Miracle-dz/UniFAR-FTUs huggingface dataset:
Drug Utilization Evaluation On Antidiabetic, Thyroid And Antithyroid Drugs

Aim: To evaluate the drug utilization of antidiabetic, thyroid and antithyroid drugs at an endocrinology speciality hospital among the ambulatory patients. Method: This was a prospective observational study conducted for a period of 6 months. Patients who were diagnosed with either diabetes mellitus (both type-I & type-II) or thyroid disorders and receiving their respective pharmacological therapy irrespective of age and gender were included in this study. Pregnant, lactating women and patients with endocrine problems other than diabetes mellitus and thyroid disorders were excluded from this study. Results: The total number of cases collected and analyzed during a period of 6 months was 246 in which 139 (56.5%) were diabetes mellitus and 107 (43.5%) were thyroid disorders. Among the parenteral hypoglycaemic agents, long acting-insulin glargine and intermediate acting + short acting- NPH + regular insulin were prescribed in almost similar frequency. In case of mono therapy, teneligliptin was the most commonly prescribed medication and this was the unique finding in this study which signified that the trend in prescribing pattern is changing and updating from time to time. In dual combination therapy Glimepiride + Metformin and in triple combination Glimepiride + Metformin + Voglibose combinations were the most commonly prescribed medication. In hypothyroidism, supplementation with levothyroxine was the only treatment alternative as it is related to the underactive thyroid, secreting insufficient amount of hormone. Irrespective of many classes of drugs available in treating hyperthyroidism carbimazole was the most frequently prescribed drug in this study. Conclusion: It was observed that some changes in the prescribing pattern of antidiabetic drugs that signified the trend in drug utilization pattern. Clinical pharmacists should play a key role in observing and identifying the trends in prescribing patterns by performing the drug utilization evaluation studies thereby providing a better pharmaceutical care in collaboration with the other health care professionals.
Ecological and evolutionary dynamics in advective environments: Critical domain size and boundary conditions

We consider the ecological and evolutionary dynamics of a reactiondiffusion-advection model for populations residing in a one-dimensional advective homogeneous environment, with emphasis on the effects of boundary conditions and domain size. We assume that there is a net loss of individuals at the downstream end with rate b ≥ 0, while the no-flux condition is imposed on the upstream end. For the single species model, it is shown that the critical patch size is a decreasing function of the dispersal rate when b ≤ 3/2; whereas it first decreases and then increases when b > 3/2. For the two-species competition model, we show that the infinite dispersal rate is evolutionarily stable for b < 3/2 and, when dispersal rates of both species are large, the population with larger dispersal rate always displaces the population with the smaller rate. For certain specific population loss rate b < 3/2, it is also shown that there can be up to three evolutionarily stable strategies. For b > 3/2, it is proved that the infinite random dispersal rate is not evolutionarily stable, and that, for some specific b > 3/2, a finite dispersal rate is evolutionarily stable. Furthermore, for the intermediate domain size, this dispersal rate is optimal in the sense that the species adopting this rate is able to displace its competitor with a similar but different rate. Finally, nine qualitatively different pairwise invasibility plots are obtained by varying the parameter b and the domain size.
Spatial Distribution of Common Pathogenic Human Intestinal Protozoa in Iran: A Systematic Review

Background: Pathogenic intestinal protozoa are considered as a serious public health problem in developing countries. This study aimed to elucidate the overall prevalence and spatial distribution of three common human pathogenic intestinal protozoan infections in Iran. Methods: Six English and Persian databases were explored for published papers on the prevalence of Entamoeba histolytica/dispar, Giardia lamblia, and Cryptosporidium spp. in the general population of Iran from 2000 to 2015. All eligible data were collected using a pre-designed data extraction form, and the overall prevalence was estimated using a random-effects meta-analysis model. We used ArcMap for mapping the prevalence of the studied protozoa and clustering analysis. Results: Altogether, 118 eligible papers from 24 provinces of Iran were included and analyzed. The weighted prevalence of E. histolytica/dispar, G. lamblia, and Cryptosporidium spp. infection among Iranian general population were calculated 1.3% (95% CI 1.1–1.5%), 10.6% (95% CI 9.6–11.5%) and 2% (95% CI 1.5–2.5%), respectively. Conclusion: Our findings indicated human intestinal protozoan infections caused by E. histolytica/dispar, G. lamblia, and Cryptosporidium spp. have still public health importance in some parts of Iran.
Deep Neural Networks Model Compression and Acceleration: A Survey

