Search Results (15,730)

This study elucidates the multi-targeted antineoplastic mechanisms of Parkia speciosa empty pod extract (PSET) against HCT-116 and HT-29 colorectal cancer (CRC) cells through integrated transcriptomic and proteomic analyses. Phytochemical profiling indicates that PSET is rich in bioactive metabolites, notably quercetin, rutin, and pyrogallol, which orchestrate its profound ability to inhibit tumor proliferation, migration, and invasion. Transcriptomic data revealed that PSET profoundly suppresses the oncogenic Wnt/β-catenin signaling axis while simultaneously activating p53-mediated cell cycle arrest. Complementary proteomic profiling uncovered critical metabolic vulnerabilities, demonstrating that PSET abrogates the Warburg effect by disrupting key glycolytic enzymes (e.g., ENO1, GAPDH, LDHA), thereby inducing metabolic starvation. Furthermore, the extract precipitated a catastrophic collapse of the cytoskeletal architecture and downregulated epithelial–mesenchymal transition (EMT) markers, effectively paralyzing the cells’ metastatic machinery. The integrated transcriptomic and proteomic signatures also highlighted an irrecoverable state of cellular stress, characterized by an overwhelming unfolded protein response and dysregulated RNA splicing, ultimately driving the cells toward apoptosis. In conclusion, this integrated omics approach provides robust molecular validation that PSET systemically dismantles colorectal cancer survival networks, highlighting its strong potential as a natural, multi-targeted therapeutic agent. Full article

Fly ash substitution for cement in Portland cement concrete (PCC) has been regarded as a sustainable solution, but its widespread application remains constrained by concerns over mechanical performance and durability of PCC, especially at higher replacement rates. This study evaluates PCC mixes incorporating fly ash Type C (FA-C) or Type F (FA-F) across cement replacement rates from 10% to 90%, tracking fresh-state workability, compressive strength, and surface electrical resistivity at 7, 14, and 28 curing days. A process-based life cycle assessment (LCA) with the TRACI 2.1 method quantified global warming potential (GWP, kg CO₂/m³) under a raw-material-plus-batching-electricity boundary for each mix. A Performance Index (PI) normalizes GWP against both compressive strength and electrical resistivity, producing a performance-weighted environmental efficiency metric (GWP/PI). A sensitivity analysis across five weighting scenarios tested the robustness of mix rankings under varying priorities for structural versus ironic transport resistance performance, and a structural threshold analysis identified mixes meeting strength requirements. FA-C at 50% cement replacement exceeded the OPC control in 28-day compressive strength (42.9 vs. 36.2 MPa) and electrical resistivity (9.88 vs. 8.50 kΩ·cm), while reducing GWP by 48.3% relative to the OPC control (40.24 kg CO₂/m³). FA-F at 30–50% replacement exhibited a distinct strength–resistivity decoupling, demonstrating that strength only evaluation underrepresents the environmental efficiency of durability-critical applications. The GWP/PI metric revealed that raw GWP reduction alone misrepresents environmental efficiency. FA-C at 50% achieved a GWP/PI of 17.73, which is a 56% improvement over the OPC control. These findings question the conventional <30% substitution ceiling at 28 days under standard moisture curing and demonstrate that performance-weighted LCA metrics provide a more informed basis for sustainable concrete mix design. Full article

With the prevalence of telemedicine and digital diagnosis, the security and integrity of medical images transmitted over open networks have become critical issues. To effectively defend against malicious tampering and ensure the reliability of diagnostic information, this study proposes a block-based image authentication and tamper detection framework (KPP-BA). This framework integrates key-dependent pixel permutation, hash-based message authentication code (HMAC)-SHA256 hash verification, and a parity-based 3-LSB minimal distortion embedding strategy. The core innovation lies in utilizing pseudo-random pixel permutation to disrupt spatial correlation within blocks, thereby effectively resisting collage and statistical analysis attacks. Furthermore, by combining the avalanche effect of HMAC-SHA256 with hybrid bit-plane feature extraction, the proposed method ensures extremely high sensitivity to subtle tampering. Experimental results on a dataset comprising 300 medical images demonstrate that the proposed method maintains superior visual quality while ensuring security, achieving an average Peak Signal-to-Noise Ratio (PSNR) of 54.15 of 0.5 bit per pixel (bpp). Moreover, against various tampering attacks—including masking, copy–paste, circle masking, and collage—the method exhibits exceptional detection capabilities with an average detection accuracy of 99.99%. Compared with seven state-of-the-art methods, the proposed framework demonstrates significant advantages in both image fidelity and tamper localization precision, validating its feasibility and robustness for secure medical image transmission applications. Full article

