Friendly User Research Articles

Tuning the structural and mechanical properties of SiC-Li and SiC-Na alloys for aerospace application: an ab initio study

Aluminum (Al) and its alloys are popular in the aerospace industry due to their high strength-to-weight ratio, corrosion resistance, and ductility. However, these properties (extreme ductility and malleability) can compromise corrosion resistance, making them susceptible to dents and scratches. Silicon Carbide (SiC) is a promising alternative to Al and it alloys due to its higher Youngs modulus and excellent wear resistance, although it has the drawbacks of brittleness and higher density. This study investigated the structural and mechanical properties of SiC alloyed with lithium (SiC-Li) or sodium (SiC-Na) using ab initio calculations with the aim of tuning the structural and mechanical properties of SiC. Modeling was done using Burai software, which offers a friendly graphical user interface for Quantum ESPRESSO, thus facilitating the creation of input files, visualization of crystal structures and analysis of results. The results from this study showed that the addition of Li and Na lowered the density as well as the mechanical properties of SiC but still being favorably better than those of Al and its alloys, suggesting that the modeled alloys could potentially replace the traditional Al and its alloys in the aerospace industry. Further experimental studies are needed to validate these findings and to explore the possibility of simultaneous alloying of SiC with both Li and Na for enhanced performance.

Interpretable machine learning model for new-onset atrial fibrillation prediction in critically ill patients: a multi-center study

BackgroundNew-onset atrial fibrillation (NOAF) is the most common arrhythmia in critically ill patients admitted to intensive care and is associated with poor prognosis and disease burden. Identifying high-risk individuals early is crucial. This study aims to create and validate a NOAF prediction model for critically ill patients using machine learning (ML).MethodsThe data came from two non-overlapping datasets from the Medical Information Mart for Intensive Care (MIMIC), with MIMIC-IV used for training and subset of MIMIC-III used as external validation. LASSO regression was used for feature selection. Eight ML algorithms were employed to construct the prediction model. Model performance was evaluated based on identification, calibration, and clinical application. The SHapley Additive exPlanations (SHAP) method was used for visualizing model characteristics and individual case predictions.ResultsAmong 16,528 MIMIC-IV patients, 1520 (9.2%) developed AF post-ICU admission. A model with 23 variables was built, with XGBoost performing best, achieving an AUC of 0.891 (0.873–0.888) in validation and 0.769 (0.756–0.782) in external validation. Key predictors included age, mechanical ventilation, urine output, sepsis, blood urea nitrogen, percutaneous arterial oxygen saturation, continuous renal replacement therapy and weight. A risk probability greater than 0.6 was defined as high risk. A friendly user interface had been developed for clinician use.ConclusionWe developed a ML model to predict the risk of NOAF in critically ill patients without cardiac surgery and validated its potential as a clinically reliable tool. SHAP improves the interpretability of the model, enables clinicians to better understand the causes of NOAF, helps clinicians to prevent it in advance and improves patient outcomes.

AIDM-CT: software for cone-beam X-ray tomography and deep-learning-based analysis

ABSTRACT Non-destructive testing (NDT) with high-resolution cone-beam X-ray computed tomography (XCT) plays a crucial role in revealing the 3D distribution and morphology of defects within an object. It’s necessary to generalise an intelligent and customised XCT-based NDT protocol, providing solutions for reconstruction quality improvement and complex defect quantitative analysis for users. We analysed the key techniques of XCT and developed the systematic software for data acquisition, reconstruction, intelligent super-resolution and segmentation in our advanced imaging and data mining laboratory (AIDM-CT). The experimental results demonstrated the software efficiently achieves the control of XCT scanning, artefact correction algorithms related to cone-beam geometrics, beam hardening, ring and radial artefacts, deep-learning based slice super-resolution and feature segmentation, and quantitative analysis. The software is programmed by Python language to provide the friendly multi-function graphical user interface (GUI), in which the programming of reconstruction and corrections combined with Computer Unified Device Architecture (CUDA) acceleration to guarantee the high efficiency of XCT task. Particularly, the multi-functional super resolution module (MF-GAN) is proposed to optimise the quality of CT image and the improved U-Net segmentation module (CBAM U-Net) is also developed to fulfill the high-precision quantitative non-destructive testing of homogeneous and variform microdefects in our AIDM-CT software.

