Search Results (68)

This study explored key predictors of teachers’ readiness for inclusive education, focusing on training, perceived success (self-efficacy), experience with students with special educational needs (SEN), and years of service. A total of 319 teachers completed questionnaires assessing professional preparation and four readiness dimensions identified through exploratory factor analysis: (F1) Teaching Adaptation and Collaborative Practices, (F2) Classroom Management and Behavioral Skills, (F3) Positive Attitudes toward Inclusion and Diversity, and (F4) Willingness to Cooperate and Comply. Multiple linear regression revealed that self-efficacy consistently predicted all four dimensions, underscoring its central motivational role in inclusive teaching. Training was positively associated with F1, while its effect on F2 was not significant. Experience with SEN predicted F2 and F4, suggesting that direct classroom exposure enhances behavioral management and collaborative engagement. Years of service predicted only F3, indicating that professional experience primarily fosters positive attitudes toward inclusion. Overall, the findings highlight that effective inclusive practices require transformative professional learning and a synergistic combination of strong self-efficacy, structured training, and experiential engagement, with each factor contributing differentially to specific aspects of teacher readiness. Full article

Within the context of global climate change, the hydrological and sediment load dynamics in the Huai River Basin are expected to continue evolving due to intensified human activities and environmental changes. Effective river management requires a clear understanding of the magnitude, causes, and characteristics of these changes, coupled with insight into the dynamic response processes of the river channel. This study applied a suite of statistical methods, including the Mann–Kendall test, Sen’s slope estimator, Pettitt’s test, double mass curve, and morphological analysis, to examine trends in streamflow and sediment load at two hydrological stations in the mid-Huai River from 1982 to 2016, and to assess channel evolution between Wujiadu and Xiaoliuxiang. The results indicate that: (1) both hydrological stations exhibited no significant decrease in annual streamflow, but a significant reduction in sediment load, with a change point detected in 1991 at Wujiadu Station; (2) compared to 1982–1990, the mean streamflow and sediment load decreased by 23% and 50% during 1991–2016, with a significant shift in the streamflow-sediment relationship; (3) while temperature and evapotranspiration increased significantly, precipitation remained relatively stable, indicating that climate change had a minor effect on hydrological elements, and sediment load reduction was primarily driven by large-scale ecological restoration and engineering activities; and (4) differential channel adjustments were observed in response to reduced sediment supply and human activities, modulated by local boundary conditions. Erosion occurred in the WJD section, resulting in a transformation from a U-shape to a V-shape cross-section, whereas the XLX section remained stable with a local adverse gradient. This study reveals the complex mechanisms of hydro-sedimentary and channel evolution under human dominance, offering scientific support for the sustainable management of the Huai River basin and similar regulated rivers. Full article

The study of tropical landscape dynamics is of critical importance, particularly within protected areas, for evaluating ecosystem functioning and the effectiveness of natural conservation efforts. This study aims to identify landscape dynamics within the Dong Nai Biosphere Reserve (including Cat Tien National Park and the Ma Da Forest) using remote sensing (Landsat and others) and geographic information system methods. The analysis is based on changes in the Enhanced Vegetation Index (EVI), land cover transformations, landscape metrics (Class area, Percentage of Landscape and others), and natural landscape fragmentation, as well as a spatio-temporal assessment of anthropogenic impacts on the area. The results revealed structural changes in the landscapes of the Dong Nai Biosphere Reserve between 2000 and 2024. According to Sen’s slope estimates, a generally EVI growth was observed in both the core and buffer zones of the reserve. This trend was evident in forested areas as well as in regions of the buffer zone that were previously occupied by highly productive agricultural land. An analysis of Environmental Systems Research Institute (ESRI) Land Cover and Land Cover Climate Change Initiative (CCI) data confirms the relative stability of land cover in the core zone, while anthropogenic pressure has increased due to the expansion of agricultural lands, mosaic landscapes, and urban development. The calculation of landscape metrics revealed the growing isolation of natural forests and the dominance of artificial plantations, forming transitional zones between natural and anthropogenically modified landscapes. The human disturbance index, calculated for the years 2000 and 2024, shows only a slight change in the average value across the territory. However, the coefficient of variation increased significantly by 2024, indicating a localized rise in anthropogenic pressure within the buffer zone, while a reduction was observed in the core zone. The practical significance of the results obtained lies in the possibility of their use for the management of the Dongnai biosphere Reserve based on a differentiated approach: for the core and the buffer zone. There should be a ban on agriculture and development in the core zone, and restrictions on urbanized areas in the buffer zone. Full article

