bluuebunny/crossref_metadata_2025 huggingface dataset:
Gate Oxide Reliability in Silicon Carbide Planar and Trench Metal-Oxide-Semiconductor Field-Effect Transistors Under Positive and Negative Electric Field Stress
Authors: Shi Limeng , Qian Jiashu , Jin Michael , Bhattacharya Monikuntala , Houshmand Shiva , Yu Hengyu , Shimbori Atsushi , White Marvin H. , Agarwal Anant K.
Published: November 2024

This work investigates the gate oxide reliability of commercial 1.2 kV silicon carbide (SiC) metal-oxide-semiconductor field-effect transistors (MOSFETs) with planar and trench gate structures. The performance of threshold voltage (Vth) and gate leakage current (Igss) in SiC MOSFETs is evaluated under positive and negative gate voltage stress. The oxide lifetimes of SiC planar and trench MOSFETs at 150 °C are measured using constant voltage Time-Dependent Dielectric Breakdown (TDDB) testing. From the test results, it is found that electron trapping and hole trapping in SiO2 caused by oxide electric field (Eox) stress affect the Vth of SiC MOSFETs. The saturation and turnaround behavior of the Vth shift during positive and negative gate voltage stresses indicates that the influence of charge trapping in the gate oxide varies with stress time. The Igss under positive and negative gate voltages depends on the tunneling barrier height for electrons and holes, respectively, which can be calculated using the Fowler–Nordheim (FN) tunneling mechanism. Moreover, the presence of near-interface traps (NITs) affects the barrier height for holes under negative gate voltages. The behavior of Vth shift and Igss under high-temperature gate bias reflects the charge trapping occurring in different regions of the gate oxide. In addition, compared to SiC planar MOSFETs, SiC trench MOSFETs with thicker gate oxide tend to exhibit higher lifetimes in TDDB tests.
Indoor Positioning Method by CNN-LSTM of Continuous Received Signal Strength Indicator
Authors: Yoon Jae-hyuk , Kim Hee-jin , Lee Dong-seok , Kwon Soon-kak
Published: November 2024

This paper proposes an indoor positioning method based on Bluetooth Low Energy signals by Convolution Neural Network-Long Short-Term Memory (CNN-LSTM). The proposed method determines a receiver location based on distances from adjacent transmitters. The CNN-LSTM model estimates the distance from each transmitter using continuous signal strengths. To train and validate the model, the signal strengths are collected in several locations within various indoor environments. The positioning technique is adaptively selected based on the highest signal strength to avoid the interfering problem due to an excessively strong signal. If the signal strength exceeds a certain threshold, the location is determined using the proximity technique, which utilizes only the strongest signal instead of triangulation. In the experimental results, the proposed method demonstrated an average error of about 2.90 m, which is 34.2% better than a triangulation-based positioning method that does not utilize neural networks.
Security Challenges in Energy Flexibility Markets: A Threat Modelling-Based Cyber-Security Analysis
Authors: Afzal Zeeshan , Ekstedt Mathias , Müller Nils , Mukherjee Preetam
Published: November 2024

Flexibility markets are crucial for balancing the decentralised and renewable-driven energy landscape. This paper presents a security evaluation of a flexibility market system using a threat modelling approach. A reference architecture for a typical flexibility market system is proposed, and attack graph-driven simulations are performed to analyse potential attack pathways where malicious actors might infiltrate the system and the vulnerabilities they might exploit. Key findings include the identification of high-risk areas, such as the Internet links between market actors. To mitigate these risks, the paper proposes and evaluates multiple protection scenarios in reducing the identified attack vectors. The findings underline the importance of multi-layered security strategies to safeguard flexibility markets from increasingly sophisticated cyber threats.
Analyzing the Relationship Between COVID-19 and Sociodemographic and Environmental Factors: A Case Study in Toronto
Authors: Yu Brian Anlan , Hu Qinmin Vivian
Published: November 2024

COVID-19 has disproportionately impacted communities based on sociodemographic and environmental factors. Previous studies have largely focused on traditional statistical models to investigate these disparities with limited attention to within-city variations. This research addresses this gap by employing advanced machine learning models to predict COVID-19 case counts at the neighborhood level within Toronto. Using algorithms such as Support Vector Regression, Random Forest, Gradient Boosting, and XGBoost, along with SHAP (SHapley Additive exPlanations) analysis, we identify key factors impacting COVID-19 transmission, including air pollution, socioeconomic status, and racialized group membership. Our results demonstrate that sociodemographic factors significantly influence sporadic cases, while environmental factors, particularly air pollutants, are critical in outbreak cases. This study highlights the value of machine learning in understanding complex interactions between risk factors with implications for targeted public health interventions to mitigate COVID-19 disparities.
Improving Churn Detection in the Banking Sector: A Machine Learning Approach with Probability Calibration Techniques
Authors: Văduva Alin-Gabriel , Oprea Simona-Vasilica , Niculae Andreea-Mihaela , Bâra Adela , Andreescu Anca-Ioana
Published: November 2024

