User-friendly Interface Research Articles

Deep learning pipeline for accelerating virtual screening in drug discovery.

In the race to combat ever-evolving diseases, the drug discovery process often faces the hurdles of high-cost and time-consuming procedures. To tackle these challenges and enhance the efficiency of identifying new therapeutic agents, we introduce VirtuDockDL, which is a streamlined Python-based web platform utilizing deep learning for drug discovery. This pipeline employs a Graph Neural Network to analyze and predict the effectiveness of various compounds as potential drug candidates. During the validation phase, VirtuDockDL was instrumental in identifying non-covalent inhibitors against the VP35 protein of the Marburg virus, a critical target given the virus's high fatality rate and limited treatment options. Further, in benchmarking, VirtuDockDL achieved 99% accuracy, an F1 score of 0.992, and an AUC of 0.99 on the HER2 dataset, surpassing DeepChem (89% accuracy) and AutoDock Vina (82% accuracy). Compared to RosettaVS, MzDOCK, and PyRMD, VirtuDockDL outperformed them by combining both ligand- and structure-based screening with deep learning. While RosettaVS excels in accurate docking but lacks high-throughput screening, and PyRMD focuses on ligand-based methods without AI integration, VirtuDockDL offers superior predictive accuracy and full automation for large-scale datasets, making it ideal for comprehensive drug discovery workflows. These results underscore the tool's capability to identify high-affinity inhibitors accurately across various targets, including the HER2 protein for cancer therapy, TEM-1 beta-lactamase for bacterial infections, and the CYP51 enzyme for fungal infections like Candidiasis. To sum up, VirtuDockDL combines user-friendly interface design with powerful computational capabilities to facilitate rapid, cost-effective drug discovery and development. The integration of AI in drug discovery could potentially transform the landscape of pharmaceutical research, providing faster responses to global health challenges. The VirtuDockDL is available at https://github.com/FatimaNoor74/VirtuDockDL .

Stress Detection System using Machine Learning and Sensors

This project presents a novel stress detection system that leverages machine learning and sensor technology to monitor and assess stress levels in real-time. As stress has become a widespread concern affecting mental health, our system integrates wearable sensors—such as heart rate monitors, galvanic skin response sensors, and accelerometers—to capture physiological data indicative of stress. The project involves several key components: data collection and preprocessing, feature extraction, and the development of machine learning models capable of accurately classifying stress levels based on the sensor data. Various algorithms, including Support Vector Machines and Neural Networks, will be employed and optimized for performance. The system is designed to provide real-time feedback to users, offering insights and personalized recommendations for stress management based on detected levels. A user-friendly interface will enable individuals to track their stress over time, fostering greater awareness and encouraging proactive coping strategies

HMicroDB: A Comprehensive Database of Herpetofaunal Microbiota Witha Focus on Host Phylogeny, Physiological Traits, and Environment Factors.

Symbiotic microbiota strongly impact host physiology. Amphibians and reptiles occupy a pivotal role in the evolutionary history of Animalia, and they are of significant ecological, economic, and scientific value. Many prior studies have found that symbiotic microbiota in herpetofaunal species are closely associated with host phylogeny, physiological traits, and environmental factors; however, insufficient integrated databases hinder researchers from querying, accessing, and reanalyzing these resources. To rectify this, we built the first herpetofaunal microbiota database (HMicroDB; https://herpdb.com/) that integrates 11,697 microbiological samples from 337 host species (covering 23 body sites and associated with 23 host phenotypic or environmental factors), and we identified 11,084 microbial taxa by consistent annotation. The standardised analysis process, cross-dataset integration, user-friendly interface, and interactive visualisation make the HMicroDB a powerful resource for researchers to search, browse, and explore the relationships between symbiotic microbiota, hosts, and environment. This facilitates research in host-microbiota coevolution, biological conservation, and resource utilisation.

