Search Results (232)

Space Station Operation Mission Planning (SSOMP) requires coordinated decisions across long-term activity allocation, mid-term logistics optimization, and short-term execution scheduling and is a key component of autonomous mission operations for high-precision space missions. Existing optimization methods have achieved substantial progress at individual planning levels, but their dependence on problem-specific models, limited support for semantic review of decision rationale, and computational cost restrict their adaptability to multi-level planning scenarios. This paper proposes a Large Language Model (LLM)-assisted framework for multi-level SSOMP. The framework combines Staged Fine-Tuning (Staged-FT), Reflective Constraint–Repair Prompting (RCRP), and LLM-Guided Evolutionary Variation (LGEV). Staged-FT uses a Cognitive-Load-Theory-informed curriculum with Low-Rank Adaptation to adapt general-purpose LLMs to SSOMP domain knowledge. RCRP couples a Deterministic Rule Engine with LLM-based semantic repair to improve hard constraint satisfaction. LGEV embeds the fine-tuned LLM into NSGA-III as a fitness-aware variation operator for multi-objective activity allocation. Three case studies are conducted on literature-derived benchmark scenarios of logistics optimization, emergency re-planning, and activity allocation with logistics design, corresponding to Flight Increment Planning, Short-Term Execution Planning, and Overall Operation Planning, respectively. Results show that Staged-FT produces solutions close to traditional algorithms, RCRP achieves full hard constraint satisfaction in the emergency re-planning and logistics planning cases, and LGEV reduces the convergence generations of NSGA-III while improving Pareto-front quality. The framework provides a constraint-aware approach with explicit reasoning traces that can support expert review of AI-assisted planning for autonomous space mission operations. Full article

The study addresses the design and development of a unified corporate information system for multimodal transport and logistics companies engaged in maritime and railway transportation. Analysis of the existing literature revealed the absence of a coherent methodological framework for the design of corporate information systems tailored to the specific operational characteristics of multimodal transport and logistics enterprises. To bridge this gap, a design methodology for corporate information system databases is proposed, intended for subsequent deployment in companies operating multimodal supply chains. The development of the unified corporate information system was guided by the principle of “total costs,” which requires that the decision-maker—when selecting transport modes, methods of transportation, carriers, routing, and auxiliary intermediaries (insurer, stevedore, bank, and customs broker)—address the problem as an integrated whole rather than optimizing individual components in isolation. The study encompasses information modeling of the business processes of multimodal transport and logistics companies, construction of an optimal model of the transport process for maritime and railway transportation using integrated computer automated manufacturing definition (IDEF) and structured analysis and design technique (SADT) modeling, and the design of a multilevel unified database structure for the coordination of different transport modes. A decision-making and support system has been developed for managing the operational activities of a multimodal transport and logistics company engaged in maritime and railway transportation. The proposed unified corporate information system enables the replacement of task resolution by local optimization criteria applied separately to each transport mode—such as freight cost and delivery time—with a single global optimization criterion for the multimodal supply chain. Full article

Background/Objectives: Structural inequalities in access to healthcare persist even within systems that have achieved near-universal coverage, reflecting the enduring influence of social determinants of health on service utilization. This study examines horizontal inequity in rehabilitation and specialist care in Türkiye and investigates whether access inequality mediates the well-documented income–depression gradient. Methods: Analyzing the nationally representative 2022 Türkiye Health Survey (adults aged ≥15; N = 22,742), we employed Latent Profile Analysis (LPA) to construct people-centered, multidimensional bodily burden profiles, and assessed need-adjusted access using survey-weighted logistic regression, Erreygers-corrected concentration-index decomposition, Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA), and restricted cubic splines, with measurement-invariance and classification-uncertainty sensitivity analyses. Statistical mediation was examined with natural-effect models and E-value sensitivity analysis. Results: Although the system demonstrated responsiveness to need—78.8% of the highest-burden profile accessed specialist services—only 13.7% of this same group reached dedicated physiotherapy or rehabilitation, revealing a profound structural bottleneck in care coordination for marginalized populations with the greatest functional impairment. A persistent pro-rich gradient was confirmed by an Erreygers-corrected concentration index of 0.058 (95% CI 0.043–0.072), driven additively by income and education. Access did not mediate the income–depression pathway (natural indirect effect OR 1.001, 95% CI 1.0003–1.002). Conclusions: The mental health burden of low income operates through pathways that equitable healthcare access alone cannot address. These findings call for macroeconomic and people-centered health system reforms—including direct physiotherapy access, transportation subsidies, and social protection interventions—to advance health equity in rehabilitation utilization. Full article

