Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital transformation: Toward new research themes and collaborations yet to be explored

This study aimed at providing an overview of research themes and collaborations in the digital transformation scholarship. The methods of co-word analysis, co-author analysis, and network analysis were employed to network-analyze the keywords, countries, and institutions of 2820 research articles published on the digital transformation topic and indexed by the Web of Science database. Our main results indicated that researchers have mostly focused on three aspects of the digital transformation phenomenon including Technological and Industrial View, Organizational and Managerial View, and Global and Social View. Also, it was realized that Technology, Sustainability, Big Data, Information and Communications Technology, Innovation, Industry 4.0, Artificial Intelligence, Business Model, Social Media, and Digitization are the most recurring themes in this field of research. Besides, Small and Medium-Sized Enterprises, Blockchain, Machine Learning, Knowledge Management, and Sustainable Development were respectively identified as the five hottest issues in the digital transformation scholarship. The contribution of our study highlights that European countries and specially the institutions of northern Europe have had better performance in the research collaborations in digital transformation.
The Role of Spontaneous Digital Play during Young Patients’ Cancer Treatment

In Europe alone, more than 120,000 children and 150,000 adolescents are diagnosed with cancer every year. Thanks to treatment innovations the survival rates of young patients’ cancer increase substantially every year, but improved prognoses are in many cases linked to longer treatments. To cope with the social, emotional, and developmental challenges associated with cancer, play and playful activities are widely recognized as fundamental for adolescents and children. This article presents the results of an exploratory study conducted to better understand the role of free digital play for young cancer patients (0–17 years). Methodology: 15 semi-structured qualitative interviews were conducted, divided into two groups. The first group consisted of four experts and the second group consisted of 11 parents of young cancer patients. Conversations with the participants revolved around children’s use of digital platforms during cancer treatment, emphasizing their motivations to play digitally, methods and patterns of use, perceived benefits, and impact on children’s social interactions, identity development, and personal narrative. The results show that digital play becomes a valuable activity for young cancer patients during three phases of the treatment: (1) inpatient care; (2) outpatient care; and (3) remission. We also identified three types of digital play patients engage with: (1) playing with digital games; (2) playfully interacting with digital technologies; and (3) the overlap between digital and non-digital play. Finally, the results also show that digital play has an impact on at least three aspects of young patients’ lives: (1) social interactions; (2) identity development; and (3) communication.
Constrained Markov Decision Process Modeling for Optimal Sensing of Cardiac Events in Mobile Health

Rapid advances in the smartphone, wearable sensing, and wireless communication provide an unprecedented opportunity to develop mobile systems for smart health management. Mobile cardiac sensing collects health-related data from individuals and enables the extraction of information pertinent to cardiac conditions. However, wireless sensors in ambulatory care settings operate on batteries. All-time sensing and monitoring will result in fast depletion of the battery in the mobile system. There is an urgent need to develop optimal sensing schemes that will reduce energy consumption while satisfying the requirements in the detection of cardiac events. In this article, we develop a constrained Markov decision process (CMDP) framework to optimize mobile electrocardiography (ECG) sensing under the constraint of the energy budget. We first characterize the cardiac states from ECG signals using the heterogeneous recurrence analysis. Second, we model the stochastic dynamics in cardiac processes as a continuous-time Markov chain (CTMC). Third, we optimize the ECG sensing through a CMDP framework under the constraint of energy budget. Finally, we validate and evaluate the performance of our CMDP policy in both simulation and real-world case studies. Experimental results demonstrate that the proposed CMDP policy significantly outperforms the traditional uniform and mean-time-to-event (MTTE) policies. Specifically, the error of state estimation is reduced by 34.0% in the real-world case study for energy-constrained sensing of cardiac events. Note to Practitioners—This article is motivated by the markedly increasing applications of mobile health (mHealth) in cardiac care. MHealth systems enable the real-time monitoring, tracking, and transmitting of heart-health information but suffer from the problem with limited battery life. This article presents a novel energy-efficient framework for mobile ECG sensing by integrating ECG signal analysis with sensing-policy optimization under the constraint of the energy budget. Experimental results demonstrate the effectiveness of the proposed framework to realize energy-efficient mobile sensing for cardiac events detection.
Seeking opportunities from crisis? China’s governance responses to the COVID-19 pandemic

