bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-Objective Optimization and Optimal Airfoil Blade Selection for a Small Horizontal-Axis Wind Turbine (HAWT) for Application in Regions with Various Wind Potential
Authors: Akbari Vahid , Naghashzadegan Mohammad , Kouhikamali Ramin , Afsharpanah Farhad , Yaïci Wahiba
Published: August 2022

The type of airfoil with small wind turbine blades should be selected based on the wind potential of the area in which the turbine is used. In this study, 10 low Reynolds number airfoils, namely, BW-3, E387, FX 63-137, S822, S834, SD7062, SG6040, SG6043, SG6051, and USNPS4, were selected and their performance was evaluated in a 1 kW wind turbine in terms of the power coefficient and also the startup time, by performing a multi-objective optimization study. The blade element momentum technique was utilized to perform the calculations of the power coefficient and startup time and the differential evolution algorithm was employed to carry out the optimization. The results reveal that the type of airfoil used in the turbine blade, aside from the aerodynamic performance, completely affects the turbine startup performance. The SG6043 airfoil has the highest power coefficient and the BW-3 airfoil presents the shortest startup time. The high lift-to-drag ratio of the SG6043 airfoil and the low inertia of the turbine blades fitted with the BW-3 airfoil make them suitable for operation in windy regions and areas with low wind speeds, respectively.
Trajectory Planning of Dual-Robot Cooperative Assembly
Authors: Chen Xuyang , You Xiaojun , Jiang Jinchao , Ye Jinhua , Wu Haibin
Published: August 2022

Efficiency can be improved through the cooperation of a dual-robot during assembly. However, how to effectively plan a simple and smooth path in a dynamic environment is a prominent problem in the process of dual-robot cooperative assembly. In this paper, a method based on RRT-Connect algorithm for trajectory planning and post-processing for trajectory optimization is proposed. This method takes full advantage of the excellent solution ability of RRT-Connect algorithm in the complex environment so as to obtain the initial path successfully. Through post-processing, the problem of RRT-Connect non-convergence to target is optimized. We use two 6-DOF industrial robots to build an experimental platform and design a dual-robot cooperative assembly system. According to the given task, the system can generate the original collision-free path based on RRT-Connect algorithm. Then the original path is simplified by Floyd algorithm and smoothed by multi-segment Bezier curve. Finally, the time parameter is sequenced for all the path points based on the iterative method, and the effective trajectory is obtained. The experimental results show that the algorithm proposed in this paper can effectively plan and optimize the trajectory of dual-robot. Compared to other methods, this approach has a higher success rate and less planning time.
Dynamics Analysis of a Predator–Prey Model with Hunting Cooperative and Nonlinear Stochastic Disturbance
Authors: Zhang Yuke , Meng Xinzhu
Published: August 2022

This paper proposes a stochastic predator–prey model with hunting cooperation and nonlinear stochastic disturbance, and focuses on the effects of nonlinear white noise and hunting cooperation on the populations. First, we present the thresholds R1 and R2 for extinction and persistence in mean of the predator. When R1 is less than 0, the predator population is extinct; when R2 is greater than 0, the predator population is persistent in mean. Moreover, by establishing suitable Lyapunov functions, we investigate the threshold R0 for the existence of a unique ergodic stationary distribution. At last, we carry out the numerical simulations. The results show that white noise is harmful to the populations and hunting cooperation is beneficial to the predator population.
Numerical Modelling and Experimental Validation of Novel Para Winglet Tape for Heat Transfer Enhancement
Authors: Rajashekaraiah Thejaraju , Bettaiah Girisha Kanuvanahalli , Rajendran Parvathy , Abbas Mohamed , Khan Sher Afghan , Saleel C. Ahamed
Published: August 2022

Heat exchangers are predominantly used in the industries of production, manufacturing, power, oil and gas, petroleum, and cooling solutions. The competence of the heat exchanger is optimized through active and passive augmented techniques. The current study revolves around the performance evaluation of Novel Para winglet tape for flow and friction characteristics. Turbulence flow properties from Re of 30,000-to-6000 were explored for three different inclinations and pitches, respectively. Experimental and numerical solutions are derived to showcase the flow behavior over Para winglet tape inserts in the double pipe heat exchanger. Appreciable results were obtained in enhancing the Nusselt number (Nup) for a better heat transfer enforcement through the DEX. All case studies also increased when compared to the smooth pipe. Experimentally, the maximum Nu and Nusselt number ratio was observed to be 398.23 and 5.05 times over the plain tube. Similarly, the maximum friction factor and its ratio were observed to be near 0.33 and 8.89 times over the plain tube. Finally, the maximum POI of 2.68 to 2.37 was achieved with 20° inclinations. The experimental and numerical outcomes of Para winglet tape with the higher inclination and shorter pitch were found to be best out of the others.
Modeling Inventory Cost Savings and Supply Chain Success Factors: A Hybrid Robust Compromise Multi-Criteria Approach
Authors: Rojas Fernando , Wanke Peter , Leiva Víctor , Huerta Mauricio , Martin-Barreiro Carlos
Published: August 2022

