Mobility Model Research Articles

Single-Molecule Pycnometry and Shape Analysis of Ions in the Gas Phase.

The analysis of ions and clusters by mobility-classified mass spectrometry provides information on the mobility of analytes in the drift gas and the analyte mass. Mass equivalent and mobility equivalent diameters of globular analytes, such as ions, poly(ethylene glycol) (PEG), and ionic liquid nanodroplets, can be correlated with good accuracy by the Stokes-Millikan mobility model. A prerequisite to such an analysis is, however, the assumption of a globular analyte shape, which then allows determination of material density for globular ions. We show that the analyte density can be evaluated with high precision, independent of any assumptions on the analyte shape, by careful analysis of analyte-PEG-cluster ions following the concept of classical pycnometry. In particular, the analyte is entrapped in a globular PEG-analyte droplet. Based on the now independently derived mobility diameter and volume equivalent diameter, it is possible to attribute two parameters, size and shape, to the analyte molecule. We demonstrate the approach for lysozyme, cyano-cobalamin (vitamin B12), and glucose, which cover two orders of magnitude in analyte mass (180···14 300 Da). The derived densities for these analytes are highly accurate, i.e., they deviate less than 1% from literature values. Our method can be applied to newly synthesized molecules, supramolecular assemblies, isolated biomolecules, and molecular clusters, where only minor amounts of materials are available. The obtained shape parameters of lysozyme and cyano-cobalamin agree well with the expected molecular shapes. Data evaluation relies only on locations of the species in the mass-mobility plane and is in principle independent of any mobility theory. Our approach is thus robust with respect to experimental uncertainties and produces identical results irrespective of the type of mobility classification and drift gas.

The mobile augmented reality acceptance model for teachers and future teachers

This study examines the factors that affect pre-service and in-service teachers’ intention to use Mobile Augmented Reality (MAR) in their teaching through the proposed Mobile Augmented Reality Acceptance Model (MARAM). The MARAM builds on the existing Technology Acceptance Model (TAM) to incorporate four other components namely perceived relative advantage, perceived enjoyment, facilitating conditions, and mobile self-efficacy. In addition, this study investigates the validity of the MARAM. Data were collected from 137 pre-service and 169 in-service teachers who completed a questionnaire after having developed their own MAR applications during an undergraduate university course and a training seminar, respectively. Structural Equation Model (SEM) analysis was conducted separately for each group, as well as for both samples together. We also conducted a multi-group analysis to examine differences across the two samples. The results for both samples together (N = 306), showed that intention was affected by attitude, perceived usefulness and facilitating conditions. In turn, attitude was affected by perceived enjoyment and perceived usefulness. Perceived usefulness was affected by perceived enjoyment and perceived relative advantage. Perceived ease of use was affected by mobile self-efficacy and facilitating conditions. However, perceived ease of use did not affect perceived usefulness or attitude. A multi-group analysis conducted on the sample of pre-service and in-service teachers produced similar results, with minor differentiations. These results have implications for the use of MAR in research and schools, as well as technology acceptance models in education.

Neural‐based online finite‐time optimal tracking control for wheeled mobile robotic system with inequality constraints

AbstractIn this study, a finite‐time online optimal controller was designed for a nonlinear wheeled mobile robotic system (WMRS) with inequality constraints, based on reinforcement learning (RL) neural networks. In addition, an extended cost function, obtained by introducing a penalty function to the original long‐time cost function, was proposed to deal with the optimal control problem of the system with inequality constraints. A novel Hamilton‐Jacobi‐Bellman (HJB) equation containing the constraint conditions was defined to determine the optimal control input. Furthermore, two neural networks (NNs), a critic and an actor NN, were established to approximate the extended cost function and the optimal control input, respectively. The adaptation laws of the critic and actor NN were obtained with the gradient descent method. The semi‐global practical finite‐time stability (SGPFS) was proved using Lyapunov's stability theory. The tracking error converges to a small region near zero within the constraints in a finite period. Finally, the effectiveness of the proposed optimal controller was verified by a simulation based on a practical wheeled mobile robot model.

