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Abstract Background OFA (Opioid-free anesthesia) has the potential to reduce the occurrence of opioid-related adverse events and enhance postoperative recovery. Our research aimed to investigate whether OFA, combining esketamine and dexmedetomidine, could serve as an alternative protocol to traditional OBA (opioid-based anesthesia) in shoulder arthroscopy, particularly in terms of reducing PONV (postoperative nausea and vomiting). Methods A total of 60 patients treated with shoulder arthroscopy from September 2021 to September 2022 were recruited. Patients were randomly assigned to the OBA group (n = 30) and OFA group (n = 30), receiving propofol-remifentanil TIVA (total intravenous anesthesia) and esketamine-dexmedetomidine intravenous anesthesia, respectively. Both groups received ultrasound-guided ISBPB(interscalene brachial plexus block)for postoperative analgesia. Results The incidence of PONV on the first postoperative day in the ward (13.3% vs. 40%, P

Abstract We present unprecedented datasets of current and future projected weather files for building simulations in 15 major cities distributed across 10 climate zones worldwide. The datasets include ambient air temperature, relative humidity, atmospheric pressure, direct and diffuse solar irradiance, and wind speed at hourly resolution, which are essential climate elements needed to undertake building simulations. The datasets contain typical and extreme weather years in the EnergyPlus weather file (EPW) format and multiyear projections in comma-separated value (CSV) format for three periods: historical (2001–2020), future mid-term (2041–2060), and future long-term (2081–2100). The datasets were generated from projections of one regional climate model, which were bias-corrected using multiyear observational data for each city. The methodology used makes the datasets among the first to incorporate complex changes in the future climate for the frequency, duration, and magnitude of extreme temperatures. These datasets, created within the IEA EBC Annex 80 “Resilient Cooling for Buildings”, are ready to be used for different types of building adaptation and resilience studies to climate change and heatwaves.

A virtual power plant (VPP) has the ability to aggregate numerous decentralized distributed energy resources using advanced control technology, offering a promising approach for low-carbon development. In order to enhance the VPP’s contribution to reducing carbon emissions, a bi-level framework is proposed that incorporates an integrated energy-carbon price response mechanism. This model allows VPPs to participate in a multi-energy system through a multi-agent Stackelberg game framework. Initially, a transaction model is established where the power distribution system operator and the gas distribution system operator act as leaders, while the virtual power plant operator acts as a follower in the multi-energy system. Subsequently, an integrated energy-carbon pricing method, rooted in carbon emission flow theory, is introduced to encourage VPPs to proactively adjust their energy-use and trading strategies within multi-energy systems, thereby promoting multi-principal interactive trading. To achieve a distributed solution among multiple entities while maintaining the privacy of each entity’s information, the adaptive step-size alternating direction multiplier method is employed. The feasibility and effectiveness of the proposed model and method are then demonstrated through case studies.

Rare earth (RE) silicate environmental barrier coatings (EBCs), with superior physicochemical properties and resistance to hot water-oxygen and calcium-magnesium-aluminosilicate (CMAS) corrosion, are ideal candidates for safeguarding the next-generation aviation engines made of SiC ceramic matrix composites (CMCs). However, improving the corrosion resistance of RE silicates remains a critical challenge due to severe environmental degradation at 1500 °C. Herein, by doping multiple RE elements and designing high entropy formulations, researchers found that multicomponent RE silicates could establish stable lattice structures, phase compositions, and reaction products while impeding the infiltration of corrosive agents. This paper reviews the modulation of their phase stability, physicochemical properties, wettability, and corrosion behaviors. It looks at how state-of-the-art optimization solutions of multicomponent and high entropy doping strategies affected corrosion resistant behaviors. In conclusion, the authors suggest further studies to optimize the corrosion resistance of multicomponent RE silicates and highlight the importance of integrating multicomponent doping with high-throughput and other optimization methods, thereby unlocking an array of possibilities for advancing RE silicate EBCs.

With the frequent occurrence of natural disasters, the problem of dam failure with low probability and high risk has gradually attracted people's attention. This paper uses flume model tests to systematically analyze the overtopping failure mechanisms of concrete face rockfill dam (CFRD) and identify its failure modes. The tests reveal that the longitudinal erosion of the CFRD breach progress through stages of soil erosion, panel failures, and water flow stabilization. Meanwhile, the cross-section breach process involves the evolution of breach size in rockfill materials, including traceable erosion, lateral broadening, and breach morphology stabilization. The fracture characteristics of the water-blocking panel are primarily evident in the flow-time curve. By analyzing the breach morphology evolution processes in longitudinal and cross sections, the flow-time curve can be subdivided into stages of burst flow formation, breach expansion with flow increase, rapid increase of breach flow discharge due to panel failures, and stabilization of breach flow and size. The primary damage process of the CFRD occurs in a cyclical stage of breach expansion, flow increase, panel failure, and rapid discharge. The rigid face plate and granular body structure contribute to partial dam failure, showing a tendency for gradual expansion of the breach. The longitudinal section illustrates dam failure resulting from panel fracture and rockfill erosion interaction, while downstream slopes exhibit failure due to lateral intrusion of rockfill and cyclic instability. These research results can serve as a reference for constructing a concrete CFRD failure prediction model and conducting disaster risk assessments.

Per- and poly-fluoroalkyl substances (PFAS) are a group of organo-fluorine compounds that have been broadly used in consumer and industrial products spanning virtually all sectors. They can be found as surfactants, coatings and liners, polymer additives, fire retardants, adhesives, and many more. The chemical stability of the carbon fluorine bond and amphiphilic nature of PFAS result in their persistence and mobility in the environment via soil porewater, surface water and groundwater, with potential for adverse effects on the environment and human health. There is an emergent and increasing requirement for fast, low-cost, robust, and portable methods to detect PFAS, especially in the field. There may be thousands of PFAS compounds present in soil and water at extremely low concentration (0.01–250 ppb) that require measurement, and traditional technologies for continuous environmental sensing are challenged due to the complexity of soil chemistry. This paper presents a comprehensive review of potentially rapid PFAS measurement methods, focused on techniques for representative sampling of PFAS in porewater from contaminated soil, and approaches for pre-treatment of porewater samples to eliminate these interferences to be ready for PFAS-detecting sensors. The review discusses selectivity, a key factor underlying pre-treatment and sensing performance, and explores the interactions between PFAS and various sensors. PFAS chemical nano-sensors discussed are categorized in terms of the detection mechanism (electrochemical and optical). This review aims to provide guidance and outline the current challenges and implications for future routine PFAS sensing linked to soil porewater collection, to achieve more selective and effective PFAS sensors.

