Search Results (4,081)

It is well-known nowadays that the usage of conventional opaque photovoltaics in agricultural practices has negative effects on crops growth, mainly due to the shading effect they cause. On the other hand, most of the emerging semi-transparent solar cells do not demonstrate the appropriate optical characteristics and scalability to attain their viable integration in agriculture, undermining their commercialization. This study deals with the development of wavelength-selective semi-transparent dye-sensitized solar cells (DSSCs) using the scalable slot-die deposition method. These devices are designed to provide high transparency in the photosynthetically active radiation (PAR) region and effectively exploit the near-ultraviolet to blue-visible spectrum for power production, simultaneously protecting cultivations from harmful short-wavelength irradiation. To this aim, a new quinoline-based dye and a highly transparent iodine-free electrolyte were employed in DSSCs, giving an external quantum efficiency of 70% for wavelengths up to 500 nm and a PAR transmittance on the level of 50% (55% crop growth factor). Additionally, the light-to-electricity conversion efficiency of these devices is high for both front- and rear-side illumination under all-weather irradiation conditions (up to 94% bifaciality factor). Finally, two new figures-of-merit (greenhouse compatibility factor, agrivoltaic performance factor) are introduced to quantify the balance of photovoltaic performance and agronomic functionality. Full article

The purpose of the present study was to evaluate the effects of zirconia material type, substructure color, cement color, and aging (before and after aging) on the color change of laminate veneers (LVs) prepared from different multilayer translucent zirconia ceramics. LV preparation was performed on a phantom tooth, and the preparation was digitized to produce resin abutments in two shades (light: A1/B1; medium: A2/A3) to simulate different tooth colors. LVs were designed using dental design software and fabricated from three multilayer zirconia ceramics (multilayered super-high-translucent 5Y-TZP zirconia, multilayered high-translucent 4Y-TZP zirconia, and multilayered ultra-translucent 5Y-TZP zirconia), resulting in 12 experimental groups (n = 10). They were then cemented with either clear or white resin cement and subsequently subjected to 10,000 cycles of thermal aging. The color parameters were measured at three time points (before cementation, before aging, and after aging), and the color change values (ΔE₀₀) were calculated. The data were statistically analyzed, and the results were compared with the perceptibility threshold (ΔE₀₀ = 0.8) and the clinical acceptability threshold (ΔE₀₀ = 1.8). Four-way ANOVA revealed an interaction among zirconia material, substructure color, cement shade, and measurement time (p = 0.02). Color change values after cementation (ΔE₀₀-1) across all groups showed a statistically significant difference between the white and clear cement groups. Before aging, the color change values observed in the 4Y-TZP (DD Cube One ML)-medium abutment–white cement (ΔE₀₀-1 = 1 ± 0.2) and 5Y-TZP (Katana UTML)-medium abutment–white cement (ΔE₀₀-1 = 1.6 ± 0.7) groups were above the perceptible threshold value but were clinically acceptable (0.8 < ΔE₀₀ < 1.8). The color change values observed after aging (ΔE₀₀-2) across all experimental groups were clinically unacceptable (ΔE₀₀ > 1.8). The color changeof ultra-translucent zirconia LVs was influenced by zirconia material type, cement shade, substructure color, and aging, with the effect of the zirconia material itself being less pronounced. Aging further reduced color stability, resulting in increased ΔE₀₀ values and clinically unacceptable color differences. Full article

Background/Objectives: Myocardial bridging is a common anatomic variant in which an epicardial coronary artery takes an intramyocardial course. A true intracameral, or intracavitary, coronary course is rare. On coronary CT angiography (CTA), multiplanar and curved planar reformations answer most of these questions, but some cases stay equivocal because of partial-volume effects, cardiac motion, and loss of depth cues. A vessel next to a thin or fat-infiltrated wall can also mimic an intracameral course. Cinematic rendering (CR) is a photorealistic three-dimensional post-processing method that uses Monte Carlo path tracing with global illumination to reproduce natural depth and soft-tissue shading. Methods: In this retrospective, single-center, four-patient case series, we describe patients in whom CR was used alongside standard two-dimensional and reformatted CTA after the planar reconstructions had been read as equivocal. The cases were purposively selected as illustrative examples rather than consecutively enrolled, and the report is intended to be hypothesis-generating. Results: In two patients with left anterior descending (LAD) myocardial bridging, CR showed the tunneled segment with depth and was useful for discussion with referring clinicians and for teaching. In two patients whose coronary segment looked intracameral on planar images, CR showed intact overlying myocardium, which favored an intramyocardial course over chamber entry. Conclusions: In these selected patients, CR did not add anatomic information that was unavailable on expert reformations. It appeared to improve perceived depth and reader confidence in these equivocal cases and was useful for communication and teaching; no quantitative image analysis or formal reader study was performed. Because this is a small, retrospective case series without a comparison group, these observations are illustrative and hypothesis-generating and cannot be extrapolated to coronary CTA in general; prospective comparative studies are needed to determine whether CR adds diagnostic value over standard reconstructions. Full article

