Detecting Scam Tokens and Backdoor Functions in EVM Based Networks

Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.The blockchain ecosystem is currently becoming an intricate landscape given the large number of attacks, scams, and hacks that occur. With such risk surrounding Blockchain DApps and users, we propose a method to help in the earlier detection of scam tokens. While blockchain technology continues to grow in multiple industries, the proliferation of fraudulent activities in the decentralized ecosystem has become a significant threat to the well-being and trustworthiness of the whole ecosystem. This scam tokens are deployed in plain sight in decentralized networks and listed in platforms such as CoinMarketCap and other token issuance platforms. In this paper, we identify over 1450 unreported scam tokens, 167 different backdoor functions, and 1428 malicious contract creators. By increasing the collaboration between industry stakeholders and web3 companies, our scam tokens dataset and detection tools can help in web3 threat detection while creating a more secure and resilient blockchain environment, fostering trust and innovation in the decentralized space.Peer reviewe

Realizing CO2 emission reduction in lime and soda ash manufacturing through anion exchange

Publisher Copyright: © 2025 The Royal Society of Chemistry.Lime (CaO) and soda ash (Na2CO3) are two foundational chemicals for modern civilization, and the CO2 emissions from their production processes are challenging to reduce. Furthermore, decarbonization of the lime industry could also reduce the CO2 emissions associated with cement production, for which lime is the key precursor. In this paper, we show that an anion exchange process to co-produce CaO and Na2CO3 from CaCO3 and NaOH can reduce the carbon footprint of both chemicals through industrial symbiosis. Heating energy and NaOH production are the major contributing factors towards the cost and CO2 emissions of this process, which can supply the global annual soda ash demand (∼65 Mt) and co-produce ∼50 Mt of lime in an economically sustainable manner (16% gross margin) while immediately reducing global CO2 emission by 37 Mt compared to current production methods. Using electrified industrial heat sources and heat pumps to reuse heating energy would further reduce the cost and CO2 emissions of the anion exchange process.Peer reviewe

Associations Between Transdiagnostic Psychological Processes and Global Symptom Severity Among Outpatients With Various Mental Disorders: A Cross-Sectional Study

Publisher Copyright: © 2025 The Author(s). Clinical Psychology & Psychotherapy published by John Wiley & Sons Ltd.OBJECTIVE: Knowledge about transdiagnostic factors associated with global symptom severity among patients diagnosed with various mental disorders remains limited. This study examined the cross-sectional associations between transdiagnostic processes including global emotion regulation and specific emotion regulation strategies (i.e., amount of physical activity and sedentary behaviour, repetitive negative thinking and sleep routines) with global symptom severity, while controlling for sociodemographic data (age, gender, employment status, relationship status, and educational level) and fear of the coronavirus. METHODS: Data from 401 outpatients, aged 42.08 years on average (SD = 13.26; 71.3% female), diagnosed with depressive disorders, non-organic primary insomnia, agoraphobia, panic disorder and/or post-traumatic stress disorder were examined. This study is a secondary analysis of a randomized controlled trial. Data were collected from 10 different study sites between March 2021 and May 2022 for cross-sectional analysis. The influence of predictors of global symptom severity was determined using three-step hierarchical multiple regression: (1) control variables, (2) global emotion regulation and (3) specific emotion regulation strategies. Global symptom severity was measured using the Global Severity Index, derived from the Brief Symptom Inventory-18. Predictors were measured using validated scales, and physical activity was additionally assessed via accelerometer-based sensors. RESULTS: In the first step, control variables accounted for 4% of variance in global symptom severity. The inclusion of global emotion regulation in the second step explained 26% of the outcome variance, and the incorporation of specific emotion regulation strategies in the third step increased the explained variance to 37%. Significant predictors included global emotion regulation (β = 0.28), repetitive negative thinking (β = 0.26) and sleep routines (β = 0.25). CONCLUSION: Global emotion regulation along with repetitive negative thinking and sleep routines as specific emotion regulation strategies are identified as transdiagnostic psychological processes that may serve as treatment targets for evidence-based interventions designed to enhance emotion regulation, particularly in transdiagnostic samples of stress-related disorders. Additional prospective longitudinal studies with transdiagnostic samples are necessary to explore possible causal relationships.Peer reviewe

