Selection Of Optimal Parameters Research Articles

Investigation of Urban Low-Altitude Long-Range Atmospheric Links FSO Coherent Communication

Due to the complexity of low-altitude atmospheric channel conditions, the commercial application of FSO communication systems is less prevalent than satellite applications. This is mainly because there is a trade-off between achieving system stability through active compensation technology and maintaining low costs. Different application scenarios and atmospheric channel states correspond to different optimal structural parameter selections, which is crucial for low-cost FSO systems without complex compensation mechanisms. Therefore, measuring actual channel conditions is essential. One existing issue is that current research does not sufficiently discuss the connection between theoretical analysis and practical experiments in FSO communication, thus, establishing models and analysis methods is necessary that can accurately predict the performance of actual FSO atmospheric communication systems. We have developed a comprehensive FSO model to evaluate the effects of channel and structural factors on system performance. This model is vital for cost-effective parameter selection in system design and for analyzing actual system performance. Additionally, we established 6.6 km and 12.7 km FSO transmission links in an urban low-altitude environment. Subsequently, we conducted tests on atmospheric attenuation, channel statistical characteristics, and DPSK/QPSK coherent communication performance. Based on the analysis that combines experiments with theory, we have deduced the atmospheric attenuation of FSO links. The channel statistical characteristics provided the statistical properties of the two links under weak, moderate, and strong turbulence, along with fitting curves. Long-term channel statistical experiments indicate that the channel is most stable during the early morning hours of the day, and the 12.7 km communication experiment without turbulence correction technology is only feasible at this time. Furthermore, we presented a method for using theory to evaluate the performance of actual FSO systems in random atmospheric channels.

Enhanced multimodal medical image fusion via modified DWT with arithmetic optimization algorithm.

Medical image fusion (MIF) techniques are proficient in combining medical images in distinct morphologies to obtain a reliable medical analysis. A single modality image could not offer adequate data for an accurate analysis. Therefore, a novel multimodal MIF-based artificial intelligence (AI) method has been presented. MIF approaches fuse multimodal medical images for exact and reliable medical recognition. Multimodal MIF improves diagnostic accuracy and clinical decision-making by combining complementary data in different imaging modalities. This article presents a new multimodal medical image fusion model utilizing Modified DWT with an Arithmetic Optimization Algorithm (MMIF-MDWTAOA) approach. The MMIF-MDWTAOA approach aims to generate a fused image with the significant details and features from each modality, leading to an elaborated depiction for precise interpretation by medical experts. The bilateral filtering (BF) approach is primarily employed for noise elimination. Next, the image decomposition process uses a modified discrete wavelet transform (MDWT) approach. However, the approximation coefficient of modality_1 and the detailed coefficient of modality_2 can be fused interchangeably. Furthermore, a fusion rule is derived from combining the multimodality data, and the AOA model is enforced to ensure the optimum selection of the fusion rule parameters. A sequence of simulations is accomplished to validate the enhanced output of the MMIF-MDWTAOA technique. The investigational validation of the MMIF-MDWTAOA technique showed the highest entropy values of 7.568 and 7.741 bits/pixel over other approaches.

Proposal of trapezoidal vibration-assisted diamond cutting for ductile-regime machining of brittle crystals: A case study on KDP crystal

Despite various vibration-assisted cutting techniques have been utilized to increase the machining performances of brittle materials, the highly dynamic variations of cutting process quantities lead to the complicated brittle-ductile transition (BDT) mechanism and the formation of undesired vibration marks on the machined surfaces. In this paper, a novel ultra-precision vibration-assisted cutting process, named trapezoidal modulation diamond cutting (TMDC), is firstly proposed for ductile-regime machining of brittle materials with significantly increased BDT cutting depth. By imposing a dedicate trapezoidal locus to the diamond tool, the unique invariable uncut chip thickness and cutting states were achieved for realizing stable vibration-assisted cutting without the formations of vibration marks. Systematic cutting experiments of KDP crystals were carried out to comprehensively investigate the influences of different process parameters on the machining performances of TMDC process. In addition, the underlying mechanisms of machining performance improvements have been discussed under the different combinations of process parameters, based on which the guidelines for optimal process parameter selection are given for increasing the BDT cutting depths. The outcomes of this study contribute to not only improving the ductile machining efficiency and machining quality of KDP crystals, but also help to deepen the understandings of BDT mechanism during vibration-assisted diamond cutting of common brittle materials.

