bluuebunny/crossref_metadata_2025 huggingface dataset:
Trends in Autism Spectrum-Related Motherhood Research: A Bibliometric Study
Authors: Hernández-González Osvaldo , González-Fernández Daniela , Spencer-Contreras Rosario , Tárraga-Mínguez Raúl , Ponce-Carrasco Viviana
Published: February 2023

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by difficulties in socialization. The amount of scientific research results on motherhood related to ASD has grown exponentially; however, there are no bibliometric studies in this field. Objective: This article aimed to analyze scientific research on motherhood related to the autism spectrum published in WoS. Articles on motherhood related to ASD were retrieved from the Web of Science (WoS). The advanced search interface used was “Title of the article, Abstract, Keywords”. The analysis and visualization of the selected documents and their data were performed using a wide range of tools and software such as MS Excel (v16.0), VOS viewer (version 1.6.15), and R packages (Biblioshiny, version 2.0). A total of 1660 articles were included in this study. Most of the publications were original articles. The United States published the most significant number of articles among the countries identified. P.R. Hastings, M.M. Seltzer, and J. Van de Water were the main authors. The Journal of Autism and Developmental Disorders was the most productive and impactful journal. The main research topics were related to mental health and social support in the role of motherhood. This desk study provides researchers with a comprehensive understanding of ASD-related maternity research trends by evaluating relevant publications in recent decades. The results of this bibliometric analysis can serve as a basis and orientation for new studies.
Research and Application of Generative-Adversarial-Network Attacks Defense Method Based on Federated Learning
Authors: Ma Xiaoyu , Gu Lize
Published: February 2023

In recent years, Federated Learning has attracted much attention because it solves the problem of data silos in machine learning to a certain extent. However, many studies have shown that attacks based on Generative Adversarial Networks pose a great threat to Federated Learning. This paper proposes Defense-GAN, a defense method against Generative Adversarial Network attacks under Federated Learning. Under this method, the attacker cannot learn the real image data distribution. Each Federated Learning participant uses SHAP to explain the model and masks the pixel features that have a greater impact on classification and recognition in their respective image data. The experimental results show that while attacking the federated training model using masked images, the attacker cannot always obtain the ground truth of the images. At the same time, this paper also uses CutMix to improve the generalization ability of the model, and the obtained model accuracy is only 1% different from that of the model trained with the original data. The results show that the defense method proposed in this paper can not only resist Generative Adversarial Network attacks in Federated Learning and protect client privacy, but also ensure that the model accuracy of the Federated model will not be greatly affected.
Music Emotion Recognition Based on a Neural Network with an Inception-GRU Residual Structure
Authors: Han Xiao , Chen Fuyang , Ban Junrong
Published: February 2023

As a key field in music information retrieval, music emotion recognition is indeed a challenging task. To enhance the accuracy of music emotion classification and recognition, this paper uses the idea of inception structure to use different receptive fields to extract features of different dimensions and perform compression, expansion, and recompression operations to mine more effective features and connect the timing signals in the residual network to the GRU module to extract timing features. A one-dimensional (1D) residual Convolutional Neural Network (CNN) with an improved Inception module and Gate Recurrent Unit (GRU) was presented and tested on the Soundtrack dataset. Fast Fourier Transform (FFT) was used to process the samples experimentally and determine their spectral characteristics. Compared with the shallow learning methods such as support vector machine and random forest and the deep learning method based on Visual Geometry Group (VGG) CNN proposed by Sarkar et al., the proposed deep learning method of the 1D CNN with the Inception-GRU residual structure demonstrated better performance in music emotion recognition and classification tasks, achieving an accuracy of 84%.
Analysis of Disparities in Internet Purchases by Individuals at the EU State Level
Authors: Bălăcescu Aniela , Pătrașcu Aurelia , Tănăsescu Ana
Published: February 2023

In recent years, the trend of digital transformation of commerce has been extremely dynamic, and the COVID-19 pandemic has generated a strong impetus for the development of digitalization. However, there is a large difference in the use of e-commerce in European Union (EU) countries. The objective of this article is to explain the complex evolution of e-commerce in terms of B2C using econometric methods, especially regarding Internet purchases made by individuals according to six social and technological factors considered basic factors in purchase decisions. Thus, we propose a conceptual model for the analysis of disparities in Internet purchases, taking into account both factors related to demand (consumer) and factors related to supply (supplier). Based on the data provided by Eurostat for all 27 EU countries for the period 2012–2021, the empirical findings of cluster analysis suggest changes in the grouping of countries at the beginning and end of the analysed period. Socioeconomic factors that can explain the transition of EU states from one group to another in terms of development level of Internet purchases by individuals were identified. The results of this study can serve as a starting point for future adaptation strategies of e-commerce in the context of global economic development with a focus on increasing digitisation, the existence of for which a digital convergence in the process of digital transformation is essential.
An Image Denoising Method for Arc-Scanning SAR for Airport Runway Foreign Object Debris Detection
Authors: Wang Yuming , Huang Haifeng , Wang Jian , Wang Pengyu , Song Qian
Published: February 2023

