bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis and Optimization Design Scheme of CMOS Ultra-Wideband Reconfigurable Polyphase Filters on Mismatch and Voltage Loss
Authors: Wang Yingze , Li Xiaoran , Wang Yuanze , Wang Xinghua , Liu Zicheng , Han Fang , Qi Quanwen
Published: February 2024

This manuscript presents an analysis and optimization scheme for the ultra-wideband passive reconfigurable polyphase filters (PPFs) to minimize I/Q (in-phase and quadrature-phase) phase/amplitude mismatch and voltage loss. By building a mathematical model of the voltage transfer, the relationship between the resonant frequency of each stage and the I/Q mismatch and the relationship between the network impedance and the voltage loss are revealed, providing a scheme for PPF optimization. The proof-of-concept 2~8 GHz wideband reconfigurable PPF is designed in a 55 nm CMOS process. The optimization scheme enables the designed PPF to achieve an I/Q phase mismatch within 0.2439° and an I/Q amplitude mismatch within 0.098 dB throughout the entire band, and it shows great robustness during Monte Carlo sampling. The maximum voltage loss is 17.7 dB, and the total chip area is 0.174 × 0.145 mm2.
Semi-Supervised Feature Selection of Educational Data Mining for Student Performance Analysis
Authors: Yu Shanshan , Cai Yiran , Pan Baicheng , Leung Man-Fai
Published: February 2024

In recent years, the informatization of the educational system has caused a substantial increase in educational data. Educational data mining can assist in identifying the factors influencing students’ performance. However, two challenges have arisen in the field of educational data mining: (1) How to handle the abundance of unlabeled data? (2) How to identify the most crucial characteristics that impact student performance? In this paper, a semi-supervised feature selection framework is proposed to analyze the factors influencing student performance. The proposed method is semi-supervised, enabling the processing of a considerable amount of unlabeled data with only a few labeled instances. Additionally, by solving a feature selection matrix, the weights of each feature can be determined, to rank their importance. Furthermore, various commonly used classifiers are employed to assess the performance of the proposed feature selection method. Extensive experiments demonstrate the superiority of the proposed semi-supervised feature selection approach. The experiments indicate that behavioral characteristics are significant for student performance, and the proposed method outperforms the state-of-the-art feature selection methods by approximately 3.9% when extracting the most important feature.
Leveraging Artificial Intelligence and Provenance Blockchain Framework to Mitigate Risks in Cloud Manufacturing in Industry 4.0
Authors: Umer Mifta Ahmed , Belay Elefelious Getachew , Gouveia Luis Borges
Published: February 2024

Cloud manufacturing is an evolving networked framework that enables multiple manufacturers to collaborate in providing a range of services, including design, development, production, and post-sales support. The framework operates on an integrated platform encompassing a range of Industry 4.0 technologies, such as Industrial Internet of Things (IIoT) devices, cloud computing, Internet communication, big data analytics, artificial intelligence, and blockchains. The connectivity of industrial equipment and robots to the Internet opens cloud manufacturing to the massive attack risk of cybersecurity and cyber crime threats caused by external and internal attackers. The impacts can be severe because the physical infrastructure of industries is at stake. One potential method to deter such attacks involves utilizing blockchain and artificial intelligence to track the provenance of IIoT devices. This research explores a practical approach to achieve this by gathering provenance data associated with operational constraints defined in smart contracts and identifying deviations from these constraints through predictive auditing using artificial intelligence. A software architecture comprising IIoT communications to machine learning for comparing the latest data with predictive auditing outcomes and logging appropriate risks was designed, developed, and tested. The state changes in the smart ledger of smart contracts were linked with the risks so that the blockchain peers can detect high deviations and take actions in a timely manner. The research defined the constraints related to physical boundaries and weightlifting limits allocated to three forklifts and showcased the mechanisms of detecting risks of breaking these constraints with the help of artificial intelligence. It also demonstrated state change rejections by blockchains at medium and high-risk levels. This study followed software development in Java 8 using JDK 8, CORDA blockchain framework, and Weka package for random forest machine learning. As a result of this, the model, along with its design and implementation, has the potential to enhance efficiency and productivity, foster greater trust and transparency in the manufacturing process, boost risk management, strengthen cybersecurity, and advance sustainability efforts.
Multi-Agent-Deep-Reinforcement-Learning-Enabled Offloading Scheme for Energy Minimization in Vehicle-to-Everything Communication Systems
Authors: Duan Wenwen , Li Xinmin , Huang Yi , Cao Hui , Zhang Xiaoqiang
Published: February 2024

