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Ti-based bulk metallic glass composites (BMGMCs) containing an in situ formed metastable β phase normally exhibit enhanced plasticity attributed to induced phase transformation or twinning. However, the underlying deformation micromechanism remains controversial. This study investigates a novel deformation mechanism of Ti-based BMGMCs with a composition of Ti42.3Zr28Cu8.3Nb4.7Ni1.7Be15 (at%). The microstructures after tension were analyzed using advanced electron microscopy. The dendrites were homogeneously distributed in the glassy matrix with a volume fraction of 55 ± 2% and a size of 1~5 μm. The BMGMCs deformed in a serrated manner with a fracture strength (σf) of ~1710 MPa and a fracture strain of ~7.1%, accompanying strain hardening. The plastic deformation beyond yielding was achieved by a synergistic action, which includes shear banding, localized amorphization and a localized BCC (β-Ti) to HCP (α-Ti) structural transition. The localized amorphization was caused by high local strain rates during shear band extension from the amorphous matrix to the crystalline reinforcements. The localized structural transition from BCC to HCP resulted from accumulating concentrated stress during deformation. The synergistic action enriches our understanding of the deformation mechanism of Ti-based BMGMCs and also sheds light on material design and performance improvement. [ABSTRACT FROM AUTHOR]

Accurately detecting voltage faults is essential for ensuring the safe and stable operation of energy storage power station systems. To swiftly identify operational faults in energy storage batteries, this study introduces a voltage anomaly prediction method based on a Bayesian optimized (BO)-Informer neural network. Firstly, the temporal characteristics and actual data collected by the battery management system (BMS) are considered to establish a long-term operational dataset for the energy storage station. The Pearson correlation coefficient (PCC) is used to quantify the correlations between these data. Secondly, an Informer neural network with BO hyperparameters is used to build the voltage prediction model. The performance of the proposed model is assessed by comparing it with several state-of-the-art models. With a 1 min sampling interval and one-step prediction, trained on 70% of the available data, the proposed model reduces the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) of the predictions to 9.18 mV, 0.0831 mV, and 6.708 mV, respectively. Furthermore, the influence of different sampling intervals and training set ratios on prediction results is analyzed using actual grid operation data, leading to a dataset that balances efficiency and accuracy. The proposed BO-based method achieves more precise voltage abnormity prediction than the existing methods. [ABSTRACT FROM AUTHOR]

Cell–cell communications is crucial for the regulation of cellular life and the establishment of cellular relationships. Most approaches of inferring intercellular communications from single-cell RNA sequencing (scRNA-seq) data lack a comprehensive global network view of multilayered communications. In this context, we propose scHyper, a new method that can infer intercellular communications from a global network perspective and identify the potential impact of all cells, ligand, and receptor expression on the communication score. scHyper designed a new way to represent tripartite relationships, by extracting a heterogeneous hypergraph that includes the source (ligand expression), the target (receptor expression), and the relevant ligand–receptor (L-R) pairs. scHyper is based on hypergraph representation learning, which measures the degree of match between the intrinsic attributes (static embeddings) of nodes and their observed behaviors (dynamic embeddings) in the context (hyperedges), quantifies the probability of forming hyperedges, and thus reconstructs the cell–cell communication score. Additionally, to effectively mine the key mechanisms of signal transmission, we collect a rich dataset of multisubunit complex L-R pairs and propose a nonparametric test to determine significant intercellular communications. Comparing with other tools indicates that scHyper exhibits superior performance and functionality. Experimental results on the human tumor microenvironment and immune cells demonstrate that scHyper offers reliable and unique capabilities for analyzing intercellular communication networks. Therefore, we introduced an effective strategy that can build high-order interaction patterns, surpassing the limitations of most methods that can only handle low-order interactions, thus more accurately interpreting the complexity of intercellular communications. [ABSTRACT FROM AUTHOR]

Previous study consistently showed that lower serum sodium (SNa) was associated with a greater risk of mortality in hemodialysis (HD) patients. However, few studies have focused on the change in SNa (ΔSNa = post-HD SNa – pre-HD SNa) during an HD session. In a retrospective cohort of maintenance HD adults, all-cause mortality and cardio-cerebrovascular event (CCVE) were followed up for a medium of 82 months. Baseline pre-HD SNa and ΔSNa were collected; time-averaged pre-HD SNa and ΔSNa were computed as the mean values within 1-year, 2-year and 3-year intervals after enrollment. Cox proportional hazards models were used to evaluate the relationships of pre-HD and ΔSNa with outcomes. Time-averaged pre-HD SNa were associated with all-cause mortality (2-year pre-HD SNa: HR [95% CI] 0.86 [0.74–0.99], p = 0.042) and CCVE (3-year pre-HD SNa: HR [95% CI] 0.83 [0.72–0.96], p = 0.012) with full adjustment. Time-averaged ΔSNa also demonstrated an association with all-cause mortality (3-year ΔSNa: HR [95% CI] 1.26 [1.03–1.55], p = 0.026) as well as with CCVE (3-year ΔSNa: HR [95% CI] 1.51 [1.21–1.88], p = <0.001) when fully adjusted. Baseline pre-HD SNa and ΔSNa didn't exhibit association with both outcomes. Lower time-averaged pre-HD SNa and higher time-averaged ΔSNa were associated with a greater risk of all-cause mortality and CCVE in HD patients. [ABSTRACT FROM AUTHOR]

Considering the voltage stability problem brought by large-scale distributed PV access to the distribution network, this paper proposes a generalized load model that considers the fault ride-through capability of distributed PV. Firstly, the detailed model of the distribution network is established, and the detailed model is calibrated based on the measured data, the simulation errors are below 1%. And then establish a generalized load model considering distributed PV high and low voltage traversal ability. The sensitivity analysis results are used to rank the parameters to be identified, and the parameters with higher sensitivity are identified. The parameters are obtained from the detailed model and measured data, and four sets of parameters are identified and simulated under different PV penetration rates and fault conditions. The calculated fitting errors are less than 1%. The results show that the generalized load gray box model of the distribution network with distributed PV high and low voltage ride-through capability can reflect the dynamic characteristics of the distribution network well. [ABSTRACT FROM AUTHOR]

Background: Sleep deprivation (SD) is a growing global health problem with many deleterious effects, such as cognitive impairment. Microglia activation‐induced neuroinflammation may be an essential factor in this. Propofol has been shown to clear sleep debt after SD in rats. This study aims to evaluate the effects of propofol‐induced sleep on ameliorating sleep quality impairment and cognitive decline after 48 h SD. Methods: Almost 8–12‐week‐old rats were placed in the SD system for 48 h of natural sleep or continuous SD. Afterwards, rats received propofol (20 mg·kg−1·h−1, 6 h) via the tail or slept naturally. The Morris water maze (MWM) and Y‐maze test assessed spatial learning and memory abilities. Rat EEG/EMG monitored sleep. The expression of brain and muscle Arnt‐like protein 1 (BMAL1), brain‐derived neurotrophic factor (BDNF) in the hippocampus and BMAL1 in the hypothalamus were assessed by western blot. Enzyme‐linked immunosorbent assay detected IL‐6, IL‐1β, arginase 1 (Arg1), and IL‐10 levels in the hippocampus. Immunofluorescence was used to determine microglia expression as well as morphological changes. Results: Compared to the control group, the sleep‐deprived rats showed poor cognitive performance on both the MWM test and the Y‐maze test, accompanied by disturbances in sleep structure, including increased total sleep time, and increased time spent and delta power in non‐rapid eye movement sleep. In addition, SD induces abnormal expression of the circadian rhythm protein BMAL1, activates microglia, and causes neuroinflammation and nerve damage. Propofol reversed these changes and saved sleep and cognitive impairment. Furthermore, propofol treatment significantly reduced hippocampal IL‐1β and IL‐6 levels, increased BDNF, Arg1, and IL‐10 levels, and switched microglia surface markers from the inflammatory M1 type to the anti‐inflammatory M2 type. Conclusion: Propofol reduces SD‐induced cognitive impairment and circadian rhythm disruption, possibly by lowering neuronal inflammation and switching the microglia phenotype from an M1 to an M2 activated state, thus exerting neuroprotective effects. [ABSTRACT FROM AUTHOR]

