bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigation of the Complexation Activity of 2,4-Dithiouracil with Au(III) and Cu(II) and Biological Activity of the Newly Formed Complexes
Authors: Marinova Petya , Stoitsov Dimitar , Burdzhiev Nikola , Tsoneva Slava , Blazheva Denica , Slavchev Aleksandar , Varbanova Evelina , Penchev Plamen
Published: July 2024

The goal of this study is to synthesize, determine the structure, and examine the antimicrobial properties of novel Cu(II) and Au(III) complexes of 2,4-dithiouracil and its derivatives. These complexes were obtained by mixing aqueous solutions of the corresponding metal salts with the ligand dissolved in DMSO and aqueous NaOH, using a metal-to-ligand ratio of 1:4:2. The structures of the new compounds were analyzed by melting point determination, microwave plasma atomic emission spectrometry (MP-AES) for Cu and Au, inductively coupled plasma optical emission spectrometry (ICP-OES) for S, attenuated total reflection (ATR), solution and solid-state NMR, and Raman spectroscopy. The data for 2,4-dithiouracil obtained from the 1H NMR, 13C NMR, distortionless enhancement by polarization transfer spectrum (DEPT-135), proton–proton homonuclear correlation spectrum (1H-1H COSY), long-range 1H-13C heteronuclear multiple bond correlation experiment (HMBC), and heteronuclear single quantum coherence spectra (HSQC) aided the interpretation of the NMR data for the gold and copper complexes. Furthermore, the antimicrobial effect of the free ligands and their complexes was assessed against Gram-positive and Gram-negative bacteria, as well as yeasts.
Design Considerations Concerning an Innovative Drive System for a Manual Wheelchair
Authors: Kończak Michał , Kukla Mateusz , Rybarczyk Dominik
Published: July 2024

Manual wheelchairs, which are the basic means of transport for people with disabilities, are usually characterized by an inefficient adaptation to the physical capabilities of their users. For this reason, it is advisable to search for solutions that will allow us to change the parameters of the mechanical power generated by human muscles. For this purpose, mechanical gearing known from other solutions, for example, from bicycles, can be used. The paper describes the design methodology and a number of issues related to the construction of an innovative wheelchair prototype using a chain transmission in its drive system. This solution allows for the implementation of a variable ratio between the wheels and the pushrims. Thus, it effectively allows for matching the demand for driving torque to the movement conditions and the physical capabilities of its user. The use of such a system provides the basis for increasing the efficiency of the manual propulsion process. Initial studies show that changing the gear ratio allows for different speeds of the wheelchair wheel. In the tests conducted, the root mean square of this value varied from 15.2 RPM to 35.5 RPM, which resulted in a change in power from 15.8 W to 40.1 W. Of course, the values of rotational speed and torque show a cyclically changing character, which results from the intermittent nature of generating drive by the wheelchair user. The average peak values of rotational speed were 31.4 ± 1.7 RPM, 44.3 ± 3.4 RPM and 57.9 ± 3.4 RPM, while the torque was 12.1 ± 0.5 Nm, 12.4 ± 0.4 Nm and 14.1 ± 0.6 Nm for Gears 1, 4 and 6, respectively.
Context-Encoder-Based Image Inpainting for Ancient Chinese Silk
Authors: Wang Quan , He Shanshan , Su Miao , Zhao Feng
Published: July 2024

The rapid advancement of deep learning technologies presents novel opportunities for restoring damaged patterns in ancient silk, which is pivotal for the preservation and propagation of ancient silk culture. This study systematically scrutinizes the evolutionary trajectory of image inpainting algorithms, with a particular emphasis on those firmly rooted in the Context-Encoder structure. To achieve this study’s objectives, a meticulously curated dataset comprising 6996 samples of ancient Chinese silk (256 × 256 pixels) was employed. Context-Encoder-based image inpainting models—LISK, MADF, and MEDFE—were employed to inpaint damaged patterns. The ensuing restoration effects underwent rigorous evaluation, providing a comprehensive analysis of the inherent strengths and limitations of each model. This study not only provides a theoretical foundation for adopting image restoration algorithms grounded in the Context-Encoder structure but also offers ample scope for exploration in achieving more effective restorations of ancient damaged silk.
Effects of Palm Kernel Shells (PKS) on Mechanical and Physical Properties of Fine Lateritic Soils Developed on Basalt in Bangangté (West Cameroon): Significance for Pavement Application
Authors: Njuikom Djoumbi Verlène Hardy , Yato Katte Valentine , Tiomo Idriss Franklin , Wouatong Armand Sylvain Ludovic
Published: July 2024

