Search Results (2,438)

The carbon emission allowance (CEA) market is a complex socio-technical and environmental-management system in which regulatory design, trading activity, liquidity conditions, and price volatility interact dynamically. Accurate systems-level tail-risk forecasting and early warning remain challenging because carbon-market losses are affected by nonlinear dependence, episodic liquidity stress, and time-varying volatility. This study proposes an Interpretable Quantile Temporal Network (IQTN) as a systems-oriented risk-monitoring framework for China’s national CEA market. By integrating a feature-gating mechanism, a causal temporal convolutional encoder, and a non-crossing quantile output layer, IQTN directly models the conditional tail distribution of future carbon-market losses. The framework produces multi-horizon Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) forecasts for 1-day, 5-day, and 10-day horizons and converts predicted tail risk into operational early-warning signals. Compared with historical simulation, EWMA, GARCH-type models, machine-learning quantile models, and deep temporal benchmarks, IQTN achieved the lowest 95% VaR pinball loss across all horizons, with values of 0.1765, 0.3958, and 0.5732. VaR backtesting showed empirical exceedance rates of 5.23%, 6.04%, and 6.94%, closest to the nominal 5% level. Interpretability analysis identified rolling volatility, maximum loss, intraday range, trading value, and illiquidity as key risk drivers. The temporal importance results also show that recent observations dominated the risk forecasts, suggesting that the risk state of the CEA market is highly sensitive to short-term market information. This supports the use of a short-horizon temporal network as a systems-oriented tool for carbon-market tail-risk monitoring and early warning. Full article

Background: Simultaneous pancreas–kidney (SPK) transplantation can restore renal function and insulin independence, but non-technical pancreas graft dysfunction remains difficult to anticipate. Methods: We conducted an exploratory single-centre retrospective biomarker-modelling study to determine whether day-zero recipient serum Fourier-transform infrared (FTIR) spectra are associated with subsequent loss of insulin independence after SPK transplantation. Results: Among 104 screened recipients, 51 met predefined sample-availability, spectral-quality, data-linkage and endpoint-adjudication criteria; 30 maintained pancreas graft function and 21 developed dysfunction. Cases dominated by early technical surgical failure were excluded. Clinical-only, FTIR-only and FTIR–clinical Naïve Bayes models were evaluated using leave-one-out cross-validation with Fast Correlation-Based Filter feature selection. In locked-feature internal validation, the best FTIR-only model used second-derivative spectra with vector normalization and nine selected wavenumbers, achieving AUC 0.997 (95% CI 0.985–1.000) and accuracy 0.961 (95% CI 0.902–1.000). A fixed-feature permutation analysis exceeded label-randomized performance (empirical p = 0.001). The secondary Group 1 versus Group 3 analysis suggested discrimination of pancreas dysfunction despite preserved kidney function (AUC 0.992; accuracy 0.930). Conclusions: Given the small cohort, high-dimensional input, non-nested feature selection, selection-bias risk and absence of external validation, serum FTIR should be considered a candidate risk-enrichment platform requiring prospective multicentre validation. Full article

