Modeling Techniques Research Articles

Modeling the Swelling Behavior of Clayey Geomaterials Across Scales: Advances and Challenges

ABSTRACTMost soils and rocks contain varying fractions of clay minerals within their solid matrix. These geomaterials can exhibit a significant swelling potential toward chemo‐thermo‐hydromechanical loadings. Several multiscale modeling techniques have been developed to ascertain their swelling behavior across various scales, with molecular dynamics (MD), micromechanics‐based approaches, and double‐porosity models being the most common. MD simulation is a computational technique that applies Newton's second law of motion to depict the movement of particles within a granular system. Micromechanics‐based approaches upscale the poro‐elasticity law from the clay layer level to the sample scale through homogenization. Dual‐porosity models are generally based on elasto‐plasticity, incorporating different hydro‐mechanical laws at two distinct scales. These models have been extensively used, particularly for clayey soils and bentonites, though their application to clayey rocks has not been reported in the literature. Although their significant contribution to the understanding of clay swelling behavior, these techniques have been insufficiently reviewed, compared, and discussed mutually in the literature. This paper aims to provide a cross‐look on these multiscale approaches by presenting the theoretical background of existing formulations, highlighting breakthrough results, discussing major differences and current challenges, and proposing future perspectives.

Quantitative characterisation of debris cloud-induced pitting damage in AL-Whipple shields of spacecraft using infrared thermography

ABSTRACT Quantitatively evaluating the debris cloud-induced pitting damage in the AL-Whipple structure of spacecraft, is still a challenge, let alone microstructural plastic deformation usually accompanied by geometric cratering defects, which may decrease material stress-carrying capacity and structural life. With this motivation, a dedicated modelling technique is proposed to gain an insight into various modalities (i.e., geometric and plastic defects) of pitting damage-induced thermal modulation for infrared inspection. Then, a sequence of infrared images is obtained and analysed using the gap smoothing algorithm and naive Bayes classification methods. On this basis, a quantitative characterization framework is proposed to characterize the damage modalities and their morphologies. This method is validated using a 3D numerical model which contains pitting damage acquired from the real-world structure. Modality classification and morphology evaluation results are well consistent with the artificial defects, in terms of the modality, diameter, and depth. Then they‘re experimentally corroborated, in which the severity and distribution of pitting damage are precisely characterized, in terms of the crater number and morphologies, as well as the size of plastic regions around these craters, which are consistent with the actual observations. This work proposes a non-destructive strategy for pitting damage evaluation, which is engineering-friendly.

Pengaruh Teknik Modeling Simbolik terhadap Peningkatan Motivasi Belajar Peserta Didik Kelas X TKI 1 SMK Negeri 3 Madiun

Learning motivation is one of the things that needs to be considered in learning because there are still many who think that motivation is not that important to improve learning achievement. The aim of this research is to study the effect of symbolic modeling techniques in guidance and counseling services on increasing students' learning motivation. This technique is a method in guidance and counseling services that uses pictures, videos or other stories to help students understand the concept of learning motivation. This research uses a guidance and counseling action research approach which involves several cycles, namely planning, action, observation and reflection. The research subjects were class X TKI 1 students at SMK Negeri 3 Madiun with low learning motivation. Data was collected from observations, interviews and special questionnaires regarding learning motivation before and after the intervention.The research results showed that students learning motivation improved significantly after intervention with symbolic modeling techniques.

