Variations In Features Research Articles

Multi-step intelligent prediction of shield machine position attitude on the basis of BWO-CNN-LSTM-GRU

Abstract Realizing automatic control of shield machine tunneling attitude is a challenging problem. Realizing multi-step intelligent prediction for attitude and position is an important prerequisite for solving this problem in the tunneling process with complex and varied geological environments. In this paper, a multi-step intelligent predictive scheme based on beluga whale optimization-convolutional neural network-Long Short-term memory-gated recurrent unit (BWO-CNN-LSTM-GRU) is proposed for shield machine position attitude. First, Pearson correlation analysis is utilized to determine the input feature variables from the construction data and temporalize the input features. Subsequently, CNN-LSTM-GRU predictive models are established for the six positional parameters, separately. Among them, CNN performs feature extraction on the input variables, and LSTM-GRU realizes the predictions for the target positional parameters. In the end, the optimization of the convolutional layer dimension, the number of convolutional layers, iterations, the learning rate, the number of neurons in the LSTM layer and GRU layer of each position predictive model is performed on the basis of BWO, separately, and the best hyperparameters found are built into a BWO-CNN-LSTM-GRU position predictive model, which realizes the multi-step intelligent predictions for the shield machine’s position. The proposed approach is examined by utilizing the Beijing Metro Line 10. The results show that the predictive deviation of the position predictive model is within 3 mm, and the positional trajectory points obtained on the basis of the predicted values and the 3D coordinate system are highly coincident with the actual trajectory points. Therefore, the approach provides a more accurate predictive result for shield attitude and position and can provide a decision-making scheme for further realizing the coordinated autonomous control of shield machine.

A deep belief network-based energy consumption prediction model for water source heat pump system

To achieve optimal energy efficiency in buildings, accurately forecasting the energy consumption of air conditioning systems is crucial. This study develops an energy consumption prediction model based on a deep belief network, which is constructed according to the principles of a restricted Boltzmann machine. Actual experimental data from a water source heat pump system are collected, and feature variables are selected. The study discusses the impact of model parameters and training set sizes on the performance of energy consumption prediction model. Additionally, the trend in model prediction performance is analyzed through parameter adjustments. The results show that the coefficient of determination (R2) for the optimized model has increased to 0.585. The mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) have been reduced to 6.311, 2.512, and 1.625, respectively. The deep belief network energy consumption prediction model outperforms other common machine learning models for water source heat pump systems.

Exploring Implicit Biological Heterogeneity in ASD Diagnosis Using a Multi-Head Attention Graph Neural Network.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder exhibiting heterogeneous characteristics in patients, including variability in developmental progression and distinct neuroanatomical features influenced by sex and age. Recent advances in deep learning models based on functional connectivity (FC) graphs have produced promising results, but they have focused on generalized global activation patterns and failed to capture specialized regional characteristics and accurately assess disease indications. To overcome these limitations, we propose a novel deep learning method that models FC with multi-head attention, which enables simultaneous modeling of the intricate and variable patterns of brain connectivity associated with ASD, effectively extracting abnormal patterns of brain connectivity. The proposed method not only identifies region-specific correlations but also emphasizes connections at specific, transient time points from diverse perspectives. The extracted FC is transformed into a graph, assigning weighted labels to the edges to reflect the degree of correlation, which is then processed using a graph neural network capable of handling edge labels. Experiments on the autism brain imaging data exchange (ABIDE) I and II datasets, which include a heterogeneous cohort, showed superior performance over the state-of-the-art methods, improving accuracy by up to 3.7%p. The incorporation of multi-head attention in FC analysis markedly improved the distinction between typical brains and those affected by ASD. Additionally, the ablation study validated diverse brain characteristics in ASD patients across different ages and sexes, offering insightful interpretations. These results emphasize the effectiveness of the method in enhancing diagnostic accuracy and its potential in advancing neurological research for ASD diagnosis.

