bluuebunny/crossref_metadata_2025 huggingface dataset:
MYB Transcriptional Factors Affects Upstream and Downstream MEP Pathway and Triterpenoid Biosynthesis in Chlamydomonas reinhardtii
Authors: Anwar Muhammad , Wang Jingkai , Li Jiancheng , Altaf Muhammad Mohsin , Hu Zhangli
Published: February 2024

Terpenoids are enormous and different types of naturally occurring metabolites playing an important role in industrial applications. Cost-effective and sustainable production of terpenoids at commercial scale is the big challenge because of its low abundance from their natural sources. Metabolic and genetic engineering in microorganisms provide the ideal platform for heterologous overexpression protein systems. The photosynthetic green alga Chlamydomonas reinhardtii is considered as a model host for the production of economic and sustainable terpenoids, but the regulation mechanism of their metabolisms is still unclear. In this study, we have investigated the genetic and metabolic synthetic engineering strategy of MYB transcriptional factors (MYB TFs) in terpenoids’ synthesis from C. reinhardtii for the first time. We heterologous overexpressed MYB TFs, specifically SmMYB36 from Salvia miltiorrhiza in C. reinhardtii. MYB upregulated the key genes involved in the 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway. Expression of the SQS gene, which is involved in the downstream triterpenoid synthesis pathway, highly accumulated in MYB-overexpression lines of C. reinhardtii. The contents of squalene increased about 90.20 μg/g in MYB-overexpressed lines. Our results propose the rerouting of the carbon flux toward the biosynthesis of triterpenoids upon overexpression of MYB TFs in C. reinhardtii. Our study suggests imperative novel understandings into the regulation mechanisms of C. reinhardtii triterpenoid metabolism through MYB TFs in photosynthetic green microalgae C. reinhardtii. The role of MYB TFs is investigated for the first time in C. reinhardtii, and provides a prodigious potential for recognizing important transcriptional regulators of the MEP pathway as goals for prospective metabolic and genetic manipulation investigation for increased production of triterpenoids.
Extraction Kinetics of Pyridine, Quinoline, and Indole from the Organic Phase with Natural Deep Eutectic Solvents and Separation Study Using a Centrifugal Extractor
Authors: Lobovich Dmitriy V. , Zinov’eva Inna V. , Milevskii Nikita A. , Kostanyan Artak E. , Zakhodyaeva Yulia A. , Voshkin Andrey A.
Published: February 2024

In this study, the kinetics of extracting pyridine, quinoline, and indole from model fuels using natural deep eutectic solvents (NaDES) composed of carboxylic acids, xylitol, and water were investigated under static conditions. This research marks the first examination of extraction kinetics in this context. The key kinetic parameters of the extraction process were identified. Notably, it was observed that the mass transfer coefficient for indole was in the range of 3.4 × 10−6 to 1.2 × 10−6, depending on NaDES. That is significantly lower, by an order of magnitude, than for pyridine and quinoline under identical experimental conditions. The study revealed that, under specific conditions, where thermodynamic equilibrium for indole cannot be reached, it becomes possible to achieve kinetic separation of the components. The presented experimental data obtained on a centrifugal extractor showed a decrease in the degree of indole extraction with increasing flow: Extraction efficiency decreased from 63% at a flow rate of 0.05 L/h to 18% at 0.8 L/h. Moreover, the research indicated that, during indole extraction, the mass transfer coefficient in a centrifugal extractor was 1.3 × 10−4, which is two orders of magnitude higher than under static conditions. The study underscores the potential utility of the proposed extraction systems based on environmentally friendly NaDES, comprised of carboxylic acids and xylitol, for the kinetic separation of various classes of heterocyclic compounds. Overall, the research provides valuable insights into the kinetics of extraction and the potential applications of ‘green’ NaDES in the separation of heterocyclic compounds from organic liquids.
Photocatalytic Degradation of Neonicotinoids—A Comparative Study of the Efficacy of Hybrid Photocatalysts
Authors: Yarbay Rahmiye Zerrin , Şimşek Veli , Bogdan Lucija , Tomašić Vesna
Published: February 2024

This study deals with the synthesis and characterization of a series of hybrid photocatalysts consisting of different loadings of TiO2, Cd, and Fe on mesoporous SBA-15 material. The prepared samples were characterized by Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), and scanning electron microscopy (SEM), and tested for the removal of the neonicotinoid insecticide imidacloprid. The results showed that uncalcined 10% Cd-SBA-15 catalyst exhibited the best photocatalytic activity. The photocatalytic degradation of the imidacloprid was carried out in a batch photoreactor at different pH values, and in the presence or absence of additional compounds such as peroxymonosulfate (PMS) and peroxydisulfate (PDS). The best degradation results were achieved at a pH value of 6.5 with 10% Cd/SBA-15. The degradation performance increased with the addition of PMS and PDS. Based on the results of the experimental measurements, Cd/SBA-15 is a good candidate that can show a reasonable degradation efficiency and reactivity, especially in the presence of PDS or PMS.
Maximizing Corrosion Resistance of HA+Ce Coated Mg Implants Using Random Forest and Whale Optimization Algorithm
Authors: Rajabi Zeinab , Afshar Taromi Faramarz , Pourmahdian Saeed , Eivaz Mohammadloo Hossein
Published: February 2024

