Training Purposes Research Articles

Games With Safety Training Purposes in the Industry: Game Design Method and Its Demonstration

Work accidents are a concern for the industry as they can generate human, material, and economic losses. One way to mitigate them is to conduct efficient safety training, but they cannot always arouse the necessary interest and engagement of those trained. Games with a purpose are tools widely used for training in different contexts. They are identified as innovative, immersive, and attractive approaches, but they are complex to develop. This article presents SafetyPlay Game Design (SpGD), a structured method for designing safety training games with well-defined steps that support translating training elements into an immersive and playful game environment. Since we seek to build an artifact as an intervention to a problem in a real context, we based the research methodology on Action Research to conduct the investigations. Therefore, the intervention in the context of security training continued with creating and demonstrating the SpGD method based on the construction of a digital game for training. In this way, the evaluation undertaken in this research considered two moments: i) the ability of SpGD to develop the game, considering when we evaluated it by volunteers regarding game experience and perception of safety training, and ii) the validated method through interviews with game designers, experts in designing training games. The results point to evidence that the SpGD method supported the design of a game that allowed a positive gaming experience considered by game designers to be viable and useful for this purpose, in addition to allowing people to learn training concepts and risks. Therefore, this research contributes to the game design field and the industry, providing opportunities for creating interactive training that can complement traditional security training.

ICEED: Integrated customized extraction of enzyme data.

Enzymes catalyze diverse biochemical reactions and are building blocks of cellular and metabolic pathways. Data and metadata of enzymes are distributed across databases and are archived in various formats. The enzyme databases provide utilities for efficient searches and downloading enzyme records in batch mode but do not support organism-specific extraction of subsets of data. Users are required to write scripts for parsing entries for customized data extraction prior to downstream analysis. Integrated Customized Extraction of Enzyme Data (iCEED) has been developed to provide organism-specific customized data extraction utilities for seven commonly used enzyme databases and brings these resources under an integrated portal. iCEED provides dropdown menus and search boxes using typehead utility for submission of queries as well as enzyme class-based browsing utility. A utility to facilitate mapping and visualization of functionally important features on the three-dimensional (3D) structures of enzymes is integrated. The customized data extraction utilities provided in iCEED are expected to be useful for biochemists, biotechnologists, computational biologists, and life science researchers to build curated datasets of their choice through an easy to navigate web-based interface. The integrated feature visualization system is useful for a fine-grained understanding of the enzyme structure-function relationship. Desired subsets of data, extracted and curated using iCEED can be subsequently used for downstream processing, analyses, and knowledge discovery. iCEED can also be used for training and teaching purposes.

An immersive virtual reality communication skills training for dietitians: A feasibility study

ObjectiveThe training of dietitians emphasizes the development of strong communication skills. Immersive virtual reality (IVR) has been successfully employed for various healthcare scenarios; however, it is yet understudied in dietetics education. Therefore, we conducted a feasibility study to investigate the usefulness of IVR for the purpose of communication skills training. MethodsWe designed a multi-user virtual hospital environment that enabled the training of professional conversations between dietitians and patients. Divided into groups of three, 30 dietetics students participated in a role-play task. Taking a qualitative approach to inquiry, we evaluated how participants assessed the benefits and limitations of the IVR training. ResultsParticipants appreciated the authenticity of the training environment and mentioned various advantages of IVR (e.g., sense of privacy, better focus on conversation) over traditional modes of instruction. On the other side, participants frequently mentioned that the lack of avatars' facial expressions might present an obstacle for effective communication skills training. ConclusionIVR enables authentic communication skills trainings for dietitians. Special consideration should be given to providing ample social cues during training. InnovationThe study demonstrates that the promising results from other healthcare professions regarding the usefulness of IVR training also apply to dietetics.

Reproducible mass spectrometry data processing and compound annotation in MZmine 3.

Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography-MS, gas chromatography-MS and MS-imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography-(IMS-)MS, gas chromatography-MS and (IMS-)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis.

