Multi-fidelity Bayesian Optimisation of Syngas Fermentation Simulators

A Bayesian optimization approach for maximizing the gas conversion rate in an industrial-scale bioreactor for syngas fermentation is presented. We have access to a high-fidelity, computational fluid dynamic (CFD) reactor model and a low-fidelity ideal-mixing-based reactor model. The goal is to maximize the gas conversion rate, with respect to the input variables (e.g., pressure, biomass concentration, gas flow rate). Due to the high cost of the CFD reactor model, a multi-fidelity Bayesian optimization algorithm is adopted to solve the optimization problem using both high and low fidelities. We first describe the problem in the context of syngas fermentation followed by our approach to solving simulator optimization using multiple fidelities. We discuss concerns regarding significant differences in fidelity cost and their impact on fidelity sampling and conclude with a discussion on the integration of real-world fermentation data

<em>Zataria multiflora</em> Boiss. essential oil against ethanol-induced gastric ulcer in rats by antioxidant properties and increase in nitric oxide production

Introduction: The present study investigated protective effect of Zataria multiflora essential oil on ethanol-induced gastric ulcer in rats along with its possible mechanism(s).Methods: Eighty male adult rats were randomly allocated into 8 groups as follows: 1: negative control (NC); 2, 3 and 4: positive control (PC, distilled water), vehicle control (VC, corn oil) and comparative control (CC, omeprazole 20 mg/kg in distilled water), respectively; 5, 6, 7 and 8: treated with 100, 200, 400 and 800 &mu;L/kg Z. multiflora essential oil. After 1 hour, gastric ulcer was induced by 4 mL/kg 75 ethanol orally to rats of groups 2-8. One hour later, blood samples were collected and then all rats were sacrificed and their stomachs were immediately removed.Results: In PC and VC groups severe lesions were observed in stomachs where mucosal lesions in CC group as well as groups treated with Z. multiflora essential oil (especially higher doses) were very mild with regard to ulcer area and number. No significant difference was observed in mucosal prostaglandin E2 (PGE2) and serum tumor necrosis factor-&alpha; (TNF-&alpha;) level among groups, gastric mucosal nitric oxide (NO) content was significantly higher in rats treated with Z. multiflora essential oil at 200, 400 and 800 &mu;L/kg as compared to PC group. Rats in CC, Z. multiflora 400 and Z. multiflora 800 groups showed higher mucosal total antioxidant capacity (TAC) as compared to PC group.Conclusion: Z. multiflora essential oil has a gastro-protective effect against ethanol-induced gastric ulcer in rats which is probably due to its antioxidant and NO production enhancing effect

Prediction of Mortar Compressive Strengths for Different Cement Grades in the Vicinity of Sodium Chloride Using ANN

AbstractThe compressive strength values of cement mortar usually affect by sodium chloride quantities, chemical admixtures and cement grades so that an artificial neural network model was performed to predict the compressive strength of mortar value for different cement grades and sodium chloride (NaCl) percent. A three layer feed forward artificial neural network (ANN) model having four input neurons such as cement grades, various water to cement ratio, sodium chloride solution content, one output neuron and five hidden neurons was developed to predict of mortar each compressive strength.To this aim, twelve different mixes under three sodium chloride solution of 0%, 5% and 10% submerged after 60 days has been adopted to measure compressive strength.Artificial neural network (ANN) analysis indicated that by using ANN as non-linear statistical data modeling tool, a strong correlation between the sodium chloride percent of cement mortar and compressive strength can be established. Moreover modeling tools has great influence on the different cement grade such as 42.5 and 32.5 MPa

DiFair: A Benchmark for Disentangled Assessment of Gender Knowledge and Bias

Numerous debiasing techniques have been proposed to mitigate the gender bias that is prevalent in pretrained language models. These are often evaluated on datasets that check the extent to which the model is gender-neutral in its predictions. Importantly, this evaluation protocol overlooks the possible adverse impact of bias mitigation on useful gender knowledge. To fill this gap, we propose DiFair, a manually curated dataset based on masked language modeling objectives. DiFair allows us to introduce a unified metric, gender invariance score, that not only quantifies a model's biased behavior, but also checks if useful gender knowledge is preserved. We use DiFair as a benchmark for a number of widely-used pretained language models and debiasing techniques. Experimental results corroborate previous findings on the existing gender biases, while also demonstrating that although debiasing techniques ameliorate the issue of gender bias, this improvement usually comes at the price of lowering useful gender knowledge of the model

