Are We Wasting Time? A Fast, Accurate Performance Evaluation Framework for Knowledge Graph Link Predictors

Carbon nanotube field-effect transistors (CNFETs) show great potential to build digital systems on advanced technology nodes with big benefits in terms of power, performance, and area (PPA). However, CNFET-specific additional features such as the number of tubes, pitch (spacing between tubes), tube position, and diameter in array of tubes play a significant role in accurate PPA evaluation. Furthermore, count and density variations in carbon nanotubes (CNTs) due to manufacturing limitations, like the presence of metallic tubes in the CNFET channel, degrade the anticipated PPA benefits. Moreover, modeling the CNFET parameters, CNT variations and etching techniques for CNTs create additional complexity during performance optimization. Hence, for realistic optimization of CNFET circuit’s performance, it is imperative to incorporate the impact of these parameters and variations. In this paper, we propose delay models [pitch-aware logical effort (PALE) and position-aware pitch factor (PAPF)] for fast and accurate performance evaluation by including the impact due to CNFET-specific parameters and CNT variations. These models are developed based on industry standard logical effort framework. Furthermore, we present an optimization tool using PALE and PAPF to minimize the delay and area of CNFET circuits. We deploy several circuit-level techniques prior to optimizing the tubes (CNTs) in the logic gates to achieve globally optimum solution. For better optimization of the circuits, we also include the impact of wire parasitic in estimating the delay of the individual gates. Our optimization tool results in the maximum and average delay improvement by 27% and 17%, respectively, and $2.5\times $ reduction in area for standard ISCAS and OpenSPARC benchmark circuits. Fast and fairly accurate delay computation in our optimization framework offers great runtime benefits as compared to state-of-the-art simulation and statistical-based methods.

Piezoelectric ultrasonic transducers with the function of energy conversation, as well as their numerous advantages in high-power density, quick response, flexible design, and service reliability, are involved in wide applications of industrial processing, precision driving, smart sensing, and medical services. The electromechanical equivalent circuit and Kirchhoff’s law indicate that mechatronics parameters are essential for performance evaluation, reliability analysis, and fault diagnosis of ultrasonic transducers. Importantly, the ultrasonic transducer is a time-variant system, data of one single parameter collected from a certain test cannot match with the data of another single parameter acquired from a different test. So, a synchronous and precise online measurement of electronic parameters is encouraged for performance evaluation and optimization design of ultrasonic transducers. With the combination of virtual instrument technology, an asynchronous measurement system of electrical excitation parameters for the ultrasonic transducers of linear driving motors was established in this study. Furthermore, the systematic measurement methods and error theory were illustrated, including the calculation methods and measuring circuits of electric signals. Experimental results proved that the proposed system and methods for measuring the input electronic power of ultrasonic transducer (e.g. effective value method for voltage and current, energy moment method for frequency, and spectrum analysis method for phase difference) are highly precise, quickly responsive, robust, and reliable for ultrasonic transducers. The findings of this study provide valuable references and suggestions for efficient, accurate, and online performance evaluation of ultrasonic transducers, particularly for piezoelectric transducers utilizing ultrasonic high-voltage exciting signals.

Quantifying the number of active sites is a crucial aspect in the performance evaluation of single metal‐atom electrocatalysts. A possible realization is using adsorbing gas molecules that selectively bind to the single‐atom transition metal and then probing their surface density using spectroscopic tools. Herein, using in situ X‐ray photoelectron spectroscopy (XPS) and near edge X‐ray absorption fine structure (NEXAFS) spectroscopy, we detect adsorbed CO gas molecules on a FeNC oxygen reduction single atom catalyst. Correlating XPS and NEXAFS, we develop a simple surface‐ and chemically‐sensitive protocol to accurately and quickly quantify the active site density. Density functional theory‐based X‐ray spectra simulations reaffirm the assignment of the spectroscopic fingerprints of the CO molecules adsorbed at Fe‐N 4 ‐C sites, and provide additional unexpected structural insights about the active site needed to explain the low‐temperature CO adsorption. Our work represents an important step towards an accurate quantitative catalytic performance evaluation, and thus towards developing reliable material design principles and catalysts.

