Dynamic Applications Research Articles

Integral theorems for the gradient of a vector field, with a fluid dynamical application

The familiar divergence and Kelvin–Stokes theorem are generalized by a tensor-valued identity that relates the volume integral of the gradient of a vector field to the integral over the bounding surface of the tensor product of the vector field with the exterior normal. The importance of this long-established yet relatively little-known result is discussed. In flat two-dimensional space, it reduces to a relationship between an integral over an area and that over its bounding curve, combining the two-dimensional divergence and Kelvin–Stokes theorems together with two related theorems involving the strain, as is shown through a decomposition using a suitable tensor basis. A fluid dynamical application to oceanic observations along the trajectory of a moving platform is given. The potential extension of the generalized two-dimensional identity to curved surfaces is considered and is shown not to hold. Finally, the paper includes a substantial background section on tensor analysis, and presents results in both symbolic notation and index notation in order to emphasize the correspondence between these two notational systems.

Dynamical Behaviors and Abundant New Soliton Solutions of Two Nonlinear PDEs via an Efficient Expansion Method in Industrial Engineering

In this study, we discuss the dynamical behaviors and extract new interesting wave soliton solutions of the two significant well-known nonlinear partial differential equations (NPDEs), namely, the Korteweg–de Vries equation (KdVE) and the Jaulent–Miodek hierarchy equation (JMHE). This investigation has applications in pattern recognition, fluid dynamics, neural networks, mechanical systems, ecological systems, control theory, economic systems, bifurcation analysis, and chaotic phenomena. In addition, bifurcation analysis and the chaotic behavior of the KdVE and JMHE are the main issues of the present research. As a result, in this study, we obtain very effective advanced exact traveling wave solutions with the aid of the proposed mathematical method, and the solutions involve rational functions, hyperbolic functions, and trigonometric functions that play a vital role in illustrating and developing the models involving the KdVE and the JMHE. These new exact wave solutions lead to utilizing real problems and give an advanced explanation of our mentioned mathematical models that we did not yet have. Some of the attained solutions of the two equations are graphically displayed with 3D, 2D, and contour panels of different shapes, like periodic, singular periodic, kink, anti-kink, bell, anti-bell, soliton, and singular soliton wave solutions. The solutions obtained in this study of our considered equations can lead to the acceptance of our proposed method, effectively utilized to investigate the solutions for the mathematical models of various important complex problems in natural science and engineering.

Progress Tracker Using NextJs Framework

Abstract : The Progress Tracker built using the Next.js framework is a dynamic web application designed to help users monitor their advancement towards specific goals or milestones. Leveraging the capabilities of Next.js, this tracker offers a seamless user experience with server-side rendering, efficient client-side navigation, and easy scalability. Key features include real-time progress updates, customizable goal setting, intuitive data visualization, and responsive design for accessibility across devices. By employing Next.js, the application ensures high performance and SEO optimization, making it an ideal choice for individuals or teams striving for continuous improvement and goal attainment.

The MATERIAL framework: Modeling and AuTomatic code Generation of Edge Real-TIme AppLications under the QNX RTOS

Modern edge real-time automotive applications are becoming more complex, dynamic, and distributed, moving away from conventional static operating environments to support advanced driving assistance and autonomous driving functionalities. This shift necessitates formulating more complex task models to represent the evolving nature of these applications aptly. Modeling of real-time automotive systems is typically performed leveraging Architectural Languages (ALs) such as Amalthea, which are commonly used by the industry to describe the characteristics of processing platforms, operating systems, and tasks. However, these architectural languages are originally derived for classical automotive applications and need to evolve to meet the needs of next-generation applications. This paper proposes an automatic framework for the modeling and automatic code generation of dynamic automotive applications under the QNX RTOS. To this end, we extend Amalthea to describe chains of communicating tasks with multiple operating modes and to consider the QNX’s reservation-based scheduler, called APS, which allows providing temporal isolation between applications co-located on the same hardware platform. Finally, an evaluation is presented to compare different implementation alternatives under QNX that are automatically generated by our code generation framework.

