Mobile Systems Research Articles

Simultaneous determination of 17 perfluoroalkyl substances in beef by EMR-Lipid dispersive solid-phase extraction and ultra performance liquid chromatography-tandem mass spectrometry

The ability to accurately analyze perfluoroalkyl substance (PFAS) levels in beef is imperative in order to effectively assess food-safety risks and ensure consumer safety because PFASs are harmful and prevalent in beef. In this study, we developed a rapid and accurate method for the simultaneously determination of the 17 PFASs in beef using dispersive solid-phase extraction (d-SPE) and ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), and optimized the mobile phase system, extraction solvent, and d-SPE materials. Samples were finally extracted using 0.1% (v/v) formic acid in acetonitrile, cleaned using d-SPE with PSA, C18, GCB, and EMR-Lipid, separated using an Acquity Premier BEH C18 column (100 mm×2.1 mm, 1.7 μm) with 0.5 mmol/L ammonium fluoride aqueous solution and methanol as the mobile phases at a flow rate of 0.3 mL/min. Analytes were detected in negative ion switching mode (ESI-) with multiple reaction monitoring (MRM) scanning, and quantitatively analyzed using the internal standard method. The 17 PFASs exhibited linearity in the 0.2-20.0 μg/L range under the optimal experimental conditions, with correlation coefficients of 0.9915-0.9999. The method delivered limits of detection (LODs) of 0.003-0.007 μg/kg and limits of quantification (LOQs) of 0.01-0.02 μg/kg. The 17 PFASs exhibited recoveries of 71.1%-127.4% with RSDs of 0.6%-14.4% when spiked at three levels (0.05, 0.5, and 1.8 μg/kg). We optimized the mobile phase system, which revealed that, compared with 2.0, 5.0, and 10.0 mmol/L ammonium formate or ammonium acetate in aqueous methanol, 0.5 mmol/L ammonium fluoride in aqueous methanol exhibited higher sensitivities for all the 17 PFASs, with PFASs bearing long-chain carboxylic acids (C10-C18) showing 1-2 fold increases in sensitivity. PFASs do not dissociate in acidic environments, favoring their entry into the organic phase. Therefore, we investigated the effect of extractant acidity, which revealed that the 17 PFASs were better extracted using 0.1% (v/v) formic acid in acetonitrile. The beef matrix has a complex composition; consequently, d-SPE adsorbents were required to purify samples and reduce matrix effects. The purification effects of four adsorbents (PSA, C18, GCB, and EMR-Lipid) toward the 17 PFASs and the amount of EMR-Lipid used were investigated, which revealed that 100 mg PSA+80 mg C18+40 mg GCB+150 mg EMR-Lipid exhibited superior matrix-purification behavior. We also investigated the effects of various injection solutions and types of syringe filter, with pure methanol selected for reconstitution and high-speed supernatant centrifugation applied prior to injection. The developed method is simple, rapid, sensitive, and reproducible, and can be used to simultaneously, rapidly, and accurately determine various perfluoroalkyl compounds in beef.

Does Prosocial Automation Increase Driver’s Well-being?

As societies transition to hybrid mobility systems, interactions between automated vehicles (AVs) and human users in public spaces become more complex, highlighting the critical role of prosocial behaviors. These behaviors are essential for the seamless operation of interdependent transportation networks, helping to address integration challenges of AVs with human-operated vehicles and enhancing well-being by creating more efficient, less stressful, and inclusive environments. This study explores the impact of receiving prosocial behaviors on the cognition, riding performance, and well-being of micromobility users in simulated urban traffic scenarios. Using a mixed-method design, the research contrasts two types of social interactions—prosocial and asocial—across three temporal conditions: relaxed, neutral, and urgent. The Structural Equation Modeling underscored the intricate relationships between prosocial behaviors, satisfaction, cognition, riding performance, and well-being. Incorporating social factors into driving systems could enhance safety and improve urban mobility solutions.