Thisstudy investigates the reliability of power-terminal solder joints in intelligent power modules (IPMs) subjected to thermal cycling, random vibration, and packaging/assembly-induced deformation. Fifty IPMs were tested under temperature cycling from −55 °C to 125 °C and random vibration from 20 to 2000 Hz, and the experimental observations were combined with finite element simulations of thermal, vibration, and deformation loads. The modules survived 200 temperature cycles in the free state, whereas functional abnormalities occurred after board-level assembly and subsequent environmental loading. Simulation results showed that random vibration produced limited solder-layer stress because the first structural mode was above the excitation range, while packaging and PCB deformation markedly increased the initial stress of the power-terminal solder joints. When local deformation reached approximately 0.5 mm, the calculated solder-pad stress reached or exceeded the shear-strength risk range, consistent with the failure tendency observed in highly deformed modules. Weibull analysis further indicated a fatigue-dominated failure process with an increasing failure rate. These findings suggest that deformation control, package stiffness improvement, and assembly flatness management are critical for improving the reliability of IPM power-terminal solder joints. Full article

Heat-treatable aluminum alloys are widely used in aerospace and automotive industries for high-performance structural applications. However, their processing through conventional fusion-based additive manufacturing is limited by solidification-related defects, such as hot cracking, porosity, and elemental segregation. To overcome these limitations, friction-based additive manufacturing (FBAM) has emerged as a promising solid-state alternative. FBAM primarily includes friction stir additive manufacturing (FSAM), additive friction stir deposition (AFSD), friction screw extrusion additive manufacturing (FSEAM), and friction rolling additive manufacturing (FRAM), which differ in feedstock form and process configuration. In these processes, feed material is consolidated through frictional heat generated below the melting temperature, enabling the formation of refined equiaxed microstructures while minimizing solidification defects. Despite these advantages, significant challenges persist in processing heat-treatable aluminum alloys, particularly the 2xxx, 6xxx, and 7xxx series. These include non-uniform microstructure and mechanical properties along the build direction; precipitation instability; process-induced defects, such as tunnel formation; and mechanical properties that are often inferior to those of the corresponding base materials (BMs). Reported FBAM builds generally exhibit equiaxed ultrafine grains below 1 μm; however, the strength and microhardness of heat-treated alloy builds commonly remain around 70–75% of the corresponding BM. Following post-heat treatment, microhardness can be nearly fully recovered, whereas UTS typically reaches about 80–85% of BMs, often with an associated ductility reduction of nearly 50%. This review critically analyzes research reported over the past decade on FBAM processing of heat-treatable aluminum alloys, covering FSAM, AFSD, FSEAM, and FRAM. The key challenges related to microstructural evolution and mechanical performance are systematically discussed for each alloy series. Furthermore, mitigation strategies proposed in the literature, including process parameter optimization, in-process cooling, post-heat treatment, and nanoparticle reinforcement (e.g., SiC, TiC, Ni and ZrO₂), are evaluated. Finally, existing research gaps are identified, and future directions are proposed to support the development of robust, scalable, and high-performance FBAM processes for heat-treatable aluminum alloys. Full article

District heating networks (DHNs) are critical infrastructure prone to physical failures such as leakage-related faults, which cause significant energy and financial losses. Traditional physics-based monitoring methods are computationally expensive and require the continual recalibration of complex mathematical models, while standard data-driven approaches often fail due to the scarcity of real-world sensor data. This study addresses these challenges by proposing a topology-aware graph neural network (GNN) architecture for fault localization. The methodology follows a two-stage process: first, a graph attention-based architecture is designed and optimized using a synthetic dataset to effectively capture multi-step neighborhood dependencies. Second, the model is adapted and evaluated on a physically simulated dataset of a real urban DHN, comprising 187 nodes and 42,570 operational states. The problem is formulated as a multi-class classification task across supply and return subnets. The results demonstrate high predictive performance, achieving an accuracy of 96% on the supply subnet and 91% on the return subnet. Analysis of prediction errors reveals a strong bias towards local topological mistakes, indicating the model’s ability to capture the physical propagation of disturbances. These findings highlight the efficacy of GNNs in handling sparse data and exploiting network topology for robust DHN monitoring. Full article

