Testing Phase Research Articles

Adaptation of the Keirsey Temperament Sorter II (KTS-II) for the Chinese vocational university students majoring in preschool education

ABSTRACT The preschool education major in vocational universities serves as the core platform for cultivating students who are preparing to become applied early childhood teachers in China, but currently its education is too unified and ignores the differences among students. In addition, there is a lack of a measurement instrument to understand student differences. The purpose of this study is to adapt the English version of the Keirsey Temperament Sorter II (KTS-II) into the Chinese version in order to measure and understand the temperament types of Chinese vocational university students majoring in preschool education, so as to make it possible to conduct differentiated education based on student temperament differences. The adaptation procedure consists of three phases: (1) the scale translation phase; (2) the scale evaluation phase, including expert evaluation and target population pretesting; and (3) the scale testing phase, including initial testing and retesting. The results show that by removing six items from the original English version of the KTS-II, 64 items in the Chinese version of the KTS-II have been proven to have acceptable validity and reliability. The conclusions suggest that the Chinese version of the KTS-II can be used by relevant stakeholders in the preschool education major of Chinese vocational universities to distinguish student differences, and the scale adaptation procedure and methods in this study can serve as a theoretical reference for scale adaptors.

Predicting viral proteins that evade the innate immune system: a machine learning-based immunoinformatics tool

Viral proteins that evade the host’s innate immune response play a crucial role in pathogenesis, significantly impacting viral infections and potential therapeutic strategies. Identifying these proteins through traditional methods is challenging and time-consuming due to the complexity of virus-host interactions. Leveraging advancements in computational biology, we present VirusHound-II, a novel tool that utilizes machine learning techniques to predict viral proteins evading the innate immune response with high accuracy. We evaluated a comprehensive range of machine learning models, including ensemble methods, neural networks, and support vector machines. Using a dataset of 1337 viral proteins known to evade the innate immune response (VPEINRs) and an equal number of non-VPEINRs, we employed pseudo amino acid composition as the molecular descriptor. Our methodology involved a tenfold cross-validation strategy on 80% of the data for training, followed by testing on an independent dataset comprising the remaining 20%. The random forest model demonstrated superior performance metrics, achieving 0.9290 accuracy, 0.9283 F1 score, 0.9354 precision, and 0.9213 sensitivity in the independent testing phase. These results establish VirusHound-II as an advancement in computational virology, accessible via a user-friendly web application. We anticipate that VirusHound-II will be a crucial resource for researchers, enabling the rapid and reliable prediction of viral proteins evading the innate immune response. This tool has the potential to accelerate the identification of therapeutic targets and enhance our understanding of viral evasion mechanisms, contributing to the development of more effective antiviral strategies and advancing our knowledge of virus-host interactions.

The convolutional neural network for Pacific decadal oscillation forecast

Abstract The Pacific Decadal Oscillation (PDO) is often described as a long-lived El Niño-like pattern of Pacific climate variability, and it has widespread climate and ecosystem impacts. PDO forecasts can provide useful information for policymakers on how to handle PDO impacts. Nevertheless, due to the long duration of the PDO cycles and their complex formation mechanisms, it remains a challenge to predict long lead time PDO. In this paper, we propose a transfer-learning-enhanced Convolutional Neural Network (CNN) to tackle complex ocean dynamic forecasting and predict PDO events with up to a one-year lead time. Our method first trains the CNN on historical simulations from Coupled Model Intercomparison Project 6 (CMIP6), covering the period from 1850 to 1972. This prior knowledge is then refined by further training the model with observational data from 1854 to 1972, ensuring robust performance on unseen data. Additionally, k-fold cross-validation is also employed to evaluate the model's performance across diverse subsets of data, enhancing its reliability. Throughout the testing phase from 1983 to 2022, the CNN model consistently outperforms existing dynamical forecast systems, exhibiting superior correlation skills in predicting annual mean PDO indices and PDO phases, including displaying resilience to seasonal variations. The transferred CNN is thus a powerful method to predict PDO events and is potentially valuable for a wide range of applications. This work directly supports the objectives of the World Climate Research Programme (WCRP) Grand Challenge on Climate Prediction.