Offloading computation-intensive tasks to mobile edge computing (MEC) servers, such as road-side units (RSUs) and a base station (BS), can enhance the computation capacities of the vehicle-to-everything (V2X) communication system. In this work, we study an MEC-assisted multi-vehicle V2X communication system in which multi-antenna RSUs with liner receivers and a multi-antenna BS with a zero-forcing (ZF) receiver work as MEC servers jointly to offload the tasks of the vehicles. To control the energy consumption and ensure the delay requirement of the V2X communication system, an energy consumption minimization problem under a delay constraint is formulated. The multi-agent deep reinforcement learning (MADRL) algorithm is proposed to solve the non-convex energy optimization problem, which can train vehicles to select the beneficial server association, transmit power and offloading ratio intelligently according to the reward function related to the delay and energy consumption. The improved K-nearest neighbors (KNN) algorithm is proposed to assign vehicles to the specific RSU, which can reduce the action space dimensions and the complexity of the MADRL algorithm. Numerical simulation results show that the proposed scheme can decrease energy consumption while satisfying the delay constraint. When the RSUs adopt the indirect transmission mode and are equipped with matched-filter (MF) receivers, the proposed joint optimization scheme can decrease the energy consumption by 56.90% and 65.52% compared to the maximum transmit power and full offloading schemes, respectively. When the RSUs are equipped with ZF receivers, the proposed scheme can decrease the energy consumption by 36.8% compared to the MF receivers.
Charge-Domain Static Random Access Memory-Based In-Memory Computing with Low-Cost Multiply-and-Accumulate Operation and Energy-Efficient 7-Bit Hybrid Analog-to-Digital Converter
Authors: Lee Sanghyun , Kim Youngmin
Published: February 2024

This study presents a charge-domain SRAM-based in-memory computing (IMC) architecture. The multiply-and-accumulate (MAC) operation in the IMC structure is divided into current- and charge-domain methods. Current-domain IMC has high-power consumption and poor linearity. Charge-domain IMC has reduced variability compared with current-domain IMCs, achieving higher linearity and enabling energy-efficient operation with fewer dynamic current paths. The proposed IMC structure uses a 9T1C bitcell considering the trade-off between the bitcell area and the threshold voltage drop by an NMOS access transistor. We propose an energy-efficient summation mechanism for 4-bit weight rows to perform energy-efficient MAC operations. The generated MAC value is finally returned as a digital value through an analog-to-digital converter (ADC), whose performance is one of the critical components in the overall system. The proposed flash-successive approximation register (SAR) ADC is designed by combining the advantages of flash ADC and SAR ADC and outputs digital values at approximately half the cycle of SAR ADC. The proposed charge-domain IMC is designed and simulated in a 65 nm CMOS process. It achieves 102.4 GOPS throughput and 33.6 TOPS/W energy efficiency at array size of 1 Kb.
An Efficient Multi-Party Secure Aggregation Method Based on Multi-Homomorphic Attributes
Authors: Gao Qi , Sun Yi , Chen Xingyuan , Yang Fan , Wang Youhe
Published: February 2024

The federated learning on large-scale mobile terminals and Internet of Things (IoT) devices faces the issues of privacy leakage, resource limitation, and frequent user dropouts. This paper proposes an efficient secure aggregation method based on multi-homomorphic attributes to realize the privacy-preserving aggregation of local models while ensuring low overhead and tolerating user dropouts. First, based on EC-ElGamal, the homomorphic pseudorandom generator, and the Chinese remainder theorem, an efficient random mask secure aggregation method is proposed, which can efficiently aggregate random masks and protect the privacy of the masks while introducing secret sharing to achieve tolerance of user dropout. Then, an efficient federated learning secure aggregation method is proposed, which guarantees that the computation and communication overheads of users are only O(L); also, the method only performs two rounds of communication to complete the aggregation and allows user dropout, and the aggregation time does not increase with the dropout rate, so it is suitable for resource-limited devices. Finally, the correctness, security, and performance of the proposed method are analyzed and evaluated. The experimental results indicate that the aggregation time of the proposed method is linearly related to the number of users and the model size, and it decreases as the number of dropped out users increases. Compared to other schemes, the proposed method significantly improves the aggregation efficiency and has stronger dropout tolerance, and it improves the efficiency by about 24 times when the number of users is 500 and the dropout rate is 30%.
Research on Knowledge Graph Construction and Semantic Representation of Low Earth Orbit Satellite Spectrum Sensing Data
Authors: Ma Yijie , Liu Ziwei , Yang Nan , Xu Huajian , Zhang Gengxin
Published: February 2024

The growth of frequency-usage devices has made the electromagnetic spectrum posture complex, resulting in an urgent demand for frequency-usage posture cognition. However, the sensing of space-based platforms is limited by the transmission capacity of the satellite-to-ground link and the satellite processing capacity, which makes on-satellite data analysis and posture generation lack the efficient means. Facing the above issues, an idea of a knowledge graph construction and semantic representation for low Earth orbit (LEO) satellite spectrum sensing data is designed in this paper. In the designed construction process, technologies such as knowledge extraction, ontology construction, knowledge fusion and knowledge visualization are utilized to efficiently analyze on-satellite sensing data. Moreover, the constructed spectrum knowledge graph can be applied in the analysis and prediction of frequency-usage behavior and intelligent spectrum management, which exhibits the effectiveness of the spectrum knowledge graph. Finally, the further development of the spectrum knowledge graph is foreseen.
Comparative Study on the Thermal Properties of Engine Oils and Their Nanofluids Incorporating Fullerene-C60, TiO2 and Fe2O3 at Different Temperatures
Authors: Galpaya Chanaka , Induranga Ashan , Vithanage Vimukthi , Mantilaka Prasanga , Koswattage Kaveenga Rasika
Published: February 2024