The utilization of an agricultural waste product known as palm kernel shells (PKS) combined with fine laterites (from basalt in Bangangté, West Cameroon) to produce low-cost and innovative materials with good bearing capacities for road pavement was investigated. Fine laterites from two soil profiles (BL31 and BL32) and made up of kaolinite, hematite, goethite, gibbsite, anatase, ilmenite and magnetite minerals were partially replaced with PKS at 15%, 25%, 35%, and 45% by weight. Physical and mechanical tests, including particle size distribution, Atterberg limits, unsoaked and soaked California Bearing Ratio (UCBR and SCBR), unconfined compressive strength (UCS), and tensile strength (Rt), were performed on the different mixtures. After the addition of PKS, a decrease in fine particle content (77 to 38%), liquidity limit (LL: 72 to 61%), plasticity index (PI: 30 to 19%), maximum dry density (MDD: 1.685 to 1.29 t/m3), and optimum moisture content (OMC: 27.5 to 24.0%) was noticed. Additionally, there was an increase in UCBR (16–72%), SCBR (14–66%), UCS (1.07–7.67 MPa), and Rt (2.24–9.71 MPa). This allows new materials suitable for the construction of base layers for low trafficked roads (T1–T2), as well as sub-base and base layers for high trafficked roads (T3), to be obtained. This newly formed material can be recommended locally for road construction works, though more in-depth studies are required.
Study of the Dependence of the Properties of the Ti EBPVD Layer Deposited on the Inner Surface of the Tube on the Position of the Evaporated Target Using the Statistical Methods
Authors: Andrejiova Miriam , Kottfer Daniel , Grincova Anna , Jus Milan
Published: July 2024

This article presents the properties of a Ti coating (angle of grain growth, thickness of the deposited layer, adhesion, and roughness of the deposited layer) deposited on the inner surface of a steel tube. The Ti coating was deposited using the EB PVD (electron beam physical vapor deposition) method. The areas in which the measurements were carried out are defined by the height of the measured area under the target (from 305 to 425 mm) and the radius of the tube (from 100 to 140 mm). The measurements showed that the angle of grain growth is in an interval from 40 to 61°. The results of the statistical analysis demonstrate that the height of the measured area and the tube radius significantly impact the angle of grain growth and adhesion. On the other hand, the impact of the height of the measured area and the tube radius on the roughness of the applied coating was not confirmed.
Developing Standard BIM Execution Plans for Complex Construction Projects
Authors: Abdelalim Ahmed Mohammed , Shawky Kamal , Alnaser Aljawharah A. , Shibeika Amna , Sherif Alaa
Published: July 2024

This study addresses the critical need for standardizing building information modeling (BIM) execution plans (BEPs) in the architecture, engineering, construction, and operations (AECO) sector. Through the analysis of 36 BEP documents from international organizations, we have identified crucial components and put forth a comprehensive framework with the objective of improving digital transformation and collaboration in intricate construction projects. This study utilizes scientometric analysis to chart the development of BEP standards and incorporates empirical data from industry surveys to verify the suggested framework. The results of our research emphasize the advantages of using standardized building execution plans (BEPs) to decrease inefficiencies and enhance project outcomes. This makes a substantial contribution to the field of building information modeling (BIM) implementation.
Potentiation with Overspeed for Jump Height Enhancement: An Analysis of Factors Distinguishing Responders from Non-Responders
Authors: Koźlenia Dawid , Domaradzki Jarosław
Published: July 2024

(1) Background: This cross-over study aimed to assess the effectiveness of jump height (JH) enhancement after post-activation performance enhancement (PAPE) protocol based on assisted band jumps and to determine factors distinguishing responders (RS) and non-responders (NRS) based on morphological and functional factors. (2) Methods: Ten males aged 20–23 years with relative strength in back squat 156 ± 14% body weight participated. The conditioning activity, based on three series of five repetitions of assisted jumps with a band (30% of body mass load reduction) with one minute rest between series, was introduced. (3) Results: The two-way repeated measures ANOVA showed a significant interaction between effects (F = 7.78; p-eta = 0.30; p < 0.01). Comparison with the Bonferroni test showed that JH was higher than the baseline in the 3rd minute (p = 0.02; ES = 0.30) in the 6th (p < 0.01; ES = 0.39), and in the (9th p < 0.01; ES = 0.32) in an experimental condition. No factor statistically significantly distinguishes RS and NRS, but due to effect size (ES) relative strength (ES = −0.80), baseline jump ability (countermovement jump ES = −0.74; squat jump =−0.59), limb symmetry index (ES = −0.56) can be considered to contribute the most to positive effects. (4) Conclusions: The provided PAPE protocol is effective in enhancing JH but optimal rest should be established individually. Individuals characterized by greater muscular strength may benefit the most, but further consideration is needed.
Calculation of Lateral Logging Response and Environmental Impact Factor Analysis for Small Borehole Array
Authors: Li Zhiqiang , Xing Shaojie
Published: July 2024

This paper refines an optimized array lateral logging tool designed for small boreholes, leveraging existing technologies. The tool features four investigation depth curves, and resistivity response curves are derived through finite element model simulations considering variables such as borehole size, mud characteristics, invasion zone features, resistive annuli, formation rock properties, and formation resistivity anisotropy. The findings included the following: (1) Increasing the wellbore diameter uniformly decreased resistivity across all four investigation depths, positively correlating with borehole size. When mud resistivity (Rm) exceeded formation resistivity (Rt), resistivity curves became distorted. (2) For high- and low-invasion models, the ratio of the deepest to the shallowest investigation depth curves ranged from 1 to 8 and 0.6 to 0.9, respectively, with maximum separation at an invasion depth of 0.5–0.8 m. (3) Under invasion conditions with annuli, an invasion zone depth and annulus width around 0.4 m yield well separated the resistivity curves for all depths. Low- and high-resistivity annuli of 2 m and 0.7 m, respectively, can cause curve intersections. (4) When the formation thickness exceeded 0.2 m, the tool accurately reflected formation resistivity variations and demonstrated effective layer identification in multi-layer conditions. (5) In anisotropic formations, resistivity was negatively correlated with the anisotropy coefficient (λ) as it changed from 2 to 4. The instrument can be fully utilized in the exploration of thin interlayers in oil and gas, significantly enhancing the accuracy of resource identification and extraction technologies.
Machine Learning in Medical Triage: A Predictive Model for Emergency Department Disposition
Authors: Feretzakis Georgios , Sakagianni Aikaterini , Anastasiou Athanasios , Kapogianni Ioanna , Tsoni Rozita , Koufopoulou Christina , Karapiperis Dimitrios , Kaldis Vasileios , Kalles Dimitris , Verykios Vassilios S.
Published: July 2024

