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User engagement metrics, including engaged sessions, average engagement time, bounce rate, and conversions, provide significant insights into online behavior. This study utilizes Google Analytics data insights and predictive statistics to analyze these metrics and apply classification models to enhance digital marketing strategies. Relationships among key metrics including event count, sessions, purchase revenue, transactions, and bounce rate, were examined using descriptive statistics, revealing factors affecting user engagement. Machine learning classifiers, such as decision trees (DTs), Naive Bayes (NB), and k-nearest neighbors (k-NN), were assessed for their effectiveness in classifying engagement levels. DTs achieved a classification accuracy of 97.98%, outperforming NB (65.00%) and k-NN (97.90%). Furthermore, techniques like pruning are applied for performance optimization. Primarily, this paper goas is to generate a series of recommendations to help the decision-makers and marketers optimizing the marketing strategies. This study highlights the significance of artificial intelligence (AI) integration in digital marketing as a best practice for optimizing decision-making processes.

Existing buildings in the European Union account for 40% of its energy consumption. To significantly reduce this impact, annual deep energy renovation rates should triple by the end of the 2020s. However, the lack of automation in the construction industry has hindered energy renovation efforts. Horizon Europe’s INPERSO project (Industrialised and Personalised Renovation for Sustainable Societies) aims to create a user-centered energy rehabilitation method based on industrialized technologies and systems, enhancing efficiency and building performance. To bridge the gap between predictions and real-world outcomes, the 22 project partners—using a multi-criteria decision analysis (MCDA) process—devised a list of key performance indicators (KPIs) for evaluating rehabilitation based on economic, energy, environmental, social, and technological factors. Adopting a human-centric approach, these project partners aim to minimize the technologies’ environmental impact while optimizing users’ comfort and experience. The indicators are designed to evaluate performance at every stage of the renovation process, enabling continuous feedback and user engagement and ultimately ensuring that projected energy savings are met throughout the building’s lifespan. The KPIs selected for INPERSO provide a solid framework for evaluating and monitoring sustainable renovation. However, challenges such as administrative reluctance and user disruption must be addressed to further boost the adoption of deep energy renovations.

Global efforts to achieve climate neutrality increasingly rely on innovative urban planning and design strategies. This study focuses on the identification and application of key performance indicators (KPIs) to support policymakers and local authorities in driving sustainable urban transitions. Using a real-life case study of European cities and countries, this research leverages data analytics and machine learning to inform decision-making processes. Specifically, the k-means clustering algorithm was employed to group countries based on socioeconomic and environmental KPIs, while principal component analysis was used to rank the most influential indicators in shaping these clusters. The analysis highlighted GDP per capita, corruption perception, and climate-related expenditure as key drivers of clustering. Additionally, time series analysis of KPI trends demonstrated the impact of policy decisions over time. This study showcases how machine learning and data-driven approaches can provide valuable insights for urban planners, offering a robust framework for evaluating and improving climate-neutrality strategies at both city and country levels.

Timeliness, budget consciousness, and quality are critical to the success of a project, and become increasingly challenging with increased project complexity. Five-dimensional building information modeling (BIM) integrates cost and schedule data with a 3D model, and enhances project management by addressing budgeting, timelines, and visualization simultaneously. However, a comprehensive assessment of 5D BIM’s impact on key performance indicators is currently lacking. This research aims to identify the critical factors influencing the adoption of 5D BIM and its impact on key project performance indicators. A thorough systematic literature review and qualitative analysis were conducted to achieve this goal. Relevant articles from the past decade (2014–2023) were examined from the Scopus and Web of Science databases, of which 222 were selected and screened using PRISMA procedures. This research found consistent and rapid updating of keywords, highlighting the dynamic evolution of 5D BIM and its expanding applications in the construction industry. Thirty critical factors affecting the adoption of 5D BIM were identified and categorized into the following six groups based on the technology–organization–environment (TOE) framework: technology, organization, environment, operator, project, and government policy. The 15 factors driving construction project performance in integrated 5D BIM were divided into cost, time, and quality performance based on key performance indicators. This review offers innovative insights into 5D BIM adoption, and can aid stakeholders in developing effective 5D BIM implementations.

Background: Pickleball is an exponentially growing sport with a lack of notation-based studies. Consequently, this research aimed to conduct a match analysis in men’s singles to enhance the understanding of the game and optimize training practices. Methods: Using observational methodology, a total of 1145 points were analyzed from the semifinal and final rounds of five Professional Pickleball Association Tour tournaments. Data were recorded with LINCE PLUS V.2.1.0 software using the OI-PICKLEBALL-S23 observational instrument. Descriptive statistical analyses were conducted with IBM-SPSS version 25.0, and gameplay patterns were detected using Theme 6.0 Edu. Statistical significance was set at p < 0.05. Results: The data indicated that service faults at the start of the game were minimal (2.4%). The server won fewer points than the returner in the overall set of analyzed points (46.6%). Most points were concluded in short rallies (1–4 shots; 43%) or medium-length rallies (5–8 shots; 44%), with the final shot predominantly occurring from striking zone 2, the area closest to the non-volley line (50.7%). Ground strokes (55.1%) and volleys (38.4%) were the most common final shots. Conclusions: The insights gained from this study can benefit high-performance players and coaches and provide a foundation for future notation-based research in pickleball.

(1) Background: The analysis of women’s tennis performance has not been extensively explored by the scientific community, necessitating further research to understand the tactical actions occurring in matches. This research aimed to examine the chance of winning a point in professional women’s tennis based on the key variables that influence performance in the sport. (2) Methods: Data from 3239 points were examined across three distinct court surfaces, sourced from the final rounds (starting with the quarterfinals) of three Grand Slam tournaments in the 2021 season. An observational methodology was employed, using various analysis techniques: descriptive and chi-square analyses, with a significance level of p < 0.05. (3) Results: The probability of winning a point on the first serve was 61% on clay, 70% on grass, and 69% on hard courts. For second serves, the probability of winning the point varied between 55% and 57%, depending on the court surface. Additionally, the majority of points, ranging from 70% to 71%, concluded with short rallies, involving one to four shots. On clay courts, the server won up to 65% of points with a first serve and a short rally, while the success rate increased to 75% on both grass and hard courts. For medium-length rallies (5–8 shots), the probability of winning the point dropped to 55–57%. The point outcome (winner, forced error, and unforced error) varied according to court surface, serve type, and rally length. (4) Conclusions: Descriptive data from this research on the probability of winning a point could assist coaches and players in developing match strategies.