The efficiency, durability, and overall performance of a car engine are influenced by several critical factors. The quality and properties of engine oil play a crucial role, and oil is used in internal combustion engines for lubrication and cooling purposes. This research study aimed to compare the impact of fullerene-C60 (99.5%), Fe2O3, and TiO2 nanoparticles on the thermal properties of C.A.L.T.E.X. red engine oil with grades 10W30, 20W40, and 20W50. This study focused on the effect of a nanoparticle concentration of 0.01 wt.% in different engine oil grades at various temperature values of 30–120 °C. The nanofluids were prepared using the two-step direct mixing method, employing a magnetic stirrer and an ultrasonicator, ensuring uniform distribution of nanoparticles in the base fluids. The thermal conductivity, thermal diffusivity, and volumetric heat capacity of the base fluids and nanofluids were measured using the FLUCON LAMBDA thermal conductivity meter. Additionally, flash points were measured using the flash point tester. It was concluded that the thermal properties of TiO2 and Fe2O3 showed considerable enhancement; in contrast, fullerene only showed a 212 °C flash point.
Achieving Techno-Economic Feasibility for Hybrid Renewable Energy Systems through the Production of Energy and Alternative Fuels
Authors: Armoo Ekua Afrakoma , Mohammed Mutala , Narra Satyanarayana , Beguedou Essossinam , Agyenim Francis Boateng , Kemausuor Francis
Published: February 2024

In developing countries like Ghana, the conversion of waste into energy is gaining greater interest among policy makers and researchers. The present study investigates the feasibility of producing electricity and/or fuels from a hybrid waste-to-energy pilot plant located in the Ashanti Region of Ghana. The plant integrates three technologies: anaerobic digestion, pyrolysis and solar PV. The plant has the potential to produce both energy and fuels such as green hydrogen, refuse derived fuels, bio-compressed natural gas and compost. Thus, this study compares the financial feasibility of three scenarios—generating electricity and fuels, generating electricity alone and generating fuels alone—by modelling their energy output and financial performance using RETSCREEN expert 6.0.7.55 and Microsoft Excel 2019 softwares. The results indicate that the multiple products of electricity and fuels provide higher investment interest with a Net Present Value in excess of EUR 13 million and a payback period of 12 years compared to the electricity-only model. Also, converting electricity into fuels alone also provides substantial benefits which can be explored. However, the Levelized Cost of Energy, ranging from 0.3 to 0.68 EUR/kWh, is far above the average residential End User tariff. Overall, this study provides an important methodology for assessing the potential products of future projects.
The Effects of Syngas Composition on Engine Thermal Balance in a Biomass Powered CHP Unit: A 3D CFD Study
Authors: Costa Michela , Piazzullo Daniele
Published: February 2024

Syngas from biomass gasification represents an interesting alternative to traditional fuels in spark-ignition (SI) internal combustion engines (ICEs). The presence of inert species in the syngas (H2O, CO2, N2) reduces the amount of primary energy that can be exploited through combustion, but it can also have an insulating effect on the cylinder walls, increasing the average combustion temperature and reducing heat losses. A predictive numerical approach is here proposed to derive hints related to the possible optimization of the syngas-engine coupling and to balance at the best the opposite effects taking place during the energy conversion process. A three-dimensional (3D) computational fluid dynamics (CFD) model is developed, based on a detailed kinetic mechanism of combustion, to reproduce the combustion cycle of a cogenerative engine fueled by syngas deriving from the gasification of different feedstocks. Numerical results are validated with respect to experimental measurements made under real operation. Main findings reveal how heat transfer mainly occurs through the chamber and piston walls up to 50° after top dead center (ATDC), with the presence of inert gases (mostly N2) which decrease the syngas lower calorific value but have a beneficial insulating effect along the liner walls. However, the overall conversion efficiency of the biomass-to-ICE chain is mostly favored by high-quality syngas from biomasses with low-ashes content.
State of the Art and Environmental Aspects of Plant Microbial Fuel Cells’ Application
Authors: Lepikash Roman , Lavrova Daria , Stom Devard , Meshalkin Valery , Ponamoreva Olga , Alferov Sergey
Published: February 2024

Environmental pollution is becoming ubiquitous; it has a negative impact on ecosystem diversity and worsens the quality of human life. This review discusses the possibility of applying the plant microbial fuel cells (PMFCs) technology for concurrent processes of electricity generation and the purification of water and soil ecosystems from organic pollutants, particularly from synthetic surfactants and heavy metals. The review describes PMFCs’ functioning mechanisms and highlights the issues of PMFCs’ environmental application. Generally, this work summarizes different approaches to PMFC development and to the potential usage of such hybrid bioelectrochemical systems for environmental protection.
LCL Trap Filter Analysis with a PFC Isolated Ćuk Converter Using SiC MOSFET for DCM
Authors: Şehirli Erdal
Published: February 2024

