Performance Evaluation Of System Research Articles

Navigating Challenges in Chinese Enterprise Management: A Focus on Budgeting and Performance Evaluation

In the ever-evolving landscape of global economic integration, comprehensive budget management and performance evaluation have emerged as critical tools for enhancing enterprise efficiency and fostering development. However, Chinese enterprises face challenges in breaking through existing management bottlenecks, hindering innovation and leading to phenomena of rapid growth followed by swift decline. This paper aims to address these challenges by exploring the relationship between budget management and performance appraisal, highlighting their vital role in enterprise total value management. By aligning budgeting with strategy and operations and establishing fair performance evaluation systems, organizations can cultivate environments conducive to internal competition, fostering a corporate culture replete with entrepreneurial and innovative spirit. The study analyzes existing issues in current budget management and performance appraisal, such as imperfect systems, rigid practices, and talent shortages, proposing strategies to strengthen enterprise budget and performance management. These strategies include enhancing organizational structures, integrating resources with strategic focus, strengthening PDCA cycles, and establishing effective assessment mechanisms. By leveraging cloud platforms and big data, and actively pursuing legislative reforms, enterprises can optimize their budget management and performance evaluation systems, ultimately driving sustained growth and competitiveness in dynamic market environments.

Dual-parameter combined measurement sensor based on tri-metal structure

Simultaneous measurement of strain and temperature in vibrating environments has always been a key technical issue in the field of structural health monitoring. To address the bottleneck of inadequate dual-parameter measurement performance in existing Fiber Bragg Grating (FBG) strain and temperature sensors, a new type of fiber optic grating sensor with anti-vibration capabilities for the measurement of both strain and temperature is proposed. The sensor utilizes a flexible hinge structure made of 304 steel with high elastic modulus for strain sensitization and a bimetallic structure composed of 7075 aluminum and 4 J36 Invar alloy for temperature sensitization, integrating both types of sensors. Theoretical analysis of the sensor was conducted, followed by the fabrication of a prototype and the establishment of a testing system for performance evaluation. The results show that the designed dual-parameter FBG sensor has a strain sensitivity of approximately 3.06 pm/µε, about 2.62 times that of a bare FBG, and a temperature sensitivity of about 50.2 pm/℃, roughly 4.97 times that of a bare FBG. In anti-vibration performance tests between 5–100 Hz, the wavelength drift of the strain and temperature sections of the FBG in the sensor were both within 1 pm, meeting the requirements for simultaneous measurement of strain and temperature in vibrating environments. These findings provide a reference for the development of similar sensors and further improvement of the measurement performance of FBG strain and temperature sensors.

Study on the Influence of Digital Transformation on The Financial Performance of Wuliangye

With the development of the Internet and big data, the digital transformation of enterprises has become a necessary means to cope with the changes of their internal and external environment and improve their survival and development. As a leader in the liquor industry, Wuliangye has implemented the digital transformation of online and offline integration. In wuliangye as the research object, from the profitability, solvency, operating ability and growth ability to build financial performance evaluation system, analysis of the financial data from 2016 to 2022, explores the dynamic change of wuliangye financial performance, and compare the digital transformation, and the relevant enlightenment, in order to provide reference for digital transformation of liquor industry.

In-event surrogate model training for regional transmission tower-line system dynamic responses prediction in realistic hurricane wind fields

Surrogate models have shown improved accuracy in predicting infrastructure responses during dynamic loadings. However, training a surrogate model for complex loading inputs across the entire hazard region remains challenging. This study provides insight into the training of surrogate models to estimate the responses of transmission tower-line structures in a coupled high-dimensional and high-resolution wind field and presents innovative methods for addressing these challenges. Four data- and physics-based spatial-temporal decoupling sampling methods are employed and cross-compared to obtain the most representative in-event wind profiles for training the surrogate model. Long Short-Term Memory (LSTM) is utilised as the surrogate model framework to predict the dynamic responses of the structure during the 2017 Hurricane Harvey. The accuracy and robustness of two transmission tower-line structure configuration surrogate models are validated by comparing the predictions with finite element analyses by using randomly distributed temporal and geospatial wind profiles throughout the hurricane. Finally, a single LSTM surrogate model is developed, trained by applying the full reference wind speed range of Hurricane Harvey for the regional-scale structural performance evaluation of the transmission tower-line system. The results demonstrate that the proposed surrogate model training methodology is general and can be applied to regional-scale structural performance evaluations.

