Search Results (23,253)

Glacier inventories are critical for monitoring glacier response to climate change, providing constraints for glacier modeling studies and for assessing the impacts of glacier retreat on ecosystems and human societies. In the Bhutanese Himalaya, an up-to-date glacier inventory and a systematic analysis of decadal-scale glacier changes is lacking. Here, we present three glacier inventories (1976, 1998, and 2024) for this region. Manual mapping of glacier outlines from multi-source satellite imagery and the Copernicus digital elevation model (DEM) are used to derive a glacier inventory with associated topographic attributes. We found that 1871 glaciers existed in this region in 1976, covering an area of 2297.07 ± 117.15 km². By 1998 this number had reduced to 1803 glaciers, covering 2106.99 ± 90.60 km². In 2024, only 1697 glaciers remained, covering 1584.18 ± 36.37 km². A total of 89 (1976–1998) and 435 (1998–2024) glaciers became extinct in the Bhutanese Himalaya during these two time periods, and glacier area decrease accelerated from ~0.38% yr⁻¹ to ~0.95% yr⁻¹. Lake-terminating glaciers retreated almost three times faster (~32.2 m yr⁻¹) than land-terminating (~10.4 m yr⁻¹) glaciers during the observation period. Debris-covered glacier area increased from 112.79 ± 11.50 km² in 1976 to 128.89 ± 10.50 km² in 2024. Glaciers on the South Bhutanese Himalaya (draining into Bhutan) experienced faster glacier retreat than the glaciers of the North Bhutanese Himalaya (draining into the Tibetan Autonomous Region). ERA5-Land reanalysis data show that summer decadal average temperature in this region increased by 0.003 °C yr⁻¹ between 1976 and 1998 and 0.020 °C yr⁻¹ between 1998 and 2024, with the increase in warming rate coinciding with accelerated glacier retreat after 1998. Our updated glacier inventories will be useful for assessments of global sea level change, mountain hazards, and water resources. Full article

Background/Objectives: Keratoconus, a condition in which the cornea becomes thinner and steeper, can cause visual problems, particularly when it is progressive. Early diagnosis is important for preserving visual acuity. Raw data, unlike preprocessed data, are unaffected by software modifications. They retain their native structure across versions, providing consistency for analytical purposes. The objective of this study was to design a deep learning-based graphical user interface for predicting the corneal ectasia score using raw optical coherence tomography data. Methods: The graphical user interface was developed using Tkinter, a Python library for building graphical user interfaces. The user is allowed to select raw data from the cornea/anterior segment optical coherence tomography Casia2, which is generated in the 3dv format, from the local system. To view the predicted corneal ectasia score, the user must determine whether the selected 3dv file corresponds to the left or right eye. Extracted optical coherence tomography images are cropped, resized to 224 × 224 pixels and processed by the modified EfficientNet-B0 convolutional neural network to predict the corneal ectasia score. The predicted corneal ectasia score value is displayed along with a diagnosis: ‘No detectable ectasia pattern’ or ‘Suspected ectasia’ or ‘Clinical ectasia’. Performance metric values were rounded to four decimal places, and the mean absolute error value was rounded to two decimal places. Results: The modified EfficientNet-B0 obtained a mean absolute error of 6.65 when evaluated on the test dataset. For the two-class classification, it achieved an accuracy of 87.96%, a sensitivity of 82.41%, a specificity of 96.69%, a PPV of 97.52% and an F1 score of 89.33%. For the three-class classification, it attained a weighted-average F1 score of 84.95% and an overall accuracy of 84.75%. Conclusions: The graphical user interface outputs numerical ectasia scores, which improves other categorical labels. The graphical user interface enables consistent diagnostics, regardless of software updates, by using raw data from the Casia2. The successful use of raw optical coherence tomography data indicates the potential for raw optical coherence tomography data to be used, rather than preprocessed optical coherence tomography data, for diagnosing keratoconus. Full article

