Data Requests Research Articles

Facilitating an ecosystem approach through open data and information packaging

Abstract Open data that can be easily incorporated into analyses are essential for developing ecosystem approaches to marine ecological management: a common goal in fisheries policy in many countries. Although it is not always clear what constitutes an ecosystem approach, it always involves scientists working with a large variety of data and information, including data from physical and oceanographic sampling, multispecies surveys, and other sources describing human pressures. This can be problematic for analysts because these data, even when available, are often held in disparate datasets that do not necessarily correspond at appropriate temporal and spatial scales. Data can often only be obtained by specific requests to individuals in governmental agencies who are delivering on an increasing number of data requests as interest grows in practical ecosystem approach implementation. This data access model is not sustainable and hinders the momentum for ecosystem approach development. We describe a data bundling R package that makes data and climate projections available at appropriate scales to facilitate development of an ecosystem approach for the Gulf of St. Lawrence, Canada. This approach integrates closely with the present workflow of most government analysts, academics in fisheries, and scientists in private industry. The approach conforms with open data initiatives and makes data easily available globally while relieving some of the burden of data provision that can fall to some individuals in government laboratories. The structure and approach are generic, adaptable, and transferable to other regions and jurisdictions.

Unmanned aerial vehicles in collaboration with fog computing network for improving quality of service

SummaryIn this paper, we study a UAV‐based fog or edge computing network in which UAVs and fog/edge nodes work together intelligently to provide numerous benefits in reduced latency, data offloading, storage, coverage, high throughput, fast computation, and rapid responses. In an existing UAV‐based computing network, the users send continuous requests to offload their data from the ground users to UAV–fog nodes and vice versa, which causes high congestion in the whole network. However, the UAV‐based networks for real‐time applications require low‐latency networks during the offloading of large volumes of data. Thus, the QoS is compromised in such networks when communicating in real‐time emergencies. To handle this problem, we aim to minimize the latency during offloading large amounts of data, take less computing time, and provide better throughput. First, this paper proposed the four‐tier architecture of the UAVs–fog collaborative network in which local UAVs and UAV–fog nodes do smart task offloading with low latency. In this network, the UAVs act as a fog server to compute data with the collaboration of local UAVs and offload their data efficiently to the ground devices. Next, we considered the Q‐learning Markov decision process (QLMDP) based on the optimal path to handle the massive data requests from ground devices and optimize the overall delay in the UAV‐based fog computing network. The simulation results show that this proposed collaborative network achieves high throughput, reduces average latency up to 0.2, and takes less computing time compared with UAV‐based networks and UAV‐based MEC networks; thus, it can achieve high QoS.

Responding to unusual government request for user data: How tech companies make sense of human rights

Data sharing practices between governments and the private sector are characterized by a lack of transparency which has potential implications for human rights. Minimal scholarship exists investigating how companies address human rights risks stemming from government requests for user data. Understanding corporate response processes to government requests is central to advancing human rights research at the intersection of tech company conduct. This becomes even more pressing as emerging technologies gather increasing amounts of data. Scholarship demonstrates that transparency reporting cannot assist in analyzing data sharing practices between the private and the public sectors due to a variety of constraints. Using semi-structured interviews with senior staff at technology companies, this paper presents an empirical analysis of how technology company representatives and external advisors seek to align their processes when responding to government requests for user data. It describes a set of six themes using human rights terminology which company representatives aim to employ when responding to requests.

Accurate assessment of land–atmosphere coupling in climate models requires high-frequency data output

