Comprehensive modelling of rotary desiccant wheel with different multiple regression and machine learning methods for balanced flow

BackgroundTransient protein-protein interactions (PPIs), which underly most biological processes, are a prime target for therapeutic development. Immense progress has been made towards computational prediction of PPIs using methods such as protein docking and sequence analysis. However, docking generally requires high resolution structures of both of the binding partners and sequence analysis requires that a significant number of recurrent patterns exist for the identification of a potential binding site. Researchers have turned to machine learning to overcome some of the other methods’ restrictions by generalising interface sites with sets of descriptive features. Best practices for dataset generation, features, and learning algorithms have not yet been identified or agreed upon, and an analysis of the overall efficacy of machine learning based PPI predictors is due, in order to highlight potential areas for improvement.ResultsThe presence of unknown interaction sites as a result of limited knowledge about protein interactions in the testing set dramatically reduces prediction accuracy. Greater accuracy in labelling the data by enforcing higher interface site rates per domain resulted in an average 44% improvement across multiple machine learning algorithms. A set of 10 biologically unrelated proteins that were consistently predicted on with high accuracy emerged through our analysis. We identify seven features with the most predictive power over multiple datasets and machine learning algorithms. Through our analysis, we created a new predictor, RAD-T, that outperforms existing non-structurally specializing machine learning protein interface predictors, with an average 59% increase in MCC score on a dataset with a high number of interactions.ConclusionCurrent methods of evaluating machine-learning based PPI predictors tend to undervalue their performance, which may be artificially decreased by the presence of un-identified interaction sites. Changes to predictors’ training sets will be integral to the future progress of interface prediction by machine learning methods. We reveal the need for a larger test set of well studied proteins or domain-specific scoring algorithms to compensate for poor interaction site identification on proteins in general.

Due to the large number of factors in churn prediction, it is challenging to choose and provide a model to do churn prediction and find the most important factors to deal with. In this paper, multiple machine learning methods will be combined to be compared to strengthen the validity of prediction. More specifically, there are four ways listed in this paper, which are Logistic Regression, Decision Tree, Light Gradient Boosting Machine, Support Vector Machine, K-means, and Random forest. The data selected from Kaggle will be the datasets in this paper to be analyzed. The data is preprocessed by turning figures into numbers so that it can be analyzed by mathematics way like polynomial or linear process. In addition, the important factors in telecom churn are also included through machine learning. The score of different kinds of machine learning will be given to choosing the best model. Experimental results indicate that Support Vector Machine with linear kernel is the best model, and the most important factors are contract and tenure.

In recent years, additive manufacturing (AM) techniques have gained increased attention. The most common AM technologies to realize complex parts are powder bed-based fusion processes, especially electron beam powder bed fusion of metals (PBF-EB/M) and laser-based powder bed fusion of metals (PBF-LB/M). Focusing on industrial applications, cyclic loading scenarios and fatigue properties of components produced by such techniques came into focus of research. The present work deals with a comparison between microstructure, hardness, density and fatigue properties of a high-alloy tool steel AISI H13 (1.2344, X40CrMoV5-1) manufactured by PBF-EB/M and PBF-LB/M. The investigated specimens are characterized by a complex phase composition containing ferrite, perlite, bainite and martensite, eventually resulting in different hardness values depending on the used AM technology. Fatigue data for PBF-EB/M AISI H13 are reported for the first time in open literature. It is shown that the fatigue behavior is significantly influenced by the specimen density. Accordingly, parts with a high density are characterized by superior fatigue strength.

