Local Mobility Research Articles

Conventional Spinal Chordomas: Investigation of SMARCB1/INI1 Protein Expression, Genetic Alterations in SMARCB1 Gene, and Clinicopathological Features in 89 Patients.

The partial loss of SMARCB1/INI1 expression has recently been reported in skull base conventional chordomas, with possible therapeutic implications. We retrospectively analyzed 89 patients with conventional spinal chordomas to investigate the differences in the immunohistochemical expression of SMARCB1/INI1 and the underlying genetic alterations in the SMARCB1 gene. Moreover, we assessed the correlation of clinicopathological features (age, gender, tumor size, tumor location, surgical margins, Ki67 labelling index, SMARCB1/INI1 pattern, previous surgery, previous treatment, type of surgery, and the Charlson Comorbidity Index) with patient survival. Our cohort included 51 males and 38 females, with a median age at diagnosis of 61 years. The median tumor size at presentation was 5.9 cm. The 5-year overall survival (OS) and 5-year disease-free survival (DFS) rates were 90.8% and 54.9%, respectively. Partial SMARCB1/INI1 loss was identified in 37 (41.6%) patients with conventional spinal chordomas (27 mosaic and 10 clonal). The most frequent genetic alteration detected was the monoallelic deletion of a portion of the long arm of chromosome 22, which includes the SMARCB1 gene. Partial loss of SMARCB1/INI1 was correlated with cervical-thoracic-lumbar tumor location (p = 0.033) and inadequate surgical margins (p = 0.007), possibly due to the high degree of tumor invasiveness in this site. Among all the considered clinicopathological features related to patient survival, only tumor location in the sacrococcygeal region and adequate surgical margins positively impacted DFS. In conclusion, partial SMARCB1/INI1 loss, mostly due to 22q deletion, was detected in a significant number of patients with conventional spinal chordomas and was correlated with mobile spine location and inadequate surgical margins.

Quantifying local mobility patterns in urban human mobility data

Understanding fine-scale dynamics of human mobility patterns is pivotal for effective urban planning, public health strategies, and retail analysis. This study introduces a novel mobility measure – the Local Mobility Index (LMI) – combining geometry-based mobility metrics and accessibility measures. The LMI can be considered a measure of ‘relative localness’ by integrating preferences into the assessment of local mobility patterns, offering a novel measure for understanding mobility behavior in urban contexts. The LMI improves upon existing measures as it captures individual choice for local destinations through measuring whether individuals select nearby destinations; accounting for the unequal spatial distribution of urban amenities. Our contribution is mainly methodological, advancing the field by introducing a metric that captures different aspects of mobility compared to conventional mobility metrics. Leveraging mobile-phone-based GPS data, we examine the LMI using 759 individuals across three cities in England. We found that the LMI captures a new and distinct dimension of urban mobility, as evidenced by its weak correlation with established metrics. Therefore, LMI's capacity to highlight previously undetected aspects of mobility behavior, underscores its importance for advancing research and urban planning.

Collaborating by Hiring: The Roles of Local Talent Mobility and Boundary-Spanning Linkages

Collaborating by Hiring: The Roles of Local Talent Mobility and Boundary-Spanning Linkages

Research on rescue priority based on high spatiotemporal resolution mobile positioning data

Estimate the key rescue areas of earthquake accurately, which is of great significance for deploying rescue forces and implementing rescue activities in post-earthquake scientifically.This paper based on the idea of first zoning, then classification, and then prioritizing rescue, taking the core area of Tangshan City as the study area, based on urban road data and mobile positioning data, combined with GIS methods to achieve street level rescue zoning, k-means clustering analysis is used to classify rescue sectors, and personnel burial model is used to conduct rescue priority classification.The results indicate that rescue priority is closely related to the time of earthquake occurrence. When the earthquake occurs between 18pm and 7pm in the next day, the number of priority rescue sector at level I and II is the highest. When the earthquake occurs between 8am and 11am on weekends, the number of priority rescue sector in residential areas increases, while the number of priority rescue zone decreases in workspace areas. This study provides refined rescue zoning and priority grading in the early stages of disaster relief with the absence of disaster information, which will help to assist in decision-making for professional force dispatch.

