bluuebunny/crossref_metadata_2025 huggingface dataset:
Experimental Tests on In Situ Combustion Using Dynamic Ignition Simulation System in High-Temperature and High-Pressure Conditions
Authors: Yin Yuchuan , Chen Xinyuan , Yu Xiaocong , Liu Deji , Chen Chao , Zhou Xiaosong , Li Xiaohui , Zhang Lidong , Kan Changbin
Published: December 2023

The study of crude oil oxidation characteristics is fundamental to the design of ignition in situ combustion. Experimentation is the most crucial method for studying the oxidation characteristics of crude oil. Aiming to address the challenges posed by high temperature, high pressure, and rapid temperature changes during the combustion of crude oil, a dynamic simulation system for high-temperature and high-pressure ignition is designed. In order to study the oxidation characteristics of the crude oil ignition process, we conducted experiments using a high-temperature and high-pressure dynamic ignition simulation device. The experiments focused on determining the ignition point of crude oil under different pressure conditions, oil–water ratios, heating rates, gas injection rates, and other relevant characteristics. The kinetic model for the oxidation process of crude oil ignition was established. The kinetic parameters were calculated for different ignition conditions and the apparent activation energy for each oxidation stage was determined. Additionally, the stability of in situ combustion was evaluated under various ignition parameters. The results show that the Arrhenius curves for crude oil exhibit noticeable differences in the HTO (high-temperature oxidation) and LTO (low-temperature oxidation) regions. The curves demonstrate good linearity in the HTO region, with correlation coefficients exceeding 0.9. Moreover, the apparent activation energies in the HTO region range from 8.01 to 26.7 kJ/mol. The apparent activation energies and finger front factors were calculated for the HTO stage under different pressure conditions. The results showed that, as the pressure increased, the autoignition point, inflection point temperature, and apparent activation energy of the crude oil decreased. This suggests that increasing the pressure can enhance the HTO of the crude oil. The spontaneous ignition point of the crude oil exhibited an upward trend as the heating rate increased. Additionally, the maximum temperature during the combustion process generally increased with the heating rate, reaching a maximum temperature of 453.1 °C. The tests demonstrated that the simulation system is capable of real-time monitoring and recording of oxidation parameters during the combustion process of crude oil. This system can provide essential data for project implementation and numerical simulation.
A Time–Frequency Residual Convolution Neural Network for the Fault Diagnosis of Rolling Bearings
Authors: Wu Chenxi , Jiang Rong , Wu Xin , Zhong Chao , Huang Caixia
Published: December 2023

A time–frequency residual convolution neural network (TFRCNN) was proposed to identify various rolling bearing fault types more efficiently. Three novel points about TFRCNN are presented as follows: First, by constructing a double-branch convolution network in the time domain and the frequency domain, the respective features in the time domain and the frequency domain were extracted to ensure the rich and complete feature representation of raw data sources. Second, specific residual structures were designed to prevent learning degradation of the deep network, and global average pooling was adopted to improve the network’s sparsity. Third, TFRCNN was better than the other models in terms of prediction accuracy, robustness, generalization ability, and convergence. The experimental results demonstrate that the prediction accuracy rate of TFRCNN, trained using mixing load data, reached 98.88 to 99.92% after optimizing the initial learning rate and choosing the optimizer and loss function. It was verified that TFRCNN can adaptively learn to extract deep fault features, accurately identify bearing fault conditions, and overcome the limitations of classical shallow feature extraction and classification methods, as well as common convolution neural networks. Hence, this investigation revealed TFRCNN’s potential for bearing fault diagnosis in practical engineering applications.
Unveiling the Dichotomy of Urinary Proteins: Diagnostic Insights into Breast and Prostate Cancer and Their Roles
Authors: Feng Yan , Huo Qingji , Li Bai-Yan , Yokota Hiroki
Published: December 2023

This review covers the diagnostic potential of urinary biomarkers, shedding light on their linkage to cancer progression. Urinary biomarkers offer non-invasive avenues for detecting cancers, potentially bypassing the invasiveness of biopsies. The investigation focuses primarily on breast and prostate cancers due to their prevalence among women and men, respectively. The intricate interplay of urinary proteins is explored, revealing a landscape where proteins exhibit context-dependent behaviors. The review highlights the potential impact of physical activity on urinary proteins, suggesting its influence on tumorigenic behaviors. Exercise-conditioned urine may emerge as a potential diagnostic biomarker source. Furthermore, treatment effects, notably after lumpectomy and prostatectomy, induce shifts in the urinary proteome, indicating therapeutic impacts rather than activating oncogenic signaling. The review suggests further investigations into the double-sided, context-dependent nature of urinary proteins, the potential role of post-translational modifications (PTM), and the integration of non-protein markers like mRNA and metabolites. It also discusses a linkage of urinary proteomes with secretomes from induced tumor-suppressing cells (iTSCs). Despite challenges like cancer heterogeneity and sample variability due to age, diet, and comorbidities, harnessing urinary proteins and proteoforms may hold promise for advancing our understanding of cancer progressions, as well as the diagnostic and therapeutic role of urinary proteins.
Double-Relief Silver Coins Minted in the Greek Colonies (444–390/340–280/270 BC) of Southern Italy Analysed by XRF
Authors: Brocchieri Jessica , Vitale Rosa , Sabbarese Carlo
Published: December 2023

