Research Question: How do EU-based nuclear energy producers align their operations with the EU Taxonomy for sustainable activities, and what are the implications for their long-term sustainability strategies and financial performance?
Motivation: The integrated analysis of sustainability disclosures, financial metrics and cross-national comparisons of firms provides a lens on how nuclear energy is recast as sustainable within a new policy paradigm.
Idea: This paper compares the deployment of nuclear energy by significant producers in the European Union (EDF, Nuclearelectrica and Vattenfall) over the 2020-2024 period. While every company has a unique approach to nuclear within their energy mix and national goals, all are viewing it as a critical component to the decarbonization strategy.
Data: We analyze how these companies comply with the EU Taxonomy and how they add to the establishment of (4.26) electricity generation from nuclear, (4.27) construction of nuclear installations and (4.28) safe decommissioning of nuclear installations.
Tools: Comparative analysis, content analysis, financial analysis.
Findings: This paper also shows how the reports of selected European Union companies are different regarding the disclosure of information on nuclear activities. It additionally provides a financial framework to facilitate future analysis of the efficiency with which these firms deploy capital and operate their nuclear assets, based on fixed assets turnover.
Contribution: In summary, the paper paints a much clearer picture of the role of nuclear energy in reaching the clean energy ambitions of Europe and the need for uniform sustainability reporting if green investment is sought.
Saida M. Ibraimova, Ilham M. Saipidinov , Natalia M. Serbulova
et al.
In this paper, we analysed the state of implementing technologies and equipment that are based on the tools of the AI economy, which allow achieving program and normative indicators of decarbonisation and thus stimulating the growth of the quality of products, which are manufactured in the conditions of environmental priorities. We proved the connection between the implementation of climate-responsible management information systems, aimed at an increase in product quality, and the efficiency of its promotion in the market. This research covered mainly the construction sphere. We identified the role of companies of digital business in ensuring mediation for consumers in the achievement of sustainable goals in the conditions of the AI economy. The goal of the work was to establish the main sectorial directions for using climate-responsible management information systems to raise product quality in support of decarbonisation in the AI economy. To reach this goal, we used the trend method, comparative analysis, statistical analysis, and the method of classification. The scientific novelty of the presented results consists in the determination of the key directions for implementing digital solutions for the sustainable growth of the quality of products that are based on artificial intelligence (AI) within the process of decarbonisation.
In response to increasingly complex social emergencies, this study realizes the optimization of logistics information flow and resource allocation by constructing the Emergency logistics information Traceability model (ELITM-CBT) based on alliance blockchain technology. Using the decentralized, data immutable and transparent characteristics of alliance blockchain technology, this research breaks through the limitations of traditional emergency logistics models and improves the accuracy and efficiency of information management. Combined with the hybrid genetic simulated Annealing algorithm (HGASA), the improved model shows significant advantages in emergency logistics scenarios, especially in terms of total transportation time, total cost, and fairness of resource allocation. The simulation results verify the high efficiency of the model in terms of timeliness of emergency response and accuracy of resource allocation, and provide innovative theoretical support and practical scheme for the field of emergency logistics. Future research will explore more efficient consensus mechanisms, and combine big data and artificial intelligence technology to further improve the performance and adaptability of emergency logistics systems.
Background and Aim: Today, information dashboards are the main tools for understanding and extracting knowledge from large data sets and can be used in various forms by healthcare providers. At the same time as the COVID-19 epidemic, several information dashboards were designed and developed. Still, due to the speed of the spread of this virus, there was no opportunity to evaluate them. Therefore, this research was conducted to evaluate the usability of the Covid-19 management dashboard.
Materials and Methods: This descriptive-cross-sectional study was conducted on the management dashboard of Shiraz University of Medical Sciences. The dashboard was evaluated using an exploratory evaluation method with the participation of three medical informatics experts. Each of the evaluators evaluated the system independently and identified its problems by using thirteen principle checklist. Then, with the presence of all evaluators, the list of identified problems was combined, repeated problems were removed from the list and a single list was prepared. In this joint meeting, any disagreements about the problems found by the evaluators were discussed and reached a common opinion. Finally, the evaluators determined and reported the severity of the problems.
