bluuebunny/crossref_metadata_2025 huggingface dataset:
Overlay Optimization Algorithm for Directed Sensor Networks with Virtual Force and Particle Swarm Optimization Synergy
Authors: Zhu Lingjian , Lin Li , Liang Qi , Lu Yaling , Tan Haonan , Ma Xuan , Zhang Dongya
Published: October 2023

In this study, a novel algorithm for optimizing the coverage of directed sensor networks is proposed. The deployment of sensor networks is typically random, leading to the potential issues of extensive coverage overlaps and blind areas. To address this challenge and enhance the effectiveness of network coverage, a directional sensor network coverage optimization algorithm is developed based on the principles of virtual force and particle swarm optimization. Firstly, the article introduces the concept of a segmented virtual negative centroid model. This model revolutionizes the configuration of the virtual negative centroid, thereby enabling a more efficient adjustment of the gravitational forces exerted by the coverage blind areas on the sensor nodes. Therefore, the influence of these blind areas on the improvement of network coverage is significantly amplified. Secondly, taking into account the characteristics of global optimization and the inherent randomness of particle swarm optimization, the algorithm synergistically combines the principles of virtual force and particle swarm optimization. This integration effectively fine-tunes the sensing direction of the sensor nodes, thereby optimizing their overall performance. The algorithm in this study incorporates an adjusted inertia weight strategy and introduces Gaussian disturbance in the local optimization enhancement phase to prevent local optimization, accelerate particle convergence, and facilitate the sensor network’s attainment of an optimal distribution for coverage optimization. Simulation experiments were conducted to verify the algorithm’s effectiveness. The initial sensor network coverage was 31.04%. After applying the algorithm, the average coverage increased to 80.16%, with a maximum coverage of 84.2%. These results verify the effectiveness of the algorithm.
A Blockchain-Based Framework for Rural Property Rights Transactions
Authors: Hua Caijian , Wu Sichao , Zhang Yan , Luo Kun , Li Miaomiao , Fu Jiaguo
Published: October 2023

Currently, there are several issues in China’s rural property rights trading, including poor credit reporting, low transaction efficiency, non-standardized transaction contracts, and high costs for rights protection. However, blockchain technology, known for its convenience, security, and traceability, is highly compatible with the requirements of standardization, convenience, and agility in rural property rights trading. Therefore, we propose a system framework for rural property rights trading based on the Hyperledger Fabric consortium blockchain. This framework significantly enhances transaction efficiency, improves the security of rural property rights trading, effectively resolves contract disputes in property rights transactions, and promotes rural revitalization and development. Furthermore, we design the information data structure on the blockchain, present the information flow, and develop smart contracts that utilize automation capabilities to automatically summarize, analyze, and issue alerts based on on-chain data, enabling risk assessment for both buyers and sellers. Additionally, we introduce an improved PBFT consensus algorithm called CA-PBFT, which is based on the PBFT consensus protocol and integrates a scoring model and simplified consensus protocol. CA-PBFT efficiently reduces transaction response latency and improves information throughput. Finally, we develop a prototype system for rural property rights trading and perform a performance comparison and analysis of CA-PBFT against other PBFT-based consensus algorithms in this prototype system. Experimental results demonstrate the feasibility of the prototype system framework for rural property rights trading.
Investigation of the Effect of Different SiNx Thicknesses on the Characteristics of AlGaN/GaN High-Electron-Mobility Transistors in Ka-Band
Authors: Hsu Che-Wei , Lin Yueh-Chin , Lee Ming-Wen , Chang Edward-Yi
Published: October 2023

The effect of different SiNx thicknesses on the performance of AlGaN/GaN high-electron-mobility transistors (HEMTs) was investigated in this paper. The current, transconductance (Gm), cut-off frequency (fT), maximum oscillation frequency (fmax), power performance, and output third-order intercept point (OIP3) of devices with three different SiNx thicknesses (150 nm, 200 nm, and 250 nm) were measured and analyzed. The DC measurements revealed an increase in both the drain-source current (IDS) and Gm values of the device with increasing SiNx thickness. The S-parameter measurement results show that devices with a higher SiNx thickness exhibit improved fT and fmax. Regarding power performance, thicker SiNx devices also improve the output power density (Pout) and power-added efficiency (PAE) in the Ka-band. In addition, the two-tone measurement results at 28 GHz show that the OIP3 increased from 35.60 dBm to 40.87 dBm as the SiNx thickness increased from 150 nm to 250 nm. The device’s characteristics improved by appropriately increasing the SiNx thickness.
Infrared and Visible Image Fusion Based on Mask and Cross-Dynamic Fusion
Authors: Fu Qiang , Fu Hanxiang , Wu Yuezhou
Published: October 2023