Arc-scanning synthetic aperture radar (AS-SAR) is an emerging technical means for detecting foreign object debris (FOD). Most FOD are small and appear as weak targets with a low signal-to-noise ratio (SNR) in AS-SAR images. Therefore, image noise is a fundamental challenge in detecting FOD on airport runways that leads to many false alarms. A weak scattering denoising method is proposed to aim at the noise caused by speckle and rough surface scattering. To enhance FOD detection, a transformation parameter concept is offered and adopted, which has different characteristics for the target and background. This paper estimates the transformation parameter through logarithms, normalization, and morphological erosion and optimizes them with edge-preserving filtering. The results show that despeckling and runway scattering suppression can be simultaneously implemented, and that field experiments validate the performance of this method.
A Model-Based Approach for Setting the Initial Angle of the Drive Axles in a 4 × 4 High Mobility Wheeled Vehicle
Authors: Kosobudzki Mariusz , Zajac Pawel , Gardyński Leszek
Published: February 2023

This article presents an analysis of the driveline operation of a high-mobility Jelcz 442.32 wheeled vehicle, which uses rigid drive axles connected to drive shafts with two universal joints (another name for the Cardan joints) due to the occurrence of kinematic incompatibility. The conditions for the correct connection of the drive shafts with two universal joints (Cardan joints) were presented, and the kinematic ratio of the complete drive shaft was defined. In the analysis of kinematic incompatibility regarding (but not limited to) the method of loading the vehicle, selected characteristic conditions of vehicle movement and the initial values of the angular setting of the rigid driving axles in relation to the vehicle body were presented. It has been shown that, in the analyzed vehicle, the kinematic incompatibility in the driveline is constantly present, and the value of this incompatibility, represented by the temporary ratio of drive shafts, depends on, among other things, the ways of loading the vehicle, the existing conditions of vehicle movement, and the type of ground. Moreover, the value of the arising kinematic incompatibility was noticed to be highly influenced by the correct manufacturing and assembly of the vehicle springs.
Green Innovation through Green and Blue Infrastructure Development: Investigation of Pollution Reduction and Green Technology in Emerging Economy
Authors: Nassani Abdelmohsen A. , Yousaf Zahid , Radulescu Magdalena , Balsalobre-Lorente Daniel , Hussain Hadi , Haffar Mohamed
Published: February 2023

Green and blue infrastructure has gained importance for energy firms around the globe due to increased focus on addressing emerging challenges raised in the achievement of green innovation (GI) and the protection of the environment. Pollution reduction and green technology are key factors that help to accomplish GI. The aim of the current research was to examine how green and blue infrastructure and green technologies impact and improve energy firms’ business GI in Pakistan. Quantitative methods were used for the collection of data from 475 managers, policymakers, senior employees, owners, and CEOs. Findings demonstrated that green and blue infrastructure and GI were positively linked. The results also showed that pollution reduction played a mediating role between blue infrastructure and GI. Furthermore, findings also revealed that green technology played a moderating role between green and blue infrastructure and GI. Pollution reduction is a crucial factor in developing the GI performance of energy firms. These new outcomes extend green and blue infrastructure and GI literature and provide empirical proof for practitioners and policymakers. Our study suggests that green technology positively influences green and blue infrastructure, which in turn is associated with GI. This study is based on critical factors that have encouraging impacts on GI.
Evaluation of Machine Learning Algorithms for Supervised Anomaly Detection and Comparison between Static and Dynamic Thresholds in Photovoltaic Systems
Authors: Klinsuwan Thitiphat , Ratiphaphongthon Wachiraphong , Wangkeeree Rabian , Wangkeeree Rattanaporn , Sirisamphanwong Chatchai
Published: February 2023

The use of photovoltaic systems has increased in recent years due to their decreasing costs and improved performance. However, these systems can be susceptible to faults that can reduce efficiency and energy yield. To prevent and reduce these problems, preventive or predictive maintenance and effective monitoring are necessary. PV health monitoring systems and automatic fault detection and diagnosis methods are critical for ensuring PV plants’ reliability, high-efficiency operation, and safety. This paper presents a new framework for developing fault detection in photovoltaic (PV) systems. The proposed approach uses machine learning algorithms to predict energy power production and detect anomalies in PV plants by comparing the predicted power from a model and the measured power from sensors. The framework utilizes historical data to train the prediction model, and live data is compared with predicted values to analyze residuals and detect abnormal scenarios. The proposed approach has been shown to accurately distinguish anomalies using constructed thresholding, either static or dynamic thresholds. The paper also reports experimental results using the Matthews correlation coefficient, a more reliable statistical rate for an imbalanced dataset. The proposed approach leads to a reasonable anomaly detection rate, with an MCC of 0.736 and a balanced ACC of 0.863.
Performance Prediction of Plate-Finned Tube Heat Exchangers for Refrigeration: A Review on Modeling and Optimization Methods
Authors: Macchitella Silvia , Colangelo Gianpiero , Starace Giuseppe
Published: February 2023

