Optimum Model Research Articles

The oxidation reaction mechanism and its kinetics for a carbonaceous precursor prepared from ethylene tar for use as an anode material for lithium-ion batteries

The oxidation reaction mechanism and its kinetics for ethylene tar were investigated in order to obtain a suitable anode material for Li-ion batteries. The oxidation of ethylene tar was divided into 3 stages (350–550, 550–700 and 700–900 K) according to the thermogravimetric curve. To reveal the oxidation reaction mechanism, the components of the gases evolved at different stages were analyzed by mass spectrometry and infrared technology. Based on these results the reaction was divided into 4 stages (323–400, 400–605, 605–750 and 750–860 K) to perform simulation calculations of the kinetics. Using the iso-conversion method (Coats-Redfern) to analyze the linear regression rates (R2) between 17 common reaction kinetics models and experimental data, an optimum reaction kinetics model for expressing the oxidation of ethylene tar was determined and the results were as follows. (1) During oxidation, the side chains of aromatic compounds first react with oxygen to form alcohols and aldehydes, leaving peroxy-radicals on aromatic rings. Subsequently, the aromatic compounds with peroxy-radicals undergo polymerization/condensation reactions to form larger molecules. (2) A fourth-order reaction model was used to describe the first 3 stages in the oxidation process, and the activation energies are 47.33, 18.69 and 9.00 kJ·mol−1 at 323–400, 400–605, 605–750 K, respectively. A three-dimensional diffusion model was applied to the fourth stage of the oxidation process, and the activation energy is 88.37 kJ·mol−1 at 750–860 K. A high softening point pitch was also produced for use as a coating of the graphite anode, and after it had been applied the capacity retention after 300 cycles increased from 51.54% to 79.07%.

Heat Transfer Performance Analysis of an E-Vehicle Cooling System Using a Hybrid Peltier System

Abstract: This research investigates the heat transfer performance of an electric vehicle (EV) cooling system utilising a hybrid Peltier as integrated system with advancements in EV battery technology, the demand for efficient cooling systems has become crucial for ensuring battery safety, longevity, and overall performance. Present research focus on the application of ethylene glycol as a coolant, exploring the thermal parameters, heat transfer rates, and overall effectiveness of the hybrid cooling system. A literature review highlights the significance of heat transfer enhancement techniques, emphasising the need for energyefficient methods in the automotive industry. The research methodology involves experimental work, analytical calculations, and the preparation of coolant samples to evaluate thermo-physical parameters. The results demonstrate the temperature drop, Overall heat transfer for different flow rates, Logarithmic Mean Temperature difference, and Comparative Power Consumption of different setup. The result showed that presence of ethylene glycol in water enhances the desirable temperature drop of the heat transfer fluid. The Overall heat transfer rate is increased by 58% for EG-100 sample as compared to pure water. Further an ANN model is also developed for prediction of temperature drop, the result showed 2 layer model with 8, 8 neurons as optimum model for prediction.

Estimating the Potential of Biogas Yield from Anaerobic Co-digestion of Organic Waste with Ensemble Machine learning

The biogas has the potential to serve as a substitute as fossil derived fuel. The present study investigates anaerobic co-digestion of organic waste for predictive modelling. Several co-digestion studies were performed with different pH, solid concentration, temperature, and co-digestion ratios. A water displacement apparatus was used to test biogas yield, and data was collected meticulously. Linear regression (LR) and Random Forest (RF) based models were built with Python-based tools and tested using statistical measures for the prediction of biogas yield. The LR showed a strong linear association, with R and R2 values of 0.9892 and 0.9785, respectively. However, RF surpassed LR, with higher R and R2 values of 0.9919 and 0.9826, respectively. Furthermore, RF had lower MSE and MAE values, indicating higher prediction accuracy and precision. RF consistently scored well in tests, demonstrating its ability to capture complicated relationships while minimizing prediction mistakes. Taylor's diagrams further demonstrated RF's excellent performance during both the training and testing periods. Overall, RF emerges as the optimum model for reliably estimating biogas output in anaerobic co-digestion systems, with important implications for waste-to-energy processes.

