bluuebunny/crossref_metadata_2025 huggingface dataset:
Deep Learning Algorithm to Predict Cryptocurrency Fluctuation Prices: Increasing Investment Awareness
Authors: Ammer Mohammed Abdullah , Aldhyani Theyazn H. H.
Published: July 2022

Digital currencies such as Ethereum and XRP allow for all transactions to be carried out online. To emphasize the decentralized nature of fiat currency, we can refer, for example, to the fact that all virtual currency users may access services without third-party involvement. Cryptocurrency price swings are non-stationary and highly erratic, similarly to the price changes of conventional stocks. Owing to the appeal of cryptocurrencies, both investors and researchers have paid more attention to cryptocurrency price forecasts. With the rise of deep learning, cryptocurrency forecasting has gained great importance. In this study, we present a long short-term memory (LSTM) algorithm that can be used to forecast the values of four types of cryptocurrencies: AMP, Ethereum, Electro-Optical System, and XRP. Mean square error (MSE), root mean square error (RMSE), and normalize root mean square error (NRMSE) analyses were used to evaluate the LSTM model. The findings obtained from these models showed that the LSTM algorithm had superior performance in predicting all forms of cryptocurrencies. Thus, it can be regarded as the most effective algorithm. The LSTM model provided promising and accurate forecasts for all cryptocurrencies. The model was applied to forecast the future closing prices of cryptocurrencies over a period of 180 days. The Pearson correlation metric was applied to assess the correlation between the prediction and target values in the training and testing processes. The LSTM algorithm achieved the highest correlation values in training (R = 96.73%) and in testing (96.09%) in predicting XRP currency prices. Cryptocurrency prices could be accurately predicted using the established LSTM model, which displayed highly efficient performance. The relevance of applying these models is that they may have huge repercussions for the economy by assisting investors and traders in identifying trends in the sales and purchases of different types of cryptocurrencies. The results of the LSTM model were compared with those of existing systems. The results of this study demonstrate that the proposed model showed superior accuracy based on the low prediction errors of the proposed system.
Exposure of Botnets in Cloud Environment by Expending Trust Model with CANFES Classification Approach
Authors: Selvaraj Nagendra Prabhu , Paulraj Sivakumar , Ramadass Parthasarathy , Kaluri Rajesh , Shorfuzzaman Mohammad , Alsufyani Abdulmajeed , Uddin Mueen
Published: July 2022

Many cloud service providers offer access to versatile, dependable processing assets following a compensation as-you-go display. Investigation into the security of the cloud focusses basically on shielding genuine clients of cloud administrations from assaults by outer, vindictive clients. Little consideration is given to restrict malicious clients from utilizing the cloud to dispatch assaults, for example, those as of now done by botnets. These assaults incorporate propelling a DDoS attack, sending spam and executing click extortion. Bots’ detection in the cloud environment is a complex process. The purpose of this study was to create a multi-layered architecture that could detect a variety of existing and emerging botnets. The goal is to be able to detect a larger range of bots and botnets by relying on several techniques called trust model. On this work, the port access verification in trust model is achieved by a Heuristic factorizing algorithm which verifies the port accessibility between client-end-user and client server. Further, back-off features are extracted from the particular node and all these structures are trained and categorized with a Co-Active Neuro Fuzzy Expert System (CANFES) classifier. The performance of the proposed bot detection system in the internet environment is analyzed latency, detection rate, packet delivery ration, energy availability and precision.
Simulation Study of Low Turn-Off Loss and Snapback-Free SA-IGBT with Injection-Enhanced p-Floating Layer
Authors: Zhang Xiaodong , Gong Ming , Pan Junfeng , Song Mingxin , Zhang Hang , Zhang Linlin
Published: July 2022

In this study, a shorted-anode IGBT with an injection-enhanced p-floating layer (IEPF-IGBT) under the N-buffer layer is proposed. Compared to conventional shorted-anode IGBT (SA-IGBT), the IEPF-IGBT has the structural characteristics of an injection-enhanced P-floating (IEPF) layer inserted into the N-buffer layer and the P+ collector region. The IEPF layer and P+ collector region pinch off the electron path during the turn-on period to suppress the snapback effect with a half-cell pitch of 10 μm. In addition, the IEPF layer acts as an injection-enhanced layer that influences the current injection of the holes. There is 56.3% reduction in the turn-off loss of the IEPF-IGBT at the same forward voltage drop.
A Comprehensive Analysis of Proportional Intensity-Based Software Reliability Models with Covariates
Authors: Li Siqiao , Dohi Tadashi , Okamura Hiroyuki
Published: July 2022

This paper focuses on the so-called proportional intensity-based software reliability models (PI-SRMs), which are extensions of the common non-homogeneous Poisson process (NHPP)-based SRMs, and describe the probabilistic behavior of software fault-detection process by incorporating the time-dependent software metrics data observed in the development process. The PI-SRM is proposed by Rinsaka et al. in the paper “PISRAT: Proportional Intensity-Based Software Reliability Assessment Tool” in 2006. Specifically, we generalize this seminal model by introducing eleven well-known fault-detection time distributions, and investigate their goodness-of-fit and predictive performances. In numerical illustrations with four data sets collected in real software development projects, we utilize the maximum likelihood estimation to estimate model parameters with three time-dependent covariates (test execution time, failure identification work, and computer time-failure identification), and examine the performances of our PI-SRMs in comparison with the existing NHPP-based SRMs without covariates. It is shown that our PI-STMs could give better goodness-of-fit and predictive performances in many cases.
Dataflow Optimization through Exploring Single-Layer and Inter-Layer Data Reuse in Memory-Constrained Accelerators
Authors: Ye Jinghao , Yanagisawa Masao , Shi Youhua
Published: July 2022

