Efficiency Of Method Research Articles

Using Affine Arithmetic to Design a Controller for Large Uncertain System

This study presents a approach for creating the supervisor of expansive uncertain systems. The Controller for a particular high order system is designed using a reduced order model. The numerator and denominator polynomial in the suggested reduction approach is derived using modified polynomial differential method. A lower order model with least ISE optimization is obtained. Assuming that the initial high-order system is stable, the proposed approach guarantees the stability of the streamlined model. Through With reference to the original high-order systems, a PID controller is created for the suggested low-order model. In order to explain the method's efficiency, a few numerical examples were taken into consideration. It has been demonstrated that applying Control from the lower order model to the higher order system is improved and the controlled system's performance. Common numerical illustrations seen in the literature have been used to test the method, and the results show that it works satisfactorily.

A Systematic Review of Applications of Generative Design Methods for Energy Efficiency in Buildings

Energy efficiency is a principle of architectural design that reduces environmental impact. Generative design can offer alternative options to improve energy efficiency in buildings, but significant gaps exist in the application due to accessing complex knowledge. This study aimed to explore publications on generative design and energy efficiency in buildings and identify generative methods for energy efficiency topics. This study conducted a systematic review using the PRISMA methodology in December 2023 by searching publications from databases including Scopus, Google Scholar, and Thai Journals Online. Descriptive analysis examined 34 articles, showing the publication year, source, and citations. Comparative qualitative and descriptive analysis identified generative methods. Publications are increasing over time, and further growth is expected related to the accessibility of computational design and practical applications. Tools and frameworks demonstrated reduced energy usage compared to prototypes or traditional design approaches. The most studied is thermal performance, which was reduced by 28%. Energy performance achieved up to a 23.30% reduction, followed by others and daylighting. In addition to single-topic studies, there are also studies with multiple topics. Evolutionary algorithms are standard. Parametric search strategies have increased. Exploration reveals rule-based and mixed methods. Machine learning and AI garner attention.

The Literature Review on Curriculum Implementation Problems

Background: Changes in knowledge and technology are continually shaping the educational environment, thereby influencing curriculum implementation. Innovations are being made to meet the evolving educational goals, which includes overcoming various barriers to curriculum implementation. Objective: The aim of this study was to examine the existing literature on curriculum implementation problems, particularly focusing on the issues faced by educational institutions in effectively applying educational theories and practices. Methods: A comprehensive literature review was conducted, utilizing databases such as PubMed, ERIC, Google Scholar, and JSTOR. Articles published from 1997 to 2022 that discussed challenges and strategies in curriculum implementation across various educational levels and settings were included. Thematic analysis was employed to synthesize the data, identifying common themes related to the challenges and solutions of curriculum implementation. Results: The review identified critical challenges related to four main areas: teachers, students, the curriculum itself, and institutional factors. Teacher-related challenges included inadequate professional development and inefficient teaching methods. Student-related challenges highlighted diverse learning needs and varying levels of engagement. Institutional challenges pointed to resource limitations and insufficient stakeholder support. Curriculum-related challenges were mostly about alignment and compatibility with educational goals. Conclusion: Effective curriculum implementation is pivotal in educational settings and requires comprehensive strategies to address the myriad of challenges identified. Enhancing teacher training, improving resource allocation, and ensuring curriculum relevance are crucial for improving educational outcomes.

Drug repositioning based on tripartite cross-network embedding and graph convolutional network

Drug-disease association prediction is an important part of drug discovery, which can help researchers uncover potential drug candidates and disease targets more accurately to deal with the drawbacks of inefficiency and high cost of traditional methods. In contrast, past drug-disease prediction processes often overlooked the associations between drugs and side effects and the impact of proteins on diseases. These issues hindered models from fully learning the potential information within the network. Hence, a method capable of comprehensively learning from multi-source heterogeneous information and extracting high-level features of biological associations is needed. To overcome these challenges, proposes a drug repositioning method based on a tripartite cross-network embedding graph convolutional network, called TGCNDR, to predict the associations between drugs and diseases. Firstly, we construct a tripartite cross-network of drug-disease associations, drug-protein associations, and drug-side effect associations. TGCNDR employs graph convolutional networks to capture information from different nodes to learn drug embedding. Subsequently, TGCNDR uses anchor links for knowledge transfer, and different networks use the Self-attention mechanism to represent drug importance. Experiments on benchmark datasets demonstrate that TGCNDR exhibits good performance (the average AUROC is 0.922, the average AUPR is 0.916, and the average F1-score is 0.860) compared with state-of-the-art methods. In the case study, we perform molecular docking experiments on the predicted drug candidates and identify newly approved drugs for osteosarcoma, which suggests that TGCNDR is an effective tool for drug repositioning.

