bluuebunny/crossref_metadata_2025 huggingface dataset:
Zero-Trust Security Authentication Based on SPA and Endogenous Security Architecture
Authors: Xu Mingyang , Guo Junli , Yuan Haoyu , Yang Xinyu
Published: February 2023

Zero-trust security architecture reconstructs the trust foundation of access control based on authentication and authorization by continuously authenticating the terminal during the authentication process and not relying solely on geographic location/user attributes as the sole basis for the trust assessment. However, due to the fine-grained verification of identity under the zero-trust security architecture, there is a need for multiple authentication and authorization processes. If a single policy engine has unknown vulnerabilities and unknown backdoors to be maliciously attacked, or DDOS attacks initiated by known vulnerabilities cannot be prevented, the policy engine based on this control center architecture cannot meet the requirements of system security and reliability. Therefore, it is proposed to apply the SPA single-package authorization and endogenous security architecture to the zero-trust authentication system, which can realize the reliability, dynamism and diversity of system defense. Through the experimental antiattack analysis and antiattack test, the test from the proposed scheme found that when the system introduces the endogenous security architecture, the security of the system can be improved due to the complexity of the attack process and the increase in the cost of the attack. The test through both the security and system overhead found that the scheme can effectively improve the security of the system while ensuring the quality of network services, compared to the traditional scheme. It was found that the scheme can effectively improve the security of the system while ensuring the quality of network services and has better adaptability than the traditional zero-trust authentication scheme.
A Comprehensive Review on AI-Enabled Models for Parkinson’s Disease Diagnosis
Authors: Dixit Shriniket , Bohre Khitij , Singh Yashbir , Himeur Yassine , Mansoor Wathiq , Atalla Shadi , Srinivasan Kathiravan
Published: February 2023

Parkinson’s disease (PD) is a devastating neurological disease that cannot be identified with traditional plasma experiments, necessitating the development of a faster, less expensive diagnostic instrument. Due to the difficulty of quantifying PD in the past, doctors have tended to focus on some signs while ignoring others, primarily relying on an intuitive assessment scale because of the disease’s characteristics, which include loss of motor control and speech that can be utilized to detect and diagnose this disease. It is an illness that impacts both motion and non-motion functions. It takes years to develop and has a wide range of clinical symptoms and prognoses. Parkinson’s patients commonly display non-motor symptoms such as sleep problems, neurocognitive ailments, and cognitive impairment long before the diagnosis, even though scientists have been working to develop designs for diagnosing and categorizing the disease, only noticeable defects such as movement patterns, speech, or writing skills are offered in this paper. This article provides a thorough analysis of several AI-based ML and DL techniques used to diagnose PD and their influence on developing additional research directions. It follows the guidelines of Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). This review also examines the current state of PD diagnosis and the potential applications of data-driven AI technology. It ends with a discussion of future developments, which aids in filling critical gaps in the current Parkinson’s study.
Impact of the Noise on the Emulated Grid Voltage Signal in Hardware-in-the-Loop Used in Power Converters
Authors: Lamo Paula , Ruiz Gustavo A. , Azcondo Francisco J. , Pigazo Alberto , Brañas Christian
Published: February 2023

This work evaluates the impact of the input voltage noise on a Hardware-In-the-Loop (HIL) system used in the emulation of power converters. A poor signal-to-noise ratio (SNR) can compromise the accuracy and precision of the model, and even make certain techniques for building mathematical models unfeasible. The case study presents the noise effects on a digitally controlled totem-pole converter emulated with a low-cost HIL system using an FPGA. The effects on the model outputs, and the cost and influence of different hardware implementations, are evaluated. The noise of the input signals may limit the benefits of increasing the resolution of the model.
Infrared and Visible Image Homography Estimation Using Multiscale Generative Adversarial Network
Authors: Luo Yinhui , Wang Xingyi , Wu Yuezhou , Shu Chang
Published: February 2023

In computer vision, the homography estimation of infrared and visible multi-source images based on deep learning is a current research hotspot. Existing homography estimation methods ignore the feature differences of multi-source images, which leads to poor homography performance in infrared and visible image scenes. To address this issue, we designed an infrared and visible image homography estimation method using a Multi-scale Generative Adversarial Network, called HomoMGAN. First, we designed two shallow feature extraction networks to extract fine features of infrared and visible images, respectively, which extract important features in source images from two dimensions: color channel and imaging space. Second, we proposed an unsupervised generative adversarial network to predict the homography matrix directly. In our adversarial network, the generator captures meaningful features for homography estimation at different scales by using an encoder–decoder structure and further predicts the homography matrix. The discriminator recognizes the feature difference between the warped and target image. Through the adversarial game between the generator and the discriminator, the fine features of the warped image in the homography estimation process are closer to the fine features of the target image. Finally, we conduct extensive experiments in the synthetic benchmark dataset to verify the effectiveness of HomoMGAN and its components. We conduct extensive experiments and the results show that HomoMGAN outperforms existing state-of-the-art methods in the synthetic benchmark datasets both qualitatively and quantitatively.
Applications of Clustering Methods for Different Aspects of Electric Vehicles
Authors: Nazari Masooma , Hussain Akhtar , Musilek Petr
Published: February 2023