Detection of Hardening in Mangosteens Using near-Infrared Hyperspectral Imaging

Mangosteens can develop a postharvest physiological disorder, called “hardening”, which affects their marketability and is not detectable using visual inspection. The hardening disorder of mangosteens was determined by firmness value using the texture analyzer. Near-infrared hyperspectral imaging (NIR-HSI) in the region of 935–1720 nm was tested as a possible rapid and non-destructive method to detect this disorder. The spectra from a region of interest of mangosteens were acquired and used for analysis. Calibration models for firmness of a similarly sized group and a mixed-size group were established using partial least squares regression (PLSR) and support vector machine regression (SVMR). Chemometric algorithms were investigated in order to determine the optimal conditions for establishing the models for firmness. The optimum model was obtained when the fruit were graded into similarly sized groups. Using partial least squares regression (PLSR), the correlation coefficient of prediction (Rp) was 0.87 and the root mean square error of prediction (RMSEP) was 6.25 N. The predictive images for firmness of the fruit were created by interpreting predicted firmness visualized as colors in every pixel. From the data, it was concluded that NIR-HSI can potentially be used to visualize hardening of individual mangosteens based on their predictive images.

Micro-electro-mechanical acoustofluidic mixing system: A response surface-metaheuristic machine learning fusion framework

Micro-mixing is an essential step in various biological and chemical analyses and synthesis processes. In the present study, an acoustofluidic micromixer is designed, modeled, and its functionality has been investigated. Intelligent acoustofluidic is proposed as an evaluation-optimization method, based on Machine Learning algorithms. The first step involves designing a parametric geometry with multiple sharp edges. Following that, mathematical modeling of the system is carried out using Response Surface Methodology (RSM) and computational fluid dynamics (CFD). Geometrical parameters including the sharp edge tip angle and the channel thickness, along with the peak-to-peak actuation voltage were investigated. It was revealed that, due to the inherent nonlinearity of acoustic streaming, each design parameter could significantly impact the flow patterns and mixing performance of the acoustofluidic mixer. The mathematical surrogate model obtained from data-based modeling procedure is then employed for the artificial intelligence multi-objective optimization algorithms, i.e. Differential Evolution (DE) and Non-dominated Sorting Genetic Algorithm two (NSGA-II). The two objective functions are the mixing index and the mechanical energy consumption index (ME). The derived optimum solutions had low Mehcanical Energy (ME) values while yielding homogeneity in a wide range of applied voltages. This ensures the controllability of the proposed system, which is an invaluable feature in synthesis processes to determine the critical physical properties of the nanoparticles. Through the algorithm evaluation process, which was conducted to distinguish the more accurate and robust optimization method, it was revealed that the DE algorithm outperformed NSGA-II in generating optimum solutions with superior functionalities. In the final section, three optimum models, operating under three different Vp-p values, 10, 20, 30, were selected, and their acoustofluidic characteristics were analyzed. It was concluded that utilizing baffles with three sharp edges at low Vp-p values results in a 96% Mixing Index and 48% decrease in the power consumption of the system. However, at high Vp-p values, baffles with one sharp edge demonstrated superior mixing functionality, with a MI of 98%. Moreover, it is revealed that utilizing baffles with three-sharp edges results in an 80% increase of Mixing Index of the acoustofluidic micromixer.

Application of Machine Learning Algorithms for Soil Erosion Susceptibility Estimation in Gumani River Basin, Eastern India

