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Data papers have been defined as scholarly journal publications whose primary purpose is to describe research data. Our survey provides more insights about the environment of data papers, i.e., disciplines, publishers and business models, and about their structure, length, formats, metadata, and licensing. Data papers are a product of the emerging ecosystem of data-driven open science. They contribute to the FAIR principles for research data management. However, the boundaries with other categories of academic publishing are partly blurred. Data papers are (can be) generated automatically and are potentially machine-readable. Data papers are essentially information, i.e., description of data, but also partly contribute to the generation of knowledge and data on its own. Part of the new ecosystem of open and data-driven science, data papers and data journals are an interesting and relevant object for the assessment and understanding of the transition of the former system of academic publishing. [ABSTRACT FROM AUTHOR]

In advanced missions, handling thin and soft membranes is difficult for robots. This task is composed of several sub-tasks, including turning and removing a sheet from a pile of papers on a table, folding paper, and sticking two pieces of paper together. In this study, a hand robot turns and removes a sheet from a pile of papers, and ensures that only one sheet is grasped. To perform this task, two robotic fingers compress the piled papers in the normal direction of the table with a specific force as they close. We therefore incorporated position-based force control into our system; for the force controller, we adopted stiffness control that calculates position modification proportionally to the difference between the target force and an external force measured by the tactile sensor. With this controller, the robot was able to remove a sheet from a pile of papers and ensure that only one sheet was removed. Using this discrimination, the maximum-minimum shearing forces are better classified than with normal force distribution deviation. [ABSTRACT FROM AUTHOR]

The proposal of Industry 5.0 has made sustainability, human-centric and resilience the core of digital manufacturing, which also puts forward new requirements for the human-machine interaction (HMI) paradigm in human-centric smart manufacturing (HCSM). In the manufacturing scenario, the process of HMI can be divided into four parts: 1) Sensors and hardware, where the environment information and input signals are collected, 2) Data processing, where the signals are converted into data, 3) Transmission mechanism, where the data is transmitted to the processing centre, and 4) Interaction and collaboration. Among them, sensors and data are expected to become breakthrough points in optimising HMI. This is not only due to the emergence of new research, innovation and technologies but also because they are closely influenced by the new design concepts brought about by Industry 5.0. This paper analyses the latest studies and technologies in the sensor field and their possible applications in HCSM scenarios. Then, opportunities and challenges of data analysis in the HMI in Industry 5.0 are discussed. Finally, based on the design concepts and requirements of Industry 5.0, this paper demonstrates how they will become the key points for future HMI development. [ABSTRACT FROM AUTHOR]

Ship collision avoidance has always been a concern and it is crucial for achieving safe navigation of ships at sea. There are many studies on ship collision avoidance in open water, but less attention on coastal waters considering the uncertainty of target ships due to the complexity of the environment and traffic flow. In this paper, collision avoidance decision-making research in coastal waters considering the uncertainty of target ships was proposed. Firstly, accurate ship trajectories are obtained by preprocessing the raw Automatic Identification System (AIS) data. Subsequently, the processed trajectories are clustered using the Ordering Points to Identify the Clustering Structure (OPTICS) algorithm and Hausdorff distance, acquiring a dataset for trajectory prediction of target ships. Then, a mixed Gaussian model is utilized to calculate the prior probability distribution of the prediction model, thus establishing a trajectory prediction model that considers the uncertainty of the target ship. Finally, ship maneuverability is simulated using the Mathematical Model Group (MMG) and Proportion Integration Differentiation (PID) models, and a collision avoidance decision-making model for ships is constructed. The proposed algorithm has been tested and verified in a case study. The results show that the approach effectively predicts the trajectory of the target ship and facilitates informed collision avoidance decision-making. [ABSTRACT FROM AUTHOR]

This paper examines the effects of digital technologies on children's rights. All children's rights ought to be reinforced, respected, and protected. Majority of children devote their time on Internet using various social media platforms which have become a key means of teaching and learning. Increased internet access by children is associated with risks and escalation of children's rights violations. Institutions dealing with children continuously struggle to represent the rights of children enshrined within the Convention on the Rights of Children. The paper is mainly focuses on children's rights such as freedom of expression and information, privacy and data protection, protection and safety. Children's rights are not fully protected online in most cases particularly in countries like Botswana. The publication of sensitive information relating to children online is a violation of their rights to privacy and may affect their childhood development. The lack of relevant expert to investigate and prosecute cybercrime leads to the increase of cybercrime activities committed against children. [ABSTRACT FROM AUTHOR]

