Mobile Signal Research Articles

Identification and Mechanism of Residents’ Regional Non-Commuting Flow Patterns Based on the Gradient Boosting Decision Tree Model: A Case Study of the Shanghai Metropolitan Area

With the improvement in residents’ living standards, non-commuting has gradually become an important daily transportation behaviour for residents. The intensity of non-commuting flow can reflect the level of urban functional services and external attractiveness and can intuitively characterise the interconnection pattern and relationship among various cities within the metropolitan area. Related research is also a key topic in the fields of urban planning and transportation geography from a humanistic perspective. Taking the Shanghai Metropolitan Area as an example, this study explored the characteristics of the non-commuting flow of residents in the region and between cities and its nonlinear influencing factors with the help of the mobile phone signalling data and the gradient lifting decision tree model. Three conclusions were identified: first, non-commuting flow within each city in the metropolitan area was concentrated in the central urban area, while non-commuting flow between cities was concentrated in the central urban area of the urban border and strong core cities. Second, the built environment had a nonlinear impact on residents’ non-commuting flow. Different types of large-scale service facilities had different impact mechanisms on non-commuting flow, and public service facilities and transportation infrastructure jointly affected residents’ non-commuting flow. Third, transportation facilities had a more significant impact on the non-commuting flow between cities. Large tourism, cultural, and medical service facilities had a more significant impact on non-commuting flow within cities, with upper or lower thresholds according to the type of facility. The planning strategy needs to conduct targeted planning, regulation, and facility configuration based on the area’s actual needs. In addition, this study identified the characteristics of non-commuter flow differentiation in street towns and the nonlinear impact of the built environment.

A kriging interpolation model for geographical flows

The kriging model can accommodate various spatial supports and has been extensively applied in hydrology, meteorology, soil science, and other domains. With the expansion of applications, it is essential to extend the kriging model for new spatial support of high-dimensional data. Geographical flows can depict the movements of geographical objects and imply the underlying mobility patterns in geographical phenomena. However, due to the bias, sparsity, and uneven quality of flow data in the real world, research about flows remains hindered by the lack of complete flow data and effective flow interpolation methods. In this study, we design a kriging interpolation model for flows based on several flow-related concepts and the autocorrelation of flows. We also analyze the second-order stationarity and anisotropy in the flow spatial random field. To illustrate the effectiveness and applicability of our method, we conduct two case studies. The former case study compares several experiments of flow density interpolation using Beijing mobile signaling data and illustrates the conditions of applicable areas. The latter case study extends our model to other flow attributes, such as travel time uncertainty, using Beijing taxi origin-destination flow data. The results of these cases demonstrate the effectiveness and high accuracy of our model.

Sustainable Traffic Regulation System in Protected Areas: Pilot Technology Testing in National Park in the Czech Republic

In the context of nature protection, there is an effort to regulate individual car traffic in protected areas. In the framework of the research, a pilot testing of a vehicle detection and identification system in the Krkonoše National Park was carried out using two selected technologies (license plate recognition and Bluetooth token detection). The research was carried out under conditions of poorer availability of mobile signal for transmission of measured data, lack of electrical power supply, and in challenging climatic conditions in the mountains. The main objective was to verify the applicability and limits of the mentioned technologies under these difficult conditions. For this purpose, two test sites were built: a fixed and a mobile point. Testing at both points was carried out using two basic methods, namely online through continuous data collection from the detectors and on-site through a local survey during the summer of 2022. The parameters evaluated were the reliability of the vehicle identification itself and the reliability of the operation of the individual detection subsystems and the tested system as a whole. The results show that the license plate recognition system using two cameras for the checkpoint shows a high recognition reliability, but it is reduced for some types of vehicles (especially motorcycles and four-wheelers). At the same time, this technology is demanding on energy resources. Detection using a Bluetooth scanner has proven to be highly reliable up to 50 km/h. A reliable power supply is necessary to achieve high reliability, which was a problem at the mobile point. Evaluation of images from cameras with motion detection showed the limits of this technology, which increased with increasing vehicle speed. The system can be used to detect traffic in protected areas, taking into account the limits specified in this article.

Urban tourists’ spatial distribution and subgroup identification in a metropolis --the examination applying mobile signaling data and latent profile analysis

Urban tourists’ spatial distribution and subgroup identification in a metropolis --the examination applying mobile signaling data and latent profile analysis

Long-Distance Movement of Solanum tuberosum Translationally Controlled Tumor Protein (StTCTP) mRNA.

