Search Results (2,041)

Optical-fibre daylighting systems (OFDS) harvest solar energy as a renewable lighting resource by delivering sunlight deep into green buildings. This emerging technology for sustainable infrastructure reduces electric-lighting demand; however, reported performance is difficult to compare across heterogeneous designs, metrics, and validation practices. Therefore, a PRISMA 2020–reported systematic literature review (SLR) of OFDS studies from three databases (Google Scholar, Scopus, and Web of Science; 2000–2025) was conducted, synthesising primary research that quantifies system- or component-level performance, with a focus on (i) plastic optical fibre (POF) transmission characteristics; and (ii) POF-based illuminance model validation. After de-duplication and screening, 106 primary studies were included, and two meta-analyses were performed where data were harmonised from 29 studies in total. Across reported POF configurations, attenuation ranged from 150 to 800 dB/km with a pooled mean of 332.8 dB/km, corresponding to a mean 1 m transmission of 92.7% and median design length scales of ∼3.7 m for 80% transmission and ∼11.6 m to half-power. Across illuminance validation datasets, models showed high linear agreement with experimental measurements (coefficient of determination ($R^2$) = 0.99; slope = 0.99) but typically underpredicted illuminance (geometric mean ratio = 1.16; mean absolute error (MAE) = 27.3 lux; mean absolute percentage error (MAPE) = 17.6%). These findings underscore the need for a standardised evaluation framework, including consistent metric definitions, robust uncertainty reporting, and reusable validation datasets to enable variance-weighted synthesis, while also identifying short-run POF routing as a key lever for improving system efficiency. In addition to providing the OFDS research agenda, this study serves as a roadmap for the industrial development of daylighting systems for green buildings based on harvesting solar energy, with its novelty lying in the PRISMA-guided evidence synthesis and quantitative meta-analytic consolidation of POF transmission and illuminance-validation performance. Full article

In recent decades, the Ucayali region, the main territory of the Asheninka communities, has experienced increasing socio-environmental pressures associated with climate change, educational inequality, and territorial vulnerability in rural and indigenous contexts. In response, this research proposes the design of a sustainable agricultural school for the Asheninka community, conceived as an educational building that integrates biophilic strategies to enhance environmental performance and spatial quality. The methodological approach comprises a literature review, site-specific environmental analysis based on hydrometeorological data, and the development of an architectural proposal focused on sustainable building design. Digital tools such as Revit and SketchUp were employed alongside official climatic data sources to support design decision-making. The proposal includes twelve biophilic agricultural classrooms incorporating passive design strategies, rainwater harvesting systems with a capacity of 22.5 m³ per day per classroom, and photovoltaic-powered public lighting systems. Results indicate that the integration of natural ventilation, green infrastructure, and locally sourced materials contributes to significant improvements in thermal comfort, humidity control, and energy autonomy within the educational facilities. The architectural complex is complemented by green corridors and collective open spaces that reinforce environmental performance at the site scale. This study demonstrates that sustainable educational buildings adapted to local ecosystems and climatic conditions can function as effective infrastructures for environmental mitigation and resilient rural development, contributing to more sustainable forms of urban and rural living. Full article

To address the challenges of unstable target localization and poor multi-module coordination in automated green pepper harvesting—caused by occlusions from branches and leaves, as well as varying lighting conditions—this paper presents the design and implementation of a modular robotic picking system. At the perception level, the system integrates a YOLOv8 detector with a RealSense D435i camera to identify and locate the calyx–ectocarp junctions of green peppers. An integrated multi-target tracking and filtering framework is proposed, which fuses multi-feature association, trajectory smoothing and coordinate denoising strategies to suppress depth noise and trajectory jitter, thereby enhancing the stability and accuracy of 3D localization. At the control and execution level, a depth-first picking sequence strategy with ID freeze-state management is implemented within a multithreaded software–hardware co-design architecture. This approach avoids task conflicts and duplicate operations while supporting continuous multi-fruit harvesting. Field experiments under natural outdoor lighting and varying occlusion levels demonstrate that the proposed system achieves recognition rates of 91.57% and 80.29% and harvesting success rates of 82.85% and 77.68% for non-occluded and lightly occluded fruits, respectively. The average picking cycle per pepper fruit is 9.8 s. This system provides an effective technical solution for addressing stability control challenges in the automated harvesting process of green peppers. Full article

