Search Results (3,838)

A structural model of health and mental well-being among academic staff and students in Thailand was constructed and validated through confirmatory factor analysis (CFA). Data were obtained from 600 online questionnaires, equally distributed between staff (n = 300) and students (n = 300). Statistical analyses were undertaken in SPSS. Descriptive statistics were generated, internal reliability was assessed, and correlations were examined. The factor structure was first extracted through exploratory factor analysis (EFA). Model fit was subsequently assessed using CFA in LISREL. Five constructs were derived and validated: mental well-being (18 items), social participation (12 items), health literacy (28 items), work–life balance (10 items), and health behaviour (30 items). Convergent validity was demonstrated across all constructs. The final CFA model was found to exhibit a robust fit (χ² = 145.14, df = 62, p < 0.001, RMSEA = 0.047). Strong convergent validity and excellent fit indices were confirmed. Empirical evidence was therefore provided to support the model’s application in assessing health and mental well-being within Thai academic contexts. Full article

With the surge in urban logistics demand, traditional surface transportation faces challenges, such as traffic congestion and environmental pollution. Leveraging metro systems in metropolitan areas for both passenger commuting and underground logistics presents a promising solution. The metro-based underground logistics system (M-ULS), characterized by extensive coverage and independent right-of-way, has emerged as a potential approach for optimizing urban freight transport. However, existing studies primarily focus on single-line scenarios, lacking in-depth analyses of multi-tier network coordination and dynamic demand responsiveness. This study proposes an optimization framework based on mixed-integer programming and an improved ICSA to address three key challenges in metro freight network planning: balancing passenger and freight demand, optimizing multi-tier node layout, and enhancing computational efficiency for large-scale problem solving. By integrating E-TOPSIS for demand assessment and an adaptive mutation mechanism based on a normal distribution, the solution space is reduced from five to three dimensions, significantly improving algorithm convergence and global search capability. Using the Nanjing metro network as a case study, this research compares the optimization performance of independent line and transshipment-enabled network scenarios. The results indicate that the networked scenario (daily cost: CNY 1.743 million) outperforms the independent line scenario (daily cost: CNY 1.960 million) in terms of freight volume (3.214 million parcels/day) and road traffic alleviation rate (89.19%). However, it also requires a more complex node configuration. This study provides both theoretical and empirical support for planning high-density urban underground logistics systems, demonstrating the potential of multimodal transport networks and intelligent optimization algorithms. Full article

Count data are present in many areas of everyday life. Unfortunately, such data are often characterized by over- and under-dispersion. In 1986, Efron introduced the Double Poisson distribution to account for this problem. The aim of this work is to examine the application of this distribution in regression analyses performed in health-related literature by means of a narrative review. The databases Science Direct, PBSC, Pubmed PsycInfo, PsycArticles, CINAHL and Google Scholar were searched for applications. Two independent reviewers extracted data on Double Poisson Regression Models and their applications in the health and life sciences. From a total of 1644 hits, 84 articles were pre-selected and after full-text screening, 13 articles remained. All these articles were published after 2011 and most of them targeted epidemiological research. Both over- and under-dispersion was present and most of the papers used the generalized additive models for location, scale, and shape (GAMLSS) framework. In summary, this narrative review shows that the first steps in applying Efron’s idea of double exponential families for empirical count data have already been successfully taken in a variety of fields in the health and life sciences. Approaches to ease their application in clinical research should be encouraged. Full article

Hydrogen has been explored as a greener alternative for greenhouse gas emissions reduction. Sodium borohydride (NaBH₄) is a favorable hydrogen carrier due to its high hydrogen content, safe handling, and rapid hydrogen release. This work presents a novel synthesis of the catalyst CoFe₂O₄/Fe₂O₃ using nanocellulose fibers (TCNF) as reactive templates for metal adsorption and subsequent calcination. The resulting material was tested for H₂ production from basic NaBH₄ aqueous solutions (10–55 °C). The catalyst’s composition is 74.8 wt% CoFe₂O₄, 25 wt% Fe₂O₃, and 0.2 wt% Fe₂(SO₄)₃ with agglomerated spheroidal particles (15–20 nm) and homogeneous Fe and Co distribution. The catalyst produced 1785 mL of H₂ in 15 min at 25 °C (50 mg catalyst, 4.0% NaBH₄, and 2.5 wt% NaOH), close to the stoichiometric maximum (2086 mL). The maximum H₂ generation rate (HGR) reached 3.55 L min⁻¹ g_cat⁻¹ at 40 °C. Activation energies were determined using empirical (38.4 ± 5.3 kJ mol⁻¹) and Langmuir–Hinshelwood (L–H) models (42.2 ± 5.8 kJ mol⁻¹), consistent with values for other Co-ferrite catalysts. Kinetic data fitted better to the L–H model, suggesting that boron complex adsorption precedes H₂ evolution. Full article

