Evaluation of a software positioning tool to support SMEs in adoption of big data analytics

Big data analytics has been widely adopted by large companies to achieve measurable benefits including increased profitability,customer demand forecasting,cheaper development of products,and improved stock control.Small and medium sized enterprises(SMEs)are the backbone of the global economy,comprising of 90%of businesses worldwide.However,only 10%SMEs have adopted big data analytics despite the competitive advantage they could achieve.Previous research has analysed the barriers to adoption and a strategic framework has been developed to help SMEs adopt big data analytics.The framework was converted into a scoring tool which has been applied to multiple case studies of SMEs in the UK.This paper documents the process of evaluating the framework based on the structured feedback from a focus group composed of experienced practitioners.The results of the evaluation are presented with a discussion on the results,and the paper concludes with recommendations to improve the scoring tool based on the proposed framework.The research demonstrates that this positioning tool is beneficial for SMEs to achieve competitive advantages by increasing the application of business intelligence and big data analytics.

基于SMES装置的车网系统低频振荡抑制策略

近年来,我国电气化铁路发生多起因牵引网网压低频振荡导致的机车保护装置误动作事故,对铁路安全运行造成严重的威胁.现有抑制策略多从机车侧开展,通过对机车控制器参数的修订实现抑制效果,但对于铁路行业的长远发展缺乏灵活性.针对车网系统低频振荡问题,提出从牵引网侧外接超导磁储能(Superconducting Magnetic Energy Storage,SMES)的动态抑制方案.首先,分析SMES抑制车网系统低频振荡的原理,根据车网系统低频振荡的走势对其进行分类,并依据振荡类型设定SMES接入准则;其次,建立车网系统等效阻抗模型,结合阻抗比判据和伯德图分析车网系统稳定性,对SMES接入车网系统前后系统稳定性对比分析,验证所提方案的有效性,并与传统抑制方法进行对比,验证所提方案的灵活性;最后,对单车及多车升弓并网情况下的车网系统进行仿真分析.结果表明:SMES装置接入车网系统后可快速有效地抑制车网系统低频振荡.

电网不平衡下SMES变流器动态演化控制策略研究

针对超导磁储能(SMES)变流器在电网电压不平衡下的工作情况,提出一种新型非线性控制策略,即动态演化控制(DEC)。该控制方法相较于传统PI控制能有效地减小SMES因电网电压不平衡而产生的三相电流畸变以及有功、无功功率产生的二次谐波。文中根据电网不平衡下SMES变流器的工作情况设置三个工作目标;建立了基于DEC的不平衡控制方程,并在Simulink中搭建模型进行仿真分析。通过与传统PI控制作对比,DEC控制具有更强的鲁棒性,更快速的响应能力,理论分析和仿真结果验证了该控制策略的可行性和有效性。

基于MPC的SMES多目标控制

模型预测控制(model predictive control,MPC)性能优越,响应迅速而且可实现多目标控制,已受到广泛关注并逐步应用于大功率变流器,特别是在具有高功重比的超导储能磁体(superconductor magnetics energy storage,SMES)控制系统中,应用前景十分广阔。本文提出的基于MPC的SMES多目标控制,不仅实现SMES线圈在储能时较高精度的电流跟踪,同时可控制SMES线圈充放电时电流变化速率,避免较大的电流冲击,而且能够提高其响应速度与精度。通过搭建仿真实验平台验证了本文提出的SMES系统储能稳流性能、功率平衡稳定性能、动态性能以及充放电速率的控制性能。并与基于PI控制的系统进行比较,结果表明基于模型预测控制的SMES系统不仅可以实现稳态时较高精度的电流跟踪,而且可以实现充放电时电流变化速率的控制。

SRv6 Policy业务可靠性方案探讨

介绍了SRv6 Policy、SBFD技术及5G承载网络SRv6 Policy业务可靠性方案。针对SBFD误触发问题,提出了在被动端生成BSID、部署引流策略及设置不同检测周期等3种完善措施,这些措施不仅提高了业务的可靠性,还显著提升了网络服务能力.

