Search Results (6,226)

Data over sound (DoS) is an established technique that has experienced a resurgence in recent years, finding applications in areas such as contactless payments, device pairing, authentication, presence detection, toys, and offline data transfer. This study introduces CardiaWhisper, a system that extends the DoS concept to the medical domain by using a medical data-over-sound (MDoS) framework. CardiaWhisper integrates wearable biomedical sensors with home care systems, edge or IoT gateways, and telemedical networks or cloud platforms. Using a transmitter device, vital signs such as ECG (electrocardiogram) signals, PPG (photoplethysmogram) signals, RR (respiratory rate), and ACC (acceleration/movement) are sensed, conditioned, encoded, and acoustically transmitted to a nearby receiver—typically a smartphone, tablet, or other gadget—and can be further relayed to edge and cloud infrastructures. As a case study, this paper presents the real-time transmission and processing of ECG signals. The transmitter integrates an ECG sensing module, an encoder (either a PLL-based FM modulator chip or a microcontroller), and a sound emitter in the form of a standard piezoelectric speaker. The receiver, in the form of a mobile phone, tablet, or desktop computer, captures the acoustic signal via its built-in microphone and executes software routines to decode the data. It then enables a range of control and visualization functions for both local and remote users. Emphasis is placed on describing the system architecture and its key components, as well as the software methodologies used for signal decoding on the receiver side, where several algorithms are implemented using open-source, platform-independent technologies, such as JavaScript, HTML, and CSS. While the main focus is on the transmission of analog data, digital data transmission is also illustrated. The CardiaWhisper system is evaluated across several performance parameters, including functionality, complexity, speed, noise immunity, power consumption, range, and cost-efficiency. Quantitative measurements of the signal-to-noise ratio (SNR) were performed in various realistic indoor scenarios, including different distances, obstacles, and noise environments. Preliminary results are presented, along with a discussion of design challenges, limitations, and feasible applications. Our experience demonstrates that CardiaWhisper provides a low-power, eco-friendly alternative to traditional RF or Bluetooth-based medical wearables in various applications. Full article

Mobile Charging Robots (MCRs), essentially high-voltage batteries mounted on mobile platforms, offer a flexible solution for electric vehicle (EV) charging, particularly in environments like supermarket parking lots with photovoltaic (PV) generation. Unlike fixed charging stations, MCRs must be strategically dispatched and recharged to maximize operational efficiency and revenue. This study investigates a Model Predictive Control (MPC) approach using Mixed-Integer Linear Programming (MILP) to coordinate MCR charging and movement, accounting for the additional complexity that EVs can park at arbitrary locations. The performance impact of EV arrival and demand forecasts is evaluated, comparing perfect foresight with data-driven predictions using long short-term memory (LSTM) networks. A slack variable method is also introduced to ensure timely recharging of the MCRs. Results show that incorporating forecasts significantly improves performance compared to no prediction, with perfect forecasts outperforming LSTM-based ones due to better-timed recharging decisions. The study highlights that inaccurate forecasts—especially in the evening—can lead to suboptimal MCR utilization and reduced profitability. These findings demonstrate that combining MPC with predictive models enhances MCR-based EV charging strategies and underlines the importance of accurate forecasting for future smart charging systems. Full article

Analyses of student mobility are typically conducted in an urban environment and are informed by socio-demographic or trip attributes. The prevailing focus is on individual modes of transport, different groups of commuters travelling to campus, students’ behavioural perceptions, and the totality of student trips. This paper starts with the identification of the determinants of student mobility that have received insufficient research attention. Utilising surveys, the study captures the mobility patterns of a sample of 1014 students and calculates their carbon footprint (CF; in kg/academic year) to assess whether the factors neglected in previous studies influence differences in the actual environmental load of student commuting. A regression analysis is employed to ascertain the significance of these factors as predictors of sustainable student mobility. This study exclusively focuses on the group of student commuters to campus and analyses the trips associated with compulsory activities at a suburban campus that is distant from the university centre and student facilities, which changes the mobility context in terms of commuting options. The under-researched factors identified in this research have not yet been quantified as CF. The findings confirm that only some of the factors neglected in previous research are statistically significant predictors of the local environmental load of student mobility. Specifically, variables such as student employment, frequency of class attendance, and propensity for ride-sharing could be utilised to forecast and regulate students’ mobility towards more sustainable patterns. However, all of the under-researched factors (including household size, region of origin (i.e., past experiences), residing at term-time accommodation while studying, and the availability of a family car) have an influence on the differences in CF magnitude in the studied campus. Full article

