Search Results (25,788)

Deep learning models based on supervised learning rely heavily on large annotated datasets and particularly in the context of medical image segmentation, the requirement for pixel-level annotations makes the labeling process labor-intensive, time-consuming and expensive. To overcome these limitations, self-supervised learning (SSL) has emerged as a promising alternative that learns generalizable representations from unlabeled data; however, existing SSL frameworks often employ highly parameterized encoders that are computationally expensive and may lack robustness in label-scarce settings. In this work, we propose a scattering-based SSL framework that integrates Wavelet Scattering Networks (WSNs) and Parametric Scattering Networks (PSNs) into a Bootstrap Your Own Latent (BYOL) pretraining pipeline. By replacing the initial stages of the BYOL encoder with fixed or learnable scattering-based front-ends, the proposed method reduces the number of learnable parameters while embedding translation-invariant and small deformation-stable representations into the SSL pipeline. The pretrained encoders are transferred to a U-Net and fine-tuned for cardiac image segmentation on two datasets with different imaging modalities, namely, cardiac cine MRI (ACDC) and cardiac CT (CHD), under varying amounts of labeled data. Experimental results show that scattering-based SSL pretraining consistently improves segmentation performance over random initialization and ImageNet pretraining in low-label regimes, with particularly pronounced gains when only a few labeled patients are available. Notably, the PSN variant achieves improvements of 4.66% and 2.11% in average Dice score over standard BYOL with only 5 and 10 labeled patients, respectively, on the ACDC dataset. These results demonstrate that integrating mathematically grounded scattering representations into SSL pipelines provides a robust and data-efficient initialization strategy for cardiac image segmentation, particularly under limited annotation and domain shift. Full article

Green space equity is increasingly recognized as a critical environmental condition for healthy aging, yet existing research often overlooks how different green space attributes—accessibility and diversity—are associated with distinct dimensions of older adults’ health. Limited attention has been paid to their nonlinear threshold effects or to the social pathways through which green spaces influence health outcomes. Using the United States county-level panel data from 2020 to 2023, this study integrates fixed-effects models, Extreme Gradient Boosting (XGBoost), and mediation analysis to examine the associations between green accessibility measured by the Two-Step Floating Catchment Area (2SFCA) method, and green diversity measured by the Shannon Index, on the general, physical, and mental health of older adults. Findings indicate that (1) higher green accessibility is associated with better general health, whereas green diversity shows a stronger association with physical health, reflecting its link to more heterogeneous ecosystem service environments. (2) Green accessibility demonstrates the threshold effect, in which the strength of association with health becomes steeper once accessibility approaches higher levels. (3) Green space equity is linked to health partly through social structures. Education clustering and marital stability mediate the associations with general health, while mental health appears to depend more on the social interaction opportunities embedded within green environments than on their physical attributes alone. The study proposes an integrated “physical environment–social structure–health outcome” framework and a threshold-oriented spatial intervention strategy, highlighting the need to prioritize improvements in green accessibility in underserved areas and prioritizing green diversity and age-friendly social functions where accessibility is already high. These findings offer evidence for designing inclusive, health-oriented urban environments for aging populations. Full article

We present an advanced statistical framework for estimating the relative intensity of astrophysical event distributions (e.g., Gamma-Ray Bursts, GRBs) on the sky tofacilitate population studies and large-scale structure analysis. In contrast to the traditional approach based on the ratio of Kernel Density Estimation (KDE), which is characterized by numerical instability and bandwidth sensitivity, this work applies a logistic regression embedded in a Bayesian framework to directly model selection effects. It reformulates the problem as a logistic regression task within a Generalized Additive Model (GAM) framework, utilizing isotropic Splines on the Sphere (SOS) to map the conditional probability of redshift measurement. The model complexity and smoothness are objectively optimized using Restricted Maximum Likelihood (REML) and the Akaike Information Criterion (AIC), ensuring a data-driven bias-variance trade-off. We benchmark this approach against an Adaptive Kernel Density Estimator (AKDE) using von Mises–Fisher kernels and Abramson’s square root law. The comparative analysis reveals strong statistical evidence in favor of this Preconditioned (Precon) Estimator, yielding a log-likelihood improvement of $\Delta \mathcal{L}\approx 74.3$ (Bayes factor $>{10}^{30}$) over the adaptive method. We show that this Precon Estimator acts as a spectral bandwidth extender, effectively decoupling the wideband exposure map from the narrowband selection efficiency. This provides a tool for cosmologists to recover high-frequency structural features—such as the sharp cutoffs—that are mathematically irresolvable by direct density estimators due to the bandwidth limitation inherent in sparse samples. The methodology ensures that reconstructions of the cosmic web are stable against Poisson noise and consistent with observational constraints. Full article

