bluuebunny/crossref_metadata_2025 huggingface dataset:
Survey of Reliability Research on 3D Packaged Memory
Authors: Zhou Shuai , Ma Kaixue , Wu Yugong , Liu Peng , Hu Xianghong , Nie Guojian , Ren Yan , Qiu Baojun , Cai Nian , Xu Shaoqiu , Wang Han
Published: June 2023

As the core carrier of information storage, a semiconductor memory device is a basic product with a large volume that is widespread in the integrated circuit industry. With the rapid development of semiconductor manufacturing processes and materials, the internal structure of memory has gradually shifted from a 2D planar packaging structure to a 3D packaging structure to meet industry demands for high-frequency, high-speed, and large-capacity devices with low power consumption. However, advanced 3D packaging technology can pose some reliability risks, making devices prone to failure, especially when used in harsh environmental conditions, including temperature changes, high temperature and humidity levels, and mechanical stress. In this paper, the authors introduce the typical structure characteristics of 3D packaged memory; analyze the reasons for device failure caused by stress; summarize current research methods that utilize temperature, mechanical and hygrothermal theories, and failure models; and present future challenges and directions regarding the reliability research of 3D packaged memory.
Forensic Analysis Laboratory for Sport Devices: A Practical Use Case
Authors: Donaire-Calleja Pablo , Robles-Gómez Antonio , Tobarra Llanos , Pastor-Vargas Rafael
Published: June 2023

At present, the mobile device sector is experiencing significant growth. In particular, wearable devices have become a common element in society. This fact implies that users unconsciously accept the constant dynamic collection of private data about their habits and behaviours. Therefore, this work focuses on highlighting and analysing some of the main issues that forensic analysts face in this sector, such as the lack of standard procedures for analysis and the common use of private protocols for data communication. Thus, it is almost impossible for a digital forensic specialist to fully specialize in the context of wearables, such as smartwatches for sports activities. With the aim of highlighting these problems, a complete forensic analysis laboratory for such sports devices is described in this paper. We selected a smartwatch belonging to the Garmin Forerunner Series, due to its great popularity. Through an analysis, its strengths and weaknesses in terms of data protection are described. We also analyse how companies are increasingly taking personal data privacy into consideration, in order to minimize unwanted information leaks. Finally, a set of initial security recommendations for the use of these kinds of devices are provided to the reader.
Knowledge Discovery in Databases for a Football Match Result
Authors: Głowania Szymon , Kozak Jan , Juszczuk Przemysław
Published: June 2023

The analysis of sports data and the possibility of using machine learning in the prediction of sports results is an increasingly popular topic of research and application. The main problem, apart from choosing the right algorithm, is to obtain data that allow for effective prediction. The article presents a comprehensive KDD (Knowledge Discovery in Databases) approach that allows for the appropriate preparation of data for sports prediction on sports data. The first part of the article covers the subject of KDD and sports data. The next section presents an approach to developing a dataset on top football leagues. The developed datasets are the main purpose of the article and have been made publicly available to the research community. In the latter part of the article, an experiment with the results based on heterogeneous groups of classifiers and the developed datasets is presented.
Expansion Joints Risk Prediction System Based on IoT Displacement Device
Authors: Park Jong-Su , Ham Hyoung-Min , Ahn Yeong-Hwi
Published: June 2023

Damage to bridge expansion joints arises from a variety of causes such as increasingly deteriorated bridges, abnormal temperatures, and increased traffic. To detect anomalies in the expansion joints, this study proposes an Artificial Intelligence (AI)-model-based diagnosis method of analyzing the vibration of the bridge bearing that supports the upper structure of a bridge. The proposed system establishes big data with the measured displacement of a bridge bearing and makes an AI-based prediction about the risk of bridge expansion joints. Replacing a bridge bearing makes it possible to manage the bridge displacement before and after construction and helps improve safety inspections and diagnosis methods. It is necessary to prepare a bridge with anomalies for the AI model training. For this reason, a bridge with a bridge bearing was simulated. In addition, a vehicle suitable for the bridge was simulated. The displacement data in normal and abnormal situations were collected, cleaned, and applied to the AI analysis model. The system was found to have over 90% accuracy of prediction about expansion joint faulting and damage.
Student Project-Based Space Vector Modulation Technique for Power Electronics Laboratory
Authors: Saribulut Lutfu , Ameen Arman
Published: June 2023

Two-level DC/AC inverter topologies are widely used for low voltage and high voltage applications in power systems and industrial areas. Space Vector Modulation (SVM) is a popular Pulse-Width Modulation technique used for controlling the inverters and providing the efficient energy conversion from DC sources. However, applications of SVM-based studies are limited in the Power Electronics Laboratory (PEL) due to the vital risks associated with high voltage applications, and it is not easily learned through mathematical analysis and visual learning without implementation by undergraduate students. A simulation and experimental setup of an SVM-controlled two-level, three-phase inverter was presented in this study for undergraduate students to learn its basics in the PEL. Several programs were used to simulate the inverter in the classroom environment and to design a power circuit and microcontroller-based printed circuit board of the inverter for PEL experiments. The two case studies were given. In the case results, the output voltage waveforms of simulation and experimental inverters were compared to show the validation of simulation results. With this study, the students’ experience is enhanced in electronic circuit design, programming, coordination with hardware and software development activities, self-learning, and teamwork. Additionally, practical applications increase undergraduate students’ interest in Power Electronics Courses and reinforce their knowledge from lecture and laboratory studies.
Design of a Baseband Signal for the 406 MHz Satellite Emergency Radio Transmitter Based on STM32
Authors: Liao Mi , Nie Guigen , Zhao Jing , He Xi
Published: June 2023

