Search Results (271,112)

Current climatic conditions are drying peatland ecosystems, compromising carbon storage through increased decomposition and vegetation shifts. Large-scale monitoring is essential to quantify climate change impacts on vegetation and hydrology. PlanetScope high-resolution imagery (3 m pixel) over seven years (2017–2023) served as proof-of-concept for a central European peatland (Rzecin, Poland). The enhanced vegetation index (EVI) was selected based on ground validation (R = 0.9 vs. 0.8 for NDVI-normalised vegetation index). Phenological metrics (SOS—start of the season; EOS—end of the season; LOS—length of the season; POS—peak of the season; EVImax; amplitude; area) were derived via DATimeS from snow-free EVI time series. Trends were analysed using pixel-wise slopes, change-point detection (break ~2020–2021), paired correlations, subarea (P1–P4) behaviour, and PCA, alongside air temperature (Tair), precipitation, and water table depth (WTD). Results revealed LOS and peak EVI increased until 2020, a 2021 break, and a 2022–2023 recovery, signalling nonlinear vegetation reorganisation. Transitional mire floating mats (Sphagnum spp.–Carex spp.–Vaccinium oxycoccus) showed the longest seasons/highest greenness but weakest hydrometeorological links, implying rising internal dynamics. Phragmites mats, fern–sedge edges, and riparian willow differed in tolerance or sensitivity to WTD and precipitation oscillations. Tair dominated EVI seasonality across types, while WTD and precipitation controlled phenology and greenness in edges, showing better results with phase-aligned means. Vascular plants outpaced mosses in peak EVI and persistence, with patch-specific shifts. Full article

Background/Objectives: Accurate preoperative characterization of ovarian masses is essential for appropriate clinical management, particularly for borderline ovarian tumors (BOTs), which are less common and often difficult to distinguish from benign or malignant lesions on ultrasound. Although expert subjective ultrasound assessment achieves high diagnostic accuracy, limited availability of highly trained sonologists restricts its widespread application. Artificial intelligence-based approaches offer a potential solution; however, the low prevalence of BOTs restricts the development of robust deep learning models due to severe class imbalance. This study aimed to develop a Convolutional Neural Network (CNN)-based classifier enhanced with Generative Adversarial Networks (GANs) to improve the discrimination of ovarian masses as benign, malignant, or BOT using ultrasound images. Methods: A total of 3816 ultrasound images from 636 ovarian masses were retrospectively analyzed, including 390 benign lesions, 202 malignant tumors, and 44 BOTs. To address class imbalance, a Deep Convolutional GAN (DCGAN) was used to generate 2000 synthetic BOT images for data augmentation. A three-class ensemble CNN model integrating VGG16, ResNet50, and InceptionNetV3 architectures was developed. Performance was assessed on an independent test set and compared with a baseline model trained without DCGAN augmentation. Results: The incorporation of DCGAN-generated BOT images significantly enhanced classification performance. The BOT F1-score increased from 68.4% to 86.5%, while overall accuracy improved from 84.7% to 91.5%. For BOT identification, the final model achieved a sensitivity of 88.2% and specificity of 85.1%. Class-specific AUCs were 0.96 for benign lesions, 0.94 for malignant tumors, and 0.91 for BOTs. Conclusions: DCGAN-based augmentation effectively expands limited ultrasound datasets and improves CNN performance, particularly for BOT detection. This approach demonstrates potential as a decision support tool for preoperative assessment of ovarian masses. Full article

Managing electronic waste (e-waste) in the Philippines is a critical challenge, no with roughly 80% handled by an informal sector using hazardous methods. This study develops a context-specific Circular Economy (CE) framework for urban Manila by quantifying the behavioral, institutional, and socio-economic factors influencing recycling efficiency. Using a hybrid methodology, quantitative data were collected from 435 informal recyclers. Structural Equation Modeling (SEM) supported 16 of 18 hypothesized pathways from the Theory of Planned Behavior (TPB), though Perceived Behavioral Control did not directly affect Intention. An Artificial Neural Network (ANN) sensitivity analysis identified economic factors, Income Level (84.01%) and Financial Incentives (82.86%), as the dominant predictors of behavior, followed by the Cultural–Cognitive Pillar (80.98%). This necessitates modifying the TPB for subsistence economies, where economic survival acts as a super-moderator. The resulting CE framework mandates inclusive policies, prioritizing “Economic First Interventions” like buy-back schemes to equitably integrate informal recyclers into formal Extended Producer Responsibility systems. Full article