Call detail record aggregation methodology impacts infectious disease models informed by human mobility.

This paper demonstrates how two different methods used to calculate population-level mobility from Call Detail Records (CDR) produce varying predictions of the spread of epidemics informed by these data. Our findings are based on one CDR dataset describing inter-district movement in Ghana in 2021, produced using two different aggregation methodologies. One methodology, "all pairs," is designed to retain long distance network connections while the other, "sequential" methodology is designed to accurately reflect the volume of travel between locations. We show how the choice of methodology feeds through models of human mobility to the predictions of a metapopulation SEIR model of disease transmission. We also show that this impact varies depending on the location of pathogen introduction and the transmissibility of infections. For central locations or highly transmissible diseases, we do not observe significant differences between aggregation methodologies on the predicted spread of disease. For less transmissible diseases or those introduced into remote locations, we find that the choice of aggregation methodology influences the speed of spatial spread as well as the size of the peak number of infections in individual districts. Our findings can help researchers and users of epidemiological models to understand how methodological choices at the level of model inputs may influence the results of models of infectious disease transmission, as well as the circumstances in which these choices do not alter model predictions.

Dynamic path planning of mobile robots using adaptive dynamic programming

Dynamic path planning has gained increasing popularity in mobile robot navigation. Some of the current path planning methods require a priori information about the motion space and are easily affected by the distribution of obstacles. To address the above limitation, this paper presents a novel dynamic method that transforms path planning into an optimal control problem and solves it dynamically through adaptive dynamic programming and artificial potential field. The proposed method can obtain optimal paths for a differentially-driven mobile robot model in an unknown environment with many irregular obstacles. First, by combining path optimization and kinematical constraints of the mobile robot, the original problem is transformed into a new problem. Second, the total distance traveled, the effect of heading angle, the distance from the target to the robot, and the resultant force of the artificial potential field are included in the new performance index function. Third, the method based on adaptive dynamic programming is developed to avoid obstacles and guarantee the safety of autonomous navigation. The convergence analysis provides theoretical guarantees for our method, and the iterative control sequence will converge to the optimal control. Furthermore, simulation results and analyses under different complexity levels demonstrate that our method has promising performance in exploring and exploiting dynamic path planning problems.

Redesigning of Mobile Content to Enhance Learning Activities Preparation by Preschool Experts

Mobile content provision at the preschool stage has grown after the post-pandemic Covid-19.Preschool teachers face varying degrees of mobile technology literacy to prepare learningactivities that enhance children’s understanding and acquisition of knowledge and skills.Nevertheless, these teachers have minimal exposure and input to mobile content elementsthat can enhance learning. This study aims to redesign the mobile content model by redefiningthe required elements to improve learning activities preparation for preschoolers’basic problem-solving skills in mathematics. This case study involves seven preschool expertswhose data was collected using the Nominal Group Technique approach. Then, InterpretiveStructural Modelling was applied to develop the model through a voting process. The techniquewas used to rank the data from the experts before running the voting session. Thefindings revealed eleven principal elements for the mobile content model that can be dividedinto three content execution streams. The results offer several implications and benefitsto the mobile learning arena, particularly in early childhood education, due to its potentialto enhance engagement, interaction, and personalized learning experiences for younglearners. These outcomes provide insights into the best practices, strategies, and pedagogicalapproaches that can optimize the use of mobile devices in early childhood education, hence,promoting effective learning outcomes.

Advances in Inversion Channel Mobility Model for 4H-SiC MOS Devices

Advances in Inversion Channel Mobility Model for 4H-SiC MOS Devices

Exploring the Impact of Labour Mobility on the Mental Health and Wellbeing of Skilled Trades Workers in Ontario, Canada.

