Japanese encephalitis is a zoonotic disease caused by Japanese encephalitis virus (JEV). It is mainly epidemic in Asia with an estimated 69,000 cases occurring per year. However, no approved agents are available for the treatment of JEV infection, and existing vaccines cannot resist various types of JEV strains. Drug repurposing is a new concept for finding new indication of existing drugs, and recently, it has been used to discover new antiviral agents. Identifying host proteins involved in the progress of JEV infection and using these proteins as targets are the center of drug repurposing for JEV infection. In this study, based on the gene expression data of JEV infection and the phenome-wide association study (PheWAS) data, we identified 286 genes participating in the progress of JEV infection using the systems biology methods. The enrichment analysis of these genes suggested that the genes identified by our methods were predominantly related to viral infection pathways and immune response-related pathways. We found that bortezomib which can target these genes may have potential effect on the treatment of JEV infection. Subsequently, we evaluated the antiviral activity of bortezomib using the JEV-infected mice model. The results showed that bortezomib can lower JEV-induced lethality in mice, alleviate suffering in JEV-infected mice and reduce the damage in brains caused by JEV infection. This work provides a new method for the development of antiviral agents.

To improve the tangerine crop yield, the work of recognizing and then disposing of specific pests is becoming increasingly important. The task of recognition is based on the features extracted from the images that have been collected from websites and outdoors. Traditional recognition and deep learning methods, such as KNN (k-nearest neighbors) and AlexNet, are not preferred by knowledgeable researchers, who have proven them inaccurate. In this paper, we exploit four kinds of structures of advanced deep learning to classify 10 citrus pests. The experimental results show that Inception-ResNet-V3 obtains the minimum classification error.

In our discipline, some numbers, facts and methods are established and unquestionable. Still, a credible source is hard to find. Likewise, some state-of-the-art papers do not have the right focus, or they do not point at current research trends and future mission statements. Lastly, reliable resources for claims and statements that are arguable are not always found in the typical journals of our field. The paper at hand solves this issue for our field. Through critical review of a vast amount of journal papers, monographs, handbooks, conference proceedings and theses as well as manuals, textbooks and tutorials, numbers, facts, methods, claims statements, state-of-the-art, research and application trends are reflected and substantiated.

The objective of this research is to ascertain the elements that have an impact on and drive domestic violence in Saudi Arabia, a phenomenon that has a prevalence rate of around 35% among women globally. The researchers administered a survey to a sample of 550 individuals and using exploratory factor analysis (EFA) to analyze the collected data. The findings revealed three factors: lack of familial unity, encouragement of detrimental characteristics, and economic turmoil. The authors examined the consequences of these characteristics on preventive and intervention programs and proposed suggestions for policy makers and researchers. This research enhances the existing body of knowledge on domestic violence by doing a statistical analysis to examine the factors that lead to it and the resulting outcomes within a particular cultural setting.

This study explores the impact of self-regulation skills on the decision-making styles of school administrators in public primary schools. There is a noticeable gap in the literature regarding how self-regulation components—foresight, will control, and self-reflection—affect the deci-sion-making processes of school leaders. A mixed-methods approach was used, integrating quantitative data from the "Self-Regulation Skills Scale for School Administrators" and the "Deci-sion-Making Styles Scale" with qualitative data from semi-structured interviews with adminis-trators and teachers. The findings reveal that administrators with strong self-regulation skills predominantly use rational and intuitive decision-making styles. This correlation leads to more effective management and improved educational outcomes. The study underscores the im-portance of professional development programs aimed at enhancing self-regulation abilities among school administrators. Such improvements in self-regulation can lead to better deci-sion-making processes, ultimately benefiting student performance and overall school success.

Integrating sustainable processes and innovative waste utilization not only addresses environ-mental challenges but also aligns with evaluating energy sustainability and creating the circular waste management. This study presents a comparative analysis of liquid fuel production from waste tires and water hyacinth using two thermochemical methods: co-pyrolysis (direct method) and co-gasification integrated with the Fischer-Tropsch (FT) process (indirect method). The As-pen Plus simulation software version 9 is employed to develop models, determining optimal operating conditions for maximum liquid fuel, and assessing economic viability. Co-pyrolysis simulations reveal an increase in oil products and a decrease in gas products with higher blending weight ratios of waste tires/water hyacinth (W/B) and lower pyrolyser temperatures. Operating at 400°C with a blending weight ratio of 75:25 provided the maximum liquid fuel rate of 6649.22 gal/day. For co-gasification, optimal conditions (W/B = 50:50, T = 800°C, steam to feed ratio (S/F) = 1) result in the highest syngas yield of 115.92 kmol/h. Fischer-Tropsch simulations demonstrate an increase in gasoline and a decrease in diesel with rising reactor temperatures. At 230°C, the maximum liquid fuel rate reaches 8817.15 gal/day. The economic and production yield analysis indicates that the indirect method better than the direct method, providing higher liquid fuel yields and investment worthiness.

Reactive powder concrete (RPC) is a modern form of cementitious material. Major components include cement with a very low water-to-binder (w/b) ratio, steel fibres, superplasticizers, and very fine additives used in place of coarse aggregate, and very little water. The primary goal of this study is to examine the impact of immersion in sulfuric acid on compressive strength and to evaluate the impact of curing procedures on the preparation of RPC with high characteristics. The experimental work includes 4 mixes, which are prepared using 30 mm hooked-end steel fibers with a ratio of 0.4 by volume, silica fume and metakaolin with a ratio of 25% of cement weight. Compressive and indirect tensile strengths, ultrasonic velocity, and bulk density tests were conducted on the RPC that was prepared to evaluate its qualities. The results showed that heat curing resulted in a greater increase in strength than water curing did, and that mechanical properties improved with increasing SF more than MK additions. Heat-cured RPC demonstrated greater resistance to sulfuric acid immersion tests on compressive strength compared to water-cured RPC.

Current conventional methods of evaluating microstructures are characterized by a high degree of subjectivity and a lack of reproducibility. Modern machine learning (ML) approaches have already shown great potential in overcoming these challenges. Once trained with representative data in combination with an objective ground truth, the ML model is able to perform a task properly in a reproducible and automated manner. However, in highly complex use cases, it is often not possible to create a definite ground truth. This study addresses this problem using the underlying showcase of microstructures of highly complex quenched and quenched and tempered (Q/QT) steels. A patch-wise classification approach combined with a sliding window technique provides a solution for segmenting entire microphotographs where pixel-wise segmentation is not applicable due to the problem of reproducibly creating unambiguous training masks. Using correlative microscopy, consisting of light optical microscope (LOM) and scanning electron microscope (SEM) micrographs as well as corresponding data from electron backscatter diffraction (EBSD), a training dataset of reference states that covers a wide range of microstructures was acquired in order to train accurate and robust ML models. Despite the enormous complexity associated with the steels treated here, classification accuracies of 88.8% in the case of LOM images and 93.7% for high-resolution SEM images were achieved. These high accuracies are close to super-human performance, especially in consideration of the reproducibility of the automated ML approaches compared to conventional methods based on subjective evaluations through experts.

Wearable sensors have become increasingly popular for assessing athletic performance, but the optimal methods for processing and analysing the data remain unclear. This study investigates the efficacy of discrete and continuous feature extraction methods, separately and in combination, for modelling countermovement jump performance using wearable sensor data. We demonstrate that continuous features, particularly those derived from Functional Principal Component Analysis, outperform discrete features in terms of model performance, robustness to variations in data distribution and volume, and consistency across different datasets. Our findings underscore the importance of methodological choices, such as signal type, alignment methods, and model selection, in developing accurate and generalisable predictive models. We also highlight the potential pitfalls of relying solely on domain knowledge for feature selection and the benefits of data-driven approaches. Furthermore, we discuss the implications of our findings for the broader field of sports biomechanics and provide practical recommendations for researchers and practitioners aiming to leverage wearable sensor data for athletic performance assessment. Our results contribute to the development of more reliable and widely applicable predictive models, facilitating the use of wearable technology for optimising training and enhancing athletic outcomes across various sports disciplines.

Bacterial infections and resistance to antibiotic drugs represent the highest challenges to public health. The search for new and promising compounds with anti-bacterial activity is a very urgent matter. In order to promote the development of platforms enabling to the discovery of compounds with anti-bacterial activity, Fourier Transformed Mid-Infrared (FT-MIR) spectroscopy associated with the machine learning algorithms were used to predict the impact of compounds extracted from Cynara cardunculus against Escherichia coli cells. According to the plant tissue (seeds, dry and fresh leaves, and flowers) and the solvents (ethanol, methanol, acetone, ethyl acetate, and water), compounds with different compositions concerning, phenol content, antioxidant, and antimicrobial activity were obtained. Principal component analysis of spectra, allowed to discriminate compounds that inhibit the E. coli growth, according to the conventional assay. The supervised classification models enabled the prediction of the compound's impact on the E. coli growth, with the accuracy for the test data: Partial least squares-discriminant analysis of 94%; Support vector machines of 89%; k-Nearest neighbor of 72%; and a Backpropagation network of 100%. According to the promising results, the integration of FT-MIR spectroscopy with machine learning presents a high potential to promote the discovery of new compounds with antibacterial activity, thereby streamlining the drug exploratory process.

As distinct human societies, three unique intentional communities are investigated regarding their preferred consensus decision-making practices. It is Identified that each has adopted a different form of consensus decision-making to solve potential group-wide interpersonal conflict. The individual attributes of these three consensus decision-making practices are considered, both from the perspective of maintaining group stability and in relation to individual member's mental health. The communities are a Canadian self-directed public senior elementary and secondary school, an annual English conference for those self-identifying as on the autistic spectrum, and a self-producing Korean popular music (K-pop) group. It is found that the intentional community and participants’ mental health are sustained regarding each of the three consensus decision-making practices. Nevertheless, the resulting decisions generate various stresses within the communities, both as a whole and concerning the individual members. To retain group cohesion and maintain individual mental health, these stresses must be recognized and understood by participants. The novel finding of this research is that, dependent on the time available for decision-making, and the members’ perspective adopted, intentional communities might practice more than one form of consensus decision-making and still support both group cohesion and individual mental health, maintaining the democracy of these distinct societies.

After reviewing the history of the struggle between human beings and plant diseases, insects and weeds, especially reviewing the thoughts of plant protection in ancient Chinese agricultural books, the target recognition of plant pests and the six types of plant protection methods and 36 subdi-vision measures were summarized. Then the development overview of pesticide application technology was focused and reviewed systematically by combining the development timeline of pesticide application and the key technologies including the performance measurement and the simulation and modeling of pesticide spraying system. And three suggestions for further research were proposed from the perspectives of human-being and environmental health, the sustainable and eco-friendly application medium, and the efficiently application equipment system in plant protection.

The emergence of non-canonical amino acids (ncAAs) has enriched the functional pool inherent in canonical amino acids (cAAs). ncAAs are important building blocks in the production of various pharmaceuticals. The biosynthesis of ncAAs has emerged as an alternative to traditional chemical synthesis because of its environmental friendliness and high efficiency. The breakthrough genetic code expansion (GCE) technique developed in recent years has allowed for the incorporation of ncAAs into target proteins, giving them special functions and biological activities. Biosynthesis of ncAAs and their incorporation into target proteins within a single microbe has become an enticing application of such molecules. Based on that, in this study we first review the biosynthesis methods for ncAAs and analyze the difficulties in biosynthesis. We then summarize the GCE methods and analyze their advantages and disadvantages. Further, we review the application progress of ncAAs and anticipate the challenges and future development directions of ncAAs.

Physicians often label anatomical structure sets in Digital Imaging and Communications in Medicine (DICOM) images with nonstandard names. As these names vary widely, the standardization of the nonstandard names in the Organs at Risk (OARs), Planning Target Volumes (PTVs), and 'Other' organs inside the area of interest is a vital problem. Prior works considered traditional machine learning approaches on structure sets with moderate success. This paper presents integrated deep learning methods applied to structure sets by integrating the multimodal data compiled from the radiotherapy centers administered by the US Veterans Health Administration (VHA) and the Department of Radiation Oncology at Virginia Commonwealth University (VCU). The de-identified radiation oncology data collected from VHA and VCU radiotherapy centers have 16,290 prostate structures. Our method integrates the heterogeneous (textual and imaging) multimodal data with Convolutional Neural Network (CNN)-based deep learning approaches like CNN, Visual Geometry Group (VGG) network, and Residual Network (ResNet). Our model presents improved results in prostate (RT) structure name standardization. Evaluation of our methods with macro-averaged F1 Score shows that our deep learning model with single-modal textual data usually performs better than the previous studies. We also experimented with various combinations of multimodal data (masked images, masked dose) besides textual data. The models perform well on the textual data alone, while the addition of imaging data shows that deep neural networks can achieve improved performance using information present in the other modalities. Additionally, using masked images and masked doses along with text leads to an overall performance improvement with the various CNN-based architectures than using all the modalities together. Undersampling the majority class leads to further performance enhancement. The VGG network on the masked image-dose data combined with CNNs on the text data performs the best and establishes the state-of-the-art in this domain.