Hybrid Machine Learning Approach for Early Stroke Prediction in Elderly People

Stroke represents a significant global health challenge, often leading to severe disability and mortality. The timely prediction and intervention of stroke are paramount in enhancing patient outcomes and reducing healthcare burdens. This project proposes a machine learning-based approach to predict stroke risk using multi-modal biosignals, specifically electrocardiogram (ECG) data. By leveraging advanced algorithms, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, the system aims to classify patient health data into critical risk categories such as Normal, Abnormal, Ischemic, and Hemorrhagic. The study utilizes a comprehensive dataset consisting of ECG signals and incorporates techniques for data preprocessing, class balancing, and feature extraction. The predictive model is trained and validated using robust evaluation metrics, including accuracy, precision, recall, and F1-score. The findings underscore the efficacy of the proposed system in providing real-time stroke risk assessments, offering a cost-effective alternative to traditional diagnostic methods. Furthermore, this research explores the integration of wearable technology with machine learning, highlighting its potential for continuous patient monitoring and early detection of stroke symptoms. By creating a user-friendly interface for healthcare professionals and patients, the system aims to facilitate prompt decision-making and intervention, ultimately improving the overall quality of care for individuals at risk of stroke

SiRNAEfficacyDB: An experimentally supported small interfering RNA efficacy database.

Small interfering RNA (siRNA) has revolutionised biomedical research and drug development through precise post-transcriptional gene silencing technology. Despite its immense potential, siRNA therapy still faces technical challenges, such as delivery efficiency, targeting specificity, and molecular stability. To address these challenges and facilitate siRNA drug development, we have developed siRNAEfficacyDB, a comprehensive database that integrates experimentally validated siRNA efficacy data. This database contains 3544 siRNA records, covering 42 target genes and 5 cell lines. It provides detailed information on siRNA sequences, target genes, inhibition efficiencies, experimental techniques, cell lines, siRNA concentrations, and incubation times. siRNAEfficacyDB offers a user-friendly web interface that makes it easy to query, browse and analyse data, enabling efficient access to siRNA-related information. In summary, siRNAEfficacyDB provides a useful data foundation for siRNA drug design and optimisation, serving as a valuable resource for advancing computer-aided siRNA design research and nucleic acid drug development. siRNAEfficacyDB is freely available at https://cellknowledge.com.cn/siRNAEfficacy for non-commercial use.

PVACview: an interactive visualization tool for efficient neoantigen prioritization and selection.

Neoantigen-targeting therapies including personalized vaccines have shown promise in the treatment of cancers, particularly when used in combination with checkpoint blockade therapy. At least 100 clinical trials involving these therapies have been initiated globally. Accurate identification and prioritization of neoantigens is crucial for designing these trials, predicting treatment response, and understanding mechanisms of resistance. With the advent of massively parallel DNA and RNA sequencing technologies, it is now possible to computationally predict neoantigens based on patient-specific variant information. However, numerous factors must be considered when prioritizing neoantigens for use in personalized therapies. Complexities such as alternative transcript annotations, various binding, presentation and immunogenicity prediction algorithms, and variable peptide lengths/registers all potentially impact the neoantigen selection process. There has been a rapid development of computational tools that attempt to account for these complexities. While these tools generate numerous algorithmic predictions for neoantigen characterization, results from these pipelines are difficult to navigate and require extensive knowledge of the underlying tools for accurate interpretation. This often leads to over-simplification of pipeline outputs to make them tractable, for example, limiting prediction to a single RNA isoform or only summarizing the top ranked of many possible peptide candidates. In addition to variant detection, gene expression, and predicted peptide binding affinities, recent studies have also demonstrated the importance of mutation location, allele-specific anchor locations, and variation of T-cell response to long versus short peptides. Due to the intricate nature and number of salient neoantigen features, presenting all relevant information to facilitate candidate selection for downstream applications is a difficult challenge that current tools fail to address. We have created pVACview, the first interactive tool designed to aid in the prioritization and selection of neoantigen candidates for personalized neoantigen therapies including cancer vaccines. pVACview has a user-friendly and intuitive interface where users can upload, explore, select, and export their neoantigen candidates. The tool allows users to visualize candidates at multiple levels of detail including variant, transcript, peptide, and algorithm prediction information. pVACview will allow researchers to analyze and prioritize neoantigen candidates with greater efficiency and accuracy in basic and translational settings. The application is available as part of the pVACtools software at pvactools.org and as an online server at pvacview.org.

