C-C motif chemokine receptor-8 (CCR8) belongs to class A of G protein-coupled receptors (GPCRs). CCR8 interacts with the specific chemokine ligand CCL1/I-309 in humans, which is produced by various cells, including tumor-associated macrophages and regulatory T cells (Treg). CCR8 is highly expressed on Treg and T-helper 2 (T_H2) cells recruited to the inflammation site and is implicated in allergy, asthma, and cancer progression. The CCR8⁺Treg has been suggested to be an important regulator in the immunosuppressive tumor microenvironment (TME); therefore, it has been desired to develop sensitive monoclonal antibodies (mAbs) for CCR8. This study developed a specific mAb for human CCR8 (hCCR8), which is useful for flow cytometry by employing the Cell-Based Immunization and Screening (CBIS) method. The established anti-hCCR8 mAb, C₈Mab-21, (mouse IgM, kappa) reacted with hCCR8-overexpressed Chinese hamster ovary-K1 (CHO/hCCR8) cells, TALL-1 (acute T lymphoblastic leukemia), CCRF-HSB2 (human T-lymphoblastic leukemia), and natural killer cells, which express endogenous hCCR8 by flow cytometry. Furthermore, C₈Mab-21 demonstrated a moderate binding affinity for CHO/hCCR8 and TALL-1 with a dissociation constant of 6.5×10^-8 M and 2.0×10^-8 M, respectively. C₈Mab-21, which was established by the CBIS method, could be a useful tool for analyzing the hCCR8-related biological response using flow cytometry.

The aim of article is to design a calm water resistance predictor based on Machine Learning Tools and development of a systematic series for battery-driven catamaran hull forms. Regression Trees (RT), Support Vector Machines (SVM), and Artificial Neural Network (ANN) regression models are applied for dataset training on developed systematic series of catamarans. A hullform optimization was implemented for various catamarans including dimensional and hull coefficient parameters based on resistance and structural weight reduction and battery performance improvement. This paper provides a diverse database of catamaran hullform. Hence, an automated Matlab program was coded for geometry generation and cost function evaluation. Design distribution based on Lackenby transformation fulfills all design space and sequentially a novel self-blending method reconstructs new hullforms based on two parents blending. Finally, a machine learning approach was conducted on generated data of case study. This study shows that ANN algorithm correlates well with the measured resistance. Accordingly, a general and unique tool is proposed for optimized and desired design in first design stage.

Determining the level of PE/VC market development and the way it operates is extremely difficult. Despite countless reports and analyses concerning the PE/VC market, there is a lack of study on that sector based on a synthetic index. The usage of taxonomic methods in the PE/VC market analysis may be extremely useful in its proper characterizing. An in-depth study allows the identification of weaknesses and strengths as well as opportunities and threats related to a specific stage of PE/VC market development in a given country, that gives the opportunity to determine public activities that could affect the acceleration of this market development in future. The main objective of the study was to conduct a taxonomic analysis of the level of PE/VC market development in Europe, in 2010–2015. It is worth mentioning that the term “level of development” does not refer only to the value of investment or its dynamics, but to a set of objectified features that create one synthetic indicator. The conducted analysis using the Hellwig method allowed to determine the ranking of European countries in terms of the level of PE/VC market development, which reveal some significant differences in the functioning of the this market within Europe. Further detailed analysis of the designated groups of countries made it possible to determine the characteristics of such clusters. The construction of the level of private equity/venture capital market development ranking over the years enabled defining the current status and indicating the direction of possible changes.

This mixed-methods research article explores the influence of ChatGPT, a state-of-the-art AI language model, on the development of maladaptive learning habits in users. The study combines quantitative and qualitative data to provide a comprehensive understanding of how engaging with ChatGPT may inadvertently foster detrimental learning behaviors. Findings suggest that while ChatGPT can be a valuable learning tool, it has the potential to exacerbate or create bad learning habits if not used mindfully.

Rheumatoid arthritis (RA) is a chronic, systemic, abnormal inflammatory immune response. It is characterized by the involvement of the synovium and multiple organs and the destruction of joints and articular cartilage. Over the past 30 years, several promising novel compounds and antibodies have been developed for the treatment of RA. The introduction of new drugs and precision medicine for all forms of RA raises several issues related to access to novel treatments by patients, optimal regimen selection, cost-effectiveness, prognosis monitoring and outcome surveillance, particularly with regarding to the development of low drug response rates, drug resistance and adverse side effects. Tremendous attention has been given to the identification of optimized drug combinations for the treatment of RA, particularly in early high-risk vulnerable and early individuals. Addressing these issues requires novel therapeutic approaches with new mechanisms and the establishment of accurate guidelines for drug selection, drug recombination, and non-chemical therapeutic efforts. In this study, we reviewed the most exciting recently established or ongoing novel drugs and methods according to the clinical trial database maintained by the United States National Library of Medicine and discussed the trends in RA drug development and challenges in the treatment, providing a reference significant for the accurate treatment of RA and the research direction in the future.

Rivaroxaban and aspirin are commonly used antithrombotic agents that are used in combination for the prevention of coronary artery disease (CAD) and atherothrombotic events in adult patients after an acute coronary syndrome (ACS) with elevated cardiac biomarkers, or with coronary artery disease (CAD) or symptomatic peripheral artery disease (PAD) in high-risk patients. The recommended dosage is 2.5 mg Rivaroxaban twice daily with 75–100 mg aspirin daily. This study aimed to develop a fixed-dose combination tablet of rivaroxaban (2.5 mg) and aspirin (50 mg) to decrease the patient's pill burden and enhance medication adherence. The product formula was developed based on compatibility studies among the active ingredients and the active ingredients' compatibility studies. The formula and the manufacturing procedure were chosen based on the risk assessment for each active substance, wet granulation with both actives intragranular was found to have faster dissolution than direct mix formulae. Furthermore, a validated reverse-phase HPLC stability indicator method was developed to detect APIs and their possible degradants in the formula.

Brushless Direct Current Motors are significantly improved for using many electrical applications. The design investigation is increased year by year and most of the motor design researchers find out the superior efficiency of the BLDC motor. The traditional motor consumed more electrical power than BLDC motor. The design of BLDC motors that are compact, small, easy to control, save energy and light weight. Due to this fact, the design of BLDC motor should be analyzed to improve the motor characteristics performance. The research focuses on the design of BLDC motor performance using Taguchi Method and Response Surface Methodology (RSM) for Finite Element Method (FEM). Firstly, the Taguchi Method is applied to identify the optimization point of design parameters and consider using the reference model of actual BLDC electric fan motor with a rated power of 26Watts design. Based on the Taguchi Method to create the RSM is used to two-step optimization design performance using in FEM analysis. The performances of motor design proposed approach to provide the effeteness for motor design investigator. In this investigation aims the better structural performances in output power, torque, efficiency and torque ripple. The final design of proposed model achieved an energy efficiency is 10% higher than the reference design of prototype modes, the output power result is 10W higher this reference design. We can reduce the loss of the proposed motor design. Furthermore, the maximum torque can also increase the 0.032Nm of that design. The design simulation results were produced and studied by the FEM analysis of JMAG software. From the evaluation results, the configurations of reference model design are compared with the proposed design by using the three methods of motor design optimization showed the best characteristics for performance of motor investigation design.

With the rapid development of the economy and the transformation of social contradictions, the economic development model in China has also changed, and a high-quality economic development model has been proposed. This paper uses the entropy method to measure the level of innovation and high-quality economic development in nine prefecture-level cities in Fujian Province from 2011 to 2020, and finds that the overall level shows an increasing trend year by year, but there are obvious regional differences. By constructing a grey correlation degree model, it is found that the innovation subsystem and the high-quality economic development are strongly correlated, the development level of innovation and the subsystem of high-quality economic development are both strongly correlated, the grey correlation degree from high to low is green, coordinated, shared, and livelihood, indicating that innovation has a significant impact on high-quality economic development. Based on the research results, countermeasures and suggestions are proposed to promote the coordinated development of innovation and high-quality economy.

China's economic development has shifted from high speed to high quality. This study constructs an evaluation index for China’s high-quality development utilizing a new development concept. The entropy method and cluster analysis were used to assess the level of high-quality development in China between 2015 and 2021. Kernel density estimation method was employed to explore spatiotemporal dynamics. The results reveal significant differences in the level of economic development among provinces in China; the eastern region is significantly better than the central, northeastern, and western regions. From the perspective of the sub-item evaluation, challenges with uneven, uncoordinated, and insufficient economic development exist in various Chinese provinces.

An imperative challenge emerges from the demand to construct a scientific method to recent agricultural and rural policies throughout the world. The objective of the present study is to conduct an ex-post quantitative evaluation of the Comprehensive Rural Village Development Project, a representative rural development project operated by the Ministry of Agriculture, Food and Rural Affairs, a central government agency in South Korea. The primary purpose of the project is to ensure sustainable rural society. This study found a moderate but significant positive impact of the policy in enhancing the standard of living in rural areas. The present study concludes with suggesting some policy implications and future directions of policy evaluation studies.

The current study sets out to develop an empirical line method (ELM) radiometric calibration framework for reducing atmospheric contributions in UAS imagery and for producing scaled remote sensing reflectance imagery. Using a MicaSense RedEdge camera flown on a custom-built octocopter, the research reported herein finds that atmospheric contributions have an important impact on UAS imagery. Data collected over the Lower Pearl River Estuary in Mississippi during five week-long missions covering a wide range of environmental conditions was used to develop and test a simplified ELM radiometric calibration framework designed specifically for the reduction of atmospheric contributions to UAS imagery in studies with limited site accessibility or data acquisition time constraints. The framework was effective in reducing atmospheric and other external contributions to UAS imagery. Unique to the proposed radiometric calibration framework is the radiance to reflectance conversion conducted externally from the calibration equations which allows for the normalization of illumination independent from the time of UAS image acquisition and from the time of calibration equations development. This paper presents the simplified ELM radiometric calibration framework that can be used as a time-effective calibration technique to reduce errors in the UAS imagery.

Toward the standardization of O. biennis sprout extract (OBS-E), we aimed to obtain indicator compounds, using a validated method. HPLC-UVD allowed simultaneous quantification of indicator compounds quercetin 3-glucuronide and ellagic acid. The method was validated in terms of specificity, linearity, precision, accuracy, and limit of detection/limit of quantification (LOD/LOQ). High specificity and linearity was demonstrated, with correlation coefficients of 1.0000 for quercetin 3-glucuronide and 0.9998 for ellagic acid. The LOD/LOQ values were 0.486/1.472 μg/mL for quercetin 3-glucuronide and 1.003/3.039 μg/mL for ellagic acid. Intra-day and inter-day variability tests produced relative standard deviation for each compound of <2%, a generally accepted precision criterion. High recovery rate were also obtained, indicating accuracy validation. The OBS-E prepared using various concentrations of ethanol were then analyzed. The 50% ethanol extract had highest content of quercetin 3-glucuronide, whereas the 70% ethanol extract possessed the lowest. However, the ellagic acid content was highest in the 70% ethanol extract and lowest in the 90% ethanol extract. Thus, quercetin 3-glucuronide and ellagic acid can be used industrially as indicator compounds for O. biennis sprout products, and our validated method can be used to establish indicator compounds for other natural products.

