In situ calibrated angle between the quantization axis and the propagating direction of the light field for trapping neutral atoms

The recently developed magic-intensity trapping technique of neutral atoms efficiently mitigates the detrimental effect of light shifts on atomic qubits and substantially enhances the coherence time. This technique relies on applying a bias magnetic field precisely parallel to the wave vector of a circularly polarized trapping laser field. However, due to the presence of the vector light shift experienced by the trapped atoms, it is challenging to precisely define a parallel magnetic field, especially at a low bias magnetic field strength, for the magic-intensity trapping of85Rb qubits. In this work, we present a method to calibrate the angle between the bias magnetic field and the trapping laser field with the compensating magnetic fields in the other two directions orthogonal to the bias magnetic field direction. Experimentally, with a constantdepth trap and a fixed bias magnetic field, we measure the respective resonant frequencies of the atomic qubits in a linearly polarized trap and a circularly polarized one via the conventional microwave Rabi spectra with different compensating magnetic fields and obtain the corresponding total magnetic fields via the respective resonant frequencies using the Breit–Rabi formula. With known total magnetic fields, the angle is a function of the other two compensating magnetic fields.Finally, the projection value of the angle on either of the directions orthogonal to the bias magnetic field direction can be reduced to 0(4)° by applying specific compensating magnetic fields. The measurement error is mainly attributed to the fluctuation of atomic temperature. Moreover, it also demonstrates that, even for a small angle, the effect is strong enough to cause large decoherence of Rabi oscillation in a magic-intensity trap. Although the compensation method demonstrated here is explored for the magic-intensity trapping technique, it can be applied to a variety of similar precision measurements with trapped neutral atoms.

A Systematic Review of Computer Vision Techniques for Quality Control in End-of-Line Visual Inspection of Antenna Parts

The rapid evolution of wireless communication technologies has underscored the critical role of antennas in ensuring seamless connectivity.Antenna defects,ranging from manufacturing imperfections to environmental wear,pose significant challenges to the reliability and performance of communication systems.This review paper navigates the landscape of antenna defect detection,emphasizing the need for a nuanced understanding of various defect types and the associated challenges in visual detection.This review paper serves as a valuable resource for researchers,engineers,and practitioners engaged in the design and maintenance of communication systems.The insights presented here pave the way for enhanced reliability in antenna systems through targeted defect detection measures.In this study,a comprehensive literature analysis on computer vision algorithms that are employed in end-of-line visual inspection of antenna parts is presented.The PRISMA principles will be followed throughout the review,and its goals are to provide a summary of recent research,identify relevant computer vision techniques,and evaluate how effective these techniques are in discovering defects during inspections.It contains articles from scholarly journals as well as papers presented at conferences up until June 2023.This research utilized search phrases that were relevant,and papers were chosen based on whether or not they met certain inclusion and exclusion criteria.In this study,several different computer vision approaches,such as feature extraction and defect classification,are broken down and analyzed.Additionally,their applicability and performance are discussed.The review highlights the significance of utilizing a wide variety of datasets and measurement criteria.The findings of this study add to the existing body of knowledge and point researchers in the direction of promising new areas of investigation,such as real-time inspection systems and multispectral imaging.This review,on its whole,offers a complete study of computer vision approaches for quality control in antenna parts.It does so by providing helpful insights and drawing attention to areas that require additional exploration.

Different coding characteristics between flight and freezing in dorsal periaqueductal gray of mice during exposure to innate threats

Background:Flight and freezing are two vital defensive behaviors that mice display to avoid natural enemies.When they are exposed to innate threats,visual cues are processed and transmitted by the visual system into the emotional nuclei and finally transmitted to the periaqueductal gray(PAG)to induce defensive behaviors.However,how the dorsal PAG(dPAG)encodes the two defensive behaviors is unclear.Methods:Multi-array electrodes were implanted in the dPAG nuclei of C57BL/6 mice.Two kinds of visual stimuli(looming and sweeping)were used to induce defensive behaviors in mice.Neural signals under different defense behaviors were recorded,and the encoding characteristics of the two behaviors were extracted and analyzed from spike firing and frequency oscillations.Finally,synchronization of neural activity during the defense process was analyzed.Results:The neural activity between flight and freezing behaviors showed different firing patterns,and the differences in the inter-spike interval distribution were mainly reflected in the 2–10 ms period.The frequency band activities under both defensive behaviors were concentrated in the theta band;the active frequency of flight was~8to 10 Hz,whereas that of freezing behavior was~6 to 8 Hz.The network connection density under both defense behaviors was significantly higher than the period before and after defensive behavior occurred,indicating that there was a high synchronization of neural activity during the defense process.Conclusions:The dPAG nuclei of mice have different coding features between flight and freezing behaviors;during strong looming stimulation,fast neuro-i nstinctive decision making is required while encountering weak sweeping stimulation,and computable planning late behavior is predicted in the early stage.The frequency band activities under both defensive behaviors were concentrated in the theta band.There was a high synchronization of neural activity during the defense process,which may be a key factor triggering different defensive behaviors.

