Regular Data Research Articles

Using heat map data to measure population development trends and urban disparities of large-scale affordable communities in China

Over the past decade, large-scale affordable communities have proliferated in the suburban areas of major Chinese cities. Most of the housing in these communities consists of affordable housing provided by the government and constructed by developers, targeted at middle to low-income individuals through restricted-price sales and rentals. With more than a decade of occupancy, it is crucial to examine the development status of these communities. This study employs regular collection of heat map data to observe the population density trends in communities from 2020 to 2024. By analyzing population density and its derived variables, the study examines changes in daytime and nighttime populations during weekdays, as well as the differences between the Spring Festival period and regular population data. Combining urban context, we use both quantitative and qualitative methods to analyze population quantity and structure trends and further explore the development disparities among cities. The study reveals a widespread increase in population across 35 communities in five cities from 2020 to 2021, peaking in 2021 and 2022, with differentiation among cities after 2023. Before 2022, there was a pattern in the development of community population quantity and structure: communities with faster population growth exhibited a simultaneous increase in commuter proportions. The impetus for community population changes is largely attributed to the varying attractiveness of cities to the floating population, with the five cities demonstrating distinct patterns. The method used in this study to collect population data, as opposed to traditional questionnaire surveys, presents advantages in terms of real-time, efficiency, convenience, and high sensitivity. This approach can offer feasible insights for establishing a real-time monitoring system for the development of large-scale affordable communities in China in the future.

IDI Search: a metadata search app for exploring New Zealand’s administrative linked data

How can we lower barriers to reuse of linked administrative data and improve quality at the same time? Linked data resources are expensive and complex, but accompanying readily-accessible metadata supports data reuse and quality control. However, metadata systems that are unsystematic or non-automated can lead to inconsistencies, making metadata use difficult even for experienced analysts. Our national statistics office (NSO) maintains a research database (RD) of administrative datasets linkable at the individual level. With the NSO’s agreement, we extracted schema information (e.g. tables and variables) from the RD and obtained data dictionaries. R was used to extract information (e.g., descriptions, human-friendly names, codings) from the data dictionaries, which identified errors and inconsistencies that the NSO then fixed. A metadata database was collated using schema and data dictionary information about variables and datasets in the RD. Finally, a public web app was developed to enable exploration of the meta database by searching specific terms or navigating the hierarchical relationships. We now work with the NSO data team to re-extract variables and add or update dictionaries ahead of the regular data updates. Metadata coverage is displayed on the app and used by our NSO to improve metadata quality and coverage. Our scripting workflow demonstrates the utility of automation in picking up errors and inconsistencies quickly, and before they can propagate by copy and paste. The web app, available to existing and new users, is now listed by the NSO as a “go-to” resource for using the linked administrative data resources.

Functional Principal Component Analysis as an Alternative to Mixed-Effect Models for Describing Sparse Repeated Measures in Presence of Missing Data.

Analyzing longitudinal data in health studies is challenging due to sparse and error-prone measurements, strong within-individual correlation, missing data and various trajectory shapes. While mixed-effect models (MM) effectively address these challenges, they remain parametric models and may incur computational costs. In contrast, functional principal component analysis (FPCA) is a non-parametric approach developed for regular and dense functional data that flexibly describes temporal trajectories at a potentially lower computational cost. This article presents an empirical simulation study evaluating the behavior of FPCA with sparse and error-prone repeated measures and its robustness under different missing data schemes in comparison with MM. The results show that FPCA is well-suited in the presence of missing at random data caused by dropout, except in scenarios involving most frequent and systematic dropout. Like MM, FPCA fails under missing not at random mechanism. The FPCA was applied to describe the trajectories of four cognitive functions before clinical dementia and contrast them with those of matched controls in a case-control study nested in a population-based aging cohort. The average cognitive declines of future dementia cases showed a sudden divergence from those of their matched controls with a sharp acceleration 5 to 2.5 years prior to diagnosis.