Shear localization in gradient high-entropy alloy at high strain rates: Crystal plasticity modeling

Gradient-structured metals have attracted a lot of attention due to their good synergy between strength and ductility. However, whether they can be used as a candidate for effective prevention of shear localization failure during high strain rate deformations is still an open question. Corresponding to the particular mechanisms of dynamic recrystallization and twinning at high strain rates, a physically based constitutive model of crystal plasticity is developed, including a new evolution equation for dislocation density and a twinning model, to investigate the detailed process of shear localization of gradient-structured CoCrFeMnNi high-entropy alloys (HEAs). A physically based strain gradient theory is considered to capture the strengthening effect of gradient structures. The competition between microstructural softening and strengthening effects is quantified to reveal the effects of gradient-structure on shear localization and shear localization can be significantly delayed in gradient-structured HEAs. This study contributes to the understanding of the influence of gradient-structure on shear localization and provides insights for further optimization of the mechanical behavior of gradient-structures at high strain rates to develop strong and ductile metals and alloys for dynamic applications.

Energy injection adaptive technology for wireless power transmission based on parameter identification

AbstractThis paper presents an innovative adaptive strategy implemented in wireless power transmission (WPT) systems to overcome efficiency challenges due to coupling structure misalignment and load variability. A novel versatile transmitter, capable of alternating between single and dual voltage source states via two MOSFETs has been designed. Initially, the system operates in a single voltage source mode, mutual inductance and load characteristics are pinpointed using a particle swarm optimization algorithm. Subsequently, the system transitions to dual voltage source mode, where it fine‐tunes the auxiliary voltage source's amplitude and phase based on the determined mutual inductance and load values to realize zero phase angle for optimal impedance matching. The experimental results show that this method can obviously improve the output power of WPT system. This method stands out for its simplicity, adaptability and rapid response, suggesting its potential for practical use in dynamic charging applications.

Study of web application development methodology for a medical clinic using React

The article explores in detail the methodologies for developing web applications for medical clinics using the popular React library. The main goal of this study is the analysis of modern approaches to the creation of web applications that meet the specific requirements of medical institutions. Particular attention is paid to the functional and non-functional characteristics that are critical to such systems, including data security, user experience, integration with existing systems, and compliance with regulatory standards. First of all, a comprehensive analysis of the requirements of medical clinics for web applications was carried out. Data security is one of the most important aspects as healthcare institutions work with sensitive information. The study examines methods for ensuring the confidentiality and integrity of medical data, including compliance with HIPAA and GDPR standards. React makes it easy to integrate modern security tools such as JWT (JSON Web Tokens) for authentication and authorization, and use secure HTTP connections for data transfer. In addition, special attention is paid to the user experience (UX/UI), which should ensure the convenience and efficiency of using the web application by both medical personnel and patients. React allows you to create dynamic and interactive interfaces that can adapt to different devices and screens, thus providing a high-quality user experience. This is especially important for medical applications, where the accuracy and speed of interaction can affect the quality of healthcare delivery. A detailed description of best practices and approaches to web application development using React is provided. A component architecture is considered, which provides modularity and code reuse, which is a key factor in creating scalable and easily maintainable applications. Different methods of state management in the application, such as the use of Context API and Redux, have been explored, which allows for efficient data management and stable operation of the application. Guidelines for configuring routing using the React Router library are described, allowing you to create dynamic and multi-page applications while providing smooth navigation and a good user experience. In addition, ways to ensure application security are considered, including protection against XSS attacks, CSRF and other types of vulnerabilities, which is critical in medical applications. The article provides specific recommendations for implementing a web application for a medical clinic. They include practical code examples that demonstrate the implementation of key functionalities such as user registration and authentication, medical record processing, and database interaction. The process of setting up the infrastructure for development, testing and deployment of the application is described, including the use of CI/CD tools to automate these processes, which contributes to increasing the efficiency of development and the quality of the final product. The article provides valuable information for developers who seek to create effective and secure web applications for medical clinics using modern technologies. The conclusions and recommendations presented in the study will help ensure high quality and compliance of the developed solutions with modern industry standards and requirements, which will contribute to increasing the efficiency of the work of medical institutions and the quality of medical services provided to patients