The baseband signal of the 406 MHz COSPAS-SARSAT emergency beacon is a 144-bit or 112-bit data with 0 and 1 code, including the information bits and error correcting check bits. In order to improve the coding efficiency of the baseband signal for the 406 MHz transmitter in the STM32 micro-controller, this paper proposes a calculation method for the first BCH error correcting code (BCH-1) and the second BCH error correcting code (BCH-2) of the COSPAS-SARAST transmitter. First, conduct the data coding process by 64-bit integral data; then, compute the BCH (127, 106) code and the BCH (63, 51) code to obtain the respective error correcting codes for improving coding rate; next, generate the baseband signal according to the GB 14391-2021 coding format; finally, modulate the baseband signal into a 406 MHz-RF signal, send it out and complete the task of distress alerting. The experimental results show that the proposed STM32 microcontroller-based baseband signal generation method is effective, and the EPIRB (Emergency Position Indicating Radio Beacon) designed based on this method has reached the industry standard.
Improved BIGRU Model and Its Application in Stock Price Forecasting
Authors: Duan Yuanshuai , Liu Yuanxin , Wang Yi , Ren Shangsheng , Wang Yibo
Published: June 2023

In order to obtain better prediction results, this paper combines improved complete ensemble EMD (ICEEMDAN) and the whale algorithm of multi-objective optimization (MOWOA) to improve the bidirectional gated recurrent unit (BIGRU), which makes full use of original complex stock price time series data and improves the hyperparameters of the BIGRU network. To address the problem that BIGRU cannot make full use of the stationary data, the original sequence data are processed using the ICEEMDAN decomposition algorithm to derive the non-stationary and stationary parts of the data and modeled with the BIGRU and the autoregressive integrated moving average model (ARIMA), respectively. The modeling process introduces a whale algorithm for multi-objective optimization, which improves the probability of finding the best combination of parameter vectors. The R2, MAPE, MSE, MAE, and RMSE values of the BIGRU algorithm, ICEEMDAN-BIGRU algorithm, MOWOA-BIGRU algorithm, and the improved algorithm were compared. An average improvement of 14.4% over the original algorithm’s goodness-of-fit value will greatly improve the accuracy of stock price predictions.
An AI-Enhanced Strategy of Service Offloading for IoV in Mobile Edge Computing
Authors: Peng Hongyu , Zhang Xiaosong , Li Hongwu , Xu Lexi , Wang Xiaodong
Published: June 2023

A full connected world is expected to be introduced in the sixth generation mobile network (6G). As a typical fully connected scenario, the internet of vehicle (IoV) enables intelligent vehicle operations via artificial intelligence (AI) and edge computing technologies. Thus, integrating intelligence into edge computing is, no doubt, a promising development trend. In the future of vehicular networks, a massive variety of services need powerful computing resources and higher quality of service (QoS). Existing computing resources are insufficient to match those increasing requirements. Most works on this problem focused on finding the power-delay’s trade-off, ignoring QoS and stable load balance. In this study, we found that the computing power and redundancy of vehicles’ in IoV is increasing. So, those redundant computing resources are possible to be used to solve the shortage of computing resource. CNN is a typical AI technique. This technology is very suitable for solving the problems in this article. So, we adopted CNN technique of AI to design and algorithm of convolutional long short-term memory (CN_LSTM) based traffic prediction (ACLBTP). ACLBTP was designed to gain the predicted number of vehicles belonging to the edge node. Secondly, according to the problem of insufficient computing resources on remote servers, we found that a large amount of redundant computing resources exist in edge nodes. So, we used edge computing technique to solve the problem of insufficient computing resources on remote servers. ASOBCL was designed to distribute computing tasks to edge nodes. Meanwhile, an intelligent service offloading framework was provided in this article. Based on the framework, an algorithm of improved gradient descent (AIGD) was created to accelerate the speed of iteration. So, the ACLBTP’s convergence of convolutional neural network (CNN) based on AIGD was able to be accelerated too. In ASOBCL, a sorting technique was adopted to speed up the offloading work. Simulation results demonstrated the fact that the prediction strategy designed in this paper had high accuracy. The low offloading time and maintaining stable load balance were gained via running ASOBCL. Low offloading time means short response time. Additionally, the QoS was guaranteed. So, these strategies designed in this paper were effective and valuable.
Machine Learning and Cochlear Implantation: Predicting the Post-Operative Electrode Impedances
Authors: Alohali Yousef A. , Fayed Mahmoud Samir , Abdelsamad Yassin , Almuhawas Fida , Alahmadi Asma , Mesallam Tamer , Hagr Abdulrahman
Published: June 2023