The factors associated with the modified Fisher grade in patients with aneurysmal subarachnoid hemorrhage.

Aneurysmal subarachnoid hemorrhage (aSAH) is a life-threatening medical condition with a high fatality and morbidity rate. There was a substantial link between the modified Fisher grade of aSAH and the neurological function deficit. This study aimed to analyze the factors associated with the modified Fisher grade of aSAH using a machine learning approach. A multi-center observational study was conducted. The patients with aSAH were recruited from five tertiary hospitals in China. The volume of hemorrhage in aSAH was measured using the modified Fisher grade scale. The risk factors responsible for the modified Fisher grade of aSAH were analyzed, which include sociodemographic factors, clinical factors, blood index, and ruptured aneurysm characteristics. We built several tree-based machine learning models (XGBoost, CatBoost, LightGBM) for prediction and used grid search to optimize model parameters. To comprehensively evaluate the model, we used Accuracy, Precision, Area Under the Receiver Operating Characteristic Curve (AUROC), Area Under the Precision-Recall Curve (AUPRC), and Brier as evaluation indicators to assess the model performance and select the best model. A total of 888 patients with aSAH were recruited, of whom 305 with modified Fisher grade of 3 and 4. The results show that the XGBoost model has the highest AUROC of 0.772, and the indicators are better than CatBoost and LightGBM. The feature importance graph shows that the top feature variables include platelet, thrombin time, fibrinogen, preadmission systolic blood pressure, activated partial thromboplastin time, and the time interval between the onset of aSAH and the first-time CT examination. The factors responsible for the modified Fisher grade of aSAH were identified, which offered valuable insights for future research and clinical intervention. These risk factors should be controlled in the treatment of unruptured aneurysms, and appropriate treatment can be given if necessary to reduce the risk of severe hemorrhage after aneurysm rupture.

Improving forest age prediction performance using ensemble learning algorithms base on satellite remote sensing data

Forest age plays a crucial role in assessing forest structure, carbon sinks, and other ecological functions. How to estimate forest age by satellite remote sensing data has been a hot research topic. This study focused on the forests of Zhejiang Province, utilizing Landsat 5 as the remote sensing data source to extract distribution information of broadleaf and coniferous forests. Then, the remote sensing feature variables were screened, and the age of broadleaf forests and coniferous forests was estimated by using the multiple linear regression model MLR, the machine learning model (K-nearest neighbor method regression model KNN, support vector regression model SVR), and the ensemble learning model (adaptive boosting model AdaBoost, random forest model RF, and eXtreme gradient boosting XGBoost). After analyzing the forest age estimation results from different models, the best-performing model was selected to create a spatial distribution map of forest age in Zhejiang Province. The study shows that the ensemble machine learning model can better realize the remote sensing inversion of forest age. The optimal model for broadleaf forests is the XGBoost model, with a coefficient of determination R2 of 0.832, a root mean square error (RMSE) of 5.823a and a relative root mean square error (rRMSE) of 21.009%. And the top model for coniferous forests is the RF model, with R2 of 0.800, RMSE of 5.076a and rRMSE of 19.782%. Compared with the MLR model, the best broadleaf and coniferous forest age inversion models improved the R2 by 75.120% and 82.500%, and reduced the RMSE by 52.674% and 47.540%, and reduced the rRMSE by 52.703% and 47.480% respectively. Additionally, the analysis revealed that 50% of the remote sensing feature variables involved in forest age inversion are texture features, indicating that texture is an important feature variable for the construction of forest age remote sensing inversion models.

