Miracle-dz/UniFAR-FTUs huggingface dataset:
COVID-19 Vaccination and Neurological Manifestations: A Review of Case Reports and Case Series

Background: With 10 vaccines approved by the WHO and nearly 48% of people fully vaccinated worldwide, we have observed several individual case studies of neurological manifestations post-COVID-19 vaccination. Through this systematic review, we aim to discern these CNS and PNS manifestations following the COVID-19 vaccine to help produce methods to mitigate them. Methods: We conducted a thorough literature search of Google Scholar and PubMed from 1 December 2020 until 10 October 2021 and included all the case studies of COVID-19 vaccine-associated neurological side effects. The literature search and data analysis were performed by two independent reviewers according to prespecified inclusion and exclusion criteria using PRISMA. Results: The most common CNS manifestation was CVST (14.47%), found in females (64%) younger than 50 years (71%) after the first AstraZeneca dose (93%). Others included CNS demyelinating disorders (TM, ADEM, MS, NMOSD) (9.30%), encephalopathy/encephalitis (3.10%), and others (4.13%). The most common PNS manifestation was GBS (14.67%) found in males (71%) older than 50 years (79%), followed by Bell’s palsy (5.24%) and others (2.10%). Most occurred with the AstraZeneca (28.55%), Pfizer-BioNTech (9.18%), and Moderna (8.16%) vaccines. Nine (64%) out of the 14 patients with CVST died. However, most cases overall (42 out of 51) were non-fatal (82%). Conclusion: Several CNS and PNS adverse events have occurred post-COVID-19 vaccination, including CVST, GBS, and TM. High vigilance with early identification and treatment leads to better outcomes. Further studies with non-vaccinated controls might help in understanding the pathophysiologic mechanisms of these neurological manifestations following COVID-19 vaccination.
Missing Data Imputation in GNSS Monitoring Time Series Using Temporal and Spatial Hankel Matrix Factorization

GNSS time series for static reference stations record the deformation of monitored targets. However, missing data are very common in GNSS monitoring time series because of receiver crashes, power failures, etc. In this paper, we propose a Temporal and Spatial Hankel Matrix Factorization (TSHMF) method that can simultaneously consider the temporal correlation of a single time series and the spatial correlation among different stations. Moreover, the method is verified using real-world regional 10-year period monitoring GNSS coordinate time series. The Mean Absolute Error (MAE) and Root-Mean-Square Error (RMSE) are calculated to compare the performance of TSHMF with benchmark methods, which include the time-mean, station-mean, K-nearest neighbor, and singular value decomposition methods. The results show that the TSHMF method can reduce the MAE range from 32.03% to 12.98% and the RMSE range from 21.58% to 10.36%, proving the effectiveness of the proposed method.
Coal Generated Synthetic Gas Operating Experience with Two 100 MW-Class Combustion Turbines

Dow Chemical, U. S. A. , is currently operating two Westinghouse W501D5 combustion turbines with synthetic gas from the commercial demonstration coal gasification unit at Plaquemine, Louisiana.The first of these units was fueled with synthetic gas in April 1987. This conversion followed several years of experimentation and prototype work by Dow and Westinghouse. The second unit began operating with synthetic gas during September 1987.Addition of synthetic gas capability to these combustion turbines, originally installed in December 1982 and May 1983, was relatively straightforward, and once installed, did not adversely affect their operation or efficiency on natural gas, or mixtures in various proportions with synthetic gas. For durability assessment, special instrumentation was maintained at the site in the months immediately following conversion. This facilitated making minor changes to improve combustor longevity.The heat recovery units were modified for synthetic gas operation to improve corrosion resistance and to handle higher temperatures. Minor adjustments were made in the controls to accommodate changing gas flow. Emission monitoring was enhanced.Copyright © 1989 by ASME
BTF Prediction Model using Unsupervised Learning

