Critical quality indicators of high-performance polyetherimide(ULTEM)over the MEX 3D printing key generic control parameters:Prospects for personalized equipment in the defense industry

Additive Manufacturing(AM)can provide customized parts that conventional techniques fail to deliver.One important parameter in AM is the quality of the parts,as a result of the material extrusion 3D printing(3D-P)procedure.This can be very important in defense-related applications,where optimum performance needs to be guaranteed.The quality of the Polyetherimide 3D-P specimens was examined by considering six control parameters,namely,infill percentage,layer height,deposition angle,travel speed,nozzle,and bed temperature.The quality indicators were the root mean square(Rq)and average(Ra)roughness,porosity,and the actual to nominal dimensional deviation.The examination was performed with optical profilometry,optical microscopy,and micro-computed tomography scanning.The Taguchi design of experiments was applied,with twenty-five runs,five levels for each control parameter,on five replicas.Two additional confirmation runs were conducted,to ensure reliability.Prediction equations were constructed to express the quality indicators in terms of the control parameters.Three modeling approaches were applied to the experimental data,to compare their efficiency,i.e.,Linear Regression Model(LRM),Reduced Quadratic Regression Model,and Quadratic Regression Model(QRM).QRM was the most accurate one,still the differences were not high even considering the simpler LRM model.

Contribution of the MERISE-Type Conceptual Data Model to the Construction of Monitoring and Evaluation Indicators of the Effectiveness of Training in Relation to the Needs of the Labor Market in the Republic of Congo

This study proposes the use of the MERISE conceptual data model to create indicators for monitoring and evaluating the effectiveness of vocational training in the Republic of Congo. The importance of MERISE for structuring and analyzing data is underlined, as it enables the measurement of the adequacy between training and the needs of the labor market. The innovation of the study lies in the adaptation of the MERISE model to the local context, the development of innovative indicators, and the integration of a participatory approach including all relevant stakeholders. Contextual adaptation and local innovation: The study suggests adapting MERISE to the specific context of the Republic of Congo, considering the local particularities of the labor market. Development of innovative indicators and new measurement tools: It proposes creating indicators to assess skills matching and employer satisfaction, which are crucial for evaluating the effectiveness of vocational training. Participatory approach and inclusion of stakeholders: The study emphasizes actively involving training centers, employers, and recruitment agencies in the evaluation process. This participatory approach ensures that the perspectives of all stakeholders are considered, leading to more relevant and practical outcomes. Using the MERISE model allows for: • Rigorous data structuring, organization, and standardization: Clearly defining entities and relationships facilitates data organization and standardization, crucial for effective data analysis. • Facilitation of monitoring, analysis, and relevant indicators: Developing both quantitative and qualitative indicators helps measure the effectiveness of training in relation to the labor market, allowing for a comprehensive evaluation. • Improved communication and common language: By providing a common language for different stakeholders, MERISE enhances communication and collaboration, ensuring that all parties have a shared understanding. The study’s approach and contribution to existing research lie in: • Structured theoretical and practical framework and holistic approach: The study offers a structured framework for data collection and analysis, covering both quantitative and qualitative aspects, thus providing a comprehensive view of the training system. • Reproducible methodology and international comparison: The proposed methodology can be replicated in other contexts, facilitating international comparison and the adoption of best practices. • Extension of knowledge and new perspective: By integrating a participatory approach and developing indicators adapted to local needs, the study extends existing research and offers new perspectives on vocational training evaluation.

Stock Prediction Based on Technical Indicators Using Deep Learning Model

Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to each other.Several traditional Stock Technical Indicators(STIs)may incorrectly predict the stockmarket trends.To study the stock market characteristics using STIs and make efficient trading decisions,a robust model is built.This paper aims to build up an Evolutionary Deep Learning Model(EDLM)to identify stock trends’prices by using STIs.The proposed model has implemented the Deep Learning(DL)model to establish the concept of Correlation-Tensor.The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange(NSE)-India,a Long Short Term Memory(LSTM)is used.The datasets encompassed the trading days from the 17^(th) of Nov 2008 to the 15^(th) of Nov 2018.This work also conducted exhaustive experiments to study the correlation of various STIs with stock price trends.The model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning one.The proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%,56.25%,and 57.95%on the datasets of HDFC,Yes Bank,and SBI,respectively.Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.