An Intelligent Scheduling Approach on Mobile OS for Optimizing UI Smoothness and Power

Mobile devices need to respond quickly to diverse user inputs. The existing approaches often heuristically raise the CPU/GPU frequency according to the empirical rules when facing burst inputs and various changes. Although doing so can be effective sometimes, the existing approaches still need improvements. For instance, raising processors’ frequency can lead to high power consumption when the frequency is over-provisioned or fails to meet user demands when the frequency is under-provisioned. To this end, we propose MobiRL, a reinforcement learning-based scheduler for intelligent adjusting CPU/GPU frequency to satisfy user demands accurately on mobile systems. MobiRL monitors the mobile system status and autonomously learns to optimize UI smoothness and power consumption by conducting CPU/GPU frequency-adjusting actions. The experimental results on the latest delivered smartphones show that MobiRL outperforms the widely used commercial scheduler on real devices – reducing the frame drop rate by 4.1% and reducing power consumption by 42.8%, respectively. Moreover, compared with a study using Q-Learning for CPU frequency scheduling, MobiRL achieves up to 2.5% lower frame drop rate and reduces power consumption by 32.6%, respectively. Our approach has been deployed in mobile phone products.

Plan Your System and Price for Free: Fast Algorithms for Multimodal Transit Operations

We study the problem of jointly pricing and designing a smart transit system, where a transit agency (the platform) controls a fleet of demand-responsive vehicles (cars) and a fixed line service (buses). The platform offers commuters a menu of options (modes) to travel between origin and destination (e.g., direct car trip, a bus ride, or a combination of the two), and commuters make a utility-maximizing choice within this menu, given the price of each mode. The goal of the platform is to determine an optimal set of modes to display to commuters, prices for these modes, and the design of the transit network in order to maximize the social welfare of the system. In this work, we tackle the commuter choice aspect of this problem, traditionally approached via computationally intensive bilevel programming techniques. In particular, we develop a framework that efficiently decouples the pricing and network design problem: Given an efficient (approximation) algorithm for centralized network design without prices, there exists an efficient (approximation) algorithm for decentralized network design with prices and commuter choice. We demonstrate the practicality of our framework via extensive numerical experiments on a real-world data set. We moreover explore the dependence of metrics such as welfare, revenue, and mode usage on (i) transfer costs and (ii) cost of contracting with on-demand service providers and exhibit the welfare gains of a fully integrated mobility system. Funding: This work was supported by the National Science Foundation [Awards CMMI-2308750, CNS-1952011, and CMMI-2144127]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0452 .

Static analysis framework for permission-based dataset generation and android malware detection using machine learning

Since Android is the popular mobile operating system worldwide, malicious attackers seek out Android smartphones as targets. The Android malware can be identified through a number of established detection techniques. However, the issues presented by modern malware cannot be met by traditional signature or heuristic-based malware detection methods. Previous research suggests that machine-learning classifiers can be utilised to analyse permissions, making it possible to differentiate between malicious and benign applications on the Android platform. There exist machine-learning methods that utilise permission-based attributes to build models for the detection of malware on Android devices. Nevertheless, the performance of these detection methods is dependent on the raw or feature datasets. Android malware research frequently faces a major obstacle due to the lack of adequate and up-to-date raw malware datasets. In this paper, we put forward a systematic approach to generate an Android permission-based dataset using static analysis. To create the dataset, we collect recent raw malware samples (APK files) and focus on the reverse engineering approach and permission-based features extraction. We also conduct a thorough feature analysis to determine the important Android permissions and present a machine-learning-based Android malware detection mechanism. The experimental result of our study demonstrates that with just 48 features, the random forest classifier-based Android malware detection model obtains the best accuracy of 97.5%.