Design and research of serial communication system based on virtual instrument technology

Abstract In the measurement and automation control system, serial communication has been connected between the acquisition hardware (lower computer) and the upper computer between the easiest to realize and the most economical way. In view of the traditional instrument itself is relatively bulky, inconvenient to carry, this paper adopts the virtual instrument development environment to realize the realization of serial communication, in order to complete the subsequent data acquisition. When compared with other software development environment, LabWindows/CVI platform comes with a function library to make the program writing simple and flexible. In this paper, we designed the realization method of serial communication based on virtual instrument Labwindows/CVI environment, completed the design and realization of serial communication between PC and lower computer (microcontroller) based on Labwindows/CVI environment, and applied it to the example of serial communication with microcontroller to realize the data communication function. Practical results prove that the serial communication system based on virtual instrument technology has the advantages of friendly user interface, high stability, convenience and reliability, and can reliably and accurately complete the communication, and provide strong technical support for the system to follow up data acquisition.

A transfer learning approach to identify Plasmodium in microscopic images.

Plasmodium parasites cause Malaria disease, which remains a significant threat to global health, affecting 200 million people and causing 400,000 deaths yearly. Plasmodium falciparum and Plasmodium vivax remain the two main malaria species affecting humans. Identifying the malaria disease in blood smears requires years of expertise, even for highly trained specialists. Literature studies have been coping with the automatic identification and classification of malaria. However, several points must be addressed and investigated so these automatic methods can be used clinically in a Computer-aided Diagnosis (CAD) scenario. In this work, we assess the transfer learning approach by using well-known pre-trained deep learning architectures. We considered a database with 6222 Region of Interest (ROI), of which 6002 are from the Broad Bioimage Benchmark Collection (BBBC), and 220 were acquired locally by us at Fundação Oswaldo Cruz (FIOCRUZ) in Porto Velho Velho, Rondônia-Brazil, which is part of the legal Amazon. We exhaustively cross-validated the dataset using 100 distinct partitions with 80% train and 20% test for each considering circular ROIs (rough segmentation). Our experimental results show that DenseNet201 has a potential to identify Plasmodium parasites in ROIs (infected or uninfected) of microscopic images, achieving 99.41% AUC with a fast processing time. We further validated our results, showing that DenseNet201 was significantly better (99% confidence interval) than the other networks considered in the experiment. Our results support claiming that transfer learning with texture features potentially differentiates subjects with malaria, spotting those with Plasmodium even in Leukocytes images, which is a challenge. In Future work, we intend scale our approach by adding more data and developing a friendly user interface for CAD use. We aim at aiding the worldwide population and our local natives living nearby the legal Amazon's rivers.

Social Perception with Graph Attention Network for Recommendation

Recommendation systems are designed to uncover users’ potential preferences and make recommendations. However, they often face challenges such as data sparsity and the cold start problem. Although the introduction of knowledge graphs has partially addressed the issue of data sparsity, the challenge of cold start has not been effectively resolved. In this paper, a novel approach called Social Perception with Graph Attention Network (SPGAT) for Recommendation is proposed. In SPGAT, we aim to leverage social perception to solve the cold start effectively for more accurate recommendations. The approach utilizes a multi-layer graph attention network to aggregate user preference features from collaborative knowledge graphs and social perception graphs. By analyzing the social network of a new user, associated friend users can be identified. The interaction data of these friend users is then provided as side information to recommend to the new user. To handle one-to-many and many-to-many relations, we introduce the TransD graph embedding model, which maps different types of relations and entities to different spaces. To optimize the proposed SPGAT, self-adversarial negative sampling is utilized to implement entity and relation embedding and generate negative samples. Experimental results demonstrate that SPGAT has achieved superior performance compared to several advanced methods.

Information control in networked discrete event systems

How to control information exchange among different users is an important problem in networked systems with many users/agents. Generally speaking, there are several considerations in control of information exchange in a networked system, including (1) to ensure a friend user has sufficient information to perform its tasks, (2) to deprive an adversary user its information to perform its tasks, (3) to minimize information exchange among friend users so that the risk of information leaking is minimized, and (4) to maximize information broadcasted to all users to achieve maximum transparency. In this paper, we investigate the information control problems in the framework of discrete event systems. Based on the problem at hand, we divide users in a networked system into two or more groups. Users in the same group are consider as friends and users in a different group are consider as adversaries. Several information control problems are investigated and solved using a systematic and rigorous approach. Methods are developed to design controllers that send minimum information to its friends to help them to perform their tasks and broadcast maximum information without helping its adversaries.

