Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of Coffee Variety and Processing on the Properties of Parchments as Functional Bioadditives for Biobased Poly(butylene succinate) Composites

Fermented polymers like biobased poly(butylene succinate) (BioPBS) have become more relevant as technical substitutes for ductile petrochemical-based polymers but require biogenic functional additives to deaccelerate undesired thermo-oxidative degradation and keep a fully biobased character. In this paper, the influence of coffee parchment (PMT) from two different varieties and processings on the thermo-oxidative stabilization and mechanical properties of poly(butylene succinate) composites up to 20 wt. -% PMT were investigated. Micronized with a TurboRotor mill, both PMT powders differ in particle size and shape, moisture ab- and adsorption behavior and antioxidative properties. It could be shown that pulped-natural PMT consists partially of coffee cherry residues, which leads to a higher total polyphenol content and water activity. The homogeneous PMT from fully washed processing has a higher thermal degradation resistance but consists of fibers with larger diameters. Compounded with the BioPBS and subsequent injection molded, the fully washed PMT leads to higher stiffness and equal tensile strength but lower toughness compared to the pulped-natural PMT, especially at lower deformation speed. Surprisingly, the fully washed PMT showed a higher stability against thermo-oxidative decomposition despite the lower values in the total phenol content and antioxidative activity. The required antioxidative stabilizers might be extracted at higher temperatures from the PMT fibers, making it a suitable biogenic stabilizer for extrusion processes.
Development and validation of a high-performance liquid chromatography method for the estimation of esomeprazole in bulk and tablet dosage form

Introduction: An accurate, simple, precise, rapid, economic and reproducible reverse-phase high-performance liquid chromatography method was developed and validated for the estimation of Esomeprazole (ESO) in bulk and tablet dosage form. Method: The separation was carried out on Finepak SIL C18T-5 column (250 × 4.6 mm, 5.0 µm i. d.) using potassium dihydrogen phosphate buffer (0.025M): ACN (20:80 v/v) and at a flow rate of 1.0 mL/min. using UV detector at 302 nm with a run time of 10 min. The method was validated for accuracy for linearity, accuracy, precision, limit of detection (LOD), limit of quantification (LOQ) and robustness. Results: The standard calibration curve was linear with R2 = 0.995. LOD and LOQ obtained for esomeprazole were 0.0001 and 0.0004 µg/mL respectively. The method was found robust for possible changes. Results of analysis of other parameters were also tested and validated as per ICH guidelines and recovery studies confirmed the accuracy of the proposed method. validation studies showed that the developed HPLC method is simple, reproducible, rapid, precise and reliable. The high recovery and low relative standard deviation confirm the suitability of the developed method for the determination of esomeprazole in the tablet dosage form. Conclusion: This method may be used as a more convenient and efficient option for the analysis of esomeprazole to establish the quality of the substance during routine analysis with consistent and reproducible results.
A New Convolution Network Based on Laplacian Eigenmap

The best is to read these instructions and follow the outline of this text. Recently, convolutional deep neural network (ConvNet) has been widely used in the field of image classification. In this work, we propose a new feedback free convolution network for image classification. The proposed network could hierarchically and effectively extract the features from an image through a manually designed convolution network without relying on back-propagation. The network is designed in a cascaded fashion, where the Laplacian Eigenmap filter is used as convolution kernel to extract features in each of the cascaded stage. The final output of the network is achieved by a simply binary hashing and histogram encoding, and could be served as distinguishing features for many classification tasks. Experiments on different database, e.g. FERET datasets for face recognition, CUReT for texture classification and MNIST for hand-written digits recognition, showed that the proposed method outperforms many other popular machine learning algorithms.
An Analysis of Respiratory Muscle Paralysis of Adult Patients in Guillain–Barré Syndrome: A Retrospective Analysis

