Miracle-dz/UniFAR-FTUs huggingface dataset:
Increased Platelet-to-Lymphocyte Ratio is an Independent Predictor of Hemorrhagic Transformation and In-Hospital Mortality Among Acute Ischemic Stroke with Large-Artery Atherosclerosis Patients

Background The platelet-to-lymphocyte ratio (PLR) is an inflammation marker of acute ischemic stroke, but its significance in patients with hemorrhage transformation (HT) after acute ischemic stroke with large-artery atherosclerosis (AIS-LAA) is unclear, and we also identified the relationship between PLR and in-hospital mortality of HT after AIS-LAA. Methods This was a retrospective analysis of patients with AIS-LAA. The PLR was calculated according to platelet and lymphocyte counts on admission. HT was defined on follow-up magnetic resonance imaging or computed tomography when neurologic deterioration worsened during hospitalization. The univariate analysis and multivariate logistic regression were performed to assess the association of PLR, HT and in-hospital mortality of HT after AIS-LAA. Results We included 328 Chinese AIS-LAA patients (mean age 67.2±11.1 years; 70.4% male). HT occurred in 38 patients (11.6%). After multivariate regression analyses, NRL (odds ratio [OR] 1.354, 95% confidence interval [CI] 1.176–1.559, P<0.001) and PLR (odds ratio [OR] 3.869, 95% confidence interval [CI] 2.233–5.702, P<0.001) were independently associated with HT after AIS-LAA. The area under the ROC curve (AUC) value of PLR (0.72, 95% CI (0.64–0.80), P<0.001) tested a greater discriminatory ability compared with neutrophil-lymphocyte ratio (NLR) (0.67, 95% CI (0.58–0.76), P<0.001). Meanwhile, PLR was found to be significantly related to HT after AIS-LAA, including in subtypes of artery-to-artery embolization (aOR 1.699, 95% CI 1.298–3.215, P<0.001), in-situ thrombosis (aOR4.499, 95% CI 1.344–9.054, P<0.001) and branch atheromatous disease (aOR3.239, 95% CI 1.098–8.354, P<0.001). Increased PLR predicts high in-hospital mortality of HT after AIS-LAA (OR 1.041, 95% CI (1.006–1.077), P=0.020; aOR 1.053, 95% CI (1.004–1.104), P=0.034). Conclusion High PLR is associated with greater risk of HT in AIS-LAA patients, including in artery-to-artery embolization, in-situ thrombosis and branch atheromatous disease. Meanwhile, increased PLR predicts high in-hospital mortality of HT after AIS-LAA.
An Efficient Energy Management in Smart Grid Considering Demand Response Program and Renewable Energy Sources

The advancement of the smart grids (SGs) is enabling consumers to schedule home appliances to respond to demand response programs (DRs) offered by distribution system operators (DSOs). This way, not only will customers save money on their energy bills and be more comfortable, but the utility company will also be able to regulate peak-hour demand and reduce carbon emissions (CE). Designing an optimization scheme to reduce the electricity bill cost, peak-to-average ratio (PAR), CO2 emission, wait time, and enhance the user comfort in terms of delay, luminance, and thermal comfort is not only the aim of this work but also the need of demand-side management. This research focuses on energy usage, scheduling, and management under the DR program of an electric utility, as well as renewable energy sources integration, i.e., solar energy (SE), thermal, controllable heat and power (CHP), and wind energy (WE). Moreover, the integration of renewable energy sources will reduce electricity bills and also lower the environmental impact of CE. In this context, a smart appliances scheduler and energy management controller (ASEMC) is proposed which is based on heuristic algorithms, i.e., genetic algorithm (GA), wind-driven optimization (WDO), particle swarm optimization (PSO), bacterial foraging optimization (BFO) and our proposed hybrid of GA, PSO, and WDO (HGPDO) algorithm. The performance of the proposed scheme and heuristic algorithms is evaluated via simulations. Results show that in Scenario 1, the proposed algorithm-based ASEMC reduces the electricity bill costs, PAR, and CE by 25.7%, 36.39%, and 20.74%, respectively, while in Scenario 2, the proposed algorithm-based ASEMC reduces the electricity bill costs, PAR, and CE by 35.25%, 31.72%, and 36.30%, respectively. Furthermore, in Scenario 1, user comfort in terms of cumulative delay, indoor air freshness quality, thermal, and visual comfort improves by 26.77%, 3.28%, 13.33%, and 31.66%, whereas in Scenario 2, user comfort improves by 23.33%, 3.30%, 10%, and 45%, respectively.
Analysis of efficacy, safety and prognostic factors of DAC-HAA treatment in Chinese pediatric patients with refractory or relapsed acute myeloid leukemia