A sample of 18 double-relief coins from different poleis of Magna Graecia and ancient Italy has been analysed using a handheld XRF spectrometer directly inside the Museo Provinciale Campano (Capua, Italy). The data analysis shows that (i) the main elements are Ag and Cu, indicating that the coins are of high fineness (average Ag 95.7%), (ii) trace elements can help to characterise the coins, (iii) a superficial chemically altered layer (corrosion) is absent, (iv) the values of ratio Ag Kα/Lα evidence the presence of an enrichment layer on the surface of silver or subaerata in some coins. Multivariate statistical analysis and graph analysis allowed the coins to be assigned to different groups with the highest possible accuracy on the basis of the chemical data obtained and models to be constructed to classify the coins according to their historical periods.
Movement, Geography, and Rabbinic Culture in High Medieval Northern Europe
Authors: Barzilay Tzafrir
Published: December 2023

Despite the distance between their different communities and the difficulties of medieval travel, the Jews of northern Europe developed typical common legal and communal traditions. Rabbinic students traveled hundreds of kilometers to study with famous rabbis, rabbis themselves often relocated from one community to another, and questions were regularly sent to faraway rabbinic authorities and were quickly answered. This article sheds light on the movement and communication patterns of medieval Jewish scholars as a social group. It includes three sections; the first focuses on the movement patterns of prominent rabbis, the second on their forms of communication, and the third on the way these practices were reflected in the organization of larger communal structures. Overall, the article highlights the major role that networks of movement and communication played in the intellectual culture of the rabbinic elite (and other Jews as well) in high medieval northern Europe.
Simulation of Dynamic Performance of DeFi Protocol Based on Historical Crypto Market Behavior
Authors: Grigorova Iveta , Karamfilov Aleksandar , Merakov Radostin , Efremov Aleksandar
Published: December 2023

In a rapidly evolving and often volatile crypto market, the ability to use historical data for simulations provides a more realistic assessment of how decentralized finance (DeFi) protocols might perform. This insight is crucial for participants, developers, and investors seeking to make informed decisions. This paper presents a comprehensive study evaluating the dynamic performance of a newly developed DeFi protocol—NOLUS. The main objective of this paper is to present and analyze the built realistic model of the platform. This model could be successfully used to analyze the stability of the platform under different environmental influences by performing various simulations and conducting experiments with different parameters that could not be realized with the real platform. In the article, the key components of the platform are presented in detail and the main dependencies between them are clarified, in addition to the ways of forming multiple variables, and the complex relations between them in the real protocol are explained. The main finding from the experimental part of the study is that the performance of the protocol representation accounts for the expected system behavior. Hence the system simulation could be successfully used to reveal essential protocol behaviors resulting from potential shifts in the crypto market environment and to optimize the protocol’s hyper parameters.
Temporal and Spatial Evolution Analysis and Correlation Measurement of Urban–Rural Fringes Based on Nighttime Light Data
Authors: Fu Bo , Xue Bing
Published: December 2023

Rural–urban fringe areas serve as crucial transitional zones within urban structures, and their spatiotemporal evolution holds significant reference value for scientifically planning urban configurations. The existing research predominantly focuses on large cities, overlooking the spatiotemporal evolution mechanisms of small- to medium-sized cities. This study employs nighttime light data as the data source to ensure continuous and consistent data, overcoming administrative boundaries. Taking Taizhou City as a case study, a combination of the threshold method and an improved Mann–Kendall algorithm is employed to reveal the evolution process of fringe areas. And a gravity model is utilized to unearth the interaction relationships among regions. The results indicate that from 2010 to 2020, the urban area expanded from 1097 km2 to 2791 km2, with fringe areas experiencing initial contraction followed by gradual expansion. The central urban areas of Jiaojiang, Luqiao, and Huangyan gradually merged, forming a concentrated region. Linhai maintained a high level of attraction, while spatial gravity weakened in other areas. This study quantitatively analyzes the expansion trends of fringe areas in small- to medium-sized cities, elucidating the attractive effects of time–cost distance and land resources on development and providing valuable support for subsequent spatial planning and governance.
Evaluation of the CRTM Land Emissivity Model over Grass and Sand Surfaces Using Ground-Based Measurements
Authors: Wang Yidan , He Wenying , Duan Minzheng , Liu Hailei , Chen Hongbin , Han Congzhen , Nan Weidong
Published: December 2023

Microwave surface emissivity is complex and variable, leading to increased difficulty in accurately retrieving atmospheric parameters and assimilating satellite microwave observations over land. The Community Radiative Transfer Model (CRTM) land emissivity model is a useful tool for providing microwave emissivity over complex surfaces. By combining the model with ground measurements from a mobile multi-surface observing system at the Xianghe site, China, the performance of the land emissivity model is evaluated over grass and sand surfaces. The simulated and measured emissivity agrees at both polarizations over the grassland surface but a more significant difference is observed at the horizontal polarization over the sand surface. To solve this problem, the Q/H module for soil reflectance roughness correction in the CRTM emissivity model was replaced with the Qp module for the sand surface. This results in a significant improvement in the horizontal polarization simulation, with the corresponding mean bias error (MBE) reducing from 0.08 in the Q/H module to less than 0.03. The adjustment demonstrates that the Qp module more effectively corrects the roughness effect on horizontally polarized emissivity for bare soil surfaces. For grassland, the CRTM emissivity model with the Q/H module demonstrates accurate simulations, showing its suitability for vegetated land surfaces.
Design and Analysis of a Cardioid Flow Tube Valveless Piezoelectric Pump for Medical Applications
Authors: Wang Jialong , Zhang Fan , Gui Zhenzhen , Wen Yuxin , Zeng Yaohua , Xie Tang , Tan Tian , Chen Bochuan , Zhang Jianhui
Published: December 2023

