift-off of the probe in real time. Firstly, the effects of lift-off and bottom cracks on amplitude of bottom echo are distinguished combining the theoretical analysis and finite element method (FEM). And then an amplitude correction method based on the fusion of EMAT and PEC signals is proposed to reduce the impact of lift-off on the defect signal. The experimental results demonstrate that the designed composite sensor can effectively detect the bottom crack as small as 0.1mm* 0.3mm. The signal fusion method can accurately correct the amplitude of defect signal and the relative error is less than ±8%.

Background: Vestibular Neuritis (VN) induces Acute Vestibular Syndrome (AVS). This study aimed to identify predictive factors of VN recovery by healing and/or compensation. Methods: In this longitudinal and prospective study, 40 patients were included. The first assessment (T0) included medical history taking (general and specific), for cardiovascular risk factors (CVRFs), a battery of diagnostic tests, bithermal caloric test (BCT), video Head Impulse Test (VHIT) and skull vibration induced nystagmus (SVIN). All patients also completed a Dizziness Handicap Inventory (DHI). All assessments were repeated 90±15 days (T3) later. Subjective criteria compensation were based on DHI total score and objective criteria compensation on the instrumental test results. Four groups of patients (A, B, C, D) were delineated by combining patients with normal vs abnormal vestibular tests and patients with normal vs abnormal DH. Results: CVRFs were associated with a poorer recovery of symptoms but not the age or BMI. Vestibular tests mainly BCT (canal paresis %), VHIT (lateral semicircular canal gain) and SVINT (nystagmus slow phase velocity) recovered within normal values in 20%, 20%, 26% of cases respectively at T3 for the 40-patient group. Conclusions: Vascular risk factors (hypercholesterolemia) are correlated with patients who do not recover their symptoms either by healing or compensation. The evolution did not show any significant difference between high or low frequency vestibular tests in patients recovering from their symptoms. However, some patients with objective recovery may continue to show persisting subjective symptoms.

The term Air Quality (AQ) pertains to the standard of air, which has a direct impact on the health and well-being of individuals. It is crucial to maintain high Air Quality (AQ), for better health and productivity. Though most of people spend more time indoors nowadays, it is not to be overlooked that the pollutant concentration in indoor air is directly linked to the outdoor air quality. Due to the changes in occupancy patterns, outdated maintenance of ventilation systems, and structural flaws in buildings, the indoor air is significantly polluted; pollutants emitted outdoors enter indoor air through open windows, ventilation systems and infiltration. In this paper, we have proposed a new technique, DTMCPM (discrete-time Markov chain (DTMC) model for the analysis and forecasting of AQ, using the power method). Specialized sensors and equipment to measure different pollutants, such as particulate matter, ozone, nitrogen dioxide, sulfur dioxide, benzene and toluene, are used to collect data by the Institute of Pakistan Air Quality Monitor in various cities; the data is converted to US EPA standard for all pollutant species. This data is used to calculate the transition matrix, steady-state values and mean return rate for the analysis of IAQ. The calculated and actual return rates have been compared and the proposed model is found to have a low average prediction error of 2.356%.

Background: Treatment at stabilization center is an important intervention to avert the huge burden of mortality for children with complicated severe acute malnutrition (SAM). Recent reviews indicated a wide range in recovery rate (34-88%) due to several context-specific factors. This study aimed to estimate time to recovery and to determine predictors of time to recovery among children aged 6-59 month with severe acute malnutrition. Method: Retrospective cohort study was used among 375 children aged 6-59 months admitted in Jimma university medical center, from September 2015 to September 2017. Kaplan Meir estimate and survival curve was used to compare the time to recovery using log-rank test among different characteristics. Cox Proportional Hazard Model was used to identify significant predictors of time to recovery. Results: Median time of recovery for cohort of SAM children’s was 19 days (95%CI: 17.95-20.05). Independent predictors of time to recovery were: Play stimulation, vaccination status, Tuberculosis, malaria, use of amoxicillin, deworming and shock. Conclusion: The findings of this study showed that the average length of stay on treatment and median time for recovery are within the sphere standard. Psychosocial stimulation, appropriate provision of routine medication and management of medical co-morbidity are needed to promote fast recovery.

This paper explores the application of machine learning (ML) in predicting functional recovery in patients with ischemic stroke. As technology advances, ML shows significant potential in the field of stroke medicine, especially in the areas of big data analytics and personalized medicine. Studies have shown that ML algorithms can improve the accuracy of stroke image analysis, subtype classification, risk assessment, treatment guidance, and prognosis prediction. However, the widespread use of ML still faces challenges such as data standardization, model validation, privacy, and bias. This paper reviews the current application status of ML in the field of stroke, discusses the challenges faced, and looks forward to the future development direction, aiming to promote the practical application of ML technology in the diagnosis and treatment of stroke to improve the prognosis and quality of life of patients.

The necessity for precise and current data concerning the dynamics of land cover change in Indonesia is crucial for efforts to reduce natural vegetation cover due to agricultural expansion. The functionality of monitoring systems that incorporate Terra-MODIS is currently compromised by the limited availability of data for the immediate future. This study seeks to assess the potential of VIIRS satellite imagery in developing an early warning system for monitoring vegetation cover change in Indonesia. The Normalized Differential Open Area Index (NDOAI) computed from the 8-day’ VIIRS data was employed to detect changes in vegetation cover based on pixel-by-pixel subtraction in the NDOAI data time series. Evaluating the pixel-level accuracy of change detection is complicated due to the fact that we evaluate a change map at a coarser resolution than the Landsat-based reference map. The results revealed that increasing the threshold percentage is associated with improved accuracy. In change detection, there is often a trade-off between accuracy and sensitivity. A threshold that is too low may result in false positives, while a threshold that is too high may lead to missed changes. This study demonstrates that when a threshold value of less than 20% is applied, Landsat can identify vegetation cover changes at an earlier stage. Conversely, when a threshold value greater than 20% is employed, VIIRS will detect the change 4.5 days earlier than Landsat. Additionally, VIIRS is capable of detecting changes 25.4 days and 54.8 days faster than Landsat, respectively, when using thresholds of 40% and 75%.

Chip pad alignment inspection is of great importance in the industrial field. However, due to the fact that chip pads are usually small, problems such as misdetection and missed detection often occur. When applying deep learning methods for chip pad detection, it is necessary to ensure accurate detection of small target chips while meeting the requirements of lightweight detection models for industrial needs. To solve the above problems, this paper proposes a lightweight model based on improved YOLOv5s. Firstly, the feature extraction part is improved to increase the network's focus on the target. Secondly, the feature fusion layer is improved to double the resolution of the prediction head, and the context-aware network is designed to enhance the context-capture ability of key features of small targets. Finally, SIoU is adopted as the loss function to improve the speed and accuracy of the regression frame. The experimental results show that the improved YOLOv5s algorithm improves the detection accuracy by 2.3% and reduces the network parameters by 81.8% compared to the original algorithm. The improved algorithm is combined with image processing techniques to design correction methods for alignment anomalies and realize real-time alignment anomaly correction in industry.

In order to achieve the dual carbon goal and simplify the decarbonization process, a CO2 recovery process of the fire-flooding exhaust was designed. The process adopted the two-stage membrane separation method to recover CO2 and obtain high purity CO2 rich flow, and then through the mixed refrigerant liquefaction and rectification column, food grade liquid CO2 was obtained. HYSYS process simulation shows that for the fire-flooding exhaust with a flow rate of 1000 kg/h and a given composition, the process can achieve a CO2 recovery of 69.02%, total product sales S=2086.8 yuan/h, and specific power consumption of 2.818 kW∙h/kg. On this basis, a hybrid simulation platform of HYSYS & Matlab was established. Taking the specific power consumption of the product as the objective function, sequential quadratic programming (SQP) algorithm was adopted to optimize the process, and the optimized specific power consumption was reduced by 18.8%. Finally, by adjusting N2/CO2 components, the adaptability of the optimization condition was analyzed.

