Search Results (348)

This study examines factors driving reliance on mobile phones as a primary information source. Using Information-Seeking Complementarity Theory (ISCT), which posits that frequent use of diverse media channels builds digital habits that reinforce mobile reliance, we analyze World Values Survey (WVS) Time Series 1981–2022 (v4.0), validated with WVS v5.0 and Integrated Values Survey (IVS). A multi-stage pipeline integrates AdaBoost (R 4.3.1), LASSO/BMA (Stata v17), Histogram Gradient Boosting (Python 3.12.7), and mixed-effects logistic regression. Missing data (DK/NA) were excluded or median-imputed. The final model (AUC-ROC > 0.85) identifies five robust predictors: age (negative), and positive associations with digital mail, online social networks, peer interaction, and radio listening—all stable across methods, datasets, and reverse causality checks. Subgroup analysis reveals stronger effects among males, unmarried individuals, urban residents, and higher education/employment groups. Nomograms enable probabilistic forecasting and policy simulation. By identifying technology-agnostic behavioral drivers validated across three decades of global survey data (1981–2022), with mobile reliance measured from 2010 onward, this work provides a transparent, replicable predictive framework with implications for emerging AI and wearable contexts. Full article

Urban residents’ transportation modes play a pivotal role in shaping transportation planning and policies for sustainable cities. Mining refined transportation modes from mobile phone location (MPL) data is a key spatiotemporal big data application for sustainable city planning and traffic management. However, key challenges persist: low recognition accuracy due to insufficient consideration of travel features of transportation modes, the positioning uncertainty of MPL data, and ineffective evaluation due to lacking validation datasets. To address these limitations, we propose an analytical framework for transportation mode recognition. First, precise moving segments are constructed through road network matching and linear interpolation, resolving the positioning uncertainty issues of MPL data. Then, we propose a comprehensive feature parameter system for transportation mode recognition and construct a transportation mode recognition model based on eXtreme Gradient Boosting (XGBoost). Finally, using synchronously collected GPS data and travel logs, we validated the framework’s recognition results, demonstrating its ability to improve the accuracy of transportation mode recognition. Full article

Background: Population ageing is a global challenge, prompting ageing-in-place policies in Hong Kong to support community-dwelling older adults while reducing healthcare costs. Yet, their impact on health-related quality of life (HRQoL) remains underexplored amid Hong Kong’s long life expectancy and growing older population. Traditional surveys are costly and time-consuming, while routinely collected registration data offers a large, efficient source for health insights. This study uses enhanced administrative data to track HRQoL trajectories and inform policy. Methods: This is a prospective, open-ended longitudinal study, enrolling adults aged 50 or older from a collaborating non-governmental organization in Hong Kong’s Southern District. Data collection, started in February 2021, occurs annually via phone and face-to-face interviews by trained social workers and volunteers using a standardized questionnaire to assess individual (e.g., socio-demographics), environmental (e.g., social support via Lubben Social Network Scale-6), biological (e.g., chronic illnesses), functional (e.g., cognition via Montreal Cognitive Assessment), and HRQoL (e.g., EQ-5D-5L) factors. A secure online system links health and service use data (e.g., service utilization like community care visits). Analysis employs descriptive statistics, group comparisons, correlations, growth modelling to identify health trajectories, and structural equation modelling to test a revised quality-of-life framework. Sample size (projected 470–580 after two follow-ups from a 2321 baseline) is based on power calculations: 300–500 for latent class growth analysis (LCGA) class detection and 200–400 for structural equation modelling (SEM) fit (e.g., RMSEA < 0.06) at 80% power/α = 0.05, simulated via Monte Carlo with a 50–55% attrition. Discussion: This is the first longitudinal HRQoL study in Hong Kong using enhanced non-governmental organization (NGO) administrative data, integrating social–ecological and HRQoL models to predict trajectories (e.g., stable vs. declining mobility) and project care demands (e.g., increase in in-home care for frailty). Unlike prior cross-sectional or inpatient studies, it offers a scalable model for NGOs, informing ageing-in-place policy effectiveness and equitable geriatric care. Full article