The microstructure of the Ti-Al binary system is an area of great interest, as it affects material properties and plasticity. Phase transformations induce microstructural changes; therefore, accurately modeling the phase transformations of the Ti-Al system is necessary to describe plasticity. Interatomic potentials can be a powerful tool to model how materials behave; however, existing potentials lack accuracy in certain aspects. While classical potentials like the Modified Embedded Atom Method (MEAM) perform adequately for modeling a dilute Al solute within Ti’s $\alpha$ phase, they struggle with accurately predicting plasticity. In particular, they struggle with stacking fault energies in intermetallics and to some extent elastic properties. This hinders their effectiveness in investigating the plastic behavior of formed intermetallics in Ti-Al alloys. Classical potentials also fail to predict the $\alpha$-to-$\beta$ phase boundary. Existing machine learning (ML) potentials reproduce the properties of formed intermetallics with density functional theory (DFT) but do not accurately capture the $\alpha$-to-$\beta$ or $\alpha$-to-D0₁₉ phase boundaries. This work uses a rapid artificial neural network (RANN) framework to produce a neural network potential for the Ti-Al binary system. This potential is capable of reproducing the Ti-Al binary phase diagram up to 30% Al concentration. The present interatomic potential ensures stability and allows results near the accuracy of DFT. Using Monte Carlo simulations, the RANN potential accurately predicts the $\alpha$-to-$\beta$ and $\alpha$-to-D0₁₉ phase transitions. The current potential also exhibits accurate elastic constants and stacking fault energies for the L1₀ and D0₁₉ phases. Full article

A conceptual design study is presented for a hemispherical, two-chamber water Cherenkov detector instrumented with bladder-embedded light traps. The detector consists of a rigid aluminium vessel enclosing a water volume that is divided into an outer, optically black chamber and a inner, reflective chamber lined by a flexible bladder. Arrays of light-trap modules, based on plastic scintillators with wavelength-shifting elements and thin silicon photomultipliers, are integrated into the bladder and selected inner surfaces. This geometry is intended to enhance muon tagging, increase acceptance for inclined air showers, and enable improved discrimination between electromagnetic and hadronic components. The study describes the mechanical and optical layout of the detector, the baseline aluminium housing, and the use of 3D-printed hexagonal prototypes to validate integration of the bladder and readout electronics. A first-order structural assessment based on thin-shell and plate theory is presented, indicating large safety margins for the hemispherical shells and identifying the flat base as the mechanically most loaded component. While GEANT4 simulations for detector response to extensive air showers in the atmosphere and performance measurements are left to future work, the present study establishes a mechanically validated, costed baseline design and outlines the steps needed to assess its impact in air-shower arrays. Full article

A major objective of this study is to investigate the incorporation of hydroxyapatite nanoparticles (nHA) in a biopolymeric matrix of alginate (Alg) and gelatin (Gel), with particular emphasis understanding how controlled variation in nHA concentration affects rheological, mechanical, printing, and biological performance. Although Alg–Gel blends and nHA-containing hydrogels have been previously explored, a systematic and quantitative correlation between nHA loading, viscoelastic recovery, yield behavior, filament fidelity, and cell viability under optimized bioprinting conditions has not been established. Here, we address this by preparing and evaluating six composite inks (0, 1, 2, 3, 4, and 5% w/v nHA). The parameters of interest included the printing accuracy, the rheological profile, including over 70% viscosity recovery after 10 s in almost all formulations, the elastic modulus, which was over 10 kPa, and the swelling degree. In addition, pre-osteoblastic cells were embedded in these formulations, subsequently bioprinted, and demonstrated viability over 70% after 7 days. The results advance our understanding on the effect of the chemical composition behind the modification of the properties of the composite materials and their applications for biofabrication. This work contributes quantitative insight into how compositional tuning influences the performance of alginate–gelatin–nHA bioinks for extrusion-based bioprinting applications. Full article