Using Machine Learning to Determine the Efficacy of Socio-Economic Indicators as Predictors for Flood Risk in London

This study examines how socio-economic characteristics predict flood risk in London,England,using machine learning algorithms.The socio-economic variables considered included race,employment,crime and poverty measures.A stacked generalization(SG)model combines randomforest(RF),support vector machine(SVM),and XGBoost.Binary classification issues employ RF as the basis model and SVM as the meta-model.In multiclass classification problems,RF and SVM are base models while XGBoost is meta-model.The study utilizes flood risk labels for London areas and census data to train these models.This study found that SVM performs well in binary classifications with an accuracy rate of 0.60 and an area under the curve of 0.62.XGBoost outperforms other multiclass classification methods with 0.62 accuracy.Multiclass algorithms may perform similarly to binary classification jobs due to reduced data complexity when combining classes.The statistical significance of the result underscores their robustness,respectively.The findings reveal a significant correlation between flood risk and socio-economic factors,emphasizing the importance of these variables in predicting flood susceptibility.These results have important implications for disaster relief management and future research should focus on refining these models to improve predictive accuracy and exploring socio-economic factors.

Modeling post-operative survival in patients with gallbladder cancer resections:The road to improved patient care?

In this letter,we discuss the article by Li et al published in the World Journal of Gastrointestinal Surgery.Gallbladder cancer is a rare but fatal cancer that is often detected unexpectedly and at an advanced stage following routine cholecystectomy.Although the prognosis is poor,curative resections often combined with postoperative chemotherapy and/or radiation therapy can improve survival.However,targeted patient selection for the appropriate therapeutic approach is critical to minimize unnecessary morbidity.Using advanced statistical techniques,the authors developed a nomogram with the potential to predict survival after gallbladder cancer resection,identifying factors associated with long-and shortterm survival.This tool could improve patient selection for surgery and postoperative treatment.In this letter,we provide background on survival nomograms including an in-depth discussion of statistical methods employed in this study,the use of nomograms in other forms of cancer,limitations to the model,and directions for future research.

Evaluation Model of Carrying Capacity of Water Resources Based on Standardized Indices of Radial Basis Function

[Objective] The aim was to study on RBF model about evaluation on carrying capacity of water resources based on standardized indices. [Method] The indices were transformed and the averages of standard values in different levels were taken as the standardized values of components of central vectors for basic functions of RBF hidden nodes. Hence, the basic functions are suitable for most indices, simplifying expression and calculation of basic functions. [Result] RBF models concluded through Monkey-king Genetic Algorithm with weights optimization are used in evaluation on water carrying capacity in three districts in Changwu County in Shaanxi Province, which were in consistent with that through fuzzy evaluation. [Conclusion] RBF, simple and practical, is universal and popular.

A Framework for Selecting Indicators to Assess the Sustainable Development of the Natural Heritage Site

Sustainable world heritage management represents an approach for managing the resources of a property by integrating environmental, economic, and social issues. It aims to provide sustainable benefits for future generations, while protect the property and minimize the possible adverse social, economic and environmental impacts. Indicators of sustainable development, which summarize information for decision-making, are invaluable to learn the efficiency and effectiveness of property management. Scientists in many fields devised several conceptual models of environmental statistics and indicators, of which, DPSIR (Driving forces – Pressure – State – Impact – Response) is thought to be the best available one in identifying and developing indicators of sustainable development. Based on the DPSIR conceptual model and indicator selection criteria, the present paper proposed a methodology framework for selecting indicators to assess the sustainable development of a natural heritage site. The proposed framework included a multi-level hierarchical structure for various indicators and indexes, a modified DPSIR frame to identify key issues in property management and a set of indicators for evaluating the sustainability in Sichuan Giant Panda Sanctuaries.

