bluuebunny/crossref_metadata_2025 huggingface dataset:
Changes in Oxidised Phospholipids in Response to Oxidative Stress in Microtubule-Associated Protein Tau (MAPT) Mutant Dopamine Neurons
Authors: Bradford Xanthe , Fernandes Hugo J. R. , Snowden Stuart G.
Published: April 2024

Microtubule-associated protein Tau (MAPT) is strongly associated with the development of neurodegenerative diseases. In addition to driving the formation of neurofibrillary tangles (NFT), mutations in the MAPT gene can also cause oxidative stress through hyperpolarisation of the mitochondria. This study explores the impact that MAPT mutation is having on phospholipid metabolism in iPSC-derived dopamine neurons, and to determine if these effects are exacerbated by mitochondrial and endoplasmic reticulum stress. Neurons that possessed a mutated copy of MAPT were shown to have significantly higher levels of oxo-phospholipids (Oxo-PL) than wild-type neurons. Oxidation of the hydrophobic fatty acid side chains changes the chemistry of the phospholipid leading to disruption of membrane function and potential cell lysis. In wild-type neurons, both mitochondrial and endoplasmic reticulum stress increased Oxo-PL abundance; however, in MAPT mutant neurons mitochondrial stress appeared to have a minimal effect. Endoplasmic reticulum stress, surprisingly, reduced the abundance of Oxo-PL in MAPT mutant dopamine neurons, and we postulate that this reduction could be modulated through hyperactivation of the unfolded protein response and X-box binding protein 1. Overall, the results of this study contribute to furthering our understanding of the regulation and impact of oxidative stress in Parkinson’s disease pathology.
The Impact of Curcumin, Resveratrol, and Cinnamon on Modulating Oxidative Stress and Antioxidant Activity in Type 2 Diabetes: Moving beyond an Anti-Hyperglycaemic Evaluation
Authors: Banaszak Michalina , Górna Ilona , Woźniak Dagmara , Przysławski Juliusz , Drzymała-Czyż Sławomira
Published: April 2024

Research indicates that up to half of the population resorts to dietary supplements for managing diseases such as type 2 diabetes rather than changing their nutritional habits. These supplements not only aim to have an anti-hyperglycaemic effect but also seek to reduce oxidative stress to prevent diabetes complications. This systematic literature systematic review aims to evaluate the efficacy of curcumin, resveratrol, and cinnamon in modulating oxidative stress and antioxidant activity in individuals with type 2 diabetes. Data were collected from PubMed, Web of Sciences, and Scopus databases regarding the impact of curcumin, resveratrol, and cinnamon on total antioxidant capacity (TAC), malondialdehyde (MDA), tumour necrosis factor α (TNF-α), interleukin 6 (IL-6), and high-sensitivity C-reactive protein (hs-CRP) levels for this review. Effect sizes for each study were calculated using Cohen’s or Hedges’s d coefficient. Parameters of oxidative stress and inflammatory status, such as TAC, MDA, TNF-α, IL-6, and hs-CRP, improved following phytochemicals. Additionally, curcumin, resveratrol, and cinnamon exhibited regulatory effects on carbohydrate metabolism by reducing glucose, insulin, and glycated haemoglobin concentrations and lipid metabolism by lowering total cholesterol (TC), low-density lipoprotein (LDL), and triglycerides (TG) and increasing high-density lipoprotein (HDL). Incorporating curcumin, resveratrol, and cinnamon into diets may be beneficial for maintaining organism homeostasis and improving metabolic control in individuals with type 2 diabetes. However, the conflicting results reported in the literature highlight the need for further detailed investigations into the effectiveness of phytochemical use for type 2 diabetes.
Modeling and Performance Analysis of a Notification-Based Method for Processing Video Queries on the Fly
Authors: Kossoski Clayton , Simão Jean Marcelo , Lopes Heitor Silvério
Published: April 2024

With the rapid growth of video data, the search for content and events in videos is becoming increasingly relevant, and many challenges arise. Various approaches have been proposed to deal with many issues. However, many open questions are still related to computational cost and latency, especially for real-time applications. Considering the need for new and efficient solutions, the so-called NOP (Notification Oriented Paradigm) could be a suitable alternative. NOP introduced a new way of thinking and developing software in which small collaborative entities perform fact execution and logical decision processing based on precise notifications. Following these concepts and practical tools, this paper proposes a new querying processing method based on NOP, focusing on search and matching in a continuous flow context. Experiments on a labeled dataset demonstrated the suitability of the proposed method for low-latency processing with polynomial complexity. The results are better than the state of the art, which works at exponential cost.
Research on the Autonomous Control Technology Used in the Slurry Mixing System of Cementing Units
Authors: Gao Xiang , Hou Guojian , Yang Huiwen , Hu Changmiao , Cui Junguo , Xiao Wensheng
Published: April 2024

