Humanizing the Culture of Technology Teams: Strategies for Creating Healthier and More Productive Work Environments

With the advancement of technology, companies are increasingly dependent on technology teams to stay competitive. However, members of these teams often work in stressful and unhealthy environments, which can undermine their productivity and well-being. The humanization of the culture of technology teams is an approach that aims to create healthier and more productive work environments for team members, promoting balance between personal and professional life. Despite the importance of a healthy and productive work environment, many companies do not invest in strategies to humanize the culture of their technology teams. This can lead to high levels of stress, staff turnover and low productivity. The objective of this project is to identify effective strategies to humanize the culture of technology teams and create healthier and more productive work environments in digital companies. For this, factors such as management styles, psychological safety, human-centered development, individual beliefs and time and energy management will be analyzed. The project’s methodology will include a literature review on the subject and qualitative data analysis will be performed using a content analysis approach. This project will contribute to the advancement of knowledge about the humanization of the culture of technology teams in digital companies. The results can be applied to companies that want to create healthier and more productive work environments for their team members.

The Impact of Human Activities on the Environment, Case of Mhondongori in Zvishavane, Zimbabwe

This study was proposed to explore the impact of human activities on the environment in Mhondongori. In this community, there are natural resources that include minerals, natural forest, wetlands, rivers and beautiful mountains. However, over the past two decades, the environmental quality of the community has deteriorated demonstrating irresponsible use of natural resources by humans. Indeed, if such poor natural resource management continues at the current rate, this would ultimately lead to extinction. Qualitative research design and convenience sampling methods were used. The study involved six participants who provided the data. An interview guide and observation techniques were used to collect data. Using content analysis data was classified into themes and a scorecard was used to determine the human activities on the environment and their impact. The results revealed that in Mhondongori, human activities that affected the environment were mining, veld fire, cutting down trees, overgrazing, excessive communal hunting and fishing, littering, water and air pollution. The impacts were the degradation of land, permanent scars on the landscape, deforestation, destruction of wildlife habitats, disappearance of wetlands, destruction of trees, grass, seed bank and seedlings, pollution of streets, bushes and rivers, disruption of migration and hibernation of animals. To conserve the environment, it was recommended that community leadership must develop appropriate environmental management strategies to mitigate these unfavourable impacts and that the state must empower Mhondongori leadership through reasonable legislative and other measures that would prevent environmental degradation.

Identification of rural courtyards’utilization status using deep learning and machine learning methods on unmanned aerial vehicle images in north China

The issue of unoccupied or abandoned homesteads(courtyards)in China emerges given the increasing aging population,rapid urbanization and massive rural-urban migration.From the aspect of rural vitalization,land-use planning,and policy making,determining the number of unoccupied courtyards is important.Field and questionnaire-based surveys were currently the main approaches,but these traditional methods were often expensive and laborious.A new workflow is explored using deep learning and machine learning algorithms on unmanned aerial vehicle(UAV)images.Initially,features of the built environment were extracted using deep learning to evaluate the courtyard management,including extracting complete or collapsed farmhouses by Alexnet,detecting solar water heaters by YOLOv5s,calculating green looking ratio(GLR)by FCN.Their precisions exceeded 98%.Then,seven machine learning algorithms(Adaboost,binomial logistic regression,neural network,random forest,support vector machine,decision trees,and XGBoost algorithms)were applied to identify the rural courtyards’utilization status.The Adaboost algorithm showed the best performance with the comprehensive consideration of most metrics(Accuracy:0.933,Precision:0.932,Recall:0.984,F1-score:0.957).Results showed that identifying the courtyards’utilization statuses based on the courtyard built environment is feasible.It is transferable and cost-effective for large-scale village surveys,and may contribute to the intensive and sustainable approach to rural land use.

