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Cu pillar bump (CPB) technology as the representative advanced packaging technology possesses the characteristics of miniaturization and high density, thus playing an important role in the reliability of electronic devices. However, the brittle intermetallic compound (IMC) layer in the joints evidently affects the service performance and reliability of CPB joints. Thus, the influence of IMC layer thickness and microstructure needs to be carefully evaluated. In this study, the reliability of CPB joints with different IMC layer thickness under the condition of isothermal storage and room-temperature electromigration was tested, and the effect of initial IMC thickness of joints on the reliability was revealed. The full-IMC joints have the best isothermal storage reliability while the half-IMC joints perform the worst. Meanwhile, the thin-IMC joints own the best electromigration reliability but the full-IMC joints show the worst because that the initial voids in full-IMC joints accelerate the failure caused by electromigration.

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.

Abstract Background China’s transition into an aging society is accelerated by the simultaneous decline in fertility rates and the prolonged life expectancy of older adults. The impact of widowhood, a significant stressor in old age, has emerged as a crucial factor affecting the quality of life among the elderly. Methods This study is based on data from the 2018 China Health and Aging Tracking Survey. Multiple linear regression models were employed to investigate the influence of widowhood on the quality of life of older adults in China. Additionally, Bootstrap mediation effects were utilized to assess the mediating role of intergenerational support, considering both financial and emotional support from children. Results The findings indicate a substantial reduction in the quality of life index among older adults following widowhood. Moreover, the impact is more pronounced among older men compared to older women. Rural older adults experience a significant decline in quality of life post-widowhood, while the effect on urban counterparts is not statistically significant. Conclusions Intergenerational financial support was identified as a partial mediator between widowhood and the quality of life among older adults. This underscores the importance of familial financial assistance in mitigating the adverse effects of widowhood on the well-being of the elderly. These results offer valuable insights into the nuanced impact of widowhood on the quality of life among older adults in China, emphasizing the need for targeted interventions, especially in rural areas. Clinical trial Not applicable.

Abstract Direct hydrogen production from inexhaustible seawater using abundant offshore wind power offers a promising pathway for achieving a sustainable energy industry and fuel economy. Various direct seawater electrolysis methods have been demonstrated to be effective at the laboratory scale. However, larger-scale in situ demonstrations that are completely free of corrosion and side reactions in fluctuating oceans are lacking. Here, fluctuating conditions of the ocean were considered for the first time, and seawater electrolysis in wave motion environment was achieved. We present the successful scaling of a floating seawater electrolysis system that employed wind power in Xinghua Bay and the integration of a 1.2 Nm3 h−1-scale pilot system. Stable electrolysis operation was achieved for over 240 h with an electrolytic energy consumption of 5 kWh Nm−3 H2 and a high purity (>99.9%) of hydrogen under fluctuating ocean conditions (0~0.9 m wave height, 0~15 m s−1 wind speed), which is comparable to that during onshore water electrolysis. The concentration of impurity ions in the electrolyte was low and stable over a long period of time under complex and changing scenarios. We identified the technological challenges and performances of the key system components and examined the future outlook for this emerging technology.

Abstract Skyrmions in existing 2D van der Waals (vdW) materials have primarily been limited to cryogenic temperatures, and the underlying physical mechanism of the Dzyaloshinskii–Moriya interaction (DMI), a crucial ingredient for stabilizing chiral skyrmions, remains inadequately explored. Here, we report the observation of Néel-type skyrmions in a vdW ferromagnet Fe3−x GaTe2 above room temperature. Contrary to previous assumptions of centrosymmetry in Fe3−x GaTe2, the atomic-resolution scanning transmission electron microscopy reveals that the off-centered FeΙΙ atoms break the spatial inversion symmetry, rendering it a polar metal. First-principles calculations further elucidate that the DMI primarily stems from the Te sublayers through the Fert–Lévy mechanism. Remarkably, the chiral skyrmion lattice in Fe3−x GaTe2 can persist up to 330 K at zero magnetic field, demonstrating superior thermal stability compared to other known skyrmion vdW magnets. This work provides valuable insights into skyrmionics and presents promising prospects for 2D material-based skyrmion devices operating beyond room temperature.

Abstract The ability to electrically manipulate antiferromagnetic magnons, essential for extending the operating speed of spintronic devices into the terahertz regime, remains a major challenge. This is because antiferromagnetic magnetism is challenging to perturb using traditional methods such as magnetic fields. Recent developments in spin-orbit torques have opened a possibility of accessing antiferromagnetic magnetic order parameters and controlling terahertz magnons, which has not been experimentally realised yet. Here, we demonstrate the electrical manipulation of sub-terahertz magnons in the α-Fe2O3/Pt antiferromagnetic heterostructure. By applying the spin-orbit torques in the heterostructure, we can modify the magnon dispersion and decrease the magnon frequency in α-Fe2O3, as detected by time-resolved magneto-optical techniques. We have found that optimal tuning occurs when the Néel vector is perpendicular to the injected spin polarisation. Our results represent a significant step towards the development of electrically tunable terahertz spintronic devices.

Basement buried hill reservoirs represent significant emerging prospects among the newly discovered growth poles in the deepwater areas of the northern South China Sea. Addressing the unclear key factors contributing to their formation, this study dissects successful global exploration cases of basement buried hill reservoirs and analyzes the common characteristics of basement reservoir accumulation under different basin types, structural backgrounds, basement lithologies, and oil and gas geological conditions. A three-element coupling relationship, termed “source-reservoir-cap”, is proposed as the dominant mechanism controlling basement buried hill reservoir formation. The genesis of these reservoirs requires adequate oil and gas supply, appropriately sized accumulation bodies, and effective sealing layers. The optimal configuration of the “source-reservoir-cap” relationship directly influences the efficient charging and preservation of oil and gas within basement buried hill reservoirs. Four configurations are identified, including circumstances such as the source-underlying low-positioned basement buried hill with a “source-reservoir cover docking migration type”, the source-border middle-positioned basement buried hill with a “source-reservoir lateral window docking migration type”, and the source-outside high-positioned basement buried hill with both “source-reservoir short-distance transport and migration type” and “source-reservoir long-distance transport and migration type”. The first to three models present favorable accumulation conditions. Based on the “source-reservoir-cap” three-element coupled model, this study identifies the Yunkai basement buried hill in the Pearl River Mouth Basin, the central depression in the Qiongdongnan Basin, and the northern and southern basement buried hills belts as crucial exploration targets in the deepwater areas of the northern South China Sea.

