Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Teacher-student Relationship in Motivation (A Case Study of Community School of Nepal)

This research paper is focused to analyze how teachers - students' relationship plays a crucial role in students' motivation in learning. This paper is based on the Gardner model of motivation. By adopting a qualitative method approach and a phenomenological research design, the research paper has been prepared. Convenience sampling technique has been used in it. Data were collected by using a questionnaire and unstructured interview via telephone. Four issues were identified between the teachers-student’s relationships throughout the unstructured interview: the importance of teacher-student relationship, teacher expectation, students' motivation, and improvement in academic learning. The delimitation of this researcher was to collect the data from the telephone call due to the high transmission and risk of communicable diseases like corona virus (COVID -19) spreading all over the world. The research found out that, the better contact and mutual relationship between teachers and students resulted higher motivation in learning.
New Ozone Measurement Systems for Autonomous Operation on Ocean Buoys and Towers

Two autonomous ozone measurement systems for use on ocean buoys and towers have been built and are discussed herein. They are based on low-power atmospheric ozone sensors from Physical Sciences Inc. (PSI) and 2B Technologies. The PSI sensor operates at 1 Hz with a precision of 1 ppb but requires about 45 W with the present data system; the 2B makes a measurement every 10 s with a precision of 1‐2 ppb and uses less than 4 W. The sensors have been packaged in watertight enclosures with a set of valves and filters to keep out seawater and aerosols. A controller uses data from the sensors and a meteorological system to determine whether sampling should proceed. If a sensor malfunction (such as an incorrect valve position or a temperature beyond its proper range) is detected, the controller attempts to correct it. Both sensors have been tested and used over the ocean, and one complete ozone measurement system (with the PSI sensor) has been successfully deployed on a buoy off Woods Hole, Massachusetts. In 2003, this system was operated at the Chesapeake Bay Lighthouse Tower for over a month with excellent results. The 2B system was also successfully tested in 2003 at a nearby offshore tower. The design of the systems and their testing and deployments are described, and data from some of the first experiments are presented.
Forecasting composite indicators with anticipated information: an application to the industrial production index

Summary. Many economic and social phenomena are measured by composite indicators computed as weighted averages of a set of elementary time series. Often data are collected by means of large sample surveys, and processing takes a long time, whereas the values of some elementary component series may be available a considerable time before the others and may be used for forecasting the composite index. This problem is addressed within the framework of prediction theory for stochastic processes. A method is proposed for exploiting anticipated information to minimize the mean‐square forecast error, and for selecting the most useful elementary series. An application to the Italian general industrial production index is illustrated, which demonstrates that knowledge of anticipated values of some, or even just one, component series may reduce the forecast error considerably.
Air Cushion Restraint and Extended Safety System with Sensor and Control Module

The importance of safety restraint systems in cars has increased during the previous ten years. Since several studies have demonstrated how airbags improve passenger safety, they are by far the most crucial component of a car’s safety system. Without question, airbags save lives, and the number of lives they have saved is continuously rising. However, the standard system airbags frequently fail to protect the driver and passengers, increasing death and high crash rates. This paper proposed an air cushion restraint system with bumper extension for smart automobiles using IoT to address this issue. By reducing the force of the vehicle impact and preventing collisions, the suggested system increases safety for the driver and passengers during a collision with the vehicle.
2D Fluid Modeling of Light Pumped Na Containing Plasma to Develop a Prototype of a Photo Electromotive Force Converter

The development of innovative technologies for direct solar energy conversion is one of the main trends of modernity. The use of solar semiconductor panels is well-known in solar photovoltaics. As an alternative to this, the use of gas photoplasma can be promising. However, research in this direction is still very fragmentary so far. This paper shows the results of a systematic study of photoplasma in mixtures of sodium and inert gases to obtain photo-EMF in a gas cell. A fluid modeling considering detailed plasma chemistry, radiation transfer, and charge diffusion processes and 2D simulation were fulfilled and main plasma parameters were obtained and analyzed. In particular, non-trivial dependence of EMF in a two-chamber gas cell containing sodium and argon on the pressure of the inert gas was obtained, reaching up to 1.3 eV by absolute value at 100 Torr of Ar and changing its sign near 5 Torr at the decrease of the pressure.
Design of the particle swarm optimization simulated annealing MPPT control method based on motion shadow