In the realm of precision agriculture, agricultural UAV path planning is challenging because the UAV must avoid obstacles, follow uneven terrain, and satisfy multiple flight constraints simultaneously. Differential evolution (DE) has been widely adopted for this problem because of its simple structure and effective optimization capability. However, existing DE-based methods often become trapped in local optima and cannot effectively balance exploration and exploitation in complex search environments. To address these issues, this paper proposes a reinforcement learning-based multi-strategy differential evolution algorithm, named PPOMSDE. By introducing Proximal Policy Optimization (PPO) to construct a multi-dimensional state pool and an action pool, PPOMSDE enables adaptive strategies for individuals, improving strategy selection during the search process. An independent multi-buffer is adopted to ensure strict data isolation and efficient learning to avoid strategy confusion. In addition, an adaptive triplet mechanism which partitions the population into fitness-based tiers (best, medium, and worst) assigns different control parameters and mutation strategies to individuals with different fitness levels, improving the balance between global exploration and local exploitation. Extensive experiments on the CEC’2014 and CEC’2017 benchmark suites demonstrate the effectiveness of PPOMSDE. The proposed method achieves the lowest average performance ranks of 1.39 on the combined 10-D and 30-D CEC’2014 benchmarks and 1.03 on the 10-D CEC’2017 benchmarks. In agricultural UAV path planning, PPOMSDE generates safer and smoother flight paths while maintaining accurate terrain-following flight, reducing the overall cost by an average of 22.42% compared with ISDE, L-SHADE, SHADE, and ISHACDE. Full article

Eclipta prostrata is a medicinal plant widely used in traditional medicine because of its antioxidant and therapeutic properties; however, comprehensive information on the effects of drying methods on tissue-specific phytochemical composition remains limited. This study evaluated the influence of freeze drying (FD), shade drying (SD), and oven drying (OD) on the antioxidant activity, phenolic and flavonoid contents, elemental composition, and metabolomic profiles of flowers, leaves, roots, and stems of E. prostrata. Antioxidant activity was determined using DPPH, ABTS, and FRAP assays; elemental composition was analyzed by ICP-OES; and untargeted metabolomic profiling was performed using LC-QTOF-MS in both positive and negative ionization modes. Freeze-dried samples consistently exhibited the highest antioxidant capacity and retained the greatest total phenolic and flavonoid contents, particularly in flowers, whereas oven drying generally resulted in the greatest reduction in antioxidant activity, especially in roots. ICP-OES analysis demonstrated tissue-dependent differences in mineral composition, with calcium, potassium, magnesium, sodium, iron, zinc, and manganese being the predominant essential elements retained after drying. Untargeted metabolomics revealed tissue-specific metabolic responses to drying, with root tissues exhibiting the greatest metabolomic variation, whereas flowers and stems remained comparatively stable. Multivariate analyses indicated that drying primarily altered the relative abundance of metabolite classes rather than the overall metabolome. The major metabolites detected belonged to flavonoids, coumestans, phenolic acids, lipid-derived metabolites, amino acid derivatives, oxylipins, and triterpenoid/saponin-related compounds, which are associated with the medicinal properties of E. prostrata. These findings demonstrate that both plant tissue and drying method significantly influence the retention of bioactive compounds and provide valuable information for optimizing post-harvest processing to preserve the phytochemical quality and therapeutic potential of E. prostrata. Full article

