Multi Parameter Research Articles

Rockfall Risk Management System Based on Multi Parameter Rating System for Humans in Inaccessible and Isolated Settings of Cultural Heritage: Application on Nisyros Island, Greece

Rockfall Risk Management System Based on Multi Parameter Rating System for Humans in Inaccessible and Isolated Settings of Cultural Heritage: Application on Nisyros Island, Greece

Radiomics based Machine Learning Models for Classification of Prostate Cancer Grade Groups from Multi Parametric MRI Images

Abstract Purpose: This study aimed to investigate the performance of multiparametric magnetic resonance imaging (mpMRI) radiomic feature-based machine learning (ML) models in classifying the Gleason grade group (GG) of prostate cancer. Methods: In this retrospective study, a total of 203 patients with histopathologically confirmed prostate cancer who underwent mpMRI before prostate biopsy were included. After manual segmentation, radiomic features (RFs) were extracted from T2-weighted, apparent diffusion coefficient, and high b-value diffusion-weighted magnetic resonance imaging (DWMRI). Patients were split into training sets and testing sets according to a ratio of 8:2. A pipeline considering combinations of two feature selection (FS) methods and six ML classifiers was developed and evaluated. The performance of models was assessed using the accuracy, sensitivity, precision, F1-measure, and the area under curve (AUC). Results: On high b-value DWMRI-derived features, a combination of FS method recursive feature elimination (RFE) and classifier random forest achieved the highest performance for classification of prostate cancer into five GGs, with 97.0% accuracy, 98.0% sensitivity, 98.0% precision, and 97.0% F1-measure. The method also achieved an average AUC for GG of 98%. Conclusion: Preoperative mpMRI radiomic analysis based on ML, as a noninvasive approach, showed good performance for classification of prostate cancer into five GGs. Advances in Knowledge: Herein, radiomic models based on preoperative mpMRI and ML were developed to classify prostate cancer into 5 GGs. Our study provides evidence that analysis of quantitative RFs extracted from high b-value DWMRI images based on a combination of FS method RFE and classifier random forest can be applied for multiclass grading of prostate cancer with an accuracy of 97.0%.

Continuous-flow degradation of Orange I using CoFe2O4-loaded activated carbon catalyst: Optimization of reaction parameters and toxicity prediction

Orange I (OI) azo dyes produce toxic aromatic compounds in water, making solutions an important issue. In this study, we employed the urea combustion method to load CoFe2O4 particles onto activated carbon (CFO-AC-500). Subsequently, the catalyst was incorporated into a column continuous flow device, testing the effects of multi water parameters and toxicity, and predicting the degradation process on the catalytic degradation of OI for peroxymonosulfate (PMS) activation. In CFO-AC-500/PMS continuous reactor, it showed a satisfied OI removal of more than 82 % of the OI at a flow rate of 73.6 mL/min in 240 min. Also, response surface methodology (RSM) was employed to optimize the operating conditions, with model fitting results demonstrating that the OI removal rate could reach 95.07 % under specific conditions. ESR analyses revealed that the degradation of OI was primarily governed by free radicals (SO4•-, HO• and O2•-) and non-radical pathways (1O2). The biological toxicity of degraded OI was significantly reduced. This innovative CFO-AC-500/PMS continuous flow system can provide an effective new method for the degradation of difficult-to-treat pollutants in practical applications.

Multiparameter Method for Developer Screening of Molecular Glass Resists via Molecular Simulations.

Efficient and accurate developer screening is critical for high-resolution lithography. In this study, a multiparameter (MP) method simultaneously based on the Hansen solubility parameters (HSP), two-component solubility parameters (TSP), and order parameter (OP) is proposed for the developer screening of molecular glass resists via molecular dynamics simulation. A customized solvent database, including 80 organic solvents, is created for the subsequent developer screening. Two diagram forms of HSP (δD-δP-δH diagram and δV-δH diagram) were investigated, and the δV-δH diagram is utilized due to its higher solubility prediction consistency with OP. Then, two solubility prediction diagrams, i.e., HSP&OP and TSP&OP diagrams, were formed using the MP method. The developer screening scheme using the MP method is illustrated for a decomposable resist, AD10BOC, and a cross-linkable resist, AD4C. Meanwhile, the feasibility of the developer screening scheme is verified by practical lithography experiments. In addition, relative solvation free energy (RSFE) calculations and dissolution experiments of six resists are implemented to further confirm the accuracy of OP in reflecting the relative solubility of the resist in various solvents. This study presents a viable approach for developer screening, which will facilitate the development of resist materials.

