Improved Scatter Search Algorithm for Multi-skilled Personnel Scheduling of Ship Block Painting

This paper focuses on the optimization method for multi-skilled painting personnel scheduling.The budget working time analysis is carried out considering the influence of operating area,difficulty of spraying area,multi-skilled workers,and worker’s efficiency,then a mathematical model is established to minimize the completion time. The constraints of task priority,paint preparation,pump management,and neighbor avoidance in the ship block painting production are considered. Based on this model,an improved scatter search(ISS)algorithm is designed,and the hybrid approximate dynamic programming(ADP)algorithm is used to improve search efficiency. In addition,the two solution combination methods of path-relinking and task sequence combination are used to enhance the search breadth and depth. The numerical experimental results show that ISS has a significant advantage in solving efficiency compared with the solver in small scale instances;Compared with the scatter search algorithm and genetic algorithm,ISS can stably improve the solution quality. Verified by the production example,ISS effectively shortens the total completion time of the production,which is suitable for scheduling problems in the actual painting production of the shipyard.

Analysis of the Digital Skill Gap of Agricultural Extension Personnel in South-East, Nigeria

The study analysed the digital skill gap of agricultural extension personnel on the use of digital technologies for extension services delivery in South-East, Nigeria. The specific objectives were to describe the socioeconomic characteristics of agricultural extension personnel in South-East Nigeria and identify the digital skill gaps among agricultural extension personnel in the area of study. Purposive sampling technique was used to select 364 Agricultural Extension personnel for the study. Data were collected through the use of structured questionnaire and were analysed using simple descriptive statistical tools such as percentages, mean score, and standard deviation. Findings indicated that most of the personnel were male (57.8%), within the age bracket of 38 - 47 years (62.9%), had B.Sc./HND as their highest educational qualification (74.7%), married (86.3%), and had a household size of 6 - 10 Persons (57.7%). It was further revealed that the majority (70.1%) were members of professional organization, earned a monthly income of N50,001.00 - N100,000.00 (65.7%), had a work experience of 11 - 15 years (51.1%), and owned a smartphone/ iPad/laptop (91.5%). Findings further indicated that they had moderate skill gap in Basic Computer skills (Mean = 4.32), and digital communication and collaboration skills (Mean = 4.26). Findings also showed that they had a high skill gap in digital technical skills (Mean = 2.46), digital data analysis skills (Mean = 2.09), digital content creation skills (Mean = 2.43), digital ethical skills (Mean = 2.79), multimedia production skills (Mean = 2.81), and video library management skills (Mean = 2.39). The study concluded that though there exists a high digital skill gap among agricultural extension personnel in South-East, Nigeria, their socioeconomic characteristics are capable of supporting the implementation of digital extension services in the area. The study recommended that the management of Agricultural Development Programs in South-East, Nigeria, should provide digital training for extension personnel to close the digital skill gap that currently exists among the personnel.

Comparative Study of the Socioeconomic Characteristics and Digital Literacy Level of Agricultural Extension Personnel in Imo and Ebonyi States, South-East, Nigeria