(1) Background: the penalty kick is an action in football that decides many matches. The aim of this research was to establish the success rate of penalty kicks in Spain between the seasons 2012/2013 and 2019/2020 considering different key performance variables. (2) Methods: A total of 941 penalty kicks were analysed from eight seasons of the first division of the Spanish men’s football league (La Liga). An observational methodology was used and, in order to obtain the results, different analytical techniques were employed: descriptive and chi square with a significance level of p < 0.05. (3) Results: The effectiveness of penalty kicks was 76.7%. Most of the penalties were taken by the home team (60.9%), with a tie on the scoreboard (44.2%) and within the last 30 min of the match (41.9%). The highest success rate was observed for balls kicked to the upper side of the goal (100%), even though the highest number of penalties were taken low, to one of the two sides of the goal (57.0%). Up to 5.6% of the penalties were not kicked inside the goal. (4) Conclusions: Effectiveness does not depend on home–visitor status, player laterality, score or remaining match time, studied independently. A loss of effectiveness is observed when the match scores of both teams are close and in critical moments of the match.

Large quantities of battery systems will be discarded from electric vehicles in the future. Non-destructive separation of used electric vehicle (EV) traction batteries enables a second life of battery components, extraction of high value secondary materials, and reduces the environmental footprint of recycling and separation processes. In this study, the key performance indicators (KPIs) for the second life application of spent EV batteries are identified. Three battery packs are analyzed in terms of the joining techniques used—and possible separation techniques—considering only direct recycling methods. The components that can be recovered from these batteries are evaluated against the KPIs. This study shows that all the batteries analyzed allow a second life in stationary and semi-stationary electrical storage systems and marine applications when used at the pack and module levels. Two packs can be reused in electric vehicles such as forklifts. However, the feasibility of re-use in micro-mobility and consumer electronics is very limited. This study shows that technically feasible separation methods are dictated and constrained by the joining techniques used. As welding and adhesive bonding pose challenges to separation processes, future efforts should prioritize ‘design for disassembly’ to ensure sustainable battery life cycle management.

Our study investigates the relevance and application of key performance indicators (KPIs) and key risk indicators (KRIs) in business management from 1992 to 2023 through a comprehensive bibliometric analysis performed in RStudio using the Bibliometrix platform and in VOSviewer. Utilizing data from the Web of Science database, we identify trends, key themes, and influential research in this domain, observing an annual growth rate of 17.76%. Our analyses include the top 10 most globally cited documents, word clouds based on authors’ keywords and Keywords Plus, clustering by coupling, co-occurrence networks, and factorial analysis. Our findings reveal a significant increase in research interest post-2004, with sustainability and corporate social responsibility emerging as central themes. We confirm positive correlations between KPIs, improved organizational performance, and effective risk management via KRIs. This research underscores the importance of international collaboration and diverse thematic exploration in advancing the field.

Cybersecurity protects cyberspace from a wide range of cyber threats to reduce overall business risk, ensure business continuity, and maximize business opportunities and return on investments. Cybersecurity is well achieved by using appropriate sets of security governance frameworks. To this end, various Information Technology (IT) and cybersecurity governance frameworks have been reviewed along with their benefits and limitations. The major limitations of the reviewed frameworks are; they are complex and have complicated structures to implement, they are expensive and require high skill IT and security professionals. Moreover, the frameworks require many requirement checklists for implementation and auditing purposes and a lot of time and resources. To fill the limitations mentioned above, a simple, dynamic, and adaptive cybersecurity governance framework is proposed that provides security related strategic direction, ensures that security risks are managed appropriately, and ensures that organizations’ resources are utilized optimally. The framework incorporated different components not considered in the existing frameworks, such as research and development, public-private collaboration framework, regional and international cooperation framework, incident management, business continuity, disaster recovery frameworks, and compliance with laws and regulations. Moreover, the proposed framework identifies and includes some of the existing frameworks’ missed and overlapped components, processes, and activities. It has nine components, five activities, four outcomes, and seven processes. Performance metrics, evaluation, and monitoring techniques are also proposed. Moreover, it follows a risk based approach to address the current and future technology and threat landscapes. The design science research method was used in this research study to solve the problem mentioned. Using the design science research method, the problem was identified. Based on the problem, research objectives were articulated; the objective of this research was solved by developing a security governance framework considering different factors which were not addressed in the current works. Finally, performance metrics were proposed to evaluate the implementation of the governance framework.

Software development has a problem with transparency/visibility. As an intangible product, software and its intermediate development results are hard to see or touch. Customers of custom software have difficulties checking progress, and risk coming out with costly but low-quality software. In the space domain with its often expensive and one-of-a-kind devices, which are developed in complex multitier supply chains, the risk is even greater. This paper presents the SKQuest data set. It contains the completed responses with 190 variables from an empirical study with over 100 software experts. The data set covers distinct aspects of measuring metrics and transparency in software projects. To show what information lies in the data set, the paper investigates, and affirms, from different perspectives, the following questions: Is transparency a problem in software development projects? Is there a desire for more transparency in projects? Can metrics contribute to improving the situation? Moreover, it attempts to replicate the results of an earlier study. The main contribution of this paper is, however, the SKQuest data set that is published with this paper in CSV formatas. It is a tool that enables systematic investigations of software metrics and allows research on how they can improve the efficiency of the software lifecycle, not limited to, but particularly with respect to transparency. Consequently, the paper may serve as a starting point for future research avenues in academia and industry and help to improve existing and future standards in software development.

Modern challenges such as the liberalization of the railway sector and growing demands for sustainability, high-quality services, and user satisfaction set new standards in railway operations. In this context, railway infrastructure managers (RIMs) play a crucial role in ensuring innovative approaches that will strengthen the position of railways in the market by enhancing efficiency and competitiveness. Evaluating their performance is essential for assessing the achieved objectives, and it is conducted through a wide range of key performance indicators (KPIs), which encompass various dimensions of operations. Monitoring and analyzing KPIs are crucial for improving service quality, achieving sustainability, and establishing a foundation for research and development of new strategies in the railway sector. This paper provides a detailed overview and evaluation of KPIs for RIMs. This paper creates a framework for RIM evaluation using various scientific methods, from identifying KPIs to applying complex analysis methods. A novel hybrid model, which integrates the fuzzy Delphi method for aggregating expert opinions on the KPIs’ importance, the extended fuzzy analytic hierarchy process (AHP) method for determining the relative weights of these KPIs, and the ADAM method for ranking RIMs, has been developed in this paper. This approach enables a detailed analysis and comparison of RIMs and their performances, providing the basis for informed decision-making and the development of new strategies within the railway sector. The analysis results provide insight into the current state of railway infrastructure and encourage further efforts to improve the railway sector by identifying key areas for enhancement. The main contributions of the research include a detailed overview of KPIs for RIMs and the development of a hybrid multi-criteria decision making (MCDM) model. The hybrid model represents a significant step in RIM performance analysis, providing a basis for future research in this area. The model is universal and, as such, represents a valuable contribution to MCDM theory.

