Miracle-dz/UniFAR-FTUs huggingface dataset:
Radiation-induced defect formation and reactivity of model TiO2 capping layers with MMA: a comparison with Ru

Our goal is to provide insights into surface processes that affect the reflectivity and lifetimes of TiO2 and Ru-capped multilayer mirrors used in extreme ultraviolet (EUV) lithography. Several surface-sensitive ultrahigh-vacuum techniques are used to characterize thermally-induced and electron-induced surface reactions of methyl methacrylate (MMA), a model for hydrocarbons found in EUV lithography vacuum chambers; low-energy electron beams are used to mimic excitations initiated by EUV radiation. Carbon film growth is measured on TiO2 surfaces during electron bombardment (at 20 eV and 100 eV) in MMA vapor; C growth rates are compared on Ru surfaces. The initial rates on the clean surfaces are very different: a C film grows more rapidly on TiO2 than on Ru. However, the limiting growth rates are the same for C thicknesses greater than ~1 to 1.5 nm, when MMA interacts with a C film. Irradiation of the C films in O2 gas, or in MMA + O2, has a mitigating effect on TiO2 surfaces.
ASSESSMENT OF ANTIOXIDANT ACTIVITIES, PHENOL AND FLAVONOID CONTENTS OF DIFFERENT EXTRACTS OF LEAVES, BARK AND ROOT FROM THE Abutilon indicum (L.) SWEET

Objective: This study evaluated the antioxidant properties of petroleum ether, chloroform, ethyl acetate and methanol extracts of Abutilon indicum (L). Methods: The different extracts of A. indicum leaves, bark and roots were antioxidant potential by using 1,1-diphenyl-2-picryl-hydrazyl (DPPH), 2, 2-Azino-bis-3-ethyl benzthiazoline-6-sulphonic acid (ABTS •+ ) , Hydrogen peroxide scavenging, Superoxide anions scavenging, Hydroxyl radical scavenging, Ferric reducing antioxidant power , Total antioxidant activity (Phosphomolybdic acid) and total phenol and flavonoid contents. Results: The highest total phenol and flavonoid contents in leaves extracts of A. indicum . The total phenol (3.08 ± 0.06) mg/ml and flavonoid (7.16 ± 0.15) mg/ml were found to be higher in ethyl acetate extract of A. indicum and free radical scavenging activities IC 50 values were calculated and compare to standard for L- ascorbic acid, (BHT) butylated hydroxytoluene and gallic acid Conclusion: The results of the study revealed that the ethyl acetate extract of A. indicum leaves can be used for the biological characterization and importance of the compounds identified and creates a platform to screen many bioactive compounds to treat many diseases. Keywords: Abutilon indicum, Free radical scavenging activity, 1,1-diphenyl-2-picryl-hydrazyl, 2-azino-bis-3-ethyl benzthiazoline-6-sulfonic acid, Ferric reducing antioxidant power.
Numerical Evaluation on Parametric Choices Influencing Segmentation Results in Radiology Images—A Multi-Dataset Study

