Digital Data Research Articles

The Corporate Digital Responsibility (CDR) Calculus: How and Why Organizations Reconcile Digital and Ethical Trade-offs for Growth

In this digital age, organizations are increasingly reliant on technology to drive business growth and innovation. With this reliance, comes an impetus for firms to responsibly and ethically manage their digital assets and data. In response, Corporate Digital Responsibility (CDR) has been espoused as a means by which firms can address the competing demands of digital innovation and transformation and ethical responsibility. Ultimately, good CDR practices have the potential to safeguard not only firms, but the various stakeholders they interact with across their digital ecosystems. However, it is recognized that the resource investment required to do so, can come at a significant cost. The CDR Calculus has been developed to assist organizations in evaluating the risks and benefits associated with digital and data practices and to determine whether they align with the organization’s CDR objectives. This paper provides a roadmap of how firms can take steps to establish a culture of CDR practice in light of these tensions. We also recognize that external regulation is required, if not needed, to enforce and cement positive CDR practices as firms respond to growing digital demands.

Long-term monitoring of desert restoration processes using historical stereoscopic imagery and laser scanning data: An example of the Błędów Desert (Poland)

This study aimed to present the spatiotemporal changes associated with the loss and subsequent regaining of the original character of the Błędów Desert (Poland) as a result of restoration efforts. The study area is one of the few places in Europe with a sandy desert character. The research period covered nearly 40 years, covering an area of approximately 1,400 ha.The methodology is based on comparing the height structure parameters by analyzing point clouds created from contemporary and historical stereoscopic images. For this purpose, four sets of stereoscopic images were used, including two historical analog images from 1982 and 1996 and contemporary digital data from 2012 and 2018. Additional datasets such as airborne laser scanning data and cadastral databases were used as auxiliary data. The results showed an increase in the area covered by tall vegetation from 2% in 1982 to 18% and 41% in 1996 and 2012, respectively, which decreased to 30% after the completion of restoration activities in 2018. The restoration process has enabled the revival of the original desert character of the study area, although the total area covered by sand decreased compared to its state in the early 1980s. These results indicate the significant potential of the presented methodology in supporting ecosystem management systems, where changes in the height structure of vegetation play a crucial role. This applies to monitoring land abandonment, forest management, and large-scale reclamation work

Blockchain-based Pipeline Custody System (BPCS) for Preserving Critical Video Evidence

<p>The explosive development of network technology has driven the evolution of civilization, leading to the higher crime rate. Hence, CCTV cameras have been widely deployed to monitor criminal behaviors. The video of CCTV is considered as significant digital evidence. Due to the property of digital data, it is easily to copy and modify without electronic footprint, unlike the physical records. In case, the law enforcement starts to employ the blockchain technique due to its advantages, decentralization, transparency, and traceability, which can improve the legal effect. Unfortunately, blockchain network produces the tremendous overhead when it processes the data preservation based on the distributed communication. Thus, the gap and burden of data synchronization are the derivative issues. In order to solve the above problems, we aim to realize a blockchain-based pipeline custody system, so-called BPCS. The critical frame determination (CFD) technique is designed to catch the potential evidence image from the monitor, which is able to mitigate the storage cost of blockchain. Especially, the pipeline and directional ring strategy is proposed to aggregate the potential evidence in a batch file. This can reduce the burden on the chain of custody and shorten the time gap. The experimental results have demonstrated that BPCS can firmly possess the features of credibility and efficient through the comparisons and storage simulation.</p> <p> </p>

Remaining useful life prediction for stochastic degrading devices incorporating quantization

Quantization has been widely employed in analog-to-digital conversion (ADC) for the acquisition of digital data, which are further utilized for prognostics. However, quantization errors are inevitable during ADC, resulting in bias in the subsequent prognosis results. Compared to the numerous researches on prognosis considering measurement noises, slight attention has been paid on remaining useful life (RUL) for degrading devices incorporating quantization errors. In this study, a Wiener-process-based model incorporating mixed random noise is utilized to describe the degradation process involving quantization errors. In order to mitigate the impact of quantization errors, a parameter identification approach and a degradation state estimation method are proposed, which integrate maximum likelihood estimation, particle filter, and Bayesian inference. Subsequently, the results of RUL prediction with and without considering quantization errors are derived, respectively. Finally, numerical examples and a case study on the degradation data from control moment gyroscopes (CMG) are provided to demonstrate the proposed method.

