Search Results (5,120)

Complex-equipment cost estimation often relies on very small historical datasets, and new targets may fall partly outside the feature ranges represented by those data. This study examines whether two observable data-state indicators can provide a transparent basis for configuring Gaussian process regression (GPR) before the target cost is known. The proposed ED-AGPR framework combines a target-specific parameter extrapolation ratio with a case-level sample-to-feature ratio to route each target among three predefined GPR configurations. The empirical assessment retains five source-defined application targets and uses case-specific leave-one-out cross-validation across the three datasets (29 pseudo-target predictions in total), matched component contrasts, conventional and composite GPR baselines, five-seed optimizer-robustness analysis, threshold sensitivity, and predictive-interval coverage diagnostics. Under leave-one-out evaluation, ED-AGPR achieved a mean absolute relative error of 48.51%, compared with approximately 52% for standard Matérn-3/2 and RBF GPR baselines. The paired case-cluster bootstrap descriptive ranges for those standard GPR differences included zero; the component contrasts showed that no single modeling ingredient was uniformly beneficial, and the predictive intervals substantially under-covered their nominal levels. The results therefore support ED-AGPR as an auditable empirical configuration heuristic for the three examined small-sample datasets rather than as an estimator with established predictive superiority or universal deployment readiness. Full article

This paper develops a finite-horizon stochastic optimal harvesting model that links constrained Hamilton-Jacobi-Bellman (HJB) control with a nonlinear Feynman-Kac/BSDE representation. Harvesting effort is bounded, yielding a projected feedback policy with lower-bound, interior, and upper-saturation regimes. Under appropriate regularity conditions, the HJB and BSDE formulations characterize the same value function and optimal feedback through the Markovian relation $Z_s=\sigma X_s{J}_X^{\ast }(s,X_s)$. Numerically, the HJB equation is solved using a monotone implicit upwind Bellman scheme with policy iteration, while the associated BSDE is approximated independently by Monte Carlo conditional-expectation regression, permitting an ex post assessment of numerical consistency. The framework is illustrated using annual capture fisheries production data for the United States, Japan, China, and Indonesia. Country-specific drift and multiplicative volatility are estimated from normalized state-relative increments. The empirical state is interpreted as a normalized capture-production index rather than a biological stock, providing a data-informed illustration of constrained harvesting under stochastic dynamics and uncertainty. Full article

Accurate prediction of droplet impact behavior is essential for applications including spray cooling, coating technologies, additive manufacturing, and inkjet printing. Conventional analytical and empirical models often have limited predictive capability because of the nonlinear interactions among liquid properties, impact conditions, and surface wettability. This study develops machine learning models to predict the maximum spreading coefficient and the critical spreading–splashing threshold velocity using an experimental dataset of more than 700 droplet impacts spanning multiple liquids and hydrophilic, hydrophobic, and superhydrophobic surfaces. Gaussian process regression (GPR) achieved the highest predictive accuracy, predicting the maximum spreading coefficient with coefficients of determination exceeding 0.99 and outperforming widely used empirical correlations. Evaluation using an externally sourced dataset demonstrated satisfactory model transferability, while a second GPR model accurately predicted the critical spreading–splashing threshold velocity within the investigated parameter space. Shapley Additive Explanations (SHAP) and Individual Conditional Expectation (ICE) analyses showed that impact velocity is the dominant predictor of maximum spreading, whereas surface tension primarily governs splash onset, consistent with established droplet-impact physics. These results demonstrate that interpretable machine learning models provide accurate, physically meaningful predictions across diverse liquid–surface systems, in regimes where the input parameters are not scarcely populated, and offer an alternative to conventional empirical correlations. Full article