Cementing is a critical link in oil and gas exploitation, in which slurry density control is particularly important. In this study, we examined a slurry mixing control system in order to solve the problem of time delays in the mixing system. The model of a slurry mixing system was built in accordance with the system’s structure. A Smith fuzzy PID (proportion integration differentiation) composite control solution is proposed herein, and the simulation results show that the adjustment time and overshoot are lower than those of the conventional PID control and Smith predictive compensation control. A genetic algorithm is utilized to optimize the quantization factor and scale factor of the Smith fuzzy PID controller. Following optimization, the rise time of the controller was found to be 0.45 s, which represents a decrease of 35.9%, the overshoot was reduced by 0.4%, and the stabilization time was reduced by 36.6%. Afterward, we built a cementing slurry mixing simulation experimental platform, and experiments were used to verify the feasibility and superiority of the Smith fuzzy PID controller optimized by the genetic algorithm in comparison with the conventional controllers. The study results thus provide a scientific basis for the engineering application of the autonomous control technology of the slurry mixing system in cementing units.
Apparent Resistivity Variation Imaging Method Based on Magnetic Field Gradient by NGO-LSSVM for the Ground-Airborne Frequency-Domain Electromagnetic Method
Authors: Liu Changsheng , Liang Jie , Liu Shuxu , Zhou Haigen
Published: April 2024

Apparent resistivity imaging is a typical rapid imaging method in the ground-airborne frequency-domain electromagnetic method. At present, the apparent resistivity is typically calculated by the measured magnetic field, however, this imaging method exhibits limited capability in recognizing the center of three-dimensional anomalies. Therefore, this paper proposed the calculation of apparent resistivity using magnetic field gradients. To solve the problem of random artificial anomalies that existed during the calculated process, this paper presents a hybrid least square support vector machine (LSSVM) and Northern Goshawk optimization (NGO) to establish the mapping relationship between the magnetic field gradient and apparent resistivity variation. This approach enables accurate prediction of apparent resistivity variations and effectively resolves the challenge of correcting background resistivity. Furthermore, three typical theoretical models and field examples are used to predict the apparent resistivity variation, the imaging results demonstrate that the proposed NGO-LSSVM algorithm is a feasible and efficient tool for predicting the apparent resistivity variation with high accuracy. This study provides a novel and efficient imaging method, which facilitates the application of ground-airborne frequency-domain electromagnetics for high-resolution detection requirements, such as mineral exploration.
Remote Sensing and Machine Learning for Safer Railways: A Review
Authors: Helmi Wesam , Bridgelall Raj , Askarzadeh Taraneh
Published: April 2024

Regular railway inspections are crucial for maintaining their safety and efficiency. However, traditional inspection methods are complex and expensive. Consequently, there has been a significant shift toward combining remote sensing (RS) and machine learning (ML) techniques to enhance the efficiency and accuracy of railway defect monitoring while reducing costs. The advantages of RS-ML techniques include their ability to automate and refine inspection processes and address challenges such as image quality and methodological limitations. However, the integration of RS and ML in railway monitoring is an emerging field, with diverse methodologies and outcomes that the research has not yet synthesized. To fill this gap, this study conducted a systematic literature review (SLR) to consolidate the existing research on RS-ML applications in railway inspection. The SLR meticulously compiled and analyzed relevant studies, evaluating the evolution of research trends, methodological approaches, and the geographic distribution of contributions. The findings showed a notable increase in relevant research activity over the last five years, highlighting the growing interest in this realm. The key methodological patterns emphasize the predominance of approaches based on convolutional neural networks, a variant of artificial neural networks, in achieving high levels of precision. These findings serve as a foundational resource for academics, researchers, and practitioners in the fields of computer science, engineering, and transportation to help guide future research directions and foster the development of more efficient, accurate, and cost-effective railway inspection methods.
Milk: A Nutritive and Healthy Food? Consumer Perspective from French and Portuguese Participants
Authors: Bréjon Marius , Tavares Francisca , Florença Sofia G. , Gonçalves João Carlos , Barroca Maria João , Guiné Raquel P. F.
Published: April 2024

Milk from different animals is a staple food consumed since immemorial times all over the world. However, there is a lack of knowledge in the scientific literature about knowledge related to milk and its effects on nutrition and health. The objective of this study was to investigate consumers’ knowledge about milk, its composition, and its effects on human health in two different countries. The study was conducted through a questionnaire survey in Portugal and France and involved 542 participants of whom 332 were French and 210 were Portuguese. For data analysis, we used basic statistics, parametric tests, tree classification, and factor analysis. The results showed that some sociodemographic variables significantly influenced the level of knowledge, namely country (p < 0.001), age (p = 0.029), and the dimension of the household (p < 0.001). Nevertheless, tree classification analyses revealed that other variables such as education, occupation, and body mass index showed some discriminating ability. Factor analysis retained 20 items of the 23 initially tested. The solution contained five factors, two of which had very good internal consistency (alpha values of 0.825 and 0.803). The mean scores for knowledge in practically all factors were consistently higher for the Portuguese as compared with the French sample. In conclusion, the level of knowledge about milk composition and its health effects differs according to several sociodemographic variables, particularly in what constitutes country differences.
A Normalization Strategy for Weakly Supervised 3D Hand Pose Estimation
Authors: Guo Zizhao , Li Jinkai , Tan Jiyong
Published: April 2024