Both single infrared and visible images have respective limitations. Fusion technology has been developed to conquer these restrictions. It is designed to generate a fused image with infrared information and texture details. Most traditional fusion methods use hand-designed fusion strategies, but some are too rough and have limited fusion performance. Recently, some researchers have proposed fusion methods based on deep learning, but some early fusion networks cannot adaptively fuse images due to unreasonable design. Therefore, we propose a mask and cross-dynamic fusion-based network called MCDFN. This network adaptively preserves the salient features of infrared images and the texture details of visible images through an end-to-end fusion process. Specifically, we designed a two-stage fusion network. In the first stage, we train the autoencoder network so that the encoder and decoder learn feature extraction and reconstruction capabilities. In the second stage, the autoencoder is fixed, and we employ a fusion strategy combining mask and cross-dynamic fusion to train the entire fusion network. This strategy is conducive to the adaptive fusion of image information between infrared images and visible images in multiple dimensions. On the public TNO dataset and the RoadScene dataset, we selected nine different fusion methods to compare with our proposed method. Experimental results show that our proposed fusion method achieves good results on both datasets.
Adaptive Spectrum Anti-Jamming in UAV-Enabled Air-to-Ground Networks: A Bimatrix Stackelberg Game Approach
Authors: Cheng Longbo , Xu Zixuan , Zhou Jianshan , Tian Daxin , Duan Xuting , Qu Kaige , Zhao Dezong
Published: October 2023

Anti-jamming communication technology is one of the most critical technologies for establishing secure and reliable communication between unmanned aerial vehicles (UAVs) and ground units. The current research on anti-jamming technology focuses primarily on the power and spatial domains and does not target the issue of intelligent jammer attacks on communication channels. We propose a game-theoretical center frequency selection method for UAV-enabled air-to-ground (A2G) networks to address this challenge. Specifically, we model the central frequency selection problem as a Stackelberg game between the UAV and the jammer, where the UAV is the leader and the jammer is the follower. We develop a formal matrix structure for characterizing the payoff of the UAV and the jammer and theoretically prove that the mixed Nash equilibrium of such a bimatrix Stackelberg game is equivalent to the optimal solution of a linear programming model. Then, we propose an efficient game algorithm via linear programming. Building on this foundation, we champion an efficacious algorithm, underpinned by our novel linear programming solution paradigm, ensuring computational feasibility with polynomial time complexity. Simulation experiments show that our game-theoretical approach can achieve Nash equilibrium and outperform traditional schemes, including the Frequency-Hopping Spread Spectrum (FHSS) and the Random Selection (RS) schemes, in terms of higher payoff and better stability.
Combining MUSHRA Test and Fuzzy Logic in the Evaluation of Benefits of Using Hearing Prostheses
Authors: Szymański Piotr , Poremski Tomasz , Kostek Bożena
Published: October 2023

Assessing the effectiveness of hearing aid fittings based on the benefits they provide is crucial but intricate. While objective metrics of hearing aids like gain, frequency response, and distortion are measurable, they do not directly indicate user benefits. Hearing aid performance assessment encompasses various aspects, such as compensating for hearing loss and user satisfaction. The authors suggest enhancing the widely used APHAB (Abbreviated Profile of Hearing Aid Benefit) questionnaire by integrating it with the MUSHRA test. APHAB, a self-completed questionnaire for users, evaluates specific sound scenarios on a seven-point scale, with each point described by a letter, percentage, and description. Given the complexities, especially for older users, we propose converting the seven-point APHAB scale to a clearer 100-point MUSHRA scale using fuzzy logic rules. The paper starts with presenting the goals of the study, focused on the assessment of the benefits of hearing aid use, especially in the case of the elderly population. The introductory part includes an overview of methods for evaluating the effectiveness of hearing aid use. Then, the methodology for the data collection is presented. This is followed by a method modification that combines the MUSHRA (MUltiple Stimuli with Hidden Reference and Anchor) test and fuzzy logic processing and the commonly used hearing aid benefit assessment questionnaire, APHAB (Abbreviated Profile of Hearing Aid Benefit). The results of such a process are examined. A summary of the findings is given in the form of fuzzy logic-based rules, followed by a short discussion. Finally, the overall conclusion and possible future directions for the method development are presented.
Anomaly Detection in a Smart Microgrid System Using Cyber-Analytics: A Case Study
Authors: Thulasiraman Preetha , Hackett Michael , Musgrave Preston , Edmond Ashley , Seville Jared
Published: October 2023

Smart microgrids are being increasingly deployed within the Department of Defense. The microgrid at Marine Corps Air Station (MCAS) Miramar is one such deployment that has fostered the integration of different technologies, including 5G and Advanced Metering Infrastructure (AMI). The objective of this paper is to develop an anomaly detection framework for the smart microgrid system at MCAS Miramar to enhance its cyber-resilience. We implement predictive analytics using machine learning to deal with cyber-uncertainties and threats within the microgrid environment. An autoencoder neural network is implemented to classify and identify specific cyber-attacks against this infrastructure. Both network traffic in the form of packet captures (PCAP) and time series data (from the AMI sensors) are considered. We train the autoencoder model on three traffic data sets: (1) Modbus TCP/IP PCAP data from the hardwired network apparatus of the smart microgrid, (2) experimentally generated 5G PCAP data that mimic traffic on the smart microgrid and (3) AMI smart meter sensor data provided by the Naval Facilities (NAVFAC) Engineering Systems Command. Distributed denial-of-service (DDoS) and false data injection attacks (FDIA) are synthetically generated. We show the effectiveness of the autoencoder on detecting and classifying these types of attacks in terms of accuracy, precision, recall, and F-scores.
An Interleaved Battery Charger Circuit for a Switched Capacitor Inverter-Based Standalone Single-Phase Photovoltaic Energy Management System
Authors: Patel Manesh , Zhou Zhongfu
Published: October 2023

