Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial and temporal subsidence characteristics in Wuhan (China), during 2015–2019, inferred from Sentinel-1 synthetic aperture radar (SAR) interferometry

Abstract. Ground subsidence is regarded as one of the most common geohazards, accompanied with the rapid urban expansion in recent years. In the last 2 decades, Wuhan, located in the alluvial Jianghan Plain, has experienced great urban expansion with increased subsidence issues, i.e., soft foundation subsidence and karst collapse. Here we investigated subsidence rates in Wuhan with 2015–2019 Sentinel-1 synthetic aperture radar (SAR) images. We found that the overall subsidence over the Wuhan region is significantly correlated with the distribution of engineering geological subregions (EGSs). We further validated the interferometric SAR (InSAR) measurements with better than 5 mm accuracy by comparing with leveling measurements. Subsidence centers in Qingling–Jiangdi, Houhu, Qingshan, and Dongxihu were identified with displacement rates of approximately 30 mm/yr. Our results demonstrated that the dominant driving factor is ongoing construction, and the fact that the subsidence centers shifted with construction intensities. The Qingling–Jiangdi area in our study is a well-known site of karst collapse. We find that the nonlinear subsidence of this area is correlated with the seasonal rainfall.
ON THE FEASIBILITY OF APPLYING ORBITAL CORRECTIONS TO SAOCOM-1 DATA WITH FREE OPEN SOURCE SOFTWARE (FOSS) TO GENERATE DIGITAL SURFACE MODELS: A CASE STUDY IN ARGENTINA

Abstract. In this work we present an orbital correction workflow developed with FOSS tools to compensate for orbital errors present in Synthetic Aperture Radar (SAR) interferograms. The technique is tested in forested areas in Argentina, using full polarimetric images from the argentinean SAR constellation SAOCOM-1 (Satélite Argentino Con Microondas). The results are contrasted with field measurements of canopy height provided by local producers, and the results show that the Root Mean Square Error (RMSE) of the satellite measurements is significantly reduced after the orbital correction. Moreover, forest plantation become more distinguishable in the retrieved Digital Surface Models, especially in those pairs with larger spatial baseline. A section of this article is also dedicated to the discussion on which are the best parameters to run the module, and how different configurations can affect the result.
Factors Associated with the Behavior of Men who Have Sex with Men in the Prevention of Human Immunodeficiency Virus Transmission

Background: The prevalence of HIV/AIDS transmission caused by the behavior of men who have sex with men (MSM) is still a concern. The purpose of this study was to determine the factors associated with MSM behavior in relation to the prevention of HIV transmission. Methods: This study used a cross-sectional design. The total sample was 176 respondents determined using consecutive sampling technique. The variables consist of demographic data, knowledge, attitude, beliefs, the accessibility of the health facilities, health-related skills, peer support, health care provider support and the prevention behavior concerning HIV transmission. The data was obtained using a questionnaire and it was analyzed using Chi-square and ordinal logistic regression. Results: From the sample of 176 MSM, the behavior of the prevention of HIV transmission was 43.75%, which is a moderate level. Through ordinal logistic regression, we found that knowledge [OR:1.171, CI: 0.414-1.929] and peer support [OR: 1.902, CI: 0.721, 3.082] are more likely to prevent HIV transmission. Conclusion: Knowledge and peer support are known to be very important factors concerning the behavior engaged in the prevention of HIV transmission. Increasing the level of knowledge and increasing the youth networks in the form of groups as a form of support can be promoted by nurses, health workers, and the community in order to prevent HIV transmission.
Innovation Capabilities and Performance of Malaysian Agricultural SMEs: The Moderating Role of Strategic Alliance

Small and Medium Enterprises (SMEs) in the agriculture sector in Malaysia are still seen to contribute only a small portion towards Gross Domestic Product (GDP) and the total number of the national workforce. As for agricultural SMEs, innovation activities are needed to produce quality agricultural-based products to meet the increasing of Malaysia and world market demand. Nevertheless, innovation among agricultural SMEs is still less encouraging. Hence, this study aims to investigate the effect of innovation capabilities towards performance of agricultural SMEs in Malaysia and determine the role of strategic alliance as a moderator in influencing the performance of agricultural SMEs. The conceptual framework of innovation capabilities is based on Resource-Based View Theory and Schumpeter Innovation Theory that considers strategic alliance will positively affect the relationship between innovation capabilities and SME performance. A total of 136 respondents from Agricultural SMEs were used for data analysis process using SmartPLS 3.0 software. The results show that innovation capabilities have a positive impact on the performance of SMEs, while strategic alliance does not affect the relationship between innovation capabilities and agricultural SME performance. Although strategic alliance has insignificant effect, this might be resulted from the emphasis of this study as it defined strategic alliances as acquisitions of companies. For agricultural SMEs in Malaysia, entrepreneurs need to look at an alliance from a positive perspective by looking it as a joint venture process in resource sharing that will benefit the enterprises, thus it is not only meant acquisition. The implication of this study is expected to contribute and enhance the role of innovation capabilities among entrepreneurs and strategic alliances in the Malaysian agricultural SMEs.
Emotion extraction from text: Analysis of four different algorithms