Abstract Research has recently grown on multi-agent systems (MAS) and their coordination and secure cooperative control, for example in the field of edge-cloud computing. MAS offers robustness and flexibility compared to centralized systems by distributing control across decentralized agents, allowing the system to adapt and scale without overhaul. The collective behavior emerging from agent interactions can solve complex tasks beyond individual capabilities. However, controlling high-order nonlinear MAS with unknown dynamics raises challenges. This paper proposes an enhanced genetic algorithm strategy to enhance secure cooperative control performance. An efficient encoding method, adaptive decoding schemes, and heuristic initialization are introduced. These innovations enable compelling exploration of the solution space and accelerate convergence. Individual enhancement via load balancing, communication avoidance, and iterative refinement intensifies local search. Simulations demonstrate superior performance over conventional algorithms for complex control problems with uncertainty. The proposed method promises robust, efficient, and consistent solutions by adapting to find optimal points and exploiting promising areas in the space. This has implications for securely controlling real-world MAS across domains like robotics, power systems, and autonomous vehicles.

Multimodal named entity recognition plays a crucial role in the construction process of knowledge graphs as it directly influences the quality of entity extraction and classification, which in turn affects the overall quality of knowledge graph construction. However, most existing multimodal named entity recognition algorithms do not consider the correlation between text and images. They either use visual features of images as the attention of the text modality or fuse them with textual features. In the case of multimodal tweets containing both text and images, three categories of data can be identified based on the correlation between the two: text that is related to images, text that is partially related to images, and text that is not related to images. Using irrelevant or partially relevant image features as text cross-modal attention can result in incorrect text representation, ultimately leading to misclassification of entities and negatively impacting the model’s performance. To address the problem of uncertainty or negative impact caused by the lack of relevance or partial correlation between text and images, this paper proposes a visually enhanced text representation algorithm based on a hybrid of dynamic gating and correlation coefficient. We conducted experiments on two benchmark datasets, namely Twitter-2015 and Twitter-2017. The experimental results were analyzed comprehensively to showcase the strengths of the proposed model.

Objective: Lactobacillus salivarius is a probiotic bacteria strain in human and animal diets. The administration of probiotics to weaned piglets may improve their growth by optimizing the gastrointestinal bacterial composition. To further investigate the effect of bacterial communication between the gastrointestinal tract and lungs on bodily immunity, we reared weaned piglets in a low-ammonia gas environment. L. salivarius was supplemented to explore its effects on pulmonary immunity and its potential for bacterial translocation. Results: One hundred sixty weaned piglets were allocated to four groups: L. salivarius-supplemented, L. reuteri-supplemented, control, and antibiotic drug (aureomycin)-supplemented. The feeding duration was 28 d. The body weights of piglets administered a strain of Lactobacillus were better than those of the control (P < 0.01). The transcription level of immune factors interleukin 2 (IL-2), IL-4, interferon α (IFN-α), and tumor necrosis factor α (TNF-α) in cells of the ileum and lung was significantly higher (P < 0.01). Lung and ileal mucus tissues were isolated to sequence the bacterial composition, which suggested a higher richness in the lungs at the phylum level, which was not significant in the ileum. Functional bacteria were more abundant in the ileum and lungs. The proportion of the genera of Lactobacillus, Prevotella, Actinobacillus, and Prevotellaceae_ NK3B31_group increased in two tissues, and a lower ratio of Streptococcus, Escherichia-Shigella, and mycoplasma was detected. The correlation between the microbial genus composition and the levels of immune factors suggests that the abundance of Lactobacillus plays the same positive role in the lungs and ileum. Mycoplasmas play a negative role in ileal and pulmonary immunity. More Lactobacillus reuteri and anaerobic probiotic bacteria were detected in the lungs. Conclusion: The colonization of Lactobacillus salivarius and Lactobacillus reuteri in the membrane of the ileum optimized the ileal microbial composition, enrolled other probiotic bacteria translating to the lung, improved the abundance of pulmonary microbiota, and enhanced immunity after exposure to low concentrations of ammonia.

ABSTRACTMetagenomic sequencing has proven to be a powerful tool in the monitoring of antimicrobial resistance (AMR). Here, we provide a comparative analysis of the resistome from pigs, poultry, veal calves, turkey, and rainbow trout, for a total of 538 herds across nine European countries. We calculated the effects of per-farm management practices and antimicrobial usage (AMU) on the resistome in pigs, broilers, and veal calves. We also provide an in-depth study of the associations between bacterial diversity, resistome diversity, and AMR abundances as well as co-occurrence analysis of bacterial taxa and antimicrobial resistance genes (ARGs) and the universality of the latter. The resistomes of veal calves and pigs clustered together, as did those of avian origin, while the rainbow trout resistome was different. Moreover, we identified clear core resistomes for each specific food-producing animal species. We identified positive associations between bacterial alpha diversity and both resistome alpha diversity and abundance. Network analyses revealed very few taxa–ARG associations in pigs but a large number for the avian species. Using updated reference databases and optimized bioinformatics, previously reported significant associations between AMU, biosecurity, and AMR in pig and poultry farms were validated. AMU is an important driver for AMR; however, our integrated analyses suggest that factors contributing to increased bacterial diversity might also be associated with higher AMR load. We also found that dispersal limitations of ARGs are shaping livestock resistomes, and future efforts to fight AMR should continue to emphasize biosecurity measures.IMPORTANCEUnderstanding the occurrence, diversity, and drivers for antimicrobial resistance (AMR) is important to focus future control efforts. So far, almost all attempts to limit AMR in livestock have addressed antimicrobial consumption. We here performed an integrated analysis of the resistomes of five important farmed animal populations across Europe finding that the resistome and AMR levels are also shaped by factors related to bacterial diversity, as well as dispersal limitations. Thus, future studies and interventions aimed at reducing AMR should not only address antimicrobial usage but also consider other epidemiological and ecological factors.

