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Abstract A permanent magnet synchronous generator‐based wind generation system has been predominantly applied in wind farms. With the wide application of wind generation, mechanical shafting attracts wide attention as torsional vibration problems may occur in its mechanical rotational system, which can further affect the power system. The paper first intentionally designs a two‐open‐loop two‐mass shaft subsystem model to investigate the interactions among wind turbine mass, generator mass, and machine‐side converter, that is, the machine‐side dynamics. Then, a bilateral damping contribution analysis is proposed to investigate the damping mechanism of these machine‐side dynamics. The impact mechanism of one dynamic on another through the damping contribution channel can be revealed by modal analysis, indicating the coupling of different oscillation modes and the complex interactions of machine‐side dynamics. The established two‐open‐loop two‐mass shaft subsystem model and the proposed bilateral damping contribution analysis with the identified damping contribution channel of the permanent magnet synchronous generator‐based wind generation system are validated.

IntroductionIn recent years, it has become increasingly evident that the population in many countries has been declining. China, which was previously the world's most populous nation and is often categorized as an emerging economy, officially entered an era of population decline in 2022. The advent of this era has make China's economic development more uncertain and aging of population more pronounced. To address the population decline, the Chinese government implemented the “Three-Child Policy” to encourage childbirth, aiming to reverse the negative population growth. However, this policy has not achieved the expected goals. Instead, it has increased the pressure on women to bear children, particularly for career women, where such pressure may conflict with their existing work and family environments, subsequently affecting their mental wellbeing.MethodsA survey was conducted to investigate the mental wellbeing status of career women in Changchun City, Jilin Province, Northeast China. It analyzes the impact of working and family environments on the mental wellbeing of these women.ResultsBased on the survey, this study draws five conclusions: A. The mental wellbeing status of career women varies across different ages, industries, and childbirth statuses. B. The perceived adverse impact of childbirth on the working environment may negatively affect the mental wellbeing of career women. C. The perceived adverse impact of childbirth on the family environment may negatively affect the mental wellbeing of career women. D. Career women are not satisfied with the effectiveness of current policies in protecting women's rights. E. Compared to working environments, there is a greater demand for career women in the family environments, particularly in reducing various family burdens.DiscussionThe pro-natalist policies introduced in response to negative population growth can worsen the mental wellbeing of career women, while the deterioration of their mental wellbeing could further accelerate population decline. Given the current challenges, this study suggests that effectively improving the mental wellbeing of career women requires building psychological resilience among childless career women, reducing the burden of family on career women, and continuously improving policies and regulations that protect the rights of career women.

[Introduction] High energy, strong current, and long pulse neutral beam negative ion source technology are one of the core technologies for achieving combustion conditions in magnetic confinement fusion reactor plasma. To meet the beam diagnostic requirements of the long pulse high-power negative ion source experimental platform under the parameters of 200 kV/20 A, a calorimeter target has been developed for intercepting negative ion beams or neutral beams, diagnosing the power density distribution and beam divergence angle of the two beams, as well as the neutralization efficiency of negative ions and other performance parameters. [Method] Based on the existing structure of the experimental platform vacuum chamber and the size of the extraction electrode, the Matlab program was used to obtain the power density distribution and beam spot size of the negative ion beam generated by the negative ion source at the front end of the calorimeter target at a beam divergence angle of 1° under this parameter. Subsequently, the physical structure of the calorimeter target with a V-shaped target plate was designed. On this basis, Workbench software was used to simulate the thermal load of the oxygen-free copper V-shaped target plate structure under full power operation and obtain the temperature distribution of the calorimeter target during long pulse operation with a water flow rate of 80 m3/h. The highest temperature was 610 °C. [Results] Based on the simulation calculation results, and in combination with the engineering structure and diagnostic requirements of the experimental platform, the engineering design of the calorimeter target was completed. [Conclusion] The calorimeter target adopts magnetic fluid vacuum sealing to achieve the opening and closing of the V-shaped target plate, and a thermocouple array is arranged on the back of the target plate to monitor the temperature of the target plate in real time. The calorimeter target engineering has a compact structure, and its installation size can be compatible with ion beam diagnostic vacuum chambers and neutral beam diagnostic vacuum chambers, meeting diagnostic requirements and enabling safe operation with long pulses.

Background Perioperative neurocognitive disorders (PND) refer to neurocognitive abnormalities during perioperative period, which are a great challenge for elderly patients and associated with increased morbidity and mortality. Our studies showed that long non-coding RNAs (lncRNAs) regulate mitochondrial function and aging-related pathologies in the aged hippocampus after anesthesia, and lncRNAs are associated with multiple neurodegenerations. However, the regulatory role of lncRNAs in PND-related pathological processes remains unclear. Methods A total of 18-month mice were assigned to control and surgery (PND) groups, mice in PND group received sevoflurane anesthesia and laparotomy. Cognitive function was assessed with fear conditioning test. Hippocampal RNAs were isolated for sequencing, lncRNA and microRNA libraries were constructed, mRNAs were identified, Gene Ontology (GO) analysis were performed, and lncRNA-microRNA-mRNA networks were established. qPCR was performed for gene expression verification. Results A total of 312 differentially expressed (DE) lncRNAs, 340 DE-Transcripts of Uncertain Coding Potential (TUCPs), and 2,003 DEmRNAs were identified in the hippocampus between groups. The lncRNA-microRNA-mRNA competing endogenous RNA (ceRNA) network was constructed with 29 DElncRNAs, 90 microRNAs, 493 DEmRNAs, 148 lncRNA-microRNA interaction pairs, 794 microRNA-mRNA interaction pairs, and 110 lncRNA-mRNA co-expression pairs. 795 GO terms were obtained. Based on the frequencies of involved pathological processes, BP terms were divided into eight categories: neurological system alternation, neuronal development, metabolism alternation, immunity and neuroinflammation, apoptosis and autophagy, cellular communication, molecular modification, and behavior changes. LncRNA-microRNA-mRNA ceRNA networks in these pathological categories were constructed, and involved pathways and targeted genes were revealed. The top relevant lncRNAs in these ceRNA networks included RP23-65G6.4, RP24-396L14.1, RP23-251I16.2, XLOC_113622, RP24-496E14.1, etc., and the top relevant mRNAs in these ceRNA networks included Dlg4 (synaptic function), Avp (lipophagy), Islr2 (synaptic function), Hcrt (regulation of awake behavior), Tnc (neurotransmitter uptake). Conclusion In summary, we have constructed the lncRNA-associated ceRNA network during PND development in mice, explored the role of lncRNAs in multiple pathological processes in the mouse hippocampus, and provided insights into the potential mechanisms and therapeutic gene targets for PND.