Comparison of the variability and diagnostic efficacy of respiratory-gated PET/CT based radiomics features with ungated PET/CT in lung lesions

ObjectivesTo investigate the variability and diagnostic efficacy of respiratory-gated (RG) PET/CT based radiomics features compared to ungated (UG) PET/CT in the differentiation of non-small cell lung cancer (NSCLC) and benign lesions. Methods117 patients with suspected lung lesions from March 2020 to May 2021 and consent to undergo UG PET/CT and chest RG PET/CT (including phase-based quiescent period gating, pQPG and phase-matched 4D PET/CT, 4DRG) were prospectively included. 377 radiomics features were extracted from PET images of each scan. Paired t test was used to compare UG and RG features for inter-scan variability analysis. We developed three radiomics models with UG and RG features (i.e. UGModel, pQPGModel and 4DRGModel). ROC curves were used to compare diagnostic efficiencies, and the model-level comparison of diagnostic value was performed by five-fold cross-validation. A P value < 0.05 was considered as statistically significant. ResultsA total of 111 patients (average age ± standard deviation was 59.1 ± 11.6 y, range, 29 – 88 y, and 63 were males) with 209 lung lesions were analyzed for features variability and the subgroup of 126 non-metastasis lesions in 91 patients without treatment before PET/CT were included for diagnosis analysis. 101/377 (26.8 %) 4DRG features and 82/377 (21.8 %) pQPG features showed significant difference compared to UG features (both P<0.05). 61/377 (16.2 %) and 59/377 (15.6 %) of them showed significantly better discriminant ability (ΔAUC% (i.e. (AUCRG – AUCUG) / AUCUG×100 %) > 0 and P<0.05) in malignant recognition, respectively. For the model-level comparison, 4DRGModel achieved the highest diagnostic efficacy (sen 73.2 %, spe 87.3 %) compared with UGModel (sen 57.7 %, spe 76.4 %) and pQPGModel (sen 63.4 %, spe 81.8 %). ConclusionRG PET/CT performs better in the quantitative assessment of metabolic heterogeneity for lung lesions and the subsequent diagnosis in patients with NSCLC compared with UG PET/CT.

Maximum pitting corrosion depth prediction of buried pipeline based on theory-guided machine learning

Buried pipelines are crucial for the transportation of oil and natural gas resources. However, pipeline failure accidents have frequently occurred due to corrosion. Therefore, an accurate corrosion depth prediction model is necessary for the reliable supply of energy. In this paper, a theory-guided machine learning (ML) model is developed for maximum pitting corrosion depth prediction, the engineering theory and domain knowledge are integrated into feature space to improve the model interpretability. Firstly, several new feature variables are constructed based on the interactions between independent variables. Then, feature importance of all feature variables is obtained using random forest (RF). A hybrid multi-objective grey wolf optimization (HMOGWO) is proposed to optimize the hyper-parameters of RF model, considering feature number, prediction accuracy, and stability simultaneously. Finally, a comprehensive pitting corrosion dataset is utilized for performance evaluation. The results indicate that the proposed theory-guided model can achieve high prediction accuracy and stability, the optimal feature subset can be determined using multi-objective optimization method simultaneously, which solves the problems of model interpretability and feature selection for traditional ML models with the single-objective optimizer. This study is of great significance to the transportation safety of buried pipelines.

Uveal melanoma distant metastasis prediction system: A retrospective observational study based on machine learning.

Uveal melanoma (UM) patients face a significant risk of distant metastasis, closely tied to a poor prognosis. Despite this, there is a dearth of research utilizing big data to predict UM distant metastasis. This study leveraged machine learning methods on the Surveillance, Epidemiology, and End Results (SEER) database to forecast the risk probability of distant metastasis. Therefore, the information on UM patients from the SEER database (2000-2020) was split into a 7:3 ratio training set and an internal test set based on distant metastasis presence. Univariate and multivariate logistic regression analyses assessed distant metastasis risk factors. Six machine learning methods constructed a predictive model post-feature variable selection. The model evaluation identified the multilayer perceptron (MLP) as optimal. Shapley additive explanations (SHAP) interpreted the chosen model. A web-based calculator personalized risk probabilities for UM patients. The results show that nine feature variables contributed to the machine learning model. The MLP model demonstrated superior predictive accuracy (Precision = 0.788; ROC AUC = 0.876; PR AUC = 0.788). Grade recode, age, primary site, time from diagnosis to treatment initiation, and total number of malignant tumors were identified as distant metastasis risk factors. Diagnostic method, laterality, rural-urban continuum code, and radiation recode emerged as protective factors. The developed web calculator utilizes the MLP model for personalized risk assessments. In conclusion, the MLP machine learning model emerges as the optimal tool for predicting distant metastasis in UM patients. This model facilitates personalized risk assessments, empowering early and tailored treatment strategies.

