Mobility Model Research Articles

An Optimization Method for Lightweight Rock Classification Models: Transferred Rich Fine-Grained Knowledge.

Rock image classification represents a challenging fine-grained image classification task characterized by subtle differences among closely related rock categories. Current contrastive learning methods prevalently utilized in fine-grained image classification restrict the model's capacity to discern critical features contrastively from image pairs, and are typically too large for deployment on mobile devices used for in situ rock identification. In this work, we introduce an innovative and compact model generation framework anchored by the design of a Feature Positioning Comparison Network (FPCN). The FPCN facilitates interaction between feature vectors from localized regions within image pairs, capturing both shared and distinctive features. Further, it accommodates the variable scales of objects depicted in images, which correspond to differing quantities of inherent object information, directing the network's attention to additional contextual details based on object size variability. Leveraging knowledge distillation, the architecture is streamlined, with a focus on nuanced information at activation boundaries to master the precise fine-grained decision boundaries, thereby enhancing the small model's accuracy. Empirical evidence demonstrates that our proposed method based on FPCN improves the classification accuracy mobile lightweight models by nearly 2% while maintaining the same time and space consumption.

Comparison of initial dental treatment decisions between in-person and asynchronous teledentistry examinations for people with special health care needs

BackgroundPeople with special health care needs in long-term care settings have difficulty accessing a traditional dental office. The goal of the authors was to assess initial treatment decision concordance between dentists conducting traditional in-person examinations using mobile equipment and additional dentists conducting examinations using asynchronous teledentistry technology. MethodsSix dentists from Access Dental Care, a North Carolina mobile dentistry nonprofit, saw new patients on-site at 12 participating facilities or asynchronously off-site with electronic dental records, radiographs, and intraoral images, all captured by an on-site dental hygienist. Off-site dentists were masked to other dentists’ treatment need decisions; 3 through 5 off-site examinations were conducted for each on-site examination. Demographic and binary treatment need category data were collected. For the 3 most prevalent treatment types needed (surgery, restorative, and new removable denture), the authors calculated the percentage agreement and κ statistics with bootstrapped CIs (1,000 replicates). ResultsThe 100 enrolled patients included 47 from nursing homes, 45 from Programs of All-Inclusive Care for the Elderly, and 8 from group homes for those with intellectual and developmental disabilities. Mean (SD) age was 73.9 (16.5) years. Among dentate participants, the percentage agreement and bootstrapped κ (95% CI) were 87% and 0.74 (0.70 to 0.78) for surgery and 78% and 0.54 (0.50 to 0.58) for restorative needs, respectively, and among dentate and edentulous participants, they were 94% and 0.78 (0.74 to 0.83), respectively, for new removable dentures. ConclusionsThe authors assessed the initial dental treatment decision concordance between on-site dentists conducting in-person examinations with a mobile oral health care delivery model and off-site dentists conducting examinations with asynchronous dentistry. Concordance was substantial for surgery and removable denture treatment decisions and moderate for restorative needs. Patient characteristics and facility type were not significant factors in the levels of examiner agreement. Practical ImplicationsThis evidence supports teledentistry use for patients with special health care needs and could help improve their access to oral health care.

A visit generation process for human mobility random graphs with location-specific latent-variables: From land use to travel demand

This research introduces a novel mathematical framework for understanding collective human mobility patterns, integrating mathematical modeling and data analysis. It focuses on latent-variable networks to investigate the dynamics of human mobility using stochastic models. By analyzing origin–destination data, the study uncovers scaling relations and explores the economic implications of mobility patterns, particularly regarding the income elasticity of travel demand. The mathematical analysis begins with the development of a stochastic model based on inhomogeneous random graphs, constructing a visitation model with multipurpose drivers for travel demand. Through this model, the study gains insights into the structural properties and dynamic correlations of human mobility networks, deriving analytical solutions for key network metrics: visit distribution, assortativity behavior and clustering coefficient. Empirically, the study validates the model’s assumptions and reveals scaling behaviors in origin–destination flows within a region, reproducing statistical regularities observed in real-world cases. Notably, the model’s application to estimating income elasticity of travel demand provides significant implications for urban and transport economics. Overall, this research contributes to a deeper understanding of the interplay between human mobility and regional demographics and economics. It sheds light on critical scaling relations across various aspects of collective human mobility and underscores the importance of incorporating latent-variable structures into mobility modeling for accurate economic analysis and decision-making in urban and transportation planning.

