Search Results (199)

This study investigates the mediating role of self-awareness within the broader framework of business acumen, emphasizing its connection to entrepreneurial accounting proficiency and financial performance in community enterprises across Thailand. The purpose is to advance theoretical understanding by integrating metacognition theory and the resource-based view (RBV), and to provide practical insights for strengthening grassroots entrepreneurship. Using survey data from 210 enterprises, a hybrid Structural Equation Modeling–Artificial Neural Network (SEM–ANN) approach is applied to capture both linear and nonlinear relationships among cognitive, technical, and financial variables. The results confirm that accounting proficiency has a significant and positive effect on self-awareness with value of 0.125. However, self-awareness does not exert a direct influence on financial performance. These findings suggest that self-awareness may function as a cognitive enabler, facilitating the translation of entrepreneurial skills into effective decision-making, rather than serving as an independent predictor of financial outcomes. Empirical patterns further reveal that commercial enterprises report higher self-awareness than service firms, unregistered enterprises show greater awareness than registered ones, and financially stable firms display lower awareness, suggesting complacency or overconfidence. In contrast, regular participation in training significantly enhances awareness, underscoring the role of continuous learning. Full article

Bioactive peptides have significant potential for applications in pharmaceuticals, the food industry, and cosmetics due to their wide spectrum of biological activities. However, their pronounced structural and functional heterogeneity complicates the classification and prediction of biological activity. This study uses data from the PepLab platform, comprising 2748 experimentally confirmed bioactive peptides distributed across 15 functional classes, including ACE inhibitors, antimicrobial, anticancer, antioxidant, toxins, and others. For each peptide, the amino acid sequence and key physicochemical descriptors are provided, calculated via the integrated DMPep module, such as GRAVY index, aliphatic index, isoelectric point, molecular weight, Boman index, and sequence length. The dataset exhibits class imbalance, with class sizes ranging from 14 to 524 peptides. An innovative methodology is proposed, combining descriptive statistical analysis, structural modeling via DEMATEL, and structural equation modeling with neural networks (SEM-NN), where SEM-NN is used to capture complex nonlinear causal relationships between descriptors and functional classes. The results of these dependencies are integrated into a multi-class machine learning model to improve interpretability and predictive performance. Targeted data augmentation was applied to mitigate class imbalance. The developed classifier achieved predictive accuracy of up to 66%, a relatively high value given the complexity of the problem and the limited dataset size. These results confirm that integrating structured dependency modeling with artificial intelligence is an effective approach for functional peptide classification and supports the rational design of novel bioactive molecules. Full article

Geopolymers are an environmentally sustainable class of low-calcium alkali-activated materials (AAMs), distinct from high-calcium C–A–S–H gel systems. Synthesized from aluminosilicate-rich precursors such as fly ash, metakaolin, slag, waste glass, and coal gasification fly ash (CGFA), geopolymers offer a significantly lower carbon footprint, valorize industrial by-products, and demonstrate superior durability in aggressive environments compared to Ordinary Portland Cement (OPC). Recent advances in thermodynamic modeling and phase chemistry, particularly in CaO–SiO₂–Al₂O₃ systems, are improving precursor selection and mix design optimization, while Artificial Neural Network (ANN) and hybrid ML-thermodynamic approaches show promise for predictive performance assessment. This review critically evaluates geopolymer chemistry and composition, emphasizing precursor reactivity, Si/Al and other molar ratios, activator chemistry, curing regimes, and reaction mechanisms in relation to microstructure and performance. Comparative insights into alkali aluminosilicate (AAS) and aluminosilicate phosphate (ASP) systems, supported by SEM and XRD evidence, are discussed alongside durability challenges, including alkali–silica reaction (ASR) and shrinkage. Emerging applications ranging from advanced pavements and offshore scour protection to slow-release fertilizers and biomedical implants are reviewed within the framework of the United Nations Sustainable Development Goals (SDGs). Identified knowledge gaps include standardization of mix design, LCA-based evaluation of novel precursors, and variability management. Aligning geopolymer technology with circular economy principles, this review consolidates recent progress to guide sustainable construction, waste valorization, and infrastructure resilience. Full article

