Search Results (446)

Recent large-kernel based SISR methods often struggle to balance global structural consistency with local texture preservation while maintaining computational efficiency. To address this, we propose the Hybrid Gated Large-kernel Network (HGLN). First, the Hybrid Multi-Scale Aggregation (HMSA) decouples features into structural and detailed streams via dual-path processing, utilizing a modified Large Kernel Attention to capture long-range interactions. Second, the Local–Global Synergistic Attention (LGSA) recalibrates features by integrating local spatial context with dual global statistics (mean and standard deviation). Finally, the Structure-Gated Feed-forward Network (SGFN) leverages high-frequency residuals to modulate the gating mechanism for precise edge restoration. Extensive experiments demonstrate that HGLN outperforms state-of-the-art methods. Notably, on the challenging Urban100 dataset ($\times 4$), HGLN achieves significant PSNR gains with extremely low complexity (only 11G Multi-Adds), proving its suitability for resource-constrained applications. Full article

The rapid increase in organic waste generation poses significant environmental challenges and highlights the limitations of conventional waste management practices. In this context, black soldier fly (Hermetia illucens) larvae (BSFL) have emerged as a promising biological tool for valorizing organic residues within circular bioeconomy frameworks. This review provides an integrated analysis of BSFL-based bioconversion systems, focusing on the biological characteristics of BSFL, suitable organic waste streams, and the key process parameters influencing waste reduction efficiency, larval biomass production, and frass (the residual material from larval bioconversion) yield. The performance of BSFL in converting organic waste is assessed with emphasis on substrate characteristics, environmental conditions, larval density, and harvesting strategies. Environmental and economic implications are discussed in comparison with conventional treatments such as landfilling, composting, and anaerobic digestion. Special attention is given to the nutritional composition of BSFL and the valorization of larvae as sustainable protein and lipid sources for animal feed and emerging human food applications, while frass is highlighted as a nutrient-rich organic fertilizer and soil amendment. Finally, current challenges related to scalability, safety, regulation, and social acceptance are highlighted. By linking waste management, resource recovery, and sustainable protein production, this review clarifies the role of BSFL and frass in resilient and resource-efficient food and waste management systems. Full article

This study contributes to the advancement of circular economy practices in polymer manufacturing by applying machine learning algorithms (MLA) to predict the tensile strength of recycled low-density polyethylene (LDPE) building films. As the construction and packaging industries increasingly seek eco-efficient and low-carbon materials, recycled LDPE offers a valuable route toward sustainable resource management. However, ensuring consistent mechanical performance remains a challenge when reusing polymer waste streams. To address this, tensile tests were conducted on LDPE films produced from recycled granules, measuring tensile strength, strain, mass per unit area, thickness, and surface roughness. Three established machine learning algorithms—feed-forward Neural Network (NN), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost)—were implemented, trained, and optimized using the experimental dataset using R statistical software (version 4.4.3). The models achieved high predictive accuracy, with XGBoost providing the most robust performance and the highest level of explainability. Feature importance analysis revealed that mass per unit area and surface roughness have a significant influence on film durability and performance. These insights enable more efficient production planning, reduced raw material usage, and improved quality control, key pillars of sustainable technological innovation. The integration of data-driven methods into polymer recycling workflows demonstrates the potential of artificial intelligence to accelerate circular economy objectives by enhancing process optimization, material performance, and resource efficiency in the plastics sector. Full article

This study examined the seasonal and size-related variations in the diet composition and feeding strategies of the robust tonguefish Cynoglossus robustus collected in the Yeosu Coast, Korea, from January to December 2024. Stomach content analysis identified amphipods, polychaetes, and brachyurans as the dominant prey items. Ontogenetic dietary shifts were evident, with individuals < 25 cm TL feeding mainly on amphipods, whereas larger individuals consumed more polychaetes and brachyurans, indicating a shift toward larger and more energy-efficient prey with growth. Amphipods, with Ampelisca sp. being predominant, were predominant in spring and summer, whereas crabs and polychaetes increased in autumn and winter, respectively. Seasonal variation was attributed to environmental factors and post-spawning feeding recovery. The estimated trophic level (3.22) suggests that C. robustus functions as a mesopredator consuming benthic invertebrates and plays an essential role in energy transfer within the coastal benthic ecosystem. These findings provide fundamental ecological insights into the trophic structure of the coastal ecosystem in the southern sea of Korea and serve as a scientific basis for sustainable fisheries resource management. Full article

