bluuebunny/crossref_metadata_2025 huggingface dataset:
Review on the Experimental Characterization of Fracture in Active Material for Lithium-Ion Batteries
Authors: Pistorio Francesca , Clerici Davide , Mocera Francesco , Somà Aurelio
Published: December 2022

Nowadays, lithium-ion batteries are one of the most widespread energy storage systems, being extensively employed in a large variety of applications. A significant effort has been made to develop advanced materials and manufacturing processes with the aim of increasing batteries performance and preserving nominal properties with cycling. Nevertheless, mechanical degradation is still a significant damaging mechanism and the main cause of capacity fade and power loss. Lithium ions are inserted and extracted into the lattice structure of active materials during battery operation, causing the deformation of the crystalline lattice itself. Strain mismatches within the different areas of the active material caused by the inhomogeneous lithium-ions concentration induce mechanical stresses, leading ultimately to fracture, fatigue issues, and performance decay. Therefore, a deep understanding of the fracture mechanics in active materials is needed to meet the rapidly growing demand for next-generation batteries with long-term stability, high safety, excellent performance, and long life cycle. This review aims to analyze the fracture mechanics in the active material microstructure of electrodes due to battery operations from an experimental point of view. The main fracture mechanisms occurring in the common cathode and anode active materials are described, as well as the factors triggering and enhancing fracture. At first, the results obtained by performing microscopy and diffraction analysis in different materials are discussed to provides visual evidence of cracks and their relation with lattice structure. Then, fatigue phenomena due to crack growth as a function of the number of cycles are evaluated to assess the evolution of damage during the life cycle, and the effects of fracture on the battery performance are described. Finally, the literature gaps in the characterization of the fracture behavior of electrode active materials are highlighted to enhance the development of next-generation lithium-ion batteries.
Wide Band Gap Devices and Their Application in Power Electronics
Authors: Kumar Amit , Moradpour Milad , Losito Michele , Franke Wulf-Toke , Ramasamy Suganthi , Baccoli Roberto , Gatto Gianluca
Published: December 2022

Power electronic systems have a great impact on modern society. Their applications target a more sustainable future by minimizing the negative impacts of industrialization on the environment, such as global warming effects and greenhouse gas emission. Power devices based on wide band gap (WBG) material have the potential to deliver a paradigm shift in regard to energy efficiency and working with respect to the devices based on mature silicon (Si). Gallium nitride (GaN) and silicon carbide (SiC) have been treated as one of the most promising WBG materials that allow the performance limits of matured Si switching devices to be significantly exceeded. WBG-based power devices enable fast switching with lower power losses at higher switching frequency and hence, allow the development of high power density and high efficiency power converters. This paper reviews popular SiC and GaN power devices, discusses the associated merits and challenges, and finally their applications in power electronics.
Feeder Losses Analysis of Marine Vessel Power Systems: A Case Study of Container Ship Power Loss Analysis Using Newton–Raphson Method
Authors: Chen Ching-Jin , Su Chun-Lien , Teng Jen-Hao , Elsisi Mahmoud
Published: December 2022

Load survey has become a routine project for shipbuilding and shipping companies to investigate electrical load characteristics to enhance the power system planning and operation of marine vessels. In this brief perspective, we will outline a few steps to feeder losses analysis based on the result conducted by the load survey. The power flow and feeder loss analysis are extracted and used to determine the critical parameters that can significantly affect the system feeder losses used in the electrical load analysis in new ships. Exploring this new research direction will provide a more thorough understanding of feeder losses in marine vessel power systems. In this paper, a case study of container ship power loss analysis using the Newton–Raphson method is presented. The analysis results can provide shipbuilding corporations and ship owners with useful information for planning, designing, operating, and controlling shipboard power systems. As an energy-saving measure for ship microgrids, the frequency converters are widely used by shipyards for seawater and freshwater cooling systems and heating, ventilation, and air conditioning (HVAC) systems, so that these systems can adjust the speed of the motor according to the actual demand of the load, so as to avoid full-load operation during the motor operation. With the proposed method, other measures, such as battery energy storage systems and energy-saving lighting equipment based on LEDs, are also utilized for shipboard power demand management.
Electrification of Steam and Thermal Oil Boilers in the Textile Industry: Techno-Economic Analysis for China, Japan, and Taiwan
Authors: Hasanbeigi Ali , Zuberi M. Jibran S.
Published: December 2022

Process heating is typically more than half of the total final energy demand in the textile industry, most of which is usually provided by fossil fuels. There is significant potential to decarbonize the textile industry by the electrification of process heating where low-carbon electricity is used. This study aims to quantify the potential for the electrification of process heating in the textile sector in three of the top textile manufacturing and exporting countries in the world. The results show that the total annual potential energy savings due to the electric steam boiler applications are estimated to be around 92, 2.4, and 2.5 PJ in China, Japan, and Taiwan, respectively, by 2050. This is equal to approximately 19% of the total boiler energy demand in the three economies. Similarly, annual potential energy savings of 8.6, 0.21, and 0.24 PJ can be realized if the existing fossil-fuel-fired thermal oil boilers are electrified in the textile industry in China, Japan, and Taiwan, respectively, by 2050. Moreover, the potential CO2 abatement resulting from the electrification is highly dependent on the carbon intensity of the electricity used. The economic analysis shows that switching from combustion boilers to electric boilers may result in higher energy costs primarily because the average electricity prices in all three economies are substantially higher than fossil fuel prices. Finally, some key recommendations that different stakeholders can take to scale up electrification in the textile industry are provided.
Provincial Carbon Emission Allocation and Efficiency in China Based on Carbon Peak Targets
Authors: Zhang Mengwan , Gao Fengfeng , Huang Bin , Yin Bo
Published: December 2022

