Committed to Safe Production China-Indonesia joint venture HBAP strives to ensure safe, stable operations while fulfilling social responsibility

Recently,PT Huadian Bukit Asam Power (HBAP) was awarded the"Zero Accident Award"by the Ministry of Manpower of the Republic of Indonesia.This national-level award for safe production in Indonesia serves to testify to the enterprise's achievements in safe and stable operations from June 1,2018,to December 31,2023.

Evaluation of Driver-Induced Human Errors in Smart Construction Tower Crane Operations Based on DEMATEL-ISM-MICMAC

With the advent of Industry 4.0, smart construction sites have seen significant development in China. However, accidents involving digitized tower cranes continue to be a persistent issue. Among the contributing factors, human unsafe behavior stands out as a primary cause for these incidents. This study aims to assess the human reliability of tower crane operations on smart construction sites. To proactively enhance safety measures, the research employs text mining techniques (TF-IDF-Truncated SVD-Complement NB) to identify patterns of human errors among tower crane operators. Building upon the SHEL model, the study categorizes behavioral factors affecting human reliability in the man-machine interface, leading to the establishment of the Performance Shaping Factors (PSFs) system. Furthermore, the research constructs an error impact indicator system for the intelligent construction site tower crane operator interface. Using the DEMATEL method, it analyzes the significance of various factors influencing human errors in tower crane operations. Additionally, the ISM-MICMAC method is applied to unveil the hierarchical relationships and driving-dependent connections among these influencing factors. The findings indicate that personal state, operating procedures, and physical environment directly impact human errors, while personal capability, technological environment, and one fundamental organizational management factor contribute indirectly. .

Research and Practice of the Blended Teaching Model of BOPPPS Teaching Method Under the Background of Digital Education: Taking Operations Research Course as an Example

The rapid development of digital education provides new opportunities and challenges for teaching model innovation.This study aims to explore the application of the BOPPPS(Bridge-in,Objective,Pre-assessment,Participatory learning,Post-assessment,Summary)teaching method in the development of a blended teaching model for the Operations Research course under the background of digital education.In response to the characteristics of the course and the needs of the student group,the teaching design is reconstructed with a student-centered approach,increasing practical teaching links,improving the assessment and evaluation system,and effectively implementing it in conjunction with digital educational technology.This teaching model has shown significant effectiveness in the context of digital education,providing valuable experience and insights for the innovation of the Operations Research course.

Optimization Task Scheduling Using Cooperation Search Algorithm for Heterogeneous Cloud Computing Systems

Cloud computing has taken over the high-performance distributed computing area,and it currently provides on-demand services and resource polling over the web.As a result of constantly changing user service demand,the task scheduling problem has emerged as a critical analytical topic in cloud computing.The primary goal of scheduling tasks is to distribute tasks to available processors to construct the shortest possible schedule without breaching precedence restrictions.Assignments and schedules of tasks substantially influence system operation in a heterogeneous multiprocessor system.The diverse processes inside the heuristic-based task scheduling method will result in varying makespan in the heterogeneous computing system.As a result,an intelligent scheduling algorithm should efficiently determine the priority of every subtask based on the resources necessary to lower the makespan.This research introduced a novel efficient scheduling task method in cloud computing systems based on the cooperation search algorithm to tackle an essential task and schedule a heterogeneous cloud computing problem.The basic idea of thismethod is to use the advantages of meta-heuristic algorithms to get the optimal solution.We assess our algorithm’s performance by running it through three scenarios with varying numbers of tasks.The findings demonstrate that the suggested technique beats existingmethods NewGenetic Algorithm(NGA),Genetic Algorithm(GA),Whale Optimization Algorithm(WOA),Gravitational Search Algorithm(GSA),and Hybrid Heuristic and Genetic(HHG)by 7.9%,2.1%,8.8%,7.7%,3.4%respectively according to makespan.

