Search Results (21,878)

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

To reduce the direct-current (DC)-side inductances of single-phase current-source pulse-width-modulation (PWM) rectifiers while improving their power densities. This paper introduces a symmetric half-bridge power decoupling circuit topology. First, the circuit structure and its operating principles are analyzed in detail, following which system performance is further enhanced, leveraging the magnetic integration technology. Furthermore, a simplified control strategy based on the indirect closed-loop control of the DC-side output current is introduced. This method requires fewer sensors and simpler calculations, thereby improving system stability, and achieves approximately a 15% increase in efficiency compared to conventional methods. Simulation results demonstrate that the proposed topology significantly suppresses second harmonic ripples commonly observed in the DC-side currents of single-phase current-source PWM rectifiers. Furthermore, it reduces the values of DC-side inductances while lowering the system volume and cost. Finally, experimental validations confirm the significant potential of the symmetric half-bridge power decoupling circuit topology in enhancing the performance of single-phase current-source PWM rectifiers. Full article

Voltage source converters (VSCs), together with their inner current and outer power/voltage control loops, are fundamental building blocks in the modern, converter-dominated power systems, particularly within high-voltage DC (HVDC) frameworks. Selecting effective control methods for VSCs is essential to ensure the stability, power quality, and dynamic performance of HVDC grids. This paper seeks to advance the current body of research by delivering an in-depth, consistent, unified framework and systematic examination of VSC control architectures within HVDC networks. It thoroughly explores various control strategies for VSCs interfacing with HVDC grids, such as grid-following and grid-forming strategies, with particular emphasis on both stationary ($\alpha \beta$) and synchronous (dq) reference frames. Moreover, the paper provides a comprehensive analysis of the theoretical underpinnings and decoupled control strategies, like the feedforward decoupling of the d- and q-axis currents in the dq frame and the inherently decoupled structure of the αβ frame. Additionally, advanced filtering techniques, including Moving Average Filter (MAF), Cascaded Delayed Signal Cancellation (DSC), and LCL filters, are analyzed. In addition, harmonic mitigation strategies, like parallel/multiple resonant (PR) terms in the αβ frame and cascaded notch filters in the dq frame, are presented. Furthermore, precise power control approaches and synchronization methods are discussed in detail. Also, this paper presents a detailed comparison of the performance characteristics of phase-locked loop (PLL) and frequency-locked loop (FLL) in response to grid frequency variations. Moreover, this paper proposes circuit representations and VSC models in both synchronous and stationary reference frames. The simulation results corroborate the theoretical insights discussed in the paper under various operational conditions, including initial responses, grid disturbances, three-phase-to-ground temporary fault scenarios, harmonic distortions, and load imbalances, in terms of overshoot, settling time, active- and reactive-power fluctuation reduction, voltage unbalance factor, total harmonic distortion, and post-fault convergence time, all evaluated in accordance with the limits defined in EN-50160. This comprehensive comparison of the presented control strategies facilitates researchers in identifying the most appropriate controller depending on their specific application requirements. Full article

Long-term energy system planning depends on projections of future wind turbine characteristics, yet existing approaches rely on either costly expert elicitation or deterministic trend extrapolation without formal uncertainty quantification. We present a Bayesian logistic framework that models the temporal evolution of hub height, rotor diameter, and specific power as physically constrained growth and decay processes, producing full posterior predictive distributions via Markov Chain Monte Carlo sampling. The framework is validated across three major onshore wind markets: Austria (534 turbines, 2000–2025), Germany (31,202 turbines, 1988–2026), and the United States (71,457 turbines, 1986–2025); spanning different market structures, regulatory environments, and data availability. Systematic benchmarking against linear, polynomial, and maximum-likelihood alternatives demonstrates superior hindcast performance, particularly for long-range projections where physical saturation constraints become relevant. Prior sensitivity analysis reveals that posteriors are robust for data-rich regions but honestly reflect prior influence for small datasets, identifying where expert knowledge is essential. We extend the framework to climate-aware energy yield estimation by propagating turbine posteriors through synthetic power curves and site-specific wind resource projections under SSP2-4.5 and SSP5-8.5, decomposing the total uncertainty into technology and climate components. When climate uncertainty is measured by scenario spread alone, technology uncertainty dominates. However, accounting for the full inter-model spread across 13 CMIP6 global climate models reveals that climate uncertainty becomes substantial (14–56%) and region-dependent, underscoring that both sources require explicit quantification. The open-source pipeline is designed for direct adoption in energy system planning workflows. Full article

