Miracle-dz/UniFAR-FTUs huggingface dataset:
Miranda Yendrembam

Background Perceived stress experiences differ amongst individuals due to psychological, physical and socio-economic factors and with the outbreak of COVID-19, the impact on mental health has been unavoidable. Another dimension of understanding stress is also put forth by Ayurveda, an ancient medicine system of India. Aim – Aims to find the association of bio-social parameters including prakriti body types with perceived stress levels amidst COVID -19 pandemic. Material and Methods – Data were collected from 390 individuals aged ranging from 18 – 45 years through an online survey. Prakriti were determined by prakriti assessment questionnaire. Stress level was assessed by using Perceived Stress Scale (PSS 10) and relevant statistical analysis were carried out. Results Significant association (p < 0.05) is found in body mass index (BMI), prakriti body types, sex, and effects of COVID–19 on mental and physical well – being with perceived stress levels. Vata prakriti (19.71%), underweight individuals (12.65%), and females (11.44%) are significantly more prone to develop high stress. Salaried individuals are significantly less likely to be affected by moderate stress. VIF is less than 5 and Tolerance is greater than 0.2. And, Nagelkerke value is found to be 29.3%. Conclusion – The study concludes that there is a significant association of biosocial parameters including prakriti body types with perceived stress levels amidst COVID–19 pandemic. 68 * Corresponding Author:
Factor structure and proposed scoring revision of the Three-Dimensional Psychological Pain Scale

The development of psychometrically sound measures to assess mental pain are important because research has consistently demonstrated a robust relationship to suicide risk. The current research evaluated the Three-Dimensional Psychological Pain Scale (TDPPS) structure, a suicide-relevant measure intended to articulate pain into affective, cognitive, and behavioral facets. As the first Western study to evaluate the TDPPS structure with non-Chinese respondents, six samples comprising 1,627 adults participated. Neither confirmatory factor analyses nor exploratory structural equation modeling supported the hypothesized three-dimensional structure of the TDPPS but, instead, identified two dimensions: pain escape and pain emotions. Scales based on these two dimensions demonstrated replicability in crossvalidation and score internal consistency reliability. Furthermore, validity for scores on these two scales was confirmed through moderate associations with another pain measure and scales of suicidal behavior and depression. Findings extend knowledge of TDPPS’s structure of psychological pain and suggest a scale scoring revision.
Playing traditional games vs. free-play during physical education lesson to improve physical activity: a comparison study

Background and Study Aim. Non-sport activities like traditional games can be a practical way to promote physical activity (PA) during physical education (PE) lessons, especially among those who are less inclined towards sports. The purpose of this study is to compare the PA variables and acceptance between playing traditional games and free-play during physical education lessons among secondary school students. Material and Methods. Fifty-six (n = 56) subjects participated in this study wore accelerometers for a total of 40 minutes during each PE lesson to record PA. In the first week, a 40-min free-play PE lesson was done while in the second week, a 40-min PE lesson incorporated with traditional games (TG-PE lesson) was done. After following both lessons, subjects filled up a questionnaire on acceptance of TG-PE and free-play PE lessons. Results. The mean total activity count (p < 0.05) and time spent in moderate-to-vigorous physical activity (MVPA) (p < 0.05) were 20% and 19% greater respectively during TG-PE lesson compared to free-play PE lesson. The mean acceptance scores showed students preferred playing TG (19.29 ± 4.21; p < 0.00) over free-play PE lesson (17.59 ± 3.13). Conclusions. In conclusion, incorporating fun and meaningful activities such as TG during PE lessons can serve as an alternative strategy to promote PA during school hours. Further studies are warranted to determine other types of TG that may promote PA among secondary school students.
Functionalization of Silica Aerogels and AlPO-5 with Molybdenum, Rhenium - A Study on Metal Introduction Methods

