Spektra: Jurnal Fisika dan Aplikasinya
Not a member yet
254 research outputs found
Sort by
Short-Term Variability of Hα Emission Line Parameters in π Aqr as a Be Star
Full text linkB-emission (Be) star is a B type star which shows emission line in its spectrum, especially Hα, caused by the disk surrounding it. One of the Be stars, π Aqr, is a binary Be star in constellation Aquarius which shows variability of double-peaked Hα emission line caused by a circumstellar disk around the primary component. We aim to study recent physical phenomenon which occurs in the disk of π Aqr star based on its spectral data from 2004 to 2024, retrieved from BeSS database, by analyzing the variability of its Hα emission line parameters: the Violet-to-Red peak ratio (V/R), Emission-to-Continuum ratio (E/C), Equivalent Width (EW), Full Width at Half Maximum (FWHM), and peak separation. The variation of V/R ratio from 2004 to 2019 shows period of 84.1 days which corresponds with orbital period of the binary system. However, in 2024, the V/R variability does not show similar cycle period in previous years as the red peak strength dominates throughout most of the year. The EW and E/C increase, indicating rising disk activity and expansion until 2024. Overall increase of red and blue peak separation roughly suggests expansion in Hα region in the disk
One-Dimensional Modeling of Magnetotelluric Data using Convolutional Neural Network-Gated Recurrent Unit Based Inversion and Its Implementation on Field Data
Full text linkThe magnetotelluric method is a geophysical method that utilizes natural variations in the electromagnetic field to map the resistivity distribution beneath the surface. In this method, inversion is the primary process used to estimate the resistivity structure from field data. This study proposes a deep learning-based approach for one-dimensional magnetotelluric inversion, combining Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) as an alternative inversion method. The dataset consists of 20 layers with resistivity and thickness values randomly selected within a specific range at 4000 meters depths and a probability value. Apparent resistivity and phase are obtained through forward modeling based on selected resistivity and thicknesses as input, while the resistivity structure is used as output, with a large data sample. The dataset was standardized and normalized using a logarithmic scale and the MinMax method to map values into the 0-1 range. The dataset was used to train the proposed CNN-GRU model, which is capable of mapping the resistivity distribution in the subsurface. The results show that the CNN-GRU model could map the resistivity distribution model and predict its thicknesses with small error based on the apparent resistivity and phase data, indicating that it can be used for one-dimensional inversion in magnetotellurics. Nevertheless, the model performed quite well on several field datasets, showing a good fit between predicted and true values
Hydrostatic Mass of Galaxy Clusters within Eddington-inspired Born Infeld Theory Modified by Generalized Uncertainty Principle
Full text linkThe mass of galaxy cluster systems can be determined by calculating the hydrostatic equation for such systems. In this study, we derive the hydrostatic mass of galaxy cluster systems within the Eddington-inspired Born-Infeld (EiBI) theory, a modified theory of gravity. The EiBI theory is further modified by incorporating the generalized uncertainty principle (GUP) into its formulation. The GUP affects the mathematical expression of the temperature in galaxy clusters, leading to modifications in the clusters' equation of state (EoS), which is also an essential mathematical tool in hydrostatic equation calculations. This incorporation is motivated by the need to explore quantum gravitational effects on cosmological scales, bridging a fundamental gap between a modified theory of gravity and quantum mechanics. This work is significant in that it introduces the effect of the GUP, implemented through a modification of the temperature, within the framework of EiBI gravity. Using the derived formulation, we calculate the mass of 12 galaxy clusters and compare the results with observational data. The calculations reveal a significant reduction in the masses of these galaxy clusters to the order of 10-19 M⊙. A result which is profoundly inconsistent with observational data, thereby challenging the physical viability of this specific EiBI-GUP framework for modelling large-scale structures like galaxy clusters
Analysis of the Effect of ZnO, FeO, MnO, and MgO Dopants on the Optical Properties of P2O5-CaO Glass System and Comparison With P2O5-Eggshell Glass System
