Search Results (12,093)

This study critically addresses the challenge of selecting optimal insulation materials for contemporary, energy-efficient building envelopes, a decision with profound environmental, structural, and occupational health consequences. The paper responds to the growing demand for sustainable, resilient solutions by comparing wool, a bio-based, regenerative material, and expanded polystyrene (EPS), a synthetic polymer widely implemented in the construction industry, and advanced laboratory testing (thermal conductivity, moisture buffering, freeze–thaw resistance) is discussed in a comprehensive synthesis of the recent literature. Also, field evaluations from European retrofits and pilot projects (UK, Denmark, Finland, Iceland, Norway, Sweden, Germany and France) further contextualize performance outcomes, and life cycle impacts are considered. Recent results reveal that wool insulation achieves a moisture buffering value (MBV) between 1.8 and 2.7 (g/m²) % RH, minimal vapor resistance (mvr = 1–2), and preserves functional and structural integrity through more than 100 freeze–thaw cycles, leading to significant stabilization of the interior microclimate and enhanced durability. In contrast, EPS delivers lower thermal conductivity (0.032–0.037 (W/mK), critical for reducing heating/cooling demand, but exhibits limited vapor permeability (lvp = 60–150 MN·s/(g·m)), increased risk of condensation and mold, and reduced compressive strength (<22% after 30 cycles), especially when ventilation details are inadequate. Hybrid envelope systems leveraging both EPS and wool are demonstrated to optimize energy efficiency (up to 23% seasonal savings) and reduce interior humidity fluctuations, while lifecycle and recycling assessments show wool panels to be markedly superior in carbon footprint reduction and circularity. The stratification of insulation layers incorporating wool for vapor and moisture control, and EPS for pure thermal resistance is emerging as best practice in sustainable retrofit and new-build projects. Recommendations highlight the necessity for rigorous laboratory validation, international standards alignment, and integrated material design for robust hygrothermal comfort and environmental performance. The review also covers wool- and EPS-based hybrid composites, showing how natural fibers can improve key mechanical properties without compromising thermal insulation performance or environmental benefits. Full article

In bank-dominated financial systems, the accumulation of non-performing loans is a recognised source of systemic vulnerability, as correlated corporate defaults can erode bank capital, impair liquidity, and propagate stress across interconnected portfolios. Firm-level default detection thus constitutes a microprudential foundation of macroprudential stability: the reliable early identification of risky borrowers reduces both individual credit losses and the aggregate exposures that drive system-level fragility. Yet the use of structured data-mining pipelines for this task remains underexplored in Central and Eastern Europe. This study applies the CRISP-DM methodology to predict corporate loan default using data on 302 Slovak corporate borrowers, combining financial ratios from publicly available financial statements with selected company and loan-related information from internal bank records. Seven individual classifiers were developed and compared: decision trees (CART, CHAID, C5.0), logistic regression, discriminant analysis, and neural networks (MLP, RBF), together with a stacked ensemble based on their outputs. Model performance was evaluated using sensitivity, overall classification accuracy, and area under the ROC curve (AUC), with sensitivity treated as the primary criterion because of the asymmetric costs of misclassification in credit risk assessment. The results confirm that historical firm-level information provides a reliable basis for default prediction, with tree-based models consistently outperforming statistical and neural network approaches. The stacked ensemble achieved the strongest overall performance, whereas C5.0 and CHAID showed that interpretable classifiers can also deliver competitive predictive accuracy. A champion–challenger deployment architecture is proposed, in which the ensemble serves as the performance-oriented champion and interpretable models act as challengers; this arrangement contributes to the operational resilience of the credit-risk assessment process and aligns with macroprudential expectations of model governance, auditability, and explainability. The study offers a replicable methodological framework for integrating data-driven decision support into credit evaluation in comparable banking settings. Full article