Long-distance signaling molecules in plants, including different RNA species, play a crucial role in the development and environmental responses. Among these mobile signals, the Translationally Controlled Tumor Protein (TCTP) mRNA is one of the most abundant. TCTP regulates cell-cycle progression and programmed cell death and is involved in responses to abiotic and biotic stress as well as plant regeneration, among other functions. Considering that the ability to induce plant regeneration is linked to a possible role of TCTP in vegetative propagation and asexual reproduction, we analyzed TCTP overexpression in a solanaceous plant model that can reproduce asexually by regeneration from stolons and tubers. Therefore, in this study, the effect of transient expression of Solanum tuberosum TCTP (StTCTP) on tuber development and vegetative propagation was described. StTCTP mRNA was shown to be transported long-distance. Additionally, transient overexpression of StTCTP resulted in sprouts with a greater diameter compared to control plants. Furthermore, the early stages of tuberization were induced compared to control plants, in which only mature tubers were observed. These results suggest a role of TCTP in vegetative propagation and asexual reproduction.

Discrimination in Online Learning During the COVID-19 Pandemic in Indonesian Higher Education

Objective: Discrimination has been significant in the COVID-19 pandemic in online learning in universities, as it affects the availability of technological devices, the training of human resources, student and parent literacy rates and institutional training.
 
 Method: Using a descriptive qualitative approach, this article aims to identify and analyze the causes of forms of discrimination occurring in Indonesian universities during the COVID-19 pandemic. Data were collected through interviews with students, teachers and parents, taking into account their characteristics, situations and particular cultures.
 
 Result: The study shows that differences in the social groups and classes of university students and mothers structured their possibilities to study online in higher education in the COVID-19 pandemic. Differences between social class have consequences on the use of online learning in the COVID-19 pandemic at the level of discrimination. Existing structural differences are an important factor in interpreting the discriminatory reality of online learning in Indonesian higher education.
 
 Conclusion: Three important factors were identified as the cause of this discrimination: limited availability of mobile devices, smartphones, tablets, laptops, signals, data packets and Internet network services; lack of technological literacy among learners and parents hampers the ability to participate; and lack of institutional training to learn online.

Erratum to: 모바일 데이터를 활용한 도시공원 이용자 핫스팟 분석 - 서울 성동구 공원을 대상으로 -

Erratum to: 모바일 데이터를 활용한 도시공원 이용자 핫스팟 분석 - 서울 성동구 공원을 대상으로 -

Mobile Signaling Peptides: Secret Molecular Messengers with a Mighty Role in Plant Life

AbstractAdaptive mechanisms for unfavorable environments have evolved in plants for thousands of generations, primarily in the form of endogenous chemical signals and the coordination of physiological processes. Signaling peptides (SPs) are diverse molecular messengers in various stress responses which have been identified in different plant families. SPs are recognized by the membrane-localized receptors and co-receptors, leading to downstream signaling for various plant responses. Progress in in silico analysis, along with other factors, has increased our understanding of the signaling peptide-mediated regulatory mechanisms underlying the entire plant life cycle. SPs mediate both long-distance (root-to-shoot-to-root) and local cell–cell communication via vascular system to communicate and coordinate with plant organs at distant locations. During abiotic stress, SPs inside plant cells perceive stress signals and transfer information at short and long physiological ranges through the signal transduction pathway, causing stress-responsive gene expression. SPs interact with pathogens and mediate cell-to-cell communication via signaling pathways. There are intriguing relationships between phytohormones and the secondary signaling cascades which are mediated by SPs. During biotic or abiotic stress, different peptides trigger jasmonic acid, ethylene, and ABA signaling, involving several secondary messengers. These messengers mediate the stress response via shared signaling components of ROS, Ca2+, and MAPKs, and they modify the gene expression for different phytohormones. In this review, we highlight current knowledge on the role of signaling peptides in plant adaptation, growth, and development. We aim to analyze the SP-receptor interactions and the significance of crosstalk between a few sample SPs and phytohormones. Potential directions on how scientists can use this information for crop improvement are also suggested.

