Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of High Throughput Flexible Ring Oscillator

Ring oscillator is one of the key building blocks of many digital and analog circuits. Due to their simplicity in design and low power consumption, they are used in voltage controlled oscillators, pseudo random number generators, and etc. Our paper introduces a circuit design for a ring oscillator that can perform several ring oscillations concurrently using a single circuit, resulting in a high throughput. The proposed nine stage ring oscillator has the flexibility to perform seven stage, five stage, and three stage ring oscillations using two control lines for maximum throughput. The throughput of the circuit is increased by using the proposed hardware design. Both the proposed and existing designs were implemented using Cadence Virtuoso with a 90nm CMOS library, as well as Xilinx Vivado with the FPGA device xc7z020clg484−1.
Triple Parallel LSTM Networks for Classifying the Gait Disorders Using Kinect Camera and Robot Platform During the Clinical Examination

This paper presents a new methodology for the data processing and classification method for gait disorders, which is observed with a Kinect camera. The study of gait and motion stability in gait disorders is one of the most interesting research areas in the field. The patient and the physician must monitor the progress of the rehabilitation process before and after surgery to obtain an objective view of the rehabilitation process. In this study, the patient is scanned with the Kinect camera placed on a mobile robotic platform. For feature extraction and feature analysis, the exercises (three walking exercises) frames are collected and saved in data folders. This study uses 84 measurements of 37 patients with complex observations based on the physician's opinion in a clinical setting to address classification problems. In the analysis of gait disorders, motion data play an essential role. Furthermore, it reduces the selection of helpful body features for assessing gait disorders. The proposed system uses a key-point detector that computes body landmarks and classifies gait disorders using triple-parallel long short-term memory (LSTM) networks. The present study demonstrates the success of the method in classification evaluation when combined with the state-of-the-art pose estimation method. Around 81 percent accuracy was achieved for given sets of individuals using velocity-based, angle-based, and position-based features.
Exploring the Role of Computer Vision in Human Emotion Recognition: A Systematic Review and Meta-Analysis

Human emotion recognition is an important area of research with numerous applications in various fields. In recent years, computer vision techniques have emerged as a promising approach for automated human emotion recognition. This systematic review and meta-analysis provide an overview of the role of computer vision in human emotion recognition research. The review found that computer vision techniques have been utilized for a range of tasks related to human emotion recognition, including facial expression recognition, body posture analysis, and speech and voice analysis. The most used computer vision techniques include deep learning, support vector machines, and principal component analysis. The accuracy of computer vision-based emotion recognition approaches varied widely across studies, with reported accuracies ranging from 60% to 99%. The review identified several factors that may influence the accuracy of these approaches, including the quality of the data used for training and testing, the complexity of the emotions being recognized, and the choice of features and algorithms used for analysis. Additionally, ethical considerations related to privacy and bias were discussed as important considerations in the development and deployment of computer vision-based emotion recognition systems. In conclusion, this systematic review and meta-analysis provides a comprehensive overview of the role of computer vision in human emotion recognition research. The findings suggest that computer vision techniques have the potential to contribute significantly to the field, but further research is needed to address the identified limitations and challenges. The results of this review could be used as a basis for future research and development of computer vision-based emotion recognition systems.
Wildfire Spread Prediction Using Attention Mechanisms in U-NET

