Characteristics Of Mobile Devices Research Articles

A MODEL FOR PHYSICS PRACTICAL WORK USING MOBILE LEARNING AND LEARNING MANAGEMENT SYSTEM (LMS) IN HIGHER EDUCATION: DESIGN, VALIDATION, AND EVALUATION

Despite the importance of physics practical work in higher education, its implementation is often hampered by various constraints and problems. Technology, such as learning management systems (LMS) and mobile learning, can offer solutions to some of these problems and enrich students' learning experiences. Therefore, this research proposes a model called Practical Works in Physics via Mobile Learning and LMS (PWP-MLMS) that exploits features of LMSs and mobile devices to overcome specific challenges encountered in physics practical works and improve students' performance in these works. The model was designed, validated, and evaluated within the teaching context of a Moroccan university. To assess the model's effectiveness,128 students in the Bachelor of Education, Physics-Chemistry specialization were randomly divided into two groups of 64 students each: an experimental group using the model for practical work on the topic of rectification and filtering in the electronics module, and a control group following the conventional method for the same practical work. The results of the evaluation showed that the proposed model can significantly reduce the time required to complete the practical work, have a positive influence on the students' technical skills, and improve the quality of their laboratory reports. Keywords: mobile learning, LMS, practical work, physics education, higher education

Challenges and Alternatives to Evaluation Methods and Regulation Approaches for Medical Apps as Mobile Medical Devices: International and Multidisciplinary Focus Group Discussion.

The rapid proliferation of medical apps has transformed the health care landscape by giving patients and health care providers unprecedented access to personalized health information and services. However, concerns regarding the effectiveness and safety of medical apps have raised questions regarding the efficacy of randomized controlled trials (RCTs) in the evaluation of such apps and as a requirement for their regulation as mobile medical devices. This study aims to address this issue by investigating alternative methods, apart from RCTs, for evaluating and regulating medical apps. Using a qualitative approach, a focus group study with 46 international and multidisciplinary public health experts was conducted at the 17th World Congress on Public Health in May 2023 in Rome, Italy. The group was split into 3 subgroups to gather in-depth insights into alternative approaches for evaluating and regulating medical apps. We conducted a policy analysis on the current regulation of medical apps as mobile medical devices for the 4 most represented countries in the workshop: Italy, Germany, Canada, and Australia. We developed a logic model that combines the evaluation and regulation domains on the basis of these findings. The focus group discussions explored the strengths and limitations of the current evaluation and regulation methods and identified potential alternatives that could enhance the quality and safety of medical apps. Although RCTs were only explicitly mentioned in the German regulatory system as one of many options, an analysis of chosen evaluation methods for German apps on prescription pointed toward a "scientific reflex" where RCTs are always the chosen evaluation method. However, this method has substantial limitations when used to evaluate digital interventions such as medical apps. Comparable results were observed during the focus group discussions, where participants expressed similar experiences with their own evaluation approaches. In addition, the participants highlighted numerous alternatives to RCTs. These alternatives can be used at different points during the life cycle of a digital intervention to assess its efficacy and potential harm to users. It is crucial to recognize that unlike analog tools, digital interventions constantly evolve, posing challenges to inflexible evaluation methods such as RCTs. Potential risks include high dropout rates, decreased adherence, and nonsignificant results. However, existing regulations do not explicitly advocate for other evaluation methodologies. Our research highlighted the necessity of overcoming the gap between regulatory demands to demonstrate safety and efficacy of medical apps and evolving scientific practices, ensuring that digital health innovation is evaluated and regulated in a way that considers the unique characteristics of mobile medical devices.