Background: Indonesia is the world’s third-largest banana producer, yet its value chains remain structurally fragmented, with pervasive information asymmetries and substantial technical inefficiencies among smallholder farmers. Research integrating relational governance determinants with frontier efficiency analysis across upstream, middle, and downstream actors remains limited. Methods: A cross-sectional survey comprising 251 respondents (237 farmers, 5 wholesalers, and 9 retailers) was conducted across Cianjur, Lumajang, and Lampung, of whom 194 provided valid complete cases (181 farmers, 5 wholesalers, and 8 retailers). Constructs used reflective multi-item scales adapted from established supply chain governance literature; validity and reliability were confirmed through confirmatory factor analysis (Cronbach’s α = 0.731–0.941; composite reliability = 0.746–0.940; average variance extracted = 0.348–0.546). Antecedents of supply chain coordination were tested using hierarchical multiple regression on the farmer subsample with complete data (n = 181), and relative technical efficiency across value chain actors was estimated using input-oriented Data Envelopment Analysis (DEA). Results: The comprehensive regression model explained 63.3% of the variance in supply chain coordination performance. Inclusivity (β = 0.251), commitment (β = 0.259), information quality (β = 0.233), and trust (β = 0.160) were significant positive predictors, and a parsimonious model identified commitment and trust as the strongest. DEA showed low mean technical efficiency among smallholder farmers (0.683) relative to retailers (0.934) and wholesalers (0.954), with only modest variation across the three regions. Conclusions: Relational governance mechanisms primarily drive farmer-level supply chain coordination, whereas structural position within the value chain shapes technical efficiency. These empirically validated determinants and efficiency benchmarks can inform targeted agricultural policy. No artificial intelligence system was developed or tested in this study; the results are offered only as candidate inputs that could support the future design of AI-enabled decision-support tools in food supply chain management. Full article

Background/Objectives: Prompt initiation of appropriate antibiotics is essential for sepsis management. While early anti-methicillin-resistant Staphylococcus aureus (MRSA) therapy benefits confirmed cases, unnecessary use in low-risk patients promotes antimicrobial resistance. Current guidelines recommend risk-based empiric coverage, but specific predictors in the emergency department (ED) remain poorly defined. Therefore, this study aims to identify clinical factors independently associated with MRSA infection in ED patients with sepsis. Methods: This retrospective analysis used data from a prospective, nationwide, multicenter cohort in the Korean Sepsis Alliance registry. The study included 11,477 adults with sepsis from 16 tertiary or university-affiliated EDs between September 2019 and December 2022. Factors independently associated with MRSA infection were identified with multivariable logistic regression. Results: MRSA infection occurred in 189 of 11,477 patients (1.65%; 95% confidence interval [CI], 1.43–1.90%), representing 2.86% (95% CI, 2.49–3.29%) of patients with a microbiologically identified causative pathogen. Catheter-related infection showed the strongest association (odds ratio [OR], 5.98; 95% CI, 1.93–18.49). Other factors independently associated with MRSA included hospitalization within 90 days (OR, 1.65; 95% CI, 1.14–2.39) and elevated C-reactive protein ≥ 6 mg/dL (data-derived cutoff; OR, 2.18; 95% CI, 1.43–3.38). Conversely, abdominal (OR, 0.19; 95% CI, 0.09–0.43) and urinary (OR, 0.40; 95% CI, 0.21–0.77) sources were linked to significantly lower risk. Conclusions: MRSA infection was uncommon (1.65%) among ED patients with sepsis. Catheter-related infection was most strongly associated with MRSA, whereas abdominal and urinary sources were associated with lower odds. These findings may help inform risk stratification and support antimicrobial stewardship in the ED. Full article

Background/Objectives: No shoulder-specific percentile reference range exists for postoperative C-reactive protein (CRP) or erythrocyte sedimentation rate (ESR) after reverse shoulder arthroplasty (RSA); clinicians rely on time-invariant thresholds borrowed from lower-extremity cohorts, and the ESR trajectory is uncharacterized. Methods: We retrospectively studied a single-surgeon registry of elective primary RSA (2018–2026); after predefined exclusions, 214 procedures (206 patients) remained, with no infection events by design. CRP and ESR were measured preoperatively and on postoperative days 2, 5, and 14 (exploratory days 1, 3, 7). We computed empirical 50th–95th percentile curves with bootstrap confidence intervals, supported by mixed-effects, quantile-regression, missingness, and within-patient fold-change analyses. Results: CRP peaked on day 2 (95th percentile 13.33 mg/dL); its day 14 95th percentile returned to, and lay numerically below, the preoperative 95th percentile (1.53 versus 2.34 mg/dL). ESR fell on day 2 (77.8% of procedures at or below their preoperative value), then rose and remained elevated at day 14 (95th percentile 56.00 mm/h); upper percentiles exceeded the 30 mm/h threshold at baseline and from day 3 onward. Conclusions: These curves describe an uncomplicated course rather than validated diagnostic thresholds; they provide a shoulder-specific normative reference and ESR trajectory for RSA, and a benchmark for future infection-event cohorts. Full article

