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

Our paper deals with an advanced statistical tool for the volatility prediction problem in financial (crypto) markets. Firstly, we consider the conventional GARCH involved volatility models. We next extend the corresponding GARCH based forecasting and calculate a specific probability associated with the predicted volatility levels. Since the probability evaluation is based on a stochastic model, we next develop an additional data-driven estimation of this probability. The proposed advanced statistical estimation uses real (historical) market data. The obtained theoretic results for the statistical probability of levels are also discussed in the framework of the integrated volatility concept. A possible application of the established probability estimation to the volatility clustering problem is also mentioned. Our paper includes a concrete practical implementation of the resulting (combined) volatility prediction tool and considers a novel module for trading and volatility estimation in crypto markets recently developed by 1ex Trading Board group in collaboration with the GoldenGate Venture. Moreover, we discuss shortly a possible using of the proposed model based and data-driven volatility prediction methodology in the financial risk management.

This study investigates the impact of thin flame combustor design on hydrogen flame characteristics and combustion performance through numerical simulations. Variations in flame shape and combustibility between 100% methane and 100% hydrogen combustion are analyzed. Three combustor header shapes (flat, concave, and convex) are modeled to assess their influence on flame behavior. Results show distinct flow patterns, with concave headers promoting strong central flows and convex headers dispersing the flow outward. Temperature field analysis indicates that hydrogen flames have higher temperatures and shorter quenching distances compared to methane flames. A comparative analysis of combustion products is conducted to evaluate combustion performance and NOx emissions. Consequently, the concave header has high combustibility and increases temperatures and NOx fraction in hydrogen combustion.

Total ankle replacement (TAR) implants are an effective option to restore the range of motion of the ankle joint for arthritic patients. An effective tool for analyzing these implants’ mechanical performance and longevity in-silico is finite element analysis (FEA). ABAQUS FEA was used to statically analyze the von Mises stress and contact pressure on the articulating surface of the bearing component in two newly installed fixed-bearing total ankle replacement implants (the Wright Medical INBONE II and the Exactech Vantage). This ultra-high molecular weight polyethylene (UHMWPE) bearing commonly fails and is the cause for surgeon revisions. Six different FEA models for various gait percentages during stance (10%-60%) were created. They varied in magnitude of the compressive load and the ankle dorsiflexion/plantarflexion angle. The results indicated that the von Mises stress and contact pressure distributions increased as compressive load increased, and the largest magnitudes occurred at high dorsiflexion angles (15-30°). High magnitudes occurred in regions where the thickness of the bearing was the least. Additionally, high contact pressures were examined in areas near the talar component's edge or at the bearing's edges. To the author’s knowledge, this is the first study available to the research community that analyzes the Vantage implant with FEA. This study lays an essential foundation for future researchers in presenting a thorough literature review of TAR and methods for a simple FEA model setup. This study provides valuable information that can be beneficial to medical company designers and orthopedic surgeons in understanding the stress response and contact pressure of TAR implants during use.

A methodology has been developed to assess the presence and dissipation of herbicides of wide range of polarities in soil using in-tube solid phase microextraction (IT-SPME) coupled on-line to capillary liquid chromatography (capLC). The compounds investigated were tritosulfuron (TRT), triflusulfuron-methyl (TRF), aclonifen (ACL) and bifenox (BF), with log octanol-water partition coefficients (log Kow) ranging from 0.62 to 4.48. The linearity, recoveries, intra and interday precision, limits of detection (LODs) and quantification (LOQs), and accuracy of the method were established. The proposed approach was successfully applied assess the degradation of the tested herbicides in different types of soil (agricultural, urban and forest) after being exposed to different laboratory and outdoor conditions. The results obtained showed a greater persistence of the most apolar compounds ACL and BF, regardless of the soil characteristics, whereas a significant degradation of highly polar herbicides TRT and TRF was observed in soils with lowest organic matter even after a few days of exposure. Those results indicate that the proposed approach can be considered an effective tool for a better understanding of soil pollution.

This research article describes the creation of a mock-up for a Smart Insole prototype that employs Force Sensitive Resistors (FSRs) and an ADXL345 accelerometer. The mock-up is an early step towards realising the concept and functionality of the planned Smart Insole system. The key factors in the mock-up's design, integration, and testing are highlighted, with a focus on the iterative refining and optimisation process. The mock-up, which was carefully planned and implemented, highlights the practicality and possible applications of Smart Insole technology in healthcare, sports performance measurement, and rehabilitation. The paper outlines the development process and implementation of a "Smart Insole" utilizing Force Sensitive Resistors (FSR) and the ADXL345 accelerometer. The project aims to create an innovative wearable technology solution for monitoring foot biomechanics and enhancing human performance. The Smart Insole prototype integrates FSRs strategically placed on the insole to capture pressure distribution across the foot, along with an ADXL345 accelerometer to track foot orientation and movement. Through a combination of hardware integration, firmware development using Arduino IDE, and visualization using Processing application, the Smart Insole offers real-time monitoring and analysis of gait dynamics, pressure distribution, and foot movement.

We estimated the risk of blood clots after COVID-19 vaccination accounting for the roles of vaccination in reducing the infection rate, and indirectly lowering the risk of blood clots caused by infection. A self-controlled case series method was used to examine the association between blood clots and COVID-19 vaccination. To deal with the bias due to under-reported infection among non-hospitalized subjects, a case-control study was used to compare the risk of blood clots in infected subjects to control subjects who were hospitalized due to physical injury. We found increased risks of blood clots after vaccination (incidence rate ratio is 1.13, 95% confidence interval (CI): [1.03, 1.24] after the first dose and 1.23, 95% CI: [1.13,1.34] after the second dose). Furthermore, vaccination attenuated the increased risk of blood clots associated with infection (odds ratio (OR) = 2.16, 95% CI: [1.93, 2.42] in unvaccinated versus OR=1.46, 95% CI: [1.25, 1.70] in vaccinated). A causal model was used to explain and weigh up possible risk factors for blood clots after COVID-19 vaccination. After accounting for vaccine efficacy against infection and the protection against infection-associated blood clots, receiving the COVID-19 vaccines decreases the risk of blood clots, especially during high infection rate period.

YouTube is a very popular social media platform in today’s digital landscape. The primary focus of this study is to explore the underlying sentiment in user comments on food-related videos on YouTube, specifically within two pivotal food product categories: Plant-Based and Hedonic. Our research involved labelling comments using sentiment lexicons such as TextBlob and VADER. Furthermore, the sentiments of the comments were classified using advanced Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), Multinomial Naive Bayes, Random Forest, Logistic Regression, and XGBoost. The evaluation of these models encompassed key macro average metrics, including accuracy, precision, recall, and f1-score. Results from VADER showcased a high accuracy level, with SVM achieving 93% accuracy in the plant-based dataset and 96% in the hedonic dataset. In addition to sentiment analysis, we delved into user interactions within the two datasets, measuring crucial metrics such as views, likes, comments, and engagement rate. The findings illuminate significantly higher levels of views, likes, and comments in the hedonic food dataset, but the plant-based dataset maintains a superior overall engagement rate.

In this paper, a multi-patch weakly singular isogeometric boundary element method (WSIGABEM) for two-dimensional elastostatics is proposed. Since the method based on the weakly singular boundary integral equation, the quadrature techniques, dedicated for the weakly singular and regular integrals, are applied in the method. A new formula for the generation of collocation points is suggested to take the full advantage of the multi-patch technique. The generated collo-cation points are essentially inside the patches without any correction. If the boundary conditions are assumed to be continuous in every patch, no collocation point lies on the discontinuous boundaries, thus simplifying the implementation. The multi-patch WSIGABEM is verified by the simple examples with analytical solutions. The feature of the present multi-patch WSIGABEM is investigated by the comparison with the traditional IGABEM. Furthermore, the combination of the present multi-patch WSIGABEM and the particle swarm optimization algorithm results in a shape optimization method in two-dimensional elastostatics. By changing some specific control points and their weights, the shape optimizations of the fillet corner, the spanner and the arch bridge are verified to be effective.

Fragile X syndrome (FXS), the most common monogenic cause of inherited intellectual disability and autism spectrum disorder, is caused by a full mutation (>200 CGG repeats) in the Fragile X Messenger Ribonucleoprotein 1 (FMR1) gene. Individuals with FXS experience a wide range of challenges related to social interaction (SI). Animal models, such as Drosophila melanogaster model for FXS where the only ortholog of human FMR1 (dFMR1) is mutated, have played a crucial role in understanding of FXS. The aim of this study was to investigate SI in the dFMR1B55 mutants using the novel Drosophila Shallow Chamber and a Python data processing pipeline based on social network analysis (SNA). In comparison with wild-type flies (w1118), SNA analysis in dFMR1B55 mutants revealed hypoactivity, fewer connections in their networks, a lower ability to efficiently transmit information, fewer alternative pathways for information transmission, a higher variability in the number of interactions they achieved, and tended to stay near the boundaries of the testing chamber. These observed alterations indicate the presence of characteristic strain-dependent social networks in dFMR1B55 flies, commonly referred to as the group phenotype. Finally, a combination of novel research tools is a valuable method for SI research in fruit flies.

Partial Information Decompositions (PIDs) aim to categorize how a set of source variables provide information about a target variable redundantly, uniquely, or synergetically. The original proposal for such an analysis used a lattice-based approach and gained significant attention. However, finding a suitable underlying decomposition measure is still an open research question, even at an arbitrary number of discrete random variables. This work proposes a solution to this case with a non-negative PID that satisfies an inclusion-exclusion relation for any f-information measure. The decomposition is constructed from a pointwise perspective of the target variable to take advantage of the equivalence between the Blackwell and zonogon order in this setting. The zonogons correspond to the Neyman-Pearson region for an indicator variable of one target state and f-information is the expected value of quantifying its boundary. We prove that the decomposition satisfies the axioms of the original decomposition framework and guarantees non-negative partial information results. We highlight that our decomposition behaves differently depending on the used information measure, which can be utilized for different applications. We additionally show how our proposal can be used to obtain a non-negative decomposition of Rényi-information at a transformed inclusion-exclusion relation, and for tracing partial information flows through Markov chains.

Introduction: Biliary Atresia (BA) is a common cause of neonatal cholestatic jaundice which, if recognized and treated early, can be life-saving for the infant. If the liver biopsy suggests the presence of BA, exploratory laparotomy is then carried out to confirm the diagnosis, along with the Kasai procedure to provide drainage to the bile secreted by the liver. The purpose of this study is to get a deep dive into all the various imaging modalities that can diagnose BA more effectively and with absolute certitude, thus helping prevent several children from a grave prognosis.Methodology: For the collection of the data, a search was done by two individuals using PubMed, Google Scholar, and Cochrane Library databases for all relevant literature. Each qualifying paper was evaluated independently by two reviewers. Each article was analyzed for the number of patients, their age, procedure modality, complexity of ethology, and incidence of the pre-decided complications. The statistical software packages RevMan (Review Manager, version 5.3), SPSS (Statistical Package for the Social Sciences, version 20), Google Sheets, and Excel in Stata 14 were used to perform the statistical analyses. The data was obtained and entered into analytic software.Results: A total of 39 studies with 3,679 subjects were selected for the study, out of which 7 studies showed sensitivity above or equal to 95%, and an astounding 14 studies showed specificity above 95%. The value of True Positive (TP) was 1280, that of True Negative (TN) was 1817, that of False Positive (FP) was 239, and that of False Negative (FN) was 343. With a confidence interval of 95%, sensitivity, specificity, and positive predictive values were calculated. The sensitivity of Ultrasonography for Biliary Atresia is 0.79, with a CI of 95% in a range of 0.77 to 0.81. The specificity of Ultrasonography for Biliary Atresia is 0.88, with a CI of 95% in a range of 0.87 to 0.90. Conclusion: Even though several radiological techniques at present help to detect BA, USG of the abdomen proves to be an effective diagnostic and a modest screening test for the detection of BA.

This article develops duality principles and numerical results for a large class of non-convex variational models. The main results are based on fundamental tools of convex analysis, duality theory and calculus of variations. More specifically the approach is established for a class of non-convex functionals similar as those found in some models in phase transition. Finally, in the last section we present a concerning numerical example and the respective software.

This paper explores the application of machine learning (ML) algorithms in predicting trends in the fixed bond market, where traditional analytical methods have proven inadequate. Focusing on various ML techniques such as supervised and unsupervised learning, neural networks, and deep learning, the study evaluates their effectiveness in forecasting market movements. It details a series of experiments in which different ML models are rigorously trained and tested against historical bond market data. The findings reveal that models employing time series analysis and advanced deep learning show marked potential in accurately predicting bond market trends. Additionally, the paper delves into the challenges and limitations inherent in these ML approaches, including data requirements and the risk of model overfitting. Finally, it proposes directions for future research, emphasizing the integration of ML into broader financial market analysis.

The article delves into the crucial realm of compressing hydrogen in its gaseous state, a pivotal process for enhancing its viability in both civil and industrial sectors. The study initiates by providing a concise overview and comparison of diverse hydrogen storage methodologies, laying the groundwork with an in-depth analysis of hydrogen's thermophysical properties. It scrutinizes plausible configurations for hydrogen compression, aiming to strike a delicate balance between energy consumption, predominantly derived from the fuel itself, and the requisite number of compression stages. Notably, to render hydrogen storage competitive in terms of volume, pressures of at least 350 bar are deemed essential, albeit at an energy cost amounting to approximately 10% of the fuel's calorific value. Multi-stage compression emerges as a crucial strategy, not solely for energy efficiency, but also to curtail temperature rise, with an upper limit set at 200°C. This nuanced approach is underlined by the exploration of compression levels commonly cited in the literature, particularly 350 bar and 700 bar. Ultimately, the study advocates for a three-stage compression system as a pragmatic compromise, capable of achieving high-pressure solutions while keeping compression work below 10 MJ/kg, a threshold indicative of sustainable energy utilization.