Deep learning‐based location prediction in VANET

AbstractIn recent years, Vehicular Ad‐hoc Network (VANET) has become an essential component of intelligent transportation systems that, along with the previous systems such as traffic condition, accident alert, automatic parking, and cruise control, use the communication of vehicle to vehicle and vehicle to the roadside unit to facilitate road transportation. Several challenges hinder efforts to improve traffic conditions and reduce traffic fatalities through VANET. A critical challenge is achieving highly accurate and reliable vehicle localization within the VANET. Additionally, the frequent unavailability of Global Positioning System (GPS), particularly in tunnels and parking lots, presents another significant obstacle. Traditional methods like Dead Reckoning offer low accuracy and reliability due to accumulating errors. Similarly, GPS positioning, map matching with mobile phone location services, and other existing solutions struggle with accuracy and economic feasibility. In this article, two Kalman filter approaches are used based on signal statistical information and the other learning‐based networks, including traditional neural network, deep neural network and LSTM (long short‐term memory) to locate the car. The prediction error of car position with root mean square measures. The squared error and distance prediction error are evaluated. It is shown that in terms of prediction time and processing time of vehicle localization, all the vehicle localization methods are efficient in terms of response time for localization, and Kalman filter methods, traditional neural network and deep neural network are faster than LSTM method. Also, in terms of localization error, Kalman filter works better than learning‐based methods, and in learning‐based methods, both deep neural network and LSTM methods perform better than traditional neural network in terms of localization error.

Non-Linear Impact of Economic Performance on Social Equity in Rail Transit Station Areas

Rail transit station areas (RSAs) are heralded as a transformative approach to urban planning, emphasizing the integration of transportation, housing, and commercial development to foster sustainable and inclusive cities. This study presents a comprehensive exploration of the interplay between transit-oriented development (TOD) economic performance and social equity in RSAs, employing advanced methodologies, like eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAPs), to decipher the complex relationships between TOD characteristics and social equity outcomes. Focused on Dalian’s urban center, this study integrates diverse datasets, including mobile location, geospatial, and economic price data, to construct a nuanced analysis framework within the NPE (node–place–economic) model. The results indicate that economic factors significantly impact overall social equity, particularly influencing key variables, such as weekday and weekend commuter population densities. Local explanatory plots reveal that economic performance variables associated with transportation development exhibit a broad non-linear impact on social equity in RSAs. This study advances equitable urban development through TOD by stressing the importance of factoring in multiple variables in RSA planning. This approach is vital for creating synergistic effects, fostering equitable spatial planning, and has both theoretical and practical benefits for improving residents’ well-being.

Ageing in networks: The unbounded geographies of non-migrant and migrant older adults

We propose ‘ageing in networks’ as an optic that shows how the social networks of older adults extend beyond their residential neighbourhoods to extra-local and transnational settings. The paper brings together literature on ageing and social networks in mobilities and migration research to identify shared thematic framings between non-migrant and migrant older adults. Our approach broadens the analytical frame to encapsulate how ageing individually and in communities takes place through local and international mobility and via digital technologies. Ageing in networks also illuminates the importance of connecting their social protection needs with those of the people in their care assemblages.

Unveiling the Reactivity and the Li‐Ion Exchange at the PEO‐Li6PS5Cl Interphase: Insights from Solid‐State NMR

Li6PS5Cl (LPSCl) argyrodites offer high room temperature ionic conductivity (>1 mS cm−1) and are among the most promising solid electrolytes. However, their chemical instability against Li metal compromises the long‐term cyclability. Using PEO‐LiTFSI as an interlayer or as a matrix for composite electrolytes is a promising strategy to address this issue. Nevertheless, the interphase of PEO‐LiTFSI and LPSCl requires further detailed investigations. This work explores the interfacial reactions between these phases using solid‐state nuclear magnetic resonance. Results show that PEO facilitates the formation of a complex with LiCl and Li3PS4 from LPSCl, resulting in an interphase material with limited local mobility, thus impeding ion transport. Although the addition of Br as a dopant can improve the ionic conductivity of LPSCl by inducing disorder and generating the Li vacancies, it makes the LPSCl more susceptible to PEO and increases the extent of the interfacial reaction. 6Li–6Li EXSY experiments demonstrate spontaneous Li‐ion exchange between the PEO and the LPSCl, yet this exchange is significantly hindered by reaction products within the PEO‐LPSCl interphase, attributable to their sluggish local dynamics. This study sheds light on the complex interfacial interaction between PEO‐LiTFSI and sulfide argyrodite, providing insights into designing solid electrolytes for the new generation of electrochemical devices.