Off-chip memory access has become the performance and energy bottleneck in memory-constrained neural network accelerators. To provide a solution for the energy efficient processing of various neural network models, this paper proposes a dataflow optimization method for modern neural networks by exploring the opportunity of single-layer and inter-layer data reuse to minimize the amount of off-chip memory access in memory-constrained accelerators. A mathematical analysis of three inter-layer data reuse methods is first presented. Then, a comprehensive exploration to determine the optimal data reuse strategy from single-layer and inter-layer data reuse approaches is proposed. The result shows that when compared to the existing single-layer-based exploration method, SmartShuttle, the proposed approach can achieve up to 20.5% and 32.5% of off-chip memory access reduction for ResNeXt-50 and DenseNet-121, respectively.
The M-Commerce of Solar Energy Applications: An Analysis of Solar Energy Consumers’ Effort Paradox
Authors: Nicolau Cristina , Henter Ramona , Comșiț Mihai , Roman Nadinne
Published: July 2022

The mobile commerce of applications integrating solar thermal collectors, together with their configuring applications, has started to develop. Such applications are not only a business opportunity but also a sustainable and feasible solution for energy consumers who are more and more digitalised. This paper focuses on understanding behaviours in niche markets formed by small-sized and/or isolated consumers who need customized, sustainable and economically efficient applications for heating water for domestic and business use. We chose the focus group interview as the method of research. Primary data were collected in Romania and analysed with Atlas.ti 8. Firstly, the results revealed that consumers’ behavioural changes needed for switching to solar energy are influenced by their attitude regarding investment in this market as well as by the perceived social influences and control. Secondly, the results showed the effort paradox of small-sized and/or isolated users of applications integrating solar thermal collectors who shall be considered by developers and sellers alike in providing them with water heating solutions. In terms of business implications, we highlight that the cost-reduction strategy within enterprises is to go green, so major investments in solar technology in order to become energy independent and self-sufficient are envisaged on the long term, whereas the use of digital applications integrating it requires a high level of staff’s digital skills and the use of smart devices.
X-Band In-Line Coaxial-to-Groove Gap Waveguide Transition
Authors: Rossi Riccardo , Vincenti Gatti Roberto
Published: July 2022

A coaxial-to-groove gap waveguide transition is proposed for the first time, addressing the lack of similar designs in the state-of-the-art literature. An in-line configuration is adopted in case of stringent space requirements in groove gap waveguide systems. This device has no dielectric and makes use of a waveguide multi-step ridged section connected to the inner conductor of a coaxial line. Three versions are presented, covering progressively wider bandwidths depending on the number of employed steps. The three configurations achieve 20-dB return loss fractional bandwidths of 2.63%, 16.79%, and 38.08%, and 30-dB return loss fractional bandwidths of 0.84%, 10.63%, and 33.49%, with a simulated insertion loss always better than 0.05 dB when a realistic metal conductivity of 2 × 10 7 S/m is assumed. A tolerance analysis on the most critical geometrical parameters is provided to assess the mechanical feasibility, preserving a remarkable performance at X band.
Machine Vision-Based Human Action Recognition Using Spatio-Temporal Motion Features (STMF) with Difference Intensity Distance Group Pattern (DIDGP)
Authors: Arunnehru Jawaharlalnehru , Thalapathiraj Sambandham , Dhanasekar Ravikumar , Vijayaraja Loganathan , Kannadasan Raju , Khan Arfat Ahmad , Haq Mohd Anul , Alshehri Mohammed , Alwanain Mohamed Ibrahim , Keshta Ismail
Published: July 2022

In recent years, human action recognition is modeled as a spatial-temporal video volume. Such aspects have recently expanded greatly due to their explosively evolving real-world uses, such as visual surveillance, autonomous driving, and entertainment. Specifically, the spatio-temporal interest points (STIPs) approach has been widely and efficiently used in action representation for recognition. In this work, a novel approach based on the STIPs is proposed for action descriptors i.e., Two Dimensional-Difference Intensity Distance Group Pattern (2D-DIDGP) and Three Dimensional-Difference Intensity Distance Group Pattern (3D-DIDGP) for representing and recognizing the human actions in video sequences. Initially, this approach captures the local motion in a video that is invariant to size and shape changes. This approach extends further to build unique and discriminative feature description methods to enhance the action recognition rate. The transformation methods, such as DCT (Discrete cosine transform), DWT (Discrete wavelet transforms), and hybrid DWT+DCT, are utilized. The proposed approach is validated on the UT-Interaction dataset that has been extensively studied by past researchers. Then, the classification methods, such as Support Vector Machines (SVM) and Random Forest (RF) classifiers, are exploited. From the observed results, it is perceived that the proposed descriptors especially the DIDGP based descriptor yield promising results on action recognition. Notably, the 3D-DIDGP outperforms the state-of-the-art algorithm predominantly.
Application of Generative Adversarial Network and Diverse Feature Extraction Methods to Enhance Classification Accuracy of Tool-Wear Status
Authors: Chen Bo-Xiang , Chen Yi-Chung , Loh Chee-Hoe , Chou Ying-Chun , Wang Fu-Cheng , Su Chwen-Tzeng
Published: July 2022