In the field of railroad safety, effective detection of surface cracks is critical, necessitating reliable, high-speed non-destructive testing (NDT) methods. This study introduces a hybrid Eddy Current Testing (ECT) probe, specifically engineered for railroad inspection, to address the common issue of ’lift-off noise’ due to varying distances between the probe and test material. Unlike traditional ECT methods, this probe integrates transmit and differential receiver coils, aiming to enhance detection sensitivity and minimize lift-off impact. The study optimizes ECT probes employing different driver coils, emphasizing three main objectives: a) quantitatively evaluating each probe using signal- to-noise ratio (SNR) and outlining a real-time data processing algorithm based on SNR methodology; b) exploring the frequency range proximal to the electrical resonance of the receiver coil; c) examining sensitivity variations across varying lift-off distances. The experimental outcomes indicate that the newly designed probe with figure 8-shape driver coil significantly improves sensitivity in detecting surface cracks on railroads. It achieves an impressive SNR exceeding 100 for defects with minimal dimensions of 1 mm in width and depth. Simulation results closely align with experimental findings, validating the investigation of optimal operational frequency and lift-off distance for selected probe performance, determined to be 0.3 MHz and 0.5 mm, respectively. The realization of this project would lead to notable advancements in enhancing railroad safety by improving crack detection efficiency.

In this paper, a proof similar to the Pusey-Barrett-Rudolph (PBR) theorem is given to prove that time in quantum theory is not epistemic but has ontological reality. For this, we first discuss how time can be encoded as an intrinsic property of a quantum system. We prepare a quantum state containing time as an intrinsic property and show by a PBR-type proof that time cannot be an epistemic notion. In fact, the PBR theorem implies an ontic notion of time. Indeed, if the quantum state is a physical property, then quantum state reduction is a physical process involving information destruction. In this case, quantum probabilities are intrinsic probabilities without epistemic origin, and they generate a genuinely new sequence of events. This novelty introduced by quantum probabilities can be interpreted as time.

Quantum gravity theories rely on a minimal measurable length for their formulations, which clashes with the classical formulation of the uncertainty principle and with Lorentz invariance from general relativity. These incompatibilities led to the development of the generalized uncertainty principle (GUP) from string theories and its various modifications. GUP and covariant formulations of the uncertainty principle are discussed, together with implications for space-time quantization.

Optimal control is a critical tool for mechanical robotic systems, facilitating precise manipulation of dynamic processes. These processes are described through differential equations governed by a control function, addressing a time-optimal problem with bilinear characteristics. Our study utilizes the classical approach complemented by Pontryagin's Maximum Principle (PMP) to explore this inverse optimal problem. The objective is to develop an exact piecewise control function that effectively manages trajectory control while considering the effects of viscous friction. Our simulations demonstrate that the proposed control law markedly diminishes oscillations induced by boundary conditions. This research not only aims to delineate the reachability set but also strives to determine the minimal time required for the process. The findings include an exact analytical solution for the stated control problem.

Accurate traffic simulation models play a crucial role in developing intelligent transport systems that offer timely traffic information to users and efficient traffic management. However, calibrating these models to represent real-world traffic conditions accurately poses a significant challenge due to the dynamic nature of traffic flow and the limitations of traditional calibration methods. This article introduces a machine learning-based approach to calibrate macroscopic traffic simulation models using real-time traffic video stream data. By leveraging computer vision technologies to extract key traffic parameters from video streams, the approach demonstrated a notable improvement in aligning the generated data from the calibrated simulation model with car sensor data, achieving an average improvement of over 50% compared to the uncalibrated macroscopic model. Moreover, there was a substantial reduction in data drift for the machine learning model integrated into the virtual transport space using vehicle-to-everything technology, resulting in a more than fourfold decrease in the average absolute error of the model.

In the last decades we have experienced great social, cultural, and educational changes, spending more time performing tasks in near vision and less hours in outdoor activities, having more overexposure to digital screens, especially the youngest and increasingly from younger ages. It is common to see cases when performing tasks in near vision the posture or position of the head is not adequate, especially the youngest, being essential to have a correct posture of the head to avoid visual, muscular, or articular problems. We present a procedure to try to control the correct position of the head in real time when performing close-up activities. The aim is to detect the inclination of the head and to know if a bad position is adopted due to a visual problem or simply to inadequate visio-postural habits, warning us of the postural anomaly to correct it.

A review discusses recent research findings spanning the last two decades concerning ultrasonic technologies applicable to the oil, gas, and coal sectors. Various experiments conducted in laboratories have demonstrated the efficacy, cost-effectiveness, and environmental friendliness of ultrasound in recovering and processing oil, bitumen, coal, and oil shale. Ultrasound enhances formation permeability, coal gas permeability, and oil viscosity, particularly when delivered in short, powerful pulses at medium frequencies. Combining ultrasound with traditional recovery methods has shown promising results, boosting recovery efficiency by up to 100%. At the same time, ultrasonic treatment reduces the use of traditional reagents, thereby reducing environmental pollution. Moreover, ultrasound treatment shows potential in tasks such as separating oil-water emulsions, desulfurization, dewaxing oil, coal enrichment, and extracting valuable metals from metal-bearing shales through hydrometallurgical leaching. However, widespread industrial implementation of ultrasonic technology necessitates further field and mathematical research.

This work introduces a new architecture for a time-domain feedback-based beamformer designed to meet real-time processing demands. The primary goal of this design is to locate reflective sources by estimating their direction of arrival (DOA) and signal range. Incorporating a feedback mechanism in this architecture is a unique aspect that refines localization precision. We conduct an in-depth analysis highlighting their respective benefits and limitations to compare the effectiveness of time-domain feedback beamforming against conventional time-domain methods. Our evaluation of the proposed architecture, based on critical performance indicators such as peak-to-sidelobe ratio, mainlobe width, and directivity factor, demonstrates its ability to improve beamformer effectiveness significantly.

Performing optimisation and scale-up studies of crystallisation systems requires accurate and computationally efficient mathematical models. The assumption of the ideal mixing conditions in batch reactors typically produce inaccurate results while the computational expense of CFD models is still prohibitively high. Therefore, in this work, a new intermediary approach is proposed that takes into account the non-ideal mixing conditions in the reactor and requires less computational resources than full CFD simulations. Starting with the Danckwerts concept of the intensity of segregation, an analogy between its application to chemical reactions and the kinetics of the crystallisation phenomena (such as nucleation and growth) has been made. As a result, the modified kinetics expressions have been derived which incorporate the effect of non-idealities present in stirred reactors. This way, based on the experimental measurements of the mixing time using the Laser Induced Fluorescence (LIF) technique, computationally more efficient mathematical models can be developed in two ways: (1) the accurate semi-empirical correlations are available for standard mixing configurations with the most often used types of impellers, (2) CFD simulations can be utilised for estimation of the mixing time; in this case it is necessary to simulate only the mixing process. The benefits offered by the LIF experimental technique have been demonstrated and some frequent problems in its application analysed. The mixing time results for configurations with and without baffles for three types of impellers and four different rotational speeds have been presented. The false shorter mixing times in the non-baffled configurations have been observed and this phenomena explained by the existence of two segregated zones in the reactor and confirmed by additional experiments. The precise measurements in these cases have been shown as difficult using the LIF technique, particularly for higher rpms. The experimental data has been compared to the preliminary simulation results obtained from the Smoothed Particle Hydrodynamics method and the standard k-ε turbulence model with the modest success. The shortcomings of the SPH model have been recognized and the directions for the future work discussed.

