bluuebunny/crossref_metadata_2025 huggingface dataset:
In Vitro/Vivo Mechanisms of Antibacterial Peptide NZ2114 against Staphylococcus pseudintermedius and Its Biofilms
Authors: Zhang Shuang , Yang Na , Mao Ruoyu , Hao Ya , Teng Da , Wang Jianhua
Published: April 2024

Staphylococcus pseudintermedius is an opportunistic pathogen commonly found in canines, and has garnered escalating interest due to its potential for zoonotic transmission and increasing antimicrobial resistance. However, the excessive use of antibiotics and the characteristic of S. pseudintermedius forming biofilms make treatment challenging. In this study, the in vivo and in vitro antimicrobial activity and mechanisms of action of NZ2114, a plectasin-derived peptide, against S. pseudintermedius were investigated. NZ2114 exhibited potent antibacterial activity towards S. pseudintermedius (minimum inhibitory concentration, MIC = 0.23 μM) with a lower probability of inducing drug-resistant mutations and efficient bactericidal action, which was superior to those of mopirucin (MIC = 0.25–0.5 μM) and lincomycin (MIC = 4.34–69.41 μM). The results of electron microscopy and flow cytometry showed that NZ2114 disrupted S. pseudintermedius’ cell membrane, resulting in cellular content leakage, cytoplasmic membrane shrinkage, and, eventually, cell death. The intracellular ROS activity and Alamar Blue detection showed that NZ2114 interferes with intracellular metabolic processes. In addition, NZ2114 effectively inhibits biofilm formation, and confocal laser scanning microscopy further revealed its antibacterial and anti-biofilm activity (biofilm thickness reduced to 6.90–17.70 μm). The in vivo therapy of NZ2114 in a mouse pyoderma model showed that it was better than lincomycin in effectively decreasing the number of skin bacteria, alleviating histological damage, and reducing the skin damage area. These results demonstrated that NZ2114 may be a promising antibacterial candidate against S. pseudintermedius infections.
α-Phenyl-N-tert-Butylnitrone and Analogous α-Aryl-N-alkylnitrones as Neuroprotective Antioxidant Agents for Stroke
Authors: Marco-Contelles José
Published: April 2024

The recent advances in research on the use of the antioxidant and neuroprotective agent α-phenyl-N-tert-butylnitrone (PBN) for the therapy of stroke have been reviewed. The protective effect of PBN in the transient occlusion of the middle cerebral artery (MCAO) has been demonstrated, although there have been significant differences in the neuronal salvaging effect between PBN-treated and untreated animals, each set of data having quite large inter-experimental variation. In the transient forebrain ischemia model of gerbil, PBN reduces the mortality after ischemia and the neuronal damage in the hippocampal cornu ammonis 1 (CA1) area of the hippocumpus caused by ischemia. However, PBN fails to prevent postischemic CA1 damage in the rat. As for focal cerebral ischemia, PBN significantly reduces cerebral infarction and decreases neurological deficit after ischemia using a rat model of persistent MCAO in rats. Similarly, the antioxidant and neuroprotective capacity of a number of PBN-derived nitrones prepared in the author’s laboratory have also been summarized here, showing their high potential therapeutic power to treat stroke.
Fast and Accurate Multi-Task Learning for Encrypted Network Traffic Classification
Authors: Park Jee-Tae , Shin Chang-Yui , Baek Ui-Jun , Kim Myung-Sup
Published: April 2024

The classification of encrypted traffic plays a crucial role in network management and security. As encrypted network traffic becomes increasingly complicated and challenging to analyze, there is a growing need for more efficient and comprehensive analytical approaches. Our proposed method introduces a novel approach to network traffic classification, utilizing multi-task learning to simultaneously train multiple tasks within a single model. To validate the proposed method, we conducted experiments using the ISCX 2016 VPN/Non-VPN dataset, consisting of three tasks. The proposed method outperformed the majority of existing methods in classification with 99.29%, 97.38%, and 96.89% accuracy in three tasks (i.e., encapsulation, category, and application classification, respectively). The efficiency of the proposed method also demonstrated outstanding performance when compared to methods excluding lightweight models. The proposed approach demonstrates accurate and efficient multi-task classification on encrypted traffic.
3D Printing Approach in Maxillofacial Surgery in Iran: An Evaluation Using the Non-Adoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) Framework
Authors: Farjaminejad Rosana , Farjaminejad Samira , Nucci Ludovica , d’Apuzzo Fabrizia , Grassia Vincenzo , Majidi Korosh , Jamilian Abdolreza
Published: April 2024

The integration of 3D printing technology in maxillofacial surgery in Iran represents a significant advancement in medical practice, offering unprecedented precision and efficiency in surgical procedures. Employing the Non-Adoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) framework and the WHO Health Systems Framework, this study analyzes the complex impact and challenges of 3D printing adoption within the Iranian healthcare sector. Maxillofacial surgery has seen transformative progress with 3D printing’s ability to produce accurate surgical models and customized implants. The results indicate a notable improvement in surgical precision and patient recovery times, alongside combatting identified barriers including technological access, cost, and skill acquisition. This conclusion underscores the critical need for targeted educational programs, policy support, and infrastructure development to overcome these obstacles. This paper highlights 3D printing’s potential to revolutionize maxillofacial surgery in Iran, provided that comprehensive strategies are implemented to address the current limitations and fully leverage this innovative technology’s benefits.
Extending Context Window in Large Language Models with Segmented Base Adjustment for Rotary Position Embeddings
Authors: Li Rongsheng , Xu Jin , Cao Zhixiong , Zheng Hai-Tao , Kim Hong-Gee
Published: April 2024

