Control System for Indoor Safety Measures Using a Faster R-CNN Architecture

This paper presents a control system for indoor safety measures using a Faster R-CNN (Region-based Convolutional Neural Network) architecture. The proposed system aims to ensure the safety of occupants in indoor environments by detecting and recognizing potential safety hazards in real time, such as capacity control, social distancing, or mask use. Using deep learning techniques, the system detects these situations to be controlled, notifying the person in charge of the company if any of these are violated. The proposed system was tested in a real teaching environment at Rey Juan Carlos University, using Raspberry Pi 4 as a hardware platform together with an Intel Neural Stick board and a pair of PiCamera RGB (Red Green Blue) cameras to capture images of the environment and a Faster R-CNN architecture to detect and classify objects within the images. To evaluate the performance of the system, a dataset of indoor images was collected and annotated for object detection and classification. The system was trained using this dataset, and its performance was evaluated based on precision, recall, and F1 score. The results show that the proposed system achieved a high level of accuracy in detecting and classifying potential safety hazards in indoor environments. The proposed system includes an efficiently implemented software infrastructure to be launched on a low-cost hardware platform, which is affordable for any company, regardless of size or revenue, and it has the potential to be integrated into existing safety systems in indoor environments such as hospitals, warehouses, and factories, to provide real-time monitoring and alerts for safety hazards. Future work will focus on enhancing the system's robustness and scalability to larger indoor environments with more complex safety hazards.

IoT-based unified child monitoring and security system

In this fast-growing modern era, mothers are likely to work soon after childbirth, which makes it hard for them to render complete care to their child. Hiring a childminder is not just costly but also unsafe, especially during a global pandemic like Covid-19. Child abuse is also a major worry. This paper introduces an IoT-based Unified child monitoring and security system without any third-party involvement, thus addressing the parents' needs and concerns. The proposed system monitors the temperature and heart rate of theinfant, humidity of the room, detects motion and sound produced by the baby and adopts suitable measures to notify the parent such as sending alert messages, live video streaming of the infant or turning on a motor to swing the cradle. This system also monitors the movement of toddlers using GPS-and GSM-enabled wrist-bands and continuously sends their live locations. A buzzer is also interfaced with the band to alert if any stranger is in close proximity with the toddler. This system enables parents to keep a watch on their children remotely and thus ensures the safety of the child from any type of abuse. An added feature of this band is that it also prompts to maintain social distancing from the toddlers. Overall, a reliable, continuous and real-time baby monitoring is ensured by the proposed system. © 2023 Scrivener Publishing LLC. All rights reserved.

A Validation Study to Confirm the Accuracy of Wearable Devices Based on Health Data Analysis

This research article presents an analysis of health data collected from wearable devices, aiming to uncover the practical applications and implications of such analyses in personalized healthcare. The study explores insights derived from heart rate, sleep patterns, and specific workouts. The findings demonstrate potential applications in personalized health monitoring, fitness optimization, and sleep quality assessment. The analysis focused on the heart rate, sleep patterns, and specific workouts of the respondents. Results indicated that heart rate values during functional strength training fell within the target zone, with variations observed between different types of workouts. Sleep patterns were found to be individualized, with variations in sleep interruptions among respondents. The study also highlighted the impact of individual factors, such as demographics and manually defined information, on workout outcomes. The study acknowledges the challenges posed by the emerging nature of wearable devices and technological constraints. However, it emphasizes the significance of the research, highlighting variations in workout intensities based on heart rate data and the individualized nature of sleep patterns and disruptions. Perhaps the future cognitive healthcare platform may harness these insights to empower individuals in monitoring their health and receiving personalized recommendations for improved well-being. This research opens up new horizons in personalized healthcare, transforming how we approach health monitoring and management.