Leveraging Gamification in Science Learning for Secondary Students

This project aims to study the usefulness of incorporating gamification for science learning, for secondary school students. Ever since, the Science subject has been a hassle to the students, as well as the teachers. Therefore, a web-based system with gamification has been proposed. The waterfall model was chosen as the project's methodology, which consists of planning, analysis, design, implementation, testing, and documentation phases. Firebase was used as the cloud database to ease data storage and analysis. The selection was made to ensure the data from interactive activities such as watching films and doing exercises was securely gathered and used. Students earned points, badges, and were ranked as they performed assignments, adding a competitive element to the learning experience. A usability test was carried out to assess the success of the gamified learning platform using PSSUQ (Post-Study System Usability Questionnaire). The survey participants were chosen randomly from a group of high school students and teachers. After using the gamified website, they were asked to submit feedback by answering 16 constructed questions. Positive feedback was gathered from the survey, as most of the questions recorded mean values between 4.0 and 4.5. Hopefully, this study will provide further information regarding the effectiveness of gamification in learning, particularly in science learning for secondary students.

Machine learning-based design of double corrugated steel plate shear walls

PurposeIn this study, machine learning (ML) algorithms were employed to predict the shear capacity and behavior of DCSWs.Design/methodology/approachIn this study, ML algorithms were employed to predict the shear capacity and behavior of DCSWs. Various ML techniques, including linear regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost) and artificial neural network (ANN), were utilized. The ML models were trained using a dataset of 462 numerical and experimental samples. Numerical models were generated and analyzed using the finite element (FE) software Abaqus. These models underwent push-over analysis, subjecting them to pure shear conditions by applying a target displacement solely to the top of the shear walls without interaction from a frame. The input data encompassed eight survey variables: geometric values and material types. The characterization of input FE data was randomly generated within a logical range for each variable. The training and testing phases employed 90 and 10% of the data, respectively. The trained models predicted two output targets: the shear capacity of DCSWs and the likelihood of buckling. Accurate predictions in these areas contribute to the efficient lateral enhancement of structures. An ensemble method was employed to enhance capacity prediction accuracy, incorporating select algorithms.FindingsThe proposed model achieved a remarkable 98% R-score for estimating shear strength and a corresponding 98% accuracy in predicting buckling occurrences. Among all the algorithms tested, XGBoost demonstrated the best performance.Originality/valueIn this study, for the first time, ML algorithms were employed to predict the shear capacity and behavior of DCSWs.

Earthquake Prediction and Alert System Using IoT Infrastructure and Cloud-Based Environmental Data Analysis

Earthquakes are one of the most life-threatening natural phenomena, and their prediction is of constant concern among scientists. The study proposes that abrupt weather parameter value fluctuations may influence the occurrence of shallow seismic events by focusing on developing an innovative concept that combines historical meteorological and seismic data collection to predict potential earthquakes. A machine learning (ML) model utilizing the ML.NET framework was designed and implemented. An analysis was undertaken to identify which modeling approach, value prediction, or data classification performs better in forecasting seismic events. The model was trained on a dataset of 8766 records corresponding to the period from 1 January 2001 to 5 October 2024. The achieved accuracy of the model was 95.65% for earthquake prediction based on weather conditions in the Vrancea region, Romania. The authors proposed a unique alerting algorithm and conducted a case study that evaluates multiple predictive models, varying parameters, and methods to identify the most effective model for seismic event prediction in specific meteorological conditions. The findings demonstrate the potential of combining Internet of Things (IoT)-based environmental monitoring with AI to improve earthquake prediction accuracy and preparedness. An IoT-based application was developed using C# with ASP.NET framework to enhance earthquake prediction and public warning capabilities, leveraging Azure cloud infrastructure. The authors also created a hardware prototype for real-time earthquake alerting, integrating the M5Stack platform with ESP32 and MPU-6050 sensors for validation. The testing phase and results describe the proposed methodology and various scenarios.