Finned tube heat exchangers are used in many technological applications in both civil and industrial sectors. Their large-scale use requires a design aimed at reaching high thermal efficiency as well as avoiding unnecessary waste of resources in terms of time and costs. Therefore, in the last decades, research in this area has developed considerably and numerous studies have been conducted on modeling in order to predict heat exchangers (HXs) performance and to optimize design parameters. In this paper, the main studies carried out on plate-finned tube HXs have been collected, analyzed, and summarized, classifying existing models by their scale approach (small, large, or multi-scale). In addition, the main methods of design optimization with a focus on circuitry configurations have been illustrated. Finally, future developments and research areas that need more in-depth analysis have been identified and discussed.
Practical Nonlinear Model Predictive Control for Improving Two-Wheel Vehicle Energy Consumption
Authors: Bello Yesid , Roncancio Juan Sebastian , Azib Toufik , Patino Diego , Larouci Cherif , Boukhnifer Moussa , Rizoug Nassim , Ruiz Fredy
Published: February 2023

Increasing the range of electric vehicles (EVs) is possible with the help of eco-driving techniques, which are algorithms that consider internal and external factors, like performance limits and environmental conditions, such as weather. However, these constraints must include critical variables in energy consumption, such as driver preferences and external vehicle conditions. In this article, a reasonable energy-efficient non-linear model predictive control (NMPC) is built for an electric two-wheeler vehicle, considering the Paris-Brussels route with different driving profiles and driver preferences. Here, NMPC is successfully implemented in a test bed, showing how to obtain the different parameters of the optimization problem and the estimation of the energy for the closed-loop system from a practical point of view. The efficiency of the brushless DC motor (BLCD) is also included for this test bed. In addition, this document shows that the proposal increases the chance of traveling the given route with a distance accuracy of approximately 1.5% while simultaneously boosting the vehicle autonomy by almost 20%. The practical result indicates that the strategy based on an NMPC algorithm can significantly boost the driver’s chance of completing the journey. If the vehicle energy is insufficient to succeed in the trip, the algorithm can guide the minimal State of Charge (SOC) required to complete the journey to reduce the driver energy-related uncertainty to a minimum.
A Comprehensive Assessment of Two-Phase Flow Boiling Heat Transfer in Micro-Fin Tubes Using Pure and Blended Eco-Friendly Refrigerants
Authors: Vidhyarthi Neeraj Kumar , Deb Sandipan , Gajghate Sameer Sheshrao , Pal Sagnik , Das Dipak Chandra , Das Ajoy Kumar , Saha Bidyut Baran
Published: February 2023

This review study examines flow boiling heat transfer in micro-fin tubes using mixed and pure refrigerants with zero ozone-depleting potential (ODP) and minimal global warming potential (GWP). This investigation focuses on the extraordinary relationship between heat transfer coefficients (HTCs) and vapor quality. Since the introduction of micro-fin heat exchanger tubes over 30 years ago, refrigerant-based cooling has improved significantly. Air conditioning and refrigeration companies are replacing widely used refrigerants, with substantial global warming impacts. When space, weight, or efficiency are limited, micro-fin heat exchangers with improved dependability are preferred. This review article discusses flow boiling concepts. The researchers used several refrigerants under different testing conditions and with varying micro-fin tube parameters. Micro-fin tubes are promising for improved heat transfer techniques. This tube increases the heat transfer area, fluid disturbance, flow speed, and direction owing to centrifugal force and HTC. As the focus shifts to improving heat transfer, pressure drop, mean vapor quality, and practical devices, this subject will grow more intriguing. A radical shift will reduce equipment size for certain traditional heat transfer systems and bring new products using micro-scale technologies. This suggested review effort helps comprehend saturation flow boiling through micro-fin tubes and find the right correlation for a given application. This domain’s challenges and future relevance are also discussed.
Numerical 3D Model of a Novel Photoelectrolysis Tandem Cell with Solid Electrolyte for Green Hydrogen Production
Authors: Giacoppo Giosuè , Trocino Stefano , Lo Vecchio Carmelo , Baglio Vincenzo , Díez-García María I. , Aricò Antonino Salvatore , Barbera Orazio
Published: February 2023

The only strategy for reducing fossil fuel-based energy sources is to increase the use of sustainable ones. Among renewable energy sources, solar energy can significantly contribute to a sustainable energy future, but its discontinuous nature requires a large storage capacity. Due to its ability to be produced from primary energy sources and transformed, without greenhouse gas emissions, into mechanical, thermal, and electrical energy, emitting only water as a by-product, hydrogen is an effective carrier and means of energy storage. Technologies for hydrogen production from methane, methanol, hydrocarbons, and water electrolysis using non-renewable electrical power generate CO2. Conversely, employing photoelectrochemistry to harvest hydrogen is a sustainable technique for sunlight-direct energy storage. Research on photoelectrolysis is addressed to materials, prototypes, and simulation studies. From the latter point of view, models have mainly been implemented for aqueous-electrolyte cells, with only one semiconductor-based electrode and a metal-based counter electrode. In this study, a novel cell architecture was numerically modelled. A numerical model of a tandem cell with anode and cathode based on metal oxide semiconductors and a polymeric membrane as an electrolyte was implemented and investigated. Numerical results of 11% solar to hydrogen conversion demonstrate the feasibility of the proposed novel concept.
An Automated and Interpretable Machine Learning Scheme for Power System Transient Stability Assessment
Authors: Liu Fang , Wang Xiaodi , Li Ting , Huang Mingzeng , Hu Tao , Wen Yunfeng , Su Yunche
Published: February 2023