A rapid approach with machine learning for quantifying the relative burden of antimicrobial resistance in natural aquatic environments

The massive use and discharge of antibiotics have led to increasing concerns about antimicrobial resistance (AMR) in natural aquatic environments. Since the dose-response mechanisms of pathogens with AMR have not yet been fully understood, and the antibiotic resistance genes and bacteria-related data collection via field sampling and laboratory testing is time-consuming and expensive, designing a rapid approach to quantify the burden of AMR in the natural aquatic environment has become a challenge. To cope with such a challenge, a new approach involving an integrated machine-learning framework was developed by investigating the associations between the relative burden of AMR and easily accessible variables (i.e., relevant environmental variables and adjacent land-use patterns). The results, based on a real-world case analysis, demonstrate that the quantification speed has been reduced from 3-7 days, which is typical for traditional measurement procedures with field sampling and laboratory testing, to approximately 0.5 hours using the new approach. Moreover, all five metrics for AMR relative burden quantification exceed the threshold level of 85%, with F1-score surpassing 0.92. Compared to logistic regression, decision trees, and basic random forest, the adaptive random forest model within the framework significantly improves quantification accuracy without sacrificing model interpretability. Two environmental variables, dissolved oxygen and resistivity, along with the proportion of green areas were identified as three key feature variables for the rapid quantification. This study contributes to the enrichment of burden analyses and management practices for rapid quantification of the relative burden of AMR without dose-response information.

Ultrasound findings and clinical characteristics in differentiating renal urothelial carcinoma from endophytic clear cell renal cell carcinoma.

This study aimed to evaluate the clinical characteristics and features of conventional ultrasound (CUS) and contrast-enhanced ultrasound (CEUS) in differentiating between renal urothelial carcinomas (RUC) and endophytic clear cell renal cell carcinomas (EccRCC). A total of 72 RUCs and 120 EccRCCs confirmed by pathology were assessed retrospectively. Both CUS and CEUS were performed within 4 weeks before the surgery. Logistic regression analyses were used to select statistically significant variables of clinical, CUS, and CEUS features for the differentiation of RUC and EccRCC. Sensitivity (SEN), specificity (SPE), and the area under the receiver-operating characteristic curve (AUC) were assessed for diagnostic performance. Inter- and intra-observer agreements of CUS and CEUS features were evaluated using the intra-class correlation coefficient(ICC). Multiple logistic regression analysis demonstrated that clinical (age >50 years old and hematuria), CUS (size <4.0 cm, hypo-echogenicity, irregular shape, hydronephrosis) and CEUS (absence of non-enhancement area, iso- /hypo-enhancement in cortical phase and absence of rim-like enhancement) features were independent factors for RUC diagnosis. When combining clinical characters with CUS and CEUS features into an integrated diagnostic criterion, the AUC reached 0.917 (95% CI 0.873-0.961), with a sensitivity of 95.8% and specificity of 87.5%. ICC ranged from 0.756 to 0.907 for inter-observer agreement and 0.791 to 0.934 for intra-observer agreement for CUS and CEUSfeatures. The combination of clinical features of age and hematuria with imaging features of CUS and CEUS can be useful for the differentiation between RUC and EccRCC.