Determining success factors for managing supply chains is a relevant aspect for companies. Then, modeling the relationship between inventory cost savings and supply chain success factors is a route for stating such a determination. This is particularly important in pharmacies and food nutrition services (FNS), where the advances made on this topic are still scarce. In this article, we propose and formulate a robust compromise (RoCo) multi-criteria model based on non-linear programming and time-dependent demand. The novelty of our proposal is in defining a score that allows us to measure the mentioned success factors in a simple way, in meeting together all three elements (RoCo multi-criteria, non-linear programming, and time-dependent demand) to state a new model, and in applying it to pharmacies and FNS. This model relates inventory cost savings for pharmacy/FNS and success factors across their supply chains. Savings of inventory costs are predicted by lot sizes to be purchased and computed by comparing optimal and true inventory costs. We utilize a system that records the movements and costs of products to collect the data. Factors, such as purchasing organization, economies of scale, and synchronized supply, are assumed using the purchase system, with these factors ranked on a Likert scale. We consider multilevel relationships between savings obtained for 79 pharmacy/FNS products, and success factor scores according to these products. To deal with the endogeneity bias of the relationships proposed, internal instrumental variables are employed by utilizing generalized statistical moments. Among our main conclusions, we state that the greatest cost savings obtained from inventory models are directly associated with low-success supply chain factors. In this association, the success factors operate as endogenous variables, with respect to inventory cost savings, given the simultaneity of their relationship with cost savings when inventory decision-making.
Language Accent Detection with CNN Using Sparse Data from a Crowd-Sourced Speech Archive
Authors: Mikhailava Veranika , Lesnichaia Mariia , Bogach Natalia , Lezhenin Iurii , Blake John , Pyshkin Evgeny
Published: August 2022

The problem of accent recognition has received a lot of attention with the development of Automatic Speech Recognition (ASR) systems. The crux of the problem is that conventional acoustic language models adapted to fit standard language corpora are unable to satisfy the recognition requirements for accented speech. In this research, we contribute to the accent recognition task for a group of up to nine European accents in English and try to provide some evidence in favor of specific hyperparameter choices for neural network models together with the search for the best input speech signal parameters to ameliorate the baseline accent recognition accuracy. Specifically, we used a CNN-based model trained on the audio features extracted from the Speech Accent Archive dataset, which is a crowd-sourced collection of accented speech recordings. We show that harnessing time–frequency and energy features (such as spectrogram, chromogram, spectral centroid, spectral rolloff, and fundamental frequency) to the Mel-frequency cepstral coefficients (MFCC) may increase the accuracy of the accent classification compared to the conventional feature sets of MFCC and/or raw spectrograms. Our experiments demonstrate that the most impact is brought about by amplitude mel-spectrograms on a linear scale fed into the model. Amplitude mel-spectrograms on a linear scale, which are the correlates of the audio signal energy, allow to produce state-of-the-art classification results and brings the recognition accuracy for English with Germanic, Romance and Slavic accents ranged from 0.964 to 0.987; thus, outperforming existing models of classifying accents which use the Speech Accent Archive. We also investigated how the speech rhythm affects the recognition accuracy. Based on our preliminary experiments, we used the audio recordings in their original form (i.e., with all the pauses preserved) for other accent classification experiments.
Machine Learning-Based Models for Shear Strength Prediction of UHPFRC Beams
Authors: Ni Xiangyong , Duan Kangkang
Published: August 2022

Estimating shear strength is a crucial aspect of beam design. The goal of this research is to develop a shear strength calculation technique for ultra-high performance fiber reinforced concrete (UHPFRC) beams. To begin, a shear test database of 200 UHPFRC beam specimens is established. Then, random forest (RF) is used to evaluate the importance of influence factors for the shear strength of UHPFRC beams. Subsequently, three machine learning (ML)-based models, including artificial neural network (ANN), support vector regression (SVR), and eXtreme-gradient boosting (XGBoost), are proposed to compute shear strength. Results demonstrate that the area of longitudinal reinforcement has the greatest influence on the shear capacity of UHPFRC beams, and ten parameters with high importance (e.g., the area of longitudinal reinforcement, the stirrup strength, the cross-section area, the shear span ratio, fiber volume fraction, etc.) are selected as input parameters. The models of ANN, SVR, and XGBoost have close accuracy, and their R2 are 0.8825, 0.9016, and 0.8839, respectively, which are much larger than those of existing theoretical models. In addition, the average ratios of prediction values of ANN, SVR, and XGBoost models to experimental results are 1.08, 1.02, and 1.10, respectively; the coefficients of variation are 0.28, 0.21, and 0.28, respectively. The SVR model has the best accuracy and reliability. The accuracy and reliability of ML-based models are much better than those of existing models for calculating the shear strength of UHPFRC beams.
Combining a Population-Based Approach with Multiple Linear Models for Continuous and Discrete Optimization Problems
Authors: Vega Emanuel , Soto Ricardo , Contreras Pablo , Crawford Broderick , Peña Javier , Castro Carlos
Published: August 2022

