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22 pages, 1217 KB  
Article
The Missing Layer in Modern IT: Governance of Commitments, Not Just Compute and Data
by Rao Mikkilineni and William Patrick Kelly
Computers 2026, 15(5), 275; https://doi.org/10.3390/computers15050275 - 24 Apr 2026
Abstract
Contemporary enterprise IT operations are largely implemented on Shannon–Turing computing models in which programs execute read–compute–write cycles over data structures, while governance—fault handling, configuration control, auditability, continuity, and accounting—is applied externally through infrastructure platforms, observability stacks, and human operational processes. This separation scales [...] Read more.
Contemporary enterprise IT operations are largely implemented on Shannon–Turing computing models in which programs execute read–compute–write cycles over data structures, while governance—fault handling, configuration control, auditability, continuity, and accounting—is applied externally through infrastructure platforms, observability stacks, and human operational processes. This separation scales analytical throughput but accumulates what we term coherence debt: locally expedient operational commitments whose provenance and revisability degrade over time until exposed by failures, security incidents, regulatory demands, or architectural transitions. This paper examines the evolution of operational computing models that integrate com-pupation with regulation at two distinct levels. First, Distributed Intelligent Managed Elements (DIME) extend the classical Turing cycle toward a supervised execution loop—read–check-with-oracle–compute–write—by incorporating signaling overlays and FCAPS (Fault, Configuration, Accounting, Performance, and Security) supervision into computation in progress. Second, the Autopoietic Management and Orchestration System (AMOS), grounded in the General Theory of Information, the Burgin–Mikkilineni Thesis, and Deutsch’s epistemic framework, fully decouples process executors from governance by treating any Turing-equivalent engine as a replaceable execution substrate while elevating knowledge structures—encoded as local and global Digital Genomes—to first-class operational state within a governed knowledge network. Using a distributed microservice transaction testbed, we demonstrate how this approach operationalizes topology-as-data, a capability-oriented control plane, decoupled application-layer FCAPS independent of infrastructure management, and policy-selectable consistency/availability semantics. Our results show that the principal benefit of AMOS is not circumventing theoretical constraints such as the Consistency, Availability, and Partition tolerance (CAP) theorem, but governing their trade-offs as explicit, auditable commitments with defined convergence pathways and controlled return to a coherent system state, thereby reducing coherence debt and improving operational reliability in distributed AI-enabled enterprise systems. Full article
(This article belongs to the Special Issue Cloud Computing and Big Data Mining)
42 pages, 3267 KB  
Systematic Review
Fiber-Optic Sensor-Based Structural Health Monitoring with Machine Learning: A Task-Oriented and Cross-Domain Review
by Yasir Mahmood, Nof Yasir, Kathryn Quenette, Gul Badin, Ying Huang and Luyang Xu
Sensors 2026, 26(9), 2641; https://doi.org/10.3390/s26092641 - 24 Apr 2026
Abstract
Structural health monitoring (SHM) plays an increasingly important role in managing aging, safety-critical infrastructure under growing environmental and operational demands. In recent years, fiber-optic sensors (FOSs) have attracted significant attention for SHM applications due to their immunity to electromagnetic interference, durability in harsh [...] Read more.
Structural health monitoring (SHM) plays an increasingly important role in managing aging, safety-critical infrastructure under growing environmental and operational demands. In recent years, fiber-optic sensors (FOSs) have attracted significant attention for SHM applications due to their immunity to electromagnetic interference, durability in harsh environments, multiplexing capability, and suitability for both localized and fully distributed measurements. In parallel, advances in machine learning (ML) have enabled new approaches for extracting actionable insights from large, high-dimensional sensing datasets. This paper presents a systematic review of FOS-based SHM systems integrated with ML across civil, transportation, energy, marine, and aerospace infrastructures. Following PRISMA 2020 guidelines, peer-reviewed studies were identified and synthesized to examine sensing principles, deployment configurations, data characteristics, and learning-based analytical strategies. Fiber optic technologies are categorized into point-based, quasi-distributed, and fully distributed systems, and their capabilities for capturing strain, temperature, and spatiotemporal structural responses are critically evaluated. ML approaches are examined from a task-oriented perspective, including damage detection, localization, severity assessment, environmental compensation, and prognosis, with emphasis on the alignment between sensing configurations and appropriate learning paradigms. Key challenges remain, particularly regarding large data volumes, environmental variability, limited labeled damage datasets, model generalization, and system-level integration. Emerging directions such as physics-informed and hybrid learning, transfer learning, uncertainty-aware modeling, and integration with digital twins are discussed as pathways toward more robust and scalable SHM systems. By jointly addressing sensing physics and data-driven intelligence, this review provides a structured reference and practical roadmap for advancing intelligent FOS-based SHM in next-generation infrastructure. Full article
(This article belongs to the Special Issue Smart Sensor Technology for Structural Health Monitoring)
46 pages, 4530 KB  
Review
Progress in Flexible and Wearable Power Sources
by Mervat Ibrahim and Hani Nasser Abdelhamid
Batteries 2026, 12(5), 152; https://doi.org/10.3390/batteries12050152 - 24 Apr 2026
Abstract
The demand for flexible and wearable electronics has intensified the need for conformable, high-performance, and self-sustaining power sources. Flexible supercapacitors (FSCs) and flexible batteries (e.g., lithium-ion and lithium–sulfur) are promising owing to their high-power density, long cycle life, and mechanical flexibility. A transformative [...] Read more.