Optimization for industrial robot joint movement in non-horizontal 3D printing application

When a robot is printing a sequence of non-horizontal goal poses, its joint values often undergo significant variations, resulting in challenges such as singularities or exceeding joint limits. This paper proposes two new methods aimed at optimizing goal poses to solve the problem. The first method, employing an analytical approach, modifies the goal poses to maintain the 4th joint value of a 6-axis industrial robot at zero. This adjustment effectively reduces the motion range of the 5th and 6th axes. The second method utilizes numerical optimization to adjust the goal poses, aiming to minimize the motion range of all joints. Leveraging the analytical method to obtain one good initial value, numerical optimization is subsequently applied to complete the entire path optimization, creating an optimization workflow. It is also possible to use only analytical methods for computational efficiency. The feasibility and effectiveness of these two methods are validated through simulation and real project case.

Adaptive Random Mac Strategy for IoT Security Through Network Forensics Investigation

In the rapidly evolving landscape of digital systems and networks, ensuring robust cyber security measures is of paramount importance. This study delves into the evaluation of a proposed security method's efficiency through a comprehensive comparative analysis conducted under diverse security breach simulations. The objective is to discern the method's effectiveness in safeguarding sensitive information and maintaining system integrity.The research methodology involves the selection of representative security breach scenarios, each emulating distinct attack vectors and potential vulnerabilities. A benchmark security protocol, widely acknowledged for its reliability, serves as a reference for the proposed method's assessment. A series of controlled breach simulations are executed, simulating real-world cyber threats, ranging from sophisticated malware intrusions to social engineering attacks. Key performance metrics, such as response time, resource utilization, and data recovery rates, are meticulously recorded and analyzed for both the benchmark and proposed methods. Furthermore, the impact on system functionality and user experience is evaluated to comprehend the trade-off between heightened security and seamless user interaction. The experiments are conducted across multiple system architectures to account for variations in hardware and software configurations. The findings reveal nuanced insights into the proposed method's efficiency under distinct breach scenarios. It showcases commendable resilience against certain attack vectors while potentially exposing limitations in others. The comparative analysis provides a comprehensive overview of the method's overall efficacy and enables the identification of scenarios where enhancements might be necessary. The performance of the suggested approach under various security breach simulations is empirically demonstrated in this paper, adding to the body of current knowledge. The insights gained from this research aid in refining the method's design and implementation, bolstering its potential to address the dynamic and evolving landscape of cyber security threats. As digital systems continue to play an increasingly integral role in our lives, the significance of such evaluations cannot be overstated, ensuring that our vital digital infrastructure remains secure and resilient.

Application of Developmental Regulators for Enhancing Plant Regeneration and Genetic Transformation.

Establishing plant regeneration systems and efficient genetic transformation techniques plays a crucial role in plant functional genomics research and the development of new crop varieties. The inefficient methods of transformation and regeneration of recalcitrant species and the genetic dependence of the transformation process remain major obstacles. With the advancement of plant meristematic tissues and somatic embryogenesis research, several key regulatory genes, collectively known as developmental regulators, have been identified. In the field of plant genetic transformation, the application of developmental regulators has recently garnered significant interest. These regulators play important roles in plant growth and development, and when applied in plant genetic transformation, they can effectively enhance the induction and regeneration capabilities of plant meristematic tissues, thus providing important opportunities for improving genetic transformation efficiency. This review focuses on the introduction of several commonly used developmental regulators. By gaining an in-depth understanding of and applying these developmental regulators, it is possible to further enhance the efficiency and success rate of plant genetic transformation, providing strong support for plant breeding and genetic engineering research.