In the realm of large language models (LLMs), extending the context window for long text processing is crucial for enhancing performance. This paper introduces SBA-RoPE (Segmented Base Adjustment for Rotary Position Embeddings), a novel approach designed to efficiently extend the context window by segmentally adjusting the base of rotary position embeddings (RoPE). Unlike existing methods, such as Position Interpolation (PI), NTK, and YaRN, SBA-RoPE modifies the base of RoPE across different dimensions, optimizing the encoding of positional information for extended sequences. Through experiments on the Pythia model, we demonstrate the effectiveness of SBA-RoPE in extending context windows, particularly for texts exceeding the original training lengths. We fine-tuned the Pythia-2.8B model on the PG-19 dataset and conducted passkey retrieval and perplexity (PPL) experiments on the Proof-pile dataset to evaluate model performance. Results show that SBA-RoPE maintains or improves model performance when extending the context window, especially on longer text sequences. Compared to other methods, SBA-RoPE exhibits superior or comparable performance across various lengths and tasks, highlighting its potential as an effective technique for context window extension in LLMs.
Magnetic Localization Method for Vehicles Based on Transformer
Authors: Lu Yi , Wei Dongyan , Yuan Hong
Published: April 2024

Magnetic positioning is a promising technique for vehicles in Global Navigation Satellite System (GNSS)-denied scenarios. Traditional magnetic positioning methods resolve the position coordinates by calculating the similarity between the measured sequence and the sequence generated from the magnetic database with criteria such as the Mean Absolute Difference (MAD), PRODuct correlation (PROD), etc., which usually suffer from a high mismatch rate. To solve this problem, we propose a novel magnetic localization method for vehicles based on Transformer. In this paper, we cast the magnetic localization problem as a regression task, in which a neural network is trained by equidistant sequences to predict the current position. In addition, by adopting Transformer to perform magnetic localization of vehicles for the first time, magnetic features are extracted, and positional relationships are explored to guarantee positioning accuracy. The experimental results show that the proposed method can greatly improve the magnetic positioning accuracy, with an average improvement of approximately 2 m.
Synergistic Effect and Phase Behavior of SCG-CAPB-H2O Ternary Compound System
Authors: Zhu Zhendong , Zhang Jiahao , Wang Feihong , Feng Wenhui , Dang Leping , Wei Hongyuan
Published: April 2024

The physical properties of sodium cocoyl glycinate (SCG) and the synergistic effects within the SCG/CAPB complex system were investigated using visible–ultraviolet spectrophotometry and a surface tension meter. Analysis of the synergistic parameters of the complex system revealed that the combination of SCG and cocamidopropyl betaine (CAPB) yielded superior results compared to the individual use of SCG. Building upon this, the ternary phase behavior of the SCG/CAPB/H2O system was further explored. Different lyotropic liquid crystal phases were identified using polarized optical microscopy (POM) and small-angle X-ray scattering (SAXS). Surfactant solutions with varying mass fractions exhibited diverse phase behaviors, including the micellar phase (L1), micelle–solid mixed phase (L1-S), hexagonal phase (H), and lamellar phase (Lα), with the insoluble solid phase predominating. The investigation of the ternary phase diagram of SCG/CAPB/H2O provided valuable guidance for detergent formulation screening, enriched the understanding of sodium cocoyl glycinate complex systems, and further promoted the utilization of amino acid-based surfactants in the field of personal care products.
A Simplified Frequency-Domain Feedback Active Noise Control Algorithm
Authors: Gao Yuan , Yu Guoliang , Gao Min
Published: April 2024

When the adaptive filter length is increased, the calculation complexity increases rapidly because the relationship between the calculation and the adaptive filter length N contains a power function with no secondary path identification algorithm. Under the basic premise of unreduced noise reduction, herein, a simplified frequency-domain feedback active noise control algorithm is proposed. To reduce the computation complexity, the total delay is adopted as the estimated secondary path; the filtered reference signal is produced in the frequency domain by using multiplication to replace convolution calculation in the time domain and then updating the adaptive filter coefficients in the frequency domain. Therefore, the computational complexity has a logarithmic function with the increased adaptive filter length in the proposed algorithm. If the adaptive filter length is 512, the existing WSMANC algorithm’s calculation is 271,360 real number multiplications, while that of the proposed algorithm is only 38,912 real number multiplications. To verify the proposed algorithm’s stability, convergence speed, and noise reduction, the single-frequency noise, narrowband white noise, and narrowband pink noise, respectively, are used as the primary noise types in the simulations. The results show that (1) the proposed SFDFBANC algorithm can obtain similar noise reduction performance to existing algorithm, (2) the convergence rate is faster than existing algorithm, and (3) if the adaptive filter length is more than 64, the proposed algorithm exhibits a lower computational complexity.
An Integrated Approach to Precedence-Constrained Multi-Agent Task Assignment and Path Finding for Mobile Robots in Smart Manufacturing
Authors: Liu Shuo , Feng Bohan , Bi Youyi , Yu Dan
Published: April 2024

