Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multimodal Framework for Video Caption Generation

Video captioning is a highly challenging computer vision task that automatically describes the video clips using natural language sentences with a clear understanding of the embedded semantics. In this work, a video caption generation framework consisting of discrete wavelet convolutional neural architecture along with multimodal feature attention is proposed. Here global, contextual and temporal features in the video frames are taken into account and separate attention networks are integrated into the visual attention predictor network to capture multiple attentions from these features. These attended features with textual attention are employed in the visual-to-text translator for caption generation. The experiments are conducted on two benchmark video captioning datasets - MSVD and MSR-VTT. The results prove an improved performance of the method with a CIDEr score of 91.7 and 52.2, for the aforementioned datasets, respectively.
Self-Supervised Divide-and-Conquer Generative Adversarial Network for Classification of Hyperspectral Images

Generative adversarial network (GAN) has been rapidly developed because of its powerful generating ability. However, imbalanced class distribution of hyperspectral images (HSIs) easily causes mode collapse in GAN. Moreover, limited training samples in HSIs restrict the generating ability of GAN. These issues may further deteriorate the classification performance of the discriminator. To conquer these issues, a novel self-supervised divide-and-conquer (SDC)-GAN is proposed for HSI classification. In SDC-GAN, a pretext cluster task with an encoder–decoder architecture is designed by leveraging abundant unlabeled samples. By transferring the learned cluster representation from the cluster task, limited labeled samples are divided effectively in the downstream classification. According to the division of clustering, SDC-GAN constructs a generic and several specific branches for both the generator and discriminator. The generator generates all- and specific-class samples by using the generic and specific branches separately and combines them adaptively. It can weaken the generation preference for the classes with large sample sizes and alleviate the mode collapse problem. Meanwhile, the classification ability of the discriminator is improved by integrating the judgment of specific branches into the generic branch. Experimental results show that SDC-GAN achieves competitive results for HSI classification compared with several state-of-the-art methods.
First-principles study of the structural and mechanical properties of substitution-doped Mg_5Si_6

The structural, mechanical, and electronic properties of the metastable β″ -Mg_5Si_6 phase doped with six elements were calculated by using DFT. Our calculated lattice constants of pristine Mg_5Si_6 are in good agreement with the experimental and other theoretical values. It is found that all doping elements preferentially occupy the Mg site in the Mg_5Si_6 structure. The independent elastic constants of Mg_9Si_12X (X = Li, Ca, Al, Ge, Cu, Zn) structures are calculated and a series of mechanical moduli (bulk modulus, shear modulus, Young’s modulus, and Poisson’s ratio) are determined. The Debye temperature and the anisotropy of doped structures are also investigated. Doping Ge can significantly improve the toughness of Mg_9Si_12Ge structure, and the doping Li and Zn atoms are beneficial to enhance the strength and hardness of Mg_9Si_12X structures. Finally, the type of interatomic bonding and charge transfer in the materials are determined by the electronic structure analysis. Electron orbital hybridization will change the types of bonds between nearby atoms and, in turn, the mechanical properties of Mg_9Si_12X structures. Graphical abstract Effect of element doping on mechanical properties of β″ -Mg_5Si_6 phase in Al–Mg–Si alloy.
A Noise-Immune Motion Sensing Technique with Low-IF CW Radars

The accuracy of motion sensing with a Continuous-Wave (CW) radar is subject to noises. The low intermediate-frequency (IF) CW radar tackles the impact of the flicker noise. However, it still suffers from other noise components which mainly include the Additive White Noise (AWN) in short-range sensing. This paper proposes a novel Noise-Immune Motion Sensing (NIMS) technique for accurate displacement motion sensing against the AWN in low-IF CW radars. It works by dividing the IF signals sequentially into segments, extracting phases from the spectra of the sequential segments, and combing the phases to reconstruct the phase evolution for the target displacement motion. The theory and working principle are presented. Simulation and experimental results show that the Signal-to-Noise Ratio (SNR) is improved for about 20.1 dB with the proposed technique and the mechanical displacement motion is accurately obtained with the root-mean-square-error (RMSE) decreased by 99.2%.
Assessing Impact of Carbon-smart Farming Practices in Rice with Mobile Crowdsensing

