bluuebunny/crossref_metadata_2025 huggingface dataset:
The Effect of Ultrasonic Alternating Loads on Restoration of Permeability of Sedimentary Rocks during Crude Paraffinic Oil Flow
Authors: Riabokon Evgenii , Gladkikh Evgenii , Turbakov Mikhail , Kozhevnikov Evgenii , Guzev Mikhail , Yin Qian
Published: October 2023

This paper presents the results of experimental studies on the filtration of reservoir fluid through the rocks under the influence of nonlinear loads. A laboratory rig is assembled that allows for modeling the flow of fluid from the reservoir into the well during the propagation of elastic waves from the well. It is shown that depending on the permeability of the rock matrix as well as on the concentration of paraffins and asphaltenes in crude oil, the effect of the nonlinear load is different. Three types of sandstone are studied: low, medium, and high permeability. The greatest influence of nonlinear loads is observed in high-permeability sandstone. The effect manifests itself in fully unblocking the pore space from paraffins and asphaltenes accumulated in pore throats and restoring the oil permeability to its original value. In the case of medium-permeability sandstone subjected to nonlinear loads, blocking of the pore space is slow. In the case of low-permeability sandstone, the impact of nonlinear loads does not have a significant effect. When studying water filtration in the presence of residual oil saturation, the effect of nonlinear loads is observed as a mobilization of additional oil not previously involved in the filtration process, which also leads to an increase in the water permeability of the rock.
Assessment Framework for the Maintainability of Sewer Pipeline Systems
Authors: Alqahtani Fahad K. , Alsharef Abdullah , Hommadi Ghalib M. , Alammari Mohammed A.
Published: October 2023

The maintainability of sewer infrastructure systems is vital for public health, environmental protection, and the overall well-being of communities. However, maintenance and repair activities for sewer pipelines are often constrained, leading to challenges in effectively managing such infrastructures. To address these challenges, this study assessed the maintainability of sewer pipelines. A total of 15 defects were identified and categorized into structural, hydraulic, and quality defects after a comprehensive literature review was conducted and sewer pipeline experts were interviewed. Each failure caused by these defects was categorized as a pipe collapse associated with structural defects, sewer system overflow (SSO), odor, and groundwater contamination associated with quality defects. Apart from assessing the defects, the study identified potential solutions. After that, the obtained data were analyzed to determine the relative significance of each probability identified and its impact on four parameters, economic, social, environmental, and detection difficulty, using the relative importance index (RII), while the risk value index (RI) was applied to prioritize the defects. Furthermore, a data reliability assessment was utilized to evaluate internal consistency. The findings indicate that the probability of joint defects in the structural category caused by weaknesses in welded joints due to the misalignment of plastic pipes or joints, especially spigot and socket joints for rigid pipes, was the highest (RII = 0.733). Additionally, the ranking showed that the dumping of FOG had the highest priority, with an RI value of 0.535. This study offers a comprehensive maintainability framework that can be utilized by agencies assessing their current sewer infrastructure systems, in particular Arab Gulf countries including Saudi Arabia, which is the subject of this study.
Rapid Discrimination of Organic and Non-Organic Leafy Vegetables (Water Spinach, Amaranth, Lettuce, and Pakchoi) Using VIS-NIR Spectroscopy, Selective Wavelengths, and Linear Discriminant Analysis
Authors: Wu Yinggeng , Wu Bing , Ma Yao , Wang Meizhu , Feng Qi , He Zhiping
Published: October 2023

Organic leafy vegetables face challenges related to potential substitution with non-organic products and vulnerability to dehydration and deterioration. To address these concerns, visible and near-infrared spectroscopy (VIS-NIR) combined with linear discriminant analysis (LDA) was employed in this study to rapidly distinguish between organic and non-organic leafy vegetables. The organic category includes organic water spinach (Ipomoea aquatica Forsskal), amaranth (Amaranthus tricolor L.), lettuce (Lactuca sativa var. ramosa Hort.), and pakchoi (Brassica rapa var. chinensis (Linnaeus) Kitamura), while the non-organic category consists of their four non-organic counterparts. Binary classification was performed on the reflectance spectra of these vegetables’ leaves and stems, respectively. Given the broad range of the VIS-NIR spectrum, stability selection (SS), random forest (RF), and analysis of variance (ANOVA) were used to evaluate the importance of the wavelengths selected by genetic algorithm (GA). According to the GA-selected wavelengths and their SS-evaluated values and locations, the significant bands for leaf spectra classification were identified as 550–910 nm and 1380–1500 nm, while 750–900 nm and 1700–1820 nm were important for stem spectra classification. Using these selected bands in the LDA classification, classification accuracies of over 95% were achieved, showcasing the effectiveness of utilizing the proposed method to rapidly identify organic leafy vegetables and the feasibility and potential of using a cost-effective spectrometer that only contains necessary bands for authenticating.
Enhancing Industrial Design Competitiveness: Research and Application of a Machine Tool Industrial Design Decision-Making Method Based on Product Family Architecture and Systematic Evaluation
Authors: Yao Zhiwei , Wu Xumin , Wu Yu , Wen Xintong
Published: October 2023