Visual secret sharing (VSS) schemes can enhance the security of image transmission over networks. Conventional VSS schemes often generate meaningless shares, which can raise suspicion among potential attackers. To address this issue, this paper proposes a novel VSS scheme that integrates information hiding techniques with quick response (QR) codes to generate meaningful shares. The first $n-1$ shares are encoded as standard QR codes, while the n-th share is embedded into a grayscale carrier image using a reversible information hiding method, ensuring the carrier remains visually meaningful. During transmission, the $n-1$ QR codes and the hidden image are distributed. At the receiver end, the hidden n-th share is extracted losslessly from the carrier image using the $n-1$ QR codes, and the original secret image is perfectly reconstructed by bitwise XORing all n shares. Experimental results demonstrate the feasibility, security, and visual quality of the proposed scheme. Full article

This study examines the systemic integration of entrepreneurship education and the culture of self employment within higher education as a component of sustainable institutional reform. Using a comparative analytical approach, it analyzes international practices across five higher education systems. Finland, the United States, Canada, the United Kingdom, and South Korea were selected to represent diverse yet mature models of entrepreneurship education integration. The findings reveal significant variation in the depth and coherence of integration across national contexts. Rather than identifying a single transferable model, the study shows that effective integration depends on the interaction of key institutional dimensions, including policy alignment, curricular embedding, faculty capacity, infrastructure, external partnerships, and impact evaluation. Finland demonstrates the most coherent configuration, while other systems exhibit partial or fragmented integration shaped by contextual factors. The study concludes that entrepreneurship education is most sustainable when embedded as a system-level institutional strategy rather than implemented through isolated initiatives. It offers an analytical framework, supported by an adapted ADKAR change model, to guide context-sensitive reform. For Arab higher education systems, the primary implication is diagnostic, emphasizing contextual adaptation over direct replication. Full article

Defect inspection of Printed Circuit Board (PCB) is essential for maintaining the safety and reliability of electronic products. With the continuous trend toward smaller components and higher integration levels, identifying tiny imperfections on densely packed PCB structures has become increasingly difficult and remains a major challenge for current inspection systems. To tackle this problem, this study proposes the Multi-scale Edge-Aware Enhanced Detection Transformer (MEE-DETR), a deep learning-based object detection method. Building upon the RT-DETR framework, which is grounded in Transformer-based machine learning, the proposed approach systematically introduces enhancements at three levels: backbone feature extraction, feature interaction, and multi-scale feature fusion. First, the proposed Edge-Strengthened Backbone Network (ESBN) constructs multi-scale edge extraction and semantic fusion pathways, effectively strengthening the structural representation of shallow defect edges. Second, the Entanglement Transformer Block (ETB), synergistically integrates frequency self-attention, spatial self-attention, and a frequency–spatial entangled feed-forward network, enabling deep cross-domain information interaction and consistent feature representation. Finally, the proposed Adaptive Enhancement Feature Pyramid Network (AEFPN), incorporating the Adaptive Cross-scale Fusion Module (ACFM) for cross-scale adaptive weighting and the Enhanced Feature Extraction C3 Module (EFEC3) for local nonlinear enhancement, substantially improves detail preservation and semantic balance during feature fusion. Experiments conducted on the PKU-Market-PCB dataset reveal that MEE-DETR delivers notable performance gains. Specifically, Precision, Recall, and mAP_50–95 improve by 2.5%, 9.4%, and 4.2%, respectively. In addition, the model’s parameter size is reduced by 40.7%. These results collectively indicate that MEE-DETR achieves excellent detection performance with a lightweight network architecture. Full article

This study examines how different programming paradigms are associated with learning experiences and cognitive challenges as encountered by non-computer science novice learners. Using a case-study approach situated within specific instructional contexts, we integrate survey data from undergraduate students with large-scale public question-and-answer data from Stack Overflow to explore paradigm-related difficulty patterns. Four instructional contexts—C, Java, Python, and Prolog—were examined as pedagogical instantiations of imperative, object-oriented, functional-style, and logic-based paradigms using text clustering, word embedding models, and interaction-informed complexity metrics. The analysis identifies distinct patterns of learning challenges across paradigmatic contexts, including difficulties related to low-level memory management in C-based instruction, abstraction and design reasoning in object-oriented contexts, inference-driven reasoning in Prolog-based instruction, and recursion-related challenges in functional-style programming tasks. Survey responses exhibit tendencies that are broadly consistent with patterns observed in public Q&A data, supporting the use of large-scale community-generated content as a complementary source for learner-centered educational analysis. Based on these findings, the study discusses paradigm-aware instructional implications for programming education tailored to non-major learners within comparable educational settings. The results provide empirical support for differentiated instructional approaches and offer evidence-informed insights relevant to curriculum-oriented teaching and future research on adaptive learning systems. Full article