Background: Agri-food by-products are increasingly recognized as valuable sources of tannins, whose antioxidant properties represent the primary driver of their biological activity across human and animal health. The strong redox-modulating capacity of condensed and hydrolysable tannins provides a unifying mechanistic explanation for their effects on inflammation, metabolism, gut integrity and neuroprotection. Methods: This narrative review synthesizes evidence obtained through a structured literature search across major databases, selecting studies that investigated antioxidant mechanisms of tannin-rich matrices from plant- and processing-derived residues. Results: Condensed tannins, particularly proanthocyanidins, consistently display potent antioxidant activity through radical scavenging, metal chelation and activation of endogenous defenses, thereby underpinning their anti-inflammatory, anti-ischemic, neuroprotective and metabolic actions. Hydrolysable tannins similarly exert strong antioxidative effects that support antimicrobial activity, enzyme modulation and protection against neuroinflammation. In animals, the antioxidant capacity of tannins translates into improved oxidative balance, enhanced immune status, reduced tissue damage, better feed efficiency and mitigation of oxidative stress-linked methane emission pathways. Conclusions: Antioxidant activity emerges as the central, cross-species mechanism through which tannins mediate diverse health benefits. Tannin-rich agri-food by-products therefore represent promising sustainable antioxidant resources, although their efficacy remains influenced by tannin class, degree of polymerization and dosage, warranting further mechanistic and translational research. Full article

Lactic acid bacteria (LAB), as the core microorganisms in silage fermentation, play a crucial role in improving silage quality and ensuring feed safety, making the screening, identification, and functional characterization of LAB strains a significant research focus. Researchers initially isolate and purify LAB from various samples, followed by identification through a combination of morphological, physiological, biochemical, and molecular biological methods. Systematic screening has been conducted to identify LAB strains tolerant to extreme environments (e.g., low temperature, high temperature, high salinity) and those possessing functional traits such as antimicrobial activity, antioxidant capacity, production of feruloyl esterase and bacteriocins, as well as cellulose degradation, yielding a series of notable findings. Furthermore, modern technologies, including microbiomics, metabolomics, metagenomics, and transcriptomics, have been employed to analyze the structure and functional potential of microbial communities, as well as metabolic dynamics during the ensiling process. The addition of superior LAB inoculants not only facilitates rapid acidification to reduce nutrient loss, inhibit harmful microorganisms, and improve fermentation quality and palatability but also demonstrates potential functions such as degrading mycotoxins, adsorbing heavy metals, and reducing methane emissions. However, its application efficacy is directly constrained by factors such as strain-crop specific interactions, high dependence on raw material conditions, limited functionality of bacterial strains, and relatively high application costs. In summary, the integration of multi-omics technologies with traditional methods, along with in-depth exploration of novel resources like phyllosphere endophytic LAB, will provide new directions for developing efficient and targeted LAB inoculants for silage. Full article

Poultry farming ranks among the most rapidly expanding sectors of global agriculture, significantly contributing to food availability, improved dietary quality, and economic stability in rural areas. The sector’s efficiency stems from short production cycles and the ability to convert agricultural by-products into high-quality protein, energy, and essential nutrients. Despite these benefits, the growing scale of poultry production raises serious environmental concerns, including intensive use of land and water, high feed demand, and impacts on greenhouse gas emissions, soil nutrient balance, and water quality. This study examines the poultry industry in the Republic of Moldova, where it forms a crucial component of the agricultural economy. Drawing on recent statistical data and scientific literature, the article reviews production dynamics, farm structures, and technological adoption, offering a comprehensive overview of the sector’s current state. The findings highlight both the sector’s essential role in strengthening food security and rural livelihoods and its susceptibility to resource limitations and environmental pressures. The analysis emphasizes the importance of implementing precision livestock farming technologies, improving biosecurity, and promoting environmentally sustainable practices as key strategies for long-term sector resilience. These insights aim to support policymakers and stakeholders in developing effective strategies to ensure a competitive and sustainable poultry industry in Moldova. Full article

