Search Results (329)

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

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

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

This study presents the diagnostic and conservation work carried out on the Roman mosaic of the South cubiculum in the Latomia dei Niccolini (Marsala, western Sicily). The mosaic, decorated with polychrome tesserae featuring a kantharos motif, presented severe structural damage, including fractures, subsurface voids, and progressive material loss. To assess the causes of deterioration and design an effective conservation strategy, an integrated approach combining non-invasive geophysical and 3D survey methods was applied. Ground-penetrating radar (GPR) was selected as the main diagnostic tool because it allows high-resolution subsurface imaging while preserving the integrity of the fragile mosaic surface. By utilizing high-frequency 2 GHz antennas and complementary video inspection, a significant subsurface cavity beneath the mosaic preparation layer was successfully mapped, determining its critical relationship with the main diagonal surface fracture. Simultaneously, laser scanning and close-range photogrammetry enabled the creation of accurate 3D models supporting both documentation and restoration planning. The conservation concluded with surface cleaning, mortar consolidation, and the successful structural detachment and relocation of the compromised section onto a lightweight support for future museum display. The findings demonstrate that integrating 3D digital and geophysical data provides a quantitative, low-risk roadmap for preserving highly vulnerable archaeological floorings, moving beyond qualitative technical documentation to establish a replicable preservation framework. Full article

To reveal the electromagnetic response characteristics and hydro-thermal evolution mechanism of frozen soil under microwave irradiation, we used remolded frozen soil prepared from undisturbed parent soil collected in Hegang, China, as the research object. We conducted dielectric parameter tests across the 715–1150 MHz and 2250–2650 MHz frequency bands and 1.5 kW microwave heating tests on specimens with three gravimetric water contents (15%, 20%, and 25%) paired with a coupled numerical simulation of electromagnetic field-heat transfer-moisture migration. The results show that water content is the dominant factor controlling the dielectric response of frozen soil. The dielectric loss and water content sensitivity of frozen soil in the low-frequency band (dominated by unfrozen water) are significantly higher than those in the high-frequency band (dominated by ice phase and soil matrix). Microwave-induced temperature rise exhibits a three-stage characteristic, as follows: slow temperature rise, isothermal plateau at the freezing point, and rapid temperature rise. Specimens with a lower initial water content show a higher temperature rise efficiency in the late heating stage, with a maximum rate of 1.112 °C·s⁻¹ for the 15% water content specimen. Mass loss is negatively correlated with initial water content, with a maximum value of 1.8 g after 120 s of irradiation. In addition, the non-uniformity of the electromagnetic field results in a temperature field pattern characterized by a high-temperature core at the specimen center and lower temperatures at the edges. This study provides fundamental theoretical support and technical guidance for the application of microwave thawing technology in geotechnical engineering, particularly for frozen soil foundation treatment in cold regions. Full article

Delayed sowing frequently occurs in the North China Plain (NCP), restricting pre-winter population establishment and reducing grain yield. To determine whether narrow row spacing can alleviate yield loss under delayed sowing by improving the pre-winter stem and tiller basis, a randomized block experiment with two row spacings and four sowing-date regimes with adjusted planting density was conducted from 2021 to 2024. Yield components, pre-winter stem and tiller number and quality, and anatomical and physiological traits of the main-stem base and tiller nodes were measured. The results showed that 7.5 cm narrow row spacing (R2) increased tiller occurrence and improved hormonal balance and non-structural carbohydrate accumulation in the main-stem base and tiller nodes, and was associated with enhanced anatomical structural integrity and pre-winter stem and tiller quality. R2 increased final spike number, grain number per spike, thousand-grain weight, and grain yield by an average of 2.8%, 8.6%, 3.9%, and 10.7%, respectively, with the greatest yield increase under moderately delayed sowing (S2, 17.7%), and structural equation modeling (SEM) supported this possible yield-increasing pathway (χ²/df = 1.38, GFI = 0.90).These results provide a theoretical basis and technical reference for stabilizing and increasing yield in delayed-sown winter wheat in the NCP. Full article