Background: Surgical pain affects postoperative sleep quality, and they jointly form a vicious cycle of mutual influence. The cycle of postoperative pain and sleep disorders could lead to delirium, cardiovascular disease, and hyperalgesia, which significantly affect patients' postoperative recovery. Thus, exploring this phenomenon is of great importance for surgical patients, and warrants further investigation. Objective: By employing bibliometric methods, this study systematically analyzes the publications on postoperative pain-sleep disorders, identifies research trends and field dynamics, and ultimately provides insights for further progress in this research area. Methods: In this study, we searched the Web of Science database for studies on postoperative pain and sleep disorders from 2013 to 2023, and analyzed the number of publications, journals, authors, institutions, country regions, and keywords by utilizing CiteSpace, VOSviewer, and Bibliometrix. Results: The 1894 retrieved publications showed a trend of increasing number of publications and correlations between postoperative pain and sleep disorders from 2013 to 2023. The top countries for publications included the USA, China, etc., establishing a global collaborative network centered around the USA, China, and Europe. The top institutions for publications included University of California System, Harvard University, etc. The top authors include Christine Miaskowski, Steven M. Paul, Qiuling Shi, etc. These publications involved multiple disciplines including surgery, neurology, and anesthesiology, and various research funds including NIH, HHS, NSFC, etc. The top journals for publications included the European Archives of Oto-Rhino-Laryngology, etc. Keywords that appear most frequently in this field include ''pain'', ''surgery'', ''quality of life'', ''sleep'', ''depression'', and ''outcomes''. The thematic map indicated that the hot topics in this area include obstructive sleep apnea, tonsillectomy, children, pain, quality of life, and sleep. The undeveloped topics with research potential included postoperative pain, analgesia and dexmedetomidine, breast cancer, fatigue, and lung cancer. Conclusion: The increased number of publications and correlations between postoperative pain and sleep disorders, and the collaborative network across the USA, China, and Europe indicate a growing global interest in this area. This study also provides valuable insights into the trend of hot topics and frontiers and shows that this is an evolving and dynamic research area.

Energy-intensive load benefits from low electricity tariff and carbon emission, since they occupy certain amounts in the total cost of the product. This paper considers energy-intensive load participation in the electricity as well as carbon trading to reduce the cost. Firstly, an electricity-carbon model is established based on the correlation value method to calculate the carbon emissions of energy-intensive load based on their electricity consumption to realize the carbon amount. Afterwards, the baseline method is used to allocate free carbon emission quotas to energy-intensive load and a reward-penalty carbon trading price mechanism considering offset is proposed. Next, the objective function to achieve maximum benefits, and to reduce output fluctuation, and to improve new energy accommodation is proposed. The case studies show that, by comparing multi-objective function optimization, the optimization target proposed in this paper can effectively reduce wind power output fluctuations and improve wind power accommodation. Through the total participation in carbon trading and electricity market income, multi-objective optimization can increase the system income while ensuring that energy-intensive load meets production requirements under the premise of reducing carbon emissions, verifying the effectiveness of the low-carbon optimal operation model proposed in this paper.

The performance of marine radar target detection is largely affected by the intricate and dynamic space–time variations of sea clutter signals, which cause substantial numbers of false and missed alarms. To improve the target detection performance of rotating scanning marine radar, this study proposes a marine radar image sequence target detection algorithm based on space–time adaptive filtering and the Hough transform algorithm. The algorithm adopts a two-stage approach of coarse detection followed by precise detection. During the coarse detection stage, the sea clutter energy in the 3-D frequency–wavenumber spectrum of the marine radar image sequence is suppressed by a sea clutter suppression algorithm in the space–time domain, space–time clutter suppression (STCS). Subsequently, moving targets are extracted from the image sequence using a target energy extraction method based on the Hough transform algorithm in the 3-D frequency–wavenumber domain. The result is a processed image sequence with sea clutter signal reduction and target signal extraction. The precise detection stage detects the target point in this processed image sequence using a constant false alarm rate method based on a real clutter background distribution model. During verification tests on real X-band marine radar data, the detection probability of the proposed method reaches 99.89% under low sea state, 95.34% under medium sea state, and 94.44% under high sea state. Compared with the WHOS-CFAR and GMOS-CFAR, the average improvement is 10.1% and 16.6%, respectively. Furthermore, compared to the STCS, there is a maximum improvement of 3.7%. The enhancement in detection performance is significant.

The performance of marine radar constant false alarm rate (CFAR) detection method is significantly influenced by the modeling of sea clutter distribution and detector decision rules. The false alarm rate and detection rate are therefore unstable. In order to address low CFAR detection performance and the modeling problem of non-uniform, non-Gaussian, and non-stationary sea clutter distribution in marine radar images, in this paper, a CFAR detection method in generalized extreme value distribution modeling based on marine radar space-time filtering background clutter is proposed. Initially, three-dimensional (3D) frequency wave-number (space-time) domain adaptive filter is employed to filter the original radar image, so as to obtain uniform and stable background clutter. Subsequently, generalized extreme value (GEV) distribution is introduced to integrally model the filtered background clutter. Finally, Inclusion/Exclusion (IE) with the best performance under the GEV distribution is selected as the clutter range profile CFAR (CRP-CFAR) detector decision rule in the final detection. The proposed method is verified by utilizing real marine radar image data. The results indicate that when the Pfa is set at 0.0001, the proposed method exhibits an average improvement in PD of 2.3% compared to STAF-RCBD-CFAR, and a 6.2% improvement compared to STCS-WL-CFAR. When the Pfa is set at 0.001, the proposed method exhibits an average improvement in PD of 6.9% compared to STAF-RCBD-CFAR, and a 9.6% improvement compared to STCS-WL-CFAR.

The goal of ''carbon peak, carbon neutral'' and the increasing expansion of new energy have helped to advance the development of energy storage. However, since the operating cost of energy storage is high, carbon emission trading and power market trading have emerged, effectively improving the efficiency. In this paper, a trading strategy and bidding framework of energy storage participation in the day-ahead joint market are studied. A market bidding model has been established in a framework based on the Stackelberg game. Finally, the ''Day-Ahead and Intra-Day and Carbon Emission Trading (CET)'' market clearing model has been constructed. It has been simplified to solve the equivalent mixed-integer linear programming (MILP) problem with equilibrium constraints through the use of the Karush–Kuhn–Tucker (KKT) optimality criterion and duality principle. The proposed model is validated through improved examples to obtain thermal unit output cuts of up to 32.2% during load trough periods, and up to 16.75% increase in clearing prices during peak load periods. The storage life is extended and the storage output variation is minimized.

Acquiring real-time status information of the distribution system forms the foundation for optimizing the management of power system operations. However, missing measurements, bad data, and inaccurate system models present a formidable challenge for distribution system state estimation (DSSE) in practical applications. This paper proposes a physics-informed graphical learning state estimation approach, to address these limitations by integrating power flow equations and GraphSAGE. The generalization ability of GraphSAGE for unknown nodes is used to perform inductive learning of measurement information. For unseen measurement points in the training set, the simulation proves that the proposed approach can still satisfactorily predict the state quantity. The training process is guided by power flow equations to ensure it has physical significance. Additionally, the possibility of applying the proposed approach to an actual distribution area is explored. Equivalent preprocessing of the three-phase voltage measurement data of the actual distribution area is conducted to improve the estimation accuracy of the transformer measurement points and simplify the computation required for state estimation.