Abstract. Land–atmosphere (L–A) interactions are important for understanding convective processes, climate feedbacks, the development and perpetuation of droughts, heatwaves, pluvials, and other land-centered climate anomalies. Local L–A coupling (LoCo) metrics capture relevant L–A processes, highlighting the impact of soil and vegetation states on surface flux partitioning and the impact of surface fluxes on boundary layer (BL) growth and development and the entrainment of air above the BL. A primary goal of the Climate Process Team in the Coupling Land and Atmospheric Subgrid Parameterizations (CLASP) project is parameterizing and characterizing the impact of subgrid heterogeneity in global and regional Earth system models (ESMs) to improve the connection between land and atmospheric states and processes. A critical step in achieving that aim is the incorporation of L–A metrics, especially LoCo metrics, into climate model diagnostic process streams. However, because land–atmosphere interactions span timescales of minutes (e.g., turbulent fluxes), hours (e.g., BL growth and decay), days (e.g., soil moisture memory), and seasons (e.g., variability in behavioral regimes between soil moisture and latent heat flux), with multiple processes of interest happening in different geographic regions at different times of year, there is not a single metric that captures all the modes, means, and methods of interaction between the land and the atmosphere. And while monthly means of most of the LoCo-relevant variables are routinely saved from ESM simulations, data storage constraints typically preclude routine archival of the hourly data that would enable the calculation of all LoCo metrics. Here, we outline a reasonable data request that would allow for adequate characterization of sub-daily coupling processes between the land and the atmosphere, preserving enough sub-daily output to describe, analyze, and better understand L–A coupling in modern climate models. A secondary request involves embedding calculations within the models to determine mean properties in and above the BL to further improve characterization of model behavior. Higher-frequency model output will (i) allow for more direct comparison with observational field campaigns on process-relevant timescales, (ii) enable demonstration of inter-model spread in L–A coupling processes, and (iii) aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.

Privacy-preserving and verifiable data aggregation for Internet of Vehicles

With the development of Internet of Vehicles (IoV), organizations or institutions are increasingly interested in aggregating vehicle perception data to perform data analysis. Nevertheless, the existing solutions are difficult to meet the comprehensive requirements of IoV scenario, such as low latency, privacy preservation and rich functions. Additionally, it is more challenging that the aggregation node may return tampered or forged aggregation results for vulnerabilities or ulterior motives. To this end, we propose a Privacy-Preserving and Verifiable Data Aggregation Scheme for IoV (PPVDA), which allows the efficient aggregation for inner product over multi-dimensional data, and the data requester can verify the correctness and integrity of the aggregation results. By exploiting homomorphic MAC and secret sharing techniques, we construct a lightweight and verifiable mechanism for results as well as data privacy protection. We also conduct a comprehensive security analysis of PPVDA under the malicious security model. Moreover, extensive theoretical analyses and experimental evaluations demonstrate that PPVDA performs efficiently while retaining more desired properties. When dealing with 4×104 VNs, 3000 RSUs and 500-dimensional vectors, each EN side only takes about 74.5 ms and the DR side only takes about 3.8 ms, is therefore suitable for practical IoV scenarios.

Navigating Gene Therapy Access: The Case of Bulgaria in the Context of the EU Regulatory Landscape.

Gene therapies (GTs) have recently emerged as revolutionary personalized therapeutic options. Despite their promising potential, challenges such as uncertainty regarding long-term health benefits and safety, along with extreme price tags, pose significant obstacles to patient access. Within the EU, the European Medicines Agency plays a pivotal role with regards to GT market authorization. However, national authorities are responsible for pricing and reimbursement, which results in fragment patient access within the EU. This study aimed to provide an overview of the complex landscape of post-market authorization accessibility for GT products in Bulgaria, comparing it with neighboring EU countries. We applied a mixed-methods approach, including desk research, public data requests, and list price comparisons. As of 1 April 2023, 14 GTs had a valid market authorization at the EU level. In Bulgaria, Kymriah® was the only GT included in the Positive Drug List (PDL), with an official list price of EUR 335,636.94. Similar results were found in Romania, whereas five GTs were included in Greece's PDL. Additionally, Zolgensma® was found accessible in Bulgaria through an alternative individual access scheme at an estimated price of EUR 1,945,000.00. In conclusion, this study emphasized targeted policy interventions to address health inequalities and to ensure timely access to GTs within the EU.

