Approach For Performance Analysis Research Articles

Design and development of photonic crystal fiber for application of milk purity detection: an approach of performance analysis

Design and development of photonic crystal fiber for application of milk purity detection: an approach of performance analysis

A method to enhance Apache Spark performance based on data segmentation and configuration parameters settings

When using modern big data processing tools, there is a problem of increasing the productivity of using modern frameworks in the context of effective setting of various configuration parameters. The object of the research is computational processes of processing big data with the use of technologies of high-performance frameworks. The subject is methods and approaches to the effective setting of configuration parameters of frameworks in the conditions of limitations of virtualization environments and local resources. The purpose of the study is to improve the performance of Apache Spark and Apache Hadoop deployment modes based on a combined approach that includes preprocess segmentation of input data and setting of basic and additional configuration parameters that take into account the limitations of the virtual environment and local resources. Achieving the set goal involves the following tasks: create a synthesized set of WordCount test data for using input data segmentation methods. Determine the composition of general and specific Apache Spark and Apache Hadoop configuration parameters that most affect the performance of frameworks in Spark Standalone and Hadoop Yarn (FIFO) deployment modes. Justify changes in the values of the configuration parameters (accepted by default) by setting the level of parallelism, the number of partitions of the input file according to the number of processor cores, the number of tasks assigned to each core and the system executor. Conduct experimental research to substantiate theoretical results and prove their use in practice. Methods. The research used the following methods: statistical analysis; a method of generating test data based on defined segmentation characteristics with arbitrary volumes of data; a systematic approach for comprehensive evaluation and analysis of performance of frameworks based on selected configuration parameters. The results. On the basis of the developed system of parameters for evaluating the performance of the studied frameworks, experiments were carried out, which include: the application of the method of segmentation of input data based on the division of the input file into paragraphs (lines) for different values of the ranges of the number of words and the number of letters in each word; setting the main parameters and specific ones, in particular, partitioning and parallelism, taking into account the characteristics of the virtual environment and the local resource. According to the obtained results, a detailed analysis of the use of the proposed methods to improve the performance of the studied frameworks with recommendations for choosing the optimal values of data segmentation parameters and configuration parameters was carried out. You are snowmen. The obtained results of the experiments allow us to conclude that the use of the proposed methods of setting the configuration parameters of Spark and Hadoop will increase the processing productivity: for small files (0.5–1 GB) on average up to 25–30%, for large ones (1.5–2.5 GB ) – up to 10–20% on average. At the same time, the average value of the execution time of one task decreased by 10-15% for files of different sizes and with different number of words in a line.

Analisis Kepuasan Mahasiswa Terhadap Laporan Kualitas Pelayanan Lab Komputer Dengan Metode Importances Perfomances Analysis (IPA)

This research aims to analyze student satisfaction with computer laboratory services at STMIK WIDURI using the Importance Performance Analysis (IPA) approach. This investigation found that the quality of service received by students did not meet expectations. High priority, high quality equipment, laboratory technician workshop, services accessible to students, availability of laboratory technicians, easy access to laboratory technicians. STMIK WIDURI Analysis of student satisfaction with computer practicum is expected to increase priorities for the first quarter.

Defining Service Quality of BRI Merchant Application Using Importance Performance Analysis

The COVID-19 pandemic has changed people's habits worldwide, accelerating the adoption of cashless payment transactions, especially in Indonesia. This situation allows Financial Technology companies and Conventional Banks to increase competition. This thesis focuses on the Digital Payments Industry in Indonesia, emphasizing the Merchant Acceptance Business, which has a crucial role as a catalyst for Cashless Payments. This research is based on previous studies regarding service quality, business model canvas, and the importance of performance analysis of the BRI Merchant Application. This research aims to define a service quality strategy for BRI merchant applications through a critical performance analysis approach. The study found that the quality of BRI merchant application services consists of changing merchant data, adding EDC, Checking transactions, reconciling Payments, filing complaints, and monitoring complaints.