In order to address the challenges posed by the integration of regional electric vehicle (EV) clusters into the grid, it is crucial to fully utilize the scheduling capabilities of EVs. In this study, to investigate the energy storage characteristics of EVs, we first established a single EV virtual energy storage (EVVES) model based on the energy storage characteristics of EVs. We then further integrated four types of EVs within the region to form EV clusters (EVCs) and constructed an EVC virtual energy storage (VES) model to obtain the dynamic charging and discharging boundaries of the EVCs. Next, based on the dispatch framework for the participation of renewable energy sources (RESs) and loads in the distribution network, we established a dual-objective optimization dispatch model, with the objectives of minimizing system operating costs and load fluctuations. We solved this model with NSGA-II and TOPSIS, which guided and optimized the charging and discharging of EVCs. Finally, the simulation results show that the system operating cost was reduced by 7.81%, and the peak-to-valley difference of the load was reduced by 3.83% after optimization. The system effectively achieves load peak shaving and valley filling, improving economic efficiency.

Radiation-induced salivary gland injury (RISGI) is a common complication of radiotherapy in patients with head and neck cancer. Intragland expression of the Sonic Hedgehog (Shh) gene may partially rescue irradiation (IR)-induced hyposalivation by preserving salivary stem/progenitor cells and parasympathetic innervation, maintaining resident macrophages, and maintaining microvascular density. Previous studies have revealed that Ad-Rat Shh transduction through the salivary glands of miniature pigs can ameliorate oxidative stress-induced microvascular dysfunction after radiotherapy. Changes in the parotid salivary flow rate were analyzed, and the parotid tissue was collected at 5 and 20 weeks after IR. Changes in the Hedgehog pathway and vascular function-related markers (vascular endothelial growth factor (VEGF) and CD31) and oxidative stress-related markers were detected via immunohistochemistry, immunofluorescence, and Western blotting. A stable Shh-overexpressing cell line was generated from human umbilical vein endothelial cells (HUVECs) and exposed to 10 Gy X-ray irradiation, after which endothelial cell proliferation, senescence, apoptosis, and vascular function were evaluated. We found that intragland expression of the Shh gene efficiently alleviated IR-induced parotid gland injury in a miniature pig model. Our results indicate that the antioxidative stress and microvascular-protective effects of the Hh pathway are regulated by nuclear factor-erythroid 2-related factor 2 (Nrf2).

Against the backdrop of the gradual advancement of China's electricity market reform, the number of Power Trading Companies in China has been increasing year by year, and as of October 2022, the number has reached more than 10,000. As an important hub connecting the electricity market and users, electricity retailers face double risks from downstream user load fluctuations and electricity market price fluctuations. Therefore, a reasonable power purchase and sale strategy is very important for an electricity retailer. In this study, a block bidding mechanism is adopted to optimize the clearing of the medium-to long-term market and a DA-RBF neural network is established for spot electricity price forecasting model based on numerical feature similarity to improve the accuracy of electricity price forecasting. Furthermore, the model considers the differences in user demand responses and investigates the optimal power purchase and sale strategy, guided by differentiated time-of-use electricity pricing. The case study analysis demonstrated that the proposed power purchase and sale optimization strategy yields favorable results, improving profitability and enhancing the stability of the power system.

Abstract In view of the existing literature, only the optimization operation of hydrogen energy–wind/light new energy is considered, and the research problems such as comprehensive utilization of water resources and load control strategies are ignored. Based on the analysis of load characteristics of offshore wind power, this paper has established an optimal operation model of power‐fresh water energy system based on “wind‐hydrogen‐water‐electricity” interaction. Meanwhile, an electrolytic hydrogen individual load control strategy is proposed to match wind power fluctuations from the perspective of internal load regulation of electrolytic hydrogen system. From the economic characteristics, operation characteristics, accommodation situation and other simulation analysis, it can reduce the total operating cost by about 3.1%, improve the utilization rate of electrolytic cell capacity, and meet the water demand of coastal users. It shows that the individual control strategy and optimal operation model have advantages, which is of great significance for realizing low‐carbon, safe, and economical operation of power grid in the future.

IntroductionIn the context of the digital economy, the emergence and application of emerging technologies have accelerated the integration of traditional social structures with new technologies, leading to the inception of the “Future Community” as an innovative urban unit. With an aging population’s rapid and sustained rise, integrating health care for older adults with modern information technology is gradually moving towards holistic governance. This approach utilizes the Future Community as a medium and aims for quality enhancement and increased efficiency, which instrumentally addresses the diversified health care needs of China’s aging era.MethodsIn this study, we employed a questionnaire survey method that covered 11 communities in Tianjin City to understand better the current status and characteristics of their health care services.ResultsThe survey results show that the means of community health care for older adults are gradually being upgraded, and the demands are shifting. Then, we arrive at three conclusions: firstly, technological innovation and smart approaches have the potential to positively influence the quality of health care in these communities. Secondly, allocating health care resources within communities can have a salutary effect on the psychological well-being of seniors. Thirdly, actively involving seniors in community life and governance can elevate their self-worth.DiscussionAt last, in conjunction with current challenges, we think that deepening multi-party collaboration, educating specialized talents, and bridging the “digital gap” would be effective ways to establish a future community for seniors.

Floating photovoltaic (FPV) power generation technology has gained widespread attention due to its advantages, which include the lack of the need to occupy land resources, low risk of power limitations, high power generation efficiency, reduced water evaporation, and the conservation of water resources. However, FPV systems also face challenges, such as a significant impact from aquatic environments on the system’s stability and safety and high operational and maintenance costs, leading to large fluctuations in the grid-connected power output. Therefore, it is necessary to integrate energy storage devices with FPV systems to form an integrated floating photovoltaic energy storage system that facilitates the secure supply of power. This study investigates the theoretical and practical issues of integrated floating photovoltaic energy storage systems. A novel integrated floating photovoltaic energy storage system was designed with a photovoltaic power generation capacity of 14 kW and an energy storage capacity of 18.8 kW/100 kWh. The control methods for photovoltaic cells and energy storage batteries were analyzed. The coordinated control of photovoltaic cells was achieved through MPPT control and improved droop control, while the coordinated control of energy storage batteries involved a droop charge–discharge mode, a constant-voltage charging mode, and a standby mode. The simulations were realized in MATLAB/Simulink and the results validated the effectiveness of the coordinated control strategy proposed in this study. The strategy achieved operational stability and efficiency of the integrated photovoltaic energy storage system.

Abstract Turbulent dissipation rate (ɛ) is a crucial parameter in turbulence theory, and an essential component of higher‐order planetary boundary layer schemes for numerical weather prediction and climate models. It is most often modeled diagnostically based on the dissipation scaling ɛ ∝ e3/2/L, where e and L are the turbulence kinetic energy (TKE) and the size of the largest turbulent eddies, respectively. Utilizing three‐month‐long vertically‐extended observations accompanied by high resolution large‐eddy simulations, scaling‐based ɛ‐models are evaluated, focusing on their stability dependence under daytime convective conditions. The analysis uncovers biases in the parameterized ɛ profiles that cannot be corrected through tuning of model constants. The biases are attributed to the limited and even opposing stability dependence of the modeled dissipation length. Close examination reveals violation of the dissipation scaling by the inclusion of TKE associated with organized convection. A self‐similar dissipation length is obtained when only the isotropic component of TKE is considered.