Fetal movement monitoring (FMM) is crucial for assessing fetal well-being, traditionally relying on clinical assessments or maternal perception, each with inherent limitations. This study presents a novel lightweight deep learning framework for real-time FMM on edge devices. Data were collected from 120 participants using a wearable device equipped with an inertial measurement unit, which captured both accelerometer and gyroscope data, coupled with a rigorous two-stage labeling protocol integrating maternal perception and ultrasound validation. We addressed class imbalance using virtual-rotation-based augmentation and adaptive clustering-based undersampling. The data were transformed into spectrograms using the Short-Time Fourier Transform, serving as input for deep learning models. To ensure model efficiency suitable for resource-constrained microcontrollers, we employed knowledge distillation, transferring knowledge from larger, high-performing teacher models to compact student architectures. Post-training integer quantization further optimized the models, reducing the memory footprint by 74.8%. The final optimized model achieved a sensitivity (SEN) of 90.05%, a precision (PRE) of 87.29%, and an F1-score (F1) of 88.64%. Practical energy assessments showed continuous operation capability for approximately 25 h on a single battery charge. Our approach offers a practical framework adaptable to other medical monitoring tasks on edge devices, paving the way for improved prenatal care, especially in resource-limited settings. Full article

The rapid industrialization and urbanization of the modern era caused widespread deforestation and ecological degradation, raising global concerns about sustainable planning, urban green space, and environmental governance. Around the turn of the 20th century, Guangdong Province in China suffered severe environmental decline due to extensive deforestation, threatening public health, ecological resilience, and urban livability. In response, returning Chinese intellectuals and foreign forestry experts introduced advanced Western forestry theories and practices to address these crises and promote green urban development. This study examines how these transnational forestry ideas were ideologically embraced, locally adapted, and institutionally embedded in modern Guangdong’s afforestation and urban greening efforts. Drawing on a systematic review of historical literature, forestry journals, and government archives, it identifies three key developments. (1) In ideology, figures such as Yat-sen Sun and German forester Fenzel played vital roles in raising public awareness of afforestation. (2) In practice, Guangdong developed a diversified greening model integrating commemorative, ecological, and aesthetic functions. This included transforming Arbor Day into a civic ritual honoring Yat-sen Sun, establishing nurseries and forest farms for large-scale afforestation, systematically planting street trees in urban centers, and creating forest parks that combined conservation, recreation, and historical commemoration. (3) In regulation, Guangdong formulated forestry laws inspired by Western models. By this way, Guangdong effectively addressed the management challenges in urban greening practices. It should also be emphasized that these modern-era practices have persisted in Guangdong, and their historical experience provides a valuable reference for present-day urban greening. Additionally, Fenzel’s methods for planning nurseries and forest farms can be seen as early prototypes of “evidence-based planning”. By highlighting a historically grounded yet under-explored case, this research offers new insights into the long-term evolution of urban greening strategies and provides lessons for current global efforts in sustainable land use and resilient urban design. Full article