Hematopoietic stem cell transplantation (HSCT) is generally considered as the only effective treatment for children with relapsed/refractory (R/R) acute myeloid leukemia (AML). Achieving remission prior to HSCT affects the efficacy of the procedure and patient survival; therefore, induction therapy in children with R/R AML prior to HSCT is very important. The aim of the present study was to evaluate the clinical efficacy, prognosis and safety of 5-aza-2-deoxycytidine (DAC) combined with homoharringtonine + cytarabine + aclarubicin (HAA regimen) in the treatment of pediatric R/R AML. A total of 53 pediatric patients with R/R AML, aged 1-14 years, were treated with DAC-HAA. The overall response rate was 83.1%, with a complete remission rate of 77.4% and a partial remission rate of 5.7%. In conclusion, DAC-HAA therapy for children with R/R AML was found to be associated with a high remission rate, a short period of bone marrow suppression and a good safety profile. Therefore, DAC-HAA may be of value as a transitional regimen prior to HSCT and is worthy of clinical consideration.
Advanced data analysis methods for dark matter research associated with the quark top at the LHC

A Machine Learning model was trained on simulated data of proton-proton collisions at the Large Hadron Collider at CERN derived using Monte Carlo methods to be used as a binary classifier. The data contained background events from the standard model and events from a beyond the standard model signal related to dark matter research. It was concluded that the use of machine learning models in the search of new physics is quite relevant since, on the simulated data, the trained model managed to get a area under the ROC curve of 0.9947 in the test dataset using Monte Carlo Dropout techniques that showed to be useful in the model optimization.
A Real-Time Car Towing Management System Using ML-Powered Automatic Number Plate Recognition

Automatic Number Plate Recognition (ANPR) has been widely used in different domains, such as car park management, traffic management, tolling, and intelligent transport systems. Despite this technology’s importance, the existing ANPR approaches suffer from the accurate identification of number plats due to its different size, orientation, and shapes across different regions worldwide. In this paper, we are studying these challenges by implementing a case study for smart car towing management using Machine Learning (ML) models. The developed mobile-based system uses different approaches and techniques to enhance the accuracy of recognizing number plates in real-time. First, we developed an algorithm to accurately detect the number plate’s location on the car body. Then, the bounding box of the plat is extracted and converted into a grayscale image. Second, we applied a series of filters to detect the alphanumeric characters’ contours within the grayscale image. Third, the detected the alphanumeric characters’ contours are fed into a K-Nearest Neighbors (KNN) model to detect the actual number plat. Our model achieves an overall classification accuracy of 95% in recognizing number plates across different regions worldwide. The user interface is developed as an Android mobile app, allowing law-enforcement personnel to capture a photo of the towed car, which is then recorded in the car towing management system automatically in real-time. The app also allows owners to search for their cars, check the case status, and pay fines. Finally, we evaluated our system using various performance metrics such as classification accuracy, processing time, etc. We found that our model outperforms some state-of-the-art ANPR approaches in terms of the overall processing time.
Design and Assist-as-Needed Control of Flexible Elbow Exoskeleton Actuated by Nonlinear Series Elastic Cable Driven Mechanism

Exoskeletons can assist the daily life activities of the elderly with weakened muscle strength, but traditional rigid exoskeletons bring parasitic torque to the human joints and easily disturbs the natural movement of the wearer’s upper limbs. Flexible exoskeletons have more natural human-machine interaction, lower weight and cost, and have great application potential. Applying assist force according to the patient’s needs can give full play to the wearer’s remaining muscle strength, which is more conducive to muscle strength training and motor function recovery. In this paper, a design scheme of an elbow exoskeleton driven by flexible antagonistic cable actuators is proposed. The cable actuator is driven by a nonlinear series elastic mechanism, in which the elastic elements simulate the passive elastic properties of human skeletal muscle. Based on an improved elbow musculoskeletal model, the assist torque of exoskeleton is predicted. An assist-as-needed (AAN) control algorithm is proposed for the exoskeleton and experiments are carried out. The experimental results on the experimental platform show that the root mean square error between the predicted assist torque and the actual assist torque is 0.00226 Nm. The wearing experimental results also show that the AAN control method designed in this paper can reduce the activation of biceps brachii effectively when the exoskeleton assist level increases.
Density gradient‐based adaptive refinement of analysis mesh for efficient multiresolution topology optimization