Using mobile technology acceptance model to explore the satisfaction and continuous use of live streaming for training

ABSTRACT During the digital era and especially the COVID-19 pandemic, live streaming emerged as a vital component of mobile learning and training. This study sought to determine whether the social presence and interactivity experienced by learners using live digital platforms could enhance their learning outcomes, satisfaction, and propensity for ongoing use.Leveraging the mobile technology acceptance model as its research framework, this study integrated interactivity and social presence as external variables. The aim was to uncover how these variables impact user satisfaction and continued use, while also exploring the mediating role of learning effectiveness in driving continued use. A live streaming system designed for mobile training was tested among 67 employees in a pilot study. The results demonstrated that:(1)Ease of use positively impacted perceptions of mobile usefulness.(2)Mobile usefulness, interactivity, and social presence had a positive effect on satisfaction, yet ease of use didn't significantly influence satisfaction.(3)Interactivity enhanced social presence.(4)Satisfaction was a key driver of continued use, with learning effectiveness acting as a partial mediator between them. The findings from this research can guide businesses looking to utilize live streaming for training purposes and serve as a reference for academic research in similar areas.

Neural Robust Control for a Mobile Agent Leader–Follower System

A controller employing a combined new strategy of output feedback linearization and a recurrent high-order neural network (RHONN) adaptive approach for a mobile agent leader–follower system is presented. The controller structure is based on feedback linearization; then, a scheme of lumping uncertainties which are estimated via the RHONN is incorporated; with this estimate, the controller is able to produce a robust control action for mobile agents so they track a prescribed reference trajectory. Moreover, the nonlinear system part is transformed into a linearizable one; then, a specific function lumps all the nonlinearities, uncertain parameters, and unmodeled dynamics of the system; this overall function is estimated via the RHONN. Thus, both parametric uncertainties and unmodeled dynamics between agents can be compensated via the controller, and, subsequently, follower agents track the reference provided by the leader. The obtained controller is such that the estimation scheme is not based on high-gain controllers. Here, it is underlined that the main contribution consists of designing a nonlinear controller and combining it with an RHONN to estimate the nonlinear uncertainties in the leader–follower system. This control action includes robust features provided by the online recurrence and the nonlinear base of the neural network in which not general but specific parametric disturbances and unmodeled discrepancies are identified or compensated. For this control scheme, only nominal values of the system parameters are required, as well as the velocities of the agents. Numeric simulation of the model and designed tracking control are carried out in which the control law is applied to a two-wheeled differential mobile robot model, obtaining satisfactory results for tracking angular velocities of the wheels.

Emergence of Complex Network Topologies from Flow-Weighted Optimization of Network Efficiency

Transportation and distribution networks are a class of spatial networks that have been of interest in recent years. These networks are often characterized by the presence of complex structures such as central loops paired with peripheral branches, which can appear both in natural and manmade systems, such as subway and railway networks. In this study, we investigate the conditions for the emergence of these nontrivial topological structures in the context of human transportation in cities. We propose a simple model for spatial networks generation, where a network lattice acts as a planar substrate and edge speeds define an effective temporal distance which we aim to optimize and quantifies the efficiency in exploring the urban space. Complex network topologies can be recovered from the optimization of edges’ speeds and we study how the interplay between a flow probability between two nodes in space and the associated travel cost influences the resulting optimal network. In the perspective of urban transportation we simulate these flows by means of human mobility models to obtain origin-destination matrices. We find that when using simple lattices, the obtained optimal topologies transition from treelike structures to more regular networks, depending on the spatial range of flows. Remarkably, we find that branches paired to large loops structures appear as optimal structures when the network is optimized for an interplay between heterogeneous mobility patterns of small range travels and longer-range ones typical of commuting. Moreover, when congestion dynamics in traffic routing is considered, we study the emergence of additional edges from the tree structure to mitigate temporal delays. Finally, we show that our framework is able to recover the statistical spatial properties of the Greater London area subway network. Published by the American Physical Society 2024

Mobile addiction treatment and harm reduction services as tools to address health inequities: a community case study of the Brockton Neighborhood Health Center mobile unit.