Rapid urbanization in semi-arid heritage cities is accelerating land use/land cover (LULC) transitions, with critical implications for local climate regulation, surface energy balance, and environmental sustainability. This study investigates Jaipur, Jodhpur, and Udaipur (Rajasthan, India) between 2018 and 2024 to assess the influence of spatio-temporal dynamics of LULC on urban surface metrics. Multi-temporal satellite datasets were used to derive the index-based built-up index (IBI), surface urban heat island intensity (SUHI), Albedo, urban thermal field variance index (UTFVI), and bare soil index (BSI). The results reveal substantial built-up expansion—most pronounced in Udaipur (+26.7%)—coupled with vegetation loss (up to −23.8% in Jaipur) and progressive albedo decline (Sen’s slope ≈ −0.002 yr⁻¹). These transformations highlight suppressed surface reflectivity and enhanced heat absorption. A key and novel finding is the emergence of a counter-intuitive surface urban cool island (SUCI) effect, whereby urban cores exhibited daytime cooling and nighttime warming relative to rural surroundings. This anomaly is attributed to the rapid heating and poor nocturnal heat retention of bare, sparsely vegetated rural soils, contrasted with the thermal inertia and shading of urban surfaces. By documenting negative SUHI patterns and explicitly linking them to LULC trajectories, this study advances the understanding of urban climate dynamics in semi-arid contexts. The findings underscore the need for climate-sensitive planning—strengthening peri-urban green belts, regulating impervious expansion, and adopting albedo-enhancing construction materials—while safeguarding cultural heritage. More broadly, the study contributes empirical evidence from climatically vulnerable yet culturally significant cities, offering insights relevant to global SUHI research and sustainable urban development. Full article

The primary objective of the presented study is to develop a comprehensive system for the acquisition of surface electromyographic (sEMG) data and to perform time–frequency analysis aimed at extracting discriminative features for the classification of hand gestures intended for the control of a simplified bionic hand prosthesis. The proposed system is designed to facilitate precise finger gesture execution in both prosthetic and robotic hand applications. This article outlines the methodology for multi-channel sEMG signal acquisition and processing, as well as the extraction of relevant features for gesture recognition using artificial neural networks (ANNs) and other well-established machine learning (ML) algorithms. Electromyographic signals were acquired using a prototypical LPCXpresso LPC1347 ARM Cortex M3 (NXP, Eindhoven, Holland) development board in conjunction with surface EMG sensors of the Gravity OYMotion SEN0240 type (DFRobot, Shanghai, China). Signal processing and feature extraction were carried out in the MATLAB 2024b environment, utilizing both the Fourier transform and the Hilbert–Huang transform to extract selected time–frequency characteristics of the sEMG signals. An artificial neural network (ANN) was implemented and trained within the same computational framework. The experimental protocol involved 109 healthy volunteers, each performing five predefined gestures of the right hand. The first electrode was positioned on the brachioradialis (BR) muscle, with subsequent channels arranged laterally outward from the perspective of the participant. Comprehensive analyses were conducted in the time domain, frequency domain, and time–frequency domain to evaluate signal properties and identify features relevant to gesture classification. The bionic hand prototype was fabricated using 3D printing technology with a PETG filament (Spectrum, Pęcice, Poland). Actuation of the fingers was achieved using six MG996R servo motors (TowerPro, Shenzhen, China), each with an angular range of ${180}^{\circ }$, controlled via a PCA9685 driver board (Adafruit, New York, NY, USA) connected to the main control unit. Full article

Inclusive music education refers to the use of musical experiences and practices as tools for promoting participation, equity, and meaningful engagement among all learners, including those with Special Educational Needs (SEN). Music education has long been recognized not only for its value in emotional expression and cultural transmission but also for its cognitive and relational benefits. This entry examines the inclusive and transformative potential of music, highlighting how it can foster equitable, accessible, and culturally relevant learning environments. Drawing from pedagogy, neuroscience, and educational technology, the entry explores how music contributes to cognitive, emotional, and social development, with a focus on learners with SEN. It emphasizes the importance of early exposure to music, the strong connections between music and language acquisition, and the need to challenge persistent misconceptions about innate musical talent. The findings demonstrate that when supported by digital tools and educational games, music education becomes a powerful driver of inclusion, enhancing participation, relational dynamics, and cognitive engagement. The entry concludes by advocating for a reimagining of music not as a secondary subject, but as a foundational component of holistic and inclusive education, capable of building more empathetic, connected, and equitable societies. Full article