A Study on Onboarding Employees and Solving Employees Queries of Amnex Infotechnologies in Ahmedabad

This study investigates the onboarding processes, employee query management, and data handling practices at Amnex Infotechnologies, highlighting key findings and offering actionable recommendations for improvement. The workforce at Amnex is predominantly below 25 years old and between 35-44 years old, with a slight male majority. Educational backgrounds are diverse, with postgraduates forming the largest group. Department representation varies, with HR and Finance departments having the highest participation. A significant majority of employees perceive the onboarding process as well-structured and feel adequately supported by the HR team. Data management practices are positively received, with most employees confident in the security of their data. However, there is potential for enhancing the onboarding experience through technology integration and structured programs. Improving query management by establishing a centralized knowledge base and responsive support systems is also recommended. Regular data audits and secure access channels can further strengthen data management practices. The study concludes that while Amnex has successfully created a supportive and engaging work environment, continuous improvements based on feedback and industry best practices can drive further organizational success and employee satisfaction

Best Practice of Moodle Implementation for E-Learning: A Perspective of Public University Lecturers

Moodle is a popular open-source learning management system (LMS) used for online learning and course management. It provides educators with tools to create online courses, manage learning content, track student progress, conduct assessments, and facilitate communication and collaboration among students and instructors. Moodle is widely used in educational institutions, corporate training settings, and various other learning environments around the world. The aim of this article was to study the best practices for implementing Moodle in e-learning, as perceived by public university lecturers. This study employed a qualitative approach, utilizing Focus Group Discussions (FGDs) among lecturers from public universities. All data were meticulously transcribed and subsequently analyzed thematically. The results show, experts agreed that three best practices were i) Optimizing the task of e-learning manager, ii) Utilize the restriction option to uphold student integrity and, iii) Implementing regular data backups. Therefore, this study enriches the field by providing practical recommendations for implementing Moodle, drawing from the insights of public university lecturers.

DPKnob: A visual analysis approach to risk-aware formulation of differential privacy schemes for data query scenarios

Differential privacy is an essential approach for privacy preservation in data queries. However, users face a significant challenge in selecting an appropriate privacy scheme, as they struggle to balance the utility of query results with the preservation of diverse individual privacy. Customizing a privacy scheme becomes even more complex in dealing with queries that involve multiple data attributes. When adversaries attempt to breach privacy firewalls by conducting multiple regular data queries with various attribute values, data owners must arduously discern unpredictable disclosure risks and construct suitable privacy schemes. In this paper, we propose a visual analysis approach for formulating privacy schemes of differential privacy. Our approach supports the identification and simulation of potential privacy attacks in querying statistical results of multi-dimensional databases. We also developed a prototype system, called DPKnob, which integrates multiple coordinated views. DPKnob not only allows users to interactively assess and explore privacy exposure risks by browsing high-risk attacks, but also facilitates an iterative process for formulating and optimizing privacy schemes based on differential privacy. This iterative process allows users to compare different schemes, refine their expectations of privacy and utility, and ultimately establish a well-balanced privacy scheme. The effectiveness of this study is verified by a user study and two case studies with real-world datasets.

ResGAT: Residual Graph Attention Networks for molecular property prediction

Molecular property prediction is an important step in the drug discovery pipeline. Numerous computational methods have been developed to predict a wide range of molecular properties. While recent approaches have shown promising results, no single architecture can comprehensively address all tasks, making this area persistently challenging and requiring substantial time and effort. Beyond traditional machine learning and deep learning architectures for regular data, several deep learning architectures have been designed for graph-structured data to overcome the limitations of conventional methods. Utilizing graph-structured data in quantitative structure–activity relationship (QSAR) modeling allows models to effectively extract unique features, especially where connectivity information is crucial. In our study, we developed residual graph attention networks (ResGAT), a deep learning architecture for molecular graph-structured data. This architecture is a combination of graph attention networks and shortcut connections to address both regression and classification problems. It is also customizable to adapt to various dataset sizes, enhancing the learning process based on molecular patterns. When tested multiple times with both random and scaffold sampling strategies on nine benchmark molecular datasets, QSAR models developed using ResGAT demonstrated stability and competitive performance compared to state-of-the-art methods.

