Search Results (6,055)

This paper proposes a micro-Doppler flash detection framework for hovering unmanned aerial vehicle (UAV) detection with linear frequency modulated continuous wave (LFMCW) radar under the dual constraints of strong ground clutter and severe thermal noise conditions. In such scenarios, conventional methods fail not only due to the spectral overlap between hovering targets and clutter but also because of the visual disappearance of micro-Doppler features under heavy noise. The framework consists of three sequential modules. A prior-template orthogonal projection (PTOP) module suppresses clutter via a single-step orthogonal projection, preserving the micro-Doppler flash signature without distortion while approximately maintaining the Gaussian noise statistics required for subsequent detection. A flash power spectrum construction module then collapses the periodic blade flash energy onto a sharp spectral peak in a one-dimensional (1D) power spectrum via Gabor transform, power projection, and fast Fourier transform (FFT). A cell-averaging constant false alarm rate (CA-CFAR) detection module with an analytically derived threshold factor finally renders a reliable detection decision. Simulations under a signal-to-clutter ratio (SCR) of $-21$ dB and signal-to-noise ratio (SNR) of $-23$ dB confirm that the proposed framework achieves reliable detection even when the micro-Doppler flash signatures are visually obscured by residual noise in the time–frequency domain. Parametric SNR sweep curves and a two-dimensional (2D) SCR–SNR detection-probability heatmap under a non-stationary clutter model further quantify the practical performance boundaries of the framework. By transforming these concealed periodic features into a sharp spectral peak, the framework provides robust detection performance where conventional range-Doppler and moving target indication (MTI)-based methods both exhibit severe performance degradation. Full article

Background: Appetite regulation is governed by a complex neuroendocrine network that integrates peripheral peptide signals with hypothalamic and brainstem circuits to coordinate energy intake and maintain energy homeostasis. Disruption of these pathways contributes to obesity and other disorders characterised by dysregulated feeding behaviour. Objective: To map and synthesise the current evidence on the role of appetite-regulating peptide hormones and central neural pathways in appetite control, obesity pathophysiology, and emerging therapeutic approaches. Methods: A scoping review of the literature was conducted to identify and synthesise evidence relating to the physiological and pathological mechanisms of appetite regulation. The review examined the actions of key peptide hormones, including ghrelin, glucagon-like peptide-1 (GLP-1), peptide YY (PYY), leptin, and insulin, their interactions within the gut–brain axis, and their effects on central appetite-regulating circuits. Results The evidence highlights the central role of the arcuate nucleus in integrating peripheral hormonal signals with neural pathways controlling feeding behaviour. Appetite regulation is mediated by the balance between orexigenic neuropeptide Y/agouti-related peptide (NPY/AgRP) neurons and anorexigenic pro-opiomelanocortin/cocaine- and amphetamine-regulated transcript (POMC/CART) neurons, with further modulation by the paraventricular, lateral, and ventromedial hypothalamic nuclei. The literature identifies hormone resistance, impaired satiety signalling, and altered neuroendocrine feedback as major contributors to obesity. Evidence on therapeutic interventions demonstrates the potential of GLP-1 receptor agonists, including liraglutide and semaglutide, and the dual incretin agonist tirzepatide, while also highlighting challenges related to treatment durability, adverse effects, and weight regain following discontinuation. Conclusions: Current evidence demonstrates that appetite regulation involves highly interconnected peripheral and central signalling pathways. The reviewed literature supports the development of multi-target and precision-based therapeutic strategies for obesity and identifies important areas for future research, including mechanisms of treatment resistance, long-term efficacy, and inter-individual variability in neuroendocrine responses. Full article

With the increasing penetration of renewable energy, power systems are facing growing challenges in voltage and frequency stability. Synchronous condensers (SCs) have attracted renewed attention due to their capabilities in providing dynamic reactive power support, improving voltage regulation characteristics, and providing rotational inertia. This paper reviews the development history and research status of six main types of SCs based primarily on their machine structures and operating principles, including conventional synchronous condensers (CSCs), superconducting synchronous condensers (SSCs), dual-excited synchronous condensers (DESCs), high-inertia synchronous condensers (HISCs), energy storage synchronous condensers (ESSCs), and phase modulation conversion of power units. A comparative analysis is further conducted in terms of cost, performance, technical advantages, and typical application scenarios, thereby clarifying the technical rationale for selecting suitable SC technologies for different power system requirements. Finally, several promising avenues for future research on SC technologies are proposed. Full article