Dynamic network conditions in precision agriculture motivate a scalable, privacy preserving federated learning architecture that tightly integrates ground-based edge intelligence with a space-assisted hierarchical aggregation layer. In Phase 1, heterogeneous tractors act as intelligent farm nodes that train local models, form capability- and task-aware clusters, and employ Network Quality Index (NQI)-driven scheduling, similarity-based check pointing, and compressed transmissions to cope with highly variable 3G/4G/5G connectivity. In Phase 2, cluster drivers synchronize with Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) satellites that perform regional and global aggregation using staleness- and fairness-aware weighting, while end-to-end Salsa20 + MAC encryption preserves the confidentiality and integrity of all model updates. Across two representative tasks—nutrient prediction and crop health assessment—our full hierarchical system matches or exceeds centralized performance (e.g., AUC 0.92 vs. 0.91 for crop health) while reducing uplink traffic by ∼90% relative to vanilla FedAvg and cutting the communication energy proxy by more than 4×. The proposed fairness-aware GEO aggregation substantially narrows regional performance gaps (standard deviation of AUC across regions reduced from 0.058 to 0.017) and delivers the largest gains in low-connectivity areas (AUC 0.74 → 0.88). These results demonstrate that coupling on-farm intelligence with multi-orbit federated aggregation enables near-centralized model quality, strong privacy guarantees, and communication efficiency suitable for large-scale, connectivity-challenged agricultural deployments. Full article

Symbiotic radio (SR) has recently emerged as a promising paradigm for enabling spectrum- and energy-efficient massive connectivity in low-power Internet-of-Things (IoT) networks. By allowing passive backscatter devices (BDs) to coexist with active primary link transmissions, SR significantly improves spectrum utilization without requiring dedicated spectrum resources. However, most existing studies on multi-tag multiple-input multiple-output (MIMO) SR systems assume homogeneous traffic demands among BDs and primarily focus on rate-based performance metrics, while neglecting system-level task completion time (TCT) optimization under heterogeneous data requirements. In this paper, we investigate a joint performance optimization framework for a multi-tag MIMO symbiotic radio network. We first formulate a weighted sum-rate (WSR) maximization problem for the secondary backscatter links. The original non-convex WSR maximization problem is transformed into an equivalent weighted minimum mean square error (WMMSE) problem, and then solved by a block coordinate descent (BCD) approach, where the transmit precoding matrix, decoding filters, backscatter reflection coefficients are alternatively optimized. Second, to address the transmission delay imbalance caused by heterogeneous data sizes among BDs, we further propose a rate weight adaptive task TCT minimization scheme, which dynamically updates the rate weight of each BD to minimize the overall TCT. Simulation results demonstrate that the proposed framework significantly improves the WSR of the secondary system without degrading the primary link performance, and achieves substantial TCT reduction in multi-tag heterogeneous traffic scenarios, validating its effectiveness and robustness for MIMO symbiotic radio networks. Full article

In the realm of wearable technology, achieving robust continuous authentication requires balancing high security with the strict resource constraints of embedded platforms. Conventional machine learning approaches and deep learning-based biometrics often incur high computational costs, making them unsuitable for low-power edge devices. To address this challenge, we propose H-PPG, a lightweight authentication system that integrates photoplethysmography (PPG) and inertial measurement unit (IMU) signals for continuous user verification. Using Hyperdimensional Computing (HDC), a lightweight classification framework inspired by brain-like computing, H-PPG encodes user physiological and motion data into high-dimensional hypervectors that comprehensively represent individual identity, enabling robust, efficient and lightweight authentication. An adaptive learning process is employed to iteratively refine the user’s hypervector, allowing it to progressively capture discriminative information from physiological and behavioral samples. To further enhance identity representation, a dimension regeneration mechanism is introduced to maximize the information capacity of each dimension within the hypervector, ensuring that authentication accuracy is maintained under lightweight conditions. In addition, a user-defined security level scheme and an adaptive update strategy are proposed to ensure sustained authentication performance over prolonged usage. A wrist-worn prototype was developed to evaluate the effectiveness of the proposed approach and extensive experiments involving 15 participants were conducted under real-world conditions. The experimental results demonstrate that H-PPG achieves an average authentication accuracy of 93.5%. Compared to existing methods, H-PPG offers a lightweight and hardware-efficient solution suitable for resource-constrained wearable devices, highlighting its strong potential for integration into future smart wearable ecosystems. Full article

