bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on the Evolution Models and Risk of Disaster-Induced Storage Tank Explosions in a Smart City
Authors: Wang Yunge , Cui Tiejun , Xu Gang
Published: May 2024

An important goal of smart cities is to ensure city safety and reduce city risks. However, because the chemical industry park is often located interior and surroundings of a city, it is easy to induce explosions in case of natural disasters, causing serious losses. To investigate the possibility of explosion damage to other storage tanks in chemical industrial parks caused by tank explosions, the evolution process of tank explosion damage was studied, and an analysis method to determine the most unfavorable process was provided. This method is based on regional grid division and shock wave overpressure calculation to obtain the probability of damage at the grid location. Based on the system fault evolution process, the space fault network model was used to calculate the explosion damage evolution process of each storage tank, and the most unfavorable situation was finally determined. The paper assumes a positive linear relationship between the damage probability of the affected storage tank and the likelihood of explosion. The paper provides a calculation method for regional grid division, tank explosion overpressure, and damage probability. A mathematical model for the evolution process of tank explosion damage was constructed, and it is believed that the damage effects of tanks are a logical superposition. The results can provide a reference for controlling the explosion process in chemical industrial parks under the most unfavorable conditions and realize intelligent analysis and prediction of regional risks.
EEG_DMNet: A Deep Multi-Scale Convolutional Neural Network for Electroencephalography-Based Driver Drowsiness Detection
Authors: Obaidan Hanan Bin , Hussain Muhammad , AlMajed Reham
Published: May 2024

Drowsy driving is one of the major causes of traffic accidents, injuries, and deaths on roads worldwide. One of the best physiological signals that are useful in detecting a driver’s drowsiness is electroencephalography (EEG), a kind of brain signal that directly measures neurophysiological activities in the brain and is widely utilized for brain–computer interfaces (BCIs). However, designing a drowsiness detection method using EEG signals is still challenging because of their non-stationary nature. Deep learning, specifically convolutional neural networks (CNNs), has recently shown promising results in driver’s drowsiness. However, state-of-the-art CNN-based methods extract features sequentially and discard multi-scale spectral-temporal features, which are important in tackling the non-stationarity of EEG signals. This paper proposes a deep multi-scale convolutional neural network (EEG_DMNet) for driver’s drowsiness detection that learns spectral-temporal features. It consists of two main modules. First, the multi-scale spectral-temporal features are extracted from EEG trials using 1D temporal convolutions. Second, the spatial feature representation module calculates spatial patterns from the extracted multi-scale features using 1D spatial convolutions. The experimental results on the public domain benchmark SEED-VIG EEG dataset showed that it learns discriminative features, resulting in an average accuracy of 97.03%, outperforming the state-of-the-art methods that used the same dataset. The findings demonstrate that the proposed method effectively and efficiently detects drivers’ drowsiness based on EEG and can be helpful for safe driving.
Piezoelectric MEMS Energy Harvester for Low-Power Applications
Authors: Muscalu George , Firtat Bogdan , Anghelescu Adrian , Moldovan Carmen , Dinulescu Silviu , Brasoveanu Costin , Ekwinska Magdalena , Szmigiel Dariusz , Zaborowski Michal , Zajac Jerzy , Stan Ion , Tulbure Adrian
Published: May 2024

With the global market value of sensors on the rise, this paper focuses on the fabrication and testing of a proof-of-concept piezoelectric energy harvester which is able to harvest mechanical energy from the ambient environment and convert it into electrical energy in order to power wireless sensor networks. We focused on obtaining a new device structure based on a comb-type array of piezoelectric MEMS cantilevers (2 × 10) for a resonant frequency in the environmental application domain (a few hundred Hz) and a chip area of only 1 cm2. The configuration of the lead-free piezoelectric cantilever consists of a Si substrate, a pair of Ti-Pt electrodes and a sputtered piezoelectric layer of 12% Sc-doped AlN with a thickness of 1000 nm, a dielectric constant of ~13 and e31,f = 1.3 C/m2. At a resonant frequency of 465.2 Hz and an acceleration of 1 g, the maximum value for the collected power was 2.53 µW for an optimal load resistance of 1 MΩ resulting in a power density of 60.2 nW/mm3 for the unpacked device, without taking into account the vibration volume. By increasing the excitation acceleration to 2 g RMS and using LTC3588-1 for the power circuitry we were able to obtain a stabilized output voltage of 1.8 V.
Transient Pressure Performance Analysis of Hydraulically Fractured Horizontal Well in Tight Oil Reservoir
Authors: Sun Lichun , Fang Maojun , Fan Weipeng , Li Hao , Li Longlong
Published: May 2024