Estimating The Health of Helicopter Turbine Engines by Means of Regression and Classification Using a Probabilistic Neural Network

This paper presents team Mad SoftMax’s approach to competing in the Conference of the Prognostics and Health Management Society 2024 Data Challenge. The competition tasked participants with estimating the health of helicopter turbine engines by means of regression and classification. Classification was used to categorize each observation within the dataset as belonging to a healthy or faulty engine while probabilistic regression was employed to estimate the torque margin at each measurement. Additionally, teams were challenged to provide a confidence metric to each of their estimations. These metrics reflected the trustworthiness of solutions and added a risk versus reward element to the competition. While the complete dataset for the challenge contained seven engines, four were provided in a training set to encourage robustness of solutions to handle the three withheld engines’ data during the testing and validation phase. The data was scrambled and identifiers to specific engines were removed to not give away patterns specific to a given engine. Open-source libraries such as TensorFlow were utilized to develop classification and regression models and the following paper opines on the process of understanding the data, data cleaning and model evaluation.

To Hear or Not to Hear: Cognitive Load Theory and Learning

ABSTRACT Background: Cognitive load is the amount of information one’s working memory can process at any given time. Key components of cognitive load include germane load, intrinsic load, and extraneous load. Learning is hindered if overload occurs, thus reducing cognitive performance. This study addresses the impact of auditory stimulation on cognitive load through repeated recognition tasks. Methods: Twenty-eight Queen’s University students were recruited and randomly allocated to control and experimental groups who completed the experiment with noise-cancelling headphones and with ambient noise, respectively. The experiment consisted of learning and testing phases. The learning phase involved memorizing a series of words. Subsequently, the testing phase required participants to recall words from the list. Each trial consisted of one learning and one testing phase, with three trials per participant. Cardiovascular measurements were taken prior to the experiment, at the beginning of each phase, and after each trial was completed. Results: Primary outcomes demonstrated that the experimental group experienced greater percent changes in heart rate and rate pressure product, higher perceived mental effort, and greater within-group variation for heart rate compared to controls. Scores decreased as trials progressed. Secondary outcomes showed that the top 33% of participants were less susceptible to changes in heart rate and blood pressure. Overall, four heart rate or blood pressure responders were identified. Conclusions: Exposure to 70 decibels of ambient noise contributes to cognitive load as noted by elevated cardiovascular measure and perceived mental effort. Results can inform educational platforms and hospital logistics.

Developing a Cloud and IoT-Integrated Remote Laboratory to Enhance Education 4.0: An Approach for FPGA-Based Motor Control

Remote laboratories are essential in addressing access and quality challenges in technical education. They enable students from various locations to engage with real equipment, overcome geographic and economic constraints, and provide solutions during crises, such as pandemics, when in-person learning is limited. As a key element of Education 4.0, remote labs promote technical skill development, enhance engineering education, and support diverse learning approaches. This study presents a remote laboratory based on Field Programmable Gate Arrays (FPGAs), developed using a waterfall methodology integrating IoT and Cloud Computing technologies to facilitate close interaction between hardware and software. The lab focuses on controlling DC, servo, and stepper motors, allowing students to apply theoretical concepts such as digital signals, pulse-width modulation (PWM), and data representation in bits in a practical setting. The testing phase involved 50 robotics and mechatronics engineering students who participated in hands-on sessions for one month, followed by a structured survey evaluating their experience, interaction, and the educational relevance of the platform. The survey shows high student satisfaction, highlighting the platform’s strengths and identifying areas for improvement. The results also underscore the system’s potential to significantly enhance the educational experience in remote environments, aligning with the United Nations Sustainable Development Goals (SDGs).

