Search Results (39,287)

It is a common phenomenon that the stairs of modern historical brick–timber buildings cannot meet existing fire protection specifications, something which has become a difficulty in their renovation. In response, this study proposes two different renovation strategies for the Hui-shaped Chinese Baroque brick–timber building in Harbin and constructs multiple fire scenarios. Using a coupled PyroSim–Pathfinder (version 2023.2.0816) simulation approach, a finite element model of the building under fire and a corresponding evacuation model are established. The aim is to investigate how variations in stair width, number, position, and overall building scale under the two renovation strategies influence evacuation movement time and the number of evacuation failures, and to compare the effectiveness of common fire protection measures. The results show that, for the same stair configuration and building mass, the fire development patterns of the two renovation strategies are similar. Increasing the stair width from the original 0.9 m to 1.1 m produces no significant improvement in evacuation performance. When the number of indoor existing stairways increases from one to two, the proportion of occupants evacuated safely rises from 68% to 91%. External corridor staircases provide the best evacuation performance, and a single such stair can satisfy the safe evacuation of all occupants. When the same additional floor area is provided, increasing the number of storeys extends the evacuation movement time by approximately twice that caused by increasing the building footprint. Automatic sprinkler systems and mechanical smoke exhaust systems exhibit more pronounced fire protection effects. Full article

To address the issue of negative damping instability easily induced by DC/DC converters under constant power load (CPL) in DC microgrids and to enhance the control robustness of the system under uncertainties such as parameter perturbations, this paper designs a controller based on the linear active disturbance rejection control (LADRC) theory. Firstly, by establishing an equivalent model of the DC microgrid with CPL, the intrinsic relationship between the equivalent incremental admittance of the hybrid load and the system damping is revealed. Subsequently, treating the nonlinear characteristics of the CPL and model parameter variations as external disturbances, the linear extended state observer (LESO) is employed to estimate and compensate for the total system disturbance in real time. This effectively eliminates the risk of negative damping instability caused by the CPL and enhances the system’s robustness against parameter variations. Then, theoretical analysis is conducted from three perspectives, the convergence of disturbance estimation error, the stability of the closed-loop system, and robustness against parameter variations, thereby ensuring the reliability of the proposed control strategy. Finally, the proposed control strategy is validated through simulations and experiments. The results confirm that, even in the presence of negative damping effects and parameter variations, the strategy can effectively maintain fast tracking and stable control of the output voltage. Full article

The intestine–liver–muscle axis plays an essential role in drug and nutrient absorption, metabolism, and energy balance. Yet in vitro models capable of recapitulating this inter-organ communication remain limited. Here, we present a pump-free, 3D-printed multi-organ-on-a-chip device that enables dynamic co-culture of Caco-2 intestinal epithelial cells, HepG2 hepatocytes, and primary human skeletal myoblasts (HSkMs) under gravity-driven oscillatory flow. The device consists of five interconnected chambers designed to accommodate Transwell cell culture inserts for intestine and muscle compartments and hydrogel-embedded hepatocyte spheroids in the central hepatic compartment. The device was fabricated by low-cost fused deposition modeling (FDM) using acrylonitrile butadiene styrene (ABS) polymers. Under dynamic rocking, oscillatory perfusion promoted inter-organ communication without the need for external pumps or complex tubing. Biological assessments revealed that dynamic co-culture significantly enhanced the characteristics of skeletal muscle, as indicated by increased myosin heavy chain expression and elevated lactate production, while HepG2 spheroids exhibited improved hepatic function with higher albumin expression compared with monoculture. Additionally, Caco-2 cells maintained stable tight junctions and transepithelial electrical resistance, demonstrating preserved intestinal barrier integrity under dynamic flow. These results establish the device as a versatile, accessible 3D-printed platform for modeling the intestine–liver–muscle axis and investigating metabolic cross-talk in drug discovery and disease modeling. Full article