Opioid overdose deaths continue to increase in the US. Recent data show disproportionately high and increasing overdose death rates among Black, Latine, and Indigenous individuals, and people experiencing homelessness. Medications for opioid use disorder (MOUD) can be lifesaving; however, only a fraction of eligible individuals receive them. Our goal was to describe our experience promoting equitable MOUD access using a mobile delivery model. We implemented a mobile MOUD unit aiming to improve equitable access in Brockton, a racially diverse, medium-sized city in Massachusetts. Brockton has a relatively high opioid overdose death rate with increasingly disproportionate death rates among Black residents. Brockton Neighborhood Health Center (BNHC), a community health center, provides brick-and-mortar MOUD access. Through the Communities That HEAL intervention as part of the HEALing Communities Study (HCS), Brockton convened a community coalition with the aim of selecting evidence-based practices to decrease overdose deaths. BNHC leadership and coalition members recognized that traditional brick-and-mortar treatment locations were inaccessible to marginalized populations, and that a mobile program could increase MOUD access. In September 2021, with support from the HCS coalition, BNHC launched its mobile initiative - Community Care-in-Reach® - to bring low-threshold buprenorphine, harm reduction, and preventive care to high-risk populations. During implementation, the team encountered several challenges including: securing local buy-in; navigating a complex licensure process; maintaining operations throughout the COVID-19 pandemic; and finally, planning for sustainability. In two years of operation, the mobile team cared for 297 unique patients during 1,286 total visits. More than one-third (36%) of patients received buprenorphine prescriptions. In contrast to BNHC's brick-and-mortar clinics, patients with OUD seen on the mobile unit were more representative of historically marginalized racial and ethnic groups, and people experiencing homelessness, evidencing improved, equitable addiction care access for these historically disadvantaged populations. Offering varied services on the mobile unit, such as wound care, syringe and safer smoking supplies, naloxone, and other basic medical care, was a key engagement strategy. This on-demand mobile model helped redress systemic disadvantages in access to addiction treatment and harm reduction services, reaching diverse individuals to offer lifesaving MOUD at a time of inequitable increases in overdose deaths.

Do languages open doors? A theoretical model of linguistic capital and (im)mobility and its application in Spanish youth migration

ABSTRACT Language skills have demonstrated their significance in migration decisions; however, their role as a potential cause of immobility has been largely overlooked. In this paper, I first propose a theoretical model that seeks to explain decisions regarding mobility and immobility based on the instrumental returns generated by an individual's linguistic capital within a given opportunity structure. Secondly, I apply this model utilizing data from university and vocational training graduates in Spain. Leveraging its particularity as a case study, which encompasses five regions where a minority's co-official language is an integral part of ethnic identification and shares a similar institutional environment that encourages its use, I find that bilingualism is primarily linked to internal immobility. Simultaneously, it affects the direction of internal migration, since bilinguals are more inclined to relocate to areas with similar language conditions. Bilingualism also presents indirect effects. The results indicate that bilingualism influences international migration by facilitating the acquisition of a foreign language, which becomes increasingly attainable as the cost of language acquisition decreases, though the effect remains relatively small.

A MILP-based power system parallel restoration model with the support of mobile energy storage systems

During bulk power system restoration, black-start (BS) units are used to crank non-BS units in such a way that the over system generation capability is maximized. However, traditional power system restoration methods rely on stationary BS units, whose number is limited due to high investment and geographic conditions, impeding a fast system restoration. To enhance restoration efficiency, this paper proposes an integrated power system parallel restoration method considering the support of mobile energy storage systems (MESSs), which can provide cranking power to non-BS units and transform them into MESS-assisted BS units. MESSs are routed and scheduled via transportation network, along with restoration planning in power network by integrating them into a mixed integrated linear programming model. Specifically, to describe the interactive behavior between MESSs and the power system, a mobility model for MESSs is proposed, which indicates the travel path, travel time and connection nodes. Then, an integrated optimization model involving BS unit location, zone partitioning, network reconfiguration and generator start-up is formulated. The proposed method was simulated in the modified IEEE 30-bus transmission system coupled with 11-node transportation system. The results show that, in contrast with a decoupled restoration method, the proposed method increases system generation capability by 6.3%.

