bluuebunny/crossref_metadata_2025 huggingface dataset:
Curve Similarity Analysis for Reducing the Temperature Uncertainty of Optical Sensor for Oil-Tank Ground Settlement Monitoring
Authors: Liu Tao , Liu Gang , Jiang Tao , Li Hong , Sun Changsen
Published: October 2023

A nonuniform temperature field can deteriorate the performance of sensors, especially those working in the field, such as an optical sensor for oil-tank ground settlement (GS) monitoring. In this case, the GS monitoring employs hydraulic-level-based sensors (HLBS), which are uniformly installed along with the oil-tank basement perimeter and are all connected by hydraulic tubes. Then, the cylinder structure of the oil tank itself can create a strong temperature difference between the sensors installed in the sunlit front and those in the shadow. Practically, this sunlight-dependent difference can be over 30 °C, by which the thermal expansion of the measuring liquid inside the connecting hydraulic tubes keeps on driving a movement and, thereby, leads to fluctuations in the final result of the oil-tank GS monitoring system. Now, this system can work well at night when the temperature difference becomes negligible. However, temperature uncertainty is generated in the GS sensors due to the large temperature difference between the sensors in the daytime. In this paper, we measured the temperature where the sensor was located. Then, we compared the results of the GS sensors with their corresponding temperatures and fitted them with two separate curves, respectively. After observing the similarity in the tendency of the two curves, we found that there was a qualitative correlative relationship between the change in temperature and the uncertainty in the sensor results. Then, a curve similarity analysis (CSA) principle based on the minimum mean square error (MMSE) criteria was employed to establish an algorithm, by which the temperature uncertainty in the GS sensors was reduced. A practical test proved that the standard deviation was improved by 73.4% by the algorithm. This work could be an example for reducing the temperature uncertainty from in-field sensors through the CSA method.
Drivers’ Comprehensive Emotion Recognition Based on HAM
Authors: Zhou Dongmei , Cheng Yongjian , Wen Luhan , Luo Hao , Liu Ying
Published: October 2023

Negative emotions of drivers may lead to some dangerous driving behaviors, which in turn lead to serious traffic accidents. However, most of the current studies on driver emotions use a single modality, such as EEG, eye trackers, and driving data. In complex situations, a single modality may not be able to fully consider a driver’s complete emotional characteristics and provides poor robustness. In recent years, some studies have used multimodal thinking to monitor single emotions such as driver fatigue and anger, but in actual driving environments, negative emotions such as sadness, anger, fear, and fatigue all have a significant impact on driving safety. However, there are very few research cases using multimodal data to accurately predict drivers’ comprehensive emotions. Therefore, based on the multi-modal idea, this paper aims to improve drivers’ comprehensive emotion recognition. By combining the three modalities of a driver’s voice, facial image, and video sequence, the six classification tasks of drivers’ emotions are performed as follows: sadness, anger, fear, fatigue, happiness, and emotional neutrality. In order to accurately identify drivers’ negative emotions to improve driving safety, this paper proposes a multi-modal fusion framework based on the CNN + Bi-LSTM + HAM to identify driver emotions. The framework fuses feature vectors of driver audio, facial expressions, and video sequences for comprehensive driver emotion recognition. Experiments have proved the effectiveness of the multi-modal data proposed in this paper for driver emotion recognition, and its recognition accuracy has reached 85.52%. At the same time, the validity of this method is verified by comparing experiments and evaluation indicators such as accuracy and F1 score.
Predicting a Fall Based on Gait Anomaly Detection: A Comparative Study of Wrist-Worn Three-Axis and Mobile Phone-Based Accelerometer Sensors
Authors: Kocuvan Primož , Hrastič Aleksander , Kareska Andrea , Gams Matjaž
Published: October 2023

Falls by the elderly pose considerable health hazards, leading not only to physical harm but a number of other related problems. A timely alert about a deteriorating gait, as an indication of an impending fall, can assist in fall prevention. In this investigation, a comprehensive comparative analysis was conducted between a commercially available mobile phone system and two wristband systems: one commercially available and another representing a novel approach. Each system was equipped with a singular three-axis accelerometer. The walk suggestive of a potential fall was induced by special glasses worn by the participants. The same standard machine-learning techniques were employed for the classification with all three systems based on a single three-axis accelerometer, yielding a best average accuracy of 86%, a specificity of 88%, and a sensitivity of 86% via the support vector machine (SVM) method using a wristband. A smartphone, on the other hand, achieved a best average accuracy of 73% also with an SVM using only a three-axis accelerometer sensor. The significance analysis of the mean accuracy, sensitivity, and specificity between the innovative wristband and the smartphone yielded a p-value of 0.000. Furthermore, the study applied unsupervised and semi-supervised learning methods, incorporating principal component analysis and t-distributed stochastic neighbor embedding. To sum up, both wristbands demonstrated the usability of wearable sensors in the early detection and mitigation of falls in the elderly, outperforming the smartphone.
Unsupervised Mixture Models on the Edge for Smart Energy Consumption Segmentation with Feature Saliency
Authors: Al-Bazzaz Hussein , Azam Muhammad , Amayri Manar , Bouguila Nizar
Published: October 2023