Structural prior-driven feature extraction with gradient-momentum combined optimization for convolutional neural network image classification

Recent image classification efforts have achieved certain success by incorporating prior information such as labels and logical rules to learn discriminative features. However, these methods overlook the variability of features, resulting in feature inconsistency and fluctuations in model parameter updates, which further contribute to decreased image classification accuracy and model instability. To address this issue, this paper proposes a novel method combining structural prior-driven feature extraction with gradient-momentum (SPGM), from the perspectives of consistent feature learning and precise parameter updates, to enhance the accuracy and stability of image classification. Specifically, SPGM leverages a structural prior-driven feature extraction (SPFE) approach to calculate gradients of multi-level features and original images to construct structural information, which is then transformed into prior knowledge to drive the network to learn features consistent with the original images. Additionally, an optimization strategy integrating gradients and momentum (GMO) is introduced, dynamically adjusting the direction and step size of parameter updates based on the angle and norm of the sum of gradients and momentum, enabling precise model parameter updates. Extensive experiments on CIFAR10 and CIFAR100 datasets demonstrate that the SPGM method significantly reduces the top-1 error rate in image classification, enhances the classification performance, and outperforms state-of-the-art methods.

Improve the efficiency and accuracy of ophthalmologists’ clinical decision-making based on AI technology

BackgroundAs global aging intensifies, the prevalence of ocular fundus diseases continues to rise. In China, the tense doctor-patient ratio poses numerous challenges for the early diagnosis and treatment of ocular fundus diseases. To reduce the high risk of missed or misdiagnosed cases, avoid irreversible visual impairment for patients, and ensure good visual prognosis for patients with ocular fundus diseases, it is particularly important to enhance the growth and diagnostic capabilities of junior doctors. This study aims to leverage the value of electronic medical record data to developing a diagnostic intelligent decision support platform. This platform aims to assist junior doctors in diagnosing ocular fundus diseases quickly and accurately, expedite their professional growth, and prevent delays in patient treatment. An empirical evaluation will assess the platform’s effectiveness in enhancing doctors’ diagnostic efficiency and accuracy.MethodsIn this study, eight Chinese Named Entity Recognition (NER) models were compared, and the SoftLexicon-Glove-Word2vec model, achieving a high F1 score of 93.02%, was selected as the optimal recognition tool. This model was then used to extract key information from electronic medical records (EMRs) and generate feature variables based on diagnostic rule templates. Subsequently, an XGBoost algorithm was employed to construct an intelligent decision support platform for diagnosing ocular fundus diseases. The effectiveness of the platform in improving diagnostic efficiency and accuracy was evaluated through a controlled experiment comparing experienced and junior doctors.ResultsThe use of the diagnostic intelligent decision support platform resulted in significant improvements in both diagnostic efficiency and accuracy for both experienced and junior doctors (P < 0.05). Notably, the gap in diagnostic speed and precision between junior doctors and experienced doctors narrowed considerably when the platform was used. Although the platform also provided some benefits to experienced doctors, the improvement was less pronounced compared to junior doctors.ConclusionThe diagnostic intelligent decision support platform established in this study, based on the XGBoost algorithm and NER, effectively enhances the diagnostic efficiency and accuracy of junior doctors in ocular fundus diseases. This has significant implications for optimizing clinical diagnosis and treatment.

The Effect of Social Factors, Prices, and Product Features on Purchase Intention of Yamaha NMAX Motorcycles at State Polytechnic of Bengkalis Students

The development of the business world in the current era of globalization is increasingly sharpening competition, especially in the management of automotive company units. One type of automotive that is widely favored by Indonesians is motorcycles. This study aims to determine the Effect Social Factors, Prices, and Product Features on Purchase Intention of Yamaha NMAX Motorcycles at State Polytechnic of Bengkalis Students. This study uses quantitative methods, data collection techniques are carried out by distributing online questionnaires with a sample of 100 respondents. The sampling technique used non- probability sampling method, data analysis used is descriptive statistics, classical assumption test, multiple regression analysis, data conclusion with T test, F test and the coefficient of determination (R2), while data processing used SPSS version 26 program. The findings indicate that social factors and product features variables have an effect, meanwhile the prices variable does not have an effect on purchase intention of Yamaha NMAX motorcycles at State Polytechnic of Bengkalis students. This study provides the insight that Yamaha can direct efforts and resources towards social marketing and product feature enhancement rather than focusing on price adjustment strategies.