Career decisions are among the most consequential choices individuals make, profoundly shaping their long-term stability and overall life satisfaction. The literature suggests that behavioral biases, specifically overconfidence, herd mentality, social comparison, status quo bias, and optimism bias, can exert considerable influence on these decisions, thereby shaping students’ future career trajectories. This study adopts a behavioral perspective to examine how these biases influence career choices within a collectivist social context. A survey of 360 undergraduate and graduate business students was conducted. The collected data were analyzed using an integrated approach that combines Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Networks (ANN), enabling the use of both linear and non-linear methods to analyze the relationship between cognitive biases and career choices. Our findings reveal that while all five biases have a measurable impact, status quo bias and social comparison are the dominant factors influencing students’ career decisions. These results underscore the need for interventions that foster self-awareness, independent decision-making, and critical thinking. Such insights can guide educators, career counselors, and policymakers in designing programs to mitigate the negative effects of cognitive biases on career decision-making, ultimately enhancing career satisfaction and workforce efficiency. Full article

Drilling-induced damage in fiber-reinforced polymer composite materials was measured excavating four laminates, basalt (B₁₄), glass (G₁₄) and their two sandwich type hybrids (B₄G₆B₄, G₄B₆G₄), with 6 mm twist drills at 1520 revolutions per minute and 0.10 mm rev⁻¹ under dry running with an uncoated high-speed steel (HSS-R), grind-coated high-speed steel (HSS-G) or physical vapor deposition-coated (high-speed steel coated with Titanium Nitride (TiN) and Titanium Aluminum Nitride (TiAlN)) drill bits. The hybrid sheets were deliberately incorporated to clarify how alternating basalt–glass architectures redistribute interlaminar stresses during drilling, while the hard, low-friction TiN and TiAlN ceramic coatings enhance cutting performance by forming a heat-resistant tribological barrier that lowers tool–workpiece adhesion, reduces interface temperature, and thereby suppresses thrust-induced delamination. Replacement of an uncoated, grind-coated, high-speed-steel drill (HSS-G) with the latter coats lowered the mechanical and thermal loads substantially: mean thrust fell from 79–94 N to 24–30 N, and peak workpiece temperatures from 112 °C to 74 °C. Accordingly, entry/exit oversize fell from 2.5–4.7% to under 0.6% and, from the surface, the SEM image displayed clean fiber severance rather than pull-out and matrix smear. By analysis of variance (ANOVA), 92.7% of the variance of thrust and 86.6% of that of temperature could be accounted for by the drill-bit factor, thus confirming that the coatings overwhelm the laminate structure and hybrid stacking simply redistribute, but cannot overcome, the former influence. Regression models and an artificial neural network optimized via meta-heuristic optimization foretold thrust, temperature and delamination with an R² value of 0.94 or higher, providing an instant-screening device with which to explore industrial application. The work reveals TiAlN- and TiN-coated drills as financially competitive alternatives with which to achieve ±1% dimensional accuracy and minimum subsurface damage during multi-material composite machining. Full article

Emotion recognition in conversations is a key task in natural language processing that enhances the quality of human–computer interactions. Although existing deep learning and Transformer-based pretrained language models have shown remarkably enhanced performances, both approaches have inherent limitations. Deep learning models often fail to capture the global semantic context, whereas Transformer-based pretrained language models can overlook subtle, local emotional cues. To overcome these challenges, we developed EmoBERTa–CNN, a hybrid framework that combines EmoBERTa’s ability to capture global semantics with the capability of convolutional neural networks (CNNs) to extract local emotional features. Experiments on the SemEval-2019 Task 3 and Multimodal EmotionLines Dataset (MELD) demonstrated that the proposed EmoBERTa–CNN model achieved F1-scores of 96.0% and 79.45%, respectively, significantly outperforming existing methods and confirming its effectiveness for emotion recognition in conversations. Full article