Impact of ICTs on Core Competences of SMEs in Bukavu, DRC

Currently, it has been noticed a strong use of ICTs in several companies. The objective of this study was to analyze and measure the impact of the implementation of ICTs on the strategy and structure of management of core competence in SMEs in Bukavu, DRC. The scale used shows the good psychometric properties (χ2 = 75.045;P = 0.16;χ2/ddl = 1.471;CFI = 0.92;IFI = 0.92;RMSEA = 0.049;NFI = 0.79;GFI = 0.94;AGFI = 0.91. This study analyzed primary data collected from 195 SMEs in the city of Bukavu. Data processing was achieved using exploratory and confirmatory factor analysis and flat sorting. The results indicate that the use of ICTs by SME managers in the city of Bukavu has a positive impact on all dimensions of core competence selected in this study. They also reveal that the views of these leaders on the importance attached to the adoption of ICTs were divided and the level of ICT use was above average.

Assessment of challenges and strategies for driving energy transitions in emerging markets:A socio-technological systems perspective

The pursuit of improved quality of life standards has significantly influenced the contemporary mining model in the 21st century.This era is witnessing an unprecedented transformation driven by pressing concerns related to sustainability,climate change,the just energy transition,dynamic operating environments,and complex social challenges.Such transitions present both opportunities and obstacles.The aim of this study is to provide an extensive literature review on energy transition to identify the challenges and strategies associated with navigating transformations in energy systems.Understanding these transformations is particularly critical in the face of the severe consequences of global warming,where an accelerated energy transition is viewed as a universal remedy.Adopting a socio-technological systems perspective,specifically through the application of Actor Network Theory(ANT),this research provides a theoretical foundation while categorising challenges into five distinct domains and outlining strategies across these different dimensions.These insights are specifically tailored for emerging market countries to effectively navigate energy transition while fostering the development of resilient societies.Furthermore,our findings highlight that energy transition encompasses more than a mere technological shift;it entails fundamental changes in various systemic socio-economic imperatives.Through focusing on the role of social structures in transitions,this study makes a significant and innovative contribution to ANT,which has historically been criticised for its limited acknowledgement of social structures.Consequently,we propose an emerging market energy transition framework,which not only addresses technological aspects,but also integrates social considerations.This framework paves the way for future research and exploration of energy transition dynamics.The outcomes of this study offer valuable insights to policymakers,researchers,and practitioners engaged in the mining industry,enabling them to comprehend the multifaceted challenges involved and providing practical strategies for effective resolution.Through incorporating the social dimension into the analysis,we enhance the understanding of the complex nature of energy system transformations,facilitating a more holistic approach towards achieving sustainable and resilient energy transitions in emerging markets and beyond.

The Polish SMEs as Intelligent Organizations in an Era of Digital Transformation

The impact that the digital transformation(DT)has on businesses,suppliers,and other third parties has increased significantly now.Digital transformation means improving traditional manufacturing processes with the help of digital technologies.The goal of digital transformation is to increase production efficiency and reduce costs,improve the quality of goods and services produced,and quickly adapt to changes in the global market.The state of industrial production is constantly changing due to the instability of global,economic and political decisions,so the adoption and expansion of digital solutions based on Industry 4.0,the Internet of things,machine learning,and other technologies of the future is accelerating.With the help of these technologies,companies are trying to change approaches and find new ways to solve problems.In this article the author analyzed the phenomenon of a complex system of knowledge management with tools as SMAC,AI,IoT and Edge computing in intelligent organizations as a part of intelligent economy.The arguments are illustrated with the results of own research conducted by the author in 2021-2022 in selected SMEs from the Polish Wielkopolska Province and their reference to the general development trends in this area.

Delivery Service Management System Using Google Maps for SMEs in Emerging Countries

This paper proposes a Delivery Service Management(DSM)system for Small and Medium Enterprises(SMEs)that own a delivery fleet of pickup trucks to manage Business-to-Business(B2B)delivery services.The proposed DSM system integrates four systems:Delivery Location Positioning(DLP),Delivery Route Planning(DRP),Arrival Time Prediction(ATP),and Communication and Data Sharing(CDS)systems.These systems are used to pinpoint the delivery locations of customers,plan the delivery route of each truck,predict arrival time(with an interval)at each delivery location,and communicate and share information among stakeholders,respectively.The DSM system deploys Google applications,a GPS tracking system,Google Map APIs,ATP algorithms(embedded in Excel Macros),Line,and Telegram as supporting tools.To improve the accuracy of the ATP system,three tech-niques are applied considering driver behaviors.The proposed DSM system has been implemented in a Thai SME.From the process perspective,the DSM system is a systematic procedure for end-to-end delivery services.It allows the interactions between planner-driver decisions and supporting tools.The supporting tools are simple,can be easily used with little training,and require low capital expenditure.The statistical analysis shows that the ATP algorithm with the three techniques provides high accuracy.Thus,the proposed DSM system is beneficial for practitioners to manage delivery services,especially for SMEs in emerging countries.