Quantum approximated cloning-assisted density matrix exponentiation

Publisher Copyright: © 2025 authors. Published by the American Physical Society. Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Classical information loading is an essential task for many processing quantum algorithms, constituting a cornerstone in the field of quantum machine learning. In particular, the embedding techniques based on Hamiltonian simulation techniques enable the loading of matrices into quantum computers. A representative example of these methods is the Lloyd-Mohseni-Rebentrost (LMR) protocol, which efficiently implements matrix exponentiation when multiple copies of a quantum state are available. However, this is a quite ideal setup, and in a realistic scenario, the copies are limited and the noncloning theorem prevents one from producing more exact copies in order to increase the accuracy of the protocol. Here, we propose a method to circumvent this limitation by introducing imperfect quantum copies, which significantly improve the performance of the LMR when the eigenvectors are known.Peer reviewe

Robot localization aided by quantum algorithms

Publisher Copyright: © 2025Localization is a critical aspect of mobile robotics, enabling robots to navigate their environment efficiently and avoid obstacles. Current probabilistic localization methods, such as the Adaptive Monte Carlo localization (AMCL) algorithm, are computationally intensive and may struggle with large maps or high resolution sensor data. This paper explores the application of quantum computing in robotics, focusing on the use of Grover's search algorithm to improve the efficiency of localization in mobile robots. We propose a novel approach to utilize Grover's algorithm in a 2D map, enabling faster and more efficient localization. Despite the limitations of current physical quantum computers, our experimental results demonstrate a significant speedup over classical methods, highlighting the potential of quantum computing to improve robotic localization. This work bridges the gap between quantum computing and robotics, providing a practical solution for robotic localization and paving the way for future research in quantum robotics.Peer reviewe

A multi-level IIOT platform for boosting mines digitalization

Publisher Copyright: © 2024This paper presents an innovative IIoT multi-level platform tailored to address the specific needs of the mining domain. The platform has been conceptualized and built in the context of the illuMINEation European project. For this purpose, mining specific use cases have been designed such as promoting underground safe areas, performing efficient mining operations or boosting predictive maintenance approaches. Then, specific requirements have been identified and, as a result, the platform has been developed. It consists of four-level layered platform: (1) edge devices layer to manage several sensors deployed in the mines; (2) edge box layer to provide in-mine operations such as filtering, streaming and processing; (3) fog layer which offers an overall perspective of each mine; and (4) cloud layer to centralize the data of all the mines and to provide powerful processing capabilities. In addition, the platform is robustly secured in terms of protecting communications confidentiality and access control and also provides a toolbox aimed at manipulating 3D complex images to obtain operable mine-domain novel user interfaces. Finally, a platform validation is proposed where three different use cases are explained to better show and demonstrate the capabilities of the platform.Peer reviewe

WoodAD: A New Dataset and a Comparison of Deep Learning Approaches for Wood Anomaly Detection

Publisher Copyright: © 2025 John Wiley & Sons Ltd.Anomaly detection is a crucial task in computer vision, with applications ranging from quality control to security monitoring, among many others. Recent technological advancements have enabled near-perfect solutions on benchmark datasets like MVTec, raising the need for novel datasets that pose new challenges for this modelling task. This work presents a novel Wood Anomaly Detection (WoodAD) dataset, which includes defects in wooden pieces that result in challenges for the most advanced techniques applied to other established datasets. This article evaluates such challenges posed by WoodAD with one-class and few-shot supervised learning approaches. Our experiments herein reveal that EfficientAD, a state-of-the-art method previously excelling on the MVTec dataset, outperforms all other one-class learning approaches. Nevertheless, there is room for improvement, as EfficientAD achieves a 0.535 pixel/segmentation average precision (AP) over the complete test set. UNet, a well-known pixel-level classification architecture, leveraged few-shot supervised learning to enhance the pixel AP score, achieving 0.862 pixel/segmentation AP over the entire test set. Our WoodAD dataset represents a valuable contribution to the field of anomaly detection, offering complex image textures and challenging defects. Researchers and practitioners are encouraged to leverage this dataset to push the boundaries of anomaly detection and develop more robust and effective solutions for more complex real-world applications. The WoodAD dataset has been made publicly available in Kaggle (https://www.kaggle.com/datasets/itiresearch/wood-anomaly-detection-one-class-classification).Peer reviewe

