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This study evaluates the spatial patterns of flows generated from geo-located Twitter data to measure human migration. Using geo-located tweets continuously collected in the U.S. from 2013 to 2015, we identified Twitter users who migrated per changes in county-of-residence every two years and compared the Twitter-estimated county-to-county migration flows with the ones from the U.S. Internal Revenue Service (IRS). To evaluate the spatial patterns of Twitter migration flows when representing the IRS counterparts, we developed a normalized difference representation index to visualize and identify those counties of over-/under-representations in the Twitter estimates. Further, we applied a multidimensional spatial scan statistic approach based on a Poisson process model to detect pairs of origin and destination regions where the over-/under-representativeness occurred. The results suggest that Twitter migration flows tend to under-represent the IRS estimates in regions with a large population and over-represent them in metropolitan regions adjacent to tourist attractions. This study demonstrated that geo-located Twitter data could be a sound statistical proxy for measuring human migration. Given that the spatial patterns of Twitter-estimated migration flows vary significantly across the geographic space, related studies will benefit from our approach by identifying those regions where data calibration is necessary. [ABSTRACT FROM AUTHOR]

Summary: The mounting requirement for advanced lithium‐ion batteries (LIBs) is based on the enhancement of their whole work life. The application of battery models is vital to improve the control and management ability of sophisticated battery system. Latest work has demonstrated that the open‐circuit potential (OCP) of a full LiFePO4‐graphite battery (LFP) which is critical to model accuracy. But the OCP is inconsistent along with the charge and discharge cycles, which also varies with different discharge C‐rates. In this study, to simulate the special discharge voltage of a commercial LiFePO4‐graphite cell, a mesoscopic model for LFP cathode solid particles is proposed, which is considering the dynamical reaction in the positive region by introducing of many‐particle model. Different with the conventional way to capture the OCP by experimental measurement or empirical function, the mesoscopic model divides the electrode solid phase into several units possessing the non‐monotonic reference potential which is employed the Margules equation. In this way, the potential can be integrally calculated, which is much approaching to the true OCP. Besides, the charge reaction resistance is regarded as equivalent direct current resistances assumed to be in line with the Gauss distribution law. We also considered the appearance and vanishment of the discharging platform during the high C‐rates discharge cycles, which can be explained by the phase changing phenomenon of LFP OCP. And for the calculation of a full cell voltage, the electrolyte and anode part of model are retained from single‐particle model with electrolyte dynamics (SPMe), so that it can be processed by Laplace transformation and followed by Padé approximation for the simplicity. The SPMe combined with our many‐particle mesoscopic model is capable to ultimately simulate the characteristic of a full LiFePO4‐graphite battery discharge voltage. [ABSTRACT FROM AUTHOR]

Real-time electrochemical state information of lithium-ion batteries attributes to a high-fidelity estimation of state-of-charge (SOC) and state-of-health (SOH) in advanced battery management systems. However, the consumption of recyclable lithium ions, loss of the active materials, and the interior resistance increase resulted from the irreversible side reactions cause severe battery performance decay. To maintain accurate battery state estimation over time, a scheme using the reduced-order electrochemical model and the dual nonlinear filters is presented in this article for the reliable co-estimations of cell SOC and SOH. Specifically, the full-order pseudo-two-dimensional model is first simplified with Padé approximation while ensuring precision and observability. Next, the feasibility and performance of SOC estimator are revealed by accessing unmeasurable physical variables, such as the surface and bulk solid-phase concentration. To well reflect battery degradation, three key aging factors including the loss of lithium ions, loss of active materials, and resistance increment, are simultaneously identified, leading to an appreciable precision improvement of SOC estimation online particular for aged cells. Finally, extensive verification experiments are carried out over the cell's lifespan. The results demonstrate the performance of the proposed SOC/SOH co-estimation scheme. [ABSTRACT FROM AUTHOR]

Generally, high bioelectroactivity of anodophilic biofilm favors high power generation of microbial fuel cell (MFC); however, it is not clear whether it can promote denitrification of MFC synchronously. In this study, we studied the impact of anodophilic biofilm bioelectroactivity on the denitrification behavior of air‐cathode MFC (AC‐MFC) in steady state and found that high bioelectroactivity of anodophilic biofilm not only favored high power generation of the AC‐MFC, but also promoted the growth of denitrifers at the anodes and strengthened denitrification. Anodophilic biofilms of AC‐MFC with various bioelectroactivity were acclimated at conditions of open circuit (OC), Rext of 1000 Ω and 20 Ω (denoted as AC‐MFC‐OC, AC‐MFC‐1000Ω, and AC‐MFC‐20Ω, respectively) and performed for over 100 days. Electrochemical tests and microbial analysis results showed that the anode of the AC‐MFC‐20Ω delivered higher current response of both oxidation and denitrification and had higher abundance of electroactive bacteria than the AC‐MFC‐OC, AC‐MFC‐1000Ω, demonstrating a higher bioelectroactivity of the anodophilic biofilms. Moreover, these electroactive bacteria favored the accumulation of denitrifers, like Thauera and Alicycliphilus, probably by consuming trace oxygen through catalyzing oxygen reduction. The AC‐MFC‐20Ω not only delivered a 61.7% higher power than the AC‐MFC‐1000Ω, but also achieved a stable and high denitrification rate constant (kDN) of 1.9 h−1, which was 50% and 40% higher than that of the AC‐MFC‐OC and AC‐MFC‐1000Ω, respectively. It could be concluded that the high bioelectroactivity of the anodophilic biofilms not only favored high power generation of the AC‐MFC, but also promoted the enrichment of denitrifers at the anodes and strengthened denitrification. This study provided an effective method for enhancing power generation and denitrification performance of the AC‐MFC synchronously. [ABSTRACT FROM AUTHOR]

