High Data Availability Research Articles

From Grapes to Clicks : A Cloud-Based Wine-Selling Solution for Single Vendors

This document outlines the design and creation of a robust wine vending application for a single vendor, which combines a web platform, an Android mobile app, and a cloud-based framework. This solution addresses the increasing demand for online wine sales while delivering a smooth, secure, and adaptable shopping experience. Essential features of the system include an intuitive web interface and an application focused on effective product searching, ordering, and payment processing. Cloud infrastructure is implemented to guarantee high availability, scalability, and secure data management. The paper discusses various architectural alternatives, such as utilizing cloud services for load balancing, data handling, and disaster recovery. It also emphasizes the necessity of strong security protocols, including encryption, user authentication, and adherence to industry standards for online transactions. Additionally, the document addresses challenges related to regulatory compliance, including age verification mandates and data protection laws. Lastly, it explores strategies for performance enhancement, improvements to user experience, and potential avenues for future expansion. The proposed solution aims to deliver a dependable, secure, and scalable platform tailored to the requirements of individual wine retailers while accommodating future growth.

Development of physics-guided neural network framework for acid-base treatment prediction using carbon dioxide-based tubular reactor

Accurate acid-base treatment prediction is necessary to achieve the required yield, given the inherent complexity, high nonlinearity, and restricted availability of data samples; to address this challenge, a data-driven approach was developed. However, the technique is constrained by the need for sufficient data to construct an accurate model and lacks both process insight and physical consistency. Therefore, this study introduces a physics-guided neural network model for acid-base treatment prediction in a dynamic tubular reactor using the fundamental physical intermediate variables obtained through the derivation process of the reaction schematic. By integrating batch experimental data, which provides key intermediate variables such as residence time and hydroxide ion concentration, the model addresses the challenge of high nonlinearity and limited data availability. The result shows that the physics-guided potential of a hydrogen predictor had outstanding performance in terms of prediction accuracy (greatest coefficient of determination value of 0.9381). The proposed model demonstrated an average improvement of 24.92% in pH prediction accuracy compared to traditional models without physical guidance, with a maximum improvement of up to 64.95% under limited data conditions. Moreover, downsampling tests revealed that the proposed model maintained robust performance with minimal accuracy reduction even when data was limited without overfitting implication.

Protecting Infinite Data Streams from Wearable Devices with Local Differential Privacy Techniques

The real-time data collected by wearable devices enables personalized health management and supports public health monitoring. However, sharing these data with third-party organizations introduces significant privacy risks. As a result, protecting and securely sharing wearable device data has become a critical concern. This paper proposes a local differential privacy-preserving algorithm designed for continuous data streams generated by wearable devices. Initially, the data stream is sampled at key points to avoid prematurely exhausting the privacy budget. Then, an adaptive allocation of the privacy budget at these points enhances privacy protection for sensitive data. Additionally, the optimized square wave (SW) mechanism introduces perturbations to the sampled points. Afterward, the Kalman filter algorithm is applied to maintain data flow patterns and reduce prediction errors. Experimental validation using two real datasets demonstrates that, under comparable conditions, this approach provides higher data availability than existing privacy protection methods for continuous data streams.

Development and validation of a natural language processing algorithm using electronic health record data to identify patients with breast cancer with low social support.

