Performance Evaluation Methodology Research Articles

Methodology for assessing the effectiveness of innovations, robotization and automation in the automotive industry

The current trend in the automotive industry confirms that innovation and process automation are increasingly helping the industry’s development and economic growth. In addition to the digitalisation of society, new technologies are also being actively developed. The economic development of a country is inextricably linked to the development of process innovation, process automation and robotisation. In this paper, the theoretical background of innovation approaches in the automotive industry and performance evaluation methodologies have been discussed. Industrial robots and robotic technological complexes, which are universally used in industry, have existed for about 50 years and at the present moment of time replace humans and displace them from the sphere of production to the sphere of services and control. To ensure balanced economic development, the Government of the Republic of Kazakhstan closely monitors the main economic sectors and endeavours to support domestic manufacturers in every possible way. The automobile industry is no exception. The purpose of this article is to conduct a brief theoretical review of the state of the automobile industry, as well as to study foreign experience. We have studied the methodology of economic efficiency assessment. The study showed that innovation, robotisation and automation undoubtedly lead to the economic development of both regions and the country as a whole.

Advances in Artificial Intelligence-aided Intraoral Imaging Analysis in Periodontics

Artificial intelligence has increasingly influenced the field of periodontology by enhancing diagnostic accuracy and treatment planning through advanced data-driven techniques. It was aimed to examine the integration of artificial intelligence, particularly deep learning and machine learning, in analyzing intraoral photographs for periodontal conditions in this review. Periodontal assessments rely on clinical and radiographic evaluations, but artificial intelligence introduces a transformative approach by analyzing large datasets to improve clinical decision-making. The review investigates the effectiveness of artificial intelligence-enhanced intraoral photograph analysis, focusing on methodologies for dataset creation, model development, training, and performance evaluation. A thorough search of databases such as PubMed, Scopus, Google Scholar, and IEEE Xplore identified 338 articles, with 16 meeting the inclusion criteria. These studies primarily utilized convolutional neural networks and architectures like DeepLabv3+ and U-Net, demonstrating high accuracy in detecting conditions such as gingivitis, dental plaque, and other periodontal issues. The dataset sizes ranged from 110 to 7220 images, affecting the models' generalizability. Most studies employed supervised learning, with models trained on labeled datasets to achieve precise diagnostic outcomes. The review highlights that while artificial intelligence and machine learning techniques, including convolutional neural networks and U-Net, offer significant improvements in periodontal diagnostics, the choice of model and the quality of the dataset are crucial for performance. Hybrid approaches that combine automated and expert-driven methods might provide a balance between efficiency and accuracy. The successful integration of artificial intelligence into clinical practice requires continuous validation and adaptation to ensure that these technologies remain accurate and relevant. Future research should focus on enhancing model robustness, expanding dataset diversity, and refining clinical applications to fully exploit the potential of artificial intelligence in periodontology.

Advancements in Asymmetric Supercapacitors: From Historical Milestones to Challenges and Future Directions.

Numerous challenges, like the uninterrupted supply of electricity, stable and reliable power, and energy storage during non-operational hours, arise across various industries due to the absence of advanced energy storage technologies. With the continual technological advancements in portable electronics, green energy, and transportation, there are inherent limitations in their innovative production. Thus, ongoing research is focused on pursuing sustainable energy storage technologies. An emerging solution lies in the development of asymmetric supercapacitors (ASCs), which offer the potential to extend their operational voltage limit beyond the thermodynamic breakdown voltage range of electrolytes. This is achieved by employing two distinct electrode materials, presenting an effective solution to the energy storage limitations faced by ASCs. The current review concentrates on the progression of working materials to develop authentic pseudocapacitive energy storage systems (ESS). Also, evaluates their ability to exceed energy storage constraints. It provides insights into fundamental energy storage mechanisms, performance evaluation methodologies, and recent advancements in electrode material strategies. The review approaches developing high-performance electrode materials and achieving efficient ASC types. It delves into critical aspects for enhancing the energy density of ASCs, presenting debates and prospects, thereby offering a comprehensive understanding and design principles for next-generation ASCs in diverse applications.