High-entropy alloys (HEAs), composed of five or more principal elements in near-equimolar ratios, have emerged as a groundbreaking class of materials for next-generation flexible electronics. This review systematically examines the unique potential of HEAs as sensing materials, moving beyond their traditional role as structural components. We first elucidate the fundamental mechanisms—core effects including lattice distortion, sluggish diffusion, and the cocktail effect—that endow HEAs with an exceptional synergy of high strength, good ductility, tunable electrical resistivity, and superior electrocatalytic activity. Subsequently, we critically analyze the state-of-the-art strategies for processing HEA-based micro/nano structures, including mechanical alloying, wet-chemical synthesis, and non-equilibrium deposition techniques, with an emphasis on their compatibility with flexible substrates. The core of the review categorizes and discusses the latest advances in HEA-based flexible sensors for strain/stress, gas, and electrochemical (e.g., glucose, biomarkers, heavy metals) detection, highlighting the structure–property–performance relationships. Representative studies have demonstrated that HEA flexible strain sensors achieve a temperature coefficient of resistance as low as 45.59 ppm/K with no signal drift over 6000 stretching cycles; room-temperature hydrogen sensors reach a detection limit down to 31 ppb with a response time of 19 s; and non-enzymatic glucose sensors deliver a sensitivity up to 3043 μA·mM⁻¹·cm⁻². Finally, we summarize the key challenges—such as manufacturing scalability, long-term stability under dynamic deformation, and cost-effectiveness—and provide a forward-looking perspective on promising research directions, including high-throughput compositional screening, multi-functional sensor arrays, and the integration of machine learning for rational material design. Full article

Background/Objectives: COVID-19, caused by the SARS-CoV-2 virus, can lead to widespread neurological and cognitive complications, even in the absence of significant structural brain abnormalities. Understanding the evolving health concerns in the context of viral infections is critical to service member readiness, fitness, and mission completion. The potential neuroprotective effects of SARS-CoV-2 vaccination remain underexplored. Methods: Using a cross-sectional, non-human primate model (female cynomolgus macaques), we employed magnetoencephalography (MEG) to assess resting-state brain activity following vaccination with escalating doses of a novel psoralen-inactivated SARS-CoV-2 vaccine (PsIV) or a combination of PsIV and a DNA vaccine (prime boost), and subsequent challenge with the Delta variant (SARS-CoV-2 B.1.617.2). MEG scans were acquired 41 days after inoculation. Source series were constructed for 42 regions of interest for each subject, and band power was computed. Results: Band power demonstrated substantial preservation of neural activity across multiple brain regions in vaccinated subjects compared to unvaccinated controls following viral challenge. Significantly lower power was observed across the brain at all bandwidths in the unvaccinated group relative to the prime boost group. As PsIV concentration increased, spectral power increased, with the prime boost group having the greatest power. Conclusions: This approach not only underscores the role of vaccination in mitigating neuropathology but also highlights the capability of MEG to detect subtle yet significant changes in brain function that may be overlooked by other imaging modalities. These findings advance our understanding of vaccine-induced neuroprotection and establish MEG as a powerful tool for monitoring brain function in the context of viral infections. Full article

Accurate remaining useful life (RUL) prediction is critical for the reliable operation of lithium-ion batteries. Traditional data-driven methods often suffer from parameter redundancy and error accumulation in state prediction. This paper proposes a hybrid data-driven RUL prediction framework based on Gaussian process regression (GPR) optimized by the lightning search algorithm (LSA). First, both local and global indirect health features (HFs) are extracted from the external characteristic parameter curves and the incremental capacity curves during battery charging/discharging. Second, the Pearson correlation coefficient is applied to select highly relevant features, forming a compact feature set. Third, a GPR model is developed, and the LSA is introduced to optimize its hyperparameters, overcoming the tendency of the conjugate gradient method to fall into local optima or fail to converge. Experimental results under identical conditions show that the proposed LSA–GPR model achieves a prediction error of 3% or less. Full article