Cochlear implantation is the common treatment for severe to profound sensorineural hearing loss if there is no benefit from hearing aids. Measuring the electrode impedance along the electrode array at different time points after surgery is crucial in verifying the electrodes’ status, determining the compliance levels, and helping to identify the electric dynamic range. Increased impedance values without proper reprogramming can affect the patient’s performance. The prediction of acceptable levels of electrode impedance at different time points after the surgery could help clinicians during the fitting sessions through a comparison of the predicted with the measured levels. Accordingly, clinicians can decide if the measured levels are within the predicted normal range or not. In this work, we used a dataset of 80 pediatric patients who had received cochlear implants with the MED-EL FLEX 28 electrode array. We predicted the impedance of the electrode arrays in each channel at different time points: at one month, three months, six months, and one year after the date of surgery. We used different machine learning algorithms such as linear regression, Bayesian linear regression, decision forest regression, boosted decision tree regression, and neural networks. The used features include the patient’s age and the intra-operative electrode impedance at different electrodes. Our results indicated that the best algorithm varies depending on the channel, while the Bayesian linear regression and neural networks provide the best results for 75% of the channels. Furthermore, the accuracy level ranges between 83% and 100% in half of the channels one year after the surgery, when an error range between 0 and 3 KΩ is defined as an acceptable threshold. Moreover, the use of the patient’s age alone can provide the best prediction results for 50% of the channels at six months or one year after surgery. This reflects that the patient’s age could be a predictor of the electrode impedance after the surgery.
Economic Optimization Scheduling Based on Load Demand in Microgrids Considering Source Network Load Storage
Authors: He Yuling , Wu Xuewei , Sun Kai , Du Xiaodong , Wang Haipeng , Zhao Jianli
Published: June 2023

As a large number of flexible elements such as distributed power and flexible load are connected to microgrids, the economic improvement of microgrids has become an important topic through rational energy distribution. In this paper, an economic scheduling model considering the load demand for a microgrid system under the mechanism of a peak–valley tariff is proposed. A mathematical model of the microgrid components is proposed to determine the exchange power between the microgrid and main network. Meanwhile, an improved War Strategy Optimization (WSO) algorithm is proposed to investigate three scenarios: (i) without batteries, (ii) with batteries and (iii) with batteries and demand response. Additionally, demand response optimization is carried out with the Particle Swarm Optimization (PSO) algorithm and the improved WSO is compared with four other algorithms in scenario (iii). The comparison shows that the improved WSO algorithm has a better optimization performance in solving the proposed scheduling model.
Emergent Cooperation and Strategy Adaptation in Multi-Agent Systems: An Extended Coevolutionary Theory with LLMs
Authors: de Zarzà I. , de Curtò J. , Roig Gemma , Manzoni Pietro , Calafate Carlos T.
Published: June 2023

The increasing complexity of Multi-Agent Systems (MASs), coupled with the emergence of Artificial Intelligence (AI) and Large Language Models (LLMs), have highlighted significant gaps in our understanding of the behavior and interactions of diverse entities within dynamic environments. Traditional game theory approaches have often been employed in this context, but their utility is limited by the static and homogenous nature of their models. With the transformative influence of AI and LLMs on business and society, a more dynamic and nuanced theoretical framework is necessary to guide the design and management of MASs. In response to this pressing need, we propose an Extended Coevolutionary (EC) Theory in this paper. This alternative framework incorporates key aspects of coevolutionary dynamics, adaptive learning, and LLM-based strategy recommendations to model and analyze the strategic interactions among heterogeneous agents in MASs. It goes beyond game theory by acknowledging and addressing the diverse interactions (economic transactions, social relationships, information exchange) and the variability in risk aversion, social preferences, and learning capabilities among entities. To validate the effectiveness of the EC framework, we developed a simulation environment that enabled us to explore the emergence of cooperation and defection patterns in MASs. The results demonstrated the potential of our framework to promote cooperative behavior and maintain robustness in the face of disruptions. The dynamics and evolution of the Multi-Agent System over time were also visualized using advanced techniques. Our findings underscore the potential of harnessing LLMs to facilitate cooperation, enhance social welfare, and promote resilient strategies in multi-agent environments. Moreover, the proposed EC framework offers valuable insights into the interplay between strategic decision making, adaptive learning, and LLM-informed guidance in complex, evolving systems. This research not only responds to the current challenges faced in modeling MASs, but also paves the way for future research in this rapidly developing field.
A Streaming Data Processing Architecture Based on Lookup Tables
Authors: Yuemaier Aximu , Chen Xiaogang , Qian Xingyu , Dai Weibang , Li Shunfen , Song Zhitang
Published: June 2023