Unveiling community adaptations to extreme heat events using mobile phone location data

The escalating frequency, duration, and intensity of extreme heat events have posed a significant threat to human society in recent decades. Understanding the dynamic patterns of human mobility under extreme heat will contribute to accurately assessing the risk of extreme heat exposure. This study leverages an emerging geospatial data source, anonymous cell phone location data, to investigate how people in different communities adapt travel behaviors responding to extreme heat events. Taking the Greater Houston Metropolitan Area as an example, we develop two indices, the Mobility Disruption Index (MDI) and the Activity Time Shift Index (ATSI), to quantify diurnal mobility changes and activity time shift patterns at the city and intra-urban scales. The results reveal that human mobility decreases significantly in the daytime of extreme heat events in Houston while the proportion of activity after 8 p.m. is increased, accompanied with a delay in travel time in the evening. Moreover, these mobility-decreasing and activity-delaying effects exhibited substantial spatial heterogeneity across census block groups. Causality analysis using the Geographical Convergent Cross Mapping (GCCM) model combined with correlation analyses indicates that people in areas with a high proportion of minorities and poverty are less able to adopt heat adaptation strategies to avoid the risk of heat exposure. These findings highlight the fact that besides the physical aspect of environmental justice on heat exposure, the inequity lies in the population's capacity and knowledge to adapt to extreme heat. This research is the first of the kind that quantifies multi-level mobility for extreme heat responses, and sheds light on a new facade to plan and implement heat mitigations and adaptation strategies beyond the traditional approaches.

Social mobility and CCT programs: The Bolsa Família program in Brazil

We investigate long-term outcomes related to social mobility and their determinants for low-income Brazilian households. More precisely, the first cohorts of beneficiaries of the Conditional Cash Transfer (CCT) program Bolsa Família (BFP); children aged between 7 and 16 in 2005, who are tracked for over a decade, until 2019. We use individual-level administrative data to analyze our two indicators of social mobility: (i) future emancipation from federal government social programs, and (ii) access to the formal labor market. We observe that formerly vulnerable children, beneficiaries of the CCT, find themselves in better socioeconomic conditions in adulthood. While 64 % of them, aged between 21 and 30 years in 2019, were no longer beneficiaries of federal government social programs, 45 % accessed the formal labor market at least once between 2015 and 2019. We also compare the characteristics of the formal employment they access with those of non-BFP beneficiaries during the same period. They have worse employment conditions, although better than informal positions typical of their parents. Furthermore, we investigate the association between local sociodemographic characteristics and individual social mobility. We find significant territorial heterogeneity associated with differences in better health and education infrastructures, and local economic activity.

Mapping the Nanoscale Heterogeneous Responses in the Dynamic Acceleration of Deformed Polymer Glasses.

Understanding the evolution of local structure and mobility of disordered glassy materials induced by external stress is critical in modeling their mechanical deformation in the nonlinear regime. Several techniques have shown acceleration of molecular mobility of various amorphous glasses under macroscopic tensile deformation, but it remains a major challenge to visualize such a relationship at the nanoscale. Here, we employ a new approach based on atomic force microscopy in nanorheology mode for quantifying the local dynamic responses of a polymer glass induced by nanoscale compression. By increasing the compression level from linear elastic to plastic deformation, we observe an increase in the mechanical loss tangent (tan δ), evidencing the enhancement of polymer mobility induced by large stress. Notably, tan δ images directly reveal the preferential effect of the large compression on the dynamic acceleration of nanoscale heterogeneities with initially slow mobility, which is clearly different from that induced by increasing temperature.

Untapping the potential of mobile location data: The opportunities and challenges for retail analytics

Smartphone technology has created a burgeoning mobile location data (MLD) marketplace. MLD has been adopted by many retailers to track consumer behaviours at a granular level across space and time. This paper provides a critical empirical case study approach to generate spatiotemporal MLD-based insights using raw smartphone data at five super-regional shopping centres. The analysis and visualization of MLD-based metrics provide the basis to rethink how we approach retail analytics and location decision-making. Many challenges need to be addressed to leverage the full potential of MLD, including data bias, spatial and temporal accuracy and significant consumer privacy concerns.

Volumetric Additive Manufacturing of Dicyclopentadiene by Solid-State Photopolymerization.