Physics-guided fault diagnosis method for proton exchange membrane fuel cells based on LSTM neural network

In this paper, we propose a novel hybrid method for fault diagnosis of Proton Exchange Membrane Fuel Cells (PEMFCs) based on the combination of a physics-based model and a long short-term memory (LSTM) neural network. By incorporating the physics-based model in the fault diagnosis algorithm, we can access to several process variables not directly measured through sensors but related to the state of the PEMFC stack. The model estimates are subsequently combined with signals measured on the PEMFC stack and inputted to a LSTM neural network. The performance of the physics-guided LSTM is evaluated on an extensive dataset comprising a thousand hours of operation of a PEMFC stack under dynamic load profiles, proving the enhanced capability of the proposed fault diagnosis method in capturing complex fault patterns. Furthermore, the effectiveness of the proposed method in dealing with PEMFC aging is tested by using data from the initial phase of stack operation for algorithm training and reserving the data from the aged stack operation for the testing phase. The experimental results reveal that the proposed physics-guided LSTM method allows for a significant amelioration over purely data-driven LSTM.

Efficient Flood Detection through Hybrid Machine Learning and Metaheuristic Methods using Sentinel-1

Abstract. Floods are considered among the most destructive natural disasters, requiring precise and timely management. Remote sensing, utilizing diverse satellite imagery data, enables effective monitoring and assessment of flood impacts. In this context, machine learning and deep learning methods, as effective and scalable approaches, can significantly enhance the accuracy of flood detection and management by analyzing remote sensing data, thereby playing a crucial role in mitigating flood-related risks. In this study, to flood detection using Sentinel-1 SAR data, machine learning algorithms, including Random Forest (RF) and Histogram-based Gradient Boosting Decision Tree, were employed, along with two metaheuristic algorithms, Harris Hawks Optimization (HHO) and Ant Colony Optimization (ACO), for hyperparameter optimization. Additionally, to enhance the models' ability to detect flooded pixels and improve overall performance accurately, a pre-trained VGG-16 Neural Network was used as a deep feature extractor. Finally, four ensemble flood detection models—RF-HHO, RF-ACO, HGBDT-HHO, and HGBDT-ACO—were implemented, and their performance was evaluated and compared based on statistical metrics. Based on the obtained results, all four ensemble flood detection models demonstrated excellent performance in the validation and testing phases. The overall accuracy of these models reached over 95% in the validation phase and exceeded 97% in the testing phase. However, the HGBDT-ACO model achieved the highest accuracy and the lowest error rate in detecting flood pixels, making it the best-performing model in this study. Generally, HGBDT models showed a relative advantage over RF models, as they required significantly less time for training while achieving comparable results. Therefore, they were efficient and performed better in terms of computational complexity.

Hybrid Classifier for Detecting Zero-Day Attacks on IoT Networks

Recently, Internet of Things (IoT) networks have been exposed to many electronic attacks, giving rise to concerns about the security of these networks, where their weaknesses and gaps can be exploited to access or steal data. These networks are threatened by several cyberattacks, one of which is the zero-day distributed denial-of-service (DDoS) attack, which is considered one of the dangerous attacks targeting network security. As such, it is necessary to find smart solutions to address such attacks swiftly. To address these attacks, this research proposed a hybrid IDS to detect cyber-attacks on IoT networks via machine learning (ML) algorithms, namely, XGBoost, K-nearest neighbors, and stochastic gradient descent (SGD), while classifiers are combined via an ML ensemble. Grid search CV was used to find the best hyperparameters for each classifier at each classification stage. Random projection was used to select the relevant features for training the model. In the evaluation and performance testing phase of the model, two cybersecurity datasets (CIC-IDS2017 and CIC-DDoS2019) were used to test the efficiency of the model in detecting zero-day threats. The best results were obtained for the CIC-DDoS2019 dataset, where 20 features out of the total selection were used. The model was able to achieve an accuracy of 99.91% and an intrusion detection time of 0.22 seconds. The confusion matrix results also revealed a reduction in false alarms. The results and their comparison with those of recent relevant studies demonstrated the effectiveness of the hybrid model in securing IoT networks from zero-day attacks as well as its superiority in terms of accuracy and intrusion detection time. This study is an important step in enhancing security in the IoT environment by presenting a new hybrid model that is capable of dealing with zero-day attacks that are difficult to detect with traditional models.

