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Understanding the intricate interplay between demographic shifts and the profound influence of social networks on the mental health of older adults is crucial amid our rapidly aging global population. As societies evolve, the significance of social connections intensifies, directly impacting the well-being of older individuals. This empirical research, encompassing 50 individuals aged 60 years and above from urban and rural areas in equal proportions, aims to comprehend sociodemographic characteristics, assess social network resilience, evaluate mental health, and investigate how sociodemographic factors shape mental well-being. Utilizing Chi-Square tests, correlation coefficients, independent samples t-tests, and frequency tables, the study reveals nuanced insights into the complex relationship between social network strength and demographic variables among older adults. The research methodology employs a comprehensive set of tools, including a self-prepared interview schedule for socio-demographic details, the Lubben Social Network Scale (LSNS-R), and the Mental Health Inventory (MHI). These instruments, recognized for their reliability and validity, offer a thorough assessment of social networks and mental health. The study's outcomes emphasize the necessity of tailored approaches that consider diverse sociodemographic factors in addressing the mental health needs of older adults. In conclusion, this research contributes significantly to the understanding of how social networks influence the mental health of older adults within the context of demographic shifts, underscoring the imperative of personalized strategies to effectively cater to the diverse mental health needs of this population in view of social work practice. [ABSTRACT FROM AUTHOR]

Background: The vegetable‐based diet alone does not provide the lysine (Lys) needed to maximize poultry productive performance. Objectives: This experiment aimed to evaluate the effects of dietary digestible Lys (dLys) level on productive and reproductive performance, egg quality, blood metabolites and immune responses in breeding Japanese quails (Coturnix japonica). Methods: The experiment was conducted in a completely randomized design with 6 treatments, 5 replicates and 15 (12 females and 3 meals) 10‐week‐old breeding Japanese quails each. A basal diet was formulated to meet nutritional requirements of breeding quails except dLys. The basal diet was supplemented with graded (+0.82 g/kg) levels of l‐Lys‐HCl, corresponding to dietary dLys levels of 0.690%, 0.755%, 0.820%, 0.885%, 0.950% and 1.015%. The experiment lasted for 12 weeks, which was divided into 3‐4‐week periods. Results: Significant differences were observed for egg production (EP), egg mass (EM) and feed efficiency (FE) in response to increasing dietary dLys concentration with quadratic trends. The highest traits were observed in the birds fed with a diet containing 0.885% dLys. However, feed intake, egg quality, reproductive performance, blood metabolites and immune responses against sheep red blood cell inoculation were not significantly affected by increasing dietary dLys concentrations. The dLys requirements during 11–14, 15–18, 19–22 and 11–22 (overall) weeks of age for optimal EP, EM and FE, based on the quadratic broken‐line regression analysis, were estimated 272, 265, 250 and 266; 293, 285, 264 and 279; and 303, 294, 281 and 293 mg/bird/day, respectively. Conclusions: The dLys requirements vary depending on the EP phase and the trait being optimized. The estimated dLys requirement for FE was higher than those for EP and EM. During the peak stage of the first laying cycle, the dietary dLys level of 0.932% and a daily intake of 303 mg dLys/bird are sufficient for optimal performance. [ABSTRACT FROM AUTHOR]

Dengue fever remains a significant epidemiological challenge globally, particularly in Brazil, where recurring outbreaks strain healthcare systems. Traditional statistical models often struggle to accurately capture the complexities of dengue case distributions, especially when data exhibit bimodal patterns. This study introduces a novel bimodal regression model based on the log-generalized odd log-logistic exponential distribution, offering enhanced flexibility and precision for analyzing epidemiological data. By effectively addressing multimodal distributions, the proposed model overcomes the limitations of unimodal models, making it well suited for public health applications. Through regression analysis of dengue case data from the Federal District of Brazil during the epidemiological weeks of 2022, the model demonstrates its capacity to improve the fit of the disease rate. The model's parameters are estimated using maximum likelihood estimation, and Monte Carlo simulations validate their accuracy. Additionally, local influence measures and residual analysis ensure the proposed model's goodness-of-fit. While this innovative regression model offers substantial advantages, its effectiveness depends on the availability of high-quality data, and further validation is necessary to confirm its applicability across diverse diseases and regions with varying epidemiological characteristics. [ABSTRACT FROM AUTHOR]

Eucalyptus is a widely planted species in plantation forests because of its outstanding characteristics, such as fast growth rate and high adaptability. Accurate and rapid prediction of Eucalyptus biomass is important for plantation forest management and the prediction of carbon stock in terrestrial ecosystems. In this study, the performance of predictive biomass regression equations and machine learning algorithms, including multivariate linear stepwise regression (MLSR), support vector machine regression (SVR), and k-nearest neighbor (KNN) for constructing a predictive forest AGB model was analyzed and compared at individual tree and stand scales based on forest parameters extracted by Unmanned Aerial Vehicle–Light Detection and Ranging (UAV LiDAR) and variables screened by variable projection importance analysis to select the best prediction method. The results of the study concluded that the prediction model accuracy of the natural transformed regression equations (R2 = 0.873, RMSE = 0.312 t/ha, RRMSE = 0.0091) outperformed that of the machine learning algorithms at the individual tree scale. Among the machine learning models, the SVR prediction model accuracy was the best (R2 = 0.868, RMSE = 7.932 t/ha, RRMSE = 0.231). In this study, UAV-LiDAR-based data had great potential in predicting the AGB of Eucalyptus trees, and the tree height parameter had the strongest correlation with AGB. In summary, the combination of UAV LiDAR data and machine learning algorithms to construct a predictive forest AGB model has high accuracy and provides a solution for carbon stock assessment and forest ecosystem assessment. [ABSTRACT FROM AUTHOR]

The widespread popularization of Internet technology has facilitated the emergence of e-commerce as a novel avenue for agricultural product sales, driven by its convenience and broad reach. Nevertheless, in Wuchang City, a well-developed agricultural region in northeastern China, some farmers still exhibit low enthusiasm for participating in agricultural product e-commerce, with limited levels of engagement. To investigate the underlying causes, this study analyzes survey data from 301 farmers in Wuchang City and uses mean difference significance tests and Logistic and Tobit regression models to explore the factors influencing farmers' adoption of e-commerce for agricultural products. The results demonstrate that gender and the number of household members involved in agricultural labor significantly influence the adoption decision and the extent of adoption. There is a significant difference in the adoption of decisions among ages. Subjective willingness and policy perception positively and significantly influence the adoption decision. Risk perception significantly and negatively impacts the extent of adoption. Infrastructure exerts a significant and negative influence on the adoption decision but a significant and positive influence on the extent of adoption. Based on these findings, this study suggests localized reforms, enhanced e-commerce promotion, and differentiated training to boost farmers' adoption, promoting sustainable development of the agricultural e-commerce economy. [ABSTRACT FROM AUTHOR]