This study employs an innovative industrial design decision-making approach based on the notion of product family architecture and structural equation modeling. The aim is to enhance the product competitiveness of machine tool enterprises, which is crucial for their development. Key domains within machine tool industrial design were systematically analyzed, leading to the creation of a design evaluation criteria system. Through quantitative data, the importance order of indicators at all levels was determined to enhance the overall competitiveness of product industrial design. First, teachers of relevant majors and professionals from machine tool companies completed a Likert scale questionnaire to validate the PFA-SEM hypothesis model. Next, a subjective–objective integrated weighting method was introduced to guide the ranking of multiple design elements. The feasibility of this method was confirmed by applying the Entropy-Based Multi-Objective Decision-Making Method (Technique for Order Preference by Similarity to Ideal Solution). These approaches helped achieve industrial design improvements in Heatking induction vertical hardening machines. This research aids in optimizing machine tool design, guiding design iterations, and serving as a reference for the development and optimization of other machine tool design solutions.
Fractal Behavior of Size Distribution and Specific Surface Area of Blasting Fragments
Authors: Li Ruize , Zhu Peng , Li Shuyi , Ding Cong , Lu Wenbo , Liu Yijia
Published: October 2023

The shape characteristics of blasting fragments are important for analyzing the blasting fragmentation mechanism and evaluating the fragmentation energy efficiency. In this study, through field blasting experiments and morphological parameter measurements, fractal theory was employed to reveal the fractal characteristics of rock blasting fragmentation. Using the fragmentation fractal dimension, the probability of rock blasting fragmentation was analyzed, and a predictive formula for the specific surface area (SSA) of blasting fragments was established. For fragments, the fractal dimensions of the particle size distribution calculated using different characteristic sizes were less than 2.0. When the fragment size distribution had dual fractal characteristics, significant differences were observed in the scale-free interval and fragmentation fractal dimension. Within the large scale-free interval, the fragmentation fractal dimension was mostly greater than 2.3, whereas within the small scale-free interval, it tended to be less than 2.0. The rock fragmentation process typically consisted of two or three breakage levels, with a similarity ratio of 0.5 and an average fragmentation probability ranging from 0.44 to 0.71. The predicted SSA of the blasting fragments closely matched the measured values. The findings provide valuable insights into the mechanism of rock blasting fragmentation and the calculation of fragmentation energy consumption.
Clinical Evaluation of a Novel Premixed Tricalcium Silicate Containing Bioceramic Sealer Used with Warm Carrier-Based Technique: A 12-Month Prospective Pilot Study
Authors: Spinelli Andrea , Zamparini Fausto , Lenzi Jacopo , Gandolfi Maria Giovanna , Prati Carlo
Published: October 2023

Background: This pilot prospective study analysed the clinical use of a new bioceramic premixed CaSi-containing sealer in association with a warm carrier-based technique. Methodology: Healthy patients (n = 38) requiring 40 root canal treatments were enrolled. Periapical X-rays were taken preoperatively, after root canal filling and after 1, 6, and 12 months. Two evaluators assessed the Periapical Index (PAI) and the sealer extrusion. The healing rate and survival rate were also evaluated. Barnard test was used to assess the relationship of each potential prognostic factor with periapical index (PAI) at 12-month follow-up. The significance level was set at 0.05. Results: Root canal treatments (n = 38) were analysed at the end-line (12 months). The total drop-out was 5% (two patients; two teeth). A total of 31 teeth (82%) (PAI 1-2) showed complete healing, while 7 (18%) are still healing. Cumulative survival was 100%. Apical extrusion of the sealers was observed in 18 cases (47%). Of these extrusions, nine (50%) resulted radiographically undetectable after 12 months. Conclusions: The study supports the use of premixed CaSi-based bioceramic sealers in association with carrier-based techniques. Periapical extrusion of the sealer and its radiographic modification or disappearance are possible events reported in the first 12 months.
Effects of Milking System Operating Conditions on the Milk-Fat-Percentage Measuring Accuracy of an Inline Light-Scattering Sensor
Authors: Khakimov Artyom R. , Dorokhov Aleksei S. , Pavkin Dmitriy Y. , Yurochka Sergey S. , Shkirin Alexey V. , Astashev Maxim E. , Ignatenko Dmitry N. , Izmailov Andrey Yu. , Gudkov Sergey V.
Published: October 2023

Contamination of the inner surface of milk hoses of milking systems with milk residues, water residues and pathogenic microflora has a negative impact on milk quality and can affect the accuracy of light-scattering sensors measuring milk quality parameters. A three-stage study of the influence of milking system operating conditions on the accuracy of a light-scattering sensor is presented. The growth of pathogenic microflora on the inner surface of milk hoses was studied, the flow of milk and milk hoses rinsing was simulated and the accuracy of the light-scattering sensor was tested under conditions of low-quality rinsing of the milking system. A significant difference in the total microbial count was detected between the milk residues of healthy animals and the milk residues of animals with subclinical mastitis (1.2 × 104 CFU/mL and 9.2 × 104 CFU/mL). The presence of Staphylococcus (aureus and epidermidis) was detected in quantities much lower than those that could interfere with the analysis of milk quality parameters. Simulation modeling allowed us to prove that even when rinsing with hot water (80 °C) without chemical solutions, pathogenic microflora in milk hoses of the milking system will be suppressed. Modeling of the milk flow in the measuring chamber of the device showed the stable replacement of milk in the measuring chamber and provided actual measurements of milk quality parameters. It has been determined that low-quality rinsing and water residues in milk hoses lead to a significant increase in measurement error up to 15–20% during the first 30–40 s after the milking system starts operating. The study results confirm that the light-scattering sensors are resistant to such factors of the milking system operation as contamination by pathogenic microflora (S. aureus and S. epidermidis) and low-quality rinsing of milk hoses.
Dynamic and Energy Efficient Cache Scheduling Framework for IoMT over ICN
Authors: Alourani Abdullah , Sardaraz Muhammad , Tahir Muhammad , Khan Muhammad Saud
Published: October 2023

