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In a drone system with dual data links, this article presents a redundant data processing algorithm that can minimize flight control instability without increasing the weight and price of the aircraft by software processing of duplicate received messages. [ABSTRACT FROM AUTHOR]

The poor level of engagement in learning mathematics is primarily caused by ineffective methods of instruction. Therefore, the purpose of this study was to investigate how context-based problem-posing and solving instructional approaches influence students’ engagement in learning data handling using a concurrent embedded quasi-experimental non-equivalent pre-test-post-test control group mixed method design. A total of 138 fifth-grade students participated in the study. The data were collected using the Students’ Engagement in Learning Data Handling Scale, interviews, and lesson observation. Quantitative data were analyzed using mean, standard deviation, percentage, paired sample t-test, and ANCOVA. Qualitative data were analyzed thematically. The study found that context-based problem-posing and solving instructional approaches have brought a significant increment in students overall engagement and its entire component’s in learning data handling. Besides, it has been observed that there is a large effect size on overall engagement and cognitive, behavioral, and medium treatment effects on emotional engagement when the context-based problem-posing and solving instructional approaches group is compared to the comparison group. Context-based problem-posing and solving instructional approaches also encouraged students to pay attention, to think in different ways, to create and solve problems, to ask, share, reflect, and work through problems more diligently than before.

Abstract In a drone system with dual data links, this article presents a redundant data processing algorithm that can minimize flight control instability without increasing the weight and price of the aircraft by software processing of duplicate received messages.

In a distributed environment, replication is the most investigated phenomenon. Replication is a way of storing numerous copies of the same data at different locations. Whenever data is needed, it will be fetched from the nearest accessible copy, avoiding delays and improving system performance. To manage the replica placement strategy in the Cloud, three key challenges must be addressed. The challenges in determining the best time to make replicas were generated, the kind of files to replicate, as well as the best location to store the replicas. This survey conducts a review of 65 articles published on data replication in the cloud. The literature review examines a series of research publications and offers a detailed analysis. The analysis begins by presenting several replication strategies in the reviewing articles. Analysis of each contributor's performance measures is conducted. Moreover, this survey offers a comprehensive examination of data auditing systems. This work also determines the analytical evaluation of replication handling in the cloud. Furthermore, the evaluation tools used in the papers are examined. Furthermore, the survey describes a lot of research issues & limitations that might help researchers support better future work on pattern mining for data replication in the cloud. [ABSTRACT FROM AUTHOR]

Fifth-generation (5G) wireless systems increased the bandwidth, improved the speed, and shortened the latency of communications systems. Various channel models are developed to study 5G. These channel models reproduce the stochastic properties of multiple-input multiple-output (MIMO) antennas by generating wireless multipath components (MPCs). The MPCs with similar properties in delay, angles of departure, and angles of arrival form clusters. The multipaths and multipath clusters serve as datasets to understand the properties of 5G. These datasets generated by the Cooperation in Science and Technology 2100 (COST 2100), International Mobile Telecommunications-2020 (IMT-2020), Quasi Deterministic Radio Channel Generator (QuaDRiGa), and Wireless World Initiative New Radio II (WINNER II) channel models are tested for their homoscedasticity based on Johansen's procedure. Results show that the COST 2100, QuaDRiGa, and WINNER II datasets are heteroscedastic, while the IMT-2020 dataset is homoscedastic. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) techniques are maturing and penetrating every aspect of the industry. With more and more design, process, structure, and property data collected, machine learning (ML) models are found to be useful to analyze the patterns in the data. The quality of datasets and the handling methods are important to the performance of these ML models. This work reviews recent publications on the topic, focusing on the data types along with the data handling methods and the implemented ML algorithms. The examples of ML applications in AM are then categorized based on the lifecycle stages, and research focuses. In terms of data management, the existing public database and data management methods are introduced. Finally, the limitations of the current data processing methods are discussed and suggestions on perspectives are given. [ABSTRACT FROM AUTHOR]

In April 2023, the superBIT telescope was lifted to the Earth's stratosphere by a helium-filled super-pressure balloon to acquire astronomical imaging from above (99.5% of) the Earth's atmosphere. It was launched from New Zealand and then, for 40 days, circumnavigated the globe five times at a latitude 40 to 50 degrees south. Attached to the telescope were four "drs" (Data Recovery System) capsules containing 5 TB solid state data storage, plus a gnss receiver, Iridium transmitter, and parachute. Data from the telescope were copied to these, and two were dropped over Argentina. They drifted 61 km horizontally while they descended 32 km, but we predicted their descent vectors within 2.4 km: in this location, the discrepancy appears irreducible below ∼2 km because of high speed, gusty winds and local topography. The capsules then reported their own locations within a few metres. We recovered the capsules and successfully retrieved all of superBIT's data despite the telescope itself being later destroyed on landing. [ABSTRACT FROM AUTHOR]

