Search Results (4,020)

The integration of solar energy into smart grids requires high-efficiency power conversion to support grid stability. However, Partial Shading Conditions (PSCs) remain a primary obstacle by inducing multiple local maxima on P–V characteristic curves. This paper presents a hardware-aware and memory-enhanced Maximum Power Point Tracking (MPPT) approach based on a modified Dragonfly Algorithm (DA) for grid-connected microinverter-based photovoltaic (PV) systems. The proposed method utilizes a quasi-switched Boost-Switched Capacitor (qSB-SC) topology, where the DA is specifically tailored by combining Lévy-flight exploration with a dynamic damping factor to suppress steady-state oscillations within the qSB-SC ripple constraints. Coupling the MPPT stage to a seven-level Packed-U-Cell (PUC) microinverter ensures that each PV module operates at its independent Global Maximum Power Point (GMPP). A ZigBee-based Wireless Sensor Network (WSN) facilitates rapid data exchange and supports ‘swarm-memory’ initialization, matching current shading patterns with historical data to seed the population near the most probable GMPP region. This integration reduces the overall response time to 0.026 s. Hardware-in-the-loop experiments validated the approach, attaining a tracking accuracy of 99.32%. Compared to current state-of-the-art benchmarks, the proposed model demonstrated a significant improvement in tracking speed, outperforming the most recent 2025 GWO implementation (0.0603 s) by approximately 56% and conventional metaheuristic variants such as GWO-Beta (0.46 s) by over 94%.These results confirmed that the modified DA-based MPPT substantially enhanced the microinverter efficiency under PSC through cross-layer parameter adaptation. Full article

This paper introduces a mathematical model to assess the polluting impact of the decarbonization options for medium-sized High-Speed Crafts in the EU, and their consequences in terms of Market-Based Measure costs and Goal-Based Measure compliance under expected regulatory scenarios. This model is applied to a particular European High-Speed Craft operating in the Canary Islands. Considering slow steaming along with High Speed Craft’s retrofitting with alternative technologies for its electricity supply, we conclude that green H₂ fuel Cells provide the greatest environmental advantage by comparison with slow steaming alone, achieving a 6.96% improvement in emissions and savings under European Market-Based Measures of 39.76% by 2033. The expected regulative progression involves a 5.90% improvement in the Market-Based Measure costs’ convergence with the actual pollution impact of High-Speed Crafts. The findings warn about the pressing need to review the implementation of On-Shore Power Supply emissions into the Fuel EU fines, and about a concerning pull effect for the most polluting European High-Speed Crafts are moved towards the outermost regions of the EU due to their permanent exceptions from the application of the European Market-Based Measures. Full article

To improve the accuracy and robustness of lithium-ion battery state of charge (SOC) estimation, this paper proposes a generalized mixture maximum correlation-entropy criterion-based adaptive extended Kalman filter (GMMCC-AEKF) algorithm, addressing the performance degradation of the traditional extended Kalman filter (EKF) under non-Gaussian noise and inaccurate initial conditions. Based on the GMMCC theory, the proposed algorithm introduces an adaptive mechanism and employs two generalized Gaussian kernels to construct a mixed kernel function, thereby formulating the generalized mixture correlation-entropy criterion. This enhances the algorithm’s adaptability to complex non-Gaussian noise. Simultaneously, by incorporating adaptive filtering concepts, the state and measurement covariance matrices are dynamically adjusted to improve stability under varying noise intensities and environmental conditions. Furthermore, the use of statistical linearization and fixed-point iteration techniques effectively improves both the convergence behavior and the accuracy of nonlinear system estimation. To investigate the effectiveness of the suggested method, experiments for SOC estimation were implemented using two lithium-ion cells featuring distinct rated capacities. These tests employed both dynamic stress test (DST) and federal test procedure (FTP) profiles under three representative temperature settings: 40 °C, 25 °C, and 10 °C. The experimental findings prove that when exposed to non-Gaussian noise, the GMMCC-AEKF algorithm consistently outperforms both the traditional EKF and the generalized mixture maximum correlation-entropy-based extended Kalman filter (GMMCC-EKF) under various test conditions. Specifically, under the 25 °C DST profile, GMMCC-AEKF improves estimation accuracy by 86.54% and 10.47% over EKF and GMMCC-EKF, respectively, for the No. 1 battery. Under the FTP profile for the No. 2 battery, it achieves improvements of 55.89% and 28.61%, respectively. Even under extreme temperatures (10 °C, 40 °C), GMMCC-AEKF maintains high accuracy and stable convergence, and the algorithm demonstrates rapid convergence to the true SOC value. In summary, the GMMCC-AEKF confirms excellent estimation accuracy under various temperatures and non-Gaussian noise conditions, contributing a practical approach for accurate SOC estimation in power battery systems. Full article

