Miracle-dz/UniFAR-FTUs huggingface dataset:
On chaotic motion of a double pendulum.

This paper presents the results of studying the chaotic motion of a virtual double pendulum. Large oscillations of this pendulum were modelled by the system of nonlinear differential equations in the Hamilton form. This system was solved on the worksheet of Computer Package Mathcad numerically, using the Runge-Kutta algorithm of fourth order. The results are illustrated by some frames of video clips visualizing the chaotic motion of a double physical pendulum. The results of this paper can be used in the teaching process of analytical mechanics for students of engineering.
Validation of the Health Consciousness Scale among the Czech Population

The COVID-19 pandemic has demonstrated the importance of maintaining good health. It became has become apparent that health consciousness is a crucial factor in promoting healthy lifestyles, disease prevention, and the overall well-being of individuals. A higher level of health consciousness is associated with healthy habits, better adherence to medical recommendations, and a higher quality of life. Therefore, health consciousness is a critical construct in health care that reflects the degree to which individuals care about their health. This study, which is based on a representative sample of the adult population (n = 1372), aims to validate the Health Consciousness Scale (HCS) to assess its reliability and validity, and evaluate the factor structure of the translated version of the scale in the Czech language. The validation of the HCS in the Czech context is a significant step forward and provides useful information for healthcare professionals, policymakers, and researchers. The findings of this study contribute to the understanding of health consciousness in the Czech population and provide unique information for the development and evaluation of health interventions aimed at promoting healthy behaviors and attitudes.
Review of the RaD and Supply of the LHC Superconducting Cables

The construction of the superconducting magnets for the LHC machine has required the supply of ~7350 km of superconducting cables. The delivery of cables which is completed at 97% has made use of a large part of the world wide production capacity. Ten contracts have been placed with firms in Europe, Japan, USA. The Nb-Ti and the Nb materials have been contracted by CERN. Before tendering and placing the contracts, a R&D program has combined studies at CERN and orders of finished cables of significant lengths to industry. The report will present the main results of the R&D program, the characteristics of the LHC cables, the encountered difficulties and the obtained successes during the long duration fabrication contracts of the highly sophisticated LHC superconducting cables
MS-Net: Multi-Segmentation Network for the Iris Region Using Deep Learning in an Unconstrained Environment

Iris segmentation is a significant phase in the iris recognition process because segmentation errors cascade into all subsequent phases. Therefore, it is important that errors in iris segmentation are minimised. The U-Net architecture that uses a deep learning approach was previously adopted for this task, but its performance was affected by the deformation of iris images caused by various noise factors in unconstrained (non-ideal) environments. Scratches, blurriness, dirt, specular reflections and other noise factors are some of the challenges faced in unconstrained environments when eyeglasses are present in the original images. Additionally, the performance of the iris segmentation was degraded due to problems of exploding gradient or vanishing gradient and the loss of information. This paper proposes a multi-segmentation network called MS-Net, based on a deep learning approach, that aims to capture high-level semantic features while maintaining spatial information to improve the accuracy of iris segmentation. MS-Net consists of three principal segments: a feature encoder network, a multi-scale context feature extractor network (MSCFE-Net) and a feature decoder network. MSCFE-Net a multi-scale context feature extractor network is constructed from a dilated residual multi-convolutional network module and a pyramid pooling residual model based on an attention convolutional module. In addition, the proposed MS-Net contains dense connections within the feature decoder network to decrease training difficulty, by using only a few training samples. The accuracy of MS-Net was evaluated on the CASIA-Iris.V4-1000 and UBIRIS.V2 databases. The performance of our proposed MS-Net method on the CASIA-Iris.V4-1000 and UBIRIS.V2 databases achieved an overall accuracy of 97.11% and 96.128%, respectively. Experiment results show that MS-Net is able to achieve better results compared to earlier methods used for the same purpose.
Public Investment in Short-Cycle Tertiary Vocational Education: Historical, Longitudinal, and Fixed-Effects Analyses of Developed and Less-Developed Countries

We use three analytic steps to examine public investment in short-cycle tertiary education. First, reviewing the historical development, the literature reveals that national and regional policies on educational development emphasized bachelor’s programs in vocational education in the early twenty-first century, especially in the EU. This historical background informs the longitudinal trend analysis in the second step of the educational and public investment variables (2000–2018) in our econometric analysis. The combined descriptive studies illuminate competitive advantages for EU and ASEAN nations in networks emphasizing open economic and academic exchange. Third, the fixed-effects analysis indicates a higher level of investment in general tertiary education per student, associated with a lower enrollment level in short-cycle vocational and technical tertiary programs. Using insights from this three-step process, we explore the implications of a nation’s capacity to invest in short-cycle tertiary programs as part of economic development and the pursuit of social equity within and across countries. Specifically, we conclude that short-cycle programs are a step toward integrating vocational education into programs in polytechnics and other higher education institutions.
Sexual and reproductive health communication between parents and adolescents: the case of Wa West District of the Upper West Region, Ghana