Under the same environmental conditions, photovoltaic arrays at different operating voltages will significantly affect the size of solar power. To obtain as much power as possible, the maximum power point tracking (MPPT) control method has been invented. Previous studies focused on the tracking problem under local shadow conditions, but the dynamic shadow problem with rapid changes in local shadow was rarely discussed. To solve the tracking problem that the maximum power point has multi-peak and fast-changing characteristics under this condition, this paper proposes a simulated annealing MPPT control method based on particle swarm optimization, which compensates the disadvantages of the memoryless Markov chain in the optimization algorithm by adaptive asymmetric memory factor, thus improving the local search ability and convergence speed in the later stage. The control effect is verified by modeling and simulation in Simulink of MATLAB, and the adaptability of the heuristic control method is reflected by simulating the tracking mode under the moving shadow condition through the actual hardware deployment on the moving car.
Calibration of timing mismatch for TIADC based on error table and fractional delay filter

In this paper, we presents a correcting method based on an error table and fractional delay filter for correcting timing mismatch in a time-interleaved analog-to-digital converter (TIADC). This method uses the ramp signal to estimate timing mismatch and the error table storage error value. A Farrow structure fractional delay filter is utilized to implement the calibration of mismatch. Simulation results show that this method can realize the correction of timing mismatch and suppress the spurious component effectively with a good correction effect.
Segmented sensor blockchain based on state and block compression

In the sensor blockchain network, sensors need to collect data continuously and upload it to the blockchain network for verification of data integrity. The deployment of smart contracts on the chain can be used to authenticate data visitors. Historical data is valuable for data visitors, but it is useless for sensor nodes that can continue to link data only with current information. With the passage of time, the data of the sensor blockchain network will continue to grow. A node data storage mode that can adapt to the continuous expansion of the blockchain scale is needed, which can encourage nodes to join the network to participate in consensus and jointly maintain the security of the blockchain data. This paper provides a segmented storage solution based on state and block compression for sensor device nodes with limited storage capacity. After experimental verification, this scheme can significantly reduce the storage cost. It allows nodes to use blockchain for a long time in sensor blockchain networks with large transaction volumes and a large number of smart contracts and allows new nodes to be deployed faster.
Random Multi-access Scheme Based on Group Testing Theory for Massive Machine-type Communication

Considering emerging massive machine-type communication with very few devices simultaneously transmitting short messages, random multiple access has currently become a research hot spot since the traditional multiple access protocol struggles to work properly. Inspired by the superimposed code, a channel assignment scheme based on non-adaptive group testing theory is proposed to allow multiple users to share a single channel at the same time. A superimposed code with threshold value d, which can recover the original code through the Boolean sum of up to d codes, is constructed by studying the problem of group testing. When the number of active users with a message to be transmitted is no more than the threshold value, active users encode their messages by the superimposed code and multiple active users transmit their messages together by sharing a single channel. The receiver can decode the mixed messages by the disjunction of the superimposed code. We focus on the design of the superimposed code by group testing theory, formulate this problem as the construction of a transversal design, and use the Latin square to form a useful transversal design. Theoretical proof shows that our design works well under limited circumstances where the number of active users does not exceed the threshold value. Additionally, our design can recover the original codes by the mixture of codes, which are more easily than the algorithms based on compressive sensing. Simulation analysis presents that our design performs better than the traditional competitive access protocols in terms of successful probability, throughput, and packet loss rate.
A novel bearing fault diagnosis method under small samples using time-frequency multi-scale convolution layer and hybrid attention mechanism module