This study presents an AI-enhanced pedestrian simulation model for subway stations, combining the Social Force Model (SFM) with LiDAR trajectory data from Samseong Station in Seoul. To reflect time-dependent behavioral differences, RMSProp-based optimization is performed separately for the morning peak, leisure hours, and evening peak, yielding time-specific parameter sets. Compared to baseline models with static parameters, the proposed method reduces prediction errors (MSE) by 50.1% to 84.7%. The model integrates adaptive learning rates, mini-batch training, and L2 regularization, enabling robust convergence and generalization across varied pedestrian densities. Its accuracy and modular design support real-world applications such as pre-construction design testing, post-opening monitoring, and capacity planning. The framework also contributes to Sustainable Urban Mobility Plans (SUMPs) by enabling predictive, data-driven evaluation of pedestrian flow dynamics in complex station environments. Full article

Robots are increasingly emerging as effective platforms to overcome a wide range of challenges in agriculture. Beyond functioning as standalone systems, agricultural robots are proving valuable as collaborative platforms, capable of supporting and integrating with humans and other technologies and agricultural activities. In this study, we designed and implemented an automated system embedded in a ground-based robotic platform to support spraying drone operations. The system consists of a robotic platform that carries the spraying drone along with all necessary support devices, including a water tank, chemical reservoirs, a mixer, generators for drone battery charging, and a top landing pad. The system is controlled with a mobile app that calculates the total amount of water and chemicals required and sends commands to the platform to prepare the application mixture. The input information in the app includes the field area, application rate, and up to three chemical dosages simultaneously. Additionally, the platform allows the drone to take off from and land on it, enhancing both safety and operability. A set of pumps was used to deliver water and chemicals as specified in the mobile app. To automate pump control, we used Arduino technology, including both the microcontroller and a programming environment for coding and designing the mobile app. To validate the system’s effectiveness, we individually measured the amount of water and chemical delivered to the mixer tank and compared it with conventional manual methods for calculating chemical quantities and preparation time. The system demonstrated consistent results, achieving high precision and accuracy in delivering the correct amount. This study advances the field of agricultural robotics by highlighting the role of collaborative platforms. Particularly, the system presents a valuable and low-cost solution for small farms and experimental research. Full article

Corrosion-related failures have emerged as a critical driver of premature support bolt failures in underground mines, emphasizing the urgency of understanding the phenomenon with respect to enhancing safety in underground environments. This study investigated key factors influencing bolt degradation through extensive experimental evaluation of cable bolts in simulated underground bolt environments. Multi-stranded cable specimens were exposed to saturated clay, coal, mine water, and grout/cement environments. Water samples were collected weekly from critical packing sections and analyzed for pH, electrical conductivity, and dissolved oxygen. The mineralogy and atmospheric conditions were identified as principal corrosion factors, and clay-rich and coal matrices accelerated corrosion, linked to high ion mobility and oxygen diffusion. Secondary factors correlated context-dependently: pH was negatively associated with corrosion in mineral-packed environments, while conductivity was correlated with non-mineral matrices. Notably, multi-stranded cables exhibited higher localized galvanic corrosion in inter-strand zones, highlighting design vulnerabilities. This work provides pioneering evidence that geological conditions are primary drivers for corrosion-related failures, offering actionable guidance for corrosion mitigation strategies in mining infrastructure. Full article

With the rapid development of wireless communication, Wireless Local Area Networks (WLANs) are widely deployed in high-density environments. Ensuring fast handovers and optimal AP selection during device roaming is critical for maintaining network throughput and user experience. However, frequent mobility, high access density, and dynamic channel fluctuations complicate throughput prediction. To address this, we propose a method combining the Snow-Melting Optimizer (SMO) with decision tree regression models to optimize feature selection and model transmission opportunities (TXOP) and AP throughput. Experimental results show that the Extreme Gradient Boosting (XGBoost) model performs best, achieving high prediction accuracy for TXOP (MSE = 1.3746, R² = 0.9842) and AP throughput (MAE = 2.5071, R² = 0.9896). This approach effectively captures the nonlinear relationships between throughput and network factors in dense WLAN scenarios, demonstrating its potential for real-world applications. Full article