Medical image classification is a fundamental task in medical image analysis and underpins a wide range of clinical applications, including dermatological screening, retinal disease assessment, and malignant tissue detection. In recent years, diffusion models have demonstrated promising potential for medical image classification owing to their strong representation learning capability. However, existing diffusion-based classification methods often rely on oversimplified prior modeling strategies, which fail to adequately capture the intrinsic multi-scale semantic information and contextual dependencies inherent in medical images. As a result, the discriminative power and stability of feature representations are constrained in complex scenarios. In addition, fixed noise injection strategies neglect variations in sample-level prediction confidence, leading to uniform perturbations being imposed on samples with different levels of semantic reliability during the diffusion process, which in turn limits the model’s discriminative performance and generalization ability. To address these challenges, this paper proposes a Confidence-Guided Adaptive Diffusion Network (CGAD-Net) for medical image classification. Specifically, a hybrid prior modeling framework is introduced, consisting of a Hierarchical Pyramid Context Modeling (HPCM) module and an Intra-Scale Dilated Convolution Refinement (IDCR) module. These two components jointly enable the diffusion-based feature modeling process to effectively capture fine-grained structural details and global contextual semantic information. Furthermore, a Confidence-Guided Adaptive Noise Injection (CG-ANI) strategy is designed to dynamically regulate noise intensity during the diffusion process according to sample-level prediction confidence. Without altering the underlying discriminative objective, CG-ANI stabilizes model training and enhances robust representation learning for semantically ambiguous samples.Experimental results on multiple public medical image classification benchmarks, including HAM10000, APTOS2019, and Chaoyang, demonstrate that CGAD-Net achieves competitive performance in terms of classification accuracy, robustness, and training stability. These results validate the effectiveness and application potential of confidence-guided diffusion modeling for two-dimensional medical image classification tasks, and provide valuable insights for further research on diffusion models in the field of medical image analysis. Full article

Lesson preparation plays a crucial role in structuring and organizing the teaching process. However, traditional lesson design and presentation creation require teachers to spend a considerable amount of time reviewing the literature and organizing materials. Therefore, developing an intelligent and multimodal technology capable of automatically generating lesson materials holds great significance. Such technology can potentially reduce teachers’ workloads and improve the efficiency and quality of lesson preparation, as indicated by teacher satisfaction and preference judgments. In this paper, we introduce LessonAgent, a multimodal and interactive pipeline that leverages large language models (LLMs) to generate lesson plans, presentations, and podcasts. Our system enhances the quality of generated materials through diverse input modalities, refined generation mechanisms, and interactive feedback with teachers. Specifically, we present the Plan10k dataset—a high-quality bilingual collection of lesson plans—and employ it to train and evaluate our framework. The pipeline consists of three main modules: a query rewriting module that handles multimodal teacher inputs (e.g., textual concepts, images, or textbook excerpts), a lesson plan generation module that produces structured content, and a chapter correction module that integrates retrieval-based tools to improve factual accuracy and contextual relevance. Furthermore, teachers can interact with intermediate results, allowing adaptive refinement throughout the generation process. Based on the generated lesson plans, the framework further produces corresponding visual presentations and podcasts, forming a comprehensive multimodal teaching assistant system. Extensive experiments and teacher evaluations demonstrate the superior performance and satisfaction of our approach. Full article

Background/Objectives: Cytoreductive surgery (CRS) combined with hyperthermic intraperitoneal chemotherapy (HIPEC) may modify the prognostic impact of established clinical and surgical factors in advanced ovarian cancer. A retrospective study was conducted to identify independent predictors of survival, recurrence patterns and major postoperative complications in the HIPEC setting. Methods: In total, 265 women with advanced-stage ovarian cancer operated on between 2015 and 2019 were included. Patients were treated with CRS, with or without HIPEC. Patients’ characteristics, oncological and follow-up information were collected. Results: In total, 62.3% underwent primary CRS, and 39.2% received HIPEC, with complete or near-complete cytoreduction (CC-0/1) achieved in 85.6%. Major complications (≥grade III) were recorded in 16.6% of the patients. HIPEC, high peritoneal cancer index (PCI), and greater intraoperative blood loss were found to independently increase the odds of major postoperative complications, while prior surgery was the only independent predictor of local-regional recurrence. The median follow-up was 34 months, with a median progression-free (PFS) and overall survival (OS) of 26 and 77 months, respectively. Multivariable analysis identified systematic lymphadenectomy and serous histology as independent predictors for PFS and also for OS, with the addition of CC-3. Survival analysis with Kaplan–Meier curves revealed that CRS plus HIPEC was associated with significantly better OS, but not PFS, compared with CRS alone. Conclusions: Systematic lymphadenectomy, serous histology, and absence of macroscopic gross residual disease emerge as key independent favorable prognostic factors in the HIPEC era, while prior surgery adversely affects loco-regional control. CRS plus HIPEC improved OS in this specific regimen’s perfusion protocol but was associated with higher major postoperative complications, underscoring the need for careful patient selection. Full article

