Miracle-dz/UniFAR-FTUs huggingface dataset:
Hypnotherapy in Weight Loss Treatment

This study investigated the effects of hypnosis as a treatment for weight loss among women. The sample consisted of 60 women between the ages of 20 and 65 who were at least 20% overweight and were not in any other treatment program. Six client variables (suggestibility, self-concept, quality of family origin, age of obesity onset, education level, and socioeconomic status) and one process variable (multi-modal imagery) were analyzed in relation to the dependent variable (weight loss). Two experimental groups, hypnosis plus audiotapes (Hy-T) and hypnosis without audiotapes (Hy), and the control group (Cont) were investigated for weight loss immediately after treatment and again after a 6-month follow-up. The primary hypothesis that hypnosis is an effective treatment for weight loss was confirmed, but the seven concomitant variables and the use of audiotapes were not significant contributors to weight loss.
Acidified milk for feeding dairy calves in tropical raising systems

According to a national survey, in Brazil about 44% of farmers use whole milk to feed calves, but when considering the use of waste milk this value is almost 80% (Santos and Bittar, 2015). Milk quality should be evaluated not only for its nutrient content, but also for its microbiological quality. Poor microbial quality may be responsible for the occurrence of diseases such as pneumonia and diarrhoea, impairing calves’performance, mainly in periods when the liquid diet is responsible for the most of energy intake (Aust et al., 2012). Microbial growth in milk tends to increase because of management failures, during milking or even during storage processes. The microbial growth may be a challenge especially in feeding programs with free access to the milk or in calf operations that do not have ways to refrigerate milk during storage (Moore et al., 2009). Although the optimal growth conditions vary due to the presence of microorganisms, the nutritional composition of milk is ideal for the growth of many different pathogens. Temperature is also important for bacterial growth. In non-refrigerated milk (25–30 °C), bacterial growth is accelerated and causes milk degradation and alteration of the nutritional quality. However even during refrigeration, ABSTRACT. The objective of this study was to evaluate the effect of milk acidification in tropical climate conditions on dairy calves’ growth, health and selected blood metabolites. Thirty-two Holstein calves were blocked according to sex, birth date and weight, and distributed to the following treatments: 1. refrigerated milk kept at 5 °C (RM) or 2. acidified milk (with added lactic acid to a pH of 4.2) kept at ambient temperature (ACM). After birth, calves were fed colostrum and from the second day received 6 l/day of RM or ACM heated to 38 °C until weaning at day 56. Calves were individually housed with free access to water and starter diet. Feed intake and health problems were monitored daily; calves were weighed and measured weekly. Blood samples were collected weekly to evaluate the levels of metabolites. Feed intake, body weight and daily gain did not differ between treatments, but heart girth and wither height was higher for animals fed RM. The faecal score was lower for RM group, however in calves fed ACM it also did not suggest a diarrheal process (1.98). In addition, the first case of diarrhoea in calves fed ACM was later than in calves fed RM (15.4 vs 8.6 days, respectively; P < 0.01). So, the acidification of milk is an adequate method of preserving milk in tropical ambient temperatures. It resulted in health benefits to calves, delaying the first case of diarrhoea. Received: 16 December 2019 Revised: 14 May 2020 Accepted: 17 September 2020
Spinal versus general anesthesia in gynecologic laparoscopy: A prospective, randomized study