Copyright of Darbai ir Dienos is the property of Vytautas Magnus University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Compared to traditional electronic maps, high-precision maps are more about providing services for vehicles. Because vehicles lack the visual recognition and logic inherent in humans, high-precision maps can assist vehicles in identifying obstacles, vehicles, etc., and can also expand their environmental awareness capabilities. This can provide vehicles with a global perspective that other sensors cannot obtain. This article includes road, traffic, and facility information outside the sensor monitoring range. This paper analyzes and defines road design data and high-precision map construction in detail, and then verifies the progressiveness of high-precision map construction methods based on road design data compared with traditional high-precision map construction methods through experimental analysis [ABSTRACT FROM AUTHOR]

This paper presents the findings of a study on readiness of the University of Zambia in participating in research data management services in the COVID-19 and post Covid eras. The study adopted a qualitative research method and employed purposive sampling. In-depth interviews were conducted with key informants in the University. Findings reveal a lack of awareness and knowledge about research data management among senior officers and inadequate skills among library staff. On a positive note, the University has a robust information and communication technology (ICT) infrastructure supported by qualified information technology (IT) staff. It was further discovered that the University did not have a research data management policy. This paper provides a framework for the University to operationalize research data management services. The study recommends development of a research data management policy; conducting awareness campaigns; investing in skills training for library staff, and constituting research teams to deal with issues of research data management. [ABSTRACT FROM AUTHOR]

Novel usages of brain stimulation combined with artificially intelligent (AI) systems promise to address a large range of diseases. These new conjoined technologies, such as brain-computer interfaces (BCI), are increasingly used in experimental and clinical settings to predict and alleviate symptoms of various neurological and psychiatric disorders. Due to their reliance on AI algorithms for feature extraction and classification, these BCI systems enable a novel, unprecedented, and direct connection between human cognition and artificial information processing. In this paper, we present the results of a study that investigates the phenomenology of human-machine symbiosis during a first-in-human experimental BCI trial designed to predict epileptic seizures. We employed qualitative semi-structured interviews to collect user experience data from a participant over a six-years period. We report on a clinical case where a specific embodied phenomenology emerged: namely, after BCI implantation, the patient reported experiences of increased agential capacity and continuity; and after device explantation, the patient reported persistent traumatic harms linked to agential discontinuity. To our knowledge, this is the first reported clinical case of a patient experiencing persistent agential discontinuity due to BCI explantation and potential evidence of an infringement on patient right, where the implanted person was robbed of her de novo agential capacities when the device was removed. • Some brain-computer interfaces predict seizures by monitoring neural data, some advise users based on specific neural data thresholds. • In the ethical literature, advisory devices are perceived as encompassing a stronger degree of autonomy because they keep users in the decisional loop. • We report on a clinical case where a BCI implantation yielded enhanced agential capacity and continuity, while device explantation caused ongoing traumatic harm and agential discontinuity • To our knowledge, the first reported case of a patient experiencing ongoing agential discontinuity due to BCI removal, raising concerns about potential patient rights violation. [ABSTRACT FROM AUTHOR]

In view of the problems existing in the analysis and evaluation of the system efficiency, this paper establishes the real-time calculation models of the surface efficiency, the downhole efficiency and the system efficiency in sucker-rod pump wells by mining the relationship between the dynamometer card, the electric power diagram and the various efficiencies of the sucker-rod pump well. And on this basis, the relationship model between system efficiency and submergence pressure is derived, and the evaluation template of system efficiency macro control chart is established to realize the real-time calculation and intelligent evaluation of sucker-rod pump well system efficiency. Through the potential evaluation and optimization effect analysis of 45 suckerrod pump wells in Shengli Oilfield in China, the results show that the system efficiency evaluation model established in this paper is scientific and reasonable, and can provide direction and guidance for the efficient operation of sucker-rod pump wells. At the same time, combined with the real-time acquisition of dynamometer card and electric power diagram, it can realize the real-time calculation of system efficiency and intelligent evaluation of production potential in sucker-rod pump wells, improve the timeliness of production analysis, which can help to reduce production management costs, and improve the production efficiency and benefits of the oilfield. [ABSTRACT FROM AUTHOR]