Policy Gradient Adaptive Dynamic Programming for Model-Free Multi-Objective Optimal Control

Dear Editor,In this letter,the multi-objective optimal control problem of nonlinear discrete-time systems is investigated.A data-driven policy gradient algorithm is proposed in which the action-state value function is used to evaluate the policy.In the policy improvement process,the policy gradient based method is employed.

Accelerating the establishment of a new science-policy panel to address the triple planetary crisis

he triple planetary crisis—climate change,biodiversity loss,and pollution—threatens planetary health.In response to these challenges,the Intergovernmental Panel on Climate Change(IPCC)was established in 1988,followed by the formation of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services(IPBES)in 2012.Ongoing global initiatives through IPCC and IPBES have significantly advanced scientific understanding,raised public awareness,and informed policy-making in relation to climate change and biodiversity loss.However,pollution remains a pressing concern in all three crises.

Research on Policy Diffusion Path and Reproduction of River Chief System

The River Chief System (RCS) has evolved from local innovative practices to a national water governance strategy to address the current challenges in China’s water environmental management. In contrast to existing research that focuses on the strengths, weaknesses, and improvements of RCS, this study uses literature study to reveal the dynamic evolution of RCS through three phases, with RCS spreading from developed coastal areas to central and western inland regions. RCS’s diffusion path involves vertical diffusion between central and local levels and horizontal diffusion among local governments. Moreover, RCS has also achieved conceptual spillover, gradually expanding into other governance domains, such as the Lake Chief System, the Field Chief System, the Forestry Chief System, and the integration of multiple chief roles. However, it is essential to scrutinize the phenomenon of applying similar governance mechanisms to different areas, as it may result in challenges such as overburdening local governments, insufficient public participation, oversimplification of differences in natural resource endowments, and limited applicability. This study also provides suggestions on how to address these challenges. The study contributes theoretical insights and policy implications, providing a foundation for practical policy innovation.

Inactivated H9N2 vaccines developed with early strains do not protect against recent H9N2 viruses:Call for a change in H9N2 control policy

H9N2 virus has been widely distributed in wild birds and poultry around the world since its first emergence in the United States of America in 1966(Gu et al.2017;Carnaccini and Perez 2020).The virus appeared in chickens in China in the early 1990s,and over the last two decades has gradually become the dominant epidemic subtype(Sun and Liu 2015;Bi et al.2020).Although H9N2 virus infection alone cannot cause severe disease or death in poultry,H9N2 virus-infected birds experience a degree of egg production drop and can be easily infected by other pathogens,thus causing economic losses for poultry industry.

Automatic Generation of Attribute-Based Access Control Policies from Natural Language Documents

In response to the challenges of generating Attribute-Based Access Control(ABAC)policies,this paper proposes a deep learning-based method to automatically generate ABAC policies from natural language documents.This method is aimed at organizations such as companies and schools that are transitioning from traditional access control models to the ABAC model.The manual retrieval and analysis involved in this transition are inefficient,prone to errors,and costly.Most organizations have high-level specifications defined for security policies that include a set of access control policies,which often exist in the form of natural language documents.Utilizing this rich source of information,our method effectively identifies and extracts the necessary attributes and rules for access control from natural language documents,thereby constructing and optimizing access control policies.This work transforms the problem of policy automation generation into two tasks:extraction of access control statements andmining of access control attributes.First,the Chat General Language Model(ChatGLM)isemployed to extract access control-related statements from a wide range of natural language documents by constructing unique prompts and leveraging the model’s In-Context Learning to contextualize the statements.Then,the Iterated Dilated-Convolutions-Conditional Random Field(ID-CNN-CRF)model is used to annotate access control attributes within these extracted statements,including subject attributes,object attributes,and action attributes,thus reassembling new access control policies.Experimental results show that our method,compared to baseline methods,achieved the highest F1 score of 0.961,confirming the model’s effectiveness and accuracy.

