Search Results (2,731)

Mass timber construction (MTC) has emerged as a sustainable alternative to conventional building systems due to its low carbon footprint, high structural performance, and alignment with the principles of a circular economy. While the environmental and structural advantages of mass timber (MT) are well-documented, its occupational safety implications remain underexplored. This study examines how construction safety professionals in the United States perceive and experience safety in MT projects, and how these perceptions compare to those in conventional concrete and steel construction. To achieve this objective, the data were collected through a national web-based survey of OSHA-authorized construction safety trainers. Analyses were conducted to explore perceptions of occupational safety in MT projects, to compare safety perceptions between MT and conventional materials, and to identify construction hazards and challenges specific to MT construction. Results show that respondents with MT experience generally perceive MT projects as safer than concrete or steel, whereas those without experience tend to be more neutral. However, even among experienced safety professionals in MT, a gap persists between observed and perceived safety hazards. High rates of near misses and non-fatal injuries further indicate operational strain during MT erection. These findings underscore the need for specialized, data-driven safety training and planning frameworks tailored to MT’s distinct workflows. Targeted safety programs can help align perception with reality, thereby improving safety outcomes in this rapidly expanding sustainable construction sector. Full article

In arid and water-stressed regions, groundwater desalination plants are critical for ensuring reliable potable water supplies, making improvements in their operational efficiency and cost effectiveness a priority for utilities. In many such facilities, lime and soda ash softening remain common pretreatment practices, which increase chemical consumption and sludge generation, prompting the need for alternative low-chemical strategies. This study evaluates the technical, operational, and economic implications of transitioning a full-scale brackish groundwater desalination plant, from lime–soda ash softening (old plan) to a low-chemical pretreatment strategy based on antiscalant dosing (new plan) upstream of reverse osmosis (RO). Key parameters, including pH, total hardness, calcium and magnesium hardness, silica, iron, alkalinity, and total dissolved solids (TDS), were measured and compared at multiple locations within the treatment plant under both the old and new plans. Removing lime and soda ash caused higher levels of hardness, alkalinity, and silica in the water before RO treatment, increasing the risk of scaling. Operationally, the feed pressure increased from 11.43 ± 0.16 bar (old plan) to a peak of 25.50 ± 0.10 bar in the new plan, accompanied by a decline in water production. Chemical cleaning effectively restored performance, reducing feed pressure to 13.13 ± 0.05 bar, confirming that fouling and scaling were the primary, reversible causes. Despite these challenges, the plant consistently produced water that complied with Saudi Standards for Unbottled Drinking Water (e.g., pH = 7.18 ± 0.09, TDS = 978.27 ± 9.26 mg/L). Economically, the new strategy reduced operating expenditure by approximately 54% (0.295 → 0.135 $/m³), largely due to substantial reductions in chemical and sludge handling costs, although these savings were partially offset by higher energy consumption and more frequent membrane maintenance. Overall, the findings emphasize the importance of systematic performance evaluation during operational transitions, providing guidance for utilities seeking to optimize pretreatment design while maintaining compliance, long-term membrane protection, and environmental sustainability. Full article

This study aims to develop an entrepreneurial literacy model for mompreneurs that contributes to Sustainable Development Goal (SDG) 1 (poverty reduction) and Sustainable Development Goal (SDG) 8 (decent work and economic growth), focusing on how entrepreneurial literacy transforms into financial behavior and fosters innovation in micro-business management. A qualitative case study was conducted in several districts of Makassar City, Indonesia. Participants were selected based on their status as mompreneurs and their type of business. Data were collected through in-depth interviews, observations, and documentation, then analyzed using a thematic spiral model. Entrepreneurial literacy, obtained through formal and informal education, translates into financial behaviors such as basic planning, financial management, and reporting. Innovation emerges through improved creativity, marketing, customer relationships, product development, and service enhancement. This study proposes a conceptual model linking entrepreneurial literacy, financial behavior, and innovation, offering insights for developing training programs that empower women in entrepreneurship. The scope of this study is limited to mompreneurs operating micro-scale businesses in Makassar City; therefore, the findings cannot be generalized to different socio-economic contexts. Nevertheless, the results provide theoretical implications for enriching entrepreneurial literacy models from an accounting perspective and practical implications for policymakers to design gender-responsive entrepreneurship and financial literacy programs. Full article