This article describes the Analytic Hierarchy Process (AHP) method which can be calculated by several methods. The AHP method is essential for the managerial decision-making to recognize which method is efficient for the calculation and to determine the proper order of criteria. In the article there are included methods that can be used in order to calculate the matrix in the AHP process for setting criteria such as Geometric mean, Arithmetic mean, Row sum of the adjusted the Saaty matrix, Reverse sums of the Saaty matrix columns and Row sums of the Saaty matrix. The paper is focused on the accuracy of the methods used. The results show that the most accurate method is the Saaty method, and the second most accurate method is the geometric mean in order to determine the ranking. This method is easier to use when the same order is achieved as in the Saaty method, followed by Geometric mean, which is favourable for fast and easy determination of alternatives using in the AHP process. The survey carried out among managers graduated from Universities and Colleges in Slovakia showed that the respondents considered the Saaty method as the most complex and also the most difficult method and the geometric mean average method the simplest method. 44% of respondents stated that they are capable to use a program to calculate the AHP. 46% of respondents said they had experience with some method related to the managerial decision-making process. 72% of managers regarded as important to manage some method for decision-making in their managerial position.

Developing new energy is critical to China's green and low-carbon development. And the New Energy Demonstration City Policy (NEDCP) is a vital innovation policy that inspires the development of new energy. Is the NEDCP facilitating green and low-carbon development, if so, how? Employing unbalanced panel data from 2003 to 2017, the impact of NEDCP on green and low-carbon development was studied by using the "difference in difference" (DID) model. We find that this policy can take significantly role. After various robustness tests, our results are still valid. According to the heterogeneity analysis, non-resource and non-old industrial base cities have a greater positive impact from this policy. The mechanism analysis denotes that the positive policy effect works through upgrading the industrial structure and stimulating urban innovation. The substantial empirical evidence presented in this article supports the continued promotion and implementation of new energy demonstration cities.

Individual investors often struggle to predict stock prices due to the limitations imposed by the computational capacities of personal laptop Graphics Processing Units (GPUs) when running intensive deep learning models. This study proposes solving these GPU constraints by integrating deep learning models with technical analysis methods. This integration significantly reduces analysis time and equips individual investors with the ability to identify stocks that may yield potential gains or losses in an efficient manner. Thus, a comprehensive buy and sell algorithm, compatible with average laptop GPU performance, is introduced in the study. This algorithm offers a lightweight analysis method that emphasizes factors identified by technical analysis methods, thereby providing a more accessible and efficient approach for individual investors. To evaluate the efficacy of this approach, we analyzed the performance of eight deep learning models: LSTM (4 layers), CNN, BiLSTM (4 layers), CNN Attention, BiGRU CNN BiLSTM Attention, BiLSTM Attention CNN, CNN BiLSTM Attention, and CNN Attention BiLSTM. These models were used to predict stock prices for Samsung Electronics and Celltrion Healthcare. The CNN Attention BiLSTM model displayed superior performance among these models, with the lowest validation mean absolute error value. In addition, an experiment was conducted using WandB Sweep to determine the optimal hyperparameters for four individual hybrid models. These optimal parameters were then implemented in each model to validate their back-testing rate of return. The CNN Attention BiLSTM hybrid model emerged as the highest-performing model, achieving an approximate rate of return of 5 percent. Overall, this study offers valuable insights into the performance of various deep learning and hybrid models in predicting stock prices. These findings can assist individual investors in selecting appropriate models that align with their investment strategies, thereby increasing their likelihood of success in the stock market.

This study examines the optimization of ground source heat pump (GSHP) systems and energy piles for sustainable urban development, with a focus on Southeast Asia. GSHPs, which utilize geothermal energy for indoor HVAC needs, offer a sustainable alternative to traditional systems by utilizing consistent subsurface temperatures for heating and cooling. The study highlights the importance of understanding thermal movement within the soil, especially in soft marine clays prevalent in Southeast Asia, to improve GSHP system efficiency. Using a one-dimensional finite-difference model, the study examines the effects of soil thermal conductivity and density on system performance. The results show that GSHP systems, especially when integrated with energy piles, significantly reduce electricity consumption and greenhouse gas emissions, underscoring their potential to mitigate the urban heat island effect in densely populated areas. Despite challenges posed by the region's hot and humid climate, which could affect long-term effectiveness, the study highlights the need for further study, including field experiments and advanced modeling techniques, to optimize GSHP configurations and fully exploit geothermal energy in urban environments. The study's insights into soil thermal dynamics and system design optimization contribute to the advancement of sustainable urban infrastructure development.

Every firm is devoted to polishing and fine-tuning its maintenance strategy. More exactly, when we develop robust and efficient preventive and conditional maintenance approaches, our objective is to combine performance and robustness to increase maintenance decision-making. This, in turn, leads to a reduction in system failures and a subsequent drop in related expenses. This impulse drives the origin of this essay: to provide fresh criteria that allow us to improve and optimize maintenance techniques. Furthermore, our study has been realistically utilized via the deployment of condition-based maintenance (CBM) techniques, which are commonly regarded as a critical lever that allows firms to gain a competitive advantage in the context of the fourth industrial revolution. Combining both perfect and imperfect maintenance actions into CBM strategies appears to be an effective technique to obtain high economic performance and robustness in the maintenance strategies of the companies. Following this idea, we propose and optimize in this paper a set of criteria enabling the combined assessment of the mean economic performance and the resilience of various kinds of maintenance techniques. The advantage of the proposed criterion is that it adapts to different types of maintenance strategies and provides access to a simple and relevant evaluation model. Thanks to this new criterion, we will be able to choose the better maintenance strategies that have more performance and robustness between these strategies for a system with more or less stable behavior. The Monte Carlo Method is incorporated into the comparative assessment of maintenance methods, adding a layer to the evaluation of the performance and robustness of each adaptation in decision-making.

The first part of the paper was aimed at analyzing the given nonlinear problem by different methods of computation of the Lyapunov exponents (Wolf method [1], Rosenstein method [2], Kantz method [3], method based on the modification of a neural network [4, 5], and the synchronization method [6, 7]) for the classical problems governed by difference and differential equations (Hénon map [8], hyper-chaotic Hénon map [9], logistic map [10], Rössler attractor [11], Lorenz attractor [12]) and with the use of both Fourier spectra and Gauss wavelets [13]. It was shown that a modification of the neural network method [4, 5] makes it possible to compute a spectrum of Lyapunov exponents, and then to detect a transition of the system regular dynamics into chaos, hyper-chaos, hyper hyper-chaos and deep chaos [14-16]. Different algorithms for computation of Lyapunov exponents were validated by comparison with the known dynamical systems spectra of the Lyapunov exponents. The carried out analysis helps comparatively estimate the employed methods in order to choose the most suitable/optimal one to study different kinds of dynamical systems and different classes of problems in both this and the next paper parts.

The presence of antibiotic residues in honey has recently become a growing public health concern due to the overuse of these products in beekeeping and their transfer to the food industry. The aim of this study was to develop and validate a simple, selective and sensitive method for the simultaneous identification and quantification of residues of twenty-two sulfonamides, trimethoprim and dapsone in honey using a rapid and reliable liquid chromatography–tandem mass spectrometry (LC–MS/MS) method. The developed method showed good linearity, specificity, precision (repeatability and intra-laboratory reproducibility) and recovery. The decision limit (CCalpha) ranged between 2.16-22.12 µg/kg for CCα, while the detection capability (CCbeta) was between 2.16- 2.67-27.36 µg/kg. The method proved to be rapid, reliable and effective for the determination of sulfonamides, trimethoprim and dapsone from honey samples. The average recoveries were estimated to be between 70 and 106%. The relative standard deviations (RSDs) were between 6-18%. In practice, the method is essential to check the honey sold on the market and ensure its safety before it reaches the consumer.

The city-port involves a decisive reality for the economic development of the territories and nations, capable of significantly influencing the conditions of well-being and quality of life, and of making the Circular City Model operational, preserving and enhancing seas and marine resources in a sustainable way, through the construction of appropriate production and consumption models, with attention to relations with the urban and territorial system. The Circular Economy paradigm identifies the ideal context in the city-port to rethink traditional development models and make ports driver areas for the regeneration of the city and metropolitan territories, in compliance with the EU Directive 2014/89 which considers maritime spatial planning as a tool for public authorities and stakeholders to achieve an integrated approach, promoting the development of maritime and coastal economies and the sustainable use of resources. The paper, starting from these assumptions, presents an adaptive decision-making process for the strategies development of the Naples (Italy) commercial port, aimed at re-establishing a sustainable city-port relationship and making operative Circular Economy principles.

The first paper of this two-part series paper discussed the development of a novel lightweight 3D wood dowel-reinforced glass epoxy hybrid composite material (3DdrFRP) and its manufacturing procedures. It also experimentally compared the performance of scaled utility poles made from conventional 2D E-glass epoxy and 3DdrFRP materials. In the second part, we developed robust finite element (FE) models in LS-DYNA, calibrated them based on experimental results, and used them to simulate the performance of equivalent 2D and 3D poles, proving the integrity of the numerical model. Additionally, we developed a simplified analytical calculation method for engi-neers to determine the stiffness of 3D-DrFRP poles accurately and quickly.

The properties of iterative splitting methods with serial versions have been analyzed since recent years, see [1] and [3]. We extend the iterative splitting methods to a class of parallel versions, which allow to reduce the computational time and keep the benet of the higher accuracy with each iterative step. Parallel splitting methods are nowadays important to solve large problems, which can be splitted in subproblems and computed independently with the dierent processors. We present a novel parallel iterative splitting method, which is based on the multi-splitting methods, see [2], [10] and [15]. Such a exibilisation with multisplitting methods allow to decompose large iterative splitting methods and recover the benet of their underlying waveform-relaxation (WR) methods. We discuss the convergence results of the parallel iterative splitting methods, while we could reformulate such an error to a summation of the individual WR methods. We discuss the numerical convergence of the serial and parallel iterative splitting methods and present dierent numerical applications to validate the benet of the parallel versions.

This paper reviews numerical methods used to simulate desiccation cracks in clayey soils. It examines five numerical approaches: Finite Element (FEM), Lattice Boltzmann (LBM), Discrete Element (DEM), Cellular Automaton (CAM), and Phase Field (PFM) Methods. The FEM is widely used to capture moisture diffusion, shrinkage, and cracking during drying. LBM is used to simulate fluid flow in clayey soils, while the DEM focuses on capturing the behavior of individual particles and their interactions. CAM simplifies crack evolution with computational efficiency, while PFM provides a continuous representation of crack formation and propagation. The author discusses the complexity of the problem, the continuum mechanics governing and constitutive equations that describe it, and the influence of various factors such as the multiphase nature of soils, heterogeneity, nonlinearities, coupling, scales of analysis, and computational aspects. The review highlights the characteristics, strengths, and limitations of each method. It emphasizes the importance of appropriate method selection for every problem depending on the aim of the analysis. The article concludes by reviewing the integration of multiple numerical methods to enhance the simulation of desiccation cracks in clayey soils.

Fixed offshore wind turbines continue to be developed for high latitude areas where not only wind and wave loads need to be considered, but also moving sea ice. Current rules and regulations for the design of fixed offshore structures in ice-covered waters do not adequately consider effects of ice loading and its stochastic nature on fatigue life of the structure. Ice crushing on such structures results in ice-induced vibrations, which can be represented by loading the structure using a variable-amplitude loading (VAL) sequence. Typical offshore load spectra are developed for wave and wind loading. Thus, a combined VAL spectrum is developed for wind, wave, and ice action. To this goal, numerical models are used to simulate the dynamic ice-, wind-, and wave-structure interaction. The stress time-history at an exemplarily selected critical point in an offshore wind energy monopile support structure is extracted from the model and translated into a VAL sequence, which can then be used as a loading sequence for the fatigue assessment or fatigue testing of welded joints of offshore wind turbine support structures. This study presents the approach to determine combined load spectra and standardized time series for wind, wave, and ice action.

