Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review on Small Power Rating PV Inverter Topologies and Smart PV Inverters

The two most critical deciding factors for power consumption are energy efficiency and cost. Power electronic circuits are widely used and play an important role in achieving high efficiency in power distribution to customers and power transfer from source to load. Furthermore, solar energy is abundant, sustainable, and pollution-free in nature. Power electronic circuits are used in high-power applications with voltages ranging from a few millivolts to thousands of volts and wattages ranging from a few mW to megawatts. This paper examines a variety of inverter topologies and their modeling, as well as a comparison of single-stage and multi-stage/inverter topologies depending on the application. The main aim of control techniques is to keep Total Harmonic Distortion (THD) to a minimum and the switching frequency within the permissible range so that inverters for renewable energy sources, electric vehicles, uninterruptible power supply (UPS) systems, and hybrid energy storage systems can work efficiently. Plug-and-play, adaptability, self-awareness, and other features should all be included in a smart inverter. Based on the findings of this comparative analysis, selection criteria are established. This comparative analysis can be used to develop selection criteria for choosing inverter circuits for the various applications described in this paper.
Cover Crop Impacts on Water Dynamics and Yields in Dryland Wheat Systems on the Colorado Plateau

On the semiarid Colorado Plateau, dryland farmers are challenged by degraded soils and unreliable precipitation. While cover crops have been shown to support soil fertility, control erosion, and enhance in soil water capture, they also use limited soil water and, thus, may impact cash crop productivity in dryland systems. Most literature on cover crops comes from relatively humid climates, where yield penalties due to cover crops may be less pronounced. Two field trials were conducted in Southwestern Colorado to assess the short-term viability of cover crops in dryland systems in this region. The effect of cover crops on subsequent winter wheat (Triticum aestivum L.) yield ranged from a decrease of 78% to an increase of 13%, depending on the amount of cover crop biomass produced in the previous year. Cover crop biomass was inversely correlated with soil nitrate levels and soil water storage at wheat planting, which decreased by 0.39 mg kg−1 and 10 mm, respectively, per 1000 kg ha−1 of cover crop biomass produced. Less available soil water and immobilized N therefore appeared to contribute to wheat yield reductions. These impacts are particularly important for semiarid environments, where decomposition of residue is water-limited and soil water recharge depends on unpredictable precipitation patterns.
Thermal Inertia Characterization of Multilayer Lightweight Walls: Numerical Analysis and Experimental Validation

The thermal inertia properties of construction elements have gained a great deal of importance in building design over the last few years. Many investigations have shown that this is the key factor to improve energy efficiency and obtain optimal comfort conditions in buildings. However, experimental tests are expensive and time consuming and the development of new products requires shorter analysis times. In this sense, the goal of this research is to analyze the thermal behavior of a wall made up of lightweight concrete blocks covered with layers of insulating materials in steady- and transient-state conditions. For this, numerical and experimental studies were done, taking outdoor temperature and relative humidity as a function of time into account. Furthermore, multi-criteria optimization based on the design of the experimental methodology is used to minimize errors in thermal material properties and to understand the main parameters that influence the numerical simulation of thermal inertia. Numerical Finite Element Models (FEM) will take conduction, convection and radiation phenomena in the recesses of lightweight concrete blocks into account, as well as the film conditions established in the UNE-EN ISO 6946 standard. Finally, the numerical ISO-13786 standard and the experimental results are compared in terms of wall thermal transmittance, thermal flux, and temperature evolution, as well as the dynamic thermal inertia parameters, showing a good agreement in some cases, allowing builders, architects, and engineers to develop new construction elements in a short time with the new proposed methodology.
Remote Sensing Road Extraction by Road Segmentation Network

Road extraction from remote sensing images has attracted much attention in geospatial applications. However, the existing methods do not accurately identify the connectivity of the road. The identification of the road pixels may be interfered with by the abundant ground such as buildings, trees, and shadows. The objective of this paper is to enhance context and strip features of the road by designing UNet-like architecture. The overall method first enhances the context characteristics in the segmentation step and then maintains the stripe characteristics in a refinement step. The segmentation step exploits an attention mechanism to enhance the context information between the adjacent layers. To obtain the strip features of the road, the refinement step introduces the strip pooling in a refinement network to restore the long distance dependent information of the road. Extensive comparative experiments demonstrate that the proposed method outperforms other methods, achieving an overall accuracy of 98.25% on the DeepGlobe dataset, and 97.68% on the Massachusetts dataset.
Midwest US Croplands Determine Model Divergence in North American Carbon Fluxes