Density is a crucial parameter for quantitatively describing the physical properties of liquids. It serves as an important indicator for scientific research, production process control, pipeline transportation, and other aspects. In oil pipeline transportation and raw material processing, real-time online measurement of liquid density is of great significance. This paper analyzes the working principle of an on-line vibrating tube densitometer and derives the fitting equation for temperature, pressure, and density；it also conducts experiments with an on-line vibrating tube liquid densitometer and establishes a traceability chain for the experimental device. The experimental setup includes a desktop densitometer system, a multi-temperature field constant temperature stirring system, a walk-in constant temperature box, an automatic blowing system, and a frequency acquisition and calculation system. The uncertainty of the device's evaluation is U= 0.08kg/m3, k=2.The experimental research on the on-line vibrating tube liquid densitometer can be carried out within the range of (650~1400) kg/m3, (10~70)℃, (0~50)bar the metrological performance of the vibrating tube densitometer can be researched by utilizing the straight tube type and the elbow type on-line vibrating tube densitometer. A preliminary study was conducted on the effects of different densities, temperatures, and pressures on the vibrating tube system's vibration cycle. The fit coefficient and error were calculated, and the experimental results were compared to the theoretical analysis to confirm the device's conformity. The study verified the device's scientific and reasonable design, and demonstrated that it is feasible to use the device for follow-up research.

Regression models are highly popular in empirical research and come in many different forms to fit virtually any distribution, variable, or research question. Usually, these models also compute how much variation in the outcome variable can be explained by all predictors, which is relevant to understanding whether the predictors are, in sum, able to explain the outcome or whether other potentially unobserved factors are more relevant. This aspect is crucial for interventions and policy as even statistically significant regression coefficients can be meaningless if they have little influence on the outcome overall. Besides having a measurement to judge the overall goodness of fit, ranking predictors by their relative importance is also relevant. For such analyses, it is necessary to decompose the total explanatory power of a model and partition it so that each explanatory variable is assigned a share. This enables the computation of a predictor variable's absolute and relative influence. Dominance analysis is a statistical approach to achieve this goal.

In order to tackle the issue of severe rocking phenomenon under seismic conditions in structures equipped with steel spring isolation bearings, this paper investigates a novel type of anti-rocking bearing. Firstly, the structural configuration and working principle of the novel anti-rocking bearing are introduced, and a design method for bearing parameters is proposed. Secondly, a finite element analysis model is established using SAP2000 software to conduct nonlinear dynamic time history analysis under seismic loading. The analysis results show that the structural arrangement of the novel anti-rocking bearing reduces both the vertical displacement difference and the rocking angle of the isolation layer. The bearing exhibits a certain level of anti-rocking effect, but it may cause significant tensile forces in some bearings, necessitating the installation of anti-tensile reinforcement. The effectiveness of the anti-rocking effect improves as the stiffness of the steel tension rod in the bearing increases; however, it also leads to higher maximum tension in individual bearings as well as the total tension in all bearings. For structures equipped with the novel anti-rocking bearing, the acceleration amplifies under most cases, with amplification coefficients ranging from 0.82 to 1.55. The performance of the novel anti-rocking bearing varies significantly for different seismic waves. The stiffness of the steel tension rod should be determined considering the reference pressure of the bearing and the maximum designed tension of the bearing. Through the finite element simulation of the bearing, the mechanical properties of the bearing are basically the same as the theoretical analysis results. For the cylinder and the joint bearing, certain structural measures need to be taken to ensure its strength and stiffness.

The ground consists of many layers of soil with different properties. The propagation speed and path of seismic waves are affected by different soil layers. It is necessary to understand that layered soil exhibits different dynamic behaviors and responses under the action of seismic waves. This study utilized weathered soil and silica sand as materials to create multi-layered soil conditions with varying degrees of compaction. By conducting 1 g shaking table test on multi-layered soil, the interaction and influence between different soil layers under different earthquake conditions were observed. The approach of numerical analysis aims to validate the experimental results and provide an in-depth understanding of the dynamic behaviors of multi-layered soil grounds during seismic events. The results of this study show that the amplification effect of seismic waves is related to the number of soil layers and the degree of compaction of the soil layers. This indicates that the multi-layered soil ground and the behavior of the soil layers play an important role in the propagation and impact of seismic waves, and this amplification effect is of great significance in actual seismic disaster risk assessment and design.

To realize the utilization of peanut shell, this study investigates the combustion behavior, chemical kinetics, and thermodynamic parameters of peanut shell using TGA under atmospheric air. Results indicate that increasing the heating rate leads to higher ignition, burnout, and peak temperatures, as observed in the TG/DTG curves shifting to the right. Analysis of combustion performance parameters suggest that higher heating rates can enhance combustion performances. Kinetic analysis using two model-free methods, KAS and FWO, shows that the activation energy (Eα) ranges from 93.30 to 109.65 kJ/mol for FWO and 89.72 to 103.88 kJ/mol for KAS. The data fit well with coefficient of determination values (R2) close to 1. Pre-exponential factors using FWO range from 2.19×106 to 8.06×107 s–1, and for KAS range from9.72×105 to 2.25×107 s–1. Thermodynamic analysis indicates a low-energy barrier (≤ ±6 kJ/mol) between activation energy and enthalpy changes, suggesting easy reaction initiation. Furthermore, variations in enthalpy (ΔH), Gibbs free energy (ΔG), and entropy (ΔS) upon conversion (α) suggest that peanut shell combustion is endothermic and non-spontaneous, with the generation of more homogeneous or well-ordered products as combustion progresses. These findings offer a theoretical basis and data support for the further utilization of agricultural biomass.

The implementation of sustainable practices, such as the use of renewable sources and efficient technologies, is crucial for reducing climate change. Energy efficiency is highly important on a global scale due to its relationship with climate change, aiming to minimize environmental impact. In Spain, renewable energy policies have increased self-sufficiency and reduced the use of fossil fuels. Optimizing energy consumption in residences, buildings, and industries is essential for improving efficiency. This was taken into consideration for conducting this study, focusing on the residential sector. The desire to understand the level of energy efficiency in Extremadura drove this project, which focuses on collecting and analyzing data on a set of homes located in the au-tonomous community of Extremadura, with the purpose of evaluating their energy efficiency. To achieve this, a computer tool will be employed to provide a detailed view of the different levels of energy efficiency present in the community. In addition to data collection and analysis, research will be conducted to identify areas for improvement. This will allow proposing a series of specific measures that users can implement to optimize energy efficiency in their homes, adapting them to the particular characteristics of each residence.

The aims of this research were to know the physicochemical parameters, texture properties and sensory profile of vegetarian sausages through the flash profile, overall liking and purchase intention. Additionally, protein, fat and carbohydrate contents, pH, water activity, color, instrumental texture and descriptive profile were quantified with the overall liking and purchase intention of four sausages: ovo-vegetarian (SO), classic vegan (SC), fine herb vegan (SH) and vegan quinoa (SQ). Regarding the physicochemical properties, significant differences (p < 0.05) were obtained. Using the flash profile, consumers described SO with attributes of elasticity, tamale smell, strange smell, characteristic flavor, pastiness, softness, pastel color. SC described as soft, pale color, characteristic odor, characteristic flavor, porous and flexible; SH and SQ characterized by the presence of ingredients, stew flavor, spices, pale and soft color. The SO sample presented greater overall liking and purchase intention compared to the other samples, and had a better texture and higher protein content. Through the HMFA analysis, a positive correlation was obtained between the texture and descriptive sensory of the flash profile, and on the other hand, a correlation between the physicochemical characteristics pH, aw, color, overall liking and purchase intention.

Balancing the efficiency and robustness of food systems is a well-known trade-off process. Over-optimization of efficiency may lead to excessive use of resources. On the other hand, under-optimization of efficiency may lead to excessive waste of resources. Using non-parametric methods can help in developing actionable frameworks through which decision-makers and stakeholders can effectively balance and improve food systems' efficiency and robustness. Under this context, this paper presents a new structured approach integrating two well-suited non-parametric methods for analyzing and balancing the interconnection between the efficiency and robustness of food systems. This approach consists of three main steps. First, the slacks-based data envelopment analysis (SBM-DEA) is utilized to analyze the efficiency of five food robustness dimensions. Second, the weighted efficiency of these dimensions is computed and analyzed to provide an insight into their influence on the food system efficiency. Finally, two search methods were developed to identify opportunities for improving efficiency and robustness. The outcomes of these methods have been analyzed and validated to ensure their effectiveness and viability. The operational and mathematical properties of the proposed approach were evaluated using data from thirty-seven food-producing countries. The weights of the robustness dimensions in this study were determined using the analytic hierarchy process (AHP) method. The input matrix of the AHP was constructed using expert judgment and consultations with stakeholders. The results of this study have shown an appropriate level of applicability and effectiveness of the proposed approach. While the first search method provided valuable insights, the second method proved to be more effective in identifying the sources of inefficacy of robustness dimensions. Several conclusions have been drawn and reported with some recommendations for future research works.

Sensory descriptive analysis was used to evaluate the aged Feng-flavored baijiu. The results indicated that there are notable differences in the samples of different ages. The samples with shorter storage times and fresh distilled Baijiu present bran flavor, and fresh green flavor. with the increase of storage time, the fragrance of honey, sweetness, flowers, and aging gradually increases. The samples of Feng-flavored baijiu were dected by Headspace solid-phase microextraction (HS-SPME) combined with comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC–TOFMS) , in total, 496 sustances were identified in all samples, mainly containing 14 types of substances, such as esters, aldehydes etc., of which 42 substances were found in Feng-flavored baijiu for the first time. Chemometrics was used to analyze the key differential compounds. 143 differential compounds closely related to aging were preliminarily screened, and principal component analysis revealed that these samples were separated by year. 65 differential compounds were selected out by partial least squares discriminant analysis(PLS-DA). Furthermore, 43 key differential compounds were selected from the aged Feng-flavored baijiu combined with variable importance in projection and Pearson correlation coefficients. Partial least squares regression (PLSR) was used to research the correlation of sensory properties and key differential compounds, and the results indicated that most compounds were closely related to the aged Baijiu . This study afford theoretical basis and reference for flavor research of Feng-flavored baijiu.

Optical communications are described and analyzed by shaped beams; we challenge the ‎effects of parameters that impact the profile of a Flat Top Gaussian (FTG) beam. When ‎the laser beam propagates throughout the atmosphere, it can be influenced by different ‎optical phenomena including scattering, absorption, and turbulence due to changes in the ‎scintillation index and the forms of intensity that are displayed in the source and receiver ‎planes. In this project, the FTG laser beam that propagates through a weak turbulent ‎region is numerically investigated using open-source software. This simulation will be ‎performed according to a mathematical model based on the split-step beam propagation ‎method. Intensity distributions at the source plane and the received average intensity in ‎atmospheric turbulence are calculated, and additional contour is in the transducer plane. ‎The scintillation index, structure constant, source size, and other parameters, are applied ‎in the Rytov method to quantify the weak turbulent model. Moreover, these parameters ‎are analyzed in near-field propagation. Also, the effects of the beam’s scintillation and ‎beam wander are determined. All results simulated are discussed and compared with the ‎TEM00 Gaussian beam. Finally, these results are compared to measurements in the ‎experimental part of the work.

The flourishing landscape of agritourism in Albania has witnessed a steady increase in destinations over the years. Recognizing their pivotal role in driving economic development in rural areas, considerable attention is directed towards the promotion and expansion of these agricultural tourism spots. To enhance interactivity with visitors, these agritourism destinations actively leverage social networks and various digital platforms. However, the wealth of information pertaining to these destinations is currently scattered across different platforms, posing a challenge to accessibility. To address this issue, these points are strategically placed on Google Maps, along with essential details collected from users, such as websites, contact numbers, visitor ratings, and comments. Nevertheless, other valuable data remains dispersed on various social networks. In response to this fragmentation, this study proposes the development of algorithms and scripts to systematically collect and analyze this dispersed information. By doing so, the goal is to centralize and streamline the data, enabling a comprehensive understanding of the agritourism landscape in Albania. This approach not only facilitates efficient access to information for potential visitors but also provides valuable insights for stakeholders involved in the development and management of these agritourism destinations. The study aims to contribute to the sustainable growth of agritourism in Albania by harnessing the power of digital platforms to create a unified and organized database. Through this integration, the research seeks to unveil patterns, trends, and opportunities that can further guide policy-making, marketing strategies, and overall decision-making processes in the realm of agritourism development.