The effectiveness of deep neural network models is intricately tied to the distribution of training data. However, in pose estimation, potential discrepancies in root joint positions and inherent variability in biomechanical features across datasets are often overlooked in current training strategies. To address these challenges, a novel Hand Pose Biomechanical Model (HPBM) is developed. In contrast to the traditional 3D coordinate-encoded pose, it provides a more intuitive depiction of the anatomical characteristics of the hand. Through this model, a data normalization approach is implemented to align the root joint and unify the biomechanical features of training samples. Furthermore, the HPBM facilitates a weakly supervised strategy for dataset expansion, significantly enhancing the data diversity. The proposed normalized method is evaluated on two widely used 3D hand pose estimation datasets, RHD and STB, demonstrating superior performance compared to the models trained without normalized datasets. Utilizing ground truth 2D keypoints as input, a reduction of 45.1% and 43.4% in error is achieved on the STB and RHD datasets, respectively. When leveraging 2D keypoints from MediaPipe, a reduction in error by 11.3% and 14.3% is observed on the STB and RHD datasets.
Computer-Guided Bone Lid Technique for Surgical Extraction of Deeply Impacted Mandibular Third Molars: A Technical Report
Authors: Sivolella Stefano , Brunello Giulia , Fincato Andrea , De Stavola Luca
Published: April 2024

The extraction of mandibular impacted third molars is one of the most common procedures in oral surgery. One of the extraction methods consists of the preparation of a buccal bone lid to obtain access and remove the tooth. This technique aims to reduce bone loss, promote bone healing distal to the second molar, and increase the accessibility to the impacted tooth, particularly in cases of concomitant pathologies or proximity to the mandibular canal. The procedure benefits from the application of 3D printing technologies to produce cutting guides that, combined with piezosurgery, allow deeply impacted third molar extractions with excellent preoperative planning and extreme localisation precision, as well as predictable repositioning and fixation of the bone lid. The objective of this article is to present two clinical cases of the application of the bone lid technique to extract deeply impacted third molars using 3D printed guides with different designs and piezosurgery.
A Temporal Methodology for Assessing the Performance of Concatenated Codes in OFDM Systems for 4K-UHD Video Transmission
Authors: Costa Thiago de A. , Macedo Alex S. , Matos Edemir M. C. , Castro Bruno S. L. , Farias Fabricio de S. , Cardoso Caio M. M. , Cavalcante Gervásio P. dos S. , Barros Fabricio J. B.
Published: April 2024

The communication channel is a critical part of the process of information degradation. In the 4K ultra-resolution video transmission domain, the communication channel is a crucial part where information degradation occurs, inevitably leading to errors during reception. To enhance the transmission process in terms of fidelity, advanced technologies such as digital video broadcasting terrestrial (DVB-T) and its evolutionary successor, digital video broadcasting terrestrial second generation (DVB-T2), are utilized to mitigate the effects of data transmission errors. Within this scenario, this research presents an innovative methodology for the temporal analysis of 4K ultra-resolution video quality under the influence of additive white Gaussian noise (AWGN) and Rayleigh channels. This analytical endeavor is facilitated through the application of concatenated coding schemes, specifically, the Bose–Chaudhuri–Hocquenghem concatenated low-density parity check (BCH-LDPC) and Reed–Solomon concatenated convolutional (RS-CONV) coders. A more comprehensive understanding of video quality can be attained by considering its temporal variations, a crucial aspect of the ongoing evolution of technological paradigms. In this study, the Structural Similarity Index (SSIM) serves as the main metric for quality assessment during simulations. Furthermore, the simulated Peak Signal-to-Noise Ratio (PSNR) values validate these findings, exhibiting consistent alignment with the SSIM-based evaluations. Additionally, the performance of the BCH-LDPC significantly outperforms that of RS-CONV under the 64-QAM modulation scheme, yielding superior video quality levels that approximate or surpass those achieved by RS-CONV under QPSK (Quadrature Phase Shift Keying) modulation, leading to an increase in spectral efficiency. This enhancement is evidenced by SSIM gains exceeding 78% on average. The computation of average gains between distinct technologies in video quality analysis furnishes a robust and comprehensive evaluation framework, empowering stakeholders to make informed decisions within this domain.
Combined Sous-Vide and High Hydrostatic Pressure Treatment of Pork: Is the Order of Application Decisive When Using Minimal Processing Technologies?
Authors: Kenesei György , Kiskó Gabriella , Dalmadi István
Published: April 2024