The Utility of Three-Dimensional Printing in Physician-Modified Stent Grafts for Aortic Lesions Repair.

Background: Three-dimensional (3D) printing is becoming increasingly popular around the world not only in engineering but also in the medical industry. This trend is visible, especially in aortic modeling for both training and treatment purposes. As a result of advancements in 3D technology, patients can be offered personalized treatment of aortic lesions via physician-modified stent grafts (PMSG), which can be tailored to the specific vascular conditions of the patient. The objective of this systematic review was to investigate the utility of 3D printing in PMSG in aortic lesion repair by examining procedure time and complications. Methods: The systematic review has been performed using the PRISMA 2020 Checklist and PRISMA 2020 flow diagram and following the Cochrane Handbook. The systematic review has been registered in the International Prospective Register of Systematic Reviews: CRD42024526950. Results: Five studies with a total number of 172 patients were included in the final review. The mean operation time was 249.95± 70.03 min, and the mean modification time was 65.38 ± 10.59 min. The analysis of the results indicated I2 of 99% and 100% indicating high heterogeneity among studies. The bias assessment indicated the moderate quality of the included research. Conclusions: The noticeable variance in the reviewed studies' results marks the need for larger randomized trials as clinical results of 3D printing in PMSG have great potential for patients with aortic lesions in both elective and urgent procedures.

Pavement Crack Detection Based on the Improved Swin-Unet Model

Accurate pavement surface crack detection is crucial for analyzing pavement survey data and the development of maintenance strategies. On the basis of Swin-Unet, this study develops the improved Swin-Unet (iSwin-Unet) model with the developed skip attention module and the residual Swin Transformer block. Based on the channel attention mechanism, the pavement crack region can be better captured while the crack feature channels can be assigned more weights. Taking advantage of the developed residual Swin Transformer block, the encoder architecture can globally model the pavement crack feature. Meanwhile, the crack feature information can be efficiently exchanged. To verify the pavement crack detection performance of the proposed model, we compare the training performance and visualization results with the other three models, which are Swin-Unet, Swin Transformer, and Unet, respectively. Three public benchmarks (CFD, Crack500, and CrackSC) have been adopted for the purpose of training, validation, and testing. Based on the test results, it can be found that the developed iSwin-Unet achieves a significant increase in mF1 score, mPrecision, and mRecall compared to the existing models, thereby establishing its efficacy in pavement crack detection and underlining its significant advancements over current methodologies.

A Mobile-Based System for Detecting Plant Leaf Diseases Using Deep Convolutional Neural Network

Plant diseases pose a significant challenge to agriculture in India, causing substantial losses in crop production each year. Limited-resource farmers often struggle with diagnosing these diseases accurately, especially when relying on visual observation of leaf symptoms. Hence, there is an urgent need to enhance the detection, monitoring, and prediction of crop diseases to mitigate agricultural losses effectively. To address this challenge, we propose a mobile-based system empowered by Machine Learning (ML) and computer vision tailored for the Indian context. Our system utilizes Convolutional Neural Networks (CNNs) as the core deep learning engine to classify 37 disease categories commonly found in Indian crops. We have curated a comprehensive dataset comprising 96,206 images of plant leaves, encompassing both healthy and infected, for training, validation, and testing purposes. The user interface of our system is designed as an Android mobile application, enabling farmers to capture images of diseased plant leaves effortlessly. Upon image capture, the system swiftly identifies the disease category and presents it to the user along with a confidence percentage. This functionality empowers farmers to take timely action to protect their crops, thereby reducing reliance on incorrect fertilizers that may exacerbate plant stress. Furthermore, we evaluated the performance of our system using various metrics, including classification accuracy and processing time. Our results indicate an impressive overall classification accuracy of 94% across the 37 disease classes prevalent in 14 different crop species commonly cultivated in India. In essence, our ML-powered mobile-based solution offers Indian farmers a robust tool for efficient crop disease diagnosis and management, ultimately contributing to the enhancement of agricultural productivity and sustainability in the country.