The COVID-19 pandemic has imposed a challenge to state capacities on all countries of the world and a genuine test of their abilities of opportunity management. In comparison, China has managed to promptly get the pandemic under effective control and firmly enhanced domestic support for the government. This article argues that China’s successful opportunity management was firmly shaped by its institutional settings, governing structures, and actor strategies. While the noncompetitive regime, unitary government, performance legitimacy, and high citizen trust afforded strong political commitment, China’s crisis management experiences and capacities facilitated quick and effective coordination. Further, top leaders made use of the crisis to demonstrate accountable leadership and push forward a grand reform agenda. The nature and functioning of these pro-success factors are inherently rooted in the unique Chinese context. Points for practitioners This study shows a successful story of opportunity management in crises in the Chinese context under the COVID-19 pandemic scenario. Political leaders and public managers should enable systematic and prompt governance responses to such major challenges by building up a broad political consensus and coordinating evidence-based emergency responses. The study shows that clear accountability in crises is a major factor determining the capability of a system to take decisive actions and should be seriously reconstructed by countries struck by the pandemic.
Coupling Impact Between a Radial Antenna and Guided Modes on Twisted-Pair Systems Operating in Millimeter-Wave

—Recent research points out that transmission at terabits per second (Tbps) is feasible over copper cables if they were used as millimeter waveguides. The challenge is how to efﬁciently couple signals to the higher-order modes of twisted-pairs. This paper investigates the effectiveness of radial antennas on the coupling of near-terahertz signals to twisted-pairs. For that, the scattering parameter of the proposed antenna and the intensity of the electric ﬁeld around the pairs are evaluated from numerical simulations. We also present the attenuation coefﬁcients of four guided modes in a twisted-pair with typical constructive parameters and evaluate aggregate data rate results through Shannon’s capacity. The results indicate the coupling efﬁciency may reach up to 71.62%, yielding an aggregate data rate over copper cables up to 0.17 Tbps at 10m.
High performance anti-corrosive epoxy–titania hybrid nanocomposite coatings

Mechanical stability, surface hydrophobicity, and thermal stability of anticorrosive polymeric coatings play a significant role in technological and industrial fields. In view of this, the present study demonstrates the design of transparent organic–inorganic hybrid nanocomposite coatings with improved mechanical strength and barrier properties. Herein, a suspension was formulated containing in situ grown titania (–O–Ti–O–) from titanium isopropoxide (TIP), diglycidyl ethers of bisphenol-A (DGEBA), and polyamide (PA). TIP simultaneously acts as a condensation agent, bridging agent (–O–Ti–O), as well as an inorganic surface modifier to epoxy resin and increases the water contact angle due to topographical improvement via optimum cross-linked surface density. The surface, morphological, thermal, and electrochemical techniques highlight broader application areas for these hybrid polymeric materials. The lowest Icorr value of 2.5211 × 10−10 for DGE–TiO2–PA hybrid coatings compared to those of DGE–PA (2.1029 × 10−7), CS (8.9131 × 10−5), and other reported coating systems in saline (3.5 and 5 weight% NaCl) environments projects DGE–TiO2–PA as a potential anticorrosive coating material.
Biochemical components and dry matter of lemon and mandarin hybrids under salt stress

) applied in five citrus rootstock genotypes (1. TSKC x CTARG - 019; 2. LRF; 3. TSKC x (LCR x TR) - 040; 4. LCRSTC and 5. LVK), with three replicates and four plants per plot. At 90 days after sowing, saline treatments started to be applied and continued until 120 days after sowing, the moment in which the plants were collected for evaluation of biochemical characteristics and phytomass accumulation. The increase in water salinity negatively affected the biochemical components and dry matter accumulation of citrus genotypes. The genotypes TSKC x (LCR x TR) - 040, LCRSTC and LVK were the least affected by salt stress, standing out as the materials most tolerant to salinity.
Evaluation of Neurosensory Deficits Following the Use of Computer Assisted Surgical Guide for Inferior Alveolar Nerve Bypass versus Inferior Alveolar Nerve Lateralization (Randomized Controlled Trial)

Objective : To evaluate the neurosensory function of inferior alveolar nerve following the placement of animplant with Bypassing versus lateralization method. Methods : Twenty six partially edentulous patientswere selected and equally grouped into 2 groups. In 1st group: implants placed by inferior alveolar nervelateralization with Piezosurgery, while in 2nd group implants placed by computer guided surgical stentto bypass the inferior alveolar nerve. Then the neurosensory deficits are evaluated by subjectively andobjectively tests. Assessment occurred at period intervals 2 ,8 & 24 weeks postoperatively. Results: therewas statistically significant difference between the IANL & IANB especially at the early post-operativefollow up, but with time there was no statistically significant difference. Conclusion: The inferior alveolarnerve lateralization technique has a higher initial rate of sensory dysfunction than the nerve bypassingtechnique with the aid of computer guided stent.
Machine Learning Classifier Approach with Gaussian Process, Ensemble boosted Trees, SVM, and Linear Regression for 5G Signal Coverage Mapping