Many repeated manual feature adjustments and much heuristic parameter tuning are required during the debugging of machine learning (ML)-based transient stability assessment (TSA) of power systems. Furthermore, the results produced by ML-based TSA are often not explainable. This paper handles both the automation and interpretability issues of ML-based TSA. An automated machine learning (AutoML) scheme is proposed which consists of auto-feature selection, CatBoost, Bayesian optimization, and performance evaluation. CatBoost, as a new ensemble ML method, is implemented to achieve fast, scalable, and high performance for online TSA. To enable faster deployment and reduce the heavy dependence on human expertise, auto-feature selection and Bayesian optimization, respectively, are introduced to automatically determine the best input features and optimal hyperparameters. Furthermore, to help operators understand the prediction of stable/unstable TSA, an interpretability analysis based on the Shapley additive explanation (SHAP), is embedded into both offline and online phases of the AutoML framework. Test results on IEEE 39-bus system, IEEE 118-bus system, and a practical large-scale power system, demonstrate that the proposed approach achieves more accurate and certain appropriate trust solutions while saving a substantial amount of time in comparison to other methods.
Open-Circuit Fault Detection and Location in AC-DC-AC Converters Based on Entropy Analysis
Authors: Morel Cristina , Akrad Ahmad
Published: February 2023

Inverters and converters contain more and more power electronics switches which may subsequently affect their reliability. Therefore, fault detection and location are essential to improve their reliability and to ensure continuous operation. In this paper, an AC−DC−AC converter with three-phase inverter is investigated under permanent, single and multiple open-circuit fault scenarios. Many entropies and multiscale entropies are then proposed to evaluate the complexity of the output currents by quantifying their entropies over a range of temporal scales. Among the multitude of entropies, only some entropies are able to differentiate healthy from open-circuit faulty conditions. Moreover, the simulation results show that these entropies are able to detect and locate the arms of the bridge with open-circuit faults.
Investigation of the Mechanical Behavior of a New Generation Wind Turbine Blade Technology
Authors: Çiftci Cihan , Erdoğan Ayşe , Genç Mustafa Serdar
Published: February 2023

Wind turbine blades are one of the largest parts of wind power systems. It is a handicap that these large parts of numerous wind turbines will become scrap in the near future. To prevent this handicap, newly produced blades should be recyclable. In this study, a turbine blade, known as the new generation of turbine blade, was manufactured with reinforced carbon beams and recycled, low-density polyethylene materials. The manufacturing addressed in this study reveals two novelties: (1) it produces a heterogeneous turbine blade; and (2) it produces a recyclable blade. In addition, this study also covers mechanical tests using a digital image correlation (DIC) system and modeling investigations of the new generation blade. For the mechanical tests, displacement and strain data of both new generation and conventional commercial blades were measured by the DIC method. Instead of dealing with the modeling difficulty of the new generation blade’s heterogeneity we modeled the blade structural system as a whole using the moment–curvature method as part of the finite element method. Then, the behavior of both the new generation and commercial blades at varying wind speeds and different angles of attack were compared. Consequently, the data reveal that the new generation blades performed sufficiently well compared with commercial blades regarding their stiffness.
Decay Branch Ratio Sampling Method with Dirichlet Distribution
Authors: Wang Yizhen , Cui Menglei , Guo Jiong , Zhang Han , Wu Yingjie , Li Fu
Published: February 2023

The decay branch ratio is evaluated nuclear data related to the decay heat calculation in reactor safety analysis. Decay branch ratio data are inherently subjected to the “sum-to-one” constraint, making it difficult to generate perturbed samples while preserving their suggested statistics in a library of evaluated nuclear data. Therefore, a stochastic-sampling-based uncertainty analysis method is hindered in quantifying the uncertainty contribution of the decay branch ratio to the decay heat calculation. In the present work, two alternative sampling methods are introduced, based on Dirichlet and generalized Dirichlet distribution, to tackle the decay branch ratio sampling issue. The performance of the introduced methods is justified by three-branch decay data retrieved from ENDF/B-VIII.0. The results show that the introduced sampling methods are capable of generating branch ratio samples and preserving their suggested statistics in an evaluated nuclear data library while satisfying their inherent “sum-to-one” constraint. These decay-branch-ratio sampling methods are expected to be alternative procedures in conducting stochastic-sampling-based uncertainty analyses of the decay branch ratio in reactor simulations.
An Adaptive, Data-Driven Stacking Ensemble Learning Framework for the Short-Term Forecasting of Renewable Energy Generation
Authors: Huang Hui , Zhu Qiliang , Zhu Xueling , Zhang Jinhua
Published: February 2023