Automatic Sleep Staging Using BiRNN with Data Augmentation and Label Redirection

Sleep staging has always been a hot topic in the field of sleep medicine, and it is the cornerstone of research on sleep problems. At present, sleep staging heavily relies on manual interpretation, which is a time-consuming and laborious task with subjective interpretation factors. In this paper, we propose an automatic sleep stage classification model based on the Bidirectional Recurrent Neural Network (BiRNN) with data bundling augmentation and label redirection for accurate sleep staging. Through extensive analysis, we discovered that the incorrect classification labels are primarily concentrated in the transition and nonrapid eye movement stage I (N1). Therefore, our model utilizes a sliding window input to enhance data bundling and an attention mechanism to improve feature enhancement after label redirection. This approach focuses on mining latent features during the N1 and transition periods, which can further improve the network model's classification performance. We evaluated on multiple public datasets and achieved an overall accuracy rate of 87.3%, with the highest accuracy rate reaching 93.5%. Additionally, the network model's macro F1 score reached 82.5%. Finally, we used the optimal network model to study the impact of different EEG channels on the accuracy of each sleep stage.

COVID-19 Pandemic Learning: The Uprising of Remote Detailing in Pharmaceutical Sector Using Sales Force Automation and Its Sustainable Impact on Continuing Medical Education

The availability of resources is vital when rapid changes and updated medical information in the provision of care are needed, such as in the fight against COVID-19, which is not a conventional disease. Continuing medical education plays an essential role in preparing for and responding to such emergencies. Workflow has improved based on the virtual meetings, online trainings, and remote detailing conducted by medical representatives in order to deliver educational content instantly through digital tools, such as salesforce automation (SFA), webinars, etc. In terms of its regulatory barriers, the pharmaceutical industry mainly targets healthcare professionals, unlike most businesses that reach end users directly. Medical representatives are equipped with an SFA to enhance customer relationship management (CRM) and closed loop marketing (CLM) capabilities in pharmaceutical companies. This study aimed to fill a gap in the literature by investigating the use of SFA in work patterns, such as health professionals' loyalty and involvement in their medical knowledge in Turkey, and how it allows for differentiating training from marketing. This study intended to compare the data on internists and medical products gathered from a well-known pharmaceutical company's SFA. The data covered the first three months of the year 2020, when medical representatives had a normal daily routine, and that of 2021, when Turkey experienced the most powerful surge of the COVID-19 pandemic. The analysis was based on simple correspondence analysis (SCA) and multiple correspondence analysis (MCA) for 11 variables. Monitoring product, physician's segment, and medical representatives' behaviors with SFA had a significant influence on the pharma-physician relationship strategy, as expected. The findings supported the view that SFA technologies can be deployed to advance the medical knowledge of physicians, in addition to managing and designing superior CRM and CLM capabilities.

Learning Strategies for Sensitive Content Detection

Currently, the volume of sensitive content on the Internet, such as pornography and child pornography, and the amount of time that people spend online (especially children) have led to an increase in the distribution of such content (e.g., images of children being sexually abused, real-time videos of such abuse, grooming activities, etc.). It is therefore essential to have effective IT tools that automate the detection and blocking of this type of material, as manual filtering of huge volumes of data is practically impossible. The goal of this study is to carry out a comprehensive review of different learning strategies for the detection of sensitive content available in the literature, from the most conventional techniques to the most cutting-edge deep learning algorithms, highlighting the strengths and weaknesses of each, as well as the datasets used. The performance and scalability of the different strategies proposed in this work depend on the heterogeneity of the dataset, the feature extraction techniques (hashes, visual, audio, etc.) and the learning algorithms. Finally, new lines of research in sensitive-content detection are presented.

COVID-19 Vaccine and Booster Shots Compatibility Analyzer using CMOS-based Monochrome Encoding

The continuous increase in COVID-19 positive cases in the Philippines might further weaken the local healthcare system. As such, an efficient system must be implemented to further improve the immunization efforts of the country. In this paper, a contactless digital electronic device is proposed to assess the vaccine and booster brand compatibility. Using Logisim 2.7.1, the logic diagrams were designed and simulated with the help of truth tables and Boolean functions. Moreover, the finalized logic circuit design was converted into its equivalent complementary metal-oxide semiconductor (CMOS) and stick diagrams to help contextualize how the integrated circuits will be designed. Results have shown that the proposed device was able to accept three inputs of the top three COVID-19 vaccine brands (Sinovac, AstraZeneca, and Pfizer) and assess the compatibility of heterologous vaccinations. With the successful results of the circuit, it can be concluded that this low-power device can be used to manufacture a physical prototype for use in booster vaccination sites. © 2022 IEEE.