Objective: To compare spinal anesthesia (SA) with general anesthesia (GA) in gynecologic laparoscopic surgery regarding anesthetic parameters and patient satisfaction together with an assessment of total oxidant, antioxidant levels, and Oxidative Stress index (OSI). Materials and Methods: Sixty patients who were planned to undergo gynecologic laparoscopy were randomized into group G (GA) and group S (SA). Demographics, adverse events and anesthetic parameters were recorded before induction, after induction, and at the 5th, 10th, 15th, 30th, 60th, 90th, and 120th minutes. Patients and surgeons completed questionnaires. Total antioxidant capacity (TAC), total oxidant level (TOL), and OSI were measured. Results: There was no difference between the groups in terms of hemodynamic parameters except heart rate at 30th minute and mean arteral pressure at 10th, 15th, 30th, and 60th minute (p<0.05). The postoperative arterial blood pH value was lower in group S (p=0.021). Intraoperative hypotension was lower in group S (p=0.038). There was more intraoperative hypotension in group S when compared with group G (p=0.038). Postoperative analgesic consumption was higher and onset of postoperative pain was shorter in group G (p=0.001 for both). There was no difference between the groups in terms of patient and surgeon satisfaction. There was no difference in terms of TAC, TOL, and OSI between the groups (p=0.862, p=0.940, and p=0.728, respectively). Conclusion: SA may become a reliable alternative to GA in gynecologic laparoscopy when hemodynamic and respiratory parameters, patient and surgeon satisfaction, as well as total oxidant, antioxidant levels, and OSI are considered.
Review of Wintertime Orographic Cloud Seeding

This review provides a sketchy background of orographic weather modification activities prior to the 1960s, followed by a more critical review of major orographic projects carried out and reported in the scientific literature during the past 25 years. In the earlier of these major projects, evaluation of results had been based largely upon comparisons of seeded and nonseeded precipitation experimental units stratified by various sounding-derived parameters in an attempt to amplify the physical significance of the seeding effects within various sub-types of orographic clouds.
Formulation and evaluation of bilayer tablets of clarithromycin and omeprazole against Helicobacter pylori infection

In the current study a successful attempt was made to formulate bilayer floating tablets of clarithromycin and omeprazole by direct compression method containing loading dose, superdisintegrants in immediate release layer and maintenance dose, rate controlling polymers and gas generating agents in floating layer. The extended release was prepared by direct compression method using HPMC K15, HPMC K4, PVP K30, as sustained release polymer and sodium bicarbonate as gas generating agent to reduce floating lag time. Immediate release layer were prepared by direct compression using sodium starch glycolate, Croscarmellose sodium, crospovidone as super disintegrant. Gastro retentive floating drug delivery systems have been designed to increase its residence time in the stomach. The granules were evaluated for bulk density, tapped density, compressibility index, and hausner’s ratio. The granules showed satisfactory flow properties. The optimized tablets were compressed to obtain bilayer tablets. The tablets were evaluated for various physicochemical parameters and dissolution study. Further, the bilayer tablets were subjected to accelerated stability study. The omeprazole tablet showing more than 99.45 % release in 15 min and clarithromycin tablet showing more than 99.76 % release at the end of 12 h of initial lag time were compressed one above the other to obtain bilayer tablet. The similar release pattern was observed with the bilayer tablets as that of individual tablets. The bilayer tablets were found to be stable at the end of 6 months storage period as per ICH guidelines. Both the layers of the bilayer formulation showed desired drug release at the end of time period.
Centre‐Based Care and Parenting Activities*

We examine the relationship between parenting activities and centre-based care using time diary and survey data for mothers in Germany. While mothers using centre-based care spend signiﬁcantly less time in the presence of their child, we ﬁnd that differences in the time spent on speciﬁc activities such as reading, talking, and playing with the child are relatively small or zero. The pattern of results is more pronounced for lower education mothers. The lack of large decreases in activities is explained by two factors: (i) that centre care replaces time that parents spend with the child but are doing other things such as housework or leisure (a small direct effect), and (ii) that evenings become relatively more activity-rich (a compensating indirect effect). For the intensive margin (full-day vs. half-day), we ﬁnd more additional reductions in parenting activities, but these are compensated for by lower education mothers during non-centre hours. Our ﬁndings represent novel evidence that activities in the home environment are a complement to centre-based care, highlighting a credible additional mechanism for child development effects of centre-based care.
The effect of the air duct tightness on the stability of the indoor air parameters