The main purpose of this research is to present a detailed comparison between the 1996-2001 Dotcom and 2014-2018 AI landscapes, by comparing the evolution of Dotcom and AI through a regression model aimed at testing the relationship between these two phenomena, which have similar characteristics, but also distinct features. In a period when the academic and the business environment discourse is focused on the potential effects of diminishing funding and interest in AI and even a bubble burst, our investigation demonstrates the contrary. Having in mind the strengths of the AI market and its interrelations with significant sectors of the world economy, our paper denies the hypothesis of a potential AI crisis, in spite of some hints that AI companies are in the early stages of high risk. Our paper brings also another novelty element related to a different type of Schumpeterian creative destruction identified in the case of AI. The big players present on the market are able to purchase the firms that Schumpeter suggested would replace the existing firms, therefore the creative destruction occurs in the case of start-ups, which are undertaken by stronger companies present on the market, even survivors of the Dotcom crisis, not vice versa. [ABSTRACT FROM AUTHOR]

This paper provides significant contributions in the line of the so-called privacy-preserving OLAP research area, via extending the previous SPPOLAP's results provided recently. SPPOLAP is a state-of-the-art algorithm whose main goal consists in computing privacy-preserving OLAP data cubes effectively and efficiently. The main innovations carried-out by SPPOLAP are represented by the novel privacy OLAP notion and the flexible adoption of sampling-based techniques in order to achieve the final privacy-preserving data cube. In line with the main SPPOLAP's results, this paper significantly extends the previous research efforts by means of the following contributions: (i) complete algorithms of the whole SPPOLAP algorithmic framework; (ii) complexity analysis and results; (iii) comprehensive experimental analysis of SPPOLAP against real-life multidimensional data cubes, according to several experimental parameters. These contributions nice-fully complete the state-of-the-art SPPOLAP's results. [ABSTRACT FROM AUTHOR]

In its entirety, a research project can be a long and sometimes challenging undertaking. However, it is important to not think of the research process as a long linear process, but rather as a collection of small tasks that build on each other. Data analysis is an important step in this process; however, choosing the right statistical method to examine data can be complex. The purpose of this paper is to discuss how choosing a statistical method for data analysis can be influenced by other aspects of the research process and to highlight considerations when choosing a statistical method more generally. This is the fourth paper in a series of articles about research methods. Previous papers in this series have focused on research paradigms (Davies & Fisher, 2018), the research process (Fisher & Bloomfield, 2019), and quantitative research methods (Bloomfield & Fisher, 2019). [ABSTRACT FROM AUTHOR]

Previous studies show that time series data about the frequency of hits for tourism-related search terms from Google (Google Trends data) is a valuable predictor for short-term tourism demand forecasting in many different tourism regions worldwide. The paper contributes to this literature in three ways. First, it shows that Google Trends data is useful for short-term predictions of monthly tourist arrivals in several German holiday regions. Second, the paper also demonstrates that the Google Trends time series we employ share certain patterns with Google Trends time series used in previous studies, including several studies totally unrelated to the tourism industry. We refer to these artefacts as "spurious patterns" and perform a detailed analysis of their negative impact on forecasting. Last, the paper proposes a method to sanitize Google Trends data and reduce the adverse impact of spurious patterns, thereby paving the way to develop statistically sound tourism demand forecasts. • Google Trends data is a valuable predictor for tourism in Germany. • The data shows "spurious patterns" that are unrelated to tourism. • The spurious patterns have a negative impact on tourism forecasts. • Data modi_cation can reduce the impact of the spurious patterns. • The spurious patterns can be seen in several previously published studies. [ABSTRACT FROM AUTHOR]

The present paper dwells on Research Data Management (RDM), its need, importance, the behaviour of the researchers in different disciplines towards data sharing and the role of libraries in extending data management services. The policies of publishers and funding bodies for sharing research data have also been described. It underlines that the library professionals should have a comprehensive understanding of the emerging issues, trends and challenges about the research data management to deploy appropriate services for the researchers. It highlights various resources on research data management, which may serve as guidelines for library professionals and researchers. The study will prove beneficial to library professionals and inspire them to extend RDM services in their organisations The paper concludes by advocating that the library professionals must update and upskill themselves with new trends, tools and techniques to provide RDM services. They should sensitise researchers to make their datasets accessible for reuse and sharing. [ABSTRACT FROM AUTHOR]