Piezoelectric pumps play an important role in modern medical technology. To improve the flow rate of valveless piezoelectric pumps with flow tube structures and promote the miniaturization and integration of their designs, a cardioid flow tube valveless piezoelectric pump (CFTVPP) is proposed in this study. The symmetric dual-bend tube design of CFTVPP holds great potential in applications such as fluid mixing and heat dissipation systems. The structure and working principle of the CFTVPP are analyzed, and flow resistance and velocity equations are established. Furthermore, the flow characteristics of the cardioid flow tube (CFT) are investigated through computational fluid dynamics, and the output performance of valveless piezoelectric pumps with different bend radii is studied. Experimental results demonstrate that CFTVPP exhibits the pumping effect, with a maximum vibration amplitude of 182.5 μm (at 22 Hz, 100 V) and a maximum output flow rate of 5.69 mL/min (at 25 Hz, 100 V). The results indicate that a smaller bend radius of the converging bend leads to a higher output flow rate, while the performance of valveless piezoelectric pumps with different diverging bends shows insignificant differences. The CFTVPP offers advantages such as a high output flow rate, low cost, small size for easy integration, and ease of manufacturing.
Enhancing Network Intrusion Detection Using an Ensemble Voting Classifier for Internet of Things
Authors: Farooqi Ashfaq Hussain , Akhtar Shahzaib , Rahman Hameedur , Sadiq Touseef , Abbass Waseem
Published: December 2023

In the context of 6G technology, the Internet of Everything aims to create a vast network that connects both humans and devices across multiple dimensions. The integration of smart healthcare, agriculture, transportation, and homes is incredibly appealing, as it allows people to effortlessly control their environment through touch or voice commands. Consequently, with the increase in Internet connectivity, the security risk also rises. However, the future is centered on a six-fold increase in connectivity, necessitating the development of stronger security measures to handle the rapidly expanding concept of IoT-enabled metaverse connections. Various types of attacks, often orchestrated using botnets, pose a threat to the performance of IoT-enabled networks. Detecting anomalies within these networks is crucial for safeguarding applications from potentially disastrous consequences. The voting classifier is a machine learning (ML) model known for its effectiveness as it capitalizes on the strengths of individual ML models and has the potential to improve overall predictive performance. In this research, we proposed a novel classification technique based on the DRX approach that combines the advantages of the Decision tree, Random forest, and XGBoost algorithms. This ensemble voting classifier significantly enhances the accuracy and precision of network intrusion detection systems. Our experiments were conducted using the NSL-KDD, UNSW-NB15, and CIC-IDS2017 datasets. The findings of our study show that the DRX-based technique works better than the others. It achieved a higher accuracy of 99.88% on the NSL-KDD dataset, 99.93% on the UNSW-NB15 dataset, and 99.98% on the CIC-IDS2017 dataset, outperforming the other methods. Additionally, there is a notable reduction in the false positive rates to 0.003, 0.001, and 0.00012 for the NSL-KDD, UNSW-NB15, and CIC-IDS2017 datasets.
Dissipation of Motor Sleep Inertia and Motor Wake Inertia in Early Relapsing–Remitting Multiple Sclerosis
Authors: Tonetti Lorenzo , Camilli Federico , Giovagnoli Sara , Lugaresi Alessandra , Natale Vincenzo
Published: December 2023

While previous studies have described the time course of the dissipation of motor sleep inertia (around 70 min after wake-up time) and motor wake inertia (around 20 min after bedtime) in healthy controls (HCs), the corresponding knowledge for persons with early relapsing–remitting multiple sclerosis (RRMS) is lacking. To fill in this knowledge gap, we carried out a secondary analysis of previously collected data in 35 persons (24 females; mean age = 31.51 ± 7.74 years) with early relapsing–remitting multiple sclerosis (RRMS) and 35 (24 females; mean age = 31.29 ± 8.02) healthy controls (HCs). Each participant wore an actigraphic Micro Motionlogger Watch (Ambulatory Monitoring, Ardlsey, NY, USA) for seven consecutive days. The Functional Linear Modeling statistical framework was adopted to compare the dissipation of motor sleep inertia as well as motor wake inertia between RRMS and HC. As regards motor sleep inertia, no significant differences in motor activity were observed in the first 70 min after the wake-up time; however, with reference to motor wake inertia, the motor activity of RRMS persons was significantly higher than HCs in approximately the first 30 min after bedtime. Despite the small sample size, this pattern of results suggests that the dissipation of motor wake inertia is only slower in persons with RRMS as opposed to HCs.
Mentoring and Monitoring of Student Teachers in Their In-School Placements—The Case of the University of Santiago de Compostela
Authors: Fernández-Morante Carmen , Leránoz-Iglesias Martin Manuel , Cebreiro-López Beatriz , Abeal-Pereira Cristina
Published: December 2023

