Results in Control and Optimization最新文献

{"title":"Theoretical and numerical study of profit in agricultural sector model using wavelet method","authors":"Yeshwanth R.,&nbsp;Kumbinarasaiah S.","doi":"10.1016/j.rico.2025.100526","DOIUrl":"10.1016/j.rico.2025.100526","url":null,"abstract":"<div><div>Agriculture is crucial in India’s economy and society, supporting rural livelihood and national food security. India is one of the world’s largest agricultural producers, with diverse crops and farming practices. This work aims to present the Chebyshev wavelet collocation method (CWCM) for assessing and producing a numerical approximation of profit in a fractional-order agriculture sector model. Together, numerical simulations and mathematical modeling analysis enhance agricultural management and understanding while offering crucial insights into the dynamics of agricultural profit. Numerical results are obtained from a mathematical and financial perspective using the model parameters for model validation. Additionally, the error and convergence analysis of the Chebyshev wavelets has been presented to assess the applicability of the proposed approach. This study aims to construct Chebyshev wavelet operational matrix of integration (OMIs) and apply them to the numerical solution of fractional differential equations representing the agriculture model. The operational matrices are used to simplify fractional differential equations to an algebraic system of equations. Finally, we graphically depict the results and offer empirical support for our theoretical conclusions through graphic representations. The CWCM approach generates precise results with better absolute error (Ae) for highly nonlinear scenarios by computing a small number of terms and avoiding data rounding. The results of the developed method, the RK4 method, and the ND solver have been compared. The numerical findings demonstrate how well (CWCM) solves the fractional order agriculture model in terms of accuracy and efficiency. Mathematica is a mathematical program used for numerical calculations and implementation.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"18 ","pages":"Article 100526"},"PeriodicalIF":0.0,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of awareness in self–monitoring of COVID-19: An optimal control approach","authors":"Piu Samui ,&nbsp;Jayanta Mondal ,&nbsp;Amar Nath Chatterjee ,&nbsp;Fahad Al Basir","doi":"10.1016/j.rico.2024.100513","DOIUrl":"10.1016/j.rico.2024.100513","url":null,"abstract":"<div><div>COVID-19 has been a significant global health concern since the last quarter of 2019, prompting ongoing research to understand the transmission patterns and develop global intervention methods. This article presents a Scompartmental model that demonstrates the effectiveness of self-monitoring among individuals as a non-pharmaceutical method in reducing infection progression. The existence of equilibria and their stability have been studied on the basic of the basic reproduction number (<span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>). We observed forward bifurcation at <span><math><mrow><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub><mo>=</mo><mn>1</mn></mrow></math></span>. We have applied optimal control theory and formulated a three control parameters optimal control problem (OCP) to study non-therapeutic measures like increasing awareness of COVID-19 among susceptible and symptomatic individuals and therapeutic measures like providing improved treatment to hospitalized individuals. The cost-effectiveness of these strategies is also analyzed, suggesting the coexistence of possible treatment routes in controlling the virus.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"18 ","pages":"Article 100513"},"PeriodicalIF":0.0,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Daily allocation of energy consumption forecasting of a power distribution company using optimized least squares support vector machine","authors":"Marzia Ahmed ,&nbsp;Mohd Herwan Sulaiman ,&nbsp;Md. Maruf Hassan ,&nbsp;Md. Atikur Rahaman ,&nbsp;Mohammad Bin Amin","doi":"10.1016/j.rico.2025.100518","DOIUrl":"10.1016/j.rico.2025.100518","url":null,"abstract":"<div><div>Accurate energy consumption forecasting is critical for efficient power distribution management. This study presents a novel approach for optimal allocation forecasting of energy consumption in a power distribution company, utilizing the Least Squares Support Vector Machine (LSSVM) optimized by novel variants of the Barnacle Mating Optimizer (BMO) such as the new Gooseneck Barnacle Optimizer and Selective Opposition-based constrained BMO. The optimized LSSVM hyper-parameters, specifically the regularization parameter (<span><math><mi>γ</mi></math></span>) and the kernel parameter (<span><math><msup><mrow><mi>σ</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>), were applied to test data to enhance accuracy guided by the Mean Absolute Prediction Error (MAPE), ensuring precise alignment of forecasted values with actual energy consumption data. The results indicate that the novel gooseneck barnacle base-optimized LSSVM provides a robust and reliable solution with accuracy 99.98% for daily energy consumption for allocation forecasting, making it a valuable tool for power distribution companies aiming to optimize their resource allocation and planning processes.