Abstract—Online learning platforms provide diverse course resources, but this often result in the issue of information overload. Learners always want to learn courses that are appropriate for their knowledge level and preferences quickly and accurately. Effective course recommendation plays a key role in helping learners select appropriate courses and improving the efficiency of online learning. However, when a user is enrolled in multiple courses, Existing course recommendation methods face the challenge in accurately recommending the target course that is most relevant to the user, because of the noise courses. In this paper, we propose a novel reinforcement learning model named Actor-Critic Hierarchical Reinforcement Learning (ACHRL). The model incorporates the Actor-Critic method to construct the profile reviser. This can remove noise courses and make personalized course recommendation effectively. Furthermore, we propose a policy gradient based on temporal difference error to reduce the variance in the training process, to speed up the convergence of the model, and improves the accuracy of the recommendation. We evaluate the proposed model on two real datasets, and the experimental results show that the proposed model is significantly outperforms the existing recommendation models (improving 3.77% to 13.66% in terms of HR@5).

The goal of this paper is to propose a dual version of the direct cosine simplex algorithm (DDCA) for general linear problems. Unlike the two-phase and the big-M methods, our technique does not involve artificial variables. Our technique solves the dual Klee-Minty problem in two iterations and solves the dual Clausen’s problem in four iterations. The utility of the proposed method is evident from the extensive computational results on test problems adapted from NETLIB. Preliminary results indicate that this dual direct cosine simplex algorithm (DDCA) reduces the number of iterations of two-phase method.

Scanning underwater areas, using magnetometers, in search of unexploded ordnance is a difficult challenge, where machine learning methods can find a significant application. However, this requires the creation of a set enabling the training of prediction models. Such a task is difficult and costly due to the limited availability of relevant data. To meet this challenge in the article, we propose the use of numerical modeling to solve this task. The conducted experiments allow us to conclude that it is possible to obtain high compliance of the numerical model with the results of physical tests. In addition, the paper discusses the methodology of simplifying the computational model, allowing for almost three times reduction of the calculation time. In addition, in the work we present the methodology of creating an appropriate data set, enabling the generation of any number of training samples.

Ship wave is of great interest for wave drag, coastal erosion and ship detection. In this paper, a highly-paralleled numerical scheme is proposed for simulating three-dimensional (3-D) nonlinear Kelvin ship waves effectively. First, a numerical model for nonlinear ship waves is established based on potential flow theory, boundary integral method and Jacobian-free Newton-Krylov (JFNK) method. In order to improve computational efficiency and reduce data storage of JFNK method, a banded preconditioner method is then developed by formulating the optimal bandwidth selection rule. After that, a Graphics Process Unit (GPU) based parallel computing framework is designed, and a GPU solver is developed by using Compute Unified Device Architecture (CUDA) language. Finally, numerical simulations of 3-D nonlinear ship waves under multiple scales are performed by using the GPU and CPU solvers. Simulation results show that the proposed GPU solver is more efficient than the CPU solver with the same accuracy. More than 66% GPU memory can be saved and the computational speed can be accelerated up to 20 times. Hence, the computation time for Kelvin ship waves simulation can be significantly reduced by applying the GPU parallel numerical scheme, which lays a solid foundation for practical ocean engineering.

Based on previous research results, we propose a new preprocessing HSS iteration method (PHSS) for the generalized Lyapunov equation. At the same time, the corresponding inexact PHSS algorithm (IPHSS) is given from the angle of application. All the new methods presented in this paper have given the corresponding convergence proof. The numerical experiments are carried out to compare the new method with the existing methods, and the improvement effect is obvious. The feasibility and effectiveness of the proposed method are proved from two aspects of theory and calculation.

The theoretical limit of link prediction is a fundamental problem in this field. Taking the network structure as object to research this problem is the mainstream method. This paper proposes a new viewpoint that link prediction methods can be divided into single or combination methods, based on the way they derive the similarity matrix, and investigates whether there a theoretical limit exists for combination methods. We propose and prove necessary and sufficient conditions for the combination method to reach the theoretical limit. The limit theorem reveals the essence of combination method that is to estimate probability density functions of existing links and nonexistent links. Based on limit theorem, a new combination method, theoretical limit fusion (TLF) method, is proposed. Simulations and experiments on real networks demonstrated that TLF method can achieve higher prediction accuracy.

Artificial intelligence algorithms have gained widespread adoption in the field of air conditioning load prediction. However, their prediction accuracy is substantially influenced by the quality of training samples. Training samples that lack relevance to the predicted moments can introduce interference into the neural network's learning process, potentially leading to a state of local convergence during its iterative process. This, in turn, diminishes the robustness and generalization capabilities of the prediction model. To enhance the prediction accuracy of air conditioning load prediction models based on artificial intelligence algorithms, this study presents an artificial intelligence algorithm prediction model based on the method of sample similarity sample screening. Initially, the comprehensive similarity coefficient between samples is computed using the gray correlation analysis method, enriched with enhancements in information entropy. Subsequently, a subset of closely related samples is extracted from the original dataset and employed as the training dataset for the artificial intelligence prediction model. Finally, the trained artificial intelligence algorithm prediction model is deployed for air conditioning load prediction. The results illustrate that the method of screening training samples based on sample similarity effectively improves the prediction accuracy of BP neural network (BPNN) and extreme learning machine (ELM) prediction models. However, it is important to note that this approach may not be suitable for genetic algorithm BPNN (GABPNN) and support vector regression (SVR) models.

Influence lines are indispensable tools for visualizing and analyzing the dynamic variations in force factors induced by external loads within structural systems. Among these methods, the energetic approach stands as a widely employed technique, rooted in the fundamental principle of work done by changing forces. It enables engineers to transform intricate structural analysis problems into manageable ones by exploring the first derivatives of the radius vector, which represent infinitesimal velocity or displacement. This methodology seamlessly interweaves concepts such as carrier motion, relative motion, and the construction of mechanisms, bringing fresh perspectives to the analysis of influence lines. In this article, we explore the nuances of these novel methods within the domain of mechanism theory. Through comprehensive elaboration and analysis, we elucidate the underlying principles and practical applications of Jacobian contours. Crucially, we introduce a straightforward, rapid, and programmable approach, promising to revolutionize influence line determination in structural engineering. This method bridges the gap between theory and practice, offering the potential to elevate the accuracy, efficiency, and adaptability of influence line analysis. As such, it represents a significant advancement in the field of structural and applied mechanics, with broad-reaching implications for engineering practice.

With the improvement in medical quality, cold mechanical rehabilitation equipment can no longer meet the psychological needs of the physically handicapped. Therefore, design for behaviour change should be viewed as a strategy for enabling lifestyle changes as soon as possible. Although how to use equipment to assist physically disabled people in hydrotherapy rehabilitation is the primary design goal, the psychological feelings of users are also one of the focuses of this study. Besides, coupled with the large-scale spread of the Coronavirus Disease (COVID-19) in the past two years. People's living habits are also forced to change, many activities that people are used to doing outdoors are being slowly completed at home. During the epidemic, it is very dangerous and inconvenient for users who need to go to institutional rehabilitation regularly. Hence, it is urgent to change past usage habits and places. In this study, a personal multi-functional hydrotherapy bucket is designed. In addition to not having to use a public spa with other people to reduce the risk of contracting the Coronavirus Disease, it can be easily placed at home by reducing its size. According to market research, it is known that the current domestic hydrotherapy buckets on the market usually only have a single function. The large medical hydrotherapy pools are not only rare in Taiwan, but this kind of equipment also takes up a lot of space. Various factors have resulted in a relatively low market share of hydrotherapy pools compared to other rehabilitation equipment. In order to take into account, the physical and psychological needs of the physically disabled, this research focus on the design of a multi-functional but small-sized hydrotherapy bucket. Taking the convenience of entering and leaving the bucket as the starting point to improve the shortcomings of the existing products, on the other hand, because most users are inconvenient to move, safety is also the focus of the design to reduce the occurrence of accidents and injuries. This study is based on the systematic design methodology of concurrent design to obtain product design results. Firstly, conduct market research and select products from different manufacturers for analysis and comparison. Through functional analysis and sorting out the problem points, the TRIZ theory and the morphological diagram method are used to find the solution to the problem. Finally, the best design scheme is obtained by the PUGH method. Besides, construct a 3D model to present the design concept that meets the design goals. The research results are hoped to be used as a reference for designing and developing related products, thereby increasing the home's multifunctional hydrotherapy buckets' market share.

In this lecture notes, we will introduce Asymptotics, then we will give a short glimpse on Perturbation theory (regular versus singular), which plays a crucial role especially in theoretical physics. Our goal is to find Asymptotic series that approximates the values of integrals depending on some parameter or the solutions of differential equations. These methods that lead to obtain more effective algorithms of numerical evaluation are called: Asymptotic Methods.

Video super-resolution, which utilizes the relevant information of several low-resolution frames to generate high-resolution images, is a challenging task. One possible solution called sliding window method tries to divide the generation of high-resolution video sequences into independent sub-tasks, and only adjacent low-resolution images are used to estimate the high-resolution version of the central low-resolution image. Another popular method named recurrent algorithm proposes to utilize not only the low-resolution images but also the generated high-resolution images of previous frames to generate the high-resolution image. However, both methods have some unavoidable disadvantages. The former one usually leads to bad temporal consistency and requires higher computational cost while the latter method always can not make full use of information contained by optical flow or any other calculated features. Thus more investigations need to be done to explore the balance between these two methods. In this work, a bidirectional frame recurrent video super-resolution method is proposed. To be specific, a reverse training is proposed that the generated high-resolution frame is also utilized to help estimate the high-resolution version of the former frame. With the contribution of reverse training and the forward training, the idea of bidirectional recurrent method not only guarantees the temporal consistency but also make full use of the adjacent information due to the bidirectional training operation while the computational cost is acceptable. Experimental results demonstrate that the bidirectional super-resolution framework gives remarkable performance that it solves the time-related problems when the generated high-resolution image is impressive compared with recurrent-based video super-resolution method.

Currently, electromagnetic and radar methods are the most common non-destructive tests for non-destructive rebar location in structures.. Both methods have some advantages, disadvantages and limitations. This article is an attempt to describe possibilities of particular methods with emphasizing their advantages and limitations. The first stage of tests included results from tests conducted on a lintel beam made of autoclaved aerated concrete (AAC) and reinforced with small-diameter rebars and rebars placed close to each other. The second stage consisted in testing nine lightweight concrete specimens, each with three bars having a diameter of 10, 16 and 20 mm at three different space arrangements, to determine the effect of bar diameter and spacing on the measurement accuracy. The reinforcement of lintels and specimens was tested with two different electromagnetic scanners and a ground-penetrating radar (GPR) device. Received results from direct tests were compared with results from non-destructive tests (NDT). NDT tests conducted in such an element were found to perform the correct assessment of the concrete cover, and at the same time to give ambiguous results with reference to shape and number of rebars.