Scientific research on deep in situ resources is highly important to the theory and technology system construction for deep in-situ resource exploitation. To obtain high-condition preserved core samples, it is vital to maintain the original material, humidity and luminous flux information inside the core. Therefore, this study proposes a research and development strategy for a high-toughness and high-barrier sealing film based on the molecular structure design and filler synergistic enhancement via a deep solid-state sealing film using in situ substance preservation (ISP), in situ moisture preservation (IMP) and in situ light preservation (ILP) coring principles. A graphene/epoxy composite sealing film with a high barrier, high strength and high toughness was developed. The oxygen permeability of the film was 0.23 cm3/(m2·d), the water vapor permeability was 1.26 g/(m2·d), and the light transmittance was 0. The tensile strength reached 15.4 MPa, and the toughness was 5242.9 kJ/m3. The results from the film substance and moisture preservation performance verification experiments showed that the sealing film had an excellent sealing effect on small molecules, such as water, alkanes and even ions, which further verified that the sealing film greatly contributed to the maintenance and preservation of deep in-situ resource reserves and abundance.

The metal fibre surface combustion structure has the characteristics of strong thermal matching ability, short response time, and strong shape adaptability. It has more advantages in the thermal test of complex hypersonic vehicle surface inlet, leading edge, etc. In this paper, a method of aerodynamic thermal simulation test based on metal fibre surface combustion is proposed. The aim of the study was to create a uniform target heat flow on the inner wall surface of a cylindrical specimen by matching the gas jet flow rate and the geometry of the combustion surface. The research adopted the optimisation design method based on the surrogate model to establish the numerical calculation model of a metal fibre combustion jet heating cylinder specimen. One hundred sample points were obtained through Latin hypercube sampling, and a database of design parameters and heat flux was established through numerical simulation. The kriging surrogate model and the non-dominated sequencing genetic optimisation algorithm with elite strategy were adopted. A bi-objective optimisation design was carried out with the optimisation objective of the coincidence between the predicted and the target heat flux on the inner wall of the specimen. The results showed that the average relative errors of heat flow density on the specimen surface were 8.8% and 6% through the leave-one-out cross-validation strategy and the validation of six test sample points, respectively. The relative error values in most regions were within 5%, which indicates that the established kriging surrogate model has high prediction accuracy. Under the optimal solution conditions, the numerical calculation results of the heat flow on the inner wall of the specimen were in good agreement with the target heat flow values, with an average relative error of less than 5% and a maximum value of less than 8%. These results show that the optimisation design method based on the kriging surrogate model can effectively match the thermal test parameters of metal fibre combustion structures.

The bird’s-eye view (BEV) method, which is a vision-centric representation-based perception task, is essential and promising for future Autonomous Vehicle perception. It has advantages of fusion-friendly, intuitive, end-to-end optimization and is cheaper than LiDAR. The performance of existing BEV methods, however, would be deteriorated under the situation of a tire blow-out. This is because they quite rely on accurate camera calibration which may be disabled by noisy camera parameters during blow-out. Therefore, it is extremely unsafe to use existing BEV methods in the tire blow-out situation. In this paper, we propose a geometry-guided auto-resizable kernel transformer (GARKT) method, which is designed especially for vehicles with tire blow-out. Specifically, we establish a camera deviation model for vehicles with tire blow-out. Then we use the geometric priors to attain the prior position in perspective view with auto-resizable kernels. The resizable perception areas are encoded and flattened to generate BEV representation. GARKT predicts the nuScenes detection score (NDS) with a value of 0.439 on a newly created blow-out dataset based on nuScenes. NDS can still obtain 0.431 when the tire is completely flat, which is much more robust compared to other transformer-based BEV methods. Moreover, the GARKT method has almost real-time computing speed, with about 20.5 fps on one GPU.

The emergence of SARS-CoV-2 variants of concern (VOCs) with increased transmissibility and partial resistance to neutralization by antibodies has been observed globally. There is an urgent need for an effective vaccine to combat these variants. Our study demonstrated that the B.1.351 variant inactivated vaccine candidate (B.1.351V) generated strong binding and neutralizing antibody responses in BALB/c mice against the B.1.351 virus and other SARS-CoV-2 variants after two doses within 28 days. Immunized K18-hACE2 mice also exhibited elevated levels of live virus-neutralizing antibodies against various SARS-CoV-2 viruses. Following infection with these viruses, K18-hACE2 mice displayed a stable body weight, a high survival rate, minimal virus copies in lung tissue, and no lung damage compared to the control group. These findings indicate that B.1.351V offered protection against infection with multiple SARS-CoV-2 variants in mice, providing insights for the development of a vaccine targeting SARS-CoV-2 VOCs for human use.

The assessment of colleges’ effectiveness heavily relies on the employment status of graduates. Government-industry-university-research (GIUR) collaboration in cultivation talent is the key to improving the employment rate of college graduates. Based on the theoretical framework of the triple helix, this study develops a tripartite evolutionary game model that encompasses government, enterprises, and university research institutions. The research findings indicate (1) the evolutionary convergence of strategies among the subjects of the three-party game. (2) The attainment of a stable ideal evolution state for (1,1,1) is possible when the requisite conditions are met. This suggests that the cultivation of aligned talent in GIUR collaborations should coordinate the interests of various stakeholders. (3) Drawing inspiration from parameter-sensitive simulation, the problem of mismatch between talent cultivation and social demand can be effectively addressed through measures such as reducing the cost of cooperation, balancing the distribution of benefits, and implementing appropriate reward and punishment mechanisms. In response to these implications, we put forward some management insights and suggestions.

Cause marketing (CM) has become an important tool for firms to fulfill their social responsibility strategies. However, in reality, although some consumers have responded positively to the firm's CM strategies, others have doubts due to their lack of trust in the effectiveness of the firm or business. Therefore, in this paper, we consider a case that the supplier is a dual-purpose corporation that engage in a CM campaign and the consumer social preference is uncertainty and is unknown initially but can be resolved by the retailer's acquisition behaviour. By examining the two information acquisition strategies: committed acquisition and contingent acquisition. We find that, under either strategy, the retailer would like to acquire information only when the cost of information acquisition is small. Moreover, compared to contingent acquisition, the retailer is more willing to prefer committed acquisition. Additionally, we show that the supplier always prefers the committed acquisition strategy. However, the retailer's preference toward these two information acquisition strategies is related to the acquire cost. Specifically, when acquisition cost is small or large, the retailer is indifferent between these two strategies, when acquisition cost is in an intermediate range, the retailer will shift her strategy from the contingent strategy to the committed strategy. We also use numerical studies to illustrate main results. These findings provide theoretical support and management insights for managers to integrate CM into business transactions.