Summary: Signal-regulatory protein alpha (SIRPα) has recently been found to be highly expressed in podocytes and is essential for maintaining podocyte function. However, its immunoregulatory function in podocytes remains elusive. Here, we report that SIRPα controls podocyte antigen presentation in specific T cell activation via inhibiting spleen tyrosine kinase (Syk) phosphorylation. First, podocyte SIRPα under lupus nephritis (LN) conditions is strongly downregulated. Second, podocyte-specific deletion of SIRPα exacerbates renal disease progression in lupus-prone mice, as evidenced by an increase in T cell infiltration. Third, SIRPα deletion or knockdown enhances podocyte antigen presentation, which activates specific T cells, via enhancing Syk phosphorylation. Supporting this, Syk inhibitor GS-9973 prevents podocyte antigen presentation, resulting in a decrease of T cell activation and mitigation of renal disease caused by SIRPα knockdown or deletion. Our findings reveal an immunoregulatory role of SIRPα loss in promoting podocyte antigen presentation to activate specific T cell immune responses in LN.

The low carbon park islanded microgrid faces operational challenges due to the high variability and uncertainty of distributed renewable energy sources. These sources cause severe random disturbances that impair the frequency control performance and increase the regulation cost of the islanded microgrid, jeopardizing its safety and stability. This paper presents a data-driven intelligent load frequency control (DDI-LFC) method to address this problem. The method replaces the conventional LFC controller with an intelligent agent based on a deep reinforcement learning algorithm. To adapt to the complex islanded microgrid environment and achieve adaptive multi-objective optimal frequency control, this paper proposes the quantum-inspired maximum entropy actor-critic (QIS-MEAC) algorithm, which incorporates the quantum-inspired principle and the maximum entropy exploration strategy into the actor-critic algorithm. The algorithm transforms the experience into a quantum state and leverages the quantum features to improve the deep reinforcement learning’s experience replay mechanism, enhancing the data efficiency and robustness of the algorithm and thus the quality of DDI-LFC. The validation on the Yongxing Island isolated microgrid model of China Southern Grid (CSG) demonstrates that the proposed method utilizes the frequency regulation potential of distributed generation, and reduces the frequency deviation and generation cost.

In space flexible solar arrays, stranded Ag-plated Cu conductors and Ag interconnectors are preferred materials for energy transmission. Parallel gap resistance welding is a solderless micro-joining process that favors the reliability of joints between stranded Ag-plated Cu conductors and Ag interconnectors. It was found the interfacial microstructure presented diversity and complexity while mechanical and electrical properties showed unusual nonlinear variations with welding voltage. This study aims to clarify the interfacial microstructure evolution and its effects on the joint properties while elucidating the microstructure formation mechanisms. A shift in the bonding mechanism at interfaces from solid-state diffusion to brazing was found as welding voltage reached a critical value (1.5 V). It eliminated micro gaps and built nano-scale interlocking structures, resulting in substantial improvement in mechanical properties. Increasing the welding voltage from 1.2 V to 1.4 V improved electrical conductivity due to the enlarged bonding area at interfaces. However, high welding voltage (1.6 V) led to degradation in the electrical conductivity of joints due to excessive Ag-Cu solid solution formed at interfaces. The key to fabricating high-strength and high-conductivity joints lies in achieving appropriate interfacial melting while reducing alloying by controlling peak temperature and shortening the duration above the Ag-Cu eutectic point.

As global temperatures rise and climate change becomes more severely. People realize that air conditioning systems as a controllable resource and play an increasingly important role in reducing carbon emissions. In the past, the operation optimization of air conditioning systems was mainly oriented to user comfort and electricity costs ignoring the long-term impact on the environment. This article aims to establish a multi-objective model of air-conditioning load to ensure user temperature comfort performance and reduce the total cost (i.e., electricity cost and carbon emission cost) simultaneously. Multi Sand Cat Swarm Optimization (MSCSO) algorithm combined with gray target decision-making (GTD) is used to explore optimal solution. Meanwhile four competitive strategies are applied to validate the effectiveness of the proposed method, i.e., genetic algorithm (GA), MSCSO-comfort objective, MSCSO-total electricity cost objective and unoptimization. The simulation results show that the MSCSO-GTD based objective method can significantly reduce total costs while taking into account appropriate indoor temperature comfort.

Reliability of joints in solar arrays significantly influences the service life of satellites. Interface between solar cell and interconnector experiences serious temperature cycling during space service which would further lead to failure. To further improve the interface joining thermal reliability, elucidation of the interface formation and corresponding microstructure evolution during thermal fatigue is necessary. In this study, parallel gap resistance welded (PGRW) multi-layered joint between GaAs solar cell and Ag foil are subjected to different temperature cycling tests (−160–120 °C, −165–160 °C) with various cycles. Obtained results confirm the joining mechanism of the joint as solid-solution interdiffusion between Ag foil and Au surface of solar cell electrode. Also, conducted temperature cycling essentially lead to thermal fatigue process at Ag/Au interface, therefore more serious interface strength degradation is generated by larger temperature cycling range. Joint failure is initiated by thermal fatigue induced dislocation and residual strain concentrations around dissimilar interface. And the large mismatch in coefficients of thermal expansion (CTE) of the multilayer structure amplifies the thermal fatigue effect.

Abstract Background Lentinan (LNT) is a complex fungal component that possesses effective antitumor and immunostimulating properties. However, there is a paucity of studies regarding the effects and mechanisms of LNT on type 1 diabetes. Objective In the current study, we investigated whether an intraperitoneal injection of LNT can diminish the risk of developing type 1 diabetes (T1D) in non-obese diabetic (NOD) mice and further examined possible mechanisms of LNT’s effects. Methods: Pre-diabetic female NOD mice 8 weeks of age, NOD mice with 140–160 mg/dL, 200–230 mg/dL or 350–450 mg/dL blood glucose levels were randomly divided into two groups and intraperitoneally injected with 5 mg/kg LNT or PBS every other day. Then, blood sugar levels, pancreas slices, spleen, PnLN and pancreas cells from treatment mice were examined. Results Our results demonstrated that low-dosage injections (5 mg/kg) of LNT significantly suppressed immunopathology in mice with autoimmune diabetes but increased the Foxp3+ regulatory T cells (Treg cells) proportion in mice. LNT treatment induced the production of Tregs in the spleen and PnLN cells of NOD mice in vitro. Furthermore, the adoptive transfer of Treg cells extracted from LNT-treated NOD mice confirmed that LNT induced Treg function in vivo and revealed an enhanced suppressive capacity as compared to the Tregs isolated from the control group. Conclusion LNT was capable of stimulating the production of Treg cells from naive CD4 + T cells, which implies that LNT exhibits therapeutic values as a tolerogenic adjuvant and may be used to reverse hyperglycaemia in the early and late stages of T1D.

