Traditional assessments of children’s health and behavioral issues primarily rely on subjective evaluation by adult raters, which imposes major costs in time and human resource to the school system. This pilot study investigates the utilization of millimeter-wave radar coupled with machine learning for the objective and semi-automatic detection and classification of children’s activity levels, defined as restlessness, within a real classroom environment. Two objectives are pursued: confirming the feasibility of restlessness detection using millimeter-wave radar and proposing an algorithm for restlessness classification through machine learning. The experiment involves a nine-day observational study, using two radar systems to monitor the activities of 14 children in a primary school. Radar data analysis involves the extraction of distinctive features for restlessness detection and classification. Results indicate the successful detection of restlessness using millimeter-wave radar, demonstrating its potential to capture nuanced body movements in a privacy-protected manner. Machine learning models trained on radar data achieve a classification accuracy of 100%, outperforming other methods in terms of non-invasiveness, lack of body restraint, multi-target applications, and privacy protection. The study’s contributions extend to children, parents, and educational practitioners, emphasizing non-invasiveness, privacy protection, and evidence-based support. Despite limitations such as a short monitoring duration and a small sample size, this pilot study lays the foundation for future research in non-invasive restlessness detection using non-contact monitoring technologies. The integration of millimeter-wave radar and machine learning offers a promising avenue for efficient and ethical trait assessments in real-world educational environments, contributing to the advancement of child psychology and education. This work supports efforts for non-contact monitoring of children’s activity holding promise such as non-invasive, privacy protection, multi-targets, objective evaluation, and computer-aided screening.

Ten years ago, the concept of the hyperloop vacuum train promised to revolutionize transportation by offering a fast, inexpensive, and eco-friendly alternative to traditional modes of travel. The key components of the hyperloop are a vacuum tube, magnetic levitation, and linear electric propulsion technology, which is envisaged to achieve surface velocities approaching the speed of sound. This paper presents the functionalities of an ideal hyperloop transportation system (HTS) with a low-cost track and a lightweight hyperloop capsule. We show how this ideal system is indeed difficult to achieve in reality. Despite the potential benefits, hyperloop technology still lacks experimental evidence at subsonic speeds to reach a higher level of technological readiness. Taking one step back, hyperloop has lessons to learn from the maglev research and experiments in the 1970s. In fact, there are many unresolved challenges associated with maglev technologies, even at moderate speeds, which need to be recognized before reaching the whole way into the subsonic speed domain. This paper will provide a status update after ten years of hyperloop research and development.

Objective: Design refinement and validation with simulated physical prostates of a planning, guidance, and feedback system for visualizing prostate biopsy in 3D perspective. Methods: A visualized prostate biopsy system (vPBx) using electromagnetic  tracking of a transrectal ultrasound (TRUS) probe, a biopsy device, and a urinary catheter placed inside the patient’s prostate was refined and integrated as a reversible retrofit to a Flex Focus 400 ultrasound machine (BK Medical). The system was verified using simulated physical prostates with 12 0.065 ml (2.5 mm radius) spheres, arranged in a double sextant template, registered to a virtual model of the prostate and used to distinguish hits and misses. Systematic prostate biopsy (sPBx) was performed on simulated prostates using the vPBx as a retrofit to the TRUS machine. Results: The vPBx system enabled an inexperienced user to sample the sPBx template locations with a template deviation below 2.5 mm. Conclusion: An electromagnetic guidance system was refined, retrofitted with a Flex Focus 400 machine and a side-cut biopsy device (Max-Core Disposable Core Biopsy Instrument; Bard) and successfully validated with simulated physical prostates. Significance: If validated with patients, the vPBx system has the potential to reduce prostate biopsy false negatives, which may lead to earlier diagnosis of clinically significant prostate cancer and improved 10-year mortality in high risk prostate cancer patients.

