bluuebunny/crossref_metadata_2025 huggingface dataset:
Neomycin Intercalation in Montmorillonite: The Role of Ion Exchange Capacity and Process Conditions
Authors: Rapacz-Kmita Alicja , Gajek Marcin , Dudek Magdalena , Kurpanik Roksana , Kluska Stanisława , Stodolak-Zych Ewa
Published: August 2024

The study examined the possibility of intercalation of montmorillonite with neomycin in an aqueous drug solution and the factors influencing the effectiveness of this process, such as the ion exchange capacity and process conditions, including the time and temperature of incubation with the drug. X-ray diffractometry (XRD), infrared spectroscopy (FTIR), thermal analysis (DSC/TG), and Zeta potential measurement were used to confirm drug intercalation as well as to investigate the nature of clay–drug interactions. The obtained conjugates with the most favorable physicochemical properties were also tested for antibacterial response against Gram-negative bacteria (Escherichia coli) to confirm that the bactericidal properties of neomycin were retained after intercalation and UV–VIS spectrophotometry was used to examine the kinetics of drug release from the carrier. The results of the conducted research clearly indicate the successful intercalation of neomycin in montmorillonite and indicate the influence of process parameters on the properties of not only the conjugates themselves but also the properties of the intercalated drug, particularly its bactericidal activity. Ultimately, a temperature of 50 °C was found to be optimal for effective drug intercalation and the conjugates obtained within 2 h showed the highest antibacterial activity, indicating the highest potential of the thus-obtained montmorillonite conjugates as neomycin carriers.
Finite Element Analysis of the Structure and Working Principle of Solid-State Shear Milling (S3M) Equipment
Authors: Wei Lingfei , Wang Chao , Duan Ruoxuan , Zhou Zehang , Lu Canhui
Published: August 2024

Solid-state shear milling (S3M) equipment is an evolution from traditional stone mills, enabling the processing of polymer materials and fillers through crushing, mixing, and mechanochemical reactions at ambient temperature. Due to the complex structure of the mill-pan, empirical data alone are insufficient to give a comprehensive understanding of the physicochemical interactions during the milling process. To provide an in-depth insight of the working effect and mechanism of S3M equipment, finite element method (FEM) analysis is employed to simulate the milling dynamics, which substantiates the correlation between numerical outcomes and experimental observations. A model simplification strategy is proposed to optimize calculation time without compromising accuracy. The findings in this work demonstrate the S-S bond breakage mechanism behind stress-induced devulcanization and suggest the structural optimizations for enhancing the devulcanization and pulverization efficiency of S3M equipment, thereby providing a theoretical foundation for its application in material processing.
Synthesis and Characterization of Highly Fluorinated Hydrophobic Rare–Earth Metal–Organic Frameworks (MOFs)
Authors: Abbas Muhammad , Murari Bhargavasairam , Sheybani Simin , Joy Monu , Balkus Kenneth J.
Published: August 2024

Tuning a material’s hydrophobicity is desirable in several industrial applications, such as hydrocarbon storage, separation, selective CO2 capture, oil spill cleanup, and water purification. The introduction of fluorine into rare-earth (RE) metal–organic frameworks (MOFs) can make them hydrophobic. In this work, the linker bis(trifluoromethyl)terephthalic acid (TTA) was used to make highly fluorinated MOFs. The reaction of the TTA and RE3+ (RE: Y, Gd, or Eu) ions resulted in the primitive cubic structure (pcu) exhibiting RE dimer nodes (RE-TTA-pcu). The crystal structure of the RE-TTA-pcu was obtained. The use of the 2-fluorobenzoic acid in the synthesis resulted in fluorinated hexaclusters in the face-centered cubic (fcu) framework (RE-TTA-fcu), analogous to the UiO-66 MOF. The RE-TTA-fcu has fluorine on the linker as well as in the cluster. The MOFs were characterized by powder X-ray diffraction, X-ray photoelectron spectroscopy, thermogravimetric analysis, and contact angle measurements.
Solving Flexible Job-Shop Scheduling Problem with Heterogeneous Graph Neural Network Based on Relation and Deep Reinforcement Learning
Authors: Tang Hengliang , Dong Jinda
Published: August 2024

Driven by the rise of intelligent manufacturing and Industry 4.0, the manufacturing industry faces significant challenges in adapting to flexible and efficient production methods. This study presents an innovative approach to solving the Flexible Job-Shop Scheduling Problem (FJSP) by integrating Heterogeneous Graph Neural Networks based on Relation (HGNNR) with Deep Reinforcement Learning (DRL). The proposed framework models the complex relationships in FJSP using heterogeneous graphs, where operations and machines are represented as nodes, with directed and undirected arcs indicating dependencies and compatibilities. The HGNNR framework comprises four key components: relation-specific subgraph decomposition, data preprocessing, feature extraction through graph convolution, and cross-relation feature fusion using a multi-head attention mechanism. For decision-making, we employ the Proximal Policy Optimization (PPO) algorithm, which iteratively updates policies to maximize cumulative rewards through continuous interaction with the environment. Experimental results on four public benchmark datasets demonstrate that our proposed method outperforms four state-of-the-art DRL-based techniques and three common rule-based heuristic algorithms, achieving superior scheduling efficiency and generalization capabilities. This framework offers a robust and scalable solution for complex industrial scheduling problems, enhancing production efficiency and adaptability.
Optimization of Desired Multiple Resonant Modes of Compliant Parallel Mechanism Using Specific Frequency Range and Targeted Ratios
Authors: Low Vin , Yeo Song Huat , Pham Minh Tuan
Published: August 2024