Effectiveness of A Nurse-Led Multimodal Intervention in Preventing Blood Culture Contamination: A Before-and-After Study.

Blood culture is crucial for accurate and timely bacteremia diagnosis and guide antibiotic therapy. However, during culture sampling, contamination can occur, resulting in misdiagnosis, unnecessary antibiotic exposure, and prolonged hospitalization. This before-and-after intervention study aimed to evaluate the effectiveness of a multimodal intervention in preventing blood culture contamination. The study was conducted in a 170-bed hospital in Portugal and included a total of 23,566 blood cultures. Contamination rates were assessed in two phases: Phase 1 (before intervention, month 0) included 10,928 cultures, and Phase 2 (after intervention, month 6) included 12,638 cultures. During the study period, a multimodal intervention targeting the nursing staff was implemented, consisting of training actions, guideline updates, regular data monitoring and feedback, and introduction of a blood culture pack. Following the intervention, blood culture contamination decreased from 6.8% (Phase 1) to 3.9% (Phase 2). A comparative analysis revealed that the risk of contamination before the intervention was nearly four times higher in first culture, OR = 3.97 (CI 2.86-5.49). Our findings suggest that the multimodal intervention enhanced nurses' adherence to recommended practices, resulting in a reduced risk of blood culture contamination, earlier identification of infectious agents, and improved accuracy of antibiotic therapy.

Manifold-based approach for neural network robustness analysis

It is important to understand the mathematical foundations of neural networks and to include robustness in model evaluation. Here, we introduce algorithms based on manifold curvature estimation to assess neural network robustness. These algorithms rely solely on training data and do not require regular or adversarial test data. Initially, a metric is proposed to measure the curvature of discrete data manifolds by introducing weighted angles concept between subspaces. Following this, a robustness measure is introduced that is independent of network architecture or model parameters. Lastly, two additional methods are introduced, utilizing curvature estimation of special manifolds formed by using gradient vectors between output and input network layers, alongside manifold curvature estimation. A comprehensive evaluation is provided on multiple network models using the CIFAR-10 dataset. Manifold geometry-based robustness analysis may lead to the development of not only accurate but also robust neural network models.

Open Data-Driven Decision Making: Observations from COVID-19 Minimal Essential Data, Canton Zürich, Switzerland.

This research delves into the relationship between open data and decision making, drawing insights from the utilization of Minimal Essential Data (MED) during the COVID-19 pandemic in Canton Zürich, Switzerland. It examines observations and lessons learned, emphasizing the pivotal role of open data in response to an emergency. Through a case study approach, this article sheds light on the practical implications and observed strategies in publishing open data from digital public health surveillance software. While it is arguable that open data has the potential to enhance data quality in the context of a public health emergency, our findings indicate that it is firstly essential that a Minimal Essential Dataset (MED) is defined and publicly published, an open data publication platform and procedure are secondly in place (ideally following FAIR data principles), and thirdly, that regular data quality reviews are conducted.

Changes in the Johne’s disease situation in GB dairy herds over 10 years, as revealed by regular milk ELISA data

