As our world becomes increasingly urbanized, smart cities are leading the way in using technology to create more efficient, connected, and sustainable environments. However, amidst all the talk of connectivity and smartness, it’s crucial not to lose sight of one of the most basic human needs: access to nature in cities. This research describes a novel open-source framework for investigating the availability and accessibility of green recreation spaces using open-source data and statistical analytic approaches. The framework includes a comprehensive set of tools for data extraction, processing, analysis, and visualization, thereby enabling reproducible geospatial research. We test our framework on five international cities: Medellin, Milan, Chicago, Singapore, and Mumbai. Through geospatial analysis and statistical modeling of data sourced from OpenStreetMaps, we explore and comprehend the characteristics and distribution of spatial accessibility related to green recreation spaces in five cities. We find significant clustering of green recreation spaces in all these cities, indicating that a majority of such spaces are located in close proximity to each other within small areas. Our findings also shed light on the potential implications of unequal distribution of green recreation spaces for the health and well-being of city residents and highlight the need for policies and initiatives that promote equitable access to green recreation spaces in smart cities.

Segmentation ambiguity in generative linguistic steganography could induce decoding errors. One existing disambiguating way is removing the tokens whose mapping words are the prefixes of others in each candidate pool. However, it neglects probability distribution of candidates and degrades imperceptibility. To enhance steganographic security, meanwhile addressing segmentation ambiguity, we propose a secure and disambiguating approach for linguistic steganography. In this letter, we focus on two questions: (1) Which candidate pools should be modified? (2) Which tokens should be retained? Firstly, we propose a secure token-selection principle that the sum of selected tokens’ probabilities is positively correlated to statistical imperceptibility. To meet both disambiguation and optimal security, we present a lightweight disambiguating approach that is finding out a maximum weight independent set (MWIS) in one candidate graph only when candidate-level ambiguity occurs. Experiments show that our approach outperforms the existing method in various security metrics, improving 25.7% statistical imperceptibility and 12.2% anti-steganalysis capacity averagely.

Cyber-physical systems are becoming more intelligent with the adoption of heterogeneous sensor networks and machine learning capabilities that deal with an increasing amount of input data. While this complexity aims to solve problems in various domains, it adds new challenges for the system assurance. One is the rise of the number of abnormal behaviors that affect system performance due to possible sensor faults and attacks. The combination of safety risks, which are usually caused by random sensor faults and security risks, which can happen at any random state of the systems, makes the full coverage testing of the cyber-physical system to be challenging. Existing techniques are inadequate to deal with complex, safety and security co-risks on cyber-physical systems. In this paper, we propose AST-SafeSec, an analysis methodology for both safety and security aspects, which utilizes reinforcement learning to identify the most-likely adversarial paths at various normal or failure states of a cyber-physical system that can influence system behavior through its sensor data. The methodology is evaluated using an autonomous vehicle scenario by incorporating security attack into the stochastic sensor elements of the vehicle. Evaluation results show that the methodology analyzes the interaction of malicious actions with random faults, and identifies the incident caused by the interactions and the most-likely path that leads to the incident.

Automatic detection of fake content in social media such as Twitter is an enduring challenge. Technically, determining fake news on social media platforms is a straightforward binary classification problem. However, manually fact-checking even a small fraction of daily tweets would only be possible due to the sheer volume of daily tweets. To address this challenge, we crawled and crowdsourced one of the most extensive ground-truth datasets containing more than 180.000 labels from 2009 to 2022 for tweets with a 5-label and 3-label classification using Amazon Mechanical Turk. We utilized multiple levels of validation to ensure an accurate ground-truth benchmark dataset. We then created and implemented numerous machine learning and deep learning algorithms, such as different variations of BERT-based models, on the data to test the accuracy of real/fake tweet detection with both categories and determine which versions gave us the highest result metrics. Further analysis is performed on the dataset by explicitly utilizing the DBSCAN text clustering algorithm combined with the YAKE keyword creation algorithm to determine topics’ clustering and relationships. Finally, we analyzed each user in the dataset, determining their Bot Score, Credibility Score, and Influence Score for a better understanding of what type of Twitter user posts and their influence with each of their tweets, and if there were any underlying patterns to be drawn from each score concerning the truthfulness of the tweet. The experimental results illustrated profound improvement for models dealing with short-length text in solving a real-life problem, such as automatically detecting fake content in social media.