The means of accurately determining tool-wear status has long been important to manufacturers. Tool-wear status classification enables factories to avoid the unnecessary costs incurred by replacing tools too early and to prevent product damage caused by overly worn tools. While researchers have examined this topic for over a decade, most existing studies have focused on model development but have neglected two fundamental issues in machine learning: data imbalance and feature extraction. In view of this, we propose two improvements: (1) using a generative adversarial network to generate realistic computer numerical control machine vibration data to overcome data imbalance and (2) extracting features in the time domain, the frequency domain, and the time–frequency domain simultaneously for modeling and integrating these in an ensemble model. The experiment results demonstrate how both proposed modifications are reasonable and valid.
Numerical Solution of the Poisson Equation Using Finite Difference Matrix Operators
Authors: Zaman Mohammad Asif
Published: July 2022

The Poisson equation frequently emerges in many fields of science and engineering. As exact solutions are rarely possible, numerical approaches are of great interest. Despite this, a succinct discussion of a systematic approach to constructing a flexible and general numerical Poisson solver can be difficult to find. In this introductory paper, a comprehensive discussion is presented on how to build a finite difference matrix solver that can solve the Poisson equation for arbitrary geometry and boundary conditions. The boundary conditions are implemented in a systematic way that enables easy modification of the solver for different problems. An image-based geometry-definition approach is also discussed. Python code of the numerical recipe is made publicly available. Numerical examples are presented that show how to set up the solver for different problems.
Error Detection and Correction of Mismatch Errors in M-Channel TIADCs Based on Genetic Algorithm Optimization
Authors: Li Yuehui , Liu Cong , Niu Guangshan , Luo Xiangdong , Ma Haocheng , Zhao Yiqiang
Published: July 2022

In order to achieve higher system performance, a digital calibration technique for the sub-channel mismatches of time-interleaved ADCs (TIADCs) is proposed in this paper. The sine-fit-based estimation algorithm is introduced to estimate the channel mismatches and a calibration algorithm is proposed to compensate for the mismatches. Subsequently, the genetic algorithm (GA) is firstly utilized to detect the mismatch errors of the outputs of sub-channels after frequency domain filtering. The detected offset error and gain error are then corrected by performing the calibration algorithm, and the time errors are corrected by fractional delay filters based on Farrow structure. The spurious-free dynamic range (SFDR) is enhanced from 19.69 dB to 108.12 dB, and the signal to noise and distortion ratio (SNDR) is enhanced from 16.02 dB to 97.63 dB. The proposed technique is further validated on the FPGA. Compared with existing calibration techniques, the proposed technique has the advantages of simple algorithm structure, low hardware resource consumption, and high calibration accuracy and can be applied to the calibration of high-resolution (18-bit) TIADCs for low-frequency inputs.
Automation of Asset Inventory for Cyber Security: Investigation of Event Correlation-Based Technique
Authors: Kotenko Igor , Doynikova Elena , Fedorchenko Andrey , Desnitsky Vasily
Published: July 2022

Asset inventory is one of the essential steps in cyber security analysis and management. It is required for security risk identification. Current information systems are large-scale, heterogeneous, and dynamic. This complicates manual inventory of the assets as it requires a lot of time and human resources. At the same time, an asset inventory should be continuously repeated because continuous modifications of system objects and topology lead to changes in the cyber security situation. Thus, a technique for automated identification of system assets and connections between them is required. The paper proposes a technique for automated inventory of assets and connections between them in different organizations. The developed technique is constructed based on event correlation methods, namely linking the system events to each other. The essence of the technique consists of the investigation of event characteristics and identifying the characteristics that arise solely together. This allows determining system assets via assigning event characteristics to specific asset types. The security risks depend on the criticality of the assets; thus, a discussion of automated calculation of the outlined assets’ criticality is provided. Outlined system objects and topology can be further used for restoring possible attack paths and security assessment. The applicability of the developed technique to reveal object properties and types is demonstrated in the experiments.
Independent Effects of Dopant, Oxygen Vacancy, and Specific Surface Area on Crystal Phase of HfO2 Thin Films towards General Parameters to Engineer the Ferroelectricity
Authors: Cui Tianning , Zhu Liping , Chen Danyang , Fan Yuyan , Liu Jingquan , Li Xiuyan
Published: July 2022