TMPAD: Time-Slot-Based Medium Access Control Protocol to Meet Adaptive Data Requirements for Trusted Nodes in Fog-Enabled Smart Cities

The popularity of fog-enabled smart cities is increasing due to the advantages provided by modern communication and information technologies, which contribute to an improved quality of life. Wireless networks make them more vulnerable when the network is under malicious attacks that cause a collision in the medium. Furthermore, diverse applications of smart cities demand a contention-free medium access control (MAC) protocol to meet adaptive data requirements. In this work, a time-slot-based medium access control protocol to meet adaptive data requirements (TMPAD) for IoT nodes in fog-enabled smart cities is proposed. TMPAD proposes a trust mechanism to differentiate malicious and legitimate data requests. In addition, it accommodates more legitimate data-requesting nodes to transfer their data during a session by applying the technique for order performance by similarity to ideal solution (TOPSIS) and 0/1 knapsack algorithm. The performance of TMPAD is compared with well-known techniques such as first come first serve (FCFS), shortest job first (SJF), and longest job first (LJF) in different prospective scenarios. The results show that TMPAD scrutinizes more data-requesting nodes in slot allocation, allowing more data transmission in a session, with better mean trust value, as compared to other algorithms.

Deep-MAD: Deep learning based multiple attack detection for secure device- to-device communication in FOG environment1

An exponential growth of users demands ubiquitous connectivity, which requires the integration of new technology. Therefore, Device to Device (D2D) communication has been considered a promising technology that utilizes effective and efficient communication. Even though numerous studies have been conducted for establishing secure D2D communication, however, existing techniques face challenges like privacy, security threats, and poor generality. To overcome these issues a novel Deep-MAD model is proposed to preserve data privacy along with its access control in the D2D network and multiple attack detection in a fog environment. A Fully Homomorphic Elliptic Curve Cryptography (FHECC) is introduced to transmit data securely in a D2D network. The data owner uses FHECC algorithm to encrypt the plain text into cipher text before storing it on the fog. Whenever the user requests data from the fog, the fog service provider confirm the user’s access control. Furthermore, the deep learning-based Bi-LSTM is used to differentiate the device as an authorized or unauthorized user. If the IP address is genuine then the inverse FHECC is used to decrypt the data for authorized users. Otherwise, the particular device is blocked and it is sent for further verification for classifying the types of attacks. The effectiveness of the proposed strategy is examined using several parameters, such as computational complexity, scalability, accuracy, and Execution time. The proposed technique improves the overall computational overhead of 31.77, 9.34, and 4.67 better than AKA protocol, lightweight cipher, and FHEEP respectively.

ECKOchain: A FAIR blockchain‐based database for long‐term ecological data

Abstract Open data practices in ecology are increasingly accepted, yet primary long‐term ecological data remain hard to find. Barriers preventing open long‐term ecological data include social and economic constructs such as a sense of data proprietorship and fear of misuse of complex datasets. To incentivise open primary ecological data and ensure long‐term preservation, we propose a decentralised data management approach using blockchain technology. The blockchain‐based database is governed by transparent and immutable data management protocols, agreed on by members of the network. Specialised protocols ensure agreement in the network before new data is accepted, and no entity can single‐handedly alter existing data. We introduce the ECKOchain, a ‘proof of concept’ ecological blockchain‐based database created with the Hyperledger Fabric framework. While metadata and access policies are distributed to all network members, primary data remains with data owners and are served on‐demand to approved parties according to specified usage licences. Details of data requests are preserved indefinitely on the blockchain and serve as auditable data usage agreements. With the distributed blockchain‐based database we advocate for open science and transparency in long‐term management of ecological data. The ECKOchain is also suitable for other scientific fields where auditability and transparency are considerations to long‐term data management.

Design of Student Service Information System to Print Grade Transcripts in the Industrial Engineering Study Program in Universitas Sebelas Maret Surakarta

The student service system at Universitas Sebelas Maret Surakarta (UNS), especially the print transcript of grades currently running, students can apply through three methods. The first method is done by filling out an application for a printed transcript. The second method is to use the Whatsapp application. The third method is done by using E-mail. Due to the large number of media used to apply for printed transcripts, the administrative unit of the study program cannot process all requests for print transcripts quickly. Sometimes there is a request to print a grade transcript that is not processed because the request data is overwritten with the new request data, so it is not readable by the study program administration unit. Based on the description of the background, it can be formulated that the problem to be solved in this research is "How to design a student service information system for printing transcripts in the Industrial Engineering study program of UNS". Limitations The problem that the author discusses in the study focuses more on the process of applying for transcripts of grades in the scope of the Industrial Engineering study program of UNS. The method used to design an information system for the print out of transcripts is using the System Development Life Cycle (SDLC) with the Waterfall model. The programming language used to design an information system for printing transcripts is PHP Native and MySQL database as data storage media. With the design of the information system for the application of printed transcripts, it is hoped that it can improve the quality of service to students, especially in the scope of the Industrial Engineering study program of UNS.