The demand for flexible and wearable electronics has intensified the need for conformable, high-performance, and self-sustaining power sources. Flexible supercapacitors (FSCs) and flexible batteries (e.g., lithium-ion and lithium–sulfur) are promising owing to their high-power density, long cycle life, and mechanical flexibility. A transformative solution lies in integrating these storage devices with mechanical energy harvesters, particularly triboelectric nanogenerators (TENGs), to create autonomous self-charging power systems (SCPSs). TENGs exhibit high output, versatile operational modes, material flexibility, and efficient energy harvesting from body movements. This review provides an overview of the recent advances in flexible energy storage technologies, encompassing carbon-based materials, MXenes, polymers, metal oxides, metal–organic frameworks (MOFs), and their hybrid architectures. It discusses the synergistic integration of these storage devices with TENGs to realize multifunctional SCPSs. It also highlights the fundamental design principles of flexible devices, the critical interplay of materials and architecture, and the journey towards monolithic system integration. The review also underscores the importance of managing harvesters’ pulsed output for efficient storage. Finally, a critical analysis of the challenges, including the energy density–flexibility compromise, environmental stability, and safety, is presented, alongside a forward-looking perspective on commercialization pathways for these technologies to power the next generation of autonomous wearable and sustainable electronic systems. Full article
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25 pages, 14205 KB  
Article
High-Resolution Data-Driven Energy Consumption Prediction for Battery-Electric Buses Using Boosting Algorithms
by Yong Wu, Zhichao Xin, Jiachang Li, Zhenliang Ma and Jianping Xing
Energies 2026, 19(9), 2058; https://doi.org/10.3390/en19092058 - 24 Apr 2026
Abstract
Accurate prediction of energy consumption is essential for the operation and charging management of battery-electric buses. Existing prediction studies are often constrained by incomplete or low-resolution input data, limiting their robustness under real-world operating conditions. This paper presents a high-resolution, sensor-rich energy consumption [...] Read more.
Accurate prediction of energy consumption is essential for the operation and charging management of battery-electric buses. Existing prediction studies are often constrained by incomplete or low-resolution input data, limiting their robustness under real-world operating conditions. This paper presents a high-resolution, sensor-rich energy consumption modeling framework using second-by-second operational data and tests on an electric bus fleet operating on Route 49 in Jinan, China. The dataset integrates synchronized measurements of vehicle kinematics, powertrain variables, and thermal conditions, providing a substantially more complete description of bus operation against previous studies. Boosting-based machine learning models are developed to predict the instantaneous power demand, and their performance is evaluated in comparison with a physics-based energy model and other variants of machine learning models. Results show that the data-driven boosting models demonstrate excellent explanatory power (R2 values of up to 0.99 (training) and 0.95 (test)) and remain reliable under nonlinear operating conditions. Feature and SHAP analyses identify physically consistent energy drivers, supporting the applicability of the approach to real-world public transport operations. Full article
(This article belongs to the Section B: Energy and Environment)
21 pages, 484 KB  
Article
Balancing Work and Life Among Manufacturing Employees: The Role of Job Conditions, Support and Well-Being
by Rasa Balvočiūtė and Rasa Švėgždienė
Sustainability 2026, 18(9), 4239; https://doi.org/10.3390/su18094239 (registering DOI) - 24 Apr 2026
Abstract
Work–life balance (WLB) has become a critical component of social sustainability, yet empirical evidence remains uneven across economic sectors. While existing research predominantly focuses on service-oriented and public-sector occupations, comparatively little is known about the determinants of WLB in manufacturing, where high job [...] Read more.