Introduction: The Panyu low uplift is located in the middle of the Central Uplift Belt of the Pearl River Mouth Basin, which has experienced multiple episodes of tectonic movements since the Cenozoic, and the faults are extensively developed there. The fault activities had a significant influence on the migration and accumulation of oil and gas, as well as the sedimentation.Methods: In order to recognize the effect of the faults on the tectono-straigraphy evolution in the Panyu low uplift, based on the high-resolution seismic and latest drilling well data, the study has comprehensively analyzed the geometry and kinematics of these faults, as well as the sedimentary filling evolution of the residual depressions.Results: The results show that two major fault systems has developed in the Panyu low uplift: the deep and shallow fault systems. The former was dominated by a series of NEE and NW-trending high-angle listric/plane faults, along with several low-angle detachment normal faults, which were under the joint control of the NW-SE and NS-trending extensional stress during the Eocene. The latter was dominated by NW-NWW trending strike-slip normal faults with tension-shear properties under the NEE trending dextral strike-slip stress field in the late Miocene. The uplift had undergone five tectonic evolution stages: the initial rifting stage (Tg-T83), the intensive rifting stage (T83-T80), the rifting and depression transitional stage (T80-T70), the thermal subsidence depression stage (T70-T35), and the tectonic reactivation stage (T35-T10).Discussion: Based on the 3D seismic phase interpretation and drilling sample analyses, the study also indicates that during the initial rifting stage, the Panyu low uplift manifested as multistage depositional center. The sedimentary strata were distributed in the downthrown wall of the depression-controlling faults, exhibiting multiple subsidence and sedimentary centers, and the depressions was supplied by multi-provenance systems. During the intense rifting period, the depression widened, the lacustrine basins were connected with each other, and the sedimentary center migrated. During the rifting and depression transitional stage, the faults controlling on deposition weakened. The thick Enping formation from the northwest provenance direction is unconformably overlaid on the Panyu low uplift. Meanwhile, the depressions was characterized by the sedimentary deposits of lacustrine basin. Various types of sedimentary facies were developed, including the delta, semi-deep lacustrine, shallow lacustrine, and shore-shallow lacustrine deposits, and the mudstone of the semi-deep lacustrine facies could serve as a high-quality source rock for the hydrocarbon production.

Because transformer plays an important role in power system, it rarely runs in fault or abnormal state. Therefore, it is difficult to obtain sufficient transformer fault samples. In this paper, aiming at the problem of less fault sample data and data imbalance, a novel data augmentation method based on kernel principal component analysis is proposed to non-linearly map the original data to a high-dimensional feature space. In this way, the new sample data retaining the feature information of the original data can be obtained. Second, the deep residual network is introduced with the identity path to construct the fault diagnosis model, which enables the weight parameters to be effectively transferred and updated. The simulation results show that the proposed method can effectively expand the data samples with high similarity with the original data, and the residual network model has strong feature extraction ability, which improves the accuracy of fault diagnosis.

The enterovirus A71 (EV71) inactivated vaccine is an effective intervention to control the spread of the virus and prevent EV71-associated hand, foot, and mouth disease (HFMD). It is widely administered to infants and children in China. The empty particles (EPs) and full particles (FPs) generated during production have different antigenic and immunogenic properties. However, the antigen detection methods currently used were established without considering the differences in antigenicity between EPs and FPs. There is also a lack of other effective analytical methods for detecting the different particle forms, which hinders the consistency between batches of products. In this study, we analyzed the application of sedimentation velocity analytical ultracentrifugation (SV-AUC) in characterizing the EPs and FPs of EV71. Our results showed that the proportions of the two forms could be quantified simultaneously by SV-AUC. We also determined the repeatability and accuracy of this method and found that both parameters were satisfactory. We assessed SV-AUC for bulk vaccine quality control, and our findings indicated that SV-AUC can be used effectively to analyze the percentage of EPs and FPs and monitor the consistency of the process to ensure the quality of the vaccine.

Abstract Current natural gas load forecasting encounters with the conundrum of unsatisfying accuracy and interpretability. To address the challenge, a multi‐variate forecasting method is proposed, which contains three phases: First, an integrate history‐climate‐holiday factor set is established to provide multi‐perspective for a more explainable forecast; Second, factor fusion interaction between features and instances is carried out based on hierarchical contrastive learning, which contributes to inter‐intra factors potential relationships exploration. Third, a multivariate forecasting model named ResRNN is trained using fused target dataset. Due to its innovation in structure and loss, forecasting accuracy is further improved. Finally, the authors’ method's superiority is confirmed by several groups of comparative experiments and results demonstrate that it outperforms mainstream methods.

Given the inherent volatility and intermittency of photovoltaic power generation, enhancing the precision of photovoltaic power predictions becomes imperative to ensure the stability of power systems and to elevate power quality. This article introduces an intelligent photovoltaic power prediction model based on the Extreme Learning Machine (ELM) with the Adaptive Spiral Dung Beetle Optimization (ASDBO) algorithm. The model aims to accurately predict photovoltaic power generation under multi-factor correlation conditions, including environmental temperature and solar irradiance. The computational efficiency in high-dimensional data feature conditions is enhanced by using the Pearson correlation analysis to determine the state input of the ELM. To address local optimization challenges in traditional Dung Beetle Optimization (DBO) algorithms, a spiral search strategy is implemented during the dung beetle reproduction and foraging stages, expanding the exploration capabilities. Additionally, during the dung beetle theft stage, dynamic adaptive weights update the optimal food competition position, and the levy flight strategy ensures search randomness. By balancing convergence accuracy and search diversity, the proposed algorithm achieves global optimization. Furthermore, eight benchmark functions are chosen for performance testing to validate the effectiveness of the ASDBO algorithm. By optimizing the input weights and implicit thresholds of the ELM through the ASDBO algorithm, a prediction model is established. Short-term prediction experiments for photovoltaic power generation are conducted under different weather conditions. The selected experimental results demonstrate an average prediction accuracy exceeding 93%, highlighting the effectiveness and superiority of the proposed methodology for photovoltaic power prediction.