Background: Body mass index (BMI) is an important consideration in perioperative evaluation. High BMI is correlated with more comorbidities and poorer operative outcomes; however, there is limited literature on the impact of BMI on patient-reported outcome measures (PROMs) after multilevel cervical disc replacement (CDR) surgery. Objective: The objective was to evaluate the relationship between BMI and PROMs in two-level CDR. Methods: A single-surgeon database was retrospectively reviewed for patients who underwent two-level CDR between August 2017 and October 2024. Patients were stratified as non-obese (BMI < 30 kg/m²; n = 44) or obese (BMI ≥ 30 kg/m²; n = 34). The final analytic cohort included 78 patients. Mean follow-up was 7.65 ± 5.53 months. Multivariable linear and logistic regression models adjusted for age, hypertension, diabetes, and Charlson Comorbidity Index. Patient-reported outcome measures (PROMs) and minimal clinically important difference (MCID) were also analyzed. Statistical analysis was conducted using Stata 18.0 (StataCorp LP, College Station, TX, USA). Results: Obese patients had a higher CCI than non-obese patients (0.90 ± 1.06, 1.87 ± 1.31, p = 0.001). Patient-reported outcomes were collected and analyzed after controlling for hypertension, diabetes, CCI scores, and age. At the final postoperative follow-up, only VR12-MCS differed, with the obese cohort having worse scores (60.7 ± 5.0 versus 53.9 ± 10.2, p = 0.040). MCID achievement rates did not differ between groups across PROMs (p > 0.183 for all). Conclusion: Patients with and without obesity demonstrated comparable early-to-midterm PROM trajectories and MCID achievement following two-level CDR. These findings suggest that obesity was not associated with substantially different patient-reported recovery during the observed follow-up period. Full article

Background/Objective: In the multifactorial etiology of asthma, environmental factors, including sun exposure, measured either by sunshine hours (SHs) or global solar surface irradiance (GSSI), are critical. The objective of this study is to estimate the association between sun exposure and the prevalence of asthma in Spanish schoolchildren (6–7 years old) and adolescents (13–14 years old) from six geographic centers in Spain (A Coruña, Bilbao, Cantabria, Cartagena, Pamplona and Salamanca) included in the Global Asthma Network (GAN) study during 2016–2019. Methods: Measurements of SHs and GSSI were obtained from the Spanish “Agencia Estatal de Meteorología”. A semi-individual cross-sectional design was used, and multilevel logistic regression models were employed, with adjustments for age, sex, temperature, relative humidity and gross domestic product, taking the center as the reference level. Permutation tests were performed to account for the low number of centers. Results: Increases in both SHs and GSSI were associated with a lower prevalence of “asthma ever” in the 6–7 and 13–14 years age groups. An increase of 100 annual sunshine hours was associated with a lower prevalence of “asthma ever” among schoolchildren (adjusted odds ratio [aOR] = 0.82; 95% confidence interval [CI], 0.79–0.85); from the crude model, permutation p = 0.08. In adolescents, the corresponding estimates were aOR = 0.90 (95% CI, 0.87–0.94); from the crude model, permutation p = 0.02. An increase of 1 kWh m⁻² day⁻¹ in GSSI was associated with a lower prevalence of “asthma ever” among schoolchildren (aOR = 0.15; 95% CI, 0.10–0.23); from the crude model, permutation p = 0.25. In adolescents, the corresponding estimates were aOR = 0.37 (95% CI, 0.25–0.54); from the crude model, permutation p = 0.01. Conclusions: The results of this study suggest that sun exposure, measured either as SHs or GSSI, may be protective against asthma in adolescents. In contrast, the association between sun exposure and the prevalence of “asthma ever” in schoolchildren was not significant, although it followed a similar trend. Confirmation of these findings in other geographical settings could improve our understanding of the role of sun exposure in asthma prevention and control. Full article

