Miracle-dz/UniFAR-FTUs huggingface dataset:
Emotions and ensuing motor performance are altered by regulating breathing frequency: Implications for emotion regulation and sport performance

Breathing interventions have been shown to improve sport performance. Although evidence exists to support the role of perceived arousal as a critical underlying mechanism of breathing interventions, methodological differences in the literature preclude clear understanding of potential contributing factors to the effectiveness of such interventions. Under neutral contexts, we have demonstrated attention, dyspnea, and hindrance may need to be considered as mediators of how breathing frequency affects motor performance. We sought to extend our previous findings to determine how breathing frequency affects motor performance under varying emotional conditions. Participants (N = 35, Mage = 21.68, SD = 2.96; 20 females) performed slow, normal, and fast metronome-paced breathing while viewing pleasant and unpleasant stimuli prior to executing a pinch grip task. Performance was assessed via reaction time (RT), variability (V) and error (AE). Assessment of indices of perceived arousal included measuring heart rate variability (HRV) and visual analog scale responses. Visual analog scales were also used to assess attention, dyspnea, and hindrance. Repeated measures ANOVAs showed slow breathing increased RT and HRV compared to normal and fast breathing under emotional conditions (all p’s < 0.05). Hierarchical multiple regression models revealed that decreased breathing frequency predicted increases in RT (β = −0.25, p < 0.05) under pleasant conditions, while predicting increases in HRV for unpleasant conditions (β = −0.45, p < 0.001). Increases in dyspnea (β = 0.29, p < 0.05) and hindrance (β = 0.35, p < 0.01) predicted increases in RT under pleasant conditions, while only increases in hindrance predicted increases in RT under unpleasant conditions (β = 0.41, p < 0.01). Decreases in breathing frequency predicted increases in HRV under unpleasant conditions (β = −0.45, p < 0.001). Overall, our findings suggest under varying emotional contexts breathing frequency differentially affects movement, potentially mediated by factors other than perceived arousal. In addition, these results inform the use of breath regulation as an antecedent emotion regulation strategy.
Potential effects of commonly applied drugs on neural stem cell proliferation and viability: A hypothesis-generating systematic review and meta-analysis

Neural stem cell (NSC) transplantation is an emerging and promising approach to combat neurodegenerative diseases. While NSCs can differentiate into neural cell types, many therapeutic effects are mediated by paracrine, “drug-like” mechanisms. Neurodegenerative diseases are predominantly a burden of the elderly who commonly suffer from comorbidities and thus are subject to pharmacotherapies. There is substantial knowledge about drug-drug interactions but almost nothing is known about a potential impact of pharmacotherapy on NSCs. Such knowledge is decisive for designing tailored treatment programs for individual patients. Previous studies revealed preliminary evidence that the anti-depressants fluoxetine and imipramine may affect NSC viability and proliferation. Here, we derive a hypothesis on how commonly applied drugs, statins and antihypertensives, may affect NSC viability, proliferation, and differentiation. We conducted a systematic review and meta-analysis looking at potential effects of commonly prescribed antihypertensive and antihyperlipidemic medication on NSC function. PubMed and Web of Science databases were searched on according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Publications were assessed against a priori established selection criteria for relevancy. A meta-analysis was then performed on data extracted from publications eligible for full text review to estimate drug effects on NSC functions. Our systematic review identified 1,017 potential studies, 55 of which were eligible for full text review. Out of those, 21 were included in the qualitative synthesis. The meta-analysis was performed on 13 publications; the remainder were excluded as they met exclusion criteria or lacked sufficient data to perform a meta-analysis. The meta-analysis revealed that alpha-2 adrenoceptor agonists, an anti-hypertensive drug class [p < 0.05, 95% confidence intervals (CI) = –1.54; –0.35], and various statins [p < 0.05, 95% CI = –3.17; –0.0694] had an inhibiting effect on NSC proliferation. Moreover, we present preliminary evidence that L-type calcium channel blockers and statins, particularly lovastatin, may reduce NSC viability. Although the data available in the literature is limited, there are clear indications for an impact of commonly applied drugs, in particular statins, on NSC function. Considering the modes of action of the respective drugs, we reveal plausible mechanisms by which this impact may be mediated, creating a testable hypothesis, and providing insights into how future confirmative research on this topic may be conducted.
Herb-induced hepatitis secondary to artemisinin: A case report