Exploring the viability of secondary absorber materials in perovskite solar cell structures through computational analysis

Perovskite solar cells (PSCs) are at the forefront of next-generation photovoltaic technology due to their high efficiency and cost-effectiveness. To further enhance their performance, we investigate the integration of a second absorber layer alongside the conventional perovskite layer. Through extensive simulation techniques, we explore the effectiveness of silicon, Copper Indium Gallium Selenide (CIGS), and additional perovskite layers in forming a Double layered absorber perovskite solar cell (DLAPSC) structure. Our analysis of key performance metrics reveals MASnI3 as the most promising second absorber material, offering superior performance attributed to favorable band alignment and enhanced charge transport properties. In contrast, CIGS and Si layers exhibit inferior performance due to comparatively narrow bandgaps, leading to increased resistive losses. The perovskite/perovskite DLAPSC shows significant promise, achieving a notable power conversion efficiency of 41.56%. This study emphasizes the importance of meticulous parameter optimization and material selection in advancing PSC technology, highlighting the potential of DLAPSCs for renewable energy applications.

A Multi-Objective Optimization Design for Enhancing Space Utilization and Minimizing Cutting Time in Nuclear Facility Component Decommissioning

According to the International Atomic Energy Agency’s requirements for the minimization of radioactive waste, nuclear facility components need to be cut into smaller blocks and packaged in waste containers during decommissioning. Therefore, it is important to increase the space utilization (SU) of the cut blocks in the containers while reducing the cutting time (CT). In engineering practice, increasing SU often leads to longer CTs, which poses a great challenge to improve the efficiency of the nuclear decommissioning process. To overcome this challenge and to determine the optimal operational parameters, this paper introduces a multi-objective optimization design method for cutting and packaging nuclear facility components. The goal is to increase SU while concurrently reducing CTs, ultimately minimizing the waste and reducing the decommissioning cost. First, we project the intricate three-dimensional (3D) irregular parts packing problem onto a two-dimensional plane to simplify the complexity inherent in 3D parts packing and to formulate a packing optimization model. Second, we employ the Morris method to perform global sensitivity analysis on critical parameters, including cutting angle, height, component radius, and plate thickness parameters, throughout the cutting process of nuclear facility components. This analysis produces global sensitivity indicators for each parameter, facilitating a precise assessment of the sensitivity values associated with different operational parameters. Finally, we formulate a multi-objective design optimization model for cutting and packing nuclear facility components that yields a series of alternative operating parameter solutions upon solving it. This methodology offers a resolution for the selection of optimal operational parameters in the decommissioning process of nuclear facility components, thereby attaining optimal outcomes in waste disposal and furnishing guidance for subsequent decommissioning activities.

Suboptimal Analysis of the Differential System of the Conceptual Trailer Air Brake Valve

Motivation: To increase the efficiency of the brake valve by adding a corrective member. Background: The speed of response and smooth transition between modes of operation in the braking system are the primary research questions. Objective and research question: Will the optimal selection of the input parameters of the differentiating part of a conceptual brake valve ensure the speed of operation and enable a smooth transition from the accelerating mode to the tracking mode? Methods: The mathematical model of the differentiating part of the brake valve uses the lumped method, and the solution was obtained by numerical means. Results: Within the assumed range of variation of spring stiffness and control piston bore throughput, the distribution maps of action times and piston lift were determined, and the optimal configuration of the analyzed input parameters was obtained by a genetic algorithm. Future research: future activities will focus on the development of a system of smooth variation of the throughput of the connecting chamber of the differential part of the valve. Conclusions: The determined maps of functional parameter distributions are the basis for the selection of components of the braking system; optimization indicates the directions of modification of the valve in order to obtain an acceptable performance system.