In therapeutic applications, ultrasound is widely used in physiotherapy, tissue repair, and cancer treatment. Regarding cancer treatment, as an emerging field for technology, significant research efforts have been devoted to the area of ultrasound therapy. The derived energy from beams can be deposited in tissues not only through heating but also through non-thermal mechanisms, whereby cancer cells are subject to cell death. Ultrasound-induced heating can generate localized temperature elevations within biological tissues, making it a subject of interest for thermal therapeutic applications. Nevertheless, excessive temperature elevations outside the primary exposure region may result in undesirable thermal effects within the surrounding tissue. In this study, we used continuous 3 MHz ultrasound waves at the powers of 0.4 to 1.4 W on ex vivo chicken breast tissue in a water bath to prevent fluctuations in temperature. The process was also numerically modeled with a maximum error of 0.4% from the measured data. Temperature measurements revealed a significant difference between the region of maximum acoustic pressure along the beam axis and deeper tissue locations (in some cases, above 3.5 °C). These findings indicate that temperature gradients can develop within homogeneous tissue during ultrasound exposure, emphasizing the importance of controlling acoustic power and exposure conditions. Moreover, increasing the temperature was significant during the first moments of treatment, which highlights the importance of precise controls for rate and precision in therapy. The numerical simulations also showed that increasing acoustic power elevates tissue temperature while simultaneously producing a less uniform temperature distribution. These observations may be useful for the optimization of future ultrasound-based thermal treatment strategies; however, direct clinical extrapolation requires further investigation using physiologically representative tissue models. 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

This paper presents a novel approach to designing and implementing an Artificial Neural Network (ANN) for maximum power point tracking (MPPT), trained solely on unshaded photovoltaic (PV) manufacturer datasheets and capable of tracking and predicting the maximum power point (MPP) under changing shading conditions. This is also known as partial shading conditions (PSC). PSC arises when shade covers sections of the PV panel due to clouds, trees, dust, or man-made objects such as tall buildings. The proposed ANN-based MPPT technique addresses a common issue faced by conventional MPPT methods under PSC: inaccurate MPPT. PSC induces oscillations on the power-to-voltage curve, resulting in multiple local maxima (LMPPs). However, existing ANN-based MPPT methods are developed and trained on shaded PV datasets. This Neural Network (NN) tracking method complicates the training, development, and implementation processes. It increases the cost of development and requires physical, real-world data collection that requires hardware and a lot of time. All this can be avoided with unshaded PV datasheets. The input parameters used to train the model are temperature (T) and irradiance (G), and the output parameters are maximum power (Pmp) and maximum voltage (Vmp). The ANN-based MPPT technique demonstrated strong performance, accurately predicting the global MPP (GMPP) under PSC with high correlation and low prediction error. Full article

Avian viruses formed the foundation of early retrovirology. The historical line extends from the discovery of the first sarcoma virus by Peyton Rous to the quantitative determination of oncogenic activity in cell culture by the focus assay. As a viral group, avian retroviruses offered exclusive advantages that allowed the assembly of a unique and powerful tool chest for the analysis of viral activity. Among the fundamental discoveries facilitated by these tools were viral and cellular oncogenes, cell surface receptors, virus-specific detection of inapparent infection, high-frequency genetic recombination between retroviruses, and the genetic maps of simple retroviruses. The work with avian viruses was soon complemented by research on mammalian retroviruses, and several oncogenes that became the basis of successful targeted therapies were defined. The field of cancer genes is at a point of transition. Future developments will be driven by new technologies and interpretations. They will also require a more comprehensive approach. Full article

Objectives: Local positioning systems (LPSs) used in indoor team sports generate a large number of external load variables, often exceeding practical monitoring capacity. The redundancy and overlap among these variables make it difficult to identify the most informative metrics for performance analysis and load management. This study aimed to reduce the dimensionality of external load variables derived from LPS data and to identify data-driven external-load observation profiles using principal component analysis and clustering techniques. Methods: A total of 188 observations from indoor team sports (basketball, handball, and futsal) were analyzed. Continuous external load variables were standardized and subjected to principal component analysis (PCA), with component retention based on a ≥90% cumulative explained variance threshold. K-means clustering was applied in both the full standardized feature space and the PCA-reduced space. The optimal number of clusters was determined using silhouette analysis and the elbow method. Agreement between clustering solutions was assessed using Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI). Cluster characteristics were further examined using descriptive statistics and variable separation analysis. Results: The first two principal components explained 53.7% of the total variance, representing high-intensity external load and neuromuscular load dimensions, while 12 components were required to exceed 90% cumulative explained variance. Clustering analysis consistently identified three moderately separated clusters in both the full and PCA-reduced spaces. The PCA-based solution demonstrated improved separation (silhouette = 0.362) compared to the full-space solution (silhouette = 0.319). Agreement between clustering approaches was high (ARI = 0.981; NMI = 0.971), indicating that dimensionality reduction largely preserved the main clustering structure within the analyzed dataset. The most discriminative variables included jump load, acceleration load, metabolic power, and anaerobic activity distance. Conclusions: A large set of external load variables can be reduced into interpretable latent dimensions that support exploratory external-load profile identification. The combination of PCA and clustering provides an exploratory and structure-preserving framework for summarizing complex external-load datasets and identifying latent load dimensions. These findings may assist future monitoring strategies; however, the practical utility of the identified profiles requires prospective validation before implementation in training-load management. Full article