In this paper, a dynamic optimization method capable of optimizing the dynamic responses of a compliant parallel mechanism (CPM), in terms of its multiple primary resonant modes, is presented. A novel two-term objective function is formulated based on the specific frequency range and targeted ratios. The first term of the function is used to optimize the first resonant mode of the CPM, within a specific frequency range. The obtained frequency value of the first mode is used in the second term to define the remaining resonant modes to be optimized in terms of targeted ratios. Using the proposed objective function, the resonant modes of a CPM can be customized for a specific purpose, overcoming the limitations of existing methods. A 6-degree-of-freedom (DoF) CPM with decoupled motion is synthesized, monolithically prototyped, and investigated experimentally to demonstrate the effectiveness of the proposed function. The experimental results showed that the objective function is capable of optimizing the six resonant modes within the desired frequency range and the targeted ratios. The highest deviation between the experimental results and the predictions among the six resonant modes is found to be 9.42%, while the highest deviation in the compliances is 10.77%. The ranges of motions are found to be 10.0 mm in the translations, and 10.8° in the rotations.
Research on a Bearing Fault Diagnosis Method Based on an Improved Wasserstein Generative Adversarial Network
Authors: Zhu Chengshun , Lin Wei , Zhang Hongji , Cao Youren , Fan Qiming , Zhang Hui
Published: August 2024

In this paper, an advanced Wasserstein generative adversarial network (WGAN)-based bearing fault diagnosis approach is proposed to bolster the diagnostic efficacy of conventional WGANs and tackle the challenge of selecting optimal hyperparameters while reducing the reliance on sample labeling. Raw vibration signals undergo continuous wavelet transform (CWT) processing to generate time–frequency images that align with the model’s input dimensions. Subsequently, these images are incorporated into a region-based fully convolutional network (R-FCN), substituting the traditional discriminator for feature capturing. The WGAN model is refined through the utilization of the Bayesian optimization algorithm (BOA) to optimize the generator and discriminator’s semi-supervised learning loss function. This approach is verified using the Case Western Reserve University (CWRU) dataset and a centrifugal pump failure experimental dataset. The results showed improvements in data input generalization and fault feature extraction capabilities. By avoiding the need to label large quantities of sample data, the diagnostic accuracy was improved to 98.9% and 97.4%.
Structural Health Monitoring of Laminated Composites Using Lightweight Transfer Learning
Authors: Azad Muhammad Muzammil , Raouf Izaz , Sohail Muhammad , Kim Heung Soo
Published: August 2024

Due to their excellent strength-to-weight ratio, composite laminates are gradually being substituted for traditional metallic materials in a variety of industries. However, due to their orthotropic nature, composite laminates are prone to several different types of damage, with delamination being the most prevalent and serious. Therefore, deep learning-based methods that use sensor data to conduct autonomous health monitoring have drawn much interest in structural health monitoring (SHM). However, the direct application of these models is restricted by a lack of training data, necessitating the use of transfer learning. The commonly used transfer learning models are computationally expensive; therefore, the present research proposes lightweight transfer learning (LTL) models for the SHM of composites. The use of an EfficientNet–based LTL model only requires the fine-tuning of target vibration data rather than training from scratch. Wavelet-transformed vibrational data from various classes of composite laminates are utilized to confirm the effectiveness of the proposed method. Moreover, various assessment measures are applied to assess model performance on unseen test datasets. The outcomes of the validation show that the pre-trained EfficientNet–based LTL model could successfully perform the SHM of composite laminates, achieving high values regarding accuracy, precision, recall, and F1-score.
Research on Multi-Objective Low-Carbon Flexible Job Shop Scheduling Based on Improved NSGA-II
Authors: Mei Zheyu , Lu Yujun , Lv Liye
Published: August 2024

To optimize the production scheduling of a flexible job shop, this paper, based on the NSGA-II algorithm, proposes an adaptive simulated annealing non-dominated sorting genetic algorithm II with enhanced elitism (ASA-NSGA-EE) that establishes a multi-objective flexible job shop scheduling model with the objective functions of minimizing the maximum completion time, processing cost, and carbon emissions generated from processing. The ASA-NSGA-EE algorithm adopts an adaptive crossover and mutation genetic strategy, which dynamically adjusts the crossover and mutation rates based on the evolutionary stage of the population, aiming to reduce the loss of optimal solutions. Additionally, it incorporates the simulated annealing algorithm to optimize the selection strategy by leveraging its cooling characteristics. Furthermore, it improves the elite strategy through incorporating elite selection criteria. Finally, by simulation experiments, the effectiveness of the improved NSGA-II algorithm is validated by comparing it with other algorithms.
Agricultural UAV Path Planning Based on a Differentiated Creative Search Algorithm with Multi-Strategy Improvement
Authors: Liu Jin , Lin Yong , Zhang Xiang , Yin Jibin , Zhang Xiaoli , Feng Yong , Qian Qian
Published: August 2024