Stem size has the greatest effect on harvester productivity, and stem sizes vary in a forest stand. How these within-stand variations influence harvester productivity is normally not considered in studies or predictions of productivity. This study suggests reasons as to why the current production and/or application of productivity models are prone to bias from stem size variations in a stand, irrespective of whether models were developed from tree-based or stand-based studies. Moreover, it also provides empirical data on the stand stem size variation's influence on stand-based modeling of harvester productivity. Data from 11 harvesters in 347 final fellings and four harvesters in 80 thinnings were used. The mean productivity was 26.7 and 11.0 m3/PMh5 in final felling and thinning, respectively, and the mean stem size explained most of the observed variation. The productivity in final felling decreased with increased levels of stand stem size variation, as well as with increases in the proportion of broadleaf trees in the stand. For thinnings, productivity increased with increases in the proportion of pine trees in the stand, but there was no significant effect of stand stem size variation or other tested factors. The results show that stand stem size variation is a relevant factor to consider when modeling and predicting harvester productivity. [ABSTRACT FROM AUTHOR]

Solid waste management is a critical issue in India as the country continues to develop. Accurately estimating the types, quantities, and distribution of solid waste is essential for effective waste management. The methods and processes for managing waste in any city, including collection, transportation, treatment, and disposal, rely heavily on accurate estimations of waste quantities. These estimates are in turn influenced by various factors, including socio-cultural, economic, environmental, political, and technological factors. The research aims to identify specific social and spatial factors that influence solid waste generation in municipal cities of the present times through a literature study. It then undertakes the study of a selected area in the city of Cuttack, Orissa, India, as a case study and formulates a model for quantifying solid waste based on the measurements of derived indicators. The research utilizes both primary and secondary data to achieve its objectives. The analysis revealed that factors such as monthly family income, house occupancy, and occupation have a strong positive correlation with the quantity of solid waste. Conversely, factors such as educational qualification and the incentive system provided to citizens exhibit a negative correlation with the amount of solid waste generated. Based on these factors, the model derived will facilitate the accurate estimation of solid waste generated in similar contexts, thereby aiding efficient waste management. By conducting this case study in Cuttack City, we aim to contribute to the existing body of knowledge on solid waste management in India and provide a comprehensive understanding of the factors affecting waste quantity. [ABSTRACT FROM AUTHOR]

Leaf chlorophyll content (LCC) is crucial for monitoring the physiological processes of crops. Many studies have utilized spectral features to develop regression models for accurate LCC estimation, enabling the quantitative assessment and evaluation of crop growth status. The selection of optimal spectral features and regression algorithms significantly affects the precision of LCC estimation. In this study, we compared and analyzed the optimal spectral features for LCC estimation, as well as the consistency of machine learning methods across different crop types, phenology periods, and sensors. First, we extracted various spectral features, including the original spectral features (OS), first-order derivative spectral features (FDS), original continuum-removed spectra (CR) along with their four related derivative spectral features, principal component variables derived from different spectral features, and highly correlated spectral features with LCC. These extracted spectral features were then employed to construct the LCC models using six common regression algorithms on different datasets. Finally, we analyzed the optimal combination of spectral features and regression algorithms for accurate LCC estimation considering various dimensions, such as crop type, phenological period, and sensor used in observation conditions. The results demonstrate that the combinations of the principal component variables of continuum-removed derivative reflectance with the top 10 correlations with LCC (PCA_CRDR_R) combined with Gaussian process regression (GPR) can be considered as the optimal choice for estimating LCC under diverse observation conditions at a canopy scale, and its R2 can reach 0.62 for sugar beet LCC estimation; thus providing valuable theoretical guidance for selecting appropriate spectral features for LCC estimation. [ABSTRACT FROM AUTHOR]

This article is focused mainly on verifying the suitability of data from the experimental milling of heat-treated beech wood and on investigating the effects of the technical and technological parameters of milling on the energy consumption of this process. The independent parameters of the machining process are the cutting speed, feed speed, rake angle, and hydrothermal modification of the experimental wood material. Based on analysis of variance, it can be argued that the cutting speed and rake angle of the tool have the greatest statistically significant effect on energy consumption, while the feed speed has the least influence. The measured data on cutting power during milling were used to build a regression model and validate it, and the most suitable type of model, with a correlation of 87%, is the classification and regression tree, followed by a model created using the random forest method. [ABSTRACT FROM AUTHOR]

The water supply network is an important component of urban lifeline engineering, a scientific and reasonable seismic vulnerability model for the water supply network is one of the key scientific issues for implementing disaster risk investigation and key hidden danger investigation projects. Especially for large-scale water supply network seismic disaster risk assessment, it is necessary to clarify the seismic vulnerability model of the water supply network zoning and classification as support. However, due to the complexity of the development process of the water supply network, many towns are still unable to provide accurate and effective basic data of the network for effective seismic vulnerability assessment. In order to establish the seismic vulnerability model of the urban water supply network without the basic data of the pipe network in southwest China, based on the established seismic vulnerability model of the urban water supply network with the basic data of the pipe network, the polynomial fitting method is used to give the relationship model between the scale and seismic vulnerability category of the urban water supply network with the basic data of the pipe network, the urban population and GDP. Then the pipe network scale and seismic vulnerability category can be estimated according to the urban population and GDP data without the basic data of the pipe network. It has laid the foundation for comprehensively carrying out the seismic disaster risk assessment of the large-scale water supply networks. [ABSTRACT FROM AUTHOR]

Degree-based topological indices are very useful tools to model and characterize the molecular structure of drugs in order to predict their physicochemical properties without going into laborious and time-consuming laboratory experiments. These indices are numerical descriptors derived for the molecular structures using the principles of graph theory. Degree-based topological indices play a vital role in the QSPR analysis of heart attack drugs by providing molecular descriptors to predict their properties. The main goal of this paper is to compute six degree-based topological indices and a regression model for seven heart attack drugs. These drugs are nitroglycerin, clopidogrel, beta-blockers (metoprolol), ACE inhibitors (lisinopril), statins (atorvastatin), (ARBs) losartan, and beta-adrenergic blockers (propranolol). Regression analysis and degree-based indices correlate with various physicochemical properties related to drug activities, such as molecular weight, complexity, melting point, and boiling point. Correlations provide insights into how the molecular structure influences these properties, helping design and optimize new drugs. In the results, various statistical parameters are used to analyze heart attack drugs. [ABSTRACT FROM AUTHOR]