The need to promote maternal and child health is a concurrent legislative agenda in many developing countries. This is particularly important in South Sudan due to protracted economic fragility. Although previous studies have examined the determinants of antenatal care (ANC) utilization and exclusive breastfeeding (EBF) among refugees and other vulnerable populations, limited empirical evidence has examined the association between ANC visits and EBF in displacement settings. Therefore, this study determined the association between ANC visits and EBF among refugees and host community members in South Sudan. The study was based on cross-sectional secondary data, which were collected in 2023 by the United Nations High Commissioner for Refugees (UNHCR), as part of the Forced Displacement Survey in South Sudan. The data were analyzed with a control function instrumental variable Poisson regression. The results showed that 61.54% of the women breastfed exclusively for six months and 8.14% did not breastfeed. Refugees reported a higher use of skilled ANC providers. The control function instrumental variable Poisson regression results confirmed the endogeneity of ANC visits, given the statistical significance of the control function parameter (p < 0.05). Also, ANC was significantly and positively associated with the number of months of EBF (p < 0.01). The results showed differences in the correlates of ANC visits and EBF duration across residence statuses. The combined analysis revealed a positive association between ANC visits and consultations with doctors and with nurses, while food problems, unchanged household income, urban residence, and some forms of delivery assistance had negative associations among some population groups. In the pooled and refugee results, civil-issued identification had a positive association with ANC visits. Also, income resilience showed different associations with EBF among host community women, and regional differences were observed among refugees. The findings highlight the need to strengthen access to adequate ANC and integrate breastfeeding counselling into maternal healthcare services delivered by skilled healthcare providers while tailoring interventions to the distinct circumstances of refugee and host community populations. Full article

This paper presents a measurement-driven study of end-to-end latency in an indoor private standalone 5G network deployed at TU Wien IFT TEC-Lab. The testbed combines pico radio units, edge computing resources, and a local 5G core to form a campus-scale private network architecture designed for low-latency communication. To characterize network performance, TCP throughput and UDP one-way latency measurements were collected in a single-user, constant-bit-rate downlink setting at seven predefined indoor locations using iPerf-based tests at six traffic levels (1–500 Mbps), with five repetitions per condition. Based on these measurements, a compact parametric model was calibrated for this deployment to describe latency as a function of achieved bandwidth and location-dependent effects. The results show that latency remained low and relatively stable at low and medium traffic levels, generally staying below 20 ms between 1 and 200 Mbps, but increased more strongly as the operating point approached the practical throughput limit of the setup. The fitted model captured the overall latency trend with an in-sample MAE of 2.52 ms, an RMSE of 3.13 ms, and an $R^2$ of 0.842, while retaining comparable predictive performance under a trial-based test split ($R^2=0.835$) and leave-one-location-out validation ($R^2=0.818$). The compact model also achieved lower out-of-sample errors than the minimal M/M/1-type and polynomial-regression baselines under both validation schemes. Overall, the findings indicate that traffic load was the main driver of latency growth in the studied environment, while spatial effects remained measurable but secondary. The resulting formulation should be understood as an interpretable empirical model of end-to-end latency for one indoor private standalone 5G deployment under single-user, constant-bit-rate downlink conditions, rather than as a general latency model for private 5G networks. Within that scope, the model provides an interpretable description of the measured latency behavior and a basis for preliminary capacity assessment in this deployment. Its applicability to another private 5G network has not been established and would require a new measurement campaign, complete parameter re-estimation, and independent validation. Full article

Due to the intersection of factors such as cultural practices, socioeconomic context and living conditions, not only the educational experiences and outcomes, but also the environmental attitudes of Roma students are potentially different from those of non-Roma students; this suggests that the commonly implemented environmental education programs and recycling awareness campaigns may not be as effective as previously assumed. Yet, empirical research focusing on Roma students’ environmental behavior remains scarce; the aim of this study is to examine the environmental attitudes and recycling behavior of Roma students to inform the existing environmental education approaches and to detect the areas that require attention. Results based on quantitative data showed that most students (57.9%) did not recycle, while the availability of recycling infrastructure emerged as a decisive factor for the respondents who reported recycling. Irrespective of their recycling engagement, most students perceived that environmental protection contributes to quality of life and acknowledged the negative impact of waste on human health. Moreover, categorical regression analysis showed that recycling engagement was associated with parents’ occupation and the place of residence. Finally, despite the socioeconomic challenges, the findings indicate an overall positive attitude towards environmental protection, challenging widespread stereotypical assumptions about this ethnic group. Full article

