基于SMART原则的三阶段教学法的中医院神经外科规培护士培训评价

目的探讨基于SMART原则的三阶段教学法对神经外科规范化培训(以下简称“规培”)护士的教学效果。方法选择2020年1月—2022年7月在广东省中医院神经外科规培的护士80人作为研究对象,采用随机数字表法分为对照组(40人)和观察组(40人)。观察组采用基于SMART原则的三阶段教学法,对照组使用常规规培方法;培训结束后比较两组理论考试、临床实践考试成绩及护士核心能力测评结果。结果观察组规培护士理论知识考核及临床实践得分均高于对照组(P<0.05);观察组与对照组在评判性思维/科研、临床护理能力、人际关系、专业发展能力、教育咨询能力及总分方面差异有统计学意义(P<0.05),而在法律/伦理实践、领导能力方面差异无统计学意义(P>0.05)。结论与传统教学方法相比,基于SMART原则的三阶段教学法更有助于提高神经外科规培护士的综合护理能力,尤其是在神经外科急危重症护理方面;另外,规培护士的领导能力培养易被忽略,今后应加强相关研究。

二维同步插补算法及其在S7-200 Smart PLC上的应用

针对插补运动系统中存在机械振动较大的问题,提出一类基于恒定加加速度的二维直线插补算法。在确定加加速度的前提下,将直线插补的运动过程分为7个不同的阶段。利用运动学定律分析每个阶段的加速度、速度和位移的表达式,获取各运动阶段的初始条件。在基于二维系统的位移要求确定二维同步关系的基础上,实现了各阶段算法的离散化,最终完成了基于PLC(可编程逻辑控制器)的算法设计。实测效果表明,该算法同步精度小于0.5%,运行时间误差小于1 s,运行效果良好,满足应用场景的需求。

3D smart mA调控技术对不同BMI患者图像采集时间质量及辐射剂量的影响

目的:分析3D智能管电流(3D smart mA)调控技术对不同体质量指数(BMI)患者图像采集时间、质量及辐射剂量的影响。方法:择取的180例行胸部CT扫描患者选自西安交通大学第一附属医院2021年6月至2022年12月期间所收治,按照BMI将患者分为三组,A组(18.5 kg/m^(2)≤BMI≤23.9kg/m^(2),n=75)、B组(23.9kg/m^(2)0.05);两位医师对肺部不同层面图像质量(IQS)评分进行评价,Kappa一致性非常好(Kappa值=0.768、0.812、0.861);三组肺部不同层面IQS评分对比,差异无统计学意义(P>0.05);三组肺部不同层面CT对比,差异有统计学意义,且随着BMI增加而下降(P<0.05),三组肺部不同层面图像标准差(SD)值对比,差异无统计学意义(P>0.05);三组容积CT剂量指数(CTDIvol)对比,差异无统计学意义(P>0.05);A组DLP、ED均低于B、C组,B组DLP、ED低于C组(P<0.05)。结论:不同BMI患者应用3D smart mA调控技术,在保证图像质量的前提下,可有效降低辐射剂量。

基于SMART数据模式的HDD硬盘状态预测方法

硬盘广泛应用于各类信息系统中,其工作状态预测对信息系统的正常运行管理有着重要意义。现有基于SMART(Self Monitoring Analysis and Reporting Technology)属性的机器学习预测算法为保证其通用性,普遍选取部分典型属性作为特征,带来一定的信息丢失。在分析SMART数据特点的基础上,提出数据模式分类后再进行机器学习预测的SMART数据处理方法。实际测试结果表明,经分类处理后,采用简单的机器学习算法即可获得与强分类器接近的性能,同时,该方法可有效简化SMART数据机器学习时的特征选择过程,有效降低算法的资源消耗。