The study explores the application of automated machine learning (AutoML) using the MIMIC-IV-ED database to enhance decision-making in emergency department (ED) triage. We developed a predictive model that utilizes triage data to forecast hospital admissions, aiming to support medical staff by providing an advanced decision-support system. The model, powered by H2O.ai’s AutoML platform, was trained on approximately 280,000 preprocessed records from the Beth Israel Deaconess Medical Center collected between 2011 and 2019. The selected Gradient Boosting Machine (GBM) model demonstrated an AUC ROC of 0.8256, indicating its efficacy in predicting patient dispositions. Key variables such as acuity and waiting hours were identified as significant predictors, emphasizing the model’s capability to integrate critical triage metrics into its predictions. However, challenges related to the complexity and heterogeneity of medical data, privacy concerns, and the need for model interpretability were addressed through the incorporation of Explainable AI (XAI) techniques. These techniques ensure the transparency of the predictive processes, fostering trust and facilitating ethical AI use in clinical settings. Future work will focus on external validation and expanding the model to include a broader array of variables from diverse healthcare environments, enhancing the model’s utility and applicability in global emergency care contexts.
Identification of Bolt Loosening Damage of Steel Truss Structure Based on MFCC-WPES and Optimized Random Forest
Authors: Jiang Zepu , Zhu Zhiwei , Zhuo Debing
Published: July 2024

In the field of bolt loosening detection, although some progress has been made, there are still challenges such as high operational complexity, single feature extraction methods, and insufficient analysis model performance, especially in large steel truss structures, where there is a lack of efficient and accurate bolt loosening identification solutions. In response to these shortcomings, this article proposes an innovative bolt loosening damage recognition method based on sound signals. This method integrates feature extraction techniques of Mel frequency cepstral coefficients (MFCCs) and wavelet packet energy spectra (WPES), and comprehensively characterizes sound signals by constructing MFCC-WPES combined features. Subsequently, the random forest (RF) algorithm optimized by genetic algorithm was used for feature selection and model training, aiming to improve recognition accuracy and robustness. The experimental results show that this method can not only accurately identify bolt loosening signals in steel truss structure bolt loosening detection, but also has strong identification ability for environmental noise. Compared with traditional methods, the proposed solution in this article shows significant improvements in both performance and practicality, providing a new perspective and solution for the technological advancement of bolt loosening detection in steel truss structures.
Design and Application of an Onboard Particle Identification Platform Based on Convolutional Neural Networks
Authors: Bai Chaoping , Zhang Xin , Zhang Shenyi , Sun Yueqiang , Zhang Xianguo , Wang Ziting , Zhang Shuai
Published: July 2024

Space radiation particle detection plays a crucial role in scientific research and engineering practice, especially in particle species identification. Currently, commonly used in-orbit particle identification techniques include telescope methods, electrostatic analysis time of flight (ESA × TOF), time-of-flight energy (TOF × E), and pulse shape discrimination (PSD). However, these methods usually fail to utilize the full waveform information containing rich features, and their particle identification results may be affected by the random rise and fall of particle deposition and noise interference. In this study, a low-latency and lightweight onboard FPGA real-time particle identification platform based on full waveform information was developed utilizing the superior target classification, robustness, and generalization capabilities of convolutional neural networks (CNNs). The platform constructs diversified input datasets based on the physical features of waveforms and uses Optuna and Pytorch software architectures for model training. The hardware platform is responsible for the real-time inference of waveform data and the dynamic expansion of the dataset. The platform was utilized for deep learning training and the testing of the historical waveform data of neutron and gamma rays, and the inference time of a single waveform takes 4.9 microseconds, with an accuracy rate of over 97%. The classification expectation FOM (figure-of-merit) value of this CNN model is 133, which is better than the traditional pulse shape discrimination (PSD) algorithm’s FOM value of 0.8. The development of this platform not only improves the accuracy and efficiency of space particle discrimination but also provides an advanced tool for future space environment monitoring, which is of great value for engineering applications.
The Influence of Probiotics in Halitosis and Cariogenic Bacteria: A Systematic Review and Meta-Analysis
Authors: Offenbächer Victoria , Lo Giudice Roberto , Nart José , Real-Voltas Francisco , Arregui María , Greethurst Alice Rose , Galletti Cosimo
Published: July 2024