Processing in memory (PIM) is a new computing paradigm that stores the function values of some input modes in a lookup table (LUT) and retrieves their values when similar input modes are encountered (instead of performing online calculations), which is an effective way to save energy. In the era of the Internet of Things, the processing of massive data generated by the front-end requires low-power and real-time processing. This paper investigates an energy-efficient processing architecture based on table lookup in phase-change memory (PCM). This architecture replaces logical-based calculations with LUT lookups to minimize power consumption and operation latency. In order to improve the efficiency of table lookup, the RISC-V instruction set has included extended lookup and data stream transmission instructions. Finally, the system architecture is validated by hardware simulation, and the performance of computing the fast Fourier transform (FFT) application is evaluated. The proposed architecture effectively improves the execution efficiency and reduces the power consumption of data flow operations.
IoT Device Identification Method Based on Causal Inference
Authors: Wang Xingkui , Wu Yunhao , Yu Dan , Yang Yuli , Ma Yao , Chen Yongle
Published: June 2023

With the development of 5G, the number of IoT (Internet of Things) devices connected to the Internet will grow explosively. However, due to the vulnerability of the devices, attackers can launch attacks on the vulnerable IoT devices, causing great impact on the security of the network environment. Fine-grained identification of IoT devices can help network administrators set up appropriate security policies based on the functionality and heterogeneity of the devices, while enabling timely updates and upgrades for devices with security vulnerabilities or the isolation of these dangerous devices. However, most of the existing IoT device identification methods rely on a priori knowledge or expert experience in selecting features, which cannot weigh the identification performance and labor cost. In this paper, we design a fine-grained identification method for IoT devices based on causal inference, which automatically extracts key features in the protocol fields of device communication from the perspective of causality and then classifies key features using a Stacking integrated learning method to achieve high-precision and fine-grained device identification. Through experimental verification, the proposed method achieves 96.3% and 97.7% device model identification accuracy under HTTP/TCP and SSH/TCP protocol clusters.
Seismic Data Query Algorithm Based on Edge Computing
Authors: Quan Tenglong , Zhang Huifeng , Yu Yonghao , Tang Yongwei , Liu Fushun , Hao Hao
Published: June 2023

Edge computing can reduce the transmission pressure of wireless networks in earthquakes by pushing computing functionalities to network edges and avoiding the data transmission to cloud servers. However, this also leads to the scattered storage of data content in each edge server, increasing the difficulty of content search. This paper investigates the seismic data query problem supported by edge computing. We first design a lookup mechanism based on bloom filter, which can quickly determine if there is the information that we need on a particular edge server. Then, the MEC-based data query problem is formulated as an optimization problem whose goal is to minimize the long-term average task delay with the constraints of computing capacity of edge servers. To reduce the complexity of problem, we further transform it as a Markov Decision Process by defining state space, action space and reward function. A novel DQN-based seismic data query algorithm is proposed to solve problem effectively. Extensive simulation-based testing shows that the proposed algorithm performances better when compared with two state-of-the-art solutions.
Physical Variable Measurement Techniques for Fault Detection in Electric Motors
Authors: Aguayo-Tapia Sarahi , Avalos-Almazan Gerardo , Rangel-Magdaleno Jose de Jesus , Ramirez-Cortes Juan Manuel
Published: June 2023

Induction motors are widely used worldwide for domestic and industrial applications. Fault detection and classification techniques based on signal analysis have increased in popularity due to the growing use of induction motors in new technologies such as electric vehicles, automatic control, maintenance systems, and the inclusion of renewable energy sources in electrical systems, among others. Hence, monitoring, fault detection, and classification are topics of interest for researchers, given that the presence of a fault can lead to catastrophic consequences concerning technical and financial aspects. To detect a fault in an induction motor, several techniques based on different physical variables, such as vibrations, current signals, stray flux, and thermographic images, have been studied. This paper reviews recent investigations into physical variables, instruments, and techniques used in the analysis of faults in induction motors, aiming to provide an overview on the pros and cons of using a certain type of physical variable for fault detection. A discussion about the detection accuracy and complexity of the signals analysis is presented, comparing the results reported in recent years. This work finds that current and vibration are the most popular signals employed to detect faults in induction motors. However, stray flux signal analysis is presented as a promising alternative to detect faults under certain operating conditions where other methods, such as current analysis, may fail.
Studies of Engine Performance and Emissions at Full-Load Mode Using HVO, Diesel Fuel, and HVO5
Authors: Smigins Ruslans , Sondors Kristaps , Pirs Vilnis , Dukulis Ilmars , Birzietis Gints
Published: June 2023