421 Background: Social support is important to the management of breast cancer treatment. Our team has developed data from electronic health record (EHR) data into structured ‘concept groups’ that will form the basis for the development of EHRsupport, a computable, EHR-based measure of social support. We report the evaluation of these concept groups against chart review as a part of our validation. Methods: We built a natural language processing (NLP) algorithm on clinical notes in 7,989 women diagnosed from January 2006 to September 2021 with invasive breast cancer. We identified and developed 10 concept groups from unstructured data: 1) living situation, 2) marital/partner status, 3) parenthood status, 4) visit support (accompanied patient to ≥1 visit, patient attended alone at ≥1 visit), 5) friends/other support, 6) explicit positive or negative mentions of social support, 7) mention of a deceased person, 8) transportation issues, 9) relationship conflict or stress, and 10) social isolation. We validated concept groups against the charts of 100 patients randomly drawn from the broader patient population (nonoverlapping with the training data set) also around the time (-1 to +3 months) of diagnosis. Results: Concept group data availability ranged from 1.3% social isolation to 98.3% for living situation. Specificity and negative predictive values were moderate to high for all concept groups. Sensitivity and positive predictive values were moderate to high for concept groups with high data availability (Table) and lower for concept groups with low data availability. Conclusions: Data on social support have been available since the advent of Epic in 2006 and our NLP-based algorithm accurately captured data within the EHR that were systematically collected supporting the development of a clinical tool that can be used to identify patients at risk of low social support. EHRsupport concept groups, data availability (n=7,989), and evaluation vs. chart review. Availability (%) Sensitivity Specificity PPV NPV Living situation (e.g., alone or not) 98.3 100 92 61 100 Partner/spouse 92.0 85 100 100 81 Parenthood status 88.8 95 80 93 83 Visit support 82.9 92 77 96 59 Positive mentions social support *46.9 75 100 100 70 Negative mentions social support 33 99 67 96 Friend/other support 46.8 81 81 79 83 Deceased person 39.9 86 97 95 90 Transportation issues 14.6 80 85 22 99 Relationship conflict/stress 7.7 25 95 29 94 *Availability of positive and negative mentions of social support.

Managing Employee Retention Concerns: Evidence from U.S. Census Data

ABSTRACT Using Census microdata on 28,000 manufacturing plants, we examine how firms manage employee retention concerns. In response to reductions in the local unemployment rate, plants take additional steps beyond increasing compensation. First, plants adjust bonus architecture to ensure bonuses can be paid. Second, plants offer more agency to employees by deploying high-involvement work practices that generate longer-term commitment. Third, plants pull these retention levers less when they have high availability and use of data as this reduces the adverse effects of employee turnover on organizational knowledge. These results are robust to using the fracking revolution as a shock increasing firms’ retention concerns. Additionally, we observe that although compensation increases tend to spill over to other plants within the same firm—aligning with theories of inequity aversion—adjustments to bonus architecture and the provision of employee agency do not, suggesting these may be more cost-effective strategies for multiplant firms. JEL Classifications: J63; M51; M54.

A New Technique to Generate Ciphertext from Reversed Plaintext Code

Information security involves safeguarding sensitive data and IT networks from unauthorized access and cyber-attacks. To address this need, various techniques and algorithms have been introduced. In this context, we propose a new technique aimed at protecting information from diverse threats, enhancing user confidentiality, and ensuring high data integrity and availability. In this paper, we introduce a method for generating cipher text by performing multiple rounds of operations. In the first round, we convert the plaintext into ASCII code, then into hexadecimal, and subsequently reverse the code. These transformed values are then stored in blocks, where various operations are performed to generate the cipher text. Keywords: Cryptography, Security, Encryption, Decryption, Key size.

Data for health system comparison and assessment in the African Region: A review of 63 indicators available in international databases.

Achieving universal health coverage in the African region requires health systems strengthening. Assessing and comparing health systems contributes to this process, but requires internationally comparable data. The European Observatory on Health Systems and Policies has produced Health Systems in Transition (HiT) reviews in Europe, Asia, North America and the Caribbean with a standardised template. This study explores data availability in international databases for the quantitative health and health system indicators in the HiT template for the WHO African region. We identified ten databases which contained data for 40 of the 80 original HiT indicators and an additional 23 proxy indicators to fill some gaps. We then assessed data availability for the resulting 63 indicators by country and time, i.e. first/last year of data, years of data available overall and since 2000, and we explored for each indicator (1) against the country with the greatest availability overall and (2) against annual availability for all years since 2000. Overall data availability was greatest in South Africa (93.0% of possible total points) and least in South Sudan (59.5%). Since 2000, Uganda (60.4%) has had the highest data availability and South Sudan (37.2%) the lowest. By topic, data availability was the highest for health financing (91.4%; median start/end date 2000/2019) and background characteristics (88.5%; 1990/2020) and was considerably lower for health system performance (54.5%; 2000/2018) and physical and human resources (44.8%; 2004/2013). Data are available for different years in different countries, and at irregular intervals, complicating time series analysis. No data are available for service provision indicators. Gaps in data in international databases across time, countries, and topics undermine systematic health systems comparisons and assessments, regional health systems strengthening, and efforts to achieve universal health coverage. More efforts are needed to strengthen national data collection and management and integrate national data into international databases to support cross-country assessments, peer learning, and planning. In tandem, more research is needed to understand the specific historical, cultural, administrative, and technological determinants influencing country data availability, as well as the facilitators and barriers of data sharing between countries and international databases, and the potential of new technologies to increase timeliness of data.

