Search Results (2,763)

As a fundamental metric for assessing carbon sequestration, Net Primary Productivity (NPP) and the mechanisms driving its spatiotemporal dynamics constitute a critical research domain within global change science. This research centered on the Huang–Huai–Hai Plain (HHHP), combining 2001–2023 MODIS-NPP data with natural (landform, temperature, precipitation, soil) and socio-economic (population density, GDP density, land use) drivers. Trend analysis, coefficient of variation, and Hurst index were applied to clarify the spatiotemporal evolution of NPP and its future trends, while geographic detectors and structural equation models were used to quantify the contribution of drivers. Key findings: (1) Across the HHHP, the multi-year average NPP ranged between 30.05 and 1019.76 gC·m⁻²·a⁻¹, with higher values found in Shandong and Henan provinces, and lower values concentrated in the northwestern dam-top plateau and central plain regions; 44.11% of the entire region showed a statistically highly significant increasing trend. (2) The overall fluctuation of NPP was low-amplitude, with a stable center of gravity and the standard deviation ellipse retaining a southwest-to-northeast direction. (3) Future changes in NPP exhibited persistence and anti-persistence, with 44.98% of the region being confronted with vegetation degradation risk. (4) NPP variations originated from the synergistic impacts of multiple elements: among individual elements, precipitation, soil type, and elevation had the highest explanatory capacity, while synergistic interactions between two elements notably enhanced the explanatory capacity. (5) Climate variation exerted the strongest influence on NPP (direct coefficient of 0.743), followed by the basic natural environment (0.734), whereas human-related activities had the weakest direct impact (−0.098). This research offers scientific backing for regional carbon sink evaluation, ecological security early warning, and sustainable development policies. Full article

Gully erosion, driven by the interplay of natural processes and human activities, results in severe soil degradation and landscape alteration, yet approaches for accurately quantifying erosion triggered by extreme precipitation using multi-source high-resolution remote sensing remain limited. This study first extracted digital surface models (DSM) for the years 2014 and 2024 using Ziyuan-3 and GaoFen-7 satellite stereo imagery, respectively. Subsequently, the DSM was calibrated using high-resolution unmanned aerial vehicle photogrammetry data to enhance elevation accuracy. Based on the corrected DSMs, gully erosion depths from 2014 to 2024 were quantified. Erosion patches were identified through a deep learning framework applied to GaoFen-1 and GaoFen-2 imagery. The analysis further explored the influences of natural processes and anthropogenic activities on elevation changes within the gully erosion watershed. Topographic monitoring in the Sandu River watershed revealed a net elevation loss of 2.6 m over 2014–2024, with erosion depths up to 8 m in some sub-watersheds. Elevation changes are primarily driven by extreme precipitation-induced erosion alongside human activities, resulting in substantial spatial variability in surface lowering across the watershed. This approach provides a refined assessment of the spatial and temporal evolution of gully erosion, offering valuable insights for soil conservation and sustainable land management strategies in the Loess Plateau region. Full article

Retinal diseases are among the leading causes of vision impairment worldwide, and early detection is essential for enabling personalized treatments and preventing irreversible vision loss. In this paper, we propose a method aimed to identify and localize retinal conditions, i.e., Age-Related Macular Degeneration, Diabetic Retinopathy, and Choroidal Neovascularization, using explainable deep learning. For this purpose, we consider seven fine-tuned convolutional neural networks: MobileNet, LeNet, StandardCNN, CustomCNN, DenseNet, Inception, and EfficientNet. Moreover, we develop a novel architecture i.e., NeoNet, specifically designed for the detection of retinal diseases, achieving an accuracy of 99.5%. Furthermore, with the aim to provide explaianability behind the model decision, we highlight the most critical regions within retinal images influencing the predictions of the model. The obtained results show the ability of the model to detect pathological features, thereby supporting earlier and more accurate diagnosis of retinal diseases. Full article

