bluuebunny/crossref_metadata_2025 huggingface dataset:
A Critical Systematic Literature Review of Global Inclusive Education Using an Affective, Intersectional, Discursive, Emotive and Material Lens
Authors: Hernández-Saca David Isaac , Voulgarides Catherine Kramarczuk , Etscheidt Susan Larson
Published: December 2023

We conducted a critical systematic literature review on global inclusive education and law. The critical review questions were: (1) how have scholars theorized, conceptualized, and studied global inclusive education? (2) How do scholars define global inclusive education? (3) And what do scholars cite as prominent international inclusive education law? We ask such questions given the ongoing global crises that situate historically marginalized groups in even more precarious positions—including students with dis/Abilities. Given this framing, we employed a critical systematic literature review that is cognizant of our positionalities, writing from the Global North, so that we can identify lines of inquiry related to global inclusive education that can disrupt global cultural hegemony. Global inclusive education was defined broadly from access to employment through a human right, systemic change, academic, social and emotional frameworks for students with dis/Abilities’ inclusion of all “regardless” of markers of difference. International inclusive education law was approached by affirming the aspirational visions of numerous United Nations’ conventions and policies that focused on social justice for Black, Indigenous and Youth of Color with dis/Abilities in education and global society, without necessarily accounting for the interactions between how macro (legal), meso (local contexts) and micro (student voices) are or are not considered in the global inclusive space.
On-Cloud Linking Approach Using a Linkable Glue Layer for Metamorphic Edge Devices
Authors: Lee Dongkyu , Park Daejin
Published: December 2023

As sensors operating at the edge continue to evolve, the amount of data that edge devices need to process is increasing. Cloud computing methods have been proposed to process complex data on edge devices that are powered by limited resources. However, the existing cloud computing approach, which provides services from servers determined at the compile stage on the edge, is not suitable for the metamorphic edge device proposed in this paper. Therefore, we have realized the operation of metamorphic edge devices by changing the service that accelerates the application in real time according to the surrounding environmental conditions on the edge device. The on-cloud linking approach separates the code for communication from the edge and server into a linkable glue layer. The separated communication code in the linkable glue layer is reconfigured in real time according to the environment of the edge device. To verify the computational acceleration of cloud computing and the real-time service change of the metamorphic edge device, we operated services that perform matrix multiplication operations with one process, two processes, and four processes in parallel on the edge–cloud system based on the on-cloud linking approach. Through the experiments, it was confirmed that the on-cloud linking approach changes the service provided in real time according to changes in external environmental data without changing the code built into the edge. When a square matrix operation with 1000 rows was loaded onto the proposed platform, the size of the code embedded into the edge device decreased by 8.88% and the operation time decreased by 96.7%.
Design and Assurance of Safety-Critical Systems with Artificial Intelligence in FPGAs: The Safety ArtISt Method and a Case Study of an FPGA-Based Autonomous Vehicle Braking Control System
Authors: Silva Neto Antonio V. , Silva Henrique L. , Camargo João B. , Almeida Jorge R. , Cugnasca Paulo S.
Published: December 2023

With the advancements in utilizing Artificial Intelligence (AI) in embedded safety-critical systems based on Field-Programmable Gate Arrays (FPGAs), assuring that these systems meet their safety requirements is of paramount importance for their revenue service. Based on this context, this paper has two main objectives. The first of them is to present the Safety ArtISt method, developed by the authors to guide the lifecycle of AI-based safety-critical systems, and emphasize its FPGA-oriented tasks and recommended practice towards safety assurance. The second one is to illustrate the application of Safety ArtISt with an FPGA-based braking control system for autonomous vehicles relying on explainable AI generated with High-Level Synthesis. The results indicate that Safety ArtISt played four main roles in the safety lifecycle of AI-based systems for FPGAs. Firstly, it provided guidance in identifying the safety-critical role of activities such as sensitivity analyses for numeric representation and FPGA dimensioning to achieve safety. Furthermore, it allowed building qualitative and quantitative safety arguments from analyses and physical experimentation with actual FPGAs. It also allowed the early detection of safety issues—thus reducing project costs—and, ultimately, it uncovered relevant challenges not discussed in detail when designing safety-critical, explainable AI for FPGAs.
Learning Adaptive Quantization Parameter for Consistent Quality Oriented Video Coding
Authors: Vu Tien Huu , Do Minh Ngoc , Nguyen Sang Quang , PhiCong Huy , Sisouvong Thipphaphone , HoangVan Xiem
Published: December 2023