Harmful stimuli trigger mutations lead to uncontrolled accumulation of hnRNPA2/B1 in the cytoplasm, exacerbating neuronal damage. Kapβ2 mediates the bidirectional transport of most substances between the cytoplasm and the nucleus. Kapβ2 guides hnRNPA2/B1 back into the nucleus and restores its function, alleviating related protein toxicity. Here, we aim to explore the involvement of Kapβ2 in neurodegeneration in rats with MCI following sevoflurane anesthesia and surgery. Firstly, novel object recognition test and Barnes maze were conducted to assess behavioral performances, and we found Kapβ2 positively regulated the recovery of memory and cognitive function. In vivo electrophysiological experiments revealed that the hippocampal theta rhythm energy distribution was disrupted, coherence was reduced, and long-term potentiation was attenuated in MCI rats. LTP was greatly improved with positive modulation of Kapβ2. Next, functional MRI and BOLD imaging will be employed to examine the AFLL and FC values of dynamic connectivity between the cortex and hippocampus of the brain. The findings show that regulating Kapβ2 in the hippocampus region enhances functional activity and connections between brain regions in MCI rats. WB results showed that increasing Kapβ2 expression improved the expression and recovery of cognitive-related proteins in the hippocampus of MCI rats. Finally, WB and immunofluorescence were used to examine the changes in hnRNPA2/B1 expression in the nucleus and cytoplasm after overexpression of Kapβ2, and it was found that nucleocytoplasmic mis location was alleviated. Overall, these data show that Kapβ2 reverses the nucleoplasmic misalignment of hnRNPA2/B1, which slows neurodegeneration towards dementia in MCI after sevoflurane anesthesia and surgery. Our findings may lead to new approaches for perioperative neuroprotection of MCI patients. [ABSTRACT FROM AUTHOR]

In the conventional dual-loop vector control strategy of Voltage Source Converter-based High Voltage Direct Current (VSC-HVDC) systems employed in offshore wind farms, challenges such as complex PI parameter-tuning and slow response speed exist. Furthermore, a single-phase bridge-arm fault in the converter station can lead to a change in system parameters, resulting in the failure of the original control strategy. Hence, this paper proposes a fault-tolerant control strategy for grid-connected offshore wind farms, based on model predictive control (MPC). Firstly, the predictive models for both normal and fault-tolerant states of the grid-side converter station are established based on the system structure of the grid-side converter station and a super-local model. Subsequently, a cost function is constructed using the power error, with the optimization objective set as the value function. This approach allows for accurate prediction of the future switching states of the grid-tied inverter to track the reference power. Finally, a simulation model of the offshore wind power grid system is established in the MATLAB/Simulink (2022a) environment. The results demonstrate that the grid-side converter station can effectively operate in a fault-tolerant manner under the proposed control strategy, thereby enhancing the disturbance resistance and fault-recovery capabilities of the offshore wind VSC-HVDC system. [ABSTRACT FROM AUTHOR]

PIKfyve is an endosomal lipid kinase that synthesizes phosphatidylinositol 3,5-biphosphate from phosphatidylinositol 3-phsphate. Inhibition of PIKfyve activity leads to lysosomal enlargement and cytoplasmic vacuolation, attributed to impaired lysosomal fission processes and homeostasis. However, the precise molecular mechanisms underlying these effects remain a topic of debate. In this study, we present findings from PIKfyve-deficient zebrafish embryos, revealing enlarged macrophages with giant vacuoles reminiscent of lysosomal storage disorders. Treatment with mTOR inhibitors or effective knockout of mTOR partially reverses these abnormalities and extend the lifespan of mutant larvae. Further in vivo and in vitro mechanistic investigations provide evidence that PIKfyve activity is essential for mTOR shutdown during early zebrafish development and in cells cultured under serum-deprived conditions. These findings underscore the critical role of PIKfyve activity in regulating mTOR signaling and suggest potential therapeutic applications of PIKfyve inhibitors for the treatment of lysosomal storage disorders. [ABSTRACT FROM AUTHOR]

The utilization of wind power all-DC systems with DC collection and transmission is an effective solution for the extensive development of wind power in deep-sea areas. However, in the event of faults occurring in wind power all-DC systems, the fault propagation speed is extremely rapid, with a wide-ranging impact, and to date, there are no complete DC engineering references available. It is crucial to research the topology and fault isolation methods applicable to large-scale offshore wind power all-DC systems in deep-sea areas. This paper proposes a novel series-connected all-DC system topology and presents corresponding fault isolation methods for internal faults in wind turbine units and faults in high-voltage DC transmission lines. The system simulation model was constructed using PSCAD/EMTDC (v4.6.3), and simulations were conducted for internal faults in the wind turbine units and DC transmission line short-circuit faults. The simulation results demonstrate that the proposed system can isolate various DC faults while maintaining stable operation, thereby validating the effectiveness of the control strategies and fault isolation methods proposed in this paper. [ABSTRACT FROM AUTHOR]

The uncertainty in the new power system has increased, leading to limitations in traditional stability analysis methods. Therefore, considering the perspective of the three-dimensional static security region (SSR), we propose a novel approach for system static stability analysis. To address the slow training speed of traditional deep learning algorithms using batch gradient descent (BGD), we introduce an improved stochastic–batch gradient descent (S-BGD) search method that combines the advantages of stochastic gradient descent (SGD) in fast training. This method ensures both speed and precision in parameter training. Moreover, to tackle the problem of getting trapped in local optima and saddle points during parameter training, we draw inspiration from kinematic theory and propose a gradient pile (GP) training method. By utilizing accumulated gradients as parameter corrections, this method effectively avoids getting stuck in local optima and saddle points, thereby enhancing precision. Finally, we apply the proposed methods to construct the static security region for the IEEE-118 new power system using its data as samples, demonstrating the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Purpose: During the period of COVID-19 pandemic, the social restrictions and isolation exerted a significant impact on the sleep quality of Chinese college students. This study aims to delve into the influence of physical activity on the sleep quality of college students as well as the mediating roles of stress and smartphone addiction. Materials and Methods: A cohort of 274 eligible college students (146 males and 128 females) were selected for the investigation. The International Physical Activity Questionnaire Short Form, Stress Perception Scale, Smartphone Addiction Scale, and Pittsburgh Sleep Quality Index were employed to assess the levels of physical activity, stress, smartphone addiction, and sleep quality among college students. For data analysis, descriptive statistics, correlation analysis, and chained mediation effect tests were performed sequentially. Results: The findings revealed: (1) a significant negative correlation between physical activity and stress, smartphone addiction, and sleep quality among college students (r = − 0.216, p < 0.001; r = − 0.224, p < 0.001; r = − 0.259, p < 0.001); (2) independent mediating roles of stress and smartphone addiction in the relationship between physical activity and sleep quality; and (3) chained mediating effects of stress and smartphone addiction in the association between physical activity and sleep quality. Conclusion: This study deepens our comprehension of how physical activity augments the quality of slumber, concurrently emphasizing that mitigating stress levels and alleviating smartphone addiction constitute effective strategies for preventing sleep issues among college students. [ABSTRACT FROM AUTHOR]