Utilizing the discrete fracture model (DFM), a transient flow model is established for fractured horizontal wells in tight oil reservoirs, accounting for threshold pressure gradient (TPG), stress sensitivity effect, hydraulic fracture parameters, and fracture distribution pattern. This model is solved using the finite-volume method (FVM), and an important sensitivity analysis is conducted. The findings reveal that the models incorporated by the threshold pressure gradient result in an upward trend in the pressure-derivative curve. As the threshold pressure gradient increases, this upward trend becomes more pronounced, rendering the distinction between flow regimes more challenging. The stress sensitivity effect predominantly impacts the pressure-derivative curve during the later flow period. Additionally, as the fracture half-length increases, the pressure performance of both fracture radial flow and formation radial flow becomes more difficult. Fracture conductivity has a significant influence during the early flow period, facilitating the identification of flow regimes with the trend of increasing fracture conductivity.
Combustion Characteristics of Sinusoidal-Shaped Walls with Catalyst Segmentation in Micro-Combustors for Micro-Thermophotovoltaic Application
Authors: Yuan Qi , Guo Zhiping , Li Yuan
Published: May 2024

The combustion characteristics of micro-combustors significantly impact the performance of micro-thermophotovoltaic (MTPV) systems. This study aims to investigate the effects of sinusoidal-shaped walls and catalyst segmentation on flame stability and combustion performance in a micro-combustor by using numerical methods. The numerical simulation with detailed gas-phase and surface reaction mechanisms is reliable, as the results of numerical simulation align with experimental data. The results show that the interplay between flame stability and sinusoidal-shaped walls is crucial, particularly because of the cavities formed by the sinusoidal-shaped walls of the micro-combustor. The gas-phase ignition position of the sinusoidal-shaped wall combustor moves upstream by 0.050 m compared to the planar-wall combustor, but the flame is stretched. The catalyst segments coated on the crest can shorten the flame length and increase the average temperature by a maximum 62 K, but delay the gas-phase ignition. Conversely, catalyst segments coated on the trough can advance ignition, but this results in flame elongation and a decrease in the average temperature. The rational combination of catalyst segmentation and sinusoidal-shaped walls facilitates moving the ignition position upstream by a maximum of 0.065 m while substantially reducing the length of the combustor required for complete fuel conversion by more than 60%. These attributes are highly beneficial for improving efficiency and minimizing the length of the micro-combustor for MTPV application.
Proposal and Verification of the Application of an Expert Inference Method to Present the Probability of Lithium-Ion Battery Thermal Runaway Risk
Authors: Shon Jong Won , Choi Donmook , Lee Hyunjae , Son Sung-Yong
Published: May 2024

This study proposes a probabilistic quantification technique that applies an expert inference method to warn of the risk of a fire developing into a thermal runaway when a lithium-ion battery fire occurs. Existing methods have the shortcomings of low prediction accuracy and delayed responses because they determine a fire only by detecting the temperature rise and smoke in a lithium-ion battery to initiate extinguishing activities. To overcome such shortcomings, this study proposes a method to probabilistically calculate the risk of thermal runaway in advance by detecting the amount of off-gases generated in the venting stage before thermal runaway begins. This method has the advantage of quantifying the probability of a fire in advance by applying an expert inference method based on a combination of off-gas amounts, while maintaining high reliability even when the sensor fails. To verify the validity of the risk probability design, problems with the temperature and off-gas increase/decrease data were derived under four SOC conditions in actual lithium-ion batteries. Through the foregoing, it was confirmed that the risk probability can be accurately presented even in situations where the detection sensor malfunctions by applying an expert inference method to calculate the risk probability complexly. Additionally, it was confirmed that the proposed method is a method that can lead to quicker responses to thermal runaway fires.
A Stabilization Control Strategy for Wind Energy Storage Microgrid Based on Improved Virtual Synchronous Generator
Authors: Huang Jingguang , Lin Xinyuan , Sun Jiahang , Xu Huixin
Published: May 2024

In high-penetration renewable-energy grid systems, conventional virtual synchronous generator (VSG) control faces a number of challenges, especially the difficulty of maintaining synchronization during grid voltage drops. This difficulty may lead to current overloads and equipment disconnections, and it has an impact on the security and reliability of the system, as well as limiting the dynamic reactive power support capability of the system. To solve this problem, in this study, a wind–solar hybrid power generation system is designed with a battery energy storage device connected on the DC side, and proposes a low voltage ride-through (LVRT) control strategy for the grid-connected inverter based on an improved VSG. The control strategy employs an integrated current limiting technique combining virtual impedance and vector current limiting to ensure that the VSG exhibits good dynamic power support characteristics during symmetrical faults by adjusting the setpoint value of reactive power. At the same time, it maintains the synchronization and power angle stability of the VSG itself to achieve the goal of LVRT. Simulation results show that the proposed control strategy can effectively suppress the renewable power fluctuations (about 30% reduction in fluctuations compared to the conventional strategy) and ensure the safe and reliable operation of the renewable energy sources and VSGs during grid-side faults. In addition, it provides a given reactive power support and stable grid voltage control (voltage dips reduced by about 20%), which significantly enhances the LVRT capability of the hybrid wind–solar-storage generation system.
Wind Power Prediction Based on EMD-KPCA-BiLSTM-ATT Model
Authors: Zhang Zhiyan , Deng Aobo , Wang Zhiwen , Li Jianyong , Zhao Hailiang , Yang Xiaoliang
Published: May 2024