Smart Automated Irrigation System using Wireless Technology

Abstract: The need for agricultural output has surged due to the exponential rise of the world population, resulting in a strain on water supplies and a need for more effective irrigation systems. The goal of this project is to optimize water utilization in agricultural settings by presenting an automated and intelligent irrigation system. Based on current soil moisture levels, the system monitors and regulates irrigation using soil moisture sensors, an Arduino Nano, and a Raspberry Pi 3B+. By converting analog signals from the soil moisture sensor into digital data, the Arduino Nano serves as an analog-to-digital converter. This digital data is sent to the Raspberry Pi 3B+, which acts as the central control unit. A MOSFET is then triggered to activate a relay that is coupled to a 5V DC water pump when the moisture levels drop below a certain threshold. The system also has wireless connection, which enables remote control and monitoring via an Android application. Users have the ability to manually override the automatic system if needed, monitor the moisture content of the soil, and turn on or off the water pump. This feature improves the usability and flexibility of the system.

Transformation of Population Administration Services in Sidoarjo Regency

Quality public services are one of the benchmarks for the success of government implementation. One of the public services which is a basic service for the community is population administration services. To ensure the implementation of improvements in the quality of population administration services efficiently and effectively, orderly population administration and increasing the scope of ownership of population documents as well as improving service performance, the Sidoarjo Regency Population and Civil Registration Service has carried out various innovations in services. The method used in this article is through literature study by exploring various sources including journals, books, official documents, digital data and official news limited by the author from 2018 to 2024. Transformation of population administration services carried out by the Population and Registration Service Sidoarjo Regency's civil services include the Peduli Dilan program in 2019, Plavon (Online Service) Dukcapil in 2021, and Integrated Ball Pick-up in 2022. The transformation of population administration services carried out by the Sidoarjo Regency Population and Civil Registration Service is able to create effective and effective services. Efficient and increasing ownership of population administration documents for the people of Sidoarjo Regency.

Digital platforms and reproductive health information: Navigating legal and ethical boundaries

In today's digital age, the dissemination of reproductive health information has shifted significantly to online platforms, offering unparalleled accessibility while presenting a myriad of legal and ethical challenges. This paper investigates the complex interplay between digital platforms and reproductive health information, navigating the intricate landscape of laws and regulations such as HIPAA and GDPR, and examining the challenges in enforcing these regulations across diverse platforms and jurisdictions. Ethical considerations loom large, necessitating a delicate balance between accuracy, cultural sensitivity, and inclusivity to meet the diverse needs and perspectives of users while safeguarding their privacy and confidentiality. Through case studies and examples, real-world challenges faced by digital platforms, including misinformation, censorship dilemmas, and data privacy breaches, are explored, highlighting the importance of clear policies, robust data protection measures, and stakeholder collaboration. Looking ahead, the paper identifies future trends and recommendations for enhancing the responsible dissemination of reproductive health information online, advocating for comprehensive sexual and reproductive health education and ongoing research to address emerging challenges effectively. In conclusion, concerted efforts from stakeholders are called for to prioritize the responsible provision of reproductive health information online, ensuring that legal, ethical, and public health considerations are balanced effectively in the digital realm.