The deployment of a green power alternative within an isolated network, powered by renewable energy sources, in the "Three North" region of China can facilitate the substitution of high-energy-consuming industrial loads with green power. However, an inadequate power supply configuration may lead to economic and reliability issues. To address this problem, effective capacity allocation within the green power alternative isolated network is proposed. The capacity allocation process starts with the design of a network structure that aligns with local conditions. Subsequently, a capacity allocation model is developed, considering economic factors, renewable energy utilization efficiency, and system reliability. The gray wolf optimizer is enhanced to establish a capacity allocation method for the green power alternative isolated network. This method is then employed to simulate and assess the performance of the network. The results indicate that the green alternative isolated grid can successfully facilitate green power substitution, satisfying the energy requirements of the loads. [ABSTRACT FROM AUTHOR]

Purpose: Increased body mass index (BMI) has been associated with poor outcomes in women with breast cancer. We evaluated the association between BMI and pathological complete response (pCR) in the I-SPY 2 trial. Methods: 978 patients enrolled in the I-SPY 2 trial 3/2010–11/2016 and had a recorded baseline BMI prior to treatment were included in the analysis. Tumor subtypes were defined by hormone receptor and HER2 status. Pretreatment BMI was categorized as obese (BMI ≥ 30 kg/m2), overweight (25 ≤ BMI < 30 kg/m2), and normal/underweight (< 25 kg/m2). pCR was defined as elimination of detectable invasive cancer in the breast and lymph nodes (ypT0/Tis and ypN0) at the time of surgery. Logistic regression analysis was used to determine associations between BMI and pCR. Event-free survival (EFS) and overall survival (OS) between different BMI categories were examined using Cox proportional hazards regression. Results: The median age in the study population was 49 years. pCR rates were 32.8% in normal/underweight, 31.4% in overweight, and 32.5% in obese patients. In univariable analysis, there was no significant difference in pCR with BMI. In multivariable analysis adjusted for race/ethnicity, age, menopausal status, breast cancer subtype, and clinical stage, there was no significant difference in pCR after neoadjuvant chemotherapy for obese compared with normal/underweight patients (OR = 1.1, 95% CI 0.68–1.63, P = 0.83), and for overweight compared with normal/underweight (OR = 1, 95% CI 0.64–1.47, P = 0.88). We tested for potential interaction between BMI and breast cancer subtype; however, the interaction was not significant in the multivariable model (P = 0.09). Multivariate Cox regression showed there was no difference in EFS (P = 0.81) or OS (P = 0.52) between obese, overweight, and normal/underweight breast cancer patients with a median follow-up time of 3.8 years. Conclusion: We found no difference in pCR rates by BMI with actual body weight-based neoadjuvant chemotherapy in this biologically high-risk breast cancer population in the I-SPY2 trial. [ABSTRACT FROM AUTHOR]

Optical encryption strategies utilizing fully coherent light have been widely explored but often face challenges such as speckle noise and beam instabilities. In this work, we introduce a novel protocol for multi-channel optical information encoding and encryption using vectorial spatial coherence engineering of a partially coherent light beam. By characterizing the beam's spatial coherence structure with a 2 × 2 coherence matrix, we demonstrate independent control over the three components of the coherence Stokes vector. This allows for three-channel optical information encoding and encryption, with applications in color image representation. Unlike existing methods based on fully coherent light modulations, our approach utilizes a two-point dependent coherence Stokes vector, proving resilient to random noise in experimental scenarios. Our findings provide a robust foundation for higher-dimensional optical encoding and encryption, addressing limitations associated with partially coherent light in complex environments. [ABSTRACT FROM AUTHOR]

Currently, the global energy revolution in the direction of green and low-carbon technologies is flourishing. The large-scale integration of renewable energy into the grid has led to significant fluctuations in the net load of the power system. To meet the energy balance requirements of the power system, the pressure on conventional power generation units to adjust and regulate has increased. The efficient utilization of the regulation capability of controllable industrial loads and energy storage can achieve the similarity between renewable energy curves and load curves, thereby reducing the peak-to-valley difference and volatility of the net load. This approach also decreases the adjustment pressure on conventional generating units. Therefore, this paper proposes a two-stage optimization scheduling strategy considering the similarity between renewable energy and load, including energy storage and industrial load participation. The combination of the Euclidean distance, which measures the similarity between the magnitude of renewable energy–load curves, and the load tracking coefficient, which measures the similarity in curve shape, is used to measure the similarity between renewable energy and load profiles. This measurement method is introduced into the source-load-storage optimal scheduling to establish a two-stage optimization model. In the first stage, the model is set up to maximize the similarity between renewable energy and the load profile and minimize the cost of energy storage and industrial load regulation to obtain the desired load curve and new energy output curve. In the second stage, the model is set up to minimize the overall operation cost by considering the costs associated with abandoning the new energy sources and shedding loads to optimize the output of conventional generator sets. Through a case analysis, it is verified that the proposed scheduling strategy can achieve the tracking of the load curve to the new energy curve, reducing the peak-to-valley difference of the net load curve by 48.52% and the fluctuation by 67.54% compared to the original curve. These improvements effectively enhance the net load curve and reduce the difficulty in regulating conventional power generation units. Furthermore, the strategy achieves the full discard of renewable energy and reduces the system operating costs by 4.19%, effectively promoting the discard of renewable energy and reducing the system operating costs. [ABSTRACT FROM AUTHOR]

Background: Network latency is the most important factor affecting the performance of telemedicine. The aim of the study is to assess the feasibility and efficacy of a novel network latency management system in 5G telesurgery. Methods: We conducted 20 telesurgery simulation trials (hitching rings to columns) and 15 remote adrenalectomy procedures in the 5G network environment. Telemedicine Network Latency Management System and the traditional "Ping command" method (gold standard) were used to monitor network latency during preoperative simulated telesurgery and formal telesurgery. We observed the working status of the Telemedicine Network Latency Management System and calculated the difference between the network latency data and packet loss rate detected by the two methods. In addition, due to the lower latency of the 5G network, we tested the alert function of the system using the 4G network with relatively high network latency. Results: The Telemedicine Network Latency Management System showed no instability during telesurgery simulation trials and formal telesurgery. After 20 telesurgery simulation trials and 15 remote adrenalectomy procedures, the p-value for the difference between the network latency data monitored by the Telemedicine Network Latency Management System and the "Ping command" method was greater than 0.05 in each case. Meanwhile, the surgeons reported that the Telemedicine Network Latency Management System had a friendly interface and was easy to operate. Besides, when the network latency exceeded a set threshold, a rapid alarm sounded in the system. Conclusion: The Telemedicine Network Latency Management System was simple and easy to operate, and it was feasible and effective to use it to monitor network latency in telesurgery. The system had an intuitive and concise interface, and its alarm function increased the safety of telesurgery. The system's own multidimensional working ability and information storage capacity will be more suitable for telemedicine work. [ABSTRACT FROM AUTHOR]