Global warming has increased large-scale natural disasters at an alarmingly greater frequency. These natural disasters affect humans and the ecosystems that all species rely upon for food and shelter. A significant byproduct of global warming is the emergence of mega-fires, which are wildfires that burn more than 100,000 acres. Mega fires have exhibited the capacity to destroy entire ecosystems and urban areas. Researchers forecast a significant increase in wildfires during the next decade. This is a fundamentally growing problem that mandates modern solutions. Currently, simulation models are employed to predict wildfire propagation and aid firefighters in suppressing them. However, these methods are computationally expensive and often inaccurate. A recent advance in this area is the usage of neural networks (NN), which are systematically more accurate and computationally efficient. This paper focuses on predicting wildfire spread using NN with an attention mechanism to improve spatial recognition and overall performance in neural networks for wildfire spread prediction. We train different models on 12 distinct observational variables derived from the Google Earth Engine catalog. Evaluation is conducted with accuracy, Dice coefficient score, ROC-AUC, and F1-score. Results show that when augmenting segmentation models with attention mechanisms, the attention component improves feature suppression and recognition, improving overall performance. Furthermore, we use ensemble modeling to reduce bias and variation, leading to more consistent and accurate predictions. The architecture presented in this research achieved a ROC-AUC score of 86.2% and an accuracy of 82.1% when inferencing on wildfire propagation at 30-minute intervals.
Application of Energy-Saving Technology for Carbon Dioxide Transcritical Wide Temperature Range Based on EMD Algorithm

Carbon dioxide transcritical wide temperature range energy-saving technology is a new type of energy-saving technology, among which the Empirical Mode Decomposition (EMD) algorithm is one of the important technologies. In order to achieve the goals of minimum energy consumption, lower cost, highest operating efficiency, and longer maintenance cycle for carbon dioxide systems, it is necessary to consider how to use EMD theory for calculation and processing work. This article mainly uses experimental and comparative methods to design and calculate an energy-saving model for carbon dioxide across the critical wide temperature range. Experimental data shows that when the temperature increases from 25 ° C to 45 ° C, the optimal high pressure increases from 7.8 MPa to 10.5 MPa. Carbon dioxide supercritical technology is a commonly used environmental protection technology in modern chemical production, but its energy consumption and environmental pressure are still bottlenecks that plague the industry. One of the feasible technologies to solve this problem is the carbon dioxide transcritical wide temperature range energy-saving technology based on EMD algorithm.
Application of Multi-objective Optimization Routing Algorithm in Machine Automatic Translation Quality Evaluation Model

Machine automated translation is now the mainstream method of translation, and more and more people are choosing machine automated translation to replace manual translation because the speed of machine automated translation is much faster than manual translation. However, the accuracy of quality evaluation for machine automated translation still needs to be improved. This article studied the application of multi-objective optimization routing algorithms in the quality evaluation model of machine automatic translation, thus aiming to improve the accuracy of machine automatic translation quality evaluation through multi-objective routing algorithms. This article experimentally tested machine automatic translation using multi-objective optimization routing algorithms, with a quality evaluation accuracy of up to 94%. The quality evaluation accuracy of traditional machine automatic translation was as high as 75%. From this experimental result, it could be seen that multi-objective optimization routing algorithms had a high degree of compatibility with machine automatic translation, which could improve the accuracy of quality evaluation for automatic translation.
Research on Material Demand Forecasting Algorithm Based on Multi-Dimensional Feature Fusion

Material demand forecasting has a profound impact on the supply chain and is an important prerequisite for manufacturing enterprises to produce. In order to accurately predict the material demand of enterprises, this paper proposes a material demand forecasting algorithm based on multi-dimensional feature fusion (DFMF). Secondly, in order to obtain the spatial features, the vector representation of the relevant materials of a material is obtained through the attention mechanism. Then, the authors aggregate the relevant material representation and material vector representation of materials to obtain the final material vector representation through aggregation function. Then the final material vector representation under different time scales is used as input, and the prediction value of material demand is obtained by using BP neural network. Finally, experiments show that the model can effectively obtain multi-dimensional features of materials for prediction, and the prediction results have high accuracy.
THE LONG-RUN RELATIONSHIP BETWEEN FISCAL POLICY AND ECONOMIC GROWTH IN TURKEY

