Miracle-dz/UniFAR-FTUs huggingface dataset:
Validation of realistic Monte Carlo plasma gamma-ray source on JET discharges

A novel modelling methodology has been developed for the creation of a realistic plasma gamma-ray source for Monte Carlo transport simulations in the tokamak JET. The methodology couples the TRANSP code for plasma transport calculations with the MCNP Monte Carlo particle transport code, thus connecting plasma physics with gamma-ray transport. This paper presents the validation of the developed source methodology by comparing calculated gamma-ray spectra with measurements performed at JET. The validation focuses on gamma-ray spectra measured by the tangential gamma-ray spectrometer during two JET three ion RF scenario discharges, performed in the JET 2019 deuterium experimental campaign. For validation the calculated plasma gamma-ray spectrum was combined with the neutron induced prompt gamma-ray background, originating in the vacuum vessel, and scaled to absolute values calculating the total number of plasma gamma-ray and neutron emitting reactions. The comparison between calculated and measured gamma-ray spectra shows good agreement with the shape of the calculated gamma-ray spectra matching that of measurements for both studied discharges. Moreover, the calculated absolute values of the gamma-ray spectra were of the same order of magnitude at the position of the gamma-ray detector located at the end of a long line-of-sight in a biological shield. The comparison has validated the developed plasma gamma-ray source methodology for MCNP photon transport calculations at JET. The validation provides a basis for the developed plasma gamma-ray source to be used as a support for the development of future tokamaks such as DEMO.
School-Level Perspectives of the Ontario Student Nutrition Program

The purpose of this study was to assess the implementation of a school snack program for children in elementary schools. School-level program volunteers’ experiences were explored using semi-structured interviews. Fieldnotes were taken during on-site school visits. Quantitative data were collected through a General Information Form and Weekly Logbooks. Seven elementary schools in Southwestern Ontario were invited and agreed to participate. Interviews (n = 27) revealed that volunteers valued the program for its universality, the excitement it created, the opportunity for students to try new foods, and the social interactions that it generated. Challenges included the burden on snack volunteers to plan, procure, and prepare foods; the competition the program posed for school priorities; limited funding; and a lack of clear purpose for the program. Suggestions for improvement included providing adequate and sustained resources and an integration of the program into the curriculum. Data obtained from 15 on-site visits, 7 General Information Forms, and 59 (out of a total of 70) Weekly Logbooks confirmed the data obtained from interviews. This research provides insights into the challenges of volunteer-led school snack programs in Canada and may guide policy makers, practitioners, and researchers in the development of a universal, nationally funded school food program.
An Apprenticeship Learning Approach for Adaptive Video Streaming Based on Chunk Quality and User Preference

Video traffic has experienced an exponential increase in current years due to the growing ubiquity of mobile equipment and the constant network improvement. Most commercial players employ adaptive bitrate (ABR) algorithms to dynamically choose bitrate for each chunk based on perceived network capacity and buffer occupancy. Unluckily, even though improving the quality of chunks with dynamic scenes can achieve more QoE gain than static scenes, current ABR algorithms usually strive to maximize the average bitrate instead of perceptual quality, leading to the QoE degradation. To overcome this obstacle, we introduce a dynamic-chunk quality-aware adaptive bitrate algorithm through apprenticeship learning called DAVS (Dynamic-chunk quality Aware Video Streaming), where higher quality is selected for the dynamic chunks without reducing the quality of static chunks extravagantly. Furthermore, we take the user’s viewing preference into account to make DAVS adapt to the QoE diversity. The experimental results demonstrate that DAVS ameliorates the quality of dynamic chunks and significantly enhances the QoE compared with several representative ABR algorithms.
Computational nanotechnology

The major research objectives in molecular nanotechnology are the design, modeling, and fabrication of molecular machines and molecular devices. While the ultimate objective must clearly be economical fabrication, present capabilities preclude the manufacture of any but the most rudimentary molecular structures. The design and modeling of molecular machines is, however, quite feasible with present technology. More to the point, such modeling is a cheap and easy way to explore the truly wide range of molecular machines that are possible, allowing the rapid evaluation and elimination of obvious dead ends and the retention and more intensive analysis of more promising designs. While it can be debated exactly how long it will take to develop a broadly based molecular manufacturing capability, it is clear that the right computational support will substantially reduce the development time. With appropriate molecular CAD software, molecular modeling software (including available computational chemistry packages, e.g., molecular mechanics, semi-empirical and ab initio programs) and related tools, one can plan the development of molecular manufacturing systems on a computer.
Developing sustainable Zn-MWCNTs composite coatings using electrochemical co-deposition method: Tribological and surface wetting behavior