Polymerization in the solid state is generally infeasible due to restrictions on mobility. However, in this work, the solid-state photopolymerization of crystalline dicyclopentadiene is demonstrated via photoinitiated ring-opening metathesis polymerization. The source of mobility in the solid state is attributed to the plastic crystal nature of dicyclopentadiene, which yields local short-range mobility due to orientational degrees of freedom. Polymerization in the solid state enables photopatterning, volumetric additive manufacturing of free-standing structures, and fabrication with embedded components. Solid-state photopolymerization of dicyclopentadiene offers a new paradigm for advanced and freeform fabrication of high-performance thermosets.

Evaluating the Effect of Morphologic Units on Fractional Sediment Mobility and Bedload Transport in a Small Pool‐Riffle Reach

AbstractThis study examines the spatial pattern of fractional sediment mobility and assesses the influence of morphologic units on bedload transport in a small pool‐riffle reach with limited supply. Using a 2D hydraulic model and a subsurface‐based sediment transport model, shear stresses, fractional sediment mobility, and bedload transport were examined for flow events ranging in magnitude between 0.2Qbf and 1.5Qbf. Results reveal that while spatial variations in shear stress decrease as discharge increases, only a small proportion of the bed experiences high transport rates. At the reach scale, riffles are the primary morphological unit contributing to fully mobile sediment for all size fractions. However, at a subunit scale, there is evidence of sediment transport reversal for grains >32 mm at flows near or exceeding bankfull discharge in association with shear stress reversal. These transport reversals are important for maintaining pools despite their infrequent occurrence in the study reach. Sediment transport maps indicate that bed morphology considerably influences sediment transport at low to moderate flows. During these events, the shear stress is sensitive to local bed topography and partial mobility is the dominant transport process. In contrast, variations in bedload transport rates decrease during high flows when the flow is less sensitive to variations in bed topography and the bed becomes fully mobile.

Generative artificial intelligence of things systems, multisensory immersive extended reality technologies, and algorithmic big data simulation and modelling tools in digital twin industrial metaverse

Research background: Multi-modal synthetic data fusion and analysis, simulation and modelling technologies, and virtual environmental and location sensors shape the industrial metaverse. Visual digital twins, smart manufacturing and sensory data mining techniques, 3D digital twin simulation modelling and predictive maintenance tools, big data and mobile location analytics, and cloud-connected and spatial computing devices further immersive virtual spaces, decentralized 3D digital worlds, synthetic reality spaces, and the industrial metaverse. Purpose of the article: We aim to show that big data computing and extended cognitive systems, 3D computer vision-based production and cognitive neuro-engineering technologies, and synthetic data interoperability improve artificial intelligence-based digital twin industrial metaverse and hyper-immersive simulated environments. Geolocation data mining and tracking tools, image processing computational and robot motion algorithms, and digital twin and virtual immersive technologies shape the economic and business management of extended reality environments and the industrial metaverse. Methods: Quality tools: AMSTAR, BIBOT, CASP, Catchii, R package and Shiny app citationchaser, DistillerSR, JBI SUMARI, Litstream, Nested Knowledge, Rayyan, and Systematic Review Accelerator. Search period: April 2024. Search terms: “digital twin industrial metaverse” + “artificial Intelligence of Things systems”, “multisensory immersive extended reality technologies”, and “algorithmic big data simulation and modelling tools”. Selected sources: 114 out of 336. Published research inspected: 2022–2024. PRISMA was the reporting quality assessment tool. Dimensions and VOSviewer were deployed as data visualization tools. Findings & value added: Simulated augmented reality and multi-sensory tracking technologies, explainable artificial intelligence-based decision support and cloud-based robotic cooperation systems, and ambient intelligence and deep learning-based predictive analytics modelling tools are instrumental in augmented reality environments and in the industrial metaverse. The economic and business management of the industrial metaverse necessitates connected enterprise production and big data computing systems, simulation and modelling technologies, and virtual reality-embedded digital twins.