A Novel Local Magnetic Fluid Hyperthermia Based on High Gradient Field Guided by Magnetic Particle Imaging.

Magnetic Particle Imaging (MPI)-guided Magnetic Fluid Hyperthermia (MFH) has the potential for widespread utilization, as it allows for the prediction of magnetothermal dosage, real-time visualization of the thermal therapy process, and precise localization of the lesion area. However, the existing MPI-guided MFH (MPI-MFH) method is insensitive to concentration gradients of magnetic nanoparticles (MNPs) and is susceptible to causing damage to normal tissues with high MNP concentrations during MFH treatment, while inadequately heating tumor tissues with lower MNP concentrations. In this work, we established a relationship between MNP concentration and heating efficiency through simulations and phantom measurements, enabling the optimal selection of MFH parameters guided by MPI. Based on these findings, we developed a high-gradient field MPI-MFH method using a field-free point (FFP) approach to achieve precise local heating. Phantom experiments and in vivo glioma model experiments were conducted to validate this proposed method. The results demonstrated that the proposed method of MPI-MFH can improve the MNP concentration gradient sensitivity to ±1 mg/ml, thereby enabling more effective lesion-site heating without damaging normal tissues. This method not only reduced glioma size effectively but also holds promise for application in various other types of cancers.

Investigating the Visual Utility of Differentially Private Scatterplots.

Increasingly, visualization practitioners are working with, using, and studying private and sensitive data. There can be many stakeholders interested in the resulting analyses-but widespread sharing of the data can cause harm to individuals, companies, and organizations. Practitioners are increasingly turning to differential privacy to enable public data sharing with a guaranteed amount of privacy. Differential privacy algorithms do this by aggregating data statistics with noise, and this now-private data can be released visually with differentially private scatterplots. While the private visual output is affected by the algorithm choice, privacy level, bin number, data distribution, and user task, there is little guidance on how to choose and balance the effect of these parameters. To address this gap, we had experts examine 1,200 differentially private scatterplots created with a variety of parameter choices and tested their ability to see aggregate patterns in the private output (i.e. the visual utility of the chart). We synthesized these results to provide easy-to-use guidance for visualization practitioners releasing private data through scatterplots. Our findings also provide a ground truth for visual utility, which we use to benchmark automated utility metrics from various fields. We demonstrate how multi-scale structural similarity (MS-SSIM), the metric most strongly correlated with our study's utility results, can be used to optimize parameter selection.

Подбор параметров моделей роста радужной форели

The purpose of the presented work is to assess the adequacy and selection of optimal parameters of mathematical models of fish growth in application to forecasting the growth of aquaculture trout in closed water supply installations (CWSI). To perform the assessment and comparison, 8 nonlinear mathematical models of fish growth were considered. Tabular (parametric) models of feed for trout cultivation in the CWSI were taken as a standard, and on their basis the selection of optimal parameters was carried out, bringing each of the mathematical models closer to the standard one. The results were compared using an estimate of the standard error, the Akaike information criterion adjusted for a small sample size, and the Bayesian information criterion. As a result, the parameters providing the best approximation of the considered mathematical models of fish growth to the reference tabular function are obtained. As a result of the error estimation, it was found that the three-parameter von Bertalanfi model demonstrates the best accuracy. At the same time, other models (with the exception of exponential) have also demonstrated accuracy sufficient for practical use. The practical significance of the work is the conclusions on the adequacy of the use of nonlinear mathematical models for modeling trout growth in the CWSI, as well as the presented model parameters that provide the best approximation to the reference tabular function.