Value Engineering Adoption’s Barriers and Solutions: The Case of Saudi Arabia’s Construction Industry

Several developing countries around the globe have launched their future visions, which include concrete strategic plans that involve many kinds of mega-construction projects. One common goal across these visions is to increase the efficiency of construction projects’ huge expenditures using recent sustainable, practical engineering tools, such as the value engineering (VE) approach. Although the VE approach has been around for a few years now, some barriers prevent the popularity of its application in different construction projects. This research investigates the barriers to adopting VE through a systematic literature review. This study has categorized the application barriers using thematic analysis under three main categories: barriers related to practitioners’ knowledge and background, barriers associated with the behavior of practitioners towards VE, and barriers related to the implementation of VE. Moreover, this research has investigated solutions to overcome these barriers and improve the application of VE in construction projects. Saudi Arabia’s construction industry is used as a case study. A survey was distributed to Saudi construction industry practitioners using the snowballing technique to evaluate the criticality of the found barriers to adopting VE and the best strategic solutions to overcome these barriers using the importance–performance analysis (IPA) approach. Based on the results of this study we have specifically recommended including a VE course in college engineering programs and an incentive clause in construction contracts to encourage VE application on construction projects at the top of the list of proposed solutions. Finally, defining the criticality of barriers and the effectiveness of solutions will help enhance the application of VE and sustainability practices in the Saudi Arabian construction industry and around the globe.

An evolutionary game-theoretic approach for analysis of green innovation and environmental performance of tech firms under stakeholders’ policies based on system dynamics

An evolutionary game-theoretic approach for analysis of green innovation and environmental performance of tech firms under stakeholders’ policies based on system dynamics

An Importance–Performance Analysis Approach to a Tourism Destination: the Case of Lisbon Metropolitan Area, Portugal

Pull factors, the attributes of a specific location, matter in tourism. These factors encompass the distinctive characteristics and alluring features that attract tourists to a destination, their importance and performance influencing the decision-making process and overall experience. A clear comprehension of this concept is crucial for destinations' economic vitality as they compete for tourists and strive for economic expansion and employment prospects. Indeed, an in-depth knowledge of these factors' importance and performance underscores the need for regions to adjust their approach to evolving tourist preferences. This adaptation is crucial to fostering responsible and sustainable tourism practices that safeguard cultural and natural resources. However, besides learning about the factors considered critical by tourists to visit a location, it is paramount to analyse how they evaluate the performance of each of these factors while staying there.This study focuses on the Lisbon Metropolitan Area (LMA), Portugal, and the findings will provide essential insights into destination competitiveness, sustainable tourism development, and the creation of unique travel experiences. As a destination that gained popularity, attracting tourists from all over the world, it is necessary to comprehend the specific factors that contribute to this vibrant region's allure and how it performs regarding them. The research will use the Importance-Performance Analysis to make data interpretation accessible and suggest relevant measures to improve competitiveness based on optimising the allocation of resources among the various factors/attributes analysed. The research incorporates numerous criteria categories, including historical significance, urban appeal, natural scenery, culinary experiences, cultural heritage, and aspects like streets' cleanliness, transport availability, or health emergency infrastructures. The study's conclusions will impact Lisbon region's (LMA) tourism and destination management. By understanding what factors are more important for tourists that visit the area and their evaluation of the performance of each of these factors, stakeholders can improve marketing, infrastructure, and the visitors' experience to promote sustainable development and maintain Lisbon's standing as a top tourist destination for rewarding and enlightening experiences. Relevant data is also provided to inform future tourism initiatives. This study adds to the academic discussion of location competitiveness and unique travel experiences.