An Approach of Skill-Based Education for Industry-Ready Electrical Engineers: Power Electronics Course as A Case Study

Recently, and particularly after the Covid19 pandemic period and during teaching different courses, it has been noticed that most of the undergraduate engineering students have rising the type of questions such as ''Why we are learning this particular course?'' and ''What are the main benefits and direct impacts of such course on our future carrier? Also as a direct impact of the new available job requirements, it becomes most importance to prepare future engineers to thrive in recent dynamic changing in employment landscape. Hence for students who want to compete and involved in promising working opportunities, it is important to bridging the gap between teaching courses and the industry requirements by focusing on the concept of ''Industry Ready Engineers Since most of recent jobs concentrate on specific required competencies, the author believes that it is important now to give more focusing on the skill-based learning methodology. This paper introduces an approach focusing on group categorization for the recent specific required skills of electrical engineers;then how to involve these skills in specific teaching courses. The main objectives of such approach is to intentionally improve such group skills (one by one) throughout the all program courses in order to introduce a final graduated engineer with great working readiness skills. The approach is validated and evaluated on teaching the power electronics course 1 as a case study. © 2023 IEEE.

Smart and functional textiles

Smart and Functional Textiles is an application-oriented book covering a wide range of areas from multifunctional nanofinished textiles, coated and laminated textiles, wearable e-textiles, textile-based sensors and actuators, thermoregulating textiles, to smart medical textiles and stimuli-responsive textiles. It also includes chapters on 3D printed smart textiles, automotive smart textiles, smart textiles in military and defense, as well as functional textiles used in care and diagnosis of Covid-19. • Overview of smart textiles and their multidirectional applications • Materials, processes, advanced techniques, design and performance of smart fabrics • Fundamentals, advancements, current challenges and future perspectives of smart textiles. © 2023 Walter de Gruyter GmbH, Berlin/Boston.

Unsupervised Numerical Information Extraction via Exploiting Syntactic Structures

Numerical information plays an important role in various fields such as scientific, financial, social, statistics, and news. Most prior studies adopt unsupervised methods by designing complex handcrafted pattern-matching rules to extract numerical information, which can be difficult to scale to the open domain. Other supervised methods require extra time, cost, and knowledge to design, understand, and annotate the training data. To address these limitations, we propose QuantityIE, a novel approach to extracting numerical information as structured representations by exploiting syntactic features of both constituency parsing (CP) and dependency parsing (DP). The extraction results may also serve as distant supervision for zero-shot model training. Our approach outperforms existing methods from two perspectives: (1) the rules are simple yet effective, and (2) the results are more self-contained. We further propose a numerical information retrieval approach based on QuantityIE to answer analytical queries. Experimental results on information extraction and retrieval demonstrate the effectiveness of QuantityIE in extracting numerical information with high fidelity.

Content and Sentiment Analysis of The New York Times Coronavirus (2019-nCOV) Articles with Natural Language Processing (NLP) and Leximancer