Determination of trace levels of thiazopyr residues in oolong tea samples using metal sieve-linked double syringe based liquid-phase microextraction coupled with gas chromatography-mass spectrometry

Thiazopyr has raised concerns about its potential impact on human health. Hence, detection of trace levels of thiazopyr herbicide has become critically important. This study presents a highly precise and accurate method to determine thiazopyr at trace levels using a combination of metal sieve-linked double syringe based liquid-phase microextraction (MSLDS-LPME) and Gas Chromatography-Mass Spectrometry (GC-MS) system. A thorough optimization of all parameters affecting the method's efficiency was performed using a univariate optimization approach, followed by an evaluation of the system's analytical performance. Employing the developed method in conjunction with the MSLDS-LPME-GC-MS system yielded a limit of detection (LOD) of 2.73 µg/kg and a limit of quantification (LOQ) of 9.11 µg/kg. To assess the method's applicability, a recovery study was conducted using oolong tea as a real sample matrix, employing a matrix-matching calibration approach. The results of recovery studies demonstrated acceptable recoveries ranging from 87.5 % to 101.9 %.

Exploring Data Input Problems in Mixed Reality Environments: Proposal and Evaluation of Natural Interaction Techniques

Data input within mixed reality environments poses significant interaction challenges, notably in immersive visual analytics applications. This study assesses five numerical input techniques: three benchmark methods (Touch-Slider, Keyboard, Pinch-Slider) and two innovative multimodal techniques (Bimanual Scaling, Gesture and Voice). An experimental design was employed to compare these techniques’ input efficiency, accuracy, and user experience across varying precision and distance conditions. The findings reveal that multimodal techniques surpass slider methods in input efficiency yet are comparable to keyboards; the voice method excels in reducing cognitive load but falls short in accuracy; and the scaling method marginally leads in user satisfaction but imposes a higher physical load. Furthermore, this study outlines these techniques’ pros and cons and offers design guidelines and future research directions.

Vitamin Deficiency Detection from eye Using Machine Learning

Vitamin deficiencies affect millions globally, with over two billion people at risk. Early detection is crucial, yet one in three youngsters lacks proper vitamin intake. Addressing this, a novel machine learning-based approach, using eye images instead of blood samples, offers a free desktop application for vitamin deficiency detection. The software, trained to distinguish healthy and deficient eyes, provides users with comprehensive reports. Early identification of deficiencies can prevent anemia, infectious illnesses, and developmental issues. Real-world trials confirm the method's superior efficiency compared to previous approaches, marking a promising advancement in global health. Keywords: Machine Learning, Opthalmology, YOLO, Deep Learning, Image preprocessing , extraction, ultralytics.

Iterative kernel regression with preconditioning

Kernel methods are popular in nonlinear and nonparametric regression due to their solid mathematical foundations and optimal statistical properties. However, scalability remains the primary bottleneck in applying kernel methods to large-scale data regression analysis. This paper aims to improve the scalability of kernel methods. We combine Nyström subsampling and the preconditioned conjugate gradient method to solve regularized kernel regression. Our theoretical analysis indicates that achieving optimal convergence rates requires only [Formula: see text] memory and [Formula: see text] time (up to logarithmic factors). Numerical experiments show that our algorithm outperforms existing methods in time efficiency and prediction accuracy on large-scale datasets. Notably, compared to the FALKON algorithm [A. Rudi, L. Carratino and L. Rosasco, Falkon: An optimal large scale kernel method, in Advances in Neural Information Processing Systems (Curran Associates, 2017), pp. 3891–3901], which is known as the optimal large-scale kernel method, our method is more flexible (applicable to non-positive definite kernel functions) and has a lower algorithmic complexity. Additionally, our established theoretical analysis further relaxes the restrictive conditions on hyperparameters previously imposed in convergence analyses.