Serious games (SGs) have been widely employed in the digital preservation and transmission of architectural heritage. However, the key determinants and underlying mechanisms driving users’ continuance intentions toward ancient-architecture cultural heritage serious games (CH-SGs) have not been thoroughly investigated. Accordingly, a conceptual model grounded in the stimulus–organism–response (S–O–R) framework was developed to elucidate the affective and behavioral effects experienced by CH-SG users. Partial least squares structural equation modeling (PLS-SEM) and artificial neural networks (ANNs) were employed to capture both the linear and nonlinear relationships among model constructs. By integrating sufficiency logic (PLS-SEM) and necessity logic (necessary condition analysis, NCA), “must-have” and “should-have” factors were identified. Empirical results indicate that cultural authenticity, knowledge acquisition, perceived enjoyment, and design aesthetics each exert a positive influence—of varying magnitude—on perceived value, cultural identification, and perceived pleasure, thereby shaping users’ continuance intentions. Moreover, cultural authenticity and perceived enjoyment were found to be necessary and sufficient conditions, respectively, for enhancing perceived pleasure and perceived value, which in turn indirectly bolster CH-SG users’ sustained use intentions. By creating an immersive, narratively rich, and engaging cognitive experience, CH-SGs set against ancient architectural backdrops not only stimulate users’ willingness to visit and protect heritage sites but also provide designers and developers with critical insights for optimizing future CH-SG design, development, and dissemination. Full article

This study investigates how university students adopt large language models (LLMs) for online consumption of sustainable products, integrating perceived value theory with the technology acceptance model (TAM). Cross-sectional survey data were analyzed using structural equation modeling (SEM) and artificial neural networks (ANNs). SEM results reveal partial mediation. Performance expectancy value (PEV) and information quality value (IQV) directly shape continue using intention (CUI). They also influence CUI indirectly through perceived ease of use (PEU) and perceived usefulness (PU). Green self-identity value (GSV) influences CUI both directly and via PEU, while trust transfer value (TTV) and green perceived value (GPV) affect CUI only via PEU. ANN findings confirm this hierarchy, as PU (86.7%) and PEU (85.7%) are the strongest predictors of CUI, followed by GSV (73.7%). Convergent evidence from both methods indicates that instrumental utility, effortless interaction, and sustainability identity congruence drive sustained LLM use in the context of online consumption of green products, whereas credibility cues and sustainability incentives play secondary roles. This study extends TAM by incorporating multidimensional value constructs and offers design recommendations for engaging and high-utility AI shopping platforms. Full article

This study explores the enhanced photocatalytic performance of boron-doped zinc oxide (ZnO) nanoparticles synthesized via a scalable mechanochemical route. Utilizing X-ray diffraction (XRD) and scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS), the structural and morphological properties of these nanoparticles were assessed. Specifically, nanoparticles with 1 wt%, 2.5 wt%, and 5 wt% boron doping were analyzed after calcination at temperatures of 500 °C, 600 °C, and 700 °C. The obtained results indicate that 1 wt% B-ZnO nanoparticles calcined at 700 °C show superior photocatalytic efficiency of 99.94% methyl orange degradation under UVA light—a significant improvement over undoped ZnO. Furthermore, the study introduces a predictive model using the artificial neural network (ANN) technique, developed in Python, which effectively forecasts photocatalytic performance based on experimental conditions with R² = 0.9810. This could further enhance wastewater treatment processes, such as heterogeneous photocatalysis, through ANN-guided optimization. Full article

Traditional coal quality assessment methods rely exclusively on the laboratory testing of physical samples, which impedes detailed stratigraphic evaluation and limits the integration of intelligent precision mining technologies. To resolve this challenge, this study investigates geophysical logging as an innovative method for coal quality prediction. By integrating scanning electron microscopy (SEM), X-ray analysis, and optical microscopy with interdisciplinary methodologies spanning mathematics, mineralogy, and applied geophysics, this research analyzes the coal quality and mineral composition of the Shanxi Formation coal seams in northern Jiangsu, China. A predictive model linking geophysical logging responses to coal quality parameters was established to delineate relationships between subsurface geophysical data and material properties. The results demonstrate that the Shanxi Formation coals are gas coal (a medium-metamorphic bituminous subclass) characterized by low sulfur content, low ash yield, low fixed carbon, high volatile matter, and high calorific value. Mineralogical analysis identifies calcite, pyrite, and clay minerals as the dominant constituents. Pyrite occurs in diverse microscopic forms, including euhedral and semi-euhedral fine grains, fissure-filling aggregates, irregular blocky structures, framboidal clusters, and disseminated particles. Systematic relationships were observed between logging parameters and coal quality: moisture, ash content, and volatile matter exhibit an initial decrease, followed by an increase with rising apparent resistivity (LLD) and bulk density (DEN). Conversely, fixed carbon and calorific value display an inverse trend, peaking at intermediate LLD/DEN values before declining. Total sulfur increases with density up to a threshold before decreasing, while showing a concave upward relationship with resistivity. Negative correlations exist between moisture, fixed carbon, calorific value lateral resistivity (LLS), natural gamma (GR), short-spaced gamma-gamma (SSGG), and acoustic transit time (AC). In contrast, ash yield, volatile matter, and total sulfur correlate positively with these logging parameters. These trends are governed by coalification processes, lithotype composition, reservoir physical properties, and the types and mass fractions of minerals. Validation through independent two-sample t-tests confirms the feasibility of the neural network model for predicting coal quality parameters from geophysical logging data. The predictive model provides technical and theoretical support for advancing intelligent coal mining practices and optimizing efficiency in coal chemical industries, enabling real-time subsurface characterization to facilitate precision resource extraction. Full article

