Miracle-dz/UniFAR-FTUs huggingface dataset:
STEM Persisters, Switchers, and Leavers: Factors Associated with 6-Year Degree Attainment for STEM Aspiring Community College Transfer Students

ABSTRACT Focusing on community college transfer students who declared a STEM major at their initial transfer to a four-year research university, this study examined the extent to which students’ sociodemographic characteristics and academic factors were related to STEM persistence and attrition. This study tracked multiple cohorts of transfer students matriculated at the four-year university between fall 2007 and fall 2014, and used data drawn from their transcript records. The findings indicated that STEM switchers, who obtained a non-STEM degree within six years of their initial transfer, were more likely to be older learners, female, and of non-Asian race/ethnicity than STEM persisters, who earned a STEM degree within six years. STEM Leavers, who did not earn any degree within six years, were more likely to be older, non-Asian, first-generation, and had a low or middle family income than STEM persisters. The study also found that STEM switchers and leavers were more likely to transfer over a lower number of courses, attempted fewer courses in the first semester, withdrew one or more courses, and received a lower semester GPA. In terms of mathematics preparation, STEM switchers and leavers were less likely to complete Calculus I or more advanced courses. The study contributes to the literature on STEM persistence and attrition, especially for STEM aspirants who transferred from community colleges to four-year universities. The findings also provide new knowledge about STEM attrition by comparing students who departed from STEM with or without earning a non-STEM degree.
MOLECULAR ABSORPTION IN ATOMSPHERE USING MILLIMETER WAVE COMMUNICATION

Millimeter wave (mmWave) communication has become the boom for manyacademic institutions and industry organisations reason for its benefits when related to the prevailing wireless communication procedures comprising of Wifi, 4G, 5G,mmWavepcommunications such as WiFi and 4G, mmWave communications implement frequency carriers at higher level including such features enormous range of bandwidth, quality, narrow beam, extreme transmissionsuperiority, and robust detection capability. Such critical situations affected by the most preferrable applications will be well tackled with the problems face by employing wireless technologies. The increase rapidly traffic can substantially lighten the wireless communication due to enhancement in the technology ofmmWave communications. Over the insight of mmWave advantageous with respect to the easy obstruction, clear handling, less wavelength, and blockings etc., to do with the major issues and concerns that utilize for the additionalamenities with enormous quantity of 5G communications with the traditional way ofstatementfor anti-impeding, dynamic control, interference supervision, spatial reuse, and system design for enhancement. This study surveys the various standards and concerns related to structural design and protocols for mmWave communicationsassessedon cellular based access and its beneficial areas has been investigated. KeywordsMillimeter (mm) wave communications; cellular access; wireless technology; 5G communication systems; Antenna array.
Development and Evaluation of Blockchain based Secure Application for Verification and Validation of Academic Certificates

Elva Leka and Besnik Selimi, “Development and Evaluation of Blockchain based Secure Application for Verification and Validation of Academic Certificates”, Annals of Emerging Technologies in Computing (AETiC), Print ISSN: 2516-0281, Online ISSN: 2516-029X, pp. 22-36, Vol. 5, No. 2, 1st April 2021, Published by International Association of Educators and Researchers (IAER), DOI: 10.33166/AETiC.2021. 02.003, Available: http://aetic.theiaer. org/archive/v5/v5n2/p3.html. Review Article
Research On Visual Question Answering Based On Deep Stacked Attention Network

Aiming at the problem that the existing visual question answering model has a language bias in high-level logical reasoning, the model describes images or answers questions with low accuracy. A visual question answering model based on deep stacked attention networks is proposed. At the same time, the attention mechanism is introduced to help the model fully mine the image information; secondly, the problem attention mechanism is introduced to pay attention to the image and the question information at the same time when the problem feature is extracted; finally, the features of the image and the question are integrated and the answer is selected in the classifier. Taking the Visual Genome and VQA2.0 data sets as examples for empirical analysis, the results show that the accuracy of predicting answers is 1.13% higher than the existing best model, which proves the effectiveness and applicability of the model.
Green Entrepreneurial Orientation and Green Innovation in Small and Medium-Sized Enterprises (SMEs)