Decentralized renewable energy (DRE) has emerged as a key tool for global energy transition and emissions reduction. While DRE has the potential to democratize energy production, evidence suggests it may cause unequal benefit distribution across population groups. This study provides the first comprehensive empirical analysis of DRE distribution patterns across all Israeli municipalities, examining policy implications for equitable energy transitions. We analyzed 16,998 rooftop solar installations across 232 municipalities between 2017 and 2022, categorized as residential and commercial installations. Using regression analysis, we examined how geographic, socioeconomic, and demographic factors associate with installation adoption rates. Findings reveal divergent patterns between installation types. For residential installations, socioeconomic status emerged as the primary determinant, with adoption rates increasing linearly with municipal wealth. These disparities widened significantly over time, contradicting expectations that declining costs would democratize access. For commercial installations, the urban–rural divide proved dominant, with rural areas showing substantially higher adoption rates. Our analysis reveals important policy implications and recommendations for global DRE deployment, emphasizing the need to integrate equity considerations into renewable energy policy design to accelerate the transition to renewable energy while minimizing socioeconomic disparities. Full article

Background/Objectives: Accurate estimation of caloric intake from breastfeeding is essential for understanding infant nutrition during early life. However, most existing models rely on fixed assumptions and do not reflect the natural variability in feeding behaviors and human milk composition. This study aims to provide a realistic estimation of breast milk (BM) caloric intake throughout infancy using a probabilistic approach based on empirical data. Methods: A probabilistic model was developed using four variables: feeding frequency, volume per feeding, caloric density, and infant weight. Systematic reviews were conducted to inform the input values of the first three variables, and meta-analyses were performed when feasible. Infant weight was based on World Health Organization (WHO) growth standards. Variables were stratified by age and integrated into the model through appropriate probability distributions. Monte Carlo simulations were conducted to estimate caloric intake per kilogram of body weight, expressed both per day and per feeding, across all age groups. Results: The model showed a progressive decline in daily caloric intake per kilogram with age, consistent with decreasing feeding frequency and the introduction of complementary foods. In contrast, caloric intake per feeding increased with age. These findings align with WHO energy intake targets during exclusive breastfeeding and reflect expected physiological changes in infant growth and feeding behavior. Conclusions: This study provides a probabilistic framework for estimating BM caloric intake across infancy, accounting for interindividual and age-related variability. It offers a valuable research tool to support future studies on infant nutrition and feeding behavior using realistic, data-driven assumptions. Full article

Exploring the impact of industrial land misallocation (ILM) on sustainable urban development (SUD) helps provide strong empirical support for SUD from the perspective of land factor allocation. Based on panel data from 283 cities between 2009 and 2021, this paper systematically analyzes the impact mechanism and spatial spillover effects of ILM on SUD from the perspective of factor misallocation. The results show that most Chinese cities face a surplus-type misallocation of industrial land, and resource allocation urgently needs optimization. During the study period, the overall level of SUD increased and exhibited a spatial gradient distribution characterized by high levels in the east and low levels in the west. ILM significantly inhibited the improvement of SUD, with the negative impact being particularly pronounced in central-western regions and non-resource-based cities. ILM also showed a significant negative spatial spillover effect. Mechanism analysis found that ILM mainly negatively affected SUD by hindering industrial transformation and upgrading as well as the progress of urban technological innovation. Further research found that the implementation of the policy for exit audits of natural resource assets alleviated the problem of ILM to a certain extent and weakened its adverse effects on SUD. Therefore, deepening efforts to correct ILM is a key measure to break resource allocation barriers and promote SUD. Full article

This paper analyzes high-frequency intraday Bitcoin data from 2019 to 2022. The Bitcoin market index exhibits two distinct periods, characterized by abrupt volatility shifts. Bitcoin returns can be described by anomalous diffusion processes, transitioning from subdiffusion for short intervals to weak superdiffusion at longer intervals. Heavy tails are captured well by q-Gaussian distributions, and the autocorrelation of absolute returns shows power law behavior. Both periods display multifractality, with Hurst exponents shifting toward 0.5 over time, indicating increased market efficiency. The time evolution of the empirical PDF of price return allows us to connect these stylized facts to the mathematical framework of multifractals and locally fractional porous medium equations. Full article

