bluuebunny/crossref_metadata_2025 huggingface dataset:
Real-World Case Series of Efgartigimod for Japanese Generalized Myasthenia Gravis: Well-Tailored Treatment Cycle Intervals Contribute to Sustained Symptom Control
Authors: Konno Shingo , Uchi Takafumi , Kihara Hideo , Sugimoto Hideki
Published: May 2024

Introduction: Myasthenia gravis (MG), an immune disorder affecting nerve-muscle transmission, often necessitates tailored therapies to alleviate longitudinal symptom fluctuations. Here, we aimed to examine and compare the treatment cycle intervals and efficacy of efgartigimod in four patients. This case series mainly offers insights into personalized treatment cycle intervals and the efficacy of efgartigimod for patients with MG in our facility in Japan. Methods: We retrospectively analyzed four patients with MG (2 patients with early-onset, 1 with late-onset, and 1 with seronegative MG, mainly managed with oral immunosuppressants as prior treatments) who completed four or more cycles of efgartigimod treatment from January 2022 to September 2023. We focused on changes in serum immunoglobulin (IgG) level, acetylcholine receptor antibody (AChR-Ab) titer, and quantitative MG (QMG) score. Results: Efgartigimod, administered at a median of 5.0 [IQR 5.0, 7.5] weeks between cycles, led to decreased serum IgG levels in all patients and reduced AChR-Ab titers in seropositive patients. All patients showed sustained MG symptom improvement, with considerably reduced QMG scores before efgartigimod treatment. None of the patients required rescue medications or developed treatment-related adverse events. Conclusions: Customized efgartigimod administration intervals effectively enhanced clinical outcomes in patients with MG without notable symptom fluctuations, demonstrating the benefits of individualized treatment approaches and validating the safety of efgartigimod during the study period.
Gut Permeability and Immune-Mediated Inflammation in Heart Failure
Authors: Perticone Maria , Gigliotti Simona , Shehaj Ermal , Maio Raffaele , Suraci Edoardo , Miceli Sofia , Andreozzi Francesco , Matera Giovanni , Perticone Francesco
Published: May 2024

Heart failure (HF) is characterized by low-grade immune-mediated inflammation due to increased Toll-like receptor (TLR) expression as response to endotoxin increase and dysregulated gut barrier permeability. We investigated TLR expression and possible gut dysbiosis in HF patients compared to a control group. We enrolled 80 Caucasian HF patients and 20 controls. Low-grade immune-mediated inflammation was evaluated by TLR expression, while gut dysbiosis by the detection of zonulin and bacterial endotoxin activity in a semi-quantitative (endotoxin activity assay [EAA]) and quantitative (limulus amebocyte lysate [LAL] test) way. Compared to controls, patients with HF showed significantly higher age and blood pressure values, worse metabolic profile and kidney function, higher inflammatory biomarkers levels, and lower levels of zonulin and endotoxin activity. When dividing failing patients in those with reduced ejection fraction (HF-rEF) and those with preserved ejection fraction (HF-pEF), HF-rEF patients showed significantly higher values of inflammatory biomarkers and TLR expression than HF-pEF patients. Gut permeability biomarkers inversely correlated with the severity of HF and positively with renal function. eGFR was retained as an independent predictor of zonulin variation in all the three groups of failing patients. Present data work to extend current knowledge about the role of gut microbiota in immune-mediated inflammation in HF.
Mycelium-Based Composites: Surveying Their Acceptance by Professional Architects
Authors: Lewandowska Anna , Bonenberg Agata , Sydor Maciej
Published: May 2024

Mycelium-based composites (MBCs) are biomaterials with scientifically proven potential to improve sustainability in construction. Although mycelium-based products are not entirely new, their use in engineering presents challenges due to the inherent properties of this fungal material. This study investigated professional architects’ and interior designers’ perceptions of MBCs, focusing on familiarity, aesthetic appeal, and willingness to use. The first phase of the survey explored respondents’ views on material-related ecological design principles. In the second phase, respondents evaluated ten small architectural objects crafted from MBCs, focusing on form, detail, and visual appeal. The last phase of the survey measured their interest in using mycelium in their design work. The results revealed that MBCs were relatively unknown among the surveyed professionals; only every second respondent knew this material. Despite this, 90% found MBCs visually appealing after seeing the examples. Interestingly, the natural, unprocessed appearance of the material was assessed as less aesthetically pleasing, with thermal treatment improving its perceived value. Architects were more receptive to using MBCs in their professional projects for customers than for personal use. This observation points to a ‘double standard’: professional architects are more open to using MBCs in projects not intended for their own use.
UAV Multi-Dynamic Target Interception: A Hybrid Intelligent Method Using Deep Reinforcement Learning and Fuzzy Logic
Authors: Xia Bingze , Mantegh Iraj , Xie Wenfang
Published: May 2024

