bluuebunny/crossref_metadata_2025 huggingface dataset:
Pixelator v2: A Novel Perceptual Image Comparison Method with LAB Colour Space and Sobel Edge Detection for Enhanced Security Analysis
Authors: Dey Somdip , Alshehabi Al-Ani Jabir , Bourazeri Aikaterini , Saha Suman , Purkait Rohit , Hill Samuel , Thompson Julian
Published: November 2024

In this paper, we introduce Pixelator v2, a novel perceptual image comparison method designed to enhance security and analysis through improved image difference detection. Unlike traditional metrics such as MSE, Q, and SSIM, which often fail to capture subtle but critical changes in images, Pixelator v2 integrates the LAB (CIE-LAB) colour space for perceptual relevance and Sobel edge detection for structural integrity. By combining these techniques, Pixelator v2 offers a more robust and nuanced approach to identifying variations in images, even in cases of minor modifications. The LAB colour space ensures that the method aligns with human visual perception, making it particularly effective at detecting differences that are less visible in RGB space. Sobel edge detection, on the other hand, emphasises structural changes, allowing Pixelator v2 to focus on the most significant areas of an image. This combination makes Pixelator v2 ideal for applications in security, where image comparison plays a vital role in tasks like tamper detection, authentication, and analysis. We evaluate Pixelator v2 against other popular methods, demonstrating its superior performance in detecting both perceptual and structural differences. Our results indicate that Pixelator v2 not only provides more accurate image comparisons but also enhances security by making it more difficult for subtle alterations to go unnoticed. This paper contributes to the growing field of image-based security systems by offering a perceptually-driven, computationally efficient method for image comparison that can be readily applied in information system security.
Hypergraph Neural Network for Multimodal Depression Recognition
Authors: Li Xiaolong , Dong Yang , Yi Yunfei , Liang Zhixun , Yan Shuqi
Published: November 2024

Deep learning-based approaches for automatic depression recognition offer advantages of low cost and high efficiency. However, depression symptoms are challenging to detect and vary significantly between individuals. Traditional deep learning methods often struggle to capture and model these nuanced features effectively, leading to lower recognition accuracy. This paper introduces a novel multimodal depression recognition method, HYNMDR, which utilizes hypergraphs to represent the complex, high-order relationships among patients with depression. HYNMDR comprises two primary components: a temporal embedding module and a hypergraph classification module. The temporal embedding module employs a temporal convolutional network and a negative sampling loss function based on Euclidean distance to extract feature embeddings from unimodal and cross-modal long-time series data. To capture the unique ways in which depression may manifest in certain feature elements, the hypergraph classification module introduces a threshold segmentation-based hyperedge construction method. This method is the first attempt to apply hypergraph neural networks to multimodal depression recognition. Experimental evaluations on the DAIC-WOZ and E-DAIC datasets demonstrate that HYNMDR outperforms existing methods in automatic depression monitoring, achieving an F1 score of 91.1% and an accuracy of 94.0%.
DistOD: A Hybrid Privacy-Preserving and Distributed Framework for Origin–Destination Matrix Computation
Authors: Kim Jongwook
Published: November 2024

The origin–destination (OD) matrix is a critical tool in understanding human mobility, with diverse applications. However, constructing OD matrices can pose significant privacy challenges, as sensitive information about individual mobility patterns may be exposed. In this paper, we propose DistOD, a hybrid privacy-preserving and distributed framework for the aggregation and computation of OD matrices without relying on a trusted central server. The proposed framework makes several key contributions. First, we propose a distributed method that enables multiple participating parties to collaboratively identify hotspot areas, which are regions frequently traveled between by individuals across these parties. To optimize the data utility and minimize the computational overhead, we introduce a hybrid privacy-preserving mechanism. This mechanism applies distributed differential privacy in hotspot areas to ensure high data utility, while using localized differential privacy in non-hotspot regions to reduce the computational costs. By combining these approaches, our method achieves an effective balance between computational efficiency and the accuracy of the OD matrix. Extensive experiments on real-world datasets show that DistOD consistently provides higher data utility than methods based solely on localized differential privacy, as well as greater efficiency than approaches based solely on distributed differential privacy.
Neural Architecture Search via Trainless Pruning Algorithm: A Bayesian Evaluation of a Network with Multiple Indicators
Authors: Lin Yiqi , Endo Yuki , Lee Jinho , Kamijo Shunsuke
Published: November 2024