Labour mobility and subsequent workers migration is an increasing trend worldwide and can be a force that counteracts Canada's shortage of skilled labour. Supercommuting allows workers facing economic challenges to pursue more financially advantageous work opportunities in other regions. This study aimed to evaluate the "supercommuting" labour mobility model and its impact on long-distance mobile workers' mental health and wellbeing. We utilized a non-experimental research design using convenience sampling from workers who participated in Blue Branch Inc.'s (Hamilton, Canada) supercommuting labour mobility model. An online questionnaire collected demographic data, work-related data, occupational stress measures related to burnout, and job-related stress data. Data collection was started on 1 April 2021, and of the total 58 participants, the majority (44, 76%) were male, born outside Canada, and had an average age of 32.8 years. Workplace Safety (95%), full-time employment opportunity (95%), career advancement possibility (95%), and income and benefits (94.9%) were found to be the most crucial factors to keep study participants working in their current position. Of the 47 participants who experienced burnout, only one showed severe burnout in each domain (personal, work-related, and colleague-related). There is a great need for preventative burnout programs and supportive employer resources for those who engage in long-distance labour commuting. The study emphasizes the need to encourage policymakers to develop solutions for training future Ontario workers to support mobile employment and long-distance labour commuting.

The impact of containment policy and mobility on COVID-19 cases through structural equation model in Chile, Singapore, South Korea and Israel.

The study aims to understand the impact of containment policy and mobility on COVID-19 cases in Chile, Singapore, South Korea and Israel. To provide experience in epidemic prevention and control. Structural equation modeling (SEM) of containment policies, mobility, and COVID-19 cases were used to test and analyze the proposed hypotheses. Chile, Israel and Singapore adopted containment strategies, focusing on closure measures. South Korea adopted a mitigation strategy with fewer closure measures, focusing on vaccination and severe case management. There was a significant negative relationship among containment policies, mobility, and COVID-19 cases. To control the COVID-19 and slow down the increase of COVID-19 cases, countries can increase the stringency of containment policies when COVID-19 epidemic is more severe. Thus, countries can take measures from the following three aspects: strengthen the risk monitoring, and keep abreast of the COVID-19 risk; adjust closure measures in time and reduce mobility; and strengthen public education on COVID-19 prevention to motivate citizen to consciously adhere to preventive measures.

Adaptive Decoding Mechanisms for UAV-Enabled Double-Uplink Coordinated NOMA

In this paper, we propose a novel adaptive decoding mechanism (ADM) for the unmanned aerial vehicle (UAV)-enabled uplink (UL) non-orthogonal multiple access (NOMA) communications. Specifically, considering a harsh UAV environment, where ground-to-ground links are regularly unavailable, the proposed ADM overcomes the challenging problem of conventional UL-NOMA systems whose performance is sensitive to the transmitter's statistical channel state information and the receiver's decoding order. To evaluate the performance of the ADM, we derive closed-form expressions for the system outage probability (OP) and system throughput. In the performance analysis section, we provide novel expressions for practical air-to-ground and ground-to-air channels, while taking into account the practical implementation of imperfect successive interference cancellation (SIC) in UL-NOMA. Moreover, the obtained expression can be adopted to characterize the OP of various systems under a Mixture of Gamma (MG) distribution-based fading channels. Next, we propose a sub-optimal Gradient Descent-based algorithm to obtain the power allocation coefficients that result in maximum throughput with respect to each location on UAV's trajectory. To determine the significance of the proposed ADM in nonstationary environments, we consider the ground users and the UAV to move according to the Random Waypoint Mobility (RWM) and Reference Point Group Mobility (RPGM) models, respectively. Accurate formulas for the distance distributions are also provided. Numerical solutions demonstrate that the ADM-enhanced NOMA not only outperforms Orthogonal Multiple Access (OMA), but also improves the performance of UAV-enabled UL-NOMA even in mobile environments.