Level of Acceptability of Computer-Assisted Assessment (CAA) and Student’s Academic Performance in Key Stage 2 of Ilalim Elementary School for The School Year 2022-2023

In this research study, the researcher aimed to determine the level of acceptability of computer-assisted assessment and learners’ academic performance of 127 Key Stage 2 Learners of Ilalim Elementary School for the School Year 2022-2023. A descriptive survey instrument was used to determine the level of acceptability adapted from the computer-based assessment acceptance model (CBAAM) and was modified by the researcher. Data revealed that the use of computer-assisted assessment of the Key Stage 2 learners was Moderately Acceptable with a mean of 4.01, indicating that the application purposively used it to improve their knowledge and skills. The learners gained an Outstanding academic performance as reflected in their general weighted average from the four grading periods. Significant differences were found in the level of acceptability of the use of computer-assisted assessment on the profile variables of the learner-respondents. Moreover, the results showed a positive correlation between the level of acceptability of the use of computer-assisted assessment on the learner’s academic performance. Based on the gathered data from the survey, computer-assisted assessment provides the learners with a positive experience when using the application through its friendly user interface. Teachers may continue to use the computer-assisted assessment application to motivate their learners to increase their engagement in learning their subject. It is recommended that teachers may further enhance the delivery of questions in the CAA application to improve learners’ academic performance in the future.

OralImmunoAnalyser: a software tool for immunohistochemical assessment of oral leukoplakia using image segmentation and classification models.

Oral cancer ranks sixteenth amongst types of cancer by number of deaths. Many oral cancers are developed from potentially malignant disorders such as oral leukoplakia, whose most frequent predictor is the presence of epithelial dysplasia. Immunohistochemical staining using cell proliferation biomarkers such as ki67 is a complementary technique to improve the diagnosis and prognosis of oral leukoplakia. The cell counting of these images was traditionally done manually, which is time-consuming and not very reproducible due to intra- and inter-observer variability. The software presently available is not suitable for this task. This article presents the OralImmunoAnalyser software (registered by the University of Santiago de Compostela-USC), which combines automatic image processing with a friendly graphical user interface that allows investigators to oversee and easily correct the automatically recognized cells before quantification. OralImmunoAnalyser is able to count the number of cells in three staining levels and each epithelial layer. Operating in the daily work of the Odontology Faculty, it registered a sensitivity of 64.4% and specificity of 93% for automatic cell detection, with an accuracy of 79.8% for cell classification. Although expert supervision is needed before quantification, OIA reduces the expert analysis time by 56.5% compared to manual counting, avoiding mistakes because the user can check the cells counted. Hence, the SUS questionnaire reported a mean score of 80.9, which means that the system was perceived from good to excellent. OralImmunoAnalyser is accurate, trustworthy, and easy to use in daily practice in biomedical labs. The software, for Windows and Linux, with the images used in this study, can be downloaded from https://citius.usc.es/transferencia/software/oralimmunoanalyser for research purposes upon acceptance.

DCP: A pipeline toolbox for diffusion connectome.

The brain structural network derived from diffusion magnetic resonance imaging (dMRI) reflects the white matter connections between brain regions, which can quantitatively describe the anatomical connection pattern of the entire brain. The development of structural brain connectome leads to the emergence of a large number of dMRI processing packages and network analysis toolboxes. However, the fully automated network analysis based on dMRI data remains challenging. In this study, we developed a cross-platform MATLAB toolbox named "Diffusion Connectome Pipeline" (DCP) for automatically constructing brain structural networks and calculating topological attributes of the networks. The toolbox integrates a few developed packages, including FSL, Diffusion Toolkit, SPM, Camino, MRtrix3, and MRIcron. It can process raw dMRI data collected from any number of participants, and it is also compatible with preprocessed files from public datasets such as HCP and UK Biobank. Moreover, a friendly graphical user interface allows users to configure their processing pipeline without any programming. To prove the capacity and validity of the DCP, two tests were conducted with using DCP. The results showed that DCP can reproduce the findings in our previous studies. However, there are some limitations of DCP, such as relying on MATLAB and being unable to fixel-based metrics weighted network. Despite these limitations, overall, the DCP software provides a standardized, fully automated computational workflow for white matter network construction and analysis, which is beneficial for advancing future human brain connectomics application research.