Pharmaceutical sector: Packaging requirement in serialization

Access to healthcare is a fundamental right for every individual, and it is the responsibility of governments to ensure the provision of quality healthcare services and infrastructure to their citizens. Over the past few decades, both governments and the healthcare industry have been grappling with the challenge of minimizing the adverse effects on public health caused by counterfeit medicines. According to the World Health Organization, approximately four out of ten medicines in developing and impoverished countries are either fake or potentially adulterated, leading to significant economic losses and reducing funds allocated for research and development (R&D) by organizations. Preventing counterfeit medicines from entering the supply chain poses a significant challenge for governments and regulatory authorities. Consequently, there is a concerted effort to establish stringent guidelines aimed at thwarting criminals and counterfeiters from infiltrating markets with fake medications. The healthcare industry recognizes the necessity for strict regulations and secure technologies to ensure the provision of safe and authentic drugs to patients. In the United States, the FDA has outlined a ten-year roadmap to implement drug traceability measures. Additionally, the Healthcare Distribution Alliance (HDA) has mandated the inclusion of multiple barcodes and human-readable data in product packaging hierarchy. Furthermore, the FDA is actively engaged in a pilot project with leading pharmaceutical manufacturers and wholesalers to explore the utilization of blockchain technology within an interoperable digital network for securing the transfer of digital traceability data among authorized trading partners.

Innovations in public health surveillance: An overview of novel use of data and analytic methods.

Innovative data sources and methods for public health surveillance (PHS) have evolved rapidly over the past 10 years, suggesting the need for a closer look at the scientific maturity, feasibility, and utility of use in real-world situations. This article provides an overview of recent innovations in PHS, including data from social media, internet search engines, the Internet of Things (IoT), wastewater surveillance, participatory surveillance, artificial intelligence (AI), and nowcasting. Examples identified suggest that novel data sources and analytic methods have the potential to strengthen PHS by improving disease estimates, promoting early warning for disease outbreaks, and generating additional and/or more timely information for public health action. For example, wastewater surveillance has re-emerged as a practical tool for early detection of the coronavirus disease 2019 (COVID-19) and other pathogens, and AI is increasingly used to process large amounts of digital data. Challenges to implementing novel methods include lack of scientific maturity, limited examples of implementation in real-world public health settings, privacy and security risks, and health equity implications. Improving data governance, developing clear policies for the use of AI technologies, and public health workforce development are important next steps towards advancing the use of innovation in PHS.

Scope and caveats: Artificial intelligence in gastroenterology

The use of Artificial intelligence (AI) has evolved from its mid-20th century origins to playing a pivotal tool in modern medicine. It leverages digital data and computational hardware for diverse applications, including diagnosis, prognosis, and treatment responses in gastrointestinal and hepatic conditions. AI has had an impact in diagnostic techniques, particularly endoscopy, ultrasound, and histopathology. AI encompasses machine learning, natural language processing, and robotics, with machine learning being central. This involves sophisticated algorithms capable of managing complex datasets, far surpassing traditional statistical methods. These algorithms, both supervised and unsupervised, are integral for interpreting large datasets. In liver diseases, AI's non-invasive diagnostic applications, particularly in non-alcoholic fatty liver disease, and its role in characterizing hepatic lesions is promising. AI aids in distinguishing between normal and cirrhotic livers and improves the accuracy of lesion characterization and prognostication of hepatocellular carcinoma. AI enhances lesion identification during endoscopy, showing potential in the diagnosis and management of early-stage esophageal carcinoma. In peptic ulcer disease, AI technologies influence patient management strategies. AI is useful in colonoscopy, particularly in detecting smaller colonic polyps. However, its applicability in non-academic settings requires further validation. Addressing these issues is vital for harnessing the potential of AI. In conclusion, while AI offers transformative possibilities in gastroenterology, careful integration and balancing of technical possibilities with ethical and practical application, is essential for optimal use.

