Miracle-dz/UniFAR-FTUs huggingface dataset:
The healthcare redesign student experience: qualitative and quantitative insights of postgraduate work-integrated learning

ABSTRACT A challenge for providers of work-integrated learning courses is delivering a meaningful learning experience for students, while collaborating with organisations to ensure adequate support. Evaluating student experience has traditionally been about collecting feedback on teaching methods, course content and learning outcomes, rather than the educational influence of student interactions with people, places and systems at their workplace. The aim of this mixed-methods study was to evaluate the student experience within a workplace-integrated healthcare redesign postgraduate course and understand how it affects student learning. Ninety-nine graduates of the course completed an online bespoke survey indicating their levels of satisfaction with the course, including delivery, support and goal achievement. In addition, 10 graduates participated in semi-structured interviews where they shared their overall experience with the course, including barriers and enablers to completion, the balance between academic requirements and workplace responsibilities, and the influence of educators on their achievements. The results suggested that social interaction, communication, and knowledge and skills development were highly valued by students, and that resourcing, course structure and organisational support were significant factors that influenced student outcomes. Cross-discipline interaction and networking opportunities further enhanced student learning. Study findings indicated that a blended model of learning involving workplace project-based activity and traditional academic delivery provided meaningful, practice-relevant learning opportunities and enriched the student experience.
Residual Learning Diagnosis Detection: An Advanced Residual Learning Diagnosis Detection System for COVID-19 in Industrial Internet of Things

Due to the fast transmission speed and severe health damage, COVID-19 has attracted global attention. Early diagnosis and isolation are effective and imperative strategies for epidemic prevention and control. Most diagnostic methods for the COVID-19 is based on nucleic acid testing (NAT), which is expensive and time-consuming. To build an efficient and valid alternative of NAT, this article investigates the feasibility of employing computed tomography images of lungs as the diagnostic signals. Unlike normal lungs, parts of the lungs infected with the COVID-19 developed lesions, ground-glass opacity, and bronchiectasis became apparent. Through a public dataset, in this article, we propose an advanced residual learning diagnosis detection (RLDD) scheme for the COVID-19 technique, which is designed to distinguish positive COVID-19 cases from heterogeneous lung images. Besides the advantage of high diagnosis effectiveness, the designed residual-based COVID-19 detection network can efficiently extract the lung features through small COVID-19 samples, which removes the pretraining requirement on other medical datasets. In the test set, we achieve an accuracy of 91.33%, a precision of 91.30%, and a recall of 90%. For the batch of 150 samples, the assessment time is only 4.7 s. Therefore, RLDD can be integrated into the application programming interface and embedded into the medical instrument to improve the detection efficiency of COVID-19.
BETA EFFECTIVE SENSITIVITY TO NUCLEAR DATA WITHIN THE APOLLO3® NEUTRONIC PLATFORM

In this paper, we present a sensitivity analysis of the beta effective to nuclear data for the UM17x17 experiment that has been performed in the EOLE reactor. This work is carried out using the APOLLO3® platform. Regarding the flux calculation, the standard two-step approach (lattice/core) is used. For what concerns the delayed nuclear data, they are processed to be directly used in the core calculation without going through the lattice one. We use the JEFF-3.1. 1 nuclear data library for cross-sections and delayed data. The calculation of k-effective and beta effective is validated against a TRIPOLI4® one while the main sensitivities are validated against direct calculation. Finally, uncertainty propagation is performed using the COMAC-V2.0 covariance library.
Distributed Planning of Multi-Agent Systems with Coupled Temporal Logic Specifications

In this paper, we investigate the multi-agent mission planning and control problem under coordination constraints such as collision avoidance and spatial coherence. We propose a sequential trajectory planning algorithm that is run by each agent in a distributed manner. Mission requirements including servicing speciﬁc regions, avoiding static and dynamic obstacles (other agents), and ﬂying close to the other agents are incorporated into the problem as Signal Temporal Logic (STL) constraints. The planning problem under STL constraints is formulated as a Mixed Integer Linear Program and an oﬀ-the-shelf tool is used to ﬁnd the high-level trajectories. The resulting high-level trajectories are then tracked by the agents via Model Predictive Control. Illustrative simulation and experiment results are presented in addition to comparing the proposed method with a centralized solution.
Chemokine Ligand 13 Expression is Abundant in the Tumor Microenvironment and Indicates Poor Prognosis of Kidney Clear Cell Carcinoma