Initial periods of adsorption kinetics play an important role in estimating the initial adsorption rate and rate constant of adsorption process. Several adsorption processes rapidly occur, and the experimental data of adsorption kinetics under the initial periods can contain potential errors. The pseudo-second-order (PSO) kinetic model has been popularly applied in the field of adsorption. The use of the nonlinear optimization method to obtain the parameters of the PSO model can minimize error functions during modelling compared to the linear method. However, the nonlinear method has limitations in that it cannot directly recognize potential errors in the experimental points of time-dependent adsorption, especially under the initial periods. In this study, for the first time, the different linear types (Types 1–6) of the PSO model are applied to discover the error points under the initial periods. The result indicated that the fitting method using its linear equations (Types 2–5) is really helpful for identifying the error (doubtful) experimental points from the initial periods of adsorption kinetics. The imprecise points lead to low adjusted R2 (adj-R2), high reduced χ2 (red-χ2), and high Bayesian information criterion (BIC) values. After removing those points, the experimental data were adequately fitted with the PSO model. statistical analyses demonstrated that the nonlinear method must be used for modelling the PSO model because its red-χ2 and BIC were lower than the linear method. Type 1 has been extensively applied in the literature because of its very high adj-R2 (0.9999) and its excellent fitting to experimental points. However, its application should be limited because the potential errors from experimental points were not identified by this type. For comparison, the other kinetic models (i.e., pseudo-first-order, pseudo-nth-order, Avrami, and Elovich) are applied. The modelling result using the nonlinear forms of those models indicated that the fault experimental points from the initial periods were not detected.

The Failure Mode and Effect Analysis (FMEA) is often used to improve a system's reli-ability. This paper proposes a new approach that aims to overcome the most critical defects of the traditional FMEA. This new methodology combines the Entropy and Best Worst Method (BWM) methodology with the EDas and System Dynamics, FMECA: The EN-B-ED Dynamic FMECA. The main innovation’s point of the proposed work is the presence of an unknown factor (Cost), that allows to obtain an objective weighted factor, a risk index when a machine failure occurs. The criticality analysis has been carried out using software (Vensim PLE x64) to simulate System Dynamics models to identify corrective actions and evaluating the possible implementation of these ac-tions. The methodology proposed is applied to a case study in a relevant Italian com-pany in the agri-food sector.

The development of computational acoustics allows simulation of sound generation and propagation in complex environment. In particular, meshfree methods are widely used to solve acoustics problems through arbitrarily distributed field points and approximation smoothness flexibility. As a Lagrangian meshfree method, smoothed particle hydrodynamics (SPH) method reduce the difficulty in solving problems with deformable boundaries, complex topologies, or multiphase medium. The traditional SPH method has been applied in acoustic simulation. This study presents the corrective smoothed particle method (CSPM), which is a combination of SPH kernel estimate and Taylor series expansion. The CSPM is introduced as a Lagrangian approach to improve accuracy in solving acoustic wave equations in the time domain. Moreover, a boundary treatment technique based on the hybrid meshfree and finite difference time domain (FDTD) method is proposed to represent different acoustic boundaries with particles. To model sound propagation in pipes with different boundaries, soft, rigid, and absorbing boundary conditions are built with this technique. Numerical results show that the CSPM algorithm is consistent and demonstrates convergence with exact solutions. Main computational parameters are discussed, and different boundary conditions are validated to be effective for benchmark problems in computational acoustics.

This article presents the possibilities of using ferrite absorption material for shielding rooms with medical devices in order to additionally protect them against the limits of the electric component of the E field, which are up to 61 V/m. These values may appear in connection with the currently launched 5G system operating in the high-band frequency range, i.e. 28 GHz and 38 GHz, and may cause negative effects on the operation of medical devices. The possibilities of using ferrite absorption material for shielding rooms with medical devices were determined based on the results of measurements of the electrical properties of the ferrite material performed using two methods: the reflection method and the free space method. In the article, the complex electrical permittivity and complex magnetic permeability were determined for a ferrite plate based on measurements of the reflection coefficient and the transmission (attenuation) coefficient. The presented calculation and measurement results were used to show the properties of the ferrite material in the frequency range from 15 GHz to 45 GHz and to show the possibility of using it for shielding rooms as a material absorbing electromagnetic waves in this frequency range.

Comparison with conventional reservoirs, the prediction of pressure in ultra-deep tight sandstone formations is difficult. The prediction of seismic pressure is more challenging than well logging pressure prediction. The main methods for seismic pressure prediction include equivalent depth method, Eaton method, Fillippone formula, and its modified versions. Among them, the Eaton method is widely used and has good effectiveness. However, this method relies on difficult-to-obtain normal compaction trend line, which leads to low prediction accuracy in space. To address this issue, a method combining the Eaton method and Collocated cokriging is proposed. The Eaton formula is used to predict formation pressure at the well, with compressional wave velocity as the covariate for predicting the main variable, formation pressure. By simulating the shear wave velocity based on seismic compressional wave velocity, the influence of various parameters on the prediction results is analyzed, and the accuracy of this method is verified by comparing it with other methods. The proposed method is then applied to predict formation pressure in the ultra-deep formations of the Junggar Basin. The simulation results show that the Collocated cokriging method achieves higher planar accuracy and better matches the experimental expectations in terms of prediction results. The application results also demonstrate the scientific effectiveness of the combined method, which has achieved good results in practical production applications.

Topological indices are invariant numerical fields of a graph that give facts about the structure of graphs and are found to be very helpful in predicting the physical properties of aluminophosphates. The characteristics of the aluminophosphates are similar to those of zeolites. Two examples of current applications are natural gas dehydration and humidity sensor. New frameworks are being synthesised by researchers in chemistry and materials science. There are many layers and holes in these substances. In this study, Vertex version of distance-based topological indices, the entropy of topological indices and their numerical analysis are explained.

Rail impedance directly affects the transmission performance of track circuit . Considering the condition of earth stratification, for the difficult to calculate the rail impedance due to the semi-infinite integration interval and the oscillation of the integrand by using the Carson formula, The truncation method is proposed to divide the impedance formula is divided into definite integral and tail integral. The integral is approximated by the spline function, and the tail integral is calculated by using the exponential integral and Euler formula. Based on it, the rail impedance calculation formula of track circuit is obtained. The electromagnetic field model of track circuit with earth stratification is simulated by finite element method, and the correctness of the method is verified. Based on the formula, the influence of current frequency, soil depth and conductivity on rail impedance is studied. The relative error between the calculated results of rail impedance and the simulation results of finite element is within 5%. It can be seen that the formula has high accuracy and correctly reflects the law of rail impedance variation with current frequency, soil depth and resistivity. It provides a reliable reference for the theoretical calculation of rail impedance of track circuit.

Due to the tremendous progress in the automotive industry, the growth of the urban population, the number of vehicles is increasing and this creates parking challenges. Intelligent parking management systems offer an optimal solution for finding empty parking space so that drivers can quickly find their car parking space. To solve these problems, it is necessary to design an intelligent parking system, in addition to providing comfort to drivers, which is also economically viable. This paper proposes an intelligent multi-storey car parking system with the help of RFID technology and examining user preferences that can effectively solve car parking problems. The proposed method is a multi-objective decision-making method to reduce the problem of car parking, which is called MODM-RPCP. Therefore, the proposed MODM-RPCP method can allocate the best space for their stopping place by using the decision-making system and based on the priorities considered by the users. The simulation results show that the MODM-RPCP reduces the average booking time more than 19.2% and 27.1%, and decreases the response time of central parking management server more than 20.1% and 29.78% compared to MOGWOLA and ODPP approaches.

In this paper, we introduce some basic idea of Variational iteration method for short (VIM) to solve the Volterra’s integro-differential equations. The VIM is used to solve effectively, easily, and accurately a large class of non-linear problems with approximations which converge rapidly to accurate solutions. For linear problems, it’s exact solution can be obtained by only one iteration step due to the fact that the Lagrange multiplier can be exactly identified. It is to be noted that the Lagrange multiplier reduces the iteration on integral operator and also minimizes the computational time. The method requires no transformation or linearization of any forms. Two numerical examples are presented to show the effectiveness and efficiency of the method. Also, we compare the result with the result from Homotopy perturbation method (HPM). Finally, we investigate the absolute difference between variational iteration method and homotopy perturbation method and draw the graph of difference function by using Mathematica.

In this paper, we consider the development of algorithms for the solution of first order initial value problems whose solution exhibits exponential behaviour. Method of interpolation and collocation of basis function to give system of nonlinear equations which is solved for the unknown parameters to give a continuous scheme. The discrete methods recovered from the continuous scheme are implemented in block form. The stability properties of the method is verified and numerical experiments show that our method is efficient in handling these problems

Solving Linear equations with large number of variable contains many computations to be performed either iteratively or recursively. Thus it consumes more time when implemented in a sequential manner. There are many ways to solve the linear equations such as Gaussian elimination, Cholesky factorization, LU factorization, QR factorization. But even these methods when implemented on a sequential platform yield slower results as compared to a parallel platform where the time consumption is reduced considerably due to concurrent execution of instructions. The above mentioned linear equation solving methods can be implemented on the parallel platform using the direct approaches such as pipelining or 1D and 2D Partitioning approach. Vedic mathematics is a very ancient approach for solving mathematical problems. These Vedic mathematical approaches are well known for quicker and faster computation of mathematical problems. Vedic Mathematics provides a very different outlook towards the approach of solving linear Equation on parallel platform. It could be considered as a better approach for reducing space consumption and minimizing the number of algebraic operations involved in solving linear equation. In future Vedic Mathematics might serve as a viable solution for solving linear equation on parallel platform.

Over the last few decades, ocean research and exploration have made underwater mechanical systems a necessity. Underwater vehicles provide a new kind of marine platforms that could represent a great necessity in many areas of oceanographic research. Until now, the underwater vehicles come in a verity of shapes, sizes and means of propulsion. Depending on these characteristics, the type and mission of the vehicle are also determined. The underwater robots are used for different inspection and intervention missions in e.g. the oil and gas industry, ocean science research. Due to multiple applications to which the vehicle can participate, it can be successfully used and to determine methods of re-use of marine energy. Environmental mapping provides accurate information about the main areas of interest of the energy, as well as the exploitation possibilities of that. Most of the time, biomimetic robots were inspired their senso structure, from different kind of animals, such as insects, fish and birds. Nowadays, the concept of a underwater robotic vehicles capable to move independently, autonomously or remotely, has a great potential and a large application. This is the reason that the last studies have been directed on biomimetic robots. The fish and other underwater animals have evolved superior swimming capabilities in many ways and represent a starting point to explain the fluid-mechanical principles. Furthermore, the underwater animals develop and achieve extraordinary propulsion efficiencies, acceleration and maneuverability. They can also achieve high speed under water. Implanting and creating a vehicle through a biomimetic approach reduces the energy used to maneuver the vehicle as it can automatically correct its position and displacement. The paper presents an examination of the state of biomimetic robotic fishes, underlining the reason why bio-inspiration can help us in the underwater locomotion technology.

Vibrations of plate structures placed in a supersonic flow is considered. The undisturbed fluid flow is parallel to the plate. Two specific problems are treated: in the first one the plate is in the form of an infinite strip and the flow is in the direction of its finite length. Rigid walls extend from the sides of the plate indefinitely. In the second problem, the plate is a finite rectangle and the flow is parallel to one of its sides. The first problem is a limiting case of the second problem. The flow is modeled by piston theory which assumes that the fluid pressure on the plate is proportional to local slope. This approximation is widely used at high speeds, and reduces the interaction between the fluid flow and the vibrations of the plate to an additional term in the vibration equation. The resulting problem can be solved by assumed mode methods. In this study, the solution is also carried out by using the collocation method. The main result is the flutter velocity of the free fluid flow under which the plate vibrations become unstable. Finally, simple expressions are proposed between the various non-dimensional parameters that allows quick estimation of flutter velocity.