Soil Organic Carbon (SOC) is an important constituent of measures that govern soil health. Sequestration of carbon in the soil is a major growing focus of sustainability initiatives world-wide to reduce carbon footprint in the atmosphere and counter the effects of global warming. India is the world's largest rice producer by area, estimated at about 44.0 million hectares. Sustainable crop and land management practices play a crucial role in enabling more carbon to be absorbed by the soils and potentially generate carbon-credits which could be traded. Mobile Crowdsensing (MCS) can play a pivotal role in this by serving as a channel to acquire the data and generate insights to take in-season decisions and get a compliance-view on carbon-smartness of farm-operations. We present our study to digitally assess through crowdsensing the carbon smartness and stock of different agricultural fields in the Cauvery Delta Zone (CDZ) of Tamil Nadu, India. A set of ten selected farmers were divided into two observation groups CSCP-1 and CSCP-2. CSCP-1 followed organic practices and CSCP-2 followed integrated nutrient management practices with inorganic components. AI in the form of imaging with deep learning was used to validate across both the groups the granularity of operations like tillage that have a significant impact on the level of carbon sequestration. A process model approach was used to simulate the temporal changes in soil organic carbon (SOC) for all the farmers to serve as a continuous feedback on carbon footprint in response to operations reported. SOC levels were obtained for the entire rice season including pre-sowing and post-harvest periods where CSCP-1 farmers sequestrated more carbon than CSCP-2 farmers in general. Results showed an increase of 0.51 tC/ha in carbon stock for CSCP-1 farmers at the end of the season while the corresponding increase was 0.23 tC/ha for CSCP-2 farmers. Within the CSCP-2 group that adopted inorganic practices, farmers following intensive tillage sequestrated even less soil organic carbon at 0.17 tC/ha. MCS coupled with AI and process models therefore can help evolve a real-time carbon-smartness profile for every farm towards better strategic and management decisions.
A Systematic Review On Various TeleHealth Techniques In Recent Times

In the recent years of technological development, the implementation of IoT has increased drastically. Automations have molded the advanced way of life of each person and thus brought about a wide scope of uses for IoT in present times. IoT has also influenced medical technologies and advancement. Many models have already been proposed to monitor a patient. Nonetheless, these advances additionally experience the ill effects of restrictive weaknesses, like security, insufficient battery duration, multi-patient checking, and many more. This survey features some innovative models that provide solutions for different problems. We have also tried to provide the merits and demerits of the reviewed works to uphold further advancement.
Advanced 1D models for fluid-structure interaction of viscous flows in thin-walled cylinders

The Carrera Unified Formulation (CUF) is well established in the literature for over a decade. CUF was originally developed in the framework of solid mechanics for the analysis of plate and shell structures [1] and related multi-field problems [2]. Recently, CUF was successfully extended to the analysis of beams [3,4] and its capability to deal with solid and thin-walled structures as well as with non-homogeneous anisotropic materials has been widely demonstrated. According to CUF, the problem solution (e.g., the 3D displacement field in the case of displacement-based finite element structural formulations) is expressed as an arbitrary expansion series of the generalized unknowns, which lay either on a curve in the case of 1D theories or on a reference surface in the case of 2D theories. By exploiting a compact notation for the governing equations in terms of fundamental nuclei, advanced theories can be formulated in an automatic manner and without the need for ad-hoc assumptions. In the present work, CUF is extended to computational fluid-dynamics [5] and used for the formulation of advanced, hierarchical 1D theories for the analysis of Stokes flows. First, the accuracy and the numerical efficiency of the present methodology in dealing with laminar, incompressible, viscous, steady flows with arbitrary velocity/pressure fields is established by comparisons with state-of-the-art finite volumes tools and analytical solutions. The 1D CUF fluid-mechanics models are subsequently coupled with 1D CUF structural theories for Fluid-Structure Interaction (FSI) analysis of flows into thin-walled deformable cylinders. The enhanced capabilities of the devised CUF/CUF FSI tool are widely supported by the results, which provide good confidence for future research in this direction
Design of Wideband Continuous Class-F Power Amplifier Using Low Pass Matching Technique and Harmonic Tuning Network

Many countries have allocated new frequency bands for fifth generation (5G) communication systems. In this paper, a wideband continuous class-F (CCF) radio frequency power amplifier (RFPA) is presented for the new 5G frequency band, from 3.3 GHz to 4.3 GHz using a 10 W Cree CGH40100F device. A unique wideband RFPA design approach for the output matching network (OMN) is also presented by applying a harmonic tuning network (HTN) for the harmonics and a low pass matching technique (LPMT) for the fundamental frequency. The RFPA is fabricated, and promising measurement results show a drain efficiency of 55.9% to 65.3% is achieved at an output power of 40 dBm (± 0.3 dBm) over a frequency range of 3.3 GHz to 4.3 GHz. A two-tone signal with a 10 MHz spacing was also applied to investigate the linearity of the RFPA.
An Agent-Based Artificial Market Model for Studying the Bitcoin Trading