This study investigates the conditions under which workplace loneliness influences employees’ help-seeking behavior. Drawing on the conservation of resources theory and the theory of planned behavior, we propose that loneliness does not uniformly discourage interpersonal engagement but can motivate help-seeking under certain circumstances. Using survey data from 260 full-time Korean employees, we find that workplace loneliness is positively associated with help-seeking when employees perceive high levels of transactive memory systems or work meaningfulness. These moderating effects suggest that the negative impact of loneliness on help-seeking can be attenuated or reversed when key contextual and motivational resources are present. We discuss the implications of these findings for understanding workplace loneliness as a potentially adaptive response rather than solely a detrimental experience. Full article

This conceptual article introduces Perceived Cognitive Assistance (PCA)—a novel psychological construct capturing how interactive support from Large Language Models (LLMs) alters investors’ perception of their cognitive capacity to execute complex trading strategies. PCA formalizes a behavioral shift: LLM-empowered retail investors may transition from intuitive heuristics to institutional-grade strategies—sometimes without adequate comprehension. This empowerment–distortion duality forms the theoretical contribution’s core. To empirically validate this model, this article outlines a five-step research agenda including psychological diagnostics, trading behavior analysis, market efficiency tests, and a Behavioral Shift Index (BSI). One agenda component—a dual-agent simulation framework—enables causal benchmarking in post-LLM environments. This simulation includes two contributions: (1) the Virtual Trader, a cognitively degraded benchmark approximating bounded human reasoning, and (2) the Digital Persona, a psychologically emulated agent grounded in behaviorally plausible logic. These components offer methods for isolating LLM assistance’s cognitive uplift and evaluating behavioral implications under controlled conditions. This article contributes by specifying a testable link from established decision frameworks (Theory of Planned Behavior, Technology Acceptance Model, and Risk-as-Feelings) to two estimators: a moderated regression for individual decisions (Equation (1)) and a composite Behavioral Shift Index derived from trading logs (Equation (2)). We state directional, falsifiable predictions for the regression coefficients and for index dynamics, and we outline an identification and robustness plan—versioned, time-locked, and auditable—to be executed in the subsequent empirical phase. The result is a clear operational pathway from theory to measurement and testing, prior to empirical implementation. No empirical results are reported here; the contribution is the operational, falsifiable architecture and its implementation plan, to be executed in a separate preregistered study. Full article

Generation Z is becoming a dominant market segment and an essential source of talent in the hospitality industry. Their digital fluency, sustainability expectations, and preference for meaningful and personalized experiences are increasingly reshaping service delivery, marketing strategies, and workforce management in the hotel industry. Following the PRISMA 2020 guideline, this review systematically analyzed 131 peer-reviewed studies published between 2011 and 2025. Performance analysis, science mapping through co-word and Leiden clustering, and trend analysis were conducted using VOSviewer (v1.6.20) and Biblioshiny in RStudio (v2025.09.2). The findings reveal a rapidly expanding but relatively young field, with key themes clustered around technology acceptance (AI, service robots), experiential and sustainable consumption, digital engagement (word-of-mouth, social media), workforce dynamics (person–environment fit, leadership, quiet quitting), and emerging topics such as experiential education, ethics, and self-efficacy. The study highlights the centrality of the Theory of Planned Behavior and technology acceptance models in explaining Gen Z’s decision-making, while also identifying substantial gaps in cross-cultural, ethical, and experiential research. Practical implications call hoteliers to integrate seamless digital services, robust sustainability initiatives, and adaptive talent management system to meet Gen Z’s evolving expectations. Full article

Oral cancer represents a life-threatening malignancy with profound implications for patient survival and quality of life. Oral squamous cell carcinoma (OSCC), the predominant histological variant of oral cancer, constitutes a substantial healthcare challenge wherein early detection remains critical for therapeutic efficacy and enhanced survival outcomes. Recent advances in deep learning methodologies have demonstrated superior performance in medical imaging applications. However, existing investigations have predominantly employed unimodal image data for oral lesion classification, thereby neglecting the potential advantages of multimodal data integration. To address this limitation, we propose a comprehensive multimodal pipeline for the classification of OSCC versus leukoplakia through the integration of histopathological imagery with tabular data encompassing anatomical characteristics and behavioral risk factors. Our methodology achieved a precision of 0.97, F1-score of 0.97, recall of 0.98, and accuracy of 0.97. These findings demonstrate the enhanced diagnostic precision and efficacy afforded by multimodal approaches in oral cancer classification, suggesting a promising avenue for improved diagnostic accuracy and treatment planning optimization. Full article