Smart Contract to Traceability of Food Social Selling

Traditionally,food sustainability has been considered solely in the stage of agricultural production.However,globalization,the expansion of the food production industry,and the emergence of supermarket chains that control the retail food market require specific significant changes in supply chains in the food sector and,therefore,we need to address the economic,social,and environmental impacts of these events.On the other hand,social selling has increased rapidly in recent years,with a further boom,following current events related to the coronavirus disease(COVID-19).This explosion of social sales,where there are usually no control and regulation entities,can bring problems associated with mishandling items.In this paper,we expose how Blockchain technology supports the traceability of social sales by validating the data provided by the chain participants such as digital health passports,production and transport data in the sale process;the proposed solution generates recommendations on productmanagement considering the agreements previously made by the network actors.To evaluate the proposed smart contracts,we useHyperledger Caliper,obtaining an average throughput of 12.6 transactions per second and an average latency of 0.3 s for the asset update process.We also use a study case to evaluate the proposed project platform’s selling-transport stage using Internet of Things(IoT)sensors.

Smart Contract Based DDoS Attack Traceability Audit Mechanism in Intelligent IoT

In this paper,we focus on providing data provenance auditing schemes for distributed denial of service(DDoS)defense in intelligent internet of things(IoT).To achieve effective DDoS defense,we introduce a two-layer collaborative blockchain framework to support data auditing.Specifically,using data scattered among intelligent IoT devices,switch gateways self-assemble a layer of blockchain in the local autonomous system(AS),and the main chain with controller participation can be aggregated by its associated layer of blocks once a cycle,to obtain a global security model.To optimize the processing delay of the security model,we propose a process of data pre-validation with the goal of ensuring data consistency while satisfying overhead requirements.Since the flood of identity spoofing packets,it is difficult to solve the identity consistency of data with traditional detection methods,and accountability cannot be pursued afterwards.Thus,we proposed a Packet Traceback Telemetry(PTT)scheme,based on in-band telemetry,to solve the problem.Specifically,the PTT scheme is executed on the distributed switch side,the controller to schedule and select routing policies.Moreover,a tracing probabilistic optimization is embedded into the PTT scheme to accelerate path reconstruction and save device resources.Simulation results show that the PTT scheme can reconstruct address spoofing packet forward path,reduce the resource consumption compared with existing tracing scheme.Data tracing audit method has fine-grained detection and feasible performance.

基于SMART原则的两癌筛查项目标准化管理研究

目的 分析基于SMART原则的两癌筛查项目标准化管理的作用和价值。方法 选取2022年1—12月在本院完成女性“两癌”筛查的患者3555例,并采取常规管理方法,将其作为对照组;选取2023年1月-7月在本院完成女性“两癌”筛查的患者2775例,并采取基于SMART原则的两癌筛查项目标准化管理,将其作为观察组,从两组筛查资料中各随机抽取500例作为研究对象,分析两组“两癌”筛查项目管理指标。结果 观察组两癌筛查记录本填写漏项率、错项率明显小于对照组,而完整率高于对照组(P<0.05);观察组宫颈癌(含癌前病变)、乳腺癌(含癌前病变)随访率高于对照组(P<0.05);观察组“两癌”筛查管理考核评分(方案、计划、组织管理、技术服务、信息管理)高于对照组(P<0.05);观察组“两癌”筛查人员服务满意率高于对照组(P<0.05)。结论 在两癌筛查过程中采取基于SMART原则的项目标准化管理,能够提高填写完整率,保证重点人员的随访率,提升两癌筛查服务质量与管理水平,有重要的应用价值。

Achieving 500X Acceleration for Adversarial Robustness Verification of Tree-Based Smart Grid Dynamic Security Assessment

Dear Editor,This letter presents a novel and efficient adversarial robustness verification method for tree-based smart grid dynamic security assessment(DSA).Based on tree algorithms technique,the data-driven smart grid DSA has received significant research interests in recent years.

Introduction to the Special Issue on ComputerModeling for Smart Cities Applications

A significant fraction of the world’s population is living in cities. With the rapid development ofinformation and computing technologies (ICT), cities may be made smarter by embedding ICT intotheir infrastructure. By smarter, we mean that the city operation will be more efficient, cost-effective,energy-saving, be more connected, more secure, and more environmentally friendly. As such, a smartcity is typically defined as a city that has a strong integration with ICT in all its components, includingits physical components, social components, and business components [1,2].