Mobile robots play an important role in smart factories, though efficient task assignment and path planning for these robots still present challenges. In this paper, we propose an integrated task- and path-planning approach with precedence constrains in smart factories to solve the problem of reassigning tasks or replanning paths when they are handled separately. Compared to our previous work, we further improve the Regret-based Search Strategy (RSS) for updating the task insertions, which can increase the operational efficiency of machining centers and reduce the time consumption. Moreover, we conduct rigorous experiments in a simulated smart factory with different scales of robots and tasks. For small-scale problems, we conduct a comprehensive performance analysis of our proposed methods and NBS-ISPS, the state-of-the-art method in this field. For large-scale problems, we examine the feasibility of our proposed approach. The results show that our approach takes little computation time, and it can help reduce the idle time of machining centers and make full use of these manufacturing resources to improve the overall operational efficiency of smart factories.
Surface Roughness of Polyetheretherketone Printed by Fused Deposition Modeling: A Pilot Study Investigating the Impact of Print Layer Thickness and Polishing Method
Authors: Soares Tânia , Fernandes Carlos , Barbosa Cláudia , Vaz Mário A. P. , Reis Tiago , Figueiral Maria Helena
Published: April 2024

Polyetheretherketone is a high-performance thermoplastic polymer that can be used in 3D printing by fused deposition modeling, and is a promising material for dental applications. Some printing parameters are sensitive and can influence the properties of the printed object. Thus, this work aims to evaluate the influence of the print layer thickness on the surface roughness of polyetheretherketone before and after polishing and to verify the effectiveness of the polishing method used, as well as to compare it with the results obtained using polymethyl methacrylate as the control group. Specimens with different impression layer thicknesses were printed with polyetheretherketone (Group A—0.1 mm and Group B—0.3 mm). Additionally, a control group with polymethyl methacrylate specimens was milled. Roughness evaluation was conducted using a contact profilometer after the specimens had been printed (before polishing). Then, silicon carbide sandpaper was used to polish the surface, and the roughness was reassessed. Differences were observed between specimens regarding the print layer thickness and the roughness, with the 0.3 mm layer thickness showing the lowest roughness values. The results of this pilot study suggest that the surface roughness of fused deposition modeling printed polyetheretherketone is influenced by print layer thickness, with the lowest roughness seen at a thickness of 0.3 mm.
Improved Long-Term Forecasting of Passenger Flow at Rail Transit Stations Based on an Artificial Neural Network
Authors: Du Zitao , Yang Wenbo , Yin Yuna , Ma Xinwei , Gong Jiacheng
Published: April 2024

When new rail stations or lines are planned, long-term planning for decades to come is required. The short-term passenger flow prediction is no longer of practical significance, as it only takes a few factors that affect passenger flow into consideration. To overcome this problem, we propose several long-term factors affecting the passenger flow of rail transit in this paper. We also create a visual analysis of these factors using ArcGIS and construct a long-term passenger flow prediction model for rail transit based on a class neural network using an SPSS Modeler. After optimizing relevant parameters, the prediction accuracy reaches 94.6%. We compare the results with other models and find that the neural network model has a good performance in predicting long-term rail transit passenger flow. Finally, the factors affecting passenger flow are ranked in terms of importance. It is found that among these factors, bicycles available for connection have the biggest influence on the passenger flow of rail stations.
A Path Planning Method Based on Improved A* and Fuzzy Control DWA of Underground Mine Vehicles
Authors: Zhang Chuanwei , Yang Xinyue , Zhou Rui , Guo Zhongyu
Published: April 2024

In order to solve the problem of low safety and efficiency of underground mine vehicles, a path planning method for underground mine vehicles based on an improved A star (A*) and fuzzy control Dynamic Window Approach (DWA) is proposed. Firstly, the environmental information is analyzed, and the proportion of obstacles is introduced into the A* algorithm to optimize the heuristic function to improve its search efficiency in different environments. Secondly, for the global path, the key node selection strategy is proposed, the node selection method is optimized, and the redundant nodes are deleted. The path is optimized by using a 3 times Clamped-B spline to improve the smoothness of the path. Then, based on the principle of fuzzy control, fuzzy rules are established, and a DWA fuzzy controller is designed to improve the environmental adaptability of the DWA algorithm. Finally, on the basis of the global path, the key node is used as the local target point of the DWA, and the fusion of the improved A* and DWA algorithm is realized. Finally, experiments are conducted to verify the effectiveness and feasibility of the proposed path-planning method. The average deviation of the path is controlled at ±0.109 m, which basically meets the path planning needs of underground mine vehicles.
AdaBoost Ensemble Approach with Weak Classifiers for Gear Fault Diagnosis and Prognosis in DC Motors
Authors: Hussain Syed Safdar , Zaidi Syed Sajjad Haider
Published: April 2024