ABSTRACT Carbon nanotube (CNT) nanofillers are a part of carbon allotrope family materials with numerous and effective features. Multiple CNTs nested one another is a low-cost and scalable category of CNTs, known as multi-walled CNTs (MWCNTs), which are well-stablised for their excellent mechanical, electrical and thermal properties. In this present work, MWCNTs nanofiller embedded Zn matrix (Zn-MWCNTs) composite coatings were fabricated by adding the different concentrations of MWCNTs (0.5, 0.1 and 0.2 g/L) in the acidic electrolyte bath using electrochemical co-deposition route with uniform embedment. The prepared composite coatings were characterised using advanced characterisation techniques (XRD, FESEM and EDS) for their obtained surface morphology and microstructures. All Zn-MWCNTs composite coatings exhibited enhanced tribo-mechanical performance compared to pure Zn coatings. Zn-MWCNT (0.2 g/L) composite coating exhibited the highest average microhardness value of 119 HV and minimum wear rate of 12.5 × 10−3 mm3N−1m−1 among all the prepared composite coatings, which attributed to the synergistic effect of microstructural refinement and superior mechanical properties of MWCNTs. Surface wetting studies reveal that MWCNTs play an important role in increasing hydrophobicity in terms of contact angle, which increased by 1.93 times for Zn-MWCNT (0.2 g/L) composite coating. The present work may be helpful for facile fabrication of low-cost biocidal Zn-MWCNTs nanocomposite coating.
Determination of Breakdown Voltage Along the Surface of a Cylindrical Insulator

This article investigates the electric breakdown along the surface of a cylindrical dielectric, part of a coaxial transmission line. The particular geometry of the insulator is very common, as it is used at the input or output of most high-power, high-voltage (HV) generators. The streamer propagation criterion is used to calculate the flashover voltage. Studies of the dependence of the flashover voltage on the distance between the electrodes obtained in tests with dc voltage and also by applying voltage impulses with different durations and rise times are reported. The highest difference between the estimated values and the experimental data is less than 15%, which proves the applicability of the criterion for determining the surface breakdown voltage within the geometry under study. In particular, the results of these studies are used in the design of a compact 0.5-MV pulsed power system currently under development.
Improving Antibacterial Activity of a HtrA Protease Inhibitor JO146 against Helicobacter pylori: A Novel Approach Using Microfluidics-Engineered PLGA Nanoparticles

Nanoparticle drug delivery systems have emerged as a promising strategy for overcoming limitations of antimicrobial drugs such as stability, bioavailability, and insufficient exposure to the hard-to-reach bacterial drug targets. Although size is a vital colloidal feature of nanoparticles that governs biological interactions, the absence of well-defined size control technology has hampered the investigation of optimal nanoparticle size for targeting bacterial cells. Previously, we identified a lead antichlamydial compound JO146 against the high temperature requirement A (HtrA) protease, a promising antibacterial target involved in protein quality control and virulence. Here, we reveal that JO146 was active against Helicobacter pylori with a minimum bactericidal concentration of 18.8–75.2 µg/mL. Microfluidic technology using a design of experiments approach was utilized to formulate JO146-loaded poly(lactic-co-glycolic) acid nanoparticles and explore the effect of the nanoparticle size on drug delivery. JO146-loaded nanoparticles of three different sizes (90, 150, and 220 nm) were formulated with uniform particle size distribution and drug encapsulation efficiency of up to 25%. In in vitro microdilution inhibition assays, 90 nm nanoparticles improved the minimum bactericidal concentration of JO146 two-fold against H. pylori compared to the free drug alone, highlighting that controlled engineering of nanoparticle size is important in drug delivery optimization.
Automated Transformation from Competency List to Tree: Way to Competency-Based Adaptive Knowledge E-Evaluation

E-learning is rapidly gaining its application. While actively adapting student-oriented learning with the competency evaluation model, the standard of competency support in existing e-learning systems is not implemented and varies. This complicated integration of different e-learning systems or transfer from one system to another might be challenging if the student had his or her competency portfolio in list form, while another system supports tree-based competency portfolios. Therefore, in this paper, we propose a transformation model dedicated to converting the competency list to a competency tree. This solution incorporates text processing and analysis, competency ranking based on Bloom’s taxonomy, and competency topic area clustering. The case analysis illustrates the model’s capability to generate a qualitative tree from the competency list, where the average accuracy of competency assignment to appropriate parent competency is 72%, but, in some cases, it reaches just 50%.
Research strength index to identify the performance of research universities: the case of Indonesia