Purpose: Using a combination of the geographical information system (GIS) and the Canadian water quality index (WQI), the current study sought to provide a long-term general assessment of the water quality of the Shatt Al-Arab River (SAAR), focusing on its suitability for living organisms. Likewise, SPSS statistics was used to develop a nonlinear WQI regression model for the study area. Design/methodology/approach: The study required four decades of data collection on some environmental characteristics of river water. After that, calculate the WQI and conduct the spatial analysis. Eight variables in total, including water temperature, dissolved oxygen, potential hydrogen ions, electrical conductivity (EC), biological oxygen demand, turbidity, nitrate and phosphate, were chosen to calculate the WQI. Findings: Throughout the study periods, the WQI values varied from 55.2 to 79.83, falling into the categories of four (marginal) and three (fair), with the sixth period (2007–2008) showing the most decline. The present research demonstrated that the high concentration of phosphates, the high EC values, and minor changes in the other environmental factors are the major causes of the decline in water quality. The variations in ecological variables' overlap are a senior contributor to changes in water quality in general. Notably, using GIS in conjunction with the WQI has shown to be very effective in reducing the time and effort spent on investigating water quality while obtaining precise findings and information at the lowest possible expense. Calibration and validation of the developed model showed that this model had a perfect estimate of the WQI value. Due to its flexibility and impartiality, this study recommends using the proposed model to estimate and predict the WQI in the study area. Originality/value: Even though the water quality of the SAAR has been the subject of numerous studies, this is the only long-term investigation that has been done to evaluate and predict its water quality. [ABSTRACT FROM AUTHOR]

Plant phenotyping is the science to quantify the quality, photosynthesis, development, growth, and biomass productivity of different crop plants. In the past, plant phenotyping employed methods such as grid count and regression models. However, the grid count method proved to be labor‐intensive and time‐consuming, while the regression model lacked accuracy in calculating leaf area. To address these challenges, a portable automatic platform was developed for precise ground‐based imaging of field plots. This platform consisted of a frame, an RGB camera, a stepper motor, a control board, and a battery. The RGB camera captured images, which were then processed using MATLAB software. Statistical analysis was performed to compare the results obtained from the grid count, regression model, and image processing techniques. The correlation coefficient (r) between the image processing technique and the regression model for sunflower was found to be 0.98 and 0.97, respectively, whereas for kidney bean it was 0.99 and 0.96, respectively. The minimum and maximum values for leaf area density (LAD) of all selected sunflower leaves were determined to be 0.132 and 0.714 m²/m³, respectively. For kidney bean leaves, the minimum and maximum mean LAD values were found to be 0.081 and 0.239 m²/m³, respectively. Ergonomic aspects of the developed automatic system were studied. The developed system had lower physiological parameters, such as working heart rate of 99 beats/min, work pulse of 18 beats/min, oxygen consumption of 786 mL/min, and energy consumption of 11.5 kJ/min compared to the grid count method. Thus, developed automatic ground‐based imaging system would significantly reduce physiological workload and associated hazards. Therefore, the developed method proved satisfactory in comparison to other techniques, offering a quick, efficient, and user‐friendly approach for determining plant phenotypes. [ABSTRACT FROM AUTHOR]

This research presents a machine learning (ML)‐based model that determines the DC and RF characteristics of InGaAs sub‐channel double gate high electron mobility transistors (DG‐HEMTs) to optimize the device structure. We employ technology computer‐aided design (TCAD) simulations to analyze the DC and RF performance of InGaAs sub‐channel DG‐HEMTs, generating a range of datasets by varying the material composition, layer width, and thickness of different layers in the device structure. We then train and optimize support vector regression (SVR) models using 5‐fold cross‐validation, varying the kernel function and degree parameters, and achieve better performance with the radial basis function (RBF) kernel. The simulated results indicate that the ML model predicts physical parameters more effectively than experimental analysis, offering a compact modeling solution that requires fewer computing resources than traditional methods. [ABSTRACT FROM AUTHOR]

This study examines the balance between technical and social focus in artificial intelligence, blockchain, cybersecurity, and privacy publications in Web of Science across countries, exploring the social factors that influence these research priorities. We use regression analysis to identify predictors of research focus and cluster analysis to reveal patterns across countries, combining these methods to provide a broader view of global research priorities. Our findings reveal that liberal democracy index, life expectancy, and happiness are significant predictors of research focus, while traditional indicators like education and income show weaker relationships. This unexpected result challenges conventional assumptions about the drivers of research priorities in digital technologies. The study identifies distinct clusters of countries with similar patterns of research focus across the four technologies, revealing previously unrecognized global typologies. Notably, more democratic societies tend to emphasize social implications of technologies, while some rapidly developing countries focus more on technical aspects. These findings suggest that political and social factors may play a larger role in shaping research agendas than previously thought, necessitating a re-evaluation of how we understand and predict research focus in rapidly evolving technological fields. The study provides valuable information for policymakers and researchers, informing strategies for technological development and international collaboration in an increasingly digital world. [ABSTRACT FROM AUTHOR]

Educational data mining (EDM) is a strategic technique for exploring data in educational environments to gain a deeper understanding of education. One of the goals of EDM is to predict things related to students in the future which can be done using a machine learning approach. In this paper, a regression model is developed to predict student performance in the first semester and the waiting period for graduate employment using machine learning approach based on informatics management (MI) and noninformatics management (non-MI) student data. Four regression models are compared for predicting student performance in the first semester and waiting period for graduate employment, including support vector regression (SVR), random forest regression (RFR), AdaBoost regression (ABR), and XGBoost regression. Based on the experiment, prediction of students' performance in the first semester, the highest R2 result produced by SVR model by value of 0.58 for MI and by RFR by value of 0.34 for non-MI. While, waiting period for graduate employment prediction, the highest R2 result produced by AdaBoost regression by value of 0.44 for MI and SVR by value of 0.39 for non-MI. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative method for enhancing time series data preprocessing by integrating a cycling layer into a self-attention mechanism. Traditional approaches often fail to capture the cyclical patterns inherent to time series data, which affects the predictive model accuracy. The proposed method aims to improve models' ability to identify and leverage these cyclical patterns, as demonstrated using the Jena Climate dataset from the Max Planck Institute for Biogeochemistry. Empirical results show that the proposed method enhances forecast accuracy and speeds up model fitting compared to the conventional techniques. This paper contributes to the field of time series analysis by providing a more effective preprocessing approach. [ABSTRACT FROM AUTHOR]

The need to increase bio-oil yield from biomass and enhance its fuel properties has driven research into optimizing the pyrolysis process. This study investigated the influence of three key process parameters—temperature, heating rate, and nitrogen flow rate—on the pyrolysis of elephant grass (Pennisetum purpureum) in a fixed-bed reactor. Response surface methodology was used to study the impact of the aforementioned variables on bio-oil yield to improve its production efficiency. Proximate analysis of the biomass revealed 79.24 wt% volatile matter, 14.22 wt% fixed carbon, and 5.86% ash, with ultimate analysis showing 45.44% carbon, 5.59% hydrogen, and 40.95% oxygen. The high volatile matter content and favourable carbon and hydrogen percentages indicate that elephant grass is a viable energy source due to its potential for high bio-oil yield and energy content. The resulting bio-oil exhibited a higher heating value of 20.9 MJ/kg, indicating its suitability for various heating applications. A second-order regression model was developed for bio-oil yield, with optimal conditions identified as a temperature of 550°C, a heating rate of 17°C/min, and a nitrogen flow rate of 6 ml/min. The study achieved an optimal bio-oil yield of 59.03 wt%, and the model's high R ² value of 0.8683 from analysis of variance analysis confirmed its predictive accuracy. This research highlights elephant grass as a sustainable feedstock for bio-oil production, offering valuable insights into optimizing pyrolysis conditions to enhance bio-oil yield, thus advancing biofuel technology. [ABSTRACT FROM AUTHOR]

