Search Results (239)

This scoping review examines the evolving landscape of European Union (EU) legislation, as it pertains to the implementation of artificial intelligence (AI) in smart grid systems. By outlining the current regulatory landscape, including the General Data Protection Regulation (GDPR), the EU Artificial Intelligence Act, the EU Data Act, the EU Data Governance Act, the ePrivacy framework, the Network and Information Systems (NIS2) Directive, the EU Cyber Resilience Act, the EU Network Code on Cybersecurity for the electricity sector, and the EU Cybersecurity Act, it highlights both constraints and opportunities for stakeholders, including energy utilities, technology providers, and end-users. The analysis delves into regulatory barriers such as data protection requirements, algorithmic transparency mandates, and liability concerns that can limit the scope and scale of AI deployment. Technological challenges are also addressed, ranging from the integration of distributed energy resources and real-time data processing to cybersecurity and standardization issues. Despite these challenges, this review emphasizes how compliance with EU laws may ultimately boost consumer trust, promote ethical AI usage, and streamline the roll-out of robust, scalable smart grid solutions. The paper further explores stakeholder benefits, including enhanced grid stability, cost reductions through automation, and improved sustainability targets aligned with the EU’s broader energy and climate strategies. By synthesizing these findings, the review offers insights into policy gaps, technological enablers, and collaborative frameworks critical for accelerating AI-driven innovation in the energy sector, helping stakeholders navigate a complex regulatory environment while reaping its potential rewards. Full article

Accurate wind power forecasting (WPF) is crucial to enhance availability and reap the benefits of integration into power grids. The time lag of wind power generation lags the time of wind speed changes, especially in ultra-short-term forecasting. The prediction model is sensitive to outliers and sudden changes in input historical meteorological data, which may significantly affect the robustness of the WPF model. To address this issue, this paper proposes a novel hybrid machine learning model for highly accurate forecasting of wind power generation in ultra-short-term forecasting. The raw wind power data were filtered and classified with the local outlier factor (LOF) and the voting tree (VT) model to obtain a subset of inputs with the best relevance. The time-varying properties of the fluctuating sub-signals of the wind power sequences were analyzed with the optimized variational mode decomposition (OVMD) algorithm. The Northern Goshawk optimization (NGO) algorithm was improved by incorporating a logical chaotic initialization strategy and chaotic adaptive inertia weights. The improved NGO algorithm was used to optimize the least squares support vector regression (LSSVR) prediction model to improve the computational speed and prediction results. The proposed model was compared with traditional machine learning models, deep learning models, and other hybrid models. The experimental results show that the proposed model has an average R² of 0.9998. The average MSE, average MAE, and average MAPE are as low as 0.0244, 0.1073, and 0.3587, which displayed the best results in ultra-short-term WPF. Full article

The relationship between socioeconomic and demographic characteristics and health effects of green spaces has been studied, suggesting that certain groups may reap more health benefits from exposure to nature. However, the link between the perceived benefits of nature and socioeconomic and demographic characteristics remains a gap in the literature. We used a subsample (n = 711, 2018–2019) from an environmental cardiovascular risk cohort to investigate the perceived benefits of nature. Participants completed an 11-item survey about their perceptions of the benefits of nature at in-person visits. Socioeconomic and demographic characteristics including income, education, race, biological sex at birth, and age, were self-reported. Generalized linear models were used to evaluate associations between the perceived benefits of nature and demographic and socioeconomic factors; odds ratios and 95% confidence intervals (CIs) are reported. Both unadjusted and fully adjusted models for race, age, sex, and education are reported. Our results suggest that participants who identified as male, a member of a minoritized population, and/or completing less education perceived nature as less beneficial. Although additional research is needed to better understand contributors to these perceptions, access to convenient, safe, and multi-use green spaces may be important in encouraging time in nature and shifting perceptions of the benefits of greenness. Full article

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disorder characterized by serious physical and cognitive impairments. Recent research underscores the role of immune dysfunction, including the role of autoantibodies, in ME/CFS pathophysiology. Expanding on previous studies, we analyzed 7542 antibody–antigen interactions in ME/CFS patients using two advanced platforms: a 1134 autoantibody Luminex panel from Oncimmune and Augmenta Bioworks, along with Rapid Extracellular Antigen Profiling (REAP), a validated high-throughput method that measures autoantibody reactivity against 6183 extracellular human proteins and 225 human viral pathogen proteins. Unlike earlier reports, our analysis of 172 participants revealed no significant differences in autoantibody reactivities between ME/CFS patients and controls, including against GPCRs such as β-adrenergic receptors. However, subtle trends in autoantibody ratios between male and female ME/CFS subgroups, along with patterns of herpesvirus reactivation, suggest the need for broader and more detailed exploration. Full article