MRI-only based material mass density and relative stopping power estimation via deep learning for proton therapy: a preliminary study

Magnetic Resonance Imaging (MRI) is increasingly being used in treatment planning due to its superior soft tissue contrast, which is useful for tumor and soft tissue delineation compared to computed tomography (CT). However, MRI cannot directly provide mass density or relative stopping power (RSP) maps, which are required for calculating proton radiotherapy doses. Therefore, the integration of artificial intelligence (AI) into MRI-based treatment planning to estimate mass density and RSP directly from MRI has generated significant interest. A deep learning (DL) based framework was developed to establish a voxel-wise correlation between MR images and mass density as well as RSP. To facilitate the study, five tissue substitute phantoms were created, representing different tissues such as skin, muscle, adipose tissue, 45% hydroxyapatite (HA), and spongiosa bone. The composition of these phantoms was based on information from ICRP reports. Additionally, two animal tissue phantoms, simulating pig brain and liver, were prepared for DL training purposes. The phantom study involved the development of two DL models. The first model utilized clinical T1 and T2 MRI scans as input, while the second model incorporated zero echo time (ZTE) MRI scans. In the patient application study, two more DL models were trained: one using T1 and T2 MRI scans as input, and another model incorporating synthetic dual-energy computed tomography (sDECT) images to provide accurate bone tissue information. The DECT empirical model was used as a reference to evaluate the proposed models in both phantom and patient application studies. The DECT empirical model was selected as the reference for evaluating the proposed models in both phantom and patient application studies. In the phantom study, the DL model based on T1, and T2 MRI scans demonstrated higher accuracy in estimating mass density and RSP for skin, muscle, adipose tissue, brain, and liver. The mean absolute percentage errors (MAPE) were 0.42%, 0.14%, 0.19%, 0.78%, and 0.26% for mass density, and 0.30%, 0.11%, 0.16%, 0.61%, and 0.23% for RSP, respectively. The DL model incorporating ZTE MRI further improved the accuracy of mass density and RSP estimation for 45% HA and spongiosa bone, with MAPE values of 0.23% and 0.09% for mass density, and 0.19% and 0.07% for RSP, respectively. These results demonstrate the feasibility of using an MRI-only approach combined with DL methods for mass density and RSP estimation in proton therapy treatment planning. By employing this approach, it is possible to obtain the necessary information for proton radiotherapy directly from MRI scans, eliminating the need for additional imaging modalities.

Towards blockchain based federated learning in categorizing healthcare monitoring devices on artificial intelligence of medical things investigative framework

Categorizing Artificial Intelligence of Medical Things (AIoMT) devices within the realm of standard Internet of Things (IoT) and Internet of Medical Things (IoMT) devices, particularly at the server and computational layers, poses a formidable challenge. In this paper, we present a novel methodology for categorizing AIoMT devices through the application of decentralized processing, referred to as "Federated Learning" (FL). Our approach involves deploying a system on standard IoT devices and labeled IoMT devices for training purposes and attribute extraction. Through this process, we extract and map the interconnected attributes from a global federated cum aggression server. The aim of this terminology is to extract interdependent devices via federated learning, ensuring data privacy and adherence to operational policies. Consequently, a global training dataset repository is coordinated to establish a centralized indexing and synchronization knowledge repository. The categorization process employs generic labels for devices transmitting medical data through regular communication channels. We evaluate our proposed methodology across a variety of IoT, IoMT, and AIoMT devices, demonstrating effective classification and labeling. Our technique yields a reliable categorization index for facilitating efficient access and optimization of medical devices within global servers.

