Miracle-dz/UniFAR-FTUs huggingface dataset:
Emergency management in higher education during COVID-19 pandemic: A phenomenology inquiry comparing a developed and developing country

Higher Education Institutions (HEIs) are facing more enormous challenges due to the quick spread of NOVEL COVID-19, which carried lockdown in the lives of people across the world. Countries are preparing to face the challenge as the pandemic may run for several months. A large number of academic institutions had shut their campuses and went online. In terms of e-education, developing countries are facing more challenges comparatively. The current study is designed to investigate the phenomena from the lens of emergency management theory, with a purpose to come up with a viable framework for taking proper actions. Another objective of the study was to examine commonalities and differences between a developed and a developing country in terms of preparedness, response, mitigation, and recovery. The study was conducted under the philosophy of social constructivism, following qualitative research design, applying phenomenology research method, with the help of interviews as a data collection technique. Thematic analysis was applied for data analysis with the help of NVivo 12. The results show there are significant differences in terms of 'preparedness', a considerable difference in terms of 'response' and 'recovery' and a minor difference in terms of 'mitigation' between developed and developing countries. The study is conducted during COVID-19 emergency and provides useful insights to understand faculty point of view and suggestions for improving the quality of e-learning and emergency preparedness.
Cancer-Related Somatic Mutations in Transmembrane Helices Alter Adenosine A1 Receptor Pharmacology

Overexpression of the adenosine A1 receptor (A1AR) has been detected in various cancer cell lines. However, the role of A1AR in tumor development is still unclear. Thirteen A1AR mutations were identified in the Cancer Genome Atlas from cancer patient samples. We have investigated the pharmacology of the mutations located at the 7-transmembrane domain using a yeast system. Concentration–growth curves were obtained with the full agonist CPA and compared to the wild type hA1AR. H78L3.23 and S246T6.47 showed increased constitutive activity, while only the constitutive activity of S246T6.47 could be reduced to wild type levels by the inverse agonist DPCPX. Decreased constitutive activity was observed on five mutant receptors, among which A52V2.47 and W188C5.46 showed a diminished potency for CPA. Lastly, a complete loss of activation was observed in five mutant receptors. A selection of mutations was also investigated in a mammalian system, showing comparable effects on receptor activation as in the yeast system, except for residues pointing toward the membrane. Taken together, this study will enrich the view of the receptor structure and function of A1AR, enlightening the consequences of these mutations in cancer. Ultimately, this may provide an opportunity for precision medicine for cancer patients with pathological phenotypes involving these mutations.
Cramér-Rao Lower Bounds of Model-Based Electrocardiogram Parameter Estimation

Clinical parameter estimation from the electrocardiogram (ECG) is a recurrent field of research. It is debated that ECG parameters estimated by human experts and machines/algorithms is always model-based (implicitly or explicitly). Therefore, all estimation algorithm used in this context have performance bounds in terms of the achievable mean squared error, which are not exceedable. These bounds depend on the adopted data-model, the estimation scheme (least-squares error, maximum likelihood, or Bayesian), and prior assumptions on the model parameters and noise distributions. In this research, we develop a comprehensive theoretical framework for ECG parameter estimation and derive the Cramér-Rao lower bounds (CRLBs) for the most popular signal models used in the ECG modeling literature, namely functional expansions (including polynomials) and sum of Gaussian functions. The developed framework is evaluated over real and synthetic data for three popular applications: T-to-R wave ratio estimation, ST-segment analysis and QT-interval estimation, using the state-of-the-art estimators in each context. The proposed framework and the derived CRLBs provide practical guidelines for the selection of data-models, sampling frequency (beyond the Nyquist rate), modeling segment length, number of beats required for ECG beat averaging, and other factors that influence the accuracy of ECG-based clinical parameter estimation.
Improved Ability in Emotional Recognition and Social Skills After Emotional Recognition Training in Children