The purpose of this study was to prove the use of content and sentiment analysis to understand public discourse on Nytimes.com around the coronavirus (2019-nCOV) pandemic. We examined the pandemic discourses in the article contents, news, expert opinions, and statements of official institutions with natural language processing methods. We analyzed how the mainstream media (Nytimes.com) sets the community agenda. As a method, the textual data for the research were collected with the Orange3 software text-mining tool via the Nytimes.com API, and content analysis was conducted with Leximancer software. The research data were divided into three categories (first, mid, and last) based on the date ranges determined during the pandemic. Using Leximancer concept maps tools, we explained concepts and their relationships by visualizing them to show pandemic discourse. We used VADER sentiment analysis to analyze the pandemic discourse. The results gave us the distance and proximity positions of themes related to Nytimes.com pandemic discourse, revealed according to their conceptual definitions. Additionally, we compared the performance of six machine learning algorithms on the task of text classification. Considering the findings, it is possible to conclude that in Nytimes.com (2019-nCOV) discourse, some concepts have changed on a regular basis while others have remained constant. The pandemic discourse focused on specific concepts that were seen to guide human behavior and presented content that may cause anxiety to readers of Nytimes.com. The results of the sentiment analysis supported these findings. Another result was that the findings showed us that the contents of the coronavirus (2019-nCOV) articles supported official policies. It can be concluded that regarding the coronavirus (2019-nCOV), which has caused profound societal changes and has results such as death, restrictions, and mask use, the discourse did not go beyond a total of 15 main themes and about 100 concepts. The content analysis of Nytimes.com reveals that it has behavioral effects, such as causing fear and anxiety in people. Considering the media dependency of society, this result is important. It can be said that the agenda-setting of society does not go beyond the traditional discourse due to the tendency of individuals to use newspapers and news websites to obtain information.

Lessons Learned from Online Teaching and Their Implications for Students' Future Careers: Combined PLS-SEM and IPA Approach

As far as students are concerned, there is a well-founded relationship between academic performance and career management from which a special professional path can result, based on the multitude of knowledge, skills, and experiences acquired during the years of study. To this end, the presence and help of teachers participating in the learning process, the teaching activities they are involved in, and their own participation are determinant factors. This research aims to highlight the impact that the above factors have on the professional future of students. For this purpose, 395 respondents, including students in the bachelor's and master's cycles, were involved in the research process, to whom a questionnaire was given in electronic format during two stages: one where the didactic activity was carried out in online format and the other carried out face-to-face. Hypotheses testing was performed using partial least squares structural equation modeling. The present study focuses on two main directions regarding the results obtained. Thus, with respect to the acquisition of knowledge and the development of student skills, it emerged that the effect of the content in the didactic activities on student skills and the development of competencies is strengthened by the skills and degree of involvement of the teaching staff from the university environment. Related to the management of students' careers, the analysis showed that the effect of the content in didactic activities is complemented by the accumulation of knowledge and the formation of student skills. The rigorous economic training resulting from didactic activities constitutes a main pillar in the students' future, even more so depending on how much they perceive that the topics covered in the university courses are of interest to them. The results of this study can serve as theoretical support for future research that addresses the topic of student career management and the implications of university activities on knowledge and skills. In addition, the results can support decisions for the management of higher education institutions regarding the development and implementation of university programs and educational strategies with the aim of increasing the involvement of teachers and students in the teaching–learning process.

Learning Peer Recommendation Based on Weighted Heterogeneous Information Networks on Online Learning Platforms

With the development of online education, there is an urgent need to solve the problem of the low completion rate of online learning courses. Although learning peer recommendation can effectively address this problem, prior studies of learning peer-recommendation methods extract only a portion of the interaction information and fail to take into account the heterogeneity of the various types of objects (e.g., students, teachers, videos, exercises, and knowledge points). To better motivate students to complete online learning courses, we propose a novel method to recommend learning peers based on a weighted heterogeneous information network. First, we integrate the above different objects, various relationships between objects, and the attribute values to links in a weighted heterogeneous information network. Second, we propose a method for automatically generating all meaningful weighted meta-paths to extract and identify meaningful meta-paths. Finally, we use the Bayesian Personalized Ranking (BPR) optimization framework to discover the personalized weights of target students on different meaningful weighted meta-paths. We conducted experiments using three real datasets, and the experimental results demonstrate the effectiveness and interpretability of the proposed method.

Enhancing the student experience in pre‐ and mid‐COVID‐19 pandemic period by upgrading the power electronics laboratory practice curriculum

Laboratory practices, which represent a vital part of electrical engineering education, have especially in the last few years been subjected to numerous challenges. The paper presents a concept of upgrading the laboratory practice curriculum in power electronics by introducing computer simulations. Due to the recognized shortcomings of the previous approach, the curriculum was closely reviewed, compared to the concepts from existing literature, and intensively upgraded by the introduction of the Ansys Simplorer computer program. The intensity of the process upgrade was enhanced by the COVID‐19 pandemic and related lockdowns. The introduced curriculum changes enabled the students to approach individual topics more gradually, reducing the gaps between the behavior of ideal and real power electronics circuits. The results of student feedback, obtained by a web‐based survey and a pre‐exam quiz, demonstrate that students recognize the new approach as being more gradual and beneficial, enabling them to improve their understanding of specific phenomena and to master the topics of power electronics with ease and satisfaction.