This study introduces a novel predictive methodology for diagnosing and predicting gear problems in DC motors. Leveraging AdaBoost with weak classifiers and regressors, the diagnostic aspect categorizes the machine’s current operational state by analyzing time–frequency features extracted from motor current signals. AdaBoost classifiers are employed as weak learners to effectively identify fault severity conditions. Meanwhile, the prognostic aspect utilizes AdaBoost regressors, also acting as weak learners trained on the same features, to predict the machine’s future state and estimate its remaining useful life. A key contribution of this approach is its ability to address the challenge of limited historical data for electrical equipment by optimizing AdaBoost parameters with minimal data. Experimental validation is conducted using a dedicated setup to collect comprehensive data. Through illustrative examples using experimental data, the efficacy of this method in identifying malfunctions and precisely forecasting the remaining lifespan of DC motors is demonstrated.
Development Characteristics and Reactivation Mechanism of a Large-Scale Ancient Landslide in Reservoir Area
Authors: Dai Liang , Jia Chaojun , Chen Lei , Zhang Qiang , Chen Wei
Published: April 2024

The intricate geological conditions of reservoir banks render them highly susceptible to destabilization and damage from fluctuations in water levels. The study area, the Cheyipin section of the Huangdeng Hydroelectric Station, is characterized by numerous ancient landslides of varying scales and ages. In June 2019, during the reservoir filling process of the Huangdeng Hydroelectric Station, a large-scale reactivation of ancient landslides occurred in this area, posing severe threats to riverside infrastructure and human safety, including ground cracking, house cracking, foundation settlement, and road collapse. The reactivation mechanism of ancient landslides at reservoir banks is highly complex due to fluid dynamics. This study conducted field investigations in the Cheyipin landslide area, monitored surface and subsurface deformations using GNSS and inclinometers, and analyzed the distribution characteristics, destruction features, and reactivation mechanisms of the landslides through correlation analysis and numerical calculations. The results indicate that the instability pattern of the slopes manifests as traction-type sliding failure. The slopes do not slide along the ancient sliding surface but along a newly formed arcuate sliding surface, with the direct impact area mainly concentrated near the waterline. The stability of the slopes in this project is closely related to the reservoir water level. It can be assumed that the lowering of the reservoir water level triggered the reactivation of the ancient landslides in the Cheyipin section, while the influence of rainfall can be ignored. To prevent the reactivation of ancient landslides, attention should be focused on the changes in reservoir water level, avoiding rapid adjustments in water level during the initial lowering and final raising of the water level.
Ride Comfort Prediction on Urban Road Using Discrete Pavement Roughness Index
Authors: Wu Difei
Published: April 2024

The prediction of ride comfort holds significant potential for enhancing the driving experience of both human drivers and autonomous vehicles, as it is closely correlated with pavement roughness. However, in urban road scenarios, the presence of shorter road segments and local irregularities introduces added complexity to ride comfort prediction. To better capture and characterize the irregularities and short road sections’ unevenness, we adopt the discrete roughness index (DRI) instead of the commonly used international roughness index (IRI) for assessing road profile unevenness, which is more suitable for urban roads. Ride comfort prediction is developed through numerical simulations using an eight-degree-of-freedom full-car model. The maximum transient vibration value (MTVV) is adopted to assess ride comfort. Through comparing the correlations between the MTVV and pavement roughness indices, it is indicated that the fitting degree of MTVV-DRI outperforms that of MTVV-IRI on short sections. Then, a set of speed-related DRI thresholds to estimate ride comfort distribution on a given road section is proposed, with considerations of vehicle speed, time period, and wheel paths. A hyperbolic-tangent-based speed control strategy is also proposed to avoid abrupt speed and acceleration changes during deceleration. This prediction method can assist drivers or autonomous vehicles in generating driving control strategies and maintaining a high level of ride comfort.
Analysis of the Load-Bearing Capacity of Pebble Aggregates
Authors: Liu Pan , Bai Peiyi , Liu Wenju
Published: April 2024

The load-bearing capacity of pebble aggregates plays a pivotal role in influencing the operational performance of uncontrolled trucks on arrester beds. The complexity of this phenomenon stems from the nonuniformity in the shapes of the pebbles and their stochastic arrangement within the beds, presenting notable challenges for traditional mathematical modelling techniques in precisely evaluating the contact dynamics of these aggregates. This study leverages the discrete element method (DEM) to extensively analyse the arrester bed aggregate of a standard truck escape ramp. The aforementioned mechanism entails the gathering of morphological parameters of irregularly shaped aggregate particles and introduces a novel method for constructing random shapes that adhere to the observed distribution characteristics. A discrete element model, grounded in the physical properties of these aggregates, is formulated. This study focuses on the aggregate’s load-bearing capabilities, scrutinising the mechanical behaviour of the aggregate particles at the macroscopic and microscopic scales. These insights offer substantial scientific contributions and practical implications for assessing the safety of escape ramps and determining essential parameters for the brake bed design.
Designing the Quality Characteristics of Berry Processing Byproducts Using Fermentation
Authors: Sady Sylwia , Ligaj Marta , Pachołek Bogdan , Błaszczyk Alfred , Płaczek Zuzanna , Dłużniewska Nikola , Kawałek Patrycja , Pakuła Karolina , Konopelski Adam , Gołaszewski Eryk
Published: April 2024