Smart meter datasets have recently transitioned from monthly intervals to one-second granularity, yielding invaluable insights for diverse metering functions. Clustering analysis, a fundamental data mining technique, is extensively applied to discern unique energy consumption patterns. However, the advent of high-resolution smart meter data brings forth formidable challenges, including non-Gaussian data distributions, unknown cluster counts, and varying feature importance within high-dimensional spaces. This article introduces an innovative learning framework integrating the expectation-maximization algorithm with the minimum message length criterion. This unified approach enables concurrent feature and model selection, finely tuned for the proposed bounded asymmetric generalized Gaussian mixture model with feature saliency. Our experiments aim to replicate an efficient smart meter data analysis scenario by incorporating three distinct feature extraction methods. We rigorously validate the clustering efficacy of our proposed algorithm against several state-of-the-art approaches, employing diverse performance metrics across synthetic and real smart meter datasets. The clusters that we identify effectively highlight variations in residential energy consumption, furnishing utility companies with actionable insights for targeted demand reduction efforts. Moreover, we demonstrate our method’s robustness and real-world applicability by harnessing Concordia’s High-Performance Computing infrastructure. This facilitates efficient energy pattern characterization, particularly within smart meter environments involving edge cloud computing. Finally, we emphasize that our proposed mixture model outperforms three other models in this paper’s comparative study. We achieve superior performance compared to the non-bounded variant of the proposed mixture model by an average percentage improvement of 7.828%.
A Comprehensive Review of Digital Twin from the Perspective of Total Process: Data, Models, Networks and Applications
Authors: Wu Honghai , Ji Pengwei , Ma Huahong , Xing Ling
Published: October 2023

With the rapid development of industrial digitalization and intelligence, there is an urgent need to accurately depict the physical world in digital space, and, in turn, regulate and optimize the behavior of physical entities based on massive data collection and analysis. As a technology that combines virtual space and physical space, digital twin can satisfy all of the above needs, and has attracted widespread attention. Due to the promising application prospects of digital twins, both academia and industry have launched research in this field, and related studies have been conducted from different perspectives. Accordingly, some articles summarizing the existing work have also been published, but they are all from a single perspective, lacking a systematic introduction and summary. Based on this, this paper conducts a comprehensive review of the existing work on digital twins from four perspectives: data, model, network and application, and strives to gain a better understanding of the development of the field from the physical to the virtual and back to the physical. Meanwhile, current research challenges and future directions for the development of digital twins are all discussed.
Recent Progress in Gas Sensors Based on P3HT Polymer Field-Effect Transistors
Authors: Cheng Si , Wang Yifan , Zhang Ruishi , Wang Hongjiao , Sun Chenfang , Wang Tie
Published: October 2023

In recent decades, the rapid development of the global economy has led to a substantial increase in energy consumption, subsequently resulting in the emission of a significant quantity of toxic gases into the environment. So far, gas sensors based on polymer field-effect transistors (PFETs), a highly practical and cost-efficient strategy, have garnered considerable attention, primarily attributed to their inherent advantages of offering a plethora of material choices, robust flexibility, and cost-effectiveness. Notably, the development of functional organic semiconductors (OSCs), such as poly(3-hexylthiophene-2,5-diyl) (P3HT), has been the subject of extensive scholarly investigation in recent years due to its widespread availability and remarkable sensing characteristics. This paper provides an exhaustive overview encompassing the production, functionalization strategies, and practical applications of gas sensors incorporating P3HT as the OSC layer. The exceptional sensing attributes and wide-ranging utility of P3HT position it as a promising candidate for improving PFET-based gas sensors.
Sound Field Reconstruction Using Prolate Spheroidal Wave Functions and Sparse Regularization
Authors: Zhang Xuxin , Lou Jingjun , Zhu Shijian , Lu Jinfang , Li Ronghua
Published: October 2023

Near-field acoustic holography (NAH) based on compressing sensing (CS) theory enables accurate reconstruction of sound fields using a limited number of sampling points. However, the successful implementation of this technique depends on two crucial factors: (1) the appropriate selection or construction of the spatial basis and (2) an effective sparse regularization process. To enhance reconstruction performance for elongated sound sources, this paper proposes a novel sound field reconstruction method that combines prolate spheroidal wave functions (PSWFs) with the orthogonal matching pursuit (OMP) algorithm. In this method, PSWFs serve as a sparse spatial basis for representing the radiated sound field. The sparse coefficients are determined by the OMP algorithm in a linear subspace composed of basic functions that best match the residual error. The OMP algorithm effectively identifies significant components before potentially selecting incorrect ones by setting an appropriate stopping rule. Numerical simulations are conducted using a line-array source model. The results show that the proposed method can accurately reconstruct the sound pressures of the elongated source model using a relatively small number of samplings. In addition, the proposed method exhibits robustness across a wide frequency range, diverse array configurations and various sampling numbers. The experimental results further validate the feasibility and reliability of the proposed method.
Perspectives in Wearable Systems in the Human–Robot Interaction (HRI) Field
Authors: Liu Tao , Liu Xiangzhi
Published: October 2023

