Miracle-dz/UniFAR-FTUs huggingface dataset:
To Investigate the Relationship between TQM Enablers Applicable In Indian Engineering Educational Institutes

The present study aims to analyze the inter-linkages between the essential enablers of total quality management applicable in Indian engineering educational institutes. The implementation of TQM in engineering institutes can be used as a means to improve the quality, academic and research performance, students’ satisfaction level, employees’ morale, etc. In this research paper, twelve enablers of TQM implementation in engineering institutes have been identified using the literature review and expert opinion from academicians. In this study an ISM based methodology has been used to understand the relationship among these enablers. This helped in identifying the power level of each enabler which can facilitate in taking appropriate action so to enhance the overall standard of engineering education. Additionally a MICMAC analysis has also been used to examine the driving power and dependence power of identified enablers. The results show that the top two enablers such as ‘Vision of Institute’ and ‘Top Management Commitment’ are having high driving power and low dependence power, thus these two should be given additional consideration for better improvement of education level.
In silico studies on pyrazine derivatives for identifying potential inhibitors of PIM-1 kinase

PIM-1 kinase (PIM-1K) modulates multiple cellular functions and is evolving as a drug target for cancer. In search of potential PIM-1 inhibitors, we report herein the 3D-QSAR as well as docking studies on 3-(pyrazin-2-yl)-1H-indazole derivatives. Based on the 3D-QSAR study, the generated pharmacophore was utilized for the virtual screening of thousands of compounds (compds) from ZINC database against PIM-1K. Four top-ranked compds, ZINC05885218, ZINC05888770, ZINC08652441, and ZINC73096248, were selected by virtual screening study. The study results of molecular docking suggested that certain key residues were significant for interactions of ligand–receptor due to the formation of hydrogen bonds with Glu171, Glu121, Lys67, Asp128, Asp131 and Asp186 of PIM-1K. Virtually screened compds displayed resemblance in binding interactions within the PIM-1Ks catalytic pocket in comparison with their corresponding crystal structures. The ADRRR.2 emerged as the potential pharmacophore hypothesis and was employed for the generation of model of 3D-QSAR which gave good statistical values of q2 and r2 using partial least square analyses (r2 = 0.922, q2 = 0.8629) for the best model. The study results revealed that ZINC73096248 could serve as a prototype compd for developing novel PIM-1K inhibitors.
A Systematic Study of the Novel Materials used in Solar Cell Technologies, Opportunities and Challenges

This study aims to review the present progress of integrating the PV cell with other prominent mainstream photovoltaic cell materials. The combination of the materials in a thin film electric cell is advantageous due to the reduction within the material usage and therefore the rise in efficiency. Amorphous Silicon (α-Si), Cadmium Telluride (CdTe) and Copper Indium Gallium Selenide (CIGS) are the three major thin film solar cells technologies. The discussion mainly focuses on the environmental factors, efficiency of the energy production and limitations of the utilization of the technology. The recent progress within the third-generation cells is being reckoned as a pathway to beat the drawbacks and therefore, the concerns regarding the present solar cell technologies. Referring to this situation there are certainly more theoretical analysis than the sensible application of the third-generation solar cells. The need of low-cost, flexible, scalable and lightweight materials has certainly been the key propulsion within the development of the third-generation solar cells. The exploration of those new materials certainly holds the aptitude to enhance the device performance and stability, resulting in its commercialization within the photovoltaic field in the coming years.
A global semi-empirical GIA model based on GRACE data

The effect of Glacial Isostatic Adjustment (GIA) on the shape and gravity of the Earth is usually described by numerical models that solve for both glacial evolution and Earth’s rheology, being mainly constrained by the geological evidence of local ice extent and globally distributed sea level data, as well as by geodetic observations of Earth’s rotation. In recent years, GPS and GRACE observations have often been used to improve those models, especially in the context of regional studies. However, consistency issues between different regional models limit their ability to answer questions from 10 global scale geodesy. Examples are the closure of the sea level budget, the explanation of observed changes in Earth’s rotation, and the determination of the origin of the Earth’s reference frame. Here, we present a global empirical model of present-day GIA, solely based on GRACE data and on geoid fingerprints of mass redistribution. We will show how the use of observations from a single space-borne platform, together with GIA fingerprints based on different viscosity profiles, allows us to tackle the questions from global scale geodesy mentioned above. We find 15 that, in the GRACE era (2003-2016), freshwater exchange between land and oceans has caused global mean sea level to rise by 1.2 ± 0.2 mm/yr, the geocentre to move by 0.4 ± 0.1 mm/yr, and the Earth’s dynamic oblateness (J2) to increase by 6.0 ± 0.4 10-11/yr.
A 1 . 3 mm Superconductor Insulator Superconductor Mixer Receiver with 40 GHz Wide Instantaneous Bandwidth

