Evaluation Techniques Research Articles

Online Exam Portal

An online test is one that is conducted via the internet or an intranet utilizing a computer system. It is a web-based exam system. It works well as a mass education evaluation solution. Our system for online exams is built on a Browser/Server architecture. The multiple choice questions that are fed into the system are examined and automatically graded by the system. Even while it is comfortable, the classic pen and paper exam format has drawbacks in the current digital learning environment. IT students find these limits even more noticeable because they are used to using internet resources and platforms. In response, this proposal suggests creating an efficient, user-friendly, and safe online assessment system especially for IT students. This project goes beyond practicality. It increases assessment quality with interesting formats, empowers students with freedom, and promotes efficiency with quicker administration and grading. Teachers receive insightful data-driven information for bettering their curricula. In the end, it welcomes the digital age and uses cutting-edge evaluation techniques to get IT students ready for success

Engineered Cementitious Composites with Super-Sulfated Cement: Mechanical, Physical, and Durability Performance

This study aimed to bridge a research gap by exploring the utilization of super-sulphated cement (SSC) in engineered cementitious composites (ECCs) as a sustainable alternative to ordinary Portland cement (OPC)-based mixtures. The SSC was designed with slag, gypsum, and a small amount of OPC. The primary objective was to investigate the effects of incorporating SSC, both with and without fly ash (FA), at various FA/SSC ratios between 0 and 1.5. A comprehensive evaluation was conducted to assess the performance of the ECC-SSC mixtures, including the compressive and flexural strengths, ductility, ultrasonic pulse velocity, rapid chloride permeability, and drying shrinkage. Additionally, advanced microstructural evaluation techniques such as scanning electron microscopy (SEM) with energy dispersive X-ray (EDX) analysis as well as X-ray diffraction (XRD) analysis were employed to analyze the reaction products in selected mixtures. The results showed that the ECC mixture produced with SSC exhibited comparable strength to the ECC-OPC. In general, all the SSC-based ECCs fulfilled the criteria for various engineering applications, especially when the fly ash to SSC ratios were 0 and 0.8. In addition, ECCs with FA/SSC ratios of 1.2 and 1.5 showed ultra-ductile performance higher than the control ECC. Interestingly, all the FA-based ECC-SSC presented lower shrinkage characteristics than the control OPC-based ECC.

OPERATE–H2: A tool for optimizing underground hydrogen storage

Underground hydrogen storage (UHS) is crucial in facilitating the transition to clean energy by reducing reliance on fossil fuels and ensuring a stable supply of emission-free energy. However, safe and efficient storage conditions require careful planning. While reservoir simulations could determine optimal storage conditions, exploring all possible scenarios can be computationally prohibitive and time-consuming. Reduced-order models (ROMs) can efficiently simulate reservoir performance and analyze multiple strategies with reduced computational cost, providing valuable insights for UHS decision-making. This paper introduces an innovative integrated assessment framework with a user-friendly graphical user interface (GUI), designed to optimize UHS operations. The GUI features several advanced functionalities, including (1) temporal evolution for visualizing and analyzing UHS performance over withdrawal cycles, (2) local sensitivity analysis to determine the impact of individual parameters on UHS sites while keeping other inputs constants, (3) uncertainty quantification to identify the key factors for effective decision-making via tornado plots, (4) global sensitivity analysis to comprehensively quantify the interactions between geologic and operational parameters and their effect on UHS performance, and (5) sites screening feature to identify the most prominent potential sites for UHS installations. The GUI is built based on ROMs and the PyQt5 packages in Python. With user-friendly design and advanced features, Optimization, Evaluation, and Risk-Assessment Techniques for Hydrogen Energy tool (OPERATE–H2) enables users to evaluate different UHS scenarios quickly and effectively, helping to accelerate the transition to a cleaner and more sustainable energy future.

