Search Results (2,140)

Adulteration detection in medicinal plant powders remains a critical challenge in quality control. In this study, we propose a hyperspectral imaging (HSI)-based method combined with deep learning models to quantitatively analyze adulteration levels in Anoectochilus roxburghii powder. After preprocessing the spectral data using raw, first-order, and second-order Savitzky–Golay derivatives, we systematically evaluated the performance of traditional machine learning models (Random Forest, Support Vector Regression, Partial Least Squares Regression) and deep learning architectures. While traditional models achieved reasonable accuracy (R² up to 0.885), their performance was limited by feature extraction and generalization ability. A single-channel convolutional neural network (CNN) utilizing individual spectral representations improved performance marginally (maximum R² = 0.882), but still failed to fully capture the multi-scale spectral features. To overcome this, we developed a multi-channel CNN that simultaneously integrates raw, SG-1, and SG-2 spectra, effectively leveraging complementary spectral information. This architecture achieved a significantly higher prediction accuracy (R² = 0.964, MSE = 0.005), demonstrating superior robustness and generalization. The findings highlight the potential of multi-channel deep learning models in enhancing quantitative adulteration detection and ensuring the authenticity of herbal products. Full article

Pulsed electric field (PEF) and high-pressure processing (HPP) are non-thermal treatments, developed to ensure preservation of food products whilst maintaining taste and valuable nutrients. In this study, we investigated their potential for the inactivation of 3 commercial exogenous pectinases (polygalacturonase, pectin transeliminase, pectin esterase) commonly used in juice processing for clarification of juices. The inactivation of these enzymes after processing is mandatory by European law. Clear apple juice was treated with both non-thermal processing methods, as well as with thermal pasteurization as the standard method. For HPP, 3 pressures (250, 450, and 600 MPa) and different holding times (from 2 to 12 min) were tested. For PEF, 3 electric field intensities (10, 13, and 15 kV/cm) and different specific energy values (from 121 to 417 kJ/kg). Standard thermal pasteurization resulted in a complete inactivation of all tested pectinases. HPP treatment only showed marginal effects on polygalacturonase and pectin transeliminase at the highest pressure and holding times, which are beyond levels used in industrial settings. For PEF, dependence upon high electric field strength and specific energy values was evident; however, here too, the effect was only moderate at the levels attainable within the scope of this study. Assuming a continued linear relationship, usable results could be achieved in an industrial setting, albeit under more extreme conditions. Full article

This study explores the role of solidarity finance in promoting local development and the empowerment of marginalized communities through financial inclusion and access to community credits. It focuses on how solidarity-based financial mechanisms provide accessible credit with fewer barriers, fostering productive activities and economic resilience. This study employed a quantitative and exploratory design, analyzing data from 51 community funds in Ecuador out of a total of 220 through a self-administered online survey, validated by auditing professionals and answered by community representatives. The 25-item questionnaire gathered data on organizational dynamics, financial practices, and perceptions of sustainability. Descriptive analysis was complemented with an analysis of variance to test hypotheses concerning associativity, self-management, and organizational performance. The results show that while associativity, self-management, and organizational management are perceived as institutional strengths, aspects such as autonomy and solidarity received lower evaluations, suggesting critical areas for strategic improvement. Notably, significant differences emerged between self-management–organization and solidarity–organization groups, emphasizing the importance of associativity (collaboration) in enhancing the sustainability of solidarity finance, which proves to be a vital mechanism for community empowerment and local development; however, its long-term sustainability depends on strengthening internal dimensions, particularly autonomy and solidarity, and reinforcing associativity as a core driver of organizational resilience. Full article