In recent years, there has been increasing interest in berry fruit processing byproducts, namely, seeds, pulp, and peel, due to the high content of nutritionally valuable ingredients. The market is seeing an increase in the popularity of fermented products, especially those from vegetables or fruits. Fermented fruit pomace can be used as an ingredient or food additive. Many studies have confirmed that the fermentation process can increase the antioxidant activity of plant extracts due to the decomposition of cell walls. The aim of this study was to evaluate the microbiological quality and antioxidant potential of fermented berry pomace (from chokeberry, blackcurrant, raspberry, and strawberry) in terms of its potential use as an alternative source of valuable ingredients for the design of new food products. The scope of this research included assessing microbiological quality, vitamin C and total phenolic compound (TPC) contents, and antioxidant activity using ABTS, DPPH, and FRAP assays. The polyphenolic compound and vitamin C contents, as well as antioxidant activity, depended on the mixture of microbial strains used for fermentation and the type of fruit pomace. The most favorable parameters for TPC, ABTS, DPPH, and FRAP were obtained for chokeberry pomace samples inoculated with yeast cultures. Chokeberry pomace exhibited the highest vitamin C content when inoculated with a mixture of bacteria.
Fault Diagnosis Method of Box-Type Substation Based on Improved Conditional Tabular Generative Adversarial Network and AlexNet
Authors: Liu Yong , Zhou Jialin , Zhang Dong , Wei Shaoyu , Yang Mingshun , Gao Xinqin
Published: April 2024

To solve the problem of low diagnostic accuracy caused by the scarcity of fault samples and class imbalance in the fault diagnosis task of box-type substations, a fault diagnosis method based on self-attention improvement of conditional tabular generative adversarial network (CTGAN) and AlexNet was proposed. The self-attention mechanism is introduced into the generator of CTGAN to maintain the correlation between the indicators of the input data, and a large amounts of high-quality data are generated according to the small number of fault samples. The generated data are input into the AlexNet model for fault diagnosis. The experimental results demonstrate that compared with the SMOTE and CTGAN methods, the dataset generated by the self-attention-conditional tabular generative adversarial network (SA-CTGAN) model has better data relevance. The accuracy of fault diagnosis by the proposed method reaches 94.81%, which is improved by about 11% compared with the model trained on the original data.
A Multi-Objective Optimal Control Method for Navigating Connected and Automated Vehicles at Signalized Intersections Based on Reinforcement Learning
Authors: Jiang Han , Zhang Hongbin , Feng Zhanyu , Zhang Jian , Qian Yu , Wang Bo
Published: April 2024

The emergence and application of connected and automated vehicles (CAVs) have played a positive role in improving the efficiency of urban transportation and achieving sustainable development. To improve the traffic efficiency at signalized intersections in a connected environment while simultaneously reducing energy consumption and ensuring a more comfortable driving experience, this study investigates a flexible and real-time control method to navigate the CAVs at signalized intersections utilizing reinforcement learning (RL). Initially, control of CAVs at intersections is formulated as a Markov Decision Process (MDP) based on the vehicles’ motion state and the intersection environment. Subsequently, a comprehensive reward function is formulated considering energy consumption, efficiency, comfort, and safety. Then, based on the established environment and the twin delayed deep deterministic policy gradient (TD3) algorithm, a control algorithm for CAVs is designed. Finally, a simulation study is conducted using SUMO, with Lankershim Boulevard as the research scenario. Results indicate that the proposed methods yield a 13.77% reduction in energy consumption and a notable 18.26% decrease in travel time. Vehicles controlled by the proposed method also exhibit smoother driving trajectories.
Composition and Basic Physical Properties of the Phobos Surface: A Comprehensive Review
Authors: Kolano Malwina , Cała Marek , Stopkowicz Agnieszka
Published: April 2024

The surface of Phobos is an intriguing subject of research for many scientists. This is associated, among other things, with the fact that it is perceived as a potential launch site for future human Mars exploration. Additionally, measurements conducted on its surface would not only deepen our knowledge about Phobos but also provide insights into geochemical processes occurring on similar small bodies in the Solar System. Therefore, understanding the physical–mechanical properties of regolith is a crucial aspect of planetary exploration. These properties are key factors needed for both planning safe landings and establishing future bases on celestial bodies. In this paper, information is compiled regarding hypotheses about its origin, the probable composition of Phobos’ surface (spectral properties and HiRISE data), as well as its morphology. The article also presents the process of regolith formation covering Phobos’ surface and its presumed physical properties. It has been established that the estimated bulk density of Phobos, compared to the densities of other asteroids and meteorites, is most similar to C-type asteroids. It was also found that C-type asteroids, in terms of total porosity, best reflect Phobos. However, determining the surface composition of Phobos and its detailed physical properties requires additional information, which could be obtained through in situ studies or sample return missions.
Activity of Methanolic and Hydrolyzed Methanolic Extracts of Ricinus communis (Euphorbiaceae) and Kaempferol against Spodoptera frugiperda (Lepidoptera: Noctuidae)
Authors: Rodríguez-Cervantes Manolo , Zavala-Gómez Carlos Eduardo , Hernández-Caracheo Karla , Campos-Guillén Juan , Rodríguez-de León Eloy , Amaro-Reyes Aldo , Rodríguez-Morales José Alberto , Jiménez-García Sandra Neli , Figueroa-Brito Rodolfo , Salinas-Sánchez David Osvaldo , Flores-Gallardo Francisco Javier , Ramos-López Miguel Angel
Published: April 2024