The radio-frequency quadrupole (RFQ) linac is often used as the first radio-frequency (rf) structure for both bunching and preaccelerating proton or heavy ion beams directly behind an ion source or a low-energy beam transport section. Typically, RFQ linacs operate at frequencies below 450 MHz and beam velocity ranges from β=0.01–0.08. Taking the MYRRHA project as an example, a 704.4 MHz continuous-wave (cw) RFQ linac has been recently proposed to realize an efficient fourfold frequency jump at β≈0.2. Due to its high frequency, small dimensions, and cw operation mode, careful design studies for the RFQ cavity structure including the water-cooling channels have been further performed, integrating the idea of using additive manufacturing technology for future construction. Systematic rf and thermal simulations and analysis demonstrated the reliability of the 704.4 MHz RFQ linac at cw operation and the frequency tuning ability via water-speed adjustments. This investigation shows that it could be a highly promising solution to perform an efficient frequency jump with a high-frequency RFQ accelerator at β≈0.2 for large-scale hadron linacs, which could potentially shorten the low- and medium-β parts of the whole facility.

Transfusion of bacterially contaminated platelets, although rare, is still a major cause of mortality and morbidity despite the introduction of many methods to limit this over the past 20 years. The methods used include improved donor skin disinfection, diversion of the first part of donations, use of apheresis platelet units rather than whole-blood derived pools, primary and secondary testing by culture or rapid test, and use of pathogen reduction. Primary culture has been in use the US since 2004, using culture 24 h after collection of volumes of 4–8 mL from apheresis collections and whole-blood derived pools inoculated into aerobic culture bottles, with limited use of secondary testing by culture or rapid test to extend shelf-life from 5 to 7 days. Primary culture was introduced in the UK in 2011 using a “large-volume, delayed sampling” (LVDS) protocol requiring culture 36–48 h after collection of volumes of 16 mL from split apheresis units and whole-blood derived pools, inoculated into aerobic and anaerobic culture bottles (8 mL each), with a shelf-life of 7 days. Pathogen reduction using amotosalen has been in use in Europe since 2002, and was approved for use in the US in 2014. In the US, recent FDA guidance, effective October 2021, recommended several strategies to limit bacterial contamination of platelet products, including pathogen reduction, variants of the UK LVDS method and several two-step strategies, with shelf-life ranging from 3 to 7 days. The issues associated with bacterial contamination and these strategies are discussed in this review.

Power system operation mode calculation (OMC) is the basis for unit commitment, scheduling arrangement, and stability analyses. In dispatch centers at all levels, OMC is usually realized by manually adjusting the parameters of power system components. In a new-type power system scenario, a large number of new energy sources lead to a significant increase in the complexity and uncertainty of a system structure, thus further increasing the workload and difficulty of manual adjustment. Therefore, improving efficiency and quality is of particular importance for power system OMC. This paper first considers generator power adjustment and line switching, and it then models the power flow adjustment process in OMC as a Markov decision process. Afterward, an improved deep Q-network (improved DQN) method is proposed for OMC. A state space, action space, and reward function that conform to the rules of the power system are designed. In addition, the action mapping strategy for generator power adjustment is improved to reduce the number of action adjustments and to speed up the network training process. Finally, 14 load levels under normal and N-1 fault conditions are designed. The experimental results on an IEEE-118 bus system show that the proposed method can effectively generate the operation mode under a given load level, and that it has good robustness.

In view of the large-scale electricity demand of magnesite electrolytic and the problem of co-generation under the restriction of “power based on heat”, given full play to the operational advantages of multi-source interconnection and overall scheduling in the integrated energy system, an integrated energy system that includes large-scale industrial electric loads and multiple types of residential loads and an optimal operation model for this system are established. Firstly, the operation model of each equipment unit is constructed and coupled to form the overall system architecture; afterwards, the economic cost of system operation is comprehensively considered, and the system economic operation optimization model is established. Finally, simulation verification and analysis of the proposed model and method have been carried out with examples.

A non-orientable surface can mirror reflecting the man travelling on it. Realizing such topological object is fascinating. Here, the authors discover that antiferromagnetic-induced polarization in a solid can realize a non-orientable Roman surface.

Abstract Apolipoprotein E ε4 (APOE4) is the primary genetic risk factor for the late-onset form of Alzheimer’s disease (AD). Although the reason for this association is not completely understood, researchers have uncovered numerous effects of APOE4 expression on AD-relevant brain processes, including amyloid beta (Aβ) accumulation, lipid metabolism, endosomal-lysosomal trafficking, and bioenergetics. In this study, we aimed to determine the effect of APOE4 allelic dosage on regional brain lipid composition in aged mice, as well as in cultured neurons. We performed a targeted lipidomic analysis on an AD-vulnerable brain region (entorhinal cortex; EC) and an AD-resistant brain region (primary visual cortex; PVC) from 14–15 month-old APOE3/3, APOE3/4, and APOE4/4 targeted replacement mice, as well as on neurons cultured with conditioned media from APOE3/3 or APOE4/4 astrocytes. Our results reveal that the EC possesses increased susceptibility to APOE4-associated lipid alterations compared to the PVC. In the EC, APOE4 expression showed a dominant effect in decreasing diacylglycerol (DAG) levels, and a semi-dominant, additive effect in the upregulation of multiple ceramide, glycosylated sphingolipid, and bis(monoacylglycerol)phosphate (BMP) species, lipids known to accumulate as a result of endosomal-lysosomal dysfunction. Neurons treated with conditioned media from APOE4/4 vs. APOE3/3 astrocytes showed similar alterations of DAG and BMP species to those observed in the mouse EC. Our results suggest that APOE4 expression differentially modulates regional neuronal lipid signatures, which may underlie the increased susceptibility of EC-localized neurons to AD pathology.

As the ownership of electric vehicles (EVs) continues to rise, EVs are becoming an integral part of urban microgrids. Incorporating the charging and discharging processes of EVs into the microgrid’s optimization scheduling process can serve to load leveling, reducing the reliance of the microgrid on external power networks. This paper proposes a novel two-stage, dual-layer distributed optimization operational approach for microgrids with EVs. The lower layer is a distributed control layer, which ensures, through consensus control methods, that every EV maintains a consistent charging/discharging and state of charge (SOC). The upper layer is the optimization scheduling layer, determining the optimal operational strategy of the microgrid using the multiagent reinforcement learning method and providing control reference signals for the lower layer. Additionally, this paper categorizes the charging process of EVs into two stages based on their SOC: the constrained scheduling stage and the free scheduling stage. By employing distinct control methods during these two stages, we ensure that EVs can participate in the microgrid scheduling while fully respecting the charging interests of the EV owners.