Existing vehicle-to-grid (V2G) applications are aimed at the power grid and the government. It is difficult for charging stations (CSs) to execute the schedules in real time. To figure out the multiple-layer energy management from the perspective of CS, the dispatch potential assessment model is constructed based on the EV users’ charging demand and Minkowski summation. And the optimal energy management schedule model of CS with ESS is proposed considering peak shaving and valley filling under the time-in-use tariff. Besides, the real-time charging control model of EVs in CS is designed under the premise of meeting the charging needs. The simulation results show that the proposed strategy can promote CS operation revenues and track the scheduling plan of CS. The arbitrage of tariffs and peak shaving ancillary services are realized while the charging loads of CSs are smoothed by the charging/discharging of ESS. The proposed strategy is applicable for the CS aggregators and can help the grid operators for dispatch schedules.

A space solar array includes GaAs solar cell, interconnector, and stranded Cu wire. Due to the preferential high working efficiency, the assembly of solar array is mostly performed using parallel gap resistance welding (PGRW). Since strengthening of the PGRW joints is the key to further extend service life of solar array, it is necessary to elucidate joining mechanism of each connection. In the present research, to clarify the controversy over PGRW joining mechanism between Ag-plated Kovar interconnector and stranded Ag-plated Cu wire, various advanced material characterization methods are conducted to joining interfaces. Experimental results confirm that, in addition to the known Ag/Cu eutectic interface, solid evidence of metallurgy bonding is also found in Cu/Cu interface. Such finding has significant implication for further enlarge the PGRW process window and produce stronger joints.

When space solar cell array is subjected to harsh temperature cycle, such as planet orbit, thermal fatigue cracks in bonding area are easily induced. With the aim of improving bonding quality and elucidating failure mechanism of parallel gap resistance welding (PGRW) joints in temperature cycling environment, the present research investigates the effect of current density on bonding quality and thermal fatigue behavior of PGRW joint between Ag interconnector and front electrode of GaAs solar cell. When current density is set at 417 A/mm2, a solid diffusion bonding is achieved at the Ag/Au interface, which also possesses adequate joint strength as ensured by both pressure and input energy of PGRW. Crack initiation by thermal fatigue is found at joint edge, which subsequently propagates along the interface as the environment temperature cycling continues. Further investigation reveals that the conducted temperature cycling generates serious tensile and compressive stress in the multi-layered joint structure. Since such reciprocating forces directly induce micro-plastic deformation and strain accumulation at joint interface, failure by crack is finally generated at the joining interface.

Magnaporthe oryzae causes rice blasts posing serious threats to food security worldwide. During infection, M. oryzae utilizes several transmembrane receptor proteins that sense cell surface cues to induce highly specialized infectious structures called appressoria. However, little is known about the mechanisms of intracellular receptor tracking and their function. Here, we described that disrupting the coat protein complex II (COPII) cargo protein MoErv14 severely affects appressorium formation and pathogenicity as the ΔMoerv14 mutant is defective not only in cAMP production but also in the phosphorylation of the mitogen-activated protein kinase (MAPK) MoPmk1. Studies also showed that either externally supplementing cAMP or maintaining MoPmk1 phosphorylation suppresses the observed defects in the ΔMoerv14 strain. Importantly, MoErv14 is found to regulate the transport of MoPth11, a membrane receptor functioning upstream of G-protein/cAMP signaling, and MoWish and MoSho1 function upstream of the Pmk1-MAPK pathway. In summary, our studies elucidate the mechanism by which the COPII protein MoErv14 plays an important function in regulating the transport of receptors involved in the appressorium formation and virulence of the blast fungus.

In this study, the magnetic behavior and magnetocaloric effect (MCE) of Dy18Er18Y18Ni26Al20 and Dy13.5Er13.5Y13.5Gd13.5Ni26Al20 high-entropy bulk metallic glasses (BMGs) were systematically investigated, as well as Dy27Er27Ni26Al20 and Dy54Ni26Al20 medium-entropy BMGs. Distinct spin glass-like behavior below the Curie temperature and large MCE at cryogenic temperature were observed in all BMGs. Competitive magnetic entropy change values of 10.6 and 10.2 J kg−1K−1 were obtained for the Dy27Er27Ni26Al20 and Dy18Er18Y18Ni26Al20 BMGs, respectively, under a magnetic field change of 5 T. Accordingly, the effect of configuration entropy on the magnetic response and MCE was discussed. It was found that the magnetic phase transition temperature, magnetic hysteresis and refrigeration capacity can be altered by utilization of configuration entropy, which can be ascribed to the change in random magnetic anisotropy and magnetic interactions. However, no distinct difference in the magnetic entropy change was observed from the ternary Dy54Ni26Al20 to senary Dy13.5Er13.5Y13.5Gd13.5Ni26Al20 BMGs with increasing configuration entropy. The results reveal that the typical cocktail effect of high-entropy alloys is not applicable to magnetic entropy changes in these BMGs. Nevertheless, all BMGs in this study show good glass-forming ability and MCE which make them promising refrigerant candidates at cryogenic temperatures.

Objective We built a prediction model of mortality risk in patients the with Acinetobacter baumannii (AB)-caused hospital-acquired (HAP) and ventilator-associated pneumonia (VAP). Methods In this retrospective study, 164 patients with AB lower respiratory tract infection were admitted to the respiratory intensive care unit (RICU) from January 2019 to August 2021 (29 with HAP, 135 with VAP) and grouped randomly into a training cohort (n = 115) and a validation cohort (n = 49). Least absolute shrinkage and selection operator regression and multivariate Cox regression were used to identify risk factors of 90-day mortality. We built a nomogram prediction model and evaluated model discrimination and calibration using the area under the receiver operating characteristic curve (AUC) and calibration curves, respectively. Results Four predictors (days in intensive care unit, infection with carbapenem-resistant AB, days of carbapenem use within 90 days of isolating AB, and septic shock) were used to build the nomogram. The AUC of the two groups was 0.922 and 0.823, respectively. The predictive model was well-calibrated; decision curve analysis showed the proposed nomogram would obtain a net benefit with threshold probability between 1% and 100%. Conclusions The nomogram model showed good performance, making it useful in managing patients with AB-caused HAP and VAP.