Foreign direct investment (FDI) has often been cast as a straightforward engine of growth, yet its record across Central and Eastern Europe tells a more tangled story where outcomes hinge on the interplay of education, governance, and the timing of external shocks. This study embeds fixed effects panel econometrics within a systems framework, treating FDI as a subsystem of socio-economic dynamics. Using a long-run panel of eleven economies from 2000 to 2023, the analysis models path dependence and regime shifts through interaction terms and period-specific dummies set against a systems-thinking backdrop. The analysis shows that for the average CEE economy, FDI’s contribution has waxed and waned: it dragged on growth during the early transition years (2000–2007), settled into a neutral role after the global financial crisis, and proved unpredictable in the pandemic era. Romania stands out, however, with a marked “FDI premium” quantified as approximately 0.7 pp of growth per pp of FDI that seems to stem from reinforcing loops between rising tertiary enrolment and productivity spillovers. Mapping these feedbacks brings to light virtuous circles where human capital and resilience make or break the benefits of foreign capital. The policy message is plain: nurture the positive loops through investment in skills and firm linkages, keep institutions nimble enough to adapt, and watch for early warning signs of systemic strain. Full article

This study addresses the critical problem of barriers hindering Green Supply Chain Management (GSCM) adoption in Egypt’s petrochemical sector, a major economic driver that produces approximately 4.5 million tons annually but generates significant GHG emissions and hazardous waste. The objective is to identify, rank, and analyze the hierarchical relationships among internal and external barriers using a mixed-methods approach. This study focuses on the full petrochemical supply chain in Egypt, encompassing upstream (raw material sourcing), midstream (manufacturing/refining processes), and downstream (distribution, waste management, reverse logistics), with an emphasis on emission/waste reduction practices. Data were collected via a structured questionnaire from 400 employees in Egyptian petrochemical firms and analyzed using Interpretive Structural Modeling (ISM). The findings showed that internal impediments, such as a lack of corporate leadership and support (IB1), a critical shortage of resources (IB6), and the absence of green initiatives (IB5), serve as driving forces that exert a cascading influence over the external barriers, which include insufficient government support (EB1), a lack of markets for recycled materials (EB5), and human resources or expertise shortages (EB7). The study contributes to the existing literature on GSCM by incorporating international trends and specifically addressing Egyptian issues, including weak policies, difficult supply chains, high energy-intensive operations, and costly operations. The study suggests that sending clear messages from the top and providing financial incentives can help push the obstacles aside and guide the industry down the path of environmentally responsible operations. Full article

Investigating how climatic and hydrological conditions in ecological resource-enriched zones of marginal seas respond to external forcing, particularly during past greenhouse climates, holds considerable significance for understanding current environmental and resource challenges driven by global warming. In marginal seas, climatic hydrological states, including salinity, redox conditions, and productivity, are key environmental parameters controlling organic matter production, preservation, and ultimately the formation of high-quality shale. Herein, high-resolution cyclostratigraphic and multi-proxy geochemical analyses were conducted on a continuous core from the upper part of Member 4 of the Eocene Shahejie Formation (Es4cu) in Well NY1, Dongying Sag, Bohai Bay Basin. Based on these data, a refined astronomical timescale was accordingly established for the studied interval. By integrating sedimentological observations with multiple proxy indicators, including elemental geochemistry (e.g., Sr/Ba and Ca/Al ratios), organic geochemistry, and mineralogical data, the evolution of climate and paleo-water mass conditions during the study period was reconstructed. Spectral analyses revealed prominent astronomical periodicities in paleosalinity, productivity, and redox proxies, indicating that sedimentation was modulated by cyclic changes in eccentricity, obliquity, and precession. It was hereby proposed that orbital forcing governed periodic shifts in basin hydrology by regulating the intensity and seasonality of the East Asian monsoon. Intervals of enhanced summer monsoon associated with high eccentricity and obliquity were typically accompanied by increased sediment supply and intensified chemical weathering. Increased precipitation and runoff raised the lake level while promoting stronger connectivity with the ocean. In contrast, during weak seasonal monsoon intervals linked to eccentricity minima, basin conditions shifted from humid to arid, characterized by reduced precipitation, lower lake level, decreased sediment supply, and a concomitant decline in proxies for water salinity. The present results demonstrated orbital forcing as a primary external driver of cyclical changes in conditions favorable for resource formation in the Eocene lacustrine strata of the Bohai Bay Basin. Overall, this study yields critical paleoclimate evidence and a mechanistic framework for predicting the spatial-temporal distribution of high-quality shale under comparable astronomical-climate boundary conditions. Full article

