Search Results (2,954)

This study proposes a vision-based framework for automated inspection and quality grading of beef steaks by integrating fat marbling distribution analysis and lean-meat color evaluation. In frozen beef products, surface frost often generates specular reflections that resemble both fat and lean regions, thereby reducing segmentation accuracy. To address this challenge, a sequential and interpretable analytical framework is developed. First, homomorphic filtering is applied to suppress frost-induced illumination artifacts, followed by curvelet transform combined with square-ring filtering to separate fat and lean regions based on their multi-scale and directional characteristics. For marbling analysis, the convex hull, skeleton, and principal axis of the steak are extracted, and a chi-square goodness-of-fit test is performed within eight predefined regions to quantitatively evaluate marbling distribution uniformity and identify localized fat accumulation. For lean-meat evaluation, RGB color features are extracted and classified using a Support Vector Machine (SVM) to determine redness levels. The resulting marbling and color information are subsequently integrated through a weighted grading strategy to estimate the final quality grade. Experimental results demonstrate a fat detection rate of 92.68%, a false-positive rate of 4.97%, and a correct classification rate of 94.09% for fat segmentation, while the SVM-based lean-meat color classifier achieves an accuracy of 96.67%. Furthermore, the proposed grading framework attains an overall grading accuracy of 90.38%, showing strong agreement with human evaluation. Full article

Spent coffee grounds (SCG) are generated in millions of tonnes annually due to rising global coffee consumption, posing significant challenges, including greenhouse gas emissions, waste-disposal problems, and the loss of valuable compounds like caffeine, dietary fibre, phenolics, antioxidants, proteins, and lipids, offering prospects for potential valorization. Its composition is influenced by several factors. This review focuses on recent advancements in the valorization of SCG across sectors such as food, nutraceuticals, bioenergy, and packaging. The emphasis is on pretreatment, extraction, and bioconversion methods, as well as current research gaps, limitations, and future directions. SCG valorization is oriented toward integrated, multi-product biorefinery systems based on green extraction and bioconversion technologies to recover high-value compounds in both the food and non-food sectors. Nonetheless, industrial scalability is limited by composition variability, energy-intensive processing, techno-economic constraints, and safety and regulatory issues that remain unresolved. The shortcomings, such as inadequate standardized characterization, toxicological validation, and pilot-scale studies, are critical gaps. Scalable, energy-efficient processes, AI-assisted optimization, and regulatory alignment development should be a priority in future research, so that sustainable and commercial deployment is possible. Full article

Tri-reforming of methane (TRM) has emerged as a promising pathway for low-carbon syngas production by integrating steam reforming, dry reforming, and partial oxidation within a single process. This coupling enables simultaneous CH₄ utilization and CO₂ valorization while enabling internal heat generation and flexible adjustment of the H₂/CO ratio for downstream synthesis. However, TRM performance cannot be adequately evaluated using conversion or energy efficiency alone, because the process involves complex interactions among competing reaction pathways, transport phenomena, catalyst stability, and thermodynamic irreversibility. This review provides a multiscale critical assessment of TRM from both first-law energy and second-law exergy perspectives, linking reaction-network fundamentals to reactor-level behavior and system-level performance. The literature evidence shows that although high temperatures and near-autothermal operation can enhance CH₄ conversion and reduce external heat demand, these conditions may simultaneously intensify deep oxidation, hotspot formation, carbon-forming tendencies, and exergy destruction. While equilibrium analyses help define feasible operating windows, they are insufficient without kinetic modeling and reactor-scale studies that capture spatial non-uniformities and pathway competition. Across reported TRM systems, exergy destruction is consistently concentrated within the reformer, identifying the reacting core as the dominant thermodynamic bottleneck. Accordingly, the key challenge in TRM is not simply to maximize conversion but to preserve chemical work potential while maintaining syngas quality and operational stability. Viewed from this perspective, TRM is better understood as an irreversibility-aware multiscale design problem in which optimal performance depends on the integrated optimization of catalyst functionality, reactor architecture, heat management, and system-level operation. Full article