Self-constructed strategy-based reinforcement LSTM approach for fiber-reinforced polymer non-linear degradation performance analysis

The performance degradation of fiber-reinforced polymer (FRP) is a typical sequential data with a highly non-linear evolution pattern. In this study, a Self-constructed strategy-based reinforcement LSTM approach (short for SCRLA) is proposed to self-accommodate the non-linear sequential data, reduce modeling burden, and improve generalization ability. SCRLA incorporates Bayesian algorithms introducing the uncertainty of hyperparameter optimization by a probabilistic distribution of implicit objectives and self-constructed a high-dimensional, reinforced machine model that can learn and predict non-linear representations. In the case study, the datasets with different properties (one consisting of finite element analysis (FEA) results and one of real experimental (EXP) data) are selected to verify the validity of the degradation performance predictions. It is shown that the reinforced LSTM based on SCRLA is more suitable for the non-linear degradation performance analysis of FRP, especially with higher prediction accuracy for EXP data. The establishment of the approach and model provides a feasible idea and framework for the prediction of composites' sequential performance.

An Exploration of Prediction Performance Based on Projection Pursuit Regression in Conjunction with Data Envelopment Analysis: A Comparison with Artificial Neural Networks and Support Vector Regression

Data envelopment analysis (DEA) is a leading approach in performance analysis and discovering newer benchmarks, and the traditional DEA models cannot forecast the future efficiency of decision-making units (DMUs). Machine learning, such as the artificial neural networks (ANNs), support vector machine/regression (SVM/SVR), projection pursuit regression (PPR), etc., have been viewed as beneficial for managers in predicting system behaviors. PPR is especially suitable for small and non-normal distribution samples, the usual cases in DEA analysis. This paper integrates DEA and PPR to cover the shortcomings we faced while using DEA and DEA-BPNN, DEA-SVR, etc. This study explores the advantages of combining these complementary methods into an integrated performance measurement and prediction model. Firstly, the DEA approach is used to evaluate and rank the efficiency of DMUs. Secondly, we establish two DEA-PPR combined models to describe the DEA efficiency scores (also called the production function) and the DEA-efficient frontier function. The first combined model’s input variables are input–output indicators in the DEA model, and the output variable is the DEA efficiency. In the second model, its input variables are input or output indicators in the DEA model, and the output variable is the optimal input indicator for input-oriented DEA or the output indicator for output-oriented DEA. We conducted positive research on two examples with actual data and virtual small, medium-sized, and large samples. Compared with the DEA-BPNN and DEA-SVR models, the results show that the DEA-PPR combined model has more vital global optimization ability, better convergence, higher accuracy, and a simple topology. The DEA-PPR model can obtain robust results for both small and large cases. The DEA-BPNN and DEA-SVR models cannot obtain robust results for small and medium-sized samples due to overfitting. For large samples, the DEA-PPR model outperforms DEA-BPNN, DEA-SVR, etc. The DEA-PPR combined model possesses better suitability, applicability, and reliability than the DEA-BPNN model, the DEA-SVR model, etc.

A Quasi-Distributed optic fiber sensing approach for interlayer performance analysis of ballastless Track-Type II plate

As an advanced high-speed railway technology, ballastless track type II slabs have found widespread application in the development of high-speed train networks. However, the performance of ballastless track type II slabs deteriorates over time, making it crucial to investigate the performance of structural interlayer forces under load. This study explores the interlayer forces in ballastless track type II slabs under shear loads, the variation in interlayer force performance within the track slab, CA mortar layer, and base plate, and the impact of increasing the cross-sectional area of shear reinforcement on the mechanical performance of ballastless track type II slabs. Quasi-distributed fiber optic sensing is employed for these investigations. The results demonstrate the effective monitoring capability of a quasi-distributed fiber optic sensing network, setting a precedent for the utilization of fiber optic sensing technology in high-speed train structural health monitoring.

A Unique Approach for Performance Analysis of a Blockchain and Cryptocurrency based Carbon Footprint Reduction System

Blockchain technology is commonly used as a replicated and distributed database in different areas. In this paper, a smart home blockchain network connects smart homes through smart devices for reducing carbon footprint and thereby earning bitcoin value in the network. The network is composed of different smart homes interconnected with smart devices. The user makes a transaction request through the network layer and matches the user’s activity with the reward table located at the incentive layer to estimate the bitcoin value. Furthermore, the miner verifies the transaction and sends the bitcoin value to the user, and adds the respective block to the network structure. The optimal parameter used to estimate the bitcoin value is computed using the proposed Improved Invasive Weed Mayfly Optimization (IIWMO) algorithm. The developed method attained higher performance with the metrics, like coins earned, Annual Carbon Reduction (ACR), and fitness as 0.00357BTC, 23.891, and 0.6618 for 200 users. For 200 users the fitness obtained by the proposed method is 14.41%, 16.68%, and 11.68% higher when compared to existing approaches namely, Without optimization, IIWO, and MA, respectively.

