Analysis Of Technical Efficiency Research Articles

A Hybrid Continuation Framework for Analyzing Nonlinear Normal Modes of Systems With Contact Nonlinearity

Abstract The prediction of the modal properties of structural systems with contact clearances and prestress presents a computational challenge, as the nonlinearity induced by piecewise-linear stiffness eliminates the use of efficient linear modal analysis techniques. The most common approach to obtaining the nonlinear normal modes (NNMs) of these structural systems is a numerical framework that integrates numerical integration, the shooting method, and the pseudo-arc-length continuation scheme. This numerical continuation framework (NCF) computes NNMs through iterative numerical calculations; thus, the computational cost of the nonlinear modal analysis of complex nonlinear systems, particularly piecewise-linear systems, becomes prohibitively expensive as the model size increases. In this study, a hybrid continuation framework (HCF) combining analytic and numerical methods is proposed to enable efficient computations of NNMs for systems with contact boundaries. This new hybrid framework utilizes a semi-analytic method to conduct the iterative shooting procedure; thus, the computational burden of the numerical continuation can be significantly reduced. The proposed method is demonstrated on spring-mass oscillators with contact elements, and the NNMs obtained using the proposed method are validated by those computed using the traditional numerical continuation framework. The modal properties of the systems can be computed using the proposed framework with significant speed-up. Furthermore, the modal properties, including internal resonance and sharp turning in NNM curves, of the piecewise-linear systems are identified and discussed.

Mibianto: ultra-efficient online microbiome analysis through k-mer based metagenomics.

Quantifying microbiome species and composition from metagenomic assays is often challenging due to its time-consuming nature and computational complexity. In Bioinformatics, k-mer-based approaches were long established to expedite the analysis of large sequencing data and are now widely used to annotate metagenomic data. We make use of k-mer counting techniques for efficient and accurate compositional analysis of microbiota from whole metagenome sequencing. Mibianto solves this problem by operating directly on read files, without manual preprocessing or complete data exchange. It handles diverse sequencing platforms, including short single-end, paired-end, and long read technologies. Our sketch-based workflow significantly reduces the data volume transferred from the user to the server (up to 99.59% size reduction) to subsequently perform taxonomic profiling with enhanced efficiency and privacy. Mibianto offers functionality beyond k-mer quantification; it supports advanced community composition estimation, including diversity, ordination, and differential abundance analysis. Our tool aids in the standardization of computational workflows, thus supporting reproducibility of scientific sequencing studies. It is adaptable to small- and large-scale experimental designs and offers a user-friendly interface, thus making it an invaluable tool for both clinical and research-oriented metagenomic studies. Mibianto is freely available without the need for a login at: https://www.ccb.uni-saarland.de/mibianto.

Comprehensive investigations of four ratiometric fluorescent chemosensors based on 4-(1H-imidazol-2-yl)benzaldehyde skeleton for malononitrile detection

Malononitrile is a very important chemical material and has wide application fields in production of medicines, pesticides, and extraction of gold. However, its nonnegligible hypertoxicity inspired researchers to develop more efficient analysis techniques to sensitively and selectively detect malononitrile. Nopinone derivatives initiated by our research group have been developed as a class of organic fluorescent chemosensors for identifying multiple analytes in recent years. Different heterocyclic compounds based on nopinone were designed and synthesized to be applied in the fields of environmental analysis, food detection and bioimaging. Nevertheless, the comparison research on the optical properties of fluorescent compounds containing the nopinyl matrix with other structural analogs including alkyl, cyclohexyl and phenyl groups was deficient. Herein, four 4-(1H-imidazol-2-yl)benzaldehyde-based ratiometric fluorescent chemosensors based on o-dimethyl cyclohexyl, phenyl and nopinyl units for recognizing malononitrile were designed and developed, and their differences in the optical properties and detection performances were investigated by using spectral analysis combined with theoretical calculations. Moreover, the nopinone-based 4-(1H-imidazol-2-yl)benzaldehyde fluorescent chemosensor NMZQ was successfully applied in the dual channel fluorescence bioimaging of malononitrile in living HeLa cells and zebrafish, which attributed to its outstanding spectral property and detection performance.