This work presents a model predictive control (MPC) scheme to achieve three-dimensional (3D) tracking control and point stabilisation of an autonomous underwater vehicle (AUV) subject to environmental disturbances. The AUV is modelled as a coupled, nonlinear system. The control scheme is developed using a linear parameter-varying (LPV) formulation of the nonlinear model in velocity form to obtain an optimisation control problem with efficient online solvers and does not require model augmentation that can potentially increase computational efforts. The control strategy inherently provides offset-free control when tracking piece-wise constant reference signals, ensures feasibility for trajectories containing unreachable points and is relatively simple to implement since parameterisation of all equilibria is not required. A simple switching law is proposed for task switching between the 3D trajectory tracking and point stabilisation. The MPC is designed to ensure closed-loop stability of the vehicle in both motion control tasks via the imposition of terminal constraints. Through simulations of the coupled nonlinear Naminow-D AUV under ocean current and wave disturbances, the effectiveness of the control strategy in trajectory tracking and point stabilisation is demonstrated.

To overcome the challenges faced by people with neurodegenerative diseases, Brain-Computer Interface (BCI) systems must make use of datasets relevant to patient's spoken languages. However, BCI research frequently faces setbacks due to the absence of such datasets for the target language population. This paper deals with the BCI datasets for one of the major Indian languages, Telugu, used by more than 90 million people, yet to obtain essential BCI datasets capturing the linguistic characteristics. To solve the unavailability of the Telugu BCI datasets, we created a dataset featuring EEG signal samples corresponding to frequently used Telugu words, aiming to fill this void and facilitate advancements in BCI research for native Telugu speakers. Through the utilization of Machine Learning and other classifiers, BCI systems can potentially translate and classify EEG signals into a display of or pronouncing Telugu words. Our dataset consists of both vocal and sub-vocal datasets of Telugu words and an English dataset of the corresponding English equivalents of the Telugu words. This IIST-BCI Dataset for Telugu is the first of its kind. It is dedicated to improving the accessibility of BCI technologies for Telugu-speaking individuals and fostering further research progress in this particular area.

The research presented here looks into the vibration properties of 3D-printed airless tires, which have the potential to revolutionize tire design and transportation efficiency. Through extensive experimentation and vibration research, three distinct tire constructions were investigated. Because of its good damping and deformation qualities, thermoplastic polyurethane (TPU) was chosen as the 3D printing material. The experimental arrangement was designed to simulate real-world road conditions, and an MPU6050 sensor captured tire vibrations in three axes. The vibrational properties of the tire structures were revealed using Fast Fourier Transform (FFT) analysis, allowing for a comparative assessment of their stability. Structure 1 was found to be the most vibration-stable, followed by Structures 3 and 2.

This article presents the design, development, and implementation of walking control for the bipedal exoskeleton prototype Co-Ex. The main objective in developing this prototype is to take a successive step towards ambulatory support via an exoskeleton with self-stabilization capability. To attain this goal, Co-Ex is equipped with 8 torque-controllable active joints to provide ambulatory support while ensuring improved environmental interaction. The development of Co-Ex led to three contributions: i) self-stabilization capability in 3D against external disturbances, ii) a locomotion control framework that provides dynamically balanced walking behavior in 3D despite the underactuated leg configuration, iii) a power-aware leg design in which most actuators are deployed around the waist for reduced leg inertia. To verify the self-stabilization and locomotion capabilities of Co-Ex, we conducted a series of experiments using a dummy manikin. As a result, Co-Ex showed self-stabilization behavior against disturbances and exhibited favorable locomotion characteristics that validated the proposed approach.

The aim of our research is to establish relationship between linearly independent vectors and the R3 Space The means for that are going to be taken from Balanced Ternary analysis e.g. triplets with entries (-1,0,1) This is not going to be any specific vector, for instance (-1,0,1) It’s going to be rather a Coordinate system with 8 quadrants and directional vectors The novelty of the proposed research lies in the fact that unlike traditional Cartesian system-vector basis our system uses 8 vectors which are at the same time a polyvector (Boas) We may define it as a multiple of the type:  ∑ (i=-1)^1∑ (j=-1)^1 ∑ (k=-1)^1〖(i,j,k)〗  

This letter tries to address the fundamental questions on the feasibility of multipath communication and achievable capacity for line-of-sight (LoS), obstructed-line-of-sight (OLoS), and non-line-of-sight (NLoS) scenarios in an outdoor setting for futuristic communication networks. In this context, a relevant measurement campaign at 300 GHz is conducted, and the results are presented in terms of the number of nontrivial propagation streams available for signal transmission. Encouraged by the obtained results, the average achievable channel capacity for such multi-stream channels is evaluated with and without passive reflecting surfaces (PRS). It is observed a multi-antenna system provides a significant improvement in average capacity compared to a single-antenna system with PRS providing additional enhancement.