Effective management of cattle infected with Johne’s Disease (JD) is crucial to minimizing transmission and within-herd prevalence. Within Great Britain (GB), the voluntary National Johne’s Management Plan (NJMP) requires farmers and a certified vet to conduct a risk assessment to determine the herd risk, examine the herd JD status and formulate a management plan. Individual milk ELISA tests for JD antibodies are widely used to monitor infection. The JD Tracker application, available within the dairy data management software InterHerd+ and other web-based environments, is being used by farmers and veterinarians to facilitate the practical use of milk ELISA data to aid JD-related management decisions. The JD Tracker application uses a herd’s milk ELISA data to calculate a collection of ‘JD parameters’ that are indicative of the current JD status of the herd alongside contemporary and retrospective drivers linked to transmission and maintenance of infection. Herein, we use milk ELISA data from 154 regularly testing herds to review the temporal trends in JD parameters from 2013 to 2022. Since 2015, JD Tracker parameters have improved in these herds, most notably average test value (ATV) and within-herd prevalence (%Pos30). Trends in driver parameters suggest that farmers are progressively less likely to serve repeat test-positive (J5) cows and are more readily removing them. The data also reveal that the burden of JD is disproportionately greater in herds with higher ATV. In 2022, the 25 % of herds with the highest ATVs accounted for 42 % of positive tests and 42 % of repeat ELISA positive (J5) cows. Retrospectively, it is not possible to identify with certainty factors that directly contributed to the trends in JD parameters, but it is notable that the introduction of the NJMP was coincided with the improving JD situation. In 2019, participation in the NJMP or an equivalent scheme became mandatory for dairy farms to be compliant with the food and farms standards assurance scheme Red Tractor, with the result that JD management plans are now completed by 95 % of UK dairy farms. As far as we know, the UK is unique in its development of a tool (the JD Tracker) which adds utility to milk ELISA data using specifically designed JD parameters. Anticipated further work includes the development of a national database of JD testing herds and application of the JD Tracker at national scale to enable more comprehensive industry-level monitoring of JD within GB dairy farms.

Abstractions for C++ code optimizations in parallel high-performance applications

Many computational problems consider memory throughput a performance bottleneck, especially in the domain of parallel computing. Software needs to be attuned to hardware features like cache architectures or concurrent memory banks to reach a decent level of performance efficiency. This can be achieved by selecting the right memory layouts for data structures or changing the order of data structure traversal. In this work, we present an abstraction for traversing a set of regular data structures (e.g., multidimensional arrays) that allows the design of traversal-agnostic algorithms. Such algorithms can easily optimize for memory performance and employ semi-automated parallelization or autotuning without altering their internal code. We also add an abstraction for autotuning that allows defining tuning parameters in one place and removes boilerplate code. The proposed solution was implemented as an extension of the Noarr library that simplifies a layout-agnostic design of regular data structures. It is implemented entirely using C++ template meta-programming without any nonstandard dependencies, so it is fully compatible with existing compilers, including CUDA NVCC or Intel DPC++. We evaluate the performance and expressiveness of our approach on the Polybench-C benchmarks.

Machine-learning prediction of kidney failure occurrence based on regular health check-up data: a nationwide cohort dataset in South Korea

Machine-learning prediction of kidney failure occurrence based on regular health check-up data: a nationwide cohort dataset in South Korea

Wave equation on general noncompact symmetric spaces

abstract: We establish sharp pointwise kernel estimates and dispersive properties for the wave equation on noncompact symmetric spaces of general rank. This is achieved by combining the stationary phase method and the Hadamard parametrix, and in particular, by introducing a subtle spectral decomposition, which allows us to overcome a well-known difficulty in higher rank analysis, namely the fact that the Plancherel density is not a differential symbol in general. Consequently, we deduce the Strichartz inequality for a large family of admissible pairs and prove global well-posedness results for the corresponding semi-linear equation with low regularity data as on hyperbolic spaces.

Tipler naked singularities in N dimensions

Abstract A spacetime singularity, identified by the existence of incomplete causal geodesics in the spacetime, is called a (Tipler) strong curvature singularity if the volume form acting on independent Jacobi fields along causal geodesics vanishes in the approach of the singularity. It is called naked if at least one of these causal geodesics is past incomplete. Here, we study the formation of strong curvature naked singularities arising from spherically symmetric gravitational collapse of general type-I matter fields in an arbitrarily finite number of dimensions. In the spirit of Joshi and Dwivedi (1993 Phys. Rev. D 47 5357), and Goswami and Joshi (2007 Phys. Rev. D 76 084026), beginning with regular initial data, we derive two distinct (but not mutually exclusive) conditions, which we call the positive root condition (PRC) and the simple positive root condition (SPRC), that serve as necessary and sufficient conditions, respectively, for the existence of naked singularities. In doing so, we generalize the results of both the aforementioned works. We further restrict the PRC and the SPRC by imposing the curvature growth condition (CGC) of Clarke and Krolak (1985 J. Geom. Phys. 2(2) 127) on all causal curves that satisfy the causal convergence condition. The CGC then gives a sufficient condition ensuring that the naked singularities implying the PRC and implied by the SPRC, are of strong curvature type and hence correspond to the inextendibility of the spacetime. Using the CGC, we extend the results of Mosani et. al. (2020 Phys. Rev. D 101 044052) (that hold for dimension N=4) to the case N=5, showing that strong curvature naked singularities can occur in this case. However, for the case N≥6, we show that past-incomplete causal curves that identify naked singularities do not satisfy the CGC. These results shed light on the validity of the cosmic censorship conjectures in arbitrary dimensions.