A differentiated creative search algorithm with multi-strategy improvement (MSDCS) is proposed for the path planning problem for agricultural UAVs under different complicated situations. First, the good point set and oppositional learning strategies are used to effectively improve the quality of population diversity; the adaptive fitness–distance balance reset strategy is proposed to motivate the low performers to move closer to the region near the optimal solution and find the potential optimal solution; and the vertical and horizontal crossover strategy with random dimensions is proposed to improve the computational accuracy of the algorithm and the ability to jump out of the local optimum. Second, the MSDCS is compared to different algorithms using the IEEE_CEC2017 test set, which consists of 29 test functions. The results demonstrate that the MSDCS achieves the optimal value in 23 test functions, surpassing the comparison algorithms in terms of convergence accuracy, speed, and stability by at least one order of magnitude difference, and it is ranked No. 1 in terms of comprehensive performance. Finally, the enhanced algorithm was employed to address the issue of path planning for agricultural UAVs. The experimental results demonstrate that the MSDCS outperforms comparison algorithms in path planning across various contexts. Consequently, the MSDCS can generate optimal pathways that are both rational and safe for agricultural UAV operations.
The Asymmetrical Distribution of a Dominant Motional Electric Field within the Martian Magnetosheath
Authors: Li Shibang , Lu Haoyu , Cao Jinbin , Wu Xiaoshu , Zhang Xiaoxin , Chen Nihan , Song Yihui , Wang Jianxuan , Cao Yuchen , Zhao Jianing
Published: August 2024

Attributed to the lack of an Earth-like global intrinsic dipole magnetic field on Mars, the induced electromagnetic field environment plays a crucial role in the evolution of its atmosphere. The dominant motional electric field (EM) induced by the bulk motion of the magnetic field within the Martian magnetosheath serves to accelerate ions toward escape velocity, thereby forming a plume escape channel. However, the distribution morphology of EM itself has received limited attention in previous research. In this study, by taking advantage of the multi-fluid Hall-MHD model cooperating with the Martian crustal field model, we focus on elucidating the physical mechanisms underlying the asymmetrical distribution of EM and examining the influence of the crustal field on this asymmetry. The results obtained from the simulation conducted in the absence of the crustal field indicate that the EM is more intense within the −ZMSE magnetosheath, where EM is directed toward Mars, primarily due to its corresponding higher velocity and a stronger magnetic field at lower solar zenith angles. The Martian crustal field has the ability to enhance the local EM around the inner boundary of the magnetosheath by amplifying both the magnetic field and its associated velocity. Accordingly, these findings provide valuable insights into the asymmetric nature of EM within the Martian magnetosheath under typical quiet-time solar wind conditions.
On General Alternating Tornheim-Type Double Series
Authors: Chen Kwang-Wu
Published: August 2024

In this paper, we express ∑n,m≥1ε1nε2mMn(u)Mm(v)nrms(n+m)t as a linear combination of alternating multiple zeta values, where εi∈{1,−1} and Mk(u)∈{Hk(u),H¯k(u)}, with Hk(u) and H¯k(u) being harmonic and alternating harmonic numbers, respectively. These sums include Subbarao and Sitaramachandrarao’s alternating analogues of Tornheim’s double series as a special case. Our method is based on employing two different techniques to evaluate the specific integral associated with a 3-poset Hasse diagram.
Operational Calculus for the 1st-Level General Fractional Derivatives and Its Applications
Authors: Alkandari Maryam , Luchko Yuri
Published: August 2024

The 1st-level General Fractional Derivatives (GFDs) combine in one definition the GFDs of the Riemann–Liouville type and the regularized GFDs (or the GFDs of the Caputo type) that have been recently introduced and actively studied in the fractional calculus literature. In this paper, we first construct an operational calculus of the Mikusiński type for the 1st-level GFDs. In particular, it includes the operational calculi for the GFDs of the Riemann–Liouville type and for the regularized GFDs as its particular cases. In the second part of the paper, this calculus is applied for the derivation of the closed-form solution formulas to the initial-value problems for the linear fractional differential equations with the 1st-level GFDs.
Optimizing Electric Vehicle (EV) Charging with Integrated Renewable Energy Sources: A Cloud-Based Forecasting Approach for Eco-Sustainability
Authors: Aldossary Mohammad , Alharbi Hatem A. , Ayub Nasir
Published: August 2024

