Search Results (200)

Food cold chains rely on refrigeration, cold storage, refrigerated transport, packaging and monitoring systems that consume electricity and fuel while preserving food safety, quality and shelf life. Although many studies propose energy-saving technologies or optimization models for individual cold-chain operations, less is known about how energy-efficiency actions are distributed across refrigeration, logistics and digital monitoring domains, which actors must collaborate to implement them, and which benefits and barriers shape adoption. This paper presents a systematic literature review supported by bibliometric and structured content analysis. Searches in Scopus and Web of Science identified 3930 records before deduplication. After removing out-of-year records and duplicates, 2368 unique records were screened; 896 reports were sought for full-text assessment; 751 reports were retrieved and assessed; 466 studies were included in the final review corpus; and 408 were coded as an applied/action corpus. The synthesis identifies ten energy-efficiency action families, seven cold-chain stage classes, multi-actor configurations, evidence types, collaboration-intensity levels, energy benefits, non-energy benefits and implementation barriers. Transport, routing and distribution is the largest action family (134 records), followed by cold storage and refrigeration technology (66), digital monitoring and information sharing (58), life-cycle assessment, energy assessment and decision support (36), energy systems and renewable cooling (34), packaging and thermal insulation (33), and inventory, and planning and coordination (27). The findings show that food cold-chain energy efficiency is not only a technical refrigeration problem but also a collaborative implementation challenge: many actions require information sharing, coordinated operating decisions, joint investment, data governance or cost/benefit-sharing mechanisms. The review contributes an action-oriented framework that links energy-saving actions to stages, actors, collaboration requirements, benefits and barriers, and it identifies future research priorities on comparable energy metrics, measured savings, renewable cooling, digital twins, demand-side flexibility and governance of collaborative energy-efficiency investments. Full article

High renewable-energy shares do not, by themselves, guarantee that renewable electricity can be absorbed, shifted, and delivered through a constrained network. This study uses openTEPES for a deterministic 8736 h assessment of Tunisia’s 2035 power system, represented by 47 domestic nodes. It compares matched cases without and with endogenous battery energy storage system (BESS) investment and evaluates eight sensitivity families. In the no-BESS case, 265.996 GWh of modelled energy not served (ENS) and 1120.263 GWh of renewable curtailment occur in disjoint hours, indicating a temporal mismatch between scarcity and surplus. Under the central assumptions, the model selects 3.590 GW/14.361 GWh of BESS, reducing modelled ENS to zero under the represented deterministic conditions and renewable curtailment to 42.362 GWh. The sensitivity analysis shows that the model-selected BESS capacity and location, together with transmission exposure, depend strongly on demand, imposed duration, renewable-profile stress, interconnection availability, and candidate siting. Within the modelled scenario space, storage, internal transmission, and cross-border exchange therefore provide complementary forms of flexibility. The central BESS quantity is interpreted as a conditional planning benchmark, not as evidence of probabilistic adequacy or a national procurement target. Full article

Background: In-center hemodialysis is the dominant kidney replacement therapy modality in Greece and generates substantial recurring expenditure for the public payer. Home hemodialysis is not currently implemented at national scale, but it may reduce long-term public expenditure if early investment in training capacity, equipment and home support is recovered over time. Objective: To evaluate, from the Greek public-payer perspective, the discounted budget impact and net present value (NPV) of implementing a national home hemodialysis program for 300 patients. Methods: We developed a deterministic investment-appraisal model comparing gradual implementation of home hemodialysis with continued in-center hemodialysis for the same projected cohort over 10 years. The in-center comparator was informed by a 2022 Greek patient-level micro-costing study. Home hemodialysis expenditure was constructed from explicit patient-flow equations, resource quantities, unit costs, capital purchases and hospital-tariff offsets. Annual incremental savings were discounted at 3% in the base case. Alternative discount rates, deterministic one-way sensitivity analyses and program-scale scenarios were examined. Fiscal benefit–cost ratio (BCR) and return on investment (ROI) were also calculated. Results: Undiscounted 10-year public expenditure was EUR 69,681,804 for home hemodialysis and EUR 86,700,267 for continued in-center hemodialysis, yielding savings of EUR 17,018,463. During the first 5 years, the program required EUR 1,724,012 in additional expenditure. At a 3% discount rate, NPV was EUR 12,696,564, the fiscal BCR was 2.89, fiscal ROI was 189.3% and discounted payback occurred during year 6. NPV remained positive at 5% (EUR 10,391,382) and across all tested one-way scenarios (range EUR 706,179 to EUR 24,686,948). Conclusions: The modeled national home hemodialysis program generated a positive 10-year public-payer NPV under the base-case and tested sensitivity assumptions. A positive NPV is not, however, a formal Greek health-system decision rule and does not capture health outcomes, patient and family costs, or equity. The findings support staged pilot implementation and prospective collection of Greek real-world data before wider rollout. Full article