As digital ecosystems evolve into increasingly complex networks, harnessing their collective potential becomes paramount. This article focuses on developing software for smart ecosystems by programming their underlying System-of-Systems (SoS)-a domain ripe with opportunities and challenges. We propose the Holon Programming Model (HPM), which adds the programmability aspect to the holonic architecture of SoS and will pave the way for more integrated and adaptable systems. Demonstrating the HPM's utility, we show how developers can program diverse behaviours and execute operations within the disaster management ecosystem, aiming to inspire and motivate software developers to explore the potential of SoS-level applications, which are fundamental for smart ecosystems.

Biological intelligence uses a “multiscale competency architecture” (MCA). It exhibits adaptive, goal directed behaviour at all scales, from cells to organs to organisms. In contrast, machine intelligence is only adaptive and goal directed at a high level. Learned policies are passively interpreted using abstractions (e.g. arithmetic) embodied in static interpreters (e.g. x86). Biological intelligence excels at causal learning. Machine intelligence does not. Previous work showed causal learning follows from weak policy optimisation, which is hindered by presupposed abstractions in silico. Here we formalise MCAs as nested “agentic abstraction layers”, to understand how they might learn causes. We show that weak policy optimisation at low levels enables weak policy optimisation at high. This facilitates what we call “multiscale causal learning” and high level goal directed behaviour. We argue that by engineering human abstractions in silico we disconnect high level goal directed behaviour from the low level goal directed behaviour that gave rise to it. This inhibits causal learning, and we speculate this is one reason why human recall would be accompanied by feeling, and in silico recall not.

In recent years, combined optimisation of Photovoltaic (PV) and Battery Energy Storage Systems (BESS) has attracted remarkable attention as a solution to increase prosumers selfconsumption while also mitigating the issues of high BESS capital costs. In this context, this work proposes a two-level optimal sizing algorithm of PV-BESS systems. The upper level is based on a Genetic Algorithm (GA) devoted to the optimisation of rated PV power and BESS power and storage capacity minimising the Levelised Cost of Energy (LCOE), whereas the lower level embeds a novel rule-based energy management system, where rules are defined considering both energy and feed-in tariffs, with tariff-dependent rules being also optimised by the GA. The algorithm is eventually tested in a Spanish reality-based case study.

As corporate sustainability initiatives have gained traction, marketing strategies under the banner of environmental, social, and governance (ESG) principles have proliferated. However, a concerning trend has emerged wherein organizations disproportionately emphasize the environmental (’E’) dimension while neglecting the social (’S’) and governance (’G’) aspects. This practice, often termed ”greenwashing,” poses significant risks to both marketing efficacy and genuine ESG progress.This paper examines the pitfalls of this selective ESG marketing focus through the lenses of consumer scepticism, regulatory scrutiny, and organizational misalignment. Synthesizing insights from recent scholarly works and industry reports, we underscore the imperative for a holistic and authentic ESG marketing approach that upholds all three pillars. Ultimately, we contend that only through balanced ESG integration can organizations safeguard marketing credibility, cultivate consumer trust, and drive meaningful sustainability transformations.The scepticism of customers toward corporate sustainability claims has intensified, particularly amid perceived greenwashing attempts. Research indicates that when organizations present an imbalanced ESG narrative emphasizing environmental achievements while downplaying social and governance aspects, consumers are more likely to view these efforts as disingenuous and self-serving, eroding trust and brand credibility [Torelli et al., 2020; Bhattacharya & Sen, 2004]. Furthermore, consumers are adept at detecting incongruities between an organization’s rhetoric and its actual practices, potentially leading to negative brand perceptions and backlash when environmental marketing claims contradict questionable social or governance conduct [Marquis et al., 2016].Regulatory bodies are also increasing scrutiny over the accuracy and completeness of ESG disclosures and marketing claims. The U.S. Federal Trade Commission has actively targeted misleading environmental marketing, while the Securities and Exchange Commission is proposing rules mandating comprehensive ESG reporting, including social and governance metrics [SEC, 2022]. Firms with imbalanced ESG practices face heightened risks of regulatory action, fines, and shareholder litigation [Delmas et al., 2013].Moreover, the selective ’E’ focus in ESG marketing can perpetuate organizational misalignment and hinder effective sustainability integration. Research demonstrates that companies pursuing holistic, integrated ESG strategies encompassing all three dimensions outperform peers with isolated environmental initiatives, experiencing operational efficiencies, financial outperformance, and superior long-term value creation [Ioannou & Serafeim, 2019; Edmans, 2011]. A fragmented approach undermines an organization’s ability to attract top talent, foster innovation, and achieve genuine sustainability impacts across environmental, social, and governance domains.To overcome these pitfalls, organizations must adopt a holistic and authentic ESG marketing strategy aligned with comprehensive sustainability practices, transparent reporting, stakeholder engagement, aligned executive incentives, and cross-functional collaboration. This entails integrating environmental stewardship with robust social responsibility and ethical governance initiatives, embracing recognized ESG reporting frameworks, actively soliciting stakeholder input, tying leadership compensation to measurable ESG metrics, and cultivating specialized ESG marketing expertise.By upholding all three pillars of ESG, organizations can enhance marketing credibility, mitigate greenwashing risks, and drive meaningful sustainability progress. This paper underscores the criticality of balanced ESG integration, contending that only through a holistic approach can businesses leverage the full potential of sustainability to create long-term value for stakeholders and catalyse positive environmental, social, and governance impacts.