Coordinated mitigation of greenhouse gases (GHGs) and air pollutants (APs) offers an effective strategy to address climate and air quality challenges, yet systematic evaluations in medium-sized industrial cities remain limited, despite their coal-dependent energy systems and emission-intensive manufacturing that disproportionately shape national emission trajectories. Thus, this study focuses on Weifang, a representative industrial city in Shandong Province, developing a high-resolution, multi-pollutant inventory and applying quantitative synergy indices to characterize emission patterns, sectoral contributions, and hotspot regions. In 2023, Weifang’s total emissions comprised 114.54 million metric tons (Mt) CO₂, 121.91 thousand metric tons (kt) CH₄, and 27.67 kt N₂O, alongside major APs including CO (662.99 kt), TSP (154.44 kt), and NOx (100.83 kt). Industrial sources and electricity-heat production contributed over 80% of CO₂ and SO₂, while agriculture dominated CH₄ (59.5%) and N₂O (40.5%). Mobile sources accounted for 66.6% of NOx, over 20% of VOCs, and 61.4% of CO. Spatially, suburban areas produced over 65% of total emissions due to heavy industry and agriculture, whereas the urban core exhibited higher intensities but lower total contributions. Bivariate and integrated synergy indices revealed stronger SO₂-NOx-CO₂ synergies in the urban core, while suburban emissions were more heterogeneous and spatially dispersed. Synergy analysis indicated strong SO₂-CO₂ co-variation from shared industrial sources but weak NOx-CO₂ correlations due to divergent origins. Hotspot mapping identified industrial parks, power plants, steel zones, and suburban agriculture as priority control areas. These findings demonstrate that source-specific measures are critical to maximizing co-benefits. The proposed methodological framework offers transferable insights for evaluating emission synergies in other industrial cities. Full article

Wood-based particleboards are a key component of sustainable building materials due to their renewable and low-carbon nature. However, their susceptibility to microbial contamination poses a significant challenge to indoor environmental quality and durability, limiting their alignment with the principles of a healthy and circular built environment. In this study, a sustainable antibacterial modification strategy was developed by employing natural montmorillonite (MMT) as a renewable mineral carrier to address the challenge. A synergistic antibacterial agent (Cu²⁺/ZnO@MMT-O) was engineered via ion exchange and co-precipitation, effectively immobilizing Cu²⁺ ions and ZnO nanoparticles within the MMT structure. This process preserved the layered structure of the carrier while simultaneously enhancing its specific surface area and mesoporosity. Antibacterial tests revealed that the Cu²⁺/ZnO@MMT-O exhibited markedly higher antibacterial activity against Escherichia coli and Staphylococcus aureus than single-component counterparts, indicating a pronounced synergistic effect. At an additive loading of 1.25%, the particleboards exhibited antibacterial rates exceeding 99% against both tested bacteria, while their mechanical properties (MOR 10.65 MPa, MOE 2304.40 MPa, and IB 0.29 MPa) and dimensional stability (24 h TS 16.31%) compliant with national standards. Overall, this work presents a practical and sustainable approach to enhancing the hygienic performance of renewable wood composites through the integration of mineral carriers with synergistic nanoscale antibacterial mechanisms, thereby contributing to healthier indoor environments and the development of green and healthy residential materials. Full article

