Approach For Performance Evaluation Research Articles

Technology adoption performance evaluation applied to testing industrial REST APIs

Testing is an important task within software development. To write test cases and integrate them into an automated test suite requires a significant amount of work. Given a set of requirements and specifications of a software, testing is needed to verify its correctness. When done manually, it is an expensive and error prone task. To facilitate such work, automated test-case generation via tools could be useful. Test-case generation can be facilitated by deterministic algorithm-driven approaches or non-deterministic approaches such as with AI (e.g., evolutionary and LLM). The different approaches come with their strengths and weaknesses, which must be considered when integrating these approaches into a product test procedure in industry. Several novel testing techniques and tools have been developed in academia and industry, but how effective they are and how to integrate them in real-world large industrial scenarios is still unclear. In this paper, a systematic approach is presented to evaluate test-case generation methodologies and integrate them into a scalable enterprise setup. The specific context is black-box testing of REST APIs, based on their OpenAPI schemas. The aim is to facilitate IT product development and service delivery. The proposed Technology Adoption Performance Evaluation (TAPE) approach is evaluated by a case study within the Group IT of Volkswagen AG. We evaluated existing tools such as OpenAPI Generator, EvoMaster and StarCoder which are built on different technologies. Our results show that these tools are of benefit for test engineers to facilitate test-case specification and design within the Group IT of Volkswagen AG.

Enhancing smart city assessment: an advanced MCDM approach for urban performance evaluation

Urban centers in the 21st century face significant challenges impacting future urban living. The numerous indicators and inherent complexity involved in assessing urban performance in smart cities necessitate the application of multi-criteria evaluation methods, which are frequently associated with fuzziness and uncertainties. Thus, this study introduces an innovative decision support approach that integrates complex spherical fuzzy set to enhance decision-making for smart city evaluation. By incorporating a consensus measurement mechanism for judgments, the reliability of results is improved. The methodology assesses smart city performance by calculating a level index using criteria of resilience, sustainability, and urban livability, along with eighteen sub-criteria for comprehensive benchmarking. Results highlight pollution control, energy and environmental resources, community well-being, eco-friendly transport, and social cohesion as critical factors for smart city performance. Singapore demonstrates high-level performance due to its integration of advanced technologies and community-focused strategies. The evaluated results obtained via the developed approach offer valuable insights for urban planners aiming to promote sustainable, resilient, and livable urban development.

A two-stage approach for modeling, performance evaluation, and decision support of disassembly processes

ABSTRACT This paper tackles an issue of disassembly lot-sizing within the framework of stochastic batch demand for multi-products featuring a two-level structure. The suggested methodology relies on a batch deterministic and stochastic Petri net as a modeling tool to address such problems in the context of stochastic processes. The primary objective is to determine the optimal sequencing of disassembly for each product type to meet stochastic batch demands of root items across multiple criteria. A novel mathematical model is introduced to minimize the cumulative costs associated with disassembly operations and extra costs, as well as holding costs for diverse products. In a subsequent stage, a decision support tool, namely the preference ranking organization method for enrichment evaluation method, is put forward for prioritizing different scenarios. For assessing the efficiency of the suggested approach, a real case study from the manufacturing industry is simulated. The simulation results prove the suggested approach’s capability to adapt to dynamic demands for leaf items throughout the planning horizon.

Enhancing Operational Efficiency through Stopwatch Time Study: A Systematic Approach for Performance Evaluation

Enhancing Operational Efficiency through Stopwatch Time Study: A Systematic Approach for Performance Evaluation

F-BWM-MARCOS approach for performance evaluation of Türkiye's top 500 industrial enterprises in the pre-pandemic and pandemic era

PurposeThe purpose of this study is to analyze the impacts of the COVID-19 pandemic on the performance of companies using a hybrid Multi-Criteria Decision-Making (MCDM) approach. Specifically, the study examines Türkiye’s Top 500 Industrial Enterprises to analyze their performance before and during the pandemic, and to capture their performance in determining investment and production strategy.Design/methodology/approachTo achieve the study’s objectives, the Fuzzy Best-Worst Method (F-BWM) was used to obtain importance levels of performance indicators, decreasing the vagueness in experts’ decision-making preferences. The Measurement Alternatives and Ranking According to Compromise Solution (MARCOS) method was used to rank enterprises based on their performance.FindingsThe COVID-19 pandemic has clearly had a substantial impact on the performance of Türkiye’s top 500 industrial enterprises. While some companies suffered decreased sales, others reported that their revenues increased or remained constant during the outbreak. The results reveal that the pandemic caused a shift in the initial ranking outcomes for the first two enterprises.Research limitations/implicationsThe study’s limitations include the sample size and the time period under consideration, which may have an impact on the generalizability of the findings.Practical implicationsDecision-makers’ investment, employment and operational decisions were influenced by the impact of the COVID-19 pandemic. The results provide insights for decision-makers on how to achieve higher growth and performance under the pressure of the pandemic.Social implicationsThe study’s practical consequences help decision-makers understand how to attain higher growth and performance in the face of the epidemic.Originality/valueThe originality of this study lies in using a hybrid MCDM approach to examine the impact of the COVID-19 pandemic on company performance. A hybrid MCDM approach is proposed to help decision-makers make the best possible investment and implementation decisions.