Due to the advantages of ease of use, less motion disturbance, and low cost, wearable systems have been widely used in the human–machine interaction (HRI) field. However, HRI in complex clinical rehabilitation scenarios has further requirements for wearable sensor systems, which has aroused the interest of many researchers. However, the traditional wearable system has problems such as low integration, limited types of measurement data, and low accuracy, causing a gap with the actual needs of HRI. This paper will introduce the latest progress in the current wearable systems of HRI from four aspects. First of all, it introduces the breakthroughs of current research in system integration, which includes processing chips and flexible sensing modules to reduce the system’s volume and increase battery life. After that, this paper reviews the latest progress of wearable systems in electrochemical measurement, which can extract single or multiple biomarkers from biological fluids such as sweat. In addition, the clinical application of non-invasive wearable systems is introduced, which solves the pain and discomfort problems caused by traditional clinical invasive measurement equipment. Finally, progress in the combination of current wearable systems and the latest machine-learning methods is shown, where higher accuracy and indirect acquisition of data that cannot be directly measured is achieved. From the evidence presented, we believe that the development trend of wearable systems in HRI is heading towards high integration, multi-electrochemical measurement data, and clinical and intelligent development.
Enhancing the Longevity and Functionality of Ti-Ag Dry Electrodes for Remote Biomedical Applications: A Comprehensive Study
Authors: Carvalho Daniel , Marques Sandra , Siqueira Giorgia , Ferreira Armando , Santos João , Geraldo Dulce , Castro Cidália R. , Machado Ana V. , Vaz Filipe , Lopes Cláudia
Published: October 2023

This study aims to evaluate the lifespan of Ti-Ag dry electrodes prepared using flexible polytetrafluoroethylene (PTFE) substrates. Following previous studies, the electrodes were designed to be integrated into wearables for remote electromyography (EMG) monitoring and electrical stimulation (FES) therapy. Four types of Ti-Ag electrodes were prepared by DC magnetron sputtering, using a pure-Ti target doped with a growing number of Ag pellets. After extensive characterization of their chemical composition and (micro)structural evolution, the Ti-Ag electrodes were immersed in an artificial sweat solution (standard ISO-3160-2) at 37 °C with constant stirring. Results revealed that all the Ti-Ag electrodes maintained their integrity and functionality for 24 h. Although there was a notable increase in electrical resistivity beyond this timeframe, the acquisition and transmission of (bio)signals remained viable for electrodes with Ag/Ti ratios below 0.23. However, electrodes with higher Ag content (Ag/Ti = 0.31) became insulators after 7 days of immersion due to excessive Ag release into the sweat solution. This study concludes that higher Ag/Ti atomic ratios result in heightened corrosion processes on the electrode’s surface, consequently diminishing their lifespan despite the advantages of incorporating Ag into their composition. This research highlights the critical importance of evaluating electrode longevity, especially in remote biomedical applications like smart wearables, where electrode performance over time is crucial for reliable and sustained monitoring and stimulation.
ECG Synthesis via Diffusion-Based State Space Augmented Transformer
Authors: Zama Md Haider , Schwenker Friedhelm
Published: October 2023

Cardiovascular diseases (CVDs) are a major global health concern, causing significant morbidity and mortality. AI’s integration with healthcare offers promising solutions, with data-driven techniques, including ECG analysis, emerging as powerful tools. However, privacy concerns pose a major barrier to distributing healthcare data for addressing data-driven CVD classification. To address confidentiality issues related to sensitive health data distribution, we propose leveraging artificially synthesized data generation. Our contribution introduces a novel diffusion-based model coupled with a State Space Augmented Transformer. This synthesizes conditional 12-lead electrocardiograms based on the 12 multilabeled heart rhythm classes of the PTB-XL dataset, with each lead depicting the heart’s electrical activity from different viewpoints. Recent advances establish diffusion models as groundbreaking generative tools, while the State Space Augmented Transformer captures long-term dependencies in time series data. The quality of generated samples was assessed using metrics like Dynamic Time Warping (DTW) and Maximum Mean Discrepancy (MMD). To evaluate authenticity, we assessed the similarity of performance of a pre-trained classifier on both generated and real ECG samples.
Sign Language Recognition Using the Electromyographic Signal: A Systematic Literature Review
Authors: Ben Haj Amor Amina , El Ghoul Oussama , Jemni Mohamed
Published: October 2023