The effectiveness of the fiscal policy on the economic growth is still a much-debated issue in the public finance literature. Current studies for Turkey analyzing this issue, have not a comprehensive econometric model with coherent results. In this vein, our study investigates the relationship between the fiscal policy and the economic growth in Turkey for the period 1980-2017. The main aim of the study is to understand how the fiscal policy affects the economic growth in Turkey both in the short and long run. We employed the autoregressive distributed lag (ARDL) approach and the bounds test to detect the long-run relationship. This is the first study, having coherent results and a correctly specified model, using ARDL to test the long-run effectiveness of the fiscal policy in Turkey. Using the budget balance indicator as the fiscal policy stance, our long-run ARDL results reveal that the fiscal policy is a significant determinant of the output in the long run. However, it is shown that the fiscal policy is not effective in the short run.
UvA-DARE (Digital Academic Repository) Follow-up effects in a parent-training trial for mothers being released from incarceration and their children

Given the at-risk status of children of incarcerated mothers for behavior problems and later delinquency, and the key role of their mothers ’ parenting behaviors, the need for parent training in this population seems obvious. Although short-term effects of parent training for this population seem promising, sustained intervention effects are unknown. Therefore, we examined follow-up effects of Incredible Years Parent Training enhanced with home visits, on parenting behavior (positive parenting and inconsistent discipline), parenting stress, and disruptive child behavior, in families with mothers being released from incarceration. Mothers of 133 two-to ten-year-old children ( M = 6.4 years; 51.1% girls) were partially randomly assigned to the intervention group — receiving group sessions and individual home visits — or a no-intervention control group. Mothers participated in eight assessments, including a follow-up assessment 8 months after intervention. Intention-to-treat latent growth analyses showed that inconsistent discipline increased and remained higher at follow-up in the control group, whereas inconsistent discipline among intervention mothers did not increase. In addition, the intervention reduced both parenting stress and children ’ s disruptive behavior, but these effects were not maintained at follow-up. In sum, the intervention had sustained effects on inconsistent discipline, however, direct bene ﬁ ts of the intervention on child behavior were no longer visible at 8-month follow-up. Maintenance of treatment gains seems dif ﬁ - cult for previously incarcerated mothers, and therefore ongoing family support is needed for these mothers.
Atacama Large Millimeter/submillimeter Array (ALMA)

Summary form only given. The Atacama Large Millimeter/submillimeter Array (ALMA) is the largest astronomical project under an international partnership of East Asia, Europe, and North America in cooperation with the Republic of Chile. ALMA will be composed of 66 high precision antennas with two antenna sizes, 54 12-m antennas and 12 7-m antennas. By reconfiguring antennas, the array can achieve an angular resolution equivalent to an antenna diameter up to 18km using aperture synthesis technique. All antennas will be equipped with front end receivers covering the frequency range from 84 GHz to 950GHz. The signals received by all antennas are transmitted through very long optical fibers to a large scale digital correlator which can process the broad band signal of 16GHz/baseline with 4096 spectral channels in real time. With unprecedented angular resolution and spectral line capabilities, ALMA is expected to enable transformational research in various areas of millimeter and submillimeter astronomy, such as detailed structure of proto-planetary disks, detection of high redshift galaxies, and evolution of matter in the Universe. At present more than 30 antennas are located at the 5000m plateau in northern Chile, a part of which are used for the science verification and the initial science operation. The general view and current status of the ALMA project will be reported with a special emphasis on antenna and propagation topics. This document gives formatting instructions for authors preparing manuscripts for ISAP2012. The authors must follow the instructions given in this document. Entire document should be in Times New Roman Font with single space.
Study of high-resolution satellite geoid and gravity anomaly data over the Bay of Bengal