Разработка и исследование подхода к обработке сигналов цифровых измерительных трансформаторов тока и напряжения

During the development of secondary systems of stations and substations, including relay protection, automation, control, electricity metering, etc., qualitative changes have recently occurred due to the introduction of digital technologies. The introduction of digital technologies is supported by innovative development programs such as “Digital Transformation 2030”. The introduction of digital technologies is associated with the use of digital data transmission channels according to uniform standards. Projects of “digital substations” are being actively implemented with the transmission of digital information at all levels, including the primary equipment, the transmission of information both from current and voltage measuring transformers, and from switching devices. Currently, digital instrument transformers are actively being developed and implemented, in which information about measured currents and voltages is presented in digital code. New approaches to measure primary currents and voltages, and transmit information pose new challenges, and open prospects for improving signal processing methods used for relay protection purposes. Thus, the relevant aim of the study is the development and research of approaches to digital processing of signals of digital instrument current and voltage transformers. To solve the problems posed within the framework of this study, analytical and numerical solution methods have been used. Numerical methods are applied using software products such as MathCAD and the Python programming language to generate signals and display them on graphs. To substantiate the reliability, verification of the results obtained by different methods is carried out. The authors have formulated an approach to digital processing of signals obtained using current sensors of a digital instrument transformer. The authors have studied the approach to determine synchronized current vector values by processing information received from primary current converters. The characteristics of the algorithm for obtaining a vector have been analyzed and the dependences of errors on the influence of distorting factors have been plotted. The proposed approach is a development of the existing approach to obtain a vector by getting information from current sensors operating on different physical principles. The results show the advantages of the proposed method. The proposed digital signal processing algorithm is more technically expensive since it contains at least two metrologically calibrated measuring channels instead of one. However, all other things being equal, it allows to increase the accuracy and speed of the digital processing algorithms taken as a prototype. The results obtained can be used to formulate algorithms for measuring relay protection devices.

Digital Subtraction Angiogram: Beginners Perspective

Digital Subtraction Angiography (DSA) is a modern technique which integrates digital data collection and computer processing to produce a medical image. X-ray signals are detected electronically rather than on film and then converted to digital form to be processed by a computer before being displayed. DSA is an integral investigation in the management of patients with neurovascular diseases. It is basically used for diagnosis, but in many instances, it may be therapeutic in the same sitting. Indications for diagnostic DSA include both extracranial and intracranial vascular diseases. A sound understanding of the principles of appropriate periprocedural care and anatomy, catheter technique and basic disease pathology are vital to use DSA as a diagnostic as well as therapeutic purposes. Bang. J Neurosurgery 2023; 12(2): 102-113

Building and evaluating the performance of a digital data transmission system over a noisy Rician fading channel using a 16-QAM modulator, Hamming source encoding, and BCH channel encoding

In today's digital age, digital data transmission methods are becoming popular. In a wireless communication environment, interference can cause data loss and distortion. In this paper, the author builds and evaluates the performance of a data transmission system using a noisy Rician fading channel. This system combines the use of 16-QAM modulators, Hamming source coding, and BCH channel coding to improve the reliability of data transmission. The performance evaluation of the system is conducted under different interference conditions. The results show that the proposed system is capable of detecting and correcting errors in transmitted data and has good anti-interference ability.

Prevention and Mitigation of Disruptions in Medical Device Supply Chains: A Policy Perspective

New technologies and their connectivity to the internet render medical devices and their supply chains a target for worldwide criminal attacks. Disruptions to a medical device supply chain (MDSC), including cybersecurity attacks, have increased rapidly, with some sophisticated attacks compromising the availability and operation of life-critical devices. Understanding the impact of disruptions on MDSCs is difficult due to the attendant supply chain complexity. The lack of a systematic classification of disruptions to MDSCs limits the transferability of mitigation strategies. Thus, there is a need for evidence-based, actionable policy guidance for corporations and government agencies that address disruption risks to MDSCs. This paper first presents a disruption classification scheme based on historical and anticipated disruptions to MDSCs. Then, it introduces a model, dubbed the Focal-firm Supply-chain Integrated Model (FSIM), that integrates a focal-firm view of supply chains and attendant disruptions, illustrated through an MDSC example. Based on lessons learned from constructing a disruption classification and creating an FSIM map, we describe the following actionable insights: a) implementing procedures and technologies that enable early-detection of disruptions, b) accurate and timely information flows, c) continual monitoring of physical inventory levels and comparing them with digital data, d) enhanced and repeated testing at various supply chain testing sites, e) standardized metrics that measure the impact of disruptions, f) developing in advance a preparedness strategy and a planning process to allocate resources effectively, equitably, and transparently during disruptions, and g) the need for a common framework that bridges the medical device regulatory gaps across countries. These insights can guide MDSC stakeholders, including manufacturers, suppliers, service providers, healthcare providers, policymakers, and government agencies.