The rapid development of renewable energy generators (REGs) has increased the uncertainties and security risks in power systems. Furthermore, extreme weather conditions impose higher demands on the secure operation range of power systems. Energy storage systems (ESSs), with fast power regulation capability, can smooth fluctuations of REGs and mitigate risks of power deficits and power flow violations under extreme events. To this end, this paper proposes an ESS siting and sizing model that considers the economic efficiency, security, and resilience requirements. First, to overcome drawbacks of existing ESS planning methods that ignore the resilience requirement under extreme events and the strong nonlinearity of power flow entropy indicator reflecting system security margins, the loading rate balance (LRB) indicator is developed to describe the safety and resilience of transmission grid and is incorporated into the ESS planning model in a first-order dispersion form to keep the optimization model linear. Second, a coordinated ESS planning and dispatch optimization model is formulated to minimize the equivalent daily planning cost, daily dispatch cost, and LRB, subject to secure operation constraints of the power system under renewable generation uncertainties. Third, a sample average approximation -based chance-constrained approach is proposed in the ESS planning model to characterize the uncertainties of wind and solar power to avoid distributional dependence and the curse of dimensionality. Detailed simulations validate the effectiveness of the proposed ESS planning method in terms of improving economic efficiency while ensuring system security and resilience. Full article

Background/Objectives: Semaglutide and tirzepatide demonstrate substantial efficacy in obesity treatment, yet individual responses vary markedly. The incretin system—comprising glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP)—is frequently dysregulated in obesity, but whether fasting incretin levels predict differential pharmacological responses remains unexplored. We investigated whether combinatorial fasting GLP-1/GIP tertile profiles predict the initial weight-loss response to semaglutide versus tirzepatide in patients with severe obesity. Methods: This prospective, parallel-group, open-label pilot study enrolled 90 treatment-naïve patients with BMI > 40 kg/m² (mean 42.5 ± 3.5 kg/m²) at the University of Catania, Italy. Fasting serum GLP-1 (0.8–50 pg/mL) and GIP (1–16 ng/mL) were measured by chemiluminescence immunoassay and distributed into tertiles, generating nine combinatorial profiles (P1–P9; n = 10 per profile). Within each profile, five patients were randomly assigned to semaglutide (escalated to 2.4 mg/week) or tirzepatide (escalated to 15 mg/week). Primary outcome was pharmacological response category at six months: low (<5% body weight reduction), intermediate (5–15%), or optimal (>15%). Results: Baseline characteristics were balanced across profiles (age 48 ± 8 years, BMI 42.5 ± 3.5 kg/m², waist circumference 134 ± 12 cm, HOMA-IR 8.5 ± 3.0; all p > 0.05). Tirzepatide achieved optimal response in profiles with low GIP tertile regardless of GLP-1 level (P1, P6, P8), while semaglutide achieved optimal response when GLP-1 was low and GIP was intermediate-to-high (P4, P5). Both drugs showed low response in the high GLP-1/high GIP profile (P3). Mean weight loss in optimal-response groups was 18.2 ± 2.1% for tirzepatide and 16.8 ± 1.9% for semaglutide. Waist circumference reductions paralleled weight loss patterns. HOMA-IR decreased significantly in all optimal-response groups (mean reduction 4.2 ± 1.1 units). Conclusions: In this hypothesis-generating pilot study, fasting GLP-1/GIP combinatorial profiling, obtained from a single fasting blood sample, was associated with differential pharmacological responses to semaglutide and tirzepatide in severe obesity. Low GIP levels were tentatively associated with optimal tirzepatide response; low GLP-1 with intermediate-to-high GIP was tentatively associated with optimal semaglutide response. These preliminary findings provide proof-of-concept for incretin-guided personalised obesity pharmacotherapy but require confirmation in larger, adequately powered randomised trials before any clinical recommendations can be made. The inability to discriminate incretin secretory deficiency from receptor resistance using fasting measurements alone, the absence of a placebo control, and the six-month follow-up (shorter than the 12–18 months at which maximal efficacy is typically observed) remain critical limitations. 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