Retraction: Comparison between 200, 400 and 600 microgram rectal misoprostol before cesarian section: A randomized clinical trial.

The above article, published online on January 7th, 2019 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal's Editor-in-Chief, Hiroaki Kajiyama, Japan Society of Obstetrics and Gynecology, and John Wiley & Sons, Australia, Ltd. The retraction has been agreed on following concerns about the validity of the presented data raised by a third party. As part of the investigation, an independent statistical expert concluded that there are inconsistencies in the data that could only be verified by examining the underlying clinical trial data. The authors have not responded to repeated requests for the original data and have not provided any explanation into the overlap between this study and one published in 2018 in a different journal. Furthermore, while the institute where the research was conducted confirmed that this clinical study was approved by an institutional ethics committee, documentation of the approval is not available. The authors have not responded when asked to agree to this retraction.

The Impact of Multiple API Connection Request to An Open-Source Android-based Currency Tracker's Performance and The Mitigation

High traffic data requests through APIs can significantly impact the performance of mobile applications, affecting both the user interface and overall system performance. The user's perception and experience while performing tasks within the application are closely tied to the perceived performance of the user interface. To understand the impact of connection requests on application performance, a study is necessary, with responsiveness being a critical factor. This study aims to investigate the potential effects on the user interface when each unit or feature in the application requires a data connection through the API. The study will examine code patterns and conduct multiple data requests through APIs. Performance profiling will be the main method used to test each unit or block of code related to data request and reception via the API. The obtained results from the testing process will reveal the performance of the currency tracker application, specifically in areas directly associated with data request and reception via the API. To optimize the request and data calling process, best practices such as AsyncTask and Coroutine will be implemented. The implementation of these best practices will be assessed based on a predefined matrix to measure improvements needed to enhance applications’ performance. Therefore, the results of this study will demonstrate the influence of data request and reception processes within applications on network activity. Furthermore, the implementation of best practices, specifically AsyncTask and Coroutine, will enhance the overall performance of the application.

Продовольственные отходы в социальных учреждениях: потери средств бюджетного финансирования

Circular agriculture considered as one of the forms of sustainable development of the agricultural sector. The essence of the proposed new approach in agriculture based on the reduction of agricultural waste and the ability to use a closed production cycle to increase economic, social and environmental efficiency. The author has previously developed an experimental business model for the circular economy of agriculture and the agri-food system using the example of the Komi Republic. The study revealed that the main problem inherent in the circular economy of agriculture, as the disposal of animal waste, is absent in the region. Taking into account the peculiarities of the region, other significant problems were found: the disposal of containers from food imported from other regions and the amount of discarded food purchased for state social institutions under Federal Law No. 44 «On the contract system in the field of procurement of goods, works, services to meet state and municipal needs». Based on surveys, it was revealed that in social institutions where free meals provided at the expense of the republican budget, the amount of food thrown away by students and patients ranges from 25 to 60%. The scientific novelty of the work lies in the fact that for the Republic of Komi, for the first time, the losses of budget funds allocated for free meals in public institutions in the context of municipalities of the region were calculated. The article calculates the losses of budget funding from discarded food (food) in the areas of education, social protection and healthcare for 2017-2022. The calculations based on the data of requests, both to the relevant ministries of the Komi Republic, and to the administration of municipalities of urban districts and municipal districts. It is revealed that the loss of budget funding due to discarded food for 2017-2022 range from 1024.3 to 3073 million rubles. The practical significance of the introduction of this model in the regional economy of the republic will ensure a reduction in food losses, a decrease in the formation of food and non-food waste, which will increase the level of self-sufficiency in basic foodstuffs, the rational use of resources in the agricultural sector and the effective use of budgetary funds.

VigiNUTRI Brasil: methods of request, data extraction, treatment and consistency analysis of individual data from adolescents monitored by the Food and Nutrition Surveillance System (Sisvan Web).

