Search Results (3,207)

This paper reviews recent work on dynamic virtual power plants (DVPPs) using an Energy–Information–Market framework. It addresses the important problem of how DVPPs can support low-inertia power system operation and feeder-level stability under high renewable penetration. First, system-level studies on low-inertia operation and frequency control are used to frame quantitative requirements on rate of change of frequency, nadir, and quasi-steady-state limits. Second, energy-layer models are surveyed, including participation-factor-based DVPP controllers, grid-forming architectures, model-free frequency regulation, and robust frequency-constrained scheduling for allocating virtual inertia and fast frequency response (FFR) across distributed energy resource fleets. Third, information-layer and market-layer models are reviewed, covering stochastic and robust bidding, distribution locational marginal price-based clearing, peer-to-peer and community markets, privacy-preserving coordination, and emerging governance and cybersecurity schemes for DVPP participation. Across these strands, much of the literature remains centred on steady-state active and reactive power dispatch, with dynamic security enforced as constraints rather than formulated as verifiable and tradable services. This review identifies gaps in dynamic metrics and benchmarks, forecasting of available inertia and FFR capacity, market-physics co-design, multi-aggregator interaction, and experimentally validated DVPP implementations. These findings suggest that DVPPs can “sell stability” at the feeder level only through co-designed control, information, and market mechanisms and outline a research roadmap for this purpose. Full article

The skin serves as the first line barrier of innate immunity, protecting the body from external influences and maintaining its homeostasis. Exogenous and endogenous stress factors alter the structure and functional properties of the skin. The search for compounds capable of counteracting these processes has allowed the identification of peptides as promising ingredients of products for medicinal and cosmetic applications. This review comprehensively examines the mechanisms of action and dermatological applications of two distinct classes of natural products—endogenous human peptides and those derived from marine organisms. Human peptides exhibit numerous biological functions, including antimicrobial and immunomodulatory ones, as well as promoting antioxidant protection and wound healing. Microbiome-associated peptides are an underestimated but powerful regulator of skin aging through immunomodulation, inflammation control, barrier function maintenance, and selection of the proper microbial community. Peptides from marine organisms exhibit significant structural diversity and a broad spectrum of biological activity, including regenerative effects and effects on antibiotic-resistant microorganisms. This review summarizes current data obtained from in vitro, ex vivo, and clinical studies demonstrating a broad potential of peptides for maintaining skin health. Both peptide classes represent powerful, targeted strategies for innovative dermatological interventions aimed at promoting skin rejuvenation, protection, and overall homeostasis. Full article

Background: Pre-eclampsia (PE) is a significant cause of maternal and perinatal morbidity and mortality globally and is characterized by impaired endothelial function and disturbances in coagulation pathways. The effects of Human Immunodeficiency Virus (HIV) on the immune and coagulation systems have been investigated during pregnancy, but there are few reports on anticoagulant factors in pregnant women who are infected with HIV and develop PE. This investigation compares plasma protein C levels in pregnant women with pre-eclampsia and those without pre-eclampsia, and compares the results based on their HIV status. Methods: A hospital-based cross-sectional study design was used for the current research, which was carried out at Charlotte Maxeke Johannesburg Academic Hospital, South Africa. A total of 83 pregnant women participated in the study and were categorized into one of four groups: normotensive HIV-negative (n = 36); normotensive HIV-positive (n = 18); pre-eclamptic HIV-negative (n = 21); and pre-eclamptic HIV-positive (n = 8). Data collected included demographic information and clinical characteristics that were abstracted from maternity records. Plasma protein C concentrations were determined by ELISA (enzyme-linked immunosorbent assay). Nonparametric statistical methods were used to compare the mean values of plasma protein C between each of the four groups, and significance was set at p < 0.05. Subgroup analyses, particularly for the pre-eclamptic HIV-positive group (n = 8), were considered exploratory due to small sample sizes. Results: As would be anticipated, both systolic and diastolic blood pressure values were significantly elevated in the pre-eclamptic group when compared to the normotensive control subjects (p < 0.0001). There were no statistically significant differences in plasma protein C concentration between the normotensive and pre-eclamptic groups, nor between the HIV-negative and HIV-positive groups. Similarly, there were no significant differences in plasma protein C concentration when comparing all four study groups (Kruskal–Wallis test p = 0.2295). Conclusions: Plasma protein C concentrations did not vary significantly according to the presence of pre-eclampsia or HIV status in this cohort. These findings suggest that protein C concentrations were not measurably altered between groups within this study population. However, due to the small sample size in key subgroups, these findings should be considered preliminary and interpreted with caution. Larger, adequately powered studies are required to further investigate potential associations between HIV infection, pre-eclampsia, and anticoagulant pathways during pregnancy. Full article