Analisis Structure, Conduct and Performance (SCP) pada Pemasaran Daging Sapi di Kabupaten Kubu Raya

<p><em>Structural problems of domestic cattle and beef marketing include: marketing infrastructure, market structure and price formation, and the existence of domestic marketing policies and regulations. The purpose of this study was to determine the description of beef marketing in Sungai Subdistrict, Kubu Raya Regency and to calculate the marketing efficiency of beef cattle using market structure, market conduct, and market performance (SCP) analysis approaches. The research was carried out in November-December 2021. Respondents were 60 farmers, and 32 sub-district traders, district traders, and retailers. The method used is a survey using a questionnaire. Descriptive analysis for marketing margins and analysis of market structure, market behavior and market appearance. The results showed that beef marketing in Sungai Raya Subdistrict consisted of 3 marketing channels, namely: 1) Breeders – Collector Traders, Sub-District Traders – Retailers – Consumers; 2) Breeders – District Collectors – Retailers – Consumers. ; 3) Jakarta Retailers-PT Agro Boga Utama Pontianak (Prov Distributor)-Regency/City Traders in West Kalimantan Province-Consumers. With a market structure approach, the beef market in Sungai Raya District tends to be less concentrated (low concentration). From market behavior, the payment system is good. Meanwhile, market performance shows that the distribution of margin, farmer's share, and profit ratio is evenly distributed in each marketing institution. From the SCP indicators above, it can be seen that beef marketing in Sungai Raya District is efficient.</em></p>

Intelligent reflecting surface‐assisted beamforming‐NOMA networks for short‐packet communications: Performance analysis and deep learning approach

AbstractThis paper investigates intelligent reconfigurable surface‐assisted non‐orthogonal multiple access (NOMA) networks for short packet communications where the source with multiple antennas utilizes beamforming technology to serve two end users including a near user and a far user. The analysis system, closed‐form expressions of the average block error rates (BLERs), latency and reliability at all users are derived under assuming perfect channel state information over quasi‐static Rayleigh fading channels. For evaluating the improvement of the network system, a deep neural network (DNN) framework is designed to predict the performance parameters in two cases: (i) BLERs prediction of the users, (ii) transmit power allocation under the constraints of BLERs at each user which satisfy the extremely high reliability requirements of uRLLCs. The extensive results demonstrate that first, the DNN‐based estimation results are more accurate than Monte‐Carlo simulation results with low execution time. Second, the allocated power for each user can be accurately predicted at the high layers. Finally, two methods of evaluating the effectiveness of the DNN model, i.e. root mean square error and mean absolute percentage error, reach low error which verify the power of the designed DNN model for future analysis.

Design of an Experimental Approach for Characterization and Performance Analysis of High-Frequency Transformer Core Materials

High-frequency transformer core materials are used in power converter applications due to high efficiency performance. Their volume and weight can be reduced when higher operating frequencies are used but at the expense of an increase in core material losses. Some studies analyzed transformer core material performance by using finite element method (FEM) analysis, while others used an experimental model. This study proposes an experimental approach to compare the high-frequency transformer efficiency performance of different core material types. In this way, newly produced core material performance can be rapidly analyzed by comparing it against a known core material type, thereby resulting in the fast identification of improved core material design. This empirical approach makes use of a standard half-bridge inverter topology to enable an analysis of high-frequency transformer core material efficiency performance. Actual voltage and current measurements are used to determine the efficiency and output power performance throughout a specified constant current load range at different switching frequencies. Initially commercial standard polycrystalline or ferrite E-core materials were used to validate the characterization jig performance measured curve trends. The usefulness of the jig is then demonstrated by comparatively analyzing and then verifying the expected performance difference between polycrystalline and nanocrystalline toroidal core materials.

