Miracle-dz/UniFAR-FTUs huggingface dataset:
Illness Behavior in the Face of COVID-19 Infection: A Qualitative Study on Homecare in Afghanistan

Background: Illness behavior refers to the ways of perceiving, assessing, and managing specific symptoms by an individual. Objectives: The present study aims to investigate illness behavior during the COVID-19 outbreak among Afghan people. Methods: The study is a thematic analysis conducted between May and September 2020. Data were collected purposefully through semi-structured telephone interviews with 17 infected people from the cities of Kabul, Bamian, and Balkh in Afghanistan. Results: As the participants believed, the COVID-19 outbreak in their country was a socio-political rather than a medical phenomenon. In this situation, the health system is unable to meet medical needs, and patients creatively take care of themselves at home with exercise, local soups, and oxygen therapy if needed. There were four themes explored, including de-hospitalization, healthcare-seeking behavior, social stigma, and the role of media. Conclusions: The illness behavior regarding disease outbreaks is a social rather than a clinical phenomenon. It includes de-pharmaceuticalization perceptions and activities. Therefore, healthcare systems are recommended to pay attention to the socio-political dimensions of illness behavior during such outbreaks.
Exceptional multifunctionality in the feeding apparatus of a mid-Cambrian radiodont

Abstract. Radiodonts (stem Euarthropoda) were ecologically diverse, but species generally displayed limited functional specialization of appendages along the body axis compared with crown group euarthropods. This is puzzling, because such functional specialization is considered to have been an important driver of euarthropod ecological diversification. One way to circumvent this constraint could have been the functional specialization of different parts of the frontal appendages, known to have been ecologically important in radiodonts. This hypothesis has yet to be tested explicitly. Here we redescribe the poorly known mid-Cambrian hurdiid radiodont Stanleycaris hirpex from the Burgess Shale (Stephen Formation) and quantitatively assess functional specialization of the frontal appendages of stem euarthropods. The appendages of Stanleycaris are composed of 14 podomeres, variously differentiated by their possession of pectinate endites, mono- to trifurcate medial gnathites, and outer spines. The oral cone is tetraradially organized and can be uniquely distinguished from those of other hurdiids by the presence of 28 rather than 32 smooth tridentate plates. Our phylogenetic analysis finds Stanleycaris in a grade of hurdiids retaining plesiomorphic raptorial appendicular functionality alongside derived adaptations for sweep feeding and large, bilaterally opposed gnathites. We conclude that the latter performed a masticatory function, convergent with gnathal structures like mandibles in various panarthropods. Taken together, Stanleycaris and similar hurdiids provide an extreme example of the evolution of division of labor within the appendage of a stem euarthropod and suggest that this innovation may have facilitated the functional transition, from raptorial to sweep feeding, at the origin of the hurdiid clade.
Radiation Effect of Urban Agglomeration’s Transportation Network: Evidence from Chengdu–Chongqing Urban Agglomeration, China

Recent years have seen the global proliferation and integration of transportation systems in urban agglomeration (UA), suggesting that transportation networks have become more prominent in the sustainable development of UA. Core cities play a radiating and driving role in affecting their adjacent cities to formulate transportation networks. Such a phenomenon is called the radiation effect of transportation networks and can be imaged using a field strength model as proposed in the study. The field strength model was verified using the Chengdu–Chongqing urban agglomeration (CCUA) as a case. Case data concerning transportation routes and traffic volume were collected for the past 20 years. The data analyses results indicate a relatively stable pattern of transportation networks in the UA. UA cities’ radiation effects follow the same compactness trend. The core cities’ radiation spheres go beyond their territories, and they can envelop the surrounding cities, highlighting the core cities’ dominance in the entire transportation network. Moreover, two development stages of UA transportation—focus and spillover—are also identified. This study contributes to the literature by providing an innovative quantitative method to detect the interaction between a city’s transportation system and peripheral cities or regions. The radiation effect of cities’ transportation systems should be considered in the UA transportation development plan, so as to meet the needs of spatial structure planning and coordinated development of the UA.
Identification and functional analysis of bacteria in sclerotia of Cordyceps militaris