Egypt, the world’s second-largest wheat importer, has been working hard to narrow the gap between its domestic wheat production and consumption. However, these efforts have been hampered by water scarcity and the negative impact of climate change on wheat production. This study seeks to analyze the influence of climatic and technical factors on wheat production in Egypt over the long and short term. Using Egypt-specific data from 1961 to 2022 and employing the Autoregressive Distributed Lag (ARDL) model and Granger-causality, the study examines the impact of factors such as harvested area, fertilizers, technology, CO₂ emissions, seasonal temperature and precipitation patterns (winter and spring) on wheat production in Egypt. The empirical results indicate that the harvested area, level of technology, and average winter temperature significantly and positively impact wheat production. Precisely, a 1% increase in these factors leads to a 1.08%, 1.49%, and 6.89% increase in wheat production, respectively. Conversely, a 1% rise in CO₂ emissions, average spring temperature, and precipitation reduced wheat production by 1.76%, 0.52%, and 0.054%, respectively. The Granger causality results indicate a bidirectional causal relationship between wheat production and harvested area. Furthermore, the technology level exhibits a significant causal influence on wheat production, cultivated area, and CO₂ emissions, highlighting its pivotal role in both the wheat production process and its environmental impact. In conclusion, this study is crucial for Egypt’s future food security. By identifying the key climatic and non-climatic factors that impact wheat production, policymakers can gain valuable insights to address climate change and resource limitations. Improving domestic production through technological advancements, effective resource utilization, and climate-resilient practices will ensure a sustainable food supply for Egypt’s expanding population in the face of global uncertainties. Full article

Wind turbines face significant operational challenges due to their complex electromechanical systems, exposure to harsh environmental conditions, and high maintenance costs. Reliable structural health monitoring and condition monitoring are therefore essential for early fault detection, minimizing downtime, and optimizing maintenance strategies. Traditional approaches typically rely on either Trend Monitoring (TM) or Change Point Detection (CPD). TM methods track the long-term behaviour of process parameters, using statistical analysis or machine learning (ML) to identify abnormal patterns that may indicate emerging faults. In contrast, CPD techniques focus on detecting abrupt changes in time-series data, identifying shifts in mean, variance, or distribution, and providing accurate fault onset detection. While each approach has strengths, they also face limitations: TM effectively identifies fault type but lacks precision in timing, while CPD excels at locating fault occurrence but lacks detailed fault classification. This review critically examines the integration of TM and CPD methods for wind turbine diagnostics, highlighting their complementary strengths and weaknesses through an analysis of widely used TM techniques (e.g., Fast Fourier Transform, Wavelet Transform, Hilbert–Huang Transform, Empirical Mode Decomposition) and CPD methods (e.g., Bayesian Online Change Point Detection, Kullback–Leibler Divergence, Cumulative Sum). By combining both approaches, diagnostic accuracy can be enhanced, leveraging TM’s detailed fault characterization with CPD’s precise fault timing. The effectiveness of this synthesis is demonstrated in a case study on wind turbine blade fault diagnosis. Results shows that TM–CPD integration enhances early detection through coupling vibration and frequency trend analysis with robust statistical validation of fault onset. Full article

Climate change is transforming energy use at the residential level by increasing temperature fluctuations and sustaining extreme weather events. This study proposes a climate-reactive, multi-objective approach to integrate the demand response (DR) with distributed generation (DG) and power quality improvement under a multi-objective framework of an integrated climate-adaptive approach to residential energy management. A cognitive neural network combination model with bidirectional long short-term memory networks (bidirectional) and a self-attention mechanism was used to successfully predict temperature-sensitive loads. The hybrid deep learning solution, which applies convolutional and bidirectional long short-term memory (LSTM) networks with attention, predicted the temperature-dependent load profiles optimized with an enhanced modified grey wolf optimizer (MGWO). The results of the experimental studies indicated significant gains in performance: in energy expenditure, the studies reduced it by 32.7%; in peak demand, they were able to reduce it by 45.2%; and in self-generated renewable energy, the results were 28.9% higher. The solution reliability rate provided by the MGWO was 94.5%, and it converged more quickly, thus providing better diversity in the Pareto-optimal frontier than that of traditional metaheuristic algorithms. Sensitivity tests with climate conditions of +2 °C and +4 °C showed strategy changes as high as 18.3%, thus establishing the flexibility of the system. Empirical evidence indicates that the energy and peak demand are to be cut, renewable integration is enhanced, and performance is strong in fluctuating climate conditions, highlighting the adaptability of the system to future resilient smart homes. Full article