This report describes a rare case of severe liver injury secondary to a herbal supplement containing artemisinin. The patient received plasma exchange with bilirubin filtration adsorption therapy. The case is unique in its severe cholestasis. Bilirubin decreased to baseline after 62 days. This will help to improve clinicians' awareness of the diagnosis and treatment of drug-induced liver injury.
Relaxion window

We investigate cosmological constraints on the original relaxion scenario proposed by Graham, Kaplan and Rajendran. We first discuss the appropriate sign choice of the terms in the scalar potential, when the QCD axion is the relaxion with a relaxion-inflaton coupling proposed in the original paper. We next derive the cosmologically consistent ranges of the mass and a coupling of the relaxion for both the QCD relaxion and non-QCD relaxion. The mass range is obtained by $10^{-5}$ eV $\ll m_{\phi} \lesssim 10^4$ eV. We also find that a strong correlation between the Hubble parameter at the relaxion stabilization and the scale $\Lambda$ of non-QCD strong dynamics, which generates the non-perturbative relaxion cosine potential. For a higher relaxion mass, a large scale $\Lambda$ becomes available. However, for its lower mass, $\Lambda$ should be small and constructing such a particle physics model is challenging.
Challenges in Region-Speciﬁc Image Captioning: A Deep Learning Approach

Region-speciﬁc image captioning is the 001 task of generating a caption from an im-002 age such that the caption is about the spe-003 ciﬁc region in that image. This paper de-004 scribes the challenges involved in region-005 speciﬁc image captioning and provides sev-006 eral methods to utilize the region-speciﬁc 007 features to enhance the quality of the cap-008 tions in addition to utilizing the features 009 from the whole image. Our experiments 010 on real-world data sets demonstrate that 011 generating region-speciﬁc captions is chal-012 lenging even after utilizing the information 013 speciﬁc to the region. We analyze the vari-014 ables impacting the quality of the captions 015 which include the bounding box size and 016 the region-speciﬁc feature extractor
Research on Intelligent Power Marketing Inspection Model Based on Knowledge Graph

The traditional topic-based auditing model lacks the ability of multi-object and multi-abnormal correlation auditing, which makes it impossible to solve the multi-scenario and multi-factor correlation-based auditing problem. This paper designs an intelligent power marketing audit model based on a knowledge graph. First, an entity identification and relationship extraction method for power marketing business based on NLP (natural language processing) and sequence annotation technology is proposed, and the description content is imported into the knowledge graph database; then, semantic disambiguation and knowledge are carried out by using bidirectional encoder representation from transformers (BERT). Link to build a knowledge map of business audit rules: Finally, an experimental analysis is carried out by taking the copying and receiving business with a large business volume in the marketing audit work as an example, and it is verified that the proposed model can effectively improve the information analysis ability and the audit accuracy of the audit work.
Key Challenges in Integrating ICTs in Education