In the industry 4.0 era, video applications such as surveillance visual systems, video conferencing, or video broadcasting have been playing a vital role. In these applications, for manipulating and tracking objects in decoded video, the quality of decoded video should be consistent because it largely affects the performance of the machine analysis. To cope with this problem, we propose a novel perceptual video coding (PVC) solution in which a full reference quality metric named video multimethod assessment fusion (VMAF) is employed together with a deep convolutional neural network (CNN) to obtain consistent quality while still achieving high compression performance. First of all, in order to achieve the consistent quality requirement, we propose a CNN model with an expected VMAF as input to adaptively adjust the quantization parameters (QP) for each coding block. Afterwards, to increase the compression performance, a Lagrange coefficient of rate-distortion optimization (RDO) mechanism is adaptively computed according to rate-QP and quality-QP models. The experimental results show that the proposed PVC solution has achieved two targets simultaneously: the quality of video sequence is kept consistently with an expected quality level and the bit rate saving of the proposed method is higher than traditional video coding standards and the relevant benchmark, notably with around 10% bitrate saving on average.
A Study of Direction-of-Arrival Estimation with an Improved Monopulse Ratio Curve Using Beamforming for an Active Phased Array Antenna System
Authors: Jung Jinwoo , Satriyotomo Bagas , Pyo Seongmin
Published: December 2023

When constructing a wireless communication network, the line of sight of radio waves is limited by the terrain features in a ground communication network. Also, satellite communication networks face capacity limitations and are vulnerable to jamming. Aviation communication networks can solve the above-mentioned problems. To construct seamless aviation communication networks, fast counterpart location estimation and efficient beam steering performance are essential. Among various techniques used for searching the counterpart’s location, the monopulse technique has the advantage of quickly estimating the location through a simplified procedure. However, the nonlinear characteristics of the monopulse ratio curve, which are inevitably caused by the general antenna beam shape, both limit the location estimation range and reduce the estimated location accuracy. To overcome these limitations, a method that improves the estimation accuracy and extends the range by correcting the sum and difference patterns using the beamforming technique of active phased array antennas was proposed. An antenna system model suitable for aviation communication networks was presented, and the proposed model was experimentally proven to be effective. An average angle error of 0.021° was observed in the estimation of the accuracy of the antenna location.
Model Reference Adaptive Observer for Permanent Magnet Synchronous Motors Based on Improved Linear Dead-Time Compensation
Authors: Chen Huipeng , Zhang Renjie , Zhu Shaopeng , Gao Jian , Zhou Rougang
Published: December 2023

Aiming at the problem that the model reference adaptive observer (MRAS) is sensitive to the parameters of the motor model, this paper designs a model reference adaptive observer with resistance adaption and considers the influence of the inverter dead zone. The method introduces the online identification of resistance on the traditional reference adaptive observer model, corrects the resistance parameters of the model’s reference adaptive observer in real time, selects PI as the adaptive law, and proves the stability of the adaptive law using Popov’s stability theorem. To study the influence of the inverter dead zone on the voltage parameters in the estimation model at low speeds, an improved linear dead zone compensation method is proposed to improve the model reference adaptive observer, which eliminates the voltage error between the estimated motor model and the actual motor model. For a given rotational speed of 300 rpm, the observed errors of sliding mode observer (SMO) and traditional MRAS in a steady state were 8.3% and 6%, respectively, and the rotational speed error was controlled to 1.6% using the compensation scheme proposed in this paper. After compensation, the resistance identification error was reduced from 17% to 1.6%. The simulation and experimental results showed that the accuracy of position estimation can be significantly improved using dead zone compensation under low-speed conditions, and the dead zone compensation can improve the accuracy of the online recognition of the resistance.
Lane Detection Based on Adaptive Cross-Scale Region of Interest Fusion
Authors: Deng Lujuan , Liu Xinglong , Jiang Min , Li Zuhe , Ma Jiangtao , Li Hanbing
Published: December 2023

Lane detection, a crucial component of autonomous driving systems, is in charge of precise lane location to ensure that cars navigate lanes appropriately. However, in challenging conditions like shadows and extreme lighting, lanes may become obstructed or blurred, posing a significant challenge to the lane-detection task as the model struggles to extract sufficient visual information from the image. The current anchor-based lane-detection network detects lanes in complex scenes by mapping anchors to images to extract features and calculating the relationship between each anchor and other anchors for feature fusion. However, it is insufficient for anchors to extract subtle features from images, and there is no guarantee that the information carried by each anchor is valid. Therefore, this study proposes the adaptive cross-scale ROI fusion network (ACSNet) to fully extract the features in the image so that the anchor carries more useful information. ACSNet selects important anchors in an adaptive manner and fuses these important anchors with the original anchors across scales. Through this feature extraction method, the features of different field-of-view ranges under complex road surfaces can be learned, and diversified features can be integrated to ensure that lanes can be well detected under complex road surfaces such as shadows and extreme lighting. Furthermore, due to the slender structure of lane lines, there are relatively few useful features in the images. Therefore, this study also proposes a Three-dimensional Coordinate Attention Mechanism (TDCA) to enhance image features. The Three-dimensional Coordinate Attention Mechanism extensively explores relationships among features in the row, column, and spatial dimensions. It calculates feature weights for each of these dimensions and ultimately performs element-wise multiplication with the entire feature map. Experimental results demonstrate that our network achieves excellent performance on the existing public datasets, CULane and Tusimple.
Adaptive Truck Platooning with Drones: A Decentralized Approach for Highway Monitoring
Authors: de Curtò J. , de Zarzà I. , Cano Juan Carlos , Manzoni Pietro , Calafate Carlos T.
Published: December 2023