Two efficient techniques for analysis and simulation of time-fractional Tricomi equation

Two efficient techniques for analysis and simulation of time-fractional Tricomi equation

The application of laser‑induced fluorescence in oil spill detection.

Over the past two decades, oil spills have been one of the most serious ecological disasters, causing massive damage to the aquatic and terrestrial ecosystems as well as the socio-economy. In view of this situation, several methods have been developed and utilized to analyze oil samples. Among these methods, laser-induced fluorescence (LIF) technology has been widely used in oil spill detection due to its classification method, which is based on the fluorescence characteristics of chemical material in oil. This review systematically summarized the LIF technology from the perspective of excitation wavelength selection and the application of traditional and novel machine learning algorithms to fluorescence spectrum processing, both of which are critical for qualitative and quantitative analysis of oil spills. It can be seen that an appropriate excitation wavelength is indispensable for spectral discrimination due to different kinds of polycyclic aromatic hydrocarbons' (PAHs) compounds in petroleum products. By summarizing some articles related to LIF technology, we discuss the influence of the excitation wavelength on the accuracy of the oil spill detection model and proposed several suggestions on the selection of excitation wavelength. In addition, we introduced some traditional and novel machine learning (ML) algorithms and discussed the strengths and weaknesses of these algorithms and their applicable scenarios. With an appropriate excitation wavelength and data processing algorithm, it is believed that laser-induced fluorescence technology will become an efficient technique for real-time detection and analysis of oil spills.

Empirical analysis of production risk and technical efficiency of sesame farmers in northwest Ethiopia

Empirical analysis of production risk and technical efficiency of sesame farmers in northwest Ethiopia

Technical Efficiency Analysis Of Home Industry Kamang Crackers In Kenagarian Kamang Hilir Kamang Magek District, Agam Regency West Sumatra Province

Technical efficiency is the ability of an industry to use minimum total input to produce maximum output. This research aims to 1) Analyze the level of technical efficiency in the Kamang cracker home industry in Kamang Hilir Kenagarian, and 2) Analyze what factors influencing the level of technical efficiency in the Kamang cracker home industry in Kamang Hilir Kenagarian. This research was carried out deliberately in Kenagarian Kamang Hilir, Kamang Magek District, Agam Regency. Respondents in this study using the Slovin formula, totaling 51 Kamang cracker home industry entrepreneurs using a simple random sampling technique. Using primary and secondary data, while the data analysis used is qualitative descriptive analysis and the Cobb-Douglass production function method which is converted into logarithmic form, then data analysis uses Stochastic Frontier. The analysis results showed that the average level of technical efficiency was 0.5792, proving that the hypothesis in this research was proven correct. Can be concluded that the Kamang cracker home industry is technically efficient and the factors that influence technical efficiency are raw materials, salt, hours people work, age, number of family members while green onions, pertalite and gas fuel, education, business experience are not significant in the Kamang cracker home industry

Technical Efficiency Analysis of Rice Farming in Deli Serdang Regency: A Data Envelopment Analysis (DEA) Approach

Deli Serdang Regency is the leading rice producer in North Sumatra Province. The fact is that productivity in Deli Serdang Regency has stagnated. The problem experienced by farmers is that farmers still apply monoculture cropping patterns, namely farming with only one type of crop each year, climate change, and rising input prices. This study aims to determine whether rice farming is run by farmers in Deli Serdang Regency, whether or not it is efficient, and to find out the socio-economic factors that influence technical efficiency in Deli Serdang Regency. Our study uses the Data Envelopments Analysis (DEA) method and Tobit regression to measure the technical efficiency of 400 rice farmers. Our study found that the value of technical efficiency based on the CRS, VRS, and SE assumptions was 0.576, 0.806, and 0.717. These three values mean that inefficient rice farming must reduce inputs by 42.6%, 19.4%, and 28.3%, respectively, so inefficient farming becomes efficient. Our study also found that factors that increase technical efficiency are education, access to credit, access to irrigation, distance to markets, experience, and crop diversification. Factors reducing technical efficiency are climate change and rising input prices.