Predicting tensile and fracture parameters in polypropylene-based nanocomposites using machine learning with sensitivity analysis and feature impact evaluation

This study examines the efficacy of decision tree and AdaBoost algorithms in predicting mechanical and fracture parameters of polypropylene nanocomposites toughened with ethylene-based and propylene-based thermoplastic elastomers and reinforced with fumed silica and halloysite nanotube nanoparticles. The essential work of the fracture approach was utilized to study the fracture parameters, including elastic and plastic works of the blended polymer nanocomposites. The data were divided into 80 % for training and 20 % for testing. AdaBoost consistently achieved superior performance compared to the decision tree model in all variables throughout both the training and testing stages. During the testing phase, the AdaBoost model obtained R2 values of 0.90 for Young's modulus, 0.93 for elongation at break, 0.87 for tensile strength, 0.86 for plastic work, and 0.60 for elastic work. Also, the mean absolute percentage error for the AdaBoost model during the test phase was 3.10 % for Young's modulus, 3.25 % for tensile strength, 10.34 % for elastic work, 13.55 % for plastic work, and 24.78 % for elongation at break. Furthermore, a sensitivity analysis examining the effects of various features such as TPO type, nanoparticles, and nanoparticle type on mechanical properties reveals that TPO has the most significant overall influence. The results also include an analysis of the impact of the key features on each mechanical property based on the sensitivity analysis.

Light and normal weight concretes shear strength estimation using tree-based tunned frameworks

Concrete component shear strength is a complex phenomenon that depends on a number of governing bodies and regulatory mechanisms. Moreover, the addition of new concrete variations like fibered concrete and light-weight concrete increases the complexity of the topic. Consequently, in order to estimate the shear resistance (Vu) of normal-weight concrete (NWC) and light-weight concrete (LWC) components, three machine learning-based methods were developed. The work gives researchers significant tools to predict concrete structural integrity. This helps design safer, more efficient buildings by accounting for complicated material qualities and concrete variants like fibered or lightweight concrete in real-world construction settings. Extreme gradient boosting (XGB) was taken into consideration for this aim. The Chaos Game Optimization (CGO), Giant Trevally Optimizer (GTO), and Mountain Gazelle Optimizer (MGO) algorithms were used in order to identify the hyperparameters effectively (abbreviated as XGBMGO,XGBGTO,andXGBCGO). The collected big data was divided into three subsets: training (75 %: 1194), validating (15 %: 256), and testing (15 %: 256). The XGBGTO, XGBMGOand XGBCGO algorithms have a considerable capacity to precisely forecast the Vuof NWC and LWC. The R2 values for theXGBCGO net were determined to be 0.9959, 0.9957, and 0.9961, respectively, throughout the training, verification, and testing phases. The reasons put forward generally suggest that the XGBCGO analysis can achieve more than the XGBGTO, and XGBMGOframeworks.