The increasing demand for efficient and safe transportation systems has led to the development of autonomous vehicles and vehicle platooning. Truck platooning, in particular, offers numerous benefits, such as reduced fuel consumption, enhanced traffic flow, and increased safety. In this paper, we present a drone-based decentralized framework for truck platooning in highway monitoring scenarios. Our approach employs multiple drones, which communicate with the trucks and make real-time decisions on whether to form a platoon or not, leveraging Model Predictive Control (MPC) and Unscented Kalman Filter (UKF) for drone formation control. The proposed framework integrates a simple truck model in the existing drone-based simulation, addressing the truck dynamics and constraints for practical applicability. Simulation results demonstrate the effectiveness of our approach in maintaining the desired platoon formations while ensuring collision avoidance and adhering to the vehicle constraints. This innovative drone-based truck platooning system has the potential to significantly improve highway monitoring efficiency, traffic management, and safety. Our drone-based truck platooning system is primarily designed for implementation in highway monitoring and management scenarios, where its enhanced communication and real-time decision-making capabilities can significantly contribute to traffic efficiency and safety. Future work may focus on field trials to validate the system in real-world conditions and further refine the algorithms based on practical feedback and evolving vehicular technologies.
Deployment and Implementation Aspects of Radio Frequency Fingerprinting in Cybersecurity of Smart Grids
Authors: Awan Maaz Ali , Dalveren Yaser , Catak Ferhat Ozgur , Kara Ali
Published: December 2023

Smart grids incorporate diverse power equipment used for energy optimization in intelligent cities. This equipment may use Internet of Things (IoT) devices and services in the future. To ensure stable operation of smart grids, cybersecurity of IoT is paramount. To this end, use of cryptographic security methods is prevalent in existing IoT. Non-cryptographic methods such as radio frequency fingerprinting (RFF) have been on the horizon for a few decades but are limited to academic research or military interest. RFF is a physical layer security feature that leverages hardware impairments in radios of IoT devices for classification and rogue device detection. The article discusses the potential of RFF in wireless communication of IoT devices to augment the cybersecurity of smart grids. The characteristics of a deep learning (DL)-aided RFF system are presented. Subsequently, a deployment framework of RFF for smart grids is presented with implementation and regulatory aspects. The article culminates with a discussion of existing challenges and potential research directions for maturation of RFF.
Throughput Optimization for Blockchain System with Dynamic Sharding
Authors: Liu Chuyi , Wan Jianxiong , Li Leixiao , Yao Bingbing
Published: December 2023

Sharding technology, which divides a network into multiple disjoint groups so that transactions can be processed in parallel, is applied to blockchain systems as a promising solution to improve Transactions Per Second (TPS). This paper considers the Optimal Blockchain Sharding (OBCS) problem as a Markov Decision Process (MDP) where the decision variables are the number of shards, block size and block interval. Previous works solved the OBCS problem via Deep Reinforcement Learning (DRL)-based methods, where the action space must be discretized to increase processability. However, the discretization degrades the quality of the solution since the optimal solution usually lies between discrete values. In this paper, we treat the block size and block interval as continuous decision variables and provide dynamic sharding strategies based on them. The Branching Dueling Q-Network Blockchain Sharding (BDQBS) algorithm is designed for discrete action spaces. Compared with traditional DRL algorithms, the BDQBS overcomes the drawbacks of high action space dimensions and difficulty in training neural networks. And it improves the performance of the blockchain system by 1.25 times. We also propose a sharding control algorithm based on the Parameterized Deep Q-Networks (P-DQN) algorithm, i.e., the Parameterized Deep Q-Networks Blockchain Sharding (P-DQNBS) algorithm, to efficiently handle the discrete–continuous hybrid action space without the scalability issues. Also, the method can effectively improve the TPS by up to 28%.
A 12~14-Bit SAR-SS Hybrid ADC with SS Bit Shifting Resolution Reconfigurable Method for Bio-Signal Processing
Authors: Moon Cheol Woo , Yoon Kwang Sub , Lee Jonghwan
Published: December 2023

This paper presents a low-power, high-resolution reconfigurable hybrid ADC for bio-electrical signal processing. The proposed ADC contains a SAR ADC for the most significant bit (MSB) and a single-slope ADC for the least significant bit (LSB). To solve the issue of exponentially increasing sampling speed based on the resolution of the single-slope ADC, the SAR ADC is designed to be reconfigurable with a resolution of 8–10-bit, while the single-slope ADC is configured with a resolution of 4-bit. To achieve this resolution reconfiguration, the bit shifting method is proposed and implemented with reconfigurable SAR logic circuit and 4-bit single-slope digital ramp generator. Measurement results demonstrate the power consumption of 34.0 uW, which includes analog power of 23.8 uW and digital power of 10.2 uW, INL/DNL of ±3.5 LSB and −1.0/+2.5 LSB. The ENOB and FoM are measured to be 10.8 bits and 53 fJ/step, respectively.
A Study on the High Reliability Audio Target Frequency Generator for Electronics Industry
Authors: Park Changsik , Han Euntack , Kim Ikjae , Shin Dongkyoo
Published: December 2023