Spodoptera frugiperda is the main pest of maize. One of the alternatives proposed for its control is the implementation of products of botanical origin, such as those derived from Ricinus communis. In this work, the insecticidal and insectistatic activities of methanolic and hydrolyzed methanolic extracts of the aerial parts of R. communis and kaempferol against S. frugiperda are evaluated. The methanolic extract presented a larval mortality rate of 55% and an accumulated mortality rate of 65% starting at 4000 ppm, with LC50 values of 3503 (larvae) and 2851 (accumulated); meanwhile, from a concentration of 1000 ppm, a decrease in pupa weight at 24 h of 20.5 mg was observed when compared to the control. The hydrolyzed methanolic extract presented a larval mortality and accumulated mortality rate of 60% from a concentration of 1000 ppm, and a decrease in pupa weight at 24 h of 35.31 mg was observed, when compared to the control. For the compound kaempferol 3-β-D-glucopyranoside, a larval mortality rate of 65% and an accumulated mortality rate of 80% were observed from 800 ppm, with LC50 values of 525.2 (larvae) and 335.6 ppm (accumulated); meanwhile, at 300 ppm, a decrease in pupa weight of 25.59 mg after 24 h was observed when compared to the control.
Memory-Enhanced Knowledge Reasoning with Reinforcement Learning
Authors: Guo Jinhui , Zhang Xiaoli , Liang Kun , Zhang Guoqiang
Published: April 2024

In recent years, the emergence of large-scale language models, such as ChatGPT, has presented significant challenges to research on knowledge graphs and knowledge-based reasoning. As a result, the direction of research on knowledge reasoning has shifted. Two critical issues in knowledge reasoning research are the algorithm of the model itself and the selection of paths. Most studies utilize LSTM as the path encoder and memory module. However, when processing long sequence data, LSTM models may encounter the problem of long-term dependencies, where memory units of the model may decay gradually with an increase in time steps, leading to forgetting earlier input information. This can result in a decline in the performance of the LSTM model in long sequence data. Additionally, as the data volume and network depth increase, there is a risk of gradient disappearance. This study improved and optimized the LSTM model to effectively address the problems of gradient explosion and gradient disappearance. An attention layer was employed to alleviate the issue of long-term dependencies, and ConvR embedding was used to guide path selection and action pruning in the reinforcement learning inference model. The overall model achieved excellent reasoning results.
Health Risk Assessment from Exposure to Ambient Volatile Organic Compounds (VOCs) at a Truck Tire Factory in the Yangtze River Delta, China
Authors: Hou Songtao , Wang Yalong , Duan Lian , Xiu Guangli
Published: April 2024

Occupational health risk assessments of exposure to VOCs still need to be extensively studied to improve the safety standards in the industry. Based on the monitoring of organic pollutants at various workstations in a truck tire factory in Jiangsu Province, both semi-quantitative and quantitative health risk assessment methods were employed to assess health risk levels. The findings indicated that VOCs were categorized into five classes, which included alkanes, aromatics, halocarbons, carbon disulfide, and oxygenated volatile organic compounds (OVOCs). The highest concentration of total volatile organic compounds (TVOCs) was found in shaping workshop; alkanes were the most abundant class (74.2%), followed by aromatics (24.02%) and OVOCs (1.96%). Although the results of the semi-quantitative risk assessment showed that most of the organic compounds had low R values, various kinds of VOCs were detected; particularly, many harmful organic compounds (such as toluene, ethyl benzene) were detected in all the sampling sites. The quantitative risk in the calendering and vulcanizing workshop exceeded the acceptable level; both the carcinogenic risk of ethylbenzene in the tire-strip storage room, and trichloroethylene and perchloroethylene in the calendering workshop were unacceptable. Thus, the calendering and vulcanizing processes in rubber tire manufacturing should be priority-controlled processes.
Vegetation–Topographic Landscape and the Influence of Water and Sediment in the Shule River Basin
Authors: Zeng Jianjun , Cui Yanqiang
Published: April 2024

The study of the effect of vegetation cover on water and sediment content is of great significance for the in-depth understanding of ecological and environmental effects in river basins and the formulation of corresponding management measures. Based on the monitoring data of rainfall and runoff of Panjiazhuang and Dangchengwan Hydrologic Stations in Shule River Basin from 2000 to 2020 and the sediment discharge of Changmabao, methods such as geographic information technology (GIS), landscape pattern analysis, land use transfer matrix, correlation analysis, principal component analysis, and linear regression analysis were used to study water and sediment change, land use pattern, vegetation change characteristics, and local water and sediment change in Shule River Basin and construct vegetation–topographic landscape factors. The main research results are as follows: (1) Vegetation coverage in the Shule River Basin increased year by year from 2000 to 2020, with a cumulative increase of 0.064 in 20 years. Vegetation cover has a significant effect on water and sediment content, and the correlation is −0.966. (2) The cultivated land area of the Shule River Basin increased by 604 km2 from 2000 to 2020, and the conversion rate was 67%. From 2000 to 2020, the water area increased by 442 km2, and the conversion rate was 51%. The area of grassland and forest increased by 198 km2 and 12 km2, respectively, and the conversion rate was 68% and 33%, respectively. Forest had the highest transfer rate (0.67). The lowest conversion rate was 0.32 for grassland. (3) The variation coefficient of water and sediment content in Shule River Basin during 1971–2020 was 45.21%, and the highest variation coefficient during 2001–2010 was 49.15%. The lowest variation coefficient was 39.73% during 2011–2020. The annual sediment transport in the Shule River Basin fluctuates greatly and has a high degree of dispersion during 1971–2020. (4) The results of the landscape index in Shule River Basin during 2000–2020 had a small difference, with a difference of less than 0.5. According to the principal component analysis of landscape index and water and sediment content, the maximum patch index (LPI) had the strongest positive correlation with water and sediment content (0.958). The diversity index SHDI had the strongest negative correlation with water and sediment content (−0.995).
Dynamic Snow Melting Process and Its Driving Factors in Northern Grasslands
Authors: Huang Kunlin , Xu Weixin , Wang Haimei , Li Hang , Li Lidong , Li Zixiang , Si Jingke , Liu Hongbin , Wu Chengna
Published: April 2024

