Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Deadlifts in Distal Biceps Brachii Tendon Ruptures: An Alternative Mechanism Described With YouTube Videos

Background: The classic injury mechanism of a distal biceps brachii tendon rupture (DBBTR) is eccentric loading to the flexed elbow when the forearm is supinated. Purpose: To determine alternative mechanisms of a DBBTR in powerlifting sports, particularly in deadlift competitions, with the use of YouTube videos. Study Design: Descriptive epidemiology study. Methods: A search on YouTube was performed using the search terms “distal biceps tendon rupture” and “distal biceps tendon injury” combined with “competition,” “deadlift,” and “powerlifting. ” The videos underwent an evaluation for accuracy by 3 surgeons according to predetermined criteria. Type of sports activity, participant sex, side of the injury, and arm positions at the time of the injury were recorded. Results: Among the videos reviewed, 35 injuries were found appropriate for an evaluation. All participants were male. The majority of the injuries (n = 25) were observed during the deadlift. Only in 1 deadlift injury were both forearms in supination. In the remaining deadlift injuries (n = 24), all elbows were in extension, with 1 forearm in supination and the other in pronation. Among the deadlift injuries in the mixed-grip position, all ruptures occurred in a supinated extremity: 25% (n = 6) of ruptures occurred on the right side, and 75% (n = 18) of ruptures occurred on the left side; this was a significant difference (P = .014). Conclusion: We described an alternative mechanism for a DBBTR, namely, eccentric loading to an extended elbow when the forearm is supinated during the deadlift. As all the ruptures occurred in a supinated extremity, holding the bar with both forearms in pronation may prevent or decrease the risk of ruptures during the deadlift.
An analysis of listening errors by Korean EFL learners from
 self-paced passage dictation

In this study, listening errors by Korean EFL learners are comprehensively analyzed from self-paced passage dictation tasks. Fifty-five Korean EFL learners participated in the study. Listeners were asked to write down dictation passages as accurately as possible, while listening to the audio as much as they needed. The results show that (i) low-proficiency learners tend to misperceive longer phrases than high-proficiency learners, (ii) function words are more often omitted or misheard than content words, and (iii) low-proficiency learners have more difficulties with content words than high-proficiency learners do. Most frequent suffix errors were omissions of past or plural suffixes. Among the function words, the most frequent errors were found with auxiliary contractions, infinitive marker to, and articles, mostly in the environment of linking and elision. It is also shown that C-V linking, C-C linking, and elision are the primary sources for the most frequent errors. C-V linking led to errors in correctly locating the word boundary, while C-C linking and elision resulted in omission. These errors show that Korean EFL listeners have difficulties in detecting fine-grained phonetic details to the extent that native speakers can do.
The Big Why of Implementing Computational Thinking In STEM Education: A Systematic Literature Review

Computational Thinking (CT) has been increasingly embraced as a reformation in STEM education. This paper discusses why the implementation of CT would have a considerable effect on STEM education. The first objective of this systematic literature review is to identify the subjects that incorporate the most elements of CT in STEM education. Secondly, it aims to provide an overview of CT practices in the classrooms. Finally, the major findings of this study seek to discuss the benefits and challenges of the use of CT in STEM education. Fifteen articles were methodically selected from Scopus, Web of Science, Dimensions, and Google Scholar databases as the relevant studies to be discussed in this systematic study, based on the PRISMA Statement (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) review technique. This review identifies current research gaps and directions for the practice and implementation of CT in STEM education. Further analysis of the articles has contributed to a conclusion that CT has become more widespread and multi-disciplinary and seems to have propagated improvements in STEM education. Still, a new study is required, especially on long-term implications.
High Capacity Blind Information Hiding Based on Autoencoder

To realize image information hiding, we train a multi-layers autoencoder includes an encoder and a decoder. Using encoder architecture, we input the secret color image and generate the cover image where the size stays the same directly. In addition, we reconstruct the secret color image by inputting the stego images passing on the decoder which accomplish the secret image blind extraction. Our method realizes a high resolution and high payload color image steganography. Experimental results show that we can realize the high capacity blind specified secret images information hiding automatically by our method.
Advanced Thermoelectric Materials for Flexible Cooling Application

Flexible cooling devices, which aim to fulfill the essential requirement of complex working environments and enable local heat dissipation, have become the cutting‐edge area of refrigeration technology. Thermoelectric (TE) material represents a promising candidate for various flexible cooling applications, including wearable personal thermoregulation devices. With the increasing interest in the Peltier effect of conductive polymers and inorganic films on flexible substrates, flexible cooling devices have undergone rapid development. Herein, the fundamental mechanisms, basic parameters, and temperature measurement techniques for evaluating the cooling performance are summarized. Moreover, recent progress on TE materials, such as flexible inorganic and organic materials for Peltier cooling studies, is reviewed. More importantly, insights are provided into the key strategies for high‐performance Peltier devices. The final part details the existing challenges and perspectives on flexible TE cooling to inspire additional research interests toward the advancement of refrigeration technology.
Multi-image encryption algorithm based on image hash, bit-plane decomposition and dynamic DNA coding

