bluuebunny/crossref_metadata_2025 huggingface dataset:
Aquatic Environments as Hotspots of Transferable Low-Level Quinolone Resistance and Their Potential Contribution to High-Level Quinolone Resistance
Authors: Miranda Claudio D. , Concha Christopher , Godoy Félix A. , Lee Matthew R.
Published: October 2022

The disposal of antibiotics in the aquatic environment favors the selection of bacteria exhibiting antibiotic resistance mechanisms. Quinolones are bactericidal antimicrobials extensively used in both human and animal medicine. Some of the quinolone-resistance mechanisms are encoded by different bacterial genes, whereas others are the result of mutations in the enzymes on which those antibiotics act. The worldwide occurrence of quinolone resistance genes in aquatic environments has been widely reported, particularly in areas impacted by urban discharges. The most commonly reported quinolone resistance gene, qnr, encodes for the Qnr proteins that protect DNA gyrase and topoisomerase IV from quinolone activity. It is important to note that low-level resistance usually constitutes the first step in the development of high-level resistance, because bacteria carrying these genes have an adaptive advantage compared to the highly susceptible bacterial population in environments with low concentrations of this antimicrobial group. In addition, these genes can act additively with chromosomal mutations in the sequences of the target proteins of quinolones leading to high-level quinolone resistance. The occurrence of qnr genes in aquatic environments is most probably caused by the release of bacteria carrying these genes through anthropogenic pollution and maintained by the selective activity of antimicrobial residues discharged into these environments. This increase in the levels of quinolone resistance has consequences both in clinical settings and the wider aquatic environment, where there is an increased exposure risk to the general population, representing a significant threat to the efficacy of quinolone-based human and animal therapies. In this review the potential role of aquatic environments as reservoirs of the qnr genes, their activity in reducing the susceptibility to various quinolones, and the possible ways these genes contribute to the acquisition and spread of high-level resistance to quinolones will be discussed.
Antifungal Activity of a Library of Aminothioxanthones
Authors: Cardoso Joana , Freitas-Silva Joana , Durães Fernando , Carvalho Diogo Teixeira , Gales Luís , Pinto Madalena , Sousa Emília , Pinto Eugénia
Published: October 2022

Fungal infections are one of the main causes of mortality and morbidity worldwide and taking into account the increasing incidence of strains resistant to classical antifungal drugs, the development of new agents has become an urgent clinical need. Considering that thioxanthones are bioisosteres of xanthones with known anti-infective actions, their scaffolds were selected for this study. A small library of synthesized aminothioxanthones (1–10) was evaluated for in vitro antifungal activity against Candida albicans, Aspergillus fumigatus, and Trichophyton rubrum; for the active compounds, the spectrum was further extended to other clinically relevant pathogenic fungi. The results showed that only compounds 1, 8, and 9 exhibited inhibitory and broad-spectrum antifungal effects. Given the greater antifungal potential presented, compound 1 was the subject of further investigations to study its anti-virulence activity and in an attempt to elucidate its mechanism of action; compound 1 seems to act predominantly on the cellular membrane of C. albicans ATCC 10231, altering its structural integrity, without binding to ergosterol, while inhibiting two important virulence factors—dimorphic transition and biofilm formation—frequently associated with C. albicans pathogenicity and resistance. In conclusion, the present work proved the usefulness of thioxanthones in antifungal therapy as new models for antifungal agents.
Canine Saliva as a Possible Source of Antimicrobial Resistance Genes
Authors: Tóth Adrienn Gréta , Tóth Imre , Rózsa Bernadett , Dubecz Attila , Patai Árpád V. , Németh Tibor , Kaplan Selçuk , Kovács Eszter Gabriella , Makrai László , Solymosi Norbert
Published: October 2022

While the One Health issues of intensive animal farming are commonly discussed, keeping companion animals is less associated with the interspecies headway of antimicrobial resistance. With the constant advance in veterinary standards, antibiotics are regularly applied in companion animal medicine. Due to the close coexistence of dogs and humans, dog bites and other casual encounters with dog saliva (e.g., licking the owner) are common. According to our metagenome study, based on 26 new generation sequencing canine saliva datasets from 2020 and 2021 reposited in NCBI SRA by The 10,000 Dog Genome Consortium and the Broad Institute within Darwin’s Ark project, canine saliva is rich in bacteria with predictably transferable antimicrobial resistance genes (ARGs). In the genome of potentially pathogenic Bacteroides, Capnocytophaga, Corynebacterium, Fusobacterium, Pasteurella, Porphyromonas, Staphylococcus and Streptococcus species, which are some of the most relevant bacteria in dog bite infections, ARGs against aminoglycosides, carbapenems, cephalosporins, glycylcyclines, lincosamides, macrolides, oxazolidinone, penams, phenicols, pleuromutilins, streptogramins, sulfonamides and tetracyclines could be identified. Several ARGs, including ones against amoxicillin–clavulanate, the most commonly applied antimicrobial agent for dog bites, were predicted to be potentially transferable based on their association with mobile genetic elements (e.g., plasmids, prophages and integrated mobile genetic elements). According to our findings, canine saliva may be a source of transfer for ARG-rich bacteria that can either colonize the human body or transport ARGs to the host bacteriota, and thus can be considered as a risk in the spread of antimicrobial resistance.
Understanding the Role of Self-Assembly and Interaction with Biological Membranes of Short Cationic Lipopeptides in the Effective Design of New Antibiotics
Authors: Stachurski Oktawian , Neubauer Damian , Walewska Aleksandra , Iłowska Emilia , Bauer Marta , Bartoszewska Sylwia , Sikora Karol , Hać Aleksandra , Wyrzykowski Dariusz , Prahl Adam , Kamysz Wojciech , Sikorska Emilia
Published: October 2022