Indian metropolitan cities such as Mumbai grapple with rapid urbanisation, extreme urban density, high built-up areas, loss of green cover, and shrinking open spaces, resulting in increased impermeable surfaces, urban heat island effects, and frequent flooding occurrences. Modern stormwater management has increasingly been characterised by integrated grey-green approaches; however, cities in the Global North benefit from established policies, technical expertise, and financial resources that enable the systematic and large-scale integration of Blue-Green Infrastructure (BGI) through district-wide geospatial assessment frameworks, unlike many cities in the Global South. Despite growing interest in nature-based stormwater solutions, there remains a dearth of geospatial empirical research from India examining the placement, distribution, performance, and functionality of BGI integrated with existing stormwater management systems in cities such as Mumbai. Furthermore, hydrological modelling using tools such as the Storm Water Management Model (SWMM) for the design, planning, and implementation of BGI in Indian cities remains largely unexplored. This study explores the role of BGI strategies in improving urban stormwater management within high-density Indian cities under a 25-year return period extreme rainfall scenario. Using an integrated approach that combines QGIS-based spatial analysis with EPA-SWMM hydrologic-hydraulic modelling, the research examines runoff behaviour, identifies flooding hotspots, and evaluates the effectiveness of Low Impact Development (LID)-based BGI measures such as permeable pavements, infiltration trenches, and green roofs applied at the ward level in Mumbai’s F/North and G/North Wards. Detailed land use classification, spatial mapping, and rainfall simulation corresponding specifically to a 25-year return period rainfall event was used to assess pre- and post-intervention conditions. The findings indicate that the applied BGI measures led to a 12.6% reduction in peak runoff (137.6 m³/s to 120.2 m³/s) and a 5.5% decrease in total runoff volume (783,510 m³ to 740,410 m³). More importantly, the peak flooding flow rate decreased by 45% (94.1 m³/s to 51.7 m³/s), demonstrating that BGI measures can efficiently reduce peak flooding flows by extending runoff hydrographs during extreme rainfall events. These findings are specifically applicable to the simulated 25-year return period extreme rainfall scenario and may vary under different rainfall intensities or return periods. Less extreme events could potentially experience even greater relative reductions or prevent flooding altogether, while also easing downstream hydraulic loads. Overall, strategically placed BGI interventions can significantly reduce surface runoff and peak flow, thereby enhancing stormwater resilience within spatially constrained urban environments. This study provides a replicable, data-driven framework for catchment-scale stormwater planning in dense Indian cities under extreme rainfall conditions, offering practical insights into methods, local contextual considerations, and spatial planning strategies for policymakers and urban planners seeking to retrofit and adapt existing infrastructure under increasing hydrologic stress and climate variability. Full article

The internal quality of watermelons has garnered extensive attention. Conventional destructive quality detection for watermelons causes fruit loss, while existing acoustic techniques often rely on a single evaluation index. To address these issues, this study proposes a non-destructive method for comprehensive watermelon quality detection using acoustic and vibration signals. Signals from two watermelon varieties were collected under impact excitation to extract six time-domain and frequency-domain features. Factor Analysis of Mixed Data (FAMD) was employed to integrate ripeness, Soluble Solids Content (SSC), firmness, and sensory scores into a Comprehensive Quality Index (CQI), categorizing samples into High-Quality, Medium-Quality, and Low-Quality groups. Following physically constrained data augmentation to mitigate small sample size and class imbalance, an Extremely Randomized Trees (Extra-Trees) model was constructed. Results demonstrate that the Extra-Trees model achieved an overall testing accuracy of 0.92, with recall rates of 0.93 and 1.00 for Low-Quality and High-Quality watermelons, respectively. Recognition for Medium-Quality samples was lower due to overlapping physical and acoustic characteristics. Ultimately, this system aligns with actual consumer demands, providing technical support for low-cost, portable, and non-destructive watermelon grading. Full article

Tire and road wear particles (TRWPs) are major sources of non-exhaust traffic emissions. However, a limited understanding of their generation mechanisms and the lack of efficient collection methods under realistic driving conditions hinder accurate assessment. This study addresses these challenges by developing a vehicle-based methodology for the controlled recovery and characterization of TRWPs in the near-field region, rather than for direct quantification of real-world emissions. An autonomous electric vehicle was employed to ensure stable driving conditions and eliminate exhaust interference. Near-field distribution of TRWPs was visualized using a high-sensitivity optical scattering system. Based on this, a sealed tire enclosure with a high-power on-vehicle vacuum collection system was designed to enhance particle containment and recovery. Controlled circular driving tests were conducted on a dedicated outdoor test track under well-defined and repeatable conditions to enable system-level evaluation of TRWP generation and collection relative to measured tire wear. Particles were analyzed by thermogravimetric analysis, microscopy, scanning electron microscopy–energy-dispersive X-ray spectroscopy, and particle imaging. The results demonstrated stable, reproducible TRWP generation with ~60% collection efficiency relative to tire mass loss. These values are reported as system-dependent recovery indicators rather than precise emission estimates. Additional tests with an expanded recovery protocol indicated that collection efficiency can increase to ~81% (range: 73–91%), highlighting the influence of collection coverage. The collected TRWPs exhibited heterogeneous morphology, bimodal size distribution, and a mixed rubber–mineral composition in the 10–100 μm range. Spatial analysis revealed that TRWPs predominantly accumulated within a narrow zone around the driving lane. While the controlled experimental configuration enables reproducible particle generation and high-efficiency recovery, it represents a simplified driving scenario and may not fully capture the variability of real-world traffic conditions, including straight-line driving and transient maneuvers. Overall, this study demonstrates a technical framework for reproducible and comparative recovery of tire-associated particles under identical, well-defined conditions. The approach is intended to support controlled characterization studies while explicitly acknowledging limitations related to representativeness, particle origin attribution, and quantitative emission relevance, rather than to establish emission factors or mechanistic descriptions of TRWP generation. Full article

