bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on the Influence of the Closing Amount of Electrical Connector Contacts on Fretting Wear under a Vibration Environment
Authors: Wen Baohua , Pan Jun , Qian Ping , Zhang Libin , Chen Wenhua , Zhang Jun
Published: May 2023

As a key component for transmitting electrical signals, the electrical contact performance of electrical connectors is directly affected by the closing amount of the socket. It is urgent to explore the relationship between contact resistance and the closing amount of cylindrical groove electric connectors, as well as the range of the closing amount when their comprehensive performance is optimal in vibration environments. Based on Hertz contact theory, the long- and short-axis parameters of the contact elliptical surface were obtained using the Boussinesq solution and the contact deformation coordination equation. An electrical contact resistance (ECR) theoretical model was obtained using Holm’s electric contact theory and the GW contact model. According to the ECR and long- and short-axis parameters of the contact elliptical surface and the theoretical model of contact resistance, an ECR model based on changes in the initial closing amount (deflection) of the socket spring was established by introducing the cantilever beam model of the socket spring. Then, the failure mechanism of fretting wear and the change mechanism of the ECR of the electrical connectors under a vibration environment were analyzed using the sinusoidal vibration test of electric connectors. The optimal range of the initial closing amount of the cylindrical-groove-closing-type electrical connector socket spring was determined, providing a reference for the reliability designs of electrical connectors.
Dataset Bias Prediction for Few-Shot Image Classification
Authors: Kim Jang Wook , Kim So Yeon , Sohn Kyung-Ah
Published: May 2023

Dataset bias is a significant obstacle that negatively affects image classification performance, especially in few-shot learning, where datasets have limited samples per class. However, few studies have focused on this issue. To address this, we propose a bias prediction network that recovers biases such as color from the extracted features of image data, resulting in performance improvement in few-shot image classification. If the network can easily recover the bias, the extracted features may contain the bias. Therefore, the whole framework is trained to extract features that are difficult for the bias prediction network to recover. We evaluate our method by integrating it with several existing few-shot learning models across multiple benchmark datasets. The results show that the proposed network can improve the performance in different scenarios. The proposed approach effectively reduces the negative effect of the dataset bias, resulting in the performance improvements in few-shot image classification. The proposed bias prediction model is easily compatible with other few-shot learning models, and applicable to various real-world applications where biased samples are prevalent, such as VR/AR systems and computer vision applications.
Open Source Monitoring and Alarm System for Smart Microgrids Operation and Maintenance Management
Authors: Izquierdo-Monge Oscar , Redondo-Plaza Alberto , Peña-Carro Paula , Zorita-Lamadrid Ángel , Alonso-Gómez Víctor , Hernández-Callejo Luis
Published: May 2023

Microgrids are becoming increasingly important for improving the dependability, stability, and quality of the electrical system, as well as for integrating renewable technologies. This paper describes a novel monitoring and alarm system that has been developed to optimize the operation and maintenance of microgrids. The system is unique in that it has been developed using open source software, including Node-RED, Home Assistant, Maria DB, and Telegram, and it allows for easy programming of alarms or warnings. Testing of the system has taken place within the CEDER microgrid and is capable of detecting faults in various components of the microgrid, such as generation units, storage systems, and loads. Upon detecting a fault, the system immediately sends a text message to the mobile phones of the microgrid operator or maintenance crew, enabling them to promptly address the problem. The software can also manage preventive maintenance of the microgrid by sending periodic reminders to the maintenance crew regarding necessary tasks.
SDSIOT: An SQL Injection Attack Detection and Stage Identification Method Based on Outbound Traffic
Authors: Fu Houlong , Guo Chun , Jiang Chaohui , Ping Yuan , Lv Xiaodan
Published: May 2023

An SQL Injection Attack (SQLIA) is a major cyber security threat to Web services, and its different stages can cause different levels of damage to an information system. Attackers can construct complex and diverse SQLIA statements, which often cause most existing inbound-based detection methods to have a high false-negative rate when facing deformed or unknown SQLIA statements. Although some existing works have analyzed different features for the stages of SQLIA from the perspectives of attackers, they primarily focus on stage analysis rather than different stages’ identification. To detect SQLIA and identify its stages, we analyze the outbound traffic from the Web server and find that it can differentiate between SQLIA traffic and normal traffic, and the outbound traffic generated during the two stages of SQLIA exhibits distinct characteristics. By employing 13 features extracted from outbound traffic, we propose an SQLIA detection and stage identification method based on outbound traffic (SDSIOT), which is a two-phase method that detects SQLIAs in Phase I and identifies their stages in Phase II. Importantly, it does not need to analyze the complex and diverse malicious statements made by attackers. The experimental results show that SDSIOT achieves an accuracy of 98.57% for SQLIA detection and 94.01% for SQLIA stage identification. Notably, the accuracy of SDSIOT’s SQLIA detection is 8.22 percentage points higher than that of ModSecurity.
Gearbox Fault Diagnosis Based on Gramian Angular Field and CSKD-ResNeXt
Authors: Liu Yanlin , Dou Shuihai , Du Yanping , Wang Zhaohua
Published: May 2023