In recent years, the residents’ demands for power supply is increasing. The load resource trading system responds to the demands through intelligent scheduling, which can effectively relieve the severe power load pressure. The load resource trading system is a type of nonlinear system because the trading price is adjustable with user’s credit, instead of being linear to the power trading volume. The adjustable load resource trading is faced with the challenges of large demands, strong user autonomy, and secure and tamper-proof transaction data. The blockchain technology has been widely concerned by industrial and academic domains due to its decentralization, strong encryption of account information, and traceability of transaction behaviour. In this paper, we propose a credible and adjustable load resource trading framework based on blockchain, which uses blockchain to achieve credible grid load resource trading. Firstly, we propose a two-layer blockchain architecture based on the alliance chain. The main chain maintains all the data of the system, and the station-area nodes constitute the alliance chain. We design a distributed trading processing mechanism based on hybrid consensus and sharding technologies, which promotes the speed of cross-station transaction consensus. Next, we propose a two-level bidding model, which determines the trading price of load resources based on the maximum benefit of users and the lowest cost of grid companies. The results of extensive experiments shows that our proposed framework can achieve the promising result.

Graphical AbstractOverview of the study design. (A) We firstly identified five GBM progression related pathways. By performing functional enrichment analysis on gene sets obtained from three perspectives, such as genes co-survival in TCGA-GBM and CGGA cohort, DEGs of high and low risk groups, and DEGs of IDH1 mutation compared with wild type, we identified five pathways significantly associated with poor prognosis in GBM patients. (B) Secondly, GBM TME-associated functional gene signatures were constructed. Based on the activity profile of these signatures, GBM patients were classified into four distinct subtypes and immunosuppressive subtypes were found. (C) the expression of hub genes from immunosuppressive subtypes were validated in three single-cell RNA-seq datasets, and cell types significantly associated with TME subtypes were identified. The interactions between certain cell types were also elaborated.

Abstract The increasing number and types of industrial tasks require factories to be more flexible in production. An improved discrete cuckoo search algorithm (CSA) is proposed and used to optimise the mixed no‐idle permutation flow shop scheduling problem (MNPFSP). This problem considers MNPFSP energy consumption (MNPFSP_EC) an optimisation objective. Firstly, according to the characteristics of the individual update formula in the two stages of the standard CSA, the paper replaces the real number calculation or vector calculation in the original update formula with a discrete operation to keep the update mechanism of each stage unchanged. The change allows the algorithm to directly find a feasible solution in the discrete solution space that significantly improves the global search capability of cuckoo search. Secondly, an adaptive‐starting local search based on quasi‐entropy (QE) is constructed using swap, insert and 2‐OPT operations with an exploitation that is adaptively executed based on QE, and QE is used to represent the diversity of population and control individuals in deciding whether to execute local search, thereby reducing computational complexity. Simulation experiments and comparisons of different instances demonstrate that the proposed algorithm can effectively solve MNPFSP_EC.

Saccharopolyspora is an important microorganism in the fermentation process of wheat qu and huangjiu, yet the mechanisms by which it performs specific functions in huangjiu remain unclear. A strain with high amylase and glucoamylase activities was isolated from wheat qu and identified as Saccharopolyspora rosea (S. rosea) A22. We initially reported the whole genome sequence of S. rosea A22, which comprised a circular chromosome 6,562,638 bp in size with a GC content of 71.71%, and 6,118 protein-coding genes. A functional genomic analysis highlighted regulatory genes involved in adaptive mechanisms to harsh conditions, and in vitro experiments revealed that the growth of S. rosea A22 could be regulated in response to the stress condition. Based on whole-genome sequencing, the first genome-scale metabolic model of S. rosea A22 named iSR1310 was constructed to predict the growth ability on different media with 91% accuracy. Finally, S. rosea A22 was applied to huangjiu fermentation by inoculating raw wheat qu, and the results showed that the total higher alcohol content was reduced by 12.64% compared with the control group. This study has elucidated the tolerance mechanisms and enzyme-producing properties of S. rosea A22 at the genetic level, providing new insights into its application to huangjiu.

Abstract Background 99mTc bone scintigraphy (BS) is still the most common approach for the evaluation of bone metastasis in China. The purpose of this study was to investigate the necessity of BS as part of a routine preoperative workup for patients with cT1N0 subsolid lung cancer. Methods This was a prospective multicenter clinical trial (NCT03689439). Patients with cT1N0 subsolid nodules who were candidates for surgical resection were consecutively enrolled into the study. BS was performed preoperatively. The surgical plan could be changed if a positive result was detected. The primary endpoint was the incidence rate of the surgical plan being changed because of positive BS results. The secondary endpoint was the rate of positive BS findings and the rate of related complications. Results From November 2018 to July 2019, 691 patients were enrolled into the study. None of the patients had positive BS results and no surgical plans were changed by BS findings. There were 222 male and 469 female patients. The average age was 54.8 ± 3.7 years old. The average tumor diameter was 14.9 ± 4.2 mm. There were 282 patients with pure GGO nodules and 409 with part‐solid nodules. A total of 470 patients had a single nodule, while 221 patients had multifocal lesions. The number of patients whose pathological diagnosis was invasive adenocarcinoma, minimally invasive adenocarcinoma, adenocarcinoma in situ and mucinous adenocarcinoma was 357, 293, 32 and nine, respectively. The number of patients who underwent lobectomy, segmentectomy and wedge resection was 234, 199 and 258, respectively. Conclusions 99mTc bone scintigraphy is unnecessary in the preoperative workup for patients with cT1N0 subsolid lung cancer. Key points Significant findings of the study In this prospective study of 691 patients with cT1N0 subsolid lung cancer, no surgical plans were affected by positive bone scan findings. What this study adds We suggest physicians consider canceling BS from preoperative workup for cT1 subsolid lung cancer patients. Clinical trial registry number: NCT03689439.

