Miracle-dz/UniFAR-FTUs huggingface dataset:
Decent living gaps and energy needs around the world

In recent years, there has been growing interest in defining what exactly constitutes “decent living standards” (DLS)—the material underpinnings of human well-being. We assess the gaps in providing decent health, shelter, nutrition, socialization, and mobility within countries, across the world. Our results show that more people are deprived of DLS than are income-poor, even when numbers are measured against medium income poverty thresholds. We estimate the cumulative energy needs for building out new infrastructure to support DLS provision for all by 2040 to be about 290 EJ, which amounts to less than three-quarters of current annual global energy demand, at the final energy level. The annual energy requirements to support decent living for the global population after 2040 is estimated to be 156 EJ yr−1. Present average energy demand levels in most countries exceed hypothetical DLS energy needs. Nevertheless, the required rate of increase in energy to provide decent living for all in the coming two decades would be unprecedented for many countries. Greater attention to equity would significantly reduce the need for growth. The per capita energy requirement of different countries to meet the same DLS levels varies by up to a factor of four due to differences in climate, urbanization, diets, and transport infrastructure. Transport energy dominates energy for decent living worldwide, while housing requirements dominate upfront energy investment needs. This study supports the claim that the increase in energy provision poverty eradication does not, in itself, pose a threat to mitigating climate change at a global scale. Distinguishing energy for affluence from energy for decent living could provide a basis for defining equitable access to sustainable development in energy terms.
Development of Automotive Air-Conditioning System Test Rig for Hybrid Electric Vehicles

Introducing nanoparticles in liquid-based mixtures began to gain attention in various industries. This is supported by previous studies to improve the performance and provide energy saving for the system. Among its uses is in the VCRS and automotive air conditioning (AAC) system. The lubricant used in this system has the potential to have a good effect on the performance. Before testing the nanolubricant enhancement performance, an automotive air conditioning (AAC) system test rig based on hybrid electric vehicles (HEV) AC system has to be developed; therefore, this paper presented the development process of AAC test rig specific for the HEV. In order to analyze the performance, 11 thermocouples, a digital pressure gauge with data logger, and AC/DC power clamp were assembled and used. After that, the experiment was conducted with five different initial refrigerant charges and three different compressor speeds. This method was applied to both pure POE lubricant and SiO2/POE nanolubricant. Then, the heat absorbs, compressor work, and coefficient of performance (COP) were evaluated. The highest average COP for SiO2/POE nanolubricant was achieved at a 40 % duty cycle (2520 RPM) speed with a value of 2.84. The highest enhancement of the COP is 25.1% at 60% duty cycle (3180 RPM) speed with 160 grams of initial refrigerant charged an average enhancement of the COP is 13.16%.
Determination of Silybin in Blood Plasma Using High Performance Liquid Chromatography with Solid Phase Extraction

HPLC method with SPE preseparation step for determination of silybin, the main constituent of silymarin, in blood plasma was optimized. A C18 sorbent for preseparation from acidified samples was used. Diastereomers of silybin in unconjugated form or total silybin are resolved by the method, the detection limit being 2 nmol/l and the limit of quantification 5 nmol/l per diastereomer. The method is suitable for silybin monitoring in human plasma after administration of drugs or food additives based on silymarin.
Improved 3D fully convolutional network based on squeeze-excitation method for liver segmentation

Due to the variable shape and size of the liver in abdominal CT and the complex surrounding tissues, accurate liver segmentation in CT remains a challenge. In the field of two-dimensional image analysis, the squeeze-excitation (SE) method effectively adjusts the input feature information. Our work is based on SE method to obtain feature information that is more relevant to the liver region through improved feature recalibration methods. This work adds the MSCR block, which can do space and channel feature recalibration, to 3D full convolution network. The 3D space part of the MSCR block re-calibrates the feature of liver voxel space. And in the channel recalibration part, it changes the original spatial compression operation according to the none-local method, so the spatial sampling results contain more global context information. Multiple metrics are used to evaluate the proposed model on the LiTS dataset, and it achieves better segmentation performance than other comparison models such as Attention U-Net. The method is further tested on the 3DIRCADb dataset, which proves its effectiveness and stability. Thus, the model proposed in the paper is effective for improving the performance of liver segmentation.
Self-powered ZrO2 nanofibers/n-Si photodetector with high on/off ratio for detecting very low optical signal