Problems such as large data volumes, slow transmission rates and low security often occur when transmitting multiple digital images over the internet. In order to protect the content security of multiple images and improve the speed of internet transmission, a multi-image encryption algorithm based on image hash, bit-plane decomposition and dynamic DNA coding is proposed in this study. First, the proposed algorithm merges several grey images and image hashing algorithm is used to generate the initial key for chaotic mapping. Second, the random sequence generated by the improved three-dimensional chaotic map is used to decompose the bit plane of the merged image and the bit-plane matrix is substituted numerically. Finally, the random sequence generated by the four-dimensional hyperchaotic system is used to perform dynamic DNA coding and calculation on the image and the ﬁnal multiple ciphertext images are generated by decomposition of the pixel matrix. The experimental results show that the proposed algorithm has large key space, strong key sensitivity, strong security and robustness, and can resist various conventional attacks such as statistical analysis, differential attack, exhaustive attack, cropping and noise attack.
On the Historical Association between NationalIQ and GDP per Capita

A remarkable, unquestioned assumption in (1–3) and subsequent studies measuring the association between national average Intellectual Quotients (IQ) and Gross Domestic Products (GDP) per capita is that a supposedly immutable1 genetic2 factor (IQ) may be correlated with a markedly fluctuant one (the wealth of nations). This short paper questions this assumption and presents the following results:
Research on Intelligent recognition system of Cotton apical Bud based on Deep Learning

Cotton topping is a key link in the whole cotton production process, which can eliminate the top growth advantage and promote cotton production and income. The automation of cotton topping can greatly reduce the topping time and labor intensity, and the high-speed and accurate identification of cotton top buds is the prerequisite and basis for automatic topping. This article designs a cotton top bud intelligent identification system based on YOLOv3 network, which can detect cotton top buds in visible light images in real time, and provide visual information for the subsequent realization of cotton top bud position measurement and mechanical control. A computer workstation (RTX2070s) was used to identify 15049 cotton top bud images under different weather and illumination conditions. The results show that the average time of single frame image recognition is controlled within 100 milliseconds, and the top bud recognition rate reaches 96%, which creates good conditions for the development of automatic cotton topping equipment and has broad application prospects.
Vascular optical spectroscopy for the detection of contrast-agent perfusion during x-ray angiography in peripheral artery disease patients undergoing a surgical intervention

In patients with peripheral artery disease (PAD), plaque is accumulating in arteries which leads to a reduction in blood supply to the extremities. In advanced stages, surgical intervention is required to reopen the arteries and restore limb perfusion. During this procedure, it is important to correctly identify which areas of the foot lack perfusion. The standard procedure to obtain this information is X-ray angiography, which is performed repeatedly during the intervention. The disadvantage of this procedure is the relatively high radiation dose and extensive use of contrast agents. To reduce this problem, we evaluate in this pilot study (involving 4 patients) the ability of vascular optical spectroscopy (VOS) to detect the X-ray contrast agent permeating the angiosomes in the foot. We show that the contrast agent can be detected by optical measurements as it temporarily replaces the blood in the different angiosomes, which leads to a 1% to 5% change in the signal amplitude. In addition, measurements of the blood pooling in the foot were performed before the intervention. We observed a strong correlation between the angiosomes that showed a worsen state in the measurement done before the intervention and the absence of angiographic contrast agent signals during the intervention itself. Among the 4 patients monitored, 2 showed a response to the contrast agent in their angiosomes and they corresponded to the patients with a relatively better perfusion in the pre-intervention measurements.
Resource Allocation for High-Speed Train Communication Based on Deep Reinforcement Learning

With the advent of high-speed railway era, users’ demands for high link capacity and high reliability are increasing. In this paper, a resource allocation algorithm based on deep reinforcement learning (DRL) is proposed in the high-speed railway (HSR) scenario. The communication link between high-speed train and high-speed train (T2T) is taken as an agent, and the link capacity and packet transmission success probability (PTSP) are taken as optimization objectives, which are mapped into the reward function. The agents gain experience by constantly interacting with the HSR communication environment to update the neural networks in DRL and eventually learned the optimization policy. The simulation shows that when the trains’ speed is 350km/h and the transmission packet size is 2385Bits, the high reliability of T2T link can still be maintained, and the capacity of high-speed train to Infrastructure (T2I) link is improved by 1Mbps compared with the baseline, which proves that the proposed algorithm can mitigate the interference introduced by spectrum reuse and doppler shift.
The Role of Leadership in Corporate Social Responsibility as a Manifestation of Organization’s Intellectual Capital: A Conceptual Framework