Abstract Sb‐based oxides electrodes have received tremendous attention owing to their high theoretical capacities and electrochemical activities. However, the huge volume change during ion insertion/extraction process and poor electronic conductivity limit their wide applications in the electrochemical energy storage. In this study, SbO2/Sb2O3 nanoparticles embedded in N‐doped carbon fibers are successfully synthesized. The composite can display a high reversible specific capacity of 622 mAh g−1 for lithium‐ion batteries and a desirable reversible capacity 307 mAh g−1 for sodium‐ion batteries at current density of 100 mA g−1. Such superior lithium and sodium storage performance can be ascribed to that carbon fibers can effectively restrict the large volume change and enhance the electrochemical stability.

Convolutional neural networks (CNN) have achieved excellent performance for the hyperspectral image (HSI) classification problem due to better extracting spectral and spatial information. However, CNN can only perform convolution calculations on Euclidean datasets. To solve this problem, recently, the graph convolutional neural network (GCN) is proposed, which can be applied to the semisupervised HSI classification problem. However, the GCN is a direct push learning method, which requires all nodes to participate in the training process to get the node embedding. This may bring great computational cost for the hyperspectral data with a large number of pixels. Therefore, in this article, we propose an inductive learning method to solve the problem. It constructs the graph by sampling and aggregating (GraphSAGE) feature from a node's local neighborhood. This could greatly reduce the space complexity. Moreover, to enhance the classification performance, we also construct the graph using spectral and spatial information (spectra-spatial GraphSAGE). Experiments on several hyperspectral image datasets show that the proposed method can achieve better classification performance compared with state-of-the-art HSI classification methods.

In the era of big data, industrial process data are often generated rapidly in the form of streams. Thus, how to process such sequential and high-speed stream data in real time and provide critical quality variable predictions has become a critical issue for facilitating efficient process control and monitoring in the process industry. Traditionally, soft sensor models are usually built through offline batch learning, which remain unchanged during the online implementation phase. Once the process state changes, soft sensors built from historical data cannot provide accurate predictions. In practice, industrial process data streams often exhibit characteristics such as nonlinearity, time-varying behavior, and label scarcity, which pose great challenges for building high-performance soft sensor models. To address this issue, an online-dynamic-clustering-based soft sensor (ODCSS) is proposed for industrial semi-supervised data streams. The method achieves automatic generation and update of clusters and samples deletion through online dynamic clustering, thus enabling online dynamic identification of process states. Meanwhile, selective ensemble learning and just-in-time learning (JITL) are employed through an adaptive switching prediction strategy, which enables dealing with gradual and abrupt changes in process characteristics and thus alleviates model performance degradation caused by concept drift. In addition, semi-supervised learning is introduced to exploit the information of unlabeled samples and obtain high-confidence pseudo-labeled samples to expand the labeled training set. The proposed method can effectively deal with nonlinearity, time-variability, and label scarcity issues in the process data stream environment and thus enable reliable target variable predictions. The application results from two case studies show that the proposed ODCSS soft sensor approach is superior to conventional soft sensors in a semi-supervised data stream environment.

In eukaryotes, Maf1 is an essential and specific negative regulator of RNA polymerase (Pol) III. Pol III, which synthesizes 5S RNA and transfer RNAs (tRNAs), is suppressed by Maf1 under the conditions of nutrient starvation or environmental stress. Here, we identified M. oryzae MoMaf1, a homolog of ScMaf1 in budding yeast. A heterogeneous complementation assay revealed that MoMaf1 restored growth defects in the ΔScmaf1 mutant under SDS stress. Destruction of MoMAF1 elevated 5S rRNA content and increased sensitivity to cell wall agents. Moreover, the ΔMomaf1 mutant exhibited reduced vegetative growth, conidiogenesis, and pathogenicity. Interestingly, we found that MoMaf1 underwent nuclear-cytoplasmic shuffling, through which MoMaf1 accumulated in nuclei under nutrient deficiency or upon the interaction of M. oryzae with rice. Therefore, this study can help to elucidate the pathogenic molecular mechanism of M. oryzae.

This paper considers two kinds of stochastic reentrant job shop scheduling problems (SRJSSP), i.e., the SRJSSP with the maximum tardiness criterion and the SRJSSP with the makespan criterion. Owing to the NP-complete complexity of the considered RJSSPs, an effective differential evolutionary algorithm (DEA) combined with two uncertainty handling techniques, namely, DEA_UHT, is proposed to address these problems. Firstly, to reasonably control the computation cost, the optimal computing budget allocation technique (OCBAT) is applied for allocating limited computation budgets to assure reliable evaluation and identification for excellent solutions or individuals, and the hypothesis test technique (HTT) is added to execute a statistical comparison to reduce some unnecessary repeated evaluation. Secondly, a reentrant-largest-order-value rule is designed to convert the DEA’s individual (i.e., a continuous vector) to the SRJSSP’s solution (i.e., an operation permutation). Thirdly, a conventional active decoding scheme for the job shop scheduling problem is extended to decode the solution for obtaining the criterion value. Fourthly, an Insert-based exploitation strategy and an Interchange-based exploration strategy are devised to enhance DEA’s exploitation ability and exploration ability, respectively. Finally, the test results and comparisons manifest the effectiveness and robustness of the proposed DEA_UHT.

Time series classification is an essential task in many real-world application domains. As a popular deep learning network, convolutional neural networks have achieved excellent performance in time series classification tasks. The filters of the convolutional neural networks are fixed length and shared by each sample. However, each time series usually has different time scale features. Therefore, convolutional neural networks are not capable of extracting multi-scale features for each sample flexibly. In this paper, we propose dynamic multi-scale convolutional neural network to extract multi-scale feature representations existing in each time series dynamically. Specifically, we design a variable-length filters generator to produce a set of variable-length filters conditioned on the input time series. To make model differentiable, we use the learnable soft masks to control the lengths of variable-length filters. Therefore, the feature representations of different time scales can be captured through the variable-length filters. Then, the max-over-time pooling is used to select the most discriminative local patterns. Finally, the fully connected layer with softmax output is employed to calculate the final probability distribution for each class. Experiments conducted on extensive time series datasets show that our approach can improve the performance of time series classification through the learning of variable-length filters. Furthermore, we demonstrate the effectiveness of dynamically learning variable-length filters for each sample through the visualization analysis.