Deep reinforcement learning (DRL) has demonstrated considerable potential for autonomous collision avoidance in unmanned surface vessels (USVs). However, its application in complex multi-agent maritime environments is often limited by challenges such as convergence issues and high computational costs. To address these issues, this paper proposes an expert-guided DRL algorithm that integrates a Dual-Priority Experience Replay (DPER) mechanism with a Hybrid Reciprocal Velocity Obstacles (HRVO) expert module. Specifically, the DPER mechanism prioritizes high-value experiences by considering both temporal-difference (TD) error and collision avoidance quality. The TD error prioritization selects experiences with large TD errors, which typically correspond to critical state transitions with significant prediction discrepancies, thus accelerating value function updates and enhancing learning efficiency. At the same time, the collision avoidance quality prioritization reinforces successful evasive actions, preventing them from being overshadowed by a large volume of ordinary experiences. To further improve algorithm performance, this study integrates a COLREGs-compliant HRVO expert module, which guides early-stage policy exploration while ensuring compliance with regulatory constraints. The expert mechanism is incorporated into the Soft Actor-Critic (SAC) algorithm and validated in multi-vessel collision avoidance scenarios using maritime simulations. The experimental results demonstrate that, compared to traditional DRL baselines, the proposed algorithm reduces training time by 60.37% and, in comparison to rule-based algorithms, achieves shorter navigation times and lower rudder frequencies. Full article

This study provides a practice-oriented sensitivity analysis of DeePC for pressure management in water distribution systems. Two public benchmark systems were used, Fossolo (simpler) and Modena (more complex). Each run fixed a monitored node and pressure reference, applied the same randomized identification phase followed by closed-loop control, and quantified performance by the mean absolute error (MAE) of the node pressure relative to the reference value. To better characterize closed-loop behavior beyond MAE, we additionally report (i) the maximum deviation from the reference over the control window and (ii) a valve actuation effort metric, normalized to enable fair comparison across different numbers of valves and, where relevant, different control update rates. Motivated by the need for practical guidance on how hydraulic boundary conditions and algorithmic choices shape DeePC performance in complex water networks, we examined four factors: (1) placement of an additional internal PRV, supplementing the reservoir-outlet PRVs; (2) the control time step $(\Delta t)$; (3) a uniform reservoir-head offset $(\Delta h)$; and (4) DeePC regularization weights ${(\lambda }_g,{\lambda }_u,{\lambda }_y)$. Results show strong location sensitivity, in Fossolo, topologically closer placements tended to lower MAE, with exceptions; the baseline MAE with only the inlet PRV was 3.35 [m], defined as a DeePC run with no additions, no extra valve, and no changes to reservoir head, time step, or regularization weights. Several added-valve locations improved the MAE (i.e., reduced it) below this level, whereas poor choices increased the error up to ~8.5 [m]. In Modena, 54 candidate pipes were tested, the baseline MAE was 2.19 [m], and the best candidate (Pipe 312) achieved 2.02 [m], while pipes adjacent to the monitored node did not outperform the baseline. Decreasing $\Delta t$ across nine tested values consistently reduced MAE, with an approximately linear trend over the tested range, maximum deviation was unchanged (7.8 [m]) across all $\Delta t$ cases, and actuation effort decreased with shorter steps after normalization. Changing reservoir head had a pronounced effect: positive offsets improved tracking toward a floor of ≈0.49 [m] around $\Delta h$ ≈ +30 [m], whereas negative offsets (below the reference) degraded performance. Tuning of regularization weights produced a modest spread (≈0.1 [m]) relative to other factors, and the best tested combination (λy, λg, λu) = (10², 10⁻³, 10⁻²) yielded MAE ≈ 2.11 [m], while actuation effort was more sensitive to the regularization choice than MAE/max deviation. We conclude that baseline system calibration, especially reservoir heads, is essential before running DeePC to avoid biased or artificially bounded outcomes, and that for large systems an external optimization (e.g., a genetic-algorithm search) is advisable to identify beneficial PRV locations. Full article