The aim of using minimal food processing technologies is to preserve the raw material or to achieve a special technological goal with the least possible impact. When several technologies are used together, the intensity of each treatment can be reduced according to Leistner’s hurdle principle. Does the order of application of the treatments result in a detectable difference? This research focuses on the effect of the combination of the sous-vide technology and the high hydrostatic pressure (HHP) treatment. The effect of the pressure level (300 and 600 MPa) and the influence of the treatment order was investigated on pork (longissimus thoracis and lumborum muscles (LTL)). Physico-chemical and microbiological measurements were carried out on day 0 and after 21-day storage at 2 °C and 8 °C. Significant differences were found for both the order of treatment and pressure level in weight loss (p < 0.001), CIELab color parameters a* and b* (p < 0.001), and denaturation enthalpy (p < 0.01). The texture (p < 0.001) and lipid oxidation TBARS values (p < 0.05) were only influenced by the pressure level. In the challenge test, the initial count of 105 CFU/g Listeria monocytogenes dropped below detection limit in all cases. Total aerobic and anaerobic viable numbers were below/near the detection limit in all combined-treated samples on day 0 and showed only slight or more notable growth after 21-day storage at 2 °C and 8 °C, respectively. An additional 300 MPa pressure treatment can increase the safety of sous-vide cooked pork samples while having only a minor effect on physicochemical properties. The 600 MPa pressure treatment results in a stable, albeit not shelf-stable product, but it also affects a considerable number of quality parameters such as color, texture, weight loss, and TBARS values.
The Potential for Restoring the Activity of Oxidoreductases and Hydrolases in Soil Contaminated with Petroleum Products Using Perlite and Dolomite
Authors: Wyszkowska Jadwiga , Borowik Agata , Zaborowska Magdalena , Kucharski Jan
Published: April 2024

The research focused on assessing the response of oxidoreductases (dehydrogenases and catalase) and hydrolases (urease, acid phosphatase, alkaline phosphatase, arylsulfatase, and β-glucosidase) to diesel oil (DO) and gasoline (G) contamination of soils subjected to phytoremediation with Zea mays. The activity of enzymes constitutes one of the fundamental mechanisms for the removal of contaminants from soil, which have the potential to contaminate not only the soil but also groundwater and water reservoirs. Additionally, correlations between enzyme activity and the basic physicochemical properties of the soil were determined. The interaction of perlite and dolomite with soil enzymes and the cultivated plant was also tested. The study was carried out in a pot experiment, where soil contaminated with DO or G was artificially treated at doses of 0, 8 cm3, and 16 cm3 kg−1. Perlite and dolomite were applied for remediation at doses of 0 and 10 g kg−1 of soil. Zea mays was found to respond to the tested pollutant with a reduction in biomass. DO affected the growth of this plant more than G. DO reduced the yield of aerial parts by 86% and G by 74%. The negative effects of these pollutants on the growth and development of Zea mays were mitigated by both perlite and dolomite. DO exerted greater pressure than G on the activity of oxidoreductases and hydrolases, as well as on the physicochemical properties of the soil. DO enhanced the activity of oxidoreductases and most hydrolases, whereas G inhibited them. The implementation of dolomite intensified the activity of all enzymes, except AcP (acid phosphatase) and Glu (ß-glucosidase), in soil contaminated with DO and G, and also improved its physicochemical properties. Perlite induced less significant effects than dolomite on soil enzymes and the physicochemical properties of the soil.
MoSe2 with Ultra-Fine Pt Decoration for Efficient Photodegradation
Authors: Chen Yong , Shao Dawei , Xu Fupeng , Huang Zhongjia , Shi Xinying
Published: April 2024

Transition metal dichalcogenides are widely studied for their photocatalytic ability due to the adjustable bandgap, high carrier mobility and possibility of foreign-element doping. In this work, multilayer molybdenum diselenide (MoSe2) was decorated with ultra-fine Pt nanoparticles through the mild hydrothermal method. MoSe2-Pt nanocomposites were synthesized and showed good structural and chemical stabilities. The incorporation of Pt nanoparticles provides plenty of active sites for MoSe2. The dominant Pt particle sizes are 1.8 nm, 1.8 nm, and 1.9 nm for the three synthesized samples, respectively. The mean crystal sizes of Pt (111) were calculated from X-ray diffraction patterns and we found that they were in accordance with the particle sizes. Both the particle sizes and mean crystal sizes are related to the synthesis conditions. X-ray photoelectron spectroscopy (XPS) characterizations revealed the formation of Se–Pt bonding. The relative contents of Pt–Se bonding were also calculated from XPS results, and they show the same trends as the optical absorption properties. Combining the XPS and optical absorption results, the effects of Se–Pt bonding during the photo-related process could be further confirmed. By degrading methylene blue (MB) under visible light, the synthesized nanocomposites proved promising for application in real-case degradation of organic pollutants. The sample synthesized with a moderate content of MoSe2 exhibited the best photodegradation efficiency, which could be explained by the maximum Pt-Se contents. Based on the experimental findings, we proposed a possible photodegradation mechanism.
Steel Arch and Rock Bolt Support in Terms of the Gateroad Stability Maintaining behind the Longwall Face
Authors: Bednarek Łukasz , Małkowski Piotr , Niedbalski Zbigniew , Mucha Kamil
Published: April 2024