A model-based approach for formal verification and performance evaluation of energy harvesting architectures in IoT systems: A case study of a long-term healthcare application

Energy harvesting plays a significant role in the Internet of Things (IoT). Indeed, although numerous approaches exist to limit the system’s power consumption, the energy provided by the battery remains constrained, thereby limiting the system’s lifetime. Energy harvesting represents an interesting technique that allows a set of devices in an IoT architecture to operate for a potentially infinite time without the need for battery replacement or recharge. This work presents a formal modeling framework for the performance evaluation of energy harvesting architectures and strategies in IoT systems. We present a model-based approach using UPPAAL to model and analyze IoT device lifetimes and capture the energy-related behavior of nodes and various energy harvesters. Furthermore, the model is calibrated using measurements acquired from real-life IoT applications to demonstrate the effectiveness of the proposed model and its ability to investigate various energy-related aspects.

An integrated multi-criteria decision-making approach for sustainable supply chain performance evaluation from a manufacturing perspective

An integrated multi-criteria decision-making approach for sustainable supply chain performance evaluation from a manufacturing perspective

An integrated cost based approach for warehouse performance evaluation: A new multiphase model

Warehouses represent key links in domestic and international commodity flows. The increasing shortage of workers and increasing costs on the one hand, and the increasing number and stricter demands of users on the other hand lead warehouse managers to realize their operations as efficiently as possible. A proposed model has an objective of enabling companies to monitor warehouse performance in an authoritative, reliable, and simple way and define appropriate corrective measures accordingly. The proposed empirical research consists of three stages, where in the first stage a combination of Principal Component Analysis-Data Envelopment Analysis methods was applied in order to determine efficient warehouses based on 90 decision making units. In the second phase, a completely new method called Interval Fuzzy Rough Pivot Pair-wise Relative Criteria Importance Assessment method used for determining criteria weights was developed and applied, which is one of the most important novelties of this study. In the last phase, the Interval Fuzzy Rough Measurement of Alternatives and Ranking according to the Compromise Solution method was applied to rank the alternatives. Twelve criteria were observed to evaluate 21 alternatives. Based on the results, it was concluded that salary stood out as the most important criterion, while amortization stood out as the least significant criterion. On the other hand, alternatives A9 and A10 stood out as the best-ranked alternatives while A1, A2, and A3 stood out as the least efficient ones. The paper provides clear scientific contributions that are reflected in the reduction of the gap that was observed after reviewing the literature where there is a lack of papers dealing with this task. Also, the combination of methods applied in the paper has not been used so far, so it can be said that this paper represents an excellent basis for further research. The model has practical contributions as it allows decision-makers to make quality decisions regarding the operation of their warehouses in different time periods or observation periods, as well as it represents a decision support tool that can be used for better warehouse management.

Analytical and Numerical Approach for Performance Evaluation of Thermoelectric Generator

Growing demands in energy requirement need more efficient methods to recover waste energy from various energy conversion systems, among them generator set (Genset) run by diesel engines in construction and mining areas are commonly used to generate electrical energy and usually these systems work at an efficiency of around 30-35 %. A large part of energy loss in these systems is resulted from heat carried away by coolant and exhaust gases. The energy lost in the form of heat in the exhaust can be harvested using various heat recovery methods like Thermoelectric Generators [TEG]. Thermoelectric generators are used to convert thermal energy into electrical energy governed by the principle of Seebeck effect. Present work draws attention towards external factors that influence the output voltage of thermoelectric generators attached to a stationary waste heat source like Gensets. Here thermoelectric generator units were modelled by attaching them to the exhaust manifold of pipe of a diesel Genset and were analyzed both analytically and numerically to obtain their dependence of output voltage on thermal conductivity of exhaust pipe material, where TEGs can be mounted. From the analysis of obtained report it was observed that efficiency of waste heat recovery improved on using higher thermal conductive materials. In this preliminary analysis, an increase in thermal conductivity of 20% has resulted in an increase in power output of 11%.