Romanian agriculture is characterized by fragmented land structures, limited capitalization, and high labour dependence, conditions that may constrain the economic performance of family farms. This study integrates two complementary components: (i) a systematic literature review conducted within the PRISMA 2020 framework, synthesizing evidence from 34 empirical studies concerning Romanian and Central and Eastern European family farms; and (ii) an original sectoral analysis based on publicly available Eurostat data for 2016–2025, including national trends and NUTS-2 regional comparisons. The reviewed evidence indicates that market integration, production specialization, technological adoption, investment capacity, and human capital are frequently associated with stronger labour-productivity outcomes, whereas land fragmentation and surplus labour are more frequently associated with weaker performance, particularly among smaller farms. The Eurostat analysis identifies an overall upward, although fluctuating, trajectory in nominal agricultural output and gross value added per AWU. The polynomial models provide a descriptive fit to these temporal patterns but do not identify their causal determinants. Overall, the findings support a size-differentiated interpretation of labour-productivity patterns and underline the relevance of targeted policies addressing the different structural conditions of Romanian family farms. Full article

Global labour shortages in the agricultural sector, combined with diminishing arable land and a growing population, are driving investment in autonomous agricultural machinery. Autonomous systems that can navigate crop environments and perform planting, treatment, and harvesting alongside humans are required, but the gap between published research and commercially deployed systems remains wide across most operational scenarios. Why are agricultural robots still not widely deployed in real farms despite decades of research in autonomous navigation and path planning, and what is preventing full farm autonomy? This paper reviews the principal enabling technologies for autonomous agricultural integration, with a specific focus on path planning as the differentiator between research-stage and deployed systems. Current research in human–robot integration, open-field navigation, row identification and following, crop sensing, and power efficiency is synthesised and evaluated against a deployability criterion. A Deployability Assessment Framework is introduced, comprising structured tables that assign Technology Readiness Levels to twelve path-planning families and benchmark eleven commercial and research platforms against field-validated accuracy data. The analysis shows that point-to-point GNSS navigation has reached TRL 9 with over one million commercial units deployed, vision-based crop row following is at TRL 5–7 depending on crop and season, and whole-farm autonomy with dynamic re-planning is at TRL 3–5. The primary barriers are the absence of standardised evaluation benchmarks, the failure of perception models to generalise across seasons and crop types, and the decoupling of terrain and slip feedback from global path planners. Our review reveals that open-field GNSS navigation is commercially mature, but true whole-farm agricultural autonomy remains unsolved because current systems are not robust enough across seasons, terrain, sensing conditions, and operational transitions. Full article

Currently, access to safe drinking water in developing countries remains a structural challenge. Despite progress, many households still lack access to clean and safe water, leading to serious consequences for health, human dignity, and the economy. This research aims to evaluate the impact of drinking water and sanitation investment projects on monetary expenditures for water consumption. Using data from the 2017 and 2023 National Household Survey (ENAHO-INEI) in Peru, a longitudinal study with a retrospective cohort design was conducted. The complex, probabilistic sampling method allowed for modeling both periods using ordered multiple response logit regression. The results show that household connections to potable water positively impacted water consumption expenditures in 2017 (n = 21,251) and 2023 (n = 19,482). Similarly, connections to sanitation facilities positively influenced expenditures in 2017, although no significant evidence was found in 2023. Having access to safe water (adequate chlorine) versus unsafe water (inadequate chlorine) showed a positive effect on consumption expenditures in both years, reflecting that families must bear a double expense to access alternatives such as bottled water. A higher monthly income of the head of household, associated with greater age and years of education, increases expenditures on potable water, excluding households in monetary poverty. A differentiated design of water policies is recommended, based on natural region (coast, highlands, and rainforest) and area (urban or rural), considering the different water access alternatives according to the geographic location of the dwelling. Full article