Fire Blackbox Control for Public Marketing

This paper presents the system for Fire Blackbox control in Public Marketing. When information is required following a fire incident, investigators or police might use the smart black box system to collect information. The black box system is used to continuously record, temperature, smoke, and other data. The information is then stored in a storage device. We are able to examine, track, and evaluate fire situations. In this paper, we propose a novel concept termed "Fire Black Box Control" (FBBC) tailored specifically for public marketing spaces. FBBC integrates cutting-edge sensing technologies, data recording, and data analytics. We present a comprehensive framework for implementing FBBC, encompassing sensor deployment strategies, data processing techniques, decision-making algorithms, and performance evaluation methodologies. Through extensive simulations and analysis, we demonstrate the effectiveness of the proposed FBC approach in mitigating fire risks and ensuring regulatory compliance in public marketing spaces

Implementing and Assessing the Performance Evaluation Methodology of Urban Street Facilities for Motorized Vehicles in the Highway Capacity Manual Using Vehicle Trajectory Data

Implementing and Assessing the Performance Evaluation Methodology of Urban Street Facilities for Motorized Vehicles in the Highway Capacity Manual Using Vehicle Trajectory Data

Research on Energy and Mass Integration of Green Electricity Hydrogen Production and Ammonia Synthesis

As we strive to attain "dual carbon goals," the chemical industry faces an immediate imperative to diminish its carbon emissions. Leveraging renewable energy to generate green hydrogen via water electrolysis for ammonia synthesis has emerged as a significant pathway towards the industry's green transformation—this not only effectively utilizes new energy but also promotes sustainable development. However, such an interconnected system encompasses a multitude of intricate processes, ranging from thermochemistry and electrochemistry to power cycles, each with varying energy input and output grades. The study seeks to elucidate the synergistic interplay of energy conversion and energy-mass exchange across these diverse processes within the integrated system. It necessitates solving optimization issues related to energy usage and heat exchange. By delving into the intricacies of the coupled system, we aim to uncover the fundamental principles and performance evaluation methodologies pertinent to system design. These insights can provide valuable guidance for the real-world application of new energy power-driven hydrogen production and green ammonia synthesis systems. Ultimately, widespread application of these renewable energy practices within the chemical industry will not only contribute to sustainable development but also help in further mitigating carbon emissions.

Equivalent damping ratio oriented investigation on tuned negative stiffness inerter damper for seismic application

Additional equivalent damping ratio (EDR) plays a crucial role in the performance evaluation and design methodology of conventional passive dampers. The inclusion of inerter elements has been extensively demonstrated to significantly enhance the control efficiency of these passive dampers. Recently, negative stiffness (NS) elements have been found potential to enhance the control performance of inerter-based dampers (IBDs). However, little research has investigated the enhancement effect of NS elements on the additional EDR of IBDs. Meanwhile, the existing design methodology for IBDs is response-based, posing challenges in ensuring optimal control efficiency and fully utilizing the essential features of inerter and NS elements. In this study, the tuned negative stiffness inerter damper (TNSID), which integrates the NS element into one of the classical IBDs, namely the tuned inerter damper (TID), is investigated in terms of EDR for seismic application. This study makes two primary contributions. Firstly, it proposes an optimization design methodology aimed at achieving optimal control performance and efficiency of the TNSID simultaneously. Secondly, it unveils the enhancement law for the control performance and efficiency of TNSID. To do this, the analytical formulas for additional EDR and EDR enhancement (EDRE) factors are established as indicators of control performance and efficiency, respectively. Closed-form expressions are derived to optimize the TNSID design parameters. The comparative studies are conducted on both single and multi-degree-of-freedom primary structures, utilizing closed-form expressions, frequency transfer functions, and time history analysis. Results demonstrate that the incorporation of the NS element and the proposed optimization design methodology can further enhance both the control performance and efficiency of TNSID, surpassing those achieved by TID and classical fixed-point optimization theory.