In the context of promoting sustainable mobility, walking and cycling have been widely recognized for their environmental and health benefits. However, a notable gap often exists between residents’ motivation to engage in these modes and their actual behavior. This study focuses on this motivation–behavior discrepancy and explores how heterogeneous information interventions—within the constraints of the existing built environment—can effectively influence residents’ travel psychology and behavior. Drawing on Protection Motivation Theory, this study aims to uncover the psychological mechanisms behind travel-mode choices and quantify the relative impacts of different types of information interventions. A travel survey was conducted in Yangzhou, China, collecting data from 1052 residents. Cluster analysis was performed using travel psychology data to categorize travel motivations and examine their alignment with actual travel behavior. A random forest model was then employed to assess the effects of individual attributes, travel characteristics, and information intervention attributes on the choice of walking and cycling. The results reveal a significant motivation–behavior gap: while 76% of surveyed residents expressed motivation to walk or cycle, only 30% actually adopted these modes. Based on this, further research shows that informational attributes exhibit a stronger effect in terms of promoting walking and cycling behavior compared to individual attributes and travel characteristics. Among these, health-related information demonstrates the maximum efficacy in areas with well-developed infrastructure. Specifically, health-related information has a greater impact on cycling (21.4%), while environmental information exerts a stronger influence on walking (7.31%). These findings suggest that leveraging information to promote walking and cycling should be more targeted. Full article

The climbing perch, Anabas testudineus, is one of the most widely distributed freshwater amphibious fishes in South and Southeast Asia, exhibiting terrestrial movements. Our experimental study aimed to investigate endocrinological and biochemical changes in the blood of climbing perch associated with their terrestrial movements. To achieve this, the fish were divided into two groups: one group was exposed to aquatic conditions for twenty minutes, while the other group was subjected to terrestrial conditions for the same duration through rapid water level decrease. In terrestrial conditions, the fish predominantly exhibit movements on land, whereas in aquatic environments, they primarily remain immobile or swim. Elevated levels of stress-induced cortisol and glucose after short-term exposure indicate a high-stress response involving both neuroendocrine and metabolic mechanisms. Changes in the activity of aspartate aminotransferase and increased concentrations of triglycerides in the blood serum suggest energy mobilization through aerobic metabolic pathways. Extreme environmental changes did not affect thyroid axis function, including deiodination, thereby maintaining essential physiological activities under new conditions. Additionally, the anaerobic metabolic pathway appears to be minimally utilized at the onset of terrestrial movement, as no significant changes in lactate dehydrogenase concentrations were observed. Overall, the terrestrial movements of the climbing perch are likely predominantly forced and associated with high stress. Full article

Handover (HO) management plays a key role in ensuring uninterrupted connectivity across evolving wireless networks. While previous generations such as 4G and 5G have introduced several HO strategies, these techniques are insufficient to meet the rigorous demands of sixth-generation (6G) networks in ultra-dense, heterogeneous smart city environments. Existing studies often fail to provide integrated HO solutions that consider key concerns such as energy efficiency, security vulnerabilities, and interoperability across diverse network domains, including terrestrial, aerial, and satellite systems. Moreover, the dynamic and high-mobility nature of smart city ecosystems further complicate real-time HO decision-making. This survey aims to highlight these critical gaps by systematically categorizing state-of-the-art HO approaches into AI-based, fuzzy logic-based, and hybrid frameworks, while evaluating their performance against emerging 6G requirements. Future research directions are also outlined, emphasizing the development of lightweight AI–fuzzy hybrid models for real-time decision-making, the implementation of decentralized security mechanisms using blockchain, and the need for global standardization to enable seamless handovers across multi-domain networks. The key outcome of this review is a structured and in-depth synthesis of current advancements, which serves as a foundational reference for researchers and engineers aiming to design intelligent, scalable, and secure HO mechanisms that can support the operational complexity of next-generation smart cities. Full article

This work presents the development of a functional real-time SLAM system designed to enhance the perception capabilities of an Automated Guided Vehicle (AGV) using only a 2D LiDAR sensor. The proposal aims to address recurring gaps in the literature, such as the need for low-complexity solutions that are independent of auxiliary sensors and capable of operating on embedded platforms with limited computational resources. The system integrates scan alignment techniques based on the Iterative Closest Point (ICP) algorithm. Experimental validation in a controlled environment indicated better performance using Gauss–Newton optimization and the point-to-plane metric, achieving pose estimation accuracy of 99.42%, 99.6%, and 99.99% in the position ($x$, $y$) and orientation ($\theta$) components, respectively. Subsequently, the system was adapted for operation with data from the onboard sensor, integrating a lightweight graphical interface for real-time visualization of scans, estimated pose, and the evolving map. Despite the moderate update rate, the system proved effective for robotic applications, enabling coherent localization and progressive environment mapping. The modular architecture developed allows for future extensions such as trajectory planning and control. The proposed solution provides a robust and adaptable foundation for mobile platforms, with potential applications in industrial automation, academic research, and education in mobile robotics. Full article

