Search Results (10,397)

Background/Objectives: Accurate nuclear segmentation is a fundamental step in computational pathology, enabling reliable estimation of cellularity and nuclear morphology. However, segmentation models are typically evaluated under ideal imaging conditions, while real-world microscopy data are affected by staining variability, noise, and image degradation. This study aims to comparatively evaluate three representative convolutional architectures for nuclei segmentation, with emphasis on robustness and clinical relevance under perturbed imaging conditions. Methods: U-Net, Attention U-Net, and U-Net++ were trained and evaluated on the BBBC038 nuclei microscopy dataset using fixed train–validation–test splits. Robustness was assessed under three types of synthetic perturbations: Gaussian blur, additive noise, and color jitter. Segmentation performance was quantified using the Dice coefficient and Intersection-over-Union (IoU). Paired Wilcoxon signed-rank tests with Holm correction and Cliff’s delta were used for statistical comparison. In addition, clinically relevant nuclear descriptors—nuclear count, median nuclear area, area interquartile range (IQR), and nuclear density—were extracted from predicted masks, and descriptor stability was analyzed as relative deviation from clean conditions. Results: Under clean imaging conditions, Attention U-Net achieved the highest mean Dice score, while paired statistical analysis indicated that U-Net++ exhibited the most consistent performance across test samples. Under image perturbations, Attention U-Net demonstrated greater robustness to blur and noise, whereas U-Net++ showed superior stability under color variations. Descriptor-based analysis further indicated that U-Net++ preserved nuclear count and density most reliably under chromatic perturbations, while U-Net exhibited larger instability in nuclear count and density, particularly under noise. Conclusions: Architectural design choices strongly influence not only pixel-level segmentation accuracy but also the stability of clinically relevant nuclear morphology descriptors. Robustness evaluation under multiple perturbation types reveals important trade-offs between architectures that are not captured by clean-image benchmarks alone. These findings highlight the necessity of multi-level evaluation strategies combining overlap metrics, statistical testing, robustness analysis, and descriptor stability assessment for future benchmarking and clinically reliable deployment of nuclei segmentation systems. Full article

Global river water quality degradation severely impairs aquatic ecosystem stability and human health, highlighting the urgency of spatiotemporal analysis for management guidance. Based on 2014–2024 monitoring data from the Quzhou Section of Qiantang River Basin, this study adopted the Water Quality Index (WQI) and statistical methods (PCA, Mann–Kendall test) to explore the spatiotemporal characteristics of water quality across the basin. Results showed an overall mean WQI of 79.26 (classified as “Good”), with general stability, localized fluctuations, and a stable-then-declining trend, mirroring an imbalance between governance effects and emerging pollution pressures. It identifies a critical governance phase focused on securing the current good water quality and curbing the trend of further deterioration. Water quality exhibited distinct variations: upper reaches > lower reaches, tributaries > mainstreams, with priority required for the Wuxi River’s declining WQI and the Qu River’s persistently low WQI. TN, TP, and NH₃-N were identified as key factors coupled with land use patterns. A differentiated strategy prioritizing nitrogen control, synergizing phosphorus–oxygen management, and reducing organics is thus proposed. This study provides scientific references for water quality assessment and targeted aquatic ecological governance in the basin and similar river networks. Full article

Antibiotic resistance gene (ARG) monitoring in environmental systems increasingly relies on DNA-based molecular approaches; however, the extent to which DNA extraction strategies bias downstream resistome interpretation remains insufficiently understood. This study systematically evaluated the effects of single versus successive DNA extraction on DNA recovery, microbial community composition, and the abundance and diversity of 385 genes related to antibiotic resistance including ARGs and mobile genetic elements (MGEs) across three contrasting matrices: water, sediment, and fish intestinal tissue. Successive extraction markedly increased DNA yield and detection of functional genes in water and sediment, particularly for low-abundance and particle-associated taxa. Enhanced recovery resulted in higher richness and abundance of ARGs and MGEs and strengthened correlations between intI1, ARGs, and bacterial taxa, indicating that single-cycle extraction may underestimate resistome magnitude and potential host associations in complex matrices. Conversely, fish intestinal tissue, used here as a representative biological matrix, showed limited benefit or even reduced gene abundance with repeated extraction, likely due to rapid depletion of extractable nucleic acids and DNA degradation. While successive extraction improves recovery efficiency, the potential inclusion of extracellular or relic DNA suggests caution in interpreting inflated ARG abundance. Overall, our findings demonstrate that DNA extraction is a matrix-dependent methodological driver that can reshape both quantitative outcomes and ecological inference. Matrix-specific optimization and careful protocol selection are therefore essential for improving data comparability and minimizing methodological underestimation in environmental resistome assessments. Full article