Identifying and reducing customer churn have become a priority for financial institutions seeking to retain clients. Our research focuses on customer churn rate analysis using advanced machine learning (ML) techniques, leveraging a synthetic dataset sourced from the Kaggle platform. The dataset undergoes a preprocessing phase to select variables directly impacting customer churn behavior. SMOTETomek, a hybrid technique that combines oversampling of the minority class (churn) with SMOTE and the removal of noisy or borderline instances through Tomek links, is applied to balance the dataset and improve class separability. Two cutting-edge ML models are applied—random forest (RF) and the Light Gradient-Boosting Machine (LGBM) Classifier. To evaluate the effectiveness of these models, several key performance metrics are utilized, including precision, sensitivity, F1 score, accuracy, and Brier score, which helps assess the calibration of the predicted probabilities. A particular contribution of our research is on calibrating classification probabilities, as many ML models tend to produce uncalibrated probabilities due to the complexity of their internal mechanisms. Probability calibration techniques are employed to adjust the predicted probabilities, enhancing their reliability and interpretability. Furthermore, the Shapley Additive Explanations (SHAP) method, an explainable artificial intelligence (XAI) technique, is further implemented to increase the transparency and credibility of the model’s decision-making process. SHAP provides insights into the importance of individual features in predicting churn, providing knowledge to banking institutions for the development of personalized customer retention strategies.
Text-Guided Unknown Pseudo-Labeling for Open-World Object Detection
Authors: Wang Xuefei , Xu Dong
Published: November 2024

Open-world object detection (OWOD) focuses on training models with partially known class labels, enabling the detection of objects from known classes while concurrently identifying objects from unknown classes. Current models often perform suboptimally in generating pseudo-labels for unknown objects based on objectness scores due to inherent biases towards known classes. To address this issue, we propose a cross-modal learning model named Text-Guided Unknown Pseudo-Labeling for Open-world Object Detection(TGOOD) building on the Featurized Query R-CNN (FQR-CNN) framework. Specifically, we introduce a module called Similarity-Random-Similarity (SRS) to guide the model in detecting unknown objects during training. Additionally, we replace the one-to-one label assignment strategy in FQR-CNN with a one-to-many (OTM) label assignment strategy to provide more supervisory information during training. Moreover, we propose the ROI features Refinement Module (RRM) to enhance the discriminability of all objects. Experimental evaluations on the PASCAL VOC, MS-COCO, and COCO-O benchmarks demonstrate TGOOD’s superior open-world detection capability.
Simplified Knowledge Distillation for Deep Neural Networks Bridging the Performance Gap with a Novel Teacher–Student Architecture
Authors: Umirzakova Sabina , Abdullaev Mirjamol , Mardieva Sevara , Latipova Nodira , Muksimova Shakhnoza
Published: November 2024

The rapid evolution of deep learning has led to significant achievements in computer vision, primarily driven by complex convolutional neural networks (CNNs). However, the increasing depth and parameter count of these networks often result in overfitting and elevated computational demands. Knowledge distillation (KD) has emerged as a promising technique to address these issues by transferring knowledge from a large, well-trained teacher model to a more compact student model. This paper introduces a novel knowledge distillation method that simplifies the distillation process and narrows the performance gap between teacher and student models without relying on intricate knowledge representations. Our approach leverages a unique teacher network architecture designed to enhance the efficiency and effectiveness of knowledge transfer. Additionally, we introduce a streamlined teacher network architecture that transfers knowledge effectively through a simplified distillation process, enabling the student model to achieve high accuracy with reduced computational demands. Comprehensive experiments conducted on the CIFAR-10 dataset demonstrate that our proposed model achieves superior performance compared to traditional KD methods and established architectures such as ResNet and VGG networks. The proposed method not only maintains high accuracy but also significantly reduces training and validation losses. Key findings highlight the optimal hyperparameter settings (temperature T = 15.0 and smoothing factor α = 0.7), which yield the highest validation accuracy and lowest loss values. This research contributes to the theoretical and practical advancements in knowledge distillation, providing a robust framework for future applications and research in neural network compression and optimization. The simplicity and efficiency of our approach pave the way for more accessible and scalable solutions in deep learning model deployment.
Robust Twin Extreme Learning Machine Based on Soft Truncated Capped L1-Norm Loss Function
Authors: Xu Zhendong , Wei Bo , Yu Guolin , Ma Jun
Published: November 2024

Currently, most researchers propose robust algorithms from different perspectives for overcoming the impact of outliers on a model, such as introducing loss functions. However, some loss functions often fail to achieve satisfactory results when the outliers are large. Therefore, the capped loss has become a better choice for researchers. The majority of researchers directly set an upper bound on the loss function, which reduces the impact of large outliers, but also introduces non-differentiable regions. To avoid this shortcoming, we propose a robust twin extreme learning machine based on a soft-capped L1-normal loss function (SCTELM). It uses a soft capped L1-norm loss function. This not only overcomes the shortcomings of the hard capped loss function, but also improves the robustness of the model. Simultaneously, to improve the learning efficiency of the model, the stochastic variance-reduced gradient (SVRG) optimization algorithm is used. Experimental results on several datasets show that the proposed algorithm can compete with state-of-the-art algorithms in terms of robustness.
Performance and Efficiency Evaluation of a Secondary Loop Integrated Thermal Management System with a Multi-Port Valve for Electric Vehicles
Authors: Bae Jaehyun , Yun Jinwon , Han Jaeyoung
Published: November 2024

