Mobile Devices Research Articles

ZoAM GameBot: A Journey to the Lost Computational World in the Amazonia

The search for alternative teaching–learning processes that attract more interest and involvement of young people has inspired the development of a game with a chatbot architecture based on interactive storytelling and multiple learning paths. Thus, we introduce in this article the GameBot ZoAm, developed for the Discord instant messaging and social platform. ZoAm offers a unique learning experience centered around storytelling, focusing on fundamental computing concepts and logical challenges that enhance computational thinking skills. Furthermore, the game also promotes an appreciation for Amazonian culture and folklore, with decision-making with human values. An action research study was conducted involving students from the last years of the end of elementary school. The research utilized a heuristic analysis based on the Gameplay Heuristics (PLAY) by Desurvire and Wiberg (ANO) and the evaluation model proposed by Korhonen and Koivisto (ANO) for mobile devices. The analysis employed a reduced and merged set of heuristics from these models, suited for the GameBot's context, focusing on (I) usability, (II) gameplay and immersion, and (III) mobility. Regarding the reliability coefficient used to evaluate the survey applied to students after playing the GameBot, Cronbach's Alpha and Guttman Lambda-6 (G6(smc)) coefficients were applied. These metrics were chosen to ensure the internal consistency and reliability of survey items, reflecting on how effectively the questions measured the focuses proposed by the heuristic analysis. The findings indicate that the game has the potential to facilitate the assimilation of the integrated concepts and sustain student interest throughout gameplay.

A Machine Learning-based Secured and Energy-efficient Data Transmission in Mobile Ad-Hoc Networks (MANET)

Mobile hoc networks (MANET) are wireless networks of mobile devices that set up Data Transmission (DT) links independently without a fixed framework. Because topology changes constantly, MANET lines are often interrupted and out of balance. So, making sure that DT works well and reliably while also making good use of network resources is a problematic issue that MANET needs to solve. The suggested solution is to create a DT method to deal with these issues. This method aims to improve DT by sending data as quickly as possible while using as little time. As learning examples, the suggested method uses a range of mobile gadgets. Support Vector Machine (SVM) classes are trained to be weak groups. Supervised SVM can sort the surrounding nodes into groups with better link quality and lower energy use. The results of weak learners are combined to create a robust classifier, which guarantees that the data transfer works well. An experimental test checks the amount of power used, the packet transfer rate, the time it takes, and the output. The number of mobile nodes and data packages will be changed during the analysis.

Enabling Intelligence on the Edge: Leveraging Edge Impulse to Deploy Multiple Deep Learning Models on Edge Devices for Tomato Leaf Disease Detection

Tomato diseases, including Leaf blight, Leaf curl, Septoria leaf spot, and Verticillium wilt, are responsible for up to 50% of annual yield loss, significantly impacting global tomato production, valued at approximately USD 87 billion. In Ghana, there is a yield gap of about 50% in tomato production, which requires drastic measures to increase the yield of tomatoes. Conventional diagnostic methods are labor-intensive and impractical for real-time application, highlighting the need for innovative solutions. This study addresses these issues in Ghana by utilizing Edge Impulse to deploy multiple deep-learning models on a single mobile device, facilitating the rapid and precise detection of tomato leaf diseases in the field. This work compiled and rigorously prepared a comprehensive Ghanaian dataset of tomato leaf images, applying advanced preprocessing and augmentation techniques to enhance robustness. Using TensorFlow, we designed and optimized efficient convolutional neural network (CNN) architectures, including MobileNet, Inception, ShuffleNet, Squeezenet, EfficientNet, and a custom Deep Neural Network (DNN). The models were converted to TensorFlow Lite format and quantized to int8, substantially reducing the model size and improving inference speed. Deployment files were generated, and the Edge Impulse platform was configured to enable multiple model deployments on a mobile device. Performance evaluations across edge hardware provided metrics such as inference speed, accuracy, and resource utilization, demonstrating reliable real-time detection. EfficientNet achieved a high training accuracy of 97.12% with a compact 4.60 MB model size, proving its efficacy for mobile device deployment. In contrast, the custom DNN model is optimized for microcontroller unit (MCU) deployment. This edge artificial intelligence (AI) technology integration into agricultural practices offers scalable, cost-effective, and accessible solutions for disease classification, enhancing crop management, and supporting sustainable farming practices.