Since environmental issues are becoming an integral part of business performances, policymakers and managers have started recognizing the importance of green innovation towards sustainable business performances. The role of the automotive parts industry is crucial in minimizing environmental degradation and promoting sustainable development. Yet few studies have focused on the connection between green entrepreneurial orientation and green innovation that may affect small and medium enterprise (SME) business performance. Therefore, this study aims to analyze the influence of green entrepreneurial orientation on green innovations, and its effects on sustainable business performances in the automotive parts industry in Thailand. The sample consists of 226 SMEs in the automotive parts industry in Thailand. The partial least square method (PLS-SEM) has been used for the analysis of data. The results of the study show that green innovations have the strongest influence on economic and environmental performances. This study contributes to resource-based view theory by incorporating green innovation as a strategic competency of SMEs’ performance. Further, green entrepreneurial orientation and green innovation can assist SME managers in understanding the factors leading to sustainable performance of businesses.
Crowdsourcing-based application to solve the problem of insufficient training data in deep learning-based classification of satellite images

Abstract In order to solve insufficient training data problem in remote sensing, a web platform was created so that registered users can generate labeled data for various classes in a dynamic structure. Users were asked to select representative pixel groups for the forest, hazelnut, shadow, soil, tea, and building classes with the polygon tool, and then assign a class label corresponding to each created polygon thanks to the help document displaying descriptive information regarding the locations, colors, textures and distributions of the classes in the image. Crowdsourcing was again used to test the accuracy of the tagged data produced by crowdsourcing. The created data set was overlaid with the original WV-2 image, and the correctness of the labels of the polygons was once visually verified. Finally, the WV-2 image, consisting of 40 patches, was classified with CNN and an average of over 95% accuracy was achieved.
Elevated Tropospheric Ozone Concentration Alters Soil CO2 Emission: A Meta-Analysis

Elevated tropospheric ozone (O3) concentration may substantially influence the below-ground processes of terrestrial ecosystems. Nevertheless, a comprehensive and quantitative understanding of O3 impacts on soil CO2 emission remains elusive, making the future sources or sinks of soil C uncertain. In this study, 77 pairs of observations (i.e., elevated O3 concentration treatment versus control) extracted from 16 peer-reviewed studies were synthesized using meta-analysis. The results depicted that soil CO2 efflux was significantly reduced under short-term O3 exposure (≤1 year, p < 0.05), while it was increased under extended duration (>1 year, p < 0.05). Particularly, soil CO2 emission was stimulated in nonagricultural ecosystems, in the free-air CO2 enrichment (FACE) experiment, and in the soils of lower pH. The effect sizes of soil CO2 efflux were significantly positively correlated with experimental duration and were significantly negatively correlated with soil pH, respectively. The ozone effect on soil CO2 efflux would be enhanced at warm temperatures and high precipitation. The duration of O3 exposure was the fundamental factor in analyzing O3 impacts on soil CO2 emission.
Multi-objective optimization of laminar flow characteristics in helical coiled tubes based on NSGA-II

To meet the requirement of energy conservation and optimize the design of helical coils in laminar flow, the pitch, the coil radius and the average inlet velocity were selected as design parameters to obtain the tendency of the heat transfer and flow resistance characteristics of the helical coils. Heat transfer coefficient and pressure drop per unit length of helical coils were taken as the optimization objective functions. A multi-objective optimization model was established, three candidate points A, B and C were obtained based on Non-dominated Sorted Genetic Algorithm-II (NSGA-II). The results showed that heat transfer coefficient and pressure drop per unit length increase with the pitch and inlet velocity whilst them decrease with coil radius, among which the influence of inlet velocity is the most obvious and the pitch is the least. It was found that the coil radius of most points in Pareto front was relatively concentrated. The comprehensive performance of candidate points A, B, C had been improved significantly by 16.3%, 28.3% and 19.6% respectively.
Studies on Nylon-66 membrane using aqueous solutions of potassium and lead nitrate salts as permeants