The seismic events of 2015 in Nepal exposed the challenges associated with constructing earthquake-resistant 2-story self-built stone masonry structures in high-elevation rural regions, especially post-disaster. The Department of Urban Development and Building Construction (DUDBC) provided designs that failed to preserve the local architectural landscape and craftsmanship while adding unfamiliar knowledge and technology. To address the challenges related to architecture, structure, and socio-economic concerns in designing and implementing post-earthquake houses, a need-based building development framework is required for standardized practice. This study presents a novel framework to assess, design, and implement a self-built house after a disaster, consisting of five stages: 1) post-disaster problem assessment, 2) need identification, 3) material selection, 4) design development, and 5) validation and implementation. Based on this framework, we proposed a novel gabions building construction technology for two-story stone masonry structures that effectively mitigate post-disaster challenges such as logistic, resilience, and socioeconomic aspects while improving disaster resiliency specifically in the high-elevation rural areas of Nepal. The proposed two-story gabion building preserves architectural values, enhances structural integrity, and provides cost-effectiveness when compared with its DUDBC peer models while providing much-needed relief to the vulnerable community. The proposed resilient house, G- 2.1, utilizes locally sourced materials and craftsmanship, and innovative gabion technology that ensures affordability and facilitates knowledge transfer. The contribution of this study includes a development framework and a two-story gabions house that is most suitable for self-built post-disaster reconstruction and resilient to local architecture and socio-economic conditions while providing structural integrity and safety.

Serine peptidases (SPs) of the chymotrypsin S1A subfamily are an extensive group of enzymes found in all animal organisms, including insects. Here we provide analysis of SPs in the yellow mealworm Tenebrio molitor transcriptomes and genomes datasets and profile their expression pattern at various stages of ontogeny. A total of 269 SPs were identified, including 137 with con-served catalytic triad residues, while others 125 lacking conservation were proposed as non-active serine peptidase homologs (SPHs). Seven deduced sequences exhibit a complex do-main organization with two or three peptidase units (domains), predicted both as active or non-active. The largest group of 84 SP and 102 SPH had no regulatory domains in the propep-tide, and the majority of them were expressed only in the feeding life stages, larvae and adults, presumably playing an important role in digestion. The remaining 53 SP and 23 SPH had differ-ent regulatory domains, showed constitutive or upregulated expression at eggs or/and pupae stages, participating in regulation of various physiological processes. The majority of polypep-tidases was mainly expressed at the pupal and adult stages. The data obtained expand our knowledge on SPs/SPHs and provide a foundation for further research on the functions of this gene family in T. molitor.

Originating in the Mediterranean, B. oleracea is grown all over the world and is highly valued for its nutritional value and variety in cooking. Its susceptibility to pests, diseases, and environmental stressors, however, poses significant challenges to sustaining consistent crop quality and productivity. The BASIC PENTACYSTEINE gene significantly impacts plant growth and responses to abiotic stresses. Nevertheless, little is known about its precise role and the underlying biological processes in B. oleracea. Eleven BPC genes in B. oleracea were identified by a thorough genome-wide investigation and categorized into three categories according to their structural traits and gene motifs. There were eight different chromosomes on which these BoBPCs were located. Comparative synteny analysis was used to reveal the evolutionary traits of potatoes, Arabidopsis, and B. oleracea. The BoBPC family genes in B. oleracea were found to have specific cis-elements linked to growth, stress responses, and hormone regulation when their promoter regions were studied. Cuticular wax synthesis expression revealed that all BoBZR1 genes, from wild to mutant, expressed at varied amounts in distinct types. Moreover, distinctive BoBPC gene expression patterns were found in the seven distinct tissues that were studied. Our thorough analysis shows that the BoBPC family is widely involved in B. oleracea developmental processes. The present findings provide an established theoretical basis for further exploration into the composition and roles of BoBPC in B. oleracea.

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.

The reaction between 5-acetylbarbituric acid and 4-dimethylthiosemicarbazide or 4-hexamethyleneiminyl thiosemicarbazide produces 5-acetylbarbituric-4-dimethylthiosemicarbazone (H2AcbDM) and 5-acetylbarbituric-4N-hexamethyleneiminyl thiosemicarbazone (H2Acbhexim). Eight new complexes with different cooper (II) salts have been prepared and characterized using elemental analysis, molar conductance, UV–vis, ESI-HRMS, FT-IR, magnetic moment, EPR and cyclic voltammetry. In addition, three-dimensional molecular structures of [Cu(HAcbDM)(H2O)2](NO3)·H2O (3a), [Cu(HAcbDM)(H2O)2]ClO4 (4), and [Cu(HAcbHexim)Cl] (6) were determined by single crystal X-ray crystallography, and was carried out an analysis of their supramolecular structure. The H-bonded assemblies were further studied energetically using DFT calculations, MEP surface and QTAIM analyses. In these complexes, the thiosemicarbazone coordinates to the metal ion in an ONS-tridentate manner, in the O-enolate/S-thione form. The electrochemical behavior of the thiosemicarbazones and their copper(II) complexes has been investigated at room temperature using the cyclic voltammetry technique in DMFA. The Cu(II)/Cu(I) redox system was found to be consistent with the quasi-reversible diffusion-controlled process.

Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. In contemporary times, the transition from conventional electricity grids to Smart Grids constitutes an area where extensive research is conducted on a global scale. Among the research challenges, the investigation of Deep Transfer Learning (DTL) in the field of electricity load forecasting represents a fundamental effort that imparts generality to Artificial Intelligence applications, due to new capabilities, such as knowledge transfer and computational power reduction. In this paper a comprehensive study is conducted for day-ahead electricity load forecasting. For this purpose, three Sequence-to-Sequence (Seq2seq) Deep Learning (DL) models are used, namely the Multilayer Perceptron (MLP), the Convolutional Neural Network (CNN) and the Ensemble Learning Model (ELM), which is consisted of the weighted combination of the outputs of MLP and CNN models. Also, the study focuses on the development of different forecasting strategies based on DTL and emphasizing the way the datasets are trained and fine-tuning for higher forecasting accuracy. In order to implement the forecasting strategies using Deep Learning models, load datasets from three Greek islands, Rhodes, Lesvos, and Chios, are used. The main purpose is to apply DTL for day-ahead predictions (1-24 hours) for each month of the year for Chios dataset after training and fine-tuning the models using the datasets of the three islands in various combinations. After several trials, four DTL strategies are illustrated. In the first strategy (DTL Case 1), each of the three DL models is trained using only Lesvos dataset, while fine-tuning is performed on the dataset of Chios island, in order to create day-ahead predictions for Chios load. In the second strategy (DTL Case 2), data from both Lesvos and Rhodes concurrently are used for the DL model training period, and fine-tuning is performed on the data from Chios. The third DTL strategy (DTL Case 3) contains the training of the DL models using Lesvos dataset, and the testing period performed directly on the Chios dataset without fine-tuning. The fourth strategy is a Multi-task Deep Learning (MTDL) approach, which has been extensively studied in recent years. In MTDL, the three DL models are trained simultaneously on all three datasets and the final predictions are made on the unknown part of the dataset of Chios. In this paper, we explore the performance of DTL and compare the results with those produced with MTDL. The results demonstrated that DTL can be applied with high efficiency for day-ahead load forecasting. Specifically, the two cases with fine-tuning (DTL Case 1 and 2) outperformed MTDL in terms of load prediction accuracy. Regarding the DL models, all three exhibit very high prediction accuracy, especially in the two cases with fine-tuning. The ELM excels compared to the single models. More specifically, for conducting day-ahead predictions, it has been concluded that the MLP model presents best monthly forecasts with a MAPE of 6.24% and 6.01% for the first two cases, the CNN model presents best monthly forecasts with a MAPE of 5.57% and 5.60% respectively and the ELM model achieves best monthly forecasts with a MAPE of 5.29% and 5.31%, respectively, indicating the very high accuracy it can achieve.

This paper introduces a rigorous and comprehensive approach for the assessment of marine shafting systems through the utilization of an advanced Pi-Theorem based scaling methodology. Integrating journal bearing similarity assessment and shaft-line scaling methodology with advanced dimensional analysis, the study aims to provide a methodology foundation for systematic replication and analysis of marine shafting systems through scaled models. The proposed scaling methodology ensures geometric and mechanical similarity in terms of shaft-line deflection, considering key scaling parameters such as shaft length, diameter, weight, loads, rotational speed, material properties, bearing locations, and offsets. The advanced dimensional analysis computes specific non-dimensional ratios to guarantee a close resemblance between a real-size system and a scaled lab model, enabling meaningful experimentation. The methodology is analytically derived and validated with numerical simulations for a case study, conducting comparative analysis, evaluating discrepancies, and utilizing the integrated framework for experimentation.

Wireless Sensor networks (WSNs) are gaining more and more research interest due to their ability to monitor large areas more or less independent. However, their reliability is an important issue, since their system reliability is influenced from a hardware, a data and an energetic point of view due to different factors like loading conditions, signal attenuation and battery lifetime. Regarding these issues, the positions of sensor nodes have to be chosen correctly to maximize the system reliability, which has to be considered during the development of new products equipped with WSNs. In this paper, an approach to estimate a WSNs system reliability during the development phase based on the analysis of the measurements on the example of strain measurements in finite elements (FE) models is presented. For that, the part under consideration is divided into regions with similar strains by the use of a Region Growing Algorithm (RGA). Afterwards, the WSN’s configuration is analyzed regarding its reliability in accordance to the data paths and measurement redundancy due to the sensor positions in the found measuring regions. This methodology is tested on an exemplary WSN configuration at an aircraft wing box under bending load and was found to work very well regarding the estimation of the hardware perspective on the system reliability. Therefore, the methodology and the developed algorithm show potentials for optimizations in sensor node positions for better reliability results.

Based on the traditional Chinese cultural belief of "male breadwinner, female homemaker," as well as the systemic and interactive characteristics of families, this study aims to explore the influence of maternal gatekeeping behavior on the quality and quantity of paternal parenting, as well as adolescent aggressive behavior. A total of 483 seventh-grade students completed questionnaires on maternal gatekeeping behavior, paternal involvement, parenting styles and aggressive behavior. Latent profile analysis identified four parenting combinations: positive, negative, mixed, and neglectful. Adolescents under negative parenting exhibited the highest aggression, while those under positive and neglectful parenting showed the least aggression. Maternal gatekeeping behavior correlated with paternal negative parenting and adolescent aggression. Paternal negative parenting mediated the relationship between maternal gatekeeping and aggression, while paternal involvement moderated this relationship. These findings highlight the role of parental interaction in adolescent behavior and support family-based interventions.

The development of transversal competences, in interaction with professional competences, is essential for individuals to adapt to change and live meaningful and productive lives. The aim of this study was to explore the mutual interaction of six transversal competences—civic, digital, entrepreneurial, global, innovation, and research, as well as their respective sub-competences—and propose possible approaches to how this dynamic of interaction can be applied to facilitate the development of transversal competences. A sample of students (N = 1575) from 21 universities in Latvia completed a self-assessment survey evaluating multiple behavioural indicators for each transversal competence and sub-competence. The results revealed that civic competence was evaluated significantly lower than other competences, and, for most transversal competences, sub-competences associated with active and systematic collaboration and networking received lower evaluations. A network analysis showed that the community involvement sub-competence of the civic competence and the initiative and critical thinking sub-competences of the innovation competence had the strongest links to the other measured sub-competences on average. The results suggest that the development of initiative and cooperation-and-collaboration-related and network-building skills are likely to have a systematic positive effect on the development of a wider set of transversal competences and the professional autonomy of students in the long term.

HIV-associated neurocognitive disorders (HAND) persist under antiretroviral therapy as a complex pathology that has been difficult to study in cellular and animal models. Therefore, we generated an ex vivo human brain slice model of HIV-1 infection from surgically resected adult brain tissue. Brain slice cultures processed for flow cytometry showed >90% viability of dissoci-ated cells within the first three weeks in vitro, with parallel detection of astrocyte, myeloid, and neuronal populations. Neurons within brain slices showed stable dendritic spine density and mature spine morphologies in the first weeks in culture, and they generated detectable activity in multi-electrode arrays. We infected cultured brain slices using patient-matched CD4+ T-cells or monocyte-derived macrophages (MDMs) that were exposed to a GFP-expressing R5-tropic HIV-1 in vitro. Infected slice cultures expressed viral RNA and developed a spreading infection up to 9-days post-infection, which were significantly decreased by antiretrovirals. We also detected infected myeloid cells and astrocytes within slices and observed minimal effect on cellular via-bility over time. Overall, this human-centered model offers a promising resource to study the cel-lular mechanisms contributing to HAND (including antiretroviral toxicity, substance use, and aging), infection of resident brain cells, and new neuroprotective therapeutics.

China is experiencing rapid urbanization. The focus on health issues has shifted from the treatment of diseases to passive or active prevention. Diseases caused by the deterioration of the urban environment, such as cardiovascular disease, diabetes, obesity and other chronic diseases and mental illness, have gradually become the main threat to public health. Studies have confirmed that urban green spaces (UGS) can help residents relieve mental stress, improve mood, reduce mental fatigue and restore attention, which has a positive effect on people's physical and mental health. In this study, the relationship between the quantity and quality factors of UGS and the physical and mental health of urban residents in Xihu District of Hangzhou is examined, and on this basis, UGS design strategies are proposed to promote the physical and mental health of residents. This study is of great significance for the future construction of UGS that are beneficial to the health of residents and the promotion of a healthy China and healthy city construction.