A variety of positioning systems have emerged for indoor localization which are based on several system strategies, location methods, and technologies while using different signals, such as radio frequency (RF) signals. Demands regarding positioning in terms of performance, robustness, availability and positioning accuracies are increasing. The overall goal of indoor positioning is to provide GNSS-like functionality in places where GNSS signals are not available. Analysis of the state-of-the-art indicates that although a lot of work is being done to combine both the outdoor and indoor positioning systems, there are still many problems and challenges to be solved. Most people moving on the city streets and interiors of public facilities have a smartphone, and most professionals working in public facilities or construction sites are equipped with tablets or smartphone devices. If users already have the necessary equipment, they should be provided with further functionalities that will help them in day-to-day life and work. In this review study, user requirements and the state-of-the-art in system development for smartphone localization are discussed. In particular, localization with current and upcoming ‘signals-of-opportunity’ (SoP) for use in mobile devices is the main focus of this paper.

In the evolving landscape of Industry 4.0, the integration of advanced wireless technologies into manufacturing processes holds the promise of unprecedented connectivity and efficiency. In particular, the data transmission in a heavy industry environment needs stable connectivity with mobile operators. This paper deals with the performance study of 4G and 5G mobile signal coverage within a complex factory environment. For this purpose, a cost-effective and portable measurement setup was realized and used to provide long-term measurement campaigns monitoring and recording several key parameter indicators (KPIs) in 4G/5G downlink and upload. To support the reproducibility of the provided study and other research activities, the measured dataset is publicly available for download. Among others findings, the obtained results show how the performance of 4G/5G is influenced by a heavy industry environment and of the time of day on the network load.

The cloudification and virtualisation of railway functions have the potential to improve railway operation efficiency, reliability, safety, and security, as well as to enhance passenger experience by offering innovative services. This paper considers issues related to the management and orchestration of railway clouds that host cloudified railway functions. A microservices-based approach to the design of railway cloud management and orchestration functionality is proposed. The basic railway cloud concepts were defined, and functionality related to the basic orchestration of the railway cloud and deployments is analysed in order to derive the requirements of platform resources and workload management. This functionality is further designed in the form of microservices, meaning that they could possibly be used in orchestration applications to enable improvements in scalability, fault isolation, and data security. The design of microservices follows the principles of the Representational State of Transfer (REST) application programming interface (API) as a set of interlinked resources. Resources related to railway cloud orchestration are identified with their associated data, relationships to other resources, and applicable methods. The resources’ methods are used in railway applications to implement the required orchestration functionality and to maintain the state of railway cloud orchestration processes. To verify the synthesised microservices, the common orchestration application logic and microservices’ logic were modelled, and it was proved that the orchestration processes, which run concurrently, expose equivalent behaviour. The proposed approach was validated using a simulation, aiming to evaluate injected latency as a key performance indicator for the reliability and safety of railway operations. Additionally, some safety and security issues related to railway cloud management and orchestration are considered.

Population growth, urbanization, and climate change have significantly contributed to environmental degradation, posing severe consequences for humans and other species. By integrating ecological objectives with human-centric goals, a path towards a sustainable, multi-species future is possible. Current sustainable design principles have shown positive environmental impacts by addressing human-centric objectives such as enhancing green infrastructure, energy efficiency, thermal comfort, and more. However, the incorporation of multi-species design criteria remains unresolved. This paper proposes a conceptual framework in which human-centric and ecological design objectives are defined and associated through the selection of key performance indicators (KPIs) represented by numerical thresholds. But, while the objective-KPI relationship is an established path in architectural design, the same does not apply for preserving and promoting biodiversity. The proposed conceptual framework identifies, defines, and associates the relevant objective-KPI relationships for all stakeholders and becomes the basis for evaluating the project computationally. Such an approach is currently lacking.

To achieve climate and energy goals in the building sector, the current pace of renovating existing structures must double, overcoming prevailing barriers. Key Performance Indicators play a pivotal role in science-based decision making, emphasizing both direct and indirect benefits of building retrofits. The authors aim to contribute to proper metric identification for multi-perspective building performance assessment and formulate a methodology supporting energy planning decisions. They introduce the Global Cost per Emission Savings (GCES), an aggregated indicator encompassing both public (CO2 emissions) and private (global cost) perspectives of diverse retrofit technologies for building HVAC systems. Applied to the Italian residential building stock via the Reference Building approach, the methodology is tested using condensing gas boilers, biomass boilers, and electric heat pumps, revealing diverse environmental and economic performances. Addressing the establishment of effective decision-support tools for policymakers, the paper explores the potential impact of various policies on the favorability of technologies. Different policy scenarios are delineated to analyze how distinct approaches may influence the attractiveness of technologies. Notably, in the baseline scenario, biomass boilers hold an advantage over heat pumps according to the GCES index. However, scenarios involving technology-specific incentives or a greenhouse gases emission tax failed to alter the technological ranking, leaving heat pumps financially uncompetitive. In contrast, the TXPM scenario positions heat pumps as the most financially appealing option, penalizing biomass boilers for high particulate matter emissions.

Maintenance work orders are commonly used to document information about wind turbine operation and maintenance. This includes details about proactive and reactive wind turbine downtimes, such as preventative and corrective maintenance. However, the information contained in maintenance work orders is often unstructured and difficult to analyze, presenting challenges for decision-makers wishing to use it for optimizing operation and maintenance. To address this issue, this work compares three different approaches to calculating reliability key performance indicators from maintenance work orders. The first approach involves manual labeling of the maintenance work orders by domain experts, using the schema defined in an industrial guideline to assign the label accordingly. The second approach involves the development of a model that automatically labels the maintenance work orders using text classification methods. Through this method, we are able to achieve macro average and weighted average F1-scores of 0.75 and 0.85 respectively. The third technique uses an AI-assisted tagging tool to tag and structure the raw maintenance information, together with a novel rule-based approach for extracting relevant maintenance work orders for failure rate calculation. In our experiments, the AI-assisted tool leads to an 88% drop in tagging time in comparison to the other two approaches, while expert labeling and text classification are more accurate in KPI extraction. Overall, our findings make extracting maintenance information from maintenance work orders more efficient, enable the assessment of reliability key performance indicators, and therefore support the optimization of wind turbine operation and maintenance.

The transient response of production systems is of significant importance especially if present advancements in Digital Twinning technology are taken into account. While the steady-state response enables long-term strategic decision making, the transient response enables more detailed simulation concerning aspects like production losses and preventive maintenance. This is especially relevant if nonhomogenous aspects of production systems are taken into account. An analytical and approximative solution to the problem of the transient response of homogenous and nonhomogenous Bernoulli production systems is developed in this paper based on the eigendecomposition of transition matrices, the eigenvalue problem, and the finite-state method. In particular, sub-resonant and resonant nonhomogeneous production lines are introduced for the first time. Also, the most significant key performance indicators are developed as functions of the time elapsed from the first cycle. Finally, the relationship between the number of eigenvalues and the accuracy of the results is inspected by employing a sensitivity analysis. The presented theoretical framework was employed in the case of a wood processing facility to present the potential application of the theory in the case of long- and short-term management of production systems.