Female genital mutilation (FGM) affects women in many countries and carries significant socio-sexual and cultural implications such as affected female sexual pleasure and a strong association with traditions which are believed to form a cultural identity. This essay explored the lack of data and discourse regarding this practice among the Dawoodi Bohra community in Pakistan. Due to the Pakistani government's lack of recognition and public dialogue on this issue, there remains no official laws, statistics, or empirical research about the ritual. Despite the rise of feminism and women's empowerment in many contemporary societies, Pakistan has not initiated any FGM debate in the domains of government, political activism, or academia. Simultaneously, FGM remains secretive and taboo within the Dawoodi Bohra community. This overall lack of awareness has left little to no resources or methods to study FGM in the country. To help bridge this data gap, this essay investigated the religious and cultural significance of FGM among Dawoodi Bohras in Pakistan, discussing factors and justifications that perpetuate the tradition. A unique interplay of patriarchal and matriarchal power structures may be driving FGM in this population, resulting in continued violation of the bodily autonomy of female children and unclear negative effects on women (e.g., physical pain, sexual problems, and psychological trauma). This paper also contrasted universalist and cultural relativist theories of FGM and recommended a research approach characterized by increased cultural competence, sensitivity, and nonjudgmental exploration. By pursuing well-designed, culturally sensitive research about FGM in Pakistan, it is possible to initiate productive public discourse and action without incurring shame upon women and entire communities. [ABSTRACT FROM AUTHOR]

Automating traceability-based decision-making can shorten the reaction time to supply chain disruptions. This paper develops a framework for choosing automated decision-making (ADM) methods based on traceability data. It contains a toolbox comprising methods suitable for ADM, respective selection criteria and a new process to select a suitable ADM method based on companies' requirements. This process is based on an evaluation matrix matching methods and criteria. As a result, the ADM framework suggests the most suitable method to automate a specifically chosen decision. The developed framework is validated in the supply chain of a globally operating truck manufacturer. [ABSTRACT FROM AUTHOR]

Similar to information and knowledge, data and especially big data are now known as one of the most vital elements in the 21st century since they provide multiple capabilities to individuals and organizations. However, in comparison to some theories about information and knowledge, there are no significant attempts in most scientific disciplines for building theories about data. This paper first reviews the different definitions provided about the concept of data in the works of scholars. It then identifies and explores the philosophical aspects as well as the multiple capabilities/features that can be derived from data. Finally, a starter list of some basic/general theories is developed based on the capabilities and features of data. Such new theories can be used as meta-theories to extend data theories for various scientific disciplines. The important notion supporting the development of theories about data is that, if data is so important and if data science is to continue flourishing in a variety of specialized fields and trends, then we need to build relevant theories about data for research and practical purposes in a multi-disciplinary context. [ABSTRACT FROM AUTHOR]

• Summarize household energy consumption data characteristics and development trends. • Analyze household energy consumption patterns based on the time dimension. • Summarize the main influencing factors of household energy consumption. • Organically combine data, household energy consumption patterns and drivers. Understanding household energy consumption (HEC) demand patterns and their influencing factors at different times can provide insights into household energy demand and consumption behavior. Combining with optimization technology can improve energy efficiency, promote energy conservation, and predict future energy demand. Obtaining comprehensive and accurate data is the basis for revealing and analyzing HEC patterns and related activities. The main purpose of this paper is to review the current methods of obtaining HEC data in different time dimensions, HEC patterns, and influencing factors at different times, and organically combine all three. We first start with HEC data. This paper provides an overview of the current methods and data types used to collect information on HEC and related activities. It includes macro panel data, micro questionnaire survey data, micro case study data, and micro smart meter and advanced sensor data. Following this, presents current studies on HEC patterns, with a focus on cross-country comparisons in the temporal dimension. Direct and indirect (time-occupancy) studies are two ways of studying HEC patterns over time. Finally, discusses the factors that influence HEC demand, such as climate, building type, and socio-demographic factors. The understanding of these factors is crucial for promoting energy conservation and efficiency in households. [ABSTRACT FROM AUTHOR]

Artificial intelligence is often framed as a tool to be used to counter violent extremism. This paper seeks to make a contribution to scholarship on the use of AI by considering the other side of the debate: how AI and algorithms themselves can and are being used to radicalize, polarize, and spread racism and political instability. The central argument of the paper is that AI and algorithms are not just tools deployed by national security agencies to prevent malicious activity online, but contributors to polarization, radicalism and political violence. Further to this, securitization processes have been instrumental in how AI has been designed, used and to the harmful otucomes that it has generated. The paper begins with an analysis of the connections between AI, polarization, radicalism and political violence. Drawing on the 'Copenhagen School' of International Relations theory, it then moves on to an empirical assessment of how AI has been securitized throughout its history, and in media and popular culture depictions, and by exploring a number of modern examples of AI having polarizing, radicalizing effects that have contributed to political violence. The third section of the article examines AI technology itself, arguing that problems exist in the design of AI, the data that it relies on, how it is used, and in its outcomes and impacts. The final section draws conclusions and policy implications, arguing that a reconceptualisation of AI-enabled security is necessary that is more attuned to the human, social and psychological impacts of the technology. • This paper analyses how AI and algorithms are being used to radicalize, polarize, and spread racism and political instability. • AI and algorithms are not just tools deployed by national security agencies but contributors to polarization, radicalism and political violence. • Securitization processes are a missing link to understanding how AI has been designed and used and to the harmful outcomes that it has generated. • Reconceptualizing AI-enabled conflict is necessary in a way that is more aware of the human, social and psychological impacts of the technology. [ABSTRACT FROM AUTHOR]