Abstract The primary goal of this study is to determine soil erosion risk susceptibility and to suggest the most appropriate techniques for soil erosion susceptibility for Gumani river basin. This research examined the point-specific values of several components derived from randomization. The susceptibility of the projected frameworks, namely the Artificial Neural Network and Support Vector Machine, was investigated with the help of the most significant causative variables and the corresponding field records. The area underneath the Receiver Operating Characteristics curve shows accuracy for ANN and SVM accordingly. For assessing susceptibility, the ANN (AUC = 0.932) and SVM (AUC = 0.915) were used for training points, whereas ANN (AUC = 0.906) and SVM (AUC = 0.882) were for validation points. The ANN model is very efficient in simulating the erosional and non-erosional regions more accurately than SVM. The outcome of ANN predicted that 19.14% area is very high, extended in the entire western parts and some parts of the southern part, 14.96% is high, extended in the entire western part, 16.01% is moderate, extended in the western and eastern parts, 18.54% low, and 31.35% very low extended in the middle and eastern parts, susceptible for soil erosion whereas the outcome of SVM predicted that 15.45% area is very high, 18.82% high, 22.05% moderate, 22.94% low, 20.74% very low, susceptible for soil erosion. The land degradation phase is not a unidirectional process. Therefore, multidimensional effects from conditioning factors must be calculated accurately by considering the maximum possible variables and choosing optimum models for particular areas. These attempts will help policymakers implement proper methods to check soil erosion in the Gumani river basin.

Leveraging hybrid ANN–AHP to optimize cement industry average inventory levels

In recent years, inventory has been critical due to the production cost and overstock risk related to the expiration date and the fluctuation price risk. This study's minimization of overstock and price fluctuation in the warehouse used a hybridized artificial neural network (ANN) and analytical hierarchy process (AHP) to produce an optimum model. The variables, such as average demand, reorder point, order quantity, factor service level, safety stock, and average inventory level, were used to obtain the optimal condition of the average inventory levels to maximize the profit. Then, the type of inventory system that guarantees the minimum risks in managing the inventory would be selected. The result shows that the data has a mean of 39.2 units, and the standard deviation (SD) was 12.9. This means that the order quantity is 20.2 units, the average inventory level is 57.3, and the average demand is 39. These conditions used the factor z, which is 97% service level. This study concludes that the optimum average inventory level is 91 units, the order quantity is 11 units with the maximum average profit is $1098, and the peak fluctuation condition maximum profit is $1463 when the average inventory level is 7.3, and the inventory policy system used to minimize the risk is the continuous review policy type. The study could be beneficial to reduce production costs and enhance overall profitability and operational efficiency in the sector by mitigating the risks associated with excessive inventory and price volatility while also minimizing the potential for expired inventory.

Hybrid Architecture Model of Genetic Algorithm and Learning Vector Quantization Neural Network for Early Identification of Ear, Nose, and Throat Diseases

Background: In 2020, the World Health Organization (WHO) estimated that 466 million people worldwide are affected by hearing loss, with 34 million of them being children. Indonesia is identified as one of the four Asian countries with a high prevalence of hearing loss, specifically at 4.6%. Previous research was conducted to identify diseases related to the Ear, Nose, and Throat, utilizing the certainty factor method with a test accuracy rate of 46.54%. The novelty of this research lies in the combination of two methods, the use of genetic algorithms for optimization and learning vector quantization to improve the level of accuracy for early identification of Ear, Nose, and Throat diseases. Objective: This research aims to produce a hybrid model between the genetic algorithm and the learning vector quantization neural network to be able to identify Ear, Nose, and Throat diseases with mild symptoms to improve accuracy. Methods: Implementing a 90:10 ratio means that 90% (186 data) of the data from the initial sequence is assigned for training purposes, while the remaining 10% (21 data) is allocated for testing. The procedural stages of genetic algorithm-learning vector quantization are population initialization, crossover, mutation, evaluation, selection elitism, and learning vector quantization training. Results The optimum hybrid genetic algorithm-learning vector quantization model for early identification of Ear, Nose, and Throat diseases was obtained with an accuracy of 82.12%. The parameter values with the population size 10, cr 0.9, mr 0.1, maximum epoch of 5000, error goal of 0.01, and learning rate (alpha) of 0.5. Better accuracy was obtained compared to backpropagation (64%), certainty factor 46.54%), and radial basic function (72%). Conclusion: Experiments in this research, successed identifying models by combining genetic algorithm-learning vector quantization to perform the early identification of Ear, Nose, and Throat diseases. For further research, it's very challenging to develop a model that automatically adapts the bandwidth parameters of the weighting functions during trainin Keywords: Early Identification, Ear-Nose-Throat Diseases, Genetic Algorithm, Learning Vector Quantization

CAM3.0: determining cell type composition and expression from bulk tissues with fully unsupervised deconvolution.

