A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

Epidemiology of Mpox Outbreaks and Implications for Surveillance in Imo State, Nigeria

Mpox disease is caused by a double-stranded DNA virus, genus Orthopoxvirus of the family Poxviridae. The incubation period is usually 6 to 13 days but can range from 5 to 21 days while symptoms and signs may persist for 2 to 5 weeks. Although, the clinical features are usually less severe when compared to the deadly smallpox, the disease can be fatal with case fatality rate between 1% and 10%. In Imo State, Nigeria, there has been a changing epidemiology of the disease in the last 6 years and the frequency and geographic distribution of cases have progressively increased. This study aims to conduct a review of the disease epidemiology between 2017 and 2023 and implications for surveillance in Imo State. Surveillance data from the Surveillance Outbreak Response and Management System (SORMAS) was extracted between January 2017 and December 2023 across the 27 Local Government Areas (LGAs) of Imo State. A line list of 231 suspected cases was downloaded into an excel template and analyzed using SPSS® version 20 software. Analysis was done using descriptive statistics and associations were tested using Fischer’s exact at 0.05 level of significance. Of the 231 suspected cases, 57.1% (132) were males, 42.9% (99) were females and the modal age group was between the ages of 0 - 4 (32.5%). Eight (8) LGAs (districts) accounted for 71% (n = 164) of all the suspected cases. 21.2% (49) were confirmed positive, 27 males (55.1%) and 22 females (44.9%) (p > 0.05). Modal age group was 20 - 24 (22.4%, n = 11), 18% (9) were children under 14 years, p > 0.05. Case fatality rate was 8% (n = 4). There was no significant association between mortality and age group. Five (5) LGAs accounted for about 60% (29) of all confirmed cases. These LGAs contribute only 20% to the total population in the State. Only 5.6% and 4% of suspected and confirmed cases, respectively, had knowledge of contact with an infectious source. The study described the epidemiology of Mpox outbreaks between 2017 and 2023 and the findings have significant implications on detection and outbreak response activities.

Assessment of Wet Season Precipitation in the Central United States by the Regional Climate Simulation of the WRFG Member in NARCCAP and Its Relationship with Large-Scale Circulation Biases

Assessment of past-climate simulations of regional climate models(RCMs)is important for understanding the reliability of RCMs when used to project future regional climate.Here,we assess the performance and discuss possible causes of biases in a WRF-based RCM with a grid spacing of 50 km,named WRFG,from the North American Regional Climate Change Assessment Program(NARCCAP)in simulating wet season precipitation over the Central United States for a period when observational data are available.The RCM reproduces key features of the precipitation distribution characteristics during late spring to early summer,although it tends to underestimate the magnitude of precipitation.This dry bias is partially due to the model’s lack of skill in simulating nocturnal precipitation related to the lack of eastward propagating convective systems in the simulation.Inaccuracy in reproducing large-scale circulation and environmental conditions is another contributing factor.The too weak simulated pressure gradient between the Rocky Mountains and the Gulf of Mexico results in weaker southerly winds in between,leading to a reduction of warm moist air transport from the Gulf to the Central Great Plains.The simulated low-level horizontal convergence fields are less favorable for upward motion than in the NARR and hence,for the development of moist convection as well.Therefore,a careful examination of an RCM’s deficiencies and the identification of the source of errors are important when using the RCM to project precipitation changes in future climate scenarios.

A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

Large-scale spatial data visualization method based on augmented reality

Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for researchers'visual perceptions of the evolution and interaction of events in the space environment.Methods A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time,and the corresponding relationships between data location features and other attribute features were established.A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data.The visualization process is optimized for rendering by merging materials,reducing the number of patches,and performing other operations.Results The results of sampling,feature extraction,and uniform visualization of the detection data of complex types,long duration spans,and uneven spatial distributions were obtained.The real-time visualization of large-scale spatial structures using augmented reality devices,particularly low-performance devices,was also investigated.Conclusions The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space,express the structure and changes in the spatial environment using augmented reality,and assist in intuitively discovering spatial environmental events and evolutionary rules.