Introduction: The circum-Mediterranean region is a global biodiversity hotspot, hosting a highly distinctive freshwater fauna with a high degree of endemism and conservation concern. However, these ecosystems are increasingly threatened by biological invasions, particularly by non-native fish species, which represent a major driver of biodiversity loss. Objective: This study aims to compile a comprehensive and updated inventory of non-native inland fish species across the circum-Mediterranean region and to identify the main taxonomic, biogeographical, and socio-environmental drivers shaping their distribution. Methodology: We conducted an extensive review of the scientific literature, online databases (including EASIN, GISD, and CABI), and technical reports to compile records of non-native fish species across inland and transitional waters of Mediterranean-climate basins. Analyses focused on species composition, taxonomic representativeness, introduction pathways, native regions, and the relationship between species richness and selected environmental and socio-economic variables. Results: A total of 151 non-native fish species were recorded across the study area. Italy, Spain, Bosnia and Herzegovina, France, and Croatia exhibited the highest numbers of established species. Taxonomic representation was uneven, with Salmoniformes and Esociformes overrepresented among established non-native species, while Siluriformes and Characiformes were underrepresented. Most introductions originated from Europe, Asia, and North America, primarily through intentional releases and escape events. Non-native species richness was positively correlated with gross domestic product, precipitation, and the number of dams, highlighting the role of economic development and habitat modification in facilitating invasions. Conclusions: Biological invasions by non-native fishes are widespread across the Mediterranean basin and are strongly driven by human activities and environmental conditions. The high invasion levels observed in this biodiversity hotspot pose a significant threat to endemic freshwater faunas. These findings underscore the need for coordinated transnational management strategies, stricter regulation of introduction pathways, and prioritization of high-risk species to mitigate further impacts. Full article

This paper addresses probabilistic and statistical methods for calculating technical energy losses in direct current (DC) networks. A DC network model is adopted as the basis for the analysis, and several approaches are compared in terms of qualitative features and computational efficiency. The load profile is described using probabilistic indicators, emphasizing the importance of accounting for correlation moments (CMs) between node powers and CMs between voltages to reduce calculation errors. A correction procedure for the mathematical expectation of node voltages is proposed, which significantly improves the accuracy of loss estimation. Simulation studies on representative four-node DC test networks show that the proposed method reduces the root mean square error in loss estimation by up to 15–20% compared with traditional approaches based solely on mean load values. The results confirm that the correction of node voltage expectations provides a good balance between accuracy and computational cost and can be recommended as an independent procedure within existing probabilistic frameworks for loss assessment. Full article