Evaluating the Durability of Perfluorosulfonic Acid Membranes in Fuel Cells Using Combined Open-Circuit Voltage-Accelerated Stability Testing

This study evaluates the chemical and mechanical durability of membranes used in proton exchange membrane fuel cells, highlighting the essential role of electrochemical tests in understanding the relationship between durability and performance. Our methodology integrates various electrochemical evaluation techniques to assess the degradation of perfluorosulfonic acid (PFSA) membranes. The results highlight the considerable improvement in the chemical and mechanical durability of annealed 3M PFSA-reinforced composite membranes (RCMs) compared with their non-annealed counterparts and other membrane types, indicating their superior resilience under challenging conditions. Moreover, the results of using a combined open-circuit voltage-accelerated stability testing protocol demonstrate that annealed 3M PFSA RCMs exhibit enhanced resilience, reaching 18,000 cycles before failure, considerably outperforming NR 211 (5000 cycles) and other membranes. In addition, membrane deterioration over time can be precisely measured by interpreting electrochemical indicators (electrochemically active surface area, circuit resistance, high-frequency resistance, and proton resistance). This approach provides a clear relationship between electrochemical data and durability, offering a comprehensive understanding of how different membranes withstand operational stresses.

Structured illumination microscopy for cancer identification in diagnostic breast biopsies.

Breast cancer is the second most common cancer diagnosed in women in the US with almost 280,000 new cases anticipated in 2023. Currently, on-site pathology for location guidance is not available during the collection of breast biopsies or during surgical intervention procedures. This shortcoming contributes to repeat biopsy and re-excision procedures, increasing the cost and patient discomfort during the cancer management process. Both procedures could benefit from on-site feedback, but current clinical on-site evaluation techniques are not commonly used on breast tissue because they are destructive and inaccurate. Ex-vivo microscopy is an emerging field aimed at creating histology-analogous images from non- or minimally-processed tissues, and is a promising tool for addressing this pain point in clinical cancer management. We investigated the ability structured illumination microscopy (SIM) to generate images from freshly-obtained breast tissues for structure identification and cancer identification at a speed compatible with potential on-site clinical implementation. We imaged 47 biopsies from patients undergoing a guided breast biopsy procedure using a customized SIM system and a dual-color fluorescent hematoxylin & eosin (H&E) analog. These biopsies had an average size of 0.92 cm2 (minimum 0.1, maximum 4.2) and had an average imaging time of 7:29 (minimum 0:22, maximum 37:44). After imaging, breast biopsies were submitted for standard histopathological processing and review. A board-certified pathologist returned a binary diagnostic accuracy of 96% when compared to diagnoses from gold-standard histology slides, and key tissue features including stroma, vessels, ducts, and lobules were identified from the resulting images.

Lithium-ion point-of-care ultrasound battery joint state of charge estimation

With recent advances in ultrasound technology, including accessibility and portability, point-of-care ultrasound systems (POCUS) are becoming ubiquitous in urban and rural health services. The efficient use of POCUS battery power is necessary due to the limited access to electricity in rural locations. Accurate estimation of the state-of-charge (SOC) of the onboard lithium-ion battery is crucial in ensuring the safe, efficient, and reliable operation of the battery management system (BMS). This research suggests a novel approach for striking a reasonable balance between the POCUS battery management systems computation time and SOC estimate accuracy. Given that the lithium-ion battery is a non-linear and time-varying system, it is modelled by a second-order RC equivalent circuit model (2-RC ECM). Real-time parametric identification is carried out using the method of recursive least squares with respect to an adaptive forgetting factor (AFFRLS). The joint SOC evaluation technique, ordering the results of real-time identification and the Extended Kalman Filter (EKF) recommended for estimating battery SOC at temperatures between 0 and 45 °C. Simulations were carried out using experimental data from the DST (Dynamic Stress Test), incremental current and, the Federal Urban Driving Program (FUDS) of the CALCE research group to evaluate the proposed approach. Based on the monitoring of dynamic changes in model parameters and the computational complexity of existing algorithms, the results reveal that the joint AFFRLS-EKF estimation proposed for the SOC strategy achieves good accuracy, high adaptability and rapid convergence of SOC estimation.