Peptide arrays represent a powerful tool for investigating a wide application field for biomedical questions. This review summarizes recent applications of peptide chips in oncology, with a focus on tumor microenvironment, metastasis, and drug mechanism of action for various cancer types. These high-throughput platforms enable the simultaneous screening of thousands of peptides. We report on recent achievements in peptide array technology for tumor microenvironments, an enhanced ability to decipher complex cancer-related signaling pathways, and characterization of cell-adhesion-mediating peptides. Furthermore, we highlight the applications in high-throughput drug screenings for development of immune therapies, e.g., the development of novel neoantigen therapies of glioblastoma. Moreover, epigenetic profiling using peptide arrays has uncovered new therapeutic targets across various cancer types with clinical impact. In conclusion, we discuss artificial intelligence-driven peptide array analysis as a tool to determine tumor origin and metastatic state, potentially transforming diagnostic approaches. These innovations promise to accelerate the development of precision cancer approaches. Full article

Green hydrogen production and the use of fuel cells (FCs) in microgrid (MG) systems have become viable and feasible solutions due to their continuous cost reduction and advancements in technology. Furthermore, green hydrogen electrolyzers and FC can mitigate fluctuations in renewable energy generation and various demand-related disturbances. Proper incorporation of electrolyzers and FCs can enhance load frequency control (LFC) in MG systems. However, they are subjected to multiple false data injection attacks (FDIAs), which can deteriorate MG stability and availability. Moreover, most existing LFC control schemes—such as conventional PID-based methods, single-degree-of-freedom fractional-order controllers, and various optimization-based structures—lack robustness against coordinated and multi-point FDIAs, leading to significant degradation in frequency regulation performance. This paper presents a new, modified, multi-degree-of-freedom, cascaded fractional-order controller for green hydrogen-based MG systems with high fluctuating renewable and demand sources. The proposed LFC is a cascaded control structure that combines a 1+TID controller with a filtered fractional-order PID controller (FOPIDF), namely the cascaded 1+TID/FOPIDF LFC control. Furthermore, another tilt-integrator derivative electric vehicle (EV) battery frequency regulation controller is proposed to benefit from EVs installed in MG systems. The proposed cascaded 1+TID/FOPIDF LFC control and EV TID LFC methods are designed using the powerful capability of the exponential distribution optimizer (EDO), which determines the optimal set of design parameters, leading to guaranteed optimal performance. The effectiveness of the newly proposed cascaded 1+TID/FOPIDF LFC control and design approach employing multi-generational-based two-area MG systems is studied by taking into account a variety of projected scenarios of FDIAs and renewable/load fluctuation scenarios. In addition, performance comparisons with some featured controllers are provided in the paper. For example, in the case of fluctuation in RESs, the measured indices are as follows: ISE (1.079, 0.5306, 0.3515, 0.0104); IAE (15.011, 10.691, 9.527, 1.363); ITSE (100.613, 64.412, 53.649, 1.323); and ITAE (2120, 1765, 1683, 241.32) for TID, FOPID, FOTID, and proposed, respectively, which confirm superior frequency deviation mitigation using the proposed optimized cascaded 1+TID/FOPIDF and EV TID LFC control method. Full article

The deployment of 5G networks has transformed the landscape of radiofrequency electromagnetic field (RF-EMF) exposure patterns, shifting from high-power macro base stations to dense networks of small, beamforming cells. This review critically assesses the validity, challenges, and research gaps of low-cost RF-EMF sensors used for 5G exposure monitoring. An analysis of over 60 studies covering Sub-6 GHz and emerging mmWave systems shows that well-calibrated sensors can achieve measurement deviations of ±3–6 dB compared to professional instruments like the Narda SRM-3006, with long-term calibration drift less than 0.5 dB per month and RMS reproducibility around 5%. Typical outdoor 5G FR1 exposure levels range from 0.01 to 0.5 W/m² near small cells, while personal device use can cause transient exposures 10–30 dB higher. Although mmWave (24–100 GHz) and Wi-Fi 7/8 (~60 GHz) are underrepresented due to antenna and component limitations, Sub-6 GHz sensing platforms, including software-defined radio (SDR)-based and triaxial isotropic designs, provide sufficient sensitivity for both citizen and institutional monitoring. Major challenges involve calibration drift, frequency band gaps, data interoperability, and ethical management of participatory networks. Addressing these issues through standardized calibration protocols, machine learning-assisted drift correction, and open data frameworks will allow affordable sensors to complement professional monitoring, improve spatial coverage, and enhance public transparency in 5G RF-EMF exposure governance. Full article