Full text linkThis study investigates the effect of ZnO, FeO, MnO, and MgO dopants on the optical properties of phosphate-based glass within the P₂O₅–CaO system and compares the results with glasses using eggshell-derived CaO as an alternative calcium source. The glass samples were synthesized using the melt quenching method, followed by characterization using Energy Dispersive X-ray (EDX) and UV-Visible spectroscopy. EDX analysis confirmed that phosphorus and oxygen were the dominant elements in all samples, with successful incorporation of each dopant as a network modifier. UV-Vis analysis revealed that the optical properties of the glass were significantly affected by both dopant type and concentration. The addition of ZnO decreases absorbance and widens the band gap up to 10 mol%, indicating improved structural regularity of the glass network. MnO exhibits a non-linear trend, with the highest absorbance observed at 5 mol% and decreasing at higher concentrations. The band gap varies from 3.37 eV to 3.59 eV, suggesting a transition from a disordered to a more stable and compact glass structure. In contrast, FeO and MgO doping reduced the band gap energy due to increased formation of non-bridging oxygen and network disruption. Additionally, comparison with eggshell-derived CaO showed higher UV absorbance compared to glass made with pure CaO, especially in the wavelength range below 400 nm, indicating that the raw material source influences the optical performance of the glass. Overall, this research highlights the potential of tuning dopant concentration and utilizing sustainable raw materials to enhance the optical characteristics of phosphate glass for use in UV-blocking, optoelectronic, and sensor applications
Theoretical Study of Positron-Electron Scattering with Thermal-Volkov Wavefunction
Full text linkThis study investigates the differential cross-section (DCS) for laser-assisted positron-electron scattering in a Gaussian wave packet, within a linearly polarized laser field in a thermal environment. For this, a theoretical model was developed with a designed thermal Gaussian Volkov wavefunction, vector potential, and scattering matrix with the application of the Bessel function. The developed model was computed using the Matlab programming language to study the nature of the developed model of DCS. The observation shows that the DCS initially increases with positron energy, reaching a peak around 0.5 eV; after that, it decreases with further increases in energy and approaches a constant at high energies. This is due to changing dynamics of positron-electron interactions with resonance occurring at specific energies. Also, the observation shows that temperature plays a significant role, especially at lower energies, with higher temperatures (325 K) enhancing the DCS due to increased thermal excitation of the target electrons. The study also explores the influence of the z-value and found that higher z-values lead to a decrease in the DCS due to the Coulombic interaction becoming stronger. Moreover, the effects of external factors such as the number of laser field photons and pulse duration are considered. The observation shows that shorter laser pulse durations and higher photon energies enhance the scattering process, while longer pulse durations result in a decrease in DCS. This study aids in optimizing technologies like PET imaging, plasma diagnostics, and particle accelerators by revealing how positron-electron scattering varies with energy, temperature, and laser parameters. It supports real-world applications in medical, space, and materials science
Comparative Study of Activation Functions and Image Resolution on ResNet-34 for Spiral Galaxy Spin Classification
Full text linkThis study investigates the application of the Residual Network (ResNet-34) architecture for classifying spiral galaxy spin directions, specifically focusing on the comparative performance of activation functions and cross-dataset generalizability using data derived from the Dark Energy Spectroscopic Instrument Legacy Survey (DESI LS) and the Hyper Suprime Cam Subaru Strategic Program (HSC-SSP) surveys. The methodology ensures robustness by training each model configuration across 10 independent runs. The results demonstrate the clear superiority of the Rectified Linear Unit (ReLU) over the Hyperbolic Tangent (Tanh); ReLU-based models achieved a mean peak accuracy of 94.7% and required only less than 60 epochs to converge, significantly faster than Tanh's 120 epochs. Crucially, we found that models trained on lower-resolution DESI LS images exhibited superior robustness and generalizability compared to high-resolution-trained models, suggesting that low-resolution training acts as effective implicit regularization. This research provides critical design recommendations for efficient machine learning pipelines, particularly for upcoming facilities like the 3.8-meter telescope at Timau National Observatory (TNO), ensuring model stability and transferability across diverse survey conditions
Influence of Polymer Matrix on The Morphology and Crystallization Behavior of Electrospun Zinc Oxide Fibers
Full text linkZnO finds widespread applications such as in photocatalysis, sensors, medicine, and other optoelectronic devices. The characteristics of ZnO can be influenced by several parameters, one of which is morphology. Fiber structures are attractive for research among various shapes and sizes due to their large effective surface area. ZnO fibers can be produced using electrospinning. However, the fiber morphology strongly depends on several important parameters, one of them is the characteristics of the polymer as a matrix. The molecular weight and concentration of the polymer and precursor material influence the solution viscosity, which is one of the crucial parameters in the electrospinning method. In this study, ZnO fibers were fabricated using three different polymers as matrices: PVP (polyvinyl pyrrolidone), PVAc (polyvinyl acetate), and PVA (polyvinyl alcohol). This research investigates the influence of polymer type on the morphology of ZnO fibers and crystallization behavior based on thermal characteristics. Based on SEM results, ZnO fibers were successfully fabricated with diameters ranging