Neural Architecture Search (NAS) has found applications in various areas of computer vision, including image recognition and object detection. An increasing number of algorithms, such as ENAS (Efficient Neural Architecture Search via Parameter Sharing) and DARTS (Differentiable Architecture Search), have been applied to NAS. Nevertheless, the current Training-free NAS methods continue to exhibit unreliability and inefficiency. This paper introduces a training-free prune-based algorithm called TTNAS (True-Skill Training-Free Neural Architecture Search), which utilizes a Bayesian method (true-skill algorithm) to combine multiple indicators for evaluating neural networks across different datasets. The algorithm demonstrates highly competitive accuracy and efficiency compared to state-of-the-art approaches on various datasets. Specifically, it achieves 93.90% accuracy on CIFAR-10, 71.91% accuracy on CIFAR-100, and 44.96% accuracy on ImageNet 16-120, using 1466 GPU seconds in NAS-Bench-201. Additionally, the algorithm exhibits improved adaptation to other datasets and tasks.
MSG-YOLO: A Lightweight Detection Algorithm for Clubbing Finger Detection
Authors: Wang Zhijie , Meng Qiao , Tang Feng , Qi Yuelin , Li Bingyu , Liu Xin , Kong Siyuan , Li Xin
Published: November 2024

Clubbing finger is a significant clinical indicator, and its early detection is essential for the diagnosis and treatment of associated diseases. However, traditional diagnostic methods rely heavily on the clinician’s subjective assessment, which can be prone to biases and may lack standardized tools. Unlike other diagnostic challenges, the characteristic changes of clubbing finger are subtle and localized, necessitating high-precision feature extraction. Existing models often fail to capture these delicate changes accurately, potentially missing crucial diagnostic features or generating false positives. Furthermore, these models are often not suited for accurate clinical diagnosis in resource-constrained settings. To address these challenges, we propose MSG-YOLO, a lightweight clubbing finger detection model based on YOLOv8n, designed to enhance both detection accuracy and efficiency. The model first employs a multi-scale dilated residual module, which expands the receptive field using dilated convolutions and residual connections, thereby improving the model’s ability to capture features across various scales. Additionally, we introduce a Selective Feature Fusion Pyramid Network (SFFPN) that dynamically selects and enhances critical features, optimizing the flow of information while minimizing redundancy. To further refine the architecture, we reconstruct the YOLOv8 detection head with group normalization and shared-parameter convolutions, significantly reducing the model’s parameter count and increasing computational efficiency. Experimental results indicate that the model maintains high detection accuracy with reduced parameter and computational requirements. Compared to YOLOv8n, MSG-YOLO achieves a 48.74% reduction in parameter count and a 24.17% reduction in computational load, while improving the mAP0.5 score by 2.86%, reaching 93.64%. This algorithm strikes a balance between accuracy and lightweight design, offering efficient and reliable clubbing finger detection even in resource-constrained environments.
Lightweight, Post-Quantum Secure Cryptography Based on Ascon: Hardware Implementation in Automotive Applications
Authors: Nguyen Hai Phong , Chen Yuhua
Published: November 2024

With the rapid growth of connected vehicles and the vulnerability of embedded systems against cyber attacks in an era where quantum computers are becoming a reality, post-quantum cryptography (PQC) is a crucial solution. Yet, by nature, automotive sensors are limited in power, processing capability, memory in implementing secure measures. This study presents a pioneering approach to securing automotive systems against post-quantum threats by integrating the Ascon cipher suite—a lightweight cryptographic protocol—into embedded automotive environments. By combining Ascon with the Controller Area Network (CAN) protocol on an Artix-7 Field Programmable Gate Array (FPGA), we achieve low power consumption while ensuring high performance in post-quantum-resistant cryptographic tasks. The Ascon module is designed to optimize computational efficiency through bitwise Boolean operations and logic gates, avoiding resource-intensive look-up tables and achieving superior processing speed. Our hardware design delivers significant speed improvements of 100 times over software implementations and operates effectively within a 100 MHz clock while demonstrating low resource usage. Furthermore, a custom digital signal processing block supports CAN protocol integration, handling message alignment and synchronization to maintain signal integrity under automotive environmental noise. Our work provides a power-efficient, robust cryptographic solution that prepares automotive systems for quantum-era security challenges, emphasizing lightweight cryptography’s readiness for real-world deployment in automotive industries.
A Paradox of LED Road Lighting: Reducing Light Pollution Is Not Always Linked to Energy Savings
Authors: Fryc Irena , Listowski Maciej , Martinsons Christophe , Fan Jiajie , Czyżewski Dariusz
Published: November 2024

A variety of LED types can be employed for street and road lighting purposes. White phosphor-converted LEDs represent the most widely used option. However, amber LEDs are increasingly being used to reduce some negative effects associated with light pollution. These LEDs can be manufactured using both phosphor-converted and monochromatic direct chip technologies. This paper addresses the link between the reduction in short-wavelength light emissions which contribute to sky glow and the energy efficiency of LED-based road lighting. This paper focuses on an example illustrating the common misconception that reducing light pollution also means saving energy. Through the consideration of how spectral power distribution influences both mesopic vision and the amount of emitted blue light, it has been concluded that while monochromatic direct amber LEDs consume more energy than their white or amber phosphor-converted counterparts, their use in outdoor lighting is justifiable due to their potential effects of reducing sky brightness.
Energy Solutions for Decarbonization of Industrial Heat Processes
Authors: Veronezi Danieli , Soulier Marcel , Kocsis Tímea
Published: November 2024