In topology optimization, the finite‐element analysis of the problem is generally the most computationally demanding task of the solution process. In order to improve the efficiency of this phase, in this article we propose to represent regions with zero density gradient by a coarser analysis mesh. The design is instead represented in a uniform mesh. We motivate the density gradient‐based adaptive refinement by discussing the topological meaning of the density gradient and how it can help avoid loss of information during projections or interpolations between design and analysis meshes. We also study the adaptiveness of the mesh and its ability to detect the topology change of the design. An a posteriori error analysis is performed as well. Furthermore, we provide theoretical and numerical considerations on the reduction of the number of degrees of freedoms of the adaptive analysis mesh with respect to the uniform case. This translates into a faster solution of the analysis, as we show numerically. Finally, we solve several test problems, including large 3D problems that we solve in parallel on computer cluster, demonstrating the applicability of our procedure in large scale computing and with iterative solvers.
A combined molecular docking and molecular dynamics simulation approach to probing the host–guest interactions of Ataluren with natural and modified cyclodextrins

ABSTRACT The objective of this study was to apply molecular docking and molecular dynamics (MDs) simulation approaches to investigate the host–guest interactions between natural Cyclodextrins (CDs) (Alpha, Beta and Gamma Cyclodextrins) and some modified CDs (hydroxypropyl, sulfobutylether and randomly substituted methyl Beta Cyclodextrins) with Ataluren as a small molecule drug designed for diseases attributable to a nonsense mutation. Molecular docking results presented that randomly substituted methyl beta Cyclodextrin (RMBCD) has the highest binding affinity and Alpha Cyclodextrin (ACD) has the lowest binding affinity to Ataluren. According to the MDs simulation results, ACD and hydroxypropyl Cyclodextrin have not proper cavity for encapsulation of Ataluren. In contrast, Ataluren was totally included into the Beta Cyclodextrin (BCD), Gamma Cyclodextrin, sulfobutylether Beta Cyclodextrin (SBECD) and RMBCD due to appropriate interior capability. Moreover, MDs results showed that SBECD has the most conformational flexibility and BCD has the most conformational stability during simulation time. The results of this paper may open new paths to use this formulation for further researches in the delivery mechanism of hydrophobic pharmaceutical molecules.
Orthogonal Constrained Meta Heuristic Adaptive Multi-View Clustering Over Multi Labeled Categorical Data Analysis

In data mining, clustering is the one of the efficient research concept in real time data analysis, evaluation of attribute representation in clustering is main issue in artificial intelligence related research areas. Multi labeled clustering gives high amount of valuable data, which describes the evaluation and representation of attribute be the trending concept in multi labeled categorical data analysis. Multi dimensional clustering is combined complementary data from different dimensions to provide efficient clustering results in various conditions. Different multi view clustering techniques are proposed traditionally but they can give output as single clustering with input data. Because of multiplicity, multi dimensional data can have different grouping data which are reasonable consist perspective attributes. So how to find measurable and reasonable cluster results which are represented in multi view labeled data is still challenging task, so that in this paper, we propose a novel approach i.e. Orthogonal Constrained Meta Heuristic Adaptive Multi-View Clustering (OCMHAMVC) to represent data as a cluster with different categories. Based on multi labeled data, first proposed approach evaluates low dimensional data using optimized matrix factorization (OMF) method and clusters the similar labeled sample data into prototype cluster of dimensional data. After that we represent data in desirable orthonormality constrained view of data using adaptive heuristic to combine complementary data from different dimensions, also provide complexity in computational analysis of data representation. Experimental results of proposed approach applied on high amount of multi view data gives scalable and efficient performance with comparison to traditional multi view related clustering approaches.
Ultrasonic Extraction of Nothaphoebe Coriacea Bark and Its Antioxidant Evaluation