Purpose: The main concept of the work is to learn, understand, describe and explain the importance of leaders characterized by special moral competences giving them a rational direction to their behaviors and thus to the development of the socially responsible organization in the context of constituting intellectual capital within the process of corporate social responsibility. Design/Methodology/Approach: The paper constitutes the conceptual research framework for further investigations on the position of the leadership in socially responsible organizations which contributes to the increasing role of intellectual capital in the above organizations. The hypotheses summarizing the deliberation based on the critical review of the literature were formulated in the paper. Findings: The preliminary results of the conceptual work carried out indicate that the higher the level of intellectual capital, the higher is the level of excellence in social responsibility. It was also assumed that the key factor shaping intellectual capital of the organization is the emotional intelligence of the leader and the emotional intelligence of the leader positively shapes interpersonal trust in the organization as well as inter-organizational trust. Practical Implications: Presented outcomes may be useful in formulating management strategies of contemporary enterprises based on the specific role of the responsible leader which then may turn into a new paradigm of managing socially responsible organization operating at higher level of responsibility awarness. Originality/value: The results of the research contribute in the cognitive sense to the enrichment of knowledge in terms of responsible leadership and developing intellectual capital of the organizations.
In the Trenches of the Solar–Stellar Connection. III. The HST/COS Ecliptic-poles Stellar Survey (EclipSS)

The Ecliptic-poles Stellar Survey (EclipSS) collected far-ultraviolet (FUV: 1160–1420 Å) spectra of 49 nearby (d ≲ 100 pc) F3–K3 main-sequence stars, located at high ecliptic latitudes (north and south), using the Cosmic Origins Spectrograph of the Hubble Space Telescope. The ecliptic poles receive higher exposures from scanning missions like the Transiting Exoplanet Survey Satellite (high-precision optical photometry) and Extended Roentgen Survey with an Imaging Telescope Array (X-ray monitoring), which can deliver crucial contextual information, not otherwise easily secured. The objective was to support theoretical studies of stellar hot outer atmospheres—chromospheres (∼104 K) and coronae (≳1 MK)—which, among other things, can adversely impact exoplanets via host-star “space weather. ” Flux–flux diagrams (e.g., C ii 1335 Å versus O i 1306 Å) were constructed for the EclipSS stars, solar Cycle 23/24 irradiances, and long-term FUV records of α Cen A (G2 V) and B (K1 V). The EclipSS cohort displays similar minimum (“basal”) fluxes to the Sun and solar twin α Cen A, in chromospheric O i 1306 Å. In hotter C ii 1335 Å, a downward slump of the basal fluxes—noted in previous, less controlled surveys—can now be explained as an effect of subsolar abundances. The consistent basal minima in chromospheric and higher temperature species at solar metallicity favor the idea that stellar analogs of the solar supergranulation network provide a baseline of high-energy emissions. The magnetic network is replenished by a “local dynamo” independently of the stellar spin. It can operate even when the starspot-spawning internal dynamo has ceased cycling, as during the Sun’s 17th century Maunder Minimum.
Learning-based Buffer Starvation Modeling for Packets Prefetching Strategies of Video Streaming Services

Improving the quality of experience (QoE) of video streaming is a significant task in the wireless network scenario. Buffer starvation in the transmission process will cause playback freeze, and a certain number of packets must be prefetched before the service restarts. Taking into account the shortcomings of buffer in video streaming services, this paper proposes a deep learning-based starvation probability calculation model and a reinforcement learning-based packet prefetching model. The deep learning approach extracts the correlation between different timing inputs through the recurrent neural network module to return an explicit result and the precise distribution of the number of buffer starvation. The reinforcement learning approach leverages a better trade-off between start-up/rebuffering delay and buffer starvation by adjusting the packet prefetching strategy, so that the long-term objective quality of experience (QoE) of the video stream is optimized. Our framework can be applied to actual scenarios including finite video streaming and long video streaming transmission.
A fully differential switched-capacitor integrator based programmable resolution hybrid ADC architecture for biomedical applications