The hand-net is the standard method for capturing mosquitoes with sylvatic diurnal activity in disease outbreaks in Brazil. However, occupational risks and biases related to the collectors´ abilities and attractiveness are important limitations. In this study, we compared hand-nets with automatic traps (CDC) associated to CO2 and BG-Lure®, in the Vassununga State Park, a Brazilian Savanna protection area. The collections carried out over 27 days, on the ground and the forest canopy. A total of 1,555 mosquitoes were obtained in 20 taxa. The diversity index ranged between 1.12 and 1.79 and the dominance index, from 0.22 to 0.40. The dominant species in the ground was Aedes scapularis (46.0%) and in the canopy, Hg. janthinomys/capricornii (31.9%). Haemagogus leucocelaenus was rare (n=2). The hand-net resulted in the greatest diversity and abundance of species in both strata, followed by traps associated with CO2. A low degree of similarity was observed between the hand-net on the ground compared to the other capture methods. The use of BG-Lure® alone resulted in a low number of specimens. In conclusion, the hand-net is still the method of choice for collecting arbovirus vectors in the diurnal period, especially yellow fever vectors.

Specialized classes of proteins, working together in a tightly orchestrated manner, induce and maintain highly curved cellular and organelles membrane morphology. Due to the various ex- perimental constraints, including the resolution limits of imaging techniques, it is non-trivial to accurately elucidate interactions among the various components involved in membrane deformation. The spatial and temporal scales of the systems also make it formidable to investigate them using simulations with molecular details. Interestingly, mechanics-based mesoscopic models have been used with great success in recapitulating the membrane defor- mations observed in experiments. In this review, we collate together and discuss the various mechanics based mesoscopic models for protein-mediated membrane deformation studies. In particular, we provide an elaborate description of a mesoscopic model where the membrane is modeled as a triangulated sheet and proteins are represented as either nematics or fila- ments. This representation allows us to explore the various aspects of protein-protein and protein-membrane interactions as well as examine the underlying mechanistic pathways for emergent behavior such as curvature-mediated protein localization and membrane deforma- tion. We also put forward current efforts in the field towards back-mapping these mesoscopic models to finer-grained particle based models - a framework that could be used to explore how molecular interactions propagate to physical scales and vice-versa. We end the review with an integrative-modeling based road map where experimental imaging micrograph and biochemical data are combined with mesoscopic and molecular simulations methods in a theoretically consistent manner to faithfully recapitulate the multiple length and time scales in the membrane remodeling processes.

The fetal heart rate (FHR) is a screening signal for preventing fetal hypoxia during labor. When experts analyze this signal, they have to position a baseline and identify decelerations and accelerations. These steps can potentially be automated and made more objective by data processing analysis, but training and evaluation datasets are required. Here, we describe a dataset of 155 FHR recordings in which a reference baseline, accelerations and decelerations have been annotated by expert consensus. 66 FHR recordings with a shared expert analysis have been included in a training dataset, and 90 other FHR recordings with a non-shared expert analysis have been included in an evaluation dataset. Researchers wishing to evaluate their automatic analysis method should submit their results for comparison with the expert consensus. The dataset also contains the results produced by 11 re-coded automatic analysis methods from the literature. All the data are available at http://utsb.univ-catholille.fr/fhr-review.

Onion (Allium cepa L.) is a strong-flavoring vegetable consumed in different ways. It is mainly due its distinctive flavor or simply pungency. Onion has also important natural compounds effective for medical functions such as inhibition of bone resorption, lower risk of cardiovascular disease and cancer. This importance is directly related to high content of organo-sulphur compounds. Shelf life of fresh onion bulb is short enough about two weeks at ambient storage conditions in Fogera district, Amhara region, Ethiopia. This is mainly due to the presence of high moisture in fresh onion bulbs. Postharvest loss of onion bulb reaches up to 50% in the production season in Fogera district. Consequently onion bulb had extreme variable market price during production and off season in the district which directly influences both the growers and consumers. In this study the effect of different drying techniques on nutritional and volatile components of onion were evaluated. Effect of different drying techniques on protein, carbohydrate, total sugar, fat, pyurvic acid, ascorbic acid, total phenol, total flovonol, rehydration ratio, color and sensory properties of onion slice were evaluated and found insignificant at (P > 0.05) for microwave and modified direct solar dryers taking fresh onion bulb as a control. But oven drying method had significant effect on onion physicochemical quality attributes at (P < 0.05) as compared to fresh onion bulbs.

Software engineers from all over the world solve independently a lot of similar problems. In this condition the problem of code or even better architecture reusing becomes an issue of the day. In this paper two phase approach to determining the functional and structural likenesses of software projects is proposed. This approach combines two methods of artificial intelligence: natural language processing techniques with a novel method for comparing software projects based on ontological representation of their architecture automatically obtained from the projects source code. Additionally several similarity metrics are proposed to estimate similarity between projects.

Detection methods have been developed to prevent transmission of zoonotic or xenozoonotic porcine viruses after transplantation of pig organs or cells to the recipient (xenotransplantation). Eleven xenotransplantation-relevant viruses including porcine cytomegalovirus, a porcine roseolovirus (PCMV/PRV), porcine lymphotropic herpesviruses -1, -2, -3 (PLHV-1, 2, 3), porcine parvovirus (PPV), porcine circovirus 2, 3, 4 (PCV2, 3, 4), hepatitis E virus genotype 3 (HEV3), porcine endogenous retrovirus-C (PERV-C) and recombinant PERV-A/C have been selected. In the past, several pig breeds, minipigs and genetically modified pigs generated for xenotransplantation had been analyzed using these methods. Here, spleen, liver and blood samples from 10 German slaughterhouse pigs were screened using both, PCR-based and immunological assays. Five viruses: PCMV/PRV, PLHV-1, PLHV-3 and PERV-C were found in all animals, and PCV3 in one animal. Some animals were latently infected with PCMV/PRV as only virus-specific antibodies were detected. Others were also PCR positive in spleen and/or liver, indicative of an ongoing infection. These results provide important information on the viruses that infect German slaughterhouse pigs and together with results of previous studies revealed that the methods and test strategies efficiently work under field conditions.

The aim of the research was to determine the most productive tree cutting methods and the factors influencing them in flat and sloping terrain in tree plantations in Brazil. The study utilized drone-captured video material from eucalyptus and pine plantations harvesting operations. In both terrains, two cutting methods were compared, differing in the felling to the side method used: either at the edge of the harvesting front or inside. In addition, on flat terrain, the efficiency of forward felling was studied in relation to the aforementioned methods. In slope terrain, the machines were also equipped with a winch assistance system. Statistical tests were used to determine the most productive cutting methods by analyzing differences in productivity. With an average tree size of 0.3 m3, cutting productivity was 45 m3/E0h in pine cuttings and 55 m3/E0h in eucalyptus cuttings. The average cutting productivity on flat terrain was about 11 m3/E0h higher than on sloping terrain, mainly due to the time spent attaching the winch assistance system, which was a necessary phase of the work in sloping terrain. The research results suggest that it would be the most productive to use sideways felling inside the harvesting front method. However, the need for further research is evident if we want to precisely identify the harvesting conditions and work phases in the tree cutting cycle affecting the differences in the productivity of the harvesting chain in tree plantations.

The core motivation for this study emanates from Voronin S.'s seminal work on the universality of the zeta function. This universality positions the zeta function as a pivotal element in the conceptual framework of global artificial intelligence, suggesting its potential as a foundational component. A critical barrier to harnessing this promising universality has been the absence of efficient algorithms capable of actualizing this concept.

Stochastic modelling of geometrically imperfect steel frame structures requires inputs of statistical values of the imperfection parameters, in some cases also with suitably selected mutual correlations between these input parameters. The paper presents a verification study of two stochastic methods to directly model the initial global geometrical imperfections of the steel frame structures. The stochastic input values of geometrical imperfections are derived from the tolerance criteria of the corresponding European Standard. Two advanced stochastic methods have been developed, referred as #RSS (random story sway) and #RSP (random story position). These methods are verified using random sampling simulations. This study provides useful provisions for the advanced numerical analyses of multi-story steel frames of various geometries. The proposed methods are verified for equidistant story heights and for structures of up to 24 stories, hence might be used for the most types of the standard steel frame structures.

To protect power lines, distance relays are used which can rely on a modified distance resistance. But usually the operating range of these relays changes as the network conditions change (network topology, load value, output value, etc.) and leads to false trips, By using methods that can process information and recognize patterns, for example, the use of micro and intelligent processor algorithms can use the new relays with high precision and thus provide adequate protection. In this study, a distance relay was modeled using a neural network, and it was observed that the neural relay had higher accuracy than the conventional relay. In addition to detecting the fault and its location, type and phase of the fault, three-stage simultaneous protection can be performed. As a result, the number of linear relays can be reduced by using relays based on neural technologies. An MLP (multilayer perceptron) neural network is used to model a sequence of distances.

Fungal pathogens pose a major threat to food production worldwide. Traditionally, chemical fungicides have been the primary means of controlling these pathogens, but many of these fungicides have recently come under increased scrutiny due to their negative effects on the health of humans, animals, and the environment. However, the use of fungicides with less of an environmental impact tends to result in the development of resistance in populations of phytopathogenic fungi. Therefore, new environmentally friendly alternatives that provide adequate levels of disease control are needed to replace chemical fungicides—if not completely, at least partially. A number of alternatives to conventional chemical fungicides have been developed, including plant defence elicitors (PDEs), biological control agents (fungi, bacteria and mycoviruses), biofungicides, RNA interference (RNAi) methods, and resistance breeding. This article reviews the conventional and alternative methods available to manage fungal pathogens, discusses their strengths and weaknesses, and identifies potential areas for future research.

Although tourism seasonality significantly influences the livelihood activities of rural tourism households, limited research has explored how they adapt their livelihood strategies to achieve optimal outcomes. Employing an exploratory sequential mixed-methods design that combines thematic analysis and fuzzy-set Qualitative Comparative Analysis (fsQCA), we investigated the relationship between the livelihood strategies and outcomes of rural tourism households under the influence of tourism seasonality at the Guilin Karst World Heritage Site. The results indicate that livelihood strategies adopted by rural tourism households include "Tourism Persistence,” "Seasonal Employment,” and “Seasonal Farming” during the off-season, and “Extending Working Hours,” “Increasing Staffing Input,” and “Increasing Capital Input” during the peak season. Furthermore, these strategies form flexible combinations to realize livelihood outcomes, with “Extending Working Hours” being a necessary condition. The findings also revealed that the four configurations of seasonal livelihood strategies in the two patterns significantly contributed to high livelihood outcomes. One is named “Peak-Season Driven Pattern,” where “Extending Working Hours” and “Increasing Staffing Input” are the core conditions; the other is named “Peak-Off Blend-Driven Pattern,” where “Extending Working Hours,” “Increasing Capital Input,” and the absence of “Tourism Persistence” are the core conditions. These findings provide theoretical and practical insights for sustainable livelihood research.