Background: This study investigated sex differences in foot arch structure and function, and their impact on postural control and energy flow during dynamic tasks. Findings aim to inform sex-specific training, movement assessment, and injury prevention strategies. Methods: A total of 108 participants (53 males and 55 females) underwent foot arch morphological assessments and performed a sit-to-stand (STS). Motion data were collected using an infrared motion capture system, three-dimensional force plates, and wireless surface electromyography. A rigid body model was constructed in Visual3D, and joint forces, segmental angular and linear velocities, center of pressure (COP), and center of mass (COM) were calculated using MATLAB. Segmental net energy was integrated to determine energy flow across different phases of the STS. Results: Arch stiffness was significantly higher in males. In terms of postural control, males exhibited significantly lower mediolateral COP frequency and anteroposterior COM peak velocity during the pre-seat-off phase, and lower COM displacement, peak velocity, and sample entropy during the post-seat-off phase compared to females. Conversely, males showed higher anteroposterior COM velocity before seat-off, and greater anteroposterior and vertical momentum after seat-off (p < 0.05). Regarding energy flow, males exhibited higher thigh muscle power, segmental net power during both phases, and greater shank joint power before seat-off. In contrast, females showed higher thigh joint power before seat-off and greater shank joint power after seat-off (p < 0.05). Conclusions: Significant sex differences in foot arch function influence postural control and energy transfer during STS. Compared to males, females rely on more frequent postural adjustments to compensate for lower arch stiffness, which may increase mechanical loading on the knee and ankle and elevate injury risk. Full article

The value of personal data can only be unlocked through efficient circulation. This study explores a multi-party collaborative mechanism for personal-data trading, aiming to improve data quality and market vitality via incentive-compatible institutional design, thereby supporting the high-quality development of the digital economy. Symmetry enhancement refers to the use of strategies and mechanisms to narrow the information gap among data controllers, operators, and demanders, enabling all parties to facilitate personal-data transactions on relatively equal footing. Drawing on evolutionary-game theory, we construct a tripartite dynamic-game model that incorporates data controllers, data operators, and data demanders. We analyze how initial willingness, payoff structures, breach costs, and risk factors (e.g., data leakage) shape each party’s strategic choices (cooperate vs. defect) and their evolutionary trajectories, in search of stable equilibrium conditions and core incentive mechanisms for a healthy market. We find that (1) the initial willingness to cooperate among participants is the foundation of a virtuous cycle; (2) the net revenue of data products significantly influences operators’ and demanders’ propensity to cooperate; and (3) the severity of breach penalties and the potential losses from data leakage jointly affect the strategies of all three parties, serving as key levers for maintaining market trust and compliance. Accordingly, we recommend strengthening contract enforcement and trust-building; refining the legal and regulatory framework for data rights confirmation, circulation, trading, and security; and promoting stable supply–demand cooperation and market education to enhance awareness of data value and compliance, thereby stimulating individuals’ willingness to authorize the use of their data and maximizing its value. Full article

Accurate diagnosis of sinusitis is essential due to its widespread prevalence and its considerable impact on patient quality of life. While multiple imaging techniques are available for detecting maxillary sinus, computed tomography (CT) remains the preferred modality because of its high sensitivity and spatial resolution. Although recent advances in deep learning have led to the development of automated methods for sinusitis classification, many existing models perform poorly in the presence of complex pathological features and offer limited interpretability, which hinders their integration into clinical workflows. In this study, we propose a hybrid deep learning framework that combines EfficientNetB0, a convolutional neural network, with the Swin Transformer, a vision transformer, to improve feature representation. An attention-based fusion module is used to integrate both local and global information, thereby enhancing diagnostic accuracy. To improve transparency and support clinical adoption, the model incorporates explainable artificial intelligence (XAI) techniques using Gradient-weighted Class Activation Mapping (Grad-CAM). This allows for visualization of the regions influencing the model’s predictions, helping radiologists assess the clinical relevance of the results. We evaluate the proposed method on a curated maxillary sinus CT dataset covering four diagnostic categories: Normal, Opacified, Polyposis, and Retention Cysts. The model achieves a classification accuracy of 95.83%, with precision, recall, and F1 score all at 95%. Grad-CAM visualizations indicate that the model consistently focuses on clinically significant regions of the sinus anatomy, supporting its potential utility as a reliable diagnostic aid in medical practice. Full article