Formation of Linear Plasmonic Heterotrimers Using Nanoparticle Docking to DNA Origami Cages.

The fabrication of complex assemblies with interesting collective properties from plasmonic nanoparticles (NPs) is often challenging. While DNA-directed self-assembly has emerged as one of the most promising approaches to forming such complex assemblies, the resulting structures tend to have large variability in gap sizes and shapes, as the DNA strands used to organize these particles are flexible, and the polydispersity of the NPs leads to variability in these critical structural features. Here, we use a new strategy termed docking to DNA origami cages (D-DOC) to organize spherical NPs into a linear heterotrimer with a precisely defined geometrical arrangement. Instead of binding NPs to the exterior of the DNA templates, D-DOC binds the NPs to either the interior or the opening of a 3D cage, which significantly reduces the variability of critical structural features by incorporating multiple diametrically arranged capture strands to tether NPs. Additionally, such a spatial arrangement of the capture strand can work synergistically with shape complementarity to achieve tighter confinement. To assemble NPs via D-DOC, we developed a multistep assembly process that first encapsulates an NP inside a cage and then binds two other NPs to the openings. Microscopic characterization shows low variability in the bond angles and gap sizes. Both UV-vis absorption and surface-enhanced Raman scattering (SERS) measurements showed strong plasmonic coupling that aligned with predictions by electrodynamic simulations, further confirming the precision of the assembly. These results suggest D-DOC could open new opportunities in biomolecular sensing, SERS and fluorescence spectroscopies, and energy harvesting through the self-assembly of NPs into more complex 3D assemblies.

Alpine Catchments’ Hazard Related to Subaerial Sediment Gravity Flows Estimated on Dominant Lithology and Outcropping Bedrock Percentage

Sediment gravity flows (SGFs) cause serious damage in the Alpine regions. In the literature, several methodologies have been elaborated to define the main features of these phenomena, mainly considering the rheological features of the flow processes by laboratory experiments or by flow simulation using 2D or 3D propagation models or considering a single aspect, such as the morphometric parameters of catchments in which SGFs occur. These very targeted approaches are primarily linked to the definition of SGFs’ propagation behavior or to identify the predisposing role played by just one feature of catchments neglecting other complementary aspects regarding phenomena and the environment in which SGFs can occur. Although the research aimed at the quantification of some parameters that drive the behavior of SGFs provides good results in understanding the flow mechanisms, it does not provide an exhaustive understanding of the overall nature of these phenomena, including their trigger conditions and a complete view of predisposing factors that contribute to their generation. This paper presents a research work based on the collection and cross-analysis of lithological, geomechanical, geomorphological and morphometrical characteristics of Alpine catchments compared with sedimentological and morphological features of SGF deposits, also taking in to account the rainfall data correlation with historical SGF events. A multidisciplinary approach was implemented, aiming at quantifying SGF causes and characteristics starting from the catchments’ features where the phenomena originate in a more exhaustive way. The study used 78 well-documented catchments of Susa Valley (Western Italian Alps), having 614 historical flow events reported, that present a great variability in geomorphological and geological features. As the main result, three catchment groups were recognized based on the dominant catchment bedrock’s lithology characteristics that influence the SGFs’ rheology, sedimentological and depositional features, triggering rainfall values, seasonality, occurrence frequency and alluvial fan architecture. The classification method was also compared with the catchments’ morphometry classification, demonstrating that the fundamental role in determining the type of flow process that can most likely occur in a given catchment is played by the bedrock outcropping percentage, regardless of the results provided by the morphometric approach. The analysis of SGF events through the proposed method led to a relative estimate of the hazard degree of these phenomena distinguished by catchment type.

