Example Of Application Research Articles

Exploring the Influence of Ionic Liquid Anion Structure on Gas-Ionic Liquid Partition Coefficients of Organic Solutes Using Machine Learning.

This article presents an in-depth investigation into the influence of anionic structures of ionic liquids (ILs) on gas-ionic liquid partition coefficients (log K) of organic solutes in three ILs. While the primary objective was to examine whether there is a relationship between the molecular structure of the IL anion component and log K, additionally it was looked at whether the molecular descriptors of the anion in the relationships encode possible molecular interactions during the miscibility and partitioning in the IL. The research involves the compilation of data series of experimental log K values, where the cation component is constant. Such representative data series were obtained for three solutes─benzene, cyclohexane, and methanol─in three ILs with a uniform cationic component of methylimidazoliums. Using multiple linear regression models enhanced with machine learning techniques, the relationship between anionic structures and log K values was successfully quantified and modeled. Systematically selected molecular descriptors describing the anion structure show that in the case of methanol log K is strongly dependent on hydrogen bonds and Coulomb-dipolar interactions with the anion component, while in the case of benzene and cyclohexane the dispersion forces of the anion component are dominant. The outlier analysis and data interpretation highlight the need for extensive experimental data. The results confirm the initial hypothesis and provide valuable information on the role of the structure of the anionic component in determining the partitioning behavior of organic solutes. This knowledge is important for the design and optimization of ILs for specific applications, particularly as solvents in various industrial processes. The research also provides useful information about molecular interactions taking place in the interfaces of IL and organic additives in complex liquid media such as multicomponent electrolyte solutions, for example in energy storage applications.

Microstructures, phase and mechanical characterisation of Al2O3-ZrO2-TiO2 coating produced by atmospheric plasma spraying

The microstructure, crystallographic phases, and mechanical properties of a newly developed Al2O3 – TiO2 – ZrO2 ternary ceramic coating were characterized. The coatings were produced by atmospheric plasma spraying as a preblended powder on Ti-6Al-4 V substrates using the new generation of the Debye-Larmor cascaded plasma torch. The 400 μm thick as-sprayed ternary ceramic coating is compact and neither delamination nor inter-/trans-granular cracks were found. The coating consists of single phase α-Al2O3, monoclinic m-ZrO2, and a nanocrystalline dual phase structure of α-Al2O3 and m-ZrO2. Ti is either present as ZrTiO4 or as solute in the dual phase. Cracking from the tip of the indent is rare and delamination was not observed after the progressive scratch test. The coating has potential in high wear applications for example in medical devices.

3D Printable κ‐Carrageenan‐Based Granular Hydrogels

Abstractκ‐carrageenan, an algae‐extracted polysaccharide known for its emulsifying properties, is widely used in food and beauty products. Because of its abundance in nature, similarity to natural glycosaminoglycans, and biocompatibility, it is a promising alternative to animal gelatin for tissue engineering. Key to the more widespread use of κ‐carrageenan for biomedical applications is its processability, which is hampered by its temperature‐dependent rheological properties. Here, a κ‐carrageenan‐based ink is introduced that can be 3D printed at room temperature through direct ink writing (DIW). This is achieved by formulating κ‐carrageenan as microgels that are covalently crosslinked through a second network, resulting in double network granular hydrogels (DNGHs). These DNGHs can be stiffened to reach stiffnesses up to 0.9 MPa under tension and 1.1 MPa under compression through the addition of metal ions and glucose. The metal/glucose reinforcement also increases the work of fracture up to 1.1 MJ m−3, exceeding that of unmodified κ‐carrageenan DNGHs 50‐fold. The potential of the ink by room temperature 3D printing cm‐sized free‐standing load‐bearing structures is demonstrated. This ink is envisaged to be a well‐suited algae‐derived alternative for the animal‐based gelatin for tissue engineering and in food applications for example as soft confectionery products.

Photostability of Phenoxazine Derivatives.

Phenoxazine is a commonly used molecular building block, for example in optoelectronic applications and pharmaceuticals. However, it is highly susceptible to rapid photodegradation, especially in halogenated solvents. In the present study, we identify the degradation products in both halogenated and non-halogenated solvents by UV/Vis absorption, NMR spectroscopy and mass spectrometry. We also propose a substitution strategy aimed at effectively suppressing the high photoreactivity. Kinetic studies show that the quantum yield of photodegradationϕdiffers by a factor of more than 1000 between trisubstituted derivatives and N-substituted phenoxazine.