In order to ensure reliability and security of power supply, the situation awareness of some elements in the distribution network is studied in this paper. Considering the influence of the interference signal, Empirical Mode Decomposition (EMD) is introduced to reduce it. Subsequently, according to operation characteristics of the actual distribution network, several evaluation indexes are proposed to describe the operation situation of the overall distribution network and internal elements, especially evaluation of fragile nodes. Based on these indexes and characteristics of actual distribution network, the comprehensive evaluation method of fragile nodes (CEMFN) is constructed, which mainly aims at the fragile nodes in the distribution network and synthesizes the single index coefficient to realize the identification of fragile nodes. Through the simulation calculation, it can be seen that the result of CEMFN is more accurate than that of the traditional evaluation model, which provides an important theoretical support for the operation and maintenance of distribution network and reliable power supply.

In order to solve the cooperative control problem among multiple distributed units in a distributed DC microgrid, a distributed control based on a consensus algorithm is firstly proposed, which can realize power distribution among distributed units. The distributed voltage control of the observer will generate steady-state errors due to problems such as time delay initial value of the observer. A voltage optimization control strategy based on the PI consistency algorithm is further proposed, which converts the voltage control problem into an optimization problem, and the optimization goal is to ensure the deviation sum of each busbar from the rated value is the smallest, and the stability of this method is proved. Finally, the effectiveness of the proposed method is verified by MATLAB/Simulink software.

Abstract Gaussian processes (GP) based extended target tracking (ETT) technique has attracted the attention of scientists since it can accurately estimate the kinematic states as well as the contour states of irregular-shape targets. Most traditional GP methods consider the ETT problem with a linear measurement model in Cartesian coordinates and ignore the input uncertainty of GP for simplicity. In many applications, however, measurements are generated with high-resolution sensors in polar coordinates. More importantly, the ignorance of input uncertainty in GP will exacerbate the tracking performance in these cases. In order to track an irregular-shape extended target in polar coordinates with input uncertainty, an improved GP-based probabilistic data association (IGP-PDA) algorithm is developed that includes the following enhancements: Firstly, a nonlinear measurement model is used and the unbiased converted measurement technique is invoked for the ETT problem. Secondly, the analytical form of statistical properties of the input uncertainty due to measurements noise and predicted error is derived. Thirdly, an IGP-PDA taking into account measurement origin uncertainty as well as input uncertainty is proposed, and three sub-optimal implementations are provided. Last, the posterior Cramer-Rao lower bound of ETT with input uncertainty is derived. Simulation results verify the effectiveness of the proposed method.

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.

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 large-scale energy storage system, the large number of energy storage units leads to inconsistent of State of Charge and unbalanced sharing of output power. In order to solve this problem, a consensus control protocol design and analysis method based on specified sampled-data is proposed. The consensus of SOC and output power (normalization) of each energy storage unit in large-scale energy storage system has been achieved. Firstly, according to the definition of lithium battery SOC and discharge mechanism, a system model of large-scale energy storage system is established. Based on specified sampled-data control, consensus control protocol of multi-agent of energy storage units is designed, and system convergence analysis is carried out. Meantime, the constraints of sampling period T are derived. It is proved that local energy storage unit agent can generate local energy storage unit control signal only when it receives the state information of adjacency energy storage unit agent at a specified sampling time point. In this way, the problem of too much computation caused by the complex communication of large-scale energy storage system is solved. Considering specified sampled-data control and system requirements, the system control parameters are designed. This method is simple in control and takes up less system communication resources. Finally, the simulation results are shown that the method is effective in different application scenarios.

In this paper, Lepista sordida polysaccharides (LSP) were separated from Lepista sordida (L. sordida) mainly using the Ultrasonic-Micro Wave Synergy Extraction (UMSE) method and purified by graded alcohol precipitation. Three polysaccharide components: 40%-LSP-UMSE, 60%-LSP-UMSE, and 80%-LSP-UMSE were obtained and further analyzed the physicochemical properties, structural characteristics, and antioxidant activity. And the effects on the proliferation of Lactobacillus casei of three polysaccharide components were studied. The characteristic absorption peaks and the β-glycosidic bond of three polysaccharide components were the direct expression at UV 200 nm using UV and FT-IR spectroscopy. The three polysaccharide components were mainly composed of glucose, mannose, galactose, and ribose using high-performance liquid chromatography (HPLC) analysis. The antioxidant activity study revealed that the polysaccharides obtained by the UMSE method had better antioxidant activity compared to the traditional “Hot Water Extraction (HWE)” method. In addition, the polysaccharide components promoted the proliferation of L. casei to some extent. 40%-LSP-UMSE, 80%-LSP-UMSE as the carbon source had better acid production than the control inulin. Three LSP-UMSE used as a carbon source compared with glucose for culturing L. casei could significantly improve its tolerance to bile salts. Results are helpful to develop the bioactive polysaccharides from Lepista sordida and beneficial to develop a unique health and functional product in the future.

High silicon aluminum alloy is a kind of high-performance electronic packaging material, due to its excellent thermal conductivity, low thermal expansion coefficient, and low density. The heat affected zone (HAZ) of conventional welding techniques is too wide to weld the high silicon aluminum alloy, as wide HAZ will improve risk of cracking. Laser welding with narrow HAZ, high joint strength, and good morphology, has been considered as a promising method to weld high silicon aluminum alloy. Herein, the surface morphology, microstructure, and microhardness of laser welding joint between Al-50wt.%Si and Al-27wt.%Si alloys were investigated. It has been found that welding speed at 3 mm–5 mm/min can significantly reduce the hydrogen pores and cracks in laser welding joints. Also, the key paraments of laser welding to control the quality of joints are power and pulse width, while the former one is mainly controlling the joint width and the latter one is significantly changing the joint depth. Along the horizontal direction of laser welding joints, the microhardness increases firstly and then decreases, with the highest microhardness of 167 HV in the weld zone. Because the laser welding method could produce uniform microstructure of joints, the microhardness of joints is uniform along the longitudinal direction. The tensile properties of welding samples are almost the same with the properties of the Al-50wt.%Si base materials.

