T2-weighted imaging-based radiomic-clinical machine learning model for predicting the differentiation of colorectal adenocarcinoma

BACKGROUND The study on predicting the differentiation grade of colorectal cancer(CRC)based on magnetic resonance imaging(MRI)has not been reported yet.Developing a non-invasive model to predict the differentiation grade of CRC is of great value.AIM To develop and validate machine learning-based models for predicting the differ-entiation grade of CRC based on T2-weighted images(T2WI).METHODS We retrospectively collected the preoperative imaging and clinical data of 315 patients with CRC who underwent surgery from March 2018 to July 2023.Patients were randomly assigned to a training cohort(n=220)or a validation cohort(n=95)at a 7:3 ratio.Lesions were delineated layer by layer on high-resolution T2WI.Least absolute shrinkage and selection operator regression was applied to screen for radiomic features.Radiomics and clinical models were constructed using the multilayer perceptron(MLP)algorithm.These radiomic features and clinically relevant variables(selected based on a significance level of P<0.05 in the training set)were used to construct radiomics-clinical models.The performance of the three models(clinical,radiomic,and radiomic-clinical model)were evaluated using the area under the curve(AUC),calibration curve and decision curve analysis(DCA).RESULTS After feature selection,eight radiomic features were retained from the initial 1781 features to construct the radiomic model.Eight different classifiers,including logistic regression,support vector machine,k-nearest neighbours,random forest,extreme trees,extreme gradient boosting,light gradient boosting machine,and MLP,were used to construct the model,with MLP demonstrating the best diagnostic performance.The AUC of the radiomic-clinical model was 0.862(95%CI:0.796-0.927)in the training cohort and 0.761(95%CI:0.635-0.887)in the validation cohort.The AUC for the radiomic model was 0.796(95%CI:0.723-0.869)in the training cohort and 0.735(95%CI:0.604-0.866)in the validation cohort.The clinical model achieved an AUC of 0.751(95%CI:0.661-0.842)in the training cohort and 0.676(95%CI:0.525-0.827)in the validation cohort.All three models demonstrated good accuracy.In the training cohort,the AUC of the radiomic-clinical model was significantly greater than that of the clinical model(P=0.005)and the radiomic model(P=0.016).DCA confirmed the clinical practicality of incorporating radiomic features into the diagnostic process.CONCLUSION In this study,we successfully developed and validated a T2WI-based machine learning model as an auxiliary tool for the preoperative differentiation between well/moderately and poorly differentiated CRC.This novel approach may assist clinicians in personalizing treatment strategies for patients and improving treatment efficacy.

Sustainability Evaluation of Modern Photovoltaic Agriculture Based on Interval Type-2 Fuzzy AHP-TOPSIS and Least Squares Support Vector Machine Optimized by Fireworks Algorithm

Photovoltaics(PV)has been combined with many other industries,such as agriculture.But there are many problems for the sustainability of PV agriculture.Timely and accurate sustainability evaluation of modern photovoltaic agriculture is of great significance for accelerating the sustainable development of modern photovoltaic agriculture.In order to improve the timeliness and accuracy of evaluation,this paper proposes an evaluation model based on interval type-2 Fuzzy AHP-TOPSIS and least squares support vector machine optimized by fireworks algorithm.Firstly,the criteria system of modern photovoltaic agriculture sustainability is constructed from three dimensions including technology sustainability,economic sustainability and social sustainability.Then,analytic hierarchy process(AHP)and technique for order preference by similarity to an ideal solution(TOPSIS)methods are improved by using interval type-2 fuzzy theory,and the traditional evaluation model based on interval type-2 Fuzzy AHP-TOPSIS is obtained,and the improved model is used for comprehensive evaluation.After that,the optimal parameters of least squares support vector machine(LSSVM)model are obtained by Fireworks algorithm(FWA)training,and the intelligent evaluationmodel for the sustainability of modern photovoltaic agriculture is constructed to realize fast and intelligent calculation.Finally,an empirical analysis is conducted to demonstrate the scientificity and accuracy of the proposed model.This study is conducive to the comprehensive evaluation of the sustainability of modern photovoltaic agriculture,and can provide decision-making support for more reasonable development model in the future of modern photovoltaic agriculture.