Ataúro Island, located in the inner Banda Arc, provides a natural laboratory to investigate the interplay between magmatic evolution, hydrothermal circulation, and near-surface weathering in an active arc–continent collision setting. This study presents the first systematic island-wide geochemical baseline for Ataúro Island, based on multi-element analyses of stream sediments integrated with updated geological, structural, and hydromorphological information. Compositional Data Analysis (CoDA–CLR–PCA), combined with anomaly mapping and spatial overlays, defines a coherent three-tier geochemical framework comprising: (i) a lithogenic component dominated by Fe–Ti–Mg–Ni–Co–Cr, reflecting the geochemical signature of basaltic to andesitic volcanic rocks; (ii) a hydrothermal component characterized by Ag–As–Sb–S–Au associations spatially linked to structurally controlled zones; and (iii) an oxidative–supergene component marked by Fe–V–Zn redistribution along drainage convergence areas. These domains are defined strictly on geochemical criteria and represent geochemical process domains rather than proven metallogenic provinces. Rare earth element (REE) systematics further constrain the geotectonic setting and indicate that the primary geochemical patterns are largely controlled by lithological and magmatic differentiation processes. Spatial integration of geochemical patterns with fault architecture highlights the importance of NW–SE and NE–SW structural corridors in focusing hydrothermal fluid circulation and associated metal dispersion. The identified Ag–As–Sb–Au associations are interpreted as epithermal-style hydrothermal geochemical enrichment and exploration-relevant geochemical footprints, rather than as evidence of confirmed or economic mineralization. Overall, Ataúro Island emerges as a compact natural analogue of post-arc geochemical system evolution in the eastern Banda Arc, where lithogenic background, hydrothermal fluid–rock interaction, and early supergene processes are superimposed. The integrated geochemical framework presented here provides a robust baseline for future targeted investigations aimed at distinguishing lithogenic from hydrothermal contributions and evaluating the potential significance of the identified geochemical enrichments. Full article

Hydraulic thrust cylinders in hard-rock tunnel boring machines (TBMs) often exhibit slow response and sluggish acceleration during start-up, which degrades early-stage tracking performance and limits overall operational accuracy. Most existing studies primarily enhance start-up behavior through advanced control algorithms, yet the achievable improvement is ultimately constrained by the system’s flow–pressure capacity. Meanwhile, reported system-level optimization approaches are either difficult to implement under practical TBM operating conditions or fail to consistently deliver high-accuracy tracking. To address these limitations, this paper proposes a “dual-pump–single-cylinder” control framework for the TBM thrust system, where a large-displacement pump serves as the main supply and a parallel small-displacement pump provides auxiliary flow compensation to mitigate the start-up flow deficit. Building on this architecture, an adaptive compensation algorithm is developed for the auxiliary pump, with its output updated online according to the system’s dynamic states, including displacement error and velocity-related error components. Comparative simulations and test-bench experiments show that, compared with a single-pump scheme, the proposed method notably accelerates cylinder start-up while effectively suppressing overshoot and oscillations, thereby improving both transient smoothness and tracking accuracy. This study provides a feasible and engineering-oriented solution for achieving “rapid and smooth start-up” of TBM hydraulic cylinders. Full article