Reducing building energy consumption is central to EU climate-neutrality targets and to sustainable development goals: buildings account for around 40% of EU final energy consumption, placing Building Energy Management Systems (BEMS) at the intersection of the European Green Deal and the EU Artificial Intelligence Act. A scoping review following PRISMA-ScR guidelines charted 61 Machine Learning (ML) for BEMS papers (2020–2026) across three sub-domains (load forecasting and energy monitoring, HVAC control, and demand response), using a nine-point Technology Readiness Level (TRL) rubric and three Trustworthy AI (TAI) dimensions (Privacy & Data Governance, Robustness, and Transparency). The review finds that 90.2% of papers remain at the development stage (TRL 4–6), with no multi-site production deployment documented. TAI coverage is heterogeneous at publication level: transparency is addressed in only 3 of 61 papers (4.9%), and privacy provisions (the best-covered ALTAI dimension) are concentrated in demand-response papers (9 of 17, 52.9%), largely via Federated Learning (6 of 9 privacy-tagged papers). A three-level EU AI Act risk classification identifies 23 borderline-candidacy papers (37.7%), predominantly Reinforcement Learning-based HVAC control systems, whose high-risk proximity cannot be resolved at abstract level; explicit compliance engagement is absent from all 61 mapped sources, including the 22 papers published after the Act entered into force in August 2024. The findings document adouble readiness gap: a TRL ceiling co-located with limited documented engagement with TAI obligations and EU AI Act compliance at publication level. Closing this gap is necessary before AI-driven building energy management can be deployed at scale under EU governance requirements. Full article

Tourism destinations consume vast quantities of energy, water, food, and materials, yet these resource flows remain largely invisible in destination planning practice. The aim of this paper is to develop a conceptual framework that reconceptualises tourism destinations as industrial ecosystems and makes their material and energy flows visible, quantifiable, and amenable to destination-scale planning. Existing frameworks prioritise governance and demand management, leaving the material dimension of sustainability unaddressed. To this end, the paper proposes a multi-scale resource-flow framework grounded in industrial ecology. This is a conceptual framework paper: it develops analytical architecture for destination-scale resource accounting rather than reporting empirical measurements. The framework organises four analytical components—actors, flows, structural configurations, and feedback mechanisms—across macro, meso, and micro scales. Three planning capabilities are advanced: supply-chain-complete environmental accounting, resource hotspot detection, and policy design along the full causal chain from structural arrangement to environmental outcome. Material flow analysis, life cycle assessment, and industrial symbiosis mapping are presented as operational tools, illustrated through reference to high-intensity coastal tourism systems. Industrial symbiosis is positioned as a structural mechanism through which by-product valorisation reduces destination-level resource throughput. The study contributes a bridging framework between governance-oriented tourism planning and the material accounting rigour of industrial ecology, distinguishing it from circular economy models that supply a design principle but no material accounting, from urban metabolism approaches that assume temporally stable flows, and from regenerative development that is values-based rather than quantitative. The framework offers a foundation for more integrated and resource-efficient destination sustainability planning. Full article

High-resolution spatiotemporal sea ice concentration (SIC) estimates are essential for Arctic navigation and ice analysis, but existing observational products are often too coarse, and physics-based models are computationally expensive. This study proposes a data-driven framework that couples Light Gradient Boosting Machine (LightGBM) temporal prediction with Kriging-based spatial interpolation to reconstruct SIC fields over the Northern Sea Route sector. LightGBM is trained on a grid-based SIC time series with engineered features representing persistence, seasonality, and short-term variability, enabling multi-horizon forecasting across large spatial grids. The predicted SIC fields are then refined using Ordinary Kriging (OK) and Co-Kriging (CK) with Gaussian and spherical semi-variogram models. Prediction performance is evaluated using root mean square error, and interpolation accuracy is assessed through cross-validation. Results show that, for high-latitude regions and resolutions finer than 0.25° × 0.25°, OK with a spherical semi-variogram achieves lower interpolation errors than CK and Gaussian-based alternatives. By sequentially coupling temporal learning and spatial refinement, the proposed framework improves temporal continuity, spatial structure, and error quantification, providing high-resolution SIC information suitable for large-scale Arctic ice analysis and navigation support. Full article