Drainage pipe defect detection is essential for maintaining the normal operation of urban infrastructure. In recent years, deep learning-based object detection methods have provided an effective technical solution for drainage pipe defect recognition. Among them, YOLO-series models have demonstrated strong potential in visual detection tasks due to their end-to-end architecture and high inference efficiency. However, directly applying baseline YOLO models may still face challenges such as limited detection accuracy, relatively high model complexity, and insufficient adaptability for lightweight deployment scenarios. To address these issues, this paper proposes a lightweight drainage pipe defect detection method based on an improved YOLO11 network. Rather than treating detection enhancement and model compression as two separate procedures, the proposed method integrates feature enhancement, adaptive pruning, and distillation-based recovery into a unified lightweight detection framework. Specifically, an improved SimAM attention mechanism is introduced into the backbone and integrated with the C3k2 module to construct the C3K2_SWS module, aiming to enhance the representation capability of critical defect features. In the neck network, a focused diffusion pyramid network with a dimension-aware selective fusion structure, termed FDPN-DASI, is designed to strengthen multi-scale feature interactions. In addition, an adaptive-threshold focal loss (ATFL) is introduced to improve the learning capability for hard samples. For efficient deployment, the LAMP pruning algorithm is further improved, and an entropy-guided entropy-adaptive magnitude-based pruning method (EA-LAMP) is proposed to enable adaptive allocation of pruning ratios across different network layers. Moreover, BCKD knowledge distillation is applied after pruning to mitigate the accuracy degradation caused by model compression. Experimental results indicate that the proposed lightweight YOLO11-SFA+EA+BCKD framework achieves a precision of 92.4%, a recall of 88.5%, and an mAP50 of 93.3%, while maintaining a compact model size of 1.6 M parameters and 4.5 G FLOPs. Compared with the baseline model, the proposed method improves precision, recall, and mAP50 by 5.9%, 5.0%, and 4.7%, respectively, while reducing the number of parameters, FLOPs, and model size by 1.0 M, 1.8 G, and 2.1 M, respectively. These results suggest that the proposed framework can improve detection performance while reducing model complexity under the current experimental setting, indicating its potential for lightweight drainage pipe defect detection tasks. Full article

Background: Microsatellite instability (MSI) and mismatch repair (MMR) deficiency are key predictive biomarkers for immune checkpoint inhibitors (ICIs) in metastatic colorectal cancer (mCRC). In real-world practice, however, diagnostic pathways often involve heterogeneous testing modalities, which may lead to discordant or inconclusive results. Methods: We conducted a retrospective study of patients with mCRC who underwent at least one MSI/MMR assessment between 2015 and 2025. Diagnostic modalities included IHC, tissue-based and liquid-based MSI testing. A predefined decision algorithm classified results as conclusive or inconclusive; discordant cases underwent adjudication that integrated a pathology review, molecular features, and technical considerations. Patients were ultimately assigned to definitive MSS or definitive MSI groups. Clinical characteristics, treatment patterns, and outcomes—particularly in relation to immunotherapy—were evaluated. Results: Among 727 evaluable patients, the MSI/MMR status was conclusive in 695 (95.6%) and inconclusive in 32 (4.4%). Inconclusive cases resulted from isolated MMR protein loss, heterogeneous or equivocal staining, inter-tumoral discordance, or discrepancies between tissue- and liquid-based assays. After adjudication, 54 patients (7.4%) were classified as definitive MSI and 673 (92.6%) as definitive MSS. Definitive MSI tumors were associated with female sex, right-sided primaries, high-grade histology, nodal involvement, and BRAF V600E mutations. Among the definitive MSI patients, 31 (57.4%) received immunotherapy, achieving a complete response rate of 48.4% and an overall response rate of 71.0%. Median PFS and OS were not reached in the definitive MSI group, whereas definitive MSS patients treated with ICIs experienced significantly poorer outcomes. Conclusive and adjudicated MSI groups demonstrated comparable responses to immunotherapy. Conclusions: In real-world practice, a meaningful proportion (4%) of mCRC patients experience inconclusive MSI/MMR assessment, with important clinical implications. Both technical and biological factors contribute to diagnostic uncertainty. Integrating orthogonal testing modalities and applying structured adjudication improves classification accuracy and ensures appropriate access to immunotherapy. Full article

The existing literature highlights that artificial intelligence (AI) creates both hope and threat perceptions among managers and workers, particularly due to concerns about potential job losses and the negative effect on continued professional development. Employee trust in AI-based systems varies depending on their features and performance. Furthermore, regardless of the performance of such systems, some individuals are inherently opposed to AI, a phenomenon known as AI aversion. In this study, an Integrative AI Adoption Framework is developed, drawing upon principles from established theories, including the technology acceptance model, behavioral decision theory, and emotion-based frameworks, to assess how perceived usefulness and perceived ease of use, along with perceived threat, trust, and AI aversion, influence human resources (HR) professionals’ attitudes and behavioral intentions to use AI-based recruitment systems. In doing so, the study conceptualizes AI-based recruitment as a socio-technical system in which a technical subsystem (the system’s instrumental and AI-specific properties) and a social subsystem (the affective and trust-related responses of HR professionals) must be jointly considered to explain adoption. The model was tested using the partial least squares structural equation modeling (PLS-SEM) approach through survey-based data collected from 242 HR professionals. The study’s findings indicate that attitude plays an important role in shaping behavioral intention, and perceived usefulness is a key driver of attitude. AI aversion negatively influences attitudes, while trust has a twofold effect of reducing AI aversion and positively influencing attitude. Additionally, perceived threat significantly increases AI aversion, which is driven by concerns over job replacement and personal development. Full article