This study investigates short cationic antimicrobial lipopeptides composed of 2–4 amino acid residues and C12-C18 fatty acids attached to the N-terminal part of the peptides. The findings were discussed in the context of the relationship among biological activity, self-assembly, stability, and membrane interactions. All the lipopeptides showed the ability to self-assemble in PBS solution. In most cases, the critical aggregation concentration (CAC) much surpassed the minimal inhibitory concentration (MIC) values, suggesting that monomers are the main active form of lipopeptides. The introduction of β-alanine into the peptide sequence resulted in a compound with a high propensity to fibrillate, which increased the peptide stability and activity against S. epidermidis and C. albicans and reduced the cytotoxicity against human keratinocytes. The results of our study indicated that the target of action of lipopeptides is the bacterial membrane. Interestingly, the type of peptide counterion may affect the degree of penetration of the lipid bilayer. In addition, the binding of the lipopeptide to the membrane of Gram-negative bacteria may lead to the release of calcium ions necessary for stabilization of the lipopolysaccharide layer.
Antibiotic Use in European Pig Production: Less Is More
Authors: Dewulf Jeroen , Joosten Philip , Chantziaras Ilias , Bernaerdt Elise , Vanderhaeghen Wannes , Postma Merel , Maes Dominiek
Published: October 2022

The goal of this study is to describe the current use of antibiotics in the European pig industry based on an extensive literature review. To achieve this, an overview of results from national (n = 15) and multi-country (n = 2) cross-sectional and longitudinal (n = 2) surveys, which describe antimicrobial use in pigs, is presented. Results are further linked to the outcome of the European Surveillance of Veterinary Antimicrobial Consumption (ESVAC) project. Overall, it was found that weaned piglets received the most antibiotics, followed by suckling piglets resulting in over 80% of the treatments being administered to animals before 10 weeks of age. Furthermore, it was observed that antibiotic use (ABU) was significantly associated across age categories, indicating that farms with a high use in piglets also used more antibiotics in their finishers. This may, among other things, be explained by farmers’ habits and behavior. However, above all, the studies showed surprisingly large differences in ABU between the countries. These differences may be related to the differences in disease prevalence and/or differences in the level of biosecurity. However, they may also reflect variations in rules and regulations between countries and/or a difference in attitude towards ABU of farmers and veterinarians that are not necessarily linked to the true animal health situation. Furthermore, it was observed that already a substantial proportion of the European pig production is able to successfully raise pigs without any group treatments, indicating that it is possible to rear pigs without systematic use of antibiotics. Based on the ESVAC data, a decline of 43.2% was observed in sales of antibiotics for animals in Europe between 2011 and 2020. To enable efficient antimicrobial quantification and stewardship, 15 European countries have already established systems for herd level monitoring ABU in pigs.
Pharmacological Activity, Pharmacokinetics, and Clinical Research Progress of Puerarin
Authors: Wang Di , Bu Tong , Li Yangqian , He Yueyue , Yang Fan , Zou Liang
Published: October 2022

As a kind of medicine and food homologous plant, kudzu root (Pueraria lobata (Willd.) Ohwi) is called an “official medicine” in Chinese folk medicine. Puerarin is the main active component extracted from kudzu root, and its structural formula is 8-β-D-grapes pyranose-4, 7-dihydroxy isoflavone, with a white needle crystal; it is slightly soluble in water, and its aqueous solution is colorless or light yellow. Puerarin is a natural antioxidant with high health value and has a series of biological activities such as antioxidation, anti-inflammation, anti-tumor effects, immunity improvement, and cardio-cerebrovascular and nerve cell protection. In particular, for the past few years, it has also been extensively used in clinical study. This review focuses on the antioxidant activity of puerarin, the therapy of diverse types of inflammatory diseases, various new drug delivery systems of puerarin, the “structure-activity relationship” of puerarin and its derivatives, and pharmacokinetic and clinical studies, which can provide a new perspective for the puerarin-related drug research and development, clinical application, and further development and utilization.
Active Disturbance Rejection Control for Speed Control of PMSM Based on Auxiliary Model and Supervisory RBF
Authors: Gao Peng , Su Xiuqin , Pan Zhibin , Xiao Maosen , Zhang Wenbo , Liu Ruoyu
Published: October 2022

