Journal of Physics: Conference Series

In the Philippines, youth unemployment continues to hinder meaningful economic development due to job-skills mismatch, lack of knowledge and skills training, and work experiences. To address the problem, Department of Education's (DepEd) K to12 Program added two (2) years of Senior High School which is an advantage for students to develop relevant skills as early as high school. One of the goals of the K to 12 Basic Education Program is to develop the competencies, work ethic, and values relevant to pursuing further education and/or joining the world of work among learners. This study involved twenty-five (25) Grade 12 STEM strand students who were immersed in a science and technology-based industries. The work immersion lasted for eighty (80) hours where students performed different tasks assigned to them by the work immersion partner institution supervisors. The students' performance appraisal and evaluation were evaluated by their respective supervisors. Results showed that most of the students were evaluated outstanding and that their performance exceeds the required standard. The positive learning gains suggest that work immersion enables the students to acquire and develop the skills of teamwork, communication, attendance and punctuality, productivity and resilience, initiative and proactivity, judgment and decision making, dependability and reliability, attitude, and professionalism. Moreover, the students perceived their experiences as an avenue to test themselves and apply what they have learned in a non-school scenario where they were not only able to apply their previous training but are also able to experience the social interactions in a work environment. Their experiences in science-based work immersion partner institutions developed many skills and values that would help them as they move from high school to real life.

Time management is very important and it may actually affect individual's overall performance and achievements. Students nowadays always commented that they do not have enough time to complete all the tasks assigned to them. In addition, a university environment's flexibility and freedom can derail students who have not mastered time management skills. Therefore, the aim of this study is to determine the relationship between the time management and academic achievement of the students. The factor analysis result showed three main factors associated with time management which can be classified as time planning, time attitudes and time wasting. The result also indicated that gender and races of students show no significant differences in time management behaviours. While year of study and faculty of students reveal the significant differences in the time management behaviours. Meanwhile, all the time management behaviours are significantly positively related to academic achievement of students although the relationship is weak. Time planning is the most significant correlated predictor.

Overfitting is a fundamental issue in supervised machine learning which prevents us from perfectly generalizing the models to well fit observed data on training data, as well as unseen data on testing set. Because of the presence of noise, the limited size of training set, and the complexity of classifiers, overfitting happens. This paper is going to talk about overfitting from the perspectives of causes and solutions. To reduce the effects of overfitting, various strategies are proposed to address to these causes: 1) "early-stopping" strategy is introduced to prevent overfitting by stopping training before the performance stops optimize; 2) "network-reduction" strategy is used to exclude the noises in training set; 3) "data-expansion" strategy is proposed for complicated models to fine-tune the hyper-parameters sets with a great amount of data; and 4) "regularization" strategy is proposed to guarantee models performance to a great extent while dealing with real world issues by feature-selection, and by distinguishing more useful and less useful features.

Assessment of discriminant validity is a must in any research that involves latent variables for the prevention of multicollinearity issues. Fornell and Larcker criterion is the most widely used method for this purpose. However, a new method has emerged for establishing the discriminant validity assessment through heterotrait-monotrait (HTMT) ratio of correlations method. Therefore, this article presents the results of discriminant validity assessment using these methods. Data from previous study was used that involved 429 respondents for empirical validation of value-based excellence model in higher education institutions (HEI) in Malaysia. From the analysis, the convergent, divergent and discriminant validity were established and admissible using Fornell and Larcker criterion. However, the discriminant validity is an issue when employing the HTMT criterion. This shows that the latent variables under study faced the issue of multicollinearity and should be looked into for further details. This also implied that the HTMT criterion is a stringent measure that could detect the possible indiscriminant among the latent variables. In conclusion, the instrument which consisted of six latent variables was still lacking in terms of discriminant validity and should be explored further.

Without substantial automation, individuals cannot manage the complexity of operations and the scale of information to be utilized to secure cyberspace. Nonetheless, technology and software with traditional fixed implementations are difficult to build (hardwired decision-making logic) in order to successfully safeguard against security threats. This condition can be dealt with using machine simplicity and learning methods in AI. This paper provides a concise overview of AI implementations of various cybersecurity using artificial technologies and evaluates the prospects for expanding the cybersecurity capabilities by enhancing the defence mechanism. We may infer that valuable applications already exist after the review of current artificial intelligence software on cybersecurity. First of all, they are used to protect the periphery and many other cybersecurity areas with neural networks. On the other hand, it was clear that certain cybersecurity problems would only be overcome efficiently if artificial intelligence approaches are deployed. In strategic decision making, for example, comprehensive information is important, and logical decision assistance is one of the still unanswered cybersecurity issues.