Background: The aim of this systematic review was to evaluate the therapeutic potential of probiotics in patients with halitosis and to assess whether probiotics can also be implemented as a preventative tool in oral health. Secondary objectives included the effect of probiotics on oral-health-related quality of life, as well as their safety. Materials and methods: An electronic literature search in Medline (PubMed), Scopus, Web of Science, and the Cochrane library was carried out for the identification and selection of relevant randomized controlled trials. Eligibility was based on inclusion criteria, which included RCTs published after 2013, and the outcome variables were volatile sulfur compound (VSC) levels, organoleptic scores, plaque, or saliva samples to assess cariogenic bacteria counts and/or pH levels. Results: Out of 192 identified records, 16 randomized controlled trials were included. Ten of those studied the effects of probiotics on halitosis and the other six analyzed the effect of probiotics on oral health parameters, such as cariogenic bacteria counts, pH levels, and salivary flow and quality. A total of 921 patients were evaluated. The risk of bias was assessed using the Cochrane risk-of-bias assessment tool version 2. Conclusions: Probiotics exhibit the potential for oral health management by reducing VSC levels, improving saliva quality, and enhancing oral-health-related quality of life. Combining probiotics with tongue scraping may sustain VSC reduction, while symbiotics show potential in reducing tongue coating. However, different bacterial strains have been used in the included studies; hence, the conclusions cannot be generalized, being one of the main limitations of this review. Future research should explore the probiotics’ potential to persist in the oral cavity post-treatment and employ standardized methodologies for conclusive efficacy assessment.
Sustainable Pavement Management: Harnessing Advanced Machine Learning for Enhanced Road Maintenance
Authors: Ijari Kshitij , Paternina-Arboleda Carlos D.
Published: July 2024

In this study, we introduce an advanced system for sustainable pavement management that leverages cutting-edge machine learning and computer vision techniques to detect and classify pavement damage. By utilizing models such as EfficientNetB3, ResNet18, and ResNet50, we develop robust classifiers capable of accurately identifying various types of pavement distress. To further enhance our dataset, we employ a Swin Transformer-based Generative Adversarial Network (GAN) to synthetically generate images of pavement cracks, thereby augmenting the training data. Our approach aims to improve the efficiency and accuracy of pavement damage assessment, contributing to more effective and sustainable road maintenance practices. This research aligns with the sustainable development goals by fostering innovative methods that extend the lifespan of infrastructure, reducing the need for resource-intensive repairs, and promoting the longevity and reliability of road networks. The outcomes of this study are discussed in terms of their potential impact on infrastructure safety and sustainability, with suggestions for future research directions. This study demonstrates how integrating advanced machine learning techniques into pavement management systems can enhance decision-making, optimize resource allocation, and improve the sustainability of infrastructure maintenance practices. By leveraging big data and sophisticated algorithms, stakeholders can proactively address pavement deterioration, extend asset lifespan, and optimize maintenance efforts based on real-time data-driven insights.
New Physically-Based Mathematical Model and Experiments for a Recently Invented Solar Pot
Authors: Rátkai Márton , Géczi Gábor , Kicsiny Richárd , Székely László
Published: July 2024

The studied solar pot is a recent invention, which is made for environmentally friendly cooking or heating (by utilizing solar energy) of foods and liquids. Its structure is similar to a double pipe heat exchanger, it has an outer mantle and an inner cooking tank. The goals of the paper are proposing a new physically-based mathematical model describing the solar pot and carrying out computer experiments with it, assembling an experimental system of the pot connected with a solar collector and performing measurements on it. Based on the results, the solar pot can successfully be used for cooking or sterilizing foods or liquids during the studied time period, in Hungary. In particular, based on measured data, the temperature level needed for heat treatment (75 °C) can be maintained in the cooking tank for several hours (~5 h, on the average) in a typical day in May.
A DFB-SOA Based Optical Vector Network Analyzer for Characterization of Bandpass Optical Devices
Authors: Deng Ye , Gao Hui , Shen Zhixiong , Li Bin , Zhang Jinping
Published: July 2024

In this paper a novel optical vector network analyzer (OVNA) utilizing a distributed feedback semiconductor optical amplifier (DFB-SOA) is introduced. The proposed OVNA is implemented by converting the transmission response of the optical device under test (ODUT) into the electrical domain. The main principle of the OVNA is predicated on the optical carrier restoration facilitated by the wavelength-selective amplification attribute of the DFB-SOA. The implemented OVNA effectively determined the transmission spectrum of an optical filter possessing a passband of 9-GHz bandwidth, achieving a commendable resolution of 25 MHz in the measurement process. The dynamic range of the OVNA can be broadened by adjust the driven current under the DFB-SOA. Additionally, the detection range of our system can be expanded through the utilization of broadband optoelectronic devices. Furthermore, the OVNA possesses considerable potential for integration onto a single chip.
The Vibration Response to the High-Pressure Gas Expansion Method: A Case Study of a Hard Rock Tunnel in China
Authors: Peng Huaide , Sheng Jia , Da Qi , Dai Bing , Zhang Lei , Tan Lihai
Published: July 2024