Recently, battery electric vehicles (BEVs) have faced various technical challenges, such as reduced driving range due to ambient temperature, slow charging speeds, fire risks, and environmental regulations. This numerical study proposes an integrated thermal management system (ITMS) utilizing R290 refrigerant and a 14-way valve to address these issues, proactively meeting future environmental regulations, simplifying the system, and improving efficiency. The performance evaluation was conducted under high-load operating conditions, including driving and fast charging in various environmental conditions of 35 °C and −10 °C. As a result, the driving efficiency was 4.82 km/kWh in high-temperature conditions (35 °C) and 4.69 km/kWh in low-temperature conditions (−10 °C), which demonstrated higher efficiency than the Octovalve-ITMS applied to the Tesla Model Y. Furthermore, in fast charging tests, the high voltage battery was charged from a 10% to a 90% state of charge in 26 min at 35 °C and in 31 min at −10 °C, outperforming the Octovalve-ITMS-equipped Tesla Model Y’s fast charging time of 27 min under moderate ambient conditions. This result highlights the superior fast-charging performance of the 14-way valve-based ITMS, even under high cooling load conditions.
Do Environmental Tax and Energy Matter for Environmental Degradation in the UK? Evidence from Novel Fourier-Based Estimators
Authors: Addai Kwaku , Al Geitany Souha Hanna , Athari Seyed Alireza , Farmanesh Panteha , Kirikkaleli Dervis , Saliba Chafic
Published: November 2024

Currently, the UK has ambitious plans to reach net zero by 2050, despite other countries such as Russia and India targeting 2060 and 2070, respectively. Assuming that the UK emissions unceasingly decline at a given rate annually towards achieving net zero by 2050, its economy would need to ensure a reduction of 105 MtCO2 per year of its emissions from the current 2021 levels. Given that global greenhouse gas emissions have not peaked and continue to rise, the UK seeks to implement costly and aggressive emission reduction policies towards fulfilling commitments under the 2021 Glasgow Climate Pact. This paper investigates the effect of environmental taxes on environmental degradation in the UK between 2000Q1 and 2019Q4 using novel Fourier approaches. Using the novel Fourier ARDL estimator, the long-run equilibrium estimates indicate that gross domestic product and environmental tax cause a fall in carbon emissions. However, in trade and primary energy use, a unit change caused rising carbon emissions in the UK. Especially, the results indicate that environmental taxes have a negative effect on environmental degradation in the UK, and ecological tax policy could be considered as an effective channel to attain environmental sustainability. The outcome provides the following policy insights: (i) The government of the UK should support international environmental tax coordination mechanisms, especially on carbon pricing, to avoid relocation of carbon-intensive investments. (ii) The UK government must note that imposing more taxes to encourage emissions reductions could bring complexity to the tax system and unnecessarily bring costly ways to deal with climate change. Higher domestic electricity prices could disproportionately hit low-income households and create distributional cost concerns, which require benefit payouts or compensation schemes. (iii) Switching to electric vehicles simultaneously requires investments in charging infrastructure and battery technologies. To avoid this chicken-and-egg problem, the government of the UK could play a coordinating role, including deploying targeted subsidies, regulations, direct government involvement, or setting higher carbon prices in special cases.
Optimization of a Gorlov Helical Turbine for Hydrokinetic Application Using the Response Surface Methodology and Experimental Tests
Authors: Pineda Juan Camilo , Rubio-Clemente Ainhoa , Chica Edwin
Published: November 2024

The work presents an analysis of the Gorlov helical turbine (GHT) design using both computational fluid dynamics (CFD) simulations and response surface methodology (RSM). The RSM method was applied to investigate the impact of three geometric factors on the turbine’s power coefficient (CP): the number of blades (N), helix angle (γ), and aspect ratio (AR). Central composite design (CCD) was used for the design of experiments (DOE). For the CFD simulations, a three-dimensional computational domain was established in the Ansys Fluent software, version 2021R1 utilizing the k-ω SST turbulence model and the sliding mesh method to perform unsteady flow simulations. The objective function was to achieve the maximum CP, which was obtained using a high-correlation quadratic mathematical model. Under the optimum conditions, where N, γ, and AR were 5, 78°, and 0.6, respectively, a CP value of 0.3072 was achieved. The optimal turbine geometry was validated through experimental testing, and the CP curve versus tip speed ratio (TSR) was determined and compared with the numerical results, which showed a strong correlation between the two sets of data.
State-of-Health Estimation for Lithium-Ion Batteries in Hybrid Electric Vehicles—A Review
Authors: Zhang Jianyu , Li Kang
Published: November 2024