In this study, the microbial strain was isolated from the humus soil to ferment Sophora flavescens and Eleutherococcus sessiliflorus extracts. The isolated microbial was identified as Bacillus genus by 16S rRNA sequence analysis. The fermented plant extracts exhibited the antifungal effects against four types of the plant pathogens such as P. carotorum, B. cinerea, C. fructicola Sau-3, and C. gloeosporioides according to incubation time. Especially, the fermented plant extracts showed the most active activities for Colletotrichum genus to inhibit mycelium growth. Metabolite changes in fermented S. flavescens and E. sessiliflorus extracts were confirmed through LC-Q-TOF/MS. Flavonoid and peptide derivatives were improved in fermented S. flavescens and E. sessiliflorus extracts compared to their unfermented one, respectively. This study suggested that the isolated Bacillus microbial fermentation could be a valuable tool in improving the bioactivity of S. flavescens and E. sessiliflorus extracts to have potential with more environmentally-friendly antifungal agents.

This paper investigates the impact of debt financing on dividend payments when they face the agency costs of free cash flow. Focusing on a sample of 120 firms listed on the Saudi Stock Exchange during the period 2011-2021. The study found a negative association between agency cost of free cash flow and dividend payment. More importantly, our research highlights the significant role of long-term debt in making more prudent use of free cash flow. The obligation to meet interest and principal payments acts as an incentive for them to steer clear of unprofitable expenses and risky investments. Concurrently, long-term debt imposes restrictive clauses in debt contracts, such as minimum dividend distribution requirements, which further encourage higher dividend payments. Since interest and debt repayments are fixed obligations, using free cash flow for dividend disbursement is considered a more profitable and beneficial approach for shareholders in the context of Saudi Arabia.

The field of domain adaptation, particularly in cross-domain sentiment classification, leverages labeled data from a source domain alongside unlabeled or sparsely labeled data from a target domain. The objective is to enhance performance in the target domain by mitigating the discrepancies in data distributions. Traditional methods in this area have focused on identifying and differentiating between pivotal sentiment words (shared across domains) and domain-specific sentiment words. In our work, we introduce a novel framework called Sentiment Domain Adapter (SDA), which incorporates a Category Attention Network (CAN) alongside a Convolutional Neural Network (CNN). This approach treats pivotal and domain-specific words as part of a collective category of attributes, which SDA learns to discern automatically, thereby enhancing domain adaptation. Additionally, SDA seeks to provide interpretative insights by learning these category attributes. Our model's optimization targets three main goals: (1) supervised classification accuracy, (2) minimizing the disparity in category feature distribution, and (3) maintaining domain invariance. Evaluations on three benchmark datasets for sentiment analysis affirm that SDA surpasses several established baselines.

Cancer is a disease caused by the abnormal growth of cells in different parts of body is one of the top causes of death globally. Microarray gene expression data plays a critical role in the identification and classification of cancer tissues. Due to recent advancements in Machine Learning (ML) techniques, researchers are analyzing gene expression data using a variety of such techniques to model the progression rate & treatment of cancer patients with great effect. But high dimensionality alongside the presence of highly correlated columns in gene expression datasets leads to computational difficulties. This paper aims to propose the use of ML classification techniques- Linear Discriminant Analysis (LDA) & Random Forest (RF) for classifying five types of cancer (breast cancer, kidney cancer, colon cancer, lung cancer and prostate cancer) based on high dimensional microarray gene expression data. Principal component analysis (PCA) was used for dimensionality reduction, and principal component scores of the raw data for classification. Six distinct categorization performance measures were used to evaluate these approaches; RF method provided us with higher accuracy than LDA method. The method and results of this article should be helpful to researchers who are dealing with many genes in microarray data.

The unprecedented production and sharing of data, opinions, and comments among people on social media and the Internet in general has highlighted sentiment analysis as a key machine learning approach in scientific and market research. Sentiment analysis can extract sentiments and opinions from user-generated text, providing useful evidence for new product decision-making and effective customer relationship management. However, there are concerns about existing standard sentiment analysis tools regarding the generation of inaccurate sentiment classification results. The objective of this paper is to determine the efficiency of off-the-shelf sentiment analysis APIs in recognizing low-resource languages, such as Greek. Specifically, we examined whether sentiment analysis performed on 300 online ordering customer reviews using the Meaning Cloud web-based tool produced meaningful results with high accuracy. According to the results of the study, we found low agreement between the web-based and the actual raters in the food delivery services related data. However, the low accuracy of the results highlights the need for specialized sentiment analysis tools capable of recognizing only one low-resource language. Finally, there is a significant need for the creation of industry-specific lexical resources that provide decision-makers with valuable insights.

This paper studies the flexural behavior of the bridge deck link slabs made with the Polyvinyl Alcohol-Strain Hardening Cementitious Composites (PVA-SHCC). Tensile and flexural properties of the PVA-SHCC with four volume fractions, i.e., 0%, 1%, 1.5%, and 2%, were evaluated first. Next, using the similarity theory, composite models with a geometric similarity ratio of 1: 5 were designed to represent the bridge deck with the link slabs. The models considered three materials for link slabs, including concrete, cement mortar and self-compacting PVA-SHCC; and two different curing ages at 7 and 56 days. Bending tests were performed to investigate the flexural behavior of the models. Based on the fractal theory, cracking characteristics of the models with different types of link slabs were compared, and the relationship between Df and the flexural behavior of the models were studied. Numerical models were built to correlate with the results from the bending tests. It is illustrated that the flexural behavior of the self-compacting PVA-SHCC link slab is better than that of concrete and cement mortar link slabs, where the crack initiation and propagation can be postponed.

We describe a new approach for integrating multiple views of data into a common latent space using non-negative tensor factorization (NTF). This approach, which we refer to as the "Integrated Sources Model" (ISM), consists of two main steps: embedding and analysis. In the embedding step, each view is transformed into a matrix with common non-negative components. In the analysis step, the transformed views are combined into a tensor and decomposed using NTF. Noteworthy, ISM can be extended to process multi-view data sets with missing views. We illustrate the new approach using two examples: the UCI digit dataset and a public cell type gene signatures dataset, to show that multi-view clustering of digits or marker genes by their respective cell type is better achieved with ISM than with other latent space approaches. We also show how the non-negativity and sparsity of the ISM model components enable straightforward interpretations, in contrast to latent factors of mixed signs. Finally, we present potential applications to single-cell multi-omics and spatial mapping, including spatial imaging and spatial transcriptomics, and computational biology, which are currently under evaluation. ISM relies on state-of-the-art algorithms invoked via a simple workflow implemented in a Jupyter Python notebook.

Fever control is essential in patients with severe traumatic brain injury (TBI). The efficacy of therapeutic hypothermia (TH) in severe TBI has been investigated over the last few decades; however, in contrast to experimental studies showing benefits, no evidence of efficacy has been demonstrated in clinical practice. In this review, the mechanisms and history of hypothermia were briefly outlined, while the results of major randomized controlled trials (RCTs) and meta-analyses investigating TH for adult TBI are introduced and discussed. The retrieved meta-analyses showed conflicting results, with a limited number of studies indicating benefits of TH. Some studies have shown benefits of long-term TH compared with short-term TH. Although TH is effective at lowering elevated intracranial pressure (ICP), reduced ICP does not lead to favorable outcomes. Low-quality RCTs overestimated the benefits of TH, while high-quality RCTs showed no difference or worse outcomes with TH. RCTs assessing standardized TH quality demonstrated the benefits of TH. As TBI has heterogeneous and complicated pathologies, applying a uniform treatment may not be ideal. A meta-analysis of young patients who underwent early cooling and hematoma removal showed better TH results. TH should not be abandoned, and its optimal should be advocated on an individual basis.

Existing research indicates that mineralization of Carlin-type gold deposit is related to geological structures, but current studies mostly focus on the qualitative association, and lack quantitative analysis of the relationship between mineralization and structural features, which limits the potential for predictive mineral exploration. In this study, the Nibao Carlin-type gold deposit was selected as the research area to conduct three-dimensional geological modeling, spatial analysis, and three-dimensional prospectivity modeling. Spatial analysis revealed that the majority of gold ore bodies are situated near the main fault, with gold mineralization primarily occurring within a spatial range of approximately -80 meters to 20 meters from the main fault plane. This spatial distribution predominantly aligns in a SW-NE direction. Spatial characteristic extraction of fault slope indicators suggests that the slope angles are conducive to the migration and accumulation of ore-forming hydrothermal fluids, with ore body metal content rapidly increasing within an angle range of approximately 20° to 60°. The shape analysis of the mainfault indicates that the principal spaces of mineralization enrichment are within -10m to 20m of the fault plane undulations, where gold resources are concentrated in a relatively flat area. However, the degree of mineralization is more pronounced in the protruding sections than in the depressed areas. The areas with significant dip changes in faults display higher variations in orebody attitudes, with mineralization noticeably weaker in areas where the fault steepness changes markedly. Ore-bearing units exhibit significantly higher grades within the -17° to 3° interval of fault steepness transition. This interval also sees a rapid accumulation of gold resources. The Nibao gold mine's mineralization is derived from deep, ore-bearing hydrothermal fluids ascending along major fractures, precipitating, and accumulating in favorable ore-hosting spaces (faults, anticline cores) and lithological combinations (sedimentary tuffs, tuffs). Three deep potential gold targets were identified, highlighting the deep western edge of the F1 fault and the deep southeast-trending lower plate as prospective areas for future exploration. This study underscores the effectiveness of 3D prospective modeling as an indispensable tool for elucidating the spatial distribution of fault-controlled Carlin-type gold deposits and their tectonic determinants.

Objectives (Purpose) This study aims to evaluate the outcomes of the Subcrestally Placed Implant (SPI) technique in single molar cases, focusing on biological stability and aesthetics. It proposes a schematic model to elucidate the interplay between the biological stability of soft tissues and bone around the SPI and the morphological requisites that Implant Abutment Prostheses (IAP) must fulfill to effectively replace natural teeth. Materials and methods I examined 20 cases using the SPI technique with Internal Platform Switching (IPS) design implants for single molar replacements to assess their stability. Routine check-ups utilized plain panoramic X-rays to measure changes in crestal bone on the mesial and distal sides. Additionally, 10 cases with CBCT X-rays taken more than one year after the initial surgery underwent a novel 3-dimensional soft tissue analysis (3DSTA). Measurements included placement depths (PD), soft tissue thickness (STT) at central (cPD, cSTT) and peripheral areas (pPD, pSTT), the length of the Transitional Zone (TZ) on mesial, distal, facial, and lingual aspects, and crestal bone thickness (CBT) at buccal and lingual sites. Results Panoramic analyses showed minimal changes in crestal bone (mean changes of pPD were less than 0.1 mm) over an average period of 3.3 years. Average measurements were as follows: cPD was 1.7 mm, pPD was 2.7 mm, cSTT was 0.2 mm, pSTT was 0.6 mm, TZ length was 3.8 mm, buccal CBT was 3.0 mm, and lingual CBT was 3.7 mm. Conclusions CBCT analyses of the topographical relationships between soft tissue and crestal bone with IAP for SPIs demonstrated stable results over an average 3-year period. The consistency in the relationship between crestal bone and surrounding soft tissue can be ascribed to biological structural integrity. The study classified peri-implant soft tissues into subcrestal and supracrestal (transitional zone – TZ) segments, exploring their characteristics through dimensional measurements and assessing mechanical resistance. Further histological study is needed to identify specific features of each segment.

A finite element model of 100m X-BOW polar exploration cruise is established. The entire ship frequency response analysis was carried out, comparing the simulated values with the test values, the maximum error is 22%, but only 0.24mm/s difference, the error can be ignored from the perspective of comfort, therefore, the simulation results meet the requirements of engineering accuracy and verify the effectiveness of the model. According to the solution method for global modal equivalent mass, combing the spatial distribution principle of local modal mass, the method for solving the equivalent mass of a single local mode in mixed mode is proposed, the equivalent mass of the local vibration region of 100m X-BOW polar exploration cruise ship is solved by this method, tuned mass dampers mass ratio parameters are calculated using the equivalent mass. According to the analysis, tuned mass dampers has 31dB vibration absorption effect at 13.4Hz when the mass ratio is 0.05, it also has certain control effect at 10Hz and 18.8Hz, the control effect is 3dB and 2dB respectively, the overall control frequency band is flat and robust.

Lysine plays a crucial role in promoting development, enhancing immune function, and improving the function of central nervous system tissues. The two configurational isomers of amino acids have significantly different effects. Currently, methods for chiral recognition of lysine all have drawbacks such as expensive equipment and complicated detection processes, whereas fluorescence analysis boasts high sensitivity, strong selectivity, and simple operation. In this study, we synthesized four novel Binaphthyl-Amine (BINAM)-based fluorescent probes capable of specifically identifying the L-configuration of lysine among the twenty amino acids constituting human proteins. The enantiomeric fluorescence enhancement ratio (ef or ΔIL/ΔID) reached up to 15.29, demonstrating high enantioselectivity. We also evaluated the probe's recognition ability under different pH, reaction times, and metal ion conditions, as well as the Limit of Detection of the probe. Our findings indicate that this probe is a highly stable tool for detecting chiral lysine.

The flux of opinions in newsletters and social media platforms significantly influences market perceptions regarding corporate entities and their financial instruments. This paper introduces an advanced model, the Sentiment Dynamics Analyzer (SDA), which leverages a refined hierarchical architecture of Transformers to effectively discern the sentiment embedded within financial texts, ranging from news headlines to microblogs. We have enhanced a RoBERTa model with specialized sentiment lexicons and an augmented layer of Transformer models tailored for nuanced sentence-level sentiment detection, aiming to assign a sentiment score from -1 to +1. Our evaluations demonstrate that SDA not only surpasses the previous best methods but also exhibits superior performance over robust baseline models. This success underscores the utility of integrating tailored contextual analyses with sector-specific sentiment insights in improving predictive accuracy.