The global rise in population and advancement in civilization have led to a substantial increase in energy demand, particularly in the industrial sector. This sector accounts for a considerable proportion of total energy consumption, with approximately three-quarters of its energy consumption being used for heat processes. To meet the Paris Agreement goals, countries are aligning policies with international agreements, and companies are setting net-zero targets. Upstream emissions of the Scope 3 category refer to activities in the company’s supply chain, being crucial for achieving its net-zero ambitions. This study analyzes heating solutions for the supply chain of certain globally operating companies, contributing to their 2030 carbon-neutral ambition. The objective is to identify current and emerging heating solutions from carbon dioxide equivalent (CO2e) impact, economic, and technical perspectives, considering regional aspects. The methodology includes qualitative and quantitative surveys to identify heating solutions and gather regional CO2e emission factors and energy prices. Calculations estimate the CO2e emissions and energy costs for each technology or fuel, considering each solution’s efficiency. The study focuses on Europe, the United States, Brazil, China, and Saudi Arabia, regions or countries representative of companies’ global supply chain setups. Results indicate that heat pumps are the optimal solution for low temperatures, while biomass is the second most prevalent solution, except in Saudi Arabia where natural gas is more feasible. For medium and high temperatures, natural gas is viable in the short term for Saudi Arabia and China, while biomass and electrification are beneficial for other regions. The proportion of electricity in the energy mix is expected to increase, but achieving decarbonization targets requires cleaner energy mixes or competitive Power Purchase Agreement (PPA) projects. Brazil, with its high proportion of renewable energy sources, offers favorable conditions for using green electricity to reduce emissions. The utilization of biomethane is promising if costs and incentives align with those in the EU. Although not the objective of this study, a comprehensive analysis of CAPEX and lifecycle costs associated with equipment is necessary when migrating technologies. Policies and economic incentives can also make these solutions more or less favorable.
An Equivalent Model for Frequency Dynamic Analysis of Large Power Grids Based on Regulation Performance Weighting Method
Authors: Zhang Junlong , Ma Junchao , Yang Xuchen , Wang Jiajia , Wang Chenxu , Yu Yiping
Published: November 2024

With the construction of the UHV (Ultra High Voltage) AC/DC hybrid power grid and the large-scale access to renewable energy such as wind power, frequency dynamic fluctuation has become a prominent problem affecting the safe and stable operation of large power grids. The expansion of the scale of the power system makes it impossible to use traditional fine modeling to analyze the power system. In order to reduce the calculation scale and storage capacity of power system frequency dynamic simulation, it is necessary to make appropriate equivalent simplification of the external system, so the appropriate dynamic equivalent method is of great significance. This paper mainly studies the equivalent model suitable for frequency dynamic analysis of large power grids. Firstly, the typical models of generator set and load are simplified, and the parameters that have a great influence on frequency in the simplified model are obtained through characteristic analysis. Then, a dynamic aggregation method of generator governor and prime mover parameters and load parameters based on regulation performance weighting (the parameters of the generator or load are weighted and summed according to its regulation ability on the system) is proposed. This method is applied to the simulation example of the East China Power Grid. The simulation proves that the frequency of the East China Power Grid before and after equivalence can be consistent under four different faults, which verifies the effectiveness of the equivalent method proposed in this paper in the frequency dynamic analysis of large power grids.
Applicability of Combined Transducers for Distorted Current Metering in Electrical Substations
Authors: Kaczmarek Michal , Wiak Slawomir
Published: November 2024

Current transducers/transformers have a significant impact on the overall accuracy of the measuring system used for electrical power metering and current measurement, especially in non-sinusoidal conditions. This research concerns an evaluation of the impact of the Rogowski coil and the combined transducer on the distorted current measuring accuracy of the digital power meter. It covers a characterization of their influence in conditions similar, at present, to the high RMS value of the distorted current in order to ensure the required test scenario for the equipment designed for the power network metering purposes. It has been established that the active component—the integration circuit (voltage-to-voltage converter or voltage-to current-converter)—has the most significant effect on the overall performance of the combined transducer. However, its impact is different depending on the selected equipment. Therefore, the presented analysis and results are essential for a conscious choice of the proper current transducer for the substation equipment.
Design of an Active Axis Wind Turbine (AAWT) That Can Balance Centrifugal and Aerodynamic Forces to Reduce Support Infrastructure While Maintaining a Stable Flight Path
Authors: Mezaal Jawad , Whale Jonathan , Schlunke Kim , Bahri Parisa Arabzadeh , Parlevliet David
Published: November 2024