The analysis and recognition of sign languages are currently active fields of research focused on sign recognition. Various approaches differ in terms of analysis methods and the devices used for sign acquisition. Traditional methods rely on video analysis or spatial positioning data calculated using motion capture tools. In contrast to these conventional recognition and classification approaches, electromyogram (EMG) signals, which measure muscle electrical activity, offer potential technology for detecting gestures. These EMG-based approaches have recently gained attention due to their advantages. This prompted us to conduct a comprehensive study on the methods, approaches, and projects utilizing EMG sensors for sign language handshape recognition. In this paper, we provided an overview of the sign language recognition field through a literature review, with the objective of offering an in-depth review of the most significant techniques. These techniques were categorized in this article based on their respective methodologies. The survey discussed the progress and challenges in sign language recognition systems based on surface electromyography (sEMG) signals. These systems have shown promise but face issues like sEMG data variability and sensor placement. Multiple sensors enhance reliability and accuracy. Machine learning, including deep learning, is used to address these challenges. Common classifiers in sEMG-based sign language recognition include SVM, ANN, CNN, KNN, HMM, and LSTM. While SVM and ANN are widely used, random forest and KNN have shown better performance in some cases. A multilayer perceptron neural network achieved perfect accuracy in one study. CNN, often paired with LSTM, ranks as the third most popular classifier and can achieve exceptional accuracy, reaching up to 99.6% when utilizing both EMG and IMU data. LSTM is highly regarded for handling sequential dependencies in EMG signals, making it a critical component of sign language recognition systems. In summary, the survey highlights the prevalence of SVM and ANN classifiers but also suggests the effectiveness of alternative classifiers like random forests and KNNs. LSTM emerges as the most suitable algorithm for capturing sequential dependencies and improving gesture recognition in EMG-based sign language recognition systems.
Application of the Fourier Transform to Improve the Accuracy of Gamma-Based Volume Percentage Detection System Independent of Scale Thickness
Authors: Mayet Abdulilah Mohammad , Guerrero John William Grimaldo , Ijyas Thafasal , Bhutto Javed Khan , Shukla Neeraj Kumar , Eftekhari-Zadeh Ehsan , Alhashim Hala H.
Published: October 2023

With the passage of time, scale gradually forms inside the oil pipeline. The produced scale, which has a high density, strongly attenuates photons, which lowers the measurement accuracy of three-phase flow meters based on gamma radiation. It is worth mentioning that the need for multiphase flow metering arises when it is necessary or desirable to meter well stream(s) upstream of inlet separation and/or commingling. In this investigation, a novel technique based on artificial intelligence is presented to overcome the issue mentioned earlier. Initially, a detection system was comprised of two NaI detectors and a dual-energy gamma source (241 Am and 133 Ba radioisotopes) using Monte Carlo N particle (MCNP) code. A stratified flow regime with varying volume percentages of oil, water, and gas was modeled inside a pipe that included a scale layer with varying thicknesses. Two detectors record the attenuated photons that could travel through the pipe. Four characteristics with the names of the amplitude of the first and second dominant signal frequencies were extracted from the received signals by both detectors. The aforementioned obtained characteristics were used to train two Radial Basis Function (RBF) neural networks to forecast the volumetric percentages of each component. The RMSE value of the gas and oil prediction neural networks are equal to 0.27 and 0.29, respectively. By measuring two phases of fluids in the pipe, the volume of the third phase can be calculated by subtracting the volume of two phases from the total volume of the pipe. Extraction and introduction of suitable characteristics to determine the volume percentages, reducing the computational burden of the detection system, considering the scale value thickness the pipe, and increasing the accuracy in determining the volume percentages of oil pipes are some of the advantages of the current research, which has increased the usability of the proposed system as a reliable measuring system in the oil and petrochemical industry.
Blood Flow Restriction Training in Nonspecific Shoulder Pain: Study Protocol of a Crossover Randomised Controlled Trial
Authors: Pavlou Kyriakos , Savva Christos , Korakakis Vasileios , Pamboris George M. , Karagiannis Christos , Ploutarchou George , Constantinou Antonis
Published: October 2023

“Nonspecific shoulder pain” encompasses various non-traumatic musculoskeletal shoulder disorders, diverging from diagnostic terminologies that refer to precise tissue-oriented clinical diagnosis. Blood flow restriction (BFR) training, involving partial arterial inflow and complete venous outflow restriction, has exhibited acute hypoalgesic effects primarily in healthy populations by increasing their pain thresholds. This study aims to examine whether a single BFR session with low-load exercises can alleviate pain perception among nonspecific shoulder pain patients. Conducted as a single-blind crossover randomised clinical trial, 48 adults (age range: 18 to 40) presenting with nonspecific shoulder pain will partake in two trial sessions. Random assignment will place participants into BFR or sham BFR groups and ask them to perform one exercise with BFR. Subsequently, participants will complete a shoulder girdle loading regimen comprising six exercises. The second session will involve participants switching treatment groups. Pain pressure thresholds (PPTs), shoulder pain and disability via the shoulder pain and disability index (SPADI), maximal voluntary isometric contraction (MVIC) of shoulder external rotators, pain during active abduction, and peak pain during shoulder external rotation will be evaluated using the numeric pain rating scale (NPRS). Immediate post-exercise assessments will include patient-perceived pain changes using the global rating of change scale (GROC) and participant-rated perceived exertion (RPE), employing a modified Borg’s scale (Borg CR10) post-BFR or sham BFR exercise session. Each session will encompass three assessment periods, and a combination of mixed-effect models and descriptive statistics will underpin the analysis. This protocol was approved by Cyprus National Bioethics Committee (ΕΕΒΚ/2023/48), and was registered in ClinicalTrials.gov (Registration number: NCT05956288). Conclusion: The anticipated outcomes of this study illuminated the acute effects of BFR training on pain perception within the context of nonspecific shoulder pain, potentially advancing strategies for managing pain intensity using BFR techniques.
Impact of Carbon Impurities on Air Stability of MOCVD 2D-MoS2
Authors: Ghiami Amir , Grundmann Annika , Tang Songyao , Fiadziushkin Hleb , Wang Zhaodong , Aussen Stephan , Hoffmann-Eifert Susanne , Heuken Michael , Kalisch Holger , Vescan Andrei
Published: October 2023