This review examines the quality of capital investment. It explores the essential components and analysis involved in financially assessing an engineering project, including an assessment of various associated risks. Additionally, the review delves into the historical progression of current capital budgeting practices and surveys academic research in the field. The evaluation of investments has been a recognized challenge for the past thirty years, but it has recently gained renewed attention from both management and scholars. Investments constitute a significant and growing portion of organizations’ capital expenditures. However, assessing the impact of investments on pursued goals is challenging. Consequently, there is a strong demand for methods and techniques that can aid in evaluating investments during the proposal stage. This paper explores several investment strategies and Examines the criteria for investment.

This study analyses the changes in land use in the Sado estuary (West-Central Portugal) based on a multitemporal analysis of cartographic data from the 19th century and remote sensing land use maps from the 21st century, updated by field work. A GIS plotting of land-use evolution is summarized in a quantitative table. The comparison shows the changes in land use, with increasing occupation by human economic activities, including extensive agriculture and forestry, as well as localized urbanization and industrialization. The main elements of the landscape impacted by anthropogenic uses were: i) hydrography - river dams affected the flow dynamics and sedimentary processes in the estuary; ii) vegetation – increasing agriculture and forestry reduced the areas of native vegetation, nowadays mostly occupied by vineyards, pine forests and cork oaks. iii) wetlands - tidal and alluvial plains are being occupied by rice cultivation, aquaculture, industries, and ports; iv) coastal dunes – new developments are occupying large areas of Holocene coastal dunes; v) natural environment – mining and dredging affected some habitats and biodiversity. This analysis is intended to help the territorial organization of present and future economic activities, as well as to reduce environmental and social problems, promoting long-term sustainability of this rapidly evolving region.

In reinforced concrete (RC) structures, splicing is required owing to the length limitations of rebars, insufficient lengths, and transportation issues. In particular, splices connect the rebar of RC structures such as walls, columns, beams, slabs, and joints. Lap splices are the most commonly used method worldwide because they do not require specific equipment or skilled workers. However, lap splices incur high construction costs because of the long splice lengths required for large-diameter rebars in megastructures, as well as issues pertaining to material supply, labor costs, constructability, and project duration. Additionally, approximately 15% more rebar is required because of the overlap. Energy saving for a sustainable built environment is possible if the disadvantage of lap splices, which generate high CO2 emissions due to the excessive use of rebar, are resolved. Hence, mechanical rebar couplers (MRCs) have been developed. However, despite their advantages, they have not been widely applied in construction sites owing to concerns regarding safety, quality, and constructability. Although various MRCs have been developed, most studies focus only on their structural performance. Therefore, a data-driven approach for selecting MRCs based on the reinforcing bar shape and structural characteristics is proposed in this study. Using a data-driven MRC selection algorithm, using the T-threaded coupler for one rebar over two floors resulted in 56% more efficient labor productivity, 15% shorter assembly time, 17% lower costs, and 26% lower CO2 emission. Using a developed algorithm, the appropriate MRC can easily and rapidly be selected for frequent design changes.

This study introduces a compact Raman spectrometer with a 1064 nm excitation laser coupled with a fiber probe and an inexpensive motorized stage, offering a promising alternative to widely used Raman imaging instruments with 785 nm excitation lasers. The benefits of 1064 nm excitation for biomedical applications include further suppression of fluorescence background and deeper tissue penetration. The performance of the 1064 nm instrument in detecting cancer in human bladder resectates is demonstrated. Raman images with 1064 nm excitation were collected ex vivo from 10 human tumor and non-tumor bladder specimens and the results are compared to previously published Raman images with 785 nm excitation. K-Means cluster (KMC) analysis is used after pre-processing to identify Raman signatures of control, tumor, necrosis, and lipid-rich tissues. Hierarchical cluster analysis (HCA) groups the KMC centroids of all specimens as input. The tools for data processing and hyperspectral analysis were compiled in an open source Python library called SpectraMap (SpMap). In conclusion, the 1064 nm Raman system can differentiate between tumor and non-tumor bladder tissues in a similar way to 785 nm Raman spectroscopy. These findings hold promise for future clinical hyperspectral imaging.

China's economic development has shifted from high speed to high quality. This study constructs an evaluation index for China’s high-quality development utilizing a new development concept. The entropy method and cluster analysis were used to assess the level of high-quality development in China between 2015 and 2021. Kernel density estimation method was employed to explore spatiotemporal dynamics. The results reveal significant differences in the level of economic development among provinces in China; the eastern region is significantly better than the central, northeastern, and western regions. From the perspective of the sub-item evaluation, challenges with uneven, uncoordinated, and insufficient economic development exist in various Chinese provinces.

Measuring the impact of land use regulation on the land market involves identifying and classifying relevant impact factors related to the land market. The objective of this study was to identify land market impact factors in the context of the introduction of land use regulation in Nepal. A desktop review was carried out to identify preliminary set of impact factors, which were reclassified through intuitive analysis based on the degree of thematic closeness. Perspective-based impact factors were identified through the qualitative analysis of primary data collected through semi-structured interviews with the Nepalese land market stakeholders. These independently derived impact factors were compared with the desktop literature review impact factors resulting in fourteen land market impact factors across four dimensions, including transaction cost, valuation, mortgage availability, taxation, and compensation across the economic dimension; lot size, subdivision restrictions, and coordination across the institutional dimension; awareness, expectation and proximity across the social dimension; and risk reduction, quality of residential land, and suitability of zoning classification across the environmental dimension. There was significant overlap and commonality across factors identified from both the literature review and semi-structured interviews. The land market impact factors determined in the study may be adapted and generalized across other countries and could be utilized to better understand the impacts of land policy decisions on urban planning and development. Further research is recommended on the process to operationalize the use of these factors to quantify the impact of land use regulation on different land markets.

Abstract: : 2,3-dihydro-5,6,7,8-tetranitro-1,4-benzodioxine (TNBD), molecular formula =C8H4N4O10 is a completely nitrated in aromatic ring 1,4-benzodioxane derivative. The con-venient method of TNBD synthesis was developed (yield=81 %). The detailed structure of this compound has been investigated by X-ray crystallography. The results of differential thermal analysis (DTA) obtained with twice re-crystallized material showed onset at 247 and isoterm at 288 0C, with further total degradation at 300 0C. The experimental density 1,85 g/cm3 of TNBD was determined by X-ray crystallography. The spectral properties of TNBD (NMR, FT-IR, Ra-man) were explored. The detonation properties of TNBD calculated by EXPLO 5 code was slightly superior in comparison to standard high energy material - tetryl (detonation velocity of TNBD - 7727 m/s; detonation pressure - 278 kbar; tetryl – 7570 m/s and 226.4 kbar at 1.614 g/cm3, or 260 kbar at higher density - 1.71 g/cm3. The obtained preliminary results might suggest TNBD can be a potential thermostable high energy and density material (HEDM).

In the last two decades, the application of composite structures in many industrial sectors has been made progressively. For the current major scientific and technological challenge, the energy transition with sustainable and eco-friendly initiatives, the polymer matrix laminated composites have been revealed as an excellent alternative for the renewable energy sector. In this context, the knowledge about mechanical behavior of multiphase materials is required. Then, researches related to macro, mesomechanical and multiscale approaches are very important to improve accuracy of the models to predict effective mechanical properties and to develop a better understanding about complex phenomena such as progressive damage, creep and fatigue. Due to theses needs, this research is a synthesis of fundamentals and advanced topics concerning to global modeling of laminated composite structures, layerwise analyzes and micro-macromechanical models, in which mainly fiber, matrix, interface and damage characteristics are studied and used in the macroscale approach. At the end of this study, some final remarks and a trend analysis are proposed based on literature consulted, in other words, expectations for the future works are made by the authors.

This paper investigates women’s empowerment and their active involvement in governance aimed at promoting sustainable development. The study focuses on examining the roles and processes of co-creation of nature-based solutions (NBS) for water management in Milan. Three research directions were explored: 1) to determine gender-based roles within local governance; 2) to detect semantic groups of NBS based on stakeholders' gender; c) to analyze gender-based linguistic and conceptual patterns of NBS. To achieve our objectives, we converted a sample of audio-video interviews of local stakeholders into textual data along with metadata indicating speakers' roles and gender. Descriptive statistics were applied to explore the governance's roles. We employed a combination of textual analysis and network analysis to identify thematic groups of NBS, and applied a word embedding approach to uncover linguistic patterns indicative of semantic sub-structures. The findings revealed significant demographic diversity. Male stakeholders perceive sustainable development as a process driven by the circular economy and the coordination of territorial authorities, while female stakeholders envision a transformative approach rooted in science, culture, and collaboration. The semantic sub-structure analysis highlighted a tendency among male governance to emphasize specialized skills, whereas female governance placed greater emphasis on problem definition rather than resolution.

Volatile organic compounds (VOCs) released from plants have been correlated with disease-status. Analysis of VOCs using GC-MS is time-consuming, laboratory-based, and requires specialist training. Electronic nose devices (E-nose) provide a portable alternative. Three different E-nose devices were compared to assess how accurately they could detect Mango Twig Tip Dieback and Panama disease in banana. The devices were initially trained on known volatiles, then pure cultures of Pantoea sp., Staphylococcus sp., and Fusarium odoratissimum, and finally, on infected and healthy mango leaves and field-collected, infected banana pseudo-stems. The experiments were repeated three times with six replicates for each host-pathogen pair. The variation between healthy and infected host materials was evaluated by principal component and linear discriminant analysis, cross-validation and chemometric data analysis. GC-MS analysis was conducted contemporaneously and identified an 80% similarity between healthy and infected plant material. The portable C 320 was 100% successful in discriminating known volatiles but had a low capability in differentiating healthy and infected plant substrates. The advanced devices (PEN 3 / MSEM 160) successfully detected healthy and diseased samples with a high variance. The results suggest that E-nose devices are more sensitive and accurate in detecting changes of VOCs between healthy and infected plants compared to headspace GC-MS.

This research explores the behaviors of Generation Z consumers in China, particularly focusing on their interaction with companies through social media for the joint creation of value. Adopting a qualitative methodology, the research initially constructs a scale for assessing co-creation of value on social media platforms. Subsequently, it delves into the motivational aspects of users' engagement in these co-creative activities. The study unveils a range of utilitarian and hedonic motivations that drive Generation Z's value co-creation actions on social media. Insights derived from the findings offer both theoretical and practical implications for the field.

The rapid expansion of China's A-share market, coupled with the burgeoning volume of financial research reports, presents a unique challenge to traditional stock price prediction methodologies. This study introduces FAST-SCAN, a novel framework designed to leverage natural language processing (NLP) and time series analysis for the efficient and accurate prediction of stock market trends. By integrating advanced sentiment analysis, primarily through the utilization of the RoBERTa model, with dynamic time series forecasting, FAST-SCAN aims to distill actionable insights from the vast corpus of financial research reports. This approach not only enhances the speed and efficiency of financial analysis but also addresses the time-sensitive nature of market-influencing factors, thereby providing a competitive edge in stock price prediction. The framework demonstrates a notable improvement in predictive performance, achieving a 20% annualized return and a 7.19% RankIC, marking a significant advancement over conventional statistical analysis methods. Through its innovative combination of NLP and predictive analytics, FAST-SCAN paves the way for a more informed and strategic approach to investment in the A-share market, emphasizing the importance of timely, data-driven decision-making in the financial industry.

This article aimed to evaluate, using statistical tools, the generation and composition of urban solid waste (MSW) in the city of Belém, Pará, from nine urban collection sectors in the municipality, and the average nominal income of the population. Approximately 900kg of urban solid waste were col-lected from the municipality in the period from 2021 to 2022. Statistical tests were carried out on hypotheses with 5% significance in comparative evaluations, and their respective average nominal incomes. The results indicated that the organic fraction corresponds to 55.57% of the waste generated in the municipality, 14.26% is inert waste (potentially contaminating) and 0.67% is miscellaneous waste. It was observed that the waste generated by families with high purchasing power tends to contain higher percentages of recyclables, while in families with lower purchasing power the highest percentages tend to be organic. Knowledge of the generation of MSW is fundamental for the choice and dimensioning of operations and processes involved in the management chain at the municipal level. The decentralization of services offered based on solid waste management would enable greater success in serving the population.

Breast cancer, when advancing to a metastatic stage, involves the liver, impacting over 50% of cases and significantly diminishing survival rates. Presently, a lack of tailored therapeutic protocols for Breast Cancer Liver Metastasis (BCLM) underscores the need for a deeper understanding of molecular patterns governing this complication. Therefore, by analyzing differentially expressed genes (DEGs) between primary breast tumors and BCLM lesions, it aimed to shed light on the diversities of this process. This research investigates Breast cancer liver metastasis relapse by employing a comprehensive approach that integrates data filtering, Gene Ontology and KEGG Pathway Analysis, Overall Survival analysis, identification of the Alteration in the DEGs, Visualization of Protein-Protein Interaction Network, Signor 2.0, identification of positively correlated genes, screening results of the function of hub genes, immune cell infiltration analysis, genetic alternation analysis, Copy number variant analysis, Gene to mRNA interaction, transcription factor analysis , autodocking and identification of Potential Treatment Target. This study’s integrative approach unveils metabolic reprogramming, suggesting altered PCK1 and LPL expression as key in breast cancer metastasis recurrence.