The recognition of emotional expressions is essential to achieving emotional regulation and social development. Therefore, stimulating this recognition could benefit children’s cognitive and social levels. The aim of this study was to assess the effects of training in emotional recognition on the recognition of emotional expressions and the social behavior of children. Typically-developing children (8-10 years old) were divided into two groups for either emotional or identity training. Before and after training, the recognition of three emotions (happiness, sadness, anger) and of identity was assessed by matching and memory tasks. In addition, a social skills scale was applied to parents. After training, only participants in the emotion group improved their accuracy (correct responses minus commission errors) in recognizing happiness and sadness, and on the identity tasks. The parents of the children in the emotion group also referred higher scores on the social skills scale. Results suggest that these children improved their recognition of emotional expressions by enhancing the attention paid to specific facial features. This improvement in emotional recognition induced by training may facilitate social interaction.
Feasibility of Silicon Quantum Dots as a Biomarker for the Bioimaging of Tear Film

This study investigated the fluorescence and biocompatibility of hydrophilic silicon quantum dots (SiQDs) that are doped with scandium (Sc-SiQDs), copper (Cu-SiQDs), and zinc (Zn-SiQDs), indicating their feasibility for the bioimaging of tear film. SiQDs were investigated for fluorescence emission by the in vitro imaging of artificial tears (TheraTears®), using an optical imaging system. A trypan blue exclusion test and MTT assay were used to evaluate the cytotoxicity of SiQDs to cultured human corneal epithelial cells. No difference was observed between the fluorescence emission of Sc-SiQDs and Cu-SiQDs at any concentration. On average, SiQDs showed stable fluorescence, while Sc-SiQDs and Cu-SiQDs showed brighter fluorescence emissions than Zn-SiQDs. Cu-SiQDs and Sc-SiQDs showed a broader safe concentration range than Zn-SiQDs. Cu-SiQDs and Zn-SiQDs tend to aggregate more substantially in TheraTears® than Sc-SiQDs. This study elucidates the feasibility of hydrophilic Sc-SiQDs in studying the tear film’s aqueous layer.
An Omnidirectional and Directional Pattern Switchable Antenna Using Characteristic Mode Analysis

In this paper, an omnidirectional and directional pattern switchable antenna operating at 2.5GHz is proposed. Characteristic mode analysis (CMA) is utilized to design the proposed antenna. Based on the CMA, certain kinds of modes can be excited to generate an omnidirectional pattern or a directional beam. To verify this, a ring patch antenna with four slots and four feeding ports is analyzed by the CMA. By changing the phase difference among the four ports, different modes are excited, resulting in varying radiation pattern. Simulation results demonstrate that the omnidirectional and directional pattern switchable characteristic is obtained by the proposed ring patch antenna. With this feature, the proposed antenna can be used in a multi-functional system.
CNN Based Water Stress Detection in Chickpea Using UAV Based Hyperspectral Imaging

Water is an important agronomic input, which plays a vital role in the health and yield of the crop. Water deficiency results in abiotic stress, early detection of water stress help in recovering the health of the crop. Hyperspectral imaging (HSI) sensors acquire rich spectral information of the objects in hundreds of narrow bands, are capable of identifying the change in canopy water content, which is crucial in predicting irrigation requirements of the crop. Due to the wide field of coverages, short revisiting periods, and high spectral resolutions, Unmanned Aerial Vehicle (UAV) based HSI techniques are suitable in precision agriculture. In this paper, water stress detection in chickpea canopy is presented using hyperspectral (HS) images acquired from UAV. The drought classification was performed in two ways, i. by considering selected water-sensitive bands, and ii. by considering the whole spectral bands of the HS images. A 3D-2D convolutional neural network (CNN) model is used to classify well-watered canopy from water-stressed one, and its performance is compared with that of a Support Vector Machine (SVM) and a 2D+1D CNN model in identifying water stress. We obtained the best classification accuracy of 95.44%, which shows the potential of HSI in successfully detecting water stress in chickpea.
Analysis of Surface Water Trends for the Conterminous United States Using MODIS Satellite Data, 2003–2019