For most rotating mechanical transmission systems, condition monitoring and fault diagnosis of the gearbox are of great significance to avoid accidents and maintain stability in operation. To strengthen the comprehensiveness of feature extraction and improve the utilization rate of fault signals to accurately identify the different operating states of a gearbox, a gearbox fault diagnosis model combining Gramian angular field (GAF) and CSKD-ResNeXt (channel shuffle and kernel decomposed ResNeXt) was proposed. The original one-dimensional vibration signal of the gearbox was converted into a two-dimensional image by GAF transformation, and the image was used as the input of the subsequent diagnosis network. To solve the problem of channel independence and incomplete information caused by group convolution, the idea of channel shuffle is introduced to enable the branches of the group convolution part to establish information exchange. In addition, to improve the semantic expression ability of the model, the convolutional kernel of the network backbone is split and replaced. The model is verified under the different working conditions of the gearbox and compared with other methods. The experimental results show that the diagnostic accuracy of the model is up to 99.75%, and the precise identification of gearbox faults is realized.
Node Selection Algorithm for Federated Learning Based on Deep Reinforcement Learning for Edge Computing in IoT
Authors: Yan Shuai , Zhang Peiying , Huang Siyu , Wang Jian , Sun Hao , Zhang Yi , Tolba Amr
Published: May 2023

The Internet of Things (IoT) and edge computing technologies have been rapidly developing in recent years, leading to the emergence of new challenges in privacy and security. Personal privacy and data leakage have become major concerns in IoT edge computing environments. Federated learning has been proposed as a solution to address these privacy issues, but the heterogeneity of devices in IoT edge computing environments poses a significant challenge to the implementation of federated learning. To overcome this challenge, this paper proposes a novel node selection strategy based on deep reinforcement learning to optimize federated learning in heterogeneous device IoT environments. Additionally, a metric model for IoT devices is proposed to evaluate the performance of different devices. The experimental results demonstrate that the proposed method can improve training accuracy by 30% in a heterogeneous device IoT environment.
Carbon Felt Composite Electrode Plates Promote Methanogenesis through Microbial Electrolytic Cells
Authors: Wu Qi , Xiao Han , Zhu Hongguang , Pan Fanghui , Lu Fulu
Published: May 2023

Bioelectrochemical systems are widely used in waste utilization processes. Among them, anaerobic digestion (AD) and microbial electrolytic cell coupling (MEC) are cost-effective and efficient waste-to-energy technologies. In this study, the proposal was made that a carbon felt composite electrode plate be applied to an AD-MEC reactor. The control experiment was conducted using an AD reactor (without the external power supply). The result shows that the carbon felt composite electrode plate increased the biogas production of the AD-MEC reactor by 15.4%, and the average methane content increased by 9.49% compared to the control AD reactor. The total methane production of the AD-MEC reactor and control reactor was 302.51 and 407.79 mL, respectively. The total methane production of the AD-MEC reactor was 34.8% higher than the control group. In addition, the authors found that Methanosarcina and Methanosaeta activities in the AD-MEC reactor were significantly increased. The carbon felt composite electrode plate applied in AD-MEC may have promoted the methanogenic microorganisms’ interspecific acetic acid transport process and increased biogas production and methane content.
Comparing Machine Learning Strategies for SoH Estimation of Lithium-Ion Batteries Using a Feature-Based Approach
Authors: Marri Iacopo , Petkovski Emil , Cristaldi Loredana , Faifer Marco
Published: May 2023

Lithium-ion batteries play a vital role in many systems and applications, making them the most commonly used battery energy storage systems. Optimizing their usage requires accurate state-of-health (SoH) estimation, which provides insight into the performance level of the battery and improves the precision of other diagnostic measures, such as state of charge. In this paper, the classical machine learning (ML) strategies of multiple linear and polynomial regression, support vector regression (SVR), and random forest are compared for the task of battery SoH estimation. These ML strategies were selected because they represent a good compromise between light computational effort, applicability, and accuracy of results. The best results were produced using SVR, followed closely by multiple linear regression. This paper also discusses the feature selection process based on the partial charging time between different voltage intervals and shows the linear dependence of these features with capacity reduction. The feature selection, parameter tuning, and performance evaluation of all models were completed using a dataset from the Prognostics Center of Excellence at NASA, considering three batteries in the dataset.
Energy and Environmental Analysis of Renewable Energy Systems Focused on Biomass Technologies for Residential Applications: The Life Cycle Energy Analysis Approach
Authors: Giama Effrosyni , Kyriaki Elli , Papaevaggelou Athanasios , Papadopoulos Agis
Published: May 2023