Geoid and gravity anomalies derived from satellite altimetry are gradually gaining importance in marine geo-scientific investigations. Very high-resolution gravity database generated from Seasat, Geosat GM, ERS-1 and TOPEX/POSEIDON altimeters data of the northern Indian Ocean, has been used in the preparation of geoid and free-air gravity maps. In the present work, we have investigated various products of satellite data of the Bay of Bengal, thereby correlated to known plate tectonic feature (Sunda subduction zone), volcanic traces (Ninetyeast and 85°E ridges) and continental margin features. Besides, Swatch of No Ground and modified continental slope due to sediment discharge from East Coast rivers are some of the finer structures observed in free-air gravity anomaly data. Furthermore, the data are compared with ship-track gravity anomalies along 14.64°N lat. for their consistencies and interpreted under the constraints of seismic results for understanding the evolution of structural features of the region. Two different wavelength (100-200 and 200-500 km) components derived from satellite gravity anomaly data have been studied to explain the geophysical anomalies associated with the Ninetyeast and 85°E ridges and two isolated structures in terms of isostatic compensation due to their loads with respect to the already evolved lithosphere. It is found that the magnitude of the flexure at crust-mantle boundary beneath the 85°E Ridge and structural high close to the margin is relatively higher than that of the Ninetyeast Ridge.
Design Aspects of an Active Electromagnetic Suspension System for Automotive Applications

This paper is concerned with the design aspects of an active electromagnet suspension system for automotive applications which combines a brushless tubular permanent magnet actuator (TPMA) with a passive spring. This system provides for additional stability and safety by performing active roll and pitch control during cornering and braking. Furthermore, elimination of the road irregularities is possible, hence passenger drive comfort is increased. Based upon measurements, static and dynamic specifications of the actuator are derived. The electro magnetic suspension is installed on a quarter car test setup, and the improved performance using roll control is measured and compared to a commercial passive system. An alternative design using a slotless external magnet tubular actuator is proposed which fulfills the derived performance, thermal and volume specifications.
@Western Scholarship@Western

: Protein phosphorylation is the most abundant post-translational modi ﬁ cation in cells. Src homology 2 (SH2) domains speci ﬁ cally recognize phosphorylated tyrosine (pTyr) residues to mediate signaling cascades. A conserved pocket in the SH2 domain binds the pTyr side chain and the EF and BG loops determine binding speci ﬁ city. By using large phage-displayed libraries, we engineered the EF and BG loops of the Fyn SH2 domain to alter speci ﬁ city. Engineered SH2 variants exhibited distinct speci ﬁ city pro ﬁ les and were able to bind pTyr sites on the epidermal growth factor receptor, which were not recognized by the wild-type Fyn SH2 domain. Furthermore, mass spectrometry showed that SH2 variants with additional mutations in the pTyr-binding pocket that enhanced af ﬁ nity were highly effective for enrichment of diverse pTyr peptides within the human proteome. These results showed that engineering of the EF and BG loops could be used to tailor SH2 domain speci ﬁ city, and SH2 variants with diverse speci ﬁ cities and high af ﬁ nities for pTyr residues enabled more comprehensive analysis of the human phosphoproteome. Statement : Src Homology 2 (SH2) domains are modular domains that recognize phosphorylated tyrosine embedded in proteins, transducing these post-translational modi ﬁ cations into cellular responses. Here we used phage display to engineer hundreds of SH2 domain variants with altered binding speci-ﬁ cities and enhanced af ﬁ nities, which enabled ef ﬁ cient and differential enrichment of the human phos-phoproteome for analysis by mass spectrometry. These engineered SH2 domain variants will be useful tools for elucidating the molecular determinants governing SH2 domains binding speci ﬁ city and for enhancing analysis and understanding of the human phosphoproteome.
A Wide Scope Modeling to Reconstruct Urban Scene