The Internet of Medical Things (IoMT) is the network of medical devices, hardware infrastructure, and software applications used to connect the healthcare information technology. Massive traffic growth and user expectations cause challenges in the current exhausting models of IoMT data. To reduce the IoMT traffic, Information Centric Network (ICN) is a suitable technique. ICN uses persistent naming multicast communication that reduces the response time. ICN in IoMT provides a promising feature to reduce the overhead due to the distribution of commonly accessed contents. Some parameters such as energy consumption, communication cost, etc., influence the performance of sensors in the IoMT network. Excessive and unbalanced energy consumption degrades the network performance and lifetime. This article presents a framework called Dynamic Cache Scheme (DCS) that implements energy-efficient cache scheduling in IoMT over ICN to reduce network traffic. The proposed framework establishes a balance between the multi-hop traffic and data item freshness. The technique improves the freshness of data; thus, updated data are provided to the end-users via the effective utilization of caching in IoMT. The proposed framework is tested on important parameters, i.e., cache-hit-ratio, stretch, and content retrieval latency. The results obtained are compared with the state-of-the-art models. Results’ analysis shows that the proposed framework outperforms the compared models in terms of cache-hit-ratio, stretch, and content retrieval latency by 59.42%, 32.66%, and 18.8%, respectively. In the future, it is intended to explore the applicability of DCS in more scenarios and optimize further.
A Mongolian-Chinese Neural Machine Translation Model Based on Soft Target Templates and Contextual Knowledge
Authors: Ren Qing-Dao-Er-Ji , Pang Ziyu , Lang Jiajun
Published: October 2023

In recent years, Mongolian-Chinese neural machine translation (MCNMT) technology has made substantial progress. However, the establishment of the Mongolian dataset requires a significant amount of financial and material investment, which has become a major obstacle to the performance of MCNMT. Pre-training and fine-tuning technology have also achieved great success in the field of natural language processing, but how to fully exploit the potential of pre-training language models (PLMs) in MCNMT has become an urgent problem to be solved. Therefore, this paper proposes a novel MCNMT model based on the soft target template and contextual knowledge. Firstly, to learn the grammatical structure of target sentences, a selection-based parsing tree is adopted to generate candidate templates that are used as soft target templates. The template information is merged with the encoder-decoder framework, fully utilizing the templates and source text information to guide the translation process. Secondly, the translation model learns the contextual knowledge of sentences from the BERT pre-training model through the dynamic fusion mechanism and knowledge extraction paradigm, so as to improve the model’s utilization rate of language knowledge. Finally, the translation performance of the proposed model is further improved by integrating contextual knowledge and soft target templates by using a scaling factor. The effectiveness of the modified model is verified by a large number of data experiments, and the calculated BLEU (BiLingual Evaluation Understudy) value is increased by 4.032 points compared with the baseline MCNMT model of Transformers.
A Brief, In-Depth Survey of Deep Learning-Based Image Watermarking
Authors: Zhong Xin , Das Arjon , Alrasheedi Fahad , Tanvir Abdullah
Published: October 2023

This paper presents a comprehensive survey of deep learning-based image watermarking; this technique entails the invisible embedding and extraction of watermarks within a cover image, aiming for a seamless combination of robustness and adaptability. We navigate the complex landscape of this interdisciplinary domain, linking historical foundations, current innovations, and prospective developments. Unlike existing literature, our study concentrates exclusively on image watermarking with deep learning, delivering an in-depth, yet brief analysis enriched by three fundamental contributions. First, we introduce a refined categorization, segmenting the field into embedder–extractor, deep networks for feature transformation, and hybrid methods. This taxonomy, inspired by the varied roles of deep learning across studies, is designed to infuse clarity, offering readers technical insights and directional guidance. Second, our exploration dives into representative methodologies, encapsulating the diverse research directions and inherent challenges within each category to provide a consolidated perspective. Lastly, we venture beyond established boundaries, outlining emerging frontiers and providing detailed insights into prospective research avenues.
A Method for Measuring the Absolute Position and Attitude Parameters of a Moving Rigid Body Using a Monocular Camera
Authors: Guo Shengjun , Zhao Zuoxi , Guo Linyang , Wei Min
Published: October 2023