The success of infrared surveillance systems is proportional to distinguishing the radiance of the target and the background. In this study, a simulation program, namely, DAMA (InfrareD SeA Modeling and Analysis) software is developed to calculate the total radiance and its components seen by an observer looking towards the sea surface. Total radiance is composed of thermal sea radiance, reflected source (sun or moon) radiance, reflected sky radiance and path radiance seen by the observer. The sea surface models are directly effective on the calculation of reflected radiances. DAMA program includes Cox and Munk, Mermelstein and Shaw-Churnside models and models for six different saltiness levels. The inputs of the software are classified as observer, time, wavelength, medium, model selection and output file parameters. The software developed by modules gives the total radiance and its components as output. The program has been run for approximately 10,000 times to obtain the effects of input parameters on total radiance and its components and the outputs are examined. DAMA is validated by SEARAD software and measurement results. By this way, for the first time in open literature, a simulation program including all sea surface and saltiness models is developed and the behavior of the total radiance and its components are examined with respect to the variation of all input parameters.

The term “chemical reactor” appears for the first time in open literature at the end of First World War, while in Germany the term “reaction technology” was coined in the 1950s, although chemical reactions and processes have been known for thousands of years. In the early days, reactors were furnaces and pots, which specialized further over the centuries. With the Industrial Revolution many reactor types were already known, and industrialization brought an enormous innovation thrust in the field of products as well as technologies. The diversity of reactors today is almost uncomprehensive and so efforts to standardize apparatus are understandable.

A test architecture to perform online testing for multiple blocks concurrently is presented in this paper. The test architecture is based on monitoring output vectors of the blocks and concurrently detecting faults. The scheme works well for critical VLSI controllers where shutting down or suspending the operation of a controller for testing is not possible and where the fault needs to be detected at the earliest, during the run time of the system. The applicability of the architecture for the control blocks in OC8051 has been demonstrated. To the best of our knowledge, the proposed architecture for concurrent online testing on multiple blocks with just a few cycles of fault latency is presented for the first time in open literature.

Precise vegetation restoration is critical in drylands, as some inappropriate restoration attempts have even increased water scarcity and degradation in afforestation areas. Potential natural vegetation (PNV) is widely used to provide a reference for the appropriate location and vegetation type of restoration programs while the appropriate restored areas remain unknown. Therefore, we proposed a PNV–potential normalized difference vegetation index (PNDVI) coupling framework based on multiple machine learning (ML) algorithms for precise dryland vegetation restoration. Taking the lower Tarim River Basin (LTRB) with a total area of 1,182 km2 as a case study, its present suitable restoration locations, area, and appropriate planting species were quantitatively estimated. The results showed that the model developed by incorporating PNDVI into PNV with easily measurable and available data such as temperature and soil properties can accurately identify dryland restoration patterns. In LTRB, the potentially suitable habitats of trees and grass are closer to the riverbank, while shrubby habitats are further away from the course, covering 1.88, 2.96, and 25.12 km2, respectively. There is still enormous land potential for further expansion of the current trees and grass in the LTRB, with 2.56 and 1.54% of existing land supposed to be trees and grass, respectively. This study's novel aspect is combining PNV and PNDVI to quantify and estimate precise restoration patterns through multiple ML algorithms. The model developed here can be used to evaluate the suitable reforestation locations, area, and vegetation types in drylands and to provide a basis for precise vegetation restoration.

The changes in the housing market are not only related to human beings’ daily life, but also have an important impact on the national economy. The prediction of housing price is one of the most widely concerned topics, which is linked to the formulation of national real estate policies and the analysis of the economic situation. In this context, this paper takes housing price prediction as the topic, selects the Eames housing price dataset in Iowa, and uses supervised multiple linear regression and machine learning algorithm to train and test the real estate price prediction model. Among them, there are 79 explanatory variables, which are related to housing attributes and the explanatory variable is housing price. 1460 data is included in the training set and 1459 in the test set. In the part of machine learning algorithm, PaddlePaddle deep learning framework is used in this paper to train and test the models with the help of AI Studio platform. The experimental results show that the scatter plots of the real values are clustered and distributed on both sides of the predicted line, and their direct differences are within 30 points. According to the analysis, the real estate price prediction model based on linear regression and machine learning is reliable and stable. This paper aims to provide some suggestions for the housing price prediction. These results shed light on guiding the reference direction for investors, so as to guide the formulation of relevant policies.