Design of an automated robotic microinjection system for batch injection of zebrafish embryos and larvae

This paper presents the design of a vision-based automated robotic microinjection system for batch injection of both zebrafish embryos and larvae. A novel visual recognition algorithm based on an automatic threshold and excessive dilatation is introduced to accurately identify the center of zebrafish embryos and larval yolks. A corresponding software system is developed using the producer-consumer model as the framework structure, and a friendly user interface is designed to allow operators to choose from a range of desired functions according to their different needs. In addition, a novel microstructural agarose device is designed and fabricated to simultaneously immobilize mixed batches of embryos and larvae. Moreover, a prototype microinjection system is fabricated by integrating hardware devices with visual algorithms. An experimental study is conducted to verify the performance of the robotic microinjection system. The results show that the reported system can accurately identify zebrafish embryos and larvae and efficiently complete batch microinjection tasks of the mixtures with an injection success rate of 92.05% in 13.88 s per sample. Compared with manual and existing microinjection systems, the proposed system demonstrates the merits of versatility, excellent efficiency, high success rate, high survival rate, and sufficient stability.

Utilization of Artificial Intelligence in Chatbot Development for New Student Admission Support at Cokroaminoto University Palopo

This research aims to develop an artificial intelligence-based chatbot as a support tool for the New Student Admission (PMB) process at Cokroaminoto University Palopo. The system development method applied is the waterfall method, which consists of analysis, design, implementation, testing, and maintenance stages. The main focus of this research is to design and implement a chatbot that is able to provide information and support to prospective new students throughout the admission process. In the analysis stage, user needs and scenarios were identified to guide the development of the chatbot. System design includes the selection of artificial intelligence algorithms and a friendly user interface. Implementation involves developing a chatbot prototype that utilizes artificial intelligence technology. The expected results can improve the efficiency and quality of new student admission services at Universitas Cokroaminoto Palopo.

Unauthorized Microphone Access Restraint Based on User Behavior Perception in Mobile Devices

Microphone has been widely integrated into mobile devices to provide physical basis for human-device voice interaction. However, the microphone may be spitefully invoked by malicious <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mobile applications</i> (apps) with arousing security and privacy concerns. In this work, to explore the issue of illegal microphone access, we develop spiteful apps through native and injection development to access the microphone viciously on a series of mobile devices. The results demonstrate that baleful apps could enable the microphone arbitrarily without any hint. To combat the unauthorized microphone access behavior, we design a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">microphone illegal access detection</i> (MicDet) scheme by constructing a request-response time model using the Unix time stamps of voice icon touched and microphone invoked. Through conducting numerical analysis and hypothesis testing to effectively verify the request-response pattern of app's normal access, we detect illegal access by analyzing whether the touch operation matches the normal pattern. For friendly user experience, we design an intuitive floating window to alert users by displaying the name of the app that illegally accessed the microphone once the illegal behavior is detected. Finally, we apply our scheme to different mobile devices and test several apps, the experimental results show that the MicDet scheme achieves a high detection accuracy.

Possibilities of Using “BUGA” Named Software for Breeding Value Estimation of Anatolian Water Buffalo Population of Istanbul

