Evaluation Of Proposed Method Research Articles

An improved method for evaluating fracture density using pulsed neutron capture logging

Fracture density is a critical fracture evaluation parameter for the optimization and production prediction of hydraulic fracturing models. Non-radioactive tracer techniques (NRT) have successfully used a quantitative fracture density method based on neutron-induced gamma ray from formation elements. Furthermore, fracture density can be calculated using the formation capture cross-section before and after hydraulic fracturing based on pulsed neutron capture logging. However, the response sensitivity of the macroscopic scattering cross section to the fractures will decrease when the fracture density is high due to the neutron self-shielding phenomenon. Therefore, an improved fracture density evaluation method is applied, which combines the macroscopic capture cross section and the neutron self-shielding correction factor. In the new method, the peak area of titanium from the captured gamma spectrum was used to obtain a neutron self-shielding correction factor, in order to improve the sensitivity of fracture density determination. Furthermore, the response of capture cross-section variation to fracture density at various tagged proppant concentrations and formation backgrounds was investigated. The findings indicate that the tagged proppant concentration influences the detection limit of fracture density and the sensitivity of fracture density identification. The accurate calculation range of fracture density using the new method has been extended from 5% to 10% under the condition that the tagged proppant concentration is 0.2%. Meanwhile, water salinity significantly impacts capture cross-section variation, while the effects of porosity, lithology, and fluid type on capture cross-section variation are negligible. A simulated fracturing example demonstrates the method's applicability in various measuring environments. The results show that fracture density and height are consistent with the model settings after correcting for water salinity, and the fracture density calculation error is less than 3%. Therefore, our proposed evaluation method for fracture density corrected for the neutron self-shielding effect improves response sensitivity and fracture density calculation accuracy.

Investigating topic modeling techniques through evaluation of topics discovered in short texts data across diverse domains

The online channel has affected many facets of an individual's identity, commercial, social policy, and culture, among others. It implies that discovering the topics on which these brief writings are focused, as well as examining the qualities of these short texts is critical. Another key issue that has been identified is the evaluation of newly discovered topics in terms of topic quality, which includes topic separation and coherence. A topic modeling method has been shown to be an outstanding aid in the linguistic interpretation of quite tiny texts. Based on the underlying strategy, topic models are divided into two categories: probabilistic methods and non-probabilistic methods. In this research, short texts are analyzed using topic models, including latent Dirichlet allocation (LDA) for probabilistic topic modeling and non-negative matrix factorization (NMF) for non-probabilistic topic modeling. A novel approach for topic evaluation is used, such as clustering methods and silhouette analysis on both models, to investigate performance in terms of quality. The experiment results indicate that the proposed evaluation method outperforms on both LDA and NMF.

Functional-Combination-Based Comprehensive Benefit Evaluation of Energy Storage Projects under Source-Grid-Load Scenarios via Super-Efficiency DEA

As an important support for power systems with high penetration of sustainable energy, the energy storage system (ESS) has changed the traditional model of simultaneous implementation of electricity production and consumption. Its installed capacity under the source-grid-load scenario is rising year by year, contributing to sustainable development, but it faces the problems of insufficient utilization and benefits. This study analyzes the functional combination of ESS under source-grid-load scenarios. A comprehensive benefit evaluation method of energy storage projects (ESPs), based on a fuzzy decision-making trial and evaluation laboratory (DEMATEL) and super-efficiency data envelopment analysis (DEA), is proposed. Firstly, the functional requirements of energy storage in source-grid-load scenarios are explored, and the characteristics of various functions are analyzed to form eight functional combination schemes. Secondly, index modeling is carried out from three aspects—the whole life cycle cost, functional combination benefits, and social and environmental benefits—and a comprehensive benefit evaluation index system of ESP is proposed. Then, the intuitionistic trapezoidal fuzzy number (ITFN) is combined with DEMATEL to form an effective analysis method for the input–output relationship of the indices, and the comprehensive evaluation is realized based on the SE-DEA model. Compared with other methods, this model can ensure the objectivity and stability of the evaluation results in ESP evaluation. Finally, the effectiveness of the proposed evaluation method and the rationality of the functional combination are verified under source-grid-load scenarios. The calculation results show that in the application scenario of source-grid-load, after adopting the functional combination scheme formulated in this article, the comprehensive investment benefits of ESPs have been improved. Moreover, the source side effect is at its best, with an efficiency value of 2.209.