Smart DIC: User-independent, accurate and precise DIC measurement with self-adaptively selected optimal calculation parameters

Existing subset-based digital image correlation (DIC) must rely on user-selected key calculation parameters (i.e., subset size and shape function) to proceed with displacement/deformation analysis. However, the lack of clear guidelines for selecting these parameters leads to varying choices among users, thus introducing artificial uncertainty in DIC measurements. Previous theoretical analyses and experimental studies revealed that optimal calculation parameters must account for both local speckle pattern quality and deformation at each required point. That means these key parameters should vary at each calculation point, posing a significant challenge for optimal parameter selection. To tackle this challenge, a novel Smart-DIC is proposed to achieve user-independent, accurate and precise displacement field measurements. This method comprehensively considers the local speckle pattern quality and local deformation at each calculation point, and gives the explicit formulas to determine optimal calculation parameters. Based on these optimal calculation parameters, Smart-DIC outputs accurate and precise displacement measurements without the need for users’ inputs. To validate its metrological performance, numerical tests were processed using data from DIC Challenge 1.0 and real images of tensile tests of a commercial AL-based alloy. The experimental results underscore the capacity of Smart-DIC to efficiently achieve accurate and precise displacement measurements across various scenarios by automatically selecting optimal subset sizes and shape functions.

Математичне моделювання руху автобронетанкової техніки з колісною формулою 4×4 при зміні тиску в шинах коліс на прикладі КрАЗ-Спартан

The authors proposed a study of the passability of trucks and armored vehicles of the National Guard of Ukraine with a 4x4 wheel formula. The movement of armored vehicles (AMT) of the National Guard of Ukraine involves movement both on public roads and off-road. The possibility of increasing cross-country ability by ensuring optimal pressure distribution in the tires of the wheels is being considered. The increase in bearing capacity in difficult operating conditions can be estimated with the help of modern mathematical tools. This allows the vehicle equipment of the National Guard of Ukraine to study the cross-country ability and select optimal parameters when performing service and combat tasks with a certain ratio of technical characteristics in different operating conditions. As a result of the full-scale war of the Russian Federation against Ukraine, more serious technical requirements are imposed on the vehicles of the Armed Forces of Ukraine (AFU) and the National Guard of Ukraine (NGU) than in peacetime. Among these requirements, maneuverability is in the first place - controllability is ensured by the ability to move at high speed over rough terrain. The patency of the AMT of the National Guard of Ukraine depends on many factors, the main ones of which are: the type and quality of the surface on which movement takes place, the pressure in the tires of the wheels, the weight of the vehicle, the order of passing the route of the convoy, etc. The central air pressure control system in wheel tires (SRPRS) is widely used in various variants of automotive equipment with high traffic intensity, but the main disadvantage of such systems is the need to minimize tire pressure to improve cross-country ability on load-bearing surfaces with low load-bearing capacity. This tolerance can cause the tire to slip off the rim, overheat or burst. This is due to the fact that the maximum speed is limited due to the risk of system failure. It is recommended to determine the influence of various factors in order to ensure the required level of support patency and analyze it using the example of the KrAZ-SPARTAN armored car with a 4×4 wheel formula.

Thermodynamic performance evaluation and optimization of a hybrid system integrating vacuum graphene-anode thermionic converters with direct carbon fuel cells