The frequency synthesizer performs a simple function of generating a desired frequency by manipulating a reference frequency signal, but stable and precise frequency generation is essential for reliable operation in mechanical equipment such as communication, control, surveillance, medical, and commercial fields. Frequency synthesis, which is commonly used in various contexts, has been used in analog and digital methods or hybrid methods. Especially in the field of communication, a precise frequency synthesizer is required for each frequency band, from very low-frequency AF (audio frequency) to high-frequency microwaves. The purpose of this paper is to design and implement a highly reliable frequency synthesizer for application to a railway track circuit systems using AF frequency only with the logic circuit of an FPGA (field programmable gate array) without using a microprocessor. Therefore, the development trend of analog, digital, and hybrid frequency synthesizers is examined, and a method for precise frequency synthesizer generation on the basis of the digital method is suggested. In this paper, the generated frequency generated by applying the digital frequency synthesizer using the ultra-precision algorithm completed by many trials and errors shows the performance of generating the target frequency with an accuracy of more than 99.999% and a resolution of mHz, which is much higher than the resolution of 5 Hz in the previous study. This highly precise AF-class frequency synthesizer contributes greatly to the safe operation and operation of braking and signaling systems when used in transportation equipment such as railways and subways.
Testing the Verification and Validation Capability of a DCP-Based Interface for Distributed Real-Time Applications
Authors: Segura Mikel , Calderón Alejandro J. , Poggi Tomaso , Barcena Rafael
Published: December 2023

Cyber–physical systems (CPS) integrate diverse elements developed by various vendors, often dispersed geographically, posing significant development challenges. This paper presents an improved version of our previously developed co-simulation interface based on the non-proprietary Distributed Co-Simulation Protocol (DCP) standard, now optimized for broader hardware platform compatibility. The core contributions include a demonstration of the interface’s hardware-agnostic capabilities and its straightforward adaptability across different platforms. Furthermore, we provide a comparative analysis of our interface against the original DCP. It is validated via various X-in-the-Loop simulations, reinforcing the interface’s versatility and applicability in diverse scenarios, such as distributed real-time executions, verification and validation processes, or Intellectual Property protection.
Optimization of Dual-Design Operation Ventilation System Network Based on Improved Genetic Algorithm
Authors: Feng Yanbo , Zhu Han , Feng Xiwen , Chen Qianru , Sun Xiangyu , Li Zhengrong
Published: December 2023

The COVID-19 pandemic has emphasized the crucial role of ventilation systems in mitigating cross-infections, especially in infectious-disease hospitals. This study introduces a dual-design operation ventilation system that can operate under two sets of ventilation conditions for normal and epidemic times. A challenge is optimizing duct diameters for required airflow while maintaining hydraulic balance. We designed an improved genetic algorithm with an adaptive penalty factor and velocity constraint, as well as the improved crossover probability and mutation probability. The improved genetic algorithm is suitable for ventilation system networks, which can find a better combination of air duct diameters to improve the hydraulic balance rate and reduce the usage of air valves, resulting in efficient hydraulic balancing commissioning. A supply air ventilation system of an actual hospital in China was selected as a case study, and the number of imbalanced air ducts was reduced from 14 to 4. Compared with the traditional genetic algorithm, it has a faster search speed and a better global search ability, which is effective for the optimal design of ventilation system networks.
Denoising Masked Autoencoder-Based Missing Imputation within Constrained Environments for Electric Load Data
Authors: Jeong Jaeik , Ku Tai-Yeon , Park Wan-Ki
Published: December 2023

With recent advancements in data technologies, particularly machine learning, research focusing on the enhancement of energy efficiency in residential, commercial, and industrial settings through the collection of load data, such as heat, electricity, and gas, has gained significant attention. Nevertheless, issues arising from hardware- or network-related problems can result in missing data, necessitating the development of management techniques to mitigate these challenges. Traditional methods for missing imputation face difficulties when operating in constrained environments characterized by short data collection periods and frequent consecutive missing. In this paper, we introduce the denoising masked autoencoder (DMAE) model as a solution to improve the handling of missing data, even in such restrictive settings. The proposed DMAE model capitalizes on the advantages of the denoising autoencoder (DAE), enabling effective learning of the missing imputation process, even with relatively small datasets, and the masked autoencoder (MAE), allowing for learning in environments with a high missing ratio. By integrating these strengths, the DMAE model achieves an enhanced performance in terms of missing imputation. The simulation results demonstrate that the proposed DMAE model outperforms the DAE or MAE significantly in a constrained environment where the duration of the training data is short, less than a year, and missing values occur frequently with durations ranging from 3 h to 12 h.
Carbon Footprint and Energy Recovery Potential of Primary Wastewater Treatment in Decentralized Areas: A Critical Review on Septic and Imhoff Tanks
Authors: Boiocchi Riccardo , Mainardis Matia , Rada Elena Cristina , Ragazzi Marco , Salvati Silvana Carla
Published: December 2023