Rams-based comprehensive evaluation method for EMU components

EMU (Electric Multiple Units) components were evaluated in multiple dimensions, i.e. reliability, availability, maintainability, and safety. A parameter estimation method, which is based on the data on the mileage between failures and quantities monitored in the actual operation, was put forward for the Weibull proportional hazard model to determine their operational reliability. A multi-index component maintainability evaluation model, which is based on empirical distribution functions, and a maintainability cluster evaluation model, which is based on whitened weight functions, were built, and the entropy weight method was introduced to determine the estimates of the indexes. A component safety evaluation method, which has the safety risk factor as its evaluation basis, was built using both the fuzzy theory and the analytic hierarchy process. A RAMS comprehensive evaluation model based on the linear weighted synthesis method was proposed. All of the models and methods are aimed at meeting the needs of this multidimensional component evaluation. This evaluation method was verified using actual gearbox operation parameters. The reliability and availability evaluation curves based on the Weibull proportional hazard model, whose parameters were determined according to the failure data, show that the EMU component reliability and availability decrease as the EMU mileage increases, thus verifying that this evaluation method is reasonable. The calculated EMU gearbox degree of safety is 0.8659, at an excellent level. The evaluated EMU gearbox maintainability is at the moderate level of III. The proposed evaluation method can reflect the relations of the component reliability and availability to the quantities monitored in operation more accurately and more comprehensively and the actual component maintainability and safety levels in a more objective way. It can provide a theoretical basis for managers and design departments to improve and evaluate the performance of the components, determine their maintenance intervals, and develop scientific preventive maintenance strategies in order to reduce their overhaul cost and increase their maintenance efficiency.

Texture feature similarity-based roughness intelligent evaluation: a case study applied to milled surfaces

Surface roughness is of great significance in maintaining mechanical performance and improving the reliability of the equipment. However, fast surface roughness evaluations that are sufficiently stable and efficient for engineering in situ use have not yet been realized. To address this issue, an image-driven roughness intelligent method is proposed in this research. By evaluating the texture similarity intelligently between the testing image and the reference image, the surface roughness of the testing image can be acquired. Firstly, with a proposed adaptive texture extraction method, the texture feature of an image can be extracted even under a complex background. Secondly, by establishing the graph structure of the texture grayscale features, the similarity between different images is evaluated. Finally, by establishing a sparrow-optimized support vector machine regression method, the mapping relationship between the similarity and the surface roughness can be acquired. The experimental results indicate that the proposed method for intelligent evaluation of roughness has superior prediction performance (the average relative prediction error of Ra and Rz are 8.8156% and 8.0571%, respectively). Therefore, this work provides a useful tool for non-contact detection of workpiece surface roughness.

Time-Dependent Fuzzy Reliability Analysis of Concrete Slab during the Initial Water Storage Period of CFRD: A Case Study