Electromigration is the massive metal atom transport due to electron flow, which could induce a disconnect in electronics. Due to the size of copper pillar bump reduction, the portion of interfacial intermetallic compound in solder joints is increasing obviously. However, there is lack of systematical research on the effects of intermetallic compound on the EM lifetime of solder joints. In this paper, the interfacial intermetallic compound of copper pillar joints is modified to extend the electromigration lifetime. The growth rate of intermetallic compound in solder joints sample is calculated firstly. From 230°C to 250°C, the growth rate of intermetallic compound increases from 0.09 μm/min to 0.19 μm/min. With a longer reaction time, the intermetallic compound layers continuously grow. Then electromigration tests were conducted under thermo-electric coupling loading of 100°C and 1.0 × 104 A/cm2. Compared with lifetime of thin and thick intermetallic compound samples, the lifetime of all intermetallic compound sample improved significantly. The lifetime of thin, thick, and all intermetallic compound samples is 400 min, 300 min, and 1,200 min, respectively. The failure mechanism for the thin intermetallic compound sample is massive voids generation and aggregation at the interface between solder joints and pads. For the thick intermetallic compound sample, the intermetallic compound distance is short between cathode and anode in solder joints, leading to lots of crack create in the middle of solder joints. As the all intermetallic compound sample can greatly reduce the number of voids generated by crystal structure transforming, the lifetime extend obviously.

The objectives of this study are to optimize the ultrasonic microwave synergistic extraction (UMSE) of anthocyanins from Lycium ruthenicum Murr (ALRM) by response surface methodology and also to investigate its antioxidant activity in vitro. Based on the single-factor experiments, we optimized the ALRM extraction process by response surface methodology assuming anthocyanin extraction rate as the response point and microwave power, ultrasonic power, extraction time, and liquid-to-material ratio as the investigating parameters as well as performed correlation analysis between DPPH·, ·OH, ABTS·, O2-· radical scavenging potential, and ferric-reducing antioxidant power. The results showed that the influence on anthocyanin yield was in the order of liquid to material ratio > microwave power > ultrasonic power > extraction time; the elicited extraction conditions were: ultrasonic power 216.253 W, microwave power 89.311 W, time 26.141 min, liquid to material ratio 17.294 mL/g, the forecast content was 10.157 mg/g, while the actual value was consistent with the preview value. Meanwhile, ALRM obtainable by UMSE was identified as yielding favorable antioxidant properties firstly, when the concentration of purified ALRM (ALRM-1) was increased, the antioxidant capacity was strengthened. Theoretically, we provide a basis for the extraction procedure of ALRM and its antioxidant activity, which serves as a promising antioxidant and free radical scavenger.

The polysaccharides derived from various deproteinization methods were prepared from Flos Sophorae Immaturus (FSI) to investigate the prebiotic efficacy of Lactobacillus fermentum (L.f ). The implications of polysaccharides from FSI (PFSI) gained after purification performed by non-deproteinization and different deproteinization processes (Savage method, papain method, and TCA method) via one-factor optimization were firstly investigated for the influences on the growth of L.f. The utilization of carbohydrate sources and the synthesis of protein and lactate during its growth were analyzed, as well as the variations of LDH, SOD, and GSH- Px enzyme dynamics. The results showed that the one-factor optimization of the deproteinization process with the protein removal rate and polysaccharide retention rate as the indexes led to the optimal methods of the Sevage method with 5 elution times, papain method with 80 U/mL concentration, and TCA method with 2.5 ratio, respectively. In addition, the PFSI obtained with or without deproteinization purification had a certain effect on promoting L.f proliferation. Moreover, the PFSI gained by the third deproteinization purification, at a concentration of 10 g/L, significantly elevated L.f biomass and growth rate compared with the blank control, and the utilization of reducing sugars and the synthesis of protein and lactic acid were higher than the control (P < 0.05); improved LDH, SOD, and GSH-Px activity in L.f (P < 0.05), and the TCA method could be effectively applied to eliminate the proteins affecting FSI in probiotics, and PFSI may be a potentially beneficial prebiotic and intestinal reinforcer.

The inhibitory effects of the anthocyanin obtained from Lycium ruthenicum Murr were tested against several food-borne pathogens were evaluated, such as Staphylococcus aureus (S. aureus), Escherichia coli (E. coli), Aspergillus niger and Penicillium sp. In general, anthocyanin had different antibacterial effect on different bacteria, and the best antibacterial effect on S. aureus, with minimal inhibitory concentration (MIC) of 3.125 mg/mL. Anthocyanin increased the surface hydrophobicity of S. aureus, discharged the intracellular K+, and reduced the total soluble protein, affecting protein synthesis. Fluorescent inverted microscope and flow cytometry (FCM) found a significant increase in fluorescence intensity and lethality relative to the control group, and the dead P3 region to 77.21%. The above suggested a correlation between the antibacterial mechanism of anthocyanin and cell membrane permeability integrity. Biofilm formation was evaluated by the crystal violet assay (CV), silver staining method and methyl thiazolyl tetrazolium (MTT). Scanning electron microscopy (SEM) showed that anthocyanins could change the structure of biofilm.

High-temperature thermal stimulation (HTS) can effectively remove the damage caused by water phase trapping during the drilling and completion of tight oil and hydraulic fracturing of gas reservoirs. Since HTS induces new rock fracture, the permeability of a reservoir will be increased to a certain extent. However, there are no standardized methodologies for pretreatment, experimental procedures, or evaluation indicators for HTS rock samples. Taking the Longmaxi Formation shale from the Sichuan Basin as the research object, an experimental process for evaluating the impact of HTS on permeability was established. The temperature increased at a rate of 5 °C/min, considering the conditions of dry and water-bearing rock samples. Three variables are included in the evaluation index for HTS of shale reservoirs: threshold temperature, raise multiples of permeability in 4 MPa confining pressure, and in-situ conditions. The results show that the threshold temperature of the dry shale is 650–700 °C, while the water-bearing shale has two threshold temperatures. Furthermore, the low temperature is 100–150 °C, while the high temperature is 450–500 °C. The permeability increase ratio of drying shale samples is 1.5–10.0 after HTS under in-situ effective stress, while that of water-bearing shale samples reach 20–50. This research has shown that using the HTS method to reduce water phase trapping damage and drastically improves well production are being conducted in a shale gas reservoir with horizontal wells and staged hydraulic fracturing. Our recommended experimental methods and evaluation indicators are effective in establishing the HTS potential of a shale reservoir.