Abstract Partial discharge (PD) detection is essential in assessing the insulation state of electrical equipment. However, PD signals are often overwhelmed by interference, resulting in inaccurate detection results. Aiming at this problem, this study proposes a PD detection method based on singular value decomposition (SVD) and improved spectral subtraction. First, the test signal is constructed as a Hankel matrix, which is used as a trajectory matrix for the SVD. Next, the singular value mutation point in the feature matrix is set as the threshold for removing the narrowband interference (NBI), and a signal containing only white noise is obtained. Finally, the improved spectral subtraction is used to remove white noise and improve the signal‐to‐noise ratio (SNR). The method proposed herein, along with the variational mode decomposition, the empirical mode decomposition, and the improved threshold wavelet method, are applied to the processing of PD signals. Also, the SNR value, waveform similarity coefficient, and mean square error of the denoizing signal of the four algorithms were calculated, considering the noise suppression and feature preservation abilities. The simulation and measurement results show that the SVD‐spectral subtraction method has a strong suppression effect on narrow‐band interference and white noise. Compared with other algorithms, this method can significantly improve the execution efficiency and has great application prospects.

The chemical industry is one of the key industrial sectors in Germany and at the same time one of the largest consumers of energy and raw materials. A successful energy transition and the development of a circular economy can only succeed if they are actively supported and shaped by the chemical industry – through the redesign of existing production processes and the exploration and implementation of new process routes. The challenge is to realize this transformation within a very short time and for many production processes, whereby a much larger number of process routes must be explored. Digital technologies are key to master this transformation towards more sustainability, climate, and environmental protection. The KEEN project aims to explore and leverage artificial intelligence (AI) opportunities in process industry. The newly developed AI methods are tested wherever possible in real working environments and production plants to prove the economic benefit, applicability, and reliability of the methods and technologies. [ABSTRACT FROM AUTHOR]

New technological developments, the availability of big data, and the creation of research platforms open a variety of opportunities to generate, store, and analyze research data. To ensure the sustainable handling of research data, the European Commission as well as scientific commissions have recently highlighted the importance of implementing a research data management system (RDMS) in higher education institutes (HEI) which combines technical as well as organizational solutions. A deep understanding of the requirements of research data management (RDM), as well as an overview of the different stakeholders, is a key prerequisite for the implementation of an RDMS. Based on a scientific literature review, the aim of this study is to answer the following research questions: "What organizational factors need to be considered when implementing an RDMS? How do these organizational factors interact with each other and how do they constrain or facilitate the implementation of an RDMS?" The structure of the analysis is built on the four components of Leavitt's classical model of organizational change: task, structure, technology, and people. The findings reveal that the implementation of RDMS is strongly impacted by the organizational structure, infrastructure, labor culture as well as strategic considerations. Overall, this literature review summarizes different approaches for the implementation of an RDMS. It also identifies areas for future research. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) detection is essential in assessing the insulation state of electrical equipment. However, PD signals are often overwhelmed by interference, resulting in inaccurate detection results. Aiming at this problem, this study proposes a PD detection method based on singular value decomposition (SVD) and improved spectral subtraction. First, the test signal is constructed as a Hankel matrix, which is used as a trajectory matrix for the SVD. Next, the singular value mutation point in the feature matrix is set as the threshold for removing the narrowband interference (NBI), and a signal containing only white noise is obtained. Finally, the improved spectral subtraction is used to remove white noise and improve the signal‐to‐noise ratio (SNR). The method proposed herein, along with the variational mode decomposition, the empirical mode decomposition, and the improved threshold wavelet method, are applied to the processing of PD signals. Also, the SNR value, waveform similarity coefficient, and mean square error of the denoizing signal of the four algorithms were calculated, considering the noise suppression and feature preservation abilities. The simulation and measurement results show that the SVD‐spectral subtraction method has a strong suppression effect on narrow‐band interference and white noise. Compared with other algorithms, this method can significantly improve the execution efficiency and has great application prospects. [ABSTRACT FROM AUTHOR]

Using personally identifiable information (PII) (patron data) to make informed decisions in academic libraries through learning analytics programs has increasingly become more commonplace. In this column, we discuss how libraries around the world have used PII to make informed decisions about hours (gate count), electronic resources (collection usage and authentication), and research assistance (virtual reference). In addition, we discuss the use of learning analytics in library environments including the benefits and concerns associated with its use. Finally, we discuss how we have used PII at our institution's library and the data metrics we plan utilize at some point in the future. [ABSTRACT FROM AUTHOR]

