bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of the Ability to Form Biofilms in KPC-Producing and ESBL-Producing Klebsiella pneumoniae Isolated from Clinical Samples
Authors: Sabença Carolina , Costa Eliana , Sousa Sara , Barros Lillian , Oliveira Ana , Ramos Sónia , Igrejas Gilberto , Torres Carmen , Poeta Patrícia
Published: July 2023

The appearance of Klebsiella pneumoniae strains producing extended-spectrum β-lactamase (ESBL), and carbapenemase (KPC) has turned into a significant public health issue. ESBL- and KPC-producing K. pneumoniae’s ability to form biofilms is a significant concern as it can promote the spread of antibiotic resistance and prolong infections in healthcare facilities. A total of 45 K. pneumoniae strains were isolated from human infections. Antibiograms were performed for 17 antibiotics, ESBL production was tested by Etest ESBL PM/PML, a rapid test was used to detect KPC carbapenemases, and resistance genes were detected by PCR. Biofilm production was detected by the microtiter plate method. A total of 73% of multidrug resistance was found, with the highest resistance rates to ampicillin, trimethoprim–sulfamethoxazole, cefotaxime, amoxicillin-clavulanic acid, and aztreonam. Simultaneously, the most effective antibiotics were tetracycline and amikacin. blaCTX-M, blaTEM, blaSHV, aac(3)-II, aadA1, tetA, cmlA, catA, gyrA, gyrB, parC, sul1, sul2, sul3, blaKPC, blaOXA, and blaPER genes were detected. Biofilm production showed that 80% of K. pneumoniae strains were biofilm producers. Most ESBL- and KPC-producing isolates were weak biofilm producers (40.0% and 60.0%, respectively). There was no correlation between the ability to form stronger biofilms and the presence of ESBL and KPC enzymes in K. pneumoniae isolates.
Dietary Palygorskite-Based Antibacterial Agent Supplementation as an Alternative to Antibiotics Improves Growth Performance, Blood Parameters, and Rumen Microbiota in Sheep
Authors: Li Shujie , Liu Yue , Zeng Hanfang , Wang Chanjian , Han Zhaoyu
Published: July 2023

This research aimed to investigate the effects of a palygorskite-based antibacterial agent (PAA) as an alternative to antibiotics on growth performance, blood parameters, and rumen microbiota in sheep. A total of 120 sheep were randomly divided into five groups of six replicates with four sheep each. Sheep were fed a basal diet, an antibiotic diet supplemented with 500 g/t chlortetracycline (CTC), and a basal diet supplemented with 500, 1000, and 2000 g/t PAA for 80 d, respectively. Supplementation with 2000 g/t PAA and 500 g/t CTC increased the average daily gain (ADG) of sheep compared with the control group (p < 0.05). Diets supplemented with 2000 g/t PAA and 500 g/t CTC reduced (p < 0.05) the feed:gain ratio (F/G ratio) in the overall periods. Dietary supplementation with 1000 g/t PAA significantly increased albumin and total protein (p < 0.05). A significant positive correlation was found between growth hormone concentration and PAA supplementation (p < 0.05). In addition, compared to the control group, the CTC group had higher growth hormone concentration and lower lipopolysaccharide concentration (p < 0.05). No difference was observed between the five groups in terms of rumen fermentation characteristics (p > 0.05). At the phylum level, the relative abundance of Proteobacteria was lower in the PAA 2000 and CTC 500 groups than in the control and PAA 500 groups (p < 0.05). At the genus level, a significant decrease (p < 0.05) in the relative abundance of RuminococcaceaeUCG-010 was observed in the PAA 1000, PAA 2000, and CTC 500 groups compared with that in the control group. In addition, the relative abundance of Prevotella1 (p < 0.05) was higher in the PAA 2000 group than in the control group. These findings indicate that dietary supplementation with PAA has ameliorative effects on growth performance, blood parameters, and rumen microbiota, with an optimal dosage of 2000 g/t for sheep.
Real Image Deblurring Based on Implicit Degradation Representations and Reblur Estimation
Authors: Zhao Zihe , Qin Man , Gou Haosong , Wang Zhengyong , Ren Chao
Published: July 2023

Most existing image deblurring methods are based on the estimation of blur kernels and end-to-end learning of the mapping relationship between blurred and sharp images. However, since different real-world blurred images typically have completely different blurring patterns, the performance of these methods in real image deblurring tasks is limited without explicitly modeling blurring as degradation representations. In this paper, we propose IDR2ENet, which is the Implicit Degradation Representations and Reblur Estimation Network, for real image deblurring. IDR2ENet consists of a degradation estimation process, a reblurring process, and a deblurring process. The degradation estimation process takes the real blurred image as input and outputs the implicit degradation representations estimated on it, which are used as the inputs of both reblurring and deblurring processes to better estimate the features of the blurred image. The experimental results show that whether compared with traditional or deep-learning-based deblurring algorithms, IDR2ENet achieves stable and efficient deblurring results on real blurred images.
LogBD: A Log Anomaly Detection Method Based on Pretrained Models and Domain Adaptation
Authors: Liu Shuxian , Deng Le , Xu Huan , Wang Wei
Published: July 2023

