Editor(s)
Prof. Koji Nagata
Korea Advanced Institute of Science and Technology, Korea.

Short Biosketch

 

ISBN 978-93-48119-11-7 (Print)
ISBN 978-93-48119-15-5 (eBook)
DOI: https://doi.org/10.9734/bpi/srnta/v6

 

This book covers key areas of scientific research, new technologies and applications. The contributions by the authors include skin bleaching, skin care, blue-gray hyperpigmentation, sensory protein signal transduction, robotic vision, carbon optimized domain, ICOD technique, hydrophobic domain identification, functional protein data, explainable augmented intelligence, ultrasonic phased array data, crack characterization, AutoNDE, SPL heat conduction, Neumann boundary condition, age-related macular degeneration, optical coherence tomography, retinal imaging, subretinal drusenoid deposits, geographic atrophy, AI-based OCT tools, sustainable mobility, theory of planned behavior, Godin Shephard’s scale, FPGA integration, image steganography, least significant bit, discrete wavelet transform, information hiding systems, homography transformation, stacked hourglass networks, video augmentation, image generation, student performance, adaptive structuration theory, machine learning algorithms, exploratory data analysis, disparities in performance. This book contains various materials suitable for students, researchers, and academicians in the fields of scientific research, new technologies and applications.


Chapters


Knowledge, Concept, and Practices of Skin Bleaching in Northeastern Nigeria

Zara William Wudiri, Mohammed Tahiru Bolori, Fatima Lawan Bukar, Taofik Ademola Oloyede, Mary Olubisi Amodu, Aisha Aliyu Abulfathi, Usman Affan Haruna, Aisha Ali Bakari, Jesse Kwayas Isa’ac, Haruna Musa Garba, Babangida Ibrahim

Scientific Research, New Technologies and Applications Vol. 6, 31 October 2024, Page 1-29
https://doi.org/10.9734/bpi/srnta/v6/2416

Background: Skin bleaching and the use of hazardous and potent substances have been linked to a range of adverse effects, from skin diseases to serious systemic problems such as diabetes, hypertension and renal diseases. Skin bleaching has become a public health concern, even though the importation and marketing of skin-bleaching products are banned or strictly regulated in many countries. Skin bleaching seems to have become common in different communities across Nigeria. It appears that the local communities in the northeastern region of Nigeria are seriously unaware of the possible risks related to the usage of bleaching products. Aim: The purpose of the study was to ascertain adults’ knowledge, beliefs, and practices of skin bleaching in northeastern Nigeria.

Methods: It was a cross-sectional descriptive study that employed the multistage sampling procedure and the use of Statistical Product and Service Solutions (SPSS) to analyze the data.

Results: It was found that skin bleaching was quite common in northeastern Nigeria. Nearly half of the respondents were not aware of the long-term hazards or negative consequences, as well as the ingredients in the skin-bleaching agents. The respondents believed that the main reason why people bleach their skin was to appear more appealing to catch their partners’ attention as well as to improve their social statuses. They also believed that bleaching the skin improved their chances of getting suitors and jobs. The primary motivation behind skin bleaching was to enhance one’s physical attractiveness in order to capture the attention of their significant other. Additionally, they thought that bleaching one's skin would raise one’s social standing and bring luck in the form of potential mates or job opportunities. The majority said that while darker complexion could be less beautiful than lighter skin, but not necessarily inferior.

Conclusion: Despite the risks posed by ignorance and false ideas, the use of skin bleaching products was found to be widespread among the populations mostly because of a strong desire for attractive appearance, attraction, and high social status.

Government agencies, partners, and other relevant stakeholders along with the stakeholders among the local community members including religious and traditional leaders should take steps to protect the public from the menace of the skin-bleaching agents through legislation, education and all other possible innovative means. Companies should consider safety measures strictly in their productions.

Real-Time FPGA Integration for Image Steganography Using Haar wavelet and Least Significant Bit Technique

Mangal Patil, Shankar Madkar, Jyoti Morbale, Harshal Hemane

Scientific Research, New Technologies and Applications Vol. 6, 31 October 2024, Page 30-51
https://doi.org/10.9734/bpi/srnta/v6/2197

The purpose of this study is to develop a robust image steganography system that enhances the security of hidden communications by employing the Least Significant Bit (LSB) and Discrete Wavelet Transform (DWT) methods. The research aims to improve the robustness of the steganographic process and evaluate the effectiveness of these techniques. The study implements image steganography using two techniques: the Least Significant Bit (LSB) method and the Discrete Wavelet Transform (DWT) method. The performance of these algorithms is evaluated using key metrics such as Mean Squared Error (MSE), Bit Error Rate (BER), Peak Signal-to-Noise Ratio (PSNR), and processing time. The LSB method achieved PSNR values ranging from 42 to 46 dB and MSE values between 1.5 and 3.5. In contrast, the DWT method demonstrated superior performance, with PSNR values ranging from 49 to 57 dB and MSE values from 0.2 to 0.7. These results indicate that the DWT method provides higher performance and robustness compared to the LSB technique. The Discrete Wavelet Transform (DWT) method outperforms the Least Significant Bit (LSB) technique, offering better PSNR and lower MSE values. This makes DWT a more robust and efficient solution for image steganography, particularly in scenarios requiring high security and minimal image distortion.

