Search Results (14,754)

This study proposes CEL-DL-Bagging (Cross-Entropy Loss-optimized Deep Learning Bagging), a multi-model fusion framework that integrates cross-entropy loss-weighted voting with Bootstrap Aggregating (Bagging). First, we develop a lightweight recognition architecture by embedding a salient position attention (SPA) mechanism into four base networks (YOLOv5s-cls, EfficientNet-B0, MobileNetV3, and ShuffleNetV2), significantly enhancing discriminative feature extraction for disease patterns. Our experiments show that these SPA-enhanced models achieve consistent accuracy gains of 0.8–1.7 percentage points, peaking at 97.86%. Building on this, we introduce DB-CEWSV—an ensemble framework combining Deep Bootstrap Aggregating (DB) with adaptive Cross-Entropy Weighted Soft Voting (CEWSV). The system dynamically optimizes model weights based on their cross-entropy performance, using SPA-augmented networks as base learners. The final integrated model attains 98.33% accuracy, outperforming the strongest individual base learner by 0.48 percentage points. Compared with single models, the ensemble learning algorithm proposed in this study led to better generalization and robustness of the ensemble learning model and better identification of rice diseases in the natural background. It provides a technical reference for applying rice disease identification in practical engineering. Full article

Soil environmental quality in arid oases is crucial for regional ecological security but faces multi-source heavy metal (HM) contamination risks. This study aimed to (1) characterize the spatial distribution of soil HMs (As, Cd, Cr, Cu, Hg, and Zn) in the Ka Shi gar oasis, Xinjiang, (2) quantify the driving effect of irrigation water, and (3) elucidate interactions between HMs, soil properties, and land use types. Using 591 soil and 12 irrigation water samples, spatial patterns were mapped via inverse distance weighting interpolation, with drivers and interactions analyzed through correlation and land use comparisons. Results revealed significant spatial heterogeneity in HMs with no consistent regional trend: As peaked in arable land (5.27–40.20 μg/g) influenced by parent material and agriculture, Cd posed high ecological risk in gardens (max 0.29 μg/g), and Zn reached exceptional levels (412.00 μg/g) in gardens linked to industry/fertilizers. Irrigation water impacts were HM-specific: water contributed to soil As enrichment, whereas high water Cr did not elevate soil Cr (indicating industrial dominance), and Cd/Cu showed no significant link. Interactions with soil properties were regulated by land use: in arable land, As correlated positively with EC/TN and negatively with pH; in gardens, HMs generally decreased with pH, enhancing mobility risk; in forests, SOM adsorption immobilized HMs; in construction land, Hg correlated with SOM/TP, suggesting industrial-organic synergy. This study advances understanding by demonstrating that HM enrichment arises from natural and anthropogenic factors, with the spatial heterogeneity of irrigation water’s driving effect critically regulated by land use type, providing a spatially explicit basis for targeted pollution control and sustainable oasis management. Full article

Seeds of Cucurbitaceae crops represent a promising yet underexplored source of bioactive compounds with potential applications beyond nutrition, particularly in the cosmetics industry. This review examines the seeds of Citrullus lanatus (watermelon), Cucumis melo (melon), and Cucurbita pepo (pumpkin), focusing on their biochemical composition and evaluating their functional value in natural cosmetic development. Although these fruits are widely consumed, industrial processing generates substantial seed by-products that are often discarded. These seeds are rich in polyunsaturated fatty acids, proteins, carbohydrates, and phytochemicals, positioning them as sustainable raw materials for value-added applications. The incorporation of seed-derived extracts into cosmetic formulations offers multiple skin and hair benefits, including antioxidant activity, hydration, and support in managing conditions such as hyperpigmentation, acne, and psoriasis. They also contribute to hair care by improving oil balance, reducing frizz, and enhancing strand nourishment. However, challenges such as environmental instability and low dermal permeability of seed oils have prompted interest in nanoencapsulation technologies to improve delivery, stability, and efficacy. This review summarizes current scientific findings and highlights the potential of Cucurbitaceae seeds as innovative and sustainable ingredients for cosmetic and personal care applications. Full article

Archaeological research fundamentally relies on detecting features to uncover hidden historical information. Airborne (aerial) LiDAR technology has significantly advanced this field by providing high-resolution 3D terrain maps that enable the identification of ancient structures and landscapes with improved accuracy and efficiency. This technical note comprehensively reviews 45 recent studies to critically examine the integration of Machine Learning (ML) and Deep Learning (DL) techniques, particularly Convolutional Neural Networks (CNNs), with airborne LiDAR derivatives for automated archaeological feature detection. The review highlights the transformative potential of these approaches, revealing their capability to automate feature detection and classification, thus enhancing efficiency and accuracy in archaeological research. CNN-based methods, employed in 32 of the reviewed studies, consistently demonstrate high accuracy across diverse archaeological features. For example, ancient city walls were delineated with 94.12% precision using U-Net, Maya settlements with 95% accuracy using VGG-19, and with an IoU of around 80% using YOLOv8, and shipwrecks with a 92% F1-score using YOLOv3 aided by transfer learning. Furthermore, traditional ML techniques like random forest proved effective in tasks such as identifying burial mounds with 96% accuracy and ancient canals. Despite these significant advancements, the application of ML/DL in archaeology faces critical challenges, including the scarcity of large, labeled archaeological datasets, the prevalence of false positives due to morphological similarities with natural or modern features, and the lack of standardized evaluation metrics across studies. This note underscores the transformative potential of LiDAR and ML/DL integration and emphasizes the crucial need for continued interdisciplinary collaboration to address these limitations and advance the preservation of cultural heritage. Full article

This study explores the use of chlorography as a natural photographic developing technique that utilizes the decomposition of chlorophyll and other plant pigments through the action of sunlight. The developed images corresponded to previous research on changes in the iconography of the indigenous Salasaka people. In this context, this experimental project on natural photography is oriented toward the conservation of the ancestral knowledge of this community and the understanding of the native flora of Ecuador. We investigated the application of the contact image transfer technique with positive transparencies on leaves and flowers of 30 different species that grow in the Ecuadorian highlands, including leaves of vascular plants, as well as rose petals. The results showed that the clarity and contrast of chlorography depended on the plant species and exposure time. It was observed that fruit-bearing species produced more visible images than the leaves of other plants and rose petals, with species from the Passifloraceae family proving particularly effective. We interpreted these findings within the framework of plant photophysical mechanisms, proposing an inverse relationship between development efficiency and species’ non-photochemical quenching (NPQ) capacity. Furthermore, we interpreted the findings in relation to the photobleaching of pigments and compared chlorography with other natural photographic processes such as anthotypes. Key factors influencing the process were identified, such as the type of leaf, the intensity and duration of light, and the hydration of the plant material. It is concluded that chlorography is a viable, non-toxic, and environmentally friendly photographic alternative with potential applications in art, education, and research, although it presents challenges in terms of image permanence and reproducibility. Full article

Background/Objectives: Adult-type diffuse gliomas, including astrocytoma and glioblastoma multiforme (GBM), are brain tumors with a very poor prognosis. While current treatment options for glioma patients are not providing satisfactory outcomes, research indicates that natural compounds could serve as alternative treatments. However, their low bioavailability requires nanotechnology solutions, such as liposomes. Methods: In this study, we propose the co-encapsulation of acteoside (ACT) with other natural compounds, cannabidiol (CBD) or naringenin (NG), in a cationic liposomal nanoformulation consisting of DOTAP and POPC lipids, which were prepared using the dry lipid film method. The liposomes were characterized by their physicochemical properties, including particle size, zeta potential, and polydispersity index (PDI), with additional analyses performed using ¹H Nuclear Magnetic Resonance (NMR). Furthermore, biological experiments were performed with U-87 MG astrocytoma and U-138 MG GBM cell lines and non-cancerous MRC-5 lung fibroblasts using the MTT assay and evaluating the expression of Bax and Bcl-xL to evaluate their potential as anticancer agents. Conclusions: The IC₅₀ values for the nanoformulations in U-138 MG cells at 48 h were 6 µM for ACT + CBD and 5 µM for ACT + NG. ACT and CBD or NG demonstrated a potential synergistic effect against GBM in a liposomal formulation. Notably, treatment with ACT + CBD (5 µM) and ACT + NG (5 µM) liposomal formulations significantly upregulated Bax protein level in U-138 cells at both 24 and 48 h. In parallel, ACT + CBD (5 µM) also modulated Bcl-xL protein level in both U-138 MG and U-87 MG cell lines at the same time points. The obtained nanoformulations were homogeneous and stable for 21 days, evidenced by a narrow particle size distribution, a low polydispersity index (PDI) < 0.3, and a positive zeta potential. Full article

Mycoplasmal pneumonia of sheep (MPS), caused by Mesomycoplasma (Mycoplasma) ovipneumoniae, profoundly impacts ovine productivity and survival. Although gut–lung microbiota interactions are increasingly recognized in respiratory diseases, whether similar crosstalk occurs between the lung and rumen microbiota in MPS-affected sheep remains unknown. To investigate alterations in the lung and rumen microbiota of sheep with MPS, the crosstalk between these microbial communities, and their impacts on growth phenotypes. From a cohort of 414 naturally infected six-month-old male Hu sheep, we selected 10 individuals with severe pulmonary pathology and 10 healthy controls for detailed phenotypic and microbiome analyses. Assessment of 359 phenotypic traits revealed that MPS significantly impairs feed efficiency and growth rate (p < 0.05). Through 16S rRNA gene sequencing, we found that MPS significantly altered the pulmonary microbiota community structure (p < 0.01), with a noticeable impact on the rumen microbiota composition (p = 0.059). Succinivibrionaceae_UCG-001 was significantly depleted in both the rumen and lungs of diseased sheep (p < 0.05) and strongly associated with reduced average daily feed intake (p < 0.05). In addition, pulmonary Pasteurella and ruminal Succinivibrionaceae_UCG-002 were significantly enriched in MPS-affected sheep, showed a strong positive correlation (p < 0.05), and were both negatively associated with feed efficiency (p < 0.05). Notably, Pasteurella multocida subsp. gallicida may act as a keystone species influencing feed efficiency. These findings point to a previously unrecognized rumen-lung microbial axis that may modulate host productivity in sheep affected by MPS. This work provides new insights into the pathogenesis of MPS and offers potential targets for therapeutic intervention and management. Full article

We investigate whether China’s Value-Added Tax (VAT) Credit Refund policy influences firms’ sustainable development capability (SDC), which reflects innovation-driven growth and green development. Exploiting the 2018 implementation of the VAT Credit Refund policy as a quasi-natural experiment, we employ a difference-in-differences (DID) approach and find causal evidence that the policy significantly enhances firms’ SDC. This suggests that fiscal instruments like VAT refunds are valued by firms as drivers of long-term sustainable and high-quality development. Our mediating analyses further reveal that the policy promotes firms’ SDC by strengthening artificial intelligence (AI) capabilities and facilitating intelligent transformation. This mechanism “AI Capability Building—Intelligent Transformation” aligns with the socio-technical systems theory (STST), highlighting the interactive evolution of technological and social subsystems in shaping firm capabilities. The heterogeneity analyses indicate that the positive effect of VAT Credit Refund policy on SDC is more pronounced among small-scale and non-high-tech firms, firms with lower perceived economic policy uncertainty, higher operational diversification, lower reputational capital, and those located in regions with a higher level of marketization. We also find that the policy has persistent long-term effects, with improved SDC associated with enhanced ESG performance and green innovation outcomes. Our findings have important implications for understanding the SDC through the lens of STST and offer policy insights for deepening VAT reform and promoting intelligent and green transformation in China’s enterprises. Full article

Zoological environments aim to promote natural behaviours and optimal welfare conditions. Over the past decade, research on the use of artificial ultraviolet-B (UV-B) exposure has improved vitamin D₃ levels and reduced incidences of metabolic bone disease in diurnal primates; however, this has not been investigated in nocturnals. Aye-ayes (Daubentonia madagascariensis), nocturnal lemurs often housed indoors in zoos with little to no exposure to natural sunlight, have been reported to have low vitamin D₃ levels. This study aims to investigate the impacts of artificial UV-B as a supplemental healthcare strategy for aye-ayes, examining its influences on vitamin D₃ levels and positional sleeping behaviour. The 25-hydroxy-vitamin D₃ (25OHD₃) blood levels were tested before and after exposure to different levels of artificial UV-B and heat sources. Statistical analysis showed no correlation between UV-B and 25OHD₃ at group parameter levels. However, one individual showed a positive correlation. Sleeping position duration analysis showed a potential basking behaviour with the use of increased ear exposure and other thermoregulatory responses. Despite representing 8.06% of the European captive aye-aye population, these findings highlight the need for further research on vitamin D₃ parameters and responses to UV-B to optimise captive conditions and support the species’ long-term health. Full article

Ternary chalcogenide-based sulfide materials with distorted morphologies such as BaZrS₃, CaZrS₃, and SrZrS₃, have recently gained much attention in optoelectronics and photovoltaics due to their high structural and thermal stability and compatibility with low-cost, earth-abundant synthesis routes. However, their relatively large bandgaps often limit their suitability for near-infrared (NIR) photodetectors. Here, we conducted a comprehensive investigation of SrHfSe₃, a ternary chalcogenide with an orthorhombic crystal structure and distinctive needle-like morphology, as a promising candidate for NIR photodetection. SrHfSe₃ exhibits a direct bandgap of 1.02 eV, placing it well within the NIR range. Its robust structure, high temperature stability, phase stability and natural abundance make it a compelling material for next-generation, self-powered NIR photodetectors. An in-depth analysis of the SrHfSe₃-based photodetector was performed using SCAPS-1D simulations, focusing on key performance metrics such as J–V behavior, photoresponsivity, and specific detectivity. Device optimization was achieved by thoroughly altering each layer thickness, doping concentrations, and defect densities. Additionally, the influence of interface defects, absorber bandgap, and operating temperature was assessed to enhance the photoresponse. Under optimal conditions, the device achieved a short-circuit current density (Jsc) of 45.88 mA/cm², an open-circuit voltage (Voc) of 0.7152 V, a peak photoresponsivity of 0.85 AW⁻¹, and a detectivity of 2.26 × 10¹⁴ Jones at 1100 nm. A broad spectral response spanning 700–1200 nm confirms its efficacy in the NIR region. These results position SrHfSe₃ as a strong contender for future NIR photodetectors and provide a foundation for experimental validation in advanced optoelectronic applications. Full article

Alexandrium spp. blooms and paralytic shellfish poisoning pose serious economic threats to coastal communities and aquaculture. This study evaluated the removal efficiency of two Alexandrium minutum strains using natural kaolinite clay (KNAC) and kaolinite with polyaluminum chloride (KPAC) at three concentrations (0.1, 0.25, and 0.3 g L⁻¹), two pH levels (7 and 8), and two cell densities (1.0 and 2.0 × 10⁷ cells L⁻¹) in seawater. PAC significantly enhanced removal, achieving up to 100% efficiency within two hours. Zeta potential analysis showed that PAC imparted positive surface charges to the clay, promoting electrostatic interactions with negatively charged algal cells and enhancing flocculation through Van der Waals attractions. In addition, the study conducted a cost estimate analysis and found that treating one hectare at 0.1 g L⁻¹ would cost approximately USD 31.75. The low KPAC application rate also suggests minimal environmental impact on benthic habitats. Full article

Constipation is a prevalent gastrointestinal disorder that significantly impairs quality of life. While pharmacological agents such as loperamide are widely used to induce constipation in experimental models, there is increasing interest in natural alternatives for alleviating intestinal dysfunction. In this study, we investigated the laxative effects of soybean powder fermented by Bacillus subtilis DKU_09 in a loperamide-induced rat model of constipation. The probiotic strain was isolated from cheonggukjang, a traditional Korean fermented soybean paste, and its identity was confirmed through 16S rRNA sequencing. Fermented soybean powder was characterized morphologically via scanning electron microscopy and chemically via HPLC to assess its isoflavone content. Rats were administered loperamide (5 mg/kg) for four days to induce constipation and were then treated with fermented soybean powder at doses of 100, 200, or 300 mg/kg. No pharmacological laxatives (e.g., PEG) were used as a positive control; instead, values from the treatment groups were compared with those from the loperamide-only constipation group. Key outcomes of fecal output, water content, colonic fecal retention, and gastrointestinal transit ratio were measured. The fermented product significantly improved stool frequency and moisture content, reduced colonic fecal retention, and restored gastrointestinal transit in a dose-dependent manner. Notably, the 300 mg/kg group demonstrated nearly complete recovery of fecal parameters without affecting body weight. Statistical analysis was performed using one-way ANOVA followed by Tukey’s post hoc test. These findings suggest that Bacillus subtilis-fermented soybean powder exerts synergistic laxative effects through the combined action of probiotic viability and fermentation-enhanced bioactive compounds such as aglycone isoflavones. This study supports the potential use of fermented soybean-based nutraceuticals as a natural and safe intervention for constipation and gastrointestinal dysregulation. Full article