This article presents modeling and design optimization of switched-capacitor charge pumps for use as cold-start circuits in photovoltaic energy harvesters. For a successful coldstart from photovoltaic energy, a certain output voltage and current must be generated by the charge pump while jointly minimizing the required input voltage and current from the energy harvesting source. We model the operation of the coldstart charge pump including its oscillator, derive expressions for required input voltage and current based on output requirements, and demonstrate a pareto-optimal design space that defines the tradeoff between the required input voltage and current to achieve cold-start. We then assess the impacts of process and temperature variation, and suggest a general design methodology for cold-start charge pump circuits. Finally, we validate models with measurements from a design fabricated in 55nm CMOS.

Understanding how and where roller coaster-related injuries occur within the human body are crucial to prevention. In turn, this paper begins by analyzing the relationship between forces that roller coasters exert on riders and related injury biomechanics research. This paper then proceeds to assess the respective risks of brain injury and spinal injury that can impact riders. Recommendations for improvement are then offered in the form of biomechanically-enhanced anthropomorphic test devices. Roller coaster manufacturers can ultimately use the data supplied by such devices in their designs to reduce rider injuries.

In this paper, we propose a novel control strategy for the optimal scheduling of an energy community constituted by prosumers and equipped with unidirectional vehicle-to-grid (V1G) and vehicle-to-building (V2B) capabilities. In particular, V2B services are provided by long-term parked electric vehicles (EVs), used as temporary storage systems by prosumers, who in turn offer the V1G service to EVs provisionally plugged into charging stations. To tackle the stochastic nature of the framework, we assume that EVs communicate their parking and recharging time distribution to prosumers, allowing them to improve the energy allocation process. Acting as selfish agents, prosumers and EVs interact in a rolling horizon control framework with the aim of achieving an agreement on their operating strategies. The resulting control problem is formulated as a generalized Nash equilibrium problem, addressed through the variational inequality theory, and solved in a distributed fashion leveraging on the accelerated distributed augmented Lagrangian method, showing sufficient conditions for guaranteeing convergence. The proposed model predictive control approach is validated through numerical simulations under realistic scenarios. This preprint has been accepted for publication in IEEE Transactions on Control Systems Technology. How to cite: N. Mignoni, R. Carli and M. Dotoli, “Distributed Noncooperative MPC for Energy Scheduling of Charging and Trading Electric Vehicles in Energy Communities,” in IEEE Transactions on Control Systems Technology.

With the advancement of technology, the development of telecommunication industries, the construction of new electrical devices, and the increasing use of electromagnetic field generators in modern industrial societies, the study of the biological effects of these waves on the growth and development of living organisms are of interest to many societies. The scientific world is placed. Today, one of the ecological problems is electromagnetic pollution in the environment. The use of mobile phones has increased in the world in recent years, which in turn adds to this pollution. Some bee species worldwide are becoming extinct, called the CCD phenomenon. Our goal in this article is to investigate the effect of electromagnetic waves and their relationship with the new phenomenon of CCD and to offer a proposal to guide bees to return to the hive by fluorescent blue light.

The first cryptocurrency was invested in 2008/09, but the Blockchain-Web3 concept is still in its infancy, and the cyber risk is constantly changing. Our cybersecurity should also be adapting to these changes to ensure security of personal data and continuation of business for organisations. This review paper starts with a comparison of existing cybersecurity standards and regulations from the National Institute of Standards and Technology (NIST) and the International Organization for Standardization (ISO) - ISO27001, followed by a discussion on more specific and recent standards and regulations, such as the Markets in Crypto-Assets Regulation (MiCA), Committee on Payments and Market Infrastructures and the International Organisation of Securities Commissions (CPMI-IOSCO), and more general cryptography and post-quantum cryptography, in the context of cybersecurity. These topics are followed up by a review of recent technical reports on cyber risk/security and a discussion on cloud security questions. Comparison of Blockchain cyber risk is also performed on the recent EU standards on cyber security, including European Cybersecurity Certification Scheme (EUCS) – cloud, and additional US standards – The National Vulnerability Database (NVD) Common Vulnerability Scoring System (CVSS). The study includes a review of Blockchain endpoint security, and new technologies e.g., IoT. The research methodology applied is a review and case study analysing secondary data on cybersecurity. The research significance is the integration of knowledge from the United States (US), the European Union (EU), the United Kingdom (UK), and international standards and frameworks on cybersecurity that can be alighted to new Blockchain projects. The results show that cybersecurity standards are not designed in close cooperation between the two major western blocks - US and EU. In addition, while the US is still leading in this area, the security standards for cryptocurrencies, internet-of-things, and blockchain technologies have not evolved as fast as the technologies have. The key finding from this study is that although the crypto market has grown into a multi-trillion industry, the crypto market has also lost over 70% since its peak, causing significant financial loss for individuals and cooperation’s. Despite this significant impact to individuals and society, cybersecurity standards and financial governance regulations are still in their infancy.

