Search Results (25,340)

This study contributes to the development of performance measurement frameworks for HEIs in resource-constrained settings. It offers valuable insights for policymakers and institutional leaders in the Balkans, emphasizing the need for a holistic approach that integrates both academic and administrative factors to improve institutional effectiveness and support strategic decision-making in public higher education. This study develops and validates a multidimensional performance framework for public higher education institutions (HEIs) in developing Balkan countries, integrating both academic and administrative dimensions of institutional performance. Using a cross-sectional design, data were collected from 162 respondents across public HEIs in Serbia, Bosnia and Herzegovina, Montenegro, North Macedonia, and Albania. A two-stage analytical approach was employed: an exploratory factor analysis (EFA) to identify the underlying structure of performance dimensions, followed by a confirmatory factor analysis (CFA) to validate the measurement model. The results confirm a robust five-factor structure, comprising three academic dimensions—teaching and learning, research, and knowledge transfer—and two administrative dimensions—resources and internationalization. Structural equation modeling (SEM) was used to assess the effects of academic and administrative performance on overall higher education performance (OHEP). Findings indicate that administrative performance exerts a stronger influence on institutional outcomes (β = 0.649, p < 0.001) compared to academic performance (β = 0.314, p < 0.001). Together, these dimensions explain 51.9% of the variance in overall institutional performance. The study contributes to the limited literature on integrated performance assessment in resource-constrained higher education systems. It offers empirically validated insights for policymakers and university leaders, emphasizing the importance of aligning academic and administrative capacities to enhance institutional effectiveness and inform strategic decision-making in public HEIs. Full article

Background: Imaging-transcriptomic analysis, through the integration of multimodal magnetic resonance imaging (MRI) and transcriptomic data, provides complementary structural, functional, and molecular information that is crucial for the early detection and mechanistic exploration of Alzheimer’s disease (AD). However, effectively extracting features from heterogeneous multimodal data and capturing the associations between microscopic molecular variations and macroscopic brain alterations remain key challenges. Recent advances in deep learning and multimodal integration have enhanced the ability to model nonlinear cross-modal relationships, enabling more accurate identification of imaging-transcriptomic biomarkers and subtypes. Developing robust multimodal frameworks is therefore essential for early AD detection, subtype identification, and advancing precision medicine in neurodegenerative diseases. Methods: In this study, a two-stage method of multimodal Feature Extraction based on Association Analysis and Graph Convolutional Network with Self-Attention and Self-Expression framework (MFEAA-GCNSASE) for early diagnosis of AD and effective identification of subtypes of MCI with different progression to AD is proposed. In the first stage, the MFEAA model is applied to integrate multiple association analysis methods on sMRI, PET, and transcriptomic data to identify key multimodal biomarkers for AD and mild cognitive impairment (MCI). In the second stage, the GCNSASE model enhances classification accuracy between AD and MCI patients through self-attention and self-expression layers. Additionally, unsupervised clustering was performed on MCI samples using top multimodal biomarkers to explore subtype heterogeneity and conversion risk. Reliable MCI subtypes were also identified through a consensus clustering approach. Results: The proposed algorithm integrates sMRI, PET, and transcriptomic data, identifying robust biomarkers including the Left Hippocampus, Left Angular Gyrus, and key genes such as SLC25A5 and GABARAP. To ensure statistical robustness given the extreme class imbalance, we employed a rigorous repeated stratified cross-validation (RSCV) framework. GCNSASE achieved state-of-the-art discrimination performance with mean AUC values ranging from 0.946 to 0.961 across feature subsets (10–50%), significantly outperforming MOGONET (mean AUC: 0.844–0.875, p < 0.001) and conventional machine learning models with tighter 95% confidence intervals, indicating superior stability despite the limited AD sample size. Clustering analysis revealed two distinct MCI subtypes with divergent molecular landscapes: Subtype A was enriched in energy metabolism and cellular maintenance pathways, whereas Subtype B was enriched in neuroinflammatory and aberrant signaling pathways. Notably, the majority of MCI patients who subsequently converted to AD were concentrated in the immune-inflammatory Subtype B. These findings highlight that neuroinflammation coupled with bioenergetic failure constitutes a critical mechanism driving the conversion from MCI to AD. Conclusions: The proposed methods not only provide the key multimodal biomarkers and enhance the accuracy of the classification model for early AD diagnosis but also identify biologically and clinically meaningful MCI subtypes with distinct molecular signatures and conversion risks. Exploring these associated multimodal biomarkers and MCI subtypes is of great significance, as they help elucidate the heterogeneous mechanisms underlying AD onset and progression, enable the identification of high-risk individuals likely to convert to AD, and provide a foundation for targeted therapeutic strategies and individualized clinical management. These findings have important implications for understanding disease heterogeneity, discovering potential intervention targets, and advancing precision medicine in neurodegenerative diseases. Full article