A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

AbstractDetecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

ChatGPT in Healthcare Facilities: An Overview and Innovations in Technical Efficiency Analysis

ChatGPT in Healthcare Facilities: An Overview and Innovations in Technical Efficiency Analysis

Unveiling the Nexus: Technological Capacity and Export Performance in Indian Manufacturing Firms through Technical Efficiency Analysis

Unveiling the Nexus: Technological Capacity and Export Performance in Indian Manufacturing Firms through Technical Efficiency Analysis

Versatile capillary electrophoresis method for the direct chiral separation of aliphatic and aromatic α-hydroxy acids, β-hydroxy acids and polyhydroxy acids using vancomycin as chiral selector

Hydroxy acids (HAs) are ubiquitous in nature and play significant roles in various industrial and biological processes. Most HAs harbor at least one chiral center, therefore the development of efficient chiral analysis techniques for HA stereoisomers is of crucial importance across a wide range of fields. A capillary electrophoresis (CE) method was developed for the chiral analysis and quantification of aliphatic and aromatic α-hydroxy acid (AHA) enantiomers, aliphatic β-hydroxy acid (BHA) enantiomers and aliphatic polyhydroxy acid (PHA) stereoisomers. Using a modified partial filling-counter current method with indirect UV detection, high resolution (Rs) was achieved with vancomycin as a chiral selector added to the background electrolyte composed of 10 mM of benzoic acid/L-histidine at pH 5 using a polyacrylamide-coated capillary. This method could be readily applied to the determination of the enantiomers of 12 aliphatic AHAs, 4 aromatic AHAs, 3 aliphatic BHAs, as well as to the determination of the stereoisomers of tartaric acid, 2,3-dihydroxybutanoic acid, 2,3,4,5-tetrahydroxypentanoic acid, and 2,3,4,5,6-pentahydroxyhexanoic acid without the need for sample derivatization. Finally, our study provides a robust and versatile strategy for the chiral and stereoselective analysis of a broad range of hydroxy acid compounds.

An efficient seismic analysis technique for PCSG assembly using sub-structuring method and homogenization method

This study significantly reduced the seismic analysis time of PCSG assembly by introducing a reduced model using homogenization and sub-structuring methods. The homogenization method was applied to the primary and secondary micro-channel sheets, and the sub-structuring method was applied to the PCSG module sets. Modal analysis and frequency response analysis were then performed to validate the accuracy of the reduced model. The analysis results were compared with the full model and it was confirmed that the reduced model provided almost the same analysis results as the full model. To verify the computational efficiency of the reduced model, the computational time was then compared with the full model, and it was confirmed that the modal analysis time was reduced by 3.42 times and the frequency response analysis time was reduced by 4.59 times.

Localizing the Thickness of Cortical Regions to Descriptor the Vital Factors for Alzheimer’s Disease Using UNET Deep Learning

Alzheimer’s disease (AD) stands as a formidable global health challenge, impacting millions of lives. Timely detection and localization of affected brain regions are pivotal for understanding its progression and developing effective treatments. This research introduces a cutting-edge approach to address these critical concerns. Traditionally, exploring the influence of AD on the human brain has been a complex task. Existing methods often face limitations in accurately localizing the most affected brain regions, impeding our understanding of the disease's focal impact. Additionally, the need for efficient and precise cortical thickness analysis techniques has driven the quest for innovative solutions. In this paper, we proposed the DL+DiReCT method, a high-precision strategy that integrates deep learning-based neuroanatomy segmentations with Diffeomorphic Registration-based Cortical Thickness (DiReCT). This approach streamlines the measurement of cortical thickness, enabling rapid and precise localization of AD-affected regions within the brain. Our method significantly contributes to enhancing our understanding of the localized effects of Alzheimer’s disease. Our extensive study, involving 434 subjects from the ADNI dataset and rigorous data augmentation and optimization, has yielded remarkable outcomes. This approach has far-reaching implications for discerning the specific regions of the brain affected by AD, shedding light on their consequences for essential physiological factors.

