Search Results (954)

In this article, we establish a constructive nonparametric scheme for monitoring the quality of services provided by a transportation company. The proposed methodology aims at achieving the diligent tracking of the underlying process and the swift detection of any potential malfunctions. The implementation of the new framework requires the construction of appropriate schemes, which follow the set-up of a Shewhart chart and are connected to ranks and multiple run decision criteria. The dispersion and the mean value of the run length distribution for the suggested distribution-free scheme are investigated for the special case $k=2$. For illustration purposes, a real-data logistics environment is discussed, whereas the proposed approach is applied for improving the quality of the provided services. Full article

Ecosystems are nonlinear systems that can shift between multiple stable states. Ecosystem service bundles (ESBs) integrate the supply and trade-offs of multiple services, yet the conditions for achieving high-supply and balanced states remain unclear from a nonlinear, threshold-based perspective. In this study, six representative ecosystem services in Fujian Province were quantified, and ESBs were identified using a Self-Organizing Map (SOM). By integrating the Multiclass Explainable Boosting Machine (MC-EBM) with the API interpretable algorithm, we propose a framework for exploring ESB driving mechanisms from a nonlinear, threshold-based perspective, addressing two key questions: (1) Which factors dominate ESB formation? (2) What thresholds of these factors promote high-supply, balanced ESBs? Results show that (i) the proportion of water bodies, distance to construction land, annual solar radiation, annual precipitation, population density, and GDP density are the primary driving factors; (ii) higher proportions of water bodies enhance and balance multiple services, whereas intensified human activities significantly reduce supply levels, and ESBs are highly sensitive to climatic variables; (iii) at the 1 km × 1 km grid scale, optimal threshold ranges of the dominant factors substantially increase the likelihood of forming high-supply, balanced ESBs. The MC-EBM effectively reveals ESB formation mechanisms, significantly outperforming multinomial logistic regression in predictive accuracy and demonstrating strong generalizability. The proposed approach provides methodological guidance for multi-service coordination across regions and scales. Corresponding land management strategies are also proposed, which deepen understanding of ESB formation and offer practical references for enhancing ecosystem service supply and reducing trade-offs. Full article

To counter the high unmet need for family planning in urban areas of Sindh province, Pakistan, Greenstar Social Marketing began implementation of The Challenge Initiative (TCI) in collaboration with the government departments of Population Welfare and Health in eight urban districts of Sindh province. This study aimed to assess the effectiveness of TCI’s Community Health Volunteers (CHVs) on public sector service provision of family planning services in eight urban districts of Sindh province, Pakistan. The Contraceptive Logistics Management Information System (cLMIS) and District Health Information System 2 (DHIS2) were used to obtain monthly contraceptive data from June 2022 to December 2024. CHVs began implementation at different time points in each district, starting from January 2023 to October 2023, when CHVs became operational in all eight districts. Descriptive statistics and two-sample t-tests were used for data analysis. CHVs significantly improved family planning service provision, particularly for short- and long-acting methods at the facility level, with greater change observed in Department of Health facilities. This study provides preliminary evidence of the effectiveness of CHVs in increasing public sector service provision of contraceptives, particularly for Department of Health facilities. CHVs bridge the gap between the community and the facility, particularly in areas uncovered by the government’s existing mobilization staff. Full article

Background: Communication difficulty restricts education, healthcare, and social participation, yet population-level data for Saudi Arabia have been scarce. This study analysed the 2017 Saudi National Disability Survey to estimate prevalence, describe severity and demographic patterns, and identify factors linked to these difficulties. Objectives: We aimed to estimate national and regional prevalence, assess severity, and gender differences, and identify socio-demographic and disability-related correlates. Methods: A cross-sectional, two-stage stratified cluster sample of 33,575 households (weighted N = 20,408,362 citizens) provided self-reported data on communication difficulty and socio-demographics. Weighted frequencies described prevalence and multivariable logistic regression identified independent correlates. Results: Among all Saudi citizens, 7.1% reported at least one functional difficulty, and of this group 15.7%—equivalent to 1.1% of the total population (n = 226,510)—had a communication difficulty; within that communication difficulty stratum, (n = 185,508) (0.9% of all citizens) experienced it alongside additional impairments, whereas (n = 41,002) (0.2% of all citizens) reported communication difficulty in isolation. The communication difficulties exhibit significant regional variation, ranging from 0.45% in Najran to 1.55% in Aseer. Most cases were classified as being associated with some difficulty (72%); females were over-represented in the extreme category despite a modest male excess overall (adjusted odds ratio [AOR] = 1.09). Higher education, married status, and bilateral first-cousin marriage (AOR = 1.22) were associated with greater risk. Chronic disease (44%) and perinatal causes (13%) predominated, and 84% of cases co-occurred with at least one other disability. Independent predictors included a long duration (AOR = 4.18), disease or delivery-related cause, and consanguinity. Conclusions: Findings highlight geographically clustered need, genetic risk factors, and substantial multimorbidity, indicating the importance of region-specific screening, premarital counselling, and integrated rehabilitation within chronic disease services. Full article