In order to improve wind power utilization efficiency and reduce wind power prediction errors, a combined prediction model of EMD-KPCA-BilSTM-ATT is proposed, which includes a data processing method combining empirical mode decomposition (EMD) and kernel principal component analysis (KPCA), and a prediction model combining bidirectional long short-term memory (BiLSTM) and an attention mechanism (ATT). Firstly, the influencing factors of wind power are analyzed. The quartile method is used to identify and eliminate the original abnormal data of wind power, and the linear interpolation method is used to replace the abnormal data. Secondly, EMD is used to decompose the preprocessed wind power data into Intrinsic Mode Function (IMF) components and residual components, revealing the changes in data signals at different time scales. Subsequently, KPCA is employed to screen the key components as the input of the BiLSTM-ATT prediction model. Finally, a prediction is made taking an actual wind farm in Anhui Province as an example, and the results show that the EMD-KPCAM-BiLSTM-ATT combined model has higher prediction accuracy compared to the comparative model.
Greenhouse Gas Emissions of a Hydrogen Engine for Automotive Application through Life-Cycle Assessment
Authors: Accardo Antonella , Costantino Trentalessandro , Malagrinò Gianfranco , Pensato Michele , Spessa Ezio
Published: May 2024

Hydrogen combustion engine vehicles have the potential to rapidly enter the market and reduce greenhouse gas emissions (GHG) compared to conventional engines. The ability to provide a rapid market deployment is linked to the fact that the industry would take advantage of the existing internal combustion engine production chain. The aim of this paper is twofold. First, it aims to develop a methodology for applying life-cycle assessment (LCA) to internal combustion engines to estimate their life-cycle GHG emissions. Also, it aims to investigate the decarbonization potential of hydrogen engines produced by exploiting existing diesel engine technology and assuming diverse hydrogen production routes. The boundary of the LCA is cradle-to-grave, and the assessment is entirely based on primary data. The products under study are two monofuel engines: a hydrogen engine and a diesel engine. The hydrogen engine has been redesigned using the diesel engine as a base. The engines being studied are versatile and can be used for a wide range of uses such as automotive, cogeneration, maritime, off-road, and railway; however, this study focuses on their application in pickup trucks. As part of the redesign process, certain subsystems (e.g., combustion, injection, ignition, exhaust gas recirculation, and exhaust gas aftertreatment) have been modified to make the engine run on hydrogen. Results revealed that employing a hydrogen engine using green hydrogen (i.e., generated from water electrolysis using wind-based electricity) might reduce GHG emission by over 90% compared to the diesel engine This study showed that the benefits of the new hydrogen engine solution outweigh the increase of emissions related to the redesign process, making it a potentially beneficial solution also for reconditioning current and used internal combustion engines.
Parameter Identification of Power Grid Subsynchronous Oscillations Based on Eigensystem Realization Algorithm
Authors: Zeng Xueyang , Chen Gang , Liu Yilin , Zhang Fang , Shi Huabo
Published: May 2024

The subsynchronous oscillation caused by the resonance between power electronic devices and series compensation devices or weak power grids introduced by large-scale renewable energy generation greatly reduces the transmission capacity of the system and may endanger the safe operation of the power system. It even leads to system oscillation instability. In this paper, based on the advantages of a simple solution, a small amount of calculation and anti-noise of ERA, a method of subsynchronous oscillation parameter identification based on the eigensystem realization algorithm (ERA) is proposed. The Hankel matrix in the improved ERA is obtained by splicing the real part matrix and the imaginary part matrix of the synchrophasor, thus solving the problem of angular frequency conjugate constraints of two fundamental components and two oscillatory components which are not considered in the existing ERA. The solution to this problem is helpful to improve the accurate parameter identification results of ERA under the data window of 200 ms and weaken the limitation caused by the assumption that the synchrophasor model is fixed. The practicability of the improved method based on PMU is verified by the synthesis of ERA and the actual measurement data. Compared with the existing ERA, the improved ERA can accurately identify the parameters of each component under the ultra-short data window and realize the dynamic monitoring of power system subsynchronous oscillation.
Standalone and Hybridised Flywheels for Frequency Response Services: A Techno-Economic Feasibility Study
Authors: Hutchinson Andrew J. , Gladwin Daniel T.
Published: May 2024

Frequency response services are one of the key components used by major electrical networks worldwide, acting to help control the frequency within set boundaries. Battery Energy Storage Systems (BESSs) are commonly deployed for this purpose; however, their potential is limited by susceptibility to cycle-based degradation and widely reported safety incidents. Flywheel Energy Storage Systems (FESSs) do not share these weaknesses and hence could be a potential candidate for longer-term participation in frequency response markets. This study presents the most in-depth and wide-ranging techno-economic analysis of the feasibility of FESSs for frequency response to date. Standalone FESSs are shown to be economically viable across a range of different specifications, achieving a positive Net Present Value (NPV) under varying economic conditions. At a capital cost of 500 GBP/kW with a discount rate of 4%, a 5C FESS can achieve an NPV of GBP 38,586 as a standalone unit. The complex trade-offs when considering hybridising FESSs and BESSs for this application are also investigated in-depth for the first time, again showing positive changes to NPV under various scenarios. Conversely, under some conditions, hybridisation can have a significant negative impact, showcasing the optimisation needed when considering hybrid systems. The impact of introducing a hybrid BESS varies from a low of decreasing the NPV of the system by GBP 97,955 to a high of increasing the NPV by GBP 119,621 depending on the configuration chosen. This comprehensive work provides the foundations for future research into FESS deployment for frequency response services and shows for the first time the circumstances under which deployment for this application would be both technically and economically viable.
Trends in Hybrid Renewable Energy System (HRES) Applications: A Review
Authors: Pérez Uc Daniel Alejandro , de León Aldaco Susana Estefany , Aguayo Alquicira Jesús
Published: May 2024