Blind to culture. How classed cognition prevents cultural policy initiatives from increasing cultural participation for children

ABSTRACT Since its first formulation in the post war years, Nordic cultural policy has had a stated aim to democratize culture, but with limited success. A growing literature argues that understanding policy failure is needed for a better functioning cultural policy. The present study uses two waves of panel data on cultural participation for parents and children in Bergen, Norway (N = 4754) to analyze the impact of three locally implemented cultural policy interventions designed in part to democratize culture for children. Using work with cultural policy failure, and tools from Bourdieu’s Sociology of Culture, this study finds that policies fail in systematic ways related to classed cognition. This leads to a ‘Matthew effect’ for cultural goods, as policies enable easier access to culture for those already invested in cultural participation, while the policies fail to even out social inequalities. There are both scientific and policy implications of the findings. First, data show that a cultural reproduction model seems a better fit than a cultural mobility model of cultural participation. Second, policy planners rarely factor in implications of the former in implementation of cultural policies designed to equalize distributions of culture. As a consequence, cultural policy initiatives to reduce inequalities in access to culture may lead to an exacerbation of cultural inequalities.

Development of Modified Random Waypoint Mobility Model of Routing Protocol for the Mobile ad-hoc Network

Mobility models are used to evaluate the network protocols of the ad hoc network using the simulation. The random waypoint model is a model for mobility that is usually used for the performance evaluation of ad-hoc mobile networks. Mobile nodes have dynamic mobility in the ad hoc network, so the mobility model plays an important role to evaluate the protocol performance. In this article, we developed modify random waypoint mobility (MRWM) model based on a random waypoint for the mobile ad hoc network. In this article, the comparative analysis of modifying random waypoint mobility and random waypoint model on the ad hoc On-Demand Distance Vector (AODV) routing protocol has been done for a large wireless ad hoc network (100 nodes) with the random mobile environment for the 1800s simulation time. To enhance the confidence in the protocol widespread simulations were accomplished under heavy traffic (i.e. 80 nodes) conditions. The proposed model protocol has been investigated with the performance metrics: throughput; packet delivery ratio; packet dropping ratio; the end-to-end delay and normalized routing overhead. The obtained results revealed that the proposed modified random waypoint mobility model reduces the mobility as compared to the random waypoint mobility model and it is trace is more realistic.

Improved Highly Mobile Membrane Mimetic Model for Investigating Protein-Cholesterol Interactions.

Cholesterol (CHL) plays an integral role in modulating the function and activity of various mammalian membrane proteins. Due to the slow dynamics of lipids, conventional computational studies of protein-CHL interactions rely on either long-time scale atomistic simulations or coarse-grained approximations to sample the process. A highly mobile membrane mimetic (HMMM) has been developed to enhance lipid diffusion and thus used to facilitate the investigation of lipid interactions with peripheral membrane proteins and, with customized in silico solvents to replace phospholipid tails, with integral membrane proteins. Here, we report an updated HMMM model that is able to include CHL, a nonphospholipid component of the membrane, henceforth called HMMM-CHL. To this end, we had to optimize the effect of the customized solvents on CHL behavior in the membrane. Furthermore, the new solvent is compatible with simulations using force-based switching protocols. In the HMMM-CHL, both improved CHL dynamics and accelerated lipid diffusion are integrated. To test the updated model, we have applied it to the characterization of protein-CHL interactions in two membrane protein systems, the human β2-adrenergic receptor (β2AR) and the mitochondrial voltage-dependent anion channel 1 (VDAC-1). Our HMMM-CHL simulations successfully identified CHL binding sites and captured detailed CHL interactions in excellent consistency with experimental data as well as other simulation results, indicating the utility of the improved model in applications where an enhanced sampling of protein-CHL interactions is desired.

A Simulator Development of Surface Warship Torpedo Defense System considering Bubble-Generating Wake Decoy

The wake-homing underwater guided weapon that detects and tracks wake generated during voyage of a surface ship is impossible to avoid with the present acoustic deception torpedo defense system. Therefore, research on bubble-generating wake decoy is necessary to deceive wake-homing underwater guided weapon. Experiments in various environments are required to verify the effective operation method and performance of the wake decoy, but performance verification through underwater experiment is limited. In this paper, we develop a simulator for an torpedo defense system of surface ship, which is applied bubble-generating wake decoy, against acoustic, wake, and hybrid homing underwater guided weapon attack. The simulator includes surface ship model, acoustic decoy(static, mobile) model, bubble-generating wake decoy model, search and motion model of underwater guided weapon and so on. By integrating various models, MATLAB GUI simulator was developed. Through the simulation results for various environmental variables by this simulator, it is judged that effective operation method and performance verification of the bubble-generating wake decoy can be performed.