Scalable codes with locality and availability derived from tessellation via [7, 3, 4] Simplex code graph

Abstract A new family of scalable codes with locality and availability for information repair in data storage systems for e-health applications was presented recently. The construction was based on a graph of the [7, 3, 4] Simplex code. In this paper it is shown that the construction can be generalized via tessellation in a Euclidian plane. The codes obtained have new interesting recoverability properties. They can in some cases repair damage to many storage nodes in multiple connected graphs via sequential decoding, which is similar to healing wounds in biological systems. The advantages of the original codes, namely the availability, functionality, efficiency and high data accessibility, will be preserved also in these new codes. The computational complexity and communication costs of their incrementation will remain constant and modest. These codes could be adapted to disaster recovery because it is straightforward to place the nodes so that the graph is easily mapped on a real structure in space.

Salzburg Intensive Care database (SICdb): a detailed exploration and comparative analysis with MIMIC-IV.

The utilization of artificial intelligence (AI) in healthcare is on the rise, demanding increased accessibility to (public) medical data for benchmarking. The digitization of healthcare in recent years has facilitated medical data scientists' access to extensive hospital data, fostering AI-based research. A notable addition to this trend is the Salzburg Intensive Care database (SICdb), made publicly available in early 2023. Covering over 27 thousand intensive care admissions at the University Hospital Salzburg from 2013 to 2021, this dataset presents a valuable resource for AI-driven investigations. This article explores the SICdb and conducts a comparative analysis with the widely recognized Medical Information Mart for Intensive Care - version IV (MIMIC-IV) database. The comparison focuses on key aspects, emphasizing the availability and granularity of data provided by the SICdb, particularly vital signs and laboratory measurements. The analysis demonstrates that the SICdb offers more detailed information with higher data availability and temporal resolution for signal data, especially for vital signs, compared to the MIMIC-IV. This is advantageous for longitudinal studies of patients' health conditions in the intensive care unit. The SICdb provides a valuable resource for medical data scientists and researchers. The database offers comprehensive and diverse healthcare data in a European country, making it well suited for benchmarking and enhancing AI-based healthcare research. The importance of ongoing efforts to expand and make public datasets available for advancing AI applications in the healthcare domain is emphasized by the findings.

A study on the mechanism of the impact of entrepreneurship opportunity collaboration on opportunity innovation: The mediating effect of resource patchwork

Purpose: This article explores the impact of opportunity collaboration on opportunity innovation based on the identification, acquisition, sharing and exchange of resources and elements among entrepreneurial entities through collaborative and interactive behaviors to explore the influence mechanism of entrepreneurial opportunity collaboration on the formation of innovativeness of opportunities and the mediating role of resource patchwork. Design/Methodology/Approach: The research conducted an empirical analysis on the impact of opportunity collaboration on opportunity innovation based on the data of 207 entrepreneurial enterprises from Beijing, Guangdong and Henan with a high entrepreneurial vitality index and vital data availability. Resource patchwork's mediation effect is discussed and empirically tested from the patchwork theory perspective. Findings: Entrepreneurial opportunity collaboration significantly positively affects opportunity innovation and resource patchwork plays an intermediary role between them. Conclusion: Innovation is an important attribute to distinguish the heterogeneity of entrepreneurial opportunities and is also the key to entrepreneurial success. Interaction among various entrepreneurial elements should be strengthened to achieve efficient and high-quality resource patchwork to obtain a significant "synergistic effect" and ultimately improve innovation in entrepreneurial opportunities. Practical Implications: This study not only helps to theoretically understand the impact of entrepreneurial opportunity collaboration on opportunity innovation differentiation but also has outstanding practical reference significance for the government's targeted policy measures to support innovative entrepreneurial activities and for entrepreneurs to identify or create innovative opportunities.

