Generative Adversarial Networks-enabled Anomaly Detection Systems: A Survey

Anomaly Detection (AD) is an important area of research because it helps identify outliers in data, enabling early detection of errors, fraud, and potential security breaches. Machine Learning (ML) can be utilized for distinct AD systems, and Generative Adversarial Networks (GANs) have emerged as a promising technique due to their ability to generate new data that closely resembles a given dataset, allowing for the creation of realistic images, videos, audio, text, and other types of synthetic data. This paper explores state-of-the-art approaches in AD using GANs. The paper starts by providing a comprehensive overview of ML techniques for AD, including supervised, unsupervised, and semi-supervised approaches. This survey also explores various AD approaches based on GANs and provides an application-based classification of GANs-based AD approaches in the Internet-of-Things (IoT), Industrial IoT, Digital Healthcare, Energy Management Systems, and Cellular Network domains. Moreover, the paper discusses several datasets used in evaluating the performance of GANs-based AD techniques such as BOT-IoT, TON-IoT, CIC-IoT, CIC-IDS, and NSL-KDD. These datasets serve as valuable resources for researchers and practitioners to develop and test AD systems, particularly in the context of IoT and network security. Furthermore, the paper discusses the challenges and limitations of GANs-based AD techniques and proposes future research directions to address these challenges

Using Frequency B-Splines for an accurate and faster calculation of adaptive transforms for electric machines diagnosis

Early detection of faults in electric motors is crucial to prevent unplanned downtime and expensive repairs. Transient analysis through time-frequency transforms reveals important information on the motor condition. Computational time of these transforms becomes a problem when dealing with thousands of motors in just one industry. Researchers focus on obtaining the best quality results, usually using Gabor functions as t-f atoms. This paper shows how Frequency B-Splines can reach the same quality with nearly 40% less computational time. To achieve this goal, the slope criterion is applied to select the optimal atoms parameters. A formula that relates the slope of the Heisenberg Box of a FBS with its parameters is deduced. The proper time interval where the FBS must be defined is also determined. Results are shown not only with lab tests, but also through a field case

Driving out risk: A taxonomy of factors influencing perceived safety in automated vehicles and the role of knowledge-based variation

Despite ongoing technological advancements, public acceptance of automated vehicles (AVs) remains limited, with perceived safety (PSAV) emerging as a pivotal determinant of trust and adoption. While prior research has identified factors such as cybersecurity, legal accountability, and functional performance as influential, these elements are often examined in isolation and without a unifying framework. Furthermore, the role of individuals' Knowledge Levels of AVs (KLAV) in shaping the salience of safety concerns remains underexplored. This study addresses these gaps through a qualitative investigation involving 66 interviews with members of the public and AV experts in the United Kingdom. We develop an empirically grounded taxonomy of PSAV comprising thirteen factors, organized into three overarching categories: Technological Safety, Psychological Safety, and Social Safety. Our findings suggest that perceptions of safety are not uniform but vary with participants’ KLAV, which is associated with differences in how safety concerns are interpreted and prioritized. The study advances theoretical understanding by reconceptualizing PSAV as a multidimensional and knowledge-sensitive construct. Practically, the taxonomy and KLAV-based insights offer actionable guidance for AV research, public engagement, and anticipatory governance, supporting more inclusive and socially responsive pathways for AV deployment

How well are national policies addressing transport poverty in Scotland?

Transport poverty can be caused by a lack of transport options that are available, reliable, affordable, accessible or safe. This study aimed to assess whether selected national transport policies were likely to achieve a population level impact on transport poverty in Scotland. We identified a long list of relevant policies from sources including the national transport strategy annual delivery plan. Transport Scotland officials prioritised 12 of these policies for review. Eight policies addressed affordability, three safety, and two accessibility, one of which addressed both accessibility and safety. We used available evidence, mainly from policy documentation and evaluations, to score whether these were: systematically applied; scaled up appropriately; resourced in the long term; and evidence based, to generate an overall assessment of likely population level impact. We scored eight policies as high population level impact, three medium and one low. The policies were all legislative or universally available for defined populations, with few barriers to uptake. We identified bus concessionary schemes as particularly important to improve affordability, but some low-income populations who could most benefit are not eligible. We assessed three legislative policies as likely to have a population impact on accessibility and/or safety. We conclude that addressing each dimension in isolation is not sufficient to reduce transport poverty. A broad transport poverty strategy addressing all dimensions of transport poverty should be developed to ensure everyone can access transport options to meet their needs

