Advanced Model Research Articles

An experimental study on quartz solubility in water under supercritical geothermal conditions

Supercritical geothermal resource is a renewable and carbon-free energy with enormous potential. Quantitative assessment of the geochemical processes involved in the future operation of this resource requires a comprehensive understanding of quartz solubility in subcritical and supercritical water. However, quartz solubility behaviors from subcritical to supercritical conditions are unclear, and the quartz solubility model requires refinement by improving the accuracy of experimental measurements. In this study, a series of in-situ sampling and measurements are conducted to investigate quartz solubility at 300–500 °C and 25–50 MPa by exploiting a novel and specifically designed experimental apparatus. The results indicate that the use of in-situ sampling and resistance continuous monitoring procedures can bring an accurate and reliable estimation of quartz solubilities near the supercritical point and in the low-density region. It also emerged that the quartz solubility in water is significantly affected by temperature and pressure both under subcritical and supercritical conditions, showing a positive correlation with pressure at isothermal and a retrograde behavior near the supercritical point. These features are interpreted by the thermodynamic theory, quartz hydrolysis process, and mathematical model. By combining our experimental results with high-quality data from the literature, a five-parameter density model is provided for the calculation of the silica concentrations in aqueous fluids up to supercritical geothermal conditions. To overcome the current limitations in the modeling of supercritical geothermal fluids, future experimental work needs to collect additional data on silica in real geofluids (e.g., saline fluid) to develop an advanced model. The findings of this study can help for a better understanding of geochemical processes in supercritical geothermal systems, as well as ore deposits and vent structures.

An advanced three-dimensional analytical model for wind turbine near and far wake predictions

Most existing analytical wake models have been designed to represent the velocity distribution of the far wake region behind a wind turbine, but fail to reproduce the trend of the near wake. In order to meet the demand for both near and far wake velocity predictions, a concise three-dimensional dual-cosine shape (3D-COU) model is proposed. Moreover, by taking into account the wind shear and ground effects in the vertical direction, this model is capable of describing the anisotropic property of the 3-D wake field and is much closer to the reality. The proposed model is then calibrated and validated through a series of test cases, including different scales of turbines that are operating under a wide range of inflow conditions, and meanwhile the reference data resource is diverse. Overall, the results demonstrate that the 3D-COU model can accurately predict the variations of the wake velocity in both the vertical and horizontal directions throughout the whole wake region. Given its good accuracy, simplicity and applicability, this model provides the potential to effectively address practical problems in wind energy projects.

Journal Vol – 19 No -1, January 2024 Journal > Journal > Journal Vol – 19 No -1, January 2024 RELIABILITY OPTIMIZATION OF A DEGRADED SYSTEM UNDER PREVENTIVE MAINTENANCE USING GENETIC ALGORITHM Authors: Shakuntla Singla, Diksha Mangla, Poonam Panwar, S Z Taj DOI NO: https://doi.org/10.26782/jmcms.2024.01.00001 admin January 20, 2024 Abstract: The reliability parameters of a Mathematical model are analyzed for

The present paper deals with the lacunary interpolation problem called the mixed values problem or (0, 3; 0, 2) problem for which known data points are function values at all the points, third derivatives at even knots, and second derivatives at odd knots of the unit interval I = [0,1]. For this problem, we obtained an interpolating function. The paper is divided into two parts, where we have shown that the spline function exists and is convergent.

Quantitative Skeletal Imaging and Image-Based Modeling in Pediatric Orthopaedics.

Musculoskeletal imaging serves a critical role in clinical care and orthopaedic research. Image-based modeling is also gaining traction as a useful tool in understanding skeletal morphology and mechanics. However, there are fewer studies on advanced imaging and modeling in pediatric populations. The purpose of this review is to provide an overview of recent literature on skeletal imaging modalities and modeling techniques with a special emphasis on current and future uses in pediatric research and clinical care. While many principles of imaging and 3D modeling are relevant across the lifespan, there are special considerations for pediatric musculoskeletal imaging and fewer studies of 3D skeletal modeling in pediatric populations. Improved understanding of bone morphology and growth during childhood in healthy and pathologic patients may provide new insight into the pathophysiology of pediatric-onset skeletal diseases and the biomechanics of bone development. Clinical translation of 3D modeling tools developed in orthopaedic research is limited by the requirement for manual image segmentation and the resources needed for segmentation, modeling, and analysis. This paper highlights the current and future uses of common musculoskeletal imaging modalities and 3D modeling techniques in pediatric orthopaedic clinical care and research.