The aim of this article is to identify and systematize the principal research directions in sustainable strategic management (SSM) at the intersection of eco-innovation and business performance. Despite the growing prominence of sustainability in management scholarship, systematic understanding of how SSM, eco-innovation, and business performance are connected in the academic literature remains limited. In particular, it is unclear whether this intersection constitutes a coherent research domain or instead reflects a set of loosely related and fragmented lines of inquiry. To address this gap, the study combines bibliometric analysis and science mapping of 181 Scopus-indexed publications (2006–2024) with a PRISMA-guided scoping review of five core papers that explicitly link SSM, eco-innovation, and business performance. VOSviewer was used to identify thematic clusters and structural gaps, including missing or weak linkages between eco-innovation and different dimensions of business performance. Building on these findings, the article proposes a dual-path conceptual model: (1) a mediated path in which eco-innovation functions as a transmission mechanism between SSM and multidimensional business performance, and (2) a direct path linking SSM to business performance without mediation. The model further distinguishes between internal organizational conditions, which predominantly support the direct path, and external business environment factors, which are critical in enabling the mediated path through eco-innovation. The main contributions are as follows: (a) a structured mapping of the SSM–eco-innovation research field and its emerging thematic architecture; and (b) a conceptual model specifying the dual role of eco-innovation in shaping business performance outcomes. The study also outlines implications for theory, managerial practice, and public policy, particularly in terms of how organizations and their environments influence the effectiveness of different strategic sustainability pathways. The proposed framework should be interpreted as an evidence-informed conceptual model derived from bibliometric patterns and focused qualitative synthesis, rather than as a statistically validated causal model. Full article

The present study examined the impact of agricultural exports on economic growth in Lambayeque, Peru, during the period 2010–2023. An ordinary least squares (OLS) econometric model was employed to analyze the relationship between gross value added (GVA) and key macroeconomic variables, including agricultural exports, private investment, real wages, terms of trade, and the real multilateral exchange rate. The findings indicate that the model possesses considerable explanatory power (R² = 0.973) and that agricultural exports exert a positive and significant influence on regional GVA. In addition, private investment and real wages demonstrate positive elasticities, while terms of trade exhibit a negative relationship with regional economic growth. This highlights Lambayeque’s vulnerability to external price shocks. The study thus underscores the pivotal role of the Olmos Project, which has been instrumental in transforming arid land into fruitful agricultural zones through the implementation of an irrigation system encompassing over 22,000 hectares. This initiative has not only augmented agricultural exports, accounting for an impressive 90% of Lambayeque’s supply, but also contributed significantly to regional economic development by supporting employment generation and poverty reduction. Nevertheless, the presence of negative terms of trade indicates that the regional economy exhibits structural vulnerability in the face of external shocks. Notwithstanding the intrinsic limitations of regional, trade, and macroeconomic statistics, an understanding of the correlation between agro-exports and economic growth in a paradigmatic region of northern Peru provides substantial evidence for formulating policies to enhance the competitiveness and sustainability of the agro-export model. Full article

Tumor mutational burden (TMB) is a key pan-cancer biomarker for immunotherapy selection, but its routine assessment by whole-exome sequencing (WES) or large next-generation sequencing (NGS) panels is costly, time-consuming, and constrained by tissue and DNA quality. In parallel, advances in computational pathology have enabled deep learning models to infer molecular biomarkers directly from hematoxylin and eosin (H&E) whole-slide images (WSIs), raising the prospect of a purely digital assay for TMB. In this comprehensive review, we surveyed PubMed and Scopus (2015–2025) to identify original studies that applied deep learning directly to H&E WSIs of human solid tumors for TMB estimation. Across the 17 eligible studies, deep learning models have been applied to predict TMB from H&E WSIs in a variety of tumors, achieving moderate to good discrimination for TMB-high versus TMB-low status. Multimodal architectures tended to outperform conventional CNN-based pipelines. However, heterogeneity in TMB cut-offs, small and imbalanced cohorts, limited external validation, and the black-box nature of these models limit clinical translation. Full article

