Miracle-dz/UniFAR-FTUs huggingface dataset:
The CannTeen Study: Cannabis use disorder, depression, anxiety, and psychotic-like symptoms in adolescent and adult cannabis users and age-matched controls

Background: Adolescence is characterised by psychological and neural development. Cannabis harms may be accentuated during adolescence. We hypothesised that adolescents would be more vulnerable to the associations between cannabis use and mental health and addiction problems than adults. Method: As part of the ‘CannTeen’ study, we conducted a cross-sectional analysis. There were 274 participants: split into groups of adolescent users (n = 76; 16–17 years old) and controls (n = 63), and adult users (n = 71; 26–29 years old) and controls (n = 64). Among users, cannabis use frequency ranged from 1 to 7 days/week, while controls had 0–10 lifetime exposures to cannabis. Adolescent and adult cannabis users were matched on cannabis use frequency (mean=4 days/week). We measured Diagnostic and Statistical Manual (DSM-5) Cannabis Use Disorder (CUD), Beck Depression Inventory, Beck Anxiety Inventory and Psychotomimetic States Inventory-adapted. Results: After adjustment for covariates, adolescent users were more likely to have severe CUD than adult users (odd ratio = 3.474, 95% confidence interval (CI) = 1.501–8.036). Users reported greater psychotic-like symptoms than controls (b = 6.004, 95% CI = 1.211–10.796) and adolescents reported greater psychotic-like symptoms than adults (b = 5.509, 95% CI = 1.070–9.947). User-group was not associated with depression or anxiety. No significant interactions between age-group and user-group were identified. Exploratory analyses suggested that cannabis users with severe CUD had greater depression and anxiety levels than cannabis users without severe CUD. Conclusion: Adolescent cannabis users are more likely than adult cannabis users to have severe CUD. Adolescent cannabis users have greater psychotic-like symptoms than adult cannabis users and adolescent controls, through an additive effect. There was no evidence of an amplified vulnerability to cannabis-related increases in subclinical depression, anxiety or psychotic-like symptoms in adolescence. However, poorer mental health was associated with the presence of severe CUD.
Drug Reaction With Eosinophilia and Systemic Symptoms Syndrome in a Patient Taking Lamotrigine: A Case Report Based Literature Review

A 29-year-old patient presented to the hospital with worsening generalized rash for the last two days from a mental health facility. The patient was commenced on lamotrigine two weeks earlier, and he developed fever and generalized macular rash on his body. His blood tests showed deranged liver function tests (LFTs) and clotting with raised eosinophil count, and he was treated for lamotrigine-induced drug reaction with eosinophilia and systemic symptoms (DRESS) syndrome. The patient was commenced on prednisolone 50 mg once daily with a proton pump inhibitor cover, and lamotrigine was suspended on advice from Dermatology. The patient showed improvement after 3-4 days of treatment. His skin biopsy showed prominent suppurative granulomatous folliculitis, mild perivascular chronic inflammation, and red blood cell extravasation, including the rare eosinophil. He was weaned off from prednisolone by 5 mg weekly and had complete resolution of symptoms.
Kalman Dynamic Adaptive Filtering to Railway Line Measurement Signals of Tilting Train

To improve the accuracy of the measurement system of tilting train and in view of the disadvantages of traditional digital filters, an effective Kalman dynamic adaptive filtering method and corresponding mathematical model is put forward, which combines time-varying parameters adaptive estimation of non-stationary random signals model and normal Kalman filtering algorithm together, therefore can effectively eliminate or minimize effects of gauss white noises. Real time modeling accuracy of non-stationary random signals is key to the results of this dynamic filtering. Comparing and analyzing various RLS algorithms with different forgetting factors, it is shown that RLSAF algorithms can strongly track non-stationary random signals because of its adaptive forgetting factor changing with statistical characters of signals. In application to signals treatment of tilting train, results show that this Kalman dynamic adaptive filtering method based on RLSAF algorithm can effectively realize filtering to the railway line measurement signals, such as gyroscope angular velocity signal.
Soybean extracts can improve plant development