Metal–organic chemical vapor deposition (MOCVD) is a key method for scalable synthesis of two-dimensional transition metal dichalcogenide (2D-TMDC) layers. However, it faces several challenges, such as the unintentional co-deposition of carbon impurities resulting from the pyrolysis of metal–organic precursors. This study investigates the chemical features of carbon and its impact on the photoluminescence property and air stability of 2D-MoS2. Using X-ray photoemission spectroscopy (XPS), it was found that the carbon impurities show characteristics similar to those of sp2-bonded graphitic carbon. Upon prolonged (20–40 weeks) exposure to the atmosphere, the incorporated carbon appears to react with 2D-MoS2, forming a MoS2−xCx solid solution. At the same time, a gradual decrease in the S/Mo ratio implies the formation of sulfur vacancies was also observed. These two processes lead to crystal degradation over time, as evidenced by the gradual quenching of the Raman and photoluminescence (PL) peaks. More detailed PL analyses suggest a charge transfer mechanism between sp2-carbon/2D-MoS2 and 2D-MoS2/air-adsorbates, which, in the short term, could alter PL emissions and appear to further intensify the degradation of 2D-MoS2 in the long-term. The findings highlight the strong impact of unintentionally co-deposited carbon on the optical properties and air stability of MOCVD 2D-MoS2 layers.
Asymmetric Activation of Frontal Brain Regions during Cognitive Reappraisal Generation—A Function of Implemented Reappraisal Strategy?
Authors: Perchtold-Stefan Corinna M. , Fink Andreas , Papousek Ilona
Published: October 2023

Prefrontal cortex activity facilitates emotion regulation by cognitive reappraisal. Yet, neuroimaging studies have yielded disparate findings as to whether relatively greater left or right prefrontal activity is more beneficial for reappraisal success. We argue that differences in hemispheric activation during reappraisal efforts may depend on the specific and diverse cognitive strategies utilized to reappraise negative stimuli. In this EEG study, n = 94 participants were randomly assigned to three groups and instructed to either generate problem-oriented reappraisals, positive reinterpretations, or distancing reappraisals for anger-eliciting situations while EEG alpha asymmetry changes in the prefrontal cortex were recorded (F3–F4, F7–F8, and Fp1–Fp2). Engaging in problem orientation yielded a right-lateralized frontal activation pattern and was linked to the highest reappraisal success (percentage of strategy-conforming reappraisals), along with the highest believability ratings. Conversely, engaging in distancing reappraisal yielded a left-lateralized frontal EEG pattern, along with the highest ideational fluency and lowest anger ratings post-reappraisal. No distinct asymmetry pattern emerged for positive reinterpretation; however, this reappraisal condition yielded the lowest reappraisal success and lowest believability ratings. For all groups, higher reappraisal capacity correlated with right-lateralized frontal activity. Frontal EEG alpha asymmetry observed during reappraisal may be a specific function of implemented reappraisal strategy rather than general ideational fluency.
Tetracycline Adsorption Performance and Mechanism Using Calcium Hydroxide-Modified Biochars
Authors: Wang Kaifeng , Yao Runlin , Zhang Dongqing , Peng Na , Zhao Ping , Zhong Yongming , Zhou Haijun , Huang Jiahui , Liu Chen
Published: October 2023

Tetracycline is frequently found in various environments and poses significant ecological risks. Calcium hydroxide-modified biochar has shown potential as a material for removing multiple classes of pollutants from wastewater streams. The tetracycline-adsorption performance and mechanism of alkali-modified biochars derived from nine wastes (corn straw, rice straw, swine manure, cypress powder, wheat straw, peanut shell, walnut shell powder, soybean straw, and corncobs) were investigated in the study. Among the four alkalis tested, calcium hydroxide exhibited the most effective modification effects at a pyrolysis temperature of 500 °C. Straw biomass was most suitable to be modified by calcium hydroxide, and calcium hydroxide-modified biochar showed the highest adsorption performance for tetracycline. The maximum adsorption capacities were 8.22 mg g−1 for pristine corn straw biochar and 93.46 mg g−1 for calcium hydroxide-modified corn straw biochar. The tetracycline adsorption mechanism by calcium hydroxide-modified corn straw biochar involved hydrogen bonding, oxygen-containing functional groups, Ca2+ metal complexation, and electrostatic attraction. Consequently, calcium hydroxide-modified corn straw biochar emerges as an environment-friendly, cost-effective, and efficient tetracycline adsorbent.
2,4-Dichlorophenol Shows Estrogenic Endocrine Disruptor Activity by Altering Male Rat Sexual Behavior
Authors: Ferris Pasquini Veronica , Hurtazo Hector , Quintanilla Francisco , Cruz-Soto Martha
Published: October 2023