With the increasing integration of wind and photovoltaic power, the security and stability of the power system operations are greatly influenced by the intermittency and fluctuation of these renewable sources of energy generation. The accurate and reliable short-term forecasting of renewable energy generation can effectively reduce the impacts of uncertainty on the power system. In this paper, we propose an adaptive, data-driven stacking ensemble learning framework for the short-term output power forecasting of renewable energy. Five base-models are adaptively selected via the determination coefficient (R2) indices from twelve candidate models. Then, cross-validation is used to increase the data diversity, and Bayesian optimization is used to tune hyperparameters. Finally, base modes with different weights determined by minimizing the cross-validation error are ensembled using a linear model. Four datasets in different seasons from wind farms and photovoltaic power stations are used to verify the proposed model. The results illustrate that the proposed stacking ensemble learning model for renewable energy power forecasting can adapt to dynamic changes in data and has better prediction precision and a stronger generalization performance compared to the benchmark models.
Applications of Electric Vehicles in Instant Deliveries
Authors: Galindo-Muro Ana Bricia , Cespi Riccardo , Vallarta-Serrano Stephany Isabel
Published: February 2023

Big cities affected by intense mobility, traffic and pollution are adopting electrification-based solutions for the reduction of the CO2 emissions of combustion engines. An interesting field in which the transition toward electrification can achieve important benefits is the area of instant deliveries. Instant deliveries deal with the mobility related to commercial trades between suppliers and customers. In this respect, optimal solutions can be considered during route planning based on the minimization of several metrics, such as distance, energy and road slope, among others. To this end, this paper presents an optimal solution to the instant deliveries problem in which the result is the optimal route, in the city under study, that minimizes energy consumption based on road slope and total distance traveled, and that gives higher priority to routes that include cycling infrastructure that the city can provide. The paper uses electric bikes since they are easily transportable and are highly versatile for instant deliveries. The results obtained were compared to a previous version of the optimal algorithm already published by the authors which minimizes the Haversine and Euclidian distances only. It was found that the shortest distance travelled between customers does not necessarily imply the least energy consumption. The latter, in combination with an energy consumption estimation approach, represent the original contribution of the work.
New Pump-In Flowback Model Verification with In-Situ Strain Measurements and Numerical Simulation
Authors: Eltaleb Ibrahim , Soliman Mohamed Y.
Published: February 2023

This study presents an analytical model for estimating minimum horizontal stress in hydraulic fracturing stimulations. The conventional Diagnostic Fracture Injection Test (DFIT) is not practical in ultra-tight formations, leading to the need for pump-in/flowback tests. However, ambiguities in the results of these tests have limited their application. The proposed model is based on the linear diffusivity equation and material balance, which is analytically solved and verified using a commercially available numerical simulator. The model generates a linear graph in which the pressure drop and its derivative are plotted versus the developed solution time function. The closure pressure is determined when the slope of the derivative deviates from linearity. The model was applied to several cycles of field flowback tests and found to eliminate the ambiguity associated with identifying the fracture closure. Furthermore, the minimum In-situ stresses estimated using this approach are verified via downhole strain measurement and synthetic data from a fully 3D commercial fracturing simulator. The proposed technique outperformed other conventional methods in analyzing challenging injection/shut-in tests, showing improved results and reducing uncertainty in estimated fracture parameters. This model is expected to scale down the need for multiple field trials and provide a reliable estimation of minimum stress.
Improving Energy Performance in Flexographic Printing Process through Lean and AI Techniques: A Case Study
Authors: Abusaq Zaher , Zahoor Sadaf , Habib Muhammad , Rehman Mudassar , Mahmood Jawad , Kanan Mohammad , Mushtaq Ray
Published: February 2023

Flexographic printing is a highly sought-after technique within the realm of packaging and labeling due to its versatility, cost-effectiveness, high speed, high-quality images, and environmentally friendly nature. A major challenge in flexographic printing is the need to optimize energy usage, which requires diligent attention to resolve. This research combines lean principles and machine learning to improve energy efficiency in selected flexographic printing machines; i.e., Miraflex and F&K. By implementing the 5Why root cause analysis and Kaizen, the study found that the idle time was reduced by 30% for the Miraflex machine and the F&K machine, resulting in energy savings of 34.198% and 38.635% per meter, respectively. Additionally, a multi-linear regression model was developed using machine learning and a range of input parameters, such as machine speed, production meter, substrate density, machine idle time, machine working time, and total machine run time, to predict energy consumption and optimize job scheduling. The results of the research exhibit that the model was efficient and accurate, leading to a reduction in energy consumption and costs while maintaining or even improving the quality of the printed output. This approach can also add to reducing the carbon footprint of the manufacturing process and help companies meet sustainability goals.
Ensemble Machine Learning Techniques for Accurate and Efficient Detection of Botnet Attacks in Connected Computers
Authors: Afrifa Stephen , Varadarajan Vijayakumar , Appiahene Peter , Zhang Tao , Domfeh Emmanuel Adjei
Published: February 2023