In this paper, a hybrid three-stage methodology based on in vitro experiments, simulations, and metaheuristic optimization is presented to enhance the corrosion resistance of hydroxyapatite (HA)-coated magnesium implants in biomedical applications. In the first stage, we add cerium (Ce) to HA and present a new coating (named HA+Ce) to improve the resistance of the coating to corrosion. Then, various HA+Ce compounds with different factors (e.g., concentration, pH, immersion time, and temperature) are generated and their propensity for corrosion is examined in a physiological environment using EIS and DC polarization tests in a simulated body fluid solution. Eventually, a comprehensive dataset comprising 1024 HA+Ce coating samples is collected. In the second stage, machine learning using random forest (RF) is used to learn the relation between the input factors of the coating and its corrosion resistance. In the third stage, a metaheuristic algorithm based on the whale optimization algorithm (WOA) is utilized to find the best HA+Ce compound with the maximum corrosion resistance, while the objective function of WOA for a new unseen coating solution is estimated using the trained RF model. Finally, the morphology and composition of the best coating solution are inspected using FE-SEM. According to the obtained results, the HA+Ce coating with an immersion time of 60 min, concentrations of 0.9 for Ce and 1.2 for HA, pH of 4.1 for solution, and temperature of 70 °C demonstrated the highest level of corrosion resistance among all experiments and simulations. The final optimized HA+Ce coating solution has obtained a corrosion resistance of 14,050 Ω·cm2, which resulted in a gain of 14.9% compared to the HA-coated Mg implants.
Inhibition Localized Corrosion of N80 Petroleum Pipeline Steel in NaCl-Na2S Solution Using an Imidazoline Quaternary Ammonium Salt
Authors: Li Shanjian , Du Te , Cui Guotao , He Haoxuan , Wu Panfeng , Li Yongfei
Published: February 2024

In this paper, the local corrosion inhibition effect of imidazoline on N80 oil pipeline steel in a NaCl-Na2S solution was studied by the simulated blocking tank cell method, and the corrosion processes of the cathode and anode in the blocking zone were simulated. The blocking corrosion behavior of the pipeline tubing steel N80 in simulated corrosion solutions without and with different concentrations of an imidazoline corrosion inhibitor was studied by chemical analysis and electrochemical analysis. The results show that in the three solution systems, after the anode polarization of the occluded cell, the solution in the occluded region is acidified, the pH value decreases sharply, the migration of Cl− and S2− increases, and the concentration is increased in the blocked area. After adding the imidazoline corrosion inhibitor, the imidazoline inhibitor can reduce the migration of small-radius anions (Cl− and S2−) to the occluded area, inhibit the acidification of the solution in the occluded area, and prevent the dissolution of metals in the occluded area. As a result, the corrosion of the occluded area is slowed down due to the change in the chemical and electrochemical state of the occluded area. In the three corrosion solution systems of 2% Na2S + 5% NaCl, 2% Na2S + 8% NaCl, and 2% Na2S + 10% NaCl, the imidazoline corrosion inhibitor can form an adsorption film on the metal surface, thereby increasing the polarization resistance and decreasing the corrosion rate. The addition of an imidazoline corrosion inhibitor can significantly increase the kinetic constant of anode polarization, which can effectively inhibit the local corrosion of N80 steel in these corrosion systems.
Pattern-Moving-Modelling and Analysis Based on Clustered Generalized Cell Mapping for a Class of Complex Systems
Authors: Li Ning , Xu Zhengguang , Li Xiangquan
Published: February 2024

Considering a class of complex nonlinear systems whose dynamics are mostly governed by statistical regulations, the pattern-moving theory was developed to characterise such systems and successfully estimate the outputs or states. However, since the pattern class variable is not computable directly, this study establishes a clustered generalized cell mapping (C-GCM) to reveal system characteristics. C-GCM is a two-stage approach consisting of a pattern-moving-based description and analysis method. First, a density algorithm, named density-based spatial clustering of applications with noise (DBSCAN), is designed to obtain cell space Ω and the corresponding classification guidelines; this algorithm is initiated after the initial pre-image cells, and the total number of entity cells amounts to Ns. Then, the GCM provides several image cells based on a cell mapping function that refers to the multivariate ARMAX model. The global dynamic analysis employing both searching and storing algorithms depend on the attractor, domain of attraction, and periodic cell groups. At last, simulation results of two examples emphasise the practicality as well as efficacy of the technique suggested. The chief aim of this study was to offer a new perspective for a class of complex systems that could inspire research into nonmechanistic principles modelling and application to nonlinear systems.
A Novel Approach to Optimizing Grinding Parameters in the Parallel Grinding Process
Authors: Yin Tengfei , Zhang Hanqian , Hang Wei , To Suet
Published: February 2024

Hard materials have found extensive applications in the fields of electronics, optics, and semiconductors. Parallel grinding is a common method for fabricating high-quality surfaces on hard materials with high efficiency. However, the surface generation mechanism has not been fully understood, resulting in a lack of an optimization approach for parallel grinding. In this study, the surface profile formation processes were analyzed under different grinding conditions. Then, a novel method was proposed to improve surface finish in parallel grinding, and grinding experiments were carried out to validate the proposed approach. It was found that the denominator (b) of the simplest form of the rotational speed ratio of the grinding wheel to the workpiece has a great influence on surface generation. The surface finish can be optimized without sacrificing the machining efficiency by slightly adjusting the rotational speeds of the wheel or the workpiece to make the value of b close to the ratio (p) of the wheel contact width to the cross-feed distance per workpiece revolution. Overall, this study provides a novel approach for optimizing the parallel grinding process, which can be applied to industrial applications.
Mechanism of Calcium Ion-Selective Channel Opening in the ChR2_L132C Mutant: A Molecular Dynamics Simulation
Authors: Xu Tao , Zhang Wenying , Yuan Shuai , Dou Yusheng
Published: February 2024