As electric vehicles (EVs) are becoming more common and the need for sustainable energy practices is growing, better management of EV charging station loads is a necessity. The simple act of folding renewable power from solar or wind in an EV charging system presents a huge opportunity to make them even greener as well as improve grid resiliency. This paper proposes an innovative EV charging station energy consumption forecasting approach by incorporating integrated renewable energy data. The optimization is achieved through the application of SARLDNet, which enhances predictive accuracy and reduces forecast errors, thereby allowing for more efficient energy allocation and load management in EV charging stations. The technique leverages comprehensive solar and wind energy statistics alongside detailed EV charging station utilization data collected over 3.5 years from various locations across California. To ensure data integrity, missing data were meticulously addressed, and data quality was enhanced. The Boruta approach was employed for feature selection, identifying critical predictors, and improving the dataset through feature engineering to elucidate energy consumption trends. Empirical mode decomposition (EMD) signal decomposition extracts intrinsic mode functions, revealing temporal patterns and significantly boosting forecasting accuracy. This study introduces a novel stem-auxiliary-reduction-LSTM-dense network (SARLDNet) architecture tailored for robust regression analysis. This architecture combines regularization, dense output layers, LSTM-based temporal context learning, dimensionality reduction, and early feature extraction to mitigate overfitting. The performance of SARLDNet is benchmarked against established models including LSTM, XGBoost, and ARIMA, demonstrating superior accuracy with a mean absolute percentage error (MAPE) of 7.2%, Root Mean Square Error (RMSE) of 22.3 kWh, and R2 Score of 0.87. This validation of SARLDNet’s potential for real-world applications, with its enhanced predictive accuracy and reduced error rates across various EV charging stations, is a reason for optimism in the field of renewable energy and EV infrastructure planning. This study also emphasizes the role of cloud infrastructure in enabling real-time forecasting and decision support. By facilitating scalable and efficient data processing, the insights generated support informed energy management and infrastructure planning decisions under dynamic conditions, empowering the audience to adopt sustainable energy practices.
Identification of Time-Wise Thermal Diffusivity, Advection Velocity on the Free-Boundary Inverse Coefficient Problem
Authors: Hussein M. S. , Dyhoum Taysir E. , Hussein S. O. , Qassim Mohammed
Published: August 2024

This paper is concerned with finding solutions to free-boundary inverse coefficient problems. Mathematically, we handle a one-dimensional non-homogeneous heat equation subject to initial and boundary conditions as well as non-localized integral observations of zeroth and first-order heat momentum. The direct problem is solved for the temperature distribution and the non-localized integral measurements using the Crank–Nicolson finite difference method. The inverse problem is solved by simultaneously finding the temperature distribution, the time-dependent free-boundary function indicating the location of the moving interface, and the time-wise thermal diffusivity or advection velocities. We reformulate the inverse problem as a non-linear optimization problem and use the lsqnonlin non-linear least-square solver from the MATLAB optimization toolbox. Through examples and discussions, we determine the optimal values of the regulation parameters to ensure accurate, convergent, and stable reconstructions. The direct problem is well-posed, and the Crank–Nicolson method provides accurate solutions with relative errors below 0.006% when the discretization elements are M=N=80. The accuracy of the forward solutions helps to obtain sensible solutions for the inverse problem. Although the inverse problem is ill-posed, we determine the optimal regularization parameter values to obtain satisfactory solutions. We also investigate the existence of inverse solutions to the considered problems and verify their uniqueness based on established definitions and theorems.
Optimization Method of Mine Ventilation Network Regulation Based on Mixed-Integer Nonlinear Programming
Authors: Wen Lixue , Zhong Deyun , Bi Lin , Wang Liguan , Liu Yulong
Published: August 2024

Mine ventilation is crucial for ensuring safe production in mines, as it is integral to the entire underground mining process. This study addresses the issues of high energy consumption, regulation difficulties, and unreasonable regulation schemes in mine ventilation systems. To this end, we construct an optimization model for mine ventilation network regulation using mixed-integer nonlinear programming (MINLP), focusing on objectives such as minimizing energy consumption, optimal regulation locations and modes, and minimizing the number of regulators. We analyze the construction methods of the mathematical optimization model for both selected and unselected fans. To handle high-order terms in the MINLP model, we propose a variable discretization strategy that introduces 0-1 binary variables to discretize fan branches’ air quantity and frequency regulation ratios. This transformation converts high-order terms in the constraints of fan frequency regulation into quadratic terms, making the model suitable for solvers based on globally accurate algorithms. Example analysis demonstrate that the proposed method can find the optimal solution in all cases, confirming its effectiveness. Finally, we apply the optimization method of ventilation network regulation based on MINLP to a coal mine ventilation network. The results indicate that the power of the main fan after frequency regulation is 71.84 kW, achieving a significant energy savings rate of 65.60% compared to before optimization power levels. Notably, ventilation network can be regulated without adding new regulators, thereby reducing management and maintenance costs. This optimization method provides a solid foundation for the implementation of intelligent ventilation systems.
Advances in Structural Health Monitoring: Bio-Inspired Optimization Techniques and Vision-Based Monitoring System for Damage Detection Using Natural Frequency
Authors: Jung Minkyu , Koo Jiyeon , Choi Andrew Jaeyong
Published: August 2024

This paper introduces the improvements in natural-frequency-based SHM by applying bio-inspired optimization methods and a vision-based monitoring system for effective damage detection. This paper proposes a natural frequency extraction method using a motion magnification-based vision monitoring system with bio-inspired optimization techniques to estimate the damage location and depth in a cantilever beam. The proposed optimization techniques are inspired by natural processes and biological evolution including genetic algorithms, particle swarm optimization, sea lion optimization, and coral reefs optimization. To verify the performance of each bio-inspired optimization method, the eigenvalues of a two-bay truss structure are used for estimating the damaged elements. Then, using the proposed video motion magnification method, the natural frequency for each undamaged and damaged cantilever beam is extracted and compared with the LDV sensor to verify the proposed vision-based monitoring system. The performance of each bio-inspired optimizer in damage detection is compared. As a result, coral reefs optimization shows the lowest average error, around 1%, in damage detection using the natural frequency.
Document-Level Event Argument Extraction with Sparse Representation Attention
Authors: Zhang Mengxi , Chen Honghui
Published: August 2024