Common-mode voltage (CMV) is a critical concern in motor drive applications employing multilevel inverters, as it can lead to significant issues such as high-frequency noise, electromagnetic interference, and motor bearing degradation. These effects can compromise the reliability, reduce the operational lifespan of electric machines, and introduce safety hazards. In this study, an enhanced Direct Torque Control (DTC) strategy incorporating Space Vector Modulation (SVM) is proposed to specifically address CMV-related challenges in induction motors (IM) driven by a three-level Neutral-Point-Clamped (NPC) inverter. The proposed DTC scheme utilizes a specialized modulation technique that effectively mitigates CMV while also minimizing current harmonic content, and torque and flux ripples with a constant switching frequency. The developed SVM algorithm simplifies the three-level space vector representation into six equivalent two-level diagrams, enabling more efficient control. The zero-voltage vector is synthesized virtually by combining two active vectors within a two-level hexagonal structure. The effectiveness of the proposed DTC approach is validated through both simulation and Hardware-In-the-Loop (HIL) testing. Compared to the conventional DTC method, the proposed solution demonstrates superior performance in CMV minimization and leakage current reduction. Notably, it limits the CMV amplitude to V_dc/6, a significant improvement over the V_dc/2 typically observed with the standard DTC approach. Full article

Objectives: This randomized, controlled, parallel-group study was conducted to evaluate the effectiveness of daily almond consumption on acne lesion counts, skin hydration, sebum production, and skin microflora composition in 18–35-year-old young adults with acne vulgaris in Mumbai, India. Methods: A defined amount of whole, unsalted almonds with skin (60 g) was provided to the experimental group (n = 36). The control group (n = 38) received isocaloric cereal-pulse-based snack varieties. The primary endpoints were changes in inflammatory, non-inflammatory, and total acne lesion counts after 20 weeks of supplementation. Secondary endpoints included changes in facial sebum, hydration levels, skin morphology and microflora, and selected biochemical parameters. Results: At week 20, the almond group showed greater reductions in total lesion counts (−22.2% vs. −9.8%), inflammatory lesion counts (−8.3% vs. +12%), and non-inflammatory lesion counts (−26.1% vs. −20.4%) than controls. Objective lesion volume, area, and height measures for both single and clustered acne decreased in the almond group (p ≤ 0.001). Microbial diversity increased, with the Shannon index (2.6 to 3.4 (p = 0.039) and the Chao1 richness index (266.9 → 835.2; p < 0.001) showing improvements at endline. Moreover, significant post-intervention changes in the psychosocial outcomes, such as the acne-related quality of life scores (p < 0.001) and anxiety symptoms (p = 0.016), were observed in the almond group. Conclusions: Daily almond consumption reduced acne lesion count and improved skin microbial diversity and acne-specific quality of life, highlighting its potential to complement standard acne treatments and support skin health. Full article

The codling moth, Cydia pomonella (Linnaeus, 1758) (Lepidoptera: Tortricidae), was first detected in Xinjiang, China, in 1953 and has since spread to nine provinces, with its distribution continuing to expand into other apple- and pear-producing regions. In this study, we combined the Biomod2 model with Monte Carlo simulations to perform a spatially explicit, pixel-level assessment of the moth’s potential habitat suitability and associated economic impacts in China’s major fruit-producing areas. Results indicate that temperature is the primary factor limiting its distribution, followed by human activities, while topography plays a regulatory role at local scales. The Loess Plateau and Bohai Rim regions were identified as core suitable areas, with moderate suitability in the Northern Cold region and Xinjiang and lower suitability in the Southwest and Yangtze River Basin. Pearson correlation analysis revealed weak spatial coupling between suitable habitats and fruit yields. Monte Carlo simulations showed that potential economic losses vary spatially across regions and crop types. These findings suggest that the codling moth’s suitability differs among regions; high-yield areas do not necessarily face higher invasion risk, but once an invasion occurs, economic losses tend to be concentrated and severe. Accordingly, early warning and region-specific, differentiated management should be prioritized in key areas to mitigate damage. Full article

The study aims to develop a conceptual model for immersive chemical hazard response training that explicitly addresses four core operational constraints: time pressure, uncertainty, teamwork, and procedural/psychomotor precision. The model responds to the need for collaborative and mistake-driven training approaches in high-risk contexts. A design-oriented, theory-informed approach is applied, combining the identification of training requirements characteristic of chemical hazard response and the formulation of core operational constraints shaping the training design with the specification of CAVE affordances, a four-dimensional instructional design framework (cognitive, emotional, social, and psychomotor), conceptual alignment of scenario components with selected Sustainable Development Goals (SDGs 3, 4, 11, and 16), and a preliminary expert-based content appraisal. Results are presented as a design-oriented outcome in the form of a conceptual framework, accompanied by an illustrative scenario-based instantiation and an expert-based content appraisal demonstrating internal coherence and practical plausibility (I-CVI = 0.80–1.00; S-CVI/Ave = 0.93). Conclusions indicate that the proposed model serves as a structured instructional and scenario-design reference for immersive chemical hazard response training, positioning CAVEs as pedagogically organised learning spaces rather than as standalone simulation technologies. Further implications relate to the transferability of the model to sustainability-oriented response training across other high-risk domains. Empirical evaluation of learning processes, performance outcomes, and transfer to operational practice is identified as a necessary next step for future research. Full article