Geospatial Technology Integration in Smart City Frameworks for Achieving Climate Neutrality by 2050: A Case Study of Limassol Municipality, Cyprus

This study investigated the integration of geospatial technologies within smart city frameworks to achieve the European Union’s climate neutrality goals by 2050. Focusing on rapid urbanization and escalating climate challenges, the research analyzed how smart city frameworks, aligned with climate neutrality objectives, leverage geospatial technologies for urban planning and climate action. The study included case studies from three leading European cities, extracting lessons and best practices in implementing Climate City Contracts across sectors like energy, transport, and waste management. These insights highlighted the essential role of EU and national authorities in providing technical, regulatory, and financial support. Additionally, the paper presented the application of a WEBGIS platform in Limassol Municipality, Cyprus, demonstrating citizen engagement and acceptance of the proposed geospatial framework. Concluding with recommendations for future research, the study contributed significant insights into the advancement of urban sustainability and the effectiveness of geospatial technologies in smart city initiatives for combating climate change.

SMART管理模式联合PDCA循环管理在提高ICU心电监护报警设置正确率中的应用

探讨SMART管理模式联合PDCA循环管理在提高ICU心电监护报警设置正确率中的应用研究。 方法 随机选取2022年6月~2023年6月收治80名患者作为研究对象。依据实施干预措施的时间进行划分,将 2022年6月至12月在我院ICU行心电监护的40例患者作为对照组,实施常规心电监护护理方法,2023年1月至6月收治的40例患者作为观察组,在对照组的基础上应用SMART管理模式联合PDCA循环管理,比较两组心电监护报警设置正确率。 结果 实施SMART管理模式联合PDCA循环管理后,心电监护报警设置正确率高于实施前,差异具有统计学意义,P<0.05。 结论 SMART管理模式联合PDCA循环管理有利于提高ICU心电监护报警设置正确率,可以帮助护理人员更好的识别患者的异常生命体征,保障患者安全。

Construction of smart propellant with multi-morphologies

Smart materials,which exhibit shape memory behavior in response to external stimuli,have shown great potential for use in biomedical applications.In this study,an energetic composite was fabricated using a UV-assisted DIW 3D printing technique and a shape memory material(SMP)as the binder.This composite has the ability to reduce the impact of external factors and adjust gun propellant combustion behavior.The composition and 3D printing process were delineated,while the internal structure and shape memory performance of the composite material were studied.The energetic SMP composite exhibits an angle of reversal of 18 s at 70°,with a maximum elongation typically reaching up to 280% of the original length and a recovery length of approximately 105%during ten cycles.Additionally,thermal decomposition and combustion behavior were also demonstrated for the energetic SMP composite.

基于Smart Design的校园光伏电站仿真研究

基于智能光伏设计软件Smart Design,对株洲市某高校图书馆屋顶的工商业光伏电站进行了设计与仿真。通过评估当地太阳能资源和气象参数,确定了光伏电站的最佳选址和倾角。利用软件的3D建模和电气设计功能,完成了光伏板排布和电气连接,采用隆基绿能双面光伏组件和华为光伏优化器及逆变器,提高了电站效率。仿真结果显示,该电站具有良好的发电量、经济效益和社会贡献。

Active and passive modulation of solar light transmittance in a uniquely multifunctional dual-band single molecule for smart window applications