The main contribution of the paper concerns the use of an LCL trap filter with a PFC isolated Ćuk converter. Further, SiC MOSFET is used with a PFC isolated Ćuk converter designed for 50 W with 42 kHz in DCM. A small-signal model of the converter is cascaded with the filter model to investigate the effect of the filter on the whole system. Moreover, large-signal and small-signal models of the converter are compared to investigate the requirement of the small-signal analysis. In addition, an LTspice simulation using SiC MOSFET of the system is conducted and the results are compared by the applications for both LC and LCL trap filters with respect to different loading conditions. Further, the LCL trap filter is compared with the LC filter regarding the PF, THD, and efficiency. Controller design considering the filter is also presented. In addition, the converter is operated and compared using linear and nonlinear loads for each filter. Parametric variation in the filter components is investigated. As a result of the simulation and applications, the THD of the grid current is 4.83% and the PF is 0.998, meeting the standards, and the overall efficiency of the system is 85% with the LCL trap filter. It can be concluded that the presented filter provides better results than the LC filter.
Ground Resistance Estimation for a DC Converter Station: Theory versus Experiment
Authors: Akef Samar , El-Morshedy Ahdab M. K. , Samy Mohamed M. , Emam Ahmed M. , El-Hawary Hadeer H.
Published: February 2024

This paper is aimed at proposing a calculation model for the ground resistance of a grounding scheme servicing a high-voltage direct-current converter station. The method is based on the equivalence of current conduction and electric field from the grounding scheme through the surrounding medium. The grounding scheme is composed of three concentric ring electrodes supported by two horizontal conductors and eight vertical rods. The calculated ground resistance is 4.8 Ω against the experimental value of 5 Ω with an error of 4.2%. The calculated ground resistance value agrees reasonably well with that of 4.7 Ω as obtained using CYMGRD software (version 7.0). The calculated surface-potential values over the ground surface agreed reasonably well with those measured experimentally, with an average deviation not exceeding 6.5%. This study is designed to investigate how ground resistance is decreased by the increase in the scheme parameters, including the rods’ diameter and length, as well as the radius of the inner and outer rings. The dependency of the ground resistance on the soil type is also investigated.
Consideration of Multi-Objective Stochastic Optimization in Inter-Annual Optimization Scheduling of Cascade Hydropower Stations
Authors: Jia Jun , Zhang Guangming , Zhou Xiaoxiong , Zhu Mingxiang , Shi Zhihan , Lv Xiaodong
Published: February 2024

There exists a temporal and spatial coupling effect among the hydropower units in cascade hydropower stations which constitutes a complex planning problem. Researching the multi-objective optimization scheduling of cascade hydropower stations under various spatiotemporal inflow impacts is of significant importance. Previous studies have typically only focused on the economic dispatch issues of cascade hydropower stations, with little attention given to their coupling mechanism models and the uncertainty impacts of inflows. Firstly, this paper establishes a coupled optimization scheduling model for cascade hydropower stations and elaborates on the operational mechanism of cascade hydropower stations. Secondly, according to the needs of actual scenarios, two types of optimization objectives are set, considering both the supply adequacy and peak-shaving capacity as indicators, with the total residual load and the peak-valley difference of the residual load as comprehensive optimization objectives. Subsequently, considering the uncertainty impact of the inflow side, a stochastic optimization model for inflow is established based on a normal distribution probability. Finally, case study analyses demonstrate that the proposed model not only effectively achieves supply stability but also reduces the peak-valley difference in load, and can achieve optimized scheduling under the uncertain environment of inflow.
Efficiency Improvement of Permanent Magnet Synchronous Motors Using Model Predictive Control Considering Core Loss
Authors: Hou Lian , Guo Youguang , Ba Xin , Lei Gang , Zhu Jianguo
Published: February 2024

The highway cycle is an important consideration in the EV’s new European driving cycles (NEDCs) range, as the steady-state efficiency improvement in such conditions can be greatly beneficial. In the model predictive control (MPC) of the permanent magnet synchronous motors (PMSMs), the predicted next-step feedback reference generated by the equivalent circuit model (ECM) will contribute directly to the voltage vector selection, therefore influencing the performance of the motor control. In the current MPC scheme, when the conventional ECM is applied, it only considers copper loss, and the core loss is usually disregarded. In some circumstances, such as the highway cycle of EVs, the motors are at high speed, the torque is low, and the core loss can be significant in the losses, thus affecting the accuracy of control and the efficiency of the system; hence, the introduction of core loss ECM into the MPC would be beneficial. This paper aims to investigate the steady-state efficiency improvement of a novel ECM of PMSM considering core loss ECM, and the comparison will be based on model predictive direct torque control (MPDTC) using the core loss ECM, which will be compared to MPDTC with the conventional ECM of the PMSM. The results demonstrate the proposed ECM’s efficiency improvement in various conditions, the limitations of the model and the simulation are discussed, and future work is proposed.
Impact of Electrode Distance in a Quasi-Uniform Model Electrode System on Lightning Impulse Breakdown Voltage in Various Insulating Liquids
Authors: Kunikowski Wiktor , Rozga Pawel , Pasternak Bartlomiej , Staniewski Jakub , Stuchala Filip , Strzelecki Konrad
Published: February 2024