(1) The in-school placement is a key scenario in linking theory and practice in the processes of initial teacher education (ITE) and constitutes a crucial element in the understanding of professional competencies and the experimentation of educational innovation proposals. For this reason, the pedagogical model that guides teaching practices and the set of learning activities that university and school mentors develop is especially relevant. This paper aims to analyze the current individualized monitoring processes of student teachers, as well as to detect difficulties and needs for improvement. (2) The center of interest corresponds to the internship mentors of the ITE degrees of the Faculty of Education Sciences at the University of Santiago de Compostela. This research was carried out through a descriptive study with a survey method and a study sample of 202 mentors. (3) Results show that there are discrepancies in the activities that university and school mentors implement with students, as well as in the observation and supervision processes. (4) We propose a learning reflective methodology based on the Technological Pedagogical Content Knowledge (TPACK) model to coordinate agents involved in the internship, connecting them through a specialized eLearning environment equipped with specific tools to facilitate the processes of individualized monitoring, reflective learning, and self-regulation.
Commodification in Urban Planning: Exploring the Habitus of Practitioners in a Neoliberal Context
Authors: Vergara-Perucich Francisco , Arias-Loyola Martín
Published: December 2023

The concept of habitus, as introduced by Pierre Bourdieu, serves as a lens to understand the subjective dispositions and perceptions that influence decision-making within the social realm. This study delves into the intricate relationship between urban planners’ habitus and the commodification processes inherent in a neoliberal society. Through thematic analysis of semi-structured interviews with 27 Chilean urban planners, this research identifies typologies that capture their disciplinary stances on three pivotal urbanism facets: the city’s conceptualization, the nuances of urban practice, and visions of utopia. A salient finding is the palpable tension urban planners experience, torn between the aspiration to foster a well-conceived city and the commodifying forces that shape decision-making. This commodification of the planner’s ethos emerges as a byproduct of entrenched neoliberal institutional practices. This study delineates three distinct habitus typologies: the public, private, and academic urban planner, each exhibiting varied perspectives on the commodification of urban planning. Collectively, these insights shed light on the profound ways in which neoliberal paradigms influence urban planning, revealing both its disciplinary boundaries and inherent contradictions.
Predicting Traffic Flow Parameters for Sustainable Highway Management: An Attention-Based EMD–BiLSTM Approach
Authors: Rui Yikang , Gong Yannan , Zhao Yan , Luo Kaijie , Lu Wenqi
Published: December 2023

The long-term prediction of highway traffic parameters is frequently undermined by cumulative errors from various influencing factors and unforeseen events, resulting in diminished predictive accuracy and applicability. In the pursuit of sustainable highway development and eco-friendly transportation strategies, forecasting these traffic flow parameters has emerged as an urgent concern. To mitigate issues associated with cumulative error and unexpected events in long-term forecasts, this study leverages the empirical mode decomposition (EMD) method to deconstruct time series data. This aims to minimize disturbances from data fluctuations, thereby enhancing data quality. We also incorporate the BiLSTM model, ensuring bidirectional learning from extended time series data for a thorough extraction of relevant insights. In a pioneering effort, this research integrates the attention mechanism with the EMD–BiLSTM model. This synergy deeply excavates the spatiotemporal characteristics of traffic volume data, allocating appropriate weights to significant information, which markedly boosts predictive precision and speed. Through comparisons with ARIMA, LSTM, and BiLSTM models, we demonstrate the distinct advantage of our approach in predicting traffic volume and speed. In summary, our study introduces a groundbreaking technique for the meticulous forecasting of highway traffic volume. This serves as a robust decision-making instrument for both sustainable highway development and transportation management, paving the way for more sustainable, efficient, and environmentally conscious highway transit.
Correction: Fränkle et al. Iron Ore Tailings Dewatering: Measurement of Adhesion and Cohesion for Filter Press Operation. Sustainability 2022, 14, 3424
Authors: Fränkle Bernd , Morsch Patrick , Kessler Christoph , Sok Thien , Gleiß Marco , Nirschl Hermann
Published: December 2023

The authors would like to make the following corrections about the published paper [...]
CnSR: Exploring Consumer Social Responsibility Using Machine Learning-Based Topic Modeling with Natural Language Processing
Authors: Jang Jisu , Kang Jiyun
Published: December 2023

This study delves into Consumer Social Responsibility (CnSR) within the fashion industry, with the goal of understanding consumers’ sustainable and responsible behavior across three major consumption stages: acquisition, utilization, and disposal. While “corporate” social responsibility (CSR) has been extensively studied in the literature, CnSR that sheds light on “individual consumers” has received less attention and is understudied. Using topic modeling, an unsupervised machine learning (ML) technique that uses natural language processing (NLP) in Python, this study analyzed textual data consisting of open-ended responses from 703 U.S. consumers. The analysis unveiled key aspects of CnSR in each of the consumption processes. The acquisition stage highlighted various ethical and sustainable considerations in purchasing and decision making. During the utilization phase, topics concerning sustainable and responsible product usage, environmentally conscious practices, and emotional sentiments emerged. The disposal stage identified a range of environmentally and socially responsible disposal practices. This study provides a solid and rich definition of CnSR from the perspective of individual consumers, paving the avenue for future research on sustainable consumption behaviors and inspiring the fashion industry to create goods and services that are in line with CnSR.
State-Space Modeling, Design, and Analysis of the DC-DC Converters for PV Application: A Review
Authors: Khan M. Usman , Murtaza Ali Faisal , Noman Abdullah M. , Sher Hadeed Ahmed , Zafar Maria
Published: December 2023