Hourly automatic snow depth stations have enhanced insights into the dynamics and spatial variability of daily snowmelt. From 2021 to 2022, we gathered hourly snow depth measurements from six Hulun Buir grassland stations. Our analysis shed light on the dynamics of snowmelt and the key drivers in this northern region. We found that in northern China’s mid-high latitude grasslands, winter snow cover persists for about 80 to 134 days. The transition to the melting phase in early March spans 5 to 12 days, with continuous and rapid phases. Snow under 3 cm quickly collapses. If the average temperature from 10:00 to 18:00 exceeds 0 °C, complete melting occurs within 36 h. Daily snow melting sees initial stability, swift decline, and gradual reduction, peaking between 11:00 and 14:00. Finally, thermal conditions primarily drive snow melt dynamics, with 14:00 ground temperature being pivotal. These findings shed light on snow dynamics and key factors in the mid-high latitude grasslands of northern China.
Lower Local Uniform Monotonicity in F-Normed Musielak–Orlicz Spaces
Authors: Liu Yanli , Xue Yangyang , Cui Yunan
Published: April 2024

Lower strict monotonicity points and lower local uniform monotonicity points are considered in the case of Musielak–Orlicz function spaces LΦ endowed with the Mazur–Orlicz F-norm. The findings outlined in this study extend the scope of geometric characteristics observed in F-normed Orlicz spaces, as well as monotonicity properties within specific F-normed lattices. They are suitable for the Orlicz spaces of ordered continuous elements, specifically in relation to the Mazur–Orlicz F-norm. In addition, in this paper presents results that can be used to derive certain monotonicity properties in F-normed Musielak–Orlicz spaces.
Response of Extremely Small Populations to Climate Change—A Case of Trachycarpus nanus in Yunnan, China
Authors: Wang Xiaofan , Wang Xuhong , Li Yun , Wu Changhao , Zhao Biao , Peng Mingchun , Chen Wen , Wang Chongyun
Published: April 2024

Climate change affects the geographical distribution of plant species. Rare Trachycarpus nanus with a narrow distribution range, high medicinal value and extremely small population is facing increasing extinction risks under global climate change. In this study, 96 recorded occurrences and 23 environmental factors are used to predict the potential suitable area of T. nanus based on the optimized MaxEnt (3.4.4) model and ArcGIS (10.7) software. The results show that when the parameters are FC = LQ and RM = 1, the MaxEnt model is optimal and AUC = 0.946. The distribution patterns were predicted in the past, present, and four future phases, i.e., 2021–2040 (2030), 2041–2060 (2050), 2061–2080 (2070), and 2081–2100 (2090). The main factors are the annual precipitation (bio12), mean temperature of the coldest quarter (bio11), temperature seasonality (bio4), precipitation of the wettest quarter (bio16), and isothermality (bio3). The potential distribution of T. nanus is primarily concentrated in central Chuxiong, encompassing a total potential suitable area of 5.65 × 104 km2. In historical periods, the total habitat area is smaller than that in the present. In the future, the potential suitable area is generally increased. The centroid analysis shows that T. nanus will move to a high-altitude area and to the southeast. But its dispersal capacity may not keep up with the climate change rate. Therefore, additional protection sites for this species should be appropriately established and the habitat connectivity should be enhanced.
Microbacterium azadirachtae CNUC13 Enhances Salt Tolerance in Maize by Modulating Osmotic and Oxidative Stress
Authors: Luo Huan , Win Chaw Su , Lee Dong Hoon , He Lin , Yu Jun Myoung
Published: April 2024

Soil salinization is one of the leading threats to global ecosystems, food security, and crop production. Plant growth-promoting rhizobacteria (PGPRs) are potential bioinoculants that offer an alternative eco-friendly agricultural approach to enhance crop productivity from salt-deteriorating lands. The current work presents bacterial strain CNUC13 from maize rhizosphere soil that exerted several PGPR traits and abiotic stress tolerance. The strain tolerated up to 1000 mM NaCl and 30% polyethylene glycol (PEG) 6000 and showed plant growth-promoting (PGP) traits, including the production of indole-3-acetic acid (IAA) and siderophore as well as phosphate solubilization. Phylogenetic analysis revealed that strain CNUC13 was Microbacterium azadirachtae. Maize plants exposed to high salinity exhibited osmotic and oxidative stresses, inhibition of seed germination, plant growth, and reduction in photosynthetic pigments. However, maize seedlings inoculated with strain CNUC13 resulted in significantly improved germination rates and seedling growth under the salt-stressed condition. Specifically, compared with the untreated control group, CNUC13-treated seedlings exhibited increased biomass, including fresh weight and root system proliferation. CNUC13 treatment also enhanced photosynthetic pigments (chlorophyll and carotenoids), reduced the accumulation of osmotic (proline) and oxidative (hydrogen peroxide and malondialdehyde) stress indicators, and positively influenced the activities of antioxidant enzymes (catalase, superoxide dismutase, and peroxidase). As a result, CNUC13 treatment alleviated oxidative stress and promoted salt tolerance in maize. Overall, this study demonstrates that M. azadirachtae CNUC13 significantly enhances the growth of salt-stressed maize seedlings by improving photosynthetic efficiency, osmotic regulators, oxidative stress resilience, and antioxidant enzyme activity. These findings emphasize the potential of utilizing M. azadirachtae CNUC13 as a bioinoculant to enhance salt stress tolerance in maize, providing an environmentally friendly approach to mitigate the negative effects of salinity and promote sustainable agriculture.
The Initial COVID-19 Reliable Interactive DNA Methylation Markers and Biological Implications
Authors: Zhang Zhengjun
Published: April 2024