This study presents findings on the influence of gap length distance on the lightning impulse breakdown voltage of three dielectric liquids of different chemical origins. The liquids were tested in a model electrode system with a quasi-uniform electric field distribution and a pressboard plate placed on the grounded electrode. The experimental studies were supported using calculations and simulations to show the individual relationships between the lightning impulse breakdown voltage and gap distance, which represent the so-called volume effect of the most stressed liquid. The results of the experiment, which involved four considered gap distances of 2, 4, 6, and 8 mm, show that a dynamic increase in lightning impulse breakdown voltage with an increase in gap distance is associated with mineral oil and bio-based hydrocarbons. However, similar trends were not observed for synthetic ester. Calculations that allowed us to assess the impact of gap length distance on lightning impulse breakdown voltage support the observations from the experimental studies. The curves obtained in this field can be considered in the process of designing insulating systems for transformers.
Targeting SWI/SNF Complexes in Cancer: Pharmacological Approaches and Implications
Authors: Dreier Megan R. , Walia Jasmine , de la Serna Ivana L.
Published: February 2024

SWI/SNF enzymes are heterogeneous multi-subunit complexes that utilize the energy from ATP hydrolysis to remodel chromatin structure, facilitating transcription, DNA replication, and repair. In mammalian cells, distinct sub-complexes, including cBAF, ncBAF, and PBAF exhibit varying subunit compositions and have different genomic functions. Alterations in the SWI/SNF complex and sub-complex functions are a prominent feature in cancer, making them attractive targets for therapeutic intervention. Current strategies in cancer therapeutics involve the use of pharmacological agents designed to bind and disrupt the activity of SWI/SNF complexes or specific sub-complexes. Inhibitors targeting the catalytic subunits, SMARCA4/2, and small molecules binding SWI/SNF bromodomains are the primary approaches for suppressing SWI/SNF function. Proteolysis-targeting chimeras (PROTACs) were generated by the covalent linkage of the bromodomain or ATPase-binding ligand to an E3 ligase-binding moiety. This engineered connection promotes the degradation of specific SWI/SNF subunits, enhancing and extending the impact of this pharmacological intervention in some cases. Extensive preclinical studies have underscored the therapeutic potential of these drugs across diverse cancer types. Encouragingly, some of these agents have progressed from preclinical research to clinical trials, indicating a promising stride toward the development of effective cancer therapeutics targeting SWI/SNF complex and sub-complex functions.
Comparative Analyses of Acacia Plastomes to Detect Mutational Hotspots and Barcode Sites for the Identification of Important Timber Species
Authors: Li Weixiong , Li Yongyao , Tembrock Luke R. , Wu Zhiqiang , Liao Xuezhu , Zhang Shuo
Published: February 2024

The genus Acacia is a crucial source of industrial papermaking and timber, possessing significant economic value. Due to their nitrogen-fixing ability, rapid growth, and high tolerance to stress, Acacia species have become integral to short-term industrial timber forests, particularly in southern China and various other regions worldwide. Nevertheless, comparative genomic analyses of Acacia species remain limited, impeding the development of molecular markers for identifying and distinguishing between distinct Acacia species as well as distinguishing their wood counterparts from other tree species. To address this gap, we generated plastomic sequences from eight species commonly cultivated in Chinese plantation forests and compared them with existing data. Our findings revealed a generally conserved structure and gene content within the plastid genomes of Acacia. Notably, the dN/dS ratio of genes from distinct functional groups varied, particularly between ribosomal proteins and photosynthesis genes. Furthermore, phylogenetic analyses and sequence comparisons suggest that the introduction of Acacia to China may have been less diverse than previously thought or that interspecies introgression has occurred more frequently than previously documented. This study offers a valuable starting point for future research in this area and for improving timber quality through marker-assisted breeding.
Identifying and Predicting the Responses of Multi-Altitude Vegetation to Climate Change in the Alpine Zone
Authors: Chen Xin , Guan Tiesheng , Zhang Jianyun , Liu Yanli , Jin Junliang , Liu Cuishan , Wang Guoqing , Bao Zhenxin
Published: February 2024