Wind energy resources in complex terrain are abundant. However, the default mesh division of various terrains often needs more specificity, particularly in wind resource analysis. The mesh division method can diminish computational efficiency and quality in intricate topographical conditions. This article presents a combined algorithm for generating Delaunay triangular meshes in mountainous terrains with significant variations in terrain. The algorithm considers the uncertainty of inner nodes and mesh quality, addressing both the advantages and drawbacks of the Delaunay triangular mesh. The proposed method combines the triangular center of gravity insertion algorithm with an incremental inserting algorithm. Its main goal is to enhance the quality and efficiency of mesh generation, specifically tailored for this type of complex terrain. The process involves discretizing boundary edges and contour lines to obtain point sets, screening boundary triangles, and comparing the triangle area to the average boundary triangle area, combining with the incremental inserting algorithm to generate a triangular mesh of complex terrain. After an initial debugging of the mesh, it is determined whether increasing the internal nodes is necessary to insert the triangle centers of gravity. Upon implementing actual mountainous terrain in the simulation software, a comparison of the resulting meshing demonstrates that the proposed method is highly suitable for complex mountainous terrain with significant variations in elevation. Additionally, it effectively improves the quality of the Delaunay triangular mesh and reduces the occurrence of deformed cells during the meshing process. [ABSTRACT FROM AUTHOR]

Background: Cerebral ischemia–hypoxia leads to excitotoxicity‐mediated neuronal damage and cognitive dysfunction, especially in the elderly. Excessive intracellular [Cl−]i accumulation weakens γ‐aminobutyric acid (GABA) compensatory effects. Sub‐anesthetic dose of propofol protected the brain against ischemia–hypoxia, which was abolished by blocking Cl− efflux transporter K+/Cl− cotransporter 2 (KCC2). We aimed to determine whether low‐dose anesthetic combined with [Cl−]i regulators could restore the compensatory GABAergic system and improve cognitive function. Methods: Chronic cerebral hypoxia (CCH) model was established by bilateral carotid artery ligation in aged rats. Sub‐dose of anesthetics (propofol and sevoflurane) with or without KCC2 agonist N‐ethylmaleimide (NEM) or Na+/K+/Cl− cotransporter 1 (NKCC1) antagonist bumetanide (BTN) was administered systemically 30 days post‐surgery. Primary rat hippocampal neuronal cultures were subjected to hypoxic injury with or without drug treatment. Memory function, hippocampal neuronal survival, GABAergic system functioning, and brain‐derived neurotrophic factor (BDNF) expressions were evaluated. Results: Sub‐anesthetic dose of combined propofol (1.2 μg mL−1) and sevoflurane [0.7 MAC (minimum alveolar concentration)] did not aggravate the hypoxic brain injury in rats or cell damage in neuronal cultures. Adding either BTN or NEM protected against hypoxic injury, associated with improved cognitive function in vivo, less intracellular accumulation of [Cl−]i, reduced cell death, restored GABAergic compensation, and increased BDNF expression both in vivo and in vitro. Conclusion: Sub‐anesthetic dose of propofol and sevoflurane is a recommended anesthesia regimen in at‐risk patients. Restoration of [Cl−]i homeostasis and GABAergic could further reduce the brain damage caused by ischemia–hypoxia. [ABSTRACT FROM AUTHOR]

In order to harness a greater share of wind energy resources, offshore wind energy projects are venturing into deep-sea locations. In this progression, the issue of grid integration control becomes increasingly challenging. Traditional Model Predictive Control (MPC) has been introduced in offshore wind energy grid integration control due to its merits, such as not requiring modulators, dispensing with decoupling, incorporating constraint handling, and facilitating online optimization. However, it heavily relies on a model and consequently experiences a substantial loss of control effectiveness in the face of system parameter variations. In light of this, this study presents an active-disturbance-rejection-based three-vector sequence model predictive control approach. This method effectively mitigates the influence caused by changes in system parameters, endowing the system with self-disturbance rejection capabilities and enhancing its fault recovery abilities. The method employs self-disturbance control to track voltage reference values and employs the concept of sequence control to eliminate weighting factors in the cost function. Furthermore, it employs three-vector control to achieve error-free operation. The simulation results confirmed that the proposed method effectively minimizes voltage and power transients. It demonstrated superior control effectiveness and shorter response times, enabling the system to rapidly restore to a stable operational state following disturbances. [ABSTRACT FROM AUTHOR]

Our study majorly utilizes network pharmacology combined with molecular docking to explore the latent active components and associated pivotal targets of Guyuan Decoction (GYD) in the treatment of frequently relapsing nephrotic syndrome (FRNS). All active components and latent targets of GYD were retrieved from TCMSP database. The target genes for FRNS in our research were obtained from the GeneCards database. The drug-compounds-disease-targets (D-C-D-T) network was established using Cytoscape 3.7.1. STRING database was applied to observe the protein interaction. Pathway enrichment analyses (GO and KEGG) were conducted in R software. Moreover, molecular docking was employed to further validate the binding activity. MPC-5 cells were treated with adriamycin to mimic FRNS in vitro and to determine the effects of luteolin on modeled cells. A total of 181 active components and 186 target genes of GYD were identified. Meanwhile, 518 targets related to FRNS were also revealed. Based on the intersection using a Venn diagram, 51 common latent targets were recognized to be associated with active ingredients and FRNS. Additionally, we identified the biological processes and signaling pathways involved in the action of these targets. Molecular docking analyses illustrated that AKT1 and CASP3 interacted with luteolin, wogonin, and kaempferol, respectively. Moreover, luteolin treatment enhanced the viability but inhibited the apoptosis of adriamycin-treated MPC-5 cells via regulating AKT1 and CASP3. Our study forecasts the active compounds, latent targets, and molecular mechanisms of GYD in FRNS, which helps us to understand the action mechanism of GYD in FRNS comprehensive treatment. [ABSTRACT FROM AUTHOR]

With the gradual development of offshore wind farms toward large-scale and long-distance trends, economically efficient methods for correcting and transmitting offshore wind energy have garnered increasing attention. The rational and effective evaluation of offshore wind power collection and transmission methods has become an urgent issue. To address this, a comprehensive evaluation model for collection and transmission systems, considering factors such as network losses, lifecycle costs, and reliability, was established. This proposed model was applied to multiple sets of typical collection and transmission scenarios, utilizing an improved TOPSIS comprehensive evaluation method based on dynamic combination weighting to achieve the comprehensive optimization of collection and transmission schemes. Case studies have validated the feasibility of the comprehensive evaluation model. The results indicate that with an increase in offshore distance, the AC/DC collection and transmission system is superior to the all-AC collection and transmission system, and the all-DC collection system exhibits potential cost advantages. [ABSTRACT FROM AUTHOR]