In this study, we examine whether dividend policy is associated differently with accrual-based earnings management and real earnings management measured through abnormal operating cash flow. We analyze 8839 firm-year observations from 569 non-financial firms listed in the Korea Composite Stock Price Index (KOSPI) market from 1996 to 2024 using firm and year fixed-effects regressions with firm-clustered standard errors. We find no consistent association between dividend yield and accrual-based manipulation, but we find a negative association with abnormal operating-cash-flow manipulation. As dividend yield rises, the within-firm relation between the two measures becomes weaker. Payout burden relative to operating profit provides the primary evidence for this pattern, while the free-cash-flow and operating-cash-flow measures provide corroborating evidence. Cash holdings provide suggestive conditioning evidence, ownership concentration has no reliable moderating role, and the exploratory chaebol analysis is statistically inconclusive because only 267 affiliated firm-years limit power. Alternative real-activity measures do not reproduce the core result, so the evidence is concentrated in abnormal operating cash flow. These findings represent conditional within-firm associations, not managerial intent or a causal effect of dividend policy. These empirical patterns suggest that boards and regulators may consider dividends together with accrual and operating-cash-flow indicators, especially when payouts absorb a large share of internal resources. Full article

Agricultural cooperatives are widely regarded as engines of rural development, yet their role in advancing the economic, social, and environmental pillars of sustainability within specialized subsectors such as beekeeping remains empirically underexplored, particularly in the Gulf Cooperation Council region. This study examines the social and economic determinants of beekeepers’ membership in agricultural cooperatives in the Al-Baha region of Saudi Arabia, home to roughly 70% of the Kingdom’s beekeeping activity. Data were collected from 200 beekeepers through a structured questionnaire and analyzed using Pearson correlation, multiple linear regression, and the Mann–Whitney U test. Only 21% of respondents were cooperative members. While age, family size, experience, hive holding size, harvest frequency, and marketing-practice adoption were all significantly correlated with membership, regression analysis showed that only years of experience, hive holding size, and adoption of marketing practices remained significant predictors when considered jointly, explaining 30.8% of the variance in membership status (R² = 0.308). Mann–Whitney tests further revealed that members differed significantly from non-members in family labor contribution, access to financing, ownership of a marketing shop, and access to extension services, with shop ownership showing the largest effect size (r = 0.497). These findings indicate that cooperative membership is driven primarily by commercial experience and institutional connectedness rather than demographic characteristics, carrying direct implications for income diversification, rural resilience, and the sustainability agenda under Saudi Vision 2030 and Sustainable Development Goals 2, 5, 8, and 15. Full article

We propose an end-to-end differentiable framework for hedonic agricultural land pricing that integrates deep learning-based land cover classification with spatial econometric modeling into a single neural architecture. Traditional hedonic pricing approaches typically separate land cover extraction from price regression, leading to suboptimal feature representations that fail to capture the spatial spillover effects inherent to agricultural markets. In our system, a Swin Transformer-based semantic segmentation network extracts pixel-level land cover features from high-resolution multispectral imagery, which are then aggregated within parcel boundaries to produce composition vectors. These features are combined with static parcel attributes and fed into a graph isomorphism network that models spatial dependencies among neighboring parcels through message passing. The central methodological innovation is a differentiable Moran’s I operator that computes spatial autocorrelation from predicted parcel prices and incorporates this statistic into the training objective as a regularizing loss term. This constraint explicitly penalizes deviations from empirically observed target levels of positive spatial autocorrelation in agricultural land markets, thereby ensuring that the learned land cover features are optimized to explain spatial price clustering rather than generic class categories. The complete pipeline, including the segmentation backbone, graph neural network, and spatial autocorrelation computation, is fully differentiable, allowing gradients from the spatial loss to flow backwards and update pixel-level features. This design transforms land cover classification from a mere preprocessing step into an economically informed feature-learning process. The unified framework thereby produces parcel valuations that are both pixel-accurate and spatially coherent, capturing complex nonlinear dependencies such as irrigation network effects or soil-type continuity that conventional spatial econometric models cannot represent. By jointly optimizing segmentation features and their spatial spillover effects on market prices, our approach represents a significant departure from the two-stage hedonic pricing methodology. Full article