Design and multi parameter performance optimization of the bionic robotic fish driven by tail fin

In this paper, we investigate the optimization of body contour parameters and the parameters of the tail fin in bionic robotic fish. A new type of robotic fish with fully flexible shell was proposed, followed by the analysis and optimization of the resistance properties of the profile. Subsequently, a dynamic model considering tail fin deformation was established. We use a combination of Fluent simulation and particle swarm optimization (PSO) algorithm to seek the key parameters for an enchanced performance. Upon multi-parameter performance optimization, the obtained results indicate that the fully flexible shell robotic fish can achieve a high swimming speed of 0.69 m/s (equivalent to 1.2 BL/s) and exhibit excellent turning maneuverability with a turning radius of 0.8 body lengths. This paper provides insights for optimizing the performance of robotic fish and enhancing its underwater operational capabilities.

Underground concrete pipe crack damage monitoring by the fuzzy analysis of microseism

Abstract Underground concrete pipe is critical for the safety of the urban infrastructure. Crack would lead to the accidents such as fluid spillage, ground subsidence, as well as waterlogging. Thus, it is of great significance to measure and monitor the crack of pipeline. In this research, a fuzzy monitoring method of the concrete pipe crack damage based on the microseism signal GMM (Gaussian Mixed Model) analysis was proposed. First, the multi parameters of microseism signal were extracted. Then, CRITIC (Criteria Importance Through Intercriteria Correlation) weight vector was constructed using the importance information of parameters. Further, the GMM membership matrix was created by the similarity of the probability density distribution. The crack condition was assessed by means of the fuzzy calculation between the weight vector and membership matrix. The experiment indicated that the proposed method could monitor and assess the different crack conditions in real time.

Multi-parameter viscoelastic material model for denture adhesives based on time-temperature superposition and multiple linear regression analysis

BackgroundRestorative solutions designed for edentulous patients such as dentures and their accompanying denture adhesives operate in the complex and dynamic environment represented by human oral physiology. Developing material models accounting for the viscoelastic behavior of denture adhesives can facilitate their further optimization within that unique physiological environment. This study aims to statistically quantify the degree of significance of three physiological variables - namely: temperature, adhesive swelling, and pH - on denture adhesive mechanical behavior. Further, based on these statistical significance estimations, a previously-developed viscoelastic material modelling approach for such denture adhesives is further expanded and developed to capture these variables’ effects on mechanical behavior.MethodsIn this study a comparable version of Denture adhesive Corega Comfort was analysed rheologically using the steady state frequency sweep tests. The experimentally derived rheological storage and loss modulus values for the selected physiological variables were statistically analyzed using multi parameter linear regression analysis and the Pearson’s coefficient technique to understand the significance of each individual parameter on the relaxation spectrum of the denture adhesive. Subsequently, the parameters are incorporated into a viscoelastic material model based on Prony series discretization and time-temperature superposition, and the mathematical relationship for the loss modulus is deduced.ResultsThe results of this study clearly indicated that the variation in both the storage and loss modulus values can be accurately predicted using the oral cavity physiological parameters of temperature, swelling ratio, and pH with an adjusted R2 value of 0.85. The R2 value from the multi-parameter regression analysis indicated that the predictor variables can estimate the loss and storage modulus with a reasonable accuracy for at least 85% of the rheologically determined continuous relaxation spectrum with a confidence level of 98%. The Pearson’s coefficient for the independent variables indicated that temperature and swelling have a strong influence on the loss modulus, whereas pH had a weak influence. Based on statistical analysis, these mathematical relationships were further developed in this study.ConclusionsThis multi-parameter viscoelastic material model is intended to facilitate future detailed numerical investigations performed with implementation of denture adhesives using the finite element method.