Enhancing microalgae classification accuracy in marine ecosystems through convolutional neural networks and support vector machines

Accurately classifying microalgae species is vital for monitoring marine ecosystems and managing the emergence of marine mucilage, which is crucial for monitoring mucilage phenomena in marine environments. Traditional methods have been inadequate due to time-consuming processes and the need for expert knowledge. The purpose of this article is to employ convolutional neural networks (CNNs) and support vector machines (SVMs) to improve classification accuracy and efficiency. By employing advanced computational techniques, including MobileNet and GoogleNet models, alongside SVM classification, the study demonstrates significant advancements over conventional identification methods. In the classification of a dataset consisting of 7820 images using four different SVM kernel functions, the linear kernel achieved the highest success rate at 98.79 %. It is followed by the RBF kernel at 98.73 %, the polynomial kernel at 97.84 %, and the sigmoid kernel at 97.20 %. This research not only provides a methodological framework for future studies in marine biodiversity monitoring but also highlights the potential for real-time applications in ecological conservation and understanding mucilage dynamics amidst climate change and environmental pollution.

Justification of fire protection measures in the NPP control room using CFD fire modeling

Introduction. The present paper provides justification of fire safety measures to protect systems of the main control room of NPP using computational fluid dynamics fire modeling.Aim. To develop fire protection measures for systems of the NPP control room using computational fluid dynamics model.Materials and methods. The study involved analysis into purpose and application scope of various methods for modeling dynamics of development and spread of fire hazards. The application of the computational fluid dynamics fire modeling for multifunctional premises was considered.Results. Following the analysis of different methods for modeling the dynamics of development and spread of fire hazards, the present paper introduces the potential of using various methods of fire modeling in the evaluation of fire hazards for main control room. The obtained computations show that the temperature at the reinforcement site remains below the critical value in the most dangerous fire development scenarios like ventilation controlled fire. Moreover, fire hazards fail to spread through the walls of an uninsulated room within three hours at any value of fire load in main control room.Conclusions. The study revealed a potential for using computational fluid dynamics fire modeling for evaluating fire hazards in various buildings and premises, as well as for justifying the sufficiency of fire resistance requirements established for building structures. This regularity is obtained under conditions of preventing the spread of fire beyond the fire zone within the estimated burnout time of the entire fire load. The results received for this particular type of premises (cable floor) indicate that the designed fire resistance of the barriers separating safety system premises and normal operation premises guarantees non-proliferation of fire. The obtained regularities can be used in the development/revision of regulatory documents on fire safety at operating NPPs and NPPs under construction

Application of system dynamics to evaluate reduction targets for the entry of solid waste into a landfill site

The waste produced around the world increases daily due to population increase and the fact that life expectancy is longer. Waste production is higher in urban environments; therefore, managing Urban Solid Waste must be more rigorous to avoid environmental problems and health effects. For this reason, standards are created to properly manage landfills so that each life cycle stage is managed responsibly. However, a solid commitment from the community and more management regulations from the government are needed to be focused on reducing, reusing, and recycling materials as much as possible. This commitment needs to be reinforced to the extent that regulatory policies are created at the household level as much as education and awareness programs are implemented. This study presents a method to evaluate how imposing waste management goals before they reach landfills can increase the lifetime of landfills and, consequently the suitableness of a treatment plant implementation.