Complex tissues are dynamic ecosystems consisting of molecularly distinct yet interacting cell types. Computational deconvolution aims to dissect bulk tissue data into cell type compositions and cell-specific expressions. With few exceptions, most existing deconvolution tools exploit supervised approaches requiring various types of references that may be unreliable or even unavailable for specific tissue microenvironments. We previously developed a fully unsupervised deconvolution method-Convex Analysis of Mixtures (CAM), that enables estimation of cell type composition and expression from bulk tissues. We now introduce CAM3.0 tool that improves this framework with three new and highly efficient algorithms, namely, radius-fixed clustering to identify reliable markers, linear programming to detect an initial scatter simplex, and a smart floating search for the optimum latent variable model. The comparative experimental results obtained from both realistic simulations and case studies show that the CAM3.0 tool can help biologists more accurately identify known or novel cell markers, determine cell proportions, and estimate cell-specific expressions, complementing the existing tools particularly when study- or datatype-specific references are unreliable or unavailable. The open-source R Scripts of CAM3.0 is freely available at https://github.com/ChiungTingWu/CAM3/(https://github.com/Bioconductor/Contributions/issues/3205). A user's guide and a vignette are provided.

Machine learning-assisted FTIR spectra to predict freeze-drying curve of food

With the demand for improving freeze-drying (FD) process efficiency and protecting product characteristics, intelligent and robust analysis of process parameters for drawing effective FD curves has become the development direction of modern FD process. In this study, a prompt and applicable prediction model of FD parameters was designed by FTIR associated with chemometrics. By using spectral preprocessing and principal component analysis, 34 feature wavenumbers were extracted as input variables for modeling to quantify FD parameters. Among the 18 parameter prediction models, artificial neural network was adopted as the optimum model for the temperature and time of pre-freezing and desorption stages (R2 = 0.91, 0.83, 0.92, 0.84, RMSE = 0.12, 0.13, 0.08, 0.10), and random forest was confirmed as the best model for the parameters of sublimation stage (R2 = 0.88, 0.77, RMSE = 0.13, 0.16). According to the model prediction, random samples were selected for verification that the experimental results were close to 96% agreement with the model output.

Identification and immunological role of cuproptosis in osteoporosis

Backgroundosteoporosis is a skeletal disorder disease features low bone mass and poor bone architecture, which predisposes to increased risk of fracture. Copper death is a newly recognized form of cell death caused by excess copper ions, which presumably involve in various disease. Accordingly, we intended to investigate the molecular clusters related to the cuproptosis in osteoporosis and to construct a predictive model. Methodswe investigated the expression patterns of cuproptosis regulators and immune signatures in osteoporosis based on the GSE56815 dataset. Through analysis of 40 osteoporosis samples, we investigated molecular clustering on the basis of cuproptosis--related genes, together with the associated immune cell infiltration. The WGCNA algorithm was applied to detect cluster-specific differentially expressed genes. Afterwards, the optimum machine model was selected by calculating the performance of the support vector machine model, random forest model, eXtreme Gradient Boosting and generalized linear model. Nomogram, decision curve analysis, calibration curves, and the GSE7158 dataset was utilizing to confirm the prediction efficiency. ResultsDifferences between osteoporotic and non-osteoporotic controls confirm poorly adjusted copper death-related genes and triggered immune responses. In osteoporosis, two clusters of molecules in connection with copper death proliferation were outlined. The assessed levels of immune infiltration showed prominent heterogeneity between the different clusters. Cluster 2 was characterized by a raised immune score accompanied with relatively high levels of immune infiltration. The functional analysis we performed showed a close relationship between the different immune responses and specific differentially expressed genes in cluster 2. The random forest machine model showed the optimum discriminatory performance due to relatively low residuals and root mean square errors. Finally, a random forest model based on 5 genes was built, showing acceptable performance in an external validation dataset (AUC = 0.750). Calibration curve, Nomogram, and decision curve analyses also evinced fidelity in predicting subtypes of osteoporosis. ConclusionOur study identifies the role of cuproptosis in OP and essentially illustrates the underlying molecular mechanisms that lead to OP heterogeneity.