Profiles of peripheral regulatory T cells forecasts the feasibility of steroid withdrawal after liver transplantation based on cellular thermal modeling

After liver transplantation, steroid therapy is often used to prevent rejection. However, long-term steroid use can lead to serious side effects, therefore, this study aimed to evaluate the feasibility of peripheral regulatory T cell profiles for steroid discontinuation after liver transplantation through cellular thermal modeling and real-time monitoring of intracellular thermodynamics. In this study, cellular thermal modeling techniques were used to simulate the thermodynamic characteristics of peripheral TREgs under different conditions. The dynamic changes of peripheral Treg in liver transplantation animal models were monitored by flow cytometry and molecular biology. Significant changes in peripheral Treg profiles were observed after initiation of steroid therapy, especially in the discontinuation group, and these changes were strongly associated with the restoration of immune homeostasis. Real-time monitoring of the thermodynamic data revealed that peripheral Treg activity showed a specific temperature dependence in the cellular thermal model. Cell thermal modeling combined with real-time monitoring of intracellular thermodynamics provides a new perspective for evaluating the feasibility of peripheral regulatory T cell profiles for steroid discontinuation after liver transplantation. Strengthening the dynamic monitoring of peripheral Treg spectrum provides an important basis for the formulation of personalized immunosuppression strategies, and improves the therapeutic effect and quality of life of liver transplant patients.

A Computational Perspective into Binding Mechanism of a potent FAK Inhibitor as Anticancer Drug Candidate: Insights from Molecular Dynamics Simulations

Focal adhesion kinase (FAK) is an important non-receptor tyrosine kinase (nRTK) with crucial role in primary cell functions. Fundamental cellular processes, such as migration, adhesion, proliferation, and survival, are regulated by FAK. The tumorigenesis of FAK is very important since it is overexpressed in advanced cancers, causes malignant features and induces cancer metastasis. The available evidence proposes FAK as a promising target for cancer therapy. At the moment, there are no FAK inhibitors (FAKIs) in the market but several small molecule FAKIs have been developed and a few of them entered into clinical studies as anticancer agents. In spite of these achievements, development of selective and potent FAKIs may be partly hampered by the lack of crystallographic or structural data. This limitation could be resolved through proposing robust ligand–protein binding models to design new FAKIs. GSK2256098 is a small molecule ATP-competitive FAKI that undergoes clinical trials as a monotherapy or in combination with other medications against advanced solid tumors. GSK2256098 have been proven to be a selective and potent agent (IC[Formula: see text] 0.4 nM) with respect to other clinical candidates. With regard to appropriate characteristics, this molecule is particularly amenable for performing computational modeling studies. In this study, we try to propose an applicable binding model of GSK2256098 inside the FAK kinase domain. For this purpose, molecular docking, all-atom 200-ns molecular dynamics simulations and free-energy-calculation methods were applied as consecutive modeling techniques. Atomic insight and dynamic evolution of the system were used to attain a stable binding mode and infer chemical interactions of GSK2256098 inside the kinase domain of FAK. Although complementary investigations need to be conducted, obtained results may offer a structural basis for the development of potent and selective FAKIs.

Do help options facilitate computer‐based L2 listening comprehension? Evidence from an activity theory perspective

AbstractDespite the proliferation of help options for computer‐based second language (L2) listening comprehension, little attention has been given to a comprehensive understanding of the pedagogical effects. To gain a thorough understanding of the overall and moderator effects, drawing on the activity theory (AT), this study conducted a meta‐analysis of 33 valid independent studies on help options for computer‐based L2 listening comprehension with 3697 participants involved. Results demonstrated that (a) compared with those traditional methods, help options are found to be more facilitative for L2 listening comprehension with a small‐to‐moderate effect of g = 0.449, 95% CI [0.303, 0.596]; and (b) the effects of help options for computer‐based L2 listening comprehension are moderated by option types, feedback types, language distance types, intervention durations and learning activity types. Motivated by the results obtained, this study also discussed pedagogical implications for future studies. Practitioner notesWhat is already known about this topic The improved pedagogical effects of help options for computer‐based second language (L2) listening comprehension have been well‐documented. A large number of studies on the use of help options for computer‐based L2 listening comprehension have been published. There has been no research aggregating the overall effects of help options for computer‐based L2 listening comprehension. Some potential factors that may moderate the effects of help options for computer‐based L2 listening comprehension remain underexplored. What this paper adds This paper aims to aggregate the overall and moderator effects from the existing publications of help options for computer‐based L2 listening comprehension. Compared with those traditional methods, help options are found to be more facilitative for L2 listening comprehension with a small‐to‐moderate effect of g = 0.449. The effects of help options for computer‐based L2 listening are moderated by option types, feedback types, language distance types, intervention durations and learning activity types. Implications for practice and/or policy Help options have been confirmed to facilitate computer‐based L2 listening comprehension, especially among learners of primary and secondary levels. Call for more empirical studies investigating how different types of options, feedback, language distance and intervention durations along with learning activity moderate the effectiveness. Further attempts should investigate help options for computer‐based L2 listening comprehension with the integration of generative AI, large language model (LLM) and natural language processing (NLP) techniques.