A single-phase bidirectional DC/DC battery charger with a P&O MPPT current control strategy for a standalone energy management system has been integrated with an interleaved switched capacitor DC/AC inverter with an RMS feedback phase-shifted unipolar sinusoidal PWM control strategy. In the published literature, P&O MPPT control is used to drive a boost converter connected in parallel to a battery charger; this modified strategy combines a P&O MPPT algorithm with current control to drive an interleaving buck-boost battery charger. This battery charger circuit is connected in parallel to a closed loop controlled interleaved inverter that feeds the AC home load. MATLAB/Simulink based simulation circuit was developed and used to validate the successful integration of the interleaved battery charger with the global system. To do this, the system is tested with varying input conditions of irradiance and temperature. The system’s response to these variable inputs is monitored and analysed. The simulation results show the proposed method is effective for standalone battery-based PV systems. The system provides a more efficient and faster response compared with both an interleaved and non-interleaved voltage-controlled battery charger circuit that is also integrated with the global system. This battery charger control strategy is also shown to protect the battery from over-charging as well as discharging below 25%, which can improve and protect the long-term battery performance. Compared with novel industry approaches, the proposed system is simpler by nature due to the reduced number of conversions and therefore a reduced number of components which provides economic advantages.
Fast Power System Transient Stability Simulation
Authors: Kumissa Teshome Lindi , Shewarega Fekadu
Published: October 2023

Power system transient stability simulation is of critical importance for utilities to assess dynamic security. Most of the commercially available tools use the traditional numerical integration method to simulate power system transient stability, which is computationally intensive and has low simulation speed. This makes it difficult to identify any insecure contingency before it happens. It is already proven that power system transient stability simulation achieved using the differential transformation method (DTM) requires less computational effort and has improved simulation speed, but it still requires further improvement regarding its accuracy and performance efficiency. This paper introduces a novel power system transient stability simulation method based on the adaptive step-size differential transformation method. Using the proposed method, the step size is varied based on the estimated local solution error at each time step. The accuracy and speed of the proposed simulation approach are investigated in comparison with the classical differential transformation method and the traditional numerical integration method using the IEEE 9 bus and 39 bus test systems. The simulation results reveal that the proposed method increases the simulation speed by 20–44.57% and 83–92% when compared with the classical DTM and traditional numerical-integration-based simulation methods, respectively. It is also proved that compared with the DTM-based simulation, the proposed method provides 45.27% to 58.85% and more than 90% accurate simulation results for IEEE 9 and IEEE 39 test systems, respectively. Therefore the proposed power system transient stability simulation method is faster and relatively more accurate and can be applied for online transient stability monitoring of power system networks.
Cooling Technologies for Internet Data Center in China: Principle, Energy Efficiency, and Applications
Authors: Huang Xiaofei , Yan Junwei , Zhou Xuan , Wu Yixin , Hu Shichen
Published: October 2023

The highlighted energy consumption of Internet data center (IDC) in China has become a pressing issue with the implementation of the Chinese dual carbon strategic goal. This paper provides a comprehensive review of cooling technologies for IDC, including air cooling, free cooling, liquid cooling, thermal energy storage cooling and building envelope. Firstly, the environmental requirements for the computer room and the main energy consumption items for IDC are analyzed. The evaluation indicators and government policies for promoting green IDC are also summarized. Next, the traditional cooling technology is compared to four new cooling technologies to find effective methods to maximize energy efficiency in IDC. The results show that traditional cooling consumes a significant amount of energy and has low energy efficiency. The application of free cooling can greatly improve the energy efficiency of IDC, but its actual implementation is highly dependent on geographical and climatic conditions. Liquid cooling, on the other hand, has higher energy efficiency and lower PUE compared to other cooling technologies, especially for high heat density servers. However, it is not yet mature and its engineering application is not widespread. In addition, thermal energy storage (TES) based cooling offers higher energy efficiency but must be coupled with other cooling technologies. Energy savings can also be achieved through building envelope improvements. Considering the investment and recovery period for IDC, it is essential to seek efficient cooling solutions that are suitable for IDC and take into account factors such as IDC scale, climate conditions, maintenance requirements, etc. This paper serves as a reference for the construction and development of green IDC in China.
Multi-Scale Seismic Measurements for Site Characterization and CO2 Monitoring in an Enhanced Oil Recovery/Carbon Capture, Utilization, and Sequestration Project, Farnsworth Field, Texas
Authors: El-kaseeh George , McCormack Kevin L.
Published: October 2023