External vibration, shock, unbalanced torque and other uncertain disturbances are mainly transmitted to the motor rotor through the bearing friction. To restrain the uncertain friction disturbances and improve the speed stability of a permanent magnet synchronous motor (PMSM), an optimized active disturbance rejection control (ADRC) algorithm is proposed in this study. Firstly, an auxiliary model of friction and a reduced-order processing method are introduced into extended state observation (ESO) to reduce the burden of single ESO and promote the compensation accuracy of disturbances. In addition, a supervisory radial basis function (SRBF) is employed to supervise and promote the error elimination efficiency of the nonlinear state error feedback rate (NLSEF). The hybrid control algorithm makes up for the deficiency of typical ADRC through the fusion of multiple control quantities. Simulation and experimental results show that the proposed algorithm has strong anti-disturbance performance and effectively solves the problem of low-speed crawling.
The Impact of Levee Openings on Storm Surge: A Numerical Analysis in Coastal Louisiana
Authors: Hu Kelin , Meselhe Ehab , Rhode Rachel , Snider Natalie , Renfro Alisha
Published: October 2022

The existence of the Mississippi River (MR) and Tributaries’ levees in coastal Louisiana could block storm surge and cause surge setup in adjacent basins. In order to reduce storm surge amplification caused by these barriers, one possible solution is to build “floodways” through the mainstem MR levees to allow surge during tropical events to cross. The primary purpose of this study is to examine if these floodways/openings can help reduce storm surge in adjacent basins. Using Hurricane Isaac (2012) as an example, a pre-validated Delft3D-based hydrodynamic model was applied to study the effect of levee openings on storm surge. Model results and flux analysis show that these levee openings were not effective in reducing storm surge in Barataria Basin and Breton Sound due to the complex interaction between the cross flow from the surge and the MR flow. During Isaac, the MR water could be diverted to Barataria and/or Breton, which resulted in an increase in storm surge, essentially defeating the primary objective of the levee openings. Overall, the impact of levee openings at the selected locations on storm surge reduction in adjacent basins of coastal Louisiana was minor and very limited.
Application of Three Types of Cinnamon Essential Oils as Natural Antifungal Preservatives in Wheat Bread
Authors: Valková Veronika , Ďúranová Hana , Galovičová Lucia , Vukovic Nenad L. , Vukic Milena , Kowalczewski Przemysław Łukasz , Kačániová Miroslava
Published: October 2022

This research represents the report on the chemical profile, antioxidant, and antifungal (Penicillium (P.) citrinum, P. expansum, and P. crustosum) activities of three types of cinnamon essential oils (EOs), namely Cinnamomum (C.) cassia EO isolated from bark (CCEO), and two C. verum EOs isolated from plant bark (CVBEO) and leaf (CVLEO). The results revealed that the major compounds of the CCEO, CVBEO, and CVLEO were (E)-cinnamaldehyde (77.1%; 44.1%) and eugenol (70.8%), respectively; the demonstrable (p < 0.05) strongest antioxidant activity was detected in CVLEO (488.0 ± 1.2 TEAC; 84.0 ± 0.3%). The strongest in vitro antifungal activities were displayed by all analyzed EOs in the highest concentration (500 μL/L) used against P. crustosum, which inhibition zones ranged from 13.00 ± 1.73 mm (CVBEO) to 14.67 ± 1.15 mm (CCEO). Values for food model (bread) water activity and moisture content were 0.946 ± 0.002 and 40.88 ± 0.88%, respectively. In situ antifungal efficacies of all EOs examined were shown to be dose-dependent with the highest growth inhibition of mycelium determined in 250 μL/L of CVBEO against P. citrinum (95.23 ± 9.17%). The obtained findings promote the potential uses of the EOs and indicate their utilization for extending the shelf-life of bakery products.
Experimental Study on Pot Damage and Contact Stress Distribution Characteristics of Oil Sunflower Plug Seedlings
Authors: Zeng Fandi , Li Xuying , Bai Hongbin , Cui Ji , Liu Xuening , Zhang Yongzhi
Published: October 2022

To reveal the collision damage mechanisms of plug seedlings and improve the quality of seedlings, the kinetics equations of the plug seedlings were established based on the generalized Hertz-theory. The influence laws of different factors on pot damage were obtained through a drop impact test. The Tekscan pressure distribution measurement system measured the collision impact force, and the orthogonal tests were conducted. The test showed that the influence of the collision impact force was on the order of plug specification > drop height > contact material. The Tekscan pressure distribution measurement system measured the change law of contact stress distribution under significant influencing factors. The test results showed that the collision contact area between the plug seedlings and contact materials from large to small was soil, steel, and ABS plastic. The collision contact area between the plug seedlings and other plug specifications was 50 plug, 72 plug, and 105 plug from the largest to the smallest. When the plug seedlings collided with contact materials, the average contact stress between the seedlings and the steel plate ranged from 19.4 kPa to 22.8 kPa. When the plug seedlings of various sizes collided with steel plates, the average contact stress was ordered as 105 plug, 72 plug and 50 plug in descending order. A linear regression model between collision impact force and matrix loss rate under different factors was established based on the pressure data collected by the Tekscan pressure distribution measurement system. This study provides a basis for exploring the impact damage mechanisms of plug seedlings and improving the seedling quality.
Integration of Object-Based Image Analysis and Convolutional Neural Network for the Classification of High-Resolution Satellite Image: A Comparative Assessment
Authors: Azeez Omer Saud , Shafri Helmi Z. M. , Alias Aidi Hizami , Haron Nuzul A. B.
Published: October 2022