CT-CloudDetect:用于遥感卫星云检测的混合模型

云检测是在遥感卫星云图中检测云的任务。近年来,人们提出了基于深度学习的云检测方法,并取得了良好的性能。然而,现有的基于深度学习的云检测模型大多还是基于卷积神经网络(convolutional neural network,CNN),由于卷积运算的固有局部性,难以捕获长距离依赖关系。针对上述问题,文章提出一个基于CNN和ViT(Vision Transformer)的混合型云检测模型,并提出一种基于CNN和ViT的编码器,使网络具备捕捉局部和全局信息的能力。为了更好地融合语义和尺度不一致的特征,提出了一个双尺度注意力融合模块,通过注意力机制有选择地融合特征。此外,提出了轻量级路由解码器,该解码器通过路由结构降低模型复杂度。在3个公开云检测数据集上对模型进行了评估。大量实验表明,所提出的模型具有比现有模型更好的性能。

Dynamic Analysis of Pipeline Lifting Operations for Different Current Velocities and Wave Heights

Pipelines are widely used for transporting oil resources in the context of offshore oil exploitation.The pipeline stress-strength analysis is an important stage in related design and ensuing construction techniques.In this study,assuming representative work environment parameters,pipeline lifting operations are investigated numerically.More specifically,a time-domain coupled dynamic analysis method is used to conduct a hydrodynamic analysis under different current velocities and wave heights.The results show that proper operation requires the lifting points are reasonably set in combination with the length of the pipeline and the position of the lifting device on the construction ship.The impact of waves on the pipeline is limited,however lifting operations under strong wind and waves should be avoided as far as possible.

Study on the Characteristics of A New Hybrid Mooring System for DualPlatform Joint Operations

As the sustainable exploitation of marine resources develops,dual-platform joint operation has caught increasing attention.Dual-platform joint operation requires smaller relative motion between the two sub-platforms,which is normally difficult to be satisfied by the traditional mooring system.Therefore,a new hybrid mooring system is developed and studied in this article.To ensure safety during platform movements,both the number of anchor chains and the relative motion between the two sub-platforms are reduced in the new hybrid mooring system.By performing numerical simulations based on three-dimensional potential flow theory in AQWA and physical experiments,the performances of both the new hybrid and traditional mooring systems under two different wave conditions(i.e.,working wave and freak wave conditions) are systematically investigated.Regarding the new hybrid mooring system,the relative stability between the two sub-platforms of the new system is better,and the platforms can restore stability faster when affected by freak waves.

A cloud optical and microphysical property product for the advanced geosynchronous radiation imager onboard China's Fengyun-4 satellites: The first version

风云四号作为中国新一代静止气象卫星,提供了高时空分辨率的监测产品。本文介绍风云四号搭载的先进地球同步轨道辐射成像仪AGRI的云光学和微物理特性产品.该产品包含了基于双光谱通道反演的云光学厚度和云粒子有效半径产品,以及基于机器学习的云识别和云相态产品。与时空匹配的主动卫星观测结果对比显示,该产品的云识别和云相态的准确率分别在95%和85%;该产品提供的云光学厚度和云有效粒径与经典的MODIS产品的相关系数达到0.76和0.63.团队将持续优化和更新该云光学和微物理特性定量产品,服务风云四号卫星定量应用。

Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space

Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://github.com/yahuiliu99/PointC onT.

基于Spring Cloud的分布式医疗数据平台建设

为解决互联网时代医疗大数据的可靠存储问题,探索异构数据的有效处理方案,加强医疗数据检索能力。提出一种基于Spring Cloud的分布式医疗数据平台建设方案,该方案通过Spring Cloud技术构建分布式架构,选取MongoDB非关系型数据库作为分布式存储,同时对数据进行标准化、分词等操作,从而实现一个异构数据可存储、系统高可用、规模易扩展、检索高效率的医疗数据平台。

基于POC CLOUD智能管理云平台的即时检验教学探索

目的探讨使用POC CLOUD智能管理云平台培养住培学员掌握即时检验(point-of-care testing,POCT)各设备的工作原理、操作技能,使其具备一定的质量控制管理能力,保证结果的准确性。方法结合《住培学员规范化培训内容与标准(2022年版)》要求及科室POCT教学开展情况,利用POC CLOUD智能管理云平台,信息化、规范化地培养住培学员。结果通过POC CLOUD智能管理云平台管理住培学员操作POCT设备的资格准入,帮助住培学员快速了解轮转科室POCT设备的使用现状,提高住培学员的学习能力及质量控制能力,帮助住培学员分析与审核检验结果。结论基于POC CLOUD智能管理云平台的即时检验教学,有助于系统化培养住培学员,提升其在POCT操作和质量控制方面的能力,确保检验结果的准确性和可靠性,提高住培学员的综合素质。

A Blockchain and CP-ABE Based Access Control Scheme with Fine-Grained Revocation of Attributes in Cloud Health