Fortifying the Digital Bastion: Pioneering Cybersecurity with Dynamic Secrets Management and CMDB Fusion in the Enterprise

In the relentless quest for digital sovereignty, organizations face an unprecedented challenge in safeguarding sensitive information, protecting against cyber threats, and maintaining regulatory compliance. This manuscript unveils a revolutionary blueprint for cyber resilience, empowering organizations to transcend the limitations of traditional cybersecurity paradigms and forge ahead into uncharted territories of data security excellence and frictionless secrets management experience. Enter a new era of cybersecurity innovation and continued excellence. By seamlessly integrating secrets based on logical environments and applications (assets), dynamic secrets management orchestrates and automates the secrets lifecycle management with other platform cohesive integrations. Enterprises can enhance security, streamline operations, fasten development practices, avoid secrets sprawl, and improve overall compliance and DevSecOps practice. This enables the enterprises to enhance security, streamline operations, fasten development & deployment practices, avoid secrets spawls, and improve overall volume in shipping software with paved-road DevSecOps Practices, and improve developers’ productivity. By seamlessly integrating secrets based on logical environments and applications (assets), dynamic secrets management orchestrates and automates the application secrets lifecycle with other platform cohesive integrations. Organizations can enhance security, streamline operations, fasten development & deployment practices, avoid secrets sprawl, and improve overall volume in shipping software with paved-road DevSecOps practices. Most importantly, increases developer productivity.

基于电子商务环境的农机管理系统设计

介绍了电子商务环境的背景和现状,详细描述了农机管理系统的实际需求和业务流程,并根据系统设计原则设计了系统总体架构,最后从人机管理、作业管理及综合信息等3个方面实现了农机管理系统。系统在测试过程中取得了较好的应用效果,是一种具有广泛应用前景的高效农机管理系统。

Fall detection system in enclosed environments based on single Gaussian model

In this paper,we propose an efficient fall detection system in enclosed environments based on single Gaussian model using the maximum likelihood method.Online video clips are used to extract the features from two cameras.After the model is constructed,a threshold is set,and the probability for an incoming sample under the single Gaussian model is compared with that threshold to make a decision.Experimental results show that if a proper threshold is set,a good recognition rate for fall activities can be achieved.

Mobile Memory Management System Based on User’s Application Usage Patterns

Currently,the number of functions to improve user convenience in smartphone applications is increasing.In addition,more mobile applications are being loaded into mobile operating system memory for faster launches,thus increasing the memory requirements for smartphones.The memory used by applications in mobile operating systems is managed using software;allocated memory is freed up by either considering the usage state of the application or terminating the least recently used(LRU)application.As LRU-based memory management schemes do not consider the application launch frequency in a low memory situation,currently used mobile operating systems can lead to the termination of a frequently executed application,thereby increasing its relaunch time.This study proposes a memory management system that can efficiently utilize the main memory space by analyzing the application usage information.The proposed system reduces the application launch time by leaving the most frequently used or likely to be run applications in the main memory for as long as possible.The performance evaluation conducted utilizing actual smartphone usage records showed that the proposed memory management system increases the number of times the applications resume from the main memory compared with the conventional memory management system,and that the average application execution time is reduced by approximately 17%.

Explainable Artificial Intelligence-Based Model Drift Detection Applicable to Unsupervised Environments

