Diabetes is a metabolic disorder often diagnosed late and needs continuous blood glucose monitoring. We introduce GlucoBreath, a user-centric, cost-effective, and portable pre-diagnostic solution to address this global challenge. GlucoBreath addresses the urgent need for an accessible and non-intrusive diabetes detection device, offering affordability, mobility, and comfortable non-invasive diabetes testing, especially among economically weaker sections of society. GlucoBreath comprises (i) a non-intrusive multi-sensor Internet of Things device comprising multiple sensors detecting volatile organic compounds in breath, (ii) BreathProfiles dataset encompasses information from 492 patients, which includes demographic details, physiological measurements, and sensor readings derived by analyzing breath samples with our device, (iii) an innovative Machine Learning-based diabetes prediction system trained on the BreathProfiles dataset, and (iv) a user-friendly web interface for seamless device interaction and viewing diabetes reports. Given a person’s breath sample, demographics, and body vitals data as input, GlucoBreath predicts (a) if the person has diabetes. (b) If the person has diabetes, then the blood glucose level (BGL) of the person is moderate or high. GlucoBreath’s groundbreaking approach supersedes current methods, achieving an impressive mean accuracy of 98.4% using a Logistic Regression-AdaBoost stack-metamodel, marking a substantial 43.3% improvement over an existing method. Due to its portability, non-intrusiveness, and rapid response, GlucoBreath is a valuable pre-diagnostic tool that can facilitate the early detection of diabetes in many individuals. Further, the BGL prediction by GlucoBreath can help alert individuals to control their sugar consumption in case of a moderate BGL or visit a physician in case of a high BGL. Â

We developed an untethered robotic system that provides walking-related sensory feedback remapped non-invasively to the wearerâ\euro™s back. Using the so-called FeetBack system, we investigated how accurately healthy individuals can monitor remapped, tactile locomotor feedback (motor awareness) and how this affects their walking characteristics (adaptation).

In the wake of unprecedented global shifts, the traditional office landscape has undergone a dramatic transformation. This study delves into the heart of the ongoing debate: Work from Home (WFH) versus Return to Office (RTO). Through a comprehensive analysis of employee productivity, job satisfaction, and work-life balance, we aim to unravel the complexities and implications of these contrasting work environments. Our research employs a mixed-method approach, incorporating quantitative surveys and qualitative interviews from a diverse pool of participants across various industries. We explore the nuanced benefits and challenges of WFH, such as increased flexibility and potential isolation, alongside the dynamics of RTO, including collaboration opportunities and potential commute stress. The findings reveal a multifaceted picture, highlighting that the ideal work setting is not one-size-fits-all but rather depends on individual preferences, job nature, and organizational culture. This study offers valuable insights for companies navigating the post-pandemic era, emphasizing the need for adaptable and empathetic work policies. Ultimately, “Home Sweet Office” sheds light on the evolving nature of work and the importance of fostering environments that resonate with the diverse needs of the modern workforce.

Berger-Levrault, like many companies, has legacy web applications that still bring great values, and cannot be easily replaced. To maintain these applications, it needs data about user navigation, backend actions and client-server data exchange. Berger-Levrault has relied on a traditional logging approach that partially collects these data, requires modifying the application code and heavily impacts its performance. To address the limitations of this logging approach, we propose to replace it by a modern software telemetry approach. Existing telemetry approaches do not meet our needs, they should be extended based on our objectives, technological constraints and industrial regulations. In this paper, we report our experience in instrumenting real, large-scale, industrial legacy web applications based on a telemetry approach.  Our goal is to automatically instrument legacy web applications to collect data fulfilling our industrial needs. We extend the automatic instrumentation capabilities of OpenTelemetry agents to instrument our applications without modifying their code. We define a telemetry architecture to integrate telemetry components with legacy web applications. Also, we empirically evaluate the performance overhead produced by our agents. The results show that there is no significant overhead when using OpenTelemetry agents. However, this overhead is sensitive to the size of data being serialized when instrumenting client-server data exchange. Moreover, we discuss lessons learned about the technical challenges we faced during the industrialization of our  approach. Also, we empirically evaluate the performance overhead produced by our agents. The results show that there is no significant overhead when using Open Telemetry agents. However, this overhead is sensitive to the size of data being serialized when instrumenting client-server data exchange. Moreover, we discuss lessons learned about some technical challenges we faced during the industrialization of our telemetry approach.