Research on autonomous decision-making of UAV swarm based on neural network algorithm

This article focuses on typical unmanned aerial vehicle (UAV) cluster autonomous collaborative reconnaissance mission scenarios, constructs UAV classes in Matlab, and effectively constructs UAV autonomous decision-making models using two different algorithms; Using Extreme Learning Machines (ELM) and introducing neural network methods to solve autonomous decision-making problems for unmanned aerial vehicles. Test the performance of the model under multiple neural network algorithms, basically achieving unmanned aerial vehicles to complete flight targets based on multiple sensor parameters in unknown environments, using reinforcement learning algorithms to achieve autonomous decision-making of unmanned aerial vehicles, achieving multi parameter fusion (with parameters greater than 4) decision-making, with a decision-making time of less than 100ms, which can ensure timely decision-making while also considering the rationality of the model.

Reproducibility of SIMPLE classification for diabetic retinopathy screening and its comparison to current Italian guidelines.

To evaluate the reproducibility of SIMPLE (Single field Image Multi Parameters defined Lesions Extent), a new Diabetic Retinopathy (DR) classification for screening of 45° single field fundus pictures of patients with diabetes (PwDM), assessing DR, Diabetic Maculopathy (DMac) and referral rate agreement and comparing it to current Italian Guidelines (IG). We conducted a retrospective, observational, multicentre study, collecting 1000 retinal 45° single field images of PwDM obtained during routine visits in two diabetes clinics. Three ophthalmologists evaluated each image, determining the presence and number of specific DR lesions and then assigning a stage according to the current IG for screening. SIMPLE staging was performed automatically via Excel software, based on the pre-specified DR characteristics observed by the graders. We analysed intra-centre, inter-centre and total inter-grader agreement for DR and DMac stage and referral rate of the two classifications. Agreement amongst the three graders was consistently higher when using SIMPLE classification than when using current IG classification. For DR, kappa (k) was 0.86 with IG and 0.95 with SIMPLE classification; for DMac, k-IG was 0.78, while k-SIMPLE was 0.96; concordance on the referral rate was 0.91 with IG and 0.99 with SIMPLE. Similar results were obtained in sub-analyses for the evaluation of intra-centre and inter-centre concordance. Our results suggest that the new SIMPLE classification has an excellent reproducibility amongst graders, comparable or superior to the current IG for DR screening proposed in 2015, improving the standardisation of the decision on referability.

Modeling and Forecasting Short-Term Electricity Demand for Libyan Electric Network

Electrical load forecasting is a method of guessing the future of the electricity demand. Load forecast is an essential factor for power system planners and demand controllers. This is to ensure that would be an adequate amount of the electricity meeting the increasing demands. Therefore, an accurate load forecasting model can effectively lead to reduction of power system cost, better budget planning, and maintenance scheduling. The future load on a power system is predicted by extrapolating a predetermined relationship between the load and its influential variables. Determination of this relationship involves modeling and estimation the coefficient of the model through the use of an efficient parameter estimation technique. This paper presents a short term forecasting model for Libyan electric network using multi parameter regression method. The proposed method shows a result with small deviation between the actual and expected load, which means Multi parameter regression emerged as a suitable model for forecasting electricity demand in Libya.

Generative adversarial network for optimization of operational parameters based on shield posture requirements

To mitigate the potential hazards of shield tunneling misalignment (STM) caused by tunneling posture deviation, a method for optimizing operational parameters tailored to tunneling posture adjustment is developed. This paper presents a generative adversarial network (GAN) framework that incorporate a conditional generative adversarial network (CGAN) and two distinct discriminators (WGAN and Path GAN) to enhance the performance of the multi operational parameter generator. Based on engineering data, comparative experiments are designed to investigate the impact of the feature extraction methods, discriminators, and training strategies on the generation performance. Research has shown that an optimal generator scheme, comprising independent convolutional neural networks (CNNs), a summation feature fusion strategy, and a shared decoder, achieves remarkable performance with an MAE of 0.009, RMSE of 0.012, and average error scope of 0.073. Applications of the model confirm its ability to provide optimization suggestions for shield tunneling posture adjustments in engineering scenarios.

