Search Results (18,822)

Background: Optimal ordering in purchasing garment products for international buying houses has become increasingly complex, particularly when companies manage a variety of products across geographically dispersed national and international locations. Existing studies focus on non-integer problems and do not address objectives that minimize the absolute value of a cost or profit function. Methods: This paper proposes a method to determine optimal order quantities for garment styles while satisfying box constraints and the required demand of the wholesale business. The approach minimizes over- and under-ordering costs by reducing total order quantities and fitting orders to the historical demand distribution of each size in international supply chain operations.Results: The model was applied to a garment company operating under cooperative purchasing agreements with international traders. The approach successfully identified optimal order quantities while accounting for operational constraints, minimum order requirements, and historical demand patterns.Conclusions: The findings demonstrate that the proposed method can effectively support purchasing decisions in international garment supply chains. By minimizing over- and under-ordering costs while meeting demand requirements, the model provides a practical framework for improving order allocation and inventory management. The results are encouraging and indicate the usefulness of the approach to international buying house operations. Full article

The relatively slow pace of progress in achieving most of the objectives set forth in the 2030 Agenda for Sustainable Development (2030 Agenda) indicates a rather pessimistic outlook for their timely implementation. This, in turn, calls into question not only the attainment of the declared targets but also the sustainability of global society amid intensifying social, economic, and environmental challenges. These circumstances necessitate a reconsideration of existing approaches and mechanisms for achieving these goals, along with the more active engagement of all relevant stakeholders and a broadening of the range of potential solutions. The study demonstrates that progress toward the Sustainable Development Goals (SDGs) can be positively influenced by the implementation of contemporary paradigms such as Industry 4.0, Industry 5.0, Society 5.0, the green and circular economy, and open innovation. However, these paradigms primarily address only specific dimensions of sustainable development. This results in the current imbalance in progress toward the SDGs. The authors argue that addressing this imbalance requires intensifying the implementation of these contemporary paradigms through the formation and development of human resources as key drivers of progress. This should be achieved by ensuring the complementarity of knowledge, skills, and competencies in core domains with sustainability-oriented perspectives. In this context, artificial intelligence technologies are considered a key enabling instrument, as their rapidly evolving capabilities open up new opportunities for enhancing the efficiency and effectiveness of human resources. Accordingly, the purpose of this study is to substantiate the role of artificial intelligence technologies in shaping and developing human resources within the framework of priority societal development paradigms. The successful implementation of these paradigms can contribute to accelerating sustainable development. This article is conceptual in nature and constitutes a systematic literature review. The research methodology is based on a comprehensive review of scholarly literature on sustainable development, contemporary economic and societal paradigms, and digital technologies, available in the Google Scholar database. As a result of the research, 89 studies were selected based on the PRISMA methodology. The study also draws upon analytical reports on the implementation of the SDGs, as well as reviews of sustainable development practices and artificial intelligence adoption. General scientific and specialized research methods were employed, including theoretical and historical analysis, induction and deduction, and comparative and gap analysis. The findings provide a theoretical substantiation of the role of artificial intelligence in generating a synergistic effect from the implementation of modern development paradigms in the economy and society, which can have a positive impact on the dynamics of the SDGs. Full article

Open-pit metal mines require carbon-management tools that connect heterogeneous operational data with process-level accounting and prospective production-plan assessment. This study developed an integrated carbon-emission assessment and decarbonization decision-support system combining a MySQL database, standardized data interfaces and batch imports, a process-level accounting model, and a production-plan-driven scenario model. The boundary covers drilling, blasting, loading, haulage, crushing, beneficiation, and tailings handling, with land-disturbance-induced carbon-stock loss assessed separately. The deployed system was evaluated at the Julong open-pit copper mine using 2025 historical records for computational verification and the 2027 monthly production plan for conditional scenario assessment. Absolute relative differences for electricity, diesel, explosives, and mine-wide emissions were below 3%, with a mean absolute relative difference of 0.99%. Under the 2027 baseline, annual emissions were 1762.53 kt CO₂ eq. The combined mitigation scenario reduced emissions by 21.65% and lowered emission intensity from 8.49 to 6.65 t CO₂ eq/t concentrate. These results show that the system integrates historical carbon-emission assessment with production-plan-driven decarbonization screening and converts process data into traceable evidence for comparing mitigation options. It therefore serves as a carbon-focused decision-support tool for sustainability-oriented mine planning, although broader applicability requires multi-mine validation and explicit uncertainty analysis. Full article