Abstract Measurements on hydrodynamic and electro-osmotic permeability of water and aqueous solutions of KNO3 and Pb(NO3)2 in the concentration (C) range of 10 -4 $10^{-4}$ M to 10 -3 $10^{-3}$ M are made across the Nylon-66 membrane. The data obtained are used to ascertain the form of transport equation using the theory of non-equilibrium thermodynamics. Conductance of membrane equilibrated with water and aqueous solutions are measured and the data are used to estimate phenomenological coefficients. These phenomenological coefficients are used to determine the average pore radius, the average number of pores and the membrane constant. Zeta potentials are evaluated using electro-osmotic permeability and membrane-permeant conductance data to understand the electrical nature of the membrane-permeant interface. It is observed that hydrodynamic permeability and electro-osmotic permeability depend linearly on the applied pressure difference and the potential difference, respectively.
Mapping and Monitoring the Multi-Decadal Dynamics of Australia's Open Waterbodies Using Landsat

Water detection algorithms are now being routinely applied to continental and global archives of satellite imagery. However, water resource management decisions typically take place at the waterbody rather than pixel scale. Here, we present a workflow for generating polygons of persistent waterbodies from Landsat observations, enabling improved monitoring and management of water assets across Australia. We use Digital Earth Australia’s (DEA) Water Observations from Space (WOfS) product, which provides a water classified output for every available Landsat scene, to determine the spatial locations and extents of waterbodies across Australia. We generated a polygon set of waterbodies that identified 295,906 waterbodies ranging in size from 3125 m2 to 4820 km2. Each polygon was used to generate a time series of WOfS, providing a history of the change in surface area of each waterbody every ~16 days since 1987. We demonstrate the applications of this new dataset, DEA Waterbodies, to understanding local through to national-scale surface water spatio-temporal dynamics. DEA Waterbodies provides new insights into Australia’s water availability and enables the monitoring of important landscape features such as lakes and dams, improving our ability to use earth observation data to make meaningful decisions.
Improving success and efficiency of underwater autonomous tasks through dynamic re-planning and episodic reasoning

In this paper, we present a novel underwater autonomy architecture that combines reasoning about prior history with dynamic selection of the best set of action options for the current environment. Prior history is recorded as a set of indexed episodes, including features best describing the environment each episode occurred in, the set of actions and their parameters, and the system’s performance during the episode. Based on previous history most related to current experience, the robot dynamically selects actions and/or parameters most likely to succeed in the immediate environment; the action space is represented as a dynamic Hierarchical Task Network (HTN). We have implemented and tested the architecture in UWSim, on a simulated Blue ROV-2 with a 4 DOF manipulator in a 3D motion planning domain, where the task goal is to touch a designated underwater object with the arm’s end effector. We have shown that after just 20 episodes of learning, the robot converges on a stable global policy that maximizes success rates of object touch task. The architecture is designed to be relatively domain-independent, and is applicable to a variety of underwater tasks, such as survey/search, manipulation, active perception, etc. We are currently extending our implementation to a survey domain.
QoS-aware CR-BM-based hybrid framework to improve the fault tolerance of fog devices