This paper represents the first investigation into the consensus problem of linear time-varying multi-agent systems utilizing an event-triggered communication scheme. First, a general event-triggered consensus control scheme is proposed for a general category of linear time-varying multi-agent systems. Under some suitable assumptions, it is demonstrated that all agents’ states will converge exponentially, with Zeno behaviour being ruled out. Second, the consensus problem in a network of linear time-varying multi-agent systems with a spanning tree is investigated using the proposed control strategy. It demonstrates that the consensus issue for the specified system can be reformulated as a stabilization problem for an error system through a time-varying linear transformation. Then, the event-triggered consensus problem is just a special instance of the general event-triggered consensus problem mentioned above. Finally, to illustrate the efficacy of the event-triggered method proposed in this study, simulation results are shown.

A decanter centrifuge has been employed to successfully separated phosphoric acid and rare-earth-element (REE)-containing particles from phosphoric acid sludge with concentrations ranging from 1,000 to 2,200 ppm REEs. Operating efficiently with up to 35 wt.% solids, the centrifuge was demonstrated to achieve approximately 95% phosphoric acid recovery and 90% REEs recovery in a single pass, eliminating the need for additional processing steps. This breakthrough supports a proposed REEs oxide recovery process integrating phosphoric acid (PA), elemental phosphorus (P4), and REEs into two potential pathways: PA-REO and PA-P4-REO. These processes aim to reintroduce recovered phosphoric acid into the main product to significantly increase output and revenue. Post-separation, phosphorus-rich particles can be converted to P4, while REEs-containing solids undergo further treatment including acid leaching, extraction/stripping, precipitation, and calcination to produce a marketable rare-earth oxide (REO) material. Techno-economic analysis indicates promising profitability, with the PA-REO process showing a delta net present value (∆NPV) of $441.8 million and an expected return within a year of construction, while the PA-P4-REO process yields a ∆NPV of $178.7 million over a 12-year return period. Both pathways offer robust financial prospects and demonstrate the feasibility of commercial-scale REEs recovery from phosphoric acid sludge.

As the adoption of renewable energy sources grows, ensuring a stable power balance across various time frames has become a central challenge for modern power systems. In line with the "dual carbon" objectives and the seamless integration of renewable energy sources, harnessing the advantages of various energy storage resources and coordinating the operation of long-term and short-term storage have become pivotal directions for future energy storage deployment. To address the complexities arising from the coupling of different time scales in optimizing energy storage capacity, this paper proposes a method for energy storage planning that accounts for power imbalance risks across multiple time scales. Initially, the Seasonal and Trend decomposition using the Loess (STL) decomposition method is utilized to temporally decouple actual operational data. Subsequently, power balance computations are performed based on the obtained data at various time scales to optimize the allocation of different types of energy storage capacities and assess the associated imbalance risks. Finally, the effectiveness of the proposed approach is validated through hourly applications using real-world data from a province in China over recent years.

Commuting is a vital travel channel for college students throughout their higher education career; nevertheless, excessive commute time negatively impacts students’ school satisfaction, academic achievement, health, and quality of life. Meanwhile, few studies have explored students’ commute time, covering a large population and scope. Therefore, this study analyzes the dwelling type that influences college and graduate students’ school commute times in the Seoul Metropolitan Area, using data from the 2016 Household Travel Diary Survey. This study uses spatial data on housing development to classify five dwelling types (i.e., new town, housing site development, Seoul, Incheon, and Gyeonggi-do). A multiple linear regression model empirically analyses students’ household and personal characteristics. The results reveal that dwelling type significantly affects students’ commute time. Specifically, Students living in Seoul have a shorter commute to school than those living in new towns. Socioeconomic characteristics have less effect on college and graduate students' school commute time. This study discusses the problem of excessive commuting of college and graduate students in new towns.

This paper proposed an optimal tracking control scheme through adaptive dynamic programming(ADP) for a class of partially unknown discrete-time nonlinear systems based on radial basis function neural network(RBF-NN). In order to acquire the unknown system dynamics, we use two RBF-NNs, the one is used to construct the identifier, and the another is used to directly approximate the steady-state control input, where a novel adaptive law is proposed to update neural network weights. While the optimal feedback control and the cost function are derived via feedforward neural networks approximating, it is proposed to regulate the tracking error, the critic network and the actor network are then trained online to obtain the solution of the associated Hamilton–Jacobi–Bellman (HJB) equation being built under the ADP framework. Simulations verify the effectiveness of the optimal tracking control technique using the neural networks.

The formation of hydrogen sulfide (H₂S) in petroleum reservoirs by anaerobic microbial activity is called biogenic souring of reservoirs and is a risk in the oil and gas industry as the compound is extremely toxic, flammable and corrosive. In this paper, we present a workflow and the tools to assess biogenic souring from a pragmatic engineering perspective. The retention of H₂S in the reservoir due to the reactions with iron-bearing rock minerals (e.g. siderite) is shown in a theoretical approach here and supported with literature data. Cases are given for two fields under secondary (waterflooding) and tertiary flooding with microbial enhanced oil recovery (MEOR). The use of the Monte Carlo method as a numerical modelling tool to incorporate uncertainties in the measured physical/chemical/ biochemical data is demonstrated as well. A list of studies conducted with different chemicals alone or in combination with various biocides to mitigate biogenic souring provides an overview of potential inhibitors as well as possible applications. Furthermore, the results of static and dynamic inhibition tests using molybdate are presented in more detail due to its promising mitigation ability. Finally, a three-step workflow for the risk assessment of biogenic souring and its possible mitigation is presented and discussed.

In an effort to enhance the efficiency and safety of open-pit mines, this study explores the optimization of end slope road parameters and slope structures, specifically focusing on unmanned driving lanes.A significant aspect of the study is the development of a truck trajectory offset model, which considers the different reaction times between automated sensors and human drivers in adapting to environmental changes. To test these concepts, the study uses numerical simulations to confirm the stability of the proposed end slope designs. Using Victory West Mine No. 1 as a case study, the research determines the optimized width for unmanned driving lanes and the maximum angle for safe steepening of end slopes. The findings indicate that the optimized unmanned lane width for NTE240 mining dump trucks is 1743 mm, allowing for a 2-degree increase in the slope angle at the south end slope. This optimization leads to a steep mining stripping volume of 3.2735 million m³ and a coal output of 2.49628 million tons, maintaining a stripping ratio of 1.31m³/t. These results demonstrate that the unmanned driving road width optimization not only ensures slope safety but also significantly boosts the economic benefits of steep mining in open-pit mines.

Inadequately managed agricultural waste significantly impacts the environment, health, and economy. This form of pollution arises from the underutilization, poor awareness, and insufficient treatment of agricultural waste. Fruit and vegetable wastes are valuable sources of bioactive compounds. This study aims to revalorize discarded waste from red habanero chili peppers (Capsicum chinense) by extracting bioactive compounds through different extraction processes: maceration (ME), maceration assisted by ultrasound (US), Soxhlet extraction (SE), supercritical fluid extraction (SFE), and supercritical fluid extraction with a co-solvent (SFEC). Extraction processes have significant effects on extraction efficiency and phytochemical profile (capsaicinoids and carotenoids recovery). The results indicate that the highest efficiency process is obtained with SFEC, also a high phytochemicals recovery (14.9 mg of total capsaicinoids and total carotenoids 292.09 µg per gram of sample). Concerning to phytochemical profile in the extract, the maceration process gets the highest concentration of compound followed by US and SFEC. These data reveal that the use of SFE and SFEC extraction is recommended for extraction of phytochemicals with biologically activity from red habanero chili pepper waste for diverse industries application.

