Analysis Model Research Articles

Cost analysis of patiromer versus sodium zirconium cyclosilicate for the treatment of hyperkalemia in Spain and the UK.

Hyperkalemia is an electrolyte abnormality with potentially life-threatening consequences. Published data have shown that potassium-binding polymer patiromer (Veltassa) is associated with reduced rates of severe edema and hospitalization for heart failure compared with sodium zirconium cyclosilicate (SZC, Lokelma) when treating hyperkalemia. The aim of this study was to evaluate the possible costs associated with these interventions in the Spanish and UK settings. A cost analysis model was developed in Microsoft Excel to compare the costs associated with patiromer and SZC for the management of hyperkalemia. Clinical event rates were taken from a published real-world comparative study, with the base case capturing the statistically significant reduction in severe edema with patiromer versus SZC and a sensitivity analysis also including the non-statistically significant reduction in hospitalization for heart failure. Country-specific costs, expressed in 2022 British pound sterling (GBP) and Euros (EUR), were evaluated from a healthcare payer perspective and included pharmacy costs and costs of clinical events. Patiromer may be associated with cost savings of EUR 107 and GBP 630 per patient-year of treatment versus SZC in Spain and the UK, respectively. The majority of cost savings were due to the possible lower daily cost of patiromer compared with SZC. Including the difference in heart failure hospitalization rates in a sensitivity analysis led to greater cost savings with patiromer over SZC, increasing to EUR 460 and GBP 902 in Spain and the UK, respectively. Extrapolation of patient-level economic outcomes to a population level found that patiromer was associated with annual cost savings of EUR 30.6 million in Spain, and GBP 801.7 million in the UK versus SZC. Patiromer has the potential to be cost saving versus SZC for the treatment of hyperkalemia in Spain and the UK based on the results of a real-world evidence analysis.

Ecosystem Services Approach in Turnicki National Park Planning: Factors Influencing the Inhabitants' Perspectives on Local Natural Resources and Protected Areas.

Despite changing paradigms in nature conservation, protected areas, such as national parks, remain key tools for nature conservation. Today, protected areas are perceived as socio-ecological systems, therefore using an ecosystem services approach may help in their designation. Here, we focus on the planned Turnicki National Park located in the far eastern part of the Polish Carpathian Mountains and conflict between proponents of the park establishment and local stakeholders. We used an ecosystem services-driven questionnaire survey among local communities to analyze interactions between the perception of ecosystem services and opinions about national parks, and the role of social and economic status in shaping these opinions. We found links between opinions towards national parks and other factors: age, life span in a municipality, level of education, and an average net income. Respondents who perceived benefits from nature were more positive towards national parks in general and the Turnicki National Park specifically; however, those who prioritized provisioning services were more skeptical. Also, we distinguished four Fuzzy-Set Qualitative Comparative Analysis models which describe factors shaping opinions on national parks, respectively. The study has shown that the ecosystem services lens perspective can help in exploring the factors crucial while establishing the protected areas in specific social and economic context. The main implication for the study is careful consideration of the role of national park to protect the local environment in harmony with social needs and economic development.

Jet classification using high-level features from anatomy of top jets

Recent advancements in deep learning models have significantly enhanced jet classification performance by analyzing low-level features (LLFs). However, this approach often leads to less interpretable models, emphasizing the need to understand the decision-making process and to identify the high-level features (HLFs) crucial for explaining jet classification. To address this, we consider the top jet tagging problems and introduce an analysis model (AM) that analyzes selected HLFs designed to capture important features of top jets. Our AM mainly consists of the following three modules: a relation network analyzing two-point energy correlations, mathematical morphology and Minkowski functionals for generalizing jet constituent multiplicities, and a recursive neural network analyzing subjet constituent multiplicity to enhance sensitivity to subjet color charges. We demonstrate that our AM achieves performance comparable to the Particle Transformer (ParT) while requiring fewer computational resources in a comparison of top jet tagging using jets simulated at the hadronic calorimeter angular resolution scale. Furthermore, as a more constrained architecture than ParT, the AM exhibits smaller training uncertainties because of the bias-variance tradeoff. We also compare the information content of AM and ParT by decorrelating the features already learned by AM. Lastly, we briefly comment on the results of AM with finer angular resolution inputs.