Three-Dimensional Space Chaotic Scrolls Generated by Memristive Hopfield Neural Network and Its Application in Robot Path Planning

Abstract In this paper, a novel three-dimensional space-scroll chaotic attractor is constructed by introducing piece-wise linear memristors into a three-neurons-based Hopfield neural network to simulate the electromagnetic influence on neural dynamics. The dynamical behaviors of the novel memristive Hopfield neural network are discussed by means of numerical methods including the Lyapunov exponent spectrum, bifurcation diagrams, phase plots and dynamic maps. Furthermore, we also investigated different neurons subject to the memristive electromagnetic influence, where the two-dimensional grid-scroll chaotic attractors can be generated by one neuron subjects to the electromagnetic influence and three-dimensional space-scroll chaotic attractors can be generated by all of the neurons subject to the memristive electromagnetic influence. Moreover, we present a three-dimensional space-scroll scroll attractor-based chaotic mobile robot system. Superiority of the proposed space-scroll-based chaotic mobile robot system compared to existing methods has been demonstrated through numerical simulations.

Analysis and Comparison of Social Media Applications Using Forensic Software on Mobile Devices

With the integration of mobile systems into daily life, social media applications used especially on Android and iOS platforms contain a significant amount of sensitive information. Social media applications on mobile systems have huge personal and sensitive content. Therefore, it is important to design effective techniques for forensic analysis of social media applications and to detect personal data. In this research, three different paid mobile forensic software and 4 different brands and models of smartphones with different operating systems were used and analyzed. The study shows that private messages, e-mails, time information, shared data, location and time information, and other personal data can be obtained by a forensic expert who performs an examination, and it is seen that one software can access the deleted data, but another software cannot access it. In proportion to the technology used in today’s world, mobile forensics incidents are increasing day by day, and a competitive environment is created among the software used to illuminate these incidents. With this competition, software companies dealing with forensic informatics are trying to obtain different data to illuminate forensic events that may occur due to the active use of social media accounts with the developing technology, and software that does not meet the needs in the face of this situation remains in the background. The criminal elements in the investigation areas of mobile forensics differ daily, and the scope of crimes in the virtual environment is expanding with the developing technology. Therefore, mobile forensic analysis applications should be successful in social media applications other than standard data.

Efficacy of Yoga in Reducing Depressive Symptoms: A Systematic Review

Introduction: The fast pace of changes in contemporary life increases the need to adapt, which leads to depressive pathologies due to psychological suffering. Yoga has therefore emerged as a complementary approach to the treatment of depression. Objective: To analyze the efficacy of yoga in reducing depressive symptoms. Materials and methods: This is a systematic literature review, which followed the Joanna Briggs Institute methodology for systematic reviews of efficacy, with the review protocol registered on Prospero under the code CRD42023448158. To devise the research question, the Pico strategy was adopted, in which “P” stands for people with depression, “I” for yoga; “C” for people who have not been submitted to yoga, and “O” for improvement/reduction of depressive symptoms. The requirements of the Prisma flowchart were followed and the search was conducted in the Medline/PubMed, Web of Science, Lilacs, BDEnf, Ibecs, PsycINFO, and Cinahl databases. The Jadad scale was used to assess methodological quality and the Cochrane Risk of Bias 2 to assess the risk of bias. Results: A total of 1138 studies were found, of which 10 were selected for data extraction, detailed reading, and qualitative synthesis. Yoga, especially mindfulness yoga, has been shown to be an effective intervention for various conditions, including depression. Its benefits include significant improvements in depression severity, health-related quality of life, motor dysfunction, mobility, spiritual well-being, and parasympathetic nervous system activity. Conclusion: The studies highlight the efficacy of yoga in reducing depressive symptoms in various populations and contexts, highlighting its effectiveness as a complementary therapeutic approach in the management of depression.