The Echo State Networks (ESNs) is an efficient recurrent neural network consisting of a randomly generated reservoir (a large number of neurons with sparse random recurrent connections) and a trainable linear layer. It has received widespread attention for its simplicity and effectiveness, especially for time series prediction tasks. However, there is no explicit mechanism in ESNs to capture the inherent multi-scale characteristics of time series. To this end, we propose a model consisting of multi-reservoir structure named long-short term echo state networks (LS-ESNs) to capture the multi-scale temporal characteristics of time series. Specifically, LS-ESNs consists of three independent reservoirs, and each reservoir has recurrent connections of a specific time-scale to model the temporal dependencies of time series. The multi-scale echo states are then collected from each reservoir and concatenated together. Finally, the concatenated echo states representations are fed to the linear regression layer to obtain the results. Experiments on two time series prediction benchmark data sets and a real-world power load data sets demonstrate the effectiveness of the proposed LS-ESNs.

Hyperspectral unmixing is an important technique for remote sensing image analysis. Among various unmixing techniques, nonnegative matrix factorization (NMF) shows unique advantage in providing a unified solution with well physical interpretation. In order to explore the geometric information of the hyperspectral data, graph regularization is often used to improve the NMF unmixing performance. It groups neighboring pixels, uses groups as graph vertices, and then assigns weights to connected vertices. The construction of neighborhood and the weights are normally determined by k-nearest neighbors or heat kernel in a deterministic process, which do not fully reveal the structural relationships among data. In this article, we introduce an adaptive graph to regularize a multilayer NMF (AGMLNMF) model for hyperspectral unmixing. In AGMLNMF, a graph is constructed based on the probabilities between neighbors. This enables the optimal neighborhood be automatically determined. Moreover, the weights of the graph are assigned based on the relationships among neighbors, which reflects the intrinsic structure of the complex data. Experiments on both synthetic and real datasets show that this method has outperformed several state-of-the-art unmixing approaches.

Federated Learning (FL) enables multiple clients to train a shared model collaboratively without sharing any personal data. However, selecting a model and adapting it quickly to meet user expectations in a large-scale FL application with heterogeneous devices is challenging. In this paper, we propose a model selection and adaptation system for Federated Learning (FedMSA), which includes a hardware-aware model selection algorithm that trades-off model training efficiency and model performance base on FL developers’ expectation. Meanwhile, considering the expected model should be achieved by dynamic model adaptation, FedMSA supports full automation in building and deployment of the FL task to different hardware at scale. Experiments on benchmark and real-world datasets demonstrate the effectiveness of the model selection algorithm of FedMSA in real devices (e.g., Raspberry Pi and Jetson nano).

Aiming at reducing the total energy consumption of three stages processing-transportation-assembly in the assembly manufacturing industry, a three-stage multiobjective integrated scheduling problem with job batch transportation considering the energy consumption (3sMISP_JBTEC) is proposed, and a comprehensive energy consumption model of multistage of 3sMISP_JBTEC with an improved turn off/on strategy in the processing stage and considering speed in the transportation stage is formulated. Then, a hybrid estimation of distribution algorithm with variable neighborhood search (HEDA_VNS) is developed to solve the scheduling problem. In the HEDA_VNS, the reasonable coding/decoding rules and speed scheduling scheme (SSS) are designed. Moreover, two local search strategies are designed to further enhance the performance of HEDA_VNS. Among them, three types of neighborhood search strategies are devised in Local Search I to improve the search efficiency while retaining the structure of the original high-quality solution. A variable neighborhood hybrid operation based on the speed scheduling set is designed in Local Search II to further improve the quality of the solution while balancing the optimization goals. Finally, simulations and comparisons show the efficiency of the proposed HEDA_VNS.

Background. MicroRNAs (miRNAs) have been demonstrated to exhibit important regulatory roles in multiple malignancies, including hepatocellular carcinoma (HCC). hsa-miR-497-5p was reported to involve in cancer progression and poor prognosis in many kinds of tumors. However, the expression and its clinical significance of hsa-miR-497-5p in HCC remain unclear. Methods. In the present study, we investigated the expression of hsa-miR-497-5p in HCC and analyzed the correction of clinical features with prognosis. The expression levels of hsa-miR-497-5p and potential target genes were analyzed in HCC and adjacent noncancerous tissues using The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) datasets. Real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to analyze hsa-miR-497-5p levels in 328 HCC tissues and 30 paired adjacent noncancer tissues. Overall survival (OS) and progression-free survival (PFS) of patients with HCC were assessed using the Kaplan-Meier method and the log-rank test. Results. The hsa-miR-497-5p expression levels were decreased, and its target genes ACTG1, CSNK1D, PPP1CC, and BIRC5 were upregulated in HCC tissues compared with normal tissues. Lower levels of hsa-miR-497-5p expression and higher levels of the four target genes were significantly associated with higher tumor diameter. Moreover, patients with lower hsa-miR-497-5p expression and higher target genes levels had shorter OS. Conclusion. The expression levels of hsa-miR-497-5p may play an important regulatory role in HCC and are closely correlated with HCC progression and poor prognosis in patients. The hsa-miR-497-5p may be a specific therapeutic target for the treatment of HCC.

This paper addresses the two-dimensional loading open vehicle routing problem with time window (2L-OVRPTW). We propose a learning whale optimization algorithm (LWOA) to minimize the total distance; an improved skyline filling algorithm (ISFA) is designed to solve the two-dimensional loading problem. In LWOA, the whale optimization algorithm is used to search the solution space and get the high-quality solution. Then, by learning and accumulating the block structure and customer location information in the high-quality solution individuals, a three-dimensional matrix is designed to guide the updating of the population. Finally, according to the problem characteristics, the local search method based on fleet and vehicle is designed and performed on the high-quality solution region. IFSA is used to optimize the optimal individual. The computational results show that the proposed algorithm can effectively solve 2L-OVRPTW.