Proposal of a scissor-based model for the non-linear analysis of RC beam-column joints strengthened by FRP

The paper presents a new simple modeling approach for studying the monotonic response of RC beam-column joints externally strengthened by Fiber Reinforced Polymer (FRP) materials. The approach assumes a parallel combination of the behavior of the joint in the unstrengthened configuration and the contribution of strengthening system. To achieve this, the common scissor model, where the behavior of joint shear panel is modeled through a rotational spring, is here modified by introducing an additional spring, arranged parallel to the concrete spring, to account for the contribution of strengthening system. Regarding the behavior of the additional spring, the authors proposed a multilinear simplified constitutive law by appropriately combining analytical models available from the literature with specific guidelines provided by Italian technical standards. The proposed model, implemented in the computer code OpenSees and validated against experimental case studies from the literature, demonstrates its strong capability to capture the contribution of the strengthening system on both the strength and ductility of joints.

Analysis of neural networks in text sentiment

The proliferation of digital media and online platforms has led to a significant increase in the production and distribution of textual data. Consequently, the study of text sentiment analysis has emerged as a crucial area of research within the domain of natural language processing. The objective of sentiment analysis is to detect and categorize emotional inclinations in text, which has substantial practical importance in comprehending public sentiment, monitoring brand reputation, and other related areas. This research does a comprehensive investigation of the utilization of neural network models in text sentiment analysis using literature review methods. It examines the difficulties encountered and the approaches employed to overcome these difficulties. The research encompasses the fundamental principles and techniques of text sentiment analysis utilizing neural networks, as well as the utilization of convolutional neural networks (CNN), recurrent neural networks (RNN)/long short-term memory networks (LSTM), attention mechanisms, and pre-trained models in the field of sentiment analysis. Furthermore, this study delves into the complexities associated with data noise, the interpretability of models, the analysis of long text and multimodal sentiment, as well as the utilization of transfer learning and domain adaptation techniques. The study's importance is in establishing a theoretical basis and practical recommendations for the advancement of sentiment analysis technology, particularly in enhancing model precision, comprehensibility, and range of applications, thereby delivering useful insights.

Seismic Behavior of Steel Reinforced Ultra-High Strength Concrete Composite Frame: Experimental and Numerical Study

The seismic behavior of steel reinforced ultra-high strength concrete (SRUHSC) composite frame was investigated through finite element analysis (FEA) modeling. A FEA model for the seismic analysis of the SRUHSC frame was first established and verified with test results. The numerical model was subsequently used to study the seismic performance of the SRUHSC frame, including the P-Δ skeleton curves, the stiffness degradation, the failure mode, the subsequence mechanisms of plastic hinges and the stress–strain distribution. Finally, a parametric study was carried out to investigate the effect of salient parameters on the behavior of the SRUHSC frame. It was found that with the increment of the concrete strength, yield strength of steel, and linear stiffness ratio of beam to column, the horizontal load-bearing capacity and the elastic stiffness of the structure were improved, but there was no significant effect on the ductility. With the increment of the volume stirrup ratio and structural steel ratio, the horizontal load-bearing capacity and the ductility of the structure were both improved. However, with the increment of the axial-load ratio, there was no obvious change in the elastic stiffness of the structure, but the horizontal load bearing capacity and the ductility of the structure decreased obviously. In addition, the accuracy of a concrete constitutive model in the different degrees of constraint for the SRUHSC frame proposed by the authors was verified with the FEA model.