Satellite imagery is commonly used to map surface water extents over time, but many approaches yield discontinuous records resulting from cloud obstruction or image archive gaps. We applied the Dynamic Surface Water Extent (DSWE) model to downscaled (250‐m) daily Moderate Resolution Imaging Spectroradiometer (MODIS) data in Google Earth Engine to generate monthly surface water maps for the conterminous United States (US) from 2003 through 2019. The aggregation of daily observations to monthly maps of maximum water extent produced records with diminished cloud and cloud shadow effects across most of the country. We used the continuous monthly record to analyze spatiotemporal surface water trends stratified within Environmental Protection Agency Ecoregions. Although not all ecoregion trends were significant (p < 0.05), results indicate that much of the western and eastern US underwent a decline in surface water over the 17‐year period, while many ecoregions in the Great Plains had positive trends. Trends were also generated from monthly streamgage discharge records and compared to surface water trends from the same ecoregion. These approaches agreed on the directionality of trend detected for 54 of 85 ecoregions, particularly across the Great Plains and portions of the western US, whereas trends were not congruent in select western deserts, the Great Lakes region, and the southeastern US. By describing the geographic distribution of surface water over time and comparing these records to instrumented discharge data across the conterminous US, our findings demonstrate the efficacy of using satellite imagery to monitor surface water dynamics and supplement traditional instrumented monitoring.
A computational study of laminar-flow secondary separation on a slender delta wing

ABSTRACT The laminar flow over a slender delta wing at incidence has been extensively studied both experimentally and theoretically using vortex sheet methods. These vortex sheet methods have generally been successful apart from the prediction of the secondary boundary-layer separation induced by the primary vortex. This paper revisits the problem using computational fluid dynamics (CFD) and focusses on the effects of the secondary flow separation. The modelling approach is briefly summarised, and the results are compared with flow measurements and results from vortex sheet methods. The computations show very good agreement with measurements for the surface pressures and total head contours. The results help to understand the complex structure of the leading edge vortex flow, and the associated secondary separation of the boundary layer. They indicate that inviscid mechanisms dominate the larger scale features, and highlight a possible mechanism for the development of an instability in the leading edge vortex sheet.
Chemical Changes in Blood and Heart Tissue in Experimental Coronary Occlusion.

This study consisted of investigations into the chemical alterations in the plasma of the dog during the first several hours following coronary occlusion, and their relationships to the alterations in the composition of the heart tissue. In conjunction with these investigations, measure­ ments have been made of retrograde blood composition, oxygen saturation, flow and pressure. The results demonstrate increases in the plasma concentrations of the following substances after coronary occlusion: lactate, total amino acids, alanine, glucose, lactic dehydrogenase (LDH), glutamic oxalo­ acetic transaminase (GO-T), calcium, and pyruvate. These changes are accompanied by a decrease in bicarbonate, a decrease followed by an increase in magnesium, and no change in chloride. The similarity between the effects of coronary occlusion and those of exogenous catechol­ amines upon the composition of the plasma strongly suggest that the changes in plasma composition following coronary occlusion are mediated by the catecholamines. The effect of dibenzyline (phenoxybenzamine HC1) pre-treatment upon the plasma composition following coronary occlusion supports this postulation. The effect of bilateral adrenalectomy upon the changes in plasma composition following coronary occlusion indicate that the catecholamines are probably liberated from post-ganglionic nerve endings. Differences between retrograde and arterial plasma concen­ trations have been noted and their relationship to myocardial metabolism discussed. Measurements of the oxygen saturation of the retrograde blood yielded Values which tended to be slightly lower than those of the arterial blood obtained at the same time. Wide variations in the retrograde blood flow and peripheral coronary pressure were observed.
Wage Rigidity, Human Capital and Non-Neutrality of Monetary Policy in the Long-Run