Design of a robust intelligent controller based neural network for trajectory tracking of high-speed wheeled robots

The control design of wheeled mobile robots is often accomplished based on the robot’s kinematics which imposes critical challenges in the motion tracking control of such systems. In the related literature, most works designed dynamic controllers based on the terms of voltage or torque; however, the velocity is often utilized in industrial and commercial applications. To address this issue, this paper focuses on the design of a velocity-based control for the trajectory-tracking problem of mobile robots in practical applications including measurement acquisition. The control design of the mobile robot is realized in two separate parts using kinematic control and dynamic control. The design of kinematic control is carried out based on the robot’s kinematics where the motion is described without considering the forces and torques. A radial basis function (RBF) neural network (NN) based on the model-free controller is designed for the dynamic controller of mobile robots without relying on the system dynamics. In the proposed dynamic control, a time-delay estimation is adopted to estimate the disturbances and noises in the mobile robot and remove them from the feedback loop control. Several typical scenarios of mobile robots with high-speed movements are conducted to assess the feasibility of the proposed NN controller-based time-delay estimation under noises and disturbances. To show the supremacy of the suggested controller, the dynamic responses of the mobile robot test system are compared with the prevalent controllers. The extensive simulation and real-time examinations reveal the capability of the proposed NN controller-based time-delay estimation to control high-speed mobile robots under high-level noises and disturbances.

The Boundaries of Twenty-first-Century Policing

Three new books by sociologists—The Policing Machine by Tony Cheng, The Minneapolis Reckoning by Michelle Phelps, and The Danger Imperative by Michael Sierra-Arévalo—offer us a view into the state of US policing leading up to and following the racial reckoning of 2020. I read these books together through the lens of boundary work, examining what they tell us about the various projects that seek to maintain, expand, or change police jurisdiction over the production of public safety. Collectively, these books suggest that the police work to preserve and extend their extant role and power. Meanwhile, boundary work through movement mobilization and political systems has aimed to contain policing through law and policy, shrink policing within municipal service systems, and replace policing through community-led models of safety. These projects point us toward important areas of inquiry in efforts to understand whether and how policing can change.

Mobile Application Development for Prepaid Water Meter Based on LC Sensor

This study presents a novel low-cost and low-power prepaid water meter system that combines tokenization and LC sensors to monitor water consumption accurately with mobile application via Bluetooth Low Energy (BLE) connectivity compared to conventional meters. Water meters play a vital role in monitoring water usage in Indonesia. Postpaid billing methods that rely on manual data recording are a source of concern due to potential inaccuracies caused by human error. This study presents the development of a prepaid water meter system that integrates LC sensors, BLE connectivity, a tokenization mechanism, and a mobile application to address this issue. The system offers a cost-effective solution by utilizing BLE + Global System for Mobile (GSM) from the user’s mobile phone. Using the design thinking methodology, the mobile application for the prepaid water meter achieved a usability testing score of 80. The load testing results for the back-end server, conducted with a sample size of 515 users, revealed a back-end latency of 1.973 milliseconds and an error rate of 8.74%. Furthermore, the LC sensors integrated into the PWM device showed an average error rate of 1.33%. The power consumption during each work cycle was measured at 129 mA and each battery is expected to last six years. Overall, with simple LC sensors, this system can precisely measure water usage.

Towards full autonomy in mobile robot navigation and manipulation

AbstractRobot autonomy is a key for automatizing industrial production plants, facilitating responsive search and rescue (SAR), and for interacting in urban settings. This work presents mobile systems capable of carrying out these tasks and summarises experience from practical deployments in three different domains for navigation. For industry, a mobile manipulator is presented, which is used for dynamic precise docking and object handling. For SAR, a semi-autonomous system is presented which is used for sampling hazardous materials, manipulating tools, and for localizing nuclear radiation sources. To improve urban logistics, a mobile robot autonomously navigates complex outdoor environments using semantic mapping techniques. Future work will explore further advancements that strive toward increased flexibility, reasoning and universal applicability of mobile robots. The goal is to reduce training, onboarding and tool changing times to reduce manual interfacing times.