The aim of the study was to determine impact of commercially available hydrotreated vegetable oil (HVO) and its mixture (HVO5, where 5% (v/v) HVO and 95% (v/v) FDD) with diesel fuel (FDD) on the power, torque, fuel consumption, and exhaust gas composition of an atmospheric internal combustion diesel engine used in off-road applications. Diesel fuel was used as the comparative fuel. Testing was realized in a full-load mode on the KOHLER KDI 1903 M 3-cylinder diesel engine on a SIERRA CP-Engineering engine test bench. The AVL SESAM FTIR exhaust gas analytical system was used to determine exhaust gas emissions, while the AVL KMA Mobile fuel consumption measuring device was used to measure fuel consumption. Research showed that the lowest power and torque readings were obtained with FDD, while HVO showed a slightly higher result compared to the fossil diesel fuel. At the same time, the highest hourly fuel consumption was observed running on HVO5, while the lowest was observed with FDD. Increases in carbon monoxide (CO), carbon dioxide (CO2), and nitrogen oxide (NOx) emissions were observed for HVO5 compared to those of FDD. The CO content in emissions increased by an average of 3.0% using HVO and by an average of 36% using HVO5, but the NOx content in the emissions increased by an average of 3.0% using HVO and by an average of 8.8% using HVO5. The reduction by an average of 60% using HVO in emissions was found in the case of hydrocarbons (HC). Research confirmed that the physicochemical properties of HVO could leave an impact on the main engine performance parameters and exhaust emissions.
The Learning Curve of People with Complete Spinal Cord Injury Using a NESs-FESs Interface in the Sitting Position: Pilot Study
Authors: Fiorin Felipe Augusto , Sartori Larissa Gomes , Méndez María Verónica González , Ferreira Christiane Henriques , França Maria Bernadete de Morais , Krueger Eddy
Published: June 2023

The use of assistive technologies, such as a non-invasive interface for neuroelectrical signal and functional electrical stimulation (NESs-FESs), can mitigate the effects of spinal cord injury (SCI), including impairment of motor, sensory, and autonomic functions. However, it requires an adaptation process to enhance the user’s performance by tuning the learning curve to a point of extreme relevance. Therefore, in this pilot study, the learning curves of two people with complete SCI (PA: paraplegic-T6, and PB: quadriplegic-C4) were analyzed, with results obtained on the accuracy of the classifier (AcCSP−LDA), repetitions of intra-day training, and number of hits and misses in the activation of FESs for sixteen interventions using the NESs-FESs interface. We assumed that the data were non-parametric and performed the Spearman’s ρ test (and p-value) for correlations between the data. There was variation between the learning curves resulting from the training of the NESs-FESs interface for the two participants, and the variation was influenced by factors both related and unrelated to the individual users. Regardless of these factors, PA improved significantly in its learning curve, as it presented lower values in all variables in the first interventions compared to the PB, although only PA showed statistical correlation (on AcCSP−LDA values in RLL). It was concluded that despite the variations according to factors intrinsic to the user and the functioning of the equipment used, sixteen interventions were sufficient to achieve a good learning effect to control the NESs-FESs interface.
Identification of Eight Pterocarpus Species and Two Dalbergia Species Using Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging (HSI)
Authors: Xue Xiaoming , Chen Zhenan , Wu Haoqi , Gao Handong , Nie Jiajie , Li Xinyang
Published: June 2023

Pterocarpus santalinus is considered among the finest luxury woods in the world and has potential commercial and medicinal value. Due to its rich hue and high price, Pterocarpus santalinus has often been substituted and mislabeled with other woods of lower economic value. To maintain the order of the timber market and the interests of consumers, it is necessary to establish a fast and reliable method for Pterocarpus species identification. In this study, wood samples of Pterocarpus santalinus and nine other wood samples commonly used for counterfeiting were analyzed by visible light/near-infrared (Vis/NIR) hyperspectral imaging (HSI). The spectral data were preprocessed with different algorithms. Principal component analysis (PCA) was applied in different spectral ranges: 400~2500 nm, 400~800 nm, and 800~2500 nm. Partial least squares discriminant analysis (PLS-DA) and square support vector machine (SVM) modeling methods were performed for effective discrimination. The best classification model was SVM combined with a normalization preprocessing method in whole spectral range (400~2500 nm), with prediction accuracy higher than 99.8%. The results suggest that the use of Vis/NIR-HSI in combination with chemometric approaches can be used as an effective tool for the discrimination of Pterocarpus santalinus.
Development of Soft Composite Based Anisotropic Synthetic Skin for Biomechanical Testing
Authors: Gupta Vivek , Singla Rohan , Singh Gurpreet , Chanda Arnab
Published: June 2023

Human skin exhibits highly varying mechanical properties, thickness, hardness, and anisotropy by virtue of changing fiber distributions and orientations, across different body locations. To date, only a few studies have computationally simulated skin anisotropy and no experimental study on synthetic skin exists which can mimic the accurate biomechanical properties of the skin. In this work, unique anisotropic synthetic skin samples were created using an elastic composite-based structure. Both single and multi-layer synthetic skin were fabricated with consistent fiber density and fiber dimensions and varying fiber angles to generate over 100 compositions. The compositions implied stress versus stretch responses in mechanical biaxial testing were compared to those of the skin of a person. Hyperelastic constitutive models were used to characterize the non-uniform test results. The created anisotropic synthetic skin must be essential for reliable Biomechanical investigation of skin free from ethical concerns, undertaking medical training and researching skin pathophysiology and injuries.
The Current State and Prospects of Recycling Silk Industry Waste into Nonwoven Materials
Authors: Sashina Elena , Yakovleva Olga
Published: June 2023