Small-signal models of dc-dc converters are often designed using a state-space averaging approach. This design can help discuss and derive the control-oriented and other frequency-domain attributes, such as input or output impedance parameters. This paper aims to model the dc-dc converters for PV application by employing a capacitor on the input side. The modeling, design, and analysis of the dc-dc converters regarding the input capacitor is limited in the literature. Five dc-dc converters, including buck, boost, buck-boost, ĆUK, and SEPIC converters, are designed and implemented using the state-space average modeling approach in MATLAB/Simulink. The circuit topology of each converter and the state-space matrices are derived considering every constraint. A rigorous and compelling analysis of the dc-dc converters is carried out to compare system stability and, ultimately, the dynamic performance. The output of the resulting small-signal models has been demonstrated in the time-domain against topology simulations. All the converters are exposed to unpredictable weather conditions and the simulations are carried out in the PSIM software. The perturb and observe (P&O) maximum power point tracking (MPPT) algorithm is applied in all the converters to ensure maximum power point (MPP) achievement. The results showcase that the boost converter outperforms all other converters in terms of stability, settling time, and overshoot.
Systematic Analysis of the Literature Addressing the Use of Machine Learning Techniques in Transportation—A Methodology and Its Application
Authors: Gal-Tzur Ayelet , Albagli-Kim Sivan
Published: December 2023

Advances in the field of machine learning (ML) have been reflected in the intensity of research studies exploiting these techniques for a better understanding of existing phenomena, and for predicting future ones, as a mean for promoting a more efficient and sustainable transportation system. The present study aims to understand the trends of utilizing diverse ML approaches to tackle issues within sub-domains of transportation and to identify underutilized potentials among them. This paper presents a methodology for the bi-dimensional classification of a large corpus of scientific articles. The articles are classified into six transport-related sub-domains, based on the definition of the Israeli Smart Transport Research Center, whose aim is a transportation system with zero externalities, and the ML techniques used in each of them is identified. A fuzzy KNN model is implemented for the multi-classification of articles into the transportation sub-domains and an ontology-based reasoning for identifying the share of each applied ML approach is employed. The application of these methodologies to a corpus of 1718 articles revealed, among other findings, an increasing share of artificial neural networks and deep learning techniques from 2018 until 2022, particularly in the traffic management sub-domain. A significant contribution of the development of these automatic methodologies is the ability to reuse them for ongoing exploration of trends regarding the use of ML techniques for transportation sub-domains.
Power Quality and Break-Even Points in the Use of Electric Motorcycles in the Case of the Thailand Residential Building
Authors: Ananwattanaporn Santipont , Ngaopitakkul Atthapol , Jettanasen Chaiyan
Published: December 2023

The use of electric motorcycles (EMCs) is rapidly increasing because EMCs are comparable in price to internal combustion engine motorcycles (ICE motorcycles), can be charged at home, and do not cause pollution. However, using EMCs in residential electrical systems is still a new issue in Thailand, as the Thai power grid was not originally designed to support electric vehicle charging. Therefore, the effect that may occur on the electrical system of a house should be studied. In this study, the power quality when charging an EMC in a residential electrical system is investigated by considering the circuits of various electrical devices according to their actual consumption behavior. Three electric motorcycles with battery capacities of 20, 30, and 40 Ah were used to investigate the effects of charging these motorcycles through the electrical system of a house. The experiment was conducted in a laboratory that replicated the electrical system of a house, and the conditions and patterns of power consumption were identical in all three cases. The test results were considered in terms of power quality, voltage harmonics, and current power system harmonics to analyze the effects on the electrical system in each circuit and to compare the charging differences of each motorcycle model. Next, it was determined that using an EMC is more cost-effective than using an ICE motorcycle. ICE motorcycles will eventually be completely replaced by EMCs, and our research will enable informed decision-making for electric motorcycle riders, researchers, and automotive corporations.
Non-Axisymmetric Bouncing Dynamics on a Moving Superhydrophobic Surface
Authors: Wang Wenhao , Yu Wenlong , Yu Zhiyuan , Chen Shuo , Cao Damin , Liu Xiaohua , Zhao Jiayi
Published: December 2023