Many factors have been confirmed to affect ferroelectric phase formation in HfO2-based thin films but there was still a lack of general view on describing them. This paper discusses the intrinsic parameters to stabilize the ferroelectric phase of HfO2 thin films to approach this general view by investigating the separate effects of dopant, oxygen vacancy (VO), and specific surface area on the crystal phase of the films. It is found that in addition to extensively studied dopants, the ferroelectric orthorhombic phase can also be formed in pure HfO2 films by only introducing sufficient VO independently, and it is also formable by only increasing the specific surface area. By analyzing the common physics behind these factors, it is found that orthorhombic phase formation is universally related to strain in all the above cases with a given temperature. To get a general view, a physical model is established to describe how the strain influences ferroelectric phase formation during the fabrication of HfO2-based films based on thermodynamic and kinetics analysis.
Piezoelectric Impedance-Based Structural Health Monitoring of Wind Turbine Structures: Current Status and Future Perspectives
Authors: Le Thanh-Cao , Luu Tran-Huu-Tin , Nguyen Huu-Phuong , Nguyen Trung-Hau , Ho Duc-Duy , Huynh Thanh-Canh
Published: July 2022

As an innovative technology, the impedance-based technique has been extensively studied for the structural health monitoring (SHM) of various civil structures. The technique’s advantages include cost-effectiveness, ease of implementation on a complex structure, robustness to early-stage failures, and real-time damage assessment capabilities. Nonetheless, very few studies have taken those advantages for monitoring the health status and the structural condition of wind turbine structures. Thus, this paper is motivated to give the reader a general outlook of how the impedance-based SHM technology has been implemented to secure the safety and serviceability of the wind turbine structures. Firstly, possible structural failures in wind turbine systems are reviewed. Next, physical principles, hardware systems, damage quantification, and environmental compensation algorithms are outlined for the impedance-based technique. Afterwards, the current status of the application of this advanced technology for health monitoring and damage identification of wind turbine structural components such as blades, tower joints, tower segments, substructure, and the foundation are discussed. In the end, the future perspectives that can contribute to developing efficient SHM systems in the green energy field are proposed.
Modeling and Analysis on AC-DC Harmonic Coupling of the Three-Phase Voltage Source Converter under Asymmetric Condition
Authors: Wu Wei , Li Jing , Mao Tian , Liu Shenquan , Zhou Baorong , Zeng Dehui
Published: July 2022

A three-phase voltage source converter (VSC) is the most common power electronic device in power systems, but its inherent nonlinearity leads to complex AC-DC harmonic coupling phenomena. Existing studies focus on the harmonic coupling characteristics of three-phase VSCs under three-phase symmetrical conditions, but the problem under asymmetrical conditions is rarely investigated. This paper proposes a practical modeling method for harmonic state space (HSS) modeling of the VSC topology, together with a classic T/4-delay-based decoupled double synchronous reference frame (DDSRF) control system. The steady-state phase shift characteristics of the T/4 delay link for each frequency component are first analyzed, and the expansion method of the DDSRF controller in the HSS frame is derived. Compared with the conventional method, the proposed technique can provide an accurate description of the steady-state effect of the delay link without introducing additional state variables and, therefore, can reduce model complexity and computation burden. Then, the small-signal close-loop HSS model of the VSC is established, and a harmonic transfer matrix between the AC positive sequence/negative sequence and DC current of different frequencies is developed, which reveals the global harmonic coupling relationship between AC and DC grids across the VSC. Analysis shows that the T/4-delay-link significantly weakens the AC-DC inter-harmonic coupling near the fundamental frequency. Finally, the electromagnetic transient simulations are carried out based on MATLAB/Simulink, and the validity of the proposed modeling method and the model and the correctness for the analysis of harmonic coupling characteristics are verified.
A Multi-Hour Ahead Wind Power Forecasting System Based on a WRF-TOPSIS-ANFIS Model
Authors: Xing Yitian , Lien Fue-Sang , Melek William , Yee Eugene
Published: July 2022

Wind is a renewable and green energy source that is vital for sustainable human development. Wind variability implies that wind power is random, intermittent, and volatile. For the reliable, stable, and secure operation of an electrical grid incorporating wind power systems, a multi-hour ahead wind power forecasting system comprising a physics-based model, a multi-criteria decision making scheme, and two artificial intelligence models was proposed. Specifically, a Weather Research and Forecasting (WRF) model was used to produce wind speed forecasts. A technique for order of preference by similarity to ideal solution (TOPSIS) scheme was employed to construct a 5-in-1 (ensemble) WRF model relying on 1334 initial ensemble members. Two adaptive neuro-fuzzy inference system (ANFIS) models were utilised to correct the wind speed forecasts and determine a power curve model converting the improved wind speed forecasts to wind power forecasts. Moreover, three common statistics-based forecasting models were chosen as references for comparing their predictive performance with that of the proposed WRF-TOPSIS-ANFIS model. Using a set of historical wind data obtained from a wind farm in China, the WRF-TOPSIS-ANFIS model was shown to provide good wind speed and power forecasts for 30-min to 24-h time horizons. This paper demonstrates that the novel forecasting system has excellent predictive performance and is of practical relevance.
MLD–MPC for Ultra-Supercritical Circulating Fluidized Bed Boiler Unit Using Subspace Identification
Authors: Yang Chen , Zhang Tao , Zhang Zonglong , Sun Li
Published: July 2022