Building-integrated photovoltaics (BIPV) have achieved a high level of technical maturity. In spite of that, the installed capacity remains limited. To stimulate the integration of solar panels in the built environment, aesthetical features like color and freedom in size and shape are of key importance for architects and building owners. Multilayer interference coatings are an attractive coloring technique for solar panels, as they are known for their high solar transmission and tuneable reflection peak. The latter gives rise to an intense metallic reflection color. In this study, the outdoor performance of colored versus non-colored BIPV panels was investigated, and a method has been developed to measure the color variation of the solar panels with respect to outdoor conditions, viewing angles and tilt angles of the setup. A limited performance loss of 15% was measured for colored solar panels compared to their black counterparts, caused by a reduction in generated photocurrent due to light loss. Outdoor color measurements showed that the cloudiness of the sky and the tilt angle of the setup are key parameters causing a color variation from yellow-green to blue-green. In addition, the developed method and tailored measurement setup have proven their value in quantifying color appearance of colored BIPV in realistic and varying outdoor conditions. Full article

The growing penetration of electric vehicles (EVs) and distributed photovoltaic (PV) generation is increasing operational uncertainty in distribution networks and intensifying long-standing challenges such as higher power losses, rising peak demand, and voltage instability. To address these issues, this paper proposes a multi-objective optimization framework for the strategic placement of smart meters equipped with demand response (DR) capability in radial distribution systems. Unlike conventional placement approaches that mainly focus on monitoring or reducing non-technical losses, the proposed method integrates active load control into the planning stage and explicitly considers the stochastic behavior of loads, PV generation, and electric vehicle charging stations (EVCSs). The problem is formulated with four objectives: minimizing total power losses, substation peak demand, voltage deviation penalty, and installation cost. A scenario-based stochastic model is employed to represent operational variability across the network. The resulting nonlinear mixed discrete optimization problem is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), an evolutionary multi-objective optimization technique that generates a set of Pareto-optimal solutions representing trade-offs among conflicting objectives. Smart meters are allowed to curtail a portion of controllable demand during critical loading conditions, which helps reduce feeder loading and improve voltage profiles. The proposed approach is evaluated on the IEEE 33-bus and IEEE 69-bus distribution systems. Simulation results demonstrate significant reductions in power losses and peak demand, with the IEEE 33-bus system achieving up to a 26.2% reduction in power losses and 52.5% reduction in substation peak demand compared with existing metaheuristic approaches. The results also indicate improved voltage stability and effective performance in the IEEE 69-bus system, confirming the importance of topology-aware DR-enabled planning. Overall, the findings show that embedding demand response capability within smart meter allocation can significantly enhance the resilience and operational efficiency of modern distribution networks with high EV and PV penetration. Full article

Mechanical Wear particle recognition is an important approach for equipment health monitoring and fault early warning. However, flow-field disturbances and high-speed particle motion in high-speed fluid environments can lead to image degradation, non-stationary electrostatic signals, and insufficient reliability of single-source recognition methods. Therefore, this study proposes a wear particle recognition method based on multi-source information fusion for high-speed fluid environments. The method establishes a multi-scale electrostatic sensing model to characterize the coupling relationship among particle material properties, motion states, and electrostatic response characteristics. Empirical mode decomposition and independent component analysis are combined for adaptive electrostatic signal denoising, and a Transformer network is used to extract multi-domain features. Meanwhile, an ECA-CNN model with an efficient channel attention mechanism is introduced to enhance the feature representation of degraded particle images. On this basis, a meta-learning-based sample-adaptive decision fusion framework is developed to achieve dynamic and complementary fusion of electrostatic and visual information. The experimental results demonstrate that the proposed method exhibits excellent recognition accuracy and robustness in the tested high-speed fluid environment of 10 m/s, achieving a fusion recognition accuracy of 96.0%, which is significantly superior to single-source recognition methods. Ablation experiments further show that removing the global scaling factor, guidance loss, interpolation loss, and category-specific weight generator decreases the average recognition accuracy by 0.7%, 1.2%, 0.4%, and 1.8%, respectively, confirming the contribution of each key module to fusion recognition performance. These findings provide a new technical approach for the online intelligent recognition of wear particles under high-speed fluid conditions and offer theoretical support and methodological guidance for condition monitoring, health assessment, and intelligent operation and maintenance of large-scale equipment. Full article