To verify the dynamic model of a paddy-field laser grader and solve the problem of the unstable high-speed operation of the grader, a method for measuring the absolute pose parameters of a moving rigid body using a monocular camera is proposed. The proposed method does not require calibration beforehand. Using more than six non-coplanar control points symmetrically arranged in the rigid-body and world coordinate systems, the matrices of rotation and translation between the camera and two coordinate systems are obtained and the absolute pose of the rigid body is measured. In this paper, formulas of the absolute pose measurement of a moving rigid body are deduced systematically and the complete implementation is presented. Position and attitude measurement experiments carried out on a three-axis precision turntable show that the average absolute error in the attitude angle of a moving rigid body measured by an uncalibrated camera at different positions changes by no more than 0.2 degrees. An analysis of the three-dimensional coordinate errors of the centroid of a moving rigid body shows little deviation in measurements taken at the three camera positions, with the maximum deviation of the average absolute error being 0.53 cm and the maximum deviation of the standard deviation being 0.66 cm. The proposed method can measure the absolute pose of a rigid body and is insensitive to the position of the camera in the measurement process. This work thus provides guidance for the repeated measurement of the absolute pose of a moving rigid body using a monocular camera.
Development of a Data-Based Machine Learning Model for Classifying and Predicting Property Damage Caused by Fire
Authors: Lee Jongho , Shin Jiuk , Lee Jaewook , Park Chorong , Sohn Dongwook
Published: October 2023

Large fires in factories cause severe human casualties and property damage. Thus, preparing more economical and efficient management strategies for fire prevention can significantly improve fire safety. This study deals with property damage grade prediction by fire based on simplified building information. This paper’s primary objective is to propose and verify a framework for predicting the scale of property damage caused by fire using machine learning (ML). Korean public datasets are collected and preprocessed, and ML algorithms are trained with only 15 input data using building register and fire scenario information. Four models (artificial neural network (ANN), decision tree (DT), k-nearest neighbor (KNN), and random forest (RF)) are used for ML. The RF model is the most suitable for this study, with recall and precision of 74.2% and 73.8%, respectively. Structure, floor, causes, and total floor area are the critical factors that govern the fire size. This study proposes a novel approach by utilizing ML models to accurately and rapidly predict the size of fire damage based on basic building information. By analyzing domestic fire incident data and creating fire scenarios, a similar ML model can be developed.
Analysis of the Performance of a Hybrid Thermal Power Plant Using Adaptive Neuro-Fuzzy Inference System (ANFIS)-Based Approaches
Authors: Kabengele Kantu T. , Olayode Isaac O. , Tartibu Lagouge K.
Published: October 2023

The hybridization of conventional thermal power plants by the incorporation of renewable energy systems has witnessed widespread adoption in recent years. This trend aims not only to mitigate carbon emissions but also to enhance the overall efficiency and performance of these power generation facilities. However, calculating the performance of such intricate systems using fundamental thermodynamic equations proves to be both laborious and time-intensive. Nevertheless, possessing accurate and real-time insights into their performance is of utmost significance to ensure optimal plant operation, facilitate decision making, and streamline power production planning. This paper explores the novel application of machine learning techniques to predict the performance of hybrid thermal power plants, specifically the integrated solar combined cycle power plant (ISCCPP). These plants combine conventional thermal power generation with renewable energy sources, making them crucial in the context of carbon reduction and enhanced efficiency. We employ three machine learning approaches: the adaptive neuro-fuzzy inference system (ANFIS), ANFIS optimized via particle swarm optimization (ANFIS-PSO), and ANFIS optimized through a genetic algorithm (ANFIS-GA). These methods are applied to the complex ISCCPP, comprising steam and gas turbine sections and a concentrated solar power system. The results highlight the accuracy of ANFIS-based models in evaluating and predicting plant performance, with an exceptional overall correlation coefficient of 0.9991. Importantly, integrating evolutionary algorithms (PSO and GA) into ANFIS significantly enhances performance, yielding correlation coefficients of 0.9994 for ANFIS-PSO and 0.9997 for ANFIS-GA, with ANFIS-GA outperforming the others. This research provides a robust tool for designers, energy managers, and decision makers, offering valuable support in assessing the performance of hybrid thermal power plants. As the world transitions to cleaner energy sources, the insights gained here are poised to have a significant impact on the growing number of these thermal power plants globally.
Unveiling the Strain Rate Sensitivity of G18NiCrMo3-6 CAST Steel in Tension/Compression Asymmetry
Authors: Çetin Barış , Bayraktar Emin , Aslan Ozgur
Published: October 2023

This research was devoted to unveiling the strain rate sensitivity (SRS) of G18NiCrMo3-6 cast steel in tension/compression asymmetry. For that purpose, detailed mechanical characterization tests were conducted providing a process window covering quasi-static and medium strain rate regimes (0.001, 0.1, 10 [s−1]) in tension and compression states. Through this experimental effort, the SRS of the material could be extracted by a function of strain and the strain rate which enabled us to create a mathematical expression to easily be implemented as a state variable for constitutive material modeling. Finally, a pressure- and rate-dependent constitutive material model on the basis of the Cocks’89 yield locus definition was created using a subroutine (UMAT) file and the material parameters were verified with respect to the experimental data. The UMAT file also takes into account the tension/compression asymmetry in yielding to handle the effect of the porous media plasticity concept. The predictions of the proposed material model are in line with the experimental outputs.
Congruence Representations via Soft Ideals in Soft Topological Spaces
Authors: Ameen Zanyar A. , Alqahtani Mesfer H.
Published: October 2023