The transmission of information, ideas, and thoughts requires communication, which is a crucial component of human contact. The utilization of Internet of Things (IoT) devices is a result of the advent of enormous volumes of messages delivered over the internet. The IoT botnet assault, which attempts to perform genuine, lucrative, and effective cybercrimes, is one of the most critical IoT dangers. To identify and prevent botnet assaults on connected computers, this study uses both quantitative and qualitative approaches. This study employs three basic machine learning (ML) techniques—random forest (RF), decision tree (DT), and generalized linear model (GLM)—and a stacking ensemble model to detect botnets in computer network traffic. The results reveled that random forest attained the best performance with a coefficient of determination (R2) of 0.9977, followed by decision tree with an R2 of 0.9882, while GLM was the worst among the basic machine learning models with an R2 of 0.9522. Almost all ML models achieved satisfactory performance, with an R2 above 0.93. Overall, the stacking ensemble model obtained the best performance, with a root mean square error (RMSE) of 0.0084 m, a mean absolute error (MAE) of 0.0641 m, and an R2 of 0.9997. Regarding the stacking ensemble model as compared with the single machine learning models, the R2 of the stacking ensemble machine learning increased by 0.2% compared to the RF, 1.15% compared to the DT, and 3.75% compared to the GLM, while RMSE decreased by approximately 0.15% compared to the GLM, DT, and RF single machine learning techniques. Furthermore, this paper suggests best practices for preventing botnet attacks. Businesses should make major investments to combat botnets. This work contributes to knowledge by presenting a novel method for detecting botnet assaults using an artificial-intelligence-powered solution with real-time behavioral analysis. This study can assist companies, organizations, and government bodies in making informed decisions for a safer network that will increase productivity.
Effect of Ammonia–Autoclave Pretreatment on the Performance of Corn Straw and Cow Manure Batch Anaerobic Digestion
Authors: Xu Yonghua , Xu Xinrui , Su Xiaohong , Liu Wei , Qu Jingbo , Sun Yong
Published: February 2023

Biomass pretreatment is a critical method for improving the anaerobic digestion (AD) performance of lignocellulosic feedstocks. In this study, an effective combined ammonia–autoclave pretreatment method was selected for the pretreatment of corn straw at 90 °C using four ammonia concentrations (7%, 9%, 11%, and 13%). The results showed that the combined pretreatment improved the substrate’s degradation efficiency and the system’s buffer capacity, and significantly improved the hydrolysis and biogas production performance of corn straw. After pretreatment, the lignin removal rate increased by 11.28–39.69%, and the hemicellulose degradation rate increased from 10.12% to 21.23%. Pretreatment of corn straw with 9% ammonia and an autoclave gave the highest methane yield of 257.11 mL/gVS, which was 2.32-fold higher than that of untreated corn straw, making it the optimal pretreatment condition for corn straw. Therefore, the combined ammonia–autoclave pretreatment technology can further improve the AD performance of corn straw.
Simulation of Low-Temperature Oxidation and Combustion of N-Dodecane Droplets under Microgravity Conditions
Authors: Frolov Sergey M. , Basevich Valentin Y.
Published: February 2023

Fires are considered among the most dangerous accidents on manned spacecraft. That is why several programs of combustion experiments were implemented at the International Space Station (ISS) since 2008. In the experiments with n-heptane and n-dodecane droplet combustion, a new phenomenon was discovered, namely, the phenomenon of the radiative extinction of a burning droplet with subsequent multiple flashes of flame. In this paper, n-dodecane droplet ignition, combustion, radiative extinction, and subsequent low-temperature oxidation with multiple flashes of cool, blue, and hot flames under microgravity conditions are studied computationally. The mathematical model takes into account multiple elementary chemical reactions in the vicinity of a droplet in combination with heat and mass transfer in liquid and gas, heat release, convection, soot formation, and heat removal by radiation. The model is based on the non-stationary one-dimensional differential equations of the conservation of mass and energy in liquid and gas phases with variable thermophysical properties within the multicomponent diffusion concept in the gas phase. Calculations confirm the important role of the soot shell formed around the droplet and low-temperature reactions in the phenomenon of droplet radiative extinction with multiple flame flashes in the space experiment at the ISS. Calculations reveal the decisive role of the blue flame, arising due to the decomposition of hydrogen peroxide, in the multiple flame flashes. Calculations with forced ignition of the droplet reveal the effect of the ignition procedure on droplet evolution in terms of the timing and the number of cool, blue, and hot flame flashes, as well as in terms of the combustion rate constant of the droplet. Calculations with droplet self-ignition reveal the possible existence of new modes of low-temperature oxidation of droplets with the main reaction zone located very close to the droplet surface and with only partial conversion of fuel vapor in it.
Numerical Study of the Effects of Asymmetric Velocity Profiles in a Curvilinear Channel on Migration of Neutral Buoyant Particle
Authors: Naito Ryo , Fukui Tomohiro
Published: February 2023