Application of Multi-Input Multi-Output Brain-Computer Interface in Cognitive Assessment for Alzheimer’s Disease

This research aims to augment human cognitive abilities, particularly memory function, through the development of innovative prosthetics. The hippocampus, a crucial brain region involved in memory encoding and recall, has been the focal point of numerous studies seeking to develop prosthetics that can restore or enhance memory function in individuals with impairments such as Alzheimer’s disease. This review examines two studies that delve into the development and application of hippocampal neural prosthetics, highlighting the potential of these prosthetics to revolutionize the treatment of memory impairments. One study utilized static neural stimulation patterns to enhance memory for specific information content, while another developed a nonlinear Multiple Input Multiple Output (MIMO) dynamic model to predict and stimulate neural activity patterns during memory encoding. Both studies demonstrated significant improvements in memory performance. Additionally, this paper discusses the clinical significance and application prospects of hippocampal neural prosthetics, including the potential for implantable prosthetics and the widespread applicability of MIMO stimulation. Furthermore, the paper explores advanced modeling and classification techniques for neural ensembles and visual memory decoding, including Volterra-type hierarchical modeling, optimal multi-unit stimulation patterns, and a sparse classification model for decoding visual memories. Finally, the paper presents the implementation and evaluation of neural prosthesis systems, including an application-specific integrated circuit (ASIC) design for a hippocampal prosthesis and a high-performance, scalable system architecture for real-time estimation of neural activity. These advancements hold promise for future research and applications in neural prosthetics for memory enhancement.

Advanced ERT techniques for methane potential evaluation in controlled dump sites: A forward modeling approach

Methane (CH4) emissions from large controlled dump sites play a significant role in global warming. However, when captured efficiently, CH4 is a valuable energy resource. This study optimizes electrical resistivity tomography (ERT) for improved CH4 detection in controlled dump sites, focusing on refining electrode array configurations. We assessed three configurations: dipole-dipole, Wenner-Schlumberger (WS), and Wenner arrays. The results were compared with synthetic data simulated using the forward modeling technique, and the model's accuracy was evaluated using the Nash-Sutcliffe model efficiency coefficient (NSE), model sensitivity, and root mean square error (RMSE). Additionally, we analyzed the model's correlation with CH4 flux measurements. The WS array demonstrated the best performance, achieving a lower RMSE (23.4 %), a higher NES (0.90), and model sensitivity (0.90), along with a moderate negative correlation (-0.50) with CH4 flux measurements. This configuration effectively identified subsurface features, such as organic waste layers and leachate zones, which are crucial for CH₄ capture and landfill gas collection. Forward modeling confirmed the WS array's ability to provide high-resolution subsurface imaging, enhancing CH₄ hotspot detection. By improving the spatial and temporal accuracy of CH₄ detection, this approach addresses limitations in traditional methods and supports the design of more efficient gas capture systems. Applied in Thailand, these findings highlight the potential of optimized ERT techniques for enhancing CH₄ recovery in waste-to-energy systems, contributing to more sustainable waste management practices and reducing greenhouse gas emissions.