This paper presents a comprehensive review of state-of-health (SoH) estimation methods for lithium-ion batteries, with a particular focus on the specific challenges encountered in hybrid electric vehicle (HEV) applications. As the demand for electric transportation grows, accurately assessing battery health has become crucial to ensuring vehicle range, safety, and battery lifespan, underscoring the relevance of high-precision SoH estimation methods in HEV applications. The paper begins with outlining current SoH estimation methods, including capacity-based, impedance-based, voltage and temperature-based, and model-based approaches, analyzing their advantages, limitations, and applicability. The paper then examines the impact of unique operating conditions in HEVs, such as frequent charge–discharge cycles and fluctuating power demands, which necessitate tailored SoH estimation techniques. Moreover, this review summarizes the latest research advances, identifies gaps in existing methods, and proposes scientifically innovative improvements, such as refining estimation models, developing techniques specific to HEV operational profiles, and integrating multiple parameters (e.g., voltage, temperature, and impedance) to enhance estimation accuracy. These approaches offer new pathways to achieve higher predictive accuracy, better meeting practical application needs. The paper also underscores the importance of validating these estimation methods in real-world scenarios to ensure their practical feasibility. Through systematic evaluation and innovative recommendations, this review contributes to a deeper understanding of SoH estimation for lithium-ion batteries, especially in HEV contexts, and provides a theoretical basis to advance battery management system optimization technologies.
A Method for Fault Localization in Distribution Networks with High Proportions of Distributed Generation Based on Graph Convolutional Networks
Authors: Ma Xiping , Zhen Wenxi , Ren Haodong , Zhang Guangru , Zhang Kai , Dong Haiying
Published: November 2024

To address the issues arising from the integration of a high proportion of distributed generation (DG) into the distribution network, which has led to the transition from traditional single-source to multi-source distribution systems, resulting in increased complexity of the distribution network topology and difficulties in fault localization, this paper proposes a fault localization method based on graph convolutional networks (GCNs) for distribution networks with a high proportion of distributed generation. By abstracting busbars and lines into graph structure nodes and edges, GCN captures spatial coupling relationships between nodes, using key electrical quantities such as node voltage magnitude, current magnitude, power, and phase angle as input features to construct a fault localization model. A multi-type fault dataset is generated using the Matpower toolbox, and model training is evaluated using K-fold cross-validation. The training process is optimized through early stopping mechanisms and learning rate scheduling. Simulations are conducted based on the IEEE 33-node distribution network benchmark, with photovoltaic generation, wind generation, and energy storage systems connected at specific nodes, validating the model’s fault localization capability under various fault types (single-phase ground fault, phase-to-phase short circuit, and line open circuit). Experimental results demonstrate that the proposed model can effectively locate fault nodes in complex distribution networks with high DG integration, achieving an accuracy of 98.5% and an AUC value of 0.9997. It still shows strong robustness in noisy environments and is significantly higher than convolutional neural networks and other methods in terms of model localization accuracy, training time, F1 score, AUC value, and single fault detection inference time, which has good potential for practical application.
Enhancing Distributed Energy Markets in Smart Grids Through Game Theory and Reinforcement Learning
Authors: Boumaiza Ameni , Maher Kenza
Published: November 2024

The rapid growth of distributed energy resources (DERs) in smart grids has necessitated innovative strategies to manage and optimize energy markets. This paper introduces an architectural framework that leverages game theory and reinforcement learning (RL) as foundational methodologies to enhance the efficiency and robustness of distributed energy markets. Through simulations and case studies, we demonstrate how these approaches can facilitate improved decision-making among market participants, leading to better energy distribution and consumption. This exploratory approach is intended to lay the groundwork for more complex implementations that account for physical and regulatory constraints. Our preliminary results indicate a 25% reduction in energy costs and a 30% improvement in energy distribution efficiency compared to traditional methods.
A Review of Building Physical Shapes on Heating and Cooling Energy Consumption
Authors: Li Jin , Liang Chao , Zhou Wenwu
Published: November 2024

The shape of a building profoundly impacts its energy consumption throughout its life and is a critical consideration in early architectural design. Despite its significance, the influence of building shape on heating and air conditioning energy usage remains insufficiently understood. This study systematically analyzes the relationship between building shape and energy consumption, grounded in objective facts about building energy performance from the perspective of architects during the initial design phases. This analysis aids designers in making informed decisions. Key parameters, notably the widely used building shape coefficient, are examined. The relationship between building shape and energy consumption across various global and China’s diverse climate zones is synthesized. Current simulation tools and methodologies are assessed to guide future research. Findings reveal a predominant reliance on simulations for comparing energy use across specific building shapes. The academic understanding of the shape−energy relationship remains superficial, complicating standardization. Future research should prioritize extensive, multi-parameter simulations to enhance understanding of building performance, thereby facilitating energy-efficient design.
Catanionic Surfactant Systems for Emulsifying and Viscosity Reduction of Shale Oil
Authors: Li Qi , Wang Xiaoyan , Li Dongping , Ge Hongjiang , Han Xiangyong , Xue Enmao
Published: November 2024

