Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Generating System with Solar and Fuel Cell using ANFIS Controller

This paper proposes a hybrid generating system which consists of the solar photovoltaic (PV) array and the fuel cell which here acts both as a source and a backup storage device to deliver the maximum power to the three phase ac load. The maximum power delivered to the load by the solar PV is achieved by MPPT controller which employs adaptive neuro-fuzzy inference system (ANFIS). The proposed ANFIS-based MPPT offers an extremely fast dynamic response with great accuracy. The proposed system consists of photovoltaic module, Fuel cell, switching system, boost converter, voltage source inverter (VSI) and ANFIS controller to control the duty cycle of boost converter switch as well as the modulation index of voltage source inverter (VSI). The entire proposed system has been modeled and simulated using MATLAB/Simulink software. The simulation results reveal that the proposed hybrid generating system is very simple, very efficient and low cost.
Visual Landmark Selection for Generating Grounded and Interpretable Navigation Instructions

Instruction following for vision-and-language navigation (VLN) has prompted signiﬁcant research efforts developing more powerful “follower” models since its inception in [1]; however, the inverse task of generating visually grounded instructions given a trajectory – or learning a “speaker” model – has been largely unexamined. This task is itself a challenging visually-grounded language generation problem akin to video or image captioning. Unlike these tasks however, instruction generation has a straight-forward notion of correctness – can a follower arrive at the correct location based on generated instructions? Further, improved speaker models can be leveraged to strengthen follower models via data augmentation or back-translation. In this abstract we present a work-in-progress “speaker” model that generates navigation instructions in two stages, by ﬁrst selecting a series of discrete visual landmarks along a trajectory using hard attention, and then second generating language instructions conditioned on these landmarks. This two-stage approach improves over prior work, while also permitting greater interpretability. We hope to extend this to a reinforcement learning setting where landmark selection is optimized to maximize a follower’s performance without disrupting the model’s language ﬂuency.
Future Educators’ Perceptions of African American Vernacular English (AAVE)

Teachers are responsible for educating students’. Unfortunately, many teachers, however, are not prepared to meet the increasing need of the students’ population that they encounter in the classroom. One area in which teachers do not receive adequate training is language diversity. In particular, African American Vernacular English is often an unfamiliar language for many educators. This study employed a language attitude scale to assess the attitudes toward African American Vernacular English of preservice teachers in a multicultural, undergraduate education course. The findings revealed that European American students expressed a belief that African American Vernacular English is a negative and inferior dialect of English, while African American and Hispanic students expressed more favorable views of African American Vernacular English. The study suggests that pre-service teachers should receive training on the different dialects of English that students may speak in the classroom.
Strategy implementation: the Critical Link between Strategic planning process and Performance of accredited Universities in Kenya

Is the concept of strategic planning falling out of fashion in the 21 st century business model or has the how and when of planning become increasingly complex? Strategic planning as a management tool though adopted by many organizations, has not yielded similar outcome. This necessitates further enquiry on causes of discrepancies. The operationalization and institutionalization of strategy during the implementation stage has been conceptualized as a possible mediator in this study. The constant increase in demand for and access to higher education has led to a need for sustainable competitive advantage addressing all university stakeholder. Results indicate that implementation is a significant intervener between planning and performance of universities as partial mediator. The study recommends that all personnel be involved in strategy from formulation to enhance their understanding and participation in implementation. It suggests a longitudinal study to establish patterns at transition from formulation to implementation for further research. Key words; Implementation, Institutionalization, Operationalization, Accredited universities, Planning process
Hybrid Solar Cells from Regioregular Polythiophene and ZnO Nanoparticles

Blends of nanocrystalline zinc oxide nanoparticles (nc‐ZnO) and regioregular poly(3‐hexylthiophene) (P3HT) processed from solution have been used to construct hybrid polymer–metal oxide bulk‐heterojunction solar cells. Thermal annealing of the spin‐cast films significantly improves the solar‐energy conversion efficiency of these hybrid solar cells to ∼ 0.9 %. Photoluminescence and photoinduced absorption spectroscopy demonstrate that charge‐carrier generation is not quantitative, because a fraction of P3HT appears not to be in contact with or in close proximity to ZnO. The coarse morphology of the films, also identified by tapping‐mode atomic force microscopy, likely limits the device performance.
Effects of manure‐ and lignocellulose‐derived biochars on adsorption and desorption of zinc by acidic types of soil with different properties