Cybersecurity increasingly relies on machine learning(ML)models to respond to and detect attacks.However,the rapidly changing data environment makes model life-cycle management after deployment essential.Real-time detection of drift signals from various threats is fundamental for effectively managing deployed models.However,detecting drift in unsupervised environments can be challenging.This study introduces a novel approach leveraging Shapley additive explanations(SHAP),a widely recognized explainability technique in ML,to address drift detection in unsupervised settings.The proposed method incorporates a range of plots and statistical techniques to enhance drift detection reliability and introduces a drift suspicion metric that considers the explanatory aspects absent in the current approaches.To validate the effectiveness of the proposed approach in a real-world scenario,we applied it to an environment designed to detect domain generation algorithms(DGAs).The dataset was obtained from various types of DGAs provided by NetLab.Based on this dataset composition,we sought to validate the proposed SHAP-based approach through drift scenarios that occur when a previously deployed model detects new data types in an environment that detects real-world DGAs.The results revealed that more than 90%of the drift data exceeded the threshold,demonstrating the high reliability of the approach to detect drift in an unsupervised environment.The proposed method distinguishes itself fromexisting approaches by employing explainable artificial intelligence(XAI)-based detection,which is not limited by model or system environment constraints.In conclusion,this paper proposes a novel approach to detect drift in unsupervised ML settings for cybersecurity.The proposed method employs SHAP-based XAI and a drift suspicion metric to improve drift detection reliability.It is versatile and suitable for various realtime data analysis contexts beyond DGA detection environments.This study significantly contributes to theMLcommunity by addressing the critical issue of managing ML models in real-world cybersecurity settings.Our approach is distinguishable from existing techniques by employing XAI-based detection,which is not limited by model or system environment constraints.As a result,our method can be applied in critical domains that require adaptation to continuous changes,such as cybersecurity.Through extensive validation across diverse settings beyond DGA detection environments,the proposed method will emerge as a versatile drift detection technique suitable for a wide range of real-time data analysis contexts.It is also anticipated to emerge as a new approach to protect essential systems and infrastructures from attacks.

智慧学习环境中的人机协同设计

作为教育数字化转型的首要任务,智慧学习环境建设过分强调技术之于教育的能力,而忽略教育主体的价值与地位,涌现出场景割裂、数据孤岛等问题。人机协同旨在充分发挥人与机器的优势,弥补彼此的劣势,成为指导智慧学习环境创设与优化的最优解。研究将人机协同视为智慧学习环境设计的基线思维,构建了由数据模型层、技术支撑层和场景应用层三个层级,包含场景、数据、模型、资源、工具与服务等六个要素的智慧学习环境概念模型。基于普瑞斯的人机功能分配决策矩阵理论,提出了AI讲师、执行型AI+人类助手、伙伴型AI+人类同侪、助教型AI+人类教练、人类导师等五种人机协同模式。在此基础上,研究制定了智慧学习环境各层级的设计原则,分析了数据模型层的决策协同设计、技术支撑层的交互协同设计和场景应用层的流程协同设计,讨论了人机协同模式中人机互信和价值对齐的建构策略,以期指导智慧学习环境中的人机协同设计。

人机环境系统智能与生产方式变革

人机环境系统智能作为新质生产力的重要组成部分,其发展深刻影响人类社会生产方式。人工智能技术不仅能提高生产效率,降低生产成本,还可能在根本上改变劳动的性质和内容,其进步将推动生产关系的调整,并使社会生产力的释放和利用更加高效。人机环境系统智能带来的人机分工不同于以往简单的体力、智力再分配,还需要考虑人、机、环境优势如何融合,这不仅意味着人们工作方式、职业结构和思维方式的改变,也加速推动各个行业的数字化和智能化进程。未来在人机环境系统智能思维下,社会将迎来生产效率更高、公平与环境可持续的新阶段。

关于智能若干问题的思考

提升人工智能系统的通用性是人类追求的目标。随着科技的发展,人工智能已经从单一任务的解决者发展为能够适应复杂环境、理解人类需求并进行自主决策的系统。但不可否认的是,当前的人工智能水平仍远未实现通用人工智能,智能问题是实现通用人工智能的基础和关键。从智能的发展状况及与其他领域的对比方面介绍了智能的内涵,提出其未来发展趋势——人机环境系统智能。通过分析并实证验证人机环境系统智能的核心概念,揭示了人机环境智能系统中人与机各自的优势、劣势及二者的互补性,为进一步优化智能系统提供了理论支持。最后对智能的发展方向进行展望,指出人机环境系统智能的不断发展将对社会、经济、科技等多个领域产生深远影响,并提出人机环境系统智能发展会面临的终极问题——生态问题。

