Search Results (78)

This study proposes a practical integration of an existing deep learning model (YOLOv9-E) and social participation GIS using multi-source remote sensing data to identify asbestos-containing materials located on the side of a building affected by light occlusions. These objects are often undetectable by traditional vertical or oblique photogrammetry, yet their precise localization is essential for effective removal planning. By leveraging the mobility and responsiveness of citizen investigators, we conducted fine-grained surveys in community spaces that were often inaccessible using conventional methods. The YOLOv9-E model demonstrated robustness on mobile-captured images, enriched with geolocation and orientation metadata, which improved the association between detections and specific buildings. By comparing results from Google Street View and field-based social imagery, we highlight the complementary strengths of both sources. Rather than introducing new algorithms, this study focuses on an applied integration framework to improve detection coverage, spatial precision, and participatory monitoring for environmental risk management. The dataset comprised 20,889 images, with 98% being used for training and validation and 2% being used for independent testing. The YOLOv9-E model achieved an mAP50 of 0.81 and an F1-score of 0.85 on the test set. Full article

The hybridization of cellular networks and GNSS systems has gained increasing attention, especially in urban canyons and indoor environments where GNSS performance degrades significantly. Hybrid localization is part of the 3rd Generation Partnership Project (3GPP) standard, offering an effective solution when satellite visibility is limited. Additional cellular measurements can enhance the accuracy and reliability of standalone UE. Hybrid methods offer multiple benefits: an improved availability, continuity, and integrity; better signal penetration due to proximity; a lower power consumption; and, in harsh environments, potentially more accurate positioning than a GNSS. Moreover, GNSS chipsets in mobile phones or smartwatches are typically power-intensive. This work presents a user-level hybridization method that enables UE to receive both GNSS and 4G/5G data and autonomously determine whether to apply hybrid positioning. The developed algorithms improve the precision and reliability, allowing user-driven decisions based on data quality. The system was tested under static conditions across various scenarios: outdoors, in urban canyons, and indoors. The results show that, while hybridization enhances positioning, the 4G-only solution often performs in terms of vertical accuracy. Standard deviation metrics help guide the selection of the most precise option in real time. Full article

This review reveals the parameters of next-generation fuel cells for portable applications such as cellular phones, laptops, automobiles, etc. Disputes over issues such as design, fluid dynamics, channel dimensions, thermal management, and water management must be overcome for practical applications. We examine techniques such as microfabrication, material selection for membranes and electrodes, and integration challenges in small-scale devices, in addition to issues like methanol crossover, low efficiency at high methanol concentrations, thermal management, and the cost of materials. The advancements in micro-DMFC stacks and prototype developments are presented, and the challenges relating to micro-DMFCs are also identified and reviewed in detail. The challenges in the development of micro-DMFC applications are also presented, including the need for a better understanding of the anode and cathode catalyst structure and for high catalyst loadings in oxidation-and-reduction reactions. Also, a comprehensive and highly valuable database for advancing innovations and enhancing the understanding of micro-DMFCs for potential applications is provided. Full article

Every time the proper functioning of the vehicles must be guaranteed, as well as safety and efficiency. To achieve this, some expensive solutions are used, with few connectivity options and that fail to meet consumer demand. This paper presents a low-cost hardware system for the design of a real-time communication protocol between the electronic control unit (ECU) of a vehicle and a remote server based in a embedded system. A dual tone multi-frequency (DTMF) approach is implemented, so error codes (DTCs) are always available on a unit equipped with this system. The vehicle-to-infrastructure (V2I) communication protocol through voice channels is provided by cellular technology infrastructure, in which primary information is shared to monitor vehicles. With real-time data transmission, communication is established through a voice phone call between the vehicle’s ECU and the destination server, communicating the DTC codes. The system shows that the communication protocol has an effectiveness of 78.23%, which means that with the use of 2G technology, which is active and operating in many regions, it allows the information with the data to be received by the receiving user. Through this implemented system, it is ensured that if a vehicle suffers an accident or stops due to a mechanical failure in a region where there is no cellular technology coverage, information or a message can be sent so that through communication the rescue can be carried out using an cellular technology coverage. Full article