To address the challenges of climate change, significantly more geologic carbon sequestration projects are beginning. The characterization of the subsurface and the migration of the plume of supercritical carbon dioxide are two elements of carbon sequestration that can be addressed through the use of the available seismic methods in the oil and gas industry. In an enhanced oil recovery site in Farnsworth, TX, we employed three separate seismic techniques. The three-dimensional (3D) surface seismic survey required significant planning, design, and processing, but produces both a better understanding of the subsurface structure and a three-dimensional velocity model, which is essential for the second technique, a timelapse vertical seismic profile, and the third technique, cross-well seismic tomography. The timelapse 3D Vertical Seismic Profile (3D VSP) revealed both significant changes in the reservoir between the second and third surveys and geo-bodies that may represent the extent of the underground carbon dioxide. The asymmetry of the primary geo-body may indicate the preferential migration of the carbon dioxide. The third technique, cross-well seismic tomography, suggested a strong correlation between the well logs and the tomographic velocities, but did not observe changes in the injection interval.
Impact of Renewable and Non-Renewable Energy Consumption and CO2 Emissions on Economic Growth in the Visegrad Countries
Authors: Suproń Błażej , Myszczyszyn Janusz
Published: October 2023

This study presents empirical results regarding the relationships between renewable and non-renewable energy consumption, CO2 emissions, and the GDP within the Visegrád Group (V4) countries. Using FMOLS/DOLS and ARDL approaches, along with causality tests based on the Toda–Yamamoto method, this study explores these relationships at a regional level. The study fills the research gaps for a comprehensive analysis of the indicated relationships in the V4 countries by using both individual and panel models. The findings indicate that renewable energy has a small positive impact on long-term economic growth, with non-renewable energy having a more significant effect. Moreover, CO2 emissions have a negative impact on economic growth, suggesting an ongoing reliance on non-renewable energy sources and a burden on economic expansion. At an individual country level, the effects vary. Poland, Slovakia, and Hungary exhibit a negative relationship between CO2 emissions and economic growth. Energy sources also differ in regards to impact: in Poland, the Czech Republic, and Slovakia, non-renewable energy significantly affects economic growth, while in Hungary, renewable energy plays a more substantial role. Causality tests reveal a causal relationship between CO2 emissions and economic growth in the Czech Republic and Poland, suggesting that CO2 emissions significantly influence economic expansion. In terms of energy production, renewable energy is causally related to economic growth in the Czech Republic and Slovakia. All countries demonstrate significant causality between non-renewable energy and economic growth. Additionally, a relationship between renewable energy and CO2 emissions is confirmed in Poland.
Early Childhood Care in Spain before the Lockdown
Authors: Álvarez-Vega Esther , Grande-de-Prado Mario
Published: October 2023

It is widely assumed that every action in Early Childhood Care (ECC) must be conducted taking into consideration the well-being of the family and relying on their active involvement. It becomes essential to implement an intervention strategy that encompasses not only the children but also their immediate environment and the entire family unit. In this context, the Family-Centred Approach (FCA) has emerged as a prominent methodology. This approach can be defined as a collaborative connection between professionals and families, characterised by mutual respect and acknowledgment of their values and choices. Moreover, it entails extending assistance when required, all aimed at fostering optimal functioning of the familial core. This article aims to understand perceptions of families and professionals concerning Early Intervention in Spain before the lockdown. For this, a scoping review was conducted by mapping articles related to Spain in Scopus or Dialnet (CIRC A+, A, B, or C) before the COVID-19 lockdown. After reviewing thirty-five chosen articles, the identified topics were categorised into emotions, diagnoses, and requirements. Many of the publications fulfilled the CIRC B criterion, with a minority of articles meeting this criterion in the latter portion of the present decade. This subject matter warrants further research. There is a need for effective Early Childhood Care (ECC), characterised by early detection and diagnosis, involving well-trained professionals, and which recognises the importance of families in interventions and children development.
Impacts of Fermentation on the Phenolic Composition, Antioxidant Potential, and Volatile Compounds Profile of Commercially Roasted Coffee Beans
Authors: Tan Yuanyuan , Wu Hanjing , Shi Linghong , Barrow Colin , Dunshea Frank R. , Suleria Hafiz A. R.
Published: October 2023