Soil pH is a critical soil property governing nutrient availability and ecosystem functioning. Digital mapping of its spatial distribution is essential for precision agriculture and sustainable land management. This study performs a comparative analysis of six tree-based models coupled with residual kriging (RK) for 30 m resolution mapping of soil pH in Shayang County, China. Specifically, random forest (RF), extremely randomized trees (ERT), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost) were used. Based on 1343 soil samples and 32 environmental variables, experimental results demonstrate that the integration of RK enhanced the prediction accuracy of all standalone models by taking the spatial dependence of residuals into account. Among the models, CatBoost-RK achieved the best performance with an R² of 0.7265, RMSE of 0.5072, and RPD of 1.9122, closely followed by ERT-RK and RF-RK. The analysis of variable importance identified soil type (ST) and mean annual precipitation (MAP) as the most critical factors affecting soil pH distribution. The generated 30 m resolution soil pH map reveals distinct patterns across different land use types, with croplands showing lower soil pH and grasslands exhibiting higher pH with greater variability. These findings confirm the effectiveness of the hybrid ML-RK framework and provide valuable insights for selecting optimal modeling strategies in digital soil mapping. Full article

Forecasting of travel demand has become increasingly important in the context of evolving mobility patterns and structural disruptions, including economic fluctuations and public health crises. Classical time series models, although well established in travel-demand analysis, are often limited in their ability to capture non-linear dependencies or adapt to abrupt regime shifts. This study develops and evaluates forecasting techniques drawn from both traditional statistical modeling and machine learning approaches. Their predictive performance and adaptability are benchmarked for U.S. outbound air travel demand across eight global destination regions, Europe, the Caribbean, Asia, South America, Central America, Oceania, the Middle East, and Africa, respectively. Using historical outbound passenger data, six forecasting models are constructed and assessed through multiple forecasting accuracy measures, including the Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Empirical results demonstrate that machine-learning-based models, particularly those incorporating adaptive learning components, consistently outperform conventional approaches in modeling structural changes in travel demand data. The study further contributes a generalizable methodological framework that enhances robustness under uncertainty and offers broad applicability to forecasting problems in transportation, tourism, and related domains. Full article

Objective: The arterial anatomy of the left upper lobe exhibits the greatest variability in branching patterns among all pulmonary lobes. This lobe is commonly described as having four segments: the fused apicoposterior segment (S1+2), the anterior segment (S3), and the lingular segments (S4 and S5). Each segment may contain subsegments with distinct vascular supplies. Although several studies have examined patterns and diversity of branching, a comprehensive assessment of the incidence of these variations has not yet been performed. Methods: This systematic review was conducted in accordance with a protocol registered in PROSPERO (CRD42024546839). The search was performed between December 2023 and February 2024. A systematic search of databases was carried out to identify publications describing arterial branching patterns supplying the left upper lobe of the lung in adults. Furthermore, we collected and analyzed data on the relationship between the different origins of the lingular arteries and the corresponding bronchial and venous patterns. The AUQA tool was used to perform bias assessment. Data extraction included study characteristics, participant demographics (listed in AUQA), and anatomical variables based on the Yamashita classification of LUL arterial patterns and the number of branches supplying the left upper lobe. Results: In total, 15 publications were included (3313 cases). Lobar vasculature was firstly categorized more broadly, analyzing the number of branches from the left pulmonary artery, which supplies the left upper lobe, most commonly four branches. Then, analysis based on the Yamashita classification was performed, and Type A (A3, A(1+2) a+b, A(1+2) c) was established as the most common variant. Conclusions: The left upper lobe is most commonly supplied by four different arterial branches, followed by three and five. Differences in arterial branching patterns between Asian and Western populations may represent an important distinguishing factor. According to Yamashita’s classification, Type A (A3, A1+2a+b, A1+2c) is the most frequently observed pattern. Further attention should be directed to the relationship between the presence of a common trunk and the origin of the lingular arteries. Detailed knowledge of this anatomy remains fundamental for segmental thoracic surgery. Full article