Experimentally Verified Retrofit Combining Hybrid Solutions for Enhancing the Seismic Response of Multistory RC Structures

ABSTRACTThis experimental and numerical study proposes hybrid retrofit alternatives involving multiple contemporary techniques to upgrade the fundamental seismic response characteristics of multistory reinforced concrete frame (MSRCF) structures. The impact of thin high‐performance reinforced concrete (HPRC) jackets on the concrete framing system's seismic response is first investigated through shake table testing (STT). The performance of the HPRC‐retrofitted frame is then compared with another STT campaign conducted for fabric‐reinforced cementitious matrix (FRCM)–retrofitted frame. The STT results indicated that at the same input ground motion intensity that caused failure for the FRCM‐retrofitted frame, the curvature ductility and interstory drift ratio of the HPRC‐retrofitted specimen were reduced by 79% and 87%, respectively. Upon verifying the fiber‐based modeling technique of HPRC and FRCM alternatives through STT, the study probabilistically assesses the seismic performance of a benchmark MSRCF building considering different hybrid retrofit alternatives by combining each technique with a self‐centering energy dissipation (SCED) bracing system. Following a systematic seismic assessment framework, it is concluded that the performance‐cost index of the less disruptive HPRC‐SCED option exceeded that of the FRCM‐SCED by 36%, confirming its preference among the considered alternatives for upgrading the seismic response of MSRCF deficient in stiffness, strength, and ductility.

Modeling the rainfall–runoff process in the Bird Creek River Basin: a comparative analysis of conceptual and artificial neural network techniques

ABSTRACT The modeling of the rainfall–runoff (RR) process is a key component for water resources projects, planning, and management for which conceptual and data-driven modeling techniques are utilized. However, these techniques in the modeling of the RR process have their own benefits, and their performance need to be explored in real basins. In this paper, five conceptual models (namely, AWBM, Sacramento, SimHyd, SMAR, and TANK) and an artificial neural network (ANN) model have been developed and their performances have been assessed using the rainfall, runoff, and other climatic data derived from the Bird Creek Basin, USA. The results obtained from the study suggest that the SimHyd performed the best among all the conceptual models during testing. The ANN model performance in simulating the RR process was found to be the best among all the models developed in this study during testing with the highest values of R = 0.941, E = 0.994 and all threshold statistics and least values of AARE = 38.9 and NRMSE = 0.031. Overall, it can be concluded that although the conceptual models are highly comprehensible, the ANN models are able to simulate the flow more accurately than any of the conceptual models developed in this study.

A robust approach to Gaussian process implementation

Abstract. Gaussian process (GP) regression is a flexible modeling technique used to predict outputs and to capture uncertainty in the predictions. However, the GP regression process becomes computationally intensive when the training spatial dataset has a large number of observations. To address this challenge, we introduce a scalable GP algorithm, termed MuyGPs, which incorporates nearest-neighbor and leave-one-out cross-validation during training. This approach enables the evaluation of large spatial datasets with state-of-the-art accuracy and speed in certain spatial problems. Despite these advantages, conventional quadratic loss functions used in the MuyGPs optimization, such as root mean squared error (RMSE), are highly influenced by outliers. We explore the behavior of MuyGPs in cases involving outlying observations and, subsequently, develop a robust approach to handle and mitigate their impact. Specifically, we introduce a novel leave-one-out loss function based on the pseudo-Huber function (LOOPH) that effectively accounts for outliers in large spatial datasets within the MuyGPs framework. Our simulation study shows that the LOOPH loss method maintains accuracy despite outlying observations, establishing MuyGPs as a powerful tool for mitigating unusual observation impacts in the large data regime. In the analysis of US ozone data, MuyGPs provides accurate predictions and uncertainty quantification, demonstrating its utility in managing data anomalies. Through these efforts, we advance the understanding of GP regression in spatial contexts.