Sok Chan Forest, located in Lao Suea Kok District, Ubon Ratchathani Province, Thailand, is frequently affected by wildfires during the dry season, resulting in significant environmental degradation and adverse impacts on the livelihoods of local communities. In this study, we outline the development of a prototype wildfire early warning system utilizing LoRa technology to address the long-distance data transmission limitations that are commonly encountered when using conventional Internet of Things (IoT) solutions. The proposed system comprises sensor nodes that communicate from peer to peer with a central node, which subsequently relays the collected data to a remote database server via the internet. Real-time alerts are disseminated through both a smartphone application and a web-based platform, thereby facilitating timely notification of authorities and community members. Field experiments in Sok Chan Forest demonstrated reliable single-hop communication with a 100% packet delivery ratio at distances up to 1500 m, positive SNR, and RSSI levels above receiver sensitivity, as well as sub-second end-to-end detection latency in both single- and two-hop configurations. A controlled alarm accuracy evaluation yielded an overall classification accuracy of 91.7%, with perfect precision for the Fire class, while a user study involving five software development experts and fifteen firefighters yielded an average effectiveness score of 3.84, reflecting a high level of operational efficacy. Full article

This study presents a detailed evaluation of how inlet air filter differential pressure (Filter DP) affects the operational performance of a gas turbine, focusing on its influence on power generation and thermal efficiency. Real operating data combined with machine learning (ML) techniques were used. Following the installation of new filters, the turbine operated for 10,000 h, and 4438 h under base-load conditions were selected for modeling. The impact of Filter DP was examined using Multiple Linear Regression (MLR), Quadratic Support Vector Regression (SVR), Regression Tree, and Artificial Neural Network (ANN) models, allowing both linear and nonlinear behavior to be captured. Results show that each 1 mbar increase in Filter DP leads to roughly a 1.67 MW drop in power output and a 0.094% reduction in thermal efficiency. Additionally, higher Filter DP raises fuel consumption and causes an extra 0.45 kgCO₂e of emissions per 1 MWh of electricity produced. These findings underline that even small increases in inlet pressure loss significantly affect economic and environmental performance. Filter fouling increases natural gas demand, CO₂e emissions, and overall carbon footprint. The ML-based approach enhances predictive maintenance by enabling early detection of filter degradation and supporting more efficient and sustainable turbine operation. Full article

Traditional villages, as carriers of agricultural civilization and ecological wisdom, represent important sites for fostering new-quality productive forces. In the context of rapid urbanization, they function as key spaces for rural development while also confronting vulnerabilities such as spatial functional imbalance and ecological degradation. Within the production–living–ecology (PLE) spaces, dependence on labor-intensive and capital-intensive agricultural models often results in resource misallocation and systemic dysfunction. New-quality productive forces, driven by innovation and green transition, provide a fresh perspective for sustainable rural spatial restructuring. However, their micro-scale mechanisms within traditional villages remain underexplored. This study focuses on 22 nationally recognized traditional villages in Foshan, China. Based on land-use and socioeconomic data from 1993, 2003, 2013, and 2023, we applied land-use transition matrices, a coupling coordination degree model, and geographical detector analysis to examine the evolution of PLE spatial patterns and their driving mechanisms. The findings show that (1) spatially, the share of living space increased significantly, while ecological and agricultural production spaces continued to shrink, reflecting heightened competition among the three; (2) the overall coupling coordination degree exhibited a declining trend, indicating weakened synergy among PLE functions; (3) key drivers of system coordination include per capita disposable income of rural residents, agricultural labor productivity, regional technological innovation capacity, and forest coverage, underscoring the synergistic role of socioeconomic and ecological factors in new countryside development. This study elucidates the micro-spatial pathways through which new rural construction and conservation mechanisms operate, providing a reference for context-sensitive conservation and high-quality development of traditional villages in rapidly industrializing regions. The analytical framework can also be extended to other rural areas undergoing transition. Full article