Burst traffic: Congestion management and performance optimization strategies in heterogeneous cognitive radio networks

AbstractIn order to solve the network congestion problem caused by burst service flows in heterogeneous cognitive radio networks (Het‐CRN), a two‐stage dual‐threshold random early detection (TD‐RED) control mechanism is proposed here. Meanwhile, a three‐state Markov modulated Poisson process (MMPP‐3) is used to model the burst service to analyze its arrival in depth. Moreover, a performance evaluation system (PES) is proposed to quantify the interference of burst services to different classes of services and their quality of service, which can comprehensively evaluate the performance enhancement of the TD‐RED control mechanism for each service. The system examines performance metrics such as packet loss rate, average delay, average captain etc. PES simulation results show that TD‐RED significantly reduces the packet rejection rate and transmission delay compared to the no congestion control (NCC) and random early detection (RED) methods, thus providing superior quality of service to all services. thereby providing a superior quality of service to cognitive users. Its lower average captain fully proves that TD‐RED can effectively alleviate congestion.

Asymmetric Performance Evaluation Under Quantity and Price Competition with Managerial Delegation

Abstract In this paper, we consider asymmetric performance evaluation contracts under different product market configurations with managerial delegation and specify the optimal decision-making by the social and relative performance evaluation firms. We present a reversal result on the owner’s choice of the social performance and relative performance evaluation contract as the product market competition type changes from quantity to price competition. Surprisingly, results indicate that the consumer surplus increases as the degree of product substitution increases under quantity competition in a specific economic environment. A firm that considers social performance evaluation produces less, charges a higher price, and earns a lower profit than a firm that uses relative performance evaluation. We also endogenize the choice of performance evaluation systems. While relative performance emerges as the endogenous choice under both modes of product market competition, it leads to lower consumer surplus and social welfare in comparison to an asymmetric performance evaluation system.

A time-domain spectral finite element method for acoustoelasticity: Modeling the effect of mechanical loading on guided wave propagation

Ultrasonic testing techniques such as guided wave-based structural health monitoring aim to evaluate the integrity of a material with sensors and actuators that operate in situ, i.e. while the material is in use. Since ultrasonic wave propagation is sensitive to environmental conditions such as pre-deformation of the structure, the design and performance evaluation of monitoring systems in this context is a complicated task that requires quantitative data and the associated modeling effort. In our work, we propose a set of numerical tools to solve the problem of mechanical wave propagation in materials subjected to pre-deformation. This type of configuration is usually treated in the domain of acoustoelasticity. A relevant modeling approach is to consider two different problems: a quasi-static nonlinear problem for the large displacement field of the structure and a linearized time-domain wave propagation problem. After carefully reviewing the modeling ingredients to represent the configurations of interest, we propose an original combination of numerical tools that leads to a computationally efficient algorithm. More specifically, we use 3D shell elements for the quasi-static nonlinear problem and the time-domain spectral finite element method to numerically solve the wave propagation problem. Our approach can represent any type of material constitutive law, geometry or mechanical solicitation. We present realistic numerical results on 3D cases related to the monitoring of both isotropic and anisotropic materials, illustrating the genericity and efficiency of our method. We also validate our approach by comparing it to experimental data from the literature.

Exploring Relationship of High-performance Work System with Knowledge Workers' Work engagement through Indirect and Indirect Effects