Accurately identifying the aging-related parameters of a lithium-ion electrochemical model is crucial for the advanced battery management systems over the cells’ service life. However, the multiparametric and highly nonlinear mathematical structures of the physical model heighten the difficulty for parameterization. Thus, analyzing the influence of degraded parameters on model output holds the key to efficient identification. In this article, a statistics-based global sensitivity analysis of overall 16 aging parameters in the pseudo-2-D model of lithium-ion batteries is investigated under both the charge process and dynamic driving cycles at 10°, 25°, and 45°. First, massive samples of the parameters are generated synchronously with the Latin hypercubes method. Then the model is simulated with the Monte Carlo technique. Finally, the sensitivity of each parameter is ranked with the partial correlation coefficient which quantifies the relation between the parameter variations and the voltage residuals. The results turn out that the sensitivity of each parameter varies at different operating conditions. Specifically, the resistance-related parameters are the most sensitive than capacity and diffusion-related parameters. To validate the effectiveness of the proposed approach, 16 aging parameters are clustered for identification. The identified model achieves low voltage root-mean-square errors across 170 cycles at three temperatures. [ABSTRACT FROM AUTHOR]

Battery fast charging strategies have gained an increasing interest toward the convenience of battery applications but may unduly degrade or damage the batteries. To harness these competing objectives, including safety, lifespan, and charging time, in this article, we propose a novel health-aware multiobjective optimal charging strategy to simultaneously shorten the charging time and relieve the battery degradation. The multiobjective optimal charging problem is formulated based on a coupled electrochemical-thermal-aging battery model. Constraints are explicitly imposed on physically meaningful state variables to avoid hazardous operations. Charging duration and battery aging process are well traded-off. Strategies for minimum-time and health-aware fast charging are investigated using different input current bounds, subject to both side reaction and temperature constraints. The experimental results validate that the presented multiobjective health-aware optimal charging algorithm is capable of reducing the charging time from its benchmarks largely without sacrificing the state-of-health of the battery. [ABSTRACT FROM AUTHOR]

Detecting irregularly shaped spatial clusters within heterogeneous point processes is challenging because the number of potential clusters with different sizes and shapes can be enormous. This research develops a novel method, expansion-based spatial clustering for inhomogeneous point processes (ESCIP), for detecting spatial clusters of any shape within a heterogeneous point process in the context of analyzing spatial big data. Statistical testing is used to find core points—points with neighboring areas that have significantly more cases than the expectation—and an expansion approach is developed to find irregularly shaped clusters by connecting nearby core points. Instead of employing a brute-force search for all potential clusters, as done in the spatial scan statistics, this approach only requires testing a small neighboring area for each potential core point. Moreover, spatial indexing is leveraged to speed up the search for nearby points and the expansion of clusters. The proposed method is implemented with Poisson and Bernoulli models and evaluated for large spatial data sets. Experimental results show that ESCIP can detect irregularly shaped spatial clusters from millions of points with high efficiency. It is also demonstrated that the method outperforms the spatial scan statistics on the flexibility of cluster shapes and computational performance. Furthermore, ESCIP ensures that every subset of a detected cluster is statistically significant and contiguous. Key Words: cyberGIS, spatial algorithm, spatial analysis, spatial clustering. [ABSTRACT FROM AUTHOR]

Radiological dirty bombs and improvised nuclear devices pose a significant threat to both public health and national security. Growing networks of radiation sensors have been deployed by a number of offices within the U.S. and international agencies. Detecting such threats while minimizing false alarm rates presents a considerable challenge to homeland security and public health. This research aims to achieve a higher probability of detection with a lower probability of false alarms. It focuses on the local spatial instability of radiation levels in order to detect radioactive materials based on robust outlier detection methods. Our approach includes a three‐step abnormality detection method consisting of one‐dimensional robust outlier detection for all gamma‐ray counts, a density‐based clustering analysis, and a two‐dimensional robust outlier detection method using a bagplot, based on spatial associations. The effectiveness of the method proposed is demonstrated through a case study, wherein radioactive materials are detected in urban environments, and its performance is compared with alternative methods employing a k‐sigma approach, local Getis–Ord (Gi*) statistic, and the goodness of fit of the Poisson distribution. [ABSTRACT FROM AUTHOR]

This paper describes a multidimensional spatial scan statistics approach to comparing spatial movement patterns based on origin-destination (OD) representation. This approach aims to evaluate differences and similarities between the spatial distributions of a pair of OD movement datasets, and detect areas where the two spatial distributions differ the most. Specifically, two OD datasets being compared are modeled as a bivariate marked spatial point process in a multidimensional space, consisting of points representing individual OD movement records. Such multidimensional space is formed by the Cartesian product of the origins’ and the destinations’ geographic spaces. With this spatial data model, one can evaluate how two movement distributions differ from each other by testing against a random labeling null hypothesis. A multidimensional Bernoulli spatial scan statistics method is developed to detect OD region pairs with abnormally high concentrations of one movement dataset over the other. The existence and the spatial extents of these OD region pairs indicate whether and where the two movement distributions differ. Two case studies were conducted to evaluate the approach by comparing morning and afternoon taxi trips (individual movements), and county-to-county migration flows between age groups (aggregated movement flows), and demonstrated that areas with the most significant spatial distribution differences could be detected from large movement datasets. [ABSTRACT FROM AUTHOR]

Tracking spatial and temporal trends of events (e.g. disease outbreaks and natural disasters) is important for situation awareness and timely response. Social media, with increasing popularity, provide an effective way to collect event-related data from massive populations and thus a significant opportunity to dynamically monitor events as they emerge and evolve. While existing research has demonstrated the value of social media as sensors in event detection, estimating potential time spans and influenced areas of an event from social media remains challenging. Challenges include the unstable volumes of available data, the spatial heterogeneity of event activities and social media data, and the data sparsity. This paper describes a systematic approach to detecting potential spatiotemporal patterns of events by resolving these challenges through several interrelated strategies: using kernel density estimation for smoothed social media intensity surfaces; utilizing event-unrelated social media posts to help map relative event prevalence; and normalizing event indicators based on historical fluctuation. This approach generates event indicator maps and significance maps explaining spatiotemporal variations of event prevalence to identify space-time regions with potentially abnormal event activities. The approach has been applied to detect influenza activity patterns in the conterminous US usingTwitterdata. A set of experiments demonstrated that our approach produces high-resolution influenza activity maps that could be explained by available ground truth data. [ABSTRACT FROM PUBLISHER]

SUMMARY Social media have experienced a spectacular rise in popularity, attracting hundreds of millions of users and generating unprecedented amount of content that increasingly contain location and place information. Collectively, the massive location information in these data provides an excellent opportunity to better understand many geographic phenomena and geospatial dynamics in a timely fashion. Recent studies capitalizing on social networking and media data show significant societal impacts in many areas including prediction of stock market and infectious disease surveillance. However, because location-based social media data are often massive, generated dynamically, and unstructured, significant computation, data, and visualization challenges need to be resolved. This research aims to demonstrate the use of massive social media data to interactively analyze spatiotemporal events across spatial and temporal scales, by establishing a data-driven framework using cyberGIS-geographic information systems (GIS) based on advanced cyberinfrastructure-to resolve aforementioned challenges. Specifically, FluMapper-an application on the CyberGIS Gateway-is employed as a case study to demonstrate the data-driven framework and seamless integration of massive location-based social media data and spatial analytical services within the online problem solving environment of the Gateway. FluMapper presents integrated results from two complementary spatial analyses: (i) an interactive exploration of spatial distribution of flu risk and (ii) dynamic mapping of movement patterns, across multiple spatial, and temporal scales. The seamless integration of these two analyses through the framework illustrates the potential of cyberGIS to resolve the compute and data challenges of analyzing near real-time social media data in an efficient and scalable manner and to support interactive visualization. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Laser speckle imaging (LSI) is a powerful tool for motion analysis owing to the high sensitivity of laser speckles. Traditional LSI techniques rely on identifying changes from the sequential intensity speckle patterns, where each pixel performs synchronous measurements. However, a lot of redundant data of the static speckles without motion information in the scene will also be recorded, resulting in considerable resources consumption for data processing and storage. Moreover, the motion cues are inevitably lost during the "blind" time interval between successive frames. To tackle such challenges, we propose neuromorphic laser speckle imaging (NLSI) as an efficient alternative approach for motion analysis. Our method preserves the motion information while excluding the redundant data by exploring the use of the neuromorphic event sensor, which acquires only the relevant information of the moving parts and responds asynchronously with a much higher sampling rate. This neuromorphic data acquisition mechanism captures fast-moving objects on the order of microseconds. In the proposed NLSI method, the moving object is illuminated using a coherent light source, and the reflected high frequency laser speckle patterns are captured with a bare neuromorphic event sensor. We present the data processing strategy to analyze motion from event-based laser speckles, and the experimental results demonstrate the feasibility of our method at different motion speeds.

We present an erratum to our Letter [Opt. Lett.46, 3885 (2021)OPLEDP0146-959210.1364/OL.430419]. This erratum corrects an inadvertent error in Eq. (4). The corrections have no influence on the results and conclusions of the original Letter.