IntroductionBay Areas are viewed as a complex region with integrated objectives and numerous functions. Despite the increasing interaction between bay area development and urban landscape transformation, the fragmented literature did not address the question: how to achieve sustainable development in the bay area? The objective of this study was to review the literature on four representative bay areas from both the West and the East, including the San Francisco Bay Area, New York Bay Area, Tokyo Bay Area, and Guangdong-Hong Kong-Macao Greater Bay Area. This study aimed to address the following two questions: What are the ongoing research trends in the field of bay areas? What are the most critical urban issues for future bay area development?MethodsThis study used a systematic review to examine publications from the Scopus database. Both descriptive and evaluative approaches were employed to analyze the publications on the four representative bay areas. The bibliometric analysis was performed using the VOS viewer to visualize research results and outcomes.ResultsThe results revealed that the majority of the bay area related studies emphasized environmental issues, focused on decentralization and policy interactions, and the field of education received limited attention.DiscussionThis study contributes to the comprehensive understanding of the city-region development and provides policy suggestions for future sustainable bay area development. The findings of the keyword-based bibliometric analysis can be applied to other realms of city studies.

Island microgrids play a crucial role in developing and utilizing offshore renewable energy sources. However, high operation costs and limited operational flexibility are significant challenges. To address these problems, this paper proposes a novel dual−layer distributed optimal operation methodology for islanded microgrids. The lower layer is a distributed control layer that manages multiple controllable distributed fuel−based microturbines (MTs) within the island microgrids. A novel adaptive consensus control method is proposed in this layer to ensure uniform operating status for each MT. Moreover, the proposed method can achieve the total output power of MTs to follow the reference signal provided by the upper layer while ensuring plug−and−play capability for MTs. The upper layer is an optimal scheduling layer that manages various forms of controllable distributed power sources and provides control reference signals for the lower layer. Additionally, a two−stage twin−delayed deterministic policy gradient (MATD3) algorithm is utilized in this layer to minimize the operating costs of island microgrids while ensuring their safe operation. Simulation results demonstrate that the proposed methodology can effectively reduce the operating costs of island microgrids, unify the operational status of MTs, and achieve plug−and−play capability for MTs.

In most of the studies focusing on the conformity of voluntary vaccination decisions, the conformity was always directly modeled as a conformity-driven strategy-updating rule. However, the utility of an individual can also be influenced by the group identity or discrimination behaviors associated with strategy conformity in realities. Thus, a novel utility model of the vaccination game is first formulated in which the influence of strategy conformity is considered. Then, we use the spatial evolutionary game theory to study the dynamics of individual vaccination strategies under the influence of strategy conformity on the scale-free network. The results show that moderate strategy conformity and a high herd immunity threshold have a significant positive effect on vaccination behaviors when the initial vaccination fraction is low. Moreover, for a high initial vaccination fraction, the strong strategy conformity and high herd immunity threshold are more conducive to encourage vaccination behavior. To analyze the model sensitivity, experiments are conducted in the small world network and square lattice network. In addition, we performed the sensitivity analysis on vaccination effectiveness. Finally, the generality of strategy conformity effect is investigated when the myopic strategy updating rule is adopted in the whole population. The result shows that vaccination behaviors can also be promoted under the condition of moderate strategy conformity and low initial vaccination fraction.

Pumped storage power plant (PSPP) has the upper hand on economy and cleanness. It also has the functions of frequency regulation, phase regulation, and spare, which have been instrumental in maintaining the stability of power system operation. But now the mechanism for PSPP to become involved in electricity market transactions in China is imperfect. How to properly establish a multi-time scale trading profit model and reasonably allocate the capacity of PSPP has been instrumental in realizing the economic operation of the power system. So, this article analyzes the mechanism for PSPP to become involved in electricity market trading by providing combined electricity supply services and ancillary services, and establishes an optimization model with respect to economic optimization. At the same time, considering the volatility of electricity prices in the spot market, the risk of PSPP becoming involved in electricity market trading is measured by conditional Value at Risk (CVaR) to achieve economic optimization while minimizing the risk. The case studies demonstrate that the proposed profit model can enhance the revenue and decrease the risk of PPSP.

Shape segmentation in urban environments forms the foundation for tasks such as classification and reconstruction. Most artificial buildings with complex structures are composed of multiple simple geometric primitives. Based on this assumption, this paper proposes a divisive hierarchical clustering algorithm that uses shape classification and outliers reassignment to segment LiDAR point clouds in order to effectively identify the various shapes of structures that make up buildings. The proposed method adopts a coarse-to-fine strategy. Firstly, based on the geometric properties of different primitives in a Gaussian sphere space, coarse extraction is performed using Gaussian mapping and the DBSCAN algorithm to identify the primary structure of various shapes. Then, the error functions are constructed after parameterizing the recognized shapes. Finally, a minimum energy loss function is built by combining the error functions and binary integer programming (BIP) to redistribute the outlier points. Thereby, the accurate extraction of geometric primitives is achieved. Experimental evaluations on real point cloud datasets show that the indicators of precision, accuracy, and F1 score of our method are 0.98, 0.95, and 0.96 (point assignment) and 0.97, 0.95, and 0.95 (shape recognition), respectively. Compared with other state-of-the-art methods, the proposed method can efficiently segment planar and non-planar structures with higher quality from building point clouds.

Hibiscus (Hibiscus syriacus L.) is known as a horticultural plant of great ornamental and medicinal value. However, the effect of NaCl stress on hibiscus seedlings is unclear. Little is known about H. syriacus ‘Duede Brabaul’ (DB) and H. syriacus ‘Blueberry Smoothie’ (BS). Here, the effects of solutions with different concentrations of NaCl on the organic osmolytes, ion accumulation, and antioxidant enzyme activity of hibiscus seedling leaves were determined. The results showed that the Na+/K+ ratio was imbalanced with increasing NaCl concentration, especially in BS (range 34% to 121%), which was more sensitive than DB (range 32% to 187%) under NaCl concentrations of 50 to 200 mM. To cope with the osmotic stress, the content of organic osmolytes increased significantly. Additionally, NaCl stress caused a large increase in O2·− and H2O2, and other reactive oxygen species (ROS), and antioxidant enzyme activity was significantly increased to remove excess ROS. The expression level of genes related to salt tolerance was significantly higher in DB than that in BS under different NaCl concentrations. Taken together, DB possessed a stronger tolerance to salt stress and the results suggest membrane stability, Na+/K+, H2O2, catalase and ascorbate peroxidase as salt tolerance biomarkers that can be used for gene transformation and breeding in future hibiscus research.

Renewable energy is gradually replacing traditional power plants to provide electricity for users, but it also brings potential risks to the safe operation of the power grid. Thus, in the planning stage, it is necessary to comprehensively evaluate the probability of violation risk of maximum frequency deviation. Planning method based on Monte Carlo simulation (MCS) is inefficient, while artificial neural network (ANN) can make fast and effective prediction by learning data. Therefore, this paper proposed an MCS-ANN algorithm to realize the rapid assessment of violation risk of regional maximum frequency deviation. Firstly, a large number of stochastic disturbances were generated, and only a small part of disturbances were used for MCS. Then, these data were sent to the neural network for training, and most of the remaining disturbances were sent to the trained neural network for output prediction. The above training and prediction processes were repeated. The average of multiple prediction results was used as the final prediction output, and the probability distribution of each risk interval was obtained. Finally, the effectiveness of the proposed MCS-ANN algorithm was verified on IEEE 10-machine 39-node system.