This research work was undertaken to examine how the implementation of a high-performance work system (HPWS) affected the level of work engagement in Indian knowledge workers by observing the both direct and indirect effects. Research was carried out involving 202 knowledge workers from India. The study employed an online survey to gather information from the participants, and the data was obtained through a structured questionnaire. According to the study's findings, Indian knowledge workers recognized and valued the advantages of an HPWS (High Performance Work System) that embraced employees’ participation in important decisions, receive ongoing training and development, access pertinent information and knowledge, and have access to a well-structured performance evaluation system. Furthermore, the study demonstrated that Indian knowledge workers also acknowledged their level of engagement in their work, indicating a willingness to take on challenges, voluntarily assist colleagues and supervisors, and fully engage with their tasks. In general, HPWS reported for 35 per cent of the variability in job engagement and exhibited a positive connection with work assignment. The study's findings also showed that HPWS reported for 31 per cent of the alteration in psychological contract fulfillment, 22 per cent of the variance in workers' perceived organizational support in terms of resources, and 33 per cent of the variance in organizational identity among Indian knowledge workers. Notably, constructs like ‘psychological contract fulfillment,’ ‘organizational identity’ and ‘perception of job resources’ influenced the Indian knowledge workers’ ‘work engagement.’ Also, findings showed that in the context of Indian knowledge workers, factors such as the ‘psychological contract fulfillment,’ ‘perception of job resources,’ and ‘organizational identity’ played a role as partial mediators between High-Performance Work Systems (HPWS) and knowledge workers’ ‘work engagement.’ Moreover, 'job satisfaction' acted as a perfect intermediary or mediator between 'fulfillment of psychological contract' and 'work engagement'.

Distributed matrix computing system for big data

In order to solve the problem of low computing efficiency in big data analysis and model construction, this paper intended to deeply explore the big data analysis programming model, DAG (Directed Acyclic Graph) and other contents, and on this basis, it adopted a distributed matrix computing system Octopus for big data analysis. Octopus is a universal matrix programming framework that provides a programming model based on matrix operations, which can conveniently analyze and process large-scale data. By using Octopus, users can extract functions and data from multiple platforms and operate through a unified matrix operation interface. The distributed matrix representation and storage layer can design data storage formats for distributed file systems. Each computing platform in OctMatrix provides its own matrix library, and it provides a matrix library written in R language for the above users. SymboMatrix provides a matrix interface to OctMatrix that is consistent with OctMatrix. However, SymboMatrix also retains the flow diagram for matrix operations in the process, and it also supports logical and physical optimization of the flow diagram on a DAG. For the DAG computational flow graph generated by SymbolMatrix, this paper divided it into two parts: logical optimization and physical optimization. This paper adopted a distributed file system based on line matrix, and obtained the corresponding platform matrix by reading the documents based on line matrix. In the evaluation of system performance, it was found that the distributed matrix computing system had a high computing efficiency, and the average CPU (central processing unit) usage reached 70%. This system can make full use of computing resources and realize efficient parallel computing.

Performance evaluation of blockchain food supply chain management system: JkBFMs

Performance evaluation of blockchain food supply chain management system: JkBFMs

Ballistic performance evaluation of 3-dimensional orthogonal composite soft armor systems

Ballistic performance evaluation of 3-dimensional orthogonal composite soft armor systems

Design and performance evaluation of an off-grid hybrid solar system in a remote location: A case study of Legundi Island

Legundi Island, located in Lampung Bay, Indonesia, has high solar energy potential but still relies on Diesel Power Plants. This research aims to design a hybrid Solar Power Plant and Diesel Power plant system that is environmentally friendly and reliable. Analysis was conducted using the Homer software.•This research designs a hybrid Solar Power Plant system with a capacity of 170.2 kWp and a battery of 1,036 kWh, integrated with three existing Diesel Power Plants units with a capacity of 200 kW.•This system can generate electricity of 296,391 kWh/year with a renewable energy fraction reaching 79.9% and an electricity surplus of 0.988%.•Fuel consumption decreased by 79.3% compared to the existing Diesel Power Plant system.