This article presents a description of a new method for solving the factorization problem, based on the gradient descent algorithm. This approach demonstrates significant improvements in efficiency compared to traditional methods proposed in previous research. This article describes a new method for solving the factorization problem, based on the gradient descent algorithm, and demonstrates the transition from algebraic methods to approaches based on functional analysis. The proposed approach not only improves the efficiency of solving the problem but also allows the application of computational algorithms of functional analysis, opening new possibilities for research and optimization.

Purpose of publication: - Preparation of bibliometric data exported from the Scilit platform on energy efficiency and conservation for further analysis to identify relevant research topics. - To identify potential issues in the processing of data exported from the Scilit platform. - Providing colleagues with the opportunity to use the prepared data and examples of their analysis for independent research on topical issues of energy efficiency and energy conservation using materials provided by the Scilit platform. Research Materials: Files in CSV and RIS formats exported from Scilit for the query "energy conservation OR efficiency" in Common Fields [Title, Abstract, Keyword], using filters: Content Type → Journal Article; Year→2021-2023; Subject → Industrial Engineering (29.8K), Energy and Fuel Technology (9.8K), Manufacturing Engineering (9.2K). A total of 30K records sorted by their relevance (10K for each year) were exported. Data are current as of 14-03-2024. Methods: Preprocessing of title, annotation, and keyword field texts using lemmatization dictionaries collected on GitHub, removal of keywords taken from GATE and spaCy, and "manual" editing. Using VOSviewer to analyze publication topics by clustering keywords based on their co-occurrence. Using Scimago Graphica to build bubble diagrams.Application of the GSDMM algorithm for clustering bibliometric records by title and annotation texts. Creation of a dictionary for this algorithm using the keyword field.Use of the Carrot2 demo version and the NMF algorithm for a more detailed analysis of the topics of the record clusters obtained from GSDMM. Results: are presented in the form of initial and interim tables and graphs obtained in the course of this study. The full tables are provided as references to the attached materials. Supplementary material for this preprint on figshare: Chigarev, Boris (2024). Supplementary material for preprint "Analyzing the Possibilities of Using the Scilit Platform to Identify Current Energy Efficiency and Conservation Issues". figshare. Dataset. https://doi.org/10.6084/m9.figshare.25574058.v1

Exoskeletons designed to assist patients with activities of daily living are becoming increasingly popular, but still are subject to research. In order to gather requirements for the design of such systems, long-term gait observation of the patients over the course of multiple days in an environment of daily living are required. In this paper a wearable all-in-one data acquisition system for collecting and storing biomechanical data in everyday life is proposed. The system is designed to be cost efficient and easy to use, using off-the-shelf components and a cloud server system for centralized data storage. The measurement accuracy of the system was verified, by measuring the angle of the human knee joint at walking speeds between 3 and 12 km/h in reference to an optical motion analysis system. The acquired data was uploaded to a cloud database via a smartphone application. Verification results showed that the proposed toolchain works as desired. The system reached an RMSE from 2.9° to 8°, which is below most of the comparable system. The system provides a powerful, scalable platform for collecting and processing biomechanical data, which can help to automize the generation of an extensive database for human kinematics.

This study delves into Personal Narratives (PN), which encompass both oral and written recounting of individual experiences, encompassing facts, events, people, and thoughts. Traditional emotion recognition and sentiment analysis primarily focus on broader categories like utterances or documents. Our research, however, centers on identifying Emotion Carriers (EC), which are specific segments within speech or text that elucidate the narrator's emotional state (e.g.,"losing a parent", "decision-making moments"). Extracting these ECs enriches the representation of a user's emotional state, thereby enhancing natural language understanding and the sophistication of dialogue models. While previous studies have utilized lexical attributes to identify ECs, we argue that incorporating spoken narratives offers a more nuanced view of the context and emotional state. This paper explores the integration of speech and textual embeddings at the word level, alongside both early and late fusion techniques, for improved EC detection in spoken narratives. We employ Residual Neural Networks (ResNet), initially pre-trained on diverse speech emotion datasets and subsequently fine-tuned for EC detection. Our experimental findings demonstrate that late fusion, in particular, significantly enhances EC detection capabilities.

The importance of conflict analysis and safety considerations during work zone intervals at signalized intersections cannot be overstated, as these periods pose unique challenges and safety risks for both motorists and pedestrians. Work zones introduce temporary changes to traffic patterns, lane configurations, and signal timings, which can disrupt the flow of traffic and increase the likelihood of conflicts between vehicles and pedestrians. By conducting comprehensive conflict analysis, it is possible to identify potential conflict points, such as merging areas, pedestrian crossings, and lane transitions, and implement targeted safety measures to mitigate risks. Additionally, considering safety considerations during work zone intervals is paramount to safeguarding the well-being of road users and minimizing the occurrence of conflicts or collisions. Measures such as enhanced signage, temporary traffic control devices, and increased enforcement efforts can help promote compliance with traffic regulations and ensure the safe passage of vehicles and pedestrians through work zones. This study investigates the dynamics of traffic behavior and safety implications within work zones at signalized intersections, aiming to address key challenges and enhance intersection management strategies. Through comprehensive analysis, varying frequencies of Vehicle-to-Vehicle (V2V) conflicts were observed across different movement directions, with significant occurrences identified in specific phases of the traffic signal cycle. Specifically, 69 V2V conflicts in phase #1, 181 in phase #2, 31 in phase #3, 207 in phase #4, 1 in phase #5, 274 in phase #6, 93 in phase #7, and 186 in phase #8 were recorded. Furthermore, the examination of Vehicle-to-Pedestrian (V2P) conflicts underscored the importance of prioritizing pedestrian safety, with notable occurrences observed in phases associated with pedestrian crossing movements. Meanwhile, despite the closure of left-turn movements, instances of red light running persisted, with 69 red light running events in phase #1, 181 in phase #2, 31 in phase #3, 207 in phase #4, 1 in phase #5, 274 in phase #6, 93 in phase #7, and 186 in phase #8.To mitigate these safety concerns during work zone intervals, proactive measures are essential. Strategies include improving communication through clear signage and advanced warning systems, enhancing enforcement efforts with red light cameras and increased police presence, and investing in infrastructure improvements such as temporary pedestrian facilities. Integration of real-time data from sensors, particularly LiDAR technology, offers significant advantages in detecting conflicts and red light runners during work zone intervals. LiDAR's precise, real-time data on vehicle and pedestrian movements, combined with its 360-degree coverage and high spatial resolution, facilitates accurate detection and tracking of potential conflict scenarios, contributing to enhanced intersection safety and traffic management efficiency. This study endeavored to conduct a traffic safety analysis during work zone intervals at a signalized intersection in Baltimore City, known for its high rate of traffic crashes, utilizing LiDAR sensor technology.

Understanding gene expression changes in chicks after vaccination against Newcastle Disease (ND) can reveal vaccine biomarkers. There is limited data on chicks’ early immune response after ND vaccination. Two trials aimed into this knowledge gap. In experiment one, forty-two 13 days-old specific pathogen free (SPF) chicks were used. Harderian glands (Hg) and tracheas (Tc) from five birds per group were sampled at 12, 24, and 48 hours post-vaccination (hpv) to evaluate the gene transcription levels by RNA sequencing (RNA-seq) and RT-qPCR. Results of RNA-seq were compared by glmFTest, while results of RT-qPCR were compared by t-test. With RNA-seq, significant up-regulation of interferon related genes along with JAK-STAT signaling pathways regulation was observed in the Hg at 24- hpv. None of the DEGs identified by RNA-seq were positive for RT-qPCR. Experiment 2 used one-hundred and twelve SPF and commercial chickens at 1 day-old and 14 days-old. Only the commercial birds had maternal antibodies for NDV. By RNA-seq, twenty core DEGs associated with innate immunity and viral genome replication inhibition were identified. Genes previously unlinked to NDV response, such as USP41, were identified. This research present genes with potential as immunity biomarkers for vaccines, yet further investigation is needed to correlate the core gene expression with viral shedding post-vaccination.

The Ebola virus disease (EVD) is an extremely contagious and fatal illness caused by the Ebola virus. Recently, Uganda witnessed an outbreak of EVD, which generated a lot of attention on various social media platforms. To ensure effective communication and implementation of targeted health interventions, it is crucial for stakeholders to comprehend the sentiments expressed in the posts and discussions on these online platforms. In this study, we used deep learning techniques to analyze the sentiments expressed in Ebola-related tweets during the outbreak. We explored the application of three deep learning techniques to classify the sentiments in 8,395 tweets as positive, neutral, or negative. The techniques examined included a 6-layer convolutional neural network (CNN), a 6-layer long-short-term memory model (LSTM), and an 8-layer Bidirectional Encoder Representations from Transformers (BERT) model. The study found that the BERT model outperformed both the CNN and LSTM-based models across all the evaluation metrics, achieving a remarkable classification accuracy of 95\%. These findings confirm the reported effectiveness of transformer-based architectures in tasks related to natural language processing, such as sentiment analysis.

This paper presents a novel approach to sentiment analysis specifically customized for predicting stock market movements, bypassing the need for external dictionaries which are often unavailable for many languages. Our methodology directly analyzes textual data, with a particular focus on context-specific sentiment words within neural network models. This specificity ensures our sentiment analysis is both relevant and accurate in identifying trends in the stock market. We employ sophisticated mathematical modeling techniques to enhance both the precision and interpretability of our models. Through meticulous data handling and advanced machine learning methods, we leverage large datasets from Twitter and financial markets to examine the impact of social media sentiment on financial trends. We achieved an accuracy exceeding 75%, highlighting the effectiveness of our modeling approach, which we further refined into a convolutional neural network model. This achievement contributes valuable insights into sentiment analysis within the financial domain, thereby improving the overall clarity of forecasting in this field.

The digital era has significantly amplified the volume of online reviews, presenting both opportunities and challenges in harnessing insights from consumer feedback. Aspect-Based Sentiment Analysis (ABSA) has emerged as a crucial tool for distilling sentiments from these reviews, providing valuable data for enhancing product and service quality. This study introduces an advanced hybrid model, AdvSentiNet, which integrates adversarial training into the state-of-the-art framework to elevate the precision of sentiment detection at the aspect level. By employing an adversarial network, where a generative model competes against a classifier by crafting highly realistic synthetic samples, we aim to bolster the model's resilience against varied data samples. This innovative approach, unexplored in its entirety within the realm of ABSA, demonstrated remarkable performance improvements on benchmark datasets. For instance, accuracy on the SemEval 2015 dataset escalated from 81.7% to 82.5%, and for the SemEval 2016 dataset, it surged from 84.4% to 87.3%.

Recent advancements in machine learning have ushered in innovative techniques for augmenting datasets, particularly through contrastive learning in the computer vision domain. This study pioneers the application of contrastive learning for sentiment analysis, introducing a novel approach termed EmoConLearn. By fine-tuning contrastive learning embeddings, EmoConLearn significantly surpasses BERT-based embeddings in sentiment analysis accuracy, as evidenced by our evaluations on the DynaSent dataset. This research further delves into the efficacy of EmoConLearn across various domain-specific datasets, highlighting its versatility. Additionally, we investigate upsampling strategies to mitigate class imbalance, further enhancing EmoConLearn's performance in sentiment analysis benchmarks.

Space-based gravitational wave detection is extremely sensitive to disturbances. The Keplerian configuration cannot accurately reflect the variations in spacecraft configuration. Planetary gravitational disturbances are one of the main sources. Numerical simulation is an effective method to investigate the impact of perturbation on spacecraft orbits. This study shows that, in the context of the Taiji project, Earth’s gravity is an essential factor in the change in heliocentric formation configuration, contributing to the relative acceleration between spacecrafts in the order of O(10−6)m·s−2. Considering 00:00:00 on October 27, 2032 as the initial orbiting moment, under the influence of Earth’s gravitational perturbation, the maximum relative change in armlengths and variation rates of armlengths for Taiji is 1.6×105km, 32m·s−1 respectively, compared with the unperturbed Keplerian orbit. Additionally, by considering the gravitational perturbations of Venus and Jupiter, the armlength and relative velocity for Taiji are reduced by 16.01% and 17.45%, respectively, compared with when only considering that of Earth. The maximum amplitude of the formation motion indicator changes with the orbit entry time. Results show that the relative velocity increase between the spacecrafts is minimal when the initial orbital moment occurs in July. Moreover, the numerical simulation results are inconsistent when using different ephemerises. The differences between ephemeris DE440 and DE430 are smaller than those between DE440 and DE421.

Hybrid Renewable Energy Systems (HRES) integrate renewable sources, storage, and optionally conventional energies, offering an eco-friendly solution to fossil fuels. Microgrids (MGs) bolster HRES integration, enhancing energy management, resilience, and reliability at various levels. This study, emphasizing the need for refined optimization methods, investigates three themes: renewable energy, microgrid, and multiobjective optimization (MOO), through a bibliometric analysis of 470 Scopus documents from 2010-2023, analyzed with SciMAT software. It segments the research into two periods, 2010-2019 and 2020-2023, revealing a surge in MOO focus, especially in the latter period, with a 35% increase in MOO-related research. This indicates a shift towards com-prehensive energy ecosystem management that balances environmental, technical, and economic elements. The initial focus on MOO, genetic algorithms, and energy management systems has expanded to include smart grids and electric power systems, with MOO remaining a primary theme in the second period. The increased application of Artificial Intelligence (AI) in optimizing HMGS within the MOO framework signals a move towards more sustainable, intelligent energy solutions. Despite progress, challenges remain, including high battery costs, the need for reliable MOO data, the intermittency of renewable energy sources, and HMGS network scalability issues, highlighting directions for future research.