Sleep staging is a crucial indicator for assessing sleep quality, which contributes to sleep monitoring and the diagnosis of sleep disorders. Although existing sleep staging methods achieve high classification performance, two major challenges remain: (1) the ability to effectively extract salient features from multi-channel sleep data remains limited; (2) excessive model parameters hinder efficiency improvements. To address these challenges, this work proposes a lightweight multi-channel sleep staging network (LMCSleepNet). LMCSleepNet is composed of four modules. The first module enhances frequency domain features through continuous wavelet transform. The second module extracts time–frequency features using multi-scale convolutions. The third module optimizes ResNet18 with depthwise separable convolutions to reduce parameters. The fourth module improves spatial correlation using the Convolutional Block Attention Module (CBAM). On the public datasets SleepEDF-20, SleepEDF-78, and LMCSleepNet, respectively, LMCSleepNet achieved classification accuracies of 88.2% (κ = 0.84, MF1 = 82.4%) and 84.1% (κ = 0.77, MF1 = 77.7%), while reducing model parameters to 1.49 M. Furthermore, experiments validated the influence of temporal sampling points in wavelet time–frequency maps on sleep classification performance (accuracy, Cohen’s kappa, and macro-average F1-score) and the influence of multi-scale dilated convolution module fusion methods on classification performance. LMCSleepNet is an efficient lightweight model for extracting and integrating multimodal features from multichannel Polysomnography (PSG) data, which facilitates its application in resource-constrained scenarios. Full article

Background/Objectives: Patients with pancreatic neuroendocrine tumors (PanNETs) often have a good prognosis with long overall survival. We evaluated quality of life (QoL) after surgery for PanNETs, using the new EORTC-specific questionnaires. Methods: PanNET patients operated on in our unit (1990–2023) received three EORTC questionnaires (QLQ-C30 and the new P.NET15 and P.NET19). We evaluated the following: (1) QLQ-C30 outcomes; (2) mixed domains from QLQ-C30, P.NET15, and P.NET19; and (3) domains from P.NET19 and P.NET15 only. Functional and symptom scales were investigated in relationship with clinical variables. Gamma regression and multivariable analyses were performed with R software. Results: The 100 patients enrolled (median time 133 months after surgery) showed a good QoL (median 83.3/100). Old age was related to worse QoL and physical functioning (p = 0.007 and p < 0.001, respectively). Diabetes negatively influenced QoL (p < 0.001), physical functioning (p = 0.005), and fatigue (p = 0.03). Patients undergoing parenchyma-sparing surgery showed less fatigue (p = 0.046), while non-insulinoma PanNET diagnosis was related to worse QoL (p = 0.039). Multiple comorbidities were negatively associated with physical functioning (p = 0.010), fatigue (p = 0.001), and pain (p = 0.021). According to the new questionnaires, the most affected outcome was muscle energy, depending on age (p = 0.042), diabetes (p = 0.014), type of surgery (p = 0.018), and non-insulinoma diagnosis (p = 0.007). Conclusions: A good QoL evaluated with EORTC questionnaires is reported in PanNET patients after surgery. Elderly and diabetic patients who underwent standard resection for gastrinoma/non-functioning PanNETs showed worse QoL outcomes. Full article

Color grading of boiled shrimp is a critical factor influencing market price, yet the process is usually conducted visually by buyers such as middlemen and processing plants. This subjective practice raises concerns about accuracy, impartiality, and fairness, often resulting in disputes with farmers. To address this issue, this study proposes a standardized and automated grading approach based on image processing and artificial intelligence. The method requires only a photograph of boiled shrimp placed alongside a color grading ruler. The grading process involves two stages: segmentation of shrimp and ruler regions in the image, followed by color comparison. For segmentation, deep learning models based on Mask R-CNN with a Feature Pyramid Network backbone were employed. Four model configurations were tested, using ResNet and ResNeXt backbones with and without a Boundary Loss function. Results show that the ResNet + Boundary Loss model achieved the highest segmentation performance, with IoU scores of 91.2% for shrimp and 87.8% for the color ruler. In the grading step, color similarity was evaluated in the CIELAB color space by computing Euclidean distances in the L (lightness) and a (red–green) channels, which align closely with human perception of shrimp coloration. The system achieved grading accuracy comparable to human experts, with a mean absolute error of 1.2, demonstrating its potential to provide consistent, objective, and transparent shrimp quality assessment. Full article