Utility-scale ground-mounted photovoltaic development is expanding across alpine dryland grasslands, creating engineered microhabitats with contrasting shading regimes, surface exposure, and soil hydro-physical conditions. Microhabitat-specific responses of vegetation and soil carbon pools remain poorly quantified in these systems. We assessed carbon-pool responses in the Talatan photovoltaic park on the northeastern Qinghai–Tibet Plateau using 109 plot-level observations across five microhabitats: reference grassland (REF), fixed-panel shaded microhabitat (FS), fixed-panel interspace microhabitat (FI), horizontal single-axis tracking microhabitat (HSA), and tilted single-axis tracking microhabitat (TSA). We estimated aboveground biomass carbon (AGB-C), belowground biomass carbon (BGB-C), 0–30 cm soil organic carbon stock (SOC stock), and total ecosystem carbon storage (TEC). Microhabitat contrasts were evaluated relative to REF, and standardized association models were used to examine relationships between SOC stock, soil moisture, soil fines, and vegetation carbon pools. Carbon responses differed by microhabitat position and carbon-pool compartment. FS showed the clearest vegetation carbon contrast, with AGB-C 29.2% lower than REF and BGB-C 29.8% lower with borderline statistical support. In contrast, FS showed smaller, more uncertain contrasts for SOC stock (−3.2%) and TEC (−6.3%). The SOC stock association model explained 46% of the variance, with positive coefficients for soil moisture (β = 0.496), soil fines (β = 0.256), AGB-C (β = 0.239), and BGB-C (β = 0.171). These findings indicate that photovoltaic carbon assessment in alpine dryland grasslands should distinguish microhabitat position, carbon-pool compartment, and soil hydro-physical background to identify where carbon responses occur and through which carbon pools they are expressed. Full article

With heatwaves becoming increasingly frequent and intense in recent years, summer comfort is now a major issue for the design of low-energy buildings. Currently, most French homes are not equipped with air conditioning. To prevent the widespread adoption of this energy-intensive equipment while limiting indoor thermal discomfort during heatwaves, it is crucial to accurately design and assess climate adaptation measures. Occupants actively employ various strategies to limit overheating inside their dwellings, adapting their actions on the specific constraints they face. However, they are still frequently treated as passive entities in building performance simulations. This paper introduces an agent-based model, MOBAPY, developed to simulate the summer adaptive behaviour of households in urban dwellings. Grounded in qualitative data derived from semi-structured interviews, the model integrates commonly reported actions for one occupant profile, including the operation of windows, solar shading devices, air conditioning, and fans, alongside clothing adjustments. MOBAPY is applied to one case study: an urban dwelling, simulated under multiple climate scenarios and varying behavioural constraints within a building energy modelling framework. The results highlight the significant impact of occupant behaviour on summer comfort results for this occupant profile in a well-insulated dwelling. Notably, under the most severe future climate scenario, the percentage of uncomfortable occupied time drops to 20% when behaviour is unconstrained, whereas it surges to 68% under highly constrained conditions. Full article

This paper presents an experimental comparative study of four advanced maximum power point tracking (MPPT) strategies—backstepping, adaptive backstepping, sliding mode control, and vision-based MPPT—combined with three wireless communication technologies: IEEE 802.15.4, Wi-Fi, and 3G. The comparison is performed on a distributed photovoltaic (PV) platform under common power-stage conditions in harmonized irradiance scenarios, including abrupt uniform-irradiance transients and controlled partial shading patterns. Under uniform irradiance, adaptive backstepping achieved the best overall dynamic behavior, with the shortest average convergence time of 0.045 s using IEEE 802.15.4, compared with 0.052 s for Wi-Fi and 0.115 s for 3G, while all tested configurations maintained tracking efficiencies above 98.8%. Under partial shading, the ranking changed substantially: the vision-based MPPT provided the best GMPP-oriented performance, reaching the highest tracking success rate and the lowest energy loss. In particular, its lost energy increased from 0.20% with IEEE 802.15.4 to 0.45% with 3G, whereas conventional backstepping increased from 0.65% to 1.35% over the same communication range. Latency measurements showed that IEEE 802.15.4 exhibited the lowest median end-to-end delay and the smallest dispersion, Wi-Fi showed intermediate behavior, and 3G introduced the largest latency and variability. The results demonstrate that MPPT performance in distributed PV systems depends on both the control strategy and the communication architecture and that a unified experimental assessment of both layers is required to identify the optimal MPPT–communication combination for each operating scenario. Full article