The objective of this paper is to simulate the trading of the currency pair BTC/USD, investigating through the theory of the genetic algorithms the best sets of trading strategies, simulating through a realistic order book the bitcoin price formation, and reproducing a bitcoin price series that exhibits some stylized facts found in real-time price series. In this artificial market model two kinds of agents, Chartists and Random traders, perform trading. Chartists trade through the application of trading rules. Specifically, a part of Chartists trades applying the best sets of trading rules selected by a genetic algorithm that simulates a trading system, based on four technical analysis indicators, searching for parameters of each indicator that guarantee the highest profits in the training period; the remaining part trades applying trading rules choosing their parameters in a random way. On the contrary random trader’s trade without applying any trading strategy, issuing in a random way sell or buy orders. Results show that the best sets of rules found to guarantee the highest profits both in the training and in the testing periods, and perform well also in the artificial market model where the Chartists who adopt the best sets of trading rules are able to achieve higher profits.
Advanced TCAD Simulation of Tunnel Oxide Degradation for EEPROM Applications

In this paper we have simulated dynamic Constant Voltage Stress (CVS) and subsequent degradation of MOS capacitor representative of Non-Volatile Memory (NVM) devices. Oxide degradation was modelled through trap defects generation at SiO2/Si interface and SiO2 bulk. A very good agreement was obtained between simulation and experimental data.
A Relational Retrieval Model for Industrial Production Systems

Industrial big data analysis is an important approach to explore the essence of industrial production processes. Traditional keyword retrieval and semantic retrieval are difficult to adapt to the industrial manufacturing systems with the characteristics of strong coupling of business associations. Therefore, according to the characteristics of the association in the industrial production system, an association retrieval model for the industrial production system is proposed in this paper. Firstly, the industrial Internet of things system is analyzed and abstracted, and the key factors of correlation retrieval are derived; then through the analysis of the relationship between key factors, the invalid relationship is screened out, the correlation relationship with practical significance is obtained, and the establishment of the relevance retrieval model is completed. Finally, the feasibility and effectiveness of the association retrieval model are verified by actual industrial scene data.
Poly(ADP-Ribose) Polymerases in Plants and Their Human Counterparts: Parallels and Peculiarities

Poly(ADP-ribosyl)ation is a rapid and transient post-translational protein modification that was described first in mammalian cells. Activated by the sensing of DNA strand breaks, poly(ADP-ribose)polymerase1 (PARP1) transfers ADP-ribose units onto itself and other target proteins using NAD+ as a substrate. Subsequently, DNA damage responses and other cellular responses are initiated. In plants, poly(ADP-ribose) polymerases (PARPs) have also been implicated in responses to DNA damage. The Arabidopsis genome contains three canonical PARP genes, the nomenclature of which has been uncoordinated in the past. Albeit assumptions concerning the function and roles of PARP proteins in planta have often been inferred from homology and structural conservation between plant PARPs and their mammalian counterparts, plant-specific roles have become apparent. In particular, PARPs have been linked to stress responses of plants. A negative role under abiotic stress has been inferred from studies in which a genetic or, more commonly, pharmacological inhibition of PARP activity improved the performance of stressed plants; in response to pathogen-associated molecular patterns, a positive role has been suggested. However, reports have been inconsistent, and the effects of PARP inhibitors appear to be more robust than the genetic abolition of PARP gene expression, indicating the presence of alternative targets of those drugs. Collectively, recent evidence suggests a conditionality of stress-related phenotypes of parp mutants and calls for a reconsideration of PARP inhibitor studies on plants. This review critically summarizes our current understanding of poly(ADP-ribosylation) and PARP proteins in plants, highlighting similarities and differences to human PARPs, areas of controversy, and requirements for future studies.
To the possibility of experimental estimation of the diffusion spinodal position of binary mixture with LCST via pulsed heating method

In this paper, we present new data on non-stationary heating of a platinum wire probe, immersed in a binary partially-miscible liquid. A pressure value and a mass fraction of the a polymer in the mixture were the experimental parameters. The characteristic heating time was from 5 to 15 ms. The object of the research was the water/polypropylene glycol-425 (PPG-425) mixture having lower critical solution temperature (LCST). The position of the diffusion spinodal was estimated based on the obtained data on the liquid-liquid binodal in the framework of the Flory-Huggins approximation. An experimental technique to estimate the position of the spinodal of two-component mixtures with LCST on the scale of the mixture component ratio was developed. It was shown that the method of isobaric pulse heating can be used for this purpose. This representation is based on the threshold change in the heat transfer pattern when crossing the phase coexistence curve and the diffusion spinodal.
Hysteresis Modeling for Torque Control of an Elastomer Series Elastic Actuator