Evaluation of the effectiveness of methodological approaches to hierarchical intelligent control of ground-air Ad-Hoc communication network

The article shows the features of intelligent management processes of a new type of ground-air communication networks that are rapidly being implemented. The dynamic nature of the conditions of operation and behavior of communication nodes, both ground and air, causes a rapid increase in the volume of service information necessary to ensure continuous and adaptive management in real time. One of the ways to solve this problem is the redistribution of management tasks at different stages of the management cycle, which is classically divided into the stages of planning, deployment and operational management. On the one hand, the increase in entropy at the planning stage complicates this process, but this approach will increase the probability of making "correct" management decisions from the standpoint of quality management (network metrics). The work investigates a new architecture of a hierarchical intelligent ground-air communication network control system based on the model-free Reinforcement learning algorithm as a Q-learning network agent and FA-OSELM online sequential extreme machine learning algorithms as node-level agents. The IСS model is presented, its adequacy is checked, and the process of its learning at the planning stage on various mobility models is shown. An important feature of the IСS training process is the application of the developed mobility model, disclosed in the article, which describes the interaction processes of communication nodes at a deeper level. The work conducted a study of the representativeness of the training sample, obtained using the developed mobility model, that was carried out relative to the existing ones, and it was determined that despite the smaller volume of the population of the initial data, it was possible to ensure a better quality of resource management.

An enhanced image based mobile deep learning model for identification of Newcastle poultry disease

An enhanced mobile deep learning model based on images is presented in this paper to identify Newcastle poultry disease. A dataset of manually annotated and labeled images of the disease was utilized to pre-train an image-based Convolutional Neural Network (CNN). An Android smartphone app was developed to communicate with the model. A local server was integrated with the generated model to do image classification. A mobile application was developed and made available, enabling users to upload a fecal photograph to a website housed on the streamlet server and obtain the model's processed findings. The user regains control over their health status. The model achieved an accuracy of 95% on the test set and was able to correctly identify specific instances of Newcastle poultry disease. The paper discusses the advantages of a mobile-based approach in comparison to traditional methods of identification and proposes the model as an effective low-cost solution for farmers and researchers.

Improving on Complexity: Ideas for Enhancing Quantitative Modeling of Climate Mobility

AbstractQuantitative climate mobility research has, so far, focused primarily on climate change impacts on migration outcomes. This focus has led to a separation between quantitative climate migration research and the broader field of migration studies. In this paper ways are proposed for quantitative research to better address the complexity in the relationship between climate change and mobility. First technical suggestions are presented to improve upon migration model setups and designs and highlight promising developments. Then it is argued that quantitative methodologies can broaden the scope of research inquiries by examining how climate mitigation and adaptation efforts influence mobility, as well as assessing how mobility itself impacts vulnerability.

A joint model of residential mobility and location choice decisions

ABSTRACT Residential relocation is a process involving the interdependent decisions of mobility (i.e. the decision to move) and location choice. Location choice is a discrete choice decision, whereas mobility has a continuous time dimension. Since households relocate along their life course for different reasons such as the birth of a child, these reasons could influence the duration of stay as competing events. This paper develops a joint model for mobility and location choice decisions. Data come from a retrospective survey conducted in the Central Okanagan of British Columbia, Canada. A reason-based competing hazard model is developed for the mobility component of the joint model. For the location choice component, a latent segmentation-based logit model is developed to capture unobserved heterogeneity. The influence of location utility on mobility is addressed through log sum parameters. The model confirms the effects of life-cycle events, parcel, socio-demographic, land use, transportation, neighbourhood and accessibility characteristics. The findings reveal that households relocating for the occurrence of life-cycle events are active in the housing market following the birth of a child, marriage and vehicle purchase. Relocation to live in a desirable neighbourhood is found to delay the move for the reason of living closer to activity points. Results also indicate that duration is likely to be shorter with the increase in expected utility from the following location. Regarding the location choice, preferences for dwellings closer to schools are evident. In the case of heterogeneity, it is observed that urban dwellers are more likely to prefer locations closer to the workplace with the addition of jobs in the household. In contrast, suburban dwellers are more likely to prefer residing far from the workplace. The developed joint model has been included in an integrated urban model, currently under development at the University of British Columbia.