Two-dimensional heat diffusion problem with heat source which is quasilinear parabolic problem is examined analytically and numerically. Periodic boundary conditions are used as a boundary conditions. Since the problem is not linear, Picard’s successive approximation theorem is used. Under certain conditions of natural regularity and consistency imposed on the input data, establish the existence, uniqueness and constant dependence of the solution on the data using the generalized Fourier method. As a numerical solution, implicit finite difference scheme is used. The results ob-tained from analytical and the numerical solutions are so close to each other.

Shear deflection effects are traditionally neglected in most structural system identification methods. Unfortunately, this assumption might lead to significant errors in some structures, like deep beams. Although some inverse analysis methods based on the stiffness matrix method including shear deformation effects have been presented in the literature, none of these methods is able to deal with actual rotations in their formulations. Recently, the observability techniques, one of the first methods for the inverse analysis of structures included the shear effects into the system of equations. In this approach, the effects of shear rotation are neglected. When actual rotations on site are used to estimate the mechanical properties in the inverse analysis, it can result in serious errors in the observed properties. This characteristic might be especially problematic in structures such as deep beams where only rotations can be measured. To solve this problem and increase the observability techniques' applicability, this paper proposes a new approach to include shear rotations into the inverse analysis by observability techniques. This modification is based on the introduction of a new iterative process. To illustrate the applicability and potential of the proposed method, the inverse analysis of several examples of growing complexity is presented.

This paper focuses on robust multi-criteria traffic network equilibrium problems with path capacity constraints. The concepts of robust vector equilibrium and robust vector equilibrium with respect to worst case are introduced, respectively. To generate a subset of robust vector equilibrium, an equivalent min-max optimization problem is constructed. In addition, a direct search algorithm, in which the step size without derivatives and redundant parameters, for solving this min-max problem is proposed. To find a subset of robust vector equilibrium with respect to worst case, an equivalent smoothing optimization problem based on a variant version of ReLU activation function is established. Numerical experiments are also presented to expound the advantages of the algorithms.

Linear elastic fracture modeling coupled with empirical material tension data result in good quantitative agreement with experimental measurements of fracture failure for both brittle and tough epoxy nanocomposites. The nanocomposites comprise diglycidyl ethers of bisphenol A cured with O,O’ bis (2-aminopropylpropylene glycol) (Jeffamine D230) and doped with rubber nanoparticles of varying concentrations. Toughness, critical load, and critical displacement in quasi-static single edge-notched three-point bending are predicted accurately using both surface-based cohesive zone (CZS) and extended finite element (XFEM) methods implemented in Abaqus software. Fracture initiation within a crack is taken at the yield stress from uniaxial tension data. Prediction of fracture processes using a generalized truncated linear traction-separation law was significantly improved by considering the case of a linear softening function. There are no adjustable parameters in the XFEM model. The CZS model requires only optimization of the element displacement at fracture parameter. Thus, these continuum methods describe these materials in mode I fracture with a minimum number of independent parameters.

This paper addresses the study of the stress field in composites continua with the multiscale approach of the DECM (Discrete Element modeling with the Cell Method). The analysis focuses on composites consisting of a matrix with inclusions of various shapes, to investigate whether and how the shape of the inclusions changes the stress field. The purpose is to provide a numerical explanation for some of the main failure mechanisms of concrete, which is precisely a composite consisting of a cement-based matrix and aggregates of various shapes. Actually, while extensive experimental campaigns detailed the shape effect of concrete aggregates in the past, so far it has not been possible to model the stress field within the inclusions and on the interfaces accurately. The reason for this lies in the limits of the differential formulation, which is the basis of the most commonly used numerical methods. The Cell Method (CM), on the contrary, is an algebraic method that provides descriptions up to the micro-scale, independently of the presence of rheological discontinuities or concentrated sources. This makes the CM useful for describing the shape effect of the inclusions, on the micro-scale. When used together with a multiscale approach, it also models the macro-scale behavior of periodic composite continua, without losing accuracy on the micro-scale. The DECM uses discrete elements precisely to provide the CM with a multiscale approach.

The main issue in this work is to study limit functions necessary for the reliability assessment of the structural steel with the use of the relative entropy apparatus. This would be done using a few different mathematical theories relevant to this relative entropy, namely those proposed by Bhattacharyya, Kullback-Leibler, Jeffreys, and Hellinger. Probabilistic analysis in the presence of uncertainty in material characteristics would be delivered using three different numerical strategies – Monte-Carlo simulation, stochastic perturbation method as well as the semi-analytical approach. All these methods are based on the Weighted Least Squares Method approximations of the structural response functions versus the given uncertainty source and they would allow efficient determination of the first two probabilistic moments of the structural responses including stresses, displacements, and strains. The entire computational implementation would be delivered using the Finite Element Method system ABAQUS and computer algebra program MAPLE, where relative entropies, as well as polynomial response functions, would be determined. This study would demonstrate that the relative entropies may be efficiently used in reliability assessment close to the widely engaged First Order Reliability Method (FORM).

The article presents a new scalable iterative method for linear programming called the “apex method”. The key feature of this method is constructing a path close to optimal on the surface of the feasible region from a certain starting point to the exact solution of linear programming problem. The optimal path refers to a path of minimum length according to the Euclidean metric. The apex method is based on the predictor-corrector framework and proceeds in two stages: quest (predictor) and target (corrector). The quest stage calculates a rough initial approximation of linear programming problem. The target stage refines the initial approximation with a given precision. The main operation used in the apex method is an operation that calculates the pseudoprojection, which is a generalization of the metric projection to a convex closed set. This operation is used both in the quest stage and in the target stage. A parallel algorithm using a Fejér mapping to compute the pseudoprojection is presented. An analytical estimation of the parallelism degree of this algorithm is obtained. Also, an algorithm implementing the target stage is given. The convergence of this algorithm is proven. An experimental study of the scalability of the apex method on a cluster computing system is described. The results of applying the apex method to solve problems from the Netlib-LP repository are presented.

In order to reduce the silique shattering loss of rapeseed mechanical harvesting process, based on the state of force on the silique during rapeseed harvesting reel branch, Ningza 1810, Zhenyou 8 and Fengyou 306 were used as research objects, and the experimental research on the factors affecting rapeseed siliques shattering was carried out using the swing impact method. The experimental analysis showed that: rapeseed varieties, silique moisture content, silique growth position, collision material, impact speed, force position and other factors had significant effects on silique shattering. The impact velocity was less than 1.5m·s-1, the difference on the effect of each factor on pod-shattering was not significant, it was not easy to shatter when the moisture content of rapeseed silique was higher. The impact resistance of the front side of rapeseed was 2 to 4 times that of the bonding surface of rapeseed petals, the shattering rate of the top rapeseed silique was twice that of the bottom siliques, when siliques were supported, they were more likely to shatter under external forces than when they were unsupported. The experimental study of the mechanical properties of rapeseed siliques was carried out using the impending fracture method, the experimental analyses showed that the support position and force position of the silique, the loading speed and the growth position of the silique had a significant effect on the mechanical properties of the silique. The maximum cracking force was higher and bending strength was stronger when the body of the silique was supported, which the range of the maximum cracking force were 3.05N to 4.16N, and the bending strength range were 8.48 MPa to 11.57 MPa. The maximum cracking force and bending strength of the silique was stronger when the front side of the silique petal was pressurized than when the bonding surface of the petal was pressurized. Based on Pearson's correlation and grey correlation analysis, the morphological characteristics of rapeseed siliques were ranked in order of their influence on the performance of siliques in terms of the angle between silique and stalk, stalk diameter, petal thickness, beak length, silique thickness, silique width and silique length. This study can be used as a reference for the design and optimization of the rapeseed harvesting reel branch mechanism and the selection of machine harvestable rapeseed varieties.

Magnetorheological fluids (MRFs) are smart materials consisting of micro-scale magnetizable particles suspended in a carrier fluid. The rheological properties of a MRF can be changed from a fluid-state to a solid-state upon the application of an external magnetic field. This study reports the development of a particle-level simulation code for magnetic solid spheres moving through an incompressible Newtonian carrier fluid. The numerical algorithm is implemented within an open-source finite-volume solver coupled with an immersed boundary method (FVM-IBM) to perform fully-resolved simulations. The particulate phase of the MRF is modeled using the discrete element method (DEM). The resultant force acting on the particles due to the external magnetic field (i.e., magnetostatic polarization force) is computed based on the Clausius-Mossotti relationship. The fixed and mutual dipole magnetic models are then used to account for the magnetic (MAG) interactions between particles. Several benchmark flows were simulated using the newly-developed FVM-IBM-DEM-MAG algorithm to assess the accuracy and robustness of the calculations. First, the sedimentation of two spheres in a rectangular duct containing a Newtonian fluid is computed without the presence of an external magnetic field, mimicking the so-called drafting-kissing-tumbling (DKT) phenomenon. The numerical results obtained for the DKT case study are verified against published data from the scientific literature. Second, we activate both the magnetostatic polarization and the dipole-dipole forces and resultant torques between the spheres for the DKT case study. Next, we study the robustness of the FVM-IBM-DEM-MAG solver by computing multi-particle chaining (i.e., particle assembly) in a two-dimensional (2D) domain for area volume fractions of 20% (260 particles) and 30% (390 particles) under vertical and horizontal magnetic fields. Finally, the fourth computational experiment describes the multi-particle chaining in a three-dimensional (3D) domain allowing to study fully-resolved MRF simulations of 580 magnetic particles under vertical and horizontal magnetic fields.

The paper presents the identification and classification of steel cord failures in the conveyor belt core based on an analysis of a two-dimensional image of magnetic field changes recorded using the Diagbelt system around scanned failures in the test belt. The obtained set of identified changes in images obtained for numerous devices parameters settings were the base for statistical analysis. It makes it possible to determine the Pearson’s linear correlation coefficient between the parameters being changed and the image of the failures. In the second stage of the research, artificial intelligence methods were applied to construct a multilayer neural network (MLP) and to teach its appropriate identification of damage. In both methods were used the same data sets, which made it possible to compare methods.

In this article, we present a comparative study between the Adomian Decomposition Method (ADM) and Variational Iteration Method (VIM). The study outlines the significant features of the two methods, for solving nonlinear Volterra-Fredholm integro-differential equations. From the computational viewpoint, the VIM is more efficient, convenient and easy to use. Moreover, we proved the existence and uniqueness results and convergence of the solution. Finally, an example is included to demonstrate the validity and applicability of the proposed techniques.

This paper presents a new numerical method for multiscale modeling of composite materials. The new numerical model, called DECM, consists in a DEM (Discrete Element Method) approach of the Cell Method (CM) and combines the main features of both the DEM and the CM. In particular, it offers the same degree of detail as the CM, on the microscale, and manages the discrete elements individually such as the DEM—allowing finite displacements and rotations—on the macroscale. Moreover, the DECM is able to activate crack propagation until complete detachment and automatically recognizes new contacts. Unlike other DEM approaches for modeling failure mechanisms in continuous media, the DECM does not require prior knowledge of the failure position. Furthermore, the DECM solves the problems in the space domain directly. Therefore, it does not require any dynamic relaxation techniques to obtain the static solution. For the sake of example, the paper shows the results offered by the DECM for axial and shear loading of a composite two-dimensional domain with periodic round inclusions. The paper also offers some insights into how the inclusions modify the stress field in composite continua.