As the world’s largest carbon emitter, China is facing great pressure to reduce emissions. With the country’s proposed timeline for carbon peaking and carbon neutralization, a new goal has been established for China’s low-carbon development. Based on the improved equal proportion allocation method, this paper allocates the overall carbon emission control goal for 2025 among 30 provinces and cities, based on 2015 figures, and measures and studies the country’s carbon emission allocation efficiency on this basis. The results show that Beijing, Tianjin, Hebei, Shandong, Zhejiang, Shanghai, Jiangsu, Guangdong and Inner Mongolia need to increase their emission reduction capacity, while Jiangxi, Guizhou, Gansu, Qinghai, Hainan and Guangxi have relatively low emission reduction targets. Based on this allocation scheme, more provinces can reduce carbon emissions by increasing their efficiency with up-to-date technology, and a new vision for national allocation that is more easily accepted by all provinces and regions can be developed. Based on the research results of this paper, each province and region can choose its own low-carbon economic development path within the constraints of China’s carbon intensity emission reduction targets, without compromising its own economic development characteristics.
Energy-Saving Potential Comparison of Different Photovoltaic Integrated Shading Devices (PVSDs) for Single-Story and Multi-Story Buildings
Authors: Shi Shaohang , Sun Jingfen , Liu Mengjia , Chen Xinxing , Gao Weizhi , Song Yehao
Published: December 2022

Building-integrated photovoltaic (BIPV) façades are a promising technique for improving building energy performance. This study develops energy simulation models of different photovoltaic-integrated shading devices (PVSDs) in single-story and multi-story office buildings. A cross-region study in China is carried out to explore the energy performance of PVSDs in five climate zones. The shading effect of the upper PVSDs is taken into account. The results show that (1) PVSDs can be applicable in hot and cold climates; shading effects lead to a notable difference in the optimal PVSDs style. The average comprehensive energy saving ratios of different PVSDs ranged from 16.12% (fixed PV louvres in the vertical plane) to 51.95% (lower single panel). The most rewarding PVSDs are for single-story buildings in Kunming and the least suitable are for multi-story buildings in Guangzhou. (2) In climate zones with little air-conditioning energy consumption, avoiding considerably increased lighting consumption by PVSDs is vital. (3) To reduce shading effects, solar panels with smaller widths or vertical placements can be adopted. In addition, the distance of the PV modules from the top edge of the windows is also critical. Building performance evaluation in the early design stage enables maximum benefits for the same input (total area of PV panels). The research methodology and data analysis presented can guide parameters design and the geographical applicability of PVSDs, providing a reference for optimal building energy performance.
Energy Transition Manifesto: A Contribution towards the Discourse on the Specifics Amid Energy Crisis
Authors: Gitelman Lazar , Kozhevnikov Mikhail
Published: December 2022

The article seeks to describe a more realistic approach to the transition to a carbon-neutral energy model in the current period of economic and geopolitical turbulence, with the high volatility of energy prices, and the disruption of geopolitical and logistic ties, and puts forward a set of fundamental principles for the energy transition. The hypothesis tested is that the development of low-carbon energy is based on the structural-technological diversification of production methods and rationalization of energy consumption through expanded electrification and application of energy demand-side management programs. The analysis of the main approaches to the energy transition across the world shows that many countries now prioritize renewable energy sources, even though, despite their obvious environmental benefits, they are less reliable due to their intermittent nature. The proposed principles of the energy transition draw from a more flexible, compromise approach that implies an optimal balance of mutually reinforcing centralized and distributed technologies of energy generation and their characteristics. The key provisions of the manifesto were verified by means of theoretical generalization and graphical interpretation of data from a number of analytical reports of international energy agencies and the results of an expert opinion survey. The survey was conducted among leading Russian experts from energy companies, who had relevant experience of developing innovation projects in this sphere, and university researchers. Most of them subscribe to the view that, despite the economic downturn, it would be unfeasible to curtail energy transition programs at this point, and even more so, it is important to continue local modernization projects. The results of the study could have a practical application when planning structural, organizational, and market transformations aimed at achieving the objectives of energy transition amid growing uncertainty, including the expansion of the structure of generating capacities in regional energy systems and the active use of low-carbon technologies in the energy sector and related industries.
Application Framework of Multi-Criteria Methods in Sustainability Assessment
Authors: Ziemba Paweł
Published: December 2022