Purpose This paper aims to identify the strengths of the universities based on the research area which are prioritized by the Government of Indonesia in the National Medium Term Development Plan 2015–2019, in the research areas of food and agriculture; energy, new and renewable energy; health and medicine; transportation; telecommunication, information and communication; defense and security technologies and advanced materials. Design/methodology/approach The mapping of the research strength in Indonesian universities is performed by using data of the university research output in the Information System for Research and Community Service, Ministry of Research, Technology and Higher Education and categorized into seven categories, i.e. accredited national journals, international journals indexed by Scopus or others, Intellectual Property ownership, textbooks, prototypes and an appropriate technology. Based on the data obtained from Information System for Research and Community Service, there are 904 universities in Indonesia conducting research activities and generating 14,188 research outputs. Findings This paper analyzes 3 of the 9 National Research Priorities, namely, food, energy and health and medicine. The data show that there are 904 universities in Indonesia conducting research activities and producing 14,188 research results. The research strength index based on National Research Priorities shows that three universities have the highest cluster strength index. Gadjah Mada with an index value of 4.95 is the highest index in the food cluster. In the energy cluster of the Institut Teknologi Bandung with the highest index value of 0.63. Meanwhile, the Universitas Indonesia reached 2.06 as the highest index value in the health and medicine cluster. Originality/value Measurement of the strength of the study was conducted using data from research results from universities in 2016 which were recorded in the Information System for Research and Community Service. The University’s R&D strength is calculated from seven categories of research results: accredited national journals, international journals indexed by Scopus or others, intellectual property rights, textbooks, prototypes and appropriate technology products.
A Siamese Network Approach to Multiple-Object Tracking (Computer Vision)

In this project, we focus on the three latter stages which is a common practice in the field of MOT. Therefore, our problem setting is: given a set of objects detected by a state-of-the-art ML detection system, perform online MOT on all frames in a video sequence. Here the term "online" means that the tracking algorithm cannot peak into the future and can only use the current and past frames when performing the tracking. Also, we will say that our algorithm uses public detections since it uses the set of objects detected by a state-of-the-art ML detection.
Recent Patterns in Maize Yield and Harvest Area across Africa

Africa’s rapidly growing population is exerting a lot of pressure on agricultural resources including maize yields and harvest area. Across Africa, access to advanced options for increasing maize yields are inadequate. This is daunting as most of the cultivation of maize is in the hands of smallholder farmers who have inadequate access to modern methods of farming. This has resulted in an increase in dependency on harvest area to increase yields. However, it is still unclear how this maize-yield-harvest-area dynamic plays out across different regions of Africa. This study uses crop yield and harvest area time series data from FAOSTAT for the period 1961–2019. The data are analyzed using linear interpolation, the normalization technique, the rate of change, the Pearson correlation coefficient, the coefficient of determination and regression analysis. The results show that maize yields and harvest area have increased by 71.35% and 60.12%, respectively across Africa. Regionally, West, Middle and East Africa witnessed a positive relationship between maize yields and harvest area while in North and Southern Africa, maize yields and harvest area have an inverse relationship. For example, in assessing the relationship between maize yield and harvest area in Africa, this work observes that North Africa has a correlation of −35% and an R2 of 12%, while Southern Africa has a correlation of −36% and R2 of 13%. On the other hand, West Africa has a correlation of 87% and an R2 of 76%, while Middle Africa recorded a correlation of 66% and an R2 of 42%. East Africa recorded a correlation of 76% and R2 of 61%. These results confirm that maize yield and harvest area have a positive relationship in West, Middle and East Africa and a negative relationship in North and Southern Africa. These results underscore the fact that in North and Southern Africa, maize production is less dependent on harvest area as is the case in the other regions of Africa. Such findings have implications for adaptation planning especially in sub-Saharan Africa where food insecurity is closely related to land and forest degradation.
Edge2Analysis: A Novel AIoT Platform for Atrial Fibrillation Recognition and Detection

Atrial fibrillation (AF) is a serious medical condition of the heart potentially leading to stroke, which can be diagnosed by analyzing electrocardiograms (ECG). Technologies of Artificial Intelligence of Things (AIoT) enable smart abnormality detection by analyzing streaming healthcare data from the sensor end of users. Analyzing streaming data in the cloud leads to challenges of response latency and privacy issues, and local inference by a model deployed on the user end brings difficulties in model update and customization. Therefore, we propose an AIoT Platform with AF recognition neural networks on the sensor edge with model retraining ability on a resource-constrained embedded system. To this aim, we proposed to combine simple but effective neural networks and an ECG feature selection strategy to reduce computing complexity while maintaining recognition performance. Based on the platform, we evaluated and discussed the performance, response time, and requirements for model retraining in the scenario of AF detection from ECG recordings. The proposed lightweight solution was validated with two public datasets and an ECG data stream simulation on an ATmega2560 processor, proving the feasibility of analysis and training on edge.
In‐Depth Evaluation of MiRS Total Precipitable Water From NOAA‐20 ATMS Using Multiple Reference Data Sets

