Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Speech Enhancement based on Full-scale Connection

In order to solve the problem that the popular monaural speech enhancement models that based on encoder-decoder do not make full use of full-scale features, a full-scale feature connected speech enhancement model FSC-SENet is proposed. Firstly, this paper constructs a speech enhancement model based on CRN architecture. Convolutional encoder and decoder are used to extract features and recover speech signals, and LSTM modules are used to extract temporal features at the bottleneck of the model. Then a full-scale connection method and multi feature dynamic fusion mechanism are proposed, so that the decoder can make full use of the full-scale features to recover clean speech in the decoding process. Experimental results on TIMIT corpus show that compared with CRN, our FSC-SENet improves PESQ score by 0.39 and STOI score by 2.8% under seen noise cases, and PESQ score by 0.43 and STOI score by 3.1% under unseen noise cases, which proves that the proposed full-scale connection and dynamic feature fusion mechanism can make CRN have better speech enhancement performance.
SACS-LSTM: A Vehicle Trajectory Prediction Method Based on Self-Attention Mechanism#

High-quality trajectory prediction is crucial for autonomous vehicles with various applications like driving safely and planning traffic trajectory in smart cities. In order to achieve high-precision trajectory prediction, not only the historical trajectory information of the target needs to be considered, but the influence of vehicle interaction on the future trajectory is also extremely important. However, (1) with the increase of the input historical trajectory sequence, it is cause the important information in the feature sequence to be overwritten or lost; (2) It is also a challenge to properly model the interaction between vehicles. In this paper, we propose a Self-Attention Convolutional Social pooling LSTM (SACS-LSTM) vehicle trajectory prediction model. It uses the self attention mechanism to make important information in the historical sequence not easily lost, and uses the self attention mechanism to improve the modeling accuracy of the convolutional social pooling layer for vehicle interaction information, so that the model can achieve the purpose of accurate modeling. We use the NGSIM data set to evaluate the model, and the results vertify that the predicted accuracy within 5s outperforms the baseline models.
Rumor Detection Based on Generalized Text Feature

This paper proposes a rumor detection method based on topic classification and content understanding in health. It can extract features from different scales of sub-datasets, comprehensively consider the correlation and difference in different topics, and combine the part of speech and word meaning to expand the model's ability to understand the text and enhance the ability to detect the rumor traps spread maliciously. The experimental results show that the method achieves good results in the field of health data sets, improving the results in new indicators such as content understanding, and have more vital generalization ability.
Cell Images Classification using Deep Convolutional Autoencoder of Unsupervised Learning

The present work proposes a classification system for the HEp-2 cell images using an unsupervised deep feature learning method. Unlike most of the state-of-the-art methods in the literature that utilize deep learning in a strictly supervised way, we propose here the use of the deep convolutional autoencoder (DCAE) as the principal feature extractor for classifying the different types of the HEp-2 cell images. The network takes the original cell images as the inputs and learns to reconstruct them in order to capture the features related to the global shape of the cells. A final feature vector is constructed by using the latent representations extracted from the DCAE, giving a highly discriminative feature representation. The created features will be fed to a nonlinear classifier whose output will represent the final type of the cell image. We have tested the discriminability of the proposed features on one of the most popular HEp-2 cell classification datasets, the SNPHEp-2 dataset and the results show that the proposed features manage to capture the distinctive characteristics of the different cell types while performing at least as well as the actual deep learning based state-of-the-art methods.
Promoting the psycho-social wellbeing of international students through mindfulness