Assimilation of GNSS tropospheric gradients into the Weather Research and Forecasting (WRF) model version 4.4.1

Abstract. In this study, we have incorporated tropospheric gradient observations from a Global Navigation Satellite Systems (GNSS) ground station network into the Weather Research and Forecasting (WRF) model through a newly developed observation operator. The experiments aim at testing the functionality of the developed observation operator and at analyzing the impact of tropospheric gradients on the sophisticated data assimilation (DA) system. The model was configured for a 0.1° mesh over Germany with 50 vertical levels up to 50 hPa. Our initial conditions were obtained from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) data at 0.25° resolution, and conventional observations were obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF), restricted to mainly surface stations and radiosondes. We selected approximately 100 GNSS stations with high data quality and availability covering Germany. We performed DA every 6 h for June and July 2021. Four experiments were conducted: (1) a control run assimilating only conventional observations; (2) an impact run assimilating zenith total delays (ZTDs) on top of the control run; (3) an impact gradient run assimilating ZTDs and gradients on top of the control run; and (4) a gradient run assimilating only gradients on top of the control run. The error for the impact run was reduced by 32 % and 10 % for ZTDs and gradients, whereas the error for the impact gradient run was reduced by 35 % and 18 %, respectively. The gradient errors for the gradient run were nearly equal to those of the impact gradient. Overall, the newly developed operator for the WRFDA system works as intended. In particular, the combined assimilation of gradients and the ZTDs led to a notable improvement in the humidity field at altitudes above 2.5 km. With the operator codes developed and freely available to the WRF users, we aim to trigger further GNSS tropospheric gradient assimilation studies.

Multisource spatiotemporal analysis of cropping patterns on dry upland: A case study in Rubaru Sub-district, Sumenep Regency

Dry upland agriculture is vital for securing food production in several countries. However, the research on evaluating cropping patterns using remote sensing techniques is completely neglected due to several factors such as the availability of clean imagery and the complexity of the landscape. This research primarily focused on the evaluation of data availability from three different satellite imageries: Sentinel-2, Landsat-8, and MODIS. The consistently high data availability demonstrated by Sentinel-2 established its potential as a reliable source for gap-filling analysis in remote sensing studies. Using a classification model, various land cover types were identified with an overall accuracy of 86.4%, indicating the model's efficiency in accurately classifying these areas. This research also analyzed the detailed cropping patterns, revealing seven distinct temporal cultivation patterns of various crops. This period is strategically positioned between the cultivation of maize, which spans an area of 5,943 ha in December, January, and February, suggesting a potential crop rotation system. The rotation indicated that nearly 83.7% of the cultivated land was planted between maize and shallot throughout the year. The study emphasizes the significance of continuous monitoring and adaptive management in agriculture to ensure sustainability and productivity.

An Onshore Deployment of Advanced Dual-Doppler Radar for Wind Energy Applications

Two advanced X-band radars have been developed and deployed by Texas Tech University to provide continuous wind measurements serving project AWAKEN, including the construction of near real-time dual-Doppler synthesized wind fields covering a large 35 km x 35 km analysis domain. This deployment represents the first overland long duration utilization of the technology, which was configured to document a wide range of wind energy relevant flows spanning turbine inflow/wake flow to regional wind flows and wind plant interactions. The overland environment coupled with technology innovation has led to very high data availability yielding a growing archive of measurements documenting diverse atmospheric phenomena including numerous instances of wind plant interaction. Through the first few project months, data availability greater than 90%, 80% and 70% has been assessed at the ranges of 8 km, 16 km, and 25 km, respectively. Robust data availability coupled with fast radar scan speeds and high along beam measurement resolution position advanced Doppler radar technology to broadly contribute to diverse wind energy measurement applications covering domains sizes that far exceed existing industry standard measurement systems.

Extraction of Coastal Levees Using U-Net Model with Visible and Topographic Images Observed by High-Resolution Satellite Sensors.