Deep neural networks for bearing fault diagnosis have become the focus of research in recent years with its excellent feature extraction capability. However, the problem of diagnosis under small samples still needs to be solved in industrial applications, because bearings rarely work in the fault state in practice, resulting in the scarcity of fault data. To solve this problem, this paper proposes a new diagnosis model, a time-frequency multi-scale attention network, which structure allows the original signal and its transformed spectrum to be used as the input in parallel. A multi-scale convolutional layer is also designed to extract information from the signal at different scales to enhance the feature extraction capability of the network. In addition, a hybrid attention mechanism is added to integrate the redundant features and realize the complementarity between features. The experimental results of seven bearing diagnosis cases from two bearings show that the proposed method can achieve high diagnostic accuracy under small samples, which proves the superiority of the proposed method. The time domain signal and frequency domain signal were respectively used as input to train the model. By comparing the accuracy with the time-frequency combined signal as input, the superiority of the time-frequency domain signal as input is proved.
Prognostic Analysis and Biomarkers Identification of Immune Infiltration in Early and Late Stage Hepatocellular Carcinoma Based on TCGA Data

Background Hepatocellular carcinoma (HCC) is a major cause of cancer death in the world. The aim of this study was to establish a new model to predict the prognosis of HCC. Materials and Methods The mRNA, miRNA and lncRNA expression profiles of early (stage I–II) and late (stage III–IV) stage HCC patients were acquired from The Cancer Genome Atlas (TCGA) database. The differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs) and lncRNAs (DElncRNAs) were identified between early and late stage HCC. Key molecules associated with the prognosis, and important immune cell types in HCC were identified. The nomogram based on incorporating age, gender, stage, and all important factors was constructed to predict the survival of HCC. Results A total of 1516 DEmRNAs, 97 DEmiRNAs and 87 DElncRNAs were identified. A DElncRNA-DEmiRNA-DEmRNA regulatory network including 78 mRNAs, 50 miRNAs and 1 lncRNA was established. Among the regulatory network, 11 molecules were significantly correlated with the prognosis of HCC based on Lasso regression analysis. Then, Preadipocytes and 3 survival-associated DEmRNAs were identified as crucial biomarkers. Subsequently, a nomogram with a differentiation degree of 0.758, including 1 immune cell, 11 mRNAs and 3 miRNAs, was generated. Conclusion Our study constructed a model by incorporating clinical information, significant biomarkers and immune cells to predict the survival of HCC, which achieved a good performance.
Stock price movement prediction based on Stocktwits investor sentiment using FinBERT and ensemble SVM

Investor sentiment plays a crucial role in the stock market, and in recent years, numerous studies have aimed to predict future stock prices by analyzing market sentiment obtained from social media or news. This study investigates the use of investor sentiment from social media, with a focus on Stocktwits, a social media platform for investors. However, using investor sentiment on Stocktwits to predict stock price movements may be challenging due to a lack of user-initiated sentiment data and the limitations of existing sentiment analyzers, which may inaccurately classify neutral comments. To overcome these challenges, this study proposes an alternative approach using FinBERT, a pre-trained language model specifically designed to analyze the sentiment of financial text. This study proposes an ensemble support vector machine for improving the accuracy of stock price movement predictions. Then, it predicts the future movement of SPDR S&P 500 Index Exchange Traded Funds using the rolling window approach to prevent look-ahead bias. Through comparing various techniques for generating sentiment, our results show that using the FinBERT model for sentiment analysis yields the best results, with an F1-score that is 4–5% higher than other techniques. Additionally, the proposed ensemble support vector machine improves the accuracy of stock price movement predictions when compared to the original support vector machine in a series of experiments.
Advances in Therapeutic Applications of Extracellular Vesicles