An Enhanced Method to Improve Proxy Mobile IPv6 Efficiency Through Software Defined Network

This suggested method, called the Software Defined Open-Flow Mechanism of PMIPv6 (SD-PMIPv6), modifies the Proxy Mobile IPv6 protocol to match the Augmented Open-Flow architecture. The flexibility aspects of the PMIPv6 components, including the Mobile Access Gateway (MAG) and Local Mobility Anchor (LMA), are broken apart and rebuilt in order to take use of the Open-Flow strategy. The LMA components, which serve as the network's Open-Flow controller for the switches, maintain the mobile node's (MN) location. The contact access entities that are capable of managing MAG signals interact with the MN. The recommended approach has two primary objectives: (a) separating the control and data planes; and (b) shortening the handover delay.

MOBILE ROBOT TRACKING SYSTEM BASED ON MACHINE VISION AND LASER RADAR

The proposed solution addresses the issue of insufficient real-time performance and accuracy in mobile robot path tracking by introducing a system that combines machine vision and laser radar. In this study, the Broadcom BCM2711 microcontroller chip is connected to the RS232 communication interface for transmitting information to the ARM embedded processor. Users can access position distance, direction, and other robot-related data through the man-machine interface's LCD display in a Windows operating system environment. By initiating an adaptive position tracking algorithm program identified by the robot within the position tracking unit, mobile position tracking of the robot is achieved. Experimental results demonstrate significant improvements in both real-time performance and accuracy of this mobile robot tracking system.

Reviewing the Adequacy of Zambia’s Legal Framework for Local Authorities Revenue Mobilization

An increase in sources of local authority’s revenue in both developed and developing countries has compelled governments to develop policies and laws aimed at guiding local authorities in revenue mobilization. The Zambian government, to be specific, has over the years developed quite many laws for this venture. In addition to the constitutional provision, other pieces of legislation guiding local authorities’ revenue mobilization in Zambia include; the Public Finance Management Act, No. 1 of 2018, Local Government Act, No. 2 of 2019, the Property Transfer Tax (Amendment) Act, 2019, Market and Bus Station Act. No.7 of 2007, the Valuation Surveyors Act, the Rating Act No. 21 of 2018, and the Personal Levy Act of 1996. This paper explores the adequacy of Zambia’s Legal Framework for Local Authorities Revenue Mobilization. The respondents were purposively sampled from the Ministry of Finance, the Ministry of Local Government, the Decentralization Secretariat, and the Local Authorities. Moreover, the various sources of local authorities’ revenue and pieces of legislation are discussed. Zambia has an adequate legal framework guiding local authority revenue mobilization. It is for this reason that local authorities are encouraged to adhere to the legal framework at all costs if they are to effectively generate revenue and play an effective role in enhancing development.

From PV to EV: Mapping the Potential for Electric Vehicle Charging with Solar Energy in Europe

The electrification of the transport sector is a key element in decarbonizing our societies. However, energy systems will have to cope with additional electricity demand due to the charging needs of electric vehicles (EVs) together with the integration of fluctuating renewable energy sources. Shifting EV charging during the day can contribute to absorbing photovoltaic (PV) production peaks and limiting the additional demand during peak periods. The EV batteries can then be discharged to the grid or home during the evening or other demand peaks. We developed a new methodology to quantify the flexibility gained from the EV-PV coupling from local mobility habits and local (or decentralized) photovoltaic production. Our approach first focuses on the geospatial modelling of mobility habits across Europe to quantify energy demand for charging. The charging demand is then distributed between residential areas, workplaces and points of interest (shopping, leisure, etc.) to model the spatiotemporal distribution of energy needs. We show and discuss a practical case in Copenhagen illustrating the impact of charging behaviour for three different scenarios. The methodology is implemented as a calculation module in the open-source online geographic tool for energy transition planning, Citiwatts.

An experienced racial-ethnic diversity dataset in the United States using human mobility data

Despite the importance of measuring racial-ethnic segregation and diversity in the United States, current measurements are largely based on the Census and, thus, only reflect segregation and diversity as understood through residential location. This leaves out the social contexts experienced throughout the course of the day during work, leisure, errands, and other activities. The National Experienced Racial-ethnic Diversity (NERD) dataset provides estimates of diversity for the entire United States at the census tract level based on the range of place and times when people have the opportunity to come into contact with one another. Using anonymized and opted-in mobile phone location data to determine co-locations of people and their demographic backgrounds, these measurements of diversity in potential social interactions are estimated at 38.2 m × 19.1 m scale and 15-minute timeframe for a representative year and aggregated to the Census tract level for purposes of data privacy. As well, we detail some of the characteristics and limitations of the data for potential use in national, comparative studies.