Anti-Ku + myositis: an acquired inflammatory protein-aggregate myopathy

Myositis with anti-Ku-autoantibodies is a rare inflammatory myopathy associated with various connective tissue diseases. Histopathological studies have identified inflammatory and necrotizing aspects, but a precise morphological analysis and pathomechanistic disease model are lacking. We therefore aimed to carry out an in-depth morpho-molecular analysis to uncover possible pathomechanisms. Muscle biopsy specimens from 26 patients with anti-Ku-antibodies and unequivocal myositis were analyzed by immunohistochemistry, immunofluorescence, transcriptomics, and proteomics and compared to biopsy specimens of non-disease controls, immune-mediated necrotizing myopathy (IMNM), and inclusion body myositis (IBM). Clinical findings and laboratory parameters were evaluated retrospectively and correlated with morphological and molecular features. Patients were mainly female (92%) with a median age of 56.5 years. Isolated myositis and overlap with systemic sclerosis were reported in 31%, respectively. Isolated myositis presented with higher creatine kinase levels and cardiac involvement (83%), whereas systemic sclerosis-overlap patients often had interstitial lung disease (57%). Histopathology showed a wide spectrum from mild to pronounced myositis with diffuse sarcolemmal MHC-class I (100%) and -II (69%) immunoreactivity, myofiber necrosis (88%), endomysial inflammation (85%), thickened capillaries (84%), and vacuoles (60%). Conspicuous sarcoplasmic protein aggregates were p62, BAG3, myotilin, or immunoproteasomal beta5i-positive. Proteomic and transcriptomic analysis identified prominent up-regulation of autophagy, proteasome, and hnRNP-related cell stress. To conclude, Ku + myositis is morphologically characterized by myofiber necrosis, MHC-class I and II positivity, variable endomysial inflammation, and distinct protein aggregation varying from IBM and IMNM, and it can be placed in the spectrum of scleromyositis and overlap myositis. It features characteristic sarcoplasmic protein aggregation on an acquired basis being functionally associated with altered chaperone, proteasome, and autophagy function indicating that Ku + myositis exhibit aspects of an acquired inflammatory protein-aggregate myopathy.

Network analysis model investigating the relationship between social support and depression symptoms in pregnant women based on personality differences

Network analysis model investigating the relationship between social support and depression symptoms in pregnant women based on personality differences

Provider perspectives on adolescent access to contraception in South Africa

Background: Poor access to contraception influences adolescent health outcomes and may lead to sexual and reproductive health challenges. Unmet sexual and reproductive health should contribute to unplanned adolescent pregnancies, sexually transmitted infections, and other conditions. Therefore, it is crucial to enable adolescents to access appropriate contraceptive methods easily.Aim: This study explored factors influencing adolescents’ access to contraceptive methods from the perspective of primary healthcare providers in South Africa.Setting: This study was conducted in two health districts of the Western Cape province in South Africa: the City of Cape Town Metropolitan Municipality and the West Coast district.Methods: Using a qualitative research design, in-depth semi-structured interviews were conducted with 24 healthcare providers who work in primary healthcare clinics. Subsequently, the interviews were transcribed and verified for errors. Braun and Clarke’s thematic analysis model guided the data analysis using ATLAS.ti software (version 22). The study adhered to the consolidated criteria for reporting qualitative studies checklist for qualitative research.Results: Four themes emerged through the data analysis: (1) personal influences, (2) community-level influences, (3) health system influences, and (4) policy-level influences. Representative quotations were used to illustrate the themes and sub-themes.Conclusion: Adolescence is shaped by various influences that affect adolescents’ ability to access contraception. These factors include their awareness of contraception, social environment, provider biases and school policies. Understanding these influences is crucial for addressing unintended pregnancies and promoting sexual and reproductive health among this age group.Contribution: This study highlights strategies that facilitate and hinder adolescents’ access to contraception.

Design and Experimental Study of Banana Bunch Transportation Device with Lifting Mechanism and Automatic Bottom-Fixing Fruit Shaft

In addressing the challenges of high labor intensity, cost, and potential mechanical damage to banana fruit in orchards, this study presents the design of a banana bunch transport device featuring a lifting mechanism and an automatic fruit shaft bottom-fixing system. The device is tailored to the planting and morphological characteristics of banana bunches, aiming for efficient, low-loss, and labor-saving mechanized transport. Key design considerations included the anti-overturning mechanism and the lifting system based on transportation conditions and the physical dimensions of banana bunches. A dynamic simulation was conducted to analyze the angular velocity and acceleration during the initial conveying stages, forming the basis for the fruit shaft bottom-fixation mechanism. A novel horizontal multi-point scanning method was developed to accurately identify and secure the fruit shaft bottom, complemented by an automated control system. Experimental results showed a 95.83% success rate in identification and fixation, validated by field trials that confirmed the necessity and stability of the fixation mechanism. To enhance the durability of the fruit shaft bottom-fixation mechanism, a multi-factor test was conducted, optimizing the device’s maximum travel speed and minimizing the banana bunch’s oscillation angle. Field tests showed an oscillation angle of 8.961°, closely matching the simulated result of 9.526°, demonstrating the reliability of the response surface analysis model. This study offers a practical and efficient solution for banana bunch transport in orchards, showcasing significant practical value and potential for wider adoption.