The present work is a critical review on the carbon footprint and energy recovery potential of septic and Imhoff tanks for primary wastewater treatment. From an online search of research papers, a lack of up-to-date research about gas emissions from Imhoff tanks emerged. Additionally, available literature data should be extended to incorporate the effect of seasonal variations, which may be relevant due to the fact that both systems work under environmental conditions. The literature generally agrees on the positive effect of temperature increase on biogas and methane production from both septic and Imhoff tanks. Additionally, sludge withdrawal is an important operational feature for gas production in these reactors. More recently, the application of electrochemical technologies and the installation of photovoltaic modules have been studied to enhance the sustainability of these decentralized solutions; in addition, sludge pretreatment has been investigated to raise the obtainable methane yields due to limited sludge biodegradability. Further research is needed to assess the effective sustainability of biogas collection and valorization from existing septic and Imhoff tanks, considering the limited biogas generation and the implementation of these systems in decentralized wastewater treatment scenarios (rural or mountain areas). Contrary to the intensive research on greenhouse gas mitigation strategies applied to centralized systems, solutions specifically designed for gas emission mitigations from septic and Imhoff tanks have not attracted the same scientific interest up to now. More generally, given the widespread application of these two options and their potential significant contribution to the overall carbon footprint of wastewater treatment technologies, much more research must be performed in the future both on the quantification of gas production and on the applicable strategies to reduce their carbon footprint.
Towards the Conservation of Monumental Taxus baccata L. Trees of Thasos Island: Genetic Insights
Authors: Malliarou Ermioni , Avramidou Evangelia V. , Ranis Georgios D. , Bountis Diamantis I.
Published: December 2023

Taxus baccata L. is a tertiary relict, long-lived, wind-pollinated dioecious tree species found throughout Europe. In the rocky mountains of Thasos island, monumental old trees create a unique area of natural beauty. In recent times, the need to implement conservation measures for key endangered species such as Taxus baccata has intensified. Exploring the genetic diversity of the species is a prerequisite for successful forest management decisions aimed at conservation. In this study, 28 monumental trees from two natural populations of Thasos were investigated using eight Simple Sequence Repeat markers in order to assess the levels of genetic diversity and genetic differentiation within the individuals, to estimate the degree of inbreeding and the effective population size of each population, and to discuss the impact this study has on conservation efforts for the species. Although the population size was small (14 individuals per population), the results showed moderate to high genetic diversity parameters. The mean expected heterozygosity was He = 0.649 and the number of effective alleles was Ne = 3.270 for both populations. Moreover, allelic richness (AR = 3.395) was high, indicating a variable genetic pool which is probably a result of a past established expansion of the species in the area. The results of the present study present a unique genetic pool harbored by specific trees, which is an important advantage for ensuring their conservation and resistance against biotic and abiotic threats. Our study paves the way towards conservation measures, which can be prioritized as follows: (a) in situ conservation, (b) seed bank establishment, and (c) in vitro propagation in order to secure future resilience and sustainability of the species.
Effect of Lithological Parameters on Combustion Characteristics of Methane Hydrate Sediments
Authors: Cui Gan , Wu Di , Li Yixuan , Yao Shupeng , Guo Tao , Yin Juerui , Xing Xiao , Liu Jianguo
Published: December 2023

In this paper, four lithological parameters, that is, thermal conductivity, particle size, porosity, and saturation, are investigated by combining experimental observations with numerical simulations to study the influence laws and mechanisms of action on the combustion process of methane hydrate sediments. The variations in combustion characteristics parameters such as flame height, effective combustion ratio, and dimensionless discharge water mass with the lithological parameters are studied. In addition, the combustion mechanism of lithologic parameters on methane hydrate deposits is revealed. Combining the experimental results and simulation calculations to optimize the combustion of methane hydrate sediments, it is recommended to use methane hydrate sediment samples with high saturation and low thermal conductivity, while the oxidant concentration and porosity of methane hydrate sediment samples should be increased.
Properties of Heat-Assisted pH Shifting and Compounded Chitosan from Insoluble Rice Peptide Precipitate and Its Application in the Curcumin-Loaded Pickering Emulsions
Authors: Yang Zhenyu , Li Zhiying , Xu Zitong , Kong Zhihao , Qiao Xin , Zhang Liwen , Dai Lei , Wang Yanfei , Sun Qingjie , McClements David Julian , Xu Xingfeng
Published: December 2023