The impressions evoked by textures are called affective textures, and are considered to be important in evaluating and judging the quality of an object. And, technologies for understanding and controlling sensory textures are needed in product design. In this study, we propose a BTF prediction method using DNN as a first attempt to generate textures based on affective texture recognition. The method uses a series of continuously varying viewpoint angles of a texture image as the input signal. This method enables the generation of texture images with continuously changing angles. We tested the validity of the proposed method by using textile, wood and paper. The results show that the proposed method is effective for predicting diffuse reflection optical properties and irregular and regular patterns.
Using Domain Knowledge for Low Resource Named Entity Recognition

In recent years, named entity recognition has always been a popular research in the field of natural language processing, while traditional deep learning methods require a large amount of labeled data for model training, which makes them not suitable for areas where labeling resources are scarce. In addition, the existing cross-domain knowledge transfer methods need to adjust the entity labels for different fields, so as to increase the training cost. To solve these problems, enlightened by a processing method of Chinese named entity recognition, we propose to use domain knowledge to improve the performance of named entity recognition in areas with low resources. The domain knowledge mainly applied by us is domain dictionary and domain labeled data. We use dictionary information for each word to strengthen its word embedding and domain labeled data to reinforce the recognition effect. The proposed model avoids large-scale data adjustments in different domains while handling named entities recognition with low resources. Experiments demonstrate the effectiveness of our method, which has achieved impressive results on the data set in the field of scientific and technological equipment, and the F1 score has been significantly improved compared with many other baseline methods.
An Unmanned Aerial System (UAS) based methodology for measuring biomass burning emission factors

Biomass burning (BB) emits large quantities of greenhouse gases (GHG) and aerosols that impact climate and adversely affect human health. Although much research has focused on quantifying BB emissions on regional to global scales, field measurements of BB emission factors (EFs) are sparse, clustered and indicate high spatio-temporal variability. EFs are generally calculated from groundor aeroplane measurements with respective potential biases towards smouldering or flaming combustion products. Unmanned aerial systems (UAS) have the potential to measure BB EFs in fresh smoke, targeting 20 different parts of the plume at relatively low cost. We propose a light-weight UAS-based method to measure EFs for carbon monoxide (CO), carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), as well as PM2.5 (TSI Sidepak AM520) and equivalent black carbon (eBC, microAeth AE51) using a combination of a sampling system with Tedlar bags which can be analysed on the ground and airborne aerosol sensors. In this study, we address the main uncertainties associated with this approach (1) the degree to which taking a limited number of samples is representative for the integral smoke plume and 25 including (2) the reliability of the lightweight aerosol sensors. This was done for prescribed burning experiments in the Kruger national park, South Africa where we compared fire-averaged EF from UAS-sampled bags for savanna fires to integrated EFs from co-located mast measurements. Both measurements matched reasonably well with linear R ranging from 0.81 to 0.94. Both aerosol sensors are not factory calibrated for BB particles and therefore require additional calibration. In a series of smoke chamber experiments, we compared the lightweight sensors to high-fidelity equipment to empirically determine specific 30 calibration factors (CF) for measuring BB particles. For the PM mass concentration from a TSI Sidepak AM520, we found an optimal CF of 0.27, using a scanning mobility particle sizer and gravimetric reference methods, albeit that the CF varied for different vegetation fuel types. Measurements of eBC from the Aethlabs AE51 aethalometer agreed well with the multihttps://doi.org/10.5194/amt-2022-77 Preprint. Discussion started: 11 March 2022 c © Author(s) 2022. CC BY 4.0 License.
Exploratory Search for Characteristic Symptoms to Distinguish Meibomian Gland Dysfunction from Dry Eye in a Population-Based Study in Japan