With the rapid development of Artificial Intelligence, AI-enabled Uncrewed Aerial Vehicles have garnered extensive attention since they offer an accessible and cost-effective solution for executing tasks in unknown or complex environments. However, developing secure and effective AI-based algorithms that empower agents to learn, adapt, and make precise decisions in dynamic situations continues to be an intriguing area of study. This paper proposes a hybrid intelligent control framework that integrates an enhanced Soft Actor–Critic method with a fuzzy inference system, incorporating pre-defined expert experience to streamline the learning process. Additionally, several practical algorithms and approaches within this control system are developed. With the synergy of these innovations, the proposed method achieves effective real-time path planning in unpredictable environments under a model-free setting. Crucially, it addresses two significant challenges in RL: dynamic-environment problems and multi-target problems. Diverse scenarios incorporating actual UAV dynamics were designed and simulated to validate the performance in tracking multiple mobile intruder aircraft. A comprehensive analysis and comparison of methods relying solely on RL and other influencing factors, as well as a controller feasibility assessment for real-world flight tests, are conducted, highlighting the advantages of the proposed hybrid architecture. Overall, this research advances the development of AI-driven approaches for UAV safe autonomous navigation under demanding airspace conditions and provides a viable learning-based control solution for different types of robots.
High-Efficiency Data Fusion Aerodynamic Performance Modeling Method for High-Altitude Propellers
Authors: Zhang Miao , Jiao Jun , Zhang Jian , Zhang Zijian
Published: May 2024

During the overall design phase of solar-powered unmanned aerial vehicles (UAVs), a large amount of high-fidelity (HF) propeller aerodynamic performance data is required to enhance design performance, but the acquisition cost is prohibitively expensive. To improve model accuracy and reduce modeling costs, this paper constructs a multi-fidelity aerodynamic data fusion model by associating data with different fidelity. This model utilizes a low-fidelity computational method to quickly determine the design space. The constrained Latin hypercube sampling based on the successive local enumeration (SLE-CLHS) method and the expected improvement (EI) criterion were adopted to achieve the efficient initialization and fastest convergence of the Co-Kriging surrogate model within the design space. This modeling framework was applied to acquire the aerodynamic performance of high-altitude propellers, and the model was evaluated using various performance indicators. The results demonstrate that the proposed model has excellent predictive performance. Specifically, when the surrogate model was constructed using 350 high-fidelity samples, there were improvements of 13.727%, 12.241%, and 5.484% for thrust, torque, and efficiency compared with the surrogate model constructed from low-fidelity samples.
A Robust Strategy for UAV Autonomous Landing on a Moving Platform under Partial Observability
Authors: Aikins Godwyll , Jagtap Sagar , Nguyen Kim-Doang
Published: May 2024

Landing a multi-rotor uncrewed aerial vehicle (UAV) on a moving target in the presence of partial observability, due to factors such as sensor failure or noise, represents an outstanding challenge that requires integrative techniques in robotics and machine learning. In this paper, we propose embedding a long short-term memory (LSTM) network into a variation of proximal policy optimization (PPO) architecture, termed robust policy optimization (RPO), to address this issue. The proposed algorithm is a deep reinforcement learning approach that utilizes recurrent neural networks (RNNs) as a memory component. Leveraging the end-to-end learning capability of deep reinforcement learning, the RPO-LSTM algorithm learns the optimal control policy without the need for feature engineering. Through a series of simulation-based studies, we demonstrate the superior effectiveness and practicality of our approach compared to the state-of-the-art proximal policy optimization (PPO) and the classical control method Lee-EKF, particularly in scenarios with partial observability. The empirical results reveal that RPO-LSTM significantly outperforms competing reinforcement learning algorithms, achieving up to 74% more successful landings than Lee-EKF and 50% more than PPO in flicker scenarios, maintaining robust performance in noisy environments and in the most challenging conditions that combine flicker and noise. These findings underscore the potential of RPO-LSTM in solving the problem of UAV landing on moving targets amid various degrees of sensor impairment and environmental interference.
Entropy Production in Reaction–Diffusion Systems Confined in Narrow Channels
Authors: Chacón-Acosta Guillermo , Núñez-López Mayra
Published: May 2024

This work analyzes the effect of wall geometry when a reaction–diffusion system is confined to a narrow channel. In particular, we study the entropy production density in the reversible Gray–Scott system. Using an effective diffusion equation that considers modifications by the channel characteristics, we find that the entropy density changes its value but not its qualitative behavior, which helps explore the structure-formation space.
Analysis of Vibration Characteristics of Bridge Structures under Seismic Excitation
Authors: Li Ling’ai , Huang Shengxiang
Published: May 2024