“Power-to-ammonia” is widely regarded as a viable solution for large-scale consumption of wind and solar power, as well as for deep decarbonization in the energy and chemical sectors. However, the intermittent nature of renewable energy requires ammonia synthesis systems to operate across a wide and varying range of loads, posing challenges to their economic viability. To address this, we develop a simulation and optimization methodology for ammonia reactor operation under varying loads. Firstly, a high-fidelity reactor model is developed based on the reactor’s structural characteristics by incorporating reaction kinetics and thermodynamic mechanisms. This reactor model is then integrated with compression and separation units. To ensure computational efficiency, surrogate models are developed to approximate the ammonia synthesis and flash separation units. A case study of an ammonia plant with a nominal production rate of 100,000 tons/year is conducted to demonstrate the effectiveness of the proposed method. The results indicate that the feasible operation region of the reactor narrows significantly as the system production load decreases. System operation parameters, including reactor inlet temperature, reactor pressure, and ammonia separation temperature, are optimized for the ammonia synthesis loop over a wide operating window from 30% to 100% of nominal capacity. It is recommended to increase the system inlet temperature as the production load decreases, thereby compensating for the reduced heat release per unit product resulting from the decreased system pressure. Full article

Limited by the computational performance limits of the CPU(Central Processing Unit), the traditional Spark architecture struggles to achieve high throughput and low latency under the dual pressure of a large data scale and real-time requirements in centralized control systems. This work uses a publicly available CNC(Computer Numerical Control) milling dataset as a functional validation proxy for time-series data processing, then extends validation to a large-scale synthetic power transmission grid dataset. Furthermore, Spark-GPU(Graphics Processing Unit) collaboration suffers from load balancing failure due to heterogeneous resource scheduling and communication overhead, thus failing to unleash its performance potential. This paper proposes a Spark-GPU fusion acceleration technology path. The path consists of three key components: first, it integrates the RAPIDS accelerator; second, it designs a GPU-aware partitioning and task co-scheduling strategy; and third, it optimizes the zero-copy data path. Together, these components realize an integrated collaboration of heterogeneous resources. Validation on real-world datasets yields the following results. In real-time aggregation scenarios, the proposed solution improves throughput by a factor of 3.7 over the pure CPU baseline and reduces end-to-end latency by 62%. Compared with the basic GPU solution, GPU utilization rises from 51.7% to 72.3%, representing a relative improvement of 39.8%. Furthermore, the solution meets industrial-grade high availability requirements. This research significantly improves the processing throughput and reduces end-to-end latency in typical centralized control scenarios, thus providing a feasible technical route for demanding concurrent centralized control scenarios such as electric power industry manufacturing with high real-time demands. Full article

Traditional air conditioning consumes substantial electricity, exacerbates the urban heat island effect, and creates a maladaptive feedback loop, necessitating a shift toward passive-first net-zero pathways. This study takes a typical six-story residential building in Nanjing’s hot summer and cold winter climate zone as a case study. Using EnergyPlus hourly simulations, three progressive passive strategy packages are designed to quantify the impact of building envelope heat exchange on cooling loads, grid stress, and heat resilience. Package A includes external shading and natural ventilation. Package B adds reflective coating and a green roof. Package C further adds night ventilation precooling and high-performance windows. The results show that Package C achieves a 62.5% reduction in peak cooling load and a 63.0% reduction in seasonal cooling load. Daytime peak inward heat gain decreases from 68 W/m² to 22 W/m², while nighttime outward heat dissipation increases from 12 W/m² to 38 W/m². Under an extreme heat day of 41.2 °C with no active cooling, indoor peak temperature drops from 36.8 °C to 29.4 °C, and heat risk hours decrease by 73.6%. Peak-hour power demand is reduced by 70.4%, with a systemic leverage factor of 1.08. Innovations include achieving over 60% load reduction using only mature passive strategies, introducing the systemic leverage factor to quantify urban heat island mitigation benefits, and establishing a vulnerability-to-resilience transformation framework. The passive-first pathway validates building envelope as the first line of defense for net-zero futures. However, the findings are based on a typical six-story residential building in Nanjing and require validation through field measurements or broader application across different climate zones and building typologies before generalization. Full article