This qualitative study explores the potential of a mindfulness programme for providing psycho-social support to international students in higher education. The article focuses on analysing the nature of emotional distress among students, how they regulate difficult emotions, and the effects of the mindfulness programme on emotion regulation. For this purpose, in-depth interviews were conducted with students before and after the mindfulness programme, and the weekly mindfulness sessions were observed. The study identified that stress, anxiety, anger, sadness, loneliness and insecurity were among the most difficult emotions experienced by international students. Through mindfulness, students improved in awareness of their emotions, learnt to relate to difficult emotions more constructively, and regulated negative emotions more effectively. Nevertheless, many suggested that they would have welcomed more attention to emotion regulation, as it was identified as a key challenge in their lives. These findings have important implications for the psycho-social well-being and academic achievement of international students.
Level of Anxiety-State as a factor in changing eating patterns in college students: NUTSAU Study

With the purpose of identifying eating patterns of university students at a federal university in the state of Rio de Janeiro, a cross-sectional analysis of food consumption data was stratified by anxiety symptom scores of 147 students enrolled in the second semester of several undergraduate courses in the second semester of 2015. The State-Trait Anxiety Inventory determined the anxiety symptoms scores. A food frequency questionnaire was applied to obtain food consumption data. Students were stratified into two groups, using the median state anxiety score as cutoff point. For each group, some dietary patterns were derived by the application of the principal component analysis. Statistical analysis was performed using the SPSS software. The university students presented a median state-anxiety score of 43 points, and it was significantly higher in women. Three dietary patterns were identified for each group according to their nutritional characteristics, among which “Mixed 1”, “Healthy” and “Rice and Beans” for the less anxious students and “Western”, “Mixed 2” and “Rice and Beans” for the most anxious students. The results showed that it was possible to identify consistent food patterns for both, using a factorial analysis method. Students with the highest anxiety-state scores had a pronounced Western eating pattern, and those least anxious students had higher consumption of healthier food groups and lower consumption of caffeinated beverages. Although this is a cross-sectional analysis, it can contribute to optimize nutritional intervention for this population.
Challenges in Developing Urban Marketing Strategies: Evidence From Ekaterinburg

2018 FIFA World Cup became the first championship held in Russia and Eastern Europe. However, at that time, Russia already had experience in hosting sports mega-events such as the 2014 Winter Olympics in Sochi, the 2013 Summer Universiade in Kazan, and before that the 1980 Olympic Games in Moscow. Hosting the championship in 11 cities at once sets the 2018 FIFA World Cup apart from all these events. It gave impetus to the socio-economic development of all cities (and regions) where the matches were held, including Ekaterinburg. On the other hand, the sports mega-event provides unique opportunities for the global marketing positioning of the city. The present study examines the challenges of developing a city marketing strategy using the case of Ekaterinburg. Theoretical foundations of place marketing were employed for the analysis. Based on the critically explored concepts of place marketing and the competitiveness of the territory, the author’s 4C + 1S model was constructed. The current state of the urban environment was evaluated using a SWOT analysis conducted in a group of students. The research also analysed cities’ experience in conducting recent sports mega-events in the world and managing their facilities and infrastructure after these events. In conclusion, we formulated the proposals for Ekaterinburg’s positioning, including the improvement of the transport system, solution to environmental problems, increase in the effectiveness of the local government, and maintenance of public consensus. As a basis for specialisation, it is suggested to consider either the industry of meetings (MICE) or industrial tourism with complementary ‘natural’ and ‘historical’ directions. The proposed 4C + 1S model can be used as a methodological framework for the creation of urban marketing strategies. Additionally, the article makes a theoretical contribution to the development of place marketing. The results can be applied in further academic urban studies.
Development of Adaptive Reclosing Scheme Using Wavelet Transform of Neutral Line Current in Distribution System

Abstract This article proposes an adaptive operating scheme for a recloser based on classified detection of transient faults and permanent faults in a distribution system. Wavelet transform is used to classify whether a fault is transient or permanent in nature. The Haar mother wavelet of the current flowing in a neutral line has more rich harmonics than other mother wavelets, such as Daubechies, Symlets, Coiflets, and Biorthogonal, so the total harmonic distortion of the Haar mother wavelet is applied for estimating the instant of fault clearance and giving a signal for operating action to the recloser (reclosing). The various simulations according to the fault clearance time and fault type are performed using the Electromagnetic Transient Program to verify the proposed algorithm. The simulation results show the effectiveness of the proposed algorithm.
Disparities in Lung Cancer: miRNA Isoform Characterization in Lung Adenocarcinoma