Medical image segmentation has gained greater attention over the past decade, especially in the field of image-guided surgery. Here, robust, accurate and fast segmentation tools are important for planning and navigation. In this work, we explore the Convolutional Neural Network (CNN) based approaches for multi-dataset segmentation from CT examinations. We hypothesize that selection of certain parameters in the network architecture design critically influence the segmentation results. We have employed two different CNN architectures, 3D-UNet and VGG-16, given that both networks are well accepted in the medical domain for segmentation tasks. In order to understand the efficiency of different parameter choices, we have adopted two different approaches. The first one combines different weight initialization schemes with different activation functions, whereas the second approach combines different weight initialization methods with a set of loss functions and optimizers. For evaluation, the 3D-UNet was trained with the Medical Segmentation Decathlon dataset and VGG-16 using LiTS data. The quality assessment done using eight quantitative metrics enhances the probability of using our proposed strategies for enhancing the segmentation results. Following a systematic approach in the evaluation of the results, we propose a few strategies that can be adopted for obtaining good segmentation results. Both of the architectures used in this work were selected on the basis of general acceptance in segmentation tasks for medical images based on their promising results compared to other state-of-the art networks. The highest Dice score obtained in 3D-UNet for the liver, pancreas and cardiac data was 0.897, 0.691 and 0.892. In the case of VGG-16, it was solely developed to work with liver data and delivered a Dice score of 0.921. From all the experiments conducted, we observed that two of the combinations with Xavier weight initialization (also known as Glorot), Adam optimiser, Cross Entropy loss (GloCEAdam) and LeCun weight initialization, cross entropy loss and Adam optimiser LecCEAdam worked best for most of the metrics in a 3D-UNet setting, while Xavier together with cross entropy loss and Tanh activation function (GloCEtanh) worked best for the VGG-16 network. Here, the parameter combinations are proposed on the basis of their contributions in obtaining optimal outcomes in segmentation evaluations. Moreover, we discuss that the preliminary evaluation results show that these parameters could later on be used for gaining more insights into model convergence and optimal solutions.The results from the quality assessment metrics and the statistical analysis validate our conclusions and we propose that the presented work can be used as a guide in choosing parameters for the best possible segmentation results for future works.
The Role of Indonesian School Principals’ Leading Distance Learning During Covid-19 Pandemic

—The focus of this study is to investigate how the principal leading distance learning during this pandemic. The aims are to get a comprehensive result of how the principal aware of the concept of distance learning and what challenges they faced during the process. A qualitative research approach was employed in this study to get a deep understanding and explanations from the participants. The participants are twenty-five school principals from different primary schools under the Ministry of Education and Ministry of Religious Affairs in Karawang district, Indonesia. It was revealed that school principals in this study were having only a limited idea about the concept of the distance learning model. They were aware of how distance learning works, even when they have only limited knowledge and skill. Principals mentioned about skills and knowledge that principals needed most in leading during the distance learning, such as providing advice on designing interesting activities for teachers, skills in operating educational applications, implementing multiple distance learning models/strategies, accessing information from the various online sources, doing the academic supervision, providing training related to online learning for teachers, and managing the well-being of teachers and students. School principals in this study responded that they faced multiple challenges during distance learning implementation. The four most common ones were their concern around the online teaching model, distance learning infrastructures, teachers’ basic qualifications explicitly oriented to technology, and adjusting the offline curriculum materials into an online model.
Decoding the Street-Based Spatiality of Urban Gyms: Implications for Healthy City Planning

Many cities face health issues that result from ineffective urban planning strategies. The chances of doing exercises in sportive venues implicate public health and citizen quality of life. With the advent of the geo-big data era, it is crucial to explore the spatial pattern of sports facilities to reflect urban health issues. This study aims to decode the street-based spatiality of gyms (one prevailing type of sportive venues) from a comprehensive perspective by both geometric methods (i.e., segment streets) and topological analytics in the context of complexity science (i.e., complex network derived from the topology of natural streets). We found that: (1) gyms are spatially clustered and distributed unevenly; (2) community-to-gym walkability fits the power-law with a heavy-tailed distribution at the 10-min and 20-min temporal scales; (3) the model for the street connectivity and the multi-distance reachability of gyms is with high polynomial fitting goodness. This article is conducive to strategies-making of healthy city planning and the further optimization of urban spatial structure.
Thin MoO3 films for sensor applications