Broader understanding of waste management has the potential to bring about broad societal change impacting the climate crisis and public health. We present existing waste management tools and commercially-available games involving waste management, highlighting the strengths and opportunities left unaddressed by these tools in educational contexts and planning use cases. A survey motivates the need for enhanced interactive tools providing clear feedback through quick-visibility performance indicators. After identifying an opportunity to build upon highly-detailed multi-criteria simulation tools, we explore the need for easy-to-read performance metrics that will bring to the field of waste management easily identifiable and measurable key performance indicators (KPIs) that vary alongside factors affecting waste management policies. Such metrics are introduced and detailed as part of a unified waste management model. We then develop a representative gamified educational tool based upon this model to be used by students, decision makers planning real-world policies, and the public. This simulator is built upon the Unity Game Engine and emulates waste management techniques and resulting KPIs within the context of a virtual city.

A well-functioning smart grid is an essential part of an efficient and uninterrupted power supply for the key enablers of smart cities. To effectively manage the operations of a smart grid, there is an essential requirement for a seamless wireless communication system that provides high data rates, reliability, flexibility, massive connectivity, low latency, security, and adaptability to changing needs. A contemporary review of the utilization of emerging 6G wireless communication for the major applications of smart grids, especially in terms of massive connectivity and monitoring, secured communication for operation and resource management, and time-critical operations, are presented in this paper. This article starts with the key enablers of the smart city, along with the necessity of the smart grid for the key enablers of it. The fundamentals of the smart city, smart grid, and 6G wireless communication are also introduced in this paper. Moreover, the motivations to integrate 6G wireless communication with the smart grid system are expressed in this article as well. The relevant literature overview, along with the novelty of this paper, is depicted to bridge the gap of the current research works. We describe the novel technologies of 6G wireless communication to effectively perform the considered smart grid applications. Novel technologies of 6G wireless communication have significantly improved the key performance indicators compared to the prior generation of the wireless communication system. A significant part of this article is the contemporary survey of the considered major applications of a smart grid that is served by 6G. In addition, the anticipated challenges and interesting future research pathways are also discussed explicitly in this article. This article serves as a valuable resource for understanding the potential of 6G wireless communication in advancing smart grid applications and addressing emerging challenges.

With the increasing demand for sustainable urban development, smart cities have emerged as a promising solution for optimizing energy usage, reducing emissions, and enhancing the quality of life for citizens. In this context, the combined use of key performance indicators (KPIs) and data analytics has gained significant attention as a powerful tool for promoting energy efficiency and emissions reduction in urban areas. This paper presents a comprehensive conceptual framework in which a series of KPIs are proposed to serve as essential metrics for guiding, monitoring, and assessing energy efficiency and emissions reduction levels in smart cities. Some of the included KPIs in the analysis are 'annual energy consumption per person', 'reduction in greenhouse gas emissions', 'public transport use', and 'adoption of renewable energy'. By incorporating these KPIs, city planners and policymakers can gain valuable insights into the effectiveness of sustainability initiatives. Furthermore, the paper explores how the integration of KPIs with data analytics can be used for monitoring and assessing the overall performance of the city in terms of energy efficiency, emissions reduction, and the enhancement of urban living conditions. Visualization tools, such as radar plots, and time series analysis forecasting methods allow data to be processed and patterns to be identified, enabling informed decision-making and efficient resource allocation. Real-life case studies of ongoing smart city projects are presented in the paper, which also provides a KPI comparison among different European cities, as well as models to forecast the evolution of KPIs related to energy usage and emissions reduction in different European cities.

Background: This article identifies and examines key performance indicators (KPIs) related to citizen mobility logistics in smart and sustainable urban areas. It begins with a comprehensive literature review to identify essential KPIs, offering valuable insights for both public and private stakeholders, including policymakers and mobility service providers. Drawing from various mobility projects in smart cities, the study extracts common KPIs and best practices. The focus of the paper then turns to Barcelona, Spain, where KPIs that matter most are analyzed. Methods: Using open data from the city council spanning from 2017 onwards, the study provides insights into the evolving mobility logistics landscape. KPIs from other European cities are also considered by utilizing similar open data sources. This comparative analysis provides valuable benchmarks and reveals disparities in mobility logistics. Throughout this investigation, the paper emphasizes the role of data quality in KPI selection. Results: Reliable open data significantly influence indicator choices and present challenges when comparing cities. Remarkably, the findings consistently highlight environmental data as an area requiring attention in sustainable mobility logistics. Conclusions: This paper makes contributions by identifying and examining KPIs relevant to citizen mobility logistics in smart and sustainable urban areas. It offers insights by applying these KPIs to Barcelona and conducting comparative analyses with other European cities. These findings serve as a valuable resource for policymakers, city planners, and mobility experts.

The present work introduces a multi-criteria approach focused on the evaluation of wine production, considering the three dimensions of sustainability: environmental, economic and social. In this sense, the most relevant key performance indicators were selected within each dimension and disaggregated into sub-indicators to address the different sustainability aspects within the wine value chain. Furthermore, the analytic hierarchy process was applied as the method to weight the relevance of the three dimensions and corresponding key performance indicators, in order to allow the producers to understand which aspects need to be covered to improve their production sustainability. Results demonstrate how the approach proposed is able to identify, for both the vineyard and winery, the strengths and weaknesses regarding the sustainability performance of their production. Additionally, this study also contributes by shedding light on the most suitable and recommended actions to increase the company’s sustainability from a sustainable perspective. Additionally, it is important to highlight that, although the developed approach is specifically designed for the wine sector in the Navarrese region, it can be replicated by adjusting the key performance indicator selection to apply this methodology to the reality in other wine production regions.

Urbanization is one of the most dominant economic and social changes of the 20th century. This phenomenon brings about rapid urban development, which is inextricably linked to transport development. In order to understand this relationship, it is important to analyze the spatial spillover effects of the phenomenon in the urban environment. This study analyzes the spatial performance, in terms of urban development, of 12 European Union regions from five European countries with coastal areas by incorporating spatial data such as length of road network, population distribution, land uses, and other factors. Key performance indicators have been developed for evaluating the structural development model of the regions (e.g., dense or sprawl development). In addition, the incorporation of spatial spillover effects in the evaluation of the regions was conducted by the extended spatial data envelopment analysis (SDEA) method. The results of SDEA identified the best and worst-performing regions in terms of urban growth. Finally, this study implements a target-setting approach where under-performing regions can best perform. Based on the target-setting approach, local authorities can set realistic targets for improving the structural model that the regions are following.

The classification of drilling conditions is a crucial task in the drilling process, playing a vital role in improving drilling efficiency and reducing costs. In this study, we propose an improved stacking ensemble learning algorithm with the objective of enhancing the performance of drilling conditions classification. Additionally, this algorithm aims to have a positive impact on automated drilling time estimation and the continuous improvement of efficiency. In our experimental setup, we employed various base learners, such as random forests, support vector machine, and the K-nearest neighbors algorithm, as initial models for the task of drilling conditions classification. To improve the model’s expressive power and feature relevance specifically for this task, we enhanced the meta-model component of the stacking algorithm by incorporating feature engineering techniques. The experimental results show that the improved ensemble learning algorithm achieves an accuracy and recall rate of 97% and 98%, respectively. Through continuous improvement in drilling operations, the average sliding time is reduced by 21.1%, and the average Rate of Penetration (ROP) is increased by 15.65%. This research holds significant importance for engineering practice in the drilling industry, providing robust support for optimizing and enhancing the drilling process.