Autonomous ship maneuvering in harbor environments is increasingly supported by advances in model predictive control, reinforcement learning, digital twins, multi-sensor fusion, berth allocation, and multi-agent coordination. However, these developments are often studied as separate technological domains, while real harbor autonomy requires coordinated operation across vessels, port infrastructure, regulatory systems, cybersecurity mechanisms, and human supervisory processes. This study presents an architecture-oriented critical review of autonomous ship and harbor maneuvering research published between 2015 and May 2026. The review synthesizes literature from control engineering, maritime artificial intelligence, sensor fusion, digital twins, port logistics, cyber-physical systems, regulation, cybersecurity, and human–AI supervision. The analysis introduces two conceptual contributions: a layered cyber-physical taxonomy and an integration maturity model. The taxonomy organizes autonomous harbor maneuvering into seven interdependent layers: physical dynamics, perception and sensor fusion, prediction and state estimation, control, decision and coordination, digital twin federation, and regulatory–supervisory governance. The maturity model distinguishes isolated vessel autonomy, assisted coordination, shared digital synchronization, agent-based coordination, and fully federated maritime cyber-physical autonomy. The reviewed evidence shows substantial progress in individual layers, especially control, perception, digital twins, and berth allocation. However, major gaps remain in cross-layer synchronization, semantic interoperability, regulation-aware decision-making, cybersecurity integration, and validated ship–shore federation. To address these gaps, this study proposes a Federated Maritime Cyber-Physical Architecture for autonomous harbor maneuvering. The architecture integrates vessel autonomy cores, port intelligence cores, semantic federation middleware, agent-based negotiation, regulatory verification, cybersecurity safeguards, and human supervisory interfaces. This review argues that future progress in autonomous harbor operations depends not only on stronger algorithms, but on interoperable, explainable, regulation-aware, and cyber-resilient ship–shore ecosystems. Full article

Robotic belt grinding is an effective and widely adopted finishing method for superalloys, offering notable advantages such as high material removal capability, low heat input, and reduced workpiece damage. In addition, robots can readily integrate multiple sensors—such as infrared radiation cameras, force sensors, and high-speed cameras—which facilitate real-time monitoring of the grinding process and thereby enhance grinding quality control. With the establishment and continuous advancement of large-scale artificial intelligence (AI) data models, new breakthroughs have emerged in the optimization of robotic grinding processes. Owing to its dexterous workspace and advantages in high flexibility and cost-effectiveness, robotic belt grinding has become a critical process for the precision forming of complex curved components such as aero-engine blades and blisks. However, factors such as the limited absolute accuracy of industrial robots, time-varying grinding contact states, and significant transient boundary effects make it difficult for the current constant-parameter open-loop machining mode to simultaneously meet the demands for high material removal efficiency and high surface integrity on complex profiles. This paper systematically reviews the technologies for precision control and process optimization of robotic belt grinding aimed at pointwise precise material removal. First, the structural composition of the robotic belt grinding system and the material removal mechanism are analyzed. Then, centered on the compensation concept, a hierarchical progressive technical framework is outlined, covering geometric calibration compensation, force/position hybrid online compensation, transient entry boundary compensation, and system-level comprehensive compensation of multi-source errors, with a comparison of the applicable scenarios and the effects on shape and property control at each level. Furthermore, under the support of effective compensation, the collaborative optimization methods of material removal modeling, multi-objective optimization of process parameters, force-constrained trajectory planning, and intelligent adaptive processes are elaborated. Finally, current technical bottlenecks are summarized, and future trends in next-generation adaptive grinding technology driven by digital twins and embodied intelligence are envisioned. This review aims to provide a systematic theoretical reference for the high-precision and intelligent upgrading of robotic precision grinding systems. Full article

Accurate obesity risk classification is often hindered by the asymmetric and heteroscedastic nature of health data, where traditional mean-based machine learning models fail to capture critical distribution tails. This study addresses this gap by proposing a robust Expectile Random Forest (ERF) model, a novel ensemble architecture that integrates an expectile learning framework via the Asymmetric Least Squares (ALS) loss function for seven-level (multilevel) classification. Utilizing a dataset of 2111 empirical records, the sensitivity analysis identifies $\tau =0.7$ as the optimal configuration, achieving an overall Accuracy of 94.6 ± 0.7% and a Macro F1-Score of 94.5 ± 0.7%. This performance represents a significant quantitative improvement over state-of-the-art benchmarks, outperforming XGBoost by 1.8% and standard Random Forest by 3.9%. Feature importance analysis identifies body weight, age, and sedentary factors as primary predictors, while the ERF model demonstrates exceptional ordinal consistency and robustness against clinical outliers. These findings provide a superior methodological framework for developing precise medical decision support systems, shifting the paradigm from central-tendency predictions to tail-sensitive health risk mapping. Full article