Remote driving, i.e., the capacity of controlling road vehicles at a distance, is an innovative transportation technology often associated with potential ethical benefits, especially when deployed to tackle urban traffic issues. However, prospected benefits could only be reaped if remote driving can be executed in a safe and responsible way. This paper builds on notions elaborated in the philosophical literature on technological mediation to offer a systematic examination of the extent to which current and emerging Human–Machine Interfaces contribute to hindering or supporting the exercise of responsibility behind the remote wheel. More specifically, the analysis discusses how video, audio, and haptic interfaces co-shape the remote driving experience and, at the same time, the operators’ capacity to drive responsibly. The multidisciplinary approach explored in this research offers a novel methodological framework to structure future empirical inquiries while identifying finely tuned multi-sensory HMIs and dedicated training as critical presuppositions to the remote drivers’ exercise of responsibility. Full article

Strategic human resource management plays a crucial role in fostering long-term organizational performance through data-driven decision-making. Human Resource Analytics (HRA), using advanced business intelligence and integrated reporting tools, provides insights that optimize decision-making and strategy alignment. Despite its potential, the impact of HRA on organizational performance remains insufficiently explored. This study addresses this gap by examining the effects of HRA on organizational performance in Ethiopian organizations. A quantitative research design was employed, utilizing a survey method to collect data from 269 valid responses across 55 organizations in Addis Ababa, Ethiopia. Structural Equation Modeling (SEM) via SmartPLS 3.0 software was used for data analysis. The findings reveal that HRA significantly enhances organizational performance, with this relationship mediated by strategic alignment between HR and organizational goals. Additionally, firm size was found to moderate the impact of HRA on performance, with larger firms deriving greater benefits. The results suggest that HRA serves as a powerful driver of enhanced organizational performance, with larger firms potentially reaping even greater benefits from its implementation. These results also underscore the importance of strategic alignment in leveraging HRA for improved performance, particularly in the context of Ethiopian organizations, where HRA adoption is still evolving. This study offers practical implications for organizations seeking to enhance workforce management and performance through data-driven HR strategies. Full article

Using adaptive learning technologies, personalized feedback, and interactive AI tools, this study investigates how these tools affect student engagement and what the mediating role of individuals’ digital literacy is at the same time. The study will target 500 students from different faculties such as science, engineering, humanities, and social sciences. With the changing trends in educational technology, it is important to know if these tools allow students to interact with learning materials. Through this study, we explore how adaptive learning technologies, which adapt content to students’ progress, are influenced by student motivation and participation during the learning process using AI tools that provide real-time feedback and interaction. Also, digital literacy is presented as a moderating factor that may either accelerate or impede the effectiveness of these tools. These findings demonstrate that more adaptive learning technologies, which have organized feedback, and interactive AI tools help improve student engagement. Additionally, students with higher levels of digital literacy are more involved with digital tools. This research recognizes that teachers should incorporate these technologies into their courses in such a manner as it synergizes with student’s digital capabilities to reap the benefits of technology on students’ engagement and learning outcomes. Full article

This study explores the crucial role of the banking industry in addressing climate change and promoting environmental sustainability. As climate change increasingly threatens global economies, ecosystems, and public health, financial experts recognize the potential for the banking sector to contribute to a low-carbon economy. By incorporating green-banking practices, which blend traditional financial services with environmental, social, and economic considerations, banks can foster sustainable development while reaping financial benefits. The research examines the dynamics of sustainable banking, focusing on its ability to drive efficiency improvements through eco-friendly programs and enhance profitability. Furthermore, the study discusses the challenges in adopting and implementing environmental sustainability, comparing the command-and-control regulatory model with voluntary approaches. The findings emphasize the importance of effective regulation and incentives for ensuring that banks adopt sustainable practices, ultimately contributing to a more resilient and low-carbon economic system. Through this analysis, this study underscores the banking industry′s pivotal role in shaping the transition towards a sustainable future. Full article