Recent research on humanoid robot actuators has shown that the use of series elastic actuators (SEAs) is necessary for accurate and robust torque control. Among the numerous implementations of an SEA, using a spring as an elastomer is considered to be the most suitable. However, a major disadvantage of this method in terms of torque control is the hysteresis of the elastomer. Although various hysteresis modeling methods have been studied to resolve the hysteresis problem of an elastomer, they are not sufficiently accurate to perform torque control. Therefore, we propose a hysteresis model and compensation method to estimate torque based on deformation for the hysteresis of an elastomer SEA to resolve the problems encountered in previous studies. Torque control is evaluated with the proposed hysteresis compensation method. Torque measurements obtained using the proposed hysteresis model improve the maximum error by up to 10% compared with that of Hooke's law, which has a maximum error of 25%. Torque control of an elastomer SEA can be performed with improved accuracy by using the proposed hysteresis model and compensation method.
Ergonomic Improvements to Agricultural Harvest Baskets to Reduce the Risk of Musculoskeletal Disorders among Farmers

Typical harvesting baskets (TB) are used in various agricultural workplaces; however, no study to date has reported their effect on the musculoskeletal system. Therefore, this study aimed to evaluate the effects of a novel basket with attached rotational handles (RHB) to help alleviate the work-related physical burden of farmers. We analyzed the surface electromyograms (EMGs) of seven muscles, evaluated the subjective discomfort levels and locally perceived discomfort (LPD) scores to investigate the discomfort in the whole body and seven hand muscles, respectively. The EMGs showed that muscle activity decreased in five muscles (flexor carpi ulnaris, extensor carpi radialis, lateral triceps, middle deltoid, and upper trapezius) and increased in two (biceps brachii and erector spinae) when the RHB was used (p < 0.05). The subjective discomfort score for the RHB decreased compared to that for TB (p < 0.001). The LPD scores also decreased, and the RHB and TB scores ranged from 1.25–1.40 and 3.1–3.25, respectively. The use of the RHB may prevent wrist bending, and reduce the activity of certain muscles while increasing the activity of other muscles. Therefore, it is necessary to conduct training and to evaluate the working posture while considering the affected muscles.
A Fully-Differential 146.6-157.4 GHz LNA Utilizing Back Gate Control to Adjust Gain in 22 nm FDSOI

This paper presents a fully-differential D-Band LNA with a measured variable peak gain of 9.0-18.0dB at 153.5 GHz over a 3 dB bandwidth of 10.8 GHz. The LNA consists of four capacitor neutralized Common-Source (CS) stages each matched with transmission lines. A simulated noise figure (NF) of 7.5 dB is achieved. Utilizing the back gate control of the 22 nm FDSOI technology enables a passive gain control of 9 dB while reducing the NF and compression point degradation and scaling the power consumption through optimized circuit biasing. The peak power consumption is less than 27.5mW and reduces to 17.5mW at minimum gain.
UAV Deployment Model in Aerial Monitoring Applications Using Camera Field of View

Unmanned aerial vehicles (UAVs) have advanced to the point where they can now be used for surveillance, delivery, agricultural, communication range expansion, and firefighting. Quality, capacity, robustness, energy efficiency, and reliability are all important factors in such deployments. Because UAVs have constraints in terms of communication range, storage, onboard power, and RF resources, their performance in ad-hoc networks suffers. Furthermore, due to the general structure of UAVs and deployment environments, path planning of the UAV while executing tasks assigned by a ground control station (GCS) is a challenging task. For surveillance missions, this paper provides a UAV-assisted communication model based on grid-based area decomposition and lawnmower-scanning approach. While designing the UAV trajectory, the area decomposition, height of the UAV, and path planning are chosen based on the UAV camera field of view (FoV), image aspect ratio, and communication quality. Deriving mathematical expressions is used to characterize the area and energy consumption. For the urban setting, a statistical propagation model is discussed to estimate path loss (PL), probability of bit-error-rate (BER), probability of line of sight (PLoS), and probability of system outage (Pout). The impact of various deployment settings on system performance is shown in numerical and simulation findings. The network designer can choose appropriate system resources using the analytical results of this work and by optimizing various parameters.
The Impact of Tax Control on Tax Revenues: An Aggregate Metric Analysis of the Case of Albania

Tax administration constantly monitors taxpayers through tax control to meet the legal obligations of the tax field. The main purpose of this function is to advise taxpayers and maintain operational control through continuous monitoring. Motivated by the importance of tax control, this paper aims to provide an empirical contribution to the analysis of tax control’s effects on the level of tax revenues. The database obtained in the analysis is both quantitative and qualitative according to the evidence of direct operational controls performed on businesses. The variables analysed are total tax revenues from value added tax (VAT), profit tax, personal income tax (PIT), withholding tax, etc.; tax control variables and macroeconomic variables. The methodology used in this paper is based on linear models with many variables as well as statistical tests to find the potential levels and equilibrium, elasticity and structural estimation of tax revenues. The findings of this paper provide a focused contribution on current issues for tax control policymakers, as well as other public entities, and they make an academic contribution to the legal and economic interpretation of the importance of tax control.