Automotive chassis components, constructed as lap joints and produced by gas metal arc welding (GMAW), require fatigue durability. The fatigue properties of the weld in a lap joint are largely determined by weld geometry factors. When there is no gap or a consistent gap in the lap joint, improving the geometry of the weld toe can alleviate stress concentration and enhance fatigue properties. However, due to machining tolerances, it is difficult to completely eliminate or consistently manage the gap in the joint. In the case of a lap-welded joint with an inconsistent gap, it is necessary to identify the weld geometry factors related to fatigue properties. Evaluating the fatigue behavior of materials and welded joints requires significant time and cost, meaning that research that seeks to predict fatigue properties is essential. More research is needed on predicting fatigue properties related to automotive chassis components, particularly studies on predicting the fatigue properties of lap-welded joints with gaps. This study proposed a regression model for predicting fatigue properties based on crucial weld geometry factors in lap-welded joints with gaps using statistical analysis. Welding conditions were varied in order to build various weld geometries in joints configured in a lap with gaps of 0, 0.2, 0.5, and 1.0 mm, and 87 S–N curves for the lap-welded joints were derived. As input variables, 17 weld geometry factors (7 lengths, 7 angles, and 3 area factors) were selected. The slope of the S–N curve using the Basquin model from the S–N curve and the safe fatigue strength were selected as output variables for prediction in order to develop the regression model. Multiple linear regression models, multiple non-linear regression models, and second-order polynomial regression models were proposed to predict fatigue properties. Backward elimination was applied to simplify the models and reduce overfitting. Among the three proposed regression models, the multiple non-linear regression model had a coefficient of determination greater than 0.86. In lap-welded joints with gaps, the weld geometry factors representing fatigue properties were identified through standardized regression coefficients, and four weld geometry factors related to stress concentration were proposed. [ABSTRACT FROM AUTHOR]

Driven piles are used in many geological environments as a practical and convenient structural component. Hence, the determination of the drivability of piles is actually of great importance in complex geotechnical applications. Conventional methods of predicting pile drivability often rely on simplified physical models or empirical formulas, which may lack accuracy or applicability in complex geological conditions. Therefore, this study presents a practical machine learning approach, namely a Random Forest (RF) optimized by Bayesian Optimization (BO) and Particle Swarm Optimization (PSO), which not only enhances prediction accuracy but also better adapts to varying geological environments to predict the drivability parameters of piles (i.e. maximum compressive stress, maximum tensile stress, and blow per foot). In addition, support vector regression, extreme gradient boosting, k nearest neighbor, and decision tree are also used and applied for comparison purposes. In order to train and test these models, among the 4072 datasets collected with 17 model inputs, 3258 datasets were randomly selected for training, and the remaining 814 datasets were used for model testing. Lastly, the results of these models were compared and evaluated using two performance indices, i.e. the root mean square error (RMSE) and the coefficient of determination (R). The results indicate that the optimized RF model achieved lower RMSE than other prediction models in predicting the three parameters, specifically 0.044, 0.438, and 0.146; and higher R² values than other implemented techniques, specifically 0.966, 0.884, and 0.977. In addition, the sensitivity and uncertainty of the optimized RF model were analyzed using Sobol sensitivity analysis and Monte Carlo (MC) simulation. It can be concluded that the optimized RF model could be used to predict the performance of the pile, and it may provide a useful reference for solving some problems under similar engineering conditions. Graphic Abstract [ABSTRACT FROM AUTHOR]

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Land degradation is a critical issue in Ethiopia, exacerbating food insecurity by reducing agricultural productivity. Soil and water conservation (SWC) practices are essential to control erosion and increase food production. However, there is a lack of comprehensive evaluations on the impact of Ethiopia's Productive Safety Net Program (PSNP) on SWC practices. This study aimed to assess the contribution of the PSNP to SWC in the Amhara Sayint Woreda. The researchers used a mixed-method approach, combining quantitative and qualitative data. Multistage sampling was used to select households, and data were collected through questionnaires, interviews, focus groups, and observations. The study provided empirical evidence that the PSNP has a positive impact on SWC practices. Key factors influencing SWC participation include age, family size, education, plot size, livestock ownership, credit service, and access to extension services. The results suggest that the PSNP should improve payment for public work participants implementing SWC, undertake institutional reform, and increase public awareness of the benefits of SWC in reversing land degradation and improving food security. This study uniquely contributes to the understanding of how the PSNP influences the varying degrees of participation in SWC practices, filling a critical research gap. The findings can inform policymakers and program managers to enhance the PSNP's effectiveness in promoting sustainable land management and food security in Ethiopia. [ABSTRACT FROM AUTHOR]

BACKGROUND AND OBJECTIVES: Jordan faces significant food security challenges due to population growth, climate change, and urbanization, straining limited water resources. Water supply expansion is constrained by economic and environmental factors, leading to a critical impact on agriculture, which stands as the largest consumer of water. The study emphasizes the crucial importance of regional water sources in ensuring food security, as the quantity and quality of irrigation water have a direct influence on crop yield and productivity. Managing irrigation is crucial for sustainability and livelihoods, given Jordan's reliance on food imports and climate change-induced production variability. This study delves into potato cultivation, examining the interconnections among irrigation water availability, crop yield, and self-relianceMETHODS: Irrigation with freshwater and blended treated wastewater in the middle and northern Jordan Valley is compared, using stepwise regression analysis and assuming other agricultural inputs to be optimal. Food security indicators, which include agricultural and socio-economic factors, along with water scarcity indicators, encompassing both water quantity and quality, were methodically chosen and examined.FINDINGS: Regression analysis of potato production in Jordan revealed that increased blended water positively impacts yields, while higher water requirements and chloride levels negatively affect them. The negative implications of effective rainfall in combined water irrigation were evident, emphasizing the necessity for precise control over the levels of water quality and quantity. The study found that higher local potato production enhances self-sufficiency, crucial for food security. Enhanced water management techniques and advancements in agricultural practices have led to an increase in potato self-sufficiency, even in the face of dwindling water resources. Challenges from stable water requirements and decreasing rainfall can be addressed with advanced irrigation techniques and adaptive practices.CONCLUSION: Improved water and crop management contribute to enhanced potato self-sufficiency in Jordan, despite varying water quality parameters. Furthermore, the results of research offer important information regarding the adjustment of potato cultivation to evolving climate patterns, including changes in precipitation temperature. These adaptation strategies can be shared and implemented in other countries facing similar climatic challenges.