Inference latency prediction for CNNs on heterogeneous mobile devices and ML frameworks

Due to the proliferation of inference tasks on mobile devices, state-of-the-art neural architectures are typically designed using Neural Architecture Search (NAS) to achieve good tradeoffs between machine learning accuracy and inference latency. While measuring inference latency of a huge set of candidate architectures during NAS is not feasible, latency prediction for mobile devices is challenging, because of hardware heterogeneity, optimizations applied by machine learning frameworks, and diversity of neural architectures. Motivated by these challenges, we first quantitatively assess the characteristics of neural architectures (specifically, convolutional neural networks for image classification), ML frameworks, and mobile devices that have significant effects on inference latency. Based on this assessment, we propose an operation-wise framework which addresses these challenges by developing operation-wise latency predictors and achieves high accuracy in end-to-end latency predictions, as shown by our comprehensive evaluations on multiple mobile devices using multicore CPUs and GPUs. To illustrate that our approach does not require expensive data collection, we also show that accurate predictions can be achieved on real-world neural architectures using only small amounts of profiling data.

Affordances of Mobile Devices and Their Effect on Content Retention: A Conceptual Review

Content Retention in education is very important especially when the content learned is to be applied in life after school. Students in school are also expected after a period of lessons to sit for examinations for assessment of retained knowledge. This makes retention of subject content the key to a successful performance and life after school. This paper highlights ways the affordances of mobile devices can enhance content retention in mobile learning for better academic performance. A theoretical literature review was done to discuss common affordances of mobile learning. The affordances were categorized based on two notable features of mobile devices which are portability or ubiquity and connectivity and applications. The paper discussed how portability and ubiquity make it easy for learning to be enhanced in various ways. This feature allows the learner to be continuously exposed to the learning content and engage in spaced practice in learning that enhances content retention. Connectivity and application also facilitate creative learning collaboration, and experiential learning. This also makes it easy for the learner to customize their learning style and contextualize the information thereby improving cognitive retention. Communication and collaboration in education make learning socio-constructive. The paper concludes that as far as learning is concerned learners have the ultimate control in exploiting all affordances of mobile devices available for productive learning.

Trends of Modern Media Industry: Mobile Applications as a New Format of Media

The study explores the specifics of the development of the media landscape in modern Russia. Attention is focused on the changes occurring in the information field, particularly on the issue of forming new channels of distribution and transforming the ways in which audiences consume journalistic products. The use of mobile applications is addressed, which complement and enhance the significance of online publications as part of the media segment. Key characteristics of mobile devices are emphasized. Functional aspects of content transmission and restrictions imposed on media content are highlighted. Stages of transforming media content for mobile applications are identified. The relevance of the research is justified by the expansion of media integration processes and information and communication technologies development, leading to modifications in mass media. A practical study of media platforms for cell phones at federal and regional levels is conducted. A comparative analysis of the mobile applications RBC Daily and LipetskMedia is carried out. It is proven that the difference in the presentation of images of reality through mobile applications and other content distribution channels depends solely on the editorial information and business policies in the realm of mobile devices. At this stage, the emergence of financially successful mass media where mobile applications serve as the sole content distribution channel is entirely possible.

Exploring the Information-seeking Behaviour of Students at the Federal University of Lafia, Nigeria, who Use Mobile Technologies to Access Information

The advent and development of ICTs brought about changes that influence users’ information-seeking by using mobile devices. This article contains a report from a doctoral thesis on a qualitative phenomenological study that explored the use of mobile devices by undergraduate students at the Federal University of Lafia, Nigeria, to seek academic-related information. A semi-structured interview schedule was employed to collect data from 21 students from years 2, 3 and 4. Owing to certain features of mobile devices, the findings suggested that mobile devices influenced the students’ information-seeking behaviour. The features that influenced the use of mobile technologies for information-seeking purposes included affordability, ease of use, portability, and convenience. However, features such as screen size and unstable networks negatively affected the use of mobile devices for information-seeking purposes. In turn, some user-related characteristics, such as a lack of information literacy skills and a lack of knowledge on how to use mobile technologies, seemed to affect the students’ willingness to use mobile technologies for information-seeking purposes.