Chlorophenols (CPs) have been extensively used worldwide as a treatment to prevent the growth and proliferation of different microorganisms, mainly in the wood and farm industries. Chlorine has been used for water disinfection, and phenol groups are water contaminants; these two groups can react with each other to form species such as 2,4-dichlorophenol (2,4-DCP). 2,4-DCP is still used as an herbicide in many countries such as Mexico. CPs have been largely analyzed, like bisphenol A, for their probable endocrine-disrupting effects in humans and aquatic animals. We still do not understand whether these endocrine responses can be manifested as an impairment in sexual behavior in rodents. With the present toxicology study, the endocrine-disrupting effects of 2,4-DCP on male sexual behavior were investigated. Sexually naïve male Wistar rats were used to assess the endocrine-disrupting effects of 2,4-DCP. The rats were divided into two groups: one control group and one experimental group that was administered 1.25 mg/day of 2,4-DCP for 45 days. After completing treatment, the male sexual behavior of the rats was evaluated. The results of this investigation demonstrated that 2,4-DCP affected male sexual behavior. A decrease in mount latency, intromission latency, and post ejaculation period compared with the control animals was found.
Modulation of the Cardiovascular Effects of Polycyclic Aromatic Hydrocarbons: Physical Exercise as a Protective Strategy
Authors: Rojas Gabriel A. , Saavedra Nicolás , Morales Cristian , Saavedra Kathleen , Lanas Fernando , Salazar Luis A.
Published: October 2023

Exposure to polycyclic aromatic hydrocarbons (PAHs) present in air pollution increases cardiovascular risk. On the contrary, physical exercise is a widely used therapeutic approach to mitigate cardiovascular risk, but its efficacy in an environment of air pollution, particularly with PAHs, remains unclear. This study investigates the effects of exercise on inflammation, endothelial dysfunction, and REDOX imbalance due to PAH exposure using a mouse model. Twenty male BALB/c mice were subjected to a mixture of PAHs (phenanthrene, fluoranthene, pyrene) in conjunction with aerobic exercise. The investigation evaluated serum levels of inflammatory cytokines, gene expression linked to inflammatory markers, endothelial dysfunction, and REDOX imbalance in aortic tissues. Furthermore, the study evaluated the expression of the ICAM-1 and VCAM-1 proteins. Exercise led to notable changes in serum inflammatory cytokines, as well as the modulation of genes associated with endothelial dysfunction and REDOX imbalance in aortic tissue. In turn, exercise produced a modulation in the protein expression of ICAM-1 and VCAM-1. The findings implicate the potential of exercise to counter PAH-induced damage, as demonstrated by changes in markers. In conclusion, exercise could mitigate the adverse effects related to exposure to PAHs present in air pollution, as evidenced by changes in inflammatory markers, endothelial dysfunction, and REDOX imbalance.
Diagnostic Potential for the Detection of Canine Visceral Leishmaniasis of an ELISA Assay Based on the Q5 Recombinant Protein: A Large-Scale and Comparative Evaluation Using Canine Sera with a Positive Diagnosis from the Dual-Path-Platform (DPP) Test
Authors: de Araújo Paz Larissa Ferreira , da Silva Adalúcia , da Silva Hemilly Rayanne Ferreira , Cavalcanti Milena Paiva , de Lima Valeria Marçal Felix , da Cunha Beltrão Maria Rosário Oliveira , Silva Maria Beatriz Araújo , de Melo Neto Osvaldo Pompílio , Medeiros Zulma Maria , Santos Wagner José Tenório dos
Published: October 2023

Dogs are considered the major domestic reservoir for human visceral leishmaniasis, a serious disease caused by the Leishmania infantum parasite. Diagnosis of canine visceral leishmaniasis (CVL) is critical for disease control, with several methods currently available. Among the serological tests, the DPP rapid test and the EIE-LVC, more commonly used in Brazil, are associated with variable sensitivity and specificity. Research with novel recombinant proteins such as the ELISA with the recombinant chimeric protein Q5 may therefore improve the CVL diagnosis. This study aimed to evaluate the true diagnostic potential of Q5 in an ELISA assay using a large number of CVL-suspected sera (406) with a previous positive diagnosis based on the rapid DPP test. Sera from the DPP-positive dogs, also assessed with the EIE-LVC test, were compared with sera from healthy dogs (n = 46) and used for ELISA tests using the recombinant Q5. The resulting data as well as the correlation with the clinical signs and the environmental characteristics of the animals were analyzed using Medal and GraphPad Prism 8.0. Overall, similar levels of lower sensitivity (67–68%) were seen for both the commercial EIE-LVC test and the Q5 ELISA when all assessed sera were considered, but a much greater sensitivity (92%) was seen for those samples from symptomatic dogs only. In contrast, many negative results were observed for the DPP-positive sera from asymptomatic dogs or those with no clinical information available. A selection of those sera were tested yet again in new ELISA assays using a second batch of the recombinant Q5, purified under milder denaturing conditions, as well as using another recombinant protein (Lci13). The results reveal a higher-than-expected incidence of likely false-positive results for DPP, reinforcing the need for other recombinant proteins, such as the chimeric Q5, to be investigated as possible alternatives to the currently used CVL diagnostic methods.
Amount of Fill Product Residues in Plastic Packagings for Recycling
Authors: Schinkel Konstantin , Küppers Bastian , Reichenbach Sven , Rohrmeier Teresa , Müller Kajetan , Fell Tanja , Sängerlaub Sven
Published: October 2023