During the past decade, deep learning-based classification methods (e.g., convolutional neural networks—CNN) have demonstrated great success in a variety of vision tasks, including satellite image classification. Deep learning methods, on the other hand, do not preserve the precise edges of the targets of interest and do not extract geometric features such as shape and area. Previous research has attempted to address such issues by combining deep learning with methods such as object-based image analysis (OBIA). Nonetheless, the question of how to integrate those methods into a single framework in such a way that the benefits of each method complement each other remains. To that end, this study compared four integration frameworks in terms of accuracy, namely OBIA artificial neural network (OBIA ANN), feature fusion, decision fusion, and patch filtering, according to the results. Patch filtering achieved 0.917 OA, whereas decision fusion and feature fusion achieved 0.862 OA and 0.860 OA, respectively. The integration of CNN and OBIA can improve classification accuracy; however, the integration framework plays a significant role in this. Future research should focus on optimizing the existing CNN and OBIA frameworks in terms of architecture, as well as investigate how CNN models should use OBIA outputs for feature extraction and classification of remotely sensed images.
A Parallel Quantum Circuit Implementations of LSH Hash Function for Use with Grover’s Algorithm
Authors: Song Gyeongju , Jang Kyungbae , Kim Hyunji , Seo Hwajeong
Published: October 2022

Grover’s search algorithm accelerates the key search on the symmetric key cipher and the pre-image attack on the hash function. To conduct Grover’s search algorithm, the target cipher algorithm should be efficiently implemented in a quantum circuit. Currently, small quantum computers are difficult to operate with large quantum circuits due to limited performance. Therefore, if a large quantum computer that can operate Grover’s algorithm appears, it is expected that a cipher attack will be possible. In this paper, we propose a parallel structure quantum circuit for the Korean hash function standard (i.e., LSH). The proposed quantum circuit designed a parallel operation structure for the message expansion (i.e., MsgExp) function and the mix function, which are the internal structures of the LSH hash function. This approach shows an efficient result for quantum circuit implementation in terms of quantum resources by reducing the depth of the quantum circuit by about 96% through the trade-off of appropriate quantum resources compared to previous work. This result can be a reference for the implementation of a parallel quantum circuit in the future and is expected to advance the attack timing of the search algorithm for Grover’s LSH hash function.
Supporting Argumentation Dialogues in Group Decision Support Systems: An Approach Based on Dynamic Clustering
Authors: Conceição Luís , Rodrigues Vasco , Meira Jorge , Marreiros Goreti , Novais Paulo
Published: October 2022

Group decision support systems (GDSSs) have been widely studied over the recent decades. The Web-based group decision support systems appeared to support the group decision-making process by creating the conditions for it to be effective, allowing the management and participation in the process to be carried out from any place and at any time. In GDSS, argumentation is ideal, since it makes it easier to use justifications and explanations in interactions between decision-makers so they can sustain their opinions. Aspect-based sentiment analysis (ABSA) intends to classify opinions at the aspect level and identify the elements of an opinion. Intelligent reports for GDSS provide decision makers with accurate information about each decision-making round. Applying ABSA techniques to group decision making context results in the automatic identification of alternatives and criteria, for instance. This automatic identification is essential to reduce the time decision makers take to step themselves up on group decision support systems and to offer them various insights and knowledge on the discussion they are participating in. In this work, we propose and implement a methodology that uses an unsupervised technique and clustering to group arguments on topics around a specific alternative, for example, or a discussion comparing two alternatives. We experimented with several combinations of word embedding, dimensionality reduction techniques, and different clustering algorithms to achieve the best approach. The best method consisted of applying the KMeans++ clustering technique, using SBERT as a word embedder with UMAP dimensionality reduction. These experiments achieved a silhouette score of 0.63 with eight clusters on the baseball dataset, which wielded good cluster results based on their manual review and word clouds. We obtained a silhouette score of 0.59 with 16 clusters on the car brand dataset, which we used as an approach validation dataset. With the results of this work, intelligent reports for GDSS become even more helpful, since they can dynamically organize the conversations taking place by grouping them on the arguments used.
Machine Learning Algorithms in Corroboration with Isotope and Elemental Profile—An Efficient Tool for Honey Geographical Origin Assessment
Authors: Hategan Ariana Raluca , Magdas Dana Alina , Puscas Romulus , Dehelean Adriana , Cristea Gabriela , Simionescu Bianca
Published: October 2022