Smartwatch technology is increasingly being used to support the management of chronic health conditions. Yet, many new digital health innovations fail because the correct foundations are not well established. This exploratory study aims to uncover the challenges experienced during the setup phase of a smartwatch intervention, to support the prototype development of a digital health intervention for children. Five children with a chronic health condition were asked to wear a smartwatch for 14 days that collects health data (pain levels, medication adherence, and physical activity performance). To explore the experiences of these children, their parents and the research team, all written records were analysed using READ’s four steps of document analysis and reported using the Standards for Reporting Qualitative Research checklist. The following three themes emerged: 1.) Infrastructure limitations: inexpensive smartphones prevented connection, and outpatient clinics’ internet black spots constrained setup and training; 2.) Personal phone restrictions: limited setup, training, and engagement; 3.) Elimination of the parent’s phone: provided children with digital support (a smartphone, pre-installed apps, cellular data) to allow active participation. Overall, we identified barriers hindering the use of smartwatch technology in clinical practice. More resources are needed to ensure paediatric preparedness for digital health support. Full article

This work investigates the impact of rainfall on cellular communication links, leveraging smartphone-collected measurements. While existing studies primarily focus on line-of-sight (LoS) microwave propagation environments, this work explores the impact of rainfall on typical signal metrics over cellular links when the LoS path is not guaranteed. We examine both small-scale and large-scale variations in signal measurements across dry and rainy days, considering diverse locations and time windows. Through statistical and spectral analysis of a large dataset, we uncover novel insights into how rainfall influences cellular communication links. Specifically, we observe a consistent daily fluctuation pattern in key cellular metrics, such as the reference signal received quality. Additionally, spectral features of key mobile metrics show noticeable changes during rainfall events. These findings, consistent across three distinct locations, highlight the significant impact of rainfall on everyday cellular links. They also suggest that the widely available by-product signals from mobile phones could be leveraged for innovative rainfall-sensing applications. Full article

Currently, as an increasing number of people have been addicted to using cellular phones, smartphone tenosynovitis has become common from long-term use of fingers for their operations. Hand exercise while playing video games, which is called exergame, can be a good solution to provide enjoyable daily exercise opportunities for its prevention, particularly, for young people. In this paper, we implemented a simple exergame system with a hand gesture recognition program made in Python using the Mediapipe library. We designed three sets of hand gestures to control the key operations to play the games as different exercises useful for tenosynovitis prevention. For evaluations, we prepared five video games running on a web browser and asked 10 students from Okayama and Hiroshima Universities, Japan, to play them and answer 10 questions in the questionnaire. Their playing results and System Usability Scale (SUS) scores confirmed the usability of the proposal, although we improved one gesture set to reduce its complexity. Moreover, by measuring the angles for maximum wrist movements, we found that the wrist flexibility was improved by playing the games, which verifies the effectiveness of the proposal. Full article

This study introduces RTEEMF (Real-Time Evaluation Electromagnetic Field)-PhoneAnts, a novel Deep Learning (DL) framework for the efficient evaluation of mobile phone antenna performance, addressing the time-consuming nature of traditional full-wave numerical simulations. The DL model, built on convolutional neural networks, uses the Near-field Electromagnetic Field (NEMF) distribution of a mobile phone antenna in free space to predict the Effective Isotropic Radiated Power (EIRP), Total Radiated Power (TRP), and Specific Absorption Rate (SAR) across various configurations. By converting antenna features and internal mobile phone components into near-field EMF distributions within a Huygens’ box, the model simplifies its input. A dataset of 7000 mobile phone models was used for training and evaluation. The model’s accuracy is validated using the Wilcoxon Signed Rank Test (WSR) for SAR and TRP, and the Feature Selection Validation Method (FSV) for EIRP. The proposed model achieves remarkable computational efficiency, approximately 2000-fold faster than full-wave simulations, and demonstrates generalization capabilities for different antenna types, various frequencies, and antenna positions. This makes it a valuable tool for practical research and development (R&D), offering a promising alternative to traditional electromagnetic field simulations. The study is publicly available on GitHub for further development and customization. Engineers can customize the model using their own datasets. Full article