Curcumin exhibits antioxidant and antitumor properties, but its poor chemical stability limits its application. Insoluble peptide precipitates formed by proteolysis of rice glutelin are usually discarded, resulting in resource waste. The coupled treatment of heat-assisted pH shifting and compounded chitosan (CS) was used to fabricate rice peptide aggregate–chitosan complexes (RPA–CS). The structure, interfacial behavior, emulsion properties, and digestibility of curcumin-loaded RPA–CS Pickering emulsions were investigated. Increasing the CS concentration led to lower interfacial tension but larger particle size, and the three-phase contact angle of the RPA–CS complexes approached 90°. Quartz crystal microbalance with dissipation (QCM–D) indicated that RPA–CS complexes with 6 g·kg−1 of CS (RPA–CS6) had the highest K1 (0.592 × 106 Hz−1) and K4 (0.487 × 106 Hz−1), suggesting that the softest interfacial layers were formed. The solid–liquid balance of RPA–RPA–CS emulsions was lower than 0.5, declaring that they had more elastic behavior than that of RPA emulsions. RPA–RPA–CS4-and RPA–CS6 emulsions had better storage stability, lower FFA release (79.8% and 76.3%, respectively), and higher curcumin bioaccessibility (65.2% and 68.2%, respectively) than RPA emulsions. This study showed that a low-value insoluble rice peptide precipitate could be used as a valuable emulsifier in foods, which may increase the economics and sustainability of the food supply.
Virtual Screening Technology for Two Novel Peptides in Soybean as Inhibitors of α-Amylase and α-Glucosidase
Authors: Tang Xiyao , Chen Xu , Wang Hong , Yang Jinyi , Li Lin , Zhu Jie , Liu Yujia
Published: December 2023

Soybean peptides (SPs) have bioactivities of enzyme inhibition that are beneficial to human health, but their mechanism is not clear. This study aimed to identify peptide fragments in SPs that simultaneously inhibit α-amylase and α-glucosidase and to explore their enzyme inhibition mechanism. Firstly, the inhibitory activity of SPs against the enzymes was determined. And two octapeptides, LDQTPRVF and SRNPIYSN, were identified for the first time by using HPLC-QTOF-MS/MS and virtual screening. Molecular simulation results showed that hydrogen bonds and π–π bonds were the key factors, and the N-terminal (Leu and Ser) and C-terminal (Phe) of peptide were important inhibiting sites. Both octapeptides were synthesized, and their IC50 values were 3.08 and 5.58 mmol/L for α-amylase, and 2.52 and 4.57 mmol/L for α-glucosidase, respectively. This study provided evidence for SPs as a potential inhibitor of α-amylase and α-glucosidase in special dietary foods.
Sensory Perception and Consumer Acceptance of Carrot Cultivars Are Influenced by Their Metabolic Profiles for Volatile and Non-Volatile Organic Compounds
Authors: Nothnagel Thomas , Ulrich Detlef , Dunemann Frank , Budahn Holger
Published: December 2023

Sensory parameters as well as the volatile and non-volatile compound profiles of sixteen carrot cultivars were recorded to obtain insight into consumer preference decisions. The sensory test was carried out with a consumer panel of 88 untrained testers allowing a clear acceptance-based differentiation of the cultivars. Five individual sensory characters (sweetness, overall aroma, bitterness, astringency and off-flavor) supported this discrimination. Chemical analyses of volatile organic compounds, polyacetylenes, phenylpropanoids and sugars enabled us to correlate the influence of these ingredients on sensory perception. Higher concentrations of α-pinene, hexanal, styrene and acetophenone correlated with a better acceptance, as well as sweetness and overall aroma perception. In contrast, a low acceptance as well as a stronger perception of bitterness, astringency and off-flavor correlated with enhanced concentrations of camphene, bornylacetate, borneol, myristicine, falcarindiol, falcarindiol-3-acetate, laserin and epilaserin. The present study should support the development of new breeding strategies for carrot cultivars that better satisfy consumer demands.
Effects of NaCl on the Physical Properties of Cornstarch–Methyl Cellulose Blend and on Its Gel Prepared with Rice Flour in a Model System
Authors: Kim Juhee , Chang Yoon Hyuk , Lee Youngseung
Published: December 2023

This study investigated the impact of NaCl on the physical properties of cornstarch–methyl cellulose (CS–MC) mixtures and their gels prepared with rice flour in a model system. Opposite trends were observed, showing that NaCl led to decreased viscosity of the CS–MC mixtures (liquid-based), whereas a more stable and robust structure was observed for the rice-flour-added gels (solid-based) with the addition of NaCl. The interference of NaCl with the CS-MS blend’s ability to form a stable gel network resulted in a thinner consistency, as the molecules of the CS-MS blend may not bind together as effectively. On the contrary, NaCl showed the potential to enhance the protein network within CS–MC gels prepared with rice flour, thereby contributing to an augmentation in the stability or firmness of the cooked gels. Careful utilization of NaCl to optimize the physical properties of the CS–MC blends, as well as the gels based on rice flour, should be performed.
Reduction and Growth Inhibition of Listeria monocytogenes by Use of Anti-Listerial Nisin, P100 Phages and Buffered Dry Vinegar Fermentates in Standard and Sodium-Reduced Cold-Smoked Salmon
Authors: Heir Even , Jensen Merete Rusås , Aasli Anette Wold , Berget Ingunn , Holck Askild Lorentz
Published: December 2023