Work–life balance (WLB) has become a critical component of social sustainability, yet empirical evidence remains uneven across economic sectors. While existing research predominantly focuses on service-oriented and public-sector occupations, comparatively little is known about the determinants of WLB in manufacturing, where high job demands, limited flexibility, and structural constraints on autonomy often characterize work. Addressing this gap, the present study examines how job characteristics, support mechanisms, and individual resources shape the likelihood of achieving WLB among manufacturing employees in a rapidly developing European economy. Drawing on the Job Demands–Resources (JD–R) framework, the study employs survey data from 361 manufacturing employees and estimates a series of Probit regression models. To facilitate a meaningful analysis, composite indices were constructed to capture job demands, job flexibility, organizational and social support, psychological boundaries, and overall well-being. Predicted probabilities were used to evaluate both direct effects and interaction patterns in the Probit models. The findings indicate that manageable job demands and individual resources, particularly well-being and effective self-management, are the strongest predictors of WLB. Job flexibility demonstrates a slight positive effect; however, when accounting for individual and structural factors, formal organizational and social support mechanisms do not show statistically significant direct effects. Furthermore, our analysis provides no empirical support for moderating effects, as the interaction terms between job characteristics and support variables are not statistically significant. This suggests that support mechanisms do not consistently modify the relationship between job demands, flexibility, and WLB within the analyzed sample. Overall, the findings underscore the importance of combining supportive organizational contexts with manageable work demands and individual resources to promote sustainable work–life balance in manufacturing. The study contributes sector-specific empirical evidence to sustainability research and offers practical insights for designing socially sustainable work environments in industrial settings. Full article
(This article belongs to the Section Social Ecology and Sustainability)
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20 pages, 4123 KB  
Article
Surveying Techniques for Built Heritage Conservation: A Comparative Perspective of Workflows for Monument Restoration
by George Cristian, Sorin Herban, Clara-Beatrice Vîlceanu, Andreea-Diana Clepe and Carmen Grecea
Sustainability 2026, 18(9), 4237; https://doi.org/10.3390/su18094237 (registering DOI) - 24 Apr 2026
Abstract
This study presents a comparative evaluation of three modern surveying techniques—UAV photogrammetry, static tripod-based LiDAR scanning, and handheld mobile LiDAR—applied in the context of historic monument restoration. The focus is on analysing workflow efficiency, data accuracy, and adaptability to complex architectural features, including [...] Read more.
This study presents a comparative evaluation of three modern surveying techniques—UAV photogrammetry, static tripod-based LiDAR scanning, and handheld mobile LiDAR—applied in the context of historic monument restoration. The focus is on analysing workflow efficiency, data accuracy, and adaptability to complex architectural features, including interior wall paintings, which are integral to the monument’s heritage value. Particular attention is given to how each technique captures surface texture, color fidelity, and material deterioration. The study also examines performance around intricate architectural elements such as vaulted ceilings, apses, cornices, columns, and carved stone portals, where occlusions, tight clearances, and fine ornamentation challenge coverage and resolution. By evaluating the strengths and limitations of each approach, the research highlights methodological considerations relevant for conservation professionals. The results indicate that the Static TLS is the most demanding workflow, requiring complex total station integration for control and station points. It produced the highest data density, with acquisition rates of one million points per second, making it the most hardware-intensive and difficult to manipulate. UAV photogrammetry provided a balanced middle-ground; it required minimal physical effort during acquisition and produced datasets that were significantly easier to manage. Handheld SLAM LiDAR emerged as the most productive solution for rapid coverage. While the handheld scanner’s image quality was lower than the photogrammetry, it still provided enough detail for the structural assessment and documentation needed. Although the point cloud lacked the extreme geometric detail provided by the TLS, the FARO Connect software made georeferencing and data manipulation significantly more efficient. Full article
24 pages, 11638 KB  
Article
Socio-Ecological Barriers to the Sustainable Management of the Andean Walnut (Juglans neotropica) and the Value Paradox in the Ecuadorian Andes: A Case Study from Imbabura Province, Ecuador
by Oscar Hernando Eraso Terán, Guillermo David Varela Jacome, Mario José Añazco Romero and Hugo Vinicio Vallejos Álvarez
Conservation 2026, 6(2), 52; https://doi.org/10.3390/conservation6020052 (registering DOI) - 24 Apr 2026
Abstract
The Andean walnut (Juglans neotropica Diels), locally known as tocte, is a keystone tree species of major socio-ecological importance in South American mountain ecosystems, facing severe anthropogenic pressure associated with genetic erosion, habitat fragmentation, and unregulated selective logging. This article presents a [...] Read more.