Photoluminescent (PL) layers and electroluminescent (EL) systems have been extensively explored for constructing flat panels, screen monitors, and lighting systems. Due to their optical properties, our group is interested in the relationship between optical properties and dielectric constant is converted due to thermal change. In this study, we prepared a zinc sulfide (ZnS) fluorescent film on barium-calcium zirconium titanate ceramics. BCZT0.85 (Ba0.85Ca0.15Zr0.1Ti0.9O3) and BCZT0.9 (Ba0.9Ca0.1Zr0.1Ti0.9O3) were prepared using the solid-state reaction method for the dielectric layer. The BCZT powders were calcined at 1200 and 1250 °C and dwell times of 2 and 4h. The phase formation and microstructure characteristics were then determined using X-ray diffraction and scanning electron microscopy, respectively. The dielectric behavior and optical properties rela-tionship was then studied to determine the composition and optimal conditions for further use as thermal detectors in electric vehicle battery packs. All BCZT powders exhibited the tetragonal phase, which was confirmed by JCPDS No. 01-079-2265. The dielectric constant increased with increasing calcining temperature or dwell time. The dielectric properties showed the BCZT0.85 ceramic sintered had the maximum dielectric constant of 15342 at calcine temperature of 1250 °C for 4h. Therefore, in this study, this condition was optimal for preparing the dielectric film. The maximum dielectric constant film is 42. The electroluminescence intensity of the samples increased in a temperature-dependent manner, with the highest EL intensity at 80 °C. Moreover, the electroluminescence intensity value was enhanced with the increase of the dielectric constant. Our results show promise for developing applications for Opto-thermal sensors.

There exists a great potential for application in soil mechanics with the use of mechanistic models such as the finite element model, and other non-local models, such as the peridynamic model, since the soil mass can be modelled as a group of particles that interact with each other. In this work, we determine the bearing capacity of deep foundations where stresses are transmitted to deep deposits, which generally present better characteristics in terms of compressibility and shear strength. One of the main elements are driven piles, which must be previously designed, made and tested before their final use. In this work, a comparison with different methods is presented. The finite element method and the peridynamic model are used. Because the effect of consolidation was not taken into account, in these examples, the assumption is made that the structures are cemented in inert soil. Likewise, it is assumed that the water table has no influence on the simulations. Failure envelopes were observed where bonds between particles present high gradients of deformation and fracture. Bearing capacities were estimated and compared with those obtained from the Terzaghi and Meyerhof methods.

The calculation of the surface tension of ordinary organic and ionic liquids, based on a computer algorithm applying a refined group-additivity method, is presented. The refinement consists of a complete breakdown of the molecules into their constituting atoms, further distinguishing them by their immediate neighbour atoms and bond constitution. The evaluation of the atom-groups’ contributions was carried out by means of a fast Gauss-Seidel fitting method founded upon the experimental data of 1895 compounds from literature. The result has been tested for plausibility using a 10-fold cross-validation (cv) procedure. The direct calculation and the cv test proved the applicability of the present method by the close similarity and excellent goodness of fit R2 and Q2 of 0.9023 and 0.8821, respectively. The respective standard deviations are ±2.01 and ±2.16 dyn/cm. Some correlation peculiarities have been observed in a series of ordinary and ionic liquids with homologous alkyl chains which are illustrated and discussed in detail, exhibiting the limit of the present method.

The digital elevation model (DEM) is one of the key geospatial datasets used in many fields of engineering and science for countless applications. In this contribution, we assess the vertical accuracy of the Advanced Land Observing Satellite (ALOS) World 3D-30m (AW3D30) DEM using the runway method (RWYM). The RWYM utilizes the longitudinal profiles of runways which are reliable and ubiquitous reference data. A reference dataset used in this project consists of 36 runways located at various points throughout the world. The same dataset was previously used to test the accuracy of WorldDEM^TM. Our study indicates that AW3D30 has a remarkably high RMSE of 1.78 m (one σ). However, while analyzing the results, it has become apparent that it also contains a widespread elevation anomaly. We conclude that this anomaly is the result of uncompensated sensor noise and the data processing algorithm (downsampling of the higher resolution data). We believe that this issue should be communicated to the user community. Also, we would like to note that the traditional accuracy assessment of a DEM, e.g., statistical assessment of the elevation differences = model – reference, does not allow for identification of these type of anomalies in a DEM.

This paper introduces an innovative approach to numerically model Structure-Soil-Structure Interaction (SSSI) by integrating the Boundary Element Method (BEM) and the Finite Element Method (FEM) in a coupled manner. To assess the accuracy of the proposed method, a comparative study is undertaken, comparing its outcomes with those generated by the conventional FEM technique. Alongside accuracy, the computational efficiency aspect is crucial for the analysis of large-scale SSSI problems. Hence, the computational performance of the coupled BEM-FEM method undergoes a thorough examination and is compared with that of the standalone FEM method. The results from these comparisons illustrate the superior capabilities of the proposed method in comparison to the FEM method. The novel approach provides more reliable results compared to traditional FEM methods, serving as a valuable tool for engineers and researchers involved in structural analysis and design.

Estimating the parameters of statistical distributions generally involves solving a system of nonlinear equations or a nonlinear equation, being a technical difficulty in their usual application in hydrology. This article presents improved approximations and, in some cases, new approximations for the estimation with the method of ordinary moments and the method of linear moments, which are useful for the direct calculation of the parameters, because the errors in the approximate estimation are similar to the use of iterative numerical methods. Thirteen statistical distributions of two and three parameters frequently used in hydrology are presented, for which parameter estimation was laborious. Thus, the approximate estimation of the parameters by the two methods is simple but also precise and easily applicable by hydrology researchers. The approximate forms characterized by very small errors are the result of the research conducted within the Faculty of Hydrotechnics to update the romanian normative standards in the hydrotechnical field.

The testing requirements of the active phased array antennas are very different from those of traditional passive antennas, due to its beam steering capability. Usually, each beam profile of the active phased array needs a separate radiation pattern test, which makes the overall testing time extremely long. Thus the traditional antenna test method can no longer meet the efficiency and cost requirements of new active phased array antennas test. In this paper, a fast test method tailored for phased array antennas is proposed that offers significantly reduced testing time at the expense of slight sacrifice of the accuracy. Using the simulated element pattern in array and ideal port excitation, the beam profile in any direction can be predicted by testing only a certain beam profile. Through theoretical derivation and experiments, the effectiveness of the method is verified, and the testing efficiency of the phased array antenna is demonstrated to be improved by ten times or even more.

DGJ (Daftardar-Gejii-Jafaris) method is used to obtain numerical solution of the third order fractional differential equation. Providing the DGJ method converges, the approximate solution is a good and effective numerical result which is close to the exact solution or the exact solution. For this,the examples of the explaning the method are presented. The proposed method is implemented for the approximation solution of the third order nonlinear fractional partial differential equations. The method was shown to be unsuitable and inconsistent for an example of a nonlinear fractional partial differential equation depend on initial-boundary value conditions. The fact that these numerical results are not consistent can be explained by the fact that the method is not convergent.

This study proposes a mobile positioning method which adopts recurrent neural network algorithms to analyze the received signal strength indications from heterogeneous networks (e.g., cellular networks and Wi-Fi networks) for estimating the locations of mobile statioThis study proposes a mobile positioning method which adopts recurrent neural network algorithms to analyze the received signal strength indications from heterogeneous networks (e.g., cellular networks and Wi-Fi networks) for estimating the locations of mobile stations. The recurrent neural networks with multiple consecutive timestamps can be applied to extract the features of time series data for the improvement of location estimation. In practical experimental environments, there are 4,525 records, 59 different base stations, and 582 different Wi-Fi access points detected in Fuzhou University in China. The lower location errors can be obtained by the recurrent neural networks with multiple consecutive timestamps (e.g., 2 timestamps and 3 timestamps); the experimental results can be observed that the average error of location estimation was 9.19 meters by the proposed mobile positioning method with 2 timestamps.

The paper presents an adaptation of numerical solution of first order linear differential equation in fuzzy environment. The numerical method is re-established and studied with fuzzy concept to estimate its uncertain parameters whose values are not precisely known. Demonstrations of fuzzy solutions of the governing methods are carried out by the approaches, namely Modified Runge Kutta method and Runge Kutta Merson method. The results are compared with the exact solution which is found using generalized Hukuhara derivative (gH-derivative) concepts. Additionally, different illustrative examples and an application in industry of the methods are also undertaken with the useful table and graph to show the usefulness for attained to the proposed approaches.

With the increasing of copper consumption, the in-use stock of copper tends to increase. This paper used the “average use life method” to quantify the amount of copper in-use stock, and calculated the average age of in-use stock. It was indicated that the total in-use stock had an overall smooth trends, and reached its peak in 2007 was about 68.9 Mt (million tons), in addition, in-use stock per capita reached its peak ,234 kg/capita in 2001. The results demonstrated that during the period 1992-2002, the average age of copper in-use stock was continually decreased, but gradually increased since the year 2003. The fixed assets depreciation method used in this paper is applied to analyze depreciated copper in-use stock, and to analyze the relationship with economic indicator (GDP). It is demonstrated that it was inconsistent with the theory of Environmental Kuznets Curve (EKC) before 2000, this might be the demand for copper in the service sector is greater than the reduced strength of alternatives. Finally, scenario analysis of future copper in-use stock and depreciated copper in-use stock per capita in the U.S. were presented. The corresponding average age of the in-use stock will have a slight rise in the next decade.

The development of mineral resources in China has gradually entered the stage of deep mining. Rock burst, collapse and other ground pressure disasters caused by deep high stress have become major hidden dangers restricting mine safety production. Microseismic monitoring technology is an important means of ground pressure risk prediction. In this paper, the wavelet coefficient threshold denoising method in time-frequency domain, STA/LTA method, AIC method, Skew and Kurtosis method are studied, and the automatic P-phase onset time picking model based on noise reduction and multiple detection indexes is established. Through the effect analysis of microseismic signals collected by microseismic monitoring system of coral Tungsten Mine in Guangxi, automatic P-phase onset time picking is realized. the reliability of the P-phase onset time picking method proposed in this paper based on noise reduction and multiple detection indexes is verified. The picking accuracy can still be guaranteed under the severe signal interference of background noise, power frequency interference and manual activity underground mine, Which is of great significance to the data processing and analysis of microseismic monitoring.

H-adaptivity is an effective tool to introduce local mesh refinement in FEM-based numerical simulation of crack propagation. The implementation of h-adaptivity could benefit the numerical simulation of fatigue or accidental load scenarios involving large structures such as ship hulls. In engineering applications, the element deletion method is frequently used to represent cracks. However, the element deletion method has some drawbacks such as strong mesh dependency and loss of mass or energy. In order to mitigate this problem, the element splitting method could be applied. In this study, a numerical method called ‘h-adaptive element splitting’ (h-AES) is introduced. The h-AES method is applied in FEM programs by combining h-adaptivity with the element splitting method. Two examples using the h-AES method to simulate cracks in large structures under linear-elastic fracture mechanics scenario are presented. The numerical results are verified against analytical solutions. Based on the examples, the h-AES method is proven to be able to introduce mesh refinement in large-scale numerical models that consist of structured coarse meshes. By employing the mesh refinement introduced in this paper, very small cracks are well represented in large structures.