This research aims to synthesize a novel hydrogel bio-composite based on natural clay, sodium alginate (Na-AL), and iota-carrageenan as adsorbents to remove phosphate ions from aqueous solutions. The adsorbents were characterized by a variety of techniques, such as Fourier-transform infrared (FTIR) spectroscopy, scanning electron microscopy coupled with energy dispersive X-rays (SEM-EDX), and the determination of point zero charge (PZC). This research investigated how the adsorption process is influenced by parameters such as adsorbent dose, contact time, solution pH, and temperature. In this study, we used four isotherms and four kinetic models to investigate phosphate ion removal on the prepared bio-composite. The results showed that the second-order kinetic (PSO) model is the best model for describing the adsorption process. The findings demonstrate that the R² values are highly significant in both the Langmuir and Freundlich models (very close to 1). This suggests that Langmuir and Freundlich models, with a diversity of adsorption sites, promote the adsorption of phosphate ions. The maximum adsorbed amounts of phosphate ions by the bio-composite used were 140.84 mg/g for H₂PO₄⁻ ions and 105.26 mg/g for HPO₄²⁻ ions from the batch system. The positive ∆H° confirms the endothermic and physical nature of adsorption, in agreement with experimental results. Negative ∆G° values indicate spontaneity, while the positive ∆S° reflects increased disorder at the solid–liquid interface during phosphate uptake. The main parameters, including adsorbent dosage (mg), contact time (min), and initial concentration (mg/L), were tuned using the Box–Behnken design of the response surface methodology (BBD-RSM) to achieve the optimum conditions. The reliability of the constructed models is demonstrated by their high correlation coefficients (R²). An R² value of 0.9714 suggests that the model explains 97.14% of the variability in adsorption efficiency (%), which reflects its strong predictive capability and reliability. Finally, the adsorption behavior of phosphate ions on the prepared bio-composite beads was analyzed using an artificial neural network (ANN) to predict the process efficiency. The ANN model accurately predicted the adsorption of phosphate ions onto the bio-composite, with a strong correlation (R² = 0.974) between the predicted and experimental results. Full article

Organic dyes are pollutants that threaten aquatic life and human health. These dyes are used in various industries; therefore, recent research focuses on the problem of their removal from wastewater. The aim of this study is to examine the clay/gum arabic nanocomposite (CG/NC) as an adsorbent to adsorb methylene blue (MB) and crystal violet (CV) dyes from synthetic wastewater. The CG/NC was characterized using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), and Brunaure–Emmett–Teller (BET). The effect of parameters that may influence the efficiency of removing MB and CV dyes was studied (dosage of CG/NC, contact time, pH values, initial concentration, and temperature), and the optimal conditions for removal were determined. Furthermore, an artificial neural network (ANN) model was adopted in this study. The results indicated that the adsorption behavior adhered to the Langmuir model and conformed to pseudo-second-order kinetics. The results also indicated that the removal efficiency reached 99%, and q_max reached 66.7 mg/g and 52.9 mg/g for MB and CV, respectively. Results also proved that CG/NC can be reused up to four times with high efficiency. The ANN models proved effective in predicting the process of the removal, with low mean squared errors (MSE = 1.824 and 1.001) and high correlation coefficients (R² = 0.945 and 0.952) for the MB and CV dyes, respectively. Full article