Abstract: Short-chain fatty acids (SCFAs) are the major microbial metabolites produced in the gut through the fermentation of non-digestible fibers. SCFAs act through free fatty acid receptor 2 and 3 (FFAR2 and 3), which are known to be expressed on glucagon-like peptide-1 (GLP-1)-secreting L-cells. This pathway is suggested to protect the intestinal mucosa. We aimed to investigate how lack of fiber affects the production of SCFAs and the vulnerability of the co-lon. Additionally, the involvement of FFAR2 and 3 in GLP-1 secretion and intestinal protection. SCFA concentrations in luminal contents from chow- or fiber-free diet-fed mice were analysed by LC‒MS. SCFAs were applied to isolated perfused colons from FFAR2/3 double KO and WT mice, and the secretion of GLP-1 was measured. Dextran sulfate sodium (DSS) colitis was induced in chow- or fiber-free diet-fed mice as well as in FFAR2/3 KO and WT mice. The removal of die-tary fiber significantly decreased total colonic SCFA concentrations, with specific decreases in acetate, propionate, and butyrate. SCFAs significantly increased colonic GLP-1 secretion but not in FFAR2/3 KO mice. In the DSS model, the removal of dietary fiber increased the severity and delayed recovery. Colitis severity was independent of FFAR2 and FFAR3 signaling.

The enormous growth in the penetration of electric vehicles (EVs), has laid the path to advancements in the charging infrastructure. Connectivity between charging stations is an essential prerequisite for future EV adoption to alleviate users’ “range anxiety”. The existing charging stations fail to adopt power provision allocation and scheduling management. To improve the existing charging infrastructure data based on real-time information and availability of reserves at charging stations could be uploaded to the users to help them locate the nearest charging station for an EV. This research article focuses on an a interactive user application developed through SQL and PHP platform to allocate the charging slots based on estimated battery parameters, which uses data communication with charging stations to receive the slot availability information. The proposed server-based real-time forecast charging infrastructure avoids waiting times and its scheduling management efficiently prevents the EV from halting on road due to battery drain out. The proposed model is implemented using a low-cost microcontroller and the system etiquette tested.

Underwater Sensor Networks (UWSNs) can enable a broad range of applications such as resource monitoring, disaster prevention, and navigation-assisted. It is especially relevant for sensor nodes location in UWSNs. Global Positioning System (GPS) is not suitable for using in UWSNs because of the underwater propagation problems. Hence some localization algorithms based on the precise time synchronization between sensor nodes have been proposed which are not feasible for UWSNs. In this paper, we propose a localization algorithm called Two-Phase Time Synchronization-Free Localization Algorithm (TP-TSFLA). TP-TSFLA contains two phases, namely, range-based estimation phase and range-free evaluation phase. In the first phase, we address a time synchronization-free localization scheme base on the Particle Swarm Optimization (PSO) algorithm to decrease the localization error. In the second phase, we propose a Circle-based Range-Free Localization Algorithm (CRFLA) to locate the unlocalized sensor nodes which cannot obtain the location information through the first phase. In the second phase, sensor nodes which are localized in the first phase act as the new anchor nodes to help realize localization. Hence in this algorithm, we use a small number of mobile beacons to help achieve location without any other anchor nodes. Besides, to improve the precision of the range-free method, an extension of CRFLA by designing a coordinate adjustment scheme is updated. The simulation results show that TP-TSFLA can achieve a relative high localization ratio without time synchronization.

A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: Place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: briefly describe the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions.

This bibliometric analysis delves into the realm of botanical gardens through the process of mapping literature and discerning research frontiers, underlying themes, and interconnections within the extant body of knowledge. The study uses a dataset of 1340 publications and employs methodologies such as authors' keywords co-occurrence data to examine the discipline's trends, patterns, and knowledge gaps. The study finds significant growth in literature from 1960 to 2023, emphasizing botanical gardens, conservation, taxonomy, biodiversity, ex-situ conservation, and diversity. Key influencers such as Sanja Kovačić, Elaissi, and Chemli have steered research directions towards ex-situ conservation and essential oils, reflecting the field's interdisciplinary nature. The study shows global institutional variation, with entities like the Chinese Academy of Sciences, the Royal Botanic Gardens UK, and the New York Botanical Garden US leading the discourse. Thematic analyses have identified core themes and emerging research topics, with ecological restoration, essential oils, invasive species, and climate change mirroring the field's expanding scope. The consistent prevalence of themes such as 'botanical gardens' and 'conservation' over time underscores their enduring significance. Emerging topics like climate change and biodiversity are gaining prominence, signaling a shift in research priorities. The expanding array of subjects, including 'invasive species' and 'genetic diversity,' indicates an amplified scope and growing complexity in botanical garden research. This study aims to bridge a crucial gap by outlining the developmental trajectory of botanical gardens and offering guidance for future research directions. Diverse research domains within botanical garden studies, including biodiversity, essential oils, taxonomy, and ex-situ conservation, present extensive avenues for exploration. Future research endeavors should delve into these impactful themes to better understand botanical ecosystems and their conservation. The study emphasizes the importance of nurturing interdisciplinary collaborations and multidisciplinary projects, advocating for a comprehensive and collaborative approach to future research in botanical garden studies.

The extent of correlation or co-movement among the returns of developed and/or emerging stock markets remains pivotal for efficiently diversifying global portfolios. This correlation is prone to variation over time as a consequence of escalating economic interdependence fostered by international trade and financial markets. In this study, we analyzed the time-varying correlation and co-movement between the JSE.JO stock market of South Africa and its developed and developing stock market partners. The Dynamic Conditional Correlation - Exponential Generalized Autoregressive Conditional Heteroscedasticity (DCC-EGARCH) methodology is employed with different multivariate distributions to explore the time-varying correlation and volatilities between the JSE.JO stock market and its partners. Based on the conditional correlation results, the JSE.JO stock market is integrated and co-moves with its partners, and the conditional correlation for all markets exhibit time-variant behavior. The conditional volatility results show that the JSE.JO stock market behaves differently from other markets, especially after 2015, indicating a positive sign for investors to diversify between JSE.JO and its partners. The highest value of conditional volatility for markets was in 2020 during the COVID-19 pandemic, representing the riskiest period that investors should avoid due to the lack of diversification opportunities during crises.

This study presents the design and comprehensive 3D multiphysics simulation of a novel microfluidic immunosensor for real-time, non-invasive monitoring of pro-inflammatory biomarkers in human sweat. The patch-like device, designed using COMSOL Multiphysics, integrates magnetofluidic manipulation with direct-field capacitive sensing. The sensor comprises two distinct units: an immunocomplex enhancement unit, employing a series of microcoils to optimize the binding efficiency, reaction kinetics, and homogeneity of biomarker-magnetic nanoparticle (MNP) interactions, thereby enhancing the sensor's specificity; and a layered capacitive sensing unit, designed to concentrate and detect biomarker-tagged MNPs, thus amplifying sensitivity. Simulations of the capacitive sensing unit revealed a substantial sensitivity increase of up to 42.48\% at an 85\% MNP concentration within the detection zone. These findings highlight the potential of the proposed immunosensor for efficient and precise real-time biomarker monitoring, which may facilitate early disease diagnosis and enable personalized healthcare interventions.

COVID-19 is fast spreading around the globe in a highly contagious manner. The results from our study showed that after intervention with successive Lockdowns, there was marked decrease in the rate of COVID-19 cases, though there was sporadic volatility in number of COVID-19 cases due to some extrinsic factors. Concomitant with reduction in rate of COVID-19 there was gradual increase in doubling time of COVID-19, steady increase in number of discharged/recovered patients from COVID-19 reaching to ≥ 58% taking the entire Indian population into consideration. Another important aspect was consistent low mortality rate was accompanied by gradual increase in recovery rate of COVID-19 in the population. The possible implication of these results in the development of protective immunity in the population has been discussed.

The phenomenon of vaccine hesitancy is a growing threat to public health with far-reaching implications. The widening gap between the vaccinated and the proportion needed for herd immunity raises two critical research questions that are of interest to practitioners, researchers, and policymakers: (1) What determines one’s decision to be vaccinated? and (2) What is the implication of COVID-19 vaccine hesitancy on economic recovery? In this study, we use empirical data in the context of South Africa to investigate factors affecting COVID-19 vaccine hesitancy and their implications for economic recovery. Findings reveal key socio-demographic and institutional drivers of COVD-9 vaccine hesitancy, which include age (the youth are more hesitant), inadequate information on the vaccine (those who perceive they have adequate information are vaccinated), trust issues in government institutions, conspiracy beliefs, vaccine-related factors, and perceived side effects associated with the vaccine. Additionally, an individual’s decision to remain hesitant about COVID-19 vaccination has implications for businesses and the economy by limiting movement and trade, increasing unemployment, and causing a resurgence of new variants. Based on the findings, action plans such as information dissemination, convenience vaccination centers, consistency communications, and targeted campaign strategies are recommended for improving vaccine intakes and a positive economic recovery.