Against China’s dual-carbon goals, carbon emissions and pollution transfer across supply chains hinder systemic low-carbon transformation; the prior literature on ESG and corporate environmental investment mainly focuses on individual firm-level effects, ignoring the unique lead goose governance function of core chain leader enterprises in supply chain networks and their internal transmission mechanisms. Drawing on stakeholder theory, this paper constructs a collaborative green supply chain governance framework led by chain leaders to fill the above research gaps. Based on a sample of Shanghai and Shenzhen A-share listed firms from 2011 to 2023, we identify chain leaders by combining official industrial chain leader lists and total asset threshold standards, with baseline ESG data from Wind and alternative Bloomberg ESG scores for robustness. Two-stage least squares instrumental variable regression addresses endogeneity, while omitted variable sensitivity analysis, indicator replacement and stepwise high-dimensional fixed effects ensure reliable empirical conclusions. The results provide evidence of a significant lead goose spillover effect: each one-unit improvement in chain leaders’ ESG performance is associated with an 8.33% increase in upstream suppliers’ environmental investment at the 1% significance level and downstream clients’ environmental investment by 3.54% at the 5% significance level, yet non-leader enterprises generate no meaningful spillover impacts, and the effect is stronger for upstream partners. Mechanism tests support two core mediating channels: chain leaders’ ESG performance stimulates supply-chain green investment by fostering environmental sensitivity salience and cutting inter-firm transaction costs. Heterogeneity analysis shows the spillover effect is amplified in polluting industries and highly concentrated supply chains. This research extends the emerging literature on supply chain ESG spillovers by documenting the lead goose effect of formally identified chain leaders on partners’ actual environmental investment, distinguishing directional asymmetry, and unveiling dual mediating mechanisms. Full article

With increasing concerns over food safety, fraud, and information asymmetry in global food supply chains, improving transparency has become a key challenge for modern agricultural systems. Traditional supply chains often lack visibility, leading to fragmented data, reduced consumer trust, and difficulties in verifying food product information. Blockchain technology has emerged as a promising solution for enabling decentralised and tamper-resistant data sharing across supply networks. However, despite its theoretical potential, empirical evidence remains limited regarding how blockchain adoption enhances transparency through specific operational mechanisms, such as traceability. Therefore, this study investigates the role of blockchain adoption in improving traceability and strengthening supply chain transparency within agri-food systems. Data were collected from Generation Z university students in Malaysia, selected due to their familiarity with digital technologies and heightened awareness of food sourcing issues. The study is based on a total of 253 usable responses analysed using IBM SPSS Statistics 27, employing reliability analysis, Exploratory Factor Analysis, Pearson correlation, multiple regression analysis, and the PROCESS Macro Model 4 with 5000 bootstrap resamples for mediation testing. The findings indicate that blockchain adoption is significantly and positively associated with traceability, which in turn is positively associated with overall supply chain transparency. Furthermore, traceability is found to act as a mediating factor, serving as the key mechanism through which blockchain generates transparency outcomes, via an indirect-only mediation pathway. Thus, based on this premise, it is evident that blockchain alone is not sufficient unless integrated with effective traceability systems, highlighting the importance of aligning digital infrastructure with physical supply chain processes to strengthen trust and resilience in agricultural systems. Full article

The increasing adoption of machine learning models in corporate valuation has substantially improved predictive accuracy but at the cost of interpretability, a critical limitation in regulated financial environments. This study investigates whether SHAP (Shapley Additive Explanations) can systematically enhance the transparency of XGBoost based valuation models and examines whether the resulting insights extend those of classical linear regression. An empirical analysis was conducted on a cross-sectional dataset of U.S. publicly listed firms (2018), including more than 200 financial indicators. After systematic preprocessing and a hybrid feature selection procedure combining XGBoost importance, mutual information, and correlation based filtering, both an OLS regression and an XGBoost model were trained and validated. XGBoost achieved substantially higher predictive performance (R² = 0.654 vs. 0.364), while SHAP values provided transparent global and local explanations of model decisions. Both models consistently identified earnings before tax and EV to sales as primary value drivers; however, SHAP additionally showed nonlinear effects, threshold behaviors, and context dependent interactions, particularly for EBITDA margin, share buybacks, and asset based indicators, that remain undetectable in linear models. These findings confirm that SHAP significantly enhances model transparency and generates economically meaningful insights beyond classical regression, supporting its application in auditable, regulatory compliant financial modeling. Full article