Group contribution method for rapid estimation of crystal growth rates

The morphological evolution of organic crystals during crystallization depends on the face-specific growth rates. Classical growth rate models relate the face-specific growth rates to the crystal lattice, energy of stable facets, growth mechanism, and supersaturation. The complexities of these models have increased over time to account accurately for solution conditions, the structure of growth units, and their attachment rates. Such advanced growth rate models require several layers of computations to obtain attachment energies of facets, nucleation rates, kink density, and attachment rates. Among these, the most intensive and time-consuming computation is for attachment rates, which require molecular dynamic simulations. This substantially increases the overall computation time to predict the absolute growth rate for even one crystallization condition. Since it is nearly impossible to iterate such a growth rate model, optimization schemes cannot be implemented to identify solution conditions that favor specific crystal growth. To reduce the computational time for attachment rate calculations, we implement a group contribution method (GCM) that relates the properties of functional groups in a molecule to their attachment rates to the crystal lattice, thereby rapidly estimating the growth rates of organic crystals. The process of molecular attachment involves partial desolvation of a solvated molecule, referred to as a transition state, followed by total desolvation via spontaneous attachment to a crystal facet. The first step in GCM is to identify the equilibrium states of fully solvated and partially desolvated solute molecules. The degree of supersaturation dictates the extent of this equilibrium and, thereby, the activation barrier for the growth of crystals, according to transition state theory. Identifying this equilibrium phenomenon allows for capturing the functional-group-specific interactions that depend on molecular motion, which could be related to operating conditions such as temperature and pressure. The stochastic optimization technique with Monte-Carlo sampling allows an efficient optimization problem solution to obtain the group interaction parameters. The GCM approach is first validated for the estimation of growth rates of glutamic acid and L-histidine, and then extended to predict growth rates of alanine and glycine rapidly. The optimized parameters and GCM scheme can be used to estimate growth rates in other crystallization systems.

Joint-Module Health Status Recognition for an Unmanned Platform: A Time–Frequency Representation and Extraction Network-Based Approach

Due to the complex structure of the joint module and harsh working conditions of unmanned platforms, the fault information is often overwhelmed by noise. Moreover, traditional mechanical health state recognition methods usually require a large amount of labeled data in advance, which is difficult to obtain for specific fault data in engineering applications. This limited amount of fault data restricts the diagnostic performance. Additionally, the characteristics of convolutional neural networks (CNNs) limit their ability to capture the relative positional information of fault features. In order to obtain more comprehensive fault information, this paper proposes an intelligent health state recognition method for unmanned platform joint modules based on feature modal decomposition (FMD) and the enhanced capsule network. Firstly, the collected vibration signals are decomposed into a series of feature modal components using FMD. Then, time–frequency maps containing significant fault features are generated based on the continuous wavelet transform (CWT). Finally, a multi-scale feature enhancement (MLFE) module and an efficient channel attention (ECA) module are proposed to enhance the feature extraction capability of the capsule network, extracting more comprehensive global and local feature information from the time–frequency maps to achieve the intelligent state recognition of joint modules. This approach enhances fault features while reducing the impact of redundant features, significantly improving the feature extraction capability without increasing the model’s computational complexity. The effectiveness and superiority of the proposed method are validated through experiments on an unmanned platform joint-module testbed. An ablation experiment demonstrates the effectiveness of the MLFE and ECA modules, and a comparison with other advanced network models proves the superiority of the proposed method for health status recognition.

Organoids as a tool to study the impact of heterogeneity in gastrointestinal epithelium on host-pathogen interactions.