This paper attempts to study the significant challenges in integrating ICTs use in education rising from environmental, cultural and educational faced by policy makers, educators, educational administrators and students in higher education. The role of Information & Communication Technology (ICT) in Education is undisputed globally. ICT have potentially powerful tool for extending educational opportunities. ICT have the potential for increasing access to and improving the relevance and quality of Education. ICT can enhance the quality of education in several ways: by increasing learner motivation and engagement, by facilitating the acquisition of basic skills and by enhancing teacher training. In 21 st century teaching learning skill underscore the need to shift from traditional teacher centered pedagogy to more learner-centered method, so that's why we need ICT facilities in our modern education system. But some barriers may discourage teachers & student to integrated ICT in education, like-availability in educational institution, maintenance, lack of knowledge, pace of change & funding. The process of using ICT is changing everyday in education that’s very complicated, because people can't know, what is it, how can we handle it, when we use it. People must know what can be done with ICT. This paper examined some key challenges faced by the higher education institutions (HEIs) in integrating ICTs into teaching and learning & key challenges of ICT envisaged in our 21 st century Education & how we overcome the problems that we face in use of ICT in 21 st century of education. The task of employment and integration of ICT in modern eduction is facing a lot of challenges. the challenges like-availability of ICT facilities in educational institution, lack of knowledge to handle ICT equipment, language problem, insufficient funds, lack of trained etc. but we can over come the challenges-to create awareness on ICT Education, to formulate policies to promote broad access to skills and competencies for learning and adopting ICT, enlarge community participation for self-sustainability in ICT application, develop supportive infrastructure facilities such as electricity, internet, etc. Government should actively. Responsible authorities have to try and overcome these challenges, so that the modern education can benefit. ICTs, in their role as tools added to pedagogical models, can become valuable resources for learning and for equipping students with appropriate personal and professional skills for a country’s development (Prieto, Quiñones, Ramírez, Fuentes, Labrada, Pérez & Montero, 2011).
Does ﬁnancial development drive economic growth ? an ARDL approach

: This paper examines the Granger-causal relationship between financial development and economic growth. India is taken as a case study. The Auto-Regressive Distributed Lag (ARDL) method (also known as the bounds testing approach) proposed by Pesaran-Shin-Smith (2001) has been employed The study finds that financial development and economic growth are cointegrated in the long run i.e. there is a long run theoretical relationship between these variables. Our findings are in line with (McKinnon (1973); King and Levine (1993a, b); Neusser and Kugler (1998); Levine (1997); Beck (2000). For a developing country like India financial development is evidenced to lead growth rather than the other way around. This has a major policy implication for the government of India. If government wants to increase economic growth, it can do so by bringing out reforms in the banking sector and capital market sector. Hence, India may consider banking sector and capital market sector as the policy variables and the authorities should take necessary steps to bring about reforms in these sectors to enhance economic growth in the economy.
Probing O-enrichment in C-rich dust planetary nebulae

The abundance of O in planetary nebulae (PNe) has been historically used as a metallicity indicator of the interstellar medium (ISM), where they originated; e.g. it has been widely used to study metallicity gradients in our Galaxy and beyond. However, clear observational evidence for O self-enrichment in low-metallicity Galactic PNe with C-rich dust has been recently reported. Here, we report asymptotic giant branch (AGB) nucleosynthesis predictions for the abundances of the CNO elements and helium in the metallicity range Z� /4 < Z < 2Z� . Our AGB models, with diffusive overshooting from all the convective borders, predict that O is overproduced in low-Z low-mass (∼1–3M� ) AGB stars and nicely reproduce the recent O overabundances observed in C-rich dust PNe. This confirms that O is not always a good proxy of the original ISM metallicity and other chemical elements such as Cl or Ar should be used instead. The production of oxygen by low-mass stars should be thus considered in galactic-evolution models.
Augmentor or Filter? Reconsider the Role of Pre-trained Language Model in Text Classification Augmentation

Text augmentation is one of the most effective techniques to solve the critical problem of insufficient data in text classification. Existing text augmentation methods achieve hopeful performance in few-shot text data augmentation. However, these methods usually lead to performance degeneration on public datasets due to poor quality augmentation instances. Our study shows that even employing pre-trained language models, existing text augmentation methods generate numerous low-quality instances and lead to the feature space shift problem in augmentation instances. However, we note that the pre-trained language model is good at finding low-quality instances provided that it has been fine-tuned on the target dataset. To alleviate the feature space shift and performance degeneration in existing text augmentation methods, we propose BOOSTAUG, which reconsiders the role of the language model in text augmentation and emphasizes the augmentation instance filtering rather than generation. We evaluate BOOSTAUG on both sentence-level text classification and aspect-based sentiment classification. The experimental results on seven commonly used text classification datasets show that our augmentation method obtains state-of-the-art performance. Moreover, BOOSTAUG is a flexible framework; we release the code which can help improve existing augmentation methods.
Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey

In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and Blockchain technologies have quickly gained pace as a new study niche in numerous collegiate and industrial sectors, notably in the healthcare sector. Recent advancements in healthcare delivery have given many patients access to advanced personalized healthcare, which has improved their well-being. The subsequent phase in healthcare is to seamlessly consolidate these emerging technologies such as IoT-assisted wearable sensor devices, AI, and Blockchain collectively. Surprisingly, owing to the rapid use of smart wearable sensors, IoT and AI-enabled technology are shifting healthcare from a conventional hub-based system to a more personalized healthcare management system (HMS). However, implementing smart sensors, advanced IoT, AI, and Blockchain technologies synchronously in HMS remains a significant challenge. Prominent and reoccurring issues such as scarcity of cost-effective and accurate smart medical sensors, unstandardized IoT system architectures, heterogeneity of connected wearable devices, the multidimensionality of data generated, and high demand for interoperability are vivid problems affecting the advancement of HMS. Hence, this survey paper presents a detailed evaluation of the application of these emerging technologies (Smart Sensor, IoT, AI, Blockchain) in HMS to better understand the progress thus far. Specifically, current studies and findings on the deployment of these emerging technologies in healthcare are investigated, as well as key enabling factors, noteworthy use cases, and successful deployments. This survey also examined essential issues that are frequently encountered by IoT-assisted wearable sensor systems, AI, and Blockchain, as well as the critical concerns that must be addressed to enhance the application of these emerging technologies in the HMS.
25-Hydroxyvitamin D Serum Levels Linked to Single Nucleotide Polymorphisms (SNPs) (rs2228570, rs2282679, rs10741657) in Skeletal Muscle Aging in Institutionalized Elderly Men Not Supplemented with Vitamin D

Sarcopenia (Sp) is the loss of skeletal muscle mass associated with aging that results in an involution of muscle function and strength. Vitamin D deficiency is a common health problem worldwide, especially among the elderly, and hypovitaminosis D leads to musculoskeletal disorders. The aim of this study was to evaluate the impact and presence of a possible linkage between Single Nucleotide Polymorphisms (SNPs) CYP2R1 (rs10741657), GC (rs2282679), and VDR (rs2228570), serum 25-OH/D concentrations and the link with the degree of sarcopenia in 19 institutionalized elderly men not supplemented with vitamin D. Levels of 25-OH vitamin D were quantified with a commercial enzyme-linked immunosorbent assay kit and 3 SNPs were genotyped with KASPar assays. Significant differences in 25-OH/D concentration were determined between the bi-allelic combinations of rs228679 and rs228570. We detected statistically significant weak positive correlations between the AA (rs10741657 and rs228570) and TT (rs228679) and alleles and 25-OH/D and the probability of having higher 25-OH/D concentrations was 2- to 3-fold higher. However, the GG alleles of the 3 SNPs showed that the probability of having optimal 25-0H/D concentrations decreases by 32% for rs10741657, 38% for rs228679, and 74% for rs228570, showing a strong negative correlation between the degree of sarcopenia and 25-OH/D levels. Allelic variations in CYP2R1 (rs10741657), GC (rs2282679), and VDR (rs10741657) affect vitamin D levels and decisively influence the degree of sarcopenia in institutionalized elderly people.
Point Clouds Classification and Segmentation Based on Local Fusion Network