Spinal Cord Involvement in Children with Acquired Demyelinating Syndromes: A Single Centre Study

Abstract Background Myelitis may be the first presentation of relapsing acquired demyelinating syndromes (ADS) such as neuromyelitis optica (NMO) or multiple sclerosis (MS) and in association with connective tissue diseases. It may also occur in the context of acute disseminated encephalomyelitis (ADEM). Early recognition of myelitis in these disorders will allow better management plan including theprognosis. Objective To evaluate the frequency of myelitis among children with acquired demyelinating neurological diseases, and the variability of clinical features, neuroimaging findings and treatments given. Methods A pilot observational study with both retrospective and prospective components was conducted on children and adolescents fulfilling the diagnostic criteria for any of the acquired demyelination syndromes admitted inChildren’s Hospital, Ain Shams University whose ages were below 18 years during the period from July 2020 to February 2022. Results Forty-three children were included in our study with mean (SD)age of onset 7.63 (4.28) years. The most common diagnosis was ADEM (n (%) =20 (46.5%), followed by transverse myelitis (TM) (n (%) =11 (25.6%) and NMO (n (%) =7(16.3%). Progression to develop encephalopathy was recorded in 55.8%of cases (100% of ADEM and autoimmune encephalitis (AE) cases). Seizures developed in 23.3% of cases (35% of ADEM, 50% of AE and 14.29% of NMO cases). MRI brain abnormalities were observed in 76.7% of cases (100% of ADEM, NMO, AE and MS cases), and abnormalities of MRI spine were seen in 59% of cases (100% of TM and MS cases, 85.7% of NMO, 33.3% of AE and 23.5% of ADEM cases). Spinal cord affection was most commonly cervical(86.9%) and mostly short segment. All cases received pulsed methylprednisolone therapy, while intravenous immunoglobulins (IVIG) were used in 32.5% of cases and plasmapheresis in 13.9% of cases as a stepwise approach. Nearly half of the cases (53.5%) had monophasic illnesses without the need for long term therapy. Conclusion Spinal cord involvement is frequently observed in childhood acute demyelinating syndromes, and is most commonly cervical and short-segment. MRI spine should be regarded as a sensitive tool for diagnosis of myelitis in the context of ADEM and MS. MRI Brain is a sensitive tool accounting forfindings in 100% of ADEM, NMO, AE and MS cases. Patients with ADEM and AE has higher risk to develop encephalopathy and seizures unlike TM, NMO and MS patients.

Multi-armed bandit algorithm for sequential experiments of molecular properties with dynamic feature selection.

Sequential optimization is one of the promising approaches in identifying the optimal candidate(s) (molecules, reactants, drugs, etc.) with desired properties (reaction yield, selectivity, efficacy, etc.) from a large set of potential candidates, while minimizing the number of experiments required. However, the high dimensionality of the feature space (e.g., molecular descriptors) makes it often difficult to utilize the relevant features during the process of updating the set of candidates to be examined. In this article, we developed a new sequential optimization algorithm for molecular problems based on reinforcement learning, multi-armed linear bandit framework, and online, dynamic feature selections in which relevant molecular descriptors are updated along with the experiments. We also designed a stopping condition aimed to guarantee the reliability of the chosen candidate from the dataset pool. The developed algorithm was examined by comparing with Bayesian optimization (BO), using two synthetic datasets and two real datasets in which one dataset includes hydration free energy of molecules and another one includes a free energy difference between enantiomer products in chemical reaction. We found that the dynamic feature selection in representing the desired properties along the experiments provides a better performance (e.g., time required to find the best candidate and stop the experiment) as the overall trend and that our multi-armed linear bandit approach with a dynamic feature selection scheme outperforms the standard BO with fixed feature variables. The comparison of our algorithm to BO with dynamic feature selection is also addressed.