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.

Addressing the significant challenge in high-speed cotton dibbling operations in Xinjiang—specifically, the duck bill’s ("Duck bill" is a type of duck-billed bunch planting mechanism or component). shortened opening duration leading to seed throwing and floating—a new crawler-type delayed hole-forming mechanism has been designed. This mechanism extends the duck bill horizontally, thereby prolonging its opening time, enabling cotton sowing speeds of up to 6 km/h. Following agronomic principles for cotton planting, the overall layout of the crawler-type hole-forming mechanism, the structural parameters of the key components and the trajectory of the hole-forming are determined. We use the Discrete Element Method (DEM) and multi-body dynamics coupling method to simulate and analyze the motion process of the device. The fixed the tilt angle of the duck bill α(A), the depth of the duck bill hole-forming into the soil h(B), and the angle of rotation of the moving duck bill β(C) were taken as the experimental factors, with the longitudinal length of cotton seed hole (Y1) and the hole-forming depth of cotton seed hole (Y2) as the response values. The parameter range of each factor and the preliminary influence on the response value were determined through pre-experiments and single-factor simulation test. Based on RecurDyn-EDEM coupling, a quadratic regression orthogonal rotation combination simulation test was carried out. The results show that the order of the primary and secondary factors affecting the Y1 of the time-lapse hole-forming device was: A > C > B. The order of primary and secondary factors affecting the Y2 of the delayed hole forming device was: B > C > A. When the A was 2.4 °, the B was 42.4 mm, and the C was 30.5 °, the performance of the hole-forming was the best, and the Y1 was 26.4 mm, and the Y2 was 25.0 mm. The bench verification test results show that under the optimized combination of operating parameters, the average Y1 was 29.1 mm, and the average Y2 was 23.2 mm. Compared with the results of the previous coupled simulation model, the average relative errors of the two are 7.23 % and 4.36 %, respectively, indicating the accuracy and effectiveness of the established DEM simulation model and virtual test analysis. The hole-forming area and the opening time of the duck bill after the stable operation of the machine were recorded and measured. The calculated hole dislocation rate Hc was 4.33 %, which did not exceed the national standard requirement of 6 %. The qualified rate of hole distance Hx was 94.33 %, which was much higher than the national standard requirement of 80 %. The average opening time of the duck bill at a speed of 6 km/h is 0.45 s, which is much longer than the time required for cotton seeds to fall from the duck bill 0.11 s. It meets the requirements of high-speed cotton planting in China and facilitating advancements in future high-speed planter technology.

Real-time object detection remains a pivotal topic in the realm of computer vision. Balancing the accuracy and speed of object detectors poses a formidable challenge for both academic researchers and industry practitioners. While recent transformer-based models have showncase the prowess of the attention mechanism, delivering remarkable performance improvements over CNNs, their computational demands can hinder their effectiveness in real-time detection tasks. In this paper, we elect to use YOLOX as our robust starting point and introduce a series of effective enhancements, culminating in a new high-performance detector named YOLOAX. To further exploit the power of the attention mechanism, we devise multi-dimensional attention-based modules that activate CNNs, emphasizing the most pertinent regions and bolstering the capacity to learn the most informative image representations from feature maps. Moreover, we introduce a novel leading label assignment strategy called STA, along with a groundbreaking loss function named GEIOU Loss, to further refine our detector's performance. We provide extensive ablation studies on the COCO and PASCAL VOC 2012 detection datasets to validate our proposed methods. Remarkably, our YOLOAX is trained solely on the MS-COCO dataset from scratch, without leveraging any prior knowledge, which achieves an impressive 54.2% AP on the COCO 2017 test set while maintaining a real-time speed of 72.3 fps, surpassing YOLOX by a significant margin of 3.0% AP. Our source code and pre-trained models are openly accessible at https://github.com/KejianXu/yoloax.

Recovery of forest after logging can be tested in many ways: the presence of particular species of fauna or flora, the similarity of the biodiversity of the recovering forest to that on neighbouring areas of undisturbed forest; or the characteristics of soils and streams whose conditions may have drastically changed during logging. Three cases of rainforest recovery after logging and clearance from Australia and Borneo exhibit different starting and different goals for recovery. Faunal indicators of recovery vary with size and with species dependence of the rainforest. Endemic forest species may have difficulty in recovering. Tree species richness and abundance may recover in two decades, but canopy closure takes longer. Compacted soils may retain low infiltration capacities for many decades. This diversity in recovery rates is confirmed when compared with those used elsewhere. Because the starting points for recovery vary, from damage by tropical cyclones and landslides, to clearance for shifting cultivation, pasture or agriculture, to post-logging conditions, universal indicators may be inappropriate. The desired endpoints of recovery also range from a “wilderness” state to a National Park for human enjoyment, biodiversity preservation, safeguarding rights of traditional forest-dwelling peoples, or a second round of selective logging.

This study presents preliminary findings on the physicochemical, calorific, and thermal properties of a newly discovered coal from Obomkpa town in Aniocha-North Local Government Area of Delta State in Nigeria. The Obomkpa (BMK) coal sample was subjected to fuel characterisation through ASTM standards and techniques for determining elemental, proximate, and calorific coal properties. The results indicated that BMK coal contains high carbon, oxygen, volatile matter, fixed carbon, and ash. The higher heating value of BMK was 19.66 MJ/kg, which indicates it could be ranked as either lignite A or subbituminous C coal. The thermal properties indicated that BMK experienced significant mass loss (M_L) under oxidative (combustion) and non-oxidative (pyrolysis) conditions due to thermal degradation of organic rock macerals (inertinite and vitrinite). Furthermore, BMK experienced mass loss (M_L=59.27%–76.56%) along with residual mass (R_M = 23.44–40.73%) under oxidative (combustion) and non-oxidative (pyrolysis) conditions. Thermal degradation occurred in three (3) stages; drying (30 °C to 200 °C), devolatilization (200 °C – 500 °C and 600 °C), and lastly, coke degradation and ash formation. Furthermore, the DTG peaks for the drying and devolatilization of BMK during the oxidative (combustion) process occurred at lower temperatures compared to the non-oxidative (pyrolysis) process. Hence, the results submit oxidative (combustion) is a more thermally efficient process compared to the non-oxidative (pyrolysis) process. Lastly, the findings indicate BMK is a potential feedstock material for future coal power generation, steel, iron ore or cement production.

Academics in healthcare grapple with the challenge of efficiently managing time while addressing diverse roles and responsibilities, leading to heightened levels of anxiety and stress within the academic community. The primary objective of this study is to offer a sustainable and systemic solution to the prevalent time-management challenges in healthcare academia, through the development of a predictive model. Employing systemic analysis software and adopting a systemic management approach, we crafted a model to address academic time-management concerns. The dynamics of time allocation for academicians are governed by three fundamental pillars: teaching, research, and administrative tasks. Significantly prioritizing administrative tasks depletes valuable academic time, making time management within academia excessively costly. This occurs as highly skilled individuals find themselves diverted towards administrative duties at the expense of their academic roles diminishing resilience levels. The proposed Timebooster Academic Systemic Model (TASM) advocates for the cultivation of soft skills in academia, en-compassing prioritization, seeking assistance, and addressing tendencies of perfectionism and procrastination. Additionally, TASM recommends the implementation of full automation, delegation of administrative responsibilities, and the mitigation of both overt and covert time-consuming elements. Furthermore, TASM reports on the pivotal role of the academic resilient manager-leaders, who actively contribute to their own satisfaction and academic growth by enhancing collaboration and teamwork. Time-management challenges emerge as a critical determinant of academic well-being and resilience. Systemic models, exemplified by TASM, offer a valuable framework for investigating prioritization concerns, identifying administrative impediments in academic processes, and managing low-value time-consuming elements. The TASM model, along with the systemic philosophy underpinning its design, presents an avenue for advancing the exploration of time-management phenomena within the context of healthcare academia.