This article starts with a study of the congruence of soft sets modulo soft ideals. Different types of soft ideals in soft topological spaces are used to introduce new weak classes of soft open sets. Namely, soft open sets modulo soft nowhere dense sets and soft open sets modulo soft sets of the first category. The basic properties and representations of these classes are established. The class of soft open sets modulo the soft nowhere dense sets forms a soft algebra. Elements in this soft algebra are primarily the soft sets whose soft boundaries are soft nowhere dense sets. The class of soft open sets modulo soft sets of the first category, known as soft sets of the Baire property, is a soft σ-algebra. In this work, we mainly focus on the soft σ-algebra of soft sets with the Baire property. We show that soft sets with the Baire property can be represented in terms of various natural classes of soft sets in soft topological spaces. In addition, we see that the soft σ-algebra of soft sets with the Baire property includes the soft Borel σ-algebra. We further show that soft sets with the Baire property in a certain soft topology are equal to soft Borel sets in the cluster soft topology formed by the original one.
Surface Pencil Couple with Bertrand Couple as Joint Principal Curves in Galilean 3-Space
Authors: Alluhaibi Nadia , Abdel-Baky Rashad A.
Published: October 2023

A principal curve on a surface plays a paramount role in reasonable implementations. A curve on a surface is a principal curve if its tangents are principal directions. Using the Serret–Frenet frame, the surface pencil couple can be expressed as linear combinations of the components of the local frames in Galilean 3-space G3. With these parametric representations, a family of surfaces using principal curves (curvature lines) are constructed, and the necessary and sufficient condition for the given Bertrand couple to be the principal curves on these surfaces are derived in our approach. Moreover, the necessary and sufficient condition for the given Bertrand couple to satisfy the principal curves and the geodesic requirements are also analyzed. As implementations of our main consequences, we expound upon some models to confirm the method.
Cerebellar Transcranial Direct Current Stimulation Applied over Multiple Days Does Not Enhance Motor Learning of a Complex Overhand Throwing Task in Young Adults
Authors: Pantovic Milan , Lidstone Daniel E. , de Albuquerque Lidio Lima , Wilkins Erik W. , Munoz Irwin A. , Aynlender Daniel G. , Morris Desiree , Dufek Janet S. , Poston Brach
Published: October 2023

Cerebellar transcranial direct current stimulation (tDCS) enhances motor skill and learning in relatively simple motor tasks, but it is unclear if c-tDCS can improve motor performance in complex motor tasks. The purpose of this study was to determine the influence of c-tDCS applied over multiple days on motor learning in a complex overhand throwing task. In a double-blind, randomized, between-subjects, SHAM-controlled, experimental design, 30 young adults were assigned to either a c-tDCS or a SHAM group. Participants completed three identical experiments on consecutive days that involved overhand throwing in a pre-test block, five practice blocks with concurrent c-tDCS, and a post-test block. Overhand throwing endpoint accuracy was quantified as the endpoint error. The first dorsal interosseous muscle motor evoked potential (MEP) amplitude elicited by transcranial magnetic stimulation was used to quantify primary motor cortex (M1) excitability modulations via c-tDCS. Endpoint error significantly decreased over the 3 days of practice, but the magnitude of decrease was not significantly different between the c-tDCS and SHAM group. Similarly, MEP amplitude slightly increased from the pre-tests to the post-tests, but these increases did not differ between groups. These results indicate that multi-day c-tDCS does not improve motor learning in an overhand throwing task or increase M1 excitability.
A Comprehensive Review of the Current Status of the Cellular Neurobiology of Psychedelics
Authors: Banushi Blerida , Polito Vince
Published: October 2023

Psychedelic substances have gained significant attention in recent years for their potential therapeutic effects on various psychiatric disorders. This review delves into the intricate cellular neurobiology of psychedelics, emphasizing their potential therapeutic applications in addressing the global burden of mental illness. It focuses on contemporary research into the pharmacological and molecular mechanisms underlying these substances, particularly the role of 5-HT2A receptor signaling and the promotion of plasticity through the TrkB-BDNF pathway. The review also discusses how psychedelics affect various receptors and pathways and explores their potential as anti-inflammatory agents. Overall, this research represents a significant development in biomedical sciences with the potential to transform mental health treatments.
LC-MS/MS Quantitation of HILIC-Enriched N-glycopeptides Derived from Low-Abundance Serum Glycoproteins in Patients with Narcolepsy Type 1
Authors: Atashi Mojgan , Reyes Cristian D. Gutierrez , Sandilya Vishal , Purba Waziha , Ahmadi Parisa , Hakim Md. Abdul , Kobeissy Firas , Plazzi Giuseppe , Moresco Monica , Lanuzza Bartolo , Ferri Raffaele , Mechref Yehia
Published: October 2023