This study conducts a wideband multi-path propagation measurement at the 95 GHz sub-terahertz band for short-range communication in a conference room desktop scenario. Regardless of the fact that the current 3rd generation partnership (3GPP) stochastic channel model (SCM) targets the frequency up to 100 GHz for various scenarios, neither detailed measurements at the 95 GHz band nor a compatible channel modeling/generation framework for indoor short-range communication scenarios have been conducted. To fill these voids, based on a real-world measurement at 95 GHz with a bandwidth of 4 GHz, this study analyzes the multi-path propagation characteristics and yields the following insights for developing a 3GPP SCM-compatible channel generation framework at this band. First, the exponential power decay with delay time and quasi-uniform azimuth angles of arrival (AAoAs) are observed, which should be revisited to develop a channel generation framework. Secondly, distribution models for root mean squared (RMS) delay/AAoA spreads and omnidirectional path loss model are derived, which serves as a foundation for developing a channel generation framework at this band. Moreover, these established models are compared with the recently conducted measurement results at the 60 GHz band in the same scenario, shedding light on the hypothesis that the models for these parameters at the 60 GHz can be generalized for the 95 GHz band. “© 2024 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Published Version:10.1109/WCNC57260.2024.10571036

Background: Given that VR is used in multiple domains, understanding the effects of cybersickness on human cognition and motor skills and the factors contributing to cybersickness is becoming increasingly important. This study aimed to explore the predictors of cybersickness and its interplay with cognitive and motor skills.Methods: 30 participants, 20–45 years old, completed the MSSQ and the CSQ-VR, and were immersed in VR. During immersion, they were exposed to a roller coaster ride. Before and after the ride, participants responded to the CSQ-VR and performed VR-based cognitive and psychomotor tasks. After the VR session, participants completed the CSQ-VR again.Results: Motion sickness susceptibility, during adulthood, was the most prominent predictor of cybersickness. Pupil dilation emerged as a significant predictor of cybersickness. Experience with videogaming was a significant predictor of cybersickness and cognitive/motor functions. Cybersickness negatively affected visuospatial working memory and psychomotor skills. Overall the intensity of cybersickness’s nausea and vestibular symptoms significantly decreased after removing the VR headset.Conclusions: In order of importance, motion sickness susceptibility and gaming experience are significant predictors of cybersickness. Pupil dilation appears to be a cybersickness biomarker. Cybersickness affects visuospatial working memory and psychomotor skills. Concerning user experience, cybersickness and its effects on performance should be examined during and not after immersion.  

While Generative AI (GAI) like ChatGPT have had tremendous impact, they need to be used cautiously and wisely to avoid any problems arising out of hallucinations. Â In the article, I show an example where ChatGPT produces wrong results for a statistical function calculation. So, I request students to use ChatGPT or other GAI judiciously as their learning companion, rather than a substitute for their hard work.

This paper focuses on multiple-access protocol design in a wireless network assisted by multiple reconfigurable intelligent surfaces (RISs). By extending the existing approaches in single-user or single-RIS cases, we present two benchmark schemes for this multi-user multi-RIS scenario. Inspecting their shortcomings, a simple but efficient method coined opportunistic multi-user reflection (OMUR) is proposed.

 The upcoming 6G will represent a complete paradigm shift for global communications. Addressing the critical research verticals toward the envisioned 2030 will require a compelling mix of enabling radio access technologies (RAT) and native softwarized, disaggregated, and intelligent conceptions such as the Open Radio Access Network (O-RAN) architecture. Integrating the Multicast/Broadcast Services (MBS) capability is an appealing feature to overcome the ever-growing traffic demands, disruptive multimedia services, massive connectivity, and low-latency applications. This article discusses the insertion of machine learning (ML)â\euro“based multicasting Radio Resource Management (RRM) solutions in the 6G O-RAN. We review the expected evolution of the MBS capability, including enabling technologies and challenges for the IMT-2030 framework. Moreover, we cover essential aspects at the intersection of MBS, ML-based RRM solutions, and the disaggregated O-RAN architecture, identifying possible scenarios as feature extensions of O-RAN. We present the outcomes of a comprehensive MBS use case simulation oriented to validate our approach, highlighting critical remarks and conclusions.Â