With the evolution of the Internet of Things (IoT), the use of smart devices has completely changed the day-to-day life of the human being. IoT devices are of flexible use which is implemented to sense the environment and data efficiently. However, these devices have some constrained capabilities concerning fault tolerance, computation cost, and storage. This requires an improved framework and algorithms for performing effective operations. In this paper, a hybrid framework is proposed, which incorporates the various IoT devices in fog environments to enhance fault tolerance. The proposed framework implements a novel QoS-aware technique based on the combination of checkpoints and replication (CR) for diagnosing faults and the bee-mutation (BM) algorithm for optimal placement of service. A fog service monitor is established to observe the performance of fog nodes. Both the CR module and BM module access the service monitor to ensure that the proposed hybrid framework is fault-tolerant. Furthermore, the proposed CR-BM-based hybrid framework has been evaluated for its performance by using various performance metrics. In the comparative analysis, it is observed that the proposed hybrid framework outperforms the existing genetic algorithm-based framework.
Design of Wave Glider Optimal Parameters Suitable for the Northwest Pacific Ocean, the North Indian Ocean, and the South China Sea

To study the optimal design of Wave Glider parameters in the wave environment of the Northwest Pacific Ocean, the North Indian Ocean, and the South China Sea, the average velocity of a Wave Glider was taken as the evaluation criterion. Wave reanalysis data from ERA5 were used to classify the mean wave height and period into five types by the K-means clustering method. In addition, a dynamic model was used to simulate the influence of umbilical length, airfoil, and maximum limited angle on the velocity of the Wave Glider under the five types of wave element. The force of the wings was simulated using FLUENT as the model input. The simulation results show that (1) 7 m is the most suitable umbilical length; (2) a smaller relative thickness should be selected in perfect conditions; and (3) for the first type of wave element, 15° is the best choice for the maximum limited angle, and 20° is preferred for the second, third, and fourth types, while 25° is preferred for the fifth type.
Synthesis of azabicycloalkanone amino acid and azapeptide mimics and their application as modulators of the prostaglandin F2α receptor for delaying preterm birth

Premature birth (<37 weeks gestation) is the major cause of perinatal mortality and morbidity and has been steadily increasing worldwide. Towards the rational design of more effective therapeutic agents for inhibiting uterine contractions and prolonging gestation (a so-called tocolytic drug), our team has targeted the prostaglandin F2α receptor (FP) employing a peptidomimetic approach designed to provide modulators of this novel target. We identified first a lead peptide (PDC113) (1) based on the sequence of the second extracellular loop of FP on the basis that the loop itself might modulate receptor activation. Systematic study of the structure−activity relationships of 1 generated hypotheses concerning the conformation and side-chains responsible for activity that led to the synthesis of PDC113.31 (2), a potent all d-amino acid peptide, which has successfully completed Phase 1b clinical trials. Employing indolizidinone amino acids, peptide mimics were developed that served to probe the mechanism of FP m.
Reconstruct fingerprint images using deep learning and sparse autoencoder algorithms

Fingerprinting is one form of biometrics, a science that can be used for personal identification. It is one of the important techniques and security measures for human authentication across the globe due to its uniqueness and individualistic characteristics. Fingerprints are made up of an arrangement of ridges, called friction ridges. Each ridge consists of pores, that are attached to the glands under the skin. Several algorithms proposed different approaches to recreate fingerprint images. However, these works encountered problems with poor quality and presence of structured noise on these images. In this paper, we present a novel fingerprint system that provides more unique and robust algorithms which are capable to distinguish between individuals effectively. A sparse autoencoder (SAE) algorithm is used to reconstruct fingerprint images. It is an unsupervised deep learning model that replicates its input at the output. The architecture is designed and trained with datasets of fingerprint images that are pre-processed to be able to fit them in the model. Three datasets of fingerprint images have been utilized to validate the robustness of the model. This dataset has been split into 70% for training and 30% for testing the model. SAE is fine-tuned and optimized with L2 and sparsity regularization, thus it increased the efficiency of learning representation for the architecture. The sparse autoencoder is a suitable deep learning model to improve the recreation of fingerprint images significantly. The proposed approach showed promising results, and it can enhance the quality of reproduced fingerprint images with a clear ridge structure and eliminating various overlapping patterns.
Mathematical Model of a Bubble Column for the Increased Growth of Arthrospira platensis and the Formation of Phycocyanin