Applying the Health Belief Model to mobile device distracted driving

Introduction: The advancement of mobile devices has resulted in constant connectivity, but at the expense of traffic safety. The goal of this study was to apply the Health Belief Model to understand the barriers preventing drivers from driving without manipulating their devices, and what they perceived would motivate them to stop driving distracted. Methods: We conducted a nationwide survey of 2,013 U.S. licensed drivers. Participants indicated how much they agreed with or supported 63 statements and concepts pertaining to the Health Belief Model constructs of threats, barriers, benefits, and cues to action related to manipulating devices while driving. Heath Belief Model constructs were compared between distracted drivers, or those who regularly did (during most or all drives in the previous 30 days) one or more mobile device tasks, and non-distracted drivers. Logistic regression evaluated the relationship between Health Belief Model constructs and distracted driver designation. Results: Those who agreed more with threats (odds ratio [OR], 1.61; 95 % confidence interval [CI], 1.27, 2.04) and disagreed more with barriers to stopping (OR, 0.36; 95 % CI, 0.31, 0.41) were more likely to not drive distracted. We identified distracted drivers’ top barriers to avoiding cellphone distractions, as well as barrier categories that were significantly greater for distracted drivers than non-distracted drivers. Distracted drivers felt most strongly that intrapersonal, interpersonal, and policy cues would motivate behavior termination, and more support of technological countermeasures was associated with regularly driving distracted after controlling for support for policy and organizational countermeasures. Conclusions: Simultaneously increasing threat perceptions, targeting the top barriers identified, and implementing policy-, interpersonal-, and technological-based countermeasures may encourage device-free driving. Practical Applications: When designing interventions or programs, state highway safety offices should find new ways to increase threat perceptions and offer solutions to the barriers most cited by distracted drivers.

Trust enhanced POI recommendation with collaborative learning

With the rapid development and popularization of smart mobile devices, users tend to share their visited points-of-interest (POIs) on the network with attached location information, which forms a location-based social network (LBSN). LBSNs contain a wealth of valuable information, including the geographical coordinates of POIs and the social connections among users. Nowadays, lots of trust-enhanced approaches have fused the trust relationships of users together with other auxiliary information to provide more accurate recommendations. However, in the traditional trust-aware approaches, the embedding processes of the information on different graphs with different properties (e.g., user-user graph is an isomorphic graph, user-POI graph is a heterogeneous graph) are independent of each other and different embedding information is directly fused together without guidance, which limits their performance. More effective information fusion strategies are needed to improve the performance of trust-enhanced recommendation. To this end, we propose a Trust Enhanced POI recommendation approach with Collaborative Learning (TECL) to merge geographic information and social influence. Our proposed model integrates two modules, a GAT-based graph autoencoder as trust relationships embedding module and a multi-layer deep neural network as a user-POI graph learning module. By applying collaborative learning strategy, these two modules can interact with each other. The trust embedding module can guide the selection of user’s potential features, and in turn the user-POI graph learning module enhances the embedding process of trust relationships. Different information is fused through the two-way interaction of information, instead of travelling in one direction. Extensive experiments are conducted using real-world datasets, and results illustrate that our suggested approach outperforms state-of-the-art methods.

La tecnología a nuestro servicio: 4 aplicaciones móviles para optimizar la gestión de necesidades cotidianas

Nowadays, mobile devices have undoubtedly become the most immediate means of accessing information. Their portability and increasingly large processing and storage capacities have been key to the expansion of mobile applications running on these devices.This article reviews the utility of these mobile applications, based on the experience gained from developing and implementing mobile applications for four real cases. In these cases, second-semester students of the Software Development program at the 'Quito Metropolitano' Higher Technological Institute, as part of their training, developed fully functional mobile applications to make them available to society. Their work was showcased at the '5 Minutes Pitch' and 'Quito Metro Tech' events organized by the institution in March 2024.The development process utilized tools included in the institution's curricular planning, such as Java programming language and frameworks like TypeScript and React Native. Additionally, databases like MySQL were used for desktop applications, and Firebase was used for cloud persistence.