Cold-smoked salmon are ready-to-eat products that may support the growth of pathogenic Listeria monocytogenes during their long shelf-life. Consumption of such contaminated products can cause fatal listeriosis infections. Another challenge and potential risk associated with CS salmon is their high levels of sodium salt. Excess dietary intake is associated with serious health complications. In the present study, anti-listerial bacteriocin (nisin), P100 bacteriophages (Phageguard L, PGL) and fermentates (Verdad N6, P-NDV) were evaluated as commercial bio-preservation strategies for increased control of L. monocytogenes in standard (with NaCl) and sodium-reduced (NaCl partially replaced with KCl) CS salmon. Treatments of CS salmon with nisin (1 ppm) and PGL (5 × 107 pfu/cm2) separately yielded significant initial reductions in L. monocytogenes (up to 0.7 log) compared to untreated samples. Enhanced additive reductions were achieved through the combined treatments of nisin and PGL. Fermentates in the CS salmon inhibited the growth of Listeria but did not lead to its eradication. The lowest levels of L. monocytogenes during storage were observed in nisin- and PGL-treated CS salmon containing preservative fermentates and stored at 4 °C, while enhanced growth was observed during storage at an abusive temperature of 8 °C. Evaluation of industry-processed standard and sodium-replaced CS salmon confirmed significant effects with up to 1.7 log reductions in L. monocytogenes levels after 34 days of storage of PGL- and nisin-treated CS salmon-containing fermentates. No differences in total aerobic plate counts were observed between treated (PGL and nisin) or non-treated standard and sodium-reduced CS salmon at the end of storage. The microbiota was dominated by Photobacterium, but with a shift showing dominance of Lactococcus spp. and Vagococcus spp. in fermentate-containing samples. Similar and robust reductions in L. monocytogenes can be achieved in both standard and sodium-replaced CS salmon using the bio-preservation strategies of nisin, PGL and fermentates under various and relevant processing and storage conditions.
Occurrence, Concentration and Toxicity of 54 Polycyclic Aromatic Hydrocarbons in Butter during Storage
Authors: Lan Jianqiang , Wu Shimin
Published: December 2023

Polycyclic aromatic hydrocarbons (PAHs) are a class of highly carcinogenic compounds with a lipophilic nature. This study investigated the characterization of PAH24 contamination in twenty-one types of butter and five types of margarines using the QuEChERS pretreatment coupled with GC-QqQ-MS. Additionally, low-temperature storage experiments were conducted to explore the variations in oxidation index as well as the PAH levels. The results revealed that PAH24 concentrations in butter and margarine were 50.75–310.64 μg/kg and 47.66–118.62 μg/kg, respectively. The PAH4 level in one type of butter reached 11.24 μg/kg beyond the EU standards. Over 160 days of storage at 4 °C, acid value (AV), peroxide value (POV), and acidity significantly increased, while malondialdehyde (MDA) content and carbonyl value (CGV) fluctuated. Concentrations of PAH24 and oxidized PAHs (OPAHs) experienced a notable reduction of 29.09% and 63.85%, respectively. The slow reduction in naphthalene (NaP) indicated the dynamic nature of PAHs during storage. However, the toxic equivalency quotients (TEQs) decreased slightly from a range of 0.65–1.90 to 0.39–1.77, with no significant difference. This study contributes to the understanding of variations in PAHs during storage, which is of great significance for food safety.
A Comparative Analysis of Laplace Residual Power Series and a New Iteration Method for Fitzhugh-Nagumo Equation in the Caputo Operator Framework
Authors: Alshehry Azzh Saad
Published: December 2023

In this paper, I utilize the Laplace residual power series method (LRPSM) along with a novel iteration technique to investigate the Fitzhugh-Nagumo equation within the framework of the Caputo operator. The Fitzhugh-Nagumo equation is a fundamental model for describing excitable systems, playing a crucial role in understanding various physiological and biological phenomena. The Caputo operator extends the conventional derivative to handle non-local and non-integer-order differential equations, making it a potent tool for modeling complex processes. Our study involves transforming the Fitzhugh-Nagumo equation into its Laplace domain representation, applying the LRPSM to derive a series solution. We then introduce a novel iteration technique to enhance the solution’s convergence properties, enabling more accurate and efficient computations. This approach offers a systematic methodology for solving the Fitzhugh-Nagumo equation with the Caputo operator, providing deeper insights into excitable system dynamics. Numerical examples and comparisons with existing methods demonstrate the accuracy and efficiency of the LRPSM with the new iteration technique, showcasing its potential for solving diverse differential equations involving the Caputo operator and advancing mathematical modeling in various scientific and engineering domains.
Pathological Fracture of the Proximal Humerus Occurred on Metastases of Probable Kidney Origin in the Absence of Primary Lesions: A Case Report
Authors: Bianco Prevot Luca , Fozzato Stefania , Cannavò Luca , Accetta Riccardo , Amadei Federico , Basile Michela , Leigheb Massimiliano , Basile Giuseppe
Published: December 2023