The use of confuser–diffuser nozzles in power machines enables efficient conversion of gas energy into mechanical work. However, traditional Laval, Venturi, and Vitoszynski nozzles are associated with shock wave formation, causing energy losses, noise, and structural loading. This study proposes innovative jet nozzles with an internal streamlined body that forms annular flow rather than a classical diffusor. A rational computational design methodology based on the Venturi effect criterion and equality of cross-sectional area variation laws was developed. A couple of configurations with spindle-toroidal and ellipsoidal streamlined bodies were generated analytically, studied numerically, and confirmed experimentally. Based on the SST turbulence model, CFD simulations for a compressible flow (air) show that the proposed designs reduce the pressure jump from 60 kPa (traditional nozzle) to 20 kPa for the spindle-toroidal configuration and eliminate it for the ellipsoidal configuration. The Reynolds number in the throat decreases by a factor of 2.6, reducing turbulence. The outlet velocity increases by 3.0% for the spindle-toroidal design, while the ellipsoidal nozzle provides expansion with slightly lower velocity but a smoother velocity profile. Experimental thrust measurements agree with simulations within 2.6–6.7%. The proposed designs enhance energy efficiency, reduce erosion and vibration, and enable adaptive flow control via axial displacement of the streamlined body. Full article

The increasing penetration of rooftop photovoltaic (RTPV) systems in low-voltage (LV) distribution networks introduces challenges such as voltage rises, reverse power flow, and reduced hosting capacity, thereby necessitating effective active power regulation (APR) in module-level micro inverters. This paper proposes a dual-layer control framework for a 250 watt-peak (Wp) three switch rooftop PV micro-inverter, integrating quantum-behaved particle swarm optimization with reinforcement learning (QPSO-RL) for accurate maximum power point tracking (MPPT) and a linear quadratic regulator (LQR) for reserve- aware APR. The QPSO-RL algorithm improves available-power estimation under varying irradiance, temperature, and partial-shading conditions, while the LQR-based controller ensures fast, well-damped, and grid-compliant power regulation. The proposed framework was developed and validated using MATLAB/Simulink 2024 for simulation studies and LabVIEW with NI myRIO 2022 for real-time hardware implementation. Both simulation and experimental results confirm that the proposed method achieves 99.5% MPPT accuracy, convergence within 20 ms, grid-injected current total harmonic distortion (THD) below 3%, and a near-unity power factor. In addition, the reserve-based regulation strategy improves feeder compliance and reduces converter stress, thereby supporting reliable rooftop PV integration. These results demonstrate that the proposed QPSO-RL + LQR framework offers a practical and intelligent solution for high-performance, grid-supportive rooftop PV micro-inverter applications. Full article

Exerkines are released in response to physical exercise and play a key role in promoting health, such as taking part in modulating brain morphology and function. Expression levels of some of them are associated with an increase in neuroplasticity and a decrease in the risk of brain-related diseases such as dementia and depression. Therefore, our objective is to investigate the response of exerkines in healthy individuals and its potential to promote brain health. The search was performed in five databases. Randomized controlled trials of humans and animals of all ages who performed acute and/or long-term exercise and assessed the effects of exerkines were included. Human data were used for quantitative analysis, and animal experiments were included as part of the qualitative analysis. No meta-analyzes were conducted on animal data; preclinical findings are presented solely to contextualize mechanisms and are not used for clinical inference. Eventually, the sample consisted of 3321 individuals, with an age range from 10 to 89 years. Meta-analysis reveals that both acute and chronic exercise induced increases in the brain-derived neurotrophic factor and insulin-like growth factor 1 in older adults. Other exerkines such as cathepsin B and vascular endothelial growth factor have also demonstrated potential power for brain health. In conclusion, physical exercise by altering the levels of exerkines may be a feasible strategy for healthy individuals aiming at healthy aging of the brain. Moreover, it is advisable to analyze additional exerkines or multiple simultaneous applications to assess the cerebral effects during physical exercise. PROSPERO registration number: CRD42023438803. Full article