In the contemporary literature on sustainability, one can notice an increasingly frequent use of Multi-Criteria Decision Analysis (MCDA) methods instead of classic sustainability indices. The MCDA method should be tailored to the specific sustainability problem and decision situation so that its results are credible and satisfactory for the decision-maker. Therefore, the following research questions arise: (1) which MCDA methods are most often used in sustainability problems, and (2) which methods should be used depending on the characteristics of a particular sustainability decision problem and its assessment. The aim of the article is to scientifically analyse the applicability of various MCDA methods in decision-making problems related to sustainability, sustainable development, and sustainability assessment. In the article, based on the analysis of the literature, a set of features has been developed that determines the possibility of using individual MCDA methods in sustainability problems. Then, the characteristics of 28 methods are presented and the framework for selecting the MCDA method for the purpose of sustainability decision problems is indicated. As a result of the conducted research, it was found that the most commonly used MCDA methods in sustainability problems are primarily methods based on arithmetic aggregation of criteria. In addition, fuzzy methods and fuzzy modifications of classical methods are used more often. Research has established that MCDA methods are more functional than classic sustainability indices. In addition, the use of MCDA methods in the assessment of sustainability gives much more flexibility than the use of classic indices. The proposed framework allows the decision-maker to independently assess the potential of using individual multi-criteria methods in specific decision-making problems related to sustainability. The framework enables the selection of an appropriate MCDA method depending on the defined needs of the decision-maker, resulting from the decision problem, its structure, and decision-making situation.
Distributionally Robust Unit Commitment with N-k Security Criterion and Operational Flexibility of CSP
Authors: Pei Younan , Han Xueshan , Ye Pingfeng , Zhang Yumin , Li Mingbing , Mao Huizong
Published: December 2022

In order to reduce the conservatism of the robust optimization method and the complexity of the stochastic optimization method and to enhance the ability of power systems to deal with occasional line fault disturbance, this paper proposes a distributionally robust unit commitment (DRUC) model with concentrating solar power (CSP) operational flexibility and N-k safety criterion under distributed uncertainty. According to the limited historical sample data, under the condition of satisfying a certain confidence level, based on the imprecise Dirichlet model (IDM), an ambiguity set is constructed to describe the uncertainty of transmission line fault probability. Through the identification of the worst probability distribution in the ambiguity set, the adaptive robust optimal scheduling problem is transformed into a two-stage robust optimization decision model under the condition of deterministic probability distribution. The CSP flexibility column and constraint generation (C&CG) algorithm is used to process the model and the main problem and subproblem are solved by using the Big-M method, linearization technique, and duality principle. Then, a mixed integer linear programming problem (MILP) model is obtained, which effectively reduces the difficulty of solving the model. Finally, case studies on the IEEE 14 bus system and the IEEE 118 bus system demonstrate the efficiency of the proposed method, such as enhancing the ability of power systems to cope with occasional line fault disturbances and reducing the conservatism of the robust optimization method.
Establishment of CNN and Encoder–Decoder Models for the Prediction of Characteristics of Flow and Heat Transfer around NACA Sections
Authors: Seo Janghoon , Yoon Hyun-Sik , Kim Min-Il
Published: December 2022

The present study established two different models based on the convolutional neural network (CNN) and the encoder–decoder (ED) to predict the characteristics of the flow and heat transfer around the NACA sections. The established CNN predicts the aerodynamic coefficients and the Nusselt number. The established ED model predicts the velocity, pressure and thermal fields to explain the performances of the aerodynamics and heat transfer. These two models were trained and tested by the dataset extracted from the computational fluid dynamics (CFD) simulations. The predictions mostly matched well with the true data. The contours of the velocity components and the pressure coefficients reasonably explained the variation of the aerodynamic coefficients according to the geometric parameter of the NACA section. In order to physically interpret the heat transfer performance, more quantitative and qualitative information are needed owing to the lack of the correlation and the resolution of the thermal fields. Consequently, the present approaches will be useful to design the NACA section-based shape giving higher aerodynamic and heat transfer performances by quickly predicting the force and heat transfer coefficients. In addition, the predicted flow and thermal fields will provide the physical interpretation of the aerodynamic and heat transfer performances.
R&D Human Capital, Renewable Energy and CO2 Emissions: Evidence from 26 Countries
Authors: Mentel Grzegorz , Tarczyński Waldemar , Azadi Hossein , Abdurakmanov Kalandar , Zakirova Elina , Salahodjaev Raufhon
Published: December 2022

This study examines the long-term relationship between carbon emissions and a number of researchers engaged in Research and Development (R&D), economic development, foreign capital inflows, renewable energy and population growth in 26 countries between 1995 and 2015. Pedroni’s panel cointegration test confirms the cointegrating relationship between the variables. Long-term elasticities are derived from FMOLS regression. Researchers in R&D and renewable energy are negatively and significantly related to carbon emissions. There is a positive and significant long-term relationship between GDPs per capita and CO2 and between the FDI and CO2. Dumitrescu and Hurlin’s panel causality test revealed unidirectional causality running from economic development to carbon emissions and feedback hypotheses between the FDI and CO2 and between renewable energy and CO2.
Towards Optimization of Energy Consumption of Tello Quad-Rotor with Mpc Model Implementation
Authors: Benotsmane Rabab , Vásárhelyi József
Published: December 2022