In this study, variance components and breeding value estimations of Anatolian water buffaloes were estimated from the data obtained within the scope of community based water buffalo improvement program of Anatolian water buffalo population of İstanbul. The "BUGA" named software, which was started to be developed with the support of Harran University Scientific Research Project Unit (HUBAP) for use in the estimation of (co)variance components and breeding value were used in this study. The BUGA software used in the research can predict genetic and environmental effects together with variance components as Best Linear Unbiased Prediction (BLUP) with the Restricted Maximum Log likelihood (REML) method. Unlike its counterparts of another software of foreign origin, it has a visual interface. The language of use is Turkish. The dataset includes records such as city, district, village, genealogy information, date of birth, gender, calving date, lactation parity, number of test day milk yield, lactation milk yield and previous calving date of 442 Anatolian buffaloes raised in this population. The records were analysed using the repeatability model with BUGA software and the additive genetic and permanent environmental effects of all animals in the dataset were estimated. Random environment variance, genotypic variance and permanent environment variance values were estimated as 107112.60 kg2, 20045.22 kg2 and 1259.50 kg2, respectively, while heritability (h2) and degree of repeatability (r) were determined as 0.1561 and 0.1659, respectively. The results obtained as an example of case study applied for the possibility of usage the BUGA software in the estimation of genetic parameters and breeding values showed that BUGA software can be easily used in farm recording conditions with the advantage of friendly user features.

The influence of peer's social networks on adolescent's cannabis use: a systematic review of longitudinal studies.

A systematic review was performed to summarize the key findings of the peer influence on cannabis use through Social Network Analysis (SNA) studies and identify limitations and gaps with the purpose of informing future research and practice. Longitudinal studies were included since they provide robust information about social relationships change over time. Adolescents' cannabis use is a global problem, which has awakened an interest in its determinants such as social influences. Research has shown the importance of these influences on cannabis uptake and use. SNA is an useful relational approach to examine socialization mechanisms related to the onset of cannabis use in adolescents. A search was conducted in PyscINFO, PubMed, Scopus and Web of Science for longitudinal articles published until February 2023, to examine cannabis use and peer's social networks. We focus on peers' influence of peers on cannabis use. Additionally, information about effect of cannabis use for peer selection was collected. The results of the included studies (n = 8) showed that friends' cannabis use was most often/strongly associated with cannabis use. There was also an increase of cannabis use when the adolescent did not feel close to the school's peers, had a higher proportion of friendships relative to the total number of ties in the neighborhood, had a central position, did not belong to any group but had ties to members of two or more groups, had cannabis user friends (especially in early ages), and lived in a neighborhood where cannabis was used. Cannabis use is mainly related to friends' use. Yet, future studies are warranted to control for relevant selection effects to further knowledge on network effects on cannabis use, improving the design, and improving the modeling of the network. This systematic review may inform about the critical aspects of preventing cannabis use among adolescents, taking into consideration their complex social environment.

Estimation of compressive strength for spiral stirrup-confined circular concrete column using optimized machine learning with interpretable techniques

The compressive strength (CS) of a concrete column confined with spiral stirrups is an important indicator for assessing the safety and stability of concrete structures. However, achieving accurate CS estimation for confined concrete remains challenging due to the complex confinement mechanism provided by spiral stirrups. In this study, three robust machine learning (ML) algorithms—support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost)—are employed to predict the CS value of the spiral stirrup-confined circular concrete columns. The hyperparameters of the ML models undergo fine-tuning via Bayesian optimization with 10-fold cross-validation, and the optimized ML models are evaluated for their predictive capabilities. Results show that compared to SVR and RF, XGBoost exhibits more stable generalization performance, achieving an average coefficient of determination (R 2) of 0.944 for the 10-fold cross-validation, and demonstrates superior accuracy on the testing dataset with an R 2 value of 0.967. To provide insights into the relationship between input features and the output CS value, Individual Conditional Exception (ICE) plots, one/two-dimensional Partial Dependence Plots (PDPs), and Shapley Additive Explanation (SHAP) techniques are utilized to interpret the optimized XGBoost model. Additionally, a friendly online graphical user interface (GUI) has been specially developed based on the optimized XGBoost model to facilitate convenient CS estimation for spiral stirrup-confined circular concrete column.

An approach for proactive mobile recommendations based on user-defined rules

In the Big Data era, context-aware mobile recommender systems are crucial in assisting citizens and tourists in making informed decisions, providing a suitable way for users to find the relevant data. These systems should be proactive, able to detect the ideal time and location to provide recommendations for a specific item or activity. To accomplish this, push-based recommender systems can be employed, utilizing context rules to determine when a recommendation should be initiated. However, there is very limited reported experience in defining and implementing such systems and a complete generic solution that adapts flexibly to the preferences of users and protects their privacy is still missing.In this paper, we present a novel approach where appropriate types of recommendations are provided automatically, without the need for user input. Our proposal allows users to easily activate, deactivate, customize, and create rules for improved personalization. Additionally, the module that, based on the context, decides the types of recommendations required is executed on the user’s mobile device, reducing wireless communication and safeguarding the user’s privacy, as context data are evaluated locally. To illustrate the approach, we have developed R-Rules, a prototype for Android devices focused on the triggering of recommendation rules, which provides a friendly user interface that facilitates user personalization. We have evaluated various technological options and demonstrated the feasibility, performance, and scalability of the proposal, as well as its suitability to users’ needs.