-Emotions play an important role in human life. In today’s internet world textual data has been proven as a main medium of communication in human-human and human-machine interaction. Thisincrease in use of text for communication makes it a need to find the emotion present in textual data. This makes it important to provide a framework that can recognize the emotions present in the communication or the emotions of the users. Emotion recognition or sentiment analysis is a natural language processing task that mine information from textual data liketwitter tweets, blogs, movie reviews and reviews from online websites and classify on the basis of polarity. Emotion Recognition Model extracts emotion from text at the sentence level. Our method uses 4 different deep learning algorithm and detects emotion from a text-input. To make the recognition more accurate, emotion-affect-bearing words and phrases were also analyzed. This experiment shows that the method could generate agood result for emotion detection from text input.
Semi-Automated Segmentation of Symptomatic Exudate-Associated Derangements (SEADs) in 3D OCT Using Layer Segmentation

We present a novel two step method to segment retinal pathologies related to the so called wet age-related macular degeneration from 3D spectral optical coherence tomography images. In the first step we segment three retinal layers by an optimal surface algorithm. The identified layers are used in the second step to constrain the segmentation of fluid filled retinal regions using GraphCut. We propose a new regularization energy term for GraphCut that permits long range effect of the manual initialization.
DEVELOPMENT OF PATTERNS OF EVOLUTIONARY METHODS FOR DIAGNOSING DIGITAL DEVICES

- The paper proposes classification of existing evolutionary methods for generating identifying sequences of digital devices as well as development the templates for constructing such methods and their algorithmic implementation, which are based on this classification. Such classification and built templates are part of the methodology for the synthesis of evolutionary methods of diagnosing digital devices aimed to accelerate the development of new methods by unifying their components.
LOGISTIC REGRESSION IN DEPRESSION CLASSIFICATION

Well known logistic regression and the other binary response models can be used in the area of statistics, biomedical data analysis and artificial intelligence. Beginning with basic properties of proposed model, we use likelihood ratio testing as a tool for the pruning of complete model to produce the best sub-model. We are focused to optimum and automated pruning which is an alternative to traditional stepwise logistic regression. The general methodology of logistic regression with optimum pruning was developed together with a library of functions in the Matlab environment. The library was used to the analysis of neuropsychological and biomedical data related to the systematic research of resistant depressions. The aim of the paper is in the selection of optimum sub-model (set of patient properties) which will help with the decision whether the patient has resistant or non-resistant form of the depression.
Adversarial Training Blocks Generalization in Neural Policies

Deep neural networks have made it possible for reinforcement learning algorithms to learn from raw high dimensional inputs. This jump in the progress has caused deep reinforcement learning algorithms to be deployed in many different ﬁelds from ﬁnancial markets to biomedical applications. While the vulnerability of deep neural networks to imperceptible speciﬁcally crafted perturbations has also been inherited by deep reinforcement learning agents, several adversarial training methods have been proposed to overcome this vulnerability. In this paper we focus on state-of-the-art adversarial training algorithms and investigate their robustness to semantically meaningful natural perturbations ranging from changes in brightness to rotation. We conduct several experiments in the OpenAI Atari environments, and ﬁnd that state-of-the-art adversarially trained neural policies are more sensitive to natural perturbations than vanilla trained agents. We believe our investigation lays out intriguing properties of adversarial training and our observations can help build robust and generalizable neural policies.
Solving Multi-Objective Energy Management of a DC Microgrid using Multi-Objective Multiverse Optimization

This paper deals with the multi-objective optimization dispatch (MOOD) problem in a DC microgrid. The aim is to formulate the MOOD to simultaneously minimize the operating cost, pollutant emission level of (NOx, SO2 and CO2) and the power loss of conversion devices. Taking into account the equality and inequality constraints of the system. Two approaches have been adopted to solve the MOOD issue. The scalarization approach is first introduced, which combines the weighted sum method with price penalty factor to aggregate objective functions and obtain Pareto optimal solutions. Whilst, the Pareto approach is based on the implementation of evolutionary multi-objective optimization solution. Single and multi-objective versions of multi-verse optimizer algorithm are, respectively, employed in both approaches to handle the MOOD. For each time step, a fuzzy set theory is selected to find the best compromise solution in the Pareto optimal set. The simulation results reveal that the Pareto approach achieves the best performances with a considerable decrease of 28.96 $/day in the daily operating cost, a slight reduction in the power loss of conversion devices from 419.79 kWh to 419.29 kWh, and in less computational time. While, it is noticing a small increment in the pollutant emission level from 11.54 kg/day to 12.21 kg/day, for the daily microgrid operation. This deviation can be fully covered when comparing the cost related to the treatment of these pollutants, which is only 5.55 $/day, to the significant reduction in the operating cost obtained using the Pareto approach.
Design of a Linearly Polarized HMSIW U-Slot Antenna

