Search Results (1,899)

Energy efficiency interventions are typically evaluated and implemented at the single-firm level, yet energy use and savings are shaped by interdependent decisions distributed across the supply chain, spanning sourcing, production, inventory, logistics, and financing. A foundational observation motivating this paper is that some energy efficiency actions are only possible through inter-firm cooperation: they require changes to partners’ processes or technologies, create benefits that accrue to different actors than those bearing the investment costs, and demand governance mechanisms (e.g., cost-sharing contract, buyer-financed supplier development, supply chain finance instruments) to be financially viable. This paper proposes CHAIN-EE (Collaborative Holistic Approach for Integrated Network Energy Efficiency), an action-oriented framework that operationalizes systems thinking into a practical roadmap for supply chain decision-makers. CHAIN-EE integrates three interconnected phases: (A) supply-chain energy diagnosis, covering boundary definition, baseline construction, and hotspot identification across nodes and flows; (B) action portfolio design, structured around a six-lever intervention taxonomy and multi-criteria evaluation embedding a cost–benefit alignment map that makes governance feasibility an explicit selection criterion; and (C) governance and continuous improvement, including incentive alignment, investment architecture and ISO 50001-compatible performance management. Evidence from four European research projects spanning the food cold chain, dairy, food-and-beverage/transport value chains, and HORECA illustrates how each phase operates in practice across different sectors and governance contexts. The paper contributes an integrative, sector-adaptable structure for supply chain energy efficiency programmes, grounded in both analytical research and applied project experience, and a targeted research agenda on cross-node rebound effects, data-enabled energy flow mapping, and multi-tier coordination mechanisms. Full article

High-precision mapping of tree species composition is essential for sustainable forest management, biodiversity assessment, and ecosystem monitoring. Airborne hyperspectral imagery provides rich spectral and spatial information that enables detailed species discrimination. However, traditional single-stream convolutional neural networks (CNNs) often fail to fully exploit multi-dimensional features and are susceptible to spectral redundancy. In this study, we propose the MS-CBAM-TSCNet (Multi-Stream Convolutional Block Attention Deep Neural Network), a novel architecture specifically designed for tree species classification using aerial hyperspectral data. The proposed model integrates three parallel processing streams: a 1D spectral branch for capturing reflectance signatures, a 2D spatial branch for modeling contextual patterns, and a 3D spectral–spatial branch enhanced with Convolutional Block Attention Modules (CBAMs) to adaptively recalibrate channel-wise and spatial–spectral features. An adaptive gated fusion mechanism with attention-based weighting is introduced to dynamically combine the complementary representations extracted from the three streams, improving robustness to spectral redundancy and class imbalance. The method was evaluated on an airborne HyMap hyperspectral dataset (125 bands, with 4 m spatial resolution) acquired over a mixed boreal forest in Karlsruhe, Germany, comprising five dominant tree species. Using five-fold cross-validation on an augmented dataset, the MS-CBAM-TSCNet achieved an overall accuracy of 96.8%, a Kappa coefficient of 0.96, and a macro F1-score of 0.966, outperforming conventional 1D, 2D, and 3D CNNs, as well as a hybrid CNN-SVM approach across all evaluation metrics. An ablation study further confirms the complementary contributions of the multi-stream architecture, CBAM attention, and adaptive gated fusion. Pixel-wise classification maps demonstrate improved boundary delineation and reduced misclassification in mixed stands, highlighting the effectiveness of the proposed framework for operational forest inventory and ecological monitoring. Full article

Enhancement of carbon dioxide (CO₂) storage capacity in subsurface formations aids in offsetting the projected increase in carbon emissions. Consequently, various injection techniques should be explored to optimize the storage process. This study delves into CO₂-foam utilization in a saline carbonate aquifer for underground carbon storage (UCS) purposes. In order to depict the subsurface flow dynamics, a carbonate saline aquifer model, which incorporates heterogeneous properties, a natural fracture network, and geochemical reactions, was developed. Various subsurface flow dynamics were considered by generating multiple natural fracture network realizations. Afterward, the developed model was utilized to numerically simulate the UCS process for two hundred years, capturing the CO₂ inventory as well as fluid–fluid and fluid–rock interactions throughout the storage process. Introducing the foam enabled the injected CO₂ to penetrate deeper around the injection zone; hence, higher trapped CO₂ can be expected at the bottom of the aquifer. Despite the heterogeneity and natural fractures, CO₂ foam helped in enhancing the dissolved CO₂ distribution in the swept volume of the aquifer. The natural fracture network realizations demonstrated that the CO₂ foam can potentially limit the influence of natural fractures during the UCS process. Furthermore, the subsurface geochemical reactions tend to be altered due to the drop in the fluid–fluid and fluid–rock interactions. Overall, the findings suggest that CO₂ foam impacts surpass the heterogeneity and natural fracture network effects during the UCS. While the performed evaluation highlighted the CO₂ foam role within a carbonate saline aquifer, the workflow and outcomes of the study can be extended to various subsurface environments wherein a compatible CO₂ foam design can lead to CO₂ storage capacity enhancement. Full article