Background: Long-term real-world data on breast cancer outcomes in North Africa remain limited, despite rising incidence and increasing access to multimodal treatment. This study reports survival outcomes, recurrence patterns, and quality-of-care performance in the largest single-center breast cancer cohort in the region. Methods: A retrospective analysis was conducted on a prospectively maintained registry of 1826 women who underwent curative-intent breast cancer surgery between 2002 and 2016. Overall survival (OS) and disease-free survival (DFS) were estimated using Kaplan–Meier methods. Prognostic factors were examined through univariate and multivariate Cox regression analysis. Adherence to selected EUSOMA quality indicators was assessed across two time periods. Results: At a median follow-up of 10 years, five-year OS and DFS were 96% and 90%, respectively, declining to 91% and 84% at 10 years. Local recurrence occurred in 6.2% of patients and distant metastasis in 11%, with bone being the most frequent metastatic site. TNM stage, tumor size, SBR grade, and tumor localization were independent predictors of OS, while younger age and TNM stage independently predicted DFS. Hormone receptor status and TNM stage were associated with local recurrence; age and tumor site predicted distant metastasis. Quality-of-care evaluation showed high adherence to EUSOMA indicators, including timeliness of surgery, proportion of single-operation resections, and use of postoperative radiotherapy after breast-conserving surgery. Limited administration of HER2-targeted therapy during the earlier years of the cohort reflected historical availability constraints rather than current practice. Conclusions: This study provides robust long-term evidence from North Africa, demonstrating that high survival rates can be achieved when standardized surgical pathways, multidisciplinary coordination, and adherence to quality indicators are maintained. The findings underline the importance of sustained investment in diagnostic access and treatment organization and highlight the expected benefits of the expanded availability of HER2-targeted therapies in the region. These results offer a valuable benchmark for strengthening breast cancer care in comparable LMIC settings. Full article

In the building sector, time and cost overruns are still ongoing difficulties; hence, good project management depends critically on accurate evaluation of project performance. Usually, project success is measured in several performance criteria: cost, schedule, quality, safety, and others as well. This research suggests the construction of a thorough Project Performance Index (PPI) methodically combining these important performance criteria. One finds the relative weight of every element by means of a frequency-based analysis of their occurrence in the current literature. The final index presents a complete method for assessing and contrasting the performance of building projects, giving researchers and practitioners trying to improve project results a helpful instrument. Full article

The development of new energy technologies is crucial for the future competitiveness of the automotive industry. Green innovation is a key driver of industrial transformation and advancement. Companies in the new energy vehicle (NEV) sector play a critical role in the automotive supply chain and demonstrate their green innovation capabilities across the industry. The dual-credit policy, a major governmental regulatory incentive, has a significant impact on the innovation performance of NEVs. Therefore, it is important to examine its influence on green innovation outcomes. This study is grounded in institutional theory and the resource-based view, and informed by the TOE analytical framework. It aims to develop a theoretical model to investigate the interplay among technological, organizational, and environmental factors in fostering green innovation. Using panel data from 21 NEV companies spanning the period 2014–2023, the research employs the dynamic fuzzy-set Qualitative Comparative Analysis (fsQCA) method to identify causal configurations associated with high green innovation performance. The results show that no single factor is necessary for achieving superior outcomes. Configuration analysis reveals 3 dominant pathways: “Technology-driven + Environment-pulled” pathway, “Technology-driven + organizational collaboration” pathway and the “Tripartite linkage” pathway. This study advances theoretical understanding by moving beyond unidimensional analyses and offering a holistic perspective on the multiple equifinal paths to high green innovation performance. It also provides practical insights for NEV firms to strategically align their technological, organizational, and environmental resources to enhance green innovation performance. Full article