Automatic target classification or recognition is a critical capability in non-cooperative surveillance with radar in several defence and civilian applications. It is a well-established research field and numerous techniques exist for recognising targets, including miniature unmanned air systems or drones (i.e., small, mini, micro and nano platforms), from their radar signatures. These algorithms have notably benefited from advances in machine learning (e.g., deep neural networks) and are increasingly able to achieve remarkably high accuracy. Such classification results are often captured by standard, generic, object recognition metrics and originate from testing on simulated or real radar measurements of drones under high signal to noise ratios. Hence, it is difficult to assess and benchmark the performance of different classifiers under realistic operational conditions. In this paper, we first review the key challenges and considerations associated with the automatic classification of miniature drones from radar data. We then present a set of important performance measures, from an end-user perspective. These are relevant to typical drone surveillance system requirements and constraints. Selected examples from real radar observations are shown for illustration. We also outline here various emerging approaches and future directions that can produce more robust drone classifiers for radar.

In this paper, we propose a novel session-based continuous authentication model using photoplethysmography (PPG). Unlike previous PPG-based authentication techniques that generate user signatures only during the initial interaction, our session-based approach tackles inter session PPG drifting by generating a user signature at the start of each session. Our model is composed by two modules: Firstly, heavy deep autoencoders (AE) are utilized for feature extraction and, secondly, a lightweight Local Outlier Factor (LOF) is employed for user authentication. Additionally, we introduce a continuous updating system for the LOF model, which automatically recovers from security breaches and can enhance authentication accuracy by more than 9%. Our experiments show that in a single-session scenario, our model achieves authentication accuracies of 93.5% and 91.8% on the CapnoBase and BIMDC benchmarking datasets, respectively, outperforming the state-of-the-art baseline model by 3.2% and 1.6% on both datasets, respectively. In multiple-session scenarios, our scheme attains an authentication accuracy of 95% when tested on the BioSec2 dataset, effectively mitigating inter-session PPG drifting and achieving an advantage of more than 8.5% in authentication accuracy over the state-of-the-art method. In terms of execution speed, our solution is seven times faster at runtime compared to competing state-of-the-art solutions.

In this article we argue that the disruptive social implications of skill-replacing technological innovations are determined neither by human characteristics, such as “low skills” or “low cognition,” nor by task characteristics, such as “routine,” as it is typically assumed in the predominant economics and management science literature, but by the cybernetic characteristics of the innovations. We also propose that the negative effects of technological disruptions on human well-being cannot be fully understood without the use of a transdisciplinary approach involving cybernetics science and occupational science, and that it is urgent that policymakers look beyond their narrow effects on productivity and on the labor force, and consider instead the complexity of the interactions between cybernetic technologies and meaningful human occupations. We offer as an example the case of the fast adoption of online food delivery services and of remote work technologies during the COVID-19 pandemic. Ethical implications are derived from the arguments.

Terrestrial data centers suffer from a growing carbon footprint that could contribute with 14% to global CO2 emissions by 2040. High Altitude Platform (HAP) is a promising airborne technology that can unleash the computing frontier in the stratospheric range by hosting a flying data center. HAP systems can endorse the sustainable green operation of data centers thanks to the naturally low atmospheric temperature that saves cooling energy and its large surface that can host solar panels covering energy requirements. Throughout this article, we define the operation limitations of this innovative solution and study the energy-efficiency-related trade-offs. Then, we shed light on the significance of the scalability of the data center-enabled HAP architecture by investigating potential bottlenecks and proposing different deployment scenarios to avoid network congestion. We also highlight the importance of the management agility of the data center-enabled HAP system by defining effective management techniques that yield high-performing data centers. Our results demonstrate that deploying a single data center-enabled HAP can save 12% of the electricity costs.