Workplace sexual harassment is a persistent human rights and public-health problem. This study analyzes in-person, online, and hybrid victimization using microdata from the EU-GBV survey (2021) across 17 countries, applying multilevel logistic models. The official indicator identifies that 31.6% of women have experienced harassment. In-person harassment is prevalent, whereas online victimization disproportionately affects the young cohort (18–34 years). Significant between-country variation is observed, indicating the influence of the national context in shaping risk. Among women under 35, urban environment and country-of-origin dynamics are relevant exposure factors. The findings show high recurrence (57.8%), low formal reporting (1.7%), and insufficient organizational preparedness (23.4% with training available). Digitalization expands the repertoire of violence without displacing its structural foundations. We conclude that there is a need to implement integrated prevention and intervention systems that address hybrid risk through a gender-sensitive perspective and a multivariable multilevel analysis attentive to intersecting axes, with models centered on main effects and the young cohort treated as an analytical stratification. Full article

Background. Recent advances in data systematization have enabled the development of machine learning models to evaluate performance in elite sports; however, studies are needed to analyze the performance of professional players in relation to their playing position. Objective. To analyze the performance of professional soccer players who competed between 2017 and 2024 by applying a multilevel classification approach that integrates different machine learning algorithms. Materials and Methods. We analyzed 9088 player-seasons from professional players during the 2017–2024 seasons. These data were extracted from standardized databases on sports performance analysis belonging to the following leagues: LaLiga (Spain), Premier League (England), Bundesliga (Germany), Serie A (Italy), and Ligue 1 (France). The final sample, distributed by performance level, was as follows: elite players (n = 1818; 20%), mid-level players (n = 2727; 30%), and low-level players (n = 4543; 50%). The average age of the players analyzed was 26.1 ± 4.0 years, distributed across three outfield playing positions: center backs (CB), central midfielders (CM), and strikers (ST); goalkeepers were excluded because the dataset contains no goalkeeper-specific performance metrics. Results. Under a leakage-controlled protocol (the label-defining indicators were excluded from the predictors and the train/test split preceded all preprocessing), a linear model (logistic regression) achieved the best overall performance (mean macro-F1 = 0.729), ahead of ensemble and kernel-based methods; this indicates that, once target leakage is removed, the classification does not require non-linear models. Strikers (ST) were the most separable position (best macro-F1 = 0.818, area under the receiver operating characteristic curve [AUC-ROC] = 0.948) and center backs (CB) the most difficult (macro-F1 = 0.602, AUC-ROC = 0.791), with midfielders (CM) intermediate (macro-F1 = 0.770, AUC-ROC = 0.916). Conclusions. The results confirmed that performance structures differ substantially depending on the position in the field, supporting the use of position-specific analytical strategies. In this context, the combination of position-stratified dimensionality reduction, handling of imbalance, and explainable artificial intelligence allowed for the identification of interpretable performance patterns associated with the profiles of elite, mid-level, and low-level players. Full article