Forecasting of energy efficiency in buildings using multilayer perceptron regressor with waterwheel plant algorithm hyperparameter

Energy consumption in buildings is gradually increasing and accounts for around forty percent of the total energy consumption. Forecasting the heating and cooling loads of a building during the initial phase of the design process in order to identify optimal solutions among various designs is of utmost importance. This is also true during the operation phase of the structure after it has been completed in order to ensure that energy efficiency is maintained. The aim of this paper is to create and develop a Multilayer Perceptron Regressor (MLPRegressor) model for the purpose of forecasting the heating and cooling loads of a building. The proposed model is based on automated hyperparameter optimization using Waterwheel Plant Algorithm The model was based on a dataset that described the energy performance of the structure. There are a number of important characteristics that are considered to be input variables. These include relative compactness, roof area, overall height, surface area, glazing area, wall area, glazing area distribution of a structure, and orientation. On the other hand, the variables that are considered to be output variables are the heating and cooling loads of the building. A total of 768 residential buildings were included in the dataset that was utilized for training purposes. Following the training and regression of the model, the most significant parameters that influence heating load and cooling load have been identified, and the WWPA-MLPRegressor performed well in terms of different metrices variables and fitted time.

Re-thinking housing inspection and regulation: using 360 technology as a new approach in environmental health practice training

Front-line professions such as environmental health require practitioners to demonstrate competence in practice-based skills typically gained only through on-the-job experience. However, practice opportunities have been more scarce, owing mainly to austerity but also to the impact of the COVID-19 pandemic, so necessitating novel approaches to teaching and learning. 360 technology has recently emerged as a possible solution, but its potential for use in an environmental health setting, such as housing inspection and regulation, is virtually unknown. This study involved the use of a 360-degree camera to record a simulated walkthrough and mock inspection of a two-storey house. These ‘photospheres’ were demonstrated to under- and postgraduate students, who were then surveyed online to explore their attitudes towards the use of this technology as a training method. Analysis of the questionnaire responses revealed four self-reported themes: experience of housing regulation and enforcement practice and training; experiences of 360 technology in housing inspection practice and enforcement; attitudes towards using 360 technology versus traditional photograph and filming methods for experiential learning, training and practice purposes; and attitudes towards the application of 360 technology for experiential learning and training purposes in housing inspection enforcement and regulation. The results indicated that students found this a helpful and flexible tool, particularly valuing its immersive nature, both for initial training and continuing professional development (CPD) in housing inspections and interventions. It has wider applicability to other frontline professional practices and requires further development to help shape new forms of training.

Touch to Learn: A Review of Haptic Technology's Impact on Skill Development and Enhancing Learning Abilities for Children

Early childhood education is critical in shaping children's intellectual and motor skills as it provides a solid foundation for cognitive, social, and emotional development, which highly depends on spatial thinking. Haptic feedback can be effectively used for educational and training purposes, particularly in fields such as physics, math, and arts, offering a more interactive learning media and supporting kinesthetic learners by its nature. Herein, different ways of implementing haptic feedback on different educational scenarios from the perspective of technological development and their impact on children's learned skills and outcomes (e.g., their motivation, their analytical or spatial thinking abilities, or fine motor skills) will be examined. This article provides an overview of how haptic feedback has been implemented in different learning scenarios for children. Particularly, it is indicated that haptics can potentially improve early childhood learning outcomes and spatial reasoning skills as it can increase children's interest, participation, performance in educational activities, and analytical ability. The major drawbacks of the current studies, such as variance in participants’ learning challenges and small sample numbers are also highlighted.

Intelligent crack identification method for high‐rise buildings aided by synthetic environments

SummaryCracks can develop in high‐rise buildings because of long‐term environmental changes and extreme loading events such as strong winds or earthquakes. Although deep learning‐based identification methods can efficiently identify cracks, the accuracy of crack identification in high‐rise buildings needs to be improved due to the lack of crack datasets specifically related to high‐rise structures. Moreover, the number of available images of cracks in high‐rise is limited. To this end, this paper establishes an intelligent crack identification method based on a photorealistic synthetic modeling technique. First, a computer graphics (CG) model of a high‐rise building with assumed damage is constructed. Subsequently, the CG model is utilized to generate a dataset that includes photorealistic images of the high‐rise building as well as corresponding labels for various components and types of damage. The generated dataset is then used to train a DeepLabv3 + neural network for structural component and damage identification, followed by validation by employing images of both synthetic and full‐scale high‐rise buildings. The trained network can accurately identify different components in images of the full‐scale, high‐rise building and identify cracks that are intentionally synthesized in those images. The results show that the synthetic dataset generated by the CG model not only allows for fast and efficient labeling for the purpose of neural network training but also outperforms methods that do not consider any application‐specific context in crack identification.