UHF Partial Discharge Detection for Power Transformer Based on Hilbert Fractal Antenna with Different Feeding Techniques

In power transformer, Partial Discharge (PD) can cause a major equipment risk and lead to further accidents. One of the PD detection techniques is Ultra-High Frequency (UHF) antenna. UHF antenna have a major advantage which is it immune to external interference. However, the choice of feeding technique used to connect the antenna to the transmission line can limit the performance of UHF antenna. This paper was conducted by comparing microstrip line feed and coaxial feed for Ultra-High Frequency (UHF) PD detection. The antenna is aimed to operate in the PD frequency range (300-3000 MHz) and there is no specific frequency band that PD radiate in a measurement. The frequency of UHF PD measurements in power transformers varies based on the individual case and is impacted by several factors, including the PD source and locations of PD in the transformer. In this work, 4 th order Hilbert fractal antenna are selected as patch antenna for UHF PD detection. The proposed design for the antenna based on the appropriate size for internal installation in the power transformer. All these results showed that either microstrip line feed or coaxial feed technique are able to capture the PD in the range of UHF for the power transformer. However, the coaxial feed antenna is preferable as it shows that the fabricated measurement is consistent with the simulation and had a higher number of resonant frequencies

Digital economy, energy trade, and the path to sustainability: A G7 perspective

This study develops an integrated analytical framework to investigate how the digital economy influences the energy trilemma, comprising energy security, energy equity, and environmental sustainability. Using a balanced panel dataset of G7 countries spanning 2000–2023, we employ panel regression techniques and extensive robustness checks to quantify the digital economy's impact on each dimension of the trilemma. Our results reveal a significant positive effect, suggesting that digitalization can enhance energy efficiency and sustainability while improving access and reliability. We further examine the mediating roles of energy trade cooperation, market openness, and energy structure adjustment in amplifying these effects. The findings underscore the strategic importance of aligning digital economy policies with energy sector reforms to address long-term energy challenges. Policy recommendations include fostering cross-border energy cooperation, enhancing digital trade infrastructure, accelerating renewable energy adoption, and implementing targeted incentives to fully leverage digitalization for sustainable energy development

Integrating ethnographic futures research with critical realism and grounded theory

This paper presents Critical Realist Grounded Ethnographic Futures Research (CRG-EFR) as a development of the Ethnographic Futures Research (EFR) method. EFR is a long-established but under documented and developed scenario building method, that directly engages with images of the future held by a defined social group. Responding to the challenge that scenario methods lack theoretical depth compared to established social research methods, CRG-EFR is systematically built to combine the qualities of both, to enhance the scenario building process. Critical realism is established as a basis for understanding present reality and future possibilities, in which predictions are possible as conjectural knowledge of the future and can be systematically developed. EFR is set out as a method for building scenarios from conjectural knowledge of the future. Grounded Theory (GT) is then introduced as a rigorous research methodology that closely resembles the overall procedure of EFR, providing an established basis for the analysis of qualitative data that maintains the emic perspective of the futures of those social groups being researched. The application of the CRG-EFR method is illustrated in a discussion of its use in an ongoing research project, demonstrating how it achieves the methodological rigour necessary to meet quality criteria for grounded theory. The paper concludes by considering the limitations of the CRG-EFR method compared to classical EFR, identifying CRG-EFR as ideal suited for transformative futures research in which culturally embedded insights are foundational to images of the future

Examining a Successful High-Performance Triathlon Environment From the Coaching Team Perspective