Document-level Event Argument Extraction (DEAE) aims to extract structural event knowledge composed of arguments and roles beyond the sentence level. Existing methods mainly focus on designing prompts and using Abstract Meaning Representation (AMR) graph structure as additional features to enrich event argument representation. However, two challenges still remain: (1) the long-range dependency between event trigger and event arguments and (2) the distracting context in the document towards an event that can mislead the argument classification. To address these issues, we propose a novel document-level event argument extraction model named AMR Parser and Sparse Representation (APSR). Specifically, APSR sets inter- and intra-sentential encoders to capture the contextual information in different scopes. Especially, in the intra-sentential encoder, APSR designs three types of sparse event argument attention mechanisms to extract the long-range dependency. Then, APSR constructs AMR semantic graphs, which capture the interactions among concepts well. Finally, APSR fuses the inter- and intra-sentential representations and predicts what role a candidate span plays. Experimental results on the RAMS and WikiEvents datasets demonstrate that APSR achieves a superior performance compared with competitive baselines in terms of F1 by 1.27% and 3.12%, respectively.
Evaluation and Optimal Design of a Balanced Diet
Authors: Chen Zijian , Cai Manyang , Cao Yongshi , Zhang Kemeng , Hu Linchao , Guo Hongpeng
Published: August 2024

Malnutrition has led to growth retardation in many adolescents and health deterioration in adults all over the world. Recently, there has been increasing attention on balanced diets as a preventive measure. This study evaluates the daily diet of a student, aiming to optimize the amino acid score (AAS) across three meals per day. By using balanced diet criteria as constraints, we established a single-objective nonlinear programming model, maximizing AAS as the objective function. The model was solved by using a simulated annealing algorithm, and we obtained a diet that is both balanced and high in AAS. This study helps to raise awareness about individual nutritional needs and provides guidance for dietary structure improvements, thereby contributing to global public health enhancement.
CL-NOTEARS: Continuous Optimization Algorithm Based on Curriculum Learning Framework
Authors: Liu Kaiyue , Liu Lihua , Xiao Kaiming , Li Xuan , Zhang Hang , Zhou Yun , Huang Hongbin
Published: August 2024

Causal structure learning plays a crucial role in the current field of artificial intelligence, yet existing causal structure learning methods are susceptible to interference from data sample noise and often become trapped in local optima. To address these challenges, this paper introduces a continuous optimization algorithm based on the curriculum learning framework: CL-NOTEARS. The model utilizes the curriculum loss function during training as a priority evaluation metric for curriculum selection and formulates the sample learning sequence of the model through task-level curricula, thereby enhancing the model’s learning performance. A curriculum-based sample prioritization strategy is employed that dynamically adjusts the training sequence based on variations in loss function values across different samples throughout the training process. The results demonstrate a significant reduction in the impact of sample noise in the data, leading to improved model training performance.
Dynamic Byzantine Fault-Tolerant Consensus Algorithm with Supervised Feedback Mechanisms
Authors: Li Anqi , Yao Yingbiao , Xu Xin
Published: August 2024

Among the existing consensus algorithms, there are very few low-latency consensus algorithms that can simultaneously take into account node dynamics, fault tolerance, and scalability, and which are applicable to large-scale open scenarios where low latency is required. Therefore, this paper proposes a low-latency scalable dynamic consensus algorithm with high fault tolerance utilizing a combination of layered and threshold signature technologies, known briefly as LTSBFT. Firstly, LTSBFT achieves linear communication complexity through the utilization of threshold signature technology and a two-layer structure. Secondly, the mutual supervision feedback mechanism among nodes enables the attainment of linear complexity for reaching consensus on the faulty upper-layer nodes during the view-change. Lastly, a node dynamic protocol was proposed for the first time to support dynamic changes in the number of nodes during the consensus process. That is to say, consensus can still be reached when the number of nodes dynamically changes without interrupting the client’s request for consensus in the network. The experimental results indicate that LTSBFT exhibits lower communication latency and higher throughput compared to the classic HotStuff and PBFT algorithms. Furthermore, in comparison to double-layer PBFT, the LTSBFT has been demonstrated to have improved fault tolerance.
Hole Appearance Constraint Method in 2D Structural Topology Optimization
Authors: Zhu Lei , Zuo Tongxing , Wang Chong , Wang Qianglong , Yu Zhengdong , Liu Zhenyu
Published: August 2024

A 2D topology optimization algorithm is proposed, which integrates the control of hole shape, hole number, and the minimum scale between holes through the utilization of an appearance target image. The distance between the structure and the appearance target image is defined as the hole appearance constraint. The appearance constraint is organized as inequality constraints to control the performance of the structure in an iterative optimization. Specifically, hole shapes are controlled by matching adaptable equivalent shape templates, the minimum scales between holes are controlled by a hole shrinkage strategy, and the hole number is controlled by a hole number calculation and filling method. Based on the SIMP interpolation topology optimization model, the effectiveness of the proposed method is verified through numerical examples.
Modeling the Transmission Dynamics and Optimal Control Strategy for Huanglongbing
Authors: Liu Yujiang , Gao Shujing , Chen Di , Liu Bing
Published: August 2024

