Journal of Physics: Conference Series

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.

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.

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.

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.

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.

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 world of education today not only requires students to be experts in the cognitive realm, but is required to be able to achieve 21st century skills. Based on the analysis and synthesis of journals, the appropriate learning model to face the 21st century is the Project Based Learning (PjBL) learning model. The purpose of this article is to identify PjBL from the characteristics, effectiveness and implementation aspects of science learning. This structured review reviewed 20 articles on PjBL for science learning based on the available Scopus database reference from 2017 to 2021. The data obtained were analyzed using content analysis methods. The results showed that on average PjBL can be categorized as a learning model that can improve student learning outcomes in science learning and train students in problemving (critical thinking). The review reveals that PjBL has an influence on student learning, especially in science learning. From this article, it can be concluded and can be recommended three recommendations related to the essential success of PjBL in schools.

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.

Preface to the Second International Conference on Space Science & Technology (ICSST 2025)

With the vision of deepening international academic exchange and broadening global space cooperation, the Second International Conference on Space Science & Technology (ICSST 2025) was convened in two phases.

Phase I, the Suzhou Venue (22-24 May 2025), was held in Suzhou, Jiangsu Province, China. It was sponsored by Beijing Institute of Technology, organized by the journal Space: Science & Technology, Aerospace Information Research Institute (CAS), and Soochow University, co-organized by Beijing Minospace Technology Co., Ltd, Harbin Institute of Technology Satellite Technology Co., Ltd, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, and Tsinghua University / China-Russia International Joint Research Center for Aerospace Innovation Technologies.

List of Committee Member 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: 157

• Number of submissions sent for review: 157

• Number of submissions accepted: 105

• Acceptance Rate (Submissions Accepted / Submissions Received × 100): 66.9

• Average number of reviews per paper: 1

• Total number of reviewers involved: 48

• Contact person for queries:

Name: Shu Miao

Email: space@science-bitpjournal.org.cn

Affiliation: Space: Science & Technology Editorial Office

Spacecraft anomaly detection has become a crucial task in aerospace engineering. Since the anomalies are quite rare in telemetry data, unsupervised learning has been applied to the anomaly detection. Previous methods have difficulty in capturing temporal dependency of non-stationary series. Besides, the most widely used criterion for anomaly detection, which is based solely on reconstruction error, is too simplistic to distinguish anomalies accurately. To address these challenges, we propose a spacecraft anomaly detection method based on multi-component self-attention and association discrepancy evaluation. Firstly, we construct a decomposed anomaly detection model, which processes the three components of time series in parallel. According to the characteristics of the series components, self-attention mechanism is adaptively optimized. Secondly, the anomaly score is designed by combining the reconstructed sequence errors and the correlation differences of residual terms. The experimental results show that, compared with existing anomaly detection models, the proposed model improves both the recall rate and the F1 score, which means it has lower miss rate. Moreover, ablation experiments further verify the effectiveness of the improved self-attention calculation in enhancing the detection performance of the model.

There is seldom research considering propeller torque on the flight characteristics of powered parafoil. To address this insufficiency, this paper establishes a multi-body dynamics model of a powered parafoil and introduces a propeller torque model to investigate the mechanism of torque effect on flight characteristics. The analysis results show that the propeller torque has a significant effect on the lateral-directional motion of the powered parafoil, which is equivalent to handling the asymmetric brake lines with up to 10% deflection. The research also shows that propeller torque will cause an additional yaw motion derived from its mounting position and installation angle. Based on the above results, this paper further proposes several strategies to mitigate the influence of propeller torque by adjusting the propeller mounting position and installation angle.