The chemokine ligand 13-chemokine receptor 5 (CXCL13-CXCR5) axis has been characterized as a critical tumor-promoting signaling pathway in the tumor microenvironment (TME) in multiple types of solid tumors. In this study, we analyzed the expression profile of CXCL13 in kidney clear cell carcinoma (KIRC) and its correlation with tumor-infiltrating immune cells (TIICs). A monoclonal antibody against CXCL13 with high affinity and purity was generated in our lab for western blot and immunohistochemistry (IHC). Bioinformatic analysis was performed based on bulk-seq data from the Cancer Genome Atlas (TCGA)-KIRC and single-cell RNA-seq data from scRNASeqDB and PanglaoDB. Results showed that high CXCL13 expression in TME was associated with shorter progression-free survival (PFS), disease-specific survival (DSS), and overall survival (OS). KIRC cell lines, as well as several other cancer cell lines, had negative CXCL13 expression. IHC staining from the Human Protein Atlas (HPA) and our tissue array indicated that CXCL13 might be mainly expressed by TIICs, but not KIRC tumor cells. CXCL13 expression was strongly and positively correlated with γδ T cell abundance in TME. Besides, γδ T cell infiltration was associated with poor survival of KIRC. Methylation 450k array data showed that CXCL13 promoter hypomethylation was common in TIICs. The methylation level of cg16361705 within the CXCL13 promoter might play an important role in modulating CXCL13 transcription. In conclusion, our study revealed that CXCL13 expression and γδ T cell infiltration in TME is associated with unfavorable survival of KIRC. TIICs, most possibly γδ T cells, are the dominant source of CXCL13 in KIRC TME.
Combined Random Forest and NSGA-II for Optimal Design of Permanent Magnet Arc Motor

This article presents an optimization design method for a double-stator hybrid excited permanent magnet arc motor (DS-HE-PMAM). The proposed optimization method combining the machine learning algorithm random forest (RF) and the nondominated sorting genetic algorithm-II (NSGA-II) contributes to achieving high average torque, low torque ripple, high back electromotive force (EMF), and low total harmonic distortion of the back EMF. First, the motor structure and working principle of the DS-HE-PMAM are illustrated. The selection of parameters to be optimized is determined based on an analytical model. Then, a variable importance measure-based new sensitivity analysis method is implemented to evaluate the influence of each structural parameter on the selected design objectives. The finite-element analysis (FEA)-based DS-HE-PMAM model is developed to obtain the sample data regarding input structural parameters and output design objectives. Based on the sample data, a powerful machine learning algorithm called RF is employed to fit the function relationship between output design objectives and input structural parameters. After that, an intelligent search algorithm named NSGA-II is introduced to search for the optimal solution to the structural parameters combination and obtain the optimal motor performances. Finally, the electromagnetic characteristics of the initial and optimized models of the DS-HE-PMAM are compared and analyzed, and both FEA and prototype experiments verify the feasibility and superiority of the proposed optimization method.
Facile Synthesis of Yolk–Shell Nanostructured Photocatalyst with Improved Adsorption Properties and Molecular‐Sieving Properties

A novel and facile method to fabricate yolk–shell nanostructured photocatalysts consisting of TiO2 nanoparticles (NPs) as the core and spherical hollow silica as the shell was developed. In the fabrication, commercial TiO2 NPs were directly incorporated into hollow silica spheres by utilizing oil‐in‐water (O/W) microemulsions as a template, followed by heat treatment to create a void space between the TiO2 core and the silica shell region. The synthesized yolk–shell nanostructured TiO2@SiO2 acts as an efficient photocatalyst with both improved adsorption properties and photocatalytic efficiency, which far outperformed those of naked TiO2 owing to the ability of the porous silica shell to adsorb/enrich small organic reactants (acetaldehyde and 2‐propanol) inside the void space and efficient transportation to the adjacent active TiO2 core. Comparative photocatalytic tests using a large organic molecule (conalbumin) revealed that the porous silica shell with an average pore diameter of 2.0 nm endowed this material with a molecular‐sieving property, demonstrating its potential application in combination with organic support materials.
Myofascial trigger-point compression therapy as an adjunct to exercise program is superior in alleviating chronic low back pain compared to exercise program alone- A retrospective non-randomized study.

Introduction: Current guidelines for the treatment of low back pain (LBP) endorse physical exercise programs and a range of non-pharmacological complementary therapies. Myofascial trigger points (MTP) compression therapy is a well-established form of manual therapy, which aims to induce temporary ischemia and over-stimulation of mechanoreceptors over a desired area in order to restore tissue normal functional conditions required for healing response. The purpose of the current study was to evaluate the effect of MTP compression therapy as an adjunct to active exercise program in alleviating chronic non-specific low back pain.Methods: One-hundred and three patients with chronic non-specific LBP were retrospectively reviewed, 45 of them were treated with MTP compression therapy as an adjunct to physical exercise program and 58 were treated with exercise program alone. Pain and functional scores were compared before initiating treatment and at 3-month follow-up. Results: Pain reduction at 3-month follow-up was 5.6 points (0-10 numerical scale) in the combined treatment group compared to 3.6 points in the exercise program alone group (p<0.01). Oswestry Disability Index improved by 36 and 22 points respectively (p<0.01) and SF-12 life quality score improved by 41 and 29 points respectively (p<0.01). Conclusions: Myofascial trigger points compression therapy as an adjunct to active exercise program provides superior pain and disability relief compared to active exercise program alone for patients with chronic LBP.
Computational modelling of process–structure–property–performance relationships in metal additive manufacturing: a review