Background Cordyceps militaris is a fungus that parasitizes insects. Compounds from C. militaris are valuable in medicine and functional food. There are many kinds of bacteria in the natural sclerotia of C. militaris. However, the community structure of microorganisms in samples from different places may be different, and their corresponding ecological functions require experimental verification. Methods We used high-throughput sequencing technology to analyze bacterial 16S rRNA gene sequences in sclerotia of three samples of C. militaris from Liaoning Province, China. We isolated, identified and verified the function of culturable bacterial strains from the sclerotia. Results Pseudomonas, Pedobacter, Sphingobacterium, and Serratia were the dominant bacterial genera in the sclerotia. And function prediction showed that Pseudomonas and Pedobacter could be heterotrophic, Sphingobacterium could decompose urea, and Serratia could reduce nitrate. Two strains of bacteria isolated from the sclerotia of C. militaris, N-2 and N-26, were identified as Stenotrophomonas maltophilia and Pseudomonas baetica, respectively, based on culture and biochemical characteristics. When these isolated strains were co-cultured with C. militaris, the mycelium biomass and mycelium pellet diameter decreased, and the content of extracellular polysaccharide increased. Strain N-26 decreased the cordycepin content in C. militaris. Conclusions Bacteria in sclerotia have an important effect on the growth of C. militaris and the production of its metabolites.
Degradability, in vitro fermentation parameters, and kinetic degradation of diets with increasing levels of forage and chitosan

Abstract Chitosan is the second most important natural biopolymer in the world, extracted from crustaceans, shrimps, and crabs and can modulate rumen fermentation. Our hypothesis is that the addition of chitosan alters the fermentation patterns of different diets for ruminants. This study aimed to evaluate the effects of different levels of chitosan and forage on in vitro dry degradation kinetics and fermentation in a gas production system. The chitosan levels (0, 1625, 3,500, or 7,500 mg/kg of dry matter [DM]) were arranged in a completely randomized block design, and for in vitro ruminal fermentation assay, we used a split splot arrangement. Into the incubator, all chitosan levels were distributed in the four jars, and the forage levels varying on 100, 65, 50, 35, and 20 on DM basis. There was an interaction effect for chitosan and forage levels (P ≤ 0.05) on IVDMD; IVOMD. IVDCP and IVDNDF. Chitosan negatively affected IVDMD in all roughage levels evaluated. The pH and ammonia concentration present effect only for roughage levels and incubation hours. The chitosan did not change (P = 0.3631) the total short-chain fatty acid concentration (overall mean = 21.19 mmol/L) and the C2:C3 ratio (overall mean = 5.85). The IVDCP showed the same decreasing quadratic behavior (P < 0.0001). The increasing chitosan addition increases (P < 0.0001) the gas production and decreases (P < 0.0001) the lag time (parameter C) of diets with greater concentrate participation, characterizing greater efficiency in the degradability of the diet, confirming its potential use in diets for ruminants. Chitosan changes in vitro dry degradation kinetics and fermentation at the minimum dose of 1,722 mg/kg DM for all diets. The roughage level influenced the in vitro nutrients degradability and cumulative gas production.
DEVELOPING AND OPTIMIZING A VALIDATED RP-HPLC METHOD FOR THE ANALYSIS OF AMLODIPINE AND EZETIMIBE WITH ATORVASTATIN IN PHARMACEUTICAL DOSAGE FORMS APPLYING RESPONSE SURFACE METHODOLOGY