The longwall system is an extraction system commonly used in coal mining in many countries, including Poland. One of the methods for reducing extraction costs is the dual use of the gateroad. In the first instance, the gateroad serves as the tailgate, and during the exploitation of the second coal panel, it functions as the headgate. Such a situation requires maintenance of the roadway behind the longwall face, which is typically challenging, due to significant stress-related loads on the support and its substantial deformation. The support design for this kind of roadway should take into consideration the dual impact of exploitation pressure and the caved zone influence behind the longwall face. This article presents the results of in-situ research conducted on two roadways behind the longwall face. In both roadways, the effectiveness of specially designed steel arch frames and rock bolt patterns were examined to minimize roadway deformations and maintain their functionality. The research project was comprised of several stages. Initially, mining and laboratory studies were conducted to determine the geomechanical parameters of the rocks. Subsequently, excavation stability and functionality forecasts were performed based on the authors’ empirical indicators. Then, numerical analyses were carried out to design support schemes (steel arches and rock bolt) in both roadways. A fully automated monitoring system with programmed data loggers was designed to check the behaviour of a specific rock mass and the support elements. The load on the steel arch support was measured with the help of load cells, while the load on the rock bolt support was carried out with the help of measurement bolts. Behind the longwall face, the loads on the wooden cribs set from the goaf side were also monitored. Additionally, the measurement station was equipped with extensometers to monitor the movement of roof layers and stress meters to determine changes in rock mass stress. Laser scanning or traditional surveying methods were also used to verify the support schemes through roadway convergence measurements. The obtained results allowed us to draw conclusions regarding the optimization of support schemes and to give recommendations for the practical application of specific reinforcements in excavations maintained behind the longwall face.
Virtual Reality for Spatial Planning and Emergency Situations: Challenges and Solution Directions
Authors: Wiltenburg Reinout , Mendoza Frida Ruiz , Hurst William , Tekinerdogan Bedir
Published: April 2024

The notion of the smart city involves embedding Industry 4.0 technologies to improve the lives of inhabitants in urban environments. Within this context, smart city data layers (SCDLs) concern the integration of extra tiers of information for the purposes of improving communication potential. Under the Industry 4.0 technology grouping, advanced communication technologies, such as virtual reality (VR), further the opportunities to model, recreate, evaluate and communicate scenarios that potentially improve citizens lives at multiple SCDL scales in a smart cities context. The use of added contextual information in SCDLs is of special interest for emergency planning situations at the building scale. In this research, a literature review to understand the current approaches for the use, development and evaluation of VR applications in the context of emergency planning was conducted. The results indicated four main categories of relevant challenges for these types of applications, for which recommendations and a roadmap for VR development are presented. In total, the study identified 10 commonly occurring challenges (e.g., optimization and discomfort) and 19 solution directions (e.g., model construction and spatial directions) in related articles when considering the development of VR for spatial planning and emergency situations.
The Effect of Varying Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Parameters on Wind Energy Prediction: A Comparative Study
Authors: Oguz Erenler Gokce , Bulus Halil Nusret
Published: April 2024

Owing to the development of technology, the majority of nations throughout the world now rely on fossil fuels and nuclear power plants to meet their energy needs. However, as academic research on this subject has shown, it has become clear that alternative energy uses are necessary due to the gradual depletion of these fuels and their significant negative effects on the environment. In order to ensure energy diversity and end the energy shortage, the development of renewable energy sources is crucial. The prediction of wind power is crucial for effectively utilizing the potential of wind energy. In this study, an adaptive neuro-fuzzy inference system (ANFIS) and an artificial neural network (ANN) have been developed for the prediction of wind power. In this study, data sets were created by taking the daily average wind speeds of the selected wind turbine, the daily average power values it produces, and the daily average wind speed values in the Velimese region. By creating single-hidden layer and multi-hidden layer ANN models, the network was trained multiple times with different activation functions and different numbers of neurons, and wind power prediction was performed. In the ANFIS model, the number of membership functions is kept constant, and wind power prediction is performed using different membership functions. With these ANFIS and ANN models developed with different parameter combinations, it is aimed to determine the most efficient model by performing daily average wind power prediction. Parameter combinations were tested to determine the appropriate models, and as a result, the ANN and ANFIS models were compared with each other.
3D Point Cloud Dataset of Heavy Construction Equipment
Authors: Park Suyeul , Kim Seok
Published: April 2024

Object recognition algorithms and datasets based on point cloud data have been mainly designed for autonomous vehicles. When applied to the construction industry, they face challenges due to the origin of point cloud data from large earthwork sites, resulting in high volumes of data and density. This research prioritized the development of 3D point cloud datasets specifically for heavy construction equipment, including dump trucks, rollers, graders, excavators, and dozers; all of which are extensively used in earthwork sites. The aim was to enhance the efficiency and productivity of machine learning (ML) and deep learning (DL) research that relies on 3D point cloud data in the construction industry. Notably, unlike conventional approaches to acquiring point cloud data using UAVs (Unmanned Aerial Vehicles) and UGVs (Unmanned Ground Vehicles), the datasets for the five types of heavy construction equipment established in this research were generated using 3D-scanned diecast models of heavy construction equipment to create point cloud data.
A PHREEQC-Based Tool for Planning and Control of In Situ Chemical Oxidation Treatment
Authors: Samborska-Goik Katarzyna , Ulańczyk Rafał , Krupanek Janusz , Pogrzeba Marta
Published: April 2024