This study presents the application of free, open-source artificial intelligence (AI) techniques to advance machine translation for low-resource Turkic languages such as Kazakh, Azerbaijani, Kyrgyz, Turkish, Turkmen, and Uzbek. This machine translation problem for Turkic languages is part of a project to generate meeting minutes from speech transcripts. Due to limited parallel corpora and underdeveloped linguistic tools for these languages, traditional machine translation approaches often underperform. The goal is to reduce digital inequality for these languages and to support scalability. We investigate the effectiveness of free open-source pre-trained specialized and general-purpose AI models for morphologically rich state Turkic languages. This research includes developing parallel corpora for six Turkic languages, fine-tuning, and performance evaluation using BLEU, WER, TER, and chrF metrics. The parallel corpora for five pair languages, each of 300,000 and 500,000 sentences, were generated and cleaned. The results for corpora 500,000 parallel sentences show significant improvements compared with baseline NLLB-200 1.3B on average: BLEU increased by 23.81 points, chrF increased by 26.05 points, and WER and TER decreased by 0.36 and 33.95, respectively, after cleaning and fine-tuning. Six Turkic-language multilingual parallel corpora of 3 885 542 sentences were developed and the fine-tuning of NLLB-200 1.3B shows the following, compared with the results for 500,000 cleaned corpus: BLEU increased by 4.3 points, chrF increased by 1.7 points, and WER and TER decreased by 0.1 and 4.75, respectively These results demonstrate the high efficiency of corpus cleaning and synthetic data generation to improve the quality of machine translation for low-resource Turkic languages using AI models. These results were confirmed by external evaluation on the FLORES 200 dataset and human evaluation. The scientific contribution of this article is the development of a methodology for generating parallel corpora using a specialized AI model of machine translation and fine-tuning the specialized AI model on the created corpora, creating new multilingual parallel corpora of Azerbaijan–Kazakh, Kyrgyz–Kazakh, Turkish–Kazakh, Turkmen–Kazakh, and Uzbek–Kazakh pairs using the proposed methodology, cleaning them, and conducting fine-tuning experiments. Full article

Increasing the permissible travel speed of suspended monorails in underground mines improves the efficiency and profitability of hard coal mining. However, increasing the maximum speed requires addressing a number of issues affecting the safety of the crew and the mine infrastructure. The concept of a new emergency braking device presented in this article is intended to protect against excessive temperature increases on friction surfaces during braking. The article presents the results of preliminary numerical simulations, the purpose of which was to calculate the temperature of a wet multi-plate brake, its propagation, and verify the condition for not exceeding the maximum permissible temperature of external surfaces in contact with a potentially explosive atmosphere. Full article

The present study aimed to analyze the differences in psychopathological symptomatology between men and women who participate in online gambling, as well as to explore the relationship between this symptomatology and different risk profiles. The sample consisted of 382 participants, all university students from a province in Spain, of whom 261 were men (68.3%) and 121 were women (31.7%), with a mean age of 21.8 years (SD = 3.2; range = 18–30 years). Psychopathological symptomatology was assessed using the SAS-45, while gambling risk profiles were determined using an ad hoc questionnaire. The results of the risk profiles were formed by categorizing the SOG-RA Scale scores into non-risk gambler, at-risk gambler, and pathological gambler. The results evidenced that gender and risk profile are determining factors in the manifestation of psychopathological symptoms. It was observed that women tend to internalize their emotional problems, presenting higher levels of depression, anxiety, and interpersonal sensitivity, while men exhibit a greater propensity to externalize their symptoms, manifesting hostility, paranoid ideation, and psychoticism. Furthermore, gamblers with high-risk profiles showed higher scores in both internalizing and externalizing symptoms. Significant correlations were identified between risk profile, psychopathological symptomatology, and cognitive distortions, suggesting the need for comprehensive interventions differentiated by gender. These findings provide valuable information for the design of specific treatments that address the emotional and cognitive needs of problem gamblers, contributing to improving the effectiveness of therapeutic strategies in the context of problem gambling. University gambling is an emerging public health issue with consequences that extend beyond the individual, affecting educational, social, and economic well-being. This study addresses a critical gap by delineating gender-specific psychopathological profiles across gambling risk categories, providing actionable evidence to inform campus-based screening and targeted prevention strategies. The findings underscore the necessity of integrating gender-responsive interventions and upstream measures—such as early detection within student health services and harm-reduction messaging—to effectively mitigate gambling-related harm. Full article