With the gradual increase of distributed energy penetration, the traditional optimization model of distribution network can no longer guarantee the stable and efficient operation of the distribution network. In order to deal with the inevitable uncertainty of distributed energy, a new robust optimal operation method is proposed for active distribution network (ADN) based on the minimum confidence interval of distributed energy Beta distribution in this paper. First, an ADN model is established with second-order cone to include the energy storage device, capacitor bank, static var compensator, on-load tap changer, wind turbine and photovoltaic. Then, the historical data of related distributed energy are analyzed and described by the probability density function, and the minimum confidence interval is obtained by interval searching. Furthermore, via taking this minimum confidence interval as the uncertain interval, a less conservative two-stage robust optimization model is established and solved for ADN. The simulation results for the IEEE 33-bus distribution network have verified that the proposed method can realize a more stable and efficient operation of the distribution network compared with the traditional robust optimization method.

By collecting the ripple current in the control coil, the self-sensing magnetic bearings can obtain rotor displacement information without a displacement sensor, which has the advantages of low cost and high integration. According to the problems of incomplete and inaccurate displacement information extraction caused by the traditional displacement estimation method using Fourier method to analyze the ripple current with non-stationary characteristics to estimate the rotor displacement with the Gibbs effect. Therefore, this paper proposes a rotor displacement estimation algorithm based on multiresolution filter bank biorthogonal spline wavelet for a magnetic bearing motor based on two-level switching power amplifier. This algorithm utilizes the biorthogonal spline wavelet with the characteristics of generalized linear phase and tight support, which can accurately demodulate the ripple current in the coil and extract the displacement information in the ripple current. Therefore, it is able to overcome the Gibbs effect in the traditional Fourier analysis estimation algorithm, to reduce the influence of ripple on rotor displacement estimation, and to improve the accuracy and stability of displacement estimation. In order to verify the correctness and effectiveness of the proposed algorithm, this paper establishes a simulation model of magnetic bearing in MATLAB Simulink and designs the proposed displacement estimator. Simulation results demonstrate that the proposed algorithm has higher accuracy and better stability than traditional displacement estimation algorithms. Experimental results show that the maximum deviation rate of the displacement estimation method proposed in this paper does not exceed 1% when the radial magnetic bearing gap is 0.5 mm, which can effectively estimate the rotor displacement.

Abstract Background Iron overload has been found to be related with various cardiometabolic disorders, like dyslipidemia, metabolic syndrome, and diabetes. The disturbance of the iron status and lipid metabolism can contribute to organ damage such as atherosclerotic plaque growth and instability. An assessment on the associations of iron status with apolipoproteins and lipid ratios would be informative for maintenance of metabolic homeostasis and hinderance of disease progression. Hence, this study aims to establish the relationships of iron status with apolipoproteins and lipid ratios. Methods A cross-sectional study of 7540 adult participants from the China Health and Nutrition Survey 2009 was conducted. Logistic regression analyses were used to investigate the relationships between indicators of iron status and the prevalence of unfavorable apolipoprotein profiles. Multivariate linear regression models were constructed to assess the dose-response correlations between serum ferritin and lipid parameters. Results After adjustment for confounding factors, in both sexes, the subjects in the top quartile of ferritin had the highest prevalence of an elevated apolipoprotein B (men: odds ratio (OR) 1.97, 95% confidence interval (CI) 1.50–2.62; women: OR 2.13, 95% CI 1.53–2.97) and an elevated apolipoprotein B/apolipoprotein A1 ratio (men: OR 2.00, 95% CI 1.50–2.66; women: OR 1.41, 95% CI 1.04–1.92) when compared with individuals in the lowest quartile. Hemoglobin were also independently associated with unfavorable apolipoprotein B and apolipoprotein B/apolipoprotein A1 ratio both in men and women. However, transferrin (men: OR 0.74, 95% CI 0.56–0.99; women: OR 0.73, 95% CI 0.56–0.95) and soluble transferrin receptor (men: OR 0.75, 95% CI 0.57–0.99; women: OR 0.71, 95% CI 0.55–0.91) were found to be negatively associated with a decreased apolipoprotein A1. Moreover, after controlling for potential confounders, the ferritin concentrations were significantly associated with the levels of lipid ratios including TG/HDL-C, non-HDL-C/HDL-C, TC/HDL-C, apoB/apoA1, and LDL-C/HDL-C ratio in men (β coefficient = 0.147, 0.061, 0.043, 0.038, 0.032, respectively, all P values

The design and implementation of printed helical spiral antenna (PHSA) and helical printed inverted-F antenna (IFA) are presented for unmanned aerial vehicle (UAV) with global positioning system (GPS) L1 band (1.57 GHz) and telemetry communication frequency band (2.33 GHz) applications. The proposed antenna is miniaturized by co-winding the radiation elements on the same ceramic rod, surface covering by dielectric sealant, inserting a high permittivity dielectric load into the helical structure, turning and meandering the radiation element of PHSA into a spiral parallelogram structure. The PHSA and helical IFA antenna radiation elements are manufactured by silver pasted on the same hollow ceramic cylinder with a diameter of 12 mm and a height of 24.7 mm. The experimental and simulation results show that the proposed PHSA and IFA can achieve the gain of 0.05dB and 1.97 dB respectively and be suitable for GPS L1 band and telemetry communication frequency band (2.33 GHz) applications separately.

In this paper, robust finite-time H∞ filtering for uncertain switched systems with state constraints and multiple time delays is investigated. In this studied plant, there are nonlinear functions and exogenous disturbances. First, a filter is described for the switched systems. A closed-loop system is earned to acquire filtering error. Secondly, piecewise Lyapunov function is designed to gain sufficient stability conditions for the closed-loop system. Not only finite-time boundedness (FTB) but also input-output finite-time stability (IO-FTS) is studied. The attempt is the first time to research. In the end, an example is shown to demonstrate the availability of this method suggested in this study.

In recent years, due to the global energy crisis, increasingly more countries have recognized the importance of developing clean energy. Offshore wind energy, as a basic form of clean energy, has become one of the current research priorities. In the future, offshore wind farms will be developed in deep and distant sea areas. In these areas, there is a new trend of floating offshore wind platforms replacing fixed wind power platforms, due to their low cost, ease of installation, and independence from the water depth. However, the stability of offshore floating platforms is poor and their power fluctuations are significant; furthermore, they are more prone to failure because of sea wind, waves, and currents. This paper summarizes and analyzes the current research progress and critical technical issues of offshore floating wind power generation, such as stability control technology, integrated wind storage technology, wind power energy management, and long-distance transmission of electricity for floating wind power generation at sea. Finally, future research directions for key offshore wind power technologies are presented.