Machine Learning Electrocardiogram for Mobile Cardiac Pattern Extraction

Background: Internet-of-things technologies are reshaping healthcare applications. We take a special interest in long-term, out-of-clinic, electrocardiogram (ECG)-based heart health management and propose a machine learning framework to extract crucial patterns from noisy mobile ECG signals. Methods: A three-stage hybrid machine learning framework is proposed for estimating heart-disease-related ECG QRS duration. First, raw heartbeats are recognized from the mobile ECG using a support vector machine (SVM). Then, the QRS boundaries are located using a novel pattern recognition approach, multiview dynamic time warping (MV-DTW). To enhance robustness with motion artifacts in the signal, the MV-DTW path distance is also used to quantize heartbeat-specific distortion conditions. Finally, a regression model is trained to transform the mobile ECG QRS duration into the commonly used standard chest ECG QRS durations. Results: With the proposed framework, the performance of ECG QRS duration estimation is very encouraging, and the correlation coefficient, mean error/standard deviation, mean absolute error, and root mean absolute error are 91.2%, 0.4 ± 2.6, 1.7, and 2.6 ms, respectively, compared with the traditional chest ECG-based measurements. Conclusions: Promising experimental results are demonstrated to indicate the effectiveness of the framework. This study will greatly advance machine-learning-enabled ECG data mining towards smart medical decision support.

Multi-scale cross-city community detection of urban agglomeration using signaling big data

ABSTRACT Many existing efforts have taken advantage of large-scale spatial-temporal data to partition cities via constructed human interaction networks. However, few studies focus on communities emerging between adjacent cities in big urban agglomerations, which we call “cross-city” communities. In this study, we introduce a novel framework to detect cross-city communities in urban agglomerations under different scales leveraging a large number of fine-grained mobile signaling data aiming to break the original administrative boundaries. Taking the Pearl River Delta (PRD) urban agglomeration in China as study area, we investigate the existence of potential communities at three scales, i.e. city-group level, city level and sub-city level. The partition results are expected to benefit transportation planning, urban zoning and administrative boundary re-delineation. The results from our study highlight the necessity of considering cross-city communities and their scale effects when examining urban spatial interactions.

The Communities Detection of the Tourist Flow Network using Mobile Signaling Data in Nanjing, China

The Communities Detection of the Tourist Flow Network using Mobile Signaling Data in Nanjing, China

Design of Angklung Music Scoring System Based on Remote Training

Angklung is a traditional musical instrument from West Java, Indonesia. To play a song using this instrument can only be played in groups. The size of the musical instrument is quite large and prone to damage if carried anywhere, making it challenging to practice except in schools or music clubs. Since the pandemic, it has become increasingly difficult for students to practice angklung due to the limitations of face-to-face learning. This study aims to make it easier for students to practice and play angklung. The system is based on online multiplayer games so that students can learn and play angklung with more fun, and players can play anywhere. Players can use the mobile version of the angklung application as a substitute for the angklung instrument. Players can perform a duet with other players in playing the same song. The results of the player's performance will be displayed as scores and stored in the database. Besides that, a real-time feedback function is always displayed for each note played. To distinguish individual players and/or notes, we use vibration sensor technology installed in each mobile application or signal processing, and the system will recognise them properly. We propose a Short-time Fourier Transform (STFT) algorithm as a scoring system and realtime-feedback performance. We use class diagrams to describe the whole application system and describe the functionality of the system in detail. We collect some research related to angklung and technology. Currently, there is a little study that could answer all the problems of learning and training angklung. The results of this study in the form of a system design are the initial research stage and are used for the following research phase. In conclusion, the design of this study can solve the existing problems so that the system will make it easier for students to practice and play angklung.

Determination of most affected areas by earthquakes based on mobile signaling data: a case study of the 2022 Mw 6.6 Luding earthquake, China

Rapid determination of the most-affected areas after an earthquake can help with emergency relief efforts, which is also an essential part of postearthquake relief work. Mobile signaling data contains rich user information, which helps determine the most-affected areas. This study investigated the 2022 Mw 6.6 Luding earthquake in Sichuan, China; the relationship between mobile signaling data and the seismically most-affected area was explored using comparative, regression, and spatial interpolation analysis. The results revealed that mobile signaling data changed abnormally following the earthquake and differed across high- and low-intensity zones. There was a significant correlation between mobile signaling data and intensity. Additionally, the most-affected areas estimated using mobile signaling data were comparable to the actual seismic damage. Therefore, mobile signaling data can provide a reference for determining the most-affected areas and assist in emergency relief efforts after an earthquake.