This article describes a tool that can be used to improve the effectiveness of the ISCO (in situ chemical oxidation) method. It is an Excel-based application that uses Visual Basic, PHREEQC, and Python. The main functions are feedback control solutions. There are several ideas that can optimise ISCO treatment when using the geochemical model: (i) looping real-time data into the geochemical model and using them to estimate the actual rate, (ii) using spatial distribution maps for delineating zones that are susceptible or resistant to oxidation, (iii) visualising the permanganate consumption that could indicate the right time for further action, and (iv) using alarm reports of the abnormal physico-chemical conditions that jeopardise successful injection.
Effect of Data Augmentation Using Deep Learning on Predictive Models for Geopolymer Compressive Strength
Authors: Nguyen Ho Anh Thu , Pham Duy Hoang , Ahn Yonghan
Published: April 2024

In recent years, machine learning models have become a potential approach in accurately predicting the concrete compressive strength, which is essential for the real-world application of geopolymer concrete. However, the precursor system of geopolymer concrete is known to be more heterogeneous compared to Ordinary Portland Cement (OPC) concrete, adversely affecting the data generated and the performance of the models. To its advantage, data enrichment through deep learning can effectively enhance the performance of prediction models. Therefore, this study investigates the capability of tabular generative adversarial networks (TGANs) to generate data on mixtures and compressive strength of geopolymer concrete. It assesses the impact of using synthetic data with various models, including tree-based, support vector machines, and neural networks. For this purpose, 930 instances with 11 variables were collected from the open literature. In particular, 10 variables including content of fly ash, slag, sodium silicate, sodium hydroxide, superplasticizer, fine aggregate, coarse aggregate, added water, curing temperature, and specimen age are considered as inputs, while compressive strength is the output of the models. A TGAN was employed to generate an additional 1000 data points based on the original dataset for training new predictive models. These models were evaluated on real data test sets and compared with models trained on the original data. The results indicate that the developed models significantly improve performance, particularly neural networks, followed by tree-based models and support vector machines. Moreover, data characteristics greatly influence model performance, both before and after data augmentation.
Phase Behavior and Rheological Properties of AES/CAPB/H2O Ternary System
Authors: Wu Xinran , Zhang Guangyan , Wang Peng
Published: April 2024

Cleaning products are often formulated as mixtures of surfactants because the properties of surfactant mixtures are easier to adjust than those of a single surfactant. Therefore, it is of great significance to study the phase diagram of surfactant mixtures. In this paper, the phase behavior of the alkyl ethoxysulfate (AES)/cocamidopropyl betaine (CAPB)/H2O ternary system was investigated at room temperature using polarizing optical microscopy (POM) and small angle X-ray scattering (SAXS), and the identified phases of the samples with various compositions were used to construct the ternary phase diagram of the AES/CAPB/H2O system which contains normal micellar phase (L1), normal hexagonal phase (H1), lamella phase (Lα), and one transition region (L1 → H1). The viscosity distribution of the AES/CAPB/H2O system was determined by a Brookfield DV2T touch screen viscometer. In addition, the effects of the weight percentage of CAPB and salts on the viscosity and rheological properties of the AES/CAPB/H2O system were also investigated. This work not only enriches the phase diagram of surfactant systems, but also has important guiding significance for the design and development of cleaning products.
Mask-Wearing Detection in Complex Environments Based on Improved YOLOv7
Authors: Feng Guang , Yang Qun , Tang Chong , Liu Yunhai , Wu Xiaoting , Wu Wenyan
Published: April 2024

Wearing masks is an effective protective measure for residents to prevent respiratory infectious diseases when going out. Due to issues such as a small target size, target occlusion leading to information loss, false positives, and missed detections, the effectiveness of face mask-wearing detection needs improvement. To address these issues, an improved YOLOv7 object detection model is proposed. Firstly, the C2f_SCConv module is introduced in the backbone network to replace some ELAN modules for feature extraction, enhancing the detection performance of small targets. Next, the SPPFCSPCA module is proposed to optimize the spatial pyramid pooling structure, accelerating the model convergence speed and improving detection accuracy. Finally, the HAM_Detect decoupled detection head structure is introduced to mitigate missed and false detections caused by target occlusion, further accelerating model convergence and improving detection performance in complex environments. The experimental results show that improved YOLOv7 achieved an mAP of 90.1% on the test set, a 1.4% improvement over the original YOLOv7 model. The detection accuracy of each category improved, effectively providing technical support for mask-wearing detection in complex environments.
Sparse Signal Recovery via Rescaled Matching Pursuit
Authors: Li Wan , Ye Peixin
Published: April 2024