Respiratory muscle paralysis is known as a very common complication of Guillain–Barré syndrome (GBS). However, most research has focused on its later stages rather than its earlier stages, including the prognosis of patients with this condition, or factors that act as early predictors of risk. Therefore, our study aimed to identify early predictors of respiratory muscle paralysis in patients with GBS and determine the short-term prognosis of such patients. We recruited 455 GBS patients (age ≥ 18) who had been hospitalized in the First Affiliated Hospital of Harbin Medical University between 2016 and 2021, retrospectively. We recorded clinical and laboratory data and used linear and logistic regression analysis to investigate the relationship between early clinical, examination results, and subsequent respiratory muscle paralysis. Among the 455 patients, 129 were assigned to a respiratory muscle paralysis group and 326 were assigned to a non-respiratory muscle paralysis group. Compared with the non-affected group, the time from onset to admission was shorter (p = 0.0003), and the Medical Research Council (MRC) score at admission and discharge was smaller in the affected group (p < 0.0001). Compared with the non-affected group, the affected group had higher Hughes and Erasmus GBS Respiratory Insufficiency Score (EGRIS) scores at admission and longer hospital stays (p < 0.0001). Patients in the affected group were more likely to have bulbar palsy and lung infections (p < 0.0001). To conclude, bulbar palsy, a higher EGRIS score and Hughes score at admission, a lower MRC score, and a shorter time between onset and admission, are all predictive risk factors for respiratory muscle paralysis in patients with GBS. An increase in any of these factors increases the risk of muscle paralysis. Patients with respiratory muscle paralysis have a poorer short-term prognosis than those without respiratory muscle paralysis. Therefore, we should attempt to identify patients with one or more of these characteristics in the early stages of admission, provide ventilation management, and administer IMV treatment if necessary.
Domain-Specific Sentiment Analysis: An Optimized Deep Learning Approach for the Financial Markets

Although different studies are caried out by deep learning models for financial markets sentiment analysis, there is a lack of specific embedding method that regards the domain. Therefore, the goal of this study is to discover what type of embedding techniques along with different classification algorithms work better for the financial markets’ sentiment analysis to present an optimized embedding method in the domain. In this paper we present a broad comparative study of multiple classification models trained for sentiment analysis and will improve their performance with an optimized embedding layer. We use a heterogeneous corpus of both formal (news headlines) and informal (tweets) text to increase the robustness and build the models with CBOW, GloVe, and BERT pre-trained embeddings as well as developing an optimized embedding layer to improve the results. The best results reported here are by our LSTM model with the fine-tuned embedding layer, which has an accuracy of 0.84 and a macro-average F1-score of 0.8. Our results give evidence that the fine-tuned embedding is superior to utilising pretrained CBOW, GloVe, and BERT embeddings for financial markets sentiment analysis. We train SVM, MLP, CNN, generic RNN and LSTM models by a comprehensive approach in input data and algorithms. As a result, a sentiment analysis model is presented with a robust performance for different datasets in the domain.
UAV Swarm Navigation in GNSS Denied Region with Formation Control

UAVs are becoming extremely essential these days for many applications such as HADR missions. During these missions, there can be a loss of GPS signal due to challenging environments and terrains, and this can hinder the missions so there is a need for a system that provides UAVs the capability to do these missions even in GPS-denied environments. In most of such missions, swarms of UAVs are working. Hence, the aim of this research paper is to propose a reliable GPS-denied navigation system for swarms. The major challenge with the swarm navigation is to achieve inter-UAV collision avoidance. So, this paper mainly focuses on prevention of inter UAV collision. Therefore, to accomplish this objective, the integration of estimations by Visual Odometry and UWB module is proposed in this paper.
Efficacy of Rigid-Angle Bronchial Blockers for Uniportal Video-Assisted Thoracoscopic Tumor Resection

Background: For uniportal video-assisted thoracoscopic surgery (VATS), which is greatly dependent on satisfactory lung collapse without lung compression from another port, few reports have elucidated the intraoperative efficacy of bronchial blockers (BBs). We hypothesized that operation time would be prolonged if BBs required more intraoperative repositioning during surgical manipulation. We compared the operation times of different surgical procedures performed using BBs with double-lumen tubes (DLTs) in uniportal VATS. Materials and Methods: Patients who underwent intubated uniportal VATS were enrolled retrospectively from March to May 2019. Data on the patient, anesthetic, and surgical factors were collected. Regression analyses were performed to determine the effect of various factors on operation time. Results: 317 patients who underwent uniportal VATS were included. Wedge resection constituted 70.7%, and anatomic resection constituted 29.3% of procedures. BBs were applied for left- and right-side wedge resection (85.6% and 78.7%, respectively) and left- and right-side anatomic resection (74.1% and 56.4%, respectively). The surgical factors significantly affecting operation time were operation procedures (P < .01), number of lymph nodes sampled (P < .001), and size of tumors (P < .01). Conclusions: The efficacy of BBs was comparable to that of DLTs for uniportal VATS wedge resection. With significantly less preference for right-side anatomic resection, the efficacy of DLTs was comparable with that of BBs, which were applied in more than half of right-side uniportal anatomic VATS. We conclude that even in uniportal VATSs, rigid-angled BBs demonstrate comparable efficacy with feasible alternatives.
What Does “Success” Mean to Students at a Selective University? Individual Differences and Implications for Well-Being