Analyzing the Performance of Thermoelectric Generators with Inhomogeneous Legs: Coupled Material–Device Modelling for Mg2X-Based TEG Prototypes

Thermoelectric generators (TEGs) possess the ability to generate electrical power from heat. As TEGs are operated under a thermal gradient, inhomogeneous material properties—either by design or due to inhomogeneous material degradation under thermal load—are commonly found. However, this cannot be addressed using standard approaches for performance analysis of TEGs in which spatially homogeneous materials are assumed. Therefore, an innovative method of analysis, which can incorporate inhomogeneous material properties, is presented in this study. This is crucial to understand the measured performance parameters of TEGs and, from this, develop means to improve their longevity. The analysis combines experimental profiling of inhomogeneous material properties, modelling of the material properties using a single parabolic band model, and calculation of device properties using the established Constant Property Model. We compare modeling results assuming homogeneous and inhomogeneous properties to the measurement results of an Mg2(Si,Sn)-based TEG prototype. We find that relevant discrepancies lie in the effective temperature difference across the TE leg, which decreases by ~10%, and in the difference between measured and calculated heat flow, which increases from 2–15% to 9–16% when considering the inhomogeneous material. The approach confirms additional resistances in the TEG as the main performance loss mechanism and allows the accurate calculation of the impact of different improvements on the TEG’s performance.

Evaluating supply chain resilience using supply chain management competencies in the garment industry: a post COVID analysis

ABSTRACT This study contributes to the complex adaptive system theory by offering a valid hierarchical model to evaluate the theory’s important features related to resilience. The garment industry in Bangladesh encountered disruption in the supply chain during the COVID-19 pandemic and the supply chain competencies played a vital role in overcoming the crisis. Limited studies are built on a solid theoretical foundation and considered supply chain competencies in assessing supply chain resilience. This study aims to develop a multi-criteria hierarchical measurement structure by considering the supply chain competencies to evaluate supply chain resilience. Fuzzy Delphi method and Fuzzy importance and performance analysis approach were applied for the study purpose. Findings reveal health and safety management, information management system, business intelligence, innovation capabilities management, technological innovation, and artificial intelligence as critical criteria, and data, information, and computing, technological innovation and adaptation are critical aspects that require improvement.

Effect of IMPACT team approach implementation processes on health supply chain management data use in Singida region: A quantitative study

The Government of Tanzania (GoT), in collaboration with development partners, has made significant improvements in the health supply chain since 2013 through developing of an electronic Logistics Management Information System (e-LMIS) and strengthening of the Logistics Management Services (LMS) component. Nevertheless, the use of reported data in the e-LMIS remained very low. In efforts to respond to low data use at all supply chain levels, the GoT introduced the Information Mobilized for Performance Analysis and Continuous Transformation (IMPACT) approach to reinforce data use culture for decision-making to improve the supply chain. Therefore, this study aimed to determine the effect of IMPACT team approach implementation processes on health supply chain data use in Singida region. This was a pre- and post-cross-section study whereby the quantitative data were obtained from e-LMIS stock imbalance reports. This data was processed using Microsoft Excel power query and analysed using Microsoft Excel 2019 pivot tables to get indicator results on availability, not-in-demand and overstocked health commodities. The overall pre (2018) and post (2020) intervention results for both Iramba district council (DC) and Singida municipal council (MC) were 66% and 81% for the availability of essential health commodities, 17.2% and 21% for not-in-demand health commodities and 17% and 20.7% for overstocked health commodities respectively. Iramba DC demonstrated a significant statistical increase in both indicators compared to Singida MC, as the P-value was less than 0.05 at a 95% confidence level (df=35). Conclusively, the IMPACT team approach positively influenced data use and availability of health commodities. However, it was not the case for overstocked and not-in-demand indicator results. Apart from the possible low quality of logistics data, further studies should explore other contributing factors behind the increasing trends of overstocks and not-in-demand health commodities in the region.