CAN Bus: The Future of Additive Manufacturing (3D Printing)

Additive Manufacturing (AM) is gaining renewed popularity and attention due to low-cost fabrication systems proliferating the market. Current communication protocols used in AM limit the connection flexibility between the control board and peripherals; they are often complex in their wiring and thus restrict their avenue of expansion. Thus, the Controller Area Network (CAN) bus is an attractive pathway for inter-hardware connections due to its innate quality. However, the combination of CAN and AM is not well explored and documented in existing literature. This article aims to provide examples of CAN bus applications in AM.

Kullback-Leibler Control in Boolean Control Networks.

This article addresses the Kullback-Leibler (KL) control problem in Boolean control networks. In the considered problem, an extended stage cost function depending on the control inputs is introduced; in contrast to a stage cost of the conventional KL control problems in the Markov decision process cannot take into consideration the control inputs. An associated Bellman equation and a matrix-based iteration algorithm are presented. The theoretical analysis shows that the proposed KL control results in an approximated form of conventional dynamic programming (DP). Furthermore, the convergence analysis is presented, with the weight parameter converging to zero and diverging to infinity. In practical application examples, a comparison of the conventional DP and proposed KL control is illustrated.

Exploring the Application of Gamification Thinking in Primary School Class Management

Young teachers in classroom management often use traditional management methods to get themselves into the quagmire of management difficulties. Gamification and gamification thinking has long been used in various fields, and as elementary school children are "fond of games", this paper analyzes the value of gamification thinking in elementary school classroom management, through the application of examples of classroom teachers to put forward specific requirements.

System Design for Sensing in Manufacturing to Apply AI through Hierarchical Abstraction Levels.

Activity recognition combined with artificial intelligence is a vital area of research, ranging across diverse domains, from sports and healthcare to smart homes. In the industrial domain, and the manual assembly lines, the emphasis shifts to human-machine interaction and thus to human activity recognition (HAR) within complex operational environments. Developing models and methods that can reliably and efficiently identify human activities, traditionally just categorized as either simple or complex activities, remains a key challenge in the field. Limitations of the existing methods and approaches include their inability to consider the contextual complexities associated with the performed activities. Our approach to address this challenge is to create different levels of activity abstractions, which allow for a more nuanced comprehension of activities and define their underlying patterns. Specifically, we propose a new hierarchical taxonomy for human activity abstraction levels based on the context of the performed activities that can be used in HAR. The proposed hierarchy consists of five levels, namely atomic, micro, meso, macro, and mega. We compare this taxonomy with other approaches that divide activities into simple and complex categories as well as other similar classification schemes and provide real-world examples in different applications to demonstrate its efficacy. Regarding advanced technologies like artificial intelligence, our study aims to guide and optimize industrial assembly procedures, particularly in uncontrolled non-laboratory environments, by shaping workflows to enable structured data analysis and highlighting correlations across various levels throughout the assembly progression. In addition, it establishes effective communication and shared understanding between researchers and industry professionals while also providing them with the essential resources to facilitate the development of systems, sensors, and algorithms for custom industrial use cases that adapt to the level of abstraction.

Problems Faced by Bank Employees in Electronic Banking Applications Example of TRNC Bank Employees

Problems Faced by Bank Employees in Electronic Banking Applications Example of TRNC Bank Employees

The core idea of hypothesis testing and its application in examples

With the development of social science and technology, probability and mathematical statistics have been widely used in many fields. It also includes significant knowledge, such as parameter estimation and hypothesis testing. Hypothesis testing plays an important role in plenty of fields. This article introduces the basic theory of hypothesis testing, such as null hypothesis and alternate hypothesis. Apply test statistics and the level of significance to select the rejection rejoin. There are also several advantages to using p-value in hypothesis testing. In head up display (HUD) display accuracy test, the accuracy test decision process is a crucial process. Applying hypothesis testing to the accuracy test decision process greatly improves HUD testing solutions. Meanwhile, the risks of both producers and users are also taken into consideration. In the field of automotive engineering, the detection of Max-torque and Max-power also requires hypothesis testing to ensure whether the productions are qualified. Thus, hypothesis testing improves the way of products are tested in the industry.