In the present work possible routes for the introduction of Mo and Re into silica aerogels and aluminophosphate AlPO-5 have been investigated. Parameter studies on the influence of synthesis conditions including metal precursor, metal molar ratio, sol pH, gelation mechanism, and post treatments have been performed on the synthesis of Me-aerogels. Parameter studies on the synthesis of MeAlPO-5 have been performed with varying metal precursor, sol preparation method, sol pH, choice of structural directing agent, and crystallization conditions. Both Mo and Re have been introduced into silica aerogels following the sol-gel route and ambient pressure drying method, in which the metal (Mo or Re) precursor was added during the sol stage before gelation. The as-prepared Mo-aerogels were all amorphous with metal content up to 0.56 wt. %, whereas all as-prepared Re-aerogels contain both amorphous and crystalline phases with metal content up to 6.61 wt. %. Direct introduction of Mo into AlPO-5 by isomorphous substitution obtained phase pure samples with metal content up to 0.12 wt. %. Direct introduction of Re into AlPO-5 produced samples either contain significant amount of extra phases, or with negligible amount of metal content. An indirect method to introduce Re into AlPO-5 through the intermediate GeAlPO-5 has been attempted in the first stage, where GeAlPO-5 was synthesized with the precursor GeO2, yielding Ge content up to 0.84 wt. %.
An Exploration of the Use of AI Planning for Predicting Stock Market Movement

Reasoning about stock market and projecting its movement is of great interest to the investors and traders. Recent advances in the stock market ﬁeld focused on designing automated agents for performing trading tasks. However, there is no diminishing interest in approaches for predicting ﬂuctuations in the market and, more importantly, to reason on different scenarios and possibilities because these not only impact the automation but also important from risk management perspective even without automation. In this work, we are exploring the use of plan recognition as planning approach via AI Planning technique to reason about the observations made from the market, and predict the direction of the market movement. The benchmark broad based stock market index being the representative of the overall market, we are using benchmark index, Nifty to project movement of Indian stock market. Our proposed approach considers the available domain knowledge and a sequence of observations derived from the overall market to produce a number of plans that could provide explanations for the observed behaviour as well as the prediction by computing posterior probability.
CRISPR / Cas inactivation of RECQ 4 and FIGL 1 changes homeologous crossover frequency in an interspecific tomato hybrid 3

Crossover formation during meiosis in plants is required for proper chromosome segregation and is essential for crop breeding as it allows an (optimal) combination of traits by mixing parental alleles on each chromosome. Crossover formation commences with the production of a large number of DNA double-strand breaks, of which only a few result in crossovers. A small number of genes, which drive the resolution of DNA crossover intermediate structures towards non-crossovers, have been identified in Arabidopsis thaliana. In order to explore the potential of modification of these genes in interspecific hybrids between crops and their wild relatives towards increased production of crossovers, we have used CRISPR/Cas9-mutagenesis in an interspecific tomato hybrid to knockout RECQ4 and FIGL1, two anti-crossover factors. Biallelic mutants for both genes were obtained in the F1 hybrid of S. lycopersicum and S. pimpinellifolium. Compared with the wild-type F1 hybrid, the F1 recq4 mutant showed a significant increase in crossovers, whereas in the F1 figl1 mutant a significant decrease was observed. These results demonstrate changes in crossover frequencies in interspecific hybrids by manipulating genes in crossover intermediate resolution pathways by directed mutagenesis via CRISPR/Cas9.
Opportunistic Actor-Critic with Clipped Triple Q-learning

: Soft Actor-Critic (SAC) and Twin Delayed Deep Deterministic Policy 1 Gradient (TD3), two of the best model-free deep reinforcement learning (RL) al-2 gorithms in the past years, have managed to solve challenging tasks in continuous 3 state-action spaces. SAC effectively addressed the problems of sample complexity 4 and convergence brittleness to hyper-parameters. It outperformed all state-of-the-5 art algorithms including TD3 in harder tasks, whereas TD3 produced moderate 6 results in all environments. However, SAC suffers from inefﬁcient exploration 7 owing to the Gaussian nature of its policy which causes borderline performance in 8 simpler tasks. This paper introduces Opportunistic Actor-Critic (OPAC), a novel 9 model-free deep RL algorithm that combines the central features of TD3 and SAC. 10 It also employs the usage of three critics instead of two, unlike TD3 and SAC. We 11 have systematically evaluated OPAC on MuJoCo environments where the results 12 show that OPAC outperforms TD3 and SAC in easy and challenging tasks as well. 13
Effect of Temperature on Production of Endoglucanase and Xylanase from Some Green Manure Fungal Decomposers

