Search Results (18,523)

Renewable energy’s growing penetration erodes traditional power systems’ inherent dynamic symmetry—balanced inertia, damping, and frequency response. This paper proposes a self-adaptive virtual synchronous generator (VSG) control strategy for a photovoltaic hybrid energy storage system (PV-HESS) based on a radial basis function (RBF) neural network. The strategy establishes a dynamic adjustment framework for inertia and damping parameters via online learning, demonstrating enhanced system stability and robustness compared to conventional VSG methods. In the structural design, the DC-side energy storage system integrates a passive filter to decouple high- and low-frequency power components, with the supercapacitor attenuating high-frequency power fluctuations and the battery stabilizing low-frequency power variations. A small-signal model of the VSG active power loop is developed, through which the parameter ranges for rotational inertia (J) and damping coefficient (D) are determined by comprehensively considering the active loop cutoff frequency, grid connection standards, stability margin, and frequency regulation time. Building on this analysis, an adaptive parameter control strategy based on an RBF neural network is proposed. Case studies show that under various conditions, the proposed RBF strategy significantly outperforms conventional methods, enhancing key performance metrics in stability and dynamic response by 16.98% to 70.37%. Full article

Rhenium is one of the rarest and most strategically important metals, indispensable in high-temperature superalloys and platinum–rhenium catalysts used across the aerospace and petrochemical industries. Owing to its limited primary reserves, recovering rhenium from secondary sources, such as spent catalysts, superalloy residues, and metallurgical dusts, has become vital to ensuring supply security. This review examines technological developments between 1998 and 2025, focusing on how operational parameters, including temperature, leaching time, reagent concentration, and solid-to-liquid ratio, govern dissolution kinetics and overall process efficiency. Comparative evaluation of hydrometallurgical, alkaline, and hybrid processes indicates that modern systems can achieve recovery rates exceeding 98% through selective oxidation, alkaline activation, or combined pyro and hydrometallurgical mechanisms. Acid–chlorine leaching facilitates rapid, low-temperature dissolution; alkaline sintering stabilises rhenium as soluble perrhenates; and hybrid smelting routes enable the concurrent separation of rhenium and osmium. Sustainable aqueous systems employing nitric and ammonium media have also demonstrated near-complete recovery at ambient temperature under closed-loop recycling conditions. Collectively, these findings highlight a technological transition from energy-intensive, acid-based pathways towards low-impact, recyclable, and digitally optimised hydrometallurgical processes. The integration of selective oxidants, phase engineering, circular reagent management, and artificial intelligence-assisted modelling is defining the next generation of rhenium recovery, combining high extraction yields with reduced environmental impact and alignment with global sustainability goals. Full article

With the increasing demand for high efficiency and high power density in photovoltaic power generation, triangular current mode (TCM) control has garnered significant attention due to its capability to achieve zero voltage switching (ZVS) for switches. However, TCM is inherently a variable-frequency control method. Traditional modeling approaches based on fixed-frequency assumptions neglect the non-linear characteristics and sideband effects introduced by frequency variations, failing to accurately describe the dynamic behavior of the system. This paper proposes a multi-frequency small-signal modeling method tailored for TCM inverters. Small-signal models characterizing the impact of duty cycle perturbations and frequency modulation perturbations on the output voltage are derived, and the joint effect of both the duty cycle and switching frequency is analyzed. On this basis, a loop gain expression incorporating sideband frequency components is derived using Mason’s gain formula. Finally, the proposed model is verified through simulation. The results demonstrate that, compared with the multi-frequency model, which only considers the effect of duty cycle control, the proposed multi-frequency model can more accurately predict the dynamic response of TCM inverters across a wide frequency range, providing a precise theoretical basis for the control system design of variable-frequency inverters. Full article

Triple-Negative Breast Cancer (TNBC) remains the most aggressive breast cancer subtype, characterized by profound heterogeneity and a lack of effective targeted therapies. Although cytotoxic chemotherapy is the standard of care, the rapid emergence of resistance driven by cancer stem cells (CSCs), metabolic plasticity, and the tumor microenvironment limits long-term survival. This review highlights the paradigm shift in TNBC treatment from 2021 to 2025, moving beyond broad cytotoxicity to precision medicine. We first examine the limitations of earlier targeted therapies, such as PI3K/AKT/mTOR inhibitors, which failed due to compensatory feedback loops and toxicity. We then discuss emerging synthetic lethality strategies targeting the G2/M checkpoint (WEE1, ATR) and mitotic kinases (PLK1, TTK) to exploit genomic instability in TP53-mutant tumors. Furthermore, we explore how novel modalities like PROTACs and Antibody–Drug Conjugates (ADCs) are unlocking the “undrugged kinome,” including targets like TNIK, PTK7, and PAK4, which were previously inaccessible. Finally, we propose that future success lies in combinatorial strategies integrating these next-generation kinase inhibitors with ADCs and immunotherapies to dismantle therapeutic resistance. Full article