A street-point method to measure the spatiotemporal relationship between walkability and pedestrian flow

Walkability indicators are a pivotal method to evaluate the role of the built environment in peoples' decisions regarding active mobility, supporting the application of public measures that contribute to more sustainable and resilient regions. Currently, data used to evaluate associations between walkability indicators and travel behavior is obtained via traditional methods of data collection, like questionnaires, that are costly and hard to scale in large urban environments. Moreover, the spatial resolution of most indicators may not be sufficient to support granular local public interventions. To face these issues, we propose a novel walkability indicator that provides a score of walkability for every one-meter street point, based on street conditions and accessibility to points of interest calculated with a Cumulative-Gaussian impedance function. Resorting to Linear and Geospatial Weighted Regressions, we evaluate the associations between walkability features and pedestrian flow data retrieved from mobile phone communication signals for a week in March 2022. The relationship between walkability features and pedestrian flow is stronger during workdays, in which accessibility to education, food amenities and government services are the most important predictors. On the weekend, the features with more explanatory power are accessibility to crosswalks and leisure amenities. Accessibility to public transport, sidewalk width and slope seem to impact pedestrian decisions independently of the day type, although the impact is stronger on weekends. This study provides policy makers and urban planners with a practical tool to effectively support the evaluation of current street conditions and access areas that are underserved, as well as plan and gauge new local interventions, while objectively understanding their impacts on pedestrian mobility.

Study on travel behavior characteristics of air passengers in an airport hinterland

Understanding the characteristics and rules of air passenger travel behavior in an airport hinterland plays an important role in forecasting air passenger travel flows, improving airport service levels, and planning urban transportation. Taking Shijiazhuang Airport as an example, this paper studied the travel characteristics of air passengers in the airport hinterland using mobile phone signaling data. The results showed that: ① Air passengers were mainly distributed in Shijiazhuang City (about 70%), and the density of air passengers in urban area was significantly higher than that in other areas. ② The Shijiazhuang Airport had a distance decay effect, and the distance decay characteristics had obvious directivity. ③ The time spent at the airport for departing passengers was longer than that of arriving passengers. And the travel duration distribution characteristics of arriving and departing passengers presented a long-tail effect. The main reason for the distance and time decay characteristics would be that the car is the main collecting and distributing mode of Shijiazhuang Airport. Finally, we provide some recommendations to optimize the single collection and distribution mode of Shijiazhuang Airport.

Root-knot nematode modulates plant CLE3-CLV1 signaling as a long-distance signal for successful infection.

Plants use many long-distance and systemic signals to modulate growth and development, as well as respond to biotic and abiotic stresses. Parasitic nematodes infect host plant roots and cause severe damage to crop plants. However, the molecular mechanisms that regulate parasitic nematode infections are still unknown. Here, we show that plant parasitic root-knot nematodes (RKNs), Meloidogyne incognita, modulate the host CLAVATA3 (CLV3)/EMBRYO SURROUNDING REGION (CLE)-CLV1 signaling module to promote the infection progression. Plants deficient in the CLE signaling pathway show enhanced RKN resistance, whereas CLE overexpression leads to increased susceptibility toward RKN. Grafting analysis shows that CLV1 expression in the shoot alone is sufficient to positively regulate RKN infection. Together with results from the split-root culture system, infection assays, and CLE3-CLV1 binding assays, we conclude that mobile root-derived CLE signals are perceived by CLV1 in the shoot, which subsequently produce systemic signals to promote gall formation and RKN reproduction.

