Profiling Public Transit Passenger Mobility Using Adversarial Learning

It is important to capture passengers’ public transit behavior and their mobility to create profiles, which are critical for analyzing human activities, understanding the social and economic structure of cities, improving public transportation, assisting urban planning, and promoting smart cities. In this paper, we develop a generative adversarial machine learning network to characterize the temporal and spatial mobility behavior of public transit passengers, based on massive smart card data and road network data. The Apriori algorithm is extended with spatio-temporal constraints to extract frequent transit mobility patterns of individual passengers based on a reconstructed personal trip dataset. This individual-level pattern information is used to construct personalized feature vectors. For regular and frequent public transit passengers, we identify similar transit mobility groups using spatio-temporal constraints to construct a group feature vector. We develop a generative adversarial network to embed public transit mobility of passengers. The proposed model’s generator consists of an auto-encoder, which extracts a low-dimensional and compact representation of passenger behavior, and a pre-trained sub-generator containing generalization features of public transit passengers. Shenzhen City is taken as the study area in this paper, and experiments were carried out based on smart card data, road network data, and bus GPS data. Clustering analysis of embedding vector representation and estimation of the top K transit destinations were conducted, verifying that the proposed method can profile passenger transit mobility in a comprehensive and compact manner

Spatial scale in environmental risk mapping: A Valley fever case study

Background. Valley fever is a fungal infection occurring in desert regions of the U.S. and Central and South America. Environmental risk mapping for this disease is hampered by challenges with detection, case reporting, and diagnostics as well as challenges common to spatial data handling. Design and Methods. Using 12,349 individual cases in Arizona from 2006 to 2009, we analyzed risk factors at both the individual and area levels. Results. Risk factors including elderly population, income status, soil organic carbon, and density of residential area were found to be positively associated with residence of Valley fever cases. A negative association was observed for distance to desert and pasture/ hay land cover. The association between incidence and two land cover variables (shrub and cultivated crop lands) varied depending on the spatial scale of the analysis. Conclusions. The consistence of age, income, population density, and proximity to natural areas supports that these are important predictors of Valley fever risk. However, the inconsistency of the land cover variables across scales highlights the importance of how scale is treated in risk mapping

Self-polarized CNT/PVDF nanocomposites with ultra-high β phase achieved via water induction for efficient piezo-catalysis

Polyvinylidene fluoride (PVDF) is promising for piezo-catalytic applications owing to its excellent biocompatibility, flexibility, and durability. However, it is limited by weak electroactivity originating from its intrinsically low β piezoelectric phase content ( 6-fold and > 19-fold better than standalone self-polarized PVDF (i.e., without CNT content) and emerging piezo-catalytic designs, respectively. This study offers a unique approach for designing PVDF-based piezo-catalyst to expedite mechanically-driven catalysis towards practical applications.Agency for Science, Technology and Research (A*STAR)Ministry of Education (MOE)Nanyang Technological UniversityHaitao Li thanks the financial support from Natural Science Foundation of Shandong Province (ZR2023QF019), Jiangsu Higher Education Institutions of China (21KJB430049) and Innovation Technology Platform Project (YZ2020268) jointly built by Yangzhou City and Yangzhou University. H.K.L. thanks the funding supports from Singapore Ministry of Education (RS13/20 and RG4/21), Agency for Science, Technology and Research, Singapore (A*STAR, A2084c0158), Center of Hydrogen Innovation, National University of Singapore (CHIP2022-05), and Nanyang Technological University start-up grants. Wangshu Tong thanks the funding supports from the NSFC (52173088)

Dual–Plasticizing Strategy for an Enhanced Performance of Degradable Chitosan-Based Triboelectric Nanogenerators

Chitosan (CS), as the polymer friction layer of triboelectric nanogenerators (TENGs), has great potential for application in the development of degradable wearable sensors. However, its mechanical properties and output performance require further improvement. Although introducing plasticizers into polymers can simultaneously increase their mechanical properties and TENG output, this strategy remains unexplored for degradable polymer TENGs, which exhibit great potential as green materials in electromechanical conversion. Herein, we used glycerol and polyethylene glycol as plasticizers to enhance tensile properties and output properties of the CS TENG. Plasticizer incorporation resulted in an improved surface roughness and the introduction of numerous −OH groups, thereby improving the tribo-positive electrical generation of CS. The maximum open–circuit voltage can reach 173 V, which was three times higher than that of pure CS-based TENGs. Moreover, reduced Young’s modulus of this film made it more advantageous for flexible sensor applications, and throat sensing and handwriting recognition were realized. Finally, the CS sensor exhibited antibacterial activity and complete degradability in soil within 36 days. Overall, this plasticizing method is expected to be extensively studied in the field of degradable, wearable polymer TENG sensors

Dual–Plasticizing Strategy for an Enhanced Performance of Degradable Chitosan-Based Triboelectric Nanogenerators

Chitosan (CS), as the polymer friction layer of triboelectric nanogenerators (TENGs), has great potential for application in the development of degradable wearable sensors. However, its mechanical properties and output performance require further improvement. Although introducing plasticizers into polymers can simultaneously increase their mechanical properties and TENG output, this strategy remains unexplored for degradable polymer TENGs, which exhibit great potential as green materials in electromechanical conversion. Herein, we used glycerol and polyethylene glycol as plasticizers to enhance tensile properties and output properties of the CS TENG. Plasticizer incorporation resulted in an improved surface roughness and the introduction of numerous −OH groups, thereby improving the tribo-positive electrical generation of CS. The maximum open–circuit voltage can reach 173 V, which was three times higher than that of pure CS-based TENGs. Moreover, reduced Young’s modulus of this film made it more advantageous for flexible sensor applications, and throat sensing and handwriting recognition were realized. Finally, the CS sensor exhibited antibacterial activity and complete degradability in soil within 36 days. Overall, this plasticizing method is expected to be extensively studied in the field of degradable, wearable polymer TENG sensors

Novel Method for the Fabrication of Flexible Film with Oriented Arrays of Graphene in Poly(vinylidene fluoride-<i>co</i>-hexafluoropropylene) with Low Dielectric Loss

Carbon–polymer nanocomposites with good dielectric properties have potential applications in the electronic and electrical industry because of their good mechanical properties and low cost. The morphology, structure, dielectric properties, and mechanical strength of reduced-graphene oxide nanosheet/poly­(vinylidene fluoride-<i>co</i>-hexafluoropropylene) nanocomposites (rGO/PVDF-HFP) were investigated. The rGO nanosheets were well dispersed and strongly oriented in the matrix, thanks to the unique spin-assistant preparation process. A dielectric constant of 54 (100 Hz) which was four times higher than that of pure PVDF-HFP was obtained when the concentration of rGO was 0.7 vol % and the dielectric loss was as low as 0.27. The good dielectric performance of the nanocomposites was attributed to the homogeneous dispersion and good alignment of rGO. The shear force provided by spin-coating, the thickness decreasing process, and thickness control were assumed to be key factors in the alignment of rGO nanosheets in the nanocomposite films. At the same time, the aligned rGO sheets increased the percolation threshold of the composite which shed light on the mechanism for obtaining low loss materials