The study comparatively analysed the socioeconomic characteristics and digital literacy level of Agricultural Extension personnel (AEP) in Ebonyi and Imo States, South-East, Nigeria. The specific objectives were to describe the socioeconomic characteristics of agricultural extension personnel in Ebonyi and Imo States, and to ascertain the digital literacy level of AEP in the studied states. Purposive sampling technique was used to select 312 Agricultural Extension personnel (132 from Ebonyi State Agricultural Development Program and 180 from Imo State Agricultural Development Program) for the study. Data were collected through the use of validated and structured questionnaire, and administered through the help of well-trained enumerators. Data were analysed using simple descriptive statistical tools such as percentages mean score, standard deviation and weighted mean. Findings indicated that they were more male in the both States (55.3% and 57.8%) for Ebonyi and Imo State respectively and that the average age of AEP in Ebonyi and Imo States were 44.7 years and 49.2 years respectively. It was further revealed that the majority (77.3% and 82.8%) had B.Sc./HND as their highest academic qualifications, belonged to professional organisations (62.1% and 75%), and were earning an average monthly income of N58,798 and N62,648 for Ebonyi and Imo State respectively. Also, it was revealed that their mean years of service were 12.4 years and 13.4 years for Ebonyi and Imo State respectively. Almost all of them (87.9% and 95.0%) own a smartphone, had access to the internet (80.3% and 90.0%), but do not own a laptop/ipad (82.6% and 72.8%) for Ebon-yi and Imo State respectively. Results further revealed that Agricultural extension personnel in both Ebonyi and Imo State respectively had low digital literacy level ( = 2.41 and 2.32). The study concluded that AEP in Ebonyi and Imo State respectively had similar socioeconomic characteristics and low level of digital literacy. The study recommended that the management of ADPs in both Ebonyi and Imo State should ensure the training of AEP in digital skills to enhance their digital literacy level to enable them use digital technologies in their work.

Detection Algorithm of Laboratory Personnel Irregularities Based on Improved YOLOv7

Due to the complex environment of the university laboratory,personnel flow intensive,personnel irregular behavior is easy to cause security risks.Monitoring using mainstream detection algorithms suffers from low detection accuracy and slow speed.Therefore,the current management of personnel behavior mainly relies on institutional constraints,education and training,on-site supervision,etc.,which is time-consuming and ineffective.Given the above situation,this paper proposes an improved You Only Look Once version 7(YOLOv7)to achieve the purpose of quickly detecting irregular behaviors of laboratory personnel while ensuring high detection accuracy.First,to better capture the shape features of the target,deformable convolutional networks(DCN)is used in the backbone part of the model to replace the traditional convolution to improve the detection accuracy and speed.Second,to enhance the extraction of important features and suppress useless features,this paper proposes a new convolutional block attention module_efficient channel attention(CBAM_E)for embedding the neck network to improve the model’s ability to extract features from complex scenes.Finally,to reduce the influence of angle factor and bounding box regression accuracy,this paper proposes a newα-SCYLLA intersection over union(α-SIoU)instead of the complete intersection over union(CIoU),which improves the regression accuracy while increasing the convergence speed.Comparison experiments on public and homemade datasets show that the improved algorithm outperforms the original algorithm in all evaluation indexes,with an increase of 2.92%in the precision rate,4.14%in the recall rate,0.0356 in the weighted harmonic mean,3.60%in the mAP@0.5 value,and a reduction in the number of parameters and complexity.Compared with the mainstream algorithm,the improved algorithm has higher detection accuracy,faster convergence speed,and better actual recognition effect,indicating the effectiveness of the improved algorithm in this paper and its potential for practical application in laboratory scenarios.

Research on the Improvement of Incentive Mechanism for Pharmaceutical Sales Personnel - Taking Company A as an Example

Objective To analyze the improvement of the incentive mechanism of sales personnel in pharmaceutical company A,and to promote the smooth operation and further development of the company in a long term.Methods Compensation incentive,performance appraisal,welfare benefit,training incentive,promotion motivation and enterprise cultural inspiration were explored through questionnaires,telephone interviews and in-person interviews.Results and Conclusion This company’s incentive mechanism has problems in two aspects:Material incentives and spiritual incentives.As to the company’s characteristics and strategic development,the optimization countermeasures of incentive mechanism are proposed from the following three aspects:constructing a reasonable incentive system,establishing an efficient spiritual incentive mechanism,and implementing the dynamic incentive and differentiated incentive simultaneously.