Due to the extensive area, lightweight construction, and hinged multi-panel connection, spacecraft solar panels exhibit low stiffness and weak damping properties, resulting in low-frequency vibration and nonlinear dynamic behavior. This paper proposes an adjustable-stiffness magnetic (ASM) joint as both structural and functional component for the drive and control of solar panels. First, joint structure design and adjustable-stiffness principle are introduced. Subsequently, a rigid, flexible coupling dynamic model of solar panel structure with ASM joints is established, considering the weight and rotational inertia of the joints. The eigen equation of the system is derived to obtain the natural frequencies and corresponding global modes of the system through Rayleigh-Ritz method. The inherent characteristics of the rigid flexible coupling system are explored. The extracted global modes effectively capture the characteristics of both rigid-body motion modes and elastic vibration modes, providing an accurate representation of its dynamic behaviour. Meanwhile, all the first four modes exhibit an increase in frequency with the increase of joint stiffness. Finally, a ground-scale experimental platform of panels with ASM joints is constructed. A significant frequency-shift phenomenon with variable stiffness is observed under impact excitation. Additionally, the accuracy of GMM is validated by experiments and finite element simulation. These findings will pave the way for further exploring vibration suppression methods for multiple spacecraft solar panels under ultra-low frequency excitation.

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.

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.

LARCH, a package of analysis tools for XAFS and related spectroscopies is presented. A complete rewrite of the ifeffit package, the initial release of larch preserves the core XAFS analysis procedures such as normalization, background subtraction, Fourier transforms, fitting of XANES spectra, and fitting of experimental spectra to a sum of feff Paths, with few algorithmic changes made in comparison to IFEFFIT. LARCH is written using Python and its packages for scientific programming, which gives significant improvements over IFEFFIT in the ability to handle multi-dimensional and large data sets, write complex analysis scripts, visualize data, add new functionality, and customize existing capabilities. Like the earlier version, larch can run from an interactive command line or in batch-mode, but larch can also be run as a server and accessed from clients using standard inter-process communication techniques available in a variety of computer languages. larch is freely available under an open source license. Examples of using larch are shown, future directions for development are discussed, and collaborations for adding new capabilities are actively sought.

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 accuracy of dynamic light scattering (DLS) measurements are compared with transmission electron microscopy (TEM) studies for characterization of size distributions of ceramic nanoparticles. It was found that measurements by DLS using number distribution presented accurate results when compared to TEM. The presence of dispersants and the enlargement of size distributions induce errors to DLS particle sizing measurements and shifts its results to higher values.

Reflectance and transmittance of graphene in the optical region are analyzed as a function of frequency, temperature, and carrier density. We show that the optical graphene properties are determined by the direct interband electron transitions. The real part of the dynamic conductivity in doped graphene at low temperatures takes the universal constant value, whereas the imaginary part is logarithmically divergent at the threshold of interband transitions. The graphene transmittance in the visible range is independent of frequency and takes the universal value given by the fine structure constant.

The input power requirements for accessing H-mode at low density and maintaining it during the density ramp in ITER is addressed by statistical means applied to the international H-mode threshold power database. Following the recent addition of new data, the improvement of existing data and the improvement of selection criteria, a revised scaling law that describes the threshold power required to obtain an L-mode to H-mode transition is presented. Predictions for ITER give a threshold power of ∼52MW in a deuterium plasma at a line average density, n_e = 0.5×10²⁰m^-3. At the nominal ITER H-mode density, n_e = 1.0×10²⁰m^-3, the threshold power required is ∼86MW. Detailed analysis of data from individual devices suggests that the density dependence of the threshold power might increase with the plasma size and the magnetic field. On the other hand, the density at which the threshold power is minimal is found to decrease with the plasma size and increase with magnetic field. The influence of these effects on the accessibility of the H-mode regime in ITER plasmas is discussed. Analyses of the confinement database show that, in present day devices, H-modes are generally maintained with powers exceeding the threshold power by a factor larger than 1.5, and that, on the other hand, good confinement can be obtained close to the threshold power although rarely demonstrated.

We review the Mathematica package LiteRed, version 1.4.

Magnetic ordering in nanosized (100 and 1500 nm) nickel oxide NiO powders, prepared by the plasma synthesis method, was studied using Raman scattering spectroscopy in a wide range of temperatures from 10 to 300 K. It was observed that the intensity of two-magnon band decreases rapidly for smaller crystallites size. This effect is attributed to a decrease of antiferromagnetic spin correlations and leads to the antiferromagnetic-to-paramagnetic phase transition.