The log data generated during operation of a software system contain information about the system, and using logs for anomaly detection can detect system failures in a timely manner. Most existing log anomaly detection methods are specific to a particular system, have cold-start problems, and are sensitive to updates in log format. In this paper, we propose a log anomaly detection method LogBD based on pretrained models and domain adaptation, which uses the pretraining model BERT to learn the semantic information of logs. This method can solve problems caused by the multiple meaning of words and log statement updates. The distance to determine anomalies in LogBD is constructed on the basis of domain adaptation, using TCNs to extract common features of different system logs and mapping them to the same hypersphere space. Lastly, experiments were conducted on two publicly available datasets to evaluate the method. The experimental results showed that the method can better solve the log instability problem and exhibits some improvement in the cross-system log anomaly detection effect.
Development of Fire Safety Assessment Model for Buildings Using Analytic Hierarchy Process
Authors: Alfalah Ghasan , Al-Shalwi Munther , Elshaboury Nehal , Al-Sakkaf Abobakr , Alshamrani Othman , Qassim Altyeb
Published: July 2023

Fires pose significant risks, encompassing loss of life, destruction of property, and substantial adverse impacts on the economy. Therefore, the prioritization of fire safety in building structures must be embraced by all relevant stakeholders, including building owners, authorities, and the general public. However, traditional fire safety assessment methods can be laborious and challenging, impeding the identification of potential fire hazards and the selection of optimal fire safety measures. To this end, this research study offers an analytic hierarchy process for assessing building fire safety. Two case studies are presented to support this model’s outperformance compared with conventional assessment techniques. The proposed method yields hazard ratings of 5.3 and 4.3 along with safety ratings of 5.5 and 5.9 for the two case studies. Additionally, the proposed model yields comprehensible, well-documented, and comparable results. Therefore, it serves as a valuable decision-making tool for evaluating fire hazards and enhancing the efficiency of building structures. As a result, decision-makers can identify current and future fire protection and prevention requirements with greater ease and precision, making the decision-making process more effective.
Hydrodynamic Modification in Channels Densely Populated with Aquaculture Farms
Authors: Cornejo Pablo , Guerrero Nicolás , Sobarzo Marcus , Sepúlveda Héctor H.
Published: July 2023

We predicted small-scale hydrodynamics, including the effect of the aquaculture farming infrastructure, for a region within the group of salmon farm concessions identified in the Chilean regulation as ACS-7. The geographical region corresponds to the Caucahue Channel, composed of two branches connected by a constriction on Caucahue Island, Inland Sea of Chiloe, Chilean Patagonia. The prediction methodology considers the interaction of a regional ocean model and a high-resolution local CFD model. The model prediction was validated using available data from ADCP. We find that the Caucahue Channel is characterized by a complex circulation and hydrodynamics, including an unstable shear flow, with meanders and turbulent structures, and retention zones. Results show the aquaculture infrastructure has a non-local hydrodynamic effect. Differences in horizontal and vertical velocity can be quite significant even far from aquaculture centers, reaching up to 300% and 170%, respectively, in simulations without taking its effects into account. The useful characteristics of this predictive approach and its potential use in particle tracking and species diffusion prediction allow for the use of projecting as a tool for strengthening the environmental and productive management of this industry.
A Novel Efficient Borehole Cleaning Model for Optimizing Drilling Performance in Real Time
Authors: Al-Rubaii Mohammed , Al-Shargabi Mohammed , Al-Shehri Dhafer , Alyami Abdullah , Minaev Konstantin M.
Published: July 2023

The drilling industry has evolved significantly over the years, with new technologies making the process more efficient and effective. One of the most crucial issues of drilling is borehole cleaning, which entails removing drill cuttings and keeping the borehole clean. Inadequate borehole cleaning can lead to drilling problems such as stuck pipes, poor cementing, and formation damage. Real-time drilling evaluation has seen significant improvements, allowing drilling engineers to monitor the drilling process and make adjustments accordingly. This paper introduces a novel real-time borehole cleaning performance evaluation model based on the transport index (TIm). The novel TIm model offers a real-time indication of borehole cleaning efficiency. The novel model was field-tested and validated for three wells, demonstrating its ability to determine borehole cleaning efficiency in typical drilling operations. Using TIm in Well-A led to a 56% increase in the rate of penetration (ROP) and a 44% reduction in torque. Moreover, the efficient borehole cleaning obtained through the use of TIm played a significant role in improving drilling efficiency and preventing stuck pipes incidents. The TIm model was also able to identify borehole cleaning efficiency during a stuck pipe issue, highlighting its potential use as a tool for optimizing drilling performance.
Enhancing Abstractive Summarization with Extracted Knowledge Graphs and Multi-Source Transformers
Authors: Chen Tong , Wang Xuewei , Yue Tianwei , Bai Xiaoyu , Le Cindy X. , Wang Wenping
Published: July 2023