This study introduces a novel approach to wind energy by investigating a novel Active Axis Wind Turbine design. The turbine is neither a horizontal nor vertical axis wind turbine but has an axis of operation that can actively change during operation. The design features a rotor with a single blade capable of dynamic pitch and tilt control during a single rotor rotation. This study examines the potential to balance the centrifugal and aerodynamic lift forces acting on the rotor blade assembly, significantly reducing blade, tower, foundation and infrastructure costs in larger-scale devices and decreasing the levelised cost of energy for wind energy. The design of a laboratory prototype rotor assembly is optimised by varying the masses and lengths in a lumped mass model to achieve equilibrium between centrifugal and lift forces acting on the turbine’s rotor assembly. The method involves an investigation of the variation of blade pitch angle to provide a balance between centrifugal and aerodynamic forces, thereby facilitating the cost advantages and opening the opportunity to improve the turbine efficiency across a range of operation conditions. The implication of this study extends to different applications of wind turbines, both onshore and offshore, introducing insight into innovation for sustainable energy and cost-effective solutions.
Lithium-Ion Battery Health Management and State of Charge (SOC) Estimation Using Adaptive Modelling Techniques
Authors: Bouchareb Houda , Saqli Khadija , M’sirdi Nacer Kouider , Oudghiri Bentaie Mohammed
Published: November 2024

Effective health management and accurate state of charge (SOC) estimation are crucial for the safety and longevity of lithium-ion batteries (LIBs), particularly in electric vehicles. This paper presents a health management system (HMS) that continuously monitors a 4s2p LIB pack’s parameters—current, voltage, and temperature—to mitigate risks such as overcurrent and thermal runaway while ensuring balanced charge distribution between cells. An improved online battery model (IOBM) is developed to enhance SOC estimation accuracy. The system utilises forgetting factor recursive least squares (FFRLS) for real-time parameter updates, an adaptive nonlinear sliding mode observer (ANSMO) for SOC estimation, and a long short-term memory (LSTM) network to dynamically adjust capacity based on operating conditions. Validation using the urban dynamometer driving schedule (UDDS) test demonstrated high accuracy, with the proposed battery model achieving a root mean square error (RMSE) of 12.13 mV and the LSTM achieving an RMSE of 0.0118 Ah. Regular updates to the battery’s current capacity, along with the proposed IOBM, significantly improved SOC estimation performance, maintaining estimation errors within 1.08%.
Exploring Hydrogen–Diesel Dual Fuel Combustion in a Light-Duty Engine: A Numerical Investigation
Authors: Scrignoli Francesco , Pisapia Alfredo Maria , Savioli Tommaso , Mancaruso Ezio , Mattarelli Enrico , Rinaldini Carlo Alberto
Published: November 2024

Dual fuel combustion has gained attention as a cost-effective solution for reducing the pollutant emissions of internal combustion engines. The typical approach is combining a conventional high-reactivity fossil fuel (diesel fuel) with a sustainable low-reactivity fuel, such as bio-methane, ethanol, or green hydrogen. The last one is particularly interesting, as in theory it produces only water and NOx when it burns. However, integrating hydrogen into stock diesel engines is far from trivial due to a number of theoretical and practical challenges, mainly related to the control of combustion at different loads and speeds. The use of 3D-CFD simulation, supported by experimental data, appears to be the most effective way to address these issues. This study investigates the hydrogen-diesel dual fuel concept implemented with minimum modifications in a light-duty diesel engine (2.8 L, 4-cylinder, direct injection with common rail), considering two operating points representing typical partial and full load conditions for a light commercial vehicle or an industrial engine. The numerical analysis explores the effects of progressively replacing diesel fuel with hydrogen, up to 80% of the total energy input. The goal is to assess how this substitution affects engine performance and combustion characteristics. The results show that a moderate hydrogen substitution improves brake thermal efficiency, while higher substitution rates present quite a severe challenge. To address these issues, the diesel fuel injection strategy is optimized under dual fuel operation. The research findings are promising, but they also indicate that further investigations are needed at high hydrogen substitution rates in order to exploit the potential of the concept.
TCN-GRU Based on Attention Mechanism for Solar Irradiance Prediction
Authors: Rao Zhi , Yang Zaimin , Yang Xiongping , Li Jiaming , Meng Wenchuan , Wei Zhichu
Published: November 2024

The global horizontal irradiance (GHI) is the most important metric for evaluating solar resources. The accurate prediction of GHI is of great significance for effectively assessing solar energy resources and selecting photovoltaic power stations. Considering the time series nature of the GHI and monitoring sites dispersed over different latitudes, longitudes, and altitudes, this study proposes a model combining deep neural networks and deep convolutional neural networks for the multi-step prediction of GHI. The model utilizes parallel temporal convolutional networks and gate recurrent unit attention for the prediction, and the final prediction result is obtained by multilayer perceptron. The results show that, compared to the second-ranked algorithm, the proposed model improves the evaluation metrics of mean absolute error, mean absolute percentage error, and root mean square error by 24.4%, 33.33%, and 24.3%, respectively.
Electronic Dynamic Switching Techniques for Efficient Drive of Asymmetric Three-Phase Motors with Single-Phase Supply
Authors: Prado Wellington do , Guimarães Geraldo Caixeta , Alves Guilherme Henrique
Published: November 2024