Ukraine’s energy strategy under martial law necessitates decentralized local energy systems to counter electricity shortages and systemic infrastructure failures. The study develops and validates an optimization model for designing the architecture of decentralized “energy islands” based on circular bioenergy models for agricultural waste use. Empirical verification was conducted using data from the Vinnytsia region in Ukraine. The model accounts for a multi-level structure that separates micro/small generation (0.1–2.0 MW) from medium generation (1–20 MW) based on the logistical radius for raw material collection. The model incorporated the Value of Lost Load (VLL), enabling the monetization of avoided socio-economic losses from energy shortages. In addition, the coefficient of energy island sustainability (I_sred) was introduced to quantitatively assess the effectiveness of investments in terms of replacing external resources. The modeling revealed the nonlinear nature of the total cost function, enabling us to determine an optimal energy-autonomy range of 40% to 50% for communities. At this threshold, the total construction and logistics costs are minimized. The potential socio-economic losses from blackouts are effectively mitigated, as confirmed by the calculated sustainability coefficient (I_sred), which ranges from 0.78 to 0.94 across the studied communities. The resource potential assessment confirms that the region’s total potential is approaching 30 million tons of oil equivalent, driven by solid biofuels, agricultural residues, and energy crops (miscanthus, switchgrass). The classification of biomass supply chains shows that exceeding the transportation radius by more than 70 km at the meso level, or deviating from the optimal logistics lever by 20%, reduces the profitability of projects below the critical limit of 15%, which justifies strict localization within raw-material clusters. This enables local communities to eliminate natural gas consumption, reduce energy supply operating costs by 15%, and ensure the autonomous and stable operation of critical infrastructure facilities during prolonged disruptions to the national power grid. Full article

Background/Objectives: Access to dental care is a multilevel challenge shaped by healthcare systems, individual characteristics, and broader social determinants. This study aimed to identify parent-reported barriers to dental care among a sample of children in Saudi Arabia using Levesque’s Conceptual Framework for Access to Healthcare. Methods: A cross-sectional survey was conducted between March and April 2023 among parents of children aged 1–18 years. The 44-item survey assessed sociodemographic, oral health, and dental care utilization, organized using Levesque’s access dimensions. Difficulty accessing needed dental care within the past 12 months served as the main outcome. The chi-square test was used to examine bivariate associations between potential barriers and difficulty accessing needed dental care. Multivariable logistic regression was used to identify factors independently associated with difficulty accessing needed dental care after adjustment for the other variables. Results: This study included 351 parents. The mean age of children was 10.7 ± 4.7 years, and 45.9% were male. One-third of parents reported difficulty accessing needed dental care for their children. The adjusted regression model identified fair/poor oral health (aOR = 13.39, 95% CI: 6.19–28.97), general health condition (aOR = 2.31, 95% CI: 1.11–4.82), and the absence of a nearby dental clinic (aOR = 2.08, 95% CI: 1.11–3.90) as factors independently associated with difficulty accessing dental care. Conclusions: Applying Levesque’s framework highlighted the multidimensional nature of barriers to accessing dental care for the study population. These findings provide preliminary evidence on parent-reported access barriers and may inform larger, representative studies and future policies to improve equitable pediatric oral healthcare in alignment with Saudi Vision 2030. Full article

Bayesian disease mapping models are formulated for aggregated areal counts, although veterinary epidemiological studies commonly collect individual-level data from animals located in geographical units. Aggregating such data to the areal level may change the estimand and make individual-level interpretation inappropriate. This study extends the original Besag–York–Mollié 2 (BYM2) model to a multilevel modeling framework for individual-level binary outcomes by embedding a BYM2 random effect within a logistic regression model. The proposed model retains individual animals as the unit of analysis, estimates covariate associations, and accounts for residual structured and unstructured areal variation. We present the work as a practical guide, covering likelihood and priors specifications, BYM2 scaling, estimation algorithm, model diagnostics, posterior predictive checks, practical model comparison, sensitivity analysis, and interpretation of fixed and areal effects. The model is directly applicable to individual-level binary outcomes in a cross-sectional study or a cohort study with a fixed follow-up period, while we also demonstrated that the proposed model can estimate associations generated from an open cohort time-to-event process under appropriate sampling frameworks including probability proportional to size sampling and incidence density sampling. This paper provides a reproducible workflow for applying BYM2 disease mapping to individual-level epidemiological data and clarifies the study designs under which the proposed binary modeling framework can be appropriately used. Full article