Before carbon capture and storage technologies can truly be promoted and applied, and nuclear or renewable energy power generation can become predominant, it is important to further develop more efficient and ultra-low emission USC units on the basis of leveraging the strengths of CFB technology. In view of this complex system with strong nonlinearity such as the boiler-turbine unit of a thermal power unit, the establishment of a model that is suitable for control is indispensable for the operation and the economics of the process. In this study the form of the nonlinear model after linearization at the steady-state point has been fully considered and an improved subspace identification method, which is based on the steady-state point deviations data, was proposed in order to identify a piecewise affine model. In addition, the construction of the excitation signal in practical applications has been fully considered. The identification results demonstrate that this method has a better adaptability to strong nonlinear systems. The identification normalized root mean square errors of each working condition were almost all less than 10%. On this basis, a framework that is widely applicable to complex system control has been established by combining with the mixed logic dynamic (MLD) model. The canonical form realization was performed in order to transfer the local models into the same state basis. The predictive control was carried out on the boiler-turbine system of a 660-MW ultra-supercritical circulating fluidized bed unit that was based on the above framework. The results indicate that the predictive control performance is closely related to the setting value of the ramp rate and, therefore, prove the effectiveness of the framework.
Medium-Voltage DC-DC Converter Topologies for Electric Bus Fast Charging Stations: State-of-the-Art Review
Authors: ElMenshawy Mena , Massoud Ahmed
Published: July 2022

With the expansion of E-mobility technology, the demand for Medium-Voltage (MV) Electric Buses (E-buses) charging infrastructure has significantly increased. In this regard, the effective connection of E-bus chargers to a medium voltage power grid is essential to provide fast charging and carry out multiple charging processes simultaneously. One of the main building blocks for E-bus charging is the DC-DC converter stage responsible for regulating the power flow and matching the different voltage and power levels. Accordingly, this paper presents a comprehensive review of DC-DC converter topologies applicable to MV E-bus fast charging. This review discusses and compares the basic isolated DC-DC converter topologies. In addition, the DC-DC converters are classified based on their conversion stages. Moreover, isolated DC-DC converter topologies applicable for MV E-bus fast charging applications, including Dual Active Bridge (DAB) modular-based structure converter and Modular Multilevel Converter (MMC)-based DAB, are discussed where the merits and demerits of each topology are highlighted. Moreover, this review illustrates how DAB converters are employed in different power level applications through the multimodule converter or the MMC-based DAB structure. Furthermore, the challenges and required features for MV DC-DC converter topologies are discussed.
Model Predictive Voltage Control of Uninterruptible Power Supply Based on Extended-State Observer
Authors: He Guofeng , Zheng Shicheng , Dong Yanfei , Li Guojiao , Zhang Wenjie
Published: July 2022

Finite-set model predictive controls have been widely used in inverter control because of the flexible target control and no need of a modulation unit. However, the mismatching of prediction model parameters produces prediction errors, resulting in a significant decline in the performance of finite-set model predictive controls. Aiming at the problem of model parameter mismatch, an extended-state observer was proposed to accurately estimate the disturbance of the system in this paper, and the obtained disturbance value was added to a finite-set model predictive control controller to compensate for the prediction error and achieve parameter robustness. By constructing a prediction model of the inverter output voltage in αβ coordinates, all the possible output voltage values were predicted by using different voltage vectors and system measurement values. A set of voltage vectors that minimized the cost function was selected, and its corresponding switching state was applied to the inverter in the next sampling period to achieve control of the output voltage quality. Both the simulation and experimental results showed that the finite-set model predictive voltage control method based on the extended-state observer can estimate the total disturbance quickly and accurately, suppress the influence of capacitance parameter disturbance, and improve the control effect of an inverter.
Limited Effects of Precipitation Manipulation on Soil Respiration and Inorganic N Concentrations across Soil Drainage Classes in Northern Minnesota Aspen Forests
Authors: Stockstad Anna B. , Slesak Robert A. , Toczydlowski Alan J. , Blinn Charles R. , Kolka Randall K. , Sebestyen Stephen D.
Published: July 2022

It is critical to gain insight into the responses of forest soils to the changing climate. We simulated future climate conditions with growing season throughfall reduction (by 50%) and winter snow removal using a paired-plot design across a soil drainage class gradient at three upland, Populus-dominated forests in northern Minnesota, USA. In situ bulk soil respiration and concentrations of extractable soil N were measured during the summers of 2020–2021. Soil respiration and N concentrations were not affected by throughfall reduction and snow removal, which was largely attributed to the limited treatment effects on soil moisture content and soil temperature. Drainage class was only a significant factor during the spring thaw period in 2021. During this period, the poorly drained plots had lower respiration rates compared to the well-drained plots, which was associated with the drainage class effects on soil temperature. The results of the companion laboratory incubation with varying levels of soil moisture also indicated no effect of the treatment on soil respiration, but effects of drainage class and moisture content on respiration were observed. Our results indicate that the combined effects of reduced summer and winter precipitation on soil respiration and N dynamics may be limited across the range of conditions that occurred in our study.
Impact of Different Combinations of Green Infrastructure Elements on Traffic-Related Pollutant Concentrations in Urban Areas
Authors: Santiago Jose-Luis , Rivas Esther , Sanchez Beatriz , Buccolieri Riccardo , Esposito Antonio , Martilli Alberto , Vivanco Marta G. , Martin Fernando
Published: July 2022