Atypical enantioseparation of a non-ionic form of allantoin with Cinchona alkaloid-based zwitterionic chiral stationary phases

Allantoin represents a compound widely employed in pharmaceutical and cosmetic fields. Its safety has been acknowledged by regulatory bodies such as the US Food and Drug Administration, the European Commission for Cosmetics and Consumer and Health and European Directorate for the Quality of Medicines & HealthCare. This justifies its wide use in dermatological/cosmetic formulations and allows their safe use.Allantoin possesses an asymmetric carbon atom, resulting in two enantiomers, with the (S)-enantiomer predominating in plants, although racemization may potentially occur during manufacturing processes. Notably, literature currently lacks enantioselective LC methods for allantoin analysis.In this study, two zwitterionic Cinchona alkaloid-based chiral stationary phases (CSPs), commercially known as CHIRALPAK® ZWIX(+) (CSP1) and CHIRALPAK® ZWIX(-) (CSP2), were utilized for the enantioseparation of allantoin under polar-ionic conditions. By employing a mobile phase consisting of acetonitrile/methanol/water/acetic acid (96:2:2:0.1, v/v/v/v), nearly complete baseline separation (with α=1.08) of allantoin enantiomers was achieved in less than 15 min with both CSPs. Due to the “pseudo-enantiomeric” nature of the two chiral selectors (quinine-based in CSP1 and quinidine-based in CSP2), an inversion of the enantiomer elution order was observed with the two CSPs under identical experimental conditions. Remarkably, this represents a rare instance where these CSPs demonstrate the ability to enantioseparate a non-ionic, non-ionizable species.The application of a molecular dynamics in silico protocol proved useful in elucidating the retention mechanism in depth, casting light on the central role of the H-bond formation and the involvement of the anionic moiety of the CSP 1.

Intragroup-to-intergroup game envelopment analysis: Multi-subgroup modeling, dual Nash equilibrium, and dynamic evaluation application

In the competitive decision-making process, the mutual competition caused by limited resources can help participants improve their decision-making effectiveness. When it comes to rational self-interest, the competing individuals and groups play mutual games with each other from a partial perspective. In other words, they focus only on the games among the related individuals or groups, ignoring the competition between their related groups or included individuals. Thus, the obtained game results could exclude parallel dependence and progressive transformation relationships between individuals and groups. To address these issues, this study first proposes the intragroup and intragroup-to-intergroup game envelopment analysis models that consider multiple subgroups as the link between individuals and groups to further realize the dual game. Next, we prove that the proposed models show Nash and dual Nash equilibrium phenomena under the multi-subgroup environment, which shows the dual stability of the optimal results after the multiple mutual game calculations. Furthermore, the optimization algorithm in the multi-subgroup environment is fused with the aforementioned models to make global decisions for the intragroup individuals and intragroup-to-intergroup subgroups in the same schedule. Finally, this study applies the new methods to make a dynamic evaluation analysis of different country's banking competitiveness. The derived results demonstrate the real effectiveness of the proposed methods.

The dynamic analysis, FPGA implementation, and adaptive synchronization control application of a multi‐vortex chaotic system based on nonlinear functions

The dynamic analysis, FPGA implementation, and adaptive synchronization control application of a multi‐vortex chaotic system based on nonlinear functions

Strength and functionalized borage biochar for effective elimination of nickel contamination: Insight into batch and dynamic flow mode treatment applications

A silica gel-modified borage biochar (BB@Si) was first produced and used as a binding agent for potentially hazardous Ni2+ ions in aqueous systems. The recommended biochar was more effective in eliminating Ni2+ than pristine biochar (BB). Its maximum qm could reach up to 1.39 × 10−3 mol/g at 30 °C, and sorption isotherms showed that the Langmuir model could more accurately define its sorption behavior. The Dubinin-Radushkevich isotherm also revealed that the average sorption energy ranged from 11.00 to 11.14 kJ/mol. Zeta potential tests, SEM images, and FT-IR scans confirmed the interactions between BB@Si and Ni2+ ions. Dynamic flow treatment studies showed high uptake effectiveness when the flow rate and amount of BB@Si were suitable. Nickel desorption yield of around 80% from BB@Si was noted with 0.01 M HCl. The BB@Si column's breakthrough and exhausted points were identified to be 45 and 352 min, respectively. Its maximum exhaustion capacity value was determined to be 52.73 mg/g. Ni2+ removal from the actual wastewater sample exceeded 75%. The resulting outcomes imply the immense potential of employing BB@Si in the treatment of Ni2+– contaminated aqueous systems.