In BIM-enabled architectural projects, information systems preserve accepted decisions far more reliably than the rejected and non-selected alternatives that shaped them. Drawings, models, specifications and common data environments record what a project became, while the reasons that eliminated competing options are dispersed across meeting notes and revision logs or lost. This asymmetry weakens design coordination, change management and cross-project knowledge reuse. This article proposes a conceptually derived and analytically evaluated recording artefact for recovering these lost decision traces within the phase-transition band from spatial coordination to technical design. A two-gate evaluation logic separates codified screening from stakeholder-mediated review and decouples the procedural location of rejection from the category family that organises its reason. Three loss types are identified: pre-stakeholder invisible loss, trace/version loss and terminal loss. These are linked to six rejection-category families, four process redirection effects and differentiated memory destinations, with a constraint-bearing layer divided into avoidance and comparative branches. A fillable eight-field decision record template, formalised as a single recording-and-routing procedure, is specified for BIM, common data environment and design review workflows, supported by a query specification. The model is illustrated through a constructed hotel-floor decision node and offers a structured basis for retaining the knowledge carried by rejected, revised and valid but non-selected architectural decisions. Full article

The increasing volume of discarded apples generated by commercial grading standards and postharvest losses represents both an environmental burden and an opportunity for sustainable valorization. Despite growing interest in circular economy strategies in the fruit-processing sector, a comprehensive review of the technological, microbiological, and nutritional factors influencing cider production from discarded apples remains limited. To address this gap, this review discusses key aspects of cider production from discarded apples, focusing on raw material characterization, nutrient management, yeast strategies, and fermentation technologies. The physicochemical and microbiological properties of discarded apples are examined, including soluble solids, acidity, phenolic composition, and microbial spoilage risks. Special attention is given to nutrient optimization, particularly yeast assimilable nitrogen (YAN), vitamins, and minerals, as deficiencies may cause sluggish fermentation and adversely affect volatile compound formation and stability. This review evaluates yeast selection, comparing Saccharomyces cerevisiae with non-Saccharomyces yeasts and mixed fermentations, highlighting their effects on chemical composition, aroma, and sensory quality. Innovative approaches such as yeast immobilization and repeated-batch fermentation are reviewed as tools to improve process performance. Key technical challenges, including variability in raw material quality, nutrient supplementation needs, contamination risks, and process scalability, are discussed alongside opportunities for valorization of cider pomace within a circular economy framework. Full article

As software systems increasingly combine machine learning, deep learning, and generative AI components with classical deterministic logic, the systematic detection of AI-based algorithmic elements in application code is becoming essential for software audit, compliance with the EU AI Act (Regulation (EU) 2024/1689), and quality assurance. This paper introduces Quality-Informed Neural Networks (QINN), an architecture in which the structured knowledge encoded in the Quality Function Deployment (QFD) House of Quality is embedded into the network topology and weight initialisation through QFD-derived binary structural masks and knowledge-calibrated initialisation—in direct analogy with Physics-Informed Neural Networks (PINNs). The QFD relationship matrices act as structural priors that constrain the hypothesis space toward quality-consistent solutions by enforcing domain-expert-validated sparsity on network connectivity, while an optional QFD-regularised loss term provides an additional soft constraint on the learned weight structure. As a proof of concept, QINN is instantiated in its masked-architecture configuration for the binary classification of software repositories as AI-enabled or classical. On the AIC-199 proof-of-concept dataset, the proposed QINN attains a cross-validated AUC of 99.47% (±1.18%), recall of 100.00% (±0.00%), and F1-score of 99.02% (±1.34%) under QFD-informed structural masking, outperforming a non-learned QFD scoring baseline by 37.37 percentage points in recall and exceeding a cross-validated Random Forest ensemble on AUC by 2.47 percentage points (W = 0, p < 0.05), while producing explanations at three QFD-grounded levels—feature salience, named Technical-Evidence activations, and per-criterion quality requirement scores—that align directly with the EU AI Act documentation obligations. Validation on larger, independently curated datasets and sensitivity analysis of the QFD elicitation process are identified as priorities for future work. A domain-general seven-phase application protocol is provided. Full article