There is limited information on zinc (Zn2+) adsorption and desorption by different types of soil, especially acidic ones following amendment with manure‐ and lignocellulose‐derived biochars. The novelty of this research is a comparison of the mechanisms of adsorption and desorption of Zn2+ by different types of acidic soil amended with biochar derived from either swine manure or rape straw. Both biochars were prepared at a pyrolysis temperature of 500°C before being incorporated into four types of soil with low pH (pHH2O from 4.61 to 5.91) at an incorporation rate of 3%. Following incubation for 360 days, we carried out batch experiments of Zn2+ adsorption and desorption. The dual‐mode model (DMM) was more suitable for describing the adsorption isotherms of the four soil types following biochar incorporation than the distributed reactivity model (DRM). Swine‐manure biochar enhanced Zn2+ adsorption by soil more than rape‐straw biochar. The increases in Zn2+ adsorption by the different soil types following biochar incorporation were in the order Psammaquent > Plinthudult > Paleudalf > Argiustoll soil, whereas it was most difficult to desorb the Zn2+ already present, in reverse order: Argiustoll > Paleudalf > Plinthudult > Psammaquent soil. This could be attributed to differences in soil pH, organic carbon, cation exchange capacity (CEC) and soil texture. Both non‐electrostatic and electrostatic adsorption mechanisms were identified in the four soil types. The negative surface charge of biochar is responsible for the electrostatic adsorption, whereas functional oxygen groups and mineral components of biochar were responsible for non‐electrostatic adsorption. Non‐electrostatic adsorption was more important for the swine‐manure biochar than for the rape‐straw biochar, which was attributed to the larger content of mineral components (phosphate and silicate anions) of the former. In conclusion, the biochar effects on Zn2+ adsorption depended on the initial Zn2+ concentration, soil pH, soil type and biochar origin.
Journal of Insects as Food and Feed

The black soldier fly has shown great promise in addressing two environmental concerns: (1) waste management; and (2) protein supplementation for use as feed for livestock, poultry, and aquaculture. Thus, tremendous efforts have been placed on mass-production of the black soldier fly. Currently, little is known about the thermal tolerance limits of black soldier fly eggs and immatures. The objective of this study was to determine the lower temperature threshold for black soldier fly development. Development time, egg eclosion and adult emergence success were measured at 12, 16 and 19 °C. We determined that the lower threshold for egg hatch was between 12 and 16 °C, taking 15 days to hatch. Furthermore, we determined that the lower temperature threshold for larvae is between 16 and 19 °C with egg hatch in 7.75 days at 19 °C. Mean development time from egg to adult at 19 °C was 72 days.
Preparation and Characterization of Poly(vinyl alcohol)-chondroitin Sulphate Hydrogel as Scaffolds for Articular Cartilage Regeneration

The aim of the study was to develop PVA-CS hydrogel scaffolds using glutaraldehyde as a cross-linking agent by chemical cross-linking method in order to obtain biomimetic scaffolds for articular cartilage regeneration. The introduction of PVA enhances the mechanical and bioadhesive properties to the native tissue while chondroitin sulphate enhances the glycosaminoglycan content of extracellular matrix. The role of hydrogel as cartilage regeneration scaffold was evaluated by swelling study, porosity, rheological behaviour, in vitro degradation, and quantification of released chondroitin sulphate. In vivo results showed that cross-linked hydrogels repaired defects with no sign of inflammation as it was well anchored to tissue in the formation of new articular surface. It may be concluded that the addition of chondroitin sulphate to the PVA polymer develops a novel composite with significant applications in cartilage tissue engineering.
Predicting and Analyzing the Performance of the IPL Cricket Using Regression Models

-- With the increasing number of matches day by day, it has become difficult to manage or extract useful information from the available data of all the matches. The paper presents a data visualization and prediction tool in which an open-source, distributed, and non-relational database. The game of cricket is played in various formats, i.e., One Day International, T20 and Test Matches. The Indian Premier League (IPL) is a Twenty-20 cricket tournament league established with the objective of promoting cricket in India and thereby nurturing young and talented players. The league is an annual event where teams representing different Indian cities compete against each other. This data is then used for visualizing the past performance of players’ performance. Additionally, the data is used to predict the outcome of a match through various machine learning approaches. The proposed tool can prove beneficial for the team managements in the player auctions for selecting the right team. We are using SVM, Random forest, Decision tree classifiers and to generate the prediction models for the problem.The prediction of the matches based on the toss wins and predicting the outcomes who will be winning the matches, addresses the problem of predicting the outcome of an IPL cricket match.
Solving Large Top-K Graph Eigenproblems with a Memory and Compute-optimized FPGA Design