Establishment of injury models in studies of biological effects induced by microwave radiation

Microwave radiation has been widely used in various fields,such as communication,industry,medical treatment,and military applications.Microwave radiation may cause injuries to both the structures and functions of various organs,such as the brain,heart,reproductive organs,and endocrine organs,which endanger human health.Therefore,it is both theoretically and clinically important to conduct studies on the biological effects induced by microwave radiation.The successful establishment of injury models is of great importance to the reliability and reproducibility of these studies.In this article,we review the microwave exposure conditions,subjects used to establish injury models,the methods used for the assessment of the injuries,and the indicators implemented to evaluate the success of injury model establishment in studies on biological effects induced by microwave radiation.

Comparative Study among Different Semi-Empirical Models for Soil Salinity Prediction in an Arid Environment Using OLI Landsat-8 Data

Salt-affected soils, caused by natural or human activities, are a common environmental hazard in semi-arid and arid landscapes. Excess salts in soils affect plant growth and production, soil and water quality and, therefore, increase soil erosion and land degradation. This research investigates the performance of five different semi-empirical predictive models for soil salinity spatial distribution mapping in arid environment using OLI sensor image data. This is the first attempt to test remote sensing based semi-empirical salinity predictive models in this area: the Kingdom of Bahrain. To achieve our objectives, OLI data were standardized from the atmosphere interferences, the sensor radiometric drift, and the topographic and geometric distortions. Then, the five semi-empirical predictive models based on the Normalized Difference Salinity Index (NDSI), the Salinity Index-ASTER (SI-ASTER), the Salinity Index-1 (SI-1), the Soil Salinity and Sodicity Index-1 and Index-2 (SSSI-1 and SSSI-2), developed for slight and moderate salinity in agricultural land, were implemented and applied to OLI image data. For validation purposes, a fieldwork was organized and different important spots-locations representing different salinity levels were visited, photographed, and localized using an accurate GPS (σ ≤ ±30 cm). Based on this a priori knowledge of the soil salinity, six validation sites were selected to reflect non-saline, low, moderate, high and extreme salinity classes, descriptive statistics extracted from polygons and/or transects over these sites were used. The obtained results showed that the models based on NDSI, SI-1 and SI-ASTER all failed to detect salinity bounds for both extreme salinity (Sabkhah) and non-saline conditions. In Fact, NDSI and SI-ASTER gave respectively only 35% dS/m and 25% dS/m in extreme salinity validation site, while SI-1 and SI-ASTER indicated 38% dS/m and 39% dS/m in non-saline validation site. Therefore, these three models were deemed inadequate for the study site. However, both SSSI-1 and SSSI-2 allowed a detection of the previous salinity bounds and furthermore described similarly and correctly the urban-vegetation areas and the open-land areas. Their predicted EC is around 10% dS/m for non-saline urban soil, about 25% dS/m for low salinity urban-vegetation soil, approximately 30% to 75% dS/m, respectively, for moderate to high salinity soils. SSSI-2 based semi-empirical salinity models was able to differentiate the high salinity versus extreme salinity in areas where both exist and was very accurate to highlight the pure salt where SSSI-1 has reach saturation for both salinity classes. In conclusion, reliable salinity map was produced using the model based on SSSI-2 and OLI sensor data that allows a better characterization of the soil salinity problem in an Arid Environment.