The concrete-faced rockfill dam (CFRD) has been widely constructed worldwide, and the reliability of the concrete panels, the most important containment structure, is critical to the safety of the CFRDs and downstream communities. Several practical projects show that concrete slab cracking usually occurs during the water storage period, which can be attributed to the rapid increase of hydrostatic pressure and uneven settlement of the dam body. In this paper, a time-dependent reliability analysis method considering the fuzziness of the failure criterion is presented to assess the slab cracking risk of the Houziyan CFRD during the water storage period. Based on the observed deformation, the material parameters are calibrated to ensure the fidelity of the numerical simulation. Based on the Drucker–Prager yield criterion and tensile strength criterion, the response surface method is utilized to construct the performance functions at different moments, and then the time-dependent reliability analysis considering the fuzzy failure criteria is implemented for the concrete slab. The case study of the Houziyan CFRD shows that, during the initial period of water storage, the reliability index of the concrete slab decreases with the rise of the water level. With a stable water level, the probability of cracking of the concrete slab slowly decreases as the deformation of the dam body and slab tend to be coordinated. Especially, considering the fuzziness of the failure criteria, the reliability index decreased by about 2%, indicating that the proposed evaluation method is biased toward safety. The method proposed in this paper can reflect the evolution law of concrete slab reliability with the operating environment and provides a new approach to evaluate the performance of the concrete slab during the water storage period.

An image-processing method based on regional separation-parameter coupling for the stability analysis of biodiesel flame

Flame stability is vital to obtain good combustion performance. Therefore, the development of flame stabilization technologies and stability evaluation methods has always received considerable research attention. However, the existing evaluation methods have low precision, which makes it difficult to accurately assess the flame stability. To address this issue, we proposed an effective flame state evaluation method in this study. We conducted relevant combustion experiments, and applied the proposed method to evaluate the flame stability of biodiesel. Furthermore, we established a numerical evaluator of the flame state based on a regional separation-parameter coupling image-processing method. This numerical evaluator combines the statistical characteristics of six flame parameters, which we derived from the flame images. The experimental results suggested that the proposed method can be used to determine some characteristic parameters that cannot be obtained by traditional methods, such as oscillation frequency and propagation velocity. Notably, these characteristic parameters are important for the evaluation of flame stability. At an oxygen concentration of 24 % and fuel pressure of 0.5 MPa, we subdivided the combustion state of biodiesel into stable, relatively stable, unstable, and extinguished states. We observed that by decreasing the airflow rate from 0 to 12.50 m3/h, the flame color changed from orange to green, then blue, and finally to yellow-green. We also observed, contraction and spreading in the flame highlight region. The color space analysis revealed that the larger the blue-to-red ratio, the more complete the combustion, which led to a blue flame. Overall, the proposed evaluation method provides a useful guide for regulating the combustion of biodiesel. Furthermore, these result can be generalized to assess the combustion states of other fuels.

Методичні особливості оцінки рівня компетентності експертів у групі при визначенні ефективності ракетно-космічних комплексів

Expert examination methods greatly facilitate the solution of difficult-to-formalize problems. However, in this case the solution is affected by a subjective factor. The decision-making theory has a number of methodological techniques that diminish its effect on the decision made. This paper presents a method of quantitative evaluation of experts’ competence from the results of an expert examination of the efficiency determination of unique, technically complex systems of special and dual purpose, in particular space-rocket complexes. In an expert examination of projects of such systems, it is suggested that the experts’ competence be quantitatively evaluated in two stages: a preliminary evaluation of the experts’ competence from their factual data and a refined evaluation of the experts’ competence just before the calculation of the expected indices of target efficiency using the results of expert examinations made by the procedure developed. The proposed method of quantitative evaluation of experts’ competence is based on evaluating the qualification of the experts involved in the target efficiency determination of a complex engineering system. A rank matrix constructed on the basis of partial criteria of technical efficiency and additional factors of indirect control is proposed as a tool to eliminate cases where at a high level of expert evaluation consistency the most accurate expert evaluations may be considered anomalous in the expert evaluation of the technical and target efficiency of space-rocket systems. The presented mathematical model of quantitative evaluation of experts’ competence includes parameters that adjust the mathematical model to specific conditions of the expert evaluation (expert evaluation methods employed, measurement scales, specific limitations, etc.). The mathematical model is constructed around the axiom that the “true” estimates of the significance of the objects under evaluation lie within the expert evaluation domain. The paper also presents an enlarged algorithm for adjustment parameter calculation from the results of expert estimate preprocessing. The presented mathematical model and algorithm make it possible to develop a computer program for determining experts’ competence from expert evaluation results.