Safe Data-Driven Lane Change Decision Using Machine Learning in Vehicular Networks

This research proposes a unique platform for lane change assistance for generating data-driven lane change (LC) decisions in vehicular networks. The goal is to reduce the frequency of emergency braking, the rate of vehicle collisions, and the amount of time spent in risky lanes. In order to analyze and mine the massive amounts of data, our platform uses effective Machine Learning (ML) techniques to forecast collisions and advise the driver to safely change lanes. From the unprocessed large data generated by the car sensors, kinematic information is retrieved, cleaned, and evaluated. Machine learning algorithms analyze this kinematic data and provide an action: either stay in lane or change lanes to the left or right. The model is trained using the ML techniques K-Nearest Neighbor, Artificial Neural Network, and Deep Reinforcement Learning based on a set of training data and focus on predicting driver actions. The proposed solution is validated via extensive simulations using a microscopic car-following mobility model, coupled with an accurate mathematical modelling. Performance analysis show that KNN yields up to best performance parameters. Finally, we draw conclusions for road safety stakeholders to adopt the safer technique to lane change maneuver.

Location Recommendation Based on Mobility Graph With Individual and Group Influences

With the rapid development of mobile technology, it is very convenient to share people's current locations by checking-in on Location-Based Social Networks (LBSNs). Using users' check-in histories to study mobility preferences and recommend new locations is a typical application to LBSNs. Most existing models explore reasonable representations for users and locations. However, a lack of behavioral mobility modeling would hamper a better understanding of users' mobility patterns. This paper proposes a location recommendation model to serve the personalized LBSNs application, called Spatio-temporal Individual mobility graph encoding network with Group Mobility Assistance (SIGMA). We design a spatio-temporal interaction enhanced graph neural network to encode the mobility graphs to represent individual mobility behaviors. Furthermore, we provide a novel stacked scoring approach to generate the recommendation score by combining the stacked individual mobility graphs with the group influences. We conduct extensive experiments on two real-world LBSNs data, Foursquare and Gowalla. The result demonstrates SIGMA outperforms ten state-of-the-art models and further confirms that both the individual and the group mobility behaviors play essential roles in the practical scenario of location recommendation.

A privacy-preserving trajectory data synthesis framework based on differential privacy

Trajectory data contains spatio-temporal information about a large number of moving objects, and analysis of this data can yield a lot of valuable information to support a variety of application requirements related to moving objects. However, publishing unprotected trajectory data carries the risk of revealing personal privacy. In the existing approaches, the utility of published trajectory data is sacrificed for privacy protection. In this paper, we propose a differential privacy mechanism DP-Std for data publication in synthetic trajectory databases. first, the DP-Std mechanism constructs a density- and distance-aware adaptive grid structure for discrete geographic regions, and adds noise in the process to satisfy differential privacy. Second, DP-Std extracts feature distributions to ensure the utility of synthetic trajectories. Third, DP-Std constructs a mobility model that samples intermediate location points. And then the trajectory dataset is synthesized by differential privacy distribution and mobility model. Finally, we conduct experiments on two datasets to evaluate our framework. The results show that DP-Std outperforms other feature-based trajectory synthesis methods in terms of data utility and achieves a trade-off between privacy and utility with strict privacy protection.

The role of deep learning in the innovation of smart classroom teaching mode under the background of internet of things and fuzzy control