Scientometric Research and Critical Analysis of Gait and Balance in Older Adults.

Gait and balance have emerged as a critical area of research in health technology. Gait and balance studies have been affected by the researchers' slow follow-up of research advances due to the absence of visual inspection of the study literature across decades. This study uses advanced search methods to analyse the literature on gait and balance in older adults from 1993 to 2022 in the Web of Science (WoS) database to gain a better understanding of the current status and trends in the field for the first time. The study analysed 4484 academic publications including journal articles and conference proceedings on gait and balance in older adults. Bibliometric analysis methods were applied to examine the publication year, number of publications, discipline distribution, journal distribution, research institutions, application fields, test methods, analysis theories, and influencing factors in the field of gait and balance. The results indicate that the publication of relevant research documents has been steadily increasing from 1993 to 2022. The United States (US) exhibits the highest number of publications with 1742 articles. The keyword "elderly person" exhibits a strong citation burst strength of 18.04, indicating a significant focus on research related to the health of older adults. With a burst factor of 20.46, Harvard University has made impressive strides in the subject. The University of Pittsburgh displayed high research skills in the area of gait and balance with a burst factor of 7.7 and a publication count of 103. The research on gait and balance mainly focuses on physical performance evaluation approaches, and the primary study methods include experimental investigations, computational modelling, and observational studies. The field of gait and balance research is increasingly intertwined with computer science and artificial intelligence (AI), paving the way for intelligent monitoring of gait and balance in the elderly. Moving forward, the future of gait and balance research is anticipated to highlight the importance of multidisciplinary collaboration, intelligence-driven approaches, and advanced visualization techniques.

Computational evaluation of PEMFC performance based on bipolar plate material types

Materials used in bipolar plates are essential for addressing the challenges associated with the large-scale commercialization of Proton Exchange Membrane Fuel Cells (PEMFCs). Therefore, it is necessary to develop effective performance evaluation approaches to evaluate the performance of materials. This work has presented a high-fidelity mathematical model that includes electrochemical, fluid flow, energy, species transport, water formation, and transport aspects to simulate the conditions in a PEMFC system and to theoretically assess the performances of commercial bipolar plate materials. Accordingly, the effects of metallic (aluminum) and carbon-based (graphite and graphite composite) bipolar plate materials on the current collection performance, thermal management, water management, and overall efficiency of PEMFCs are analyzed. The findings suggest that the current density, power density, and operating voltage of each PEMFC are greatly affected by the bipolar plate material used. When compared to graphite bipolar plates, the aluminum and graphite composite bipolar plates have exhibited 16.5 % and 7.5 % higher power densities, respectively. The change in operating conditions and thermal environments caused by a change of bipolar plate materials gives rise to distinct water transport phenomena according to their respective capabilities in the balancing of liquid water movements across the membrane, which in turn affects the species distribution pattern. Specifically, the high imbalance between the electro-osmotic drag and back diffusion in the case of aluminum bipolar plate leads to a severe membrane dry-out on the anode side, limiting the hydrogen concentration in the region.

Application of Random Forest Algorithm in English Innovative Talent Cultivation Mode in Colleges and Universities

This research investigates the integration of the Random Forest Algorithm within the framework of English Innovative Talent Cultivation Mode in higher education institutions. The study uses a performance evaluation approach to examine the algorithm's impact on student learning outcomes and instructional methodologies. The algorithm's usefulness in improving outcomes and personalizing instructional approaches is examined using descriptive statistical analysis. The findings demonstrate the algorithm's potential to change English language education by providing useful insights into student performance that can drive instructional decisions. This study emphasizes the relevance of refining talent development tactics and cultivating innovative English language skills among students, thus contributing to the growth of educational practices in colleges and universities. As a result, using the Random Forest Algorithm in this scenario has the potential to revolutionize teaching practices while also encouraging a culture of continual improvement and innovation in English language acquisition.

Optimized Meta-path extracted graph neural network for embedded computer performance evaluation model