Shale oil resources are abundant in the second member of the Kongdian Formation, Cangdong Sag, Bohai Bay Basin, China. However, the shale oil here has high viscosity and poor fluidity, resulting in low recovery and huge difficulty in development, gathering, and transporting. This study assembled a catanionic surfactant (PSG) through electrostatic interactions between cetyltrimethylammonium bromide (CTAB) and α-olefin sulfonate (AOS) in an aqueous phase, which can be used as an effective emulsifying and viscosity-reducing agents for shale oils of Dagang oilfield. The interfacial activity and emulsification performance of PSG can be optimized by changing the molar ratio of CTAB to AOS. Notably, the PSG assembled at the molar ratio of 6:4 shows the best performance, with ultra-high surface activity and excellent salt resistance. At an oil/water ratio of 1:1 and 50 °C, an aqueous solution of 0.2% PSG can emulsify five types of shale oil, making it form shale oil-in-water (O/W) emulsion with a viscosity of less than 35 mPa·s, thereby reducing the viscosity of shale oil and improving its flowability. Importantly, shale oil and water can be separated by simple sedimentation without adding demulsifiers. This study has important guiding significance for the efficient development and transportation of shale oil.
Effect of Different Partial Pressures on H2 Production with Parageobacillus thermoglucosidasius DSM 6285
Authors: Ardila Magda Stephania , Aliyu Habibu , de Maayer Pieter , Neumann Anke
Published: November 2024

The ability of Parageobacillus thermoglucosidasius to produce H2 from CO via the water–gas shift (WGS) reaction makes it a compelling microorganism for biofuels research. Optimizing this process requires evaluating parameters such as pressure. This study aimed to understand how H2 production is affected by increasing CO, N2, and H2 partial pressures to 1.0, 2.0, and 3.0 bar. Increasing CO partial pressure can improve the solubility of the gas in the liquid phase. However, raising CO partial pressure to 3.0 bar had an inhibitory effect, delaying and reducing H2 production. By contrast, increasing N2 and H2 partial pressures to 3.0 bar had positive effects, reaching a H2 production of 9.2 mmol and 130 mmol, respectively. Analysis of the electron balance at the end of the fermentation process showed that the selectivity toward H2 production reached 95%, with the remainder of electrons deriving from CO and glucose directed at organic acid production, mainly acetate, followed by formate.
AI-Driven Neuro-Monitoring: Advancing Schizophrenia Detection and Management Through Deep Learning and EEG Analysis
Authors: Paraschiv Elena-Anca , Băjenaru Lidia , Petrache Cristian , Bica Ovidiu , Nicolau Dragoș-Nicolae
Published: November 2024

Schizophrenia is a complex neuropsychiatric disorder characterized by disruptions in brain connectivity and cognitive functioning. Continuous monitoring of neural activity is essential, as it allows for the detection of subtle changes in brain connectivity patterns, which could provide early warnings of cognitive decline or symptom exacerbation, ultimately facilitating timely therapeutic interventions. This paper proposes a novel approach for detecting schizophrenia-related abnormalities using deep learning (DL) techniques applied to electroencephalogram (EEG) data. Using an openly available EEG dataset on schizophrenia, the focus is on preprocessed event-related potentials (ERPs) from key electrode sites and applied transfer entropy (TE) analysis to quantify the directional flow of information between brain regions. TE matrices were generated to capture neural connectivity patterns, which were then used as input for a hybrid DL model, combining convolutional neural networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks. The model achieved a performant accuracy of 99.94% in classifying schizophrenia-related abnormalities, demonstrating its potential for real-time mental health monitoring. The generated TE matrices revealed significant differences in connectivity between the two groups, particularly in frontal and central brain regions, which are critical for cognitive processing. These findings were further validated by correlating the results with EEG data obtained from the Muse 2 headband, emphasizing the potential for portable, non-invasive monitoring of schizophrenia in real-world settings. The final model, integrated into the NeuroPredict platform, offers a scalable solution for continuous mental health monitoring. By incorporating EEG data, heart rate, sleep patterns, and environmental metrics, NeuroPredict facilitates early detection and personalized interventions for schizophrenia patients.
Numerical Computation-Based Analysis of Blasting Vibration Effects and Slope Stability in an Open-Pit Quarry
Authors: Fu Botao , Ji Huaijun , Pei Jingjing , Wei Jie
Published: November 2024

With the large-scale exploitation of mineral resources, the safety problems of open-pit slopes and dumps become increasingly prominent. In this work, the safety demonstration of the interaction between the eastside slope of an open-pit quarry and the dump was carried out. Firstly, the influence of blasting vibration on the stability of the eastside slope and the dump was analyzed, and the propagation law of blasting vibration wave and the attenuation law of velocity were obtained. Secondly, the finite element analysis of the seepage field of the slope was carried out, and the basic seepage parameters of the rock and soil were determined. Then, the limit equilibrium analysis method was used to quantitatively calculate the stability of the eastside slope of the stope under the load of the dump to assess landslide risk. The results show that no landslides would occur at the dump or affect the performance of the slope, and that the slope was able to reach the required safety reserve factor. Then, through the finite element analysis of the 3-3 profile, it was found that the deformation caused by the excavation unloading of the eastside slope was mainly concentrated in the lower part of the slope, and the displacement variation caused by the top area of the slope was about 0~10 mm. The research verified the stability of slope and dump, which provides technical support for slope project design and blast operation in open-pit quarries.
Length–Weight Relationship and Spatiotemporal Distribution Pattern of Three Schizothoracinae Fishes Along the Nujiang River in the Qinghai–Tibetan Plateau, China
Authors: Liu Mingdian , Xu Weitong , Zhu Fengyue , Duan Xinbin , Liu Shaoping , Chen Daqing
Published: November 2024