Combination Computing of Support Vector Machine, Support Vector Regression and Molecular Docking for Potential Cytochrome P450 1A2 Inhibitors

The computational approaches of support vector machine （SVM）, support vector regression （SVR） and molecular docking were widely utilized for the computation of active compounds. In this work, to improve the accuracy and reliability of prediction, the strategy of combining the above three computational approaches was applied to predict potential cytochrome P450 1A2 （CYP1A2） inhibitors. The accuracy of the optimal SVM qualitative model was 99.432%, 97.727%, and 91.667% for training set, internal test set and external test set, respectively, showing this model had high discrimination ability. The R2 and mean square error for the optimal SVR quantitative model were 0.763, 0.013 for training set, and 0.753, 0.056 for test set respectively, indicating that this SVR model has high predictive ability for the biolog-ical activities of compounds. According to the results of the SVM and SVR models, some types of descriptors were identi ed to be essential to bioactivity prediction of compounds, including the connectivity indices, constitutional descriptors and functional group counts. Moreover, molecular docking studies were used to reveal the binding poses and binding a n-ity of potential inhibitors interacting with CYP1A2. Wherein, the amino acids of THR124 and ASP320 could form key hydrogen bond interactions with active compounds. And the amino acids of ALA317 and GLY316 could form strong hydrophobic bond interactions with active compounds. The models obtained above were applied to discover potential CYP1A2 inhibitors from natural products, which could predict the CYPs-mediated drug-drug inter-actions and provide useful guidance and reference for rational drug combination therapy. A set of 20 potential CYP1A2 inhibitors were obtained. Part of the results was consistent with references, which further indicates the accuracy of these models and the reliability of this combinatorial computation strategy.

The Development and Application of 2BDQ-8 Rice Direct Sowing Machine

[Objective] The aim was to introduce the development and application of 2BDQ-8 rice direct sowing machine and provide a theoretical basis for rice mechanization production. [Method] 2BDQ-8 rice direct sowing machine was used for the promotion test in field of several cities and counties in Jiangsu Province,and artificial rice planting and mechanization rice planting were compared to explore the production and economic situation. [Result] 2BDQ-8 rice direct sowing machine had advantages such as high efficiency and low cost,the rice direct sowing machine saved about 30% compared to the artificial rice planting and mechanization rice planting,and the overall efficiency was significant. [Conclusion] 2BDQ-8 rice sowing machine was a production technology that had low cost and high efficiency,which should be widely applied.

Predictive value of bilirubin and serum γ-glutamyltranspeptidase levels in type-2 diabetes mellitus patients with acute coronary syndrome

BACKGROUND Cardiovascular disease is a major complication of diabetes mellitus(DM).Type-2 DM(T2DM)is associated with an increased risk of cardiovascular events and mortality,while serum biomarkers may facilitate the prediction of these outcomes.Early differential diagnosis of T2DM complicated with acute coronary syndrome(ACS)plays an important role in controlling disease progression and improving safety.AIM To investigate the correlation of serum bilirubin andγ-glutamyltranspeptidase(γ-GGT)with major adverse cardiovascular events(MACEs)in T2DM patients with ACS.METHODS The clinical data of inpatients from January 2022 to December 2022 were analyzed retrospectively.According to different conditions,they were divided into the T2DM complicated with ACS group(T2DM+ACS,n=96),simple T2DM group(T2DM,n=85),and simple ACS group(ACS,n=90).The clinical data and laboratory indices were compared among the three groups,and the correlations of serum total bilirubin(TBIL)levels and serumγ-GGT levels with other indices were discussed.T2DM+ACS patients received a 90-day follow-up after discharge and were divided into event(n=15)and nonevent(n=81)groups according to the occurrence of MACEs;Univariate and multivariate analyses were further used to screen the independent influencing factors of MACEs in patients.RESULTS The T2DM+ACS group showed higherγ-GGT,total cholesterol,low-density lipoprotein cholesterol(LDL-C)and glycosylated hemoglobin(HbA1c)and lower TBIL and high-density lipoprotein cholesterol levels than the T2DM and ACS groups(P<0.05).Based on univariate analysis,the event and nonevent groups were significantly different in age(t=3.3612,P=0.0011),TBIL level(t=3.0742,P=0.0028),γ-GGT level(t=2.6887,P=0.0085),LDL-C level(t=2.0816,P=0.0401),HbA1c level(t=2.7862,P=0.0065)and left ventricular ejection fraction(LEVF)levels(t=3.2047,P=0.0018).Multivariate logistic regression analysis further identified that TBIL level and LEVF level were protective factor for MACEs,and age andγ-GGT level were risk factors(P<0.05).CONCLUSION Serum TBIL levels are decreased andγ-GGT levels are increased in T2DM+ACS patients,and the two indices are significantly negatively correlated.TBIL andγ-GGT are independent influencing factors for MACEs in such patients.