Modern power grids with high renewable energy penetration are vulnerable to fast voltage disturbances caused by grid faults. Among these, voltage sags are critical because they develop within milliseconds and require rapid reactive current support to maintain grid stability and power reliability. Reinforcement learning has previously shown potential for reactive current injection control during voltage sag events due to its fast response and adaptability to changing system conditions. However, existing approaches rely on separate policies for specific subsets of the operating space, which limits their ability to provide optimal actions when the system operates across broader or combined state regions. To address this limitation, this paper proposes a unified Soft Actor–Critic (SAC) target policy trained over the full state and action space by integrating multi-source transfer learning with potential-based reward shaping approach. Results show that the proposed multi-source transfer approach enables the target agent to converge faster and reach a higher reward solution than the baseline SAC and single-source transfer approach. The trained policy also improved prediction accuracy, achieving reactive-current errors below 0.2 A with respect to the ground-truth reference generated through extensive simulations over the full observation and action space. The reference follows the grid-code requirement for minimum reactive current injection during faults and provides a benchmark for evaluating prediction accuracy. This can help distributed generation sources respond more effectively during severe perturbations such as voltage sags, support voltage recovery, and reduce the risk of cascaded disconnections that could lead to unwanted blackouts. Additionally, the inference execution time is also sufficiently fast to satisfy the response-time requirement of voltage sag events, confirming the real-time feasibility of the proposed controller. Full article

This article provides the theoretical foundation for upcoming primary research on the formation of ‘integral generative AI (GenAI) innovation ecosystems’ in the Chinese higher education context. Based on an adaptation of Gramsci’s idea of the ‘integral state’, which informs the move beyond Western civil society/market-led and Chinese political state-led innovation ecosystem models, key features of an integral innovation GenAI ecosystem are elaborated upon. An expanded framework builds on previously published work on socialised GenAI systems comprising a multi-level approach, with particular emphasis on ‘thickened’ meso-institutional layers (e.g., supportive local investment, institutional governance frameworks and critical practices) mediating between an enhanced macro-strategic direction and upscaled micro-level practices. Theorising the institutional meso-system helps analyse challenges facing non-elite Chinese universities in moving from a ‘low-technological-baseline equilibrium’ (LTBE) constraining GenAI development to demonstrating features of GenAI innovation ecosystem ‘readiness’. The framework also draws on Lury’s ‘problem space’ research methodology, with a particular focus on its ‘within/without’ contextual factors, while also contributing a chrono-dimension to reinforce its conceptual role over time. The article concludes with an outline of a primary research strategy to investigate the challenges of building integral GenAI innovation ecosystems in Chinese higher education institutions more broadly. Full article

The trend in the power electronics industry toward higher power density and efficiency has brought high-frequency transformers (HFTs) to the forefront of critical applications, including isolated DC–DC converters, electric vehicle chargers, and solid-state transformers. This paper focuses on the leakage inductance of HFTs and presents a systematic comparative framework that evaluates five surrogate modeling and hybrid optimization approaches for the rapid and accurate estimation of leakage inductance. A comprehensive parametric dataset was constructed, comprising 1210 finite element analysis simulations conducted via finite element analysis in the ANSYS Maxwell 2024 R1 environment, varying the number of winding turns, primary winding thickness, and secondary winding thickness of the HFT. All five methods were trained and evaluated on the same dataset under identical conditions. The comparative evaluation demonstrates that the proposed hybrid Gray Wolf optimizer–artificial neural network (GWO-ANN) framework achieved the highest prediction accuracy (R² = 0.9832, MSE = 0.01780, MAE = 0.0935 µH) and the fastest convergence among all tested approaches. The generalization capability of the proposed model was confirmed through blind validation tests across six geometric configurations spanning the full range of the design space, yielding a maximum prediction error of 8.15% and an average error of 2.14%. The functional validity of the proposed parameters was further tested in a third validation layer using MATLAB/Simulink R2024b transformer circuit studies, demonstrating a theoretical efficiency of 96.06%. This three-layer validation approach proves both the parametric and functional reliability of the proposed framework for HFT designs. Full article