The majority of SIS mixers at millimeter and submillimeter wavelengths have been designed for radioastronomy applications where low-noise and high sensitivity are of paramount importance. These include double sideband, single sideband, and 2 SB mixer receivers. Historically, the development of low noise IF amplifiers has enabled the instantaneous frequency coverage of these receivers to be extended from about 500 MHz or so available bandwidth using L Band amplifiers, centered typically at 1.5 GHz, to 8 GHz and beyond. For example, many of the receivers incorporated into the Atacama Large Millimeter Array provide IF output from 4 – 12 GHz, which is similar to that of receivers operating at the IRAM 30 m radio telescope and those at the SMA, which now provide IF output across the 4 – 16 GHz frequency range. However, in all of these recent designs, IF output below 4 GHz is not generally processed. In this paper, we present an SIS mixer receiver with an IF bandwidth of 20 GHz, which also makes use of the frequency range below 4 GHz with low noise. To achieve this, we use an IF diplexer with two outputs, the first is used to couple IF from ~ 100 MHz to 4 GHz to a commercially available low-noise SiGe amplifier, and the second couples IF above 4 GHz to a separate commercially available low noise amplifier via a wideband circulator produced in-house. In this way, the instantaneous frequency response of the receiver extends over an almost continuous 40 GHz band, with only a small gap, about 100 MHz wide, centered at the local oscillator frequency. Referring to figure 1, the receiver noise performance at low IF is similar to that at intermediate IF. However, at high IF, the receiver noise increases as a result of increased IF noise contribution, which is partly due to non-optimal mixer tuning and partly due to higher IF noise. In addition, a standing wave with period of about 1 GHz occurs at high IF. A more optimized mixer tuning coupled with improved circulator performance is expected to reduce this.
ASEAN Child Face Recognition System with FaceNet

— Current researches show that deep learning is the state-of-art techniques in machine learning which outperform the human level performance. It is also the popular technique which gives high accuracy in computer vision. Face recognition is one of the ongoing researches in biometric authentication and identification system. In this paper, we develop ASEAN child face recognition system using FaceNet. We use FaceNet as feature extractions, dlib as preprocessing and classifier as K-Nearest Neighbors (KNN). FaceNet produces 128 embeddings per face as feature vectors and calculate Euclidean distance between faces in order to measure face similarity. The proposed system achieves state-of-art face recognition performance on ASEAN child face dataset with high accuracy.
What Do Millennials Expect From Their Workplaces ? 1 An Exploratory Study in Indian Context

Workplace spirituality meets a deep desire of individuals and society to find ultimate purpose in life and to live accordingly. It is fundamentally related with listening to the inner voice in the midst of a turbulent workplace; about recognizing a need for self-integration; about connecting with one's community and family; and for developing a path toward achievement with satisfaction and security. Spirituality as a construct is a better option than religion for understanding the relationship between individual and the modern workplace. The present paper describes workplace spirituality as an element of the organizational culture which affects organizational and employees' well-being. Spirituality at workplace leads to improved ethical behaviours at a personal level and an enhanced ethical climate/culture at an organizational level.
Beginner's guide to microbiome analysis: Bioinformatics guidelines and practical concepts for amplicon-based microbiome analysis.

The advent of next-generation sequencing (NGS) allows to study living organisms by reading genetic materials in a high-throughput manner. The technology has opened up a field of microbial research in several areas such as medicine, agriculture, energy, and environment, to study a whole microbial community in an environment of interest without culturing. Bioinformatics analysis is a need in order to characterize and analyze microbiota in the studied samples. In this tutorial, we will give an overview of microbiome analysis based on high-throughput 16S rRNA genes sequencing, a commonly-used target sequence to classify bacteria and archaea. With biological and technology backgrounds, microbiome data from short-read sequencing platform will be elucidated followed by all important computational steps for microbiome analysis. The steps include data preprocessing, amplicon sequence variant analysis, taxonomy assignment, data normalization, and diversity analyses. Practical concepts and codes for the microbiome analysis will be demonstrated step by step providing a basic guideline for beginner.
Transdisciplinary Convergence: Intelligent Infrastructure for Sustainable Development