Symptom overlap between meibomian gland dysfunction (MGD) and dry eye (DE) makes it difficult to distinguish between these two conditions on the basis of symptoms alone. We searched for characteristic symptoms that might help to distinguish MGD from DE on the basis of a population-based study. Subjects comprised 311 residents of Takushima island (18 to 96 years), including 117 individuals with MGD and 114 with DE. Responses to a symptom-related questionnaire (19 items) were subjected to factor analysis, and univariate regression analysis was performed to identify ocular surface parameters associated with characteristic symptoms of MGD. Factor analysis revealed aggregation of symptoms according to three factors: Factor 1 related to Symptom Score, Factor 2 to DE, and Factor 3 to MGD. Symptoms associated with DE included 11 items, whereas the only item related to MGD was tearing sensation. Pearson’s correlation analysis revealed that tearing sensation was associated with tear meniscus height (TMH), noninvasive tear-film breakup time, fluorescein staining score, meiboscore, meibum grade, and Schirmer value. Subjects with MGD experienced significantly more tearing and had a larger TMH than did those without MGD (p = 0.0334). Tearing sensation may thus be a characteristic symptom of MGD. Physicians should suspect MGD who complain of tearing sensation.
Preservation of Smooth Muscle Cell Integrity and Function: A Target for Limiting Abdominal Aortic Aneurysm Expansion?

(1) Abdominal aortic aneurysm (AAA) is a silent, progressive disease with significant mortality from rupture. Whilst screening programmes are now able to detect this pathology early in its development, no therapeutic intervention has yet been identified to halt or retard aortic expansion. The inability to obtain aortic tissue from humans at early stages has created a necessity for laboratory models, yet it is essential to create a timeline of events from EARLY to END stage AAA progression. (2) We used a previously validated ex vivo porcine bioreactor model pre-treated with protease enzyme to create “aneurysm” tissue. Mechanical properties, histological changes in the intact vessel wall, and phenotype/function of vascular smooth muscle cells (SMC) cultured from the same vessels were investigated. (3) The principal finding was significant hyperproliferation of SMC from EARLY stage vessels, but without obvious histological or SMC aberrancies. END stage tissue exhibited histological loss of α-smooth muscle actin and elastin; mechanical impairment; and, in SMC, multiple indications of senescence. (4) Aortic SMC may offer a therapeutic target for intervention, although detailed studies incorporating intervening time points between EARLY and END stage are required. Such investigations may reveal mechanisms of SMC dysfunction in AAA development and hence a therapeutic window during which SMC differentiation could be preserved or reinstated.
Problematic Internet Pornography Use and Psychological Distress among Emerging Adults in Malaysia: Gender as a Moderator

Internet pornography use (IPU) refers to Internet-based sexually explicit materials that are ultimately used to elicit sexual feelings or thoughts. The accessibility of Internet pornography could lead to excessive exposure to pornographic messages, posing a risk to heavy users’ psychological health. This paper offers a preliminary understanding of the relationship between Internet pornography use and psychological distress among emerging adults and the moderating role of gender in the association. This cross-sectional study has taken a purposive sampling approach to recruit 144 emerging adult pornography users via the online survey method. The results indicated that males reported having more problematic Internet pornography use, and there were no gender differences in psychological distress. Meanwhile, gender is a significant moderator between Internet pornography use and psychological distress. The females were found to be more psychologically affected by their problematic Internet pornography use than the males. Overall, this study has provided a novel finding of the moderating role of gender in problematic Internet pornography use and psychological distress in the Malaysian context. This study also calls for a gender-focused sexual health programme for Malaysian emerging adults. Furthermore, the scores of problematic IPU in this study raise a concern over the effectiveness of current sex education in Malaysia. The scores may highlight the need to provide education targeting Internet pornography use.
Leisure Constraint Negotiation Strategies among Serious Leisure Participants in Swimming: Experiences of Facility Use Restriction Due to COVID-19