Fourier-transform spectrometers are widely used for spectral measurements. Changes in the field of view during measurement introduce oscillations into the measured spectra known as scene-change artifacts. Field-of-view changes also introduce uncertainty about which target the measured spectrum represents. Though scene-change artifacts are often present in dynamic data, their significance is disputed in the current literature. This work presents a theoretical framework and experimental validation for scene-change artifacts. Field-of-view changes introduce variable interferogram offsets, which standard processing techniques assume are constant. The error between the interferogram offset and its estimate is Fourier-transformed, yielding scene-change artifacts, often confused with noise, in the calibrated spectrum. Previous theoretical models ignored the effect of the interferogram offset in generating SCAs, leading to an underestimation of the scene-change artifact significance. Smooth offset correction removes these artifacts by estimating the variable interferogram offset using locally weighted scatter-plot smoothing. Updating the interferogram offset estimate resulted in the same accuracy expected for static conditions. The resulting spectra resemble the zero path difference spectra, similar to earlier theoretical predictions. These results indicate that Fourier-transform spectroscopy accuracy with variable scenes can be significantly improved with minor modifications to data processing. Full article

Background: Sarcomas are rare, aggressive malignancies with limited therapeutic options in advanced stages. This is the first real-world study in the MENA region evaluating the clinical utility of Next-Generation Sequencing (NGS) in guiding sarcoma treatment and improving outcomes. Methods: We retrospectively reviewed sarcoma patients who underwent NGS at a major referral center (2021–2024), comparing clinical and molecular outcomes between those who received NGS-based treatment adjustments (NBTA) and those who did not. Results: Seventy-eight patients were included (60% male; median age 44 years). Soft tissue sarcomas accounted for 70.5% of cases (n = 55), while bone sarcomas represented 29.5% (n = 23). Prior to NGS, 64.1% of patients had received a median of one line of systemic therapy. NGS was performed late in the disease course in 73% of cases. At least one mutation was detected in 87% (median 3 mutations). Targetable alterations were identified in 33% at the time of testing, rising to 42% with updated genomic knowledge and therapeutic advances. Overall, 20.5% received NBTA. Among non-NBTA patients, 67% had no actionable targets, 17% had no detectable mutations, and 16% were ineligible due to cost, limited access, or clinical deterioration. Tumor Mutational Burden was low in 79%, intermediate in 19%, and high in 2%, and all tumors were microsatellite stable. Patients receiving NBTA had a longer median Progression-Free Survival (9 vs. 2 months; p = 0.023). Median Overall Survival was longer in the NBTA group (74 vs. 48 months), though not statistically significant (p = 0.207). Genomic alterations were subtype-specific: EWSR1 rearrangements (Ewing and Desmoplastic small round cell tumors), CDK4 and MDM2 amplifications (Liposarcoma and Osteosarcoma), TP53 and RB1 mutations (Leiomyosarcoma), CDKN2A/B deletions (Undifferentiated Pleomorphic Sarcoma and Chondrosarcoma), and SS18 rearrangements (Synovial Sarcoma). Conclusions: Genomics-guided therapy in sarcoma is feasible and impactful. Expanding timely access to molecular profiling is essential for advancing precision oncology in the MENA region. Full article

In the era of big data and multimedia communication, securing color images against unauthorized access and attacks is a pressing challenge. While quaternion-based models provide a unified representation for color images, most existing encryption schemes rely on single-image frameworks or lack the mathematical rigor to ensure both security and feasibility. To bridge this gap, this paper introduces a system of generalized Sylvester-type quaternion matrix equations as a novel encryption model. By using the equivalence canonical forms of five matrices arranged in a specific array, we provide necessary and sufficient conditions for the solvability of the generalized Sylvester-type quaternion matrix equation system, depending on the rank of the coefficient matrix. Numerical examples are provided to validate the obtained results. As an example of applications, we develop an encryption scheme for color images based on the proposed quaternion matrix equation system. Experimental results confirm the high feasibility of the proposed scheme. Notably, the proposed model supports dynamic key updates and multi-image secure transmission, making it highly adaptable for real-world applications. By integrating advanced quaternion matrix theory with practical image encryption, this work offers a scalable, secure, and mathematically sound approach to color image protection. Full article