Population-based approaches have given us new search strategies and ideas in order to solve optimization problems. Usually, these methods are based on the performance carried out by a finite number of agents, which by the interaction between them they evolve and work all over the search space. Also, it is well-known that the correct employment of parameter values in this kind of method can positively impact their performance and behavior. In this context, the present work focuses on the design of a hybrid architecture which smartly balances the population size on run-time. In order to smartly balance and control the population size, a modular approach, named Linear Modular Population Balancer (LMPB), is proposed. The main ideas behind the designed architecture include the solving strategy behind a population-based metaheuristic, the influence of learning components based on multiple statistical modeling methods which transform the dynamic data generated into knowledge, and the possibilities to tackle both discrete and continuous optimization problems. In this regard, three modules are proposed for LMPB, which concern tasks such as the management of the population-based algorithm, parameter setting, probabilities, learning methods, and selection mechanism for the population size to employ. In order to test the viability and effectiveness of our proposed approach, we solve a set of well-known benchmark functions and the multidimensional knapsack problem (MKP). Additionally, we illustrate promising solving results, compare them against state-of-the-art methods which have proved to be good options for solving optimization problems, and give solid arguments for future work in the necessity to keep evolving this type of proposed architecture.
A New Similarity Measure of Fuzzy Signatures with a Case Study Based on the Statistical Evaluation of Questionnaires Comparing the Influential Factors of Hungarian and Lithuanian Employee Engagement
Authors: Kóczy László T. , Susniene Dalia , Purvinis Ojaras , Konczosné Szombathelyi Márta
Published: August 2022

Similarity between two fuzzy values, sets, etc., may be defined in various ways. The authors here attempt introducing a general similarity measure based on the direct extension of the Boolean minimal form of equivalence operation. It is further extended to hierarchically structured multicomponent fuzzy signatures. Two versions of this measure, one based on the classic min–max operations and one based on the strictly monotonic algebraic norms, are proposed for practical application. A real example from management science is chosen, namely the comparison of employee attitudes in two different populations. This example has application possibilities in the evaluation and analysis of employee behaviour in companies as, due to the complex aspects in analysing multifaceted behavioural paradigms in organizational management, it is difficult for companies to make reliable decisions in creating processes for better social interactions between employees. In the paper, the authors go through the steps of building a model for exploring a set of different features, where a statistical pre-processing step enables the identification of the interdependency and thus the setup of the fuzzy signature structure suitable to describe the partially redundant answers given to a standard questionnaire and the comparison of them with help of the (pair of the) new similarity measures. As a side result in management science, by using an internationally applied standard questionnaire for exploring the factors of employee engagement and using a sample of data obtained from Hungarian and Lithuanian firms, it was found that responses in Hungary and Lithuania were partially different, and the employee attitude was thus in general different although in some questions an unambiguous similarity could be also discovered.
Hankel Determinant Containing Logarithmic Coefficients for Bounded Turning Functions Connected to a Three-Leaf-Shaped Domain
Authors: Shi Lei , Arif Muhammad , Raza Mohsan , Abbas Muhammad
Published: August 2022

The purpose of this study was to obtain the sharp Hankel determinant H2,1Ff/2 and H2,2Ff/2 with a logarithmic coefficient as entry for the class BT3L of bounded turning functions connected with a three-leaf-shaped domain. In this study, we developed a novel method to prove the bound sharpness. Although the calculations are much easier using numerical analysis, all the proofs of our results can be checked with a basic knowledge of calculus.
Applying Regressive Machine Learning Techniques in Determination of COVID-19 Vaccinated Patients’ Influence on the Number of Confirmed and Deceased Patients
Authors: Baressi Šegota Sandi , Lorencin Ivan , Anđelić Nikola , Musulin Jelena , Štifanić Daniel , Glučina Matko , Vlahinić Saša , Car Zlatan
Published: August 2022

Vaccinations are one of the most important steps in combat against viral diseases such as COVID-19. Determining the influence of the number of vaccinated patients on the infected population represents a complex problem. For this reason, the aim of this research is to model the influence of the total number of vaccinated or fully vaccinated patients on the number of infected and deceased patients. Five separate modeling algorithms are used: Linear Regression (LR), Logistic Regression (LogR), Least Absolute Shrinkage and Selection Operator (LASSO), Multilayer Perceptron (MLP), and Support Vector Regression (SVR). Cross-correlation analysis is performed to determine the optimal lags in data to assist in obtaining better scores. The cross-validation of models is performed, and the models are evaluated using Mean Absolute Percentage Error (MAPE). The modeling is performed for four different countries: Germany, India, the United Kingdom (UK), and the United States of America (USA). Models with an error below 1% are found for all the modeled cases, with the best models being achieved either by LR or MLP methods. The obtained results indicate that the influence of vaccination rates on the number of confirmed and deceased patients exists and can be modeled using ML methods with relatively high precision.
A Real-Time Crowdsensing Framework for Potential COVID-19 Carrier Detection Using Wearable Sensors
Authors: Mankodiya Harsh , Palkhiwala Priyal , Gupta Rajesh , Jadav Nilesh Kumar , Tanwar Sudeep , Neagu Bogdan-Constantin , Grigoras Gheorghe , Alqahtani Fayez , Shehata Ahmed M.
Published: August 2022