Background/Objectives: Workplace violence (WPV) is a major occupational health hazard and psychosocial safety concern in psychiatric nursing, yet evidence integrating exposure, worker well-being, and workforce stability within a single theory-informed model remains limited in Saudi Arabia. This study examined associations between WPV and proximal occupational outcomes (burnout, job satisfaction, and absenteeism) and distal outcomes (quality of life [QoL] and turnover intentions) among psychiatric nurses and tested burnout as a statistical mediator of the WPV–turnover intentions association. Methods: A multicentre cross-sectional census survey was conducted across three psychiatric facilities in northern Saudi Arabia between June and August 2025 (N = 171). Participants reported WPV exposure over the preceding 12 months using Arabic versions of standardised instruments, including validated Arabic versions where available and a forward–backward-translated WVSQ. Linear, negative binomial, and logistic regression models examined outcome associations and factors associated with WPV exposure; the PROCESS macro (Model 4, 5000 bootstrap resamples) tested statistical mediation. Results: Most nurses reported at least one WPV incident in the preceding 12 months, most commonly verbal abuse. Younger age, male sex, shorter psychiatric nursing experience, lower perceived staffing adequacy, and inadequate security were associated with higher WPV exposure. WPV exposure was associated with lower QoL across all WHOQOL-BREF domains, higher burnout, lower job satisfaction, higher absenteeism, and stronger turnover intentions. Burnout showed a partial statistical mediation pattern in the WPV–turnover intentions association. Because the study was not specifically powered for the indirect effect, the mediation result should be interpreted with caution. Conclusions: In this cross-sectional study, WPV was a pervasive occupational exposure among Saudi psychiatric nurses and was associated with poorer QoL and higher turnover intentions, with burnout showing a partial statistical mediation pattern. Multilevel prevention, safer staffing, and structured post-incident psychological support are needed to support psychiatric nurses’ well-being and workforce retention. Full article

Human Biomonitoring (HBM) is a key tool for assessing human exposure to environmental contaminants and identifying early biological changes potentially associated with adverse health effects. However, as contaminated sites are characterized by multiple pollutants, exposure pathways and biological targets, a structured decision-support process is required to guide exposure assessment, biomarker selection, and early health-risk evaluation. This manuscript does not report biomonitoring results but describes the methodological process used to design the INSINERGIA_RT HBM study in two contaminated areas of Tuscany, Italy. The study design integrated environmental evidence, toxicological knowledge, epidemiological priorities, biomarker relevance, analytical feasibility, logistical considerations, and public health needs. Instead of proposing a universally applicable framework, the manuscript presents a structured approach that may support the design of HBM studies in similarly complex settings. Biomarkers are organized within a hierarchical model reflecting successive stages of the biological response to environmental exposure. A central feature is a mechanistic, multi-level approach encompassing upstream pathways (oxidative stress and inflammation), early markers of adverse effects and subclinical organ damage, including cardiovascular, renal, and respiratory alterations, and integrative indicators of cumulative biological responses, such as telomere length, epigenetic changes, and metabolomic profiles. Applicability is illustrated through an ongoing HBM study in Livorno and Piombino (Tuscany). Full article