Microgrids and hybrid renewable energy systems play a crucial role in today’s energy transition. They enable local power generation and distribution, reducing dependence on large centralized infrastructures, can operate independently or connected to a grid, and can provide backup power, thus increasing system resilience. In addition, they combine multiple renewable energy sources, such as solar, wind, hydro, and biomass, to maximize the efficiency and reliability of the supply, and are also adaptable to location-specific conditions, taking advantage of locally available energy resources and reducing the need for energy imports. Moreover, they contribute to decarbonization goals by offering a cleaner and more sustainable alternative. In this article, a documentary review is presented on the interaction of Homer Pro software 3.16.2 (July 2023), used for the design of hybrid renewable energy systems (HRES), with other methods of optimization or sizing. Allusion is made to the type of architecture in the most prominent clean and fossil source configurations, the levelized cost, net annual cost, and maintenance and capital investment cost. A comparison is made among the works reported in the last five years regarding the use of this software tool, based on load demand, geographical area, renewable energy sources, fossil sources, and objective functions, applied to the educational, rural, and industrial sectors. It is shown that India is one of the countries that has reported the most number of HRES techno-economic environmental analysis works, and that the case studies have focused approximately 47% on rural areas, 20% on educational agencies, 14% on commerce and industry, and 29% on urban buildings.
Research on the Improvement of Cable Ampacity in Dense Cable Trench
Authors: Zhang Han , Yu Shangyu , Liu Zhenguo , Cheng Xiangmao , Zeng Yanqi , Shu Jian , Liu Gang
Published: May 2024

Due to the influence of many factors, distribution cables are often densely placed at the bottom of the cable trench. As a result, it is easy for distribution cables to become the thermal bottleneck of the whole transmission line. To address this dilemma, this paper establishes a finite element simulation model of a cable trench to analyze the hot spots of cables with different arrangements in the cable trench. Then, the model’s accuracy is verified based on real temperature rise experiments. For an arrangement with overheating risk, the ampacity improvement method of filling the cable trench with high-thermal-conductivity material was proposed, and the ampacity improvement effect under different filling ratios was assessed. Finally, combined with the analysis of economic benefit and cost, the method of determining the optimal filling ratio was used, and the impact resistance of the cables under the impact of new energy load was analyzed. The results indicate that, for the case of the optimal filling ratio, the cables in the dense cable trench showed superior impact resistance. The investigations in this paper make significant contributions to the promotion of the maximum utilization of cables.
Research on Bearing Fault Identification of Wind Turbines’ Transmission System Based on Wavelet Packet Decomposition and Probabilistic Neural Network
Authors: Cao Li , Sun Wenlei
Published: May 2024

In order to improve the reliability and life of the wind turbine, this paper takes the rolling bearing in the experimental platform of the wind turbine as the research object. In order to obtain the intrinsic mode function (IMF) of each fault type, the original signals of different fault states of the rolling bearing on the experimental platform are decomposed by using the overall average empirical mode decomposition method (EEMD) and the wavelet packet decomposition method (WPD), respectively. Then the energy ratio of the IMF component of the different types of faults to the total energy value is calculated and the eigenvectors of different types of faults are constructed. The extreme learning machine (ELM) and probabilistic neural network (PNN) are used to learn fault types and eigenvector samples to identify the faults of the rolling bearing. It is found that the bearing fault characteristics obtained by the WPD method are more obvious, and the results obtained by the same recognition method are ideal; and the PNN method is obviously superior to the extreme learning machine method in bearing fault recognition rate.
Predicting the Remaining Useful Life of Supercapacitors under Different Operating Conditions
Authors: Qi Guangheng , Ma Ning , Wang Kai
Published: May 2024

With the rapid development of the new energy industry, supercapacitors have become key devices in the field of energy storage. To forecast the remaining useful life (RUL) of supercapacitors, we introduce a new technology that integrates variational mode decomposition (VMD) with a bidirectional long short-term memory (BiLSTM) neural network. Firstly, the aging experiments of supercapacitors under various temperatures and voltages were carried out to obtain aging data. Then, VMD was implemented to decompose the aging data, which helped to eliminate disturbances, including capacity recovery and test errors. Then, the hyperparameters of BiLSTM were adjusted, employing the sparrow search algorithm (SSA) to improve the consistency between the input data and the network structure. After obtaining the optimal hyperparameters of BiLSTM, the decomposed aging data were input into BiLSTM for prediction. The experimental results showed that the VMD-SSA-BiLSTM model proposed in this paper has high prediction accuracy and high robustness under different temperatures and voltages, with an average RMSE of 0.112519, a decrease of 44.3% compared to BiLSTM, and a minimum of 0.031426.
Optimal Operation of Energy Storage Facilities in Incentive-Based Energy Communities
Authors: Zanvettor Giovanni Gino , Casini Marco , Vicino Antonio
Published: May 2024