Machine Learning Models in Type 2 Diabetes Risk Prediction:Results from a Cross-sectional Retrospective Study in Chinese Adults

Type 2 diabetes mellitus (T2DM) has become a prevalent health problem in China,especially in urban areas.Early prevention strategies are needed to reduce the associated mortality and morbidity.We applied the combination of rules and different machine learning techniques to assess the risk of development of T2DM in an urban Chinese adult population.A retrospective analysis was performed on 8000 people with non-diabetes and 3845 people with T2DM in Nanjing.Multilayer Perceptron (MLP),AdaBoost (AD),Trees Random Forest (TRF),Support Vector Machine (SVM),and Gradient Tree Boosting (GTB) machine learning techniques with 10 cross validation methods were used with the proposed model for the prediction of the risk of development of T2DM.The performance of these models was evaluated with accuracy,precision,sensitivity,specificity,and area under receiver operating characteristic (ROC) curve (AUC).After comparison,the prediction accuracy of the different five machine models was 0.87,0.86,0.86,0.86 and 0.86 respectively.The combination model using the same voting weight of each component was built on T2DM,which was performed better than individual models.The findings indicate that,combining machine learning models could provide an accurate assessment model for T2DM risk prediction.

Sentiment Analysis Using E-Commerce Review Keyword-Generated Image with a Hybrid Machine Learning-Based Model

In the context of the accelerated pace of daily life and the development of e-commerce,online shopping is a mainstreamway for consumers to access products and services.To understand their emotional expressions in facing different shopping experience scenarios,this paper presents a sentiment analysis method that combines the ecommerce reviewkeyword-generated imagewith a hybrid machine learning-basedmodel,inwhich theWord2Vec-TextRank is used to extract keywords that act as the inputs for generating the related images by generative Artificial Intelligence(AI).Subsequently,a hybrid Convolutional Neural Network and Support Vector Machine(CNNSVM)model is applied for sentiment classification of those keyword-generated images.For method validation,the data randomly comprised of 5000 reviews from Amazon have been analyzed.With superior keyword extraction capability,the proposedmethod achieves impressive results on sentiment classification with a remarkable accuracy of up to 97.13%.Such performance demonstrates its advantages by using the text-to-image approach,providing a unique perspective for sentiment analysis in the e-commerce review data compared to the existing works.Thus,the proposed method enhances the reliability and insights of customer feedback surveys,which would also establish a novel direction in similar cases,such as social media monitoring and market trend research.

Absorption and Photocataly tic Degradation of Machine Oil by Exfoliated Graphite-Supported Nanometer TiO2 Material

By loading nanometer anatase onto exfoliated graphite with the sol-gel method, exfoliated graphite-TiO2 composite （EG-TiO2） can be prepared, which can adsorb oil and can also degrade oil. In a technologic condition for preparing EG-TiO2, the impregnated number of times is the most important factor to influence oil-adsorbing capability, that is, when the impregnated number of times increases, the amount of saturation-adsorbed oil decreases. The study of EG-TiO2 photocatalytic degradation of machine oil based on the weight-loss method and infrared spectrum method indicates that EG-TiO2 has obvious effect of photocatalytic degradation for machine oil. Its performance is superior to pure nanometer TiO2 powder because nanometer TiO2 in EG-TiO2 has three-dimension laminar structure and comparatively high adsorption capability.