Abstract The fast-developing intelligent infrastructure landscape catalyzes transformative new relationships of human, technology, and environment and requires new socio-technical configurations of information practice and knowledge work. With a focus on data as the source of intelligence, this paper aims to explore the shifting scenarios and indicative features of data science solutions for intelligent system applications and identify the evolving knowledge spaces and integrative learning practices in the “smart” landscape. It projects and discusses the democratization of data science platforms, the distribution of data intelligence on the edge, and the transition from vertical to horizontal data solutions in solving intelligent system problems. Through mapping the changing data research landscape, this work further reveals essential new roles of knowledge architects and social engineers in enabling dynamic data linking, interaction, and exploration for transdisciplinary data convergence.
An Image Segment-based Classification for Chest X-Ray Image

In late 2019, the first case of COVID-19 was confirmed in Wuhan, China. The number of cases has been rapidly growing since then. Molecular and antigen testing methods are very accurate for the diagnosis of COVID-19. However, with sudden increases of infected cases, laboratory-based molecular test and COVID-19 test kits are in short supply. Because the virus affects an infected patient’s lung, interpreting images obtained from Computed Tomography Scanners and Chest X-ray Radiography (CXR) machines can be an alternative for diagnosis. However CXR interpretation requires experts and the number of experts is limited. Therefore, automatic detection of COVID-19 from CXR images is required. We describe a system for automatic detection of COVID-19 from CXR images. It first segmented images to select only the lung. The segmented part was then fed into a multiclass classification module, which worked well with samples obtained from various sources, which had different aspect ratios, contrast and viewpoints. The system also handled the unbalanced dataset—only a small fraction of images showed COVID-19. Our system achieved 92% of F1-score and 88.1% Marco F1-score on the 3rd Deep Learning and AI Summer/Winter School Hackathon Phase 3—Multi-class COVID-19 Chest X-ray challenge public leaderboard.
Predicting Dihydroartemisinin Resistance in Plasmodium falciparum using Pathway Activity Inference

Drug resistance threatens the effectiveness of treatments of infectious diseases, particularly on the global scale where mutation is rapid, mechanisms of resistance are developing or unknown, and limited data is available. Pathway activity inference is a dimensionality reduction method with proven effectiveness in classifying cancer types and drug responses based on transcription data. We propose a novel application of pathway activity inference to predict dihydroartemisinin resistance in the Plasmodium falciparum strain of malaria, a global infectious disease. Optimized pathway activity inference models outperform untransformed gene expression models in both in vitro regression (p = 0.03) and in vivo classification tasks (p = 2 × 10− 9). Optimal methods were found to be mostly ensemble (5 of 12) and/or kernel-based (7 of 12), providing the first evidence of the effectiveness of kernel methods for predicting drug resistance in infectious diseases. Performance metrics of the optimal in vitro model on in vivo data (accuracy , area under receiver operating characteristic curve = 0.63) affirmed the low empirical correlation between resistance measures in the two settings.
Spatiophylogenetic modelling of extinction risk reveals evolutionary distinctiveness and brief flowering period as risk factors in a diverse hotspot plant genus

Comparative models used to predict species threat status often combine variables measured at the species level with spatial variables, causing multiple statistical challenges, including phylogenetic and spatial non-independence. We present a novel bayesian approach for modelling threat status that simultaneously deals with both forms of non-independence and estimates their relative contribution, and we apply the approach to modelling threat status in the Australian plant genus Hakea. We find that after phylogenetic and spatial effects are accounted for, species with greater evolutionary distinctiveness and a shorter annual flowering period are more likely to be threatened. The model allows us to combine information on evolutionary history, species biology, and spatial data, to calculate latent extinction risk (potential for non-threatened species to become threatened), and estimate the most important drivers of risk for individual species. This could be of value for proactive conservation decision-making that targets species of concern before they become threatened.
The Pan-Arctic Continental Slope: Sharp Gradients of Physical Processes Affect Pelagic and Benthic Ecosystems