The ramping requirement in new power systems primarily stems from net load variations and forecast errors of renewable energy and load. Designing an equitable cost allocation mechanism for ramping services based on these factors facilitates incentives for generation and load to actively reduce ramping demands, thereby alleviating system ramping pressure. Accordingly, this paper proposes a fair ramping cost allocation mechanism based on the ramping responsibility coefficients of market participants. Under this mechanism, a market-oriented operation model for wind–hydro-storage joint operation is established to verify its effectiveness in market applications. First, a ramping cost allocation mechanism is constructed based on ramping responsibility coefficients. According to the responsibility coefficients of market participants for deterministic and uncertain ramping requirements, ramping costs are allocated to the corresponding contributors in proportion to the ramping demands caused by net load variations, load forecast deviations, and renewable energy forecast deviations. Specifically, for costs arising from renewable energy forecast errors, an allocation mechanism is designed based on the difference between the declared error range and the actual error. Second, within this allocation framework, hydropower and storage (including cascade hydropower and hybrid pumped storage) are utilized as flexible resources to mitigate wind power uncertainty and reduce its ramping costs. A two-stage day-ahead and real-time bi-level game model for wind–hydro-storage cooperative decision-making is developed. The upper level optimizes bilateral trading and market bidding strategies for wind–hydro-storage, while the lower level simulates the market clearing process. Through Stackelberg game modeling, joint optimal operation of wind–hydro-storage is achieved, ensuring mutual benefits. Finally, simulation results validate that the proposed ramping cost allocation mechanism can guide renewable energy to improve output controllability through economic signals. Furthermore, the bilateral trading and coordinated market participation of wind–hydro-storage realize win–win outcomes, reduce the ramping cost allocation for wind power by 23.10%, effectively narrow peak-valley price differences, and enhance market operational efficiency. Full article

Nonlinear state estimation in electric power systems remains challenging under mixed-measurement conditions due to the coexistence of legacy SCADA and PMU data with markedly different reliability levels, the sensitivity of classical Gauss–Newton-type methods to heterogeneous noise and numerical conditioning, and the increasing complexity of large-scale grids. To address these issues, this paper proposes ST-ResGAT, a spatio-temporal residual graph attention framework for nonlinear state estimation under heterogeneous sensing conditions. The proposed method models the problem on an augmented heterogeneous factor graph, employs a reliability-aware heterogeneous graph attention mechanism with residual propagation to adaptively fuse measurements of different quality, and further refines the graph-based estimates through a lightweight LSTM post-processing module that exploits short-term temporal continuity. All datasets are generated using pandapower on the IEEE 30-bus, IEEE 118-bus, and IEEE 1354-bus benchmark systems to ensure full reproducibility of the experimental pipeline. Experimental results show that the proposed method consistently achieves lower estimation errors than WLS, DNN, GAT, and PINN baselines across all three systems, while also exhibiting more compact node-level error distributions and stronger spatial consistency. Multi-seed ablation studies further indicate that residual propagation, reliability-aware attention, and temporal refinement play complementary roles across different system scales. Robustness experiments additionally show that, under random measurement exclusion as well as bias, Gaussian, and mixed corrupted-measurement settings, ST-ResGAT exhibits smooth and progressive degradation, including on the newly added large-scale IEEE 1354-bus benchmark. These results suggest that the proposed framework is a promising direction for data-driven state estimation under controlled mixed-measurement benchmark conditions. Full article

This study investigated neural oscillatory dynamics underlying visual word recognition in university students with dyslexia using a portable brain–computer interface (BCI) EEG system. The sample included university students with dyslexia (N = 12) and matched controls (N = 14) who completed auditory discrimination and visual word recognition tasks, with and without musical accompaniment. Through these experimental conditions, the researchers assessed (a) the cortical activation across frequency bands, (b) the modulatory effect of background music, and (c) the relationship between emotional states and brain activity. Results revealed significant group differences in oscillatory patterns, with reduced β- and γ-band activity in the left occipito-temporal cortex among participants with dyslexia, confirming disrupted temporal coordination in posterior reading networks. Compensatory right-hemisphere activation was observed, particularly under musical conditions, accompanied by increased α-band power and reduced δ activity, indicating enhanced attentional engagement and reduced cognitive fatigue. Emotional assessment using the DASS-21 revealed higher stress and anxiety scores in the dyslexic group, suggesting that affective factors may modulate oscillatory dynamics. The presence of background music appeared to attenuate these effects, supporting improved emotional regulation and cognitive focus. These findings demonstrate that dyslexia reflects a distributed disruption in neural synchrony and cross-frequency coupling, influenced by both cognitive and affective mechanisms. The integration of portable EEG technology with rhythmic auditory stimulation offers new insights into the neurophysiological and emotional aspects of dyslexia, highlighting the potential of rhythm- and music-based approaches for both diagnostic and therapeutic applications. Full article