The increasing demand for innovative strategies in sustainable food production—driven by rapid global population growth, particularly in sub-Saharan Africa (SSA)—necessitates urgent attention to agricultural resilience. Recent technological advancements have enhanced crop productivity, post-harvest preservation, and environmentally sustainable farming practices. However, three critical bottlenecks remain: (i) the lack of accurate, maize-specific yield prediction methods tailored to SSA; (ii) limited multimodal modeling approaches capable of capturing complex, nonlinear interactions among heterogeneous data sources; and (iii) a lack of explainability mechanisms, which render high-performing models “black boxes” and hinder stakeholder trust. To address these gaps, this study presents an explainable machine learning framework for smart maize yield prediction. We integrate multimodal SSA-specific soil, crop, and weather data to capture the multi-dimensional drivers of maize productivity. Six diverse algorithms—including extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), categorical boosting (CatBoost), support vector machine (SVM), random forest (RF), and an artificial neural network (ANN) combined with a k-nearest neighbors (kNN)—were benchmarked to evaluate predictive performance. To ensure robustness against spatial heterogeneity, we employed a Leave-One-Plot-Out (LOPO) cross-validation strategy. Empirical results on unseen test data identify CatBoost as the best-performing model, achieving a coefficient of determination of ($R^2 =~76\%$), demonstrating its ability to capture complex, nonlinear relationships in agricultural data. To enhance transparency and stakeholder trust, we integrated Local Interpretable Model-agnostic Explanations (LIME), providing plot-level insights into the physiological and environmental drivers of maize yield. Together, these contributions establish a scalable and interpretable modeling framework capable of supporting data-driven agricultural decision-making in SSA. Full article

Accurate tea-shoot detection in real tea gardens is essential for intelligent harvesting, yet dynamic illumination (low light, strong light, and shadows) can cause brightness/contrast fluctuations and feature distribution shifts, degrading detection stability and localization accuracy. This paper proposes LA-YOLO, a dynamic-light tea-shoot detector based on YOLOv11. First, we construct a dynamic-light benchmark dataset and a difficulty-stratified evaluation protocol with four single-light subsets (A–D) and a mixed-light subset (E). Second, we design LA-CSNorm, an input-side brightness-adaptive preprocessing module that applies gated enhancement to dark samples followed by channel-selective normalization to reduce illumination-induced drift. Third, we propose RECA, a residual efficient channel-attention module to enhance discriminative channels and improve localization stability. Ablation studies show that LA-CSNorm and RECA provide complementary gains, and their combination improves the YOLOv11 baseline to 0.831 mAP@0.5 and 0.621 mAP@0.5:0.95, with only 0.01 M additional parameters. On the mixed-light subset E, LA-YOLO achieves 0.816 mAP@0.5 and 0.613 mAP@0.5:0.95, and consistently outperforms mainstream YOLO variants (e.g., YOLOv11m) under dynamic lighting conditions. These results demonstrate that LA-YOLO offers a robust and deployment-friendly solution for tea-shoot detection in complex natural illumination. Full article

Solar-driven interfacial evaporation is a sustainable technology for freshwater production; however, the rational design of photothermal materials that simultaneously achieve full-spectrum solar absorption, minimized thermal loss, and efficient energy utilization remains a formidable challenge. Herein, we report a “post-treatment” defect engineering strategy to fabricate highly active, non-stoichiometric BTO (black TiO_2−x) via a hydrothermal-assisted atmospheric deoxygenation process. The precise modulation of oxygen vacancies (O_v) within the TiO₂ lattice effectively narrows its bandgap, facilitating a dramatic enhancement in both light-harvesting capacity and photothermal conversion efficiency. By integrating the BTO into a polyvinyl alcohol (PVA) hydrogel framework, we developed a 3D evaporator (TPVA) that synergistically couples superior optical trapping with attenuated thermal conductivity. Consequently, the O_v-enriched TPVA architecture achieves an impressive solar absorption of 94.3%, enabling a high-performance evaporation rate of 2.492 kg m⁻² h⁻¹ under 1 sun irradiation, which is approximately 5.0 times higher than that of direct seawater evaporation under the same conditions. This work underscores the efficacy of defect engineering in optimizing semiconductor photothermal materials and provides a promising strategy for the advancement of next-generation solar desalination technologies. Full article