GTO: a comprehensive gene therapy omnibus.

Gene therapy, which involves the delivery of genetic material into cells to correct an underlying genetic problem, has emerged as a promising approach for treating various conditions. To promote research in this rapidly evolving field, we developed the Gene Therapy Omnibus (GTO) (http://www.inbirg.com/gto/), a comprehensive resource containing detailed clinical trial data and molecular information related to gene therapy. The GTO includes 6333 clinical trial records and 3466 transcriptome profiles, with information on 614 altered genes and 22 types of gene therapy, including DNA therapies, RNA therapies and genetically-modified cell therapies. For each gene therapy product in a clinical trial, detailed information, such as altered gene name, structural components, indication, vector information, phase of the clinical trial, clinical outcomes and adverse effects, is provided when available. Additionally, 345 comparison datasets, including 29 single-cell RNA-sequencing datasets comprising information on both gene therapy and control samples, were established. Differential gene expression and downstream functional enrichment analyses were performed through standardized pipelines to elucidate the molecular alterations induced by gene therapy. The user-friendly interface of the GTO supports efficient data retrieval, visualization and analysis, making it an invaluable resource for researchers and clinicians performing clinical research on gene therapy and the underlying mechanisms.

Creating a Quasi-Resonant Induction Cooktop Integrating Zero-Voltage Switching (ZVS) and Load Management

This study aims to elucidate the development and construction of a durable induction cooktop, with key considerations including efficiency, power customization, and safety features. The intricate processes involved in crafting a 3.5 kW induction burner are thoroughly examined, encompassing simulations for quasi-resonant inverters, the meticulous selection of induction coils and capacitors, the implementation of practical Analog-to-Digital Converter (ADC) filtration, pulse width modulation (PWM) driving techniques, and the integration of protection mechanisms. Leveraging an ARM-based microcontroller enabled the attainment of diverse objectives such as Zero-Voltage Switching (ZVS), safeguarding IGBTs, facilitating user interaction through a user-friendly interface, and enabling load detection capabilities. Furthermore, the capability to gauge and adjust output power based on user preferences was incorporated. Subsequently, rigorous testing was conducted to evaluate the functionality and applicability of the device in real-world scenarios.

The EURISCO-EVA Information System, an innovative approach to the data management of multi-site crop evaluation data

This paper introduces EURISCO-EVA, an extension of the European Search Catalogue for Plant Genetic Resources (EURISCO), facilitating standardized data collection, sharing, and analysis for plant genetic resources for food and agriculture (PGRFA) characterization and evaluation. In the framework of the European Evaluation Network (EVA), a private-public partnership aiming at the evaluation of crop accessions conserved in European genebanks, this information system addresses the need for a standardized data repository for multi-site evaluations of different crops. Through centralizing metadata maintenance, EURISCO-EVA ensures uniformity in trait definitions, experimental designs, and passport data, promoting efficient exchange of observed phenotypic data. EURISCO-EVA currently stores more than half a million phenotypic data points for 4,845 PGRFA accessions from six genera and 17 species, collected through 382 phenotypic experiments conducted at 115 experimental locations across 33 countries, involving 89 project partners. This platform offers a user-friendly web interface, empowering its users with features such as map-based filtering of trial locations, statistical overviews, and customizable reports. EURISCO-EVA’s robust administrative functionalities, coupled with standardization efforts, enhance data quality and harmonization, providing a robust tool for storage of and access to crop evaluation data that could be further enhanced by adding analysis modules. EURISCO-EVA also formed the basis for the data management of two research projects (AGENT and INCREASE) under the Horizon 2020 funding program, providing the background organization of complex datasets used to address future challenges in European agriculture.