An important impediment to the incorporation of artificial intelligence-based tools into healthcare is their association with so-called black box medicine, a concept arising due to their complexity and the difficulties in understanding how they reach a decision. This situation may compromise the clinician’s trust in these tools, should any errors occur, and the inability to explain how decisions are reached may affect their relationship with patients. Explainable AI (XAI) aims to overcome this limitation by facilitating a better understanding of how AI models reach their conclusions for users, thereby enhancing trust in the decisions reached. This review first defined the concepts underlying XAI, establishing the tools available and how they can benefit digestive healthcare. Examples of the application of XAI in digestive healthcare were provided, and potential future uses were proposed. In addition, aspects of the regulatory frameworks that must be established and the ethical concerns that must be borne in mind during the development of these tools were discussed. Finally, we considered the challenges that this technology faces to ensure that optimal benefits are reaped, highlighting the need for more research into the use of XAI in this field. Full article

The current technological developments within the offshore wind industry reveal a trend towards larger wind turbine MW-classes for both bottom-fixed and floating support structures. Furthermore, bottom-fixed designs are modified and enhanced to also serve deeper offshore sites. Apart from these technological developments, another trend of a competitive nature, related to politics and other stakeholders, can be observed: ever-higher targets are specified for offshore wind energy, while national offshore water areas are limited and divided among various stakeholders in terms of their use. This situation raises the following questions, which are discussed in this paper: 1. Should and could floating offshore wind be extended to shallow-water regions? 2. What benefits can be gained when going beyond traditional floating wind technologies, and what does this mean in detail? 3. What are the motivations, challenges, and solutions for coexistence options? The investigations reveal that floating solutions are more than just options for supporting offshore wind turbines at very-deep-water sites. By extending the traditional application ranges of floating wind turbine systems and going beyond traditional floating offshore wind technologies, additional benefits can be reaped, and worldwide climate and renewable energy targets can be met in harmony with other stakeholders. Full article

Edge, fog, and cloud computing provide complementary capabilities to enable distributed processing of IoT data. This requires offloading mechanisms, decision-making mechanisms, support for the dynamic availability of resources, and the cooperation of available nodes. This paper proposes a novel 3-tier architecture that integrates edge, fog, and cloud computing to harness their collective strengths, facilitating optimised data processing across these tiers. Our approach optimises performance, reducing energy consumption, and lowers costs. We evaluate our architecture through a series of experiments conducted on a purpose-built testbed. The results demonstrate significant improvements, with speedups of up to 7.5 times and energy savings reaching 80%, underlining the effectiveness and practical benefits of our cooperative edge-fog-cloud model in supporting the dynamic computational needs of IoT ecosystems. We argue that a multi-tier (e.g., edge-fog-cloud) dynamic task offloading and management of heterogeneous devices will be key to flexible edge computing, and that the advantage of task relocation and offloading is not straightforward but depends on the configuration of devices and relative device capabilities. Full article

This article proposes the application of the AHP method in an office furniture remanufacturing company, with the aim of optimizing knowledge retention and management. In particular, it seeks to establish the optimal retrieval route for returned products. To this end, a bibliographic analysis was first carried out, which revealed the scarcity of previous studies on the subject, thus validating the relevance of this work. Subsequently, a practical application of the AHP method was carried out to define the weighting matrix of the evaluation criteria, applied to three specific pieces of furniture, which confirmed the effectiveness of the tool. In a complementary manner, Nonaka and Takeuchi’s SECI model of knowledge management was used, guaranteeing the continuous updating of the matrices and the adequate retention of knowledge in the company. This methodology will increase the volume of remanufactured products and improve operating margins. By reaping both the economic and environmental benefits of this practice, the company will be able to reduce costs, generate additional revenue, improve its corporate image, and build customer loyalty. At the same time, this study promotes the sustainability and sustainable development of this practice within the company and, by extension, in the broader office furniture manufacturing industry. It can serve as a reference for other companies in this sector across different countries. Full article