This research is a comparative analysis of applying different machine-learning methods to health care data. The data used is from the interRAI home care assessment instrument, collected in central British Columbia, Canada. The primary dataset used contains more than 100,000 records each with 423 attributes. We built models for predicting home care usage in the three weeks following an assessment by applying different regression and classification machine learning algorithms. The main regression algorithms used in the process were multiple linear regression, lasso, ridge, decision tree and ensemble methods, with the last being the most promising. In the area of classification, KNN, logistic regression, decision tree and ensemble methods were used. Apart from the technical machine learning algorithms, both patient partners and health systems experts participated and provided feedback regarding home care practices and issues. These formed essential element in designing the research question, selecting variables, and improving the models. The highest accuracy achieved was 84.3% which was achieved through a random forest classifier and evaluated using K-fold cross validation.

Investigation of essential genes is significant to comprehend the minimal gene sets of cell and discover potential drug targets. In this study, a novel approach based on multiple homology mapping and machine learning method was introduced to predict essential genes. We focused on 25 bacteria which have characterized essential genes. The predictions yielded the highest area under receiver operating characteristic (ROC) curve (AUC) of 0.9716 through tenfold cross-validation test. Proper features were utilized to construct models to make predictions in distantly related bacteria. The accuracy of predictions was evaluated via the consistency of predictions and known essential genes of target species. The highest AUC of 0.9552 and average AUC of 0.8314 were achieved when making predictions across organisms. An independent dataset from Synechococcus elongatus, which was released recently, was obtained for further assessment of the performance of our model. The AUC score of predictions is 0.7855, which is higher than other methods. This research presents that features obtained by homology mapping uniquely can achieve quite great or even better results than those integrated features. Meanwhile, the work indicates that machine learning-based method can assign more efficient weight coefficients than using empirical formula based on biological knowledge.

Glutamine metabolism (GM) is known to play a critical role in cancer development, including in lung adenocarcinoma (LUAD), although the exact contribution of GM to LUAD remains incompletely understood. In this study, we aimed to discover new targets for the treatment of LUAD patients by using machine learning algorithms to establish prognostic models based on GM-related genes (GMRGs). We used the AUCell and WGCNA algorithms, along with single-cell and bulk RNA-seq data, to identify the most prominent GMRGs associated with LUAD. Multiple machine learning algorithms were employed to develop risk models with optimal predictive performance. We validated our models using multiple external datasets and investigated disparities in the tumor microenvironment (TME), mutation landscape, enriched pathways, and response to immunotherapy across various risk groups. Additionally, we conducted in vitro and in vivo experiments to confirm the role of LGALS3 in LUAD. We identified 173 GMRGs strongly associated with GM activity and selected the Random Survival Forest (RSF) and Supervised Principal Components (SuperPC) methods to develop a prognostic model. Our model's performance was validated using multiple external datasets. Our analysis revealed that the low-risk group had higher immune cell infiltration and increased expression of immune checkpoints, indicating that this group may be more receptive to immunotherapy. Moreover, our experimental results confirmed that LGALS3 promoted the proliferation, invasion, and migration of LUAD cells. Our study established a prognostic model based on GMRGs that can predict the effectiveness of immunotherapy and provide novel approaches for the treatment of LUAD. Our findings also suggest that LGALS3 may be a potential therapeutic target for LUAD.

Total organic carbon (TOC) is directly associated with total porosity and gas content and is a critical factor in assessing the potential of unconventional reservoirs. TOC content is only known at the depths where the laboratory measurements on recovered core samples are performed. However, reliable estimation of potential resources can only be based on information about vertical and lateral distribution of organic matter throughout the prospective gas shale reservoir. This information is commonly obtained from conventional wireline logs, such as gamma ray, density, transit time and resistivity. Due to the complexity of unconventional reservoirs, traditional methods based on distinct differences of resistivity, density and sonic velocity of organic matter from those of the inorganic matrix are not always successful. We investigate the best way to predict the TOC using gamma-ray, density, porosity, resistivity and sonic transit time log responses by applying machine learning methods such as Artificial Neural Network (ANN) and Support Vector Machine (SVM). The analysis is done on the data from seven wells drilled through onshore unconventional reservoirs in the McArthur Basin (Northern Territory) and Georgina Basin (Northern Territory and Queensland), Australia. The prediction quality of traditional, multiple liner regression (MLR) and machine learning methods was compared. The most accurate TOC estimates were generated by ANN- and SVM-based nonlinear predictors, followed by the MLR and traditional models. This indicates that geologic complexity affects the relationship between the log response and TOC in the area of interest.KeywordsTOCunconventional gas reservoirregressionANNSVM AcknowledgmentsThe log data collection used in this study was kindly provided by the Northern Territory Geological Survey (http://www.nt.gov.au/d/Minerals_Energy/).