Irrigation efficiency projects aim to conserve water for in-stream flow and agricultural use by reducing water losses throughout the system. Piping irrigation canals is a common irrigation efficiency method that results in trade-offs: while it increases efficiency of irrigation water conveyance, it reduces incidental groundwater recharge. This paper focuses on the data and decision-making of canal piping, focusing primarily on understanding the potential impacts of reduced canal leakage on shallow wells. By conducting a spatial analysis of shallow wells in the basin at risk of being impacted by canal piping, and combining this with interviews with water managers in central Oregon's Upper Deschutes Basin, we demonstrate the complex socio-natural dynamics and politics of water conservation decision-making. The research finds that irrigation canal piping is fully supported by water managers in the study area as a means of physically shifting flows of water towards particular valued uses, yet at the same time there is not enough data to understand the potential impacts of canal piping on water users reliant on canal seepage. Given the lack of localized shallow groundwater monitoring data, water managers are reliant upon basin-scale model predictions when defining the trade-offs in canal piping. Broadly, the research demonstrates that well-intended water management decisions can have trade-offs and impacts that are not well understood, pointing to the need for more groundwater monitoring and critical attention to the multiple scales of irrigation efficiency to inform the co-management of surface water and groundwater for the many water users within a basin. • Irrigation efficiency can impact basins in ways that are not understood. • Water managers rely on incomplete evidence when making efficiency decisions. • Irrigation efficiency efforts may negatively impact shallow domestic wells. • The politics of data gaps must be taken seriously in water management. • Critical lenses help to understand the complexity of irrigation efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Romanian Review of Private Law / Revista Română de Drept Privat is the property of Universul Juridic Publishing House and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The manufacturing industry faces immense challenges for maintaining and increasing their productivity and flexibility. In this context, it is important for companies to ensure that their employees have the relevant data, information and knowledge necessary to make well-informed decisions. Due to recent development with Industry 4.0 enabling technologies that create new possibilities, the amount of available data, information and knowledge increase rapidly, but the insights into how to utilize it to its full potential are still lacking. In this paper, a human-centred perspective has been applied, aiming at improving how to cognitively support humans at work with new Industry 4.0 enabling technologies. Heavy emphasis is placed on people's requirements and preferences of data, information and knowledge for enhancing their performance and satisfaction at work. This paper examines the relationship between existing literature on dissemination of data, information and knowledge within the manufacturing industry with state-of-the-art research on Industry 4.0. The outcome of the research recognizes the increased importance of utilizing data, information and knowledge for people at work, facilitated by exploiting the new possibilities from Industry 4.0. To accomplish this, it is concluded that there exists an urgency to design: both a holistic framework for identifying and accommodating individuals' needs and expectations of relevant data, information and knowledge; and demonstrators and concepts to simplify the implementation of Industry 4.0 enabling technologies that support the aforementioned dissemination of data, information and knowledge. [ABSTRACT FROM AUTHOR]

In recent years, the Unmanned Aerial System (UAS) has experienced rapid development and has been frequently deployed on the battlefield. The UAS for the export of military trade in China, represented by the CH-4 Medium Altitude Long Endurance (MALE) UAS, has been equipped in the air forces of many countries in the Middle East. When the UAS is deployed and operated, it produces a large amount of data and information of Integrated Logistical Support (ILS). Recording, processing, and applying this information and data are important contents of ILS. At present, the information and data sources of ILS are various and difficult to manage. Also, it is difficult to effectively integrate and use, so it cannot effectively analyze and evaluate equipment performance and support efficiency nor provide insight into the market demand and profit model. According to the requirements of information and data management for ILS of UAS under the military trade model, this paper puts forward an information data management platform for ILS of UAS. Firstly, the design ideas and principles of the platform is determined. Then, a general framework of the platform is established. Finally, the design contents and requirements are provided, with a detailed description of the hardware layer, software layer, data layer, function layer, and interface layer. The content of this paper can help the research and development of the information data management platform for ILS of UAS. [ABSTRACT FROM AUTHOR]