Multifunctional Deep Eutectic Solvent-Catalyzed Synthesis of Dihydropyrimidinethiones: A Sustainable Approach for Green and Efficient Reactions

Background: This study investigates the synthesis of dihydropyrimidinethiones using a multifunctional deep eutectic solvent (DES) composed of Choline chloride (ChCl) and ammonium thiocyanate. This DES serves as a catalyst, solvent, and reagent, providing a simple, highyielding, and environmentally friendly method for dihydropyrimidinethione synthesis. The use of DES in this capacity offers several advantages, including reduced environmental impact, high efficiency, and ease of use, highlighting its potential as a sustainable alternative in organic synthesis. Objective: The objective of this study is to investigate the application of a deep eutectic solvent (DES) composed of ChCl and ammonium thiocyanate as a catalytic solvent and reagent system for synthesizing dihydropyrimidinethiones. The aim is to simplify the reaction setup, improve yields, and enhance the green metrics of the process. Methods: ChCl and ammonium thiocyanate were combined to form a DES catalyst-solvent system. Dihydropyrimidinethiones were synthesized in one-pot reactions at ambient temperature. Green metrics and DES recovery were evaluated. Comparative analysis with traditional methods was conducted. Results: The DES efficiently catalyzed dihydropyrimidinethione synthesis with high yields. Simplified reaction setup, safe solvent properties, and favorable green metrics. DES was recoverable and reusable, outperforming traditional methods in efficiency and eco-friendliness. Conclusion: The ChCl and ammonium thiocyanate DES demonstrated remarkable efficiency and eco-friendliness in dihydropyrimidinethione synthesis. The toxicity-free, multifunctional roles of the DES, serving as a catalyst, solvent, and reagent, highlight its novelty and potential as a sustainable alternative in organic chemistry. This study simplifies the synthesis process and improves yields and green metrics, showcasing the DES as a promising candidate for future research and industrial applications.

Adaptive variational low-rank dynamics for open quantum systems

We introduce a model-independent method for the efficient simulation of low-entropy systems, whose dynamics can be accurately described with a limited number of states. Our method leverages the time-dependent variational principle to efficiently integrate the Lindblad master equation, dynamically identifying and modifying the low-rank basis over which we decompose the system's evolution. By dynamically adapting the dimension of this basis, and thus the rank of the density matrix, our method maintains optimal representation of the system state, offering a substantial computational advantage over existing adaptive low-rank schemes in terms of both computational time and memory requirements. We demonstrate the efficacy of our method through extensive benchmarks on a variety of model systems, with a particular emphasis on multiqubit bosonic codes, a promising candidate for fault-tolerant quantum hardware. Our results highlight the method's versatility and efficiency, making it applicable to a wide range of systems characterized by arbitrary degrees of entanglement and moderate entropy throughout their dynamics. We provide an implementation of the method as a Julia package, making it readily available to use. Published by the American Physical Society 2024

Research on the calculation and evaluation method of transmission efficiency of noncircular planetary gear

The torque fluctuation characteristics of noncircular planetary gear (NPG) systems are difficult to obtain, and there is no clear analysis method and evaluation method for transmission efficiency, which seriously affects their service performance. Given that, this article establishes a torque fluctuation model for NPG systems based on the principle of gear meshing and differential geometry theory and methods, taking into account factors such as actual working conditions and speed loads. And proposed, the comprehensive transmission rate, a new method for evaluating the efficiency of NPG systems. By analyzing the actual working conditions of NPG systems, the influence mechanism of alternating load and speed on torque fluctuation and transmission efficiency was explored. The research results indicate that the alternating load can cause an increase in system impact and vibration. The comprehensive transmission rate of NPG systems decreases with increasing rotational speed and increases with increasing load, and is more sensitive to changes in rotational speed. The trend of comprehensive transmission rate and transmission efficiency of NPG systems is basically consistent, indicating that the proposed method of using comprehensive transmission rate to evaluate transmission efficiency is a practical and feasible analysis method. The research results can provide a new method for dynamic measurement and evaluation of torque and transmission efficiency of non-circular gear transmission systems.