In this article, we explore the use of neural networks (NNs) to enhance the performance evaluation of Universal Filtered Multi-Carrier (UFMC) systems, a key technology for modern communication networks such as 5G and beyond. Traditional methods of evaluating the Bit Error Rate (BER) and system efficiency in UFMC system can be computationally intensive and less accurate under dynamic conditions. To address these challenges, we propose a NN based approach that not only improves the accuracy of BER prediction but also significantly optimizes the system's overall efficiency. Channel state estimation (CSE) plays a major role for UFMC system to address the phenomenon of multipath channel fading. In order to achieve a high data rate using UFMC technology, it is necessary to have an effective CSE and very accurate signal detection. Recently, there has been significant interest in utilizing deep learning (DL) to enhance channel estimations. This article introduces a new method for channel estimation (CE) in UFMC system. The suggested approach utilizes DL models to improve the CE. For the UFMC system, we propose a detector based on bidirectional long short-term memory (Bi-LSTM). To identify the transmitted symbols, the suggested detector uses DL training data directly. Currently, a significant drawback of UFMC systems is the presence of a high peak-to-average power ratio (PAPR). The approach aims to reduce the BER and enhances the efficiency of the UFMC system. This is achieved by dynamically setting the constellation mapping and symbol damping on each subcarrier and sub-symbol. The results illustrate that the proposed model can accurately and efficiently recognize UFMC signals. The suggested model is being compared to Least Square (LS), LSTM, and Minimum Mean Square Error (MMSE) channel estimators. Through extensive simulations, our results demonstrate that the NN model reduces BER and enhances efficiency. The proposed model gives more effective performance in terms of enhanced efficiency and reduction of BER. The findings offer valuable insights for the design and optimization of next-generation communication systems, where accurate and efficient performance evaluation is critical.

Purpose Existing supply chain (SC) performance models are not able to cope with the potential of intensive SC digitalisation and establish a relationship between decisions and decision criteria. The purpose of this paper is to develop an integrated knowledge-based system (KBS) that creates a link between decisions and decision criteria (attributes) and evaluates the overall SC performance. Design/methodology/approach The proposed KBS is grounded on the fuzzy analytic hierarchy process (fuzzy AHP), which establishes a relationship between short-term and long-term decisions and SC performance criteria (short-term and long-term) for accurate and integrated Overall SC performance evaluation. Findings The proposed KBS evaluates the overall SC performance, establishes a relationship between decisions (long-term and short-term) and decision criteria of SC functions and provides decision makers with a view of the impact of their short-term or long-term decisions on overall SC performance. The proposed system was implemented in a case company where the authors were able to develop a SC performance monitoring dashboard for the company’s top managers and operational managers. Practical implications The proposed KBS assists organisations and decision makers in evaluating their overall SC performance and helps in identifying underperforming SC functions and their associated criteria. It may also be considered as a tool for benchmarking SC performance against competitors. It can efficiently point to improvement directions and help decision makers improve overall SC performance. Originality/value The proposed KBS provides a holistic and integrated approach, establishes a relationship between decisions and decision criteria and evaluates overall SC performance, which is one of the main limitations in existing supply chain performance measurement systems.

Accurate and complete performance evaluation is playing a major role in the further development of concentrating solar collectors. To ensure dependable test results, an appropriate testing and evaluation procedure is required. Moreover, the selection and installation of suitable measurement instrumentation are essential for obtaining reliable data for the performance evaluation. The quality of the measurement instrumentation greatly influences the representativeness of the test results. Details on the measurement instrumentation recommended for the testing of low-temperature solar collectors have already been provided in the testing standard EN ISO 9806:2013.