Transportation infrastructures, especially roadway bridges, play a pivotal role in socioeconomic development. Recently, bridge engineers are increasingly facing the challenge in terms of shifting their strategy from building new facilities to maintaining the existing aging infrastructures, to preserve their service performance during the operational stage. In fact, the infrastructure administrators lack a quality control (QC) strategy for the existing roadway bridges, which leads to the decision-making application and tool being still minor. To overcome those challenging issues, this paper proposes a quality control framework for roadway bridge management using key performance indicators (KPIs). The case study methodology is suggested to be used and then conducted for several bridges, mostly in European countries. In which the performance indicators (PIs) and goals (PGs) are defined, after assessing the bridges and vulnerable zones, the derivation KPIs from those PIs are introduced and developed considering time functions and different maintenance scenarios. Eventually, a two-stage quality control framework will be proposed in which the static stage includes preparatory works, inspection responsibilities, and a quick assessment of KPIs; while the dynamic stage helps the decision maker in estimating the time remaining of the bridge service life, managing the evolution of KPIs as well as planning the best possible maintenance strategy. The selected two case studies are present and curated, which show the excellent potential to develop a long-term strategy for roadway bridge management on a lifecycle level.

In terms of uncertain business conditions, the ability of an enterprise to bounce back after severe disruptions, or simply resilience, may be seen as one of the major features needed to sustain successful business operations. This research has the objective of proposing an algorithm for the organizational resilience assessment in industrial companies and conducting an analysis of the relationship between the organizational Resilience Factors and Key Performance Indicators recovery times. As the variables that are an integral part of the research are exposed to a high degree of uncertainty, they are modeled using fuzzy set theory. The methodology used for the research is an enhanced fuzzy Delphi, where the fuzzy geometric mean is employed as an aggregation operator. The relationship between the organizational resilience factors and Key Performance Indicators’ recovery time is based on the correlation analysis. The proposed model is based on real data from one complex industrial enterprise. The main finding of the research is that calculations indicate a significant negative correlation between treated variables.

Mobile broadband (MBB) services in Lagos, Nigeria are marred with poor signal quality and inconsistent user experience, which can result in frustrated end-users and lost revenue for service providers. With the introduction of 5G, it is becoming more necessary for 4G LTE users to find ways of maximizing the technology while they await the installation and implementation of the new 5G networks. A comprehensive analysis of the quality of 4G LTE MBB services in three different locations in Lagos is performed. Minimal optimization techniques using particle swarm optimization (PSO) are used to propose solutions to the identified problems. A methodology that involves data collection, statistical analysis, and optimization techniques is adopted to measure key performance indicators (KPIs) for MBB services in the three locations: UNILAG, Ikorodu, and Oniru VI. The measured KPIs include reference signal received power (RSRP), reference signal received quality (RSRQ), received signal strength indicator (RSSI), and signal-to-noise ratio (SINR). Specific statistical analysis was performed, and the mean, standard deviation, skewness, and kurtosis were calculated for the measured KPIs. Additionally, the probability distribution functions for each KPI were plotted to infer the quality of MBB services in each location. Subsequently, the PSO algorithm was used to optimize the KPIs in each location, and the results were compared with the measured data to evaluate the effectiveness of the optimization. Generally, the optimization process results in an improvement in the quality of service (QoS) in the investigated environments. Findings also indicated that a single KPI, such as RSRP, is insufficient for assessing the quality of MBB services as perceived by end-users. Therefore, multiple KPIs should be considered instead, including RSRQ and RSSI. In order to improve MBB performance in Lagos, recommendations require mapping and replanning of network routes and hardware design. Additionally, it is clear that there is a significant difference in user experience between locations with good and poor reception and that consistency in signal values does not necessarily indicate a good user experience. Therefore, this study provides valuable insights and solutions for improving the quality of MBB services in Lagos and can help service providers better understand the needs and expectations of their end users.

The digital twinning concept stands as a remarkable opportunity to integrate sophisticated mathematical models within the context of existing manufacturing systems. Such models may provide shipyard management with predictive analytics, improving the final results at the strategic, tactical, and operational levels. Therefore, the possibility of integrating the Markovian-framework-based finite-state method into the context of ship production is presented in this study, including its outline, digital thread, and factory-floor data reliance. First, the predictive analytics problem is addressed by the finite-state method in the case of the shipyard’s fabrication line, and the obtained results are validated afterward using a numerical model through discrete-event theory. The predictive analytics indicate an almost ideal balancing of the fabrication line, except for the buffers storing stiffeners before the coat-dying and marking operations. In addition, the improvability analysis of the shipyard’s fabrication lines extended the scope of the predictive analytics using bottleneck identification and affecting the key performance indicators through a digital thread, as well as by improved maintenance strategies.

Recently, the appeal of Hybrid Energy Storage Systems (HESSs) has been growing in multiple application fields, such as charging stations, grid services, and microgrids. HESSs consist of an integration of two or more single Energy Storage Systems (ESSs) to combine the benefits of each ESS and improve the overall system performance, e.g., efficiency and lifespan. Most recent studies on HESS mainly focus on power management and coupling between the different ESSs without a particular interest in a specific type of ESS. Over the last decades, Redox-Flow Batteries (RFBs) have received significant attention due to their attractive features, especially for stationary storage applications, and hybridization can improve certain characteristics with respect to short-term duration and peak power availability. Presented in this paper is a comprehensive overview of the main concepts of HESSs based on RFBs. Starting with a brief description and a specification of the Key Performance Indicators (KPIs) of common electrochemical storage technologies suitable for hybridization with RFBs, HESS are classified based on battery-oriented and application-oriented KPIs. Furthermore, an optimal coupling architecture of HESS comprising the combination of an RFB and a Supercapacitor (SC) is proposed and evaluated via numerical simulation. Finally, an in-depth study of Energy Management Systems (EMS) is conducted. The general structure of an EMS as well as possible application scenarios are provided to identify commonly used control and optimization parameters. Therefore, the differentiation in system-oriented and application-oriented parameters is applied to literature data. Afterwards, state-of-the-art EMS optimization techniques are discussed. As an optimal EMS is characterized by the prediction of the system’s future behavior and the use of the suitable control technique, a detailed analysis of the previous implemented EMS prediction algorithms and control techniques is carried out. The study summarizes the key aspects and challenges of the electrical hybridization of RFBs and thus gives future perspectives on newly needed optimization and control algorithms for management systems.