AI enhances aquatic resource management by automating species detection, optimizing feed, forecasting water quality, protecting species interactions, and strengthening the detection of illegal, unreported, and unregulated fishing activities. However, these advancements are inconsistently employed, subject to domain shifts, limited by the availability of labeled data, and poorly benchmarked across operational contexts. Recent developments in technology and applications in fisheries genetics and monitoring, precision aquaculture, management, and sensing infrastructure are summarized in this paper. We studied automated species recognition, genomic trait inference, environmental DNA metabarcoding, acoustic analysis, and trait-based population modeling in fisheries genetics and monitoring. We used digital-twin frameworks for supervised learning in feed optimization, reinforcement learning for water quality control, vision-based welfare monitoring, and harvest forecasting in aquaculture. We explored automatic identification system trajectory analysis for illicit fishing detection, global effort mapping, electronic bycatch monitoring, protected species tracking, and multi-sensor vessel surveillance in fisheries management. Acoustic echogram automation, convolutional neural network-based fish detection, edge-computing architectures, and marine-domain foundation models are foundational developments in sensing infrastructure. Implementation challenges include performance degradation across habitat and seasonal transitions, insufficient standardized multi-region datasets for rare and protected taxa, inadequate incorporation of model uncertainty into management decisions, and structural inequalities in data access and technology adoption among smallholder producers. Standardized multi-region benchmarks with rare-taxa coverage, calibrated uncertainty quantification in assessment and control systems, domain-robust energy-efficient algorithms, and privacy-preserving data partnerships are our priorities. These integrated priorities enable transition from experimental prototypes to a reliable, collaborative infrastructure for sustainable wild capture and farmed aquatic systems. Full article

The sustainable future of dairy farming will depend on how trade-offs between environmental impact, economic viability, and animal welfare are managed. Dairy production contributes significantly not only to human nutrition but also to greenhouse gas (GHG) emissions, ammonia release, and water pollution. Comprehensive sustainability assessments are essential for addressing these impacts, also in light of evolving regulations like the EU Corporate Sustainability Reporting Directive. However, existing research on sustainable dairy farming and intensification often overlooks trade-offs with other ecological aspects like biodiversity, economic viability, or animal welfare. This study evaluated the sustainability performance of Austrian dairy farms using a tool called NEU.rind, which combines life cycle assessment (LCA) with other indicators. Applied to 170 dairy farms, the tool identified four sustainability clusters across the dimensions of environmental conditions, efficiency, animal health, and sustainability: (1) Alpine farms (high cow longevity, medium-to-high emissions per kg milk), (2) efficient low-input farms (low emissions, high cow longevity), (3) high-output lowland farms (high productivity, lower animal welfare), and (4) input-intensive lowland farms (high emissions, especially per hectare; inefficient use of resources). The analysis revealed fundamental trade-offs between production intensity, environmental impact, and animal welfare, particularly when comparing product-based (per kg milk) versus hectare-based indicators. Key improvement strategies include increasing the use of regional feed and pasture as well as adapting manure management. For policymakers, these findings underline the importance of site-specific sustainability assessments and the need for region-specific incentive schemes that reward both environmental efficiency and animal health performance. In this context, NEU.rind provides farm-specific recommendations with minimal data input, making sustainability assessments practical and feasible. Full article