Large-scale eigenvalue computations on sparse matrices are a key component of graph analytics techniques based on spectral methods. In such applications, an exhaustive computation of all eigenvalues and eigenvectors is impractical and unnecessary, as spectral methods can retrieve the relevant properties of enormous graphs using just the eigenvectors associated with the Top-K largest eigenvalues.In this work, we propose a hardware-optimized algorithm to approximate a solution to the Top-K eigenproblem on sparse matrices representing large graph topologies. We prototype our algorithm through a custom FPGA hardware design that exploits HBM, Systolic Architectures, and mixed-precision arithmetic. We achieve a speedup of 6.22x compared to the highly optimized ARPACK library running on an 80-thread CPU, while keeping high accuracy and 49x better power efficiency.
Recognition of Learners’ Cognitive States using Facial Expressions in E-learning Environments

Technological developments in e-learning systems provide new opportunities for students to enhance academic growth and improve access to education. E-learning is on the rise because it has advantages over conventional learning. The need for time is a faster and simpler learning experience. The spread of the coronavirus disease (COVID-19) pandemic has resulted in school closures around the world. More than one billion students are out of the classroom worldwide. As a consequence, education has taken on a new shape, with a major increase in e-learning, whereby teaching is carried out online and on digital platforms. The scope of this research is to identify the facial expressions of the students and then link these expressions to the cognitive states. This paper presents a hybrid-CNN model to recognize a learner's cognitive state using the manually engineered features and features extracted from the convolutional neural network. In addition, the performance of the model is compared with the manual feature extraction method and CNN methods separately. The proposed method trained and tested with the spontaneous database(DAiSEE) created exclusively for the elearning environment, and with the state-of-the-art datasets such as JAFFE and CK+. The model has achieved 53.4%, 71.4%, and 99.95% respectively.
Defining Privacy for Data Mining-An Overview

: PPDM (Privacy preserving data mining) in receipt of valid data mining results without learning the original or essential data values. It has been receiving attention in the research scoiety and beyond. That time it was unclear what privacy preserving means. This paper provides a study framework and metrics for discussing the meaning of privacy preserving data mining, as groundwork for extra research in this field.
α-Cyclodextrin Modified Screen Printed Graphite Electrodes for Detection of Phenols

A screen printed graphite electrode has been developed for a simple and sensitive determination of phenolic compounds in an aqueous solution. The electrode developed uses a simple and effective screen printing technique with α-Cyclodextrin(α-CD) modified graphite ink. Phenols were captured on the surface of the αCD modified electrode through complex formation. The phenol/α-CD complex was deposited and quantified electrochemically using cyclic voltammetry (CV), differential pulse voltammetry (DPV) and square wave voltammetry (SWV). The optimization of the experimental parameters was performed in regard to electrode composition, pH, temperature, sample preconcentration time. Interferences from other organic compounds were investigated. The detection limit for phenols was 500 ± 7 nM for DPV, with the linear range of 0.5 µM 25.0 µM and 30 ± 2 nM for SWV, with the linear range of 30 nM - 50µM, respectively.
Simulation of Two-Dimensional Random Structure Through Inversely Generated Delaunay Subgraphs

—Subgraphs taken from Delaunay triangulations are used as a basis for the representation of spacing in two-dimensional random structure. Stochastic simulations are presented in which Delaunay triangulations are used to determine sets of points rather than vice versa. Reversal of the usual tri-angulation problem allows the simulations to be based on side-length probability distribution functions. A convenient ﬁnding is that, for triangulations with a standard deviation of less than or equal to approximately one-half of the minimum spacing, simulations tend to reproduce the original distributions without using conditional probabilities on the generated side lengths. It is also shown that, for distributions of much larger variance, simulations may be readily tuned based on the introduction of an assumed, possibly ﬁctitious, conditional probability for the third side in each triangle. Two distributions are used as numerical examples, one which shows that the scatter in ﬁber density in carbon-ﬁber composites can be readily simulated from the data obtained by image-processing of micrographs of ply cross-sections and a second which shows that an arbitrary distribution may be simulated through introduction of the aforementioned assumed conditional probability on the third side in each triangle.
Towards a Process for Migrating Legacy Systems into Microservice Architectural Style