Personnel Localization Method in Transformer Substation Based on Factor Graph

A SINS/GNSS location method based on factor diagram is proposed to meet the requirement of accurate location of substation construction personnel. In this paper, the inertial autonomous positioning, carrier motion information acquisition and satellite positioning technologies are integrated. The factor graph method is adopted to abstract the measurement information received by inertial navigation and satellite into factor nodes, and the state information into variable nodes, so as to construct the SINS/GNSS construction personnel positioning fusion factor graph model. The Gauss-Newton iterative method is used to implement the recursive updating of variable nodes, and the optimal estimate of the location information of the construction personnel is calculated, which realized the high precision location of the construction personnel. The factor graph method is verified by pedestrian navigation data. The results show that the factor graph method can continuously and stably output high-precision positioning results, and realize non-equidistant fusion of SINS and GNSS. The positioning accuracy is better than Kalman filter algorithm, and the horizontal positioning accuracy is less than 1 m. Therefore, the factor graph method proposed can provide accurate location information for substation construction personnel.

Visualization Techniques viaMLBS for Personnel Management in Major Events

Mobile location-based services(MLBS)refer to services around geographic location data.Mobile terminals use wireless communication networks(or satellite positioning systems)to obtain users’geographic location coordinate information based on spatial databases and integrate with other information to provide users with required location-related services.The development of systems based on MLBS has significance and practical value.In this paper a visualization management information system for personnel in major events based on microservices,namely MEPMIS,is designed and implemented by using MLBS.The system consists of a server and a client app,and it has some functions including map search and query,personnel positioning and scheduling,location management,messaging,and location service.Managers of the events can quickly search and locate the staff on the specific area of the map in real-time,and make broadcasting messages to the staff,and manage the staff.The client app is developed on the Android system,by which staff users can send the positions information to the server timely.The client users can search fuzzily near their peers and list their locations,and also call near peers through sending messages or query the history record of staff locations.In the design of the system,several new proposed techniques,including visual annotation method for overlapping locations,correcting trajectory drift algorithm,microservices-based overall system architecture methodology and other new techniques,which are applied to the implementation of the system.Also,HTML5,JQuery,MLBS APIs(Application Program Interfaces)related programming techniques have been used and combined with loading Ajax asynchronously and Json data encapsulation,map marker optimization techniques,that can improve the positioning accuracy and the performance of the system.The developed system with practical functions can enhance the efficiencies of the organization and management of major events.

陆军飞行人员领悟社会支持与疲劳、焦虑的相关性研究

目的探讨陆军飞行人员领悟社会支持与疲劳、焦虑发生的相关性,为精准干预研究奠定基础。方法于2022年7月10-13日随机抽取某部飞行人员,采用领悟社会支持、多维疲劳和焦虑量表进行问卷调查(n=88);利用座谈和个别交流方式补充了解陆军飞行人员领悟社会支持现状及其与疲劳、焦虑状态的关系;通过SPSS 20.0软件对该人群上述指标进行相关分析,计量资料采用描述性分析,计数资料采用频率分析,Pearson分析变量间的相关性。结果纳入分析问卷85份(每份含领悟社会支持、多维疲劳和焦虑量表)。其中男性83人(97.65%),女性2人(2.35%);飞行员80人(94.11%),领航员2人(2.35%),空中机械师3人(3.53%)。20~39岁82人(96.47%)。高社会支持(分值61~84)人数占比为68.23%(58/85),低、中焦虑度(分值<30分)人数占比为100%(85/85),非显著疲劳状态(55.71±8.48)人数占比为98.82%(84/85)。领悟社会支持程度(62.22±14.67)与焦虑(14.44±3.86)呈显著负相关(P<0.01),但与疲劳无显著相关性(P>0.05),焦虑与疲劳之间相关性也不显著(P>0.05)。结论陆军飞行人员领悟社会支持程度可能会引发焦虑,进而影响作业能力和健康水平;应对该人群领悟社会支持进行精准干预以完善航卫保障。

面向双创教育的高校档案人才的培养体系建构

创新创业教育是高等教育改革发展的重要方向,也是培养高素质档案专门人才的必然要求。在数字经济时代背景下,档案工作面临数字化转型与创新发展的新形势、新挑战,迫切需要一批具有创新意识、创业精神和实践能力的复合型档案人才。基于创新创业教育理念,围绕档案学专业人才培养体系,提出亟须通过更新人才培养理念,优化专业课程体系,创新人才培养模式,强化实践教学环节,完善人才培养评价来统筹推进档案学专业创新创业教育教学改革。