Community multi-building energy management is a key approach for reducing carbon emissions from the building sector and alleviating peak grid pressure. However, load coupling among buildings and coordinated energy-storage operation make control-policy design highly challenging. To address the limitation of the standard multi-agent proximal policy optimization (MAPPO) algorithm, in which the centralized critic simply concatenates building observations and therefore struggles to model inter-building interactions, this paper proposes an improved MAPPO algorithm with a multi-head-attention-enhanced centralized critic, referred to as a multi-head-attention MAPPO (MHA-MAPPO). Without changing the decentralized execution framework, the proposed method improves the critic network in three aspects. First, a dual-branch gated embedding module is designed to adaptively fuse local building observations and global interaction information. Second, an interaction-attention path is constructed to explicitly capture pairwise dependencies among buildings through multi-head attention. Third, a context-attention path is introduced to extract high-level community-level global features by means of learnable query vectors. These improvements enable the critic to estimate the joint-state value more accurately and provide more reliable advantage estimates for all agents. Experiments in the CityLearn environment show that, compared with the original MAPPO, MHA-MAPPO improves the mean evaluation reward by approximately 19.2%, reduces the reward standard deviation by one order of magnitude, and decreases peak net load and total net load by approximately 15.4% and 35.5%, respectively. The results verify the effectiveness of multi-head attention for coordinated multi-building scheduling. The proposed method provides a useful reference for improving multi-agent reinforcement learning algorithms in community energy management. Full article

Under the “dual carbon” targets, using green finance to support renewable energy use is an important way to reduce extreme climate risks. This study builds a balanced panel dataset of 271 Chinese cities from 2010 to 2021. We measured the level of Green Finance (GF) and renewable energy utilization (RE). Employing two-way fixed effects, the Spatial Durbin Model (SDM), and the Heterogeneous Spatial Autoregressive (HSAR) model, we systematically examine the promoting effects, transmission mechanisms, spatial heterogeneity, and economic–environmental consequences of GF on RE. The empirical results reveal that GF significantly enhances RE and generates pronounced positive spatial spillovers. Mechanism analysis indicates that R&D investment and environmental regulation serve as the primary transmission channels. The promotion effect is more pronounced in the eastern and central regions, as well as in areas with higher R&D investment and stricter environmental regulation, whereas the spatial spillover effect is particularly evident in coastal regions. Further consequence analysis demonstrates that GF contributes to reducing conventional energy intensity, improving green total factor productivity, and alleviating extreme climate events. Building on these findings, this study proposes spatially differentiated and sustainability-oriented policy strategies to advance China’s energy transition and foster coordinated economic and environmental sustainability. Full article

A fuel cell (FC)/supercapacitor (SC) hybrid powertrain is proposed for rubber tire gantry (RTG) cranes, aiming to address their characteristics of high peak/low average power demand and huge potential energy recovery. Unlike conventional design methods that neglect the coupling effects of energy management strategies (EMSs), this paper adopts a joint optimization (JO) for the powertrain parameters’ design. Parameters are preliminarily sized based on routine container handling tasks, then refined via a dynamic programming (DP)-based EMS for secondary optimization to minimize the total crane operation costs that cover hydrogen consumption as well as FC degradation. Iterations of the optimization process continue until targets are met. The results indicate that the JO framework achieves dual energy-economic goals, exhibiting a 57.33% enhancement in fuel economy compared to diesel-powered cranes through port validation while concurrently decreasing the SC’s capacity redundancy by 12.7%. These findings aid FC/SC RTG crane configuration design in ports. Additionally, the theoretical optimal operation cost obtained by the DP-based EMS can be used as a benchmark for evaluating other EMSs. Full article