This discussion paper examines the evolution of freshwater distribution networks from smart to intelligent and ultimately meta-intelligent or wise systems, highlighting the transition from human-supervised operation to autonomous adaptive management. Smart systems integrate monitoring, automation and remote control through information technologies. Intelligent systems extend these capabilities by adding predictive analytics, demand forecasting and automated operational optimization. Wise systems further evolve through adaptive learning mechanisms that allow continuous self-improvement while minimizing dependence on operators. Evidence from Greek water utilities demonstrates practical applications and operational outcomes. The analysis discusses implementation challenges including investment costs, system complexity, data governance and resilience. Finally, the paper proposes design principles for scalable adaptive water networks applicable to utilities with different sizes, resources and levels of technological maturity. Full article

The global agricultural sector faces the dual challenge of increasing productivity while mitigating environmental impacts caused by synthetic agrochemicals and massive agro-industrial waste. This review examines the transition to “Biostimulants 4.0,” a circular economy paradigm driven by the valorization of biomass residues into high-value biological inputs through nanotechnology and Artificial Intelligence (AI). Our analysis highlights that green extraction methods, specifically enzymatic hydrolysis, preserve bioactive integrity and reduce carbon emissions by up to 23.2 times compared to synthetic nitrogen production. Furthermore, waste-derived formulations and nanoscale smart-delivery systems dramatically enhance crop performance; for instance, chitosan nanoparticles can achieve up to a 471% increase in specific growth metrics through sustained-release pathways. To move the industry beyond empirical trial-and-error, the integration of AI-driven predictive models now achieves up to 87% accuracy in forecasting biostimulant efficacy. Finally, we contrast global regulatory frameworks and evaluate the monetization of biostimulant-driven carbon sequestration, capable of generating high-integrity credits priced up to $35 per tonne, as a critical economic pathway to accelerate commercial adoption and incentivize a resilient, decarbonized agricultural system. Full article

Obesity is increasingly recognized as a complex systemic disorder rather than a simple consequence of excess energy intake and fat accumulation. This review presents a systems biology framework that examines how obesity-driven disruption of inter-organ communication networks contributes to chronic disease susceptibility, with particular emphasis on colorectal cancer (CRC). Disrupted signaling among the brain, adipose tissue, liver, skeletal muscle, gut, and immune system generates maladaptive feedback loops that promote chronic metabolic inflammation (metaflammation), loss of physiological resilience, and progressive metabolic dysfunction. Within this framework, obesity is redefined as a network disease characterized by neuroendocrine dysregulation, adipose tissue remodeling, immune dysfunction, impaired organ crosstalk, and alterations in the gut microbiome. A central feature of this dysregulation is persistent low-grade inflammation driven by immune-metabolic reprogramming and sustained activation of inflammatory pathways. Obesity-associated metaflammation is further linked to accelerated biological aging through mechanisms involving cellular senescence, mitochondrial dysfunction, oxidative stress, and impaired metabolic resilience. These interconnected processes create a tumor-promoting environment by enhancing oncogenic signaling, disrupting intestinal barrier integrity, altering microbial and metabolic signaling, impairing immune surveillance, and promoting epithelial dysfunction, thereby increasing susceptibility to CRC. The review also examines how behavioral, circadian, environmental, and socioeconomic factors influence metabolic health and cancer risk. Finally, emerging translational opportunities, including biomarker-guided risk stratification, precision prevention, metabolic network restoration, and integrative lifestyle and pharmacological interventions, are discussed. Collectively, this review reframes obesity as a whole-body regulatory disorder and provides an integrated conceptual framework linking metabolism, inflammation, aging, and colorectal carcinogenesis to inform future prevention and therapeutic strategies. Full article

Beekeeping is highly affected by climate change, which alters environmental conditions and challenges colony stability. In this context, digital monitoring technologies can enhance apiary resilience. This study presents the development and field validation of a low-cost hive monitoring system based on a customizable Raspberry Pi architecture, integrating temperature and weight sensors with robust data continuity features. The system was evaluated over one year in Southern Europe (Serbia) against a commercial reference. Results show that correlation between systems depends on both the monitored parameter and the biological state of the colony. For weight, strong agreement was observed only during winter, when reduced biological activity allows reliable comparison, whereas correlations were weak in more active periods. Conversely, temperature monitoring exhibited the highest correlation over long-term datasets, indicating that extended time scales are required for reliable sensor validation. These findings highlight the importance of a context-aware validation approach in apiculture. The proposed system provides a cost-effective and reliable solution for continuous hive monitoring, supporting data-driven management and improved resilience under climate variability. Full article