Schisming as an Interactional Resource for Learning at the Frontiers of Work and Vocational Training — A Secondary Publication

This paper examines the sorts of interactional competencies and institutional demands required from students as they engage in complex forms of participation combining work and training purposes. It focuses on a series of empirical cases, recorded through video data and analyzed from a conversation analytic perspective, in which mentors make the decision to intervene during work sessions moderated by students. Such interventions do not interrupt the student’s activity and lead to the emergence of two distinct but not impermeable interactional spaces. This complex participation framework, known as “schisming,” contributes to overcoming practical issues within multiparty settings. Our study shows how schisming constitutes a particular sequential phenomenon where participants reorganize the interaction and co-construct a social and cognitive interactional space, thus enabling a shared understanding of the specific training context. Empirical data from the practical training of medical radiographers are used to illustrate how schisming may contribute to learning in the conditions of guided practice.

의미연결망 분석을 통한 유보통합 관련 기사 분석: 윤석열 정부 출범 이후

This study analyzed news articles using semantic network analysis to identify the actual and direction of the Yoon administration's integration of the early childhood education and care policy. To do this, we collected news articles from national daily newspapers from May 10, 2022 to December 31, 2023, and identified semantic structures through semantic linkage network and agglomeration group analysis. The analysis categorized the articles on integration of the early childhood education and care during Yoon administration's into five subgroups: opinions from the frontline, the purpose of reservation integration, teacher training and qualifications, administrative unification, and financial and legal system unification. Comparing this to the UNESCO report's integration factors, we found that while there was strong news coverage of administrative, financial, and legal systems, and teacher training and qualifications, there was a lack of mention of curriculum and service types and accessibility. Based on these findings, we discussed the implications for the promotion of integration of the early childhood education and care and future directions.

CYBER SECURITY ATTACK DETECTION MODEL USING SEMI-SUPERVISED LEARNING

The increasing digitalization of our society has brought about numerous benefits, enabling seamless communication, convenient transactions, and efficient operations. However, with this growing reliance on interconnected systems and information technology, the risk of cyber-attacks has also surged. Cyber threats, such as data breaches, ransomware, and sophisticated malware, have become more prevalent, threatening the confidentiality, integrity, and availability of critical data and services. Organizations across industries face the daunting challenge of defending against a wide array of cyber-attacks that continue to evolve in complexity and stealth. In response to this ever-changing cyber threat landscape, Cyber Security Risk Management (CSRM) and attack detection have become critical components of any comprehensive cybersecurity strategy. The ability to identify and mitigate cyber risks and swiftly detect malicious activities is paramount for safeguarding sensitive information, preserving business continuity, and maintaining the trust of customers and stakeholders. A novel approach to Cyber Security Risk Management through an Attack Detection Model that utilizes Semi-Supervised Learning Auto-Encoders in conjunction with Probabilistic Bayesian Networks. The study compares the performance of Multi Connect Variational Auto-Encoder (MC-VAE), Probabilistic Bayesian Networks (PBN), and a combined model of MC-VAE and PBN. The study employs the NUSW-NB15_GT dataset for training and evaluation purposes. Notably, the Semi-Supervised Learning with Probabilistic Bayesian Networks (SSL-PBN) model demonstrates exceptional results, achieving a precision rate of 94% and a recall rate of 90%. The F1 score of 0.9191 highlights the SSL-PBN model's efficacy in achieving a balanced trade-off between precision and recall, critical for minimizing false positives and false negatives...