The green energy transition calls for various solutions to enhance environmental sustainability. One of these is represented by renewable energy communities, which may help transition from centralized energy production to distributed renewable generation. European countries are actively promoting incentive schemes for energy communities to foster local electricity self-consumption in order to balance demand and renewable generation. In this context, energy storage facilities can be employed to gather the energy production surplus and use it in periods of low generation. In this paper, we focus on the optimal operation of an incentive-based energy community in the presence of energy storage systems. A centralized optimization problem was formulated to optimally operate storage systems at the community level. Starting from this solution, distributed charging/discharging commands were found to optimally operate the single storage units. Moreover, conditions guaranteeing the convenience of using energy storage systems inside the community were derived. Numerical simulations were performed to validate the reported results and to evaluate the potential benefits of energy storage facilities inside renewable energy communities.
Biochar Addition Increased Soil Carbon Storage but Did Not Exacerbate Soil Carbon Emission in Young Subtropical Plantation Forest
Authors: Sun Nan , Sarkar Binoy , Li Saiqiang , Tian Youhua , Sha Longbing , Gao Yongchao , Luo Xu , Yang Xiaodong
Published: May 2024

Forests play a crucial role in mitigating global warming, contributing approximately 46% of the global terrestrial carbon sink. However, it remains uncertain whether the addition of biochar to forests enhances the ecosystem’s carbon sink capacity. This study aims to address this scientific question by investigating whether biochar application increases carbon storage, potentially leading to an overall rise in carbon emissions by influencing soil respiration and identifying the underlying mechanisms. A controlled experiment was conducted in a young plantation forest that had grown for three years, where soil CO2 efflux rate and physicochemical properties, photosynthesis, and plant growth traits were measured across varying biochar addition rates (0, 5, and 10 t/ha) over five seasons. Then, statistical methods including one-way ANOVA, regression analysis, and structural equation modeling (SEM) were employed to assess differences in biological and abiotic factors among biochar addition gradients and understand the influencing mechanisms of soil CO2 efflux change. The findings revealed that biochar addition significantly increased the contents of soil organic carbon (SOC) and microbial biomass carbon (MBC), consequently promoting photosynthesis and plant growth (p < 0.05). Biochar addition accounted for 73.8% of the variation in soil CO2 efflux by affecting soil physicochemical properties, photosynthesis, and plant basal diameter growth. However, the net effect of biochar addition on soil CO2 efflux was found to be low. The positive effects of biochar addition on soil CO2 efflux via factors such as soil bulk density, total nitrogen (TN), MBC, and photosynthesis were counteracted by its negative impact through soil total phosphorus (TP), water content, pH, SOC, and plant basal diameter growth. Overall, our findings indicate that there was no significant increase in soil CO2 efflux in the short term (totaling 16 months) over the biochar addition gradient. However, we observed a substantial increase in soil carbon storage and an enhancement in the soil’s capacity to act as a carbon sink. Therefore, adding biochar to forests may be a feasible strategy to increase carbon sinks and mitigate global climate change.
Response of Soil Microbial Community Structure and Diversity to Mixed Proportions and Mixed Tree Species in Bamboo–Broad-Leaved Mixed Forests
Authors: Zhang Meiman , Guan Fengying , Fan Shaohui , Zhang Xuan
Published: May 2024

Bamboo and broad-leaved mixed forests have been widely recognized for their advantages in maintaining ecological balance, improving soil fertility, and enhancing biodiversity. To understand the effects of mixed broad-leaved tree species and mixing ratios on soil microbial communities in bamboo and broad-leaved mixed forests, we quantified the structure and diversity responses of soil microbial communities to tree species and mixing ratios using high-throughput sequencing of the 16 S rRNA gene. Three bamboo and broad-leaved tree mixed forests were studied, including bamboo–Castanopsis chinensis Hance mixed forest (CCB), bamboo–Alniphyllum fortune (Hemsl.) Makino mixed forest (AFB), and bamboo–Choerospondias axillaris (Roxb.) B. L. Burtt & A. W. Hill mixed forest (CAB). We assessed the impact of tree species and mixing ratios on soil microbial communities by measuring soil properties and the diversity and composition of soil microbes. The results indicate that soil properties and the diversity and composition of microbial communities are highly dependent on broad-leaved tree species in mixed forests. The mixing ratios had a more pronounced effect on microbial diversity than on richness. In CAB, diversity peaked at mixing ratios of 10%–20% and 20%–40%. The presence of broad-leaved trees significantly altered the relationships among soil bacteria, with CAB showing the highest stability, likely due to the increased diversity and quantity of litter from Choerospondias axillaris. Our results show that the choice of broad-leaved tree species and their mixing ratios significantly influence soil microbial diversity and composition in bamboo–broad-leaf mixed forests. An optimal mixing ratio in CAB can maximize bacterial diversity and stability, providing insights for forest management and promoting ecosystem health and sustainability.
Dynamic Spatial–Temporal Self-Attention Network for Traffic Flow Prediction
Authors: Wang Dong , Yang Hongji , Zhou Hua
Published: May 2024