The growing penetration of electric vehicles can pose several challenges for power systems, especially distribution systems, due to the introduction of significant uncertain load. Analysis of these challenges becomes computationally expensive with higher penetration of electric vehicles due to various preferences, travel behavior, and the battery size of electric vehicles. This problem can be addressed using clustering methods which have been successfully used in many other sectors. Recently, there have been several studies published on applying clustering methods for various aspects of electric vehicles. To summarize the existing efforts and provide future research directions, this contribution presents a three-step analysis. First, the existing clustering methods, including hard and soft clustering, are discussed. Then, the recent literature on the application of clustering methods for different aspects of electric vehicles is reviewed. The review concentrates on four major aspects of electric vehicles: the behavior of the user, driving cycle, used batteries, and charging stations. Then, several representative studies are selected from each category and their merits and demerits are summarized. Finally, gaps in the existing literature are identified and directions for future research are presented. They indicate the need for further research on the impact on distribution circuits, charging infrastructure during emergencies, equity and disparity in rebate allocations, and the use of big data with cluster analysis to assist transportation network management.
Multi-Mode Channel Position Attention Fusion Side-Scan Sonar Transfer Recognition
Authors: Wang Jian , Li Haisen , Huo Guanying , Li Chao , Wei Yuhang
Published: February 2023

Side-scan sonar (SSS) target recognition is an important part of building an underwater detection system and ensuring a high-precision perception of underwater information. In this paper, a novel multi-channel multi-location attention mechanism is proposed for a multi-modal phased transfer side-scan sonar target recognition model. Optical images from the ImageNet database, synthetic aperture radar (SAR) images and SSS images are used as the training datasets. The backbone network for feature extraction is transferred and learned by a staged transfer learning method. The head network used to predict the type of target extracts the attention features of SSS through a multi-channel and multi-position attention mechanism, and subsequently performs target recognition. The proposed model is tested on the SSS test dataset and evaluated using several metrics, and compared with different recognition algorithms as well. The results show that the model has better recognition accuracy and robustness for SSS targets.
A Novel Bidirectional-Switched-Capacitor-Based Interlaced DC-DC Converter
Authors: Shyu Kuo-Kai , Yu Yi-Chang , Lin Xin-Lan , Lee Lung-Hao , Lee Po-Lei
Published: February 2023

This study proposes a novel bidirectional-switched-capacitor-based interleaved converter. In view of the shortcomings of the two well-known unidirectional-switched-capacitor-based interleaved converters, this study improves such converters through combining the novel structure of a switched capacitor circuit. The first effort was to overcome the drawback of the Cockcroft–Walton-based interleaved converter, whose circuit impedance and ripple cause a serious output voltage drop. The second was to solve the Dickson-based interleaved converter with its capacitors subjected to high-voltage stress. The third was to relax the unidirectional boost function of the Cockcroft–Walton- or Dickson-based interleaved converter. This study avoided not only high-circuit impedance and ripple, as in the case of the Cockcroft–Walton converter, but also it had lower component stress than the Dickson converter. In addition, this study redesigned the unidirectional boost function of the Cockcroft–Walton- or Dickson-based interleaved converter, such that the switch-capacitor-based interleaved converters became bidirectional DC-DC converters. Finally, the experimental results are provided to verify the feasibility of the proposed method.
Self-Supervised Graph Attention Collaborative Filtering for Recommendation
Authors: Zhu Jiangqiang , Li Kai , Peng Jinjia , Qi Jing
Published: February 2023

Due to the complementary nature of graph neural networks and structured data in recommendations, recommendation systems using graph neural network techniques have become mainstream. However, there are still problems, such as sparse supervised signals and interaction noise, in the recommendation task. Therefore, this paper proposes a self-supervised graph attention collaborative filtering for recommendation (SGACF). The correlation between adjacent nodes is deeply mined using a multi-head graph attention network to obtain accurate node representations. It is worth noting that self-supervised learning is brought in as an auxiliary task in the recommendation, where the supervision task is the main task. It assists model training for supervised tasks. A multi-view of a node is generated by the graph data-augmentation method. We maximize the consistency between its different views compared to the views of the same node and minimize the consistency between its different views compared to the views of other nodes. In this paper, the effectiveness of the method is illustrated by abundant experiments on three public datasets. The results show its significant improvement in the accuracy of the long-tail item recommendation and the robustness of the model.
Flexible Frequency Response Strategy with Smooth Rotor Speed Recovery of a DFIG-Based Wind Turbine
Authors: Xue Xiaocen , Sang Shun , Huang Jiejie
Published: February 2023