Adaptive Resource Allocation Considering Power-Consumption Outage: A Deep Reinforcement Learning Approach

A major feature of mobile devices in the future wireless network is the high data rate. However, the newly proposed power-consumption outage indicates that the heat generated by high data rate devices has an influence on the performance. This correspondence investigates a novel resource allocation scheme considering power-consumption outage. Specifically, based on the analysis of the heat transfer model in the smartphone, we formulate the problem to jointly allocate the downlink power and bandwidth while considering the impact of power-consumption outage. According to the dynamic feature of the problem, the Markov decision process (MDP) is utilized to model it. Furthermore, dut to the continuity of action space, the problem is handled by normalized advantage function (NAF), a deep reinforcement learning (DRL) algorithm. The effectiveness of the proposed scheme is corroborated in the simulation results on the basis of which its applicability for services with different features is further discussed.

Accurate integrated position measurement system for mobile applications in GPS-denied coal mine

The automatic positioning of underground mobile applications plays a crucial role in enabling intelligent coal mining. However, due to the diverse kinematics and dynamics of these applications, various positioning methods have been proposed to match different targets. Nonetheless, the accuracy and applicability of these methods still fall short of meeting the requirements for field applications. Based on the vibration characteristics of underground mobile devices, a multi-sensor fusion positioning system is developed to enhance the accuracy of positioning in long and narrow global positioning system denied (GPS-denied) underground coal mine roadways. The system combines inertial navigation (INS), odometer, and ultra wide band (UWB) technologies through extended Kalman filter (EKF) and unscented Kalman filter (UKF). This approach enables accurate positioning by recognizing target carrier vibrations and facilitating fast conversion between multi-sensor fusion modes. The proposed system is tested on both a small unmanned mine vehicle (UMV) and a large roadheader, demonstrating that UKF enhances stability for roadheaders with strong nonlinear vibrations while EKF is more suitable for flexible UMVs. Detailed results confirm that the proposed system achieves an accuracy level of 0.15 m, meeting most coal mine application requirements.

State of the Art of Mobile Learning in Jordanian Higher Education: An Empirical Study

A new approach to learning is mobile learning (m-learning), which makes use of special features of mobile devices in the education sector. M-learning is becoming increasingly common in higher education institutions all around the world. The use of mobile devices for education and learning has also gained popularity in Jordan. Unlike studies about Jordan, there are many studies that thoroughly analyze the situation of m-learning in other countries. Thus, it is important to understand the current situation of m-learning at Jordanian universities, especially in light of the COVID-19 pandemic. While there have been some studies conducted prior to COVID-19 and a few studies after COVID-19, there is a need for a comprehensive study that provides an in-depth exploration of the current situation, student adoption, benefits, disadvantages, and challenges, particularly following COVID-19. Therefore, this study utilizes a sequential exploratory mixed research method to investigate the current state of the art of m-learning in Jordanian higher education with a particular focus on student adoption, benefits, disadvantages, and challenges. Firstly, the study explores the existing literature on m-learning and conducts 15 interviews with educators and learners in three Jordanian universities to gain insights into their experiences with m-learning. The study then distributes a survey to students at four Jordanian universities, representing both public and private universities, to generalize the results from the qualitative study. Additionally, the study investigates the relationship between student enrollment in public/private universities and the adoption of m-learning. The study came to the conclusion that students have a positive opinion of m-learning and are also willing to use it. However, there are a number of disadvantages and challenges to its adoption. Additionally, there is a relationship between student enrolment in public/private universities and the adoption of m-learning. These findings have important implications for institutions that want to incorporate m-learning into their undergraduate and graduate degree programs, as they aid decision-makers in these universities in creating frameworks that may be able to meet the needs of m-learning.