Evolutionary Multitasking With Global and Local Auxiliary Tasks for Constrained Multi-Objective Optimization

Constrained multi-objective optimization problems(CMOPs) include the optimization of objective functions and the satisfaction of constraint conditions, which challenge the solvers.To solve CMOPs, constrained multi-objective evolutionary algorithms(CMOEAs) have been developed. However, most of them tend to converge into local areas due to the loss of diversity. Evolutionary multitasking(EMT) is new model of solving complex optimization problems, through the knowledge transfer between the source task and other related tasks. Inspired by EMT, this paper develops a new EMT-based CMOEA to solve CMOPs, in which the main task, a global auxiliary task, and a local auxiliary task are created and optimized by one specific population respectively. The main task focuses on finding the feasible Pareto front(PF), and global and local auxiliary tasks are used to respectively enhance global and local diversity. Moreover, the global auxiliary task is used to implement the global search by ignoring constraints, so as to help the population of the main task pass through infeasible obstacles. The local auxiliary task is used to provide local diversity around the population of the main task, so as to exploit promising regions. Through the knowledge transfer among the three tasks, the search ability of the population of the main task will be significantly improved. Compared with other state-of-the-art CMOEAs, the experimental results on three benchmark test suites demonstrate the superior or competitive performance of the proposed CMOEA.

Electric-controlled pressure relief valve for enhanced safety in liquid-cooled lithium-ion battery packs

The liquid-cooled battery energy sto rage system(LCBESS) has gained significant attention due to its superior thermal management capacity.However,liquid-cooled battery pack(LCBP) usually has a high sealing level above IP65,which can trap flammable and explosive gases from battery thermal runaway and cause explosions.This poses serious safety risks and challenges for LCBESS.In this study,we tested overcharged battery inside a commercial LCBP and found that the conventionally mechanical pressure relief valve(PRV) on the LCBP had a delayed response and low-pressure relief efficiency.A realistic 20-foot model of an energy storage cabin was constructed using the Flacs finite element simulation software.Comparative studies were conducted to evaluate the pressure relief efficiency and the influence on neighboring battery packs in case of internal explosions,considering different sizes and installation positions of the PRV.Here,a newly developed electric-controlled PRV integrated with battery fault detection is introduced,capable of starting within 50 ms of the battery safety valve opening.Furthermore,the PRV was integrated with the battery management system and changed the battery charging and discharging strategy after the PRV was opened.Experimental tests confirmed the efficacy of this method in preventing explosions.This paper addresses the safety concerns associated with LCBPs and proposes an effective solution for explosion relief.

Resource Allocation for IRS Assistedmm Wave Wireless Powered Sensor Networks with User Cooperation

In this paper,we investigate IRS-aided user cooperation(UC)scheme in millimeter wave(mmWave)wirelesspowered sensor networks(WPSN),where two single-antenna users are wireless powered in the wireless energy transfer(WET)phase first and then cooperatively transmit information to a hybrid access point(AP)in the wireless information transmission(WIT)phase,following which the IRS is deployed to enhance the system performance of theWET andWIT.We maximized the weighted sum-rate problem by jointly optimizing the transmit time slots,power allocations,and the phase shifts of the IRS.Due to the non-convexity of the original problem,a semidefinite programming relaxation-based approach is proposed to convert the formulated problem to a convex optimization framework,which can obtain the optimal global solution.Simulation results demonstrate that the weighted sum throughput of the proposed UC scheme outperforms the non-UC scheme whether equipped with IRS or not.