Fill product residues in packagings are equivalent to product losses. They are washed out after sorting and before commencing recycling processes. Not much data have been published about how much fill product is still present in packagings dedicated for recycling. Results are often from laboratory trials. Therefore, several hundred packagings from a sorting plant of a dual system in Germany were analysed to determine the amount of fill product residues. Approximately 10 wt. % of highly viscous fill products in tubes were lost as residue. In the case of packagings that were easy to empty, such as cups, and in the case of low-viscosity fill products, such as water, less than 1 wt. % of the fill products remained in the packagings. The mean amount of residue in relation to clean packaging was 0.9 g residue in 1 g of packaging material (without residue) in tubes and 0.07 g in PET bottles. These values were significantly lower for low-viscosity fill products compared to high-viscosity fill products, as expected.
Robust Shared Control for Four-Wheel Steering Considering Driving Comfort and Vehicle Stability
Authors: Zhang Chuanwei , Liu Haoxin , Dang Meng
Published: October 2023

Although the four-wheel steering system expands the flexibility of vehicle control, it also brings the problem of difficult coordination between driver comfort and vehicle stability. To this end, this paper proposes robust coordinated control for a four-wheel steering (4WS) vehicle considering driving comfort and vehicle stability. First, the vehicle dynamics model is constructed to reflect the lateral motion characteristics of a 4WS vehicle. Then, the driver model is coupled into the 4WS vehicle model to describe the driver’s handling characteristics. To suppress the system perturbation caused by the uncertainties of driver behavior and vehicle states, the Takagi-Sugeno fuzzy robust control method is developed to design the human-machine co-driving system. Moreover, the robust positive invariant set theory is used to guarantee the stability and safety constraints of the vehicle. Finally, the proposed human-machine shared robust control for 4WS vehicle is verified through the driving simulator platform. The results indicate that the fuzzy robust shared control approach comprehensively improves the driving comfort, vehicle stability, and path tracking.
Influence of the Blunt Trailing-Edge Thickness on the Aerodynamic Characteristics of the Very Thick Airfoil
Authors: Pei Zhen , Xu He-Yong , Deng Lei , Li Ling-Xiao
Published: October 2023

In this paper, the NWT600 airfoil with a thickness ratio of 60% is taken as the research object. The aerodynamic performance of the airfoil is analyzed by experiments and numerical simulations. The results simulated by various turbulence models used in the 2D steady-state RANS method are compared, including the Spalart–Allmaras model, k-ω SST model, k-ε realizable model, and Reynolds stress (linear pressure-strain) model. The influence of blunt trailing-edge thickness on aerodynamic characteristics is studied by adding thickness symmetrically. The results show that even under the low subsonic flow with a Mach number of 0.149, the airflow is prone to severe separation. The aerodynamic performance of the airfoil is very different from that of the conventional thin airfoil. Although the 2D steady-state RANS models overestimate the pressure on the surface of the airfoil in most cases, it is qualitatively acceptable to predict the pressure distribution of the very thick airfoil. Numerical results simulated by the Reynolds stress model are in the best agreement with the experimental data. It is also found that symmetrically thickening the trailing edge effectively improves the maximum lift coefficient and reduces the drag coefficient at a small angle of attack.
INTERNATIONAL PARTNERSHIPS OF LOCAL AND REGIONAL PUBLIC ADMINISTRATION BODIES OF KOŠICE REGION AND PREŠOV REGION WITH PARTNERS IN UKRAINE
Authors: Cirner Michal , Székely Gabriel
Published: October 2023

Despite the absence of a law on cross-border cooperation in the Slovak Republic, it can be concluded that the existing legal frameworks in Slovakia and Ukraine provide sufficient and diverse formal opportunities for cooperation between regional and local authorities on both sides of the border. At the beginning of the 90s of the 20th century, real self-governing structures, the business environment, and the non-governmental sector were just starting to take shape, and the relations between the then-new states of Slovakia (founded in 1993) and Ukraine (founded in 1991) were just being formed anew. The legislative environment was gradually formed. From this period, if we do not count legislative frameworks at national levels, almost the only formal evidence of cooperation between regional and local actors on both sides of the border is official documents on international partnerships of cities and municipalities. Of course, cooperation existed, especially in areas where it still works. In the field of culture, sports, tourism, education, and possibly also other, but practical matters, for example, related to transport connections or trade. We believe that, given the circumstances, the intensity of cooperation was at a lower level than at present. The paper deals with mapping international partnerships of local and regional public administration bodies in the Prešov and Košice regions with partners in Ukraine, from 1993 until now. The text deals with the legislative and institutional framework for the cooperation of local and regional actors across the border. Part of the article is also an overview of the contractual relations between the examined local and regional public administration bodies. The paper also offers political considerations and recommendations for the further development of the investigated relations. The paper also includes an interpretation of the results of the author‘s survey and the sociological survey carried out for the SIBSU project.