The quantile estimation of extreme wind speed is needed in various environmental fields such as climatology, design of structures, renewable energy sources and agricultural operations. These calculations are crucial for the coding of wind speed. In this study, the required wind speed series of 16 stations in Khyber Pakhtunkhwa, Pakistan, was obtained from the NASA official website and measured in meters per second (m/s) at a 10-meter distance. A Regional Frequency Analysis of 16 AMWS stations was performed using L-moments. The quantile estimates of extreme wind speed are needed for various areas of interest using Regional Frequency Analysis (RFA) and extreme value theory. These calculations are crucial for the coding of wind speed. The data was taken from the NASA official website at a 10-meter distance and measured in meter per second (m/s). A Regional Frequency Analysis of AMWS using L-moments is performed utilizing wind speed data from sixteen sites (16) in Pakistan's Khyber Pakhtunkhwa province. There are no sites that are found to be discordant. The wards method is used to construct a homogenous region and make two homogenous regions from 16 sites. The heterogeneity test justifies that both clusters are homogeneous. The most appropriate probability distribution from the Generalized Normal (GNO), Generalized Logistic (GLO), Pearson Type-3 (P3), Generalized Pareto (GPA), and Generalized Extreme Value (GEV) distributions are chosen to calculate regional quantiles. According to the L-moments diagram and Z statistics, GEV for Cluster- Ι and GLO for Cluster- ΙΙ are the best suggestions from the others. Both clusters’ robustness is measured utilizing Relative Bias (RB) and Relative Root Mean Square Error (RRMSE). Overall, GEV distribution is fit for cluster-Ι, and the GLO distribution is fit for cluster-ΙΙ. Utilizing the site mean and median as index parameters, we can also find at-site quantiles from regional quantiles. The study’s quantile estimates can be employed in codified structural designs with policy consequences.

Recently, 3D printing techniques gained much attention in solid dosage form production. Even though the methods are increasingly studied, there are still many challenges in establishing an accurate content of active ingredients in the final products. In the present study, new experimental design techniques and statistical software were developed to identify and optimize a formula to predict the mass of the tablet to be 3D printed before it actually is. The first step was to develop a virtual 3D object in the FreeCad Software, then, using a CURA slicer software, the G-code Format was obtained. The tablets were printed using a Cartesian 3D printer based on the generated data. Finally, adapting the object sequencing mode, a predicted drug mass value was obtained. As expected, the oblong tablet's scaling directly influences its mass. The software generates a correlation coefficient (r) of 0.84, confirming the directly proportional relationship between scaling and tablet weight. However, the r values progressively decreased, indicating a poor correlation between tablet weight and other factors: infill density percent (r = 0.403) and wall thickness (r = 0.185). After repeating the 3D printing with the values ​​suggested by the software, tablets with a practical value of 1066 mg were obtained. Considering that the confidence interval calculated is [834.95—1165.05] and the experimental value is 1066 mg, it can be certified that the proposed model is valid.

Analytical mechanics is the most basic discipline. The basic principles of analytical mechanics should also be applicable to general deformed objects. However, the virtual displacement principle proposed by analytical mechanics is only applicable to particle systems and rigid body systems, but not to general deformed objects. In this paper, the generalized virtual displacement principle of general deformed objects (such as, elastic objects, plastic objects, elasto-platic objects and flexible objects etc ) is derived by the method of analytical mechanics, which is also applicable to particle systems and rigid body systems. First of all, according to the method of analytical mechanics, the external force, internal force, constraint reaction force and elastic recovery force of the deformed object system under the equilibrium state are analyzed, and the concepts of virtual displacement, ideal constraint and virtual work are introduced, and the generalized virtual displacement principle (also called generalized virtual work principle) of deformed objects is proposed; secondly, vector form, coordinate component form and generalized coordinate form of generalized virtual displacement principle of deformed object are given; thirdly, as the application of the principle, the virtual displacement principle of deformed objects in plane polar coordinate system, space cylindrical coordinate system and spherical coordinate system is given; finally, a brief conclusion is drawn. This work unifies the virtual displacement principle of elastic object, plastic, elastoplastic etc deformed object systems and rigid object systems with the basic method of analytical mechanics. It is a basic principle for dealing with the static problems of deformed objects. This work also lays a foundation for the further study of the dynamics of deformed object systems.

As indicated by standards of modern dentistry, the essence of endodontic treatment is chemo-mechanical disinfection of root canal system. The infected tooth should be eradicated of microorganisms and sealed tight with biocompatible materials. Vital element of this process is irrigation of canals with designated solutions as well as their activation. This review article summarizes various techniques of activation of irrigants for endodontic treatment and compares them. Results were gathered using PubMed and Google Scholar databases by searching keywords: irrigation, activation, sodium hypochlorite, irrigation protocols.

Since the discovery of graphene, due to its excellent optical, thermal, mechanical and electrical properties, it has a broad application prospect in energy, materials, biomedicine, electromagnetism and other fields. A great quantity of researches on the physical mechanism of graphene has been applied to engineering in electromagnetism and optics. To study the properties of graphene, different kinds of numerical methods have been developed for simulating the electromagnetic field effects of graphene, and equivalent boundary conditions have been employed to replace graphene in the computational domain. In this work, the numerical methods with equivalent boundary conditions are reviewed, and some examples are provided to illustrate their applicability.

Global research on the Covid-19 vaccine’s effectiveness is using methods that are misleading the scientific community and public opinion. There is a golden rule in medicine: to measure the effectiveness of an experimental medicine you need to have two groups. The group that will take medicine and the group that will not take medicine. You follow the health status of both groups for a few months and you will get objective results. This is the only proper methodology to verify the effectiveness of a new medicine. Articles cited from 1-5 did not use the gold rule. They develop different kinds of methodologies that all have no statistical significance. On the basis of their methodologies, they conclude that Covid-19 vaccines have a positive effect on public health. By comparing graphs of the intensity of vaccination and the rate of mortality we see that after the period of intense vaccination follows the period of higher excess mortality. Basic statistical data are confirming that Covid-19 vaccines increased the mortality rate.

In the last decade, we have witnessed an upsurge in nuclei-based studies, particularly coupled with next-generation sequencing. Such studies aim at understanding the molecular states that exist in heterogeneous cell populations by applying increasingly more affordable sequencing approaches, in addition to optimized methodologies developed to isolate and select nuclei. Although these powerful new methods promise unprecedented insights, it is important to understand and critically consider the associated challenges. Here, we provide a comprehensive overview of the rise of nuclei-based studies and elaborate on their advantages and disadvantages. Improved designs and appropriate use of the various experimental strategies will result in acquiring biologically accurate and meaningful information.

Abstract This essay presents a real-world demonstration of the evidence-based science communication process, showing how it can be used to create scientific data visualizations for public audiences. Visualizing research data can be an important science communication tool. Maximizing its effectiveness has the potential to benefit millions of viewers. As with many forms of science communication, creators of such data visualizations typically rely on their own judgments and the views of the scientists providing the data to inform their science communication decision-making. But that leaves out a critical stakeholder in the communications pipeline: the intended audience. Here, we show the practical steps that our team - the Advanced Visualization Lab at the University of Illinois at Urbana-Champaign - has taken to shift towards more evidence-based practice to enhance our science communication impact. We do this using concrete examples from our work on two scientific documentary films, one on the theme of ‘solar superstorms’ and the other focusing on the black hole at the center of the Milky Way galaxy. We used audience research with each of these films to inform our strategies and designs. We describe how such research evidence informed our understanding of ‘what works and why’ with cinematic-style data visualizations for the public. We close the essay with our key ‘take home’ messages from this evidence-based science communication process.

Coronavirus disease since December 2019 has significantly affected millions of people. Given the effect this disease has on the pulmonary systems of humans, there is a need for chest radiographic imaging (CXR) for monitoring the disease and preventing further deaths. Several studies have been shown that Deep Learning models can achieve promising results for COVID-19 diagnosis towards the CXR perspective. However, this research field is at an initial stage since there is a limited number of large CXR repositories regarding COVID-19. In this study, five deep learning models were analyzed and evaluated with the aim of identifying COVID-19 from chest X-Ray images. More specifically, we utilized the ResNet50, ResNet101, DenseNet121, DenseNet169 and InceptionV3 using Transfer Learning. All models were trained and validated on the largest publicly available repository for COVID-19 CXR images. Furthermore, they were evaluated on unknown data as well, that was not used for training or validation, authenticating their performance, and clarifying their usage in a medical scenario. All models achieved satisfactory performance where ResNet101 was the superior model achieving 96% in Precision, Recall, and Accuracy. Our outcomes show the potential of deep learning models on COVID-19 medical offering a promising way for the deeper understanding of COVID-19.

Soil working tools, implements, and machines are inevitable in mechanized agriculture. The soil-tool/machine interaction is a multivariate, dynamic, and intricate process. The accurate interpretation, description, and modeling of a soil-machine interaction is key to providing a solution to sustainable crop production by reducing energy input, excessive soil pulverization, and compaction. The traditional method provides insight into soil-machine interaction but often provides inadequate solutions and lacks broad applicability. Computational intelligence (CI) is a comprehensive class of approaches that rely on approximate information to solve complex problems. The CI method has been extensively studied and applied in soil tillage and traction domain in recent decades. The study critically reviews the CI techniques implemented in soil-machine interactions, especially in the context of tillage, traction, and compaction. The traditional methods and their limitation are discussed. The fundamental of CI methods and a detailed overview of the most popular methods are provided. The study reviews and summarizes the 50 selected articles on soil-machine interaction studies where CI methods were employed. It discusses the strength and limitations of employed CI methods. It also suggests the emergent CI methods and future applications are discussed. The outlined study would serve as a concise reference and a quick and systematic way to understand the applicable CI methods that allow crucial farm management decision-making.

Organisms release their nucleic acid in the environment including the DNA and RNA which can be used to detect their presence. eDNA/eRNA techniques are being used in different sectors to identify organisms from soil, water, air, and ice since long. The advancement in technology led to easier detection of different organisms without impacting the environment and the organism itself. These methods are being employed in different areas including surveillance, history, and conservation. eDNA and eRNA methods are being extensively used in aquaculture and fisheries setting to understand the presence of different fish species and pathogens in water. However, there are some challenges associated with the reliability of the results because of the degradation of nucleic acid by several factors. In aquaculture there are several diseases and parasites detected with these methods. In this review we discuss different aquaculture diseases and parasites detected with eDNA/eRNA approach and the fate of these nucleic acids when subjected to different water quality and environmental parameters. This review intends to help the researcher about the potential of eDNA/eRNA based detection of pathogens in aquaculture; this will be useful to predict the potential outbreak before it occurs. Along with that this paper intends to make people understand several factors that degrade and can hamper the detection of these nucleic acids.

Biostimulation is an animal management practice that helps improve reproductive parameters by modulating animal sensory systems. Chemical signals, mostly known as pheromones, have a great potential in this regard. This study was conducted to determine the influence of short-term female rabbit exposure to different conditions, mainly pheromone-mediated, on reproductive parameters of inseminated does. Groups of 60 females/each were exposed to 1) female urine, 2) male urine, 3) seminal plasma and 4) female-female interaction, just before artificial insemination, and compared to isolated females controls (female-female separated). The following reproductive parameters were analyzed for each group: receptivity (vulvar color), fertility (calving rate), prolificacy and number of born alive and dead kits ⁄ litter. Our results showed that the biostimulation methods employed in this experiment did not significantly improve any of the analyzed parameters. However, female doe exposure to urine, especially to male urine, slightly increased fertility levels when compared to the rest of the experimental conditions. Female-female interaction before artificial insemination, which is a common practice in rabbit farms, did not have any effect, which suggests its removal to avoid unnecessary animal management and time cost. On the other hand, fertility ranges were lower for animals with pale vulvar color whereas no differences were noticed among the other three colours which measure receptivity (pink, red, purple), thus suggesting that these three colours could be grouped together. Additionally, equine chorionic gonadotropin injection could be replaced with various biostimulation methods, therefore reducing or replacing current hormonal treatments, and contributing to animal welfare and to a natural image of animal production.

The task of unsupervised anomalous sound detection (ASD) is challenging for detecting anomalous sounds from a large audio database without any annotated anomalous training data. Many unsupervised methods were proposed, but previous works have confirmed that the classification-based models far exceeds the unsupervised models in ASD. In this paper, we adopt two classification-based anomaly detection models: (1) Outlier classifier is to distinguish anomalous sounds or outliers from the normal; (2) ID classifier identifies anomalies using both the confidence of classification and the similarity of hidden embeddings. We conduct experiments in task 2 of DCASE 2020 challenge, and our ensemble method achieves an averaged area under the curve (AUC) of 95.82% and averaged partial AUC (pAUC) of 92.32%, which outperforms the state-of-the-art models.