Radiotherapy for head-and-neck cancers frequently causes long-term hypofunction of salivary glands that severely compromises quality of life and is difficult to treat. Here, we studied effects and mechanisms of Sphingosine-1-phosphate (S1P), a versatile signaling sphingolipid, in preventing irreversible dry mouth caused by radiotherapy. Mouse submandibular glands (SMGs) were irradiated with or without intra-SMG S1P pretreatment. The saliva flow rate was measured following pilocarpine stimulation. The expression of genes related to S1P signaling and radiation damage was examined by flow cytometry, immunohistochemistry, quantitative RT-PCR, Western blotting, and/or single-cell RNA-sequencing. S1P pretreatment ameliorated irradiation-induced salivary dysfunction in mice through a decrease in irradiation-induced oxidative stress and consequent apoptosis and cellular senescence, which is related to the enhancement of Nrf2-regulated anti-oxidative response. In mouse SMGs, endothelial cells and resident macrophages are the major cells capable of producing S1P and expressing the pro-regenerative S1P receptor S1pr1. Both mouse SMGs and human endothelial cells are protected from irradiation damage by S1P pretreatment, likely through the S1pr1/Akt/eNOS axis. Moreover, intra-SMG-injected S1P did not affect the growth and radiosensitivity of head-and-neck cancer in a mouse model. These data indicate that S1P signaling pathway is a promising target for alleviating irradiation-induced salivary gland hypofunction.

As water scarcity is becoming a growing threat to human development, finding effective solutions has become an urgent need. To make better use of water resources, seawater desalination and storage systems using renewable energy sources (RES) are designed and implemented around the world. In this paper, an optimal capacity planning method for RES-pumped storage-seawater desalination (RES-PS-D) system is introduced. The configuration of the RES-PS-D system is clarified first, after which a cost-benefit analysis is performed using all cost and benefit components. A function for determining maximum economic benefits of the RES-PS-D system is then established, and the constraints are proposed based on various limitations. The mixed-integer linear programming algorithm is applied to solve the optimal function. A case study is introduced to validate the feasibility and effectiveness of the method. The conclusion shows that the strategy is suitable for solving the configuration optimization problem, and finally both merits and defects of the method are discussed. Keywords: Renewable energy source, Seawater desalination, Pumped storage, Maximum economic benefit

The ubiquitous power internet of things(UPIoT) is a complex multi-network system composed of a power system and a combination of intelligent terminal sensors, communication networks, artificial intelligence, and cloud platform technologies. It is with the characteristics of holographic perception, ubiquitous connectivity, open sharing, and fusion innovation. This paper firstly expounded the basic concepts and characteristics of UPIoT, and analyzed its architecture in detail, including the technical architecture, standard architecture, and application architecture. Next, it discussed the key technologies of the ubiquitous power internet of things including intelligent chips, 5G and LPWA, internet of things platform. Afterwards, key difficulties such as business barriers, cyber security, data analytics, and business modes were explored. The actual needs and application prospects of ubiquitous power internet of things were studied.

Wind energy is a kind of sustainable energy with strong uncertainty. With a large amount of wind power injected into the power grid, it will inevitably affect the security, stability and economic operation of the power grid. High-precision wind power spot prediction and fluctuation interval information can provide more adequate decision-making support for grid scheduling and optimization. Hence, this paper proposes a K-Means-long short-term memory (K-Means-LSTM) network model for wind power spot prediction, and a nonparametric kernel density estimation (KDE) model with bandwidth optimization for wind power probabilistic interval prediction. The long short-term memory (LSTM) network has a strong memory function, which can establish the correlation between the data before and after, so as to improve the prediction accuracy. The K-Means clustering method forms different clusters of wind power impact factors to generate a new LSTM sub-prediction model. The optimization of the bandwidth in the nonparametric KDE is implemented by the mean integrated squared error criterion. In addition, a part of the dataset is deliberately demarcated from the wind power historical dataset to generate reasonable wind power prediction errors. The simulation results show that the proposed K-Means-LSTM network model has higher prediction accuracy than the back propagation (BP) neural networks, Elman neural networks, support vector regression (SVR) and LSTM network models. Compared with the KDE model with random bandwidth and the Gaussian distribution model, the bandwidth optimization model proposed in this paper has more narrow prediction intervals with higher interval coverage rates.

Magnetic coupled resonance wireless power transfer system has the advantages of long transfer distance and high efficiency. However, it needs a high-frequency AC power, and the influence of the circuit parasitic elements at high frequency cannot be ignored, which brings challenges to the research and the design of the high-frequency power for the wireless power transfer system. In this paper, the effect of the parasitic elements in the full-bridge inverter and the reasons of high-frequency ringing generated are analyzed in detail. The influences of device packaging and the printed circuit board (PCB) layout on circuit parasitic elements are studied. An optimized inverter PCB layout design that aims to reduce the parasitic elements and to provide a stable and high-quality AC power for the wireless power transfer system is presented. Finally, the performance of the conventional PCB layout design and the proposed optimized PCB layout design are compared through experiments. The experiment results show that the proposed design not only reduces the parasitic inductance in the circuit, but also reduces the electromagnetic interference noises and improves the stability of the inverter at 900kHz switching frequency.

In order to make full use of the vibration energy in the process of attenuating vibration, an array piezoelectric coupled disc damper is developed, which works by converting part vibration energy into electrical energy. The piezoelectric damper is made of a pair of piezoelectric coupled discs built in a case cylinder. Its energy harvesting behavior is studied by a series of forced-vibration experiments and simulations. The influences of some factors, such as the excitation frequency, substrate thickness, the size of the piezoelectric patch, the paste form of the piezoelectric patch and the load resistance, on the energy harvesting behavior of the damper are analyzed and concluded. The experimental results show that the maximum peak-to-peak voltage and average power from one piezoelectric patch with an inner diameter of 35 mm, an outer diameter of 80 mm, and a thickness of 1 mm can reach up to 163 V and 161 mW, respectively. This research provides a practical piezoelectric damper attenuating harmful vibration by converting them into useful electric power, and the corresponding theoretical models are derived to predict its electrical output.

Summary: Cellular inflammasome activation causes caspase-1 cleavage of the pore-forming protein gasdermin D (GSDMD) with subsequent pyroptotic cell death and cytokine release. Here, we clarify the ambiguous role of the related family member gasdermin E (GSDME) in this process. Inflammasome stimulation in GSDMD-deficient cells led to apoptotic caspase cleavage of GSDME. Endogenous GSDME activation permitted sublytic, continuous interleukin-1β (IL-1β) release and membrane leakage, even in GSDMD-sufficient cells, whereas ectopic expression led to pyroptosis with GSDME oligomerization and complete liberation of IL-1β akin to GSDMD pyroptosis. We find that NLRP3 and NLRP1 inflammasomes ultimately rely concurrently on both gasdermins for IL-1β processing and release separately from their ability to induce cell lysis. Our study thus identifies GSDME as a conduit for IL-1β release independent of its ability to cause cell death.