Abstract. The present study aimed to determine the impact of a gradual and lineal increase of the air duct leakage factor on various indoor air stability parameters in ventilation systems across the following scenarios: 1) air leakage effect on the air pressure and volume; 2) air leakage effect on the indoor air parameters when the ductwork passes through an uninsulated and unheated premise. A galvanized steel air duct was used for the air leakage measurement sessions, and orifices were cut in the duct before each subsequent measurement session, thus, consecutively decreasing the air tightness factor of the duct over the measurement sessions. The results indicated that the ductwork air tightness affects the stability of the air parameters such as air temperature, relative humidity and CO2 concentration, however, up until certain point, the impact was either non-detectable or negligible. The transition in the behavior of the air stability parameters occurred in line with the introduction of orifices 4→5, resulting in perceptible effects on air pressure, volume etc. Although, this factor may be attributed to the errors caused by low instrument sensitivity or by relatively small number and size of the orifices, authors suggest that minor air duct leakage such as presented in this study may result in the disruption of air stability parameters, which in certain instances is critical.
Progress in the Study of Phylogeny of Microgastrinae(Hymenoptera:Braconidae)

Much progress has been made on the phylogeny of Microgastrinae recently.Mainly cladistics analysis and moculecular methods were applied to the examination of the appropriateness of Mason’s classification system prevailed globally by scholars of many countries.The study suggested that Mason’s abandonment of Apanteles was appropriate,with scholars application of morphology and male,female genitalia to support Mason’s framework of two main clades(Apantelini+Microgastrini) and(Fornicini+Cotesiini+Microplitini) in the Microgastrinae.The tribes Apantelini,Fornicini,and Microplitini as monophyletic groups were also supported,but other two tribes,Microgastini and Cotesiini,defined by Mason were paraphyletic groups by the cladistic analysis.Howevert,he research resulted from the morphology alone or morphology intergrating with molecular data were not consistent with the tribe units defined by Mason,with an exception of the genus unit,after clasification and verification.Obviously,a large number of data are needed to determine the relationship between the genera and tribes of Microgastrinae.
A multiplex reverse transcription-nested polymerase chain reaction for detection and differentiation of wild-type and vaccine strains of canine distemper virus

A multiplex reverse transcription-nested polymerase chain reaction(RT-nPCR) was developed for detection and differentiation of wild-type and vaccine strains of canine distemper virus(CDV). A pair of primers P1 and P4 corresponding to the highly conserved regions of CDV genome was used as common primers in the first round PCR.Primers P2 or P3 specific for CDV wild-type strain or vaccine strain respectively were used as the forward primer together with the common reverse primer P4 in the second round PCR.A PCR fragment of 177 bp from vaccine strain genome and a fragment of 247 bp from wild-type strain genome were amplified by the RT-nPCR.Two fragments of 247 bp and 177 bp were amplified from mixed genomic samples of vaccine strain and wild-type strain.No PCR products were detected for uninfected cells,or cells infected with newcastle disease virus(NDV),canine parvovirus(CPV),canine coronavirus(CCV),rabies virus(RV),canine adenovirus(CAV). The RT-nPCR method was successfully used to detect for suspected CDV infection in 20 clinical samples from Heilongjiang and Jilin Provinces,of which 15 samples were found infected by wild type CDV,and 5 by vaccine strains.This method was effective for detecting and differentiating wild-type CDV and vaccine strains in dogs,thus could be useful for the clinical detection and the epidemiological studies.It also can be used for monitoring the vaccinal immunity.
GitEvolve: Predicting the Evolution of GitHub Repositories

Software development is becoming increasingly open and collaborative with the advent of platforms such as GitHub. Given its crucial role, there is a need to better understand and model the dynamics of GitHub as a social platform. Previous work has mostly considered the dynamics of traditional social networking sites like Twitter and Facebook. We propose GitEvolve, a system to predict the evolution of GitHub repositories and the different ways by which users interact with them. To this end, we develop an end-to-end multi-task sequential deep neural network that given some seed events, simultaneously predicts which user-group is next going to interact with a given repository, what the type of the interaction is, and when it happens. To facilitate learning, we use graph based representation learning to encode relationship between repositories. We map users to groups by modelling common interests to better predict popularity and to generalize to unseen users during inference. We introduce an artificial event type to better model varying levels of activity of repositories in the dataset. The proposed multi-task architecture is generic and can be extended to model information diffusion in other social networks. In a series of experiments, we demonstrate the effectiveness of the proposed model, using multiple metrics and baselines. Qualitative analysis of the model's ability to predict popularity and forecast trends proves its applicability.
Identifying Risk of Opioid Use Disorder for Patients Taking Opioid Medications with Deep Learning