This study investigates the gendered implications of Pakistan’s Special Economic Zones (SEZs) under CPEC 2.0, focusing on risks and opportunities for women in the country’s green industrial transition. Using a mixed-methods approach that combines secondary research with multi-stakeholder consultations, including engagement with SEZ authorities, Chinese investors, and women’s professional networks, the paper examines how legal ambiguity, defeminization, and occupational segregation restrict women’s participation and mobility in SEZs. Drawing on global comparative evidence and Pakistan’s specific legal and institutional gaps, the paper argues that SEZs can support gender-equitable industrialization if reforms are integrated into their design and governance. It recommends introducing mandatory gender equity plans in zone licensing, providing targeted skills training for women in high-tech sectors, operationalizing Pakistan’s National Gender Policy Framework within SEZ development, and embedding the Zone Social Responsibility (ZSR) framework across all SEZs to ensure long-term inclusion and empowerment. Full article

Large-scale transitions to eco-friendly vehicle fleets present complex capital budgeting challenges, requiring the integration of extensive operational data with financial modeling while balancing economic profitability and environmental sustainability. Traditional approaches often struggle to manage this complexity and quantify the inherent trade-offs. This study develops and applies an innovative integrated accounting-and-finance framework to evaluate the economic and environmental implications of green fleet transition projects, explicitly quantifying the trade-off between profitability and sustainability. Focusing on waste vehicle replacement of Iren Spa, a leading European multi-utility company, we employ the recently developed Split-Screen Approach, a unified accounting-and-finance framework grounded in the laws of motion and conservation. It automatically reconciles pro forma financial statements and generates internally consistent valuation metrics, eliminating the manual adjustments and inconsistencies of traditional models. Its built-in diagnostic checks and scalability for highly complex datasets overcome the manual adjustments and inconsistencies inherent in traditional financial models. We process 2303 inputs across multiple “green” scenarios. This methodology integrates an Engineering Model, describing fleet evolution, operating costs, and CO₂ reduction, with a HookUp Model, which serves to transform scenarios into well-defined projects. The latter model is then integrated with a Financial Model that generates pro forma financial statements, incorporates financing and payout policies, and assesses economic profitability through Net Present Value (NPV) and consistent accounting rates of return. Together, these elements form a robust framework for managing complex data integration and analysis. Our research reveals a fundamental trade-off: enhanced environmental sustainability (measured by Net Green Value, NGV), which quantifies CO₂ reduction, is achieved at the expense of economic profitability, measured by NPV. This financial sacrifice is captured by the Net Value Curve, a Pareto frontier, while the NPV-to-NGV ratio provides “shadow prices” for CO₂ reduction, revealing the financial cost per unit of sustainability gained. Based on 21 project scenarios and additional sensitivity analyses on financial inputs and energy prices, the results confirm a decreasing relationship between NGV and NPV. This study makes three main contributions: (1) it demonstrates the practical application of the Split- Screen Approach for capital budgeting under complexity, (2) it introduces the Net Value Curve framework as a useful tool for visualizing and quantifying the trade-off between profitability and sustainability, (3) it provides managers and policymakers actionable insights, supporting more informed decisions in green fleet transition planning where economic and environmental objectives may conflict. The findings provide managers and policymakers with a rigorous and transparent accounting-and-finance framework that enhances the reliability of capital budgeting decisions compared with traditional financial modeling, while offering a Paretian frontier for evaluating environmental trade-offs. Full article