Sustainability and resilience are major challenges for the building sector in order to meet energy efficiency and low carbon emissions goals. Based on the defined and quantified targets of the EU climate change policy, Renewable Energy Systems (RESs) are among the top-priority measures for accomplishing the target of decarbonization in buildings. Nevertheless, the choice of the type of RES is not a one-dimensional problem, and the optimal combination may not be unique. The aim of this paper is the energy and environmental evaluation of renewable energy technologies with emphasis on biomass and solar thermal systems for heating applications in residential buildings. More specifically, and aiming at the maximum possible contribution of renewable energy sources in the total final energy consumption for the needs of zero energy buildings, different scenarios are presented based on a Life Cycle Energy Analysis (LCEA) approach. The methodology is based on quantifying the environmental impacts (midpoint analysis), as well as endpoint analysis, in order to define the impact on human health, ecosystem damage, and resource depletion. The LCEA has been conducted, supported by the SimaPro tool, ensuring the environmental impact assessment result. A combination of RES technologies based on solar and biomass are examined and compared to conventional fossil fuel heating systems according to technical, energy, and environmental criteria. Finally, the energy system technologies were compared in correlation to a building’s thermal insulation level. The first set of simulations fulfilled the minimum thermal insulation requirements, according to the national energy performance regulation, whilst the second set of simulations was based on increased levels of insulation. The point of this analysis was to correlate the impact of thermal insulation to RES technologies’ contribution. The results determined that the best available energy solution, focusing on technical and environmental criteria, is the combination of biomass and solar thermal systems for covering the heating processes in residential buildings. More specifically, the combined biomass–solar system has a lower overall environmental impact, due to the reduction in gaseous pollutant emissions, as well as the reduction in the amount of used fuel. The reduction in the total environmental impact amounts to a percentage of approximately 43%.
Robust Placement and Control of Phase-Shifting Transformers Considering Redispatch Measures
Authors: Santos Allan , Steinke Florian
Published: May 2023

Flexible AC transmission systems (FACTSs) can maximize capacity utilization under time-varying grid usage patterns by actively controlling the power flow of the transmission lines, e.g., with phase-shifting transformers (PST). In this paper, we propose an algorithm to determine the minimum number of PSTs and their location such that the grid can operate robustly for any realization of the (active) power set points from a known, continuous uncertainty set. As we show in our experiments, only considering a few extreme grid scenarios cannot provide this guarantee. The proposed algorithm considers the trade-offs between PST placement and operational decisions, such as PST control and redispatch. By minimizing the worst-case redispatch cost, it yields two affine linear control policies for these as a byproduct. Power flow is modeled as a constrained linear system, and the control design and actuator minimization tasks are formulated as a mixed-integer linear program (MILP). We also design a greedy algorithm, whose optimal value differs less than 20% from the MILP solution while being one to two orders of magnitude faster to compute. The proposed algorithm is evaluated for a small demonstrative 3-bus example and the IEEE 39 bus test system.
A Data-Driven Approach for Generating Vortex-Shedding Regime Maps for an Oscillating Cylinder
Authors: Cann Matthew , McConkey Ryley , Lien Fue-Sang , Melek William , Yee Eugene
Published: May 2023

This study presents a data-driven approach for generating vortex-shedding maps, which are vital for predicting flow structures in vortex-induced vibration (VIV) wind energy extraction devices, while addressing the computational and complexity limitations of traditional methods. The approach employs unsupervised clustering techniques on subsequences extracted using the matrix profile method from local flow measurements in the wake of an oscillating circular cylinder generated from 2-dimensional computational fluid dynamics simulations of VIV. The proposed clustering methods were validated by reproducing a benchmark map produced at a low Reynolds number (Re = 4000) and then extended to a higher Reynolds number (Re = 10,000) to gain insights into the complex flow regimes. The multi-step clustering methods used density-based and k-Means clustering for the pre-clustering stage and agglomerative clustering using dynamic time warping (DTW) as the similarity measure for final clustering. The clustering methods achieved exceptional performance at high-Reynolds-number flow, with scores in the silhouette index (0.4822 and 0.4694) and Dunn index (0.3156 and 0.2858) demonstrating the accuracy and versatility of the hybrid clustering methods. This data-driven approach enables the generation of more accurate and feasible maps for vortex-shedding applications, which could improve the design and optimization of VIV wind energy harvesting systems.
Neural Network Applications in Electrical Drives—Trends in Control, Estimation, Diagnostics, and Construction
Authors: Kaminski Marcin , Tarczewski Tomasz
Published: May 2023

Currently, applications of the algorithms based on artificial intelligence (AI) principles can be observed in various fields. This can be also noticed in the wide area of electrical drives. Consideration has been limited to neural networks; however, the tasks for the models can be defined as follows: control, state variable estimation, and diagnostics. In the subsequent sections of this paper, electrical machines, as well as power electronic devices, are assumed as the main objects. This paper describes the basics, issues, and possibilities related to the used tools and explains the growing popularity of neural network applications in automatic systems with electrical drives. The paper begins with the overall considerations; following that, the content proceeds with the details, and two specific examples are shown. The first example deals with a neural network-based speed controller tested in a structure with a synchronous reluctance motor. Then, the implementation of recurrent neural networks as state variable estimators is analyzed. The achieved results present a precise estimation of the load speed and the shaft torque signals from a two-mass system. All descriptions in the article are considered in the context of the trends and perspectives in modern algorithm applications for electrical drives.
Applications of Machine Learning in Mechanised Tunnel Construction: A Systematic Review
Authors: Shan Feng , He Xuzhen , Xu Haoding , Armaghani Danial Jahed , Sheng Daichao
Published: May 2023