A Time–Frequency Image Quality Evaluation Method Based on Improved LIME

Deep learning has the characteristics of high efficiency, high accuracy and low knowledge dependence, and has become a hot method in the research field of rotating machinery fault diagnosis. The time–frequency transform can show both the time and frequency characteristics of the vibration signal, so the time–frequency image is often used as the input of deep learning networks. At present, there are many time–frequency transform methods, and how to choose one is a problem worth discussing. This paper proposed a time–frequency image quality evaluation method based on improved local interpretable model-agnostic explanations (LIME). With the input of deep learning networks as the application background, this method evaluates the time–frequency image quality of rotating machinery vibration signals from two aspects: the accuracy of the diagnosis results and the consistency of the interpretation results with prior knowledge. The feasibility of the proposed evaluation method is verified by experiments on the measured data set, and engineers’ trust in the deep learning model is improved.

Point Cloud Registration Method Based on Geometric Constraint and Transformation Evaluation.

Existing point-to-point registration methods often suffer from inaccuracies caused by erroneous matches and noisy correspondences, leading to significant decreases in registration accuracy and efficiency. To address these challenges, this paper presents a new coarse registration method based on a geometric constraint and a matrix evaluation. Compared to traditional registration methods that require a minimum of three correspondences to complete the registration, the proposed method only requires two correspondences to generate a transformation matrix. Additionally, by using geometric constraints to select out high-quality correspondences and evaluating the matrix, we greatly increase the likelihood of finding the optimal result. In the proposed method, we first employ a combination of descriptors and keypoint detection techniques to generate initial correspondences. Next, we utilize the nearest neighbor similarity ratio (NNSR) to select high-quality correspondences. Subsequently, we evaluate the quality of these correspondences using rigidity constraints and salient points' distance constraints, favoring higher-scoring correspondences. For each selected correspondence pair, we compute the rotation and translation matrix based on their centroids and local reference frames. With the transformation matrices of the source and target point clouds known, we deduce the transformation matrix of the source point cloud in reverse. To identify the best-transformed point cloud, we propose an evaluation method based on the overlap ratio and inliers points. Through parameter experiments, we investigate the performance of the proposed method under various parameter settings. By conducting comparative experiments, we verified that the proposed method's geometric constraints, evaluation methods, and transformation matrix computation consistently outperformed other methods in terms of root mean square error (RMSE) values. Additionally, we validated that our chosen combination for generating initial correspondences outperforms other descriptor and keypoint detection combinations in terms of the registration result accuracy. Furthermore, we compared our method with several feature-matching registration methods, and the results demonstrate the superior accuracy of our approach. Ultimately, by testing the proposed method on various types of point cloud datasets, we convincingly established its effectiveness. Based on the evaluation and selection of correspondences and the registration result's quality, our proposed method offers a solution with fewer iterations and higher accuracy.

Evaluation of surface quality and error compensation for optical aspherical surface grinding

Optical aspherical lenses are widely used in the aerospace, electronics, and optical communication industries. Grinding is the primary processing method used to ensure their precision. It is difficult for traditional evaluation indexes to fully reflect the optical properties of optical surfaces after grinding. This paper presents a novel method for characterizing surface quality of optical aspherical surface oriented to the application of optical properties. The proposed evaluation indicators include the profile error, surface roughness, slope error, and projection-area scale-deviation value, which can be used to comprehensively evaluate the surface quality. Subsequently, a novel grinding compensation method for the optical aspherical surface is proposed, and its compensation precision is evaluated using the abovementioned evaluation indicators. The results show that the maximum surface-shape error decreases to less than 150 nm; additionally, the central cusp point is restrained. The precision and efficiency of error compensation for the optical aspherical surface are significantly better than those of the conventional method. The proposed evaluation indicators can be applied to reflect the features of optical aspheric surface directly. The high efficiency and precision compensation of optical aspherical surface can be achieved with the proposed evaluation method and modified error compensation algorithm.