Colorectal cancer remains a leading cause of cancer-related mortality worldwide, with automated polyp classification from endoscopic images offering a promising avenue for improving early detection. Existing approaches rely on single convolutional neural network (CNN) backbones with manually designed classification heads, limiting both representational capacity and deployment flexibility. This paper presents a swarm intelligence-augmented multi-backbone deep learning framework for eight-class gastrointestinal lesion classification on the Kvasir benchmark. Four CNN backbones (ResNet50, DenseNet121, MobileNetV2, EfficientNetB3) are independently fine-tuned using a two-phase transfer learning protocol and their penultimate-layer features concatenated into a 5888-dimensional representation, reduced to 256 dimensions via PCA. Five swarm intelligence algorithms—Particle Swarm Optimization, Artificial Bee Colony, JADE, L-SHADE, and CMA-ES—are benchmarked on the classification head architecture search task; all independently converge to tanh activation, a consistent pattern across independently initialized algorithms that is suggestive of, though not conclusive evidence for, particular geometric properties of PCA-transformed deep feature spaces. The PSO-optimized single-layer head (284 units, tanh) outperforms a manually designed three-layer baseline by 0.75% while using 67% fewer parameters. SI-guided class weight optimization yields targeted F1 improvements on the two most clinically significant classes (polyps: +0.015, ulcerative-colitis: +0.013). The fixed-head classifier trained on fused four-backbone features achieves 91.08% accuracy on Kvasir v2 (multi-seed mean 91.47% ± 0.49 across nine converging seeds; one seed failed to converge and is disclosed rather than excluded), below end-to-end DenseNet121 (92.25%; Wilcoxon p = 0.31, not statistically significant), while enabling classifier updates in under 30 s; a three-backbone subset dropping the weakest backbone (EfficientNetB3) reaches 92.33%, exceeding the full four-backbone fusion. Cross-dataset evaluation on Kvasir v1-to-v2 confirms near-zero generalization gaps across dataset scales; a restricted two-class evaluation on HyperKvasir (the only two of eight classes with usable labeled data) reaches 96.28% accuracy, and dual Grad-CAM with SI minimal sufficient region analysis, validated quantitatively against Kvasir-SEG ground-truth masks, provides spatially grounded, clinically interpretable explanations. Full article

Smart mobility strategies often emphasize digital information, applications, and data-driven transport management, while giving less attention to the stop-level conditions that shape passengers’ everyday experience. This study develops and applies a Smart Bus Stop Readiness Index (SBSRI) to assess seven interchange bus stops in Jeddah, Saudi Arabia. The index integrates five weighted dimensions: Passenger Information and Digital Readiness; Physical and Thermal Comfort Provision; Pedestrian Accessibility and Universal Design; Safety and Security; and Land-Use and Activity Integration. Data were collected through field audits, spatial mapping, passenger observations, and a short survey of 71 users. The results indicate that the selected stops have operational interchange importance but generally limited readiness. The mean SBSRI score was 39.07/100; under the adopted planning-oriented classification scheme, only Al-Balad Main Station A achieved moderate readiness, while the remaining stops were classified as showing low or very low readiness. Physical and Thermal Comfort Provision was the weakest dimension, particularly in relation to shade, seating, shelter, and shaded waiting areas. Passenger information and pedestrian accessibility also showed substantial deficiencies. Sensitivity analysis indicated that the principal stop rankings remained stable under alternative weighting scenarios, although category labels were more responsive to threshold selection. This study concludes that smart bus stop readiness should be assessed as a socio-technical condition integrating digital systems with climate-responsive waiting provision, pedestrian accessibility, safety, and the surrounding urban context. Full article