The objective of this study is to determine the relationship between body condition score (BCS) and body carcass fat (BCF) of ewes at two key stages of the production cycle. Data were collected from 88 Coopworth ewes including BCS measurements manually evaluated by the farm manager, BCF measurements determined by computerized tomography (CT) and body parameter measurements automatically determined by an image processing application. Artificial neural network (ANN) statistical analysis method was used to develop a relationship between the body parameters and the BCF with an r2=0.96 and RMSE=1.01 using all top and side variables.

Despite the extensive literature on missing data theory and cautionary articles emphasizing the importance of realistic analysis for healthcare data, a critical gap persists in incorporating domain knowledge into missing data problem formulation, assumption specification, and method development. In this paper, we highlight the gap particularly for observational data from healthcare facilities. We address this gap by identifying ten fundamental missingness scenarios arising during data measurement, recording, and pre-processing in observational health data, influenced by physicians, patients, healthcare facilities, and data scientists. We analyze the effect of scenarios on estimand formulation, missing data identification, estimation, and sensitivity analysis. To emphasize how domain-informed analysis can improve method reliability, we conduct simulation studies under the influence of various missingness scenarios. We compare the results of three common methods in medical data analysis (complete-case analysis, Missforest imputation, and inverse probability weighting estimation) for two estimands (variable mean estimation and classification accuracy). We advocate for our analysis approach as a reference for the analysis of observational health data. Furthermore, we posit that the proposed analysis framework is applicable to other medical domains, including medical wearable data analysis.

Infant mortality remains a major global concern. Sudden unexpected death in infancy (SUDI) is reported globally and in the Western Cape of South Africa it accounted for 40% of all infant deaths between 2012 and 2016. Research highlighting molecular typing of respiratory viruses in SUDI cases is lacking. A total of 116 PCR-positive human rhinovirus (HRV) and respiratory syncytial virus (RSV) swab samples collected from the lungs and trachea of SUDI cases admitted to Tygerberg Medico-legal Mortuary in Cape Town between 2015 and 2019 were included and underwent sequencing and phylogenetic analysis. Three distinct HRV species were identified; HRV-A (n=28), HRV-C (n=11), and HRV-B (n=4), including eight HRV-A, seven HRV-C and one HRV-B genotypes. For RSV, both RSV-A (n=5) and -B (n=5) were detected, but no RSV-A. After amino acid alignment indicating 20 amino acid duplication and nine substitutions, two BA9 genotypes were confirmed. This study describes the first molecular and phylogenetic characterisation of respiratory viruses in SUDI cases in South Africa. The rapid decline in viral viability in post-mortem samples and the retrospective study design employed for the current study did not allow for any correlations between viral genotypes and cause of death and future prospective studies should investigate temporality and specific viral strains associated with clinical disease severity.

Mutual exclusion algorithms are at the heart of concurrent, distributed/parallel, real-time and operating systems. It is well-known that such algorithms are very difficult to analyze and in the literature different conjectures about starvation-freedom and the number of by-passes (also said the overtaking factor), which in turn affects the (hopefully) bounded waiting time that a process competing for entering the critical section has to suffer before accessing the shared resource, have been formulated for specific algorithms. This paper proposes a novel modelling approach based on Timed Automata and the Uppaal toolset, which proves effective for studying all the properties of a mutual exclusion algorithm for N≥2 processes, by exhaustive model checking. Although the approach, as already confirmed by similar experiments reported in the literature, is not scalable due to state explosion problems, and can be practically applied until N≤5, it is of great value for revealing true properties of analyzed algorithms. For dimensions N>5 the statistical model checker of Uppaal can be used which, although based on simulations, can confirm properties by estimations and probabilities. The paper describes the proposed modelling and verification method and applies it to several mutual exclusion algorithms, thus retrieving known properties but also showing new results about properties often studied only by peer-review and intuitive reasoning.

The incessant evolution of online platforms has ushered in a multitude of shopping modalities. Within the food industry, however, assessing the delectability of meals can only be tentatively determined based on consumer feedback encompassing aspects such as taste, pricing, packaging, service quality, delivery timeliness, hygiene standards, and environmental considerations. Traditional text data mining techniques primarily focus on consumers' emotional traits, disregarding pertinent information pertaining to the online products themselves. In light of these aforementioned issues in current research methodologies, this paper introduces the Bert BiGRU Softmax model combined with multimodal features to enhance the efficacy of sentiment classification in data analysis. Comparative experiments conducted using existing data demonstrate that the accuracy rate of the model employed in this study reaches 90.9%. In comparison to single models or combinations of three models with the highest accuracy rate of 7.7%, the proposed model exhibits superior accuracy and proves highly applicable to online reviews.

To get more data concerning the influence of fertilizers (organic and mineral) on different forage plants existing in North-eastern Romanian grassland, the mass fractions of 14 essential, enzymatic, or considerd toxic elements was determined by Instrumental Neutron Activation Analysis together with the amount of crude proteins, ash, fibres as well as ether fact extract. Final results showed a significant variance of the content of analysed elements on organic as well as on mineral fertilized experimental plots, i.e. the content increased in 24% of plots, decreased in 23%, and remained unchanged in 45% while in 8% of plots their content was below the detection limits, as the case of Se. At the same time, an increase content of crude protein and ether extract was evidenced in fertilized grasses for all applied fertilizers while other global indicators such as neutral and acid fibre of sufuric lignin content decreased, suggesting significant higher nutritional values of fertilized forage plant.

The Drinking Water Directive (EU) 2020/2184 includes the parameter microcystin-LR, a cyanotoxin, which drinking water producers need to analyze if the water source has potential for cyanobacterial blooms. In light of the increasing occurrences of cyanobacterial blooms worldwide and given that more than 50 percent of the drinking water in Sweden is produced from surface water, both fresh and brackish, the need for improved knowledge about cyanotoxin occurrence and cyanobacterial diversity has increased. In this study a total of 98 cyanobacterial blooms were sampled in 2016-2017 and identified regarding their toxin production and taxonomical compositions. The surface water samples from freshwater lakes throughout Sweden including brackish water from 8 east coast locations along the Baltic Sea were analyzed for their toxin content with LC-MS/MS and taxonomic composition with 16S rRNA amplicon sequencing. Both the extracellular and the total toxin content was analyzed. Microcystin´s prevalence was highest with presence in 82% of blooms, of which as free toxins in 39% of blooms. Saxitoxins were found in 36% of blooms in which the congener decarbamoylsaxitoxin (dcSTX) was detected for the first time in Swedish surface waters at four sampling sites. Anatoxins were most rarely detected, followed by cylindrospermopsin, which were found in 6% and 10% of samples, respectively. As expected, nodularin was detected in samples collected from the Baltic Sea only. The cyanobacterial operational taxonomic unit (OTU) with the highest abundance and prevalence could be annotated to Aphanizomenon NIES-81 and the second most profuse cyanobacterial taxon to Microcystis PCC-7914. In addition, two correlations were found, one between Aphanizomenon NIES-81 and saxitoxins and another between Microcystis PCC-7914 and microcystins. This study is of value to drinking water management and scientists involved in recognizing and controlling toxic cyanobacteria blooms.

In this work, a novel approach is presented for the multivariate prediction of hydrological variables, employing a specialized type of recurrent neural network (RNN) known as the long-term memory network (LSTM).The central objective is to develop a flood prediction model using data collected from two hydrological stations situated along the Atrato River in the Choc´o Department, Colombia. The interconnected variables of water level, water flow, and water precipitation are analyzed in this model to enhance flood prediction accuracy. The LSTM model, integrated into an RNN structure, is designed to capture the complex dynamics and cross-correlations inherent in these hydrological variables. Validation involves a comparison with linear and nonlinear Nonlinear AutoRegressive with eXogenous input (NARX) models, considering factors such as estimation error and computational time. Additionally, the study conducts further analysis for water level prediction under two scenarios: utilizing only outflow and inflow measurements or predicting outflow alongside measuring inflow, with the objective of utilizing the proposed approach as a flood early warning system. † This article is a revised and expanded version of a paper entitled Multivariable NARX based Neural Networks Models for Short-term Water Level Forecasting, which was presented at International Conference on Time Series and Forecasting (ITISE-2023), Gran Canaria, Spain, 12th-14th July, 2023.

This study investigates the mean annual water temperature trends at the confluence of the Sava and Danube Rivers, along with air temperature trends at the Belgrade meteorological station, spanning from 1956 to 2020. Results reveal a consistent increase in temperature across all three measuring stations, with the Danube experiencing a rise of 0.34°C/10 years, the Sava at 0.44°C/10 years, and Belgrade's air temperature increasing by 0.39°C/10 years. Employing the RAPS method, sharp rises in water temperature were pinpointed in 1989 for the Sava and 1990 for the Danube, while Belgrade's air temperature surge began in 1998. The highest intensity of air temperature increase within the recent period (1998-2020) was observed at the Belgrade observatory, reaching 0.76°C/10 years. Notably, the Sava exhibited a faster increase in water temperature over the last thirty years compared to the Danube. August marked the peak average water temperature for both rivers, while July recorded the highest average air temperature in Belgrade. Despite differing flow rates, both rivers exhibit similar hydrological regimes, with maximum flows occurring in April and minimum flows in August for the Sava, and October for the Danube. Seasonal temperature increases were most pronounced in summer, notably in August, with the smallest rises occurring during cold periods. Additionally, an inverse proportional relationship between mean annual water temperatures and discharges was observed at both river stations.

Glycosidic linkage analysis was conducted on the unfractionated polysaccharides in the alcohol-insoluble residues (AIRs) prepared from six red seaweeds (Gracilariopsis sp., Prionitis sp., Mastocarpus papillatus, Callophyllis sp., Mazzaella splendens, and Palmaria palmata) using GC-MS/FID analysis of partially methylated alditol acetates (PMAAs). The cell walls of P. palmata primarily contained mixed-linkage xylans and small amounts of sulfated galactans and cellulose. In contrast, the unfractionated polysaccharides of the other five species were rich in galactans displaying diverse 3,6-anhydro-galactose and galactose linkages with varied sulfation patterns. Different levels of cellulose were also observed. This glycosidic linkage method offers advantages for cellulose analysis over traditional monosaccharide analysis that is known for underrepresenting glucose in crystalline cellulose. Relative linkage compositions calculated from GC-MS and GC-FID measurements showed that anhydro sugar linkages generated more response in the latter detection method. This improved linkage workflow presents a useful tool for studying polysaccharide structural variations across red seaweed species. Furthermore, for the first time, relative linkage compositions from GC-MS and GC-FID measurements, along with normalized FID and total ion current (TIC) chromatograms without peak assignments, were analyzed using principal component analysis (PCA) as a proof-of-concept demonstration of the technique's potential to differentiate various red seaweed species.

Robotic systems have been widely used in various industries, so the security of communication between robots and their components has become an issue that needs to be focused on. As a framework for developing robotic systems, the security of ROS2 can directly affect the security of the upper-level robotic systems. Therefore, it is a worthwhile research topic to detect and analyze the security of ROS2. In this paper, we adopt a formal approach to analyze the security of the communication mechanism of ROS2. First, we use a state transition system to model the potential vulnerabilities of ROS2 based on the ROS2 communication mechanism and the basic process of penetration testing. Secondly, we introduce the CIA model on the basis of the established vulnerability model and use LTL to define its security properties. Then, we design and implement a vulnerability detection tool for ROS2 applications based on the vulnerability model and security properties. Finally, we experimentally test some ROS2-based applications, and the results show that ROS2 has vulnerabilities without additional protection safeguards.

In order to study the key factors that induce green travel among urban residents under the carbon inclusive mechanism, analyze the formation mechanism of green travel intention and reduce urban transportation carbon emissions. First, an SEM model of urban residents’ green travel under the carbon inclusive mechanism was constructed (the 8 latent variables including Subjective Norms (SN), Attitude (ATT), Perceived Behavioral Control (PBC), Habits (HAB), Perceived Usefulness (PU), Perceived Ease of Use (PEU), Continuous Intention of Carbon Inclusive Platform application (CI), Behavioral Intention (BI)). Then, based on the data analysis and path analysis results of 354 valid sample data obtained through online surveys, the interrelationships and corresponding parameters between latent variables and manifest variables were obtained. The results show that SN, ATT, PBC, HAB, PU, PEU and CI all have varying degrees of effects on BI. Among them, in the direct influence relationship, the influence path coefficients of CI, PBC and ATT on BI are 0.529, 0.299 and 0.146 respectively; The influence coefficient of HAB on BI is 0.152, but P>0.05, it fails the test, so the influence of HAB on BI is Not significant; in the indirect impact relationship, the coefficient of HAB indirectly affecting BI through ATT is 0.102, and the coefficient of PU indirectly affecting BI through CI and SN is 0.450. It can be seen that under the carbon inclusive mechanism, useful methods such as increasing the value of carbon points and the breadth of application, improving perceived behavioral control, increasing publicity and social awareness, and cultivating green travel attitudes can promote the willingness of urban residents’ green travel behavioral intention.