Heterogeneously elevated branched-chain/aromatic amino acids among new-onset type-2 diabetes mellitus patients are potentially skewed diabetes predictors

BACKGROUND The lack of specific predictors for type-2 diabetes mellitus(T2DM)severely impacts early intervention/prevention efforts.Elevated branched-chain amino acids(BCAAs:Isoleucine,leucine,valine)and aromatic amino acids(AAAs:Tyrosine,tryptophan,phenylalanine)show high sensitivity and specificity in predicting diabetes in animals and predict T2DM 10-19 years before T2DM onset in clinical studies.However,improvement is needed to support its clinical utility.AIM To evaluate the effects of body mass index(BMI)and sex on BCAAs/AAAs in new-onset T2DM individuals with varying body weight.METHODS Ninety-seven new-onset T2DM patients(<12 mo)differing in BMI[normal weight(NW),n=33,BMI=22.23±1.60;overweight,n=42,BMI=25.9±1.07;obesity(OB),n=22,BMI=31.23±2.31]from the First People’s Hospital of Yunnan Province,Kunming,China,were studied.One-way and 2-way ANOVAs were conducted to determine the effects of BMI and sex on BCAAs/AAAs.RESULTS Fasting serum AAAs,BCAAs,glutamate,and alanine were greater and high-density lipoprotein(HDL)was lower(P<0.05,each)in OB-T2DM patients than in NW-T2DM patients,especially in male OB-T2DM patients.Arginine,histidine,leucine,methionine,and lysine were greater in male patients than in female patients.Moreover,histidine,alanine,glutamate,lysine,valine,methionine,leucine,isoleucine,tyrosine,phenylalanine,and tryptophan were significantly correlated with abdominal adiposity,body weight and BMI,whereas isoleucine,leucine and phenylalanine were negatively correlated with HDL.CONCLUSION Heterogeneously elevated amino acids,especially BCAAs/AAAs,across new-onset T2DM patients in differing BMI categories revealed a potentially skewed prediction of T2DM development.The higher BCAA/AAA levels in obese T2DM patients would support T2DM prediction in obese individuals,whereas the lower levels of BCAAs/AAAs in NW-T2DM individuals may underestimate T2DM risk in NW individuals.This potentially skewed T2DM prediction should be considered when BCAAs/AAAs are to be used as the T2DM predictor.

Interplay of serum biomarkers bilirubin andγ-glutamyltranspeptidase in predicting cardiovascular complications in type-2 diabetes mellitus

This editorial synthesizes insights from a series of studies examining the interplay between metabolic and oxidative stress biomarkers in cardiovascular disease(CVD),focusing particularly on type-2 diabetes mellitus(T2DM)and acute coronary syndrome(ACS).The central piece of this synthesis is a study that investigates the balance between oxidative stress and antioxidant systems in the body through the analysis of serum bilirubin andγ-glutamyltranspeptidase(γ-GGT)levels in T2DM patients with ACS.This study highlights serum bilirubin as a protective antioxidant factor,while elevatedγ-GGT levels indicate increased oxidative stress and correlate with major adverse cardiovascular events.Complementary to this,other research contributions revealγ-GGT’s role as a risk factor in ACS,its association with cardiovascular mortality in broader populations,and its link to metabolic syndrome,further elucidating the metabolic dysregulation in CVDs.The collective findings from these studies underscore the critical roles ofγ-GGT and serum bilirubin in cardiovascular health,especially in the context of T2DM and ACS.By providing a balanced view of the body’s oxidative and antioxidative mechanisms,these insights suggest potential pathways for targeted interventions and improved prognostic assessments in patients with T2DM and ACS.This synthesis not only corroborates the pivotal role ofγ-GGT in cardiovascular pathology but also introduces the protective potential of antioxidants like bilirubin,illuminating the complex interplay between T2DM and heart disease.These studies collectively underscore the critical roles of serum bilirubin andγ-GGT as biomarkers in cardiovascular health,particularly in T2DM and ACS contexts,offering insights into the body’s oxidative and antioxidative mechanisms.This synthesis of research supports the potential of these biomarkers in guiding therapeutic strategies and improving prognostic assessments for patients with T2DM and some CVD.