In this paper, a characteristic modal analysis and a design guide of a linearly polarized HMSIW U-slot antenna is proposed. The presented characteristic modal analysis of the antenna explains its multi-modal behavior in the centimeter frequency band. Further, in this paper there is a design guide of the antenna for several dielectric substrates. Exploitation of the design guide is demonstrated on the design of several antenna examples which are finally experimentally verified in a laboratory environment.
Application of model fitting technique to enhance bacterial regrowth potential (BRP) measurement for drinking water supply monitoring

The bacterial regrowth potential (BRP) method was utilised to indirectly measure the assimilable organic carbon (AOC) as an indicator for the assessment of the microbial regrowth potential in drinking water distribution systems. A model using various microbial growth parameters was developed in order to standardise the experimental interpretation for BRP measurement. This study used 82 experimental BRP data sets of water samples collected from the water treatment plant to locations (customer taps) in the distribution system. The data were used to model the BRP process (growth curve) by a data fitting procedure and to obtain a best-fitted equation. Statistical assessment and validation of the model obtained equation by fitting these 82 sets of data were conducted, and the results show average R2 values were 0.987 for treated water samples (collected at the plant prior to chlorination) and 0.983 for tap water (collected at the customer taps). The F values obtained from the F-test are all exceeded their corresponding F critical values, and the results from the t-test also showed a good outcome. These results indicate this model would be successfully applied in modelling BRP in drinking water supply systems.
The Mediating Effect of e-Satisfaction and Trust on the Influence of Brand Image and e-Loyalty

Loyalty to an e-commerce is difficult to be gained even though factors influencing it have been studied by many. Among others, brand image, e-satisfaction, and trust are the factors. This study aims at analyzing mediating effect of e-satisfaction and trust on the influence of brand image and e-loyalty in the case of Tokopedia, an e-marketplace with strong brand image in Indonesia. Questionnaires to 114 buyers who are categorized as generation Y and Z were distributed using Google form. The Mediating Regression Analysis (MRA) test was applied to see mediating effect of e-satisfaction and trust. The data were tested for their normality, multicollinearity, and heteroscedasticity before the regression. The results indicate only brand image and e-satisfaction that affect e-loyalty positively and significantly. E-satisfaction is also found to mediate brand image and e-loyalty. Since trust is insignificantly affect e-loyalty, it thus does not mediate brand image and e-loyalty.
Towards an open, zoomable atlas for invasion science and beyond

Biological invasions are on the rise, and their global impacts on ecosystems, economies and human health are a major challenge. Invasion science is critical to mitigate invader impacts, yet due to the strong increase of data and information in this area, it has become difficult to acquire and maintain an overview of the field. As a result, existing evidence is often not found, knowledge is too rarely transferred to practice, and research is sometimes conducted in pursuit of dead ends. We propose to address these challenges by developing an interactive atlas of invasion science that can be extended to other disciplines in the future. This online portal, which we aim to create in the course of the project described here, will be an evolving knowledge resource and open for anyone to use, including researchers, citizen scientists, practitioners and policy makers. Users will be able to zoom into the major research questions and hypotheses of invasion science, which are connected to the relevant studies published in the field and, if available, the underlying raw data. The portal will apply cutting-edge visualization techniques, artificial intelligence and novel methods for knowledge synthesis.
Policy Failures with Learner-Centred Pedagogy: Case Studies from the Zimbabwean Experiment on Project-Based Learning

Globally, policy reform in education has recommended learner-centered pedagogy for more than a century, but its practical implementation remains an illusion in many classrooms. This study describes history teachers’ experiences while experimenting on project-based learning (PjBL) in Zimbabwe’s current curriculum reform initiative. Project-based learning has gained acceptance as an indispensable approach in developing learners’ 21st century skills of creativity, critical thinking, research, and problem-solving. Using a qualitative multiple-case study, four history teachers were observed and interviewed while implementing PjBL at four secondary schools in Zimbabwe. Results indicate policy failures with learner-centered pedagogy. Where some participants and their students had made significant progress, others had done very little at the time the policy reformers withdrew the projects. Feasibility studies could have circumvented some of the pitfalls experienced while enacting PjBL.
TraViA: a Traffic data Visualization and Annotation tool in Python

In recent years, multiple datasets containing traffic recorded in the real world and containing human-driven trajectories have been made available to researchers. Among these datasets are the HighD, pNEUMA, and NGSIM datasets. TraViA, an open-source Traffic data Visualization and Annotation tool was created to provide a single environment for working with data from these three datasets. Combining the data in a single visualization tool enables researchers to easily study data from all sources. TraViA was designed in such a way that it can easily be extended to visualize data from other datasets and that specific needs for research projects are easily implemented.
How Effective Is Tax Policy In Attracting Foreign Direct Investments In Transition Countries?