The epithelium of the gastrointestinal (GI) tract has been extensively characterized using advanced histological and RNA sequencing techniques, which has revealed great cellular diversity. Pathogens, such as viruses and bacteria, are highly adapted to their host and often exhibit not only species-specificity but also a preference or tropism for specific GI segments or even cell types-some of these preferences are so specific, that these pathogens still cannot be cultured invitro. Organoid technology now provides a tool to generate human cell types, which enables the study of host cell tropism. Focussing on the GI tract, we provide an overview about cellular differentiation in vivo and in organoids and how differentiation in organoids and their derived models is used to advance our understanding of viral, bacterial, and parasitic infection. We emphasize that it is central to understand the composition of the model, as the alteration of culture conditions yields different cell types which affects infection. We examine future directions for wider application of cellular heterogeneity and potential advanced model systems for GI tract infection studies.

TBACkp: HER2 expression status classification network focusing on intrinsic subenvironmental characteristics of breast cancer liver metastases

The HER2 expression status in breast cancer liver metastases is a crucial indicator for the diagnosis, treatment, and prognosis assessment of patients. And typical diagnosis involves assessing the HER2 expression status through invasive procedures like biopsy. However, this method has certain drawbacks, such as being difficult in obtaining tissue samples and requiring long examination periods. To address these limitations, we propose an AI-aided diagnostic model. This model enables rapid diagnosis. It diagnoses a patient's HER2 expression status on the basis of preprocessed images, which is the region of the lesion extracted from a CT image rather than from an actual tissue sample. The algorithm of the model adopts a parallel structure, including a Branch Block and a Trunk Block. The Branch Block is responsible for extracting the gradient characteristics between the tumor sub-environments, and the Trunk Block is for fusing the characteristics extracted by the Branch Block. The Branch Block contains CNN with self-attention, which combines the advantages of CNN and self-attention to extract more meticulous and comprehensive image features. And the Trunk Block is so designed that it fuses the extracted image feature information without affecting the transmission of the original image features. The Conv-Attention is used to calculate the attention in the Trunk Block, which uses kernel dot product and is responsible for providing the weight for the self-attention in the process of using convolution induced deviation calculation. Combined with the structure of the model and the method used, we refer to this model as TBACkp. The dataset comprises the enhanced abdominal CT images of 151 patients with liver metastases from breast cancer, together with the corresponding HER2 expression levels for each patient. The experimental results are as follows: (AUC: 0.915, ACC: 0.854, specificity: 0.809, precision: 0.863, recall: 0.881, F1-score: 0.872). The results demonstrate that this method can accurately assess the HER2 expression status in patients when compared with other advanced deep learning model.

Evaluation of TIEGCM based on GOCE neutral density

The Thermosphere Ionosphere Electrodynamic General Circulation Model (TIEGCM), as one of the most advanced physical models of the Earth’s thermosphere and ionosphere, is not only widely used in scientific research, but also has essential reference value in aerospace operations. In this study, we use Gravity field and steady-state Ocean Circulation Explorer (GOCE) neutral density to evaluate the accuracy of the TIEGCM. The assessment is performed on both time and spatial scales. The time scales are conducted annually, monthly, and daily, while the spatial scales are carried out in terms of altitude, latitude, and local time. On the time scales, the performance of the TIEGCM on the monthly time scale is better than that on the annual time scale. Also, the performance on the daily time scale is better than that on the monthly time scale. The relative deviation shows a significant seasonal variation, that is, larger in winter and summer and smaller in spring and autumn. In addition, the relative deviation shows a negative correlation with F10.7 and Ap. On the spatial scale, with the increase in altitude, the average relative deviation of the model becomes larger in general. The relative deviation is usually larger at middle latitudes in the Northern Hemisphere and high latitudes in the Southern Hemisphere. Finally, on the scale of local time, the relative deviation changes more dramatically in local morning than at dusk.