In this work, a light-sensitive photodetector based on ZrO2 nanofibers (NFs) formed onto n-Si derived by the electrospinning technique was reported. According to the I–V measurements in the dark, the ZrO2 NFs/n-Si has an ohmic current conduction mechanism at low voltages and the SCLC conduction mechanism at medium voltages, while the Fowler–Nordheim tunneling conduction model is dominant at high voltages. The dark current and the rectifying ratio were found to be 9.4 × 10−10 A (at −1.0 V) and 10 351 (at ±1.0 V), respectively. The shunt resistance (R sh) (at −1 V) and series resistance (R s) (at +2 V) values were determined as 2.0 GΩ and 5.5 kΩ, respectively from the dark I–V plot. The device was shown to be highly sensitive to light and exhibits self-powering characteristics under illumination. The ON/OFF ratio was about 1.0 × 106 at zero-bias (self-powered mode) suggested a good response of the device to the light intensity. The maximum responsivity and specific detectivity were calculated to be 1.44 mA W−1 and 4.5 × 108 Jones, respectively. Finally, capacitance and conductance versus bias voltage characteristics of the device were analyzed at various frequencies.
Industry 4.0 Readiness: a new framework for maturity evaluation based on a bibliometric study of scientific articles from 2001 to 2020

This article presents the scientific production over the last two decades concerning the terms Industry 4.0 and Maturity and introduces a conceptual framework for maturity evaluation based on the concept of industry 4.0 readiness. A bibliometric study based on the keywords Industry 4.0 and Maturity is conducted and main journals, authors, countries, and trends of maturity models are analyzed. Based on the analysis, a framework for measuring maturity to evaluate a company’s readiness for Industry 4.0 is introduced. The assessment enables companies to systematically increase the quality of their transformation by providing insights about their readiness level. The theoretical model suggests that the evaluation, when carried out with the detailed model, has the potential to allow companies to systematically increase the quality of their transformation by providing information on their level of readiness
Analysis of Obata’s Differential Equations on Pointwise Semislant Warped Product Submanifolds of Complex Space Forms via Ricci Curvature

$e present paper studies the applications of Obata’s differential equations on the Ricci curvature of the pointwise semislant warped product submanifolds. More precisely, by analyzing Obata’s differential equations on pointwise semislant warped product submanifolds, we demonstrate that, under certain conditions, the base of these submanifolds is isometric to a sphere.We also look at the effects of certain differential equations on pointwise semislant warped product submanifolds and show that the base is isometric to a special type of warped product under some geometric conditions.
Mitochondrial dysfunction compromises ciliary homeostasis in astrocytes

Astrocytes, often considered as secondary responders to neurodegenerative processes, are emerging as primary drivers of brain disease. The underlying pathogenic mechanisms are, however, insufficiently understood. Here we show that pathogenesis of mitochondrial spongiotic encephalopathy, a severe manifestation of mitochondrial brain diseases, involves abnormal maintenance of the astrocytic primary cilium, a major signaling organelle of a cell. We show that progressive respiratory chain deficiency in astrocytes activates FOXJ1 and RFX transcription factors and master regulators of motile ciliogenesis. Consequently, a wide aberrant nuclear expression program with FOXJ1 and RFX target genes, encoding motile cilia components, is induced in astrocytes. While the affected astrocytes still retain a single cilium, these organelles elongate and become remarkably distorted. Multiciliated ventricle-lining ependymal cells show no overt cilia morphology defects despite similar mitochondrial dysfunction. We propose that the chronic activation of the integrated mitochondrial stress response (ISRmt), specifically induced in astrocytes, drives anabolic metabolism and promotes ciliary growth. Collectively, our evidence indicate that 1) an active signaling axis exists between astrocyte mitochondria and primary cilia; 2) ciliary signaling is part of ISRmt in astrocytes; 3) metabolic ciliopathy is a novel pathomechanism for mitochondria-related neurodegenerative diseases.
Chapter 4 Paths to formulaicity: How do L2 speakers internalise new formulaic material?