Global climate change has affected vegetation cover in alpine areas. In this paper, we analyzed the correlation between Leaf Area Index (LAI) and climate factors of the Yarlung Tsangpo River basin, and identified their contributions using the quantitative analysis method. The results show that the vegetation cover in the study area generally exhibited an increasing trend. Grassland in the middle- and high-altitude areas was the main contributing area. Temperature is the dominant climatic factor affecting the change, the effect of which increases with the rise in elevation. The influences of precipitation and radiation had obvious seasonality and regionality. The vegetation illustrated a lag response to climate drivers. With the change in the elevation band, the response time to precipitation was significantly less than that to air temperature in the low-elevation area, while the opposite trend was observed in the high-elevation area. In the future, the LAI of the watershed will show different characteristics at different time points, with the increases in the LAI in autumn and winter becoming the main factors for the future increase in the LAI. This provides a crucial basis upon which to explore hydrological and ecological processes as important components of the Third Pole region.
Mechanistic Insights into Nitrite Degradation by Metabolites of L. plantarum A50: An LC-MS-Based Untargeted Metabolomics Analysis
Authors: An Jiangbo , Sun Lin , Liu Mingjian , Dai Rui , Si Qiang , Ge Gentu , Wang Zhijun , Jia Yushan
Published: February 2024

Nitrites are universally acknowledged natural toxic substances that frequently lead to poisoning in humans and animals. During fermentation, certain microorganisms utilize a portion of the nitrogen element and reduce nitrates to nitrites through specific metabolic pathways. In this study, a highly effective lactic acid bacterial strain, Lactiplantibacillus plantarum A50, was isolated and screened from alfalfa silage for its remarkable ability to degrade nitrites. L. plantarum A50 exhibits exceptional nitrite removal capacity, with a degradation rate of 99.06% within 24 h. Furthermore, L. plantarum A50 demonstrates normal growth under pH values ranging from 4 to 9 and salt concentrations of 5%, displaying excellent tolerance to acidity, alkalinity, and salinity. Additionally, it undergoes fermentation using various carbon sources. Within the first 6–12 h of culture, L. plantarum A50 primarily achieves nitrite degradation through non-acidic processes, resulting in a degradation rate of 82.67% by the 12th hour. Moreover, the metabolites produced by L. plantarum A50 exhibit a synergistic interaction with acidity, leading to a nitrite degradation rate of 98.48% within 24 h. Notably, both L. plantarum A50 and MRS broth were found to degrade nitrites. Consequently, a non-targeted metabolomic analysis using LC-MS was conducted to identify 342 significantly different metabolites between L. plantarum A50 and MRS broth. Among these, lipids and lipid-like molecules, organic acids and derivatives, organic oxygen compounds, and organoheterocyclic compounds emerged as the main constituents. Lipids and lipid-like molecules, derivatives of glucose and galactose, amino acids and their derivatives, as well as organoheterocyclic compounds, are likely to play a role in nitrite elimination. Through the enrichment analysis of differential metabolic pathways using KEGG, nine distinct pathways were identified. These pathways provide essential nutrients, maintain cellular structure and function, participate in substance transport, regulate metabolic activities, and enhance resistance against pathogenic microorganisms in L. plantarum A50.
State of the Art Technologies for High Yield Heterologous Expression and Production of Oxidoreductase Enzymes: Glucose Oxidase, Cellobiose Dehydrogenase, Horseradish Peroxidase, and Laccases in Yeasts P. pastoris and S. cerevisiae
Authors: Crnoglavac Popović Milica , Stanišić Marija , Prodanović Radivoje
Published: February 2024

Oxidoreductase (OXR) enzymes are in high demand for biocatalytic applications in the food industry and cosmetics (glucose oxidase (GOx) and cellobiose dehydrogenase (CDH)), bioremediations (horseradish peroxidase (HRP) and laccase (LAC)), and medicine for biosensors and miniature biofuel cells (GOx, CDH, LAC, and HRP). They can be used in a soluble form and/or within the yeast cell walls expressed as chimeras on the surface of yeast cells (YSD), such as P. pastoris and S. cerevisiae. However, most of the current studies suffer from either low yield for soluble enzyme expression or low enzyme activity when expressed as chimeric proteins using YSD. This is always the case in studies dealing with the heterologous expression of oxidoreductase enzymes, since there is a requirement not only for multiple OXR gene integrations into the yeast genome (super transformations), and codon optimization, but also very careful design of fermentation media composition and fermentation conditions during expression due to the need for transition metals (copper and iron) and metabolic precursors of FAD and heme. Therefore, scientists are still trying to find the optimal formula using the above-mentioned approaches; most recently, researcher started using protein engineering and directed evolution to increase in the yield of recombinant enzyme production. In this review article, we will cover all the current state-of-the-art technologies and most recent advances in the field that yielded a high expression level for some of these enzymes in specially designed expression/fermentation systems. We will also tackle and discuss new possibilities for further increases in fermentation yield using cutting-edge technologies such as directed evolution, protein and strain engineering, high-throughput screening methods based on in vitro compartmentalization, flow cytometry, and microfluidics.
Impacts of Harvest Date and Concurrent Alkali Pretreatment and Ensiling on Anaerobic Digestion of Pennycress Biomass
Authors: Yang Liangcheng , Lubna Tuba Yasmin , Moklak Michael A. , Gautam Barsanti , Heller Nicholas J. , Rhykerd Robert L. , Kopsell David E. , Sedbrook John C.
Published: February 2024