Measuring the gap between optimal and observed energy structure: Evidence from 36 OECD countries

Sustainable development has always been given priority by economic authorities, therefore pursuing both environmental emissions reduction and economic growth at the same time is required. However, energy structure is often disregarded in existing performance evaluation methodologies, despite its essential function in determining environmental efficiency or productivity. This study shows how ignoring the energy structure can lead to distorted policy recommendations as well as evaluation results. The present study provides a non-parametric estimating approach to quantify the difference between the optimal (theoretical) energy structure required to achieve sustainable development and the actual (observed) energy structure. This method may assess the energy transition process and evaluate the observed and optimal energy structure of a country or region. We demonstrate that using the countries of the Organization for Economic Co-operation and Development as an example, that only a limited number of countries are able to meet their objectives for carbon reduction and economic development simultaneously without transforming their energy structure. The relationship between energy structure and green total factor productivity (GTFP) was further studied using a two-stage least squares regression on the observed energy structure and GTFP. The findings indicate that most countries need to increase their share of renewable energy in order to achieve green growth.

E-Commerce Recommendation System

In today's digitally driven marketplace, E-commerce Product Recommendation Systems (EPRS) play a pivotal role in enhancing user experience, facilitating decision-making, and boosting sales. This paper offers a comprehensive analysis of various methodologies, techniques, and advancements in EPRS. Beginning with an overview of the significance of personalized recommendations in driving user engagement and satisfaction, the paper delves into the underlying principles and algorithms employed in EPRS, including collaborative filtering, content-based filtering, and hybrid approaches. Furthermore, this research explores the challenges associated with EPRS, such as data sparsity, cold start problem, and scalability issues, along with the strategies and innovations proposed to address these challenges. It examines the role of machine learning, deep learning, and artificial intelligence in improving the accuracy and relevance of recommendations, thereby optimizing user experience and maximizing conversion rates. Moreover, the paper investigates the impact of contextual factors, such as user demographics, browsing history, and social interactions, on recommendation quality and effectiveness. It discusses the ethical considerations and privacy concerns surrounding data collection, user profiling, and algorithmic bias in EPRS implementation, emphasizing the need for transparency, fairness, and user consent. Additionally, this research evaluates the performance metrics and evaluation methodologies used to assess the effectiveness and efficiency of EPRS, including precision, recall, coverage, and serendipity. It highlights the importance of continuous evaluation and refinement of recommendation algorithms to adapt to evolving user preferences and market dynamics. In conclusion, this paper provides valuable insights into the state-of-the-art techniques, challenges, and future directions of E-commerce Product Recommendation Systems. By understanding the intricacies and advancements in this field, businesses can leverage EPRS to enhance customer satisfaction, foster brand loyalty, and drive sustainable growth in the competitive landscape of ecommerce. keyword Recommendation Algorithms,

Performance Evaluation of DGNSS Receiver for Dynamic Military Applications

In military applications, a highly accurate and precise navigation system is necessary for some ground-based combat system applications to navigate the vehicle and mark the location of the fuses and pickets laid by the mobile equipment. Few companies across the globe have developed expertise in manufacturing Global Navigation Satellite System (GNSS) receivers with Real Time Kinematics (RTK) capability along with integrated INS. On mobile equipment, it is not always possible to precisely place the Differential GNSS (DGNSS) antenna and GNSS receiver at the point for which the data needs to be marked. For such applications, lever arm calculation needs to be implemented. Also, due to uneven terrain conditions and slopes, the vehicle undulates up to 10o about its longitudinal and transverse axis. This dynamic condition induces considerable errors in the actual data. During the fuse laying process, there is also a continuous requirement for real-time location data in the GNSS-denied environment for a short period. INS was integrated with the GNSS receiver. We discuss test methodology for GNSS receiver performance evaluation. Experimentations were performed, taking into consideration these requirements. Data collected are also analyzed and discussed. Test results confirmed that the module is accurate with an accuracy of a few centimeters.