In large infrastructures such as dams, which have a relatively high economic value, ensuring the proper operation of the associated hydraulic facilities in different operating conditions is of utmost importance. To ensure the correct and successful operation of the dam's hydraulic equipment and prevent possible damages, including gates and downstream tunnel, to build laboratory models and perform some tests are essential (the advancement of the smart sensors based on artificial intelligence is essential). One of the causes of damage to dam bottom outlets is cavitation in downstream and between the gates, which can impact on dam facilities, and air aeration can be a solution to improve it. In the present study, six dams in different provinces in Iran has been chosen to evaluate the air entrainment in the downstream tunnel experimentally. Three artificial neural networks (ANN) based machine learning (ML) algorithms are used to model and predict the air aeration in the bottom outlet. The proposed models are trained with genetic algorithms (GA), particle swarm optimization (PSO), i.e., ANN-GA, ANN-PSO, and ANFIS-PSO. Two hydrodynamic variables, namely volume rate and opening percentage of the gate, are used as inputs into all bottom outlet models. The results showed that the most optimal model is ANFIS-PSO to predict the dependent value compared with ANN-GA and ANN-PSO. The importance of the volume rate and opening percentage of the dams' gate parameters is more effective for suitable air aeration.

The use of low-intensity ultrasound has gotten surprising consideration over the last decade as a method for enhancing the catalytic activity of the enzyme. Ultrasounds have the potential to significantly influence the activity of the enzymatic processes, provided that the energy input is not too high to inactivate the enzyme. By providing the variation in parameters, various physical and chemical effects can be attained that can enhance the enzymatic reaction. Ultrasonic reactors are known for their application in bioprocesses. However, the potential of their applications is still limited broadly due to the lack of proper information about their operational and performance parameters. In this review, the detailed information about ultrasonic reactors is provided by defining the different types of reactors, number and position of ultrasonic transducers. Also, it includes the mechanism of intensification and influence of ultrasonic parameters (intensity, duty cycle and frequency) and enzymatic factors (enzyme concentration, temperature and pH) on the catalytic activity of enzyme during ultrasound treatment.

G-protein coupled receptors (GPCRs) are large protein families known to be important in many cellular processes. They are well known for their allosteric activation mechanisms. They are drug targets for several FDA-approved drugs. We have investigated the diversity of the ligand binding site for these class of proteins against their cognate ligands using computational docking, even if their structures are known in the ligand-complexed form. The cognate ligand of some of these receptors dock at allosteric binding site, with better score than the binding at the conservative site. Further, ligands obtained from GLASS database, which consists of experimentally verified GPCR ligands, also show allosteric binding to GPCRs. The allosteric binders show strong affinity to the binding site, though the residues at the binding site are not conserved across GPCR subfamilies.

Several problems of radiative transfer are yet unsolved because of the difficulties of the calculations involved in them, especially if the intervening shapes are geometrically complex. The main goal of our investigation in this domain is to convert the formulas that were previously derived, into a graphical interface based on the projected solid-angle principle. Such procedure is now feasible by virtue of several widely diffused programs for Algorithms Aided Design (AAD). Accuracy and reliability of the process is controlled by means of the analytical software DianaX developed at an earlier stage by the authors. With this new approach the often cumbersome procedure of lighting and thermal exchange calculations can be simplified and made available for the neophyte, with the undeniable advantage of reduced computer time.

The purpose of this paper is to prove strong convergent theorems for Browder's type iterations and Halpern's type iterations of a family of nonexpansive mappings in a complete geodesic space with curvature bounded above by a positive number. Moudafi's viscosity type methods are also discussed without the nice projection property.

Aggregating data is the main line of any discipline dealing with fusion of information from the knowledge-based systems to the decision-making. The purpose of aggregation methods is to convert a list of objects, all belonging to a given set, into a single representative object of the same set usually by an n-ary function, so-called aggregation operator. Since the useful aggregation functions for modeling real-life problems are limit, the basic problem is to construct a proper aggregation operator for each situation. During the last decades, a number of construction methods for aggregation functions have been developed to build new classes based on the well-known operators. This paper reviews some of these construction methods where they are based on transformation, composition and weighted rule.

Use of Machine Learning (ML) methodologies in optical communications has paved a new pathway. In this paper, firstly, we discuss the use of ML methodologies for reducing optical fiber nonlinearities, nonlinearity compensation, fault detection and optical performance monitoring. Then we present our recent work where we compare RL-SARSA and SVM based method with conventional method. The results show that RL-SARSA and SVM methods are successful candidates in mitigating the nonlinearities in proposed system as compared to conventional optical communication system.

This paper investigates the inverse option problems (IOP) in the extended Black--Scholes model arising in financial markets. We identify the volatility and the drift coefficient from the measured data in financial markets using a Bayesian inference approach, which is presented as an IOP solution. The posterior probability density function of the parameters is computed from the measured data. The statistics of the unknown parameters are estimated by a Markov Chain Monte Carlo (MCMC) algorithm, which exploits the posterior state space. The efficient sampling strategy of the MCMC algorithm enables us to solve inverse problems by the Bayesian inference technique. Our numerical results indicate that the Bayesian inference approach can simultaneously estimate the unknown trend and volatility coefficients from the measured data.

The railway industry focus in the past years was to research, find and develop methods to mitigate noise and vibration resulted from wheel/rail contact along track infrastructure. This resulted in a wide range of abatement measures that are available for the professionals of the industry today. However, although there are many options in the market, their practical implementations depend upon general constraints that affect most technological application in the engineering world. The progression of these technologies have facilitated the selection of more adequate methods for each best case scenario, but further studies are ought to be made to proper assess if each one is fit for their purpose. Every method implementation must be analyzed through budget and timeframe limitations, which includes building, maintenance and inspection costs and time allocation, while also aiming to meet different benefits, such as environmental impact control and wear of the whole infrastructure. There are several situations and facilities in a railway project design that need noise and vibration mitigation methods and each design allocates different priorities for each one of them. Traditionally the disturbance caused by railways to the community are generated by wheel/rail contact sound radiation that expresses in different ways, depending on the movement of the rolling stock and track alignment, such as rolling noise, impact noise and curve noise. More specifically, in special trackworks such as turnouts, the main area of this study, there are two noises types that must be evaluated: impact noise and screeching noise. With respect to the second, it is similar to curve squeals and, being such, its mitigation methods are to be assigned as if it was to abate curve squeal in turnouts and crossings. The impact noise on the other hand, emerges from the sound made by the rolling stock moving through joints and discontinuities (i.e. gaps) that composes these special components of a railway track. A life cycle analysis is therefore substantial for this reality and in this case will be applied to Squeal and Impact Noise on Special Trackwork. The evaluation is based on a valid literature review and the total costs were assumed by industry reports to maintain coherency. The period for a life cycle analysis is usually of 50 years, hence it was the value assumed. As for the general parameters, an area with high density of people was considered to estimate the values for a community with very strict limits for noise and vibration.

Accurate agricultural commodity price models enable efficient allocation of limited natural resources, leading to improved sustainability in agriculture. Because of climate change, price volatility and uncertainty for the sector are expected to increase in the future, increasing the need for improved price modeling. With the emergence of machine learning (ML) algorithms, novel tools are now available to enhance the modeling of agricultural commodity prices. This research uses both univariate and multivariate ML techniques to perform probabilistic price prediction modelling for the Canadian beef industry, taking into account beef production, commodity market, and international trade features to enhance accuracy. We apply multivariate algorithms by using support vector regression (SVR), random forest (RF), and adaboost (AB) and univariate algorithms by using autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA), and the seasonal autoregressive integrated moving average with exogenous factors (SARIMAX). We apply these models to monthly fed steer price data between January 2005 and September 2023 and compare predicted prices with observed prices using several validation metrics. The outcomes indicate that both random forest (RF) and adaboost (AB) in multivariate modeling show superior performance in accurately predicting Alberta fed steer prices in comparison to other algorithms. To better account for the variance of the best model performance, we subsequently adopted a probabilistic approach by considering uncertainty in our best-selected ML model. The beef industry can use these improved price models to minimize resource waste and inefficiency in the sector and improve the long-term sustainability prospects for beef producers in Canada.

In this paper, we design two inertial-type subgradient extragradient algorithms with line-search process for solving the pseudomonotone variational inequality problems (VIPs) and common fixed-point problem (CFPP) of finite Bregman relatively nonexpansive mapping and a Bregman relatively demicontractive mapping in p-uniformly convex and uniformly smooth Banach spaces, which are more general than Hilbert spaces. Under mild conditions, we derive weak and strong convergence of the suggested algorithms to a common solution of the VIPs and CFPP, respectively. Additionally, an illustrated example is furnished to back up the feasibility and implementability of our proposed methods.

PVDF has special piezo/pyro/ferroelectric, flexibility, low weight, biocompatibility, economical, good chemical/thermal, and high mechanical properties such as excellent nontoxic fiber/film formation. It has polar and nonpolar phases of α, β, γ, ε, and δ that the nonpolar α phase is the most stable one, but the β phase is the best of all because it has good piezo/pyro/ferroelectric properties. Copolymers are attractive because of their low weight, nontoxic, chemical acid resistance, flexibility, and ease of processing. These aspects result in their applications in many fields. They are used for piezoelectric nanogenerators, cooling/heating sensors, electronic devices (fuel cells, lithium-ion batteries (as separators), dye sensitive solar cells), filtration, oil/water separation, and photoelectric nanodevices. This review highlights the main aspects of the last decade's articles, and the focus is on the synthesis methods of PVDF nanofibers and their properties which results in their application in different fields of industry and especially focuses on finding ways to increase the output of PVDF nanofibers nanogenerators (weight/acoustic pressure nanogenerators

Firstly, the author do dynamic analysis for reaction-diffusion Gilpin-Ayala competition model with Dirichlet boundary value, involved in harmful species. Existence of multiple stationary solutions is verified by way of Mountain Pass lemma, and the local stability result of the null solution is obtained by employing linear approximation principle. Secondly, the author utilize variational methods and LMI technique to deduce the LMI-based global exponential stability criterion on the null solution which becomes the unique stationary solution of the ecosystem with delayed feedback under a reasonable boundedness assumption on population densities. Particularly, LMI criterion is involved in free weight coefficient matrix, which reduces the conservatism of the algorithm. In addition, a new impulse control stabilization criterion is also derived. Finally, two numerical examples show the effectiveness of the proposed methods. It is worth mentioning that the obtained stability criteria of null solution presented some useful hints on how to eliminate pests and bacteria.

Urban areas are centres of business and innovation. Freight transport is indispensable for the proper functioning of any modern urban society. Urban areas can’t function without an appropriate freight transport system, due to the need to replenish stocks of food and other goods in retail shops. The complexity of the decisions concerning implementation of measures to improve the movement of goods in the city requires tools designed to support this process. The purpose of this article is to introduce the possibility of applying the Analytic Hierarchy Process (AHP) as well as the Decision Making Trial and Evaluation Laboratory Method (DEMATEL) in choosing a set of measures and in analysing in the field of distribution logistics, which will help to solve the delivery problems and streamline the cargo flows in Szczecin, in the context of sustainable development.

The emission of sulphur dioxide (SO2) from mining activities presents a substantial risk to public health, especially in populations located near mine industrial regions. This study investigated the potential correlation between exposure to sulphur dioxide (SO2) and its effects on respiratory health in Kankoyo Township, Zambia. A variety of methodologies were employed in this study, encompassing community participation, expert interviews, spatial analysis, and a retrospective examination of health and SO2 data for Kankoyo spanning 15 years. The results of our research indicated a worrisome correlation between the community and the presence of SO2, which has had a significant impact on their lives. The expert interviews also emphasized the effects of SO2, with respiratory cases accounting for approximately 75% of the reported cases. Additionally, the clinic officer observed that asthma cases have decreased following the implementation of a monitoring station in the community. Furthermore, overall emissions of SO2 have decreased since the upgrade of the smelter operation. A probable association was identified in the retrospective analysis between exposure to SO2 and the occurrence of asthma, respiratory non-pneumonia, and pneumonia infections. The findings of the retrospective research revealed a substantial association between SO2 concentrations and the susceptibility of individuals aged 5 years and older to asthma, respiratory non-pneumonia, and pneumonia infections. The results of the spatial study indicated that Kankoyo exhibited a significant level of SO2 pollution, above the established limitations set by the Zambian Environmental Management Agency (ZEMA) by an alarming margin of 1713%. In response to the challenges identified, this study proposed several measures. These measures encompassed the implementation of a requirement for regulators such as ZEMA and local communities to have access to real-time pollution data from mines, to enhance transparency. Additionally, the deployment of affordable air quality sensors, the resolution of medication shortages, the establishment of specialized respiratory health clinics, the initiation of IT-driven public awareness campaigns, and the expansion of research to investigate additional pollutants and confounding factors were also recommended.