Pennycress (Thlaspi arvense L.) is an annual cover crop known for its exceptional cold tolerance and high oil and protein yields. Pennycress can be integrated into a corn–soybean rotation in the U.S. However, the utilization of pennycress biomass remains largely unexplored, including assessing compositional changes through its growth and organic matter digestibility. This study harvested pennycress at three growth stages, characterized the biomass for anaerobic digestion (AD), and tested the effects of concurrent alkali pretreatment and ensiling on the biomass methane yield. Results showed that the biomass harvested when the plants were undergoing senescence (“third-harvest”) had higher contents of acid detergent fiber, neutral detergent fiber, and lignin, while the biomass harvested when 80–90% of the pods were fully-sized (“second-harvest”) had the highest protein content. The AD experiments showed that the first-harvest biomass (90% of flowers opened) failed to produce biogas due to a drop in the pH and alkalinity, the second-harvest biomass was inhibited for methane production (45.74 ± 0.20 L/kg-VS), and the third-harvest biomass had a methane yield of 171.80 ± 4.82 L/kg-VS. After the alkali pretreatment and ensiling, a methane yield of 270.4 ± 3.10 L/kg-VS was obtained from the second-harvest biomass, representing a significant 4.5-fold increase (adjusted for the organic matter loss) relative to the untreated second-harvest biomass.
A QoS-Aware IoT Edge Network for Mobile Telemedicine Enabling In-Transit Monitoring of Emergency Patients
Authors: Mukhopadhyay Adwitiya , Remanidevi Devidas Aryadevi , Rangan Venkat P. , Ramesh Maneesha Vinodini
Published: February 2024

Addressing the inadequacy of medical facilities in rural communities and the high number of patients affected by ailments that need to be treated immediately is of prime importance for all countries. The various recent healthcare emergency situations bring out the importance of telemedicine and demand rapid transportation of patients to nearby hospitals with available resources to provide the required medical care. Many current healthcare facilities and ambulances are not equipped to provide real-time risk assessment for each patient and dynamically provide the required medical interventions. This work proposes an IoT-based mobile medical edge (IM2E) node to be integrated with wearable and portable devices for the continuous monitoring of emergency patients transported via ambulances and it delves deeper into the existing challenges, such as (a) a lack of a simplified patient risk scoring system, (b) the need for architecture that enables seamless communication for dynamically varying QoS requirements, and (c)the need for context-aware knowledge regarding the effect of end-to-end delay and the packet loss ratio (PLR) on the real-time monitoring of health risks in emergency patients. The proposed work builds a data path selection model to identify the most effective path through which to route the data packets in an effective manner. The signal-to-noise interference ratio and the fading in the path are chosen to analyze the suitable path for data transmission.
A Case Study on the Evacuation of People during a Fire in the Workshop of a Large Factory
Authors: Fan Yuru , Cui Hao , Qin Jiawen , Liu Changcheng , Huang Que
Published: February 2024

A workshop, as a crowded place, is quite easy to cause serious casualties and economic losses once there is a fire. In this paper, Pathfinder software was used to simulate fire emergency evacuation in a workshop of a large factory with building structural symmetry. According to the simulation results, several obstacles to the evacuation were discovered and further analyzed. The results showed that the main factors affecting the evacuation were the width of exits, the distribution of occupants and the effective evacuation width of stairs. Among them, only changing the width of exits had little influence on shortening evacuation time. While changing the effective evacuation width of stairs could greatly relieve the evacuation pressure, every increase of 0.5 m in the width of the staircase could shorten the evacuation time by 30.0 s. Meanwhile, the larger the number of people in high-rise buildings, the longer the evacuation time was. Therefore, the means of restricting people from entering the high-rise buildings in batches could be used to prevent personnel from being evacuated in time when a fire incident occurs.
Flame Stabilisation Mechanism for Under-Expanded Hydrogen Jets
Authors: Takeno Keiji , Kido Hikaru , Takeda Hiroki , Yamamoto Shohei , Shentsov Volodymyr , Makarov Dmitriy , Molkov Vladimir
Published: February 2024

A hydrogen under-expanded jet released from a high-pressure vessel or equipment into the atmosphere through a 0.53 mm diameter orifice results in a sustained lifted flame for pressures above 4 MPa and flame blow-out at pressures below 3 MPa. Knowledge of whether the leaked hydrogen creates a sustained flame or is extinguished is an important issue for safety engineering. This study aims to clarify, in detail, a mechanism of flame stabilisation and blow-out depending on the spouting pressure. The model of flame stabilisation is derived using measurements and observations at the flame base location by means of high-speed schlieren images, laser diagnostics, and electrostatic probe techniques. The sustained stable flame originating from the 0.53 mm orifice is characterised by the existence of the spherical flame structures with a diameter of about 5 to 7 mm that appear one after another at the flame base and outside the streamlines of the hydrogen jet. As the spouting pressure reduces to 3.5 MPa, the sustained lifted flame becomes quasi-steady with higher fluctuations in amplitude of the flame base (lift-off height). In addition to that, flame structures are moving further from the hydrogen jet outlet, with a further decrease of spouting pressure leading to blow-out. The existence of spherical flame formations plays an important role in flame stabilisation. Based on the measurements of OH radicals using the PLIF method and ion currents, multiple flame surfaces were found to be folded in the flame structures. The hydrogen jet generates the vortex-like flow near its outer edge, creating flamelets upon ignition, ultimately forming the observed in the experiments spherical flame structures.
Research on Sequential Decision-Making of Major Accidents with Incomplete Information
Authors: Xia Dengyou , Chen Changlin , Zheng Ce , Xin Jing , Zhu Yi
Published: February 2024