Optimal directed acyclic graph federated learning model for energy-efficient IoT communication networks

Federated learning (FL) stimulates distributed on-device computation systems to process an optimum technique efficiency by communicating local process upgrades and global method distribution from aggregation averaging procedure. On-device FL is a standard application in wireless environments, with several mobile devices participating as nodes in the FL network. Managing extensive multi-dimensional process upgrades and resource-constrained computations in large-scale heterogeneous IoT cellular networks can be challenging. This article introduces a Lifetime Maximization using Optimal Directed Acyclic Graph Federated Learning in IoT Communication Networks (LM-ODAGFL) technique. The proposed LM-ODAGFL technique utilizes FL and metaheuristic optimization algorithms for energy-effective IoT networks. The Direct Acyclic Graph (DAG) model addresses device asynchrony in FL while minimizing additional resource usage. The Archimedes Optimization Algorithm (AOA) is designed to optimize the DAG model by reducing both user energy consumption and the training loss of the FL model. The performance validation of the LM-ODAGFL technique is performed by utilizing a series of experimentations. The obtained results of the LM-ODAGFL model demonstrate superior performance by consuming significantly less energy than SDAGFL and ESDAGFL, with values ranging from 0.373 to 0.485 kJ per round on the FMNIST-Clustered dataset and 16.27 to 20.34 kJ per round on the Poets dataset, compared to 0.000 to 1.442 kJ and 0.00 to 63.89 kJ respectively.

High-Performance Grape Disease Detection Method Using Multimodal Data and Parallel Activation Functions

This paper introduces a novel deep learning model for grape disease detection that integrates multimodal data and parallel heterogeneous activation functions, significantly enhancing detection accuracy and robustness. Through experiments, the model demonstrated excellent performance in grape disease detection, achieving an accuracy of 91%, a precision of 93%, a recall of 90%, a mean average precision (mAP) of 91%, and 56 frames per second (FPS), outperforming traditional deep learning models such as YOLOv3, YOLOv5, DEtection TRansformer (DETR), TinySegformer, and Tranvolution-GAN. To meet the demands of rapid on-site detection, this study also developed a lightweight model for mobile devices, successfully deployed on the iPhone 15. Techniques such as structural pruning, quantization, and depthwise separable convolution were used to significantly reduce the model’s computational complexity and resource consumption, ensuring efficient operation and real-time performance. These achievements not only advance the development of smart agricultural technologies but also provide new technical solutions and practical tools for disease detection.

The Role of Cyber Security for Mobile Device: Implication and Challenges

The Role of Cyber Security for Mobile Device: Implication and Challenges

Modeling the Effect of Cell Orientation on the Performance of an Air-Breathing Polymer Electrolyte Fuel Cell with a Columnar Cathode Plate

Air-breathing polymer electrolyte fuel cells (PEMFCs) are expected to play an important role in the development of sustainable portable and mobile devices, such as unnamed aerial vehicles, motorbikes or laptops. In this work, the performance of air-breathing PEMFCs with a columnar cathode plate design is examined by means of a 3D two-phase, non-isothermal model, and compared against experimental data. The results show that the performance is highly influenced by the cell orientation, namely horizontal (material plane perpendicular to gravity) versus vertical (material plane parallel to gravity). Oxygen transport in horizontal cells is mainly dominated by diffusion, leading to oxygen shortage toward the cell center and an inhomogeneous current density distribution. In contrast, oxygen transport in vertical cells is assisted by natural convection, enhancing oxygen transport throughout the cell and increasing the current density homogeneity. Water saturation is preferentially accumulated close to the cell center, being larger in horizontal cells due to the lower water removal rate of the cathode plate.

Automated analysis of digital medical images in cervical cancer screening: A systematic review.

