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In the evolving global market, new business conditions necessitate that enterprises adapt and construct organizational structures grounded in new principles and the implementation of contemporary management methods. This is particularly crucial for enterprises in transitional economies, which need to be highly flexible and innovative to meet the increasing demands of users swiftly, employ modern management techniques, and gain a competitive edge. The modern business environment assumes that there are very few products, technologies, services, knowledge areas, or procedures unavailable to interested groups worldwide. This study examines the influence of modern management methods and techniques (MMMTs), human resource management (HRM), quality management (QM), and intellectual capital management (ICM) on the sustainable market performance (SMPC) of these enterprises. A structured survey was conducted among 146 managers from various Serbian industrial enterprises, and the data were analyzed using descriptive statistics, Pearson correlation analysis, linear regression, and multicollinearity tests. The results revealed significant positive correlations between MMMTs, HRM, QM, ICM, and SMPC, with quality management having the highest impact. These findings provide valuable insights for improving business competitiveness in Serbia’s industrial sector. The results also support the development of an integrated model for sustainable management practices in transitional economies. Full article

The development of current sexing methods largely depends on the use of adequate sources of data and adjustable classification techniques. Most sex estimation methods have been based on linear measurements, while the angles have been largely ignored, potentially leading to the loss of valuable information for sex discrimination. This study aims to evaluate the usefulness of cranial angles for sex estimation and to differentiate the most dimorphic ones by training machine learning algorithms. Computed tomography images of 154 males and 180 females were used to derive data of 36 cranial angles. The classification models were created by support vector machines, naïve Bayes, logistic regression, and the rule-induction algorithm CN2. A series of cranial angle subsets was arranged by an attribute selection scheme. The algorithms achieved the highest accuracy on subsets of cranial angles, most of which correspond to well-known features for sex discrimination. Angles characterizing the lower forehead and upper midface were included in the best-performing models of all algorithms. The accuracy results showed the considerable classification potential of the cranial angles. The study demonstrates the value of the cranial angles as sex indicators and the possibility to enhance the sex estimation accuracy by using them. Full article

The increase in traffic caused by new development affects the change in traffic conditions on the surrounding roads, and shopping centres are significant traffic generators. The development of local travel generation rates and their characteristics for individual land uses from the aspect of traffic demand is a reliable way to plan traffic in order to come up with preventive solutions to traffic problems, that is, prevention of possible negative consequences on traffic conditions in the street network occurring due to the construction of shopping centres. One of the main aims of this paper is to develop a model for objective assessment of the generated traffic demand for significant changes in land use, such as the construction of shopping centres in medium-sized towns. All these would be steps in the right direction for the promotion of reliable traffic planning and adoption of TIA for every new development before a decision regarding the change in land purpose has been made. This kind of process still has not been established systematically in either Bosnia and Herzegovina and the Republic of Serbia, or in surrounding countries. This paper focuses on the formulation of a model for determining the volume of traffic generated by shopping centres in medium-sized towns in two countries of the Southeast Europe region. The survey was conducted in eight different locations (cities) where there are shopping centres with common facilities. The analysis showed that the number of visitors and vehicles attracted by the shopping centre zone can be determined by a model based on a linear regression analysis. The analysis included exploring several different factors of trip generation in shopping centres, including the relationship between trip generation and combinations of several independent variables. The verification of the model was conducted in real conditions of the traffic flow generated by a shopping centre which was not the analysis subject when forming the forecasting model. In this way, the validity of the proposed model is credibly assessed. The developed model can be applied in the procedures of planning the construction of shopping centres in medium-sized cities in the Republic of Serbia and Bosnia and Herzegovina, and wider, in the region of Southeast Europe, in order to estimate the volume of generated traffic demand, that is, its impact on the conditions of traffic on the surrounding traffic network. Full article