Edible Grass (EG) is a hybrid vegetable variety valued for its high biomass and protein content, garnering significant interest in recent years for its potential in food, feed, and health product applications. However, in subtropical climates, intense light and high temperatures severely affect the growth and development of Edible Grass (EG), leading to substantial reductions in yield and quality. This study was conducted in the subtropical humid monsoon climate zone of Changsha, Hunan, China, comparing two growth conditions: natural light (CK) and shading treatment (ST). High light-aggravated heat damage under CK significantly reduced EG yield and quality (p < 0.05), with severe cases leading to plant death. and could even lead to plant death in severe cases. Specifically, maximum air and leaf temperatures under CK reached 38.85 °C and 38.14 °C, respectively, well exceeding the plant’s optimal growth range. Shading treatment (ST) effectively alleviated this damage, significantly increasing the net photosynthetic rate, stomatal conductance, and intercellular CO₂ concentration, while decreasing leaf temperature and transpiration rate (p < 0.001). The analysis of physiological and biochemical indicators indicates that after ST, the activities of SOD, CAT, and POD in the leaves decreased, while the contents of MDA and H₂O₂ were significantly lower compared to the CK group (p < 0.001). The transcriptome sequencing results indicate that a total of 8004 DEGs were identified under shading treatment (ST) relative to natural light (CK), with 3197 genes upregulated and 4807 genes downregulated. Significantly enriched Gene Ontology (GO) terms include ‘cell membrane’, ‘extracellular region’, and ‘protein kinase activity’, while significantly enriched KEGG metabolic pathways include ‘plant hormone signal transduction’, ‘photosynthesis–antenna proteins’, and ‘glutathione metabolism’. Compared to CK, the expression of genes associated with oxidative stress (e.g., CAT1, OXR1, APX, GPX) was significantly downregulated in ST, indicating a relief from light-aggravated heat stress. This transcriptional reprogramming was corroborated by metabolomic data, which showed reduced accumulation of key flavonoid compounds, aligning with the downregulation of their biosynthetic genes as well as genes encoding heat shock proteins (e.g., Hsp40, Hsp70, Hsp90). It indicated that plants switch from a ‘ROS stress–high energy defense’ mode to a ‘low oxidative pressure–resource-saving’ mode. Collectively, ST significantly alleviated the physiological damage of forage grasses under heat stress by modulating the processing of endoplasmic reticulum heat stress proteins, plant hormones, and related genes and metabolic pathways, thereby improving photosynthetic efficiency and yield. The findings provide a theoretical basis for optimizing the cultivation management of EG, particularly in subtropical regions, where shade treatment serves as an effective agronomic strategy to significantly enhance the stress resistance and yield of EG. Full article

The demand for food is increasing with the rise in the human population. Among foods, meat is an essential part of human nutrition. Meat provides good-quality protein and all the micronutrients needed by humans. In addition, it also contains some bioactive compounds that are good for human health. Increasing demand, together with concerns over food safety, requires new approaches to guarantee a sustainable, safe, and healthy meat supply chain. The only way to get over these challenges is through technological innovations that are capable of enhancing the safety, quality, and sustainability of meat. Herein, this review identifies the need for new methods of rapid microbial detection, biosensors, AI-based monitoring, innovative processing and preservation techniques, precision livestock farming, resource-efficient feed and water management, alternative protein sources, and circular economy approaches. In particular, this review examines some meat analogs like cultured meat, hybrid products, and microbial proteins as environmentally friendly and nutritionally balanced alternatives. These changes in technology can also bring benefits to consumers in terms of their health. The health benefits of these technological innovations for consumers go beyond just safety, including improved nutritional profiles, functional bioactive ingredients, and the prevention of antimicrobial resistance. The review further analyzes policies, regulatory frameworks, and ethical considerations necessary to achieve consumer trust and social acceptance, including the global alignment of standards, certification, labeling, and all issues related to ethics. Furthermore, AI, IoT, Big Data, and nutritional technologies represent new emerging trends able to unleash new opportunities for the optimization of production, quality control, and personalized nutrition. Full article

The progressive depletion of high-grade ore bodies has shifted attention toward the exploitation of lower-grade deposits as viable sources of value. In recent years, there has been growing emphasis on mining and processing methods that incorporate sustainability by addressing both environmental and socio-economic considerations. To maximize resource recovery, integrated strategies that combine exploration, grade control drilling, mine planning, and processing are essential. Within this framework, particle sorting has emerged as an effective coarse separation method that can upgrade low-grade feed prior to the more energy-demanding milling and subsequent processing stages. Incorporating screening before particle sorting not only assists in identifying the distribution of metals but also determines the most suitable particle size ranges for sorting performance. This study reports on the applicability of sensor-based sorting technologies to low-grade gold and nickel ores from Australia, with a focus on grade deportment by particle size. The results demonstrate that substantial upgrading of low-grade ores is possible, achieving 70%–80% metal recovery within approximately 30%–40% of the original mass through the use of induction and XRT sensors. Overall, the findings indicate that both induction and XRT sorting methods are broadly effective across ore types, offering enhanced upgrading capability and improved processing efficiency. Full article