This paper presents a low-power hardware implementation of an AES-CCM coprocessor for secure chips in embedded systems. The proposed design performs key expansion only once and stores the round keys in an on-chip RAM to avoid redundant computations. Meanwhile, the S-box module is shared between the key expansion and encryption to reduce hardware overhead. A dual-mode architecture supporting parallel (two-core) and serial (single-core) operations is implemented to adapt to high-throughput and low-power scenarios. The design supports AES-128, with a 1.25 Kb RAM used to store the 10 round keys. Experimental results using TSMC 40 nm technology show that the parallel mode achieves a 5.4% power reduction at the cost of 12.8% area overhead compared with the reference design. The energy efficiency reaches 2.11 pJ/bit in the parallel mode and 2.17 pJ/bit in the serial mode. Full article

The integration of high-penetration renewable energy creates new requirements for cross-timescale peak shaving and for system robustness under extreme meteorological conditions. This study develops a dual-timescale capacity allocation method for pumped hydro storage (PHS), combining 8760 h chronological production simulation with monthly typical-day retrospective analysis. The model represents the operating limits of conventional units, nuclear power, hydropower, wind power, photovoltaic generation, tie-line exchange, and PHS energy shifting. On this basis, a stepwise capacity-sensitivity framework is established to minimize annualized comprehensive system cost while controlling renewable energy curtailment within a predefined planning threshold, rather than treating zero curtailment as an unconditional monthly hard constraint. Using long-term planning data from a coastal provincial power grid in southeastern China, the study compares the 2035 and 2040 planning scenarios. The results show that isolated typical-day models tend to overestimate PHS requirements because they disconnect chronological continuity and cross-day reservoir buffering. In 2035, the system presents a two-level seasonal capacity structure: 15,000 MW can support normalized operation in stable months, whereas the rigid boundary rises to 19,000 MW under extreme autumn high-wind conditions. In 2040, wind and photovoltaic capacity increase by approximately 20.01 GW compared with 2035, deepening low-net-load valleys and compressing seasonal regulation margins. Under the assumed planning boundary, the recommended PHS capacity converges to 23,000 MW. The proposed framework provides a practical reference for flexible resource planning in coastal power grids with deep renewable energy integration. Full article

Circulating ketone bodies (KBs), particularly β-hydroxybutyrate (β-HB), have emerged as metabolites with dual roles as both oxidative fuels and metabolic signaling molecules. Beyond serving as an alternative energy substrate, β-HB regulates diverse pathways involved in oxidative stress, inflammation, and mitochondrial function. However, the clinical implications of circulating KBs remain uncertain. This review summarizes current evidence regarding the potential role of KBs in glycemic progression and diabetic cardiomyopathy (DCM). Epidemiologic and experimental studies report conflicting associations between KB levels and the progression to hyperglycemia or type 2 diabetes, with some findings suggesting that elevated KB levels may reflect a metabolically favorable phenotype or a compensatory mechanism, whereas others indicate links to worsening glycemia. Similarly, studies in DCM have produced divergent results, with β-HB reported to improve mitochondrial function and cardiac performance in some models while contributing to metabolic inflexibility and adverse cardiac remodeling in others. We discuss potential mechanisms underlying these discrepancies and propose that the metabolic effects of β-HB are context-dependent, influenced by factors such as circulating concentration, the mode of ketosis induction, and the underlying metabolic or disease stage. Understanding these contextual determinants may help clarify whether β-HB represents an adaptive metabolic signal or a maladaptive substrate shift in cardiometabolic disease. Full article