How are new formulaic expressions acquired and stored by L2 learners? Defining formulaicity with respect to the individual speaker’s storage and processing of a given expression as a single holistic unit (Myles & Cordier 2017; Wray 2002), two potential routes are explored: the “fusion” over time of individual words and “holistic acquisition”, where an expression is internalised as a single unit from the start. Two studies exploring the route to acquisition are reported. L2 speakers are presented with novel target expressions to memorise, and their ease of recall, accuracy and fluency over time is monitored. These delivery features are used in combination to indicate particular stages of acquisition that may be associated with each route. Study 1 contrasts analytical and holistic methods for introducing the targets. Study 2 explores methods for determining the holisticity and processing automaticity of the target expressions in the learners’ output. Drawing on the results of these, a model for the acquisition and storage of formulaic expressions based on the “superlemma” model of Sprenger et al. (2006) is presented and discussed in relation to fusion and holistic acquisition.
ROBUST ESTIMATION IN ROBOT VISION AND PHOTOGRAMMETRY: A NEW MODEL AND ITS APPLICATIONS

Abstract. Robust estimation (RE) is a fundamental issue in robot vision and photogrammetry, which is the theoretical basis of geometric model estimation with outliers. However, M-estimations solved by iteratively reweighted least squares (IRLS) are only suitable for cases with low outlier rates (< 50%); random sample consensus (RANSAC) can only obtain approximate solutions. In this paper, we propose an accurate and general RE model that unifies various robust costs into a common objective function by introducing a “robustness-control” parameter. It is a superset of typical least-squares, l1-l2, Cauchy, and Geman-McClure estimates. We introduce a parameter-decreasing strategy into the IRLS to optimize our model, called adaptive IRLS. The adaptive IRLS begins with a least-squares estimate for initialization. Then, the “robustness-control” parameter is decreased along with iterations so that the proposed model acts as different robust loss functions and has different degrees of robustness. We also apply the proposed model in several important tasks of robot vision and photogrammetry, such as line fitting, feature matching, image orientation, and point cloud registration (scan matching). Extensive simulated and real experiments show that the proposed model is robust to more than 80% outliers and preserves the advantages of M-estimations (fast and optimal). Our source code will be made publicly available in https://ljy-rs.github. io/web.
PREDICTION OF THE PERSONAL ATTRIBUTE OF TWITTER USER USING MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING

: In modern society a large number of communication and interaction take place on various social media platforms such as Twitter and Facebook. For privacy reasons personally identifiable information like gender, age and occupation class of people is not available publicly. But precise prediction of such information is relevant in the area of advertising, forensics and business intelligence. In recent years user generated text and content has grown exponentially mainly in the form of blogs, tweets, comments and social networking messages. The rise in textual data has sparked interest in inducing user attribute such as age, gender, occupation using various machine learning algorithms and pre-processing of natural language processing.
A Novel Long Short-Term Memory Predicted Algorithm for BDS Short-Term Satellite Clock Offsets