城市道路网联混驾车辆分阶段动态轨迹控制方法

随着城市的进步和不断发展,智能驾驶车辆逐渐代替路段中的部分人工驾驶车辆,但在未来较长时间内人工驾驶车辆并不会被完全取代,此时出现网联车与人工驾驶车辆的混驾环境,即目前以及未来时间内我们面临的驾驶环境。网联车与人工驾驶车辆驾驶行为在路段内相互干扰,造成混合车流行驶效率低下。为减弱2种车辆间的相互作用,提出一种分离混驾环境下网联车和人工驾驶车辆的分阶段动态车道引导算法(dynamic lane guidance algorithm for separating CAVs and HDVs in mixed traffic environment,SCHME)。通过该算法分离在交叉口上游路段的混合流车辆集合,调整智能驾驶车辆的行驶路线并进行实时动态更新,在满足运动学约束收敛的条件下,人工驾驶车辆根据网联车的动态路线进行相应调整,实现在每辆车广义安全损失成本最小的情况下提高路段内混驾环境下车辆运行效率。通过MATLAB模拟车辆在进入交叉口前的车辆运行状态,结果表明,SCHME算法可在广义安全损失成本最小的情况下提高路段内平均车辆通行效率(17.29%),同时当车辆优化数组越大,车辆集合距离交叉口越远时,智能驾驶车辆渗透率越低,每辆车的道路广义安全损失成本越低。

AGI时代人机共生景观下的媒介化生存:新的延伸、相互形塑与潜在异化

随着Sora的横空出世,人类在某种意义上探索出了AGI时代的入口。当前人类社会已进入深度媒介化阶段,人机协同共生是实现价值最大化的必然路径。本文以媒介环境学派为线索,考察了媒介变迁史与社会结构演变的联系,并从中推演人工智能接入媒介后可能对社会产生的作用。在人机共生景观中,人的媒介化生存需要沿着三重视角展开:一是人与媒介之间超越中介的全新延伸,二是人与媒介彼此塑造共生的复合关系,三是警惕人工智能对人的反向异化。

护理工作负荷的概念分析和启示

目的 探讨护理工作负荷的概念及内涵,为护理人力资源管理提供参考。方法 系统检索国内外数据库中护理工作负荷的相关文献,采用经典概念分析法进行分析。结果 归纳出护理工作负荷概念的定义属性包括:完成护理工作,具有一定的工作数量和强度,消耗相应的生理、认知和情绪。结论 护理工作负荷内涵复杂,受到患者、护士和环境的影响。护理管理者应全面梳理护理工作,明确护理工作负荷的内涵;加强护理人才培养,优化护理工作环境,以减少不良因素对护理工作负荷的负性影响;构建科学测量工具,指导人力资源合理配置。

“人-机-环境”共融的工业数字孪生系统智能优化方法

工业数字孪生系统是智能制造应用实施的重要手段,但现有工业数字孪生系统主要面向机械系统探索研究理论和关键技术,缺乏从“人-机-环境”全局开展系统建模和优化。鉴于此,分析了“人-机-环境”系统建模和智能优化研究进展,总结了“人-机-环境”共融的工业数字孪生系统基本特征,建立了系统参考架构。然后,提出了“人-机-环境”共融的工业数字孪生系统智能优化方法与关键技术体系,主要包括数据驱动的快速动态建模与仿真方法、基于生成式人工智能(AI)的数字孪生快速建模方法、多模态感知与智能交互方法、虚实同步映射与模型校正方法、基于分布式计算的运行优化控制方法、“人-机-环境”安全监测与态势感知方法和基于AI agent的系统自学习与优化控制方法等。最后,通过典型案例展示了智能优化方法的应用效果。所提出的工业数字孪生系统智能优化方法为实现智能制造过程中的“人-机-环境”共融提供了重要支撑。