Artificial intelligence has been utilized extensively in the healthcare sector for the last few decades to simplify medical procedures, such as diagnosis, prognosis, drug discovery, and many more. With the spread of the COVID-19 pandemic, more methods for detecting and treating COVID-19 infections have been developed. Several projects involving considerable artificial intelligence use have been researched and put into practice. Crowdsensing is an example of an application in which artificial intelligence is employed to detect the presence of a virus in an individual based on their physiological parameters. A solution is proposed to detect the potential COVID-19 carrier in crowded premises of a closed campus area, for example, hospitals, corridors, company premises, and so on. Sensor-based wearable devices are utilized to obtain measurements of various physiological indicators (or parameters) of an individual. A machine-learning-based model is proposed for COVID-19 prediction with these parameters as input. The wearable device dataset was used to train four different machine learning algorithms. The support vector machine, which performed the best, received an F1-score of 96.64% and an accuracy score of 96.57%. Moreover, the wearable device is used to retrieve the coordinates of a potential COVID-19 carrier, and the YOLOv5 object detection method is used to do real-time visual tracking on a closed-circuit television video feed.
Antibacterial and Anticancer Activities of Pleurocidin-Amide, a Potent Marine Antimicrobial Peptide Derived from Winter Flounder, Pleuronectes americanus
Authors: Hsu Hui-Chen , Chen Ming-Hsin , Yeh Ming-Lung , Chen Wei-Jung
Published: August 2022

The extensive use of conventional antibiotics has led to the growing emergence of many resistant strains of pathogenic bacteria. Evidence suggests that cationic antimicrobial peptides (AMPs) have the greatest potential to serve as traditional antibiotic substitutes. Recent studies have also reported that certain AMPs have selective toxicity toward various types of cancer cells. The electrostatic attraction between the negatively charged membrane components and AMPs is believed to play a crucial role in the disruption of bacterial and cancer cell membranes. In the current study, we used a potent AMP called Pleurocidin (Ple) derived from winter flounder Pleuronectes americanus and its C-terminal-amidated derivative Pleurocidin-amide (Ple-a), and evaluated their antibacterial and anticancer activities. Our results indicated that both Ple and Ple-a exhibited significant antibacterial activity against a broad spectrum of Gram-positive and Gram-negative bacteria, especially marine pathogens, with MIC values ranging from 0.25 to 32 μg/mL. These peptides are also potent against several multidrug-resistant (MDR) bacterial strains, with MIC values ranging from 2 to 256 μg/mL. When used in combination with certain antibiotics, they exhibited a synergistic effect against MDR E. coli. Ple and Ple-a also showed notable cytotoxicity toward various cancer cell lines, with IC50 values ranging from 11 to 340 μM, while normal mouse fibroblast 3T3 cells were less susceptible to these peptides. Ple-a was then selected to study its anticancer mechanism toward A549 human lung adenocarcinoma cells. Western blot analysis and confocal microscopy showed that Ple-a could inhibit autophagy of A549 cells, and induce apoptosis 48 h after treatment. Our findings provided support for the future application of Ple-a as potential therapeutic agent for bacterial infections and cancer treatment.
Linoleate-Rich Safflower Oil Diet Increases Linoleate-Derived Bioactive Lipid Mediators in Plasma, and Brown and White Adipose Depots of Healthy Mice
Authors: Snoke Deena B. , Angelotti Austin , Borkowski Kamil , Cole Rachel M. , Newman John W. , Belury Martha A.
Published: August 2022

Polyunsaturated fats are energy substrates and precursors to the biosynthesis of lipid mediators of cellular processes. Adipose tissue not only provides energy storage, but influences whole-body energy metabolism through endocrine functions. How diet influences adipose–lipid mediator balance may have broad impacts on energy metabolism. To determine how dietary lipid sources modulate brown and white adipose tissue and plasma lipid mediators, mice were fed low-fat (15% kcal fat) isocaloric diets, containing either palm oil (POLF) or linoleate-rich safflower oil (SOLF). Baseline and post body weight, adiposity, and 2-week and post fasting blood glucose were measured and lipid mediators were profiled in plasma, and inguinal white and interscapular brown adipose tissues. We identified over 30 species of altered lipid mediators between diets and found that these changes were unique to each tissue. We identified changes to lipid mediators with known functional roles in the regulation of adipose tissue expansion and function, and found that there was a relationship between the average fold difference in lipid mediators between brown adipose tissue and plasma in mice consuming the SOLF diet. Our findings emphasize that even with a low-fat diet, dietary fat quality has a profound effect on lipid mediator profiles in adipose tissues and plasma.
Automated Denudation of Oocytes
Authors: Zhai Rongan , Shan Guanqiao , Dai Changsheng , Hao Miao , Zhu Junhui , Ru Changhai , Sun Yu
Published: August 2022