The irregularity and sparsity of point clouds make it challenging to directly process raw 3D points using existing deep learning frameworks for classification and segmentation tasks. Aiming to solve this challenge, an end-to-end learning network is proposed. The key component of the proposed network is Local Fusion Block (LFB), which can be easily embedded into other networks. The function of the block is to fully integrate features from local points and a single point. By stacking the blocks, the network can obtain a larger receptive field, and achieve an effect similar to image convolution. Experiments on two representative shape classification and part segmentation datasets show that the proposed method achieves competitive results compared with some representative approaches in terms of accuracy.
Deep learning model based on MobileNetV2 for mask detection with Raspberry Pi

Deep learning has proven to be a very powerful tool for all kinds of real life applications in different fields. Object detection has had a great evolution based on deep learning models, where it presents optimistic results to detect and classify objects. The COVID-19 pandemic causes a global health crisis that impacts all aspects of life. One of the effective protection methods is to use a mask in public areas according to the World Health Organization (WHO), for this reason the objective of this article is to implement a lightweight and efficient model to classify images in real time in embedded systems and determine whether or not they have a mask. In the article, three models are proposed to evaluate their execution when implemented in Raspberry Pi.
Mission Planning for Mobile Communication Passive Radar via an Evolutionary Algorithm

The spatial arrangement of receiver and transmitter sites defining the layout of a multistatic radar system has crucial influence on the system’s performance in achievable estimation accuracy. In this work, we focus on mission planning as the process of determining an optimal layout by selecting a combination of discrete receiver and transmitter sites from fixed size sets of suitable sites. In this context we apply an Evolutionary Algorithm (EA) to determine an optimal layout with regard to target state estimation, where we utilize the Cramér-Rao Lower Bound (CRLB) as performance metric. Due to a typically large number of combinations, exhaustive search becomes increasingly expensive. The presented approach provides means to find solutions within significantly less time than exhaustive search, making it therefore a viable tool in the context of mission planning for a passive radar system.
Origin and evolution of the elements

This comprehensive volume reviews current understanding of the origin and evolution of elements, from stellar nucleosynthesis to the chemical evolution of the cosmos. With chapters by leading authorities in the field, it describes models of how the elements are produced by stars, the nuclear processes involved, and how the quantity of elements evolved in our Galaxy and distant galaxies. The observed chemical composition of stars in defferent locations within our Galaxy and nearby galaxies is discussed, as are the compositions of hot and cold gases, of dust grains found between stars and in meteorites, and of the integrated light from distant galaxies and quasars. This authoritative volume is a valuable resource for graduate students and professional research astronomers.
A Real-Time Smoke and Fire Warning Detection Method Based on an Improved YOLOv5 Model

Smoke and fire detection technology has been very mature with the development of deep learning. Still, the early warning detection methods for fires in some factories and gas stations are insufficient. Especially, no clear advancement has been seen in terms of small targets. So, we propose a smoke and fire detection method based on YOLOv5 network as a solution to address this problem. We combine the original YOLOv5 backbone network with an attention mechanism to improve the ability of small object detection. Existing smoke and fire detection methods encounter some challenges in practical industrial applications. Deep neural network learning models require powerful computing processors and servers; secondly, the models trained by general neural networks are too large to be deployed. The improved network can reduce the model size while maintaining high detection accuracy, thereby reducing the storage space of edge devices. In addition, this study uses Deepstream SDK to push streaming fire video, and combines with Jetson NX Xavier edge device for model deployment to realize real-time fireworks warning.
A Concept of a Multiband Passive Radar System for Air Traffic Control on General Aviation Airfields

The paper is devoted to a concept of a low-cost passive radar system intended to support traffic control in small general aviation airfields. The requirements related to the application of such a radar system and the main assumptions concerning the architecture, components, and signal processing are discussed. The considerations are summed up on the basis of the passive radar system being under construction in the general aviation airfield in Przasnysz, Poland. The first preliminary results of passive radar signal processing using this system are also presented.