The satellite clocks carried on the BeiDou navigation System (BDS) are a self-manufactured hydrogen clock and improved rubidium clock, and their on-orbit performance and stabilities are not as efficient as GPS and Galileo satellite clocks caused of the orbital diversity of the BDS and the complexity of the space operating environment. Therefore, the existing BDS clock product cannot guarantee the high accuracy demand for precise point positioning in real-time scenes while the communication link is interrupted. To deal with this problem, we proposed a deep learning-based approach for BDS short-term satellite clock offset modeling which utilizes the superiority of Long Short-Term Memory (LSTM) derived from Recurrent Neural Networks (RNN) in time series modeling, and we call it QPLSTM. The ultrarapid predicted clock products provided by IGS (IGU-P) and four widely used prediction methods (the linear polynomial, quadratic polynomial, gray system (GM (1,1)), and Autoregressive Integrated Moving Average (ARIMA) model) are selected to compare with the QPLSTM. The results show that the prediction residual is lower than clock products of IGU-P during 6-hour forecasting and the QPLSM shows a greater performance than the mentioned four models. The average prediction accuracy has improved by approximately 79.6, 69.2, 80.4, and 77.1% and 68.3, 52.7, 66.5, and 69.8% during a 30 min and 1-hour forecasting. Thus, the QPLSTM can be considered as a new approach to acquire high-precision satellite clock offset prediction.
Intention determination of sharing economy business provider in the theory of planned behavior model using partial least square (case study: Airbnb Indonesia)

The Sharing economy is also known as collaborative consumption. It means the practice of sharing resources and replacing traditional ownership with sharing, lending and borrowing. Besides, Internet technology development has a role as transaction facility that present information to individuals which creates the optimization of resources. To motivate more people to take part in sharing economy, this research should have an understanding of why people would take part in it. Especially in the side of provider’s intention, nowadays people start to think use value of goods instead of the ownership. It means they admit the new concept of “use”. According to the characteristics of the sharing economy and the previous studies, This research believe the TPB (Theory of Planned Behavior) model would be appropriate to study this case. So far, the Theory of Planned Behavior has many research bibliographies and most of researches have shown that it is a very powerful and predictive model for explaining human behavior. This research proposes model that determine intention of business provider in participating of sharing economy that will be analyzed using Partial Least Square.
Seed-Derived Microbial Colonization of Wild Emmer and Domesticated Bread Wheat (<italic toggle='yes'>Triticum dicoccoides</italic> and <italic toggle='yes'>T. aestivum</italic>) Seedlings Shows Pronounced Differences in Overall Diversity and Composition

The composition of the plant microbiota may be altered by ecological and evolutionary changes in the host population. Seed-associated microbiota, expected to be largely vertically transferred, have the potential to coadapt with their host over generations. Strong directional selection and changes in the genetic composition of plants during domestication and cultivation may have impacted the assembly and transmission of seed-associated microbiota. Nonetheless, the effect of plant speciation and domestication on the composition of these microbes is poorly understood. Here, we have investigated the composition of bacteria and fungi associated with the wild emmer wheat (Triticum dicoccoides) and domesticated bread wheat (Triticum aestivum). We show that vertically transmitted bacteria, but not fungi, of domesticated bread wheat species T. aestivum are less diverse and more inconsistent among individual plants compared to those of the wild emmer wheat species T. dicoccoides. We propagated wheat seeds under sterile conditions to characterize the colonization of seedlings by seed-associated microbes. Hereby, we show markedly different community compositions and diversities of leaf and root colonizers of the domesticated bread wheat compared to the wild emmer wheat. By propagating the wild emmer wheat and domesticated bread wheat in two different soils, we furthermore reveal a small effect of plant genotype on microbiota assembly. Our results suggest that domestication and prolonged breeding have impacted the vertically transferred bacteria, but only to a lesser extent have affected the soilderived microbiota of bread wheat. IMPORTANCE Genetic and physiological changes associated with plant domestication have been studied for many crop species. Still little is known about the impact of domestication on the plant-associated microbiota. In this study, we analyze the seed-associated and soil-derived bacterial and fungal microbiota of domesticated bread wheat and wild emmer wheat. We show a significant difference in the seedassociated, but not soil-derived, bacterial communities of the wheat species. Interestingly, we find less pronounced effects on the fungal communities. Overall, this study provides novel insight into the diversity of vertically transmitted microbiota of wheat and thereby contributes to our understanding of wheat as a “metaorganism. ” Insight into the wheat microbiota is of fundamental importance for the development of improved crops.
Role of Machine Learning in WSN and VANETs