Financial Calculation Problems and Countermeasure Analysis of Large-Scale Engineering Construction Projects

The financial aspects of large-scale engineering construction projects profoundly influence their success.Strengthening cost control and establishing a scientific financial evaluation system can enhance the project’s economic benefits,minimize unnecessary costs,and provide decision-makers with a robust financial foundation.Additionally,implementing an effective cash flow control mechanism and conducting a comprehensive assessment of potential project risks can ensure financial stability and mitigate the risk of fund shortages.Developing a practical and feasible fundraising plan,along with stringent fund management practices,can prevent fund wastage and optimize fund utilization efficiency.These measures not only facilitate smooth project progression and improve project management efficiency but also enhance the project’s economic and social outcomes.

Gleaning insights from German energy transition and large-scale underground energy storage for China’s carbon neutrality

The global energy transition is a widespread phenomenon that requires international exchange of experiences and mutual learning.Germany’s success in its first phase of energy transition can be attributed to its adoption of smart energy technology and implementation of electricity futures and spot marketization,which enabled the achievement of multiple energy spatial–temporal complementarities and overall grid balance through energy conversion and reconversion technologies.While China can draw from Germany’s experience to inform its own energy transition efforts,its 11-fold higher annual electricity consumption requires a distinct approach.We recommend a clean energy system based on smart sector coupling(ENSYSCO)as a suitable pathway for achieving sustainable energy in China,given that renewable energy is expected to guarantee 85%of China’s energy production by 2060,requiring significant future electricity storage capacity.Nonetheless,renewable energy storage remains a significant challenge.We propose four large-scale underground energy storage methods based on ENSYSCO to address this challenge,while considering China’s national conditions.These proposals have culminated in pilot projects for large-scale underground energy storage in China,which we believe is a necessary choice for achieving carbon neutrality in China and enabling efficient and safe grid integration of renewable energy within the framework of ENSYSCO.

ISHD:Intelligent Standing Human Detection of Video Surveillance for the Smart Examination Environment

In the environment of smart examination rooms, it is important to quickly and accurately detect abnormal behavior(human standing) for the construction of a smart campus. Based on deep learning, we propose an intelligentstanding human detection (ISHD) method based on an improved single shot multibox detector to detect thetarget of standing human posture in the scene frame of exam room video surveillance at a specific examinationstage. ISHD combines the MobileNet network in a single shot multibox detector network, improves the posturefeature extractor of a standing person, merges prior knowledge, and introduces transfer learning in the trainingstrategy, which greatly reduces the computation amount, improves the detection accuracy, and reduces the trainingdifficulty. The experiment proves that the model proposed in this paper has a better detection ability for the smalland medium-sized standing human body posture in video test scenes on the EMV-2 dataset.

Treatment and surveillance for non-muscle-invasive bladder cancer:a clinical practice guideline(2021 edition)

Non-muscle invasive bladder cancer(NMIBC)is a major type of bladder cancer with a high incidence worldwide,resulting in a great disease burden.Treatment and surveillance are the most important part of NIMBC management.In 2018,we issued“Treatment and surveillance for non-muscle-invasive bladder cancer in China:an evidencebased clinical practice guideline”.Since then,various studies on the treatment and surveillance of NMIBC have been published.There is a need to incorporate these materials and also to take into account the relatively limited medical resources in primary medical institutions in China.Developing a version of guideline which takes these two issues into account to promote the management of NMIBC is therefore indicated.We formed a working group of clinical experts and methodologists.Through questionnaire investigation of clinicians including primary medical institutions,24 clinically concerned issues,involving transurethral resection of bladder tumor(TURBT),intravesical chemotherapy and intravesical immunotherapy of NMIBC,and follow-up and surveillance of the NMIBC patients,were determined for this guideline.Researches and recommendations on the management of NMIBC in databases,guideline development professional societies and monographs were referred to,and the European Association of Urology was used to assess the certainty of generated recommendations.Finally,we issued 29 statements,among which 22 were strong recommendations,and 7 were weak recommendations.These recommendations cover the topics of TURBT,postoperative chemotherapy after TURBT,Bacillus Calmette–Guérin(BCG)immunotherapy after TURBT,combination treatment of BCG and chemotherapy after TURBT,treatment of carcinoma in situ,radical cystectomy,treatment of NMIBC recurrence,and follow-up and surveillance.We hope these recommendations can help promote the treatment and surveillance of NMIBC in China,especially for the primary medical institutions.