Grid frequency must be regulated in its nominal range to guarantee the stable operation of an electric power grid. Excessive grid frequency excursions result in load shedding, grid frequency instability, or even synchronous generator damage. With the growing wind penetration, there is an increasing issue about the reduction in inertia response. This paper addresses a self-adaptive inertial control strategy for improving the frequency nadir and smoothly regaining the rotor speed to the initial working condition without causing a second frequency drop (SFD). The first objective is achieved by determining the incremental power considering the maximum rate of change of frequency; the secondary goal is realized by smoothly decreasing the power reference based on the decreasing function. Simulation results verify that the proposed control strategy not only boosts the frequency nadir but also guarantees the smooth rotor speed recovery with a negligible SFD.
Ultra-Low-Power Voice Activity Detection System Using Level-Crossing Sampling
Authors: Faghani Maral , Rezaee-Dehsorkh Hamidreza , Ravanshad Nassim , Aminzadeh Hamed
Published: February 2023

This paper presents an ultra-low-power voice activity detection (VAD) system to discriminate speech from non-speech parts of audio signals. The proposed VAD system uses level-crossing sampling for voice activity detection. The useless samples in the non-speech parts of the signal are eliminated due to the activity-dependent nature of this sampling scheme. A 40 ms moving window with a 30 ms overlap is exploited as a feature extraction block, within which the output samples of the level-crossing analog-to-digital converter (LC-ADC) are counted as the feature. The only variable used to distinguish speech and non-speech segments in the audio input signal is the number of LC-ADC output samples within a time window. The proposed system achieves an average of 91.02% speech hit rate and 82.64% non-speech hit rate over 12 noise types at −5, 0, 5, and 10 dB signal-to-noise ratios (SNR) over the TIMIT database. The proposed system including LC-ADC, feature extraction, and classification circuits was designed in 0.18 µm CMOS technology. Post-layout simulation results show a power consumption of 394.6 nW with a silicon area of 0.044 mm2, which makes it suitable as an always-on device in an automatic speech recognition system.
Preemptive Zone Reset Design within Zoned Namespace SSD Firmware
Authors: Jung Siu , Lee Seungjin , Han Jungwook , Kim Youngjae
Published: February 2023

Zoned Namespace (ZNS) SSDs address the disadvantages that come from supporting the block interface within conventional SSDs, granting more control over data management to host systems, while also relieving heavy duties from device firmware. However, with the removal of on-device garbage collection, host systems must explicitly send zone reset requests to free up storage space, which may incur multiple NAND block erase operations according to the configured zone size, resulting in increased tail latency. In this article, we propose a Preemptive Zone Reset scheduling design, which we implemented within the firmware of our ZNS SSD prototype, and compare it to an intuitive Zone Mapping Table method, which we consider as the state-of-the-art. The main idea is to service high priority foreground I/O requests while preempting block erase operations induced by zone resets. Our proposed approach, opposed to the baseline method, as much as halved tail latency for write-only workloads, and reduced read tail latency by up to 1.76 times in a mixed workload.
Condition-Based Maintenance of an Anaerobic Reactor Using Artificial Intelligence
Authors: Juárez-Barojas Isaias , Posada-Gómez Rubén , Alvarado-Lassman Alejandro , Rodríguez-Jarquín José Pastor
Published: February 2023

This paper proposes a condition-based maintenance system based on artificial intelligence for an online monitoring system of the support bed expansion in a 30-liter pilot-scale inverse fluidized bed reactor (IFBR). The main scope is to achieve a condition-based maintenance strategy using a single-level sensor for a biofilm inverse fluidizing bed as source for virtual sensors. The implementation of an artificial neural network was performed on an embedded electronic system (Raspberry Pi 4), both working together in real time. The signals estimated by the neural network are compared against the signals measured by the hardware sensors and, in case of detecting a failure in the physical measurement system, the artificial intelligence-based system then uses the signal estimated by the artificial neural network to maintain the correct operation of the IFBR. This system uses an artificial neural network to estimate the COD concentration of the effluent and the biogas production flow of a bioreactor, from the measurement of pH, the COD concentration of the influent, the inflow to the bioreactor and the signal coming from each of the conductivity sensors installed inside the reactor, which provide information about support media expansion in a pilot-scale inverse fluidized bed reactor. In addition, a fuzzy PI controller is presented, which was implemented in a Raspberry Pi electronic card, to regulate the COD concentration in the effluent of the bioreactor used as a case study.
Camouflaged Insect Segmentation Using a Progressive Refinement Network
Authors: Wang Jing , Hong Minglin , Hu Xia , Li Xiaolin , Huang Shiguo , Wang Rong , Zhang Feiping
Published: February 2023