Tunnel Boring Machines (TBMs) have become prevalent in tunnel construction due to their high efficiency and reliability. The proliferation of data obtained from site investigations and data acquisition systems provides an opportunity for the application of machine learning (ML) techniques. ML algorithms have been successfully applied in TBM tunnelling because they are particularly effective in capturing complex, non-linear relationships. This study focuses on commonly used ML techniques for TBM tunnelling, with a particular emphasis on data processing, algorithms, optimisation techniques, and evaluation metrics. The primary concerns in TBM applications are discussed, including predicting TBM performance, predicting surface settlement, and time series forecasting. This study reviews the current progress, identifies the challenges, and suggests future developments in the field of intelligent TBM tunnelling construction. This aims to contribute to the ongoing efforts in research and industry toward improving the safety, sustainability, and cost-effectiveness of underground excavation projects.
Impact of Crown Closure on Cone Production and Effective Number of Parents in Natural Stands of Taurus Cedar (Cedrus libani A. Rich.)
Authors: Yazici Nilufer , Bilir Nebi
Published: May 2023

This study was carried out to estimate the effect of crown closure classes (degraded forest, low crown coverage, medium crown coverage, and full crown coverage) and growth characteristics (three heights, diameter at base, diameter at breast height, and crown diameter) on cone production and to estimate fertility variation and its allied parameters in 130-year-old natural populations of Taurus cedar, also called cedar of Lebanon (Cedrus libani A. Rich.). The effect of biotic (e.g., growth characteristics, crown closure) and abiotic (e.g., light penetration, temperature, humidity, and soil characteristics) factors on cone production, fertility variation, and gene diversity was evaluated in two consecutive years of Taurus cedar. The factors, viz., altitude, temperature, aspect, and rainfall, varied, while some of them could be managed by cultural operations such as management of the crown and stand density. The impact of crown closure on cone production, fertility variation, and related parameters were observed in Taurus cedar’s natural populations sampled from southern Turkey. Maximum cone productions of 29 and 40/tree were recorded with full crown closure in two consecutive years, due to significant differences among individuals within crown closure class. For instance, cone productions were between 10 and 67 in the full crown closure of the first year. The sibling coefficient, which is a measure of the fertility variation in a stand, was the highest (1.16) for the full crown closure in the first (meaning 86% fertile trees) and the second years (1.55, 65%), while it was the lowest for the medium crown closure (1.09, 92%) in both years. Gene diversity decreased from a degraded patch (0.987) to that with full crown closure (0.984). Results of variance analysis showed a significant (p ≤ 0.05) difference between crown closure classes for cone production within the second year and between years. Significant positive (p ≤ 0.05) correlations were noted between years for cone production (r = 0.22) and between cone production and crown closure in the first and second years (r = 0.29). However, growth characteristics had no effect on cone productions in individual crown closure classes, while there were significant (p ≤ 0.05) correlations between the diameter at breast height and cone production for both years (r = 0.15 and 0.17) in pooled populations.
Identifying the Minimum Number of Flood Events for Reasonable Flood Peak Prediction of Ungauged Forested Catchments in South Korea
Authors: Yang Hyunje , Lim Honggeun , Moon Haewon , Li Qiwen , Nam Sooyoun , Choi Byoungki , Choi Hyung Tae
Published: May 2023

The severity and incidence of flash floods are increasing in forested regions, causing significant harm to residents and the environment. Consequently, accurate estimation of flood peaks is crucial. As conventional physically based prediction models reflect the traits of only a small number of areas, applying them in ungauged catchments is challenging. The interrelationship between catchment characteristics and flood features to estimate flood peaks in ungauged areas remains underexplored, and evaluation standards for the appropriate number of flood events to include during data collection to ensure effective flood peak prediction have not been established. Therefore, we developed a machine-learning predictive model for flood peaks in ungauged areas and determined the minimum number of flood events required for effective prediction. We employed rainfall-runoff data and catchment characteristics for estimating flood peaks. The applicability of the machine learning model for ungauged areas was confirmed by the high predictive performance. Even with the addition of rainfall-runoff data from ungauged areas, the predictive performance did not significantly improve when sufficient flood data were used as input data. This criterion could facilitate the determination of the minimum number of flood events for developing adequate flood peak predictive models.
A Birch Tree as a Witness in a Murder and Cannibalism Case
Authors: Cedro Anna
Published: May 2023

Fifteen years after a murder and an act of cannibalism, the police in Szczecin have conducted an investigation, in which neither the time of the murder, nor the victim identity were known, no body was found, and the defendants have not confessed. Due to the circumstantial nature of the investigation and the trial, a number of analyses were performed, and expert opinions were requested as part of the inquiry. A dendrochronological study of a birch tree growing on the shore of Lake Żabie was one of the analyses lending credibility to the testimony of one of the defendants and pointing to the murder site. The aim of this study was to determine the age and size of the birch tree in the 1998–2002 period, and to determine whether the tree was a distinctive feature of the landscape through the period in question. Field work was performed in April 2019, under the supervision of the police. The birch tree was measured and samples collected using a Pressler borer. The measurements and observations revealed that the birch tree, due to its size, and the absence of other trees of this species in this segment of the lake shore, may have been a characteristic landscape element in the late 1990s and early 2000s. According to the police officer and the prosecutor in charge of the case, the results of the dendrological analysis have lent a strong credibility to the testimony of one of the defendants and other findings of the investigation.
Automatic 10 m Forest Cover Mapping in 2020 at China’s Han River Basin by Fusing ESA Sentinel-1/Sentinel-2 Land Cover and Sentinel-2 near Real-Time Forest Cover Possibility
Authors: Wang Xia , Zhang Yihang , Zhang Kerong
Published: May 2023