The Qinghai–Tibet Plateau (QTP) is a unique ecological area that has faced issues like diminishing ecosystem stability and increasing pressures on resources and the environment. These issues have arisen as a result of the combined impact of global warming and human activities in recent times. The study of the growth and distribution patterns of schizothoracinae fishes can support guiding policy decisions about the conservation of aquatic species and ecological habitats in the QTP. The investigation on fish resources was carried out in the QTP section of the Nujiang River during the spring and autumn seasons of 2017, 2018, and 2019. A total of seven sampling sites were established based on variations in elevation. According to length–weight relationship (LWR) analysis, Schizothorax nukiangensis mainly displayed a negative allometric growth while Ptychobarbus kaznakovi and Schizopygopsis thermalis mainly showed near isometric growth or positive allometric growth in the QTP section of the Nujiang River. Due to temperature and food abundance, the three schizothoracinae fishes showed better growth performance in autumn than spring. Spatial heterogeneity exhibited a greater influence on the LWR of S. nukiangensis and P. kaznakovi than seasonal variation. In contrast, seasonal variation on S. thermalis showed greater influence than spatial heterogeneity. According to the linear mixed effect model (LMM), both spatial factors and seasons had influence on fish growth in the QTP. Schizothorax nukiangensis was identified as the predominant species from CWL to BS, spanning an altitude range of 1800 to 2700 m. Ptychobarbus kaznakovi was identified as the main species at LL, BB, and BR, occupying an altitude range of 2700 to 3800 m. Schizopygopsis thermalis is primarily distributed at altitudes beyond 4000 m and along the tributary river Yuqu. Principal coordinates analysis (PCOA) and nonmetric multidimensional scaling (NMDS) divided schizothoracinae fish populations into three clusters by spatial differences. Redundancy analysis (RDA) and Monte Carlo Permutation analysis revealed that habitat elevation and water temperature had a significant impact on schizothoracinae fish distribution. This article enhances our understanding of the distribution and environmental adaptation of indigenous fish in the Qinghai–Tibet Plateau.
Effects of Carnosine Addition in Low-Fishmeal Feed on the Growth Performance, Muscle Antioxidant Capacity and Flesh Quality of Orange-Spotted Grouper (Epinephelus coioides)
Authors: Li Dong , Chen Weijun , Yin Yanxia , Yang Lulu , Chen Mingfan , Sun Yunzhang , Ye Jidan
Published: November 2024

Carnosine is a natural dipeptide made up of L-histidine and β-alanine which is rich in muscle tissues and has multiple physiological functions. The current research aimed to investigate the effects of varied carnosine concentrations in low-fishmeal feed on the growth, muscle antioxidant capacity and flesh quality of orange-spotted grouper. Carnosine was supplemented at doses of 0, 10, 20, 40, 80, 160, and 320 mg/kg in low-fishmeal feed. Seven groups with three tanks of fish (11.4 ± 0.1 g/fish) were allotted one of the diets during the 8-week feeding trial. The growth rate, body protein content, muscle activities of superoxide dismutase and catalase, and muscle adhesiveness showed positive linear response and/or an open upward parabola with increasing carnosine concentrations, with a peak at 160 mg/kg of carnosine. Feed utilization, serum total protein content, gut trypsin activity, muscle glutathione peroxidase, total antioxidant capacity, muscle hardness, gumminess, chewiness and resilience followed the same pattern as the growth rate, reaching a peak at 320 mg/kg of carnosine; while the opposite trend was observed, reaching a minimum at 320 mg/kg for muscle malondialdehyde and 160 mg/kg for muscle liquid and water loss. The results indicated that appropriate carnosine addition could improve growth performance, muscle antioxidant capacity and flesh quality of grouper. The suitable inclusion concentration was estimated to be 195.14 mg/kg to achieve the best percent weight gain.
The Influence of Different ECMO Cannulation Site and Blood Perfusion Conditions on the Aortic Hemodynamics: A Computational Fluid Dynamic Model
Authors: Gramigna Vera , Palumbo Arrigo , Fragomeni Gionata
Published: November 2024

Extracorporeal Membrane Oxygenation (ECMO) is a medical device used to support patients with severe cardiac and/or respiratory failure. It is being used more frequently to offer percutaneous mechanical circulatory support, even though the intricate interactions between ECMO and the failing heart, as well as its impact on hemodynamics and perfusion, are not yet fully understood. Within the two main types of ECMO support (the veno-venous ECMO (VV-ECMO), which is used to support only the lungs) and the veno-arterial ECMO (VA-ECMO), which is used to support the lungs and heart), consideration is given solely to the second approach. Indeed, this study focuses on the impact of different ECMO cannulation site and blood perfusion conditions on the aortic hemodynamics and organ perfusion in VA-ECMO. Using computed tomography (CT) images, we reconstructed specific aortic models based on clinical cannula configurations and placements. A detailed cannula-aorta integration model was developed to simulate the VA-ECMO blood supply environment. Employing computational fluid dynamics (CFD), we analyzed how varying ECMO perfusion levels and ECMO cannulation sites affect flow characteristics. This study provides insights into optimizing ECMO therapy by understanding its effects on blood flow and potential damage to blood and organs.
Exposure Scenarios for Estimating Contaminant Levels in Healthy Sustainable Dietary Models: Omnivorous vs. Vegetarian
Authors: Ramos Helena , Reis-Mendes Ana , Silva Marta , Ribeiro Mafalda , Araújo Ana Margarida , Borges Cristiane , Viegas Olga , Melo Armindo , Martins Zita , Faria Miguel A. , Ferreira Isabel M. P. L. V. O.
Published: November 2024