This research paper presents a smart IoT-based COVID-19 vaccine supply chain, monitoring, and control system. This proposed system is designed to efficiently and effectively monitor COVID-19 vaccine storage sites by tracking their temperature, humidity, quantity, and location on a map across various supply chain categories. It ultimately aims to monitor and control temperatures outside the range at the tracked location. The approach utilized temperature, humidity, and ultrasonic sensors, a GPS module, a Wi-Fi module, and an Arduino Uno microcontroller. The system was designed and implemented using Arduino and Proteus integrated design environments (IDEs) and coded using the embedded C/C++ programming language. A real-life working system prototype was designed and implemented. The measured sensor readings can be viewed via a computer system capability or any mobile device, such as an Android phone, iPhone, iPad, or laptop, with the aid of a cloud-based platform, namely, Thingspeak.com. The experimentally measured sensor readings are stored in a data log file for subsequent download and analysis whenever the need arises. The data aggregation and analytics are coded using MATLAB and viewed as charts, and the location map of vaccine carrier coordinates is sent to the web cloud for tracking. An alarm message is sent to the monitoring and control system if an unfavorable vaccine environment exists in either the store or the carrier container. A suitable sensor-based interface architecture and web portal are provided, allowing health practitioners to remotely monitor the vaccine supply chain system. This method encourages health workers by reducing the high levels of supervision required by vaccine supervisors to ensure the smooth supply of vaccines to vaccine collection centers, by using a wireless sensor network and IoT technology. Experimental results from the implemented system prototype demonstrated the benefits of the proposed approach and its possible real-life health monitoring applications. Full article

Mobile networking in 4G and 5G remains vulnerable against fake base stations. A fake base station can inject and manipulate the radio resource control (RRC) communication protocol to disable the user equipment’s connectivity. To motivate our research, we empirically show that such a fake base station can cause an indefinite hold of the user equipment’s connectivity using our fake base station prototype against an off-the-shelf phone. To defend against such threat, we design and build an anomaly detection system to detect the fake base station threats. It detects any base station’s deviations from the 4G/5G RRC protocol, which supports both the connectivity provision case (all works well and the user receives connectivity) and the connection-release case (cannot provide connectivity at the time and thus releases connections). Our scheme based on unsupervised machine learning dynamically and automatically controls and sets the detection parameters, which vary with mobility and the communication channel, and utilizes greater information to improve its effectiveness. Using software-defined radios and srsRAN, we implement a prototype of our scheme from sensing to data collection to machine-learning-based detection processing. Our empirical evaluations demonstrate the detection effectiveness and adaptability; i.e., our scheme accurately detects fake base stations deviating from the set protocol in mobile scenarios by adapting its model parameters. Our scheme achieves 100% accuracy in static scenarios against the fake base station threats. If the dynamic control is disabled, i.e., not adapting to mobility and different channel environments, the accuracy drops to 65–76%, but our scheme adjusts the model via dynamic training to recover to 100% accuracy. Full article

Time reversal techniques have been investigated for ultrasound and electromagnetic waves. They offer some advantages, particularly in cluttered and inhomogeneous environments, for point-to-point applications. The instrumentation usually employed for electromagnetic time reversal involves costly vector network analyzers, different interconnected generators and receivers, or a base station for mobile phones. This article explores the use of a low-cost commercial software-defined radio, in frequencies between 700 MHz and 2100 MHz, with indoor tests showing its performance and observed voltage gains for the received pulse. Full article

The emergence of mobile private networks (MPNs) has enabled tailored communication solutions for industries, enterprises, and specialized applications, fostering improved control, security, and flexibility. With the rapid advancements in software-defined radio (SDR) platforms, implementing MPNs using cost-effective and versatile hardware has become increasingly feasible. Analyzing the radio coverage of such networks is critical for optimizing performance, ensuring reliable connectivity, and addressing site-specific challenges in deployment. This paper investigates the radio coverage of a 4G MPN implemented using as radio front-end an SDR platform from the Universal Software Radio Peripheral (USRP) family and the srsRAN-4G open-source software suite. Using the HTZ Communication software as simulation tool and field-test measurements performed using an off-the-shelf mobile phone as user equipment (UE), an analysis is made to evaluate the accuracy of various propagation models in predicting network coverage, in several different frequency bands. The results provide valuable insights into the design and deployment of MPNs, highlighting the importance of accurate coverage estimation in achieving robust and efficient network operation. Full article