Background: Osteoarthritis (OA) is a major global health issue, increasing with aging and obesity. Current therapies mainly address symptoms without modifying disease progression. Platelet-rich growth factor (PRGF) therapy has potential regenerative effects through high cytokines and growth factors, but the outcomes of these therapies remain heterogeneous. This study explores the relationship between patient nutritional status, PRGF characteristics, and clinical outcomes in knee OA treatment. Methods: Baseline anthropometric, metabolic, and nutritional assessments of 41 patients with knee OA who underwent PRGF treatment were conducted. Blood samples were analyzed for metabolic and inflammatory markers. PRGF composition was assessed by protein content and extracellular vesicle (EV) markers. KOOS and VAS pain scores were collected at 2, 6, and 12 months. Responders improved KOOS by ≥10 points. An elastic-net regularized logistic model allowed the identification of the predictors of treatment response. Results: KOOS and VAS scores improved significantly at all follow-ups. At 2 months, the PRGF of responder patients showed higher PRGF G-CSF levels; at 12 months, increased CD49e and HLA-ABC expression. Higher BMI correlated with increased IL-6, IL-1ra, and resistin in PRGF samples. Hypercholesterolemic patients displayed altered EV profiles, with elevated levels of CD8 but reduced CD49e, HLA-ABC, CD42a, and CD31. Multivariate analysis identified BMI, biceps fold, fat percentage, red blood cell, platelet, and neutrophil counts as predictors of early response. Conclusions: Metabolic and immunological factors influence PRGF composition and clinical efficacy in knee OA. Baseline body composition and hematological parameters as key predictors of response, highlighting the potential of personalized PRGF therapy. Full article

Remote sensing provides a viable alternative for understanding landscape modifications attributed to beaver activity. The central objective of this study is to integrate multi-source remote sensing observations in tandem with a deep learning (DL) (convolutional neural net or transformer) model to automatically map beaver-influenced floodplain inundations (BIFI) over large geographical extents. We trained, validated, and tested eleven different model configurations in three architectures using five ResNet and five B-Finetuned encoders. The training dataset consisted of >25,000 manually annotated aerial image tiles of BIFIs in Connecticut. The YOLOv8 architecture outperformed competing configurations and achieved an F1 score of 80.59% and pixel-based map accuracy of 98.95%. SegFormer and U-Net++’s highest-performing models had F1 scores of 68.98% and 78.86%, respectively. The YOLOv8l-seg model was deployed at a statewide scale based on 1 m resolution multi-temporal aerial imagery acquired from 1990 to 2019 under leaf-on and leaf-off conditions. Our results suggest a variety of inferences when comparing leaf-on and leaf-off conditions of the same year. The model exhibits limitations in identifying BIFIs in panchromatic imagery in occluded environments. Study findings demonstrate the potential of harnessing historical and modern aerial image datasets with state-of-the-art DL models to increase our understanding of beaver activity across space and time. Full article

The pea aphid, Acyrthosiphon pisum, possesses horizontally acquired fungal carotenoid biosynthesis genes, enabling de novo production of carotenoids. Although carotenoids are known to contribute to photo-protection and coloration, their potential role in energy metabolism and population fitness under thermal stress is still unclear. This study investigated the interactive effects of temperature and light intensity on energy homeostasis and life-history traits in A. pisum. Using controlled environmental regimes, we demonstrate that light intensity significantly influenced the ATP content, development, and reproductive output of A. pisum at 12 °C, but not at 22 °C. Under cold stress (12 °C), high light intensity (5000 lux) increased ATP content by 240%, shortened the pre-reproductive period by 46%, extended reproductive duration by 62%, and enhanced the net reproductive rate (R₀) and intrinsic rate of increase (rₘ) compared to low light intensity (200 lux). These effects were abolished at the optimal temperature (22 °C), indicating a temperature-gated, light-dependent mechanism. Demographic analyses revealed that carotenoid-associated solar energy harvesting significantly improves fitness under cold conditions, likely compensating for metabolic depression. Our findings reveal a novel ecological adaptation in aphids, where horizontally transferred genes may enable light-driven energy supplementation during thermal stress. This study provides new insights into the physiological mechanisms underlying insect resilience to climate variability and highlights the importance of light as a key environmental factor in shaping life-history strategies in temperate agroecosystems. Full article