Tissue engineering is entering a new era, one defined not by passive scaffolds but by smart, adaptive biomaterials that can sense, think, and respond to their surroundings. These next-generation materials go beyond simply providing structure; they interact with cells and tissues in real time. Recent advances in mechanically responsive hydrogels and dynamic crosslinking have demonstrated how materials can adjust their stiffness, repair themselves, and transmit mechanical cues that directly influence cell behavior and tissue growth. Meanwhile, in vivo studies are demonstrating how engineered materials can harness the body’s own mechanical forces to activate natural repair programs without relying on growth factors or additional ligands, paving the way for minimally invasive, force-based therapies. The emergence of electroactive and conductive biomaterials has further expanded these capabilities, enabling two-way electrical communication with excitable tissues such as the heart and nerves, supporting more coordinated and mature tissue growth. Meanwhile, programmable bioinks and advanced bioprinting technologies now allow for precise spatial patterning of multiple materials and living cells. These printed constructs can adapt and regenerate after implantation, combining architectural stability with flexibility to respond to biological changes. This review brings together these cross-cutting advances, dynamic chemical design, mechanobiology-guided engineering, bioelectronic integration, and precision bio-fabrication to provide a comprehensive view of the path forward in this field. We discuss key challenges, including scalability, safety compliance, and real-time sensing validation, alongside emerging opportunities such as in situ stimulation, personalized electromechanical sites, and closed loop “living” implants. Taken together, these adaptive biomaterials represent a transformative step toward information-rich, self-aware scaffolds capable of guiding regeneration in patient-specific pathways, blurring the boundary between living tissue and engineered material. Full article

Wearable healthcare is shifting from passive tracking to active, closed-loop care by integrating polymeric three-dimensional (3D)-printed microneedle arrays (MNAs) with soft electronics and wireless modules. This review surveys the design, materials, and the manufacturing routes that enable skin-conformal MNA wearables for minimally invasive access to the interstitial fluid and precise but localized drug delivery. Looking ahead, the converging advances in multimaterial printing, nano/biofunctional coatings, and artificial intelligence (AI)-driven control are promising “wearable clinics” that can personalize monitoring and therapy in real time, thus accelerating the translation of MNA-integrated wearables from laboratory prototypes to clinically robust, patient-centric systems. Overall, this review identifies a clear transition from proof-of-concept MNA devices toward integrated, wearable, and closed-loop therapeutic platforms. Key challenges remain in scalable manufacturing, drug dose limitations, long-term stability, and regulatory translation. Addressing these gaps through advances in hollow MNA architectures, system integration, and standardized evaluation protocols is expected to accelerate clinical adoption. However, the realization of closed-loop wearable MNA-based systems remains constrained by challenges related to power consumption, real-time data latency, and the need for robust clinical validation. Full article

The servo drive system serves as the core power unit in high-end equipment such as industrial robots and computerized numerical control (CNC) machine tools, where mechanical resonance and shaft torque ripple induced by elastic deformation and backlash severely degrade motion accuracy and system stability. Conventional resonance suppression approaches, predominantly based on PI control and notch-filter-augmented PI control, suffer from critical limitations: high sensitivity to resonant frequency variations, inability to systematically enforce physical shaft torque constraints, poor robustness against parameter uncertainties and external disturbances, and significant degradation of dynamic performance when resonance is aggressively suppressed. This paper establishes a two-inertia elastic system model to investigate the effects of elastic deformation and backlash nonlinearities, revealing the mechanisms of mechanical resonance and torque ripple, and proposes control strategies for resonance suppression and shaft torque ripple limitation. A novel hierarchical control architecture is designed, consisting of a Luenberger-observer-based model predictive control (MPC) speed controller, and a super-twisting sliding mode controller (ST-SMC) for the current loop. Luenberger observer-based MPC with ST-SMC strategy is to simultaneously obtain: (a) enhanced robustness via state estimation, (b) superior dynamic performance via SMC, and (c) guaranteed shaft torque constraint satisfaction via MPC. Compared with conventional PI control and notch-filter-based PI control, simulation results demonstrate that Luenberger observer-based MPC with ST-SMC strategy effectively suppresses resonance, limits shaft torque ripple, and enhances the system’s disturbance rejection capability. Full article