The eye, in a dominant sense, can suffer disorders, such as myopia or nearsightedness, because of VDU radiation exposure. One symptom which is often caused by excessive use of VDU is eye strain. It is usually marked by an increase in the sensitivity of the eyes to light. It is known by comparing the diameter of the normal eye’s pupil and the strained eye’s pupil. People can prevent this disorder by detecting changes in the pupil’s diameter compared to the iris. Changes in the iris and pupil can be detected by using the Hough transformation to detect their shape and train perceptron neural network algorithms to recognize the patterns. As a VDI tool, an eye strain detection application can determine the condition of the user’s eyes. The level of accuracy of the method used to detect the iris and pupil using the Hough transformation is 100% for brown irises, 50% for blue irises, 33.3% for green irises, and it has a 100% accuracy in detecting an iris that is similar to the pupil and a 28.6% accuracy in detecting a pupil that is a similar color to the iris. There is also a difference in the level of accuracy of these case studies when different detection tools are used. The smartphone camera showed a 100% accuracy in detecting the iris and 28.6% accuracy in detecting the pupil. The SLR camera had a 100% accuracy in detecting the irises and 71.4% accuracy in detecting pupils, while the digital camera had 14.28% accuracy in detecting irises and a 0% accuracy in detecting a pupil. The accuracy of the perceptron algorithm in recognizing a pattern of eye strain is 70% with 20 sets of test data. Full article

This paper presents an initial contribution to a broader research initiative focused on the aggregate calibration of travel demand sub-models using low-cost and widely accessible data. Specifically, this first phase investigates methods and algorithms for the aggregate calibration of destination choice models, with the objective of assessing the possible utilization of an external observed matrix, eventually derived from opportunistic data. It can be hypothesized that such opportunistic data may originate from processed mobile phone data or result from the application of data fusion techniques that produce an estimated observed trip matrix. The calibration problem is formulated as a simulation-based optimization task and its implementation has been tested using a small-scale network, employing an agent-based model with a nested demand structure. A range of optimization algorithms is implemented and tested in a controlled experimental environment, and the effectiveness of various objective functions is also examined as a secondary task. Three optimization techniques are evaluated: Simultaneous Perturbation Stochastic Approximation (SPSA), Particle Swarm Optimization (PSO), and Adaptive Moment Estimation (ADAM). The application of the ADAM optimizer in this context represents a novel contribution. A comparative analysis highlights the strengths and limitations of each algorithm and identifies promising avenues for further investigation. The findings demonstrate the potential of the proposed framework to advance transportation modeling research and offer practical insights for enhancing transport simulation models, particularly in data-constrained settings. Full article

Ports with Mediterranean-like traffic profiles combine dense passenger, cargo, touristic, and local operations in confined waters where many small craft sail without AIS, increasing collision risk. Nature of such traffic in often unpredictable, due to often and sudden course corrections or changes. In such situations, it is possible that larger ships cannot manoeuvre to avoid collisions with small vessels. Hence, it is important to the port authority to develop a fast and adoptable mean to reduce collision risks. We present an end-to-end shore-based framework that detects and tracks vessels from fixed cameras (YOLOv9 + DeepSORT), estimates speed from monocular lateral video with an artificial neural network (ANN), and visualises collision risk in augmented reality (AR) for VTS/port operators. Validation in the Port of Split using laser rangefinder/GPS ground truth yields MAE 1.98 km/h and RMSE 2.18 km/h (0.605 m/s), with relative errors 2.83–21.97% across vessel classes. We discuss limitations (sample size, weather), failure modes, and deployment pathways. The application uses stationary port camera as an input. The core calculations are performed at user’s computer in the building. Mobile application uses wireless communication to show risk assessment at augmented reality smart phone. For training of ANN, we used The Split Port Ship Classification Dataset. Full article

Accurately predicting the next location of urban individuals is a central issue in human mobility research. Human mobility exhibits diverse patterns, requiring the integration of spatiotemporal contexts for location prediction. In this context, multi-view learning has become a prominent method in location prediction. Despite notable advances, current methods still face challenges in effectively capturing non-spatial proximity of regional preferences, complex temporal periodicity, and the ambiguity of location semantics. To address these challenges, we propose a representation-enhanced multi-view learning network (ReMVL-Net) for location prediction. Specifically, we propose a community-enhanced spatial representation that transcends geographic proximity to capture latent mobility patterns. In addition, we introduce a multi-granular enhanced temporal representation to model the multi-level periodicity of human mobility and design a rule-based semantic recognition method to enrich location semantics. We evaluate the proposed model using mobile phone data from Fuzhou. Experimental results show a 2.94% improvement in prediction accuracy over the best-performing baseline. Further analysis reveals that community space plays a key role in narrowing the candidate location set. Moreover, we observe that prediction difficulty is strongly influenced by individual travel behaviors, with more regular activity patterns being easier to predict. Full article