Secure software engineering remains a perennial topic of discussion and exploration within the research and scientific communities. This paper delves into the historical evolution of software strategy and its intersection with the realm of secure software engineering. Past approaches have ranged from life cycle solutions to taxonomy-based identification of attack origins. In light of these approaches, we systematically examine the challenges that have been encountered over time, spanning various aspects of software engineering. A key emphasis of this review is on illuminating novel insights and methodologies that pave the way for the present and future of secure software engineering. By scrutinizing the origins of flaws, errors, and faults in software development, we propose a distinctive approach for early-stage detection and mitigation. Our work highlights the importance of identifying vulnerabilities in the nascent stages of the software life cycle. Key findings of this study encompass a nuanced taxonomy that categorizes vulnerabilities based on their inception points and their evolution throughout the software development process. Furthermore, we elucidate the critical significance of integrating cybernetics principles into secure software engineering practices.

In this paper, we present a novel digital-like defect-oriented Built-In Self-Test (BIST) methodology for Analog and Mixed-Signal (AMS) circuits. The core idea of this approach centers around the segmentation of complex AMS circuits into smaller, more manageable units for analysis. Emphasizing resource utilization efficiency, we highlight the necessity of employing purely digital circuits for both injectors and monitors within the BIST framework. We demonstrate the effectiveness of this approach through the development of a BIST system for a 12-bit Successive Approximation Register Analog-to-Digital Converter (SAR ADC). Notably, our methodology achieves 100% defect coverage without introducing additional BIST circuitry for subcircuit testing, relying solely on digital monitors for sampling switch evaluation. Furthermore, our proposed approach incurs minimal area overhead, resulting in a fast and comprehensive defect-oriented BIST solution. This versatile test method can be deployed post-manufacturing or in-field, offering flexibility in its application timing.

This research paper titled “Enhanced Wildfire Detection using AI/ML: Harnessing Multi-spectral Satellite Imagery with Convolutional Neural Networks” aims to advance the capabilities of wildfire detection by employing Artificial Intelligence (AI), specifically Convolutional Neural Networks (CNNs). Given the escalating threat of wildfires exacerbated by climate change and human activity, traditional detection methods, though effective, are both costly and time-consuming. To counter these limitations, the study taps into multi-resolution satellite imagery, particularly from the VIIRS and Sentinel-2 satellites. The primary data source, VIIRS, offers comprehensive spectral bands and frequent global coverage. In contrast, Sentinel-2 provides high-resolution optical image data vital for detailed wildfire detection. The research processes the collected data, refining and categorizing them for training and testing. A Convolutional Neural Network is then employed to classify images as either “fire” or “nofire.” Two main architectures, Deep CNN and a simplified MobileNet-like CNN, were explored. Among the models tested, the Deep CNN using the Adam optimizer was found to be the most accurate, although it hinted at possible overfitting. The paper also points out several limitations, such as reliance on the visible spectrum that could be obstructed by atmospheric conditions and the temporal gaps in image captures that could delay real-time detection. The study concludes by emphasizing the transformative potential of integrating AI with satellite technology for early wildfire detection. Future advancements could harness multispectral bands and refine spatial and temporal resolutions to further enhance the early detection and intervention of wildfires. The research received support from the Network of Resources (NoR) at ESA, which facilitated expanded access to the SentinelHub platform.

A novel circuit-computational electromagnetic (CEM) hybrid simulation methodology, which results in vast improvements in the accuracy of high-frequency photovoltaic (PV) modelling between 1 Hz and 50 MHz, is proposed. At low frequencies, the impedance of a PV module is dominated by the behaviour of the PV cells, while at high frequencies it is dominated by the layout of the inter-cell connections. The proposed methodology considers both the low and high-frequency behaviour of a PV module in a single simulation. Firstly, the impedances of two PV modules are carefully measured using two vector network analyser configurations. Secondly, literature-based models of PV modules are adapted to the proposed methodology. Thirdly, improved models of each PV module are introduced in four ascending levels of complexity. These improved models comprise additional novel aspects, including distributed cell layouts and implicit CEM-calculated self-inductances. Fourthly, a feature-selective method is used to compare the measured results with the literature-based and improved models. This analysis yielded comparable error reductions of up to a factor of 400 – highlighting the value of the improved methodology and models. Penultimately, the required level of computational resources is introduced, and the trade-off between simulation time and accuracy is discussed. Finally, a review discussion is presented.