Channelrhodopsin-2 (ChR2) is an important tool for optogenetics, and some of its mutants are Ca2+-selective channels. However, the mechanism for Ca2+-selective permeation is still unclear. In this study, molecular dynamic (MD) simulations for the Ca2+ permeation of the CatCh mutant were carried out to investigate the fundamental features of the selectivity of Ca2+. Research on the conformational changes in the key residues near the central gate (CG) of the channel suggested that E83, E90, and D253 play an important role in Ca2+ conductivity. The clustering analysis indicates that the above “EED triad” acts as a filter, and Ca2+ can only pass through if the EED is in a certain conformation. It was also found that hydrated Ca2+ can be coordinated with carboxyl groups, resulting in the loss of part of the water molecules in the hydrated shell and a reduction in ionic radius, which helps Ca2+ enter the channel.
Adaptive Neural Network Tracking Control of Robotic Manipulators Based on Disturbance Observer
Authors: Li Tianli , Zhang Gang , Zhang Tan , Pan Jing
Published: February 2024

This article presents an adaptive neural network (ANN) control scheme based on a disturbance observer that can achieve trajectory tracking control of robotic manipulators under external disturbances and dynamic model uncertainties. Firstly, an ANN controller based on full-state feedback is derived using the backstepping technique to achieve an online approximation of uncertainty. The integral sliding mode surface with a position error is introduced into the controller, which reduces the steady-state error of the system and enhances robustness. Then, a novel disturbance observer is designed to estimate both the approximation errors of the ANN and external disturbances, and to provide compensation for the controller, effectively suppressing the trajectory tracking errors caused by approximation errors and disturbances. Subsequently, the Lyapunov stability theory is utilized to demonstrate the stability of the developed control strategy and the boundedness of all closed-loop signals. Finally, numerical simulations are used to confirm the efficacy of the proposed control method.
Enzymatic Pretreatment of Slaughterhouse Wastewater: Application of Whole Lipolytic Cells of Rhizopus oryzae Produced from Residual Vegetable Oil
Authors: Reis Willian S. M. , Bento Heitor B. S. , Carvalho Ana K. F. , Pereira Ernandes B.
Published: February 2024

This study assessed the application of whole lipolytic cells in the pretreatment of slaughterhouse wastewater to reduce its lipid content. The fungal biomass of Rhizopus oryzae was evaluated in the hydrolysis of slaughterhouse wastewater containing high lipid concentrations, focusing on the biomass’s concentration and the effect of using an emulsifier and surfactant. The use of the whole-cells lipase of Rhizopus oryzae grown in a residual vegetable oil medium proved effective in the hydrolysis of slaughterhouse wastewater, generating concentrations of free fatty acids (FFA) ranging from 40.36 to 90.14 mM. The action of lipase in the hydrolysis of slaughterhouse residues indicated its effectiveness in pretreating lipid-rich liquid residues, potentially boosting the microbiota of this anaerobic treatment. The results showed that lipase activity without surfactant exhibited a similar performance to that of Triton X-100 in the hydrolysis of liquid residues. However, the combination of lipase and surfactant could represent a promising strategy to optimize free fatty acid production from slaughterhouse residues, strengthening anaerobic treatment processes and potentially enhancing the overall efficiency of waste management systems.
Real-Time Multi-Robot Mission Planning in Cluttered Environment
Authors: Lu Zehui , Zhou Tianyu , Mou Shaoshuai
Published: February 2024

Addressing a collision-aware multi-robot mission planning problem, which involves task allocation and path-finding, poses a significant difficulty due to the necessity for real-time computational efficiency, scalability, and the ability to manage both static and dynamic obstacles and tasks within a complex environment. This paper introduces a parallel real-time algorithm aimed at overcoming these challenges. The proposed algorithm employs an approximation-based partitioning mechanism to partition the entire unassigned task set into several subsets. This approach decomposes the original problem into a series of single-robot mission planning problems. To validate the effectiveness of the proposed method, both numerical and hardware experiments are conducted, involving dynamic obstacles and tasks. Additionally, comparisons in terms of optimality and scalability against an existing method are provided, showcasing its superior performance across both metrics. Furthermore, a computational burden analysis is conducted to demonstrate the consistency of our method with the observations derived from these comparisons. Finally, the optimality gap between the proposed method and the global optima in small-size problems is demonstrated.
Enhanced Knowledge Distillation for Advanced Recognition of Chinese Herbal Medicine
Authors: Zheng Lu , Long Wenhan , Yi Junchao , Liu Lu , Xu Ke
Published: February 2024