The study of precipitation amount at different time scales constitutes an important issue in climate research and risk assessment. When dealing with daily totals, a frequent but sometimes underestimated problem is at what time the observation day begins. The choice of different starting times may lead to incompatibility between stations and incorrect identification of extreme events. In this work, the problem of temporal misalignment between precipitation datasets characterized by different starting time of the observation day is analyzed. The most widely used adjustment methods (1day and uniform shift) and two methods based on reanalysis (NOAA and ERA5) are evaluated in terms of temporal alignment, precipitation statistics and percentile distributions. As test series, the precipitation amount collected from 9 a.m. local time (09 LT) on the previous day to 09 LT on the target day (9-9 datasets) of the Padua and nearby stations in the period 1993-2022 have been selected. Results show that the reanalysis-based methods, in particular ERA5, outperform the others in temporal alignment, regardless the station. But, for the periods in which reanalysis data are not available, “1day” method, which shifts the daily amount back one calendar day, and “unif” method, which distributes uniformly the daily total from a 2-day moving window surrounding the target date, can be considered valid alternatives. On the other side, concerning the precipitation statistics, the reanalysis-based methods are not the best option, as they increase the precipitation frequency and reduce the mean value over wet days, NOAA much more than ERA5. Nevertheless, the uniform method provides a larger deviation from the original daily series. The use of the series of a station nearby the target one, which is mandatory in case of missing data, gives similar or better results than applying any adjustment method to the 9-9 series. General conclusions can hardly be drawn as they depend on the method and station. For the Padua dataset, the analysis was repeated at monthly and seasonal resolution. In general, the adjustment series show the most relevant changes in the precipitation statistics in summer and less temporal alignment with the original series in summer and autumn, the two seasons mainly affected by heavy rains in Padua. Finally, the percentiles distribution, analyzed for all the methods and stations, indicates that any adjustment method underestimates the percentile values, except ERA5. Only Legnaro, the station most correlated with Padua, gives results like ERA5.

A series of water diversion projects to address the uneven distribution of water resources in China involve the construction of a large number of hydraulic tunnels. As the lining structure that maintains the stability and durability of the tunnels, it is prone to durability damage during the operation process, which in turn affects the water transmission safety and water supply capacity. Therefore, it is important to evaluate the durability of hydraulic tunnel lining structure. Considering the randomness and fuzziness of the factors affecting the durability of hydraulic tunnel lining structure, this paper proposes a comprehensive evaluation model based on the coupling of set-pair analysis and extension. The G1 method and the simple correlation function method are used to determine the subjective and objective weights of the evaluation indexes respectively, and the combination weight is assigned based on the principle of minimum entropy; the set-pair analysis principle is used to establish the linkage affiliation function, calculate the comprehensive linkage affiliation of the object to be evaluated, and the maximum affiliation principle is used to judge the durability level of the hydraulic tunnel lining structure. Finally, taking a section of hydraulic tunnel as an example, the model proposed in this paper is used to calculate its durability grade as Class III, with the set-pair potential SHI(H) = 7.5856, which is consistent with the actual engineering practice, and a comparative study is done in combination with the AHP-Extenics method. It is verified that the evaluation model can scientifically and reasonably evaluate the durability of hydraulic tunnel lining structure, providing a basis for subsequent maintenance and reinforcement.

Recent advance in electric propulsion systems have demonstrated that these engines can be used for for long-duration interplanetary voyages. Constant specific impulse engine described as a thrust-limited engine is an example of this type of engine, processing the ability to operate at a constant level of impulse. The determination of minimum-fuel, planar heliocentric Earth-to-Mars low-thrust trajectories of spacecraft using a constant specific impulse is discussed considering the first-order necessary conditions derived from Lawden’s primer vector theory. The minimum-fuel low-thrust Earth-to-Mars optimization problem is then solved in two-dimensional, heliocentric frame using both indirect and direct methods. In the indirect method, two-point-boundary-value problems are derived to solve boundary value problems for ordinary differential equations. In the direct method, a general-purpose optimal control software called GPOPS-II is adopted to solve these optimal control problems. Numerical examples using two different optimization methods are presented to demonstrate the characteristics of minimum-fuel planar low-thrust trajectories with on-off-on thrust sequences at three chosen flight times and available maximum powers. The results are useful for broad trajectory search in the preliminary phase of mission designs.

Clinical agreement between two quantitative measurements on a group of subjects is generally assessed with the help of the Bland-Altman (B-A) limits. The interpretation regarding agreement is based on whether B-A limits are within the pre-specified clinical tolerance. Thus, clinical tolerance limits are necessary for this method. We argue in this communication that such limits of clinical tolerance can be directly used for assessing agreement and plead that this nonparametric approach is simple and robust to the distribution pattern and outliers. Such direct use of clinical tolerance limits has more flexibility, and it is more effective in assessing the extent of agreement.

Magnetometric Resistivity(MMR) Method, which can use the power supply method of the traditional apparent resistivity method to measure the magnetic field. At present, the application research abroad is relatively extensive, but the domestic(China) research on the application of the MMR method is very few, and it is not even well-known. This paper is based on the MMR theoretical method under the point source DC condition, combined with the abnormal potential method and the modified Biot-Savart law, and using the three-dimensional numerical calculation method of the finite difference method to calculate the abnormal potential field, electric field, and magnetic field on the matlab2018a platform. Calculate, realize the multi-physics simulation in the electromagnetic field through Matlab platform programming, and verify the correctness of the algorithm by the spherical anomaly model with an analytical solution. Through the visual simulation of the three-dimensional data volume of the multi-physics field in the electromagnetic field, we can better understand the response mechanism of the electromagnetic field under DC conditions and grasp their three-dimensional spatial distribution rules. It is hoped that the research in this article can help the research of MMR Personnel better use this method for exploration.

The deep Cretaceous tight sandstone in Kuqa Depression of Tarim foreland basin is an ultra-low porosity and ultra-deep gas-bearing reservoir, which is characterized by small pores, fine throats, and poor connectivity. The wireline logging responses are so complex, and especially, it is difficult to identify fluid types from resistivity logs. Based on acoustic, density, and neutron logs response differences in gas and water layers, effective fluid sensitivity factors are constructed for gas layer identification. From conventional logs, acoustic-neutron porosity difference, density-neutron porosity difference, and triple-porosity ratio are all sensitive parameters to the gas layer. From the NMR logging response mechanism, the density and NMR porosity difference, and T2 geometric mean of the movable fluid are also two sensitive parameters to the gas layer. Based on these parameters, a series of fluid typing charts are constructed and their adaptabilities are analyzed and compared. By contrast, NMR log interpretation is better, and triple-porosity comprehensive method from conventional logs is also effective when NMR logging is not available. Finally, the comprehensive fluid typing strategy by combining some methods for ultra-low porosity tight sandstone is summarized and optimized. This study is another alternative for fluid identification using non-electrical logs.

In common model testing practise, the measured values of the self propulsion test are split into the characteristics of the hull, the propeller and into the interaction factors. These coefficients are scaled separately to the respective full scale values and subsequently reassembled to give the power prediction. The accuracy of this power prediction depends {\em inter alia} on the accuracy of the measured values and the scaling procedure. An inherent problem of this approach is, that it is virtually impossible to verify each single step, because of the complex nature of the underlying problem. In recent years the scaling of the open-water characteristics of propeller model tests attracted a renewed interest, fuelled by competitive tests, which became the norm due to requests of the customer. This paper will show the influence of different scaling procedures on the predicted power. The prediction is compared to the measured trials data and the quality of the prediction will be judged. The procedures examined are the standard ITTC~1978 procedure plus derivatives of it: the Meyne, the strip method and the βᵢ-method.

In this paper, we first discuss the research status and application progress of the finite element method and the smoothed particle method. By analyzing the advantages of the smoothed particle method and the finite element method, a new coupling algorithm, namely FEM-SPH algorithm, is proposed. By the method of comparison, it shows that finite element method and SPH method in the simulation of large deformation problems each have advantages and disadvantages, the finite element method smoothed particle coupling algorithm is effective to achieve the performance of high computational efficiency and can naturally simulate large deformation problems across. In the process of calculation, the large deformation unit can be freely into an algorithm to facilitate the calculation accuracy and efficiency of three methods of numerical simulation. Through the study found, FEM-SPH algorithm not only overcome the defect of smooth particle tensile instability, but also overcomes the problem of low efficiency of finite element computation. To further test the FEM-SPH algorithm has advantages in the practical engineering, we have carried out the actual test to the example of the super high speed collision, concluded that, since the target of most of the computational domain is always finite element, smoothed particle focused only in contact with the projectile and target of local area, particle number is not much, the whole calculation process just ten minutes, computational efficiency has been greatly improved, at the same time in the simulation of large deformation, the advantage is very obvious .This provides a criterion for the actual project, depending on the specific material deformation mode and choose a more appropriate conversion algorithm.

Multi-parameter families of Lax pairs for the modified Korteweg-de Vries (mKdV) equation are defined by applying a direct method developed in the present study. The gauge transformations converting the defined Lax pairs to some simpler forms and reducing the variety of the Lax pairs this way are found. The direct method and its possible applications to other types of evolution equations are discussed.

Owing to its properties, boron carbide has been applied in scientific and industrial fields. For this reason, B4C powders should be characterized by high purity and homogeneity in grain size and shape. In this study, boron carbide was prepared using precursors obtained by a hydrothermal method, and the grain morphology of the product was investigated. Boric acid and the saccharides glucose, fructose, inulin, and sorbitol were used as the precursors. Two precursor dehydration methods, freeze-drying and recrystallization, were compared. The precursors were subjected to DLS grain size and FT-IR spectrophotometric studies, and SEM observations of the precursors and products were performed, confirming that boron carbide powders with submicron spherical grains could be successfully synthesized.

In this work, the analytical treatment of Fourth order pd equations has been discussed with the variational iteration algorithm with an auxiliary parameter. The technique has the capability of reducing the size of computational work and easily overcomes the difficulty of the perturbation method or Adomian polynomials. Comparison with the classic variational iteration algorithm- is carried out, showing that the modification is more efficient and reliable.

C-X-C motif chemokine receptor 3 (CXCR3, CD183) is a G-protein-coupled receptor for CXCL9, CXCL10, and CXCL11. CXCR3 signaling induces chemotaxis of immune cells to inflammation sites and promotes inflammation in inflammatory diseases. Various mouse models to mimic the pathogenesis of each disease have been developed to understand mechanisms and evaluate therapeutics for these diseases. Although CXCR3 is an attractive target to suppress inflammation, anti-CXCR3 therapeutic agents have not been approved. In this study, we established a novel anti-mouse CXCR3 (mCXCR3) monoclonal antibody, Cx₃Mab-4 (rat IgG₁, kappa), using the Cell-Based Immunization and Screening method. Flow cytometric analysis demonstrated that Cx₃Mab-4 bound to mCXCR3-overexpressed Chinese hamster ovary-K1 (CHO/mCXCR3) cells, but did not react to parental CHO-K1 cells. The dissociation constant of Cx₃Mab-4 was determined as 1.3 × 10^-9 M, indicating that Cx₃Mab-4 possesses a high affinity to mCXCR3-expressing cells. Cx₃Mab-4 could be useful for targeting CXCR3-expressing cells in preclinical mouse models.