Deep groundwater dynamics, influenced by multiple factors, exhibit multi-scale periodicity, nonlinearity, and non-stationarity, posing significant challenges for reliable groundwater level prediction and anomaly detection. To address this issue, this study develops a hybrid deep learning model for simulating and predicting normal groundwater dynamics, which integrates multi-source observations—including barometric pressure, solid tide, and historical groundwater levels—with convolutional neural networks (CNNs), Long Short-Term Memory (LSTM), and Attention mechanisms. An Exponentially Weighted Moving Average (EWMA) control chart is then applied to the prediction residuals for statistical anomaly detection. The results show that CNN kernels capture multi-period local features induced by barometric and tidal effects; LSTM gating mechanisms learn long-term temporal dependencies; and the Attention mechanism dynamically assigns weights to both temporal steps and input features. Compared with single-source inputs or standalone LSTM/CNN models, the hybrid model enhances prediction accuracy (MAE < 0.02, RMSE < 0.03, R² > 0.99), while integration with the EWMA control chart helps to identify the possible anomalies. Collectively, these results provide a reliable means for predicting deep groundwater levels and detecting hydrological anomalies under complex multi-scale periodic forcings, holding significant practical value in groundwater resource management and disaster warning. Full article

Dali’s lakeside and mountain landscapes, historic communities, and Bai cultural heritage support a diverse homestay sector in which competitiveness increasingly depends on the guest experience. This study examines the association between Homestay Experiencescape and Tourist Loyalty toward the Focal Homestay and the distinct roles of Perceived Value of the homestay tourism experience and Tourist Satisfaction within this association. Data were obtained from 480 Chinese domestic tourists who had stayed overnight in a Dali homestay during the previous 12 months. Respondents were recruited across four homestay categories through purposive quota sampling that combined onsite QR-code surveys with targeted online invitations. Partial least squares structural equation modeling was used to estimate direct and indirect relationships. A more favorable Homestay Experiencescape was associated with greater Perceived Value and Tourist Satisfaction. Perceived Value was positively associated with Tourist Satisfaction, and both constructs were positively related to property-specific loyalty. Bootstrap analysis supported indirect associations through each evaluation separately and an ordered indirect association through Perceived Value and Tourist Satisfaction. Within the purposively sampled Dali homestay context, the findings show how a six-dimensional Homestay Experiencescape is associated with property-specific loyalty through distinct value and satisfaction evaluations, while the observed relationships should not be interpreted as nationally representative of homestays across China. Full article

Several thousands of osseous artefacts are stored in museum collections, yet older finds often remain overlooked or dismissed as mere typological references with limited research potential. Advances in analytical methods have improved the ability to study these specimens, even when their contextual information is lost. This paper examines three rediscovered antler axe/adze heads from the Historical Museum at Lund University, without known morphological parallels in other Swedish collections. Through radiocarbon dating, ZooMS analysis, and technological- and micro-wear studies, they contribute to our understanding of manufacturing traditions, resource availability and movement in postglacial landscapes. The study brings into focus the interaction between osseous and lithic axes/adzes in Preboreal Scania, Sweden, 9700–8500 BCE, focusing on raw material availability and tool morphology. Archeologically investigated sites from this period are scarce in Scandinavia, and poor preservation of organic materials hinders detailed assessments of chronologies and the relative importance and exploitation patterns of different species. The Eurasian moose (Alces alces) has been proposed as one of the first postglacial species to immigrate, and as a significant prey species for humans. It subsequently decreased substantially in the archaeological record during the Mesolithic. Here we compile faunal data to evaluate patterns of raw material choices. Although axes were typically crafted from lithic materials, osseous implements resembling axes have been found at sites with good preservation in northeastern Europe. The results suggest previously undocumented craft influences among Preboreal forest dwellers of Scania, which may have been of relevance for the continuous change of “becoming Mesolithic”. Full article