Consumers are regularly exposed to well-known food contaminants (FCs), which are typically assessed for risk on an individual basis. However, there is limited knowledge about the overall levels and combinations of these compounds depending on dietary choices. The goal of this study was to estimate the real-life mixtures of FCs in different dietary models by integrating extensive data from the scientific literature concerning the reliable quantification of FCs in foods. A FAIR database detailing the occurrence of 73 FCs in 16 foods commonly consumed was built. The data were integrated into an omnivorous and a vegetarian dietary model. A weighted estimate of the 25th, 50th, and 75th percentiles of FCs in both dietary models revealed that the omnivorous model presented slightly higher levels of FCs than the vegetarian. At the 25th percentile, the FC levels in both dietary models fall within the European Food Safety Authority (EFSA) reference exposure levels for chemical hazards, except for arsenic, lead, cadmium, fumonisin B1, and OTA. At the 75th percentile, the FC levels exceed the EFSA reference levels for those FCs and additional mycotoxins. Using in vitro models, the 25th percentile can mimic real-life FC exposure, while the 75th percentile simulates a possible worst-case scenario.
Nutrients, Phytochemicals, and Antioxidant Capacity of Red Raspberry Nectar Fermented with Lacticaseibacillus paracasei
Authors: Shi Feng , Qin Yin , Qiu Shuyi , Luo You
Published: November 2024

Fresh raspberries are highly perishable, but lactic acid bacteria fermentation offers a favourable method for developing healthy products. This study investigated the effects of Lacticaseibacillus paracasei fermentation on the nutrients and phytochemicals of red raspberry nectar using widely targeted metabolomics, as well as its antioxidant activity. The fermentation notably disrupted the raspberry tissue structure, reshaped its non-volatile composition, and increased its DPPH and hydroxyl free radical scavenging abilities. A total of 261 compounds showed significant differences, with 198 upregulated and 63 downregulated. Among these, certain flavonoid glucosides (e.g., pelargonid-in-3-O-rutinoside, delphinidin-3-O-rutinoside-7-O-glucoside, and kaempferol-3-O-glucoside) were significantly downregulated, while some bioactive phenolic acids (e.g., 3-(4-Hydroxyphenyl)-propionic acid and DL-3-phenyllactic acid), alkaloids (e.g., deoxymutaaspergillic acid and indole-3-lactic acid), amino acids (e.g., L-phenylalanine and L-glutamine), and B vitamins (e.g., VB6, VB7, and VB3) were substantially upregulated. Furthermore, the Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation and enrichment analysis revealed that metabolic pathways and the biosynthesis of secondary metabolites contributed significantly to the new profile of fermented red raspberry nectar. These findings provide valuable insights for developing fermented raspberry products using Lacticaseibacillus paracasei, which can help minimise fresh raspberry loss and enhance their valorisation.
Polysaccharide-Based Composite Films: Promising Biodegradable Food Packaging Materials
Authors: Li Shengzi , Ren Yu , Hou Yujie , Zhan Qiping , Jin Peng , Zheng Yonghua , Wu Zhengguo
Published: November 2024

With growing concerns about environmental protection and sustainable development, the development of new biodegradable food packaging materials has become a significant focus for the future of food packaging. Polysaccharides, such as cellulose, chitosan, and starch, are considered ideal biodegradable packaging materials due to their wide availability, good biocompatibility, and biodegradability. These materials have garnered extensive attention from researchers in food packaging, leading to considerable advancements in the application of polysaccharide-based food packaging films, coatings, aerogels, and other forms. Therefore, this review focuses on the application of polysaccharide-based packaging films in food storage and preservation and discusses their preparation methods, application progress, challenges, and future development directions. Through an in-depth analysis of the existing literature, this review aims to provide sustainable and environmentally friendly solutions for the food packaging industry.
Correction: Santoso et al. Effects of Laccase and Transglutaminase on the Physicochemical and Functional Properties of Hybrid Lupin and Whey Protein Powder. Foods 2024, 13, 2090
Authors: Santoso Teguh , Ho Thao M. , Vinothsankar Geerththana , Jouppila Kirsi , Chen Tony , Owens Adrian , Lazarjani Masoumeh Pourseyed , Farouk Mustafa M. , Colgrave Michelle L. , Otter Don , Kam Rothman , Le Thao T.
Published: November 2024

In the original publication [...]
Fermentation for Revalorisation of Fruit and Vegetable By-Products: A Sustainable Approach Towards Minimising Food Loss and Waste
Authors: Salas-Millán José Ángel , Aguayo Encarna
Published: November 2024