Intelligent Transportation Systems (ITSs) play a vital role in improving urban and regional mobility by reducing traffic congestion and enhancing trip planning. A key element of ITS is travel-time prediction, which supports informed decisions for both travelers and traffic management. While non-parametric models offer flexibility, they often require large datasets and significant computation. Parametric models, though easier to fit and interpret, are less adaptable. Fuzzy logic models, by contrast, provide robustness and scalability, adjusting to new data and changing conditions. This paper proposes a cascaded fuzzy logic system for highway travel-time prediction, using the Greenshields model as its reasoning foundation. The system consists of multiple fuzzy subsystems, each representing a highway segment. These subsystems transform traffic flow and density inputs into speed predictions through fuzzification, Greenshields-based rules, and defuzzification. The approach enables localized and segment-specific predictions, enhancing route planning and congestion avoidance. The system’s accuracy is evaluated by comparing its predictions with those of a regression model using real traffic data from the Sun Yat-Sen Highway in Taiwan. Simulation results confirm that the proposed model achieves reliable, adaptable travel-time forecasts, including for long-distance trips. Full article

Against the backdrop of global climate change and rapid urbanization, understanding the spatiotemporal dynamics and driving mechanisms of vegetation net primary productivity (NPP) is critical for ensuring regional ecological security and achieving carbon neutrality goals. This study focuses on the Yangtze River Delta Urban Agglomeration (YRDUA) and integrates multi-source remote sensing data with socioeconomic statistics. By combining interpretable machine learning (XGBoost-SHAP) with multiscale geographically weighted regression (MGWR), and incorporating Theil–Sen trend analysis and Mann–Kendall significance testing, we systematically analyze the spatiotemporal variations in NPP and its multiscale driving mechanisms from 2001 to 2020. The results reveal the following: (1) Total NPP in the YRDUA shows an increasing trend, with approximately 24.83% of the region experiencing a significant rise and only 2.75% showing a significant decline, indicating continuous improvement in regional ecological conditions. (2) Land use change resulted in a net NPP loss of 2.67 TgC, yet ecological restoration and advances in agricultural technology effectively mitigated negative impacts and became the main contributors to NPP growth. (3) The results from XGBoost and MGWR are complementary, highlighting the scale-dependent effects of driving factors—at the regional scale, natural factors such as elevation (DEM), precipitation (PRE), and vegetation cover (VFC) have positive impacts on NPP, while the human footprint (HF) generally exerts a negative effect. However, in certain areas, a dose–response effect is observed, in which moderate human intervention can enhance ecological functions. (4) The spatial heterogeneity of NPP is mainly driven by nonlinear interactions between natural and anthropogenic factors. Notably, the interaction between DEM and climatic variables exhibits threshold responses and a “spatial gradient–factor interaction” mechanism, where the same driver may have opposite effects under different geomorphic conditions. Therefore, a well-balanced combination of land use transformation and ecological conservation policies is crucial for enhancing regional ecological functions and NPP. These findings provide scientific support for ecological management and the formulation of sustainable development strategies in urban agglomerations. Full article

The current methods for removing thick clouds from remote-sensing images face significant limitations, including the integration of thick cloud images with synthetic aperture radar (SAR) ground information, the provision of meaningful guidance for SAR ground data, and the accurate reconstruction of textures in cloud-covered regions. To overcome these challenges, we introduce SAR-DeCR, a novel method for thick cloud removal in satellite remote-sensing images. SAR-DeCR utilizes a diffusion model combined with the transformer architecture to synthesize accurate texture details guided by SAR ground information. The method is structured into three distinct phases: coarse cloud removal (CCR), SAR-Fusion (SAR-F) and cloud-free diffusion (CF-D), aimed at enhancing the effectiveness of the thick cloud removal. In CCR, we significantly employ the transformer’s capability for long-range information interaction, which significantly strengthens the cloud removal process. In order to overcome the problem of missing ground information after cloud removal and ensure that the ground information produced is consistent with SAR data, we introduced SAR-F, a module designed to incorporate the rich ground information in synthetic aperture radar (SAR) into the output of CCR. Additionally, to achieve superior texture reconstruction, we introduce prior supervision based on the output of the coarse cloud removal, using a pre-trained visual-text diffusion model named cloud-free diffusion (CF-D). This diffusion model is encouraged to follow the visual prompts, thus producing a visually appealing, high-quality result. The effectiveness and superiority of SAR-DeCR are demonstrated through qualitative and quantitative experiments, comparing it with other state-of-the-art (SOTA) thick cloud removal methods on the large-scale SEN12MS-CR dataset. Full article