The production of reactive oxygen species (ROS) is a ubiquitous defense response in plants. Adapted pathogens evolved mechanisms to counteract the deleterious effects of host-derived ROS and promote infection. How plant pathogens regulate this elaborate response against ROS burst remains unclear. Using the rice blast fungus Magnaporthe oryzae, we uncovered a self-balancing circuit controlling response to ROS in planta and virulence. During infection, ROS induces phosphorylation of the high osmolarity glycerol pathway kinase MoOsm1 and its nuclear translocation. There, MoOsm1 phosphorylates transcription factor MoAtf1 and dissociates MoAtf1-MoTup1 complex. This releases MoTup1-mediated transcriptional repression on oxidoreduction-pathway genes and activates the transcription of MoPtp1/2 protein phosphatases. In turn, MoPtp1/2 dephosphorylate MoOsm1, restoring the circuit to its initial state. Balanced interactions among proteins centered on MoOsm1 provide a means to counter host-derived ROS. Our findings thereby reveal new insights into how M. oryzae utilizes a phosphor-regulatory circuitry to face plant immunity during infection.

Clayey sand is widely distributed and commonly encountered in geotechnical engineering practice. To understand its bearing capacity behavior under unsaturated conditions, plate load tests are performed on sand–kaolin mixture samples with varying water tables. The distributions of suction and volumetric water content with depth are measured by vibrating wire piezometers and soil moisture sensors, respectively. It is shown by the test results that the bearing capacity increases when the water table in the soil sample drops. The influence of suction on the bearing capacity is found to be dependent on the height of the water table and the hydraulic loading history of the soil sample. The plate load test results are interpreted using bearing capacity equations. Good agreement is obtained between measured and calculated bearing capacities. This study provides a simple method to estimate the bearing capacity of in situ unsaturated soil foundations.

The lack of online sensors for Mooney viscosity measurement has posed significant challenges for enabling efficient monitoring, control, and optimization of industrial rubber mixing process. To obtain real-time and accurate estimations of Mooney viscosity, a novel soft sensor method, referred to as multimodal perturbation- (MP-) based ensemble just-in-time learning Gaussian process regression (MP-EJITGPR), is proposed by exploiting ensemble JIT learning. This method employs perturbations on similarity measure and input variables for generating the diversity of JIT learners. Furthermore, a set of accurate and diverse JIT learners are built through an evolutionary multiobjective optimization by balancing the accuracy and diversity objectives explicitly. Moreover, all base JIT learners are combined adaptively using a finite mixture mechanism. The proposed method is applied to an industrial rubber mixing process for Mooney viscosity prediction, and the experimental results demonstrate its effectiveness and superiority over traditional soft sensor methods.

The no-wait flow-shop scheduling problem with sequence-dependent setup times and release times (i.e., the NFSP with SSTs and RTs) is a typical NP-hard problem. This paper proposes an enhanced differential evolution algorithm with several fast evaluating strategies, namely, DE_FES, to minimize the total weighted tardiness objective (TWT) for the NFSP with SSTs and RTs. In the proposed DE_FES, the DE-based search is adopted to perform global search for obtaining the promising regions or solutions in solution space, and a fast local search combined with three presented strategies is designed to execute exploitation from these obtained regions. Test results and comparisons with two effective meta-heuristics show the effectiveness and robustness of DE_FES.

The multi-compartment vehicle routing problem (MCVRP) has been applied in fuel or food delivery, waste collection, and livestock transportation. Ant colony optimization algorithm (ACO) has been recognized as an efficient method to solve the VRP and its variants. In this paper, an improved hybrid ant colony optimization algorithm (IHACO) is proposed to minimize the total mileage of the MCVRP. First, a probabilistic model is designed to guide the algorithm search towards high-quality regions or solutions by considering both similar blocks of customers and customer permutations. Then, a heuristic rule is presented to generate initial individuals to initialize the probabilistic model, which can drive the search to the high-quality regions faster. Moreover, a new local search using the geometry optimization is developed to execute exploitation from the promising regions. Finally, two types of variable neighborhood descent (VND) techniques based on the speed-up search strategy and the first move strategy are devised to further enhance the local exploitation ability. Comparative numerical experiments with other algorithms and statistical analyses are carried out, and the results show that IHACO can achieve better solutions.

To assess the potential of China's industrial restructuring on energy conservation and greenhouse gas (GHG) emission reduction in 2020, this study proposes an input–output multi-choice goal programming model and method. In this model, the goals include the maximization of gross domestic product (GDP), minimization of energy consumption and GHG emission. They are subjected to the input–output balance, economy development, energy supply, and industry diversity. And four scenarios with different decision preferences are taken into accounted in the solutions of the industrial structure optimization model. The results demonstrate that industrial restructuring has potential in energy saving and emission reducing. First, after optimization, energy consumption intensity and GHG emission intensity can drop by 13.88246% and 5.33767% over 2012, and GDP can grow up at annual growth rate 6.6% from 2013–2020. Second, promoting the development of the low energy-intensive and low GHG emission intensive sectors is an effective method for energy conservation and emission reduction. Three, compared to energy intensity reduction, GHG emission intensity reduction is less effective for four scenarios. Four, there are several difficulties to achieve the amounts and intensity control targets of energy conservation and GHG emission reduction simultaneously. It is suggested that China had better strive to promote progress of technologies of energy conservation and GHG emission reduction while adjusting the industrial structure.

A compact interference cancellation device (ICD) offering a great decoupling for a pair of closely overlapped antennas and enhancing the throughput of the multi-input-multi-output (MIMO) system is proposed. The device is implemented using semi-lumped components with a compact size of 1.6 mm $\times \,\, 0.8$ mm $\times \,\, 0.6$ mm with the help of the low temperature co-fired ceramics multilayer fabrication technology. The proposed topology is applied to enhance the overall data throughput of a MIMO system with two overlapped antennas sharing a reduced clearance region. A MIMO antenna system consisting of two conventional coupled antennas locating at the same plane, and its counterpart containing two overlapped antennas integrated with the ICD chip are fully investigated. The experimental results demonstrate that around 10% overall throughput improvement of the MIMO system is obtained across the 2.4 GHz band while the ground clearance area of the antennas has been shrunk by 50%. It is also proved that the presented ICD device is a good candidate in applications of mobile terminals and wireless communication devices with very compact volumes.

The resistance and capacitance parameters of a resistance–capacitance divider may change due to factors such as long-term operation, internal insulation flashover, and dielectric breakdown, which will affect the measurement characteristics of the resistance–capacitance divider. Since it is difficult to separate the voltage divider, and because improper disassembly will damage the insulation of the equipment, measuring the resistance and capacitance parameters of a voltage divider non-destructively has always been a problem. In this paper, an indirect method for evaluating the resistance and capacitance parameters is proposed, and the uncertainty of measurement of this method is determined. Simulation and actual test results show that this method can be used to estimate the resistance–capacitance parameters and has a good level of measurement accuracy. Besides, through the uncertainty analysis, it is concluded that the proposed method can overcome measurement errors within a certain range and has high practicability. Finally, a very practical application scenario of the proposed method is given, showing that the proposed method has good economic significance.