The importance of sambhasa (debate method) for increasing the self-confidence of the younger generation of hindu in Bali

Sambhasa is the art of debate and logic in India, equipped with a structured debating method. Hindu followers have three ways to resolve issues: first is Vada (discussion), second is Samvada (dialogue), and third is Tarka (Debate). Debate is a highly intellectual and refined skill aimed at elevating Hindus from various impure beliefs and thoughts that can be detrimental to Hindu Dharma. It serves the noble purpose of training the younger generation in analytical skills, using real facts to gain understanding of an issue, and comparing different perspectives on the matter. Debating becomes a fascinating discipline, inclusive of pramana, which involves seeking fundamental truths through four methods of observation: pratyaksa, anumana, upamana, and sabda, recognized by the Nyaya system as the four valid sources of knowledge. Debate is an engaging activity for the youth believed to boost confidence and shape positive character. However, a drawback lies in the dominance of participants who are confident and well-versed, overshadowing those who lack confidence and expertise in competitive debate.

Automated Facial Authentication Attendance System

Abstract: This research paper introduces a novel approach to automate attendance tracking in educational institutions through the implementation of a Face Recognition-based attendance system using Python. Traditionally, attendance management has relied on manual processes, prone to errors and time-consuming activities such as roll-call or name calling. The primary objective of this project is to revolutionize attendance management by developing an automated system that utilizes facial recognition technology. By leveraging modern advancements in computer vision, this system aims to streamline the attendancetaking process, enhancing efficiency and accuracy while reducing administrative burdens.Implemented within the classroom environment, the system captures student information including name, roll number,admission number, class, department, and photographs for training purposes. Utilizing OpenCV for image extraction and processing.The workflow involves initial face detection using a Haarcascade classifier, followed by facial recognition utilizing the LBPH (Local Binary Pattern Histogram) Algorithm. Upon recognition, the system cross-references the captured data with an established dataset to automatically mark attendance. Furthermore, to facilitate easy record-keeping, an Excel sheet is dynamically generated and updated at regular intervals with attendance information, ensuring seamless integration with existing administrative processes. This research provides a practical solution for attendance management and also helps in broader discourse on leveraging emerging technologies for optimizing educational and organizational workflows

Development and testing of immersive virtual reality environment for safe unmanned aerial vehicle usage in construction scenarios

Robotics and autonomous systems are increasingly being used on construction sites to complete tasks and complement human effort. One such autonomous system is unmanned aerial vehicles (UAV), although their wrong use could be potentially hazardous to humans and the environment. Given its relative novelty in construction, there remains a dearth of knowledge about safety risks associated with their use as well as knowledge on skills and protocols for their safe operation. With construction being one of the most dangerous and accident-prone sectors, there is even more impetus to develop UAV safety competence. In this study, we explore the application of immersive virtual reality (VR) use for purposes of safety training pertaining to the use of UAVs in construction scenarios. Whereas, previous studies, focussed on effectiveness of VR as a tool for training individuals on UAV operations and safety risks in general, this study evaluates its effectiveness across distinctive themes of safety in order to compare its effectiveness in different domains of UAV safety. The study adopts design science for development of bespoke UAV safety training tool in VR using Building Information Modelling (BIM) and game engine-driven virtual prototyping. The immersive training tool was then applied in an experiment of participants (n = 100) with (n = 50) constituting a control group using more traditional training methods and a series of pre/post-test assessments. The findings confirmed the relative superiority of VR training over traditional methods, improving performance and retention by up to 22 % on average. The immersive VR was found to be most effective on participants retention of knowledge in general flight preparation, air traffic documentation, visibility management and warning signals. Further usability tests were performed on all participants (n = 100) revealing general presence and positive attitudes towards VR based UAV safety training when compared to traditional training. From a practice perspective this research proofs the effectiveness of a VR approach as more cost effective and safe approach to UAV training for both academia and practice with added advantage of being more realistic simulation of the UAV risk scenarios.

Creating VR Content for Training Purposes

Creating VR Content for Training Purposes