Urban air quality is a major problem for human health and green infrastructure (GI) is one of the potential mitigation measures used. However, the optimum GI design is still unclear. The purpose of this study is to provide some recommendation that could help in the design of the GI (mainly, the selection of locations and characteristics of trees and hedgerows). Aerodynamic and deposition effects of each vegetation element of different GI scenarios are investigated. Computational fluid dynamics (CFD) simulations of a wide set of GI scenarios in an idealized three-dimensional urban environment are performed. In conclusion, it was found that trees in the middle of the avenue (median strip) reduce street ventilation, and traffic-related pollutant concentrations increase, in particular for streets parallel to the wind. Trees in the sidewalks act as a barrier for pollutants emitted outside, specifically for a 45° wind direction. Regarding hedgerows, the most important effect on air quality is deposition and the effects of green walls and green roofs are limited to their proximity to the building surfaces.
Enhancement of 2,3-Butanediol Production by Klebsiella pneumoniae: Emphasis on the Mediation of sRNA-SgrS on the Carbohydrate Utilization
Authors: Sun Rui , Kang Jie , Wang Xuemeng , Xiu Baolin , Ping Wenxiang , Ge Jingping
Published: July 2022

The demand for renewable energy is increasing. Klebsiella pneumoniae is one of the most promising strains to produce 2,3-butanediol (2,3-BD). Compared with chemical methods, the biological production of 2,3-BD has the characteristics of substrate safety, low cost, and low energy consumption. However, excessive glucose concentrations can cause damage to cells. Therefore, this study investigated the effect of sRNA-SgrS as a sugar transport regulator on the fermentative production of 2,3-BD by K. pneumoniae in response to sugar stress. We designed multiple mutants of K. pneumoniae HD79 to redistribute its carbon flux to produce 2,3-BD. It was found that the 2,3-BD yield of sgrS overexpressed strain decreased by 44% compared with the original strain. The results showed that a high concentration of sRNA-SgrS could accelerate the degradation of ptsG mRNA (encoding the glucose transporter EIICBGlc) and downregulate the expression levels of the budA gene (encoding the α-acetyllactate decarboxylase) and the budB gene (encoding the α-acetyllactate synthase) and budC gene (encoding the 2,3-BD dehydrogenase) but had no effect on the ack gene (encoding the acetate kinase) and the ldh gene (encoding the lactate dehydrogenase). It provides a theoretical basis and a technical reference for understanding the complex regulation mechanism of sRNA in microorganisms and the genetics and breeding in industrial fermentation engineering.
Continuous Fermentation by Lactobacillus bulgaricus T15 Cells Immobilized in Cross-Linked F127 Hydrogels to Produce ᴅ-Lactic Acid
Authors: Guo Yongxin , Wang Gang , Chen Huan , Zhang Sitong , Li Yanli , Guo Mingzhu , Liu Juan , Chen Guang
Published: July 2022

Lignocellulose biorefinery via continuous cell-recycle fermentation has long been recognized as a promising alternative technique for producing chemicals. ᴅ-lactic acid (D-LA) production by fermentation of corn stover by Lactobacillus bulgaricus was proven to be feasible by a previous study. However, the phenolic compounds and the high glucose content in this substrate may inhibit cell growth. The immobilization of cells in polymer hydrogels can protect them from toxic compounds in the medium and improve fermentation efficiency. Here, we studied the production of D-LA by L. bulgaricus cells immobilized in cross-linkable F127 bis-polyurethane methacrylate (F127-BUM/T15). The Hencky stress and Hencky strain of F127-BUM/T15 was 159.11 KPa and 0.646 respectively. When immobilized and free-living cells were cultured in media containing 5-hydroxymethylfurfural, vanillin, or high glucose concentrations, the immobilized cells were more tolerant, produced higher D-LA yields, and had higher sugar-to-acid conversion ratios. After 100 days of fermentation, the total D-LA production via immobilized cells was 1982.97 ± 1.81 g with a yield of 2.68 ± 0.48 g/L h, which was higher than that of free cells (0.625 ± 0.28 g/L h). This study demonstrated that F127-BUM/T15 has excellent potential for application in the biorefinery industry.
The Significance of Multi-Size Carbon Fibers on the Mechanical and Fracture Characteristics of Fiber Reinforced Cement Composites
Authors: Abdellatef Mohammed , Heras Murcia Daniel , Hogancamp Joshua , Matteo Edward , Stormont John , Taha Mahmoud M. Reda
Published: July 2022

One of the main challenges of using a high fiber volume content in a cement composite is the narrow margin of fiber volume content beyond which fibers can cause an adverse effect on the mechanical properties. In this paper, the significance of fiber size distribution and fiber volume content of different proportions of chopped and milled carbon microfibers are investigated. The mixes’ flowability showed improvement with altering the fiber size distribution despite having a high fiber content. Uniaxial compression cylinders and unnotched and notched beams were cast and then tested at 7 and 28 days of age. It was found that the compressive strength is significantly affected by fiber size distribution more than fiber volume content. On the other hand, the modulus of rupture and fracture toughness are proportional to the fiber volume content with little effect of fiber size distribution. Finally, neither high fiber volume content nor altered fiber size distribution significantly affected the elastic modulus of the fiber cement composites.
The Impact of Atmospheric Plasma/UV Laser Treatment on the Chemical and Physical Properties of Cotton and Polyester Fabrics
Authors: Ayesh Maram , Horrocks A. Richard , Kandola Baljinder K.
Published: July 2022