Abstract Leaf chlorophyll content (LCC) is crucial for monitoring the physiological processes of crops. Many studies have utilized spectral features to develop regression models for accurate LCC estimation, enabling the quantitative assessment and evaluation of crop growth status. The selection of optimal spectral features and regression algorithms significantly affects the precision of LCC estimation. In this study, we compared and analyzed the optimal spectral features for LCC estimation, as well as the consistency of machine learning methods across different crop types, phenology periods, and sensors. First, we extracted various spectral features, including the original spectral features (OS), first-order derivative spectral features (FDS), original continuum-removed spectra (CR) along with their four related derivative spectral features, principal component variables derived from different spectral features, and highly correlated spectral features with LCC. These extracted spectral features were then employed to construct the LCC models using six common regression algorithms on different datasets. Finally, we analyzed the optimal combination of spectral features and regression algorithms for accurate LCC estimation considering various dimensions, such as crop type, phenological period, and sensor used in observation conditions. The results demonstrate that the combinations of the principal component variables of continuum-removed derivative reflectance with the top 10 correlations with LCC (PCA_CRDR_R) combined with Gaussian process regression (GPR) can be considered as the optimal choice for estimating LCC under diverse observation conditions at a canopy scale, and its R2 can reach 0.62 for sugar beet LCC estimation; thus providing valuable theoretical guidance for selecting appropriate spectral features for LCC estimation.

Despite stiff competition in the construction industry, housing quality remains a problem. From the consumer’s perspective, these quality problems are called defects. Homeowners experience inconvenience and suffering due to home defects, and developers and builders also experience severe damage in time, costs, and reputation due to defect repairs. In Korea, lawsuits are increasing due to the rise in housing defects, and the cost of repairing defects determined by lawsuits is of great concern. Litigation is a burden to consumers and producers, requiring a hefty court fee, as well as attorneys and specialist firms, and takes some years. Suppose it is possible to predict the repair costs based on the outcome of a lawsuit and present it as objective supporting data. In that case, it can be of great help in bringing a settlement between consumers and producers. According to previous studies on housing repair costs, linear regression models were mainly used. Accordingly, in this study, a linear regression model was adopted as a method to predict housing repair costs. We analyzed the defect repair costs in 100 cases in which lawsuits were filed and the verdict was finalized for housing complexes in Korea. Previous studies investigated using the following independent variables: elapsed period, litigation period, claim amount, home warranty deposit, total floor area, households, and main building’s quantity, construction cost, region, and highest floor. Among these, the floor area, elapsed period, and litigation period were determined to be valid independent variables. In addition, the construction period was discovered as a valid independent variable. The present research model, which combines these independent variables, was compared with previous research models. The results showed that the earlier research model was found to have a multicollinearity issue among some independent variables. Also, the coefficients of some independent variables were not statistically significant. This research model did not have a multicollinearity problem; all independent variables’ coefficients were statistically significant, and the coefficient of determination was higher than other linear research models. Our proposed regression model, which accounts for the interaction of each independent variable, is a significant step forward in our research. This model, using the number of households multiplied by the construction period, the construction period multiplied by the litigation period, and the litigation period multiplied by the litigation period as independent variables, has been rigorously tested and found to have no multicollinearity issue. The coefficients of all independent variables are statistically significant, further bolstering the model’s reliability. Additionally, the explanatory power of this model is comparable to the previous model, suggesting its potential to be used in conjunction with the existing model. Therefore, the linear regression model predicting the repair cost of housing defects following litigation in this study was considered the best. Utilizing the model proposed in this study is expected to play a major role in reconciling disputes between consumers and producers over housing defects.

The assessment of income-producing properties - considered as the bulk of the existing assets - has rapidly increased. An efficient assessment of the market value of this kind of properties requires an adequate involvement of the main risk factors of the local real estate market for the determination of the capitalization rate for the income approach application. The aim of the work is to identify the most significant local real estate risk factors related to the market, the tenant and the context on the residential capitalization rate. The development of a regressive methodological approach applied to the residential sector of the city of Rome (Italy) is proposed. The obtained results show the susceptibility of the analyzed capitalization rate to the variation of the local real estate risk factors, in particular the per capita income and the variation of the rental values, by also considering the influences of the exogenous shocks and the expectation of the investors. The practical implications of the work consist in the possibility for evaluators to assess the likely changes in the capitalization rate in different residential contexts if variations occur in the most influential local risk factors identified by the proposed model.

Abstract Air pollution is a significant and pressing environmental and public health concern in urban areas, primarily driven by road transport. By gaining a deeper understanding of how traffic dynamics influence air pollution, policymakers and experts can design targeted interventions to tackle these critical issues. In order to analyse this relationship, a series of regression algorithms were developed utilizing the Google Project Air View (GPAV) and Dublin City’s SCATS data, taking into account various spatiotemporal characteristics such as distance and weather. The analysis showed that Gaussian Process Regression (GPR) mostly outperformed Support Vector Regression (SVR) for air quality prediction, emphasizing its suitability and the importance of considering spatial variability in modelling. The model describes the data best for particulate matter (PM2.5) emissions, with R-squared (R2) values ranging from 0.40 to 0.55 at specific distances from the centre of the study area based on the GPR model. The visualization of pollutant concentrations in the study area also revealed an association with the distance between intersections. While the anticipated direct correlation between vehicular traffic and air pollution was not as pronounced, it underscores the complexity of urban emissions and the multitude of factors influencing air quality. This revelation highlights the need for a multifaceted approach to policymaking, ensuring that interventions address a broader spectrum of emission sources beyond just traffic. This study advances the current knowledge on the dynamic relationship between urban traffic and air pollution, and its findings could provide theoretical support for traffic planning and traffic control applicable to urban centres globally.

Hospital construction projects require the integration of innovative technologies, making them complex and challenging to estimate accurately. Without reliable guidelines, lists of considerations, or predictive models, decision-makers may choose an unsuitable contractor, leading to cost overruns and project delays. This research used quantitative and qualitative methods, including expert opinions and focus groups, to address this issue and identify critical factors affecting cost estimation in hospital construction. Using multi-regression analysis, historical data was analyzed to establish a cost-predictive model. The study identified 24 factors that determine the most critical criteria affecting the cost estimation of hospital construction. This model can evaluate the estimated direct cost provided to decision-makers in the early stages of the project with over 95% accuracy. The research results can assist professionals in the construction industry in better estimating and managing the costs of hospital construction projects, reducing risks, and improving project outcomes. Key findings indicate that specific components such as site and electrical works show average percentage errors of 8.02% and 5.11%, respectively, highlighting areas prone to cost estimation inaccuracies. This insight directs focus on improving accuracy in these critical sectors, thus further refining cost management strategies for hospital construction projects.