We analyse a lossy transmission line and the Johnson-Nyquist noise generated therein. A representation as a noisy two-port with a voltage and a current noise sources on one end of a noiseless two-port is given. An expression for the noise properties is given for an arbitrary temperature profile along the transmission line. Compatibility of the general expression found here with expectations for special cases calculable using thermodynamics is demonstrated.

In this research, we examine a special property of some prime numbers that goes beyond separating primes from odd composites. Rather, this property serves to divide the set of primes into two groups: those having it and those lacking it. Incredibly, these two groups portray an almost equal distribution of primes amongst them. This phenomenon is explored in order to reveal some aspects concerning the basic characteristics and structure of prime numbers. We also provide further insight on what this trait implies within prime number theory through rigorous analysis as well as mathematical inspection which shines a new light on the enigmatic properties of these fundamental mathematical entities.

Objective: Clinical adoption of innovative EEG technology is contingent on the non-inferiority of the new devices relative to conventional ones. We present the four key results from testing the signal quality of Zeto’s WR19 EEG system against a conventional EEG system conducted on patients in a clinical setting. Methods: We performed 30-minute simultaneous recordings using the Zeto WR19 (zEEG) and a conventional clinical EEG system (cEEG) in a cohort of 15 patients. We compared the signal quality between the two EEG systems by computing time domain statistics, spectral density, and signal-to-noise ratio. Results: All the statistical comparisons resulted in signal quality non-inferior relative to cEEG. (i) Time domain statistics, including the Hjorth parameters, showed equivalence between the two systems, except for a significant reduction of sensitivity to electric noise in zEEG relative to cEEG. (ii) The point-by-point waveform correlation between the two systems was acceptable (r>0.6; P<0.001). (iii) Each of the 15 datasets showed a high spectral correlation (r>0.99; P<0.001) and overlapping spectral density across all electrode positions, indicating no systematic signal distortion. (iv) The mean signal-to-noise ratio (SNR) of the zEEG system exceeded that of the cEEG by 4.82 dB, equivalent to 16% improvement. Conclusion: In terms of signal quality, the zEEG system is non-inferior to conventional clinical EEG systems with respect to all relevant technical parameters that determine EEG readability and interpretability. Significance: Zeto’s WR19 wireless dry electrode system has signal quality in the clinical EEG space equivalent to traditional cEEG recordings.

Unlike terrestrial wireless stations, using drones as aerial wireless stations allows for improved wireless services due to their 3D mobility. Therefore, model interactions play a pivotal role in determining the locations and trajectories of drones and their performance. In this paper, we discuss the interactions between models in the Internet of drones under different real-life scenarios. These models include: the channel path loss models, intelligent reflecting surface (IRS) placement model, solar energy model, wireless power transfer (WPT) model, and power consumption model. Moreover, we discuss learned lessons and highlight future research directions relevant to these models.

A document by Brian J. Vaughn. Click on the document to view its contents.