Sustainable governance of linear industrial heritage involves not only the preservation of physical fabric, but also the dynamic coordination of historical meaning, continued use, spatial transformation, and public participation. Taking the Jingzhang Railway as a case, this study draws on participatory heritage conservation and knowledge translation to examine the relationships among institutional framing, professional knowledge translation, public interpretation, and spatial differentiation. The analysis integrates 13 policy and planning documents, 119 academic publications, and 27,646 valid Weibo posts, using TF-IDF, CiteSpace, LDA, MacBERT-based emotion classification, and cross-corpus interpretive comparison. The findings show that the institutional positioning of the Jingzhang Railway has expanded from statutory protection toward conservation-and-use integration, urban renewal, regional coordination, and public-space development, while professional discourse translated these objectives into operational heritage practices centred on corridors, heritage parks, spatial renewal, and interpretive nodes. Across the 2022 observation boundary, transport and engineering meanings remained persistent, while themes of historical memory, commemoration, spatial renewal, and public experience became more prominent. In the emotion analysis, neutral expression accounted for 68.21% of the full corpus and joy for 17.17%; at Qinglongqiao, the joy-classified share increased from 21.9% to 30.7%, illustrating node-level differentiation in affective expression. Based on these findings, the study proposes a recursive governance framework of institutional framing, professional translation, public interpretation, and adaptive feedback. It further argues that participatory heritage conservation should be understood as an ongoing process of interpretation, negotiation, and feedback among plural heritage values, professional knowledge, and locally situated public meanings. For linear industrial heritage, sustainable governance therefore requires both the maintenance of corridor-scale historical continuity and differentiated conservation and reuse pathways that respond to the spatial, functional, and social contexts of individual nodes. Full article

The widespread adoption of lithium-ion batteries as energy storage components necessitates accurate prediction of their remaining useful life, which is critical to ensuring operational safety and enabling timely maintenance. To address this challenge, this study proposes a data-driven deep multiple kernel extreme learning machine (DMK-ELM) model, optimized by an improved chaotic sparrow search algorithm (ICSSA), for remaining useful life prediction of lithium-ion batteries. The model constructs a mapping from historical health features to future capacity degradation, strictly adhering to a causal prediction protocol that avoids information leakage. The proposed ICSSA enhances the quality of the initial population and bolsters the global search capability through an improved Tent chaotic sequence initialization and a Gaussian variation strategy. This optimization framework is employed to fine-tune the hyperparameters of the DMK-ELM model. To comprehensively evaluate battery degradation, experimental data from ten cells across two distinct datasets are analyzed. Health features, including constant current charging time (CCCT), constant voltage charging time (CVCT), internal resistance, and incremental capacity peak value (ICCP), are extracted and refined using a causally implemented Hampel filter combined with Pearson correlation analysis performed exclusively on training data. The results confirm the accuracy and robustness of the proposed approach, offering a practical strategy for precise remaining useful life estimation in lithium-ion batteries through the extraction of highly correlated health features. Full article

Research centers and institutes in university and entrepreneurial settings are vital to advancing science, translating evidence into practice, and sustaining mission-driven programs. However, they face pressures that threaten organizational infrastructure and especially external funding. Further, there is limited literature about transformational change and sustainability within research organizations. To help fill this gap, we present two historical case studies, spanning 1984–2025, with practical applications of a university research institute, a nonprofit research institute, and a center within a national research institute. A heuristic framework grounded in organizational change and sustainability literature is presented to examine transformational change and sustainability over time and across research settings. We discuss challenges and strategies to overcome restrictions to change and sustainability, as well as successes, failures, and lessons learned. Case study limitations and future research recommendations are also presented. These case studies contribute to organizational change scholarship and offer practical guidance for sustaining research organizations. Full article

Background/Objectives: Early systemic antibiotics are central to open-fracture care, but a 60 min operational target does not establish a biological infection threshold. We examined how time origin, cutoff, and outcome definition affect interpretation of reported associations. Methods: Evidence was reconstructed from two preserved bibliographic inventories containing 24 entries. Complete database-search exports were unavailable, and selection, extraction, ROBINS-E, and GRADE were not independently duplicated. The protocol was archived retrospectively on OSF (doi:10.17605/OSF.IO/7J8QN). Estimates were described separately by time origin, cutoff, adjustment, outcome, and follow-up for a target population aged at least 18 years. No pooled estimate was retained. Results: The historical evidence map contained 16 reports, including contextual reports that did not establish applicability to the target population. This is not a verified final included-study count. Within the preserved inventory, no threshold-by-time-origin group supplied two compatible adjusted estimates. A 191-patient cohort reported an adjusted odds ratio of 1.02 (95% CI 0.39–2.68) at 60 min after emergency-department arrival. Recorded appraisals indicated predominantly high or very high risk of bias and very low certainty; these judgements were not independently verified. Conclusions: Distinguishing care-process targets from causal thresholds improves interpretation of the available reports. However, incomplete searching and single-investigator assessment limit the reliability and completeness of this synthesis. The observational associations establish neither causal thresholds nor the safety of delay and do not justify postponing indicated prophylaxis. Full article

