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.

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.

Endospores staining is the type of staining to recognize the presence spore in bacterial vegetative cells. The bacterial endospores need a staining which can penetrate wall thickness of spore bacteria. A method of endospores staining is Schaeffer Fulton method that used Malachite Green. It is an alkaline substance staining that can staining the spore bacteria. In this research, it have found the alternative staining that can replace Malachite Green solution in spore bacterial stain. The alternative staining used is Methylene Blue solution (0,5 %, 0,7%, and 1% concentration) with pH variation (10, 11, and 12), and varyous heating time (3, 4, and 5 minutes). The all treatments staining have been effect on bacterial spores staining results. The warming time greatly affect the dye to penetrate the walls of bacterial spores, this can be seen in the results with various concentration at pH 10, indicates that the not long warm-up time 3 and 4 minutes, bacterial spores are not stained, while in the longer heating time is 5 minutes bacterial spores stained. This is caused because the longer heating time can make the pores of spore wall is open so that can facilitate the dye to get into the bacterial spores.

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.

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.

The Physiological condition of cardiovascular system is analyzed by arterial blood pressure pulse wave. The arterial pulse wave displays the genetic traits of the heart, average records of a heartbeat and variation in pressure as the heart spouts blood. This pulse monitoringis a standard process used to assess the cardiovascular system’s medical history. A waveform ofthe Arterial blood pressure usually involves a systolic level, diastolic occurrence, and dicrotic spike and dicrotic notch. The cardiac cavity contracting and relaxing leads to systolic and diastolic blood pressure respectively. The dicrotic notch which is a drop on the down slope shows systole termination and depicts the aorta closure of successive backward stream. The position of the dicrotic notch throughout the cardiac activity differs as per the duration of aortic closure. Dicrotic notch plays an essential part in sclerosis, occlusion, stenosis, arterial spasm and erythromelalgia diagnostic test. Hence Discrete Wavelet transform is utilized in this proposed work to examine and assess the dicrotic notch in arterial pulse wave form. Arterial pulse data are processed using a data acquisition system consisting of multiple channels sensor signal processing and a computer to collect the necessary data for future examination. The uniform peer group of 22 patients has been evaluated utilizing two distinct Haar and Daubuchies4 (db4) wavelet transformations. The peripheral wave in the patients seems to have a sharp rise and a notch on dropping slope, has been identified. The data collected are contrasted between the two techniques, and the Haar wavelet is observed to reasonably represent the best outcome.

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 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.

About ICEAST 2025

Aim & Scope

The aim of the International Conference on Engineering Advancements, Science and Technology (ICEAST) is to promote interdisciplinary collaboration among engineers, scientists, researchers, and industry professionals, providing a global platform for presenting cutting-edge research and technological innovations.

List of General Chairman Message ICEAST, ICEAST 2025 Chair Message, ICEAST 2025 Programme, ICEAST Keynote Speakers, ICSETS 2025, ICSETS 2025 Keynote Speakers, ICSETS 2025 Invited Speakers, ICSETS 2025 Workshops, ICSETS 2025 Discussion panels 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: 252

• Number of submissions sent for review: 253

• Number of submissions accepted: 130

• Acceptance Rate (Submissions Accepted / Submissions Received × 100): 51.6

• Average number of reviews per paper: 2

• Total number of reviewers involved: 115

• Contact person for queries:

Name: Mohamed Elkhatib

Email: mohamed.elkhatib@mtc.edu.om

Affiliation: Military Technological College

Industrial automation has been transformed by the Internet of Things (IoT) through real time monitoring, predictive maintenance and increased operating efficiency. An IoT-based monitoring system is introduced in this paper for three-phase induction motor serving industrial sector. Conventional motor maintenance includes a regular hand-checking inspection, which is laborious, and sometimes causes random failure. Real time data collection and analysis are possible by introducing IoT technology so that motor reliability can increase and downtime can decrease drastically.

The developed system uses a variety of sensors to monitor the most important parameters (temperature, vibration, voltage and current). Data is processed with a microcontroller and is sent to a cloud-based platform using Wi Fi or cellular network. “This enables remote monitoring and predictive maintenance and early detection of faults, and thus improved effectiveness of industrial operations. The system is also equipped with an alarm system to which users may be alerted when the normal operation state is breached, mitigating catastrophic failure of the same.