This article investigates the effects of electromagnetic field (EMF) from mobile phones on human tissues and implanted medical devices. The intensity of the electric field (E) is evaluated based on simulations and measurements of various exposure scenarios. An area of interest is the case of a person with an implanted device (heart pacemaker) who may be affected by this exposure. Due to the rapid development of communication technologies and the growing awareness of the potential health risks of radio frequency (RF) EMF, the International Commission on Non-Ionizing Radiation Protection (ICNIRP) has established exposure limits within the European Union. Our study models and analyses EMF values in human tissues in an ideal environment, in a situation where a person uses a mobile phone in the DCS (Digital Cellular System) band, including the case of a person with an implanted pacemaker. Pilot simulations were verified by experimental measurements. Based on them, specific human models with the best matching results were selected for modelling other possible interactions of exogenous EMF and cardiac pacemaker in the same situations and locations. Full article

The aim of the electric tricycle is to bring increased mobility to impaired persons. Presently, hand-powered tricycles are used by numerous members of the impaired community, but some current users of hand-powered tricycles do not have the physical strength or collaboration to propel themselves on the tricycle with their arms and hands. The aim of the proposed paper is to add electric power to the current hand-powered tricycle to provide tricycle users with improved mobility, providing them with more freedom and making a donation to the community. This paper develops an inclusive and cost-effective electric tricycle designed specifically for individuals with mobility challenges. The proposed tricycle is equipped with a 350-watt motor and has a cargo capacity of over 100 kg. Using IoT technology, the proposed system includes features similar to real-time position shadowing, on/off announcements through mobile and dispatch, and clear on/off suggestions. This innovative result addresses the unique requirements of hindered individuals, promoting availability, autonomy, and enhanced mobility in a socially conscious manner. Full article

Signaling data are records of the interactions of users’ mobile phones with their nearest cellular stations, which could provide long-term and continuous-time location data of large-scale citizens, and therefore have great potential in intelligent transportation, smart cities, and urban sensing. However, utilizing the raw signaling data often suffers from two problems: (1) Low positioning accuracy. Since the signaling data only describes the interaction between the user and the mobile base station, they can only restore users’ approximate geographical location. (2) Poor data quality. Due to the limitations of mobile signals, user signaling may be missing and drifting. To address the above issues, we propose a graph-enhanced trajectory recovery network, GenTrajRec, to recover precise trajectories from signaling data. GenTrajRec encodes signaling data through spatiotemporal encoders and enhances the traveling semantics by constructing a signaling transition graph. In fusing the spatiotemporal information as well as the deep traveling semantics, GenTrajRec can well tackle the challenge of poor data quality, and recover precise trajectory from raw signaling data. Extensive experiments have been conducted on two real-world datasets from Mobile Signaling and Geolife, and the results confirm the effectiveness of our approach, and the positioning accuracy can be improved from 315 m per point to 82 m per point for signaling data using our network. Full article

Background/Objectives: Gaucher Disease type 1 (GD1) is a recessively inherited lysosomal storage disorder caused by a deficiency in the enzyme β-glucocerebrosidase. Enzyme replacement therapy (ERT) has become the standard of care for patients with GD. However, over 10% of patients experience an incomplete response or partial loss of response to ERT, necessitating the exploration of alternative approaches to enhance treatment outcomes. The present feasibility study aimed to determine the feasibility of using a second-generation artificial intelligence (AI) system that introduces variability into dosing regimens for ERT to improve the response to treatment and potentially overcome the partial loss of response to the enzyme. Methods: This was an open-label, prospective, single-center proof-of-concept study. Five patients with GD1 who received ERT were enrolled. The study used the Altus Care™ cellular-phone-based application, which incorporated an algorithm-based approach to offer random dosing regimens within a pre-defined range set by the physician. The app enabled personalized therapeutic regimens with variations in dosages and administration times. Results: The second-generation AI-based personalized regimen was associated with stable responses to ERT in patients with GD1. The SF-36 quality of life scores improved in one patient, and the sense of change in health improved in two; platelet levels increased in two patients, and hemoglobin remained stable. The system demonstrated a high engagement rate among patients and caregivers, showing compliance with the treatment regimen. Conclusions: This feasibility study highlights the potential of using variability-based regimens to enhance ERT effectiveness in GD and calls for further and longer trials to validate these findings. Full article