An efficient hybrid system integrating a direct carbon fuel cell (DCFC) with a vacuum graphene-anode thermionic converter (VGTC) is proposed for cogeneration. The VGTC efficiently recycles waste heat released from the DCFC and generate additional electricity, significantly improving energy utilization efficiency and economic performance. A comprehensive mathematical model is developed to quantitatively assess the thermodynamic performance of the hybrid system, taking into account the overpotential losses of the DCFC and the irreversible energy losses occurring within the system. The results show that at an operating temperature of 923 K, the hybrid system can achieve a maximum power density of 466 W/m2, which is approximately 1.34 times that of a single DCFC, indicating a significant improvement in output performance. Besides, the optimal operating region and parameter selection criteria for the hybrid system are determined using finite-time thermodynamic optimization theory. Furthermore, the effects of essential parameters on the output performance of the hybrid system, such as the operating temperature of the DCFC, the heat transfer coefficient, the Fermi level of graphene, the work function of the cathode, the thermal emissivity, and the reflectivity of the back mirror, are investigated. Finally, the comparative study shows that the proposed hybrid system outperforms other previously reported hybrid systems regarding output electric power and conversion efficiency due to the efficient high-grade waste heat recovery and energy conversion by the VGTC.

Study on the influence of collecting parameters of deep-sea seafloor massive sulfide on ore collecting characteristics

The commercialization of deep-sea mining has put forward strict requirements for ore collection output, ore collection efficiency, and environmental disturbance. However, the research of seafloor massive sulfide collection is in the initial stage. The influence of ore-collecting working parameters on the ore-collecting performance is not clear. In order to realize the commercial exploitation of deep-sea minerals, it is urgent to carry out the analysis and research on the influence of ore-collecting parameters on ore-collecting characteristics. The computational fluid dynamics-discrete element method is adopted in this paper to carry out numerical simulation research. Through the qualitative and quantitative analysis method, the influence law of six ore collecting parameters on ore collecting characteristics was analyzed. On this basis, the prediction model of ore-collecting characteristics and the optimal selection of ore-collecting parameters are proposed. Through the research, it is suggested to select 133.2 kg/s flow suction for ore collecting hard pipe; 60 rpm rotation speed is recommended for the mining head. It is recommended to select mining areas with the mineral density of 3000 kg/m3 for collection. The collection stage should be broken as far as possible to the 25 mm chip size. The recommended cutting depth is 224 mm if the strength and system metrics are met. The research of this paper provides a theoretical basis for the practice of deep-sea ore collection and has guiding significance for the selection of ore collection parameters, structural optimization design, and other engineering problems.

Learning-Assisted Fast Determination of Regularization Parameter in Constrained Image Reconstruction.

To leverage machine learning (ML) for fast selection of optimal regularization parameter in constrained image reconstruction. Constrained image reconstruction is often formulated as a regularization problem and selecting a good regularization parameter value is an essential step. We solved this problem using an ML-based approach by leveraging the finding that for a specific constrained reconstruction problem defined for a fixed class of image functions, the optimal regularization parameter value is weakly subject-dependent and the dependence can be captured using few experimental data. The proposed method has four key steps: a) solution of a given constrained reconstruction problem for a few (say, 3) pre-selected regularization parameter values, b) extraction of multiple approximated quality metrics from the initial reconstructions, c) predicting the true quality metrics values from the approximated values using pre-trained neural networks, and d) determination of the optimal regularization parameter by fusing the predicted quality metrics. The effectiveness of the proposed method was demonstrated in two constrained reconstruction problems. Compared with L-curve-based method, the proposed method determined the regularization parameters much faster and produced substantially improved reconstructions. Our method also outperformed state-of-the-art learning-based methods when trained with limited experimental data. This paper demonstrates the feasibility and improved reconstruction quality by using machine learning to determine the regularization parameter in constrained reconstruction. The proposed method substantially reduces the computational burden of the traditional methods (e.g., L-curve) or relaxes the requirement of large training data by modern learning-based methods, thus enhancing the practical utility of constrained reconstruction.