Data science has emerged as an integral part of enterprise business operations, aiming to uncover data-driven insights. However, volatility and variability are two major issues of these systems. The latter indicates the need to consider a multitude of conditions when assessing the accuracy of predictions made by analytics systems. This complexity stems from the understanding that each of these circumstances can alter at any given time. Consequently, the challenge is to estimate the predictive accuracy of data science systems even in the midst of uncertainty and variability. Such an estimate can enable good planning and potentially dynamic resource allocation for such projects. In this work, we present a study of various machine learning and deep learning models to estimate the performance of data science projects deployed in Apache Spark, a popular and flexible distributed analytics platform. Moreover, we create experiments for the purpose of gathering data, as well as assessing feature importance to find which input configuration contributes the most. We demonstrate the process of training such a model, from data collection to training and testing, and we systematically compare the various alternatives to help decision-makers choose the best one. Thus, by providing insights into the performance of data science projects under uncertain and variable conditions, this work offers valuable contributions to both research and practical applications to make informed decisions, plan effectively, and allocate resources dynamically in data science projects. Our results show that LSTM and MLP outperform other models in terms of response time models and throughput models.

A multiple-access protocol in a wireless network assisted by multiple reconfigurable intelligent surfaces (RISs)

To better comprehend the upper-limb needs and challenges stroke survivors face, and the issues associated with existing technology, the authors conversed with 153 members of the ecosystem (60 neuro patients, 30 caregivers, and 63 medical providers). Patients fell into two populations depending on their upper-limb impairment: spastic (stiff, clenched hands) and flaccid (limp hands). The authors chose to focus on the second category and developed a set of design constraints based on the information collected through customer discovery. With these in mind, they designed and prototyped OrthoHand Flex, a 3D-printed powered wrist-hand grasping orthosis (exoskeleton) to aid in recovery. The orthosis is easily custom-sized based on two parameters and derived anatomical relationships. The researchers tested the prototype on a survivor of stroke and modeled the kinematic behavior of the orthosis with and without load. The prototype neared or exceeded the target design constraints and was able to grasp objects consistently and stably, as well as exercise the patientsâ\euro™ hands. In particular, donning time was only 42 seconds, as compared to the next fastest time of 7 minutes reported in literature. This device has the potential to revolutionize neurorehabilitation by making assistive devices accessible to patients at home, and it lays the foundation for clinical trials and further device development

Disease forecasting is a longstanding problem for the research community, which aims at informing and improving decisions with the best available evidence. Specifically, the interest in respiratory disease forecasting has dramatically increased since the beginning of the coronavirus pandemic, rendering the accurate prediction of influenza-like-illness (ILI) a critical task. Although  methods for short-term ILI forecasting and nowcasting have achieved good accuracy, their performance worsens at long-term ILI forecasts. Machine learning models have outperformed conventional forecasting approaches enabling to utilize diverse exogenous data sources, such as social media, internet users’ search query logs, and climate data. However, the most recent deep learning ILI forecasting models use only historical occurrence data achieving state-of-the-art results. Inspired by recent deep neural network architectures in time series forecasting that benefit from self-attention, this work proposes the Regional Influenza-Like-Illness Forecasting (ReILIF) method for regional long-term ILI prediction. The proposed architecture takes advantage of diverse exogenous data, that are, meteorological and population data, introducing an efficient intermediate fusion mechanism to combine the different types of information with the aim to capture the variations of ILI from various views. The efficacy of the proposed approach compared to state-of-the-art ILI forecasting methods is confirmed by an extensive experimental study following standard evaluation measures.

Long-term time-series forecasting (LTSF) has received an increasing attention for its significant challenges and real-world applications. However, the previous studies under-explore the hierarchical timestamp information in LTSF. This information is crucial, especially for LTSF as failing to incorporate it may result in missing the global perspective of time series and important long-term trending effects, such as weekly and seasonal patterns. Therefore, we propose an interpretable hierarchical model called VH-NBEATS, which advances the N-BEATS model by addressing the aforementioned problem. VH-NBEATS comprises two essential blocks: the hierarchical timestamp block and the harmonic seasonal block to capture multi-diluted and trending effects. To address the high variability of time series, VH-NBEATS involves a stochastic autoencoder which significantly improves the standard deterministic approach. The experimental results are evaluated on five real-world datasets, showing state-of-the-art results for LTSF. We also prove that the VH-NBEATS framework can be easily incorporated into other ones, such as PathTST, leading to enhanced performance.