Significant weather-induced delays often plague construction projects in India’s extremely cold regions, yet comprehensive studies addressing this issue remain scarce. This study aims to fill this gap by identifying key delay factors and proposing mitigation strategies for the construction industry. Through an extensive literature review, 42 delay factors were identified and categorized into four groups. A survey of 83 experts from cold regions was conducted to evaluate these factors’ significance to contractors and subcontractors. Employing exploratory factor analysis (EFA), structural equation modeling (SEM), and artificial neural networks (ANN), the study analyzed the relationships between these factors and ranked their impact. The findings reveal that snowfall, rainfall, and low temperatures are the most significant contributors to delays, with snowfall being the most influential (significance: 1.000), followed by rainfall (0.890) and low temperatures (0.790). This research establishes a risk hierarchy and develops a predictive model to facilitate the proactive scheduling of challenging tasks during favorable seasons. This study advances the understanding of weather-induced delays in India’s cold regions and offers valuable insights for project management in such climates. However, it underscores the importance of clearly articulating its novel contributions to differentiate it within the existing literature on weather-related construction delays. Full article

The present study deals with the fabrication of activated carbon from longan peels (LPAC) using a phosphoric acid (H₃PO₄) activation method and an evaluation of LPAC’s capability for the adsorption of Reactive Black 5 (RB5) dye from aqueous solutions. The synthesized LPAC was characterized using XRD, SEM, FT-IR, and EDX, confirming a porous, carbon-rich structure with the dominant elemental composition of carbon (85.21%) and oxygen (12.43%), and a surface area of 1202.38 m²/g. Batch adsorption experiments revealed that optimal performance was achieved at pH 3.0, with equilibrium reached after 240 min. The experimental data were well fitted to the Elovich model p, suggesting a heterogeneous adsorption process with diffusion limitations. The intraparticle diffusion model further supported a multi-stage mechanism involving both film diffusion and intraparticle transport. Isotherm studies conducted at varying temperatures (293–323 K) showed a maximum adsorption capacity exceeding 370 mg/g. The adsorption data fit best with the Freundlich (R² = 0.962) and Temkin (R² = 0.970) models, indicating multilayer adsorption on a heterogeneous surface. Thermodynamic analysis revealed that the adsorption process was spontaneous and endothermic, with ΔG° values ranging from −23.15 to −26.88 kJ/mol, ΔH° = 14.23 kJ/mol, and ΔS° = 0.127 kJ/mol×K, consistent with physisorption as the dominant mechanism. Predictive modeling using an artificial neural network (ANN) achieved superior accuracy (R² = 0.989 for RRE; R² = 0.991 for q) compared to multiple linear regression (MLR). Calculation from ANN indicated that pH and contact time were the most influential factors for RB5 removal efficiency, while initial dye concentration and temperature were most critical for adsorption capacity. Furthermore, LPAC demonstrated excellent reusability, retaining over 83% removal efficiency after five adsorption–desorption cycles. These findings confirm that LPAC is an efficient and renewable adsorbent for the treatment of RB5 dye in wastewater treatment applications. Full article

Nowadays, reverse osmosis (RO) desalination has become a highly effective and economical solution to address water scarcity worldwide. The membranes used in this type of separation are influenced by both pre-treatment operations and feed water quality, leading to fouling, a complex phenomenon responsible for reducing treatment performance and shortening membrane lifespan. In this study, an autopsy of a RO membrane from the Boujdour plant was performed, and a fouling prediction tool based on transmembrane pressure (TMP) was developed using MATLAB/Simulink (R2015a) with an artificial neural network (ANN) model. The impact of membrane fouling on treatment performance was also examined through one year of monitoring. A detailed analysis of the fouled membrane was conducted using SEM/EDS techniques to characterize the fouling on the membrane’s surface and cross-section. The results revealed significant fractures on the membrane surface, with fouling predominantly consisting of organic deposits (characterized by a high oxygen concentration of 39.69%) and inorganic fouling, including Si (7.99%), Al (2.79%), Mg (1.56%), Fe (1.27%), and smaller quantities of K (0.87%), S (0.36%), and Ca (0.12%). The ANN model for predicting transmembrane pressure was successfully developed, achieving a high R² value of 92.077% and a low mean square error (MSE) of 0.005657. This predictive model demonstrates the ability to forecast future TMP cycles based on historical data. The research provides a detailed understanding of the types of fouling affecting RO membranes and contributes to the development of preventive strategies to mitigate membrane fouling. Full article