Research and Evaluation on Key Technological Indicators for Airborne and Shipborne Gravimetry

Gravimetry technical guides are the scientific basis for air-sea gravimetry. However, the existing technical guides in China are behind the application requirements. This study analyzed the most important indicators of air-sea gravimetry, including the density of survey lines, gravimetry accuracy and space resolution, stability and reliability of the air-sea gravimeter, and proposed a gravimetry accuracy assessment system consisting of gravity RMS of error, systematic error and mean error, and an assessment system for the gravimeter stability consisting of the relative accuracy of the scale value, monthly zero-drift, RMS of the monthly nonlinear zero-drift variation and the threshold of the monthly nonlinear zero-drift variation. The mathematic models for the measurement point determination in shipborne gravimetry, E tv s correction for airborne gravimetry, platform tilt correction and evaluation air-sea gravimetry were also analyzed and modified. This work will provide technology support for the composition of the military-civil air-sea gravimetry technical guides.

Bayesian methods in reporting and managing Australian clinical indicators

Sustained clinical improvement is unlikely without appropriate measuring and reporting techniques. Clinical indicators are tools to help assess whether a standard of care is being met. They are used to evaluate the potential to improve the care provided by healthcare organisations(HCOs). The analysis and reporting of these indicators for the Australian Council on Healthcare Standards have used a methodology which estimates, for each of the 338 clinical indicators, the gains in the system that would result from shifting the mean proportion to the 20 th centile. The results are used to provide a relative measure to help prioritise quality improvement activity within clinical areas, rather than simply focus on "poorer performing" HCOs. The method draws attention to clinical areas exhibiting larger between-HCO variation and affecting larger numbers of patients. HCOs report data in six-month periods, resulting in estimated clinical indicator proportions which may be affected by small samples and sampling variation. Failing to address such issues would result in HCOs exhibiting extremely small and large estimated proportions and inflated estimates of the potential gains in the system. This paper describes the 20 th centile method of calculating potential gains for the healthcare system by using Bayesian hierarchical models and shrinkage estimators to correct for the effects of sampling variation, and provides an example case in Emergency Medicine as well as example expert commentary from colleges based upon the reports. The application of these Bayesian methods enables all collated data to be used, irrespective of an HCO's size, and facilitates more realistic estimates of potential system gains.

Spatial Modeling of Soil Lime Requirements with Uncertainty Assessment Using Geostatistical Sequential Indicator Simulation

This work presents and analyses a geostatistical methodology for spatial modelling of Soil Lime Requirements (SLR) considering punctual samples of Cation Exchange Capacity (CEC) and Base Saturation (BS) soil properties. Geostatistical Sequential Indicator Simulation is used to draw realizations from the joint uncertainty distributions of the CEC and the BS input variables. The joint distributions are accomplished applying the Principal Component Analyses (PCA) approach. The Monte Carlo method for handling error propagations is used to obtain realization values of the SLR model which are considered to compute and store statistics from the output uncertainty model. From these statistics, it is obtained predictions and uncertainty maps that represent the spatial variation of the output variable and the propagated uncertainty respectively. Therefore, the prediction map of the output model is qualified with uncertainty information that should be used on decision making activities related to the planning and management of environmental phenomena. The proposed methodology for SLR modelling presented in this article is illustrated using CEC and BS input sample sets obtained in a farm located in Ponta Grossa city, Paraná state, Brazil.

Proposing a novel geo-structural model for Torbat-e-Jam-Fariman plain(Northeast of Iran),based on Geomorphic indices calculation,conjugating the field evidences

There are various faults in northern and southern margins of Torbat-e-Jam-Fariman plain which show the probability of enormous earthquake in the future.In present study the geomorphic indices contain Asymmetry Function(Af),Sinuosity of mountain front(Smf),Valley floor index(Vf),Hypsometric index(Hi),Mean Axial slope of channel index(MASC)and Drainage Basin Shape(Bs),have been utilized to determine the relative tectonic activity index(IAT)to recognize,eventually,the geo-structural model of the study area.Faults and folds control the geo-structural activities of the study area,and the geomorphic indices are being affected in consequence of their activities.The intensity of these activities is different throughout the plain.There are many geomorphic evidences,related to active transform fault which are detectable all over the study area such as deviated rivers,quaternary sediments transformation,fault traces.Therefore,recognition of geo-structural model of the study area is extremely vital.Field study,then,approved the results of geomorphic indices calculation in determining the geo-structural model of the study area.Results depicted that the geostructural model of the study area is a kind of Horsetail splay form which is in accordance to the relative tectonic activity of the study area.Based on the above mentioned results it can be predicted that the splays are the trail of Neyshabour fault.