Microservice architectural style is a paradigm to develop systems as a suite of small and autonomous services, communicating thought a lightweight protocol. Currently, the most common way of adopting microservice architectures is by the modernization of legacy monolith systems. The migration of a legacy system into a microservice architecture is not a trivial task. In addition, there is a lack of recommendation or guidelines on how to perform such process. In view of this, this paper presents a preliminary process for conducting the migration of legacy systems into microservice architectures. This process was defined by analyzing and discussing pieces of work on the topic. As result, we propose a process composed of eight steps, grouped in four phases, which we describe together with their common input and output.
Protein–mineral hybrid capsules from emulsions stabilized with an amphiphilic protein

Biomineralization comprises a most promising process to produce novel materials in an energy-efficient way. We present a straightforward route for the production of hybrid capsules composed of the amphiphilic protein hydrophobin and hydroxyapatite; the synthesis is water-based and can be performed without toxic ingredients. Emulsion droplets stabilized with hydrophobin serve as templates for the growth of a mineral shell, permeable for small molecules. The system is sensitive to the choice of oil and the mineralization time, offering ample opportunities to tune capsule properties like shell thickness and morphology. The mineral phase as well as the behaviour of the capsules upon thermal treatment is investigated. The resulting capsules could find applications as microreactors or in the field of drug delivery.
Generative Adversarial Networks and Continual Learning ∗

There is a strong emphasis in the continual learning literature on sequential classiﬁ-cation experiments, where each task bares little semblance to previous ones. While certainly a form of continual learning, such tasks do not accurately represent many continual learning problems of the real-world, where the data distribution often evolves slowly over time. We propose using Generative Adversarial Networks (GANs) as a potential source for generating potentially unlimited datasets of this nature. We also identify that the dynamics of GAN training naturally constitute a continual learning problem, and show that leveraging continual learning methods can improve performance. As such, we show that techniques from both continual learning and GAN, typically studied separately, can be used to each other’s beneﬁt.
Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications

Recently, interest in Internet of Vehicles’ (IoV) technologies has significantly emerged due to the substantial development in the smart automobile industries. Internet of Vehicles’ technology enables vehicles to communicate with public networks and interact with the surrounding environment. It also allows vehicles to exchange and collect information about other vehicles and roads. IoV is introduced to enhance road users’ experience by reducing road congestion, improving traffic management, and ensuring the road safety. The promised applications of smart vehicles and IoV systems face many challenges, such as big data collection in IoV and distribution to attractive vehicles and humans. Another challenge is achieving fast and efficient communication between many different vehicles and smart devices called Vehicle-to-Everything (V2X). One of the vital questions that the researchers need to address is how to effectively handle the privacy of large groups of data and vehicles in IoV systems. Artificial Intelligence technology offers many smart solutions that may help IoV networks address all these questions and issues. Machine learning (ML) is one of the highest efficient AI tools that have been extensively used to resolve all mentioned problematic issues. For example, ML can be used to avoid road accidents by analyzing the driving behavior and environment by sensing data of the surrounding environment. Machine learning mechanisms are characterized by the time change and are critical to channel modeling in-vehicle network scenarios. This paper aims to provide theoretical foundations for machine learning and the leading models and algorithms to resolve IoV applications’ challenges. This paper has conducted a critical review with analytical modeling for offloading mobile edge-computing decisions based on machine learning and Deep Reinforcement Learning (DRL) approaches for the Internet of Vehicles (IoV). The paper has assumed a Secure IoV edge-computing offloading model with various data processing and traffic flow. The proposed analytical model considers the Markov decision process (MDP) and ML in offloading the decision process of different task flows of the IoV network control cycle. In the paper, we focused on buffer and energy aware in ML-enabled Quality of Experience (QoE) optimization, where many recent related research and methods were analyzed, compared, and discussed. The IoV edge computing and fog-based identity authentication and security mechanism were presented as well. Finally, future directions and potential solutions for secure ML IoV and V2X were highlighted.