The National Oceanic and Atmospheric Administration (NOAA) Microwave Integrated Retrieval System (MiRS) algorithm has been producing retrieval products from NOAA‐20 Advanced Technology Microwave Sounder (ATMS) data since shortly after launch in late 2017. Retrieval products from the Suomi National Polar‐orbiting Partnership (SNPP) ATMS data have been similarly available since 2012. Among the products routinely generated is Total Precipitable Water (TPW), based on the vertical integral of the MiRS retrieved water vapor profile. In this study, TPW data from NOAA‐20 ATMS have been the subject of an in‐depth evaluation. The evaluation included characterization of dependence on orbital node (ascending/descending), season, surface type, scan angle, and meridional (zonally averaged) variation. Due to the similarity between NOAA‐20 and SNPP derived TPW, the emphasis here is on the NOAA‐20 evaluation. The globally averaged values of TPW for 2019 are 25.41 and 25.39 mm from NOAA‐20 and SNPP ATMS, respectively, for combined orbits (ascending and descending). For combined orbits, the bias and standard deviation of MiRS NOAA‐20 ATMS TPW with respect to European Centre for Medium‐Range Weather Forecasts/Global Data Assimilation System (ECMWF/GDAS) analyses are 0.79/1.76 mm and 3.65/3.55 mm, respectively, at 10°N where part of annual mean Intertropical Convergence Zone (ITCZ) is located, while at 40°N, corresponding bias and standard deviation values are 0.84/0.67 mm and 3.00/2.55 mm, respectively. The yearly averaged horizontal distribution of MiRS TPW retrieved from NOAA‐20 ATMS is highly correlated with that of ECMWF and GDAS analyses data, with a correlation coefficient of ∼1.
Can Radiomics Analyses in 18F-FDG PET/CT Images of Primary Breast Carcinoma Predict Hormone Receptor Status?

Objectives: This study aimed to investigate the role of preoperative 18fluorine-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) radiomics features and metabolic parameters of primary breast tumors in predicting hormone receptor (HR) positivity. Methods: A total of 153 patients with breast carcinoma who underwent preoperative 18F-FDG PET/CT were included. All PET/CT images were retrospectively reevaluated. Radiomics features of primary breast lesions reflecting tumor heterogeneity as well as standardized uptake value (SUV) metrics (SUVmin, SUVmean, SUVmax, and SUVpeak) and volumetric parameters such as metabolic tumor volume and total lesion glycolysis (TLG) were extracted by commercial texture analysis software package (LIFEx; https://www.lifexsoft.org/ index.php). WEKA and SPSS were used for statistical analysis. Binary logistic regression analysis was used to determine texture features predicting HR positivity. Accuracy, F-measure, precision, recall, and precision-recall curve area were used as data-mining performance criteria of texture features to predict HR positivity. Results: None of the radiomics parameters were significant in predicting HR status. Only SUV metrics and TLG were statistically important. Mean ± standard deviations for SUVmean, SUVmax, and SUVpeak for the HR-negative group were significantly higher than those in the HR-positive group (6.73±4.36 vs. 5.20±3.32, p=0.027; 11.55±7.42 vs. 8.63±5.23, p=0.006; and 8.37±6.81 vs. 5.72±4.86; p=0.012). Cut-off values of SUVmean, SUVmax, and SUVpeak for the prediction of HR positivity were 4.93, 8.35, and 6.02, respectively. Among data-mining methods, logistic regression showed the best performance with accuracy of 0.762. Conclusion: In addition to the relatively limited number of patients in this study, radiomics parameters cannot predict the HR status of primary breast cancer. SUV levels of the HR-negative group were significantly higher than those of the HR-positive group. To clarify the role of metabolic and radiomics parameters in predicting HR status in breast cancer, further studies involving a larger study population are needed.
Research on the Impact of Environmental Regulation on Green Technology Innovation from the Perspective of Regional Differences: A Quasi-Natural Experiment Based on China’s New Environmental Protection Law

Environmental regulations have a certain impact on regional green technology innovation affected by regional differences. Using the panel data of 30 provincial-level administrative regions in China (excluding Tibet, Hong Kong, Macao, and Taiwan) from 2011 to 2019, we consider China’s new environmental protection law (NEPL) as a quasi-natural experiment to evaluate the impact of environmental regulation on green technology innovation in a difference-in-differences (DID) framework and further analyze the influences of regional differences. The results indicate that environmental regulations can promote regional green technology innovation, and that regional differences have a significant impact on this issue. Furthermore, environmental regulations in regions with high and low levels of economic development and education, and regions with medium and low levels of energy consumption have a significant impact on green technology innovation. The government should reasonably formulate environmental regulation policies on the basis of regional differences, encourage cross-regional exchanges and cooperation, and more efficiently stimulate regional green technology innovation to achieve sustainable development.