Background: Sexual and reproductive health communication is an important conversation challenge between parents and their adolescent children. Studies have established that parent-adolescent communication about sex can greatly reduce adolescents' risky sexual behaviour. Factors such as attitude, religious beliefs, perceived behavioural control, and behavioural intentions have been reported to affect parent-adolescent communication. Objective: This study assessed the communication practices between adolescents and their parents in the Wa West District in the Upper West Region of Ghana and factors that may be associated with this communication process. Methods: A community-based cross-sectional study design was employed using a four-stage sampling technique. A total of 420 parents with adolescents aged 10to 19years were recruited to answer structured questions using Somers and Canivez's sexual communication tool. The tool yielded sexual communication mean scores. The analysis of variance test was used to compare mean scores. Binary logistic regression was used to determine possible factors affecting SRH communications. All analyses were done using IBM SPSS Statistics (Version 25). Results: The study found that most of the respondents had good communication on sexual and reproductive health with their adolescents. There was a significant difference between Dagaabas and the other ethnic groups (p=0.025, 95% confidence interval (95% CI) = 0.05-1.25), between public sector employees and farmers (p=0.008, 95% CI = 0.07-0.90), and between Christians and Muslims (p=0.032, 95% CI= 0.01-0.38) on SRH communication. Conclusion: Though parents' communication with adolescents was good, health stakeholders need to empower parents with information on sexual and reproductive health to ensure effective counselling of their adolescents
Artificial Intelligence for Automatic Monitoring of Respiratory Health Conditions in Smart Swine Farming

Simple Summary This paper provides a review of recent studies exploring the application of artificial intelligence (AI) in the early detection and monitoring of respiratory disease in swine, emphasizing the significance of early detection for preventing economic losses. The studies primarily focus on utilizing coughing sounds as a feature in disease recognition, comparing different AI models and methodologies. A commercially available AI system that integrates temperature and humidity sensors with audio technologies for respiratory health monitoring through cough-sound identification is also assessed. However, the limitations of the current technology are identified, highlighting the need for further advancements to develop smarter AI solutions for swine respiratory health monitoring. Abstract Porcine respiratory disease complex is an economically important disease in the swine industry. Early detection of the disease is crucial for immediate response to the disease at the farm level to prevent and minimize the potential damage that it may cause. In this paper, recent studies on the application of artificial intelligence (AI) in the early detection and monitoring of respiratory disease in swine have been reviewed. Most of the studies used coughing sounds as a feature of respiratory disease. The performance of different models and the methodologies used for cough recognition using AI were reviewed and compared. An AI technology available in the market was also reviewed. The device uses audio technology that can monitor and evaluate the herd’s respiratory health status through cough-sound recognition and quantification. The device also has temperature and humidity sensors to monitor environmental conditions. It has an alarm system based on variations in coughing patterns and abrupt temperature changes. However, some limitations of the existing technology were identified. Substantial effort must be exerted to surmount the limitations to have a smarter AI technology for monitoring respiratory health status in swine.
Spray-On Colorimetric Sensors for Distinguishing the Presence of Lead Ions

Sprayable stimuli-responsive material coatings represent a new class of detection system which can be quickly implemented to transform a surface into a color-responsive sensor. In this work, we describe a dipicolylamine-terminated diacetylene-containing amphiphile formulation for spray coating on to a simple paper substrate to yield disposable test strips that can be used to detect the presence of lead ions in solution. We find the response to be very selective to only lead ions and that the sensitivity can be modulated by altering the UV curing time after spraying. Sensitive detection to at least 0.1 mM revealed a clear color change from a blue to red phase. This represents the first demonstration of a spray-on sensor system capable of detection of lead ions in solution.
Optimization of Technological Parameters when Plasma Nitriding the Gear Working Surface

This paper presents the results of optimizing the technological parameters when plasma nitriding the gear working surface. 27 experiments were carried out on an H4580 Eltrolab instrument to obtain a working surface hardness database table, and the Minitab statistical software was used to process the experimental results. A regression equation was identified to analyze the influence of temperature, time, and gas permeation concentration on the hardness of the working surface. An analysis of variance (ANOVA) showed that all these nitriding parameters influence the regression equation. The results showed that the permeation temperature TL has the greatest influence on the hardness, while the permeation time h and the first gas permeation concentration G1 had less influence. An optimal set of technological parameters was established, where the permeation temperature was TL=550°C, permeation time was h=8h, and the first gas permeation concentration was G1=4l/h. Experiments were carried out using this set of optimal parameters to evaluate the microstructure and the depth of the hardening layer of the part surface. Finally, this study presented the whole process for the permeation and plasma nitriding of 18XГT steel.
Application of improved target detection algorithm in sports robot motion behavior