Treatment trends of muscle invasive bladder cancer: Evidence from the Surveillance, Epidemiology, and End Results database, 1988 to 2013

Objective:Guidelines for muscle-invasive bladder cancer(MIBC)recommend that patients receive neoadjuvant chemotherapy with radical cystectomy as treatment over radical cystectomy alone.Though trends and practice patterns of MIBC have been defined using the National Cancer Database,data using the Surveillance,Epidemiology,and End Results(SEER)program have been poorly described.Methods:Using the SEER database,we collected data of MIBC according to the American Joint Commission on Cancer.We considered differences in patient demographics and tumor charac-teristics based on three treatment groups:chemotherapy(both adjuvant and neoadjuvant)with radical cystectomy,radical cystectomy,and chemoradiotherapy.Multinomial logistic regression was performed to compare likelihood ratios.Temporal trends were included for each treatment group.Kaplan-Meier curves were performed to compare cause-specific sur-vival.A Cox proportional-hazards model was utilized to describe predictors of survival.Results:Of 16728 patients,10468 patients received radical cystectomy alone,3236 received chemotherapy with radical cystectomy,and 3024 received chemoradiotherapy.Patients who received chemoradiotherapy over radical cystectomy were older and more likely to be African American;stage III patients tended to be divorced.Patients who received chemotherapy with radical cystectomy tended to be males;stage II patients were less likely to be Asian than Caucasian.Stage III patients were less likely to receive chemoradiotherapy as a treatment op-tion than stage II.Chemotherapy with radical cystectomy and chemoradiotherapy are both un-derutilized treatment options,though increasingly utilized.Kaplan-Meier survival curves showed significant differences between stage II and III tumors at each interval.A Cox proportional-hazards model showed differences in gender,tumor stage,treatment modality,age,andmarital status.Conclusion:Radical cystectomy alone is still the most commonly used treatment for muscle-invasive bladder cancer based on temporal trends.Significant disparities exist in those who receive radical cystectomy over chemoradiotherapy for treatment.

The Contribution of the Xpert® MTB/RIF Assay to the Surveillance of Drug-Resistant Tuberculosis in the Central African Republic

Introduction: The Central African Republic is one of the 30 high Tuberculosis burden countries in the world, with an incidence of 540 cases per 100,000 population and a mortality of 91 deaths per 100,000 population. Since 2020, following WHO recommendations, the National Reference Laboratory for Tuberculosis has been using the Xpert® MTB/RIF assay as a first-line diagnostic test for the early detection of Drug Resistance Tuberculosis. The goal of this study was to evaluate the contribution of the Xpert® MTB/RIF assay to the surveillance of rifampicin resistance in new and previously treated tuberculosis cases. Materials and Methods: The data relative to the Xpert® MTB/RIF assay carried out on various categories of tuberculosis patients registered at the National Reference Laboratory for Tuberculosis in 2020 were analyzed retrospectively. The categories of tuberculosis patients were new cases, failed treatment cases, relapse cases, lost-to-follow-up cases and multidrug-resistant tuberculosis contact cases. Results: A total of 1404 tuberculosis patients were registered at the NRL-TB in 2020;the mean age was 39.2 years (2 - 90 years) and the male-to-female sex ratio was 1.16:1. Overall, 32.7% (454/1404) proved infected with tuberculosis, of which 22.5% (102/454) cases showed resistance to rifampicin. The primary resistance rate was 9.1% (27/298) and the secondary resistance rate was 46.6% (75/161). Treatment failures and relapsed cases were significantly associated with rifampicin resistance (p 0.005). Conclusion: Large-scale use of Xpert® MTB/RIF, especially in the provinces of the Central African Republic, will help the Ministry of Health to better control Drug Resistance Tuberculosis in the country.