Predicting the unpredictable: a robust nomogram for predicting recurrence in patients with ampullary carcinoma

ObjectiveTo screen the risk factors affecting the recurrence risk of patients with ampullary carcinoma (AC)after radical resection, and then to construct a model for risk prediction based on Lasso-Cox regression and visualize it.MethodsClinical data were collected from 162 patients that received pancreaticoduodenectomy treatment in Hebei Provincial Cancer Hospital from January 2011 to January 2022. Lasso regression was used in the training group to screen the risk factors for recurrence. The Lasso-Cox regression and Random Survival Forest (RSF) models were compared using Delong test to determine the optimum model based on the risk factors. Finally, the selected model was validated using clinical data from the validation group.ResultsThe patients were split into two groups, with a 7:3 ratio for training and validation. The variables screened by Lasso regression, such as CA19-9/GGT, AJCC 8th edition TNM staging, Lymph node invasion, Differentiation, Tumor size, CA19-9, Gender, GPR, PLR, Drinking history, and Complications, were used in modeling with the Lasso-Cox regression model (C-index = 0.845) and RSF model (C-index = 0.719) in the training group. According to the Delong test we chose the Lasso-Cox regression model (P = 0.019) and validated its performance with time-dependent receiver operating characteristics curves(tdROC), calibration curves, and decision curve analysis (DCA). The areas under the tdROC curves for 1, 3, and 5 years were 0.855, 0.888, and 0.924 in the training group and 0.841, 0.871, and 0.901 in the validation group, respectively. The calibration curves performed well, as well as the DCA showed higher net returns and a broader range of threshold probabilities using the predictive model. A nomogram visualization is used to display the results of the selected model.ConclusionThe study established a nomogram based on the Lasso-Cox regression model for predicting recurrence in AC patients. Compared to a nomogram built via other methods, this one is more robust and accurate.

Using genetic algorithms in panel Data modeling: the relationship between stock prices and financial performance of Saudi arabia’s listed companies

This study aims to use Genetic Algorithms in Panel Data modeling optimization, so we applied it to measure the relationship between the stock price and financial performance indicators of ten of leading listed companies in Saudi Arabia depending on annual time series of the last twelve years. Thus, we formulated a Mixed Effects Panel Data model containing Stock price related to: Return on Equity, Return on Assets, Earning per Share, Financial Leverage and Current Ratio; then we estimated this model by maximizing an adapted Quasi-Likelihood function using Genetic Algorithms depending on Evolver software, In addition we estimated Fixed Effects and Random effects models using usual econometric methods, all the models indicate significant impact of: ROE, ROA and EPS. The optimum Mixed Effects model estimated by Genetic Algorithms shows more accurate results with relatively better statistical characteristics and allows more usage flexibility. Special issue: The use of decision-making tools and methods in economics

Contemporary Legal Problems of Sharia Insurance Regulation in Indonesia

Objective: This study aims to examine the evolving structure of the insurance industry in Indonesia, particularly its transition from traditional insurance to dual insurance systems, highlighting the presence of the Sharia insurance sector within the national legislative framework.
 
 Theoretical framework: In the realm of Islamic economic studies, fundamental principles such as justice, mutual assistance, the avoidance of oppression, prohibition of riba (interest), and the elimination of gharar elements are central. Sharia-compliant insurance institutions are expected to adhere to these principles while also developing an independent, integrated, and professionally managed insurance framework that aligns with Islamic Shariah. To ensure compliance, each insurance company must establish a Shariah Supervisory Board (Dewan Pengurus Syariah).
 
 Method: This research adopts a normative legal research approach, analyzing primary, secondary, and tertiary legal resources sourced from academic papers and libraries to address the legal challenges of establishing Sharia insurance under existing positive legislation.
 