Background: Breast cancer comprises 31.4% of all female cancers in Saudi Arabia (2020 Cancer Registry). Despite free national screening services existing since 2005, mammography utilization remains critically low. This study assessed breast cancer knowledge, attitudes, and practices (KAP) among females in Saudi Arabia and identified independent predictors of screening behavior. Methods: A cross-sectional study (December 2024–February 2025) enrolled 426 females aged ≥20 years from all 13 Saudi administrative regions using a quota-based design combining facility-based and online recruitment. Attitude and barrier domains were adapted from Champion’s Health Belief Model Scale (CHBMS), validated in Arabic; knowledge items used validated regional instruments. Knowledge-score reliability: KR-20 = 0.45; attitude subscale: α = 0.74. Binary logistic regression identified independent predictors of screening uptake (outcome: any screening in the preceding five years, coded as screened = 1; not screened = 0). Results: Mean composite knowledge score: 4.51 ± 1.52/7 (KR-20 = 0.45); 54.0% achieved high knowledge (≥5). Mammography uptake was 30.5% overall and 52.2% among women aged ≥40 (n = 136; the recommended target group). Predominant barriers: Fear of diagnosis (83.6%), belief in incurability (76.3%), radiation concern (73.2%), and pain anxiety (72.3%). Logistic regression (χ²(8) = 188.96, p < 0.001; McFadden’s pseudo R² = 0.323) identified older age (OR = 1.52; 95% CI: 1.21–1.92), higher income (OR = 1.57; 95% CI: 1.25–1.99), transportation barriers (OR = 3.39; 95% CI: 1.95–5.89), and family discouragement (OR = 3.03; 95% CI: 1.72–5.34) as significant predictors (all p < 0.001). Conclusions: A significant knowledge–practice gap persists across all 13 Saudi regions. These findings suggest several implications for a multi-level public health response to be evaluated through future intervention research; multi-level strategies targeting CHBMS Barriers are needed. Full article

While individual determinants of students’ well-being are well established, less is known about the association with the institutional context. This study evaluates institutional-level factors associated with students’ mental health while controlling for individual characteristics. The cross-sectional analysis used data from 12 German institutions (n = 13,715) collected in the StudiBiFra survey on study conditions and student mental health. Individual-level variables included gender, age, study subject group, and four mental health variables (general well-being, depressiveness, cognitive stress, and exhaustion). Institution-level variables comprised institution type, excellence status, multi-campus structure, size, and satisfaction with the quality of health promotion services. Multilevel binary logistic regression models were applied to examine associations between institutional characteristics and mental health outcomes, adjusting for individual factors. Students enrolled at universities of applied sciences showed a lower likelihood of reporting depressiveness and exhaustion. Higher levels of depressiveness and cognitive stress were observed among students at medium-sized institutions compared to small ones. Students not enrolled at institutions with excellence status had lower risks of depressiveness, stress, and exhaustion. Additionally, higher satisfaction with institutional health promotion services was associated with reduced odds of depressiveness. Institutional factors are related to students’ mental health beyond individual characteristics, highlighting the need for a holistic, setting-based approach. Full article

Background: Agricultural logistics in Kazakhstan is critical for export-oriented supply chains, but its resilience is limited by infrastructure constraints, fluctuating export demand, and insufficient coordination between market and logistics processes. Methods: This study develops a conceptual multi-level model of the agricultural logistics system and a hybrid simulation model combining system dynamics and discrete-event simulation to analyze intermodal transportation under demand and capacity constraints. The model integrates demand formation, storage, transport, and export operations, as well as feedback mechanisms between fulfilled demand, repeat orders, and logistics performance. The model is implemented in AnyLogic 8.9. Results: The conceptual model structures the interaction of key participants, logistics facilities, and infrastructure levels within Kazakhstan’s agricultural logistics system. Simulation experiments reproduce cyclic logistics behavior and show that reduced logistics capacity increases the demand gap and system pressure, while stronger market signals intensify demand and infrastructure load. The results confirm that resilience depends on the balance between demand activation, logistics capacity, and replenishment policy. Conclusions: The proposed approach provides a tool for analyzing the resilience of agricultural intermodal logistics in Kazakhstan and supports scenario-based evaluation of infrastructure and market factors. The novelty lies in combining a conceptual multi-level logistics model with hybrid simulation of demand and logistics flows. Full article