Accurately segmenting an insect from its original ecological image is the core technology restricting the accuracy and efficiency of automatic recognition. However, the performance of existing segmentation methods is unsatisfactory in insect images shot in wild backgrounds on account of challenges: various sizes, similar colors or textures to the surroundings, transparent body parts and vague outlines. These challenges of image segmentation are accentuated when dealing with camouflaged insects. Here, we developed an insect image segmentation method based on deep learning termed the progressive refinement network (PRNet), especially for camouflaged insects. Unlike existing insect segmentation methods, PRNet captures the possible scale and location of insects by extracting the contextual information of the image, and fuses comprehensive features to suppress distractors, thereby clearly segmenting insect outlines. Experimental results based on 1900 camouflaged insect images demonstrated that PRNet could effectively segment the camouflaged insects and achieved superior detection performance, with a mean absolute error of 3.2%, pixel-matching degree of 89.7%, structural similarity of 83.6%, and precision and recall error of 72%, which achieved improvements of 8.1%, 25.9%, 19.5%, and 35.8%, respectively, when compared to the recent salient object detection methods. As a foundational technology for insect detection, PRNet provides new opportunities for understanding insect camouflage, and also has the potential to lead to a step progress in the accuracy of the intelligent identification of general insects, and even being an ultimate insect detector.
A Checkpointing Recovery Approach for Soft Errors Based on Detector Locations
Authors: Yang Na , Wang Yun
Published: February 2023

Soft errors are transient errors caused by single-event effects (SEEs) resulting from a strike by high-energy particles acting on sensitive areas of integrated circuits. Soft errors frequently occur in the space environment, adversely affecting the reliability of aerospace-based computing. A recovery process is launched to recover the program when soft errors are detected. A periodic checkpointing recovery approach is widely utilized to prevent soft errors. However, this approach does not consider the detector locations, resulting in a large time overhead. This paper proposes a checkpointing recovery approach for soft errors based on detector locations called DLCKPT. DLCKPT reduces the time overhead by considering detector locations. The experimental results show that the percentage decrease in the time overhead between the DLCKPT and the periodic checkpointing recovery approach is 13.4%. The average recovery rate and average space overhead are 99.3% and 44.4% for the periodic checkpointing recovery approach and 99.4% and 34.6% for the DLCKPT. These results show that the DLCKPT and the periodic checkpointing recovery approach produce comparable results for the recovery rate. The DLCKPT has a lower time overhead and a slightly lower space overhead than the periodic checkpointing recovery approach, demonstrating its effectiveness.
Patch-Based Difference-in-Level Detection with Segmented Ground Mask
Authors: Nonaka Yusuke , Uchiyama Hideaki , Saito Hideo , Yachida Shoji , Iwamoto Kota
Published: February 2023

Difference-in-level detection in outdoor scenes has various possible applications, including walking assistance for blind people, robot walking assistance, and mapping the hazards of factory premises. It is difficult to detect all outdoor differences in level, such as RGB or RGB-D images, not only including road curbs, which are often targeted for detection in automated driving, but also differences in level on factory premises and sidewalks, because the pattern of outdoor differences in level is abundant and complex. This paper proposes a novel method for detecting differences in level from RGB-D images with segmented ground masks. First, image patches of differences in level were extracted from outdoor images to create the dataset. The change in the normal vector of the contour part on the detected plane is used to generate image patches of the difference in level, but this method strongly depends on the accuracy of planar detection, and it detects only some differences in level. Then, we created the dataset, consisting of image patches and including the extracted differences in level. The dataset is used for training a deep learning model for detecting differences in level in outdoor images without limitations. In addition, because the purpose of this paper is to detect differences in level in outdoor walking areas, regions in the image other than the target areas were excluded by the segmented ground mask. For the performance evaluation, we implemented our algorithm using a modern smartphone with a high-performance depth camera. To evaluate the effectiveness of the proposed method, the results from various inputs, such as RGB, depth, grayscale, normal, and combinations of them, were qualitatively and quantitatively evaluated, and Blender was used to generate synthetic test images for a quantitative evaluation of the difference in level. We confirm that the suggested method successfully detects various types of differences in level in outdoor images, even in complex scenes.
A Methodology for Accelerating FPGA Fault Injection Campaign Using ICAP
Authors: Ferlini Frederico , Viel Felipe , Seman Laio Oriel , Pettenghi Hector , Bezerra Eduardo Augusto , Leithardt Valderi Reis Quietinho
Published: February 2023

The increasing complexity of System-on-Chip (SoC) and the ongoing technology miniaturization on Integrated Circuit (IC) manufacturing processes makes modern SoCs more susceptible to Single-Event Effects (SEE) caused by radiation, even at sea level. To provide realistic estimates at a low cost, efficient analysis techniques capable of replicating SEEs are required. Among these methods, fault injection through emulation using Field-Programmable Gate Array (FPGA) enables campaigns to be run on a Circuit Under Test (CUT). This paper investigates the use of an FPGA architecture to speed up the execution of fault campaigns. As a result, a new methodology for mapping the CUT occupation on the FPGA is proposed, significantly reducing the total number of faults to be injected. In addition, a fault injection technique/flow is proposed to demonstrate the benefits of cutting-edge approaches. The presented technique emulates Single-Event Transient (SET) in all combinatorial elements of the CUT using the Internal Configuration Access Port (ICAP) of Xilinx FPGAs.
Interacting Multiple Model for Lithium-Ion Battery State of Charge Estimation Based on the Electrochemical Impedance Spectroscopy
Authors: Huang Ce , Wu Haibin , Li Zhi , Li Ran , Sun Hui
Published: February 2023