Evaluation of Hand Washing Procedure Using Vision-Based Frame Level and Spatio-Temporal Level Data Models

Hand hygiene is obligatory for all healthcare workers and vital for patient care. During COVID-19, adequate hand washing was among recommended measures for preventing virus transmission. A general hand-washing procedure consisting several steps is recommended by World Health Organization for ensuring hand hygiene. This process can vary from person to person and human supervision for inspection would be impractical. In this study, we propose computer vision-based new methods using 12 different neural network models and 4 different data models (RGB, Point Cloud, Point Gesture Map, Projection) for the classification of 8 universally accepted hand-washing steps. These methods can also perform well under situations where the order of steps is not observed or the duration of steps are varied. Using a custom dataset, we achieved 100% accuracy with one of the models, and 94.23% average accuracy for all models. We also developed a real-time robust data acquisition technique where RGB and depth streams from Kinect 2.0 camera were utilized. Results showed that with the proposed methods and data models, efficient hand hygiene control is possible.

Approach to the Impact of Digital Technologies on Sustainability Reporting through Structural Equation Modeling and Artificial Neural Networks

The motivation for study derives from the requirements imposed by the European Union Corporate Sustainability Reporting Directive, which increases the sustainability reporting scope and the need for companies to use emerging digital technologies. The research aim is to evaluate the digital transformation impact of the European Union companies on sustainability reporting expressed through three sustainable performance indicators (economic, social, and ecological) based on a conceptual model. The data were collected from Eurostat for 2011–2021. The study proposes a framework for sustainable performance analysis through linear regression models and structural equations. Additionally, a hierarchy of digitization indicators is created by modeling structural equations, depending on their impact on sustainability performance indicators, which is validated using neural networks. The results indicate that the company's digital transformation indicators positively influence economic and social performance and lead to an improved environmental protection (a decrease in pollution), proving the established hypotheses' validity. The proposed model can be the basis for companies to create their dashboards for analyzing and monitoring sustainable performance. This research can be the basis of other studies, having a significant role in establishing economic and environmental strategies to stimulate an increase of companies that carry out sustainability reporting.

COVID-19 SOP Compliance and Monitoring Electronic System for Business and Public Places Using Arduino Uno

COVID-19 Corona virus disease is a rapidly spreading contagious disease that is causing a global public health crisis. In December 2019, the coronavirus was identified in Wuhan, China. COVID-19 is causing severe disease issues and many people are losing their lives daily. SARS-CoV-2 (Severe Acute Respiratory Syndrome coronavirus 2) is a severe infectious disease that is spreading very fast and is currently inflicting a healthcare crisis across the globe. The lethal coronavirus was founded in Wuhan, China in December 2019. The symptoms of this disease are fever, cough, fatigue, no taste or smell, stinging throat, headache, and difficulty in breathing. This deadly disease, COVID-19, is difficult to identify and spread. The vaccination process is still going on around the world. There are some existing strategies to minimize the spread of the COVID-19 virus by monitoring the temperature rise using sensors, wearing masks, and sanitizing their hands frequently. The proposed system comprises of an RFID reader, an IR sensor, a temperature sensor, a buzzer, a laptop or a personal computer with a web cam. A person on entry gets detected for their body temperature, wearing a face mask and then sanitizing their hands. If the temperature of the person is below 37.6 degrees, i.e., below the acceptance limit, then mask detection takes place by using MATLAB followed by spraying the sanitizer. Now the door will open automatically. Otherwise, the door will not open and the buzzer will sound. With these precautionary steps, people can survive this pandemic situation. © 2023 IEEE.