We propose the Rescaled Matching Pursuit (RMP) algorithm to recover sparse signals in high-dimensional Euclidean spaces. The RMP algorithm has less computational complexity than other greedy-type algorithms, such as Orthogonal Matching Pursuit (OMP). We show that if the restricted isometry property is satisfied, then the upper bound of the error between the original signal and its approximation can be derived. Furthermore, we prove that the RMP algorithm can find the correct support of sparse signals from random measurements with a high probability. Our numerical experiments also verify this conclusion and show that RMP is stable with the noise. So, the RMP algorithm is a suitable method for recovering sparse signals.
Photon-Added Deformed Peremolov Coherent States and Quantum Entanglement
Authors: Berrada Kamal
Published: April 2024

In the present article, we build the excitedcoherent states associated with deformed su(1,1) algebra (DSUA), called photon-added deformed Perelomov coherent states (PA-DPCSs). The constructed coherent states are obtained by using an alterationof the Holstein–Primakoff realization (HPR) for DSUA. A general method to resolve of the problem of the unitary operator was developed for these kinds of quantum states. The Mandel parameter is considered to examine the statistical properties of PA-DPCSs. Furthermore, we offer a physical method to generate the PA-DPCSs in the framework of interaction among fields and atoms. Finally, we introduce the concept of entangled states for PA-DPCSs and examine the entanglement properties for entangled PA-DPCSs.
Correction: Satam et al. Next-Generation Sequencing Technology: Current Trends and Advancements. Biology 2023, 12, 997
Authors: Satam Heena , Joshi Kandarp , Mangrolia Upasana , Waghoo Sanober , Zaidi Gulnaz , Rawool Shravani , Thakare Ritesh P. , Banday Shahid , Mishra Alok K. , Das Gautam , Malonia Sunil K.
Published: April 2024

We are very thankful to the commentator for pointing out the issues in the review article by Satam et al [...]
The Cerebrospinal Fluid Free-Glycans Hex1 and HexNAc1Hex1Neu5Ac1 as Potential Biomarkers of Alzheimer’s Disease
Authors: Krüger Lynn , Biskup Karina , Schipke Carola G. , Kochnowsky Bianca , Schneider Luisa-Sophie , Peters Oliver , Blanchard Véronique
Published: April 2024

Alzheimer’s disease (AD) is the most common neurodegenerative disorder, affecting a growing number of elderly people. In order to improve the early and differential diagnosis of AD, better biomarkers are needed. Glycosylation is a protein post-translational modification that is modulated in the course of many diseases, including neurodegeneration. Aiming to improve AD diagnosis and differential diagnosis through glycan analytics methods, we report the glycoprotein glycome of cerebrospinal fluid (CSF) isolated from a total study cohort of 262 subjects. The study cohort consisted of patients with AD, healthy controls and patients suffering from other types of dementia. CSF free-glycans were also isolated and analyzed in this study, and the results reported for the first time the presence of 19 free glycans in this body fluid. The free-glycans consisted of complete or truncated N-/O-glycans as well as free monosaccharides. The free-glycans Hex1 and HexNAc1Hex1Neu5Ac1 were able to discriminate AD from controls and from patients suffering from other types of dementia. Regarding CSF N-glycosylation, high proportions of high-mannose, biantennary bisecting core-fucosylated N-glycans were found, whereby only about 20% of the N-glycans were sialylated. O-Glycans and free-glycan fragments were less sialylated in AD patients than in controls. To conclude, this comprehensive study revealed for the first time the biomarker potential of free glycans for the differential diagnosis of AD.
The Exploitation of the Glycosylation Pattern in Asthma: How We Alter Ancestral Pathways to Develop New Treatments
Authors: Muchowicz Angelika , Bartoszewicz Agnieszka , Zaslona Zbigniew
Published: April 2024

Asthma has reached epidemic levels, yet progress in developing specific therapies is slow. One of the main reasons for this is the fact that asthma is an umbrella term for various distinct subsets. Due to its high heterogeneity, it is difficult to establish biomarkers for each subset of asthma and to propose endotype-specific treatments. This review focuses on protein glycosylation as a process activated in asthma and ways to utilize it to develop novel biomarkers and treatments. We discuss known and relevant glycoproteins whose functions control disease development. The key role of glycoproteins in processes integral to asthma, such as inflammation, tissue remodeling, and repair, justifies our interest and research in the field of glycobiology. Altering the glycosylation states of proteins contributing to asthma can change the pathological processes that we previously failed to inhibit. Special emphasis is placed on chitotriosidase 1 (CHIT1), an enzyme capable of modifying LacNAc- and LacdiNAc-containing glycans. The expression and activity of CHIT1 are induced in human diseased lungs, and its pathological role has been demonstrated by both genetic and pharmacological approaches. We propose that studying the glycosylation pattern and enzymes involved in glycosylation in asthma can help in patient stratification and in developing personalized treatment.
The Efficacy of Hispidin and Magnesium Nanoparticles against Zearalenone-Induced Fungal Toxicity Causing Polycystic Ovarian Syndrome in Rats
Authors: Alenazi Amenah , Virk Promy , Almoqhem Reem , Alsharidah Amani , Al-Ghadi Muath Q. , Aljabr Waleed , Alasmari Fawaz , Albasher Gadah
Published: April 2024