This study investigated how college students (N = 376) at a private, selective university (a) define success in college in their own words, and how conceptions of success (b) relate to differences in motivation and other individual differences, and (c) predict well-being. A thematic analysis suggested that students’ definitions of success were multifaceted, but the most common themes reflected academic and social criteria for success. Students who defined success in terms of Experience (e.g., trying new things) and Future-Readiness (e.g., preparedness to handle life after college) tended to be higher in mastery orientation and intrinsic motivation. Success definitions varied with demographic characteristics, including gender, class year, race, first-generation status, academic interests, and more, in ways that likely reflect differences in students’ cultural backgrounds and interests. Finally, well-being was higher in students who defined success in terms of Intellectual Growth (e.g., learning, being challenged, determining academic interests). We discuss the implications of these findings for practice (e.g., for university administrators, faculty, and advisors) and for future research.
PARAMETER ESTIMATION FOR PARETO DISTRIBUTION AND TYP E-I I FUZZY LOGIC

While parameter estimation by the classical methods there are a number of assumptions need to be satisfied, in the linear regression analysis. One of them is errors are normally distributed. In this work, the case that independent variable has Pareto distribution to be discussed and an algorithm using adaptive networks suggested to parameter estimation where the k which is one of the parameters of the fuzzy membership functions is fuzzy. Also the parameter of fuzzy membership function is fuzzy the estimation process is based on type-II fuzzy logic.
Unified Framework for Diversity and Coding Gains Over a Broad Gaussian Class of Fading Channels

Multipath fading is a major hindrance to the performance of wireless communication systems. For various blends of common propagation aspects—wave clustering, medium nonlinearity, correlation, scattering, and specular components—, the resulting fading model or the corresponding performance analysis is unavailable in a closed form, a drawback that becomes more prominent when source and destination are connected via multiple links. In those cases, little, if any, insight is gained into the overall system behavior. Herein, we shed light on this subject by providing a novel asymptotic framework at high signal-to-noise ratio that yields simple, general, and unified closed-form expressions for the diversity and coding gains in the symbol error rate and outage probability over a broad Gaussian class of fading channels. The proposed framework reveals how each physical aspect of fading and the number of diversity links primarily affect wireless communications. The analysis also resolves the long-standing problem of how each physical element of generalized Gaussian-class fading channels impacts multibranch diversity receivers. The strength and generality of our approach are illustrated by assessing the performance of emerging millimeter-wave systems. Finally, representative numerical results are presented and discussed to demonstrate that all the addressed fading aspects and system parameters affect the coding gain, whereas only the clustering, nonlinearity, and number of links affect the diversity gain.
Discrete T–S fuzzy systems with time-varying delays: a new discrete sliding mode control approach

The control of nonlinear systems has been the subject of extensive research. This interest is mainly due to its potential for real applications. In this paper, we investigated discrete sliding mode control for a class of nonlinear time-delay systems represented by T–S fuzzy models. In most existing fuzzy sliding mode control, a common input matrix is considered for all subsystems. This assumption is very restrictive. Therefore, we proposed a new sliding surface, which takes account of the system state and the control input in order to exclude the restrictive assumption. Furthermore, we have improved the latter sliding mode control scheme, by adding delayed states. Based on formulation of linear matrix inequalities, the parameters of the sliding function are obtained. Therefore, to further reduce the conservatism in the existing results, the Wirtinger-based integral inequality and Jensens inequality are employed. To show the applicability and effectiveness of the proposed controller design methodology, a numerical example is given for illustration.
Predicting tree failure likelihood for utility risk mitigation via a convolutional neural network

ABSTRACT Critical to the resilience of utility power lines, tree failure assessments have historically been performed via costly manual inspections. In this paper, we develop a convolutional neural network (CNN) to predict tree failure likelihood categories (Probable, Possible, Improbable) under three classification strategies. The CNN produced the best performance under the Probable/Possible vs. Improbable strategy, achieving a recall score of 0.82. We also perform a visual analysis of the predictions via Grad-CAM++ heatmaps, indicating an approach for incorporating interpretability into model selection. Benchmarking the results of our model against those produced by two state-of-the-art CNNs (ResNet-50 and Inception-v3), we show that our relatively simple model produces better results in a computational time that is three times faster. Via this novel framework, we demonstrate the potential of artificial intelligence to automate and consequently reduce the costs of tree failure likelihood assessments in proximity to power lines, thereby promoting sustainable infrastructure.
Employee Attrition Prediction using Artificial Neural Networks