Machine learning-based ionic liquids design and process simulation for CO_(2) separation from flue gas

Rational design of ionic liquids(ILs),which is highly dependent on the accuracy of the model used,has always been crucial for CO_(2)separation from flue gas.In this study,a support vector machine(SVM)model which is a machine learning approach is established,so as to improve the prediction accuracy and range of IL melting points.Based on IL melting points data with 600 training data and 168 testing data,the estimated average absolute relative deviations(AARD)and squared correlation coefficients(R^(2))are 3.11%,0.8820 and 5.12%,0.8542 for the training set and testing set of the SVM model,respectively.Then,through the melting points model and other rational design processes including conductor-like screening model for real solvents(COSMO-RS)calculation and physical property constraints,cyano-based ILs are obtained,in which tetracyanoborate[TCB]-is often ruled out due to incorrect estimation of melting points model in the literature.Subsequently,by means of process simulation using Aspen Plus,optimal IL are compared with excellent IL reported in the literature.Finally,1-ethyl-3-methylimidazolium tricyanomethanide[EMIM][TCM]is selected as a most suitable solvent for CO_(2)separation from flue gas,the process of which leads to 12.9%savings on total annualized cost compared to that of 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)amide[EMIM][Tf_(2)N].

Accelerated prediction of Cu-based single-atom alloy catalysts for CO_(2) reduction by machine learning

Various strategies,including controls of morphology,oxidation state,defect,and doping,have been developed to improve the performance of Cu-based catalysts for CO_(2) reduction reaction(CO_(2)RR),generating a large amount of data.However,a unified understanding of underlying mechanism for further optimization is still lacking.In this work,combining first-principles calculations and machine learning(ML)techniques,we elucidate critical factors influencing the catalytic properties,taking Cu-based single atom alloys(SAAs)as examples.Our method relies on high-throughput calculations of 2669 CO adsorption configurations on 43 types of Cu-based SAAs with various surfaces.Extensive ML analyses reveal that low generalized coordination numbers and valence electron number are key features to determine catalytic performance.Applying our ML model with cross-group learning scheme,we demonstrate the model generalizes well between Cu-based SAAs with different alloying elements.Further,electronic structure calculations suggest surface negative center could enhance CO adsorption by back donating electrons to antibonding orbitals of CO.Finally,several SAAs,including PCu,AgCu,GaCu,ZnCu,SnCu,GeCu,InCu,and SiCu,are identified as promising CO_(2)RR catalysts.Our work provides a paradigm for the rational design and fast screening of SAAs for various electrocatalytic reactions.

The Designed Operation of the Machine Control System on HL-2A Tokamak

The Ethernet and field-bus communications are used in the machine control system （MCS） of HL-2A. The control net, with a programmable logic controller （PLC） as its logic control master, an engineering control management station as its net server, and a timing control PC connected to a number of terminals, flexibly and freely transfers information among the nodes on it with the Ethernet transmission techniques. The PLC masters the field bus, which carries small pieces of information between PLC and the field sites reliably and quickly. The control net is connected into the data net, where Internet access and sharing of more experimental data are enabled. The communication in the MCS guarantees the digitalization, automation and centralization. Also provided are a satisfactory degree of safety, reliability, stability, expandability and flexibility for maintenance.