Online prediction of the enzymatic hydrolysis efficiency of crop straw

The extent of removal of lignin and hemicellulose are crucial indicators for evaluating the efficiency of enzymatic hydrolysis of crop straw. Numerous factors influence these two indices. Establishing a quantitative model that correlates these factors with hydrolysis efficiency is essential, as it can guide efficient hydrolysis. In this study, a predictive method for enzymatic hydrolysis efficiency in crop straw was proposed using Grey relational analysis (GRA), Kernel principal component analysis (KPCA), and a least squares support vector machine (LSSVM). The authors collected a dataset from actual production data and developed an efficiency predictive model using GRA for variable selection, KPCA for dimensionality reduction, and LSSVM for model training. This model allows for the direct estimation of the final enzymatic hydrolysis efficiency based on production condition variables, which can include enzyme amount, temperatures, pH, time, agitation, and straw dimensions. Extensive experimental testing validated the effectiveness of the proposed method, resulting in minimal errors, a high degree of fit, and exceptional performance. The methodology described in this study can serve as a foundation for optimising the design of efficient enzymatic hydrolysis production processes for crop straw. Additionally, it offers valuable soft measurements to support efficient control of the enzymatic hydrolysis process.

Joint identification method for thermal–mechanical coupling loads

The crucial role in evaluating structural fatigue life, implementing health monitoring, and optimizing lightweight and reliable design is played by load identification technology. With the development of the aviation industry towards high-speed aircraft, the challenges under high temperature and high load conditions have been intensified. Unfortunately, in environments characterized by the coexistence of force and heat, difficulties are faced by existing load identification methods in achieving key decoupling and accurate identification of force and temperature fields. To address this gap, the Joint Identification Method (JIM), a new method that integrates force and thermal strain patterns, is introduced in this article. This method has been developed through in-depth research on “joint physical fields” and “joint basis functions.” Multiple physical fields for load identification are systematically decomposed by our method, each field is theoretically deduced, and they are seamlessly reassembled using the same structural spatial field. Through the method, mechanical strain and thermal strain are separated in high-precision temperature field reconstruction, and a basis function method for identifying the full field load distribution under a few measurement points is obtained. To verify the accuracy of our proposed Joint Identification Method (JIM), five experiments were designed, and the results consistently demonstrated the superiority of our method in accuracy (with an error always below 5%), efficiency (avoiding a large number of redundant calculations in traditional Duhamel methods), and reliability in static and dynamic load static heat identification. In the end, accurate, efficient, and reliable identification of the structural strain field, structural load field, and temperature field were achieved by our method.

Elimination of Toxic Solvents from Analytical Methods in Food Analysis: Caffeine Determination in Tea as an Example.

This study presents an innovative method for caffeine determination in tea, employing ethanol as the sole organic solvent for both SPE sample preparation and chromatographic analysis. This approach aligns with green chemistry principles, as confirmed by a comparative study highlighting ethanol's safety and eco-friendliness compared to traditional solvents. The experiments validate ethanol's efficacy in caffeine extraction and chromatographic analysis, minimizing environmental impact and eliminating toxicity risks. Utilizing a reduced chromatography column enhances the method's efficiency and sustainability, resulting in a low limit of quantitation (0.125 μg/mL) and good reproducibility (RSD < 2.5%). Based on tea from the Polish market, the findings reveal the caffeine content (19.29-37.69 mg/g) and endorse ethanol's role in enhancing sustainable chemical analysis in food science.

MCDNet: Multilevel cloud detection network for remote sensing images based on dual-perspective change-guided and multi-scale feature fusion