This article presents an analysis of the behavior of an asymmetric machine subjected to dynamic switching at different load levels. The authors herein present theoretical and practical results regarding the drive of a 2 HP asymmetric three-phase induction motor, allowing for conclusions to be drawn concerning its operation in such a configuration using capacitive dynamic switching. Previous research has demonstrated that the efficiency of single-phase powered asymmetric motors is only viable when the applied load matches the rated load of the motor. Given that this mode of operation is shown to hold practical infeasibilities for asymmetric three-phase motors, the proposal of a solution is put forward that involves varying the capacitance according to demand. This approach results in a significant improvement in efficiency regardless of whether the motor is running on a full or reduced load.
Simulation of Small-Break Loss-of-Coolant Accident Using the RELAP5 Code with an Improved Wall Drag Partition Model for Bubbly Flow
Authors: Lee Young Hwan , Ryu Nam Kyu , Kim Byoung Jae
Published: November 2024

The RELAP5 code is a computational tool designed for transient simulations of light water reactor coolant systems under hypothesized accident conditions. The original wall drag partition model in the RELAP5 code has a problem in that the bubble velocity is predicted to be faster than the water velocity in the fully developed flow in a constant-area channel. The wall drag partition model, based on the wetted perimeter concept, proves insufficient for accurately modeling bubbly flows. In this study, the wall drag partition model was modified to account for the physical motion of fluid particles. After that, the modified RELAP5 code was applied to predict the SBLOCA of a full-scale nuclear power plant. Considering the SBLOCA scenario, the behavior change in the loop seal clearing phenomenon was clearly shown in the analysis by the model change. Upon the termination of natural circulation, the loop seals were cleared, allowing the steam trapped within the system to discharge through the break. The modified model was confirmed to have an impact at this time. It mainly affected the timing and shape of the loop seal clearing and delayed the overall progress of the accident. It was observed that the flow rate of the bubbly phase decreased as the modified model accounted for wall friction during dispersed flow in the horizontal section, impacting the two-phase flow behavior at the conclusion of the natural circulation phase.
Performance Characterization of Hardware/Software Communication Interfaces in End-to-End Power Management Solutions of High-Performance Computing Processors
Authors: del Vecchio Antonio , Ottaviano Alessandro , Bambini Giovanni , Acquaviva Andrea , Bartolini Andrea
Published: November 2024

Power management (PM) is cumbersome for today’s computing systems. Attainable performance is bounded by the architecture’s computing efficiency and capped in temperature, current, and power. PM is composed of multiple interacting layers. High-level controllers (HLCs) involve application-level policies, operating system agents (OSPMs), and PM governors and interfaces. The application of high-level control decisions is currently delegated to an on-chip power management unit executing tailored PM firmware routines. The complexity of this structure arises from the scale of the interaction, which pervades the whole system architecture. This paper aims to characterize the cost of the communication backbone between high-level OSPM agents and the on-chip power management unit (PMU) in high performance computing (HPC) processors. For this purpose, we target the System Control and Management Interface (SCMI), which is an open standard proposed by Arm. We enhance a fully open-source, end-to-end FPGA-based HW/SW framework to simulate the interaction between a HLC, a HPC system, and a PMU. This includes the application-level PM policies, the drivers of the operating system-directed configuration and power management (OSPM) governor, and the hardware and firmware of the PMU, allowing us to evaluate the impact of the communication backbone on the overall control scheme. With this framework, we first conduct an in-depth latency study of the communication interface across the whole PM hardware (HW) and software (SW) stack. Finally, we studied the impact of latency in terms of the quality of the end-to-end control, showing that the SCMI protocol can sustain reactive power management policies.
Allelochemicals from Moso Bamboo: Identification and Their Effects on Neighbor Species
Authors: Wang Anke , Huang Kaiwen , Ning Yilin , Bi Yufang
Published: November 2024

Moso bamboo, which is essential to China’s economy, is currently facing significant threats due to declining profits. Inadequate management of moso bamboo can negatively impact the surrounding ecosystems. This study investigated allelopathy in moso bamboo forests by identifying potential allelochemicals and their effects on coexisting plants. Fresh leaves and litter from moso bamboo were collected to examine allelochemicals released through natural processes such as rainwater leaching and litter decomposition. Seven substances with potential allelopathic effects were identified using liquid chromatography–mass spectrometry (LC–MS). Four of these substances—DBP, PHBA, citric acid, and CGA—were selected for a detailed analysis of their effects on the photosynthetic and antioxidant systems of two naturally coexisting plants, Phoebe chekiangensis and Castanopsis sclerophylla. The results indicated that the four chemicals influenced P. chekiangensis and C. sclerophylla through different patterns of interference. DBP, PHBA, and citric acid negatively impacted the transfer of electrons during photosynthesis in both plants but had a lesser effect on the antioxidant system-related indicators in P. chekiangensis. In C. sclerophylla, these four chemicals led to a significant accumulation of reactive oxygen species (ROS) and increased malondialdehyde (MDA) content and catalase (CAT) activity to varying degrees. Furthermore, the relative abundance of fungi and bacteria in the soil was also affected by the DBP treatment. The identification of allelochemicals from moso bamboo, along with the investigation of their mechanisms, provides valuable insights into competitive interactions among plant species, particularly between moso bamboo and other species, along with the expansion of moso bamboo forests.
Identifying Persons of Interest in Digital Forensics Using NLP-Based AI
Authors: Adkins Jonathan , Al Bataineh Ali , Khalaf Majd
Published: November 2024