Evaluation of technical efficiency traditional agriculture among smallholder maize farmers in East Java

The low productivity of maize in traditional agriculture has worsened the demand and supply of maize production. This gap indicates the important role of the small farmer sector in producing maize. However, this also indicates that there will be serious consequences for food security in Indonesia if maize farmers experience crop failure. Because of these conditions, this article will analyze the technical efficiency of traditional maize farming using a randomly selected sample of 70 farmers. This research uses a stochastic frontier model of technical efficiency analysis through a linearized Cobb-Douglas production function to determine the input production elasticity coefficient, technical efficiency, and the determining factors of technical efficiency. This study found that maize production responded positively to increasing the amount of seed, urea fertilizer, and NPK fertilizer. Efficiency analysis shows that traditional farming activities for smallholder maize farmers are classified as technically efficient with a Technical Efficiency value of 0.821, which means farmers can combine inputs to produce optimal output. The results of this research empirically prove that small-scale agriculture in maize farming activities has a better level of technical efficiency. The results of this research also highlight the role of farmer groups in improving technical efficiency through better agricultural extension services and other access.

Analysis of technical efficiency and yield gap of potato farmers in Nepal

The productivity of potato production in Nepal is low compared to neighbouring countries because of a range of production constraints. Potatoes are an important staple crop and commodity for many communities throughout Nepal, thus imperative to improve their performance for the benefit of smallholder producers and consumers. This study aims to identify the technical efficiency and yield gap of potato farmers. Farm-level data from 300 potato farmers randomly selected from three districts of Nepal is used to estimate a stochastic frontier model. The model allows us to estimate the production elasticity coefficients of inputs, determinants of efficiency and technical efficiency of potato farmers. Results showed that among the production variables, seed rate and labour used were positive towards potato production, whereas seed source, extension contact, variety type, and irrigation used were negatively significant factors that influences the technical inefficiency. The mean technical efficiency value of potato farmers was 68%, and there was clear scope to increase potato production by 32% with the better use of available resources. The potential yield gap can be reduced if the adoption of proper agricultural practices such as the use of improved potato varieties with formal seed sources with recommended seed rates along with irrigation application. Such efforts from policymakers and other concerned stakeholders would help improve domestic production and reduce the dependency on potato imports.

Exploring Applications and Practical Examples by Streamlining Material Requirements Planning (MRP) with Python

Background: Material Requirements Planning (MRP) is critical in Supply Chain Management (SCM), facilitating effective inventory management and meeting production demands in the manufacturing sector. Despite the potential benefits of automating the MRP tasks to meet the demand for expedited and efficient management, the field appears to be lagging behind in harnessing the advancements offered by Artificial Intelligence (AI) and sophisticated programming languages. Consequently, this study aims to address this gap by exploring the applications of Python in simplifying the MRP processes. Methods: This article offers a twofold approach: firstly, it conducts research to uncover the potential applications of the Python code in streamlining the MRP operations, and the practical examples serve as evidence of Python’s efficacy in simplifying the MRP tasks; secondly, this article introduces a conceptual framework that showcases the Python ecosystem, highlighting libraries and structures that enable efficient data manipulation, analysis, and optimization techniques. Results: This study presents a versatile framework that integrates a variety of Python tools, including but not limited to Pandas, Matplotlib, and Plotly, to streamline and actualize an 8-step MRP process. Additionally, it offers preliminary insights into the integration of the Python-based MRP solution (MRP.py) with Enterprise Resource Planning (ERP) systems. Conclusions: While the article focuses on demonstrating the practicality of Python in MRP, future endeavors will entail empirically integrating MRP.py with the ERP systems in small- and medium-sized companies. This integration will establish real-time data synchronization between the Python and ERP systems, leading to accurate MRP calculations and enhanced decision-making processes.