In the realm of physical activity and sports science, motion tracking emerges as an important tool for specialists aiming to analyze movement data, whether for daily life activities or enhancing athletic performance. This study introduces an innovative approach to motion tracking through the development of a fully embedded smart shoe sensor system, which employs an inertial measurement unit (IMU) affixed to the shoe sole, coupled with six pressure sensors, to provide real-time calculation of individual stride lengths along with other vital movement statistics. Traditional motion tracking methods, which typically rely on various body-attached sensors, often necessitate substantial post-processing, delaying the feedback loop. In contrast, our system is designed to overcome these limitations by offering high-accuracy, real-time feedback with a minimal error rate of 1.88% from our experiments. Such prompt data acquisition facilitates immediate interventions by sports performance coaches or healthcare professionals, significantly contributing to injury prevention and performance enhancement. The experimental outcomes of this study underscore the system’s capability to deliver precise stride length measurements instantaneously, marking a significant advancement in motion tracking technology and its application in sports science and healthcare.

Structural health monitoring has been studied by a number of researchers as well as various industries to keep up with the increasing demand for preventive maintenance routines. This work presents a novel method for reconstruct prompt, informed strain/stress responses at the hot spots of the structures based on strain measurements at remote locations. The structural responses measured from usage monitoring system at available locations are decomposed into modal responses using empirical mode decomposition. Transformation equations based on finite element modeling are derived to extrapolate the modal responses from the measured locations to critical locations where direct sensor measurements are not available. Then, two numerical examples (a two-span beam and a 19956-degree of freedom simplified airfoil) are used to demonstrate the overall reconstruction method. Finally, the present work investigates the effectiveness and accuracy of the method through a set of experiments conducted on an aluminium alloy cantilever beam commonly used in air vehicle and spacecraft. The experiments collect the vibration strain signals of the beam via optical fiber sensors. Reconstruction results are compared with theoretical solutions and a detailed error analysis is also provided.

This article develops a multi-perspective view on motivations and methods for tobamovirus purification through the ages, and presents a novel efficient, easy-to-use approach that can be well-adapted to different species of native and functionalized virions. We survey the various driving forces prompting researchers to enrich tobamoviruses: from the search for the causative agents of mosaic diseases in plants to their increasing recognition as versatile nanocarriers in biomedical and engineering applications. Best practice and also rarely applied options for the serial processing steps required for successful isolation of tobamoviruses are then reviewed. Adaptations for distinct particle species, pitfalls and 'forgotten' or underrepresented technologies are considered as well. The article is topped off with our own development of a method for virion preparation, rooted in historical protocols. It combines selective re-solubilization of polyethylene glycol (PEG) virion raw precipitates with density step gradient centrifugation in biocompatible iodixanol formulations, yielding ready-to-use particle suspensions. This newly established protocol and some considerations for perhaps worthwile further developments could serve as putative stepping stones towards preparation procedures appropriate for routine practical uses of the multivalent soft-matter nanorods.

In this paper, we study the semilinear pseudo-parabolic equations $\displaystyle u_{t} - \triangle_{\mathbb{B}}u - \triangle_{\mathbb{B}}u_{t} = \left|u\right|^{p-1}u$ on a manifold with conical singularity, where $\triangle _{\mathbb{B}}$ is Fuchsian type Laplace operator investigated with totally characteristic degeneracy on the boundary $x_{1} = 0$. Firstly, we discuss the invariant sets and the vacuum isolating behavior of solutions with the help of a family of potential wells. Then, we derive a threshold result of existence and nonexistence of global weak solution: for the low initial energy $J(u_{0})<d$, the solution is global in time with $I(u_{0}) >0$ or $\displaystyle\Vert \nabla_{\mathbb{B}}u_{0}\Vert_{L_{2}^{\frac{n}{2}}(\mathbb{B})} = 0$ and blows up in finite time with $I(u_{0}) < 0$; for the critical initial energy $J(u_{0}) = d$, the solution is global in time with $I(u_{0}) \geq0$ and blows up in finite time with $I(u_{0}) < 0$. The decay estimate of the energy functional for the global solution and the estimates of the lifespan of local solution are also given.

In this paper, we study the asymptotic behavior of the sequential empirical process and the sequential empirical copula process, both constructed from residuals of multivariate stochastic volatility models. Applications for the detection of structural changes and specification tests of the distribution of innovations are discussed. It is also shown that if the stochastic volatility matrices are diagonal, which is the case if the univariate time series are estimated separately instead of being jointly estimated, then the empirical copula process behaves as if the innovations were observed; a remarkable property. As a by-product, one also obtains the asymptotic behavior of rank-based measures of dependence applied to residuals of these time series models.

With the developments of smart technologies, the use of rare earth elements (REEs) knows a widespread use, thus, their importance as valuable and strategic metals boosted to levels never known before. Due to their scarcity in Earth crust, the recovery of these elements from secondary resources also knows further developments. Traditional pyrometallurgical and hydrometallurgical processing, though of wide use, in the recovery of these REEs, have some environmental drawbacks, thus, the development of new recovery approaches are under developments. Here, Solvometallurgy appeared as a friendly environmental technology, and ionic liquids and deep eutectic solvents re-sulted as a first-row chemicals to be used in the different unit operations used in solvometallurgical processing: leaching, liquid-liquid extraction, ion exchange (adsorption), precipitation and elec-trolysis. This manuscript reviewed the most recent literature concerning the use of ionic liquids and deep eutectic solvents in the recovery of REEs from various sources.

This study assesses the biomethane (CH4) generation and greenhouse gas (GHG) emissions resulting from municipal solid waste landfilling in Phnom Penh, Cambodia, with a focus on the impact of fugitive CH4 emissions and operation processes in four landfilling scenarios: simple dumping (S1), improved management with leachate treatment (S2), engineered landfill with flaring (S3), and engineered landfill with energy recovery (S4). The study also considered the environmental benefits of carbon sequestration and landfill gas utilization. The LandGEM and IPCC FOD models were used to calculate CH4 generation over the period of 2009-2022, and it was found that approximately 18 and 21 M kg/year of CH4 are released, respectively. The energy potential from CH4 recovery was 51–61 GWh/year. Overall GHG emissions in S2 were the highest, amounting to 409–509 M kg CO2-eq/year, while S1 had lower emissions at 397–496 M kg CO2-eq/year. Flaring captured CH4 in S3 could reduce GHG emissions by at least 55%, and using captured CH4 for electricity production in S4 could mitigate at least 83% of GHG emissions. Electricity recovery (S4) could avoid significant amounts of GHG emissions (˗104.5 kg CO2-eq/tMSW). The study suggests that landfill gas-to-energy could significantly reduce GHG emissions.

The coronavirus pandemic will deeply affect the tourism and travel sector. It is already clear now that its economic impact would be more severe that in the case of the Severe Acute Respiratory Syndrome (SARS) in 2002-2003. Although not as deadly as SARS, coronavirus infection has a longer incubation period and leaves about 85% of the infected without any (or with just mild) symptoms which makes it more difficult to track and to contain. Moreover, it appears to be much more contagious than its predecessor. The goods news is that most people recover from the disease and develop antibodies that can protect them from getting infected again (natural vaccination). Those cured might become the key element for the post-virus recovery strategies of tourism organisations. People with the acquired immunity to the virus would be capable of travelling freely without spreading the disease. Airlines, hotels and gastronomy should aim at this group offering them discounts and special offers. However, the problem is how to effectively ensure that everyone who claims to be cured from COVID-19 is telling the truth. Health tracking bracelets, apps, and other advanced technological solutions should be put in place. Recent best practices from Hong Kong, mainland China, or India might be applied.