Comparative Performance Analysis of Time-Frequency Domain Images and Raw Signal Data for Classification of ECG Signals

ECG signals are one of the most common tools used to diagnose cardiovascular diseases. ECG signals are obtained by measuring electrical changes on the skin surface. Arrhythmias occurring in the heart are diagnosed because the expert evaluates ECG signals. This diagnosis depends on the experience of the specialist and is a subjective evaluation. With the widespread use of computer-aided diagnostic systems, evaluations dependent on the expert's experience are objectified, and support is provided to the physician for diagnosis. For computer-aided ECG classification, beats are detected from ECG signals, and arrhythmias are detected by analyzing the structure of these beats. In recent years, deep learning models have been successful in classifying ECG signals. The data to be used in the classification process is realized with the help of morphological features or images of the signal. The main objective of this study is to compare the classification performance of digital and visual heartbeat data for ECG signal classification. For this purpose, 1D-CNN and 2D-CNN architectures are used for the type of ECG signals. As inputs of the 1D-CNN model, numerical values of the heartbeat signal and hand-crafted features obtained from these numerical values were used. The inputs of the 2D-CNN model are the raw signal image, spectrogram, scalogram, Mel-spectrogram, GFCC, and CQT images, which are visual representations of the heartbeat signal. The results show that the numerical model of the ECG signal fails for classification, while the hand-crafted features provide 85.2% accuracy. The results obtained with the visual representation of the signal provided over 99% classification accuracy for all images. The highest success rate was 99.9% with the visualization of the raw signal. In line with these findings, the 2D-CNN architecture and the visual representation of the heartbeat signal were found to be the most suitable method for classifying ECG signals.

A decolonizing approach to digital 3D visualizations and 3D printed models of human skeletal remains as demonstrative evidence in court: a view from Aotearoa New Zealand

ABSTRACT This research examines the impact of creating and presenting 3D visualizations and 3D printed models of human skeletal remains in relation to the cultural and ethical values of Māori, the indigenous people of Aotearoa New Zealand. Within Aotearoa, there is a complex history where Māori have not been consulted regarding the introduction of new technologies and systems, which ultimately had negative consequences for Māori. Using a decolonizing research approach, interviews (N = 12) and a focus group (N = 2) were conducted to identify key concepts regarding the sovereignty of these novel forms of digital data through a Māori cultural framework. For Māori, human skeletal remains are sacred; this research explored key questions including Māori Data Sovereignty and Rights, and what the digital data from human remains represents for Māori. Particular themes were identified regarding consultation with whānau (extended family) about the usefulness of the data to justice outcomes, and the rights of whānau to access, storage and disposal of the digital data, and subsequent printed 3D models. The core discussion across all participants related to tikanga Māori (Māori protocols) regarding the creation and interpretation of data, and the need for wider consultation with forensic scientists, lawyers, judges, Police and iwi (tribes) across Aotearoa.

Facebook as an Avenue to News: A Comparison and Validation of Approaches to Identify Facebook Referrals

ABSTRACT Given that Facebook is still the most widely used social networking site in the world, its influence on democratic processes is under constant scrutiny. Academics have put a special focus on Facebook’s role in inhibiting or enhancing citizens’ news exposure. Recent studies using digital behavioral data have analyzed the prevalence and effects of “Facebook news referrals.” Using a web tracking tool that captures general browsing behavior as well as public posts seen on Facebook, this paper lays the groundwork for the field by assessing the validity of previously proposed operationalizations. We validate news referrals by investigating whether different measures actually reflect exposure to a news URL a user saw on Facebook. We furthermore assess the effects of news referrals on central outcomes in extant literature, contingent on different operationalizations. The results show that the most precise measure of news referrals are click identifiers attached to news URLs by Facebook. Different operationalizations of referrals have theoretically impactful consequences for the substantive understanding of Facebook’s role in high-choice online environments. The paper demonstrates the need for academics to constantly innovate in order to measure citizens’ online behavior in an ecologically valid manner.