Numerical and Experimental Analyses of the Effect of Water Injection on Combustion of Mg-Based Hydroreactive Fuels

The energy release process of the Mg-based hydroreactive fuels directly affects the performance of water ramjet engines, and the burning rate is one of the key parameters of the Mg-based hydroreactive fuels. However, there is not enough in-depth understanding of the combustion process of Mg-based hydroreactive fuels within the chamber of water ramjet engines, and there is a lack of effective means of prediction of the burning rate. Therefore, this paper aims to examine the flame structure of Mg-based hydroreactive fuels with a high metal content and analyze the impact of the water injection velocity and droplet diameter on the combustion property. A combustion experiment system was designed to replicate the combustion of Mg-based hydroreactive fuels within water ramjet engines, and the average linear burning rate was calculated through the target line method. On the basis of the experiment, a combustion–flow coupling solution model of Mg-based hydroreactive fuels was formulated, including the reaction mechanism between Mg/H2O and the decomposition products from an oxidizer and binder. The model was validated through experimental results with Mg-based hydroreactive fuels at various pressures and water injection velocities. The mean absolute percentage error (MAPE) in the experimental results was less than 5%, proving the accuracy and validity of the model. The resulting model was employed for simulating the combustion of Mg-based hydroreactive fuels under different water injection parameters. The addition of water injection resulted in the creation of a new high-temperature region, namely the Mg/H2O non-premixed combustion region in addition to improving the radial diffusion of the flame. With the increasing water injection velocity, the characteristic distance of Mg/H2O non-premixed combustion region is decreased, which enhances the heat transfer to burning surface and accelerates the fuel combustion. The impact of droplet parameters was investigated, revealing that larger droplets enhance the penetration of the fuel-rich gas, which is similar to the effect of injection velocity. However, when the droplet size becomes too large, the aqueous droplets do not fully evaporate, resulting in a slight decrease in the burning rate. These findings enhance the understanding of the mechanisms behind the burning rate variation in Mg-based hydroreactive fuels and offer theoretical guidance for the optimal selection of the engine operating parameters.

Application of Copula Models in Stock Market Analysis

Objectives. The objective of the study is to use copula models to analyze shares of the Russian stock market and describe changes in the relationship between the shares before and during the coronavirus infection (COVID-19).Methods. An algorithm for using copulas and functions of the R programming language in its implementation is presented. To model the dynamics of financial series the ARMA-GJR-GARCH process (autoregressive moving average Glosten-Jagannathan-Runkle model with generalized autoregressive conditional heteroskedasticity) is used. The selection of optimal families and parameters of copula models is carried out. The adequacy of the obtained models is checked and the results of the study of the relationship between the series are analyzed.Results. An algorithm has been developed for a relatively new approach to using copulas in conjunction with the ARMA-GJR-GARCH model. The approach was used to study the impact of coronavirus in the context of the Russian economy. It is revealed that during the COVID-19 period the dependence between different stocks increases. It is shown that the effect of volatility in financial series increases after the outbreak of the pandemic.Conclusion. The research algorithm using copula models in conjunction with the ARMA-GJR-GARCH process has shown its feasibility. This approach can be used with other GARCH-type models to study finance and other areas.

Computational analysis of the simultaneous application of ultrasound and electric fields in a lipid bilayer

The combined application of electric fields and ultrasonic waves has shown promise in controlling cell membrane permeability, potentially resulting in synergistic effects that can be explored in the biotechnology industry. However, further clarification on how these processes interact is still needed. The objective of the present study was to investigate the atomic-scale effects of these processes on a DPPC lipid bilayer using molecular dynamics simulations. For higher electric fields, capable of independently forming pores, the application of an ultrasonic wave in the absence of cavitation yielded no additional effects on pore formation. However, for lower electric fields, the reduction in bilayer thickness induced by the shock wave catalyzed the electroporation process, effectively shortening the mean path that water molecules must traverse to form pores. When cavitation was considered, synergistic effects were evident only if the wave alone was able to generate pores through the formation of a water nanojet. In these cases, sonoporation acted as a mean to focus the electroporation effects on the initial pore formed by the nanojet. This study contributes to a better understanding of the synergy between electric fields and ultrasonic waves and to an optimal selection of processing parameters in practical applications of these processes.