Bridges may undergo structural vibration responses when exposed to seismic waves. An analysis of structural vibration characteristics is essential for evaluating the safety and stability of a bridge. In this paper, a signal time-frequency feature extraction method (NTFT-ESVD) integrating standard time-frequency transformation, singular value decomposition, and information entropy is proposed to analyze the vibration characteristics of structures under seismic excitation. First, the experiment simulates the response signal of the structure when exposed to seismic waves. The results of the time-frequency analysis indicate a maximum relative error of only 1% in frequency detection, and the maximum relative errors in amplitude and time parameters are 5.9% and 6%, respectively. These simulation results demonstrate the reliability of the NTFT-ESVD method in extracting the time-frequency characteristics of the signal and its suitability for analyzing the seismic response of the structure. Then, a real seismic wave event of the Su-Tong Yangtze River Bridge during the Hengchun earthquake in Taiwan (2006) is analyzed. The results show that the seismic waves only have a short-term impact on the bridge, with the maximum amplitude of the vibration response no greater than 1 cm, and the maximum vibration frequency no greater than 0.2 Hz in the three-dimensional direction, indicating that the earthquake in Hengchun will not have any serious impact on the stability and security of the Su-Tong Yangtze River Bridge. Additionally, the reliability of determining the arrival time of seismic waves by extracting the time-frequency information from structural vibration response signals is validated by comparing it with results from seismic stations (SSE/WHN/QZN) at similar epicenter distances published by the USGS. The results of the case study show that the combination of dynamic GNSS monitoring technology and time-frequency analysis can be used to analyze the impact of seismic waves on the bridge, which is of great help to the manager in assessing structural seismic damage.
A TCN-Linear Hybrid Model for Chaotic Time Series Forecasting
Authors: Wang Mengjiao , Qin Fengtai
Published: May 2024

The applications of deep learning and artificial intelligence have permeated daily life, with time series prediction emerging as a focal area of research due to its significance in data analysis. The evolution of deep learning methods for time series prediction has progressed from the Convolutional Neural Network (CNN) and the Recurrent Neural Network (RNN) to the recently popularized Transformer network. However, each of these methods has encountered specific issues. Recent studies have questioned the effectiveness of the self-attention mechanism in Transformers for time series prediction, prompting a reevaluation of approaches to LTSF (Long Time Series Forecasting) problems. To circumvent the limitations present in current models, this paper introduces a novel hybrid network, Temporal Convolutional Network-Linear (TCN-Linear), which leverages the temporal prediction capabilities of the Temporal Convolutional Network (TCN) to enhance the capacity of LSTF-Linear. Time series from three classical chaotic systems (Lorenz, Mackey–Glass, and Rossler) and real-world stock data serve as experimental datasets. Numerical simulation results indicate that, compared to classical networks and novel hybrid models, our model achieves the lowest RMSE, MAE, and MSE with the fewest training parameters, and its R2 value is the closest to 1.
A Convolutional Neural Network-Based Quantization Method for Block Compressed Sensing of Images
Authors: Gong Jiulu , Chen Qunlin , Zhu Wei , Wang Zepeng
Published: May 2024

Block compressed sensing (BCS) is a promising method for resource-constrained image/video coding applications. However, the quantization of BCS measurements has posed a challenge, leading to significant quantization errors and encoding redundancy. In this paper, we propose a quantization method for BCS measurements using convolutional neural networks (CNN). The quantization process maps measurements to quantized data that follow a uniform distribution based on the measurements’ distribution, which aims to maximize the amount of information carried by the quantized data. The dequantization process restores the quantized data to data that conform to the measurements’ distribution. The restored data are then modified by the correlation information of the measurements drawn from the quantized data, with the goal of minimizing the quantization errors. The proposed method uses CNNs to construct quantization and dequantization processes, and the networks are trained jointly. The distribution parameters of each block are used as side information, which is quantized with 1 bit by the same method. Extensive experiments on four public datasets showed that, compared with uniform quantization and entropy coding, the proposed method can improve the PSNR by an average of 0.48 dB without using entropy coding when the compression bit rate is 0.1 bpp.
Hilbert Space Delocalization under Random Unitary Circuits
Authors: Turkeshi Xhek , Sierant Piotr
Published: May 2024

The unitary dynamics of a quantum system initialized in a selected basis state yield, generically, a state that is a superposition of all the basis states. This process, associated with the quantum information scrambling and intimately tied to the resource theory of coherence, may be viewed as a gradual delocalization of the system’s state in the Hilbert space. This work analyzes the Hilbert space delocalization under the dynamics of random quantum circuits, which serve as a minimal model of the chaotic dynamics of quantum many-body systems. We employ analytical methods based on the replica trick and Weingarten calculus to investigate the time evolution of the participation entropies which quantify the Hilbert space delocalization. We demonstrate that the participation entropies approach, up to a fixed accuracy, their long-time saturation value in times that scale logarithmically with the system size. Exact numerical simulations and tensor network techniques corroborate our findings.
Analytic Formulae for T Violation in Neutrino Oscillations
Authors: Yasuda Osamu
Published: May 2024

Recently, a concept known as μTRISTAN, which involves the acceleration of μ+, has been proposed. This initiative has led to considerations of a new design for a neutrino factory. Additionally, leveraging the polarization of μ+, measurements of T violation in neutrino oscillations are also being explored. In this paper, we present analytical expressions for T violation in neutrino oscillations within the framework of standard three-flavor neutrino oscillations, a scenario involving nonstandard interactions, and a case of unitarity violation. We point out that examining the energy spectrum of T violation may be useful for probing new physics effects.
Photosensitive Control and Network Synchronization of Chemical Oscillators
Authors: Carballosa Alejandro , Gomez-Varela Ana I. , Bao-Varela Carmen , Flores-Arias Maria Teresa , Muñuzuri Alberto P.
Published: May 2024