The field of digital forensics relies on expertise from multiple domains, including computer science, criminology, and law. It also relies on different toolsets and an analyst’s expertise to parse enormous amounts of user-generated data to find clues that help crack a case. This process of investigative analysis is often done manually. Artificial Intelligence (AI) can provide practical solutions to efficiently mine enormous amounts of data to find useful patterns that can be leveraged to investigate crimes. Natural Language Processing (NLP) is a subdomain of research under AI that deals with problems involving unstructured data, specifically language. The domain of NLP includes several tools to parse text, including topic modeling, pairwise correlation, word vector cosine distance measurement, and sentiment analysis. In this research, we propose a digital forensic investigative technique that uses an ensemble of NLP tools to identify a person of interest list based on a corpus of text. Our proposed method serves as a type of human feature reduction, where a total pool of suspects is filtered down to a short list of candidates who possess a higher correlation with the crime being investigated.
Numerical Study of Homogenous/Inhomogeneous Hydrogen–Air Explosion in a Long Closed Channel
Authors: Zhang Jiaqing , Zhu Xianli , Guo Yi , Teng Yue , Liu Min , Li Quan , Wang Qiao , Wang Changjian
Published: November 2024

Hydrogen is regarded as a promising energy source for the future due to its clean combustion products, remarkable efficiency and renewability. However, its characteristics of low-ignition energy, a wide flammable range from 4% to 75%, and a rapid flame speed may bring significant explosion risks. Typically, accidental release of hydrogen into confined enclosures can result in a flammable hydrogen–air mixture with concentration gradients, possibly leading to flame acceleration (FA) and deflagration-to-detonation transition (DDT). The current study focused on the evolutions of the FA and DDT of homogenous/inhomogeneous hydrogen–air mixtures, based on the open-source computational fluid dynamics (CFD) platform OpenFOAM and the modified Weller et al.’s combustion model, taking into account the Darrieus–Landau (DL) and Rayleigh–Taylor (RT) instabilities, the turbulence and the non-unity Lewis number. Numerical simulations were carried out for both homogeneous and inhomogeneous mixtures in an enclosed channel 5.4 m in length and 0.06 m in height. The predictions demonstrate good quantitative agreement with the experimental measurements in flame-tip position, speed and pressure profiles by Boeck et al. The characteristics of flame structure, wave evolution and vortex were also discussed.
Effects of Dietary Protein and Lipid Levels on the Growth Performance and Serum Biochemical Indices of Juvenile Furong Crucian Carp
Authors: He Zhigang , Tian Xing , Li Jinlong , Guo Jiarong , Cheng Xiaofei , Wang Dongwu
Published: November 2024

The impact of dietary protein and lipid levels on the growth performance, feed utilization, and serum biochemical indices of Furong crucian carp was examined. Five hundred and forty carp (2.35 ± 0.08 g) were randomly assigned to nine groups and fed diets with three different protein levels (30.0, 35.0, and 40.0%) and three different lipid levels (4.0, 7.0, and 10.0%) for 60 days. The current findings revealed that the interaction effect between dietary lipid and protein levels exhibited significance for the final average weight (FAW), weight gain rate (WGR), specific growth rate (SGR), feed efficiency (FE), energy deposition rate (EDR), whole-fish energy, ash, and fat content (p < 0.05). Specifically, there was a significant reduction in FAW, WGR, and SGR with increasing dietary fat supplementation. Conversely, FE, EDR, and protein efficiency ratios were significantly decreased with increasing dietary protein levels (p < 0.05). Furthermore, serum albumin and globulin levels exhibited significant increases in response to dietary lipid inclusion (p < 0.05). The findings collectively indicate that Furong crucian carp fed a diet comprising 4% lipid and 30% protein exhibited the optimal growth and feed utilization. Conversely, excessive protein and lipid supplementation were detrimental to growth and resulted in the aggravation of metabolic disorders.
Integrate Analysis of Eyestalk Proteome and Metabolome in Precocious and Formal Juvenile Female Eriocheir sinensis
Authors: Pan Tingshuang , Yang Min , Li Tong , Jiang He , Ling Jun
Published: November 2024