Governments frequently partner with the private sector to provide infrastructure and public services. These cooperations, known as public–private partnerships (PPPs), have often failed. Sometimes, due to the problem’s complexity, the public sector cannot choose the right partner for these projects, which is one of the main reasons for failures. Complexity in such problems is associated with a large number of indicators, imprecise judgments of decision-makers or problem owners, and the unpredictability of the environment (under conditions of uncertainty). Therefore, presenting a simplified algorithm for this complicated process is the primary goal of the current research so that it can consider the problem’s various dimensions. While many researchers address the critical risk factors (CRFs) and others focus on key performance indicators (KPIs), this research has considered both CRFs and KPIs to choose the best private-sector partner. In addition, we used single-valued neutrosophic sets (SVNSs) to collect decision-makers’ views, which can handle ambiguous, incomplete, or imprecise information. Next, by defining the ideal alternative and using the similarity measure, we specified the ranks of the alternative. Additionally, to face the uncertain environment, we examined the performance of options in four future scenarios. The steps of the proposed algorithm are explained in the form of a numerical example. The results of this research showed that by employing a simple algorithm, even people who do not have significant operations research knowledge could choose the best option by paying attention to the dimensions of the problem complexity.

( Background ) Over the years, there has been increasing interest in adopting a quality approach in radiology, leading to the strategic pursuit of specific and key performance indicators (KPIs). These indicators in radiology can have significant impacts ranging from radiation protection to integration into digital healthcare. ( Purpose ) This study aimed to conduct a narrative review on the integration of key performance indicators (KPIs) in radiology with specific key questions. ( Methods ) This review utilized a standardized checklist for narrative reviews, including the ANDJ Narrative Checklist, to ensure thoroughness and consistency. Searches were performed on PubMed, Scopus, and Google Scholar using a combination of keywords related to radiology and KPIs, with Boolean logic to refine results. From an initial yield of 211 studies, 127 were excluded due to a lack of focus on KPIs. The remaining 84 studies were assessed for clarity, design, and methodology, with 26 ultimately selected for detailed review. The evaluation process involved multiple assessors to minimize bias and ensure a rigorous analysis. ( Results and Discussion ) This overview highlights the following: KPIs are crucial for advancing radiology by supporting the evolution of imaging technologies (e.g., CT, MRI) and integrating emerging technologies like AI and AR/VR. They ensure high standards in diagnostic accuracy, image quality, and operational efficiency, enhancing diagnostic capabilities and streamlining workflows. KPIs are vital for radiological safety, measuring adherence to protocols that minimize radiation exposure and protect patients. The effective integration of KPIs into healthcare systems requires systematic development, validation, and standardization, supported by national and international initiatives. Addressing challenges like CAD-CAM technology and home-based radiology is essential. Developing specialized KPIs for new technologies will be key to continuous improvement in patient care and radiological practices. ( Conclusions ) In conclusion, KPIs are essential for advancing radiology, while future research should focus on improving data access and developing specialized KPIs to address emerging challenges. Future research should focus on expanding documentation sources, improving web search methods, and establishing direct connections with scientific associations.

This paper discusses and integrates the concept of complexity in the industrial performance measurement and management systems (PMM) theory, providing a comprehensive overview of the different methodologies used within the decision systems research area. It also discusses the importance of introducing Key Activity Indicators (KAI) within PMM, specifically related to the Operations and Supply Chain management research and industrial areas. Moreover, it provides validation of the methodology through a case study concerning the production environment of a multinational pharmaceutical company. The main research objective is to design appropriate industrial PMM systems with the aim of increasing the industrial efficiency and effectiveness of manufacturing and service organizations. An analysis of the central industrial performance measurement systems design methods is conducted, classifying them into macro-categories and conducting a comparative study. Based on the analysis of the different proposed methods, organisations will be able to choose the best one based on their needs to design effective decision systems. The research work allows organisations to evaluate, assess, and design effective industrial performance measurement systems. Moreover, the proposed methodology can be easily integrated within an Industry 4.0 context, and benefit from the digitalization environment to obtain continuous feedback on the effectiveness of the industrial PMM.

Strategic planning (SP) requires attention and constant updating and is a crucial process for guaranteeing the efficient performance of companies. This article proposes a novel approach applied in a case study whereby a balanced scorecard (BSC) was generated that integrated sentiment analysis (SA) of social media (SM) and took advantage of the valuable knowledge of these sources. In this study, opinions were consolidated in the main dataset to incorporate sentiments regarding the strategic part of a restaurant in a tourist city. The proposed methodology began with the selection of the company. Information was then acquired to apply pre-processing, processing, evaluation, and validation that is capitalized in a BSC to support strategic decision-making. Python support was used in the model and comprised lexicon and machine learning approaches for the SA. The significant knowledge in the comments was automatically oriented toward the key performance indicators (KPIs) and perspectives of a BSC that were previously determined by a group of opinion leaders of the company. The methods, techniques, and algorithms of SA and SP showed that unstructured textual information can be processed and capitalized efficiently for optimal management and decision-making. The results revealed an improvement (reduced effort and time) to produce a more robust and comprehensive BSC with the support and validation of experts. Moreover, new resources and approaches were developed to implement more efficient SP. The model was based on the efficient coupling of both fields of study.

Assembly lines are one of the cornerstones of modern production systems, significantly affecting the global society, economy, and other ancillary sectors. This is why the evaluation of assembly lines is particularly significant. Hence, the research on modeling approaches is presented in this paper, yielding an efficient mathematical tool that enables the evaluation of the steady-state performance of assembly lines at low CPU cost. First, the analytical model and the transition matrix were developed for the general case, and second, dimensionality issues and demanding computational requirements were tackled by applying the finite state method. Both approaches were employed in different theoretical cases in order to validate the finite state method against the analytical solution. Additionally, the developed evaluation framework was applied in the case of a realistic assembly system, and the obtained results were successfully validated against the factory floor measurements. The comparison of the obtained results proves the finite state method as a reliable and CPU-efficient method, suitable for the evaluation of its key performance indicators as well as implementation within more sophisticated design procedures. This kind of predictive analytics is intended to support production management and enhance the reliability of long- and short-term decision-making in the context of the digital twinning of production systems.

The development of Smart Local Energy Systems (SLES) in the UK is part of the energy transition tackling the energy trilemma and contributing to achieving the Sustainable Development Goals (SDGs). Project developers and other stakeholders need to independently assess the performance of these systems: how well they meet their aims to successfully deliver multiple benefits and objectives. This article describes a step undertaken by the EnergyREV Research Consortium in developing a standardised Multi-Criteria Assessment (MCA) tool—specifically a discrete choice experiment (DCE) to determine the weighting of key performance indicators (KPIs). The MCA tool will use a technology-agnostic framework to assess SLES projects, track system performance and monitor benefit realisation. In order to understand the perceived relative importance of KPIs across different stakeholders, seven DCEs were conducted via online surveys (using 1000minds software). The main survey (with 234 responses) revealed that Environment was considered the most important criterion, with a mean weight of 21.6%. This was followed by People and Living (18.9%), Technical Performance (17.8%) and Data Management (14.7%), with Business and Economics and Governance ranked the least important (13.9% and 13.1%, respectively). These results are applied as weightings to calculate overall scores in the EnergyREV MCA-SLES tool.