Cervical cancer screening programs are poorly implemented in LMICs due to a shortage of specialists and expensive diagnostic infrastructure. To address the barriers of implementation researchers have been developing low-cost portable devices and automating image analysis for decision support.However, as the knowledge base is growing rapidly, progress on the implementation status of novel imaging devices and algorithms in cervical cancer screening has become unclear. The aim of this project was to provide a systematic review summarizing the full range of automated technology systems used in cervical cancer screening. A search on academic databases was conducted and the search results were screened by two independent reviewers. Study selection was based on eligibility in meeting the terms of inclusion and exclusion criteria which were outlined using a Population, Intervention, Comparator and Outcome framework. 17 studies reported algorithms developed with source images from mobile device, viz. Pocket Colposcope, MobileODT EVA Colpo, Smartphone Camera, Smartphone-based Endoscope System, Smartscope, mHRME, and PiHRME. While 56 studies reported algorithms with source images from conventional/commercial acquisition devices. Most interventions were in the feasibility stage of development, undergoing initial clinical validations. Researchers have proven superior prediction performance of computer aided diagnostics (CAD) in colposcopy (>80% accuracies) versus manual analysis (<70.0% accuracies). Furthermore, this review summarized evidence of the algorithms which are being created utilizing portable devices, to circumvent constraints prohibiting wider implementation in LMICs (such as expensive diagnostic infrastructure). However clinical validation of novel devices with CAD is not yet implemented adequately in LMICs.

Modeling the Effect of Cell Orientation on the Performance of an Air-Breathing Polymer Electrolyte Fuel Cell with a Columnar Cathode Plate

Air-breathing polymer electrolyte fuel cells (PEMFCs) are expected to play an important role in the development of sustainable portable and mobile devices, such as unnamed aerial vehicles, motorbikes or laptops. In this work, the performance of air-breathing PEMFCs with a columnar cathode plate design is examined by means of a 3D two-phase, non-isothermal model, and compared against experimental data. The results show that the performance is highly influenced by the cell orientation, namely horizontal (material plane perpendicular to gravity) versus vertical (material plane parallel to gravity). Oxygen transport in horizontal cells is mainly dominated by diffusion, leading to oxygen shortage toward the cell center and an inhomogeneous current density distribution. In contrast, oxygen transport in vertical cells is assisted by natural convection, enhancing oxygen transport throughout the cell and increasing the current density homogeneity. Water saturation is preferentially accumulated close to the cell center, being larger in horizontal cells due to the lower water removal rate of the cathode plate.

Clinical Significance of Pose Estimation Methods Compared with Radiographic Parameters in Adolescent Patients with Idiopathic Scoliosis.

Human pose estimation, a computer vision technique that identifies body parts and constructs human body representations from images and videos, has recently demonstrated high performance through deep learning. However, its potential application in clinical photography remains underexplored. This study aimed to establish photographic parameters for patients with adolescent idiopathic scoliosis (AIS) using pose estimation and to determine correlations between these photographic parameters and corresponding radiographic measures. We conducted a study involving 42 patients with AIS who had undergone spinal correction surgery and conservative treatment. Preoperative photographs were captured using an iPhone 13 Pro mounted on a tripod positioned at the head of an X-ray tube. From the outputs of pose estimation, we derived five photographic parameters and subsequently conducted a statistical analysis to assess their correlations with relevant conventional radiographic parameters. In the sagittal plane, we identified significant correlations between photographic and radiographic parameters measuring trunk tilt angles. In the coronal plane, significant correlations were found between photographic parameters measuring shoulder height and trunk tilt and corresponding radiographic measurements. The results suggest that pose estimation, achievable with common mobile devices, offers potential for AIS screening, early detection, and continuous posture monitoring, effectively mitigating the need for X-ray radiation exposure. Level of Evidence: 3.