The Chinese mitten crab (Eriocheir sinensis) is an economically important crustacean. With the development of the E. sisnensis industry, precocity has become a significant challenge in juvenile crab culturing. In this study, the eyestalks of female E. sinensis from precocious (PE) and normal juvenile (NE) groups were used for proteome and metabolome analyses. In total, 731 up-regulated and 657 down-regulated differentially expressed proteins (DEPs) were identified in the PE and NE groups. In addition, 110 differentially expressed metabolites (DMs) were up-regulated and 256 were down-regulated in the PE group. An integrated analysis showed 5667 significant correlations between the metabolites and proteins and 109 common pathways in the proteome and metabolome. The proteins were mostly associated with the mechanistic target of rapamycin (mTOR) pathway, longevity regulation, autophagy, and the pyrimidine and purine metabolism pathways. The metabolites were primarily enriched in amino acid and lipid metabolisms. These results demonstrated the differences in the PE and NE groups at two omics levels and will be useful for the E. sinensis industry.
Evaluation of a Method for the Quantification of Cadmium, Lead, and Zinc in Craft Beers Manufactured in Quito, Ecuador
Authors: Yépez-Basantes Steward , Fernández Lenys , Maldonado Emerson , Leon-Cueva Wilson , León-Cueva Ricardo , Cedeño-Sares Luis , Espinoza-Montero Patricio
Published: November 2024

The brewing process of craft beer can introduce contamination by heavy metals such as Cd, Pb, and Zn from various sources. Cadmium and lead metals are particularly worrisome because of their harmful effects on human health. This study aimed to evaluate a method for quantifying the levels of Cd, Pb, and Zn in craft beer brands sold in the northern region of the Metropolitan District of Quito, Ecuador, using atomic absorption spectroscopy. For confidentiality, the brands were anonymized as Brands A to I. Standard addition curves were employed for metal quantification, with repeatability assessed via the coefficient of variation (CV%) and accuracy determined by recovery (R%). The Cd content in Brands B–G and I was below the threshold established by the Mercosur Resolution Nº 12/11. Additionally, Brands A and D–H complied with the Ecuadorian INEN 2262 standard for maximum Pb concentration in beer. All samples showed Zn levels substantially below the maximum levels permitted by Ecuadorian regulations. Brands A, B, C, and H exhibited the highest concentrations of Cd and Pb.
Research Progress on the Physicochemical Properties of Starch-Based Foods by Extrusion Processing
Authors: Qiu Chao , Hu Han , Chen Baicun , Lin Qianzhu , Ji Hangyan , Jin Zhengyu
Published: November 2024

Extrusion is a crucial food processing technique that involves mixing, heating, shearing, molding, and other operations to modify the structures and properties of food components. As the primary energy source material, the extrusion process induces significant physical and chemical changes in starch that impact the quality of final products. This review paper discusses novel technologies for starch extrusion and their influence on the physical and chemical properties of starch-based foods, such as gelatinization and retrogradation properties, structural characteristics, and digestion properties. Additionally, it examines the application of extrusion in starch processing and the interactions between starch and other food components during extrusion. This information sheds light on the structural and property alterations that occur during the extrusion process to create high-quality starch-based foods.
Coupled Effect of Nutritional Food Molecules and Lactobacillus reuteri Surface Protein Interaction on the Bacterial Gastrointestinal Tolerance
Authors: Zhang Ao , Ou Mingjuan , Wu Peng , Zheng Kaige , Zhang Haiqian , Yu Yixing , Guo Yuxing , Zhang Tao , Pan Daodong , Wu Zhen
Published: November 2024

Lactobacillus reuteri, which is present in fermented foods, can produce LPxTG motif proteins (LMPs) to help the strain resist gastrointestinal fluid environmental stress and enhance the adherence and colonizing properties. Intestinal nutrient small molecules can interact with LMPs and cooperate with Lactobacillus to exert probiotic effects in the host intestine. However, the mechanism of their correlation with gastrointestinal tolerance needs to be further studied. In this study, different kinds of nutritional food molecules, such as intestinal phenols, sugars, and acids, were screened and the interaction between the LPxTG proteins and small molecules was explored via the molecular docking approach. The docking results showed that phenols and oligosaccharides were more likely to bind to the LPxTG protein (B3XKV5), with the benzene ring, phenolic hydroxyl group, and glycosidic bond in the small molecule more easily binding to the active site of B3XKV5. Furthermore, the gastrointestinal tolerance was enhanced under the rutin, myricetin, quercetin phenols, and stachyose-treated L. reuteri strain groups, especially the phenol group, which revealed the relationship between the molecular interaction of the strain with the small molecules and strain tolerance mechanism. All the findings illustrated the gastrointestinal tolerance escape effect of the Lactobacillus strain under enriched intestinal nutrient small molecular conditions, and they also provide insight regarding the small molecules for the Lactobacillus strain under abnormal growth environments.
Investigating the Dynamics of a Unidirectional Wave Model: Soliton Solutions, Bifurcation, and Chaos Analysis
Authors: Alraqad Tariq , Suhail Muntasir , Saber Hicham , Aldwoah Khaled , Eljaneid Nidal , Alsulami Amer , Muflh Blgys
Published: November 2024