The article presents a comprehensive methodology for optimizing ground impact zones of spent rocket stages based on the integration of geoinformation analysis, remote sensing of Earth, ballistic modeling, and ecosystem sustainability assessment. An information and analytical system (IAS) has been developed and tested, providing automated selection of environmentally sustainable landing points within acceptable dispersion zones. The methodology includes the use of the NDVI, digital terrain models, soil quality assessments, fire hazard assessments, and environmental damage calculations. For the first time, a system for classifying operational-territorial units according to their level of resilience to man-made impacts has been formed. The results suggest the potential for the reduction of the dangerous impact zone under modeled conditions. The system architecture is designed to be scalable and applicable to other spaceports located in continental regions. The presented methodology contributes to the development of an environmentally oriented approach to aerospace infrastructure management. Full article

Traditional air conditioning consumes substantial electricity, exacerbates the urban heat island effect, and creates a maladaptive feedback loop, necessitating a shift toward passive-first net-zero pathways. This study takes a typical six-story residential building in Nanjing’s hot summer and cold winter climate zone as a case study. Using EnergyPlus hourly simulations, three progressive passive strategy packages are designed to quantify the impact of building envelope heat exchange on cooling loads, grid stress, and heat resilience. Package A includes external shading and natural ventilation. Package B adds reflective coating and a green roof. Package C further adds night ventilation precooling and high-performance windows. The results show that Package C achieves a 62.5% reduction in peak cooling load and a 63.0% reduction in seasonal cooling load. Daytime peak inward heat gain decreases from 68 W/m² to 22 W/m², while nighttime outward heat dissipation increases from 12 W/m² to 38 W/m². Under an extreme heat day of 41.2 °C with no active cooling, indoor peak temperature drops from 36.8 °C to 29.4 °C, and heat risk hours decrease by 73.6%. Peak-hour power demand is reduced by 70.4%, with a systemic leverage factor of 1.08. Innovations include achieving over 60% load reduction using only mature passive strategies, introducing the systemic leverage factor to quantify urban heat island mitigation benefits, and establishing a vulnerability-to-resilience transformation framework. The passive-first pathway validates building envelope as the first line of defense for net-zero futures. However, the findings are based on a typical six-story residential building in Nanjing and require validation through field measurements or broader application across different climate zones and building typologies before generalization. Full article

Soil organic carbon (SOC) plays a critical role in climate regulation, soil fertility, and ecosystem resilience, making its accurate spatial quantification essential for sustainable land management and greenhouse gas (GHG) reporting. However, mapping SOC in heterogeneous agroforestry systems remains challenging due to vegetation cover and landscape complexity. In this study, we develop and evaluate a hybrid bare soil modelling- Digital Soil Mapping supported by ML framework to generate high-resolution soil properties predictions in Mediterranean agroforestry systems (Extremadura, Spain). A dual modelling approach was implemented, combining (i) Bare Soil modelling using Sentinel-2 multi-temporal reflectance composites and (ii) Digital Soil Mapping (DSM) supported by environmental covariates (climate, terrain, vegetation) following the SCORPAN framework. Machine learning models, namely Quantile Regression Forests (QRF) and Extreme Gradient Boosting (XGBoost), were applied and optimised using automated hyperparameter tuning (FLAML). A total of 107 LUCAS topsoil samples and 36 complementary points from the Forest ICP Level I were used for calibration and validation, with a 70/30 train–test split. Results show that Sentinel-2-based modelling can effectively capture SOC spatial variability in bare soil conditions, while DSM improves predictions in vegetated areas. Model performance reached R² values up to 0.76 (QRF, pH) and RMSE as low as 0.03 (XGBoost, N), with uncertainty quantified using the Prediction Interval Ratio (PIR) and performance further supported by RPIQ values up to 3.15. However, prediction accuracy remains sensitive to vegetation structure and sample density. The proposed framework provides a scalable and uncertainty-aware approach for SOC mapping, supporting Tier-3 GHG inventories and emerging Monitoring, Reporting, and Verification (MRV) systems. The results highlight the importance of integrating multi-source datasets and hybrid modelling strategies for reliable SOC estimation in complex landscapes. Full article