Natural fibres are the preferred options for garment, technical and medical textiles, nonwovens and composites. Their sustainability is a considerable advantage, though the nature of silk production and processing involves a large amount of waste. The present review explores the current issues of recycling silk waste into nonwovens for various purposes. The article proposes obtaining nonwovens from short fibres using electrospinning of fibroin solutions in volatile solvents. Longer fibres are proposed to be processed into needle-punched nonwoven materials with a selection of an effective antistatic treatment.
Virtual Cold Chain Method to Evaluate the Effect of Rising Temperature on the Quality Evolution of Peach Fruit
Authors: Liu Hui , Lv Zhenzhen , Yang Wenbo , Li Ang , Liu Jiechao , Zhang Qiang , Jiao Zhonggao
Published: June 2023

Poor temperature management along a cold chain leads to fruit quality deterioration and loss. In order to determine the threshold value of temperature fluctuation in a cold chain, peach fruits were stored in four different virtual cold chains applying different temperature–time scenarios. Core temperature profiling, the physicochemical qualities, and the activities of the peaches’ antioxidant enzymes were monitored during cold storage and shelf life. Abusive temperature management (temperature increased to 20 and 15 °C three times) resulted in a significant increase in a peach’s core temperature to the highest temperature measured: 17.6 °C. The ethylene production rate at the end of the shelf life of peaches under these temperatures was 21.03–28.16% higher than the constant-temperature group and accompanied by significantly lower levels of flesh firmness, titratable acid content, total phenol and flavonoid content, and peroxidase (POD) and catalase (CAT) activities (p < 0.05). The results of a principal component analysis (PCA) and heatmap confirmed the results. Limited temperature increases (10 °C) in a cold chain had little impact on the quality of the peaches, while temperature increases higher than 15 °C three times would negatively affect the quality of the peaches significantly. The temperature of a cold chain needs to be controlled precisely to reduce the loss of peaches.
Phenolic Profiles, Antioxidant, and Hypoglycemic Activities of Ribes meyeri Fruits
Authors: Zhang Le , Wang Qiang , Zhao Yayun , Ge Juan , He Dajun
Published: June 2023

Ribes meyeri is a Ribes genus in the Saxifragaceae family, which is used as both medicine and food. However, the active components and biological activities of R. meyeri fruits are still unknown. In this paper, the phenolic components and their antioxidant and hypoglycemic activities of R. meyeri fruits were studied. Firstly, a total of 42 phenolic components of R. meyeri fruits, including 26 anthocyanins, 9 flavonoids, and 7 phenolic acids, were tentatively identified using HPLC-QTOF-MS/MS, and the main four anthocyanins were quantified using UPLC-MS/MS. The result indicated that cyanidin-3-O-rutinoside is the main anthocyanin in the R. meyeri fruits. The anthocyanin fraction of R. meyeri fruits exhibited significant inhibitory activity on α-amylase and α-glucosidase. The anthocyanin fraction from R. meyeri fruits significantly increased the glucose uptake of 3T3-L1 adipocytes. This is the first study of a qualitative and quantitative analysis of the phenolics of R. meyeri fruits.
Optimization of Fermentation Conditions and Metabolite Profiling of Grape Juice Fermented with Lactic Acid Bacteria for Improved Flavor and Bioactivity
Authors: Kuerban Dilinu , Lu Jing , Huangfu Zekun , Wang Liang , Qin Yanan , Zhang Minwei
Published: June 2023

To enrich the flavor compounds and retain the content of polyphenolics in grape juice (GJ) under long-term storage, Lactiplantibacillus plantarum, Lactobacillus acidophilus, Lacticaseibacillus casei, and Lacticaseibacillus paracasei, were screened and the optimal fermentation conditions were determined as fermentation temperature of 41.2 °C for 24 h with an initial LAB density of 8.5 × 106 CFU/mL. Surprisingly, the retention rates of TPC still remained at 50% after storage for 45 days at 4 °C. Moreover, 251 different metabolites were identified, include 23 polyphenolics, 11 saccharides, and 9 organic acids. Most importantly, the total content of polyphenolics reserved was 92.65% at the end of fermentation. Among them, ephedrannin A content significantly decreased; however, 2′,6′-Di-O-acetylononin gradually increased with the fermentation time, which resulted in FGJ maintaining excellent bioactivity. Meanwhile, organic acid content (palmitoylethanolamide, tetraacetylethylenediamine) increased with saccharides (linamarin) decreasing, which leads to FGJ having a unique taste. Furthermore, a total of 85 Volatile organic compounds (VOCs) were identified, mainly including esters, aldehydes, and alcohols. Interestingly, key VOCs could be formed by carboxylic acids and derivatives, and fatty acyls via complex metabolic pathways.
Impact of Homegrown School Feeding Program on Smallholders’ Farmer Household Food Security in Northeastern Nigeria
Authors: Barnabas Bulus , Agyemang Sylvester , Zhllima Edvin , Bavorova Miroslava
Published: June 2023