Huanglongbing (HLB), also known as citrus greening disease, represents a severe and imminent threat to the global citrus industry. With no complete cure currently available, effective control strategies are crucial. This article presents a transmission model of HLB, both with and without nutrient injection, to explore methods for controlling disease spread. By calculating the basic reproduction number (R0) and analyzing threshold dynamics, we demonstrate that the system remains globally stable when R0<1, but persists when R0>1. Sensitivity analyses reveal factors that significantly impact HLB spread on both global and local scales. We also propose a comprehensive optimal control model using the pontryagin minimum principle and validate its feasibility through numerical simulations. Results show that while removing infected trees and spraying insecticides can significantly reduce disease spread, a combination of measures, including the production of disease-free budwood and nursery trees, nutrient solution injection, removal of infected trees, and insecticide application, provides superior control and meets the desired control targets. These findings offer valuable insights for policymakers in understanding and managing HLB outbreaks.
Cross-Validated Functional Generalized Partially Linear Single-Functional Index Model
Authors: Rachdi Mustapha , Alahiane Mohamed , Ouassou Idir , Alahiane Abdelaziz , Hobbad Lahoucine
Published: August 2024

In this paper, we have introduced a functional approach for approximating nonparametric functions and coefficients in the presence of multivariate and functional predictors. By utilizing the Fisher scoring algorithm and the cross-validation technique, we derived the necessary components that allow us to explain scalar responses, including the functional index, the nonlinear regression operator, the single-index component, and the systematic component. This approach effectively addresses the curse of dimensionality and can be applied to the analysis of multivariate and functional random variables in a separable Hilbert space. We employed an iterative Fisher scoring procedure with normalized B-splines to estimate the parameters, and both the theoretical and practical evaluations demonstrated its favorable performance. The results indicate that the nonparametric functions, the coefficients, and the regression operators can be estimated accurately, and our method exhibits strong predictive capabilities when applied to real or simulated data.
Machine Learning Based Extraction of Boundary Conditions from Doppler Echo Images for Patient Specific Coarctation of the Aorta: Computational Fluid Dynamics Study
Authors: Punabantu Vincent Milimo Masilokwa , Ngoepe Malebogo , Mishra Amit Kumar , Aldersley Thomas , Lawrenson John , Zühlke Liesl
Published: August 2024

Patient-specific computational fluid dynamics (CFD) studies on coarctation of the aorta (CoA) in resource-constrained settings are limited by the available imaging modalities for geometry and velocity data acquisition. Doppler echocardiography is considered a suitable velocity acquisition modality due to its low cost and safety. This study aims to investigate the application of classical machine learning (ML) methods to create an adequate and robust approach to obtain boundary conditions (BCs) from Doppler echocardiography images for haemodynamic modelling using CFD. Our proposed approach combines ML and CFD to model haemodynamic flow within the region of interest. The key feature of the approach is the use of ML models to calibrate the inlet and outlet BCs of the CFD model. In the ML model, patient heart rate served as the crucial input variable due to its temporal variation across the measured vessels. ANSYS Fluent was used for the CFD component of the study, whilst the Scikit-learn Python library was used for the ML component. We validated our approach against a real clinical case of severe CoA before intervention. The maximum coarctation velocity of our simulations was compared to the measured maximum coarctation velocity obtained from the patient whose geometry was used within the study. Of the 5 ML models used to obtain BCs, the top model was within 5% of the maximum measured coarctation velocity. The framework demonstrated that it was capable of taking into account variations in the patient’s heart rate between measurements. Therefore, it allowed for the calculation of BCs that were physiologically realistic when the measurements across each vessel were scaled to the same heart rate while providing a reasonably accurate solution.
Mixotrophic Cultivation of Arthrospira platensis (Spirulina) under Salt Stress: Effect on Biomass Composition, FAME Profile and Phycocyanin Content
Authors: Russo Nicola Pio , Ballotta Marika , Usai Luca , Torre Serenella , Giordano Maurizio , Fais Giacomo , Casula Mattia , Dessì Debora , Nieri Paola , Damergi Eya , Lutzu Giovanni Antonio , Concas Alessandro
Published: August 2024

Arthrospira platensis holds promise for biotechnological applications due to its rapid growth and ability to produce valuable bioactive compounds like phycocyanin (PC). This study explores the impact of salinity and brewery wastewater (BWW) on the mixotrophic cultivation of A. platensis. Utilizing BWW as an organic carbon source and seawater (SW) for salt stress, we aim to optimize PC production and biomass composition. Under mixotrophic conditions with 2% BWW and SW, A. platensis showed enhanced biomass productivity, reaching a maximum of 3.70 g L−1 and significant increases in PC concentration. This study also observed changes in biochemical composition, with elevated protein and carbohydrate levels under salt stress that mimics the use of seawater. Mixotrophic cultivation with BWW and SW also influenced the FAME profile, enhancing the content of C16:0 and C18:1 FAMES. The purity (EP of 1.15) and yield (100 mg g−1) of PC were notably higher in mixotrophic cultures, indicating the potential for commercial applications in food, cosmetics, and pharmaceuticals. This research underscores the benefits of integrating the use of saline water with waste valorization in microalgae cultivation, promoting sustainability and economic efficiency in biotechnological processes.
Comparison of Dye Spread Pattern and Nerve Involvement between Suprainguinal and Infrainguinal Fascia Iliaca Blocks with Different Injectate Volumes: A Cadaveric Evaluation
Authors: Cho Tae-Hyeon , Jun Byongnam , Yang Hun-Mu , Kim Shin Hyung
Published: August 2024