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"18 ","pages":"Article 100518"},"PeriodicalIF":0.0,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal control applied to a stage-structured cassava mosaic disease model with vector feeding behavior","authors":"Eva Lusekelo ,&nbsp;Mlyashimbi Helikumi ,&nbsp;Salamida Daudi ,&nbsp;Steady Mushayabasa","doi":"10.1016/j.rico.2025.100522","DOIUrl":"10.1016/j.rico.2025.100522","url":null,"abstract":"<div><div>Cassava remains Sub-Saharan Africa’s second most crucial staple food crop after maize. However, production of sufficient yields is hampered by pests and diseases. In particular, the whitefly (<em>Bemisia tabaci</em>) has the potential to reduce expected yields by 50% since it directly damages cassava leaves by feeding on phloem, causing chlorosis and abscission. This study develops a novel mathematical model for cassava mosaic disease that incorporates immature and adult whitefly populations. Additionally, the model includes vector feeding behavior since prior studies have shown that vectors exhibit preferences to settle for either healthy or infected hosts. We determined the offspring number and carried out its sensitivity analysis. Additionally, we carried out an optimal control study on the use of insecticides and plant roguing as disease control measures against cassava mosaic disease. Our results show that vector preference and efficiency of disease control strategies plays an important role in shaping the short and long-term dynamics of cassava mosaic disease, which subsequently impacts the design of its optimal control strategies.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"18 ","pages":"Article 100522"},"PeriodicalIF":0.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143176330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sensitivity analysis and uncertainty quantification of climate change effects on Tanzanian banana crop yield","authors":"Sabas Patrick ,&nbsp;Silas Mirau ,&nbsp;Isambi Mbalawata ,&nbsp;Judith Leo","doi":"10.1016/j.rico.2025.100519","DOIUrl":"10.1016/j.rico.2025.100519","url":null,"abstract":"<div><div>Concerns about the impact of climate change on agricultural systems have heightened, particularly in regions where crop cultivation is essential for economic stability and sustenance. This research addresses a critical gap in understanding by investigating how climate change influences Tanzania’s bananas, a vital component of the country’s agricultural sector. The study used a multiple regression model to analyze the correlation between bananas and key climate variables in Tanzania, the results showed gradual decrease in bananas. Specifically, the climate variables, including precipitation (<span><math><msub><mrow><mi>X</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>), soil moisture (<span><math><msub><mrow><mi>X</mi></mrow><mrow><mn>2</mn></mrow></msub></math></span>), minimum temperature (<span><math><msub><mrow><mi>X</mi></mrow><mrow><mn>3</mn></mrow></msub></math></span>), maximum temperature (<span><math><msub><mrow><mi>X</mi></mrow><mrow><mn>4</mn></mrow></msub></math></span>), and relative humidity (<span><math><msub><mrow><mi>X</mi></mrow><mrow><mn>5</mn></mrow></msub></math></span>) have coefficients 0.0206, −0.0085, 4.8328, −1.6594, and −0.0991, respectively. In this case, a large positive coefficient and a negligible negative coefficient show that the independent variable greatly influences the yield of the bananas. Additionally, the study utilize two powerful global sensitivity analysis methods, Sobol’ Sensitivity Indices and Response Surface Methodology, to comprehensively explore the sensitivity of bananas to climate variables. So, these methods revealed that minimum temperature, precipitation and soil moisture have the most impact on bananas and affect the crop’s production variability. Uncertainty quantification was performed using Monte Carlo simulation, estimating uncertainties in model parameters to enhance the reliability of the findings. This research not only contributes to our broader understanding of how climate change impacts bananas but also offers practical policy suggestions tailored to Tanzania’s unique context, ensuring resilience and sustainability in the face of environmental changes. The outcomes of this study carry significance for policymakers, stakeholders, and farmers, providing actionable insights to shape adaptive agricultural strategies. By bridging the gap between climate change and bananas, this research offers valuable contributions to the broader field of agricultural sustainability.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"18 ","pages":"Article 100519"},"PeriodicalIF":0.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143176331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fractional optimal control problem modeling bovine tuberculosis and rabies co-infection","authors":"Boubacar Diallo ,&nbsp;Munkaila Dasumani ,&nbsp;Jeconia Abonyo Okelo ,&nbsp;Shaibu Osman ,&nbsp;Oumar Sow ,&nbsp;Nnaemeka Stanley Aguegboh ,&nbsp;Walter Okongo","doi":"10.1016/j.rico.2025.100523","DOIUrl":"10.1016/j.rico.2025.100523","url":null,"abstract":"<div><div>Bovine tuberculosis (bTB) and rabies are eminent zoonotic afflictions that significantly impact global economic stability and public health, with pronounced effects in developing nations. These diseases continuously pressure public health systems and obstruct efforts to improve livestock productivity and export capabilities. Studying the joint dynamics of bTB and rabies involves notable mathematical