The objective of this research was to develop a mathematical model for batch photoautotrophic cultivation of Arthrospira platensis and to validate it against data obtained in experiments. All trials were carried at 30°C, under a light intensity of 60 or 120 µmol m -2 s -1 . The purpose of the model was to determine the optimal concentration of carbon dioxide, as well as to investigate the formation of phycocyanin. For the experimental conditions in this study, the optimal concentration carbon dioxide (0.8% CO 2 , v/v) was predicted using the model according to the initial bicarbonate level, the carbon uptake by the microalga, the pH, and the mass transfer process. The use of this optimal value in the gas inlet seems to be a suitable option for maintaining the optimal pH (9.5), thereby eliminating the need for a pH controller in the bioreactor system. According to the simulations, the mass fraction of the phycocyanin formation rate seems to depend on the internal light level. The percentage of adjustment obtained (R 2 ) was ≥75%. The velocity of ph ycocyanin formation was enhanced at intensities up to 120 µmol m -2 s -1 . However, the actual internal irradiance values were lower than the light compensation point (4.5 µmol m -2 s -1 ), so phycocyanin formation ceased. The mathematical model may facilitate the examination of optimal carbon delivery, as well as the light input, in several A. platensis culture conditions aimed at phycocyanin production.
Multiobjective fuzzy vehicle routing using Twitter data: Reimagining the delivery of essential goods

The world faced a major disruption in the form of the coronavirus disease (COVID‐19) pandemic, which caused many countries to impose severe restrictions on movement, popularly known as “lockdown. ” These lockdowns impacted transportation adversely, leading to massive disruptions in global and local supply chains. As the local markets were shut down, more people started turning to e‐commerce logistics platforms offering doorstep deliveries of essential items (food and medicines). This resulted in an explosion in demand for such services, and businesses struggled to complete their deliveries. Additionally, the volume of real‐time text data suddenly increased, as these customers started sharing their feedback on social media platforms. The availability of real‐time raw text data and its popularity for solving complex business problems motivated the development of the approach proposed herein to address last‐mile delivery issues. Thus, this paper suggests the use of Twitter data to identify the various grievances of customers about e‐commerce logistics platforms. Natural language processing, a popular tool for text analytics, is employed to extract consumer tweets from the Twitter profiles of such businesses and subsequently to clean, process, and analyse them. Issues are categorized and used as objectives in a multiobjective fuzzy vehicle routing problem (VRP). An integrated hybrid fuzzy VRP is developed and coded to solve last‐mile delivery issues. Experimental results and comparative analyses highlight the benefits of the novel approach. Managerial insights and scope for future research assist in the further development of the idea.
Interactions of Biological Buffers with Macromolecules: The Ubiquitous “Smart” Polymer PNIPAM and the Biological Buffers MES, MOPS, and MOPSO

Proteins are practically never in a buffer-free solution. We therefore studied the effects of some important biological buffers 2-(N-morpholino)ethanesulfonic acid (MES), 3-(N-morpholino)propanesulfonic acid (MOPS), and 3-morpholino-2-hydroxypropanesulfonic acid (MOPSO) on the lower critical solution temperature (LCST) phase transition of poly(N-isopropylacrylamide) (PNIPAM), an isomer of polyleucine, as a model compound for protein. The results from dynamic light scattering (DLS) analysis showed that the LCST of PNIPAM aqueous solutions were decreased significantly with increasing the buffer concentration, due to the presence of the buffers destroying the hydration structures and subsequent aggregation of PNIPAM. Based on density functional theory (DFT) and molecular mechanics calculations, these buffers are highly polar compounds and, therefore, strongly interact with water molecules, causing buffering out of PNIPAM. The buffer’s affinity to water follows the order MOPS > MES > MOPSO. The LCST values de.