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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.

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.

The encryption mode of WinRAR3 which does not encrypt the file name uses encryption and compression, the password recovery complexity is high. The existing cracking systems crack on a single CPU or GPU platform. Because the decryption algorithm is slow on the CPU platform, while the decompression algorithm is slow on the GPU platform, the overall performance of the cracking algorithm is not high. This paper studies the mode of CPU and GPU collaborative computing, and proposes an efficient cracking method of encrypted WinRAR3 without encrypting filename. By using the CPU + GPU pipeline cooperation method, the waiting time in the calculation is reduced, and the performance of the algorithm is improved; by using the magic number matching method of compressed files, the decompression calculation can be effectively reduced. The experimental results show that the speed of the cracking algorithm proposed by this paper for 8-digit passwords is 24423/s, which is 2.3 times as fast as before.

This paper presents the design and simulation of a 30 kW DC to AC inverter. The inverter is designed based on the Zeverlution Pro 33K three-phase DC to AC inverter. Currently, there are 6 units of the inverters installed at 180 kW solar power plant located at Mukim Utan Aji, Perlis. So, in this paper, the results from the computer simulation will be compared to the site measurement conducted from this power plant. In this design, pulse with modulation (PWM) is used as the switching technique. Even though PWM offers the ease of LC filter design and low Total Harmonic Distortion (THD), the voltage amplitude of the sine wave output fails to achieve the required national grid parameters, i.e. 240 Vrms. To overcome this problem, a three-phase transformer has to be incorporated in the design to obtain the desired outputs. Results from computer simulation using SIMULINK show that the targeted AC parameters for all phases were achieved after comparing with the site measurement.

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.

The Ranking is one of the big issues in various information retrieval applications (IR). Various approaches to machine learning with various ranking applications have new dimensions in the field of IR. Most work focuses on the various strategies for enhancing the efficiency of the information retrieval system as a result of how related questions and documents also provide a ranking for successful retrieval. By using a machine learning approach, learning to rank is a frequently used ranking mechanism with the purpose of organizing the documents of different types in a specific order consistent with their ranking. An attempt has been made in this paper to position some of the most widely used algorithms in the community. It provides a survey of the methods used to rank the documents collected and their assessment strategies.

The importance of Super-capacitors (SCs) stems from their distinctive properties including long cycle life, high strength and environment friendly, they are sharing similar fundamental equations as the traditional capacitors; for attaining high capacitances SC using electrodes materials with thinner dielectrics and high specific surface area. In this review paper, all types of SCs were covered, depending on the energy storage mechanism; a brief overview of the materials and technologies used for SCs is presented. The major concentration is on materials like the metal oxides, carbon materials, conducting polymers along with their composites. The composites’ performance was examined via parameters like capacitance, energy, cyclic performance power and the rate capability also presents details regarding the electrolyte materials.

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 International Conference on Artificial Intelligence and Materials (ICAIM 2025), sponsored by China University of Petroleum and co-sponsored by Sustainable Solid Energy, took place in Qingdao, China, from April 18 to 20, 2025, uniting global experts at the forefront of AI-driven materials innovation. This premier event highlighted transformative advancements where artificial intelligence intersects with materials science, offering a dynamic platform for researchers to present peer-reviewed breakthroughs, debate emerging methodologies, and forge cross-disciplinary collaborations poised to redefine both fields.

The event featured an inaugural ceremony, technical sessions showcasing cutting-edge research, and a closing plenary synthesizing key insights, with keynote lectures anchoring each day’s agenda. Academician Tong-Yi Zhang from Shanghai University’s Materials Genome Institute opened the discourse with “Materials Science and Engineering in the AI Era: Paradigm Shifts and Challenges,” followed by Prof. Shiyu Du of China University of Petroleum elucidating “AI-Driven Autonomous Networks for Next-Generation Communications.” Prof. Bing-Yi Jing from Southern University of Science and Technology then explored “Distributed Computing Architectures for Large-Scale AI Modeling,” while Prof. Jian Sun of Beijing Institute of Technology concluded the keynote series with groundbreaking insights into “Ionic Liquid-Mediated Synthesis of Advanced Functional Materials.”

Beyond fostering intellectual exchange, the conference’s success was driven by three pillars: the committee’s meticulous coordination, rigorously implemented peer review processes, and the steadfast dedication of the guest editor, Prof. Huiqiu Deng from Hunan University—efforts that not only ensured seamless operations but also propelled ICAIM’s mission to accelerate AI-powered materials discovery.

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: Single Anonymous

? Conference submission management system: Morressier

? Number of submissions received: 46

? Number of submissions sent for review: 45

? Number of submissions accepted: 34

? Acceptance Rate (Submissions Accepted / Submissions Received × 100): 73.9

? Average number of reviews per paper: 1

? Total number of reviewers involved: 5

? Contact person for queries:

Name: Huiqiu Deng

Email: icaim2025@163.com

Affiliation: Hunan University

Remote Sensing Image Change Captioning (RSICC) is a burgeoning task that aims to articulate change scenarios in bi-temporal remote sensing images using natural languages. The existing methods effectively capture feature differences between bi-temporal remote sensing images and realistic language decoders for accurate interpretation. Notably, not all regions exhibit changes in bi-temporal images, and the presence/absence of changes inherently imposes distinct difficulty levels on the RSICC tasks. Although several existing approaches have discussed this issue, they frequently exhibit the problem of unstable classification outcomes and feature loss during spatiotemporal joint modeling. This paper optimizes the classifier and implements a siamese network and dual-temporal image features fusion module, correlating spatial structures across temporal sequences comprehensively. The proposed framework enables efficient and reliable detection of changed bi-temporal image pairs and generates precise textual descriptions of the identified alterations. The proposed method achieves superior performance on public datasets compared to state-of-the-art methods.

Currently, there has been limited research on causal discovery for Symmetric Positive Definite (SPD) manifold data, primarily due to constraints in existing models and learning algorithms. This paper introduces the Manifold Data Causal Model (MDCM), which features a novel causal generative mechanism designed to produce non-Euclidean SPD manifold data exhibiting causal relationships. The core concept of the MDCM is first to transform SPD manifold data into Euclidean space. We then apply a functional causal model to generate causal data and subsequently map the generated data back to the original manifold space. Within the MDCM framework, we propose a hybrid causal discovery approach based on partial distance correlation. We evaluate the practical effectiveness of the MDCM through trend prediction on a real-world power equipment dataset. The experimental results demonstrate the efficacy of the proposed MDCM approach.

The problem of area coverage in unknown environments is crucial in applications such as environmental monitoring, disaster response, and autonomous navigation. Traditional methods struggle to balance exploration, obstacle avoidance, and efficient coverage. In this paper, we propose a deep reinforcement learning-based multi-agent approach that optimizes coverage efficiency while adapting to unknown obstacles. We integrate a two-layer architecture, where the upper layer employs reinforcement learning for global path planning, while the lower layer handles local obstacle avoidance and movement execution. Experimental results demonstrate that our algorithm effectively converges in unknown environments and completes the area coverage task.