Reinforcement learning for Multi-Flight Dynamic Pricing

Dynamic pricing is essential for airline revenue management, requiring quick adaptation to fluctuating market environments and complex customer behaviors. This study addresses the Multi-Flight Dynamic Pricing (MFDP) problem, which presents unique challenges due to interdependent demand between multiple flights and high dimensionality. Traditional studies often assume that the demand function modeling customer behavior is either known in advance or follows a predefined structure, failing to capture the dynamic nature of pricing decisions. To fill this gap, we develop deep reinforcement learning (DRL) algorithms—Deep Q-Network (DQN), Advantage Actor-Critic (A2C), Proximal Policy Optimization (PPO), and Trust Region Policy Optimization (TRPO). By formulating the MFDP problem as a Markov Decision Process (MDP), we design an innovative utility function for the Multinomial Logit (MNL) model that captures realistic features of the airline market, such as competition from high-speed rail, the effect of reference fares, and travel time. We compare the performance of our DRL algorithms with traditional algorithms, including Dynamic Programming (DP), Price Pooling (PP), Inventory Pooling (IP), and Inventory and Price Pooling (IPP). Our experiments demonstrate that DRL algorithms alleviate the curse of dimensionality faced by traditional algorithms, expedite the learning process, and deliver satisfactory performance without relying on predefined demand functions. Among these algorithms, TRPO shows superior performance, achieving 99% of the theoretical optimal revenue, proving its adaptability and stability in dynamic pricing applications. We also highlight the importance of considering the null price in the action space of MFDP problems. The larger the market scale, the more pronounced the effect of the null price in accelerating RL algorithm convergence, leading to more efficient computational resource utilization.

Nonstoichiometry-Induced Self-Activated Phosphors for Dynamic Anti-counterfeiting Applications.

Optical signals with distinctive properties, such as contactless, fast response, and high identification, are harnessed to realize advanced anti-counterfeiting. However, the simultaneous attainment of multi-color, -temporal, and -modal luminescence performance remains a compelling and imperative pursuit. In our work, a temperature/photon-responded dynamic self-activated luminescence originating from nonstoichiometric Zn2GeO4 is developed with the modulation of intrinsic defects. The increased concentration of oxygen vacancies (VO••) contributes to an enhanced recombination of ZnGe″-VO••, ultimately improving the self-activated luminescence performance. Additionally, the photoluminescence (PL) color of the representative Zn2.2GeO4 sample changes from green to blue-white with the increased ultraviolet (UV) irradiation time. Concurrently, the emission color undergoes a variation from blue to green as the ambient temperature raises from 280 to 420 K. Remarkably, green long persistent luminescence (LPL) and photostimulated luminescence (PSL) behaviors are observed. Herein, this study elucidates a sophisticated anti-counterfeiting approach grounded in the dynamic luminescent attributes of nonstoichiometric Zn2GeO4, presenting a promising frontier for the evolution of anti-counterfeiting technologies.

Regional-scale spatiotemporal landslide probability assessment through machine learning and potential applications for operational warning systems: a case study in Kvam (Norway)

The use of machine learning models for landslide susceptibility mapping is widespread but limited to spatial prediction. The potential of employing these techniques in spatiotemporal landslide forecasting remains largely unexplored. To address this gap, this study introduces an innovative dynamic (i.e., space–time-dependent) application of the random forest algorithm for evaluating landslide hazard (i.e., spatiotemporal probability of landslide occurrence). An area in Norway has been chosen as the case study because of the availability of a comprehensive, spatially, and temporally explicit rainfall-induced landslide inventory. The applied methodology is based on the inclusion of dynamic variables, such as cumulative rainfall, snowmelt, and their seasonal variability, as model inputs, together with traditional static parameters such as lithology and morphologic attributes. In this study, the variables’ importance was assessed and used to interpret the model decisions and to verify that they align with the physical mechanism responsible for landslide triggering. The algorithm, once trained and tested against landslide and non-landslide data sampled over space and time, produced a model predictor that was subsequently applied to the entire study area at different times: before, during, and after specific landslide events. For each selected day, a specific and space–time-dependent landslide hazard map was generated, then validated against field data. This study overcomes the traditional static applications of machine learning and demonstrates the applicability of a novel model aimed at spatiotemporal landslide probability assessment, with perspectives of applications to early warning systems.