Results: In this evaluation, a total of 79 usability problems were identified. The highest number of problems was related to the feature “Help and Documentation” (12 problems), and the lowest number of problems was related to the features “Aesthetic and Minimalist Design” (2 problems) and “Privacy” (1 problem). 45.58% of the identified problems were in the category of major problems. The average degree of severity of the problems was from 2.05 (minor problem) related to the feature of “Pleasurable and Respectful Interaction with the User” to 2.99 (major problem) related to the feature of “User Control and Freedom”. Also, the average severity of problems was calculated as 2.5, classified in the range of minor problems.
Conclusion: The heuristic evaluation method identifies user interface problems of information systems and dashboards using predetermined standards. If these problems are not resolved, they will cause users’ time wasted, errors to increase, information quality to decrease, and users’ dissatisfaction and confusion.
Research on social bot detection plays a crucial role in maintaining the order and reliability of information dissemination while increasing trust in social interactions. The current mainstream social bot detection models rely on black-box neural network technology, e.g., Graph Neural Network, Transformer, etc., which lacks interpretability. In this work, we present UnDBot, a novel unsupervised, interpretable, yet effective and practical framework for detecting social bots. This framework is built upon structural information theory. We begin by designing three social relationship metrics that capture various aspects of social bot behaviors: Posting Type Distribution, Posting Influence, and Follow-to-follower Ratio. Three new relationships are utilized to construct a new, unified, and weighted social multi-relational graph, aiming to model the relevance of social user behaviors and discover long-distance correlations between users. Second, we introduce a novel method for optimizing heterogeneous structural entropy. This method involves the personalized aggregation of edge information from the social multi-relational graph to generate a two-dimensional encoding tree. The heterogeneous structural entropy facilitates decoding of the substantial structure of the social bots network and enables hierarchical clustering of social bots. Thirdly, a new community labeling method is presented to distinguish social bot communities by computing the user's stationary distribution, measuring user contributions to network structure, and counting the intensity of user aggregation within the community. Compared with ten representative social bot detection approaches, comprehensive experiments demonstrate the advantages of effectiveness and interpretability of UnDBot on four real social network datasets.
The software development and management (SDM) practice helps organizations to ensure that their software products are developed methodically and delivered in accordance with the stakeholders’ requirements. The main purpose of the SDM is to map out the management tasks and sequences them rationally. Software must be developed in a systematic manner to ensure that it is developed on time, within budget and with all functional and non-functional capabilities. SDM practice plays a significant role in ensuring that the software is developed in a systematic manner, traditionally using the models such as waterfall model, agile model, and V-model. There are many modern software development models such as DevSecOps that extends the capability of existing models and makes it more cloud friendly. Web-based development and delivery of software applications as well as Low-Code/No-Code (LCNC) are becoming increasingly popular and valuable due to availability of tools and technology for SDM. Selecting the right model for developing software ensures that all the disciplines of that model are implemented and monitored during the SDM process. Hence, SDM practices strategically manage how software is being developed, tested, and deployed and creates the business value for the stakeholders.
Rúsbel Domínguez Domínguez, Omar A. Flores Laguna, José A. Sánchez-Valdez
This research shows the analysis of multiple factors that inhibit the implementation of an Information Security Management System (ISMS). The research data were collected from 143 respondents from two universities in northeastern Mexico, in faculties of engineering in related areas. In this study, the Information Security Management System Measurement Instrument (IM-ISMS) was validated. A scale of 24 items was obtained, divided into four factors: organizational policies and regulations, privacy, integrity and authenticity. The results of this study agree with the results found by [10] in which they pre-sent a model that complies with ISO/IEC 27002:2013 controls and security and privacy criteria to improve the ISMS. [48], Mentioned that the implementation of controls based on ISO standards can meet the requirements for cybersecurity best practices.A scale of 24 items was obtained, divided into four factors: organizational policies and regulations, privacy, integrity and authenticity. This version of the instrument meets the criteria established for its validity (KMO, Bartlett's test of sphericity). An extraction was performed by the minimum residuals method, an oblique rotation was performed by the promax method, when performing the rotation 17 of the 24 items were grouped in the corresponding factor. The final reliability of the scale was calculated by the Omega coefficient, in all the dimensions the coefficients were greater than .70, therefore the re-liability of the instrument is good.