The identification and classification of traditional Chinese herbal medicines demand significant time and expertise. We propose the dual-teacher supervised decay (DTSD) approach, an enhancement for Chinese herbal medicine recognition utilizing a refined knowledge distillation model. The DTSD method refines output soft labels, adapts attenuation parameters, and employs a dynamic combination loss in the teacher model. Implemented on the lightweight MobileNet_v3 network, the methodology is deployed successfully in a mobile application. Experimental results reveal that incorporating the exponential warmup learning rate reduction strategy during training optimizes the knowledge distillation model, achieving an average classification accuracy of 98.60% for 10 types of Chinese herbal medicine images. The model boasts an average detection time of 0.0172 s per image, with a compressed size of 10 MB. Comparative experiments demonstrate the superior performance of our refined model over DenseNet121, ResNet50_vd, Xception65, and EfficientNetB1. This refined model not only introduces an approach to Chinese herbal medicine image recognition but also provides a practical solution for lightweight models in mobile applications.
Self-Advocacy in Inclusive Research
Authors: Krueger Courtney , van Heumen Lieke , van den Helder Claire
Published: February 2024

The literature on inclusive research has established its relationship with self-advocacy for people with intellectual disabilities. Self-advocacy has been described as both a requirement and a result of inclusive research. Additionally, the process of becoming an inclusive researcher can be seen as self-advocacy for people with intellectual disabilities. As inclusive research continues to become more prominent, and more people with intellectual disabilities become inclusive researchers, we need to continue to consider this fundamental relationship and how self-advocacy and inclusive research can inform and support each other. In this paper, we first discuss the history of self-advocacy and inclusive research and what inclusive researchers have shared about the relationship between self-advocacy and inclusive research. We then present the experiences of an inclusive researcher with intellectual disability with self-advocacy and how the process of becoming an inclusive researcher impacted those experiences. We conclude the paper with reflections on how future inclusive research should consider the role of self-advocacy.
Deep-SDM: A Unified Computational Framework for Sequential Data Modeling Using Deep Learning Models
Authors: Pokhrel Nawa Raj , Dahal Keshab Raj , Rimal Ramchandra , Bhandari Hum Nath , Rimal Binod
Published: February 2024

Deep-SDM is a unified layer framework built on TensorFlow/Keras and written in Python 3.12. The framework aligns with the modular engineering principles for the design and development strategy. Transparency, reproducibility, and recombinability are the framework’s primary design criteria. The platform can extract valuable insights from numerical and text data and utilize them to predict future values by implementing long short-term memory (LSTM), gated recurrent unit (GRU), and convolution neural network (CNN). Its end-to-end machine learning pipeline involves a sequence of tasks, including data exploration, input preparation, model construction, hyperparameter tuning, performance evaluations, visualization of results, and statistical analysis. The complete process is systematic and carefully organized, from data import to model selection, encapsulating it into a unified whole. The multiple subroutines work together to provide a user-friendly and conducive pipeline that is easy to use. We utilized the Deep-SDM framework to predict the Nepal Stock Exchange (NEPSE) index to validate its reproducibility and robustness and observed impressive results.
Towards an Affective Intelligent Agent Model for Extrinsic Emotion Regulation
Authors: Pico Aaron , Taverner Joaquin , Vivancos Emilio , Botti Vicente , García-Fornes Ana
Published: February 2024

Emotion regulation is the human ability to modulate one’s or other emotions to maintain emotional well-being. Despite its importance, only a few computational models have been proposed for facilitating emotion regulation. None of them prepare a plan of all the actions necessary for emotion regulation customized to the needs of a specific individual. To address this gap, we propose a computational model for an intelligent agent which, grounded in a multidimensional emotion representation, facilitates emotion regulation in individuals. This computational model is based on J. Gross’s theoretical framework of emotion regulation. An intelligent agent selects the most appropriate regulation strategy to maintain an individual’s emotional equilibrium considering the individual’s personality traits. A dynamic planner prepares a plan of emotion regulation actions which is dynamically adapted according to the emotional changes observed in the individual after applying the previous emotion regulation actions. This refinement of the initial regulatory action plan allows the proposed emotion regulation agent to adapt the plan to the specific characteristics of the individual, facilitating the individual to improve their emotion regulation capabilities and improve their emotional health.
Visualization of Spatial–Temporal Epidemiological Data: A Scoping Review
Authors: Kim Denisse , Cánovas-Segura Bernardo , Campos Manuel , Juarez Jose M.
Published: February 2024

In recent years, the proliferation of health data sources due to computer technologies has prompted the use of visualization techniques to tackle epidemiological challenges. However, existing reviews lack a specific focus on the spatial and temporal analysis of epidemiological data using visualization tools. This study aims to address this gap by conducting a scoping review following the PRISMA-ScR guidelines, examining the literature from 2000 to 2024 on spatial–temporal visualization techniques when applied to epidemics, across five databases: PubMed, IEEE Xplore, Scopus, Google Scholar, and ACM Digital Library until 24 January 2024. Among 1312 papers reviewed, 114 were selected, emphasizing aggregate measures, web platform tools, and geospatial data representation, particularly favoring choropleth maps and extended charts. Visualization techniques were predominantly utilized for real-time data presentation, trend analysis, and predictions. Evaluation methods, categorized into standard methodology, user experience, task efficiency, and accuracy, were observed. Although various open-access datasets were available, only a few were commonly used, mainly those related to COVID-19. This study sheds light on the current trends in visualizing epidemiological data over the past 24 years, highlighting the gaps in standardized evaluation methodologies and the limited exploration of individual epidemiological data and diseases acquired in hospitals during epidemics.
Measurement of Light-Duty Vehicle Exhaust Emissions with Light Absorption Spectrometers
Authors: Giechaskiel Barouch , Melas Anastasios , Franzetti Jacopo , Valverde Victor , Clairotte Michaël , Suarez-Bertoa Ricardo
Published: February 2024