Image-based analysis of electrochromic materials: Gamma correction with a LEGO luminance checker

Publisher Copyright: © 2025 Elsevier LtdThis study explores the use of smartphone RGB cameras for reflectance measurements in electrochromic systems, using tungsten trioxide (WO₃) electrodes. The work highlights the challenges and inaccuracies introduced by gamma correction. While gamma correction enhances image aesthetics, it introduces significant non-linearities in RGB data, complicating scientific analyses. We propose an innovative calibration method using LEGO® pieces as a reliable luminance reference, allowing for accurate gamma correction. Our methodology ensures consistent and precise measurements across varying lighting conditions, bridging the gap between traditional spectrophotometry and accessible image-based analysis. By comparing results from RGB cameras with UV-Vis spectroscopy, we demonstrate that corrected RGB data can reliably replicate spectrophotometric data in terms of contrast ratio and response time, making this approach viable for low-cost, portable diagnostics, as well as for diverse scientific applications. This work demonstrates the potential of RGB cameras in scientific research and proposes image analysis protocols for their use in electrochromic studies.Peer reviewe

Tool Used to Assess Co-Benefits of Nature-Based Solutions in Urban Ecosystems for Human Wellbeing: Second Validation via Measurement Application

Publisher Copyright: © 2025 by the authors.In recent years, nature-based solutions have been used in urban regeneration interventions to improve the adaptation and resilience of these places, contributing to improved environmental quality and cultural ecosystem functions, including people’s physiological, social, and mental health and wellbeing. However, when it comes to the assessment of psychological wellbeing and social benefits (psychosocial co-benefits), the existing evidence is still limited. To contribute to the advancement of knowledge on nature’s contribution to people in relation to this type of benefit, it is necessary for us to develop and test assessment tools to contribute to the development of a robust nature-based solutions monitoring framework. In this paper, the second phase of the validation of a psychosocial co-benefit assessment tool for nature-based urban interventions is presented. This tool is structured around two dimensions: the perceived health and wellbeing and social co-benefits. The first validation was carried out with experts using the Delphi method. The second validation presented in this paper was based on a sample of users, evaluating a set of eight urban spaces at different levels of naturalisation and openness. The results indicate that the tool is sensitive to the differences in naturalisation and openness in the public urban places analysed. The most relevant contextual variables to explain the psychosocial co-benefits are openness, the surfaces covered by tree branches, the water surface area, and naturalisation.Peer reviewe

Polycarbonate Nanofiber Filters with Enhanced Efficiency and Antibacterial Performance

Publisher Copyright: © 2025 by the authors.The need for clean and safe air quality is a global priority that extends to diverse environments, including households, industrial spaces, and areas requiring respiratory personal protection. In this study, polycarbonate (PC) nanofiber filters coated with a coating containing a silver salt were prepared by the electrospinning process and a subsequent dipping–extraction method. These nanofiber filters presented the enhancement of air filtration efficiency and reinforcement of antibacterial properties. The research includes diverse PC filter structures, assessing beaded and non-beaded structures and varying areal weights. The study evaluated filtration efficiency across NaCl particle sizes (50–400 nm) and pressure drop outcomes. In addition, the antibacterial activity of the coated filters against E. coli and other coliforms was investigated by the filtration membrane method. Repetitive testing consistently yields high efficiencies, reaching 100% in thicker filters, and minimal air resistance in beaded filters, presenting an advantage over the current systems. Furthermore, the new properties of the filters will enhance environmental safety, and their time of use will be increased since they prevented the growth of bacteria, and no significant colonies were seen. Considering all these factors, these filters presented promising application in environments with harmful microorganisms, for the development of advanced industrial filtering systems or even hygienic masks.Peer reviewe