Given the increasingly fragmented forest landscapes, it is necessary to map forest cover with fine spatial resolution in a large area. The European Space Agency (ESA) released the 10 m global land cover map in 2020 based on Sentinel-1 and Sentinel-2 images, and Dynamic World provides near real-time possibilities of many land cover classes based on Sentinel-2 images, but they are not designed particularly for forest cover. In this research, we aimed to develop a method to automatically estimate an accurate 10 m forest cover map in 2020 by fusing the ESA forest cover map and Dynamic World near real-time forest cover possibilities. The proposed method includes three main steps: (1) generating stable forest samples, (2) determining the threshold T and (3) producing the fused forest cover map. China’s Han River Basin, dominated by complex subtropical forests, was used as the study site to validate the performance of the proposed method. The results show that the proposed method could produce a forest cover map with the best overall accuracy of 98.02% ± 1.20% and more accurate spatial details compared to using only one of the two data sources. The proposed method is thus superior in mapping forest cover in complex forest landscapes.
Spermosphere Bacteria Promote Ormosia henryi Seed Germination by Activating Metabolic Pathways
Authors: Ge Meng , Wei Xiaoli
Published: May 2023

Spermosphere microorganisms are affected by seed exudates (carbon deposits) and interact with each other around the germinating seeds. The interactions in this region will have an important impact on seed germination, plant growth, and development. Ormosia henryi Prain is a rare and endangered tree species with hardseed in China that generally shows low levels of natural regeneration. However, good levels of natural regeneration were observed under a few mother trees in Guanling County (Guizhou Province, China). Based on this phenomenon: we raise the question: Is the seed germination of Ormosia henryi in high-level natural regeneration areas driven by the spermosphere microbe and what is the driving mechanism? The purpose of this study was to explore the promoting role of spermosphere bacteria in different stages of germination, so as to provide a theoretical basis for screening strong colonization ability spermosphere functional bacteria. Thus, in this study, we designed a germination experiment simulating the natural soil (sterilized and unsterilized) of Guanling County. We used 16S rRNA gene sequencing and widely targeted multiple reaction monitoring (MRM) metabolomic analysis to analyze bacterial microbial diversity and the metabolic profiles of seed exudates at the four stages of seed germination: imbibition, radicle protrusion, radicle elongation, and cotyledon protrusion. The results indicated that some spermosphere bacteria: taxa of Bacillus, Acidobacteriales, Nitrospira, and Ellin6067, and other functional bacteria may play important roles in promoting seed germination depending largely on their functional characteristics and abundance. The critical period for spermosphere bacteria to promote seed germination was at the radicle protrusion stage in the unsterilized soil group, levels of metabolites such as sugars and amino acids of seed exudates were increased in this stage, more respiratory pathways were activated and seed respiratory metabolism was enhanced. Spermosphere bacteria of wild Ormosia henryi in the Guanling area appeared to promote the germination of O. henryi seeds, mainly through activating galactose metabolism and the lysine degradation pathway to provide energy for germination.
Improved Extraction of High Value-Added Polyphenols from Pomegranate Peel by Solid-State Fermentation
Authors: Buenrostro-Figueroa José Juan , Nevárez-Moorillón Guadalupe Virginia , Chávez-González Mónica Lizeth , Sepúlveda Leonardo , Ascacio-Valdés Juan Alberto , Aguilar Cristóbal Noé , Pedroza-Islas Ruth , Huerta-Ochoa Sergio , Arely Prado-Barragán Lilia
Published: May 2023

Pomegranate peel is an important source of polyphenols of remarkable interest in the food, pharmaceutical and cosmetic industry. The improved extraction of total polyphenolic compounds (TPC) from pomegranate peel by solid-state fermentation (SSF) was achieved. The Box, Hunter and Hunter (BHH) followed by the central composite design (CCD) processes were performed to assess the effect of the process variables on TPC release. The statistical designs indicate that the best TPC extraction (234.85 mg GAE/gdm) by means of SSF occurs at 42 °C, 50% moisture, 5.0 pH, mineral solution (g/L): NaNO3 (3.83), KH2PO4 (1.52), MgSO4 (4.66) and KCl (1.52) at 36 h. Under the best fermentation conditions TPC (248.78 ± 1.24 mgGAE/gdm) increased 5.96-fold more than values previously reported and antioxidant activity (AA) increased 5.81-fold compared to the value obtained before the SSF optimization. High-value citric acid, α and β punicalin, α and β punicalagin, punigluconin, galloyl-HHDP hexoside and ellagic acid molecules were identified. The increased extraction of TPC by SSF provides a suitable alternative for the valorization of pomegranate peel through the recovery of molecules with high added value with potential use in the food, pharmacy and cosmetic industries; a diversification in the use of food agroindustry by-products is obtained as an approach to the circular economy model through biotechnological processes.
Impact of Waste as a Substrate on Biomass Formation, and Optimization of Spent Microbial Biomass Re-Use by Sustainable Metabolic Engineering
Authors: Stikane Anna , Baumanis Matiss Ricards , Muiznieks Reinis , Stalidzans Egils
Published: May 2023