X-ray diffraction profile analysis of green synthesized ZnO and TiO2 nanoparticles

Abstract ZnO and TiO2 nanoparticles were prepared through a plant-extract mediated hydrothermal synthesis. X-ray diffraction (XRD) study confirmed the crystalline nature and the phase characteristic of the obtained nanoparticles. X-ray diffraction profile analysis models such as Williamson-Hall analysis, size-strain plot and Rietveld analysis were further used in evaluating the microstructural parameters of the obtained materials. The calculated particle size for all the models was in great agreement, with values in the range of 55.46-87.6 nm recorded for ZnO, while for TiO2, the crystallite size was in the range of 33.82-41.9 nm. The calculated crystal microstrain varied based on the model, while the stress in the ZnO and TiO2 nanoparticles was evaluated at 3.8 and 13.7 MPa, respectively. Furthermore, TiO2 nanoparticles had a higher energy density of 8.96 × 10-8 KJ/m3 compared to 7.12 × 10-8 KJ/m3 obtained for ZnO.

Study on internal load distribution of deep groove ball bearings considering raceway surface waviness

Abstract A full circumferential contact mechanics modeling study considering the effect of waviness mesoscopic profile and rolling element contact relationship on bearing load distribution is carried out. In the model, a sinusoidal function is adopted to simulate the profile of the waviness section. According to the contact tangency condition, a model of the contact deformation relationship between a single rolling element and the raceways is established. On this basis, a full circumferential rolling elements contact load distribution model of the bearing is established by introducing the contact deformation of all rolling elements with the raceway surfaces. Focusing on contact deformation of a full circumferential rolling elements with the raceways, this model can conduct more accurate modeling and reflect the load distribution state inside the bearing more realistically. The correctness of the mechanical analysis model proposed in this paper is proved by an average error of 3.38%, which is the comparison result between the mechanical analysis model and the finite element model. Meanwhile, computational efficiency is improved. Further, based on the model, the influence of the waviness parameters on the load distribution inside the bearing under the action of different types of loads is analyzed.

What affects customers online shopping behavior, research that applied machine learning to Amazon product reviews

The application of Natural Language Processing (NLP) in marketing has undergone significant evolution, with machine learning algorithms playing a crucial role in extracting valuable insights from complex textual data. This study focuses on comparing the performance of Support Vector Machine (SVM), Random Forest (RF), Nave Bayes (NB), and a specialized sentiment analysis model, Latent Dirichlet Allocation (LDA), in the context of online platform reviews. While previous research has delved into individual algorithms, there is a paucity of horizontal comparisons. Suitable algorithms for sentiment analysis on online platform reviews, specifically for Amazon, were filtered in this work. A dataset from Kaggle (https://www.kaggle.com/datasets/arhamrumi/amazon-product-reviews) comprising 500,000 reviews and 10 columns was utilized, overcoming time and resource constraints by opting for secondary data analysis. The primary objective was to assess the performance metrics of SVM, RF, NB, and LDA in classifying reviews into positive, neutral, and negative sentiments. Despite the massive size of the dataset posing challenges to the accuracy of the algorithms, nuanced results in precision, recall, and F-score were observed, not replicated in prior studies. Attempts to enhance accuracy by switching vectorizers yielded marginal improvements. Interestingly, LDA emerged as a transformative model, leveraging its ability to generate WordClouds for a systematic analysis of customers' emotional attachments. In addition to sentiment analysis, an investigation into the identification of factors influencing consumer purchasing behavior on Amazon was conducted. By training the LDA model on positive, neutral, and negative comments, distinctive features associated with each sentiment category were extracted. This analysis aims to unravel the underlying product features that contribute significantly to customer decision-making processes. In conclusion, this work provides a comprehensive evaluation of SVM, RF, NB, and LDA in the realm of sentiment analysis on Amazon product reviews. The findings shed light on the challenges posed by large datasets, the limitations of traditional vectorizers, and the unique capabilities of LDA in uncovering emotional nuances. Moreover, the investigation into consumer purchasing behavior offers valuable insights for marketers seeking to understand the factors influencing online shopping decisions.