The current work investigates a recently introduced unidirectional wave model, applicable in science and engineering to understand complex systems and phenomena. This investigation has two primary aims. First, it employs a novel modified Sardar sub-equation method, not yet explored in the literature, to derive new solutions for the governing model. Second, it analyzes the complex dynamical structure of the governing model using bifurcation, chaos, and sensitivity analyses. To provide a more accurate depiction of the underlying dynamics, they use quantum mechanics to explain the intricate behavior of the system. To illustrate the physical behavior of the obtained solutions, 2D and 3D plots, along with a phase plane analysis, are presented using appropriate parameter values. These results validate the effectiveness of the employed method, providing thorough and consistent solutions with significant computational efficiency. The investigated soliton solutions will be valuable in understanding complex physical structures in various scientific fields, including ferromagnetic dynamics, nonlinear optics, soliton wave theory, and fiber optics. This approach proves highly effective in handling the complexities inherent in engineering and mathematical problems, especially those involving fractional-order systems.
Soliton Solutions and Chaotic Dynamics of the Ion-Acoustic Plasma Governed by a (3+1)-Dimensional Generalized Korteweg–de Vries–Zakharov–Kuznetsov Equation
Authors: Hamza Amjad E. , Rabih Mohammed Nour A. , Alsulami Amer , Mustafa Alaa , Aldwoah Khaled , Saber Hicham
Published: November 2024

This study explores the novel dynamics of the (3+1)-dimensional generalized Korteweg–de Vries–Zakharov–Kuznetsov (KdV-ZK) equation. A Galilean transformation is employed to derive the associated system of equations. Perturbing this system allows us to investigate the presence and characteristics of chaotic behavior, including return maps, fractal dimension, power spectrum, recurrence plots, and strange attractors, supported by 2D and time-dependent phase portraits. A sensitivity analysis is demonstrated to show how the system behaves when there are small changes in initial values. Finally, the planar dynamical system method is used to derive anti-kink, dark soliton, and kink soliton solutions, advancing our understanding of the range of solutions admitted by the model.
A Simple Preparation of Crosslinked, Highly Alkaline Diallyldimethylammonium Hydroxide Hydrogel Particles via Inverse Static Anion Exchange
Authors: Mrohs Tim B. , Weichold Oliver
Published: November 2024

Highly alkaline hydrogels are gaining increasing attention in building materials research. Specifically, cationic alkaline hydrogels based on diallyldimethylammonium hydroxide (DADMAOH) as the monomer have been effectively used to seal water-bearing cracks or serve as coupling media for electrochemical chloride extraction. However, the residual halogen content and challenges in scaling up monomer production have hindered broader application. Attempts to use a commercially available cation-selective membrane for ion exchange achieved up to 90% chloride-to-hydroxide switch, but the approach proved ineffective due to significant monomer decomposition during the process. By contrast, neutral gels and gel particles can be readily prepared from diallyldimethylammonium chloride (DADMAC) in large quantities and with a wide range of compositions. It is demonstrated here that these neutral gel particles undergo inverse static anion exchange when suspended in NaOH solution, generating DADMAOH particles with residual halide contents of <0.3%, without the need for ion-selective or dialysis membranes. This corresponds to an up to 100-fold reduction in residual chloride content compared to particles produced directly from alkaline monomer solutions, thereby significantly enhancing the efficiency of hydroxide ion release. The swelling behaviour of the particles is primarily influenced by the initial monomer concentration, while conductivity remains largely unaffected, indicating that charge transport occurs mainly along the particle surface. Despite the pronounced increase in swelling with decreasing particle radii, the specific conductivity of 2.8 Ω−1 m−1 is still sufficient for their use as coupling media in concrete applications. In summary, the alkaline particles prepared via inverse static anion exchange meet all necessary requirements for building materials applications, offering a broader range of tuneable properties and greater ease of production compared to gels or particles derived from DADMAOH.
Effect of Freezing Temperature on the Thermal, Rheological, and Gelatinization Properties of Freeze-Thaw-Dehydrated Potato Powder
Authors: Duan Xinyan , Zhang Tingting , Liu Qiannan , Zhang Liang , Liu Wei , Zhao Ruixuan , Hu Honghai
Published: November 2024

To promote the application of freeze-thaw-dehydrated (FTD) potatoes and their gels, this study aimed to investigate the effects of freezing temperature on the physicochemical and gel properties of FTD potato powder and their correlation. The results revealed that, as the freezing temperature decreased, the solubility exhibited an overall downwards trend resulting from soluble solids and amylose liberation. Owing to the better cell integrity at −20 °C, the solubility was greater than that of the other treatment groups. In contrast, the trough viscosity and melting enthalpy increased, and the final viscosity, and setback first increased but then decreased. Regarding the properties of the FTD potato powder gel, the storage modulus, loss modulus, hardness, adhesiveness, chewiness, and consistency first increased but then decreased with decreasing freezing temperature. At a moderate freezing temperature (−20 °C), the solubility and stability of the FTD potato powder were well maintained, and the final viscosity, setback, and hardness reached their highest values. Correlation analysis revealed that, with decreasing freezing temperature, the amount of FTD potato powder initially increased, followed by a decrease in the final viscosity and setback. This trend was positively correlated with the hardness of the FTD potato gel (r = 0.98, r = 0.93).