PMT Fluorescence Signal Denoising Processing Based on Wavelet Transform and BP Neural Network

Air is the environmental foundation for human life and production, and its composition changes are closely related to human activities. Sulfur dioxide (SO2) is one of the main atmospheric pollutants, mainly derived from the combustion of fossil fuels. But SO2 is a trace gas in the atmosphere, and its concentration may be less than one part per billion (ppb). This paper is based on the principle of photoluminescence and uses a photomultiplier tube (PMT) as a photoelectric converter to develop a device for real-time detection of SO2 concentration in the atmosphere. This paper focuses on the impact of noise interference on weak electrical signals and uses wavelet transform to denoise the signals. At the same time, considering that the photoelectric system is susceptible to temperature changes, a multi parameter fitting model is constructed, and a BP neural network is used to further process the signal, separating the real data from the original data. Finally, a high-precision and wide-range trace level sulfur dioxide concentration detection device and algorithm were obtained.

PHREEQ C Modelling Tool Application to Determine the Effect of Anions on Speciation of Selected Metals in Water Systems within Kajiado North Constituency in Kenya

Heavy metals and anions in water pose a significant risk to living organisms, including humans, and must therefore be carefully monitored. There are numerous approaches and methods of analysis to choose from. Using the PHREEQ C chemical modeling technique, this study looked at the effect of certain anions on the speciation of selected metals in the water environment of Kajiado North. Twenty-five sampling points were used in the study, which covered five Kajiado North election wards. Samples were taken twice during the dry and rainy seasons, in November 2019 and March 2020. Using standard methods, physicochemical characteristics were analyzed, concentrations of selected heavy metals using the (ICP- OES) 7000 series, and selected anions using a multi parameter photometer, their concentration values compared to the specified maximum contaminant level and speciation analysis done using the Computer-based technique PHREEQ C. The range observed for pH, temperature, electrical conductivity, sulphate, chloride, fluoride, nitrate and carbonates was 7.43- 9.30, 23.0 -28.0oC, 335-1265μS/cm, 4.54-361.41mgL-1, 5.19-184.6 mgL-1, 0.01-2.90 mgL-1, 0.1-8.6 mgL-1, and 3.18-55.19 mgL-1 respectively. The range observed for heavy metals was 0.06-1.08 mgL-1 , 0.00 -0.05082 mgL-1, 0.01-0.02597 mgL-1, 0.01-0.01610 mgL-1, 0.00 -0.0327 mgL-1, 0.00-42.7842 mgL-1, and below detection limit, for Fe, Mn, Cu, Zn, Pb, Mg, and Cd respectively. The findings revealed that Cd, Zn, Cu, Cl-, NO2-, and eElectrical cconductivity concentrations were below the WHO levels. At certain sampling points, however, SO42-, F-, Pb, Fe, and Mn were above WHO limits. In some water systems, traces of SO42-, F-, Cu, Fe, Cd, and Mn were found to pose significant health risks, according to PHREEQ C speciation analysis. Anion concentrations, pH, temperature, and metal concentrations are all important variables in modeling. Metals formed anion complexes and the total abundance of metal species was classified at the highest levels as Fe(OH)3, PbCO3, Zn(CO3)2, Cu(CO3)2, Mn+2, MgF-, and Mg+2. At a water system temperature of 25oC without anions, it was found to have the highest chemical species. CuCO3, CuNO3, and CuOH+ complexes are also formed at low pH. Based on the data for different metal species at different pH values, the bioavailability of metal species definitely depends on pH.