Renewable energy generation is a manifestation of global economic and societal advancement and serves as a fundamental assurance for humanity's pursuit of sustainable development. However, recent years have witnessed several instances of high-frequency resonance events in high-voltage direct current (HVDC) transmission systems based on modular multilevel converters (MMC), which have resulted in converter station tripping and significant repercussions on the alternating current (AC) grid. This paper addresses the mid-to-high frequency resonance issues prevalent in flexible DC transmission systems employing modular multilevel converters (MMC-HVDC). To tackle these concerns, an impedance model for MMC's AC side is established. Utilizing impedance analysis, the essential factors contributing to the negative damping characteristics of MMC are identified as delay and voltage feedforward loops, predominantly causing negative damping in the frequency range exceeding 400 Hz. In response, a suppression strategy is proposed, involving the incorporation of a multi-band stop filter and virtual impedance. This strategy ensures that within the 0–2000 Hz frequency range, only the impedance phase within 230–430 Hz slightly surpasses 90°. Consequently, the phase difference between MMC's positive-sequence impedance and the AC system impedance is reduced from 222° to 174.7°, thus guaranteeing secure grid operation. Lastly, the accuracy and effectiveness of the theoretical analysis and suppression methodology are verified through the development of an electromagnetic transient model in MATLAB/Simulink, considering delay fluctuations of ±10%. [ABSTRACT FROM AUTHOR]

Metabolic flexibility of muscle tissue describes the adaptive capacity to use different energy substrates according to their availability. The disruption of this ability associates with metabolic disease. Here, using a Drosophila model of systemic metabolic dysfunction triggered by yorkie-induced gut tumors, we show that the transcription factor REPTOR is an important regulator of energy metabolism in muscles. We present evidence that REPTOR is activated in muscles of adult flies with gut yorkie-tumors, where it modulates glucose metabolism. Further, in vivo studies indicate that sustained activity of REPTOR is sufficient in wildtype muscles to repress glycolysis and increase tricarboxylic acid (TCA) cycle metabolites. Consistent with the fly studies, higher levels of CREBRF, the mammalian ortholog of REPTOR, reduce glycolysis in mouse myotubes while promoting oxidative metabolism. Altogether, our results define a conserved function for REPTOR and CREBRF as key regulators of muscle energy metabolism. Obesity and cancer-induced cachexia are linked to an impairment in the ability of muscle to use glucose or lipids interchangeably as energy substrates. Here, the authors propose that Drosophila REPTOR and its mammalian ortholog CREBRF act as key transcriptional regulators of fuel choice in muscle. [ABSTRACT FROM AUTHOR]

Monitoring agricultural abandonment is essential in understanding the effects on the environment and food security. Polarimetric synthetic aperture radar (PolSAR) is an efficient approach for the monitoring of large-scale agricultural land cover in cloudy and rainy areas. However, previous studies have not taken advantage of the valuable phase information and not fully utilized the spatiotemporal features of farmland parcels, which has seriously limited the abandoned land identification accuracy. In this study, we developed a new method for the mapping of abandoned land based on the spatiotemporal features from PolSAR Single Look Complex (SLC) images via deep learning methods. First, backscattering coefficients (σ0VV, σ0VH) were derived, and the polarimetric parameters (entropy, anisotropy and mean alpha angle) were obtained based on Cloude–Pottier polarimetric decomposition. Then, the VGG16 deep convolutional network was innovatively used to extract spatial features from both the backscattering coefficients and polarimetric parameters. Next, the separability index was calculated to select the most effective spatial features. Finally, LSTM classifications were conducted based on the time series of backscattering features, the polarimetric parameters, the extracted spatial features and their combinations. The results showed that the introduction of multitemporal polarimetric parameters and spatial features both led to an improvement in the abandoned land identification accuracy. The combination of backscattering features, polarimetric parameters and spatial features yielded the best performance in identifying abandoned land, with producer's accuracy of 88.29% and user's accuracy of 84.03%. This study demonstrated the potential of polarimetric parameters and validated the effectiveness of spatiotemporal features in abandoned land identification. It provided a practical method for the production of a highly reliable abandoned land mapping in cloudy and rainy areas. [ABSTRACT FROM AUTHOR]

HVDC is an important part of reducing energy transmission losses and maintaining energy sustainability. Commutation failure is the most common fault in HVDC systems, but existing commutation failure analysis approaches for HVDC systems do not consider the effects of instantly increasing direct current on the turn-off angle after an asymmetric fault in the AC system. To address this problem, we developed a commutation failure analysis approach that considers instantaneous variation of the DC current and AC voltage after asymmetrical faults. Firstly, the effects of the AC voltage and the DC current on the turn-off angle and the coupling relationship between the two are analyzed. Secondly, an equivalent mathematical model of the DC line, which covers the reactance, is built in Laplace space. Combined with the phase angle offset generated by the voltage after an asymmetric fault, a single relation expression containing only the AC voltage and turn-off angle is obtained by decoupling the DC current and AC voltage. The critical instantaneous AC voltage leading to system commutation failure is then derived based on the critical turn-off angle. Lastly, based on the CIGRE HVDC model built in the PSCAD electromagnetic transient simulation software (PSCAD v46), the accuracy of the proposed commutation failure analysis method compared with the other two methods is verified via simulation experiments under different grounding impedance values, and the applicability of the proposed method is further verified using simulation experiments with different smoothing reactor parameters. [ABSTRACT FROM AUTHOR]

Background Increasing evidence suggests a high prevalence of cerebral small-vessel disease (CSVD) in hemodialysis patients. Variable ultrafiltration during hemodialysis may contribute to brain lesions by inducing hemodynamic instability. We aimed to investigate the effect of ultrafiltration on CSVD and relative outcome in this population. Methods In a prospective cohort of maintenance hemodialysis adults, three features of CSVD including cerebral microbleed (CMB), lacunae and white matter hyperintensity (WMH) were measured by brain magnetic resonance imaging. Ultrafiltration parameters included the difference between annual average ultrafiltration volume (UV, kg) and 3%–6% of dry weight (kg), respectively, and the percentage of UV to dry weight (UV/W). The effect of ultrafiltration on CSVD and the risk of cognitive decline were investigated by multivariate regression analysis. Cox proportional hazards model was used to assess mortality over 7 years of follow-up. Results In the 119 study subjects, the frequency of CMB, lacunae and WMH was 35.3%, 28.6% and 38.7%, respectively. All ultrafiltration parameters were associated with the risk of CSVD in the adjusted model. There was a 37%, 47% and 41% greater risk of CMB, lacunae, and WMH, respectively, per 1% increment of UV/W. Ultrafiltration showed different effects on different distributions of CSVD. Restricted cubic splines depicted a linear relationship between UV/W and the risk of CSVD. At follow-up, lacunae and WMH were associated with cognitive decline, CMB and lacunae were associated with all-cause mortality. Conclusions UV/W was associated with the risk of CSVD in hemodialysis. Reducing UV/W might protect hemodialysis patients from CSVD and subsequent cognitive decline and mortality. [ABSTRACT FROM AUTHOR]