This study investigated the impact of information and communication technology (ICT) on electricity intensity, incorporating electricity prices, financial development, and population growth in Korea from 1990 to 2020, using the ARDL (autoregressive distributed lag) model. Three cases were considered, each relating to a different ICT proxy: Internet use, mobile cellular phone use, and exports of ICT-related products. The results varied depending on the proxy used to represent ICT. An increase in mobile cellular phone use leads to an increase in electricity intensity in the long run; however, the short-run effects of this change are unclear. An increase in Internet use also leads to an increase in electricity intensity in the long run but induces a decrease in electricity intensity in the short run. Increments in the exports of ICT-related products lead to an increase in electricity intensity in the short run; however, this effect is negligible in the long run. Electricity prices do not affect electricity intensity in all cases, and financial development reduces the intensity of electricity in the cases of the use of both mobile cellular phones and the Internet as proxies for ICT, whereas population growth increases electricity intensity in all cases. Full article

The fifth-generation (5G) mobile cellular network is vulnerable to various security threats. Radio frequency fingerprint (RFF) identification is an emerging physical layer authentication technique which can be used to detect spoofing and distributed denial of service attacks. In this paper, the performance of RFF identification is studied for 5G mobile phones. The differential constellation trace figure (DCTF) is extracted from the physical random access channel (PRACH) preamble. When the database of all 64 PRACH preambles is available at the gNodeB (gNB), an index-based DCTF identification scheme is proposed, and the classification accuracy reaches 92.78% with a signal-to-noise ratio of 25 dB. Moreover, due to the randomness in the selection of preamble sequences in the random access procedure, when only a portion of the preamble sequences can be trained, a group-based DCTF identification scheme is proposed. The preamble sequences generated from the same root value are grouped together, and the untrained sequences can be identified based on the trained sequences within the same group. The classification accuracy of the group-based scheme is 89.59%. An experimental system has been set up using six 5G mobile phones of three models. The 5G gNB is implemented on the OpenAirInterface platform. Full article

Soil microbial fuel cells (SMFCs) are bioelectrical devices powered by the oxidation of organic and inorganic compounds due to microbial activity. Seven soils were randomly selected from Bergen Community College or areas nearby, located in the state of New Jersey, USA, were used to screen for the presence of electrogenic bacteria. SMFCs were incubated at 35–37 °C. Electricity generation and electrogenic bacteria were determined using an application developed for cellular phones. Of the seven samples, five generated electricity and enriched electrogenic bacteria. Average electrical output for the seven SMFCs was 155 microwatts with the start-up time ranging from 1 to 11 days. The highest output and electrogenic bacterial numbers were found with SMFC-B1 with 143 microwatts and 2.99 × 10⁹ electrogenic bacteria after 15 days. Optimal electrical output and electrogenic bacterial numbers ranged from 1 to 21 days. Microbial DNA was extracted from the top and bottom of the anode of SMFC-B1 using the ZR Soil Microbe DNA MiniPrep Protocol followed by PCR amplification of 16S rRNA V3-V4 region. Next-generation sequencing of 16S rRNA genes generated an average of 58 k sequences. BLAST analysis of the anode bacterial community in SMFC-B1 demonstrated that the predominant bacterial phylum was Bacillota of the class Clostridia (50%). However, bacteria belonging to the phylum Pseudomonadota (15%) such as Magnetospirillum sp. and Methylocaldum gracile were also part of the predominant electrogenic bacterial community in the anode. Unidentified uncultured bacteria accounted for 35% of the predominant bacterial community. Bioelectrical devices such as MFCs provide sustainable and clean alternatives to future applications for electricity generation, waste treatment, and biosensors. Full article