Continental slopes – steep regions between the shelf break and abyssal ocean – play key roles in the climatology and ecology of the Arctic Ocean. Here, through review and synthesis, we find that the narrow slope regions contribute to ecosystem functioning disproportionately to the size of the habitat area (∼6% of total Arctic Ocean area). Driven by inflows of sub-Arctic waters and steered by topography, boundary currents transport boreal properties and particle loads from the Atlantic and Pacific Oceans along-slope, thus creating both along and cross-slope connectivity gradients in water mass properties and biomass. Drainage of dense, saline shelf water and material within these, and contributions of river and meltwater also shape the characteristics of the slope domain. These and other properties led us to distinguish upper and lower slope domains; the upper slope (shelf break to ∼800 m) is characterized by stronger currents, warmer sub-surface temperatures, and higher biomass across several trophic levels (especially near inflow areas). In contrast, the lower slope has slower-moving currents, is cooler, and exhibits lower vertical carbon flux and biomass. Distinct zonation of zooplankton, benthic and fish communities result from these differences. Slopes display varying levels of system connectivity: (1) along-slope through property and material transport in boundary currents, (2) cross-slope through upwelling of warm and nutrient rich water and down-welling of dense water and organic rich matter, and (3) vertically through shear and mixing. Slope dynamics also generate separating functions through (1) along-slope and across-slope fronts concentrating biological activity, and (2) vertical gradients in the water column and at the seafloor that maintain distinct physical structure and community turnover. At the upper slope, climatic change is manifested in sea-ice retreat, increased heat and mass transport by sub-Arctic inflows, surface warming, and altered vertical stratification, while the lower slope has yet to display evidence of change. Model projections suggest that ongoing physical changes will enhance primary production at the upper slope, with suspected enhancing effects for consumers. We recommend Pan-Arctic monitoring efforts of slopes given that many signals of climate change appear there first and are then transmitted along the slope domain.
Towards Automated Comprehension and Alignment of Cardiac Models at the System Invariant Level

The study of cardiac arrhythmias has spurred the development of models across a variety of formulations and scales and designed for different purposes, each with distinct configuration spaces. Nevertheless, these models should be able to exhibit equivalent behavior when their contexts overlap. Configuring models to both support this context equivalence and still exhibit intended behavioral characteristics can be challenging. Due to the complexity of this problem, automation can be desirable. We present a framework aimed at automating the comprehension and alignment of cardiac model behaviors. For model comprehension, we mine a set of properties (invariants) that a model with given configuration will exhibit when executed. Comprehension can be extended to model alignment: we perform comprehension of one model, and then mine a set of configurations for a second, each of which produces invariants aligned to the invariants of the first. The configuration spaces of the two models under study need not be related in any way; rather, the systems are compared by means of the system invariants that they each exhibit. We model system invariants as association rules, a well-studied representation used in the field of data mining. We apply our methodology to two one-dimensional models of cardiac tissue. One model is the well-known differential-equations-based Fenton-Karma model representing the electrophysiology of interconnected cardiac cells, while the other is a timed automaton representation of cardiac tissue designed to enable formal analysis. We demonstrate alignment of the models with respect to activation rates and path conductance. We expect this methodology can be generalized beyond cardiac models.
Inside Brokers: Internet Appendix

We use Form 144 trades in this paper, which have not been thoroughly studied in the literature. So we first conduct a test on the relationship between these trades and future stock performance. We find that Form 144 trades – which are all insider sales – are followed by significant negative returns. In Panel A of Table IA.2, we construct a calendar-time portfolio that shorts the stocks with Form 144 trades in the past one month and longs all other stocks. The portfolio is re-balanced monthly. We report both the equal-weighted and value-weighted monthly Carhart (1997) four-factor alphas. In Panel B, we run FamaMacBeth regressions of next month returns on two different measures of Form 144 trades, controlling for other usual cross-sectional stock return predictors. Results are consistent. This suggests that Form 144 trades are indeed informative for future firm prospects.
The Case for a Long-Lived and Robust Yellowstone Hotspot

The Yellowstone hotspot is recognized as a whole-mantle plume with a history that extends to at least 56 Ma, as recorded by offshore volcanism on the Siletzia oceanic plateau. Siletzia accreted onto the North American plate at 51–49 Ma, followed by repositioning of the Farallon trench west of Siletzia from 48 to 45 Ma. North America overrode the hotspot, and it transitioned from the Farallon plate to the North American plate from 42 to 34 Ma. Since that time, it has been genetically associated with a series of aligned volcanic provinces associated with ageprogressive events that include Oligocene high-K calc-alkaline volcanism in the Oregon backarc region with coeval adakite volcanism localized above the hot plume center; midMiocene bimodal and flood-basalt volcanism of the main-phase Columbia River Basalt Group; coeval collapse of the Nevadaplano associated with onset of Basin and Range extension and minor magmatism; and late Miocene to recent bimodal volcanism along two coeval but antithetical rhyolite migration trends—the Yellowstone–Snake River Plain hotspot track to the ENE and the Oregon High Lava Plains to the WNW.
The Role of Identity and Values

Social information programs are increasingly used to nudge behavioural change, and the literature documents strong heterogeneity in their impact along household and individual traits. Using data from a large field experiment on household energy conservation, we combine electricity metering and survey data to study how environmental values affect the impact of the program. We then leverage the role of values by augmenting social information messages with an environmental self-identity prime. We find that social information is more effective when environmental self-identity is made more salient, but only among individuals who acted pro-environmentally in the past.