With engineering architecture being shifted to meet the requirements of sustainable development, the need for optimized design solutions places precise engineering methods at the core of the contemporary industrial transition toward data-driven strategies. A timely conversion to lightweight components in drivetrain systems has led to the prominent use of high-strength polymer gears, establishing them as a critical point of interest in the field of power transmission. However, as the conversion to polymer gears relies on expensive and time-consuming laboratory testing, there is a standstill in evaluating the structural properties specific to polymer gear design. In addition, one of the major concerns in the development of polymer-based gear drives is linked with their operational performance and dynamic response under fault conditions influenced by surface wear. To address these difficulties, a framework for surface wear prediction is developed, enabling precise design optimization for specific drivetrain requirements. Computations of wear progression over multiple duty cycles are built upon the mathematical background of Archard’s wear theory, while internal changes in gear contact pressure distribution are constructed on Winkler’s surface model. The framework provides an innovative support for polymer gear systems, as it imports the three-dimensional (3D) scanning data of gear geometry, therefore enabling the analysis of actual flank surfaces with designated surface modifications and manufacturing errors. The framework’s effectiveness, confirmed by experimental validation, demonstrates a superior estimation of contact parameters and overall performance compared to traditional design methods, highlighting scalable solutions that contribute to ongoing industrial engineering objectives. Full article

A growing body of literature argues in favor of the influence of organizational culture (OC) on firm performance (FP). Yet this consensus often emanates from studies that over-emphasize the direct culture–performance relationship, with methodologies that are deficient in revealing causal mechanisms and prone to giving ambiguous results. To address these gaps, this study proposes and tests an integrated theoretical framework, synthesizing the Schema Theory, Resource-Based View/Capability theory, and Contingency Theory of Firm Performance. This framework establishes a foundational influence mechanism of OC on performance, moving from cognitive schemas to actualized capabilities and environmental fit. Using data from 249 construction firms in Ghana, we employed a three-stage analytical process; using cluster analysis, we identified five cultural clusters, dominated by Clan and Adhocracy culture types (Organic cultures). Cross-tabulation revealed that large and resource-rich firms (D1K1 and D2K2) were more likely to exhibit balanced cultural profiles. Initial analysis using Kruskal–Wallis H Test showed no significant performance difference between balanced and organic clusters. However, when multiple regression was employed to control for firm classification and adverse industry conditions, the Balanced Culture profile emerged as a statistically significant predictor of superior performance. Consequently, we argue that while an Appropriate Culture, one dominated by organic traits and values, provides survival in a challenged environment, the Balanced Culture profile serves as a critical enabler of superior firm performance, once resource constraints and industry stressors are neutralized. Our findings hold particular importance for international–local joint ventures, where cultural alignment is a critical success factor. Additionally, the proposed framework establishes a robust theoretical foundation for future studies, especially those conceptualizing organizational culture as a foundational, independent variable. Full article