In terms of the dynamic changes of battery model parameters in a single-model filtering algorithm, the filter estimation accuracy can be poor, and filtering is scattered due to the different internal state parameters of lithium-ion batteries in different aging states, which affects the state of charge (SOC). In order to address these issues, an Interacting Multiple Model (IMM) algorithm was proposed in this study, which adopted an Unscented Kalman Filter (UKF) to better approximate the nonlinear characteristics of the state equation while better stabilizing the filter and having lower computational requirements. Accordingly, the IMM was used to solve the problem of the accurate estimation of the SOC under the dynamic change of model parameters. Moreover, an electrochemical impedance spectrum was used to establish the electrochemical model, after which the lithium-ion equivalent electrochemical circuit model was established, which improved the complexity problem due to its high accuracy but complicated the calculation of the multi-order equivalent circuit model. By conducting experiments and simulations, the algorithm of IMM-UKF was shown to achieve an effective estimation of the battery SOC, even when the state parameters of lithium-ion batteries were uncertain.
Deep Learning Recommendations of E-Education Based on Clustering and Sequence
Authors: Safarov Furkat , Kutlimuratov Alpamis , Abdusalomov Akmalbek Bobomirzaevich , Nasimov Rashid , Cho Young-Im
Published: February 2023

Commercial e-learning platforms have to overcome the challenge of resource overload and find the most suitable material for educators using a recommendation system (RS) when an exponential increase occurs in the amount of available online educational resources. Therefore, we propose a novel DNN method that combines synchronous sequences and heterogeneous features to more accurately generate candidates in e-learning platforms that face an exponential increase in the number of available online educational courses and learners. Mitigating the learners’ cold-start problem was also taken into consideration during the modeling. Grouping learners in the first phase, and combining sequence and heterogeneous data as embeddings into recommendations using deep neural networks, are the main concepts of the proposed approach. Empirical results confirmed the proposed solution’s potential. In particular, the precision rates were equal to 0.626 and 0.492 in the cases of Top-1 and Top-5 courses, respectively. Learners’ cold-start errors were 0.618 and 0.697 for 25 and 50 new learners.
Reconfigurable Architecture for Noise Cancellation in Acoustic Environment Using Single Multiply Accumulate Adaline Filter
Authors: Ezilarasan M. R. , Britto Pari J. , Leung Man-Fai
Published: February 2023

The creation of multiple applications with a higher level of complexity has been made possible by the usage of artificial neural networks (ANNs). In this research, an efficient flexible finite impulse response (FIR) filter structure called ADALINE (adaptive linear element) that makes use of a MAC (multiply accumulate) core is proposed. The least mean square (LMS) and recursive least square (RLS) algorithms are the most often used methods for maximizing filter coefficients. Despite outperforming the LMS, the RLS approach has not been favored for real-time applications due to its higher design arithmetic complexity. To achieve less computation, the fundamental filter has utilized an LMS-based tapping delay line filter, which is practically a workable option for an adaptive filtering algorithm. To discover the undiscovered system, the adjustable coefficient filters have been developed in the suggested work utilizing an optimal LMS approach. The 10-tap filter being considered here has been analyzed and synthesized utilizing field programmable gate array (FPGA) devices and programming in hardware description language. In terms of how well the resources were used, the placement and postrouting design performed well. If the implemented filter architecture is compared with the existing filter architecture, it reveals a 25% decrease in resources from the existing one and an increase in clock frequency of roughly 20%.
Merchant Recommender System Using Credit Card Payment Data
Authors: Yoo Suyoun , Kim Jaekwang
Published: February 2023

As the size of the domestic credit card market is steadily growing, the marketing method for credit card companies to secure customers is also changing. The process of understanding individual preferences and payment patterns has become an essential element, and it has developed a sophisticated personalized marketing method to properly understand customers’ interests and meet their needs. Based on this, a personalized system that recommends products or stores suitable for customers acts to attract customers more effectively. However, the existing research model implementing the General Framework using the neural network cannot reflect the major domain information of credit card payment data when applied directly to store recommendations. This study intends to propose a model specializing in the recommendation of member stores by reflecting the domain information of credit card payment data. The customers’ gender and age information were added to the learning data. The industry category and region information of the settlement member stores were reconstructed to be learned together with interaction data. A personalized recommendation system was realized by combining historical card payment data with customer and member store information to recommend member stores that are highly likely to be used by customers in the future. This study’s proposed model (NMF_CSI) showed a performance improvement of 3% based on HR@10 and 5% based on NDCG@10, compared to previous models. In addition, customer coverage was expanded so that the recommended model can be applied not only to customers actively using credit cards but also to customers with low usage data.
Review on Plasma-Assisted Ignition Systems for Internal Combustion Engine Application
Authors: Choi Yong Hyun , Hwang Joonsik
Published: February 2023