Exploring Carbon Disclosure Research for Future Research Agenda: A Bibliometric Analysis

Globally, people are becoming more interested in the issue of climate change, particularly as it relates to carbon emissions. Stakeholders are putting pressure on firms to disclose more details about their ecological footprints and greenhouse gas emissions. Finding authors, publications, journals, and discoveries in the literature on emission carbon disclosure is the aim of this endeavor. To do this, we employ a review methodology that makes use of the bibliometric study technique to pinpoint a few pertinent elements found in the literature. Utilizing tools such as VOSviewer and biblioshiny from RStudio, we examine output by year, authors, articles, and publications that have received more citations. We outline the key developments in the field of carbon disclosure together with the most widely applied ideas. Furthermore, we formulate the notion of carbon disclosure as suggested by Borghei (2021), concentrating on six domains and primary patterns within the field: Provide answers to the following questions: strategic climate, carbon disclosure determinants, carbon disclosure guarantee, quality of carbon disclosure, and carbon disclosure consequences. The incorporation of sustainability dimensions into the reporting system has replaced the evaluation techniques of the disclosures as the research issue. It is discovered that a number of factors influence the corporation's choice to release its environmental accounting information. Based on its findings and analysis of current research trends and themes, this study offers suggestions for further investigation.

Usability of Standards for Scaffolding in a Health Sciences Programme: A feasibility Study

BackgroundStandards contribute to comprehensive and programmatic implementation of educational strategies, such as scaffolding. Although the development of educational standards follows a rigorous consensus approach, they are socially constructed and could result in varied interpretations by users. Reports of varied implementation of standards in health professions education underscore the need to test the developed standards for scaffolding in health sciences programmes. Usability entails determining whether a product like standards works as intended under the expected conditions and contexts. This study aimed to describe the usability of standards for scaffolding in a health sciences programme through a pilot study.MethodsA multi-method design employing user and expert-based usability evaluation techniques sought to describe the usability of the standards for scaffolding in a three-year pre-registration nursing programme. The user sample of nurse educators drawn from the programme, conducted a self-assessment on scaffolding practices in the programme using a developed standards checklist. For the expert sample, three-panel members with an understanding of the discipline and programme context were purposively sampled. These panelists studied the users’ self-assessment reports before completing an author-generated heuristics checklist to support or refute any of the standards. Descriptive statistics, comparative and content analysis were applied to analyse data from users’ interviews and expert’s completed heuristics checklist, determining the standards’ usability, and identifying the usability flaws or strengths.ResultsThe users had three or more years of teaching experience in the competency-based curriculum for nursing. The experts shared an average of 16 years of experience in teaching in higher education, and seven years of experience in quality assurance and programme accreditation. The four standards had a usability score of above average (68%). Seven usability strengths and four usability flaws were identified. Usability flaws related to misinterpretation of some criteria statements and terminologies, multiple meanings, and users’ challenges in generating evidence for some criteria.ConclusionsThe pilot study revealed the context-based ‘truth’ regarding the fidelity of a health sciences programme evaluation on scaffolding, as well as identifying the ideal contextual conditions in which the standards for scaffolding health sciences programmes would work best. The identified usability flaws highlighted the need for further revisions of the standards. Future research on the feasibility of the standards in other health sciences programmes and contexts is recommended.

Radon Exposure Assessment in Occupational and Environmental Settings: An Overview of Instruments and Methods

Radon is a naturally occurring noble radioactive gas that poses significant health risks, particularly lung cancer, due to its colorless, odorless, and tasteless nature, which makes detection challenging without formal testing. It is found in soil, rock, and water, and it infiltrates indoor environments, necessitating regulatory standards and guidelines from organizations such as the Environmental Protection Agency, the World Health Organization, and the Occupational Health and Safety Agency to mitigate exposure. In this paper, we present various methods and instruments for radon assessment in occupational and environmental settings. Discussion on long- and short-term monitoring, including grab sampling, radon dosimetry, and continuous real-time monitoring, is provided. The comparative analysis of detection techniques—active versus passive—is highlighted from real-time data and long-term exposure assessment, including advances in sensor technology, data processing, and public awareness, to improve radon exposure evaluation techniques.