Nanomilling-driven effects on polyamorphic transitions are examined in tetra-arsenic biselenide As₄Se₂ alloy, which is at the boundary of the glass-forming region in the As-Se system, using multifrequency temperature-modulated DSC-TOPEM^® technique, supported by X-ray powder diffraction (XRPD) and micro-Raman spectroscopy analysis. As shown by XRPD analysis, this alloy reveals a glassy–crystalline nature due to rhombohedral As and cubic As₂O₃ (arsenolite) inclusions, which especially grew after milling in a PVP (polyvinylpyrrolidone) water solution. At the medium-range structure level, nanomilling-driven changes are revealed as the disruption of intermediate-range ordering and enhancement of extended-range ordering. The generalized molecular-to-network amorphization trend in this alloy is confirmed by the microstructure response revealed in the broadened and obscured features in micro-Raman scattering spectra collected for nanomilled specimens. Thermophysical heat-transfer phenomena are defined by molecular-to-network polyamorphic transformations activated under nanomilling. The domination of thioarsenide-type As₄Se_n entities in this alloy results in an abnormous nanomilling-driven network-enhanced glass transition temperature increase. The nanomilled alloys become notably stressed owing to the destruction of molecular thioarsenide and incorporation of their remnants into the newly polymerized arsenoselenide network. This effect is more pronounced in As₄Se₂ alloy subjected to dry nanomilling, while it is partly counterbalanced when this alloy is additionally subjected to wet milling in a PVP water solution, accompanied by the stabilization of the As₄Se₂/PVP nanocomposite. Full article

Social media has increasingly relied on memes as a tool for expressing opinions, making meme sentiment analysis an emerging area of interest for researchers. While much of the research has focused on English-language memes, under-resourced languages, such as Bengali, have received limited attention. Given the surge in social media use, the need for sentiment analysis of memes in these languages has become critical. One of the primary challenges in this field is the lack of benchmark datasets, particularly in languages with fewer resources. To address this, we used the MemoSen dataset, designed for Bengali, which consists of 4368 memes annotated with three sentiment labels: positive, negative, and neutral. MemoSen is divided into training (70%), test (20%), and validation (10%) sets, with an imbalanced class distribution: 1349 memes in the positive class, 2728 in the negative class, and 291 in the neutral class. Our approach leverages advanced deep learning techniques for multimodal sentiment analysis in Bengali, introducing three hybrid approaches. SentimentTextFormer is a text-based, fine-tuned model that utilizes state-of-the-art transformer architectures to accurately extract sentiment-related insights from Bengali text, capturing nuanced linguistic features. SentimentImageFormer is an image-based model that employs cutting-edge transformer-based techniques for precise sentiment classification through visual data. Lastly, SentimentFormer is a hybrid model that seamlessly integrates both text and image modalities using fusion strategies. Early fusion combines textual and visual features at the input level, enabling the model to jointly learn from both modalities. Late fusion merges the outputs of separate text and image models, preserving their individual strengths for the final prediction. Intermediate fusion integrates textual and visual features at intermediate layers, refining their interactions during processing. These fusion strategies combine the strengths of both textual and visual data, enhancing sentiment analysis by exploiting complementary information from multiple sources. The performance of our models was evaluated using various accuracy metrics, with SentimentTextFormer achieving 73.31% accuracy and SentimentImageFormer attaining 64.72%. The hybrid model, SentimentFormer (SwiftFormer with mBERT), employing intermediate fusion, shows a notable improvement in accuracy, achieving 79.04%, outperforming SentimentTextFormer by 5.73% and SentimentImageFormer by 14.32%. Among the fusion strategies, SentimentFormer (SwiftFormer with mBERT) achieved the highest accuracy of 79.04%, highlighting the effectiveness of our fusion technique and the reliability of our multimodal framework in improving sentiment analysis accuracy across diverse modalities. Full article