For the last decade, there has been great interest in studying dynamic control for unmanned aerial vehicles, but drones—although a useful technology in different areas—are prone to several issues, such as instability, the high energy consumption of batteries, and the inaccuracy of tracking targets. Different approaches have been proposed for dealing with nonlinearity issues, which represent the most important features of this system. This paper focuses on the most common control strategy, known as model predictive control (MPC), with its two branches, linear (LMPC) and nonlinear (NLMPC). The aim is to develop a model based on sensors embedded in a Tello quad-rotor used for indoor purposes. The original controller of the Tello quad-rotor is supposed to be the slave, and the designed model predictive controller was created in MATLAB. The design was imported to another embedded system, considered the master. The objective of this model is to track the reference trajectory while maintaining the stability of the system and ensuring low energy consumption. The case study in this paper compares linear and nonlinear model predictive control (MPC). The results show the efficiency of NLMPC, which provides more promising results compared to LMPC. The comparison concentrates on the energy consumption, the tracked trajectory, and the execution time. The main finding of this research is that NLMPC is a good solution to smoothly track the reference trajectory. The controller in this case processes faster, but the rotors consume more energy because of the increased values of control inputs calculated by the nonlinear controller.
Load Frequency Model Predictive Control of a Large-Scale Multi-Source Power System
Authors: Afaneh Tayma , Mohamed Omar , Abu Elhaija Wejdan
Published: December 2022

With increased interests in affordable energy resources, a cleaner environment, and sustainability, more objectives and operational obligations have been introduced to recent power plant control systems. This paper presents a verified load frequency model predictive control (MPC) that aims to satisfy the load demand of three practical generation technologies, which are wind energy systems, clean coal supercritical (SC) power plants, and dual-fuel gas turbines (GTs). Simplified state-space models for the two thermal units were constructed by concepts of subspace identification, whereas the individual wind turbine integration was implicated by the Hammerstein–Wiener (HW) model and then augmented from the output to simulate the effect of a wind farm, assuming similar power harvesting from all turbines in the farm. A practical strategy of control was then suggested, which was as follows: with a changing load demand, the available harvested wind energy must be fully admitted to the network to cover part of the load demand with the free energy, and the resultant load signal will then be instructed to the MPCs designed for the coal and gas units for the coordination of generation. The load signal, after being penetrated by wind, has more transients and faster changes, and needs a more sophisticated control in order to follow the load demand of the flexible coal and gas units. Furthermore, as the level of wind penetration increases, the power system frequency excursions are higher. The simulation results show an acceptable performance for linear MPCs embedded to the GT and coal units, with around a 90 MW share of wind without exceeding the safe restrictions of the plants and allowable reasonable frequency excursions. The complete simulation framework can be used to facilitate wind energy penetration in such power systems and train the operators and future engineers with subsequent power system frequency simulation studies.
Investigation of Electromagnetic Losses Considering Current Harmonics in High-Speed Permanent Magnet Synchronous Motor
Authors: Lee Ju-Hyeong , Sung Soyoung , Cho Han-Wook , Choi Jang-Young , Shin Kyung-Hun
Published: December 2022

This paper presents a characteristic analysis and experimental verification for predicting the electromagnetic losses in high-speed permanent magnet synchronous motors. To predict the operating characteristics (such as speed and input current), dynamic modeling is conducted that combines models for the space vector pulse width modulation (SVPWM) inverter and high-speed permanent magnet synchronous motor (HPMSM). By applying the predicted harmonic currents to the electromagnetic analysis, DC and AC copper losses of the stator winding, and eddy current loss of the rotor sleeve and rotor permanent magnet, are comprehensively analyzed using the finite element (FE) method. In particular, by analyzing the magnetic field behavior of magnetic flux density according to harmonics, a core loss analysis technique was presented. The validity of the hybrid analysis, which combines the stator copper loss and rotor eddy current loss derived from the FE analysis and the proposed core loss analysis, was verified through comparison with the experimental results under various operating conditions. Compared with the experimental results, the error of total losses using the hybrid analysis with a sinusoidal current was about 47.39%, and total losses using the hybrid analysis with a harmonic current was significantly improved to within 3.7%.
Energy Balance Data-Based Optimization of Louver Installation Angles for Different Regions in Korea
Authors: Choe Seung-Ju , Han Seung-Hoon
Published: December 2022

A louver is a traditional environmental control device and passive architectural element based on an ecofriendly concept. Louvers are architectural elements that can be used to regulate natural lighting, thermal environment, and building energy use. To realize these integrated functionalities of louvers, they must be designed considering the climate and geographical characteristics of the target region. However, these aspects are typically not considered during building design in Korea, resulting in lovers being used as design elements with simple natural lighting control functions. Therefore, the objective of this study was to promote the integrated use of louvers by optimizing the louver angle according to the microclimate in Korea from the viewpoint of thermal energy use. We performed load and energy simulation planning and calculation and conducted optimization studies for the louver angle and range of motion for each region. The energy consumption in central and southern Korean regions was minimized when the angles of the fixed louvers were 45°–75° and 60°–90°, respectively. Kinetic louvers could enhance thermal energy management when installed at 30°–75° in spring, 135°–165° in summer, 75°–165° in autumn, and 45°–75° in winter. These findings can promote the realization of integrated functionalities of louvers from the perspective of indoor environment comfort based on the microclimates of the Korean regions.
Dynamic DNR and Solar PV Smart Inverter Control Scheme Using Heterogeneous Multi-Agent Deep Reinforcement Learning
Authors: Lim Se-Heon , Yoon Sung-Guk
Published: December 2022