Abstract To explore the clinical characteristics of patients infected with SARS-CoV-2 nationwide, especially the effect factors of asymptomatic infection and disappearance of clinical symptoms. A total of 66,448 COVID-19 patients in China who have been diagnosed by nucleic acid test or rapid antigen test were surveyed online (December 24, 2022 to January 16, 2023). Our cross-sectional study used descriptive analyses and binary Logistics regression model to assess the correlation between the clinical characteristics and relative factors, including age, gender, pre-existing conditions, reinfection, vaccination and treatment. A total of 64,515 valid questionnaires were collected. Among included participants, 5969 of which were asymptomatic. The symptoms were mainly upper respiratory symptoms, including dry and itchy throat (64.16%), sore throat (59.95%), hoarseness (57.90%), nasal congestion (53.39%). In binary Logistics regression model, we found that male, no pre-existing conditions, reinfection and vaccination have positive correlations with the appearance of asymptomatic COVID-19 patients. In Cox proportional-hazards regression model, considering all clinical symptoms disappeared in 14 days as outcome, we found that ≤ 60 years old, male, no pre-existing conditions, vaccination and adopted treatment have positive correlations with rapid amelioration of clinical symptoms in COVID-19 patients. The clinical symptoms of the participants were mainly upper respiratory symptoms which were according with the infection of Omicron variant. Factors including age, gender, pre-existing conditions and reinfection could influence the clinical characteristics and prognosis of COVID-19 patients. Importantly, vaccination has positive significance for the prevention and treatment of COVID-19. Lastly, the use of Chinese medicine maybe beneficial to COVID-19 patients, however, reasonable guidance is necessary.

Several regression models for predicting returns of pink salmon in the Kamchatka region are presented. The data for 1990–2023 were analyzed. Among available climatic and oceanological indices, the most suitable for using as predictors for forecasting of pink salmon returns were the Pacific Decadal Oscillation (PDO) index, Western Pacific Cyclonic Index (WP), Arctic Oscillation (AO) index, and the sea surface temperature anomaly in the North Pacific. Multi-dimensional models of the «stock–recruitment» type were built on identified statistical patterns, which allowed to estimate potential abundance of the pink salmon returns to northeastern and western Kamchatka. Besides, methods for predicting the abundance of pink salmon returns on the data of fish counting in the sea are considered, using the materials of TINRO trawl surveys conducted in the Bering and Okhotsk Seas in the fall seasons of 2012–2023. To determine the abundance of pink salmon originated from West Kamchatka, genetic identification of regional composition of juveniles in mixed trawl catches was used. All tested methods have a high level of determination, but simpler regressive models are more prospective for practical forecasting of general trend in dynamics of pink salmon stocks in the Kamchatka region due to very weak generalization ability of more complicated models.

Aims: Transfusion after primary total hip arthroplasty (THA) has become rare, and identification of causative factors allows preventive measures. The aim of this study was to determine patient-specific factors that increase the risk of needing a blood transfusion. Methods: All patients who underwent elective THA were analyzed retrospectively in this single-centre study from 2020 to 2021. A total of 2,892 patients were included. Transfusion-related parameters were evaluated. A multiple logistic regression was performed to determine whether age, BMI, American Society of Anesthesiologists (ASA) grade, sex, or preoperative haemoglobin (Hb) could predict the need for transfusion within the examined patient population. Results: The overall transfusion rate was 1.2%. Compared to the group of patients without blood transfusion, the transfused group was on average older (aged 73.8 years (SD 9.7) vs 68.6 years (SD 10.1); p = 0.020) and was mostly female (p = 0.003), but showed no significant differences in terms of BMI (28.3 kg/m2 (SD 5.9) vs 28.7 kg/m2 (SD 5.2); p = 0.720) or ASA grade (2.2 (SD 0.5) vs 2.1 (SD 0.4); p = 0.378). The regression model identified a cutoff Hb level of < 7.6 mmol/l (< 12.2 g/dl), aged > 73 years, and a BMI of 35.4 kg/m² or higher as the three most reliable predictors associated with postoperative transfusion in THA. Conclusion: The possibility of transfusion is predictable based on preoperatively available parameters. The proposed thresholds for preoperative Hb level, age, and BMI can help identify patients and take preventive measures if necessary. Cite this article: Bone Jt Open 2024;5(7):560–564.

Background: An abnormal variation in blood electrolytes, such as potassium, contributes to mortality in children admitted to intensive care units. Continuous and real-time monitoring of potassium serum levels can prevent fatal arrhythmias, but this is not currently practical. The study aims to use machine learning to estimate blood potassium levels with accuracy in real time noninvasively. Methods: Hospitalized patients in the Pediatric Department of the Rajaie Cardiology and Medical Research Center and Tehran Heart Center were recruited from December 2021 to June 2022. The electrocardiographic (ECG) features of patients were evaluated. We defined 16 features for each signal and extracted them automatically. The dimension reduction operation was performed with the assistance of the correlation matrix. Linear regression, polynomials, decision trees, random forests, and support vector machine algorithms have been used to find the relationship between characteristics and serum potassium levels. Finally, we used a scatter plot and mean square error (MSE) to display the results. Results: Of 463 patients (mean age: 8 ± 1 year; 56% boys) hospitalized, 428 patients met the inclusion criteria, with 35 patients having a high noise of ECG were excluded. After the dimension reduction step, 11 features were selected from each cardiac signal. The random forest regression algorithm showed the best performance with an MSE of 0.3. Conclusion: The accurate estimation of serum potassium levels based on ECG signals is possible using machine learning algorithms. This can be potentially useful in predicting serum potassium levels in specific clinical scenarios.

Abstract In this research, the star gold structure with beta graphene is thoroughly examined. We mainly focus on computing degree-based topological indices, which provide information about the network’s connectivity and complexity as well as structural features. In addition, we compute an entropy measure to represent the uncertainty, information richness, and degree of unpredictability in the network. Furthermore, this study explores the relationships between topological descriptors and entropy using regression models that are logarithmic, linear, and quadratic. By merging these regression models, we uncover hidden patterns and understand the underlying ideas governing the network’s behaviour. Our findings shed light on the connection between topological indices and entropy. This work improves our understanding of star gold structure dynamics and provides a visual framework for interpreting their behaviour.