Seismic Performance Evaluation of Multi-Storey Residential Building with Friction Pendulum Bearings: Indonesia case study

The methodology for seismic performance evaluation of a residential building in Indonesia with the use of seismic isolation is considered. An 8-storey reinforced concrete frame residential building with shear wall structural system was selected as a case study. Nonlinear methods of seismic response analysis were used to calculate the response of the structure: nonlinear static (Pushover) and Nonlinear-Time History Analysis, NLTHA. The analysis is performed in STERA 3D freeware. The nonlinear time history analysis was performed for seven pairs of horizontal components of earthquake ground motions, selected according to the parameters of possible earthquakes for the considered site (Bandung city). The selected earthquake records were modified using the spectral matching procedure for design spectrum. Friction-pendulum bearings developed by Nippon Steel Corporation of Japan were used as seismic isolation. The results of nonlinear time history analysis show that shallow earthquakes result in greater damage compared to megathrust earthquakes, with both scenarios providing a life safety (LS) performance level. The use of seismic isolation can reduce seismic loads, as evidenced by the reduction in top-level accelerations and shear forces at the base.

Applying AHP for Assessing Zenbo Division Production Employee Performance

This research proposes an employee assessment system utilizing the Analytical Hierarchy Process (AHP) method to evaluate productivity levels. The absence of such a system poses challenges in gauging employee performance and its impact on company productivity. Through AHP, this study aims to provide decision support for performance evaluation, thereby maintaining high productivity levels. Results indicate significant shifts in employee rankings, highlighting the effectiveness of the proposed methodology in identifying and enhancing productivity. This research contributes to the discourse on employee performance assessment, offering practical implications for decision-makers aiming to optimize workforce productivity. Highlights : Enhanced decision-making: The utilization of AHP provides a structured approach for evaluating employee performance, facilitating informed decision-making processes. Improved productivity rankings: Implementation of the assessment system results in notable shifts in employee rankings, indicating its effectiveness in identifying high-performing individuals. Practical implications: Findings offer valuable insights for organizations seeking to optimize workforce productivity through systematic performance evaluation methodologies. Keywords: Employee productivity, Assessment system, Analytical Hierarchy Process (AHP), Decision support, Workforce optimization

A Survey on Heterogeneity Taxonomy, Security and Privacy Preservation in the Integration of IoT, Wireless Sensor Networks and Federated Learning.

Federated learning (FL) is a machine learning (ML) technique that enables collaborative model training without sharing raw data, making it ideal for Internet of Things (IoT) applications where data are distributed across devices and privacy is a concern. Wireless Sensor Networks (WSNs) play a crucial role in IoT systems by collecting data from the physical environment. This paper presents a comprehensive survey of the integration of FL, IoT, and WSNs. It covers FL basics, strategies, and types and discusses the integration of FL, IoT, and WSNs in various domains. The paper addresses challenges related to heterogeneity in FL and summarizes state-of-the-art research in this area. It also explores security and privacy considerations and performance evaluation methodologies. The paper outlines the latest achievements and potential research directions in FL, IoT, and WSNs and emphasizes the significance of the surveyed topics within the context of current technological advancements.

AI-ML algorithm for enhanced performance management: A comprehensive framework using Backpropagation (BP) Algorithm

In the era of economic globalization and heightened market competition, organizations face the imperative to establish robust performance evaluation mechanisms that drive both organizational development and individual employee motivation. This article delves into the multifaceted factors influencing employee performance, encompassing personal attributes, interpersonal relations, and work standards. The study takes a deep dive into the transformative integration of AI-ML algorithms, proposing a comprehensive framework for elevated performance management. Through the application of machine learning algorithms, this research seeks to revolutionize performance appraisals, impacting crucial HR processes such as employee selection, promotions, terminations, training initiatives, and remuneration adjustments. The investigation provides nuanced insights into the synergy between artificial intelligence, machine learning, and traditional performance evaluation methodologies, offering profound perspectives on contemporary organizational practices amid evolving challenges.