Housing market research has traditionally emphasized forecasting continuous price levels, often overlooking the discrete regime shifts that more directly capture cyclical risk and market turning points relevant to construction planning and investment decisions. This study develops an interpretable machine learning framework to forecast monthly U.S. housing market expansion and deceleration regimes one month ahead by integrating macroeconomic, financial, and construction-related indicators with national housing price data spanning January 1993 through March 2025. Feature selection and hyperparameter tuning are conducted entirely within the training sample using time-series cross-validation, ensuring that all reported performance metrics reflect genuine out-of-sample generalization. Recursive feature elimination combined with variance inflation factor screening yields a compact, seven-variable predictor set, with no macro-financial variable contributing an incremental discriminatory signal. Five classification models spanning linear and tree-based ensemble families are benchmarked under a strict temporal 80%:20% train–test split. Tree-based ensemble models consistently outperform the linear baseline, with Random Forest achieving the highest holdout AUC (0.932) and the most consistent deceleration detection across nested cross-validation folds. To ensure methodological transparency, explainable AI techniques, including SHapley Additive exPlanations, partial dependence, and individual conditional expectation analyses, are employed to interpret both global and local predictive associations underlying regime classification. Construction-related indicators, particularly Construction Put in Place and Building Permits at short lag horizons, emerge as the dominant supply-side predictive signals, outperforming macro-financial variables in regime discrimination by a margin of 0.184 in training CV AUC. By shifting the analytical focus from price forecasting to one-month-ahead regime prediction and integrating predictive accuracy with economic interpretability, this study provides a transparent and scalable framework for monitoring housing market cycles with direct applications to construction risk management, procurement timing, and project planning. Full article

Parental education, as institutionalized family cultural capital, is significantly associated with children’s academic performance. Using provincial-level academic quality monitoring data, this study employs hierarchical linear models and path analysis to examine how parental education levels and matching patterns relate to urban primary school students’ academic performance. Key findings: Higher overall parental or paternal education is positively associated with performance; however, maternal education exhibits an inverted U-shaped relationship. Compared to both parents non-highly-educated families, students perform significantly better when both parents are highly educated or when fathers are highly educated and mothers are not, but significantly worse in families where the mother is highly educated while the father is not. Path analysis indicates that the negative association in mother-hypergamy families is partly statistically associated with reduced paternal learning involvement, which in turn is related to heightened academic pressure and poorer outcomes. The study suggests that the lower academic performance observed in mother-hypergamy families may be partly associated with reduced paternal learning involvement, highlighting the importance of family educational configuration and balanced co-parenting dynamics. These findings suggest that the association between parental education and children’s academic performance is not straightforward. While consistent with cultural reproduction and family investment perspectives, the detrimental effect of the mother-hypergamy pattern highlights the importance of family configuration and points to the potential roles of role conflict, co-parenting dynamics, and expectation-pressure mechanisms that warrant direct empirical investigation in future research. Full article

This study investigates and explores the volatility dynamics of the South African rand against the US dollar (ZAR/USD) using the Family GARCH (fGARCH) model and the First-Order Beta-Skew-T-Generalised Autoregressive Conditional Heteroskedasticity (Beta-Skew-T-EGARCH) model. Currency volatility across the globe, uncertainties, and instability in emerging markets have become increasingly consequential for trade flows, investment allocation, and macroeconomic management. The ZAR/USD serves as a benchmark of South Africa’s economic wealth and vulnerability to external shocks and is one of the most valued, significant, and heavily traded pairings of emerging market currencies. Simple standard GARCH (sGARCH) is one of the most useful models for exchange rate volatility; however, the sGARCH model has some limitations: it fails to accommodate or allow the long memory effects, skewness distribution, and leverage dynamics consistently observed in emerging-market currency returns. This study addresses these limitations by using the fGARCH model, which includes the most popular GARCH models and Beta-Skew-T-EGARCH for daily ZAR/USD returns ranging from 5 January 2000 to 1 October 2024. Five innovation distributions are used for evaluation and comparison under fGARCH and sGARCH, namely generalised hyperbolic (GH), generalised error (GED), skewed Student’s t (SSTD), skewed generalised error (SGED), and Student’s t (STD), with model fitness criteria assessed using the Shibata criterion (SIC), Hannan–Quinn criterion (HQ), Bayesian information criterion (BIC), and Akaike information criterion (AIC), choosing the specification with the lowest overall penalty. It is found that the fGARCH(1,1) model fitted to return-frequency data under the SSTD achieves the lowest AIC, outperforming sGARCH. The study also includes an analysis among covariates, which are day, month, trend, oil, and platinum; the trend variable is a statistically significant predictor, with p = 0.007, showing a positive influence on ZAR/USD volatility. The Beta-Skew-T-EGARCH model with two components divides volatility into long-run and short-run components, which is found to deliver a superior fit over the one-component variant, evidenced by a lower BIC (3.068435) and a higher log-likelihood (−748.464826). The two components confirm that the model captures declining conditional volatility, whereas the one-component model sustains persistence in the evaluated estimates. Full article