Simple Summary Post-transcriptional modification events in miRNA molecules have revealed a more complex layer to cancer biology. Such modifications have created a novel path to developing and testing potential cancer biomarkers. Here, we concurrently profiled canonical and non-canonical miRNA molecules in White American (W) and Black or African American (B/AA) lung adenocarcinoma (LUAD) patients from The Cancer Genome Atlas (TCGA) cohort. We identified distinct potential post-transcriptional modifications in lung cancer tissues from W versus B/AA patients. Our results suggested the relevance of miRNA isoforms as potential biomarkers in lung cancer. Abstract Despite the development of targeted therapeutics, immunotherapy, and strategies for early detection, lung cancer carries a high mortality. Further, significant racial disparities in outcomes exist for which the molecular drivers have yet to be fully elucidated. The growing field of Epitranscriptomics has introduced a new layer of complexity to the molecular pathogenesis of cancer. RNA modifications can occur in coding and non-coding RNAs, such as miRNAs, possibly altering their gene regulatory function. The potential role for such modifications as clinically informative biomarkers remains largely unknown. Here, we concurrently profiled canonical miRNAs, shifted isomiRs (templated and non-templated), and miRNAs with single-point modification events (RNA and DNA) in White American (W) and Black or African American (B/AA) lung adenocarcinoma (LUAD) patients. We found that while most deregulated miRNA isoforms were similar in W and B/AA LUAD tissues compared to normal adjacent tissues, there was a subgroup of isoforms with deregulation according to race. We specifically investigated an edited miRNA, miR-151a-3p with an A-to-I editing event at position 3, to determine how its altered expression may be associated with activation of divergent biological pathways between W and B/AA LUAD patients. Finally, we identified distinct race-specific miRNA isoforms that correlated with prognosis for both Ws and B/AAs. Our results suggested that concurrently profiling canonical and non-canonical miRNAs may have potential as a strategy for identifying additional distinct biological pathways and biomarkers in lung cancer.
Counteracting Hoax in Social Media Through Tabayyun By Islamic Student Community

This study aims to find out hoaxes on social media and how the form of tabayyun and its implementation can be a method of preventing hoaxes spread on social media. Using this type of research mixed methods with the design of the Sequential Exploratory Strategy. The subjects in this study N Total = 514 who were Muslim students at universities in Palembang City which were determined using purposive sampling technique. The data collection method used an online survey. Data analysis using coding techniques (open coding, axial coding and selective coding) version 9 of the Atlas.ti program and product moment analysis and testing for level categorization of the IBM SPSS version 24 program. This study found, first, the form of hoaxes on social media: 1) News lie; 2) False information; 3) Does not match the facts. Second, students have not implemented tabayyun optimally in responding to news on social media. The form of the application of tabayyun is to seek the truth by thinking critically, observing and confirming the news and information obtained.
The prevention and treatment of Covid-19 and related development during pandemic

When the outbreak of human novel coronavirus was first reported in Wuhan, China at the end of 2019, the epidemic spread rapidly around the world and finally became a pandemic in 2020. In order to seek effective drugs to treat the Covid-19 infected patients for emergent use and for the disease prevention, researchers examined numerous existed antiviral drugs that may have the potential for Covid-19 treatment. At the same time, antibody treatment and vaccines development were ongoing simultaneously. The aim of this review is to introduce antibody therapy, vaccine development and potential antiviral treatments on Covid-19 and to discuss the future perspective on the Covid-19 pandemic.
Nutritional Supplementation Reduces Lesion Size and Neuroinflammation in a Sex-Dependent Manner in a Mouse Model of Perinatal Hypoxic-Ischemic Brain Injury