Due to the depletion of conventional petroleum-based fuels and increasing environmental concerns, industries have been developing new combustion technologies with acceptable cost ranges and minimum system modifications for consumers. Among many approaches, the utilization of plasma ignition systems is considered as a promising pathway to achieve greener transportation while maintaining conventional internal combustion engine systems. Plasma contains highly reactive radicals, and those have a great potential of enhancing chemical reactions that are beneficial for reducing carbon emissions. The primary objective of this paper is to provide an overview of currently available plasma-assisted combustion systems including recent achievements in research and development, and technical challenges for successfully implementing a new ignition system. This review will introduce various plasma-assisted combustion approaches from worldwide projects, covering non-thermal and thermal plasma systems in internal combustion engines.
Evaluating the Efficacy and Safety of Long-Acting GLP-1 Receptor Agonists in T1DM Patients
Authors: Mohandas Deene , Calma Jamie , Gao Catherine , Basina Marina
Published: February 2023

(1) Background: Glucagon-like peptide 1 receptor agonists (GLP-1 RA) are a class of therapeutic agents that mimic the endogenous incretin hormone GLP-1. While this class of agents is not approved for Type 1 Diabetes (T1DM) due to concern of increased diabetic ketoacidosis (DKA) risk, long-acting GLP-1 medications are being commonly prescribed off label for T1DM in clinical practice. Several studies addressed the efficacy and safety of short-acting GLP-1 agonists therapy in patients with T1DM, but the data on long-acting agents are lacking. In our study, we aim to fill in this gap and help healthcare providers in their clinical decision making on the use of these agents for T1DM patients. (2) Methods: We conducted a retrospective chart review of T1DM patients on a long-acting GLP-1 for at least six months. Our retrospective chart review included information starting two years prior to starting GLP-1, and six or more months after starting GLP-1. Parameters collected included HbA1c, 14-day Continuous Glucose Monitor (CGM) and blood glucose (BG) data, and metabolic data (weight, systolic and diastolic blood pressure, and cholesterol levels). Statistical analysis was conducted using paired t-tests on R and Excel with α of 0.05. (3) Results: Our cohort consisted of 54 participants with T1DM on a long-acting GLP-1 (semaglutide, dulaglutide, exenatide extended-release [ER], albiglutide). Mean GLP-1 treatment duration was 23.85 ± 15.46 months. HbA1c values decreased significantly by an average of 0.71% percentage points (%-points, p = 0.002) comparing pre-therapy vs. on GLP-1 treatment. Similarly, for pre-therapy vs. on GLP-1 treatment values, CGM results were significant for increased time in range by 12.15%-points (p = 0.0009) showing a decreased average time in hyperglycemia (BG > 180 mg/dL) by a mean difference of 11.97%-points (p = 0.006), decreased 14-day mean BG by 19 mg/dl (p = 0.01), decreased 14-day BG standard deviation by 8.45 mg/dl (p = 0.01), decreased incidence of DKA hospitalization, and a decrease in weight by 3.16 kg (p = 0.007). (4) Conclusions: As more data emerges on cardiovascular and renal benefits of long acting GLP-1 in type 2 diabetes, there have been no reported outcomes in T1DM. Our study is the first to demonstrate glycemic and metabolic benefits of this class of medication as an adjunct therapy to insulin in T1DM, and safety of its use over an average of 1.5–2 years’ time. This study represents real life experience and the data warrants confirmation by additional prospective studies.
Characterisation of Lacto-Fermented Cricket (Acheta domesticus) Flour and Its Influence on the Quality Parameters and Acrylamide Formation in Wheat Biscuits
Authors: Bartkiene Elena , Zokaityte Egle , Kentra Evaldas , Starkute Vytaute , Klupsaite Dovile , Mockus Ernestas , Zokaityte Gintare , Cernauskas Darius , Rocha João Miguel , Guiné Raquel P. F.
Published: February 2023

The aim of this study was to evaluate the influence of different amounts (40, 80 and 100 g) of non-fermented and fermented (with Lactiplantibacillus plantarum No. 122 and Lacticaseibacillus casei No. 210) cricket flour (Cr) on the quality characteristics and acrylamide formation in wheat biscuits (WB). The main formula for WB preparation consisted of 280 g of wheat flour, 100 g of margarine, 50 g of saccharose, 3 g of vanilla sugar, 50 g of eggs, 1.5 g of salt and 2.0 g of baking powder. It was established that the highest lactic acid bacteria (LAB) number was achieved in 48 h with No. 122 fermented Cr (11.8 log10 CFU/g) and the lowest pH (4.34) was obtained after 48 h of Cr fermentation with both the tested LAB strains. The total colour differences were in the range of 17.54 to 22.08 and, in every case, fermented samples were clearly distinguished from untreated ones. Fermentation increased tyramine content in Cr (from 13.0 to 29.2 times). The main FAs in Cr were palmitic acid, stearic acid, octadec-9-enoic acid and linoleic acid. The lowest acrylamide content (84.1 µg/kg) was found in WB with 40 g of Cr fermented with No. 210. Significant differences in WB overall acceptability were not found. However, the highest intensity of emotion “happy” was elicited by WB with 80 g of Cr fermented with No. 122. Due to the demonstrated decrease of acrylamide content, fermented Cr can be considered a beneficial ingredient for the manufacture of WB.
Main Components of Fish Immunity: An Overview of the Fish Immune System
Authors: Mokhtar Doaa , Zaccone Giacomo , Alesci Alessio , Kuciel Michal , Hussein Manal , Sayed Ramy
Published: February 2023