Modeling of an all-solid-state battery with a composite positive electrode

All solid-state batteries are considered as the most promising battery technology due to their safety and high energy density. This study presents an advanced mathematical model that accurately simulates the complex behavior of all-solid-state lithium-ion batteries with composite positive electrodes. The partial differential equations of ionic transport and potential dynamics in the electrode and electrolyte are solved and reduced to a low-order system with Padé approximation. Moreover, the imperfect contact and the electrical double layers at the solid-solid interface are also taken into consideration. Subsequent experiments are conducted for the blocked cell and half-cells to extract parameters. Next, the parameterized model is validated with extensive experimental data from NCM811/LPSC/Li4.4Si batteries, illustrating the superior capability of predicting cell voltage with an average RMSE of 19.5 mV for the discharging/charging phases and 2.8 mV for the end of relaxation under a total of 15 conditions. From the simulations, it can be concluded that the limiting factors for battery performance are overpotentials caused by concentration polarization within positive particles and interface reactions. Finally, through a parameter sensitivity analysis, we offer strategic guidelines for optimizing battery performance, thus enhancing the development efficiency of ASSBs.

ChatGPT-A Generative Pre-Trained Transformer

ChatGPT is an advanced conversational AI model developed by OpenAI. It is designed to engage in natural and coherent conversations with users, providing human-like responses to a wide range of topics and questions. It leverages deep learning technique. ChatGPT uses a combination of machine learning techniques, including deep learning and natural language processing, to understand and generate human-like text. The model has been trained on a vast amount of internet text data to ensure its ability to generate relevant and contextually accurate responses. ChatGPT has applications in customer service, virtual assistants, and other conversational interfaces, offering a powerful tool for natural language understanding and generation. It is most likely used to generate human like responses and makes the communication interactive.

Identifying key soil characteristics for Francisella tularensis classification with optimized Machine learning models

Francisella tularensis (Ft) poses a significant threat to both animal and human populations, given its potential as a bioweapon. Current research on the classification of this pathogen and its relationship with soil physical–chemical characteristics often relies on traditional statistical methods. In this study, we leverage advanced machine learning models to enhance the prediction of epidemiological models for soil-based microbes. Our model employs a two-stage feature ranking process to identify crucial soil attributes and hyperparameter optimization for accurate pathogen classification using a unique soil attribute dataset. Optimization involves various classification algorithms, including Support Vector Machines (SVM), Ensemble Models (EM), and Neural Networks (NN), utilizing Bayesian and Random search techniques. Results indicate the significance of soil features such as clay, nitrogen, soluble salts, silt, organic matter, and zinc , while identifying the least significant ones as potassium, calcium, copper, sodium, iron, and phosphorus. Bayesian optimization yields the best results, achieving an accuracy of 86.5% for SVM, 81.8% for EM, and 83.8% for NN. Notably, SVM emerges as the top-performing classifier, with an accuracy of 86.5% for both Bayesian and Random Search optimizations. The insights gained from employing machine learning techniques enhance our understanding of the environmental factors influencing Ft’s persistence in soil. This, in turn, reduces the risk of false classifications, contributing to better pandemic control and mitigating socio-economic impacts on communities.

Driver-Centric Urban Route Planning: Smart Search for Parking

With urbanization, there is a growing need for mobility. Challenges for urban drivers include finding available parking spaces. Searching for a parking spot can be a frustrating experience, often time consuming and costly. Also, the increasing number of vehicles on the roads leads to an additional strain on traffic flow, while the search for parking spaces lowers the level of service. In inner cities, vehicles circulate in search of an available parking space, leading to an increase in travel time, fuel consumption, pollutant emissions, and a decrease in traffic safety. The search for a free parking space generates a significant increase in traffic in urban areas. To solve the parking search problem, it is necessary to develop certain strategies and measures that minimize circling in search of a parking space. The implementation of intelligent transportation systems stands out. By applying intelligent transport systems, drivers are provided with information about free parking spaces, which reduces the circulation of vehicles in search of free parking. Although initially ITS systems mainly provided services for closed parking lots and garages, with the further development of the system, the service was extended to street parking lots or open-type parking lots. These measures not only solve traffic challenges but also promote sustainability in urban areas. This article analyzes the effect of a cooperative approach of guiding vehicles to available parking spaces compared to a standard model of searching for an available parking space. Within the framework of the advanced model for searching for available space, four parking demand scenarios were defined and simulated. Based on the created traffic simulation, a comparative analysis was made between the classic and cooperative approach, while the primary differences are manifested in the load of the traffic flow A simulation model was developed using the road network from the urban center of Zagreb.