Limited-Entry Liner (LEL) technology has emerged as a transformative solution for enhancing hydrocarbon recovery in unconventional reservoirs while addressing challenges in carbon sequestration. This review examines the role of LEL in optimizing acid stimulation, hydraulic fracturing and production optimization, focusing on its ability to improve fluid distribution uniformity in horizontal wells through precision-engineered orifices. By integrating theoretical models, experimental studies, and field applications, we highlight LEL’s potential to mitigate the heel–toe effect and reservoir heterogeneity, thereby maximizing stimulation efficiency. Based on a comprehensive review of existing literature, this study identifies critical limitations in current LEL models—such as oversimplified annular flow dynamics, semi-empirical treatment of wormhole propagation, and a lack of quantitative design guidance—and aims to bridge these gaps through integrated multiphysics modeling and machine learning-driven optimization. Furthermore, we explore its adaptability for controlled CO₂ injection in geological storage, offering a sustainable approach to energy transition. This work provides a comprehensive yet accessible overview of LEL’s significance in both energy production and environmental sustainability. Full article

In this article, we propose a goodness-of-fit test for a one-parameter Lindley distribution based on energy statistics. The Lindley distribution has been widely used in reliability studies and survival analysis, especially in applied sciences. The proposed test procedure is simple and more powerful against general alternatives. Under different settings, Monte Carlo simulations show that the proposed test is able to be well controlled for any given nominal levels. In terms of power, the proposed test outperforms other existing similar methods in different settings. We then apply the proposed test to real-life datasets to demonstrate its competitiveness and usefulness. Full article

This research investigates the statistical correlation between Vickers hardness and tensile properties of helical submerged arc welded high-strength low-alloy (HSLA) API X70 pipeline steel. Tensile tests were performed on cross-weld joints from 138 pipe specimens. Vickers hardness measurements were also conducted on 138 samples to evaluate the hardness distribution across the base metal, fusion zone, and heat-affected zone. Results show that the fusion zone exhibits the highest hardness, correlating with enhanced tensile strength (R² = 82%). Linear regression models indicate that base metal hardness significantly influences yield strength (R² = 71%), while moderate negative correlations exist with elongation (R² = 54%). These findings suggest that hardness measurements can serve as a non-destructive predictive tool for tensile properties, improving weld quality and mechanical performance. This research provides empirical models that enhance the application of API X70 in critical engineering applications, improving pipeline safety and reliability. Full article

This study examines the dynamic relationship between the gold markets of India and the United States from 2005 to 2025. Recognising gold’s role as a hedge and safe-haven during market uncertainty, we employ the Autoregressive Distributed Lag (ARDL) model to assess long-term co-integration and apply the Toda–Yamamoto causality test to evaluate directional influences. Additionally, the Generalised Autoregressive Conditional Heteroskedasticity (GARCH) (1, 1) model is applied to examine volatility spillovers. Results reveal no long-term co-integration between the two markets, suggesting they function independently over time. However, unidirectional causality is observed from the U.S. to the Indian gold market, and the GARCH model confirms bidirectional volatility transmission, indicating interconnected short-run dynamics. These findings imply that gold market shocks in one country may affect short-term pricing in the other, but not long-term trends. From a portfolio diversification and risk management perspective, investors may benefit from allocating assets across both markets. This study contributes a novel empirical framework by integrating ARDL, Toda–Yamamoto Granger causality, and GARCH(1, 1) models over a two-decade period (2005–2025), incorporating post-COVID market dynamics. The combination of these methods, applied to both an emerging (India) and developed (U.S.) economy, provides a comprehensive understanding of gold market interdependence. In doing this, the paper offers valuable insights into causality, volatility transmission, and diversification potential. The econometric rigour of the study is enhanced through residual diagnostic tests, including tests of normality, autocorrelation, and other heteroscedasticity tests, as well as VAR stability tests. These ensure strong inference and model validity; more specifically, they are pertinent to the analysis of financial time series. Full article