An Efficient Attention-Based Strategy for Anomaly Detection in Surveillance Video

In the present technological world,surveillance cameras generate an immense amount of video data from various sources,making its scrutiny tough for computer vision specialists.It is difficult to search for anomalous events manually in thesemassive video records since they happen infrequently and with a low probability in real-world monitoring systems.Therefore,intelligent surveillance is a requirement of the modern day,as it enables the automatic identification of normal and aberrant behavior using artificial intelligence and computer vision technologies.In this article,we introduce an efficient Attention-based deep-learning approach for anomaly detection in surveillance video(ADSV).At the input of the ADSV,a shots boundary detection technique is used to segment prominent frames.Next,The Lightweight ConvolutionNeuralNetwork(LWCNN)model receives the segmented frames to extract spatial and temporal information from the intermediate layer.Following that,spatial and temporal features are learned using Long Short-Term Memory(LSTM)cells and Attention Network from a series of frames for each anomalous activity in a sample.To detect motion and action,the LWCNN received chronologically sorted frames.Finally,the anomaly activity in the video is identified using the proposed trained ADSV model.Extensive experiments are conducted on complex and challenging benchmark datasets.In addition,the experimental results have been compared to state-ofthe-artmethodologies,and a significant improvement is attained,demonstrating the efficiency of our ADSV method.

Microstructure,mechanical properties and fracture behaviors of large-scale sand-cast Mg-3Y-2Gd-1Nd-0.4Zr alloy

In order to improve the ductility of commercial WE43 alloy and reduce its cost,a Mg-3Y-2Gd-1Nd-0.4Zr alloy with a low amount of rare earths was developed and prepared by sand casting with a differential pressure casting system.Its microstructure,mechanical properties and fracture behaviors in the as-cast,solution-treated and as-aged states were evaluated.It is found that the aged alloy exhibited excellent comprehensive mechanical properties owing to the fine dense plate-shapedβ'precipitates formed on prismatic habits during aging at 200℃for 192 hrs after solution-treated at 500℃for 24 hrs.Its ultimate tensile strength,yield strength,and elongation at ambient temperature reach to 319±10 MPa,202±2 MPa and 8.7±0.3%as well as 230±4 MPa,155±1 MPa and 16.0±0.5%at 250℃.The fracture mode of as-aged alloy was transferred from cleavage at room temperature to quasi-cleavage and ductile fracture at the test temperature 300℃.The properties of large-scale components fabricated using the developed Mg-3Y-2Gd-1Nd-0.4Zr alloy are better than those of commercial WE43 alloy,suggesting that the new developed alloy is a good candidate to fabricate the large complex thin-walled components.

Knowledge,perceptions,and practice towards adverse events following immunization surveillance among vaccination workers in Zhejiang province,China

Objective:To access the level of knowledge,perceptions,and practice towards adverse events following immunization(AEFI)surveillance among vaccination workers in Zhejiang province,China.Methods:This was a cross-sectional survey involving 768 vaccination workers.Data were collected using self-administered questionnaires and analyzed by using SAS 9.3 software.Knowledge,perceptions,and practice on AEFI surveillance were summarized using frequency tables.The mean±SD value was used as the cut-off for defining good(values≥mean)and poor(values<mean)knowledge,perceptions or practice.Binary logistic regression analysis was used to determine sociodemographic variables associated with knowledge,perceptions,and practice towards AEFI.Results:The proportions of good knowledge,perceptions and practice on AEFI surveillance were 78.13%,57.81%and 66.15%,respectively.Having a higher education background,longer years of experience,previous training on AEFI and≥30 years of age were factors associated with good knowledge,perceptions and practice on AEFI surveillance among vaccination workers.Conclusions:Over half of the respondents had good knowledge,perceptions and practice on AEFI surveillance work.Interventions on improving the vaccination workers’knowledge,perceptions and practice on AEFI surveillance should be considered in order to develop a more effective surveillance system.