Hydrogen embrittlement poses a critical threat to the durability of metallic components in emerging hydrogen energy infrastructure. Reliable fatigue life assessment in hydrogen-rich environments is, however, severely constrained by the high cost and low throughput of high-pressure testing, resulting in characteristically sparse experimental datasets. Conventional empirical fatigue models struggle to capture hydrogen–mechanical coupling effects, while purely data-driven approaches often suffer from severe overfitting under data-scarce conditions. To address this challenge, this study develops a physics-enhanced learning framework that integrates established fracture mechanics principles with machine learning. Using high-strength GS80A steel as a case study, two complementary strategies are introduced. First, a physically augmented input strategy reformulates raw experimental variables into dimensionless physical descriptors derived from the Basquin and Goodman relations, thereby reducing the complexity of the learning space. Second, a physics-regularized ensemble strategy combines deterministic physical predictions with neural network outputs through a dual-pathway inference scheme, ensuring physically admissible behavior during extrapolation. An automated hyperparameter selection module is further employed to establish a robust data-driven baseline. Comparative evaluation against optimized multi-layer perceptron and support vector regression models demonstrates that the proposed framework significantly improves predictive robustness in small-sample regimes. Specifically, the coefficient of determination (R²) exceeds 0.975, with the root mean square error (RMSE) reduced by approximately 70% compared to the pure data-driven baseline. By systematically embedding mechanistic priors into the learning process, the proposed approach provides a reliable and interpretable tool for fatigue assessment of metallic components operating in hydrogen environments. Full article

In the field of remote sensing object detection (RSOD), significant challenges remain, including the vast field of view in remote sensing images, the diverse array of target categories, and complex backgrounds. Traditional methods for processing remote sensing images face limitations in this context. While convolutional neural networks (CNNs) can expand the receptive field by utilizing kernels of different sizes, larger kernels increase the number of parameters and introduce noise. Vision Transformers (ViT) achieve global receptive fields through their global attention mechanism. However, their quadratic computational complexity struggles with high-resolution images. Recently, Mamba has gained prominence in image processing. Its unique four-directional scanning mechanism allows focusing on regions of interest from multiple angles while maintaining linear model complexity and achieving global receptive fields. In this work, we propose a new CNN–Mamba network (CMNet) that synergistically exploits the advantages of both architectures. Specifically, we employ VMamba(VM) to extract global semantic features from images. Moreover, we design a multi-scale local feature extraction (MLFE) module, which captures local texture information and edge details through the local feature extraction (LFE) and the global attention module (GAM). The synergy between VMamba and MLFE creates complementary global–local features. To address the representational differences between these two kinds of features, we further design a feature cross-complementary (FCC) module. This module achieves cross-complementarity of features, solving feature disparity issues. Our CMNet achieves 79.38% mAP50 on the DOTA v1.0 dataset and 90.60% mAP50 on the HRSC dataset, outperforming existing state-of-the-art approaches. Full article

This paper introduces the Sextuple Helix Innovation Model as an extension of the Quintuple Helix Innovation Model by Carayannis and Campbell. The epistemic perspective considers the understanding of generative AI (GenAI) as a sixth helix of knowledge production in sustainable innovation ecosystems. Accordingly, the knowledge economy of GenAI will be discussed in the context of the innovation processes of cultural and creative industries. While GenAI is largely described in social discourses as a disruptive tool that potentially replaces human creativity and thus destroys jobs, this paper discusses GenAI as an entity with a specific knowledge economy that contributes to creative innovation processes in exchange with the five established helices of science, politics, economy, the media- and culture-based public, and the natural environment of societies. With the help of a scoping review, a comprehensive evaluation of academic literature from the fields of creative industries, cultural policy, and innovation research, based on a constructivist epistemological approach and knowledge economy theory, confirmed that the positioning of GenAI as an epistemic actor in the Sextuple Helix Innovation Model reframes and redefines discourses beyond the prevailing narratives of disruption and regulation. Full article