Nothaphoebe coriacea (in Kalimantan, Indonesia, commonly known as gemor) is a native wetland-plants that can grow well in swampy areas. This tree is distributed throughout South and Central Kalimantan, Indonesia. The bark of Nothaphoebe coriacea is used for bioactive compound source for larvacide, mosquito repellent, hio and glue. The objective of this research was to investigate the ultrasound-assisted extraction methods for the extraction of antioxidants compounds of Nothaphoebe coriacea. The barks were subjected to ultrasound‐assisted ethanolic and hexane extraction followed by fractionation (using ethyl acetate, chloroform, and hexane) and the antioxidant activity test was performed. The activity of antioxidant was carried out through a DPPH method. The highest antioxidant activities are shown in ethanol extract with IC50 value of 72.36 μg/mL and chloroform fraction of ethanol extract with IC50 value of 54.18 μg/mL.
No Mendelian Genes in Psychiatry?

To date, no gene following Mendelian inheritance (e.g., monogenic variant) has been discovered for any common psychiatric disorder. This unfortunate circumstance has delayed meaningful inroads into the pathophysiology of psychiatric disease that has otherwise enabled advances into so many other fields of medicine. New methods and approaches can now find these putative genes offering the same potential for foundational impact on psychiatry as has occurred in diverse fields such as metabolism, hematology, and cancer. If unsuccessful, a significant impediment in progress toward mitigating the suffering from mental illness will result.
Proximal Fibular Osteotomy for Medial Joint Osteoarthritis of the Knee: A Prospective Cohort Study

Background and objective Osteoarthritis (OA) is a polyarticular disease that most commonly afflicts the knee joint. Established operative treatment options for medial joint OA of the knee include high tibial osteotomy, unicompartmental knee arthroplasty, and total knee arthroplasty. Proximal fibular osteotomy (PFO) is a relatively new procedure for treating medial joint OA of the knee. The objective of this study was to describe the functional and radiological outcomes at one year in patients undergoing PFO for medial joint OA of the knee. Materials and methods The study included 21 patients with medial joint OA of the knee who underwent PFO. Visual analog scale (VAS) score, medial to lateral knee joint space ratio (ML ratio), Kellgren-Lawrence (KL) grade, and the American Knee Society Score (AKSS) (clinical and functional) were recorded preoperatively. VAS score, ML ratio, and AKSS (clinical and functional) were documented again at the three-month and one-year follow-ups. Results The mean age of the patients was 58.85 ±6.94 years; 12 (57.1%) were female and nine (42.9%) were males. The mean VAS score for pain decreased from 7.86 ±0.66 at baseline to 5.14 ±1.15 at three months (p<0.001) and 3.78 ±1.26 at one year (p<0.001). The mean clinical AKSS was 56.49 ±6.95 at baseline, which increased to 63.41 ±6.20 at three months (p<0.001) and 72.71 ±9.87 at one year (p<0.001). The mean functional AKSS at baseline was 48.24 ±14.31, which increased to 60.10 ±14.81 at three months (p<0.001) and 71.46 ±15.18 at one year (p<0.001). The mean ML ratio at baseline was 0.33 ±0.19, which increased to 0.41 ±0.20 at three months (p<0.01) and 0.51 ±0.22 at one year (p<0.001). Conclusion In patients who undergo PFO for OA of the knee, improvements in VAS score for pain, AKSS (functional and clinical), and ML ratio were observed to be maintained for a period of one year postoperatively.
Enhancer promoter interactome and Mendelian randomization identify network of druggable vascular genes in coronary artery disease

Coronary artery disease (CAD) is a multifactorial disorder, which is partly heritable. Herein, we implemented a mapping of CAD-associated candidate genes by using genome-wide enhancer-promoter conformation (H3K27ac-HiChIP) and expression quantitative trait loci (eQTL). Enhancer-promoter anchor loops from human coronary artery smooth muscle cells (HCASMC) explained 22% of the heritability for CAD. 3D enhancer-promoter genome mapping of CAD-genes in HCASMC was enriched in vascular eQTL genes. By using colocalization and Mendelian randomization analyses, we identified 58 causal candidate vascular genes including some druggable targets ( MAP3K11 , CAMK1D , PDGFD , IPO9 and CETP ). A network analysis of causal candidate genes was enriched in TGF beta and MAPK pathways. The pharmacologic inhibition of causal candidate gene MAP3K11 in vascular SMC reduced the expression of athero-relevant genes and lowered cell migration, a cardinal process in CAD. Genes connected to enhancers are enriched in vascular eQTL and druggable genes causally associated with CAD.
Prescribed Performance Control for the Upper-Limb Exoskeleton System in Passive Rehabilitation Training Tasks