In the context of accelerated digitalization, enterprises are increasingly adopting information-driven solutions to support managerial decision-making, with Enterprise Resource Planning (ERP) systems playing a crucial role in organizational development. Despite its importance, ERP adoption varies significantly across industries, particularly between developed and developing economies, where technological and structural differences persist. This paper proposes and validates a classification framework for assessing industry readiness for ERP adoption, based on an integrated PLS-SEM-MCDA methodological approach. PLS-SEM identified statistically significant factors and transformed them into weights to compare ERP user attitudes across eleven industries in Serbia and Slovenia. In addition, the TOPSIS-Sort method classified industries into high, moderate, and low readiness as predefined order classes. Finally, sensitivity analysis and comparative analysis are performed with AHP expert weights and the PROMETHEE-FlowSort method to determine the robustness of the PLS-SEM-TOPSIS-Sort results. The results show that the IT industry is the most consistent in adopting ERP systems. In contrast, other industries exhibit varying levels of readiness, depending on their degree of digital maturity and organizational preparedness. The proposed framework’s methodological flexibility allows it to be adapted to various contexts, making it suitable for future academic research and comparative studies. Additionally, the practical implications of the research are twofold. For ERP suppliers, the findings provide guidance on how to approach market segmentation and strategic positioning tailored to the specific needs of individual industries. For ERP users, their success in ERP adoption can be amplified by using the research insights as a benchmarking model. Full article

This paper presents the results of a structured scoping review (SSR) that explores the integration of the Internet of Things (IoT) and embedded systems in creating a sustainable and interconnected technological ecosystem. The study focuses on water quality monitoring, an area where these technologies have demonstrated significant potential. The SSR follows a meticulous methodology, covering planning, execution, and documentation stages to ensure a comprehensive and unbiased review of the existing literature. Key research questions guide the review, focusing on extracting and analyzing water sample characteristics, using machine learning algorithms for classification, and the technologies utilized in these systems. The search process involved multiple databases, yielding 343 articles, of which 8 met the stringent inclusion and exclusion criteria. The review highlights the widespread use of IoT for real-time data collection and artificial intelligence (AI) for analyzing complex patterns in water quality data. Our findings underscore the significance of temperature, pH, turbidity, and conductivity, commonly utilized in water classification. In addition, prevalent machine learning techniques for analyzing water quality data include K-Nearest Neighbors (KNN) and artificial neural networks (ANN). Despite the advances, challenges such as implementation costs, connectivity in remote areas, and the interpretability of AI models remain. This review underscores the transformative potential of IoT and AI in water quality monitoring, with implications for ensuring safe drinking water and sustainable water resource management. Full article

Background/Objectives: Nurses’ professional values are closely linked to job satisfaction, organizational commitment, and establishing objectives at work. During the COVID-19 pandemic, these relationships became especially crucial, yet they remain underexplored in the context of Southeast Asia. This study aimed to examine how these organizational and motivational factors influence professional values among nurses in a large public hospital in Thailand. Methods: A cross-sectional survey was conducted with 336 registered nurses who had at least six months of experience caring for COVID-19 patients. Standardized instruments were employed to measure job satisfaction, organizational commitment, establishing objectives at work, and professional values, and correlation analyses were conducted to assess associations between variables. Results: Job satisfaction correlated positively with professional values (r = 0.505, β = 0.097), while organizational commitment showed the strongest association (r = 0.620, β = 0.528). Establishing objectives at work was also positively related, though to a lesser extent (r = 0.236, β = 0.086). Conclusions: These findings underscore the importance of supportive work environments that foster motivation, recognition, and career development in sustaining nurses’ professional values, especially in times of crisis. This study also serves as a reference for the development of the nursing workforce in post-pandemic healthcare systems, with implications for international health policy and workforce planning. Full article