This paper reports on the preparation, surface, structural and sorption characterization of as-deposited MoO 3 thin films elaborated by r.f. magnetron sputtering. Characterization of the films was carried out by different techniques. The surface analysis of MoO 3 was performed using X-ray Photoemission Spectroscopy and Scanning Electron Microscopy. X-ray Diffraction was used to study the structure. The refractive index and thickness were determined from ellipsometric measurements. The layers were studied with the objective of developing an NH 3 sensor. The MoO 3 sensitivity to NH 3 was estimated using a Quartz Crystal Microbalance (QCM) with thin MoO 3 films sputtered on it. Deposition took place at a deposition rate of 20 nm/min. The sorption property of a QCM with a thin MoO 3 layer was defined as of function of the mass-loading, resulting in a frequency change of the QCM Δf after exposure to different NH 3 concentrations. In our experiments, the NH 3 concentrations was changed in the range 50 to 1000 ppm and frequency changes from 96 to 312 Hz were registered. On the basise of the measured Δf for each concentration, the actual sorbed mass was calculated. The obtained results on the MoO 3 sorption properties can be successfully applied in the development of a NH 3 sensor.
The Influence of Social Media Marketing, Electronic Word of Mouth and Consumer Engagement to Brand Loyalty in Indonesia Grab Company

This study aims to analyze social media marketing, electronic word of mouth, and consumer attachment to brand loyalty in Grab Indonesia. The research design used is quantitative research with a causal approach that examines the effect of exogenous variables (social media marketing, electronic word of mouth, and consumer attachment) on endogenous variables (brand loyalty). The population in this study were consumers of social media followers (Instagram) grab with an infinite number, therefore sampling was carried out using purposive sampling with the consideration of respondents as followers of Instagram grab with a minimum period of following is 6 months and has given "likes" to the grab Instagram posts. The sample used in this study were consumers of online transportation companies (grab) as many as 300 respondents. This research analysis tool uses a structural equation model (SEM). The results showed that the exogenous variables (social media marketing, electronic word of mouth, and consumer attachment) positively and significantly affect endogenous variables (brand loyalty). The variable that has the highest value in influencing brand loyalty is social media marketing. Meanwhile, the variable that directly influences brand loyalty with the lowest regression coefficient is electronic word of mouth.
Generalized Correntropy Sparse Gauss–Hermite Quadrature Filter for Epidemic Tracking on Complex Networks

The dynamic estimation of epidemic spreading on networks is essential for controlling morbidity. Nonlinear Kalman filters, which are capable of estimating the hidden state of a nonlinear system, can be utilized for dynamic state estimation of epidemic spreading. Traditional nonlinear Kalman filters perform optimization using the minimum mean square error (MMSE) criterion. Since observable measurements are generally corrupted by non-Gaussian noise, maximum correntropy criterion (MCC)-based nonlinear Kalman filters have been used for improving robustness against non-Gaussian noise. In comparison with the MCC, generalized correntropy is more robust and flexible in the presence of non-Gaussian noise. In this article, epidemic spreading is described by a compartmental model, i.e., susceptible-infected-recovered-susceptible model. Based on the compartmental model, the generalized correntropy-based sparse Gauss–Hermite quadrature filter (GCSGHQF) is proposed for epidemic tracking on homogeneous networks. The GCSGHQF approximates the Gaussian-weighted integrals by utilizing the sparse Gauss–Hermite quadrature rule. In addition, the generalized correntropy is applied to enhancing robustness in the presence of non-Gaussian noise. Simulation results show that the GCSGHQF exhibits a higher filtering accuracy and improves robustness compared to traditional nonlinear Kalman filters.
Moderation in all things: Industry-university-research alliance portfolio configuration and SMEs’ innovation performance in China

ABSTRACT Industry-university-research (IUR) alliance portfolio configurations are vital for small- and medium-sized enterprises (SMEs). Our study extends the knowledge-based view and transaction cost theory on SMEs’ IUR alliance portfolio configurations and their deployment in different institutional conditions. We analyze these IUR alliance portfolio configurations in a panel data of listed SMEs in China’s manufacturing industry. Our findings indicate that both breadth and depth dimensions of these IUR alliance portfolio configurations have an inverted U-shaped association with SMEs’ innovation performance. Moreover, developed institutional conditions, characterized by high degrees of regional marketization, strengthen the positive effect of both breadth and depth, thereby, intensifying the inverted U-shaped relationship. Consequently, our evidence contributes to the literature on IUR alliances and firm innovation performance, suggesting that medium levels of IUR alliance portfolio breadth and depth in SMEs result in higher firm innovation performance, thereby, offering several useful insights for both SME managers and policymakers in emerging economies.
Information-driven Supply Chain During a Catastrophic Event: COVID-19 Pandemic