THE EFFECT OF HEALTHCARE TECHNOLOGY ON HEALTH EXPENDITURES

This study aimed to determine the effect of Magnetic Resonance Imaging and Computed Tomography imaging services, which are within the scope of health technologies, on Health Expenditures. Panel analyses was used for the analyses. In the study, health expenditures were determined as the dependent variable, and the number of MR imaging and CT imaging within the scope of health technology as the independent variable, and 16 countries with regular data for the years 2007-2018 were included in the analysis. These countries were Australia, Belgium, Canada, Chile, The Czech Republic, Denmark, France, Germany, Iceland, Israel, Korea, Latvia, Lithuania, Luxembourg, Slovak Republic, and Slovenia. As a result of the analyses, it was determined that a one-unit increase in the number of Computed Tomography imaging increased health expenditures by 3.23 units, and a one-unit increase in the number of Magnetic Resonance imaging increased health expenditures by 21.9 units. The results of the study revealed that there was a positive and long-term relationship between health expenditures and the number of Computed Tomography and Magnetic Resonance Imaging, and there was a causal relationship in different directions between the variables. In addition, it has been determined that the number of Magnetic Resonance Imaging has increased more than the number of Computed Tomography imaging over the years, and that the number of Magnetic Resonance Imaging has a higher impact on health expenditures than Computed Tomography. When these two results were evaluated together, it is predicted that evaluating Magnetic Resonance Imaging and developing remedial activities will reduce health expenditures.

Invariant Gibbs measure for a Schrödinger equation with exponential nonlinearity

We investigate the invariance of the Gibbs measure for the fractional Schrödinger equation of exponential type (expNLS) i∂tu+(−Δ)α2u=2γβeβ|u|2u on d-dimensional compact Riemannian manifolds Md, for a dispersion parameter α>d, some coupling constant β>0, and γ≠0. (i) We first study the construction of the Gibbs measure for (expNLS). We prove that in the defocusing case γ>0, the measure is well-defined in the whole regime α>d and β>0 (Theorem 1.1 (i)), while in the focusing case γ<0 its partition function is always infinite for any α>d and β>0, even with a mass cut-off of arbitrary small size (Theorem 1.1 (ii)). (ii) We then study the dynamics (expNLS) with random initial data of low regularity. We first use a compactness argument to prove weak invariance of the Gibbs measure, in the defocusing case γ>0, in the whole regime α>d and β>0 (Theorem 1.4 (i)). In the large dispersion regime α>2d, we can improve this result by constructing a local deterministic flow for (expNLS) for any β>0 and γ≠0. Using the Gibbs measure, we prove that solutions are almost surely global for any β,γ>0, and that the Gibbs measure is invariant (Theorem 1.4 (ii)). (iii) Finally, in the particular case d=1 and M=T, we are able to exploit some probabilistic multilinear smoothing effects to build a global probabilistic flow for (expNLS) for 1+22<α≤2 for any β,γ>0 (Theorem 1.6).