This paper proposes a multi-strategy-enhanced Octopus Optimization Algorithm (OOA) for hyperparameter optimization in hyperspectral image classification. Hyperspectral images pose significant challenges due to their numerous spectral bands, high dimensionality, and complex spectral differences between classes, which complicate classification modeling. The classification performance of support vector machine (SVM) classifiers is also highly dependent on parameter settings. The original OOA is extended by incorporating an initialization strategy based on elite backpropagation, a multi-stage nonlinear adaptive parameter control mechanism, an elite-guided differential mutation strategy, a Lévy flight restart mechanism with stagnation monitoring, and a stable boundary handling strategy. These enhancements constitute the IOOA-SVM parameter optimization framework. The proposed method is evaluated against OOA, Particle Swarm Optimization (PSO), Sand Cat Swarm Optimization (SCSO), Salp Swarm Algorithm (SSA), Grey Wolf Optimizer (GWO), Arithmetic Optimization Algorithm (AOA), Differential Evolution (DE) and Linear Population Size Reduction Success-History Based Adaptive Differential Evolution (L-SHADE) on the CEC2017 test set, achieving superior results on most of the 29 test functions, IOOA achieved the best results on average for 27 of the 29 test functions, outperforming the original OOA on all 29 test functions and demonstrating superior performance on most stability metrics. Different improvement strategies yield varying degrees of performance gains for the algorithm; among them, the elite-guided differential mutation strategy produces the most significant performance improvement. The synergy and complementarity among multiple strategies play a major role in enhancing the performance of the Improved Octopus Optimization Algorithm. Experimental results show that the SVM classifier optimized using the improved OOA achieves a classification accuracy of 97.3731%, representing a 0.2278 percentage point improvement over the original algorithm and demonstrating strong overall optimization performance. Full article

Monitoring vegetation-index dynamics in tropical regions remains challenging due to persistent cloud contamination, landscape heterogeneity, and the lack of standardised and reproducible analytical workflows. This paper presents an open-source, fully reproducible end-to-end methodology for multitemporal vegetation monitoring using Sentinel-2 Level-2A (L2A) Bottom-of-Atmosphere (BOA) reflectance imagery. The methodology was applied to Military Grid Reference System (MGRS) tile T18NVL in the Colombian Eje Cafetero region (4.43°N–5.43°N, 74.91°W–75.90°W) for the 2017–2025 period. The proposed workflow integrates storage-efficient direct extraction of spectral reflectance from compressed Standard Archive Format for Europe (SAFE) archives using the Geospatial Data Abstraction Library (GDAL) /vsizip/ interface, per-pixel cloud and shadow masking based on the Sentinel-2 Scene Classification Layer (SCL), computation of Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil-Adjusted Vegetation Index (SAVI), and Normalized Difference Moisture Index (NDMI) spectral indices, a diagnostic Random Forest experiment based on threshold-labelled spectral classes, non-parametric Mann–Kendall trend analysis with Sen’s slope estimation, and external agreement assessment against European Space Agency (ESA) WorldCover 10 m 2020 and Google Earth Pro reference data. The methodology reduced per-scene I/O time by approximately 98% without additional disk overhead while retaining a median of 57.9% valid pixels under a mean scene cloud fraction of 35.4%. Mann–Kendall analysis detected no statistically significant long-term trend in any of the four vegetation indices. Seasonal NDVI peaks during September–November were consistent with the bimodal regional precipitation regime, supporting temporal coherence in the satellite-derived vegetation-index response. External agreement was low, with an Overall Accuracy (OA) of 20.2% against ESA WorldCover and 19.9% against Google Earth Pro, indicating systematic over-prediction of woody and mixed-canopy vegetation classes. These results show that single-date optical spectral indices are insufficient for reliable thematic separation of shade-grown coffee, secondary forest, and dense forest within heterogeneous tropical landscapes. The complete version-controlled codebase is publicly available to support methodological reproducibility and adaptation across data-scarce tropical regions. Full article