—Deep Neural Networks (DNNs) have achieved state-of-the-art results across various applications in the recent years, with some of the best results obtained with deeper networks and large training sets. On the other hand, the increasing size of such models restricts their deployability for consumer applications on resource-limited devices such as mobile and portable devices. As a result, there has been a lot of interest in different methods to perform model compression and acceleration, and tremendous progress has been made in this area in the past few years. In this paper, we provide a comprehensive survey of recent advanced techniques for deep convolutional neural network (CNN) compression and acceleration. Speciﬁcally, we provide insightful analysis of the techniques categorized as the following: network quantization, network pruning, low-rank approximation, knowledge distillation and compact network design. Then, we select some of the more successful methods and study them more in details, providing meaningful evaluation of the model and its performance. Finally, we discuss the topic’s challenges and possible applications of the surveyed methods.
The History of the American Mind in the 20th Century: A Scenario to Explain IQ Gains Over Time and a Case for the Irrelevance of g

The history of how American minds have adapted to social demands in the 20th century can be written only if the evaluation of IQ gains is distanced from g. The relationship between IQ gains and g can be clarified only if a causal scenario about the former is stated. Until now, I have been reluctant to endorse or state a causal scenario. However, the mathematical model of William T. Dickens (Dickens & Flynn, 2001) has made plausibility possible because it does two things: suggests how environmental change could be potent enough to cover the magnitude of IQ gains, and identifies a bridge between IQ gains and g that illuminates the significance of whether the two correlate or not. I should add that Dickens is not to be held responsible for the detail of the scenario I offer or the implications drawn herein. The scenario has two dominant themes: the industrial revolution as the creator of exogenous causes, and the reciprocal causality of IQ and environment as the multiplier of effects. The latter requires some explanation.
A Data Recovery Method for Community Detection in Feature-Rich Networks

The problem of community detection in a network with features at its nodes takes into account both the graph structure and node features. The goal is to find relatively dense groups of interconnected entities sharing some features in common. We apply the so-called data recovery approach to the problem by combining the least-squares recovery criteria for both, the graph structure and node features. In this way, we obtain a new clustering criterion and a corresponding algorithm for finding clusters one-by-one, so that the process can be interpreted as that of detecting communities indeed. We show that our proposed method is effective on real-world data, as well as on synthetic data involving either only quantitative features or only categorical attributes or both. In the cases at which attributes are categorical, state-of-the-art algorithms are available. Our algorithm appears competitive against them.
Effect of comfort nursing combined with health education in the recovery of children with polydactyly and the influence on the treatment compliance and nursing satisfaction of children

To study the effect of health education combined with comfort care on treatment compliance, nursing satisfaction, and postoperative recovery of children with polydactyly, 80 children with polydactyly were selected as the study subjects and divided into a control group (CG) and an observation group (OG). Children in the CG received routine health education nursing, and children in the OG received psychological comfort nursing combined with music intervention on this basis. The changes of anxiety before and after surgery in the two groups were studied with the distribution scale, and the treatment compliance, nursing satisfaction, and postoperative recovery of the children under different modes of nursing intervention were compared. The results showed that the preoperative and postoperative anxiety scores of children in the OG were significantly lower than those in the CG (P<0.05). The treatment compliance score of children in the OG was significantly higher than that in the CG (P<0.05), and the nursing satisfaction score of children in the OG was significantly higher than that in the CG (P<0.01). The postoperative range of motion and angular deformity in the OG were significantly different from those in the CG (P<0.01), and the aesthetic score and excellent recovery rate of the children in the OG were significantly higher than those in the CG (P<0.05). Therefore, comfort nursing combined with health education can effectively improve the treatment compliance of children with polydactyly deformity, increase the satisfaction of nursing work, and improve the degree of recovery after surgery.
The effect of successive uses of oak barrels on the extraction of oak‐related volatile compounds from wine