Longan is an important economic fruit in tropical and subtropical regions, whose harvesting primarily relies on manual labor. Automated longan harvesting is key to improving the industry’s economic benefits but faces core challenges: mature pericarp is highly similar in color to fruiting mother branches, plus dense branches and severe leaf occlusion, leading to difficult cluster detection and fruiting branch segmentation. Herein, we propose a picking point localization method named PLYS-Longan integrating three customized core modules: Dynamic Convolution, Convolutional Gated Linear Unit (CGLU), and Dynamic Hyperbolic Tangent Activation (DYT) are introduced into YOLongan module to enhance the model’s ability to detect longan clusters. For SELongan module, Depthwise Over-parameterized Convolution (DO-Conv) and Ultra-light Subspace Attention (ULSA) are adopted to improve main branch segmentation precision. The PCLongan module then performs morphological erosion on the segmentation masks and calculates centroids to precisely determine the picking points. Experimental results show that the improved model achieves a mAP@50 of 90.1% (3.3% higher than baseline model) in object detection and a mIoU of 77.24% (1.75% improvement) in semantic segmentation, outperforming the various model significantly. This study provides an efficient and robust solution for longan picking point localization, laying a solid foundation for subsequent automated harvesting. Full article

Mechanized timber harvesting on steep slopes causes soil disturbance; however, comprehensive post-harvest assessment remains challenging because terrain complexity and safety constraints render traditional field-based methods labor-intensive, spatially limited, and difficult to implement systematically. In this study, we developed and evaluated a convolutional neural network-based semantic segmentation model for detecting soil disturbances using high-resolution unmanned aerial vehicle (UAV) imagery in a steep-slope harvested area (2.50 ha, mean slope of 53.4%) in Republic of Korea. A U-Net semantic segmentation model was trained on manually annotated orthomosaic tiles incorporating RGB and digital elevation model (DEM) inputs. Ensemble predictions at an optimized threshold of 0.65 achieved Intersection over Union (IoU) of 0.55 and F1-score of 0.71. Although moderate, these values reflect the inherently challenging conditions of steep-slope forest terrain compared to similar studies conducted under gentler terrain. DEM-derived depth estimation enabled severity classification of the detected disturbances, with light disturbances predominating. Field validation using 38 pinboard measurements demonstrated reliable spatial detection (ρ = 0.567, RMSE = 6.45 cm). This approach provides an effective alternative to traditional monitoring practices in mountainous forests, where systematic trail planning is impractical, and may support evidence-based assessment of harvesting impacts for sustainable forest management. Full article

Reducing input costs, especially feed ingredients, remains a priority for production agriculture. Identifying and selecting nutritionally dense ingredients is vital to maximize animal performance. Objectives of the current study were to evaluate the impact of biochar supplementation on goat carcass characteristics and fresh meat quality. Goats (N = 36) were allocated to a diet concentration formulated with or without (Control, Low, Medium, or High g/kg) biochar. After 60 days of feeding, goats were harvested, and carcass measurements were collected. Subprimals from the leg were fabricated into steaks for laboratory analysis of surface color, cook loss, and instrumental tenderness. Biochar supplementation did not alter organ weights (p = 0.0614), dressing percentage (p = 0.8139), loin eye area (p = 0.9570), or tenderness (p = 0.0144). However, marbling scores were lower in goats fed at the medium biochar supplementation rate (p = 0.0114) and high supplementation (p = 0.0102) compared to the control. An interaction between storage day and biochar supplementation was recorded for instrumental surface color lightness (L*; p = 0.0016), redness (a*; p = 0.0547), hue angle (p = 0.0313), and red-to-brown (p = 0.0591). Steaks from the 0.052% supplementation group exhibited greater (p = 0.0003) redness (a*) during a 7-day refrigerated display and increased chroma values (p < 0.0001). Storage duration influenced all color traits, with steak surface discoloration increasing as storage time increased (p < 0.0001). Results conclude that biochar supplementation does not negatively impact all carcass quality or tenderness traits, but may influence fat deposition and improve meat color stability. Full article