Integrating the cellular vortex method with remote sensing and geographical information systems in the modelling of coastal flooding around Niger Delta

Coastal areas are increasingly vulnerable to flooding, necessitating accurate simulation methods to understand flood dynamics and their potential impacts. This study employed a Lagrangian framework integrating the cellular vortex method with remote sensing and GIS to simulate flood height distribution in a coastal region. Leveraging climatic and remotely sensed data, alongside ArcMap 10.6.1 for map processing, the research estimated flood magnitude and frequency using the L-moment approach, applied to a forty-year tidal record dataset. Essential input parameters, such as the roughness coefficient and curve number, were derived from land use and land cover characteristics. Additionally, river flow velocity was observed at 0.12m/s, with measured wind speed and direction recorded at 4m/s in the northwest direction. Notably, analysis of the initial flood height distribution map revealed a significant expansion of wetland areas, attributed to observed land use changes between May 2002 and July 2005. Projections for flood height distribution in 2025 and 2050 highlighted the emergence of tidal floods, emphasizing the critical role of considering future climate and land use scenarios in flood dynamics assessment. This research contributes to advancing understanding of flood modeling techniques and underscores the urgency of adaptive measures to mitigate the potential impacts of coastal flooding.

Modeling phosphorus transport in soil columns amended with polyaluminum chloride and anionic polyacrylamide water treatment residuals: implications for stormwater bioretention systems

ABSTRACT Understanding phosphorus transport in soil columns amended with polyaluminum chloride and anionic polyacrylamide water treatment residuals (PAC-APAM WTRs) is crucial for the effective recycling of PAC-APAM WTRs into traditional soil-based stormwater bioretention systems. Phosphorus transport in columns containing three distinct soil types amended with PAC-APAM WTRs under saturated steady-state flow conditions was effectively modeled using three different models: the convection-dispersion equation (CDE) model with linear isotherm, the CDE model with Langmuir isotherm, and the chemical non-equilibrium two-site model (TSM). In Soils 1 and 2, amended with PAC-APAM WTRs, the primary mechanism governing phosphorus transport transitioned from instantaneous adsorption to two-site adsorption as flow rate increased. In contrast, Soil 3 amended with PAC-APAM WTRs was predominantly governed by two-site adsorption throughout the experiments. An increase in flow rate reduced the solid–liquid distribution coefficient and the fraction of equilibrium adsorption sites, resulting in decreased phosphorus adsorption and increased phosphorus mobility. This study strongly recommends the selection of soil amended with PAC-APAM WTRs that exhibits higher instantaneous phosphorus adsorption. Additionally, this study emphasizes the importance of appropriately designing the depth of the ponding layer, utilizing the TSM model, and refining modeling techniques to optimize phosphorus retention and mitigate pollution risks in stormwater management.

Exploring Twitter discourse with BERTopic: topic modeling of tweets related to the major German parties during the 2021 German federal election

AbstractWe present a study in the context of computational social science that explores the topics debated in the context of the 2021 German Federal Election by using the topic modeling technique BERTopic. The corpus consists of German language tweets posted by political party accounts of the major German parties, as well as tweets by the general public mentioning the party accounts. We examined the textual content of the tweets but also included the text in images that were posted into the analysis by extracting the text using optical character recognition (OCR). Our results show that the most frequently discussed topics are party-oriented policies (including call-to-action content), climate policy and financial policy, with these topics being discussed in tweets by both, the political party accounts and tweets by accounts mentioning them. In addition, we observed that some topics were discussed consistently throughout the year, such as the COVID-19 pandemic, climate policy or digitization, while other topics, such as the return to power of the Taliban in Afghanistan or Israel were debated to a greater extent at limited time frames during the election year.