As the popularity of large language models (LLMs) has risen over the course of the last year, led by GPT-3/4 and especially its productization as ChatGPT, we have witnessed the extensive application of LLMs to text summarization. However, LLMs do not intrinsically have the power to verify the correctness of the information they supply and generate. This research introduces a novel approach to abstractive summarization, aiming to address the limitations of LLMs in that they struggle to understand the truth. The proposed method leverages extracted knowledge graph information and structured semantics as a guide for summarization. Building upon BART, one of the state-of-the-art sequence-to-sequence pre-trained LLMs, multi-source transformer modules are developed as an encoder, which are capable of processing textual and graphical inputs. Decoding is performed based on this enriched encoding to enhance the summary quality. The Wiki-Sum dataset, derived from Wikipedia text dumps, is introduced for evaluation purposes. Comparative experiments with baseline models demonstrate the strengths of the proposed approach in generating informative and relevant summaries. We conclude by presenting our insights into utilizing LLMs with graph external information, which will become a powerful aid towards the goal of factually correct and verified LLMs.
Study on Disturbance Mechanism of Squeezed and Non-Squeezed Soil Piles on Soft Soil Foundation
Authors: Lin Chengyuan , Wang Ruyi , Huang Mengshuang , Huang Lebin , Tan Qinwen
Published: July 2023

The construction process of pile foundations can significantly disrupt the soil. Therefore, it is necessary to limit the degree of soil disturbance caused by pile foundation construction to an acceptable level. This paper examines the disturbance effects of pile driving on soft soil foundations, specifically analyzing the squeezing effect of squeezed soil piles and the unloading effect of non-squeezed soil piles. To investigate these effects, two typical squeezed soil piles, a hydrostatic pile, and a bag grouting pile, as well as a typical non-squeezed soil pile (a bored pile) are selected. Specifically, a novel construction method for numerical models, which simulates the mechanical processes of different pile types under standard grids, is proposed. Three crucial indicators—soil displacement field, stress field, and disturbance influence range—are chosen to compare the disturbance effects of three types of piles on the soil. Results indicate that the two types of squeezed soil piles cause significant disturbance to the soil displacement field, especially in the horizontal direction, while causing a relatively slight disturbance to the soil stress field. Among the two of them, the disturbance magnitude and range of the hydrostatic pile are greater than those of the bag grouting pile. For the non-squeezed soil pile, the soil displacement field changes minimally and the stress field remains basically unchanged during the pile driving process of the bored pile. To compare and quantify the disturbance effects of three types of piles on soil, the soil disturbance range in the horizontal direction of each pile is normalized by its radius. Results indicate that the horizontal disturbance values of maximum horizontal stress for all three types of piles are approximately 1/5 of the pile length above the pile tip, with normalized values of 7.6, 5.5, and 3.5, respectively. The maximum horizontal deformation disturbance range in the horizontal direction occurs near the ground surface and has normalized values of 15.2, 7.5, and 1.1 for the three types of piles, respectively. Therefore, the hydrostatic pile has the greatest disturbance effect, followed by the bag grouting pile and the bored pile. However, within the allowable range of disturbance in practical engineering, the optimal piling method can be selected by comprehensively considering factors such as the construction difficulty and economic costs.
Mechanical Characterization of Soft Membranes with One-Shot Projection Moiré and Metaheuristic Optimization
Authors: Boccaccio Antonio , Lamberti Luciano , Santoro Lorenzo , Trentadue Bartolomeo
Published: July 2023

Mechanical characterization of soft materials is a complicated inverse problem that includes nonlinear constitutive behavior and large deformations. A further complication is introduced by the structural inhomogeneity of tested specimens (for example, caused by thickness variations). Optical methods are very useful in mechanical characterization of soft matter, as they provide accurate full-field information on displacements, strains and stresses regardless of the magnitude and/or gradients of those quantities. In view of this, the present study describes a novel hybrid framework for mechanical characterization of soft membranes, combining (i) inflation tests and preliminary in-plane equi-biaxial tests, (ii) a one-shot projection moiré optical setup with two symmetric projectors that project cross-gratings onto the inflated membrane, (iii) a mathematical model to extract 3D displacement information from moiré measurements, and (iv) metaheuristic optimization hybridizing harmony search and JAYA algorithms. The use of cross-gratings allows us to determine the surface curvature and precisely reconstruct the shape of the deformed object. Enriching metaheuristic optimization with gradient information and elitist strategies significantly reduces the computational cost of the identification process. The feasibility of the proposed approach wassuccessfully tested on a 100 mm diameter natural rubber membrane that had some degree of anisotropy in mechanical response because of its inhomogeneous thickness distribution. Remarkably, up to 324 hyperelastic constants and thickness parameters can be precisely identified by the proposed framework, reducing computational effort from 15% to 70% with respect to other inverse methods.
Laboratory Study on Influence of Blending Conditions on Chemo-Thermal Characteristics of Lignin-Modified Bitumen
Authors: Rezazad Gohari Ali , Lamothe Sébastien , Bilodeau Jean-Pascal , Mansourian Ahmad , Carter Alan
Published: July 2023