An evaluative technique for drought impact on variation in agricultural LULC using remote sensing and machine learning.

Drought events threaten freshwater reservoirs and agricultural productivity, particularly in semi-arid regions characterized by erratic rainfall. This study evaluates a novel technique for assessing the impact of drought on LULC variations in the context of climate change from 2018 to 2022. Various data sources were harnessed, encompassing Sentinel-2 satellite imagery for LULC classification, climate data from the CHIRPS and AgERA5 databases, geomorphological data from JAXA's ALOS satellite, and a drought indicator (Vegetation Health Index (VHI)) derived from MODIS data. Two classifier models, namely gradient tree boost (GTB) and random forest (RF), were trained and assessed for LULC classification, with performance evaluated by overall accuracy (OA) and kappa coefficient (K). Notably, the GTB model exhibited superior performance, with OA > 90% and a K > 0.9. Over the period from 2018 to 2022, Fez experienced LULC changes of 19.92% expansion in built-up areas, a 34.86% increase in bare land, a 17.86% reduction in water bodies, and a 37.30% decrease in agricultural land. Positive correlations of 0.81 and 0.89 were observed between changes in agricultural LULC, rainfall, and VHI. Furthermore, mild drought conditions were identified in the years 2020 and 2022. This study emphasizes the importance of AI and remote sensing techniques in assessing drought and environmental changes, with potential applications for improving existing drought monitoring systems.

Acute muscle loss assessed using panoramic ultrasound in critically ill adults: a prospective observational study.

Panoramic ultrasound is one of the recently introduced ultrasound evaluation techniques. We herein examined the relationship between the cross-sectional area of the rectus femoris muscle on panoramic ultrasound and its volume based on the gold standard computed tomography (CT) evaluation. This was a single-center prospective observational study. A panoramic ultrasound assessment of the cross-sectional area of the rectus femoris muscle and a simple CT evaluation of its volume were performed on days 1 and 7 of hospitalization. Physical functions were assessed at discharge. Twenty patients were examined. The rate of change in the cross-sectional area of the rectus femoris muscle on panoramic ultrasound correlated with that in its volume on CT (correlation coefficient 0.59, p = 0.0061). In addition, a correlation was observed between the absolute value for the rectus femoris muscle cross-sectional area on panoramic ultrasound and physical functions at discharge. Rectus femoris muscle distances did not correlate with either. In the acute phase of critical illness, the cross-sectional area of the rectus femoris muscle on panoramic images correlated with its volume on CT and, thus, it is a valid method for assessing muscle mass.

Advancing Language Assessment and Evaluation: Innovative Strategies for Specialized English Teaching

This research paper examines the field of language assessment and evaluation. We shall primarily concentrate on the sophisticated techniques used in the specialized domain of English instruction. With the growing need for enhanced language training, educators are striving to enhance assessment techniques in order to precisely gauge language competency and provide focused instructional interventions. We acknowledge that the process of acquiring language is always evolving. Therefore, our study advocates for the use of adaptable and varied evaluation techniques that surpass conventional approaches. We are exploring the use of technology, artificial intelligence, and data analytics to develop customized assessment tools. These technologies would enable the assessment of individual student performance and provide targeted support for their unique learning requirements. Furthermore, the research explores the use of performance-based evaluations and proposes that instructors have to design activities that emulate authentic language scenarios. This strategy enhances the credibility of evaluations and provides vital insights into a learner's practical language application abilities. We use case studies and practical examples to demonstrate the effective implementation of these advanced tactics in various specialized English teaching contexts.