The determination of geotechnical correlations and coefficients relies on the assumption of error-free training data used for empirical models, but this assumption may not always hold true. Empirical-risk assessments based on noisy data cannot guarantee the accuracy of regression results. The statistical robustness of empirical-design parameters is influenced both by the soil properties and regression models used. This study proposed a non-stochastic regression method for predicting the hydraulic conductivity of sandy soils based on relevant soil parameters. No changes in content have been made. The approach involved the following steps: data preprocessing, regression-algorithm selection, model optimization, uncertainty estimation, and model selection. The study identified trends in hydraulic conductivity and pore-structure characteristics based on specific model parameters that were derived from empirical data. The paper presents a compilation of a regression model and methods for fine-tuning parametric representations. The prediction results highlighted the best-fitting model and parameter combination with the lowest residuals, comparing favorably to empirical regression models. Machine-learning-based regression models suggest an optimal combination of properties while considering model performance and handling missing values for uncovering the relationships between hydraulic conductivity and multiple, influential, soil properties. [ABSTRACT FROM AUTHOR]

Palm fat has uniquely optimal melting characteristics that are difficult to replace in products such as baked goods and chocolate-based items. This study investigates the efficacy of using Pickering emulsions derived from Swiss plant oils and their micromilled press cakes. Emulsification was carried out at both the lab and pilot scales using sunflower- and rapeseed-based recipes, with and without additional surfactants, for both oil-in-water and water-in-oil emulsions. The resulting emulsions were measured for viscosity and short- and long-term stability and linked to the properties of the raw materials. The results indicated that the contact angle, size, and macronutrient composition of the particles significantly impact emulsion quality, though differences in oil pressing methods might predominate these effects. The combination of particles and surfactants demonstrated a clear advantage with respect to interface stabilisation, with a suggested link between the wax content of the oil and particles and the resulting emulsion quality and stability. [ABSTRACT FROM AUTHOR]

Recently, there has been a shift in the global energy landscape to move to reliable, clean, and eco-friendly renewable energy sources to address global issues such as climate change and greenhouse gas emissions. One such energy source is wave energy; researchers attempt to develop models that can accurately forecast the availability of wave energy as an alternative energy source. In this paper, an Artificial Neural Network (ANN) model along with statistical models such as time series models, and regression models are proposed for forecasting wave energy at a site in Fiji using the wave height and wave period as the independent variables. The performance of the proposed models developed is compared using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the goodness-of-fit ($R^2$R2) value. The proposed model is then further benchmarked with the naïve model. The empirical results reveal that the proposed ANN model outclassed all the other models and was more efficient and accurate in forecasting wave energy than the regression and time series models. By accurate wave modelling and by incorporating impedance matching, maximum power generation can be achieved. [ABSTRACT FROM AUTHOR]

Summary: The time until an event occurs is often known to have a skewed distribution. To model this, a statistical distribution called the two‐parameter flexible Weibull extension (FWE) has been proposed. In this paper, the FWE distribution is used to model datasets through the use of generalised additive models for location, scale and shape (GAMLSS) distributional regression. GAMLSS is the only regression technique that can examine the effects of both categorical and numeric predictors on all the parameters of the distribution used to fit the dependent variable. To make it easier to use the FWE distribution through GAMLSS, the RelDists R package is proposed. A simulation study shows that FWE modelling through GAMLSS provides reliable parameter estimates even in the presence of factors that affect the distribution. [ABSTRACT FROM AUTHOR]

Presently, the main trend in thin-sheet steel production, including the high-strength automobile steel, is to reduce production costs while retaining essential properties and quality. A considerable part of the cost of high-strength steel comes from alloying with expensive chemical elements such as vanadium and niobium. Simply reducing the content of these elements in finished products would compromise the mechanical properties of rolled products, leading to inferior quality and defects. This work, based on statistical analysis for steel grade S355MC, demonstrated the potential to reduce alloying while maintaining the required yield strength by adjusting the hot rolling conditions. It was also revealed that, in addition to the concentration of chemical elements and the hot rolling mode, the thickness of the finished product affects the tensile strength. Therefore, the analysis was performed exclusively on products with a thickness of 4 mm. The study resulted in a regression equation that illustrates the dependence of yield strength on vanadium content and hot rolling parameters. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) from road traffic is a non-negligible part of global greenhouse gas (GHG) emissions, and it is a challenge for the world today to accurately estimate road traffic CO2 emissions and formulate effective emission reduction policies. Current emission inventories for vehicles have either low-resolution, or limited coverage, and they have not adequately focused on the CO2 emission produced by new energy vehicles (NEV) considering fuel life cycle. To fill the research gap, this paper proposed a framework of a high-resolution well-to-wheel (WTW) CO2 emission estimation for a full sample of vehicles and revealed the unique CO2 emission characteristics of different categories of vehicles combined with vehicle behavior. Based on this, the spatiotemporal characteristics and influencing factors of CO2 emissions were analyzed with the geographical and temporal weighted regression (GTWR) model. Finally, the CO2 emissions of vehicles under different scenarios are simulated to support the formulation of emission reduction policies. The results show that the distribution of vehicle CO2 emissions shows obvious heterogeneity in time, space, and vehicle category. By simply adjusting the existing NEV promotion policy, the emission reduction effect can be improved by 6.5%-13.5% under the same NEV penetration. If combined with changes in power generation structure, it can further release the emission reduction potential of NEVs, which can reduce the current CO2 emissions by 78.1% in the optimal scenario. [ABSTRACT FROM AUTHOR]

Regression models often require constraints that can be expressed as non-negativity constraints. This could be because it makes sense for the underlying modeling context, or it could be necessary to prevent the fitted values violating the natural constraints of the response distribution. Examples of the latter include log-link binomial regression and additive variance regression, while an example of the former is non-negative linear regression. Non-negativity constraints may apply directly to the regression parameters, in which case we call the model parameter-constrained, or they may apply to the linear predictor, in which case we call the model predictor-constrained. In this article, we show that it is possible to fit the predictor-constrained model using a parameter-constrained method applied to a model with additional dummy parameters, a technique called parameter expansion. This is advantageous because parameter-constrained models are often easier and more reliable to fit. After considering a range of models in which predictor-constrained estimation is desirable, we study the application of parameter expansion to a general regression model that includes the specific models as special cases. We then undertake a more in-depth study of parameter expansion for the log-link binomial model. Simulation results are presented demonstrating the computational advantages of parameter expansion. [ABSTRACT FROM AUTHOR]

In this paper, we present the unit-power half-normal distribution, derived from the power half-normal distribution, for data analysis in the open unit interval. The statistical properties of the unit-power half-normal model are described in detail. Simulation studies are carried out to evaluate the performance of the parameter estimators. Additionally, we implement the quantile regression for this model, which is applied to two real healthcare data sets. Our findings suggest that the unit power half-normal distribution provides a robust and flexible alternative for existing models for proportion data. [ABSTRACT FROM AUTHOR]