Cloud detection plays a crucial role in the preprocessing of optical remote sensing images. While extensive deep learning-based methods have shown strong performance in detecting thick clouds, their ability to identify thin and broken clouds is often inadequate due to their sparse distribution, semi-transparency, and similarity to background regions. To address this limitation, we introduce a multilevel cloud detection network (MCDNet) capable of simultaneously detecting thick and thin clouds. This network effectively enhances the accuracy of identifying thin and broken clouds by integrating a dual-perspective change-guided mechanism (DPCG) and a multi-scale feature fusion module (MSFF). The DPCG creates a dual-input stream by combining the original image with the thin cloud removal image, and then utilizes a dual-perspective feature fusion module (DPFF) to perform feature fusion and extract change features, thereby improving the model's ability to perceive thin cloud regions and mitigate inter-class similarity in multilevel cloud detection. The MSFF enhances the model's sensitivity to broken clouds by utilizing multiple non-adjacent low-level features to remedy the missing spatial information in the high-level features during multiple downsampling. Experimental results on the L8-Biome and WHUS2-CD datasets demonstrate that MCDNet significantly enhances the detection performance of both thin and broken clouds, and outperforms state-of-the-art methods in accuracy and efficiency. The code of MCDNet is available in https://github.com/djw-easy/MCDNet.

Mechanisms on the removal of gram-negative/positive antibiotic resistant bacteria and inhibition of horizontal gene transfer by ferrate coupled with peroxydisulfate or peroxymonosulfate

The existence of antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs) has been a global public environment and health issue. Due to the different cell structures, gram-positive/negative ARB exhibit various inactivation mechanisms in water disinfection. In this study, a gram-negative ARB Escherichia coli DH5α (E. coli DH5α) was used as a horizontal gene transfer (HGT) donor, while a gram-positive ARB Bacillus as a recipient. To develop an efficient and engineering applicable method in water disinfection, ARB and ARGs removal efficiency of Fe(VI) coupled peroxydisulfate (PDS) or peroxymonosulfate (PMS) was compared, wherein hydroxylamine (HA) was added as a reducing agent. The results indicated that Fe(VI)/PMS/HA showed higher disinfection efficiency than Fe(VI)/PDS/HA. When the concentration of each Fe(VI), PMS, HA was 0.48 mM, 5.15 log E. coli DH5α and 3.57 log Bacillus lost cultivability, while the proportion of recovered cells was 0.0017 % and 0.0566 %, respectively, and HGT was blocked. Intracellular tetA was reduced by 2.49 log. Fe(IV) and/or Fe(V) were proved to be the decisive reactive species. Due to the superiority of low cost as well as high efficiency and practicality, Fe(VI)/PMS/HA has significant application potential in ARB, ARGs removal and HGT inhibition, offering a new insight for wastewater treatment.

Waste-Based Vertical Planting System Proposal to Increase Productivity in Sustainable Horticulture; “PETREE”

The problems experienced with the decrease in agricultural areas per capita against the uncontrolled population growth in the World and in Turkey are becoming increasingly evident. Especially the manpower engaged in horticulture is increasingly experiencing the problems of migration, economic reasons, and inefficient production methods. For healthy and efficient production, agricultural lands need a sequential cycle that includes cultivation and fallow periods. Agricultural lands, the salt they contain, etc. When left fallow, the soil area needs to rest to re-establish the mineral balance. It is now vital for small producers, whose production areas are gradually shrinking due to global conditions, to continue production while their lands are resting to produce more continuously and efficiently. In the face of increasing demands, decreasing production amounts and areas, the constantly increasing prices of economically simple agricultural products “fresh vegetables” and affecting the easy accessibility of local products, it has become inevitable to seek new and alternative solutions. This study includes an innovative solution proposal to increase the production efficiency of low-scale producers and individual gardeners, especially those producing in small agricultural areas. Critical problems of large-scale greenhouse systems, interactions of agricultural practices within the framework of urban life, water and energy efficiency in agriculture, and sustainability and waste management of the proposed system are examined. To meet changing conditions and maintain productivity in small areas, it is envisaged that mobile and vertical production stations, which can produce by increasing productivity even in fallow areas while the soil is resting, can be an alternative solution to the problems of small producers. The vertical planting system “PETREE” develops mobile units for sustainable agriculture by collecting 5th LT-PET packages, which have a natural lifespan of about 90 years, and reusing them as plant pots and some recycled plastic pieces as structures. With efficient and environmentally friendly design suggestions, the system also examined the possibilities of more efficient gardening with mobile production stations in small agricultural areas, and efficient and enjoyable gardening that suits the needs of urban consumers “production with local seeds and seedlings” with the increase in environmental awareness and food safety concerns.