In the scenario where renewable energy sources (RESs) are connected to the power system (PS) through a flexible high-voltage direct current (HVDC) transmission system, their output becomes highly intermittent and volatile due to meteorological factors like wind direction and speed. This variability poses significant challenges to the real-time power balance and control of the PS. To address the uncertainties in system operation and the challenges of RES consumption, this paper proposes an artificial intelligence (AI) algorithm-driven collaborative optimization strategy for virtual power plants (VPPs) considering RESs transmitted by flexible HVDC. Firstly, a self-attention mechanism and multiple gated structures are integrated into a long short-term memory (LSTM) deep learning model. This enhancement improves the model’s ability to capture multi-timescale characteristics of RESs, increasing forecasting accuracy and robustness. Based on these forecasts, a total cost optimization model for VPP operation is developed, which includes high penalty costs for wind and solar curtailment. By embedding economic constraints that prioritize RESs usage, the model can reduce waste caused by traditional cost-driven scheduling. Additionally, to solve the high-dimensional nonlinear optimization problem in VPP scheduling, an improved population-based incremental learning (PBIL) algorithm is introduced. It incorporates an elite retention strategy and an adaptive mutation operator to boost global search efficiency and convergence speed. Simulations based on an VPP incorporating typical offshore wind and solar RESs transmitted via flexible HVDC demonstrate that the improved LSTM reduces MAPE by 7.14% for wind and 4.27% for PV compared to classical LSTM, and the proposed method achieves the lowest curtailment rates (wind 10.74%, PV 10.23%) and total cost (43,752 RMB), outperforming GA, PSO, and GW by 10–18% in cost reduction. Simulation results show that the proposed strategy enhances RESs consumption while maintaining system economy under flexible HVDC transmission. This work offers theoretical and practical insights for optimizing PS with high RES penetration and supports the low-carbon transition of new-type PS. Full article

Grid-forming (GFM) battery energy storage system (BESS) inverters are becoming a cornerstone of resilient microgrids, where severe voltage sags and abrupt operating shifts can challenge both voltage regulation and controller stability. Finite-set model predictive control (FS-MPC) offers fast transient response and multi-objective coordination, yet conventional designs rely on static cost-function weights that are typically tuned offline and may become suboptimal under disturbance-driven regime changes. This paper proposes a forecast-guided KAN-adaptive FS-MPC framework that (i) formulates the inner-loop predictive control in the stationary αβ frame, thereby avoiding PLL dependency and mitigating loss-of-lock risk under extreme sags, and (ii) introduces an Operating Stress Index (OSI) that fuses load forecasts with reserve-margin or percent-operating-reserve signals to quantify grid vulnerability and trigger resilience-oriented control adaptation. A lightweight Kolmogorov–Arnold Network (KAN), parameterized by learnable B-spline edge functions, is embedded as an online weight governor to update key FS-MPC weighting factors in real time, dynamically balancing voltage tracking and switching effort. Experimental validation under high-frequency microgrid scenarios shows that, under a 50% symmetrical voltage sag, the proposed controller reduces the worst-case voltage deviation from 0.45 p.u. to 0.16 p.u. (64.4%) and shortens the recovery time from 35 ms to 8 ms (77.1%) compared with static-weight FS-MPC. In the islanding-like transition case, the proposed method restores the PCC voltage within 18 ms, whereas the static baseline fails to recover within 100 ms. Moreover, the deployed KAN governor requires only 6.2 μs per inference on a 200 MHz DSP, supporting real-time embedded implementation. These results demonstrate that forecast-guided adaptive weighting improves transient resilience and power quality while maintaining DSP-feasible computational complexity. Full article