Rainfall-induced Landslide in the Lesser Himalaya: A Case Study of the Pallotari Landslide, Lamjung, Central-West Nepal

The Pallotari area, located in the Marsyangdi Rural Municipality, Lamjung, central-west Nepal, within the Lesser Himalaya, was impacted by a series of rainfall-induced landslides in July 2020. This study focuses on the Pallotari landslide, one of the major landslides in the area. Geological and engineering geological studies were performed to identify the causes of this landslide, and its impacts to the local community. Our results indicate the major causative factors to be intensive rainfall, steep topography with loose colluvial soil, and some anthropogenic activities such as the construction of the rural roads through the crown area. The study area still has hanging colluvial material on the crown as well as on the left flank of the landslide, and the numerical model for slope stability analysis indicates it to be unstable even in the unsaturated condition. The colluvial material in the middle portion of the landslide also has the risk of failure in the saturation condition which can be due to intensive rainfall. However, the rock slope stability analysis using the kinematic analysis does not show any contribution to the orientation of discontinuities on the slope failure. The presence of several transverse and longitudinal cracks in the surrounding of the landslide are signs of future failure.

Bayesian Hierarchical Models for Subgroup Analysis.

In conventional subgroup analyses, subgroup treatment effects are estimated using data from each subgroup separately without considering data from other subgroups in the same study. The subgroup treatment effects estimated this way may be heterogenous with high variability due to small sample sizes in some subgroups and much different from the treatment effect in the overall population. A Bayesian hierarchical model (BHM) can be used to derive more precise, and less heterogenous estimates of subgroup treatment effects that are closer to the treatment effect in the overall population. BHM assumes exchangeability in treatment effect across subgroups after adjusting for effect modifiers and other relevant covariates. In this article, we will discuss the technical details for applying one-way and multi-way BHM using summary-level statistics, and patient-level data for subgroup analysis. Four case studies based on four new drug applications are used to illustrate the application of these models in subgroup analyses for continuous, dichotomous, time-to-event, and count endpoints.

Drug-drug interactions and synergy: from pharmacological models to clinical application.

This review explores the concept of synergy in pharmacology, emphasizing its importance in optimizing treatment outcomes through the combination of drugs with different mechanisms of action. Synergy, defined as an effect greater than the expected additive effect elicited by individual agents according to specific predictive models, offers a promising approach to enhance therapeutic efficacy while minimizing adverse events. The historical evolution of synergy research, from ancient civilizations to modern pharmacology, highlights the ongoing quest to understand and harness synergistic interactions. Key concepts such as concentration-response curves, additive effects, and predictive models are discussed in detail, emphasizing the need for accurate assessment methods throughout translational drug development. While various mathematical models exist for synergy analysis, selecting the appropriate model and software tools remains a challenge, necessitating careful consideration of experimental design and data interpretation. Furthermore, this review addresses practical considerations in synergy assessment, including preclinical and clinical approaches, mechanism of action, and statistical analysis. Optimizing synergy requires attention to concentration/dose ratios, target site localization, and timing of drug administration, ensuring that the benefits of combination therapy detected at bench-side are translatable into clinical practice. Overall, the review advocates for a systematic approach to synergy assessment, incorporating robust statistical analysis, effective and simplified predictive models, and collaborative efforts across pivotal sectors such as academic institutions, pharmaceutical companies, and regulatory agencies. By overcoming critical challenges and maximizing therapeutic potential, effective synergy assessment in drug development holds promise for advancing patient care. Significance Statement Combining drugs with different mechanisms of action for synergistic interactions optimizes treatment efficacy and safety. Accurate interpretation of synergy requires the identification of the expected additive effect. Despite innovative models to predict the additive effect, consensus in drug interaction research is lacking, hindering the bench-to-bedside development of combination therapies. Collaboration among science, industry, and regulation is crucial for advancing combination therapy development, ensuring rigorous application of predictive models in clinical settings.