Desert expressway corridors exposed to strong wind hazards often rely on single high-grade routes, with limited alternatives, high detour costs, and low network redundancy. These constraints make it difficult to maintain traffic operation resilience through route substitution alone. Taking the Hami–Tuyugou section of the G30 Lianhuo Expressway in Xinjiang, China, as a case study, this study investigates the formation and evaluation of traffic operation resilience in a wind-hazard-affected, low-redundancy desert expressway corridor. A hierarchical indicator system was constructed with four first-level, fourteen second-level, and thirty-one third-level indicators. Fuzzy DEMATEL(Decision Making Trial and Evaluation Laboratory)–ISM(Interpretive Structural Modeling) was used to identify causal relationships and hierarchical transmission paths; fuzzy DANP(DEMATEL-based Analytic Network Process)–AHP(Analytic Hierarchy Process) was applied to determine indicator weights; and a cloud model was employed to evaluate the overall resilience level. The results show that institutional adaptability, organizational learning, monitoring and information support, and multi-actor collaboration are the main upstream drivers. The corridor was evaluated as Grade IV, indicating a relatively high resilience level approaching Grade V. Sensitivity analyses confirm the robustness of the substantive conclusion. The findings suggest that, under low-redundancy conditions, resilience depends less on structural redundancy and more on adaptive governance, information support, and coordinated response. Full article

Global container liner shipping networks (GCLSNs) underpin world trade, yet their organization is increasingly reshaped by geopolitical fragmentation. Existing studies often model GCLSNs as single-layer networks, overlooking how carriers’ geopolitical affiliations structure both connectivity and disruption risk. This study constructs a weighted carrier–geopolitical multiplex network in which layers are defined by carriers’ geopolitical affiliations and coupled through shared port calls. Structural analysis reveals pronounced asymmetry in layer size, cohesion, and inter-layer dependence, with overlap concentrated in a limited set of shared hubs. Using the Red Sea crisis as an empirical stress-test scenario, we develop a load–capacity propagation model, incorporating intra-layer load redistribution, rerouting to substitute shared hubs, and inter-layer resource squeeze at same-port layer copies. Results show that direct losses concentrate in corridor-exposed layers, while indirect losses propagate selectively through bridge hubs, especially Singapore, Shanghai, Shenzhen, and Port Klang. Sensitivity analysis indicates nonlinear amplification when low tolerance, strong inter-layer squeeze, and elevated rerouting pressure coincide. These findings show that multiplexity does not imply resilience by itself; cross-layer connectivity buffers disruption only when spare capacity is distributed but amplifies vulnerability when it converges on a narrow set of shared hubs. The paper contributes a carrier–geopolitical perspective to shipping network analysis and a dynamic framework for studying disruption propagation in complex logistics systems. Full article

Wildfires are increasingly complex emergencies driven by climate variability, the expansion of wildland–urban interfaces, and the interaction between fire events and hazardous environments. These factors pose significant challenges for emergency management, particularly in the presence of cascading effects and multi-hazard interactions. This review examines the potential contribution of Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) frameworks to wildfire emergency management, focusing on preparedness and operational response. A narrative analysis of interdisciplinary literature was conducted to identify conceptual and operational overlaps between fire science and CBRNE-based approaches, with particular attention to command structures, hazard assessment, and response coordination. The analysis indicates that wildfire management systems often remain fragmented, with variability in procedures, training, and the integration of monitoring technologies. Evidence from CBRNE operational models suggests that structured command systems, field-based analytical capabilities, and interoperable procedures support improved situational awareness and decision-making. The review highlights how selected CBRNE principles, including structured command systems, zoning strategies, hazard characterization, and interoperability mechanisms, may address persistent gaps in complex wildfire emergency management, providing a basis for improved coordination, operational effectiveness, and system resilience. Full article