Summary Changes in the concentration of furfuryl compounds, guaiacol and 4-methylguaiacol, cis- and trans-oak lactone, and vanillin were studied during the maturation of wines in oak barrels of different origins (French oak and American oak). The rate at which these volatile compounds accumulated in the wines followed a curve that fits to mathematical models. The changes in concentration were usually consistent with diffusion kinetics. When the barrels are new, the concentration gradient between the wood surface and the wine is high, so diffusion occurs quickly, especially in the case of lactones, the extraction rate of which could be fitted to a power curve. After some time of maturation, the concentration gradient is not so large and furthermore, the wood surface may be exhausted of some compounds so the rate of extraction decreases. During the second fill, the rate of accumulation of these compounds in the wine was almost linear, especially during the first 6 months of storage, and the quantities measured in wine were much lower. The concentration of some compounds, increased during the final 3 months (between days 180 and 270), exhibiting a quadratic curve when plotted. Furfuryl aldehydes behaved differently as they are extracted very rapidly from the first millimetres of wood surface and transformed into furfuryl alcohol, a compound with no organoleptic significance.
The catalytic evaluation of in situ grown Pd nanoparticles on the surface of Fe3O4@dextran particles in the p-nitrophenol reduction reaction

We report the catalytic activity evaluation of in situ grown Pd nanoparticles on the surface of superparamagnetic Fe3O4 particles coated with the natural polymer dextran, in the reduction of p-nitrophenol (Nip), in water, by sodium borohydride. The catalyst was fully characterized; its activity evaluated through kinetic studies and compared with recently reported catalysts using a new activity parameter. Applying a pseudo-monomolecular surface reaction approach, the reaction was also investigated by means of a theoretical adsorption model, which revealed that the reaction follows the Langmuir–Hinshelwood mechanism, where both molecules adsorb on the catalyst surface prior to undergoing a bimolecular reaction. Moreover, the catalyst could be reused five times without significant loss of Pd and with a high degree of Nip conversion.
Effect of Feeding Graded Levels of Leaf Meal on Digestibility, Clinical Chemistry and Rumen Fermentation of Lambs

In order to study the effect of replacing conventional concentrates with graded levels of leaf meal (LM) mixture in total mixed rations (TMR), 20 Corriedale lambs were divided randomly into four equal groups. The animals of control group (T1) were maintained on the TMR comprising of oats straw and concentrate mixture while in T2, T3 and T4 groups, the concentrate mixture in TMR was replaced with LM @ 15, 30 and 45%, respectively. Blood analysis was carried out at the start and end of the 120d experiment. Rumen studies was carried out at the end of the trial period. DM intake was significantly (P<0.05) improved in groups fed LM at 30 and 45% of concentrate mixture. The lambs fed rations with concentrate mixture replaced at 0 (T1) and 30% (T3) level by LM had significantly (P<0.05) higher gain in weight. Mean values of Hb and PCV, total protein, albumin, globulin, urea, creatinine and cholesterol, Ca and P and SGOT, SGPT and ALP were similar among the groups except for significant variation seen in serum urea. The ruminal pH, TVFA, NH3-N and NPN varied significantly among the groups. Cost of production lowered significantly (P<0.01) due to inclusion of LM mixture with highest percent reduction in feed cost per kg LW gain (19.27) at 30% and lowest (2.77) at 15% replacement. It is concluded that replacement of concentrate mixture in TMR by LM had no adverse effects on health, growth and rumen fermentation pattern in lambs and better results could be obtained at 30% replacement level.
SULFUR RECOVERY WITH REDUCED EMISSIONS, LOW CAPITAL INVESTMENT AND HYDROGEN CO-PRODUCTION