Denudation is a technique for removal of the cumulus cell mass from oocytes in clinical intracytoplasmic sperm injection (ICSI). Manual oocyte denudation requires long training hours and stringent skills, but still suffers from low yield rate and denudation efficiency due to human fatigue and skill variations across operators. To address these limitations, this paper reports a robotic system for automated oocyte denudation. In this system, several key techniques are proposed, including a vision-based contact detection method for measuring the relative z position between the micropipette tip and the dish substrate, recognition of oocytes and the surrounding cumulus cells, automated calibration algorithm for eliminating the misalignment angle, and automated control of the flow rate based on the model of oocyte dynamics during micropipette aspiration and deposition. Experiments on mouse oocytes demonstrated that the robotic denudation system achieved a high yield rate of 97.0 ± 2.8% and denudation efficiency of 95.0 ± 0.8%. Additionally, oocytes denuded by the robotic system showed comparable fertilization rate and developmental competence compared with manual denudation. Our robotic denudation system represents one step towards the automation and standardization of ICSI procedures.
Ramie Fabric Treated with Carboxymethylcellulose and Laser Engraved for Strain and Humidity Sensing
Authors: Shi Shangxuan , Liang Jiao , Qu Chenkai , Chen Shangbi , Sheng Bin
Published: August 2022

Wearable fabric sensors have attracted enormous attention due to their huge potential in human health and activity monitoring, human–machine interaction and the Internet of Things (IoT). Among natural fabrics, bast fabric has the advantage of high strength, good resilience and excellent permeability. Laser engraving, as a high throughput, patternable and mask-free method, was demonstrated to fabricate fabric sensors. In this work, we developed a simplified, cost-effective and environmentally friendly method for engraving ramie fabric (a kind of bast fabric) directly by laser under an ambient atmosphere to prepare strain and humidity sensors. We used carboxymethylcellulose (CMC) to pretreat ramie fabric before laser engraving and gained laser-carbonized ramie fabrics (LCRF) with high conductivity (65 Ω sq−1) and good permeability. The strain and humidity sensors had high sensitivity and good flexibility, which can be used for human health and activity monitoring.
Tunable, Low–Cost, Multi–Channel, Broadband Liquid Crystal Shutter for Fluorescence Imaging in Widefield Microscopy
Authors: Gong Yan , Li Bo , Yao Cheng-You , Yang Weiyang , Fan Qi Hua , Qiu Zhen , Li Wen
Published: August 2022

Bistable liquid crystal (LC) shutters have attracted much interest due to their low energy consumption and fast response time. In this paper, we demonstrate an electrically tunable/switchable biostable LC light shutter in biological optics through a three–step easy–assembly, inexpensive, multi–channel shutter. The liquid crystal exhibits tunable transparency (100% to 10% compared to the initial light intensity) under different voltages (0 V to 90 V), indicating its tunable potential. By using biomedical images, the response time, resolution, and light intensity changes of the LC under different voltages in three common fluorescence wavelengths are displayed intuitively. Particularly, the shutter’s performance in tumor images under the near–infrared band shows its application potential in biomedical imaging fields.
The Influence of Noise Floor on the Measurement of Laser Linewidth Using Short-Delay-Length Self-Heterodyne/Homodyne Techniques
Authors: Zhao Zhongan , Bai Zhenxu , Jin Duo , Chen Xiaojing , Qi Yaoyao , Ding Jie , Yan Bingzheng , Wang Yulei , Lu Zhiwei , Mildren Richard P.
Published: August 2022

Delayed self-heterodyne/homodyne measurements based on an unbalanced interferometer are the most used methods for measuring the linewidth of narrow-linewidth lasers. They typically require the service of a delay of six times (or greater) than the laser coherence time to guarantee the Lorentzian characteristics of the beat notes. Otherwise, the beat notes are displayed as a coherent envelope. The linewidth cannot be directly determined from the coherence envelope. However, measuring narrow linewidths using traditional methods introduces significant errors due to the 1/f frequency noise. Here, a short fiber-based linewidth measurement scheme was proposed, and the influence of the noise floor on the measurement of the laser linewidth using this scheme was studied theoretically and experimentally. The results showed that this solution and calibration process is capable of significantly improving the measurement accuracy of narrow linewidth.
Influence of Gut–Liver Axis on Portal Hypertension in Advanced Chronic Liver Disease: The Gut Microbiome as a New Protagonist in Therapeutic Management
Authors: Santopaolo Francesco , Coppola Gaetano , Giuli Lucia , Gasbarrini Antonio , Ponziani Francesca Romana
Published: August 2022

Clinically significant portal hypertension is associated with most complications of advanced chronic liver disease (ACLD), including variceal bleeding, ascites, spontaneous bacterial peritonitis, hepatorenal syndrome, and hepatic encephalopathy. Gut dysbiosis is a hallmark of ACLD with portal hypertension and consists of the overgrowth of potentially pathogenic bacteria and a decrease in autochthonous bacteria; additionally, congestion makes the intestinal barrier more permeable to bacteria and their products, which contributes to the development of complications through inflammatory mechanisms. This review summarizes current knowledge on the role of the gut–liver axis in the pathogenesis of portal hypertension, with a focus on therapies targeting portal hypertension and the gut microbiota. The modulation of the gut microbiota on several levels represents a major challenge in the upcoming years; in-depth characterization of the molecular and microbiological mechanisms linking the gut–liver axis to portal hypertension in a bidirectional relationship could pave the way to the identification of new therapeutic targets for innovative therapies in the management of ACLD.
Subspecies Classification and Comparative Genomic Analysis of Lactobacillus kefiranofaciens HL1 and M1 for Potential Niche-Specific Genes and Pathways
Authors: Wang Sheng-Yao , Chen Yen-Po , Huang Ren-Feng , Wu Yi-Lu , Ho Shang-Tse , Li Kuan-Yi , Watanabe Koichi , Chen Ming-Ju
Published: August 2022