Decision Support System for Assessment & Selection of Optimum Mass Transit Option

Many cities in the world are aspiring to have a world-class, sustainable public transport system. However, the cost of building new mass transport systems, especially metro projects, is so high it can lead to the postponement of such projects. New mass transit modes such as Bus Rapid Transit (BRT), Trackless Tram (TT) and suspended systems are continuously emerging. Some of the new emerging modes can deliver high capacity that can be equivalent to Light Rail Transit and Metro Systems but with significantly less cost. This research is meant to develop a comprehensive approach for comparing & assessing several aspects of different mass transit modes. Different research tools, including in-depth literature review, content, and case study analysis, multicriteria assessment, benchmarking and efficiency analysis, are used to reach conclusions for optimum mass transit options from different perspectives.

A quantitative study of disruptive technology policy texts:An example of China’s artificial intelligence policy

Purpose:The transformative impact of disruptive technologies on the restructuring of the times has attracted widespread global attention.This study aims to analyze the characteristics and shortcomings of China’s artificial intelligence(AI)disruptive technology policy,and to put forward suggestions for optimizing China’s AI disruptive technology policy.Design/methodology/approach:Develop a three-dimensional analytical framework for“policy tools-policy actors-policy themes”and apply policy tools,social network analysis,and LDA topic model to conduct a comprehensive analysis of the utilization of policy tools,cooperative relationships among policy actors,and the trends in policy theme settings within China’s innovative AI technology policy.Findings:We find that the collaborative relationship among the policy actors of AI disruptive technology in China is insufficiently close.Marginal subjects exhibit low participation in the cooperation network and overly rely on central subjects,forming a“center-periphery”network structure.Policy tool usage is predominantly focused on supply and environmental types,with a severe inadequacy in demand-side policy tool utilization.Policy themes are diverse,encompassing topics such as“Intelligent Services”“Talent Cultivation”“Information Security”and“Technological Innovation”,which will remain focal points.Under the themes of“Intelligent Services”and“Intelligent Governance”,policy tool usage is relatively balanced,with close collaboration among policy entities.However,the theme of“AI Theoretical System”lacks a comprehensive understanding of tool usage and necessitates enhanced cooperation with other policy entities.Research limitations:The data sources and experimental scope are subject to certain limitations,potentially introducing biases and imperfections into the research results,necessitating further validation and refinement.Practical implications:The study introduces a three-dimensional analysis framework for disruptive technology policy texts,which is significant for formulating and enhancing disruptive technology policies.Originality/value:This study utilizes text mining and content analysis techniques to quantitatively analyze disruptive technology policy texts.It systematically evaluates China’s AI policies quantitatively,focusing on policy tools,policy actors,policy themes.The study uncovers the characteristics and deficiencies of current AI policies,offering recommendations for formulating and enhancing disruptive technology policies.

Mapping Climate Services for Disaster Risk Management: A Systematic Review and Research Gaps from a Policy Process Perspective

Climate services (CS) are crucial for mitigating and managing the impacts and risks associated with climate-induced disasters. While evidence over the past decade underscores their effectiveness across various domains, particularly agriculture, to maximize their potential, it is crucial to identify emerging priority areas and existing research gaps for future research agendas. As a contribution to this effort, this paper employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to review the state-of-the-art in the field of climate services for disaster risk management. A comprehensive search across five literature databases combined with a snowball search method using ResearchRabbit was conducted and yielded 242 peer-reviewed articles, book sections, and reports over 2013-2023 after the screening process. The analysis revealed flood, drought, and food insecurity as major climate-related disasters addressed in the reviewed literature. Major climate services addressed included early warning systems, (sub)seasonal forecasts and impact-based warnings. Grounded in the policy processes’ theoretical perspective, the main focus identified and discussed three prevailing policy-oriented priority areas: 1) development of climate services, 2) use-adoption-uptake, and 3) evaluation of climate services. In response to the limitations of the prevalent supply-driven and top-down approach to climate services promotion, co-production emerges as a cross-cutting critical aspect of the identified priority areas. Despite the extensive research in the field, more attention is needed, particularly pronounced in the science-policy interface perspective, which in practice bridges scientific knowledge and policy decisions for effective policy processes. This perspective offers a valuable analytical lens as an entry point for further investigation. Hence, future research agendas would generate insightful evidence by scrutinizing this critical aspect given its importance to institutions and climate services capacity, to better understand intricate facets of the development and the integration of climate services into disaster risk management.