OBJECTIVE The United States is experiencing an opioid epidemic. In recent years, there were more than 10 million opioid misusers aged 12 years or older annually. Identifying patients at high risk of opioid use disorder (OUD) can help to make early clinical interventions to reduce the risk of OUD. Our goal is to develop and evaluate models to predict OUD for patients on opioid medications using electronic health records and deep learning methods. The resulting models help us to better understand OUD, providing new insights on the opioid epidemic. Further, these models provide a foundation for clinical tools to predict OUD before it occurs, permitting early interventions. METHODS Electronic health records of patients who have been prescribed with medications containing active opioid ingredients were extracted from Cerner's Health Facts database for encounters between January 1, 2008, and December 31, 2017. Long short-term memory models were applied to predict OUD risk based on five recent prior encounters before the target encounter and compared with logistic regression, random forest, decision tree, and dense neural network. Prediction performance was assessed using F1 score, precision, recall, and area under the receiver-operating characteristic curve. RESULTS The long short-term memory (LSTM) model provided promising prediction results which outperformed other methods, with an F1 score of 0.8023 (about 0.016 higher than dense neural network (DNN)) and an area under the receiver-operating characteristic curve (AUROC) of 0.9369 (about 0.145 higher than DNN). CONCLUSIONS LSTM-based sequential deep learning models can accurately predict OUD using a patient's history of electronic health records, with minimal prior domain knowledge. This tool has the potential to improve clinical decision support for early intervention and prevention to combat the opioid epidemic.
Derived equivalences and equivariant Jordan decomposition.

The Bonnafe-Rouquier equivalence can be seen as a modular analogue of Lusztig's Jordan decomposition for groups of Lie type. In this paper, we show that this equivalence can be lifted to include automorphisms of the finite group of Lie type. Moreover, we prove the existence of a local version of this equivalence which satisfies similar properties.
GRF: Learning a General Radiance Field for 3D Scene Representation and Rendering

We present a simple yet powerful implicit neural function that can represent and render arbitrarily complex 3D scenes in a single network only from 2D observations. The function models 3D scenes as a general radiance field, which takes a set of posed 2D images as input, constructs an internal representation for each 3D point of the scene, and renders the corresponding appearance and geometry of any 3D point viewing from an arbitrary angle. The key to our approach is to explicitly integrate the principle of multi-view geometry to obtain the internal representations from observed 2D views, guaranteeing the learned implicit representations meaningful and multi-view consistent. In addition, we introduce an effective neural module to learn general features for each pixel in 2D images, allowing the constructed internal 3D representations to be remarkably general as well. Extensive experiments demonstrate the superiority of our approach.
HENIN: Learning Heterogeneous Neural Interaction Networks for Explainable Cyberbullying Detection on Social Media

In the computational detection of cyberbullying, existing work largely focused on building generic classifiers that rely exclusively on text analysis of social media sessions. Despite their empirical success, we argue that a critical missing piece is the model explainability, i.e., why a particular piece of media session is detected as cyberbullying. In this paper, therefore, we propose a novel deep model, HEterogeneous Neural Interaction Networks (HENIN), for explainable cyberbullying detection. HENIN contains the following components: a comment encoder, a post-comment co-attention sub-network, and session-session and post-post interaction extractors. Extensive experiments conducted on real datasets exhibit not only the promising performance of HENIN, but also highlight evidential comments so that one can understand why a media session is identified as cyberbullying.
Recursive Top-Down Production for Sentence Generation with Latent Trees