Large uncertainties in North American terrestrial carbon fluxes hinder regional climate projections. Terrestrial biosphere models (TBMs), the essential tools for understanding continental‐scale carbon cycle, diverge on whether temperate forests or croplands dominate carbon uptake in North America. Evidence from novel photosynthetic proxies, such as those based on chlorophyll fluorescence, has cast doubt on the “weak cropland, strong forest” carbon uptake patterns simulated by most TBMs. However, no systematic evaluation of TBMs has yet been attempted to pin down space‐time patterns that are most consistent with regional CO2 observational constraints. Here, we leverage atmospheric CO2 observations and satellite‐observed photosynthetic proxies to understand emergent space‐time patterns in North American carbon fluxes from a large suite of TBMs and data‐driven models. To do so, we evaluate how well the atmospheric signals resulting from carbon flux estimates reproduce the space‐time variability in atmospheric CO2, as is observed by a network of continuous‐monitoring towers over North America. Models with gross or net carbon fluxes that are consistent with the observed CO2 variability share a salient feature of growing‐season carbon uptake in Midwest US croplands. Conversely, the remaining models place most growing‐season uptake in boreal or temperate forests. Differences in model explanatory power depend mainly on the simulated annual cycles of cropland uptake—especially, the timing of peak uptake—rather than the distribution of annual mean fluxes across biomes. Our results suggest that improved model representation of cropland phenology is crucial to robust, policy‐relevant estimation of North American carbon exchange.
The impact of digital transformation on the economic security of Ukraine

The article is devoted to the study of the impact of digital transformations on the economic security of Ukraine. The article examines the essence of the digital economy, defines the features of the functioning of the economy in the context of digitalization. Based on the indicators of the international consulting company McKinsey and the World Bank, the authors analyzed the impact of digitalization on the economy, highlighted the main advantages and disadvantages of functioning in the digital economy, systematized the problems of economic security in the digital economy and highlighted systemic, structural and industry problems, as well as businesses and individuals. The main issues of economic security include the problems of "digital inequality", the lack of their own element base, changes in the labour market, industrial espionage, manipulation of personal data and others. The authors also identified a tool for monitoring the development of a networked digital society – the Network Readiness Index. The practical contribution was the development of a mechanism to smooth the impact of digital transformation on the economic security of Ukraine.
Information and information resources in COVID-19: Awareness, control, and prevention

This study was conducted with the aim of evaluating the role of information and information resources in the awareness, control, and prevention of COVID-19. This study was a descriptive-analytical survey in which 450 participants were selected for the study. The data collection instrument was a researcher-made questionnaire. Descriptive and inferential statistics were used to analyze the data through SPSS. The findings show that a wide range of mass media has become well known as information resources for COVID-19. Other findings indicate a significant statistical difference in the rate of using information resources during COVID-19 based on age and gender; however, this difference is not significant regarding the reliability of information resources with regard to age and gender. Health information has an undisputable role in the prevention and control of pandemic diseases such as COVID-19. Providing accurate, reliable, and evidence-based information in a timely manner for the use of resources and information channels related to COVID-19 can be a fast and low-cost strategic approach in confronting this disease.
Investigation of dependences of the morphological composition of body and amino acid composition of trout meat proteins (Oncorhynchus mykiss) on levels of the energy value of feeds

The article studies the effect of using complete compound feeds with different energy levels on the morphological composition of the body and the amino acid composition of trout meat proteins. The experiment aimed to establish the influence of different levels of energy nutrition of commercial rainbow trout on the morphological composition of their body and the amino acid composition of meat proteins. For this purpose, five experimental groups were formed using the analog method. The study lasted 210 days and was divided into two periods: comparative (10 days) and main (200 days). During the comparative period, the study fish consumed compound feed of the control group. During the main period, the energy level in experimental compound feeds for different experimental trout groups ranged from 16 to 20 mJ per 1 kg. It was found that with an increase in the mass of two-year-old trout, the mass of muscle tissue and the yield of edible parts probably increases. Feeding fish with an increased amount of metabolic energy (20 mJ.kg-1) resulted in a significant change in the weight of internal organs, including the heart, liver, and kidneys. Increasing the metabolic energy in the compound feeds of fish from 18 mJ.kg-1 to 19 – 20 mJ.kg-1 leads to a significant increase in bowel mass by 13.3 – 5.0%. An increase in the level of metabolic energy in rainbow trout diets from 18 mJ.kg-1 to 19 – 20 mJ.kg-1 contributed to a likely increase in the methionine content in meat. A similar pattern was observed for the tryptophan content. It was found that the content of most essential amino acids in the protein of rainbow trout meat exceeds the corresponding values in the "ideal" protein, except for the content of isoleucine and leucine, which refers them to limiting amino acids.
Dynamic modelling and analysis of smart carbon nanotube-based hybrid composite beams: Analytical and finite element study