In this paper, we study a one-dimensional Bresse-Cattaneo system with infinite memories and time-dependent delay term (the coefficient of which is not necessarily positive) in the internal feedbacks. First, it is proved that the system is well-posed by means of the Hille-Yosida theorem under suitable assumptions on the relaxation functions. Then, without any restriction on the speeds of wave propagations, we establish the exponential or general decay result by introducing suitable energy and Lyapunov functionals.

A time-sequential working, spatially multiplexed autostereoscopic 3D display design consisting of a fast switchable RGB-color filter array is presented. The wavelength-selective filter barrier emits the light from a display over a larger active area than common autostereoscopic barrier displays. An optical modelling of wavelength-selective barriers has been used for instance to calculate the light ray distribution properties of that arrangement. To find well working display designs, automated searches by simulation and computational evaluation has been proceeded. Wavelength-selective filter barrier arrangements exhibit characteristics different from common barrier displays with similar barrier pitch and ascent. In particular, these constructions show strong angular luminance dependency under barrier inclination specified by correspondent slant angle. In time-sequential implementation it is important to avoid that quick eye or eyelid movement lead to visible color artifacts. In the millisecond regime tunable liquid crystal Fabry-Pérot color filters for the colors red, green and blue are presented. They consist of a sub-micrometer thick nematic layer sandwiched between dielectric mirrors and ITO-electrodes. These cells shall switch narrow-banding light of red, green or blue. An array for a glasses-free 3D display has to be equipped with several thousand switchable filter elements having different color apertures. The newly introduced design is usable as a multi user display as well as a single user system with user adaptive control.

The degree of ore rock fragmentation of mine blasting is an important index for evaluating the effect of engineering blasting. In order to study the effect of millisecond delayed blasting on the internal fragmentation of the ore rock, LS-DYNA numerical simulation software was used to obtain the damage characteristics of the ore rock in the blasting area using the RHT model. The Mohr-Coulomb strength damage criterion was applied to analyze the internal stress state of the ore rock, and the effects of 25ms, 50ms and 75ms delay time conditions on ore rock fragmentation were studied. The results show that, within a certain range of delay time, the degree of rock fragmentation increases with the increase of delay time. The stress state in the ore rock due to blasting is strengthened with the increase of delay time in the direction of the blast hole, which shows that the stress concentration is strengthened from the excavation face to the interior of the ore rock. The damage and the degree of fragmentation show a characteristic relationship of 25ms＜50ms＜75ms, which is verified by carrying out blasting experiments on site.

This paper proposes an innovative mechatronic piezo-actuated module to control vibrations in modern machine tools. Vibrations represent one of the main issues that compromise seriously the quality of the workpiece. The active vibration control (AVC) device is composed by a host part integrated with sensors and actuators synchronized by a regulator, able to make a self-assessment and adjust to the environmental alteration. This study presents the mechatronic model based on the kinematic and dynamic analysis of the AVC device. To ensure a real time performance, a H2-LQG controller has been developed and validated by simulations involving machine tool, PZT actuator and controller models. The Hardware-in-the-loop (HIL) architecture is adopted to control and attenuate the vibrations. A set of experimental tests has been performed to validate the AVC module on a commercial machine tool. The feasibility of the real time vibration damping is demonstrated and the simulation accuracy is evaluated.

This paper presents a new semi-explicit algorithm for parameters estimation in a time-fractional generalization of dual-phase-lag heat conduction model with the Caputo fractional derivatives. It is shown that this model can be derived from a general linear constitutive relation for the heat transfer by conduction when the heat conduction relaxation kernel contains the Mittag-Leffler function. The model can be used to describe heat conduction phenomena in a material with power-law memory. The proposed algorithm of parameters estimation is based on the time integral characteristics method. The explicit representations of the thermal diffusivity and the fractional analogues of the thermal relaxation time and the thermal retardation are obtained via a Laplace transform of the temperature field and utilized in the algorithm. An implicit relation is derived for the order of fractional differentiation. In the algorithm, this relation is resolved numerically. An example illustrates the proposed technique.

Venous and arterial thromboembolism (VTE/ATE) often coexist with onco-hematologic diagnosis. The study aimed to assess the time relationship between the diagnosis of VTE/ATE and blood cancers. The second aim was to identify VTE/ATE risk factors related to the type of hematology disease and cardiac history. 1283 patients underwent cardio-oncology evaluation at the Institute of Hematology and Transfusion Medicine in Warsaw from March 2021 through March 2023 (2 years), and 101 (7.8%) cases were identified with VTE/ATE. ATE compared to VTE significantly more often occurred before the diagnosis and treatment of hematologic malignancy: 33/47 (70.2%) vs. 15/54 (27.8%), p<0.0001. The risk of a VTE episode is exceptionally high in the first months after the diagnosis of an onco-hematological disease. The higher frequency of VTE was associated with acute myeloid leukemia (17 cases/270 patients/6.30%/p = 0.055), acute lymphocytic leukemia (7 cases/76 patients/9.21%/p = 0.025) and chronic myeloproliferative disease (7 cases/48 patients/14.58%/p=0.0003). Only the risk of VTE was significantly increased before (OR=6.79; 95%CI: 1.85-24.95; p=0.004) and after diagnosis of myeloproliferative disease (OR=3.12; 95%CI: 1.06-9.16; p=0.04). ATEs occur more often than VTE before a diagnosis of blood cancer. The risk of VTE is exceptionally high before and after diagnosis of chronic myeloproliferative disease.

Digital photogrammetry has attracted widespread attention in the field of geotechnical and geological survey due to its low-cost, ease of use and contactless mode. In this work, with the purpose of studying the progressive block surficial detachments of a landslide we developed a monitoring system based on fixed multi-view time-lapse cameras. Thanks to a new-developed photogrammetric algorithm based on the comparison of photo sequences through a structural similarity metric and the computation of the disparity map of two convergent views we can quickly detect the occurrence of collapse events, determine their location and calculate the collapse volume. With the field data obtained at the Perarolo landslide site (Belluno Province, Italy), we have preliminary tested the effectiveness of the algorithm and its accuracy in the volume calculation. The method of quickly and automatically obtaining collapse information proposed in this paper can extend the potentials of landslide monitoring system based on videos or photo sequence and it will be of great significance for further research on the link between the frequency of collapse events and the driving factors.

The implementation of systems condition monitoring is of utmost importance in ensuring effective maintenance, optimal efficiency, and adherence to established design criteria. This research presents a novel and economically efficient Digital Twin Model (DTM) designed for the purpose of Live Condition Monitoring (LCM) and Predictive Lifecycles. This study discusses the enhancement of DTs modelling by decreasing the number of dimensions from five to three: Physical, Digital, and Connection entities. The resulting model exhibits a notable improvement in accuracy and efficiency. The suggested Dynamic Time Model (DTM) enhances the empirical pre-set average load for both simulation and experimentation by 35.7%, which is equivalent to a 1.6-fold increase. The DTM demonstrates a significant enhancement in the empirical predefined average lifecycles of the system, as shown in the real-life case study. Specifically, the DTM achieves a 12-fold improvement compared to the simulated results and a nine-fold improvement compared to the experimental findings. The suggested Dynamic Time Management (DTM) demonstrates a significant enhancement in the average lifecycles of the system, with a 19.7% improvement (equivalent to 1.2 times greater) compared to the wireless DTM results obtained through the application of the real system load. The suggested Dynamic Time Warping (DTW) method's accuracy and efficiency are shown via a practical case study using the suspension system of a Peugeot 3008 vehicle.