The Belousov–Zhabotinsky (BZ) reaction has long been a paradigmatic system for studying chemical oscillations. Here, we experimentally studied the synchronization control within photochemically coupled star networks of BZ oscillators. Experiments were carried out in wells performed in soda-lime glass constructed using novel laser technologies. Utilizing the inherent oscillatory nature of the BZ reaction, we engineered a star network of oscillators interconnected through photochemical inhibitory coupling. Furthermore, the experimental setup presented here could be extrapolated to more complex network architectures with both excitatory and inhibitory couplings, contributing to the fundamental understanding of synchronization in complex systems.
Analysing Conversation Pathways with a Chatbot Tutor to Enhance Self-Regulation in Higher Education
Authors: Martins Ludmila , Fernández-Ferrer Maite , Puertas Eloi
Published: May 2024

Chatbots can have a significant positive impact on learning. There is a growing interest in their application in teaching and learning. The self-regulation of learning is fundamental for the development of lifelong learning skills, and for this reason, education should contribute to its development. In this sense, the potential of chatbot technologies for supporting students to self-regulate their learning activity has already been pointed out. The objective of this work is to explore university students’ interactions with EDUguia chatbot to understand whether there are patterns of use linked to phases of self-regulated learning and academic task completion. This study presents an analysis of conversation pathways with a chatbot tutor to enhance self-regulation skills in higher education. Some relevant findings on the length, duration, and endpoints of the conversations are shared. In addition, patterns in these pathways and users’ interactions with the tool are analysed. Some findings are relevant to the analysis of the link between design and user experience, but they can also be related to implementation decisions. The findings presented could contribute to the work of other educators, designers, and developers interested in developing a tool addressing this goal.
An Innovative Approach to Radiality Representation in Electrical Distribution System Reconfiguration: Enhanced Efficiency and Computational Performance
Authors: Cortés Sanabria Pablo José , Tabares Pozos Alejandra , Álvarez-Martínez David , Noriega Barbosa Diego Alejandro
Published: May 2024

The reconfiguration problem (DPSR) in electrical distribution systems is a critical area of research, aimed at optimizing the operational efficiency of these networks. Historically, this problem has been approached through a variety of optimization methods. Regarding mathematical models, a key challenge identified in these models is the formulation of equations that ensure the radial operation of the system, along with the nonlinear equations representing Kirchhoff’s laws, the last often necessitating complex relaxations for practical application. This paper introduces an alternative representation of system radiality, which potentially surpasses or matches the existing methods in the literature. Our approach utilizes a more intuitive and compact set of equations, simplifying the representation process. Additionally, we propose a linearization of the current calculation in the power flow model typically used to solve DPSR. This linearization significantly accelerates the process of obtaining feasible solutions and optimal reconfiguration profiles. To validate our approach, we conducted rigorous computational comparisons with the results reported in the existing literature, using a variety of test cases to ensure robustness. Our computational results demonstrate a considerable improvement in computational time. The objective functions used are competitive and, in many instances, outperform the best reported results in the literature. In some cases, our method even identifies superior solutions.
DER Control and Management Strategies for Distribution Networks: A Review of Current Practices and Future Directions
Authors: Smith Edward J. , Robinson Duane A. , Elphick Sean
Published: May 2024

It is widely recognised that improving the visibility and controllability of distributed energy resources (DERs) within electricity distribution networks will have significant benefits, particularly for the management of low-voltage (LV) and medium-voltage (MV) networks. Much work within the electricity distribution industry is currently focused on improving the visibility of DERs on LV networks. From a control-theoretic perspective, this enables closing the loop between the DER and the control room and enables a shift towards utilising data-driven model-based control strategies for DERs. The result is a system-wide performance that is closer to the theoretical optimal. In the Australian context, several jurisdictions are trialling techniques such as dynamic operating envelopes to enhance DER hosting capacity, using IEEE 2030.5-based architectures, with the implementation of distributed energy resource management (DERMS) systems at the enterprise level still quite limited. While there is significant activity focused on DER behaviour and control techniques by way of inverter grid codes and standards, the core issue of interoperability with distribution management systems (DMSs), market operators or participants, electric vehicles (EVs) or other DERs is still a work in progress. Importantly, this is also an impediment to realising distributed architectures for DER control in the grid. The unique characteristics of Australian distribution networks highlights several challenging problems for DER control and management. The objective of this paper is to provide a broad overview of DER control and management strategies in the Australian context, with an application focus on DER control in distribution network management.
Investigation of Fluid Flow and Heat Transfer Characteristics of an Internally Channeled Tube Heat Exchanger under Laminar Flow Conditions
Authors: Al-Lami Abbas J. S. , Kenig Eugeny Y.
Published: May 2024