Environmental approaches in the asphalt industry have focused on utilizing waste materials as modifiers. Lignin is a high-potential bitumen modifier due to its characteristics; however, the blending process with bitumen is critical. This study investigates the chemo-thermal characteristics of lignin-modified bitumen under two different blending protocols, including a mechanical and high-shear mixer to evaluate its performance as a modifier. According to the protocols, 5, 10, and 20% of Kraft lignin was added to a PG 58S−28 bitumen. The samples were subjected to analysis using Brookfield Rotational Viscosity (BRV), Dynamic Shear Rheometer (DSR), Fourier-Transform Infrared Spectroscopy (FTIR), Environmental Scanning Electron Microscopy (ESEM), Thermogravimetric Analysis (TGA), and Differential Scanning Calorimetry (DSC) tests. The BRV and DSR test results indicate a remarkable alteration in the rheological properties of lignin-modified bitumen under blending conditions. The FTIR analysis indicated that Kraft lignin did not produce new functional groups. The fibril structures of the bitumens are affected by Kraft lignin content and blending conditions due to ESEM. The Kraft lignin and blending conditions influence the thermal behavior of bitumen. The findings highlight Kraft lignin’s potential as a bitumen modifier, and the fact that its characteristics are influenced by the blending protocol and Kraft lignin content.
Multi-Objective Collaborative Control Method for Multi-Axle Distributed Vehicle Assisted Driving
Authors: Wang Weichen , Li Junqiu , Li Xiaohan , Li Zhichao , Guo Ningyuan
Published: July 2023

For human–machine collaborative driving conditions, a hierarchical chassis multi-objective cooperative control method is proposed in this paper. Firstly, based on the phase plane theory, vehicle dynamics analysis is carried out to complete the definition of vehicle stability region. Secondly, based on the linear time-varying (LTV) system model, a cooperative control strategy combining fuzzy control with model predictive control (MPC) is proposed in the upper layer. In this strategy, the assisted driving weight adjustment coefficient and the stability weight adjustment coefficient are obtained by fuzzy mapping combining human–machine cooperation index and the vehicle stability region, respectively, and the optimization objectives of MPC are designed based on the above coefficients. In the lower layer torque allocation strategy, the stability weight adjustment coefficient is introduced to achieve multi-objective optimization of tire load rate and energy efficiency. For energy efficiency optimization, an optimal energy efficiency point-based tracking method is proposed to avoid nonlinearity caused by the introduction of motor loss models. Simulation analysis results show that the proposed strategy can effectively alleviate human–machine conflicts and improve vehicle handing stability. It also can achieve smaller tire load rate optimization through torque allocation and can reduce energy consumption by approximately 8% compared with the inter-axle torque allocation strategy. This study helps to promote the improvement of the comprehensive performance of assisted driving vehicles in human–machine cooperation, handling stability, and energy-saving torque distribution.
A COP Prediction Model of Hybrid Geothermal Heat Pump Systems based on ANN and SVM with Hyper-Parameters Optimization
Authors: Shin Jihyun , Lee Jinhyun , Cho Younghum
Published: July 2023

When the geothermal heat pump system is operated due to an imbalance in the heating and cooling load, the system performance is lowered due to the occurrence of a thermal environment problem in the ground. To solve the performance degradation, a hybrid geothermal heat pump system with an added auxiliary heat source is used. For the efficient operation of the system, it is necessary to check the performance coefficient of the hybrid geothermal system. The coefficient of performance can be monitored based on a mathematical model using a measuring instrument. However, in the case of mathematical models, there are a lot of input data required, and many measurement sensors are required for this. If there is an input factor that is omitted among the necessary input factors, the accuracy of the predicted performance coefficient is lowered or a problem occurs that it is impossible to predict. In this study, we intend to create a model that predicts the coefficient of performance (COP) by using ANNs and SVMs that can accurately predict at low cost using small input factors. Hyper-parameter optimization is performed to increase prediction accuracy in machine learning models. We compared the accuracy of ANN and SVM-based prediction models. In this study, the ANN model showed higher CvRMSE by 5.4% and SVM by 8%. It is expected that the predictive model will be able to be used in the operation of the hybrid geothermal system in the future.
Generalized Spoof Detection and Incremental Algorithm Recognition for Voice Spoofing
Authors: Guo Jinlin , Zhao Yancheng , Wang Haoran
Published: July 2023

Highly deceptive deepfake technologies have caused much controversy, e.g., artificial intelligence-based software can automatically generate nude photos and deepfake images of anyone. This brings considerable threats to both individuals and society. In addition to video and image forgery, audio forgery poses many hazards but lacks sufficient attention. Furthermore, existing works have only focused on voice spoof detection, neglecting the identification of spoof algorithms. It is of great value to recognize the algorithm for synthesizing spoofing voices in traceability. This study presents a system combining voice spoof detection and algorithm recognition. In contrast, the generalizability of the spoof detection model is discussed from the perspective of embedding space and decision boundaries to face the voice spoofing attacks generated by spoof algorithms that are not available in the training set. This study presents a method for voice spoof algorithms recognition based on incremental learning, taking into account data flow scenarios where new spoof algorithms keep appearing in reality. Our experimental results on the LA dataset of ASVspoof show that our system can improve the generalization of spoof detection and identify new voice spoof algorithms without catastrophic forgetting.
Degradation of Hydraulic System due to Wear Particles or Medium Test Dust
Authors: Novak Nejc , Trajkovski Ana , Kalin Mitjan , Majdič Franc
Published: July 2023