The efficient heat dissipation of electronic equipment is very important, its heat dissipation performance directly determines the life of the equipment itself. A hand-held electronic communications equipment, when used in surface temperature is exorbitant, need to heat dissipation equipment efficiently, to ensure that the use of comfort in the handheld. In accordance with this requirement, this article presents a flexible composite material based on nano-efficient cooling methods that can keep the layout, through the improvement of internal thermal path, it can achieve the effective heat dissipation. The network thermal resistance method is used to analyze the heat transfer in the equipment, and the thermal analysis of the local thermal resistance is carried out. At the same time, through the modeling of electronic equipment and the analysis of finite elements, the temperature drop of the equipment after improvement is accurately judged. Finally, the device experimental performance comparison before and after the optimization of the standby mode and working mode is verified. The results show that the optimized equipment heat source temperature can be reduced by up to 8.5°C, the surface temperature of the equipment can be reduced by about 5°C~7°C, and the final control equipment in the steady standby state of the temperature of about 39±0.5°C, to ensure the comfort of use, and also improved the service life of the equipment. The efficient thermal design of electronic equipment based on flexible nanocomposites can provide a convenient and reliable cooling solution for high-heat flow density devices.

Amblyopia is a common eye disease characterized by impaired best-corrected visual acuity. It starts in early childhood and leads to permanent vision reduction if left untreated. Even though many young patients with amblyopia are well treated in clinical practice, the underlying mechanism remains to be elucidated, which limits not only our understanding of this disease but also the therapeutic approach. To investigate the molecular mechanism of amblyopia, primate and rodent models of monocular-deprived amblyopia were created for mRNA screening and confirmation. We obtained 818 differentially expressed genes from the dorsal lateral geniculate nucleus (dLGN) of a primate model of amblyopia. After Gene Ontology and kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses, the main enriched pathways were related to neural development. Interestingly, a particular neurotransmitter pathway, the dopaminergic pathway, was identified. The downregulation of dopamine receptor D1 (DRD1) was confirmed in both monkey and mouse samples. Furthermore, the immunofluorescence staining indicated that DRD1 expression was downregulated in both ventrolateral region of the contralateral dLGN and the dorsomedial region of the ipsilateral dLGN in the mouse model. The regions with downregulated expression of DRD1 were the downstream targets of the visual projection from the amblyopic eye. This study suggested that the downregulation of DRD1 in the LGN may be a cause for amblyopia. This may also be a reason for the failure of some clinical cases of levodopa combined with carbidopa applied to amblyopes.

Ensuring the control of power systems is crucial for their safe operation. This paper analyses the robust controllability of complex power systems from the structural sighta structural point of view. Stressing the dominant role of generators in the control of power systems, we propose three kinds of controllable networks by generator nodes. Additionally, the satisfied conditions and the relevant proof of zero forcing set in the controllable networks by generator nodes and extra nodes are given. Besides, tThe satisfied conditions and the relevant proofs of the largest set of removable edges that have no -effect on the strong structural controllability in three kinds of controllable networks by generator nodes are also proposed. Finally, the robustness of strong structural controllability of IEEE 39 bus system and IEEE 14 bus system have been analyzed. The zero-forcing set and the largest set of removable edges of IEEE 39 bus system and IEEE 14 bus system are provided.

Abstract In the presence of power disturbance, the test accuracy of power is not good, in order to improve the performance of power testing, it is necessary to carry out the power disturbance detection design. A power disturbance detection algorithm based on wavelet analysis and neural network is proposed. The time domain and frequency domain decomposition are used to decompose the characteristic of the power disturbance signal, and the wavelet analysis method is used to improve the adaptive focusing performance of the power disturbance signal. The one-dimensional power disturbance function is mapped to the two-dimensional function of time scale and time shift by continuous wavelet transform, and the higher-order spectral characteristic quantity of the power disturbance signal is extracted, and the extracted characteristic quantity is automatically classified by neural network. The optimal identification and detection of power disturbance are realized. The simulation results show that the algorithm has higher accuracy and higher recognition ability, which improves the accurate probability of power disturbance detection and the anti-disturbance ability of power test.

With the advent of the “Internet plus” era, the Internet of Things (IoT) is gradually penetrating into various fields, and the scale of its equipment is also showing an explosive growth trend. The age of the “Internet of Everything” is coming. The integration and diversification of IoT terminals and applications make IoT more vulnerable to various intrusion attacks. Therefore, it is particularly important to design an intrusion detection model that guarantees the security, integrity and reliability of the IoT. Traditional intrusion detection technology has the disadvantages of low detection rate and poor scalability, which cannot adapt to the complex and changeable IoT environment. In this paper, we propose a particle swarm optimization-based gradient descent (PSO-LightGBM) for the intrusion detection. In this method, PSO-LightGBM is used to extract the features of the data and inputs it into one-class SVM (OCSVM) to discover and identify malicious data. The UNSW-NB15 dataset is applied to verify the intrusion detection model. The experimental results show that the model we propose is very robust in detecting either normal or various malicious data, especially small sample data such as Backdoor, Shellcode and Worms.

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 With the development of high‐voltage transmission and artificial intelligence technology, unmanned line inspection has become the inevitable trend of current electric power inspection. A new recognition algorithm for high‐voltage lines is proposed based on colour (Red, Green, Blue) RGB image to support the unmanned line inspection. Firstly, in order to solve the problem of missing weak edges in image edge detection, an improved Canny algorithm is proposed. Fourier transform Gaussian filter is introduced to enhance the high‐frequency signal of the image, which makes the extracted edge information more complete. At the same time, an improved line segment detector (LSD) algorithm is developed to extract the high‐voltage line. The complementary edge information of the three channels of the colour RGB image is analyzed, and the calculation formula of the horizontal line angle is improved, which greatly reduces the possibility of false detection and missed detection in the high‐voltage line extraction. In addition, the convolution neural network (CNN) is used to accurately recognize the extracted high‐voltage lines, which reduces the interference of non–high‐voltage lines. Simulation results show that the proposed algorithm has high recognition accuracy and strong robustness in the complex environment.

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.