Key words: public administration, international partnerships, local and regional authorities, Slovakia, Ukraine
Targeting long non-coding RNAs as new modulators in anti-EGFR resistance mechanisms
Authors: Akbarzadeh-Khiavi Mostafa , Safary Azam , Omidi Yadollah
Published: October 2023

Epidermal growth factor receptor (EGFR) is a cell surface protein that plays a vital role in regulating cell growth and division. However, certain tumors, such as colorectal cancer (CRC), can exhibit an overexpression of EGFR, resulting in uncontrolled cell growth and tumor progression. To address this issue, therapies targeting and inhibiting EGFR activity have been developed to suppress cancer growth. Nevertheless, resistance to these therapies poses a significant obstacle in cancer treatment. Recent research has focused on comprehending the underlying mechanisms contributing to anti-EGFR resistance and identifying new targets to overcome this striking challenge. Long non-coding RNAs (lncRNAs) are a class of RNA molecules that do not encode proteins but play pivotal roles in gene regulation and cellular processes. Emerging evidence suggests that lncRNAs may participate in modulating resistance to anti-EGFR therapies in CRC. Consequently, combining lncRNA targeting with the existing treatment modalities could potentially yield improved clinical outcomes. Illuminating the involvement of lncRNAs in anti-EGFR resistance mechanisms of cancer cells can provide valuable insights into the development of novel anti-EGFR therapies in several solid tumors.
Essential role of CD38 in platelet aggregation through the PKC- mediated internalization and activation
Authors: Mushtaq Mazhar , Mahmood Maira , Jabbar Uzma , Kim Uh-Hyun
Published: October 2023

Introduction: CD38 is a multifunctional enzyme with a potent Ca2+ mobilizing effect, cyclic ADP-ribose (cADPR), and nicotinic acid adenine dinucleotide phosphate (NAADP). Here, we aimed to demonstrate the role of CD38 in platelets via protein kinase C (PKC)-mediated internalization and activation. Methods: Mouse platelets were used in this study. Thrombin, an agonist of platelet function, provoked a prompt and long-lasting increase in intracellular Ca2+ concentration ([Ca2+]i), resulting from an interplay of multifold Ca2+ mobilizing messengers.The signaling pathway was delineated using different inhibitors and techniques such as platelet aggregation assay, intracellular calcium measurements, immunoprecipitation, immunoblotting, and flow cytometry. Results: We observed a sequential formation of cADPR and NAADP through CD38 activation by PKC of non-muscle myosin heavy chain IIA (MHCIIA), resulting in phospholipase C (PLC) activation in the thrombin-stimulated platelets. These findings reveal that PKC is fundamental in activating CD38 and elicits a physiological response in the murine platelets. Conclusion: PKC is involved in many signaling pathways. Specifically, PKC is involved in the internalization of CD38 via MHCIIA in CD38+/+ wild-type (WT) and CD38-/- knockout mice (KO). CD38 generates calcium-mobilizing agents that act on specific receptors of the calcium stores. Calcium triggered platelet aggregation while serving as a secondary messenger.
Benchmarking different docking protocols for predicting the binding poses of ligands complexed with cyclooxygenase enzymes and screening chemical libraries
Authors: Shamsian Sara , Sokouti Babak , Dastmalchi Siavoush
Published: October 2023

Introduction: Non-steroidal anti-inflammatory drugs (NSAIDs) constitute an important class of pharmaceuticals acting on cyclooxygenase COX-1 and COX-2 enzymes. Due to their numerous severe side effects, it is necessary to search for new selective, safe, and effective anti-inflammatory drugs. In silico design of novel therapeutics plays an important role in nowadays drug discovery pipelines. In most cases, the design strategies require the use of molecular docking calculations. The docking procedure may require case-specific condition for a successful result. Additionally, many different docking programs are available, which highlights the importance of identifying the most proper docking method and condition for a given problem. Methods: In the current work, the performances of five popular molecular docking programs, namely, GOLD, AutoDock, FlexX, Molegro Virtual Docker (MVD) and Glide to predict the binding mode of co- crystallized inhibitors in the structures of known complexes available for cyclooxygenases were evaluated. Furthermore, the best performers, Glide, AutoDock, GOLD and FlexX, were further evaluated in docking-based virtual screening of libraries consisted of active ligands and decoy molecules for cyclooxygenase enzymes and the obtained docking scores were assessed by receiver operating characteristics (ROC) analysis. Results: The results of docking experiments indicated that Glide program outperformed other docking programs by correctly predicting the binding poses (RMSD less than 2 Å) of all studied co-crystallized ligands of COX-1 and COX-2 enzymes (i.e., the performance was 100%). However, the performances of the other studied docking methods for correctly predicting the binding poses of the ligands were between 59% to 82%. Virtual screening results treated by ROC analysis revealed that all tested methods are useful tools for classification and enrichment of molecules targeting COX enzymes. The obtained AUCs range between 0.61-0.92 with enrichment factors of 8 – 40 folds. Conclusion: The obtained results support the importance of choosing appropriate docking method for predicting ligand-receptor binding modes, and provide specific information about docking calculations on COXs ligands.