Logical methods of forensic expertise of Android devices

Mobile devices are now so ubiquitous that they have revolutionized the way we do most of our activities. As a result, the mobile device is now a huge repository of confidential and personal information about its owner. This has led to the development of mobile forensics, which focuses on the recovery and investigation of mobile data. The purpose of the diagnostic process is to retrieve and recover any information from a mobile device without changing the data on the device. Over the years, digital forensics has expanded with the rapid growth of mobile devices. There are different branches of digital forensics depending on the type of digital device, such as computer forensics, network forensics, mobile forensics, etc. Data obtained from telephones becomes an invaluable source of evidence for investigations in criminal and civil cases. It's rare to conduct digital forensics without using a phone. Third-party applications are an integral part of the investigation mobile device. This requires understanding where app data is stored on the device, what app data is stored for that platform, and which tool best helps uncover the evidence. Although some commercial tools, such as Magnet IEF, are known for supporting application parsing, no tool is perfect, and it is nearly impossible for tools to keep up with the frequent updates that are released for each application. The most commonly available commercial tools analyze the most popular programs on the market. For example, when Facebook acquired WhatsApp, Cellebrite, IEF, and Oxygen Forensics started supporting the app. This is where all programs differ.

Unlocking potential: understanding mobile device use patterns among year 9–11 students for learning

ABSTRACT The study examines the nature of smartphones for learning use patterns among students in grades 9–11 and its contexts in relation to personalised learning extracting risk factors associated with less mobile device use in learning. A latent class analysis (LCA) was employed. Various covariates were used to highlight groups of students at risk for effective smartphone-supported instruction. Participants were recruited for a cross-sectional online survey within the Monitoring of digital transformation series conducted in Russia in 2020/21. We identified four distinct profile groups of mobile device use among school students. Examining profile groups through the lenses of the Personalisation–Pluralisation Quadrant (PPQ) developed by Kucirkova and Littleton and the Substitution, Augmentation, Modification and Redefinition (SAMR) framework of Puentedura, we show that heterogeneous guidance should be provided to help students maximise the potential of smartphones for learning. This study represents a first attempt to examine the heterogeneous structure of mobile use among school students, using a latent class analysis and can be used as a reference to better understand the use of personal mobile devices among school students for educational purposes and personalised learning. The findings also result in targeted approaches to the teachers’ instruction according to the constructivism principles.

Self-powered temperature-changing system driven by wind energy

Research on outdoor, mobile, and self-powered temperature-control devices has always been highly regarded. These devices can reduce energy consumption for cooling and heating, and they have broad market prospects. On this basis, a rotary disc-shaped triboelectric nanogenerator (TENG) with a maximum open-circuit voltage of 6913 V, a maximum short-circuit current of 85 μA, and a maximum transferred charge of 1.3 μC was prepared. We synthesized a ferroelectric ceramic composed of 0.15PbTiO3–0.85PbSc0.5Ta0.5O3 (0.15PT–0.85PST), which exhibited excellent electrothermal effects at room temperature. By quenching, the electrothermal effect (Δ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta$$\\end{document}Tmax) and energy harvesting properties of the device were 1.574 K and 0.542 J/cm3, respectively. Then, for the first time, we proposed a self-powered temperature quantification control system with a rotary disc-shaped TENG. This device effectively harnessed wind and water energy, in addition to other types of energy. The system consisted of energy collecting cups, a rotating disc-shaped FEP–rabbit fur TENG, a circuit management module, and a ferroelectric ceramic chip array. Through the circuit management module, the system converted external wind energy into a high-voltage electric field at the two ends of the 0.15PT–0.85PST ceramic chip to fully stimulate the electrothermal effect. At a speed of 200 rpm, the temperature change in the insulated cup within 276 s was 0.49 K, and the volume of the insulated cup was 300 times greater than that of the 0.15PT–0.85PST ceramic chip. Compared with the results reported in previous work, the cooling and heating times were both reduced by 31%, and the temperature changes for both cooling and heating increased by 81%. Moreover, the heating and cooling temperatures of the device optimized on this basis were increased to 1.19 K and 0.93 K, respectively. The great improvement in the temperature variation performance confirmed the great potential of the device for commercialization. This research could serve as a reference for reducing energy consumption for cooling and heating, and it meets the international energy policies of carbon dioxide emission peaking and carbon neutrality.

Advanced Fraud Threats and Detection in Browser, Android, and iOS Environments

As digital technologies evolve, so do the methods employed by cybercriminals to exploit vulnerabilities within browser and mobile environments. This paper explores advanced fraud threats such as VPN detection evasion, browser spoofing, mobile device factory resets, jailbroken phones, user-initiated app cloning, and Android emulators and tampering. We discuss how these threats are misused and outline advanced detection techniques to mitigate risks. The paper emphasizes the importance of implementing robust security measures to protect against sophisticated fraud tactics in browser, Android, and iOS platforms.

