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.
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Dharel P. Acut et al 2021 J. Phys.: Conf. Ser. 1835 012013
S N A M Razali et al 2018 J. Phys.: Conf. Ser. 995 012042
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.
Jamal I. Daoud 2017 J. Phys.: Conf. Ser. 949 012009
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.
Avi Ben P. Andalecio et al 2020 J. Phys.: Conf. Ser. 1529 032067
Jeepney is one of most used public transportation in the Philippines wherein Filipino passengers used Jeepney as their primary mode of transportation in their everyday lives to get to their desired destination. However, due to its negative issues such as lack of maintenance system, ensuring the safety of the passengers and drivers, negative effects to the environment and improper loading and unloading of passengers, the government decided to implement the Jeepney Modernization program which old regular jeepneys were phased out and replaced with modernized jeepneys. This study centered on the implementation, challenges and repercussions of modernized jeepneys in the Philippines. This study aimed to determine different perceptions between three (3) stakeholders involved which were jeepneys operator/driver, passengers and modernized jeepney manufacturers included the government initiatives and agreement that is acceptable to all parties. The mixed method approach was also utilized in this study to gather data based from the views and opinions of the different stakeholders. The result showed that miscommunication and diffusion of wrong information was main cause of inconsistent perceptions, as well as the different demand of each stakeholders. The findings aimed to contribute to the upward mobility of implementation of modernized jeepneys and provided all factual data to the affected stakeholders in this study.
Rammanohar Das and Raghav Sandhane 2021 J. Phys.: Conf. Ser. 1964 042072
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.
M R Ab Hamid et al 2017 J. Phys.: Conf. Ser. 890 012163
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.
Xue Ying 2019 J. Phys.: Conf. Ser. 1168 022022
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.
M Sekarwinahyu et al 2019 J. Phys.: Conf. Ser. 1157 022099
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.
Azmi Alvian Gabriel et al 2021 J. Phys.: Conf. Ser. 1858 012028
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.
Jafar Alzubi et al 2018 J. Phys.: Conf. Ser. 1142 012012
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.
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2024 J. Phys.: Conf. Ser. 2747 011001
The 2023 International Conference on Applied Mathematics and Digital Simulation (AMDS 2023) was held successfully on 28-29 December 2023 in Beijing, China. The contributions are the results of submissions from the conference participants, who provide readers with a broader view of mathematical, statistical and computational science methods, ideas and tools.
There are 92 participants in AMDS 2023, in which the program includes 6 keynote reports (35-45 minutes), 48 oral presentations (15 mins+3 Q&A) included in four sessions (Mathematics and Computational Theory, Mathematical Modelling and Machine Learning Methods Statistics Modelling and Data Analysis, and Fluid Dynamics and Mechanics), 15 E-posts and 23 listeners. The point of the conference emphasizes the development of theories and applications and promotes interdisciplinary endeavors among mathematicians, statisticians, scientists, engineers and researchers from other related disciplines.
This volume comprises selected papers from the AMDS2023 conference. Accepted manuscripts were peer-reviewed by 2 to 4 reviewers in a double-blind review process. A total of 48 reviewers participated in this conference and provided their professional comments to authors to ensure the quality of the manuscripts. There are six topic partitions, including Mathematics and Computational Theory, Statistics Modelling and Data Analysis, Mathematical Modelling and Machine Learning Methods, Fluid Dynamics and Mechanics, Mathematical Modelling in Physics and Control and Automation, which constitutes an up-to-date account of principles, techniques, and tools for mathematical simulation and modelling in a wide range of research fields.
We would like to thank the authors of all papers and participants; the members of the Scientific Committee who assisted with the review of the papers submitted and presented at the conference; the four session chairs: Prof. Yichuan Zhao, Prof. Quanxin Zhu, Prof. P.M. Siva Raja, Prof. Rao Li; the six keynote speakers: Prof. Alexander G. Ramm, Prof. Ovidiu Radulescu, Prof. Hajime Urakawa, Prof. Yanjun Liu, Prof. Grienggrai Rajchakit and Prof. Oscar Eduardo Ruiz Salguero; the organzier and sponsors: the Hubei Mengshun Exhibition Service Center, the Research Center for Stochastic Configuration Machines (SCM), the National Laboratory for Scientific Computing (LNCC); the publisher: IOP Publishing Ltd agrees to publish the AMDS 2023 proceedings. We are grateful for your attendance and contribution to a successful AMDS 2023.
Organizing Cmmittee of AMDS 2023
Jiang Zhu (Advisory Chair)
National Laboratory for Scientific Computing-LNCC, Brazi
List of Conference Committees are available in this Pdf.
2024 J. Phys.: Conf. Ser. 2747 011002
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: 101
• Number of submissions sent for review: 91
• Number of submissions accepted: 76
• Acceptance Rate (Submissions Accepted / Submissions Received × 100): 75.2
• Average number of reviews per paper: 2.07
• Total number of reviewers involved: 48
• Contact person for queries:
Name: Elaine Jiang
Email: amds_2022@163.com
Affiliation: Research Center for Stochastic Configuration Machines (SCM), China
Na Fang et al 2024 J. Phys.: Conf. Ser. 2747 012001
This paper constructs a pseudo-random signal generator based on a dual memristor chaotic system. Firstly, the chaotic characteristics of the dual memristor chaotic system were explored through theoretical analysis and MATLAB numerical simulation, and a MULTISIM simulation circuit for the dual memristor chaotic system was constructed. Then, based on the threshold decision quantization method, a pseudorandom signal generator quantization circuit is formed by connecting an inverter amplifier and a hysteresis comparator on the output side of the analog circuit. Finally, the randomness of the pseudorandom sequence is discussed by NIST test and correlation analysis. The analysis results show that the pseudorandom sequence generated by the pseudo-random signal generator based on the dual memristor chaotic system exhibits good randomness.
Tong Yu et al 2024 J. Phys.: Conf. Ser. 2747 012002
Constraining a matrix by adding additional conditions to determine the matrix if given eigenpairs is called the eigenvalue inverse problem for matrices. The eigenvalue inverse problem of a matrix can be based on a given combination of different eigenvectors and real numbers to inverse a matrix method, and the method of inversion varies for different types of matrices. In this paper, we investigate the eigenvalue inverse problem for a class of X-type matrices characterized by linear relations, Invert the matrix based on its characteristics, determine eigenvalues and eigenvectors, and finally, prove that the solution of the problem exists and is unique, derive a series of expressions and recursive formulas, and we also check the algorithm's accuracy correctness by giving different instances.
Jing Gao et al 2024 J. Phys.: Conf. Ser. 2747 012003
The grid connection of a mass of distributed power sources in the AC/DC hybrid micro-grid will cause harmonics, voltage sags, voltage fluctuations, and flicker in the grid, which seriously affects the power quality of the grid. Therefore, the power quality problems such as the resonance generated by the AC/DC hybrid micro-grid need to be studied urgently. In this paper, a resonance detection technology based on the combination of wavelet transform (WT) and fast Fourier transform (FFT) is proposed. This method uses wavelet transform to process the high-frequency and low-frequency parts of the signal separately, and eliminates part of the interference signal. The method then uses the FFT to obtain the resonance content. The AC/DC hybrid micro-grid resonance detection method based on WT and FFT can not only determine the time and amplitude of the resonance signal, but also filter the interference signal and improve the accuracy of frequency domain analysis. It is verified by simulation that the algorithm can detect the resonance of the AC/DC hybrid micro-grid.