The Andean walnut (Juglans neotropica Diels), locally known as tocte, is a keystone tree species of major socio-ecological importance in South American mountain ecosystems, facing severe anthropogenic pressure associated with genetic erosion, habitat fragmentation, and unregulated selective logging. This article presents a case study applying a qualitative phenomenological approach to examine the power relations and institutional failures shaping the sustainable management of its value chain in Imbabura Province, Ecuador. Drawing on 21 in-depth semi-structured interviews with key actors (including woodcarvers, sawyers, traders, and environmental authorities) conducted between March and September 2025 until theoretical saturation was achieved, and supported by thematic analysis in ATLAS.ti, we identified five thematic categories revealing the tension between cultural valuation and market pressure. The findings confirm the existence of a value paradox, whereby high timber demand paradoxically accelerates resource depletion rather than incentivizing conservation, as premature harvesting of young trees undermines the viability of non-timber forest products such as nuts and accelerates the loss of local genetic resources. We conclude that the long-term conservation of the species requires a transition toward polycentric stewardship, community forestry enterprises, and integrated landscape management in which the standing tree is formally recognized as carrying greater ecological and economic value than harvested timber. Full article
(This article belongs to the Special Issue Forest Ecosystem Restoration)
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18 pages, 2605 KB  
Article
Bioherbicidal Activity of Aromatic Plants’ Hydrodistillation Water Residues Against Avena sterilis and Echinochloa crus-galli, with Selectivity for Zea mays
by Pinelopi N. Liontou, Anastasia V. Badeka, Thomas K. Gitsopoulos, Georgios Patakioutas and Nicholas E. Korres
Agronomy 2026, 16(9), 858; https://doi.org/10.3390/agronomy16090858 - 24 Apr 2026
Abstract
The demand for sustainable weed management and the limited discovery of new herbicide molecules have led to high interest in plant-derived bioherbicides, such as the water residues (WRs) from the hydrodistillation of aromatic plants, which contain biologically active secondary metabolites. Here, the bioherbicidal [...] Read more.
The demand for sustainable weed management and the limited discovery of new herbicide molecules have led to high interest in plant-derived bioherbicides, such as the water residues (WRs) from the hydrodistillation of aromatic plants, which contain biologically active secondary metabolites. Here, the bioherbicidal activity of WRs of four aromatic plant species was investigated. Chemical composition of WRs was determined by solid-phase microextraction (SPME) coupled to gas chromatography–mass spectrometry (GC-MS), and their effect was assessed on seed germination and seedling growth characteristics of Avena sterilis, Echinochloa crus-galli, and Zea mays. Five concentrations, i.e., 0, 10, 20, 50, and 100% (v/v), with 100% representing pure WR, were tested. Phenolic monoterpenes dominate WRs in oregano and thyme, and oxygenated monoterpenes in laurel and lavender. Germination and growth responses were dose-dependent and species-specific. Oregano and lavender WRs exhibited the strongest inhibitory effect, reducing weed germination by 82% and 79%, respectively. In contrast, laurel extracts showed weaker germination inhibition. Across all tested species, germination delays were observed, making WRs a promising candidate for weed control. The results also showed that WR reduced root growth by up to 95% and shoot growth by 70–80%. Maize exhibited greater tolerance than the weed species, maintaining higher germination. Overall, WRs represent a promising tool for integrated weed management. Full article
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13 pages, 554 KB  
Article
The Genetics of Iron Metabolism on Biochemical and Hematological Phenotypes of Heart Failure
by Mário Barbosa, Laura Aguiar, Ana Matias, Joana Ferreira, João Caldeira, Ana Melício, Paula Faustino, Luiz Menezes Falcão, Manuel Bicho and Ângela Inácio
Int. J. Mol. Sci. 2026, 27(9), 3778; https://doi.org/10.3390/ijms27093778 - 23 Apr 2026
Abstract
Heart failure (HF) is frequently associated with iron deficiency and anemia, negatively impacting patient outcomes. This study aimed to investigate the contribution of genetic variation in iron metabolism-related genes to biochemical and hematological phenotypes in HF. An HF population of 182 patients with [...] Read more.