Hyperspectral classification is an important technique for remote sensing image analysis. For the current classification methods, limited training data affect the classification results. Recently, Conditional Variational Autoencoder Generative Adversarial Network (CVAEGAN) has been used to generate virtual samples to augment the training data, which could improve the classification performance. To further improve the classification performance, based on the CVAEGAN, we propose a Self-Attention-Based Conditional Variational Autoencoder Generative Adversarial Network (SACVAEGAN). Compared with CVAEGAN, we first use random latent vectors to obtain more enhanced virtual samples, which can improve the generalization performance. Then, we introduce the self-attention mechanism into our model to force the training process to pay more attention to global information, which can achieve better classification accuracy. Moreover, we explore model stability by incorporating the WGAN-GP loss function into our model to reduce the mode collapse probability. Experiments on three data sets and a comparison of the state-of-art methods show that SACVAEGAN has great advantages in accuracy compared with state-of-the-art HSI classification methods.

By using nuclear magnetic resonance (NMR) and CT scanning technologies, hydration experiments have been conducted on shale samples from the Lower Silurian Longmaxi Formation in Zhaotong area in North Yunnan and Guizhou Provinces under the confining pressure of 10 MPa to study the effect of hydration on the propagation of pores and natural fractures in shale formation. The results show that the hydration not only offsets the permeability drop caused by confining pressure, but makes the fracture network more complicated, the connection between fractures and pores better with larger volume, and permeability higher by facilitating the dilation, propagation and cross-connection of primary pores, natural fractures, and newly created micro-fractures; hydration damage mainly occurs along the bedding plane or the direction of primary fractures; samples with better-developed primary pores and fractures are most affected by hydration, samples with best-developed primary pores and natural fractures are less affected by hydration, samples with only pores are least affected by hydration; and the hydration intensity of shale plugs is affected by the development of primary pores and fractures, clay content, brittleness index, confining pressure and the hydration duration jointly. Therefore, in shale reservoir stimulation, it is suggested that the pumping schedule, shut-in operation or clean-up with small choke during early flow-back process be considered according to the features of shale reservoir to enhance the complexity and connection of facture network and improve the stimulation effect. Key words: shale reservoir, stimulation, flow-back process, hydration, NMR, CT scanning, pores and natural fractures

In this paper, we deal with the intersymbol interference (ISI) cancellation problem induced by the faster-than-Nyquist (FTN) signaling. In the traditional FTN signaling, the detection delay at the receiver depends on the number of states of the ISI trellis. In this case, the corresponding Viterbi algorithm or the BCJR algorithm would be far too complex and introduce a huge delay when the ISI tap set is large. In this paper, we propose a novel interference cancellation scheme to combat the ISI for the FTN communication system which enables the parallel computations. Our proposed scheme adopts a pre-coding at the transmitter and a decomposition detection at the receiver. Particularly, with the help of the parallel computations, the running time of our proposed scheme is independent of the ISI trellis, which allows the application of a more severe FTN system with a smaller time acceleration factor. Besides, based on the pre-coding scheme and the decomposition detection, an adaptive transmission strategy is developed, which can improve the performance of the proposed scheme dramatically. Finally, we compare our scheme with the offset BCJR algorithm and the offset Viterbi algorithm benchmarks. The simulation results verify that our scheme can outperform previous decoders with a better bit error rate and a much less delay.

An improved scheme of a dual-switch forward converter is proposed in this paper. Compared with the original converter, the new one can provide higher resetting voltage during the hold-up time interval under the action of an additional reset capacitor. Namely, it allows the converter's duty cycle to be larger than 50%. As a result, the proposed method can transfer more energy stored in the bulk capacitor to the load side during the hold-up interval. Therefore, a smaller bulk capacitor can be used for the converter's hold-up time requirement. Since the additional reset capacitor acts only as a compensation, it would not affect other performance of the converter. The additional circuit can be applied for the dual-switch forward converter when small-volume and high power density are required. The working principle and the performance of the converter are analysed, and the relevant theory formulas are deduced. Then the parameters of key components are given. Finally, the theoretical analysis is validated through a 200 W experimental prototype.

The Internet of things (IoT), including power meters, water meters, natural gas meters, and meter collectors in an open metering system (OMS), which is dispersed around the user side, relies on wireless virtual private networks (VPNs) to communicate with head end, and thus it is exposed to malicious cyber attacks. The General Packet Radio Service (GPRS), which is vulnerable to rogue femtocells, is widely used for communication among meter collectors and the head end. Because telecommunication fraud related to rogue femtocells is a serious offence, rogue femtocells will be turned on for some time and immediately turned off and moved from here to there to escape from being caught. The signal strength (SS) of rogue femtocells is characterized by abrupt changes. Because meter collectors and lawful femtocells are deployed at the fixed location, there is a notable difference between signal strength profile of lawful and rogue femtocells. Prior knowledge of variation of signal strength is utilized to formulate rules to detect rogue femtocells. An expert system is developed to detect rogue femtocells and prevent meter collectors from attaching to them. Numerical simulation indicates that the proposed approach can detect both stationary and moving rogue femtocells online. Since computation load of the proposed approach is not high, it can be implemented in existing IoT meter collectors with limited computation resource and the proposed approach can harden cyber security of OMS.

Measurement accuracy is an important performance indicator for high-voltage direct current (HVDC) voltage dividers. The temperature rise effect for a HVDC voltage divider’s internal resistance has an adverse effect on measurement accuracy. In this paper, by building a solid model of a DC voltage divider, the internal temperature rise characteristic and error caused by the temperature rise in a resistance voltage divider were theoretically simulated. We found that with the increase in height and working time, the internal temperature of the voltage divider increased. The results also showed that the lowest temperature was near the lower flange and the highest temperature was near the upper flange in the middle of the voltage divider. The error caused by the temperature rise increased first and then decreased gradually with divider height, increasing with its working time. The measurement error caused by the internal temperature difference in steady state reached a maximum of 158.4 ppm. This study provides a theoretical basis to determine the structure and accuracy improvement for a resistive voltage divider, which is helpful for the selection of components and the optimization of the heat dissipation structure.