The conventional volt-VAR control (VVC) in distribution systems has limitations in solving the overvoltage problem caused by massive solar photovoltaic (PV) deployment. As an alternative method, VVC using solar PV smart inverters (PVSIs) has come into the limelight, which can respond quickly and effectively to solve the overvoltage problem by absorbing reactive power. However, the network power loss, that is, the sum of line losses in the distribution network, increases with reactive power. Dynamic distribution network reconfiguration (DNR), which hourly controls the network topology by controlling sectionalizing and tie switches, can also solve the overvoltage problem and reduce network loss by changing the power flow in the network. In this study, to improve the voltage profile and minimize the network power loss, we propose a control scheme that integrates the dynamic DNR with volt-VAR control of PVSIs. The proposed control scheme is practically usable for three reasons: Primarily, the proposed scheme is based on a deep reinforcement learning (DRL) algorithm, which does not require accurate distribution system parameters. Furthermore, we propose the use of a heterogeneous multiagent DRL algorithm to control the switches centrally and PVSIs locally. Finally, a practical communication network in the distribution system is assumed. PVSIs only send their status to the central control center, and there is no communication between the PVSIs. A modified 33-bus distribution test feeder reflecting the system conditions of South Korea is used for the case study. The results of this case study demonstrates that the proposed control scheme effectively improves the voltage profile of the distribution system. In addition, the proposed scheme reduces the total power loss in the distribution system, which is the sum of the network power loss and curtailed energy, owing to the voltage violation of the solar PV output.
Kinetic Modeling of an Enzyme Membrane Reactor for the Selective Production of Oligosaccharides
Authors: Asano Shusaku , Muranaka Yosuke , Maki Taisuke , Ikeda Koki , Mae Kazuhiro
Published: December 2022

An enzyme membrane reactor is an attractive tool for producing oligosaccharides from biomass-based polysaccharides. However, kinetic modeling and reactor design based on the rate equations have rarely been reported for enzyme membrane reactors because of the difficulty in tracing the depolymerization process. In this study, a simplified reaction model based on Michaelis–Menten-type kinetics has been built to simulate the enzyme membrane reactor. Ramping various species into reactant, target, and byproduct worked well for discussing reactor performance. The use of a membrane with a molecular weight cut-off (MWCO) of 10 kDa with continuous feeding of the reactant was suggested for the efficient production of chitosan hexamer and pentamer by enzymatic hydrolysis of chitosan.
Use of Yarrowia lipolytica to Obtain Fish Waste Functional Hydrolysates Rich in Flavoring Compounds
Authors: Gottardi Davide , Ciccone Marianna , Siroli Lorenzo , Lanciotti Rosalba , Patrignani Francesca
Published: December 2022

Fishery processing industries generate large amounts of by-products. These by-products come from fish heads, skin, bones, thorns, and viscera. The disposal of these wastes represents an increasing environmental and health problem. Nowadays, there is a growing interest in how to utilize fish materials that are not used for human consumption. Among the different solutions proposed, the use of proteolytic and lipolytic microorganisms represents a green solution for waste valorization. In this work, first we screened several conventional and non-conventional microorganisms for their proteolytic and lipolytic functions. Then, the most promising strains (Yarrowia lipolytica YL2, Y. lipolytica YL4, Bacillus amyloliquefaciens B5M and B. subtilis B5C) were tested on a fish waste-based solution. After 72 h incubation at room temperature, the supernatants obtained using the strains of Y. lipolytica showed the highest degree of hydrolysis (10.03 and 11.80%, respectively, for YL2 and YL4), the strongest antioxidant activity (86.4% in DPPH assay for YL2) and the highest formation of aldehydes (above 50% of the total volatile compounds detected). Hydrolysates of fish waste obtained with Y. lipolytica may be reused in feed and food formulations for their functional and flavoring characteristics.
Flexural Capacity of Fire-Affected Concrete Members with Recycled Glass Aggregate and Glass Pozzolan
Authors: Tapia Enrique Gonzalez , Yazdani Nur
Published: December 2022

Adding recycled glass components to concrete mixes is a novel and sustainable option. Prior studies have recommended replacement ratios of 30% glass aggregates and 20% ground glass pozzolan as optimum substitutions in concrete mixes. The compressive strength of glass concrete has been shown to increase when glass components are used with these proportions. Less information is available on the flexural strength of such concrete, and no previous research has been conducted on the flexural capacity of glass concrete exposed to high temperatures. To bridge this knowledge gap, cured concrete cylinders and beam samples using various glass coarse aggregate and ground glass pozzolan replacement ratios were heat-treated in a furnace. The heat-affected samples were then tested for their compressive and flexural capacities. It was found that glass pozzolan increased the mix workability, while glass aggregates reduced it. The compressive strengths were modesty increased and the flexural capacities were drastically reduced (up to 93%) after heat exposure. Therefore, recycled sustainable glass concrete may be efficiently used in concrete compressive members and in properly designed flexural members. It can also be used efficiently in architectural non-load-bearing members for insulating and aesthetic effects.
Development of an Immunoassay Detection System for Koi Herpesvirus Using Recombinant Single-Chain Variable Fragments
Authors: Seo Haneul , Lubis Andre Ditya Maulana , Choi Tae-Jin , Jung Tae-Sung , Lee Taek-Kyun , Lee Sukchan
Published: December 2022