IoT-enhanced infant incubator monitoring system with 1D-CNN temperature prediction model

This research aims to develop a monitoring system and temperature prediction model in neonatal premature infant incubators by applying the internet of things (IoT) concept and the 1-dimensional convolutional neural network (1D-CNN) method. The system is designed by integrating sensors, actuators, and microcontrollers connected through Wi-Fi network with message queue telemetry transport (MQTT) protocol. Sensor data in the incubator is stored in a database and displayed in real-time on a web application. The data in the database is also used for creating a temperature prediction model in the incubator. Test results indicate that the best model configuration consists of 5 neurons in the first layer, 20 neurons in the second layer, and a dense layer with 100. The evaluation of this model yields a high level of accuracy with an root mean square error (RMSE) of 0.200 °C, MSE of 0.004 °C, mean absolute error (MAE) of 0.152 °C, and mean absolute percentage error (MAPE) of 0.4%. Based on the error values obtained between the predicted and actual values from each evaluation technique in the model, it can be concluded that the range between the real and predicted values is approximately 0.2 °C. Overall, this research contributes to improving the quality of care for premature infants.

Multicriteria Model to Support the Hiring of Road Freight Transport Services in Brazil

The transportation of goods plays a fundamental role in the global economy. In Brazil, specifically, a significant portion of what is transported goes through highways, and the provision of this service is carried out through the hiring of specialized companies or independent drivers, who end up serving companies from various sectors. This study successfully constructed a decision model to support the hiring of road freight transport services in Brazil. To achieve this, the multicriteria decision analysis (MCDA) approach was used, employing the Measuring Attractiveness By a Categorical-Based Evaluation Technique (MACBETH) method. The development of this model was informed by a comprehensive literature review, interviews with three transportation professionals, and a simulation involving eight anonymous Brazilian companies. This initiative aims to create a practical framework for effectively selecting logistics operators in the road transport sector to meet the needs of companies dependent on these services.

Reliability evaluation technique with the integration of wind power fluctuation through considering frequency regulation and failure restoration

The adequacy of power system can be elevated with the increasing grid-connected capacity of wind farm, and meanwhile, the power system reliability is also affected by the intermittent power fluctuation of wind farm. However, the traditional reliability evaluation technique cannot take the power fluctuation into account. Therefore, for considering the positive and negative influence of wind energy on the power system reliability, this paper proposes a reliability evaluation technique through considering static frequency regulation and failure restoration process. A failure restoration strategy which can be integrated into the reliability evaluation model is presented through designing system load curtailment, system frequency restoration, and system load restoration strategies. A series reliability calculating models which depended on the different failure restoration conditions are established. Case studies for different systems are investigated, and the results verify that the proposed method can consider both aspects of the positive and negative influence of wind farm on the power system reliability.

Barely visible impact damage detection in CFRP composite using vibrothermography with narrowband sweep excitation

The extensive use of carbon fiber-reinforced plastic (CFRP) composite materials in various industries has posed new challenges for conducting effective structural health monitoring and nondestructive evaluation techniques. Based on local defect resonance (LDR), the vibrothermography with narrowband sweep excitation is proposed to detect BVIDs in the CFRP composite. The response spectrum of displacement at the impact crater was collected by a laser Doppler vibrometer, which was used to determine the frequency range of narrowband sweep excitation. The frequency range was further validated by the analysis of local defect resonance for thermal responses, and the sweep duration was also optimized. Subsequently, the proposed vibrothermography was employed to detect BVIDs with varying severity. The impact damage could be efficiently activated and a significant temperature rise was generated in the defective areas, which enhances the temperature contrast of the thermal images and facilitates the quantification of defects.

Enhancing Odor Analysis with Gas Chromatography-Olfactometry (GC-O): Recent Breakthroughs and Challenges.

Gas chromatography-olfactometry (GC-O) has made significant advancements in recent years, with breakthroughs in its applications and the identification of its limitations. This technology is widely used for analyzing complex odor patterns. The review begins by explaining the principles of GC-O, including sample preparation, separation methods, and olfactory evaluation techniques. It then explores the diverse range of applications where GC-O has found success, such as food and beverage industries, environmental monitoring, perfume and aroma development, and forensic analysis. One of the major breakthroughs in GC-O analysis is the improvement in separation power and resolution of odorants. Techniques like rapid GC, comprehensive two-dimensional GC, and multidimensional GC have enhanced the identification and quantification of odor-active chemicals. However, GC-O also has limitations. These include the challenges in detecting and quantifying trace odorants, dealing with matrix effects, and ensuring the repeatability and consistency of results across laboratories. The review examines these limitations closely and discusses potential solutions and future directions for improvement in GC-O analysis. Overall, this review presents a comprehensive overview of the recent advances in GC-O, covering breakthroughs, applications, and limitations. It aims to promote the wider usage of GC-O analysis in odor analysis and related industries. Researchers, practitioners, and anyone interested in leveraging the capabilities of GC-O in analyzing complex odor patterns will find this review a valuable resource. The article highlights the potential of GC-O and encourages further research and development in the field.