Abstract Extracellular vesicles (EVs) are nanoscale bilayer phospholipid membrane vesicles released by cells. Contained large molecules such as nucleic acid, protein, and lipid, EVs are an integral part of cell communication. The contents of EVs vary based on the cell source and play an important role in both pathological and physiological conditions. EVs can be used as drugs or targets in disease treatment, and changes in the contents of EVs can indicate the progression of diseases. In recent years, with the continuous exploration of the structure, characteristics, and functions of EVs, the potential of engineered EVs for drug delivery and therapy being constantly explored. This review provides a brief overview of the structure, characteristics and functions of EVs, summarizes the advanced application of EVs and outlook on the prospect of it. It is our hope that this review will increase understanding of the current development of medical applications of EVs and help us overcome future challenges.
The NP-Hardness of Covering Points with Lines, Paths and Tours and their Tractability with FPT-Algorithms

Given a problem for which no polynomial-time algorithm is likely to exist, we investigate how to attack this seemingly intractable problem based on parameterized complexity theory. We study hard geometric problems, and show that they are fixed-parameter tractable (FPT) given an instance and a parameter k. This allows the problems to be solved exactly, rather than approximately, in polynomial time in the size of the input and exponential time in the parameter. Although the parameterized approach is still young, in recent years, there have been many results published concerning graph problems and databases. However, not many earlier results apply the parameterized approach in the field of computational geometry. This thesis, therefore, focuses on geometric NP-hard problems. These problems are the Line Cover problem, the Rectilinear Line Cover problem in higher dimensions, the Rectilinear Minimum-Links Spanning Path problem in higher dimensions, the Rectilinear Hyperplane Cover problem, the Minimum-Bends Traveling Salesman Problem and the Rectilinear Minimum-Bends Traveling Salesman Problem, in addition to some other variants of these problems. The Rectilinear Minimum-Links Spanning Path problem in higher dimensions and the Rectilinear Hyperplane Cover problem had been the subject of only conjectures about their intractability. Therefore, we present the NP-completeness proofs for these problems. After verifying their hardness, we use the fixed-parameter approach to solve the two problems. We focus on solving the decision version of the problems, rather than solving the optimizations. However, with the Line Cover problem we demonstrate that it is not difficult to adapt algorithms for the decision version to algorithms for the optimization version. We also implement several algorithms for the Line Cover problem and conduct experimental evaluations of our algorithms with respect to previously known algorithms. For the remaining problems in the thesis, we will establish only the fundamental results. That is, we determine fixed-parameter tractability of those problems.
Variables predictoras de la duración de la amnesia postraumática tras un traumatismo craneoencefálico

Introducción. La duración de la amnesia postraumática (APT) ayuda a estimar el nivel de recuperación tras un traumatismo craneoencefálico (TCE). Sin embargo, apenas se dispone de información sobre qué variables sociodemográficas y clínicas influyen en su duración. Este estudio pretende identificarlas y crear un modelo predictivo que permita estimar el tiempo que un paciente tarda en emerger de la APT. Pacientes y métodos. La muestra estaba formada por 40 pacientes adultos en APT ingresados en un centro neurorrehabilitador. El 89,7% había sufrido un TCE grave; el 10,3% restante, un TCE moderado. Su estado cognitivo se valoró mediante el Galveston Orientation and Amnesia Test (GOAT) –escala que permite determinar en qué momento se puede considerar que un paciente emerge de la APT–. Resultados. Se encontró una ecuación de regresión (F = 8,511; p < 0,001; R2 = 0,415), en la que las siguientes variables (clasificadas por su orden de importancia) explicaron el 41,5% de la variabilidad observada en la duración de la APT: a) GOAT administrado a la llegada al centro neurorrehabilitador; b) años de formación reglada del paciente; y c) días que estuvo en APT antes de ingresar en el centro neurorrehabilitador. La ecuación de regresión derivada fue la siguiente: 104,284 + (–0,708 × GOAT-inicial) + (–4,124 × años formación reglada) + (0,219 × APT-hospital agudos). Conclusiones. El tiempo que un paciente tarda en emerger de la APT está condicionado por la puntuación que obtiene en el primer GOAT administrado al ingresar en el centro neurorrehabilitador, el nivel de estudios, y los días que permanece en APT entre el momento del TCE y el ingreso en el centro neurorrehabilitador.
Novel Prognostic Nomogram to Predict Progression-Free Survival of Patients with Hepatocellular Carcinoma After Transarterial Chemoembolization