CLASSIFICATION OF SKIN AUTOFLUORESCENCE SPECTRUM USING SUPPORT VECTOR MACHINE IN TYPE 2 DIABETES SCREENING

Advanced glycation end products(AGEs)are a complex and heterogencous group of compounds that have been implicated in diabetes related complfcations.Sk in autofluorescence was recently introduced as an altemative tool for skin AGEs accumulation assessment in diabetes.Sucossful optical diagnosis of diabetes requires a rapid and accurate classification algorithm.In order to improve the performance of noninvasive and optical diagnosis of type 2 diabetes,support vector machines(SVM)algorithm was implemented for the clasification of skin autofluorescence from diabetics and control subjects.Cross-validation and grid optimization methods were employed to calculate the optimal parameters that ma ximize classification accuracy.Classification model was set up according to the training set and then veri fied by the testing set.The results show that radical basis fiunction is the best choice in the four common kernels in SVM.Moreover,a diagnostic accuracy of 82.61%,a sensitivity of 69.57%,and a specificity of 95.65%for discriminating diabetics from control subjects were achieved using a mixed kemel function,which is based on liner kernel function and radical basis function.In comparison with fasting plasma glucose and HbAue test,the clasifcation method of skin autofuorescence spectrum based on SVM shows great potential in screening of diabetes.

Physics-embedded machine learning search for Sm-doped PMN-PT piezoelectric ceramics with high performance

Pb(Mg_(1/3)Nb_(2/3))O_(3)–PbTiO_(3)(PMN-PT)piezoelectric ceramics have excellent piezoelectric properties and are used in a wide range of applications.Adjusting the solid solution ratios of PMN/PT and different concentrations of elemental doping are the main methods to modulate their piezoelectric coefficients.The combination of these controllable conditions leads to an exponential increase of possible compositions in ceramics,which makes it not easy to extend the sample data by additional experimental or theoretical calculations.In this paper,a physics-embedded machine learning method is proposed to overcome the difficulties in obtaining piezoelectric coefficients and Curie temperatures of Sm-doped PMN-PT ceramics with different components.In contrast to all-data-driven model,physics-embedded machine learning is able to learn nonlinear variation rules based on small datasets through potential correlation between ferroelectric properties.Based on the model outputs,the positions of morphotropic phase boundary(MPB)with different Sm doping amounts are explored.We also find the components with the best piezoelectric property and comprehensive performance.Moreover,we set up a database according to the obtained results,through which we can quickly find the optimal components of Sm-doped PMN-PT ceramics according to our specific needs.

Machine Learning: An Overview

Machine learning is an Artificial Intelligence (or AI) application, an idea that came into being by giving machines access to data and letting them learn by themselves. AI has been making headlines, especially since ChatGPT was introduced. Malaysia has taken many significant steps to embrace and integrate the technology into various sectors. These include encouraging large companies to build AI infrastructure, creating AI training opportunities (for example, the local media reported Microsoft and Google plan to invest USD 2.2 billion and USD 2 billion, respectively, in the said activities), and, as part of AI Talent Roadmap 2024-2030, establishing AI faculty in one of its public universities (i.e., “Universiti Teknologi Malaysia”) leading the way in the integration and teaching of AI throughout the country. This article introduces several products developed by the author (for the energy and transportation industries) and recommends their improvement by incorporating Machine learning.

Modelling of a post-combustion CO2 capture process using extreme learning machine

This paper presents modelling of a post-combustion CO2 capture process using bootstrap aggregated extreme learning machine （ELM）. ELM randomly assigns the weights between input and hidden layers and obtains the weights between the hidden layer and output layer using regression type approach in one step. This feature allows an ELM model being developed very quickly. This paper proposes using principal component regression to obtain the weights between the hidden and output layers to address the collinearity issue among hidden neuron outputs. Due to the weights between input and hidden layers are randomly assigned, ELM models could have variations in performance. This paper proposes combining multiple ELM models to enhance model prediction accuracy and reliability. To predict the CO2 production rate and CO2 capture level, eight parameters in the process were utilized as model input variables： inlet gas flow rate, CO2 concentration in inlet flow gas, inlet gas temperature, inlet gas pressure, lean solvent flow rate, Jean solvent temperature, lean loading and reboiler duty. The bootstrap re-sampling of training data was applied for building each single ELM and then the individual ELMs are stacked, thereby enhancing the model accuracy and reliability. The bootstrap aggregated extreme learning machine can provide fast learning speed and good generalization performance, which will be used to optimize the CO2 capture process.