The phenomenon of droplet impact on moving surfaces is widely observed in fields such as transportation, rotating machinery, and inkjet printing. Droplets exhibit non-axisymmetric behavior due to the motion of solid surfaces which significantly determines core parameters such as contact time, maximum spreading radius, and bounding velocity, thereby affecting the efficiency of related applications. In this study, we focus on the kinetics and morphology of the non-axisymmetric bouncing behaviors for droplets impacting on a moving superhydrophobic surface (SHPS) within the normal (Wen) and tangential (Wet) Weber numbers. Considering the influences of the moving surface on the contact area and contact time, the previous scaling formula for the horizontal velocity of droplets has been improved. Based on the velocity superposition hypothesis, we establish a theoretical model for the ratio of the maximum spreading radius at both ends depending on Wen and Wet. This research provides both experimental and theoretical evidence for understanding and controlling the non-axisymmetric behavior of droplets impacting on moving surfaces.
Effects of Fireworks Burning on Air Quality during the Chinese Spring Festival—Evidence from Zhengzhou, China
Authors: Liu Xinzhan , Yang Ling , Wang Yan , Yan Pengfei , Lu Yimeng
Published: December 2023

Fireworks burning significantly degrades air quality over a short duration. The prohibition of fireworks burning (POFB) policy of 2016 and the restricted-hours fireworks burning (RHFB) policy of 2023 in Zhengzhou City provide an ideal opportunity to investigate the effects of such policies and of fireworks burning on air quality during the Spring Festival period. Based on air quality ground-based monitoring data and meteorological data for Zhengzhou City, the article analyzes the impact of the POFB policy and the RHFB policy on air quality. The results show that: (1) The ban on fireworks burning significantly affects Spring Festival air quality, with a decrease of 16.0% in the Air Quality Index (AQI) value in 2016 compared to 2015 and a 74.9% increase in 2023 compared to 2022. (2) From 2016 to 2022, the Spring Festival period witnessed a substantial decrease in average concentration of main pollutants, along with a delayed occurrence of peak concentrations, indicating a noticeable “peak-shaving” effect. However, in 2023, there was an increase in pollutant concentrations, volatility, and a significant surge in hourly concentration. (3) The POFB policy and RHFB policy notably impacted PM2.5 and PM10, with a decrease of 16.1% and 23.6% in PM2.5 and PM10 concentrations, respectively, in 2016 compared to 2015, but an increase of 74.5% and 79.2%, respectively, in 2023 compared to 2022. (4) The contribution of fireworks burning to PM2.5 concentrations significantly decreased during the fireworks burning period (FBP) in 2016 after the POFB policy and increased significantly in 2023 during FBP after the implementation of the RHFB policy. Unfavorable meteorological conditions will undoubtedly exacerbate air quality pollution caused by fireworks burning.
Production of AFB1 High-Specificity Monoclonal Antibody by Three-Stage Screening Combined with the De-Homologation of Antibodies and the Development of High-Throughput icELISA
Authors: Pang Chengchen , Liu Qiang , Chen Lin , Yuan Bei , Zha Chuanyun , Nie Kunying , Xu Haitao , Ren Keyun , Yu Chunlei , Guo Yemin , Yang Qingqing
Published: December 2023

To achieve accurate detection of AFB1 toxin content in agricultural products and avoid false-positive rates in the assays, the specificity of mAbs is critical. We improved the specificity of the prepared monoclonal antibodies by modifying the traditional limiting dilution subcloning method. The traditional finite dilution method was modified with three-stage screening (the trending concentration of standards used in the screening is low–high–low) to achieve high specificity in pre-cell screening and increased the number of subclones to 10 to achieve the de-homologation of antibodies. A modified limiting dilution obtained a highly specific AFB1 monoclonal cell line, ZFG8, with a 50% inhibition concentration (IC50) of 0.3162 ng/mL. Notably, it exhibited the highest specificity compared to anti-AFB1 monoclonal antibodies prepared by other investigators. The maximum cross-reactivity of the mAb with structural analogues for AFB2, AFG1, AFG2, and AFM1 was 0.34%. The results showed that this type of screening improves the monoclonal antibodies’ specificity. Based on this ZFG8 monoclonal antibody, an icELISA assay was established with an IC50 of 0.2135 ng/mL for AFB1. The limit of the linear detection range of icELISA is 0.0422–1.29267 ng/mL with reasonable specificity and precision. The recoveries of AFB1 in samples of corn flour and wheat meal ranged from 84 to 107%, with CVs below 9.3%. The recoveries of structural analogues (AFB2, AFM1, AFG1, and AFG2) were less than 10% in both corn flour and wheat meal. The results showed that the prepared AFB1 monoclonal antibody could accurately and specifically recognize AFB1 residues in agricultural products while ignoring the effects of other structural analogues.
Snake Venom: A Promising Source of Neurotoxins Targeting Voltage-Gated Potassium Channels
Authors: AlShammari Altaf K. , Abd El-Aziz Tarek Mohamed , Al-Sabi Ahmed
Published: December 2023

The venom derived from various sources of snakes represents a vast collection of predominantly protein-based toxins that exhibit a wide range of biological actions, including but not limited to inflammation, pain, cytotoxicity, cardiotoxicity, and neurotoxicity. The venom of a particular snake species is composed of several toxins, while the venoms of around 600 venomous snake species collectively encompass a substantial reservoir of pharmacologically intriguing compounds. Despite extensive research efforts, a significant portion of snake venoms remains uncharacterized. Recent findings have demonstrated the potential application of neurotoxins derived from snake venom in selectively targeting voltage-gated potassium channels (Kv). These neurotoxins include BPTI-Kunitz polypeptides, PLA2 neurotoxins, CRISPs, SVSPs, and various others. This study provides a comprehensive analysis of the existing literature on the significance of Kv channels in various tissues, highlighting their crucial role as proteins susceptible to modulation by diverse snake venoms. These toxins have demonstrated potential as valuable pharmacological resources and research tools for investigating the structural and functional characteristics of Kv channels.
Epicyclic Oscillations and Circular Orbits in Hairy Black Holes: Testing by High-Frequency Quasi-Periodic Oscillations Observed in Microquasars
Authors: Vrba Jaroslav
Published: December 2023