Multiobjective Differential Evolution for Higher-Dimensional Multimodal Multiobjective Optimization

In multimodal multiobjective optimization problems(MMOPs),there are several Pareto optimal solutions corre-sponding to the identical objective vector.This paper proposes a new differential evolution algorithm to solve MMOPs with higher-dimensional decision variables.Due to the increase in the dimensions of decision variables in real-world MMOPs,it is diffi-cult for current multimodal multiobjective optimization evolu-tionary algorithms(MMOEAs)to find multiple Pareto optimal solutions.The proposed algorithm adopts a dual-population framework and an improved environmental selection method.It utilizes a convergence archive to help the first population improve the quality of solutions.The improved environmental selection method enables the other population to search the remaining decision space and reserve more Pareto optimal solutions through the information of the first population.The combination of these two strategies helps to effectively balance and enhance conver-gence and diversity performance.In addition,to study the per-formance of the proposed algorithm,a novel set of multimodal multiobjective optimization test functions with extensible decision variables is designed.The proposed MMOEA is certified to be effective through comparison with six state-of-the-art MMOEAs on the test functions.

Constraints Separation Based Evolutionary Multitasking for Constrained Multi-Objective Optimization Problems

Constrained multi-objective optimization problems(CMOPs)generally contain multiple constraints,which not only form multiple discrete feasible regions but also reduce the size of optimal feasible regions,thus they propose serious challenges for solvers.Among all constraints,some constraints are highly correlated with optimal feasible regions;thus they can provide effective help to find feasible Pareto front.However,most of the existing constrained multi-objective evolutionary algorithms tackle constraints by regarding all constraints as a whole or directly ignoring all constraints,and do not consider judging the relations among constraints and do not utilize the information from promising single constraints.Therefore,this paper attempts to identify promising single constraints and utilize them to help solve CMOPs.To be specific,a CMOP is transformed into a multitasking optimization problem,where multiple auxiliary tasks are created to search for the Pareto fronts that only consider a single constraint respectively.Besides,an auxiliary task priority method is designed to identify and retain some high-related auxiliary tasks according to the information of relative positions and dominance relationships.Moreover,an improved tentative method is designed to find and transfer useful knowledge among tasks.Experimental results on three benchmark test suites and 11 realworld problems with different numbers of constraints show better or competitive performance of the proposed method when compared with eight state-of-the-art peer methods.

DeBERTa-GRU: Sentiment Analysis for Large Language Model

Modern technological advancements have made social media an essential component of daily life.Social media allow individuals to share thoughts,emotions,and ideas.Sentiment analysis plays the function of evaluating whether the sentiment of the text is positive,negative,neutral,or any other personal emotion to understand the sentiment context of the text.Sentiment analysis is essential in business and society because it impacts strategic decision-making.Sentiment analysis involves challenges due to lexical variation,an unlabeled dataset,and text distance correlations.The execution time increases due to the sequential processing of the sequence models.However,the calculation times for the Transformer models are reduced because of the parallel processing.This study uses a hybrid deep learning strategy to combine the strengths of the Transformer and Sequence models while ignoring their limitations.In particular,the proposed model integrates the Decoding-enhanced with Bidirectional Encoder Representations from Transformers(BERT)attention(DeBERTa)and the Gated Recurrent Unit(GRU)for sentiment analysis.Using the Decoding-enhanced BERT technique,the words are mapped into a compact,semantic word embedding space,and the Gated Recurrent Unit model can capture the distance contextual semantics correctly.The proposed hybrid model achieves F1-scores of 97%on the Twitter Large Language Model(LLM)dataset,which is much higher than the performance of new techniques.

Electromagnetic Tomography System for Defect Detection of High-Speed Rail Wheel

A novel electromagnetic tomography(EMT)system for defect detection of high-speed rail wheel is proposed,which differs from traditional electromagnetic tomography systems in its spatial arrangements of coils.A U-shaped sensor array was designed,and then a simulation model was built with the low frequency electromagnetic simulation software.Three different algorithms were applied to perform image reconstruction,therefore the defects can be detected from the reconstructed images.Based on the simulation results,an experimental system was built and image reconstruction were performed with the measured data.The reconstructed images obtained both from numerical simulation and experimental system indicated the locations of the defects of the wheel,which verified the feasibility of the EMT system and revealed its good application prospect in the future.