The importance of knowing the advance time in drip irrigation lines lies in its precision, as this influences the proper management of water and nutrients delivery. The mathematical calculation to determine the advance time is based on the general hydraulic flow equation, which relates the fluid velocity and the cross-sectional area of the pipe to the volume of water flowing through it. However, in irrigation pipes, where the flow is a mixture of dripper properties, its pressure, the approach to solving the advance time is different, as it is the sum of individual advance times for each segment. To address this issue, Python 3.11 was used along with various libraries such as Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn to create a modeling environment and run algorithms that could predict outcomes. A program was developed that calculates the advance time for each segment of the drip line using partial velocities, and a dataset was generated that was used to train and test machine learning models. Several machine learning algorithms such as Linear Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Decision Trees, and Random Forests were implemented to predict the advance time. Additionally, SciPy optimize was used to obtain multivariable equations that describe the advance time in drip irrigation lines. Results showed that the dripper flow has the greatest influence on the advance time, followed by the diameter and distance. Decision Tree and SVM models had the best accuracy with scores above 98%. Equations were found to calculate the advance time in the complete drip line and in 95% of its length, with coefficients of determination close to 99.33%. This study demonstrated the importance of understanding the relationship between dripper parameters and travel time in drip irrigation, as well as the utility of machine learning and optimization tools for predicting and modeling this phenomenon.

Durum wheat is an important staple food used to obtain several products. At first the wheat is milled to obtain different products: bran, semolina and flour. These products are the base of several artifacts with varying properties both from a nutritional point of view and flavoring characteristics. It is known that most elements concentrate in the outer layers of the wheat seed (pericarp and aleurone) so that the content of the elements vary a lot in the ground products. The present study investigates the characterizing elements of the milled products and the effect of cultivation protocol applied. We measured, by ICP-OES, the concentration of 28 elements in the whole seed and in any grinding products; the results show that only few elements characterize each product. Few elements, but different for each product, permit to disclose the kind of agri-cultural method used: organic or conventional protocol. Five elements: B, Cd, Cu, K, Se, are the most important to distinguish between organic and conventional agriculture by PCA and PLS analysis; these elements also permit some differentiation of products.

China Xihu Longjing tea famous for its good flavor and quality. However, the related metabolites information except flavonoids is largely deficient. Different processing methods of China Xihu Longjing tea fixing both by machine at first and second step(A1), first step by machine and second step by hand(A2), first step by hand and second step by machine(A3), both by hand at first and second step (A4) were compared using a UHPLC-QE-MS-based metabolomics approach. Liquid chromatography-mass spectrometry was used to analyze the metabolic profiles of the processed samples. A total of 490 metabolites (3 alkaloids, 3 anthracenes, 15 benzene and substituted derivatives, 2 benzopyrans, 13 coumarins and derivatives, 128 flavonoids, 4 furanoid lignans, 16 glycosides and derivatives, 5 indoles and derivatives, 18 isocoumarins and derivatives, 4 chalcones and dihydrochalcones, 4 naphthopyrans, 3 nucleosides, 78 organic acids and derivatives, 55 organooxygen compounds, 5 phenols, 109 prenol lipids, 3 saccharolipids, 3 steroids and steroid derivatives, 17 tannins) were identified. The different metabolism profiles were distinguished using PCA and OPLS-DA. There were differences in the types and contents of metabolites, especially flavonoids, furanoid lignans, glycosides and derivatives, organic acids and derivatives and organooxygen compounds. There was a positive correlation between flavonoid metabolism and amino acid metabolism. However, there was a negative correlation between flavonoid metabolism and amino acid metabolism, which had the same trend as prenol lipids metabolism, tannin.This study provides new valuable information for regarding differences in the metabolite profile of China Xihu Longjing tea processed based on machine fixing and manual fixing methods.

The flower of Syringa pubescens Turcz. is a Chinese folk medicine and also used as flower tea for healthcare. The effects of five drying methods on the active compound contents, the antioxidant abilities, anti-inflammatory properties and enzyme inhibitory activities were evaluated. The plant materials were treated using shade-drying, microwave drying, sun-drying, infrared drying and oven drying. The seven active compounds were simultaneously determined by an HPLC method. Furthermore, the chemicals differences were assessed using Scanning electron microscopy, UV spectroscopy and infrared spectroscopy. The antioxidant capacities and protective effects on L02 cells induced by hydrogen peroxide were measured. The anti-inflammatory effects on lipopolysaccharide-induced RAW264.7 cells were investigated. The enzyme inhibitory activities were determined against α-amylase, α-glucosidase cholinesterases and tyrosinase. The results indicated that drying methods had significantly influences on the active compound contents and biological properties. In consideration of the contents of bioactive compound, the suitable drying methods were SHD, SD, OD and MD. The SHD sample exhibited stronger radical scavenging ability on DPPH, ABTS, hydroxyl and superoxide anions and higher reducing power compared with other samples prepared by SD, OD, MD and IRD. Moreover, SHD sample had strong protective effects against L02 cells damage induced by H2O2. SPF samples, especially SHD sample, significantly inhibited inflammatory factor secretion in LPS-stimulated RAW 264.7 cell. All studied SPF sample exhibited evidently inhibitory effects on the four enzymes. SHD sample possessed the highest inhibitory activity on α-glucosidase and α-amylase. While SHD and SD samples had the potential inhibitory effects on cholinesterases and tyrosinase. In consideration of active compound contents and biological activities, it was recommended that SHD and SD were applied for drying SPF at an industrial scale.

The internet of things and growing demand for smaller and advanced devices has created the problem of high heat production in the electronic equipment which greatly reduces the work performance and life of the electronic instruments. Thermal interface materials (TIMs) are placed in between heat generating micro-chip and the heat dissipater to conduct all the produced heat to the heat sink. Development of suitable TIMs with excellent thermal conductivity (TC) in both in-plane and through-plane directions is a very important need at present. For efficient thermal management, polymer composites are potential candidates. But in general their thermal conductivity is low compared to that of metals. The filler integration into the polymer matrix is one of the two approaches used to increase the thermal conductivity of polymer composites and is also easy to scale up for industrial production. Another way to achieve this is to change the structure of polymer chains, which fall out of the scope of this work. In this review, considering the first approach, the authors have summarized recent development in many types of fillers with different scenarios by providing multiple cases with successful strategies to improve through-plane thermal conductivity (TPTC) (K⊥). For better understanding of TC the comprehensive background is also presented. In the end, it is given a detailed conclusion which provides drawbacks of some filler, multiple significant routes recommended by other researchers to build Thermally Conductive Polymer Composites, future aspects along with direction so that the researchers can get a guideline to design an effective polymer based Thermal interface materials. A number of methods to improve the effective (out-plane) thermal conductivity of polymer composites are also discussed.

The positional deviation of the Automatic Emergency Call System (AECS) under collision conditions brings difficulties for post-rescue operations. Currently, there is a lack of analysis on the system operating conditions during collisions in the reliability assessment methods for the Global Navigation Satellite System (GNSS) deployed in AECS. Therefore, this paper establishes an in-vehicle collision environment simulation model for emergency calls to explore the influence of parameters such as temperature and vibration on the data deviation of the simulated GNSS system. We also propose environmental limits applicable to comprehensive AECS tests, which can objec-tively evaluate the reliability of AECS and provide data support for AECS bench test. Through the satellite signal-based semi-physical simulation, AECS is subjected to bench test under different operating conditions. The findings of this study demonstrate that the occurrence of random vibra-tion and impact stress, induced by vibration, exerts considerable disruptive effects on positional signal data during collisions. Consequently, it leads to substantial interference with the accurate detection of post-collision satellite positioning information. When the simulation operates under a 2.4gRMS vibration condition, the maximum phase noise error in the positioning system is 8.95%, which does not meet the AECS test accuracy requirements. On the other hand, the semi-simulation system is less affected by temperature changes, and at the maximum allowable temperature dif-ference of the equipment, the maximum phase noise error in the simulated signal is 2.12%. Therefore, based on the influence of phase noise variation on the accuracy of satellite signal simulation, nec-essary environmental conditions for AECS test are obtained, including the temperature consistent with the maximum operating temperature of the vector generator and the vibration Power Spectral Density (PSD) lower than 1.2gRMS.

Background: There are ongoing workforce challenges with the delivery of long-term care (LTC), such as staffing decisions based on arbitrary standards. The Synergy tool, a resident-centered approach to staffing, pro-vides objective, real-time acuity and dependency scores (Synergy scores) for residents. The purpose of this study was to implement and evaluate the impact of the Synergy tool on LTC delivery. Methods: A longitudinal mixed methods study took place within two publicly-funded LTC homes in British Columbia, Canada. Quantitative data included weekly Synergy scores for residents (24 weeks), monthly aggregated resident falls data (18 months) and a six-month economic evaluation. Qualitative data were gathered from family caregivers and thematically analyzed. Results: Quantitative findings from Synergy scores revealed considerable variability for resident acuity/dependency needs within and across units; and falls decreased during implementation. The six-month economic evaluation demonstrated some cost savings by comparing Synergy tool training and implementation costs with savings from resident fall rates reductions. Qualitative analyses yielded three positive impacts themes (improved care delivery, better communication, and improved resident-family-staff relationships), and two negative structural themes (language barrier and staff shortages). Conclusions: The Synergy tool provides useful data for enhancing a ‘fit’ between resident needs and available staff.

Bullying makes learning difficult for nursing students, and using real-life scenarios during training can improve the understanding of workplace bullying. Thus, to mitigate bullying experienced by nurses, this study developed and evaluated a cognitive rehearsal education program that constituted of training nursing students through role-play simulations. A mixed-method research design was used to evaluate 39 nursing students from two universities. A quasi-experimental research design was applied to assess symptoms, knowledge, and perceptions of bullying, and a focus group interview was conducted with six participants. Quantitative analyses revealed that the program improved participants’ knowledge and perceptions, but not their symptoms. The focus group interview revealed that the program improved participants’ coping skills and desire for education. This program could be effective in raising awareness of workplace bullying and increasing relevant coping skills. It can be further developed as part of an overall strategy to manage workplace bullying and its consequences in hospital settings.

Purpose: This study investigates the impact of different intensity normalization (IN) methods on the overall survival (OS) radiomics models’ performance of MR sequences in primary (pHGG) and recurrent high-grade glioma (rHGG). Methods: MR scans acquired before radiotherapy were retrieved from two independent cohorts (rHGG C1: 197, pHGG C2: 141) from multiple scanners (15, 14). The sequences are T1 weighted (w), contrast-enhanced T1w (T1wce), T2w, and T2w-FLAIR. Sequence-specific significant features (SF) associated with OS, extracted from the tumour volume, were derived after applying 15 different IN methods. Survival analyses were conducted using Cox proportional hazard (CPH) and Poisson regression (POI) models. A ranking score was assigned based on the 10-fold cross-validated (CV) concordance index (C-I), mean square error (MSE), and the Akaike information criterion (AICs), to evaluate the methods’ performance. Results: Scatter plots of the 10-CV C-I and MSE against the AIC showed an impact on the survival predictions between the IN methods and MR sequences (C1/C2 C-I range: 0.62-0.71/0.61-0.72, MSE range: 0.20-0.42/0.13-0.22). White stripe showed stable results for T1wce (C1/C2 C-I: 0.71/0.65, MSE: 0.21/0.14). Combat (0.68/0.62, 0.22/0.15) and histogram matching (HM, 0.67/0.64, 0.22/0.15) showed consistent prediction results for T2w models. They were also the top-performing methods for T1w in C2 (Combat: 0.67, 0.13; HM: 0.67, 0.13), however, only HM achieved high predictions in C1 (0.66, 0.22). After eliminating IN impacted SF using Spearman’s rank-order correlation coefficient, a mean decrease in the C-I and MSE of 0.05 and 0.03 was observed in all four sequences. Conclusion: The IN method impacted the predictive power of survival models. Thus, performance is sequence-dependent.

Accurately monitoring insecticide resistance in target mosquito populations is important to combating malaria and other vector-borne diseases, and robust methods are key. The “WHO susceptibility bioassay” has been used for +60 years: mosquitoes of known physiological status are exposed to a discriminating concentration of insecticide. Several changes to the test procedures have been made historically which may seem minor but could impact bioassay results. The published test procedures and literature for this method were reviewed for methodological details. Areas where there was room for interpretation in the test procedures or where the test procedures were not being followed were assessed experimentally for impact on bioassay results: covering or uncovering of the tube end during exposure, number of mosquitoes per test unit, and mosquito age. Many publications do not cite the most recent test procedures, methodological details are reported which contradict the test procedures referenced or methodological details are not fully reported. As a result, the precise methodology is unclear. Experimental testing showed that using fewer than the recommended 15-30 mosquitoes per test unit significantly reduced mortality, covering the exposure tube had no effect, and using mosquitoes older than 2-5 days old increased mortality, particularly in the resistant strain. Recommendations are made for better reporting of experimental parameters.