Empirical heat transfer correlations of high-pressure transcritical fluids at low Reynolds numbers

Empirical heat transfer correlations serve as indispensable tools for predicting and optimizing the thermal performance of a wide range of engineering devices, like for example in energy conversion and propulsion applications. Although a remarkable number of correlations exist for systems at low (atmospheric) pressures, accurately quantifying heat transfer under high-pressure transcritical conditions poses a notable challenge, especially at low Reynolds numbers due to the scarcity of correlations available in the literature. This difficulty has prompted the widespread use of general empirical correlations, which are inadequate for such flow conditions, and hold significant implications, for instance, in fields like microfluidics technology. In this regard, the aim of this study is to develop novel heat transfer correlations tailored for high-pressure transcritical fluids at relatively low Reynolds numbers. These correlations are derived from a dataset of 18 direct numerical simulations of carbon dioxide confined between differentially heated walls, which induce a transcritical trajectory through the pseudo-boiling region. The correlations are developed for both the cold liquid-like and hot gas-like regions, resulting in a total of 6 correlations that encompass the laminar, transitional, and turbulent flow regimes. In particular, as a function of Reynolds, Prandtl, Eckert and Mach numbers, the proposed empirical correlations provide accurate estimations with relative errors below 8%, which significantly surpass existing literature correlations.

On the Synthesis and Structure of 'Naked' Ga(I) and In(I) Salts and the Surprising Stability of Simple Ga(I) and In(I) Salts in the Coordinating Solvents Ether and Acetonitrile.

In this work, we present the solid-state structures of solvent-free Ga[pf] and In[pf] salts ([pf]-=[Al(ORF)4]-; RF=C(CF3)3), which are very rare examples of salts with truly 'naked' metal cations. Both salts may serve as starting materials for subvalent gallium and indium chemistry with very weakly coordinating ligands providing the freedom of choice for solvents and ligands for the future. On the other hand, we report and rationalize the formation and isolation of [M(OEt2)2][pf] and [M(MeCN)2][pf] (M=Ga, In), underlining the surprising stability of these subvalent group 13 M+ ions against disproportionation. Unexpectedly, dicoordinate and carbene analogous [M(L)2]+ ions with the [pf]- counterion are stable in L=acetonitrile and diethyl ether at room temperature, opening up possible applications for example in organic synthesis and catalysis.

A comparison of methods for estimating parameters of the stochastic Lomax process: through simulation study

In this paper, we study the problem of parameter estimation of the stochastic Lomax diffusion process, this process was introduced in [A. Nafidi, I. Makroz, and R. Gutiérrez Sánchez, A stochastic lomax diffusion process: Statistical inference and application, Mathematics, 2021][14], and the authors suggested the method of simulated annealing to find the maximum likelihood estimators of this process. In this work, we propose alternative methods for finding the maximum likelihood estimators, namely Genetic algorithm and Nelder-Mead, we also investigate the use of Markov Chain Monte Carlo method to determine the model parameters. Finally, an example of application through the simulation of paths for the process is suggested. Then, a comparison is made between the application of three algorithms based on their accuracy and time of execution.

Clustering analysis for Pythagorean fuzzy sets and its application in multiple attribute decision making

Pythagorean fuzzy sets, as a generalization of intuitionistic fuzzy sets, have a wide range of applications in many fields including image recognition, data mining, decision making, etc. However, there is little research on clustering algorithms of Pythagorean fuzzy sets. In this paper, a novel clustering idea under Pythagorean fuzzy environment is presented. Firstly, the concept of feature vector of Pythagorean fuzzy number (PFN) is presented by taking into account five parameters of PFN, and some new methods to compute the similarity measure of PFNs by applying the feature vector are proposed. Furthermore, a fuzzy similarity matrix by utilizing similarity measure of PFNs is established. Later, the fuzzy similarity matrix is transformed into a fuzzy equivalent matrix which is utilized to establish a novel Pythagorean fuzzy clustering algorithm. Based on the proposed clustering algorithm, a novel multiple attribute decision making (MADM) method under Pythagorean fuzzy environment is presented. To illustrate the effectiveness and feasibility of the proposed technique, an application example is offered.