Tourism plays an increasingly important role in promoting economic growth and rural revitalization in ecologically fragile regions. However, tourism systems in Tibet–related areas of Sichuan, China, are highly vulnerable to natural disasters, ecological degradation, and regional development imbalances, posing challenges to sustainable tourism development. This study aims to evaluate tourism system resilience and identify its key influencing factors from a sustainability perspective. Based on the regional characteristics of Tibet-related areas in Sichuan, a comprehensive evaluation framework is constructed covering four subsystems: tourism infrastructure and scale, economy, society, and ecology. An integrated entropy weight–analytic hierarchy process (AHP) model, coupling coordination model, and obstacle degree model are employed to assess tourism system resilience and examine subsystem interactions using panel data from 2011 to 2020. The results indicate that: (1) the resilience levels of tourism subsystems show no clear spatial or temporal regularity across the study areas; (2) ecological resilience remains significantly lower than tourism, economic, and social resilience, representing the weakest component of the tourism system; (3) the coupling coordination among subsystems remains at a low level, suggesting insufficient synergy for sustainable regional development; and (4) ecological constraints are the primary limiting factors affecting overall tourism system resilience. This study contributes to sustainable tourism research by revealing the critical role of ecological governance and subsystem coordination in enhancing tourism resilience in ecologically sensitive regions. Policy implications include strengthening ecological protection, improving tourism infrastructure, promoting digital tourism marketing, and advancing rural revitalization to achieve long-term sustainable development. However, this study is limited by data availability and the spatial scope of the selected case-study areas, which may affect the generalizability of the findings. Full article

The health system is now more complex, uncertain, interdependent, and dynamically interconnected than ever, making traditional planning decisions based on static, reductionist models increasingly impracticable. Systems analytics approaches such as system dynamics, agent-based modeling, and network analysis are often deployed in isolation and fail to capture cross-level interactions and emergent system behavior. This study proposes an integrated multi-layer systems analytics framework that combines these analytical paradigms within a unified architecture to support adaptive health systems planning under uncertainty. The proposed framework introduces an Adaptive Policy Intelligence Layer (APIL), which enables continuous feedback-driven policy adaptation through dynamic monitoring, scenario evaluation, and real-time adjustment mechanisms. The model is evaluated under multiple simulation scenarios, including baseline conditions, demand shocks, resource constraints, and digital transformation environments. The findings provide strong quantitative and analytical evidence of improved system performance and resilience. More specifically, the digital transformation scenario achieved the lowest mean system pressure (0.128) and the highest resilience index (0.887), while the demand shock scenario produced the highest peak system pressure (0.306). The results demonstrate enhanced system resilience, more efficient resource deployment, and superior policy responsiveness compared with traditional single-method approaches. The originality of this study lies in integrating multi-level systems analytics with adaptive policy intelligence into a unified, feedback-driven decision-support framework for resilient health systems governance. The study contributes to systems analytics literature by advancing a synergistic and adaptive modeling paradigm capable of supporting policymakers in navigating complex and unstable healthcare environments. Full article

As global ecosystems and food systems face unprecedented anthropogenic and climatic challenges, there is a demand for an integrated understanding of biological systems. Proteomics has emerged as a definitive approach offering a direct view of the molecular phenotype, yet it is traditionally separated into plant and animal disciplines. With recent advances in mass spectrometry (MS) and bioinformatics tools, this prospective review proposes that combining a One Health proteomics approach with deep-learning data analysis can revolutionize global food security, animal productivity, and ecosystem health by uncovering proteoform signatures that drive resilience across life. The potential of a unified One Health proteomic framework, highlighting major developments, including 4D proteomics, Data-Independent Acquisition (DIA), and single-cell resolution, and emphasizes their capacity to resolve the complex proteoform landscape across kingdoms. Review emphasizes the applications of proteogenomics as a cross-disciplinary tool to improve genome annotations, explain evolutionary differences, discover biomarkers in animals and resolve complex signaling networks in plants under stress. Nevertheless, contemporary proteogenomics methods still show limitations in their ability to comprehensively resolve proteoforms due to the fact that the use of peptide-based approaches makes it difficult to fully appreciate the post-translational modifications specific to each protein isoform. We show that One Health proteomics will provide a transformative roadmap for deciphering the functional proteoform signatures that underpin resilience across the tree of life. Full article