The first published description of intraovarian platelet-rich plasma (PRP) appeared in mid-2016, when a new experimental technique was successfully used in adult human ovaries to correct the reduced fertility potential accompanying advanced maternal age. Considering the potential therapeutic scope of intraovarian activated PRP and/or condensed platelet cytokines would likely cover both menopause treatment and infertility, the mainstream response has ranged from skeptical disbelief to welcome astonishment. Indeed, reports of restored menses in menopause (as an alternative to conventional hormone replacement therapy) and healthy term livebirths for infertility patients (either with IVF or as unassisted conceptions) after intraovarian PRP injection continue to draw notice. Yet any proper criticism of ovarian PRP applications will be difficult to rebut given the heterogenous patient screening, varied sample preparations, wide differences in platelet incubation and activation protocols, surgical/anesthesia techniques, and delivery methods. Notwithstanding these features, no adverse events have been reported thus far and ovarian PRP appears well tolerated by patients. Here, early research guiding the transition of ‘ovarian rejuvenation’ from experimental to clinical is outlined. Likely mechanisms are presented to explain results observed in both veterinary and human ovarian PRP research. Current and future challenges for intraovarian cytokine treatment are also discussed.

Learning programming is a complex and challenging task for many students. It in-volves both understanding theoretical concepts and acquiring practical skills. Hence, analyzing learners’ data from online learning environments alone fails to capture the full breadth of stu-dents’ actions if part of their learning process takes place elsewhere. Moreover, existing studies on learning analytics applied to programming education have mainly relied on frequency analysis to classify students according to their approach to programming or to predict academic achieve-ment. However, frequency analysis provides limited insights into the individual time-related characteristics of the learning process. The current study examines students’ strategies when learning programming, combining data from the learning management system and from an au-tomated assessment tool. To gain an in-depth understanding of students’ learning process as well as of the types of learners, we used learning analytics methods that account for the temporal order of learning actions. Our results show that students have special preferences for specific learning resources when learning programming, namely slides that support search, and copy and paste. We also found that videos are relatively less consumed by students, especially while working on programming assignments. Lastly, students resort to course forums to seek help only when they struggle.

Studies have shown that nearly half of rural toddlers in China have cognitive delays due to an absence of stimulating parenting practices, such as early childhood reading, during the critical first three years of life. However, few studies have examined the reasons behind these low levels of stimulating parenting, and no studies have sought to identify the factors that limit caregivers from providing effective early childhood reading practices (EECRP). This mixed-methods study investigates the perceptions, prevalence and correlates of EECRP in rural China, as well as associations with child cognitive development. We use quantitative survey results from 1,720 caregiver-child dyads across 100 rural villages/townships in northwestern China and field observation and interview data with 60 caregivers from these same sites. The quantitative results show significantly low rates of EECRP despite positive perceptions of early reading and positive associations between EECRP and cognitive development. Qualitative results suggest that low rates of EECRP in rural China are not due to the inability to access books, financial or time constraints, or the absence of aspirations. Rather, the low rate of book ownership and absence of reading to young children is driven by the insufficient and inaccurate knowledge of EECRP among caregivers, which leads to their delayed, misinformed reading decisions with their young children, ultimately contributing to developmental delays.

Background: The SARS-CoV-2 virus is a new highly pathogen virus that can suppress cell immune response (Poly ADPR process, and interferon releasing in infected cell) by viral macrodomain and create fatal pneumonia. Cytokine storm phenomenon which can create consequence of SARS COV-2 by increase of the performance of human TRPM2, is a dangerous condition for human and its body organ such as kidney, heart, and liver. it seems like prevention of beginning of these events is necessary. Material and methods: in this study drug and drug like database was used for inhibition of the viral macrodomain and human TRPM2 that can initiate of the viral cell cycle infection and human cytokine storm respectively. Ligand base drug designed, pharmacophore modeling, docking and molecular dynamic simulation was performed Results: among up to billion compounds, 20 compounds were screened by infinisee program. Then 7 compounds were selected by pharmacophore modeling. between 7 compounds, 5 compound was removed because of high similarity to ADPR and possibility of toxic effect for human RNA and DNA polymerase. One compound was losartan and selected for docking and molecular dynamic simulation. Conclusion: losartan earned a proper dock score and binding affinity to create the complexes with TRPM2 and macrodomain. Molecular dynamic simulation has shown losartan had an adequate binding free energy for human trpm2 and viral macrodomain. The inhibitory effect of losartan on these protein has shown and it could interfere in several points (PARP, PARG- macrodomain and TRPM2) and decreases oxidative stress, apoptosis, cytokine storm in COVID-19.

A drug repositioning effort supported the possible use of the anti-HIV drug etravirine as a treatment for Friedreich Ataxia (FRDA). Etravirine increases frataxin protein and corrects the biochemical defects in cells derived from FRDA patients. Because of these findings, and since etravirine displays a favorable safety profile, we conducted this pilot open-label phase 2 clinical trial assessing the safety and potential efficacy of etravirine in FRDA patients. Thirty-five patients were stratified into 3 severity groups and randomized to etravirine 200 mg/day or 400 mg/day. They were treated for 4 months. Efficacy endpoints were represented by changes in peak oxygen uptake and workload as measured by incremental exercise test, SARA score, cardiac measures, measures of QoL and disability. Data were collected 4 months before the start of the treatment (T-4), at the start (T0), at the end (T4) and 4 months after the termination of the treatment (T+4). Etravirine was well tolerated. Etravirine completely stopped the progression of the SARA score during the 4-months treatment period, compared to the 4 months pre and post treatment. It increased peak workload, while the improvement of peak oxygen uptake was not statistically significant. No changes in the cardiac measures were observed. Health and QoL measures showed a worsening at the suspension of the drug. In this open trial etravirine significantly improved neurological function and was generally safe and reasonably tolerated. This suggests that etravirine represents a potential therapeutic agent in FRDA deserving testing in a randomized placebo controlled clinical trial.

Cocoyam is a highly nutritious root crop and very promising with vast industrial potentials. The effect of some processing methods on the proximate composition of cocoyam (Xanthosoma sagittifolium) flour was investigated by subjecting the cormels to three pre-treatment methods (soaking, fermentation and steaming) and subsequently, to two different milling methods (wet and dry-milling methods). The proximate composition namely moisture content, crude protein, crude fat, fibre, ash and carbohydrate contents of the flours obtained was carried out. The fermented samples of the wet- and dry-milled flours retained the highest nutrients compared with the soaked and steamed samples. However, the fermented dry-milled flour had the highest crude protein (5.83-6.98 %), crude fat (0.55-2.21%), ash (3.75-4.29%), and carbohydrate (83.75-87.62%), which made it the most acceptable of the samples. The milling methods (wet and dry) had significant effects (p≤0.05) on the proximate composition of the flour for the selected pretreatment methods (soaking, fermentation and steaming). Fermentation as a pretreatment method was therefore, recommended for better nutrient retention for cocoyam flour.

The prediction of tertiary RNA structures is significant to the field of medicine (e.g. mRNA vaccines, genome editing), and the exploration of viral transcripts. Though many RNA folding software exist, few studies have condensed their locus of attention solely to viral pseudoknotted RNA. These regulatory pseudoknots play a role in genome replication, gene expression, and protein synthesis. This study explores five RNA folding engines that compute either the minimum free energy (MFE) or the maximum expected accuracy (MEA). These folding engines were tested against 26 experimentally derived short pseudoknotted sequences (20-150nt) using metrics that are commonly applied to software prediction accuracy (e.g. F1 scoring, PPV). This paper reports higher accuracy RNA prediction engines (pKiss) when compared to previous iterations of the software, and when compared to older folding engines. They show that MEA folding software does not always outperform MFE folding software in prediction accuracy when assessed with metrics such as percent error, sensitivity, PPV, and F1 scoring when applied to viral pseudoknotted RNA. Moreover, the results suggest that thermodynamic model parameters will not ensure accuracy if auxiliary parameters such as Mg2+ binding, dangling end options, and H-type penalties are not applied. The observations reported in this paper highlight the quality between different ab initio prediction methods while enforcing the idea that a better understanding of intracellular thermodynamics is necessary for a more efficacious screening of RNAs.

The paper aims to develop and test an algorithm for classifying Russian-language text reviews of patients’ experiences with medical facilities and physicians, extracted from social media. This is motivated by the limitations of conventional methods of surveying consumers to assess their satisfaction with the quality of services, which are being replaced by automatic processing of text data from social media. This approach enables to get more objective results due to the increased representativeness and independence of the sample of service consumers. The authors have tested machine learning methods using various neural network architectures. A hybrid method was developed to classify text reviews of medical facilities posted by patients on the two most popular physician review websites in Russia. Overall, more than 60,000 reviews were analysed. The main results are as follows: 1) the classification algorithm developed by the authors has a high efficiency, the best result being achieved by the GRU-based architecture (val_accuracy = 0.9271); 2) applying the named entity search method to text messages following their partitioning improved the classification efficiency for each of the classifiers based on artificial neural networks. To further enhance the classification quality, reviews need to be semantically partitioned by target and sentiment and the resulting fragments need to be analysed separately.

3D Printing (3DP) technology has revolutionized the field of the use of bioceramics for maxillofacial and periodontal applications, offering unprecedented control over the shape, size, and structure of bioceramic implants. In addition, bioceramics have become attractive materials for these applications due to their biocompatibility, biostability, and favorable mechanical properties. However, despite their advantages, bioceramic implants are still associated with inferior biological performance issues after implantation, such as slow osseointegration, inadequate tissue response, and increased risk of implant failure. To address these challenges, researchers have been developing strategies to improve the biological performance of 3D printed bioceramic implants. The purpose of this review is to provide an overview of 3DP techniques and strategies for bioceramic materials designed for bone regeneration. The review also addresses the use and incorporation of active biomolecules in 3D printed bioceramic constructs to stimulate bone regeneration. By controlling the surface roughness, and chemical composition of the implant, the construct can be tailored to promote osseointegration and reduce the risk of adverse tissue reactions. Additionally, growth factors, such as bone morphogenic proteins (rhBMP-2) and pharmacologic agent (dipyridamole), can be incorporated to promote the growth of new bone tissue. Incorporating porosity into bioceramic constructs can improve bone tissue formation and the overall biological response of the implant. As such, by employing surface modification, combining with other materials, and incorporation of 3DP workflow can lead to better patient healing outcomes.

Soil improvement methods can result in changes in the microbial community in blueberry soil. However, there have been few reports on the impacts of different soil improvement methods on the microbial function, particularly on endophytic microbe. In this study, we analyzed the response of microbial community composition, microbial function and nitrogen (N) cycle to different improvement methods using high throughput sequencing. We aimed to investigate the best soil improvement method from a microbial perspective. The results showed that the highest microbial diversity was observed in the T4 treatment (peat combined with mushroom bran), followed by the T2 treatment (peat combined with acidified rice husk) both in the rhizosphere and roots. The dominant phyla were Proteobacteria and Actinobacteria, which were most abundance in the T4 and T1 (peat combined with sulfur) treatments in the rhizosphere soil, respectively, but showed the opposite trend in the root endophytic bacterial community. Interestingly, Acidobacterium and Paludibaculum, belonging to the Acidobacteria phylum, were found to have the highest influence according to the correlation network analysis. And these bacteria were most abundant in the T2 treatment in the rhizosphere soil. The rhizosphere soil microbial communities were clustered into two categories: one for T1 and T2 treatments, and another for T3 (mushroom bran) and T4 treatments. Compared to the other treatments, the T1 treatment had the most significant impact on microbial functional pathways in the blueberry roots. T2 treatment promoted the growth of N fixation functional bacteria both in the rhizosphere soil and roots. At the module level, the T2 treatment increased the relative abundance of N fixation and decreased the relative abundance of assimilating nitrate reduction reaction (ANRA), dissimilating nitrate reduction reaction (DNRA), denitrification and completed nitrification in the blueberry rhizosphere soil. Additionally, the T2 treatment increased the abundance of root endophytic microbes involved in N fixation. Overall, our findings suggest that the addition of peat combined with acidified rice husk is the optimal soil improvement method for blueberry cultivation.