Generating customer wealth by mobile-based agricultural extension services: BOP farmers in the emerging markets

Purpose This research was motivated by the recognition that the use of mobile-based agricultural extension services (AES) applications has become increasingly popular among the bottom of the pyramid segment in recent years. However, users’ adoption is determined not only by their perception of the technology but also by its perceived wealth. In other words, even though a technology may be perceived as advanced, if it does not fit users’ wealth perception benefits, they may not adopt it. Using an extended unified theory of acceptance and use of technology (UTAUT) perspective, this study aims to explore potential predictors of behavioral intention toward AES such as effort expectancy, performance expectancy, customer wealth (CW), social influence and perceived characteristics of innovation. Design/methodology/approach Potential causal connections between key UTAUT factors and users’ intentions to use the services are hypothesized. An integrated theoretical model was developed and tested against the empirical data collected from 336 AES users. A measurement model and structural equation model were tested using AMOS 22.0 and confirmed all 14 hypothesized relationships. Findings The results were consistent with the recent literature on mobile technology acceptance, confirming an unmoderated relationship between CW and the adoption of mobile-based AES. This research proposes a mobile AES user adoption model by integrating CW with the unified theory of acceptance and usage of technology (UTAUT). Originality/value This study establishes CW as an outcome of various relationships.

Adaptive Mobility-Based IoT LoRa Clustering Communication Scheme

Long Range (LoRa) as a low-power wide-area technology is distinguished by its robust long-distance communications tailored for Internet of Things (IoT) networks. Because LoRa was primarily designed for stationary devices, when applied to mobile devices, they become susceptible to frequent channel attenuation. Such a condition can result in packet loss, higher energy consumption, and extended transmission times. To address these inherent challenges posed by mobility, we propose an adaptive mobility-based IoT LoRa clustering communication (AMILCC) scheme, which employs the 2D random waypoint mobility model, strategically partitions the network into optimal spreading factor (SF) regions, and incorporates an adaptive clustering approach. The AMILCC scheme is bolstered by a hybrid adaptive data rate (HADR) mechanism categorized into two approaches, namely intra-SF and inter-SF region HADRs, derived from the standard network-based ADR mechanism for stationary devices, to ensure efficient resource allocation for mobile IoT LoRa devices. Evaluation results show that, based on simulations at low mobility speeds of up to 5 m/s, AMILCC successfully maximizes the packet success ratio to the gateway (GW) by over 70%, reduces energy consumption by an average of 55.5%, and minimizes the end-to-end delay by 47.62%, outperforming stationary schemes. Consequently, AMILCC stands as a prime solution for mobile IoT LoRa networks by balancing the high packet success ratio (PSR) with reliability with energy efficiency.

Detecting synthetic population bias using a spatially-oriented framework and independent validation data

Models of human mobility can be broadly applied to find solutions addressing diverse topics such as public health policy, transportation management, emergency management, and urban development. However, many mobility models require individual-level data that is limited in availability and accessibility. Synthetic populations are commonly used as the foundation for mobility models because they provide detailed individual-level data representing the different types and characteristics of people in a study area. Thorough evaluation of synthetic populations is required to detect data biases before the prejudices are transferred to subsequent applications. Although synthetic populations are commonly used for modeling mobility, they are conventionally validated by their sociodemographic characteristics, rather than mobility attributes. Mobility microdata provides an opportunity to independently/externally validate the mobility attributes of synthetic populations. This study demonstrates a spatially-oriented data validation framework and independent data validation to assess the mobility attributes of two synthetic populations at different spatial granularities. Validation using independent data (SafeGraph) and the validation framework replicated the spatial distribution of errors detected using source data (LODES) and total absolute error. Spatial clusters of error exposed the locations of underrepresented and overrepresented communities. This information can guide bias mitigation efforts to generate a more representative synthetic population.