Chinese Stock Price and Volatility Predictions with Multiple Technical Indicators

While a large number of studies have been reported in the literature with reference to the use of Regression model and Artificial Neural Network (ANN) models in predicting stock prices in western countries, the Chinese stock market is much less studied. Note that the latter is growing rapidly, will overtake USA one in 20 - 30 years time and thus be-comes a very important place for investors worldwide. In this paper, an attempt is made at predicting the Shanghai Composite Index returns and price volatility, on a daily and weekly basis. In the paper, two different types of prediction models, namely the Regression and Neural Network models are used for the prediction task and multiple technical indicators are included in the models as inputs. The performances of the two models are compared and evaluated in terms of di- rectional accuracy. Their performances are also rigorously compared in terms of economic criteria like annualized return rate (ARR) from simulated trading. In this paper, both trading with and without short selling has been consid- ered, and the results show in most cases, trading with short selling leads to higher profits. Also, both the cases with and without commission costs are discussed to show the effects of commission costs when the trading systems are in actual use.

Bile acid indices as biomarkers for liver diseases Ⅱ: The bile acid score survival prognostic model

BACKGROUND Cholestatic liver diseases are characterized by an accumulation of toxic bile acids(BA)in the liver,blood and other tissues which lead to progressive liver injury and poor prognosis in patients.AIM To discover and validate prognostic biomarkers of cholestatic liver diseases based on the urinary BA profile.METHODS We analyzed urine samples by liquid chromatography-tandem mass spectrometry and investigated the use of the urinary BA profile to develop survival models that can predict the prognosis of hepatobiliary diseases.The urinary BA profile,a set of non-BA parameters,and the adverse events of liver transplant and/or death were monitored in 257 patients with cholestatic liver diseases for up to 7 years.The BA profile was characterized by calculating BA indices,which quantify the composition,metabolism,hydrophilicity,formation of secondary BA,and toxicity of the BA profile.We have developed and validated the bile-acid score(BAS)model(a survival model based on BA indices)to predict the prognosis of cholestatic liver diseases.RESULTS We have developed and validated a survival model based on BA(the BAS model)indices to predict the prognosis of cholestatic liver diseases.Our results demonstrate that the BAS model is more accurate and results in higher truepositive and true-negative prediction of death compared to both non-BAS and model for end-stage liver disease(MELD)models.Both 5-and 3-year survival probabilities markedly decreased as a function of BAS.Moreover,patients with high BAS had a 4-fold higher rate of death and lived for an average of 11 mo shorter than subjects with low BAS.The increased risk of death with high vs low BAS was also 2-4-fold higher and the shortening of lifespan was 6-7-mo lower compared to MELD or non-BAS.Similarly,we have shown the use of BAS to predict the survival of patients with and without liver transplant(LT).Therefore,BAS could be used to define the most seriously ill patients,who need earlier intervention such as LT.This will help provide guidance for timely care for liver patients.CONCLUSION The BAS model is more accurate than MELD and non-BAS models in predicting the prognosis of cholestatic liver diseases.

Geometrical Model Based Refinements in Nanotube Chiral Indices

There is demonstrated how it is possible to refine graphitic nanotubes’ chiral indices based on the appropriate geometrical model for their structure.