: Microbial biodiversity of an environment can contribute to plant growth and increase crop yield. Plant extracts from soybean ( Glycine max (L.) Merrill) were investigated on soybean plants grown after inoculation with these extracts. Soil samples were collected from two important Brazilian soybean-growing regions to produce the extracts used in the experiments. The extracts were produced with material collected from aboveground biomass and rhizosphere of soybean plants cultivated in a controlled greenhouse (phase 1). The extracts produced in phase 1 were applied in a sequential experiment (phase 2). Phase 2 was conducted to examine the plant microbiome after the microbial alteration process in the greenhouse through seed inoculation with the extracts produced previously. Samples of aboveground biomass were collected to determine root dry matter and crop yield. Bacterial 16S rRNA sequences were processed to determine the final microbial content of soybean. The inoculated treatments had lower species diversity; however, the phyla Firmicutes and Bacteroidetes were more abundant in the treatments than in the non-inoculated treatment. The soybean plant stem in the inoculated treatment also had a positive response to enrichment of the bacterial classes Betaproteobacteria, Bacilli and Flavobacteria. Inoculation affected the microbial composition of soybean plants. The alteration of microbiome changes revealed differences for crop yield between the inoculated and non-inoculated treatments, with up to 93.5 % higher crop yields per plant according to the extract applied.
Application of Solid-Phase Extraction and High-Performance Liquid Chromatography with Fluorescence Detection to Analyze Eleven Bisphenols in Amniotic Fluid Samples Collected during Amniocentesis

Amniocentesis involves taking a sample of the amniotic fluid in order to perform a karyotype test and diagnose any genetic defects that may affect the fetus. Amniotic fluid has been collected from patients with an indication for amniocentesis in the 15–26th week of pregnancy. A simple and sensitive high-performance liquid chromatography with fluorescence detection (HPLC-FLD) method for identification and quantification of eleven selected bisphenols in amniotic fluid samples is proposed. The proposed method involved protein precipitation using acetonitrile, and next the extraction and concentration of analytes by solid-phase extraction (SPE). The solid-phase extraction (SPE) procedure with application of Oasis HLB SPE columns performed well for the majority of the analytes, with recoveries in the range of 67–121% and relative standard deviations (RSD%) less than 16%. The limits of detection (LODs) and quantification (LOQs) of all the investigated analytes were in the range of 0.8–2.5 ng mL−1 and 2.4–7.5 ng mL−1 (curves constructed in methanol) and 1.1–5.2 ng mL−1 and 3.2–15.6 ng mL−1 (curves constructed in the amniotic fluid), respectively. The method was validated at the following two concentration levels: 10 ng mL−1 (2 × LOQ) and 20 ng mL−1 (4 LOQ). The results confirm the validity of the SPE procedure and HPLC-FLD method for identification and quantification of bisphenols in amniotic fluid samples collected during an amniocentesis. The result obtained show that HPLC-FLD is a useful method for determination of bisphenol residues at nanogram per milliliter concentrations in amniotic fluid samples. Residues of five analytes (BADGE·2H2O, BPAF, BADGE, BADGE·H2O·HCl and BADGE·2HCl) were detected in amniotic fluid samples. Additionally, the harmfulness of bisphenols as potential pathogens that may cause karyotype disorders and contribute to preterm birth was estimated.
DISTORTED MIRROR OF ADVERTISING IN A MUSLIM SOCIETY: A study of television commercials by global companies in Pakistan