The Role of Carbon in Sensory Protein Signal Transduction: Implications for Robotic Vision

Rajasekaran Ekambaram, Devprakash Rajasekaran, Indupriya Rajasekaran, Meenal Rajasekaran

Scientific Research, New Technologies and Applications Vol. 6, 31 October 2024, Page 52-64
https://doi.org/10.9734/bpi/srnta/v6/2504

This study applies the carbon code to predict the hydrophobic framework of retina-related proteins, focusing on carbon's role in proton absorption for signal transduction. Our results show that carbon frameworks in amino acid clusters within proteins are critical for forming optimized hydrophobic domains, influencing proton uptake into the cell core. In membrane-bound proteins involved in photon capture, specific amino acids regulate proton signaling and phototransduction. Mutations within these internal structures can significantly alter protein function, highlighting the importance of carbon-based frameworks and amino acid interactions in visual signal processing.

Active Paths to Sustainability: Unlocking Mobility in University Students

Foteini Mikiki, Ermioni Katartzi, Andreas Oikonomou

Scientific Research, New Technologies and Applications Vol. 6, 31 October 2024, Page 65-96
https://doi.org/10.9734/bpi/srnta/v6/2061

Aims: This study aimed at investigating behavioral aspects related to sustainability mobility practices in a specific target group, known for being physically active: the students of the Physical Education and Sport Science Department in Serres, Northern Greece. This work aimed to shed light on gender differences in weekly physical activity levels, as well as gender’s respective effect on mobility practices.

Study Design: It was a cross-sectional study, using a tested research instrument, grounded on the Theory of Planned Behaviour within a specific population group.

Place and Duration of Study: Data were collected during the first semester of the academic year before the outburst of the COVID-19 pandemic and the restrictive measures imposed by governments. University students filled in the questionnaires once during a typical academic week.

Methodology: 259 students from first year to nearing graduation from the Department of Physical Education and Sport Science in Serres, a medium-sized Greek city which is active in mobility issues, filled in a questionnaire, based on Ajzen’s theory of planned behavior and Godin–Shephard Leisure Time Physical Activity Questionnaire for reporting physical activity weekly uptake.

Results: The results confirmed higher levels of physical activity in male students, although their attitude towards physical activity was less positive than that of their female classmates. Further positive attitudes in women were recorded towards sustainable mobility choices, although the evidence demonstrated a similar gap between the answers of the two genders. Car possession was higher in men, whereas car purchase intention was slightly lower in women, who had a lower income in general. Moreover, income impacted gender mobility preferences.

Conclusion: University student mobility research calls for behavioral approaches to ground relevant interventions. Recommendations can be guided by students’ sports preferences and can be gender-sensitive, taking income into account. The findings could provide implications for policy and practice, informing strategies for promoting sustainable mobility among students.

This paper describes an image processing method that can facilitate skill learning in karate using recorded karate competition videos. The proposed method superimposes a partially filmed karate competition court in the input video image onto an overall model of a karate court via a homography transform. This method utilizes the Stacked Hourglass Network, a deep neural network proposed for estimating human poses, to estimate the corresponding points needed for the homography transform. To evaluate our method, a player-focused video was augmented with complete competition field information. The augmented video would be useful for observing both players’ actions as well as the player positioning within the entire competition court. The evaluation of the proposed method by a university karate club showed that it was useful for skill learning.

The paper explores using machine learning algorithms and big data analytics to enhance academic and psycho-socio performance among students. It suggests that South African higher education can benefit from appropriate technology and data application to address issues like lack of personalised support, varied student readiness, and resource constraints affecting performance. Employing adaptive structuration theory (AST), derived from Giddens' structuration theory, the study examines how institutions incorporate new technologies to improve student outcomes adaptively. Through a thorough literature review encompassing student performance factors, teaching methodologies, technology integration, and big data analytics (BDA), the research proposes establishing an IT infrastructure capable of integrating diverse student data types for machine learning analysis. This data-driven approach aims to personalise curricula, identify at-risk students, and enhance pedagogy to bolster learning outcomes. By bridging technological capabilities with practical implementation, the study offers a framework for local universities to make informed, data-driven decisions tailored to their challenges. It underscores the potential for data analytics to create supportive, personalised learning environments conducive to student success within the South African higher education context. The key findings show that it is possible to accurately predict student performance through machine learning algorithms even with different data sets. The analysis of the collected data showed that students' marital status and academic achievements in the previous years affected the study results. Applying big data analytics in South African higher education institutions can potentially enhance student support and resource distribution. However, there are issues of data heterogeneity and ethical issues.