In the context of small, battery-powered systems, a lightweight, reusable architecture is needed for integrated measurement, visualization, and cloud telemetry that minimizes hardware complexity and energy footprint. Existing solutions require high resources. This limits their applicability in Internet of Things (IoT) devices with low power consumption. The present work demonstrates the process of design, implementation and experimental evaluation of a single-cell lithium-ion battery monitoring prototype, intended for standalone operation or integration into other systems. The architecture is compact and energy efficient, with a reduction in complexity and memory usage: modular architecture with clearly distinguished responsibilities, avoidance of unnecessary dynamic memory allocations, centralized error handling, and a low-power policy through the usage of deep sleep mode. The data is stored in a cloud platform, while minimal storage is used locally. The developed system combines the functional requirements for an embedded external battery monitoring system: local voltage and current measurement, approximate estimation of the State of Charge (SoC) using a look-up table (LUT) based on the discharge characteristic, and visualization on a monochrome OLED display. The conducted experiments demonstrate the typical U(t) curve and the triggering of the indicator at low charge levels (LOW − SoC ≤ 20% and CRITICAL − SoC ≤ 5%) in real-world conditions and the absence of unwanted switching of the state near the voltage thresholds. Full article

Power transformers are critical assets in power systems, and their reliable operation is essential for grid stability. Conventional fault diagnosis methods suffer from delayed response and limited adaptability, while existing artificial intelligence-based approaches face challenges related to data heterogeneity, limited interpretability, and weak integration of physical mechanisms. To address these issues, this paper proposes a physics-informed enhanced transformer-based framework for power transformer fault diagnosis. A unified temporal representation scheme is developed to integrate heterogeneous monitoring data using Dynamic Time Warping and physics-guided feature projection. Physical priors derived from thermodynamic laws and gas diffusion principles are embedded into the attention mechanism through multi-physics coupling constraints, improving physical consistency and interpretability. In addition, a multi-task diagnostic strategy is adopted to jointly perform fault classification, severity assessment, and fault localization. Experiments on 3000 samples from 76 power transformers demonstrate that the proposed method achieves high diagnostic accuracy and superior robustness under noise and interference, indicating its effectiveness for practical predictive maintenance applications. Full article

This work presents the development of a differential-drive wheeled mobile robot educational prototype, manufactured using 3D additive techniques. The robot is powered by an embedded ARM-based computing system and uses open-source software. To validate the prototype, a trajectory-tracking task was successfully implemented. The aim of this contribution is to provide an easily replicable prototype for teaching automatic control and related engineering topics in academic settings. Full article

Cross-regional mega infrastructure (CrMI) is vital for sustaining economic vitality and social connectivity but is increasingly threatened by climate extremes and fragmented management. This study develops a targeted and interpretable evaluation system integrating 5 dimensions and 37 indicators. And social network analysis (SNA) with clustering methods is applied to the Hong Kong–Zhuhai–Macao Bridge as a representative case. Key indicators are classified into “Management Focuses,” “Management Challenges,” and “Management Sensitives,” reflecting varying levels of influence, feedback efficiency, and control capacity. The results reveal that the sustainable operation and maintenance management of CrMI should prioritize economic development while simultaneously strengthening resilience and intelligence. However, environmental protection remains a major challenge, and public attention and inter-regional cooperation are critical for management sensitivity. By embedding resilience intelligence into sustainable evaluation, this study advances sustainability theory and offers a more feasible and forward-looking pathway to sustaining CrMI under conditions of accelerating uncertainty. Full article

Aiming at the insufficient capture of temporal dependence and weak coupling of external factors in multivariate load forecasting, this paper proposes a Transformer model integrating timestamp-based positional embedding and convolution-augmented attention. The model enhances temporal modeling capability through timestamp-based positional embedding, optimizes local contextual representation via convolution-augmented attention, and achieves deep fusion of load data with external factors such as temperature, humidity, and electricity price. Experiments based on the 2018 full-year load dataset for a German region show that the proposed model outperforms single-factor and multi-factor LSTMs in both short-term (24 h) and long-term (cross-month) forecasting. The research results verify the model’s accuracy and stability in multivariate load forecasting, providing technical support for smart grid load dispatching. Full article