Background: Serum ferritin (SF) has been correlated with one or more metabolic syndrome features associated with an increased risk for cardiovascular disease (CVD). This study explored the associations between SF and CVD risk factors among different body size phenotypes that were based on metabolic status and body mass index (BMI) categories.Methods: A cross-sectional study was performed using a cohort of 7,549 Chinese adults from the China Health and Nutrition Survey. Participants did not exhibit acute inflammation, were not underweight and were stratified based on their metabolic status and BMI categories. The metabolically at-risk status was defined as having two or more criteria of the Adult Treatment Panel-III metabolic syndrome definition, excluding waist circumference.Results: Compared with individuals without high SF, subjects with high SF had an increased risk of diabetes in the metabolically at-risk normal-weight (MANW) and metabolically at-risk overweight/obesity (MAO) groups. The multivariate-adjusted odds ratios (ORs) were 1.52 [95% confidence interval (Cls): 1.02, 2.28] and 1.63 (95% Cls: 1.27, 2.09), respectively. Adjusted ORs for hyperuricemia from high SF in metabolically healthy normal-weight (MHNW), metabolically healthy overweight/obesity (MHO), MANW, and MAO phenotypes were 1.78 (95% Cls: 1.26, 2.53), 1.42 (95% Cls: 1.03, 1.95), 1.66 (95% Cls: 1.17, 2.36), and 1.42 (95% Cls: 1.17, 1.73), respectively. Similarly, positive correlations of high SF with triglycerides, non-high-density lipoprotein cholesterol, and apolipoprotein B100 were observed in all phenotypes. No association between high SF and elevated low-density lipoprotein cholesterol were observed among participants who were metabolically at-risk, regardless of their BMI categories. However, the ORs for elevated low-density lipoprotein cholesterol from high SF were 1.64 (95% Cls: 1.29, 2.08) in the MHNW group and 1.52 (95% Cls:1.22, 1.91) in the MHO group, significantly. This study demonstrated that the highest ORs were in MAO with a high SF group for all unfavorable CVD risk factors except low-density lipoprotein cholesterol (all p < 0.001).Conclusions: The associations of high SF with the prevalence of CVD risk factors, including diabetes, dyslipidemia, and hyperuricemia, vary in individuals among different body size phenotypes. In the MAO group, subjects with high SF levels exhibited worse CVD risk profiles than individuals without high SF.

At 15:48 on March 21, 2018, a large-scale blackout occurred in Brazil, the power grid in the north and northeast of Brazil were disconnected from main power grid. The load shedding was 19760 MW, accounted for 25% of the total load of the power grid, and 85% of the states were affected, resulting in huge economic losses. In order to avoid severe large-scale blackouts, the main process of blackouts in history was introduced, and lessons learned from blackouts were summarized from the aspects of natural conditions, equipment faults, manual mistakes, strategic mistakes and so on. The main causes of large power outage accidents were analyzed and the corresponding measures were put forward. Finally, combined with several development and research highlights of current power systems, such as renewable energy integration, AC/DC hybrid power systems, energy internet, cyber-physical power systems, and artificial intelligence, the main research directions of power system large scale blackouts were proposed.

Nowadays, the emerging Internet technology plays an essential role in pulling entrepreneurs from financial dilemmas and promoting their social capital development. Using the crawling technology, we obtained 2214 entrepreneurial social networks and entrepreneurial project data from Indiegogo.com, which helps to investigate the influence of entrepreneurs' information sharing behavior on entrepreneurial financial outcomes. We discovered that entrepreneurs' trust-building behavior comes with different stages; in personal social network stage, personal-related trust building can boost better financial outcomes, while project-related information sharing can bring about superior financial results in the socialized social network stage. The two relationships reflected above are also affected by the entrepreneurs' online entrepreneurial experience. Taking a dynamic view instead of a static one in entrepreneurial social capital development, this research sets up a paradigm for entrepreneurial events by proposing entrepreneurial information sharing and Internet venture financing model. It also has a strong practical implication for Internet entrepreneurship by offering the two-stage trust-building tactics, which can be further conducive to understanding and applying the information sharing mechanism in the context of e-commerce and sharing economy.

The flexible job shop scheduling problem has always been the focus of research in the manufacturing field. However, most of the previous studies focused more on efficiency and ignored energy consumption. Energy, especially non-renewable energy, is an essential factor affecting the sustainable development of a country. To this end, this paper designs a flexible job shop scheduling problem model with energy consideration more in line with the production field. Except for the processing stage, the energy consumption of the transport, set up, unload, and idle stage are also included in our model. The weight property of jobs is also considered in our model. The heavier the job, the more energy it consumes during the transport, set up, and unload stage. Meanwhile, this paper invents an adaptive population non-dominated sorting genetic algorithm III (APNSGA-III) that combines the dual control strategy with the non-dominated sorting genetic algorithm III (NSGA-III) to solve our flexible job shop scheduling problem model. Four flexible job shop scheduling problem instances are formulated to examine the performance of our algorithm. The results achieved by the APNSGA-III method are compared with five classic multi-objective optimization algorithms. The results show that our proposed algorithm is efficient and powerful when dealing with the multi-objective flexible job shop scheduling problem model that includes energy consumption.

A common idea is that some dishonest businessmen often disguise Citrus reticulata Blanco varieties as Citrus reticulata ‘Chachi’, which places consumers at risk of economic losses. In this work, we combined high-resolution ion mobility (U-shaped mobility analyzer) with high-resolution mass spectrometry to rapidly distinguish Citrus reticulata ‘Chachi’ from other Citrus species. The samples were analyzed directly through simple extraction and the analytes were separated in one second. It only took about 1 min to perform a cycle of sample analysis and data acquisition. The results showed that polymethoxylated flavones and their isomers were separated easily by the ion mobility analyzer and preliminarily identified according to the accurate mass. Moreover, the collision cross-section values of all analytes, which could be used as auxiliary parameters to characterize and identify the compounds in the samples, were measured. Twenty-four samples were grouped as two clusters by multivariate analysis, which meant that Citrus reticulata ‘Chachi’ could be effectively differentiated. It was confirmed that the developed method had the potential to rapidly separate polymethoxylated flavones and distinguish between Citrus reticulata ‘Chachi’ and other Citrus reticulata Blanco varieties.

Power big data-based artificial intelligence or data mining methods, which can be used to analyze electricity consumption behavior, have been widely applied to provide targeted marketing services for electricity consumers. However, the traditional clustering algorithm has difficulty in judging new electricity consumption patterns. Deep neural networks usually need large amounts of labeled data. However, there are few comparable electricity consumption features or basic data, and the labeled data cannot meet the actual needs. Therefore, an intelligent classification framework for electricity consumption behavior based on an improved k-means and long short-term memory (LSTM) is proposed, which not only extracts features effectively, but also establishes a mapping relationship between unlabeled electricity consumption behavior characteristics and user types. The features can be labeled to train the deep neural network to judge the electricity consumption behavior of new users. Firstly, nine typical characteristics were selected from aspects including electricity price sensitivity and load fluctuation rate. Secondly, the k value and initial clustering centers of the k-means algorithm were optimized. Thirdly, the users were labelled based on the clustering results, together with the features, and a dataset was formed, which was input into LSTM to train the classification model. Finally, the analysis of users in Shenyang, China, showed the results based on the proposed method were consistent with the actual situation. Moreover, compared to other methods, the efficiency and accuracy were higher.