Journal of Physics: Conference Series

This research article derives a nonlinear generalization of Maxwell's equations from a variational approach, when the action measures the variability of the metric tensor. The proper space is a Weyl space, where the covariant derivative of the metric tensor does not need to vanish. The Lorentz force law is derived from the same metrics as a geodesic equation. The charge density is shown to obey a covariant wave equation, which indicates that charge density is a field, which propagates at the speed of light. This viewpoint promotes the wave-picture of the electron. The results indicate that the Dirac equation is a geometric equation as well. As the electrodynamic force, i.e. the Lorentz force can be related directly to the metrical structure of spacetime, it directly leads to the explanation of the Zitterbewegung phenomenon and quantum mechanical waves as well.

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.

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.

This study aims to determine the quality of questions in multiple-choice test for School Final Exam of Physics subject at senior high school Pidie academic year 2018/2019, reviewed based on validity, reliability, level of difficulty, differentiating question level, and function of distracting questions. This non-experimental quantitative descriptive study involved year 12 students of there senior high school in Pidie academic year 2018/2019, namely senior high school Unggul Sigli, senior high school 1 Mila, and senior high school 1 Muara Tiga which were selected by stratified random sampling. The data source comes from the entire school final exam question sheet physics subjects for the 2018/2019 school year, answer keys and student answer sheets. The results of the package A and B final exam questions showed that not all of the questions were valid, the reliability of the questions was high, the differentiating question level was not the same, the difficulty level of the questions was different, and the effectiveness of the question was generally good and some of distracting questions need to be revised.

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 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.

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.

Reflective thinking skill is needed by prospective and in-service teachers. A study about developing problem based learning for online tutorial program using Gibbs' reflective cycle and e-portfolio was conducted to enhance reflective thinking skills of biology education students who participated in Plant Development. Development research used in this study was conducted through preliminary study, program development, trial of the program, program revision, and program implementation. This paper will discuss about the results of the program development and the trial of the program that were conducted in 2017. The Program development is conducted through program design development, instrument development, validation of program design and instrument by experts, and program development based on the revised program design. The trial of the program is conducted three times with different strategies to see which strategy is the most effective to be implemented. Based on the results of expert's validation, research results show that the design of programs and instruments can be used as references in the development of the program with some improvements. Based on the trial of the program, the results obtained that the program needs to be improved in terms of setting access between sub-initiation and between initiations.

The International Conference on Physics and Technology of Advanced Materials (ICPTAM) 2024, held in conjunction with the 8^th Nanoscience and Nanotechnology Symposium (The 8^th NNS), took place from 7-10 October 2024 in Bali, Indonesia. With the overarching theme of "Advanced materials for future technology", this event brought together leading scientists, engineers, and professionals to discuss cutting-edge advancements in materials science and nanotechnology in celebration of international year of quantum science and technology. The event features 2 keynote speakers, 6 plenary speakers, 14 Invited speakers, and 90 contributed oral presenters, which come from several countries: Japan, Malaysia, Indonesia, South Korea, United Kingdom, Thailand, Taiwan, China, and India.

This year's conference provided a unique platform for participants to exchange knowledge, foster collaboration, and showcase innovative research on topics ranging from functional advanced materials, nanomaterials and nanotechnology, computational materials and modelling, nanoelectronics and nanodevices, energy materials, environmental and green materials, biomaterials and biodevices, quantum computing. This event also highlighted the crucial intersection of physics and materials science in shaping the future of technology and society.

We would like to express our sincere appreciation to Prof. Ir. Wahyu Srigutomo, Dean of the Faculty of Mathematics and Natural Sciences (FMIPA), Bandung Institute of Technology (ITB), for his invaluable support and guidance in preparing this scientific event. We also extend our heartfelt gratitude to the Physical Society of Indonesia, MRS-id, and the Department of Electrical Engineering, Udayana University, for their generous support and collaboration, which played a pivotal role in ensuring the success of this conference and creating an engaging and stimulating environment.

List of Advisory Board, Organizing Committee and Co-Organizing Committee are available in this Pdf.

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: 44

• Number of submissions sent for review: 44

• Number of submissions accepted: 38

• Acceptance Rate (Submissions Accepted / Submissions Received × 100): 97.7

• Average number of reviews per paper: 2

• Total number of reviewers involved: 8

• Contact person for queries:

Name: Fitri Aulia Permatasari

Email: fauliap@itb.ac.id

Affiliation: Institut Teknologi Bandung

To substitute an expensive inorganic dye, we green synthesized silver nanoparticles (AgNP) from Henna leaf extract as photosensitizers in DSSC. Henna dye was macerated for 24 hours using ethanol and filtered. To synthesize AgNPs, 10 ml of Henna dye was added to 90 ml of AgNO₃ solution (0.01 M, 0.05 M, and 0.3 M) and stirred. The FTIR analysis of the Henna dye showed three main Lawsone functional groups (O-H phenol, C=O, and C=C). As for AgNP, the new peak with high intensity emerged at a frequency of 1381 cm⁻¹, indicating the existence of Ag. The FTIR results upheld by the diffractogram of the AgNP that showed the crystalline peak at the 2θ position of (29.5, 32.6, 38.0, and 44.1)° associated with Miller indices of [210],[122],[111], and [200], respectively. The evaporation process increases the crystallinity of the AgNP. The nanoparticle size increased from 124.8 nm to 189.1 nm with the AgNO₃ concentration from 0.01M to 0.1M characterized by PSA. From the Henna dye absorbance spectrum, the peak at (238 and 370) nm originated from Lawsone molecules. AgNP showed the absorbance peak at 420 nm and 430 nm for unevaporated and evaporated Henna, respectively. These peaks' intensity increased with the irradiation time of up to 72 hours, associated with the shift of the plasmon surface resonance to the lower energy. The energy gap ranges from (2.08-2.09) eV and (2.58-3.69) eV for Henna dye and AgNP, respectively. It is in the range for light harvester material in DSSC.

The catalyst plays a key role in the growth of carbon nanotubes (CNTs) functioning as a guiding medium in the growth process. In this research, a nickel (Ni) catalyst was on a 7101 glass substrate using the thermal evaporation method. First, the effect of the thickness of the layer on its morphology was investigated, with thicknesses of 30, 40, 50, and 60 nm examined. An optimum density was obtained at 50 nm, at which the Ni particles attained the highest average diameter of 4.23 nm, and polydispersity index lower than the other samples, at 5.87%. Energy-dispersive X-ray spectroscopy results also showed a positive linear relationship between the thickness of the layer and both its mass and purity. Samples at this optimum thickness were annealed at 300 °C, 400 °C, or 500 °C for 4 hours to refine the crystal structure of the catalyst and produce small, uniform features. The samples annealed at 500 °C had the greatest average particle diameter of 5.92 nm and the lowest polydispersity of 5.76%. With a Ni catalyst that has a low polydispersity and thus a high level of uniformity, CNTs with a good level of uniformity and quality can also be obtained.

This study investigates the fundamental performance of Fe₃O₄/Polyvinylidene Fluoride (PVDF) magnetic nanofiber membranes as a magnetic field sensor. The nanofiber membranes were fabricated using the electrospinning method. The success of nanofiber formation was confirmed by XRD and FTIR characterizations. The nanofiber had an average diameter of 544.7 nm. The results of the magnetic property analysis showed that the nanofiber was superparamagnetic with a saturation magnetization value of 6.1 emu/g. Furthermore, the sensor sensitivity was also evaluated with a value of 0.98 mV/mT. Interestingly, the sensor's sensitivity shows good and prospective performance, making it suitable for new magnetic sensor candidates in the future.