涉农高校助力乡村振兴的新型组织模式探索与实践——以吉林农业科技学院为例

乡村振兴战略是党的十九大提出的解决“三农”问题、加快农业农村现代化发展的重要举措。涉农高校在乡村振兴战略实施中具有独特优势,且肩负着重要使命。以吉林农业科技学院为例,提出涉农高校助力乡村振兴战略实施“五结合”新型组织模式,即“线上+线下”“集中+分散”“学校+企业”“项目+成果”“工作站+产业学院”,可为涉农高校服务乡村振兴战略提供借鉴,为乡村振兴发展提供新的思路和方法。

AIGC时代学生设计思维培养的挑战与应对

以ChatGPT为代表的AIGC对学生设计思维的培养提出了新的挑战。从主体性的回归、艺术性的呼唤与创造性的提升3个角度对学生设计思维培养的困境进行解析,并通过理论阐释与教学,探讨对AIGC时代学生设计思维的培养进行批判性的思考。一味追随智能技术并不能有效培养学生的设计思维,而技术依赖则容易导致设计教学中主体性的消解、艺术性的背离和创新性思维能力的减退。面向AIGC时代应当正视学生设计思维培养的多元价值,通过人文教育加强学生的主体性,并以美育教育为基础,聚焦批判性思维的提升,以实现设计创新人才培养的可能。

基于改进YOLOv7的矿井人员检测算法

矿井人员的实时检测是建设智慧矿山必不可少的内容,通过视频监测井下人员,从而实现危险区域预警及联动控制,对于矿井安全生产具有重要意义。现阶段可见光图像识别技术针对井下昏暗环境中人员的辨识还有待完善。针对井下光照不均、煤尘干扰严重导致监控视频存在噪声多、图像模糊等问题,提出一种改进YOLOv7的矿井人员检测算法。首先,针对ELAN模块直接拼接形成通道隔离的问题,提出基于通道重组与特征关注的复杂场景检测方式;其次,针对特征融合结果未侧重预期目标且模型缺乏针对性策略提升小目标检测性能,在颈部多尺度融合网络添加ACmix模块,兼顾全局特征和局部特征,提升了算法对小目标的检测能力;最后,引入Efficient IOU Loss提升算法收敛速度的同时减小目标框及先验框高度和宽度的差值,实现更加精准的定位。通过公开行人数据集及自建矿井人员检测数据集验证表明:该算法较YOLOv7模型相比,检测精度提升了3.1%,达到89.4%;召回率提升了3.8%,达到86.4%;速度提升了15.8%,达到68.8FPS;满足矿井人员实时检测的工作要求。

嵌入治理的激励与培养:乡镇人事结构再生产机制研究

乡镇人事结构是乡镇治理的深层基础。面对乡镇的一线治理场景,行政包干的人事整合机制具有社会激励与团队培养的效应,赋予乡镇人事结构以灵活性。而人事分流拓展了乡镇干部激励与培养的空间,且维系了人事结构的稳定性和公共性。乡镇人事结构再生产嵌入一线治理场景,实现了激励、培养与治理的统一,从而适应了乡镇治理的现实需要。乡镇机构改革须立足乡镇治理情境,在推进乡镇治理机制创新时注重发挥乡镇干部的主体性和能动性,优化乡镇人事结构,激发乡镇治理活力。