In this work, the carbon nanotube-based hybrid carbon fibre-reinforced composite smart beam constraining the layer of an active constrained layer damping treatment is investigated using an in-house finite element model based on first-order shear deformation theory. The effect of in-plane and transverse-plane actuation of the integrated active constrained layer damping treatment layer on the damping characteristics of the novel smart cantilever hybrid carbon fibre-reinforced composite beam is considered. The parameters affecting the damping characteristics of the hybrid carbon fibre-reinforced composite substrate beam such as the volume fraction of both carbon nanotubes and carbon fibre, and the aspect ratio are also studied. Besides, the micromechanical model based on the mechanics of materials approach is developed to estimate the effective elastic coefficient of novel hybrid carbon fibre-reinforced composite lamina. The effective properties of hybrid carbon fibre-reinforced composite are predicted quantitatively by considering non-bonded interaction formed between carbon nanotubes and the polymer matrix. It is revealed that due to the incorporation of carbon nanotubes into the epoxy matrix, the effective longitudinal, transverse and shear properties of the hybrid carbon fibre-reinforced composite lamina are significantly enhanced. Our outcomes explore that the damping performance of the laminated hybrid carbon fibre-reinforced composite smart beam considering the incorporation of carbon nanotubes shows substantial improvement as compared to the base composite. To bring more clarity, the quantitative relative performance of hybrid carbon fibre-reinforced composite and base composite is presented. Our fundamental analysis sheds the light on the opportunities of developing efficient, high-performance and lightweight carbon nanotubes-based micro-electro-mechanical systems smart structures such as sensors, actuators and distributors.
Willingness to Vaccinate against COVID-19: The Role of Assumptions on the World’s Orderliness and Positivity

Abstract This research investigates why people refuse the COVID-19 vaccine despite medical argumentation and dangerousCOVID-19 consequences. As the global pandemic development is beyond each person’s control, we predicted that two basic assumptions about the world, namely its order and positivity, would play an important role. Two studies on the Polish population took place in December 2020 and January 2021. The most interesting finding was that in both studies, belief in world orderliness negatively moderated, i.e., hampered, the positive relationship between belief in the world’s positivity and willingness to vaccinate. It seems that the COVID-19 vaccination might evoke a feeling of disruption in biological and social natural functioning. If we generalize, any idea undermining our habits and shared beliefs is the more challenged and opposed we have strong faith in the world as an ordered and predictable reality. Believing in the world’s positivity may even aggregate this attitude. In discussing these results, we propose how to introduce new ideas or innovative products to consumers.
Design an Infusion Device Analyzer with Flow Rate Parameters using Photodiode Sensor

Infusion devices are the basis for primary health care, that is to provide medicine, nutrition, and hydration to patients. One of the infusion devices is a syringe pump and an infusion pump. This device is very important to assist the volume and flow that enters the patient's body, especially in situations related to neonatology or cancer treatment. Therefore, a comparison tool is needed to see whether the equipment is used or not. The purpose of this research is to make an infusion device analyzer (IDA) design with a flow rate parameter. The contribution of this research is that the tool can calculate the correct value of the flow rate that comes out of the infusion pump and syringe pump. The water released by the infusion pump or syringe pump will be converted into droplets which are then detected by the sensor. This tool uses an infrared sensor and a photodiode. The results obtained by the sensor will come by Arduino nano and code it to the 16x2 Character Liquid Crystal Display (LCD) and can be stored on an SD Card so that it can be analyzed further. In setting the flow rate for the syringe pump of 100 mL / hour, the error value is 3.9, 50 ml / hour 0.02, 20 mL / hour 0.378, 10 mL / hour 0.048, and 5 mL / hour 0.01. The results show that the average error of the syringe pump performance read by the module is 0.87. The results obtained from this study can be implemented for the calibration of the infusion pump and the syringe pump so that it can be determined whether the device is suitable or not
Remaining Useful Life Prediction of Cutting Tools Using an Inverse Gaussian Process Model