The growing demographic trend of an aging population, particularly in remote rural areas, exacerbates social isolation and limits access to essential goods and services. This vulnerability highlights a pressing need to develop sustainable solutions for their mobility and support. Using Geographic Information Systems (GISs) and network analysis, a workflow was developed to optimize road-based transport for the elderly. The analysis utilized an electric vehicle, with its range limitations, influenced by road slopes, being a critical variable for assessing route efficiency. Two potential solutions were investigated: (1) the delivery of goods and medicines and (2) the transport of passengers and medicines. The methodology was tested using the Municipality of Seia, Portugal, as a case study, with a defined weekly visit frequency. The results demonstrate that both proposed solutions are technically viable for implementation, with the transport of passengers and medicines being the most effective option. This study provides a foundational framework for developing practical, demand-oriented, sustainable transport and logistics services to support isolated elderly populations. Full article

The massive volumes of data and the intensification of digital transformation are reshaping recruitment practices within organizations, particularly for specialized information technology (IT) profiles. However, existing studies have often remained conceptual, focused on developed economies, or limited to a narrow set of factors, thereby leaving important gaps in emerging contexts. Moreover, there are few studies that critically assess how Data Mining is impacting the IT recruitment process, and none that assess this in the context of Morocco. This study employs an extensive literature review to explore the role of Data Mining in facilitating the recruitment of top IT candidates, focusing on its ability to improve selection quality, reduce costs, and optimize decision-making procedures. The study provides empirical evidence from the Moroccan aeronautical and digital services sectors, an underexplored context where IT talent scarcity and rapid technological change pose critical challenges. Primary data comes from a survey of 200 IT recruitment professionals operating in these sectors in Morocco, allowing an assessment of the impact of Data Mining on IT talent acquisition initiatives. The findings reveal that a range of capabilities resulting from the application of Data Mining significantly and positively influences the success of IT recruitment processes. The novelty of the article lies in integrating six key determinants of algorithmic recruitment into a unified framework and demonstrating their empirical significance through binary logistic regression. The focus on the Moroccan context adds value to the international discussion and extends the literature on HR analytics beyond its conventional geographical and theoretical boundaries. The article thus contributes to the emerging literature on the role of digital technologies in IT recruitment that will be of interest to industry practitioners and other researchers in this field. Full article

In the rapidly evolving global business landscape, servitization and digitalization have emerged as key strategies for enhancing firm competitiveness and performance. This study examines their impact, along with the moderating role of government support, in the Indonesian shipping industry. Drawing on the resource-based view (RBV), servitization and digitalization are conceptualized as internal drivers of performance, while Resource Dependence Theory (RDT) positions government support as an external factor that reduces environmental uncertainty and strengthens these relationships. Using data from 345 shipping companies, analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM), the results show that both servitization and digitalization positively affect competitiveness and performance. Furthermore, government support significantly enhances these effects by providing resources such as infrastructure and financial incentives, facilitating the adoption of digital strategies and service-based models. Beyond firm outcomes, these transformations align with broader sustainability objectives by improving resource efficiency, reducing waste and delays, and potentially lowering the environmental footprint of logistics activities. This study advances theoretical understanding by demonstrating the central role of external resources—particularly government support—in enabling successful digital and service transformations. For policymakers, the findings emphasize the need for targeted incentives and infrastructure to accelerate industry-specific innovation and sustainability goals. For practitioners, they highlight the importance of aligning strategic initiatives with government policies to maximize the benefits of servitization and digitalization. Full article