Data-driven approach to optimizing property management strategies: spatial modeling analytics of short-term rentals

Purpose This study aims to explore the impact of locational and seasonal factors on the financial performance of short-term rental properties in Margaret River, Western Australia. It seeks to address the gap in understanding how these factors influence key financial metrics such as average daily rate (ADR) and occupancy rates, providing insights for property managers, investors and policymakers. Design/methodology/approach The research uses a mixed-method approach, integrating advanced predictive modeling techniques, such as Random Forests and Gradient Boosting, with spatial clustering algorithms like density-based spatial clustering of applications with noise (DBSCAN) and ordering points to identify the clustering structure (OPTICS). The study analyzes a comprehensive data set of short-term rental properties between 2012 and 2019. It focuses on locational attributes, seasonal variations and financial outcomes. Findings The findings reveal that properties located near tourist attractions and amenities consistently achieve higher ADRs and occupancy rates, confirming the critical role of location in driving rental demand. Seasonal analysis indicates significant fluctuations in both ADR and occupancy rates, with peaks during high tourist seasons and troughs in off-peak periods. The study underscores the importance of dynamic pricing strategies to optimize revenue and sustain occupancy across different seasons. In addition, it highlights the influence of property features, such as the number of bedrooms and bathrooms, on ADR, while noting that larger properties do not necessarily achieve higher occupancy rates. Research limitations/implications Future research could expand the scope to include different locations and explore the long-term impacts of locational and seasonal factors on property performance. Originality/value This research contributes to the literature by integrating spatial analysis with advanced predictive modeling techniques to provide a nuanced understanding of how locational and seasonal factors impact financial performance in the short-term rental market. It offers a novel application of data analytics within the context of tourism and hospitality management, bridging theoretical frameworks with practical insights.

Experimental and numerical investigation of heat transfer characteristics of radiator using Graphene Amine-based nano coolant

This work presents experimental and numerical investigations on enhancing the heat transfer performance of automobile radiators with nano-coolant flowing with different mass flow rate and at different inlet temperatures conditions. Thermal management is critical as it dictates a system's overall power output, performance, efficiency and energy utilisation. Radiators and Thermal management systems for batteries are vital in keeping automobiles operating under optimal conditions. Conventional coolants lack the ability to keep up with the increasing performance requirements of thermal management systems. Nanocoolants represent a cutting-edge advancement in heat transfer fluid, boosting conductivity without compromising essential fluid dynamics of the cooling solutions. Several investigators have worked on various nanocoolants, but less work is done on radiators. In view of this, experimental and numerical investigations were done on radiator heat transfer performance using Graphene Amine based nanocoolant with varied coolant temperatures (50 °C–80 °C) and flow rates (3 l/min to 9 l/min). Thermophysical properties of nanocoolants, such as density, thermal conductivity, viscosity, specific heat, and Prandtl number, were determined by experimental and mathematical modelling techniques and were comparable with an average deviation of 5 %. Compared to the base fluids of De-ionised water and ethylene glycol combinations, Graphene Amine based nanocoolant has exhibited advancements in heat transfer properties, showing elevated Nusselt numbers, heat transfer coefficients and heat transfer rates. The investigation into the thermal transfer properties of nanocoolants utilizing theoretical and real-world experiments shows consistency with an average deviation of 20 %. Maximum heat transfer enhancement of 154.3 % and maximum heat transfer coefficient of 3209.52 W/m2K was found for Graphene Amine at 80 °C with coolant flowing at 9 l/min, which is 83.1 % higher when compared to De-ionised Water.