Perinatal hypoxia-ischemia (HI) is a major cause of neonatal brain injury, leading to long-term neurological impairments. Medical nutrition can be rapidly implemented in the clinic, making it a viable intervention to improve neurodevelopment after injury. The omega-3 (n-3) fatty acids docosahexaenoic acid (DHA, 22:6n-3) and eicosapentaenoic acid (EPA, 20:5n-3), uridine monophosphate (UMP) and choline have previously been shown in rodents to synergistically enhance brain phospholipids, synaptic components and cognitive performance. The objective of this study was to test the efficacy of an experimental diet containing DHA, EPA, UMP, choline, iodide, zinc, and vitamin B12 in a mouse model of perinatal HI. Male and female C57Bl/6 mice received the experimental diet or an isocaloric control diet from birth. Hypoxic ischemic encephalopathy was induced on postnatal day 9 by ligation of the right common carotid artery and systemic hypoxia. To assess the effects of the experimental diet on long-term motor and cognitive outcome, mice were subjected to a behavioral test battery. Lesion size, neuroinflammation, brain fatty acids and phospholipids were analyzed at 15 weeks after HI. The experimental diet reduced lesion size and neuroinflammation specifically in males. In both sexes, brain n-3 fatty acids were increased after receiving the experimental diet. The experimental diet also improved novel object recognition, but no significant effects on motor performance were observed. Current data indicates that early life nutritional supplementation with a combination of DHA, EPA, UMP, choline, iodide, zinc, and vitamin B12 may provide neuroprotection after perinatal HI.
Landmine Clearance and Peacebuilding: Evidence from Somaliland

The mine action sector has struggled to demonstrate the socioeconomic benefits of mine clearance. Previous academic studies have made important contributions but have been limited in offering in-depth discussions of causal pathways. This paper seeks to fill that gap. It proposes a new framework, the Mine Clearance and Peacebuilding Synergies (MPS) framework that combines the Humanitarian Mine Action Peacebuilding Palette, the Mine Action- Sustainable Development Goals (SDGs) framework and theoretical considerations from the Infrastructure as Peacebuilding framework to interrogate this interaction. Using Somaliland's post-conflict reconstruction as a case study, we analyze qualitative and quantitative data to map both the direct and indirect benefits of mine clearance in relation to infrastructure development. We find that mine clearance can influence both economic and physical reconstruction through its impact on dominant economic sectors as well as critical strategic infrastructure, including ports and roads, and demonstrate the synergies therein with an array of SDGs.
Predicting Stock Closing Prices in Emerging Markets with Transformer Neural Networks: The Saudi Stock Exchange Case

Deep learning has transformed many fields including computer vision, self-driving cars, product recommendations, behaviour analysis, natural language processing (NLP), and medicine, to name a few. The financial sector is no surprise where the use of deep learning has produced one of the most lucrative applications. This research proposes a novel fintech machine learning method that uses Transformer neural networks for stock price predictions. Transformers are relatively new and while have been applied for NLP and computer vision, they have not been explored much with time-series data. In our method, self-attention mechanisms are utilized to learn nonlinear patterns and dynamics from time-series data with high volatility and nonlinearity. The model makes predictions about closing prices for the next trading day by taking into account various stock price inputs. We used pricing data from the Saudi Stock Exchange (Tadawul) to develop this model. We validated our model using four error evaluation metrics. The applicability and usefulness of our model to fintech are demonstrated by its ability to predict closing prices with a probability above 90%. To the best of our knowledge, this is the first work where transformer networks are used for stock price prediction. Our work is expected to make significant advancements in fintech and other fields depending on time-series forecasting. Keywords—Stock price prediction; time-series forecasting; transformer deep neural networks; Saudi Stock Exchange (Tadawul); financial markets
Effect of grazing on composition, fatty acid profile and nutritional
indices of the goat milk and cheese