The microstructure and suspended particle behavior should be considered when studying the flow properties exhibited by particle suspension. In addition, particle migration, also known as Segré–Silberberg effects, alters the microstructure of the suspension and significantly affects the viscosity properties of the suspension. Therefore, particle behavior with respect to the changes in mechanical factors should be considered to better understand suspension. In this study, we investigated the particle behavior in asymmetric velocity profiles with respect to the channel center numerically using the lattice Boltzmann method and a two-way coupling scheme. Our findings confirmed that the final equilibrium position of particles in asymmetric velocity profiles converged differently between the outer and inner wall sides with respect to the channel center. This indicates that the mechanical equilibrium position of particles can be changed by asymmetric velocity profiles. In addition, centrifugal force acting on the particles is also important in the study of equilibrium position. These results suggest that the microstructure and viscosity characteristics of a suspension in a pipe could be handled by changes in velocity profiles.
Emulsion-Based Coatings for Preservation of Meat and Related Products
Authors: Gautam Shweta , Lapčík Lubomír , Lapčíková Barbora , Gál Robert
Published: February 2023

One of the biggest challenges faced by the meat industry is maintaining the freshness of meat while extending its shelf life. Advanced packaging systems and food preservation techniques are highly beneficial in this regard. However, the energy crisis and environmental pollution demand an economically feasible and environmentally sustainable preservation method. Emulsion coatings (ECs) are highly trending in the food packaging industry. Efficiently developed coatings can preserve food, increase nutritional composition, and control antioxidants’ release simultaneously. However, their construction has many challenges, especially for meat. Therefore, the following review focuses on the essential aspects of developing ECs for meat. The study begins by classifying emulsions based on composition and particle size, followed by a discussion on the physical properties, such as ingredient separation, rheology, and thermal characteristics. Furthermore, it discusses the lipid and protein oxidation and antimicrobial characteristics of ECs, which are necessary for other aspects to be relevant. Lastly, the review presents the limitations of the literature while discussing the future trends. ECs fabricated with antimicrobial/antioxidant properties present promising results in increasing the shelf life of meat while preserving its sensory aspects. In general, ECs are highly sustainable and effective packaging systems for meat industries.
Vacuum-Assisted Block Freeze Concentration Studies in Cheese Whey and Its Potential in Lactose Recovery
Authors: Gil Noelia , Quinteros Gisela , Blanco Monica , Samsuri Shafirah , Amran Nurul Aini , Orellana-Palma Patrico , Schwinden Elane , Hernández Eduardo
Published: February 2023

Block freeze concentration (BFC) is considered an emerging technology which allows the acquiring of high quality organoleptic products, due to the low temperatures employed. In this study we have outlined how the vacuum-assisted BFC of whey was investigated. The effects of vacuum time, vacuum pressure, and the initial solids concentration in whey were studied. The results obtained show that the three variables significantly affect each of the following parameters analysed: solute yield (Y) and concentration index (CI). The best Y results were obtained at a pressure of 10 kPa, 7.5 °Bx, and 60 min. For CI parameter, the highest values were given at 10 kPa, 7.5 °Bx, and 20 min, respectively. In a second phase, by applying the conditions that provide higher solute yield to three different types of dairy whey, Y values of 70% or higher are reached in a single step, while that the CI of lactose are higher than those of soluble solids. Therefore, it is possible to recover, in a single step, at least 70% of the lactose contained in the initial whey samples. This suggests that vacuum-assisted BFC technology may be an interesting alternative for the recovery of lactose contained in whey.
Fruit-Based Fermented Beverages: Contamination Sources and Emerging Technologies Applied to Assure Their Safety
Authors: Avîrvarei Alexandra Costina , Salanță Liana Claudia , Pop Carmen Rodica , Mudura Elena , Pasqualone Antonella , Anjos Ofelia , Barboza Natalia , Usaga Jessie , Dărab Cosmin Pompei , Burja-Udrea Cristina , Zhao Haifeng , Fărcaș Anca Corina , Coldea Teodora Emilia
Published: February 2023

The food and beverage market has become broader due to globalization and consumer claims. Under the umbrella of consumer demands, legislation, nutritional status, and sustainability, the importance of food and beverage safety must be decisive. A significant sector of food production is related to ensuring fruit and vegetable conservation and utilization through fermentation. In this respect, in this review, we critically analyzed the scientific literature regarding the presence of chemical, microbiological and physical hazards in fruit-based fermented beverages. Furthermore, the potential formation of toxic compounds during processing is also discussed. In managing the risks, biological, physical, and chemical techniques can reduce or eliminate any contaminant from fruit-based fermented beverages. Some of these techniques belong to the technological flow of obtaining the beverages (i.e., mycotoxins bound by microorganisms used in fermentation) or are explicitly applied for a specific risk reduction (i.e., mycotoxin oxidation by ozone). Providing manufacturers with information on potential hazards that could jeopardize the safety of fermented fruit-based drinks and strategies to lower or eliminate these hazards is of paramount importance.
Mediterranean Diet: The Role of Phenolic Compounds from Aromatic Plant Foods
Authors: Delgado Amélia , Gonçalves Sandra , Romano Anabela
Published: February 2023