Development and validation of a new asphalt mixture to reduce tyre-road contact noise in interurban areas

Road transport is one of the most important sources of noise emissions having a serious impact on human health. Thus, the objective of this study is to develop an innovative asphalt mixture that decreases noise from tyre and pavement interaction on highways with average speeds of about 80 km/h. To achieve this, a series of mechanical and functional tests were conducted both in the laboratory and at the Gustave Eiffel University Accelerated Pavement Testing (APT) facility. The new experimental mixture achieves a balance between high void content and low macro-texture (wavelengths of 5–50 mm), which improves noise reduction while maintaining good mechanical performance. This is achieved thanks to its two-layer structure, with a top layer of reduced maximum aggregate size (4 mm) and a thickness suitable for optimum sound absorption. Various mechanical tests, including the particle loss test and the water sensitivity test, along with functional evaluations focusing on texture, flow resistivity and sound absorption properties, were performed. Measurements on the APT were taken at three different stages of the pavement service lifetime during the testing phase. The outcome was the production of an asphalt mixture that reduced noise up to 6.8 dB in comparison to an asphalt concrete road.

Does concomitant diazepam and ethanol use modulate age-related cognitive decline in mice?

BackgroundConcomitant use of alcohol and benzodiazepines are described among elderly, raising concerns about their combined impact on memory. We aimed to evaluate the long-term impact of chronic diazepam use associated with ethanol intoxication on memory in aging mice. MethodsTwelve-month-old male C57BL6 mice were assigned into 4 groups: ethanol (OH), diazepam (DIA), diazepam + ethanol (DOH) and control (CTL). For 16 weeks, ethanol was available ad libitum and diazepam was mixed with food. Behavioral testing, performed during and after treatment cessation included working memory and visual recognition memory assessment. The second session was implemented with spatial reference learning and memory assessment in the Barnes maze test. In vivo magnetic resonance spectroscopy (MRS) acquisitions were performed to quantify hippocampal metabolites during and after cessation treatment. ResultsDuring treatment, visual recognition memory was significantly different between groups with the DIA group exhibiting the worst performance. MRS acquisition highlighted higher glutamate and choline levels in OH and DOH groups in comparison to CTL and DIA groups. After treatment wash-out, there was no difference between in the different memories evaluated. Only the learning phase of the spatial reference memory test differed significantly with worst performance in OH groups. Three months after treatment cessation, there was no remanent effect of diazepam + ethanol on hippocampal metabolites changes. ConclusionsWe did not evidence additive effect of ethanol and diazepam on memory and hippocampal metabolite levels. The disturbances observed during treatment were no remanent, highlighting the benefits of discontinuing these substances.

Decisions to seek cognitive performance feedback: Potential determinants of feedback value and consequences for learning

Performance feedback is essential for effective learning. Feedback contains both informational and affective properties. Following negative feedback (indicating an incorrect response), the unpleasant experience of being wrong can diminish the value of constructive information that feedback also provides. This can hinder motivation to seek feedback, which can impede learning. Therefore, research into factors that directly shape the subjective value of feedback is critical. The current study investigated potential behavioral and physiological contributors to feedback valuation and to subsequent feedback-seeking behavior. Fifty-nine participants completed a willingness-to-pay associative memory task that measured feedback valuation via trial-wise decisions to either purchase or forgo feedback during a learning phase in service of maximizing a performance-contingent monetary reward during a future test phase. Skin conductance response (SCR) was also measured during feedback decisions. Lower confidence in response accuracy significantly predicted higher likelihood of purchasing feedback during learning. Neither self-reported emotional responses to feedback nor SCR during feedback decisions predicted feedback purchases. Purchase decisions yielding negative feedback significantly predicted better performance during test. These results suggest that confidence during learning significantly impacts performance feedback valuation and should be considered when devising methods to motivate feedback-seeking in settings where learning is critical to success.