from 20–90 nm. The different characteristics are related to the type of polymer matrices and heating treatment. Only the PVA polymer could produce fibers before and after calcination, whereas the PVAc polymer-based fiber vanished after calcination. The disappearance of the fiber morphology is probably caused by the relatively high precursor (ZnAc) concentration, which leads to damage to the fibers formed during the calcination process. PVP failed to produce fibers, possibly due to its low polymer molecular weight, necessitating adjustment of other parameters. The removal of organic compounds through calcination continued until a temperature of 450ºC was reached. However, organic compounds were still identified in the samples based on FTIR characteristics. The ZnO/PVA fibers have hydrophobic surfaces, with the contact angle of water droplets being 117.75º. This characteristic is ideal for several applications such as antibacterial compounds or self-cleaning materials. Considering the inherent properties of ZnO, it can function as both an antibacterial and a photocatalytic agents simultaneously
Technical and Environmental Performance Evaluation of Fuel Switching from Coal to Biomass Wood Chip in Circulating Fluidized Bed Boiler
Full text linkPresidential Regulation No. 112 of 2022 regulates the preparation of a road map to accelerate the termination of operating time for steam power plants (PLTU). Biomass’s potential to reduce emissions compared to coal explains why PLTU was chosen for this study. The operation of a PLTU requires the replacement of non-renewable electricity fuel sources with renewable ones. PLTUs are dominated by the use of coal. In accordance with presidential regulations, this research carried out fuel switching from coal to biomass. This research was conducted at PLTU Bolok, Kupang Regency. PLTU Bolok has a capacity of 2×16.5 MW. Fuel switching from coal to wood chip biomass in a Circulating Fluidized Bed (CFB) type boiler was carried out directly (direct co-firing), with five combustion treatments, namely, 100% coal and 0% biomass, 75% coal and 25% biomass, 50% coal and 50% biomass, 25% coal and 75% biomass and 0% coal and 100% biomass. The results of this research show that the performance of the biomass fuel switching caused the PLTU unit to experience a derating of 2 mW/hour. The results of other parameter analysis are FEGT 845.33 °C, furnace pressure -35 Pa and furnace temperature 947.04 °C. NOx emissions were reduced by 11.3 mg/Nm3, SO2 by 45.8 mg/Nm3 and CO2 12.5 mg/Nm3. The environmental benefit is the reduction emissions
Stem-base Rot Disease Detection in Oil Palm using RGB (Red, Green, Blue) and OCN (Orange, Cyan, NIR) Image Fusion Method Based on ResNet50
Full text linkCurrent image acquisition and processing methods still need to be improved to effectively detect oil palm diseases. A precise and fast method to detect stem base rot disease in oil palm trees can be developed using drone technology and image processing approaches. An OCN (Orange, Cyan, NIR) camera is added to a standard drone and equipped with an RGB (Red, Green, Blue) camera. Combining the two cameras is proposed to generate multispectral imagery using an image fusion method called early fusion. A Multispectral Convolution Neural Network (MCNN) is also introduced to detect stem base rot disease by analysing the leaf patterns of oil palms. Healthy and unhealthy leaf samples were collected from oil palm plantations in Bogor. The images that have passed the image processing stage with the fusion method become inputs for modelling to identify stem base rot disease in oil palm. The results of the research using the multispectral image fusion method (RGB and OCN) based on the ResNet50 architecture can be used to identify stem base rot disease in oil palm effectively, as evidenced by the training and validation accuracy of 97.75% and 96.48%
Enhanced Optical Properties of Ce-Doped ZnO Nanoparticles via A Green Plant-Based Synthesis Approach
Full text linkThis study investigated the optical properties of Ce-doped ZnO nanoparticles (Ce/ZnO), synthesized using Pandanus ammaryllifolius leaf extract via a green biosynthesis method. Ce doping concentrations of 1%, 2%, and 3% were applied, and the nanoparticles were annealed at 400 °C for two hours. UV-Vis analysis showed a redshift in the annealed samples, with the wavelength increasing from 368 nm to 370 nm, likely due to particle growth after thermal treatment. In contrast, the unannealed samples exhibited a blueshift, with the maximum absorbance wavelength decreasing from 361 nm to 356 nm. The absorbance values were higher in the annealed samples than in the unannealed ones. The band gap energy of doped ZnO samples decreased slightly after annealing, from 3.20–3.22 eV to 3.15–3.19 eV, indicating improved optical properties. FTIR analysis revealed the presence of Ce–O bonds and functional groups, such as O–H and C–H, with sharper peaks in the annealed samples. The novelty of this study lies in utilizing Pandanus ammaryllifolius leaf extract as a natural reducing and stabilizing agent, providing a sustainable and eco-friendly alternative to conventional chemical synthesis methods. The findings suggest that Ce doping enhances the optical properties of ZnO nanoparticles, making them suitable for specific applications in environmental remediation, such as the degradation of organic pollutants, and in technological fields like photocatalytic devices and UV-absorbing materials