This study assesses the intrinsic hazards of an energy-integrated gas oil hydrocracking process from a sustainability-oriented process safety perspective. Hydrocracking units are essential in modern refineries for upgrading heavy gas oil fractions into higher-value fuels; however, they operate under severe conditions involving high temperatures, elevated pressures, hydrogen-rich environments, complex process structures, and large inventories of hazardous substances. In this context, improving energy efficiency through heat integration must be evaluated together with its implications for inherent safety and sustainable process design. The Inherent Safety Index (ISI) methodology was applied at the conceptual design stage to quantify the intrinsic risk level of the process and identify the main contributors to chemical and process-related hazards. The results yielded a total ISI value of 46, composed of a chemical safety index of 26 and a process safety index of 20, indicating a high intrinsic hazard level. The most significant contributors were toxic exposure (I_TOX = 6), inventory magnitude (I_I = 5), and process structure (I_ST = 5), while the large ISBL inventory of 2838.3 t, together with operating conditions reaching 456.4 °C and 166.8 bar, substantially increased the inherent risk of the system. Although energy integration contributes to improved thermal performance, the results indicate that it does not significantly reduce the intrinsic hazard level. Sensitivity analysis showed that optimization of operating temperature and pressure could reduce the ISI from 46 to approximately 43. These findings demonstrate that the intrinsic risk of energy-integrated hydrocracking systems is primarily governed by operating severity, hazardous material inventories, and toxicity, highlighting the importance of incorporating inherent safety principles during the conceptual design stage to achieve safer, more resilient, and more sustainable refinery operations. Full article

Life cycle assessment (LCA) studies of polypropylene (PP) processing commonly rely on generic secondary databases, while primary industrial inventories for plastic conversion processes remain scarce. This study addresses this gap by quantifying the cradle-to-gate carbon footprint of polypropylene processing using anonymised primary industrial data collected in 2024 from four European polypropylene processing facilities. Unlike previous studies relying mainly on generic secondary inventories, the proposed approach combines primary industrial data with sensitivity and scenario analyses to identify practical priorities for emission reduction. The baseline carbon footprint was estimated at 1.44 tCO₂e per tonne of finished product, with material production and energy-intensive processing identified as the major emission hotspots. One-Factor-at-a-Time (OFAT) sensitivity analysis showed that polypropylene type, process efficiency, renewable electricity use, and process waste management were the most influential parameters, whereas water consumption and additive use had only a minor effect on overall emissions. Scenario analysis indicated that combining recycled polypropylene, improved process efficiency and renewable electricity reduced emissions by 45.8%, while reducing process waste and fully recycling production residues achieved a 42.2% reduction compared with the baseline. By integrating primary industrial inventory data with sensitivity and scenario analyses, this study provides a more representative assessment of real industrial polypropylene processing conditions than approaches based solely on generic databases and identifies practical priorities for industrial carbon mitigation. Full article

This study extends a cradle-to-gate life cycle assessment (LCA) of an aluminium-reinforced concrete bridge by incorporating degradation processes and maintenance cycles, enabling a cradle-to-use comparison with a conventional steel-reinforced concrete bridge. The objective is to evaluate how deterioration assumptions and reinforcement replacement strategies influence environmental performance. The analysis shows that these parameters strongly shape the results, producing distinct behavioural patterns. Replacement-driven activities, such as deck demolition and reinforcement replacement, emerge as the main contributors to impact variation. The wide impact ranges observed indicate a high sensitivity of the model to deterioration assumptions and highlight the importance of accurate use phase inventories. Overall, the findings demonstrate that bridge deterioration and maintenance strategies have a substantial impact on LCA use phase modelling and its outcomes. They emphasise the need for transparent bridge deck deterioration modelling and inventory data for maintenance activities in steel-reinforced concrete bridges and further validation of the assumed maintenance-free lifetime for the aluminium-reinforced concrete bridge in order to support robust decision-making in future construction projects. Full article