Accurate parameters extraction of photovoltaic models with multi-strategy gaining-sharing knowledge-based algorithm

The determination of photovoltaic (PV) model parameters has essential theoretical and practical significance for the performance evaluation, power monitoring, and power generation efficiency calculation of PV systems. In this paper, a multi-strategy gaining-sharing knowledge-based algorithm (MSGSK) is developed to determine these parameters. In our previous work, it has been demonstrated that gaining-sharing knowledge-based algorithm (GSK) is well suited for solving the concerned problem. To enhance its performance, a parameter adjustment strategy is developed to adjust the knowledge rate and knowledge ratio of GSK. Besides, a backtracking differential mutation strategy by combining the mutation scheme of differential evolution and the updating scheme of backtracking search optimization algorithm is developed to enrich the population diversity. Furthermore, a strategy selection mechanism is introduced to integrate the former two strategies to balance exploration and exploitation in different stages of the evolutionary process. The suggested MSGSK algorithm is applied to five PV cases (SDM, DDM, Photowatt-PW201, STM6-40/36, and STP6-120/36). From the experimental data, it can be observed that MSGSK extracts the PV model parameters more precisely than the basic GSK. Furthermore, it exhibits faster convergence speed and higher accuracy compared to other advanced algorithms found in the literature.

RFSoC Gen3-Based Software-Defined Radio Characterization for the Readout System of Low-Temperature Bolometers

This work reports the performance evaluation of an SDR readout system based on the latest generation (Gen3) of AMD’s Radio-Frequency System-on-Chip (RFSoC) processing platform, which integrates a full-stack processing system and a powerful FPGA with up to 32 high-speed and high-resolution 14-bit Digital-to-Analog Converters and 14-bit Analog-to-Digital Converters. The proposed readout system uses a previously developed multi-band, double-conversion IQ RF-mixing board targeting a multiplexing factor of approximately 1000 bolometers in a bandwidth between 4 and 8 GHz, in line with state-of-the-art microwave SQUID multiplexers. The characterization of the system was performed in two stages, under the conditions typically imposed by the multiplexer and the cold readout circuit: first, in transmission, showing that noise and spurious levels of the generated tones are close to the values imposed by the cold readout, and second, in RF loopback, presenting noise values better than −100 dBc/Hz totally in agreement with the state-of-the-art readout systems. It was demonstrated that the RFSoC Gen3 device is a suitable enabling technology for the next generation of superconducting detector readout systems, reducing system complexity, increasing system integration, and achieving these goals without performance degradation.

Performance evaluation and optimization of the cascade refrigeration system based on the digital twin model

Cascade refrigeration systems (CRSs) have become the priority choice in low-temperature applications, causing severe energy waste due to the lack of practical control strategies. Currently, refrigeration simulations are mainly based on the simplified thermodynamic or Artificial Intelligence (AI) model. However, the former is insufficiently accurate, while the latter is of unclear thermodynamic significance. By connecting the physical and digital space, the digital twin technology provides exciting opportunities for combining thermodynamic and AI methods. In this paper, an integrated digital twin model for the CRS is innovatively proposed based on the modified semi-empirical model of the twin-screw refrigeration compressor. As a major update, the identification and update method of characteristic parameters is developed based on real-time operating data to ensure performance consistency between digital and physical twins. By comparing with the simplified model, results show that the digital twin model can improve prediction accuracy by 7.42%–23.62%. Founded on the proposed model, the thermodynamic performance of the CRS is evaluated. To facilitate on-site applications, the definition of flat intermediate temperature is given, which is fitted to a control correlation with several easily observed variables as the inputs. Taking a food factory as a case study, simulation results reveal that the optimum intermediate temperature should be significantly lower than the original, and the coefficient of performance (COP) has an average improvement potential of 9.1%. By deploying the optimum strategy under the on-site condition, a superior energy-saving rate of up to 13.1% is attained according to test reports.

Performance evaluation of electro-mechanical railway interlocking system for digital twin application

The industries engaged healthcare, manufacturing, and construction automated with exquisite creativity and innovation at the doorstep of Industry 4.0. In this process, digital twins are diligently used to supplement industrial processes, reduce costs, monitor assets, streamline maintenance, reduce downtime, and enable the creation of connected goods. The research is aimed at finding the digital representation of electro-mechanical railway interlocking elements are simulated and its functionality is verified accordingly. With respect to carry out this research, the Indian Railways Mangalagiri Railway Station blueprints were collected to represent the physical Model which consists of signals, points, a crank handle, an LC gate, and many more elements. In this paper, three interlocking elements such as points, crank handle, and LC gate, are analysed, modelled, and simulated by using Model Sim ME of Libero SoC software. Representation of these digital elements were developed using a smart design canvas. Therefore, the proposed methodology will enhance the safety measures and security levels of the train movements and reduces the time required to replace the faulty relay. Similarly, in future, the proposed model could be dumped into the FPGA kit to verify the real-time data for finding the faulty relay in the relay-based interlocking system.