complexities due to the differences in their transmission mechanisms. Moreover, while there is some overlap among animal populations at risk for bTB and rabies, the exact proportion of animals susceptible to both diseases remains unspecified. In this work, we provide a simplified fractional-order optimal control model that integrates the dynamics of bTB and rabies co-infection. We determine the basic reproduction numbers for bovine tuberculosis <span><math><mfenced><mrow><msubsup><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow><mrow><mi>T</mi></mrow></msubsup></mrow></mfenced></math></span> and rabies <span><math><mrow><mo>(</mo><msubsup><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow><mrow><mi>R</mi></mrow></msubsup><mo>)</mo></mrow></math></span>, as well as the overall reproduction number for the model <span><math><mrow><mi>R</mi><mo>=</mo><mo>max</mo><mrow><mo>{</mo><msubsup><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow><mrow><mi>T</mi></mrow></msubsup><mo>,</mo><msubsup><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow><mrow><mi>R</mi></mrow></msubsup><mo>}</mo></mrow></mrow></math></span>. The qualitative analysis reveals that when <span><math><mrow><mi>R</mi><mo>&lt;</mo><mn>1</mn></mrow></math></span>, the disease-free equilibrium is locally asymptotically stable. We implement optimal control analysis to identify the best strategies for preventing each disease separately and their co-infection. The optimal control problem is solved numerically utilizing a forward–backward predict-evaluate correct-evaluate (PECE) algorithm implemented in Matlab software. The simulation results show that <strong>strategy E</strong> (<span><math><mrow><mi>i</mi><mo>.</mo><mi>e</mi><mo>.</mo></mrow></math></span>, implementation of all optimal controls) is significantly more effective in managing bovine tuberculosis but less effective in controlling rabies and co-infection. Conversely, <strong>strategy B</strong> (<span><math><mrow><mi>i</mi><mo>.</mo><mi>e</mi><mo>.</mo></mrow></math></span>, applying vaccination and removal of optimal controls for animals affected by rabies) provides satisfactory optimal control results across the three infection scenarios.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"18 ","pages":"Article 100523"},"PeriodicalIF":0.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143176334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Droop control in grid-forming converters using a fractional-order PI controller: A power system transient analysis","authors":"Luis L. Chiza ,&nbsp;Diego Benítez ,&nbsp;Rommel Aguilar ,&nbsp;Oscar Camacho","doi":"10.1016/j.rico.2025.100517","DOIUrl":"10.1016/j.rico.2025.100517","url":null,"abstract":"<div><div>The integration of renewable energy sources in modern power grids introduces challenges in ensuring stable and efficient operation, especially during transient conditions and disturbances. One of the primary issues is the inadequate transient response of conventional droop control strategies in grid-forming (GFM) converters, which can impair system stability and performance under unbalanced load conditions. This article addresses these issues by introducing a fractional-order PI (FOPI) control strategy for droop control of GFM converters, aimed at improving the transient response and enhancing the overall stability of the system. The FOPI controller’s design allows for more flexible tuning of dynamic behaviors compared to traditional integer-order controllers, making it particularly effective for bolstering stability and fault tolerance. To optimize the parameters of the FOPI controller, continuous Monte Carlo simulation is used, focusing on performance under unbalanced load disturbances. The controller’s effectiveness is assessed using the Integral of Squared Error (ISE) and Integral of Squared Control Output (ISCO) metrics to balance accuracy and control effort. The simulation results under two fault scenarios demonstrate that the FOPI controllers significantly enhance the transient response and fault tolerance. In case 1, replacing the PI controllers with the FOPI controllers reduces error by 65% and improves energy efficiency by 16%. In case 2, FOPI controllers achieve an error reduction of 83% and an improvement in energy efficiency 15%. These findings highlight the effectiveness of FOPI controllers in improving control accuracy and efficiency in fault conditions.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"18 ","pages":"Article 100517"},"PeriodicalIF":0.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143176328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient task scheduling and computational offloading optimization with federated learning and blockchain in mobile cloud computing","authors":"Matheen Fathima G,&nbsp;Shakkeera L","doi":"10.1016/j.rico.2025.100524","DOIUrl":"10.1016/j.rico.2025.100524","url":null,"abstract":"<div><div>Smartphones and other mobile device users are becoming increasingly susceptible to malicious applications or apps that compromise user privacy. Malicious applications are more invasive than required because they require less authorization to operate them. The Android platform is more vulnerable to attacks since it is open-source, allows third-party app stores and it has extensive app screening. Thus the usage of mobile cloud applications has also expanded due to android platform. The mobile apps are useful for e-transportation, augmented reality, 2D and 3D