AI-Based Environmental Monitoring System with Farm Automation

This project presents an innovative approach to environmental monitoring in agricultural settings using AI-based solutions integrated with IoT technology. The system leverages data collected from various sensors such as soil moisture, gas sensors, and other environmental indicators to monitor the conditions in real-time. By utilizing an Arduino microcontroller, ESP module, and ThingSpeak server, the project is able to capture, store, and transmit sensor data effectively. The highlight of the project is the AI-based weather prediction module implemented using a Convolutional Neural Network (CNN) in Python, providing critical insights into weather conditions and allowing for data-driven decision-making. The system also includes a user-friendly web interface to display real-time monitoring and predictions, enabling farmers to better manage their resources and plan ahead for changes in environmental conditions

A mobile electrical stimulator for therapeutic modulation of the vestibular system — design, safety, and functionality

Low-intensity noisy galvanic vestibular stimulation (nGVS) is a promising non-invasive treatment for enhancing vestibular perceptual performance and postural control in patients with chronic vestibular hypofunction. However, this approach has so far been studied mainly under laboratory conditions. Evidence indicates that continuous application of nGVS in daily life is necessary for it to be effective. To address this need, we have developed a mobile nGVS stimulator and conducted a series of pilot studies to evaluate its safety, tolerability, functionality, and therapeutic effects. The device is a lightweight, compact, and portable AC stimulator featuring a user-friendly interface for the individualized adjustment of nGVS parameters. It includes an integrated motion sensor that automatically activates stimulation during body movement and deactivates it during inactivity, optimizing its practical use in real-world settings. The stimulator adheres to strict safety standards and, in initial long-term use, has exhibited only mild side effects (e.g., skin irritation and headaches), likely attributable to the current electrode placement, which requires further optimization. As expected, the device consistently elicits known vestibular sensorimotor reflex responses in healthy individuals. Importantly, further pilot studies in healthy participants demonstrate that the device can reliably replicate known facilitating effects on vestibular perception and postural control. Together, these findings suggest that this mobile stimulation device can facilitate the translation of nGVS into therapeutic everyday use.

Abstract A029: Novel high-purity CTC isolation technology for downstream analysis: A “Cell- Circuit” magnetophoretic-microfluidic hybrid device

Abstract Circulating Tumor Cells are the only blood analytes that contain comprehensive multi-omic information regarding living tumor cells. As such, CTCs are an essential tool for the precise diagnosis of cancer. This has led to increased interest in both enumeration, and more significantly, the downstream analysis of bulk and single CTCs. One primary technical limitation that limits adoption of CTCs into routine clinical practice is the ability to effectively isolate CTCs from non-target cells. While several technologies have been developed and commercialized including initial clinical adoption, most technologies have fundamental limitations in sample purity which limits the quality of downstream analysis. Another limitation is the number of steps needed to complete enrichment, especially in high purity isolation where 2 techniques are usually required to be performed in sequence. The authors propose a novel, single-cell isolation technology called L:Cell™. This technology utilizes precise micro-magnetic patterns to allow immuno-magnetically labeled cells to be isolated individually and directed to user-defined locations. Such “Cell-Circuits” enable parallel, computer-circuit-like manipulation of a large array of single cells – combining both precision and throughput. Passive and active single-cell control is made possible by circuit components such as gates, single-cell capacitors, and transistors. The technology also enables the classification of cells based on size in addition to the presence of magnetic beads. A "passive component" performs its function under an external rotating magnetic field, manipulating cells without the need for an electrical current. The majority of circuitry on-chip are such passive components, which enable the device's key advantage of parallelized control. In contrast, an "active component" is designed in conjunction with passive components to create a local magnetic field at specific locations. This allows cells to be rerouted or manipulated on-demand. Using active components, users can selectively isolate individual cells (e.g., based on size, morphology, or fluorescence). While micro-magnetophoretic technology has been explored by various research teams, the authors have advanced this technology further by micro-magnetic simulation and integrating microfluidic technology. This integration improves the inlet and outlet structures for cell samples and the alignment of particles’ flow, thereby maximizing the commercialization potential of this sophisticated technology. In lab-scale modeling, the authors demonstrated a high separation purity level of 92%. By allowing manipulation at single-cell level using magnetic fields only, the technology minimizes any stress on the cells during isolation processes. The authors developed a user- friendly interface utilizing microfluidic channels for the extraction of CTCs from whole blood using this hybrid device. Additionally, the authors are conducting further adaptation studies to apply this technology to various downstream techniques, such as genomic analysis and protein profiling. Citation Format: Chanhee Lee, Hun Heo, Youngwoo Park, Donghee Ko. Novel high-purity CTC isolation technology for downstream analysis: A “Cell- Circuit” magnetophoretic-microfluidic hybrid device [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A029.