In various countries of the global south, school feeding programs are implemented in order to alleviate short-time hunger in children, improved their nutrition and provide employment for food vendors. The impact of these programs is important not only in terms of pupils’ nutrition but also in improving farmers’ livelihoods productivity and food security. This study analyzes the impact of the school feeding program on smallholder farmers’ household food security based on data collected in 2021 from 240 farmers surveyed in northeast Nigeria. Differently from other studies, several econometric methods are used to analyze the data, namely, binary probit regression, propensity score matching, inverse probability weighted adjusted regression, and endogenous switching regression. The results show that about 40% of the beneficial smallholder farmers are food secure compared to 20% of the nonbeneficiary households. Findings revealed that the Homegrown school feeding program (HGSF) positively improved smallholder farmers household food security status across all the models. Results provide evidence for the need to expand school feeding programs vis-a-vis interventions in facilitating farmers’ access to capital and capacity building for better integration in the supply chain.
The Application of Ultraviolet Treatment to Prolong the Shelf Life of Chilled Beef
Authors: Teng Shuang , Gan Junlan , Chen Yu , Yang Liyuan , Ye Keping
Published: June 2023

This study simulated the storage conditions of chilled beef at retail or at home, and the sterilization and preservation effects of short-time ultraviolet irradiation were studied. The conditions of different irradiation distances (6 cm, 9 cm, and 12 cm) and irradiation times (6 s, 10 s, and 14 s) of ultraviolet (UV) sterilization in chilled beef were optimized, so as to maximally reduce the initial bacterial count, but not affect the quality of the chilled beef. Then, the preservation effect on the chilled beef after the optimized UV sterilization treatment during 0 ± 0.2 °C storage was investigated. The results showed that UV irradiation with parameters of 6 cm and 14 s formed the optimal UV sterilization conditions for the chilled beef, maximally reducing the number of microorganisms by 0.8 log CFU/g without affecting lipid oxidation or color change. The 6 cm and 14 s UV sterilization treatment of the chilled beef was able to reduce the initial microbial count, control the bacterial growth, and delay the increase in the TVB-N values during storage. Compared with the control group, the total bacterial count decreased by 0.56–1.51 log CFU/g and the TVB-N value decreased by 0.20–5.02 mg N/100 g in the UV-treated group. It was found that the TBARS value of the UV treatment group increased during late storage; on days 9–15 of storage, the TBARS values of the treatment group were 0.063–0.12 mg MDA/kg higher than those of the control group. However, UV treatment had no adverse impact on the pH, color, or sensory quality of chilled beef. These results prove that UV treatment can effectively reduce the microbial count on the surface of beef and improve its microbial safety, thus maintaining the quality of beef and prolonging its shelf life. This study could provide a theoretical basis for the preservation technology of chilled beef in small-space storage equipment.
Lincomycin HCl-Loaded Borneol-Based In Situ Gel for Periodontitis Treatment
Authors: Puyathorn Napaphol , Lertsuphotvanit Nutdanai , Chantadee Takron , Pichayakorn Wiwat , Phaechamud Thawatchai
Published: June 2023

Solvent exchange-induced in situ forming gel (ISG) has emerged as a versatile drug delivery system, particularly for periodontal pocket applications. In this study, we developed lincomycin HCl-loaded ISGs using a 40% borneol-based matrix and N-methyl pyrrolidone (NMP) as a solvent. The physicochemical properties and antimicrobial activities of the ISGs were evaluated. The prepared ISGs exhibited low viscosity and reduced surface tension, allowing for easy injection and spreadability. Gel formation increased the contact angle on agarose gel, while higher lincomycin HCl content decreased water tolerance and facilitated phase separation. The drug-loading influenced solvent exchange and matrix formation, resulting in thinner and inhomogeneous borneol matrices with slower gel formation and lower gel hardness. The lincomycin HCl-loaded borneol-based ISGs demonstrated sustained drug release above the minimum inhibitory concentration (MIC) for 8 days, following Fickian diffusion and fitting well with Higuchi’s equation. These formulations exhibited dose-dependent inhibition of Staphylococcus aureus ATCC 25923, Escherichia coli ATCC 8739, and Prophyromonas gingivalis ATCC 33277, and the release of NMP effectively inhibited Candida albicans ATCC 10231. Overall, the 7.5% lincomycin HCl-loaded 40% borneol-based ISGs hold promise as localized drug delivery systems for periodontitis treatment.
A Lycopene ε-Cyclase TILLING Allele Enhances Lycopene and Carotenoid Content in Fruit and Improves Drought Stress Tolerance in Tomato Plants
Authors: Petrozza Angelo , Summerer Stephan , Melfi Donato , Mango Teresa , Vurro Filippo , Bettelli Manuele , Janni Michela , Cellini Francesco , Carriero Filomena
Published: June 2023