The vibration of rock breaking in tunnel excavation may cause serious damage to nearby buildings if it is not controlled properly. With reference to a hard rock tunnel in China, the vibration response to the high-pressure gas expansion method (HPGEM), an emerging rock-breaking approach, was investigated with field tests, theoretical derivations, and numerical simulations, then comparisons with the traditional dynamite blast were performed. Firstly, the vibration velocity prediction formulas of the two methods were fitted based on the field tests. Subsequently, the accuracy of the formula was verified by numerical simulation, and the vibration attenuation law of the HPGEM was explored. Comparisons were made between the blast and HPGEM, particularly the differences in peak particle velocity (PPV) for different agent qualities, distance from the blasting center, and engineering conditions. Furthermore, this study also analyzed the relationship between the agent qualities and the rock-breaking volume under different cases, finding that the HPGEM has slight vibration and good rock-breaking effect. The HPGEM is thus fully capable of replacing dynamite blasting to carry out rock-breaking operations in certain special areas.
Advanced State of Charge Estimation Using Deep Neural Network, Gated Recurrent Unit, and Long Short-Term Memory Models for Lithium-Ion Batteries under Aging and Temperature Conditions
Authors: El Fallah Saad , Kharbach Jaouad , Vanagas Jonas , Vilkelytė Živilė , Tolvaišienė Sonata , Gudžius Saulius , Kalvaitis Artūras , Lehmam Oumayma , Masrour Rachid , Hammouch Zakia , Rezzouk Abdellah , Ouazzani Jamil Mohammed
Published: July 2024

Accurate estimation of the state of charge (SoC) of lithium-ion batteries is crucial for battery management systems, particularly in electric vehicle (EV) applications where real-time monitoring ensures safe and robust operation. This study introduces three advanced algorithms to estimate the SoC: deep neural network (DNN), gated recurrent unit (GRU), and long short-term memory (LSTM). The DNN, GRU, and LSTM models are trained and validated using laboratory data from a lithium-ion 18650 battery and simulation data from Matlab/Simulink for a LiCoO2 battery cell. These models are designed to account for varying temperatures during charge/discharge cycles and the effects of battery aging due to cycling. This paper is the first to estimate the SoC by a deep neural network using a variable current profile that provides the SoC curve during both the charge and discharge phases. The DNN model is implemented in Matlab/Simulink, featuring customizable activation functions, multiple hidden layers, and a variable number of neurons per layer, thus providing flexibility and robustness in the SoC estimation. This approach uniquely integrates temperature and aging effects into the input features, setting it apart from existing methodologies that typically focus only on voltage, current, and temperature. The performance of the DNN model is benchmarked against the GRU and LSTM models, demonstrating superior accuracy with a maximum error of less than 2.5%. This study highlights the effectiveness of the DNN algorithm in providing a reliable SoC estimation under diverse operating conditions, showcasing its potential for enhancing battery management in EV applications.
Temperature Behavior in Headlights: A Comparative Analysis between Battery Electric Vehicles and Internal Combustion Engine Vehicles
Authors: Schlürscheid Tabea , Khanh Tran Quoc , Buck Alexander , Weber Stefan
Published: July 2024

In the context of a global shift towards renewable energies and climate change mitigation, the market for electric vehicles has experienced a remarkable upswing, with battery electric vehicles (BEVs) leading this transformative wave. The appeal of BEVs lies in their ability to significantly curtail CO2 emissions by supplanting the traditional internal combustion engine (ICE) with an electric motor. This pivotal change in vehicular technology extends its influence to various subsystems, including automotive lighting. Headlights are particularly sensitive to the thermal environment they operate in, which can profoundly affect their functionality and durability. The removal of an ICE in BEVs typically results in a reduction in heat exposure to headlight components, prompting a potential reevaluation of their design. This article presents a comprehensive analysis of temperature distributions within headlight units, comparing BEVs and ICE vehicles. The study encompasses a robust dataset of nearly 30,000 vehicles from around the globe, taking into account the impact of ambient temperature on headlight operation. The investigation delineates the distinct thermal behaviors of the two vehicle categories and offers strategic recommendations for conceptual modifications of headlights in BEVs. These adjustments are aimed at enhancing headlight efficacy, prolonging lifespan, and furthering the sustainability objectives of electric mobility.
Oxidative Modifications of Proteins and Lipids of Dried Semen, Urine, and Saliva Stains as a Function of Age in Forensic Context
Authors: Achetib Nihad , Otto Rosa E. , Aalders Maurice C. G. , van Dam Annemieke
Published: July 2024

Knowledge of the time of deposition is pivotal in forensic investigations. Recent studies show that changes in intrinsic fluorescence over time can be used to estimate the age of body fluids. These changes have been attributed to oxidative modifications caused by protein–lipid interactions. This pilot study aims to explore the impact of these modifications on body fluid fluorescence, enhancing the protein–lipid model system for age estimation. Lipid and protein oxidation markers, including protein carbonyls, dityrosine, advanced glycation end-products (AGEs), malondialdehyde (MDA), and 4-hydroxynonenal (HNE), were studied in aging semen, urine, and saliva over 21 days. Surface plasmon resonance imaging (SPRi), enzyme-linked immunosorbent assay (ELISA), and fluorescence spectroscopy were applied. Successful detection of AGE, dityrosine, MDA, and HNE occurred in semen and saliva via SPRi, while only dityrosine was detected in urine. Protein carbonyls were measured in all body fluids, but only in saliva was a significant increase observed over time. Additionally, protein fluorescence loss and fluorescent oxidation product formation were assessed, showing significant decreases in semen and saliva, but not in urine. Although optimization is needed for accurate quantification, this study reveals detectable markers for protein and lipid oxidation in aging body fluids, warranting further investigation.
Research on the Twin-Link Stepping Temporary Support Mechanism and the Repeatable Supportability of the Roof in the Underground Excavation Roadway of Coal Mine
Authors: Wang Dongjie , Cheng Jiameng , Li Rui , Qu Yuanyuan , Jiang Hai , Shen Yang , Zhao Sihai , Wu Miao
Published: July 2024