Background: Dashboards can support data-driven quality improvements in health care. They visualize data in ways intended to ease cognitive load and support data comprehension, but how they are best integrated into working practices needs further investigation.Objective: This paper reports the findings of a realist evaluation of a web-based quality dashboard (QualDash) developed to support the use of national audit data in quality improvement.Methods: QualDash was co-designed with data users and installed in 8 clinical services (3 pediatric intensive care units and 5 cardiology services) across 5 health care organizations (sites A-E) in England between July and December 2019. Champions were identified to support adoption. Data to evaluate QualDash were collected between July 2019 and August 2021 and consisted of 148.5 hours of observations including hospital wards and clinical governance meetings, log files that captured the extent of use of QualDash over 12 months, and a questionnaire designed to assess the dashboard's perceived usefulness and ease of use. Guided by the principles of realist evaluation, data were analyzed to understand how, why, and in what circumstances QualDash supported the use of national audit data in quality improvement.Results: The observations revealed that variation across sites in the amount and type of resources available to support data use, alongside staff interactions with QualDash, shaped its use and impact. Sites resourced with skilled audit support staff and established reporting systems (sites A and C) continued to use existing processes to report data. A number of constraints influenced use of QualDash in these sites including that some dashboard metrics were not configured in line with user expectations and staff were not fully aware how QualDash could be used to facilitate their work. In less well-resourced services, QualDash automated parts of their reporting process, streamlining the work of audit support staff (site B), and, in some cases, highlighted issues with data completeness that the service worked to address (site E). Questionnaire responses received from 23 participants indicated that QualDash was perceived as useful and easy to use despite its variable use in practice.Conclusions: Web-based dashboards have the potential to support data-driven improvement, providing access to visualizations that can help users address key questions about care quality. Findings from this study point to ways in which dashboard design might be improved to optimize use and impact in different contexts; this includes using data meaningful to stakeholders in the co-design process and actively engaging staff knowledgeable about current data use and routines in the scrutiny of the dashboard metrics and functions. In addition, consideration should be given to the processes of data collection and upload that underpin the quality of the data visualized and consequently its potential to stimulate quality improvement.International Registered Report Identifier (irrid): RR2-10.1136/bmjopen-2019-033208. [ABSTRACT FROM AUTHOR]

The heart is a vital organ in the human body. In the process of diagnosing heart diseases, several data are often generated and various data mining procedures are often utilized in sieving out the most useful data for ascertaining the presence or absence of the disease, as a final decision. The set of useful data which is eventually utilised in making this final decision often contains hidden patterns. The excavation and analysis of these hidden patterns is often carried out, using data classification techniques and comparative analysis between different data classification techniques in order to determine the most efficient one for this purpose, constitute an aspect of data mining where there exists unabated research efforts. In this paper, a heart disease diagnosis decision support system has been developed. The system makes use of 13 attributes and four machine learning algorithms (Xgboost, Catboost, LGBM and KNN) in carrying out the data classification process for determining the presence of heart diseases. A performance analysis process carried out amongst these four data classification techniques in detecting heat diseases, revealed that Xgboost has the best performance, as it gives a higher rate of true positives and lower rate of false negatives, as well as a higher level of accuracy, compared to the other three classification techniques considered. [ABSTRACT FROM AUTHOR]

Copyright of Journal of the Cukurova University Institute of Social Sciences is the property of Cukurova University Institute of Social Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Economics is a branch of social science that touches upon many aspects of our lives and has important effects on the well-being of all people. Within economics, human well-being is significantly contingent on the process of growth. Production takes place via a combination of human and physical capital; therefore, human capital is expected to be a main contributor to economic growth. However, many studies have failed to uncover a significant association between human capital and growth, and others have found only marginal contributions therein. Several economists have tried to explain this paradox. This study brings forth skill mismatch as another potential explanation of the weak relationship between education and growth, and shows that skill mismatch can nullify the potential advantage of increased human capital. Most countries have failed to take skill mismatch seriously; therefore, there is no systematic data on the indicators that relate to it. This paper emphasizes the need for reliable and sufficient data on the indicators of skill mismatch, so that planning for the better utilization of human capital can take place. [ABSTRACT FROM AUTHOR]