Background. Orthotic and prosthetic devices for general-use and activity-specific function can provide critical preventive health benefits for individuals with limb loss, limb difference, and mobility impairments, and yet coverage remains inconsistent across U.S. states. Objective. To evaluate the fiscal impact of clarifying insurance coverage for orthotic and prosthetic devices across 23 states lacking comprehensive coverage. Methods. A cost consequence analysis was conducted using data from the U.S. Census Bureau, Kaiser Family Foundation, Government Accountability Office, and a recent actuarial analysis informing baseline cost, coverage, and prevalence assumptions. Per-member-per-month (PMPM) cost increases were compared against device enabled preventive health savings to estimate net fiscal impact. Sensitivity analyses modeled three scenarios based upon a combination of uptake (% eligible individuals accessing device) and physical activity equivalent annual cost saving, respectively: conservative (25% uptake, $1000), moderate (50% uptake, $2500), and high-impact (75% uptake, $5000). Return on investment (ROI) was calculated for the moderate scenario as the ratio of annual savings to implementation cost. Results. Under the assumptions of the moderate scenario, projected ROI remained positive across all states, ranging from approximately 1.5× in Florida to over 114× in Vermont, with 78% of states (18 of 23 states) demonstrating returns greater than 4×. Moderate scenario annual net savings ranged from approximately $10.8 million in Vermont to $437.0 million in California, with substantial projected savings also observed in Florida ($235.5 million), New York ($225.2 million), and Virginia ($143.8 million). PMPM cost increases for 70% of states range between $0.03 and $0.43, with all modeled states remaining below $1.46. Discussion. In our healthcare system dominated by high-cost and reactive care, the ROI obtained by this cost-consequence analysis (CCA) using evidence-based assumptions supports orthotic and prosthetic coverage clarification as preventive interventions to restore function. Full article

The rapid expansion of e-commerce into rural areas has emerged as a prominent policy initiative aimed at promoting digital economic development and facilitating structural transformation in developing countries. However, empirical evidence on whether and how such policies affect rural labor markets remains limited. This paper investigates the impact of China’s E-commerce into Rural Areas Policy on non-agricultural employment among rural residents. We develop a theoretical framework in which an individual’s utility derives from commodity consumption, leisure, and social recognition associated with e-commerce participation. The model predicts that reducing the investment price of e-commerce activities—the primary intervention of the policy—increases non-agricultural labor supply through both direct and indirect channels. Using panel data from the China Family Panel Studies (CFPS) spanning 2010 to 2022 and exploiting the staggered rollout of the Rural E-commerce Demonstration Pilot as a quasi-natural experiment, we employ a difference-in-differences (DID) approach to estimate the policy’s causal effects. The results show that the policy marginally significantly increases the probability of non-agricultural employment by approximately 1.1 percentage points, an effect that remains robust after parallel trend tests, placebo tests, and controlling for concurrent policies. Mechanism analysis reveals that the policy operates through two distinct channels: promoting regional industrial development, particularly the growth of the tertiary sector, and generating village-level peer economic incentives that encourage participation through social networks and information spillovers. Heterogeneity analysis indicates that the policy’s effects are larger for males, younger individuals, those with higher educational attainment, and residents of poor counties or regions with e-commerce potential. These findings contribute to the literature on digital development and rural labor markets by providing rigorous causal evidence and identifying the mechanisms underlying the policy’s effectiveness. The results also offer practical insights for policymakers seeking to leverage digital technologies for rural employment generation and structural transformation. Full article

Developing-country construction sectors continue to record disproportionately high occupational accident rates, partly attributable to the slow adoption of digital safety technologies, including Building Information Modeling (BIM) and Internet of Things (IoT) systems. While prior empirical research has established the population-level factors that explain stakeholder adoption intention through survey-based frameworks, the ability to classify individual stakeholder readiness for targeted, pre-deployment intervention remains methodologically unaddressed. This study fills that gap by applying three supervised machine learning classifiers (Random Forest [RF], XGBoost (XGB), and Support Vector Machine (SVM)) to a dataset of 107 construction professionals purposively sampled from large-scale infrastructure projects in Pakistan, including China−Pakistan Economic Corridor (CPEC) packages and the Barakahu Bypass project. Five construct-level features derived from an integrated Technology Acceptance Model and Technology−Organization−Environment (TAM-TOE) survey instrument were used to classify stakeholders into High, Moderate, and Low readiness tiers. XGBoost achieved the best classification performance (accuracy = 93%, macro F1 = 0.93), followed by RF (91%, F1 = 0.91) and SVM (87%, F1 = 0.87). The convergent performance across three structurally different algorithm families indicates that the readiness signal reflects a consistent attitudinal pattern rather than an artifact of any single modeling assumption. Feature importance analysis consistently identified Perceived Benefits (32%) and Technology Awareness (25%) as the dominant predictive features, followed by Organizational Readiness (20%), Perceived Barriers (15%), and Respondent Profile (8%). Attitudinal readiness mapping classified 62% of stakeholders as High readiness, 28% as Moderate, and 10% as Low, providing an exploratory attitudinal segmentation framework to assist construction managers in prioritizing capacity-building investments, subject to longitudinal behavioral validation. The study also finds that awareness of digital technology consistently outpaces Organizational Readiness for implementation, a pattern consistent with findings from analogous developing-country construction contexts. Full article