Among the various microorganisms reported for their capabilities of cellulases and xylanases production, fungi are the most potent producers. This study reports the production of xylanolytic and cellulolytic enzymes by potential decomposing fungal isolates using a cheap medium containing green manure substrate and chemically defined basal medium under solid-state culture. A number of decomposing fungi were screened for cellulolytic and xylanolytic potential. The enzyme production with purified carboxy methyl cellulose (CMC) and birch wood xylan was studied and found to be promising. Aspergillus niger produced the highest amount of endoglucanase and xylanase viz., 3.635 U mg -1 protein min -1 and 2498.20 U mg -1 protein min -1 , respectively at 25ºC. Growth and enzyme production was affected by the alterations in temperature. Highest production of endoglucanse and xylanase were noted at 25ºC and 30ºC.
Package ‘ DoseFinding ’ June 25 , 2021 Type Package

June 25, 2021 Type Package Title Planning and Analyzing Dose Finding Experiments Version 1.0-1 Date 2021-06-24 Depends lattice, mvtnorm, R (>= 2.15.0) Suggests numDeriv, Rsolnp, quadprog, parallel, multcomp, ggplot2, knitr, rmarkdown, MASS, testthat Maintainer Bjoern Bornkamp <bbnkmp@mail.de> Description The DoseFinding package provides functions for the design and analysis of dose-finding experiments (with focus on pharmaceutical Phase II clinical trials). It provides functions for: multiple contrast tests, fitting non-linear dose-response models (using Bayesian and non-Bayesian estimation), calculating optimal designs and an implementation of the MCPMod methodology (Pinheiro et al. (2014) <doi:10.1002/sim.6052>). VignetteBuilder knitr License GPL-3 LazyLoad yes NeedsCompilation yes Author Bjoern Bornkamp [aut, cre] (<https://orcid.org/0000-0002-6294-8185>), Jose Pinheiro [aut], Frank Bretz [aut], Ludger Sandig [aut] Repository CRAN Date/Publication 2021-06-25 11:20:10 UTC
Native Language Identiﬁcation from Speech Transcriptions

We present the use of natural and spoken language features to correctly identify the native language of an author, given a transcript of spoken responses to an English-language examination. Our clas-siﬁcation method explored the use of n-gram features, GloVe vectors, POS tagging, and PCFG rule-based features fed through a linear classiﬁer, fully-connected neural network, and RNN. We found optimal performance came from word-based n-grams ( n = 1 , 2 , 3 ) plus tf-idf transformation with a linear SVC classiﬁer: an F1 score of 0.59, or an 0.7 gain over the base-line unigram approach.
Anterior-segment optical coherence tomography for the detection and therapeutic monitoring of corneal disorders

Background: Over recent years a revolutionary trend happened on imaging technologies to diagnose and monitor treatment of a varied group of ophthalmic pathologies. Recent reports have analyzed the microstructural changes of various ocular surface and corneal disorders, particularly ocular surface squamous neoplasia (OSSN) and keratoconus using anterior-segment optical coherence tomography (AS-OCT). Aim of this short communication is to elaborate on clinical applications AS-OCT for the detection and therapeutic monitoring of corneal disorders. Methods: We performed an English literature search without a time limit and intending to identify articles related to the ASOCT applications in the detection and therapeutic monitoring of corneal disorders. The most relevant articles were selected. practical points of selected papers and advantages and disadvantages of AS-OCT were retrieved from them and summarized. Results:. Many records reported the AS-OCT applications for diagnosing many ocular surface disorders, the microstructural changes of different inflammatory, infectious, degenerative, and dystrophic corneal disorders. Its applications in identifying disease activity and therapeutic monitoring of various corneal pathologies, including stromal edema associated with angleclosure glaucoma, Fuchs endothelial dystrophy, infectious keratitis, and bullous keratopathy, are promising. The percentage of diagnostic sensitivity, specificity, and accuracy of artificial intelligence methodologies applied to AS-OCT imaging analysis today has reached 94% to 100%. Moreover, AS-OCT is very useful for analyzing the extension of scar and leukoma depth for surgical planning of partial or total corneal transplantation. Conclusions: There is a clear prospect for expanding application of corneal OCT imaging technology, a rapid, non-invasive, and now a promising lower-cost device, which is becoming an in-office standard-of-care tool for the assessment of different corneal and ocular surface pathologies.
Indian-COVID-19 CT Dataset and Analysis of Chest CT Scans of COVID-19 Patients Using Lightweight CNN