Statistical CSI-based Beamforming for RIS-Aided Multiuser MISO Systems using Deep Reinforcement Learning

The paper presents a joint beamforming algorithm using statistical channel state information (S-CSI) for reconfigurable intelligent surfaces (RIS) for multiuser MISO wireless communications. We used S-CSI, which is a long-term average of the cascaded channel as opposed to instantaneous CSI utilized in most existing works. Through this method, the overhead of channel estimation is dramatically reduced. We propose a proximal policy optimization (PPO) algorithm which is a well-known actor-critic based reinforcement learning (RL) algorithm to solve the optimization problem. To test the efficacy of this algorithm, simulation results are presented along with evaluations of key system parameters, including the Rician factor and RIS location, on the achievable sum rate of the users

Affective Norms for 362 Persian Words

Background: During the past two decades, a great deal of research has been conducted on developing affective norms for words in various languages, showing that there is an urgent need to create such norms in Persian language, too. The present study intended to develop a set of 362 Persian words rated according to their emotional valence, arousal, imageability, and familiarity so as to prepare the ground for further research on emotional word processing. This was the first attempt to set affective norms for Persian words in the realm of emotion.  Methods: Prior to the study, a multitude of words were selected from Persian dictionary and academic books in Persian literature. Secondly, three independent proficient experts in the Persian literature were asked to extract the suitable words from the list and to choose the best (defined as grammatically correct and most often used). The database normalization process was based on the ratings by a total of 88 participants using a 9-point Likert scale. Each participant evaluated about 120 words on four different scales.  Results: There were significant relationships between affective dimensions and some psycholinguistic variables. Also, further analyses were carried out to investigate the possible relationship between different features of valences (positive, negative, and neutral) and other variables included in the dataset.  Conclusion: These affective norms for Persian words create a useful and valid dataset which will provide researchers with applying standard verbal materials as well as materials applied in other languages, e.g. English, German, French, Spanish, Portuguese, Dutch, etc

Effect of oral administration and topical application of melissa officinalis ethanolic extract on wound healing and serum biochemical changes in alloxan-induced diabetic rats

Background and purpose: Diabetes mellitus is a metabolic disorder with several complications, such as delayed wound healing. The aim of this study was to evaluate the efficacy of oral administration and topical application of hydroalcoholic extract of Melissa officinalis on cutaneous wound healing and serum biochemical changes in alloxan-induced diabetic rats. Materials and methods: In this experimental study thirty-six Wistar rats were randomly divided into three groups of control, diabetic control, and diabetic treatment. After anesthesia, full-thickness pieces of skin (25×25 mm) were removed from upper dorsal part of the rats. Subsequently, 24 h after the operation, the wounds of the diabetic group were locally treated with topical application of 5% cream and oral administration of Melissa officinalis extract (2500 mg/kg) was performed by gavage, daily for three weeks. The control and diabetic control groups received no treatment. The wound surface areas were measured using linear and photographic methods on days 4, 7, 14, and 21. Incisional biopsies were performed to evaluate the wound healing rate and for histopathologic examination. Finally, blood samples were taken to measure the serum glucose level and biochemical factors including triglycerides, total cholesterol, high-density lipoprotein, serum glutamic pyruvic transaminase, and serum glutamic oxaloacetic transaminase using standard methods. Results: According to the results, administration of Melissa officinalis extract significantly reduced glucose, total cholesterol, low-density lipoprotein, and creatine phosphokinase levels in the diabetic group (P<0.05). Additionally, the histopathological study showed that the collagen fibers density and wound healing increased in the diabetic treatment group. Conclusion: As the findings indicated, oral and topical administrations of Melissa officinalis extract accelerated the wound healing process and may act as an cardioprotective agent