Contamination in hydraulic systems is the cause of 70% of failures. This study highlights the performance degradation caused by solid particle contamination of hydraulic components: hydraulic gear pump, 4/3 valve, and orbital motor. Experimental durability tests of components with wear particles and test dust are used to investigate the effects of accelerated wear caused by these two types of contaminants. Results show that oil contaminated with wear particles reduces the volumetric efficiency of the gear pump by 18% and the hydraulic valve by only 0.8%, while oil contaminated with test dust reduces the efficiency of the pump by 76% and the hydraulic valve by 0.9%. This research provides insights for accelerating hydraulic component testing to improve system reliability and longevity.
Pre-Season ACL Risk Classification of Professional and Semi-Professional Football Players, via a Proof-of-Concept Test Battery
Authors: Tsarbou Charis , Liveris Nikolaos I. , Xergia Sofia A. , Tsekoura Maria , Fousekis Konstantinos , Tsepis Elias
Published: July 2023

This study aimed to identify football players at high risk (HR) for anterior cruciate ligament (ACL) injury via a four-test battery and assess possible factors affecting classification. Ninety-one professional and semi-professional male athletes participated in a field-based pre-season screening. The cut-off points of the test battery were 10% acknowledged inter-limb asymmetry limit for quadriceps and hamstring isometric strength and single-leg triple hop for distance, in addition to the upper limit of 6 errors in the landing error scoring system (LESS). Additional assessment of hip adductors and core stability completed the global functional profile of the players. Sixty-one players were identified as HR and thirty as low-risk (LR) for ACL injury. Discriminant analysis showed that the proposed test battery classified accurately 78.0% of them, with 91.8 sensitivity and 61.9% specificity. Only four misclassified players in the LR group were at risk of inadequate treatment. All participants were considered healthy, and history of a previously rehabilitated injury did not interfere with the results. LESS seems to be the best predictor for injury-risk grouping. Prone bridge time was also a whole-body variable discriminating between groups. The proposed test battery provides a promising option for field-based pre-season ACL risk assessment of football players and needs to be studied prospectively.
Impact of Nanoparticles from Ball-Milled Date Palm Biochar on the Hydro-Physical Characteristics of Sandy Soils
Authors: Al-Omran Abdulrasoul M. , Awad Mohammed M. , Alghamdi Abdulaziz G. , Alkhasha Arafat
Published: July 2023

Water management in sandy soils (Typic Torripsamments) is crucial in sustaining agricultural production. The main goal of this research was to assess the impact of date palm biochar on the physical properties of sandy soil with different particle sizes of biochar (macro and nano). For nano-biochar preparation, stick chips were established into a tubular furnace with nitrogen air and heated to 400–450 °C, which was accompanied by a holding period of 4 h. The ball-milled biochar was inclined via ball grinding in a model number PQN2.110 planetary mill and within jars (500 mL), and the biochar-to-sphere mass ratio was 1:100. The sphere-milling apparatus was processed at a speed of 300 rpm for 13 h. Laboratory experiments were carried out at one rate—biochar 5%—and three depths (0.0–5, 5–10, and 10–15 cm). Applying macro-biochar reduced cumulative evaporation compared to the control by 4%, 24%, and 14% for the macro-biochar particles at soil depths. In contrast, biochar reduced cumulative evaporation compared to the control by 8%, 12%, and 4% for the nano-biochar particles at the soil depths tested. Adding biochar significantly raised the amount of retained water, with the highest level recorded at the 5–10 cm depth, while the variations were significantly lower between the macro and nano-biochar when in the direction of the soil surface (0–5 cm), indicating the significance of mixing biochar with the top 10 cm of the soil to increase its ability to reduce evaporation and increase the amount of water retained in the soils. It could be concluded that applying at the top of the coarse soil can positively impact soil hydro-physical properties and increase soil water availability to plants.
Remote Spectral Identification in the THz Band with Reflection Spectroscopy in an Open Atmosphere
Authors: Walczakowski Michał , Maciejewski Marcin , Pałka Norbert
Published: July 2023

Spectroscopy in the terahertz (THz) band has been discussed as a promising tool for identifying substances such as mold in food, narcotics, or explosive materials. Other than the technological limitations, the most important difficulty is the presence of water vapor in the atmosphere, which affects THz measurements. In this paper, we present a systematic approach to the challenging subject of remote identification. We start with a brief analysis of the technical capabilities of the THz components and report the choice of devices for designing an experimental setup for reflection spectroscopy. We follow with the presentation of the transmission THz spectrometer working in an open atmosphere. Research conducted on the transmission configuration provides findings that are implemented in the experimental setup working in a reflective configuration. The final phase is an experiment providing data measured in the reflection configuration with the presence of water vapor, allowing the use of spectra in the identification of the measured samples.
Application of Immobilized Biocatalysts in the Biotransformation of Non-Steroidal Anti-Inflammatory Drugs
Authors: Nowak Agnieszka , Dzionek Anna , Wojcieszyńska Danuta , Guzik Urszula
Published: July 2023