We learn painful lessons these days about the hidden weaknesses in the supply chains, while the surge reflects a new inflection point of the COVID-19 outbreak. Healthcare experts, politicians, academics, and supply chain practitioners need to collaborate to build a reform agenda and roadmap for our humanitarian supply chain. This study aims at investigating and assessing risks on the supply chain, especially for health care supplies. The theme of the article is based on the development of an information-driven model to help in the assessment of supply chain risk. An integrative review technique has been used to analyze literature to develop prepositions for the model. The main actors of the model are supply chain preparedness, supply chain risk, supply chain response, and supply chain recovery. The findings identify that timely preparation and assessment of supply chain risk can result in accurate adoption of the practices that can not only reduce supply chain risk but also enhance supply chain recovery during any catastrophic event.
The Effects of School Climate on Sixth Form Teachers’ Self-Efficacy in Malaysia

In Malaysia, sixth form is a post-secondary education that is comparable to pre-university programs such as A-level and Foundation qualifications. Enrolment in sixth forms has dwindled over the past ten years due to assumptions that the curriculum is difficult and because it is offered in regular secondary schools. Thus, the sixth form transformation program was introduced in 2015 with the purpose to rebrand sixth form education to a new setting comparable to other pre-university education, with a focus on improving the school climate and increasing teacher self-efficacy. Therefore, this study was conducted to identify the level and relationship of the school climate on teacher self-efficacy. This survey was carried out using a questionnaire instrument, involving 695 sixth form teachers currently in Selangor. The findings showed that the level of school climate and teacher self-efficacy in all sixth form centers were at a high level. There was also a strong positive relationship between the school climate and sixth form teachers’ self-efficacy. Social and academic dimensions contributed significantly to the teacher self-efficacy of 38%. Hence, further studies looking at aspects that suggestively contribute to the school climate should be given due attention to ensure that transformation planning can be implemented as intended.
Consistency of permutation tests of independence using distance covariance, HSIC and dHSIC

The Hilbert–Schmidt independence criterion (HSIC) and its d‐variable extension dHSIC are measures of (joint) dependence between random variables. While combining these statistics with a permutation test has become a popular method of testing the null hypothesis of (joint) independence, it had thus far not been proved that this results in a consistent test. In this work, we provide a simple proof that the permutation test with the test statistic HSIC or dHSIC is indeed consistent when using characteristic kernels. That is, we prove that under each alternative hypothesis, the power of these permutation tests indeed converges to 1 as the sample size converges to infinity. Since the test is consistent for each number of permutations, we further give a brief discussion of how the number of permutations relates to the power of the test and how the number of permutations may be selected in practice.
Importance of respiratory syncytial virus as a predictor of hospital length of stay in bronchiolitis

Introduction : Bronchiolitis is the leading cause of hospitalization in children. Estimate potentially preventable variables that impact the length of hospital stay are a priority to reduce the costs associated with this disease. This study aims to identify clinical variables associated with length of hospital stay of bronchiolitis in children in a tropical middle-income country Methods: We conducted a retrospective cohort study in 417 infants with bronchiolitis in tertiary centers in Colombia. All medical records of all patients admitted through the emergency department were reviewed. To identify factors independently associated we use negative binomial regression model, to estimate incidence rate ratios (IRR) and adjust for potential confounding variables Results : The median of the length of hospital stay was 3.68 days, with a range of 0.74 days to 29 days, 138 (33.17%) of patients have a hospital stay of 5 or more days. After modeling and controlling for potential confounders age <6 months, comorbidities (CHD or neurological), BPD, chest indrawing, detection of RSV, and C-reactive protein were independent predictors of LOS Conclusions : Our results show that in infants with bronchiolitis, detection of RSV, age <6 months, comorbidities (CHD or neurological), BPD, chest indrawing, and C-reactive protein were independent predictors of LOS. As a potentially modifiable risk factor, efforts to reduce the probability of RSV infection can reduce the high medical cost associates with prolonged LOS in bronchiolitis.
Diagnosis of Pneumatic Systems on Basis of Time Series and Generalized Feature for Comparison with Standards for Normal Working Condition