A dataset for fatigue estimation during shoulder internal and external rotation movements using wearables

Wearable sensors have recently been extensively used in sports science, physical rehabilitation, and industry providing feedback on physical fatigue. Information obtained from wearable sensors can be analyzed by predictive analytics methods, such as machine learning algorithms, to determine fatigue during shoulder joint movements, which have complex biomechanics. The presented dataset aims to provide data collected via wearable sensors during a fatigue protocol involving dynamic shoulder internal rotation (IR) and external rotation (ER) movements. Thirty-four healthy subjects performed shoulder IR and ER movements with different percentages of maximal voluntary isometric contraction (MVIC) force until they reached the maximal exertion. The dataset includes demographic information, anthropometric measurements, MVIC force measurements, and digital data captured via surface electromyography, inertial measurement unit, and photoplethysmography, as well as self-reported assessments using the Borg rating scale of perceived exertion and the Karolinska sleepiness scale. This comprehensive dataset provides valuable insights into physical fatigue assessment, allowing the development of fatigue detection/prediction algorithms and the study of human biomechanical characteristics during shoulder movements within a fatigue protocol.

Crossmixed convolutional neural network for digital speech recognition.

Digital speech recognition is a challenging problem that requires the ability to learn complex signal characteristics such as frequency, pitch, intensity, timbre, and melody, which traditional methods often face issues in recognizing. This article introduces three solutions based on convolutional neural networks (CNN) to solve the problem: 1D-CNN is designed to learn directly from digital data; 2DS-CNN and 2DM-CNN have a more complex architecture, transferring raw waveform into transformed images using Fourier transform to learn essential features. Experimental results on four large data sets, containing 30,000 samples for each, show that the three proposed models achieve superior performance compared to well-known models such as GoogLeNet and AlexNet, with the best accuracy of 95.87%, 99.65%, and 99.76%, respectively. With 5-10% higher performance than other models, the proposed solution has demonstrated the ability to effectively learn features, improve recognition accuracy and speed, and open up the potential for broad applications in virtual assistants, medical recording, and voice commands.

Airborne Platform Three-Dimensional Positioning Method Based on Interferometric Synthetic Aperture Radar Interferogram Matching

As the demand for precise navigation of aircraft increases in modern society, researching high-precision, high-autonomy navigation systems is both theoretically valuable and practically significant. Because the inertial navigation system (INS) has systematic and random errors, its output information diverges. Therefore, it is necessary to combine them with other navigation systems for real-time compensation and correction of these errors. The SAR matching positioning and navigation system uses synthetic aperture radar (SAR) image matching for platform positioning and compensates for the drift caused by errors in the inertial measurement unit (IMU). Images obtained by SAR are matched with digital landmark data, and the platform’s position is calculated based on the SAR imaging geometry. However, SAR matching positioning faces challenges due to seasonal variations in SAR images, the need for typical landmarks for matching, and the lack of elevation information in two-dimensional SAR image matching. This paper proposes an airborne platform positioning method based on interferometric SAR (InSAR) interferogram matching. InSAR interferograms contain terrain elevation information, are less affected by seasonal changes, and provide higher positioning accuracy and robustness. By matching real-time InSAR-processed interferograms with simulated interferograms using a digital elevation model (DEM), three-dimensional position information about the matching points has been obtained. Subsequently, a three-dimensional positioning model for the platform has bene established using the unit line-of-sight vector decomposition method. In actual flight experiments using an FMCW Ku-band Interferometric SAR system, the proposed platform positioning framework demonstrated its ability to achieve precise positioning in the absence of signals from the global navigation satellite system (GNSS).