Functional materials may change color by heat and electricity separately or simultaneously in smart windows.These materials have not only demonstrated remarkable potential in the modulation of solar radiation but are also leading to the development of indoor environments that are more comfortable and conducive to improving individuals'quality of life.Unfortunately,dual-responsive materials have not received ample research attention due to economic and technological challenges.As a consequence,the broader utilization of smart windows faces hindrances.To address this new generational multistimulus responsive chromic materials,our group has adopted a developmental strategy to create a poly(NIPAM)n-HV as a switchable material by anchoring active viologen(HV)onto a phase-changing poly(NIPAM)n-based smart material for better utility and activity.These constructed smart windows facilitate individualistic reversible switching,from a highly transparent state to an opaque state(thermochromic)and a red state(electrochromic),as well as facilitate a simultaneous dual-stimuli response reversible switching from a clear transparent state to a fully opaque(thermochromic)and orange(electrochromic)states.Absolute privacy can be attained in smart windows designed for exclusive settings by achieving zero transmittance.Each unique chromic mode operates independently and modulates visible and near-infrared(NIR)light in a distinct manner.Hence,these smart windows with thermal and electric dual-stimuli responsiveness demonstrate remarkable heat regulation capabilities,rendering them highly attractive for applications in building facades,energy harvesting,privacy protection,and color display.

A Review of Deep Learning-Based Vulnerability Detection Tools for Ethernet Smart Contracts

In recent years,the number of smart contracts deployed on blockchain has exploded.However,the issue of vulnerability has caused incalculable losses.Due to the irreversible and immutability of smart contracts,vulnerability detection has become particularly important.With the popular use of neural network model,there has been a growing utilization of deep learning-based methods and tools for the identification of vulnerabilities within smart contracts.This paper commences by providing a succinct overview of prevalent categories of vulnerabilities found in smart contracts.Subsequently,it categorizes and presents an overview of contemporary deep learning-based tools developed for smart contract detection.These tools are categorized based on their open-source status,the data format and the type of feature extraction they employ.Then we conduct a comprehensive comparative analysis of these tools,selecting representative tools for experimental validation and comparing them with traditional tools in terms of detection coverage and accuracy.Finally,Based on the insights gained from the experimental results and the current state of research in the field of smart contract vulnerability detection tools,we suppose to provide a reference standard for developers of contract vulnerability detection tools.Meanwhile,forward-looking research directions are also proposed for deep learning-based smart contract vulnerability detection.

The Contribution of AI-Powered Mobile Apps to Smart City Ecosystems

In the groundbreaking study “The Contribution of AI-powered Mobile Apps to Smart City Ecosystems,” authored by Zaki Ali Bayashot, the transformative role of artificial intelligence (AI) in urban development is meticulously examined. This comprehensive research delineates the multifaceted ways in which AI-powered mobile applications can significantly enhance the efficiency, sustainability, and livability of urban environments, marking a pivotal step towards the realization of smart cities globally. Bayashot meticulously outlines the critical areas where AI-powered apps offer unprecedented advantages, including urban mobility, public safety, energy management, and environmental monitoring. By leveraging AI’s capabilities, these applications not only streamline city operations but also foster a more sustainable interaction between city dwellers and their environment. The paper emphasizes the importance of data-driven decision-making in urban planning, showcasing how AI analytics can predict and mitigate traffic congestion, optimize energy consumption, and enhance emergency response strategies. The author also explores the social implications of AI in urban settings, highlighting the potential for these technologies to bridge the gap between government entities and citizens. Through engaging case studies, Bayashot demonstrates how participatory governance models, enabled by AI apps, can promote transparency, accountability, and citizen engagement in urban management. A significant contribution of this research is its focus on the challenges and opportunities presented by the integration of AI into smart city ecosystems. Bayashot discusses the technical, ethical, and privacy concerns associated with AI applications, advocating for a balanced approach that ensures technological advancements do not come at the expense of civil liberties. The study calls for robust regulatory frameworks to govern the use of AI in public spaces, emphasizing the need for ethical AI practices that respect privacy and promote inclusivity. Furthermore, Bayashot’s research underscores the necessity of cross-disciplinary collaboration in the development and implementation of AI technologies in urban contexts. By bringing together experts from information technology, urban planning, environmental science, and social sciences, the author argues for a holistic approach to smart city development. This interdisciplinary strategy ensures that AI applications are not only technologically sound but also socially and environmentally responsible. The paper concludes with a visionary outlook on the future of smart cities, posited on the seamless integration of AI technologies. Bayashot envisions a world where AI-powered mobile apps not only facilitate smoother urban operations but also empower citizens to actively participate in the shaping of their urban environments. This research serves as a critical call to action for policymakers, technologists, and urban planners to embrace AI as a tool for creating more sustainable, efficient, and inclusive cities. By presenting a detailed analysis of the current state of AI in urban development, coupled with practical insights and forward-looking recommendations, “The Contribution of AI-powered Mobile Apps to Smart City Ecosystems” stands as a seminal work that is poised to inspire and guide the evolution of urban landscapes worldwide. Its comprehensive exploration of the subject matter, combined with its impactful conclusions, make it a must-read for anyone involved in the field of smart city development, AI technology, or urban policy-making.