The pathological mechanism of autism spectrum disorder (ASD) exhibits dual heterogeneity: abnormal local energy metabolism and brain-wide high-order topological failure. To synergistically characterize these complex signals captured by advanced neuroimaging sensors, we propose the Quantum-Enhanced Graph Convolutional Wide Residual Network (QG-WRN), a modality-specific, decoupled parallel dual-stream architecture. In the classical branch, to accurately capture the spatial distribution of local metabolic abnormalities, we employ a wide residual network (WRN) to extract amplitude of low-frequency fluctuation (ALFF) features, leveraging its expanded feature channels to effectively mine regional neurodynamic properties. Furthermore, to overcome the representational bottlenecks of classical linear operators in parsing hidden, long-range network connections, we introduce quantum computing, exploiting its exponentially expansive state space and intrinsic low-parameter regularization mechanism. Guided by these properties, the quantum branch utilizes a variational quantum graph convolutional (QGCN) module—featuring a trainable circular encoding strategy and a hardware-efficient 4-qubit configuration—with a 2-layer nested message passing structure to process the functional connectivity (FC) matrix, harnessing quantum interference in Hilbert space to parse complex topology while effectively mitigating overfitting on small-sample medical data. A unified training scheme achieves full-dimensional fusion of node activity and topology. Achieving 68.49% accuracy, our method outperforms 10 classic and recent new baselines, providing a powerful computational intelligence tool for sensor-based ASD clinical diagnosis. Furthermore, interpretability analysis successfully maps core disease hubs to standard AAL116 atlas coordinates, providing a powerful tool for computationally aided ASD diagnosis. Full article

Background: Endocrine therapy for hormone receptor-positive breast cancer is associated with accelerated bone loss and increased fracture risk. Vertebral fractures (VFs) are frequently asymptomatic and remain underdiagnosed, potentially leading to underestimation of fracture risk. Methods: We conducted a cross-sectional real-world study that included 172 women with breast cancer (mean age 58.2 ± 12.0 years), the majority receiving aromatase inhibitors. Vertebral fractures were assessed using vertebral fracture assessment (VFA) during dual-energy X-ray absorptiometry (DXA). Bone mineral density (BMD), trabecular bone score (TBS), quantitative ultrasound (QUS), and FRAX^® scores were evaluated. Results: Vertebral fractures were identified in 13% of patients, and 78% of these occurred in women with normal or osteopenic BMD. Age was independently associated with VFs, while conventional densitometric and non-densitometric parameters showed limited discriminatory ability. The incorporation of VFA-detected fractures into FRAX significantly increased estimated fracture risk (hip fracture risk: 0.8% vs. 4.1%, p = 0.008). Conclusions: Vertebral fractures are common and frequently unrecognized in women receiving endocrine therapy and are not adequately captured by BMD. Routine use of VFA during DXA substantially improves fracture risk assessment and leads to a clinically meaningful reclassification of FRAX estimates. These findings support a more comprehensive approach to skeletal risk evaluation in this population. Full article

Non-Intrusive Load Monitoring (NILM) enables estimation of the energy use of individual appliances in smart buildings from a single aggregate meter. In practice, however, this task is not straightforward. Signals from different appliances can overlap, and the measured data may also include distortions such as spikes, drops, and noise. To address these issues, this study presents a multi-task triple-hybrid deep learning framework that handles appliance classification and anomaly detection together. The model brings together 1D-CNN, BiLSTM, and Transformer Attention so that local patterns, temporal dependencies, and wider contextual information can be learned within the same structure. It also uses a dual-output design to classify appliance categories and detect anomaly types simultaneously. Experiments were carried out on Building 1 of the UK-DALE dataset with four appliances: kettle, microwave, washer dryer, and fridge freezer. For the anomaly task, synthetic disturbances were added to segmented signal windows and grouped as normal, spike, drop, and noise. To check how well the proposed framework handled different scenarios, it was tested on both the UK-DALE and REDD datasets. Looking at the main UK-DALE results, the model correctly identified appliances 99.48% of the time and spotted anomalies with 98.80% accuracy. A secondary test on the REDD dataset yielded an 86.44% classification score. This proves the architecture can adjust to completely new power grid environments without losing its edge. On top of that, when pitted against standard benchmark models like Seq2Point, this triple-hybrid design clearly does a better job of mapping out complex signal changes. As a result, it yields much stronger anomaly detection metrics. Full article