The coal mine’s underground heading roadway suffers from a lack of effective temporary support, which contributes to low efficiency and inadequate safety in excavation operations. This paper presents a novel approach for roadway excavation, support, and anchoring, utilizing the twin-link stepping temporary support method. To address the stability requirements of the roadway roof in this support method, a numerical model of the excavation face was developed using the BBM (Bonded Block Model) method in 3DEC 7.0 software. Through numerical simulation, the repeatability of the twin-link stepping temporary support process was assessed for various roadway roof rock types, thicknesses, and burial depths. By considering aspects such as roof fragmentation, roof convergence, and relative displacement between the direct roof and the bedrock, the range of roadway roof repeatability was determined. The proposed twin-link stepping temporary support process, along with its applicable roof conditions, establishes a theoretical foundation for implementing the parallel operation mode in coal mine underground heading roadways, thereby significantly enhancing safety and efficiency.
ADPSCAN: Structural Graph Clustering with Adaptive Density Peak Selection and Noise Re-Clustering
Authors: Du Xinyu , Li Fangfang , Li Xiaohua , Yu Ge
Published: July 2024

Structural graph clustering is a data analysis technique that groups nodes within a graph based on their connectivity and structural similarity. The Structural graph clustering SCAN algorithm, a density-based clustering method, effectively identifies core points and their neighbors within areas of high density to form well-defined clusters. However, the clustering quality of SCAN heavily depends on the input parameters, ϵ and μ, making the clustering results highly sensitive to parameter selection. Different parameter settings can lead to significant differences in clustering results, potentially compromising the accuracy of the clusters. To address this issue, a novel structural graph clustering algorithm based on the adaptive selection of density peaks is proposed in this paper. Unlike traditional methods, our algorithm does not rely on external parameters and eliminates the need for manual selection of density peaks or cluster centers by users. Density peaks are adaptively identified using the generalized extreme value distribution, with consideration of the structural similarities and interdependencies among nodes, and clusters are expanded by incorporating neighboring nodes, enhancing the robustness of the clustering process. Additionally, a distance-based structural similarity method is proposed to re-cluster noise nodes to the correct clusters. Extensive experiments on real and synthetic graph datasets validate the effectiveness of our algorithm. The experiment results show that the ADPSCAN has a superior performance compared with several state-of-the-art (SOTA) graph clustering methods.
2FAKA-C/S: A Robust Two-Factor Authentication and Key Agreement Protocol for C/S Data Transmission in Federated Learning
Authors: Huang Chao , Wang Bin , Bao Zhaoyang , Qi Wenhao
Published: July 2024

As a hot technology trend, the federated learning (FL) cleverly combines data utilization and privacy protection by processing data locally on the client and only sharing model parameters with the server, embodying an efficient and secure collaborative learning model between clients and aggregated Servers. During the process of uploading parameters in FL models, there is susceptibility to unauthorized access threats, which can result in training data leakage. To ensure data security during transmission, the Authentication and Key Agreement (AKA) protocols are proposed to authenticate legitimate users and safeguard training data. However, existing AKA protocols for client–server (C/S) architecture show security deficiencies, such as lack of user anonymity and susceptibility to password guessing attacks. In this paper, we propose a robust 2FAKA-C/S protocol based on ECC and Hash-chain technology. Our security analysis shows that the proposed protocol ensures the session keys are semantically secure and can effectively resist various attacks. The performance analysis indicates that the proposed protocol achieves a total running time of 62.644 ms and requires only 800 bits of communication overhead, showing superior computational efficiency and lower communication costs compared to existing protocols. In conclusion, the proposed protocol securely protects the training parameters in a federated learning environment and provides a reliable guarantee for data transmission.
Performance Evaluation and Mechanism Study of Solid Waste-Based Cementitious Materials for Solidifying Marine Soft Soil under Seawater Mixing and Erosion Action
Authors: Zhang Zheng , Guan Cheng , Hua Sudong , Zhang Yanan , Zhang Dongrui , Bao Youzhi , Yuan Zhizhou
Published: July 2024