Introducing GWAStic: A user-friendly, cross-platform solution for genome-wide association studies and genomic prediction

Abstract Motivation Advances in genomics have created an insistent need for accessible tools that simplify complex genetic data analysis, enabling researchers across fields to harness the power of genome-wide association studies and genomic prediction. GWAStic was developed to bridge this gap, providing an intuitive platform that combines artificial intelligence with traditional statistical methods, making sophisticated genomic analysis accessible without requiring deep expertise in statistical software. Results We present GWAStic, an intuitive, cross-platform desktop application designed to streamline genome-wide association studies and genomic prediction for biological and medical researchers. With a user-friendly graphical interface, GWAStic integrates machine learning and traditional statistical approaches to support genetic analysis. The application accepts inputs from standard text-based Variant Call Formats and PLINK binary files, generating clear graphical outputs, including Manhattan plots, quantile-quantile plots, and genomic prediction correlation plots, to enhance data visualization and analysis. Availability and implementation Project page: https://github.com/snowformatics/gwastic_desktop GWAStic documentation: https://snowformatics.gitbook.io/product-docs PyPI: https://pypi.org/project/gwastic-desktop/

TranscriptDB: a transcript-centric database to study eukaryotic transcript conservation and evolution.

Eukaryotic genes can encode multiple distinct transcripts through the alternative splicing (AS) of genes. Interest in the AS mechanism and its evolution across different species has stimulated numerous studies, leading to several databases that provide information on AS and transcriptome data across multiple eukaryotic species. However, existing resources do not offer information on transcript conservation and evolution between genes of multiple species. Similarly to genes, identifying conserved transcripts-those from homologous genes that have retained a similar exon composition-is useful for determining transcript homology relationships, studying transcript functions and reconstructing transcript phylogenies. To address this gap, we have developed TranscriptDB, a database dedicated to studying the conservation and evolution of transcripts within gene families. TranscriptDB offers an extensive catalog of conserved transcripts and phylogenies for 317 annotated eukaryotic species, sourced from Ensembl database version 111. It serves multiple purposes, including the exploration of gene and transcript evolution. Users can access TranscriptDB through various browsing and querying tools, including a user-friendly web interface. The incorporated web servers enable users to retrieve information on transcript evolution using their own data as input. Additionally, a REST application programming interface is available for programmatic data retrieval. A data directory is also available for bulk downloads. TranscriptDB and its resources are freely accessible at https://transcriptdb.cobius.usherbrooke.ca.

Implementation of Wastewater Monitoring System using Low-Cost Water Sensor and IoT-based SCADA

Wastewater monitoring is crucial for ensuring the wastewater quality is in the allowable range. This paper presents the implementation of a wastewater monitoring system using low-cost water sensors and Internet of Things (IoT)-based Supervisory Control and Data Acquisition (SCADA). The low-cost water sensor comprises the pH, temperature, turbidity, and Dissolved Oxygen (DO) sensors. The sensors are connected to the ESP32 microcontroller that is equipped with the WiFi module for communicating with the master unit, namely the Haiwell CBOX. Since the Haiwell CBOX is an IoT-based device, it is connected to the IoT cloud server. Thus, it can be accessed remotely through the Internet. The experimental results show that the user-friendly graphical interface is easy to develop and provides the user with useful and helpful information for monitoring and analyzing wastewater parameters. Further, the communication time interval of four seconds between the ESP32 and CBOX achieves a stable connection.

CRISPRepi: a multi-omic atlas for CRISPR-based epigenome editing.

