Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Different Pre-Treatment on the Microstructure and Intumescent Properties of Rice Husk Ash-Based Geopolymer Hybrid Coating

Despite the growing popularity of rice husk ash (RHA) in various applications, limited research has been devoted to identify the influence of silica content in RHA on the intumescent properties. The present work aims to introduce a novel and economical geopolymer hybrid fire retardant coating by utilizing the use of RHA. The silica from Rice husk (RH) was extracted using distilled water and hydrochloric acid as leaching agents and subjected to pyrolysis treatment. X-ray fluorescence (XRF) analysis indicated that RH that underwent HCl pre-treatment at 600 °C for one hour produced a high purity amorphous silica content of 93.92%. XRD measurements revealed that HCl pretreatment increased the crystallization temperature of RHA to 1000 °C and retained the amorphous state of silica for 2 h. In a fire resistance test, temperature at the equilibrium and time taken to reach 200 °C for sample S3 (93.92% wt. silica) showed 5.83% and 3.48% improvement compared to sample S1 (87.49% wt. silica). The microstructure analysis showed that sample S1 possessed bigger pores on the coating surface while an increment in silica content in sample S3 produced a dense foam structure. Results from a fire resistance test were supported by the Energy dispersive X-ray (EDX) analysis of the sample. The oxygen-to-carbon ratio of S1 and S3 coating samples were 1.695 and 1.622 respectively, which indicated that lower oxygen–to-carbon ratio in sample S3 coating resulted in better anti-oxidant properties. Interestingly, the increment of SiO2 content in RHA efficiently improved the compactness of the char layer, which resulted in a relatively higher fire-retardant efficiency. RHA proved to be a promising environmentally friendly strategy to replace halogenated fire retardant materials.
The Effects of Chewing Gum on the Formation of Plaque on the Smooth Teeth Surface

Iran Abstract Background and Aim: Gingivitis is the most common gingival inflammatory disease. Plaque control methods play a major role in the prevention of periodontal disease and dental caries, which include mechanical and chemical technique. The present study was conducted to investigate the effect of chewing gum on dental plaque on smooth teeth surface. Materials and Methods: A cross-over clinical trial was conducted on 18 subjects. The study was conducted in two 2-day duration with a 4-day wash out period. At the beginning of the study, subjects received professional prophylaxis. During the study period, they refused to brush and floss. Volunteers in the test group were instructed to chew one pellet four times a day for 20 min. On the third day, O'Leary and the Bleeding Point Index were measured. In the second stage, two groups were displaced. Finally, the data were subjected to statistical analysis by paired t-test and Wilcoxon. Results: There was no significant reduction in plaque and bleeding index in test group (P =0.187) compared to control group. Conclusion: In the absence of oral health procedure, chewing gum has no significant effect on plaque reduction in the smooth teeth
Comparative analysis of various methods for modelling surface permanent magnet machines

In this study, six different modelling methods for permanent magnet electric machines are compared in terms of their computational complexity and accuracy. The methods are based primarily on conformal mapping, mode matching, and harmonic modelling. In the case of conformal mapping, slotted air gap of a complex machine geometry is transformed to a smooth slotless air gap where analytical expression for field solution is available. The solution in the canonical domain is then mapped back to the original slotted air-gap domain. Mode matching or subdomain method, as it is called in different sources, is using a solution of Laplace's equation to model the slotted air gap. In harmonic modelling, the machine cross-section is divided into homogeneous regions that are represented using Fourier series and coupled with each other using boundary conditions. The boundary value problems in both the mode matching and harmonic models are solved to obtain the field solutions. The performance of the modelling methods are evaluated by comparing the global parameters such as cogging torque, electromagnetic torque, back-emf as well as the simulation time with the results of finite-element transient analysis.
Ga1−yInyAs/InAsxP1−x (y>0.53, x>0) pin photodiodes for long wavelength regions (λ>2μm) grown by hydride vapour phase epitaxy

pin photodiodes with a 2.3 mu m absorption edge are presented, using hydride vapour phase epitaxy. A Ga/sub 1/-/sub y/In/sub y/As (y=0.72) absorption layer, lattice-mismatched to the InP substrate, was grown on an InAs/sub x/P/sub 1-x/ (x=0-0.33) graded composition buffer layer. Typical dark current was 5 mu A (0.03 A/cm/sup 2/) at -6 V. Effective carrier lifetime of 0.05 mu s was estimated from I/V characteristics.
A Review on Bio-inspired Optimization Method for Supervised Feature Selection