This article offers a thorough analysis of the machine learning classifiers approaches for the collected Received Signal Strength Indicator (RSSI) samples which can be applied in predicting propagation loss, used for network planning to achieve maximum coverage. We estimated the RMSE of a machine learning classifier on multivariate RSSI data collected from the cluster of 6 Base Transceiver Stations (BTS) across a hilly terrain of Uttarakhand-India. Variable attributes comprise topology, environment, and forest canopy. Four machine learning classifiers have been investigated to identify the classifier with the least RMSE: Gaussian Process, Ensemble Boosted Tree, SVM, and Linear Regression. Gaussian Process showed the lowest RMSE, R-Squared, MSE, and MAE of 1.96, 0.98, 3.8774, and 1.3202 respectively as compared to other classifiers.
How to Improve the Performance of Electrochemical Sensors via Minimization of Electrode Passivation

It follows from critical evaluation of possibilities and limitations of modern voltammetric/amperometric methods that one of the biggest obstacles in their practical applications in real sample analysis is connected with electrode passivation/fouling by electrode reaction products and/or matrix components. This review summarizes possibilities how to minimise these problems in the field of detection of small organic molecules and critically compares their potential and acceptability in practical laboratories. Attention is focused on simple and fast electrode surface renewal, the use of disposable electrodes just for one and/or few measurements, surface modification minimising electrode fouling, measuring in flowing systems, application of rotating disc electrode, the use of novel separation methods preventing access of passivating particles to electrode surface and the novel electrode materials more resistant toward passivation. An attempt is made to predict further development in this field and to stress the need for more systematic and less random research resulting in new measuring protocols less amenable to complications connected with electrode passivation.
Influence of Oxidative Stress Markers on Coronary No-reflow After Primary Percutaneous Coronary Intervention for Patients with Acute Myocardial Infarction

Background: ST-elevation myocardial infarction (STEMI) is one of the leading causes of mortality and morbidity worldwide. However, survival after acute STEMI has considerably improved due to increasing symptom recognition, accurate diagnosis and effective timely reperfusion. This study aimed to investigate the relation between the level of oxidative stress markers and coronary no-reflow after primary percutaneous coronary intervention for patients with acute myocardial infarction. Patients & Methods: This prospective cohort study included 90 patients admitted with acute STEMI at cardiovascular medicine department Naser Institute hospital, during the period from June 2018 till 12 months. Patients were divided into 2 groups according to the post primary PCI thrombolysis in myocardial infarction (TIMI) flow score into: Group I: 45 patients with no-reflow phenomenon. Group II: 45 patients with TIMI flow ≥ 2 after primary PCI. They all underwent primary PCI within 24 hours of presentation. Results: This study showed increased concentrations of Malondialdehide (MDA) in the circulation of patients with no-reflow indicating increased lipid peroxidation which could be attributed to a deficiency of antioxidant defense mechanism. In group I, pt with coronary no-reflow MDA level ranged from 2.8-4.5 nmol/mL with mean 3.9±1.5 nmol/mL, while in group II control group, MDA level ranged from 1.1 – 2.1 nmol/mL with mean 1.55±0.4 nmol/mL, there was statistically significant difference between the two groups (P value<0.004). Conclusions: no-reflow phenomenon after primary PCI can be predicted using the oxidative stress markers.
Bridging the Gap Between Voltage Over-Scaling and Joint Hardware Accelerator-Algorithm Closed-Loop

Voltage over-scaling (VOS) optimizes energy while causing timing errors due to an unsustainable clock frequency. Many algorithms, such as in multimedia and machine learning applications, are capable of tolerating such errors. VOS has never been investigated in hardware accelerators running closed-loop algorithms. As the errors impact most decisions and actions in the subsequent steps, closed-loops dynamically change the execution flow. Timing errors should be evaluated by an accurate gate-level simulation, but a large gap still remains: how these timing errors propagate from the underlying hardware all the way up to the entire algorithm run, where they just may degrade the performance and quality of service of the application at stake? This paper tackles this issue showing a framework for VOS investigation, embracing any kind of application. Our framework simulates the VOS-induced timing errors at gate-level, dynamically linking the hardware result with the algorithm and vice versa during the evolution of the runtime of the application. The state-of-the-art VOS literature for video encoding application fails to assess the ultimate impacts of VOS-induced timing errors, as current works open the encoding loops. Unlike those, our work investigates the ultimate impact of a hardware accelerator dynamically carrying through to the video encoder all VOS-induced timing errors and preserving the full compliance to the standard. We employ a parallel sum of absolute differences (SAD) hardware accelerator as a case study. We assess the performance of the overall encoder under varying timing guardbands. Next, it is demonstrated that, under VOS, the ultimate impact in compression efficiency is related to the video’s motion intensity. Additionally, the advantages of timing guardband controlled reduction are clearly quantified in our results by virtue of the framework. Reducing at maximum 9.5% the clock frequency, energy savings (up to 16.5% in energy/operation) are achieved in SAD for video compression.
Application of machine learning techniques in railway demand forecasting