Biomass residue and waste stream bioconversion is a key pillar for successful transition toward sustainable bioeconomy. Spent microbial biomass (SMB) is a unique type of nutrient-rich residue generated from fermentation. This study addresses the waste–SMB–substrate cycle in fermentation. Data from a range of published fermentation processes using waste and non-waste substrates are analyzed for a variety of fermentation products including alcohols and biofuels, amino acids, polymers (PHA), and organic acids. On average, fermentation of waste substrates produces similar, or up to two–three times higher, amounts of SMB compared to purified substrates. SMB production from waste substrates is further illustrated with data from PHA production. The amino acid composition of SMB from 6 industrially relevant microorganisms is compared and shows relatively low variety (2–8%). The return of SMB as a (co-)substrate in fermentation is then considered by building upon the novel concept of sustainable metabolic engineering (SME). SME incorporates economic, environmental, and social sustainability criteria in its optimization algorithm to select microbial strain designs resulting in the most sustainable products. An example of SME application for SMB amino acid re-use by engineered Escherichia coli is demonstrated and discussed. A design with dual production of succinate and ethanol was found to be the most sustainable.
Modelling Time-Dependent Flow through Railway Ballast
Authors: Alrdadi Raed , Meylan Michael H.
Published: May 2023

In this study, a numerical simulation of fluid flow through railway ballast in the time domain is presented, providing a model for unsteady-state flow. It is demonstrated that the position of the free surface with respect to time can also be used to solve the steady flow case. The effect of ballast fouling is included in the model to capture the realistic behavior of railway ballast, which is critical to understanding the impact of flooding. A thorough comparison with a range of previous studies, including theoretical and experimental approaches, is made, and very close agreement is obtained. The significant impact of ballast fouling on fluid flow and its potential consequences for railway infrastructure are highlighted by the simulation. Valuable insights into the behavior of water flow through porous media and its relevance to railway ballast management are offered by this study.
Untargeted Lipidomics and Chemometric Tools for the Characterization and Discrimination of Irradiated Camembert Cheese Analyzed by UHPLC-Q-Orbitrap-MS
Authors: Tomaiuolo Michele , Nardelli Valeria , Mentana Annalisa , Campaniello Maria , Zianni Rosalia , Iammarino Marco
Published: May 2023

In this work, an investigation using UHPLC-Q-Orbitrap-MS and multivariate statistics was conducted to obtain the lipid fingerprint of Camembert cheese and to explore its correlated variation with respect to X-ray irradiation treatment. A total of 479 lipids, categorized into 16 different lipid subclasses, were measured. Furthermore, the identification of oxidized lipids was carried out to better understand the possible phenomena of lipid oxidation related to this technological process. The results confirm that the lipidomic approach adopted is effective in implementing the knowledge of the effects of X-ray irradiation on food and evaluating its safety aspects. Furthermore, Partial Least Squares-Discriminant Analysis (PLS-DA) and Linear Discriminant Analysis (LDA) were applied showing high discriminating ability with excellent values of accuracy, specificity and sensitivity. Through the PLS-DA and LDA models, it was possible to select 40 and 24 lipids, respectively, including 3 ceramides (Cer), 1 hexosyl ceramide (HexCer), 1 lysophosphatidylcholine (LPC), 1 lysophosphatidylethanolamine (LPE), 3 phosphatidic acids (PA), 4 phosphatidylcholines (PC), 10 phosphatidylethanolamines (PE), 5 phosphatidylinositols (PI), 2 phosphatidylserines (PS), 3 diacylglycerols (DG) and 9 oxidized triacylglycerols (OxTG) as potential markers of treatment useful in food safety control plans.
Degradation Dynamics and Residue Analysis of Four Propiconazole Stereoisomers in “Fengtang” Plum during Storage by LC-MS/MS
Authors: Deng Pengyu , Mou Lianhong , Ou Guipeng , Luo Xin , Hu Deyu , Zhang Yuping
Published: May 2023