AI-based efficiency analysis technique for photovoltaic renewable energy system

Artificial intelligence (AI) in renewable energy technologies plays a crucial part due to its modeling and performance forecasting. Therefore, a novel AI-based evolving generative adversarial Fuzzy network (EGAFN) has been presented in this paper as a forecasting tool for the efficiency analysis of renewable solar energy for four distinct regions. The power ratings from environmental parameters and solar panels were monitored and recorded for a year. The data pre-processing is primarily applied to improve the system’s function using a data filter. Furthermore, the data’s energy estimation accuracy is enhanced using feature extraction and selection by a multi-objective lionized manta-ray foraging optimizer (MLMRFO). Finally, the hyperparameters of the EGAFN method are optimized by multi-objective optimization. The proposed technique uses an optimized multi-objective algorithm to enhance the energy efficiency of PV systems for solar power production forecasting. The findings show that the suggested technique’s prediction performance is better than earlier methods. Thus, the proposed methodology can assist in increasing energy efficiency and making better use of renewable energy sources.

A systematic review and meta-regression analysis of technical efficiency in Ethiopian wheat farming

Replications are important for science, both statistically and philosophically. A plethora of empirical studies that have been done so far show that there are always variations, errors and inconclusive findings on the technical efficiency (TE) scores of wheat output in Ethiopia. In this study, we intend to contribute to these controversies by providing the first thorough investigation of the magnitude of mean-effect size and the underlying factors of mean technical efficiency variations in wheat production across studies in Ethiopia. These efficiency scores are retrieved from 31 studies, or a household size of 12,754 over the years 2011–2022. The analysis of meta-regression was done using a random effect model using Comprehensive Meta-analysis (CMA-4) software. The Begg and Mazumdar rank-correlation test, egger test, and funnel plot were used to determine publication bias across studies. Since 2022, we have utilized the classic fail-safe-N test. One issue with publication bias is the omission of some non-significant studies from the analysis, which, if they were included, would cancel out the observed effect. Further, the missing study problem or “file drawer” problem, of the study would also be computed using the “Trim and Fill” estimation approach. To evaluate heterogeneity, a forest plot is used. The effect size is also determined using the random effect model along with the I-squared statistic, the tau-squared and tau, and the Q-test. As a result, a mean effect size of 71.6% with a 95% confidence interval of 67.9%−75.3% is observed. The meta-regression demonstrates that the 31 mean technical efficiency studies included in the research had varying efficiency scores due to variations in sample size, methodologies used, publication status, and study range. It suggests that, with all other factors held constant, a 1% change in these regressors causes a 0.01%, 10.88%, 21.22%, and 6.74% change, respectively, in the mean technical efficiency scores across studies in Ethiopia. In addition, the technical efficiency score across studies is also affected by the location in which the study is conducted.

Hybrid modeling for the multi-criteria decision making of energy systems: An application for geothermal district heating system

The efficiency analysis technique with output satisficing (EATWOS) is a successful tool for determining the most efficient design for energy systems. Since EATWOS is rationally based on the maximal output throughout the minimal inputs, the weights of input and output values considerably affect the analysis results. Therefore, the impact ratio of each input and output term should be sensitively determined. In this study, the artificial neural network (ANN) modeling was used to determine the weights of the input values due to the quantitative effects of these values, whereas the analytical hierarchic process (AHP) was used for the output values due to qualitative effects. A new hybrid method was formed, embedding the ANN and AHP results into EATWOS. The new hybrid model was then applied to a sample geothermal district heating system for optimization. In this aim, 148 designs were formed throughout the different inlet parameters and evaluated by exergoeconomic and exergoenvironmental analysis to conduct the outputs. For the optimum case, the exergy efficiency was calculated as 20.25 %, whereas the SI was determined as 1.25, with the highest score. 1/r and 1/rb were determined as 0.002337 and 0.001677, respectively. The NPV value was determined as 4.44 million $.