Analysis of Closed Loop Current Controlled BLDC Motor Drive

Brushless direct current (BLDC) motors are mostly preferred for dynamic applications such as automotive industries, pumping industries, and rolling industries. It is predicted that by 2030, BLDC motors will become mainstream of power transmission in industries replacing traditional induction motors. Though the BLDC motors are gaining interest in industrial and commercial applications, the future of BLDC motors faces indispensable concerns and open research challenges. Considering the case of reliability and durability, the BLDC motor fails to yield improved fault tolerance capability, reduced electromagnetic interference, reduced acoustic noise, reduced flux ripple, and reduced torque ripple. To address these issues, closed-loop vector control is a promising methodology for BLDC motors. In the literature survey of the past five years, limited surveys were conducted on BLDC motor controllers and designing. Moreover, vital problems such as comparison between existing vector control schemes, fault tolerance control improvement, reduction in electromagnetic interference in BLDC motor controller, and other issues are not addressed. This encourages the author in conducting this survey of addressing the critical challenges of BLDC motors. Furthermore, comprehensive study on various advanced controls of BLDC motors such as fault tolerance control, Electromagnetic interference reduction, field orientation control (FOC), direct torque control (DTC), current shaping, input voltage control, intelligent control, drive-inverter topology, and its principle of operation in reducing torque ripples are discussed in detail. This paper also discusses BLDC motor history, types of BLDC motor, BLDC motor structure, Mathematical modeling of BLDC and BLDC motor standards for various applications. Key Words: Brushless direct current, industries, direct torque control.

Mortality Prediction by Kinetic Parameters of Lactate and S-Adenosylhomocysteine in a Cohort of Critically Ill Patients.

Tissue hypoxia is associated with the development of organ dysfunction and death in critically ill patients commonly captured using blood lactate. The kinetic parameters of serial lactate evaluations are superior at predicting mortality compared with single values. S-adenosylhomocysteine (SAH), which is also associated with hypoxia, was recently established as a useful predictor of septic organ dysfunction and death. We evaluated the performance of kinetic SAH parameters for mortality prediction compared with lactate parameters in a cohort of critically ill patients. For lactate and SAH, maxima and means as well as the normalized area scores were calculated for two periods: the first 24 h and the total study period of up to five days following ICU admission. Their performance in predicting in-hospital mortality were compared in 99 patients. All evaluated parameters of lactate and SAH were significantly higher in non-survivors compared with survivors. In univariate analysis, the predictive power for mortality of SAH was higher compared with lactate in all forms of application. Multivariable models containing SAH parameters demonstrated higher predictive values for mortality than models based on lactate parameters. The optimal models for mortality prediction incorporated both lactate and SAH parameters. Compared with lactate, SAH displayed stronger predictive power for mortality in static and dynamic application in critically ill patients.

Rapidly damping hydrogels engineered through molecular friction

Hydrogels capable of swift mechanical energy dissipation hold promise for a range of applications including impact protection, shock absorption, and enhanced damage resistance. Traditional energy absorption in such materials typically relies on viscoelastic mechanisms, involving sacrificial bond breakage, yet often suffers from prolonged recovery times. Here, we introduce a hydrogel designed for friction-based damping. This hydrogel features an internal structure that facilitates the motion of a chain walker within its network, effectively dissipating mechanical stress. The hydrogel network architecture allows for rapid restoration of its damping capacity, often within seconds, ensuring swift material recovery post-deformation. We further demonstrate that this hydrogel can significantly shield encapsulated cells from mechanical trauma under repetitive compression, owing to its proficient energy damping and rapid rebound characteristics. Therefore, this hydrogel has potential for dynamic load applications like artificial muscles and synthetic cartilage, expanding the use of hydrogel dampers in biomechanics and related areas.