The C-X-C motif chemokine receptor-1 (CXCR1) is a rhodopsin-like G-protein-coupled receptor, expressed on the cell surface of immune cells and tumors. CXCR1 interacts with some C-X-C chemokines, such as CXCL6, CXCL7, and CXCL8/interleukin-8, which are produced by various cells. Since CXCR1 is involved in several diseases including tumors and diabetes mellitus, drugs targeting CXCR1 has been developed. Therefore, the development of sensitive monoclonal antibodies (mAbs) for CXCR1 has been desired for the diagnosis and treatment. This study established a novel anti-mouse CXCR1 (mCXCR1) mAb; Cx₁Mab-1 (rat IgG₁, kappa), using the Cell-Based Immunization and Screening (CBIS) method. Cx₁Mab-1 reacted with mCXCR1-overexpressed Chinese hamster ovary-K1 (CHO/mCXCR1) and mCXCR1-overexpressed LN229 glioblastoma (LN229/mCXCR1) in flow cytometry. Cx₁Mab-1 demonstrated a high binding affinity for CHO/mCXCR1 and LN229/mCXCR1 with a dissociation constant of 2.6×10^-9 M and 2.1×10^-8 M, respectively. Furthermore, Cx₁Mab-1 could detect mCXCR1 by western blot analysis. These results indicated that Cx₁Mab-1 is useful for detecting mCXCR1, and provides a possibility for targeting mCXCR1-expressing cells in vivo experiments.

The paper presents a method for determining the mutual impedance of two dipoles located on a multilayer dielectric structure. Determining the mutual impedance of linear antennas is a complex issue and we are unable to obtain an exact solution, it is influenced by many elements (location, dimensions, electrical parameters of the earth, thickness of the substrate, thickness and antenna conductivity, etc.) In the case of antennas on a dielectric substrate, the coupling analysis is even more complicated, because the operation of these antennas is significantly influenced by the occurrence of surface waves conducted through an open dielectric structure. The problem was solved by the method of moments using the Galerkin method as its special case. The work uses a method developed by Richmond for obtaining the elements of the impedance matrix, based on the concept of reaction between sections of the base and testing functions. Their mutual interaction was described by the reaction of the electric field caused by the current flowing through one dipole with the current flowing through the other dipole. Based on this analysis, the mutual impedance of two arbitrarily oriented dipoles located on a multilayer dielectric was determined, where the current distribution and antenna lengths are arbitrary. A computer program was developed that allowed to determine the mutual impedance for freely oriented dipoles.

Understanding how climatic variables impact the reference evapotranspiration (ET0) is essential for water resource management, especially considering potential fluctuations due to climate change. Therefore, we used the Sobol method to analyze the spatiotemporal variations of Penman-Monteith ETo sensitivity to the climatic variables: downward solar radiation, relative humidity, maximum and minimum air temperature, and wind speed. The Sobol’s indices variances were estimated by Monte Carlo integration, setting the sample limits to 2.5 and 97.5 percentiles of the daily data of 33 automatic weather stations located in the state of Mato Grosso, Brazil. The results of the Sobol analysis indicate considerable spatiotemporal variations in the sensitivity of ETo to climatic variables and their interactions. The dominant climatic variable responsible for ET0 fluctuations in Mato Grosso is incident solar radiation, which has a more significant impact in humid environments, as observed in the areas of the Amazon biome in the state. Air relative humidity and wind speed have higher sensitivity indices during the dry season in the areas of the Cerrado biome (Savanna) in Mato Grosso. Our findings show that changes in solar radiation, relative humidity, and wind speed cannot be ignored when analyzing changes in reference evapotranspiration.

Antioxidants are very beneficial for health because they protect the body from the effects of free radicals on various degenerative diseases caused by food contamination, air pollution, sunlight, etc. In general, methods for measuring the capacity of antioxidants generally use accurate meth-ods such as spectrophotometry and chromatography. Still, it takes time, sample preparation and must be done in a laboratory with particular expertise. Therefore, a new, more practical method needs to be developed for determining antioxidants, namely the electrochemical method. The electrochemical method is promising to develop because it has several advantages, including high sensitivity and fast response. The electrochemical method discussed in this article reviews sensors, biosensors, and nanosensors. This paper comprehensively analyzes contemporary developments in electrochemical biosensor techniques and antioxidant evaluation methodologies. The discussion centers on utilizing multiple biosensors. Electrochemical biosensors have been determined to be prevalent in analyzing food quality, assessing active factor functionality, and screening practical components. The present study outlines the difficulties linked with electrochemical bio-sensor technology and provides insights into the potential avenues for future research in this domain.

The control of alumina morphology is crucial yet challenging for its various applications. Un-fortunately, traditional methods for preparing alumina particles suffer from several limitations such as irregular morphology, poor dispersibility, and restricted application areas. In this study, we develop a novel method for preparing spherical mesoporous alumina using chitin and Plu-ronic P123 as mixed templates. The effects of reaction temperature, time, and the addition of mixed templates on the phase structure, micromorphology, and optical absorption properties of the samples were investigated. The experimental results indicate that lower temperature and shorter reaction time facilitated the formation of spherical mesoporous alumina with excellent CO2 adsorption capacity. The periodic density functional theory (DFT) calculations demonstrate that both (110) and (100) surfaces of γ-Al2O3 can strongly adsorb CO2. The difference in the amount of CO2 adsorbed by Al2O3 is mainly due to the different surface areas, which give dif-ferent number of exposed active sites. This approach introduces a novel strategy for utilizing bi-ological compounds to synthesize spherical alumina and greatly enhances mesoporous alu-mina's application efficiency in adsorption fields. Moreover, the study explored the electro-chemical performance of the synthesized product using cyclic voltammetry and discovered im-proved loading of electrocatalysts and enhanced electrocatalytic activity.

This report discusses the application of approximate dynamic programming (ADP) and Krylov subspace methods in solving sequential decision-making problems in machine learning. ADP is used when dealing with large state spaces or when the exact dynamics are unknown. The paper explores various ADP methods such as temporal difference learning and least-squares policy evaluation. The authors also focus on the use of Krylov subspace methods for solving the Bellman equation in ADP. The report provides insights into linear approximation, stochastic algorithms, and the Arnoldi algorithm for orthonormal basis. Overall, the paper highlights the theoretical foundation provided by ADP and its connection with Krylov subspace methods, shedding light on their application in computer science.

The environmental pollution by microplastics (MPs) has become a growing concern, and measures are being taken in many countries. Long-term and extensive MPs investigations involving not only professional researchers but also the citizenry are needed to understand the pollution situation and to confirm the decreasing trend of MPs pollution as a result of pollution control measures. In this study, the author evaluated the accuracy of a simple method of investigating MPs on sandy beaches that can be conducted even by high school students. In a land survey using such simple tools as a tape measure and cardboard, 70% of the deviations were within approximately 20 cm when multiple surveys of approximately 20 m distance were performed. Even without heavy liquid, 89% of MPs could be recovered using only seawater. An investigation of MPs content by sampling 0.5 cm of the surface layer of sand could explain more than half of the MPs content when the sand was sampled to a depth of approximately 50 cm below the surface layer. A method in which the recovered MPs are not visually sorted but floating matter after boiling is considered as MPs is acceptable. If there was no concern about pumice contamination, the overestimation was within approximately 1.5 times. Simple laboratory equipment such as buckets, sieves, seawater, hot plates, dryers, and electronic balances could achieve lower limits of quantification of MPs of 13 mg-MPs/m2-sand and 2 mg-MPs/kg-sand.

60\% of the global population is expected to be in cities by 2050. At the same time the climate is changing at an accelerated rate. In this short paper we discuss the conflation of these two phenomena and how it may change the cities of the future. We formulate this as an optimization problem that could help answer important questions such as where are they likely to be located? How are they to be powered? What would be their demand for mobility and how might this be met? How over or under ground might these cities be? This paper first looks at a historical paradigm of cities in the absence of the climate crisis and then proposes the new paradigm to look at cities of the future faced by the challenge of the climate change crisis.

In this study, the elastic properties of composite materials are investigated, considering the effects of separation of fiber-matrix joint boundary and matrix failure. In this method, by assuming periodic microstructure and using a linear approximation of the displacement field by applying continuity and equilibrium conditions, the composite fiber composite relation is determined. The effect of separation is assumed by introducing tangential and normal scalar parameters in the equations by assuming the displacement field jump at the common boundary. In order to express the effect of matrix micro-cracks, the fracture mechanics framework of continuous environments was used and the micro-cracks parallel to the fibers, perpendicular to the fibers and in the thickness direction with scalar parameters were expressed. At the end of the effect of these parameters the results are presented in graphs. The results show that the presence of defective joint at the joint boundary and the matrix micro-components reduce the hardness of the composite and thus it’s bearing load, which is more significant at the defective joint state.

This article discusses the results of a series of experiments on pulse ultrasonic cure monitoring of carbon fiber reinforced plastics applied to the pultrusion process. The aim of this study is to validate the hypothesis that pulse ultrasonic cure monitoring can be applied a) for profiles having small cross sections such as 7x0.5 mm and b) within the environment of the pultrusion process. Ultrasonic transducers are adhesively bonded to the pultrusion tool as actuators and sensors. The time-of-flight and the amplitude of an ultrasonic wave are analyzed to deduce the current curing state of the epoxy matrix. The experimental results show that ultrasonic cure monitoring is indeed applicable even to very thin cross sections. However, significant challenges can be reported when the techniques are used during the pultrusion process.

In this paper, Akbari-Ganji's and Taylor series methods are applied to find analytical solutions to nonlinear differential equations that arise in an immobilized-cell photobioreactor. Approximate analytical expressions for substrate and product concentrations and both liquid and gas phases for various parameter values are derived using both methods. Efficiency, accuracy, and convergence of the two methods relative to highly accurate numerical methods are investigated to establish reliable profiles of these two methods for solving general nonlinear equations that model various physical phenomena. Numerical simulations are presented to validate the theoretical investigations.

This paper aims to analyze arbitrarily loaded isotropic rectangular thin plates with two opposite edges supported (simply supported or clamped), from which one or both are clamped, and the other edges have arbitrary support conditions. Rectangular thin plates with two opposite edges simply supported are routinely analyzed using for the deflection the simple trigonometric series of Lévy or the double trigonometric series of Navier, while the loads are expanded in Fourier series. In this paper the flexibility method (or force method) of the linear beam theory was applied whereby the unknowns were the bending moments along the opposite edges; in this regard, the primary system was the plate simply supported along the above-mentioned opposite edges and subjected to the external loads, system solved using the Lévy solution, and the redundant system was the plate simply supported along the opposite edges and subjected to bending moments along those edges. In the redundant system, a modified Lévy solution was introduced to account for the edge moments. The compatibility equations (vanishing of the slopes at selected positions of the opposite edges) were set to determine the unknowns and so the efforts in the plate. The results obtained were in good agreement with the exact results.

A considerable number of patients with angina or myocardial ischemia have no significant coronary artery disease on invasive angiography. In recent years, several steps towards a better comprehension of the pathophysiology of these conditions, angina or ischemia with non-obstructive coronary arteries (ANOCA/INOCA) have been made. Nevertheless, several gaps in knowledge still remain. This review is intended to provide a comprehensive overview of ANOCA and INOCA with a particular focus on pathophysiology, recent diagnostic innovations, gaps in knowledge and treatment modalities.

We firstly present two popular space discretization models of the Nonlinear Schrödinger Equation in dark solitons motion : the Direct-Discrete model and the Ablowitz-Ladik model. Applying the midpoint scheme to the space discretization models, we get two time-space discretization models: the Crank-Nicolson method and the New-Difference method. Secondly, we demonstrate that the solutions of the two space discretization models converge to the solution of the Nonlinear Schrödinger Equation. Also, we prove that the convergence order of the two time-space discretization models are O(h2+τ2) in discrete L2-norm error estimates. Finally, the numerical experiments agree well with the proven theoretical results.