In a world increasingly focused on sustainability and integrated resource use, the revalorisation of horticultural by-products is emerging as a key strategy to minimise food loss and waste while maximising value within the food supply chain. Fermentation, one of the earliest and most versatile food processing techniques, utilises microorganisms or enzymes to induce desirable biochemical transformations that enhance the nutritional value, digestibility, safety, and sensory properties of food products. This process has been identified as a promising method for producing novel, high-value food products from discarded or non-aesthetic fruits and vegetables that fail to meet commercial standards due to aesthetic factors such as size or appearance. Besides waste reduction, fermentation enables the production of functional beverages and foods enriched with probiotics, antioxidants, and other bioactive compounds, depending on the specific horticultural matrix and the types of microorganisms employed. This review explores the current bioprocesses used or under investigation, such as alcoholic, lactic, and acetic acid fermentation, for the revalorisation of fruit and vegetable by-products, with particular emphasis on how fermentation can transform these by-products into valuable foods and ingredients for human consumption, contributing to a more sustainable and circular food system.
Leader-Following Output Feedback H∞ Consensus of Fractional-Order Multi-Agent Systems with Input Saturation
Authors: Xing Hong-Shuo , Boutat Driss , Wang Qing-Guo
Published: November 2024

This paper investigates the leader-following H∞ consensus of fractional-order multi-agent systems (FOMASs) under input saturation via the output feedback. Based on the bounded real lemma for FOSs, the sufficient conditions of H∞ consensus for FOMASs are provided in α∈0,1 and 1,2, respectively. Furthermore, the iterative linear matrix inequalities (ILMIs) approaches are applied for solving quadratic matrix inequalities (QMIs). The ILMI algorithms show a method to derive initial values and transform QMIs into LMIs. Mathematical tools are employed to transform the input saturation issue into optimal solutions of LMIs for estimating stable regions. The ILMI algorithms avoid the conditional constraints on matrix variables during the LMIs’ construction and reduce conservatism. The approach does not disassemble the entire MASs by transformations to the Laplacian matrix, instead adopting a holistic analytical perspective to obtain gain matrices. Finally, numerical examples are conducted to validate the efficiency of the approach.
Effect of Thermal Cracking on the Tensile Strength of Granite: Novel Insights into Numerical Simulation and Fractal Dimension
Authors: Pan Jiliang , Zhang Leiming , Ma Yichen , Zhang Ying , Xi Xun
Published: November 2024

This study investigates the effect of thermal cracking on the tensile strength of granite through a combination of experimental testing and numerical simulations. The primary objective is to understand how thermal stress, induced by heat treatment at various temperatures (25 °C to 600 °C), influences crack initiation, propagation, and tensile strength changes. The granite specimens were subjected to Brazilian splitting tests after heat treatment, and the load–displacement curves and tensile strength variations with heat treatment temperature were analyzed. A grain-based model (GBM) was developed to simulate the complex cracking behavior, incorporating the mineral compositions and thermal expansion properties of the granite. The fractal dimension of the cracks was quantified using the box-counting method, and the relationship between fractal dimension and tensile strength was discussed. The results show that the GBM can effectively simulate the microcracking behavior and tensile fracture properties of heat-treated granite, accounting for mineral composition and thermal expansion. Thermal cracks are mainly intergranular tensile cracks, which increase in number with higher temperatures, while under mechanical loading failure is primarily due to intragranular tensile cracks. Higher heat treatment temperatures lead to denser crack networks with greater fractal complexity, reducing tensile strength and creating more tortuous crack propagation paths.
Advanced Differential Equations with Canonical Operators: New Criteria for the Oscillation
Authors: Bazighifan Omar , Alshammari Nawa , Al-Ghafri Khalil S. , Iambor Loredana Florentina
Published: November 2024

In this study, we use the integral averaging methodology, comparison with second-order differential equations, and the Riccati technique to determine the Philos-type and Hille–Nehari-type oscillation conditions of fourth-order advanced differential equations with canonical operators. In essence, these techniques supplement and generalize a wide range of established oscillation conditions. Two instance cases demonstrate the importance of our outcomes and their significant improvement.
In Vitro Investigation of 3D Printed Hydrogel Scaffolds with Electrospun Tidemark Component for Modeling Osteochondral Interface
Authors: Effanga Victoria Effiong , Akilbekova Dana , Mukasheva Fariza , Zhao Xiao , Kalyon Dilhan M. , Erisken Cevat
Published: November 2024

Osteochondral (OC) tissue plays a crucial role due to its ability to connect bone and cartilage tissues. To address the complexity of structure and functionality at the bone–cartilage interface, relevant to the presence of the tidemark as a critical element at the bone–cartilage boundary, we fabricated graded scaffolds through sequential 3D printing. The scaffold’s bottom layer was based on a gelatin/oxidized alginate mixture enriched with hydroxyapatite (HAp) to create a rougher surface and larger pores to promote osteogenesis. In contrast, the upper layer was engineered to have smaller pores and aimed to promote cartilage tissue formation and mimic the physical properties of the cartilage. An electrospun ε-polycaprolactone (PCL) membrane with micrometer-range pores was incorporated between the layers to replicate the function of tidemark—a barrier to prevent vascularization of cartilage from subchondral bone tissue. In vitro cell studies confirmed the viability of the cells on the layers of the scaffolds and the ability of PCL mesh to prevent cellular migration. The fabricated scaffolds were thoroughly characterized, and their mechanical properties were compared to native OC tissue, demonstrating suitability for OC tissue engineering and graft modeling. The distance of gradient of mineral concentration was found to be 151 µm for grafts and the native OC interface.