Automated surface defect detection in steel manufacturing is pivotal for ensuring product quality, yet it remains an open challenge owing to the extreme heterogeneity of defect morphologies—ranging from hairline cracks and microscopic pores to elongated scratches and shallow dents. Existing approaches, whether classical vision pipelines or recent deep-learning paradigms, struggle to simultaneously satisfy the stringent demands of industrial scenarios: high accuracy on sub-millimeter flaws, insensitivity to texture-rich backgrounds, and real-time throughput on resource-constrained hardware. Although contemporary detectors have narrowed the gap, they still exhibit pronounced sensitivity–robustness trade-offs, particularly in the presence of scale-varying defects and cluttered surfaces. To address these limitations, we introduce MBY (MBDNet-Attention-YOLO), a lightweight yet powerful framework that synergistically couples the MBDNet backbone with the YOLO detection head. Specifically, the backbone embeds three novel components: (1) HGStem, a hierarchical stem block that enriches low-level representations while suppressing redundant activations; (2) Dynamic Align Fusion (DAF), an adaptive cross-scale fusion mechanism that dynamically re-weights feature contributions according to defect saliency; and (3) C2f-DWR, a depth-wise residual variant that progressively expands receptive fields without incurring prohibitive computational costs. Building upon this enriched feature hierarchy, the neck employs our proposed MultiSEAM module—a cascaded squeeze-and-excitation attention mechanism operating at multiple granularities—to harmonize fine-grained and semantic cues, thereby amplifying weak defect signals against complex textures. Finally, we integrate the Inner-SIoU loss, which refines the geometric alignment between predicted and ground-truth boxes by jointly optimizing center distance, aspect ratio consistency, and IoU overlap, leading to faster convergence and tighter localization. Extensive experiments on two publicly available steel-defect benchmarks—NEU-DET and PVEL-AD—demonstrate the superiority of MBY. Without bells and whistles, our model achieves 85.8% mAP@0.5 on NEU-DET and 75.9% mAP@0.5 on PVEL-AD, surpassing the best-reported results by significant margins while maintaining real-time inference on an NVIDIA Jetson Xavier. Ablation studies corroborate the complementary roles of each component, underscoring MBY’s robustness across defect scales and surface conditions. These results suggest that MBY strikes an appealing balance between accuracy, efficiency, and deployability, offering a pragmatic solution for next-generation industrial quality-control systems. Full article

The eastern Tianshan region in the Central Asian Orogenic Belt (CAOB) is characterized by multiple complex tectonic activity of uncertain historical contribution to the construction of the CAOB. This study utilizes a multi-proxy geochemical approach to characterize I-type monzogranite pluton rocks and their associated hornblende-rich dioritic enclaves to decipher the tectonic and magmatic evolution of the Xigebi area, eastern Tianshan. Zircon geochronology indicates a Triassic and Permian crystallization age of ca. 224.2 ± 1.7 Ma and ca. 268.3 ± 3.0 Ma for the host monzogranites and the dioritic enclaves, respectively. Major, trace and rare earth element distribution, together with Hf isotope systematics displaying noticeable positive εHf(t) anomalies for both rock types, point to partial melting of meta-mafic rocks in an intraplate extensional setting. The diorite was formed by the melting of lower crustal meta-igneous rocks mixed with mantle melts, and the monzogranite, predominantly from deep crustal meta-basalts contaminated by shallow metasedimentary rocks, with some degree of mixing with deeply sourced mantle magma. While both the host monzogranites and their dioritic enclaves are the products of upwelling magma, the younger Triassic monzogranites captured and preserved fragments of the dioritic Permian lower continental crust during crystallization. These multiple stages of magmatic underplating and crustal reworking associated with vertical stratification of the juvenile paleo-continental crust suggest the monzogranites and diorites indicate a change from a post-collisional setting to a regional intraplate regime on the southern margin of the CAOB. Full article

In this work, we introduce a novel method to extract correlations in diagonal quantum states in multi-particle quantum systems, addressing a significant limitation of traditional approaches that require prior knowledge of the density matrices of quantum states. Instead of relying on classical information processing, our method is based on weak couplings and ancillary systems, eliminating the need for classical communication, optimization, and complex calculations. The concept of mutually unbiased bases is intrinsically linked to symmetry, as it entails the uniform distribution of quantum states across distinct bases. Within the framework of our theoretical model, mutually unbiased bases are employed to facilitate weak measurements and to function as the post-selected states. To quantify the correlations in the initial state, we employ the trace distance between the initial state and the product of its marginal states, and illustrate the feasibility and effectiveness of our approach. We generalize the approach to accommodate high-dimensional multi-particle systems for potential applications in quantum information processing and quantum networks. Full article

