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Understanding anomalous behavior and spatial changes in an urban parking area can enhance decision-making and situational awareness insights for sustainable urban parking management. Decision-making relies on data that comes in overwhelming velocity and volume, that one cannot comprehend without some layer of analysis and visualization. This work presents a mobile application that performs time series analysis and anomaly detection on parking lot data for decision-making. The mobile application allows users to add pins in the parking lot and analyze the pin data over a period of time. Our approach uses parking pins to identify each vehicle and then collect specific data, such as temporal variables like latitude, longitude, time, date, and text (information from the license plate), as well as images and videos shot at the location. Users have the option of placing pins at the location where their car is parked, and the information collected can be used for time series analysis. By examining the data pattern, we may quickly identify vehicles parked in restricted spaces but without authorization and vehicles parked in disabled spaces but owned by regular users. This time series analysis enables the extraction of meaningful insights, making it useful in the identification of recurring patterns in parking lot occupancy over time. This information aids in predicting future demands, enabling parking administrators to allocate resources efficiently during peak hours and optimize space usage. It can be used in detecting irregularities in parking patterns, aiding in the prompt identification of unauthorized or abnormal parking and parking violations which includes parking of the wrong type of vehicle, and parking at restricted or reserved areas. 

Crime data visualization plays a key role in understanding and dealing with criminal activities. This paper focuses on the integration of mixed reality (MR) and crime data analysis. There are many barriers and challenges when developing MR three-dimensional (3D) environments for visualization and inspection. The main problem is the lack of commonly shared data structures and interfaces between them. The rise in crime rates over the past few years is a huge source of issue for police departments and law enforcement organizations. As the crime rates significantly changed throughout time, both upward and downward, these changes are then compared to external factors, such as population, unemployment, and poverty. There is a need for visualizing the multiple crime datasets in multiple states with external factors. This work proposes a novel interactive approach for loading crime datasets into the HoloLens 2 device and displaying them in a mixed-reality setting for data analysis. By allowing people to engage and analyze datasets in a 3D space, the suggested system seeks to close the gap between data analysis and machine learning. Users can import many datasets, such as spatial, category, and numerical data, into the HoloLens 2 device and interactively visualize crime data for different states simultaneously. The system offers user-friendly capabilities for interactive data visualization in mixed reality once the data has been imported. The dataset is manipulated and transformed by users, who can also rotate, scale, and position it in 3D. To depict various characteristics and dimensions of the data, the system also supports a variety of visual encoding techniques, such as color mapping, size scaling, and spatial layout with the use of the imported datasets and the HoloLens 2’s visualization capabilities, users can discover new insights and intricate linkages within the data. Natural movements and voice instructions allow users to engage with the visible data, enabling a hands-free and immersive data exploration experience. This paper also visualizes the crime data for four different cities: Chicago, Baltimore, Dallas, and Denton. Analyzing crime against factors such as population, employment, unemployment rate, and poverty rates provides information about the complex relationship between social factors and criminal behavior. The results and outcomes of this work will help the police department and law enforcement organizations better understand crime issues and supply insight into factors affecting crime that will help them deploy resources and help their decision-making process. 

Abstract Lake ice phenology is a critical component of the cryosphere and a sensitive indicator of climate change that has some of the longest records related to climate science. Records commenced for numerous reasons including navigation, hydropower development, and individual curiosity, demonstrating the value of lake ice as a seasonal event of significant importance to a broad swath of peoples and countries. At the same time, lake ice loss has been rapid and widespread with lakes losing ice at an average rate of 17 days per century. In this Perspective, we examine the earliest known records of ice cover and the scientific studies that developed from that practice of record keeping. Studies in lake ice began in the nineteenth Century and have included relationships between climate, biology, and ice cover. Early studies developed some of the foundational principles that limnologists and climate scientists are still exploring, such as the relationship between ice phenology and climate variables, large‐scale climate oscillations, and morphological characteristics, with implications for lake ice physical structure and under‐ice ecosystems in a warming climate. We conclude with an examination of the state of the field and how these centuries‐long lake ice records can continue to inform cutting edge science by validating satellite remote sensing techniques, in addition to modeling approaches and collaborations across disciplines, that can improve our understanding of the loss of lake ice in a warmer world. 