Microfiltration membranes are widely used in drinking water treatment due to their high efficiency. However, long-term operation of polymeric membranes may lead to deterioration of hydraulic properties as a result of fouling and material aging. This study aims to determine the impact of long-term aging on hydraulic permeability and separation properties, and to determine the lifespan of microfiltration polyvinylidene fluoride (PVDF) membranes. The practical and industrial novelty of this study lies in providing an authentic, 11-year operational baseline for a full-scale microfiltration system treating highly variable surface water. The study evaluates membranes installed in a full-scale plant in Jarosław (Poland), treating surface water from the San River. The system includes 120 PVDF capillary modules (0.1 μm). After 11 years, the membranes maintained very high separation efficiency, ensuring almost complete removal of turbidity and microorganisms. However, membrane resistance increased nearly threefold, while permeability decreased by about 86%. Maintaining capacity required a gradual increase in transmembrane pressure. The permeability loss exceeded the commonly accepted replacement threshold of 70%, suggesting that membrane replacement after more than a decade of operation is technically and economically justified. Full article

Proper handling during harvesting and subsequent postharvest management is essential to reduce losses in fruits and vegetables, particularly because these products remain metabolically active after harvest. Physiological processes such as respiration, transpiration, ethylene production, softening, physiological disorders, and postharvest diseases determine quality deterioration, shelf life, and marketability. However, these processes do not affect all commodities in the same way; for example, climacteric fruits are strongly influenced by ethylene during ripening, whereas non-climacteric fruits generally show lower ethylene production and different postharvest behavior. In Mexico, postharvest management is especially relevant because fruit and vegetable producers differ widely in terms of production scale, infrastructure, access to technology, financing capacity, and market destination. Producers with limited access to technology require practical and low-cost alternatives, while more technologically advanced producers may use specialized systems but still experience postharvest losses due to physiological deterioration, handling conditions, logistics, and market constraints. Therefore, this review summarizes the main postharvest physiological processes affecting fruits and vegetables and discusses their implications for knowledge transfer, technology adoption, and sustainability among local producers in Mexico. The review highlights that reducing postharvest losses requires commodity-specific management, continuous technical support, low-cost and locally adaptable technologies, and coordinated participation among researchers, extension personnel, producers, government institutions, industry, and market actors. Strengthening postharvest knowledge transfer to small and local producers is essential to reduce losses, improve marketability, and promote more sustainable fruit and vegetable systems in Mexico. Full article

Weak grid infrastructure and the absence of flexible storage are among the principal barriers to reliable, low-carbon energy access in sub-Saharan transmission systems. This paper proposes a hierarchical multi-objective framework for the optimal siting and sizing of battery energy storage systems (BESSs), applied to the 130-bus Mali transmission network within the EMERGE project. The upper level employs NSGA-II to simultaneously maximize daily price arbitrage revenue and minimize active power losses; the lower level solves a network-constrained DC optimal power flow with thermal branch limits enforced as hard linear inequalities via the Power Transfer Distribution Factor (PTDF) matrix. Over 500 generations, the framework identifies Bus 91 (SIRAKORO II, 150 kV) as the dominant storage location, achieving a maximum daily revenue of approximately €10,033 at a marginal loss increment of $6.7\times {10}^{-3}$ MWh. The resulting Pareto front gives Mali system planners a quantitative tool for trading off private investment returns against grid-level environmental impact, demonstrating that rigorous network-constrained BESS planning is technically tractable and economically viable in the resource-constrained context of sub-Saharan energy transitions. Full article