Application of Union of Fuzzy Automata on Target Tracking

For better applications of fuzzy automata on target tracking, this paper presents an associated method of fuzzy automata by discussing the relation between fuzzy automata. The equivalence is mainly discussed regarding these fuzzy automata. The target tracking based on the associated method of fuzzy automata is given. Moreover, the simulation result shows that the associated method is better than single fuzzy automaton relatively. The development of these researches in this paper in turn can quicken the applications of the fuzzy automata in various fields.

Application of Neural Network to Game Algorithm

Intelligent was very important for command decision model, and it was also the key to improve the quality of simulation training and combat experiment. The decision-making content was more complex in the implementation of tasks and the nature of the problem was different, so the demand for intelligence was high. To solve better the problem, this paper presented a game method and established a game neural network model. The model had been successfully applied in the classification experiment of winning rate between chess game, which had good theoretical significance and application value.

Application of FRS on Target Recognition

In order to let machine better imitate thinking method of people to perform recognition and classification for fuzzy and uncertain thing, this paper puts forward a fuzzy and rough association method to deal with the problem. However, the application of fuzzy rough sets (FRS) will be introduced mainly on pattern recognition. Some related theories on FRS would be discussed, and some fuzzy rough mathematical methods on pattern-recognition will be given. Then, concrete applications of FRS on image processing and recognition will be introduced. Simulation result signifies that this fuzzy and rough association method is not only fast but also closer to nature attribute of thing for processing and recognizing image by comparing with the single neural network and other recognition device. The recognition rate is about 95.78%.

Challenges and prospects for room temperature solid-state sodiumsulfur batteries

Room temperature sodium-sulfur(Na-S)batteries,known for their high energy density and low cost,are one of the most promising next-generation energy storage systems.However,the polysulfide shuttling and uncontrollable Na dendrite growth as well as safety issues caused by the use of organic liquid electrolytes in Na-S cells,have severely hindered their commercialization.Solid-state electrolytes instead of liquid electrolytes are considered to be the most direct and effective solution to solve the above problems.However,its practical application is still greatly challenged due to the poor interfacial compatibility between the all-solid-state electrolytes and the anode/cathode,ionic conductivity,and the shuttle effect caused by the presence of liquid phase in the quasi-solid-state electrolytes.This paper presents a comprehensive review of solid-state Na-S batteries from the perspective of regulating interfacial compatibility and improving ionic conductivity as well as suppressing polysulfide shuttle.According to different components,solid-state electrolytes were divided into five categories:solid inorganic electrolytes,solid polymer electrolytes,polymer/inorganic solid hybrid electrolytes,gel polymer electrolytes,and liquid–solid inorganic hybrid electrolytes.Finally,the prospect of developing high performance solid-state electrolytes to improve the cycling stability of room temperature Na-S cells is envisaged.

Uniquely Decomposable Constellation Group-Based Sparse Vector Coding for Short Packet Communications

Sparse vector coding(SVC)is emerging as a potential technology for short packet communications.To further improve the block error rate(BLER)performance,a uniquely decomposable constellation group-based SVC(UDCG-SVC)is proposed in this article.Additionally,in order to achieve an optimal BLER performance of UDCG-SVC,a problem to optimize the coding gain of UDCG-based superimposed constellation is formulated.Given the energy of rotation constellations in UDCG,this problem is solved by converting it into finding the maximized minimum Euclidean distance of the superimposed constellation.Simulation results demonstrate the validness of our derivation.We also find that the proposed UDCGSVC has better BLER performance compared to other SVC schemes,especially under the high order modulation scenarios.

Typology and working mechanism of a hybrid power router based on power-frequency transformer electromagnetic coupling with converters

The power router(PR)is a promising piece of equipment for realizing multi-voltage level interconnection and flex-ible power control in the future distribution power grid.In this paper,a hybrid PR(HPR)topology based on power-frequency transformer electromagnetic coupling with converters is proposed for the medium distribution power grid.The power-frequency transformer is used to undertake power transmission,voltage conversion,and other main tasks,while the power electronic converters are combined to achieve active control.Equivalent magnetic and electrical circuit models are established to help discuss the operating principle of the proposed HPR.Additionally,the power flow and control principle of the HPR in different operating conditions are analyzed,with the control system design scheme presented.The theoretical analysis results are verified by MATLAB/Simulink+Plecs simulation and a controller hardware-in-the-loop study,as well as a down-scale experimental test,indicating that the proposed HPR is flexible in active voltage support and current control.