The so-called internally channeled tube (ICT) is an innovative heat exchanger design proposed in our recent publications based on a channels-in-tube principle. A general, three-dimensional numerical model was suggested to describe fluid dynamics and heat transfer in the ICT. This model has already been validated for turbulent flow. The current paper presents an experimental investigation of the ICT and the model validation under laminar flow conditions. The experimental set-up and measurement procedure are given in detail and the maldistribution issue is addressed. The deviation between simulated and measured values is below 11% for the pressure drop and below 8% for the wall and bulk temperatures. Furthermore, the ICT performance was evaluated using performance evaluation criterion (PEC) including both heat transfer rate and pressure drop. Enhanced heat transfer in the ICT surpasses the associated pressure drop increase, yielding a PEC greater than one.
Analysis of Breaking Characteristics of C4F7N/CO2 Mixture Gas in Circuit Breaker
Authors: Li Xiaolong , Liu Lei , Wang Wen , Geng Zhenxin
Published: May 2024

In recent years, the C4F7N mixed gas has attracted considerable attention for its outstanding insulation and arc-extinguishing capabilities, positioning it as a potential substitute for sulfur hexafluoride, SF6. However, there remains a limited understanding of the arc-extinguishing and insulation performance of C4F7N/CO2 mixed gas. In addition, there is limited research on high-current breaking in circuit breakers. Therefore, this study aims to investigate the arc characteristics and breaking behavior of 10%C4F7N/90%CO2 and 15%C4F7N/85%CO2 mixed gases using a magnetohydrodynamic model based on the 252kV air pressure circuit breaker. The dynamic characteristics of this mixed gas are compared with pure SF6 under short-circuit current breaking conditions, while analyzing different parameters of the C4F7N configuration. The results indicate that the mixed gas exhibits lower levels in terms of arc temperature, axial diffusion distance and pressure difference at the moment of arc initiation compared to pure SF6. Furthermore, increasing the inflating pressure can effectively enhance the breaking performance of the circuit breaker with 0.6 MPa, making it more suitable. Additionally, increasing the proportion of C4F7N in the mixed gases will cause the arc temperature to rise slightly at the initial arc and current crossing zero, but decrease at the peak current. The core pressure also rises significantly, with a greater pressure difference established in the compressor at moment of arc initiation. This study provides a reference for the design of an environmentally friendly circuit breaker and the selection of the mixed gas ratio.
A Multistage Physics-Informed Neural Network for Fault Detection in Regulating Valves of Nuclear Power Plants
Authors: Lai Chenyang , Ahmed Ibrahim , Zio Enrico , Li Wei , Zhang Yiwang , Yao Wenqing , Chen Juan
Published: May 2024

In Nuclear Power Plants (NPPs), online condition monitoring and the fault detection of structures, systems and components (SSCs) can aid in guaranteeing safe operation. The use of data-driven methods for these tasks is limited by the requirement of physically consistent outcomes, particularly in safety-critical systems. Considering the importance of regulating valves (e.g., safety relief valves and main steam isolation valves), this work proposes a multistage Physics-Informed Neural Network (PINN) for fault detection in such components. Two stages of the PINN are built by developing the process model of the regulating valve, which integrates the basic valve sizing equation into the loss function to jointly train the two stages of the PINN. In the 1st stage, a shallow Neural Network (NN) with only one hidden layer is developed to estimate the equivalent flow coefficient (a key performance indicator of regulating valves) using the displacement of the valve as input. In the 2nd stage, a Deep Neural Network (DNN) is developed to estimate the flow rate expected in normal conditions using inputs such as the estimated flow coefficient from the 1st stage, the differential pressure, and the fluid temperature. Then, the residual, i.e., the difference between the estimated and measured flow rates, is fed into a Deep Support Vector Data Description (DeepSVDD) to detect the occurrence of faults. Moreover, the deviation between the estimated flow coefficients of normal and faulty conditions is used to interpret the consistency of the detection result with physics. The proposed method is, first, applied to a simulation case implemented to emulate the operating characteristics of regulating the valves of NPPs and then validated on a real-world case study based on the DAMADICS benchmark. Compared to state-of-the-art fault detection methods, the obtained results from the proposed method show effective fault detection performance and reasonable flow coefficient estimation, thus guaranteeing the physical consistency of the detection results.
Improved Model-Free Deadbeat Predictive Current Controller for PMSMs Based on Ultralocal Model and H∞ Norm
Authors: Fang Yiming , Chen Junlei
Published: May 2024

This article proposes an improved model-free deadbeat predictive current control (MFCC) method for permanent magnet synchronous motors (PMSMs) based on the ultralocal model and H∞ norm. Firstly, the traditional deadbeat predictive current control (DPCC) method is introduced and a theoretical analysis is conducted on its sensitivity to parameters. Building upon this, the limitations of model dependence and the limited robustness of the deadbeat predictive current control method based on the extended state observer (ESO-DPCC) are theoretically analyzed. Furthermore, an improved MFCC method based on the ultralocal model is proposed, and the influence of the observer on MFCC is theoretically analyzed. This study combined the proposed method with the H∞ norm, and the optimal coefficients of the observer were tuned to enhance the robustness and dynamic performance of the current loop. Finally, the proposed algorithms were validated on a 400 W PMSM platform.
Biofuels in Aviation: Exploring the Impact of Sustainable Aviation Fuels in Aircraft Engines
Authors: Khujamberdiev Ramozon , Cho Haeng Muk
Published: May 2024