This paper aims to analyze the non-neutrality of monetary policy incorporating the Lucas (1988) type endogenous growth model in the standard New Keynesian macroeconomic model with nominal wage rigidities. It is shown that the monetary policy summarized in the level of trend inflation is non-neutral in the long-run economic growth in the presence of nominal wage rigidities. The growth-inflation nexus depends on the degree of nominal rigidities and the degree of differentiation of the labor services.
Unsupervised Change Detection in Very High Resolution Multi-Spectral Images

Change detection for remote sensing images is a critical task especially for the applications including monitoring the environment, urban planning, investigating agriculture, disaster management, etc. Although most of the exiting deep models for change detection are supervised in nature, unsupervised learning-based models are also gaining popularity because of the scarcity of the annotated training examples. In this paper, we have pro-posed an unsupervised change detection algorithm for VHR (Very High Resolution) multispectral images. It uses a deep CNN model [6] to generate the deep change vector which is then subjected to Slow Feature Analysis (SFA) module [2] to suppress the unchanged components and highlight the transformed features’ changed components. An appropriate thresholding technique is then applied to generate the required binary changed map. Experimental observation reveals that the proposed unsupervised model shows competitive performance with the state-of-the-art methods. Our proposed approach has achieved fifth place 1 in Dynamic Earth Net Challenge Track 1 of EARTHVISION 2021.
Output synchronization of networked passive systems

In this thesis we study the problem of output synchronization for a class of multi-input/multi-output nonlinear networked systems (agents, for short) whose dynamics are input-output passive. The agents are said to output synchronize if their outputs are asymptotically pairwise convergent. The agents exchange information over a network whose topology is described by an interconnection graph. We present synchronization results for both fixed and arbitrarily switching graphs. We treat both linear interconnections on balanced, directed graphs and nonlinear interconnections on undirected graphs. We also treat the realistic case of time delay in the communication of information among agents. We show that the passivity assumption allows synchronization for arbitrary constant (bounded) time delays in communication. Our results unify several existing results from the literature on multi-agent systems. We next apply our results to the problem of synchronization of Kuramoto oscillators. The traditional Kuramoto model, which consists of N oscillators with possibly distinct natural frequencies and all-to-all connectivity, is said to synchronize if the angular frequencies of all oscillators converge to the mean natural frequency of the group. Our main result demonstrates exponential oscillator synchronization to the mean frequency in the Kuramoto model with distinct natural frequencies. Our results are also used to address the general problem of synchronization of multiple Lagrangian systems. The problems of multi-robot coordination and of bilateral teleoperation with time delay are shown to be special cases of our general results.
Studying the ability to use sport watches when running - insights from a small pilot study

Digitalization is a phenomenon that seems to affect all aspects of contemporary society. The sports domain is by no means excluded from this development. In sports, the sport watch might be one of the most iconic symbols in how digitalization has enhanced, changed and developed how physical activities are experienced and measured. Despite the massive adoption of sport watches among runners and other athletes, few studies have explored how sports watches are used in the specific activity they are designed for. The study presented in this research in progress paper is small and should be viewed as a small attempt to outline an approach to study digital technology use in sports activities.
Conceptualising Management of Artificial Intelligence in Terms of People, Process, Data and Technology

Artificial Intelligence (AI) is an area of organisational activity that attempts to build human intelligence and intelligent behaviours into information and computer systems. As a domain of study AI is characterised by, diverse opinions as to its nature, and an industry approach with limited research reporting the outcome of proposed frameworks in areas such as urban innovation, good society, and management in public industry. In this paper, we examine the use of people, process, data, and technology (2PDT) as a possible means for bringing structure to the study of the AI domain. We demonstrate its effective use in the formulation of research questions, research design, data collection, and analysis. We argue that AI is a domain of interest to the information systems discipline and that formulation of 2PDT seems to offer a useful conceptual framework examining this phenomenon.
A REVIEW ON OBJECT DETECTION IN THE HEALTHCARE USING DEEP LEARNING TECHNIQUES