SMSPROTECT: An automatic smishing detection mobile application

Short Messaging Service (SMS) has grown to become the most widely used feature in mobile devices. The technological advancements that birthed other alternative messaging applications have not been able to phase out the use of the SMS. However, hackers have been exploiting this SMS feature to perpetrate smishing acts. Existing research has focused on how spam SMS could be detected and separated from ham messages but have not really done much at preventing the act of smishing. Therefore, this research presents a mobile application that used a rule-based SMS service to detect and prevent smishing attacks. Specifically, the developed SMS service allows the developed SMS mobile application to intercept incoming SMS to a smartphone. The intercepted messages were then forwarded through an Application Programming Interface (API) to the rule-based machine learning model. The model uses the carefully selected rules to analyze the retrieved message and asserts if it is a spam or ham. The result of the analysis is then forwarded to the mobile application through the API. However, the final decision to retain or discard the spam or ham depends on the user after receiving notification from the user.

Transfer Reinforcement Learning for Adaptive Task Offloading Over Distributed Edge Clouds

In the Big Data era, resource-constrained mobile devices generate an overwhelmingly large amount of data with complex tasks that demand distributed execution. Offloading computation-intensive tasks to nearby edge clouds is promising to solve this problem. However, mobile end devices cannot handle heterogeneous or delay-sensitive tasks. These end devices are also energy constrained with weak adaptability to environment changes. To address and tackle these problems, we present a two-module <i>transfer reinforcement learning</i> (TRL) framework for adaptive task offloading. A domain adaptation module is used to align heterogeneous characteristics of mobile devices. The TRL makes offloading decisions with a <i>deep reinforcement learning</i> (DRL) module. We evaluate the performance of TRL through real-world experiments on edge clouds. Our experiment results show that TRL reduces the task processing time by a factor of 20&#x0025; from using three well known DRL methods. Our method achieved (15.4<inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>40)&#x0025; reduction in task drop rate over these methods. With domain adaptation, the TRL results in (50<inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>80)&#x0025; reduction in model convergence time. These advantages in using the TRL framework make it appealing in real-life edge computing applications.

Elevation accuracy improvement in mobile devices by implementing artificial neural networks

ABSTRACT An important feature of mobile devices relates to positioning, mainly relying on the global navigation satellite system sensor. In optimal conditions, this sensor provides horizontal positioning sufficient for most location-based services. The elevation, on the other hand, still lacks sufficient accuracy and reliability – mostly due to mobile device inadequacies that stem from technological limitations and environmental and physical conditions, which impact the observations quality. We suggest augmenting the elevation measurements of this sensor with measurements from supplementary embedded mobile device sensors, such as barometers and accelerometers, and with data from external mapping and environmental databases, namely topography and weather. We developed an artificial neural network deep-learning model that identifies parameter values for producing the highest predictive accuracy of the elevation value while relying on a comprehensive set of measurements. Our findings indicate very promising results, whereby we enhanced the elevation accuracy of testing data by 428%, while significantly reducing the elevation variance. These results show that using supplementary measurements and data improves elevation values while significantly reducing errors commonly associated with mobile device global navigation satellite system sensors. The proposed method has the capacity to improve outdoor kinematic positioning for location-based services, with a focus on urban and concealed areas.

Alert Now or Never: Understanding and Predicting Notification Preferences of Smartphone Users

Notifications are an indispensable feature of mobile devices, but their delivery can interrupt and distract users. Prior work has examined interventions, such as deferring notification delivery to opportune moments, but has not systematically studied how users might prefer an intelligent system to manage their notifications. Hence, we directly probed Android smartphone users’ notification preferences via a one-week experience-sampling study ( N = 35). We found that users prefer mitigating undesired interruptions by suppressing alerts over deferring them and referred to notification content factors more frequently than contextual factors for explaining their preferences. Then we demonstrated the challenges and potentials of leveraging user actions to help predict notification preferences. Specifically, we showed that a model personalized using user actions achieved a performance gain of 39% than a generic model. This improvement is similar to the 42% performance gain using labels solicited from the user while using observable user actions causes no extra disruption.