Glycoproteomic analysis is always challenging because of low abundance and complex site-specific heterogeneity. Glycoproteins are involved in various biological processes such as cell signaling, adhesion, and cell–cell communication and may serve as potential biomarkers when analyzing different diseases. Here, we investigate glycoproteins in narcolepsy type 1 (NT1) disease, a form of narcolepsy characterized by cataplexy—the sudden onset of muscle paralysis that is typically triggered by intense emotions. In this study, 27 human blood serum samples were analyzed, 16 from NT1 patients and 11 from healthy individuals serving as controls. We quantified hydrophilic interaction liquid chromatography (HILIC)-enriched glycopeptides from low-abundance serum samples of controls and NT1 patients via LC-MS/MS. Twenty-eight unique N-glycopeptides showed significant changes between the two studied groups. The sialylated N-glycopeptide structures LPTQNITFQTESSVAEQEAEFQSPK HexNAc6, Hex3, Neu5Ac2 (derived from the ITIH4 protein) and the structure IVLDPSGSMNIYLVLDGSDSIGASNFTGAK HexNAc5, Hex4, Fuc1 (derived from the CFB protein), with p values of 0.008 and 0.01, respectively, were elevated in NT1 samples compared with controls. In addition, the N-glycopeptide protein sources Ceruloplasmin, Complement factor B, and ITH4 were observed to play an important role in the complement activation and acute-phase response signaling pathways. This may explain the possible association between the biomarkers and pathophysiological effects.
MicroRNA-Mediated Regulation of Histone-Modifying Enzymes in Cancer: Mechanisms and Therapeutic Implications
Authors: Szczepanek Joanna , Tretyn Andrzej
Published: October 2023

In the past decade, significant advances in molecular research have provided a deeper understanding of the intricate regulatory mechanisms involved in carcinogenesis. MicroRNAs, short non-coding RNA sequences, exert substantial influence on gene expression by repressing translation or inducing mRNA degradation. In the context of cancer, miRNA dysregulation is prevalent and closely associated with various stages of carcinogenesis, including initiation, progression, and metastasis. One crucial aspect of the cancer phenotype is the activity of histone-modifying enzymes that govern chromatin accessibility for transcription factors, thus impacting gene expression. Recent studies have revealed that miRNAs play a significant role in modulating these histone-modifying enzymes, leading to significant implications for genes related to proliferation, differentiation, and apoptosis in cancer cells. This article provides an overview of current research on the mechanisms by which miRNAs regulate the activity of histone-modifying enzymes in the context of cancer. Both direct and indirect mechanisms through which miRNAs influence enzyme expression are discussed. Additionally, potential therapeutic implications arising from miRNA manipulation to selectively impact histone-modifying enzyme activity are presented. The insights from this analysis hold significant therapeutic promise, suggesting the utility of miRNAs as tools for the precise regulation of chromatin-related processes and gene expression. A contemporary focus on molecular regulatory mechanisms opens therapeutic pathways that can effectively influence the control of tumor cell growth and dissemination.
The Role of Soluble Adenylyl Cyclase in the Regulation of Flagellar Motility in Ascidian Sperm
Authors: Shiba Kogiku , Inaba Kazuo
Published: October 2023

Flagellar motility in sperm is activated and regulated by factors related to the eggs at fertilization. In the ascidian Ciona intestinalis, a sulfated steroid called the SAAF (sperm activating and attracting factor) induces both sperm motility activation and chemotaxis. Cyclic AMP (cAMP) is one of the most important intracellular factors in the sperm signaling pathway. Adenylyl cyclase (AC) is the key enzyme that synthesizes cAMP at the onset of the signaling pathway in all cellular functions. We previously reported that both transmembrane AC (tmAC) and soluble AC (sAC) play important roles in sperm motility in Ciona. The tmAC plays a major role in the SAAF-induced activation of sperm motility. On the other hand, sAC is involved in the regulation of flagellar beat frequency and the Ca2+-dependent chemotactic movement of sperm. In this study, we focused on the role of sAC in the regulation of flagellar motility in Ciona sperm chemotaxis. The immunochemical analysis revealed that several isoforms of sAC protein were expressed in Ciona sperm, as reported in mammals and sea urchins. We demonstrated that sAC inhibition caused strong and transient asymmetrization during the chemotactic turn, and then sperm failed to turn toward the SAAF. In addition, real-time Ca2+ imaging in sperm flagella revealed that sAC inhibition induced an excessive and prolonged Ca2+ influx to flagella. These results indicate that sAC plays a key role in sperm chemotaxis by regulating the clearance of [Ca2+]i and by modulating Ca2+-dependent flagellar waveform conversion.
Self-Assembled Amphiphilic Chitosan Nanomicelles: Synthesis, Characterization and Antibacterial Activity
Authors: Qi Yi , Chen Qizhou , Cai Xiaofen , Liu Lifen , Jiang Yuwei , Zhu Xufeng , Huang Zhicheng , Wu Kefeng , Luo Hui , Ouyang Qianqian
Published: October 2023