Using the Termux mobile application for forensic expertise of Android devices

Mobile device logical data extraction methods extract the data present on the device by interacting with the operating system and accessing the file system. These methods are important because they provide valuable data, work on most devices, and are easy to use. Again the concept of rooting comes into play when extracting data. Boolean methods don't actually require root access to extract data. However, having root access on the device allows you to access all the files present on the device. This means that some data can be extracted on a non-rooted device, while root access will unlock the device and give access to all files present on the device. Therefore, having a rooted device will greatly affect the amount and type of data that can be retrieved using the logical methods. The adb utility is often used to logistically retrieve data from the device. It facilitates various actions with the device, such as installing and debugging programs, copying data, creating system backups. adb provides access to the phone shell, which can be used to execute various commands on the device. But if to work with this and other necessary utilities from a mobile phone, using the Termux terminal, in some cases it significantly saves time and effort. Termux allows to read, process, analyze data from a target device using just phone, providing a highly mobile way of working with data.

Mobile Devices in Forest Mensuration: A Review of Technologies and Methods in Single Tree Measurements

Mobile devices such as smartphones, tablets or similar devices are becoming increasingly important as measurement devices in forestry due to their advanced sensors, including RGB cameras and LiDAR systems. This review examines the current state of applications of mobile devices for measuring biometric characteristics of individual trees and presents technologies, applications, measurement accuracy and implementation barriers. Passive sensors, such as RGB cameras have proven their potential for 3D reconstruction and analysing point clouds that improve single tree-level information collection. Active sensors with LiDAR-equipped smartphones provide precise quantitative measurements but are limited by specific hardware requirements. The combination of passive and active sensing techniques has shown significant potential for comprehensive data collection. The methods of data collection, both physical and digital, significantly affect the accuracy and reproducibility of measurements. Applications such as ForestScanner and TRESTIMATM have automated the measurement of tree characteristics and simplified data collection. However, environmental conditions and sensor limitations pose a challenge. There are also computational obstacles, as many methods require significant post-processing. The review highlights the advances in mobile device-based forestry applications and emphasizes the need for standardized protocols and cross-device benchmarking. Future research should focus on developing robust algorithms and cost-effective solutions to improve measurement accuracy and accessibility. While mobile devices offer significant potential for forest surveying, overcoming the above-mentioned challenges is critical to optimizing their application in forest management and protection.

A Highly Accurate Adverse Drug Reactions (ADR) Detection from Medical Forum Comments Using Long Short-Term Memory Networks

Purpose: Adverse Drug Reactions (ADR) classification is useful in modern medical diagnostics and related applications. ADR is an example of how medical information is frequently accessible on social media platforms for healthcare, where people can share their experiences with treatments on desktop computers and mobile devices. Many researchers are interested in gathering valuable medical data from social media for the ADR system training and classification process. Materials and Methods: This research explores the effects of three aspects on recognizing ADR mentions in social media for the medical field and proposes a deep neural network of Long Short-Term Memory (LSTM) neural networks to do so. The comments are collected from various social media platforms to implement the ADR system with proper training and testing processes. The texts from the dataset are initially preprocessed by using a data filtering and clustering process to remove the input data's redundant information to increase the training process's quality. Characteristic features, such as semantic features and text statistics, are extracted from the input text using the American Standard Code for Information Interchange (ASCII) array. Further, the features are converted and fed to LSTM networks for training and validation. Results and Conclusion: This work is evaluated using two datasets, CODEC, and ADR Corpus datasets are used to evaluate the performance of the proposed ADR technique via multiple angles. Via extensive experiments, this work achieved 99.79 accuracy, 98.37 sensitivity, 97.63 specificity, 99.72 precision, 98.39 recall, 97.62 F1-score for the CODEC dataset, 98.16 for accuracy, 99.19 for sensitivity, 98.49 for specificity, 99.49 for precision, 96.72 for recall, and 93.16 for F1-score for ADR corpus, respectively.