The global goat milk production was approximately 19.9 mln t in 2019 and relatively continuously has been increasing during the last decades (FAO, 2021). Goat milk production has been growing, but the quantity of different types of goat milk products (typically cheese and yoghurt) has been also increasing year by year. In recent years, goat cheeses have gained increased popularity among consumers due to their nutritional value and health advantages. Small-scale farmers generally produce goat cheeses. This way, cheeses and other goat milk-based products have played an important role in the economic viability of farmers in the world. It is also necessary to add that goat cheeses are traditionally one of the crucial goat milk products produced, traded and consumed in large amounts in the world. Goat breeding and milk production are generally based on natural pastures in rural areas. After forests, grasslands and natural pastures are the most important ecosystems in the world because approximately 37% of the Earth’s terrestrial land surface (more than 50 mln km2, excluding Greenland and Antarctica) is covered by grasslands and natural pastures (O’Mara, 2012). Natural pastures are an essential resource for grazing-based animal production worldwide. In addition, sustainable small ruminant species (such as goat) breeding and keeping play an important role in the economy of marginal ABSTRACT. Twenty lactating Hungarian native goats were randomly divided into two groups to evaluate the effect of extensive grazing on the somatic cell counts (SCC), composition and fatty acid profile of milk and semi-hard cheese. The following important nutritional indices of goat milk and cheese fat were calculated: atherogenic index (AI), health-promoting index (HPI), hypercholesterolemic saturated fatty acids (HFA), hypocholesterolemic/hypercholesterolemic ratio (H/H), linoleic acid/α-linolenic acid ratio (LA/ALA) and thrombogenicity index (TI). The experimental period lasted for 38 days, encompassing the first four weeks as the period of adaptation to the natural pasture and the last ten days as the experimental period. The extensive grazing resulted in significantly higher concentrations of rumenic acid and lower n-6/n-3 ratio in milk (0.49 vs 0.67%, P < 0.01; 3.97 vs 1.49, P < 0.001) and in cheese (0.44 vs 0.63%, P < 0.001; 3.96 vs 1.54, P < 0.001). The extensive grazing significantly decreased the AI, HFA and LA/ALA, but on the other hand, increased the HPI and H/H indices in goat milk and cheese, respectively. The study results suggest that consumers have greater nutraceutical benefits from consuming milk and cheese from goats kept on natural pasture. Received: 20 August 2021 Revised: 11 November 2021 Accepted: 13 December 2021
Fast algorithms for high frequency interconnect modeling in VLSI circuits and packages

Fast Algorithms for High Frequency Interconnect Modeling in VLSI Circuits and Packages. (December 2009) Yang Yi, B.S., Shanghai Jiaotong University, China; M.En. , Shanghai Jiaotong University, China Chair of Advisory Committee: Dr. Weiping Shi Interconnect modeling plays an important role in design and verification of VLSI circuits and packages. For low frequency circuits, great advances for parasitic resistance and capacitance extraction have been achieved and wide varieties of techniques are available. However, for high frequency circuits and packages, parasitic inductance and impedance extraction still poses a tremendous challenge. Existing algorithms, such as FastImp and FastHenry developed by MIT, are slow and inherently unable to handle multiple dielectrics and magnetic materials. In this research, we solve three problems in interconnect modeling for high frequency circuits and packages. 1) Multiple dielectrics are common in integrated circuits and packages. We propose the first Boundary Element Method (BEM) algorithm for impedance extraction of interconnects with multiple dielectrics. The algorithm uses a novel equivalentcharge formulation to model the extraction problem with significantly fewer unknowns. Then fast matrix-vector multiplication and effective preconditioning techniques are applied to speed up the solution of linear systems. Experimental results show that the algorithm is significantly faster than existing methods with sufficient accuracy. 2) Magnetic materials are widely used in MEMS, RFID and MRAM. We present