The process of evaluating the effectiveness, speed, and general capabilities of a computer system that is embedded inside a larger device or system and created for a particular purpose is known as embedded computer performance evaluation. Specialized computing systems called embedded systems are built into a wide range of gadgets, including medical, industrial, , automotive and consumer electronics. In this manuscript, Optimized Meta-path extracted graph neural network for embedded computer performance evaluation model(MEGNN-EC-PEM) is proposed. Initially, the input data is obtained from real-time sensor measurements and system metrics for model training and testing. The input image is pre-processed using Orthogonal Master-slave Adaptive Notch Filter (OMANF) and it removes the noise from the collected data. Then, the pre- processed data are fed to embedded computer performance using Meta-path extracted graph neural network (MEGNN). In general, MEGNN does not express adapting optimization techniques to determine optimal parameters to assure precise embedded computer performance evaluation model. Hence, the Hunter–prey optimization algorithm (HPOA) is used to optimize Meta-path extracted graph neural network which accurately categorized embedded computer performance. Then, the proposed MEGNN-EC-PEM is implemented and the performance metrics like Modelling Accuracy, latency, Throughput, memory cost and Energy Consumption are analyzed. The performance of the MEGNN-EC-PEM approach attains 19.41%, 20.08% and 32.57% higher modelling accuracy, 22.41%, 23.08% and 24.57% lower latency and 23.01%, 23.08% and 24.07% higher throughput when analyzed through existing techniques like a state-based modelling approach for effective performance evaluation of embedded system architectures at transaction level (SMA-EPE-ESAT), Evaluating the performance of pre-trained convolutional neural network for audio classification on embedded schemes for anomaly detection in smart cities (PCNN-ACES-ADSC) and Animal behaviour classification via deep learning on embedded systems (ABC-VDL-ES) methods respectively.

Enhanced Yolov8 with OpenCV for Blind-Friendly Object Detection and Distance Estimation

The development of computer technology and computer vision has had a significant positive impact on the daily lives of blind people, especially in efforts to improve their navigation abilities. This research has the main aim of introducing a superior object detection method, especially in supporting the sustainability and effectiveness of navigation for the blind. The main focus of the research is the use of YOLOv8, the latest version of YOLO, as an object detection method, and distance measurement technology from OpenCV. The main challenge to be addressed involves improving the accuracy and performance of object detection, which is an important key to ensuring safe and effective navigation for blind people. In this context, blind people often face obstacles in their mobility, especially when walking around environments that may be full of obstacles or obstacles. Therefore, better object detection methods become essential to ensure the identification of nearby objects, which may involve obstacles or potential threats, thereby preventing possible accidents or difficulties in daily commuting. Involving YOLOv8 as an object detection method provides the advantage of a high level of accuracy, although with a slight increase in detection duration and GPU power consumption compared to previous versions. The research results show that YOLOv8 provides a low error rate, with an average error percentage of 3.15%, indicating very optimal results. Using a combined performance evaluation approach of YOLOv8 and OpenCV distance measurement metrics, this research not only seeks to improve accuracy but also efficiency in detection time and power consumption. This research makes an important contribution in presenting technological solutions that can help improve mobility and safety for blind people, bringing a real positive impact in facilitating their daily lives.

Understanding the multi-source uncertainties effect on the seismic performance assessment of deeply hydraulic tunnels based on the generalized PDEM

One crucial element of a seismic performance evaluation approach is to appropriately account for the stochastic characteristics of SGMs and the uncertainty associated with material properties. Furthermore, the incidence angle of seismic waves may be influenced by topographic and geological factors, leading to uncertainty and randomness. This variability in incident angles has the potential to cause unforeseen structural damage. However, the current seismic design code of underground structures has commonly assumed the vertical or horizontal seismic input method in underground engineering. From the uncertain point of view, the multi-source uncertainties that incorporate the nonstationary SMGs, seismic input angles, and material parameters utilized to conduct the seismic performance assessment of HTs remain a challenge in current seismic design and performance evaluation. To overcome this challenge, the Generalized F-discrepancy method, the generalized PDEM, and the equivalent extreme-value event are introduced to conduct stochastic dynamic analysis and develop the appropriate fragility curves of HTs considering the multi-source uncertainties. The results demonstrate that the probability of damage of the HT obtained by multi-source uncertainties is significantly different in analyzing the single uncertain and two uncertainties. Moreover, it can be concluded that the multi-source uncertainties can cause more seismic demand than the single uncertain and two uncertainties under different earthquake intensity levels for the HT. In light of this, it is strongly suggested that seismic design and performance assessment of HTs take into account the relevant aspects, such as the input angles, the random features of seismic waves, and the material parameters.

Implementation of language models within an infrastructure designed for Natural Language Processing

This paper explores cost-effective alternatives for resource-constrained environments in the context of language models by investigating methods such as quantization and CPUbased model implementations. The study addresses the computational efficiency of language models during inference and the development of infrastructure for text document processing. The paper discusses related technologies, the CLARIN-PL infrastructure architecture, and implementations of small and large language models. The emphasis is on model formats, data precision, and runtime environments (GPU and CPU). It identifies optimal solutions through extensive experimentation. In addition, the paper advocates for a more comprehensive performance evaluation approach. Instead of reporting only average token throughput, it suggests considering the curve’s shape, which can vary from constant to monotonically increasing or decreasing functions. Evaluating token throughput at various curve points, especially for different output token counts, provides a more informative perspective.