Integrated Model for Comprehensive Performance Investigation of Solar Concentrated Photovoltaic‐Thermal System Embedded with Microchannel Heat Sinks

Concentrated photovoltaic (CPV) is a well‐established renewable energy technology. A significant challenge in CPV systems is their low efficiency, majorly due to localized heating yielded from concentration, often requiring the use of cooling systems. The accurate performance analysis and cooling system design for CPV systems require a multiphysics model. Herein, an integrated optical–thermal–electrical model is proposed for the performance evaluation of CPV systems incorporated with different configurations of microchannel heat sinks. The heat sink configuration includes 116 parallel and counter microchannels (Configuration A and B), a single wide microchannel (Configuration C), and single wide minichannel (Configuration D) heat sinks. This study involves 3D Monte‐Carlo ray tracing, finite volume method, and cell‐based electrical modeling. The results indicate that the flux profile over the absorber is highly nonuniform in nature. Furthermore, the CPV system with a Configuration C heat sink achieves a better uniformity in temperature, providing an optimized thermal performance of 64.63%. Moreover, the integrated model considers the impact of PV cell current mismatch that becomes prominent at increasing incidence angles. A maximum deviation of 6.9% in electrical power is obtained while comparing predicted numerical results against experimental results available in literature.

Construction and Analysis of Network Cloud Security Situation Awareness System Based on DBN-DE Algorithm

With the wide application of cloud computing, network security has become the primary issue in cloud environment. This study focuses on constructing a system focusing on network cloud security situation awareness, and combines deep belief network with differential evolution algorithm to improve the perception and analysis capability of network cloud security. Differential evolution algorithm is used to optimize DBN weights and parameters to improve system performance and generalization ability. In the aspect of system performance evaluation, the effectiveness of the system is verified by a series of experiments. The experimental results show that the system based on DBN-DE has excellent performance in network attack detection and can quickly identify various potential threats. The system also has a low false alarm rate, which can reduce the frequency of administrator intervention, and improve the efficiency and reliability of network cloud security.

Leveraging and exercising caution with ChatGPT and other generative artificial intelligence tools in environmental psychology research.

Generative Artificial Intelligence (GAI) is an emerging and disruptive technology that has attracted considerable interest from researchers and educators across various disciplines. We discuss the relevance and concerns of ChatGPT and other GAI tools in environmental psychology research. We propose three use categories for GAI tools: integrated and contextualized understanding, practical and flexible implementation, and two-way external communication. These categories are exemplified by topics such as the health benefits of green space, theory building, visual simulation, and identifying practical relevance. However, we also highlight the balance of productivity with ethical issues, as well as the need for ethical guidelines, professional training, and changes in the academic performance evaluation systems. We hope this perspective can foster constructive dialogue and responsible practice of GAI tools.

When Taekwondo Meets Artificial Intelligence: The Development of Taekwondo

This study explores the comprehensive understanding of taekwondo, the application of fourth industrial revolution technologies in various kinds of sports, the development of taekwondo through artificial intelligence (AI), and essential technology in the fourth industrial revolution while suggesting advanced science directions through a literature review. Literature was sourced from six internet search electronic databases, consisting of three English databases and three Korean databases, from January 2016 to August 2023. The literature indicated cases of sports convergence with the application of fourth industrial revolution technologies, such as the game of go, golf, table tennis, soccer, American football, skiing, archery, and fencing. These sports not only use big data but also virtual reality and augmented reality. Taekwondo is a traditional martial art that originated in Republic of Korea and gradually became a globally recognized sport. Since taekwondo’s competition analysis is an analysis in which researchers manually write events, it takes a very long time to analyze, and the scale of the analysis varies depending on the researcher’s tendencies. This study presented the development of an AI Taekwondo performance improvement analysis and evaluation system and a metaverse-based virtual Taekwondo pumsae/fighting coaching platform through an AI-based motion tracking analysis method.