The application of artificial intelligence for the development of recognition models for food and beverages differentiation has benefited from increasing attention in recent years. For this scope, different machine learning (ML) algorithms were used in order to find the most suitable model for a certain purpose. In the present work, three ML algorithms, namely artificial neural networks (ANN), support vector machines (SVM) and k-nearest neighbors (KNN), were applied for constructing honey geographical classification models, and their performance was assessed and compared. A preprocessing step consisting of either a component reduction method or a supervised feature selection technique was applied prior to model development. The most efficient geographical differentiation models were obtained based on ANN, when a subset of features corresponding to the markers having the highest discrimination potential was used as input data. Therefore, when the samples aimed to be classified at an intercountry level, an accuracy of 95% was achieved; namely, 99% of the Romanian samples and 73% of the ones originating from other countries were correctly predicted. Promising results were also obtained for the intracountry honey discrimination; namely, the model built for classifying the Transylvanian samples from the ones produced in other Romanian regions had an 85% accuracy.
Introducing Mobile Collaborative Robots into Bioprocessing Environments: Personalised Drug Manufacturing and Environmental Monitoring
Authors: Mathew Robins , McGee Robert , Roche Kevin , Warreth Shada , Papakostas Nikolaos
Published: October 2022

Personalised therapeutic drugs are the future of the medical drug sector. For manufacturers, this will require the flexibility to produce many different unique batches within a given facility. This research paper aims to demonstrate the potential of mobile collaborative robots for improving current manufacturing practices in personalised therapeutics. The benefits and challenges of introducing robots in biologics are explored, including current practices, limitations, likely future practices, and the market outlook. Experiments demonstrating the application of a mobile collaborative robot to perform three different routine tasks is presented. These experiments include the transport of centrifugal tubes, manipulation of infusion bags, and scanning of Petri dishes for environmental monitoring. The investigations highlight the potential of collaborative mobile robotic platforms for automating the routine tasks carried out within the biomanufacturing sector.
Wavelet Analysis for Evaluating the Length of Precast Spliced Piles Using Low Strain Integrity Testing
Authors: Loseva Elizaveta , Lozovsky Ilya , Zhostkov Ruslan , Syasko Vladimir
Published: October 2022

The difficulties with the application of low strain integrity testing for evaluating the length of driven precast piles of two sections justify the need for new data acquisition and analysis techniques. The standard time domain analysis of the recorded signals may not allow for distinguishing the desired responses from a pile toe and a splice. In this paper, we propose the use of a set of hammers of different weights and tip materials that will provide various sensitivities of the test to a pile splice. To further analyze the collected data, we study the distributions of phase angles obtained using complex continuous wavelet transform. The characteristic phase shifts that distribute from higher to lower frequencies can be interpreted as responses from a pile toe and a splice. To verify the proposed approaches, a series of numerical simulations were performed using the finite element method for the driven pile models with the different properties of a splice zone. Numerical simulation results show that the pile splices are clearly identified when using the shorter input pulses which can be generated by light hammers with a hard head material. The total length of a simulated pile with a 1 mm air gap between sections was undetectable by standard data analysis approaches and was evaluated when analyzing the wavelet phase angle distributions. Numerically validated data acquisition and analysis techniques were applied to field data analysis and allowed us to confidently identify the length of two-section piles grouped with a pile cap.
Research and Application of Combined Reactive Power Compensation Device Based on SVG+SC in Wind Power Gathering Area
Authors: Peng Yinzhang , Wang Haiyun , Zhao Qi , Nan Dongliang , Li Wenxin
Published: October 2022

With the increasing proportion of wind power access year by year, it brings many challenges to the voltage stability of power systems. In order to maintain the stability of the voltage in the power grid, it is impossible to take into account the regulation ability and economy when a single reactive power compensation device is installed. In this paper, a combined reactive power compensation device was installed, which is composed of a static var generator (SVG) and a parallel capacitor bank. The SVG has the characteristics of fast and smooth adjustment, and the application of the capacitor bank reduces the overall investment cost and has a great economy. The modal analysis method was used to find the optimal installation position for the reactive power compensation device. The improved particle swarm algorithm was used to optimize the capacity of the optimal reactive power compensation device to ensure the best performance of the compensation device. Finally, by formulating the control strategy of the combined reactive power compensation system, the reliable switching of the compensation device is controlled. The PSCAD simulation software was used to model the power grid in the Hami area, and six different configuration programs were set for static voltage stability simulation verification and three different configurations. The program was simulated and verified for transient voltage stability, and comparative analysis showed that the proposed method was correct, which strongly supports the voltage stability of the region and meets the demand of reactive power compensation of the power grid. This provides a good reference program for other wind power gathering areas.
Prediction of the Yield Strength of RC Columns Using a PSO-LSSVM Model
Authors: Wang Bochen , Gong Weiming , Wang Yang , Li Zele , Liu Hongyuan
Published: October 2022