Demand forecasting lies at the heart of any revenue management system. It aims to estimate the quantity of a product or service that will be purchased in the future. In this paper, we perform railway demand forecasting for a major European railroad company by taking various contributing parameters into account. To have multipurpose results, the current problem is explored in two different aggregation levels. At the high level, the problem is defined as prediction of the total number of bookings for all trains departing on a specific departure date and within a certain time range. Moreover, in a more disaggregated level, the prediction models aim to compute the total number of bookings within each booking period for all trains leaving in a specific time range of a certain departure date. Using state-of-the-art machine learning methods and various heuristic feature construction techniques, remarkable results with high forecast accuracy and reasonable computational complexity are achieved in both aggregation levels. This paper aims to contribute to the application of ML techniques in RM by introducing new heuristic feature engineering techniques, exploring the importance of accurate clustering, and implementing state-of-the-art machine learning methods in the context of railway industry.
The Social Ecology of COVID-19 Cases and Deaths in New York City: The Role of Walkability, Wealth, and Race

The present research examined the zip code level (177 zip codes) prevalence of and deaths associated with COVID-19 in New York City as of May 22, 2020. Walkable zip codes had consistently lower prevalence of (r = −.49) and deaths (r = −.15) associated with COVID-19. The mediation analysis showed that the degree of reduction in actual geographical mobility during the lockdown (measured by smartphone GIS data) accounted for geographical variations in the number of confirmed cases and deaths. Residents in wealthy zip codes and walkable zip codes were able to limit geographical mobility, whereas residents in poor zip codes and Black and Hispanic dominant zip codes were not. Finally, the spatial lag regression analysis showed that walkability was a robust predictor of zip code–level prevalence of and deaths associated with COVID-19. Overall, walkability seems to have provided protection against the spread of COVID-19.
Identifying a Minimum Sequence of High-Level Changes Between Workflows

Adaptive workflow management systems allow workflows to be changed in both the modeling and runtime stages, resulting in many workflow variants. Identifying a minimum sequence of high-level changes between two workflows represents a fundamental yet critical issue. The state-of-the-art approach utilizes digital logic to seek the optimal solution; however, this approach may face difficulties when advanced workflow patterns (e.g., loops) are involved, and it does not scale well. To address this problem, we first propose a naive approach that applies all valid changes to one workflow until the other workflow is found. Then, the approach is optimized from two aspects. First, we present advanced heuristics that significantly reduce the search space without pruning the optimal solution. Second, we employ the A<inline-formula><tex-math notation="LaTeX">$^\ast$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mo>*</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="song-ieq1-3054036.gif"/></alternatives></inline-formula> search algorithm to direct the search procedure. Because the heuristic function used in the A<inline-formula><tex-math notation="LaTeX">$^\ast$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mo>*</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="song-ieq2-3054036.gif"/></alternatives></inline-formula> algorithm is problem specific, we devise a consistent heuristic function to approximate the edit distance between two workflows, thereby accelerating the search. We implement our approach in a prototype tool and conduct extensive experiments on two data sets to evaluate its effectiveness and efficiency. The experimental results demonstrate that our approach outperforms the state of the art in terms of both application scope and scalability.
On Properties of Energetic Coronal Mass - Flare Events

ABSTRACT In the present paper, the properties of more energetic coronal mass ejections (ECMEs) (kinetic energy ≥1E + 30 ergs) observed between the years from 1996 to 2006 were studied. Through this period and under certain time-based and spatial conditions 253 Flare-ECMEs were selected . These 253 energetic CME-Flare events separated into two groups according to their time of detection, Before Flare-ECME events and After Flare–ECME event. This study indicates that the mean CME width for both before and after Flare-ECMEs are 90 and 100 degrees, respectively. Also, it was found that the before Flare-ECMEs are in average faster than the after Flare-ECMEs. The results show also both before and after Flare-ECMEs are wider than other CMEs. Besides it was found that the before Flare-ECMEs are in average faster than the after Flare-ECMEs, while the after Flare-ECMEs are in average more massive than before Flare – ECMEs. Finally it was found that both after and before Flare-ECMEs has negative mean acceleration.
Homebased Standing Core Exercise Training Improves Femoral Blood Flow but not Arterial Stiffness in Middle-Aged to Older Adults

1Department of Athletics, National Taiwan University, No 1, Sec 4, Roosevelt Rd., Taipei 10617, Taiwan 2Department of Athletic Sport, National Chung Cheng University, Chiayi, Taiwan 3Department of Medicine, National Yang-Ming University School of Medicine, Taipei, Taiwan 4Center for Evidence-based Medicine, Taipei Veterans General Hospital, Taipei, Taiwan 5Human Informatics Research Institute, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan