Miracle-dz/UniFAR-FTUs huggingface dataset:
APPLICATION OF MACHINE LEARNING IN ANALYSING HISTORICAL AND NON-HISTORICAL CHARACTERISTICS OF HERITAGE PRE-WAR SHOPHOUSES

Real estate is complex and its value is influenced by many characteristics. However, the current practice in Malaysia shows that historical characteristics have not been given primary consideration in determining the value of heritage properties. Thus, the accuracy of the values produced is questionable. This paper aims to determine whether the historical characteristics of the pre-war shophouses at North-East Penang Island, Malaysia contribute any significance to their value. Several Machine Learning algorithms have been developed for this purpose namely Random Forest, Decision Tree, Lasso Regression, Ridge Regression and Linear Regression. The result shows that the Random Forest Regressor with historical characteristics is the best fitting model with higher values of R-squared (R²) and lowest value of Root Mean Square Error (RMSE). This indicates that the historical characteristics of the heritage property under study contribute to its significant value. By considering the historical characteristics, the property’s value can be better predicted.
The Effect of Alteration of Vertical Dimension of Occlusion on Brain Activity in Complete Denture Wearers

BACKGROUND: The establishment of appropriate vertical dimension of occlusion (VDO) is paramount for successful and functioning complete dentures (CDs). AIM: The aim of this study was to assess the effect of alteration of VDO on the brain activity in CD wearers. MATERIALS AND METHODS: Ten completely edentulous patients participated in this study. Each patient received three sets of CDs: one control set and two duplicates’ CDs. The control CDs was designed with appropriate VDO (control VDO), while the duplicate CDs were designated as followed: one with VDO lowered by –3 mm (low VDO) the other one VDO was increased by +5 mm (high VDO). The effect of alteration of VDO on brain activity was assessed by measuring alpha waves changes using electroencephalogram (EEG) before and after chewing gum. Statistical analysis was carried out using one-way ANOVA and dependent t-test. The significance level was set at p < 0.05. RESULTS: There was a significant increase in the alpha waves mean values after chewing (p < 0.05) gum compared to before except for low VDO CDs (p > 0.05). The control-VDO revealed a statistically significant higher mean values compared to the duplicate dentures. CDs with high VDO showed higher mean value than low VDO dentures, however, it was statistically insignificant (p > 0.05). CONCLUSION: Appropriate determination of VDO is not only important for the success of CDs but also for the functional augmentation of brain activity. Edited by: Aleksandar Iliev Citation: Elmorsy AA, Zaki M, Elbahnaswi H, Moussa AR, Elboraey AN. The Effect of Alteration of Vertical Dimension of Occlusion on Brain Activity in Complete Denture Wearers. Open Access Maced J Med Sci. 2021 Jul 29; 9(D):108-112. https://doi.org/10.3889/oamjms.2021. 6178
A new hybrid conjugate gradient algorithm for optimization models and its application to regression analysis

The hybrid conjugate gradient (CG) method is among the efficient variants of CG method for solving optimization problems. This is due to their low memory requirements and nice convergence properties. In this paper, we present an efficient hybrid CG method for solving unconstrained optimization models and show that the method satisfies the sufficient descent condition. The global convergence prove of the proposed method would be established under inexact line search. Application of the proposed method to the famous statistical regression model describing the global outbreak of the novel COVID-19 is presented. The study parameterized the model using the weekly increase/decrease of recorded cases from December 30, 2019 to March 30, 2020. Preliminary numerical results on some unconstrained optimization problems show that the proposed method is efficient and promising. Furthermore, the proposed method produced a good regression equation for COVID-19 confirmed cases globally.
Bone X-Rays Classification and Abnormality Detection using Xception Network

: Computer aided diagnosis (CAD) has a vital role and becomes an urgent demand nowadays. Bone fractures cases are considered from the most frequently occured dieases among individuals. Moreover, the incorrect diagnosis of the bone fractures cases may cause disability for the patient. Hence, CAD system for bone fractures has become a must. This paper proposes a two-stage classifcation method for bone type classification and bone abnormality detection. Xception pre-trained model is considered for all experiments. Two different approaches are utilized for the testing phase: 1) Singl-view and 2) Multi-view approachs. The enhanced images are fed into the first stage to be classified into one of the seven classes: shoulder, humerus, forearm, elbow, wrist, hand and finger. Thereafter, the classified bones are fed into the second stage to detect whether the bone is normal or
INFERRING HUMAN PERSONALITY FROM WRITTEN MEDIA A DISSERTATION SUBMITTED TO THE GRADUATE DIVISION OF THE UNIVERSITY OF HAWAI‘I AT MĀNOA IN IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTORATE OF PHILOSOPHY

This work explores the association between human personality and language features consisting of sequences of tokens. My work reveals that there are such features that are predictive of personality over multiple corpora taken from different populations of English speakers. I gathered written text authored by 50 individuals who participated on a bodybuilding web forum (the Forum corpus). Also I administered a personality questionnaire following the protocol provided by the International Personality Item Pool (IPIP). For comparison across other populations I also obtained text corpora from three other research groups, along with the results of personality assessments: the EAR corpora consisting of transcripts of the speech of 96 participants as they go about their daily lives, Essays written by 2,588 undergraduates at the University of Texas and posts by 244 Facebook users. After performing part-of-speech (POS) tagging on the text for all the participants in these corpora, I extracted unigrams, bigrams and trigrams (n-grams) of tokens and their POS tags, and counted every word/tag permutation that appeared. I considered only features appearing one or more times per 1000 words in the Forum corpus because there was not enough data to consider sparser features. I found 766 such features. From among those features I explored which were relevant across both my Forum corpus and at least one of the borrowed corpora, since those are the most promising, robust features that illustrate the possibility of building models across various corpora using the same language features. 75 of the features were associated with one or more personality dimensions across both the Forum corpus and at least one additional corpus. I devised explanations as to why some of the features are correlated with a given personality dimension. That task establishes that although some of the features may have arisen randomly, one can confidently proceed with the conclusion that English speakers consistently express their personalities through their language usage. In addition, to show that it is possible to use these features for prediction, I generated multiple linear regression models for each corpora-personality
Multi Gender Diversity and Earnings Quality in Islamic Banks: The Moderating Role of IFRS’ Adoption and Culture

Purpose – This paper firstly measure the impact of Board of Directors (BOD); audit committee (AC) and Sharia Supervisory Board (SSB) composition (gender diversity) on earnings quality (EQ) in the Islamic banks (IBs). Secondly, we measure to what extent adoption of IFRS and national culture, which is proxies by Femininity can, moderates the relationship between diversity and EQ. Design/methodology/approach – Hypotheses are tested using pooled OLSregression models. Our sample comprises 122 IBs across 21 different countries from 2010 to 2017. Our sample include 46 IBs adopter of IFRS and 76 IBs that non-adopter of IFRS as local or Islamic standards. Findings – Evidence reveals that gender diversity in BOD, AC, and SSB is positively associated with EQ activities. Further, adoption IFRS standards by IBs significantly moderate the positive link between board diversity and EQ while shows significantly moderate the negative association between board diversity and EQ for banks that are non-adopter of IFRS. Concerned with the impact of culture; we find that culture dimension of femininity serve as informal institutional forces that influence the association between diversity and EQ. Some results hold and others altered after a number of robustness checks. Originality/value – This study is unique in providing IBs evidence for the moderating effect of accounting standards' adoption and culture on the link between the demographic features of BOD, AC and SSB members and firm outcome. The present study provides a new and far-reaching contribution into the debate about EQ in the context of ethical institutions as IBs as well as contribution into the debate about role of women in board in the context on countries characterized by masculinity community as MENA region or across Muslim countries. Dr. Sherif El-Halaby, Dr. Mona Abd Al Hakim Multi Gender Diversity and Earnings Quality 61 ةعجا رملاو ةبساحملا ةلجم ةيبرعلا تاعماجلا داحتلا ثلا ددعلا ىنا 2021 Practical implications – The findings have important implications for policymakers, regulators, and investors in their attempts to enhance EQ practices and improve financial reporting quality for IBs. Keywords– Earnings quality; Islamic banks; Diversity in BOD; AC and SSB; IFRS; Culture; Femininity
Definitely Islands? Experimental investigation of definite islands

Experimental work on islands has used formal acceptability judgment studies to quantify the severity of different island violations. This current study uses this approach to probe the (in-)violability of definite islands, an understudied island, in offline and online measures. We conducted two acceptability judgment studies and find a modest island effect. However, rating distributions appear bimodal across definites and indefinites. We also conducted a self-paced reading experiment, but found no significant effects. Overall, offline, definite islands differ from other uniform islands, but online, the results are more complicated.
Drying technologies for biopharmaceutical applications: Recent developments and future direction

ABSTRACT Drying is a commonly used processing technique for the manufacture of biotherapeutic products. Removal of water provides numerous benefits, including ease of handling and storage, reduction in transportation costs, and improved stability, to name a few. Typically, drying is accomplished through freeze-drying, as evidenced by the availability of several lyophilized products on the market. There are, however, several drawbacks to lyophilization, including the lengthy process time required for drying, low energy efficiency, and the high cost of purchasing and maintaining the equipment. Furthermore, lyophilization is a batch process and may be challenging to adapt and implement within a continuous manufacturing process scheme. These limitations have led to the search for next-generation drying technologies that can be applied to the manufacture of biotherapeutic products. Several alternative drying methods are reviewed herein with particular emphasis on the advantages and disadvantages of each technology in comparison to lyophilization and the potential of each to be utilized for drying biotherapeutic compounds.
Mathematical background for statistical games 2 . 1 . Compositional probability , concretely and abstractly

We introduce the concepts of Bayesian lens, characterizing the bidirectional structure of exact Bayesian inference, and statistical game, formalizing the optimization objectives of approximate inference problems. We prove that Bayesian inversions compose according to the compositional lens pattern, and exemplify statistical games with a number of classic statistical concepts, from maximum likelihood estimation to generalized variational Bayesian methods. This paper is the rst in a series laying the foundations for a compositional account of the theory of active inference, and we therefore pay particular attention to statistical games with a free-energy objective.
Predictive Modeling Applied to Structured Clinical Data Extracted from Electronic Health Records: An Architectural Hypothesis and A First Experiment

Corresponding Author: Francesca Fallucchi, Guglielmo Marconi University, 44 Plinio Street, 00193 Rome, Italy Email: f.fallucchi@unimarconi.it Abstract: Predictive analysis is one of current important issues in the healthcare context. A lot of patients’ input data can be obtained from their Electronic Health Records. In our research, we propose a general architecture named Health Prediction Architecture. Initially, we consider that data refer to strongly structured health datasets (no free text). Our objectives are related to exploring some problems in the prediction context for healthcare. In particular, we consider dataset heterogeneity, accuracy together explain ability, dataset for benchmarking. After a presentation of Electronic Health Record and some useful related standards, we propose our architecture based on two principal modules. First module produces features extraction and it implements a Convolutional Neural Network or alternatively a Multi-Layer Perceptron. Second module produces predictions and it implements alternatively one from Graph Convolutional Network, Simplified Graph Transduction Game, Nearest Nodes and Classes Graph. We define the datasets randomly so to have the possibility to manage data sufficiently heterogeneous and useful for a benchmarking, without any privacy problem too. In this study, we experiment a first instantiation of the architecture, based on Multi-Layer Perceptron as first module and Simplified Graph Transduction Game as second module, considering health data related to type 2 diabetes risk, generated according to a healthcare rule. We try the architecture by slightly increasing both cardinalities of datasets and extracted features. As first results of our research, in this study we produce training and testing randomized datasets and we obtain a testing accuracy behavior generally better than using only Multi-Layer Perceptron (best accuracy with 200 labelled elements). Our architecture aims to evolve to be used as a general solution in healthcare predictions context. We are also interested in studying our solution in future works from the explain ability point of view, with particular interest in explaining the results in terms of input attributes.
5G cognitive radio system design with new algorithm asynchronous spectrum sensing

Received Feb 2, 2021 Revised Apr 26, 2021 Accepted May 25, 2021 Due to the rapid increase in wireless applications and the number of users, spectrum scarcity, energy consumption and latency issues will emerge, notably in the fifth generation (5G) system. Cognitive radio (CR) has emerged as the primary technology to address these challenges, allowing opportunist spectrum access as well as the ability to analyze, observe, and learn how to respond to environmental 5G conditions. The CR has the ability to sense the spectrum and detect empty bands in order to use underutilized frequency bands without causing unwanted interference with legacy networks. In this paper, we presented a spectrum sensing algorithm based on energy detection that allows secondary user SU to transmit asynchronously with primary user PU without causing harmful interference. This algorithm reduced the sensing time required to scan the whole frequency band by dividing it into n sub-bands that are all scanned at the same time. Also, this algorithm allows cognitive radio networks (CRN) nodes to select their operating band without requiring cooperation with licensed users. According to the BER, secondary users have better performance compared with primary users.
The Liquid Phase Deposition of ZnPtBNs: Study on Structural, Morphology, and Their Sheet-Resistant

This paper reports ZnPt bimetallic nanoparticles (ZnPtBNs) synthesis through the liquid phase deposition (LPD) of of Zn(NO3)2.6H2O onto the indium-titanium oxide (ITO) substrates at various concentrations. The Effects of growth solution, the morphology, structural, and sheet resistance were studied. After preparation, the materials were characterized by using field emission electron microscopy (FESEM), energy dispersive X-ray (EDX), X-ray diffraction (XRD) and Four Point Probe (FPP) measurement by using Keithley 2401 source-meter. By inserting a growth solution into the ITO substrate the ZnPtBNs was successfully in-situ prepared. The synthesized ZnPtBNs exhibited homogeneous, fibrous at the (111) orientation with an average diameter of 100-700 nm. The atomic ratio of Zn:Pt and sheet resistance of ZnPtBNs decreased with the increase of Zn(NO3)2.6H2O concentration. The optimal elemental composition of the sample was at a ratio of Zn:Pt (1:25) obtained at 0.467 mM of Zn(NO3)2.6H2O. It showed the smallest sheet resistance (13.41 Ω) which was 38% lower than the ITO sheet resistance (18.44 Ω).
The Role of Interference in Implicit Learning of the Stroop Stimuli Sequence