The Access control scheme is an effective method to protect user data privacy.The access control scheme based on blockchain and ciphertext policy attribute encryption(CP–ABE)can solve the problems of single—point of failure and lack of trust in the centralized system.However,it also brings new problems to the health information in the cloud storage environment,such as attribute leakage,low consensus efficiency,complex permission updates,and so on.This paper proposes an access control scheme with fine-grained attribute revocation,keyword search,and traceability of the attribute private key distribution process.Blockchain technology tracks the authorization of attribute private keys.The credit scoring method improves the Raft protocol in consensus efficiency.Besides,the interplanetary file system(IPFS)addresses the capacity deficit of blockchain.Under the premise of hiding policy,the research proposes a fine-grained access control method based on users,user attributes,and file structure.It optimizes the data-sharing mode.At the same time,Proxy Re-Encryption(PRE)technology is used to update the access rights.The proposed scheme proved to be secure.Comparative analysis and experimental results show that the proposed scheme has higher efficiency and more functions.It can meet the needs of medical institutions.

MCWOA Scheduler:Modified Chimp-Whale Optimization Algorithm for Task Scheduling in Cloud Computing

Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).

基于Spring Cloud微服务架构的非物质文化遗产展示与学习系统的设计与实现

针对目前人民群众对非物质文化遗产认识不够全面,以及市面上利用系统软件来有效保护和传承非物质文化遗产的软件很缺乏等问题,结合相应软件开发技术和数字可视化技术,设计和实现了一款非物质文化遗产展示与学习系统以实现对非物质文化遗产项目的传承和保护创新。本系统以江西省范围内的主要非物质文化遗产项目为例,设置有移动App端和PC端,采用前后端分离的开发模式,以当前主流的Spring Cloud微服务架构技术为基础,结合SpringBoot框架,在华为云服务器上搭建起弹性可伸缩的后端服务。使用Uni-App开发移动端,以解决无法在Android、IOS及各大小程序平台运行的问题。经测试,本系统具有操作简单,易于使用、维护和扩展,高并发性等优点。

Automatic Extraction Method of 3D Feature Guidelines for Complex Cultural Relic Surfaces Based on Point Cloud

Cultural relics line graphic serves as a crucial form of traditional artifact information documentation,which is a simple and intuitive product with low cost of displaying compared with 3D models.Dimensionality reduction is undoubtedly necessary for line drawings.However,most existing methods for artifact drawing rely on the principles of orthographic projection that always cannot avoid angle occlusion and data overlapping while the surface of cultural relics is complex.Therefore,conformal mapping was introduced as a dimensionality reduction way to compensate for the limitation of orthographic projection.Based on the given criteria for assessing surface complexity,this paper proposed a three-dimensional feature guideline extraction method for complex cultural relic surfaces.A 2D and 3D combined factor that measured the importance of points on describing surface features,vertex weight,was designed.Then the selection threshold for feature guideline extraction was determined based on the differences between vertex weight and shape index distributions.The feasibility and stability were verified through experiments conducted on real cultural relic surface data.Results demonstrated the ability of the method to address the challenges associated with the automatic generation of line drawings for complex surfaces.The extraction method and the obtained results will be useful for line graphic drawing,displaying and propaganda of cultural relics.

Security Implications of Edge Computing in Cloud Networks

Security issues in cloud networks and edge computing have become very common. This research focuses on analyzing such issues and developing the best solutions. A detailed literature review has been conducted in this regard. The findings have shown that many challenges are linked to edge computing, such as privacy concerns, security breaches, high costs, low efficiency, etc. Therefore, there is a need to implement proper security measures to overcome these issues. Using emerging trends, like machine learning, encryption, artificial intelligence, real-time monitoring, etc., can help mitigate security issues. They can also develop a secure and safe future in cloud computing. It was concluded that the security implications of edge computing can easily be covered with the help of new technologies and techniques.