Nonintrusive Load Monitoring (NILM) models often suffer from significant performance degradation when deployed across different households and datasets, primarily because of distribution discrepancies. To address this challenge, this study proposes an adversarial hierarchical distribution alignment unsupervised domain adaptation network for nonintrusive load disaggregation. The network aims to reduce the distribution divergence between the source and target domains in both the feature and label spaces, enabling effective adaptation to transfer learning scenarios in which the source domain has limited labeled data and the target domain has abundant unlabeled data. The proposed method integrates adversarial training with a hierarchical distribution alignment strategy that uses Correlation Alignment (CORAL) to align global marginal distributions. It employs Multi-Kernel Maximum Mean Discrepancy (MK-MMD) to constrain the conditional distributions of individual appliances, thereby enhancing cross-domain generalization. Extensive experiments on three public datasets demonstrate that, in both in-domain and cross-domain settings, the proposed method consistently reduces Mean Absolute Error (MAE) and Signal Aggregation Error (SAE), outperforming baseline approaches in cross-domain generalization. Full article

Urban sustainability is increasingly defined by the resilience of the built environment against hazards. While Agent-Based Models (ABMs) are commonly used to simulate these dynamics, their predictive capacity is often limited by a lack of empirical behavioral data. This study addresses this gap by introducing a Human-Centric Digital Twin framework that integrates procedural generation with immersive Virtual Reality (VR) to quantify ‘spatial sustainability’, defined as the capacity of an urban form to support life safety without compromising its morphological identity. In this framework, VR serves as a controlled environment for observing navigation under stress, while procedural generation creates structurally distinct urban morphologies (orthogonal vs. organic) to enable universal calibration. The approach was validated through evacuation experiments with 37 participants under varying visibility conditions. Results reveal that while performance was similar in daylight, significant behavioral divergence emerged at night; the organic layout (Type A) exhibited greater variability and longer evacuation times compared to the orthogonal grid (Type B). These findings confirm that spatial configuration dictates resilience when sensory inputs degrade. Consequently, this study offers a transferable, data-independent protocol for measuring and monitoring urban resilience in data-scarce environments. Full article

The exponential growth of digital data and the escalating sophistication of cyber threats have intensified the demand for secure yet computationally efficient encryption methods. Conventional algorithms (e.g., AES-based schemes) are cryptographically strong and widely deployed; however, some implementations can face performance bottlenecks in large-scale or real-time workloads. While many modern systems seed from hardware entropy sources and employ standardized cryptographic PRNGs/DRBGs, security can still be degraded in practice by weak entropy initialization, misconfiguration, or the use of non-cryptographic deterministic generators in certain environments. To address these gaps, this study introduces FileCipher. This novel file-encryption framework integrates a chaos-enhanced Cryptographically Secure Pseudorandom Number Generator (CPRNG) based on the State-Based Tent Map (SBTM). The proposed design achieves a balanced trade-off between security and efficiency through dynamic key generation, adaptive block reshaping, and structured confusion–diffusion processes. The SBTM-driven CPRNG introduces adaptive seeding and multi-key feedback, ensuring high entropy and sensitivity to initial conditions. A multi-threaded Java implementation demonstrates approximately 60% reduction in encryption time compared with AES-CBC, validating FileCipher’s scalability in parallel execution environments. Statistical evaluations using NIST SP 800-22, SP 800-90B, Dieharder, and TestU01 confirm superior randomness with over 99% pass rates, while Avalanche Effect analysis indicates bit-change ratios near 50%, proving strong diffusion characteristics. The results highlight FileCipher’s novelty in combining nonlinear chaotic dynamics with lightweight parallel architecture, offering a robust, platform-independent solution for secure data storage and transmission. Ultimately, this paper contributes a reproducible, entropy-stable, and high-performance cryptographic mechanism that redefines the efficiency–security balance in modern encryption systems. Full article