Digital Twins in the Marine Industry

The ocean holds abundant resources, but the utilization of those resources for the marine economy presents a complex and dynamic industrial situation. Exploring sustainable development in this industry is of practical value, as it involves the rational use of marine resources while protecting the environment. This study provides an innovative review of the current application status of Digital Twins Technology (DTT) in various sectors of the marine industry, including the ship-building industry (SBI), Offshore Oil and Gas Industry, marine fishery, and marine energy industry. The findings reveal that DTT offers robust support for full life cycle management (LCM) in SBI, including digital design, intelligent processing, operation, and error management. Furthermore, this work delves into the challenges and prospects of DTT application in the marine industry, aiming to provide reference and direction for intelligent systems in the industry and guide the rational development and utilization of marine resources in the future.

Blockchain-Based Trusted Federated Learning with Pre-Trained Models for COVID-19 Detection

COVID-19 is a serious epidemic that not only endangers human health, but also wreaks havoc on the development of society. Recently, there has been research on using artificial intelligence (AI) techniques for COVID-19 detection. As AI has entered the era of big models, deep learning methods based on pre-trained models (PTMs) have become a focus of industrial applications. Federated learning (FL) enables the union of geographically isolated data, which can address the demands of big data for PTMs. However, the incompleteness of the healthcare system and the untrusted distribution of medical data make FL participants unreliable, and medical data also has strong privacy protection requirements. Our research aims to improve training efficiency and global model accuracy using PTMs for training in FL, reducing computation and communication. Meanwhile, we provide a secure aggregation rule using differential privacy and fully homomorphic encryption to achieve a privacy-preserving Byzantine robust federal learning scheme. In addition, we use blockchain to record the training process and we integrate a Byzantine fault tolerance consensus to further improve robustness. Finally, we conduct experiments on a publicly available dataset, and the experimental results show that our scheme is effective with privacy-preserving and robustness. The final trained models achieve better performance on the positive prediction and severe prediction tasks, with an accuracy of 85.00% and 85.06%, respectively. Thus, this indicates that our study is able to provide reliable results for COVID-19 detection.

XXII International Conference and School on Quantum Electronics: "Laser Physics and Applications”

The International School on Quantum Electronics "Laser Physics and Applications” was held for the first time as far back as 1980. Since then it has taken place biennially and has become an important international event in the field of laser physics and laser applications attracting participants from many countries, especially from south-eastern Europe. Traditionally, its program includes lectures delivered by prominent scientists dealing with investigations of basic physical phenomena, processes of interaction of laser radiation with matter and latest scientific results obtained in the research areas of quantum electronics and optics, as well as the technological practical applications of new ideas, devices, instruments and laser systems. Special attention is paid to the active participation of students and young scientists who have the opportunity to present their results and meet and share experience with outstanding professionals in their particular fields of research.The topics include the following:• Laser-matter interactions• Laser spectroscopy and metrology• Laser remote sensing and ecology• Lasers in biology and medicine• Laser systems and nonlinear optics• Alternative techniques for material synthesis and processingThe 22nd edition of the ICSQE was held as a virtual forum due to the restrictions related to COVID-19 pandemic from September 19th to 23rd, 2022. The Institute of Electronics, Bulgarian Academy of Sciences, located in Sofia, Bulgaria, hosted the conference organization. The Big Blue Button on-line system was used as a technical platform for the meeting. The technical sessions of the International School on Quantum Electronics included 22 invited talks (30 min + 5 min Q&A), a Mini-Symposium "Extreme light infrastructure”, 11 oral contributions (30 min + 5 min Q&A) and in total 51 poster presentations divided into 5 sessions (1 hour each). The platform was available 24 hours, allowing discussions in addition to the technical program. The total number of participants was 90 from 16 countries.The XXII International Conference and School on Quantum Electronics: "Laser Physics and Applications” was held by the financial support from the Bulgarian National Science Fund under Project No. KP-06-MNF/4, 20.07.2022.List of Committees, International Advisory Committee, Program Committee, Local Organizing Committee, Lecturers, Oral Presentations, Poster Presentations are available in this pdf.