Hypoxia is one of the common abiotic stresses that negatively affects the development and productivity of agricultural crops. Quercetin is used to protect plants from oxidative stress when exposed to environmental stressors. O2 deficiency leads to impaired development and morphometric parameters in wheat varieties Orenburgskaya 22 (Triticum aestivum) and varieties Zolotaya (Triticum durum). Cytological analysis revealed various types of changes in the cytoplasm under conditions of hypoxia and treatment with quercetin. The most critical changes in the cytoplasm occur in the Zolotaya variety during pretreatment with quercetin followed by hypoxia, and in the Orenburgskaya 22 variety during hypoxia. Quercetin has a protective effect only on the Orenburgskaya 22 variety, and also promotes more effective recovery after exposure to low O2 content. Hypoxia causes an increase in ROS and activates the antioxidant system. It has been shown that the most active components of the antioxidant system in the Orenburgskaya 22 variety are MnSOD and Cu/ZnSOD, and in the Zolotaya variety GSH. We have shown that quercetin provides resistance only to the wheat genotype Orenburgskaya 22, as a protective agent against abiotic stress, which indicates the need for a comprehensive study of the effects of exogenous protectors before use in agriculture.

The study of rare diseases is important not only for the individuals affected but also for the advancement of medical knowledge and a deeper understanding of human biology and genetics. The wide repertoire of structural information now available from reliable and accurate prediction methods provides the opportunity to investigate the molecular origins of most of the rare diseases reviewed in the Orpha.net database. Thus, it has been possible to analyze the topology of the missense mutations found in the 2,535 proteins involved in Mendelian rare diseases (MRD), which form the database for our structural bioinformatics study. The amino acid substitutions responsible for MRD show different mutation site distributions at different three-dimensional protein depths. We then predicted the depth-dependent effects of mutations for the 20,248 pathogenic variants that are present in our database. The results of this structural bioinformatics investigation are relevant, as they provide additional clues to mitigate the damage caused by MRD.

Permafrost covers about 23% ~ 25% of the land in the northern hemisphere, mainly in Russia, Canada, Alaska, Northeast and Qinghai Tibet Plateau (QTP) in China. Many engineering especially the transportation has been in the permafrost regions above. Under the action of highways construction and global warming, permafrost under infrastructures is degraded and causes serious damage. Two phases closed thermosyphon (TPCT) is a widely accepted green countermeasure against the problem in permafrost regions. Although it has been applied to prevent permafrost degradation, their application presents significant challenges on account of the stronger endothermic action of asphalt pavement. This paper focused on the thermosyphon technology and application in the permafrost. Moreover, the article highlighted the excellent working performance of the TPCT that improving the stability of the infrastructures and prevent it degrading due its excellent efficiency of heat transfer. The industrial applications of the TPCT were also summarized, along with their limitations. Finally, the results reported in this paper can provide a valuable guidance for the design of the TPCT and construction of the permafrost regions in the future.

The paper examines the various uncertainties encountered in high-frequency trading (HFT) environments and delves into the multiple challenges faced by HFT firms in navigating the Dodd-Frank Wall Street Reform and Consumer Protection Act (referred to as the "Dodd-Frank Act"), particularly during the initial stages of its enactment. These challenges include the ambiguity surrounding the definition of HFT, the lack of clarity regarding regulatory requirements and boundaries, inconsistencies in enforcement resulting from deviations in understanding the content, and the absence of detailed descriptions of the Act’s provisions. These hurdles significantly impact not only the daily operations of HFT firms but also pose higher demands on their long-term strategic planning and risk management. Drawing upon the Data, Information, Knowledge, Wisdom, and Purpose (DIKWP) model, this study employs an innovative analytical framework. Through the comprehensive application of conceptual space, cognitive space, and semantic space, it provides a systematic methodology for identifying and analyzing the aforementioned issues. This approach not only aids firms in better comprehending and adhering to complex regulatory requirements but also enables them to explore new business opportunities and competitive advantages while ensuring compliance.

The exploration and understanding of sentiments and emotions through textual analysis are foundational to advancements in natural language processing (NLP). While sentiment analysis, which involves discerning the polarity of text, has been extensively explored, the nuanced detection of complex emotions within textual content is still an emerging area of study. In this paper, we introduce EmoLeverage, a model leveraging Transformer architecture and enhanced with a novel Fusion Adapter module. This model aims to deepen emotion detection capabilities by drawing insights from fundamental sentiment analysis tasks, focusing exclusively on textual data. EmoLeverage sets a new standard in the field by demonstrating that deep, nuanced understanding of emotions is achievable through text alone, challenging and surpassing existing methodologies in emotion identification on key datasets. This is a significant step forward, considering the complexity of human emotions and the subtleties involved in their expression through language. By concentrating on textual input, EmoLeverage offers a promising avenue for applications where audio or visual data is not available, highlighting the versatility and potential of text-based emotional analysis. Our approach illustrates the untapped potential of leveraging sophisticated NLP techniques to enhance the detection and comprehension of a wide array of emotions from text. The success of EmoLeverage in outperforming conventional models in emotion recognition tasks underscores the model's effectiveness in parsing and understanding the intricate expressions of human emotions through linguistic cues alone. This achievement paves the way for more accurate, efficient, and nuanced emotion detection tools, potentially transforming various sectors by providing deeper insights into human emotional states through text.

Background: Mental capacity is a fundamental aspect that enables patients to fully par-ticipate in various healthcare procedures. To assist healthcare professionals (HCPs) in assessing patients' capacity, especially in the mental health field, several standardized tools have been developed. These tools include the MacArthur Competence Assessment Tool for Treatment (MacCAT-T), the MacArthur Competence Assessment Tool for Clinical Research (MacCAT-CR), and the Competence Assessment Tool for Psychiatric Advance Directives (CAT-PAD). The core dimensions explored by these tools include Understanding, Appreciation, Reasoning, and Expression of a choice. Objective: This meta-analysis aimed to investigate potential differences in deci-sion-making capacity within the healthcare context among groups of patients with bi-polar disorder (BD) and schizophrenia spectrum disorder (SSD). Methods: A systematic search was conducted on Medline/Pubmed, and Scopus. Addi-tionally, Google Scholar was manually inspected, and a manual search of emerging re-views and reference lists of the retrieved papers was performed. Eligible studies were specifically cross-sectional, utilizing standardized assessment tools, and involving pa-tients diagnosed with BD and SSD. Data from the studies were independently extracted and pooled using random-effect models. Hedges g was used as a measure for outcomes. Results: Six studies were identified, with three studies using the MacCAT-CR, two studies the MacCAT-T, and one the CAT-PAD. The participants included 189 individuals with BD and 324 individuals with SSD. The meta-analysis revealed that patients with BD performed slightly better, and the difference was statistically significant, compared to patients with SSD, particularly in the domain of Appreciation (ES=0.23, 95% CI: 0.01 to 0.04, p=0.037). There was no statistically significant difference between the two groups for Understanding (ES=0.09, 95% CI: - 0.10 to 0.27, p=0.352), Reasoning (ES=0.18, 95% CI: - 0.12 to 0.47, p=0.074), and Expression of a choice (ES = 0.23, 95% CI: - 0.01 to 0.48, p=0.60). In the sensitivity analysis, furthermore, when considering only studies involving patients in symptomatic remission, the difference for Appreciation also resulted in non-significant (ES=0.21, 95% CI: -0.04 to 0.46, p=0.102). (ES=0.21, 95% CI: -0.04 to 0.46, p=0.102). Conclusions: These findings indicate that there are no significant differences between patients with BD and SSD during remission phases, while differences are minimal during acute phases. The usefulness of standardized assessment of capacity at any stage of the illness should be considered, both for diagnostic-therapeutic phases and for research and advance directives. Further studies are necessary to understand the reasons for the overlap in capacity between the two diagnostic categories compared in this study.

With the inclusion of Internet Gaming Disorder (IGD) in both the DSM-5 and ICD-11, understanding the prevalence and diagnostic discrepancies is crucial for developing appropriate interventions. This study presents a meta-analysis of the prevalence of IGD based on two diagnostic criteria. We systematically searched PubMed, Web of Science databases. 17 studies were included in the final analysis, The analysis incorporated studies employing the DSM-5 and ICD-11 criteria and focused on the impact of various factors, including study location, sample size, and quality score, on the reported prevalence rates using a random-effects model. Heterogeneity and publication bias were analyzed using an I2 test and Galbraith plots , The prevalence of IGD varied among the selected studies, with a general pooled prevalence of 5% (95% CI: 3%–7%). Subgroup analysis indicated that the prevalence rates of IGD (DSM-5 criteria) and GD (ICD-11 criteria) do not show significant differences. DSM-5-based scales yielded varied results, while heterogeneity among the studies was high (I2 = 98.50). In contrast, ICD-11-based scales produced more consistent and less variable findings. Our findings highlight that assessment scales, not diagnostic criteria, impact IGD prevalence. This underscores the importance of adopting standardized methodologies to guide public health interventions. However, given the limited research based on ICD-11 diagnostic criteria, further investigation is necessary to determine the variations in prevalence rates of IGD under different diagnostic standards.

An analysis of form and size of the fatigue wear of gear wheel top layer of the prototype oil pump (type 3PW-BPF-24) is presented in the publication. The object of research was subject to endurance tests, so called labour resource test. During this test the pump was loaded in cycles with maximal working pressure. Additionally, continuous monitoring tests of exploitational and environmental parameters were conducted. After a completion of the endurance test the noticed superficial defects due to a phenomenon of the top layer fatigue and previously applied thermochemical treatment were measured and described. The work was finished with an analysis of obtained results and conclusions concerning a selection optimization of the thermal treatment parameters. Taking into consideration the presented results (suggestions) in the technology of improving strength prop-erties of gear wheels (of this type oil pumps) may render it possible to obtain an appropriate fa-tigue endurance of the top layer, i.e. a minimization of observed superficial defects.

The attitude dynamics and orbit dynamics of large rigid space structures are coupled with each other under gravity gradient, which may affect the stability of the large rigid space structures. In this paper, the gravity gradient stability of a large rigid space structure is studied under both small and large disturbances. Based on the rigid body dynamics and orbit dynamics, an accurate dynamic model without any linearization of the large rigid space structure is established via the natural coordinate formulation (NCF), which is able to describe the large overall motions of the structures. By using the generalized-α algorithm, the gravity gradient stability of the large rigid space structure is simulated and analyzed via various examples, including the influence of large disturbance angles, the positions at the stabilization and unstabilization regions. Finally, the relationship between spinning stability and gravity gradient stability is also investigated via a large spinning space structure.

The focus of this article is the evaluation of the quality and the degree of community engagement in an expert-based, public-health communicative intervention for Alcohol Use Disorder (AUD). In 2023 the Canadian Alcohol Use Disorder Society collaborated with a grass-roots community health group, a regional health authority, and an academic participatory researcher to organize a working group to mount a social-marketing campaign to increase community support for AUD medical treatment in family-practice settings in a rural community in British Columbia. The partnership working group conducted a series of activities in the school system, the town council, and the community. This article covers the formative evaluation and the summative evaluation of the working group's effectiveness. The formative evaluation ("lessons learned" and "recommendations") consists of consensus evaluations of the quality of each activity using a variety of data sources. The formative evaluation followed improvement science methods and used consensus meetings to provide and reflect on formal and informal feedback. Formative results reflect the consensus of the participants in the working group and they record the reactions in the community. The summative evaluation measures evidence of community engagement found in transcribed text from meeting minutes. The summative evaluation used theory-based frameworks and text data visualization to assess the engagement dynamics of the working group. The summative evaluation helps to put the formative evaluation into perspective as communicative action. The summative evaluation validates the formative evaluation by revealing the communicative dynamics of engagement which resulted in the community’s increased capacity to understand and support Alcohol Use Disorder Treatment options. The working group and the entire intervention was seen by the participants and the public as a success and capable of replication. In a small community, it instilled new understandings and attitudes, bringing an emerging awareness of medication as an optional health mitigation strategy for Alcohol Use Disorder. Our study furthers this overall assessment by using theoretical constructs and analysis of communicative records to trace engagement characteristics of interest to researchers, societies, and community groups contemplating similar interventions in the future. The study also validates the effectiveness of communicative analysis as a measure of social-marketing for expert-based public health interventions.

We conducted a pilot study to analyze the differential methylation status of 20 primary acinar adenocarcinomas of the lungs. These adenocarcinomas had to be wild type in mutation analysis and had either high (TPS>50%; n=10) or low (TPS<1%; n=10) PD-L1 status to be integrated into our study. To examine the methylation of 866,895 specific sites, we utilized the Illumina Infinium EPIC bead chip array. Both hypermethylation and hypomethylation play significant roles in tumor development, progression, and metastasis. They also impact the formation of the tumor microenvironment, which plays a decisive role in tumor differentiation, epigenetics, dissemination, and immune evasion. The gained methylation patterns were correlated with PD-L1 expression. Our analysis has identified distinct methylation patterns in lung adenocarcinomas with high and low PD-L1 expression. After analyzing the correlation between methylation results of genes and promoters with their pathobiology, we found that tumors with high expression of PD-L1 tend to exhibit oncogenic effects through hypermethylation. On the other hand, tumors with low PD-L1 expression show loss of their suppressor functions through hypomethylation. The suppressor functions of hypermethylated genes and promoters are ineffective compared to simultaneously activated dominant oncogenic mechanisms. The tumor microenvironment supports tumor growth in both groups.