Equipped with one degree of freedom in one-dimensional translation of the base, a mobile dual-arm robot (MDAR) is proposed in this paper, and the two arms and the base move simultaneously. As a result, the motion of the base has a significant influence on the motion of both end-effectors at the same time, and the relative positions of the two end-effectors change all the time. Therefore, this paper focuses on the main issues related to the presented MDAR in two key areas: the relative dynamics and relative force/position hybrid control. First, based on the D-H parametric method, the relative kinematics of the proposed MDAR is established, and the relative Jacobian matrix of the robot is derived. Secondly, the dynamic model of the proposed MDAR is constructed using the Lagrangian method. Furthermore, a closed-loop control strategy for relative force/position hybrid control of the MDAR based on the relative dynamics is proposed to enable the two end-effectors of the MDAR to track the planned trajectory accurately. Finally, a simulation is carried out on a dual-arm cutting robot (DACR) for a coal mine to prove the effectiveness of the proposed relative dynamics and the proposed relative force/position hybrid control law in terms of the absolute error (AE) and root mean square error (RMSE). The results show that the proposed relative dynamic model and relative force/position hybrid control can significantly reduce error of the DACR, effectively improve the adaptability and operation accuracy of the system to complex environment, and verify the feasibility and superiority of the method in practical application. Full article

Artificial intelligence (AI) is rapidly transforming anesthesiology practice across perioperative settings. This review explores the evolution and implementation of hybrid decision-making models that integrate AI capabilities with human clinical expertise. From historical foundations to current applications, we examine how machine learning algorithms, deep learning networks, and big data analytics are enhancing anesthetic care. Key applications include perioperative risk prediction, AI-assisted patient education, automated analysis of clinical records, airway management support, predictive hemodynamic monitoring, closed-loop anesthetic delivery systems, and pain management optimization. In procedural contexts, AI demonstrates promising utility in regional anesthesia through anatomical structure identification and needle navigation, monitoring anesthetic depth via EEG analysis, and improving quality control in endoscopic sedation. Educational applications include intelligent simulators for procedural training and academic productivity tools. Despite significant advances, implementation challenges persist, including algorithmic bias, data security concerns, clinical validation requirements, and ethical considerations regarding AI-generated content. The optimal integration model emphasizes a complementary approach where AI augments rather than replaces clinical judgment—combining computational efficiency with the irreplaceable contextual understanding and ethical reasoning of the anesthesiologist. This hybrid paradigm reinforces the anesthesiologist’s leadership role in perioperative care while enhancing safety, precision, and efficiency through technological innovation. As AI integration advances, continued emphasis on algorithmic transparency, rigorous clinical validation, and human oversight remains essential to ensure that these technologies enhance rather than compromise patient-centered anesthetic care. Full article

Cervical cancer, primarily caused by high-risk Human Papilloma Virus (HPV), remains a global health concern. Prognostic biomarkers reflecting systemic inflammation and immune response—the Systemic Immune-Inflammation Index (SII) and the Systemic Inflammation Response Index (SIRI)—have recently attracted interest for their potential predictive value in cervical cancer. We conducted a retrospective observational study including 344 patients who underwent loop electrosurgical excision of cervical intraepithelial neoplasia at Semmelweis University, Budapest, Hungary, between 2021 and 2024. Demographic, cytologic, histologic, and laboratory data were collected, and SII and SIRI were calculated. Statistical analyses, including Receiver Operating Characteristic (ROC) analyses, were performed. Higher SII and SIRI values were significantly associated with higher-grade lesions and invasive carcinoma. ROC analyses indicated good discriminatory performance, with negative predictive values of 96–100%, suggesting potential utility in ruling out malignant transformation. SII and SIRI are simple, cost-effective, and minimally invasive biomarkers that correlate with lesion severity in cervical disease. Their high negative predictive value supports a potential role as complementary rule-out tools in diagnostic evaluation. Further prospective studies are needed to validate these findings and to define clinically meaningful cut-off values for routine use. Full article

New nuclear power plant (NPP) designs, particularly advanced reactors and small modular reactors (SMRs), are expected to be highly automated, changing the job demands and shifting the roles and responsibilities of operators. The expanded capabilities of machines and their more prominent role in plant operation means that operators need new information to support effective human–automation teaming and the maintenance of situation awareness. To understand the impact of new automation and artificial intelligence (AI) technology in NPP control rooms, a literature review on function allocation (FA) methods was conducted. This review focused on four areas: (1) Identifying trends in the prevalence of quantitative, qualitative, and mixed methodologies. (2) Developments in levels of automation frameworks. (3) Revisions to the Fitts List. (4) Enabling factors for improved access to data-driven approaches. The review was limited to work occurring after 1983, when the U.S. Nuclear Regulatory Commission published research on FA. The results of the review demonstrate that many of the post-1983 methods are qualitative and descriptive. The review also identified several themes for managing human-out-of-the-loop issues. The discussion closes with proposed future work leveraging large language models and simulator-based approaches to enhance the existing FA methods. Full article