In the current review, an exceptional view on the multi-scale integrated computational modelling and data-driven methods in the Additive manufacturing (AM) of metallic materials in the framework of integrated computational materials engineering (ICME) is discussed. In the first part of the review, process simulation (P-S linkage), structure modelling (S-P linkage), property simulation (S-P linkage), and integrated modelling (PSP and PSPP linkages) are elaborated considering different physical phenomena (multi-physics) in AM and at micro/meso/macro scales (multi-scale modelling). The second part provides an extensive discussion of a data-driven framework, which involves extracting existing data from databases and texts, data pre-processing, high throughput screening, and, therefore, database construction. A data-driven workflow that integrates statistical methods, including ML, artificial intelligence (AI), and neural network (NN) models, has great potential for completing PSPP linkages. This review paper provides an insight for both academic and industrial researchers, working on the AM of metallic materials.
Patients Age is a critical Factor in determining the Severity of Osteoarthritis of the Shoulder

Background: Osteoarthritis (OA) is one of the most common musculoskeletal disorders in the aging population. The correlation of radiographic OA severity, disability and pain is variable and inconsistent for the different joints. This study aims to elucidate the relationship between histological and radiological signs of shoulder OA with pain sensation and functional impairment to potentially adapt the recommendation for surgical treatment for primary total shoulder arthroplasty (TSA). Methods: Forty-four patients with shoulder OA undergoing TSA using an anatomic stemless implant were included in this study. The radiological OA severity was scored preoperatively on true ap X-rays according to the Kellgren-Lawrence-Score (KL-Score). Acromial types according to Bigliani were defined by preoperative radiological images. The histological OA severity was determined according to the OARSI-Score using bone-cartilage sections from loaded areas of the humeral head. Pain was quantified using the Visual Analog Scale (VAS). The functional status was assessed by the items “mobility” and “strength” out of the Constant-Murley-Score (CS-Score). Demographic data including BMI, age, gender, diabetes mellitus and smoking were recorded. Results: There was no correlation between radiographic and histological severity in shoulder OA. However, a correlation of age and the severity of radiological changes was observed. Further, pain did not correlate with histological or radiological scores, whereas it correlated with age and the presence of diabetes mellitus. The functional shoulder status (mobility, strength) correlated with the severity of radiological changes, but not with the histologic scoring, which correlated with nicotine abuse. Conclusion: This study shows that increased age is the main determinant of radiological changes in shoulder OA, as well as pain. Therefore, age and pain sensation should be considered as important parameters for the recommendation for TSA.
Cross interference suppression methodology by printed circuit board type metamaterial in multi‐frequency multi‐load magnetically coupled resonant wireless power transfer system

The prosperity of market in multi-charging devices has made the researches on multiload magnetically coupled resonant (MCR) wireless power transfer (WPT) technique be an urgent issue nowadays. For the sake of load recognition and controllable power distribution, the concept of multi-frequency excitation was introduced into multi-load MCR WPT systems. However, the transferred power components at different frequencies inevitably suffer the cross interference at receivers, which negatively affects the accuracy of load recognition and power distribution. To suppress the cross interference, a methodology by using the metamaterial composed of a printed circuit board (PCB) spiral inductor and a lumped capacitor was proposed. The concept of equivalent mutual inductance ( M eq ) was presented to quantitatively describe the inﬂuence of metamaterials on the couplings between the transmitting (Tx) and receiving (Rx) coils. By magnetic ﬁeld simulation, a series of M eq curves versus operating frequency under different metamaterial parameters were plotted and an optimal design was adopted to suppress the power components at non-targeted frequencies. Moreover, a detailed circuit model was built to characterize the output power of multiple loads after introducing the metamaterial. Finally, the experiments from a prototype was carried out and the experimental results veriﬁed the effectiveness of proposed suppression methodology.
Viscosity of secondary organic material and related atmospheric implications