Herein, an accurate and sensitive method was developed for detecting four stereoisomers of propiconazole in “Fengtang” plum by LC-MS/MS. The mean recovery of four propiconazole stereoisomers ranged from 79.42 to 104.10% at three adding levels with reasonable RSD of 1.54–11.68%, and the LOD and LOQ of the four stereoisomers was 0.0005 mg/kg and 0.004 mg/kg, respectively. In addition, the residue and selective degradation of propiconazole stereoisomers in plums were investigated by storage at 20 °C and 4 °C. The half-lives of propiconazole stereoisomeric during storage were 9.49–15.40 d at 20 °C, and 21.00–28.88 d at 4 °C. The degradation of (2R,4R)-propiconazole and (2R,4S)-propiconazole in stored plums was slightly slower than that of the corresponding enantiomers (2S,4S)-propiconazole and (2S,4R)-propiconazole. The total residues of propiconazole were 0.026–0.487 mg/kg in the plum storage period, and the water washing could remove 49.35% to 54.65% of the propiconazole residue in plum. The hardness of plums treated with propiconazole was generally higher than that of control in the middle and late stages of storage. The effects of propiconazole on the total soluble solid content of plums were different at 20 °C and 4 °C. This study provides a scientific reference for the food safety evaluation of the “Fengtang” plum after the application of propiconazole during the storage period.
Physicochemical Properties and Tissue Structure of High Kernel Elongation Rice (Oryza sativa L.) Varieties as Affected by Heat Treatment
Authors: Halim Anna Arina Bt Ab. , Rafii Mohd Y. , Osman Mohamad B. , Chukwu Samuel C. , Oladosu Yusuff
Published: May 2023

Heat treatment could affect the structure and properties of rice varieties. The present study was conducted in order to determine the effects of heat treatment on the physicochemical properties and tissue structure of Mahsuri Mutan, Basmati 370 and MR219 rice varieties. The three rice varieties were subjected to heat treatment (ageing) at 90 °C, using an oven, for 3 h. After the heat treatment, the samples were cooled at room temperature (25 °C) for 1 h. Physicochemical properties, such as alkali digestion value, water uptake ratio, solids in cooking water, high kernel elongation ratio and amylose contents, were determined. The procedure used in determining both apparent and absolute amylose involved measuring the iodine affinity of defatted whole starch. Ahigh-performance anion-exchange chromatograph was used to analyse branch chain length distribution of amylopectin quantitatively. The starch structure of the rice samples was observed under a scanning electron microscope. Data collected on physicochemical traits, heat treatment and control (ageing and non-ageing) were subjected to an analysis of variance using SAS software version 9.4. In this study, Mahsuri Mutan and Basmati 370 showed superior high kernel elongation as compared to their respective rice progenies. This study also found that heat treatment directly affected the increasingly high kernel elongation for both populations. The phenotypic correlation co-efficient indicated that there was a high positive correlation between high kernel elongation and water uptake ratio, implying that selection for water uptake ratio would increase the high kernel elongation characteristic. The heat treatment showed significant difference in all the physicochemical traits of the varieties studied. Heat treatment also affected the very long branch chains of starch, such as amylose. Observation under an electron microscope showed that the samples subjected to heat treatment had more cracks on the tissue structure compared to normal rice samples. The hexagon structure in Mahsuri Mutan produced a greater elongation effect on its kernel. The findings from this study could be useful to breeders in the selection and development of a new high kernel elongation rice variety.
Strategies to Assess the Impact of Sustainable Functional Food Ingredients on Gut Microbiota
Authors: de Carvalho Nelson Mota , Oliveira Diana Luazi , Costa Célia Maria , Pintado Manuela Estevez , Madureira Ana Raquel
Published: May 2023

Nowadays, it is evident that food ingredients have different roles and distinct health benefits to the consumer. Over the past years, the interest in functional foods, especially those targeting gut health, has grown significantly. The use of industrial byproducts as a source of new functional and sustainable ingredients as a response to such demands has raised interest. However, the properties of these ingredients can be affected once incorporated into different food matrices. Therefore, when searching for the least costly and most suitable, beneficial, and sustainable formulations, it is necessary to understand how such ingredients perform when supplemented in different food matrices and how they impact the host’s health. As proposed in this manuscript, the ingredients’ properties can be first evaluated using in vitro gastrointestinal tract (GIT) simulation models prior to validation through human clinical trials. In vitro models are powerful tools that mimic the physicochemical and physiological conditions of the GIT, enabling prediction of the potentials of functional ingredients per se and when incorporated into a food matrix. Understanding how newly developed ingredients from undervalued agro-industrial sources behave as supplements supports the development of new and more sustainable functional foods while scientifically backing up health-benefits claims.
Impact of Processing Method on AQF Functionality in Bakery Items
Authors: Edleman Dana , Hall Clifford
Published: May 2023

Aquafaba (AQF) has the unique ability to foam like egg whites and is a waste product of cooked chickpea that is not currently utilized by the food industry. Thus, the goal of this research was to concentrate the solids by reverse osmosis (cAQF) followed by drying. Dried AQF was prepared by cooking chickpea in excess water. After removal of the chickpea, the liquid AQF was subjected to reverse osmosis followed by freeze, tray, or spray drying. The resulting AQF products were incorporated into standard cake mix and sugar cookie formulas. Hardness, gumminess, and chewiness of cakes made with eggs were significantly higher compared to the cakes made with AQF. Spread factor was significantly greater for cookies made with AQF compared to eggs while hardness was significantly lower in cookies with AQF. Higher flavor and overall acceptability scores were observed in cookies made with AQF compared to cookies made with egg. However, sensory characteristics were generally not different among cakes. In general, cAQF and spray-dried AQF tended to produce cakes and cookies with the best quality and sensory characteristic. This research supports the use of RO and drying methods in producing AQF ingredients for baking applications.
Parameters Estimation in a Time-Fractiona Parabolic System of Porous Media
Authors: Koleva Miglena N. , Vulkov Lubin G.
Published: May 2023