Adaptive Fault-Tolerant Control of Mobile Robots with Fractional-Order Exponential Super-Twisting Sliding Mode

Industrial mobile robots easily experience actuator loss of some effectiveness and additive bias faults due to the working scenarios, resulting in unexpected performance degradation. This article proposes a novel adaptive fault-tolerant control (FTC) strategy for nonholonomic mobile robot systems subject to simultaneous actuator lock-in-place (LIP) and partial loss-of-effectiveness (LOE) faults. First, a nominal fractional-order sliding mode controller based on the designed exponential super-twisting reaching law is investigated to reduce the reaching phase time and eliminate the chattering. To address the time-varying LIP faults and uncertainties, a novel barrier function (BF)-based gain is explored to assist the super-twisting law. An estimator is designed to estimate the lower bound of the time-varying partial LOE fault coefficients, thus without requiring the boundary information of faults that is commonly requested in traditional FTC schemes. Combined with the nominal controller clubbed with BF and estimator-based LOE fault compensation term, the fault-tolerant controller is finally constructed. The proposed FTC scheme achieves fast convergence and the sliding variables can be confined in a predetermined neighborhood of the sliding manifold under actuator faults. The results show that the proposed controller has superior tracking performance under faulty conditions compared with other state-of-the-art adaptive FTC approaches.

Resource optimization of multi-UAV assisted communication system based on user scheduling

In order to improve the transmission rate of the multi-user mobile communication downlink wireless transmission system, a resource optimization method for multi-unmanned aerial vehicle (UAV) assisted communication system based on user scheduling and trajectory optimization is proposed. The proposed method establishes an optimization problem with the maximization of the total multi-user throughput as the criterion under the constraints of user scheduling, total energy consumption of UAVs and user service quality requirements. In order to solve this non-convex problem, the original non-convex problem is decomposed into three easy-to-handle non-convex sub-problems by using the block coordinate descent method (BCD). The sub-problems are transformed and solved by introducing slack variables, first-order Taylor expressions, continuous convex approximation (SCA) and other methods, and then alternately iteratively optimized to obtain an approximate suboptimal solution to the original non-convex problem. Simulation results show that the proposed algorithm can effectively improve the total system throughput and has good convergence in both single and multi-UAV communication systems.

Towards sustainable IoT-based smart mobility systems in smart cities

Towards sustainable IoT-based smart mobility systems in smart cities

Reengineering Web applications into pervasive information systems

In today’s mobile-driven world, organizations face the challenge of adapting legacy Web applications to pervasive information systems, such as mobile applications, ensuring seamless accessibility across devices. This paper presents a systematic, three-phase approach to reengineering Web applications into mobile applications: Reverse-engineering, Transformation, and Forward-engineering. In the reverse-engineering phase, high-level conceptual models of the Web application are generated to capture its architecture and functionality. During the transformation phase, these models are refined using Unified Modeling Language (UML) profiles to align with the requirements of mobile systems. The forward-engineering phase utilizes the Acceleo technology, along with predefined mapping rules and code patterns, to automate the generation of mobile application code. A case study is conducted to demonstrate the effectiveness of this framework, revealing a significant reduction in manual effort throughout the reengineering process. This approach offers a scalable and efficient solution for transforming Web applications into mobile applications, contributing to advancements in model-driven engineering and software reengineering.

The Transition Pathways to Sustainable Urban Mobility: Could They Be Extended to Megacities?

Population concentration in urban areas has placed cities at the forefront of the global struggle to achieve the Sustainable Development Goals. Within cities, current mobility patterns are responsible for a significant proportion of environmental emissions. As a result, cities across the world are seeking to develop transitions towards new and greener mobility systems. This paper analyses the different pathways that explain the readiness, or otherwise negation, of cities in achieving sustainable mobility. Based on a sample of 65 cities from all over the world, and with the application of Set-Theoretic Multi-Method Research and Necessary Conditions Analysis, it is demonstrated that there are necessary conditions for the achievement of sustainable mobility, as well as different terms that explain its achievement and its denial. Moreover, the analysis confirms that both the necessary conditions for sustainable mobility and one of the terms explaining its denial reflect the existence of causal mechanisms. The paper confirms the necessity for an analysis of the transitions towards sustainable mobility to take into account the characteristics of the context. Furthermore, it is not possible to explain the behaviour of megacities on the basis of generalised statements.