Swine wastewater is a high-strength agricultural effluent characterized by high organic loading, elevated ammonium nitrogen and phosphorus concentrations, and frequently low C/N ratios, which make simultaneous pollutant removal and resource recovery challenging. Conventional physicochemical, anaerobic, and aerobic treatment technologies are widely used, but they are often constrained by high energy demand, ammonia inhibition, insufficient nitrogen recovery under low C/N conditions, and limited resource valorization. This review comparatively evaluates these conventional technologies alongside microalgal and algal–bacterial symbiotic (ABS) systems for swine wastewater treatment and resource recovery. Particular attention is given to algal–bacterial interactions, oxygen and carbon exchange, nitrogen and phosphorus removal pathways, reactor configurations, key operational parameters, and biomass valorization routes. The reviewed evidence shows that conventional anaerobic–aerobic systems generally achieve stable COD removal (>80%) but often provide limited nitrogen recovery, whereas microalgal systems can remove 80–90% of nitrogen and phosphorus but remain restricted by ammonia toxicity, light attenuation, and biomass harvesting costs. Under optimized conditions, ABS granular systems have achieved >90% COD removal, >80% total nitrogen removal, and 70–95% total phosphorus removal, while also improving biomass settleability and process stability. Overall, ABS systems offer a promising route to shift swine wastewater treatment from discharge-oriented pollution control toward resource-oriented management. Future research should prioritize reactor scale-up, long-term operational stability, biological monitoring, and economically viable biomass valorization. Full article

One-dimensional hybrid organic–inorganic semiconductors enable band-edge engineering through reduced dimensionality and interfacial orbital hybridization. Nevertheless, the electronic physics of Cu/Ag-based systems has received limited attention. Here, we perform high-throughput first-principles calculations on 90 Cu/Ag halide HOISs derived from experimentally reported parent structures to elucidate bonding-dependent electronic behavior. We uncover a clear transition from electronically isolated inorganic chains in ionic hybrids to strongly hybridized band edges in covalent and mixed-bonding hybrid frameworks, where ligand p orbitals cooperatively couple with Cu-derived states and halogen p orbitals. This hybridization produces p-orbital-dominated band edges, enhanced dispersion, and light-hole effective masses along the 1D chains. Guided by this bonding-driven mechanism, we further identify four Cu-based compounds, which are helpful for tuning light-harvesting properties in low-dimensional hybrid semiconductors. Full article

This study investigated the effects of four LED light qualities, red+blue+far-red (WRS-LED), blue+red (BR-LED), blue (B-LED), and red (R-LED), and exogenous L-glutamic acid at 10 ppm on the growth and quality of red mustard spinach (Brassica rapa var. perviridis) cultivated in a plant factory using a recirculating deep-flow hydroponic system. Plants were exposed to four LED light quality treatments at 180 ± 10 μmol·m⁻²·s⁻¹ PPFD for 28 days after transplanting. L-glutamic acid at 10 ppm was applied once to the recirculating nutrient solution 15 days after transplanting, resulting in 13 days of exposure prior to final harvest on day 28. All growth and quality parameters were measured at the final harvest after 28 days of cultivation. WRS-LED promoted the greatest biomass production. Additionally, vitamin C content, DPPH radical scavenging activity, and total phenolic content were highest under BR-LED and B-LED conditions. Notably, under B-LED, L-glutamic acid treatment increased total phenolic content to approximately twice that of the control. Leaf redness, expressed as Hunter a* values, was observed exclusively under BR-LED. Principal component analysis revealed that LED light quality was the primary determinant of treatment responses, with growth-related traits associated with WRS-LED and R-LED, and quality-related traits with B-LED and BR-LED. Overall, BR-LED combined with L-glutamic acid represents the most suitable treatment for red mustard spinach cultivation in plant factories, achieving a favorable balance between growth and nutritional quality. Full article