Aerosols are ubiquitous throughout the Earth’s atmosphere, and secondary organic material (SOM), which is produced from the oxidation of volatile organic compounds, is estimated to constitute a significant fraction of atmospheric aerosol mass. Furthermore, particles containing SOM can cause negative health outcomes, and affect Earth’s climate, both directly by scattering solar radiation, and indirectly by acting as nuclei for cloud droplets. Despite the importance of particles containing SOM, their physical properties, such as viscosity, are poorly constrained. To address this knowledge deficit, a technique to measure the viscosity of small samples of material, similar to that produced in environmental simulation chambers, was developed and validated. This technique was then used to measure the viscosity of SOM produced via the ozonolysis of α-pinene in an environmental simulation chamber. The viscosity of this material was found to depend strongly on the relative humidity (RH) used when measuring viscosity and the concentration of SOM mass at which the SOM was produced. A difference between the viscosity of the water-soluble component of SOM and the total SOM (water-soluble and water-insoluble components) was also observed. The viscosity of saccharides and a tetraol were subsequently measured, with these compounds serving as proxies of highly oxidized components of SOM found in the atmosphere. For saccharides, viscosity was determined to increase by at least four orders of magnitude as molar mass doubled. In addition, the tetraol was determined to have a viscosity at least two orders of magnitude lower than that of SOM produced via the oxidation of isoprene, in which the tetraol has been identified. Finally, literature viscosity data for organic compounds was used to demonstrate that saturation vapour concentration, the mass based equiv-
Why most Chinese fans of American superhero movies are girls: A gendered local fandom of a global Hollywood icon

ABSTRACT American superhero movies are generally perceived as primarily targeted at a male audience. In China, however, females far outnumber males in the genre’s fan community. This phenomenon suggests that a popular text’s local negotiation can take on different paths when the text is consumed transnationally. To explore this process, this study examines how Chinese consumers of the Marvel franchise negotiate their gendered fandom in a global context. Drawing from in-depth interviews with 29 Chinese fans of the Marvel Cinematic Universe, this study considers the power dynamics at work in the franchise’s global spread and local consumption.
Convolutional Neural Network for Malware Classification Based on API Call Sequence

Malicious software is constantly being developed and improved, so detection and classification of malicious applications is an ever-evolving problem. Since traditional malware detection techniques fail to detect new or unknown malware, machine learning algorithms have been used to overcome this disadvantage. We present a Convolutional Neural Network (CNN) for malware type classification based on the Windows system API (Application Program Interface) calls. This research uses a database of 5385 instances of API call streams labeled with eight types of malware of the source malicious application. We use a 1-Dimensional CNN by mapping API call streams as categorical and term frequency-inverse document frequency (TF-IDF) vectors respectively. We achieved accuracy scores of 98.17% using TF-IDF vector and 95.40% via categorical vector. The proposed 1-D CNN outperformed other traditional classification techniques with overall accuracy score of 91.0%.
Design and Key Performance of 2G HTS Coils for High Speed Superconducting Maglev Application

Superconducting material can be utilized into linear synchronous motors (LSMs) in magnetically levitated maglev trains, considering its high current density without generating any Joule loss. The development of second-generation (2G) high-temperature superconducting (HTS) tapes simplifies design of superconducting coils as its operating temperature can be raised up to 40 K. In the scheme of HTS coils, evaluation of critical current (Ic0), which can be modeled based on finite element method using Maxwell's equation, is a crucial part. In this paper, the prototype of HTS coils is designed and optimized which will be further applied to a linear propulsion system. In view of a conduction cooling method, each magnet pole is composed of two coil modules. Two double racetrack coils wounded by REBCO tapes build up one coil module. Its critical current is figured and compared in liquid nitrogen temperature (i.e., 77 K) by experiments as well as simulations, based on H- and T-A formulation. Performance of Ic0 at 30 K and 40 K are also estimated through the same models. Finally, with the design results, its electromagnetic properties are analyzed by two finite element simulation software, and results meet requirements of the prototype.
The neural bases of multimodal sensory integration in older adults

Although hearing often declines with age, prior research has shown that older adults may benefit from multisensory input to a greater extent when compared to younger adults, a concept known as inverse effectiveness. While there is behavioral evidence in support of this phenomenon, less is known about its neural basis. The present functional MRI (fMRI) study examined how older and younger adults processed multimodal auditory-visual (AV) phonemic stimuli which were either congruent or incongruent across modalities. Incongruent AV pairs were designed to elicit the McGurk effect. Behaviorally, reaction times were significantly faster during congruent trials compared to incongruent trials for both age-groups, and overall older adults responded more slowly. The interaction was not significant, suggesting that older adults processed the AV stimuli similarly to younger adults. Although there were minimal behavioral differences, age-related differences in functional activation were identified: Younger adults elicited greater activation than older adults in primary sensory regions including superior temporal gyrus, the calcarine fissure, and left postcentral gyrus. In contrast, older adults elicited greater activation than younger adults in dorsal frontal regions including middle and superior frontal gyri, as well as dorsal parietal regions. These data suggest that while there is age-related stability in behavioral sensitivity to multimodal stimuli, the neural bases for this effect differed between older and younger adults. Our results demonstrated that older adults underrecruited primary sensory cortices and had increased recruitment of regions involved in executive function, attention, and monitoring processes, which may reflect an attempt to compensate.