To describe the methods for requesting, extracting data, processing and analyzing the consistency of anthropometric and food consumption data of adolescents monitored by Sisvan Web. Methodological study with individualized data from Sisvan web between 2008 and 2018. The modules of anthropometry and consumption, made available by the Ministry of Health, had a unique identifier for linkages. Implausible values and individuals outside the age range were excluded. Consistency analyses, with corrections for imputations and descriptive statistics, were performed using Stata 16.0 software. A database was obtained with 18,812,232 observations of anthropometric data between 2008 and 2018 and 440,534 records of food consumption between 2015 and 2018; after merging the banks, 64,976 observations were obtained. The combination of anthropometry and food consumption databases made it possible to link individual adolescent data and build a database with information for future analyzes relating to the dietary and nutritional profile of the same individual. The proposal for processing individual data from the Food and Nutrition Surveillance System (Sisvan Web) generated 18 million observations for anthropometric data and 65,000 observations after merging the anthropometry and food consumption databases. This work will enable replication of the methodology for processing data available in Sisvan Web, allowing for enhanced analyzes by healthcare teams and researchers in the field of public health. It is expected that these procedures will assist researchers, managers and healthcare professionals in handling and analyzing the information generated by Sisvan Web.

VigiNUTRI Brasil: methods of request, data extraction, treatment and consistency analysis of individual data from adolescents monitored by the Food and Nutrition Surveillance System (Sisvan Web)

RESUMO Objetivo Descrever os métodos para a solicitação, extração de dados, tratamento e análise de consistência de dados antropométricos e de consumo alimentar de adolescentes acompanhados pelo Sisvan Web. Métodos Estudo metodológico com dados individualizados do Sisvan Web entre 2008 e 2018. Os módulos de antropometria e consumo, disponibilizados pelo Ministério da Saúde, possuíam um identificador único para linkages. Excluíram-se valores implausíveis e indivíduos fora da faixa etária. Análises de consistência, com correções por imputações e estatísticas descritivas, foram realizadas pelo software Stata 16.0. Resultados Obteve-se uma base com 18.812.232 observações de dados antropométricos entre 2008 e 2018 e 440.534 registros de consumo alimentar entre 2015 e 2018; após a junção dos bancos, foram obtidas 64.976 observações. Conclusão A junção dos bancos de antropometria e consumo alimentar permitiu vincular dados individuais dos adolescentes e construir um banco com informações para análises futuras relativas ao perfil alimentar e nutricional do mesmo indivíduo.

Camguard: Preventing unauthorised camera access for women’s saftey

Online harassment is a serious problem that can have significant negative impacts on the mental health and well-being of its victims. To address this issue, an Android app has been developed that is accessible to a wide range of users worldwide. The app is built on a script backend process, providing a stable and efficient environment for data management and request processing. The app's API is integrated into the script backend process, ensuring seamless and efficient operation.During the installation process, the app requires users to go through a verification process that includes providing their Google Account, mobile number, and an OTP. This ensures that only legitimate users have access to the app's features and functions. The app also requires users to create a strong master password, which is directly connected to the Google bot, enhancing its security. Biometrics are recommended to protect the master password, ensuring that only authorized users can access their data.The app's security measures are crucial in ensuring the privacy and protection of user data. The use of biometrics and direct connection to the Google bot enhances security, and the app's API provides access to relevant information and enhances the user experience. Users can change their password securely, ensuring that only authorized users can access their data.Overall, the app is a reliable and efficient solution for managing and protecting user data. Its compatibility with a wide range of devices and seamless integration of API make it accessible to a large percentage of Android users worldwide. The app's verification process, use of biometrics, and direct connection to the Google bot enhance its security, ensuring that only authorized users have access to the app's functionalities. The development of this app is a critical step in addressing the issue of online harassment and protecting the privacy and well-being of its users.