<p>To meet the sustainable development goals and enable protection of surface waters, there is a strong need to plan and align forest management with the needs of the environment. The number one tool to succeed in sustainable spatial planning is accurate and detailed maps. High resolution soil moisture mapping over spatial large extent remains a consistent challenge despite its substantial value in practical forestry and land management. Here we present a novel technique combining LIDAR-derived terrain indices and machine learning to model soil moisture at 2 m spatial resolution across the Swedish forest landscape with high accuracy. We used field data from about 20,000 sites across Sweden to train and evaluate multiple machine learning (ML) models. The predictor features included a suite of terrain indices generated from national LIDAR digital elevation model and other ancillary environmental features, including surficial geology, climate, land use information, allowing for adjustment of soil moisture maps to regional/local conditions. In our analysis, extreme gradient boosting (XGBoost) outperformed the other tested ML methods (Kappa = 0.69, MCC= 0.68), namely Artificial Neural Network, Random Forest, Support Vector Machine, and Naïve Bayes classification. The depth to water index, topographic wetness index, and wetlands derived from Swedish property maps were the most important predictors for all models. With the presented technique, it was possible to generate a multiclass model with 3 classes with Kappa and MCC of 0.58. Besides the classified moisture maps, we also investigated the potential of producing a continuous map from dry to wet soils. We argue that the probability of a pixel being classified as wet from the 2-class model can be used as an index of soil moisture from 0% – dry to 100% – wet and that such maps hold more valuable information for practical forest management than classified maps.</p><p>The soil moisture map was developed to support the need for land use management optimization by incorporating landscape sensitivity and hydrological connectivity into a framework that promotes the protection of soil and water quality. The soil moisture map can be used to address fundamental considerations, such as;</p><ul><li>(i) locating areas where different land use practices can be conducted with minimal impacts on water quality;</li> <li>(ii) guiding the construction of vital infrastructure in high flood risk areas;</li> <li>(iii) designing riparian protection zones to optimize the protection of water quality and biodiversity.</li> </ul>

Spinal cord injury (SCI) is a prevalent and serious complication among patients with spinal tuberculosis (STB) that can lead to motor and sensory impairment and potentially paraplegia. This research aims to identify factors associated with SCI in STB patients and to develop a clinically significant predictive model. Clinical data from STB patients at a single hospital were collected and divided into training and validation sets. Univariate analysis was employed to screen clinical indicators in the training set. Multiple machine learning (ML) algorithms were utilized to establish predictive models. Model performance was evaluated and compared using receiver operating characteristic (ROC) curves, area under the curve (AUC), calibration curve analysis, decision curve analysis (DCA), and precision-recall (PR) curves. The optimal model was determined, and a prospective cohort from two other hospitals served as a testing set to assess its accuracy. Model interpretation and variable importance ranking were conducted using the DALEX R package. The model was deployed on the web by using the Shiny app. Ten clinical characteristics were utilized for the model. The random forest (RF) model emerged as the optimal choice based on the AUC, PRs, calibration curve analysis, and DCA, achieving a test set AUC of 0.816. Additionally, MONO was identified as the primary predictor of SCI in STB patients through variable importance ranking. The RF predictive model provides an efficient and swift approach for predicting SCI in STB patients.

Recognition and prediction of individual thermal comfort requirement based on local skin temperature