The sustainable development of valley-type historic towns is increasingly challenged by tourism growth, infrastructure modernization, and community change. Effective responses require a systematic understanding of how spatial morphology mediates the trade-offs between heritage conservation and socio-economic vitality. However, existing studies rarely connect configurational analysis with actionable sustainability strategies in topographically constrained settings. This study addresses this gap by developing a multi-level comparative framework that integrates GIS-based environmental assessment, space syntax configurational modelling, and building-fabric fieldwork. The framework is applied to three historic towns in the upper Yellow River Basin—Qingcheng, Hekou, and Hongcheng—which share similar valley constraints but diverged in historical functions (commercial, defensive, and mixed). Four dimensions are examined, mountain–water patterns, street networks, building fabric, and public-space systems, using indicators including elevation, slope, distance to water, axial integration, intelligibility, shop-front proportion, street aspect ratio, and public-node service radius. The results reveal two complementary layers of spatial logic. At the environmental level, all three towns exhibit shared valley-adaptive traits—near-water locations on relatively gentle terrain, low-slope terrace occupation, axial elongation, and concentrated public spaces. Flood risk was not directly assessed in this study. At the functional level, marked differentiation emerges. Hekou shows the highest network intelligibility (R² = 0.900) and commercial frontage (35% shop-front proportion), which may be interpreted in relation to its commercial and transportation history; however, socio-economic vitality was not directly measured. Hongcheng displays lower intelligibility (R² = 0.556) and thicker defensive walls (0.6–0.8 m), which may be interpreted in relation to its defensive history; however, cultural resilience and economic adaptability were not directly measured. Qingcheng presents a mixed pattern with a single high-integration axis and diverse public nodes, which may be interpreted as functional layering; this interpretation is theoretical and was not directly tested. Rather than offering a universal model, this study provides an evidence-based framework for diagnosing the sustainability implications of spatial form in valley-type historic towns. The findings translate morphological indicators into differentiated management strategies—prioritising commercial corridor continuity in Hekou, preserving defensive spatial sequences in Hongcheng, and balancing functional layering along the historic axis in Qingcheng. This framework contributes to sustainable heritage management by demonstrating how spatial analysis can inform context-sensitive planning that reconciles conservation with development in environmentally constrained settings. Full article

(1) Background: Research on non-software small and medium-sized enterprises (SMEs) uses agile to describe named management practices, related approaches such as Lean, and organizational capabilities. Conflating these constructs obscures what firms actually do. (2) Methods: We revisited a systematic search of Scopus and Web of Science that identified 502 records and yielded an original set of 53 studies. We linked each record to its PDF and extracted study context, design, principal finding, and reported enabling or limiting factors. (3) Results: The map distinguishes 11 practice-focused records, 8 on adjacent approaches, 25 on agility as a capability, and 9 retained in a separate peripheral category. These are thematic strata of the historical set; the 44 non-peripheral records form the domain-focused synthesis. Some practice-focused records assess awareness, feasibility, or proposed models rather than observed adoption. (4) Conclusions: The literature supports a differentiated account of agile practices and agility in non-software SMEs; it does not establish a general causal effect of Agile Methodology adoption on SME performance. Historical screening gaps and the absence of an independently verified risk-of-bias appraisal limit certainty. Full article

Flood seasonality strongly influences flood risk and its management worldwide. Anthropogenic climate change and land-surface human activities are widely recognized as the two major drivers of its evolution, yet their relative contributions remain poorly quantified. In this study, the factorial experiment design of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) was used to disentangle the individual and combined effects of these two drivers on global flood seasonality. Using observation-driven ISIMIP2a simulations, the ability of the ISIMIP framework to reproduce two key seasonality metrics—mean flood timing and flood timing concentration—was first evaluated. Historical and future changes in flood seasonality under RCP6.0 and RCP8.5 were then assessed at multiple spatial scales, including global grid cells, 32 major river basins, and Köppen–Geiger climate zones. Quantitative results show that north of 40° N, under the influence of land-surface human activities, the proportion of grid cells exhibiting earlier mean flood timing under RCP6.0 increases from 54.43% (climate-only) to 65.76%, with the largest change observed in the cold climate zone (median: −0.0708). In contrast, the tropical zone shows a delayed trend (median: +0.0051). Spatially, a pronounced polarization pattern emerges: high-latitude cold regions experience widespread earlier and more dispersed flood timing, while low-latitude tropical regions exhibit later and more concentrated flood timing. At the basin scale, under RCP8.5, the Pearl River Basin shows the strongest delay (ΔF = +0.1356), whereas the St. Lawrence River Basin shows the strongest advance (ΔF = −0.07445). Land-surface human activities amplify or even reverse the direction of changes in mean flood timing. When human activities are included, 31 out of 32 major basins exhibit earlier flood timing, with seven basins reversing from delay to advance. The anthropogenic climate change signal emerges over most of the globe during the first half of the 21st century, and the timing of emergence is closely linked to the warming threshold. This study reveals the modulating role of land-surface human activities in the evolution of flood seasonality and quantifies the emergence time and warming conditions associated with the anthropogenic signal, providing a scientific basis for basin-scale flood-risk management and climate adaptation strategies. Full article