Automating Everyday Activities: A Study on the Usage of Text-Based AutoCorrect among Age Groups

Automation has infiltrated nearly all areas of our lives. Many technologies have not been designed considering older adult populations. Smartphone usage is pervasive among older individuals, but whether age-related differences exist in the use of automated features of mobile devices continues to be researched. This study examined how younger, middle-aged, and older adults use AutoCorrect (AC) to send mobile messages. A total of 138 participants were asked about their perceptions of AC and to write messages in response to four scenarios that varied in complexity. Overall, younger adults had a more favorable perception of AC compared to middle-aged and older adults. Also, when complexity was both high and low, older adults were more likely to proofread their messages, manually correct AC, find AC distracting, and perceive AC to be helpful. Our findings suggest that older adults do engage with automation differently than their younger counterparts, which should be considered by automation designers.

Research and design of flow standard device for field service master meter method

Most of the traditional flow standard devices are fixed, with very large volumes. When the enterprise has the measurement demand, the client needs to remove all the flowmeters and send them to the location of the standard device, take them back after testing and reinstall them on the production line, resulting in low production efficiency. However, while pursuing the mobility of the device, the overall space, weight, and layout of the device are bound to be limited, thus affecting the stability of the water source and measurement accuracy. Therefore, this paper analyzes the characteristics of mobile devices, in the pursuit of lightweight at the same time design and building water source loop, branch, the overall electrical system, and write the host computer calibration software, and finally through the automatic adjustment of the solenoid valve with PID control scheme to adjust the flow, to improve the stability of water sources.

Mobile learning: research context, methodologies and future works towards middle-aged adults - a systematic literature review.

Over the past several years, mobile learning concepts have changed the way people perceived on mobile devices and technology in the learning environment. In earlier days, mobile devices were used mainly for communication purposes. Later, with many new advanced features of mobile devices, they have opened the opportunity for individuals to use them as mediated technology in learning. The traditional way of teaching and learning has shifted into a new learning dimension, where an individual can execute learning and teaching everywhere and anytime. Mobile learning has encouraged lifelong learning, in which everyone can have the opportunity to use mobile learning applications to gain knowledge. However, many of the previous studies on mobile learning have focused on the young and older adults, and less intention on middle-aged adults. In this research, it is targeted for the middle-aged adults which are described as those who are between the ages of 40 to 60. Middle-aged adults typically lead very active lives while at the same time are also very engaged in self-development programs aimed at enhancing their spiritual, emotional, and physical well-being. In this paper, we investigate the methodology used by researchers based on the research context namely, acceptance, adoption, effectiveness, impact, intention of use, readiness, and usability of mobile learning. The research context was coded to the identified methodologies found in the literature. This will help one to understand how mobile learning can be effectively implemented for middle-aged adults in future work. A systematic review was performed using EBSCO Discovery Service, Science Direct, Google Scholar, Scopus, IEEE and ACM databases to identify articles related to mobile learning adoption. A total of 65 journal articles were selected from the years 2016 to 2021 based on Kitchenham systematic review methodology. The result shows there is a need to strengthen research in the field of mobile learning with middle-aged adults.

Application of Mobile Virtual Reality Technology Combined with Neural Network in Facial Expression Recognition.

In recent years, in the field of virtual reality, in more and more scenes, users interact with hardware or programs through facial expressions. In order to give full play to the advantages of program interaction between virtual reality devices and users, this paper proposes a mobile virtual reality expression recognition system combined with convolution neural network. Based on the optimized AlexNet network, an expression recognition algorithm is constructed and combined with LBP feature mapping technology to improve the performance of the algorithm. At the same time, according to the nature and characteristics of mobile virtual reality devices, the user face information acquisition algorithm is optimized. The performance test results of the expression recognition system show that the recognition accuracy of the system is higher than that of the traditional convolution neural network expression recognition algorithm, and the maximum difference is greater than 10%. At the same time, the average running speed of the whole system is about 37 ms, which can meet the accuracy and real-time requirements of expression recognition in virtual reality interaction. The experimental results show that the expression recognition system proposed in this paper can be applied to mobile virtual reality devices. At the same time, it also provides new ideas for industry researchers to optimize the identification function.