Purpose A retrospective analysis of hepatocellular carcinoma (HCC) patients treated with transarterial chemoembolization (TACE) to identify risk factors was conducted, and a novel predictive nomogram model was constructed. Patients and Methods A total of 346 HCC patients who underwent TACE as initial treatment were retrospectively included, of which 208 were randomly allocated to the derivation cohort and 138 were allocated to the validation cohort. Progression-free survival (PFS) was used as the follow-up endpoint according to mRECIST. Kaplan‒Meier analysis and the Cox regression model screened out some indicators associated with short-term prognosis, and R language was further used to construct a nomogram model. The nomogram was compared with the classical BCLC staging system. Results The independent predictors affecting PFS in HCC patients undergoing TACE included the following: 1. Baseline indicators: age (P=0.013), albumin-bilirubin (ALBI) grade (grade 2 vs grade 1, P=0.029; grade 3 vs grade 1, P<0.001), and portal vein tumour thrombus (PVTT, P<0.001); 2. Indicators at the 1-month follow-up: Neutrophil To Lymphocyte Ratio (NLR, P=0.032) and changes in alpha-fetoprotein (AFP, P<0.05) and des-γ-carboxy prothrombin (DCP, P<0.001); and 3. Cumulative treatment numbers of TACE in 6 months (P=0.007). In the derivation cohort, the calibration curve of the nomogram showed a high consistency between the predicted and actual PFS probability, and the nomogram outperformed the BCLC staging system (P=0.004). This result was also confirmed in the validation cohort (P=0.012). Conclusion The constructed nomogram was suggested to have good predictive efficacy and could be used as a complementary assessment to predict the survival and prognosis of HCC patients treated with TACE.
Identification of an 85-kb Heterozygous 4p Microdeletion With Full Genome Analysis in Autosomal Dominant Charcot-Marie-Tooth Disease

Background and Objectives Charcot-Marie-Tooth disease (CMT) is a syndrome of a hereditary neurodegenerative condition affecting the peripheral nervous system and is a single gene disorder. Deep phenotyping coupled with advanced genetic techniques is critical in discovering new genetic defects of rare genetic disorders such as CMT. Methods We applied multidisciplinary investigations to examine the neurophysiology and nerve pathology in a family that fulfilled the diagnosis of CMT2. When phenotype-guided first-tier genetic tests and whole-exome sequencing did not yield a molecular diagnosis, we conducted full genome analysis by examining phased whole-genome sequencing and whole-genome optical mapping data to search for the causal variation. We then performed a systematic review to compare the reported patients with interstitial microdeletion in the short arm of chromosome 4. Results In this family with CMT2, we reported the discovery of a heterozygous 85-kb microdeletion in the short arm of chromosome 4 (4p16.3)[NC_000004. 12:g. 1733926_1819031del] spanning 3 genes [TACC3 (intron 6-exon 16), FGFR3 (total deletion), and LETM1 (intron 10-exon14)] that cosegregated with disease phenotypes in family members. The clinical features of peripheral nerve degeneration in our family are distinct from the well-known 4p microdeletion syndrome of Wolf-Hirschhorn syndrome, in which brain involvement is the major phenotype. Discussion In summary, we used the full genome analysis approach to discover a new microdeletion in a family with CMT2. The deleted segment contains 3 genes (TACC3, FGFR3, and LETM1) that likely play a role in the pathogenesis of nerve degeneration.