This comprehensive review examines the role of sustainable aviation fuels (SAFs) in promoting a more environmentally responsible aviation industry. This study explores various types of biofuels, including hydroprocessed esters and fatty acids (HEFAs), Fischer–Tropsch (FT) fuels, alcohol-to-jet (ATJ) fuels, and oil derived from algae. Technological advancements in production and processing have enabled SAF to offer significant reductions in greenhouse gas emissions and other pollutants, contributing to a cleaner environment and better air quality. The review addresses the environmental, economic, and technical benefits of SAF, as well as the challenges associated with their adoption. Lifecycle analyses are used to assess the net environmental benefits of SAF, with a focus on feedstock sustainability, energy efficiency, and potential impacts on biodiversity and land use. Challenges such as economic viability, scalability, and regulatory compliance are discussed, with emphasis on the need for supportive policies and international collaboration to ensure the long-term sustainability of SAF. This study also explores current applications of SAF in commercial airlines and military settings, highlighting successful case studies and regional differences driven by policy frameworks and government incentives. By promoting technological innovation and addressing regulatory and economic barriers, SAF has the potential to play a crucial role in the aviation industry’s transition toward sustainability.
Real-Time Co-Simulation and Grid Integration of PMSG-Based Hydrokinetic Energy Conversion Systems via Power-Hardware-in-the-Loop Technics
Authors: Jasso-Ruiz Ubaldo , Rodríguez-Rodríguez Juan Ramón , Mendoza Edgar , Echeverría Carlos , Salgado-Herrera Nadia Maria
Published: May 2024

Ocean energy sources are a promising source of energy. However, simulating a hydrokinetic farm with multiple units requires significant computational resources, while physical experimentation on site is expensive. Therefore, the scientific challenge is to develop analytical and experimental tools that consider real aspects of areas with generation potential in a controlled laboratory environment. This paper presents a theoretical and experimental tool for analysing the interconnection of a hydrokinetic energy farm comprising 20 generation units. The test bench is a Power Hardware in the Loop type, consisting of one physical prototype generator to scale and 19 discrete averaged models operating in real-time. The system allows generators to interact through an amplifier, emulating the impact of power injection in a small electrical network. This is based on the variability of the marine resource, specifically the current velocities in the Cozumel-Mexico channel. Unlike other publications, the most significant contribution of this work is a complete feasible emulation of a marine current plant interconnected to an electrical grid, where the objective is to have a global analysis of the operation of each generation unit and the impact of the interconnection as a whole, considering that such information is of utmost importance for the execution of future projects of power generation from the sea.
Numerical Dissipation Control in High-Order Methods for Compressible Turbulence: Recent Development
Authors: Yee H. , Sjögreen Björn
Published: May 2024

This comprehensive overview presents our continued efforts in high-order finite difference method (FDM) development for adaptive numerical dissipation control in the long-time integration of direct numerical simulation (DNS), large eddy simulation (LES), and implicit LES (ILES) computations of compressible turbulence for gas dynamics and MHD. The focus is on turbulence with shock wave numerical simulations using the adaptive blending of high-order structure-preserving non-dissipative methods (classical central, Padé (compact), and dispersion relation-preserving (DRP)) with high-order shock-capturing methods in such a way that high-order shock-capturing methods are active only in the vicinity of shock/shear waves, and high-gradient and spurious high-frequency oscillation regions guided via flow sensors. Any efficient and high-resolution high-order shock-capturing methods are good candidates for the blending of methods procedure. Typically, the adaptive blending of more than one method falls under two camps: hybrid methods and nonlinear filter methods. They are applicable to unstructured finite volume, finite element, discontinuous Galerkin, and spectral element methods. This work represents the culmination of over 20 years of high-order FDM developments and hands-on experience by the authors and collaborators in adaptive numerical dissipation control using the “high order nonlinear filter approach”. Extensions of these FDM versions to curvilinear nonuniform, freestream-preserving moving grids and time-varying deforming grids were also developed. By examining the construction of these two approaches using the high-order multistage type of temporal discretization, the nonlinear filter approach is made more efficient and less CPU-intensive while obtaining similar accuracy. A representative variety of test cases that compare the various blending of high-order methods with standalone standard methods is illustrated. Due to the fact that our nonlinear filter methods are not well known in compressible turbulence with shock waves, the intent of this comprehensive overview is for general audiences who are not familiar with our nonlinear filter methods. For readers interested in the implementation of our methods into their computer code, it is hoped that the long overview will be helpful.
APHS-YOLO: A Lightweight Model for Real-Time Detection and Classification of Stropharia Rugoso-Annulata
Authors: Liu Ren-Ming , Su Wen-Hao
Published: May 2024