Light-duty vehicle emission regulations worldwide set limits for the following gaseous pollutants: carbon monoxide (CO), nitric oxides (NOX), hydrocarbons (HCs), and/or non-methane hydrocarbons (NMHCs). Carbon dioxide (CO2) is indirectly limited by fleet CO2 or fuel consumption targets. Measurements are carried out at the dilution tunnel with “standard” laboratory-grade instruments following well-defined principles of operation: non-dispersive infrared (NDIR) analyzers for CO and CO2, flame ionization detectors (FIDs) for hydrocarbons, and chemiluminescence analyzers (CLAs) or non-dispersive ultraviolet detectors (NDUVs) for NOX. In the United States in 2012 and in China in 2020, with Stage 6, nitrous oxide (N2O) was also included. Brazil is phasing in NH3 in its regulation. Alternative instruments that can measure some or all these pollutants include Fourier transform infrared (FTIR)- and laser absorption spectroscopy (LAS)-based instruments. In the second category, quantum cascade laser (QCL) spectroscopy in the mid-infrared area or laser diode spectroscopy (LDS) in the near-infrared area, such as tunable diode laser absorption spectroscopy (TDLAS), are included. According to current regulations and technical specifications, NH3 is the only component that has to be measured at the tailpipe to avoid ammonia losses due to its hydrophilic properties and adsorption on the transfer lines. There are not many studies that have evaluated such instruments, in particular those for “non-regulated” worldwide pollutants. For this reason, we compared laboratory-grade “standard” analyzers with FTIR- and TDLAS-based instruments measuring NH3. One diesel and two gasoline vehicles at different ambient temperatures and with different test cycles produced emissions in a wide range. In general, the agreement among the instruments was very good (in most cases, within ±10%), confirming their suitability for the measurement of pollutants.
The Effects of Ambient Temperature and Atmospheric Humidity on the Diffusion Dynamics of Hydrogen Fluoride Gas Leakage Based on the Computational Fluid Dynamics Method
Authors: Zhou Zhengqing , Liu Yuzhe , Jiang Huiling , Bai Zhiming , Sun Lingxia , Liu Jia , Zhao Wenwen
Published: February 2024

In order to investigate the impact of environmental temperature and atmospheric humidity on the leakage and diffusion of hydrogen fluoride (HF) gas, this study focused on the real scenario of an HF chemical industrial park. Based on the actual dispersion scenario of HF gas, a proportionally scaled-down experimental platform for HF gas leakage was established to validate the accuracy and feasibility of numerical simulations under complex conditions. Using the validated model, the study calculated the complex scenarios of HF leakage and diffusion within the temperature range of 293 K to 313 K and the humidity range of 0% to 100%. The simulation results indicated that different environmental temperatures had a relatively small impact on the hazardous areas (the lethal area, severe injury area, light injury area, and maximum allowable concentration (MAC) area) formed by HF gas leakage. At 600 s of dispersion, the fluctuation range of hazardous area sizes under different temperature conditions was between 3.11% and 13.07%. In contrast to environmental temperature, atmospheric relative humidity had a more significant impact on the dispersion trend of HF leakage. Different relative humidity levels mainly affected the areas of the lethal zone, light injury zone, and MAC zone. When HF continued to leak and disperse for 600 s, compared to 0% relative humidity, 100% relative humidity reduced the lethal area by 35.7%, while increasing the light injury area and MAC area by 27.26% and 111.6%, respectively. The impact on the severe injury area was relatively small, decreasing by 1.68%. The results of this study are crucial for understanding the dispersion patterns of HF gas under different temperature and humidity conditions.
Chiral Analysis of Pesticides and Emerging Contaminants by Capillary Electrophoresis—Application to Toxicity Evaluation
Authors: García-Cansino Laura , Marina María Luisa , García María Ángeles
Published: February 2024

Chiral analysis is a very relevant topic in environmental chemistry. This is due to the different properties of the stereoisomers of chiral compounds. In the case of agrochemicals, the desired activity, degradation rate, or toxicity, among other characteristics, may differ between stereoisomers, and the same is true for emerging contaminants, such as pharmaceuticals and cosmetics. Therefore, the development of chiral analytical methodologies enabling their determination in samples of environmental interest is paramount. Although other techniques have been widely employed to carry out chiral separations, such as HPLC, GC, and SFC, capillary electrophoresis (CE) has attracted a lot of attention in the field of chiral analysis due to its simplicity, flexibility, and low cost. In fact, chromatographic columns are not needed, and the consumption of reagents and samples is very low due to the small dimensions of the separation capillaries. This article reviews the characteristics of the chiral methodologies developed by CE for the stereoselective analysis of pesticides and emerging contaminants in environmental samples (water and soil), as well as pesticides in food samples and commercial agrochemical formulations. Applications of the developed CE methodologies in stability and toxicity studies of these chiral contaminants are also reviewed.
Assessing Benzene and TVOC Pollution and the Carcinogenic and Noncarcinogenic Risks to Workers in an Industrial Plant in Southeastern Romania
Authors: Barbeş Sebastian-Barbu , Bărbulescu Alina , Barbeș Lucica
Published: February 2024