One major influence on the future deployment of cellular networks will be a continuous increase in traffic inside mobile broadband systems. Moreover, traditional macrocell-based mobile communication networks will struggle to keep up with the enormous expansion in the demand for communications services in the future. Densification of networks is required if we are to meet the comprehensive needs for end terminals for a wide range of applications. One of the leading concepts in this competitive environment is the Ultra-Dense Network (UDN) where the access nodes and/or the communication links per unit area are densified, with the aim of improving overall network performance. The location of the UDN nodes meets the criteria for symmetry with a high probability. Ultra-dense cell deployment aims to reduce the physical distance between the transmitter and receiver in order to boost system performance and generally optimize the values of a wide variety of key performance indicators (KPIs). This paper aims to provide a taxonomy of UDNs and specifically of UDN-related KPIs. Initially, we address the complex questions “What is the current understanding of what ultra-dense networks are and what they should be, and how can we measure their performance?” by shedding light on the fundamental characteristics of UDNs.

Humanity faces serious problems related to water supply, which will be aggravated by population growth. The water used in human activities must be treated to make it available again without posing risks to human health and the environment. In this context, Wastewater Treatment Plants (WWTPs) have gained importance. The treatment process in WWTPs is complex, consisting of several stages, which consume considerable amounts of resources, mainly electrical energy. Minimizing such energy consumption while satisfying quality and environmental requirements is essential, but it is a challenging task due to the complexity of the processes carried out in WWTPs. One form of evaluating the performance of WWTPs is through the well-known Key Performance Indicators (KPIs). The KPIs are numerical indicators of process performance, being a simple and common way to assess the efficiency and eco-efficiency of a process. By applying KPIs to WWTPs, techniques for monitoring, predicting, controlling, and optimizing the efficiency and eco-efficiency of WWTPs can be created or improved. However, the use of computational methodologies that use KPIs (KPIs-based methodologies) is still limited. This paper provides a literature review of the current state-of-the-art of KPI-based methodologies to monitor, control and optimize energy efficiency and eco-efficiency in WWTPs. In this paper, studies presented on 21 papers are identified, assessed and synthesized, 12 being related to monitoring and predicting problems, and 9 related to control and optimization problems. Future research directions relating to unresolved problems are also identified and discussed.

Project-oriented manufacturing companies aim to produce high-quality products according to customer requirements and a minimum rate of complaints. In order to achieve this, performance indicators, especially those related to product quality, must be measured and monitored by managers. This research proposes a fuzzy multi-criteria model for the selection of key performance indicators that are critical to product quality. The uncertainties in the relative importance of decision-makers, performance indicators, and their values are described by sets of natural language words that are modeled by the interval-valued intuitionistic fuzzy numbers. The assessment of the relative importance of the decision-makers and the determination of their weights are based on the inclusion comparison probability between the closeness intuitionistic fuzzy sets. The determination of the weights vector of performance indicators is based on the integration of an interval-value fuzzy weighted geometric operator and the inclusion comparison probability between the closeness intuitionistic fuzzy sets. TOPSIS expanded with interval-valued intuitionistic fuzzy numbers for ranking performance indicators is proposed. The developed model was tested on the real data collected from three manufacturing companies in the Republic of Serbia. Based on the obtained results, the top-ranked performance indicators were marked as critical for product quality and selected as quality key performance indicators.

Large retail companies routinely gather huge amounts of customer data, which are to be analyzed at a low granularity. To enable this analysis, several Key Performance Indicators (KPIs), acquired for each customer through different channels are associated to the main drivers of the customer experience. Analyzing the samples of customer behavior only through parameters such as average and variance does not cope with the growing heterogeneity of customers. In this paper, we propose a different approach in which the samples from customer surveys are represented as discrete probability distributions whose similarities can be assessed by different models. The focus is on the Wasserstein distance, which is generally well defined, even when other distributional distances are not, and it provides an interpretable distance metric between distributions. The support of the distributions can be both one- and multi-dimensional, allowing for the joint consideration of several KPIs for each store, leading to a multi-variate histogram. Moreover, the Wasserstein barycenter offers a useful synthesis of a set of distributions and can be used as a reference distribution to characterize and classify behavioral patterns. Experimental results of real data show the effectiveness of the Wasserstein distance in providing global performance measures.

New methods for state estimation are required due to the complexity of the topology of transmission and distribution systems, and the predictability in the management of prosumer dispatch. This paper describes a pilot project in Greece that, in accordance with OneNet’s architecture, addresses the challenges of congestion and balancing management that system operators face due to the high penetration of renewable energy sources. The respective data requirements and the IT/OT environment are described, as well as the interconnections among the various modules and functionalities. Available resources of the grid’s flexibility are identified, and the implementation of an integrated monitoring system based on efficient forecasting of volatile generation and demand is addressed. Congestion management and frequency and voltage control are in the center of interest of the demonstrator where, in close collaboration with system operators, respective network models are being developed.

In areas with scarce water resources, population growth and climate change scenarios will mean that there is increasingly less water available for agricultural activity. Thus, optimizing crop irrigation water management is an absolute necessity. To address this situation, the SUPROMED project (sustainable production in water-limited environments of Mediterranean agro-ecosystems), available in an online platform, brings together a series of models and methodologies designed to promote more efficient management of water, energy and fertilizers. A two-year trial (2020–2021) was implemented in the Castilla-La Mancha region (Spain), with the aim of showing the effectiveness of SUPROMED as a farm management support tool. The trial was conducted on two of the region’s most important crops (oats and garlic). A series of productive, economic, and environmental key performance indicators (KPIs) were analyzed to measure the impact of transferring MOPECO (model for the economic optimization of irrigation water use at farm level), the irrigation scheduling model integrated in the SUPROMED platform, to farmers. In 2020, the management plan proposed by SUPROMED achieved a higher yield for oat than that generated by traditional management, using 40% less water. In the case of garlic, the same yield was obtained, using 30% less water. Gross margin and gross economic irrigation water productivity were improved for both crops. In 2021, one of the selected farmers was trained to use the SUPROMED platform to work with garlic crop. This management improved most of the KPIs analyzed compared to previous management without the SUPROMED platform, obtaining similar results to those obtained by SUPROMED in 2020. The results demonstrate that the tools and models included in SUPROMED have been properly adapted and can be easily used by farmers, improving the economic and environmental sustainability of Mediterranean agroecosystems.