Multi-attribute decision making based on VIKOR with probabilistic linguistic term sets: An application to the risk evaluation of foreign direct investment.

The multiple global environments have triggered changes in the international environment, leading to a sharp decline of foreign direct investment (FDI) compared to pre-pandemic level. To evaluate the investment risk of FDI and make optimal investment decision becomes the most important issue for investors. This paper focuses on the evaluation of investment risk for FDI. First, an index system for risk evaluation of FDI is constructed. Then, we introduce the probabilistic linguistic entropy and cross entropy measures, based on which, a programming model is developed to identify the objective attribute weights. A composite weight derivation method, which takes both the objective attribute weights and the subjective attribute weights into account, is further introduced. In view of attributes' uncertainty and fuzziness and the conflicting characteristics of some attributes, the VIKOR (the Serbian name: VlseKriterijumska Optimizacija I Kompromisno Resenje, means multi-criteria optimization and compromise solution) method is used to evaluate the risk of FDI under the probabilistic linguistic environment. Furthermore, a case study is presented to illustrate the proposed method. The comparative analysis and some further discussions verify the validity of the proposed method for the FDI risk evaluation.

A comprehensive resilience evaluation method for power distribution systems based on weighted Page-Rank algorithm

Resilience evaluation plays a critical role in the decarbonized development of modern power distribution systems since it objectively assesses how the distribution system performs in the face of stochastic renewable generation, increasing load demand, and promising to cut carbon emissions. This study proposed a comprehensive resilience evaluation method for regional distribution systems. The proposed resilience evaluation system comprises three sub-groups: performance, economy, and connections. To improve the objectivity of the proposed evaluation method, the weighting factors are calculated based on the Analytic Hierarchy Process (AHP) and weighted Page-ranking algorithm, respectively. Specifically, AHP is adopted to calculate the weighting factors for different metrics. At the same time, the original Page-ranking algorithm is improved according to the importance of the buses and then used to calculate the weighting factors for different buses within the same metric. The proposed method is applied to a real-world distribution network in China. Numerical results show that the proposed method can objectively evaluate the overall resilience level of the distribution network, including the source side, the network, and the load side. The evaluation results can help the decision-makers to make resilience enhancement in an economical and targeted way.

Dynamic Assessment of Photovoltaic-Storage Integrated Energy Stations Health Incorporating Subjective and Objective Characteristics

Photovoltaic-storage integrated systems, which combine distributed photovoltaics with energy storage, play a crucial role in distributed energy systems. Evaluating the health status of photovoltaic-storage integrated energy stations in a reasonable manner is essential for enhancing their safety and stability. To achieve an accurate and continuous assessment of the health status of photovoltaic-storage integrated energy stations, a dynamic evaluation method is proposed in this study. This method integrates both subjective and objective characteristics. Initially, considering the evaluation needs of low-carbon operation and health status for photovoltaic-storage integrated energy stations, a comprehensive health status evaluation system is developed. The significance of each indicator is subjectively analyzed, while also considering objective characteristics and sensitivity of indicators. The integration of subjective and objective characteristics is achieved using principles from game theory. Subsequently, through the establishment of the Grey-TOPSIS evaluation model, both positive and negative correlations of the health status of photovoltaic-storage integrated energy stations are determined, resulting in the derivation of a health status vector. Furthermore, the introduction of time-weight vectors and the incorporation of a time dimension enable dynamic evaluation and the comprehensive observation of health status. Finally, the scientific validity and effectiveness of the proposed evaluation method are demonstrated through practical examples, with comparisons made to traditional evaluation methods. The results clearly indicate that this method offers higher sensitivity when evaluating the health of photovoltaic-storage integrated energy stations.