[Purpose/Significance] The development of open science still faces many data ethical problems, and there is an urgent need to explore the ethical guidelines for data handling that are compatible with the open science innovation ecology. The establishment of ethical guidelines is essential to create a favorable open science innovation ecology for data handling that match the construction of open science innovation ecology, and guide the participants in the open science innovation ecology to act in accordance with legal regulations and ethical norms in the process of data creation, sharing, dissemination, and utilization, and restraining data handling behaviors for the good and benefit of society. [Method/Process] This study first uses literature research and content analysis, based on relevant laws, guidelines, draft frameworks, policies and typical data management models on data handling ethics, from a universal perspective, based on the practice of data acquisition, storage, management, use and destruction, for social data in a broad sense, while taking into account a part of scientific research data, correlating and integrating different guidelines, seeking common themes and contents among existing guidelines, and summarizing the general dimensions and highly recognized guidelines. This study summarizes the general dimensions of ethical guidelines for data handling and the contents of the guidelines with high recognition. Second, this study combines the concept of open science and the needs of national social development, and distills the needs of ethical guidelines for data handling in the open science innovation ecosystem from the latest domestic planning policies and the connotation of open science. Finally, this study synthesizes and compares the two results through matrix analysis, and analyzes the framework of ethical guidelines for data handling in the construction of open science innovation ecology from the perspectives of commonality and characteristics, and synergy between guideline contents and policies and concepts. [Results/Conclusions] The existing ethical guidelines for data handling have high applicability in the open science innovation ecology, but still need dynamic expansion. Combining the concept of open science and the development needs of national society, this study proposes a framework of ethical guidelines for data handling in the open science innovation ecology, including three dimensions and 15 elements of long-term governance, coordination and order, and systematic science, aiming to serve the further development of the text of ethical guidelines for data handling adapted to the development of the open science innovation ecology in China and to provide some reference for data management in institutions.

Aviation is characterized by many stakeholders, long lifespans of its assets, and high requirements regarding safety, security, and documentation. To meet these requirements as well as customer needs, aircraft are regularly retrofitted with new cabins. During the planning and execution of this cabin retrofit, handling the needed and available data poses a challenge to the engineers. While much of the required data is available in some form, generally there is a lack of a digitally usable dataset of the specific aircraft—a virtual representation of the physical asset is missing. To support the implementation of such a digital twin and, thus, the overall process of retrofitting aircraft, an approach to model-driven data handling tailored to the unique circumstances and requirements of aviation is introduced. The methodology consists of a combination of systems engineering and data science techniques framed by an overarching procedure that iteratively creates and enhances a digitally accessible dataset of the relevant data, hence supporting the retrofit engineers by easing access to needed information. Besides the presentation of the research background and the methodology, a simplified example is shown, demonstrating the approach using abstracted but realistic information provided by partners from the industry. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a self‐configuring, intelligent system in which autonomous things connect to the Internet and communicate with each other. As 'things' are autonomous, it may raise privacy concerns. In this study, the authors describe the background of IoT systems and privacy and security measures, including (a) approaches to preserving privacy in IoT‐based systems, (b) existing privacy solutions, and (c) recommending privacy models for different layers of IoT applications. Based on the results of our study, it is clear that new methods such as Blockchain, Machine Learning, Data Minimisation, and Data Encryption can greatly impact privacy issues to ensure security and privacy. Moreover, it makes sense that users can protect their personal information easier if there is fewer data to collect, store, and share by smart devices. Thus, this study proposes a machine learning‐based data minimisation method that, in these networks, can be very beneficial for privacy‐preserving. [ABSTRACT FROM AUTHOR]

The general objective of this research article is to identify the most used frameworks in the implementation of good information technology governance (GTI) practices in small and medium-sized companies starting from the analysis of publications in the databases of academic journals of the last six years. To develop this research, a systematic review was carried out in the GOOGLE ACADEMICO, SCOPUS AND SCIELO databases. Then we proceeded with the application of the exclusion and inclusion criteria which resulted in a total of 13 articles in which the main IT governance frameworks applied in SMEs are detailed, which serve to answer the research questions. Raised. The framework that is most used in the IT governance of SMEs is COBIT since it is more flexible and can be adapted for optimal implementation in companies. [ABSTRACT FROM AUTHOR]

Ferronickel contains 60–80 wt% Fe and 40–20 wt% Ni and is a feedstock for manufacturing stainless steel and other ferrous alloys. The primary pyrometallurgical route to produce ferronickel from laterite nickel ores is the Rotary Kiln-Electric Furnace (RKEF) process. In the RKEF process, minerals undergo calcination and partial reduction in a rotary kiln. Large amounts of mineral dust leave the kiln entrained in flue gases. Dust insufflation is a potential solution in which dust particles enter directly into the burner flame. The aim is that the insufflated dust softens, agglomerates, and finally joins the minerals stream that goes into the electric furnace. We applied data analytics techniques to a 1-year operation database to assess the operation before and after dust insufflation in an industrial rotary kiln furnace. Bootstrapping revealed statistically significant differences in the operation with and without dust insufflation. Principal Component Analysis (PCA) and k-means clustering were used as exploratory techniques. PCA showed subsets of variables that significantly influence the dataset variance, and k-means allowed distinguishing operation conditions for dust insufflation with encouraging results for the calcine-to-fresh mineral and the natural gas-to-calcine ratios. These results pave the way towards successfully implementing dust insufflation in the RKEF process.

In April 2023, the superBIT telescope was lifted to the Earth’s stratosphere by a helium-filled super-pressure balloon to acquire astronomical imaging from above (99.5% of) the Earth’s atmosphere. It was launched from New Zealand and then, for 40 days, circumnavigated the globe five times at a latitude 40 to 50 degrees south. Attached to the telescope were four “drs” (Data Recovery System) capsules containing 5 TB solid state data storage, plus a gnss receiver, Iridium transmitter, and parachute. Data from the telescope were copied to these, and two were dropped over Argentina. They drifted 61 km horizontally while they descended 32 km, but we predicted their descent vectors within 2.4 km: in this location, the discrepancy appears irreducible below ∼2 km because of high speed, gusty winds and local topography. The capsules then reported their own locations within a few metres. We recovered the capsules and successfully retrieved all of superBIT’s data despite the telescope itself being later destroyed on landing.

Data handling is a general objective of education, regardless of the educational level (middle school, high school, university or postgraduate) and branch. Being at the intersection between the humanities and the exact sciences, the field of design requires a continuous summation and overlapping of information from specialists. Obtaining data is essential for perceiving the current situation, but also for adapting the solution proposed in the planning process to the given situation, to the particularities and main characteristics of the context. How difficult it is to obtain information, but especially their overlap and correlation to obtain indicators specific to target areas, depends in most cases on the experience of the specialist in the field, but for a recent graduate, the training received during the years of university training is perhaps his most important support. However, there may be data that are predominantly available during university training and data that can be obtained predominantly outside the academic environment (financial data, communication with public institutions). The paper describes a proposal for a support platform to assist the process of architectural design, urban planning and landscaping in Romania, but which can also support adjacent studies and analyzes. The purpose of the platform is to support the design and planning process of heritage and landscape, architecture and urban planning, by simplifying the data acquisition process and also directing the design/planning process to a long-term perspective, which is based on resilient solutions for natural and built heritage and for the conservation of the local cultural landscape. [ABSTRACT FROM AUTHOR]

The ever-increasing growth of semiconductor industries owing to nano sizing of modern electronic devices intensifies the need to handle enormous data. It is necessary to rely on the process of creating algorithms that extract useful information from data, automatically. The majority of data found in the real world are conflicting in nature and must be optimized to attain the demanded target. In the proposed work a Machine Learning (ML) based framework is developed for constructing the best Tunnel FETs on replacing the computationally intensive TCAD simulations. To optimize the design parameters and objectives, a Multi-Objective Optimization (MOO) technique based on Machine Learning and a natural selection approach of non-dominated sorting genetic algorithm-II (NSGA-II) is presented. TFETs optimum design together with the tradeoff between Natural Length, and Vertical Electric Field are automatically identified. The acquired results are compared to TCAD results for demonstrating the ML wrapped TFETs design in the MOO framework is advanced and applied to forecast optimal solutions for the design of TFETs. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence (AI) into the diagnosis and treatment of autism spectrum disorder (ASD) represents a promising frontier in healthcare. This review explores the current landscape and future prospects of AI technologies in ASD diagnostics and interventions. AI enables early detection and personalized assessment of ASD through the analysis of diverse data sources such as behavioural patterns, neuroimaging, genetics, and electronic health records. Machine learning algorithms exhibit high accuracy in distinguishing ASD from neurotypical development and other developmental disorders, facilitating timely interventions. Furthermore, AI-driven therapeutic interventions, including augmentative communication systems, virtual reality-based training, and robot-assisted therapies, show potential in improving social interactions and communication skills in individuals with ASD. Despite challenges such as data privacy and interpretability, the future of AI in ASD holds promise for refining diagnostic accuracy, deploying telehealth platforms, and tailoring treatment plans. By harnessing AI, clinicians can enhance ASD care delivery, empower patients, and advance our understanding of this complex condition., Competing Interests: Declaration of Competing Interest None Declared, (Copyright © 2024 Elsevier B.V. All rights reserved.)