Koi herpesvirus (KHV) is a highly contagious virus that causes high mortality in koi and common carp, leading to a reduction in production worldwide. Recent diagnostic tests based on molecular methods alone (nucleic acid amplification) and indirect immunoassay methods (antibody detection) can be confirmed over KHV infections or prior exposure and latent infections. Unfortunately, there is no established method to detect KHV virus particles, especially when virus titers are low. Therefore, we propose an alternative, direct immunoassay method for viral detection using a single-chain variable fragment (scFv), a specific region of IgG antibodies that binds specifically to KHV particles. The results of functional analyses indicated that four putative scFv candidates, C5, F8, F6, and E4, were specific to KHV, but only F6 and C5 had a high binding affinity. The binding characteristics were confirmed by indirect competitive and sandwich enzyme-linked immunosorbent assays, which indicated that F6 and C5 have a broad penetration area to the binding region and share a similar epitope with commercial KHV monoclonal antibodies. These characteristics were further confirmed by their interactions with purified KHV coat protein by indirect ELISA and Western blot analyses. In conclusion, the F6 and C5 scFvs have adequate binding affinity to KHV particles to permit their use in immunoassays.
Evaluation of the Effectiveness of Marking Juvenile Takifugu obscurus Otoliths with Strontium
Authors: Gu Lingling , Zhang Hui , Feng Guangpeng , Liu Yong , Han Zhiqiang , Zhao Feng , Ye Qing , Hu Wangjiao , Song Chao
Published: December 2022

Strontium marking technology is commonly used for extensive marking in stock enhancement and releasing activities. In order to explore the feasibility of this technology for juvenile Takifugu obscurus, five different concentrations of strontium (0, 12, 18, 36, and 72 mg/L) were set up by strontium chloride hexahydrate (SrCl2·6H2O) and T. obscurus were immersed for 7 days. Then, T. obscurus were reared in non-additive water for 25 days. The results showed that the survival rate of all groups was 100%, except the 72 mg/L group, after 7 days of immersion. Moreover, the growths of all the marked groups were better than that of the control group. There was no significant difference between the control and marked groups, except for the 18 mg/L group, indicating that the appropriate concentration of strontium might have positive effect for T. obscurus. The strontium to calcium (Sr/Ca) ratios of otoliths in the marked groups increased with increasing concentration and time, which were higher than the baseline, respectively. Then, the Sr/Ca ratios returned to the original level, but the peak value was still retained, suggesting that the success rate of the strontium marking reached 100%. Notably, the residual strontium content of 18 mg/L group was insufficient for causing negative effects for T. obscurus after 25 days. Therefore, in consideration of the growth, survival, and effect of strontium marking on otolith, 18 mg/L is an appropriate concentration for strontium marking of juvenile T. obscurus. This study confirmed the feasibility of strontium marking for T. obscurus and provided a new approach to rationally and scientifically evaluate the stock enhancement and releasing efficiency of T. obscurus.
Cloning of Two HSP Genes of Eriocheir hepuensis and Their Expression under Vibrio parahaemolyticus Stress
Authors: Fu Qianni , Liu Jinxia , Ren Tianjiao , Zhang Zining , Ma Zihang , Lan Zhenyu , Duan Yitao , Liang Ziwei , Chen Boyu , Zhang Yan , Zhu Peng , Liao Yongyan
Published: December 2022

Heat shock proteins (HSPs) are molecular chaperone proteins that can help maintain cellular protein homeostasis, assist in correcting the folding of cellular proteins, and protect organisms from stress when the body is under stress conditions such as temperature changes or bacterial infections. In this study, the HSP10 and HSP40 genes of Eriocheir hepuensis were cloned and named Eh-HSP10 and Eh-HSP40. The results show that the coding sequence length of the HSP10 and HSP40 genes of E. hepuensis was 309 bp and 1191 bp, encoding 102 and 396 amino acids, respectively. The results of protein domain prediction show that Eh-HSP10 has a Cpn10 domain. The Eh-HSP40 protein contains a DnaJ domain, which is characteristic of the HSP40 gene family. The results of qRT-PCR show that the Eh-HSP10 and Eh-HSP40 genes were expressed in different normal tissues, with the highest expression in the heart. Under Vibrio parahaemolyticus stress, the Eh-HSP10 genes peaked at 6 h, and the Eh-HSP40 peaked at 9 h in the hepatopancreas. In the gill, Eh-HSP10 showed a double peak at 24 and 48 h, and the expression of Eh-HSP40 was time-dependent. In the heart, the expression of Eh-HSP10 increased first and then decreased, whereas Eh-HSP40 peaked at 48 h. The results indicate that the Eh-HSP10 and Eh-HSP40 proteins may play a role in protecting E. hepuensis under V. parahaemolyticus infection and that they may be involved in the innate immune response of E. hepuensis against bacteria.
Machine Learning on Fault Diagnosis in Wind Turbines
Authors: Ng Eddie Yin-Kwee , Lim Jian Tiong
Published: December 2022