Prostate cancer (PCa) remains one of the most prevalent malignancies in men and a leading cause of cancer-related mortality worldwide. Over the last century, PCa modelling has evolved from basic cell-based to more complex systems. Despite this, the clinical translation of research findings is limited by the constraints of current preclinical models. In this review, rat and zebrafish models are highlighted due to their long-standing and emerging translational relevance, respectively. Rat models have played a pivotal role in understanding carcinogenesis and supporting the preclinical evaluation of drugs currently approved for clinical use, such as antiandrogens and androgen-deprivation agents. In parallel, zebrafish models are increasingly recognized as powerful complementary tools for studying tumor biology, metastasis, and drug response, offering unique advantages for high-throughput and personalized medicine approaches. We summarize historical milestones, current advances, and translational perspectives, emphasizing how combining multiple model systems can bridge the gap between molecular research and clinical application. Collectively, the development and refinement of these models represent essential steps toward more predictive and ethically responsible PCa research. Full article

In this work, a substrate-integrated waveguide (SIW) filtering power divider with a modified complementary split-ring resonator (CSRR) is reported. Firstly, by integrating the meander-shaped slots with the conventional CSRR, the proposed inner-meander-slot CSRR (IMSCSRR) can enlarge the total length of the defected slot and increase the width of the split, thus enhancing the equivalent capacitance and inductance. In this way, the fundamental resonant frequency of the IMSCSRR can be effectively decreased without enlarging the circuit size, which can generally help to reduce the physical size by over 35%. Subsequently, to further reduce the circuit size, two IMSCSRRs are separately loaded on the top and bottom metal covers to constitute a broadside-coupled IMSCSRR, which is combined with the SIW. To verify the efficacy of the proposed SIW-IMSCSRR unit cell, a two-way filtering power divider is implemented. It combines the band-selection function of a filter and the power-distribution property of a power divider, thereby enhancing system integration and realizing size compactness. Experimental results show that the proposed filtering power divider achieves a center frequency of 3.53 GHz, a bandwidth of about 320 MHz, an in-band insertion loss of (3 + 1.3) dB, an in-band isolation of over 21 dB, and a size reduction of about 30% compared with the design without broadside-coupling, as well as good magnitude and phase variations. All the results indicate that the proposed filtering power divider achieves a good balance between low loss, high isolation, and compact size, which is suitable for system integration applications in microwave scenarios. Full article

We present E-CAHORS—a compact mid-infrared open-path diode-laser spectrometer designed for the simultaneous measurement of carbon dioxide, methane, and water vapor concentrations in the near-surface atmospheric layer. These measurements, combined with simultaneous data from a three-dimensional anemometer, can be used to determine fluxes using the eddy-covariance method. The instrument utilizes two interband cascade lasers operating at 2.78 µm and 3.24 µm within a novel four-pass M-shaped optical cell, which provides high signal power and long-term field operation without requiring active air sampling. Two detection techniques—tunable diode laser absorption spectroscopy (TDLAS) and a simplified wavelength modulation spectroscopy (sWMS)—were implemented and evaluated. Laboratory calibration demonstrated linear responses for all gases (R² ≈ 0.999) and detection precisions at 10 Hz of 311 ppb for CO₂, 8.87 ppb for CH₄, and 788 ppb for H₂O. Field tests conducted at a grassland site near Moscow showed strong correlations (R = 0.91 for CO₂ and H₂O, R = 0.74 for CH₄) with commercial LI-COR LI-7200 and LI-7700 analyzers. The TDLAS mode demonstrated lower noise and greater stability under outdoor conditions, while sWMS provided baseline-free spectra but was more sensitive to power fluctuations. E-CAHORS combines high precision, multi-species sensing capability with low power consumption (10 W) and a compact design (4.2 kg). Full article