Today’s global food system aggravates climate change while failing in meeting SDG2 and more. Yet, some sustainable food cultures, such as the Mediterranean Diet (MD), are simultaneously safe, healthy, and rooted in biodiversity. Their wide range of fruits, herbs, and vegetables convey many bioactive compounds, often associated with colour, texture, and aroma. Phenolic compounds are largely responsible for such features of MD’s foods. These plant secondary metabolites all share in vitro bioactivities (e.g., antioxidants), and some are evidenced in vivo (e.g., plant sterols lower cholesterol levels in blood). The present work examines the role of polyphenols in the MD, with respect to human and planetary health. Since the commercial interest in polyphenols is increasing, a strategy for the sustainable exploitation of Mediterranean plants is essential in preserving species at risk while valuing local cultivars (e.g., through the geographical indication mechanism). Finally, the linkage of food habits with cultural landscapes, a cornerstone of the MD, should enable awareness-raising about seasonality, endemism, and other natural constraints to ensure the sustainable exploitation of Mediterranean plants.
Comparative Compositions of Grain of Bread Wheat, Emmer and Spelt Grown with Different Levels of Nitrogen Fertilisation
Authors: Lovegrove Alison , Dunn Jack , Pellny Till K. , Hood Jessica , Burridge Amanda J. , America Antoine H. P. , Gilissen Luud , Timmer Ruud , Proos-Huijsmans Zsuzsan A. M. , van Straaten Jan Philip , Jonkers Daisy , Ward Jane L. , Brouns Fred , Shewry Peter R.
Published: February 2023

Five cultivars of bread wheat and spelt and three of emmer were grown in replicate randomised field trials on two sites for two years with 100 and 200 kg nitrogen fertiliser per hectare, reflecting low input and intensive farming systems. Wholemeal flours were analysed for components that are suggested to contribute to a healthy diet. The ranges of all components overlapped between the three cereal types, reflecting the effects of both genotype and environment. Nevertheless, statistically significant differences in the contents of some components were observed. Notably, emmer and spelt had higher contents of protein, iron, zinc, magnesium, choline and glycine betaine, but also of asparagine (the precursor of acrylamide) and raffinose. By contrast, bread wheat had higher contents of the two major types of fibre, arabinoxylan (AX) and β-glucan, than emmer and a higher AX content than spelt. Although such differences in composition may be suggested to result in effects on metabolic parameters and health when studied in isolation, the final effects will depend on the quantity consumed and the composition of the overall diet.
Microbial Profiling of Biltong Processing Using Culture-Dependent and Culture-Independent Microbiome Analysis
Authors: Karolenko Caitlin , DeSilva Udaya , Muriana Peter M.
Published: February 2023

Biltong is a South African air-dried beef product that does not have a heat lethality step, but rather relies on marinade chemistry (low pH from vinegar, ~2% salt, spices/pepper) in combination with drying at ambient temperature and low humidity to achieve microbial reduction during processing. Culture-dependent and culture-independent microbiome methodologies were used to determine the changes in the microbial community at each step during biltong processing through 8 days of drying. Culture-dependent analysis was conducted using agar-based methods to recover viable bacteria from each step in the biltong process that were identified with 16S rRNA PCR, sequencing, and BLAST searching of the NCBI nucleotide database. DNA was extracted from samples taken from the laboratory meat processing environment, biltong marinade, and beef samples at three stages of processing (post-marinade, day 4, and day 8). In all, 87 samples collected from two biltong trials with beef obtained from each of three separate meat processors (n = six trials) were amplified, sequenced with Illumina HiSeq, and evaluated with bioinformatic analysis for a culture-independent approach. Both culture-dependent and independent methodologies show a more diverse population of bacteria present on the vacuum-packaged chilled raw beef that reduces in diversity during biltong processing. The main genera present after processing were identified as Latilactobacillus sp., Lactococcus sp., and Carnobacterium sp. The high prevalence of these organisms is consistent with extended cold-storage of vacuum-packaged beef (from packers, to wholesalers, to end users), growth of psychrotrophs at refrigeration temperatures (Latilactobacillus sp., Carnobacterium sp.), and survival during biltong processing (Latilactobacillus sakei). The presence of these organisms on raw beef and their growth during conditions of beef storage appears to ‘front-load’ the raw beef with non-pathogenic organisms that are present at high levels leading into biltong processing. As shown in our prior study on the use of surrogate organisms, L. sakei is resistant to the biltong process (i.e., 2-log reduction), whereas Carnobacterium sp. demonstrated a 5-log reduction in the process; the recovery of either psychrotroph after biltong processing may be dependent on which was more prevalent on the raw beef. This phenomenon of psychrotrophic bloom during refrigerated storage of raw beef may result in a natural microbial suppression of mesophilic foodborne pathogens that are further reduced during biltong processing and contributes to the safety of this type of air-dried beef.