The article aims to analyze the pollution with Volatile Organic Compounds (VOC) emitted from the biggest refinery in Romania, using the daily and monthly series registered for two years in two sites on the industrial platform, and the carcinogenic and noncarcinogenic risks for workers at the industrial plant. Since the values of the basic statistics (minimum, maximum, and average) and outliers indicate that most recorded values exceed the maximum admissible limits established by law, the Peaks Over Threshold (POT) method was utilized to model the maximum values of the series and determine the return levels for benzene and total VOC (TVOC). Given the high values obtained for relatively short return periods, indicating potential danger for the workers, we assessed the noncarcinogenic and carcinogenic risks to benzene and TVOC exposure by computing the hazard index (HI) and lifetime cancer risk (LCR). The results indicate that 43.75% of the HI values are above 1, indicating a relatively high noncarcinogenic risk for different categories of workers. LRC indicates a high LRC for 93.75% of the workers in all considered categories exposed to TVOC.
Microarray and Functional Pathway Analyses Revealed Significantly Elevated Gene Expressions Associated with Metabolic Resistance to Oxamyl (Vydate) in Lygus lineolaris
Authors: Zhu Yu-Cheng , Du Yuzhe , Liu Xiaofen , Portilla Maribel , Chen Jian , Wang Yanhua
Published: February 2024

The tarnished plant bug (TPB, Lygus lineolaris) remains a major pest for a variety of crops. Frequent sprays on row crops, especially cotton, prompted resistance development in field populations. To maintain chemical control as an effective tool against the pest, knowledge of global gene regulations is desirable for better understanding and managing the resistance. Novel microarray expressions of 6688 genes showed 685 significantly upregulated and 1382 significantly downregulated genes in oxamyl-selected TPBs (Vyd1515FF[R]) from a cotton field. Among the 685 upregulated genes (participated in 470 pathways), 176 genes code 30 different enzymes, and 7 of the 30 participate in 24 metabolic pathways. Six important detoxification pathways were controlled by 20 genes, coding 11 esterases, two P450s, two oxidases, and three pathway-associated enzymes (synthases, reductase, and dehydrogenase). Functional analyses showed substantially enhanced biological processes and molecular functions, with hydrolase activity as the most upregulated molecular function (controlled by 166 genes). Eleven esterases belong to the acting on ester bond subclass of the 166 hydrolases. Surprisingly, only one GST showed significant upregulation, but it was not involved in any detoxification pathway. Therefore, this research reports a set of 20 genes coding 6 enzyme classes to detoxify a carbamate insecticide oxamyl in Vyd1515FF. Together with three previous reports, we have obtained the best knowledge of resistance mechanisms to all four conventional insecticide classes in the economically important crop pest. This valuable finding will greatly facilitate the development of molecular tools to monitor and manage the resistance and to minimize risk to environment.
The “Drake Equation” of Exomoons—A Cascade of Formation, Stability and Detection
Authors: Szabó Gyula M. , Schneider Jean , Dencs Zoltán , Kálmán Szilárd
Published: February 2024

After 25 years of the prediction of the possibility of observations, and despite the many hundreds of well-studied transiting exoplanet systems, we are still waiting for the announcement of the first confirmed exomoon. We follow the “cascade” structure of the Drake equation but apply it to the chain of events leading to a successful detection of an exomoon. The scope of this paper is to reveal the structure of the problem, rather than to give a quantitative solution. We identify three important steps that can lead us to discovery. The steps are the formation, the orbital dynamics and long-term stability, and the observability of a given exomoon in a given system. This way, the question will be closely related to questions of star formation, planet formation, five possible pathways of moon formation; long-term dynamics of evolved planet systems involving stellar and planetary rotation and internal structure; and the proper evaluation of the observed data, taking the correlated noise of stellar and instrumental origin and the sampling function also into account. We highlight how a successful exomoon observation and the interpretations of the expected further measurements prove to be among the most complex and interdisciplinary questions in astrophysics.
Current Understanding of the Immune Response after COVID-19 Vaccination
Authors: Ning Shunbin , Firinu Davide
Published: February 2024

The global vaccination campaign against SARS-CoV-2, the virus responsible for COVID-19, has been a monumental endeavor, marked by unprecedented collaboration between scientific researchers and pharmaceutical companies [...]
Potential Intersections between lncRNA, Vascular Cognitive Impairment, and Immunization Strategies: Insights and Future Directions
Authors: Fan Yishu , Xiao Bo , Zhang Mengqi
Published: February 2024