Cancer of unknown primary (CUP) origin represents a diagnostic and therapeutic challenge. These tumours spread to different parts of the body even if the site of origin has not been identified. When renal metastases are observed without an obvious primary lesion, it is important to exclude the possibility of a primary kidney tumour that may be unknown or too small to be detected. The diagnosis of CUP is established after a careful clinical evaluation and diagnostic tests, including blood chemistry and laboratory tests, instrumental exams (CT, MRI, PET, bone scan), biopsy, and molecular and cytogenetic analysis. Once the diagnosis of CUP with kidney metastases is confirmed, treatment depends on the location of the metastases, the patient’s health status, and available treatment options. The latter includes surgery to remove metastases, radiation therapy, or systemic treatment such as chemotherapy or immunotherapy. It is important that patients with CUP are evaluated by a multidisciplinary team of specialists, who can contribute to planning the most appropriate treatment. In this article, we report the clinical case of a patient with a pathological fracture of the proximal humerus which occurred on metastases of probable renal origin in the absence of primary lesions.
Assessing the Potential and Risks of AI-Based Tools in Higher Education: Results from an eSurvey and SWOT Analysis
Authors: Denecke Kerstin , Glauser Robin , Reichenpfader Daniel
Published: December 2023

Recent developments related to tools based on artificial intelligence (AI) have raised interests in many areas, including higher education. While machine translation tools have been available and in use for many years in teaching and learning, generative AI models have sparked concerns within the academic community. The objective of this paper is to identify the strengths, weaknesses, opportunities and threats (SWOT) of using AI-based tools (ABTs) in higher education contexts. We employed a mixed methods approach to achieve our objectives; we conducted a survey and used the results to perform a SWOT analysis. For the survey, we asked lecturers and students to answer 27 questions (Likert scale, free text, etc.) on their experiences and viewpoints related to AI-based tools in higher education. A total of 305 people from different countries and with different backgrounds answered the questionnaire. The results show that a moderate to high future impact of ABTs on teaching, learning and exams is expected by the participants. ABT strengths are seen as the personalization of the learning experience or increased efficiency via automation of repetitive tasks. Several use cases are envisioned but are still not yet used in daily practice. Challenges include skills teaching, data protection and bias. We conclude that research is needed to study the unintended consequences of ABT usage in higher education in particular for developing countermeasures and to demonstrate the benefits of ABT usage in higher education. Furthermore, we suggest defining a competence model specifying the required skills that ensure the responsible and efficient use of ABTs by students and lecturers.
Heart Rate Variability Measurement through a Smart Wearable Device: Another Breakthrough for Personal Health Monitoring?
Authors: Li Ke , Cardoso Cristiano , Moctezuma-Ramirez Angel , Elgalad Abdelmotagaly , Perin Emerson
Published: December 2023

Heart rate variability (HRV) is a measurement of the fluctuation of time between each heartbeat and reflects the function of the autonomic nervous system. HRV is an important indicator for both physical and mental status and for broad-scope diseases. In this review, we discuss how wearable devices can be used to monitor HRV, and we compare the HRV monitoring function among different devices. In addition, we have reviewed the recent progress in HRV tracking with wearable devices and its value in health monitoring and disease diagnosis. Although many challenges remain, we believe HRV tracking with wearable devices is a promising tool that can be used to improve personal health.
Managing Non-Cancer Chronic Pain in Frail Older Adults: A Pilot Study Based on a Multidisciplinary Approach
Authors: Figueiredo Teodora , Midão Luís , Sampaio Rute , Carrilho Joana , Coelho Constantino , Cerullo Giovanni , Di Paola Antonella , Carfì Angelo , Onder Graziano , Costa Elísio
Published: December 2023

Considering the multidimensionality of chronic pain, it is crucial to develop comprehensive strategies for its effective management. However, establishing well-defined, evidence-based guidelines for such approaches remains challenging. To overcome this, we present the finding from a 4-month intervention to enhance the management of non-cancer chronic pain in older adults with pre-frailty and frailty. The intervention’s core elements comprised a multidisciplinary individualized plan, a case manager, and patient education. This pilot study involved 22 participants (≥65 years). It assessed changes in pain frequency and intensity (pain scale), frailty (Fried frailty phenotype criteria), and medication adherence (Brief Adherence Rating Scale) before and after the 4-month intervention. The results were encouraging: pain frequency and intensity and frailty score tended to decrease, and medication adherence showed significant improvement. This preliminary small-scale pilot study provides a foundation for further research and for exploring the potential scalability of this multidisciplinary patient-centred intervention.
Causal Relationships between Oil Prices and Key Macroeconomic Variables in India
Authors: Upadhyaya Kamal P. , Nag Raja , Mixon Franklin G.
Published: December 2023

India is among the largest and fastest-growing economies in the world. To continue its growth, energy is and will continue to be one of its most important considerations. With a population of over one billion, India is the third largest consumer of petroleum on the globe. To maintain this ranking, India imports a large percentage of its total oil consumption. Given India’s current position as a large importer of oil, how does oil price volatility affect the Indian economy? This paper examines the effect of oil price volatility on inflation, economic growth, and the stock market in India. Statistical tests suggest that the overall price level, the real effective exchange rate, and oil prices are negatively related to aggregate output in the long run. Granger causality test results derived from a vector error correction model support bidirectional causality between oil prices and aggregate output, indicating that a change in oil prices also affects aggregate output in the short run.