In distributed and vectorized computing there is a large number of highly different supercomputing platforms an application could run on. Therefore most traditional parallel codes are ill equipped to collect data about their resource usage or their behavior at run time and the corresponding data are rarely published and few scientists attack the planning of an application and its platform systematically. As an improvement over the current state of the art, we propose an integrated approach to performance evaluation, modeling and prediction for different platforms. Our approach uses a combination of analytical modeling and systematically designed experimentation with full application runs, reduced application kernels and some benchmarks. We studied our methodology of performance assessment with Opal, an example code in molecular biology, developed at our institution to run on our four Cray J90 ``Classic'' Vector SMPs. Besides a detailed assessment of performance achieved on the J90s, the primary goal of our study was to find the most suitable and most cost effective hardware platform for the application, in particular to check the suitability of this application for slow CoPs, SMP CoPs and fast CoPs, three flavors of Clusters of PCs built with off-the-shelf Intel Pentium processors. A performance assessment based on our model is much easier than porting and parallelizing the application for a new target machine and so we could easily obtain and include performance estimates for a T3E-900, a high end MPP system. The predicted execution times and speedup figures indicate that a well designed cluster of PCs achieves similar if not better performance than the J90 vector processors currently used and that the computational efficiency compares favorably to the T3E-900 for that particular application code.

The performance of complex systems is closely related to their security and reliability. Accurate performance evaluation is critical for timely system maintenance. Since the system monitoring information obtained usually contains noise, using this information to evaluate the performance of the device may result in inaccurate results. Interval values are used to represent the uncertainty of the monitoring information containing noise in the case of small samples. The uncertainty in the evaluation results is divided into two categories: the uncertainty of the evaluation model and the uncertainty of the monitoring information. The effects of the two categories on the evaluation results and the monotonicity of the output results on the monitoring information and model parameters are quantitatively analyzed. To avoid the problem of interval expansion during model optimization, an IER optimization model based on the belief degrees of monitoring information is built, and the whole evaluation process is solved and optimized uniformly. The monotonicity analysis results simplify the solving process of the IER optimization model, which reduces the difficulty and complexity. Validity and normalization tests are performed on the belief structure of the evaluation results, eliminating the invalid and redundant parts, and obtaining the best interval range of the evaluation results. The effectiveness of the proposed method is verified by its application in an electric servo mechanism.

Computer simulation is widely-used in investigating the performance of existing and proposed systems in many areas of science, engineering, operations research, and management science, especially in applications characterized by complicated geometries and interaction probabilities, and for dealing with system design in the presence of uncertainty. This is particularly true in the case of computer systems and computer networks. Therefore, it is very important to have efficient, reliable, and accurate methodologies for simulating these systems to ensure effective, reliable, and accurate evaluation and analysis of performance and to create the optimum design parameters for communication and computer networks. Although practical experiments and simulations are the most widely used, several efforts have also been directed towards mathematical models. The main objectives of this chapter are to: present the methodologies and techniques used to evaluate and analyze the performance of communication and computer networks routers, such as mathematical analysis, computer simulations techniques, and empirical measurements; identify the workload required for accomplishing a simulation model or mathematical analysis; identify the main metrics used to evaluate, manage, and control the performance of the computer networks; present the advantage and disadvantage of these techniques; and identify the challenges facing these different methodologies.

Extending service of Light Water Reactors (LWRs) to beyond 60 years will demand high integrity of materials and components in the reactors. The accurate evaluation and prediction of materials performance under anticipated operating conditions are of particular importance for ensuring the safety of nuclear power plants over their extended lifetime. Stainless steels are extensively used as structural alloys in light water reactor (LWR) systems because of their excellent combination of mechanical properties and corrosion resistance. Austenitic stainless steel welds and cast austenitic stainless steels (CASS) contain significant amounts of ferrite ~5-30% and are ubiquitous in LWR piping (elbows, pump casings, valves) and internal structural components. During their lifetime, these components are subjected to thermal aging at the temperature range of 288-327°C for pressurized water reactors (PWR). A phase transformation within the ferrite phase of these materials leads to embrittlement and degradation in corrosion resistance, which undermines the materials selection criteria.This detrimental phase transformation occurs in the temperature range of 204°C - 538°C in Cr rich ferritic alloys. A miscibility gap in the Fe-Cr system causes α-α’ phase separation within the ferrite grains through either spinodal decomposition or nucleation and growth. The resulting Cr-rich (α’) and Cr-poor (α) regions produce a material with degraded mechanical properties. Although α-α’ phase separation is limited to the ferrite grains, the loss of mechanical properties is observed in the overall dual phase material. The reduction of corrosion resistance can be severe, enhancing the likelihood of leaks or failure in these components. Small changes in alloy composition can alter the rate of phase separation and property degradation.This project proposed enhancements to MARMOT in order to predict the mechanical and corrosion properties of dual-phase stainless steels as a function of composition, aging time and temperature. The model was trained and validated by an extensive experimental database of duplex stainless steel alloys, where aging, mechanical properties, and atom probe characterization were already completed. Additional corrosion experiments were performed on aged alloys in PWR water chemistry to provide the corrosion data training set. The phase field capabilities of MARMOT were also parameterized with Density Functional Theory (DFT) simulations to provide kinetic information at the atomistic level. This multi-pronged approach has resulted in several outcomes: 1) relationships between microstructure, property and corrosion resistance of thermally aged materials, 2) fundamental understanding of interactions of ionic species with metal surfaces through first principles (DFT) calculations, and 3) mechanistic understanding of corrosion kinetics and corrosion mitigation strategies of thermally aged materials in representative exposure conditions. These outcomes will be impactful in evaluating components for plant life extension and by adding to the predictive capabilities of MARMOT.