—Feature selection is a technique that is commonly used to prepare particular significant features or produce understandable data for improving the task of classification. Bio-inspired optimization algorithms have been successfully used to perform feature selection techniques. The exploration and exploitation mechanism that is based on the inspiration of living things to find a food source and the biological evolution in nature. Nevertheless, irrelevant, noisy, and redundant features persist from the situation of fall into local optima in case of high dimensionality. Thus, this review is conducted to shed some light on techniques that have been used to overcome the problem. The taxonomy of bio-inspired algorithms is presented, along with its performances and limitations, followed by the techniques used in supervised feature selection in term of data perspectives and applications. This review paper has also included the analysis of supervised feature selection on large dataset which showed that recent studies focus on metaheuristic methods because of their promising results. In addition, a discussion of some open issues is presented for further research.
Assessing the Tsetse Fly Microbiome Composition and the Potential Association of Some Bacteria Taxa with Trypanosome Establishment

The tsetse flies, biological vectors of African trypanosomes, harbour a variety of bacteria involved in their vector competence that may help in developing novel vector control tools. This study provides an inventory of tsetse bacterial communities in Cameroon and explores their possible associations with trypanosome establishment in Glossina palpalis palpalis. High throughput sequencing of the V3-V4 hypervariable region of the bacterial 16S rRNA gene, with subsequent metagenomic, multivariate, and association analyses, were used to investigate the levels and patterns of microbial diversity in four tsetse species. Overall, 31 bacterial genera and four phyla were identified. The primary symbiont Wigglesworthia dominated almost all the samples, with an overall relative abundance of 47.29%, and seemed to be replaced by Serratia or Burkholderia in some G. tachinoides flies. Globally, significant differences were observed in the microbiome diversity and composition among tsetse species and between teneral and non-teneral flies, or between flies displaying or not displaying mature trypanosome infections. In addition, differential abundance testing showed some OTUs, or some bacteria taxa, associated with trypanosome maturation in tsetse flies. These bacteria could be further investigated for an understanding of their mechanism of action and alternatively, transformed and used to block trypanosome development in tsetse flies.
Graph Matching with Distance-Preserving Crossover

Graph models are fundamental to any kind of application on structured real-world problems. Any comparison between graphs by a graph distance measure requires the solution of the inexact graph matching problem, which constitutes a hard combinatorial optimization problem. An inexact matching problem includes in its formulation robustness to any type of perturbation, such as, for instance, noise, inherently present in real-world environments. In this paper, we introduce the concept of distance-preserving crossover operators for genetic algorithms for this task. For large graphs, our algorithm outperforms any state-of-the-art approximate algorithm—in particular, genetic algorithms with alternative crossover operators, which are to the best of our knowledge currently limited to no more than 50 nodes. We use a two-level local search heuristic to further enhance the results, pushing the limits to up to 300 nodes: a first local search step is directly integrated into the crossover operator; another one is applied independently during offspring generation.
Stacked plate capacitor design technique for filters constructed on multilayer substrates

A stacked plate-capacitor design technique for filters constructed on multilayer substrates is proposed. With this technique, the parasitic effects between capacitors and grounds as well as the parasitic effects between different capacitors can be significantly reduced. The proposed layout can be used in multlayered filters design as a block and the design procedure for filters can be simplified. The proposed layout is analysed in detail. A multilayered filter is designed to demonstrate the design procedure. Both simulation results and measurement results show the effectiveness of the proposed technique.
Multileveled ALPR using Block-Binary-Pixel-Sum Descriptor and Linear SVC

—Automatic license plate recognition (ALPR) is es- sential component of security and surveillance. ALPR mainly aims to detect and prevent the crime and fraud activities; it also plays an important role in trafﬁc monitoring. An algorithm is proposed for recognizing license plate candidates. The proposed work aimed to recognize the license plate of a car. Proposed work is designed in multilevel for more accurate License Plate (LP) recognition, At level 1 algorithm produced 93.5% accuracy and in level 3 algorithm gives 96% accuracy. For training and testing purpose, LP images were used from Medialab cars dataset, kaggle car dataset and goggle map images. These images in the dataset is formulated at various angles and illumination. Proposed algorithm for LP recognition is done by using the Block Binary Pixel descriptors (BBPS) and Linear Support Vector Classiﬁcation (SVC). Proposed algorithm is novel and produces higher accuracy in minimal processing time of an average 0.42 milliseconds with 96% accuracy when compared with state-of-the art methods.
Evaluation of Abattoir Practices in Ijebu Ode, Nigeria