Background: Intensity‐Modulated Radiation Therapy (IMRT) has been the standard of care for many types of tumors. However, treatment planning for IMRT is a time‐consuming and labor‐intensive process. Purpose: To alleviate this tedious planning process, a novel deep learning based dose prediction algorithm (TrDosePred) was developed for head and neck cancers. Methods: The proposed TrDosePred, which generated the dose distribution from a contoured CT image, was a U‐shape network constructed with a convolutional patch embedding and several local self‐attention based transformers. Data augmentation and ensemble approach were used for further improvement. It was trained based on the dataset from Open Knowledge‐Based Planning Challenge (OpenKBP). The performance of TrDosePred was evaluated with two mean absolute error (MAE) based scores utilized by OpenKBP challenge (i.e., Dose score and DVH score) and compared to the top three approaches of the challenge. In addition, several state‐of‐the‐art methods were implemented and compared to TrDosePred. Results: The TrDosePred ensemble achieved the dose score of 2.426 Gy and the DVH score of 1.592 Gy on the test dataset, ranking at 3rd and 9th respectively in the leaderboard on CodaLab as of writing. In terms of DVH metrics, on average, the relative MAE against the clinical plans was 2.25% for targets and 2.17% for organs at risk. Conclusions: A transformer‐based framework TrDosePred was developed for dose prediction. The results showed a comparable or superior performance as compared to the previous state‐of‐the‐art approaches, demonstrating the potential of transformer to boost the treatment planning procedures. [ABSTRACT FROM AUTHOR]

Background: Single-cell RNA sequencing (scRNA-seq) strives to capture cellular diversity with higher resolution than bulk RNA sequencing. Clustering analysis is critical to transcriptome research as it allows for further identification and discovery of new cell types. Unsupervised clustering cannot integrate prior knowledge where relevant information is widely available. Purely unsupervised clustering algorithms may not yield biologically interpretable clusters when confronted with the high dimensionality of scRNA-seq data and frequent dropout events, which makes identification of cell types more challenging. Results: We propose scSemiAAE, a semi-supervised clustering model for scRNA sequence analysis using deep generative neural networks. Specifically, scSemiAAE carefully designs a ZINB adversarial autoencoder-based architecture that inherently integrates adversarial training and semi-supervised modules in the latent space. In a series of experiments on scRNA-seq datasets spanning thousands to tens of thousands of cells, scSemiAAE can significantly improve clustering performance compared to dozens of unsupervised and semi-supervised algorithms, promoting clustering and interpretability of downstream analyses. Conclusion: scSemiAAE is a Python-based algorithm implemented on the VSCode platform that provides efficient visualization, clustering, and cell type assignment for scRNA-seq data. The tool is available from https://github.com/WHang98/scSemiAAE. [ABSTRACT FROM AUTHOR]

In order to integrate various operation information and accurately explore the operation situation of a power system, a dynamic comprehensive evaluation method considering index screening is proposed, and based on this, a situation awareness (SA) system based on the new energy power system is established. Firstly, aiming at the problem of information overlap among indicators, the existing indicator system of the new energy power system is quantitatively screened and reconstructed by the indicator screening method based on Measure of Sampling Adequacy (MSA) and Kaiser–Meyer–Olkin (KMO) test statistics. Afterwards, the dynamic evaluation method incorporating the subjective and objective is used to calculate the evaluation score and identify the stable operating state of the system. Finally, qualitative trend analysis and judgment are carried out on the evaluation scores, and the effect of power system situational awareness is verified by the improved IEEE 39 system. The results demonstrate that this method can be useful for improving the stability state sensing accuracy of a new energy power system. [ABSTRACT FROM AUTHOR]

Exosomes cargo tumour-characterized biomolecules secreted from cancer cells and play a pivotal role in tumorigenesis and cancer progression, thus providing their potential for non-invasive cancer monitoring. Since cancer cell-derived exosomes are often mixed with those from healthy cells in liquid biopsy of tumour patients, accurately measuring the purity of tumour cell-derived exosomes is not only critical for the early detection but also essential for unbiased identification of diagnosis biomarkers. Here, we propose 'ExosomePurity', a tumour purity deconvolution model to estimate tumour purity in serum exosomes of cancer patients based on microribonucleic acid (miRNA)-Seq data. We first identify the differently expressed miRNAs as signature to distinguish cancer cell- from healthy cell-derived exosomes. Then, the deconvolution model was developed to estimate the proportions of cancer exosomes and normal exosomes in serum. The purity predicted by the model shows high correlation with actual purity in simulated data and actual data. Moreover, the model is robust under the different levels of noise background. The tumour purity was also used to correct differential expressed gene analysis. ExosomePurity empowers the research community to study non-invasive early diagnosis and to track cancer progression in cancers more efficiently. It is implemented in R and is freely available from GitHub (https://github.com/WangHYLab/ExosomePurity). [ABSTRACT FROM AUTHOR]

This paper takes advantage of the high control flexibility and fast response time of the interfacing power electronic converter for doubly fed wind turbine grid-connected systems to address inter-area oscillations caused by inadequate system damping in power systems. A reactive-power-coordinated damping controller for a doubly fed induction generator (DFIG) is proposed, and it makes use of second-order sliding-mode technology. The suggested controller improves damping performance by controlling the reactive power. It provides benefits such as a quicker damping rate and resilience to modeling errors and parameter changes. The simulation results indicate the system's improved performance in inter-area oscillation damping and the robustness of the suggested control technique over a broad range of functional areas. [ABSTRACT FROM AUTHOR]

The twisted Laguerre–Gaussian Schell-model (TLGSM) beam is a novel type of partially coherent beam embedded with both the second-order twist phase and the classical vortex phase. The intriguing properties induced by the interaction of the two types of phases have been demonstrated theoretically quite recently. In this work, we introduce a flexible way to experimentally synthesize a TLGSM beam with controllable twist strength. The protocol relies on the discrete pseudo-mode representation for the cross-spectral density of a TLGSM beam, in which the beam is viewed as an incoherent superposition of a finite number of spatially coherent modes. We show that all these pseudo modes endowed with random phases are mutually uncorrelated and can be encoded into a single frame of a dynamic computer-generated hologram. By sequentially displaying dynamic holograms on a single spatial-light modulator, the controllable TLGSM beam can be synthesized experimentally. The joint effect of the two phases on the propagation and self-reconstruction characteristics of the synthesized beam has also been studied in the experiment. [ABSTRACT FROM AUTHOR]

The electronic structure, magnetic properties, and atomic structure of SrVO 3 / PbTiO 3 superlattices along different crystallographic orientations are investigated using density functional theory. It is revealed that Ti ions in the PbTiO 3 layers exhibit a weak ferromagnetic order. Such ferromagnetism in the PbTiO 3 layers near the interface is related to charge transfer from V to Ti and the ferroelectric polarization of PbTiO 3. The electronic structure of the superlattices indicates that spin-polarized two-dimensional polar metals are obtained for all orientations. In addition, different superlattice orientations correspond to different bandwidths of 3 d orbitals of SrVO 3 due to the dimensional confinement effect. The present work suggests a possible scenario by which two-dimensional polar metals and the coupling between the structural, electronic, and magnetic degrees of freedom in oxide superlattices can be extensively explored. [ABSTRACT FROM AUTHOR]