Social Advertising of global business corporations is extensively investigated in various cultural settings for its modifying effect on host cultures, particularly promoting Western capitalist values. The same has raised concerns for Muslim societies where majority cultural values being derived from Islam are at loggerheads with Western values, a situation which may have ramifications for these societies. This study explored the very notion in Pakistan, and investigated the depiction of culturally consistent as well as inconsistent values LQ(cid:3)JOREDO(cid:3)FRPSDQLHV•(cid:3)WHOHYLVLRQ(cid:3)FRPPHUFLDOV(cid:3)UXQQLQJ(cid:3)LQ(cid:3)WKH(cid:3)FRXQWU\(cid:17)(cid:3)7KH(cid:3)FRQWHQW(cid:3)RI(cid:3)SULPH(cid:3)WLPH(cid:3)WHOHYLVLRQ(cid:3)DGV(cid:3) by these companies from April 2017 to March 2018 (N=131) was analyzed for the use of advertising appeals, applying Hofste GH•V(cid:3)GLPHQVLRQDO(cid:3)PRGHO(cid:3)IRU(cid:3)QDWLRQDO(cid:3)FXOWXUHV(cid:3)DQG(cid:3)DGYHUWLVLQJ(cid:3)DSSHDOV(cid:3)E\(cid:3) Pollay (1983) with modifications to include regionally standardized appeals, in the perspective of distorted mirror theorizing by Pollay (1986). The descriptive and statistical analysis of content found majority value appeals used in these ads reflecting values contrary to the cultural tendencies of Pakistani society, including appeals for regional standardization. It also found significant association of major inconsistent appeals with commercials of products used by youth and women. Appeal family was found the most frequently used DPRQJ(cid:3)FXOWXUDOO\(cid:3)FRQVLVWHQW(cid:3)DSSHDOV(cid:17)(cid:3))LQGLQJV(cid:3)SURYLGHG(cid:3)D(cid:3)XQLTXH(cid:3)ZD\(cid:3)RI(cid:3)H[WUDSRODWLQJ(cid:3)3ROOD\•V(cid:3)WKHRUL]LQJ(cid:3) that mirror of advertising is distort HG(cid:3)WR(cid:3)WKH(cid:3)VHOOHUV•(cid:3)LQWHUHVW(cid:17) with the cultural values of Pakistani society, (b) is there a prevalence of culturally incongruent messages in pursuit of regional standardization of television commercials, and (c) which audience segments are the major recipients of culturally inconsistent messages. The study aims to reason the dynamics of marketing strategy of these companies within peculiarities of Pakistani culture, which is at odds with their cultures of origin in many respects. Being first to explore the congruity of cultural values in global advertising in Pakistan, this study provides a reference point to such academic endeavors in future. Based upon the findings of this analysis, this paper presents policy recommendations. dimension and Ir =. 87 for coding of ads for appeals). Pearson Chi-square test was applied to explore association of appeals with product categories.
DIGITAL ELEVATION MODEL ALTERNATIVES ASSESSMENT FOR DEFORMATION ANALYSIS PURPOSES USING GNSS AND INSAR

Digital Elevation Model (DEM) is the starting point in the analysis performed to explain the deformation pattern changes from the Earth's surface. The estimated value of deformation based on point-wise GPS and InSAR data with a better spatial resolution must be defined in a reference frame system that reflects the phenomenon of deformation of the real physical world, e.g., orthometric height for the vertical component. Therefore, this study aims to provide alternative DEM models based on a suitable combination between the Global Geopotential Model of Earth Geopotential Model 2008 (EGM2008) and global terrain models, providing position changes with respect to the orthometric height. The alternative DEM models are (i) the global elevation model of ETOPO1 (DEM1), (ii) the modified global elevation model of SRTM30_PLUS (DEM2), and (iii) the regional elevation model of DEMNAS (DEM3). These alternative models comply with each other for the land areas with mean difference values lower than 1 meter. While for the ocean areas, we found that DEM1 and DEM2 have apparent differences due to the different types of data used. However, a similar assessment could not be performed for DEM3 as it only covers the land areas. Additionally, we compared the orthometric height from these terrain models with leveling observations for the coinciding locations. DEM3 achieves the highest accuracy with the estimated standard deviation of 11.2745 meters and is followed by DEM2 and DEM1 with the respective standard deviation of 29.4498 and 37.6872 meters. We found that these models can be used as a starting position determination for horizontal and vertical deformation analysis.
Design of Stator-Magnet Moving-Iron Transverse-Flux Linear Oscillatory Machine Considering Asymmetric Saturation