Spatial and temporal distribution of physicochemical parameters of water in Gorgan Bay (the southeast of the Caspian Sea - Iran): Fundamentals for the monitoring of the Caspian Sea coastal areas

Gorgan Bay as a main part of the Miankaleh (a natural biosphere reserve registered by UNESCO) is one of the richest ecological area in the West Asia and very important internationally recognized refuge for the wildlife. To date, multi physicochemical parameters have not been examined on a large scale. To fill this knowledge gap, the present study aimed to explore the seasonal and spatial variability of water quality parameters of the bay. The results showed that except for depth and transparency, there are significant variations in most parameters across the four seasons. The patterns of these changes in the bay vary, as evidenced by a comparison of the distribution maps of the various factors throughout the year. Notably, alkalinity declined from east to west, reaching its highest levels at important entry points such as the Qarasu River, Bandar-Gaz, and the pier. TDS, on the other hand, increased westward, reaching its highest concentration in the shallow western regions. Maximum depth (310 cm) and transparency (250 cm) were observed in the central bay. While the pH was higher in deeper areas, the distribution of PO4 was more uniform. With lower levels in the east (salinity = 0.40 ‰) and higher levels in the west (salinity = 28.9 ‰), the salinity showed a coherent gradient. Agricultural land use in the basin of the bay and fluxes of nutrients and sediments of the rivers entering the bay has significant contribution to the bay pollution situation. These results will serve as a guide for improving our understanding of the Gorgan Bay ecosystem. They also have implications for informed conservation and management plans adapted to the specifics of this special region within the Caspian Sea.

Dynamic event-triggered fault detection for multi time scale systems: Application to grid connected converters

This paper deals with the fault detection problem for multi time scale systems. To save network resources, a novel dynamic event-triggered protocol with more generality and higher flexibility of parameters is proposed. The main novelties are condensed as follows, firstly, a modified fault detection observer and a quadratic residual evaluation function are synthetically designed; secondly, the sufficient condition is proved to guarantee that the estimation error system is asymptotically stable and satisfies the specified H∞ performance index; finally, a Lyapunov function is designed to composite the multi time scale perturbation parameter and the influence of the dynamic event-triggered scheme. Finally, a practical experiment on a type of grid connected converters is given to verify the proposed approach.

Special function form solutions of multi-parameter generalized Mittag-Leffler kernel based bio-heat fractional order model subject to thermal memory shocks.

The primary objective of this research is to develop a mathematical model, analyze the dynamic occurrence of thermal shock and exploration of how thermal memory with moving line impact of heat transfer within biological tissues. An extended version of the Pennes equation as its foundational framework, a new fractional modelling approach called the Prabhakar fractional operator to investigate and a novel time-fractional interpretation of Fourier's law that incorporates its historical behaviour. This fractional operator has multi parameter generalized Mittag-Leffler kernel. The fractional formulation of heat flow, achieved through a generalized fractional operator with a non-singular type kernel, enables the representation of the finite propagation speed of heat waves. Furthermore, the dynamics of thermal source continually generates a linear thermal shock at predefined locations within the tissue. Introduced the appropriate set of variables to transform the governing equations into dimensionless form. Laplace transform (LT) is operated on the fractional system of equations and results are presented in series form and also expressed the solution in the form of special functions. The article derives analytical solutions for the heat transfer phenomena of both the generalized model, in the Laplace domain, and the ordinary model in the real domain, employing Laplace inverse transformation. The pertinent parameter's influence, such as α, β, γ, a0, b0, to gain insights into the impact of the thermal memory parameter on heat transfer, is brought under consideration to reveal the interesting results with graphical representations of the findings.

Data Compression and Damage Evaluation of Underground Pipeline With Musicalized Sonar GMM

The underground cement pipeline network is critical in urban infrastructure, such as sewage drainage, fluid transportation, etc. Pipelines are deeply buried, so the damage is difficult to be detected. It is of great significant to monitor the crack of pipeline in real time. In this research, a data compression and damage evaluation method of underground pipeline using musicalized sonar GMM (Gaussian Mixed Model) was proposed. First, the sonar echo signal was sampled and discretized into four musicalized indicators by musicalized model. Then, the distribution of multi parameters were analyzed by GMM. The Frechet similarity was constructed to compare the difference between each working condition and baseline condition, so that the damage state could be identified. The engineering experiment shown that this method could greatly compress the monitoring data and evaluate four crack levels.