ENCRYPTED QR CODE FOR BRAND AUTHENTICATION: ENQRURE MOBILE APPLICATION DEVELOPMENT AND EVALUATION

Counterfeit products pose a growing concern for brand owners and consumers due to their adverse effects on user experience and brand value, leading to unsatisfied customers, negative publicity, and even potential health risks. This paper addresses the growing issues surrounding fake goods, which negatively affect customers and brand owners, including harmed user experiences, tarnished reputations, and potential health risks. The work proposed an encrypted QR code emphasising encryption techniques embedded in a mobile application with a friendly user interface and seamless interaction with existing authentication mechanisms to resolve this problem. The application aims to detect counterfeit goods, protect brand reputation, and increase consumer confidence by guaranteeing product authenticity. The study conducted a usability evaluation with 30 volunteers. The results indicated that enQRure proved to be a valuable and user-friendly application. Respondents expressed satisfaction with the application’s feature that facilitates product authentication. Feedback analysis on features offered by enQRure, such as product registration, product authentication, and encrypted QR code generation processes, revealed that most respondents found them straightforward and useful. They perceived enQRure as an effective means to authenticate products, aligning with their preferences and needs.

An open-source natural language processing toolkit to support software development: addressing automatic bug detection, code summarisation and code search.

This paper aims to introduce the innovative work carried out in the Horizon 2020 DECODER project - acronym for "DEveloper COmpanion for Documented and annotatEd code Reference" - (Grant Agreement no. 824231) by linking the fields of natural language processing (NLP) and software engineering. The project as a whole addresses the development of a framework, namely the Persistent Knowledge Monitor (PKM), that acts as a central infrastructure to store, access, and trace all the data, information and knowledge related to a given software or ecosystem. This meta-model defines the knowledge base that can be queried and analysed by all the tools integrated and developed in DECODER. Besides, the DECODER project offers a friendly user interface where each of the predefined three roles (i.e., developers, maintainers and reviewers) can access and query the PKM with their personal accounts. The paper focuses on the NLP tools developed and integrated in the PKM, namely the deep learning models developed to perform variable misuse, code summarisation and semantic parsing. These were developed under a common work package - "Activities for the developer" - intended to precisely target developers, who can perform tasks such as detection of bugs, automatic generation of documentation for source code and generation of code snippets from natural languages instructions, among the multiple functionalities that DECODER offers. These tools assist and help the developers in the daily work, by increasing their productivity and avoiding loss of time in tedious tasks such as manual bug detection. Training and validation were conducted for four use cases in Java, C and C++ programming languages in order to evaluate the performance, suitability, usability, etc. of the developed tools.

Challenges Associated with E-Learning and Measures to Curb them on Colleges of Education Students’ in the Upper West Region

The aim of the study was to assess the challenges of e-learning on the Colleges of Education students in Ghana using the Nusrat Jahan Ahmadiyya and Mccoy College of Education. The research design was a descriptive survey; the study used a structured questionnaire guide as its main tools for data gathering. A total of 100 students were recruited for the study. The study found that there is a lack of computers, mobile phones, etc., and the necessary skills for e-learning programs among most of the students. Also, the study results showed that students sometimes get lost or confounded about course exercises and deadlines. Again, the study revealed that students sometimes feel distant or disengaged from the instructor. The study found that ensuring a friendly user interface will help solve the challenges in e-learning. The study also found that computer games can be used adequately as a learning resource by educators. It was also revealed that starting e-learning with a mix of both online and offline learning will help eliminate the e-learning challenges. The study finally recommended that, since students perceive e-learning as not being self-motivating, the tutors need to develop assignments that create effective feedback and have interactive elements included within them.