Recently, Ovalle and his collaborators proposed an exact solution to Einstein’s equations. In this study, we investigate the main characteristics of the spherically symmetric spacetime determined by the hair parameter l, with a specific focus on circular orbits, particularly the innermost circular orbits (ISCOs), and the epicyclic oscillatory motion along these orbits. To assess the validity of this novel geometry, we employ the frequencies derived from the epicyclic resonance model of high-frequency quasi-periodic oscillations (HF QPOs) observed in microquasars, as well as the ISCOs. By analyzing the observed data from three selected microquasars, we establish constraints on the parameter l. Our findings suggest that this geometric framework can encompass the phenomena associated with HF QPOs and offer a partial explanation for the observed shift in the ISCOs, which is commonly attributed to the rotation of the black hole.
Kilonova-Targeting Lightcurve Classification for Wide Field Survey Telescope
Authors: Liang Runduo , Liu Zhengyan , Lei Lei , Zhao Wen
Published: December 2023

With the enhancement of the sensitivity of gravitational wave (GW) detectors and capabilities of large survey facilities, such as the Vera Rubin Observatory Legacy Survey of Space and Time (LSST) and the 2.5 m Wide Field Survey Telescope (WFST), we now have the potential to detect an increasing number of distant kilonova (KN). However, distinguishing KN from the plethora of detected transients in ongoing and future follow-up surveys presents a significant challenge. In this study, our objective is to establish an efficient classification mechanism tailored for the follow-up survey conducted by WFST, with a specific focus on identifying KN associated with GW. We employ a novel temporal convolutional neural network architecture, trained using simulated multi-band photometry lasting for 3 days by WFST, accompanied by contextual information, i.e., luminosity distance information by GW. By comparison of the choices of contextual information, we can reach 95% precision and 94% recall for our best model. It also performs good validation of photometry data on AT2017gfo and AT2019npv. Furthermore, we investigate the ability of the model to distinguish KN in a GW follow-up survey. We conclude that there is over 80% probability that we can capture true KN in 20 selected candidates among ∼250 detected astrophysical transients that have passed the real–bogus filter and cross-matching.
Susceptibility and Resistance of SARS-CoV-2 Variants to LCB1 and Its Multivalent Derivatives
Authors: Jin Hongliang , Gong Yani , Cheng Lin , Zhu Yuanmei , Zhang Zheng , He Yuxian
Published: December 2023

LCB1 is a computationally designed three-helix miniprotein that precisely targets the spike (S) receptor-binding motif (RBM) of SARS-CoV-2, exhibiting remarkable antiviral efficacy; however, emerging SARS-CoV-2 variants could substantially compromise its neutralization effectiveness. In this study, we constructed two multivalent LCB1 fusion proteins termed LCB1T and LCB1T-Fc, and characterized their potency in inhibiting SARS-CoV-2 pseudovirus and authentic virus in vitro. In the inhibition of various SARS-CoV-2 variants, the two LCB1 fusion proteins exhibited markedly improved inhibitory activities compared to LCB1 as anticipated; however, it was observed that relative to the D614G mutation hosting variant, the variants Delta, Lambda, and Omicron BQ.1.1, XBB, XBB.1.5, and EG.5.1 caused various degrees of resistance to the two fusion proteins’ inhibition, with XBB, XBB.1.5, and EG.5.1 variants showing high-level resistance. Moreover, we demonstrated that bat coronavirus RaTG13 and pangolin coronavirus PCoV-GD/PCoV-GX were highly sensitive to two LCB1 fusion proteins, but not LCB1, inhibition. Importantly, our findings revealed a notable decrease in the blocking capacity of the multivalent LCB1 inhibitor on the interaction between the virus’s RBD/S and the cell receptor ACE2 when confronted with the XBB variant compared to WT and the Omicron BA.1 variant. In conclusion, our studies provide valuable insights into the antiviral profiling of multivalent LCB1 inhibitors and offer a promising avenue for the development of novel broad-spectrum antiviral therapeutics.
Interspecies Papillomavirus Type Infection and a Novel Papillomavirus Type in Red Ruffed Lemurs (Varecia rubra)
Authors: Paietta Elise N. , Kraberger Simona , Regney Melanie , Custer Joy M. , Ehmke Erin , Yoder Anne D. , Varsani Arvind
Published: December 2023