Electronic components are rapidly updated in the context of expanding application requirements, and communication protocols used in combination with various electronic devices are also emerging. On this basis, IoT technology has developed a variety of sensor devices and gateways, which are widely used in cities. In the field of wisdom, applying IoT technology to classrooms can effectively improve the deficiencies of traditional teaching models. Fuzzy control theory is usually based on fuzzy sets in mathematics, and is combined with neural network, genetic and probability algorithms to form a calculation method. Fuzzy calculation has the ability to simplify the system input of a variety of complex variables, and its applications in the field of education are mainly: provide evaluation of teachers' teaching effectiveness. The advancement of science and technology has promoted the change and updating of the teaching mode. With the continuous advancement of basic education curriculum reform and the continuous deepening of classroom teaching reform, classroom teaching is also in urgent need of reform, from traditional classrooms to smart classrooms. Smart classrooms combine advanced technology with teachers' teaching. Through the dynamic data, the analysis instantly understands the student's learning situation, and then integrates it into education and teaching in a targeted manner. This paper conducts a questionnaire survey on the current situation of smart classroom teaching, and summarizes the current teaching problems. Then, combining the Internet of Things, fuzzy control and deep learning technology, from the two aspects of school teachers and students, it is proposed for smart classroom to promote students' learning effect. With its novel and new-style teaching advantages, smart classroom has gradually entered the public's vision and gained the attention and support of the majority of educators. Taking Grand Wisdom Classroom as an example, it uses the “Internet +" way of thinking and the new generation of information technology such as big data and cloud computing to create intelligent and efficient classrooms, realizing the whole process of application before, during and after class, and promoting the development of students' wisdom. Under the mobile Internet model, students and teachers can communicate anytime and anywhere. Combined with the analysis and application of our big data technology, data-based precision teaching becomes possible. In a real sense, learning before teaching can be realized and teaching can be determined by learning.

Prêt-à-quitter: Career mobility and entrepreneurship in the global high-end fashion industry

We test a general model of career mobility and entrepreneurship based on the premise that job transitions between organizations are influenced by the unique role of the organizational founder. Three related ideas inform our inquiry. First, individuals in that role are endowed with the right to act as representatives of their organizations which increases commitment and deters external mobility. Second, the founder role, because of its uniqueness, defines how entrepreneurs think of themselves thus aligning person and position. Repeat entrepreneurship occurs because after a founder leaves, this alignment is disrupted and the need to restore it leads to becoming a founder again. Third, we see the founder role as imbued with charismatic authority. This creates an aura of deference and a propensity to emulate the founder that inspires organizational members working alongside the founder to themselves become entrepreneurs. We investigate these ideas empirically in the context of the global high-end fashion industry through a research design that allows us to compare leading designers' career histories as both founders and members and their transitions to and out of entrepreneurship.

The impact of COVID-19 pandemic on the fundamental urban mobility theories using transit data from Singapore

The COVID-19 pandemic has caused changes in urban mobility patterns due to the temporary movement restrictive policies imposed by local governments and the long-term hybrid work arrangements adapted by companies. This study re-examines the fundamental theories of human mobility in Singapore at the pre-pandemic, pandemic and endemic stages. In particular, we estimated three well-established mobility models in complexity science: gravity model, radiation model and visitation law for investigating the effects of COVID-19 pandemic in different stages, using the transit Origin-Destination (O-D) data collected in Singapore. Overall, the results show that the three mobility theories remain valid throughout the different periods with visitation law outperforming the gravity and the radiation models. At the pre-pandemic stage, the performance of the gravity and radiation models were comparable. The performance of the gravity model has improved compared to its pre-pandemic performance, showing 6–18% improvement at the pandemic stage and 9–15% improvement at the post pandemic stage, whereas the performance of the radiation model hasn’t changed much, making gravity model a better-performing model than the radiation model for these periods. Our results also show that in the gravity and radiation models, gross floor area is a better predictor for transit flows than the population, whereas in the visitation law use of population variable results in better predictions. Our study is a first attempt in providing a revised understanding of the performance of the fundamental human mobility laws in the post-pandemic world.

Research on mobile terminal sketch 3D modeling technology based on interactive design

Research on mobile terminal sketch 3D modeling technology based on interactive design