Accuracy prediction of the yield strength and displacement of reinforced concrete (RC) columns for evaluating the seismic performance of structure plays an important role in engineering the structural design of RC columns. A new hybrid machine learning technique based on the least squares support vector machine (LSSVM) and the particle swarm optimization (PSO) algorithm is proposed to predict the yield strength and displacement of RC columns. In this PSO-LSSVM model, the LSSVM is applied to discover the mapping between the influencing factors and the yield strength and displacement, and the PSO algorithm is utilized to select the optimal parameters of LSSVM to facilitate the prediction performance of the proposed model. A dataset covering the PEER database and the available experimental data in relevant literature is established for model training and testing. The PSO algorithm is then evaluated and compared with other metaheuristic algorithms based on the experiment’s database. The results indicate the effectiveness of the PSO employed for improving the prediction performance of the LSSVM model according to the evaluation criteria such as the root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). Overall, the simulation demonstrates that the developed PSO-LSSVM model has ideal prediction accuracy in the yield properties of RC columns.
Combining Knowledge and a Data Driven Method for Identifying the Gas Kick Type in a Fractured Formation
Authors: Yin Hu , Si Menghan , Cui Hongwei , Li Qian , Liu Wei
Published: October 2022

The main forms of gas kicks into the wellbore during drilling in fractured carbonate reservoirs are underbalanced pressure and gravity displacement. These two forms of gas kicks have different mechanisms of gas entry into the wellbore and different well control measures, which require the timely identification of the type of gas kick when it occurs. A two-phase flow model with a wellbore-formation coupling was developed, based on the gas kick rate models. The variation characteristics of the bottomhole gas influx rate, the wellbore free gas, the bottomhole pressure, the bottomhole pressure change rate, the pit gain and the outlet flow rate during an underbalanced pressure gas kick and a gravity displacement gas kick, were compared and analyzed. Combining the dynamic time warping (DTW) and the wellbore-formation, coupled with a two-phase flow model, an identification method of the gas kick type, based on the DTW was proposed. Following the detection of the gas kick, the identification is performed by calculating the DTW distance between the surface parameter time series, obtained from the two-phase flow simulation and the surface parameter time series, measured in real time. The field example results show that the method can identify the type of gas kick, based on the real-time surface measurement parameters and provide a basis for taking targeted well control measures.
OffshoringDSS: An Automated Tool of Application Maintenance Offshoring
Authors: Rahman Hanif Ur , Raza Mushtaq , Alzayed Asaad , Afsar Palwasha , Alharbi Abdullah , Alosaimi Wael , Khan Umar Hayat
Published: October 2022

The rapid spread of the internet over the last two decades has prompted more and more companies to deploy their work internationally. The offshoring strategy enables organizations to cut down costs, boost shareholder value, acquire a competitive advantage, reduce cycle time, increase workforce flexibility, generate revenue and focus on their core business. The number of worldwide software development projects has increased due to globalization. Global Software Development (GSD) projects are forecast to grow by 20% to 30% in countries like India and China. The outsourcing experts choose one of the suitable models from the available global delivery options to deliver services in the global software paradigm. However, adopting the appropriate model for application maintenance is a complicated process. In addition, the right model is selected based on various influencing factors, type of the project and client requirements. Additionally, sufficient domain expertise is necessary for the decision making of offshore outsourcing. Currently, there is no dynamic and automated tool for the decision making of application maintenance offshoring. Therefore, this study presents an Offshoring Decision Support System (OffshoringDSS), an automated and novel tool to make the offshoring decisions of application maintenance. The suggested tool is based on the Analytic Hierarchy Process (AHP) technique. The tool automatically performs all the calculations involved in the decision making and ranks the sourcing models.
Assessment of Cardiometabolic Risk Reveals Elevated C-Reactive Protein Levels in Patients with Deficit Schizophrenia after Adjustment for Lifestyle Characteristics
Authors: Cyran Agnieszka , Piotrowski Patryk , Samochowiec Jerzy , Samochowiec Agnieszka , Tyburski Ernest , Łaczmański Łukasz , Misiak Błażej
Published: October 2022

Previous studies investigating cardiovascular indicators in people with deficit schizophrenia (D-SCZ) have provided mixed findings. None of these studies controlled for the effects of lifestyle characteristics. We aimed to compare cardiometabolic parameters between patients with deficit schizophrenia (D-SCZ), those with nondeficit schizophrenia (ND-SCZ) and healthy control subjects (HCs) while taking into consideration lifestyle characteristics. A total of 168 participants were assessed. The following parameters were determined from serum samples: total cholesterol, low- and high-density lipoproteins, triglycerides, insulin resistance index (HOMA-IR) and C-reactive protein (CRP). The levels of CRP, but not other parameters, were significantly higher in patients with D-SCZ compared with those with ND-SCZ and HCs after adjustment for body mass index, adherence to Mediterranean diet, level of physical activity, nicotine dependence and dosage of antipsychotics. Higher levels of CRP were associated with lower attention in patients with schizophrenia as well as lower immediate memory and global cognition in healthy controls. Moreover, a significant positive relationship between the concentration of CRP and severity of negative symptoms was observed. These findings suggest that individuals with D-SCZ show subclinical inflammation that cannot be simply explained by an unhealthy lifestyle. Elevated CRP levels might be associated with cognitive impairment in schizophrenia.
Research on Lightweight Lithology Intelligent Recognition System Incorporating Attention Mechanism
Authors: Zhang Zhiyu , Li Heng , Lei Zhen , Liu Haoshan , Zhang Yifeng
Published: October 2022