Assessing User Perceptions and Realities in FinTech Application Performance: A Comparative Analysis

Financial services have been fundamentally transformed by FinTech, an innovation in technology that has caused significant shifts in the financial sector. This study delves into the impact of two prominent FinTech platforms, Stripe and Square, on the banking industry. Stripe specializes in online payments, customization, and agility, with a focus on in-person transactions, catering to technologically adept startups. Conversely, Square offers tools tailored for small retail and restaurant enterprises. The study aims to bridge the gap between user perceptions and actual performance, focusing on security and reliability concerns. Despite the convenience offered by FinTech, concerns over security persist, highlighting the need for improved user confidence through robust security standards and transparency. The research explores the historical evolution of FinTech, emphasizing the integration of advanced technologies like AI and blockchain. The research analyzes FinTech performance evaluation methodologies, including key performance indicators (KPIs), to showcase the significance of assessing financial success, customer satisfaction, and user experience. The study outlines the criteria for selecting Stripe and Square, considering industry prominence, market share, a diverse user base, service offerings, innovation, accessibility, and financial inclusion initiatives. The methodology employs a qualitative approach to systematically analyze the performance and user experience of both platforms. Features, pricing, target audience, security, and user experience influence user preferences, according to key findings. The comparative analysis between Stripe and Square indicates a market trend where businesses choose platforms based on operational nature, settlement speed, and specific financial needs. The study underscores the transformative role of FinTech within the financial sector, driven by technological innovation, financial inclusion initiatives, diverse user bases, user-centric design, and robust security measures. The implications suggest a future FinTech landscape characterized by continued emphasis on innovation, inclusivity, user-centric approaches, and security. The research concludes by highlighting the significant impact of Stripe and Square on the financial sector and providing insights for stakeholders to adapt strategies in the dynamic FinTech landscape. Further research opportunities lie in exploring evolving user preferences, emerging technologies, and regulatory developments shaping the future of FinTech.

Collaborative Filtering Algorithms in Financial Performance Evaluation Indicators for Intelligent Innovation Dynamic Research

Abstract This study develops a financial performance evaluation system for S Group, employing a collaborative filtering algorithm to address the limitations inherent in traditional financial performance evaluation methodologies. Improvements have been made to the similarity measure method, data imputation technique, and rating prediction approach within the model. Subsequently, the financial status of S Group is scrutinized, with a thorough exploration of the input and output indicators, leading to the calculation of the efficiency values within the financial indicator system. Through sensitivity analysis, this research investigates the impact of input and output indicators on the outcomes derived from collaborative filtering. Based on the study, recommendations are formulated in alignment with the evaluation outcomes. The findings reveal that the average comprehensive efficiency of S Group over the past decade is 0.772, with a median comprehensive efficiency of 0.788, indicative of a robust financial condition. This research provides a valuable reference for the formulation of financial performance evaluation systems and the analysis of financial performance across various industries.

Innovative Multi-Criteria Decision-Making Approach for Supplier Evaluation: Combining TLF, Fuzzy BWM, and VIKOR

When confronted with underperforming suppliers, the need to evaluate and improve supplier performance becomes apparent. However, the inherent inaccuracies in information introduce complexity, especially when subjective human judgment is involved in the supplier evaluation process. Associated with such problem, this study presents a novel methodology for supplier performance evaluation in the crumb rubber industry, integrating the Taguchi Loss Function (TLF), fuzzy Best-Worst Method (BWM), and VIKOR technique in group decision-making environment. Aimed at addressing the challenges in industries with variable supplier quality and performance, such as the crumb rubber industry in Indonesia, the methodology was empirically tested to demonstrate its practical utility. The process involved identifying evaluation criteria through literature review tailored to the needs of decision makers (DMs), applying TLF to quantify losses from supplier performance deviations, using fuzzy BWM to determine criteria weights based on the DMs judgment, and employing the VIKOR technique for comprehensive supplier ranking. The findings underscore the methodology's effectiveness in enhancing decision-making, offering a unified metric that accommodates diverse criteria and balances precise data with subjective assessments. This approach simplifies the evaluation process, particularly in situations with conflicting interests among decision-makers. Demonstrating its practical application in the crumb rubber industry, the study highlights the methodology's potential for broader industrial applicability. Future research could explore comparative analyses with other analytical methods, further establishing the methodology's robustness and adaptability in different management contexts.