基于“人机料法环测”管理模式的疑似白喉在医疗机构中的防控策略

白喉是由白喉棒状杆菌引起的死亡率高的急性呼吸道传染病,是我国《传染病防治法》规定的乙类传染病。感染者咽喉部可出现白色伪膜,同时出现发热、声音嘶哑、犬吠样咳嗽、呼吸困难等症状,严重感染患者可能会产生全身性中毒症状,甚至诱发周围神经麻痹和心肌炎,曾严重危害人民健康。虽然现阶段白喉在我国属于罕见传染病,但国内仍有散发病例出现,与此同时,非产毒菌分离率呈上升趋势。本研究通过对西安市某三甲医院检出的疑似白喉患者的防控策略进行梳理,旨在有力、有序推进医疗机构内发现疑似白喉等罕见传染病的防控策略,为医疗卫生行业疑似白喉的医院感染防控提供先进成熟经验。

基于区块链和人机交互的工程质量验收系统

针对传统工程质量验收中存在信息查阅耗时、数据易被篡改等问题,在工程质量验收管理需求分析的基础上,综合利用区块链技术的信息溯源和防篡改特性,以及人机交互技术的高效处理和响应信息能力,开发一种基于区块链和人机交互的工程质量验收系统,用实际案例数据验证该模型的实用性,并从读写延迟和数据存储两个维度测试了系统的性能.结果表明:该系统在无网络延迟条件下,有关工程质量数据的读延迟和写延迟分别小于1.5 s和3.5 s;该系统因有关工程质量验收事务数据增加而产生的存储容量在可接受范围;该系统满足既有研究对区块链系统提出的性能要求.

ChatGPT类智能工具对我国高等教育的冲击及其应对

人工智能与高等教育结合是社会发展的必然趋势,利用人工智能推动高等教育革新是高等教育发展的必然结果。Chat GPT在全世界的现象级“出圈”以及展现出来的强大功能,鲜明地突显了其在高等教育领域的价值。对高校智能化建设、课堂教学、教师创造性激发以及大学生素养提升带来便捷。然而,Chat GPT的兴起也使技术主义在高等教育中进一步蔓延,对高校师生的价值观念、高校科研创新、教育伦理、教师的职业素养和大学生的学习提出了挑战。如何对待Chat GPT类智能工具成为当前亟需研究的一项课题,通过加快自主研发,完善顶层设计;加强网络空间治理,构筑良好网络环境;强化师生数字素养,共建和谐共生的人机协同模式;坚持立德树人,恪守育人本位,能够积极应对Chat GPT类智能工具对高等教育的冲击。

绿色人力资源管理实施的影响因素研究——以江西省企业为例

随着环境保护意识的普及和可持续发展的推进,绿色人力资源管理(GHRM)已成为企业管理的一个重要分支。以江西省的企业为研究对象,目的在于研究得到影响企业实施GHRM的关键因素。通过问卷调查和统计软件的分析,采用因素分析法,对17个影响因素进行分析,揭示了宏观环境、权力影响和企业运营环境3个主要因素对企业GHRM实施的影响。结果表明,这些因素相互作用,对企业采纳绿色人资策略起着关键性作用。研究结果为江西省乃至其他非经济强省的企业提供了实施GHRM的理论依据和实践指南,促进企业与环境的协调融洽发展,并指出了可持续发展道路上的具体行动方向。

新能源汽车中人工智能技术应用探究

新能源汽车的推广应用与创新发展,离不开人工智能技术的支撑与保障。本文简要介绍了新能源汽车与人工智能技术,分析了二者之间的密切关系,并尝试围绕开放道路复杂场景的驾驶决策及规划、车辆智能节能、车辆预测安全控制、人机协同、充电管理、特种车辆应用等维度,探讨新能源汽车中人工智能技术的应用策略与要点,希望能起到一定的参考作用。