Deep learning comes under the class of machine learning algorithms which made a huge impact in areas of healthcare, such as cancer diagnosis, precision medicine, Diabetic Retinopathy, Gastrointestinal (GI) Diseases Detection, Cardiac Imaging, Tumor Detection etc. It uses several layers for feature extraction and transformation. The output of one layer is given as input to the successive layers. Deep Learning Algorithms are based on the unsupervised machine learning of multiple levels of features of the data. It uses some form of gradient descent for training. Deep learning has shown impressive performance in various fields such as image segmentation, image classification and object detection. In this paper we focused on deep learning and its tool i.e. convolution neural network in generic object detection architectures along with some modifications and tricks to improve detection performance further. Finally we have given future research directions
Teaching the Machine: How People Teach Algorithms to Replace Teaching the Machine: How People Teach Algorithms to Replace People People

Automation of work via artificial intelligence is becoming a significant issue for societies. This ethnographic study presents a case study that details the process that takes place to teach the algorithms that will eventually replace the need for human effort. The author was employed as a data quality analyst and a part of a process where algorithms were taught to generate better content and eventually replace human content creators. The study proposes a three-stage process of automation where the relationship between the humans and algorithms progresses from symbiotic to cannibalistic: the first phase is the commencement phase, where the human employees and algorithms live in symbiosis, reliant on each other. The symbiosis is followed by the expansion phase, where more work is delegated to the algorithms, and the final phase is the automaton phase, where human employees are no longer needed.
EXPOSURE OF MISCHIEF TRICKS IN DEPENDENCE PROVIDENCE IN DELAY TOLERANT NETWORKS

— The network consists of numerous nodes, some nodes represents malicious and self-seeking behavior. This gives the heavy hazard for the routing in Delay Tolerant Networks (DTNs). The DTNs have unique feature so designing a mischief exposure proposal is very complex. The probabilistic mischief discovery scheme that is iTrust, is conventional for secure routing in DTN. The iTrust introduce a Trusted Authority (TA) for detecting the node’s behavior. By collecting the routing evidence from the nodes the TA checks the node about its behavior then performs the apt actions for the behavior of nodes. TA gives the precautions of DTN routing at lower cost. The detection probability is interrelated with a node’s reputation gives the dynamic recognition probability based on the trust of users. The simulation results show that the proposed format is efficient for establishing trust with the DTNs.
"Two-step cloning and expression in Escherichia coli of the DNA restriction-modification system StyLTI of Salmonella typhimurium."

The StyLTI restriction-modification system is common to most strains of the genus Salmonella, including Salmonella typhimurium. We report here the two-step cloning of the genes controlling the StyLTI system. The StyLTI methylase gene (mod) was cloned first. Then, the companion endonuclease gene (res) was introduced on a compatible vector. A strain of S. typhimurium sensitive to the coliphage lambda was constructed and used to select self-modifying recombinant phages from a ResMod+ S. typhimurium genomic library in the lambda EMBL4 cloning vector. The methylase gene of one of these phages was then subcloned in pBR328 and transferred into Escherichia coli. In the second step, the closely linked endonuclease and methylase genes were cloned together on a single DNA fragment inserted in pACYC184 and introduced into the Mod+ E. coli strain obtained in the first step. Attempts to transform ModE. coli or S. typhimurium strains with this Res+ Mod+ plasmid were unsuccessful, whereas transformation of Mod+ strains occurred at a normal frequency. This can be understood if the introduction of the StyLTI genes into naive hosts is lethal because of degradation of host DNA by restriction activity; in contrast to most restriction-modification systems, StyLTI could not be transferred into naive hosts without killing them. In addition, it was found that strains containing only the res gene are viable and lack restriction activity in the absence of the companion mod gene. This suggests that expression of the StyLTI endonuclease activity requires at least one polypeptide involved in the m.