The purpose of this study was to explore the leisure constraint negotiation strategies adopted by the people participating in swimming as a serious leisure activity in the context of COVID-19 by using qualitative research methods. The study was conducted over five months, from September 2020 to January 2021, via the use of snowball sampling. Six unmarried employees in their 30s living in Seoul and Incheon, Korea, were the participants of this study. Data collection was conducted through in-depth interviews, two or three times among each participant. For data analysis, all the recorded contents stated by the participants were transcribed. The results are as follows: first, among the leisure constraint negotiation strategies, the cognitive constraint negotiation strategies were “supernatural attitude toward swimming” and “positive attitude toward the post-COVID-19 period”; and second, among the leisure constraint negotiation strategies, behavioral constraint negotiation strategies were derived as “substitution of activity and motivation” and “participating facilities and crowding management. ” Based on these results, it can be concluded that the special leisure experiences and leisure constraint negotiation strategies only for swimming within the framework of facility use restrictions were caused due to COVID-19 and further suggested ways to improve the practical management of leisure and sports activities based on the facilities.
A Randomized Link Transformer for Diverse Open-Domain Dialogue Generation

A major issue in open-domain dialogue generation is the agent’s tendency to generate repetitive and generic responses. The lack in response diversity has been addressed in recent years via the use of latent variable models, such as the Conditional Variational Auto-Encoder (CVAE), which typically involve learning a latent Gaussian distribution over potential response intents. However, due to latent variable collapse, training latent variable dialogue models are notoriously complex, requiring substantial modification to the standard training process and loss function. Other approaches proposed to improve response diversity also largely entail a significant increase in training complexity. Hence, this paper proposes a Randomized Link (RL) Transformer as an alternative to the latent variable models. The RL Transformer does not require any additional enhancements to the training process or loss function. Empirical results show that, when it comes to response diversity, the RL Transformer achieved comparable performance compared to latent variable models.
Motivating Interactive Self-Organisation

Understanding, engineering and controlling self-organising systems, i.e. of large numbers of interwoven autonomous agents, pose numerous scientiﬁc and practical challenges. A considerable body of works aims at theoretical approaches as well as empirically identiﬁed solutions to address them— with a focus on interaction among the agents and resulting emergent, global effects. However, only few works systematically consider the means of interaction of these systems with the expert user. In this paper, we address this perspective by introducing the notion of interactive self-organisation . To this end, we step through the development life cycle of a self-organising system, we emphasise the need for accessible software solutions for modelling and simulation, we present a concrete application scenario, and highlight the great challenges on this path.
Directed Probabilistic Watershed Supplemental

In this section, we prove Lemma 3.4 of the main paper, which we rename here as Lemma A.2. It provides a closed form for w ( F − →s2 − →s1 ) and w ( F − →s2 − →s1,q ) in terms of the in-trees of G rooted at the seeds. We will use the Matrix Tree Theorem extension for directed graphs (Theorem 2.1 in the main paper), referred to as MTTdir, and the Determinant Lemma (Lemma A.1) which we state without proof. Lemma A.1 (Determinant Lemma [7]). Given an invertible matrix A ∈ Rm×m and u, v ∈ R we have det(A+ uv>) = det(A)(1 + v>A−1u). Lemma A.2. Let G = (V,E,w) be a directed graph. Let l ij represent the entry ij of the matrix ( L )−1 for any v ∈ V . Given s1, s2 and q:
Usability of Cryptocurrency Wallets Providing CoinJoin Transactions

—Over the past years, the interest in Blockchain technology and its applications has tremendously increased. This increase of interest was however accompanied by serious threats that raised concerns over user data privacy. Prominent examples include transaction traceability and identification of senders, receivers, and transaction amounts. This resulted in a multitude of privacy-preserving techniques that offer different guarantees in terms of trust, decentralization, and traceability. CoinJoin [22] is one of the promising techniques that adopts a decentralized approach to achieve privacy on the Unspent Transaction Output (UTXO) based blockchain. Despite the advantages of such a technique in obfuscating user transaction data, making them usable to common users requires considerable development and integration efforts. This paper provides a comprehensive usability study of three main Bitcoin wallets that integrate the CoinJoin technique, i.e., Joinmarket, Wasabi, and Samourai. A cognitive walkthrough was conducted in order to evaluate the ease of use of these wallets using usability and fundamental design criteria. The study findings will enable privacy wallet developers to gain valuable insights into a better user experience.
The principal disconnection of chikungunya infection from non-human primates in Malaysia