Faults are unwanted events in any industrial production system. Early detection and diagnosis of faults in automated systems is important in order to prevent equipment damage, loss of performance and profits. For this purpose, more and more sophisticated and complex systems for observation and monitoring of basic characteristics in automated processes are being built. Preconditions for increasing their efficiency are processing and analysis of process information is obtained through a significant number of sensors. For pneumatic systems in addition to the identification of certain faults that may affect the normal production process, it is important to consider the possibilities to improve their energy efficiency. In this regards, the work focuses on the detection of leaks. The fault detection is based on the measurement of the compressed air consumption at the inlet of the pneumatic module and synchronization with signal from the PLC to the valve, and controlled the pneumatic cylinder. The experimental study aims to develop methods for automatic detection and classification of leaks that may be used in machine learning algorithms.
Convolutional neural network based metal artifact reduction method in dental CT image

In dental CT, the presence of metal objects introduces various artifacts caused by photon starvation and beam hardening. Although several metal artifacts reduction methods have been proposed, they still have limitations in terms of reducing the metal artifacts. In this work, we proposed a method to reduce the metal artifacts with convolutional neural network (CNN). The proposed method is comprised of two steps. In STEP 1, we acquired a more accurate prior image, which is used in normalized metal artifact reduction (NMAR) technique through the CNN. The metal artifacts in output image from STEP 1 are reduced by CNN training, which provides more accurate prior images. In STEP 2, the NMAR is conducted with the acquired prior image from CNN result. To validate the proposed method, we used dental CT images containing metals and without metal to evaluate that the proposed method could significantly reduce the metal artifacts compared to the NMAR method.
China’s leadership in the hydropower sector: identifying green windows of opportunity for technological catch-up

From the sectoral systems of innovation perspective, the windows of opportunity (hereafter referred to as WoOs) for industrial latecomers to catch-up could be opened up through abrupt changes in the technological, market, and institutional dimensions. Existing literature discusses different dimensional changes in isolation. Nevertheless, for green industries, the systemic interplay of these dimensions is of key importance; yet few studies have probed into this. These limitations in the literature are largely rooted in the lack of novel methods to detect and specify these abrupt changes, especially in a quantitative way. This paper, therefore, proposes a framework combining natural language processing methods with experts’ knowledge to detect these abrupt changes—named turbulences—by using multi-source heterogeneous data, in order to better identify the co-occurrences and interactions of turbulences across the technological, market, and institutional dimensions that have a high probability to open up WoOs. We apply this framework to analyze China’s hydropower sector as a case study. The hydropower sector is considered a “green” energy sector, in which China, as this study finds, has recently gained technological leadership. By analyzing the interactions between these multiple dimensions of WoOs, we discover that institutional turbulences proactively intertwine with other turbulences, and collectively form Green WoOs for the successful catch-up of China’s hydropower sector.
A Contribution to Safe Railway Operation: Evaluating the Effect of Electromagnetic Disturbances on Balise-to-BTM Communication in Railway Control Signaling Systems

Electromagnetic (EM) disturbances and compatibility issues are the most common problems affecting communication and signaling systems. The duration of field testing to solve these railway EM interference (EMI) problems may vary between three and 12 months, with the cost of the complete process between €25,000 and €1.5 million [1]. Currently, the railway industry demands the building of strategies and tools to promote paths toward zero on-site testing [2] and reduce the duration of field tests; however, there is a lack of laboratory testing resources for these approaches.