Peatlands store substantial amounts of carbon (C) in the form of peat, but are increasingly threatened by drought and shrub encroachment under climate warming. However, how peat decomposition and its temperature sensitivity (Q₁₀) vary with depth and plant litter input under these stressors remains poorly understood. We incubated peat from two depths with different degrees of decomposition, either alone or incubated with Sphagnum divinum shoots or Betula ovalifolia leaves, under five temperature levels and two moisture conditions in growth chambers. We found that drought and Betula addition increased CO₂ emissions in both peat layers, while Sphagnum affected only shallow peat. Deep peat alone or with Betula exhibited higher Q₁₀ than pure shallow peat. Drought increased the Q₁₀ of both depths’ peat, but this effect disappeared with fresh litter addition. The CO₂ production rate showed a positive but marginal correlation with microbial biomass carbon, and it displayed a rather similar responsive trend to warming as the microbial metabolism quotient. These results indicate that both deep and dry peat are more sensitive to warming, highlighting the importance of keeping deep peat buried and waterlogged to conserve existing carbon storage. Additionally, they further emphasize the necessity of Sphagnum moss recovery following vascular plant encroachment in restoring carbon sink function in peatlands. Full article

Agricultural production in southern Angola faces challenges due to unsustainable practices, including inefficient use of water, fertilizers, and machinery, resulting in low yields and environmental degradation. Therefore, clear and measurable indicators are needed to guide farmers toward more sustainable practices. The scientific literature insufficiently addresses this issue, leaving a significant gap in the evaluation of key performance indicators (KPIs) that can guide good agricultural practices (GAPs) adapted to the context of southern Angola, with the goal of promoting a more resilient and sustainable agricultural sector. So, the objective of this study is to identify and assess KPIs capable of supporting the selection of GAPs suitable for maize, potato, and tomato cultivation in the context of southern Angolan agriculture. A systematic literature review (SLR) was conducted, screening 2720 articles and selecting 14 studies that met defined inclusion criteria. Five KPIs were identified as the most relevant: gross margin, net profit, water use efficiency, nitrogen use efficiency, and machine energy. These indicators were analyzed and standardized to evaluate their contribution to sustainability across different GAPs. Results show that organic fertilizers are the most sustainable option for maize, drip irrigation for potatoes, and crop rotation for tomatoes in southern Angola because of their efficiency in low-resource environments. A clear, simple, and effective representation of the KPIs was developed to be useful in communicating to farmers and policy makers on the selection of the best GAPs in the cultivation of different crops. The study proposes a validated KPI-based methodology for assessing sustainable agricultural practices in developing regions such as southern Angola, aiming to lead to greater self-sufficiency and economic stability in this sector. Full article

China’s economy is shifting from an era of rapid expansion to one focused on high-quality development, making it imperative to tackle environmental degradation linked to energy use. Understanding how New Quality Productive Forces (NQPF) interact with energy efficiency, along with the mechanisms driving this relationship, is essential for economic transformation and long-term sustainability. This study establishes an evaluation framework for NQPF, integrating technological, green, and digital dimensions. We apply fixed-effects models, the spatial Durbin model (SDM), a moderation model, and a threshold model to analyze the influence of NQPF on Green Total Factor Energy Efficiency (GTFEE) and its spatial implications. This underscores the necessity of distinguishing it from traditional productivity frameworks and adopting a new analytical perspective. Furthermore, by considering dimensions such as input, application, innovation capability, and market efficiency, we reveal the moderating role and heterogeneous effects of artificial intelligence (AI). The findings are as follows: The development of NQPF significantly enhances GTFEE, and the conclusion remains robust after tail reduction and endogeneity tests. NQPF has a positive spatial spillover effect on GTFEE; that is, while improving the local GTFEE, it also improves neighboring regions GTFEE. The advancement of AI significantly strengthens the positive impact of NQPF on GTFEE. AI exhibits a significant U-shaped threshold effect: as AI levels increase, its moderating effect transitions from suppression to facilitation, with marginal benefits gradually increasing over time. Full article

Soil salinization is a major environmental constraint that poses a significant threat to global agricultural productivity and food security. Coix lacryma-jobi L., a minor cereal crop that is valued for its nutritional and medicinal properties, displays moderate susceptibility to salinity stress. Although exogenous treatments have been demonstrated to enhance plant resilience against various biotic and abiotic stresses, the potential of nano-silicon (NaSi), melatonin (MT), and their combined application in mitigating salinity-induced damage, particularly in relation to the medicinal properties of this medicinal and edible crop, remains poorly understood. This study investigated the effects of exogenous NaSi and MT application on Coix under salinity stress using two varieties with contrasting salinity tolerances. The plants were subjected to salinity stress and treated with NaSi, MT, or a combination of both. The results revealed that salinity stress significantly impaired the agronomic traits, physiological performance, and accumulation of medicinal compounds of Coix. Exogenous MT application effectively alleviated salinity-induced damage to agronomic and physiological parameters, exhibiting superior protective effects compared to NaSi treatment. Strikingly, the combined application of MT and NaSi demonstrated synergistic effects, leading to substantial improvements in growth and physiological indices. However, the medicinal components were only marginally affected by exogenous treatments under both control and salinity-stressed conditions. Further clarification of the molecular mechanisms underlying salinity stress responses and exogenous substance-induced effects is critical to achieving a comprehensive understanding of these protective mechanisms. Full article

Reactive oxygen species (ROS) play a dual role as both essential signaling molecules and harmful mediators of damage. Imbalances in the redox state of the liver can overwhelm antioxidant defenses and promote mitochondrial dysfunction, oxidative damage, and inflammation. Complex feedback loops between ROS and immune signaling pathways are a hallmark of pathological liver conditions, such as hepatic ischemia–reperfusion injury (IRI). This is a major cause of liver transplant failure and is of increasing significance due to the increased use of marginally discarded livers for transplantation. This review outlines the major enzymatic and metabolic sources of ROS in hepatic IRI, including mitochondrial reverse electron transport, NADPH oxidases, cytochrome P450 enzymes, and endoplasmic reticulum stress. Hepatocyte injury activates redox feedback loops that initiate immune cascades through DAMP release, toll-like receptor signaling, and cytokine production. Emerging regulatory mechanisms, such as succinate accumulation and cytosolic calcium–CAMKII signaling, further shape oxidative dynamics. Pharmacological therapies and the use of antioxidant and immunomodulatory approaches, including nanoparticles and redox-sensitive therapeutics, are discussed as protective strategies. A deeper understanding of how redox and immune feedback loops interact is an exciting and active area of research that warrants further clinical investigation. Full article

This research presents DuSAFNet, a lightweight deep neural network for fine-grained bird audio classification. DuSAFNet combines dual-path feature fusion, spectral–temporal attention, and a multi-band ArcMarginProduct classifier to enhance inter-class separability and capture both local and global spectro–temporal cues. Unlike single-feature approaches, DuSAFNet captures both local spectral textures and long-range temporal dependencies in Mel-spectrogram inputs and explicitly enhances inter-class separability across low, mid, and high frequency bands. On a curated dataset of 17,653 three-second recordings spanning 18 species, DuSAFNet achieves 96.88% accuracy and a 96.83% F1 score using only 6.77 M parameters and 2.275 GFLOPs. Cross-dataset evaluation on Birdsdata yields 93.74% accuracy, demonstrating robust generalization to new recording conditions. Its lightweight design and high performance make DuSAFNet well-suited for edge-device deployment and real-time alerts for rare or threatened species. This work lays the foundation for scalable, automated acoustic monitoring to inform biodiversity assessments and conservation planning. Full article