Traffic flow prediction is considered to be one of the fundamental technologies in intelligent transportation systems (ITSs) with a tremendous application prospect. Unlike traditional time series analysis tasks, the key challenge in traffic flow prediction lies in effectively modelling the highly complex and dynamic spatiotemporal dependencies within the traffic data. In recent years, researchers have proposed various methods to enhance the accuracy of traffic flow prediction, but certain issues still persist. For instance, some methods rely on specific static assumptions, failing to adequately simulate the dynamic changes in the data, thus limiting their modelling capacity. On the other hand, some approaches inadequately capture the spatiotemporal dependencies, resulting in the omission of crucial information and leading to unsatisfactory prediction outcomes. To address these challenges, this paper proposes a model called the Dynamic Spatial–Temporal Self-Attention Network (DSTSAN). Firstly, this research enhances the interaction between different dimension features in the traffic data through a feature augmentation module, thereby improving the model’s representational capacity. Subsequently, the current investigation introduces two masking matrices: one captures local spatial dependencies and the other captures global spatial dependencies, based on the spatial self-attention module. Finally, the methodology employs a temporal self-attention module to capture and integrate the dynamic temporal dependencies of traffic data. We designed experiments using historical data from the previous hour to predict traffic flow conditions in the hour ahead, and the experiments were extensively compared to the DSTSAN model, with 11 baseline methods using four real-world datasets. The results demonstrate the effectiveness and superiority of the proposed approach.
Critically Small Contemporaneous Effective Population Sizes Estimated for Stocks of the African Bonytongue in Western Africa
Authors: Hurtado Luis A. , Mateos Mariana , Caballero Isabel C. , Oladimeji Tofunmi E. , Adite Alphonse , Awodiran Michael O. , Winemiller Kirk O. , Hamilton Matthew B.
Published: May 2024

Inland capture fisheries play a critical role in supporting food security and livelihoods in Africa. Therefore, it is important to evaluate the genetic health of exploited fish populations. The African bonytongue, Heterotis niloticus, supports important commercial and subsistence fisheries in western Africa. However, sharp declines in stocks have been reported. Herein, we estimate contemporary effective population sizes (Ne) of four Heterotis populations in Nigeria, three in Benin, and five in Cameroon using Linkage Disequilibrium methods. Ne estimates were used to assess genetic short-term (i.e., inbreeding depression) and long-term (i.e., loss of evolutionary potential) risks. Ne point estimates obtained with the best estimator (out of 16), as determined by computer simulations, were <50 (range = 5.1–36.2) for nine of the twelve populations examined, which is below the minimum recommended for avoiding the potential deleterious effects of inbreeding depression (original criterion Ne ≥ 50, revised to Ne ≥ 100); and well below the minimum recommended for populations to retain evolutionary potential (original criterion Ne ≥ 500; revised to Ne ≥ 1000). The lower bound of the confidence interval for two of the remaining populations was below the minimum recommended to retain evolutionary potential (with the point estimate of one of them also below this threshold), and for some methods, values were lower than the minimum recommended to avoid inbreeding depression. Accordingly, our results suggest that urgent conservation and management plans are needed to guarantee the persistence and sustainability of the H. niloticus populations examined.
Development and Validation of Near-Infrared Reflectance Spectroscopy Prediction Modeling for the Rapid Estimation of Biochemical Traits in Potato
Authors: Chaukhande Paresh , Luthra Satish Kumar , Patel R. N. , Padhi Siddhant Ranjan , Mankar Pooja , Mangal Manisha , Ranjan Jeetendra Kumar , Solanke Amolkumar U. , Mishra Gyan Prakash , Mishra Dwijesh Chandra , Singh Brajesh , Bhardwaj Rakesh , Tomar Bhoopal Singh , Riar Amritbir Singh
Published: May 2024

Potato is a globally significant crop, crucial for food security and nutrition. Assessing vital nutritional traits is pivotal for enhancing nutritional value. However, traditional wet lab methods for the screening of large germplasms are time- and resource-intensive. To address this challenge, we used near-infrared reflectance spectroscopy (NIRS) for rapid trait estimation in diverse potato germplasms. It employs molecular absorption principles that use near-infrared sections of the electromagnetic spectrum for the precise and rapid determination of biochemical parameters and is non-destructive, enabling trait monitoring without sample compromise. We focused on modified partial least squares (MPLS)-based NIRS prediction models to assess eight key nutritional traits. Various mathematical treatments were executed by permutation and combinations for model calibration. The external validation prediction accuracy was based on the coefficient of determination (RSQexternal), the ratio of performance to deviation (RPD), and the low standard error of performance (SEP). Higher RSQexternal values of 0.937, 0.892, and 0.759 were obtained for protein, dry matter, and total phenols, respectively. Higher RPD values were found for protein (3.982), followed by dry matter (3.041) and total phenolics (2.000), which indicates the excellent predictability of the models. A paired t-test confirmed that the differences between laboratory and predicted values are non-significant. This study presents the first multi-trait NIRS prediction model for Indian potato germplasm. The developed NIRS model effectively predicted the remaining genotypes in this study, demonstrating its broad applicability. This work highlights the rapid screening potential of NIRS for potato germplasm, a valuable tool for identifying trait variations and refining breeding strategies, to ensure sustainable potato production in the face of climate change.
In Vitro Digestion and Fermentation of Different Ethanol-Fractional Polysaccharides from Dendrobium officinale: Molecular Decomposition and Regulation on Gut Microbiota
Authors: Xu Lei , Zhu Hua , Chen Peng , Li Zhenhao , Yang Kai , Sun Peilong , Gu Fangting , Wu Jianyong , Cai Ming
Published: May 2024