Coupling the internal electric field and ferroelectric polarization via the modulation of surface electronic structures to achieve high carrier separation and migration efficiency is attractive but challenging in solar-to-chemical energy conversion. Herein, we report that coating ultrathin ZnIn2S4 nanoflakes on BiFeO3 polyhedron microparticles constructs a ferroelectric polarization and internal electric field enhanced Z-scheme heterojunction photocatalyst (BiFeO3/ZnIn2S4). Benefiting from the efficient photogenerated carrier separation and migration processes triggered by powerful spontaneous polarization and internal electric field, respectively, the nanocomposites exhibit strong half-reaction activity over hydrogen and oxygen evolution. Importantly, the optimal nanohybrid photocatalyst (ZB-3) delivered superior photocatalytic overall water splitting activity (H2: 87.3 μmol g−1 h−1, O2: 42.3 μmol g−1 h−1) with an apparent quantum efficiency of 1.12% at 420 nm in pure water under visible light irradiation, the highest activity reported thus far for ferroelectric-based heterojunction photocatalysts. The novel strategy of synergizing internal electric field and ferroelectric polarization via hybridization has been proposed to boost the separation and transfer efficiency of photogenerated charges in ferroelectric materials. [ABSTRACT FROM AUTHOR]

Photodynamic therapy (PDT) has been used clinically to treat cancer for more than 40 years. Some solid tumors, including esophageal cancer, lung cancer, head and neck cancer, cholangiocarcinoma, and bladder cancer, have been approved for and managed with PDT in many countries globally. Notably, PDT for gastric cancer (GC) has been reported less and is not currently included in the clinical diagnosis and treatment guidelines. However, PDT is a potential new therapeutic modality used for the management of GC, and its outcomes and realization are more and more encouraging. PDT has a pernicious effect on tumors at the irradiation site and can play a role in rapid tumor shrinkage when GC is combined with cardiac and pyloric obstruction. Furthermore, because of its ability to activate the immune system, it still has a specific effect on systemic metastatic lesions, and the adverse reactions are mild. In this Review, we provide an overview of the current application progress of PDT for GC; systematically elaborate on its principle, mechanism, and the application of a new photosensitizer in GC; and focus on the efficacy of PDT in GC and the prospect of combined use with other therapeutic methods to provide a theoretical basis for clinical application. [ABSTRACT FROM AUTHOR]

The advances in single-cell ribonucleic acid sequencing (scRNA-seq) allow researchers to explore cellular heterogeneity and human diseases at cell resolution. Cell clustering is a prerequisite in scRNA-seq analysis since it can recognize cell identities. However, the high dimensionality, noises and significant sparsity of scRNA-seq data have made it a big challenge. Although many methods have emerged, they still fail to fully explore the intrinsic properties of cells and the relationship among cells, which seriously affects the downstream clustering performance. Here, we propose a new deep contrastive clustering algorithm called scDCCA. It integrates a denoising auto-encoder and a dual contrastive learning module into a deep clustering framework to extract valuable features and realize cell clustering. Specifically, to better characterize and learn data representations robustly, scDCCA utilizes a denoising Zero-Inflated Negative Binomial model-based auto-encoder to extract low-dimensional features. Meanwhile, scDCCA incorporates a dual contrastive learning module to capture the pairwise proximity of cells. By increasing the similarities between positive pairs and the differences between negative ones, the contrasts at both the instance and the cluster level help the model learn more discriminative features and achieve better cell segregation. Furthermore, scDCCA joins feature learning with clustering, which realizes representation learning and cell clustering in an end-to-end manner. Experimental results of 14 real datasets validate that scDCCA outperforms eight state-of-the-art methods in terms of accuracy, generalizability, scalability and efficiency. Cell visualization and biological analysis demonstrate that scDCCA significantly improves clustering and facilitates downstream analysis for scRNA-seq data. The code is available at https://github.com/WJ319/scDCCA. [ABSTRACT FROM AUTHOR]

Single cell RNA sequencing (scRNA-seq) enables researchers to characterize transcriptomic profiles at the single-cell resolution with increasingly high throughput. Clustering is a crucial step in single cell analysis. Clustering analysis of transcriptome profiled by scRNA-seq can reveal the heterogeneity and diversity of cells. However, single cell study still remains great challenges due to its high noise and dimension. Subspace clustering aims at discovering the intrinsic structure of data in unsupervised fashion. In this paper, we propose a deep sparse subspace clustering method scDSSC combining noise reduction and dimensionality reduction for scRNA-seq data, which simultaneously learns feature representation and clustering via explicit modelling of scRNA-seq data generation. Experiments on a variety of scRNA-seq datasets from thousands to tens of thousands of cells have shown that scDSSC can significantly improve clustering performance and facilitate the interpretability of clustering and downstream analysis. Compared to some popular scRNA-deq analysis methods, scDSSC outperformed state-of-the-art methods under various clustering performance metrics. Author summary: Single cell RNA sequencing (scRNA-seq) data has been widely used in neuroscience, immunology, oncology and other research fields. Cell type recognition is an important goal of scRNA-seq data analysis, in which clustering analysis is commonly used. However, single cell clustering still remains great challenges due to its high noise, dimension and increasing data scale. Considering the advantages of subspace manifold in processing high-dimensional data and the powerful representation learning ability of deep neural network, we proposed a novel single-cell data clustering method scDSSC, which imitates the generation of scRNA-seq data and reduces the dimension and noise of the data at the same time, and finally outputs the clustering results. Experiments on a variety of scRNA-seq datasets from thousands to tens of thousands of cells have shown that scDSSC can significantly improve downstream analysis, including clustering analysis, cell visualization, differential expression analysis and trajectory inference. In addition, scDSSC has good scalability and can handle large-scale scRNA-seq data. [ABSTRACT FROM AUTHOR]

With the rapid expansion of new energy in China, the large-scale grid connection of new energy is increasing, and the operating safety of the new energy power system is being put to the test. The static security and stability region (SSSR) with hyper-plane expression is an effective instrument for situational awareness and the stability-constrained operation of power systems. This paper proposes a hybrid improved particle swarm optimization (IPSO) and recursive least square (RLS) approach for rapidly approximating the SSSR boundary. Initially, the operating point data in the high-dimensional nodal injection space is examined using the IPSO algorithm to find the key generators, equivalent search space, and crucial points, which have a relatively large impact on static stability. The RLS method is ultimately utilized to fit the SSSR border that best suits the crucial spots. Consequently, the adopted algorithm technique was used to rapidly approximate the SSSR border in power injection spaces. Finally, the suggested algorithm is confirmed by simulating three kinds of generators of the new energy 118 bus system using the DIgSILENT/Power Factory. As a result, this method accurately characterized the stability border of the new energy power system and created the visualization space of the SSSR. Using the SSSR, a rapid state analysis could be undertaken on a variety of parameters, such as security evaluation with diverse energy supply capacities. This study's findings confirmed the accuracy and efficacy of the suggested modeling for the considered system and may thus give technical support for the new energy power system's stability. [ABSTRACT FROM AUTHOR]