This research aims to investigate an environment designed by a head coach with a track record of both highly successful talent development and world-class performance outcomes in the sport of triathlon. A qualitative approach involving semistructured interviews with the head coach, the psychologist, and the nutritionist was used. Key considerations regarding the selection of athletes were discussed, providing evidence for the key role of psychology and prolonged trials that allowed thorough understanding of the athlete, and insight into development capacity, as well as their contribution to the wider squad. In addition, a coaching team was embedded utilizing a multidisciplinary approach, and significant others were actively incorporated in the development process. Furthermore, the complexities of managing long-term development were highlighted, and the use of challenge as a mechanism of development was evident

Workflow evaluation of environmental contamination with hazardous drugs during compounding and administration in an UK hospital

Introduction: Exposure of healthcare workers to hazardous drugs may result in adverse health effects underscoring the importance of validating working procedures and safety precautions to minimise the risk. The objective was to monitor environmental contamination caused by the hazardous drug workflow: from drug vials, compounding process, to patient administration. Methods: Surface wipe samples were collected from potentially contaminated surfaces in the compounding department and in the administration department. The outside of drug vials, compounded syringes, bags, elastomeric pumps, and gloves used by the nurses for administration were also monitored. Stationary air samples were collected near the isolators and above the bench top. Personal air samples were collected from pharmacy technicians, pharmacists, and nurses. Monitoring was performed in three trials during two-months. Samples were analysed for cyclophosphamide, 5-fluorouracil, docetaxel, and paclitaxel using liquid chromatography tandem mass spectrometry. Results: Contamination was mainly found for 5-fluorouracil and cyclophosphamide on isolator surfaces, bench top, trays, and compounded products. Lower levels of contamination were measured in the administration department on trays, trolley arms and gloves of the nurses. Paclitaxel and docetaxel were incidentally detected. Air contamination was found for paclitaxel in the compounding department in one trial, and 5-fluorouracil was detected once in front of an isolator. Docetaxel was found in one air sample of a nurse. Conclusions: Contamination was mainly found for 5-fluorouracil and cyclophosphamide on the products compounded in the isolators. Contamination was further spread along the workflow towards the administration department causing surfaces in between being contaminated too

Modelling lung permeability of pharmaceuticals: The effectiveness of biomimetic open tubular capillary electrochromatography and immobilised artificial membrane chromatography coupled with mass spectrometry

In this study, the potential of mass spectrometry (MS) −compatible biomimetic chromatography (BMC) was explored to assess drug permeability across biological membranes, pioneering a comparison of its application to model pulmonary absorption. Two BMC techniques were evaluated i.e., immobilised artificial membrane liquid chromatography (IAM-LC) and open-tubular capillary electrochromatography (OT-CEC) on fused silica capillaries coated with phospholipid vesicles. This application was validated on a dataset of 53 structurally diverse compounds whose pulmonary permeability is already evidenced in scientific literature.The IAM-LC model exhibited a stronger correlation with conventional n-octanol/water partitioning metrics (log Po/w and log D7.4) than OT-CEC. Analytical retention appeared to be influenced by a complex interplay of hydrophobic, electrostatic, and structural factors, leading to weaker correlations particularly with log Po/w.Coupling these techniques with MS enabled high-throughput analysis of mixtures and allowed detection of compounds lacking UV chromophores. The MS-based IAM-LC approach demonstrated excellent robustness with data obtained using a C setup with UV detection (R2 = 0.95). On the other hand, stable phospholipid coatings were achieved in OT-CEC-MS providing effectiveness across varying liposomal compositions.IAM-LC, mimicking a phosphatidylcholine (PC) −based lipid bilayer, displayed a strong correlation between log kwIAM and log Papp, with an R2 value of 0.72 observed for compounds with molecular masses > 300 g mol−1 where paracellular diffusion is negligible. Meanwhile, OT-CEC-MS allowed for the incorporation of phospholipids other than PC in the stationary phase, offering complementary insights into drug–membrane interactions beyond partitioning. The strongest correlations between IAM-LC and OT-CEC parameters were observed for cationic species with log KD > 1.5. These techniques demonstrated significant potential to support drug development programmes in both industrial and academic settings by facilitating high-throughput permeability screening and pharmacokinetics −focused lead optimisation