Optimizing thermal insulation in building facades: An examination of human-environment dynamics in Nanjing city, Eastern Asia

The external building walls are significant in mitigating adverse weather conditions. Current research on the thermal insulation of exterior walls often overlooks crucial factors such as human comfort, seasonal changes and environmental dynamics. This study introduced a performance evaluation approach for external walls that considered the human thermal zone, annual air temperature and solar radiation. Taking Nanjing City as a case study, the thermal insulation performance of five distinct types of building exterior walls was investigated. The findings highlight the impact of various insulated wall systems on thermal insulation. While different insulated walls exhibited significant variations in effectiveness during extreme weather episodes, these variations were minor over the course of the year. Analysis revealed a spectrum of performance for insulated exterior walls, ranging from excellent to poor: Outer Insulated Wall > Inside Insulated Wall = Sandwich Insulated Wall = Self-Insulated Wall > Mortar-Insulated Wall. The disparity in thermal insulation performance amongst four wall types was minimal, with the mortar-insulated wall demonstrating the lowest performance. The highest temperature recorded for the mortar-insulated wall surpassed 32.2°C over 68 days. This research contributes insights into the nuanced performance of various insulated walls, paving the way for decision-making in climate resilience strategies.

A holistic approach for performance evaluation of wastewater treatment plants: integrating grey water footprint and life cycle impact assessment.

Wastewater treatment plants (WWTPs) have positive and negative impacts on the environment. Therefore, life cycle impact assessment (LCIA) can provide a more holistic framework for performance evaluation than the conventional approach. This study added water footprint (WF) to LCIA and defined ϕ index for accounting for the damage ratio of carbon footprint (CF) to WF. The application of these innovations was verified by comparing the performance of 26 WWTPs. These facilities are located in four different climates in Iran, serve between 1,900 and 980,000 people, and have treatment units like activated sludge, aerated lagoon, and stabilization pond. Here, grey water footprint (GWF) calculated the ecological impacts through typical pollutants. Blue water footprint (BWF) included the productive impacts of wastewater reuse, and CF estimated CO2 emissions from WWTPs. Results showed that GWF was the leading factor. ϕ was 4-7.5% and the average WF of WWTPs was 0.6 m3/ca, which reduced 84%, to 0.1 m³/ca, through wastewater reuse. Here, wastewater treatment and reuse in larger WWTPs, particularly with activated sludge had lower cumulative impacts. Since this method takes more items than the conventional approach, it is recommended for integrated evaluation of WWTPs, mainly in areas where the water-energy nexus is a paradigm for sustainable development.

Performance evaluation approach for accelerating rate calorimeters by means of Joule heat

Currently, the commonly used performance evaluation approach for accelerating rate calorimeters (ARCs) is based on di‑tert‑butyl peroxide (DTBP). However, DTBP is not a certified reference material that contains untraceable and inaccurate thermodynamic and kinetic parameters. To address this issue, an evaluation approach using Joule heat was proposed. The key point of this approach is the use of Joule heat to simulate the exothermic process of a chemical reaction. First, an evaluation apparatus with reaction rate feedback control was developed. Subsequently, exothermic reaction experiments using the DTBP/toluene solution were simulated. The experimental results indicated that the thermodynamic and kinetic parameters obtained using this approach align with the preset values and exhibit good repeatability. This approach is traceable and can be traced back to the International System of Units, which facilitates reliable performance evaluation of commercial ARCs.

A novel regret-rejoice cross-efficiency approach for energy stock portfolio optimization

As climate change intensifies, more and more people realize that it is necessary and urgent to optimize energy structure through constructing energy asset portfolios. However, most of existing literature on energy asset portfolio models neglects real market restrictions, and the related portfolio performance evaluation approaches ignore the impact of the psychological factors of decision makers (DMs) on evaluation results, which limit their applications. To overcome these limitations, we first present a mean-WMCVaR (MWMC) model for energy stock portfolio selection with real features. Then, we employ the proposed MWMC model and its closely related works to yield a set of investment schemes and determine their characteristic index values including mean, WMCVaR, carbon intensity and ESG. Subsequently, we present a novel regret-rejoice cross-efficiency (NRRCE) approach to evaluate the performance of these investment schemes, and then make a comparison with the ones of the other seven competitive evaluation approaches by using an empirical example. Empirical results show that the proposed MWMC model can yield an optimal investment scheme with a better trade-off between economic and ecological benefits, and our NRRCE approach can effectively provide reasonable and reliable evaluation results for different investment schemes.