Traditional intensive agricultural system impedes ecological functions, such as nutrient cycling and biodiversity conservation, resulting in excessive nitrogen discharge, CH₄ emission, and ecosystem service losses. To enhance critical ecosystem services and mitigate environmental externalities in paddy fields, we developed a multi-objective agricultural system (MIA system), which combines two eco-functional units: paddy wetlands and Beitang (irrigation water collection pond). Pilot study results demonstrated that the MIA system enhanced biodiversity and inhibited pest outbreak, with only a marginal reduction in rice production compared with the control. Additionally, the paddy wetland effectively removed nitrogen, with removal rates of total nitrogen and dissolved inorganic nitrogen ranging from 0.06 to 0.65 g N m⁻² d⁻¹ and from 0.02 to 0.22 g N m⁻² d⁻¹, respectively. Continuous water flow in the paddy wetland reduced the CH₄ emission by 84.4% compared with the static water conditions. Furthermore, a simulation experiment indicated that tide flow was more effective in mitigating CH₄ emission, with a 68.3% reduction compared with the drying–wetting cycle treatment. The emergy evaluation demonstrated that the MIA system outperformed the ordinary paddy field when considering both critical ecosystem services and environmental externalities. The MIA system exhibited higher emergy self-sufficiency ratio, emergy yield ratio, and emergy sustainable index, along with a lower environmental load ratio. Additionally, the system required minimal transformation, thus a modest investment. By presenting the case of the MIA system, we provide a theoretical foundation for comprehensive management and assessment of agricultural ecosystems, highlighting its significant potential for widespread application. Full article

Anthropogenic infrastructure, such as inland wind turbines commonly found in agricultural fields, has substantially increased subterranean vibratory noise in the past decades. Plants, being rooted in soil, are continuously exposed to these vibrations, yet we have little understanding of how vibrational noise affects plant development and, consequently, plant–insect interactions. Here, we examine the impact of windmill-like vibrational noise on the growth of Pisum sativum and its full-factorial interaction with the generalist herbivore Spodoptera exigua. Plants were exposed to either high or low vibrational noise from seed germination to the seed production stage. We recorded germination, flowering, fruiting time, and daily shoot length. Additionally, we measured herbivory intensity by Spodoptera exigua caterpillars placed on a subset of plants. Plants exposed to high vibrational noise grew significantly faster and taller than those in the low-noise treatment. Additionally, we found a marginally significant trend for earlier flowering in plants exposed to high noise. We did not find a significant effect of vibrational noise on herbivory. Our results suggest that underground vibrational noise can influence plant growth rates, which may potentially have ecological and agricultural implications. Faster growth may alter interspecific competition and shift trade-offs between growth and defense. Understanding these effects is important in assessing the broader ecological consequences of renewable energy infrastructure. Full article

In this study, the aim was to determine the levels of aflatoxins and ochratoxin A (OTA) in baby food consumed in Hatay using fluorescence-detector HPLC (HPLC-FLD) and to reveal the health risks that may occur in babies through consumption of these foods. To determine the dietary intake and to reveal the health risk assessment, the estimated daily intake (EDI) for all mycotoxins, the margin of exposure (MOE) for aflatoxin B₁ (AFB₁), aflatoxin M₁ (AFM₁) and OTA, the hazard index (HI) and the consumption-related hepatocellular cancer risk for AFM₁ were calculated. It was reported that 11.5% and 8.2% of the analyzed samples exceeded the legal limit set for AFB₁ and OTA, respectively. However, it was found that AFM₁ concentrations in all samples did not exceed the legal limit. Based on the estimated consumption amounts of the baby foods, the HI values calculated for AFM₁ were below 1, and the MOE values calculated for AFB₁ and AFM₁ were above 10.000, indicating that the consumption of baby foods does not pose a risk regarding AFB₁ and AFM₁ for babies. However, it was determined in all other products, except for toddler formula, that the MOE values calculated for OTA were below 10.000, indicating that their consumption may pose serious health problems in babies. Full article