Integrating IT systems in the automotive industry remains a technically complex and costly process, particularly in environments based on legacy ERP and WMS platforms. Existing research discusses QR codes and process automation separately. However, little attention is paid to non-intrusive integration architectures that combine standardized identification and intelligent automation. This study develops and implements a universal logistics integration model based on GS1-compliant QR codes and JSON data structures combined with intelligent process automation (IPA). A case study from an automotive company is presented. Analysis indicates that the main loss factors include misidentification of assets, manual data entry, and lack of feedback on delivery status. The architecture proposed in this manuscript consists of three modules: INTELOGBOT (2.07), IPABOT (1.79), and APIBOT (1.79). A structured QR-JSON identifier schema was also designed to ensure platform-independent data exchange. This eliminated manual data re-entry and enabled real-time inventory and delivery synchronization. Furthermore, it also automated logistics documentation and reduced identification errors in inbound and outbound operations. The research contribution consists of developing a solution that enables the connection of QR codes with intelligent IPA-based bots, providing a repeatable framework for advanced automation of logistics processes in complex supply chains. Full article

As a fundamental and highly challenging task in the field of computer vision, industrial object counting plays a critical role in smart manufacturing, inventory management, and production process monitoring. Over the past fifteen years (2010–2025), this field has undergone a profound technological transformation, shifting from traditional machine vision methods relying on handcrafted features to a data-driven paradigm based on deep learning. This paper aims to provide a comprehensive and systematic review of this rapidly evolving research area, with technological evolution as the core narrative thread. First, we review early traditional methods, analyzing the application of sensor-based and template-matching technologies in controlled environments, as well as their core limitations in complex industrial scenarios. Subsequently, this paper focuses on exploring how the introduction of deep learning has reshaped the landscape of counting tasks, and elaborates on the breakthrough progress of convolutional neural networks (CNNs), Transformer architectures, the recently emerging Mamba state space model, and Large Foundation Models in addressing key challenges including occlusion, object overlap, multi-scale variation, and dense object counting. In particular, this paper conducts an in-depth analysis of the paradigm shift from Class-Specific Counting to Class-Agnostic Counting (CAC) and Exemplar-Free Counting. This trend significantly reduces the reliance on large-scale annotated data and greatly enhances the generalization ability of models in open-world scenarios. Additionally, this paper systematically organizes mainstream datasets in the field, including FSC-147, NWPU-MOC, and OmniCount-191, and compares core evaluation metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the PrACo metric system. In response to the core technical challenges faced by current methods, including high annotation costs, weak cross-domain adaptability, and strict real-time requirements in industrial scenarios, this paper proposes key future research directions including lightweight model design, unsupervised learning, multi-modal fusion, and Prompt-based interactive counting. This review intends to provide researchers in both academia and industry with a complete technical blueprint so as to promote the continuous development of industrial object-counting technology toward a more efficient and intelligent direction. Full article

Long-term wildfire susceptibility mapping represents a significant component of disaster prevention and the protection of human communities, public health, and local ecosystems. In this study, a wildfire inventory was developed through multi-sensor fusion of satellite data (MODIS and VIIRS), comprising 153,305 fire events across China for the period 2001–2024. In addition to historical incidents, 14 predictive variables were processed, representing geomorphological, climatological, hydrological, vegetative, and anthropogenic conditions. This study evaluates long-term spatial wildfire susceptibility based on long-term mean environmental and climatic conditions. Methodologically, the research applies six models from machine learning (ML), deep learning (DL), and transformer-based approaches: Random Forest (RF), Extreme Gradient Boosting (XGBoost), Deep Neural Network (DNN), Fourier Multi-Layer Perceptron (F-MLP), Kolmogorov–Arnold Network (KAN), and Feature Tokenizer (FT) Transformer. The results were integrated into an ensemble susceptibility map with a spatial resolution of 500 m using Geographic Information Systems (GIS), indicating that 7.4% of China’s territory is classified as having a very high wildfire susceptibility. In addition to the national-scale assessment, a local differentiation was conducted across 34 province-level divisions, revealing that Fujian Province (86.8%) and the Guangxi Zhuang Autonomous Region (82.9%) had the largest shares of areas classified as high and very high wildfire susceptibility. Performance evaluation under spatial block-based validation demonstrated that the Random Forest model achieved the highest predictive power, with an area under the curve (AUC) of 87.8%, followed by XGBoost (87.3%) and Fourier MLP (86.6%). Based on the combined SHAP (Shapley additive explanations) analysis of all applied models, soil moisture, elevation, and terrain slope were identified as the most influential factors affecting wildfire occurrence in China. Overall, the findings contribute to more effective wildfire prevention and risk management strategies at both the local and national levels. Full article