Fermented coffee beans are believed to have significantly different compositions of phenolic and volatile compounds and physicochemical properties compared to unfermented coffee beans. This study evaluated the effects of fermentation on coffee beans at a commercially roasted level by characterizing their phenolic compounds and semi-quantifying their volatile compounds using liquid chromatography–electrospray ionization quadrupole time-of-flight mass spectrometry (LC-ESI-QTOF-MS/MS) and headspace/gas chromatography–mass spectrometry (HS-SPME-GC-MS). Coffee beans from two varieties of Coffea arabica, Geisha (G) and Bourbon (B), both fermented beans had higher contents of total phenolic compounds (G: 33.52 mg/g; B: 29.95 mg/g), total flavonoid (G: 0.42 mg/g; B: 0.35 mg/g), total tannins (G: 3.49 mg/g; B: 3.18 mg/g), and higher antioxidant potential in all assays. In total, 131 phenolic compounds were tentatively characterized via LC-ESI-QTOF-MS/MS, where 73 and 65 phenolic compounds were characterized from fermented Geisha and Bourbon, respectively. Regarding GC-MS, the fermented coffee beans had higher levels of phenols, pyrazines, furan, and furanic compounds. These findings substantiated that fermented coffee beans exhibit elevated levels of phenolic and volatile compounds and greater antioxidant activity, which could contribute to relatively higher nutritional values and organoleptic properties.
Inadequate Sampling Frequency and Imprecise Taxonomic Identification Mask Results in Studies of Migratory Freshwater Fish Ichthyoplankton
Authors: Pompeu Paulo Santos , Wouters Lídia , Hilário Heron Oliveira , Loures Raquel Coelho , Peressin Alexandre , Prado Ivo Gavião , Suzuki Fábio Mineo , Carvalho Daniel Cardoso
Published: October 2023

In South America, knowledge of major spawning sites is crucial for maintaining migratory fish populations. In this study, we aimed to understand the spatio-temporal distribution of fish eggs in the upper São Francisco River using high sampling frequency and DNA metabarcoding identification. We evaluated the possible effects of the non-molecular identification of eggs and decreased sampling frequency on the determination of spawning sites and major breeding periods. Collections were carried out every three days from November 2019 to February 2020. We found that, if we had assumed that all of the free and non-adhesive sampled eggs belonged to migratory species, as is usual in the literature, this assumption would have been wrong for both the spawning sites and the breeding periods. Moreover, any decrease in the frequency of sampling could dramatically affect the determination of the major spawning rivers, and the spawning events of some of the migratory species may not have been detected. Therefore, without the proper identification and adequate sampling frequency of eggs, important spawning sites may be overlooked, leading to ineffective or inappropriate conservation measures.
Numerical Simulation of Taylor—Couette—Poiseuille Flow at Re = 10,000
Authors: Gavrilov Andrey , Ignatenko Yaroslav
Published: October 2023

A fully developed turbulent flow in a concentric annulus, Re =10,000, ri/ro=0.5, with an inner rotating cylinder in the velocity range N=Uω/Ub=0÷4, is studied via a large-eddy simulation. Also, for comparison, simulations by steady-state, unstatiounary RANS k-ω SST (URANS), and Elliptic Blending Model (EBM) were made. The main focus of this study is on the effect of high rotation on the mean flow, turbulence statistics, and vortex structure. Distribution of the tangential velocity and the Reynolds stress tensor change their behaviour at N>0.5∼1. With rotation increases, the production of tangential fluctuation becomes dominant over axial ones and the position of turbulent kinetic energy maximum shifts towards the wall into the buffer zone. URANS and EBM approaches show good agreement with LES in mean flow, turbulent statistics, and integral parameters. The difference in pressure loss prediction between LES and URANS does not exceed 20%, but the average difference is about 11%. The EBM approach underestimates pressure losses up to 9% and on average not more than 5%. Vortex structures are described well by URANS.
Recent Trends in Valorization of Food Industry Waste and By-Products: Encapsulation and In Vitro Release of Bioactive Compounds
Authors: Borah Mriganka Shekhar , Tiwari Ajita , Sridhar Kandi , Narsaiah Kairam , Nayak Prakash Kumar , Stephen Inbaraj Baskaran
Published: October 2023

Food by-products and waste are a boundless source of bioactives, nutraceuticals, and naturally occurring substances that are good for human health. In fact, a lot of by-products and wastes are generated by several food businesses. Therefore, waste management and by-product utilization are the most important aspects of the food sector. According to various studies, many bioactive compounds such as phenolics, carotenoids, and proteins can be recovered as feed stock from various industries’ by-products and wastes using potential technologies. As a result, current trends are shifting attention to the sustainable valorisation of food sector waste management and by-products utilization. Thus, the circular economy principles have been applied to the field of food science. The aim of the circular economy is to ensure environmental protection and promote economic development while minimizing the environmental impact of food production. All of these aspects of the circular economy, at present, have become a challenging area of research for by-product valorisation as well. Hence, this review aims to highlight the emerging trends in the efficient utilization of food industry waste and by-products by focusing on innovative encapsulation techniques and controlled release mechanisms of bioactive compounds extracted from food industry waste and by-products. This review also aims to suggest future research directions, and addresses regulatory and toxicity considerations, by fostering knowledge dissemination and encouraging eco-friendly approaches within the food industry. This review reveals the role of encapsulation strategies for the effective utilization of bioactive compounds extracted from food industry waste and by-products. However, further research is needed to address regulatory and toxicity considerations of encapsulated bioactive compounds and health-related concerns.
Epidemiological Data Mining for Assisting with Foodborne Outbreak Investigation
Authors: Tao Dandan , Zhang Dongyu , Hu Ruofan , Rundensteiner Elke , Feng Hao
Published: October 2023