Background and Objectives: Fascia iliaca compartment block (FICB) is an effective and relatively safe technique in perioperative pain management for hip surgery. However, blockade of the obturator nerve (ON) using this technique remains controversial. This study aimed to compare dye spread patterns and nerve involvement in the suprainguinal FICB (S-FICB) and infrainguinal FICB (I-FICB) approaches using different volumes of dye. Materials and Methods: Following randomization, 6 S-FICBs and 6 I-FICBs were performed on the left or right sides of 6 unembalmed cadavers. For each block, 30 mL or 60 mL of dye solution was injected. The extent of dye spreading and the staining pattern in the lumbar plexus branches were investigated using anatomical dissection. Results: Twelve injections were successfully completed. The lateral femoral cutaneous nerve (LFCN) and femoral nerve (FN) were consistently stained in all injections. Extended dye spread toward lumbar plexus branches was observed volume-dependently in S-FICBs. However, I-FICBs with an increased volume only showed dye spreading in the caudad direction limited to within the fascia iliaca. When 30 mL of dye was used, the ON was not stained with either approach. A stained ON was only observed in S-FICBs when 60 mL of dye was used. Conclusions: In this cadaveric evaluation, the ON was not stained in either FICB approach with the volume of injectate commonly used in clinical practice. The S-FICBs but not I-FICBs using a high volume of injectate resulted in extended spreading to the lumbar plexus branches.
A Prevalence Anatomic-Imaging Study of the Posterior Inferior Cerebellar Artery’s Origin
Authors: Davidoiu Ana-Maria , Rusu Mugurel Constantin , Toader Corneliu , Rădoi Petrinel Mugurel
Published: August 2024

Background and Objectives: Typically, the vertebral arteries (VAs) enter the posterior fossa through dural rings and further unite, forming the basilar artery. The posterior inferior cerebellar artery (PICA) is usually a branch of the V4 segment of the VA (intradural origin). It may also leave the V3 suboccipital segment of the VA (extradural origin). The transdural origin of the PICA within the VA’s dural ring has been consistently overlooked. A study was designed to determine the topographical patterns of the PICA’s origin. Materials and Methods: Determinations were performed in a retrospective sample of 225 computed tomography angiograms. Four types of PICA origin were documented: type 0, absent PICA; type 1, the extradural origin of the PICA from the V3 segment of the VA; type 2, the transdural origin of the PICA within the dural ring; and type 3, the intradural origin of the PICA from the V4 segment of the VA. The bilateral symmetry of types was also investigated. Results: Out of 450 VAs, type 0 (absent PICA) was found in 36%, type 1 (extradural) in 0.44%, type 2 (transdural) in 5.56%, and typical type 3 in just 58%. In types 1 and 2, the PICA entered the posterior fossa through the dural ring and the marginal sinus. In the overall group (N = 225), the type combinations 1_1, 1_2 and 1_3 were not found. Bilaterally absent PICAs occurred in 18.67%. The bilateral combinations 0_1/0_2/0_3/2_2/2_3/3_3 were found, respectively, in 0.89%/3.11%/30.67%/1.78%/4.44%/40.44%. Four of the seventy-eight PICAs opposite to an absent one, three intradural and one transdural, were true bihemispheric PICAs. Conclusions: The PICAs with extradural or transdural origins are facultative contents of the dural ring and are at risk during neurosurgical approaches in the foramen magnum. Rare bihemispheric PICAs could originate either intradurally or within the dural ring.
Correction: Clarkson et al. Evaluating Strategies to Increase the Number of Women Working in the UK Surveying Profession. Merits 2023, 3, 263–296
Authors: Clarkson Sinéad , Hind Lucy , Zulu Sambo Lyson
Published: August 2024

In the original publication [...]
On the Mechanical Behavior of LP-DED C103 Thin-Wall Structures
Authors: Colón Brandon , Pourjam Mehrdad , Demeneghi Gabriel , Hazeli Kavan , Mireles Omar , Medina Francisco
Published: August 2024

Laser Powder Directed Energy Deposition (LP-DED) can produce thin-wall features on the order of 1 mm. These features are essential for large structures operating in extreme environments such as regeneratively cooled nozzles and heat exchangers, which often make use of refractory metals. In this work, the mechanical behavior of LP-DED C103 was investigated via quasi-static tensile testing and low cycle fatigue (LCF) testing. The effects of vacuum stress relief (SR) and hot isostatic pressing (HIP) heat treatments were investigated for specimens in the vertical and horizontal build orientations during tensile testing. The AB and SR properties were lower than literature values for wrought and laser powder bed fusion (L-PBF) bulk components but higher than electron beam powder bed fusion (EB-PBF). The application of a HIP cycle improved strength by 7% and ductility by 27% past the initial as-built condition. Fracture images reveal that interlayer stress concentration sites are responsible for fracture in specimens in the vertical orientation. Meanwhile, fracture in the horizontal specimens mainly propagates at a slanted angle typical of plane stress conditions. The LCF results show cycles to failure ranging from 100 cycles to 8000 cycles for max strain levels of 2% and 0.5%, respectively. Fractography on the fatigue specimens reveals an increasing propagation zone as max strain levels are increased. The impact of these findings and future work are discussed in detail.
Effect of Carbon Content on the Phase Composition, Microstructure and Mechanical Properties of the TiC Layer Formed in Hot-Pressed Titanium-Steel Composites
Authors: Grad Marius , Zentgraf Jan , Schultheiss Ulrich , Esper Lukas , Diemar Andreas , Noster Ulf , Spiess Lothar
Published: August 2024