In order to solve the problem of emergency decision-making with incomplete information and deal with the accident information in different time series at the scenes of major accidents, this paper proposes a method of sequential decision-making by utilizing the relevant knowledge of D-S evidence theory and game theory. Firstly, we took an oil tank fire accident as an example and sorted out historical cases and expert experiences to establish a logical relationship between key accident scenes and accident scene symptoms in the accident. Meanwhile, we applied the logistic regression analysis method to obtain the basic probability distribution of each key accident scene in the oil tank fire, and on this basis, we constructed an evidence set of the fire. Secondly, based on the D-S evidence theory, we effectively quantified the knowledge uncertainty and evidence uncertainty, with the incomplete and insufficient information taken as an evidence system of the development of key accident scenes to construct a situation prediction model of these accident scenes. Thirdly, based on the game theory, we viewed emergency decision-makers and major accidents as two sides of the game to compare and analyze accident states at different time points and solve the contradiction between loss costs of decision-making and information collection costs. Therefore, this paper has provided a solution for the optimization of accident schemes at different time stages, thus realizing the sequential decision-making at the scenes of major accidents. Furthermore, we combined the situation prediction model with sequential decision-making, with the basic steps described below: (1) We drew up an initial action plan in the case of an extreme lack of information; then, we (2) started to address the accident and constructed a framework of accident identification, (3) collected and dealt with the continuously added evidence information with the evolution of the accident, (4) calculated the confidence levels of key accident scenarios after evaluating different evidence and then predicted the accident state in the next stage, and (5) calculated the profit–loss ratio between the current decision-making scheme and the decision-making scheme of the next stage. Finally, we (6) repeated steps (3) to (5) until the accident completely vanished. We verified the feasibility of the proposed method with the explosion accident of the Zhangzhou P.X. project in Fujian on 6 April used as an example. Based on the D-S evidence theory, this method employs approximate reasoning on the incomplete and insufficient information obtained at the scenes of major accidents, thus realizing the situation prediction of key scenes of these accidents. Additionally, this method uses the game theory to solve the contradiction between decision-making loss costs and information collection costs, thus optimizing the decision-making schemes at different time stages of major accidents.
Changes in Physiological Indices, Amino Acids, and Volatile Compounds in Vitis vinifera L. cv. Pinot Noir under UV-B Radiation and Water Deficit Conditions
Authors: Sun Meng , Zhu Yifan , Jordan Brian , Wang Tao
Published: February 2024

UV-B radiation and water deficit can challenge Pinot noir growth and fruit quality. The aim of this work is to determine the effects of UV-B and water deficit on the physiological indices, amino acids, and volatile compounds of Pinot noir vine and fruit. The results showed that both individual and combined treatments caused a decrease in the leaf SPAD, with the largest amplitude being observed in the combined treatment. Water deficit also decreased the leaf water potential and increased the juice δ13C‰ at harvest, which was the opposite of the latter under UV-B radiation. Interestingly, most of the physiological indices under combined stresses did not show significant changes compared with that under no UV-B and the well-watered control treatment. Moreover, the concentrations of amino acids and volatile compounds in the berries were determined at harvest. The amino acid contents were significantly increased by the combined treatment, particularly proline (Pro), aspartate (Arg), alanine (Ala), and threonine (Thr). There were slight increases in volatile compounds. This research substantially contributed to improve our scientific understanding of UV-B and water deficit responses in an important commercial species. In addition, it highlighted some future research to produce high-quality wines with the anticipated specific characteristics.
Fixed-Time Synchronization for Fractional-Order Cellular Inertial Fuzzy Neural Networks with Mixed Time-Varying Delays
Authors: Sun Yeguo , Liu Yihong , Liu Lei
Published: February 2024

Due to the widespread application of neural networks (NNs), and considering the respective advantages of fractional calculus (FC), inertial neural networks (INNs), cellular neural networks (CNNs), and fuzzy neural networks (FNNs), this paper investigates the fixed-time synchronization (FDTS) issues for a particular category of fractional-order cellular-inertial fuzzy neural networks (FCIFNNs) that involve mixed time-varying delays (MTDs), including both discrete and distributed delays. Firstly, we establish an appropriate transformation variable to reformulate FCIFNNs with MTD into a differential first-order system. Then, utilizing the finite-time stability (FETS) theory and Lyapunov functionals (LFs), we establish some new effective criteria for achieving FDTS of the response system (RS) and drive system (DS). Eventually, we offer two numerical examples to display the effectiveness of our proposed synchronization strategies. Moreover, we also demonstrate the benefits of our approach through an application in image encryption.