Appropriate level of development of in-situ building information modeling for existing building energy modeling implementation

Expert landmark correspondences are widely reported for evaluating deformable image registration (DIR) spatial accuracy. In this report, we present a framework for objective evaluation of DIR spatial accuracy using large sets of expert-determined landmark point pairs. Large samples (>1100) of pulmonary landmark point pairs were manually generated for five cases. Estimates of inter- and intra-observer variation were determined from repeated registration. Comparative evaluation of DIR spatial accuracy was performed for two algorithms, a gradient-based optical flow algorithm and a landmark-based moving least-squares algorithm. The uncertainty of spatial error estimates was found to be inversely proportional to the square root of the number of landmark point pairs and directly proportional to the standard deviation of the spatial errors. Using the statistical properties of this data, we performed sample size calculations to estimate the average spatial accuracy of each algorithm with 95% confidence intervals within a 0.5 mm range. For the optical flow and moving least-squares algorithms, the required sample sizes were 1050 and 36, respectively. Comparative evaluation based on fewer than the required validation landmarks results in misrepresentation of the relative spatial accuracy. This study demonstrates that landmark pairs can be used to assess DIR spatial accuracy within a narrow uncertainty range.

Predicting the performance of photovoltaic (PV) systems in industrial applications has become increasingly important due to the complex and non-linear behavior of PV modules. Accurate performance prediction is essential for optimizing energy production and ensuring system reliability. However, a significant challenge arises from the lack of critical information in manufacturer datasheets, which often do not include all the necessary details for precise PV module modeling. This limitation can hinder accurate analysis and system performance evaluation. Optimization methods are recommended to accurately determine the electrical and physical characteristics of PV cells and modules to address this issue. These methods enable detailed and precise analysis of current-voltage (I-V) and power-voltage (P-V) characteristics across various PV models, improving the overall modeling accuracy. Among these methods, the Rao-1 optimization technique has been selected and evaluated in this paper to solve this issue. It is specifically used to extract the unknown parameters of photovoltaic models, including the photo-generated current (Iph), diode ideality factor (n), series resistance (Rs), reverse saturation current (I0), and shunt resistance (Rsh). The effectiveness of the Rao-1 optimization method has been demonstrated through its successful application in estimating PV parameters across a wide range of datasets, ensuring its reliability and adaptability for various industrial scenarios.

Unveiling the quantification minefield in electrocatalytic urea synthesis

Accurate business performance evaluation is a key to success for enterprises. The purpose of this research was to analyze the current evaluation system for Taiwan thin film transistor liquid crystal display (TFT–LCD) manufacturers. Using Grey Relational Analysis (GRA) in combination with information entropy, more objective and reasonable evaluation results are obtained. This paper introduces entropy into the grey relational analysis weighting calculation method to improve the precision. The proposed method provides more flexible and objective information in determining the criteria weights vector. The study results show that the combined method has certain scientific rationality.