This study presents DEDICA (Database of Hydrogeological Instability Events in Calabria, southern Italy), developed by the District Basin Authority of the Southern Apennines (ABDAM) in collaboration with the CNR-IRPI. The database integrates digitized historical sources, chronicle-based records, and previously unpublished archival data that were systematically analyzed, validated, and georeferenced within a GIS environment. After two years of development, DEDICA includes 5329 landslides, 2097 flood events, and 1711 urban flooding occurrences spanning the period 1900–2025. The system supports continuous data updating, enabling both the integration of recent events and the refinement of historical records. The database provides a comprehensive tool for identifying areas prone to geo-hydrological hazards based on historical recurrence, supporting hazard assessment, land-use planning, and risk management strategies. The methodological framework, database structure, and data processing workflow are described in detail. Spatio-temporal analyses highlight the distribution of instability processes, identifying the most affected sectors and revealing seasonal patterns and long-term trends. DEDICA represents a pilot initiative within a broader program aimed at extending the inventory to all regions under ABDAM jurisdiction, ultimately contributing to the development of a unified geo-hydrological hazard database for southern Italy. Full article

Amid the escalating challenges of global climate change, promoting the sustainable and low-carbon transformation of wastewater treatment systems has become a critical pathway toward achieving carbon neutrality and sustainable urban infrastructure development. However, existing low-carbon technologies for wastewater treatment still lack systematic and sustainability-oriented evaluation approaches, which constrains the scientific selection of technologies and the optimization of low-carbon transition pathways. In this study, a comprehensive inventory of 30 low-carbon technologies was established across five categories, including equipment energy saving, process improvement, intelligent control, energy recovery, and resource recycling. Based on three dimensions, namely carbon reduction potential, economic performance, and technology readiness level, a Carbon Reduction–Economy–Technology Coupling Index (CRETCI) was developed to enable systematic quantitative evaluation and sustainability-oriented assessment of low-carbon technologies in wastewater treatment systems. The analysis of carbon reduction potential indicated that process improvement technologies exhibited the highest average carbon reduction potential, reaching approximately 0.136 kg CO₂e/m³, demonstrating significant advantages in deep emission reduction. Economic analysis revealed that energy recovery technologies showed the best economic performance, with all marginal abatement costs being negative, indicating strong synergistic benefits between economic returns and carbon mitigation. The technological maturity assessment demonstrated that both intelligent control and energy recovery technologies achieved a Technology Readiness Level (TRL) of 9, indicating a well-established foundation for engineering application. The TCECI evaluation results showed that energy recovery technologies achieved the highest comprehensive score (0.71), significantly outperforming process improvement technologies (0.57). This finding suggests that the current low-carbon technology system for wastewater treatment is characterized by a structural trade-off between high carbon reduction potential and high technological maturity. Overall, this study establishes a multidimensional sustainability evaluation framework integrating environmental benefits, economic feasibility, and technological applicability, thereby providing important theoretical support and practical decision-making guidance for sustainable wastewater management, low-carbon technology selection, and carbon-neutral transition pathway optimization in the wastewater treatment sector. Full article

Personal names carry cultural and linguistic identity, yet most African countries lack large-scale, structured name datasets suitable for natural language processing (NLP) research and computational social science. We present CongoNames, the first large-scale corpus of personal names from the Democratic Republic of the Congo (DRC), derived from publicly released national secondary-school examination palmarès (result lists) 8,053,983 published annually by the DRC Ministry of Education. The corpus comprises name records spanning 16 examination years (2008–2023) across 12 provinces and 304 sub-provincial regions, each enriched with a reported sex marker (M/F) and regional provenance metadata. We describe a fully deterministic, layered processing pipeline (bronze–silver–gold architecture) that converts raw protable document format (PDF) documents into structured comma-separated values (CSV) datasets without manual annotation or machine-learning-based inference. The dataset is validated against school-level census counts extracted from the same source PDFs, yielding extraction error rates below 2% for all years except 2023 (7.81%, flagged due to a layout change). Descriptive analyses document name length and token-count distributions, character-level n-gram profiles, provincial diversity indices, and inter-provincial name-inventory overlap, collectively establishing the dual linguistic origin—locally rooted Bantu components and Christian/French-origin components—that characterize modern Congolese naming practice. The dataset, processing code, and documentation are released openly to support research in African natural language processing (NLP), onomastics, and computational social science. Full article