Vascular cognitive impairment (VCI) encompasses a wide range of cognitive disorders stemming from cerebrovascular issues, such as strokes or small vessel disease. These conditions often pose challenges to traditional diagnostic approaches due to their multifactorial nature and varied clinical presentations. Recently, next-generation sequencing (NGS) technologies have provided detailed analyses of long non-coding RNAs (lncRNAs) in the molecular pathobiology of VCI. These new findings help with molecular-based diagnostics and treatments of VCI. Within this realm, the concept of immune modulation, especially through specific vaccinations, emerges as a promising therapeutic strategy in VCI mitigation. In this review, we comprehensively elucidate the function of lncRNAs in VCI, emphasizing the advanced understanding of VCI’s molecular underpinnings made possible through NGS technologies. Significant focus is placed on the immune system’s role in VCI, particularly the neuroinflammatory processes which are consequential to cerebrovascular abnormalities. We believe that lncRNAs participate in regulating these immunological pathways, potentially guiding the development of vaccines targeting VCI. In this context, we propose a novel perspective: using knowledge about lncRNA profiles and functions to guide vaccine development, we can potentially exploit the body’s immune response to mitigate or prevent VCI. This approach has the potential to revolutionize VCI management by introducing targeted immunization strategies informed by molecular signatures, a concept that remains largely unexplored in current research endeavors. In addition, we summarize current progress and propose future directions, advocating for robust, interdisciplinary studies to validate the potential intersections between lncRNA landscapes, VCI pathology, and immunology. This review aims to spur innovative research and promote the development of lncRNA-informed vaccine strategies as proactive interventions against the cognitive consequences of VCI.
Distributed Intelligent Vehicle Path Tracking and Stability Cooperative Control
Authors: Deng Zhaoxue , Zhang Yangrui , Zhao Shuen
Published: February 2024

To enhance the path tracking capability and driving stability of intelligent vehicles, a controller is designed that synergizes active front wheel steering (AFS) and direct yaw moment (DYC), specifically tailored for distributed-drive electric vehicles. To address the challenge of determining the weight matrix in the linear quadratic regulator (LQR) algorithm during the path tracking design for intelligent vehicles on conventional roads, a genetic algorithm (GA)-optimized LQR path tracking controller is introduced. The 2-degree-of-freedom vehicle dynamics error model and the desired path information are established. The genetic algorithm optimization strategy, utilizing the vehicle’s lateral error, heading error, and output front wheel steering angle as the objective functions, is employed to optimally determine the weight matrices Q and R. Subsequently, the optimal front wheel steering angle control (AFS) output of the vehicle is calculated. Under extreme operating conditions, to enhance vehicle dynamics stability, while ensuring effective path tracking, the active yaw moment is crafted using the sliding mode control with a hyperbolic tangent convergence law function. The control weights of the sliding mode surface related to the center-of-mass lateral declination are adjusted based on the theory of the center-of-mass lateral declination phase diagram, and the vehicle’s target yaw moment is calculated. Validation is conducted through Matlab/Simulink and Carsim co-simulation. The results demonstrate that the genetic algorithm-optimized LQR path tracking controller enhances vehicle tracking accuracy and exhibits improved robustness under conventional road conditions. In extreme working conditions, the designed path tracking and stability cooperative controller (AFS+DYC) is implemented to enhance the vehicle’s path tracking effect, while ensuring its driving stability.
Constitucionalismo contemporáneo vigencia del Estado social de derecho en Colombia
Authors: Muñoz Martha Oliva
Published: February 2024

El presente trabajo busca generar en el lector la inquietud acerca de la relevancia,  actualidad y necesidad material respecto a políticas públicas que profundicen y hagan realidad el Estado social de derecho, establecido por el constituyente en la Constitución de 1991 en Colombia.  A pesar de los treinta años de su promulgación y frente a los aún no resueltos problemas de inequidad y de amplios cinturones de pobreza que viven algunos sectores del país (situación que impacta en la convivencia y equilibrio social), se hace necesario establecer políticas públicas que afiancen los postulados constitucionales de convivencia pacífica, de prosperidad general, entre otros; es necesario establecerlas hoy más que nunca, toda vez que la crisis sobreviniente, generada por el covid-19, ha llevado el país a unos niveles inusitados de desempleo y pobreza. Se considera que las directrices que emanan del Estado social de derecho, adoptado por la Constitución de 1991, son una buena herramienta para combatir tanto los problemas propios de la crisis como los históricos flagelos que todavía experimenta la nación y lo ponen al borde del inicio de otra crisis institucional.
Experience of Clinical Adaptation among Nurses in Intensive Care Unit
Authors: Hong Jin Young , Sohn Sue Kyung
Published: February 2024

Purpose : This study aimed to explore and describe intensive care unit (ICU) nurses’ experience of clinical adaptation.Methods : The participants were 14 ICU nurses with more than two years of working experience in the ICU. Data were collected through in-depth individual interviews conducted between July and October 2021. Theoretical sampling was used to the point of theoretical saturation. Data were analyzed using the Strauss and Corbin method.Results : A total of 79 concepts, 37 subcategories, and 16 categories were identified through open coding. Axial coding based on the paradigm model revealed that the central phenomenon was “The harsh adversity faced in the nursing field where life and death are determined” and the core category was “Enduring the adversity of caring for critically ill patients and achieving self-realization.” ICU nurses’ clinical adaptation process was explained in five phases: “confrontation period,” “turbulent period,” “seeking period,” “struggling period,” and “stabilized period.” The five phases that affect interventional conditions were “Support from reliable people,” “Recognition of administrative and financial support.”Conclusion : This study provided novel insights for a comprehensive understanding of ICU nurses’ clinical adaptation processes. Furthermore, the findings are expected to be used as basic data to develop multifaceted strategies to help ICU nurses’ adaptation to critical care.
Effects of Intensive Care Experience on Post-Intensive Care Syndrome among Critical Care Survivors : Partial Least Square-Structural Equation Modeling Approach
Authors: Cho Young Shin , Kang Jiyeon
Published: February 2024