As a student modeling technique, knowledge tracing is widely used by various intelligent tutoring systems to infer and trace the individual's knowledge state during the learning process. In recent years, various models were proposed to get accurate and easy-to-interpret results. To make sense of the wide Knowledge tracing (KT) modeling landscape, this paper conducts a systematic review to provide a detailed and nuanced discussion of relevant KT techniques from the perspective of assumptions, data, and algorithms. The results show that most existing KT models consider only a fragment of the assumptions that relate to the knowledge components within items and student's cognitive process. Almost all types of KT models take "quize data" as input, although it is insufficient to reflect a clear picture of students' learning process. Dynamic Bayesian network, logistic regression and deep learning are the main algorithms used by various knowledge tracing models. Some open issues are identified based on the analytics of the reviewed works and discussed potential future research directions. [ABSTRACT FROM AUTHOR]

This viewpoint paper is based on desktop research focusing on the discourse of knowledge management (KM). We explore the situation in South Africa with reference to this global COVID-19 apocalypse that the world is being confronted with. By using a qualitative research approach, the concepts of data, information and knowledge are unpacked in the context of this pandemic. KM is evident in the creation, storing, using and effective sharing of information and knowledge in light of COVID-19. The sharing and dissemination of information are, however, technologically orientated as advances in this realm continue to elevate opportunities for transforming KM. All information that has been created and is available to inform society about the virus is shared and distributed through technological means such as electronic and print media, the Internet and social media. As people adjust to the new normal, they will have a hefty repository of pandemic stories, enriched with new vocabulary about the virus and centred on the three concepts of data, information and knowledge. Understanding this new terminology plays a key role in helping to prevent the spread of COVID-19. Incorrect use of this vocabulary can lead to misinformation and could endanger the lives of others. Both the public and government now need "moral knowledge" about the pandemic in the fight against this virus. [ABSTRACT FROM AUTHOR]

The deep sea has a large strategic depth, in which target detection is a crucial link, that is, how to detect the target submarine. For this reason, since this century, countries represented by the United States have innovated ideas, innovated underwater detection technology, and enhanced new underwater detection capabilities. In this paper, by consulting more than 100 literatures, the data acquisition technology in underwater acoustic detection in recent years has been investigated in detail, passive detection and active detection have been comprehensively reviewed, and the collaborative detection technology has also been summarized. In order to provide reference for related research. [ABSTRACT FROM AUTHOR]

This article presents and discusses different kinds of data documents, including data sets, data studies, data papers and data journals. It provides descriptive and bibliometric data on different kinds of data documents and discusses the theoretical and philosophical problems by classifying documents according to the DIKW model (data documents, information documents, knowledge documents and wisdom documents). Data documents are, on the one hand, an established category today, even with its own data citation index (DCI). On the other hand, data documents have blurred boundaries in relation to other kinds of documents and seem sometimes to be understood from the problematic philosophical assumption that a datum can be understood as "a single, fixed truth, valid for everyone, everywhere, at all times". [ABSTRACT FROM AUTHOR]

The heart is a vital organ in the human body. In the process of diagnosing heart diseases, several data are often generated and various data mining procedures are often utilized in sieving out the most useful data for ascertaining the presence or absence of the disease, as a final decision. The set of useful data which is eventually utilised in making this final decision often contains hidden patterns. The excavation and analysis of these hidden patterns is often carried out, using data classification techniques and comparative analysis between different data classification techniques in order to determine the most efficient one for this purpose, constitute an aspect of data mining where there exists unabated research efforts. In this paper, a heart disease diagnosis decision support system has been developed. The system makes use of 13 attributes and four machine learning algorithms (Xgboost, Catboost, LGBM and KNN) in carrying out the data classification process for determining the presence of heart diseases. A performance analysis process carried out amongst these four data classification techniques in detecting heat diseases, revealed that Xgboost has the best performance, as it gives a higher rate of true positives and lower rate of false negatives, as well as a higher level of accuracy, compared to the other three classification techniques considered. [ABSTRACT FROM AUTHOR]

Vibration data consists of batches of time series which if accumulated over a period of time is a huge collection of numeric data. Reducing such data for use in deep learning models for computational effciency is a challenge. Combinatorial and discrete approaches, on the other hand, is not an extensively explored area when it comes to datasets. This paper aims to identify feature reduction techniques based on discrete approaches such as euclidean distance using dot products on vibration data samples from accelerometers fitted on bearings. In this limited experimentation, the procured dataset by this approach is considerably smaller in size as compared to the actual complete data, and with comparable results in prediction models, it can be used as a smaller representation of a sensor timeline. The results based on different models show that such reductions can be considered in building IoT applications in industries based on sensors. [ABSTRACT FROM AUTHOR]