Chikungunya is a mosquito borne disease caused by chikungunya virus (CHIKV) . The virus is transmitted to human by Aedes genus mosquitoes. Transmission cycles of CHIKV can be man mosquito man (urban cycle) or animal mosquito man (sylvatic cycle). Sylvatic transmission cycle of CHIKV has been described in Africa and may play a role in re-emergence of CHIKV infection. In Malaysia, CHIKVneutralizing antibodies have been detected among wild monkeys in mid 1960s but so far CHIKV has never been isolated in monkeys.
COLORIZATION OF BLACK AND WHITE IMAGES: A SURVEY

: One of the most exciting Deep Learning applications is color grading black and white pictures. Coloring a grayscale image is a simple exercise for the human mind in general; we learn to fill in missing shades in colouring books from a young age. This task used to necessitate a lot of human involvement and hardcoding, but now, thanks to AI and Deep Learning, the entire process can be automated from start to finish. In this review, I looked at a number of publications that presented various AI and Deep Learning methodologies. I'll go over each approach and tactic that has been utilised to investigate different Neural Networks in Deep Learning and how they may be used to achieve incredible outcomes.
Autonomous Vehicle System using CNN

: Our proposed model provides an end-to-end autonomous vehicle simulation. We trained a Convolutional Neural Network (CNN) to directly map raw pixels to the steering commands from a single front-facing camera. This end-to-end approach proved to be surprisingly strong. The machine learns to drive in traffic on local roads with or without lane markings and highways with minimal training data from humans. It also works in places where the visual direction is not obvious, such as parking lots and on unpaved roads. The model learns internal representations of the required processing steps automatically, such as detecting useful road features with only the human steering angle as the training signal. We have never specifically conditioned it to detect the outline of paths, for example. Our end-to-end method optimizes all processing steps simultaneously as compared to the explicit decomposition of the problem, such as lane marking detection, route planning, and control. We claim that in the end, this will lead to better results and simpler systems. It will result in better performance because the internal components are self-optimizing to maximize overall system performance, rather than optimizing intermediate criteria chosen by humans e.g. Detecting roads. Such criteria are selected understandably for ease of human interpretation which does not automatically guarantee maximum performance of the system. Smaller networks are possible because with the minimal number of processing steps the machine learns to solve the problem.
Belonging to Three Worlds: Somali Adolescent–Parent Relationships in the United States and Implications for Tobacco Prevention

Immigrant family relationships help to buffer the adolescent adoption of health risk behaviors but can be strained by post-immigration structural and cultural barriers. This study qualitatively examines how Somali adolescent–parent relationship factors influence Somali adolescent tobacco use and identifies areas for further family support to prevent Somali adolescent tobacco use. We conducted fifteen key informant interviews with professionals serving the Somali community in clinical, educational, religious, or other community organization roles in one Minnesota metropolitan region. Data were collected and analyzed using approaches rooted in Grounded Theory. Key informants contrasted parenting experiences in Somalia with those in the United States and described how four key factors—structural and cultural barriers, multicultural identity formation, evolving parental expectations and responsibilities, and shifting family resources and support—have influenced Somali parent–child relationship quality and function following immigration. Informants shared the implications of these factors on parental ability to address adolescent tobacco use and discussed potential strategies to support parents that fell into two categories: assisting parents in adapting their parenting approaches to a new context and supporting knowledge and skill development in addressing tobacco use prevention specifically. Incorporating strategies that support Somali parents in their evolving parental roles and attend to structural and cultural barriers to tobacco prevention are essential to consider when developing family-centered tobacco prevention interventions in this population.