A Multi-Agent Driving-Simulation Approach for Characterizing Hazardous Vehicle Interactions between Autonomous Vehicles and Manual Vehicles

The advent of autonomous vehicles (AVs) in the traffic stream is expected to innovatively prevent crashes resulting from human errors in manually driven vehicles (MVs). However, substantial safety benefits due to AVs are not achievable quickly because the mixed-traffic conditions in which AVs and MVs coexist in the current road infrastructure will continue for a considerably long period of time. The purpose of this study is to develop a methodology to evaluate the driving safety of mixed car-following situations between AVs and MVs on freeways based on a multi-agent driving-simulation (MADS) technique. Evaluation results were used to answer the question ‘What road condition would make the mixed car-following situations hazardous?’ Three safety indicators, including the acceleration noise, the standard deviation of the lane position, and the headway, were used to characterize the maneuvering behavior of the mixed car-following pairs in terms of driving safety. It was found that the inter-vehicle safety of mixed pairs was poor when they drove on a road section with a horizontal curve length of 1000 m and downhill slope of 1% or 3%. A set of road sections were identified, using the proposed evaluation method, as hazardous conditions for mixed car-following pairs consisting of AVs and MVs. The outcome of this study will be useful for supporting the establishment of safer road environments and developing novel V2X-based trafficsafetyinformation content that enables the enhancement of mixed-traffic safety.

A proposed method for quality evaluation of COVID-19 reusable face mask

During the time of COVID-19, the use of face masks has become an essential control and prevention measure. The wide usage of disposable face masks has presented a great challenge to governments to face the impact of plastic waste on humans health and the environment. The quality of reusable face masks was assessed in previous research based on general measures such as the fabric, size fit, arrival time, price, and convenience, and based on more-specific measures related to the number of layers and the included filters. However, as the quality of reusable face masks includes several other dimensions, the general measures cannot be enough to ensure a comprehensive evaluation of these products during the COVID-19 outbreak. Nowadays, however, digital social media has provided venues and convenient tools for users to share their opinions, preferences, and experiences on the quality of reusable face masks. Considering reusable face masks, several types have been launched on online platforms to meet the increasing demand during COVID-19. The main goal of this study is to investigate how reusable face masks can be evaluated through online customers’ reviews. This study proposes a combination of qualitative (text mining) and quantitative (survey-based analysis) approaches to provide the researchers with a method of data analysis to inspect the most influential quality factors for the evaluation of reusable face masks. We performed a literature review on the previous works and also collected online customers’ reviews from Amazon.com to find the quality factors of reusable face masks. The review of previous literature on reusable face masks and the result of online reviews analysis indicated that several factors impact customers’ experiences, including filteration efficiency, fabric quality, breathability, design, functionality, environmental impact, comfort, easy to use, easy to clean, economic impact, donning/doffing, quality of seal, vision, communication and safety protection. The presented framework can be complementary to the existing evaluation research methods, which use the strengths of one method to overcome the shortcomings of the other.

A single-subject research design evaluating a co-created yoga program for adults with gynecologic cancer: feasibility study protocol

BackgroundWorldwide, > 1.3 million adults are diagnosed with a gynecologic cancer each year, affecting their wellbeing and quality of life. This manuscript describes the protocol for a study that sought to assess the feasibility, acceptability, and fidelity of a community-based co-created yoga program and proposed evaluative methods, and estimate program effects on self-reported outcomes.MethodsUsing a multiple baseline single-subject research design with a follow-up phase (ABA), quantitative and qualitative data were collected from program participants and the instructor. Participants were randomly assigned to varying baseline lengths and completed weekly surveys for 3–5 weeks pre-program. Then, participants engaged in a bi-modal 12-week hatha yoga program consisting of 2 60-min group classes a week, with optional supplemental features (January–April, 2023). Participants completed surveys after classes 1, 12, and 24. All yoga classes were audio- and video-recorded. Post-program, participants completed surveys 1, 4, and 8 weeks after the last class and took part in a semi-structured interview 1 week after to discuss program acceptability, suitability, relevance, and potential benefits. Feasibility outcomes (i.e., recruitment, retention, and program adherence rates, engagement with optional program features) were tracked by the instructor assistant and study team during the study. The yoga instructor was interviewed about their experience delivering the program 2 weeks after the last class. Planned analysisFeasibility outcomes will be analyzed using descriptive statistics. Interview transcripts will be coded using reflexive thematic analysis. Class recordings will be coded using duration and frequency coding. Survey responses for self-reported outcomes will be analyzed visually and using multilevel modeling.Expected outcomesData will help determine refinements, if any, required to the program and instructor guidebook, implementation approach, and proposed evaluation methods before scale-up projects and definitive trials are started.Trial registrationClinicalTrials.gov NCT05610982. November 3, 2022.