Evaluating Mobile LiDAR Intensity Data for Inventorying Durable Tape Pavement Markings.

Good visibility of lane markings is important for all road users, particularly autonomous vehicles. In general, nighttime retroreflectivity is one of the most challenging marking visibility characteristics for agencies to monitor and maintain, particularly in cold weather climates where agency snowplows remove retroreflective material during winter operations. Traditional surface-applied paint and glass beads typically only last one season in cold weather climates with routine snowplow activity. Recently, transportation agencies in cold weather climates have begun deploying improved recessed, durable pavement markings that can last several years and have very high retroreflective properties. Several dozen installations may occur in a state in any calendar year, presenting a challenge for states that need to program annual repainting of traditional waterborne paint lines, but not paint over the much more costly durable markings. This study reports on the utilization of mobile mapping LiDAR systems to classify and evaluate pavement markings along a 73-mile section of westbound I-74 in Indiana. LiDAR intensity data can be used to classify pavement markings as either tape or non-tape and then identify areas of tape markings that need maintenance. RGB images collected during LiDAR intensity data collection were used to validate the LiDAR classification. These techniques can be used by agencies to develop accurate pavement marking inventories to ensure that only painted lines (or segments with missing tape) are repainted during annual maintenance. Repeated tests can also track the marking intensity over time, allowing agencies to better understand material lifecycles.

The Effectiveness of Tiktok in Increasing Brand Awareness Among Users

Technological advancements have revolutionized marketing strategies, particularly in the realm of social media. As businesses recognize the importance of brand awareness in consumer decision-making, platforms like TikTok have emerged as powerful tools for enhancing brand visibility. Despite its rapid rise in popularity, TikTok's effectiveness in increasing brand awareness remains underexplored. This study investigates "The Effectiveness of TikTok in Increasing Brand Awareness Among Users" with three primary objectives: (RO1) to identify the effect of Mobile Application Values, Information Quality, System Quality, and Influencer Trust on Brand Awareness; (RO2) to identify the effect between Brand Awareness and Word of Mouth; and (RO3) to analyze the effect between Brand Awareness on Purchase Intention. The research adopts a quantitative approach, utilizing a questionnaire distributed to B2C TikTok users. A sample size of 343 respondents was determined using stratified sampling. Descriptive and regression analyses were conducted to interpret the data. The results indicate that Mobile Application Value, System Quality, and Influencer Trust significantly influence Brand Awareness on TikTok, while Information Quality does not exhibit a significant effect. Moreover, Brand Awareness positively impacts both Word of Mouth and Purchase Intention, underscoring its pivotal role in consumer behavior. This study contributes to a deeper understanding of TikTok's potential as a branding tool and offers practical insights for marketers seeking to leverage the platform effectively. Despite limitations in sample size and survey biases, the findings provide a foundation for future research and the refinement of marketing strategies in the dynamic landscape of social media.

Élő method based mobile TV recommendation system

AbstractStreaming services spread rapidly. Among these services there are the linear TV, video library or program review system, while the online platform offering these contents is called mobile TV. A recommendation system may not only keep existing clients, but may also generate further turnover, should it introduce new content to the users. In this paper a recommendation system based on the Élő point calculation method is addressed. It is detailed how the programs should be grouped into different dimensions and what type of categories should be considered. Further, the idea of punch cards is introduced. Besides, the user profiles are set. The match system introduced by Élő is applied to the present situation. The system is introduced at a local mobile TV provider with 20,000 users.