Diseases caused by the consumption of food are a significant but avoidable public health issue, and identifying the source of contamination is a key step in an outbreak investigation to prevent foodborne illnesses. Historical foodborne outbreaks provide rich data on critical attributes such as outbreak factors, food vehicles, and etiologies, and an improved understanding of the relationships between these attributes could provide insights for developing effective food safety interventions. The purpose of this study was to identify hidden patterns underlying the relations between the critical attributes involved in historical foodborne outbreaks through data mining approaches. A statistical analysis was used to identify the associations between outbreak factors and food sources, and the factors that were strongly significant were selected as predictive factors for food vehicles. A multinomial prediction model was built based on factors selected for predicting “simple” foods (beef, dairy, and vegetables) as sources of outbreaks. In addition, the relations between the food vehicles and common etiologies were investigated through text mining approaches (support vector machines, logistic regression, random forest, and naïve Bayes). A support vector machine model was identified as the optimal model to predict etiologies from the occurrence of food vehicles. Association rules also indicated the specific food vehicles that have strong relations to the etiologies. Meanwhile, a food ingredient network describing the relationships between foods and ingredients was constructed and used with Monte Carlo simulation to predict possible ingredients from foods that cause an outbreak. The simulated results were confirmed with foods and ingredients that are already known to cause historical foodborne outbreaks. The method could provide insights into the prediction of the possible ingredient sources of contamination when given the name of a food. The results could provide insights into the early identification of food sources of contamination and assist in future outbreak investigations. The data-driven approach will provide a new perspective and strategies for discovering hidden knowledge from massive data.
Development of a Low-Cost Artificial Vision System as an Alternative for the Automatic Classification of Persian Lemon: Prototype Test Simulation
Authors: Granados-Vega Bridget V. , Maldonado-Flores Carlos , Gómez-Navarro Camila S. , Warren-Vega Walter M. , Campos-Rodríguez Armando , Romero-Cano Luis A.
Published: October 2023

In the present research work, an algorithm of artificial neural network (ANN) has been developed based on the processing of digital images of Persian lemons with the aim of optimizing the quality control of the product. For this purpose, the physical properties (weight, thickness of the peel, diameter, length, and color) of 90 lemons selected from the company Esperanza de San José Ornelas SPR de RL (Jalisco, Mexico) were studied, which were divided into three groups (Category “extra”, Category I, and Category II) according to their characteristics. The parameters of weight (26.50 ± 3.00 g), diameter/length (0.92 ± 0.08) and thickness of the peel (1.50 ± 0.29 mm) did not present significant differences between groups. On the other hand, the color (determined by the RGB and HSV models) presents statistically significant changes between groups. Due to the above, the proposed ANN correctly classifies 96.60% of the data obtained for each of the groups studied. Once the ANN was trained, its application was tested in an automatic classification process. For this purpose, a prototype based on the operation of a stepper motor was simulated using Simulink from Matlab, which is connected to three ideal switches powered by three variable pulse generators that receive the information from an ANN and provide the corresponding signal for the motor to turn to a specific position. Manual classification is a process that requires expert personnel and is prone to human error. The scientific development presented shows an alternative for the automation of the process using low-cost computational tools as a potential alternative.
Effect of Process Variables and Ingredients on Controlled Protein Network Creation in High-Moisture Plant-Based Meat Alternatives
Authors: Sengar Animesh Singh , Beyrer Michael , McDonagh Ciara , Tiwari Uma , Pathania Shivani
Published: October 2023

The market has observed a rapid increase in the demand for plant-based foods as an alternative to animal meat products. Technologies such as high-moisture extrusion (HME) have the potential to develop anisotropic structures using alternative protein ingredients. This article discusses the different possible mechanisms responsible for structure formation and the effect of extrusion process parameters and outlines the recent advances in the long cooling dies (LCDs) used for meat alternative development. The role of different protein ingredients and the impact of combining them with other biopolymers were also evaluated. The underlying mechanism behind anisotropic structure formation during HME is a synergistic effect, with substantial dependence on the source of ingredients and their processing background. Formulation including proteins derived from plants, insects, animals, and microalgae with other biopolymers could pave the way to develop structured meat alternatives and fill nutritional interstices. Dynamic or rotating annular gap cooling dies operating at freely controllable shear and static annular gap dies are recent developments and assist to produce layered or fibrous structures. The complex chemical sites created during the HME of plant protein favour flavour and colour retention. This paper summarises the recent information published in the scientific literature and patents, which could further help researchers to fill the present knowledge gaps.
A Two-Step Approach to Orange Peel Waste Valorization: Consecutive Extraction of Pectin and Hesperidin
Authors: Figueira Onofre , Pereira Verónica , Castilho Paula C.
Published: October 2023