СОЗДАНИЕ СИСТЕМЫ ДЛЯ РЕШЕНИЯ ПРОБЛЕМ НА РЫНКЕ АГРЕГАЦИИ СКИДОК

The article is devoted to the creation of an innovative tool for aggregating discounts and promotions, designed to simplify the process of finding discounts for consumers and provide companies with the ability to monitor competitors’ discounts. The purpose of creating the tool is to react quickly to market changes and increase customer loyalty by providing more attractive offers. In the process of designing the discount aggregation system, attention is paid to all aspects necessary to create an efficient and reliable tool that meets the needs of users and provides a high level of security. The objectives of the system to be developed are formed: automation of the process of collecting and processing discount data, data filtering and validation, data classification and normalisation, efficient storage and processing of user requests. Key business processes such as data collection, data filtering and validation, classification, storage, user request processing, data personalisation and provisioning, analysis and monitoring are defined. The general algorithm of the information system is developed, input and output information arrays are allocated, the organisational and logical essence of the discount aggregation system problem solving is designed.

Defogger: A Visual Analysis Approach for Data Exploration of Sensitive Data Protected by Differential Privacy.

Differential privacy ensures the security of individual privacy but poses challenges to data exploration processes because the limited privacy budget incapacitates the flexibility of exploration and the noisy feedback of data requests leads to confusing uncertainty. In this study, we take the lead in describing corresponding exploration scenarios, including underlying requirements and available exploration strategies. To facilitate practical applications, we propose a visual analysis approach to the formulation of exploration strategies. Our approach applies a reinforcement learning model to provide diverse suggestions for exploration strategies according to the exploration intent of users. A novel visual design for representing uncertainty in correlation patterns is integrated into our prototype system to support the proposed approach. Finally, we implemented a user study and two case studies. The results of these studies verified that our approach can help develop strategies that satisfy the exploration intent of users.

Blockchain‐Based Piecewise Regressive Kupyna Cryptography for Secure Cloud Services

Cloud computing (CC) is a network‐based concept where users access data at a specific time and place. The CC comprises servers, storage, databases, networking, software, analytics, and intelligence. Cloud security is the cybersecurity authority dedicated to securing cloud computing systems. It includes keeping data private and safe across online‐based infrastructure, applications, and platforms. Securing these systems involves the efforts of cloud providers and the clients that use them, whether an individual, small‐to‐medium business, or enterprise uses. Security is essential for protecting data and cloud resources from malicious activity. A cloud service provider is utilized to provide secure data storage services. Data integrity is a critical issue in cloud computing. However, using data storage services securely and ensuring data integrity in these cloud servers remain an issue for cloud users. We introduce a unique piecewise regressive Kupyna cryptographic hash blockchain (PRKCHB) technique to secure cloud services with higher data integrity to solve these issues. The proposed PRKCHB method involves user registration, cryptographic hash blockchain, and regression analysis. Initially, the registration process for each cloud user is performed. After registering user particulars, Davies–Meyer Kupyna’s cryptographic hash blockchain generates the hash value of data in each block. When a user requests data from the server, a piecewise regression function is used to validate their identity. Furthermore, the Gaussian kernel function recognizes authorized or unauthorized users for secure cloud information transmission. The regression function results in original data by enhanced integrity in the cloud. An analysis of the proposed PRKCHB technique evaluates different existing methods implemented in Python. The results contain different metrics: data confidentiality rate, data integrity rate, authentication time, storage overhead, and execution time. Compared to conventional techniques, findings corroborate the assertion that the proposed PRKCHB technique improves data confidentiality and integrity by up to 9% and 9% while lowering storage overhead, authentication time, and execution time by 10%, 12%, and 12%.

Cleaning Up the Neighborhood: White Influx and Differential Requests for Services

Visible signs of disorder serve as markers of difference across urban space. Sociological theory suggests that variation in collective social control efforts contributes to variation in physical disorder. However, how structural characteristics shape differences in informal social control remains underexplored because of limited data on disorder and social control. Using city service request data and a novel dataset drawing on Google Street View imagery and computer vision methods, the authors examine the neighborhood characteristics associated with propensities to request trash-related services across five large U.S. cities. The authors find that socioeconomically advantaged neighborhoods and those with fewer minority and foreign-born residents have higher propensities. However, an increase in White residents, but not necessarily an increase in high–socioeconomic status residents, is strongly associated with greater propensities. The authors argue that incoming White residents introduce unique dynamics of social control that are not necessarily collective, thereby affecting spatial inequality and power relations within their new neighborhoods.