CFSA-Net:Efficient Large-Scale Point Cloud Semantic Segmentation Based on Cross-Fusion Self-Attention

Traditional models for semantic segmentation in point clouds primarily focus on smaller scales.However,in real-world applications,point clouds often exhibit larger scales,leading to heavy computational and memory requirements.The key to handling large-scale point clouds lies in leveraging random sampling,which offers higher computational efficiency and lower memory consumption compared to other sampling methods.Nevertheless,the use of random sampling can potentially result in the loss of crucial points during the encoding stage.To address these issues,this paper proposes cross-fusion self-attention network(CFSA-Net),a lightweight and efficient network architecture specifically designed for directly processing large-scale point clouds.At the core of this network is the incorporation of random sampling alongside a local feature extraction module based on cross-fusion self-attention(CFSA).This module effectively integrates long-range contextual dependencies between points by employing hierarchical position encoding(HPC).Furthermore,it enhances the interaction between each point’s coordinates and feature information through cross-fusion self-attention pooling,enabling the acquisition of more comprehensive geometric information.Finally,a residual optimization(RO)structure is introduced to extend the receptive field of individual points by stacking hierarchical position encoding and cross-fusion self-attention pooling,thereby reducing the impact of information loss caused by random sampling.Experimental results on the Stanford Large-Scale 3D Indoor Spaces(S3DIS),Semantic3D,and SemanticKITTI datasets demonstrate the superiority of this algorithm over advanced approaches such as RandLA-Net and KPConv.These findings underscore the excellent performance of CFSA-Net in large-scale 3D semantic segmentation.

Comparison of Pile-Soil-Structure Interaction Modeling Techniques for A 10-MW Large-Scale Monopile Wind Turbine Model Under Wind and Wave Conditions

Considering the large diameter effect of piles,the influence of different pile-soil analysis methods on the design of monopile foundations for offshore wind turbines has become an urgent problem to be solved.Three different pile-soil models were used to study a large 10 MW monopile wind turbine.By modeling the three models in the SACS software,this paper analyzed the motion response of the overall structure under the conditions of wind and waves.According to the given working conditions,this paper concludes that under the condition of independent wind,the average value of the tower top x-displacement of the rigid connection method is the smalle st,and the standard deviation is the smallest under the condition of independent wave.The results obtained by the p-y curve method are the most conservative.

A Multilevel Hierarchical Parallel Algorithm for Large-Scale Finite Element Modal Analysis

The strict and high-standard requirements for the safety and stability ofmajor engineering systems make it a tough challenge for large-scale finite element modal analysis.At the same time,realizing the systematic analysis of the entire large structure of these engineering systems is extremely meaningful in practice.This article proposes a multilevel hierarchical parallel algorithm for large-scale finite element modal analysis to reduce the parallel computational efficiency loss when using heterogeneous multicore distributed storage computers in solving large-scale finite element modal analysis.Based on two-level partitioning and four-transformation strategies,the proposed algorithm not only improves the memory access rate through the sparsely distributed storage of a large amount of data but also reduces the solution time by reducing the scale of the generalized characteristic equation(GCEs).Moreover,a multilevel hierarchical parallelization approach is introduced during the computational procedure to enable the separation of the communication of inter-nodes,intra-nodes,heterogeneous core groups(HCGs),and inside HCGs through mapping computing tasks to various hardware layers.This method can efficiently achieve load balancing at different layers and significantly improve the communication rate through hierarchical communication.Therefore,it can enhance the efficiency of parallel computing of large-scale finite element modal analysis by fully exploiting the architecture characteristics of heterogeneous multicore clusters.Finally,typical numerical experiments were used to validate the correctness and efficiency of the proposedmethod.Then a parallel modal analysis example of the cross-river tunnel with over ten million degrees of freedom(DOFs)was performed,and ten-thousand core processors were applied to verify the feasibility of the algorithm.