The demand for real-time land cover mapping from high-resolution remote sensing (HR-RS) imagery motivates lightweight segmentation models running directly on satellites. By processing on-board and transmitting only fine-grained semantic products instead of massive raw imagery, these models provide timely support for disaster response, environmental monitoring, and precision agriculture. Many recent methods combine convolutional neural networks (CNNs) with Transformers to balance local and global feature modeling, with convolutions as explicit information aggregation modules. Such heterogeneous hybrids may be unnecessary for lightweight models if similar aggregation can be achieved homogeneously, and operator inconsistency complicates optimization and hinders deployment on resource-constrained satellites. Meanwhile, lightweight Transformer components in these architectures often adopt aggressive channel compression and shallow contextual interaction to meet compute budgets, impairing boundary delineation and recognition of small or rare classes. To address this, we propose SatViT-Seg, a lightweight semantic segmentation model with a pure Vision Transformer (ViT) backbone. Unlike CNN-Transformer hybrids, SatViT-Seg adopts a homogeneous two-module design: a Local-Global Aggregation and Distribution (LGAD) module that uses window self-attention for local modeling and dynamically pooled global tokens with linear attention for long-range interaction, and a Bi-dimensional Attentive Feed-Forward Network (FFN) that enhances representation learning by modulating channel and spatial attention. This unified design overcomes common lightweight ViT issues such as channel compression and weak spatial correlation modeling. SatViT-Seg is implemented and evaluated in LuoJiaNET and PyTorch; comparative experiments with existing methods are run in PyTorch with unified training and data preprocessing for fairness, while the LuoJiaNET implementation highlights deployment-oriented efficiency on a graph-compiled runtime. Compared with the strongest baseline, SatViT-Seg improves mIoU by up to 1.81% while maintaining the lowest FLOPs among all methods. These results indicate that homogeneous Transformers offer strong potential for resource-constrained, on-board real-time land cover mapping in satellite missions. Full article

While there is very limited information on the cost of production (COP) for the emerging 100% grass-fed organic dairy sector, this study (1) estimates the COP using primary data collected from on-farm surveys, (2) assesses the correlation between COP and key production and management factors, (3) examines how land, feed and labor efficiency, and production scale affect the COP, and (4) derives recommendations for enhancing the economic efficiency of grass-fed organic dairy farms. Data collected via annual surveys in the Northeastern United States from 2019 to 2022 were analyzed through descriptive statistics, correlation analysis, hypothesis tests, and regression analysis. At an average cost of USD 45.91 per hundredweight equivalent of milk, the marginal impacts of the cows managed per full time equivalent labor and milk sold per cow on the COP were −USD 0.166 and −USD 0.003, respectively. Conversely, the COP increased by USD 1.44 when the crop acres per cow increased by one unit, and the COP of small farms with less than 45 cows was USD 6.20 higher than other farms. As farms are significantly different in resource endowment and other factors, the strategies for reducing the COP and improving the economic returns should be identified for individual farms. However, our analyses highlight the importance of enhancing labor efficiency in forage production, land management, milking and feeding, improving herd management and optimizing nutrition and dry matter intake to support high milk productivity. This study may help existing grass-fed dairy farms improve their farm management and reduce COP and help prospective farms assess their suitability for transitioning to grass-fed operation. Full article

Indonesia’s beef cattle sector plays a central role in achieving food security, enhancing rural livelihoods, and fostering economic resilience. However, it faces fragmented governance, import dependence, and persistent challenges of low productivity levels. To capture the evolving evidence base, this study conducted a scoping review of 61 peer-reviewed publications (2019–2025) drawn from six major databases. This study employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Scoping Review Protocol and Arksey and O’Malley’s framework. Key patterns, advances, and gaps, along with evidence and research recommendations, were identified using the PAGER analytical approach. The dominant themes include production efficiency, environmental sustainability, policy, market linkages, and technological innovation. The results show that most studies employed quantitative or system modelling designs. In the global literature, multidimensional sustainability frameworks have shifted away from production-centric ones, with regional studies highlighting different emphases, such as carbon metrics in South America and market access and livelihood resilience in Asia and Africa. Integrated crop, livestock, and forestry systems; legume-based nutrient management; genotype-specific feeding and breeding; and enabling policies within inclusive markets were revealed through the synthesis of the PAGER framework as four calculated levers for sustainable transformation. However, actors inadequately integrate feed, genetic, climate interactions, and governance mechanisms. According to this review, technological innovation must align with adaptive governance. Climate-resilient, low-carbon beef systems also require the development of inclusive institutional frameworks. Indonesia’s experience demonstrates the benefits of integrating science, policy, and the market to improve productivity, resource stewardship, and equity in tropical livestock systems, thereby enhancing a resilient agri-food supply chain in Indonesia. Full article