An integrated modeling approach for variable cycle engine performance analysis

The present study aims to address the issue of accuracy degradation in the traditional zero-dimensional variable cycle engine (VCE) model due to the multi-angle characteristics in the core driven fan stage (CDFS). To this end, a two-dimensional model was established through the application of the streamline curvature method. Subsequently, a multi-level VCE model was developed incorporating three zooming strategies to integrate the CDFS characteristics. The simulation results reveal that the proposed multi-level modeling approach is relatively time-efficient, requiring only half the computational time of fully coupled strategies. The results indicate a significant improvement in terms of optimal thrust and specific fuel consumption (sfc) when compared to the traditional zero-dimensional VCE model, with differences in maximum thrust and sfc of 3.11% and 1.24%, respectively. Furthermore, the average time required to solve one working point remains less than one second.

A coupled quantum-molecular mechanics approach for performance analysis of defective Silicon based photovoltaic solar cells

Although, molecular mechanics (MM) based approaches are capable of simulating the dynamic charge motion of atoms over time scales up to femto-seconds, the accuracy is an issue. As a result, quantum-mechanics (QM) based approaches are a choice to predict accurate dynamic motion of atomic charges. However, computational cost of QM simulations is significantly higher than that of MM simulations. In this study, a computationally efficient coupled QM/MM model is developed by combining the QM and MM approaches, enabling simulation of larger domains with accurate estimates. The proposed methodology is implemented with the help of QMMM package available in large-scale atomic/molecular massively parallel simulator (LAMMPS), to investigate the dynamic charge motion in the presence of cracks in Silicon. A Silicon domain of dimensions 48.87 Å × 48.87 Å × 5.43 Å is considered in the simulations. Furthermore, a small domain around the crack tip, with dimensions 4 Å × 4 Å is identified for carrying out QM analysis and denoted as embedded region (ER). Simulations are performed considering four different cases: (i) pristine Silicon, (ii) Silicon with an initial edge crack, (iii) pristine Silicon with Graphene deposition, and (iv) Graphene deposited Silicon containing an initial edge crack. In the coupled model, first, for the given load step, considering the minimum energy criteria molecular dynamics simulations are performed over the entire domain. This is followed by QM simulations over an identified ER based on first principle studies using the plane wave density functional theory. The latest atom positions from the QM simulations are updated in the MM domain before proceeding to the next load step. The electrical performance of Silicon solar cells is studied by estimating the effective Bader charge and total electric power. The effective Bader charge for atoms in QM domain is observed to be significantly higher indicating charge accumulation around the crack tip. This is further evidenced through the total electric power estimations, where pristine Silicon with Graphene deposition is observed to possess the highest power followed by cases iv, ii and i.

Efficiency analysis technique with input and output satisficing approach based on Type-2 Neutrosophic Fuzzy Sets: A case study of container shipping companies

This work tries to discuss and evaluate the advantages and superiorities of the extended Efficiency Analysis Technique with Input and Output Satisficing (EATWIOS) method based on Type-2 Neutrosophic Fuzzy Numbers (T2NFNs). The suggested model is maximally stable and robust by considering sensitivity analysis results which demonstrates a new performance analysis approach based on T2NFN sets. The proposed model deals with the input and output criteria and considers existing uncertainties arising from insufficient information and the dynamic structure of the industries. The model's basic algorithm has a unique structure compared to the previous performance analysis technique, and it does not require applying additional weighting techniques to identify the criteria weights. To the best of our knowledge, the extended version of the EATWIOS technique based on the T2NFN set is presented for the first time. The developed model provides reasonable and logical results to practitioners because it deals with satisfactory outputs instead of optimal outputs. This model is an immensely strengthened version of the EATWIOS technique, as the T2NFN sets treat predictable and unpredictable uncertainties. The suggested T2NFN-EATWIOS is then applied to a real-world assessment problem in the container shipping industry. The obtained results are pretty reasonable and logical. Moreover, the results of a comprehensive sensitivity analysis with three stages approve the robustness of the suggested model.