Dynamic nature of Vehicular Ad-hoc Networks (VANETs) and Wireless Sensor Networks (WSN) makes them hard to deal accordingly. For such dynamicity, Machine learning (ML) approaches are considered favourable. ML can be described as the process or method of self-learning without human intervention that can assist through various tools to deal with heterogeneous data to attain maximum benefits from the network. In this paper, a quick summary of primary ML concepts are discussed along with several algorithms based on ML for WSN and VANETs. Afterwards, ML based WSN and VANETs application, open issues, challenges of rapidly changing networks and various algorithms in relation to ML models and techniques are discussed. We have listed some of the ML techniques to take additional consideration of this emergent field. A summary is given for ML techniques application with their complexities to cover on open issues to kick start further research investigation. This paper provides excellent coverage of state-of-the-art ML applications that are being used in WSN and VANETs with their comparative analysis.
In situ recycle of PbI2 as a step towards sustainable perovskite solar cells

The development of organometal halide perovskite solar cells has grown rapidly and the highest efficiency of the devices has recently surpassed 22%. Because these solar cells contain toxic lead, a sustainable strategy is required to prevent environmental pollution and avoid healthy hazard caused by possible lead outflow. Here, in situ recycling PbI2 from thermal decomposition CH3NH3PbI3 perovskite films for efficient perovskite solar cells was developed. The thermal behavior of CH3NH3PbI3 perovskite and its individual components were examined by thermogravimetric analysis. By optimizing the process of thermal decomposition CH3NH3PbI3 film, the complete conversion from CH3NH3PbI3 to pure PbI2 layer with a mesoporous scaffold was achieved. The mesoporous structure readily promotes the conversion efficiency of perovskite and consequently results in high‐performance device. A perovskite crystal growth mechanism on the mesoporous PbI2 structure was proposed. These results suggest that in situ recycled PbI2 scaffolds can be a new route in manipulating the morphology of the perovskite active layer, providing new possibilities for high performance. Meanwhile, the risk of lead outflow can be released, and the saving‐energy fabrication of efficient solar cells can be realized.
POTHOLE DIMENSIONS AND GEO LOCATION CALCULATION APP

INTRODUCTION: Roads make a crucial contribution to economic development and growth and bring important social benefits. They are of vital importance in order to make a nation grow and develop. In addition, providing access to employment, social, health and education services makes a road network crucial in fighting against poverty. Roads open up more areas and stimulate economic and social development. For those reasons, road infrastructure is the most important of all public assets. It helps in generating employment and guaranteeing a better dispersal of wealth. For improved growth in the economy, the transport investments are essential. It is not only facilitating inexpensive and more efficient movements of goods and people but also affects the delivery of commercial activity across the cities If a bad patch remains unattended to, water seeps in through the cracks during the monsoon. If the water gets accumulated, the strata beneath the road weakens and subsequently the size of the pothole widens Water gets through those cracks and weakens the soil under the road. That leads to even more cracking and eventually a pothole So treating and filling them at the earliest is of utmost importance. So in order to cater to this need we analysed various existing methods, measured accuracy of each one and finally came up with our idea of measuring Potholes.
Is the Long Memory Factor Important for Extending the Fama and French Five-Factor Model: Evidence from China

)is paper proposes a new factor model, which is built upon the marriage of the Fama and French five-factor model and a long memory factor based on the monthly data of the A-share market in the Chinese stock market from January 2010 to July 2020. We first examine the explanatory power of the Fama and French five-factor model. We find strong market factor return of market (RM), size factor small minus big (SMB), and value factor high minus low (HML) but weak factor robust minus weak (RMW) and investment factor conservative minus aggressive (CMA). )en, both the Hurst exponent and the momentum factors (MOM) are added to the model to test the improvement of the explanatory power of these two new factors. We find that both the momentum factor and the Hurst exponent factor can effectively improve the explanatory power of the model. )e momentum factor captures the short-term trend, but it cannot completely replace the Hurst exponent, which reflects the long memory effect.