Among the micropollutants identified in the environment, non-steroidal anti-inflammatory drugs (NSAIDs) dominate more and more often. This is due to both the high consumption and low efficiency of biological wastewater treatment plants, where the initial transformation of NSAIDs most often takes place. The solution to the problem may be using preparations supporting activated sludge in sewage treatment plants in the biodegradation of NSAIDs. Therefore, the research aimed to develop a biopreparation stimulating the activated sludge of the sewage treatment plant to decompose paracetamol and selected NSAIDs. This biopreparation is based on strains of Stenotrophomonas maltophilia KB2, Planococcus sp. S5, Bacillus thuringiensis B1(2015b), and Pseudomonas moorei KB4 immobilized on a plant sponge. As a result of the tests, it was shown that the optimal species composition of the proposed preparation includes all tested strains immobilized on a carrier with a mass of 1.2 g/L. The system optimization showed that the optimal amount of strains on the carrier was 17 mg/g of the carrier, 15 mg/g of the carrier, 18 mg/g of the carrier, and 20 mg/g of the carrier for KB4, B1(2015b), KB2, and S5, respectively. The presence of phenol stimulated the degradation of the tested drugs, and this effect deepened with increasing phenol concentration. At the same time, the degradation rate of the mixture of NSAIDs in the presence of phenol did not depend on the amount of biomass. The lack of inhibition in the presence of an additional co-contaminant, i.e., phenol, indicates that the preparation constructed in this way has a chance of being used in sewage treatment plant systems, where introduced strains are exposed to various aromatic compounds.
Predictors of Speed and Agility in Youth Male Basketball Players
Authors: Čaušević Denis , Čović Nedim , Abazović Ensar , Rani Babina , Manolache Gabriel Marian , Ciocan Cătălin Vasile , Zaharia Gabriel , Alexe Dan Iulian
Published: July 2023

Player performance in an intense sport such as basketball is known to be related to attributes such as speed, agility, and power. This study presents a comparative analysis of associations between anthropometric assessment and physical performance in different age groups of elite youth basketball players, while simultaneously identifying the predictors for speed and agility in these players. U14 (n = 44), U15 (n = 45), and U16 (n = 51) players were tested for anthropometry, lower-body power, speed, and agility. U16 players were found to be taller, heavier, more muscular than U14 and U15 players. In addition, the U16 group showed better performance in all performance tests. Age had a significant positive correlation with countermovement (CMJ) and drop jump (DJ) performance in U14 players, and a significant negative correlation with 15m and 20m sprint times in the U15 group. CMJ and DJ emerged as the most significant predictors for sprint and agility variables, respectively. Body fat percentage was found to be a significant predictor for the speed and agility tests in all age groups, but a negative lower-body power predictor. Therefore, besides all sport-specific and fitness tests, it is essential to place emphasis on the percentage of body fat when designing players’ individualized training programs, and during team selection.
An Evaluation of the Usability of Argon Plasma-Treated Bacterial Cellulose as a Carrier for Controlled Releases of Glycoside Hydrolases PelAh and PslGh, Which Are Able to Eradicate Biofilm
Authors: Charęza Magdalena , Ekiert Ewa , Moszyński Dariusz , Madej Mariusz , Jędrzejczak-Silicka Magdalena , Drozd Radosław
Published: July 2023

Bacterial cellulose is a unique biopolymer that has found numerous biomedical applications, such as being an excellent wound-dressing material or a carrier for delivering active compounds. The purpose of this study was to analyze the ability of modified bacterial cellulose (BC) using low-pressure Ar plasma to control the release of glycoside hydrolases with antibiofilm activity, namely PelAh and PslGh, from Pseudomonas aeruginosa. The chemical composition and morphology of the BC surfaces were characterized using photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). The analyses revealed significant changes in the chemical composition of the BC surface due to the introduction of charged functional groups and the conversion of its well-ordered structure into a more amorphous form. The release profiles of enzymes from both forms of the carrier were different and depended on their structural properties. However, a significant impact of BC modification on protein release behavior from the carrier was observed only for PslGh. Both enzymes, when immobilized on pristine and argon plasma-modified BC, retained their ability to effectively reduce biofilm levels, similarly to their soluble form. Ar plasma-modified BC with immobilized specific hydrolases can be used as an effective tool for inhibiting P. aeruginosa biofilm development.
HELPFuL: Human Emotion Label Prediction Based on Fuzzy Learning for Realizing Artificial Intelligent in IoT
Authors: Zhang Lingjun , Zhang Hua , Wu Yifan , Xu Yanping , Ye Tingcong , Ma Mengjing , Li Linhao
Published: July 2023