Stator-magnet moving-iron transverse-flux linear oscillatory machine (SMTLOM) is a promising reciprocating drive for linear actuator due to its merits of simple structure, high reliability, low fabrication cost, etc. However, for the special topology and operation principle of SMTLOM, the asymmetric saturation would occur during its operation, which has significant influence on the electromagnetic performance. Hence, in this paper, the design method for SMTLOM is presented with the full consideration on saturation effect. Firstly, the axial-asymmetric saturation is described in details, and consequently, the influence of such effect on the main parameters selection is investigated based on the introduced saturation coefficient. Furthermore, the demagnetization calibration is also carried out to illustrate how the axial-asymmetric saturation impacts the operation point of the magnets on flux-weaken-side stator. Finally, a prototype is manufactured and tested by both static and dynamic experiments, which have validated the theoretical analysis and simulation.
Hybrid Summarization with Semantic Weighting Reward and Latent Structure Detector

Text summarization has been a signiﬁcant challenge in the Nature Process Language (NLP) ﬁeld. The approach of dealing with text summarization can be roughly divided into two main paradigms: extractive and abstractive manner. The former allows capturing the most representative snippets in a document while the latter generates a summary by understanding the latent meaning in a material with a language generation model. Recently, studies found that jointly employing the extractive and abstractive summarization models can take advantage of their complementary advantages, creating both concise and informative summaries. However, the reinforced summarization models mainly depend on the ROUGE-based reward, which only has the ability to quantify the extent of word-matching rather than semantic-matching between document and summary. Meanwhile, documents are usually collected with redundant or noisy information due to the existence of repeated or irrelevant information in real-world applications. Therefore, only depending on ROUGE-based reward to optimize the reinforced summarization models may lead to biased summary generation. In this paper, we propose a novel deep Hy brid S ummarization with semantic weighting R eward and latent structure D etector (HySRD). Speciﬁcally, HySRD introduces a new reward mechanism that simultaneously takes advantage of semantic and syntactic information among documents and summaries. To effectively model the accuracy semantics, a latent structure detector is designed to incorporate the high-level latent structures in the sentence representation for information selection. Extensive experiments have been conducted on two well-known benchmark datasets CNN/Daily Mail (short input document) and BigPatent (long input document). The automatic evaluation shows that our approach signiﬁcantly outperforms the state-of-the-art of hybrid summarization models.
Ethical Issues in AI-enabled Disease Surveillance: Perspectives from Global Health

Infectious diseases, as COVID-19 is proving, pose a global health threat in an interconnected world. In the last 20 years, resistant infectious diseases such as SARS, MERS, H1N1, Ebola, Zika and now COVID-19 have been impacting global health defences, and aggressively flourishing within the rise of global travel, urbanization, climate change and ecological degradation. In parallel, this extraordinary episode in global human health highlights the potential for artificial intelligence (AI)-enabled disease surveillance to collect and analyse vast amounts of unstructured and real-time data to inform epidemiological and public health emergency responses. The uses of AI in these dynamic environments are increasingly complex, challenging the potential for human autonomous decisions. In this context, our study of qualitative perspectives will consider a responsible AI framework to explore its potential application to disease surveillance in a global health context. Thus far, there is a gap in the literature in considering these multiple and interconnected levels of disease surveillance and emergency health management through the lens of a responsible AI framework.
GATSBI: Generative Adversarial Training for Simulation-Based Inference

Simulation-based inference (SBI) refers to statistical inference on stochastic models for which we can generate samples, but not compute likelihoods. Like SBI algorithms, generative adversarial networks (GANs) do not require explicit likelihoods. We study the relationship between SBI and GANs, and introduce GATSBI, an adversarial approach to SBI. GATSBI reformulates the variational objective in an adversarial setting to learn implicit posterior distributions. Inference with GATSBI is amortised across observations, works in high-dimensional posterior spaces and supports implicit priors. We evaluate GATSBI on two SBI benchmark problems and on two high-dimensional simulators. On a model for wave propagation on the surface of a shallow water body, we show that GATSBI can return well-calibrated posterior estimates even in high dimensions. On a model of camera optics, it infers a high-dimensional posterior given an implicit prior, and performs better than a state-of-the-art SBI approach. We also show how GATSBI can be extended to perform sequential posterior estimation to focus on individual observations. Overall, GATSBI opens up opportunities for leveraging advances in GANs to perform Bayesian inference on high-dimensional simulation-based models.
Effect of Probiotic Bacillus subtilis Endospore on The Immune System of Leukocytes Respiratory Burst Activity (RBA) in Grouper (Epinephelus coioides)