In the scenario of climate change, the availability of genetic resources for tomato cultivation that combine improved nutritional properties and more tolerance to water deficiency is highly desirable. Within this context, the molecular screenings of the Red Setter cultivar-based TILLING platform led to the isolation of a novel lycopene ε-cyclase gene (SlLCY-E) variant (G/3378/T) that produces modifications in the carotenoid content of tomato leaves and fruits. In leaf tissue, the novel G/3378/T SlLCY-E allele enhances β,β-xanthophyll content at the expense of lutein, which decreases, while in ripe tomato fruit the TILLING mutation induces a significant increase in lycopene and total carotenoid content. Under drought stress conditions, the G/3378/T SlLCY-E plants produce more abscisic acid (ABA) and still conserve their leaf carotenoid profile (reduction of lutein and increase in β,β-xanthophyll content). Furthermore, under said conditions, the mutant plants grow much better and are more tolerant to drought stress, as revealed by digital-based image analysis and in vivo monitoring of the OECT (Organic Electrochemical Transistor) sensor. Altogether, our data indicate that the novel TILLING SlLCY-E allelic variant is a valuable genetic resource that can be used for developing new tomato varieties, improved in drought stress tolerance and enriched in fruit lycopene and carotenoid content.
Methane Concentration Forecasting Based on Sentinel-5P Products and Recurrent Neural Networks
Authors: Psomouli Theofani , Kansizoglou Ioannis , Gasteratos Antonios
Published: June 2023

The increase in the concentration of geological gas emissions in the atmosphere and particularly the increase of methane is considered by the majority of the scientific community as the main cause of global climate change. The main reasons that place methane at the center of interest, lie in its high global warming potential (GWP) and its lifetime in the atmosphere. Anthropogenic processes, like engineering geology ones, highly affect the daily profile of gasses in the atmosphere. Should direct measures be taken to reduce emissions of methane, immediate global warming mitigation could be achieved. Due to its significance, methane has been monitored by many space missions over the years and as of 2017 by the Sentinel-5P mission. Considering the above, we conclude that monitoring and predicting future methane concentration based on past data is of vital importance for the course of climate change over the next decades. To that end, we introduce a method exploiting state-of-the-art recurrent neural networks (RNNs), which have been proven particularly effective in regression problems, such as time-series forecasting. Aligned with the green artificial intelligence (AI) initiative, the paper at hand investigates the ability of different RNN architectures to predict future methane concentration in the most active regions of Texas, Pennsylvania and West Virginia, by using Sentinel-5P methane data and focusing on computational and complexity efficiency. We conduct several empirical studies and utilize the obtained results to conclude the most effective architecture for the specific use case, establishing a competitive prediction performance that reaches up to a 0.7578 mean squared error on the evaluation set. Yet, taking into consideration the overall efficiency of the investigated models, we conclude that the exploitation of RNN architectures with less number of layers and a restricted number of units, i.e., one recurrent layer with 8 neurons, is able to better compensate for competitive prediction performance, meanwhile sustaining lower computational complexity and execution time. Finally, we compare RNN models against deep neural networks along with the well-established support vector regression, clearly highlighting the supremacy of the recurrent ones, as well as discuss future extensions of the introduced work.
Water Erosion Risk Assessment for Conservation Planning in the East Hararghe Zone, Ethiopia
Authors: Woldemariam Gezahegn Weldu , Yasin Kalid Hassen , Iguala Anteneh Derribew
Published: June 2023

Water erosion is accelerating soil loss rates in the East Hararghe Zone due to inappropriate human activities and their complex and intertwined interactions with natural factors, particularly in sensitive agroecosystems that lack soil and water conservation (SWC) measures. Although these dynamic processes cause prolonged impacts, a comprehensive assessment of the risk of soil erosion has not yet been undertaken at the zonal level. To bridge this gap, we employed the revised universal soil loss equation (RUSLE) prediction model, along with remote sensing and geographic information systems (GIS), to estimate annual soil erosion rates, analyze the temporal-spatial patterns of erosion risk, and evaluate the potential of standard conservation practices to reduce soil loss in croplands. Total soil erosion (in millions of tonnes/year; Mt yr−1) was estimated to be 9 in 1990, 14 in 2000, 12 in 2010, and 11 in 2020, with average rates of 33, 50, 44, and 39 t ha−1 yr−1, respectively. This suggests an overall 18% increase in soil erosion from 1990 to 2020. Over 75% of the area showed a tolerable soil loss rate (<10 t ha−1 yr−1) and low susceptibility to erosion risk. A mountainous landscape in the northwest presents extremely high erosion (>120 t ha−1 yr−1), which accounts for more than 80% of soil loss, making SWC planning a priority. Analysis of land-use land-cover change (LULCC) confirmed a higher increase in soil loss for LULCC that involved conversion to croplands, with average rates of 36.4 t ha−1 yr−1 (1990–2000), 70 t ha−1 yr−1 (2000–2010), and 36 t ha−1 yr−1 (2010–2020). The results have further revealed that implementing supportive practices such as terracing, stripping, and contouring could reduce average soil erosion by approximately 87%, 65%, and 29%, respectively, compared to the baseline model’s prediction. Therefore, a rigorous cost–benefit analysis is essential to design and implement optimal location-specific practices that maximize investment returns in SWC efforts and ecological restoration. However, we acknowledge the limitations of this study, associated with an empirical model that does not account for all forms of erosion, as well as reliance mainly on secondary data, which may affect the accuracy of the predicted outcomes.