Optimal Placement of Multi DG Units Including Different Load Models Using PSO

This paper proposes a multi-objective index-based approach to optimally determine the size and location of multi-distributed generators (DG) units in distribution system with different load models. It is shown that load models can significantly affect the optimal location and sizing of DG resources in distribution systems. The proposed multi-objective function to be optimized includes a short circuit level parameter to represent the protective device requirements. The proposed function also considers a wide range of technical issues such as active and reactive power losses of the system, the voltage profile, the line loading and the MVA intake by the grid. The optimization technique based on particle swarm optimization (PSO) is introduced. The analysis of continuation power flow to determine the effect of DG units on the most sensitive buses to voltage collapse is carried out. The proposed algorithm is tested using the 38-bus radial system and the IEEE 30-bus meshed system. The results show the effectiveness of the proposed algorithm.

Empirical Models for the Evaluation of Global Solar Radiation for the Site of Abeche in the Province of Ouaddaï, in Chad

The global sustainability plan for future development relies on solar radiation which is the main source of renewable energy. Thus, this work studies the performance of six models to estimate global solar radiation on a horizontal surface for the Abeche site in Chad. The data used in this work were collected at the General Directorate of National Meteorology of Chad. The reliability and accuracy of different models for estimating global solar radiation were validated by statistical indicators to identify the most accurate model. The results show that among all the models, the Sabbagh model has the best performance in estimating the global solar radiation. The average is 6.354 kWh/m2 with an average of -3.704%. This model is validated against NASA data which is widely used.

Evaluation of the Eta Simulations Nested in Three Global Climate Models

To provide long-term simulations of climate change at higher resolution, Regional Climate Models (RCMs) are nested in global climate models (GCMs). The objective of this work is to evaluate the Eta RCM simulations driven by three global models, the HadGEM2-ES, BESM, and MIROC5, for the present period, 1961-1990. The RCM domain covers South America, Central America, and Caribbean. These simulations will be used for assessment of climate change projections in the region. Maximum temperatures are generally underestimated in the domain, in particular by MIROC5 driven simulations, in summer and winter seasons. Larger spread among the simulations was found in the minimum temperatures, which showed mixed signs of errors. The spatial correlations of temperature simulations against the CRU observations show better agreement for the MIROC5 driven simulations. The nested simulations underestimate precipitation in large areas over the continent in austral summer, whereas in winter overestimate occurs in southern Amazonia, and underestimate in southern Brazil and eastern coast of Northeast Brazil. The annual cycle of the near-surface temperature is underestimated in all model simulations, in all regions in Brazil, and in most of the year. The temperature and precipitation frequency distributions reveal that the RCM and GCM simulations contain more extreme values than the CRU observations. Evaluations of the climatic extreme indicators show that in general hot days, warm nights, and heat waves are increasing in the period, in agreement with observations. The Eta simulations driven by HadGEM2-ES show wet trends in the period, whereas the Eta driven by BESM and by MIROC5 show trends for drier conditions.

Student Academic Performance Predictive Model Based on Dual-stream Deep Network

Blended teaching is one of the essential teaching methods with the development of information technology.Constructing a learning effect evaluation model is helpful to improve students’academic performance and helps teachers to better implement course teaching.However,a lack of evaluation models for the fusion of temporal and non-temporal behavioral data leads to an unsatisfactory evaluation effect.To meet the demand for predicting students’academic performance through learning behavior data,this study proposes a learning effect evaluation method that integrates expert perspective indicators to predict academic performance by constructing a dual-stream network that combines temporal behavior data and non-temporal behavior data in the learning process.In this paper,firstly,the Delphi method is used to analyze and process the course learning behavior data of students and establish an effective evaluation index system of learning behavior with universality;secondly,the Mann-Whitney U-test and the complex correlation analysis are used to analyze further and validate the evaluation indexes;and lastly,a dual-stream information fusion model,which combines temporal and non-temporal features,is established.The learning effect evaluation model is built,and the results of the mean absolute error(MAE)and root mean square error(RMSE)indexes are 4.16 and 5.29,respectively.This study indicates that combining expert perspectives for evaluation index selection and further fusing temporal and non-temporal behavioral features that for learning effect evaluation and prediction is rationality,accuracy,and effectiveness,which provides a powerful help for the practical application of learning effect evaluation and prediction.