If used properly, abattoirs can help with disease management, ante- or postmortem investigation of animals and the meat, and can protect the population from most zoonotic illnesses that can develop after eating unsanitary, unsafe, or unwholesome meat or meat products. This underscores the need to examine and explore present abattoir practises in order to better understand the current situation of meat production in Ijebu Ode, Nigeria. Data was retrieved from three (3) government-approved abattoirs using self-administered structured questionnaires, personal interviews, and observations, and the data were computed descriptively using a statistical software for social scientists. Findings from the empirical observation and evidence indicated significant deficiencies in the essential hygiene facilities and services, with the lack of a veterinary laboratory for microbiological confirmation of suspected meat, and all the butchers reported no utilisation of protective gear during slaughtering activities. 66.7% of the abattoirs disposed of their solid waste by open dumping in the nearby bushes, while the wastewater was discharged into public drains. The study revealed that the abattoir practises in Ijebu Ode are unsatisfactory, with unsanitary practises and shortfalls in fundamental hygiene facilities and services, which are critical for producing healthy meat for public consumption. As a result, standardisation of facilities, including the closure of substandard ones, and educational training of abattoir workers were proposed as ways to improve hygiene facilities and practises.
EFFECT OF KNOWLEDGE MANAGEMENT ON FIRM PERFORMANCE AND COMPETITIVENESS

Purpose: The purpose of this study was to investigate the effect of knowledge management on firm performance and competitiveness. Theoretical relations are tested through an empirical study carried out on 5 companies. Results: The results showed how the ﬁrms that adopt knowledge management practices obtain better results than their competitors. The research limitations/implications were that the subject of principles has not been considered a dimension of knowledge management. New avenues of inquiry are opened considering this dimension. Unique contribution to theory, practice and policy: The practical implications were that the study only considered practices that have a positive incidence on ﬁrm performance. Further, the conceptualization of knowledge management practices represents atheoretical innovation. This scale can be used in other knowledge-intensive industries. The analysis concludes that knowledge management practices have a positive incidence on ﬁrm performance
THE CONCEPT OF COMPUTABLE GENERAL EQUILIBRIUM MODELS

This paper contributes to the existing literature on the general concept on use of the Computable general equilibrium (CGE) models of importance in developing processes. Computable general equilibrium (CGE) models are used widely in policy analysis, especially in developed-country academic settings and also for the purpose of sharing these lessons with potential users in developing countries. The range of issues on which CGE models have had an influence is quite wide, and includes structural adjustment policies, international trade, public finance, agriculture, income distribution, and energy and environmental policy. This paper describes how to build multi sector computable general equilibrium models for policy analysis. The article presents the social accounting matrix (SAM) that provides the conceptual framework linking together different components of the model and furnishes much of the data as well.
The Effect of the Platform Horizontal Displacement on the Performance of HAP Communication System

Platform position maintaining is a major challenge for the airship or aircraft based High Altitude Platform(HAP) communication systems.The platform maintaining a stationary position will introduce a high price.Hence,a position cylinder is suggested for the position maintaining of the platform.This paper derives the computation formula for the maximum distance of the HAP horizontal displacement when a fixed pointing antenna is adopted at the terminal,and simultaneously the handover probability in a cellular of the service area under limited and un-limited average call durations is analyzed.The study results show that the maximum distance for the HAP horizontal displacement increases with the user beam-width.Under the condition of the same beam-width,the higher the altitude of the platform,and the smaller the maximum distance for the platform displacement is required.Moreover,the horizontal displacement can only generate handover in the area near the edge of the cellular.The calls which are nearer to the cell edge have higher handover probability.The average handover probability increases with the maximum distance for the HAP displacement.The larger the HAP displacement during the call duration,the greater the average handover probability is.When the call duration time and the platform speed tend to infinite,the handover probability reaches to the maximum value.
URM: A Unified RAM Management Scheme for NAND Flash Storage Devices

In NAND flash storage devices, the random access memory (RAM) is composed of a data buffer and mapping cache that play critical roles in storage performance. Furthermore, as the capacity growth rate of RAM chips lags far behind that of flash memory chips, determining how to take advantage of precious RAM is still a crucial issue. However, most existing buffer management studies on storage devices report performance degradation since these devices cannot refine reference regularities such as sequential, hot, or looping data patterns. In addition, most of these studies focus only on separately managing the data buffer or mapping cache. Compared with the existing buffer/cache management schemes (BMSs), we propose a unified RAM management (URM) scheme for not only the mapping cache but also the data buffer in NAND flash storage devices. URM compresses the mapping table to save memory space, and the remaining dynamic RAM space is used for the data buffer. For the data buffer part, we utilize the program counter-technique in the host layer that provides automatic pattern recognition for different applications, in contrast to existing BMSs. The program counter-technique in our design is able to distinguish four patterns. According to these patterns, the data buffer is divided into four size-adjustable zones. Therefore, our approach is linked to multimodal data and used in a data-intensive system. In particular, in URM, we use a multivariate classification to predict prefetching length in mapping buffer management. Our multivariate classification is transformed into multiple binary classifications (logistic regressions). Finally, we extensively evaluate URM using various realistic workloads, and the experimental results show that, compared with three data buffer management schemes, CFLRU, BPLRU, and VBBMS, URM can improve the hit ratio of data buffer and save response time by an average to 32% and 18%, respectively.
EVALUATING PSYCHOMOTOR SKILLS OF THE SÜLEYMAN DEMIREL UNIVERSITY FACULTY OF DENTISTRY STUDENTS BY 9-HOLE PEG TEST