Since the beginning of the 21st century, there has been an increase in the agricultural area devoted to olive growing and in the consumption of extra virgin olive oil (EVOO). The continuous change in cultivation techniques implemented poses new challenges to ensure environmental and economic sustainability. In this context, precision oliviculture (PO) is having an increasing scientific interest and impact on the sector. Its implementation depends on various technological developments: sensors for local and remote crop monitoring, global navigation satellite system (GNSS), equipment and machinery to perform site-specific management through variable rate application (VRA), implementation of geographic information systems (GIS), and systems for analysis, interpretation, and decision support (DSS). This review provides an overview of the state of the art of technologies that can be employed and current applications and their potential. It also discusses the challenges and possible solutions and implementations of future technologies such as IoT, unmanned ground vehicles (UGV), and machine learning (ML).
Stefano Bilotta, Enrico Collini, Paolo Nesi
et al.
Nowadays, traffic management and sustainable mobility are central topics for intelligent transportation systems (ITS). Thanks to new technologies, it is possible to collect real-time data to monitor the traffic situation and contextual information by sensors. An important challenge in ITS is the ability to predict road traffic flow data. The short-term predictions (10-60 minutes) of traffic flow data is a complex nonlinear task that has been the subject of many research efforts in past few decades. Accessing traffic flow data is mandatory for a large number of applications that have to guarantee a high level of services such as traffic flow analysis, traffic flow reconstruction, which in their turn are used to compute predictions needed to perform what-if analysis, forecast routing, conditioned routing, predictions of pollutant, etc. This paper proposes a solution for short-term prediction of traffic flow data by using a architecture capable to exploit Convolutional Bidirectional Deep Long Short Term Memory neural networks (CONV-BI-LSTM). The solution adopts a different architecture and features, so as to overcome the state-of-the-art solutions and provides precise predictions addressing traffic flow data in cities, which are tendentially very noisy with respect to the ones measured in high-speed roads, the latter being the validation context for the majority of state-of-the-art solutions. The proposed solution has been developed and validated in the city context and data via Sii-Mobility, a smart city mobility and transport national project and it is currently in use in other contexts such as in Snap4City PCP EC, TRAFAIR CEF, and REPLICATE H2020 SCC1, and it is operative in those areas.
With the construction of new power systems, distributed power sources are connected in large numbers and the possibility of faults increases. The optimal allocation of repair resources is important to improve the fault management efficiency and the quality of power supply services in the producer–consumer community. Using a large number of historical fault resources accumulated in the producer–consumer community, we first preprocess the fault information by the rough set theory, then establish an optimal allocation model that minimizes the total fault loss, consider fault risk classification and repair response capability, and finally use the improved gray wolf optimization algorithm to perform the optimal calculation. To address the problems of the traditional gray wolf algorithm, tent mapping is introduced in the generation of the initial population to enhance the uniformity of the initial population. The cooperative competition mechanism is introduced to improve the utilization of effective information among individuals. Finally, the feasibility and superiority of the algorithm are verified through the analysis of calculation cases. Finally, the feasibility of this configuration method is verified through the analysis of arithmetic cases.
Nonconvex optimization plays a key role in multi-user information theory and related fields, but it is usually difficult to solve. The rate region of the Gray--Wyner source coding system (or almost equivalently, the mutual information region) is a typical example in nonconvex optimization, whose single-letter expression was given by Gray and Wyner. However, due to the nonconvexity of the optimization involved in this expression, previously, there was none nontrivial discrete source for which the analytic expression is known. In this paper, we propose a new strategy to solve nonconvex optimization problems. By this strategy, we provide the analytic expression for the doubly symmetric binary source (DSBS), which confirms positively a conjecture of Gray and Wyner in 1974. We also provide the analytic expression of the mutual information region for the Gaussian source, and provide (or recover) the analytic expressions of the lossy Gray--Wyner region for both the DSBS and Gaussian source. Our proof strategy relies on an auxiliary measure technique and the analytical expression of the optimal-transport divergence region.