The evolution of smart grids (SG) has been rapid and ubiquitous with the advent of information and communication technology. SGs enable utilities and prosumers to monitor energy consumption in real-time, thereby possessing effective supply and demand management. The subsets of SGs, namely smart homes/smart buildings, are tailored to reap the benefits of SGs. These smart homes continuously record energy consumption data through smart meters, sensors, and smart appliances, and enable consumers to track and manage their energy usage in real-time. Usually, the energy consumption of renewable energy-integrated smart homes depends on consumer behavior and weather conditions. These aspects lead to deviation in the recorded energy consumption data from the desired levels. This variance in energy consumption impacts pattern-finding, forecasting, financial risk, decision-making, and several other grid functionalities. Hence, comprehension of variance in energy consumption is essential to properly manage energy. With this aim, this paper proposes the use of variance analysis on smart home energy consumption readings using a statistical method named “Analysis of Variance (ANOVA)”. It is implemented on the Tracebase dataset, which is a smart city database and contains data for ten months. The data were collected in the city of Darmstadt, Germany, in 2012. The proposed ANOVA is applied to all these months’ data. As an initial step, the energy consumption readings recorded for every month at each day and at each hour are enumerated and this information is further used to perform day-wise variance analysis using ANOVA. The results show that there is a significant variance in several days of each month. Furthermore, it is revealed that out of ten months, two months have high variability. Thus, this proposed variance analysis helps the stakeholders of SGs take the necessary precautions for smooth grid functionalities as well as properly estimate future energy requirements. Full article

Worldwide, the development of massive hydropower projects is becoming more common, especially when it comes to attempts to mitigate environmental degradation and increase a nation’s energy capacity. People affected by projects (PAPs) are forced to relocate in order to support large-scale development initiatives, which puts their lives and livelihoods at jeopardy. In comparison to the value of infrastructure development, which is mostly reaped by distant stakeholders, it comes at a high cost. In relation to CSR/resettlement and rehabilitation programs carried out by construction corporations in Pakistan’s hydropower development, this study on the quality-of-life (QoL) and happiness indicators of PAPs is being conducted. The analysis of factors affecting happiness and other aspects of quality-of-life indicators, including job and livelihood opportunities, housing, health, infrastructure, social interactions, environmental sustainability, inclusion, equity, and diversity, is the goal of this study. Using a questionnaire survey approach, data were directly gathered from PAPs, and about 424 replies were obtained to help with the model’s development. Structural equation modelling has been applied in conjunction with multivariate statistical analysis to analyse data. The outcome demonstrates the essential connections between the concepts that were taught in light of human, social, environmental, physical, and economic problems. The results also show that project supporters’ relocation and rehabilitation efforts fell short of what was needed to improve the quality of people’s lives. As a result, a conceptual framework specifically tailored to the hydropower construction region has been created and verified to provide PAPs with a high-quality living environment. Full article

Introduction: Antimicrobial resistance (AMR) is a global health threat requiring immediate attention as it is set to cause ten million deaths worldwide by 2050, overtaking that of cancer. Continuation of overuse and/or misuse of these crucial medicines will prevent future generations from reaping the benefits, as the pandemic of AMR spirals out of control. Aims: The primary aim of this study was to investigate antimicrobial prescribing patterns in General Practices throughout Northern Ireland. A secondary aim was to analyse the impact of the COVID-19 pandemic on antimicrobial prescribing and consumption patterns in GP practices in Northern Ireland. Methods: A retrospective, cross-sectional quantitative study was designed to measure, analyse, and evaluate the antimicrobial prescribing patterns within GP practices in Northern Ireland, using open access Business Services Organisation (BSO) data. Results: A total of 3,168.78 kg of antibacterial drugs were prescribed in primary care throughout the duration of the study. Penicillins were the most prescribed class (59.79%), followed by tetracyclines (10.68%) and macrolides (9.53%). Access group antibiotics were the most frequently prescribed (79.35%), followed by Watch group antibiotics (20.64%), with Reserve group antibiotics equating to nearly 0% despite being prescribed. The Derry GP Federation prescribed and dispensed the greatest amount of antibiotics overall in Northern Ireland (10.90%). Despite there being no significant difference in antibiotic prescribing amongst GP federations prior to and during the COVID-19 pandemic (unpaired t-test, p > 0.05), there were differences in prescribing of individual drug classes throughout this period. Conclusions: Despite meeting World Health Organisation (WHO) targets, GP practices within Northern Ireland must achieve more to further reduce antimicrobial consumption. Although antibiotic prescribing rates here are on the decline, there was no significant difference in prescribing amongst GP federations pre- and midst-COVID-19 pandemic, thus sufficient strategies such as increased communication between colleagues and supportive measures must be implemented within GP practices to enhance antimicrobial stewardship (AMS) across Northern Ireland. Full article