Background: Vision impairment (VI) in Saudi Arabia is common, largely preventable or treatable, and increasingly linked to chronic disease rather than solely to the causes that have historically dominated blindness. Recent Saudi-specific evidence places adult VI prevalence at approximately 17% and blindness at under 2%, with diabetic retinopathy affecting roughly one in three Saudi adults with diabetes; however, no study has yet examined how this burden has trended nationally over time using the full 1990–2023 window made available by the Global Burden of Disease (GBD) 2023 round. This study quantified trends in the burden of VI in Saudi Arabia by severity, cause, sex, and age, and characterised the contribution of population ageing to the observed pattern. Methods: This repeated cross-sectional trend analysis used GBD 2023 national-level modelled estimates for Saudi Arabia (1990–2023). The estimated annual percentage change (EAPC) was calculated via log-linear regression on age-standardised prevalence and years-lived-with-disability (YLD) rates. A Durbin-Watson check confirmed autocorrelation in every annual series, and because GBD point estimates carry their own reported uncertainty, EAPC confidence intervals were derived two further ways alongside the naive ordinary-least-squares (OLS) interval: a Newey-West heteroskedasticity- and autocorrelation-consistent (HAC) interval, and a Monte Carlo interval that resampled each annual estimate within its GBD-reported 95% uncertainty interval. A trend is reported here as confirmed only where all three methods agreed on direction, and as suggestive where they did not. Segmented-regression sensitivity checks, cause decomposition, and comparison of age-standardised against crude rates were used to characterise breakpoints and the contribution of ageing. Results: Age-standardised blindness and severe VI declined substantially and robustly (blindness prevalence EAPC = −2.60%/year, confirmed decreasing in females and males separately, both p < 0.05 on a formal sex-difference test), while moderate VI rose robustly overall and among males specifically (EAPC = +0.61%/year); the female-specific rise in moderate VI, and the decline in presbyopia, were directionally consistent but not robust to the choice of uncertainty method. Diabetes-attributable rates increased faster than any other tracked cause and did so robustly under every uncertainty specification (crude prevalence EAPC ≈ +4.47%/year), and males carried a disproportionate, formally sex-different share of diabetes-attributable blindness relative to females; these tracked causes covered only 44–51% of the total VI envelope, so cause-specific conclusions are restricted to the causes examined. The largest proportional increases in case counts were concentrated in working-age adults (30–59 years) rather than older adults, and the gap between crude and age-standardised trends is consistent with an important contribution from Saudi Arabia’s changing age structure, although the study design does not support assigning a specific numerical fraction to that contribution. Segmented-regression analysis further indicated that the national decline in blindness and the rise in moderate VI were concentrated in 1990–2019 and 1990–2008 respectively, with the post-breakpoint segments no longer showing a statistically distinguishable trend. Conclusions: Saudi Arabia’s VI burden is shifting in composition rather than simply declining. Diabetes-related causes, sex differences, and working-age adults emerge as priorities for vision-health policy, and the post-breakpoint plateaus identified here warrant continued monitoring in future GBD rounds. Full article

This study characterized mercury (Hg) associated with PM_2.5 in Xinxiang, China, using samples collected throughout 2015, providing a historical perspective on its temporal variation, relationships with meteorological conditions, gaseous pollutants, carbon components, water-soluble ions, and trace elements. The annual mean mercury concentration in PM_2.5 in Xinxiang was 0.399 ng/m³. Significant seasonal differences were observed (p < 0.001), with Hg concentrations in winter significantly higher than those in other seasons. The mercury concentration in PM_2.5 during the heating period was higher than that in the non-heating period. Mercury was significantly positively correlated with SO₂, CO, PM₂.₅, OC, EC, Cl⁻, Zn, and Ca, and negatively correlated with temperature, O₃, and V (p < 0.05). The non-carcinogenic risk assessment showed that ingestion was the dominant exposure pathway, followed by dermal contact and inhalation. The mean hazard index was 2.06 × 10⁻³ for children and 8.46 × 10⁻⁴ for adults, both well below 1, suggesting a low estimated non-carcinogenic risk under the exposure assumptions used in this assessment. Full article