How to achieve high-precision detection and real-time deployment of the lithology intelligent identification system has significant engineering implications in the geotechnical, geological, water conservation, and mining disciplines. In this study, a lightweight lithology intelligent identification model is proposed to overcome this problem. The MobileNetV2 model is utilized as the basic backbone network to decrease network operation parameters. Furthermore, channel attention and spatial attention methods are incorporated into the model to improve the network’s extraction of complicated and abstract petrographic elements. In addition, based on the findings of network training, computing power performance, test results, and Grad-CAM interpretability analysis and comparison tests with Resnet101, InceptionV3, and MobileNetV2 models. The training accuracy of the proposed model is 98.59 percent, the training duration is 76 min, and the trained model is just 6.38 megabytes in size. The precision (P), recall (R), and harmonic mean (FI-score) were, respectively, 89.62%, 91.38%, and 90.42%. Compared to the three competing models, the model presented in this work strikes a better balance between lithology recognition accuracy and speed, and it gives greater consideration to the rock feature area. Wider and more uniform, strong anti-interference capability, improved robustness and generalization performance of the model, which can be deployed in real-time on the client or edge devices and has some promotion value.
Acceleration of an Electron Bunch with a Non–Gaussian Transverse Profile in Proton-Driven Plasma Wakefield
Authors: Liang Linbo , Xia Guoxing , Pukhov Alexander , Farmer John Patrick
Published: October 2022

Beam-driven plasma wakefield accelerators typically use the external injection to ensure controllable beam quality at injection. However, the externally injected witness bunch may exhibit a non-Gaussian transverse density distribution. Using particle-in-cell simulations, we show that the common beam quality factors, such as the normalized RMS emittance and beam radius, do not strongly depend on the initial transverse shapes of the witness beam. Nonetheless, a beam with a highly-peaked transverse spatial profile can achieve a higher fraction of the total beam charge in the core. The same effect can be seen when the witness beam’s transverse momentum profile has a peaked non-Gaussian distribution. In addition, we find that an initially non-axisymmetric beam becomes symmetric due to the interaction with the plasma wakefield.
A Numerically Sensitive Study of Two Continuous Heavy-Pollution Episodes in the Southern Sichuan Basin of China
Authors: Chen Li , Zhou Chunhong , Zhang Lei , Wang Shigong
Published: October 2022

To explore the causes of pollution formation and changes in the complex topography of the Sichuan Basin, China, and improve the comprehensive simulation capability of pollution models, we use two online coupling models, WRF/Chem and WRF/CUACE, to simulate two heavy pollution episode that successively occurred in the southern part of Sichuan Province from 15 December 2016 to 11 January 2017 in this paper. Additionally, two sets of meteorological physics parameterization schemes MET1 and MET2 are designed, and four groups of experiments are carried out. The results suggest that the two models are good at simulating the static weather parameters such as temperature, low speed of winds and boundary layer height. The four groups of tests can accurately simulate the beginning, maintenance and turning point of the two pollution episodes’ life cycles. CUACE shows better performance in terms of higher correlation coefficients and lower errors in most of the particles and particulate components evaluations. It also performs better in the competitive mechanism of sulfate and nitrate against ammonium in the thermodynamic equilibrium mechanism. In addition, the evaluation of PM2.5 and the component simulation show that CUACE is more capable of simulating the mechanisms of heavy pollutions in southern Sichuan. Meanwhile, MET2 scheme is more appropriate for the simulation than MET1 dose. Based on the simulated concentrations of components and their precursors, the models overestimate the conversion of NO2 to nitrate but underestimate the conversion of SO2 to sulfate, which is the essential cause of the general overestimation of nitrate. Therefore, reducing the overestimation of nitrate is one major target for future model improvement.
A Qualitative Investigation of Clients, Significant Others, and Clinicians’ Experiences of Using Wireless Microphone Systems to Manage Hearing Impairment
Authors: Scarinci Nerina , Nickbakht Mansoureh , Timmer Barbra H. , Ekberg Katie , Cheng Bonnie , Hickson Louise
Published: October 2022