The pore architecture and textural properties of heterogeneous catalysts affect their intrinsic and extrinsic kinetics, selectivity, and resistance to deactivation. Modelling allows the cheaper and quicker design of new catalyst products, and the optimization of the operation of existing ones. This work particularly reviews major and recent developments in pore network models (PNMs), including image-derived versions, which are a key tool for determining the impact of pore structure and mass transport on catalyst performance. It also briefly considers related areas of multi-scale modelling, first-principles modelling of active sites with DFT, intermediate-scale microkinetic modelling, and recent developments in machine-learning-based approaches. It has been seen that, for some reaction systems, PNMs can predict effectiveness factors a priori, and deliver optimized pore network designs. However, this survey also highlights issues with current models including omission of key controlling structures due to insufficient prior pore characterization, lack of the often-substantial evolution of the pore structure over the catalyst life-stages due to various on-going physical processes, and the neglect of the often-heterogeneous spatial distribution of active sites. Further, this review also considers novel experimental techniques that demonstrate, and remedy, gaps often left out from the current modelling approaches. Full article

Background/Objectives: Metabolic syndrome (MetS) is a pressing global health challenge comprising obesity, hyperglycemia, hypertension, and hyperlipidemia. Conventional polypharmacy often presents long-term compliance issues and side effects. Eucommia ulmoides Oliv., a traditional medicinal and edible plant rich in iridoids, lignans, flavonoids, and polysaccharides, has emerged as a promising natural intervention. This review aims to systematically summarize the bioavailability and multifaceted pharmacological mechanisms of E. ulmoides and its bioactive components in alleviating MetS. Methods: We comprehensively reviewed the recent in vitro and in vivo literature to map the functional evidence, specific signaling pathways, and gut microbiota–host interactions associated with E. ulmoides extracts and its key phytochemicals (e.g., asperuloside) against various metabolic dysfunctions. Results: Current evidence indicates that E. ulmoides operates through a “multi-component, multi-target, and multi-pathway” paradigm. For hyperlipidemia and obesity, it activates hepatic lipid metabolism (PPARα/CPT1A, FXR/CYP7A1) and mitigates oxidative stress (Nrf2/ARE). Furthermore, it dose-dependently reshapes the gut microbiota by enriching beneficial bacteria like Akkermansia and increasing butyrate production, exerting profound gut–liver axis regulation. It also ameliorates hypertension by activating the ACE2-Ang-(1–7)-Mas axis, improves insulin resistance via the AMPK/PI3K/Akt cascade, and manages hyperuricemia by modulating XOD and renal transporters. Notably, the low oral bioavailability of its glycosides highlights the crucial role of gut microbial hydrolysis in its efficacy. Conclusions: E. ulmoides holds substantial therapeutic potential as a multi-target natural supplement for MetS. However, future translational applications necessitate large-scale randomized clinical trials, multi-omics studies to further clarify host–microbiome interactions, and the development of standardized formulations to ensure clinical efficacy. Full article

Persistent organic contaminants and complex wastewater matrices challenge conventional treatment because parent-compound removal does not necessarily imply mineralization, detoxification, or improved environmental safety. Advanced oxidation processes can address these limitations, but practical effectiveness is often constrained by oxidant activation, gas–liquid mass transfer, reagent distribution, light penetration, catalyst contact, energy demand, and matrix scavenging. This work critically examines hydrodynamic cavitation-assisted advanced oxidation processes for water and wastewater treatment, including systems based on hydrogen peroxide, ozone, Fenton and Fenton-like reactions, persulfate, peroxydisulfate, peroxymonosulfate, UV irradiation, photocatalysis, cold plasma, multi-hybrid configurations, and emerging reduction-oriented approaches. The discussion covers reactor configurations, target contaminants, real matrices, and sustainability-related performance metrics. The central argument is that hydrodynamic cavitation is not automatically sustainable as a stand-alone treatment. It becomes relevant as a sustainable intensification module only when measurable improvements are demonstrated in oxidant activation, mass transfer, treatment depth, biodegradability, toxicity reduction, process integration, or scale-up at acceptable energy and chemical cost. A reporting framework is proposed based on mineralization, COD/TOC reduction, by-products, toxicity, biodegradability, normalized energy consumption, chemical efficiency, real-matrix validation, reproducibility, and cost-relevant indicators. Future progress should move from isolated degradation tests to integrated, controllable, and scalable treatment frameworks. Full article