CRISPR-based epigenome editing integrates the precision of CRISPR with the capability of epigenetic mark rewriting, offering a tunable and reversible gene regulation strategy without altering the DNA sequences. Various epigenome editing systems have been developed and applied in different organisms and cell types; however, the detailed information is discrete, making it challenging to evaluate the precision of different editing systems and design the optimal sgRNAs for further functional studies. Herein, we developed CRISPRepi (http://crisprepi.maolab.org/ or http://crisprepi.lilab-pkuhsc.org/), a pioneering platform that consolidates extensive sequencing data from 671 meticulously curated RNA-seq, ChIP-seq, Bisulfite-seq and ATAC-seq datasets in 87 cell types manipulated by 74 epigenome editing systems. In total, we have curated 5962 sgRNAs associated with 283 target genes from 2277 samples across six species. CRISPRepi incorporates tools for analyzing editing outcomes and assessing off-target effects by analyzing gene expression changes pre- and post-editing, along with the details of multi-omic epigenetic landscapes. Moreover, CRISPRepi supports the investigation of editing potentials for newly designed sgRNA sequences in a cell/tissue-specific context. By providing a user-friendly interface for searching and selecting optimal editing designs across multiple organisms, CRISPRepi serves as an integrated resource for researchers to evaluate editing efficiency and off-target effects among diverse CRISPR-based epigenome editing systems.

Closha 2.0: a bio-workflow design system for massive genome data analysis on high performance cluster infrastructure.

The explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and significant computational challenges. As the cost of next-generation sequencing (NGS) has decreased, the amount of genomic data has surged globally. However, the cost and complexity of the computational resources required continue to be substantial barriers to leveraging big data. A promising solution to these computational challenges is cloud computing, which provides researchers with the necessary CPUs, memory, storage, and software tools. Here, we present Closha 2.0, a cloud computing service that offers a user-friendly platform for analyzing massive genomic datasets. Closha 2.0 is designed to provide a cloud-based environment that enables all genomic researchers, including those with limited or no programming experience, to easily analyze their genomic data. The new 2.0 version of Closha has more user-friendly features than the previous 1.0 version. Firstly, the workbench features a script editor that supports Python, R, and shell script programming, enabling users to write scripts and integrate them into their pipelines. This functionality is particularly useful for downstream analysis. Second, Closha 2.0 runs on containers, which execute each tool in an independent environment. This provides a stable environment and prevents dependency issues and version conflicts among tools. Additionally, users can execute each step of a pipeline individually, allowing them to test applications at each stage and adjust parameters to achieve the desired results. We also updated a high-speed data transmission tool called GBox that facilitates the rapid transfer of large datasets. The analysis pipelines on Closha 2.0 are reproducible, with all analysis parameters and inputs being permanently recorded. Closha 2.0 simplifies multi-step analysis with drag-and-drop functionality and provides a user-friendly interface for genomic scientists to obtain accurate results from NGS data. Closha 2.0 is freely available at https://www.kobic.re.kr/closha2 .

The Development of a 3D Magnetic Field Scanner Using Additive Technologies

Visualizing magnetic fields is essential for studying the operation of electromagnetic systems and devices that use permanent magnets or magnetic particles. However, commercial devices for this purpose are often expensive due to their complex designs, which may not always be necessary for specific research needs. This work presents a method for designing an automated laboratory setup for magnetic cartography, utilizing a 3D printer to produce structural plastic components for the scanner. The assembly process is thoroughly described, covering both the hardware and software aspects. Spatial resolution and mapping parameters, such as the number of data points and the collection time, were configured through software. Multiple tests were conducted on samples featuring flat inductive coils on a printed circuit board, providing a reliable model for comparing calculated and measured results. The scanner offers several advantages, including a straightforward design, readily available materials and components, a large scanning area (100 mm × 100 mm × 100 mm), a user-friendly interface, and adaptability for specific tasks. Additionally, the integration of a pre-built macro enables connection to any PC running Windows, while the open-source microcontroller code allows users to customize the scanner’s functionality to meet their specific requirements.