Abstract Test-particle simulations are an important tool for magnetospheric and heliophysics research. In this paper, we present the Space Plasma and Energetic Charged particle TRansport on Unstructured Meshes (SPECTRUM) software as a novel tool for performing these types of simulations in arbitrary astrophysical environments, specified either analytically or numerically (i.e., on a grid). We discuss and benchmark SPECTRUM’s interface with meshed magnetohydrodynamic backgrounds, including output from the Block Adaptive Tree Solar-wind Roe-type Upwind Scheme (BATS-R-US) code. We also investigate the effects of field discretization on both deterministic and stochastic particle motion, with emphasis on space science applications, concluding that the discretization error typically enhances the diffusive behavior of the ensemble. 

Rapid advancement in inverse modeling methods have brought into light their susceptibility to imperfect data. This has made it imperative to obtain more explainable and trustworthy estimates from these models. In hydrology, basin characteristics can be noisy or missing, impacting streamflow prediction. We propose a probabilistic inverse model framework that can reconstruct robust hydrology basin characteristics from dynamic input weather driver and streamflow response data. We address two aspects of building more explainable inverse models, uncertainty estimation (uncertainty due to imperfect data and imperfect model) and robustness. This can help improve the trust of water managers, handling of noisy data and reduce costs. We also propose an uncertainty based loss regularization that offers removal of 17% of temporal artifacts in reconstructions, 36% reduction in uncertainty and 4% higher coverage rate for basin characteristics. The forward model performance (streamflow estimation) is also improved by 6% using these uncertainty learning based reconstructions. 

Abstract Himalayan lakes represent critical water resources, culturally important waterbodies, and potential hazards. Some of these lakes experience dramatic water-level changes, responding to seasonal monsoon rains and post-monsoonal draining. To address the paucity of direct observations of hydrology in retreating mountain glacial systems, we describe a field program in a series of high altitude lakes in Sagarmatha National Park, adjacent to Ngozumba, the largest glacier in Nepal. In situ observations find extreme (>12 m) seasonal water-level changes in a 60-m deep lateral-moraine-dammed lake (lacking surface outflow), during a 16-month period, equivalent to a 5$$\times 10^6$$ $\times {10}^6$m$$^3$$ ${}^3$volume change annually. The water column thermal structure was also monitored over the same period. A hydraulic model is constructed, validated against observed water levels, and used to estimate hydraulic conductivities of the moraine soils damming the lake and improves our understanding of this complex hydrological system. Our findings indicate that lake level compared to the damming glacier surface height is the key criterion for large lake fluctuations, while lakes lying below the glacier surface, regulated by surface outflow, possess only minor seasonal water-level fluctuations. Thus, lakes adjacent to glaciers may exhibit very different filling/draining dynamics based on presence/absence of surface outflows and elevation relative to retreating glaciers, and consequently may have very different fates in the next few decades as the climate warms. 

We report the discovery of a novel form of Ruddlesden−Popper (RP) nickelate that stands as the first example of long-range, coherent polymorphism in this class of inorganic solids. Rather than the well-known, uniform stacking of perovskite blocks ubiquitously found in RP phases, this newly discovered polymorph of the bilayer RP phase La3Ni2O7 adopts a novel stacking sequence in which single-layer and trilayer blocks of NiO6 octahedra alternate in a “1313” sequence. Crystals of this new polymorph are described in space group Cmmm, although we note evidence for a competing Imam variant. Transport measurements at ambient pressure reveal metallic character with evidence of a charge density wave transition with an onset at T ≈ 134 K. The discovery of such polymorphism could reverberate to the expansive range of science and applications that rely on RP materials, particularly the recently reported signatures of superconductivity in bilayer La3Ni2O7 with Tc as high as 80 K above 14 GPa. 

Data management plans (DMPs) are required from researchers seeking funding from federal agencies in the United States. Ideally, DMPs disclose how research outputs will be managed and shared. How well DMPs communicate those plans is less understood. Evaluation tools such as the DART rubric and the Belmont scorecard assess the completeness of DMPs and offer one view into what DMPs communicate. This paper compares the evaluation criteria of the two tools by applying them to the same corpus of 150 DMPs from five different NSF programs. Findings suggest that the DART rubric and the Belmont score overlap significantly, but the Belmont scorecard provides a better method to assess completeness. We find that most DMPs fail to address many of the best practices that are articulated by librarians and information professionals in the different evaluation tools. However, the evaluation methodology of both tools relies on a rating scale that does not account for the interaction of key areas of data management. This work contributes to the improvement of evaluation tools for data management planning.