Heart failure (HF) is frequently associated with iron deficiency and anemia, negatively impacting patient outcomes. This study aimed to investigate the contribution of genetic variation in iron metabolism-related genes to biochemical and hematological phenotypes in HF. An HF population of 182 patients with functional iron deficiency (ID) and anemia was stratified by sex and heart failure subtype, including HF with reduced ejection fraction (HFrEF) and HF with non-reduced ejection fraction (HFnrEF). Genetic variants in HFE (rs1799945), SLC40A1 (rs1439816, rs2304704), and TMPRSS6 (rs855791) were evaluated. Variants in HFE and SLC40A1 were associated with differences in serum iron, ferritin, transferrin saturation, hemoglobin, and RDW. The phenotypic impact of these variants was modulated by sex and heart failure subtype, highlighting the influence of iron availability, inflammatory burden, and erythropoietic demand. In contrast, no significant associations were observed for the TMPRSS6 variant. In conclusion, genetic variation in key regulators of iron metabolism contributes to the heterogeneity of iron-related biochemical and hematological phenotypes in HF. These findings emphasize the interplay between genetic background, sex, and heart failure physiology and support the relevance of personalized approaches to iron assessment and management in heart failure. Full article
(This article belongs to the Special Issue Genes and Human Diseases: 3rd Edition)
20 pages, 1159 KB  
Article
Coordinated Dynamic Restoration of Resilient Distribution Networks Using Chance-Constrained Optimization Under Extreme Fault Scenarios
by Yudun Li, Kuan Li, Maozeng Lu and Jiajia Chen
Processes 2026, 14(9), 1355; https://doi.org/10.3390/pr14091355 - 23 Apr 2026
Abstract
Extreme disasters often induce multiple simultaneous faults in distribution networks, posing significant risks to power supply reliability. Although network reconfiguration and intentional islanding are critical strategies for enhancing system resilience, existing studies typically address them separately and fail to adequately account for the [...] Read more.
Extreme disasters often induce multiple simultaneous faults in distribution networks, posing significant risks to power supply reliability. Although network reconfiguration and intentional islanding are critical strategies for enhancing system resilience, existing studies typically address them separately and fail to adequately account for the uncertainties associated with renewable energy generation and load demand. To address these limitations, this paper presents a collaborative optimization model for resilient distribution network restoration. A multi-time-step dynamic restoration framework is developed to coordinate network reconfiguration, emergency repair scheduling, distributed generation dispatch, and load shedding. This framework enables unified decision-making for island formation and topology reconfiguration, and incorporates an island integration mechanism to broaden the feasible solution space. To manage source–load uncertainties, chance-constrained programming is introduced, transforming probabilistic security constraints into deterministic equivalents using risk indicator variables, thereby striking a balance between operational security and economic efficiency. In addition, the model optimizes repair sequences under multi-fault conditions to enhance resource utilization. Simulations on a modified IEEE 33-node system validate the effectiveness of the proposed approach in reducing load curtailment, accelerating restoration, and achieving a favorable trade-off between operational risk and economic performance. Full article
(This article belongs to the Section Energy Systems)
25 pages, 9045 KB  
Systematic Review
Systematic Review of Advanced Optimization Techniques and Multi-Asset Integration in Home Energy Management Systems
by Rabia Mricha, Mohamed Khafallah and Abdelouahed Mesbahi
Electricity 2026, 7(2), 38; https://doi.org/10.3390/electricity7020038 - 23 Apr 2026
Abstract
Home Energy Management Systems (HEMS) are increasingly positioned at the center of residential flexibility, particularly as homes integrate photovoltaics, battery storage, electric vehicles, and responsive loads. This systematic review examines recent advances in optimization and multi-asset coordination for HEMS. Searches were conducted in [...] Read more.