This work contextualizes the possibility of deriving a unifying artificial intelligence framework by walking in the footsteps of General, Explainable, and Verified Artificial Intelligence (GEVAI): by considering explainability not only at the level of the results produced by a specification but also considering the explicability of the inference process as well as the one related to the data processing step, we can not only ensure human explainability of the process leading to the ultimate results but also mitigate and minimize machine faults leading to incorrect results. This, on the other hand, requires the adoption of automated verification processes beyond system fine-tuning, which are essentially relevant in a more interconnected world. The challenges related to full automation of a data processing pipeline, mostly requiring human-in-the-loop approaches, forces us to tackle the framework from a different perspective: while proposing a preliminary implementation of GEVAI mainly used as an AI test-bed having different state-of-the-art AI algorithms interconnected, we propose two other data processing pipelines, LaSSI and EMeriTAte+DF, being a specific instantiation of GEVAI for solving specific problems (Natural Language Processing, and Multivariate Time Series Classifications). Preliminary results from our ongoing work strengthen the position of the proposed framework by showcasing it as a viable path to improve current state-of-the-art AI algorithms. Full article

Understanding how energy use and economic activity shape carbon emissions is pivotal for achieving global climate targets. This study quantifies the dynamic nexus between disaggregated energy consumption, economic growth, and CO₂ emissions in India and China—two economies that together account for more than one-third of global emissions. Using annual data from 1990 to 2021, we implement Long Short-Term Memory (LSTM) neural networks, which outperform traditional linear models in capturing nonlinearities and lagged effects. The dataset is split into training (1990–2013) and testing (2014–2021) intervals to ensure rigorous out-of-sample validation. Results reveal stark national differences. For India, coal, natural gas consumption, and economic growth are the strongest positive drivers of emissions, whereas renewable energy exerts a significant mitigating effect, and nuclear energy is negligible. In China, emissions are dominated by coal and petroleum use and by economic growth, while renewable and nuclear sources show weak, inconsistent impacts. We recommend retrofitting India’s coal- and gas-plants with carbon capture and storage, doubling clean-tech subsidies, and tripling annual solar-plus-storage auctions to displace fossil baseload. For China, priorities include ultra-supercritical upgrades with carbon capture, utilisation, and storage, green-bond-financed solar–wind buildouts, grid-scale storage deployments, and hydrogen-electric freight corridors. These data-driven pathways simultaneously cut flagship emitters, decouple GDP from carbon, provide replicable models for global net-zero research, and advance climate-resilient economic growth worldwide. Full article

This study examines non-standard comparative and superlative adjective forms in Ukrainian and English, emphasizing their evaluative meanings and grammatical deviations. While prescriptive grammar dictates conventional comparison patterns, modern discourse—particularly in advertising, informal communication, and literary texts—exhibits an increasing prevalence of innovative comparative structures. Using a corpus-based approach, this research identifies patterns of positive and negative evaluative meanings, revealing that positive evaluations dominate non-standard comparatives in both languages, particularly in advertising (English: 78.5%, Ukrainian: 80.2%). However, English exhibits a higher tolerance for grammatical flexibility, while Ukrainian maintains a more restricted use, primarily in commercial and expressive discourse. The findings highlight the pragmatic and evaluative functions of such constructions, including hyperbolic emphasis, rhetorical contrast, and branding strategies. These insights contribute to research on comparative grammar, sentiment analysis, and natural language processing, particularly in modeling evaluative structures in computational linguistics. Full article