Reading comprehension is a critical cognitive competency in higher education, although learners demonstrate substantial variability in responsiveness to metacognitive instructional interventions. The study focused on individual cognitive-response processes within the framework of the adaptive metacognitive reading system, which was realized through precision-learning architecture, which integrates latent learner-response phenotyping, explainable machine learning, Markov transition analysis, Bayesian adaptive inference, and reinforcement-learning optimization. The study employed a quasi-experimental longitudinal design involving an eight-week structured metacognitive reading intervention delivered through planning, monitoring, evaluation, strategic flexibility, and reading self-regulation activities. The psychometric analyses demonstrated satisfactory reliability of the adapted Metacognitive Awareness Inventory (MAI), with Cronbach’s α ranging from 0.83 to 0.89. A latent-profile model revealed significant heterogeneity of learner-response patterns among learners, with four learner-response phenotypes: High Responders, Strategic Improvers, Monitoring-Dependent Learners, and Low Responders. Explainable machine-learning models performed well in predicting individualized comprehension gains, with the model with the highest predictive accuracy being XGBoost (R² = 0.61). Markov transition modeling identified exploratory learner-state redistribution patterns following the intervention. Bayesian adaptive inference and reinforcement-learning optimization were subsequently conducted as post hoc simulation procedures to estimate hypothetical adaptive instructional calibration scenarios rather than as real-time instructional decision systems. Overall, the proposed Adaptive Cognitive Intervention Architecture (ACIA) should be interpreted as an exploratory computational framework for modeling learner heterogeneity, predicting comprehension gains, and simulating post hoc computational optimization in higher-education learning environments. Full article

Background: Long COVID is a heterogeneous post-viral condition in which persistent neurological, autonomic, cognitive, and psychometric symptoms often occur without clear isolated biomarker abnormalities. This mismatch suggests that disease persistence may be driven not only by changes in individual markers, but by longitudinal reorganization of biological and clinical interactions. Materials and Methods: This observational longitudinal study evaluated patients with persistent symptoms after confirmed SARS-CoV-2 infection at 3 and 6 months. Clinical assessment included neurological examination, Hospital Anxiety and Depression Scale, Beck Depression Inventory, and COMPASS-31. Biomarkers representing hypoxia signaling, oxidative/redox stress, endothelial and renin–angiotensin system activity, glycation-related processes, and complement regulation were analyzed. Correlation analysis, association-level biomarker–clinical network modeling, and complementary Graphical LASSO-based sparse network estimation were used to compare network density, community organization, centrality, and edge rewiring between time points. Results: Conventional paired analysis identified HIF-1$\alpha$ as the only continuous variable with a statistically significant longitudinal change (Wilcoxon statistic = 610.0, $p=0.000350$), whereas association-level network analysis revealed a broader systems-level signal. The association-level biomarker–clinical network preserved a similar global size at 3 and 6 months, with 16 nodes, 27 versus 26 edges, and densities of 0.225 versus 0.217. However, this apparent stability concealed substantial rewiring: 19 edges were shared, 8 were lost, and 7 emerged. Complementary Graphical LASSO analysis with 1000 bootstrap resamples supported this pattern by identifying a conservative sparse conditional-dependency core, including seven shared conditional-dependency edges across time points and selective weakening of four early conditional dependencies. The C3–C4 relationship reversed from negative to positive correlation ($r=-0.618$ to $r=0.618$), indicating marked remodeling of complement-associated regulation. A psychometric–autonomic module involving Beck, HADS I, HADS II, and COMPASS-31 remained stable across both assessments. Conclusions: Long COVID progression was characterized by dynamic remodeling of immune, endothelial/RAS, oxidative-redox, hypoxia-related, autonomic, and psychometric interactions. Longitudinal network analysis identified a systems-level interaction structure that was not captured by isolated biomarker comparisons alone and that was further supported by complementary sparse conditional-dependency analysis. Full article

Currently, the global manufacturing industry faces significant challenges due to increasingly competitive and constantly changing markets. Therefore, adapting to customer needs and improving efficiency and productivity are essential to compete internationally. Plant design and layout play a crucial role in production, material handling, time, and operational costs. The objective of this research was to implement the Systematic Layout Planning (SLP) methodology, supported by CAD and quality tools, to free up 280 m² for production processes in a horizontal blind manufacturing company. AutoCAD was used to model the facilities and visualize pre- and post-improvement scenarios, while ABC classification and root cause analysis supported problem identification in inventory areas. Results show a released expansion area of 340 m², corresponding to 21.5% above the initial space requirement, and a reduction in material travel distance from 317 m to 109 m, equivalent to 65.6%. These improvements enhanced workflow continuity and operational efficiency. The integration of SLP with CAD and quality tools provides a replicable framework for layout optimization in manufacturing environments, while future research should validate the approach under dynamic production conditions. Full article