Although amphiphilic chitosan has been widely studied as a drug carrier for drug delivery, fewer studies have been conducted on the antimicrobial activity of amphiphilic chitosan. In this study, we successfully synthesized deoxycholic acid-modified chitosan (CS-DA) by grafting deoxycholic acid (DA) onto chitosan C2-NH2, followed by grafting succinic anhydride, to prepare a novel amphiphilic chitosan (CS-DA-SA). The substitution degree was 23.93% for deoxycholic acid and 29.25% for succinic anhydride. Both CS-DA and CS-DA-SA showed good blood compatibility. Notably, the synthesized CS-DA-SA can self-assemble to form nanomicelles at low concentrations in an aqueous environment. The results of CS, CS-DA, and CS-DA-SA against Escherichia coli and Staphylococcus aureus showed that CS-DA and CS-DA-SA exhibited stronger antimicrobial effects than CS. CS-DA-SA may exert its antimicrobial effect by disrupting cell membranes or forming a membrane on the cell surface. Overall, the novel CS-DA-SA biomaterials have a promising future in antibacterial therapy.
sFlt-1 Levels as a Predicting Tool in Placental Dysfunction Complications in Multiple Pregnancies
Authors: Giardini Valentina , Grilli Leonora , Terzaghi Alessandra , Todyrenchuk Lyudmyla , Zavettieri Caterina , Mazzoni Giulia , Cozzolino Sabrina , Casati Marco , Vergani Patrizia , Locatelli Anna
Published: October 2023

Background: several studies have demonstrated that angiogenic markers can improve the clinical management of hypertensive disorders (HDs) and fetal growth restriction (FGR) in singleton pregnancies, but few studies have evaluated the performance of these tests in multiple pregnancies. Our aim was to investigate the role of soluble fms-like tyrosine kinase 1 (sFlt-1) in predicting adverse obstetric outcomes in hospitalized multiple pregnancies with HD (preeclampsia/gestational hypertension/uncontrolled chronic hypertension) and/or FGR in one or more fetuses. Methods: A retrospective analysis of multiple pregnancies with HD/FGR occurring after the 20th gestational week. Pregnant women were divided into two groups: women with high levels of sFlt-1 and those with low levels of sFlt-1. A value of sFlt-1 greater than or equal to 15,802 pg/mL was considered arbitrarily high, as it is equivalent to two times the 90th percentile expected in an uncomplicated full-term singleton pregnancy based on data from a prospective multicenter study (7901 pg/mL). Results: The cohort included 39 multiple pregnancies. There were no cases of birth <34 weeks, HELLP syndrome, ICU admission, and urgent cesarean sections for HD/FGR complications reported among women with low levels of sFlt-1. Conclusions: A cut-off value of sFlt-1 ≥ 15,802 pg/mL could represent a valuable tool for predicting adverse obstetric outcomes in multiple pregnancies hospitalized for HD/FGR disorders, regardless of gestational age and chorionicity.
Risk and Protective Factors of Depression in Family and School Domains for Chinese Early Adolescents: An Association Rule Mining Approach
Authors: Wang Chen , Zhou Ting , Fu Lin , Xie Dong , Qi Huiying , Huang Zheng
Published: October 2023

Depression is one of the most common psychological problems in adolescence. Familial and school-related factors are closely related to adolescents’ depression, but their combined effects need further examination. The purpose of this study was to explore the combined effects of risk/protective factors of depression in family and school domains using a sample of Chinese adolescents differing in gender, age group and left-behind status. A total of 2455 Chinese students in primary and secondary school participated in the cross-sectional survey and reported multiple risk/protective factors in family and school environments and depressive symptoms. Association rule mining, a machine learning method, was used in the data analyses to identify the correlation between risk/protective factor combinations and depression. We found that (1) Family cohesion, family conflict, peer support, and teacher support emerged as the strongest factors associated with adolescent depression; (2) The combination of these aforementioned factors further strengthened their association with depression; (3) Female gender, middle school students, and family socioeconomic disadvantages attenuated the protective effects of positive relational factors while exacerbating the deleterious effects of negative relational factors; (4) For individuals at risk, lack of mental health education resources at school intensified the negative impact; (5) The risk and protective factors of depression varied according to gender, age stage and left-behind status. In conclusion, the findings shed light on the identification of high-risk adolescents for depression and underscore the importance of tailored programs targeting specific subgroups based on gender, age, or left-behind status.
‘Days of Frustration’: A Qualitative Study of Adolescents’ Thoughts and Experiences of Schooling after Early Dropout
Authors: Ottosen Karl Ottar , Goll Charlotte Bjørnskov , Wynn Rolf , Sørlie Tore
Published: October 2023

School dropout increases the risk of unemployment, health problems, and disability benefits. Employing an ecological-developmental perspective, we analyzed the interviews of thirteen students from a peripheral Norwegian county, aiming to explore the possible influence of upbringing and schooling on dropout. The analysis revealed that dropout was associated with an unstable family situation, lack of structure in everyday life, unresolved complex learning difficulties, bullying, and a tough existence in a rented room. The participants conveyed a sense of defeat, frustration, and an absence of meaningful alternatives. However, two participants had actively chosen to discontinue their education; this was because they preferred work practice to allow them time to mature and re-orientate in relation to future educational and career choices. Their families and social networks contributed actively to the implementation of their future plans. The findings point to the importance of studying interventions that may prevent school dropout, and that address central factors in the process of school dropout, such as social support, academic achievement, and parental involvement.
When the Going Gets Challenging—Motivational Theories as a Driver for Workplace Health Promotion, Employees Well-Being and Quality of Life
Authors: Coco Lisa Karolin , Heidler Petra , Fischer Holger Adam , Albanese Valeria , Marzo Roy Rillera , Kozon Vlastimil
Published: October 2023