Wazuh SIEM for Cyber Security and Threat Mitigation in Apparel Industries

Security of IT infrastructure is critical in the modern digital environment, particularly for industries that manufacture clothing. The application of the Wazuh Security Information and Event Management (SIEM) system to improve security monitoring and compliance for an IT system in the apparel industry is the main topic of this study. To improve the apparel industry cyber threat monitoring system in real-time monitoring, detecting the threat vectors that support the centralized management system, to meet these objectives of the company, an SIEM security management system has been installed and configured with the components of Wazuh manager, indexer, Wazuh agent, and Wazuh dashboard as well. An Oracle virtual lab environment was created to install and configure the Wazuh system a robust Linux operating system was installed at the server level and Windows 10 was installed on the client end to the authenticity failure of the security system. To implement the Wazuh system, the project management guideline was followed which ensured the planning and implementation of the project, testing phases, and along with review and maintenance. Wazuh security management system successfully identified the authentical failure reports and vulnerabilities and generated automated reports from the system that showed the result of 1 critical, 19 high, and 5 medium levels of vulnerabilities shown in Figure 6, and the necessary patch recommended to solve the critical issues. On the other hand, the system also generated security events as well as integrity monitoring system reports. The deployment of the Wazuh security management system ensured the robust cost-effective security management system with high quality for detecting cyber threats which supports the regulatory and compliance requirements and is a viable tool for the apparel industry to protect the stakeholders’ interest.

Creating demand for unmet needs: Agile Storytelling

IntroductionThe translational gap from the discovery of evidence-based solutions to their implementation in healthcare delivery organizations derives from an incorrect assumption that the need for change among executive, administrative, or clinical personnel is the same as the demand for change. For sickle cell disease (SCD), implementation of evidence-based guidelines is often delayed or obstructed due to lack of demand. This challenge allows for the persistence of resource limitations and care delivery models that do not meet the community's unique needs. Agile Storytelling is a process built on the scientific foundations of behavioral economics, complexity science, and network science to create local demand for the implementation of evidence-based solutions.MethodsAgile Storytelling includes a design phase and a testing phase. The design phase converts the evidence-based solution into a minimally viable story of a hero, a villain, struggle, drama, and a resolution. The testing phase evaluates the effectiveness of the story via a series of storytelling sprints in the target local healthcare delivery organization. The efficacy of Agile Storytelling was tested in an iterative n-of-1 case study design.ResultsAgile Storytelling was used in a large, urban, healthcare system within the United States to facilitate implementation of national SCD best-practice guidelines. After repeated failures attempting to use national and local data regarding the high societal need to hire a SCD-specific social worker, an Agile change conductor using Agile Storytelling was able to create demand for the new position within a week. This decision has ultimately improved patient outcomes and led to the adoption of a specialized collaborative care team for SCD within the health network.DiscussionAgile Storytelling can lead to structured, effective, and informed storytelling to create local demand within healthcare delivery organizations.

Cyclic Performance of Reinforced Concrete Columns Strengthened with Basalt-Fibre-Reinforced Cementitious Matrix (BFRCM)

The purpose of this experimental investigation was to confirm whether the Basalt-Fibre-Reinforced Cement Matrix (BFRCM) effectively enhances the cyclic performance of columns made of reinforced concrete (RC). The BFRCM system, which comprises basalt fabric mesh reinforced with a cementitious matrix containing polyvinyl alcohol (PVA) fibre, has significant practical implications. In the testing phase, concrete and steel reinforcement were used to create RC column specimens, which were then strengthened with three, four, and five layers of BFRCM. The horizontal cyclic and constant axial loads were applied and tested to evaluate the performance of RC columns with and without strengthening. By improving the energy dissipation by approximately 9 to 32% and increasing stiffness by roughly 24 to 44%, the column specimen with three, four, and five layers of BFRCM performed better than the control specimen. Furthermore, incorporating short fibre into the matrix effectively improved the tensile properties of the FRCM systems and decreased the shrinkage-induced cracks. The increased stiffness indicates that the column with BFRCM has better structural strength because it can sustain higher loads with less deflection. The thorough comparative analyses examined the RC column specimens’ failure modes, hysteretic responses, stiffness degradation, and energy dissipation, providing a reliable basis for the conclusions. The test results confirmed that the BFRCM effectively enhanced the seismic capabilities and has been promised a way to strengthen RC elements, providing valuable insights for civil engineering and materials science.