Provincial-level analysis of electrification feasibility and climate policy interactions

Improving electrification feasibility is essential for reducing emissions from non-electric energy sources,thereby enhancing air quality and public health.Concurrently,climate mitigation actions,such as carbon pricing policies,have significant potential to alleviate increasing carbon dioxide(CO_(2))and other co-emitted air pollutants.However,the interactions between climate policy and the improvement of electrification feasibility at the provincial level remain unclear,collectively impacting the net-zero transition of energy-intensive sectors.Here we combine a technologically rich economic-energy-environment model with air quality modeling across China to examine the health,climate,and economic implications of large-scale upgrades in electrification feasibility and climate policies from 2017 to 2030.The results indicate that advancing electrification feasibility,coupled with adopting carbon pricing policies,is likely to facilitate a transition towards electricity-dominant energy systems.Improved electrification feasibility is projected to yield a 7-25%increase in nationwide climate benefits and a 5-14%increase in health benefits by 2030.These incremental benefits,coupled with reduced economic costs,result in a 22-68%increase in net benefits.However,regionally,improvements in electrification feasibility will lead to heightened power demand and unintended emissions from electric energy production in certain provinces(e.g.,Nei Mongol)due to the coal-dominated power system.Additionally,in major coal-producing provinces like Shanxi and Shaanxi,enhanced electrification feasibility exacerbates the negative economic impacts of climate policies.This study provides quantitative insights into how improving electrification feasibility reshapes energy evolution and the benefit-cost profile of climate policy at the provincial level.The findings underscore the necessity of a well-designed compensation scheme between affected and unaffected provinces and coordinated emission mitigation across the power and other end-use sectors.

Geographical Distribution of Atmospheric Nitrogen Deposition in China and Its Response to Emission Control Policy

Atmospheric nitrogen(N)deposition has experienced significant change because of anthropogenic emissions,thereby exert-ing a pronounced impact on global ecosystem services.With the rapid development of industry and agriculture and the swift expansion of urban areas in China since the 1980s,reactive nitrogen(Nr)emissions and N deposition have substantially increased.In pursuit of im-proving air quality,China has implemented a series of environmental protection policies and undertaken diverse measures to reduce pol-lutant emissions.This paper is a review of multivariate data sources of atmospheric N deposition based on the results of literature from 1980 to 2023,and the original data from 1980 to 2020 are summarized,counted and calculated.The main findings are as follows:1)the annual average atmospheric N deposition ranged from approximately 20-40 kg/(ha·yr),with the variability primarily linked to different assessment methods;2)regional disparities were evident in the spatial distribution of N deposition,with elevated values concentrated in areas with intense Nr emissions;3)atmospheric N deposition significantly declined after 2010,particularly the deposition of oxidized N,while reduced N deposition remained stable.These results reflect the effects of China's serious control policies on nitrogen oxide(NO.)emissions and strengthen the importance of agricultural NH3 emission mitigation.This study contributes to a comprehensive understand-ing of the N dynamics in the emission-deposition process,and provides a scientific foundation for the research of environmental protec-tion,climate change,and sustainable development.

Perception Enhanced Deep Deterministic Policy Gradient for Autonomous Driving in Complex Scenarios

Autonomous driving has witnessed rapid advancement;however,ensuring safe and efficient driving in intricate scenarios remains a critical challenge.In particular,traffic roundabouts bring a set of challenges to autonomous driving due to the unpredictable entry and exit of vehicles,susceptibility to traffic flow bottlenecks,and imperfect data in perceiving environmental information,rendering them a vital issue in the practical application of autonomous driving.To address the traffic challenges,this work focused on complex roundabouts with multi-lane and proposed a Perception EnhancedDeepDeterministic Policy Gradient(PE-DDPG)for AutonomousDriving in the Roundabouts.Specifically,themodel incorporates an enhanced variational autoencoder featuring an integrated spatial attention mechanism alongside the Deep Deterministic Policy Gradient framework,enhancing the vehicle’s capability to comprehend complex roundabout environments and make decisions.Furthermore,the PE-DDPG model combines a dynamic path optimization strategy for roundabout scenarios,effectively mitigating traffic bottlenecks and augmenting throughput efficiency.Extensive experiments were conducted with the collaborative simulation platform of CARLA and SUMO,and the experimental results show that the proposed PE-DDPG outperforms the baseline methods in terms of the convergence capacity of the training process,the smoothness of driving and the traffic efficiency with diverse traffic flow patterns and penetration rates of autonomous vehicles(AVs).Generally,the proposed PE-DDPGmodel could be employed for autonomous driving in complex scenarios with imperfect data.