Purpose - The purpose of this paper is to lay out the data competence maturity model (DCMM) and discuss how the application of the model can serve as a foundation for a measured and deliberate use of data in secondary education. Design/methodology/approach - Although the model is new, its implications, and its application are derived from key findings and best practices from the software development, data analytics and secondary education performance literature. These principles can guide educators to better manage student and operational outcomes. This work builds and applies the DCMM model to secondary education. Findings - The conceptual model reveals significant opportunities to improve data-driven decision making in schools and local education agencies (LEAs). Moving past the first and second stages of the data competency maturity model should allow educators to better incorporate data into the regular decision-making process. Practical implications - Moving up the DCMM to better integrate data into their decision-making process has the potential to produce profound improvements for schools and LEAs. Data science is about making better decisions. Understanding the path laid out in the DCMM to helping an organization move to a more mature data-driven decision-making process will help improve both student and operational outcomes. Originality/value - This paper brings a new concept, the DCMM, to the educational literature and discusses how these principles can be applied to improve decision making by integrating them into their decisionmaking process and trying to help the organization mature within this framework. [ABSTRACT FROM AUTHOR]

Digital content is most often stored in files, which may be thought of as structured containers for data. This structure facilitates the processing and rendering of the data for human or machine consumption, and also enables the storage of metadata related to the stored content. A side effect of this structured container approach is that the stored file contains more information, sometimes much more, than the actual data that is rendered or available to the receiving human or machine. Additionally, these structures have gaps and other areas where additional data may be stored, unknown to the file owner or subsequent processor. In this paper we propose and test a nonalgorithmic and file-type independent approach for hiding persistent and stealthy data in files. This approach may be used to surreptitiously tag files for attribution or tracing purposes, as well as to search for data hidden in existing files. Our approach is not algorithmic like steganography and cryptography. Rather, we take a black box approach to find candidate hiding locations, then we test each of these locations for file integrity and persistence. For our tests, we hid data in MS Word documents using the Office Open Extensible Markup Language (OOXML) format, although our work easily generalizes to other formats. We found multiple locations which allowed for the persistent and benign storage of additional data under various usage scenarios. The main contributions of this paper are: a methodology for identifying conditions favorable for hiding benign and persistent data in arbitrary file types, a methodology for testing these conditions, and empirical results using OOXML formatted files. [ABSTRACT FROM AUTHOR]

Environmental horticulture firms supply products and services related to the design, installation, and maintenance of commercial and residential landscapes. An important issue in the environmental horticulture sector is the reduction and utilization of horticultural waste including plastic containers, trays, plastic greenhouse covers and field covers, and plastic bags. Using data collected through a survey in 2013, this paper identifies factors that influence the sector’s decision to participate in waste recycling programs in Georgia, USA. The study employs a decision model. Results indicate that significant determinants of the recycle decision include the firm size, motivation for recycling, current recycling situation, as well as owner/manager education level. To help firms to recycle, local governments, the environmental horticulture sector, and environmental protection organizations need to focus on lower-revenue and small size firms, broadly disseminate recycle program information, and enhance the local recycler’s services. [ABSTRACT FROM AUTHOR]

Leveraging artificial intelligence (AI) approaches in animal health (AH) makes it possible to address highly complex issues such as those encountered in quantitative and predictive epidemiology, animal/human precision-based medicine, or to study host × pathogen interactions. AI may contribute (i) to diagnosis and disease case detection, (ii) to more reliable predictions and reduced errors, (iii) to representing more realistically complex biological systems and rendering computing codes more readable to non-computer scientists, (iv) to speeding-up decisions and improving accuracy in risk analyses, and (v) to better targeted interventions and anticipated negative effects. In turn, challenges in AH may stimulate AI research due to specificity of AH systems, data, constraints, and analytical objectives. Based on a literature review of scientific papers at the interface between AI and AH covering the period 2009–2019, and interviews with French researchers positioned at this interface, the present study explains the main AH areas where various AI approaches are currently mobilised, how it may contribute to renew AH research issues and remove methodological or conceptual barriers. After presenting the possible obstacles and levers, we propose several recommendations to better grasp the challenge represented by the AH/AI interface. With the development of several recent concepts promoting a global and multisectoral perspective in the field of health, AI should contribute to defract the different disciplines in AH towards more transversal and integrative research. [ABSTRACT FROM AUTHOR]