Indian-COVID-19 CT is the chest Computed Tomography (CT) images from 1 COVID-19 patients from India. It has been collected and curated to aid in the 2 diagnosis of COVID-19 and other chest CT analysis tasks using machine learning 3 algorithms. Currently it consists of 6174 images from 142 patients COVID-19, 4 obtained from a single hospital with same image acquisition clinical settings. The 5 dataset will be regularly updated to include more data and the original 3D volumes 6 of dicoms will also be made available. It does not include normal or any other 7 pneumonia images like other similar repositories. It would provide researchers 8 opportunities to develop generalizable and robust models for COVID-19 detection 9 and for developing models for other lung disease detection tasks. To the best of our 10 knowledge, this is the only dataset available from Indian population making it a 11 valuable addition to other similar repositories. Here we also propose a lightweight 12 Convolutional Neural Network (CNN) model to classify chest CT scans into three 13 classes, viz., Normal, non-Covid Pneumonia and COVID-19. The model has been 14 trained and validated on publicly available dataset COVIDx-CT dataset [1]. Perfor-15 mance of the model is evaluated on both COVIDx-CT and Indian-COVID-19 CT 16 datasets and is observed to be comparable, with accuracy slightly lower on Indian-17 COVID-19
IDENTIFYING THE FACTORS AFFECTING FINANCIAL CONDITION OF MALAYSIA

This research focuses on the domestic and external determinants of Malaysia’s financial condition, ranging from January 2003 and March 2019. To represent the domestic determinants, a total amount of twelve financial indicators are adopted to compute three indices using the Principal Component Approach (PCA). Three of the indices embody three respective financial segments, particularly the banking system, the foreign exchange market, and the capital market within the Malaysian financial system. These three segments offer a glimpse of the Malaysian financial condition. For the external determinant, the US total assets of all Federal Reserve Banks are adopted as the proxy for the size of the US balance sheet. This research intends to evaluate the validity of the long-run association and its corresponding level of effect between the computed indices and the US balance sheet size via the conduct of the ARDL bounds test approach. The research findings have verified the validity of the long-run association between the Malaysian financial condition and the US balance sheet size. There is a substantial role played by the US balance sheet size in affecting the well-being of Malaysian financial condition, given the statistical significance is shown in the association between the US balance sheet size and the financial-segment-based indices, whereby its strongest influence is found on the banking system condition.
Room-temperature fully recyclable carbon fibre reinforced phenolic composites through dynamic covalent boronic ester bonds

The cross-linked structures of traditional thermosetting resins not only endow carbon fibre-reinforced polymer (CFRP) composites with excellent performance, but also make their efficient recovery a real challenge. The goal of this paper is to create reversible thermosets using dynamic boronic ester bonds as cross-links in replacement of traditional irreversible covalent bonds. Specifically, we used phenylboronic acid (PBA) to crosslink traditional novolac resin (NR), namely PBNR. By combining the reprocessability of thermoplastics with the desirable chemical and thermal stability of conventional thermosets, controlled degradation and full recycling of cross-linked resins and CFRP composites under ambient conditions were realized through a gentle alcoholysis process. The PBNR/CFRP composites fabricated by hot-pressing showed excellent mechanical properties. The multiple recycling experiments revealed near-total recovery of the clean fibre cloth and binder materials, which could be reprocessed into composites with similar mechanical properties to fresh materials. Dynamic boronate bonds have been demonstrated to open up a new pathway for full recycling of thermosetting resins and CFRP composites, which has a great impact in aeronautical, astronautics, adhesive, coating, or electronic packaging fields.
Combining Analogy with Language Models for Knowledge Extraction