Brucellosis is caused by Gram-negative coccobacilli of the Brucella genus, with cattle mainly infected with Brucella abortus. The disease burden is a threat to socioeconomic development (agriculture/tourism) as well as to animal health, biodiversity and to human health due to the zoonotic nature of this pathogen. In South Africa (S.A), the prevalence of the disease in cattle and livestock in general is mostly unknown in communal farms. A cross-sectional study with a multistage sampling strategy was applied in communal areas from three district municipalities, i.e., Mopani, Capricorn and Sekhukhune of Limpopo province, South Africa. Sera (n = 1133) were collected and screened for antibodies against the Brucella species using the Rose Bengal Test (RBT) and confirmation of positive reactors with a Complement Fixation Test (CFT). The brucellosis seroprevalence was found to be 0.79% (95% CI: 0.38–1.45) by a CFT. Univariate analysis indicated that only the frequency of birth was significantly associated with CFT positivity (OR = 20; 95% Cl: 1.61–247.99; p = 0.039). The multivariable logistic regression model revealed that the frequency of birth, age, breed, gender, municipality and district were not statistically significant predictors at 0.05 level. However, some variables like cattle aged more than five years, had higher odds of CFT positivity compared to those younger than five years (OR = 5.66; 95% CI: 0.36–87.97), although the association was not statistically significant (p = 0.215). All positive reactors detected originated from the Mopani district municipality. Overall, the findings reveal a much lower seroprevalence of brucellosis in the communal farms of Limpopo province than previously assumed. We are of the opinion that the low seroprevalence is attributed to effective control strategies implemented by the Limpopo provincial veterinary services and hence provide important information to assist the regulatory bodies in the control and eradication of the disease. Full article

The primary goal of Transportation systems is to provide transportation accessibility to opportunities. Equitable access to essential destinations encompassing social, recreational, educational, and civic opportunities needs to be more consistent across different social groups. This study evaluates the disparities in social justice using social equity as a measure of transit access and walk access to non-work amenities. These non-work amenities include grocery stores, personal services, retail outlets, recreational venues, entertainment centers, and healthcare facilities in the U.S. Logistic regression models are developed using the 2017 National Community Livability Survey data. The results indicate regressive public transit access for socially disadvantaged groups, including older citizens, non-drivers, Medicare/Medicaid beneficiaries, and non-metropolitan residents. Walk access inequities similarly affect older individuals, non-drivers, the physically disabled, the unemployed, students, women, and non-metropolitan residents. This research emphasizes the importance of addressing transit and walk-access inequities to non-work amenities within transportation systems. By acknowledging the disparities in transportation equity, decision-makers and communities can foster more inclusive and equitable access to essential destinations, thereby promoting social cohesion and overall community well-being. Full article

The scientific site selection for pallet pooling systems is pivotal to enhancing logistics efficiency and environmental performance. However, previous studies mainly adopt single-objective optimization approaches, which fail to simultaneously account for economic, environmental, and operational performance factors. The contribution of this paper lies in proposing an integrated decision-making method based on BWM-GIS-DEA to address the site selection problem for pallet pooling service centers. First, the Best-Worst Method (BWM) determines the weights of 13 criteria across 5 dimensions: economic, transportation, geographical location, technological, and service coverage. These criteria include factors such as the distribution density of pallet manufacturers and potential customers. Then, suitability maps are generated using Geographic Information System (GIS) spatial overlay technology to identify 6 alternative cities. Finally, a two-layer Data Envelopment Analysis (DEA) model is applied to measure the efficiency of the alternative sites. This method is applied in Inner Mongolia, China, and Ejin Horo Banner is identified as the optimal site with an efficiency score of 1.156, demonstrating superior resource allocation characterized by lower land costs and higher pallet turnover rates. The proposed framework not only fills a methodological gap in sustainable facility location research but also provides a replicable and policy-ready tool to guide practical decision-making. Full article