Tuberculosis remains a major public health concern in Uganda, one among the thirty high TB burden countries globally. Despite national progress, gaps persist due to asymptomatic disease, diagnostic limitations, and uneven access to healthcare within the country. This study implemented the Epi-control platform, an AI-driven predictive modelling tool, to predict community-level hotspots and support data-driven active case-finding (ACF). Using retrospective chest X-ray screening data, we integrated demographic, environmental, and human development indicators from open-source databases to model TB risk at sub-parish level. A proprietary Bayesian modelling framework was deployed and validated by comparing TB yields between predicted hotspots and non-hotspot locations. Across Uganda, the model identified significantly higher TB yields in hotspot areas (risk ratio = 1.69, 95% CI 1.41–2.02; p < 0.001). The Central and Western regions showed the highest concentrations of hotspots, consistent with their population density and urbanization patterns. The results show that the model prioritized areas with higher observed ACF yield in this retrospective dataset, supporting its potential operational use for screening prioritization under similar implementation conditions. The results demonstrate that AI-based predictive modelling can enhance the efficiency of ACF by targeting high-risk areas for screening. Integrating such predictive tools within national TB programmes may support screening planning and resource prioritization; prospective evaluation and external validation are needed to assess generalisability and incremental impact. Full article

Epilepsy affects over 50 million people worldwide, yet automated seizure detection systems either achieve moderate sensitivity with excessive false alarms or rely on uninterpretable deep networks. This study presents a patient-independent EEG-based seizure detection framework that achieved zero false alarms in 24 h with 95% sensitivity in a retrospective evaluation on a CHB–MIT pediatric cohort (n = 6 seizure-positive patients). The pipeline extracts 27 time-, frequency-, and nonlinear-domain features from 5 s windows and trains five ensemble classifiers (XGBoost, CatBoost, LightGBM, Extra Trees, Random Forest) using strict leave-one-subject-out cross-validation. All models achieved segment-level AUC ≥ 0.99. Under zero-false-alarm constraints, XGBoost attained perfect specificity with 0.922 sensitivity. SHAP and LIME analyses suggested candidate EEG biomarkers that appear consistent with known ictal signatures, including temporo-parietal theta-band power, amplitude variability (IQR, RMS), and Hjorth activity. External validation on the Siena Scalp EEG Database (12 adult patients, 37 seizures) demonstrated cross-dataset generalization with 95% event-level sensitivity (Extra Trees) and AUC of 0.86 (Random Forest). Temporal lobe channels dominated feature importance in both datasets, confirming consistent biomarker identification across pediatric and adult populations. These findings demonstrate that calibrated gradient-boosted ensembles using interpretable EEG features achieve clinically safe seizure detection with cross-dataset generalizability. Full article

Background/Objectives: An impediment to successful drug discovery is the potential for off-target liabilities to eliminate otherwise promising candidates. As the drug discovery process is time-consuming and expensive, the use of artificial intelligence (AI) methods such as machine learning (ML) has drastically increased. It is invaluable to generate models that can quickly differentiate between successful and unsuccessful small-molecule drug candidates. Previous efforts established that molecular similarity could be used with other metrics to inform predictions of potential activity against a protein target. Similar methods were pursued here to combine similarity and machine learning for a collection of models called OPLE. Methods: Models were trained with proprietary and publicly available data to predict the likelihood of a given compound to be active against targets present in existing experimental SafetyScreen panels 18 and 44. Two-dimensional (2D) Tanimoto similarity from extended-connectivity fingerprints (ECFPs) and trained ML models were combined to obtain predictions. Results: Using all training data, a relationship between similarity and activity was established by fitting a probability assignment curve. Calibrated ML label assignment likelihoods were joined with the predictions from ECFP Tanimoto similarity to known active compounds using the belief theory formula, which maintains that activity prediction increases when both pieces of evidence support it. When assessing the performance of OPLE models for SafetyScreen 18 and 44 targets with external data from ChEMBL, more than 80% of the models had recall values greater than 0.8. This indicated favorable predictive ability to identify active molecules while limiting false negative predictions. Conclusions: Predicting and experimentally verifying safety liabilities is insightful at every stage of small-molecule drug discovery. This early detection tool can help project teams save resources that could be better deployed on series with no predicted or measured off-target liabilities. Full article