Development of a Phantom and Analysis Program for Assessment of Positional Accuracy of Image-guided Radiation Therapy

We created a phantom and analysis program for the assessment of IGRT positional accuracy. We verified the accuracy of analysis and the practicality of this evaluation method at several facilities. End-to-end test was performed using an in-house phantom, and EPID images were acquired after displacement by an arbitrary amount using a micrometer, with after image registration as the reference. The difference between the center of the target and the irradiated field was calculated using our in-house analysis program and commercial software. The end-to-end test was conducted at three facilities, and the IGRT positional accuracy evaluation was verified. The maximum difference between the displacement of the target determined from the EPID image and the arbitrary amount of micrometer displacement was 0.24 mm for the in-house analysis program and 0.30 mm for the commercial software. The maximum difference between the center of the target and the irradiation field on EPID images acquired at the three facilities was 0.97 mm. The proposed evaluation method using our in-house phantom and analysis program can be used for the assessment of IGRT positional accuracy.

Improvement of the Quality of Question Papers for Online Examinations Toward Simultaneous Enhancement of Students’ Learning

The present study is related to the problem associated with student assessment with online examinations at higher educational institutes (HEIs). With the current COVID-19 outbreak, the majority of educational institutes are conducting online examinations to assess their students, where there would always be a chance that the students go for malpractice. It is difficult to set a question paper for any technical course with great novelty. Under these circumstances, safeguarding academic integrity has become a challenge for HEIs. This study is aimed at improving the quality of questions for online exams to increase the accountability of HEIs by proper evaluation of their students. A detailed procedure with suggestions for setting the questions for technical courses, in the format of assertion and reason, matching, multiple select types, etc., has been discussed with adequate examples. It deals with a strategy for ensuring that all the students are held to the standards that are reflected in their grades. The proposed evaluation method has been implemented on a test batch and presented the results along with a comparison with that of traditional question papers. It is witnessed that there is a simultaneous enhancement of students' learning as an additional benefit of implementing the proposed learning-oriented assessment method.

Methods of evaluating code generators

In the ever-evolving landscape of software development, code generators have emerged as vital instruments, fostering innovation and efficiency. These automated maestros translate abstract requirements into functional code, significantly reducing development time and bridging gaps across varied programming environments. This article categorizes code generators based on their operational domain, input specificity, and the nature of the output they produce, unraveling a diverse spectrum from foundational scaffolding creators to sophisticated, language-specific assistants. Selecting an apt code generator is pivotal to the success of a project. To assist developers in this critical choice, the article presents an evaluation method that weighs essential factors such as adaptability, efficiency, and ease of integration with existing systems. This systematic approach ensures the selection of a tool that not only meets immediate project needs but also supports future scalability and complexity. The discussion extends beyond functionality to explore the transformative role of code generators. By automating routine coding tasks, they allow developers to focus on more creative aspects of software design, enhancing code quality and fostering a culture of innovation. The generators thus become indispensable allies in the creative process of software development. In essence, the article advocates for an informed selection of code generators, viewing them as cornerstones for modern coding practices. The proposed evaluation method serves as a strategic framework, empowering developers to navigate the myriad of options and align their choice with both current requirements and future aspirations. It is an invitation to embrace the synergy between human creativity and machine precision, crafting code that is both efficient and elegant.