Polyamorphic transitions driven by high-energy mechanical milling (nanomilling) are studied in thioarsenide As₄Se_n-type glassy alloys obtained by melt quenching deviated from arsenic triselenide As₂Se₃ stoichiometry towards tetraarsenic pentaselenide (g-As₄Se₅) and tetraarsenic tetraselenide (g-As₄Se₄). This employs a multiexperimental approach based on powder X-ray diffraction (XRD) analysis complemented by thermophysical heat transfer, micro-Raman scattering (micro-RS) spectroscopy, and revised positron annihilation lifetime (PAL) analysis. Microstructure scenarios of these nanomilling-driven transformations in arsenoselenides are identified by quantum-chemical modeling using the authorized modeling code CINCA (the Cation Interlinked Network Cluster Approach). A straightforward interpretation of a medium-range structure response of a nanomilling-driven polyamorphism in the arsenoselenides is developed within the modified microcrystalline model. Within this model, the diffuse peak-halos arrangement in the XRD patterning is treated as a superposition of the Bragg-diffraction contribution from inter-planar correlations supplemented by the Ehrenfest-diffraction contribution from inter-atomic (inter-molecular) correlations related to derivatives of network As₂Se₃-type and molecular As₄Se₄-type conformations. Changes in the medium-range structure of examined glassy arsenoselenides subjected to nanomilling occur as an interplay between disrupted intermediate-range ordering and enhanced extended-range ordering. The domination of network-forming conformations in arsenoselenides deviated from As₂Se₃ stoichiometry (such as g-As₄Se₅) results in rather slight changes in their calorimetric heat-transfer and micro-RS responses. At the atomic-deficient level probed by PAL spectroscopy, these changes are accompanied by reduced positron trapping rate of agglomerated multiatomic vacancies and vacancy-type clusters in an amorphous As-Se network. Under an increase in As content beyond the g-As₄Se₅ composition approaching g-As₄Se₄, nanomilling-driven polyamorphic transitions, which can be classified as reamorphization (amorphous I-to-amorphous II) phase transitions, are essentially enhanced due to the higher molecularity of these glassy alloys enriched in thioarsenide-type As₄Se₄ cage-like molecular entities and their low-order network-forming derivatives. Full article

Chaohu Lake, as an important freshwater lake in China, mainly relies on surface runoff for water replenishment, and the environmental quality of the surrounding towns directly impacts the environment of Chaohu Lake. Given the characteristics of rich water resources and extensive river networks in the lake area, this paper utilizes the GEE platform and selects Landsat data from 1992 to 2022, taking Feidong County, one of the lake’s inlets, as the study area. We used the water benefit-based ecological index (WBEI) to monitor and evaluate the ecological quality of the study area and employ the Sen+MK trend analysis method to analyze the spatial-temporal characteristics of ecological quality changes. To explore the driving forces behind the spatial-temporal changes in the WBEI, this study selects land use type, elevation, slope, aspect, potential evapotranspiration, annual average precipitation, annual average temperature, and five characteristic factors used in the construction of the WBEI as influencing factors. Using the geo-detector method, the study analyzes the driving forces behind the spatial-temporal changes in the WBEI in the study area. Results show that the WBEI, considering water efficiency, integrates waterbody information into regional environmental quality assessments, comprehensively reflecting the ecological environment of lakeside cities. From 1992 to 2022, the WBEI of the study region shows an increasing trend, with an improved area accounting for 1110.42 km², or 51.21% of the total area. Among these, the significantly improved area covers 372.9789 km² or 17.2% of the total area, while the slightly improved area covers 737.4411 km², corresponding to 34.01% of the total area. Interaction types of influencing factors include bivariate enhancement and nonlinear enhancement, with the primary interactive factors affecting the ecological environment quality change in Feidong County being CLCD∩RVI; changes in land use and vegetation cover are the main driving forces behind the changes in ecological and environmental quality in Feidong County. From 1992 to 2022, the main land type transformations in the study area were from arable land to other land types, with a significant conversion of arable land to construction land, which is the main reason for the degradation of local ecological and environmental quality. The results of this study can provide practical references and theoretical support for ecological environment assessment, governance, and improvement in areas with abundant water resources. Full article