Artificial Intelligence in Age-Related Macular Degeneration: Advances, Applications, and Future Directions

Virginia Mares, Marcio B. Nehemy, Hrvoje Bogunovic, Sophie Frank, Gregor S. Reiter, Ursula Schmidt-Erfurth

Scientific Research, New Technologies and Applications Vol. 6, 31 October 2024, Page 123-147
https://doi.org/10.9734/bpi/srnta/v6/1942

Age-related macular degeneration (AMD) is a leading cause of blindness worldwide and is expected to affect approximately 288 million people globally by 2040. While multimodal imaging has traditionally been the gold standard for diagnosing AMD, optical Coherence Tomography (OCT) provides high-resolution, non-invasive imaging of the retina and has become central to routine disease management. Artificial intelligence (AI) has rapidly emerged as a transformative force across various domains, with its impact particularly notable in ophthalmology and retina imaging, which has opened new avenues for improving diagnostic accuracy, predicting disease progression, and optimizing treatment plans. AI-based algorithms hold great potential for accurately quantifying biomarkers, such as fluid volume and geographic atrophy area in OCT images, predicting disease progression, and assisting in treatment decisions both in clinical practice and academic research. This chapter provides an overview of the current state of AI applications in AMD, highlighting its potential, the challenges encountered, and future prospects in the field.

Development of an Explainable Augmented Intelligence (AI) System for Crack Characterization Using Ultrasonic Phased Array Data

Larissa Fradkin, Sevda Uskuplu Altinbasak, Michel Darmon

Scientific Research, New Technologies and Applications Vol. 6, 31 October 2024, Page 148-176
https://doi.org/10.9734/bpi/srnta/v6/2680

Crack characterisation is one of the central tasks of NDT&E (Non-Destructive Testing and Evaluation) of industrial components and structures. A novel code containing a decision tree, that is, an explainable AI has been designed and developed for characterizing single large planar cracks. For the component surfaces whose undulation errors can be described using a normal distribution, a method for automatic estimation of the degree of the interpolating polynomial was developed. These days data necessary for carrying out this task are often collected using ultrasonic phased arrays. Many ultrasonic phased array inspections are automated but interpretation of the data they produce is not. This chapter offers an approach to designing an explainable AI (Augmented Intelligence) to meet this challenge. It describes a novel C++ code called AutoNDE, which contains a signal-processing module based on a modified total focusing method that creates a sequence of two-dimensional images of an evaluated specimen; an image-processing module, which filters and enhances these images; and an explainable AI module - a customized decision tree, which selects images of possible cracks, groups those of them that appear to represent the same crack and for each group, produces a possible inspection report for perusal by a human inspector. AutoNDE has been trained on 16 datasets collected in a laboratory by imaging steel specimens with large smooth planar notches, both embedded and surface-breaking, establishing values of various model parameters by trial and error It has been tested on two other similar datasets. The chapter presents results of this training and testing and describes in detail an approach to dealing with the main source of error in ultrasonic data - undulations in the specimens’ surfaces. AutoNDE locates a number of points on the inspected surfaces and effects a polynomial surface interpolation. Under the assumption that the error in the location of these points obeys a normal distribution, a novel method is presented for automatic estimation of the polynomial degree. Notwithstanding various challenges, AutoNDE shows great promise, demonstrating the feasibility of an explainable AI, suitable for applications in industrial NDE, increasing its accuracy and efficiency.

Solution of SPL Heat Conduction Model under Neumann Boundary Condition

T. N. Mishra, Atesh Singh

Scientific Research, New Technologies and Applications Vol. 6, 31 October 2024, Page 177-196
https://doi.org/10.9734/bpi/srnta/v6/2176

In this study, we will investigate the thermal wave propagation either by taking symmetric or asymmetric heating at the boundaries of finite thin film. Non-Gaussian heat sources are modelled as time-varying and spatially-decaying laser incidences. In this study, we present an unconditionally stable accurate finite difference scheme for Neumann (insulated) boundary condition which avoids the need of ghost point outside the boundary, unlike to the other finite difference schemes. The dimensionless form of model has been used for the complete analysis of model.