We review time expansion wave theory and refine our model showing it to accurately match observed cosmological luminosity data. The theory augments general relativity to create a container within which quantum effects can be explained by treating time as an artefact of a fourth spatial dimension that we observe as the expansion of the universe. This leads us to explain mass creation, the speed of light limit, gravity, black holes without singularities and other macroscopic effects. Our model leads to an expansion velocity of 6.87 0.36 × 106 ms-1, which results in a Hubble expansion rate constant of 71.3 3.7 km/s/Mpc. Because of the finite window of time in which we exist, an extra a degree of freedom is available leading to interesting interpretations at the subatomic level. At this size range, then a spin is allowed within the time dimension explaining why wave particle duality occurs and explains the electromagnetic force and other quantum mechanical properties. It provides a solution to the double slit conundrum and can explain how quantum entangled partners can behave in a quantum way and pass seemingly time-defying information. The potential quantitative origin of the fine structure constant which governs the size of the electromagnetic force is also strongly indicated by this theory.

The success of deep learning (DL) in offline brain-computer interfaces (BCIs) has not yet translated into efficient online applications. This is due to two limiting factors: the need for large amounts of training data in DL and the fact that current DL solutions are primarily developed for offline decoding. To enable real-time decoding, even across subjects without calibration data, we first introduce a novel method real-time adaptive pooling (RAP) to tune existing offline DL models towards online decoding by modifying the pooling layers. To reduce the amount of training data required, our approach leverages different levels of domain adaptation. We show how different settings enable different adaptation solutions. Our results demonstrate that our approach is both powerful and can be calibration-free, providing a robust and practical solution for real-time BCI applications. These findings pave the way for the development of co-adaptive and highly efficient DL-based BCI systems.

This study aimed to explore the experiences of teachers in the recovery of educational skills. Specifically, ten teachers of Andap National High School are selected for the in-depth interview. This study employed a qualitative-phenomenological research design of which in-depth interview (IDI) was used in gathering the data. The study utilized phenomenological approach to study a particular phenomenon concerning the post-pandemic experiences of teachers in the implementation of the face-to-face classes. After gathering sufficient amount of data through the conduct of in-depth interviews, thematic analysis was used to analyse further the results and to check the disparities and similarities of all responses gathered. This was the time wherein responses were categorized and organized into themes. The findings of the study revealed that the challenges encountered of educational skills recovery were sources of learning materials, learning gaps, learners characteristics, difficulty with comprehension and lack of interest. It was also revealed that the most challenging teaching experiences in the recovery of educational skills were catching attention, learning gaps, communicating to students, need for remedial instruction, retroactive interference and difficulty in understanding English Language. They frequently have to quickly convert lesson material they had intended to teach in a classroom setting to any learning modality implemented. Further, adjust and learn new trend, provide opportunities to develop new skills, helps to manage stress, improved educational outcomes, become well-rounded teacher, networking and learning and continue doing responsibilities helped in these insights had in confronting challenges brought by the pandemic.

Based on a rich data set of recoveries donated by a debt collection business, recovery rates for non-performing loans taken from a single European country are modelled using linear regression, linear regression with Lasso, beta regression and inflated beta regression. We also propose a two-stage model: beta mixture model combined with a logistic regression model. The proposed model allows us to model the multimodal distribution we find for these recovery rates. All models are built using loan characteristics, default data and collections data prior to purchase by the debt collection business. The intended use of the models is to estimate future recovery rates for improved risk assessment, capital requirement calculations and bad debt management. They are compared using a range of quantitative performance measures under K-fold cross validation. Among all the models, we find that the proposed two-stage beta mixture model performs best.

Monitoring training load, recovery, and wellness in maturation ages is even more relevant than in adulthood, especially during congested schedules. The objective of this study was to quantify and describe the differences in external and internal load, recovery, and perceived wellness in elite under-16 female players during a period of three competitions in seven days, based on their participation during matches. An observational design was used to quantify external load (total distance, high-speed distance, and PlayerLoad), internal load (differential sRPE), as well as recovery and wellness of 18 international under-16 players during a development tournament. The external load pattern of the starting players was undulating, with matches showing the highest internal load values compared to training sessions and non-starters. Non-starters players (whether training or matches) showed similar external load and differential sRPE values throughout the tournament (p&gt;0.05). Starting players experienced higher internal load than non-starters during matches (p&lt;0.037), without differences between training sessions (p&gt;0.05). No changes were seen in wellness or recovery (all p-values &gt;0.05), except for non-starters in session 3 compared to session 7 (p=0.024). Starting players had more weekly total minutes (p=0.018), differential sRPE (breath: p=0.002; leg: p=0.002; cognitive: p=0.003), total distance (p=0.003), and PlayerLoad (p=0.008) than non-starters. Our results indicate differing load patterns between starters and non-starters, which do not affect wellness and recovery differently. Starters accumulate more weekly total load. Coaches can use these findings to guide decisions and load compensations in high competition contexts for young female soccer players.

Catastrophic mass flows originating from the high mountain cryosphere often cause hazard cascades. With increasing human activities in the alpine region and the sensitivity of glaciers, snow and permafrost to climate warming, the cryospheric hazard risks tend to be more active in the Tibetan Plateau(TP). The remoteness and inaccessibility of high mountain regions make the remote sensing technology the most effective approach for monitoring and investigating the cryospheric hazards. Exploring the evolution process and impact of ice avalanches and subsequent hazards are crucial to understand drivers and nature of mass flow process chain in order to prevent and mitigate potential hazard consequences. In this study, the glacier and ice-rock avalanches occurred in Arutso and Sedongpu basins in Tibet were investigated on the basis of Sentinel-2 time-series images and in-situ investigation, the evolution process of these events were reconstructed, and impacts and driving factors were analyzed. The massive Chamoli rock-ice avalanche occurred in February 2021 in the western Himalayas was also reviewed using using same methods. The study shows that Arutso glacier No. 53 (Arutso-53) avalanche completely melted away in July 2018 after two years from occurrence to final disappearance, while Arutso glacier No. 50 (Arutso-50) avalanche completely melted by the end of August 2023 after lasting for seven years. Four large-scale ice-rock avalanche and debris flow events in the Sedongpu basin in 2017 and 2018 not only had significant impacts on the river flow, landscape and geomorphologic shape in the basin, but also caused serious disasters in the basin and downstream. In March 2021, a small-scale ice-rock avalanche and debris flow event occurred again in the Sedongpu basin. The glacier and ice-rock avalanches occurred in Tibet area were mainly induced by climate warming, heavy precipitation and seismic activity, which acting on specific topographic and geomorphic structure of glacier properties in the high mountain regions. Catastrophic hazard events originating from the high-elevation glacier and ice-rock collapses in the mountain cryosphere and subsequent hazard cascades in the TP and Himalayas highlight the importance of mountain hazard monitoring, early warning systems and sustainable mountain development.

The introduction of organic and inorganic substances to the environment is a result of human activities such as agriculture, domestic and industrial wastewater which leads to pollution. Treatment processes of these wastewaters are being conducted globally to eliminate easily settled materials and recover nutrients in an attempt to release clear and apparently clean effluent into natural waters. Lack of removing inorganic nitrogen and phosphorus nutrients is the greatest cause of eutrophication in water bodies which inhibits the life of other organisms as well as pose a threat to human life and loss of the economy. Different technologies have been applied and are being developed to recover nutrients as well as heavy metals from wastewater to meet the permissible limits before discharging effluents. Wastewater treatment using microalgae offers an opportunity to provide tertiary bio-treatment and production of valuable biomass. Microalgae use the available inorganic nitrogen and phosphorus for their growth which are then harvested for various uses. Additionally, they have the ability to remove heavy metals and some toxic compounds. The main specific microalgae species in this study is the Chlorella sorokiniana with the Aequorea victoria jellyfish This paper reviews some of the wastewater treatment processes and focus on the use of microalgae and some of the shortcomings of the technologies and how they can be improved to achieve maximum nutrient recovery economically with low energy demand.