With the improvement in wind turbine (WT) operation and maintenance (O&M) technologies and the rise of O&M cost, fault diagnostics in WTs based on a supervisory control and data acquisition (SCADA) system has become among the cheapest and easiest methods to detect faults in WTs.Hence, it is necessary to monitor the change in real-time parameters from the WT and maintenance action could be taken in advance before any major failures. Therefore, SCADA-driven fault diagnosis in WT based on machine learning algorithms has been proposed in this study by comparing the performance of three different machine learning algorithms, namely k-nearest neighbors (kNN) with a bagging regressor, extreme gradient boosting (XGBoost) and an artificial neural network (ANN) on condition monitoring of gearbox oil sump temperature. Further, this study also compared the performance of two different feature selection methods, namely the Pearson correlation coefficient (PCC) and principal component analysis (PCA), and three hyperparameter optimization methods on optimizing the performance of the models, namely a grid search, a random search and Bayesian optimization. A total of 3 years of SCADA data on WTs located in France have been used to verify the selected method. The results showed the kNN with a bagging regressor, with PCA and a grid search, provides the best R2 score, and the lowest root mean square error (RMSE). The trained model can detect the potential of WT faults at least 4 weeks in advance. However, the proposed kNN model in this study can be trained with the Support Vector Machine hybrid algorithm to improve its performance and reduce fault alarm.
Blade Drag Resistance in Windage Operating of Low Pressure Steam Turbines
Authors: Mambro Antonio , Congiu Francesco , Galloni Enzo , Lanni Davide
Published: December 2022

On the basis of previous experimental and numerical studies, the windage operation of low-pressure turbine rear stage is investigated. The state of the steam within the rotor channel was correlated to measurements carried out downstream of the blades for different ventilation regimes. Considering very-low-volume flow conditions, the ventilation power was related to the drag force acting on the moving blades. A correlation was identified between the drag coefficient and a Reynolds number relative to the reverse flow height. This correlation can be used in order to predict the power loss of a last-stage moving blade operating at low load.
Promoting Effect and Potential Mechanism of Lactobacillus pentosus LPQ1-Produced Active Compounds on the Secretion of 5-Hydroxytryptophan
Authors: Zeng Yixiu , Song Jiajia , Zhang Yuhong , Huang Yechuan , Zhang Feng , Suo Huayi
Published: December 2022

5-hydroxytryptophan (5-HTP) is an important substance thought to improve depression. It has been shown that Lactobacillus can promote the secretion of 5-HTP in the body and thus ameliorate depression-like behavior in mice. However, the mechanism by which Lactobacillus promotes the secretion of 5-HTP is unclear. In this study, we investigated the promoting effect and mechanism of Lactobacillus, isolated from Chinese fermented foods, on the secretion of 5-HTP. The results showed that Lactobacillus (L.) pentosus LPQ1 exhibited the strongest 5-HTP secretion-promoting effect ((9.44 ± 0.69)-fold), which was dependent on the mixture of compounds secreted by L. pentosus LPQ1 (termed SLPQ1). In addition, the results of the RNA sequencing (RNA-seq) and quantitative real-time polymerase chain reaction (qRT-PCR) analyses indicated that SLPQ1 alters the TNF and oxidative phosphorylation signaling pathways. Moreover, the SLPQ1 ultrafiltration fraction (>10 kDa) showed a similar 5-HTP promoting effect as SLPQ1. Furthermore, reverse-phase liquid chromatography-tandem mass spectrometry (RPLC-MS/MS) identified 29 compounds of >10 kDa in SLPQ1, including DUF488 domain-containing protein, BspA family leucine-rich repeat surface protein, and 30S ribosomal protein S5, which together accounted for up to 62.51%. This study reports new findings on the mechanism by which L. pentosus LPQ1 promotes 5-HTP production in some cell lines in vitro.
Strategy and Mechanism of Rice Bran Protein Emulsion Stability Based on Rancidity-Induced Protein Oxidation: An Ultrasonic Case Study
Authors: Zhou Qi , Li Helin , Li Fang , Zhang Benpeng , Wu Xiaojuan , Wu Wei
Published: December 2022

To provide a strategy for improving the stability of rice bran protein emulsion (RBPE), rice bran proteins (RBPs) with different oxidation extents were prepared from fresh rice bran (RB) stored for different times (0, 1, 3, 5, 10 d), and RBPE was prepared with ultrasonic treatment. The ultrasonic conditions were optimized according to the results of the RBPE’s stability (when RB stored for 0, 1, 3, 5, 10 d, the optimal ultrasonic treatment conditions of RBPE were 500 w and 50 min, 400 w and 30 min, 400 w and 30 min, 300 w and 20 min, 500 w and 50 min, respectively). Additionally, the structural characteristics and the flexibility of RBPE interface protein were characterized, and the results showed that compared with native protein and excessive oxidized protein, the unfolded structure content and flexibility of interface protein of RBPE prepared by moderate oxidized protein under optimal ultrasonic intensity was higher. Furthermore, the correlation analysis showed that the RBPE stability was significantly correlated with the structural characteristics and flexibility of the RBPE interface protein (p < 0.05). In summary, ultrasonic treatment affected the interface protein’s structural characteristics and flexibility, improving the stability of RBPE prepared from oxidized RBP.
Does Carbon Footprint Play a Relevant Role in Food Consumer Behaviour? A Focus on Spanish Beef
Authors: Lami Olda , Mesías Francisco J. , Balas Celia , Díaz-Caro Carlos , Escribano Miguel , Horrillo Andrés
Published: December 2022