As 6G networks become increasingly complex and heterogeneous, effective classification of network slicing is essential for optimizing resources and managing quality of service. While recent advances demonstrate high accuracy under controlled laboratory conditions, a critical gap exists between algorithm performance evaluation under idealized conditions and their actual effectiveness in realistic deployment scenarios. This study presents a comprehensive comparative analysis of two distinct preprocessing methodologies for 6G network slicing classification: Pure Raw Data Analysis (PRDA) and Literature-Validated Realistic Transformations (LVRTs). We evaluate the impact of these strategies on algorithm performance, resilience characteristics, and practical deployment feasibility to bridge the laboratory–reality gap in 6G network optimization. Our experimental methodology involved testing eleven machine learning algorithms—including traditional ML, ensemble methods, and deep learning approaches—on a dataset comprising 10,000 network slicing samples (expanded to 21,033 through realistic transformations) across five network slice types. The LVRT methodology incorporates realistic operational impairments including market-driven class imbalance (9:1 ratio), multi-layer interference patterns, and systematic missing data reflecting authentic 6G deployment challenges. The experimental results revealed significant differences in algorithm behavior between the two preprocessing approaches. Under PRDA conditions, deep learning models achieved perfect accuracy (100% for CNN and FNN), while traditional algorithms ranged from 60.9% to 89.0%. However, LVRT results exposed dramatic performance variations, with accuracies spanning from 58.0% to 81.2%. Most significantly, we discovered that algorithms achieving excellent laboratory performance experience substantial degradation under realistic conditions, with CNNs showing an 18.8% accuracy loss (dropping from 100% to 81.2%), FNNs experiencing an 18.9% loss (declining from 100% to 81.1%), and Naive Bayes models suffering a 34.8% loss (falling from 89% to 58%). Conversely, SVM (RBF) and Logistic Regression demonstrated counter-intuitive resilience, improving by 14.1 and 10.3 percentage points, respectively, under operational stress, demonstrating superior adaptability to realistic network conditions. This study establishes a resilience-based classification framework enabling informed algorithm selection for diverse 6G deployment scenarios. Additionally, we introduce a comprehensive explainable artificial intelligence (XAI) framework using SHAP analysis to provide interpretable insights into algorithm decision-making processes. The XAI analysis reveals that Packet Loss Budget emerges as the dominant feature across all algorithms, while Slice Jitter and Slice Latency constitute secondary importance features. Cross-scenario interpretability consistency analysis demonstrates that CNN, LSTM, and Naive Bayes achieve perfect or near-perfect consistency scores (0.998–1.000), while SVM and Logistic Regression maintain high consistency (0.988–0.997), making them suitable for regulatory compliance scenarios. In contrast, XGBoost shows low consistency (0.106) despite high accuracy, requiring intensive monitoring for deployment. This research contributes essential insights for bridging the critical gap between algorithm development and deployment success in next-generation wireless networks, providing evidence-based guidelines for algorithm selection based on accuracy, resilience, and interpretability requirements. Our findings establish quantitative resilience boundaries: algorithms achieving >99% laboratory accuracy exhibit 58–81% performance under realistic conditions, with CNN and FNN maintaining the highest absolute accuracy (81.2% and 81.1%, respectively) despite experiencing significant degradation from laboratory conditions. Full article

Background/Objectives: Timely initiation of antenatal care (ANC) services is crucial for ensuring maternal and fetal well-being. Despite the importance of ANC, research regarding its initiation remains limited in the Jazan region of Saudi Arabia, an area with notable adverse birth outcomes. Therefore, this study aimed to assess pregnant women’s initiation of ANC and identify associated factors and significant barriers for timely initiation. Methods: A cross-sectional study was conducted among 369 Saudi pregnant women in their third trimester attending ANC clinics in the Jazan region in 2024. A structured questionnaire was used to collect data. Andersen’s behavioral model of healthcare utilization provided the framework for the study. Descriptive statistics, chi-square tests, and binary logistic regression were used to analyze the data. Results: The majority of women (78.9%) initiated ANC in the first trimester. Higher maternal education was positively associated with early ANC initiation (aOR = 2.369, 95% CI: 1.154–4.901), whereas higher paternal education was negatively associated with early ANC initiation (aOR = 0.350, 95% CI: 0.175–0.699). When modeled independently, the positive association of higher maternal education was attenuated but was not significant, while the negative association of higher husband’s education remained the same. Those living more than three km from health facilities (aOR = 0.510, 95% CI: 0.276–0.941) and seeking care for reasons other than routine follow-up were less likely to initiate ANC early. Most women received essential services, but only 37.1% had ultrasound tests. Conclusions: While ANC initiation in Jazan showed promising trends, factors like geographical accessibility remain a significant barrier. Targeted interventions should address these identified barriers, which fall within predisposing, enabling, need, and external environmental factors. Further investigations of pregnant women’s familial decision-making and low ultrasound test utilization in relation to ANC are recommended. Full article