3D model of city is one of most important and valuable in many areas, such as civil engineering, traffic engineering, education, amusement. Such models describes the shape, textures and locations of constructions i.e. buildings, bridges so on. Those models are made manually through CG modeling tools usually, hence, enormous costs must be paid and restricts a utilization of those models strongly. A more efficient or automated way must be considered to validate those models. One of promising way is to apply vision techniques, such as shape from stereo, motion. Especially, shape from motion analysis is efficient, as objects are existing widely, and it's easy to observe through vehicle running on a road. In this paper, we propose a method to modeling of city scene. We aim at sufficiently describe constructions, by their surface's depth from a road, textures, and locations. The technique we propose is based on Extended EPI (Epipolar Plane Image) that was improved over EPI that had been proposed by Bolles. The output of our method is like a range image with texture, along with the observer's locus Le. the road that run through. We name this expression as "depth-map". The set depth map apart from general range data is that depth-map has range from camera path. So depth-map has structure of data like a belt, and there are three data each upper, left and right that cope with one street. Conbining these depth-map and digital map, leads to many applications such as VR, walk-through systems.
Interaction between surface and groundwater in the flooding of riparian wetlands: Biebrza wetlands case study.

The Biebrza Wetlands is located in a marginal-ice valley in north-eastern Poland and covers some 195 000 hectares in area. The most characteristic feature of the Biebrza River is flooding, which appears there almost every year. In the period 1999-2002, investigation of the flooding phenomena was carried out in order to understand the main processes involved in the inundation of the river valley and to combine them with characteristic vegetation patterns. The investigation used Landsat images, a hydrodynamic 1-D model of river and flood plain flow, and chemical analysis of flood waters. It was shown that river water is responsible for inundation of part of the valley only. Other parts of the valley were inundated by groundwater seepage or in situ snow melt. These observations agree with qualitative descriptions of water sources for the particular plant communities.
TITLE: Role of leptin in the association between body adiposity and persistent asthma: a longitudinal study

Objective: Obesity is a likely risk factor for asthma. However, underlying mechanisms by which obesity affects asthma activity remain poorly understood. We aimed to investigate the role of leptin, an adipocyte-derived pro-inflammatory protein, as a mediator in the association between body adiposity (assessed using body mass index (BMI), waist circumference (WC), and body fat percentage (BFP)) and persistent asthma. Methods: A causal approach to mediation analysis was used to disentangle total and direct effects, and the indirect effect mediated by leptin, using data from the French prospective EGEA study (baseline: 2003–2007; follow-up: 2011–2013; mean follow-up time: 7 years). 331 participants with current asthma at baseline were included. Results: Per 1-SD increment in BMI, WC and BFP, the adjusted odds ratios (ORs) of the total effect were 1.59 (95% CI: 0.95, 2.97), 2.06 (1.06–4.00), and 3.25 (1.01– 9.41), respectively; the ORs of the indirect effect mediated by leptin were 1.68 (1.09, 2.46), 1.55 (0.99–2.57), and 1.99 (0.94–4.83), respectively. Conclusions: Leptin partly (>60%) mediated the association between high body adiposity and persistent asthma over time. Using a newly developed analytic approach, this longitudinal study brought new insight into one mechanism by which obesity may affect asthma activity.
A computational predicting of possible inhibitors of the main SARS-CoV-2 protease found in Algerian herbal medicines

COVID-19 is a zoonotic viral disease caused by the SARS-CoV-2 virus. Its abrupt outbreak has caused a tremendous challenge to public health systems due to the rapid spread of the virus. In this sense, a great deal of work has been focused on finding substances from herbal plants to be used against this virus. In order to investigate the molecular interactions between natural metabolites from Algerian herbal plants and the SARS-CoV-2 protease Mpro, computational docking and molecular dynamics were used, also the drug likeness degree and in silico ADMET prediction were carried out in this study. warfarin and catalponol preferentially binds to a pocket of the SARS-Cov-2 Mpro active site that is made up of residues His 41 to Glu 166 and Leu 27 to His 163 with a relatively low binding energy of -7.1 and -6.6 kcal/mol respectively. Dynamic molecular assay further established that only warfarin managed to stay in the active site. The results suggest that warfarin may be an interesting candidate for development as a medical treatment of COVID-19 and more research is proposed, without disregarding its toxicity which deserves to be well studied.