The simultaneous estimation of coefficients and the initial conditions for model fractional parabolic systems of porous media is reduced to the minimization of a least-squares cost functional. This inverse problem uses information about the pressures at a finite number of space time points. The Frechet gradient of the cost functional is derived. The application of the conjugate gradient method for numerical parameter estimation is also discussed. Computational results with noise-free and noisy data illustrate the efficiency and accuracy of the proposed algorithm.
Preparation and Characterization of Biocompatible Iron/Zirconium/Polydopamine/Carboxymethyl Chitosan Hydrogel with Fenton Catalytic Properties and Photothermal Efficacy
Authors: Zheng Xiaoyi , Wu Hang , Wang Shige , Zhao Jiulong , Hu Lianghao
Published: May 2023

In recent years, multifunctional hydrogel nanoplatforms for the synergistic treatment of tumors have received a great deal of attention. Here, we prepared an iron/zirconium/polydopamine/carboxymethyl chitosan hydrogel with Fenton and photothermal effects, promising for future use in the field of synergistic therapy and prevention of tumor recurrence. The iron (Fe)–zirconium (Zr)@ polydopamine (PDA) nanoparticles were synthesized by a simple one-pot hydrothermal method using iron (III) chloride hexahydrate (FeCl3•6H2O), zirconium tetrachloride (ZrCl4), and dopamine, followed by activation of the carboxyl group of carboxymethyl chitosan (CMCS) using 1-(3-Dimethylaminopropyl)-3-ethylcarbodiimide hydrochloride (EDC)/N(4)-hydroxycytidine (NHS). Finally, the Fe–Zr@PDA nanoparticles and the activated CMCS were mixed to form a hydrogel. On the one side, Fe ions can use hydrogen peroxide (H2O2) which is rich in the tumor microenvironment (TME) to produce toxic hydroxyl radicals (•OH) and kill tumor cells, and Zr can also enhance the Fenton effect; on the other side, the excellent photothermal conversion efficiency of the incorporated PDA is used to kill tumor cells under the irradiation of near-infrared light. The ability of Fe–Zr@PDA@CMCS hydrogel to produce •OH and the ability of photothermal conversion were verified in vitro, and swelling and degradation experiments confirmed the effective release and good degradation of this hydrogel in an acidic environment. The multifunctional hydrogel is biologically safe at both cellular and animal levels. Therefore, this hydrogel has a wide range of applications in the synergistic treatment of tumors and the prevention of recurrence.
The Landscape of Telomere Length and Telomerase in Human Embryos at Blastocyst Stage
Authors: Wang Fang , McCulloh David H. , Chan Kasey , Wiltshire Ashley , McCaffrey Caroline , Grifo James A. , Keefe David L.
Published: May 2023

The telomere length of human blastocysts exceeds that of oocytes and telomerase activity increases after zygotic activation, peaking at the blastocyst stage. Yet, it is unknown whether aneuploid human embryos at the blastocyst stage exhibit a different profile of telomere length, telomerase gene expression, and telomerase activity compared to euploid embryos. In present study, 154 cryopreserved human blastocysts, donated by consenting patients, were thawed and assayed for telomere length, telomerase gene expression, and telomerase activity using real-time PCR (qPCR) and immunofluorescence (IF) staining. Aneuploid blastocysts showed longer telomeres, higher telomerase reverse transcriptase (TERT) mRNA expression, and lower telomerase activity compared to euploid blastocysts. The TERT protein was found in all tested embryos via IF staining with anti-hTERT antibody, regardless of ploidy status. Moreover, telomere length or telomerase gene expression did not differ in aneuploid blastocysts between chromosomal gain or loss. Our data demonstrate that telomerase is activated and telomeres are maintained in all human blastocyst stage embryos. The robust telomerase gene expression and telomere maintenance, even in aneuploid human blastocysts, may explain why extended in vitro culture alone is insufficient to cull out aneuploid embryos during in vitro fertilization.
Prediction of Diagnosis-Related Groups for Appendectomy Patients Using C4.5 and Neural Network
Authors: Chiang Yi-Cheng , Hsieh Yin-Chia , Lu Long-Chuan , Ou Shu-Yi
Published: May 2023

Due to the increasing cost of health insurance, for decades, many countries have endeavored to constrain the cost of insurance by utilizing a DRG payment system. In most cases, under the DRG payment system, hospitals cannot exactly know which DRG code inpatients are until they are discharged. This paper focuses on the prediction of what DRG code appendectomy patients will be classified with when they are admitted to hospital. We utilize two models (or classifiers) constructed using the C4.5 algorithm and back-propagation neural network (BPN). We conducted experiments with the data collected from two hospitals. The results show that the accuracies of these two classification models can be up to 97.84% and 98.70%, respectively. According to the predicted DRG code, hospitals can effectively arrange medical resources with certainty, then, in turn, improve the quality of the medical care patients receive.