The classification of Stropharia rugoso-annulata is currently reliant on manual sorting, which may be subject to bias. To improve the sorting efficiency, automated sorting equipment could be used instead. However, sorting naked mushrooms in real time remains a challenging task due to the difficulty of accurately identifying, locating and sorting large quantities of them simultaneously. Models must be deployable on resource-limited devices, making it challenging to achieve both a high accuracy and speed. This paper proposes the APHS-YOLO (YOLOv8n integrated with AKConv, CSPPC and HSFPN modules) model, which is lightweight and efficient, for identifying Stropharia rugoso-annulata of different grades and seasons. This study includes a complete dataset of runners of different grades in spring and autumn. To enhance feature extraction and maintain the recognition accuracy, the new multi-module APHS-YOLO uses HSFPNs (High-Level Screening Feature Pyramid Networks) as a thin-neck structure. It combines an improved lightweight PConv (Partial Convolution)-based convolutional module, CSPPC (Integration of Cross-Stage Partial Networks and Partial Convolution), with the Arbitrary Kernel Convolution (AKConv) module. Additionally, to compensate for the accuracy loss due to lightweighting, APHS-YOLO employs a knowledge refinement technique during training. Compared to the original model, the optimized APHS-YOLO model uses 57.8% less memory and 62.5% fewer computational resources. It has an FPS (frames per second) of over 100 and even achieves 0.1% better accuracy metrics than the original model. These research results provide a valuable reference for the development of automatic sorting equipment for forest farmers.
Antibacterial Activities of Agaricus bisporus Extracts and Their Synergistic Effects with the Antistaphylococcal Drug AFN-1252
Authors: Jankov Milica , Léguillier Vincent , Gašić Uroš , Anba-Mondoloni Jamila , Ristivojević Maja Krstić , Radoičić Aleksandra , Dimkić Ivica , Ristivojević Petar , Vidic Jasmina
Published: May 2024

Agaricus bisporus, commonly known as the button mushroom, has attracted attention for its biological properties, including antimicrobial activities. Here, we evaluated the efficacy of ethanolic and acetonic extracts from white and brown A. bisporus against different bacterial strains, including antibiotic-resistant strains. Bioautography and principal component analysis identified the most active antibacterial compounds for each of the tested bacteria and indicated the main markers responsible for the strain-specific effects. In addition, the mushroom extracts demonstrated a synergistic impact when combined with the antistaphylococcal antibiotic AFN-1252.
Supercritical CO2 Extraction of Terpenoids from Indocalamus latifolius Leaves: Optimization, Purification, and Antioxidant Activity
Authors: Chen Yadan , Wang Yanbin , He Liang , Wang Liling , Zhao Jianchen , Yang Zhenya , Li Qin , Shi Rui
Published: May 2024

This study aimed to investigate the efficacy of supercritical CO2 (SC-CO2) extraction in enhancing the extraction rate, purity, and antioxidant activity of Indocalamus latifolius (Keng) McClure (Poaceae) leaf terpenoids (ILLTs). Crude extracts obtained from leaves were subjected to qualitative and quantitative analyses, revealing neophytadiene, phytol, β-sitosterol, β-amyrone, squalene, and friedelin as the primary terpenoid constituents, identified through gas chromatography–mass spectrometry (GC-MS). Compared with steam distillation extraction (SD), simultaneous distillation extraction (SDE), ultra-high pressure-assisted n-hexane extraction (UHPE-Hex), ultra-high pressure-assisted ethanol extraction (UHPE-EtOH), ultrasound-assisted n-hexane extraction (UE-Hex), and ultrasound-assisted ethanol extraction (UE-EtOH), SC-CO2 exhibited a superior ILLT extraction rate, purity, and antioxidant activity. Scanning electron microscopy (SEM) observations of the residues further revealed more severe damage to both the residues and their cell walls after SC-CO2 extraction. Under optimal parameters (4.5 h, 26 MPa, 39 °C, and 20% ethyl alcohol), the ILLT extraction rate with SC-CO2 reached 1.44 ± 0.12 mg/g, which was significantly higher than the rates obtained by the other six methods. The subsequent separation and purification using WelFlash C18-l, BUCHI-C18, and Sephadex LH-20 led to an increase in the purity of the six terpenoid components from 12.91% to 93.34%. Furthermore, the ILLTs demonstrated cytotoxicity against HepG2 cells with an IC50 value of 148.93 ± 9.93 μg/mL. Additionally, with increasing concentrations, the ILLTs exhibited an enhanced cellular antioxidant status, as evidenced by reductions in both reactive oxygen species (ROS) and malondialdehyde (MDA) levels.
Multivalued Variational Inequalities with Generalized Fractional Φ-Laplacians
Authors: Le Vy Khoi
Published: May 2024

In this article, we examine variational inequalities of the form ⟨A(u),v−u⟩+⟨F(u),v−u⟩≥0,∀v∈Ku∈K,, where A is a generalized fractional Φ-Laplace operator, K is a closed convex set in a fractional Musielak–Orlicz–Sobolev space, and F is a multivalued integral operator. We consider a functional analytic framework for the above problem, including conditions on the multivalued lower order term F such that the problem can be properly formulated in a fractional Musielak–Orlicz–Sobolev space, and the involved mappings have certain useful monotonicity–continuity properties. Furthermore, we investigate the existence of solutions contingent upon certain coercivity conditions.
Ablation of Vitamin D Signaling in Cardiomyocytes Leads to Functional Impairment and Stimulation of Pro-Inflammatory and Pro-Fibrotic Gene Regulatory Networks in a Left Ventricular Hypertrophy Model in Mice
Authors: Zupcic Ana , Latic Nejla , Oubounyt Mhaned , Ramesova Alice , Carmeliet Geert , Baumbach Jan , Elkjaer Maria L. , Erben Reinhold G.
Published: May 2024