In this study, a model-free adaptive sliding mode control method was developed in combination with the prescribed performance method. On this basis, this study attempted to fulfill the joint position tracking trajectory task for the one-degree of freedom (DOF) upper-limb exoskeleton in passive robot-assisted rehabilitation. The proposed method is capable of addressing the defect of the initial error in the controller design and the application by adopting a tuning function, as compared with other prescribed performance methods. Moreover, the method developed here was not determined by the dynamic model parameters, which merely exploit the input and output data. Theoretically, the stability exhibited by the proposed controller and the tracking performance can be demonstrated. From the experimental results, the root mean square of the tracking error is equal to 1.06 degrees, and the steady-state tracking error converges to 1.91 degrees. These results can verify the expected performance of the developed control method.
QoS prediction for service selection and recommendation with a deep latent features autoencoder

The number of services on the Internet has increased rapidly in recent years. This makes it increasingly difficult for users to find the right services from a large number of the functionally equivalent candidate. In many cases, the number of services invoked by a user is quite limited, resulting in a large number of miss ing QoS values and sparseness of data. Consequently, predicting QoS values of the services is important for users to find the exact service among many functionally similar services. However, improving the accuracy of QoS prediction is still a prob lem. Despite the successful results of the proposed QoS prediction methods, there are still a set of issues that should be addressed, such as Sparsity and Overfitting. T address these issues and improve prediction accuracy. In this paper, we propose a novel framework for predicting QoS values and reduce prediction error. This frame work named auto-encoder for neighbor features (Auto-NF) consists of three steps. In the first step, we propose an extended similarity computation method based on Euclidean distance to compute the similarity between users and find similar neigh bors. In the second step, we form clusters of similar neighbors and partition the initial matrix into sub-matrices based on these clusters to reduce the data sparsity problem. In the third step, we propose a simple neural network autoencoder that can learn deep features and select an ideal number of latent factors to reduce the over fitting phenomenon. To validate and evaluate our method, we conduct a series of experiments use a real QoS dataset with different data densities. The experimental results demonstrate that our method achieves higher prediction accuracy compared to existing methods.
Intuitionistic Fuzzy Rough TOPSIS Method for Robot Selection using Einstein operators

Rough set and intuitionistic fuzzy set are very vital role in the decision making method for handling the uncertain and imprecise data of decision makers. The technique for order preference by similarity to ideal solution (TOPSIS) is very attractive method for solving the ranking and multi-criteria decision making (MCDM) problem. The primary goal of this paper is to introduce the Extended TOPSIS for industrial robot selection under intuitionistic fuzzy rough (IFR) information, where the weights of both, decision makers (DMs) and criteria are not-known. First, we develop Intuitionistic fuzzy rough (IFR) aggregation operators based on Einstein T-norm and T-conom, For this firstly we give the idea of intuitionistic fuzzy rough Einstein weighted averaging (IFREWA), intuitionistic fuzzy rough Einstein hybrid averaging (IFREHA) and intuitionistic fuzzy rough ordered weighted averaging (IFREOWA) aggregation operators. The fundamental properties of the proposed operators are described in detail. Furthermore to determine the unknown weights, a generalized distance measure are defined for IFRSs based on intuitionistic fuzzy rough entropy measure. Following that, the intuitionistic fuzzy rough information-based decision-making technique for multi-criteria group decision making (MCGDM) is developed, with all computing steps depicted in simplest form. For considering the conflicting attributes, our proposed model is more accurate and effective. Finally, an example of efficient industrial robot selection is presented to illustrate the feasibility of the proposed intuitionistic fuzzy rough decision support approaches, as well as a discussion of comparative outcomes, demonstrating that the results are feasible and reliable.