Reconstruction of dense time series high spatial resolution NDVI data using a spatiotemporal optimal weighted combination estimation model based on Sentinel-2 and MODIS

A regular, dense time series of the normalized difference vegetation index (NDVI) is a crucial remote sensing parameter that provides insights into the growth status of plants across different temporal and spatial scales. However, the production of high-resolution, equal-interval, and dense-time-series NDVI products currently faces technical challenges, limiting their application in agriculture and forestry-related fields. This study proposes a new method for reconstructing a dense Sentinel-2 NDVI time series based on the spatiotemporal optimal weighted combination estimation model (SOWCEM). The SOWCEM is developed using state-of-the-art spatial and temporal reconstruction algorithms. This model considers the distribution characteristics of errors in spatiotemporal dimensions, allowing for adaptive determination of the optimal combination weight at the pixel level. Then, the reconstructed regular dense-time-series NDVI data covering the region for five days/time are evaluated through a comparative analysis. The experimental results demonstrate that the regional NDVI reconstruction image based on the SOWCEM algorithm has a determination coefficient (R2) of 0.9082, a root mean square error (RMSE) of 0.0403, and a comprehensive overall error (ERGAS) of 5.7726. Compared with single spatiotemporal dimension models, the SOWCEM algorithm shows an average improvement of 5.78% in R2, while the RMSE and ERGAS decrease by 6.5% and 6.86%, respectively. Moreover, the stability and robustness of the models are also enhanced. These results indicate that the SOWCEM algorithm can effectively achieve a high-quality fusion reconstruction of regional images, significantly enhancing the accuracy of high-spatial-resolution NDVI dense temporal spectral reconstruction. Furthermore, it exhibits clear superiority and good applicability. This algorithmic framework provides a feasible approach for the dense reconstruction of high-spatial-resolution regular time-series NDVI data, enabling more accurate and efficient high-resolution NDVI remote sensing monitoring services in related field applications. The core code of SOWCEM is available at https://github.com/GISerZkun/SOWCEM.

Improving the Performance Management System for Power Plant Operators in Caprycoal Mining Company

Caprycoal Mining Company, located in East Kalimantan, Indonesia, strives to be a leading coal supplier. The performance management system of Caprycoal Mining Company for power plant operators is the subject of this research, which critically evaluates and suggests a redesign. It incorporates the Resource-Based View (RBV), Performance Management Theory, Goal-Setting Theory, and Management by Objectives (MBO), focusing on strategic resources, clear objectives, and structured management. Using Soft System Methodology (SSM) and semi-structured interviews, significant gaps were identified, such as the lack of Key Performance Indicators (KPIs) and mechanisms for managing competency data. Existing training programs were unstructured and misaligned with operational demands. The study proposes a comprehensive overhaul, including a structured KPI framework based on SMART goals aligned with organizational objectives and a digital competency management system for efficient data handling. Regular, structured feedback and advanced data analytics are emphasized for continuous performance monitoring. Additionally, a formal rewards program is recommended to motivate and retain high-performing employees by linking performance outcomes with individual and team rewards.

Enhanced heart disease prediction in remote healthcare monitoring using IoT-enabled cloud-based XGBoost and Bi-LSTM

The advancement of medical technology has brought about a significant transformation in remote healthcare monitoring, which is crucial for providing customized care and ongoing observation. This is especially important when it comes to controlling long-term illnesses like high blood pressure, which raises the risk of heart disease considerably, especially in older people. This methodology achieves greater accuracy by combining regular medical monitoring and Electronic Clinical Data (ECD) from complete medical records with physical data from patients' routine medical monitoring. This innovative technique enhances the area of cardiac disease prediction. A technique that uses cutting-edge machine learning models and IoT technology to meet this demand. In particular, we use the powerful Extreme Gradient Boosting (XGBoost) algorithm to effectively examine big datasets and extract important characteristics to improve prediction accuracy. The deep learning model Bidirectional Long Short-Term Memory (Bi-LSTM) is used to further enhance prediction skills to extract complex temporal patterns from patient data. It outperformed naive Bayes, decision trees, and random forests with our approach, achieving a greater prediction accuracy of 99.4 %. With the combination of Internet of Things technologies and sophisticated machine learning models, this paper offers a novel approach to remote healthcare monitoring.