Rural areas often lack convenient delivery logistics services, which has become a major obstacle to their economic development. Network design initiatives that synergize passenger and freight transport have been identified as effective solutions to address this challenge. Building upon this initiative, this study investigates a novel three-echelon location-routing problem that synergizes trucks and fixed-route buses (3E-LRP-TF). The model is designed with an innovative operational mode that enables fixed-route buses and trucks to travel in a parallel manner, representing a valuable extension to traditional integrated passenger–freight distribution network design. A mixed-integer nonlinear programming model with the objective of minimizing the total network cost is constructed to formulate the problem. Furthermore, a bottom-up three-phase adaptive large neighborhood search (ALNS) algorithm is designed to solve the problem. A final empirical study was conducted, with Qingchuan County in China serving as a case study, with the aim of validating the effectiveness of the proposed model and algorithm. The results show that, compared with using trucks alone, the synergistic network system has the potential to reduce costs by more than 5% for parcel pickup and delivery services. The proposed algorithm can address larger-scale problems and exhibits better performance with regard to solution quality and efficiency. Sensitivity analysis indicates that the parcel transport capacity of bus routes exerts a nonlinear effect on total costs, and changes in service radius result in trade-offs between cost and accessibility. These findings provide actionable insights for policymakers and logistics operators. Full article

This study develops a multi-agent evolutionary game model that incorporates both retailers and heterogeneous logistics providers, extending beyond prior dyadic models that typically isolate either channel choice or logistics competition. By comparing scenarios with and without the BOPS channel, the framework captures the dynamic interactions between retailers and logistics providers. The results show that introducing In-Store Pickup significantly increases market demand and retailer revenue by reducing consumer waiting time, but it also produces a revenue crowding effect for slow logistics providers. For fast providers, the impact depends on their ability to adjust service quality: lowering service levels helps retain market share, while efficiency improvement enhances profitability. Furthermore, consumer product valuation plays a critical role in driving retailers toward dual-provider or hybrid strategies. The methodological innovation lies in integrating heterogeneous logistics service differentiation with channel strategy selection into a unified evolutionary game framework. The study contributes by proposing a dynamic “efficiency threshold–channel selection” mechanism, offering both theoretical advancement in omnichannel retailing research and managerial insights for retailers and logistics providers seeking to optimize logistics capabilities and channel collaboration. Full article

Background: The Hajj pilgrimage, a significant religious event, presents unique health challenges due to the large number of participants and the physical demands of the pilgrimage. Despite efforts by Saudi health authorities to provide comprehensive health services, the effectiveness of these interventions relies heavily on pilgrims’ knowledge of available services and emergency measures. Objectives: The aim of this study is to assess the level of knowledge of health services among Hajj pilgrims in Saudi Arabia during the 1445 H (2024 G) season and its associated demographic and health-related factors. Methods and Materials: This cross-sectional study was conducted during the 1445 H (2024 G) Hajj season with 1215 pilgrims, 51.7% male. Participants were randomly selected from selected pilgrimage accommodations or passing through the airport in Jeddah. Data were collected through face-to-face interviews using a structured questionnaire covering demographics, health characteristics, and knowledge of health services. Results: The study found that 62.1% of participants correctly believed that health services during Hajj were free of charge. Furthermore, 44.4% were aware of the emergency health number, with 31.5% exhibiting good knowledge. Bivariate analysis showed that younger participants had better knowledge (p < 0.001), and males were more likely than females to demonstrate partial or good knowledge (p = 0.011). Participants with university education were significantly more likely to have good knowledge, with 29.6% demonstrating good knowledge compared to 7.4% of illiterate participants (p < 0.001). Geographic region was also a factor, with 48.9% of Middle Eastern participants exhibiting good knowledge, compared to 27.1% of African and 23.6% of Asian participants (p < 0.001). However, multivariable logistic regression, revealed that only younger age (OR = 0.98, p = 0.005), university education (OR = 1.96, p = 0.024), and being from the Middle East (OR = 1.61, p = 0.009) were significant predictors of good knowledge of health services. Conclusions: The study identified significant gaps in pilgrims’ knowledge of health services during Hajj, with younger age, higher education, and Middle Eastern geographic region identified as independent predictors of better knowledge. These findings suggest the need for targeted health education initiatives, particularly for older pilgrims, those with lower educational attainment, and individuals from regions with lower awareness levels, to improve knowledge and potentially enhance health outcomes during Hajj. Full article