Purpose : Post-intensive care syndrome (PICS) is characterized by a constellation of mental health, physical, and cognitive impairments, and is recognized as a long-term sequela among survivors of intensive care units (ICUs). The objective of this study was to explore the impact of intensive care experience (ICE) on the development of PICS in individuals surviving critical care.Methods : This secondary analysis utilized data derived from a prospective, multicenter cohort study of ICU survivors. The cohort comprised 143 survivors who were enrolled between July and August 2019. The original study's participants completed the Korean version of the ICE questionnaire (K-ICEQ) within one week following discharge from the ICU. Of these, 82 individuals completed the PICS questionnaire (PICSQ) three months subsequent to discharge from hospital. The influence of ICE on the manifestation of PICS was examined through Partial Least Squares-Structural Equation Modeling (PLS-SEM).Results : The R2 values of the final model ranged from 0.35 to 0.51, while the Q2 values were all greater than 0, indicating adequacy for prediction of PICS. Notable pathways in the relationship between the four ICE dimensions and the three PICS domains included significant associations from ‘ICE-awareness of surroundings’ to ‘PICS-cognitive’, from ‘ICE-recall of experience’ to ‘PICS-cognitive’, and from ‘ICE-frightening experiences’ to ‘PICS-mental health’. Analysis found no significant moderating effects of age or disease severity on these relationships. Additionally, gender differences were identified in the significant pathways within the model.Conclusion : Adverse ICU experiences may detrimentally impact the cognitive and mental health domains of PICS following discharge. In order to improve long-term outcomes of individuals who survive critical care, it is imperative to develop nursing interventions aimed at enhancing the ICU experience for patients.
Relationship between the Perceptions of ICU Nurses on the Disclosure of Patient Safety Incidents and Communication Barriers
Authors: Cho In Sun , Choi Su Jung
Published: February 2024

Purpose : This study sought to explore intensive care unit (ICU) nurses’ perceptions regarding the disclosure of patient safety incidents (DPSI) and identify the relationship between the perception of DPSI and communication barriers.Methods : This study used a descriptive research design. A total of 110 ICU nurses from a tertiary hospital were surveyed online between September 14 and October 5, 2022. The mean DPSI score ranged between 1.0 and 4.0, with a higher score indicating a higher perception of DPSI.Results : The mean score for ICU nurses’ perceptions of DPSI was 2.92 (SD=0.37). Among the characteristics of ICU nurses, differences were observed in perceptions of DPSI according to gender, age, total work experience, and ICU work experience. Communication barriers among ICU nurses were negatively correlated with negative results as a sub-factor of perceptions of DPSI. Ambiguity in the nurse’s position, lack of confidence, differences in perspectives with patients, and inadequate nurse-patient relationships as sub-factors of communication barriers exhibited a negative correlation with negative results as a sub-factor of perceptions of DPSI.Conclusions : ICU nurses’ perceptions of DPSI and the sub-factors related to communication barriers are negatively related to DPSI. To improve ICU nurses’ perceptions, open and non-punitive circumstances, staff education, practical guidelines, and support systems are required.
Factors influencing the clinical competence of nursing students
Authors: Kang Kino , Im Mihae
Published: February 2024

Purpose : The purpose of this study was to identify factors influencing the clinical competence of nursing students.Methods : The participants of this study were 209 nursing students who were in 3rd and 4th grade. The collected data were analyzed using one-way ANOVA, Kruskal-Wallis test, Mann-Whitney test, Pearson’s correlation coefficients, and stepwise multiple regression.Results : Clinical competence had positive relationships with self-leadership (r=.59, p ＜.001) and resilience (r=.64, p ＜.001). Clinical competence was influenced by resilience (β=.45), self-leadership (β=.20), and grade (β=.20), with an explanatory power of 44.9%.Conclusions : Based on these results, it is suggested that future nursing education should develop a curriculum that enhances students’ personal abilities such as self-leadership and resilience, rather than only teaching knowledge and skills.
Doğaltaş Ocaklarında Kullanılan Ayırma/Gevşetme Yöntemlerinin AHP ile Karşılaştırılması
Authors: AHMADI Mohammad Tamım , KARAABAT VAROL Merve , DEMİRDAĞ Servet
Published: February 2024

Türkiye’ de doğal taş üretimi ihracat açısından önemlidir. Bu nedenle doğal taş ocaklarında üretilen blokların ve bloklardan elde edilen plaka/levhaların ekonomik değere sahip boyut ve kalitede olmasına dikkat edilmektedir. Blok üretimi sırasında uygulanan işlemler, hazırlık, kesme/ayırma, sayalama, taşıma ve stoklama işlemlerinden oluşmaktadır. Bu çalışmada ekonomik değer taşıyan bloklara ulaşmak için yaygın olarak kullanılan ayırma/gevşetme yöntemlerinden hidrolik kırıcılar, piroteknik kaya kırıcılar, genleşme harçları ve geleneksel patlayıcılar incelenerek en uygun yöntem tanımlanmaya çalışılmıştır. Bu yöntemler; fiyat, zaman, atık malzeme, ticari blok kalitesi, çevresel etkiler ve güvenlik kriterleri açısından Çok Kriterli Karar Verme Yöntemlerinden biri olan AHP yöntemi ile karşılaştırılmıştır. Bu karşılaştırma sonucunda piroteknik kaya kırıcılar ile ayırma/gevşetme yönteminin en uygun yöntem olduğu sonucuna varılmıştır