Carbon footprint has become a reference indicator of the environmental impact of food production. Governments are increasingly demanding a trend towards low-carbon-footprint production in order to reduce the impact on climate change. In this sense, the study of consumers’ preferences and assessment of products from the perspective of their carbon footprint is crucial to achieve a green and circular economy. This paper specifically attempted to assess consumer preference and willingness to pay for beef that has been carbon-footprint-labelled as an attribute. In order to attain this objective, a choice experiment was designed and applied to a total of 362 Spanish consumers. The results revealed a positive preference towards beef produced in low-carbon-footprint systems. In addition, the segmentation of these consumers revealed the existence of a group of consumers who prioritise environmental impact over product price.
Detection of Coconut Clusters Based on Occlusion Condition Using Attention-Guided Faster R-CNN for Robotic Harvesting
Authors: Divyanth L. G. , Soni Peeyush , Pareek Chaitanya Madhaw , Machavaram Rajendra , Nadimi Mohammad , Paliwal Jitendra
Published: December 2022

Manual harvesting of coconuts is a highly risky and skill-demanding operation, and the population of people involved in coconut tree climbing has been steadily decreasing. Hence, with the evolution of tree-climbing robots and robotic end-effectors, the development of autonomous coconut harvesters with the help of machine vision technologies is of great interest to farmers. However, coconuts are very hard and experience high occlusions on the tree. Hence, accurate detection of coconut clusters based on their occlusion condition is necessary to plan the motion of the robotic end-effector. This study proposes a deep learning-based object detection Faster Regional-Convolutional Neural Network (Faster R-CNN) model to detect coconut clusters as non-occluded and leaf-occluded bunches. To improve identification accuracy, an attention mechanism was introduced into the Faster R-CNN model. The image dataset was acquired from a commercial coconut plantation during daylight under natural lighting conditions using a handheld digital single-lens reflex camera. The proposed model was trained, validated, and tested on 900 manually acquired and augmented images of tree crowns under different illumination conditions, backgrounds, and coconut varieties. On the test dataset, the overall mean average precision (mAP) and weighted mean intersection over union (wmIoU) attained by the model were 0.886 and 0.827, respectively, with average precision for detecting non-occluded and leaf-occluded coconut clusters as 0.912 and 0.883, respectively. The encouraging results provide the base to develop a complete vision system to determine the harvesting strategy and locate the cutting position on the coconut cluster.
Investigating the Probiotic Properties and Antimicrobial Activity of Lactic Acid Bacteria Isolated from an Iranian Fermented Dairy Product, Kashk
Authors: Saboori Bahareh , Shahidi Fakhri , Hedayati Sara , Javadmanesh Ali
Published: December 2022

In the present study, kashk samples were collected from two regions of Iran, the Fars (Abadeh) and Razavi Khorasan (Kalat) provinces. Fifteen bacteria were isolated and physiological and biochemical assays were performed. After identification to the genus level, eight isolates were identified as lactic acid bacteria (LAB) and subjected to molecular identification and probiotic properties assays. The results revealed that the isolates were Enterococcus faecium KKP 3772 (KF1), Enterococcus faecium C1 (KF2), Pediococcus pentosaceus H11 (KF3), Pediococcus pentosaceus VNK-1 (KK4), Lactococcus lactis RSg (KK1), Enterococcus faecalis P190052 (KK2), Enterococcus mundtii CECT972T (KK3), and Lactiplantibacillus plantarum PM411 (KK5). Only the numbers of L. lactis RSg (KK1) and Lpb. Plantarum PM411 (KK5) decreased to below 9 Log CFU/mL after acidic conditions (pH = 3) and showed weak antibacterial activity. Enterococcus mundtii CECT972T (KK3) and E. faecium C1(KF2) were highly susceptible to bile salts, while P. pentosaceus VNK-1 (KK4) and P. pentosaceus H11 (KF3) showed the highest resistance. All of the isolates were resistant to tetracycline and sensitive to chloramphenicol and gentamicin. The antimicrobial activity of P. pentosaceus VNK-1 (KK4) and P. pentosaceus H11 (KF3) was higher than other isolates and consequently, their inhibition zones were larger. The adhesion capabilities of LAB isolates to intestinal epithelial cells were evaluated by examining the auto-aggregation factor and cell surface hydrophobicity. The highest and lowest cell surface hydrophobicity and auto-aggregation were obtained from P. pentosaceus VNK-1 (KK4) and E. mundtii CECT972T (KK3), respectively. In general, P. pentosaceus VNK-1 (KK4) and P. pentosaceus H11 (KF3) have shown better probiotic properties as compared to other isolates.