Foreign Direct Investments (FDI) has been considered an important source of economic growth and technological development in transition economies. The previous empirical literature has shown that FDI promote economic growth via complementary effects on domestic investments, increases in productivity and overall economic efficiency, giving rise to an increasing interest in understanding the key determinants of FDI. Apart from traditional FDI determinants, favourable tax policy has been considered an important factor influencing MNCs’ location decisions. The goal of this paper is to investigate the impact of corporate income tax on FDI in the context of less advanced transition economies and to analyse whether the tax effect is conditional on the level of economic development. A small number of studies exist analysing the importance of tax policy regime in attracting FDI covering SouthEast European countries. In this study, we rely on panel gravity econometric framework and examine the impact of tax policy on FDI using bilateral FDI flows between 8 home and 8 South East Europe host countries in the period 2000–2018. We estimate the regression using Prais-Winsten correlated panels corrected standard errors PSCE method to obtain robust estimates of individual effects in the presence of heteroscedasticity and serial correlation. The seven SEE host countries included in the sample are considered of similar economic structures and institutional transformation, which seems important in analysing tax policy effectiveness and minimising biases associated with econometric modelling of FDI determinants. Finally, we study this relationship in an integrated framework considering traditional gravity forces as well as a number of additional FDI determinants, including institutional factors. We show that, although tax policy seems an important determinant of FDI, its effects seem to be conditional on the level of technological development. Given these findings, reducing corporate income tax may be considered an effective tool in promoting FDI, which seems to be of particular importance for less developed transition economies. The results are robust to different model specifications and consideration of endogeneity. Implications for Central European audience: The direct implications of this research for business policymakers in CEE include the need to revise and optimise the levels of corporate income tax and incorporate this specific policy instrument in FDI strategies. In particular, the results of this research indicate that tax cuts have been more effective in attracting FDI in countries that are at a lower level of technological sophistication. The managers could seek
New antibiotics in the treatment of urinary tract infections

Antibiotics are very often prescribed for the indication urinary tract infections (UTIs). Therefore, UTIs are an important field for the development of antibiotic resistance. A number of new antibiotics have been tested for the indication complicated UTI / pyelonephritis. These are cephalosporins and carbapenems, in combination with new beta-lactamase inhibitors, cefiderocol as a new type of cephalosporin, plazomicin a new aminoglycoside, eravacycline a new tetracycline, and intravenous fosfomycin. Not all antibiotics are approved in Europe. Although the development of these new substances is promising, these new antibiotics should be used very carefully to avoid the development of new antibiotic resistance against these new substances.
Relationship Between Personality and Music Preferences Among University Students

This study analysed the relationship between Big Five personality traits and music preferences among university students. Big Five Inventory (BFI) and Short Test of Music Preferences (STOMP) was used to assess personality traits and music preferences. Questionnaires were distributed through social media platforms to college and university students aged 19 to 26. A total number of 145 respondents participated in this study. The results showed Agreeableness, Conscientiousness, and Openness were the most prevalent personality traits among respondents and Energetic-Rhythmic (ER) was the most preferred music. The correlation analysis showed that there is a significant correlation between Openness and energetic-rhythmic (ER) music. On the other hand, there was no significant correlation between other traits (Conscientiousness, Extraversion, Agreeableness, and Neuroticism) with music preference dimensions such as Intense-Rebellious (IR) and Energetic-Rhythmic (ER). The inconsistencies in the literature and our findings suggested more studies are needed to understand the influence of personality on music preferences.
BREAST CANCER DETECTION USING KERNEL NEURAL NETWORK

- Among the assorted treatment strategies obtainable for carcinoma ,Histopathological Classification may be a normal for diagnosis of cancer. Carcinoma classification is based totally on pictures of the tissue within the tumour. We tend to classify the pictures of tissue into 2 major classes of tumour : Benign and Malignant exploitation KNN . Existing works use CNN , we tend to use KNN over CNN since it provides indefinite complicated actions of the human recognition system by creating use of the kernel trick. KNN may be a generalized version of convolution which may enhance the model ’ s extent and might capture higher order of traits exploitation of reinforcement kernel functions .