Home Energy Management Systems (HEMS) are increasingly positioned at the center of residential flexibility, particularly as homes integrate photovoltaics, battery storage, electric vehicles, and responsive loads. This systematic review examines recent advances in optimization and multi-asset coordination for HEMS. Searches were conducted in Scopus, Web of Science, IEEE Xplore, and ScienceDirect for studies published between 2020 and 2025; after screening and eligibility assessment, 90 studies were included. The findings indicates that deterministic optimization remains well suited to structured scheduling problems, whereas metaheuristic, hybrid, and learning-based methods are better able to address nonlinearity, uncertainty, and real-time adaptation. Across the reviewed literature, multi-asset integration generally improves cost, peak demand, self-consumption, and, in some cases, user comfort and emissions. Yet the field remains dominated by simulation-based validation. Future progress of HEMS will depend on real-world validation, interoperable system design, explainable control, and stronger alignment with user behavior, communication constraints, and regulatory frameworks. Full article
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19 pages, 903 KB  
Article
Dynamic Collection Routing Optimization for Domestic Waste with Mixed Fleets
by Manna Huang, Ting Qu, Ming Wan and George Q. Huang
Systems 2026, 14(5), 461; https://doi.org/10.3390/systems14050461 - 23 Apr 2026
Abstract
Influenced by factors such as residents’ living habits, commuting patterns, and commercial activity cycles, the generation of domestic waste exhibits a distinct double-peak distribution. To meet the high demand during peak periods, collection companies typically deploy excess transportation capacity, which leads to severe [...] Read more.
Influenced by factors such as residents’ living habits, commuting patterns, and commercial activity cycles, the generation of domestic waste exhibits a distinct double-peak distribution. To meet the high demand during peak periods, collection companies typically deploy excess transportation capacity, which leads to severe resource idleness during off-peak periods, imposing significant economic and environmental burdens. To address this issue, this study develops a dynamic smart waste collection routing model aimed at minimizing the coordinated collection cost between self-owned and outsourced multi-compartment vehicles, and designs a two-phase algorithm to solve it. In the pre-optimization phase, an improved Harris Hawks Optimization algorithm integrated with multiple heuristic algorithms is employed to generate initial collection routes. In the re-optimization phase, a hybrid strategy combining periodic and continuous re-optimization is used to dynamically update collection routes. Finally, the effectiveness of the proposed model and algorithm is validated through case studies. Furthermore, a systematic sensitivity analysis is conducted to investigate the impact of key parameters, yielding practical insights for waste collection management. Full article
15 pages, 4945 KB  
Article
Evaluation of Deep Learning Models for Image-Based Classification of Timber Logs by Market Value
by Matevž Triplat, Žiga Lukančič and Vasja Kavčič
Forests 2026, 17(5), 518; https://doi.org/10.3390/f17050518 (registering DOI) - 23 Apr 2026
Abstract
The identification of standing tree species, timber logs, and on-site assessment of their quality and value using images holds significant potential for forestry applications, including inventory management, traceability under EU regulations like the Deforestation Regulation, and market valuation amid growing demands for sustainable [...] Read more.
The identification of standing tree species, timber logs, and on-site assessment of their quality and value using images holds significant potential for forestry applications, including inventory management, traceability under EU regulations like the Deforestation Regulation, and market valuation amid growing demands for sustainable practices. This study addresses this by classifying images of timber logs by tree species and market value using the Orange data mining software, which leverages pre-trained convolutional neural networks (Inception v3 and SqueezeNet) to generate embeddings from a dataset of 5549 images collected at a real timber auction in Slovenia, followed by logistic regression image classification. Results show high accuracy for tree species classification (up to 92.6%), but substantially lower accuracy for market value classification (40%–55%), reflecting the greater complexity of value determination from visual features. These findings underscore the promise of deep learning for species identification while indicating the need for further methodological advancements to enhance value classification reliability, which offers the practical impact for operational forestry and bioeconomy value chains. Full article
(This article belongs to the Special Issue Sustainable Forest Operations: Technology, Management, and Challenges)
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20 pages, 2176 KB  
Article
Estimation and Prediction Methods for the Amount of Ship-Sourced Water Pollutant in Port Areas
by Xiaofeng Ma, Yanfeng Li, Chaohui Zheng, Hongjia Lai and Lin Wei
Sustainability 2026, 18(9), 4207; https://doi.org/10.3390/su18094207 - 23 Apr 2026
Abstract
To address ship-sourced water pollutant issues resulting from shipping industry growth and achieve precise supervision and effective management in coastal ports, this study develops a method for calculating and predicting the generation volume of oily sewage, domestic sewage and solid waste based on [...] Read more.