This paper deals with multiple response simultaneous optimization using the Derringer’s desirability function for the development of a reversedphase HPLC methods for the simultaneous determination of Amlodipine and Ezetimibe with atorvastatin in commercial pharmaceutical preparations. The ranges of the independent variables used for the optimization were MeCN: 35-40%, buffer conc. : 10-20 mM and flow rate: 0.8-1.2 ml/min. The influence of these independent variables on the output responses: capacity factor of the first peak (k 1), resolutions (Rs 2,3), and Retention time (tR 4) were evaluated. Using this strategy, mathematical model were defined and response surface were derived for the separation. The coefficients of determination R2 were more than 0.8871 for all the models. The three responses were simultaneously optimized by using Derringer’s desirability functions. Optimum conditions chosen for assay were MeCN, MeOH, 20 mM K 2HPO 4
Population Dynamics of Pratylenchus brachyurus in Maize Treated with Inorganic Fertilizer

Field studies were conducted in the early and late season of 2010 in the tropical rainforest zone of Nigeria to investigate the effects of NPK fertilizer as an inorganic soil amendment at 75 kg/ha and 150 kg/ha in four replications on population densities of Pratylenchus brachyurus and on crop yield of maize. Seeds of maize variety, DMR LSR-Y were collected from Institute of Agricultural Research and Training, Ibadan. The experimental field was naturally infested with Pratylenchus spp. due to subsequent continuous cultivation of susceptible crop on the field. Seeds were planted and NPK was applied around the roots of maize plants 3 weeks after seedling emergence. Soil samples were taken on treatment plots at planting and harvest to determine the percentage change in nematode population. At the end of experiment, NPK application resulted in the increase in the nematode population. However, the grain yield of maize was not reduced by the increased nematode densities. The results of this study suggests that the usage of NPK caused a surge in the nematode reproduction on the field due to the availability of more feeding sites on the actively growing root system of the crop. Original Research Article Adesuyi et al.; IJPSS, 33(10): 12-16, 2021; Article no.IJPSS. 67714 13
Complementary Substrates-Brewery Wastewater and Piggery Effluent—Assessment and Microbial Community Profiling in a Hybrid Anaerobic Reactor

A hybrid anaerobic reactor was operated under the complementary effluents concept to reduce the unbalanced/inhibitory capacity of the provided piggery effluent. Brewery wastewater was chosen to complement piggery effluent (60:40% v/v, respectively). The HRT reduction from 6.7 to 3.0 days allowed the testing of an organic load increase from 4.5 to 10.0 g COD/L·d, which resulted in the almost doubling of biogas production. Biogas volumes (1.2 and 2.1 L/L·d, respectively) associated with its quality (>77% CH4) revealed that the hybrid anaerobic reactor responded positively to the operational changes and that piggery effluent can be advantageously digested using the brewery wastewater as the complementary effluent. The unit bottom and the packing bed were the main functional sections recognized in the hybrid. At the beginning of anaerobic digestion, bacterial populations belonged mostly to Bacteroidales (33%) and Clostridiales (35%). The process stability and the biogas quality at 3-d HRT were related to a change in the structure composition, since Flavobacteriales (18%), Bacillales (7%), Pseudomonadales (11%) and members of the Alcaligenaceae family (5%) also integrated the microbial communities. An evident change had also occurred in archaeal populations at this phase. Methanosaeta became the dominant genus (95%), confirming that acetoclastic methanogenesis was the main way for methane production.
Evaluation of Emerging Antimicrobials Resistance in nosocomial infections caused by E. coli: The comparison results of observed cases and compartmental model