The Papillomaviridae are a family of vertebrate-infecting viruses of oncogenic potential generally thought to be host species- and tissue-specific. Despite their phylogenetic relatedness to humans, there is a scarcity of data on papillomaviruses (PVs) in speciose non-human primate lineages, particularly the lemuriform primates. Varecia variegata (black-and-white ruffed lemurs) and Varecia rubra (red ruffed lemurs), two closely related species comprising the Varecia genus, are critically endangered with large global captive populations. Varecia variegata papillomavirus (VavPV) types −1 and −2, the first PVs in lemurs with a fully identified genome, were previously characterized from captive V. variegata saliva. To build upon this discovery, saliva samples were collected from captive V. rubra with the following aims: (1) to identify PVs shared between V. variegata and V. rubra and (2) to characterize novel PVs in V. rubra to better understand PV diversity in the lemuriform primates. Three complete PV genomes were determined from V. rubra samples. Two of these PV genomes share 98% L1 nucleotide identity with VavPV2, denoting interspecies infection of V. rubra by VavPV2. This work represents the first reported case of interspecies PV infection amongst the strepsirrhine primates. The third PV genome shares <68% L1 nucleotide identity with that of all PVs. Thus, it represents a new PV species and has been named Varecia rubra papillomavirus 1 (VarPV1). VavPV1, VavPV2, and VarPV1 form a new clade within the Papillomaviridae family, likely representing a novel genus. Future work diversifying sample collection (i.e., lemur host species from multiple genera, sample type, geographic location, and wild populations) is likely to uncover a world of diverse lemur PVs.
Artificial Intelligence Approaches for Advanced Battery Management System in Electric Vehicle Applications: A Statistical Analysis towards Future Research Opportunities
Authors: Lipu M. S. Hossain , Miah Md. Sazal , Jamal Taskin , Rahman Tuhibur , Ansari Shaheer , Rahman Md. Siddikur , Ashique Ratil H. , Shihavuddin A. S. M. , Shakib Mohammed Nazmus
Published: December 2023

In order to reduce carbon emissions and address global environmental concerns, the automobile industry has focused a great deal of attention on electric vehicles, or EVs. However, the performance and health of batteries can deteriorate over time, which can have a negative impact on the effectiveness of EVs. In order to improve the safety and reliability and efficiently optimize the performance of EVs, artificial intelligence (AI) approaches have received massive consideration in precise battery health diagnostics, fault analysis and thermal management. Therefore, this study analyzes and evaluates the role of AI approaches in enhancing the battery management system (BMS) in EVs. In line with that, an in-depth statistical analysis is carried out based on 78 highly relevant publications from 2014 to 2023 found in the Scopus database. The statistical analysis evaluates essential parameters such as current research trends, keyword evaluation, publishers, research classification, nation analysis, authorship, and collaboration. Moreover, state-of-the-art AI approaches are critically discussed with regard to targets, contributions, advantages, and disadvantages. Additionally, several significant problems and issues, as well as a number of crucial directives and recommendations, are provided for potential future development. The statistical analysis can guide future researchers in developing emerging BMS technology for sustainable operation and management in EVs.
Battery Electric Vehicles: How Many Gears? A Technical–Economic Analysis
Authors: Bertucci Emmanuele , Bucchi Francesco , Ceraolo Massimo , Frendo Francesco , Lutzemberger Giovanni
Published: December 2023

The large majority of electric cars have a single-speed gearbox, because electrified powertrains provide maximal power across a wide operating range, and single-speed simplifies construction and reduces capital costs. Nevertheless, multi-speed transmissions have also been developed for electric cars, and some of them have recently appeared as commercial products. This paper aims to compare, through some practical examples, solutions with single-speed and dual-speed transmissions. In particular, given the very smooth driving of electric cars, for dual-speed solutions, a dual-clutch gearbox was considered. Finally, a continuously variable transmission (CVT) was also used. Different solutions were analyzed from a technical–economic point of view, based on a simulation of the vehicle under standardized driving cycles, thus evaluating the capital and running electricity costs. The obtained results show that the comparison between the two solutions is very open, and in the majority of cases, the advantages in terms of efficiency overcome the disadvantages due to the additional capital costs. For a rather low battery cost of 150 €/kWh, the total cost reduction moves from about 100–150 € up to 1500–2000 €, depending on the electricity cost, along the whole vehicle lifespan.
Assessment of Huixian Karst Wetland for Local Water Augmentation in Guilin, China
Authors: Qi Jihong , Xu Yongxin , Kanyerere Thokozani , Lu Ming , Zhang Zhixiang , Shen Haoyong , Zou Shengzhang , Jia Feng
Published: December 2023

Due to the rapid exploitation of water resources in the Huixian karst wetland in the southwest of China in the past decades, the wetland has suffered from shrinkage in size and serious degradation of ecological functioning. To assess how much water within the wetland could still be taken out for local supply purposes while the wetland can still be kept in its normal ecological functioning. Through the use of multi-methods, including field surveys by a multi-disciplinary team, water balance, Remote Sensing, GIS and numerical simulation, this paper characterizes the wetland regime of the study area and finally determines scenarios of water resource utilization for local water supply within acceptable parameters of wetland ecological health. Through the analysis of the methods, it was found that the hydrological characteristics of the study area were conditioned by not only the karst water but also the regional precipitation fluctuations. A zone of mobile watersheds for Lake Mudong was established as opposed to a conventional single watershed. If the wetland ecosystem is kept at the current status of class III, a scenario of withdrawal of up to 20% of lake inflows could be accommodated. The results and their approaches would provide much-needed information for the protection of the wetland and its sustainable water utilization per se. It would offer a basic reference for similar problems in karst areas of southwest China and other areas alike.