The system was tested in a field industrial environment and proved to be successful in real-time fault detection and industrial operation performance. A notable reduction in maintenance cost and unexpected breakdowns is evidence in practice compared to traditional monitoring systems that were demonstrated. The paper also analyses the possibility of using AI and ML algorithms to enhance predictive maintenance opportunities.

Potholes are one of the most common types of road damage and can cause accidents and damage vehicles. Therefore, detecting potholes promptly and accurately is crucial for their repair. In recent years, computer vision techniques have been used for automatic pothole detection, and deep learning models have shown promising results in this task. This paper presents a comparative analysis of four popular You Only Look Once (YOLO) models, namely YOLOv5, YOLOv6, YOLOv7, and YOLOv8, for pothole detection. The YOLO models are state-of-the-art deep learning models that can detect multiple objects in an image with high accuracy and speed. These models are evaluated on a dataset of real-world road images that contain potholes. The dataset comprises images captured under various camera settings, lighting conditions, and weather conditions. Experiments show that YOLOv5 and YOLOv8 performed well compared to other models, achieving the highest mean Average Precision (mAP@0.5) of 0.641 and 0.607, respectively. In addition, the detection speed of each model is compared, and the trade-offs between accuracy and speed are discussed. The results demonstrate that YOLOv5 achieves the highest precision and has the fastest detection speed compared to other models, making it an ideal choice for real-time pothole detection applications.

The integration of blockchain technology with the Internet of Things (IoT) has paved the way for a secure and decentralized communication framework among IoT devices. However, despite its advantages, this combination presents significant privacy concerns due to blockchain’s inherent transparency and immutability. To tackle these challenges, this paper introduces a robust framework designed to enhance privacy preservation in IoT-enabled blockchain systems. Our approach incorporates a hierarchical model that categorizes transactions at the IoT level as either public or private. By leveraging lightweight cryptographic techniques alongside an efficient consensus mechanism, our framework ensures both data confidentiality and privacy within the blockchain network. Through extensive simulations and practical experiments, we validate the effectiveness and feasibility of our proposed system. The results demonstrate that our model successfully protects IoT data privacy while retaining the core attributes of security and transparency within blockchain systems. This privacy-centric approach offers a promising solution to address IoT-related privacy concerns in blockchain environments, contributing to the advancement of secure and decentralized IoT communications.

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.

A cost effective and environment friendly technique for green synthesis of silver nanoparticles has been reported. Silver nanoparticles have been synthesized using ethanol extract of fruits of Santalum album (Family Santalaceae), commonly known as East Indian sandalwood. Fruits of S.album were collected and crushed. Ethanol was added to the crushed fruits and mixture was exposed to microwave for few minutes. Extract was concentrated by Buchi rotavaporator. To this extract, 1mM aqueous solution of silver nitrate (AgNO₃) was added. After about 24 hr incubation Ag⁺ ions in AgNO₃ solution were reduced to Ag atoms by the extract. Silver nanoparticles were obtained in powder form. X-ray diffraction (XRD) pattern of the prepared sample of silver nanoparticles was recorded The diffractogram has been compared with the standard powder diffraction card of JCPDS silver file. Four peaks have been identified corresponding to (hkl) values of silver. The XRD study confirms that the resultant particles are silver nanoparticles having FCC structure. The average crystalline size D, the value of the interplanar spacing between the atoms, d, lattice constant and cell volume have been estimated. Thus, silver nanoparticles with well-defined dimensions could be synthesized by reduction of metal ions due to fruit extract of S.album.

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.

The LHCb simulation application, Gauss, is based on the Gaudi framework and on experiment basic components such as the Event Model and Detector Description. Gauss also depends on external libraries for the generation of the primary events (PYTHIA 6, EvtGen, etc.) and on GEANT4 for particle transport in the experimental setup. The application supports the production of different types of events from minimum bias to B physics signals and particle guns. It is used for purely generator-level studies as well as full simulations. Gauss is used both directly by users and in massive central productions on the grid. The design and implementation of the application and its evolution due to evolving requirements will be described as in the case of the recently adopted Python-based configuration or the possibility of taking into account detectors conditions via a Simulation Conditions database. The challenge of supporting at the same time the flexibililty needed for the different tasks for which it is used, from evaluation of physics reach to background modeling, together with the stability and reliabilty of the code will also be described.