Photovoltaic Power Generation Related Physical Quantity Mining Method Based On Historical Time Series Data Analysis

Abstract The impact of distributed photovoltaic grid-connected on distribution network security, power quality and system stability cannot be ignored. In order to better cope with the uncertainty and output of new energy output and understand the characteristics of distributed new energy power output, it is necessary to predict photovoltaic power. The historical time series data of photovoltaic power is large in dimension and quantity. If the data output is not carried out, a large amount of redundant data will affect the accuracy of photovoltaic prediction. Therefore, this paper proposes a feature selection method based on MIC most mutual information coefficient, which filters out the most relevant data of photovoltaic power generation from the original feature variables, and then performs the feature dimension reduction method of linear discriminant analysis (LDA) to map the high-dimensional data to a lower dimension space. Finally, using the prediction simulation example, using the long and short time neural network (LSTM) prediction comparison, the feature dimension reduction method of MIC feature selection and LDA effectively improves the accuracy of photovoltaic power generation prediction.

Cross-Combination Analyses of Random Forest Feature Selection and Decision Tree Model for Predicting Intraoperative Hypothermia in Total Joint Arthroplasty

BackgroundIn total joint arthroplasty patients, intraoperative hypothermia (IOH) is associated with perioperative complications and an increased economic burden. Previous models have some limitations and mainly focus on regression modeling. Random forest (RF) algorithms and decision tree modeling are effective for eliminating irrelevant features and making predictions that aid in accelerating modeling and reducing application difficulty. MethodsWe conducted this prospective observational study using convenience sampling and collected data from 327 total joint arthroplasty patients in a tertiary hospital from March 4, 2023, to September 11, 2023. Of those, 229 patients were assigned to the training and 98 to the testing sets. The Chi-square, Mann–Whitney U, and t-tests were used for baseline analyses. The feature variables selection used the RF algorithms, and the decision tree model was trained on 299 examples and validated on 98. The sensitivity, specificity, recall, F1 score, and area under the curve were used to test the model’s performance. ResultsThe RF algorithms identified the preheating time, the volume of flushing fluids, the intraoperative infusion volume, the anesthesia time, the surgical time, and the core temperature after intubation as risk factors for IOH. The decision tree was grown to 5 levels with 9 terminal nodes. The overall incidence of IOH was 42.13%. The sensitivity, specificity, recall, F1 score, and area under the curve were 0.651, 0.907, 0.916, 0.761, and 0.810, respectively. The model indicated strong internal consistency and predictive ability. ConclusionsThe preheating time, the volume of flushing fluids, the intraoperative infusion volume, the anesthesia time, the surgical time, and the core temperature after intubation could accurately predict IOH in total joint arthroplasty patients. By monitoring these factors, the clinical staff could achieve early detection and intervention of IOH in total joint arthroplasty patients.

Comprehensive quality evaluation of Indian chili powder using physiochemical indicators coupled with multivariate analysis

Chili is an important spice prone to economically motivated adulteration. This study aimed at verifying regulatory compliance of chili powder available in the Indian market. A total of 123 samples from different regions of India were analyzed for specific quality and safety requirements laid down in Indian regulations like moisture, total ash, non-volatile ether extract (NVEE), crude fiber content, and added color. Non-compliance was seen in 43 % of branded samples, with 17 % exhibiting deviation in NVEE and 16 % testing positive for non-permitted Sudan dye. Amongst non-branded samples, 65 % were non-conforming and 51 % of samples were adulterated with Sudan dye. Principle component analysis was carried to understand the percentage variability of the target variable with respect to its principal components derived using feature variables. The total variance explained by the first two components was 88.13 % for selected brands and 88.36 % for source of origin. The results demonstrated that most of the violations were due to the presence of intentionally added coloring matter, therefore there should be an increased frequency of monitoring of Sudan dye in chili powder. The data generated in the present study can be used for estimating national prevalence of adulteration, risk assessments, and providing guidance to industry.