Background/Objectives: Cardiovascular regulation complexity (CRC) is an underexplored health marker in the context of type 2 diabetes mellitus (T2DM). Additionally, associating CRC with physical and cardiorespiratory fitness variables could provide greater insight into how physical conditioning impacts cardiovascular health in the context of T2DM. This study aims to investigate whether the relationship between physical and cardiorespiratory fitness and CRC differs according to the presence or absence of T2DM. Methods: Sixty-eight men were equally divided into the T2DM group (T2DMG; 57 ± 6 years old and 28.4 ± 3.1 kg/m²) and the control group (CG; 52 ± 5 years old and 25.1 ± 2.8 kg/m²). Participants underwent a resting cardiovascular data collection and a cardiopulmonary exercise test on a cycle ergometer. For each group, the relative peak power (W/kg_PEAK) and peak oxygen consumption (VO_2PEAK) were correlated with the CRC indices, namely, Shannon entropy, the complexity index, the normalized complexity index, and the sample entropy from heart period (HP) and systolic arterial pressure (SAP) series. A partial correlation was performed for each group, controlling for age, physical activity level, and metabolic cart. Results: Only the CG showed positive and significant correlations between relative VO_2PEAK and W/kg_PEAK and CRC indices derived from the HP series (0.354 ≤ r ≤ 0.548 and 0.001 ≤ p ≤ 0.047). Correlations with the SAP series were not significant, regardless of the groups. Conclusions: In this sample, there was no positive relationship between physical and cardiorespiratory fitness variables and CRC indices among individuals with T2DM. Further large sample studies are needed to elucidate the factors involved in T2DM that impact CRC. Full article

Soaking bovine tooth blocks in demineralization solution is a widely used method to simulate caries-like demineralization for further experimental studies. The objective of this study was to evaluate the degree and depth of mineral loss in bovine enamel and dentin blocks under various controlled conditions and to investigate the relationships between these factors and mineral loss, providing guidance for researchers to achieve targeted demineralization outcomes. A total of 54 enamel blocks and 54 dentin blocks were divided into 18 groups according to the exposed area and solution volume and then immersed in demineralization solution. Micro-CT scans were performed before immersion, as well as after 1, 2, 3, 7, and 10 days of immersion. The results were analyzed using data analysis software and subsequently summarized into graphical representations. The analysis revealed that soaking time and solution volume showed positive correlations with mineral loss, whereas the exposed area was negatively correlated with mineral loss. Mean mineral loss increased significantly with immersion time in all groups (e.g., from 6314 to 25,670 vol%·μm in the dentin 3 × 3 mm², 50 mL group, p < 0.05). After 7 days, specimens immersed in larger solution volumes showed significantly greater mineral loss than those immersed in smaller volumes (p < 0.05). In addition, larger exposed areas resulted in greater mineral loss after 3 days of immersion. Mean mineral loss followed a power function relationship with time when the solution volume was sufficiently high relative to the exposed surface area. In contrast, when the solution volume was limited, a logarithmic relationship between time and mineral loss was observed. Given its superior stability, the mean mineral loss appears to be a more reliable indicator for assessing tooth demineralization. Based on our results, more controlled and reproducible demineralization conditions can be achieved, which may contribute to improving the reliability of in vitro caries models and facilitating the evaluation of preventive and therapeutic strategies. Full article

Because the introduction of a vibro-impact structure can widen the bandwidth and improve the harvesting efficiency of the vibration energy harvesting (VEH) systems, an analytical method for a VEH system based on vibro-impact is proposed to employ the stochastic response and stability. Firstly, the piezoelectric control equation is decoupled by the generalized harmonic transformation, which obtains an uncoupled equivalent system. Secondly, the Itô stochastic differential equation with amplitude is analytically derived by applying the proposed analytical method. Furthermore, the influence of crucial parameters on the mean square voltage (MSV) and the mean output power is explored, such as the coupling factors and restitution coefficient. Finally, the top Lyapunov exponent (TLE) can be derived based on the linearized averaged Itô equations and the condition for the stability with probability one is obtained. It turned out that restitution coefficient r and time constant ratio $\mu$ have remarkable effects on the system’s stability. Full article

The centrifugal compressor is the heart that drives the operation of natural gas pipeline systems. Under low-throughput conditions, natural gas often returns back to the compressor through the surge control valve to increase the flow rate and avoid surge. However, how to reasonably determine the opening of the surge control valve is still an important problem in production. To predict the opening of the surge control valve in a centrifugal compressor, this work proposes a BP neural network optimized by the grey wolf optimizer (GWO). Five key parameters, including compressor shell vibration, power turbine speed, compressor inlet pressure, compressor outlet temperature, and gas turbine power, are determined to be key factors correlated to the opening of the surge control valve, and the relationships of these parameters are physically analyzed from a physical perspective. Compared with the other five parallel models, the GWO–BP method effectively optimizes the initial weights and thresholds of the neural network, reduces the probability of falling into a local optimum, and significantly improves prediction accuracy and stability. The root mean square error (RMSE), determination coefficient (R-square), and mean absolute error (MAE) of the GWO–BP model are all the best fit, and the predicted and actual openings of the surge control valve match well, with the average relative deviation being 4.65%, indicating that the GWO–BP model proposed in this paper has a good ability to predict the opening of surge control valves. Full article