Genetically modified (GM) plants have revolutionized agriculture for more than three decades. The production of a GM plants is a complex, multi-stage process. Several key methods are available for generating GM plants. The choice of transformation method depends on the type of plant (dicotyledonous or monocotyledonous), the objective (large-scale production versus studying a specific gene in particular cells or tissues), and whether stable or transient transformation is desired. Following successful transformation, the next step is the regeneration of a whole plant from a single cell in tissue culture, which is a labor-intensive and time-consuming process. Currently, numerous genes that confer desirable traits have been identified. These traits include stress tolerance, herbicide and pest resistance, and improved consumer qualities (such as flavor, appearance, shelf life, and nutritional value). In this review, we describe the main methods for producing GM plants and provide examples of trait genes utilized in agricultural biotechnology. Despite the fact that GM plants represent one of the most significant biotechnological advances, they also remain among the most contentious issues in contemporary food safety and agricultural policy. Here, we discuss the advantages and disadvantages of using GM plants for humans. Full article

Environmental disasters demand structured monitoring systems capable of linking ecological responses to adaptive governance. This study proposes an integrated indicator framework designed to support ecosystem preservation and environmental risk mitigation following large-scale contamination events. The proposed framework combines multi-source environmental data, intrinsic risk classification, multivariate statistical validation, and a dashboard-based decision-support architecture. When the model was applied to Restinga ecosystems impacted by mining tailings deposition, the results revealed significant spatial heterogeneity between the monitoring stations, with ~33% of sites classified under high or critical ecological risk during at least one monitoring period. Of the metals evaluated, 46.15% were above the reference levels, while for biological response indicators such as primary productivity, a 23.53% reduction in danger alerts was observed in 2019 across the evaluated sites when comparing the rainy and dry seasons. The composite “Danger Alert” indicator was triggered in all sampling campaigns during the evaluated period, demonstrating persistent ecological pressure throughout seasonal cycles. Sensitivity analyses confirmed the robustness of the risk classifications under alternative baseline and aggregation scenarios, and an uncertainty assessment indicated stable trends across temporal variability ranges. The proposed framework enhances the interpretability of complex environmental datasets by structuring inferential ecological associations between environmental pressures and biological responses, which can then be translated into actionable governance outputs. Beyond the case study, the architecture is structurally transferable to other ecosystems, provided that ecological indicators and thresholds are contextually recalibrated. The proposed approach contributes to sustainability-oriented environmental governance by integrating statistical validation, adaptive risk thresholds, and decision-support visualization within a unified monitoring system. Full article

Green hydrogen is increasingly discussed as an energy carrier that can link electricity, gas, heat, and transport sectors. However, many existing reviews address this topic from separate viewpoints, such as hydrogen production technologies, Artificial Intelligence (AI) applications, or system integration, with less attention to how policy and market conditions affect deployment. This review brings these related aspects together in one structured discussion. The paper first reviews the hydrogen supply chain, including production, storage, transport, and utilization. It then discusses an integrated multi-energy architecture in which hydrogen interacts with electricity, natural gas, heat, and cooling networks. Policy instruments in five major economies, including the European Union, the United States, China, Japan, and India, are compared. The review also summarizes the main barriers to large-scale deployment, including high production costs, limited infrastructure, technological challenges, regulatory uncertainty, and supply-chain constraints. In addition, the current market structure and selected large-scale hydrogen projects planned in the United States are reviewed. The paper also examines the role of artificial intelligence in green hydrogen systems. AI applications are grouped into four main stages of the hydrogen value chain: forecasting renewable energy generation, improving electrolyzer design and operation, optimizing storage and distribution, and supporting system-level techno-economic assessment. Recent Machine Learning (ML) studies are compared based on their methods and their contributions to operation and planning. Overall, this review highlights the role of AI in enabling green hydrogen integration within multi-energy systems. Full article