This paper attempts to establish the accurate and timely forecasting model for the total output value of agriculture, forestry, animal husbandry, and fishery (TOVAFAF) in various provinces of China using NPP-VIIRS nighttime light (NTL) remote sensing data and machine learning algorithms. It can provide important data references for timely assessment of agricultural economic development level and policy adjustment. Firstly, multiple NTL indices for provincial-level administrative regions of China were constructed based on NTL images from 2013 to 2023 and various statistics. The results of correlation analysis and significance test show that the constructed total nighttime light index (TNLI), luminous pixel quantity index (LPQI), luminous pixel ratio index (LPRI), and nighttime light squared deviation sum index (NLSDSI) are highly correlated with the TOVAFAF. Subsequently, using the relevant data from 2013 to 2020 as the training set, the four NTL indices were separately taken as single independent variable to establish the linear model, exponential model, logarithmic model, power exponential model, and polynomial model. And all the four NTL indices were taken as the input features together to establish the multiple linear regression (MLR), extreme learning machine (ELM), and particle swarm optimization-ELM (PSO-ELM) models. The relevant data from 2021 to 2022 were taken as the validation set for the adjustment and optimization of the model weight parameters and the preliminary evaluation of the modeling effect. Finally, the established models were employed to forecast the TOVAFAF in 2023. The experimental results show that the ELM and PSO-ELM models can better explore and characterize the potential nonlinear relationship between NTL data and the TOVAFAF than all the models established based on single NTL index and the MLR model, and the PSO-ELM model achieves the best forecasting effect in 2023 with the MRE value for 32.20% and the R² values of the linear relationship between the actual values and the forecasting values for 0.6460. Full article

The research focuses on incorporating microgrids into engineering curricula for achieving voltage stability in today’s power systems. This helps to meet the increasing demand for engineers to integrate distributed power generation and renewable energy sources. Some limitations of the current literature include the absence of models outlining approaches to microgrid education and limited insight into teaching strategies for electrical power systems. The research used a quantitative methodology to survey 100 engineering students enrolled in a microgrid modeling class to achieve the study’s objectives. The data analysis involved machine learning models such as Random Forest, Gradient Boosting, K-Means, hierarchical clustering, and regression models. The major findings identified exam score as the most significant determiner of student performance (weight ≈ 0.40). Based on the clustering analysis, it was found that microgrid systems can be grouped into four operational states. It was also seen that linear regression models were highly accurate and better than other highly complex models, like Decision Tree, with a model accuracy of R² ≈ 0.4. One of the study’s major strengths is the potential impact of the proposed framework for integrating microgrids into engineering education on the professional training of engineers. This framework, based on theoretical knowledge and practical experience as well as on developing advanced analytical skills, can significantly enhance the professional training of engineers to deal with the complexities of contemporary power systems, including microgrids and sustainable energy progress. Full article

This work applied three machine learning (ML) models—linear regression (LR), random forest (RF), and support vector regression (SVR)—to predict the lattice parameters of the monoclinic B19′ phase in two distinct training datasets: previously published ZrO₂-based shape-memory ceramics (SMCs) and NiTi-based high-entropy shape-memory alloys (HESMAs). Our findings showed that LR provided the most accurate predictions for a_c, a_m, b_m, and c_m in NiTi-based HESMAs, while RF excelled in computing β_m for both datasets. SVR disclosed the largest deviation between the predicted and actual values of lattice parameters for both training datasets. A combination approach of RF and LR models enhanced the accuracy of predicting lattice parameters of martensitic phases in various shape-memory materials for stable high-temperature applications. Full article

Equivalent circuit models represent one of the most efficient virtual representations of battery systems, with numerous applications supporting the design of electric vehicles, such as powertrain evaluation, power electronics development, and model-based state estimation. Due to their popularity, their parameter extraction and model parametrization procedures present high interest within the research community, with novel approaches at an elementary level still being identified. This article introduces and compares in detail two novel parameter extraction methods based on the distinct application of least squares linear regression in relation to the autoregressive exogenous as well as the state-space equations of the double polarization equivalent circuit model in an iterative optimization-type manner. Following their application using experimental data obtained from an NCA Sony VTC6 cell, the results are benchmarked against a method employing differential evolution. The results indicate the least squares linear regression applied to the state-space format of the model as the best overall solution, providing excellent accuracy similar to the results of differential evolution, but averaging only 1.32% of the computational cost. In contrast, the same linear solver applied to the autoregressive exogenous format proves complementary characteristics by being the fastest process but presenting a penalty over the accuracy of the results. Full article