Coastal levees play a role in protecting coastal areas from storm surges and high waves, and they provide important input information for inundation damage simulations. However, coastal levee data with uniformity and sufficient accuracy for inundation simulations are not always well developed. Against this background, this study proposed a method to extract coastal levees by inputting high spatial resolution optical satellite image products (RGB images, digital surface models (DSMs), and slope images that can be generated from DSM images), which have high data availability at the locations and times required for simulation, into a deep learning model. The model is based on U-Net, and post-processing for noise removal was introduced to further improve its accuracy. We also proposed a method to calculate levee height using a local maximum filter by giving DSM values to the extracted levee pixels. The validation was conducted in the coastal area of Ibaraki Prefecture in Japan as a test area. The levee mask images for training were manually created by combining these data with satellite images and Google Street View, because the levee GIS data created by the Ibaraki Prefectural Government were incomplete in some parts. First, the deep learning models were compared and evaluated, and it was shown that U-Net was more accurate than Pix2Pix and BBS-Net in identifying levees. Next, three cases of input images were evaluated: (Case 1) RGB image only, (Case 2) RGB and DSM images, and (Case 3) RGB, DSM, and slope images. Case 3 was found to be the most accurate, with an average Matthews correlation coefficient of 0.674. The effectiveness of noise removal post-processing was also demonstrated. In addition, an example of the calculation of levee heights was presented and evaluated for validity. In conclusion, this method was shown to be effective in extracting coastal levees. The evaluation of generalizability and use in actual inundation simulations are future tasks.

COVID-19 Pandemic – Battle of Narratives: Social Constructivism as a Defence Mechanism

The article analyses the development of the COVID-19 pandemic situation in Slovakia, Czechia and Slovenia in the frame of expert, political and administrative narratives. The argumentative part mainly focuses on the development of the narrative in Slovenia and uses the examples from the Czech Republic and Slovakia as the reference frame. Information was collected from the various national media outlets and organized into the comparative time frames and compared with the epidemiological data. Slovenian inconsistencies in communication patterns developed into the complete disintegration of the pandemic crisis management and into the struggle for the supremacy of personal political agenda. As a case study, the article shows the constructivist relativism through the comparison of data and government-media narrative. Thus, the article addresses the issues of relativism on the one hand and of narrative absolutism on the other. The main aim is a critical presentation of “a crisis event” in connection to the government authorities spin on an event according to their political goals. The given case, due to a rather short period, strong media coverage and high data availability, shows inconsistencies of epidemiological data interpretation, resulting in multiple realities, causing multiple responses that paralysed effective decision-making as well as effective policy measures.

Appointment Booking and Drug Inventory System in Healthcare Services Using Blockchain Technology

Blockchain technology has the potential to revolutionize the healthcare industry by improving data security, reducing administrative inefficiencies, and enabling the seamless sharing of medical information. In the healthcare sector, blockchain can be applied to a wide range of use cases, such as medical record-keeping, clinical trials, drug traceability, and telemedicine. By using a secure, decentralized system, healthcare organizations can ensure that sensitive patient data is kept confidential and can be easily accessed by authorized parties. Additionally, the use of smart contracts can contribute to reducing the risk of errors and saving time and resources. In the proposed work, a decentralized application integrates healthcare services with blockchain technology to ensure transparency and security and prevent tampering with electronic medical records. The three main functionalities implemented in this work are to provide a transparent appointment booking system where patients can view the real-time availability status of the doctors and book an appointment with the doctor of their choice, and to store and retrieve data in an efficient and secure manner. To maintain a transparent and tamper-resistant medical inventory to prevent the unauthorized sale of medicines and drugs and also verify the availability of the drug. This helps the patients get the drugs in the quickest manner possible. Compared to existing centralized storage techniques, the proposed decentralized storage technique provides higher data availability, the fastest response time, and immutable storage of existing data. Experimental results show that, compared to existing work, the proposed work provided better results in throughput and latency. The communication cost of the proposed technique is 7% lesser than the existing Telecare Medicine Information system.

Data Security Requirements of the Medical Information System

This document presents the conclusions and insights regarding the requirements for an information system project and its secure component in the context of cloud technologies and the QMS project. It emphasizes significant factors contributing to the project's success, including the necessity to deliver tangible value, ensuring global access to resources, and the use of advanced security technologies. The document discusses the role of aspects such as SSL connections, reliable authentication and authorization mechanisms, password encryption, and CSRF tokens. It also focuses on the importance of constant system updates and high data availability. Ultimately, analyzing the implementation of these requirements in the Quality Medical System (QMS), the document illuminates how, through intensive integration with various third-party services, QMS successfully addressed the defined challenges, providing a high-quality, scalable, and adaptable service.