As the largest exporter in the global solid tire market, Sri Lanka’s natural rubber supply chain plays a critical role in global production, yet its social dimension remains largely unaddressed. Our study aims to assess the social performance of a Sri Lankan natural rubber supply chain in solid tire manufacturing using social life cycle assessment (S-LCA) in a cradle-to-gate approach. Study adapts “More Good and Less Bad” method which captures both positive and negative social impacts, addressing traditional S-LCAs’ focus on negative impacts solely. It applies to updated methodological sheets to distinguish “good” and “bad” social conditions across subcategories based on baseline compliance. Social impacts were quantified using a Social Performance Index (SPI), calculated by multiplying social performance levels by working hours at the organizational level, comprising SPI_good for good social impacts and SPI_bad for bad social impacts. Data was collected through stakeholder interviews, with working hours calculated using a “working hour model”. Results showed mixed social performance across 39 subcategories, identifying six social hotspots: promoting social responsibility (27.67% less bad, 72.32% more good), wealth distribution (26.87% less bad, 73.13% more good), commitment to sustainability issues (100% less bad), social benefits (100% less bad), safe and healthy living conditions (100% less bad), and hours of work (88.74% less bad, 11.26% more good). Full article

Forecasting the spatio-temporal evolutions of cellular traffic is crucial for urban management. However, achieving accurate forecasting is challenging due to “complex correlation modeling” and “model-blindness” issues. Specifically, cellular traffic is generated within complex urban systems characterized by an intricate structure and human mobility. Existing approaches, often based on proximity or attributes, struggle to learn the latent correlation matrix governing traffic evolution, which limits forecasting accuracy. Furthermore, while substantial knowledge about urban systems can supplement the modeling of correlations, existing methods for integrating this knowledge—typically via loss functions or embeddings—overlook the synergistic collaboration between data and knowledge, resulting in weak model robustness. To address these challenges, we develop a data–knowledge collaborative learning framework termed the knowledge-empowered spatio-temporal neural network (KESTNN). This framework first extracts knowledge triplets representing urban structures to construct a knowledge graph. Representation learning is then conducted to learn the correlation matrix. Throughout this process, data and knowledge are integrated collaboratively via backpropagation, contrasting with the forward feature injection methods typical of existing approaches. This mechanism ensures that data and knowledge directly guide the dynamic updating of model parameters through backpropagation, rather than merely serving as a static feature prompt, thereby fundamentally alleviating the “model-blindness” issue. Finally, the optimized matrix is embedded into a forecasting module. Experiments on the Milan dataset demonstrate that the KESTNN exhibits excellent forecast performance, reducing RMSE by up to 23.91%, 16.73%, and 10.40% for 3-, 6-, and 9-step forecasts, respectively, compared to the best baseline. Full article

Hierarchical Federated Learning (HFL) alleviates the trade-off between communication overhead and privacy protection in mobile scenarios via multi-level aggregation and mobility consideration. However, its idealized convex loss assumption and full-dimension parameter upload deviate from real-world non-convex tasks and edge channel constraints, causing excessive energy consumption, high communication cost, and compromised convergence that hinder practical deployment. To address these issues in mobile/UAV networks, this paper proposes an energy-efficient optimization scheme for HFL under non-convex loss, integrating a dynamically adjustable partial-dimension model upload mechanism. By screening key update dimensions, the scheme reduces uploaded data volume. We construct a total energy minimization model that incorporates communication/computation energy formulas related to upload dimensions and introduces an attendance rate constraint to guarantee learning performance. Using Lyapunov optimization, the long-term optimization problem is transformed into single-round solvable subproblems, with a step-by-step strategy balancing minimal energy consumption and model accuracy. Simulation results show that compared with the original HFL algorithm, our proposed scheme achieves significant energy reduction while maintaining high test accuracy, verifying the positive impact of mobility on system performance. Full article