A process calculus SMrCaIT for IoT

AbstractWith the rapid popularization of smart devices, the applications and technologies of the Internet of Things (IoT) are in high demand worldwide, especially in improving development efficiency and ensuring system quality, reliability, and security. Formal methods have been successfully used to specify, verify, and analyze software and hardware systems, effectively alleviating the above problems in these systems. However, most of the existing works mainly focus on the practical applications of IoT, and there is a lack of research on modeling and analyzing IoT systems from the perspective of formal methods. In this paper, we first propose a secure mobile real‐time process calculus for specifying and reasoning about IoT systems, called SMrCaIT, which supports not only value‐passing communication but also name‐passing communication. In addition, this calculus can strictly separate process actions and mobility modeling by providing parametric mobility models. Subsequently, we present the operational semantics of this calculus, in particular, the rules on how to secure channel communication by closing the scope of a channel and handling scope extrusion. Taking vehicle ad hoc network (VANET) as a case, the application details of SMrCaIT and its operational semantics are fully demonstrated. By using the rewrite engine Real‐Time Maude, we further implement SMrCaIT and its operational semantics and verify some properties of the VANET case to indicate the effectiveness of our calculus in real‐world scenes.

Simulating Smart Cities Under Different Scenarios and Factors

In this research, we suggest building or utilizing a virtual environment of a smart city where fixed and mobile sensors are arranged in a certain way, using particular movement models and techniques for transmitting and sharing data via the virtual city. This proposal proposes to carry out several experiments for various movement models, such as:(Individual Mobility Model,Exponential movement model and Rayleigh Flight movement model), A specific data distribution (Gaussian, Littice, Power-law, Exponential) and a certain data routing methodology (Spray and Wait, Probabilistic Flooding, and Epidemic) were used to examine the impact of this on the use of network resources. In this proposal, we also want to look at the usage of various resources, including electricity and data. A movement model, an item distribution strategy, and a data routing protocol were all combined to create each experiment that was intended to be conducted in this proposal. All submitted experiments will ultimately be compared to one another, and recommendations that assist the research and design community in conserving network resources will be made. the findings show that the traffic models that were used with mobile nodes are considered one of the important factors in determining the consumption of network resources. In addition to the influence of traffic models on the consumption of network resources, algorithms and methods of routing data also have an impact. The Human Mobility Model, which accurately expresses human movement behavior, is considered one of the most stable models in terms of performance.

The mediating effect of allostatic load on the association between life course socioeconomic disadvantage and chronic pain: a prospective finding from the National Survey of Midlife Development in the United States.

Socioeconomic disadvantages (SEDs) are associated with chronic pain (CP) and allostatic load (AL). Few prospective population-based studies have examined the relationship between life course SED, CP interference, and CP widespreadness, and there is no prospective population-based study on whether AL mediates the association between SED and CP. In this study, we investigated whether the prospective effect of SED on CP at Midlife in the United States (MIDUS) 3 is consistent with the accumulation of risk model and social mobility model, using the National Survey of MIDUS (n = 593). To prepare for the mediation analysis, we tested (1) whether SED would be prospectively associated with AL in the MIDUS 2 biomarker project, (2) whether AL would be prospectively associated with CP, and (3) whether childhood, as a critical period, moderated the association between AL and CP. In addition, the mediating effect of AL on the association between SED and CP was examined. SED was measured using cumulative scores and disadvantage trajectories derived from latent class trajectory modeling (LCTM). After multiple imputations, analyses were conducted using multinomial logistic regression for CP and negative binomial regression for AL, respectively. Finally, mediation analyses and moderated mediation analyses were performed. LCTM identified three SED trajectories, namely, constant low, high to low, and medium to high. The results showed that proximal cumulative SED was associated with high-interference CP. Furthermore, compared with the group with constant low SED, the group with medium-to-high SED was significantly associated with high-interference pain and experienced pain in at least three different sites. Cumulative SED and deteriorating SED trajectories were associated with higher AL, consistent with previous studies. Furthermore, childhood SED moderated the effect of AL on CP widespreadness and unexpectedly demonstrated a protective effect, while other associations between AL and CP were not significant. Subsequent mediation analysis did not yield statistically significant evidence. People who experienced more recent SED or increasing disadvantage throughout their lives were more likely to suffer from CP, and this association was not mediated by physiological system dysregulation caused by chronic stress. Therefore, measures to alleviate AL may not be effective in protecting socioeconomically disadvantaged populations from CP.