Short-term Classification of Strong Solar Energetic Particle Events Using Multivariate Time-series Classifiers

Solar energetic particle (SEP) events are one of the most crucial aspects of space weather that require continuous monitoring and forecasting. Their prediction depends on various factors, including source eruptions. In the present work, we use the Geostationary Solar Energetic Particle data set covering solar cycles 22, 23, and 24. We develop a framework using time-series-based machine-learning (ML) models with the aim of developing robust short-term forecasts by classifying SEP events. For this purpose, we introduce an ensemble learning approach that merges the results from univariate time series of three proton channels (E ≥10, 50, and 100 MeV) and the long-band X-ray flux (1–8 Å) channel from the Geostationary Operational Environmental Satellite missions and analyze their performance. We consider three models, namely, time series forest, supervised time series forest (STSF), and Bag-of-Symbolic Fourier Approximation Symbols. Our study also focuses on understanding and developing confidence in the predictive capabilities of our models. Therefore, we utilize multiple evaluation techniques and metrics. Based on that, we find STSF to perform well in all scenarios. The summary of metrics for the STSF model is as follows: the area under the ROC curve = 0.981, F 1-score = 0.960, true skill statistics = 0.919, Heidke skill score = 0.920, Gilbert skill score = 0.852, and Matthew’s correlation coefficient = 0.920. The Brier score loss of the STSF model is 0.077. This work lays the foundation for building near-real-time short-term SEP event predictions using robust ML methods.

A Combined Decision-Making Analysis Under Single-Valued Neutrosophic Set for Selecting Favorable Knowledge Management Strategy

The Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are pivotal methods in multi-criteria decision-making, addressing causal relationships among criteria and ranking alternatives, respectively. This paper introduces a novel approach, integrating DEMATEL and TOPSIS under the framework of single-valued neutrosophic set (SVNS) to handle indeterminacy in decision-making. Applied to a knowledge management strategy case, the method utilizes eight criteria and four alternatives derived from the literature. The process involves DEMATEL for expert weight determination and direct-relation matrix creation through linguistic variable influence. Subsequently, TOPSIS is employed to rank alternatives based on distance measures. The combined method identifies the ‘Human Resource Department’ as the most crucial in knowledge management strategy. This integrated approach facilitates organizations in pinpointing vital criteria and alternatives for effective knowledge management. Comparative analyses with existing methods are also presented.

INDONESIAN VERSION OF SATISFACTION WITH LIFE SCALE, A PSYCHOMETRIC EVALUATION WITH RASCH MODEL

Previous studies on The Satisfaction with Life Scale (SWLS) showed inconsistency, primarily related to the justice principle based on gender when using Confirmatory Factor Analysis (CFA). Therefore, this study aims to evaluate the psychometric property of the Indonesian version of SWLS using the Rasch model. This model can be used as an evaluation technique for psychological instruments. The Rasch model showed a more detailed analysis than CFA, explaining person-item fit statistics, rating scale diagnosis, item calibration, and differential item functioning. The total participants after person fit checking were 1,154 university students who completed an online survey consisting of demographic data and a five-item Indonesian version of SWLS. The Rasch Rating Scale Analysis (RSM) showed that the Indonesian version of SWLS fulfilled unidimensional and local independence assumptions. The items delivered a good item fit index with a five-point rating scale, and there were no gender biases in moderate level and high differential item functioning (DIF). Therefore, the Indonesian version of SWLS is recommended for further research measuring life satisfaction. This research implies that using the Indonesian version of SWLS, especially in Indonesia, can use a five-point rating scale for future research.