To supply stable and high-quality power according to the advancement of industrial growth, electric power companies have performed maintenance of power facilities using various meth-ods. In the case of domestic power distribution facilities, there are limitations in performing di-agnostic management on all facilities owing to the large number of facilities; therefore, old fa-cilities are managed by the health index assessment method. The health index assessment com-prises only facility operation data and external environmental data and is managed only for four types of distribution facilities, including overhead/underground transformers and switchgear. In the case of extra high–voltage overhead wires, there is no standard for old replacement, despite the large number of wires, such as transformers and switchgear, and the large ripple effect of power failure in the event of a power outage. Therefore, in this paper, a health index assessment methodology for extra-high voltage overhead cables was developed. In this paper, we devel-oped an economic health index assessment methodology that additionally considers risk cost, which is different from the existing health index assessment method that uses only operational data and external environmental data to determine facility performance evaluation and aging replacement criteria. Using the health index assessment methodology developed in this paper, it is possible to expect a reduction in facility operating costs and investment costs from the per-spective of electric power companies through the replacement of old extra-high voltage over-head cables. In addition, from the perspective of consumers, it is expected to increase power re-liability and reduce the ripple effect of failure by preferentially replacing equipment with a high probability of failure.

The selectin of a demand forecasting method is critical for companies aiming to avoid manufacturing overproduction or shortages in pursuit of sustainable development. Various qualitative and quantitative criteria with different weights must be considered during the evaluation of a forecasting method. The qualitative criteria and criteria weights are usually assessed in linguistic terms. Aggregating these various criteria and linguistic weights for evaluating and selecting demand forecasting methods in sustainable manufacturing is a major challenge. This paper proposes an extension of fuzzy Elimination and Choice Translating Reality (ELECTRE) I to resolve this problem. In the proposed method, fuzzy weighted ratings are defuzzified with the signed distance to develop a crisp ELECTRE I model. Moreover, an extension to ELECTRE I is developed by suggesting an extended modified discordance matrix and a closeness coefficient for ranking alternatives. The proposed extension can overcome the problem of information loss which can lead to incorrect ranking result when using the Hadamard product to combine concordance and modified discordance matrices. A comparison is conducted to show the advantage of the proposed extension. Finally, a numerical example is used to demonstrate the feasibility of the proposed method; furthermore, a numerical comparison is made to display the advantage of the proposed method.

Numerous reservoirs that play a significant role in worldwide petroleum production and reserves contain fractures. Typically, the fractures must form a connected network for a reservoir to be classified as naturally fractured. Characterizing the reservoir with a focus on its fracture network is crucial for modeling and predicting production performance. To simplify the solution, dual continuum modeling techniques are commonly employed. However, to use continuum-scale approaches, properties such as average aperture, permeability, and matrix fracture interaction parameters must be assigned, making it necessary to improve fracture depiction and modeling methods. This study investigates a fractured reservoir with a low matrix permeability and a well-connected fracture network. The focus is on the impact of the hyrachical fracture network on the production performance of gas-based enhanced oil recovery methods. The Discrete Fracture Model (DFN) was utilized to create comprehensive two-dimensional models for three processes: Gas Injection (GI), Water Alternating Gas (WAG), and Foam Assisted Water Alternating Gas (FAWAG). Moreover, dimensionless numbers were employed to establish connections between properties across the entire fracture hierarchy, spanning from minor to major fractures and encompassing the fracture intensity. The results indicate that the FAWAG is more sensitive to fracture types and networks than the WAG and GI processes. Hence, the sensitivity of the individual EOR method to the fracture network requires a respective depth of description of the fracture network. However, other factors, such as reservoir fluids properties and fracture properties, might influence the recovery when the minor fracture networks are excluded. This study has determined that, among the enhanced oil recovery (EOR) techniques examined, the significance of the hyrachical depth of fracture networks diminishes as the ratio of major (primary fracture) aperture to the aperture of medium and minor fractures increases. Additionally, the impact of the assisted-gravity drainage method was greater with increased reservoir height; however, as the intensity ratio increased, the relative importance of medium and minor fracture networks decreased.

A simulation test system for the two-dimensional (2D) fuze overload loading process was designed to address the loading issues of recoil overload and centrifugal overload in fuze safety and arming (S&amp;A) devices. By incorporating centrifuge rotation energy storage, impact acceleration simulation, and equivalent centrifugal rotation simulation, a block equipped with a fuze S&amp;A device was accelerated instantly upon being impacted by a centrifuge-driven impact hammer, simulating recoil overload loading. The impact hammer was retracted instantaneously by adopting an electromagnetic brake, which resulted in the centrifugal rotation of the block around its track, to simulate the centrifugal overload loading. The dynamic equations of the simulation test system and the equations of impact hammer motions were established, whereby the rotation speed of the centrifuge and the braking force of the electromagnetic brake were calculated and selected. A dynamic model of the collision between the impact hammer and block was established using ANSYS/LS-DYNA software for simulation analysis. The acceleration curves of the recoil overload and centrifugal overload with variations in the centrifuge speed, cushion material, and buffer thickness were obtained, which verified the feasibility of the proposed loading simulation method. 2D overload loading simulation tests were performed using the developed simulation system, and the acceleration curves of the recoil overload and centrifugal overload were measured. The test results indicated that the proposed system can accomplish 2D overload loading simulations for a recoil overload of several 10000 g and centrifugal overload of several 1000 g.

In this study, we meticulously compared the practical performance of four bootstrap methods for assessing the significance of causal analysis in time series data, recognizing that their evaluation has not been sufficiently conducted in the field. The methods investigated were uncorrelated Phase Randomization Bootstrap (uPRB), which generates surrogate data with no cross-correlation between variables by randomizing the phase in the frequency domain; Time Shift Bootstrap (TSB), which generates surrogate data by randomizing the phase in the time domain; Stationary Bootstrap (SB), which calculates standard errors and constructs confidence regions for weakly dependent stationary observations; and AR-sieve bootstrap (ARSB), a resampling method based on autoregressive (AR) models that approximates the underlying data-generating process. Our study found that the AR-sieve bootstrap (ARSB) method outperformed the others in detecting both self-excitation and causality among variables. In contrast, the uncorrelated phase-randomized bootstrap (uPRB) and Stationary Bootstrap (SB) methods demonstrated limitations in specific scenarios. This detailed comparison highlights the need for selecting suitable bootstrap methods to ensure accurate results, ultimately guiding researchers in their choice of method for real data analysis.

The sinking of cavities in urban areas is a very important geological risk to which many towns in Puglia are exposed. The study, mapping, geological and speleological description, classification and cataloging of forms and types of cavities is of fundamental importance for each urban center because it is linked to past local anthropic and natural processes, unique to each site. All this could lead to an enhancement of existing underground cavities in urban areas through conservation and continuous monitoring. Unfortunately in many cases these underground cavities have been used as landfills and subsequently abandoned. One of these cavities at the end of March 2007 collapsed in the inhabited center of Gallipoli causing damage to the structures and fortunately not to human lives. In the area relating to the collapsed cavity, a series of geophysical investigations were undertaken with the use of ground penetrating radar methodology in an attempt to delimit the area of collapse and understand the possible types of interventions for restoring the area. In the same area the measures were repeated 16 years later (December 2022) due to another collapse. The results show a much more widespread danger situation.

Mesoscale imperfections, such as pores and voids, can strongly modify the properties and the mechanical response of materials under extreme conditions. Tracking the material response and microstructure evolution during void collapse is crucial for understanding its performance. In particular, imperfections in ablator materials, such as voids, can limit the efficiency of the fusion reaction and ultimately hinder ignition. To characterize how voids influence the response of materials during dynamic loading and seed hydrodynamic instabilities, we have developed a tailored fabrication procedure for designer targets with voids at specific locations. Our procedure uses SU-8 as a proxy for ablators materials and hollow silica microspheres as proxy for voids and pores. By using photolithography to design the targets’ geometry, we demonstrate precise and highly reproducible placement of a single void within the sample, which is key for a detailed understanding of its behavior under shock compression. This fabrication technique will benefit high-repetition rate experiments at x-ray and laser facilities. Insight from shock compression experiments will provide benchmarks for the next generation of microphysics modelling.

Beyond semantic segmentation,3D instance segmentation(a process to delineate objects of interest and also classifying the objects into a set of categories) is gaining more and more interest among researchers since numerous computer vision applications need accurate segmentation processes(autonomous driving, indoor navigation, and even virtual or augmented reality systems…) This paper gives an overview and a technical comparison of the existing deep learning architectures in handling unstructured Euclidean data for the rapidly developing 3D instance segmentation. First, the authors divide the 3D point clouds based instance segmentation techniques into two major categories which are proposal based methods and proposal free methods. Then, they also introduce and compare the most used datasets with regard to 3D instance segmentation. Furthermore, they compare and analyze these techniques performance (speed, accuracy, response to noise…). Finally, this paper provides a review of the possible future directions of deep learning for 3D sensor-based information and provides insight into the most promising areas for prospective research.

The silanol activity and trace metal detection methods for chemically bonded silica gels were evaluated in silico. Test compounds with large molecular sizes may demonstrate negative results because of the possibility of indirect hydrogen bonding via short alkyl groups or siloxane of the silica gels. This hypothesis was based on the observation of weak hydrogen bonding energy values similar to those observed in the study of the alkyl group effect on the hydrogen bonding of alkanols. Consequently, smaller molecules may be a better choice for the analysis of bonded-phase quality.

Extracellular vesicles (EVs) are membranous vesicles secreted by both prokaryotic and eukaryotic cells and play a vital role in intercellular communication. EVs are classified into several subtypes based on their origin, physical characteristics, and biomolecular makeup. Exosomes, a subtype of EVs, are released by the fusion of multivesicular bodies (MVB) with the plasma membrane of the cell. Several methods have been described in literature to isolate exosomes from biofluids including blood, urine, milk, and cell culture media among others. While differential ultracentrifugation (dUC), has been widely used to isolate exosomes, other techniques including ultrafiltration, precipitating agents such as poly-ethylene glycol (PEG), immunoaffinity capture, microfluidics and size exclusion chromatography (SEC) have emerged as credible alternatives with pros and cons associated with each. In this review, we provide a summary of commonly used exosomal isolation techniques with a focus on SEC as an ideal methodology. We evaluate the efficacy of SEC to isolate exosomes from an array of biological fluids, with a particular focus on its application to adipose tissue-derived exosomes. We argue that exosomes isolated via SEC are relatively pure and functional, and that this methodology is reproducible, scalable, inexpensive, and does not require specialized equipment or user expertise.

Previously, a conductivity invariance phenomena (CIP) has been discovered – at a certain lift-off, the inductance change of the sensor due to a test sample is immune to conductivity variations, i.e. the inductance – lift-off curve passes through a common point at a certain lift-off, termed as conductivity invariance lift-off. However, this conductivity invariance lift-off is fixed for a particular sensor setup, which is not convenient for various sample conditions. In this paper, we propose using two parameters in the coil design – the horizontal and vertical distances between the transmitter and the receiver to control the conductivity invariance lift-off. The relationship between these two parameters and the conductivity invariance lift-off is investigated by simulation and experiments and it has been found that there is an approximate linear relationship between these two parameters and the conductivity invariance lift-off. This is useful for applications where the measurements have restrictions on lift-off, e.g. uneven coating thickness which limits the range of the lift-off of probe during the measurements. Therefore, based on this relationship, it can be easier to adjust the configuration of the probe for a better inspection of the test samples.

We have an unprecedented ability to analyze and map the Earth’s surface, as deep learning technologies are applied to an abundance of Earth observation systems collecting images of the planet daily. In order to realize the potential of these data to improve conservation outcomes, simple, free, and effective methods are needed to enable a wide variety of stakeholders to derive actionable insights from these tools. In this paper we demonstrate simple methods and workflows using free, open computing resources to train well-studied convolutional neural networks and use these to delineate objects of interest in publicly available Earth observation images. With limited training datasets (<1000 observations), we used Google Earth Engine and Tensorflow to process Sentinel-2 and National Agricultural Imaging Program data, and use these to train U-Net and DeepLab models that delineate ground mounted solar arrays and parking lots in satellite imagery. The trained models achieved 81.5% intersection over union between predictions and ground-truth observations in validation images. These images were generated at different times and from different places from those upon which they were trained, indicating the ability of models to generalize outside of data on which they were trained. The two case studies we present illustrate how these methods can be used to inform and improve the development of renewable energy in a manner that is consistent with wildlife conservation.