Design and Optimization of Smart Campus Framework Based on Artificial Intelligence22

In this study, an artificial intelligence (AI)--based smart campus framework is built and optimized with the aim of improving user happiness, raising AI model performance, maximizing resource utilization, and promoting smart campus adoption. The study technique employs a mixed-methods approach that combines quantitative data analysis and qualitative user feedback in order to completely evaluate the effectiveness of the framework. Literature reviews, Questionnaires of 544, interviews of 56 persons, and observations are used to collect data on user satisfaction, AI model performance, optimization strategies, and adoption of smart campuses AI models are built using statistical methodology and AI techniques for performance evaluation. In the Smart Campus Framework based on Artificial Intelligence, we gathered the data by constructing IoT sensor networks for real-time monitoring and merging student data to provide insights into academic performance and student engagement. The findings indicate that, on average, users are satisfied, and the performance ratings for the AI models vary from 7.25 to 8.25. The smart campus framework is effective, as evidenced by the optimization metric's 7.53 average score. A score of 7.4 for smart campus adoption combines user knowledge, perceived utility, and perceived ease of use. The practical implications include better user experience, cost optimization, and smart campus architecture. Theoretical implications include the verification of the mixed-methods strategy and the creation of a framework for AI model optimization. The study's findings act as a model for upcoming smart campus research, spurring creativity and change in institutions of higher learning. The study’s limitations suggest that results can be generalized with minor contextual change and this is the biggest challenge for researchers and policy makers.

Efficient treatment of recalcitrant biologically treated coking wastewater using Cu-doped LaFeO3/peroxymonosulfate activator: Performance evaluation and response surface methodology modeling

Biologically treated coking wastewater (BTCW) frequently contains many kinds of refractory and toxic organic pollutants, which is far from meeting the discharge standard of GB16171–2012. In the present work, the peroxymonosulfate (PMS) activation processes via Cu-doped LaFeO3 (LFC) prepared with Bis (hexamethylene triaminepenta(methylenephosphonic acid) (BHMTMP) as the complexing agent was first utilized to degrade BTCW. The degradation reaction rate constant (kobs) of LaFe0.5Cu0.5O3 (LFC5)/PMS for CODCr and NH3-N was 3.65 times and 3.63 times for LFO (LaFeO3)/PMS. Moreover, the kobs of LFC5/PMS system prepared using BHMTMP as the complexing agent surpassed that of the LFCC/PMS system prepared with citric acid (CA) as the complexing agent, and the former was 2.3 times and 1.8 times greater than that of the latter for the removal of CODCr and NH3-N, respectively. The leaching of Cu and Fe of LFC5 was much less than that of LFCC, the strong chelation performance of BHMTMP with various metal ions can greatly reduce the leaching of metal ions and improve the stability of the catalyst. Hydroxyl radicals, sulfate radicals, and singlet oxygen were generated in the LFC5/PMS system, with singlet oxygen playing a dominant role. HPLC, UV absorption spectra, and 3D-EEM fluorescence spectra were used to analyze pollutant components in BTCW before and after degradation, demonstrating the effective removal of certain residual contaminants. In addition, the degradation process of BTCW was modeled using a 3-factor-3-level Response Surface Methodology (RSM) model. RSM model fitted the true experimental results well. This research offers a new approach for the comprehensive treatment of BTCW to meet standard discharge requirements and promote a more cost-effective operating process.

Performance evaluation of underwater vision systems

The article describes a methodology for performance evaluation of vision systems for remotely operated underwater vehicles. The methodology is based on a system approach and uses mathematical models of the aqueous medium where an optical signal propagates, the underwater object image registration system, and the mathematical model of the human visual system. The detection and recognition probabilities of underwater object image at a given registration range are used as performance evaluation indicators of underwater vision systems. The mathematical model of the aqueous medium developed by the authors allows quantitative evaluation of the influence of backscattering interference arising during objects illumination on the underwater vision system performance. The results of numerical experiments presented in the paper illustrate the possibility of using the proposed technique to optimize the underwater object image registration system parameters in order to achieve the required values of detection or recognition probabilities at the given ranges.