Robust monitoring machine:a machine learning solution for out‑of‑sample R_(2)‑hacking in return predictability monitoring

The out-of-sample R^(2) is designed to measure forecasting performance without look-ahead bias.However,researchers can hack this performance metric even without multiple tests by constructing a prediction model using the intuition derived from empirical properties that appear only in the test sample.Using ensemble machine learning techniques,we create a virtual environment that prevents researchers from peeking into the intuition in advance when performing out-of-sample prediction simulations.We apply this approach to robust monitoring,exploiting a dynamic shrink-age effect by switching between a proposed forecast and a benchmark.Considering stock return forecasting as an example,we show that the resulting robust monitoring forecast improves the average performance of the proposed forecast by 15%(in terms of mean-squared-error)and reduces the variance of its relative performance by 46%while avoiding the out-of-sample R^(2)-hacking problem.Our approach,as a final touch,can further enhance the performance and stability of forecasts from any models and methods.

Machine Learning-based Stable P2P IPTV Overlay

Live video streaming is one of the newly emerged services over the Internet that has attracted immense interest of the service providers.Since Internet was not designed for such services during its inception,such a service poses some serious challenges including cost and scalability.Peer-to-Peer(P2P)Internet Protocol Television(IPTV)is an application-level distributed paradigm to offer live video contents.In terms of ease of deployment,it has emerged as a serious alternative to client server,Content Delivery Network(CDN)and IP multicast solutions.Nevertheless,P2P approach has struggled to provide the desired streaming quality due to a number of issues.Stability of peers in a network is one of themajor issues among these.Most of the existing approaches address this issue through older-stable principle.This paper first extensively investigates the older-stable principle to observe its validity in different scenarios.It is observed that the older-stable principle does not hold in several of them.Then,it utilizes machine learning approach to predict the stability of peers.This work evaluates the accuracy of severalmachine learning algorithms over the prediction of stability,where the Gradient Boosting Regressor(GBR)out-performs other algorithms.Finally,this work presents a proof-of-concept simulation to compare the effectiveness of older-stable rule and machine learning-based predictions for the stabilization of the overlay.The results indicate that machine learning-based stability estimation significantly improves the system.

Multifractal estimation of NMR T_(2) cut-off value in low-permeability rocks considering spectrum kurtosis: SMOTE-based oversampling integrated with machine learning

The transverse relaxation time (T_(2)) cut-off value plays a crucial role in nuclear magnetic resonance for identifying movable and immovable boundaries, evaluating permeability, and determining fluid saturation in petrophysical characterization of petroleum reservoirs. This study focuses on the systematic analysis of T_(2) spectra and T_(2) cut-off values in low-permeability reservoir rocks. Analysis of 36 low-permeability cores revealed a wide distribution of T_(2) cut-off values, ranging from 7 to 50 ms. Additionally, the T_(2) spectra exhibited multimodal characteristics, predominantly displaying unimodal and bimodal morphologies, with a few trimodal morphologies, which are inherently influenced by different pore types. Fractal characteristics of pore structure in fully water-saturated cores were captured through the T_(2) spectra, which were calculated using generalized fractal and multifractal theories. To augment the limited dataset of 36 cores, the synthetic minority oversampling technique was employed. Models for evaluating the T_(2) cut-off value were separately developed based on the classified T_(2) spectra, considering the number of peaks, and utilizing generalized fractal dimensions at the weight <0 and the singular intensity range. The underlying mechanism is that the singular intensity and generalized fractal dimensions at the weight <0 can detect the T_(2) spectral shift. However, the T_(2) spectral shift has negligible effects on multifractal spectrum function difference and generalized fractal dimensions at the weight >0. The primary objective of this work is to gain insights into the relationship between the kurtosis of the T_(2) spectrum and pore types, as well as to predict the T_(2) cut-off value of low-permeability rocks using machine learning and data augmentation techniques.