Citrus consumption translates into large amounts of residue, the disposal of which is associated with environmental issues and high costs. Current trends in citrus waste focus on the extraction of highly valued bioactive compounds via single-compound extraction. There is a lack of knowledge on how these methodologies can be introduced into extraction schemes of bioactive compounds, maximizing the residue potential and reducing its amount. The present work aimed to address this issue by designing a consecutive extraction of pectin and hesperidin from orange peel waste. A novel method for extraction and precipitation of hesperidin with an eco-friendly approach is also presented. After neutral pretreatment, pectin extraction was conducted under acidic conditions, followed by hesperidin extraction with a drastic pH change. Pectin had a high AUA content (66.20 ± 1.25%), meeting the criteria for use in the food industry. The best-tested conditions for hesperidin extraction (30 min, 70 °C, 1:10 (w/v)) provided a yield of 1% and a purity of 84%. The designed extraction scheme shows the potential of citrus waste as a source of bioactive compounds of good quality and high interest in the food industry while following the principles of green chemistry and circular economy.
Use of K2CO3 to Obtain Products from Starch-Oil Mixtures by Extrusion
Authors: Włodarczyk-Stasiak Marzena
Published: October 2023

Mixtures of potato starch with oils (rapeseed and sunflower) were extruded. To improve the complexation of edible oils, a catalyst was added in amounts of 3 g, 6 g, and 9 g per 100 g of sample. The aim was to obtain potato starch extrudates with a high degree of complexation and edible oils during physical modification (extrusion) with the innovative use of K2CO3 as a catalyst. Selected functional properties (water solubility index and fat absorption index) and technological properties of the obtained extrudates (radial expansion index); color in the L*, a*, and b* systems, and the specific surface area was determined from the water vapor adsorption isotherm (SBET). The fat content was determined as external, internal, or bound, and complexed by amylose to assess the degree and manner of fat complexation during extrusion. Iodine-binding capacity and the complexing index were determined to confirm the formation of amylose-lipid complexes. The incorporation of edible oils resulted in a decrease in the radial expansion index and water solubility index compared to control samples. The extrudates were dark orange. Extrudates obtained at the temperature profile L: 80/80/80/60/60/50 °C, depending on the cooking oil, complexed from 48–79% of the introduced rapeseed oil and from 36–40% of the sunflower oil. The extrusion temperature profile (H: 100/100/100/75/75/60 °C) reduced the amount of bound lipid fractions. Using potassium carbonate in the extrusion of starch-lipid systems gives hope for further increasing the share of lipids in extruded mixtures.
Fractional Order Weighted Mixed Sensitivity-Based Robust Controller Design and Application for a Nonlinear System
Authors: Ilten Erdem
Published: October 2023

This paper focuses on fractional-order modeling and the design of a robust speed controller for a nonlinear system. An induction motor (IM), widely used in Electrical Vehicles (EVs), is preferred in this study as a well-known nonlinear system. The major challenge in designing a robust speed controller for IM is the insufficiency of the machine model due to inherent machine dynamics. Fractional calculus is employed to model the IM using the small-signal method, accounting for model uncertainties. In this context, experimental data is approximated using a fractional-order small-signal transfer function. Consequently, a mixed sensitivity problem is formulated with fractional-order weighting functions. The primary advantage of these weighting functions is their greater flexibility in solving the mixed sensitivity problem by involving more coefficients. Hereby, three robust speed controllers are designed using the PID toolkit of the Matlab program and solving the H∞ mixed sensitivity problem, respectively. The novelty and contribution of the proposed method lie in maintaining the closed-loop response within a secure margin determined by fractional weighting functions while addressing the controller design. After evaluating the robust speed controllers with Bode diagrams, it is proven that all the designed controllers meet the desired nominal performance and robustness criteria. Subsequently, real-time implementations of the designed controllers are performed using the dsPIC microcontroller unit. Experimental results confirm that the designed H∞-based fractional-order proportional-integral-derivative (FOPID) controller performs well in terms of tracking dynamics, exhibits robustness against load disturbances, and effectively suppresses sensor noise compared to the robust PID and fixed-structured H∞ controller.
Spatiotemporal Dengue Fever Incidence Associated with Climate in a Brazilian Tropical Region
Authors: Machado Nadja Gomes , Lotufo Neto Névio , da Silva Lotufo Juliana Barbosa , dos Santos Luiz Octavio Fabrício , Biudes Marcelo Sacardi
Published: October 2023

Dengue is a serious infectious disease worldwide and a climate-sensitive disease. Thus, our goals were to (i) evaluate the relationship between dengue incidence and meteorological variables (rainfall and air temperature); (ii) identify the spatiotemporal pattern of dengue incidence in the municipalities of Mato Grosso from 2001 to 2020; and (iii) verify the spatial dependence of dengue incidence in the dry and wet seasons. We used dengue data from 2001 to 2020, monthly rainfall estimates from GPM, and daily air temperature estimates from ERA-5. The municipalities of the Mato Grosso state are included in 16 healthcare territories. The seasonal rainfall pattern indicates that the peak of the dengue endemic occurred in the wet season. However, drier and/or warmer places had a lower incidence of dengue in the dry season. Furthermore, a lagged effect of meteorological variables on dengue incidence has been identified, ranging from 0 to 7 months. Hotspot areas were identified which might have the potential for an intense spreading of dengue in Mato Grosso. They were mainly concentrated in the healthcare territory of Teles Pires (ID 14) in the dry season, while they were concentrated in the healthcare territories of Garças Araguaia (ID 5), Oeste (ID 11), and Teles Pires (ID 14) in the wet season. In addition, they are located in the Am climate and in the Amazon Forest and Brazilian savanna biomes, which have higher dengue incidence values. These results help to highlight which municipalities decision-makers must intervene in the public health system to prevent and control future epidemics.
Sexuality in Women with Fibromyalgia Syndrome: A Metasynthesis of Qualitative Studies
Authors: Granero-Molina José , Jiménez-Lasserrotte María del Mar , Dobarrio-Sanz Iria , Correa-Casado Matías , Ramos-Rodríguez Carmen , Romero-Alcalá Patricia
Published: October 2023