Mobil Uygulamalarda Hibrit Sistemlerin Oluşturduğu Kullanım Kolaylıklarının İncelenmesi

Smartphone apps support people to adopt healthy lifestyles. Therefore, it is useful to understand the persuasive design strategies involved in hybrid mobile applications that facilitate behavior changes. The aim of our research is to show the ease of use of hybrid mobile applications compared to Web and Native applications. Hybrid mobile apps combine the features of Web apps and Native mobile apps. Like web applications, they are implemented in portable, platform-independent languages such as HTML and JavaScript. They directly access local device resources such as native apps. Transition to hybrid applications has been increasing in recent years with the rapid progress of technology. Although it has performance disadvantages compared to native applications, it is more preferred by software developers due to its ease of use. From the results, it has been determined that the user interface for hybrid applications is simpler to develop with its dynamic structure and it uses mobile device features better in terms of performance when used in hybrid applications.

Architecture for Fault Tolerance in Mobile Cloud Computing using Disease Resistance Approach

The mobile cloud computing (MCC) is one of the emerging fields in the distributed computing. MCC is an integration of both mobile computing and cloud computing. The limitations of the mobile devices are storage, battery and processing proficiency.These sensitive characteristics of mobile devices can be effectively handled with the introduction of cloud computing. The increasing functionality of the cloud and complexity of the applications causes resource failures in the cloud computing and it reduces the overall performance of the MCC environment. On the other hand, the existing approaches for resource scheduling in MCC proposed several architectures and they are only concentrated on the allocation of resources. The existing architectures are lack of fault tolerance mechanism to handle the faulty resources. To overcome the issues stated above, this paper proposes architecture for fault tolerance in MCC using Disease Resistance approach (DRFT). The main aim of the DRFT approach is to effectively handle the faultyVMs in the MCC. This DRFT approach utilizes the human disease resistance mechanism which is used as materials and methods in the proposed model. The DRFT is capable of identifying the faulty virtual machines and reschedules the tasks to the identified suitable virtual machines. This procedure ultimately leads to minimization of makespan value and it improves the overall performance of the scheduling process. To validate the effectiveness of the proposed approach, a series of simulations has been carried out using CloudSim simulator. The performance of the proposed DRFT approach is compared with the Dynamic group based fault tolerance approach (DGFT-approach). The makespan value of DRFT is reduced to 7% and the performance of DRFT is increased when compare to the DGFT approach. The experimental results show the effectiveness of the proposed approach.

A Sustainable Transport System—The MMQUAL Model of Shared Micromobility Service Quality Assessment

Shared micromobility is a new phenomenon being observed in urban transport. It is a response to the problems associated with congestion and environmental pollution. Small electric vehicles such as e-scooters are highly suitable for crowded city centres, often providing an alternative to private motor vehicles or public transport, and serve as a good first- and last-mile transport option. While they have become a feature of sustainable transport systems in cities, their impact on the environment often depends on the services offered by operators of this mode of personal transport. There are many tools available to measure the quality of transport, e-services and shared mobility services. However, no specific mechanism has been designed for vehicles in the field of shared e-scooters (research gap). The aim of the article is to verify whether the three dimensions identified by the authors: mobile application functions, device features, and customer service are valid for examining the quality of shared e-micromobility factors on the example of e-scooters. Based on the obtained results, the authors created the MMQUAL (MicroMobility QUALity) model, which accurately describes the quality of the studied phenomenon. The results of the study can serve as a platform for researchers interested in further exploring the issue and improving the proposed model. They may also be of commercial value to operators, who could use this tool to boost the competitiveness of their services by enhancing those features that have the greatest impact on their quality.