 Results and conclusion: The findings of this study include First, there is no specific law that governs Sharia insurance, there are no implementation rules for Law No. 40/2014, which governs Sharia insurance, and there is no regulation regarding the typical Sharia policy. These current legal issues make it difficult to set up Sharia insurance under positive law. Second, the specialized arrangement in a Law (lex specialis) of Law No. 40 of 2014 is the optimum Sharia insurance arrangement model following the favorable legal prospects.
 
 Originality/value: This research contributes to the understanding of the legal challenges and intricacies of Sharia insurance regulation in the context of Indonesia's insurance industry transformation. It provides valuable insights into the intersection of Islamic economic principles and contemporary insurance practices, offering a basis for further exploration and policy development in this critical area.

Simultaneous well spacing and completion optimization using an automated machine learning approach. A case study of the Marcellus Shale reservoir, northeastern United States

Optimizing unconventional field development requires simultaneous optimization of well spacing and completion design. However, the conventional practice of using cross plots and sensitivity analysis via Monte Carlo simulations for independent optimization of well spacing and completion design has proved inadequate for unconventional reservoirs. This is due to the inability of cross plots to capture non-linear cross-correlations between parameters affecting hydrocarbon production, and the computational expense and difficulty of Monte Carlo simulations. Recently, automated machine learning (AutoML) workflows have been used to tackle complex problems. However, applying AutoML workflows to engineering problems presents unique challenges, as achieving high accuracy in forecasting the physics of the problem is crucial. To address this issue, a new physics-informed AutoML workflow based on the TPOT open-source tool developed that guarantees the physical plausibility of the optimum model while minimizing human bias and uncertainty. The workflow has been implemented in a Marcellus Shale reservoir with over 1500 wells to determine the optimal well spacing and completion design parameters for both the field and each well. The results show that using a shorter stage length and a higher sand-to-water ratio is preferable for this field, as it can increase cumulative gas production by up to 8%. Additionally, it is observed that fifty-percentile cumulative gas predictions are in close agreement with actual field productions. Thematic collection: This article is part of the Digitally enabled geoscience workflows: unlocking the power of our data collection available at: https://www.lyellcollection.org/topic/collections/digitally-enabled-geoscience-workflows

Criticality-Based Management of Facility Assets

Effective facility asset management requires specific skills and tools to optimize the use of limited resources, making a decision support system essential. This research introduces a comprehensive decision support system, which is a framework organized into three models: the criticality model, the rehabilitation model, and the optimum criticality model to manage the rehabilitation of facility assets. The criticality model utilizes the Analytical Hierarchy Process (AHP) to assess the group of assets. Emphasizing criticality as a central management factor, this model lays the foundation for subsequent decision-making. The rehabilitation model employs an Artificial Neural Network (ANN), integrating Customer Level of Service (CLoS), Technical Level of Service (TLoS), and asset criticality to determine appropriate rehabilitation actions. NeuralTools 7.5 is leveraged for precise predictions of rehabilitation strategies tailored to specific assets. The third model, optimum criticality, focuses on prioritizing rehabilitation activities within the constraints of limited budgets. Lingo 20.0 is utilized to optimize rehabilitation activities, considering budget limitations and other constraints, offering a strategic approach to maximize the impact of available resources. This integrated framework provides decision-makers with a systematic and data-driven approach to facility management, enhancing the efficiency and effectiveness of rehabilitation actions. An academic building was chosen as a hypothetical example to implement the three models and suggest the essential considerations for managing both the academic building itself and other infrastructure assets. The results obtained demonstrate that the principles and methodologies encapsulated in this project can be extrapolated and scaled up for application to large-scale infrastructure assets, ensuring the sustenance of the requisite level of service and the management of acceptable risk on a broader scale.

Anomaly Detection on the Edge Using Smart Cameras under Low-Light Conditions

The number of cameras utilised in smart city domains is increasingly prominent and notable for monitoring outdoor urban and rural areas such as farms and forests to deter thefts of farming machinery and livestock, as well as monitoring workers to guarantee their safety. However, anomaly detection tasks become much more challenging in environments with low-light conditions. Consequently, achieving efficient outcomes in recognising surrounding behaviours and events becomes difficult. Therefore, this research has developed a technique to enhance images captured in poor visibility. This enhancement aims to boost object detection accuracy and mitigate false positive detections. The proposed technique consists of several stages. In the first stage, features are extracted from input images. Subsequently, a classifier assigns a unique label to indicate the optimum model among multi-enhancement networks. In addition, it can distinguish scenes captured with sufficient light from low-light ones. Finally, a detection algorithm is applied to identify objects. Each task was implemented on a separate IoT-edge device, improving detection performance on the ExDark database with a nearly one-second response time across all stages.