(1) Background: Strains HL1 and M1, isolated from kefir grains, have been tentatively identified, based on their partial 16S rRNA gene sequences, as Lactobacillus kefiranofaciens. The two strains demonstrated different health benefits. Therefore, not only the genetic factors exerting diverse functionalities in different L. kefiranofaciens strains, but also the potential niche-specific genes and pathways among the L. kefiranofaciens strains, should be identified. (2) Methods: Phenotypic and genotypic approaches were employed to identify strains HL1 and M1 at the subspecies level. For the further characterization of the probiotic properties of both strains, comparative genomic analyses were used. (3) Results: Both strains were identified as L. kefiranofaciens subsp. kefirgranum. According to the COG function category, dTDP-rhamnose and rhamnose-containing glycans were specifically detected in the L. kefiranofaciens subsp. Kefirgranum genomes. Three unique genes (epsI, epsJ, and epsK) encoding glycosyltransferase in the EPS gene cluster, and the ImpB/MucB/SamB family protein encoding gene were found in HL1 and M1. The specific ability to degrade arginine via the ADI pathway was found in HL1. The presence of the complete glycogen metabolism (glg) operon in the L. kefiranofaciens strains suggested the importance of glycogen synthesis to enable colonization in kefir grains and extend survival under environmental stresses. (4) Conclusions: The obtained novel information on the potential genes and pathways for polysaccharide synthesis and other functionalities in our HL1 and M1 strains could be applied for further functionality predictions for potential probiotic screening.
Need for a Standardized Translational Drug Development Platform: Lessons Learned from the Repurposing of Drugs for COVID-19
Authors: Assmus Frauke , Driouich Jean-Sélim , Abdelnabi Rana , Vangeel Laura , Touret Franck , Adehin Ayorinde , Chotsiri Palang , Cochin Maxime , Foo Caroline S. , Jochmans Dirk , Kim Seungtaek , Luciani Léa , Moureau Grégory , Park Soonju , Pétit Paul-Rémi , Shum David , Wattanakul Thanaporn , Weynand Birgit , Fraisse Laurent , Ioset Jean-Robert , Mowbray Charles E. , Owen Andrew , Hoglund Richard M. , Tarning Joel , Lamballerie Xavier de , Nougairède Antoine , Neyts Johan , Sjö Peter , Escudié Fanny , Scandale Ivan , Chatelain Eric
Published: August 2022

In the absence of drugs to treat or prevent COVID-19, drug repurposing can be a valuable strategy. Despite a substantial number of clinical trials, drug repurposing did not deliver on its promise. While success was observed with some repurposed drugs (e.g., remdesivir, dexamethasone, tocilizumab, baricitinib), others failed to show clinical efficacy. One reason is the lack of clear translational processes based on adequate preclinical profiling before clinical evaluation. Combined with limitations of existing in vitro and in vivo models, there is a need for a systematic approach to urgent antiviral drug development in the context of a global pandemic. We implemented a methodology to test repurposed and experimental drugs to generate robust preclinical evidence for further clinical development. This translational drug development platform comprises in vitro, ex vivo, and in vivo models of SARS-CoV-2, along with pharmacokinetic modeling and simulation approaches to evaluate exposure levels in plasma and target organs. Here, we provide examples of identified repurposed antiviral drugs tested within our multidisciplinary collaboration to highlight lessons learned in urgent antiviral drug development during the COVID-19 pandemic. Our data confirm the importance of assessing in vitro and in vivo potency in multiple assays to boost the translatability of pre-clinical data. The value of pharmacokinetic modeling and simulations for compound prioritization is also discussed. We advocate the need for a standardized translational drug development platform for mild-to-moderate COVID-19 to generate preclinical evidence in support of clinical trials. We propose clear prerequisites for progression of drug candidates for repurposing into clinical trials. Further research is needed to gain a deeper understanding of the scope and limitations of the presented translational drug development platform.
Production of Refractory Materials Using a Renewable Source of Silicon Dioxide
Authors: Zharmenov Abdurassul , Yefremova Svetlana , Satbaev Baimakhan , Shalabaev Nurgali , Satbaev Serik , Yermishin Sergey , Kablanbekov Askhat
Published: August 2022

Organization of environmentally-friendly production of refractory materials based on the principles of cost-effective use of energy and material resources through use of energy-saving technologies and replacement of natural raw materials with industrial and agricultural waste is gaining relevance. Scientists are increasingly interested in creating high-temperature materials using silica of plant origin. Its source is rice husk, a multi-tonnage waste from rice production. Organo-mineral in its nature, rice husk determines the uniqueness of the structure and properties of the materials obtained from it. Use of this waste allows us to produce porous, high-strength silicon carbide refractories with properties corresponding to classical analogs, while benefiting from environmental, economic and technological aspects. The lack of industrial production of refractories using rice husk ash, despite the positive results of scientific research, indicates insufficient study of the issue with certain gaps in this area. This review is intended to help researchers to identify existing problems and outline further actions necessary to ensure that the scientific results are implemented in production.
Carbothermic Reduction Roasting of Cathode Active Materials Using Activated Carbon and Graphite to Enhance the Sulfuric-Acid-Leaching Efficiency of Nickel and Cobalt
Authors: Ahn Youngjin , Koo Wonbeom , Yoo Kyoungkeun , Alorro Richard Diaz
Published: August 2022