Operations of Knapsack Sprayer and Its Impacts on Physiological Parameters of Selected Operators

The study was aimed at determining the impacts of operating Manually Operated Hand Lever Knapsack Sprayers (MOHLKS) on physiological responses of the operators as dependent on anthropometric variations and sex. Twenty eight subjects, (4 female, 24 male) Mean ± SD: Age 22.5 ± 1.92, 24.29 ± 2.2 years;Body Mass Index 24.6 ± 4.8, 21.7 ± 2.4 kg/m2 were employed in the study. Selected anthropometric parameters of weight and height were used to determine body mass index (BMI), with these are arm-reach forward, elbow to fingertip, hand length and hand width were measured to establish human variations in diversity. Subjects undertook the operation at 5 replicates each, before and after which information about operators’ body pain locations and body physiological changes of heart rates were obtained. Measured parameters were used in the determination of expended energy (EE), physiological cost (PC), oxygen intake (VO2) and aerobic power (VO2max). Alongside with these were operational parameters of stroke, pace and time taken to get the operation done and environmental factors of temperature and relative humidity. The results revealed on the average that the BMI (24.61 ± 4.78 kg/m2) in female operators was higher, this corresponded to PC and VO2, while the VO2max (34.83 ± 3.30 ml/min/kg) in males is higher. More EE was obtained in female subjects (3.53 ± 3.76 kCal/min) as compared to male subjects (3.42 ± 7.48 kCal/min). The main effects plot of operational factors on EE displayed the stroke made by the subjects during spraying operation as parameter with largest effect on EE. Regression equation for EE and PCI is given as PCI = 1.97 + 25.2 EE, while the P-value at α = 0.05 is 0.000 and R2 = 98.8%. Post operational body pain showed that 19 out of 28 subjects incurred at least one type of body pain, with shoulder pain as most frequent. The results of the study suggest that early incidence of fatigue may occur in female operators as compared to the males, and in addition, cumulative trauma at shoulder, back, and upper and lower arm may result over time. Hence, it is recommended that the tank volume should be reduced and the straps for the shoulders should be supported with additional cushion.

Fractal Fractional Order Operators in Computational Techniques for Mathematical Models in Epidemiology

New fractional operators, the COVID-19 model has been studied in this paper. By using different numericaltechniques and the time fractional parameters, the mechanical characteristics of the fractional order model areidentified. The uniqueness and existence have been established. Themodel’sUlam-Hyers stability analysis has beenfound. In order to justify the theoretical results, numerical simulations are carried out for the presented methodin the range of fractional order to show the implications of fractional and fractal orders.We applied very effectivenumerical techniques to obtain the solutions of themodel and simulations. Also, we present conditions of existencefor a solution to the proposed epidemicmodel and to calculate the reproduction number in certain state conditionsof the analyzed dynamic system. COVID-19 fractional order model for the case of Wuhan, China, is offered foranalysis with simulations in order to determine the possible efficacy of Coronavirus disease transmission in theCommunity. For this reason, we employed the COVID-19 fractal fractional derivative model in the example ofWuhan, China, with the given beginning conditions. In conclusion, again the mathematical models with fractionaloperators can facilitate the improvement of decision-making for measures to be taken in the management of anepidemic situation.

Novel Investigation of Stochastic Fractional Differential Equations Measles Model via the White Noise and Global Derivative Operator Depending on Mittag-Leffler Kernel

Because of the features involved with their varied kernels,differential operators relying on convolution formulations have been acknowledged as effective mathematical resources for modeling real-world issues.In this paper,we constructed a stochastic fractional framework of measles spreading mechanisms with dual medication immunization considering the exponential decay and Mittag-Leffler kernels.In this approach,the overall population was separated into five cohorts.Furthermore,the descriptive behavior of the system was investigated,including prerequisites for the positivity of solutions,invariant domain of the solution,presence and stability of equilibrium points,and sensitivity analysis.We included a stochastic element in every cohort and employed linear growth and Lipschitz criteria to show the existence and uniqueness of solutions.Several numerical simulations for various fractional orders and randomization intensities are illustrated.

AN EXPLANATION ON FOUR NEW DEFINITIONS OF FRACTIONAL OPERATORS

Fractional calculus has drawn more attentions of mathematicians and engineers in recent years.A lot of new fractional operators were used to handle various practical problems.In this article,we mainly study four new fractional operators,namely the CaputoFabrizio operator,the Atangana-Baleanu operator,the Sun-Hao-Zhang-Baleanu operator and the generalized Caputo type operator under the frame of the k-Prabhakar fractional integral operator.Usually,the theory of the k-Prabhakar fractional integral is regarded as a much broader than classical fractional operator.Here,we firstly give a series expansion of the k-Prabhakar fractional integral by means of the k-Riemann-Liouville integral.Then,a connection between the k-Prabhakar fractional integral and the four new fractional operators of the above mentioned was shown,respectively.In terms of the above analysis,we can obtain this a basic fact that it only needs to consider the k-Prabhakar fractional integral to cover these results from the four new fractional operators.