In manufacturing, cutting tools gradually wear out during the cutting process and decrease in cutting precision. A cutting tool has to be replaced if its degradation exceeds a certain threshold, which is determined by the required cutting precision. To effectively schedule production and maintenance actions, it is vital to model the wear process of cutting tools and predict their remaining useful life (RUL). However, it is difficult to determine the RUL of cutting tools with cutting precision as a failure criterion, as cutting precision is not directly measurable. This paper proposed a RUL prediction method for a cutting tool, developed based on a degradation model, with the roughness of the cutting surface as a failure criterion. The surface roughness was linked to the wearing process of a cutting tool through a random threshold, and accounts for the impact of the dynamic working environment and variable materials of working pieces. The wear process is modeled using a random-effects inverse Gaussian (IG) process. The degradation rate is assumed to be unit-specific, considering the dynamic wear mechanism and a heterogeneous population. To adaptively update the model parameters for online RUL prediction, an expectation–maximization (EM) algorithm has been developed. The proposed method is illustrated using an example study. The experiments were performed on specimens of 7109 aluminum alloy by milling in the normalized state. The results reveal that the proposed method effectively evaluates the RUL of cutting tools according to the specified surface roughness, therefore improving cutting quality and efficiency.
Using TDFragMapper: a high-performance tool to assess experimental parameters in top-down proteomics

Here we present a new software-tool for visualizing fragment ions and sequence coverage of intact proteins in top-down mass spectrometry. TDFragMapper combines the data arising from multiple and diverse tandem mass spectrometry experiments of intact proteins. Our tool maps fragment ions onto the protein backbone sequence and allows for a rapid comparison of the results obtained when varying experimental parameters in tandem mass spectrometry (MS/MS) experiments of intact proteins: fragmentation method, selected precursor charge state, activation level, technical replicate. In summary, TDFragMapper is a unique software-tool that allows an easy selection of the best experimental conditions leading to the highest confidence in intact protein sequence identification.
Real-Time Sentiment Analysis of Twitter Data

: The Internet has been a promising forum for online learning, exchanges of ideas and sharing views with the emerging social networking era and its development. Social media provide a large quantity of feeling information in emails, blogs, status updates and posts. This article uses the most common twitter micro blogging site. The Twitter emotion analysis is an extension of Twitter (tweet) feel analysis to extract views and feelings from users. The key aim is to examine how methods of text analysis may be used to delve through some of the information in a series of articles that concentrate on tweet language patterns, tweet twitter volumes. Experimental assessments demonstrate the efficiency and precision of the proposed machine classificators. Python implements the proposed algorithm. The paper focuses about how a feeling study has been designed and a large number of tweets extracted. In this development prototyping is used. Results characterise the view of consumers by tweets as 3 types of positive and 3 types of hate as seen in a pie chart and in an HTML tab.
Bullying at Workplace affecting Motivation and Organizational Citizenship Behavior of Branch Banking Staff in Major Cities of Pakistan

Banking Sector has been considered as one of the top career priority choices of University Graduates in Pakistan over the past decades. But since the Bank’s got privatized branch banking has mainly observed as one of the most hatred areas of deployment for any employee. The objective of this investigation was to analyze that Bullying affects motivation and employees consider it as part of the routine and stays motivated and committed to their organization, which is possible because of several reasons in which one could be fewer job opportunities and financial issues. Results of this investigation will help the Banks management for decision making in order to improve their working culture and will also trigger future researchers to further investigate these findings. We took Branch Banking Staff from all banks across Pakistan as a population and could manage a sample size of 271 (validated as 248) from different cities and brands of Banks under the snowball sampling technique by using cross sectional research design. Data was collected through structured questionnaires via google forms with help of social media forums. Data Analysis was conducted through SPSS and in initial analysis, we didn’t find any symptoms of multicollinearity in our data therefore the linear relationship between Motivation (IV) and OCB (DV) was found. Further Pearson correlation reflected a significant and positive relationship between motivation and ocb. 71% variation was explained through the regression model of motivation and ocb with positive relationship, whereas Moderation of Bullying was proved with 35% variation explained.
The reaction of H + CH2O: addition vs. abstraction

The reaction of H and CH2O, which plays an important role in combustion of hydrocarbons and alcohols, was studied in details using high-level coupled cluster calculations to construct a potential energy surface. This is followed by E,J-resolved master equation analyses to obtain rate constants as a function of both temperature and pressure. It is observed for the first time that the H-addition mechanism plays only a minor role (<10%), and can be neglected at high temperature in combustion environments. The direct H-abstraction pathway exclusively yields H2 plus CHO. The calculated rate constants from the first principles agree reasonably well with experiments for a wide range of temperatures between 250 and 3500 K. When there are no experimental data, we provide highly accurate rate constants for kinetics modelling. GRAPHICAL ABSTRACT