Carbothermic reduction-roasting tests of NCM (nickel, cobalt, and manganese) cathode active materials with carbon sources such as activated carbon and graphite followed by sulfuric acid leaching were performed to investigate the effects of roasting temperature, molar mixing ratio of cathode active materials and carbon sources, and type of cathode active materials. When the virgin NCM622 materials were roasted with activated carbon, the peaks of Ni and Co metals were observed in the XRD data. The leaching efficiencies of Li, Ni, Co, and Mn increased to over 99.9% within 120 min in all samples roasted at 600 °C–900 °C, but, at the beginning of leaching, the leaching efficiencies increased more slowly with increasing roasting temperature. The leaching efficiencies of Ni and Co decreased with decreasing the molar mixing ratio of active cathode materials and carbon sources, but the leaching efficiencies were more than 99.9% in all ratios. These results indicate that roasting can enhance the leaching of cathode active materials and improve the conventional leaching process using hydrogen peroxide.
Quantitative Evaluation of Faults by Combined Channel Wave Seismic Transmission-Reflection Detection Method
Authors: Wu Yanhui , Zhu Guowei , Wang Wei , Zhang Mengbo , Gao Zhen
Published: August 2022

The quantitative detection of faults using the channel wave seismic method has been a major but challenging area of interest. In this study, we adopted an effective technical process to evaluate fault attribution. First, we use integrated transmission and reflection channel wave information to improve the accuracy of extraction velocity. Then, the location of the fault is determined by the elliptical tangent offset method, and feature extraction and fault location extension determination are achieved through logistic regression and a neural network. This is combined with the prior geological information, the fractional dimension D to the quantitative analysis of the fault throw. Data regarding the 4203 working face of a mine in Shanxi, China, are considered as an example. Two groups of faults were predicted, with the location error in the f30 fault position as 6.7 m. In addition, the f29 fault throw first increased, and then gradually decreased from the return airway to the haulage gateway. These predicted results have been drill-verified and were used to modify the original design. The proposed method has good stability and promising application prospects for fault evaluation.
Hyperbolic Secant Subsidence Prediction Model under Thick Loose Layer Mining Area
Authors: Zhang Jinman , Yan Yueguan , Dai Huayang , Xu Liangji , Li Jiewei , Xu Ruirui
Published: August 2022

In China, as a major resource, coal has made great contributions to national energy security and social development. The mining of coal resources can cause surface subsidence damage, and in particular, the mining of coal resources in thick loose layer mines is the most serious. How to accurately predict the surface subsidence caused by coal mining in thick loose layer mines has become an urgent problem to be solved. To solve this problem, numerical simulations based on the measured data were used to reveal that the thickness of the loose layer is the intrinsic mechanism that affects the value of the surface subsidence and the large range of subsidence. On this basis, the hyperbolic secant function is used as the influence function of unit mining to derive the expected model of subsidence under thick loose layer conditions: the hyperbolic secant subsidence prediction model. Compared with the probability integral method, the hyperbolic secant subsidence prediction model’s prediction accuracy RMSE value is improved by 38%. The hyperbolic secant subsidence prediction model can realize accurate estimation of the subsidence value in the thick loose layer mine area. This greatly enriches the mining subsidence prediction theory and provides a scientific basis for the assessment of surface damage and ecological environment restoration after coal seam mining under a thick loose seam mining area.
Virtual Screening for FDA-Approved Drugs That Selectively Inhibit Arginase Type 1 and 2
Authors: Detroja Trishna Saha , Samson Abraham O.
Published: August 2022

Arginases are often overexpressed in human diseases, and they are an important target for developing anti-aging and antineoplastic drugs. Arginase type 1 (ARG1) is a cytosolic enzyme, and arginase type 2 (ARG2) is a mitochondrial one. In this study, a dataset containing 2115-FDA-approved drug molecules is virtually screened for potential arginase binding using molecular docking against several ARG1 and ARG2 structures. The potential arginase ligands are classified into three categories: (1) Non-selective, (2) ARG1 selective, and (3) ARG2 selective. The evaluated potential arginase ligands are then compared with their clinical use. Remarkably, half of the top 30 potential drugs are used clinically to lower blood pressure and treat cancer, infection, kidney disease, and Parkinson’s disease thus partially validating our virtual screen. Most notable are the antihypertensive drugs candesartan, irbesartan, indapamide, and amiloride, the antiemetic rolapitant, the anti-angina ivabradine, and the antidiabetic metformin which have minimal side effects. The partial validation also favors the idea that the other half of the top 30 potential drugs could be used in therapeutic settings. The three categories greatly expand the selectivity of arginase inhibition.
Continuous-Flow Synthesis of Nitro-o-xylenes: Process Optimization, Impurity Study and Extension to Analogues
Authors: Song Qiao , Lei Xiangui , Yang Sheng , Wang Sheng , Wang Jianhui , Chen Jiujun , Xiang Yong , Huang Qingwu , Wang Zhouyu
Published: August 2022