Atmospheric plasma treatment can modify fabric surfaces without affecting their bulk properties. One recently developed, novel variant combines both plasma and UV laser energy sources as a means of energising fibre surfaces. Using this system, the two most commonly used fibres, cotton and polyester, have been studied to assess how respective fabric surfaces were influenced by plasma power dosage, atmosphere composition and the effects of the presence or absence of UV laser (308 nm XeCl) energy. Plasma/UV exposures caused physical and chemical changes on both fabric surfaces, which were characterised using a number of techniques including scanning electron microscopy (SEM), radical scavenging (using 2,2-diphenyl-1-picrylhydrazyl (DPPH)), thermal analysis (TGA/DTG, DSC and DMA), electron paramagnetic resonance (EPR) and X-ray photoelectron spectroscopy (XPS). Other properties studied included wettability and dye uptake. Intermediate radical formation, influenced by plasma power and presence or absence of UV, was key in determining surface changes, especially in the presence of low concentrations of oxygen or carbon dioxide (20%) mixed with either nitrogen or argon. Increased dyeability with methylene blue indicated the formation of carboxyl groups in both exposed cotton and polyester fabrics. In the case of polyester, thermal analysis suggested increased cross-linking had occurred under all conditions.
Turbulence Modulation by Slender Fibers
Authors: Di Giusto Davide , Marchioli Cristian
Published: July 2022

In this paper, we numerically investigate the turbulence modulation produced by long flexible fibres in channel flow. The simulations are based on an Euler–Lagrangian approach, where fibres are modelled as chains of constrained, sub-Kolmogorov rods. A novel algorithm is deployed to make the resolution of dispersed systems of constraint equations, which represent the fibres, compatible with a state-of-the-art, Graphics Processing Units-accelerated flow-solver for direct numerical simulations in the two-way coupling regime on High Performance Computing architectures. Two-way coupling is accounted for using the Exact Regularized Point Particle method, which allows to calculate the disturbance generated by the fibers on the flow considering progressively refined grids, down to a quasi-viscous length-scale. The bending stiffness of the fibers is also modelled, while collisions are neglected. Results of fluid velocity statistics for friction Reynolds number of the flow Reτ=150 and fibers with Stokes number St = 0.01 (nearly tracers) and 10 (inertial) are presented, with special regard to turbulence modulation and its dependence on fiber inertia and volume fraction (equal to ϕ=2.12·10−5 and 2.12·10−4). The non-Newtonian stresses determined by the carried phase are also displayed, determined by long and slender fibers with fixed aspect ratio λtot=200, which extend up to the inertial range of the turbulent flow.
A Minimally Invasive Approach for Preventing White Wine Protein Haze by Early Enzymatic Treatment
Authors: Benucci Ilaria , Lombardelli Claudio , Muganu Massimo , Mazzocchi Caterina , Esti Marco
Published: July 2022

Protein stability in bottled white wine is an essential organoleptic property considered by consumers. In this paper, the effectiveness of an early enzymatic treatment was investigated by adding a food-grade microbial protease at two different stages of winemaking: (i) at cold settling, for a short-term and low temperature (10 °C) action prior to alcoholic fermentation (AF); (ii) at yeast inoculum, for a long-lasting and medium temperature (18 °C) action during AF. The results reveal that protease sufficiently preserved its catalytic activity at both operational conditions: 10 °C (during cold settling) and 18 °C (during AF). Furthermore, protease addition (dosage 50–150 μL/L) raised the alcoholic fermentation rate. The treatment at yeast inoculum (dosage 50 μL/L) had a remarkable effect in preventing haze formation, as revealed by its impact on protein instability and haze-active proteins. This minimally invasive, time and resource-saving enzymatic treatment, integrated into the winemaking process, could produce stable white wine without affecting color quality and phenol content.
Development of Loop-Mediated Isothermal Amplification (LAMP) Assays for the Rapid Authentication of Three Swimming Crab Species
Authors: Benjakul Soottawat , Saetang Jirakrit
Published: July 2022

Blue swimming crab meat is easily adulterated by other crab meats with a lower price. A potential authentication method is required to prevent mislabeling. LAMP assays were established to identify the meat of blue swimming crab, crucifix crab, and three spotted swimming crab. The primers were designed using PrimerExplorer V5. The specificity of the LAMP assay was tested compared to the PCR method. The sensitivity was conducted at the DNA concentrations of 0.4–50 ng/reaction. The results demonstrated that both LAMP and PCR could discriminate all species of crabs. LAMP showed a superior sensitivity to PCR in the three spotted swimming crab, while a similar result between LAMP and PCR was obtained in blue swimming crab. No changes in the detection efficacy were attained when boiled and steamed crab meats were applied. Therefore, the LAMP assay developed could potentially be applicable to detect the adulteration or mislabeling of raw or cooked crab meat in markets.
Elaboration of Cookies Using Oils and Flours from Seeds and Nuts: Effects on Technological, Nutritional and Consumer Aspects
Authors: Martínez Elena , Álvarez-Ortí Manuel , Rabadán Adrián , Millán Cristina , Pardo José E.
Published: July 2022