During the hot pressing of pure titanium and different carbon steels in a temperature range of ϑ = 950–1050 °C, a compound layer up to dL≈10 μm thick is formed at the titanium–steel interface. With a higher carbon content of the used steel, the layer thickness increases. The carbon concentration within the layer is in the range of stoichiometry for TiC. Apart from TiC, no other phases can be detected by X-ray diffraction (XRD) measurements inside the formed layer. The calculation of the activation energy for the TiC layer formation is Q = 126.5–136.7 kJ mol−1 and is independent of the carbon content of the steel. The resulting microstructure has a grain size gradient, wherein the mechanical properties, such as hardness and Young‘s modulus, are almost constant. Statistical analysis using Response Surface Methodology (RSM) indicates that the carbon content of the steel has the most significant influence on layer thickness, followed by annealing temperature and annealing time. By selecting the appropriate carbon steel and the subsequent removal of the steel, it is possible to produce targeted TiC layers on titanium substrates, which holds enormous potential for this material in wear-intensive applications.
Effects of the Primary Carbide Distribution on the Evolution of the Grain Boundary Character Distribution in a Nickel-Based Alloy
Authors: Xia Shuang , Ma Yuanye , Bai Qin
Published: August 2024

Grain boundary engineering (GBE) was carried out on a nickel-based alloy (GH3535, Ni-16Mo-7Cr-4Fe), which intrinsically has many strings of primary molybdenum carbides. The strings induce inhomogeneous grain size distributions and increase the difficulties in achieving a GBE microstructure. In this work, the effects of the primary carbide distribution on the grain boundary network (GBN) evolution were investigated. A higher proportion of Σ3n grain boundaries (GBs) associated with extensive multiple twinning events was achieved in the specimen with more dispersive and finer primary carbides, which are the results of cross-rolling, i.e., cold rolling with a changed direction. In a starting microstructure with many strings of primary carbides, the dense and frequent occurrence of particle-stimulated nucleation (PSN) around the carbides induced more general high-angle GBs into the GBN, and the inhibition of GB migrations by the carbide strings suppressed the formation of large-sized highly twinned grain clusters. As a consequence, the Σ3n GBs could not be effectively enhanced.
A New Critical Plane Multiaxial Fatigue Criterion with an Exponent to Account for High Mean Stress Effect
Authors: Abasolo Mikel , Pallares-Santasmartas Luis , Eizmendi Martin
Published: August 2024

The mean stress effect remains a critical aspect in multiaxial fatigue analysis. This work presents a new criterion that, based on the classical Findley criterion, applies a material-dependent exponent to the mean normal stress term and includes the ultimate tensile stress as a fitting parameter. This way of considering the non-linear effect of the mean stress, with a material-dependent rather than a fixed exponent, is totally innovative among the multiaxial fatigue criteria found in the literature. In order to verify its accuracy, the new criterion has been checked against an extended version of the Papuga database of multiaxial experimental tests with 485 results, and compared with the criteria by Findley, Robert, and Papuga. The new criterion provides outstanding results for pure uniaxial cases, with multiaxial performance similar to the Robert criterion with a smaller range of error and a conservative trend, even surpassing the popular Papuga method in several relevant loading scenarios. These features enhance the applicability and versatility of the criterion for its use in the fatigue design of structural components.
Evaluation of the Antifungal and Biochemical Activities of Fungicides and Biological Agents against Ginseng Sclerotinia Root Rot Caused by Sclerotinia nivalis
Authors: Feng Shi , Wang Chunlin , Xu Zhaoyang , Dou Baozhu , Wang Xue , Yang Lina , Lu Baohui , Gao Jie
Published: August 2024

The objective of this study was to identify effective agents for the prevention and control of ginseng Sclerotinia root rot disease caused by Sclerotinia nivalis. The inhibitory effects of 16 chemical fungicides and 10 biocontrol agents (strains) on mycelial growth and sclerotium formation in S. nivalis were determined using a plate confrontation essay. The results showed that the best chemical agents for inhibiting the mycelial growth and sclerotium formation of S. nivalis were fluconazole and fludioxonil, while Bacillus amyloliquefaciens FS6 and B. subtilis (Kono) were the best biocontrol agents (strains). The results of field trials in 2022 and 2023 showed that the control effects of fluconazole and fludioxonil on ginseng Sclerotinia root rot disease were 90.60–98.16%, and those of the biocontrol agents B. amyloliquefaciens FS6 and B. subtilis (Kono) were 94.80–97.24%, respectively. Chemical agents produced abnormal and twisted mycelia, while the biocontrol agents increased mycelial branching, dilated the mycelium tip, and revealed an abnormal balloon. All of the fungicides decreased the ergosterol content, changed the cell membrane permeability, and increased the protein and nucleic acid permeability. These results suggest that these are potential agents for controlling ginseng Sclerotinia root rot disease, and their biochemical mechanisms of chemical and biocontrol of this disease were demonstrated.