Hepatocyte nuclear factor 4 α (HNF4α) is a master transcriptional regulator essential for the maintenance of epithelial cell identity and function. Beyond its well-established role in controlling metabolic and differentiation programs, recent evidence highlights HNF4α as a key determinant of epithelial epigenetic reprogramming. Through direct interaction with chromatin modifiers and pioneer factors, HNF4α contributes to the establishment, maintenance, and dynamically reshaping of epithelial-specific transcriptional programs at epigenetic level. In this review, we summarize current knowledge on how HNF4α shapes chromatin organization by recruiting chromatin modifiers, modulating nucleosome positioning and regulating chromatin loop formation, thus directing tissue-specific gene expression. We also examine its direct regulation of epigenetic modifiers, as well as of epi-miRNAs and epi-lncRNAs, underscoring its role in coordinating chromatin remodeling with transcriptional networks. Finally, we address how dynamic HNF4α occupancy and activity influence context-dependent transcriptional outputs, and how disease-related alterations of its expression and function can contribute to epithelial dysfunction. Understanding the epigenetic functions of HNF4α provides new insights into epithelial biology and reveals potential therapeutic opportunities for restoring epithelial homeostasis in disease contexts. Full article

The growing demand for robust food authentication methods has driven the establishment of fast, sensitive, and field-based detection systems for identifying meat species. This study presents a colorimetric-based PSR approach for identifying yak (Bos grunniens) meat within fresh, thermally processed, and blended meat samples. Targeting the mitochondrial D-loop locus, the assay incorporates a simple alkaline lysis (AL) procedure for efficient DNA extraction, eliminating the requirement for specialized instrumentation. The PSR assay demonstrated high specificity, showing no evidence of cross-reactivity with closely associated food animals such as buffalo, cattle, goat, sheep, mithun, and pig. Sensitivity assessment revealed the assay’s capability to detect 1 pg of yak DNA, with reliable performance in samples exposed to thermal conditions up to 121 °C. Additionally, the technique detected yak meat down to a concentration of 0.1% in binary beef mixtures. This method provides a significant improvement in sensitivity over end-point PCR and is particularly well-suited for field applications due to its practical simplicity, affordability, as well as no reliance on sophisticated instrument. This is, to the best of our understanding, the first reported PSR-based approach developed for the identification of yak meat, offering a robust tool for food origin verification, regulatory enforcement, and product integrity monitoring. Full article

The characteristics of the dynamic reactive power demand of a hybrid multi-infeed HVDC transmission system during the post-fault recovery period are analyzed and a coordinated control strategy to improve the fault recovery characteristics of the hybrid multi-infeed HVDC transmission system is proposed in this paper. During the process of fault recovery, the LCC-HVDC adopts a progressive staggering recovery strategy. At the same time, according to the reactive power shortage of LCC-HVDC, the dynamic power limiter is used to adjust the upper and lower limit values of the outer loop power controller of VSC-HVDC, and the reactive power generated by the VSC-HVDC can be rapidly adjusted. Therefore, the problem of excessive reactive power demand during the recovery process can be solved and the reactive power demand can be satisfied with the proposed strategy. Moreover, the ability of VSC-HVDC to provide reactive power support can be fully utilized. Finally, a simulation model of a hybrid tri-infeed HVDC system is built using PSCAD/EMTDC (Version 4.6.2) software to verify the effectiveness of the proposed control strategy. Full article

The antimicrobial resistance crisis necessitates novel therapeutics. Selenium nanoparticles (SeNPs) offer promise, but their precise bactericidal mechanism remains poorly defined. This study aimed to define the antibacterial action of SeNPs synthesized via a green method with ascorbic acid and sodium citrate. The resulting SeNPs were monodisperse (17.8 ± 0.66 nm), crystalline, and highly stable (zeta potential: −69.9 ± 4.3 mV), exhibiting potent bactericidal activity against multidrug-resistant E. coli. To generate a mechanistic hypothesis, we integrated phenotypic analyses with a preliminary, single-replicate proteomic profiling. Recognizing this as an exploratory step, we focused our analysis on proteins with the most substantial changes. This revealed a coherent pattern of a targeted dual assault on core cellular processes. The data indicate that SeNPs simultaneously induce oxidative stress while severely depleting key components of the primary antioxidant glutathione system; key detoxification enzymes—glutathione S-transferase and glutaredoxin 2—were depleted 18- to 19-fold, while the stress protein HchA was reduced by over 63-fold. Concurrently, the patterns point toward a crippling of central energy metabolism; iron–sulfur enzymes in the TCA cycle, including aconitate hydratase (8.1-fold decrease) and succinate dehydrogenase (13.9-fold decrease), were severely suppressed, and oxidative phosphorylation was impaired (e.g., 4.7-fold decrease in NADH dehydrogenase subunit B). We propose that this coordinated disruption creates a lethal feedback loop leading to metabolic paralysis. Consequently, this work provides a detailed and testable mechanistic hypothesis for SeNPs action, positioning them as a candidate for a potent, multi-targeted antimicrobial strategy against drug-resistant pathogens. Full article