Contamination by fungi and the toxins they secrete is a worldwide health concern. One such toxin is zearalenone (Zea), which is structurally similar to the hormone estrogen, interferes with its action on the reproductive system, and is therefore classified as an endocrine disruptor. This study aims to determine the effectiveness of hispidin and magnesium nanoparticles (MgONPs) against zearalenone-induced myotoxicity, which causes polycystic ovary syndrome (PCOS) in rats. A three-month exposure study was performed using female Wistar rats (n = 42) with an average weight of 100–150 g. The animals were divided into six groups (I to VI) of seven rats each. Group I was administered distilled water as a negative control. Group II was exposed to Zea 0.1 mg/kg b.w. through gavage daily. Group III was treated with 0.1 mg/kg of hispidin through gavage daily. Group IV was given 150 µg/mL MgONPs orally each day. Group V was treated with Zea 0.1 mg/kg b.w. + 0.1 mg/kg hispidin orally each day. Group VI was treated with Zea 0.1 mg/kg b.w. and the combination treatment of 0.1 mg/kg hispidin + 150 µg/mL MgONPs through gavage every day. The effectiveness of hispidin and MgONPs against Zea toxicity was evaluated in terms of ovarian histological changes, gene expression, oxidative stress biomarkers, biochemical variables, and hormone levels. The findings showed that exposure to Zea promotes PCOS in rats, with Zea-treated rats displaying hyper-ovulation with large cysts; elevated testosterone, luteinizing hormone, insulin, and glucose; and reduced sex hormone-binding globulin. In addition, qRT-PCR for aromatase (Cyp19α1) showed it to be downregulated. Treatment with hispidin improved the histopathological and hormonal situation and rescued expression of Cyp19α. Our data indicate the potential therapeutic effects of hispidin against Zea-induced Fungal Toxicity.
Effects of Anionic Liposome Delivery of All–Trans–Retinoic Acid on Neuroblastoma Cell Differentiation
Authors: Minò Antonio , Lopez Francesco , Barbaro Roberto , Barile Maria , Ambrosone Luigi , Colella Matilde
Published: April 2024

All–trans–retinoic acid (ATRA) has long been known to affect cell growth and differentiation. To improve ATRA’s therapeutic efficacy and pharmacodynamics, several delivery systems have been used. In this study, free ATRA and anionic–liposome–encapsulated ATRA were compared for their effects on SK–N–SH human neuroblastoma cell growth and differentiation. Anionic liposomes made of L–α–phosphatidylcholine (PC) and L–α–phosphatidic acid (PA), empty (PC–PA) and loaded with ATRA (PC–PA–ATRA), were characterized by dynamic light scattering (DLS) and electrophoretic mobility measurements, and drug entrapment efficiency (EE%) was measured to evaluate the applicability of the new colloidal formulation. The results of brightfield microscopy and cell growth curves indicated that ATRA, whether free or encapsulated, reduced growth and induced differentiation, resulting in SK–N–SH cells changing from epithelioid to neuronal–like morphologies, and producing a significant increase in neurite growth. To further characterize the neuro-differentiation of SK–N–SH cells, the expression of βIII–Tubulin and synaptophysin and mitochondria localization were analyzed via immunofluorescence. Increased expression of neuronal markers and a peculiar localization of mitochondria in the neuritic extensions were apparent both in ATRA– and PC–PA–ATRA–differentiated cells. As a whole, our results strongly indicate that ATRA treatment, by any means, can induce the differentiation of parent SK–N–SH, and they highlight that its encapsulation in anionic liposomes increases its differentiation ability in terms of the percentage of neurite–bearing cells. Interestingly, our data also suggest an unexpected differentiation capability of anionic liposomes per se. This work highlights the importance of developing and carefully testing novel delivery nanocarriers, which are a necessary first “step” in the development of new therapeutic settings.
Efficacy of LSVT LOUD® on Phonatory Control and Voice Quality in Patients with Primary Progressive Apraxia of Speech: Case Studies
Authors: Choi Yee Nam Candice , Martel-Sauvageau Vincent , Breton Myriam , Lavoie Monica , Laforce Robert , Bouvier Liziane
Published: April 2024

Primary progressive apraxia of speech (PPAOS) is a neurodegenerative syndrome characterized by the progressive and initially isolated or predominant onset of difficulties in the planning/programming of movements necessary for speech production and can be accompanied by dysarthria. To date, no study has used an evidence-based treatment to address phonation control in patients with PPAOS. The aim of this study was to evaluate the feasibility and efficacy of LSVT LOUD® as a treatment for phonatory control in speakers with PPAOS. Three speakers with PPAOS received LSVT LOUD® therapy, and changes in phonatory control, voice quality and prosody were measured immediately, and one, four and eight weeks after the end of the treatment. Overall, the results suggest that the treatment is feasible and could improve voice quality, intensity, and control in some patients with PPAOS. The generalization of the results is also discussed.