Cultural ecosystem services (CES) represent fundamental expressions of human-environment interactions. A comprehensive assessment of CES supply and demand offers a robust scientific foundation for optimizing the transformation of ecosystem service values to improve human well-being. This study integrates multi-source datasets and employs Maximum Entropy (MaxEnt) modeling with the ArcGIS platform to analyze the spatial distribution of CES supply and demand in Hunan Province, a typical mountain tourism regions in China. Furthermore, geographical detector methods were used to identify and quantify the driving factors influencing these spatial patterns. The findings reveal that: (1) Both CES supply and demand demonstrate pronounced spatial heterogeneity. High-demand areas are predominantly concentrated around prominent scenic locations, forming a “multi-core, clustered” pattern, whereas high-supply areas are primarily located in urban centers, water systems, and mountainous regions, exhibiting a gradient decline along transportation corridors and river networks. (2) According to the CES supply-demand pattern, Hunan Province can be classified into demand, coordination, and enhancement zones. Coordination zones dominate (45–70%), followed by demand zones (20–30%), while enhancement zones account for the smallest proportion (5–20%). (3) Urbanization intensity and land use emerged as the primary drivers of CES supply-demand alignment, followed by vegetation cover, distance to water bodies, and population density. (4) The explanatory power of two-factor interactions across all eight CES categories surpasses that of any individual factor, highlighting the critical role of synergistic multi-factorial influences in shaping the spatial pattern of CES. This study provides a systematic analysis of the categories and driving factors underlying the spatial alignment between CES supply and demand in Hunan Province. The findings offer a scientific foundation for the preservation of ecological and cultural values and the optimization of spatial patterns in mountain tourist areas, while also serving as a valuable reference for the large-scale quantitative assessment of cultural ecosystem services. Full article

Background and Objectives: Public health needs collaborative, privacy-preserving analytics, but centralized AI is constrained by data sharing and governance. Federated learning (FL) enables training without moving sensitive data. This review assessed how FL is used for disease prevention in population and public health, and mapped benefits, challenges, and policy implications. Methods: Following PRISMA 2020, we searched PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar for peer reviewed English-language studies from January 2020–30 June 2025, applying FL to surveillance, outbreak detection, risk prediction, or policy support. Two reviewers screened and extracted data with third-reviewer arbitration. Quality was appraised with a tool adapted from MMAT and AI reporting frameworks. No meta-analysis was performed. Results: Of 5230 records identified (4720 after deduplication), 200 full texts were assessed and 19 were included. Most used horizontal FL across multiple institutions for communicable diseases, COVID-19, tuberculosis and some chronic conditions. Reported gains included privacy preservation across sites, better generalizability from diverse data, near real-time intelligence, localized risk stratification, and support for resource planning. Common barriers were non-IID data, interoperability gaps, compute and network limits in low-resource settings, unclear legal pathways, and concerns about fairness and transparency. Few studies linked directly to formal public-health policy or low-resource deployments. Conclusions: FL is promising for equitable, secure, and scalable disease-prevention analytics that respect data sovereignty. Priorities include robust methods for heterogeneity, interoperable standards, secure aggregation, routine fairness auditing, clearer legal and regulatory guidance, and capacity building in underrepresented regions. Full article

Purpose—This study explores the determinants of entrepreneurial intention (EI) among university students, analyzing entrepreneurial motivation (EM) as a mediator and perceived institutional support (PIS) as a moderator within the Theory of Planned Behavior (TPB) framework. Design/Methodology/Approach—Using Partial Least Squares Structural Equation Modeling (PLS-SEM), data from 128 students at the Polytechnic Institute of Cávado and Ave, Portugal, were analyzed to assess direct, indirect, and moderating effects of entrepreneurial attitudes, education, and social norms. Findings—EM significantly mediates the relationship between attitude concerning entrepreneurship (ACE), perceived social norms (PSN), entrepreneurial education (EE), and EI, reinforcing its role in bridging individual and educational influences with entrepreneurial behavior. However, PIS does not significantly moderate the EM-EI relationship, suggesting institutional support alone is insufficient to enhance motivation’s impact on EI. This challenges assumptions about institutional effectiveness and highlights the importance of entrepreneurial ecosystems, social capital, and mentorship networks as alternative enablers. Implications—The study extends TPB by incorporating mediation and moderation effects, offering a deeper understanding of personal, social, and institutional influences on EI. This study contributes by simultaneously modeling entrepreneurial motivation as mediator and perceived institutional support as moderator within a TPB framework. Such integration remains rare, particularly in Southern European higher education contexts, and our findings nuance current assumptions by revealing when institutional supports may fail to strengthen motivational pathways. The findings emphasize the need for education policies that integrate experiential learning, entrepreneurial ecosystems, and mentorship to foster entrepreneurial mindsets. Originality/Value—This research challenges the assumed role of institutional support, highlighting motivation as a key driver of EI and providing new insights into policy-driven entrepreneurship promotion in higher education. Full article