Light availability is a key environmental factor influencing plant functional traits and ecological strategies. To investigate how natural populations of Iris pumila respond to contrasting irradiance, we conducted an in situ reciprocal transplant experiment using clonal genotypes from two natural populations, each originating from an open dune and a shaded forest habitat. Leaves collected from each of the replanted and transplanted genotypes were analyzed for structural (specific leaf area—SLA, leaf dry matter content—LDMC), physiological (specific leaf water content—SLWC, photosynthetic pigments) and biochemical (peroxidase—POD, glutathione reductase—GR, phenolics and anthocyanins) traits. Shade-grown individuals developed thinner leaves with higher SLA and chlorophyll content, enhancing light-harvesting efficiency, whereas sun-exposed plants exhibited greater LDMC, increased POD and GR activities and higher anthocyanin levels—traits consistent with enhanced photoprotection under high irradiance. All genotypes exhibited pronounced plasticity to light intensity, with habitat exerting a stronger influence on trait expression than population origin. To evaluate oxidative balance, we proposed the ODAC index (Oxidative Damage to Antioxidant Capacity), which integrates lipid peroxidation with antioxidant capacity. ODAC values revealed consistent population-level differences, with higher values in Dune genotypes across habitats, indicating a constitutively elevated oxidative load relative to antioxidant protection and suggesting differentiation in redox regulation between populations. Overall, leaf trait variation in I. pumila appears to be primarily driven by plastic responses to light conditions, while differentiation in oxidative physiology contributes to functional divergence between populations. Full article

Light spectral composition plays a central role in regulating plant growth, morphology, nutrient uptake, and pigment biosynthesis, particularly in controlled-environment agriculture. This study investigated the effects of targeted LED spectral modulation, focusing on green light deprivation and different red-to-blue (R:B) ratios at constant photon flux density, on morphological traits, mineral composition, and photosynthetic pigments in Salvia officinalis L. and Cannabis sativa L. grown under controlled conditions. Plants were cultivated under three LED treatments providing equal light intensity but differing in spectral composition. Morphological parameters, mineral nutrients, inorganic anions, and photosynthetic pigments were assessed at harvest. Total biomass production was not significantly affected by the light treatments in either species; however, clear species-specific responses were observed. In S. officinalis, higher R:B ratios promoted stem elongation without affecting leaf number or fresh weight, whereas in C. sativa, the higher R:B ratio significantly increased leaf number. Green light deprivation and red–blue enrichment generally enhanced mineral accumulation and nitrogen content, although the magnitude and direction of these effects varied between species. Photosynthetic pigment responses were more pronounced in hemp, with increased chlorophylls and carotenoids under green light deprivation, while salvia showed a selective increase in carotenoids under higher R:B ratios. Overall, these findings emphasize the importance of species-specific LED spectral optimization to improve physiological performance and nutritional quality in indoor cultivation of medicinal plants. Full article

Dye-sensitized solar cells (DSCs) have garnered significant attention due to their high power conversion efficiency and low production cost-effectiveness. In this study, we developed a hierarchically structured three-layer TiO₂ photoanode via hydrothermal synthesis to significantly enhance DSC performance. The optimized device achieved a short-circuit current density of 16.92 mA/cm² and a photoelectric conversion efficiency of 8.34%, representing improvements of 15.67% and 20.5%, respectively, compared to traditional DSCs with a single-layer TiO₂ photoanode in our study. The significance lies in the rational design principle rather than absolute efficiency. This performance enhancement stems from the complementary functions of each architectural layer: (1) a bottom layer of TiO₂ nanocrystals providing high surface area for dye adsorption, (2) an intermediate layer of vertically aligned TiO₂ nanorods enabling efficient electron transport, and (3) a top layer of TiO₂ microspheres simultaneously boosting dye loading and light harvesting through enhanced light scattering. Our findings demonstrate that rational design of multi-layered photoanode architectures can effectively address the competing demands of surface area, charge transport, and light management in high-performance DSCs. Full article