Smart Farming for Sustainable Rice Production:An Insight into Application,Challenge,and Future Prospect

Rice has a huge impact on socio-economic growth,and ensuring its sustainability and optimal utilization is vital.This review provides an insight into the role of smart farming in enhancing rice productivity.The applications of smart farming in rice production including yield estimation,smart irrigation systems,monitoring disease and growth,and predicting rice quality and classifications are highlighted.The challenges of smart farming in sustainable rice production to enhance the understanding of researchers,policymakers,and stakeholders are discussed.Numerous efforts have been exerted to combat the issues in rice production in order to promote rice sector development.The effective implementation of smart farming in rice production has been facilitated by various technical advancements,particularly the integration of the Internet of Things and artificial intelligence.The future prospects of smart farming in transforming existing rice production practices are also elucidated.Through the utilization of smart farming,the rice industry can attain sustainable and resilient production systems that could mitigate environmental impact and safeguard food security.Thus,the rice industry holds a bright future in transforming current rice production practices into a new outlook in rice smart farming development.

Optimization and Construction of Collaborative Office under the Background of Smart Campus in Universities

At present, with the continuous development of technologies such as the Internet, big data, and artificial intelligence, smart campuses in universities are being rapidly constructed. Improving the informatization level of administrative management work is also an important content. The collaborative office work in multiple departments requires more standardized, convenient, intelligent, and secure office systems. In response to this issue, this article analyzes the optimization and construction process of collaborative office systems based on the development of university informatization, summarizes the operational results, and explores the prospects of smart office.

Mitigating Blackhole and Greyhole Routing Attacks in Vehicular Ad Hoc Networks Using Blockchain Based Smart Contracts

The rapid increase in vehicle traffic volume in modern societies has raised the need to develop innovative solutions to reduce traffic congestion and enhance traffic management efficiency.Revolutionary advanced technology,such as Intelligent Transportation Systems(ITS),enables improved traffic management,helps eliminate congestion,and supports a safer environment.ITS provides real-time information on vehicle traffic and transportation systems that can improve decision-making for road users.However,ITS suffers from routing issues at the network layer when utilising Vehicular Ad Hoc Networks(VANETs).This is because each vehicle plays the role of a router in this network,which leads to a complex vehicle communication network,causing issues such as repeated link breakages between vehicles resulting from the mobility of the network and rapid topological variation.This may lead to loss or delay in packet transmissions;this weakness can be exploited in routing attacks,such as black-hole and gray-hole attacks,that threaten the availability of ITS services.In this paper,a Blockchain-based smart contracts model is proposed to offer convenient and comprehensive security mechanisms,enhancing the trustworthiness between vehicles.Self-Classification Blockchain-Based Contracts(SCBC)and Voting-Classification Blockchain-Based Contracts(VCBC)are utilised in the proposed protocol.The results show that VCBC succeeds in attaining better results in PDR and TP performance even in the presence of Blackhole and Grayhole attacks.

Smart Energy Management System Using Machine Learning

Energy management is an inspiring domain in developing of renewable energy sources.However,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and demand.The major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental effects.Modern energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user comfort.To address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve it.An inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy costs.Machine Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)network.The IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource management.In this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management problem.The proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.