Background Early detection of Antimicrobials Resistance outbreaks is one of the most important goals of the World Health Organization. In this study, by comparing the observed cases of resistance with its expected cases, the outbreak of these resistances was investigated. It should be noted that this subject was not done in the country until the time of the study. Methods This study is a hospital-based study. Data related to all the university general of Iran (57 hospitals) were used. In these hospitals, all the patients who were infected by E.coli in time period of March 21 2017 to March 20 2018 were enrolled in the study. Then, using an index called the SIR; the observed cases of resistant E.coli were compared with the expected ones. This index is achieved from dividing the observed cases by its expected cases. If the obtained number is greater than one, it indicates the greater observed cases rather than the expected cases, which can represent Emerging. In order to compute SIR index, we divided the number of observed cases of Antimicrobials Resistance E.coli by the number of expected cases of each Antimicrobials Resistance E coli. To predict the expected cases of each Antimicrobials Resistance E coli, we developed one compartmental model. In this model, the number of patients is estimated using equations. Berkeley Madonna version 8.3. 23 software was used to manipulate these equations. Results The SIR index for E.coli resistant to Ampicillin, Ceftazidime and Colistin were 1.2(1.1–1.3), 1.1(1.02–1.2) and 1.7(1.02–2.3) respectively. This index for E.coli resistant to Meropenem was . 8 (.6-.9). In other cases, the calculated index was not statistically significant. Conclusions Ampicillin-resistant E.coli and Ceftazidime-resistant E.coli observed cases among nosocomial infections were greater than the expected cases. Hence it is necessary to reconsider using such type of antibiotics in treatment of nosocomial infections caused by E.coli. The results of this study could be important for health policy makers. In the future, outbreaks of this type of infection can be investigated with the help of the results of this study.
Handling Noise in Distributional Semantic Models for Large Scale Text Analytics and Media Monitoring - Abstract

The use of word embeddings in NLP tasks has exploded in the past decade. Distributional Semantic models of this kind have achieved notoriety in the research community, through projects such as GloVe (Pennington et al., 2014) and Word2Vec (Mikolov et al., 2013), and been successfully deployed as components of commercial systems, such as Gavagai’s Living Lexicon1 (Sahlgren et al., 2016). While undeniably powerful, the authors have generally found many of the standard pre-trained vector packages to be lacking in their handling of noisy data. This is very important to our use case, which involves large scale analysis of imperfect social media and other online data. Such text is full of variations, misspellings and corruptions. The popular open-sourced representations tend to be trained on high quality texts, such as Wikipedia dumps or news articles. While this is, of course, comprehensible, the resulting embeddings consequentially lack representations for many noisy terms. Those embeddings that are indeed learned from Social Media usually contain a pre-processing step (spell-checking) to avoid incorporating noise into the model. From our perspective, noise in the model is not only inevitable, but necessary for achieving high coverage. We capture representations of noisy terms by leaving imperfections in the data used for training. Through capturing the semantic similarity between, for example, words and their misspellings, we can more accurately model topics and entities across corpora of messy text. In experiments for customers, we have found that the robustness extends to handling OCR errors: (”clear” and ”dear” are distributionally similar). Inflectional variants of a lemma, in contrast, are not2.
Application of the BORUTA Algorithm to Input Data Selection for a Model Based on Rough Set Theory (RST) to Prediction Energy Consumption for Building Heating

Energy prediction used for building heating has attracted particular attention because it is often required in the development of various strategies to improve the energy efficiency of buildings, especially those undergoing thermal improvements. The complexity, dynamics, uncertainty, and nonlinearity of existing building energy systems create a great need for modeling techniques. One of them is machine learning models, which are based on input data consisting of features that describe the objects under study. The data describing actual buildings used to build the model may be characterized by missing values, duplicate or inconsistent features, noise, and outliers. Therefore, an extremely important aspect of the prediction model development effort is the proper selection of features to simplify the prediction of energy consumption for heating. In this connection, the goal was to evaluate the usefulness of a model describing the final energy demand rate for building heating using groups of features describing actual residential buildings undergoing thermal retrofit. The model was created by combining two algorithms: the BORUTA feature selection algorithm, which prepares conditional variables corresponding to features for a prediction model based on rough set theory (RST). The research was conducted on a group of 109 multi-family buildings from the end of the last century (made in large-panel technology), thermomodernized at the beginning of the 21st century. Evaluation metrics such as MAPE, MBE, CV RMSE, and R2, which are adopted as statistical calibration standards by ASHRAE, were used to assess the quality of the developed prediction model. The analysis of the obtained results indicated that the model based on RST, based on the features selected by the BORUTA algorithm, gives a satisfactory prediction quality with a limited number of input variables, and thus allows to predict energy consumption (after thermal improvement) for this type of buildings with high accuracy.
Does time-varying illiquidity matter for the Indian stock market? Evidence from high-frequency data