Abstract The main disadvantage of the Claus process is that by introducing air as oxidant a large volume of tail gas is produced. This must be treated to reduce atmospheric emissions of sulfur-containing gases. The costs of the tail-gas unit are a significant fraction of the total capital and operating costs for sulfur recovery. A new process uses thermal decomposition of hydrogen sulfide in the presence of carbon dioxide instead of air oxidation. The products of this reaction are hydrogen, carbon monoxide, elemental sulfur, water vapor and carbonyl sulfide. Carbonyl sulfide is easily converted to H2S and C02 by liquid- or vapor-phase hydrolysis. Unreacted H2S and C02 are recovered by absorption and recycled to the reactor. Since no air is introduced, there is no tail gas and the tail-gas unit is eliminated, giving a substantial reduction in capital investment. The concentrations of sulfur-containing gases in the product streams depend only on the operation of the absorber and stripper units and can be co.
A General Framework Combining Generative Adversarial Networks and Mixture Density Networks for Inverse Modeling in Microstructural Materials Design

Microstructural materials design is one of the most important applications of inverse modeling in materials science. Generally speaking, there are two broad modeling paradigms in scientific applications: forward and inverse. While the forward modeling estimates the observations based on known parameters, the inverse modeling attempts to infer the parameters given the observations. Inverse problems are usually more critical as well as difficult in scientific applications as they seek to explore the parameters that cannot be directly observed. Inverse problems are used extensively in various scientific fields, such as geophysics, healthcare and materials science. However, it is challenging to solve inverse problems, because they usually need to learn a one-to-many non-linear mapping, and also require significant computing time, especially for high-dimensional parameter space. Further, inverse problems become even more difficult to solve when the dimension of input (i.e. observation) is much lower than that of output (i.e. parameters). In this work, we propose a framework consisting of generative adversarial networks and mixture density networks for inverse modeling, and it is evaluated on a materials science dataset for microstructural materials design. Compared with baseline methods, the results demonstrate that the proposed framework can overcome the above-mentioned challenges and produce multiple promising solutions in an efficient manner.
Development and Validation of a Stability Indicating UV- Spectrophotometric Assay Method for the Determination of Naratriptan Hydrochloride

The research in this paper discusses about the development and validation of a novel, cost effective, simple, reproducible and accurate UV-spectrophotometric method to estimate naratriptan hydrochloride in bulk as well as pharmaceutical formulations. Naratriptan hydrochloride was analyzed at 223 nm in simulated saliva (pH 6.8) and phosphate buffer (pH 7.4). Linearity was present in the concentration range of 1–40 μg/ml for both media. The method was validated as per different parameters according to International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) guidelines of Q2 (R1). The suggested method was successfully used for the analysis of naratriptan hydrochloride in-house oral film formulation as linearity values for the same were found to be 1–40μg/mL. The results revealed the suitability of the technique for the estimation of naratriptan hydrochloride in the oral films for its accuracy, precision, and reproducibility.
Spectroscopic study of the [O iii]λ5007 profile in Seyfert 1 galaxies