The scorpion family Buthidae, renowned for its neurotoxin-rich venoms, dominates toxinology, while non-buthid venoms remain largely unexplored. Here, we present a comprehensive proteomic and biochemical characterization of the Amazonian chactid scorpion Brotheas amazonicus venom (BamazV), with emphasis on molecular complexity, proteolytic processing, and peptide diversity. Using an integrative venomics approach that combines molecular mass-based fractionation, reversed-phase chromatography, high-resolution mass spectrometry, N-terminal sequencing, and functional and immunological analyses, we reveal an unexpectedly complex venom profile enriched in high-molecular-weight components and extensively processed peptides, with more than 40 venom peptides sequenced by MS/MS and Edman degradation. The data provide evidence for non-canonical proteolytic events, including the generation of peptides from precursor regions not classically associated with mature venom components. In contrast to the venom of Tityus serrulatus, BamazV displays a “hydrolase-rich, neurotoxin-poor” profile, featuring a catalytically active Group III phospholipase A₂ (BamazPLA₂), a highly active hyaluronidase, metalloproteases, low-mass peptides, and potassium channel toxins. Our results suggest a hydrolytic prey-subjugation strategy, and limited cross-reactivity with commercial antivenom highlighted its distinct structural landscape. Overall, this study advances the understanding of venom evolution and proteolytic diversification in underexplored scorpion lineages, positioning B. amazonicus as a valuable model for investigating alternative venom strategies and identifying novel biotechnological scaffolds. Full article

The continuous emission of persistent and bioaccumulative pollutants into aquatic environments has become a critical global issue. Among these, per- and polyfluoroalkyl substances (PFASs) are of particular concern due to their exceptional stability, extensive industrial use, and adverse impacts on ecosystems and human health. Their resistance to conventional physical, chemical, and biological treatments stems from the strength of the carbon–fluorine bond, which prevents efficient degradation under standard conditions. This review provides a concise and updated assessment of emerging advanced oxidation processes (AOPs) for PFAS remediation, with emphasis on heterogeneous photocatalysis and electrochemical oxidation. Photocatalytic systems based on In₂O₃, Bi-based oxyhalides, and Ga₂O₃ exhibit high PFAS degradation under UV light, while heterojunctions and MOF-derived catalysts improve defluorination under solar irradiation. Electrochemical oxidation—particularly using Ti₄O₇ reactive electrochemical membranes and BDD anodes—achieves near-complete mineralization with comparatively low specific energy demand. Energy consumption (E_EO) was calculated from literature data for UV- and simulated-solar-driven photocatalytic systems, enabling a direct comparison of their energy performance. Although solar-driven processes offer clear environmental advantages, they generally exhibit higher E_EO values, mainly due to lower apparent quantum yields and less efficient utilization of the incident solar photons compared to UV-driven systems. Hybrid systems coupling photocatalysis and electro-oxidation emerge as promising strategies to enhance degradation efficiency and reduce energy requirements. Overall, the review highlights key advances and future research directions toward scalable, energy-efficient, and environmentally sustainable AOP-based technologies for PFAS removal. Full article

Space–air–ground systems employing free-space optical (FSO) communication leverage high-altitude platform stations (HAPS) to deliver seamless and ubiquitous connectivity. Although FSO links offer high capacity, they are highly susceptible to cloud extinction, which severely degrades link availability. Hybrid FSO/radio-frequency (RF) transmission and cloud-aware HAPS trajectory optimization can enhance resilience. However, the conventional cloud-aware hybrid FSO/RF transmission system based on hard-switching (HS) between the FSO and RF links leads to frequent link transitions and unstable throughput. To address these challenges, we propose a joint optimization framework that integrates soft-switch between FSO and RF links with deep reinforcement learning (DRL) for HAPS trajectory optimization. Soft-switching based on rateless codes (RCs) enables simultaneous transmission over both links, where the receiver accumulates packets until successful decoding with a single feedback. The feedback frequency of RC is sparse, which avoids feedback storms but also poses challenges to HAPS trajectory optimization. The DRL agent proactively optimizes HAPS trajectories to avoid cloud cover and maintain link availability. To address the sparse feedback of RCs for DRL training, a reward-shaped proximal policy optimization (PPO)-based agent is developed to jointly optimize throughput and trajectory smoothness. Simulations using realistic ERA5 data show that RC-PPO achieves higher throughput and smoother trajectories compared to the HS-PPO baseline. Full article