Labeled graph partitioning scheme for distributed edge caching

Distributed edge caching could address latency and congestion problems in large-scale data access effectively, improving system throughput and performance. However, the lack of specialized edge caching solutions for graph data resulted in low cache hit rates and high data access latency. This limitation stemmed from existing graph partitioning schemes that overlooked the specific requirements of edge caching, such as communication overhead between edge servers, data query frequency, and query patterns. To overcome these challenges, we proposed LGPE, a labeled graph partitioning scheme for distributed edge caching based on frequent query patterns. LGPE generated frequent query patterns according to the user’s historical query subgraphs and then performed labeled graph partitioning based on the frequent pattern, ensuring that the labeled graph divided into edge servers had a high hit rate for frequent user queries while satisfying a minimum edge cut. Evaluation on real datasets from various application domains demonstrated that LGPE achieved approximately 40% higher cache hit rates compared to benchmarks like METIS and SCOTCH, and it also performed well in terms of edge cuts in special graph cases.

Optimisation of the investment strategy of the Norwegian Sovereign Wealth Fund by adjusting the real estate quota

Purpose This paper compares the benefits of direct international real estate investments in a mixed asset portfolio from the perspective of a passive investor with high and low bond allocation.Design/methodology/approach Due to high data availability and its professionalism, the Norwegian sovereign wealth fund was used as a representative example. Real estate indices from 8 countries were used for the portfolio analysis. The data were desmoothed according to Geltners’s 1993 approach.Findings The optimal real estate ratio in the present case is around 20–55%. However, this is strongly dependent on the bond ratio of the multi-asset portfolio. Portfolios with a high equity ratio benefit more from the additional direct real estate investments than portfolios with high bond ratios.Research limitations/implications A rebalancing of individual stocks and bonds was not analysed. Only indexes from MSCI (Morgan Stanley Capital International) were available.Practical implications Concludes that the weighting of stocks and bonds has a strong influence on the optimal real estate ratio and therefore structural changes that affect this weighting.Originality/value The originality of the paper lies in the analysis with different weights of stocks and bonds, the consideration of 8 real estate markets and the observation period. The results of the work highlight areas of interest for further research.

Measuring health equity in the ASEAN region: conceptual framework and assessment of data availability

BackgroundExisting research on health equity falls short of identifying a comprehensive set of indicators for measurement across health systems. Health systems in the ASEAN region, in particular, lack a standardised framework to assess health equity. This paper proposes a comprehensive framework to measure health equity in the ASEAN region and highlights current gaps in data availability according to its indicator components.MethodsA comprehensive literature review was undertaken to map out a core set of indicators to evaluate health equity at the health system level. Secondary data collection was subsequently conducted to assess current data availability for ASEAN states in key global health databases, national health accounts, and policy documents.ResultsA robust framework to measure health equity was developed comprising 195 indicators across Health System Inputs and Processes, Outputs, Outcomes, and Contextual Factors. Total indicator data availability equated to 72.9% (1423/1950). Across the ASEAN region, the Inputs and Processes sub-component of Health Financing had complete data availability for all indicators (160/160, 100%), while Access to Essential Medicine had the least data available (6/30, 20%). Under Outputs and Outcomes, Coverage of Selected Interventions (161/270, 59.63%) and Population Health (350/350, 100%) respectively had the most data available, while other indicator sub-components had little to none (≤ 38%). 72.145% (384/530) of data is available for all Contextual Factors. Out of the 10 ASEAN countries, the Philippines had the highest data availability overall at 77.44% (151/195), while Brunei Darussalam and Vietnam had the lowest data availability at 67.18% (131/195).ConclusionsThe data availability gaps highlighted in this study underscore the need for a standardised framework to guide data collection and benchmarking of health equity in ASEAN. There is a need to prioritise regular data collection for overlooked indicator areas and in countries with low levels of data availability. The application of this indicator framework and resulting data availability analysis could be conducted beyond ASEAN to enable cross-regional benchmarking of health equity.