This article examines variations in illiquidity in the Indian stock market, using intraday data. Panel regression reveals prevalent day-of-the-week, month, and holiday effects in illiquidity across industries, especially during exogenous shock periods. Illiquidity fluctuations are higher during the second and third quarters. The ranking of most illiquid stocks varies, depending on whether illiquidity is measured using an adjusted or unadjusted Amihud measure. Using pooled quantile regression, we note that illiquidity plays an important asymmetric role in explaining stock returns under up- and down-market conditions in the presence of open interest and volatility. The impact of illiquidity is more severe during periods of extreme high and low returns. JEL Classification: G10, G12
Material Generation Algorithm: A Novel Metaheuristic Algorithm for Optimization of Engineering Problems

A new algorithm, Material Generation Algorithm (MGA), was developed and applied for the optimum design of engineering problems. Some advanced and basic aspects of material chemistry, specifically the configuration of chemical compounds and chemical reactions in producing new materials, are determined as inspirational concepts of the MGA. For numerical investigations purposes, 10 constrained optimization problems in different dimensions of 10, 30, 50, and 100, which have been benchmarked by the Competitions on Evolutionary Computation (CEC), are selected as test examples while 15 of the well-known engineering design problems are also determined to evaluate the overall performance of the proposed method. The best results of different classical and new metaheuristic optimization algorithms in dealing with the selected problems were taken from the recent literature for comparison with MGA. Additionally, the statistical values of the MGA algorithm, consisting of the mean, worst, and standard deviation, were calculated and compared to the results of other metaheuristic algorithms. Overall, this work demonstrates that the proposed MGA is able provide very competitive, and even outstanding, results and mostly outperforms other metaheuristics.
Title A memory saving vector fast multipole algorithm for solving theaugmented

An augmented EFIE (A-EFIE)[9], [10] has been proposed to separate the contributions of the vector potential and the scalar potential for avoiding the imbalance at low frequencies. The corresponding low frequency fast multipole algorithm (LFFMA) [11] was also developed for solving the A-EFIE. Instead of the factorization of the scalar Green’s function by using scalar addition theorem in the LF-FMA, we adopt the vector addition theorem for the factorization of the dyadic Green’s function to realize memory savings. We are to develop a vector fast multipole algorithm for solving the A-EFIE.
Hierarchical Scheduling Mechanisms in Multi-Level Fog Computing

Delivering cloud-like computing facilities at the network edge provides computing services with ultra-low-latency access, yielding highly responsive computing services to application requests. The concept of fog computing has emerged as a computing paradigm that adds layers of computing nodes between the edge and the cloud, also known as micro data centers, cloudlets, or fog nodes. Based on this premise, this article proposes a component-based service scheduler in a cloud-fog computing infrastructure comprising several layers of fog nodes between the edge and the cloud. The proposed scheduler aims to satisfy the application’s latency requirements by deciding which services components should be moved upwards in the fog-cloud hierarchy to alleviate computing workloads at the network edge. One communication-aware policy is introduced for resource allocation to enforce resource access prioritization among applications. We evaluate the proposal using the well-known iFogSim simulator. Results suggest that the proposed component-based scheduling algorithm can reduce average delays for application services with stricter latency requirements while still reducing the total network usage when applications exchange data between the components. Results have shown that our policy was able to, on average, reduce the overload impact on the network usage by approximately 11 percent compared to the best allocation policy in the literature while maintaining acceptable delays for latency-sensitive applications.