A comparative study on the Lattice Boltzmann Method and the VoF-Continuum method for oxygen transport in the anodic porous transport layer of an electrolyzer

The optimization of Polymer Electrolyte Membrane (PEM) electrolyzers necessitates an intimate knowledge of the oxygen flows within the anodic porous transport layers (PTLs) to determine any possible reduction in performance. In this field, as experimental studies are cumbersome and expensive, numerical modeling has arisen as a viable alternative for studying the oxygen transport within the Anodic PTLs of a PEM electrolyzer. Amongst the various numerical modeling techniques, the Lattice Boltzmann Method (LBM) is gaining prominence for its effectiveness in analyzing fluid transport within porous media due to its mesoscopic nature and ease of implementation. This study utilizes the Shan-Chen LBM methodology to model the flow of oxygen within the Anodic PTL of a PEM electrolyzer and compares it against the Volume-of-Fluid-based Continuum Model. The results show that LBM can not only replicate the experimental studies accurately, but can also maintain its high accuracy at progressively shrinking length scales of PTLs, even at length scales where the VoF-based Continuum Model would run into accuracy issues. The high accuracy of the LBM model, combined with the simplicity of the LB algorithm makes LBM a powerful technique for simulating the microfluidic flows such as the flow of oxygen within the Anodic PTL of a PEM electrolyzer.

Online adaptive critic designs with tensor product B-splines and incremental model techniques

As an effective optimal control scheme in the field of reinforcement learning, adaptive dynamic programming (ADP) has attracted extensive attention in recent decades. Neural networks (NNs) are commonly employed in ADP algorithms to realize nonlinear function approximation. However, the learning process with NN approximation typically requires a substantial amount of training data and places a heavy computational burden. To improve learning efficiency and alleviate computational load, this paper develops a novel model-free ADP approach based on multivariate splines and incremental control techniques, aimed at achieving online learning of nonlinear control. By utilizing input and output data, an incremental linear approximation model is identified online without any prior knowledge of system dynamics. To improve nonlinear approximation capabilities and lessen computational demands, tensor product B-splines, instead of NNs, are integrated into the critic module to approximate the value function, and the recursive least squares temporal difference (RLS-TD) algorithm is employed to update the weights of spline bases. Convergence analysis of the proposed control scheme is conducted based on the dual-timescale stochastic approximation theory. To illustrate the effectiveness and feasibility of the proposed control scheme, numerical simulations are performed in solving the online nonlinear control problem within the context of the inverted pendulum system.

Extended magenta aurora as revealed by citizen science

During large geomagnetic storms, red auroras are typically observed from low-latitude countries such as Japan. The color arises from a specific emission line of oxygen atoms at high altitudes. However, during the May 10-11th 2024 superstorm, magenta auroras were observed above Japan instead of the typical red. In this study, we demonstrate that the magenta hue is created by a mixture of red (O) and a blue (N2+) aurora at extremely high altitudes. The blue color originates from the N2+ first negative emission band caused by both resonant scattering of the upwelling molecular ions and heavy particle precipitation during the storm. This study is primarily driven by observations from citizen scientists, and confirmed and quantified with observations from spacecraft and modeling techniques. Additionally, we show that high solar activity, terrestrial season, and the preheating of the atmosphere all contribute to the occurrence of magenta aurora. This study showcases the value and richness of citizen science, and we anticipate that such approaches will become increasingly important in the future.

The Influence of Brand Image, Brand Trust, Service Quality through Customer Satisfaction on Honda Brand Motorcycle Brand Loyalty in DKI Jakarta

The automotive industry in Indonesia plays a crucial role in the country's economic development, with motorcycles being one of the most popular modes of transportation. Honda motorcycles have a significant market share among various brands due to their strong brand image, trust, and service quality. This study analyses the influence of brand image, trust, and service quality on Honda brand motorcycle brand loyalty through customer satisfaction in DKI Jakarta. Using a quantitative approach with a survey method, data was collected from 200 respondents who used Honda motorcycles in the DKI Jakarta area. Data was analysed using the Structural Equation Modeling (SEM) technique. The study results show that brand image, trust, and service quality significantly affect customer satisfaction, impacting brand loyalty. Customer satisfaction is essential to strengthening the relationship between independent variables and brand loyalty. These findings underscore the importance of improving service quality, strengthening the image, and building trust to create loyal customers for the Honda brand.