Objectives: This study aimed to investigate knowledge, attitudes, and practices related to pre-prepared foods among college students in Central China. Methods: From the end of May 2024 to June 2024, we completed a cross-sectional study using an online questionnaire. A total of 1676 questionnaires were distributed online, and 1566 valid questionnaires were collected. Data were analyzed using Kruskal–Wallis tests or Wilcoxon rank-sum tests for univariate analysis. A multiple linear regression model was employed with knowledge, attitudes, and practices scores as dependent variables to identify factors associated with the scores on pre-prepared food knowledge, attitudes, and practices. Results: The survey results showed that 56.7% of the participants had high knowledge scores, 4% of the participants had high attitudes scores, and only 0.4% of the participants had high practices scores. Multiple linear regression analysis showed that ethnicity, the number of children in the family, academic qualifications, and monthly living expenses were associated with college students’ knowledge of pre-prepared foods (p < 0.05). Gender and the satisfaction with school catering services were associated with college students’ attitudes of pre-prepared foods (p < 0.05). Gender, knowledge and attitudes were associated with practices of pre-prepared foods (p < 0.05). Conclusions: College students have a relatively high level of knowledge of pre-prepared foods. However, they have more negative attitudes and practices towards pre-prepared foods, and the overall KAP levels are low. Full article

In this research, the doping of SrTiO₃ with Mn⁴⁺ was performed in order to evaluate the potential application as a photocatalyst for the degradation of organic dye pollutants. Since photocatalytic activity depends on grain microstructure, fractal analysis was used to estimate the Hausdorff dimension to provide a more thorough investigation of Mn@SrTiO₃ morphology. Structural analysis by infrared spectroscopy indicated the incorporation of Mn⁴⁺ into the SrTiO₃ lattice, while by using x-ray diffraction, the crystallite size of 44 nm was determined. The photocatalytic activity test performed on complex ethyl violet organic dye revealed potential for Mn@SrTiO₃ application in water treatment. Based on fractal regression analysis, a good estimate was obtained for the reconstruction of grain shape, with a Hasudorff dimension of 1.13679, which was used to find the best kinetics model for the photodegradation reaction. The experimental data showed a nearly linear fit with fractal-like pseudo-zero order. These findings and applications of fractal dimensions could contribute to future characterizations of photocatalysts, providing a deeper understanding of surface properties and their influence on photocatalytic activity. Full article

Background/Objectives: Emotional intelligence may help nurses to cope with demanding work environments where the need to improve the quality and safety of the care provided, as well as the care of the chronically ill, prevails. Although it is well known that emotional intelligence is positively related to work performance, the literature on nurses is limited. The aim of our study was to examine the impact of emotional intelligence on work performance in a sample of nurses in Greece. Methods: We conducted a cross-sectional study with 318 nurses. We collected data from a convenience sample of nurses during January 2024. Since we conducted an online survey through social media, our sample could not be representative of all nurses in Greece. For instance, older nurses may be underrepresented in our study due to limited access on social media. We measured emotional intelligence with the Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF) and work performance with the Individual Work Performance Questionnaire (IWPQ). We created multivariable linear regression models adjusted for sex, age, educational level, and work experience. We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Results: We found that the four streams of emotional intelligence (i.e., well-being, self-control, emotionality, sociability) increased nurses’ work performance. In particular, we found a positive relationship between well-being and task performance (adjusted beta = 0.210, 95% CI = 0.140 to 0.281, p-value < 0.001) and contextual performance (adjusted beta = 0.135, 95% CI = 0.050 to 0.221, p-value = 0.002). Similarly, there was a positive relationship between self-control and task performance (adjusted beta = 0.136, 95% CI = 0.030 to 0.241, p-value = 0.012). Additionally, sociability increased task performance (adjusted beta = 0.223, 95% CI = 0.151 to 0.295, p-value < 0.001) and contextual performance (adjusted beta = 0.198, 95% CI = 0.111 to 0.286, p-value < 0.001). Moreover, emotionality (adjusted beta = −0.198, 95% CI = −0.319 to −0.076, p-value = 0.002) and sociability (adjusted beta = −0.133, 95% CI = −0.221 to −0.044, p-value = 0.003) reduced counterproductive work behavior. Conclusions: Our multivariable models identified a positive impact of emotional intelligence on nurses’ work performance. Nurse managers and healthcare organizations should adopt appropriate interventions to improve nurses’ emotional intelligence. Enhancing emotional intelligence among nurses can improve work performance and, thus, healthcare outcomes. Moreover, higher levels of emotional intelligence may empower nurses’ compassion and resilience, fostering a supportive work environment. In this context, the well-being of both nurses and patients may improve. Full article