We model the recursive production property of context-free grammars for natural and synthetic languages. To this end, we present a dynamic programming algorithm that marginalises over latent binary tree structures with N leaves, allowing us to compute the likelihood of a sequence of N tokens under a latent tree model, which we maximise to train a recursive neural function. We demonstrate performance on two synthetic tasks: SCAN, where it outperforms previous models on the LENGTH split, and English question formation, where it performs comparably to decoders with the ground-truth tree structure. We also present experimental results on German-English translation on the Multi30k dataset, and qualitatively analyse the induced tree structures our model learns for the SCAN tasks and the German-English translation task.
Short-Term Solar Irradiance Forecasting Using Calibrated Probabilistic Models

Advancing probabilistic solar forecasting methods is essential to supporting the integration of solar energy into the electricity grid. In this work, we develop a variety of state-of-the-art probabilistic models for forecasting solar irradiance. We investigate the use of post-hoc calibration techniques for ensuring well-calibrated probabilistic predictions. We train and evaluate the models using public data from seven stations in the SURFRAD network, and demonstrate that the best model, NGBoost, achieves higher performance at an intra-hourly resolution than the best benchmark solar irradiance forecasting model across all stations. Further, we show that NGBoost with CRUDE post-hoc calibration achieves comparable performance to a numerical weather prediction model on hourly-resolution forecasting.
Automorphisms of Generalized Fermat manifolds

Let $d \geq 1$, $k \geq 2$ and $n \geq d+1$ be integers. A compact complex manifold $M$ of dimension $d$ is a generalized Fermat manifold of type $(d;k,n)$ if it admits a group $H \cong {\mathbb Z}_{k}^{n}$ of holomorphic automorphisms such that there is a Galois holomorphic branched covering $\pi:M \to {\mathbb P}^{d}$, with $H$ as its deck group, whose branch locus consists of $n+1$ hyperplanes in general position (each one of branch order $k$); we also say that $H$ is a generalized Fermat group of type $(d;k,n)$. It is well known from the general theory that, for $d \leq 2$, $M$ is algebraic. If $d=1$, then $M$ a suitable fiber product of classical Fermat curves of degree $k$ and, for $(k-1)(n-1)>2$, it has a unique generalized Fermat group of type $(1;k,n)$. In this paper we generalize the above for $d\geq 2$. More precisely, (i) we note that $M$ is algebraic and we provide explicit equations, in terms of the branch hyperplanes, being a suitable fiber product of Fermat hypersurfaces of degree $k$ and dimension $d$ and (ii) we prove that $M$ has a unique generalized Fermat group of type $(d;k,n)$, for $(d;k,n) \notin \{(2;2,5), (2;4,3)\}$. This also permits us to describe the (coarse) moduli space as a complex orbifold, this is of dimension $d(n-d-1)$ given as a quotient $X_{n}/{\mathfrak S}_{n+1}$, where $X_{n}$ is a domain in ${\mathbb C}^{d(n-d-1)}$.
Connection Pruning for Deep Spiking Neural Networks with On-Chip Learning

Long training time hinders the potential of the deep, large-scale Spiking Neural Network (SNN) with the on-chip learning capability to be realized on the embedded systems hardware. Our work proposes a novel connection pruning approach that can be applied during the on-chip Spike Timing Dependent Plasticity (STDP)-based learning to optimize the learning time and the network connectivity of the deep SNN. We applied our approach to a deep SNN with the Time To First Spike (TTFS) coding and has successfully achieved 2.1x speed-up and 64% energy savings in the on-chip learning and reduced the network connectivity by 92.83%, without incurring any accuracy loss. Moreover, the connectivity reduction results in 2.83x speed-up and 78.24% energy savings in the inference. Evaluation of our proposed approach on the Field Programmable Gate Array (FPGA) platform revealed 0.56% power overhead was needed to implement the pruning algorithm.
Negative mode problem of false vacuum decay revisited

We study negative modes around the Coleman-de Luccia bounce solution. The conditions for the appearance of an infinite number of the negative modes do not coincide between Lagrangian and Hamiltonian formulations in the literature, and we discuss the origin of this difference in detail. We show how different choices of the variable for the fluctuation around the bounce solution affect the negative mode condition in the Hamiltonian approach, and point out that there exists a choice which gives the same negative mode condition as the Lagrangian approach.