Target detection in classroom education scene often brings some difficulties to target detection based on YOLO due to the large detection range and small detection target in classroom. In this study, target detection methods DPM and R-FCN were integrated into YOLO and an improved neural network structure was designed. The feature extraction mode included a fully connected layer and pooling and then convolution to reduce the loss of feature information. Then, a sliding window merging algorithm based on RPN was designed to form a feature extraction algorithm based on improved YOLO. In this study, a context detection platform for educational robot was built to clarify the overall workflow of context detection for educational robot. the comparison with the YOLO algorithm shows that the proposed algorithm is superior to the YOLO algorithm in recognition accuracy.
Combination of high hoop stress tolerance and a small screening current-induced field for an advanced Bi-2223 conductor coil at 4.2 K in an external field

An advanced Bi-2223 conductor with Ni–Cr reinforcement is a likely candidate to achieve a compact super-high field nuclear magnetic resonance (NMR) magnet capable of operation beyond 1 GHz (23.5 T). However, the conductors must show both high hoop stress tolerance, typically >300 MPa, and a small screening current-induced magnetic field, both of which are essential for a compact magnet generating a highly accurate field. These two conditions have not yet been demonstrated in a working coil. This is the first paper to systematically investigate both characteristics for a layer-wound coil made with the advanced Bi-2223 conductor operated mainly at 4.2 K in an external field of ≤17 T. The coil tolerated a hoop stress of 370 MPa, even though the conductor had a bending strain corresponding to a diameter of 120 mm. On the other hand, the coil showed a notable screening current-induced field in a low external field, which may be explained by weak-link and direct contacts between highly packed Bi-2223 filaments in the silver matrix. The field sharply decreased with increasing external field between 0–1 T. Thus, the conductor should be useful for inner coils in compact super-high field NMR magnets.
Robust passivity, feedback passification and global robust stabilisation for switched non-linear systems with structural uncertainty

This study examines the issues of robust passivity, feedback passification and global stabilisation for a class of switched non-linear system with structural uncertainty. First, a sufficient condition for such a system to be robust passive is given by the design of a state-dependent switching law. Second, a state-dependent switching law and state feedback controllers are designed to render the resulting closed-loop system robust passive. Finally, a recursive feedback passification design technique is developed to achieve the global stabilisation problem when the subsystems have any uniform vector relative degree. A numerical example is presented to illustrate the effectiveness of the proposed approach.
Building a Digital Educational Escape Room Using an Online Design-Thinking Process

There is increasing interest in the application of game-based learning approaches to education. Educators across a wide range of contexts are using digital games such as educational escape rooms to promote learner motivation and support skills development. Whilst the literature describes multiple game-based learning theories that can underpin such strategies, there is little practical guidance on how to integrate such conceptual elements into the design of digital educational escape rooms. This study aims to address this gap, outlining the use of an online design-thinking process to plan, build, and test a digital educational escape room. Our findings suggest that this process provides an effective way of harnessing team collaboration and innovation in the development of digital educational resources. The process provides structure for game design teams, enabling them to address complex or “messy” educational development problems. In utilising an online design-thinking process to design games for learning, we make a number of recommendations. These include taking time to establish psychological safety within the design team so as to facilitate creative team processes and supporting team members to adopt a design-thinking mindset throughout (e.g., regularly taking the perspective of the game user, and testing game prototypes early and frequently). Finally, our study offers a detailed description of how the online design-thinking process can be applied in an education context with the aim of offering guidance to educators and students who may want to design, build, and test their own digital educational escape rooms.
Development of 300kW twin torch plasma arc furnace for medical waste treatment

Summary form only given. The twin torch plasma arc can generate a wide space of high temperature and treat non-conducting material, and also a mixture because it is a non-transfer type. Therefore, the twin torch plasma arc is a more effective device for treatment of hazardous waste. In this paper, a 300 kW twin torch plasma arc furnace was developed. This facility was designed to enable to be mounted on commercial trucks. A set of two DC power supplies is used to generate the arc. The arc is generated between a tungsten tip cathode with a nozzle and a hollow carbon anode. The electrode angle is 60 degree, and it is adjustable. The crucible in an inner diameter of 1 m can rotate in order to treat the waste easily. The crucible and a chamber (length=2 m, width=1.5 m, height=1.5 m) have firebrick in order to keep the high temperature. The off gas can be treated by bag filter and scrubber. The gas is analyzed by using a GC-MS. The arc temperature is measured by spectroscopy. As a result, the arc temperature is 11,000 K. The SS and SUS were melted at 200 A, 200 V. The injection needle and diaper were treated at 200 A, 100 V in success. The off gas does not violate relevant law
Polymorphisms of the GCLC Gene Are Novel Genetic Markers for Susceptibility to Psoriasis Associated with Alcohol Abuse and Cigarette Smoking