Human emotion label prediction is crucial to Artificial Intelligent in the Internet of Things (IoT). Facial expression recognition is the main technique to predict human emotion labels. Existing facial expression recognition methods do not consider the compound emotion and the fuzziness of emotion labels. Fuzzy learning is a mathematical tool for dealing with fuzziness and uncertainty information. The advantage of using fuzzy learning for human emotion recognition is that multiple fuzzy sentiment labels can be processed simultaneously. This paper proposes a fuzzy learning-based expression recognition method for human emotion label prediction. First, a fuzzy label distribution system is constructed using fuzzy sets for representing facial expressions. Then, two fuzzy label distribution prediction methods based on fuzzy rough sets are proposed to solve the compound emotion prediction. The probability that a sample is likely and definitely belongs to an emotion is obtained by calculating the upper and lower approximations. Experiments show the proposed algorithm not only performs well on human emotion label prediction but can also be used for other label distribution prediction tasks. The proposed method is more accurate and more general than other methods. The improvement of the method on the effect of emotion recognition extends the application scope of artificial intelligence in IoT.
Optimization of Indirect CAP Exposure as an Effective Osteosarcoma Cells Treatment with Cytotoxic Effects
Authors: Stache Alexandru Bogdan , Mihăilă Ilarion , Gerber Ioana Cristina , Dragoș Loredana Mihaiela , Mihai Cosmin Teodor , Ivanov Iuliu Cristian , Topală Ionuț , Gorgan Dragoș-Lucian
Published: July 2023

Over the past decade, cold atmospheric plasma (CAP) has undergone extensive research as a promising therapeutic approach in oncology, with different treatment methods and exposure configurations being investigated and resulting in various biological effects, most of them after long exposure or treatment durations. This study aimed to evaluate the potential of a custom-made CAP generation source to produce plasma-activated medium (PAM) with cytotoxic effects and subsequently to establish the optimal exposure and treatment parameters. The exposure’s electrical parameters, as well as pH and NO2− content of PAM were analyzed. The cytotoxic potential and optimal parameters of the treatment were established by evaluating the viability of human osteosarcoma cells (HOS cell line) and human osteoblasts (HOB cell line) treated with PAM under different conditions. Our results showed that indirect treatment with CAP presents selective dose-dependent cytotoxic effects, while the cell viability decrease was not found to be correlated with the PAM acidification due to CAP exposure. The Griess assay revealed very high and long-term stable concentrations of NO2− in PAM. Overall, this study presents a simple and faster method of producing PAM treatment with cytotoxic effects on HOS cells, by using a custom-built CAP source.
Customer Sentiment Recognition in Conversation Based on Contextual Semantic and Affective Interaction Information
Authors: Huang Zhengwei , Liu Huayuan , Zhu Jun , Min Jintao
Published: July 2023

In the e-commerce environment, conversations between customers and businesses contain a multitude of useful information about customer sentiment. By mining that information, customer sentiment can be validly identified, which is helpful in accurately identifying customer needs and improving customer satisfaction. Contextual semantics information and inter-speaker affective interaction information are two key factors for identifying customers’ sentiments in conversation. Unfortunately, none of the existing approaches consider the two factors simultaneously. In this paper, we propose a conversational sentiment analysis method based on contextual semantic and affective interaction information. The proposed approach uses different bidirectional gated recurrent unit (BiGRU) combined with attention mechanisms to encode the contextual semantic information of different types of conversational texts. For modeling affective interactions, we use directed graph structures to portray the affective interactions between speakers and encode them with affective interaction features using graph convolutional neural networks (GCN). Finally, the two features are afused to recognize customer sentiment. The experimental results on the JDDC dataset show that our model can more accurately recognize customer sentiment than other baseline models in customer service conversation.
A Well-Known Plant and New Therapeutic Strategies: Turmeric and Its Components in Oral Inflammatory Diseases Treatment
Authors: Wojtyłko Monika , Kunstman Paweł , Bartylak Hanna , Raszewski Łukasz , Osmałek Tomasz , Froelich Anna
Published: July 2023

Turmeric has been known for centuries as a spice and an important element of traditional medicine. Nowadays, plant-derived compounds are still an object of extensive scientific investigations aiming at the development of novel drugs and dosage forms. Turmeric and its most important component, curcumin, reveal numerous interesting biological properties, including antioxidant, anti-inflammatory and antimicrobial activity. Numerous scientific studies focusing on various aspects of the activity of turmeric-derived compounds show that curcuminoids display an enormous potential as active pharmaceutical ingredients useful in a wide spectrum of medical conditions. Oral diseases comprising both mild inflammations and severe life-threatening conditions are classified as the most common ones, affecting an enormous part of the global population. In this review, the current research regarding turmeric and its constituents in oral diseases is summarized and discussed, with special attention paid to novel findings and future directions regarding scientific exploration of curcuminoids.
A Microscopic Traffic Model Considering Driver Reaction and Sensitivity
Authors: Ali Faryal , Khan Zawar Hussain , Gulliver Thomas Aaron , Khattak Khurram Shehzad , Altamimi Ahmed B.
Published: July 2023