Single-molecule RNA capture-assisted droplet digital loop-mediated isothermal amplification for ultrasensitive and rapid detection of infectious pathogens

To minimize and control the transmission of infectious diseases,a sensitive,accurate,rapid,and robust assay strategy for application on-site screening is critical.Here,we report single-molecule RNA capture-assisted digital RT-LAMP(SCADL)for point-of-care testing of infectious diseases.Target RNA was captured and enriched by specific capture probes and oligonucleotide probes conjugated to magnetic beads,replacing laborious RNA extraction.Droplet generation,amplification,and the recording of results are all integrated on a microfluidic chip.In assaying commercial standard samples,quantitative results precisely corresponded to the actual concentration of samples.This method provides a limit of detection of 10 copies mL−1 for the N gene within 1 h,greatly reducing the need for skilled personnel and precision instruments.The ultrasensitivity,specificity,portability,rapidity and user-friendliness make SCADL a competitive candidate for the on-site screening of infectious diseases.

Self-Sustaining of Critical Park Microgrids Integrating Mobile Emergency Generators Subjective to Major Outage

In the event of a major power outage,critical park microgrids(PMGs)could be self-sustaining if mobile emergency generators(MEGs)are stationed to share energy.However,the need for privacy protection and the value of flexible power support on minute-time scales have not been given enough attention.To address the problem,this paper proposes a new self-sustaining strategy for critical PMGs integrating MEGs.First,to promote the cooperation between PMG and MEG,a bi-level benefit distribution mechanism is designed,where the participants'multiple roles and contributions are identified,and good behaviors are also awarded.Additionally,to increase the alliance benefits,three loss coordination modes are presented to guide the power exchange at the minute level between the MEG and PMG,considering the volatility of renewable generation and load.On this basis,a multi-time scale power-energy scheduling strategy is formulated via the alternating direction method of multipliers(ADMM)to coordinate the PMG and MEG.Finally,a dimensionality reduction technology is designed to equivalently simplify the optimization problem to facilitate the adaptive-step-based ADMM solution.Simulation studies indicate that the proposed strategy achieves the self-sustaining of PMGs integrating MEGs while increasing the economy by no less than 3.1%.

An LCC-HVDC Adaptive Emergency Power Support Strategy Based on Unbalanced Power On-Line Estimation

HVDC auxiliary power control can significantly improve the transient stability of AC/DC power grid.An HVDC adaptive emergency power support method based on unbalanced power on line estimation is proposed in this paper.By establishing the extended state equation of the system,the on line dynamic estimation of unbalanced power of the system was realized.On this basis,power support was realized based on the principle of the ladder increment.The optimal DC was selected by the power support factor,and the emergency power support controller was installed on the DC.This emergency power support method can realize dynamic optimal power support with minimized control cost.The three infeed HVDC system was built on PSCAD.The simulation results show the effectiveness of the proposed method.

Synthesis of Co_(4)S_(3)/Co_(9)S_(8) nanosheets and comparison study toward the OER properties induced by different metal ion doping

Regulation of chemical composition and nanostructure, such as the introduction of dopant into two-dimensional nanomaterials, is a general and valid strategy for the efficient electrocatalyst design. In this work, Co_(4)S_(3)/Co_(9)S_(8) nanosheets, with an ultrathin layer structure, were successfully synthesized via an efficient solvothermal process combined with ultrasonic exfoliation. Different metal ions (M = Fe^(3+), Cr^(3+), Mn^(2+) and Ni^(2+)) were then doped by a simple cation exchange method and the effects of different dopants on the OER activities of Co_(4)S_(3)/Co_(9)S_(8) NS were further investigated in alkaline media. The corresponding results implied that M-doped Co_(4)S_(3)/Co_(9)S_(8) NS (M = Fe^(3+), Cr^(3+), Mn^(2+) and Ni^(2+)) exhibited different electrocatalytic properties. Evidenced by XPS spectra, the different OER activities were mainly aroused by the redistribution of charge at the interface due to an electronic interaction between the doped metal ions and Co_(4)S_(3)/Co_(9)S_(8) NS.