Plastics were commonly used as packaging materials for primary, secondary, and tertiary needs. However, the continuous use of plastic was inadequate for the environment. The research that was developing to address the use of conventional plastics is bioplastics. Bioplastics undergo faster degradation but had low mechanical strength and were hydrophilic. One of the main ingredients of bioplastics was starch. This study aimed to examine the effect of using starch-based materials on the quality parameters of bioplastic tensile strength and elongation quality. The tensile strength and elongation values of bioplastic from various treatments showed a relatively large range of results. Glycerol was the most widely used plasticizer because Glycerol has the best interaction ability compared to other plasticizers when combined with starches with different characters, either by adding various types of fillers or without adding fillers. The types of fillers that were commonly used are chitosan, clay, and ZnO. The use of plasticizers and fillers gives an opposite contribution to the bioplastic quality of tensile strength and Elongation.

The current SMAC (Social, Mobile, Analytic, Cloud) technology trend paves the way to a future in which intelligent machines, networked processes and big data are brought together. This virtual world has generated vast amount of data which is accelerating the adoption of machine learning solutions & practices. Machine Learning enables computers to imitate and adapt human-like behaviour. Using machine learning, each interaction, each action performed, becomes something the system can learn and use as experience for the next time. This work is an overview of this data analytics method which enables computers to learn and do what comes naturally to humans, i.e. learn from experience. It includes the preliminaries of machine learning, the definition, nomenclature and applications' describing it's what, how and why. The technology roadmap of machine learning is discussed to understand and verify its potential as a market & industry practice. The primary intent of this work is to give insight into why machine learning is the future.

The Sun is source of abundant energy. We are getting large amount of energy from the Sun out of which only a small portion is utilized. Sunlight reaching to Earth's surface has potential to fulfill all our ever increasing energy demands. Solar Photovoltaic technology deals with conversion of incident sunlight energy into electrical energy. Solar cells fabricated from Silicon aie the first generation solar cells. It was studied that more improvement is needed for large absorption of incident sunlight and increase in efficiency of solar cells. Thin film technology and amorphous Silicon solar cells were further developed to meet these conditions. In this review, we have studied a progressive advancement in Solar cell technology from first generation solar cells to Dye sensitized solar cells, Quantum dot solar cells and some recent technologies. This article also discuss about future trends of these different generation solar cell technologies and their scope to establish Solar cell technology.

The research aimed to produce natural wrapping paper from banana (Musa Paradisiaca Linn.) peel waste with additive essentials oils. The method used in this research was alkalization. The delignification process is done with the use of NaOH 4% at the temperature of 100°C for 1.5 hours. Additive materials in the form of essential oils are added as a preservative and aroma agent, namely cinnamon oil, lemon oil, clove oil and lime oil respectively 2% and 3%. Chemical and physical properties of the produced papers are tested included water content (dry-oven method SNI ISO 287:2010), pH (SNI ISO 6588-1.2010), grammage (SNI ISO 536:2010) and brightness (SNI ISO 2470:2010). Testing results of each paper were compared with commercial wrapping paper. The result shows that the natural paper from banana peel waste with additive essential oil meets the standard of ISO 6519:2016 about Basic Paper for Laminated Plastic Wrapping Paper within the parameter of pH and water content. The paper produced also meet the standard of ISO 8218:2015 about Food Paper and Cardboard within the grammage parameter (high-grade grammage), except the paper with 2% lemon oil. The paper which is closest to the characteristic of commercial wrapping paper is the paper with the additive of 2% cinnamon oil, with pH of 6.95, the water content of 7.14%, grammage of 347.6 gram/m² and the brightness level of 24.68%.

In regression analysis it is obvious to have a correlation between the response and predictor(s), but having correlation among predictors is something undesired. The number of predictors included in the regression model depends on many factors among which, historical data, experience, etc. At the end selection of most important predictors is something objective due to the researcher. Multicollinearity is a phenomena when two or more predictors are correlated, if this happens, the standard error of the coefficients will increase [8]. Increased standard errors means that the coefficients for some or all independent variables may be found to be significantly different from In other words, by overinflating the standard errors, multicollinearity makes some variables statistically insignificant when they should be significant. In this paper we focus on the multicollinearity, reasons and consequences on the reliability of the regression model.

On behalf of the organizing committee, we are delighted to present the proceedings of the Optics and Photonics in Africa Conference held on 6-10 November 2023 in White River, South Africa. This conference brought together researchers from around the globe to share their latest advancements, insights, and challenges in the field of Optics and Photonics.

The conference featured a diverse program, including plenary and invited speakers, technical sessions for contributed talks, and poster presentations. These proceedings compile the contributions of the authors whose work was selected through a rigorous peer-review process.