Background: Patients with single-ventricle physiology represent a high-risk group for heart transplantation. Due to complex anatomical and physiological challenges, including multiple prior sternotomies, pulmonary artery abnormalities, and systemic consequences of altered circulation, they represent both a surgical and a clinical challenge. We aimed to analyze perioperative challenges, as well as early and long-term complications, in this specific group of patients. Methods: We performed a retrospective data analysis of a high-volume heart transplant center, focusing on patients with single-ventricle physiology who were scheduled for heart transplantation due to end-stage heart failure. We retrospectively analyzed the period from the beginning of the transplant program in November 1985 to the end of November 2024. Results: Among 1553 transplanted patients (adults and children), 29 were transplanted due to congenital heart disease (congenital valvular disease not included). In this group, nine patients were transplanted due to end-stage heart failure in the course of single-ventricle physiology. Age at transplantation ranged from 7 to 31 years (median, 17 years), and body weight ranged from 15 to 69 kg (median, 47.9 kg). All nine patients referred for heart transplantation presented with single-ventricle physiology. Their underlying congenital heart defects were heterogeneous and included hypoplastic left heart syndrome (HLHS), double-outlet left ventricle (DOLV), transposition of the great arteries (TGA) with associated ventricular septal defects (VSDs), atrial septal defects (ASDs), valvular abnormalities such as tricuspid and or pulmonary valve atresia or stenosis, systemic or atrioventricular valve regurgitation, and vascular abnormalities, including right-sided aortic arch, aortic coarctation, and pulmonary artery hypoplasia, stenosis, or occlusion, as well as associated pulmonary vascular abnormalities such as left pulmonary artery stenosis and MAPCAs. All patients had previously undergone staged palliative procedures, including Norwood, Hemi-Fontan, Fontan, bidirectional Glenn, modified Blalock–Taussig shunts, Bjork–Fontan, or pulmonary artery banding, often with repeated interventions such as balloon angioplasty, stent placement, or MAPCA closure. Extracardiac comorbidities were common and included coagulopathies, protein-losing enteropathy, hepatic dysfunction, and chronic venous insufficiency. Preoperative functional status was markedly impaired in all patients (NYHA III-IV, INTERMACS 3-4), with severely reduced exercise capacity and thrombotic events in several individuals. Perioperative transplant surgical strategies included femoral cannulation in four cases and standard aortic and caval cannulation in five cases. Pulmonary artery reconstruction was required in all patients. Extended donor pulmonary arteries were applied in eight cases, while a bifurcated Dacron prosthesis was utilized in one patient. Perioperative mortality was 33%, with three deaths attributed to bleeding and hemodynamic instability, while overall mortality was 44% including one late death unrelated to transplantation. Protein-losing enteropathy, although persistent in the immediate postoperative period, resolved in all surviving patients, underscoring the transformative impact of transplantation. Conclusions: These findings emphasize the importance of individualized surgical planning, extended donor pulmonary artery harvesting, and careful preoperative coordination. Heart transplantation remains a viable and life-extending option for selected single-ventricle patients, despite the significant technical and clinical challenges involved. Full article

This study investigates regime-dependent forecasting of the Saudi stock market by combining macro-controlled dependence analysis with nonlinear predictive modeling. Using daily data from September 2010 to August 2025, we analyze the interaction between the Tadawul All Share Index (TASI) returns and crude oil returns while controlling for inflation and interest-rate dynamics. A four-variable VAR with macro controls is estimated separately in pre- and post-COVID regimes to characterize directional predictability and changes in transmission lags. We then evaluate out-of-sample return forecasting performance across econometric benchmarks (ARIMA, ARIMAX, and VAR) and machine learning models (LSTM and XGBoost) under a strictly time-ordered expanding-window design with sequential train/validation/test partitioning. The results indicate that traditional linear benchmarks exhibit limited predictive ability in both regimes, with negative out-of-sample explanatory power. By contrast, XGBoost delivers the strongest overall performance, achieving positive out-of-sample R² in both regimes (0.046 in pre-COVID and 0.010 in post-COVID), together with the lowest forecast errors (RMSE = 0.0081 pre-COVID; 0.0078 post-COVID). Interpretability analysis further reveals a regime-sensitive shift in drivers: short-horizon equity lag dynamics dominate during stable periods, whereas oil-related and macro-financial variables gain importance under turbulent conditions. Economic-value evaluation supports the practical relevance of these gains, showing that XGBoost-based signals yield superior risk-adjusted trading outcomes and remain favorable under downside-risk and drawdown-based assessment. Overall, these findings highlight that forecasting in oil-linked emerging markets is inherently regime-dependent and that nonlinear ensemble learners, particularly XGBoost, provide a more robust and economically meaningful approach under structural change. Full article