Context: The Importance of Dynamic Variability Management in Dynamic Software Product Lines. Objective: Define a protocol for conducting a systematic mapping study to summarize and synthesize evidence on dynamic variability management for Dynamic Software Product Lines in self-adaptive systems. Method: Application the protocol to conduct a systematic mapping study according the guidelines of K. Petersen. Results: A validated protocol to conduct a systematic mapping study. Conclusions: First findings show that it is necessary to visualize new ways to manage variability in dynamic software product lines.
Mitchell, Ellen M H, Adejumo, Olusola Adedeji, Abdur-Razzaq, Hussein
et al.
BackgroundThe greatest risk of infectious disease undernotification occurs in settings with limited capacity to detect it reliably. World Health Organization guidance on the measurement of misreporting is paradoxical, requiring robust, independent systems to assess surveillance rigor. Methods are needed to estimate undernotification in settings with incomplete, flawed, or weak surveillance systems. This study attempted to design a tuberculosis (TB) inventory study that balanced rigor with feasibility for high-need settings.
ObjectiveThis study aims to design a hybrid TB inventory study for contexts without World Health Organization preconditions. We estimated the proportion of TB cases that were not reported to the Ministry of Health in 2015. The study sought to describe TB surveillance coverage and quality at different levels of TB care provision. Finally, we aimed to identify structural-, facility-, and provider-level barriers to notification and reasons for underreporting, nonreporting, and overreporting.
MethodsRetrospective partial digitalization of paper-based surveillance and facility records preceded deterministic and probabilistic record linkage; a hybrid of health facilities and laboratory census with a stratified sampling of HFs with no capacity to notify leveraged a priori knowledge. Distinct extrapolation methods were applied to the sampled health facilities to estimate bacteriologically confirmed versus clinical TB. In-depth interviews and focus groups were used to identify causal factors responsible for undernotification and test the acceptability of remedies.
ResultsThe hybrid approach proved viable and instructive. High-specificity verification of paper-based records in the field was efficient and had minimal errors. Limiting extrapolation to clinical cases improved precision. Probabilistic record linkage is computationally intensive, and the choice of software influences estimates. Record absence, decay, and overestimation of the private sector TB treatment behavior threaten validity, meriting mitigation. Data management demands were underestimated. Treatment success was modest in all sectors (R=37.9%–72.0%) and did not align with treatment success reported by the state (6665/8770, 75.99%). One-fifth of TB providers (36/178, 20%) were doubtful that the low volume of patients with TB treated in their facility merited mastery of the extensive TB notification forms and procedures.
ConclusionsSubnational inventory studies can be rigorous, relevant, and efficient in countries that need them even in the absence of World Health Organization preconditions, if precautions are taken. The use of triangulation techniques, with minimal recourse to sampling and extrapolation, and the privileging of practical information needs of local decision makers yield reasonable misreporting estimates and viable policy recommendations.
A mathematical representation of a photovoltaic (PV) solar cell and module performances is demonstrated in this paper. One diode based on the Shockley diode equation model is adopted for simulation and extract the performance indications. The simulated solar module is a particular 235W solar module operating in an existing 806.52 kWp grid connected photovoltaic power plant located at Assa, southern Morocco. The model demonstrates power-voltage (P-V) and current-voltage (I-V) characteristic curves with weather conditions changes. These external conditions include temperature and solar irradiance level (represented by sun unit 1sun=1000w/m2) which greatly affect the performance of the PV system. This paper also presents PV simulation with a maximum power point tracking (MPPT) converter and the general model was implemented in Matlab/Simulink software and used as an example verifying the performance of the tested module under Assa city weather variable.