Polysaccharides from Dendrobium officinale have garnered attention for their diverse and well-documented biological activities. In this study, we isolated three ethanol-fractionated polysaccharides from Dendrobium officinale (EPDO) and investigated their digestive properties and effects on gut microbiota regulation in vitro. The results indicated that after simulating digestion in saliva, gastric, and small intestinal fluids, three EPDOs, EPDO-40, EPDO-60 and EPDO-80, with molecular weights (Mw) of 442.6, 268.3 and 50.8 kDa, respectively, could reach the large intestine with a retention rate exceeding 95%. During in vitro fermentation, the EPDOs were broken down in a “melting” manner, resulting in a decrease in their Mw. EPDO-60 degraded more rapidly than EPDO-40, likely due to its moderate Mw. After 24 h, the total production of short-chain fatty acids (SCFAs) for EPDO-60 reached 51.2 ± 1.9 mmol/L, which was higher than that of EPDO-80. Additionally, there was an increase in the relative abundance of Bacteroides, which are capable of metabolizing polysaccharides. EPDO-60 also promoted the growth of specific microbiota, including Prevotella 9 and Parabacteroides, which could potentially benefit from these polysaccharides. Most notably, by comparing the gut microbiota produced by different fermentation carbon sources, we identified the eight most differential gut microbiota specialized in polysaccharide metabolism at the genus level. Functional prediction of these eight differential genera suggested roles in controlling replication and repair, regulating metabolism, and managing genetic information transmission. This provides a new reference for elucidating the specific mechanisms by which EPDOs influence the human body. These findings offer new evidence to explain how EPDOs differ in their digestive properties and contribute to the establishment of a healthy gut microbiota environment in the human body.
Identification of Dendrobium Using Laser-Induced Breakdown Spectroscopy in Combination with a Multivariate Algorithm Model
Authors: Zhang Tingsong , Liu Ziyuan , Ma Qing , Hu Dong , Dai Yujia , Zhang Xinfeng , Zhou Zhu
Published: May 2024

Dendrobium, a highly effective traditional Chinese medicinal herb, exhibits significant variations in efficacy and price among different varieties. Therefore, achieving an efficient classification of Dendrobium is crucial. However, most of the existing identification methods for Dendrobium make it difficult to simultaneously achieve both non-destructiveness and high efficiency, making it challenging to truly meet the needs of industrial production. In this study, we combined Laser-Induced Breakdown Spectroscopy (LIBS) with multivariate models to classify 10 varieties of Dendrobium. LIBS spectral data for each Dendrobium variety were collected from three circular medicinal blocks. During the data analysis phase, multivariate models to classify different Dendrobium varieties first preprocess the LIBS spectral data using Gaussian filtering and stacked correlation coefficient feature selection. Subsequently, the constructed fusion model is utilized for classification. The results demonstrate that the classification accuracy of 10 Dendrobium varieties reached 100%. Compared to Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN), our method improved classification accuracy by 14%, 20%, and 20%, respectively. Additionally, it outperforms three models (SVM, RF, and KNN) with added Principal Component Analysis (PCA) by 10%, 10%, and 17%. This fully validates the excellent performance of our classification method. Finally, visualization analysis of the entire research process based on t-distributed Stochastic Neighbor Embedding (t-SNE) technology further enhances the interpretability of the model. This study, by combining LIBS and machine learning technologies, achieves efficient classification of Dendrobium, providing a feasible solution for the identification of Dendrobium and even traditional Chinese medicinal herbs.
Dietary Strategies to Improve Exercise Performance by Modulating the Gut Microbiota
Authors: Zhang Li , Li Haoyu , Song Zheyi , Liu Yanan , Zhang Xin
Published: May 2024