Introduction: AQUArestore is a three-year project focused on promoting adaptive ecological restoration strategies for river ecosystems in the vulnerable cross-border region of Portugal. The project responds to pressing environmental challenges across the territory, including severe habitat degradation, climate vulnerability, declining water security, and biodiversity loss, with particular concern for freshwater fish communities, making river restoration essential to preserve native species and freshwater ecosystem services. Objective: The project aims to develop a replicable framework for restoration of Mediterranean transboundary riverine habitats, supporting the objectives of the EU Nature Restoration Law (NRL, Regulation 2024/1991). The consortium AQUArestore will develop (1) robust restoration indicators, (2) implement living labs for restoration experimentation, and (3) establish capacity-building and training programs for technicians and citizens. Methodology: The project kick-off meeting was used to operationalize project tasks, detail the implementation calendar and milestones, and clarify responsibilities of each project member and partner institutions within the different work tasks. The meeting gathered consortium members from the coordinating institution CEF-ISA (researchers at the Instituto Superior de Agronomia) and partners WWF Portugal (an environmental NGO) and Mushmore Cooperative, each one contributing according to their respective expertise and institutional objectives. Results: The AQUArestore project kick-off meeting took place in January 2026 at ISA, Lisbon, and included a presentation of the NRL and a detailed discussion of project task development. In detail, the activities will begin with the compilation of information on previously restored sites (Task 1). This will support the development and validation of environmental and biodiversity indicators of restoration outcomes, including those linked to freshwater fish assemblages and riparian vegetation (Task 2). The project will then establish two living labs as platforms to test nature-based solutions in collaboration with stakeholders and local communities (Task 3). In parallel, AQUArestore will strengthen technical capacity through training for practitioners and public authorities (Task 4). Finally, dissemination will be supported through citizen science, communication activities, and stakeholder engagement, fostering a broader impact (Task 5). Together, these tasks provide an integrated, science-based, and participatory framework aiming to support adaptive river restoration under climate and environmental changes. Conclusions: By integrating ecological restoration, biodiversity and environmental monitoring, and stakeholder engagement, AQUArestore is expected to contribute to the recovery of Mediterranean freshwater ecosystems and improve habitat quality and connectivity for native fish communities, enhancing resilience to climate change and other anthropogenic pressures. Full article

Designing low-voltage (LV) power distribution systems for mass-sensitive electric vehicles involves several unresolved technical challenges, including parasitic $I^{2}R$ losses, excessive mass of commercial off-the-shelf distribution units, and difficulties in isolating thermal phenomena during vehicle operation. In dynamic racing conditions, temperature measurements of LV components are strongly influenced by external heat sources such as traction batteries, motors, and inverters, complicating accurate assessment of conductor self-heating and distribution losses. This work presents a load-driven methodology for the specification, implementation, and validation of LV architectures, demonstrated using a Formula Student electric race car. The proposed approach combines harness current mapping, resistive loss modeling, and component-level topology optimization to support the development of lightweight and electrically robust systems. Within this framework, a mass-optimized programmable solid-state power distribution unit (PDU), an auxiliary battery system with a battery management system (BMS), and an optimized LV wiring harness were developed and experimentally validated through controlled subsystem tests and in-vehicle operation. The proposed methodology enabled reduction in PDU mass by 40–80% relative to commercially available solutions while maintaining programmable protection, integrated current sensing, and stable thermal operation under representative racing loads. This reduction was achieved through load-driven conductor sizing, application-specific protection threshold optimization, and elimination of redundant protection and interconnection hardware. The developed PDU achieved a mass of 155 g with measured channel resistances of 40–70 m$\mathsf{\Omega }$. The auxiliary battery pack exhibited an average internal resistance of 64.2 m$\mathsf{\Omega }$ at a total mass of 2190 g, while the optimized harness demonstrated resistivity in the range of 14.72–33.98 m$\mathsf{\Omega }$/m. Experimental validation confirmed stable operation below critical thermal limits under both nominal and off-nominal load conditions. The obtained results demonstrate that the proposed methodology enables measurable reductions in both system mass and resistive power losses through application-specific optimization of the LV architecture. However, the presented approach is primarily suited for motorsport and other highly mass-constrained applications, where reduced packaging volume, efficiency, and weight justify the increased design complexity and lower universality compared to commercial off-the-shelf solutions. Full article