Employee attrition is a significant concern for businesses as it can negatively impact productivity, profitability, and overall success. Employee turnover can be costly and time-consuming, and it can also result in the loss of valuable talent and knowledge. It is important for businesses to predict and understand employee attrition so that they can take proactive measures to retain employees. During Covid, the organisations faced issues due to employees leaving uncertainly, making it difficult for the HR Department to hire new people quickly and the companies had to spend a huge fortune to fill the void. It is important for businesses to monitor and evaluate the effectiveness of retention strategies over time to ensure that they are achieving the desired outcomes. Artificial Intelligence has proved to be of great use in predicting how likely an employee is to leave the organization. By using predictive analytics to identify employees who are at risk of leaving and developing targeted retention strategies, businesses can reduce the negative impact of employee attrition and create a more stable and productive workforce. Our system assists with foreseeing the rate at which employees are stopping position in light of getting logical information available and utilize different Artificial Neural Networks to diminish prediction error. The main objective of this model is to study employee attrition in an organization, to find out the issues of the representatives in the enterprise, and to distinguish how maintenance procedure lessens worker turnover.
Deep Neighborhood Structure-Preserving Hashing for Large-Scale Image Retrieval

Deep hashing integrates the advantages of deep learning and hashing technology, and has become the mainstream of the large-scale image retrieval field. However, when training the deep hashing models, most of the existing approaches regard the similarity margin of image pairs as a constant. Once similarity distance exceeds the fixed margin, the network will not learn anything, which easily results in model collapses. In this paper, we address this dilemma with a novel unified deep hashing framework, termed Deep Neighborhood Structure-preserving Hashing (DNSH), to generate the similarity-preserving and discriminative hash codes. Specifically, by extracting the discriminative object characteristics with large variances, we design an adaptive margin quadruplet loss to further explore the underlying similarity relationship between image pairs, reflecting the correct semantic structure among its neighbors. Based on the quadruple form, we develop a quadruple regularization to decrease quantization errors between binary-like embedding and hashing codes. Furthermore, through learning bit balance and bit independent terms jointly, we present the binary code constraint loss to alleviate redundancy in different bits. Extensive evaluations on four popular benchmark datasets demonstrate that our proposed deep hashing framework achieves an excellent performance than the comparison methods.
Strong Correlation Fuzzy Clustering Analysis of Regional Industrial Structure Network Based on Floyd Improved Algorithm

According to the complex network modeling theory, the input-output direct consumption coefficient is used to construct the industrial structure network model, and the traditional Floyd algorithm is improved to construct a strong correlation matrix, so that the matrix has convergence and uniqueness. The main purpose of this study is to explore the relationship between China's regional industrial structure and economic development. The research question is: is there a strong correlation between the regional industrial structure network and economic development? To answer this question, we use Floyd's improved algorithm (Floyd, 2001) to cluster the data set into groups with different similarities. In addition, we also use fuzzy clustering technology (fuzzy clustering algorithm, FCA) to analyze the factors that affect the similarity of each group. Finally, we compared our results with those obtained by other researchers in similar studies.
Finite-time antisaturation control for spacecraft rendezvous and docking with safe constraint

This thesis studies control schemes for spacecraft safe rendezvous and docking considering input saturation. Based on the spacecraft attitude and orbit coupled model, by using fast terminal sliding mode method, a finite-time antisaturation controller and an adaptive finite-time antisaturation controller are designed for the situations of known and unknown upper bound of external disturbances, respectively. In controller design, a novel continuous and differentiable collision avoidance potential function is presented to restrict motion area and guarantee the safety of the spacecrafts. Meanwhile, the saturation function and auxiliary system are introduced to deal with the input saturation. Lyapunov theory is utilized to prove that the error states of the system under the proposed controllers are finite-time convergent, and the rendezvous and docking without collision can be accomplished. The numerical simulation results indicate that the chaser can realize the rendezvous and docking with input saturation and safe constraint, which can further illustrate the effectiveness of the designed controllers.
FSNet: A Hybrid Model for Seasonal Forecasting

Load forecasting with low prediction error is essential to keep minimizing costs in generating and supplying power. It has many applications in energy production, distribution, and infrastructure construction. Because of the high autocorrelation and strong seasonality in load data, it is difficult to build robust and generalizable forecasting models. To address the problem, we propose a hybrid model, the Fourier Split NET (FSNET). The proposed model consists of two phases. A deseasonalization phase where the model uses the Fourier transform to isolate the seasonal component from the data using the fast Fourier transform. The second phase consists of training a simple linear model to replicate the seasonal behavior of the data and training a group of LSTM neural networks on different clusters of the data. The model uses statistical features to build separate LSTM models for different groups of data. We experimented on open datasets and obtained higher accuracy results compared to other forecasting approaches using different accuracy metrics.