Measuring Interest: Development and Application of a Three-Dimensional Situational Interest Short Scale

<p>Situational interest is an important factor that has a great influence on learning success in both in-school and out-of-school learning situations. Although there has been extensive research on interest in its diverse forms for decades, an evaluated measurement instrument for situational interest that covers the three theoretically defined components of interest (emotional, cognitive, value-related) is still missing. Therefore, in this study, based on person-object theory of interest, a short scale was developed that can be used in a variety of learning programs independent of content or methods. In study 1, eight suitable items were selected and their structure was examined using exploratory methods. In study 2, the results of study 1 were verified using confirmatory factor analyses. Study 3 shows an example of a practical application of the newly developed scale in two different learning settings. The findings provide evidence that the scale developed here is a practical instrument to measure situational interest taking into account all its components. On the one hand, the scale can help teachers evaluate their educational programs; on the other hand, it can be used by researchers to empirically investigate the construct of interest. Thus, the scale makes an important contribution to research and practice.</p>

Event-Triggered Adaptive Saturated Fault-Tolerant Control for Unknown Nonlinear Systems With Full State Constraints

This paper investigates the issue of event-triggered adaptive saturated fault-tolerant control (ESFC) for uncertain nonlinear systems with time-varying full state constraints (TFSCs), actuator saturation and faults as well as unknown control direction. A bounded function with an auxiliary variable is constructed by utilizing a novel dynamics of the auxiliary system, which contributes to reducing the adverse impact of actuator saturation. Different from the previous backstepping-based event-triggered control methods such specifications by either using fuzzy approximation or by employing neural approximation techniques, this paper skillfully addresses the unknown nonlinearities, actuator saturation and faults without involving any approximation structures, and thus, we proposes the ESFC on the basis of low-complexity design framework as contributing to communication and computational resource reduction. A rigorous theoretical analysis shows that the proposed control method is an effective way to handle with the problems of actuator saturation and faults, full state constraints, and unknown system uncertainties, while simultaneously simplifying the backstepping design and avoiding the issue of explosion of complexity. The asymptotic stability of the closed-loop system is guaranteed and the Zeno behavior can be effectively removed. We present an application example of a linear motor scenario to illustrate the effectiveness of the method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Since state constraints, actuator saturation and faults, unknown mechanism model, and limited bandwidths exist extensively in practical engineering systems, which constantly degrade the operation performance of the plant. To handle these disadvantages, this paper is focus on providing simple but effective ESFC methods to ensure the asymptotic stability and enhance reliability. Compared to existing results, the presented method only uses the state signals of system without using system dynamic functions under mild conditions, which provides a theoretical basis, and has the advantages of low-complexity design, and easy implementation in practical engineering. Preliminary physical experimental comparisons demonstrate that this method is applicable to practical liner-motor platform, and achieves satisfactory control performance.

Composing MPC With LQR and Neural Network for Amortized Efficiency and Stable Control

Model predictive control (MPC) is a powerful control method that handles dynamical systems with constraints. However, solving MPC iteratively in real time, i.e., implicit MPC, remains a computational challenge. To address this, common solutions include explicit MPC and function approximation. Both methods, whenever applicable, may improve the computational efficiency of the implicit MPC by several orders of magnitude. Nevertheless, explicit MPC often requires expensive pre-computation and does not easily apply to higher-dimensional problems. Meanwhile, function approximation, although scales better with dimension, still requires pre-training on a large dataset and generally cannot guarantee to find an accurate surrogate policy, the failure of which often leads to closed-loop instability. To address these issues, we propose a triple-mode hybrid control scheme, named Memory-Augmented MPC, by combining a linear quadratic regulator, a neural network, and an MPC. From its standard form, we derive two variants of such hybrid control scheme: one customized for chaotic systems and the other for slow systems. The proposed scheme does not require pre-computation and is capable of improving the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">amortized</i> running time of the composed MPC with a well-trained neural network. In addition, the scheme maintains closed-loop stability with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">any</i> neural networks of proper input and output dimensions, alleviating the need for certifying optimality of the neural network in safety-critical applications. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article was motivated by the need to reduce the amortized cost of MPC in repetitive industrial robotic applications, where long-term operational cost is important and safety is critical. Examples of such applications include factory robotic arm manipulation and fixed-route quadcopter payload transport. Unlike explicit MPC or function approximation, our approach does not require any pre-computation or pre-training. Rather, it attains task proficiency over time by learning a surrogate neural network on the spot and by gradually replacing the costly MPC with the more efficient surrogate model so long as safety permits. Consequently, the proposed scheme incurs a learning cost during the initial phase of the deployment but usually becomes more adept on the task afterwards, leading to amortized efficiency.