To address ship-sourced water pollutant issues resulting from shipping industry growth and achieve precise supervision and effective management in coastal ports, this study develops a method for calculating and predicting the generation volume of oily sewage, domestic sewage and solid waste based on Automatic Identification System (AIS) data. First, a questionnaire survey (“Survey on Ship Water Pollutants”) is designed and implemented. Through analysis of questionnaire data, the ranges of values for the generation of oily sewage, domestic sewage, and solid waste from different ship types at China’s coastal ports are established. Additionally, onboard sampling is conducted to determine average emission factors for domestic sewage and oily sewage from typical ship types. Second, ship activities are derived from AIS data and combined with the established generation volume ranges for spatiotemporal calculation. Finally, a ConvLSTM (Convolutional Long Short-Term Memory) model is developed to predict the generation volume of water pollutant based on their spatiotemporal characteristics. Taking a major Chinese port area as a case study, the results indicate that pollutant generation volumes are significant in coastal port zones and main navigation channels, particularly between 15:00 and 16:00. chemical oxygen demand (COD), suspended solids (SS), and 5-day biochemical oxygen demand (BOD5) levels in domestic sewage exceeded China’s national regulatory limits by 0.35 times, 2.88 times and 1.07 times, respectively, which can easily lead to a decrease in dissolved oxygen content in the water, affecting the respiration and survival of aquatic organisms. Petroleum content in oily sewage remained below the standard threshold. For pollutant generation volume prediction, the proposed ConvLSTM model achieved MAE and RMSE values of 0.0824 and 0.1433, respectively, outperforming other prediction models such as LSTM and CNN-LSTM. This research provides technical support for the prevention and control of water pollution from ships in coastal ports. The proposed AIS-driven framework and ConvLSTM prediction method are transferable and globally applicable, offering a reference for the environmental sustainability of port ecosystems, the global maritime pollution prevention, and the sustainable development of the shipping industry worldwide. Full article
(This article belongs to the Section Pollution Prevention, Mitigation and Sustainability)
22 pages, 566 KB  
Article
Towards Sustainable Inventory Systems: Multi-Objective Optimisation of Economic Cost and CO2 Emissions in Multi-Echelon Supply Chains
by Joaquim Jorge Vicente
Sustainability 2026, 18(9), 4205; https://doi.org/10.3390/su18094205 - 23 Apr 2026
Abstract
Effective supply chain planning increasingly requires balancing cost-efficiency with environmental responsibility, particularly as organisations face growing pressure to reduce the carbon footprint of logistics operations. This study develops a mixed-integer linear programming model to optimise inventory and transportation decisions in a multi-echelon distribution [...] Read more.
Effective supply chain planning increasingly requires balancing cost-efficiency with environmental responsibility, particularly as organisations face growing pressure to reduce the carbon footprint of logistics operations. This study develops a mixed-integer linear programming model to optimise inventory and transportation decisions in a multi-echelon distribution network comprising a central warehouse, regional warehouses, and retailers. The model integrates a continuous-review (r,Q) replenishment policy, stochastic demand, safety stock requirements, transportation lead times, and stockout behaviour, enabling a detailed representation of operational dynamics under uncertainty and environmental concerns. Unlike most sustainable inventory models—which typically treat environmental impacts and replenishment control separately or rely on simplified service assumptions—this study provides an integrated framework that jointly embeds (r,Q) policies, stochastic demand, stockouts and distance-based CO2 metrics within a unified optimisation structure. The model advances prior work by explicitly integrating continuous-review (r,Q) replenishment policies with distance-based CO2 metrics under stochastic demand, a combination rarely addressed in sustainable multi-echelon inventory models. A multi-objective formulation captures the trade-off between economic performance and CO2 emissions, allowing the identification of Pareto-efficient strategies that reconcile financial and environmental goals. Reducing emissions by over 90% requires an additional cost of only about 4%, demonstrating that substantial emission reductions can be achieved at relatively low additional cost. The findings offer practical insights for managers seeking to design more sustainable and cost-effective distribution policies, highlighting the value of integrated optimisation approaches in contemporary logistics systems. Full article
(This article belongs to the Special Issue Green Supply Chain and Sustainable Economic Development—2nd Edition)
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