The main purpose of this research is to develop a solid waste-based cementitious material (SWC) instead of cement for solidifying a large amount of marine soft soil with high water content and low bearing capacity in coastal areas. This aims to solve the problems encountered in the practical application of cement soil, such as slow strength growth and poor durability. The SWC includes ground granulated blast furnace slag (GGBS), dust ash (DA), and activated cinder powder (ACP), with admixtures of naphthalene sulfonate formaldehyde condensate (NS) and compound salt early strength agent (SA). Both the 7 d and 28 d compressive strength values of the SWC formulations G4 and G7 are about twice as strong as those of cement soil (GC), even when mixed with seawater. Immersion tests revealed that stabilized soil had superior resistance to seawater corrosion compared to cement soil. X-ray diffraction, scanning electron microscopy, infrared spectroscopy, and thermogravimetric analysis explained that the main hydration products in cement soil are C-S-H and CH, while in stabilized soil, SWC generates a large amount of C-A-S-H with gelling properties and AFt with filling properties. These hydration products have better effects on strength and seawater erosion resistance.
An Emerging Role of Long Noncoding RNAs as Novel Biomarkers for Breast Cancer Metastasis
Authors: Derlatka Kamila , Kulczycka Marika , Prendecka-Wróbel Monika , Homa-Mlak Iwona , Małecka-Massalska Teresa
Published: July 2024

Breast cancer is a leading cause of cancer-related deaths among women both worldwide and in Poland. Consequently, ensuring equitable access to diagnostic tests for all populations is crucial, alongside the urgent need to develop new, minimally invasive methods for early cancer detection with a particular focus on metastasis. Long noncoding RNAs (lncRNAs) have emerged as critical regulators of cancer metastasis. This review aims to investigate the potential of lncRNAs as novel biomarkers for breast cancer, focusing on their mechanisms, clinical relevance, and therapeutic implications. A comprehensive literature search was conducted using PubMed and Google Scholar databases, targeting publications from 2013 to 2024. Keywords included “lncRNA”, “biomarker”, “breast cancer”, “metastasis”, “prognosis”, and “diagnosis”. A total of 111 articles were selected based on their relevance and quality. Recent studies have identified numerous lncRNAs such as HOTAIR, MALAT1, BANCR, NEAT1, H19, and GAS5 as key regulators of various metastatic processes in breast cancer. They can be both upregulated and downregulated. Clinical studies have shown that abnormal lncRNA expression correlates with poor prognosis, higher metastatic potential, and therapy resistance in breast cancer patients. LncRNAs have significant potential as novel biomarkers for breast cancer metastasis because of their regulatory roles in metastasis-related processes and detectability in body fluids. Further research is essential to validate these findings in larger clinical studies and to develop lncRNA-based diagnostic and therapeutic tools, ultimately improving patient outcomes in breast cancer.
Print Quality Analysis of Stone Paper and Coated Sticker Paper Used in Screen Printing
Authors: Akpolat Cem , Akgül Ahmet
Published: July 2024

The sustainable use of natural resources is becoming an increasingly important issue today. Stone paper, produced as an alternative to cellulose-based paper from the forest, is rich in minerals and produced without cellulose and water. This study focuses on the behavior of screen-printing ink on two different papers, stone paper and coated sticker paper. Properties such as ink adhesion, rubbing resistance, optical printing ink density, ink consumption, and lightfastness were measured on these surfaces. Solvent- and UV-based inks were used, and printing was carried out on cellulose-based (coated sticker paper) and mineral-based (stone paper) paper layers using three different mesh counts (90, 120, and 140 tpc). The rubbing resistance and lightfastness of the papers were also measured. The present findings revealed that stone paper had the same printability properties as cellulose-based paper. The study concluded that using a 140 tpc mesh with both types of ink results in a high-lightfastness ink layer and lower ink consumption. UV-based inks exhibited high rub resistance across all mesh counts. Additionally, when printing with stone paper, there will be a reduction in ink consumption, thereby achieving cost savings. Based on the present findings, it was concluded that water- and oil-resistant stone paper can be considered an essential alternative in many fields, including the printing industry.
Revolutionary Art and the Creation of the Future: The Afrofuturist Texts of José Antonio Aponte and Martin R. Delany
Authors: Fisher James J.
Published: July 2024

Afrofuturism (an artistic perspective in which Black voices tell alternative narratives of culture, technology, and the future) and the Dark Fantastic (interrupting negative depictions of Black people through emancipatory interpretations of art) are two interrelated concepts used by Black artists in the Atlantic World to counter negative images and emphasize a story from a Black perspective. Likewise, these concepts have been used to recreate and re-narrate history with an eye towards subverting white supremacist historical narratives. By using Afrofuturism and the Dark Fantastic as lenses through which texts by authors from the African Diaspora in the Atlantic World are examined, an alternative narrative of Black histories and futures concerned with revolution, liberation, and justice can be seen. The two texts that are the subject of this research include José Antonio Aponte’s descriptions of his book of paintings under interrogation in 1812–1813, and Martin Delany’s novel Blake; or the Huts of America (1859–1862), providing images of enslavement that run counter to a white supremacist telling of history. They both imagine alternative pasts and futures for Africa and the Afro-Diaspora involving revolution and magic. These works, though produced at different times and locations in the nineteenth century, offer new ways in which to discuss liberation and freedom in the context of the artistic production of the Atlantic World.
The Limiting Behaviors of the Gutman and Schultz Indices in Random 2k-Sided Chains
Authors: Tao Chen , Tang Shengjun , Geng Xianya
Published: July 2024