This study aimed to explore the perceptions and experiences of adults with hearing impairment (HI), their significant others (SOs), and clinicians regarding the use and provision of wireless microphone systems (WMS). A qualitative descriptive methodology was used, with a total of 43 participants across three groups: (1) 23 adults with HI who used WMS; (2) 7 SOs of adults who used WMS; and (3) 13 clinicians who provided WMSs to adults with HI. Participants completed an individual semi-structured in-depth interview to explore their experiences, with the data analysed using thematic analysis. The analysis revealed five themes encompassing the perceptions and experiences of WMSs: (1) with experience and clear expectations, users believe that WMS can make a difference; (2) the trial and decision-making process is important; (3) clients’ experiences using WMS; (4) issues with WMS and technology; and (5) users require ongoing training and support to use WMS. These findings highlight the complexities of providing and using WMS with adults with HI. However, clients, SOs, and clinicians all reported that, with appropriate experience, expectations, training, and support, WMS can make a real difference in listening and communicating in different situations. There is also an opportunity to involve SOs more throughout the rehabilitation process.
The Effect of Salinity Stress on Enzyme Activities, Histology, and Transcriptome of Silver Carp (Hypophthalmichthys molitrix)
Authors: Jiang Yuhan , Yuan Chen , Qi Ming , Liu Qigen , Hu Zhongjun
Published: October 2022

A 56-day study was performed to examine the effect of freshwater (FW) and brackish water (BW 6‰ salinity) on the antioxidant ability, Na+/K+-ATPase (NKA) activities, histology, and transcriptome of the gill and kidney tissue in juvenile silver carp (Hypophthalmichthys molitrix). The results show that when juvenile silver carp were exposed to 6‰ salinity, the activities of superoxide dismutase (SOD) and catalase (CAT) were shown to be substantially increased (p < 0.05), while glutathione peroxidase (GSH-PX) activities in gill were not significantly affected (p < 0.05). In kidney tissue, SOD, CAT, and GSH-PX, enzyme activities peaked at 24, 8, and 4 h, respectively, but were not significantly different compared with the control group (p < 0.05). In addition, significant effects of salinity were observed for the NKA level in both the gills and kidney tissues (p < 0.05). The gill filaments of juvenile silver carp under the BW group all underwent adverse changes within 72 h, such as cracks and ruptures in the main part of the gill filaments, bending of the gill lamellae and enlargement of the gaps, and an increase in the number of mucus and chloride-secreting cells. Transcriptome sequencing showed 171 and 261 genes in the gill and kidney tissues of juvenile silver carp compared to the BW group, respectively. Based on their gene ontology annotations, transcripts were sorted into four functional gene groups, each of which may play a role in salt tolerance. Systems involved in these processes include metabolism, signal transduction, immunoinflammatory response, and ion transport. The above findings indicate that the regulation processes in juvenile silver carp under brackish water conditions are complex and multifaceted. These processes and mechanisms shed light on the regulatory mechanism of silver carp osmolarity and provide a theoretical foundation for future research into silver carp growth in brackish water aquaculture area.
Comparative Transcriptomics and Proteomics of Cancer Cell Lines Cultivated by Physiological and Commercial Media
Authors: Wang Junyao , Peng Wenjing , Yu Aiying , Fokar Mohamed , Mechref Yehia
Published: October 2022

Aiming to reduce the gap between in vitro and in vivo environment, a complex culture medium, Plasmax, was introduced recently, which includes nutrients and metabolites with concentrations normally found in human plasma. Herein, to study the influence of this medium on cellular behaviors, we utilized Plasmax to cultivate two cancer cell lines, including one breast cancer cell line, MDA-MB-231BR, and one brain cancer cell line, CRL-1620. Cancer cells were harvested and prepared for transcriptomics and proteomics analyses to assess the discrepancies caused by the different nutritional environments of Plasmax and two commercial media: DMEM, and EMEM. Total RNAs of cells were extracted using mammalian total RNA extract kits and analyzed by next-generation RNA sequencing; proteomics analyses were performed using LC-MS/MS. Gene oncology and pathway analysis were employed to study the affected functions. The cellular invasion and cell death were inhibited in MDA-MB-231BR cell line when cultured in Plasmax compared to DMEM and EMEM, whereas the invasion, migration and protein synthesis of CRL-1620 cell line were activated in Plasmax in relative to both commercial media. The expression changes of some proteins were more significant compared to their corresponding transcripts, indicating that Plasmax has more influence upon regulatory processes of proteins after translation. This work provides complementary information to the original study of Plasmax, aiming to facilitate the selection of appropriate media for in vitro cancer cell studies.
Generalization Performance of Quantum Metric Learning Classifiers
Authors: Kim Jonathan , Bekiranov Stefan
Published: October 2022

Quantum computing holds great promise for a number of fields including biology and medicine. A major application in which quantum computers could yield advantage is machine learning, especially kernel-based approaches. A recent method termed quantum metric learning, in which a quantum embedding which maximally separates data into classes is learned, was able to perfectly separate ant and bee image training data. The separation is achieved with an intrinsically quantum objective function and the overall approach was shown to work naturally as a hybrid classical-quantum computation enabling embedding of high dimensional feature data into a small number of qubits. However, the ability of the trained classifier to predict test sample data was never assessed. We assessed the performance of quantum metric learning on test ants and bees image data as well as breast cancer clinical data. We applied the original approach as well as variants in which we performed principal component analysis (PCA) on the feature data to reduce its dimensionality for quantum embedding, thereby limiting the number of model parameters. If the degree of dimensionality reduction was limited and the number of model parameters was constrained to be far less than the number of training samples, we found that quantum metric learning was able to accurately classify test data.