Cellular immune responses consist of innate and adaptive cell-mediated immune mechanisms, where all leukocyte subpopulations are included. Among these are vital processes such as cell-mediated cytotoxicity and phagocytosis. The main cellular constituents of the fish immune system are macrophages, granulocytes, dendritic cells, NK cells, and cytotoxic T cells. This review provides the latest information on cellular defense mechanisms of fish and provides an overview of the function of the mucosal immune system in maintaining the general health of fish. Here, we discuss the fundamental ideas that underpin mucosal immune responses in teleosts, as well as the innate and adaptive immune cells and the molecules that play a role in these immune responses. Moreover, cytokine molecules and pathways in teleosts have been reported to focus on several kinds of associated immunity. Importantly, we also review antigen processing and presentation. The knowledge reported here will enable better understanding, determination, and modulation of the pathways responsible for protective immunity, thus consequently improving the health of the fish in aquaculture.
Otolith Morphometric and Shape Distinction of Three Redfin Species under the Genus Decapterus (Teleostei: Carangidae) from Sulu Sea, Philippines
Authors: Morales Christian James C. , Barnuevo Kyle Dominic E. , Delloro Emmanuel S. , Cabebe-Barnuevo Roxanne A. , Calizo Jenylle Kate S. , Lumayno Sanny David P. , Babaran Ricardo P.
Published: February 2023

Otoliths are paired calcified structures in the inner ear of teleosts that function in hearing and balance. In this study, the use of otolith morphometrics and shape analysis were explored to delineate the redfin species under the genus Decapterus from the Sulu Sea, Philippines, namely, Decapterus kurroides, D. smithvanizi, and D. tabl. Results showed that the mean otolith shapes are unique within species (p < 0.001). The otolith size-related (OW—otolith weight, OL—otolith length, OH—otolith height, OA—otolith area, and OP—otolith perimeter) and shape-related (RE—rectangularity, SQ—squareness, EL—ellipticity, RO—roundness, AR—aspect ratio, FF—form factor, CO—compactness, and CI—circularity) morphometric indices also showed significant variations (p < 0.001) among the three species. Distinct regions differentiating D. kurroides from the other two species were seen along the dorsal margin of the otoliths, which was supported by its significantly higher values for OH (size-related index) and RO (shape-related index). In addition, the morphometric indices showed significant variations (p < 0.001), except for RE. Furthermore, it was revealed that the size-related indices were able to separate D. kurroides from the other two species, which turned out to be indifferent. The results provided relevant data on the use of otoliths as an additional confirmatory tool in species delineation of the identified redfin species, and, therefore, offer opportunities of applying the same method in delineation and, eventually, identification of species from other genera, especially those that closely resemble each other.
Opportunities and Challenges of Understanding Community Assembly in Spontaneous Food Fermentation
Authors: Mudoor Sooresh Maanasa , Willing Benjamin P. , Bourrie Benjamin C. T.
Published: February 2023

Spontaneous fermentations that do not rely on backslopping or industrial starter cultures were especially important to the early development of society and are still practiced around the world today. While current literature on spontaneous fermentations is observational and descriptive, it is important to understand the underlying mechanism of microbial community assembly and how this correlates with changes observed in microbial succession, composition, interaction, and metabolite production. Spontaneous food and beverage fermentations are home to autochthonous bacteria and fungi that are naturally inoculated from raw materials, environment, and equipment. This review discusses the factors that play an important role in microbial community assembly, particularly focusing on commonly reported yeasts and bacteria isolated from spontaneously fermenting food and beverages, and how this affects the fermentation dynamics. A wide range of studies have been conducted in spontaneously fermented foods that highlight some of the mechanisms that are involved in microbial interactions, niche adaptation, and lifestyle of these microorganisms. Moreover, we will also highlight how controlled culture experiments provide greater insight into understanding microbial interactions, a modest attempt in decoding the complexity of spontaneous fermentations. Further research using specific in vitro microbial models to understand the role of core microbiota are needed to fill the knowledge gap that currently exists in understanding how the phenotypic and genotypic expression of these microorganisms aid in their successful adaptation and shape fermentation outcomes. Furthermore, there is still a vast opportunity to understand strain level implications on community assembly. Translating these findings will also help in improving other fermentation systems to help gain more control over the fermentation process and maintain consistent and superior product quality.
Iron from Co-Encapsulation of Defatted Nannochloropsis Oceanica with Inulin Is Highly Bioavailable and Does Not Impact Wheat Flour Shelf Life or Sensorial Attributes
Authors: Bhatnagar Rohil , Lei Xin-Gen , Miller Dennis , Padilla-Zakour Olga
Published: February 2023