The increase in CO2 concentration in the atmosphere has prompted the research community to find solutions for this environmental problem, which causes climate change and global warming. CO2 removal through the use of negative emissions technologies could lead to global emission levels becoming net negative towards the end of this century. Among these negative emissions technologies, direct air capture (DAC), in which CO2 is captured directly from the atmosphere, could play an important role. The captured CO2 can be removed in the long term and through its storage can be used for chemical processes, allowing closed carbon cycles in the short term. For DAC, different technologies have been suggested in the literature, and an overview of these is proposed in this work. Absorption and adsorption are the most studied and mature technologies, but others are also under investigation. An analysis of the main key performance indicators is also presented here and it is suggested that more efforts should be made to develop DAC at a large scale by reducing costs and improving efficiency. An additional discussion, addressing the social concern, is indicated as well.

Scarcity of water resources is one of the main constraints on agricultural activity in arid and semi-arid areas. Despite the great technological development over recent decades, farmers are demanding methodologies and tools adapted to their training, management restrictions, and economic capacity. To tackle these challenges, the sustainable production in water-limited environments of Mediterranean agroecosystems (SUPROMED) project combines, in an online platform, a set of models and methodologies for more efficient management of water, energy, and fertilizers. A two-year trial (2020–2021) was conducted in the Castilla-La Mancha region (Spain) to demonstrate, for a barley crop, the effectiveness of SUPROMED as a farm management support tool. The impact of transferring the model for the economic optimization of irrigation water use at farm level (MOPECO model) irrigation scheduling among other methods and tools, integrated in the SUPROMED platform, to farmers was determined by analyzing a set of productive, economic, and environmental key performance indicators (KPIs). The KPIs were selected to show farmers how the efficient use of productive factors could improve the profitability of their farms, thus reducing the impact of agriculture on the environment. In 2020, the management plan proposed by SUPROMED achieved the same yield as traditional management using 32% less water and resulting in a 13% and 66% improvement in gross margin and gross economic irrigation water productivity, respectively. In 2021, the management implemented by a farmer trained in the use of the tools and methodologies in the SUPROMED platform showed improvements in most of the KPIs analyzed, achieving similar results to those obtained by SUPROMED during 2020. The results are promising, indicating that the tools and models proposed in SUPROMED can be easily used by farmers and can improve the economic and environmental sustainability of Mediterranean agroecosystems. The involvement of public administrations, together with local researchers and technicians, is required for the effective promotion and use of these methodologies by the productive sector.

In Nepal, case investigation and contact tracing (CICT) was adopted as an important public health measure to reduce COVID-19 transmission. In this study, we assessed the performance of CICT in Madhesh Province of Nepal against national benchmarks, using routine programmatic data reported by district CICT teams. Between May and July 2021, 17,943 COVID-19 cases were declared in the province, among which case investigation was performed for 30% (95% CI: 29.6–31.0%) within 24 h (against 80% benchmark). As a result of case investigations, 6067 contacts were identified (3 contacts per 10 cases), of which 40% were traced and tested for SARS-CoV-2 infection (against 100% benchmark). About 60% of the contacts tested positive. At most 14% (95% CI: 13.1% to 14.9%) of traced contacts underwent a 14-day follow-up assessment (against 100% benchmark). We found the performance of the CICT program in Madhesh Province to be sub-optimal and call for corrective measures to strengthen CICT in the province and the country at large. Similar studies with wider geographical scope and longer time frames are needed to identify and address deficiencies in data recording and reporting systems for COVID-19, in low- and middle-income countries like Nepal and others.

The success of modular multilevel converters (MMCs) in high-voltage direct current (HVDC) applications has fueled the research on modular converter topologies. New modular converter topologies are often proposed, discussed, and sometimes applied in HVDC, as well as other industrial application such as STATCOMs, DC/DC HVDC, medium-voltage direct current (MVDC), etc. The performance evaluation of new modular converter topologies is a complex and time-consuming process that typically involves dynamic simulations and the design of a control system for the new converter topology. Sadly, many topologies do not progress to the implementation stage. This paper proposes a set of key performance indicators (KPIs) related to the cost and footprint of the converter and a procedure designed to rapidly evaluate these indicators for new converter topologies. The proposed methodology eliminates the need for dynamic simulations and control-system design, and is capable of identifying whether a particular converter is worth considering or not for further studies of a specific application, depending on the operating requirements. Thanks to the method outlined in this work and via the key parameters quantifying the “relevance” of the analyzed converters, promising topologies were easily identified, while the others could be rapidly discarded, resulting in saving valuable time in the study of the solutions that have a real potential. The proposed method is first described from a general point of view and then applied to a case study of the new converter topology—Open-Delta CLSC—and its application in two use cases.

In this article, we report on the design and implementation of a reportability tool using Microsoft Power BI embedded with Python script to assess opportunistic grouping schemes under a preventive maintenance policy. The reportability tool is based on specially developed indicators based on current maintenance standards for better implementation and considers a formerly developed grouping strategy with poor embedded performance indicators as an implementation case for the tool. Performance indicators were carefully developed considering a stochastic perspective when possible; this enables decisions to be influenced by risk assessment under a costs view. Reporting is focused on six maintenance sub-functions, enabling the decision maker to easily assess performance of any maintenance process, thereby improving the quality of decisions. The developed tool is a step forward for grouping (or any scheduling scheme) strategies due to its flexibility to be implemented in almost any case, enabling comparison between different grouping algorithms.

Adaptive control of traffic engineering (TE) based on 5G network function virtualization (NFV) authorizes the efficient and dynamic network resource allocation, whose utilization is increasingly wide and will become more widespread. In this paper, we first devise an adaptive control scheme for data-driven traffic migration engineering (TME) on the 5G virtual network. The proposed TME technology focuses on a 5G enhancing mobile broadband (eMBB) network application scenario and takes the network operating expenditure (OPEX) as the main research target. Firstly, we predict the network traffic of the virtual network through the constructed traffic predicted mathematical model. Then, based on the triangle inequality violation (TIV) theorem, some local network traffic is adaptively migrated when the predicted link traffic exceeds the peak rate. Consequently, the migrations of logical links in the virtual network layer are completed. Finally, our experiments show that the proposed protocol can effectively improve the key performance indicators (KPIs) of the reconfigured network, such as throughput, delay and energy consumption. Furthermore, the Fridman and Holm statistical hypothesis tests are also used to analyze the simulation data, which proves that the proposed approximate TME algorithm has statistical significance.

The virtualization and automation of network functions will be key features of future high-speed railway networks, which have to provide dependable, safe, and secure services. The virtualization of railway network functions will enable functions such as train control, train integrity protection, shunting control, and trackside monitoring and maintenance to be virtualized and to be run on general-purpose hardware. Network function virtualization combined with edge computing can deliver dynamic, low-latency, and reliable services. The automation of railway operations can be achieved by embedding intelligence into the network to optimize the railway operation performance and to enhance the passenger experience. This paper presents an innovative railway network architecture that features distributed intelligence, function cloudification and virtualization, openness, and programmability. The focus is on time-tolerant and time-sensitive intelligent services designed to follow the principles of service-oriented architecture. The interaction between identified logical identities is illustrated by use cases. The paper provides some details of the design of the interface between distributed intelligent services and presents the results of an emulation of the interface performance.