The papers in this volume cover a wide range of topics, including Applied Optics and Photonics, Biomedical optics, Fiber & Integrated Photonics, Green Photonics, Lasers & Light Sources, LIDAR and Photogrammetry, Light-matter Interaction, Nanophotonics, Nonlinear & Quantum Optics, Optical Instrumentation, Optical Metrology, Optical Materials, Photovoltaic Systems, Photonics for Aerospace and Spectroscopy.

Each paper provides a unique perspective and contribution to advancing knowledge and innovation in the respective fields. We hope these proceedings will serve as a valuable resource for the scientific community, fostering further collaboration and research.

We express our gratitude to the authors for their excellent contributions and to the reviewers for their time and expertise in ensuring the quality of the content. Our heartfelt thanks also go to the keynote speakers, session chairs, and sponsors, whose support and participation were essential to the success of this event.

Finally, we would like to thank all participants for their enthusiasm and engagement, which made the Optics and Photonics in Africa a memorable and fruitful experience. We look forward to seeing you at future editions of this conference.

Sincerely,

The Editorial Team

Dr Yaseera Ismail, Stellenbosch University, South Africa (Chair-OPA2023)

Dr Christine Steenkamp, Stellenbosch University, South Africa

Prof Malik Maaza, UNISA UNESCO Chair in Nanophotonics/iThemba Labs, South Africa

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing.

• Type of peer review: Double Anonymous

• Conference submission management system: Morressier

• Number of submissions received: 32

• Number of submissions sent for review: 30

• Number of submissions accepted: 22

• Acceptance Rate (Submissions Accepted / Submissions Received × 100): 68.8

• Average number of reviews per paper: 2

• Total number of reviewers involved: 27

• Contact person for queries:

Name: Yaseera Ismail

Email: yaseeraismail@sun.ac.za

Affiliation: Stelllenbosch University

The organic materials have attracted many attentions particularly in terms of their nonlinear optical (NLO) performance, due to their growing and prospective applications in possible photonic devices. In this study, we used the density functional theory to study the polarizability (<α>), the anisotropy of the polarizability (<Δα>), ground- state dipole moment (μ) and the first-order hyperpolarizability (β) of the lycopene, caffeine and capsaicin molecules. We used the B3LYP levels of the theory with 6-311G(d,p) basis set. Also, the electronic properties of molecules, such as the electron density map, the highest occupied orbital, and the lowest unoccupied orbital were calculated. The quantum chemical descriptor of molecules such as HOMO-LUMO energy gap (ΔE), electron affinity (A), ionization potential (I), global hardness (η), electronegativity (χ), chemical potential (π), and global electrophilicity index (ω) are investigated.

This manuscript explores the synthesis and characterization of tungsten trioxide (WO₃) nanoparticles and their nanocomposites with reduced graphene oxide (rGO) via an in situ hydrothermal method. Through systematic investigation of WO₃-rGO nanocomposites with varying weight ratios, denoted as WO₃-rGO3%, WO₃-rGO7%, and WO₃-rGO15. X-ray diffraction and Raman spectroscopy confirm the crystallinity of WO₃ and the successful integration of rGO. Transmission electron microscopy reveals well-dispersed WO₃ nanoparticles on rGO sheets, and diffuse reflectance spectroscopy indicates a slight reduction in the band gap energy for WO₃-rGO nanocomposites. Fluorescence emission spectra exhibit significant quenching, suggesting enhanced interfacial contact between WO₃ particles and rGO nanosheets. These findings underscore the potential of WO₃-rGO nanocomposites for diverse applications, positioning them as promising candidates in the burgeoning field of advanced composite nanomaterials.

In this study, the synthesis of yttrium oxide (Y₂O₃) nanoparticles was made using an eco-friendly methodology with a natural extract. The fruit of doum (Hyphaene thebaica) was used as both an effective chelating as well as a stabilizing agent for synthesizing Y₂O₃ nanoparticles (NPs). Once the NPs were obtained, diverse characterization techniques were used to determine the physicochemical and optical properties of the Y₂O₃ NPs; The Optical bandgap was calculated by employing the Kubelka-Munk function and was found to be ~ 5.65 eV, the Fourier Transform Infrared spectrum of the Y₂O₃ NPs shows the presence of the Y–O–Y and O–Y–O stretching vibrational modes in the synthesized Y₂O₃ nanoparticles bonds at 437 and 580 cm⁻¹, XRD results indicated that the Y₂O₃ NPs exhibit a body-centered cubic structure with average size approximately 30 nm, While TEM images revealed that the NPs had a quasi-spherical structure, the nanoparticles present a quasi-spherical morphology with sizes ranging from 5 nm to 15 nm, indicating an influence of the concentration of the H. thebaica extract in the synthesis process of Y₂O₃ NPs.