Research on the short-term wind power prediction with dual branch multi-source fusion strategy

The smooth operation of the power system imposes high requirements on wind power forecasting. Given the forecasting challenges brought by the coexistence of strong and weak disturbances in wind power data, this paper proposes a hierarchical anti-disturbance mechanism to raise the accuracy of single-point wind power prediction. In the first-level disturbance processing, we prioritize the treatment of strong disturbances in wind power data through a dual-branch multi-source fusion prediction structure. The wind power subsequences with distinct time–frequency characteristics are divided into two sets, processed by the stationary set prediction branch and the drastic set prediction branch, respectively. For the drastic set, we incorporate microenvironmental multi-source data surrounding the wind turbine as input and propose a parallel-structured attention fusion module CA (Convolution attention fusion block) to provide effective data support for the prediction module, then extract rich fusion features through deep temporal LSTM algorithm and ultimately achieve fine-grained forecasting. For weak disturbances that have not been adequately addressed in the previous step, we conduct a secondary treatment through error correction strategy. By employing the hierarchical processing approach, this paper accomplishes a comprehensive handling of disturbances with different intensities. Experimental results on four data sets show that the proposed model DEWFM (Dual-branch and error correction wind power forecast) demonstrates remarkable performance advantages in comparing with the baseline model and other advanced models, the average values of MAE reaching 1.679, revealing the effectiveness of the proposed method in enhancing the precision of short-term wind power prediction.

An advanced decision-support system for ranking unbalanced bids

Awarding a contract to a bidder who has unbalanced the bid may cost more money to the owner. An owner who determines that a bid has been unbalanced has the legal right to reject the bid. Although several models have been developed to detect unbalanced bids, some researchers still assert that the number of cases in which owners mistakenly identify balanced bids as unbalanced is too great to ignore. One of the reasons of this failure is the use of an inappropriate benchmark against which the offered unit prices are compared. In this paper it is argued that the use of the geometric mean of the unit prices provided by the bidders as a benchmark helps to obtain robust solutions and an advanced decision-making model is proposed in which the Information Entropy and VIKOR methods are used to detect unbalanced bids. The results obtained from an example project drawn from the literature demonstrate how owners may be misled if they use an inappropriate benchmark in detecting unbalanced bids. This study contributes to the body of knowledge by highlighting the negative impacts of using flawed benchmarks in this process, an area of research that is mostly ignored in the unbalanced bidding literature.

Nanosphere Chain Model of Magnetic Fluid and Its Dynamic Performance.

The magnetorheological effect is a critically important mechanical property of magnetic fluids. Accurately capturing the macroscopic properties of magnetorheological fluids with elongated particle forms, such as nanosphere chains, remains a challenging task, particularly due to the complexities arising from particle asymmetry. Traditional particle dynamics primarily utilize spherical particles as computational units, but this approach can lead to significant inaccuracies, especially when analyzing nonspherical magnetorheological fluids, due to the neglect of particle asymmetry. In this work, an advanced particle dynamics model has been developed by integrating the rotation and collision of these asymmetric particles, specifically tailored for the configuration of nanosphere chains. This model exhibits a significant reduction in error by a factor of 3.883, compared to conventional particle models. The results demonstrate that alterations in the geometric characteristics of magnetic nanosphere chains can cause changes in mesoscopic structures and magnetic potential energy, thereby influencing the mechanical properties at the macroscopic level. This work has developed an accurate mesoscopic simulation method for calculating chain-type magnetorheological fluids, establishing a connection between mesoscopic structures and macroscopic properties, and unveiling the tremendous potential for accelerating the design of next-generation magnetic fluids using this approach.