Earlier research has established the existence of reliable interactive genomic biomarkers. However, reliable DNA methylation biomarkers, not to mention interactivity, have yet to be identified at the epigenetic level. This study, drawing from 865,859 methylation sites, discovered two miniature sets of Infinium MethylationEPIC sites, each having eight CpG sites (genes) to interact with each other and disease subtypes. They led to the nearly perfect (96.87–100% accuracy) prediction of COVID-19 patients from patients with other diseases or healthy controls. These CpG sites can jointly explain some post-COVID-19-related conditions. These CpG sites and the optimally performing genomic biomarkers reported in the literature become potential druggable targets. Among these CpG sites, cg16785077 (gene MX1), cg25932713 (gene PARP9), and cg22930808 (gene PARP9) at DNA methylation levels indicate that the initial SARS-CoV-2 virus may be better treated as a transcribed viral DNA into RNA virus, i.e., not as an RNA virus that has concerned scientists in the field. Such a discovery can significantly change the scientific thinking and knowledge of viruses.
Germination Requirement and Suitable Storage Method of Hydrocharis dubia Seeds
Authors: Zhao Suting , Jiang Hongsheng , Liu Yang , Xian Ling , Fu Wenlong , Yuan Saibo , Yin Liyan , Li Wei
Published: April 2024

Understanding of seed germination requirements and storage methods is very important to successfully conserve and restore aquatic vegetation. The main question addressed by the research was germination requirements and suitable seed storage methods of Hydrocharis dubia seeds. Furthermore, the water content and respiration rate of H. dubia seeds were studied under different storage conditions. The study found that light and high seed clustering density had a positive effect on germination, while burial had a negative effect. Germination percentages were 60.67 ± 6.11% and 28.40 ± 6.79% in light and dark conditions, respectively. Under clustering densities of 1 and 50, germination percentages were 6.00 ± 2.00% and 59.33 ± 0.67%, respectively. Germination percentages were 50.40 ± 5.00%, 3.20 ± 3.20%, and 0.80 ± 0.80% at depths of 0, 2, and 3 cm, respectively. Oxygen, water level, and substratum had no significant effect on seed germination. Storage method had a significant effect on seed germination, moisture content, and respiration rate. The germination percentages were 64.00 ± 1.67%, 85.20 ± 5.04%, and 92.80 ± 4.27% under the storage conditions of 4 °C-Dry, 4 °C-Wet, and Ambient water temperature-Wet for 2 years, respectively. The seeds had no germination under the storage conditions of Ambient air temperature-Wet and Ambient air temperature-Dry. Overall, the study indicates that seed germination of H. dubia is restricted by light, burial depth, and seed clustering density. Additionally, it was found that H. dubia seeds can be stored in wet environmental conditions at ambient water temperature, similar to seed banks. Specifically, the seeds can be stored in sand and submerged underwater at ambient water temperatures ranging from 4 °C to 25 °C. This study will help with the conservation and restoration of aquatic plants, such as H. dubia.
Modulation of Apoptosis by Bovine Gammaherpesvirus 4 Infection in Bovine Endometrial Cells and the Possible Role of LPS in This Process
Authors: Romeo Florencia , Delgado Santiago , Yavorsky Marisol , Martinez Cuesta Lucía , Pereyra Susana , González Altamiranda Erika , Louge Uriarte Enrique , Pérez Sandra , Verna Andrea
Published: April 2024

The prevalent pathogens associated with bovine uterine infections are bacteria that appear to increase the host’s susceptibility to secondary infections with other bacteria or viruses, among which BoGHV4 is the most frequently found. In this work, the study of the pathways of apoptosis induction was carried out on an experimental model of primary culture of endometrial cells, in order to know the implication of BoGHV4 and the presence of bacterial LPS in the pathogenesis of the bovine reproductive tract. For this, different staining techniques and molecular analysis by RT-PCR were used. The results obtained allowed us to conclude that the level of cell death observed in the proposed primary culture is directly related to the time of viral infection and the presence of LPS in BoGHV4 infection. The apoptosis indices in cells infected with BoGHV4 and BoGHV4 + LPS revealed a maximum that correlated with the appearance of cytopathic effects and the maximum viral titers in the model studied. However, morphological, biochemical, and molecular changes were evident during both early and late stages of apoptosis. These findings provide information on the factors that may influence the pathogenesis of BoGHV4 and help to better understand the mechanisms involved in virus infection.