面向国家能源战略需求的卓越工程人才培养新体系构建与实践

行业特色高校由其办学属性决定,在培养卓越工程师等高端人才方面具有得天独厚的优势。中国石油大学(北京)作为一所油气能源特色高校,面对保障国家能源安全和能源战略转型发展双重使命挑战,坚持“强优、拓新、创一流”的学科建设思路,构建了学科发展新格局,以此为龙头,依托产学研合作办学优势,打造“思政引领、创新驱动、融合发展”的工程人才培养新模式,并建立了“内部保证、外部认证、智能赋能”的教育教学质量保障体系,取得了显著的人才培养成效,其创新成果具有较大的应用推广价值。

项目驱动“新工科”背景下高分子化学实验的教学改革与探索

基于“新工科”大背景下对应用型人才的需求,我们结合高分子化学课程的培养目标,采用项目驱动课堂教学新模式,提出“赛教融合”、“挑战杯”、“互联网+”、科研开发、校企联培等典型项目来源助力高分子化学实验教学,提升学生兴趣及应用知识能力。我们结合具体实施路径,挖掘学生潜力,全方位提升学生的创新思维,激发学生主观能动性,以期培养高质量应用型高分子专业人才。

产教融合视域下新商科高技能人才培养创新实践

高技能人才是中国人才队伍中实践能力最强的队伍之一,党中央、国务院高度重视劳动者素质提高和高技能人才培养。本文就产教融合背景下,如何加快新商科高技能人才队伍建设进行分析探讨。政府、学校、企业既要明确各自的角色,又要互联互通、携手合作,找到三者融合的突破口,实现产教深度融合,校企紧密合作,探索多种办学体制,建设知识型、技能型、创新型劳动者大军,使学生学习和掌握一定的知识技术,以适应商界的转型升级。

双碳目标下林业专硕人才培养探究——以安徽农业大学林业专硕为例

林业是我国生态建设和经济建设的重要组成部分,特别是碳达峰、碳中和理念的提出给林业赋予了新使命。以安徽农业大学林业专硕学生的培养为例,并结合“双碳”目标的实施,分析学生在培养过程中遇到的问题和挑战以及在提高专业技能和实践创新能力的同时如何结合“双碳”目标的实施进行改革,从而培养满足国家对林业绿色发展所需的高层次、应用型、复合型人才,满足“双碳”目标对社会高质量发展和生态文明建设的需要。

江苏职业教育教学资源库建设成效、特色与优化策略——基于2022年校级质量年报的分析

职业教育教学资源库是服务智慧化学习和终身学习的重要载体,对于高技能人才培养具有重要意义。以江苏省20所国家级“双高计划”院校为研究对象,发现江苏省高职院校在教学资源库建设方面取得多重成效,具有多元主体协同共建、多平台路径扩大供给、和强化经费投入基础保障的特色。面对职业教育数字化发展,需要推行“五大行动计划”,高质量建设多维立体教学资源;对接智慧教学模式需要,高水平打造数字化教学资源;主动利用信息技术,赋能职业教育“课堂革命”策略,提高职业教育教学资源库的建设质量和使用效果。

面向林学专业群的有机化学实验教学内容改革实践与探索

作为高等学校公共基础课,有机化学实验因教学资源不足,普遍存在教学内容千篇一律、与专业特色契合度低等问题,导致学生学习动力不足。本文结合西南林业大学实际,尝试将具有植物资源培养、开发、利用的相关专业组成林学专业群,并针对该专业群,构建了以天然产物提取、分离、纯化、鉴定为主线的有机化学实验教学体系。学生期末考试表现及问卷调查结果表明,新教学体系对提升学生学习兴趣、改善学生培养质量具有显著的推进作用,同时也为在教学体系中融入农林特质提供了思路。

人工智能背景下大数据与会计专业技能型人才培养模式研究

随着人工智能时代的到来,财会领域也面临着前所未有的挑战和机遇,新技术引起财会岗位及财会人员技能需求发生不同程度的变化。面对当前大数据与会计专业技能人才培养存在的问题,文章从技能人才培养模式、专业课程体系、教学资源整合、师资队伍建设、案例研究和实训项目教学等多方面提出改革路径与实践方法,以期提高职业院校大数据与会计专业技能型人才培养质量。