MeitY, Government of India Abstract A novel switched‐capacitor integrator based programmable resolution analog to digital converter (ADC) architecture is proposed. The proposed hybrid ADC architecture can be switched between successive approximation register (SAR) and delta‐sigma modulator (DSM) mode in 8‐bit to 15‐bit resolution. A mathematical relationship showing the effect of mismatch of capacitors on ADC linearity is derived. A fully differential folded cascode operational transconductance amplifier (OTA) operating in a weak inversion region is designed using gm/ID technique with programmable unity gain bandwidth and slew rate. The designed OTA offers 83 dB DC gain. The proposed ADC, designed and laid out in UMC 180 nm standard CMOS technology, occupies an area of 0.228 mm. The ADC resolution is programmable from 8‐bit to 15‐bit using a 3‐bit control bus (res[2 : 0]). The hybrid ADC operates in SAR mode from 8‐bit to 11‐bit resolutions and as the first‐order DSM with a multi‐bit quantizer in 12‐bit to 15‐bit resolutions. The dynamic performance of the proposed ADC is verified through post‐layout simulations with a supply voltage of 1.8 V. It exhibits a signal‐to‐noise and distortion ratio of 45–86 dB and consumes a power of 0.86–98 μW across target resolutions (8–15 bits).
Force Evaluation of Tendon Routing for Underactuated Grasping

While the modeling analysis of the kinetostatic behavior of underactuated tendon-driven robotic fingers has been largely addressed in the literature, tendon routing is often not considered by these theoretical models. The tendon routing path plays a fundamental role in defining joint torques, and subsequently, the force vectors produced by the phalanges. However, dynamic tendon behavior is difficult to predict and is influenced by many external factors including tendon friction, the shape of the grasped object, the initial pose of the fingers, and finger contact points. In this paper, we present an experimental comparison of the force performance of nine fingers, with different tendon routing configurations. We use the concept of force-isotropy, in which forces are equal and distributed on each phalanx as the optimum condition for an adaptive grasp. Our results show only some of the finger designs surveyed exhibited a partial adaptive behavior, showing distributed force for the proximal and distal phalanxes throughout grasping cycles, while other routings resulted in only a single phalanx remaining in contact with the object.
Design, Modeling and Performance Evaluation of Mobile Robot Based on Eccentric Paddle Mechanism

In order to improve climbing performance of the robot in complex environments, the wheeled-legged mechanism is gradually being widely used. In this paper, we use computer-aided modeling technology to design a 2-DOF ePaddle-based robot of high step-climbing capacity. Climbing strategies of the robot under the circle-wheeled (CW) mode and active-paddle-legged (APL) mode are carried out. We discuss determinants for success in climbing by establishing the climbing posture models. Simulation results processed with MATLAB tools reveal that paddle’s motion can significantly improve the climbing performance to avoid slipping. The experimental results verify the robot is capable of climbing over 300mm obstacle APL modes, which is 2.14 times the wheel radius.
THERMOSENSITIVE HYDROGELS–A POTENTIAL CARRIER FOR THE DELIVERY OF DRUGS AND MACROMOLECULES

In this review, the authors have discussed scientific advances in thermosensitive hydrogels over the past two decades. The ability of the thermo-sensitive hydrogel to undergo rapid changes with response to temperature makes it an attractive candidate for many biomedical applications such as targeted drug delivery, wound healing, soft contact lenses, sensors, tissue regeneration, gene, and protein delivery. This review aims to deliver a brief overview of gelation properties, merits, and demerits of various natural and synthetic thermo-sensitive polymers that have significant clinical relevance. The report emphasizes the importance of injectable thermosensitive hydrogels, as it can offer improved solubility of hydrophobic drugs and site-specificity, extended-release of drugs and macromolecules, improved safety, and local administration of drugs. The authors has also provided a commentary on the delivery of drugs or macromolecules from thermo-sensitive hydrogels through various approaches. This review highlights the current status of research in thermo-sensitive hydrogels and emphasizes the importance of developing nontoxic thermo-sensitive hydrogels, dual responsive, and multi-responsive hydrogel systems.
Stick-Slip Classification Based on Machine Learning Techniques for Building Damage Assessment

ABSTRACT Accelerometers in smart devices have been used to successfully provide valuable information such as early warnings of earthquake activity and health monitoring of buildings. The next important step of using the acceleration measurements from smart devices is to assess building seismic damage, which is a more challenging application. A main challenge is related to the sliding motions of smart devices, which prevents acceleration measurements from directly representing the movement of underlying building floors. To detect and remove sliding motions in acceleration measurements, this paper presents an accurate and robust accelerometer-based stick-slip motion classification framework based on machine learning techniques. Three types of machine learning algorithms are introduced, and their classification performance are compared; support vector machine (SVM), multilayer perception (MLP), and recurrent neural networks (RNN). For the SVM and MLP, three classification conditions are considered: feature selection, non-linear discriminating analysis and classifier comparison. For the RNN, three hyperparameters are considered to find the best performing classification algorithm. Each algorithm is trained and validated with experimental acceleration data from a shaking table test.