Light-YOLO: A Lightweight and Efficient YOLO-Based Deep Learning Model for Mango Detection

This paper proposes a lightweight and efficient mango detection model named Light-YOLO based on the Darknet53 structure, aiming to rapidly and accurately detect mango fruits in natural environments, effectively mitigating instances of false or missed detection. We incorporate the bidirectional connection module and skip connection module into the Darknet53 structure and compressed the number of channels of the neck, which minimizes the number of parameters and FLOPs. Moreover, we integrate structural heavy parameter technology into C2f, redesign the Bottleneck based on the principles of the residual structure, and introduce an EMA attention mechanism to amplify the network’s emphasis on pivotal features. Lastly, the Downsampling Block within the backbone network is modified, transitioning it from the CBS Block to a Multi-branch–Large-Kernel Downsampling Block. This modification aims to enhance the network’s receptive field, thereby further improving its detection performance. Based on the experimental results, it achieves a noteworthy mAP of 64.0% and an impressive mAP0.5 of 96.1% on the ACFR Mango dataset with parameters and FLOPs at only 1.96 M and 3.65 G. In comparison to advanced target detection models like YOLOv5, YOLOv6, YOLOv7, and YOLOv8, it achieves improved detection outcomes while utilizing fewer parameters and FLOPs.

Equilibrium points and their stability of COVID-19 in US

This study aims to develop an advanced mathematic model and investigate when and how will the COVID-19 in the US be evolved to endemic. We employed a nonlinear ordinary differential equations-based model to simulate COVID-19 transmission dynamics, factoring in vaccination efforts. Multi-stability analysis was performed on daily new infection data from January 12, 2021 to December 12, 2022 across 50 states in the US. Key indices such as eigenvalues and the basic reproduction number were utilized to evaluate stability and investigate how the pandemic COVD-19 will evolve to endemic in the US. The transmissional, recovery, vaccination rates, vaccination effectiveness, eigenvalues and reproduction numbers (R0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}_{0}$$\\end{document} and R0end\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}_{0}^{end}$$\\end{document}) in the endemic equilibrium point were estimated. The stability attractor regions for these parameters were identified and ranked. Our multi-stability analysis revealed that while the endemic equilibrium points in the 50 states remain unstable, there is a significant trend towards stable endemicity in the US. The study's stability analysis, coupled with observed epidemiological waves in the US, suggested that the COVID-19 pandemic may not conclude with the virus's eradication. Nevertheless, the virus is gradually becoming endemic. Effectively strategizing vaccine distribution is pivotal for this transition.

Deep Learning in Carbon Neutrality Forecasting

With the growing urgency of global climate change, carbon neutrality, as a strategy to reduce greenhouse gas emissions into the atmosphere, is increasingly seen as a critical solution. However, current forecasting models still face significant challenges and limitations in accurately and effectively predicting carbon emissions and their associated effects. These challenges largely stem from the complexity of carbon emission data and the interplay of anthropogenic and natural factors. To overcome these obstacles, the authors introduce an advanced forecasting model, the SSA-Attention-BIGRU network. This model ingeniously integrates an external attention mechanism, bidirectional GRU, and SSA components, allowing it to synthesize various key factors and enhance prediction accuracy when forecasting carbon neutrality trends. Through experiments on multiple datasets, the results demonstrate that, compared to other popular methods, the SSA-Attention-BIGRU network significantly excels in prediction accuracy, robustness, and reliability.

Global Sensitivity Analysis of the Advanced ORYZA-N Model with Different Rice Types and Irrigation Regimes.

Identifying important parameters in crop models is critical for model application. This study conducted a sensitivity analysis of 23 selected parameters of the advanced rice model ORYZA-N using the Extended FAST method. The sensitivity analysis was applied for three rice types (single-season rice in cold regions and double-season rice (early rice and late rice) in subtropical regions) and two irrigation regimes (traditional flood irrigation (TFI) and shallow-wet irrigation (SWI)). This study analyzed the parameter sensitivity of six crop growth outputs at four developmental stages and yields. Furthermore, we compared the variation in parameter sensitivity on model outputs between TFI and SWI scenarios for single-season rice, early rice, and late rice. Results indicated that parameters RGRLMX, FRPAR, and FLV0.5 significantly affected all model outputs and varied over developmental stages. Water stress in paddy fields caused by water-saving irrigation had more pronounced effects on single-season rice than on double-season rice.