Numerous research studies have shown that moderate physical exercise exerts positive effects on gastrointestinal tract health and increases the variety and relative number of beneficial microorganisms in the intestinal microbiota. Increasingly, studies have shown that the gut microbiota is critical for energy metabolism, immunological response, oxidative stress, skeletal muscle metabolism, and the regulation of the neuroendocrine system, which are significant for the physiological function of exercise. Dietary modulation targeting the gut microbiota is an effective prescription for improving exercise performance and alleviating exercise fatigue. This article discusses the connection between exercise and the makeup of the gut microbiota, as well as the detrimental effects of excessive exercise on gut health. Herein, we elaborate on the possible mechanism of the gut microbiota in improving exercise performance, which involves enhancing skeletal muscle function, reducing oxidative stress, and regulating the neuroendocrine system. The effects of dietary nutrition strategies and probiotic supplementation on exercise from the perspective of the gut microbiota are also discussed in this paper. A deeper understanding of the potential mechanism by which the gut microbiota exerts positive effects on exercise and dietary nutrition recommendations targeting the gut microbiota is significant for improving exercise performance. However, further investigation is required to fully comprehend the intricate mechanisms at work.
When Abnormality Becomes Perennial in a Reduced Population: The Case of Altudostephanus longicostis gen. et sp. nov. (Valanginian Ammonites, South-Eastern France)
Authors: Bert Didier , Bersac Stéphane , Beltran Bernard , Canut Léon
Published: May 2024

The discovery of the new ammonite Altudostephanus longicostis gen. et sp. nov. around the lower/upper Valanginian boundary (Lower Cretaceous) with a true longitudinal ribs pattern oriented in the direction of coiling is reported here for the first time for the Cretaceous. This character rises questions as this type of ornamentation is mainly represented in the Paleozoic or old Mesozoic taxa. Its study shows that it is not a ‘shell accident’ and that it belongs to a particular lineage of Olcostephaninae, in a restricted geographical area and in a progenetic evolutionary context derived from Olcostephanus, which justifies the introduction of the new monophyletic genus Altudostephanus gen. nov. The discovery of a specimen of Passendorferia sp. (Oxfordian, Upper Jurassic) with the same pattern allows us to consider that this capacity could have a genetic cause. It seems that the fixation of this character, otherwise strongly recessive, in A. longicostis gen. et sp. nov. could take place thanks to a founder effect and the drastic numerical reduction in the population. The geological reference section PIG5 (Moriez area, South-Eastern France) is described, and the problem of the conservation of ‘pyritic’ s.l. ammonites is addressed with a proposed methodology allowing their long-term conservation.
Rheology of Suspensions of TEMPO-Oxidised and Cationic Cellulose Nanofibrils—The Effect of Chemical Pre-Treatment
Authors: Alves Luís , Magalhães Solange , Pedrosa Jorge F. S. , Ferreira Paulo J. T. , Gamelas José A. F. , Rasteiro Maria Graça
Published: May 2024

Cellulose nanofibrils (CNFs) are particles with a high aspect ratio. Typically, chemically pre-treated CNFs (containing anionic or cationic charged groups) consist of long fibrils (up to 2 μm) with very low thickness (less than 10 nm). Derived from their high aspect ratio, CNFs form strong hydrogels with high elasticity at low concentrations. Thus, CNF suspensions appear as an interesting rheology modifier to be applied in cosmetics, paints, foods, and as a mineral suspending agent, among other applications. The high viscosity results from the strong 3D fibril network, which is related to the good fibrillation of the material, allowing the nanofibrils to overlap. The overlap concentration (c*) was found to vary from ca. 0.13 to ca. 0.60 wt.% depending on the type and intensity of the pre-treatment applied during the preparation of the CNFs. The results confirm the higher tendency for the fibres treated with (3-chloro-2-hydroxypropyl) trimethylammonium chloride (CHPTAC) and 2,2,6,6-tetramethylpiperidine 1-oxyl (TEMPO) to form a 3D network, resulting in the lowest c*. For the TEMPO-oxidised CNF suspensions, it was also found that aggregation is improved at acidic pH conditions due to lower charge repulsion among fibrils, leading to an increase in the suspension viscosity as well as higher apparent yield stresses. TEMPO CNF suspensions with a low content of carboxylic groups tend to precipitate at moderately acidic pH values.
Agriculture 4.0: Polymer Hydrogels as Delivery Agents of Active Ingredients
Authors: Mikhailidi Aleksandra , Ungureanu Elena , Tofanica Bogdan-Marian , Ungureanu Ovidiu C. , Fortună Maria E. , Belosinschi Dan , Volf Irina
Published: May 2024

The evolution from conventional to modern agricultural practices, characterized by Agriculture 4.0 principles such as the application of innovative materials, smart water, and nutrition management, addresses the present-day challenges of food supply. In this context, polymer hydrogels have become a promising material for enhancing agricultural productivity due to their ability to retain and then release water, which can help alleviate the need for frequent irrigation in dryland environments. Furthermore, the controlled release of fertilizers by the hydrogels decreases chemical overdosing risks and the environmental impact associated with the use of agrochemicals. The potential of polymer hydrogels in sustainable agriculture and farming and their impact on soil quality is revealed by their ability to deliver nutritional and protective active ingredients. Thus, the impact of hydrogels on plant growth, development, and yield was discussed. The question of which hydrogels are more suitable for agriculture—natural or synthetic—is debatable, as both have their merits and drawbacks. An analysis of polymer hydrogel life cycles in terms of their initial material has shown the advantage of bio-based hydrogels, such as cellulose, lignin, starch, alginate, chitosan, and their derivatives and hybrids, aligning with sustainable practices and reducing dependence on non-renewable resources.