Multi-Parameter Analysis of Textile Materials

Advanced constructions in the automotive, military, and aircraft sectors often employ textile materials. This is due to its advantageous mechanical characteristics, appealing supporting components, affordable manufacture, and simple handling. For architectural layout, textile composite analysis becomes crucial. It is costly and time-consuming to experimentally determine the rigidity and durability of textile materials. Due to the impossibility of disintegration, experimental testing can only provide the characteristics of a single textile layer. The disadvantage of a textile layer made up of various fibre orientations is that it is likely to display anisotropic physical performance. Based on the physical reaction of glass and epoxy resin materials, also the design of the textile materials, a multi-parameter (MP) analysis is used to identify the substance behaviour of textile materials via virtual testing. An estimate for just one layer or a whole textile prototype may be created using these simulated trials. Pressure-dependent and nonlinear responses of materials in the multi-parameter analysis are material models created especially for this purpose and are used to simulate. The simulations are contrasted with the outcomes of experimental tests, which reveal the properties of the textile materials. The findings stated that the proposed model has provided rigidity-dislocation of 0.30 in the simulation results

Prediction of Rock Fragmentation in Bench Blasting Operations Based on Multi Parameters—A Case Study

Prediction of Rock Fragmentation in Bench Blasting Operations Based on Multi Parameters—A Case Study

Recent Progresses on Pathophysiology, Diagnosis, Therapeutic Modalities, and Management of Non-alcoholic Fatty Liver Disorder

Abstract: Non-alcoholic fatty liver disease (NAFLD) is currently the utmost common chronic liver disorder that happens through all age groups and is identified to occur in 14%-30% of the general population, demonstrating a critical and grossing clinical issue because of the growing incidence of obesity and overweight. From the histological aspect, it looks like alcoholic liver damage, but it happens in patients who avoid remarkable alcohol usage. NAFLD comprises a broad spectrum, ranging from benign hepatocellular steatosis to inflammatory nonalcoholic steatohepatitis (NASH), different levels of fibrosis, and cirrhosis. Patients with NASH are more susceptible to more rapid progression to cirrhosis and hepatocellular carcinoma. There is no single factor that drives proceeding from simple steatosis to NASH. However, a combination of multi parameters such as genetic background, gut microflora, intake of high fat/ fructose dietary contents or methionine/choline-deficient diet, and consequently accumulated hepatocellular lipids mainly including triglycerides and also other bio-analytes, such as free fatty acids, cholesterol, and phospholipids display a crucial role in disease promotion. NAFLD is related to overweight and insulin resistance (IR) and is regarded as the hepatic presentation of the metabolic syndrome, an amalgamation of medical statuses such as hyperlipidemia, hypertension, type 2 diabetes, and visceral obesity. Despite the increasing prevalence of this disease, which imposes a remarkable clinical burden, most affected patients remain undiagnosed in a timely manner, largely related to the asymptomatic entity of NAFLD patients and the unavailability of accurate and efficient noninvasive diagnostic tests. However, liver biopsy is considered a gold standard for NAFLD diagnosis, but due to being expensive and invasiveness is inappropriate for periodic disease screening. Some noninvasive monitoring approaches have been established recently for NAFLD assessment. In addition to the problem of correct disease course prediction, no effective therapeutic modalities are approved for disease treatment. Imaging techniques can commonly validate the screening and discrimination of NAFLD; nevertheless, staging the disease needs a liver biopsy. The present therapeutic approaches depend on weight loss, sports activities, and dietary modifications, although different insulin-sensitizing drugs, antioxidants, and therapeutic agents seem hopeful. This review aims to focus on the current knowledge concerning epidemiology, pathogenesis, and different biochemical experiments and imaging modalities applied to diagnose the different grades of NAFLD and its management, as well as new data about pharmacological therapies for this disorder.