games, e-health care and education. Consequently, maintaining MCC security and optimization of resources according to the task becomes significant task. Though recent research has been focused in the area of task scheduling, supporting multiple objectives still becomes a significant issue due to the Non-deterministic Polynomial (NP) hard problem. In this paper, Federated Learning with Blockchain Technology (FLBCT) is introduced for Microservice-based Mobile Cloud Computing Applications (MSCMCC). Mobile app permissions dataset has to be offloaded to a mobile cloud and protected using FL and BCT. FL permit mobile users to train models without sending raw data to third-party servers. FL is also used to trains the data across various decentralized devices holding of samples without exchanging them. BCT is introduced for enhancing data traceability, trust, security and transparency among participating companies. Resource matching, task sequencing, and task scheduling are major steps of Optimization Task Scheduling based Computational Offloading (OTSCO) framework. OTSCO framework increases application efficiency and gives the successful resource constraints to increase application-based efficiency, tasks are executed under deadline, and minimize application cost. The proposed system has a lower overhead of 20.14%, lesser boot time of 20.47 ms, lesser CPU usage of 0.45%, failure task ratio of the suggested system is 2.52%. It shows that the proposed system is easily applicable to Task Scheduling, and gives more security on MCC.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"18 ","pages":"Article 100524"},"PeriodicalIF":0.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On generation of Julia sets, Mandelbrot sets and biomorphs via a modification of the viscosity approximation method","authors":"Rimsha Babar,&nbsp;Wutiphol Sintunavarat","doi":"10.1016/j.rico.2025.100516","DOIUrl":"10.1016/j.rico.2025.100516","url":null,"abstract":"<div><div>Iterative methodology has been demonstrated to be an achievement in the age of fractals. A novel method based on the viscosity approximation approach, one of the most popular iterative techniques for identifying non-linear operator fixed points, for visualizing Mandelbrot and Julia sets for a complex polynomial <span><math><mrow><mi>G</mi><mrow><mo>(</mo><mi>z</mi><mo>)</mo></mrow><mo>=</mo><msup><mrow><mi>z</mi></mrow><mrow><mi>m</mi></mrow></msup><mo>+</mo><mi>a</mi><mi>z</mi><mo>+</mo><mi>b</mi></mrow></math></span>, where <span><math><mi>z</mi></math></span> is a complex variable, <span><math><mrow><mi>m</mi><mo>∈</mo><mi>N</mi><mo>∖</mo><mrow><mo>{</mo><mn>1</mn><mo>}</mo></mrow></mrow></math></span> and <span><math><mrow><mi>a</mi><mo>,</mo><mspace></mspace><mi>b</mi><mo>∈</mo><mi>ℂ</mi></mrow></math></span> are parameters, is presented in this paper. Using the proposed approximation method, we establish a novel escape criterion for producing Julia and Mandelbrot sets. This method yields biomorphs for any complex function. Additionally, we visualize the sets using the escape time approach and the suggested iteration. Then, using graphical and numerical experiments, we explore how the shape of the resulting sets changes depending on the iteration parameters. The examples show that this transformation can be highly complex, and we can acquire a wide range of shapes.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"18 ","pages":"Article 100516"},"PeriodicalIF":0.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143176329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measurement-based characterization of an RF Transmitter to offset the effects of nonlinearities","authors":"Fadia Noori Hummadi ,&nbsp;Ekhlas Kadhum Hamza ,&nbsp;Ali M.J. Zalzala ,&nbsp;Ahmad H. Sabry","doi":"10.1016/j.rico.2025.100521","DOIUrl":"10.1016/j.rico.2025.100521","url":null,"abstract":"<div><div>Power amplifier characterization is an important process that is used to design and optimize PA circuits, and to troubleshoot problems with existing PA circuits. Measured input and output signals are used to determine the key parameters of a PA. However, PA characterization can be challenging due to the nonlinearity of PAs and the high frequencies at which they are often operated. This paper presents a comprehensive characterization of a power amplifier (PA) using measurement-based techniques. The study focuses on comparing the performance of memoryless and polynomial memory models in accurately predicting the PA's behavior. Results demonstrate that the polynomial memory model consistently outperforms the memoryless model, particularly at lower bandwidths. Key findings include the significant impact of memory effects on the PA's nonlinearity and the moderate influence of bandwidth on model accuracy. The proposed methodology provides a valuable framework for characterizing PAs and optimizing their performance in various applications. The results obtained indicated that for the OFDM signals with varying bandwidths, the time-based error as a percentage RMS for the nonlinear memoryless model was approximately 6 %, which is roughly greater than the inaccuracy of the polynomial memory model by three times, which is approximately 2 %.</div></div>","PeriodicalId":34733,"journal":{"name":"Results in Control and Optimization","volume":"18 ","pages":"Article 100521"},"PeriodicalIF":0.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143176332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}