Quantitative Stock Selection Model Using Graph Learning and a Spatial–Temporal Encoder

In the rapidly evolving domain of finance, quantitative stock selection strategies have gained prominence, driven by the pursuit of maximizing returns while mitigating risks through sophisticated data analysis and algorithmic models. Yet, prevailing models frequently neglect the fluid dynamics of asset relationships and market shifts, a gap that undermines their predictive and risk management efficacy. This oversight renders them vulnerable to market volatility, adversely affecting investment decision quality and return consistency. Addressing this critical gap, our study proposes the Graph Learning Spatial–Temporal Encoder Network (GL-STN), a pioneering model that seamlessly integrates graph theory and spatial–temporal encoding to navigate the intricacies and variabilities of financial markets. By harnessing the inherent structural knowledge of stock markets, the GL-STN model adeptly captures the nonlinear interactions and temporal shifts among assets. Our innovative approach amalgamates graph convolutional layers, attention mechanisms, and long short-term memory (LSTM) networks, offering a comprehensive analysis of spatial–temporal data features. This integration not only deciphers complex stock market interdependencies but also accentuates crucial market insights, enabling the model to forecast market trends with heightened precision. Rigorous evaluations across diverse market boards—Main Board, SME Board, STAR Market, and ChiNext—underscore the GL-STN model’s exceptional ability to withstand market turbulence and enhance profitability, affirming its substantial utility in quantitative stock selection.

Spatiotemporal analysis of within-country imported malaria in Brazilian municipalities, 2004-2022.

Human mobility has challenged malaria elimination efforts and remains difficult to routinely track. In Brazil, administrative records from the Ministry of Health allow monitoring of mobility locally and internationally. Although most imported malaria cases are between municipalities in Brazil, detailed knowledge of patterns of mobility is limited. Here, we address this gap by quantifying and describing patterns of malaria-infected individuals across the Amazon. We used network analysis, spatial clustering, and linear models to quantify and characterize the movement of malaria cases in Brazil between 2004 and 2022. We identified sources and sinks of malaria within and between states. We found that between-state movement of cases has become proportionally more important than within-state, that source clusters persisted longer than sink clusters, that movement of cases into sinks was seasonal while movement out of sources was not, and that importation is an impediment for subnational elimination in many municipalities. We elucidate the vast travel networks of malaria infected individuals that characterize the Amazon region. Uncovering patterns of malaria case mobility is vital for effective microstratification within Brazil. Our results have implications for intervention stratification across Brazil in line with the country's goal of malaria elimination by 2035.

Shear Mechanism of a Novel SFCBs-Reinforced Composite Shear Connector: Experimental, Theoretical Investigations and Numerical Model

Traditional stud and perfobond leiste (PBL) shear connectors are commonly used as load-transferring components in steel-concrete composite structures. Composite shear connectors fully utilize the advantages of traditional stud and PBL shear connectors. In order to maximize the advantages of composite shear connectors, a novel shear connector for complex environments was proposed. The steel-FRP composite bars (SFCBs) with excellent fatigue resistance and corrosion resistance were introduced to replace the steel bars. This study discussed the failure modes, load–slip curves, and load–strain curves of the composite shear connector. In addition, a finite element analysis (FEA) model was developed to analyze the influence of various factors on its shear behavior. Results showed that compared with traditional composite shear connectors, the introduction of SFCB resulted in a promotion of 7.85% in shear stiffness, and it also led to a significant increase of 63.61% in ductility, further enhancing the mechanical performance. Meanwhile, FEA models were well fitted to the test results, and parametric analysis showed variate effects on shear bearing capacity. In the end, an equation was established to calculate the shear capacity of composite shear connectors, which could provide a reference for further research and engineering applications.

An Economic Performance Improving and Analysis for Offshore Wind Farm-Based Islanded Green Hydrogen System

When offshore wind farms are connected to a hydrogen plant with dedicated transmission lines, for example, high-voltage direct current, the fluctuation of wind speed will influence the efficiency of the alkaline electrolyzer and deteriorate the techno-economic performance. To overcome this issue, firstly, an additional heating process is adopted to achieve insulation for the alkaline solution when power generated by wind farms is below the alkaline electrolyzer minimum power threshold, while the alkaline electrolyzer overload feature is used to generate hydrogen when wind power is at its peak. Then, a simplified piecewise model-based alkaline electrolyzer techno-economic analysis model is proposed. The improved economic performance of the islanded green hydrogen system with the proposed operation strategy is verified based on the wind speed data set simulation generated by the Weibull distribution. Lastly, the sensitivity of the total return on investment to wind speed parameters was investigated, and an islanded green hydrogen system capacity allocation based on the proposed analysis model was conducted. The simulation result shows the total energy utilization increased from 62.0768% to 72.5419%, and the return on investment increased from 5.1303%/month to 5.9581%/month when the proposed control strategy is adopted.