Being characterized by high numbers of physical and mental health issues, the veterinary sector faces some negative peculiarities compared to other professions. To evaluate and possibly improve employees’ well-being and to reward according to individual motivation, managing staff and the profession in general must understand every individual’s motivation. Defining the motivation of veterinary professionals in a multidisciplinary way can be the first step in increasing the well-being of employees. To assemble current theories of work motivation and adapt and extend them to serve the veterinary profession, basic knowledge of the classical general psychological motivational theories and work motivational theories is inevitable. The authors aimed to illustrate the different motivational theories’ key aspects and historical evolution to provide veterinary personnel with broad knowledge. In addition, the availability of already existing literature should be evaluated. A scoping review was performed. Methodological, empirical, review, and theoretical articles were evaluated. Only a minority of the studies (1.3%) evaluated motivational theories in veterinary health care. This reflects that there is a need for research in this field. Still, it is illustrated that the veterinarian profession would benefit from the implementation of general psychological as well as motivational work theories.
Short-Term Forecasting of Daily Electricity of Different Campus Building Clusters Based on a Combined Forecasting Model
Authors: Wu Wenyu , Deng Qinli , Shan Xiaofang , Miao Lei , Wang Rui , Ren Zhigang
Published: October 2023

In the building field, campus buildings are a building group with great energy-saving potential due to a lack of reasonable energy management policies. The accurate prediction of power energy usage is the basis for energy management. To address this issue, this study proposes a novel combined forecasting model based on clustering results, which can achieve a short-time prediction of daily electricity based on a campus building’s electricity data over the past 15 days. Considering the diversity of campus buildings in energy consumption and functional aspects, the selected campus buildings are firstly classified into three categories using K-Means clustering in terms of their daily power consumption. Compared with the mainstream building energy consumption prediction models, i.e., LSTM and SVR, the results show that the combined forecast model is superior to other models. Furthermore, an average percentage fluctuation (APF) index is found to be close to the MAPE, which can reflect the prediction accuracy in advance.
Study on the Bending Performance of High-Strength and High-Ductility CRE-Reinforced Concrete Beams
Authors: Zhang Xiao , Sun Youkun , Yang Xiaoxia , Sun Lele , Wang Peijun
Published: October 2023

Constant resistance energy (CRE) steel reinforcement has a yield strength of up to 750 MPa and an ultimate elongation of more than 20%. CRE reinforcement overcomes the contradiction between high yield strength and high uniform elongation of ordinary high-strength bars. This paper explores the flexural performance and load-carrying mechanisms of CRE-reinforced concrete beams through a series of experiments, while also presenting a theoretical analytical method for such specimens. Flexural tests on six CRE-reinforced concrete beams and two control tests on hot-rolled ribbed bar 400 (HRB400)-reinforced concrete beams were conducted in this paper. The study examines the influence of the shear–span ratio and reinforcement type on the mechanical response of the beams, including cracking load, yield load, and ultimate load, while analyzing the variation patterns of concrete strain and reinforcement strain. The experimental results demonstrate that as the shear–span ratio decreases, the crack resistance and load-carrying capacity of CRE-reinforced concrete beams improve. Under equivalent conditions, CRE-reinforced concrete beams exhibit higher load-carrying capacity compared to HRB-reinforced concrete beams, surpassing the latter by approximately 43% in terms of ultimate load. Additionally, this paper proposes a calculation method for the mechanical response of NPR-reinforced concrete beams and compares the theoretical values with the experimental values. The differences between the two are within 13%, which proves the reliability of the calculation method.
Preparation of Fenton Catalysts for Water Treatment
Authors: Chen Wei , Zeng Mei , Yang Junjiao
Published: October 2023

In the heterogeneous Fenton reaction, a solid catalyst reacts with H2O2 to generate highly oxidizing free radicals, that degrade organic pollutants in aqueous solutions. In this study, impregnation calcination was used to modify activated carbon and load it with various metal compounds. The synergistic catalysis of the various metal compounds showed improved catalytic activity, and the prepared heterogeneous Fenton catalyst exhibited high catalytic activity, a wide pH range, and good stability. The concentration ratios of the Fenton catalyst impregnation solutions-were as follows: Fe3+, Cu2+, Mn2+, and Ce3+ at 0.45, 0.72, 0.19, and 0.11 mol/L, respectively. The optimal sintering temperature of AC impregnation was determined through TGA/DSC, SEM, SEM-EDS, XPS, and XRD testing. At a final calcination temperature of 900 °C, the degradation efficiency of 10 ppm methylene blue reached 98.25% at pH 5 with 5 mM H2O2. After ten soaking cycles, the degradation efficiency exceeded 90%. The structure and performance of the catalysts were characterized using EPR, BET, ICP, and UV spectroscopy, demonstrating the excellent performance of the catalyst and providing an improved treatment plan for solving wastewater problems.