The spectra of active galactic nuclei usually exhibit wings in some emission lines, such as [OIII]λλ5007,4959, with these wings generally being blueshifted and related to strong winds and outflows. The aim of this work was to analyse the [OIII] emission lines in broad line Seyfert 1 (BLS1) galaxies in order to detect the presence of wings, and to study the [OIII] line properties and their possible connection with the central engine. In addition, we attempted to compare the black hole mass distribution in both BLS1 galaxies with symmetric and blue-asymmetric [OIII] profiles. For this purpose, we carried out a spectroscopic study of a sample of 45 nearby southern BLS1 galaxies from the 6 Degree Field Galaxy survey. The [OIII] emission lines were well fitted using a single Gaussian function in 23 galaxies, while 22 objects presented a wing component and required a double-Gaussian decomposition. By computing the radial velocity difference between the wing and core centroids (i.e. ∆v), we found 18 galaxies exhibiting blueshifted wings, 2 objects presenting red wings and 2 galaxies showing symmetric wings (∆v= 0). Moreover, ∆v was slightly correlated with the black hole mass. In addition, we computed the radial velocity difference of the blue-side full extension of the wing relative to the centroid of the core component through the blue emission parameter, which revealed a correlation with black hole mass, in agreement with previous results reported for narrow line galaxies. Finally, in our sample, similar black hole mass distributions were observed in both BLS1 galaxies with symmetric and blueshifted asymmetric [OIII] profiles.
Analysis and tuning of hierarchical topic models based on Renyi entropy approach

Hierarchical topic modeling is a potentially powerful instrument for determining topical structures of text collections that additionally allows constructing a hierarchy representing the levels of topic abstractness. However, parameter optimization in hierarchical models, which includes finding an appropriate number of topics at each level of hierarchy, remains a challenging task. In this paper, we propose an approach based on Renyi entropy as a partial solution to the above problem. First, we introduce a Renyi entropy-based metric of quality for hierarchical models. Second, we propose a practical approach to obtaining the “correct” number of topics in hierarchical topic models and show how model hyperparameters should be tuned for that purpose. We test this approach on the datasets with the known number of topics, as determined by the human mark-up, three of these datasets being in the English language and one in Russian. In the numerical experiments, we consider three different hierarchical models: hierarchical latent Dirichlet allocation model (hLDA), hierarchical Pachinko allocation model (hPAM), and hierarchical additive regularization of topic models (hARTM). We demonstrate that the hLDA model possesses a significant level of instability and, moreover, the derived numbers of topics are far from the true numbers for the labeled datasets. For the hPAM model, the Renyi entropy approach allows determining only one level of the data structure. For hARTM model, the proposed approach allows us to estimate the number of topics for two levels of hierarchy.
CAA : Channelized Axial Attention for Semantic Segmentation

Self-attention and channel attention, modelling the semantic interdependencies in spatial and channel dimensions respectively, have recently been widely used for semantic segmentation. However, computing self-attention and channel attention separately and then fusing them directly can cause conﬂicting feature representations. In this paper, we propose the Channelized Axial Attention (CAA) to seamlessly integrate channel attention and axial attention with reduced computational complexity. After computing axial attention maps, we propose to channelize the intermediate results obtained from the transposed dot-product so that the channel importance of each axial representation is optimized across the whole receptive ﬁeld. We further develop grouped vectorization, which allows our model to be run in the very limited GPU memory with a speed comparable with full vectorization. Comparative experiments conducted on multiple benchmark datasets, including Cityscapes, PASCAL Context and COCO-Stuff, demonstrate that our CAA not only requires much less computation resources but also outperforms the state-of-the-art segmentation models based on ResNet-101 on all tested datasets.
Clustering future scenarios based on predicted range maps

Predictions of biodiversity trajectories under climate change are crucial in order to act effectively in maintaining the diversity of species. In many ecological applications, future predictions are made under various global warming scenarios, as described by a range of different climate models. We propose a clustering methodology to synthesize and interpret the outputs of these various predictions. We propose an interpretable and flexible two‐step methodology to measure the similarity between predicted species range maps and to cluster the future scenario predictions utilizing a spectral clustering technique. We implement and provide code for this method. We find that clustering based on predicted species range maps is mainly driven by the amount of warming rather than climate model or future scenario. We contrast this with clustering based only on predicted climate variables, which is driven primarily by climate models, that is, scenarios of the same climate model are clustered together, even when the amount of warming input to the models is varied. The differences between species‐based and climate‐based clusterings illustrate that it is crucial to incorporate ecological information to understand the relevant differences between climate models. Our findings can be used to better synthesize forecasts of biodiversity change under the wide spectrum of results that emerge when considering potential future scenarios.