İşletmelerde Stratejik Planlama ve Bütçeleme ile Finansal Modelleme Uygulama Örneği

Bu çalışmada, işletmelerin finansal tablolarının birbiri ile entegrasyonu ve böylelikle denetiminin daha kolay yapılması ve işletme yönetimine raporlanması; bu yolla gelecek dönem için alınması gereken önlemlerin önceden tespiti amaçlanmış, örnek bir uygulama yapılarak finansal modellemenin bir işletmede nasıl yapılması gerektiği üzerinde durulmuştur. Stratejik planlama doğrultusunda işletme bütçesinin yapılması, nakit akış tablosu, yatırım raporu ve raporlama tablolarının entegre edilmesi suretiyle işletme için daha verimli ve etkin kullanılabilecek ‎bir finansal modelleme ‎sistemi tasarlanıp, uygulamalı olarak anlatılmıştır. Çalışmada ilk olarak “İşletmelerde stratejik planlama” ‎kavramı ele alınıp anlatılmıştır, stratejik ‎planlama ile işletmenin GZFT/SWOT analizi ve işletmelerde bütçeleme konusuna değinilmiştir.‎ Çalışmanın amacı, işletmelerin en büyük problemi olan geleceği ön görememelerine çözüm olarak işletmelerde finansal modelleme sistemiyle gelecek dönemler için işletmenin misyon ve vizyonu doğrultusunda belirlenen hedeflerin takibinin yapılması ve yönetime rapor edilerek gerekli önlemlerin önceden alınmasını sağlamaktır. Finansal modelleme özelinde işletme bütçelerinin yönetim aracı olarak sahip olduğu potansiyel gücü göstermek de çalışmanın amaçları arasında yer almakta ve çalışmada kurgusal örnek bir uygulama çalışması yapılarak daha iyi anlaşılması sağlanmıştır.

Dispersion state analysis in hot melt extruded, highly drug-loaded 3D printing filaments applying Raman microscopy

Abstract Objectives Highly drug-loaded polymer formulations are favorable intermediates in pharmaceutical applications, for example, for the individualized production of medicines via 3D printing with the maximum flexibility regarding dose strength and drug combination in a single dosage form. However, high-disperse drug loads are challenging for the process itself and the (intermediate) product properties, making the knowledge about the dispersion state of the drug particles achieved by the production process helpful to overcome such challenges. Methods Therefore, a novel dispersion state analysis technique based on scanning Raman microscopy is proposed in the present study and applied to HPMC filaments loaded with 20 wt%, 40 wt%, and 60 wt% theophylline. The qualitative and quantitative evaluations of the scans were correlated to melt viscosities, process data, and mechanical properties of the filaments. Key findings The analyses revealed not only an increasing particle size reduction with increasing drug load and thus increasing viscosity and mechanical energy input. The particle size reduction also caused a change in the filaments’ mechanical properties from elastic ductile to rigid brittle. Furthermore, an alignment of the elongated drug particles with the frozen three-dimensional flow pattern of the extruder and not only with the extrusion direction was elucidated. Conclusions Therefore, the necessity to investigate the dispersion state further is highlighted for future studies on highly drug-loaded polymer formulations, providing a novel measuring technique applicable to challenging pharmaceutical formulations.

Optimal Opacity-Enforcing Supervisory Control of Discrete Event Systems on Choosing Cost

To ensure opacity, it is optimal to retain as many as possible occurring event sequences. Contrary to this problem, the other optimal goal is to preserve the minimal occurring event sequences. Based on the choosing cost, an optimal opacity-enforcing problem with minimal discount choosing cost is presented under two constraints in this paper. The first constraint is the opacity of the controlled system. The second is the retention of the secret to the maximum. To solve the model, two scenarios on opacity are considered. For the two scenarios, some algorithms are presented to achieve the optimal solution for the model by using the method of dynamic programming. Then, the solutions produced by the algorithms are proved to be correct by theoretical proof. Finally, some illustrations and an application example on location privacy protection for the algorithms are given.