A new microscopic traffic model is proposed that characterizes driver response according to reaction and sensitivity. Driver response in the intelligent driver (ID) model is based on a fixed acceleration exponent and so does not follow traffic physics. This inadequate characterization results in unrealistic traffic behavior. With the proposed model, drivers can be aggressive, sluggish, or typical. It is shown to be string stable, and for appropriate distance headway and velocity (speed), the traffic flow is smooth. Furthermore, the proposed model has better stability than the ID model because it is based on driver reaction and sensitivity, while the ID model is based on a fixed exponent. The ID and proposed models are evaluated on a circular road of length 1200 m with a platoon of 21 vehicles for 150 s. The results obtained show that the proposed model characterizes traffic more realistically than the ID model.
A Non-Electrolysis Bioelectric Effect for Gingivitis and Hygiene Contamination Biofilm Removal
Authors: Kim Young Wook , Lee Jihyun , Han Sang Kuy , Koo Bon-Sang , Park Taeguen , Park Hyun Mok , Lee Byoungdoo
Published: July 2023

A combinatorial biofilm treatment involving a low dose of antibiotics along with a small amount of electricity is known as the bioelectric effect (BE). When an external electric field or current is applied, biofilms can be affected by the resulting electrostatic force. Our group is researching the maximization of electrostatic force through the integration of both alternating and direct currents, with a voltage below the electrolysis threshold (0.82 V). To validate the efficacy of this technology, in the present work we investigated two major biofilm applications: (1) dentistry for oral biofilm infection and (2) hygiene for aerobic biofilm contamination. For each application, testing devices were developed in the form of a toothbrush and an evaporator cleaner, respectively. The dental clinical results demonstrated a 75% reduction in gingivitis compared to the non-BE applied group (n = 40, ANOVA, paired t-test, p < 0.05). Meanwhile, the hygiene testing result demonstrated an 81.8% increase in biofilm removal compared to the initial untreated sample (n = 6, ANOVA, paired t-test, p < 0.05). In conclusion, this new BE technology showed efficacy in both dental- and hygiene-associated biofilms without causing electrolysis. Further investigation and development of the BE system should continue in both the medical and hygiene fields.
Automated Sampling System for Monitoring 85Kr in Air
Authors: Cha Hyemi , Jang Mee , Lim Jong-Myoung , Lee Wanno , Kim Hyuncheol
Published: July 2023

Radioactive krypton-85 (85Kr) gas, a chemically inert and non-proliferation indicator, is derived from fission products. Its detection relies on the Budesamt für Strahlenschutz–Institute of Atmospheric Radioactivity (BfS-IAR) method, which necessitated impurity removal using soda lime, silica gel, and liquid nitrogen for cryogenic adsorption. This manual process requires frequent replacements, posing challenges for its automation. To address this, we developed a prototype krypton sampling system as an interim research product for the fully automated remote monitoring of covert nuclear activity. The system incorporates a hollow fiber membrane for impurity removal, a computer-controlled multi-position valve for sampling, and an electric cooler for adsorption. The impurity removal modules demonstrated high efficiencies, removing H2O and CO2 at 99.8% and 97.8% rates, respectively. Further, the custom-made sampling system can process 16 samples in a single run without analyst intervention. We conducted experiments to verify the automatic krypton sampling capability. The activity concentration of 85Kr in ambient air was measured using the BfS-IAR processing and detection system. The system exhibited a recovery rate of ~7.8% for krypton in 1000 L air, demonstrating good continuous remote monitoring capability. This study promotes the development of an automated analysis system for the detection of 85Kr in ambient air.
Limiting Behaviors of Stochastic Spread Models Using Branching Processes
Authors: Hong Jyy-I
Published: July 2023

In this paper, we introduce a spread model using multi-type branching processes to investigate the evolution of the population during a pandemic in which individuals are classified into different types. We study some limiting behaviors of the population including the growth rate of the population and the spread rate of each type. In particular, the work in this paper focuses on the cases where the offspring mean matrices are non-primitive but can be decomposed into two primitive components, A and B, with maximal eigenvalues ρA and ρB, respectively. It is shown that the growth rate and the spread rate heavily depend on the conditions of these two maximal eigenvalues and are related to the corresponding eigenvectors. In particular, we find the spread rates for the case with ρB>ρA>1 and the case with ρA>ρB>1. In addition, some numerical examples and simulations are also provided to support the theoretical results.
Forecasting Water Consumption in the Yangtze River Delta Based on Deformable Cumulative Multivariable Grey Model
Authors: Qiao Zhengran , Yang Wei
Published: July 2023

The intensified contradiction between water resources and social development has restricted the development of the Yangtze River Delta. Due to the importance of water consumption in relieving this contradiction, this paper proposes a novel cumulative multivariable grey model with a high performance to predict the water consumption. Firstly, the grey correlation analysis is applied to study the influencing factors, and then the DGM(1,N) with deformable accumulation (DDGM(1,N) model) is constructed and used to predict the water consumption. The results show that the resident population has a significant impact on the water consumption, and the performance of the DDGM(1,N) model is better than the other two grey models. Secondly, the proposed novel grey model is applied to predict the water consumption in 17 cities in the Yangtze River Delta, and the predicted water consumption in Zhejiang and Shanghai indicates a downward trend, while the predicated water consumption in some cities of the Anhui Province presents an upward trend, such as Chizhou, Chuzhou, Wuhu and Tongling. Finally, some policy implications are provided that correspond to the population growth and three major industries in different situations. This paper enriches the research method and prediction analysis used for the water consumption, and the findings can provide some decision-making references for water resources management.