The steel surface defect detection is crucial for steel quality and usage safety. The high computational cost and low detection accuracy are still the main issues in current steel detection models. To efficiently address the issues above, this paper proposes a new steel surface defect detection model named DIDW-YOLOv11. In the proposed DIDW-YOLOv11, the YOLOv11 C3k2 module is first innovatively improved by C3K2-DIMB, which integrates C3K2 and DIMB by introducing DynamicInceptionDWConv2d (DIDW) to sufficiently strengthen the detailed feature extraction for tiny defects and weak-texture defects, improving the matching degree of multi-scale receptive fields. Then the YOLOv11 SPPF module is enhanced by integrating the IDWFSPPF module for optimizing the fusion of local and global information, which combines average pooling and max pooling to enhance the model’s multi-scale feature fusion capability. An auxiliary detection head (ADH) is finally proposed with an additional coarse loss function to process shallow feature information into the model, which uses extra supervision for shallow features to suppress background noise and reduce false detections. Experimental results on the NEU-DET and GC10-DET datasets show that DIDW-YOLOv11 achieves 4.9% and 3.8% improvements in mAP@0.5 compared to the baseline model YOLOv11s. Our research indicates that DIDW-YOLOv11 exhibits stronger recognition ability and robustness in complex and diverse defect detection, providing an effective solution for steel defect detection in industrial production. In addition, experimental results show that our model offers improved performance over the baseline methods. Full article

Heat stress (HS) is at the top of the challenges facing modern dairy production, with annual losses according to global projections, under high-emission scenarios, reaching US$14.7–40.0 billion by the end of the century. This review emphasizes three interconnected topics that account for most of the proportion of the productive and reproductive losses during HS. First, the physiological consequences of HS are reviewed, with emphasis on the pair-fed thermal neutral (PFTN) paradigm, which established that reduced dry matter intake (DMI) accounts for only 35–50% of the observed milk yield decline, with the remainder arising from tissue-level effects of hyperthermia on mammary function, metabolism, and reproductive performance. Second, HS-induced microbiome disruption is examined as an active pathophysiological amplifier, whereby rumen dysbiosis compromises intestinal barrier integrity and drives systemic endotoxaemia, chronically amplifying the immune suppression already imposed by the thermal insult. Third, we focus on the integration of multi-omics platforms as a management approach, since single-omics analyses capture only a fraction of the biological complexity underlying the HS response. As the available datasets expand in coverage and scale, their integration through AI-driven analytical frameworks has the potential to substantially advance beyond the current fragmented picture, progressively building toward a systems-level model of thermal stress. Evidence-based mitigation strategies spanning environmental cooling, targeted nutritional supplementation, and genomic selection are critically evaluated within this framework, with emphasis on equity of access to evidence-based solutions across global dairy production systems. Full article

The increasing adoption of Plant Factories with Artificial Lighting (PFAL) has intensified the reliance on Internet of Things (IoT) technologies for real-time monitoring and control of environmental and operational variables. While IoT-based architectures enable precise resource management and productivity optimization, PFAL systems remain highly vulnerable to component failures, sensor malfunctions, communication faults, and energy disruptions, which may compromise crop integrity and system reliability. These risks are particularly critical in low-cost and small-scale PFAL implementations, where maintenance capacity and redundancy are often limited. Existing IoT-based PFAL monitoring systems typically address either hardware or software redundancy in isolation and rarely incorporate a dedicated maintenance-oriented fault detection layer validated under realistic multi-failure scenarios. This study addresses these challenges by proposing a low-cost redundant system architecture for PFAL applications that simultaneously integrates (1) hardware redundancy through multi-sensor configurations; (2) analytical redundancy based on residual generation and threshold-based fault isolation; and (3) a maintenance-oriented fault detection layer capable of identifying abnormal internal device conditions. Experimental validation was conducted using four hardware configurations—Arduino Nano with Ethernet, ESP32, STM32 with Wi-Fi, and STM32 with Ethernet—evaluated across five fault scenarios: dust accumulation, water exposure, high temperature, fire detection, and physical impact. The STM32 with Ethernet configuration consistently achieved the fastest fault detection response times across all tested scenarios. Future work will focus on the integration of machine learning-based predictive maintenance algorithms, multi-node PFAL network deployments, and long-term field validation. Full article