The study of complex networks with topological indices has flourished in recent years. The aim of this paper is to study the limiting behaviors of Gutman and Schultz indices in random polygonal chains, whose graph-theoretic mathematical properties and their future applications have attracted the interest of scientists. By applying the concepts of symmetry and asymptotics as well as the knowledge of probability theory, we obtain explicit analytic expressions for the Gutman and Schultz indices of n random 2k-vertex chains and prove that they converge to a normal distribution, which contributes to a deeper understanding of the structural features of random polygonal chains and plays a crucial role in the study of the limiting behavior of topological indices and their applications.
Effects of Non-Uniform Temperature Distribution on the Degradation of Liquid-Cooled Parallel-Connected Lithium-Ion Cells
Authors: Iriyama Takuto , Carter Muriel , Cavalheiro Gabriel M. , Poudel Pragati , Nelson George J. , Zhang Guangsheng
Published: July 2024

Our previous work on an air-cooled stack of five pouch-format lithium-ion (Li-ion) cells showed that non-uniform temperature can cause accelerated degradation, especially of the middle cell. In this work, a stack of five similar cells was cycled at a higher C-rate and water-cooled to create a larger temperature gradient for comparison with the air-cooled stack. It was hypothesized that the larger temperature gradient in the water-cooled stack would exacerbate the degradation of the middle cell. However, the results showed that the middle cell degraded slightly slower than the side cells in the water-cooled stack. This trend is opposite to that in the air-cooled stack. This difference could be attributed to the combined effects of a smaller temperature rise and larger temperature gradient in the water-cooled stack than in the air-cooled stack. Post-mortem analysis of cycled cells and a fresh cell showed that the degradation mainly came from the anode. Increased lithium plating and decreased porosity in the side cells are possible mechanisms for the faster degradation compared with the middle cell. It was also found that all the cells in the water-cooled stack experienced a phenomenon of capacity drop and recovery after a low C-rate reference performance test and extended rest. This phenomenon can be attributed to lithium diffusion between the anode active area and the anode overhang area.
Gaze-Based Detection of Thoughts across Naturalistic Tasks Using a PSO-Optimized Random Forest Algorithm
Authors: Rahnuma Tarannum , Jothiraj Sairamya Nanjappan , Kuvar Vishal , Faber Myrthe , Knight Robert T. , Kam Julia W. Y.
Published: July 2024

One key aspect of the human experience is our ongoing stream of thoughts. These thoughts can be broadly categorized into various dimensions, which are associated with different impacts on mood, well-being, and productivity. While the past literature has often identified eye movements associated with a specific thought dimension (task-relatedness) during experimental tasks, few studies have determined if these various thought dimensions can be classified by oculomotor activity during naturalistic tasks. Employing thought sampling, eye tracking, and machine learning, we assessed the classification of nine thought dimensions (task-relatedness, freely moving, stickiness, goal-directedness, internal–external orientation, self-orientation, others orientation, visual modality, and auditory modality) across seven multi-day recordings of seven participants during self-selected computer tasks. Our analyses were based on a total of 1715 thought probes across 63 h of recordings. Automated binary-class classification of the thought dimensions was based on statistical features extracted from eye movement measures, including fixation and saccades. These features all served as input into a random forest (RF) classifier, which was then improved with particle swarm optimization (PSO)-based selection of the best subset of features for classifier performance. The mean Matthews correlation coefficient (MCC) values from the PSO-based RF classifier across the thought dimensions ranged from 0.25 to 0.54, indicating above-chance level performance in all nine thought dimensions across participants and improved performance compared to the RF classifier without feature selection. Our findings highlight the potential of machine learning approaches combined with eye movement measures for the real-time prediction of naturalistic ongoing thoughts, particularly in ecologically valid contexts.
Investigation of Biomechanical Differences in Level Walking between Patients with Bilateral and Unilateral Total Knee Replacements
Authors: Yocum Derek , Ovispo-Martinez Alejandro , Valenzuela Kevin A. , Wen Chen , Cates Harold , Zhang Songning
Published: July 2024

Due to the high risk of a bilateral total knee arthroplasty (TKR) following unilateral TKR, this study was performed to investigate bilateral TKR patients. Specifically, we examined biomechanical differences between the first replaced and second replaced limbs of bilateral patients. Furthermore, we examined bilateral TKR effects on hip, knee, and ankle biomechanics, compared to the replaced and non-replaced limbs of unilateral patients. Eleven bilateral patients (70.09 ± 5.41 years, 1.71 ± 0.08 m, 91.78 ± 13.00 kg) and fifteen unilateral TKR patients (65.67 ± 6.18 years, 1.73 ± 0.10 m, 87.72 ± 15.70 kg) were analyzed while performing level walking. A repeated measures one-way ANOVA was performed to analyze between-limb differences within the bilateral TKR group. A 2 × 2 (limb × group) ANOVA was used to determine differences between bilateral and unilateral patients. Our results showed that the second replaced limb exhibited a lower peak initial-stance knee extension moment than the first replaced limb. No other kinematic or kinetic differences were found. Bilateral patients exhibited lower initial-stance knee extension moments, knee abduction moments, and dorsiflexion moments, compared to unilateral patients. Bilateral patients also exhibited lower push-off peak hip flexion moments and vertical GRF. The differences between the first and second replaced limbs of bilateral patients may indicate different adaptation strategies used following a second TKR. The significant group differences indicate that adaptations are different between these groups, and it is not recommended to use patients with unilateral and bilateral TKR together in gait analyses.