From frustration to fulfilment

Transaction analysis, the process of discerning which accounts are impacted (either increased or decreased) by a given transaction, is a fundamental accounting principle. While proficiency in this concept is vital for introductory accounting students, it poses a significant challenge for many. We addressed this hurdle using Excel and Excel VBA to develop a fully automated Journal Entry Learning Object (JELO) spreadsheet designed to aid first-year accounting students in grasping the intricacies of analyzing business transactions. Grounded in cognitive load theory (CLT), our spreadsheet based JELO provides a user-friendly interface that helps students develop their skills without becoming overwhelmed. Cognitive load theory, pioneered by John Sweller, suggests that learning is optimized when instructional materials reduce unnecessary cognitive load, allowing students to allocate their cognitive resources efficiently toward understanding and mastering the task at hand. CLT distinguishes between three types of cognitive load: intrinsic, extraneous, and germane (Sweller, Ayres, & Kalyuga, 2011). Intrinsic cognitive load refers to the inherent difficulty associated with a specific instructional topic (Sweller, J. 2010). In transaction analysis, the intrinsic load is naturally high due to the complexity of understanding the impact of transactions on various accounts. To manage this, JELO has three tiers of transaction questions. Students are allowed to progress to more complex transactions only after demonstrating proficiency with the lower levels. Extraneous cognitive load is generated by inefficient presentation of information to learners (Paas & Sweller, 2014). JELO minimizes extraneous load through its intuitive and interactive design. By providing clear instructions, a logical interface, and eliminating unnecessary information, JELO ensures that students can focus on the essential aspects of transaction analysis without being distracted by irrelevant information. Germane cognitive load refers to the mental effort required to create schemas and integrate new information into existing knowledge structures (Sweller et al., 2011). JELO enhances germane load by offering scaffolded and immediate feedback. This feedback is designed to reinforce correct responses and guide students through their errors with hints and explanations, facilitating deeper cognitive processing and better schema construction. Our goal was to make skill acquisition enjoyable and, more realistically, to reduce the pain of learning transaction analysis. The JELO offers an alternative to traditional “drill and practice” methods by providing an easy-to-use, interactive learning tool that dynamically adjusts the difficulty of its questions to match the student's skill level. This adaptive learning approach ensures that students are continually challenged just enough to promote learning without becoming overwhelmed. Additionally, JELO's feedback mechanism aligns with CLT principles by providing immediate positive reinforcement and corrective guidance. By doing so, it helps students build confidence and competence in transaction analysis, leading to a more effective and engaging learning experience. By incorporating principles of cognitive load theory, JELO ensures that students are neither under-challenged nor overwhelmed. This careful balancing of students’ overall cognitive load facilitates a more effective learning environment, fostering both proficiency and satisfaction in mastering transaction analysis.

Development of a Smart Lock System using QR Code Technology

A smart lock system refers to a modern security solution that enables users to remotely control access to their homes or businesses using a smart device such as smartphone, tablet, or computer. This paper presents the development of a smart lock system using Quick Response (QR) code technology to address the problem of insecurity, inconvenience, and inflexibility in access control. The proposed system eliminates the need for physical keys or complex authentication and verification methods. The system integrates an ESP8266 microcontroller, barcode scanner, 16x2 liquid-crystal display (LCD), solenoid lock, relay, and a 12V power supply. The system allows authorised users to generate unique QR codes representing their access credentials through a user-friendly interface. These QR codes are scanned by the system, which processes the codes, verifies access permissions via a central server and provides real-time feedback through the LCD. Upon successful verification, the system triggers a relay to control the solenoid lock, to grant or deny access. The implementation and testing of the smart lock system demonstrated successful operation. The performance indices include authentication speed, system reliability, accuracy and response time, which were evaluated and found to be satisfactory. The obtained results showed that the proposed system outperformed traditional methods such as keys, and fingerprint authentication, with an access speed of 2.00 seconds, which is significantly faster than the alternatives. Comparative analysis with existing access control techniques demonstrated the developed system's enhanced security features and user-friendly operation, making it applicable for residential, commercial, and industrial use. Additionally, the system's scalability and adaptability allow it to be customised for various environments, offering a versatile solution to diverse security challenges. This work contributes to the field of security technology by providing an efficient and scalable smart lock system with novel algorithms, which enhances the convenience, robustness of access control and seamless user experience.