A Framework of Travel Mode Identification Fusing Deep Learning and Map-Matching Algorithm

The ubiquity of mobile phone signaling data (MPSD) allows us to study travel mode identification (TMI) of a larger scale of population in cities than GPS data and travel survey. Existing studies suffers from deficient data cleaning, ignoring low spatial accuracy of data and lack of the common datasets labeled with travel modes. This study proposes a novel TMI framework with MPSD and pseudo MPSD by designing hidden markov model (HMM)-based map-matching algorithm and bi-directional long short-term memory (Bi-LSTM)-based deep neural network (DNN). In this framework, conventional data cleaning method is modified to eliminate outliers in raw MPSD to improve data quality. In addition, HMM-based map-matching algorithm is designed to estimate the actual position of travelers from MPSD for spatial-temporal accuracy improvement. Specifically, pseudo MPSD is reconstructed by multi-source data aiming at overcoming the facts lacking of labeled MPSD. Both MPSD and labeled pseudo MPSD are processed to features, where the latter is used to train model based on Bi-LSTM, and the former is input to the trained model for TMI application validation. To evaluate the framework performance, a case study is carried out. Experimental results show that our framework outperforms baseline models on the same datasets. Furthermore, the accuracy improvement of designed approaches is demonstrated by a series of algorithm comparisons. The framework has good generality and flexibility, and can be extended to other cities for TMI.

On the Estimation of Spatial Density From Mobile Network Operator Data

We tackle the problem of estimating the spatial distribution of mobile phones from Mobile Network Operator (MNO) data, namely CDR or signalling data. The process of transforming MNO data to a density map requires to perform geolocation of radio cells to determine their spatial footprint. Traditional geolocation solutions rely on Voronoi tessellations and approximate cell footprints by mutually disjoint regions. Recently, some pioneering work started to consider more elaborated geolocation methods with partially overlapping (non-disjont) cell footprints coupled with a probabilistic model for phone-to-cell association. Estimating the spatial density in such a probabilistic setup is currently an open research problem and is the focus of the present work. We start by reviewing three different estimation methods proposed in literature and provide novel analytical insights that unveil some key aspects of their mutual relationships and properties. Furthermore, we develop a novel estimation approach for which a closed-form solution can be given. Numerical results based on semi-synthetic data are presented to assess the relative accuracy of each method. Our results indicate that the estimators based on overlapping cells have the potential to improve spatial accuracy over traditional approaches based on Voronoi tessellations.

Role of NPR1 in Systemic Acquired Stomatal Immunity.

Stomatal immunity is the primary gate of the plant pathogen defense system. Non-expressor of Pathogenesis Related 1 (NPR1) is the salicylic acid (SA) receptor, which is critical for stomatal defense. SA induces stomatal closure, but the specific role of NPR1 in guard cells and its contribution to systemic acquired resistance (SAR) remain largely unknown. In this study, we compared the response to pathogen attack in wild-type Arabidopsis and the npr1-1 knockout mutant in terms of stomatal movement and proteomic changes. We found that NPR1 does not regulate stomatal density, but the npr1-1 mutant failed to close stomata when under pathogen attack, resulting in more pathogens entering the leaves. Moreover, the ROS levels in the npr1-1 mutant were higher than in the wild type, and several proteins involved in carbon fixation, oxidative phosphorylation, glycolysis, and glutathione metabolism were differentially changed in abundance. Our findings suggest that mobile SAR signals alter stomatal immune response possibly by initiating ROS burst, and the npr1-1 mutant has an alternative priming effect through translational regulation.

The Spatial Association between Residents’ Leisure Activities and Tourism Activities Using Colocation Pattern Measures: A Case Study of Nanjing, China

With the increasing trend of residents and tourists sharing urban spaces, the boundary between leisure spaces and tourism spaces is gradually being blurred. However, few studies have quantified the spatiotemporal correlation patterns of residents’ leisure activities and tourists’ activities. To fill this gap, this paper takes Nanjing as an example to study the temporal and spatial correlation between residents’ leisure activities and tourists’ activities based on mobile phone signal data. First, through kernel density analysis, it is found that there is a spatial overlap between residents’ leisure activities and tourists’ activities. Then, the spatial and temporal correlation patterns of residents’ leisure activities and tourists’ activities are analyzed through the colocation quotient. According to our findings, (1) residents’ leisure activities and tourists’ activities are not spatially correlated, indicating that they are relatively independent in space both during the week and on weekends. (2) On weekday afternoons, the spatial correlation between residents’ and tourists’ leisure activities is strongest. On weekends, the night is when residents’ leisure activities and tourists’ activities are most closely related. (3) The correlation area is mainly distributed in areas near famous scenic spots, as well as public spaces such as parks and squares. Based on the above analysis, this paper aims to study the resident-tourist interaction in the spatial context to provide directions for improving the attractiveness of cities, urban transportation, services, and marketing strategies.