Groundwater salinization poses a critical threat to sustainable development in arid and semi-arid rurbanizing regions, exemplified by Kerman Province, Iran. This region experiences groundwater ecosystem degradation as a result of the rapid conversion of rural agricultural land to urban areas under chronic drought conditions. This study aims to enhance Groundwater Pollution Risk (GwPR) mapping by integrating the DRASTIC index with machine learning (ML) models, including Random Forest (RF), Boosted Regression Trees (BRT), Generalized Linear Model (GLM), Support Vector Machine (SVM), and Multivariate Adaptive Regression Splines (MARS), alongside hydrogeochemical investigations, to promote sustainable water management in Kerman Province. The RF model achieved the highest accuracy with an Area Under the Curve (AUC) of 0.995 in predicting GwPR, outperforming BRT (0.988), SVM (0.977), MARS (0.951), and GLM (0.887). The RF-based map identified new high-vulnerability zones in the northeast and northwest and showed an expanded moderate vulnerability zone, covering 48.46% of the study area. Analysis revealed exceedances of WHO standards for total hardness (TH), sodium, sulfates, chlorides, and electrical conductivity (EC) in these high-vulnerability areas, indicating contamination from mineralized aquifers and unsustainable agricultural practices. The findings underscore the RF model’s effectiveness in groundwater prediction and highlight the need for stricter monitoring and management, including regulating groundwater extraction and improving water use efficiency in riverine aquifers. Full article

Additive Manufacturing (AM) is a pivotal technology for transforming complex geometries with minimal tooling requirements. Among the several AM techniques, Wire Arc Additive Manufacturing (WAAM) is notable for its ability to produce large metal components, which makes it particularly appealing in the aerospace sector. However, precise control of the bead geometry, specifically bead width and height, is essential for maintaining the structural integrity of WAAM-manufactured parts. This paper introduces a methodology using a Deep Neural Network (DNN) model for forecasting the bead geometry in the WAAM process, focusing on gas metal arc welding cold metal transfer (GMAW-CMT) WAAM. This study addresses the challenges of bead geometry prediction by developing a robust predictive framework. Key process parameters, such as the wire travel speed, wire feed rate, and bead dimensions of the previous layer, were monitored using a Coordinate Measuring Machine (CMM) to ensure precision. The collected data were used to train and validate various regression models, including linear regression, ridge regression, regression, polynomial regression (Quadratic and Cubic), Random Forest, and a custom-designed DNN. Among these, the Random Forest and DNN models were particularly effective, with the DNN showing significant accuracy owing to its ability to learn complex nonlinear relationships inherent in the WAAM process. The DNN model architecture consists of multiple hidden layers with varying neuron counts, trained using backpropagation, and optimized using the Adam optimizer. The model achieved mean absolute percentage error (MAPE) values of 0.014% for the width and 0.012% for the height, and root mean squared error (RMSE) values of 0.122 for the width and 0.153 for the height. These results highlight the superior capability of the DNN model in predicting bead geometry compared to other regression models, including the Random Forest and traditional regression techniques. These findings emphasize the potential of deep learning techniques to enhance the accuracy and efficiency of WAAM processes. Full article