The aim of this pilot study was to investigate whether single nucleotide polymorphisms (SNP) in the gene encoding the catalytic subunit of glutamate cysteine ligase (GCLC) are associated with the risk and clinical features of psoriasis. A total of 944 unrelated individuals, including 474 patients with a diagnosis of psoriasis and 470 healthy controls, were recruited for the study. Six common SNPs in the GCLC gene were genotyped using the MassArray-4 system. Polymorphisms rs648595 (OR = 0.56, 95% CI 0.35–0.90; Pperm = 0.017) and rs2397147 (OR = 0.54, 95% CI 0.30–0.98; Pperm = 0.05) were associated with susceptibility to psoriasis in males. In the male group, diplotype rs2397147-C/C × rs17883901-G/G was associated with a decreased risk of psoriasis (FDR-adjusted p = 0.014), whereas diplotype rs6933870-G/G × rs17883901-G/G (FDR-adjusted p = 0.045) showed an association with an increased disease risk in females. The joint effects of SNPs with tobacco smoking (rs648595 and rs17883901) and alcohol abuse (rs648595 and rs542914) on psoriasis risk were observed (Pperm ≤ 0.05). We also found multiple sex-independent associations between GCLC gene polymorphisms and various clinical features such as earlier disease onset, the psoriatic triad, and specific localizations of skin lesions. The present study is the first to show that polymorphisms of the GCLC gene are significantly associated with the risk of psoriasis and related to its clinical features.
AB-MTEDeep Classifier Trained with AAGAN for the Identification and Classification of Alopecia Areata

Artificial Intelligence (AI) is widely used in dermatology to analyze trichoscopy imaging and assess Alopecia Areata (AA) and scalp hair problems. From this viewpoint, the Attention-based Balanced Multi-Tasking Ensembling Deep (AB-MTEDeep) network was developed, which combined the Faster Residual Convolutional Neural Network (FRCNN) and Long Short-Term Memory (LSTM) network with cross residual learning to classify scalp images into different AA classes. This article presents a new data augmentation model called AA-Generative Adversarial Network (AA-GAN) to produce a huge number of images from a set of input images. The structure of AA-GAN and its loss functions are comparable to those of standard GAN, which encompasses a generator and a discriminator network. To generate high-quality AA structure-based images, the generator was trained to extract the 2D orientation and confidence maps along with the bust depth map from real hair and scalp images. The discriminator was also used to separate real from generated images, which were provided as feedback to the generator to create synthetic images that are extremely close to the real input images. The created images were used to train the AB-MTEDeep model for AA classification. Finally, the experimental results exhibited that the AA-GAN-AB-MTEDeep achieved 96.94% accuracy.
Joint Particle Swarm Optimization of Power and Phase Shift for IRS-Aided D2D Underlaying Cellular Systems

Device-to-device (D2D) communication is a promising wireless communication technology which can effectively reduce the traffic load of the base station and improve the spectral efficiency. The application of intelligent reflective surfaces (IRS) in D2D communication systems can further improve the throughput, but the problem of interference suppression becomes more complex and challenging due to the introduction of new links. Therefore, how to perform effective and low-complexity optimal radio resource allocation is still a problem to be solved in IRS-assisted D2D communication systems. To this end, a low-complexity power and phase shift joint optimization algorithm based on particle swarm optimization is proposed in this paper. First, a multivariable joint optimization problem for the uplink cellular network with IRS-assisted D2D communication is established, where multiple DUEs are allowed to share a CUE’s sub-channel. However, the proposed problem considering the joint optimization of power and phase shift, with the objective of maximizing the system sum rate and the constraints of the minimum user signal-to-interference-plus-noise ratio (SINR), is a non-convex non-linear model and is hard to solve. Different from the existing work, instead of decomposing this optimization problem into two sub-problems and optimizing the two variables separately, we jointly optimize them based on Particle Swarm Optimization (PSO). Then, a fitness function with a penalty term is established, and a penalty value priority update scheme is designed for discrete phase shift optimization variables and continuous power optimization variables. Finally, the performance analysis and simulation results show that the proposed algorithm is close to the iterative algorithm in terms of sum rate, but lower in power consumption. In particular, when the number of D2D users is four, the power consumption is reduced by 20%. In addition, compared with PSO and distributed PSO, the sum rate of the proposed algorithm increases by about 10.2% and 38.3%, respectively, when the number of D2D users is four.