Key word: Grouper Feed Probiotic Bacillus subtilis Immune System Leukocytes Probiotics are live microbes that can help protect and maintain the health of the host by modifying the microbial community or associating with the host, increasing the response to disease, improving nutrition, and utilizing feed. Probiotics have properties to increase feed efficiency and increase non-specific immunity in fish. Probiotic administration allows fish to achieve optimal growth and increase immunity to disease. The purpose of this study was to determine the effect of probiotic Bacillus subtilis endospore administration on the leukocytes respiratory burst activity (RBA) immune system in grouper (Epinephelus coioides). The results of the research showed that the administration of probiotics in the grouper (Epinehelus coioides) feed with the probiotic dose of Bacillus subtilis in treatment B (0.1% Bacillus subtilis) and treatment C (1 % Bacillus subtilis) had a significantly effect on Leukocytes Respiratory Burst Activity (RBA) compared to treatment A (0% Bacillus subtilis). The RBA values in treatment B (1.91) and treatment C (1.98) were significantly different start from 10 days of rearing time. While the best dose for the RBA value is treatment C (5.03) with an elapsed time 30 days. Article history: Received: 16/10/2021 Revised: 02/12/2021 Accepted: 27/12/2021
SAT-based techniques for Approximate Circuit Design

: As energy efficiency becomes a crucial concern in every kind of digital application, a new design paradigm called Approximate Computing (AC) gains popularity as a potential answer to this ever-growing energy quest. AC provides a different view to the design of digital circuits, by adding accuracy to the set of design metrics. So, while traditionally one could sacrifice area for delay, for example, or energy for area, etc, now the idea is to play with accuracy also, and pay a small loss in accuracy for a large improvement in energy consumption. This is particularly suited for error-resilient applications, where such small losses in accuracy do not represent a significant reduction in the quality of the result. While Approximate Computing can be applied at different levels – from software to hardware – in our group we are particularly interested in the design of approximate boolean circuits. In particular, we are research Approximate Logic Synthesis, which is the process of automatically generating, given an exact circuit and a tolerated error threshold, an approximate circuit counterpart where the error is guaranteed to be below the given threshold. The resulting circuit will be a functional modification of the original one, where parts will be substituted, or even completely removed.
Analysis for Recovered Compact Binary Coalescence Hardware Injections

This paper is about the study of gravitational waves with the data from LIGO’s 5th science run (S5). It provides basic background information about gravitational waves, its source and the LIGO. It described the preliminary goal of research, the following process during the half of year and the final researching target. The methodology of how to recover the compact binary coalescence injections and how to create the plots is mentioned, as well as a brief analysis based on these resources. The reader is assumed to be familiar with basic physical knowledge, having interests in the cutting-edge area of physics, but not necessarily has any background information about LIGO or gravitational waves. Liang ! 1
A Competitive Clustering on Bigdata

: The vast increase in the volume of data has created a need for new applications and algorithms to quickly analyse the large scale of data. Many of the cluster analysis techniques like K-Means are used to compute the data in distributed systems, its accuracy depends on the initial seeding of centroids. The improvisation of K-Means algorithm shows good initial seeding but it suffers with the serial nature i.e., it takes long time on large data sets. In this paper we propose a new algorithm with Map Reduce implementation to address the above problems. Our algorithm provides parallel processing of data by dividing it into number of subsets using Hadoop with MapReducing methods. Our work provides good initial centers in a less time and also produces fast and accurate cluster analysis on large scale data.