Introduction. The effect of interference can help identify essential characteristics of the process of acquiring new knowledge. This study is the first to provide empirical evidence on the use of the effect of interference to dissociate explicit and implicit learning. We tested the hypothesis that the effect of interference arising in response to the Stroop test stimuli reduces the efficiency of explicit sequence learning and does not significantly affect implicit sequence learning. Methods. A sample of 80 respondents took part in this study (mean age = 22.7 years). In the experiment, we used the serial reaction time tasks, when subjects were instructed to respond quickly and accurately to the sequences of stimuli. Some subjects (n = 40) viewed series of colour names written in congruent (corresponding) font colours; others (n = 40) viewed series of colour names written in incongruent (non-corresponding) font colours (Stroop stimuli). The subjects were asked to respond to font colours, without reading words. To identify explicit sequence learning, we used the recognition test. Results. We found a significant sequence learning effect among the subjects who performed the task under congruent and incongruent conditions. Meanwhile, all subjects demonstrated a low level of explicit sequence learning (less than 51.9 % of correct responses in the recognition test). We discovered that implicit sequence learning eliminates the effect of interference (a delay in response time to incongruent stimuli). Discussion. The results confirmed the assumption that the effect of interference does not reduce the efficiency of implicit sequence learning. The absence of significant differences between the groups that responded to congruent and incongruent stimuli makes it impossible to fully evaluate the impact of interference on explicit sequence learning. In general, the findings from this study speak in favour of the fact that the effect of interference impedes the explication of the
Sustainable Mortar Made with Local Clay Bricks and Glass Waste Exposed to Elevated Temperatures

The present study involved assessing the replacement of fine aggregate in the mortar with sustainable local materials like clay bricks and glass included 168 specimens (cubes and prisms). Seven mixtures were cast for this work, one control mix (R1) with 100% natural sand whereas mixes from R2 to R5 have 10% and 20% replacing natural sand with waste clay bricks and waste glass separately and respectively. Mix R6 was included 20% replacing sand with combination waste materials (10% waste clay bricks with 10% waste glass). Mix R7 has involved the same percent of replacing the previous mix R6 but with adding Polypropylene fibers 1% by volume. The samples have put in an electrical oven for one hour at 200, 400, and 600 oC then cooled to room temperature to be tested and compared with samples at normal temperature 24 oC. Different mechanical tests were adopted involved flow tests, density, weight loss, compressive strength, flexural strength, and water absorption. The results at different temperatures were discussed where many findings were specified. The flexural strength at 400 oC was showed improving by 56% for 20% waste clay brick and 69% with 10% waste glass, as well all combination mixes illustrated higher strength than the control.
Computational fluid dynamic (CFD) modelling in anaerobic digestion: General application and recent advances

ABSTRACT Nowadays, thanks to the enhancement in computational power and software development, advanced mathematical modelling based on computational fluid dynamics (CFD) allows us to represent almost any system. Anaerobic bioreactors correspond to a complex biosystem where multiple reactions, in parallel and/or in series, take place. Besides this biological complexity, the actual digester operation increases the system's complexity given the number of transport phenomena that also occur; therefore, few real applications may be found in which mathematical models are properly harnessed. This review presents a general assessment of the CFD applications that have been applied in anaerobic digestion processes starting with the model set-up up to the post-processing of the results. In regards to the model pre-processing and setup, the generation and evaluation of the mesh and the model specifications such as multiphase flow, turbulence regimen, rheology characterization and impeller motion, are addressed. Due to the importance of the evaluation of the model's outcome, topics such as error assessment and origin, model calibration and validation are discussed. The current challenges and future perspectives of this type of mathematical modeling are also addressed, with particular emphasis on the integration of biological reactions with the conventional fluid dynamic modeling.
Affective Engagement of Higher Education Students in an Online Course

The present research studies the factors that have impacted the affective engagement of university students in an educational online course. It examines how the type of interaction (learner-learner, learner-instructor, and learner-content) and the type of engagement (behavioural, cognitive and affective) have influenced the affective engagement of the students in the online course. Nineteen university students majoring in teaching mathematics, who were enrolled in the course Mathematics Teaching Methods, participated in the present research. Two data collection tools were used: semi-structured interviews and reflections. To analyse the texts resulting from the interviews and reflections, inductive and deductive qualitative content analysis was used. The research results indicated that university students have experienced more positive than negative affective engagement in the three communicational channels used in this course to facilitate online learning, which were: synchronous lectures, forums and assignments. The results also indicated that these three types of interaction have positively influenced students’ affective engagement in the three channels, with that influence being different from one channel to the other based on the interaction type taking place. We suggest that specific types of engagement need to be attended to for positive affect to occur.