In this study, three-year-old seed-derived seedlings of Horsfieldia hainanensis Merr. were subjected to 0%, 25%, 50%, and 75% shade treatments in a nursery shade-net experiment from October 2025 to June 2026. Each treatment included three replicates, with three seedlings per replicate. The results showed that the light environment significantly affected growth. Seedlings under 0% shade showed severe wilting and mortality; therefore, this treatment was treated as a mortality outcome and excluded from subsequent statistical comparisons, which were conducted among the 25%, 50%, and 75% shade treatments. Within this range, plant height, ground diameter, crown width, leaf traits, and above- and belowground biomass increased with shade level, with the highest values observed under 75% shade. The 75% shade treatment was associated with higher maximum net photosynthetic rate (Pmax), apparent quantum yield (AQY), initial carboxylation efficiency (α), and CO₂-saturated net photosynthetic rate (Amax), together with lower light compensation point (LCP) and carbon dioxide compensation point (CDCP), indicating improved light- and CO₂-response performance within the tested shade range. Leaf thickness, epidermal thickness, and palisade and spongy parenchyma thicknesses increased with shade level, whereas variation in stomatal traits was mainly reflected in stomatal density. Chlorophyll a, chlorophyll b, total chlorophyll, and carotenoids increased under stronger shading, showing clear variation in pigment accumulation along the shade gradient. Growth, biomass, photosynthetic capacity, leaf anatomical traits, and pigment contents were positively correlated, whereas the light saturation point (LSP), LCP, carbon dioxide saturation point (CDSP), and CDCP were generally negatively associated with these traits. Among the surviving treatments tested, the 75% shade treatment produced the best seedling performance and may be suitable for nursery cultivation, although further validation at different sites and in different seasons is needed. These findings provide practical guidance for artificial propagation, seedling production, plantation establishment, and light-environment management of this species. Full article

Shade stress severely constrains soybean yield in soybean-maize intercropping systems, yet the intensity threshold triggering shade avoidance responses and the underlying hormonal mechanisms remain elusive. Here, through two-year field experiments, we demonstrate that shading coverage rate exceeding 80% is the critical threshold initiating shade avoidance, increasing plant height by 16–61%. The seventh internode is the initial responsive site where epidermal cells elongate by 69.86% longitudinally, while radial growth is broadly suppressed (cell area reduced by approximately 41%). Furthermore, integrated hormonal profiling and gene expression analyses demonstrate that shade promotes active gibberellin (GA) accumulation via dual metabolic reprogramming: upregulating biosynthetic genes GmGA20ox1 and GmGA3ox1 and downregulating catabolic genes GmGA2ox–7a and GmGA2ox–7b, leading to a 56.87% increase in GA₁ content. Notably, exogenous GA fully mimics the shade-induced phenotype, with genotype sensitivity ranking BX10 > BD2 > W82, indicating that GA plays a central role in mediating the shade response. Yield analysis shows that shade inhibits soybean biomass accumulation and grain yield and preferentially suppresses reproductive rather than vegetative growth, reducing effective pod number and seeds per plant by ~50% whereas 100-seed weight only decreases by 6.55%. Together, these findings reveal that shade modulates GA homeostasis via “enhanced biosynthesis and suppressed catabolism” to remodel internodes, providing a theoretical basis for shade-tolerant soybean breeding and optimizing intercropping systems. Full article

Hyperspectral image (HSI) clustering assigns unlabeled pixels to land-cover groups by jointly exploiting spectral and spatial observations. Existing Vision Transformer-based deep clustering captures global dependencies through self-attention. However, the quadratic computational complexity of self-attention restricts practical applications in large HSI scenes. Furthermore, illumination variation and topographic shading shift spectral amplitude of co-class pixels toward divergent directions in feature space, enlarging intra-class distances and reducing inter-class separability in learned embeddings. To address the above limitations, we propose a self-supervised Spatial–Frequency Interaction and Amplitude–Phase Decoupling framework, termed SFI-APD, which integrates a High-Order Spatial–Frequency Interaction Module (HSFIM), a Frequency Feature Attention Block (FFAB), and a Frequency-Domain Vision Transformer (FreqViT) into a unified architecture. Specifically, HSFIM couples local convolutions with Fourier filtering to extract enriched spectral–spatial representations. FFAB then decouples amplitude and phase components to suppress brightness variations, yielding illumination-robust embeddings. Finally, FreqViT performs attention modulation across spectral channels, reducing token aggregation complexity from $\mathcal{O}\left(N^{2}D\right)$ to $\mathcal{O}\left(ND\log N\right)$. On the Indian Pines, Salinas, Pavia University, and Yangzhou datasets, SFI-APD achieves OAs of 57.47%, 79.38%, 54.57%, and 64.11%, respectively, outperforming state-of-the-art self-supervised methods for large HSIs. Full article