Livestock production in Mexico takes place in a wide range of agroecological regions, with approximately one-third of the cattle population raised under tropical conditions, where heat stress and disease pressure limit the performance of poorly adapted animals. The Mexican Sardo Negro cattle breed (Bos indicus) is environmentally resilient and is used for both meat and milk production; however, information regarding its population structure and reproductive management remains limited. Therefore, the genetic diversity and population structure of this breed were evaluated through pedigree analysis to support conservation strategies. Genealogical records from 8653 animals belonging to six herds located in the states of Veracruz and Chiapas, Mexico, were analyzed using ENDOG V4.8, PopRep and GRain software. The average inbreeding coefficient was 2.5%, with an increase of 0.9% per generation, a mean generational interval of 7.9 years, and a maximum pedigree depth of nine generations, although pedigree completeness was low in distant generations. The difference between the effective number of ancestors (32) and founders (37) suggests the absence of bottlenecks; however, the fact that only 21 individuals account for 50% of the genetic variability is indicative of a founder effect. Overall, the population exhibits an acceptable level of inbreeding, highlighting the importance of planned mating strategies to maintain genetic diversity and ensure the long-term conservation of the Sardo Negro breed. Full article

Background: Medication-related osteonecrosis of the jaw (MRONJ) is a grave complication in patients with cancer treated with antiresorptive agents, particularly after invasive dental procedures. Identifying reliable clinical factors to assess MRONJ risk remains a clinical challenge. Methods: The retrospective observational study comprised 61 oncologic patients undergoing dental extractions during antiresorptive therapy. Preoperative serum levels of 25-hydroxyvitamin D and β-C-terminal telopeptide cross-link (β-CTx), along with relevant clinical variables, were measured. The analyses included comparative tests, multivariate logistic regression to detect independent predictors of MRONJ, and ROC curve analysis to assess the model’s predictive performance. Results: MRONJ was diagnosed in 18 patients (29.5%). Low preoperative vitamin D levels were significantly associated with MRONJ and remained an independent predictor in the multivariate analysis (OR = 8.74, p = 0.005). The mandibular extraction site was also identified as a significant risk factor (OR = 7.94, p = 0.007). In contrast, β-CTX levels, age, sex, cancer type, and the number of extracted teeth did not show a significant link to MRONJ development in this cohort. The comprehensive multivariate model demonstrated good discrimination capacity (AUC = 0.806). Conclusions: Preoperative vitamin D deficiency is an important independent predictor of MRONJ after dental extractions in patients with cancer receiving antiresorptive agents. Integrating metabolic biomarkers and clinical variables into predictive models may improve risk assessment and support the development of more effective preoperative prevention strategies. Full article

Background: The Callista Roy Adaptation Model posits that adaptation in later life emerges from the interaction among physical, psychological, and social dimensions. However, empirical evidence integrating these domains through predictive approaches remains limited. The aim of this study was to identify the main predictors of adaptive classification in older adult women using functional and subjective well-being measures. Methods: A predictive study was conducted in older adult women enrolled in community-based exercise programs. Assessments included the Senior Fitness Test and the SF-12 and WHO-5 questionnaires. Multiclass classification models were trained, with Random Forest selected due to superior performance. Model evaluation incorporated oversampling strategies and robustness analyses without oversampling, using metrics resilient to class imbalance (macro-F1 and balanced accuracy). Model interpretability was examined through variable importance analysis, partial dependence, and ICE plots. Results: Under the oversampling framework, the Random Forest model achieved an overall accuracy of 74% and a macro-F1 score of 0.73, with reduced performance observed in robustness analyses, particularly for the minority “High” class. The most influential predictors were the physical component of the SF-12, the 2 min step test, the mental component of the SF-12, and the chair sit-and-reach test. Conclusions: The findings highlight the joint contribution of physical and psychosocial factors to adaptive processes, in alignment with the Roy Adaptation Model. This study provides exploratory evidence supporting the integrated use of the SFT, SF-12, and WHO-5; however, external validation and longitudinal evaluation are required prior to clinical implementation. Full article