Despite stiff competition in the construction industry, housing quality remains a problem. From the consumer's perspective, these quality problems are called defects. Homeowners experience inconvenience and suffering due to home defects, and developers and builders also experience severe damage in time, costs, and reputation due to defect repairs. In Korea, lawsuits are increasing due to the rise in housing defects, and the cost of repairing defects determined by lawsuits is of great concern. Litigation is a burden to consumers and producers, requiring a hefty court fee, as well as attorneys and specialist firms, and takes some years. Suppose it is possible to predict the repair costs based on the outcome of a lawsuit and present it as objective supporting data. In that case, it can be of great help in bringing a settlement between consumers and producers. According to previous studies on housing repair costs, linear regression models were mainly used. Accordingly, in this study, a linear regression model was adopted as a method to predict housing repair costs. We analyzed the defect repair costs in 100 cases in which lawsuits were filed and the verdict was finalized for housing complexes in Korea. Previous studies investigated using the following independent variables: elapsed period, litigation period, claim amount, home warranty deposit, total floor area, households, and main building's quantity, construction cost, region, and highest floor. Among these, the floor area, elapsed period, and litigation period were determined to be valid independent variables. In addition, the construction period was discovered as a valid independent variable. The present research model, which combines these independent variables, was compared with previous research models. The results showed that the earlier research model was found to have a multicollinearity issue among some independent variables. Also, the coefficients of some independent variables were not statistically significant. This research model did not have a multicollinearity problem; all independent variables' coefficients were statistically significant, and the coefficient of determination was higher than other linear research models. Our proposed regression model, which accounts for the interaction of each independent variable, is a significant step forward in our research. This model, using the number of households multiplied by the construction period, the construction period multiplied by the litigation period, and the litigation period multiplied by the litigation period as independent variables, has been rigorously tested and found to have no multicollinearity issue. The coefficients of all independent variables are statistically significant, further bolstering the model's reliability. Additionally, the explanatory power of this model is comparable to the previous model, suggesting its potential to be used in conjunction with the existing model. Therefore, the linear regression model predicting the repair cost of housing defects following litigation in this study was considered the best. Utilizing the model proposed in this study is expected to play a major role in reconciling disputes between consumers and producers over housing defects. [ABSTRACT FROM AUTHOR]

This study is structured to address the problem of mapping the bottom of shallow and ultra-shallow inland water bodies using high-resolution satellite imagery. These environments, with their diverse distribution of optically relevant components, pose a challenge to traditional mapping methods. The study was conducted on several research issues, each focusing on a specific aspect of the SDB, related to the selection of spectral bands and regression models, regression models creation, evaluation of the influence of the number and spatial distribution of reference soundings, and assessment of the quality of the bathymetric surface, with a focus on microtopography. The study utilized basic empirical techniques, incorporating high-precision reference data acquired via an unmanned surface vessel (USV) integrated with a single-beam echosounder (SBES), and Global Navigation Satellite System (GNSS) receiver measurements. The performed investigation allowed the optimization of a methodology for bathymetry acquisition of such areas by identifying the impact of individual processing components. The first results indicated the usefulness of the proposed approach, which can be confirmed by the values of the obtained RMS errors of elaborated bathymetric surfaces in the range of up to several centimeters in some study cases. The work also points to the problematic nature of this type of study, which can contribute to further research into the application of remote sensing techniques for bathymetry, especially during acquisition in optically complex waters. [ABSTRACT FROM AUTHOR]

Air pollution is a significant and pressing environmental and public health concern in urban areas, primarily driven by road transport. By gaining a deeper understanding of how traffic dynamics influence air pollution, policymakers and experts can design targeted interventions to tackle these critical issues. In order to analyse this relationship, a series of regression algorithms were developed utilizing the Google Project Air View (GPAV) and Dublin City's SCATS data, taking into account various spatiotemporal characteristics such as distance and weather. The analysis showed that Gaussian Process Regression (GPR) mostly outperformed Support Vector Regression (SVR) for air quality prediction, emphasizing its suitability and the importance of considering spatial variability in modelling. The model describes the data best for particulate matter (PM2.5) emissions, with R-squared (R2) values ranging from 0.40 to 0.55 at specific distances from the centre of the study area based on the GPR model. The visualization of pollutant concentrations in the study area also revealed an association with the distance between intersections. While the anticipated direct correlation between vehicular traffic and air pollution was not as pronounced, it underscores the complexity of urban emissions and the multitude of factors influencing air quality. This revelation highlights the need for a multifaceted approach to policymaking, ensuring that interventions address a broader spectrum of emission sources beyond just traffic. This study advances the current knowledge on the dynamic relationship between urban traffic and air pollution, and its findings could provide theoretical support for traffic planning and traffic control applicable to urban centres globally. [ABSTRACT FROM AUTHOR]

In the dynamic landscape of the automotive industry, innovation is the key driver of success. Companies that actively engage in idea generation and have efficient idea management systems in place often gain a competitive edge. This article explores the importance and necessity of idea generation in the context of the automotive industry in Romania, shedding light on the economic effects it can have. Based on a review of scientific literature, the paperwork adds value by filling in a gap on continuous improvement process, idea management, and idea management system concepts, by describing in detail how Idea Management System can be introduced, and by performing a quantitative analysis, using complex statistical methods and machine learning on big data collected to observe the effects of Idea Management System use on the results and the level of employee involvement, as well as finding out a predictor of potential savings for automotive company. Very few academic papers take into consideration the economic effect of Idea Management System especially for the company' performance (KPIs), focusing rather on performance metrics for idea management. [ABSTRACT FROM AUTHOR]

To clarify the relationships between physicochemical properties of freshly harvested tobacco leaves and the quality of flue-cured tobacco leaves, the harvest maturity test coupled with different nutrient conditions was conducted in the field. The differences in the physicochemical properties of fresh leaves and their correlations with appearance and major chemical compositions of the cured leaves were analyzed. Regression prediction models of chemical compositions in the flue-cured leaves were established based on physicochemical indexes of the fresh leaves. The results showed that: 1) The physicochemical properties of the fresh leaves with different maturity degrees were different, and the rules of middle and upper leaves were basically the same. The nitrogen and moisture contents (mass fractions) in unripe fresh leaves were relatively higher, while their starch contents were lower. There was little difference in the physicochemical properties between mature and ripe fresh leaves. 2) Within the scope of this study, the curing loss rate of the fresh leaves increased and the oiliness of the flue-cured leaves decreased with the increase of total nitrogen and nicotine contents in the fresh leaves, while the curing loss rate of the fresh leaves decreased and the oiliness of the flue-cured leaves increased with the increase of starch content in the fresh leaves. 3) The coefficients of determination of the established regression prediction model based on total nitrogen content in the fresh leaves for reducing sugar and total nitrogen contents in the flue-cured leaves were 0.629 and 0.579, respectively; and that of the regression prediction model based on starch content in the fresh leaves for reducing sugar content in the flue-cured leaves was 0.632. 4) The total nitrogen content in the fresh leaves could be used to accurately predict the total nitrogen content in the flue-cured leaves with the average relative error less than 7%. Based on the starch and total nitrogen contents in the fresh leaves, the prediction model for reducing sugar content and sugar-nicotine ratio of the flue-cured leaves could be established with the average relative errors less than 14% and 18%, respectively. The physicochemical properties of the fresh tobacco leaves were closely related to the quality of the flue-cured tobacco leaves, the total nitrogen and starch contents in the fresh leaves can be used to predict the total nitrogen and reducing sugar contents and sugar-nicotine ratio of the flue-cured leaves. [ABSTRACT FROM AUTHOR]