Legal view on the concept of optimum models of public-private partnership in the security sector of Ukraine

The article is devoted to the study of public-private partnership in the security sector of Ukraine. It was established that public-private partnership is the highest form of interaction (cooperation) between the state and society, characterized by the involvement and exchange of resources provided by the non- state sector in order to increase innovation and efficiency in the public sector, as well as the development of certain areas.
 Attention is drawn to the fact that despite the fact that the Law of Ukraine «On Public-Private Partnership» does not single out the security sector as an area where interaction between the state and society is possible, the analysis of current legislation in the defense sphere and daily practice shows that the security sector is also sphere of implementation of public-private partnership. It is specified that a) the security of the state is a good that is necessary for the proper and full functioning of society; b) the sphere of state security contributes to the provision of public interest, which determines the need for proper interaction of the private and public sectors; c) the field of security, within which the public-private partnership is implemented, includes cyber security, public safety, protection of critical infrastructure facilities, border security and territorial integrity of Ukraine, etc.
 It is established that the study of public-private partnership models is impossible without taking into account the experience of the United States of America (Anglo-American legal system) and the European Union (Romano-Germanic legal system). It was concluded that the most optimal models of public-private partnership in the security sector for Ukraine are 1) implementation of priority projects in the field of infrastructure of local and state importance through the tender procedure (practice of the United States of America); 2) attraction of state assets by private companies, which makes it possible to implement many joint projects in the field of security (practice of the European Union); 3) contract for management and operation (of the European Union); 4) concession agreement.

Fractional Optimal Control Model and Bifurcation Analysis of Human Syncytial Respiratory Virus Transmission Dynamics

In this paper, the Caputo-based fractional derivative optimal control model is looked at to learn more about how the human respiratory syncytial virus (RSV) spreads. Model solution properties such as boundedness and non-negativity are checked and found to be true. The fundamental reproduction number is calculated by using the next-generation matrix’s spectral radius. The fractional optimal control model includes the control functions of vaccination and treatment to illustrate the impact of these interventions on the dynamics of virus transmission. In addition, the order of the derivative in the fractional optimal control problem indicates that encouraging vaccination and treatment early on can slow the spread of RSV. The overall analysis and the simulated behavior of the fractional optimum control model are in good agreement, and this is due in large part to the use of the MATLAB platform.

Data-driven approach for prediction of band diagram of locally resonant sonic crystal

The present study focuses on the prediction of the band diagram of locally resonance sonic crystal (LRSC) using machine learning models from structural, lattice material parameters. The data set for the study is generated from the numerical simulations of LRSC for wide ranges of structural, lattice material parameters using COMSOL multiphysics solver. Three machine learning algorithms namely, support vector machine (SVM), artificial neural network (ANN), and random forest regression (RFR), are employed for the study. A total of 23040 samples are simulated and included in the training of the models. Hyperparameter tuning and data set size optimization are carried out to find the optimum machine learning model given the minimum required data set. The R2 score, root mean square error (RMSE), computational time, and inference time are used to evaluate the performance of machine learning algorithms. The feature importance scores and partial dependence plots (PDPs) are used to understand each input feature’s critical role in predicting the band diagram. Among all the machine learning models studied, RFR outperformed the ANN and SVM models. RFR with 50 decision trees performed well. However, RFR with 10 decision trees also performed well with slight higher RMSE offering lower inference time compared to RFR with 50 decision trees. Due to the importance of the first two band gaps in noise attenuation, the width and center frequency of the first two bandgaps are predicted through the band diagram. The feature importance scores and PDPs of RFR models show that the structural and lattice parameters play a crucial role in predicting the first and second bandgap, aligning with physical significance.