New York city taxi demand forecast based on ARIMA model

The competition in the taxi market is becoming increasingly fierce, but there is a gap between the demand and the number of taxis in some time periods, which not only intensifies ineffective competition among taxis but also brings inconvenience to passengers. This study aims to establish a predictive model to predict the demand for taxis in different time periods in the city. The data was collected from New York City yellow taxi data which was from June 1st, 2022 to June 6th, 2022. After processing the raw data, the optimal parameter selection of the model is determined through ADF testing to improve accuracy. Through ACF and PACF calculation, the data and images are analyzed to find the most suitable p and q values. Use ARIMA model to fit the data and obtain a model with robust fitting parameters. The distribution of predicted values is very consistent with actual data. The model was used for 50 periods of prediction, and through analysis of the research results, the fitting effect of the prediction was good. It was found that the accuracy of the model was high, proving its ability to predict short-term taxi demand.

Feasibility and optimization of 19F MRI on a clinical 3T with a large field-of-view torso coil

Objective. The objective of this work is to: (1) demonstrate fluorine-19 (19F) MRI on a 3T clinical system with a large field of view (FOV) multi-channel torso coil (2) demonstrate an example parameter selection optimization for a 19F agent to maximize the signal-to-noise ratio (SNR)-efficiency for spoiled gradient echo (SPGR), balanced steady-state free precession (bSSFP), and phase-cycled bSSFP (bSSFP-C), and (3) validate detection feasibility in ex vivo tissues. Approach. Measurements were conducted on a 3.0T Discovery MR750w MRI (GE Healthcare, USA) with an 8-channel 1H/19F torso coil (MRI Tools, Germany). Numerical simulations were conducted for perfluoropolyether to determine the theoretical parameters to maximize SNR-efficiency for the sequences. Theoretical parameters were experimentally verified, and the sensitivity of the sequences was compared with a 10 min acquisition time with a 3.125 × 3.125 × 3 mm3 in-plane resolution. Feasibility of a bSSFP-C was also demonstrated in phantom and ex vivo tissues. Main Results. Flip angles (FAs) of 12 and 64° maximized the signal for SPGR and bSSFP, and validation of optimal FA and receiver bandwidth showed close agreement with numerical simulations. Sensitivities of 2.47, 5.81, and 4.44 ms−0.5mM−1 and empirical detection limits of 20.3, 1.5, and 6.2 mM were achieved for SPGR, bSSFP, and bSSFP-C, respectively. bSSFP and bSSFP-C achieved 1.8-fold greater sensitivity over SPGR (p < 0.01). Significance. bSSFP-C was able to improve sensitivity relative to simple SPGR and reduce both bSSFP banding effects and imaging time. The sequence was used to demonstrate the feasibility of 19F MRI at clinical FOVs and field strengths within ex-vivo tissues.

Optimizing Concession Agreement Terms and Conditions: Stakeholder Interest Alignment in the Petrochemical Sector

This article is devoted to the examination of models and the selection of optimal parameters for concession agreements pertaining to construction and operation projects within the pipeline infrastructure of the petrochemical sector. Pipelines are underscored as capital-intensive assets crucial for the organization of complex petrochemical production processes. These processes play a vital role in generating added value, tax revenue, employment opportunities, and fostering territorial development while upholding environmental quality standards. This study aims to ascertain the economic parameters of concession agreements, with a focus on achieving a balance of economic interests between the government and businesses. Through a comparative analysis of fundamental economic and mathematical models of concession agreements, the authors model economic parameters to determine the government’s share in investments and concession fees concerning pipeline projects. Subsequently, an oil product pipeline project is discussed as a case study. The results gleaned from this analysis can be harnessed to optimize the parameters of concession agreements and enhance the economic efficiency of project implementation. Economically viable parameters not only facilitate the execution of concession agreements but also foster the generation of added value, social benefits, and environmental oversight, thus aligning with the principles of sustainable development.