Learning structured knowledge from natural language text has been a long-standing challenge. Previous work has focused on speciﬁc domains, mostly extracting knowledge about named entities (e.g. countries, companies, or persons) instead of general-purpose world knowledge (e.g. information about science or everyday objects). In this paper we combine the Companion Cognitive Architecture with the BERT Language Model to extract structured knowledge from text, with the goal of automatically inferring missing common-sense facts from an existing knowledge base. Using the principles of distant supervision, the system learns functions called query cases that map statements expressed in natural language into knowledge base relations. Afterwards, the system uses such query cases to extract structured knowledge using analogical reasoning. We run experiments on 2,679 Simple English Wikipedia articles, where the system is able to learn high precision facts about a variety of subjects from a few training examples, outperforming strong baselines.
TWO IMPROVED ARCHITECTURES BASED ON PROTOTYPE NETWORK FOR FEW-SHOT BIOACOUSTIC EVENT DETECTION

In this technical report, we describe our submission system for DCASE2021 Task5: few-shot bioacoustic event detection. Few improvements are investigated to better the baseline of deep learning prototypical network. Including the N -way 5 -shot classiﬁcation prototypical network training strategy, data augmentation techniques, the proposed embedding propagation and attention similarity approaches. On the ofﬁcial validation set, we demonstrate that the proposed method achieves the overall F-measure score of 54 . 7 % on the validation set.
A Novel Cellular Decomposition Approach to Nonholonomic Multi-robot Coverage Path Planning

This study aims to establish an efﬁcient solution to the Multi-robot Coverage Path Planning (MCPP), for environments with a highly irregular shape and obstacles. A critical aspect of an MCPP algorithm is cellular decomposition, of which to the authors best knowledge, a novel approach, referred to as overhang decomposition, is proposed. The overhang decomposition is robust for any shaped environment with obstacles, and requires minimal pre-processing steps. The decomposed area is then used by a Genetic algorithm to compute the optimal coverage paths. Our initial experiments show that the genetic algorithm can both optimize signiﬁcantly for the total distance travelled for a single robot, and additionally for idle time minimization regarding the multi-robot coverage problem.
DISCOVERING BUSINESS PROCESS MAP OF MOST FREQUENT RUNNING CASE IN EVENT LOG Digest of paper 1

: Recently, in the area of business information systems, process mining is widely considered as a new paradigm focusing on the extraction of knowledge to build event logs from data which are recorded in process aware information systems. The purpose of the mining process is to extract information about business processes and process discovery is one of the most challenging process mining tasks. Moreover, From the event logs, the complete process model is often too complicated to understand by business users for discovering behavioral patterns and analyzing business processes. In this paper a method is presented to discover process model of most frequent running case in event log using bupaR framework in R studio. A scenario example has been applied to show the applicability of the approach. The result shows that the local process model is less complicated and easy readable process model to discover the behavioral patterns.
Barriers and Opportunities for Beneficial Reuse of Sediment to

19 As urbanization and climate change alter sediment fluxes, relative sea level and coastal erosion around 20 the world, management of sediment as a resource is increasingly important. Sediment is needed to 21 enhance marsh accretion rates, raise the grade elevation of development, and build up beaches and 22 dunes. Beneficial reuse of sediment refers to the repurposing of local sources of sediment for these 23 applications, material typically available from dredging or sediment capture infrastructure, and represents 24 a more sustainable approach compared to the status-quo involving transport to and from distant 25 locations. However, in many locations, beneficial reuse remains a concept or is constrained to small-scale 26 applications. In this paper, we draw on interviews with coastal sediment managers and regulators in 27 Southern California to identify barriers to beneficial reuse and opportunities to overcome them. 28 Interviewees reported numerous regulatory, technical, psychological, financial, and interorganizational 29 barriers in their watersheds. By highlighting these barriers, we aim to identify systemic changes that would 30 make beneficial reuse a realistic and accessible option for Southern California and elsewhere. Most 31 prominently, a more flexible regulatory framework that allows sediment management practices to adapt 32 over time, pilot studies to understand how beneficial reuse works in various settings, and educational 33 programs for regulators and the public could make beneficial reuse a more widespread approach. 34 Ulibarri et al. 2020 Ocean & Coastal Management 2