Microbial fuel cell (MFC) technology represents a promising bioelectrochemical approach for the simultaneous generation of electricity and treatment of high-strength wastewater. However, the performance of MFCs strongly depends on the metabolic potential and synergistic interactions of the inoculated microbial community. This study evaluated the electrochemical activity and COD removal efficiency of three individual bacterial strains, Lysinibacillus sphericus A1, Bacillus cereus A2 and Lactococcus lactis A4, compared with a developed consortium under long-term operation using poultry slaughterhouse wastewater as a substrate. All inocula were tested in dual-chamber MFCs for 30 days, and performance indicators included power output, voltage, and removal of chemical oxygen demand (COD). The consortium showed the highest power of 170 mW/m² and the optimal voltage–current ratio at a current of 900 mA/m² and 245 mV under decreasing external resistance from 1000 to 50 Ω. The highest COD removal (84.4%) was also recorded, surpassing all pure cultures and demonstrating a significant improvement compared with B. cereus A2 and L. lactis A4. Meanwhile, the lowest power of 52 mA/m² was recorded during testing of L. lactis A4, at 650 mA/m² and 120 mV. Compared with single cultures, the consortium produced approximately 15% higher power density than L. sphericus A1, about 29% higher than B. cereus A2, and more than threefold higher than L. lactis A4. This study highlights the potential of a consortium as an efficient biocatalyst for MFC-mediated wastewater treatment and suggests that selecting complementary strains with diverse metabolic functions can substantially improve system performance. Full article

Unmanned aerial vehicle (UAV) swarms offer an efficient solution for data collection from widely distributed ground users (GUs). However, incomplete environment information and frequent changes make it challenging for standard centralized planning or pure reinforcement learning approaches to simultaneously maintain global solution quality and local flexibility. We propose a hierarchical data collection framework for heterogeneous UAV-assisted wireless sensor networks (WSNs). A small set of high-capability UAVs (H-UAVs), equipped with substantial computational and communication resources, coordinate regional coverage, trajectory planning, and uplink transmission control for numerous resource-constrained low-capability UAVs (L-UAVs) across power-Voronoi-partitioned areas using multi-agent deep reinforcement learning (MADRL). Specifically, we employ Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to enhance H-UAVs’ decision-making capabilities and enable coordinated actions. The partitions are dynamically updated based on GUs’ data generation rates and L-UAV density to balance workload and adapt to environmental dynamics. Concurrently, a large number of L-UAVs with limited onboard resources perform self-organized data collection from GUs and execute opportunistic relaying to a remote access point (RAP) via H-UAVs. Within each Voronoi cell, L-UAV motion follows a weighted Vicsek model that incorporates GUs’ age of information (AoI), link quality, and congestion avoidance. This spatial decomposition combined with decentralized weak-swarm control enables scalability to large-scale L-UAV deployments. Experiments demonstrate that the proposed strong and weak agent MADDPG (SW-MADDPG) scheme reduces AoI by 30% and 21% compared to No-Voronoi and Heuristic-HUAV baselines, respectively. Full article

Silicon carbide (SiC) power converters are increasingly used in automotive, renewable energy, and industrial applications. While reliability assessments are typically performed at either the device or system level, an integrative approach that simultaneously evaluates both levels remains underexplored. This article presents a novel system-level simulation method with two strategies to evaluate the reliability of power devices and a resonant converter under varying temperatures and total ionizing doses (TIDs). Temperature-sensitive electrical parameters (TSEPs), such as on-state resistance ($R_{\mathrm{ON}}$) and threshold voltage shift ($\Delta V_{\mathrm{TH}}$), are calibrated and analyzed using a B1505A curve tracer. These parameters are incorporated into the system-level simulation of a 300 W resonant converter with a boosting cell. Both Silicon (Si) and SiC-based power resonant converters are assessed for power application in space engineering and harsh environments. Additionally, gate-oxide degradation and $\Delta V_{\mathrm{TH}}$-related issues are discussed based on the simulation results. The thermal-strategy results indicate that SiC MOSFETs maintain a more stable conduction loss at elevated temperatures, exhibiting higher reliability due to their high thermal conductivity. Conversely, increased TIDs result in a negative shift in conduction losses across all SiC devices under the radiation strategy, affecting the long-term reliability of the power converter. Full article