Aim: To measure and evaluate the effects of the curriculum applied in the Faculty of Dentistry on the psychomotor skills of the students.Design:The study was carried on 416 volunteer students. Participants were put to the "9-hole peg test" (9-HPT). The results were classified by considering the students' grades (1-5), gender (male and female), and the test hand (dominant/non-dominant) variables. Methods: Although there is an improvement in the 9-HPT completion time among the dominant (DH) and non-dominant hands (NDH) of the participants in the second and third grades, it is not statistically significant.Results: In this study, psychomotor proficiency development, considered a basic competence in the dentistry profession, was measured by the "9-HPT". The results are significant in measuring, comparing, and developing this competence by different educational methods.
A Millimeter-Wave Variable-Gain Power Amplifier With P₁ dB Improvement Technique in 65-nm CMOS

A millimeter-wave (MMW) variable-gain power amplifier (VGPA) with <inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {1\,dB}}$ </tex-math></inline-formula> improvement technique is proposed. The proposed VGPA consists of a postdistortion power amplifier (PDPA) and a variable-gain amplifier (VGA), for realizing high output power 1-dB compression point (<inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {1\,dB}}$ </tex-math></inline-formula>) and variable gain, respectively. To improve the <inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {1\,dB}}$ </tex-math></inline-formula> performances, a postdistortion technique with peaking-mode gain response shaping is proposed. This new approach has less amplitude-to-amplitude (AM–AM) distortion. Fabricated in 65-nm CMOS process, the proposed VGPA achieves 16-dBm output <inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {1\,dB}}$ </tex-math></inline-formula> with 29.5% power added efficiency (PAE) and 16.7-dBm saturated output power (<inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {sat}}$ </tex-math></inline-formula>) with 34% peak PAE. The maximum gain reaches 33.1 dB with 3-dB bandwidth from 23.1 to 29 GHz and 31.1-dB dynamic range of gain adjustment.
The Psychology of Personalization in Digital Environments: From Motivation to Well-Being – A Theoretical Integration

The personalization of digital environments is becoming ubiquitous due to the rise of AI-based algorithms and recommender systems. Arguably, this technological development has far-reaching consequences for individuals and societies alike. In this article, we propose a psychological model of the effects of personalization in digital environments, which connects personalization with motivational tendencies, psychological needs, and well-being. Based on the model, we review studies from three areas of application—news feeds and websites, music streaming, and online dating—to explain both the positive and the negative effects of personalization on individuals. We conclude that personalization can lead to desirable outcomes such as reducing choice overload. However, personalized digital environments without transparency and without the option for users to play an active role in the personalization process potentially pose a danger to human well-being. Design recommendations as well as avenues for future research that follow from these conclusions are being discussed.
A stacking denoising auto-encoder with sample weight approach for order remaining completion time prediction in complex discrete manufacturing workshop

ABSTRACT Accurate order remaining completion time (ORCT) prediction provides an essential criterion for dynamically triggering the adjustment of production plans and establishment of dispatching strategies, which helps to improve the plan rationality and production efficiency, thus guaranteeing delivery orders on time. With the extensive deployment of Industrial Internet of Things in the workshop, the data for ORCT prediction is perceived in real-time. However, data quality, knowledge inconsistency, and data distribution variation make ORCT prediction more difficult. Hence, a stacking denoising auto-encoder with sample weight (SW-SDAE) method is proposed to improve the robustness and applicability of ORCT prediction. Firstly, a four-layer SDAEs is constructed to extract high-level and robust features. Secondly, a dynamic updating method of sample weight for regression prediction is designed to guide the training of prediction model parameters and improve the prediction accuracy. Thirdly, model-based transfer learning is employed to adapt to the data distribution change over time and ensure the prediction applicability. Finally, different prediction models are applied to an actual case for comparison. The experimental results show that the proposed prediction method is effective for ORCT prediction and superior to other methods.