The consumption of cookies is widely extended throughout the world, although their formulas contain ingredients such as saturated fats or refined flours that are considered harmful to health. In addition, cookies are generally made from wheat flour, and nowadays there is a growing concern about gluten intolerance, thus the demand for gluten-free products is increasing. In this regard, the aim of the present study is to reformulate traditional cookies by replacing wheat flour and butter by flours and oils from nuts and seeds. Within these seeds, poppy or chia are not commonly consumed ingredients as they can cause rejection by consumers. Thus, a study was performed to evaluate the neophobia level of consumers and the consumer acceptance for the inclusion of these novel ingredients in cookies. The results have been obtained by measuring physical parameters, proximate composition and consumer evaluation of five batches of cookies. By replacing butter and wheat flour with maize flour, almond, walnut, chia or poppy seed flours and oils, an increase of protein, fat and fiber has been observed as well as a decrease in carbohydrate content; thus, the resultant cookies would be a good source of vegetal protein as well as a source of oleic and linoleic acid with potential benefits on health. The cookies in general have similar physical properties and a positive consumer acceptance in texture, taste and external aspect. The Food Neophobia Scale results suggest that non-neophobic consumers gave higher scores than neophobic consumers in all the parameters. The resultant product would be a functional product able to substitute traditional ones not only directed to celiac people but all type of consumers because of their beneficial composition.
Active Antioxidant Packaging from Essential Oils Incorporated Polylactic Acid/Poly(butylene adipate-co-terephthalate)/Thermoplastic Starch for Preserving Straw Mushroom
Authors: Gui Hang , Zhao Meiyan , Zhang Shiqi , Yin Ruoyu , Hu Changying , Fan Min , Li Li
Published: July 2022

The short-term shelf life of straw mushrooms (Volvariella volvacea) is a major challenge, hampering their wide distribution. Thus, the aim of this work was to develop a novel active packaging composed of essential oils (EOs), particularly clove oil (CO) and peppermint oil (PO), to reduce autolysis of straw mushrooms. The morphological characterizations, mechanical properties, barrier properties, and antioxidant activities of the films were characterized. The suppressive effects of the EOs on straw mushroom autolysis were estimated during storage at 16 ± 1 °C for 96 h. The results indicated that the addition of EOs weakened the mechanical and barrier properties of the films. The radical-scavenging activities of polylactic acid/poly(butylene adipate-co-terephthalate)/thermoplastic starch-peppermint oil (PLA/PBAT/TPS-PO) and polylactic acid/poly(butylene adipate-co-terephthalate)/thermoplastic starch-clove oil (PLA/PBAT/TPS-CO) films for 2,2-diphenyl-1-picrylhydrazyl were 56.0% and 91.3%, respectively. However, the PLA/PBAT/TPS-PO film was more effective in reducing polyphenol oxidase activity and maintaining the total phenol content of straw mushrooms, demonstrating better antioxidative activity. Mushrooms packaged with the PLA/PBAT/TPS-PO film exhibited the lowest autolysis rate (42.29%, p < 0.05) after 96 h of storage. Thus, PO is a good preservative agent for straw mushroom.
Isolation and Identification of Lactococcus lactis and Weissella cibaria Strains from Fermented Beetroot and an Investigation of Their Properties as Potential Starter Cultures and Probiotics
Authors: Maślak Ewelina , Złoch Michał , Arendowski Adrian , Sugajski Mateusz , Janczura Izabela , Rudnicka Joanna , Walczak-Skierska Justyna , Buszewska-Forajta Magdalena , Rafińska Katarzyna , Pomastowski Paweł , Białczak Dorota , Buszewski Bogusław
Published: July 2022

The presence of certain microorganisms in dairy products or silage is highly desirable. Among them are probiotic strains of lactic acid bacteria (LAB), which show many beneficial features, including antimicrobial properties that support the development of beneficial microflora; in addition, owing to their biochemical activity, they influence the nutritional, dietary, and organoleptic properties of food products. Before being placed on the market, each strain requires separate testing to determine its probiotic properties and effectiveness. The aim of this study was to isolate LAB strains from a pickled beetroot sample that could be used in the dairy industry and with the potential to be considered as a probiotic in the future. Two strains identified using the MALDI technique were selected—Lactococcus lactis and Weissella cibaria. The optimal growth conditions of the strains were determined, and their proteolytic properties were assessed with the use of the o-PA reagent and spectrophotometry. The lipid profile was analyzed using the SALDI (surface-assisted laser desorption/ionization) technique and silver nanoparticles. High-performance liquid chromatography was used to assess the ability of the strains to synthesize beneficial metabolites, such as B vitamins (B2, B3, and B9) or lactic acid, and gas chromatography was used to analyze the substances responsible for organoleptic properties. Moreover, the ability to inhibit the growth of pathogenic strains was also tested in the selected strains. Both tested strains demonstrated the desired properties of starter cultures for future use in functional food production, showing that fermented plant products can serve as valuable potential probiotic sources.