Traffic congestion poses a significant challenge to modern urban environments, impacting both driver satisfaction and road safety. This paper investigates the effectiveness of a smart traffic light system (STL), a solution developed under the Intelligent Transportation System (ITS) initiative by the Ministry of Works Malaysia, to address these issues in Malaysia. The system integrates a network of sensors, AI-enabled cameras, and Automatic Number Plate Recognition (ANPR) technology to gather real-time data on traffic volume and vehicle classification at congested intersections. This data is utilized to dynamically adjust traffic light timings, prioritizing traffic flow on heavily congested roads while maintaining safety standards. To evaluate the system’s performance, a comprehensive study was conducted at a selected intersection. Traffic patterns were automatically analyzed using camera systems, and the performance of the STL was compared to that of traditional traffic signal systems. The average travel time from the start to the end intersection was measured and compared. Preliminary findings indicate that the STL significantly reduces travel times and improves overall traffic flow at the intersection, with average travel time reductions ranging from 7.1% to 28.6%, depending on site-specific factors. While further research is necessary to quantify the full extent of the system’s impact, these initial results demonstrate the promising potential of STL technology to enhance urban mobility and more efficient and safer roadways by moving beyond traditional traffic signal functionalities. Full article

An accurate assessment of sidewalk conditions is critical for ensuring compliance with the Americans with Disabilities Act (ADA), particularly to safeguard mobility for wheelchair users. This paper presents a novel 3D reconstruction framework based on neural radiance field (NeRF), which utilize a monocular video input from consumer-grade cameras to generate high-fidelity 3D models of sidewalk environments. The framework enables automatic extraction of ADA-relevant geometric features, including the running slope, the cross slope, and vertical displacements, facilitating an efficient and scalable compliance assessment process. A comparative study is conducted across three surveying methods—manual measurements, LiDAR scanning, and the proposed NeRF-based approach—evaluated on four sidewalks and one curb ramp. Each method was assessed based on accuracy, cost, time, level of automation, and scalability. The NeRF-based approach achieved high agreement with LiDAR-derived ground truth, delivering an F1 score of 96.52%, a precision of 96.74%, and a recall of 96.34% for ADA compliance classification. These results underscore the potential of NeRF to serve as a cost-effective, automated alternative to traditional and LiDAR-based methods, with sufficient precision for widespread deployment in municipal sidewalk audits. Full article

The invention of antibacterial agents (antibiotics) was a significant event in the history of the human race, and this invention changed the way in which infectious diseases were cured; as a result, many lives have been saved. Recently, antibiotic resistance has developed as a result of excessive use of antibiotics, and it has become a major threat to world health. ARGs are spread across biomes and taxa of bacteria via lateral or horizontal gene transfer (HGT), especially via conjugation, transformation, and transduction. This review concerns transduction, whereby bacteriophages or phages facilitate gene transfer in bacteria. Bacteriophages are just as common and many times more numerous than their bacterial prey, and these phages are much more influential in controlling the population of bacteria. It is estimated that 25% of overall genes of Escherichia coli have been copied by other species of bacteria due to the HGT process. Transduction may take place via a generalized or specialized mechanism, with phages being ubiquitous in nature. Phage and virus-like particle (VLP) metagenomics have uncovered the emergence of ARGs and mobile genetic elements (MGEs) of bacterial origins. These genes, when transferred to bacteria through transduction, confer resistance to antibiotics. ARGs are spread through phage-based transduction between the environment and bacteria related to people or animals, and it is vital that we further understand and tackle this mechanism in order to combat antimicrobial resistance. Full article

This paper presents a novel approach to determine dynamic safety and comfort zones in human–robot interactions (HRIs), with a focus on service robots operating in dynamic environments with people. The proposed proxemic model leverages the golden ratio-based comfort zone distribution and ISO safety standards to define adaptive proxemic boundaries for robots around humans. Unlike traditional fixed-threshold approaches, this novel method proposes a gradual and context-sensitive modulation of robot behaviour based on human position, orientation, and relative velocity. The system was implemented on an NVIDIA Jetson Xavier NX platform using a ZED 2i stereo depth camera Stereolabs, New York, USA and tested on two mobile robotic platforms: Go1 Unitree, Hangzhou, China (quadruped) and Scout Mini Agilex, Dongguan, China (wheeled). The initial verification of proposed proxemic model through experimental comfort validation was conducted using two simple interaction scenarios, and subjective feedback was collected from participants using a modified Godspeed Questionnaire Series. The results show that the participants felt comfortable during the experiments with robots. This acceptance of the proposed methodology plays an initial role in supporting further research of the methodology. The proposed solution also facilitates integration into existing navigation frameworks and opens pathways towards socially aware robotic systems. Full article