Multi-terminal voltage-source-converter-based HVDC (VSC-MTDC) systems are increasingly used to integrate renewable energy and interconnect asynchronous AC grids, but conventional fixed-coefficient droop control cannot simultaneously limit DC-voltage deviations, reduce operating losses, and preserve converter power margins during operating-point switching. This paper hypothesizes that a rule-based fuzzy adjustment of the droop slope can provide smooth multi-objective coordination without inter-station communication. A dual Mamdani fuzzy controller is developed: one controller adjusts the weighting between loss-oriented and power-margin-oriented droop coefficients according to converter power margin, while the other introduces a voltage-deviation correction according to DC-bus voltage. The controller is implemented and verified in a five-terminal MMC-based VSC-MTDC model built in PSCAD/EMTDC, where simulation data are generated under heavy-load, light-load, and power-reference switching scenarios using specified line and converter parameters. Compared with conventional droop control, the proposed strategy improves power-margin utilization, reduces operating-point discontinuities, and raises the minimum DC voltage from 370.2 kV to 381.4 kV in the severe switching case. The results confirm that fuzzy-slope droop control can achieve smoother operating-point switching and better coordinated optimization among voltage stability, operating loss, and converter reserve margin. Full article

Conventional fire monitoring systems frequently exhibit high false alarm rates, delayed response times, and a lack of closed-loop control capabilities, which severely constrain their deployment in complex real-world environments. To address these issues, this paper proposes an embedded fire detection, tracking, and extinguishing system based on multimodal information fusion and a lightweight neural model. The system follows a “Perception–Decision–Execution–Feedback” closed-loop paradigm and is implemented on a heterogeneous cooperative computing architecture comprising OpenMV4 H7 Plus and STM32F103C8T6 microcontrollers. The perception layer implements a decision-level RGB-infrared fusion mechanism that incorporates a pruned, INT8-quantized lightweight FOMO model, enabling real-time fire detection with an inference latency of 210 ms and a model size of merely 1.8 MB under resource-constrained embedded conditions. The decision layer employs a Bayesian inference-based multimodal fusion framework that effectively suppresses spurious fire interference. The vision-only false detection rate is 15.3%. After infrared fusion verification, the system-level false alarm rate is reduced to 2.0% on the interference test set. In the execution layer, a sixth-degree polynomial jet trajectory model was established and combined with an improved PID–PI dual-loop controller to enable dynamic optimization of spray angle and flow rate in real time. Experimental results demonstrate that the proposed system achieves an average fire recognition accuracy of 95.6% with a false alarm rate as low as 1.4%. Furthermore, it realizes an extinguishing accuracy better than ±5 cm within an effective operating range of 10–60 cm and completes the entire perception-to-extinguishing cycle within 8.5 s under illumination conditions ranging from 50 to 100,000 lux. These results demonstrate the excellent real-time capability, robustness, and energy efficiency of the proposed system, providing a practical and scalable solution for autonomous embedded fire-fighting applications in household, industrial, and warehouse environments. Full article

Background and Objectives: Routine laboratory markers may support diagnostic risk stratification for osteoporosis, but their incremental value beyond the Osteoporosis Self-Assessment Tool for Asians (OSTA) remains uncertain in referral-based practice. We evaluated whether serum uric acid, albumin, alkaline phosphatase (ALP), and systemic inflammatory indices improve prediction of DXA-defined osteoporosis beyond OSTA in postmenopausal women. Materials and Methods: This retrospective cross-sectional study included 3504 postmenopausal women referred for DXA between January 2021 and May 2025. Osteoporosis was defined as the lowest T-score ≤ −2.5 at the lumbar spine, total hip, or femoral neck. Sequential exclusions removed patients with chronic hepatobiliary disease, chronic systemic inflammatory disease, bone-active medication exposure, systemic glucocorticoid use, abnormal liver biochemistry, or missing required variables. Multivariable logistic regression assessed associations, and OSTA-based prediction models were internally validated using stratified 10-fold cross-validation. Results: Osteoporosis was present in 1660 women (47.4%). Higher BMI, uric acid, and albumin were independently associated with lower odds of osteoporosis, whereas ALP and calcium were associated with higher odds. OSTA alone achieved an AUC of 0.679. Adding uric acid, albumin, and ALP increased AUC to 0.695 and slightly improved the Brier score, with good calibration. Adding the systemic immune-inflammation index did not materially improve performance. Conclusions: Routine laboratory variables provided only modest incremental value beyond OSTA. The model should be interpreted as an exploratory referral-pathway prioritization approach, not as a standalone population-screening tool. It should not be used as a diagnostic surrogate for DXA or as a fracture-risk model. Full article