The chronic kidney disease-mineral and bone disorder（CKD-MBD) is known to be associated with increased mortality in dialysis patients, but whether current global guidelines for CKD-MBD, which were primarily developed from hemodialysis, are suitable for peritoneal dialysis (PD) patients practice require further investigation. This is a single-center retrospective cohort study. In total 491 prevalent PD patients (median follow-ups: 34 months) from Peking Union Medical College Hospital (PUMCH) from January 2004 to December 2017 were included and followed until 30 June 2018. In the first dialysis year, the average levels of serum calcium, albumin-corrected calcium (CorCa), phosphorus, and parathyroid hormone (PTH) levels were the interested predictors in Cox proportional regression model. Of these PD patients (age 58 ± 17 years), 52% were male and 36% had diabetic nephropathy. In Cox regression over first-year mean parameters, PTH <100 pg/mL (HR = 1.97, 95% CI 1.32 to 2.94, p < 0.001) and ≥300 pg/mL (HR = 2.24, 95% CI 1.32 to 3.81, p = 0.003) were associated with increased all-cause mortality than that of PTH 100–200 pg/mL. Patients with albumin-corrected serum calcium level < 2.13 mmol/L also had higher risk of death than patients with level of 2.13 to 2.38 mmol/L (HR = 2.06, 95% CI 1.06 to 4.01, p = 0.02). Serum phosphorus ≥1.45 mmol/L were associated with increased all-cause mortality. However, lacking of data on 25-hydroxy vitamin D, alkaline phosphatase, and activated vitamin-D are limitations of our analysis. As one of the largest PD cohort study focusing on CKD-MBD, we demonstrated that the level of CKD-MBD markers in the first PD year are independent predictors of all-cause mortality. PTH 100–300 pg/mL might be the best target for Chinese PD patients. [ABSTRACT FROM AUTHOR]

In order to solve the problem of heat dissipation in the process of producing hydrogen from electrolytic water and generating electricity from fuel cells, an independent photohydrogen fuel cell thermoelectric combined supply system was designed according to the principle of water cooling heat dissipation between electrolytic cell and fuel cell. Based on this model, a control strategy was formulated to coordinate the output order and size of each module, which solved the inefficiency of electrolytic cell in low power operation zone. The capacity allocation model was established with the objective function of optimal comprehensive cost, and the optimal capacity allocation result of the system was obtained by using the particle swarm algorithm. The simulation results show that the overall cost of the configuration scheme proposed can be reduced by 2.2% compared with the traditional configuration scheme, and it can meet the same starting and ending states of the hydrogen storage tank and the heat storage tank during the operation cycle. The long-term energy supply effect of the system is excellent, which can provide a reference value for the capacity configuration of the current stand-alone photohydrogen fuel cell cogeneration system. [ABSTRACT FROM AUTHOR]

With the increasing proportion of wind power access year by year, it brings many challenges to the voltage stability of power systems. In order to maintain the stability of the voltage in the power grid, it is impossible to take into account the regulation ability and economy when a single reactive power compensation device is installed. In this paper, a combined reactive power compensation device was installed, which is composed of a static var generator (SVG) and a parallel capacitor bank. The SVG has the characteristics of fast and smooth adjustment, and the application of the capacitor bank reduces the overall investment cost and has a great economy. The modal analysis method was used to find the optimal installation position for the reactive power compensation device. The improved particle swarm algorithm was used to optimize the capacity of the optimal reactive power compensation device to ensure the best performance of the compensation device. Finally, by formulating the control strategy of the combined reactive power compensation system, the reliable switching of the compensation device is controlled. The PSCAD simulation software was used to model the power grid in the Hami area, and six different configuration programs were set for static voltage stability simulation verification and three different configurations. The program was simulated and verified for transient voltage stability, and comparative analysis showed that the proposed method was correct, which strongly supports the voltage stability of the region and meets the demand of reactive power compensation of the power grid. This provides a good reference program for other wind power gathering areas. [ABSTRACT FROM AUTHOR]

In Zhu et al. (SIAM J Sci Comput 43: A3009–A3031, 2021), we proposed a new framework of troubled-cell indicator (TCI) using K-means clustering for the discontinuous Galerkin (DG) methods. However, there are two user-tunable parameters in the framework that depend on the polynomial degree of the solution space, the indication variable and even the test problem, which circumscribe the application of the framework. To overcome this drawback, we introduce two simple techniques in this paper: one is to modify the indication variables and the other is to apply a statistical normalization to the modified values. Coupled with four different indication variables, the modified framework is tested via the classical benchmark problems and produces close results under the same setting of the parameters. The discontinuities are overall well captured and the solutions are free of spurious oscillations. The numerical results demonstrate the effectiveness and flexibility of the modified framework and the success in unifying the parameters. Existing TCIs/limiters for the DG methods can be easily implemented into this framework. [ABSTRACT FROM AUTHOR]

This study presents an adaptive proportional-integral (PI) and radial basis function neural network proportional-integral-derivative (PID) current control solution for permanent magnet synchronous motor (PMSM) drives. The proposed controller includes four controls: a decoupling term, a PI term, a supervision term, and a radial basis neural network-PID (RBFNN-PID) term. The first control term makes up the nonlinear factors, the second automatically adjusts the control gains, the third guarantees the system stability, and the fourth optimizes the PID parameters to achieve optimal system performance. Unlike off-line tuned PID controllers, the adaptive controller includes an adaptive tuning method for the on-line adjustment of control gain based on the gradient descent strategy. Therefore, it can be adjusted and handle the uncertainty of any system parameters. The program is not only simple and easy to be implemented but also ensures the accuracy and rapidity of the tracking speed. The control system has proven to be asymptotically stable. To verify the theory and application of the algorithm, a comparative experiment between the adaptive PI + RBFNN-PID controller and traditional PI + PID controller was conducted, demonstrating good robustness, which can effectively improve the PMSM. The results confirm that the proposed design achieves excellent control stability (i.e., quicker transient responding and smaller steady state error) in the presence of parametric uncertainty compared to conventional PID methods. [ABSTRACT FROM AUTHOR]

We selected the Tanchong River in Hefei as the site to test the efficacy of graphene visible‐light photocatalysis (GVLP), a new water treatment technology to improve water quality in black‐odorous rivers in urban landscapes. A model coupling the hydrodynamic force and water quality of the Tanchong River was constructed using the MIKE11 model. The numerical simulation method was used to model the concentrations of the main pollutants—chemical oxygen demand (COD), ammonia nitrogen (NH3–N) and total phosphorus (TP) concentrations. The simulation of water quality in the river section treated by GVLP was verified by the Tanchong River water quality improvement project experiment. The results showed that the MIKE11 model can effectively simulate the effect of GVLP technology on water quality improvement. The removal rates of the main pollutants'—COD, NH3–N, and TP by GVLP were 43.9%, 82.1%, and 76.8%, respectively, thus proving GVLP's viability as a treatment for controlling water pollution in similar black‐odorous rivers. Recommendations for Resource Managers: Described the treatment effect of a new type of water environment treatment technology—graphene photocatalysis technology in actual black‐odorous water. Experiments verified that the technology has a good treatment effect on black‐odorous rivers.MIKE11 model can be used to simulate the pollutant control effect of graphene visible‐light photocatalysis treatment technology in urban landscape rivers, and the simulation results can be used in the design optimization of the actual engineering projects. [ABSTRACT FROM AUTHOR]