Methylphenidate (MPH) and its active enantiomer, dexmethylphenidate, are widely prescribed as first-line therapies for attention deficit hyperactivity disorder (ADHD), yet their increasing non-medical use highlights significant clinical and toxicological challenges. MPH blocks dopamine (DAT) and norepinephrine (NET) transporters, thereby elevating synaptic catecholamine levels. While this underpins therapeutic efficacy, prolonged or abusive exposure has been associated with mitochondrial impairment, disrupted bioenergetics, and excessive reactive oxygen species (ROS) production, which collectively contribute to neuronal stress and long-term neurotoxicity. Growing evidence suggests that the gut–brain axis may critically influence MPH outcomes: diet-induced shifts in microbiome composition appear to regulate oxidative stress, neuroinflammation, and drug metabolism, opening potential avenues for dietary or probiotic interventions. From a forensic perspective, the detection and monitoring of MPH misuse require advanced methodologies, including enantioselective LC–MS/MS and analysis of alternative matrices such as hair or oral fluids, which enable retrospective exposure assessment and improves abuse surveillance. Despite its established therapeutic profile, MPH remains a compound with a narrow balance between clinical benefit and toxicological risk. Future directions should prioritize longitudinal human studies, biomarker identification for abuse monitoring, and the development of mitochondria-targeted therapies to minimize adverse outcomes and enhance safety in long-term treatment. Full article

Coasts are critical ecological, economic and social interfaces between terrestrial and marine systems. The current upsurge in the acquisition and availability of remote sensing datasets, such as Landsat remote sensing data series, provides new opportunities for analyzing multi-decadal coastal changes and other components of coastal risk. The emergence of machine learning-based techniques represents a new trend that can support large-scale coastal monitoring and modeling using remote sensing big data. This study presents a comprehensive multi-decadal analysis of coastal changes for the period from 1990 to 2024 using Landsat remote sensing data series along the eastern and southern coasts of Peninsular Malaysia. These coastal regions include the states of Kelantan, Terengganu, Pahang, and Johor. An innovative approach combining deep learning-based shoreline extraction with the Digital Shoreline Analysis System (DSAS) was meticulously applied to the Landsat datasets. Two semantic segmentation models, U-Net and DeepLabV3+, were evaluated for automated shoreline delineation from the Landsat imagery, with U-Net demonstrating superior boundary precision and generalizability. The DSAS framework quantified shoreline change metrics—including Net Shoreline Movement (NSM), Shoreline Change Envelope (SCE), and Linear Regression Rate (LRR)—across the states of Kelantan, Terengganu, Pahang, and Johor. The results reveal distinct spatial–temporal patterns: Kelantan exhibited the highest rates of shoreline change with erosion of −64.9 m/year and accretion of up to +47.6 m/year; Terengganu showed a moderated change partly due to recent coastal protection structures; Pahang displayed both significant erosion, particularly south of the Pahang River with rates of over −50 m/year, and accretion near river mouths; Johor’s coastline predominantly exhibited accretion, with NSM values of over +1900 m, linked to extensive land reclamation activities and natural sediment deposition, although local erosion was observed along the west coast. This research highlights emerging erosion hotspots and, in some regions, the impact of engineered coastal interventions, providing critical insights for sustainable coastal zone management in Malaysia’s monsoon-influenced tropical coastal environment. The integrated deep learning and DSAS approach applied to Landsat remote sensing data series provides a scalable and reproducible framework for long-term coastal monitoring and climate adaptation planning around the world. Full article

The construction industry, as a major contributor to greenhouse gas emissions, urgently requires sustainable development solutions to achieve the Net Zero Emission Goal. Magnesium oxide (MgO)-based cementitious composites have emerged as promising alternatives due to their ability to reduce environmental impact and their potential to enhance structural integrity. Despite these advantages, limitations such as poor resistance to harsh environmental conditions and concerns over long-term durability continue to restrict their broader application. To better understand these strengths and limitations, this review investigates the influence of MgO; supplementary cementitious materials (SCMs) such as fly ash, silica fume, and rice husk ash. It also examines fibers, including polyethylene (PE), polypropylene (PP), polyvinyl alcohol (PVA), glass, sisal, and cellulose, and their effect on the mechanical and durability properties of MgO-based composites. Mechanical performance is assessed through compressive and tensile strength, while durability is evaluated in terms of porosity, permeability, water absorption, shrinkage (autogenous and drying), and carbonation resistance. Key challenges and future research directions to promote the use of MgO composites in sustainable construction are also identified. Full article