This study examined the integraftion of airborne Light Detection and Ranging (LiDAR), Simultaneous Localization and Mapping (SLAM)-based handheld LiDAR, and iPhone LiDAR to inspect forest road networks following forest operations. The goal is to overcome the challenges posed by dense canopy cover and ensure accurate and efficient data collection and mapping. Airborne data were collected using the DJI Matrice 300 RTK UAV equipped with a Zenmuse L2 LiDAR sensor, which achieved a high point density of 285 points/m² at an altitude of 80 m. Ground-level data were collected using the BLK2GO handheld laser scanner (HPLS) with SLAM methods (LiDAR SLAM, Visual SLAM, Inertial Measurement Unit) and the iPhone 13 Pro Max LiDAR. Data processing included generating DEMs, DSMs, and True Digital Orthophotos (TDOMs) via DJI Terra, LiDAR360 V8, and Cyclone REGISTER 360 PLUS, with additional processing and merging using CloudCompare V2 and ArcGIS Pro 3.4.0. The pairwise comparison analysis between ALS data and each alternative method revealed notable differences in elevation, highlighting discrepancies between methods. ALS + iPhone demonstrated the smallest deviation from ALS (MAE = 0.011, RMSE = 0.011, RE = 0.003%) and HPLS the larger deviation from ALS (MAE = 0.507, RMSE = 0.542, RE = 0.123%). The findings highlight the potential of fusing point clouds from diverse platforms to enhance forest road mapping accuracy. However, the selection of technology should consider trade-offs among accuracy, cost, and operational constraints. Mobile LiDAR solutions, particularly the iPhone, offer promising low-cost alternatives for certain applications. Future research should explore real-time fusion workflows and strategies to improve the cost-effectiveness and scalability of multisensor approaches for forest road monitoring. Full article

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

Over a three-year R&D project, the perception of mobile phone use in Spanish secondary schools shifted from initial tolerance to increasingly prohibitive policies. Drawing on the Actor–Network Theory, this study examines how mobile phones—alongside institutional discourses and school and family concerns—acted as dynamic actants, shaping public and political responses. The research adopted a qualitative design combining policy and media document analysis, nine semi-structured interviews with key stakeholders, ten regional case studies, and twelve focus groups. The study concluded with a public multiplier event that engaged the broader educational community. The Madrid region, among the first to adopt a restrictive stance, contributed two school-based case studies and three focus groups with teachers, students, and families. Findings suggest that the turn toward prohibition was motivated less by pedagogical evidence than by cultural anxieties, consistent with what it conceptualizes as a techno-pessimistic shock. This shift mirrors the historical patterns of societal reaction to disruption and technological saturation. Rather than reinforcing binary framings of promotion versus prohibition, such moments invite critical reflection. The study argues for nuanced, evidence-based, and multilevel governance strategies to address the complex role of mobile technologies in education. Full article

In the era of homogenized competition, brand knowledge has become a critical factor that influences consumer purchasing decisions. However, traditional single-layer network models fail to capture the multi-dimensional semantic relationships embedded in brand-related textual data. To address this gap, this study proposes a BKMN framework integrating TF-IDF and TextRank algorithms for comprehensive brand knowledge discovery. By analyzing 19,875 consumer reviews of a mobile phone brand from JD website, we constructed a tri-layer network comprising TF-IDF-derived keywords, TextRank-derived keywords, and their overlapping nodes. The model incorporates co-occurrence matrices and centrality metrics (degree, closeness, betweenness, eigenvector) to identify semantic hubs and interlayer associations. The results reveal that consumers prioritize attributes such as “camera performance”, “operational speed”, “screen quality”, and “battery life”. Notably, the overlap layer exhibits the highest node centrality, indicating convergent consumer focus across algorithms. The network demonstrates small-world characteristics (average path length = 1.627) with strong clustering (average clustering coefficient = 0.848), reflecting cohesive consumer discourse around key features. Meanwhile, this study proposes the Mul-LSTM model for sentiment analysis of reviews, achieving a 93% sentiment classification accuracy, revealing that consumers have a higher proportion of positive attitudes towards the brand’s cell phones, which provides a quantitative basis for enterprises to understand users’ emotional tendencies and optimize brand word-of-mouth management. This research advances brand knowledge modeling by synergizing heterogeneous algorithms and multilayer network analysis. Its practical implications include enabling enterprises to pinpoint competitive differentiators and optimize marketing strategies. Future work could extend the framework to incorporate sentiment dynamics and cross-domain applications in smart home or cosmetic industries. Full article