This work is devoted to the investigation of dielectric permittivity which is influenced by electronic, ionic, and dipolar polarization mechanisms, contributing to the material’s capacity to store electrical energy. In this study, an extended dataset of 86 polymers was analyzed, and two quantitative structure–property relationship (QSPR) models were developed to predict dielectric permittivity. From an initial set of 1273 descriptors, the most relevant ones were selected using a genetic algorithm, and machine learning models were built using the Gradient Boosting Regressor (GBR). In contrast to Multiple Linear Regression (MLR)- and Partial Least Squares (PLS)-based models, the gradient boosting models excel in handling nonlinear relationships and multicollinearity, iteratively optimizing decision trees to improve accuracy without overfitting. The developed GBR models showed high R² coefficients of 0.938 and 0.822, for the training and test sets, respectively. An Accumulated Local Effect (ALE) technique was applied to assess the relationship between the selected descriptors—eight for the GB_A model and six for the GB_B model, and their impact on target property. ALE analysis revealed that descriptors such as TDB09m had a strong positive effect on permittivity, while MLOGP2 showed a negative effect. These results highlight the effectiveness of the GBR approach in predicting the dielectric properties of polymers, offering improved accuracy and interpretability. Full article

The purpose of this study was to identify the correlates of depressive symptoms and the prevalence of depression, distress, and demoralization among patients with cancer in Taiwan in relation to their sociodemographics. A cross-sectional study design with convenience sampling was used to recruit 191 consecutive patients with cancer from the Cancer Center of a teaching hospital in northern Taiwan. Multiple linear regression was applied to analyze the determinants of depressive symptoms. The prevalence rates of depression (including suspected cases), distress, and demoralization were 17.8%, 36.1%, and 32.5%, respectively. The regression model explained 42.2% of the total variance, with significant predictors including marital status, life dependence, comorbidity, demoralization, and distress. The results demonstrated that higher levels of distress and demoralization were associated with more depressive symptoms. Demoralization and distress played vital roles in moderating depressive symptoms among patients with cancer. Nursing interventions should integrate appropriate mental health services, such as alleviating distress and demoralization, to prevent the occurrence of depression in patients with cancer. Full article

Hurricane incidents have become increasingly frequent along the coastal United States and have had a negative impact on the mangrove forests and their ecosystem services across the southeastern region. Mangroves play a key role in providing coastal protection during hurricanes by attenuating storm surges and reducing erosion. However, their resilience is being increasingly compromised due to climate change through sea level rises and the greater intensity of storms. This article examines the role of remote sensing tools in studying the impacts of hurricanes on mangrove forests in the coastal United States. Our results show that various remote sensing tools including satellite imagery, Light detection and ranging (LiDAR) and unmanned aerial vehicles (UAVs) have been used to detect mangrove damage, monitor their recovery and analyze their 3D structural changes. Landsat 8 OLI (14%) has been particularly useful in long-term assessments, followed by Landsat 5 TM (9%) and NASA G-LiHT LiDAR (8%). Random forest (24%) and linear regression (24%) models were the most common modeling techniques, with the former being the most frequently used method for classifying satellite images. Some studies have shown significant mangrove canopy loss after major hurricanes, and damage was seen to vary spatially based on factors such as proximity to oceans, elevation and canopy structure, with taller mangroves typically experiencing greater damage. Recovery rates after hurricane-induced damage also vary, as some areas were seen to show rapid regrowth within months while others remained impacted after many years. The current challenges include capturing fine-scale changes owing to the dearth of remote sensing data with high temporal and spatial resolution. This review provides insights into the current remote sensing applications used in hurricane-prone mangrove habitats and is intended to guide future research directions, inform coastal management strategies and support conservation efforts. Full article