The current industrial automation landscape faces considerable challenges due to the increasing growth of Industrial Internet of Things (IIoT) devices, cloud services, IT/OT convergence, along with evolving hyper-scaler technologies such as Kubernetes and distributed computing. This paper provides an in-depth review of the existing ISA-95 Model and its current role in supporting manufacturing systems and their interactions. Additionally, it examines how emerging technologies are impacting the security, design, and management of Operational Technology (OT) networks. As existing perimeter-based models, such as ISA-95, are pushed to their limits, the concepts of Zero Trust architectures, and policy based or software-defined networks are being explored as the next generation of OT network design. This paper aims to provide a high-level introduction to the concepts and disruptive technologies, and introduce the potential implementation risks and challenges of these principles within traditional IT/OT converged solutions.

The ShuttlePump is a novel implantable total artificial heart (TAH) concept based on a Linear-Rotary Actuator (LiRA) and currently under development at the Power Electronic Systems Laboratory, ETH Zurich in close partnership with Charit´e Berlin and the Medical University of Vienna. This paper presents the analysis, design and realization of the ShuttlePump Linear Actuator (LA) part, which is necessary to provide about 45 N of axial actuation force. Design criteria are minimization of volume and generated power losses in the winding, which could result in excess heating and/or blood damage, i.e. protein denaturation and aggregation. The LA is implemented as a Tubular LA (TLA) to maximize the active area for linear/axial force generation. After a preliminary analysis based on first principles, the TLA is optimized in detail with the aid of FEM simulations. The experimental measurements conducted on the realized TLA prototype verify the FEM simulation results and confirm the suitability for the realization of the ShuttlePump TAH.

Early design stages of modern Switched Reluctance Machines (SRMs) are well-known for demanding the analysis of thousands of candidates. The need for reducing the computation time is, in turn, fostering the interest in design-oriented analytical approaches capable to maintain sufficient accuracy for SRMs featuring different geometries and rated operating conditions. To this end, this work proposes a novel design-oriented analytical model that predicts the flux linkage loci, i.e., a set of curves expressing the phase flux linkage as a function of both phase current and rotor position, from which the main performance are attained. The model comprises two main parts, each of them containing a novel scientific contribution: 1) a new interpolation technique for the flux loci based on second-order Fröhlich-Kennelly equations, and 2) an analytical model that caters for the flux linkage in partial overlap and saturated core conditions. Finally, the model is validated against Finite Element results of four SRMs, along with the experimental results of one of them, and its implementation in a design routine is discussed.

Today, one of the biological problems is electromagnetic pollution in the environment. The intensity of electromagnetic radiation is so pervasive that it is now known as a form of latent and silent pollution. The study of the biological effects of these waves on the growth and development of living insects - such as bees - are considered by many scientific communities around the world. However, the response of honey/wild bees to traps designed to selectively stimulate bee light receptors by fluorescent light indicated that selective arousal of the blue light receptor type was highly attractive. In this paper, to selectively stimulate the blue light receiver in bee species in order to guide the bee navigation system to return to its hive and prevent the colony collapse disorder (CCD) phenomenon, high-efficiency continuous laser emission by second-harmonic line width of 456 nm Nd:GdVO4 laser is proposed.

We develop a deep neural network (DNN) modeling methodology to predict the radiated emissions of a shielding enclosure in terms of its aperture attributes including aperture shape, size, pitch, and quantity. The target structure is the inside of a three-dimensional (3D) enclosure comprised of perfect electric conductor (PEC) boundaries with dimensions of a desktop personal computer (PC) containing thermal dissipation apertures on the surface of its back panel. The DNN model is developed to compute the radar cross section (RCS) as a function of aperture attributes and to enable the efficient assessment of the PC’s electromagnetic compatibility (EMC). To generate training data for machine learning (ML), we implement the modified equivalent current approximation (MECA) method and validate it against analytical methods and a commercial field-solver. We use MECA to compute RCS data for approximately 55,000 experiments across a wide range of aperture attributes. We examine numerous DNN models across parameters such as number of layers and nodes per layer, activation function, optimization algorithm, loss function, batch size, and epoch, to identify the optimal DNN model based on (a) accuracy, (b) computation time, and (c) memory usage. Results show excellent agreement between MECA and DNN predictions for previously unseen cases.