An efficient continuous-flow nitration process of o-xylene at pilot scale was demonstrated. The effects of parameters such as temperature, ratio of H2SO4 to HNO3, H2SO4 concentration, flow rate, and residence time on the reaction were studied. Under the optimal conditions, the yield of products reached 94.1%, with a product throughput of 800 g/h. The main impurities of this continuous-flow nitration process were also studied in detail. Compared with batch process, phenolic impurity decreased from 2% to 0.1%, which enabled the omission of the alkaline solution washing step and thus reduced the wastewater emission. The method was also successfully applied to the nitrification of p-xylene, toluene, and chlorobenzene with good yields.
Recent Overview of Resveratrol’s Beneficial Effects and Its Nano-Delivery Systems
Authors: Bohara Raghvendra A. , Tabassum Nazish , Singh Mohan P. , Gigli Giuseppe , Ragusa Andrea , Leporatti Stefano
Published: August 2022

Natural polyphenols have a wide variety of biological activities and are taken into account as healthcare materials. Resveratrol is one such natural polyphenol, belonging to a group known as stilbenoids (STBs). Resveratrol (3,5,4′-trihydroxy-trans-stilbene) is mainly found in grapes, wine, nuts, and berries. A wide range of biological activities has been demonstrated by resveratrol, including antimicrobial, antioxidant, antiviral, antifungal, and antiaging effects, and many more are still under research. However, as with many other plant-based polyphenol products, resveratrol suffers from low bioavailability once administered in vivo due to its susceptibility to rapid enzyme degradation by the body’s innate immune system before it can exercise its therapeutic influence. Therefore, it is of the utmost importance to ensure the best use of resveratrol by creating a proper resveratrol delivery system. Nanomedicine and nanodelivery systems utilize nanoscale materials as diagnostic tools or to deliver therapeutic agents in a controlled manner to specifically targeted locations. After a brief introduction about polyphenols, this review overviews the physicochemical characteristics of resveratrol, its beneficial effects, and recent advances on novel nanotechnological approaches for its delivery according to the type of nanocarrier utilized. Furthermore, the article summarizes the different potential applications of resveratrol as, for example, a therapeutic and disease-preventing anticancer and antiviral agent.
Dry Powder Inhalers for Proteins Using Cryo-Milled Electrospun Polyvinyl Alcohol Nanofiber Mats
Authors: Ito Takaaki , Yamazoe Eriko , Tahara Kohei
Published: August 2022

To enable the efficient delivery of drugs to the lungs, the drug particle design for most dry powder inhalers (DPIs) involves reducing the aerodynamic particle size to a few microns using methods such as spray-drying or jet-milling. Stresses, including heat and the shear forces generated by the preparation processes, may result in the degradation and denaturation of drugs such as those based on peptides and proteins. Here, we showed that cryo-milled polyvinyl alcohol nanofiber mats loaded with α-chymotrypsin by electrospinning exhibited suitable inhalation properties for use in DPIs, while maintaining enzymatic activity. The cryo-milled nanofiber mats were porous to fine particles, and the particle size and drug stability depended on the freezing and milling times. The median diameter of the milled fiber mats was 12.6 μm, whereas the mass median aerodynamic diameter was 5.9 μm. The milled nanofiber mats were successfully prepared, while retaining the enzymatic activity of α-chymotrypsin; furthermore, the activity of milled fiber mats that had been stored for 6 months was comparable to the activity of those that were freshly prepared. This novel method may be suitable for the DPI preparation of various drugs because it avoids the heating step during the DPI preparation process.
Synthesis of Electron-Rich Porous Organic Polymers via Schiff-Base Chemistry for Efficient Iodine Capture
Authors: Tian Peng , Ai Zhiting , Hu Hui , Wang Ming , Li Yaling , Gao Xinpei , Qian Jiaying , Su Xiaofang , Xiao Songtao , Xu Huanjun , Lu Fei , Gao Yanan
Published: August 2022

As one of the main nuclear wastes generated in the process of nuclear fission, radioactive iodine has attracted worldwide attention due to its harm to public safety and environmental pollution. Therefore, it is of crucial importance to develop materials that can rapidly and efficiently capture radioactive iodine. Herein, we report the construction of three electron-rich porous organic polymers (POPs), denoted as POP-E, POP-T and POP-P via Schiff base polycondensations reactions between Td-symmetric adamantane knot and four-branched “linkage” molecules. We demonstrated that all the three POPs showed high iodine adsorption capability, among which the adsorption capacity of POP-T for iodine vapor reached up to 3.94 g·g−1 and the removal rate of iodine in n-hexane solution was up to 99%. The efficient iodine capture mechanism of the POP-T was investigated through systematic comparison of Fourier transform infrared spectroscopy (FT-IR), Raman spectroscopy and X-ray photoelectron spectroscopy (XPS) before and after iodine adsorption. The unique π-π conjugated system between imine bonds linked aromatic rings with iodine result in charge-transfer complexes, which explains the exceptional iodine capture capacity. Additionally, the introduction of heteroatoms into the framework would also enhance the iodine adsorption capability of POPs. Good retention behavior and recycling capacity were also observed for the POPs.