Defatted green microalgae Nannochloropsis oceanica (DGM) is a rich source of bioavailable iron. However, its use in foods results in unacceptable color and taste development. Therefore, the purpose of this study was to investigate strategies to enhance the use of DGM in foods. DGM and inulin were encapsulated (EC) in an oil-in-water emulsion using high-pressure homogenization. To confirm iron bioavailability, C57BL/6 mice were fed an iron-deficient diet (ID) for 2 weeks. The mice were then fed one of the four diets: ID, ID + DGM (DGM), ID + EC (EC50 or EC100) for 4 weeks. To test the stability of DGM as an iron fortificant at two different fortification rates of 17.5 mg Fe/kg (50%) or 35 mg Fe/kg (100%), whole (DGM50/DGM100), encapsulated (EC50/EC100) and color-masked (CM50/CM100) DGM were added to wheat flour (WF) at two different temperatures: 20 °C and 45 °C and were examined for 30 days. Acceptability studies were conducted to determine sensory differences between rotis (Indian flat bread) prepared from WF/EC50/CM50/EC100. The mice consuming EC50/EC100 diets showed comparable iron status to DGM-fed mice, suggesting that encapsulation did not negatively impact iron bioavailability. Addition of EC to wheat flour resulted in the lowest Fe2+ oxidation and color change amongst treatments, when stored for 30 days. There were no differences in the overall liking and product acceptance of rotis amongst treatments at both day 0 and day 21 samples. Our results suggest that EC50 can be effectively used as an iron fortificant in WF to deliver highly bioavailable iron without experiencing any stability or sensory defects, at least until 30 days of storage.
Effect of Different Drying Methods on the Quality and Nonvolatile Flavor Components of Oudemansiella raphanipes
Authors: Shen Qiulian , He Zedong , Ding Yangyue , Sun Liping
Published: February 2023

Different drying methods affect the quality of foods. The aim of this study is to explore the effects of seven drying methods, including hot air drying at 60 °C and 80 °C, ultrasound-assisted hot air drying at 60 °C and 80 °C, microwave drying, vacuum microwave drying, and vacuum freeze-drying, on the quality and nonvolatile flavor components of Oudemansiella raphanipes. The vacuum freeze-drying resulted in minimal collapse, mild shrinkage at the macroscopic level, and the formation of uniform pores at the microscopic level on the surfaces of O. raphanipes mushrooms. In addition, vacuum freeze-drying can improve the color attributes of the mushrooms. Therefore, the appearance and shape of vacuum freeze-drying treated O. raphanipes were closest to those of fresh mushrooms. We found that ultrasound-assisted treatment can effectively shorten the drying time of O. raphanipes. The drying time of ultrasound-assisted hot air drying at 60 °C was 20% shorter than that of hot air drying at 60 °C, and the drying time of ultrasound-assisted hot air drying at 80 °C was 37.5% shorter than that of hot air drying at 80 °C. The analysis of the nonvolatile flavor components showed that the ultrasound-assisted hot air drying at 60 °C of the O. raphanipes sample had the highest content of free amino acids (83.78 mg/g) and an equivalent umami concentration value (1491.33 monosodium glutamate/100 g). The vacuum freeze-drying treated O. raphanipes had the highest 5′-nucleotide content of 2.44 mg/g. Therefore, vacuum freeze-drying and ultrasound-assisted hot air drying at 60 °C, followed by vacuum microwave drying, might protect the flavor components of O. raphanipes to the greatest extent. However, microwave drying, hot air drying at 80 °C, and ultrasound-assisted hot air drying at 80 °C could destroy the flavor components of O. raphanipes during drying. The results of this study provided data support for the industrial production of dried O. raphanipes.
Development of a Novel Low-Calorie Lime Juice-Based Prebiotic Beverage Using a Combined Design Optimization Methodology
Authors: Fakhri Leila Abolghasemi , Ghanbarzadeh Babak , Falcone Pasquale M.
Published: February 2023

A novel lime-juice based low-calorie functional beverage was developed by using D-optimal combined design optimization. For the preparation of the beverage, the following functional ingredients were used: lime juice, lime peel essential oil (LEO) as a flavoring agent and bioactive component, sucralose as a low-calorie sweetener, an inulin/polydextrose (I/P) mixture as prebiotic fibers, pectin as a thickening agent and soluble dietary fiber, lutein as a carotenoid colorant and antioxidant, and peppermint extract (ME) as a flavoring agent and bioactive component. A combined design consisting of one mixture factor (LEO/ME ratio), one numeric factor (lutein concentration), and one categoric factor (presence or absence of prebiotics) was used for optimizing the functional beverage based on the sensory quality. Regression models were adequately fitted to the data of sensory acceptance with a determination coefficient >90%. The sample containing a mixture of prebiotics, 2:3 (v/v) ratio of LEO: ME, and 3 mg/100 mL lutein was selected as the best formulation among the six optimal beverages which was suggested by Design-Expert software. This final optimum sample showed the highest total phenolic (44.22 mg gallic acid equivalents/L) and flavonoid (25.49 mg quercetin equivalents/L) contents, and its antioxidant activity (as 2,2-diphenyl-1-picrylhydrazyl radical (DPPH•) scavenging) was 38.30%. The newly designed beverage has the potential to promote health benefits and in therapeutic applications.