In situ preparation routes have become central to advancing lead sulfide (PbS) quantum dots (QDs) for solar-energy conversion, owing to their ability to create strongly coupled QD/oxide interfaces that are difficult to achieve with ex situ colloidal methods, along with their simplicity and potential for low-cost, scalable processing. This review systematically examines the fundamental mechanisms, processing levers, and device implications of the dominant in situ approaches successive ionic layer adsorption and reaction (SILAR), voltage-assisted SILAR (V-SILAR), and chemical bath deposition (CBD). These methods enable conformal QD nucleation within mesoporous scaffolds, improved electronic coupling, and scalable low-temperature fabrication, forming the materials foundation for high-performance PbS-based architectures. We further discuss how these in situ strategies translate into enhanced solar-energy applications, including quantum-dot-sensitized solar cells (QDSSCs) and photoelectrochemical (PEC) hydrogen production, highlighting recent advances in interfacial passivation, scaffold optimization, and bias-assisted growth that collectively suppress recombination and boost photocurrent utilization. Representative device metrics reported in recent studies indicate that in-situ-grown PbS quantum dots can deliver photocurrent densities on the order of ~5 mA cm⁻² at applied potentials around 1.23 V versus RHE in photoelectrochemical systems, while PbS-based quantum-dot-sensitized solar cells typically achieve power conversion efficiencies in the range of ~4–10%, depending on interface engineering and device architecture. These performances are commonly associated with conformal PbS loading within mesoporous scaffolds and quantum-dot sizes in the few-nanometer regime, underscoring the critical role of morphology and interfacial control in charge transport and recombination. Recent studies indicate that performance improvements in PbS-based solar-energy devices are primarily governed by interfacial charge-transfer kinetics and recombination suppression rather than QD loading alone, with hybrid heterostructures and inorganic passivation layers playing a key role in modifying band offsets and surface trap densities at the PbS/oxide interface. Remaining challenges are associated with defect-mediated recombination, transport limitations in densely loaded porous scaffolds, and long-term chemical stability, which must be addressed to enable scalable and durable PbS-based photovoltaic and photoelectrochemical technologies. Full article

Background: Primary plasma cell leukemia (pPCL) represents the most aggressive plasma cell dyscrasia with a poor prognosis and survival of <3 years. The International Myeloma Working Group (IMWG) adopted more inclusive diagnostic criteria for pPCL in 2021, including patients with 5% or more circulating plasma cells (down from 20%). Most published studies of pPCL do not include patients who meet the criteria for pPCL based on the newer diagnostic guidelines, and the data on the optimal treatment of pPCL is scarce. In our multi-center retrospective analysis, we report data on treatment regimens used in 67 pPCL patients to characterize outcomes in this population. Methods: We included patients with newly diagnosed pPCL between 2010 and 2023 based on the 2021 IMWG definition at one of three academic centers. Results: Our results suggest significant improvement in overall response rate (ORR) and progression-free survival (PFS) with the use of autologous stem cell transplant, but without additional benefit for a tandem transplant. The presence of high-risk cytogenetics was an independent risk factor for progression in the cohort. Conclusions: Our dataset represents one of the largest cohorts to date using the expanded definition of pPCL adopted by the IMWG in 2021 and stresses the importance of taking pPCL patients to transplant. Unfortunately, our study was not powered to determine the efficacy of individual induction and maintenance regimens, and many patients diagnosed with pPCL are ineligible for transplant based on end-organ damage at diagnosis or from disease that is refractory to induction therapy, underscoring the need for early diagnosis and treatment in hopes of preserving transplant eligibility. Full article

Laser-driven ion acceleration is a rapidly developing branch of plasma physics and laser science whose primary practical goal is to provide a physical and technological basis for the construction and development of new types of ion accelerators. Laser-driven accelerators can be less complex and more compact than currently used RF-driven accelerators, while the intensities, fluences, and powers of laser-accelerated ion beams can potentially exceed those achieved in RF accelerators. This paper focuses on the generation of very intense ion beams driven by a multi-PW femtosecond laser. The acceleration mechanisms enabling the generation of such beams are characterized, and the properties of multi-PW laser-driven uranium ion beams are discussed in detail based on the results of advanced particle-in-cell numerical simulations. The feasibility of generating sub-picosecond, multi-GeV, mono-charge uranium beams with extreme intensities (~>10²⁰ W/cm²) and fluences (~>GJ/cm²) is demonstrated, and methods for controlling the beam parameters are identified. It is shown that using such beams, extreme states of matter with parameters unattainable with ion beams from conventional accelerators can be created. The prospects for applications of ultra-intense laser-driven ion beams in high-energy density physics, inertial confinement nuclear fusion, and in certain areas of nuclear physics are outlined. Full article