The development of ultra-large-scale energy storage system(ESS) is beneficial to integrate the real-time renewable energy generation with uncertainty and intermittent features and provide effective capacity support for the power grid. Since renewable energy has strongly decentralized feature, it is effective way to develop distributed dispatch strategy for storage devices to integrate them. This paper focuses on built a multi-timescale dispatch model for ESS. The goal of the proposed model is to minimize the day-aheadoperation cost with better charging/discharging operation allocation for each ESS in each dispatch interval during one day, and smooth the real-time fluctuation s of renewable energy. To address this issue, this paper presents a distributed multi-timescale and discrete-time-gradient optimization algorithm by combining consensus and optimization theories. By using this method, each ESS can response to the change of renewable generation and obtain the optimal operation by using local information share, resulting in better flexibility, robustness, scalability and privacy, etc. Thus, it is suitable for distributed model development and optimal dispatch for ultra-large-scale ESS. Finally, the paper is tested on a system with eleven ESSs and five renewable energy generations. The simulation results show the feasibility and effectiveness of the proposed model and algorithm.

Android robots capable of emotional interactions with humans have considerable potential for application to research. While several studies developed androids that can exhibit human-like emotional facial expressions, few have empirically validated androids’ facial expressions. To investigate this issue, we developed an android head called Nikola based on human psychology and conducted three studies to test the validity of its facial expressions. In Study 1, Nikola produced single facial actions, which were evaluated in accordance with the Facial Action Coding System. The results showed that 17 action units were appropriately produced. In Study 2, Nikola produced the prototypical facial expressions for six basic emotions (anger, disgust, fear, happiness, sadness, and surprise), and naïve participants labeled photographs of the expressions. The recognition accuracy of all emotions was higher than chance level. In Study 3, Nikola produced dynamic facial expressions for six basic emotions at four different speeds, and naïve participants evaluated the naturalness of the speed of each expression. The effect of speed differed across emotions, as in previous studies of human expressions. These data validate the spatial and temporal patterns of Nikola’s emotional facial expressions, and suggest that it may be useful for future psychological studies and real-life applications.

Abstract Background Human endogenous retroviruses (HERVs), suspected to be transposition-defective, may reshape the transcriptional network of the human genome by regulatory elements distributed in their long terminal repeats (LTRs). HERV-K (HML-2), the most preserved group with the least number of accumulated of mutations, has been associated with aberrant gene expression in tumorigenesis and autoimmune diseases. Because of the high sequence similarity between different HERV-Ks, current methods have limitations in providing genome-wide mapping specific for individual HERV-K (HML-2) members, a major barrier in delineating HERV-K (HML-2) function. Results In an attempt to obtain detailed distribution information of HERV-K (HML-2), we utilized a PCR-based target enrichment sequencing protocol for HERV-K (HML-2) (PTESHK) loci, which not only maps the presence of reference loci, but also identifies non-reference loci, enabling determination of the genome-wide distribution of HERV-K (HML-2) loci. Here we report on the genomic data obtained from three individuals. We identified a total of 978 loci using this method, including 30 new reference loci and 5 non-reference loci. Among the 3 individuals in our study, 14 polymorphic HERV-K (HML-2) loci were identified, and solo-LTR330 and N6p21.32 were identified as polymorphic for the first time. Conclusions Interestingly, PTESHK provides an approach for the identification of the genome-wide distribution of HERV-K (HML-2) and can be used for the identification of polymorphic loci. Since polymorphic HERV-K (HML-2) integrations are suspected to be related to various diseases, PTESHK can supplement other emerging techniques in accessing polymorphic HERV-K (HML-2) elements in cancer and autoimmune diseases.

Unit commitment is an intractable issue aiming to reduce the overall economic cost of power system operation while maintaining the system constraints. Due to the emerging scenario of global warming, many countries are vigorously developing renewable energy to replace the traditional fossil power plant, in order to reduce the environmental and carbon emission. The increasing penetration of renewable generation significantly challenge the economic and security of power system operation. In this paper, a low carbon multi-objective objective unit commitment model considering economic cost, environmental cost and, more importantly, the carbon emission is established, integrating wind and solar power and therefore generating a multi-objective, high-dimensional, strong non-linear, multi-constraint and mixed integer optimization problem. The non-dominated sorting genetic algorithm-III is tailored and adopted for solving the proposed challenging task, where the decision-making scheme is designed according to the normalization method and weighted sum function. Numerical results show that the proposed complex many-objective low carbon unit commitment model can be successfully solved by the proposed algorithm and the carbon emission is effectively reduced by the integration of renewable generations.

In the vision task of a self-driving system, the use of visible light images to segment an object often loses its functionality at night or in harsh weather. The far-infrared image shows different pixel values according to the thermal radiation quantity of the object itself, so it can be adapted to perform well at night and in harsh weather conditions. However, at the same time, it has insufficient texture features, blurred object boundaries and temperature inversion, which has a great impact on the segmentation task of traditional algorithms. In response to the above problems, this article proposes a far-infrared object segmentation algorithm using deep learning. In the current popular encoding-decoding structure, multi-scale pooling layers are used to obtain receptive fields of different sizes. This is used to solve the effects caused by the blurring of infrared objects. The feature enhancement module is designed for the multi-receptive field feature map, which can filter out the most versatile and highly semantic feature channels to reduce the effect of temperature inversion on segmentation. The obtained high semantic feature map is guided into the decoding structure and is fused with the features obtained by the encoder and the decoder. This allows richer information to be obtained between different feature maps. Finally, we also release a new low-resolution far-infrared segmentation dataset. Experiments are performed on three datasets, and the segmentation result of the mIoU(mean Intersection over Union) reaches 70.59%, 30.98% and 60.67%. A large number of experiments confirm the effectiveness and robustness of the network in far-infrared images and verify that the dataset released in this article has strong reference significance.

This paper focuses on the optimal scheduling of the district energy system with multiple energy supply modes and flexible loads. For multi-energy system (MES), the energy hub (EH) model including energy storage system and integrated electric vehicle (EV) is established. Based on the model, the influence of pollutant trading market on total operation cost is analyzed, and the optimal scheduling strategy is further put forward to realize the minimum purchase cost and emission tax cost of the MES. Finally, this paper compares the economic benefit of the fixed mode and the response mode, and discusses the contribution of the energy storage device and the multi-energy complementary mode to energy utilization efficiency. The simulation results indicate that optimal scheduling strategy of the EH can coordinate various energy complementary modes reasonably. Meanwhile, the proposed strategy is able to improve the operation economy of the EH, and ensure the better response effect of the demand side. The sensitivity analysis demonstrates the impact of pollutant emission price change on emission reduction.