Fibromyalgia syndrome (FMS) is a nonarticular rheumatic syndrome which presents as chronic musculoskeletal pain, stiffness and body aches. FMS affects approximately 2.5% of the population, mostly women. FMS causes physical and psychological problems and reduces quality of life. The objective of this study is to identify qualitative evidence about experiences of women diagnosed with FMS about their sexuality. Methods: Metasynthesis of qualitative studies. The search included articles published between 2000 and June 2023 on the PubMed, WOS, CINAHL, SCOPUS, and SCIELO databases. Results: 450 articles were found through the initial search, of which, only nine fulfilled the criteria and were included in the thematic synthesis. From this analysis, three main themes emerged: (1) “I want to, but I can’t”: FMS causes a shift in feminine sexuality. (2) Resetting sex life and intimacy. (3) Taking charge of a “new sexuality.” Conclusions: Women with FMS suffer from limitations of their sexuality that affect their partner. Pain, stiffness and a loss of desire make sexual encounters difficult. Becoming aware of this and striving not to lose their sexuality is key to coping with this problem. Women and their sexual partners can change roles and encourage communication, games, foreplay or touching. The use of lubricants, physical exercise and complementary therapies, along with social, professional and partner support, are key to coping with FMS.
Do Differences Exist in Impact Test Domains between Youth Athletes with and without an Anterior Cruciate Ligament Injury?
Authors: Gureck Ashley E. , Crockett Zack , Barsky Brandon W. , Samuels Shenae , Frank Jeremy S. , Storer Stephen K. , Fazekas Matthew L.
Published: October 2023

Poor baseline reaction time, as measured via the Immediate Post-Concussion Assessment and Cognitive Test (ImPACT), has been associated with anterior cruciate ligament (ACL) injury risk in adult athletes. Our study sought to determine whether the reaction time and impulse control ImPACT test domains differed between ACL injured and uninjured pediatric athletes. A total of 140 high-school aged athletes comprising 70 athletes who went on to sustain an ACL injury between 2012 and 2018 and 70 age- and sex-matched uninjured controls were included in the study. Mean reaction times were similar for the injured (0.67 s) and uninjured (0.66 s) athletes (p = 0.432), and the impulse control scores were also similar for those with (5.67) and without (6.07) an ACL injury (p = 0.611). Therefore, neurocognitive risk factors for sustaining an ACL injury in adults cannot necessarily be extrapolated to adolescent athletes. Further research is needed to understand why differences exist between injury risk in youth and adult athletes.
Serum Anti-Spike Antibodies Are Not Affected by Immunosuppressants in SARS-CoV-2 Vaccinations Given to Brazilian Patients with Inflammatory Bowel Disease
Authors: Pereira Magno Luís Costa , Moreira Jessica Pronestino de Lima , Porto Luís Cristóvão , Souza Vania Maria Almeida de , Gonçalves Beatriz Cunta , Sampaio Amanda de Barros , Moutela Matheus Figueiredo , Farha Larissa dos Reis , Esberard Bárbara Cathalá , Amorim Renata Fernandes de , Souza Heitor Siffert Pereira de , Carvalho Ana Teresa Pugas
Published: October 2023

This study aimed to evaluate humoral responses after vaccination against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) of patients with inflammatory bowel disease (IBD). Patients with IBD enrolled in a tertiary outpatient unit were followed up between September 2021 and September 2022 via serial blood collection. Immunoglobulin G antibody titers against SARS-CoV-2 were measured before administration and 1 and 6 months after the administration of two doses of different vaccination regimens. The results were compared with those of a healthy control group obtained during the same period. The mean pre-vaccination antibody titers were 452.0 and 93.3 AU/mL in the IBD (n = 42) and control (n = 89) groups, respectively. After two doses of the vaccine, the titers significantly increased in both groups (IBD, 8568.0 AU/mL; control, 7471.0 AU/mL; p < 0.001). One month after the second dose, no significant differences were observed between the two groups (p = 0.955). Significant differences between vaccination schemes in the IBD group were observed, with higher titers in those who received Pfizer, younger patients (p < 0.005), and those with a previous coronavirus disease 2019 (COVID-19) infection (p < 0.012). The use of immunosuppressants and immunobiologicals did not affect the overall humoral response to COVID-19 vaccine in patients with IBD, but specific vaccine regimens, age, and previous coronavirus infection significantly did. This study reinforces the positive impact of booster doses and the safety of SARS-CoV-2 vaccination.