The association between vitamin D deficiency and cardiovascular disease remains a controversial issue. This study aimed to further elucidate the role of vitamin D signaling in the development of left ventricular (LV) hypertrophy and dysfunction. To ablate the vitamin D receptor (VDR) specifically in cardiomyocytes, VDRfl/fl mice were crossed with Mlcv2-Cre mice. To induce LV hypertrophy experimentally by increasing cardiac afterload, transverse aortic constriction (TAC) was employed. Sham or TAC surgery was performed in 4-month-old, male, wild-type, VDRfl/fl, Mlcv2-Cre, and cardiomyocyte-specific VDR knockout (VDRCM-KO) mice. As expected, TAC induced profound LV hypertrophy and dysfunction, evidenced by echocardiography, aortic and cardiac catheterization, cardiac histology, and LV expression profiling 4 weeks post-surgery. Sham-operated mice showed no differences between genotypes. However, TAC VDRCM-KO mice, while having comparable cardiomyocyte size and LV fibrosis to TAC VDRfl/fl controls, exhibited reduced fractional shortening and ejection fraction as measured by echocardiography. Spatial transcriptomics of heart cryosections revealed more pronounced pro-inflammatory and pro-fibrotic gene regulatory networks in the stressed cardiac tissue niches of TAC VDRCM-KO compared to VDRfl/fl mice. Hence, our study supports the notion that vitamin D signaling in cardiomyocytes plays a protective role in the stressed heart.
Patterns of the Predicted Mutation Burden in 19,778 Domesticated Barley Accessions Conserved Ex Situ
Authors: Fu Yong-Bi
Published: May 2024

Long-term conservation of more than 7 million plant germplasm accessions in 1750 genebanks worldwide is a challenging mission. The extent of deleterious mutations present in conserved germplasm and the genetic risk associated with accumulative mutations are largely unknown. This study took advantage of published barley genomic data to predict sample-wise mutation burdens for 19,778 domesticated barley (Hordeum vulgare L.) accessions conserved ex situ. It was found that the conserved germplasm harbored 407 deleterious mutations and 337 (or 82%) identified deleterious alleles were present in 20 (or 0.1%) or fewer barley accessions. Analysis of the predicted mutation burdens revealed significant differences in mutation burden for several groups of barley germplasm (landrace > cultivar (or higher burden estimate in landrace than in cultivar); winter barley > spring barley; six-rowed barley > two-rowed barley; and 1000-accession core collection > non-core germplasm). Significant differences in burden estimate were also found among seven major geographical regions. The sample-wise predicted mutation burdens were positively correlated with the estimates of sample average pairwise genetic difference. These findings are significant for barley germplasm management and utilization and for a better understanding of the genetic risk in conserved plant germplasm.
Neuro-Adipokine Crosstalk in Alzheimer’s Disease
Authors: Chen Bandy , Schneeberger Marc
Published: May 2024

The connection between body weight alterations and Alzheimer’s disease highlights the intricate relationship between the brain and adipose tissue in the context of neurological disorders. During midlife, weight gain increases the risk of cognitive decline and dementia, whereas in late life, weight gain becomes a protective factor. Despite their substantial impact on metabolism, the role of adipokines in the transition from healthy aging to neurological disorders remains largely unexplored. We aim to investigate how the adipose tissue milieu and the secreted adipokines are involved in the transition between biological and pathological aging, highlighting the bidirectional relationship between the brain and systemic metabolism. Understanding the function of these adipokines will allow us to identify biomarkers for early detection of Alzheimer’s disease and uncover novel therapeutic options.
DNA Methylation Mediates Sperm Quality via piwil1 and piwil2 Regulation in Japanese Flounder (Paralichthys olivaceus)
Authors: Zong Wenyu , Wang Yapeng , Zhang Lingqun , Lu Wei , Li Weigang , Wang Fengchi , Cheng Jie
Published: May 2024

DNA methylation is an important way to regulate gene expression in eukaryotes. In order to reveal the role of DNA methylation in the regulation of germ cell-specific piwi gene expression during spermatogenesis of Japanese flounder (Paralichthys olivaceus), the expression profiles of piwil1 (piwi-like 1) and piwil2 (piwi-like 2) genes in the gonads of female, male, and sex-reversed pseudo-male P. olivaceus were analyzed, and the dynamic of DNA methylation was investigated. As a result, piwil1 and piwil2 genes were highly expressed in the testis of both male and pseudo-male P. olivaceus, with significant variation among male individuals. The DNA methylation levels in the promoter regions of both piwil1 and piwil2 were negatively correlated with their expression levels, which may contribute to the transcriptional regulation of piwi genes during spermatogenesis. There was also sperm quality variation among male P. olivaceus, and the sperm curvilinear velocity was positively correlated with the expression of both piwil1 and piwil2 genes. These results indicated that the DNA methylation in piwil1 and piwil2 promoter regions may affect the initiation of piwi gene transcription, thereby regulating gene expression and further affecting the spermatogenesis process and gamete quality in P. olivaceus.