Federated unlearning removes a client’s influence from a trained federated model without full retraining, which is required by data deletion regulations but remains difficult due to gradient coupling and recovery instability. Existing methods often rely on historical training records or suffer from severe utility degradation and model reverting after recovery. We propose FedRazor, a two-stage federated unlearning framework that achieves stable client-level unlearning through representation divergence and gradient direction control. In Stage I, FedRazor weakens dependence on forgotten data using two complementary objectives. A Divergence-Smoothing Loss reduces prediction confidence on forgotten labels, while a Feature Mean Divergence loss pushes forgotten representations away from the retained feature center. To protect retained performance, we introduce PCGrad Razor, which trims gradient components that conflict with retained gradients during aggregation. This stage produces an intermediate unlearned model without storing historical updates. In Stage II, FedRazor restores retained utility using directional gradient trimming. Gradients aligned with the unlearning displacement direction are removed, preventing forgotten information from re-entering the model during recovery. Experiments on MNIST, CIFAR-10, and CIFAR-100 under IID and non-IID settings show that FedRazor consistently reduces attack success rate to near zero while preserving retained accuracy. On CIFAR-10 Pat-50, FedRazor achieves ASR = 0.026 with retained accuracy 0.659 after post-training, outperforming strong baselines in stability and unlearning robustness. Full article

This paper examines how hour-ahead forecasting uncertainty propagates to microgrid operation under intermittent renewable generation. Using hourly public data for Ontario and focusing on the FSA K0K in 2018, we evaluate four representative months (January, April, July, and December) to capture seasonal dynamics. We benchmark three univariate forecasting approaches for load demand, photovoltaic (PV) generation, and wind generation under a consistent 24-to-1 input setup, including GRU, LSTM, and a persistence baseline. We report point-forecast metrics (RMSE, MAE, and $R^2$) and also provide 90% prediction intervals (PI90) using conformal calibration to quantify uncertainty. To assess downstream impact, forecasts are coupled with a dual-branch MATLAB/Simulink microgrid model. One branch uses True profiles and the other uses forecast-driven Pred inputs, while both branches share the same rule-based EMS and BESS constraints. System performance is evaluated using time-series comparisons and monthly key performance indicators (KPIs) covering grid import and export, grid peak power, battery throughput, and state-of-charge (SoC) statistics. We further report an illustrative cost sensitivity under a flat tariff and a throughput-based degradation proxy. Results show that forecasting performance is target dependent. GRU achieves the best overall point accuracy for load and PV, whereas wind is strongly driven by short term persistence at the one hour horizon, and in this measurement only setup without meteorological covariates the persistence baseline can match or outperform the deep learning models. In the microgrid simulations, Pred and True trajectories remain qualitatively consistent, and SoC-related indicators and peak power remain comparatively consistent across months. In contrast, energy-flow indicators, especially grid export and battery throughput, show larger deviations and dominate the observed cost sensitivity. Overall, the findings suggest that compact hour-ahead forecasts can be adequate to preserve operational reliability under a constraint-driven EMS, while forecast improvements mainly translate into economic efficiency gains rather than reliability-critical benefits. Full article

With rapid advances in nanotechnology and synthetic biology, biological nanonetworks are emerging for biomedical and environmental applications within the Internet of Bio-NanoThings. While they rely on molecular communication, experimental validation remains limited, especially for non-ideal effects such as molecular accumulation. In this work, we present a novel table-top experimental system that emulates the core functionalities of a biological nanonetwork and is straightforward to reproduce in standard laboratory environments, also making it suitable for educational demonstrations. To the best of our knowledge, this is the first experimental platform that incorporates two end nodes capable of acting interchangeably as transmitter and receiver, thereby enabling true bidirectional molecular communication. Information transfer is realized through controlled release, advection and diffusion of molecules, using molecular concentration coding analogous to concentration shift keying, while the receiver decodes messages by comparing measured concentrations against predefined thresholds. Based on the measurements reported herein, the drop-based algorithm substantially outperforms the threshold-based scheme. Specifically, it reduces first-message latency by more than 2.5× across the tested volumes and reduces latest-message latency by up to 71%, providing approximately 3.7× better message delivery. A key experimental outcome is the observation of channel saturation: beyond a certain operating period, residual molecules accumulate and effectively saturate the medium, inhibiting reliable further message exchange until sufficient clearance occurs. This saturation-induced “channel memory” emerges as a fundamental practical constraint on sustained communication and achievable data rates. Overall, the proposed platform provides a scalable, controllable, and experimentally accessible testbed for systematically studying signal degradation, saturation, clearance dynamics, and throughput limits, thereby bridging the gap between theoretical models and practical implementations in the Internet of Bio-NanoThings era. Full article