Three Dimensional Variational data assimilation or analysis (3DVAR) is one of most classical methods for providing the initial values for numerical models. In this method, the dimensions of the background error covariance and the observational error covariance matrices are large. Therefore, it is difficult to get the inverse of the covariance matrices and to reduce the orders of these matrices without information loss. With the use of the Sylvester Equation, on the basis of a new linear regression, a new cost function for 3DVAR was given. For the first-guess m×n field, there is an approximate 1−(m²+n²)/(mn×mn) reduction with m>1 & n>1 by using the cost function. The results of the numerical experiments show that the effect of this algorithm is no worse than that of the old cost function for 3DVAR.

Endocrine disrupting compounds (EDCs) are contaminants with estrogenic or androgenic activity that negatively impact human and animal communities. These compounds have become one of the significant concerns for wastewater treatment in recent decades. Several studies have evaluated EDC removal methods from wastewater across the globe including the United Kingdom (UK). Accordingly, the current study reviews EDC removal methods from municipal/domestic wastewater in the United Kingdom (UK) for the period of 2010–2017. The research highlights that despite the relative efficacy of existing chemical and physical methods for removing certain EDCs from wastewater there is emerging evidence supporting the need for more widespread application of nature-based and biological approaches, particularly the use of biofilms. The analysis reveals that there have been relatively few research studies on EDC removal methods have been carried out in the UK in the 2010–2017 period and none of the research focused on EDC removal using biofilms. Finally, this review suggests that more research is needed to remove EDCs, particularly through the application of biofilms, from municipal wastewater in current scenarios.

Four methods in two different families have been constructed to derive the exact solutions to Benjamin-Bona-Mahony equation in two space dimensions. Simply defined hyperbolic tangent, hyperbolic secant and hyperbolic cosecant ansatzes and the expansion method based on the Sine-Gordon equation in two dimensions are directly substituted into the governing ODE reduced from the two dimensional BBM equation. Classical algebraic method is used to find the relations among the target parameters representing the nonzero coefficients in the predicted solutions and the wave transform parameters. Some complex and real solutions have been constructed in explicit forms.

The class of A-stable symmetric one-step Hermite-Obrechkoff (HO) methods introduced in [1] for dealing with Initial Value Problems is analyzed. Such schemes have the peculiarity of admitting a multiple knot spline extension collocating the differential equation at the mesh points. As a new result, it is shown that these maximal order schemes are conjugate symplectic which is a benefit when the methods have to be applied to Hamiltonian problems. Furthermore a new efficient approach for the computation of the spline extension is introduced, adopting the same strategy developed in [2] for the BS linear multistep methods. The performances of the schemes are tested in particular on some Hamiltonian benchmarks and compared with those of the Gauss Runge-Kutta schemes and Euler-Maclaurin formulas of the same order.

This paper presents a method for a decentralised peer-to-peer software license validation system using cryptocurrency blockchain technology to ameliorate software piracy, and to provide a mechanism for software developers to protect copyrighted works. Protecting software copyright has been an issue since the late 1970's and software license validation has been a primary method employed in an attempt to minimise software piracy and protect software copyright. The method described creates an ecosystem in which the rights and privileges of participants are observed.

The adipose tissue is responsible for fat storage and is the major producer of exosomes. Biological content transported by the exosomes provides information, such as immunometabolic alterations. This study aimed to isolate whole exosomes from plasma and supernatant of differentiated 3T3-L1 cells using three isolation techniques. Plasma from 120 individuals categorized by body adipose tissue proportion and differentiated 3T3-L1 cells were used in this study. The exosome isolation techniques were differential centrifugation (DC), size exclusion chromatography (SEC), and a commercial kit (Invitrogen), and characterized by cryo-TEM, TEM, and western blot for the tetraspanins CD9 and CD81. In the three isolation techniques, cryo-TEM and TEM analysis showed similar quality, quantity, and morphology of exosomes; at the same time, no difference was observed between individuals categorized by proportion of body fat. According to our results, the concentration of the circulating exosomes is not different between normal and high-fat percentage individuals, but rather their composition. The SEC and kit commercial proved that compile with most of the advantages of an exosome isolation technique, nerveless we emphasize the importance of selecting the appropriate methodology depending on the specific research objectives.

Emergence of multi drug resistant (MDR) and extensively drug resistant (XDR) Mycobacterium tuberculosis poses a serious threat to TB control as the available treatment options are less effective in these cases. Therefore, new strategies are required for identification of new drugs and drug targets. The M. tuberculosis dihydrodipicolinate synthase is a putative drug target with no potent inhibitor. Here in this study, we have used a comprehensive computational approach to identify small molecule inhibitors and validated them in vitro. As similar structures tend to have similar functions, therefore 3 approaches, namely, (a) known inhibitor-based approach, (b) substrate analogues, (c) product analogues were used for compound screening from various chemical libraries. The fourth approach we have taken is fragment library screening.In vitro studies revealed that β-hydroxypyruvic acid tartronic acid are inhibitors of Mtb-rDapA with maximum inhibition 48% and 51% at 500µM concentrations respectively. We have also observed that β-hydroxypyruvic acid is a time dependent inhibitor of Mtb-rDapA. Further, thermal shift assays showed that β-hydroxypyruvic acid interacts with Mtb-rDapA and the thermal stability of Mtb-rDapA shifted by 3˚C. Among the product analogues of Mtb-DapA, dipicolinic acid showed to bind to Mtb-rDapB shifting thermal stability of Mtb-rDapB by 0.5˚C in binding. Additionally, we have designed a new approach by combining substrate and product analogues to achieve better inhibitions. In vitro assays validated that this combination approach increased maximum inhibition up to 100%. We envision that these compounds would serve as potent leads for tuberculosis drug discovery.

Machine safety is not only a prerequisite for successful production, but also the foundation for sustainability and growth of any manufacturing organisation. The latest approaches in this rapidly developing field are the integration of effective risk management tools and strategies into occupational health and safety (OHS) systems. In this article, using a specific example from practice, we will show how using multi-criteria decision making (AHP) support Machinery Safety Decision Making (MSDM) from the point of reducing losses. Using Classification and Regression Tree Analysis (CART), we estimated the efficiency, cost-effectiveness and thus the sustainability level of the relevant safety measures. These were proposed risk reduction measures that typically raised uncertainty among managers regarding the estimation of cost-effectiveness. The advantage of application decision trees approach is possibility to identify and establish relatively homogeneous groups of undesirable events and their impact on the organisation's objectives. A comprehensive model has been developed to support management decision making in manufacturing organizations in implementing and improving safety measures in line with manufacturing sustainability goals.

This article presents the possibilities of using ferrite absorption material for shielding rooms with medical devices in order to additionally protect them against the limits of the electric component of the E field, which are up to 61 V/m. These values may appear in connection with the currently launched 5G system operating in the high-band frequency range, i.e. 28 GHz and 38 GHz, and may cause negative effects on the operation of medical devices. The possibilities of using ferrite absorption material for shielding rooms with medical devices were determined based on the results of measurements of the electrical properties of the ferrite material performed using two methods: the reflection method and the free space method. In the article, the complex electrical permittivity and complex magnetic permeability were determined for a ferrite plate based on measurements of the reflection coefficient and the transmission (attenuation) coefficient. The presented calculation and measurement results were used to show the properties of the ferrite material in the frequency range from 15 GHz to 45 GHz and to show the possibility of using it for shielding rooms as a material absorbing electromagnetic waves in this frequency range.

Individual investors often struggle to predict stock prices due to the limitations imposed by the computational capacities of personal laptop Graphics Processing Units (GPUs) when running intensive deep learning models. This study proposes solving these GPU constraints by integrating deep learning models with technical analysis methods. This integration significantly reduces analysis time and equips individual investors with the ability to identify stocks that may yield potential gains or losses in an efficient manner. Thus, a comprehensive buy and sell algorithm, compatible with average laptop GPU performance, is introduced in the study. This algorithm offers a lightweight analysis method that emphasizes factors identified by technical analysis methods, thereby providing a more accessible and efficient approach for individual investors. To evaluate the efficacy of this approach, we analyzed the performance of eight deep learning models: LSTM (4 layers), CNN, BiLSTM (4 layers), CNN Attention, BiGRU CNN BiLSTM Attention, BiLSTM Attention CNN, CNN BiLSTM Attention, and CNN Attention BiLSTM. These models were used to predict stock prices for Samsung Electronics and Celltrion Healthcare. The CNN Attention BiLSTM model displayed superior performance among these models, with the lowest validation mean absolute error value. In addition, an experiment was conducted using WandB Sweep to determine the optimal hyperparameters for four individual hybrid models. These optimal parameters were then implemented in each model to validate their back-testing rate of return. The CNN Attention BiLSTM hybrid model emerged as the highest-performing model, achieving an approximate rate of return of 5 percent. Overall, this study offers valuable insights into the performance of various deep learning and hybrid models in predicting stock prices. These findings can assist individual investors in selecting appropriate models that align with their investment strategies, thereby increasing their likelihood of success in the stock market.

Abstract: Beetroot (Beta vulgaris L) has been known for being a rich source of phytochemicals, minerals and vitamins. This study presents a model system for how the combination of extrac-tion/chromatography/mass spectrometry and NMR offers an efficient way to profile metabolites in the extracts of a natural product, beetroot . Such combination may lead to the identification of more nutritional or medicinal compounds in natural products, and it is essential for our ongoing investigation to study the selective adsorption/desorption of these metabolites on nanoparticles. The aqueous and organic extracts underwent analyses using UV-vis spectroscopy; GC-MS; LC-MS; 1H, 13C, TOCSY, HSQC, and selective TOCSY NMR experiments. Polar Extract: The two forms of betalain pigment were identified by UV-vis and LC MS. Fourteen amino acids, sucrose, and other compounds among which is riboflavin, were identified by LC-MS. 2D TOCSY showed the spin coupling correlations corresponding to some of these compounds. The HSQC spectrum showed 1H/13C spin correlation in sucrose confirming the high abundance of sucrose in beetroot. Organic Extract: GC-MS data enabled the identification of several compounds including six fatty acid methyl esters (FAME) with higher than, on average, 90% similarity score. Selective TOCSY NMR data showed the spin coupling pattern corresponding to oleic, linoleic, and linolenic fatty acids. 31P NMR spectra indicate that phospholipids exist in both the organic and aqueous phases. Keywords: Beetroot (Beta vulgaris L); Total Correlation Spectroscopy (TOCSY); Gas Chromatog-raphy-Mass Spectrometry (GC-MS); selective TOCSY, Homonuclear single quantum correlation (HSQC); fatty acid methyl esters (FAME).

Abstract: Interrupted-sampling repeater jamming (ISRJ) is a kind of intra-pulse coherent decep-tion jamming that can generate false target peaks in the range profile and interfere with the de-tection and tracking of real targets. In this paper, an anti-ISRJ method based on the intra-pulse orthogonal waveform is proposed, which can recognize common interference signals by com-paring sub-signal matched filtering results. For some special scenes where real targets cannot be directly differentiated from false targets, a new recognition method based on the energy discon-tinuity of the interference signal in the time domain is proposed in this paper. The method pro-posed in this paper can recognize real and false targets in all ISRJ modes without any prior in-formation, such as jammer parameters, with a small amount of calculation, which is suitable for actual radar systems. Simulation experiments using different interference parameters show that although this method has a 3dB loss of pulse compression gain, it can completely suppress dif-ferent kinds of ISRJ interference when the SNR before pulse compression is higher than -20dB, with 100 % target detection probability.

We formulate a more straightforward, symmetry-based techniques in manually computing the determinant of any n×n matrix by revisiting Dodgson’s condensation method and strategically applying elementary row operations and the definition and properties of determinants. The result yields a more simplified mnemonical algorithm than the usual reduction by elementary row operations and co-factor expansion and with minimal terminal divisions performed compared to Dodgson’s internal divisions.

The article describes the methodology of the experimental analysis of operational and cost-effective assessment using geographic information system (GIS) technologies, as opposed to the assessment of the tourism potential of the nature of the Fergana Valley in Uzbekistan using long term and costly classical approaches. Based on the ArcGIS (10.8) grid index approach of 20 natural geographical indicators of the valley, such as geographical location, relief, climate, hydrographic and biological, the level of tourism comfort was determined and the location of 6 tourist-recreation zones were defined. The level of accuracy of the tourist-recreational zones all situated utilizing GIS technologies was confirmed by field-expedition approaches.