Family medicine has attracted increasing policy and institutional support across the Gulf Cooperation Council (GCC) countries through health system reform, expansion of the healthcare workforce, and sustained public investment. Nevertheless, important challenges continue to affect the strength of primary healthcare systems, access to care, and the management of non-communicable diseases. The aim of the narrative review is to identify future trends, directions, perspectives, and opportunities that can strengthen implementation of family medicine across GCC countries and improve healthcare delivery. This review is based on a structured search of major databases such as PubMed, Scopus, and Google Scholar. The focus was evaluation of literature associated with family medicine and primary healthcare development in GCC countries. Regional priorities now include improving medical education and training, expanding the family medicine workforce, strengthening links with communities, promoting more equitable access to healthcare, and managing treatment costs through workforce development and digital health initiatives. Family medicine practice across the GCC is being supported increasingly by electronic health records, telemedicine, and interprofessional education. Policy directions in the region also suggest growing interest in value-based research, international collaboration, multidisciplinary care, and innovation in healthcare delivery. The future of development of family medicine in the GCC will depend on better integration of digital health, more effective use of data in planning and policy, continued investment in training, and broader adoption of patient-centred models of care. In general, strengthening family medicine through sustained investment in workforce development, primary healthcare infrastructure, research capacity, and digital health integration is essential for achieving resilient, equitable, and patient-centered healthcare systems across the GCC. Full article

Driven by the Confucian cultural ideal of “wang zi cheng long”—the fervent hope that one’s child will rise like a dragon (i.e., achieve extraordinary success)—Chinese parents commonly engage in intensive academic involvement, such as frequent homework checking. However, the mechanisms through which this high-intensity monitoring affects adolescent mental health, and whether its effects are culturally specific, remain underexplored. Drawing upon the stimulus–organism–response (SOR) theory and the stress process model, this study used data from the 2022 China Family Panel Studies (CFPS) on 1831 adolescents aged 9–15 to examine the impact of parental homework checking frequency on adolescent loneliness, the mediating role of academic pressure, and the moderating role of parental educational expectations. The results show that parental homework checking frequency was positively associated with academic pressure, which in turn was positively associated with loneliness. The mediating role of academic pressure was significant. Parental educational expectations significantly and negatively moderated the relationship between homework checking and academic pressure, and the moderated mediation was significant. Simple slope analysis indicated that the positive association between homework checking and academic pressure was stronger. In the Confucian cultural context that emphasizes academic achievement and filial responsibility, frequent parental homework checking is associated with adolescent loneliness through increased academic pressure. Unexpectedly, high parental expectations served as a buffer—a pattern that differs from typical findings in Western individualistic cultures, where high expectations often directly increase psychological distress. These findings suggest that interventions in Chinese family education should distinguish controlling from supportive monitoring and transform high expectations into emotional support and resource investment, thereby reducing adolescents’ academic pressure and loneliness. Full article

This study examines the risk–return performance of Nifty100 ESG and the Nifty Enhanced ESG equity indices listed on India’s National Stock Exchange (NSE) relative to the conventional Nifty 100 benchmark from April 2011 to June 2023. Rather than asserting formal “abnormal returns” in the asset-pricing sense as per CAPM or multi-factor alpha estimation, this study documents systematic return outperformance and time-varying volatility dynamics using unit root tests, month-of-the-year dummy regressions with ARIMA error correction, and GARCH-family conditional volatility models. The two ESG indices delivered cumulative returns much higher than the broader Nifty 100 index. Conditional volatility peaked for ESG indices when compared to Nifty 100 during the April 2020 COVID-19 shock, indicating marginally greater ESG resilience. A distinct March effect, which is analogous to the January effect in developed markets, is observed to be significant for the ESG indices. Our findings underscore the growing importance of responsible investing and time-varying risk premia in the Indian equity market. Full article