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.

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.

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.

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.

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.

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.

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.

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.

2023 The International Conference on Electronic, Eelectrical and Information Technology (EEIT 2023), the Online International Conference to be held on November 24-26, 2023. Aiming at providing an excellent international academic forum for all the researchers and practitioners, the conference enjoys a wide spread participation among all over the universities and research institutes.

It was provide opportunities for the delegates to exchange new ideas and application experiences, to establish business or research relations and to find global partners for future collaboration. Papers and topic concentrates on related fields of Electronic, Eelectrical and Information Technology are presented.

EEIT2023 proceeding tends to collect the most up-to-date, comprehensive, and worldwide state-of-art knowledge on Electronic, Eelectrical and Information Technology and other related topics. All the accepted papers have been submitted to strict peer-review by 2-3 international expert referees, and selected based on originality, significance and clarity for the purpose of the conference. The conference program is extremely rich, profound and featuring high-impact presentations of selected papers and additional late-breaking contributions. I sincerely hope that the conference would not only show the participants a broad overview of the latest research results on related fields, but also provide them with a significant platform for academic connection and exchange. The Technical Program Committee members have been working very hard to meet the deadline of review. The final conference received over 64 submissions, and 36 papers have been accepted.

I would like to express our sincere gratitude to all the keynote speakers, editors, TPC members and organizers for their hard work, precious time and endeavor preparing for the conference. Our deepest thanks also go to the volunteers and staffs for their long-hours work and generosity they've given to the conference. The last but not least, we would like to thank each and every of the authors, speakers and participants for their great contributions to the success of EEIT2023.

The Organizing Committee of EEIT2023

List of Committee is 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: Open Review

• Conference submission management system: Morressier

• Number of submissions received: 43

• Number of submissions sent for review: 38

• Number of submissions accepted: 36

• Acceptance Rate (Submissions Accepted / Submissions Received × 100): 83.7

• Average number of reviews per paper: 1.25

• Total number of reviewers involved: 52

• Contact person for queries:

Name: Josh Sheng

Email: eeitconf@163.com

Affiliation: Wuhan Zhuoxin Yuantou Information Consulting Co. LTD

This research paper aims to explore the development path of e-commerce logistics in the context of carbon neutrality. With the increasing concerns about environmental sustainability, the e-commerce industry has been pushed to find innovative solutions to reduce carbon emissions and promote sustainable practices. This study examines various strategies and approaches that can be adopted to achieve a greener and more efficient e-commerce logistics system. By analyzing the current trends, challenges, and opportunities, this paper provides insights and recommendations for policymakers, companies, and stakeholders in the e-commerce logistics sector. It requires comprehensive planning and decomposition of the entire process of e-commerce logistics. There are several key points to consider in achieving carbon neutrality for e-commerce platforms, such as green warehousing, green packaging, green transportation, and technological innovation, some top logistics companies achieved a cumulative carbon reduction. Adding a touch of "green" to the performance report of ecommerce promotions has become a proactive choice for an increasing number of platforms.

The paper presents a method for achieving constant illumination control by integrating various technologies, including Arduino technology, LED driver technology, and control technology. The HV9910B constant current drive chip is used to design a high-power LED driver in the form of an Arduino expansion board. The performance of the driver is tested, including efficiency testing and waveform testing. The PID control library of Arduino is utilized to write the control program rapidly. Illumination control experiments are conducted with expected results.

The introduction of the dead time of the inverter solves the straight-arm conduction problem of the upper and lower power devices of the inverter, but also leads to the deviation of the actual output voltage of the inverter from the given voltage. Dead time increases the harmonic components of phase voltage and phase current, which is also an important factor leading to motor torque ripple. In this paper, the mechanism of the deviation of phase voltage and phase current caused by the dead time of voltage space vector modulation is analyzed, and the effect of dead time on phase voltage and phase current harmonic components is studied. A simple and effective dead time compensation method based on phase voltage division and phase current polarity determination is proposed to directly compensate the dead time. This method can suppress the current harmonic and torque ripple in the running state of the motor, and can greatly simplify the calculation process of the traditional dead time compensation method. Through the system modeling and simulation, it is verified that the method can significantly improve the current waveform distortion, and has good practical value.