Miracle-dz/UniFAR-FTUs huggingface dataset:
SPELLING CHECKER FOR DYSLEXIC SECOND LANGUAGE ARAB LEARNERS

People with dyslexia have a lifetime of a poor spelling problem. Several researchers have tried to solve this problem through spell checkers. However, most of these spell checkers are provided to native language owners and do not take into account second-language learners with dyslexia, especially since most of the mistakes they make real mistakes lead to unintended but correct words. So far, there is no root solution to this problem. This research is proposed a spelling checker (DYS-EnSC) based on n-gram technique, look up dictionary, and Damerau-Levenshtein, To create a list of candidates and choose the most suitable candidate for each misspelled word, to detect and correct misspellings of Arab second language learners with dyslexia. The results of this study are included two parts. In the first part, we focus on comparing the performance of GSC (Microsoft Word (MW), A spell, and Language Tool) with the performance of the proposed spelling checker DYS-EnSC. Standard measures (Recall, Precision, and Accuracy) were used. Results suggest that the proposed DYS-EnSC spelling checker was useful in correcting misspellings of second language learners with dyslexia, achieving an accuracy of 93% in detecting misspellings and corrected about 86% outperforming to MW, A spell and Language Tool. In Section 2 of the results of this study, we focus on how successful students with dyslexia were in correcting their misspellings with and without the proposed DYS-EnSC system. Findings suggest that students could correct 9.3% of their misspellings without DYS-EnSC and 86% with it.
How to deal with the relationship between hypoxia and radiotherapy in the hypofractionated radiotherapy era

Hypoxia, a common phenomenon in solid tumors can promote dysfunctional vascular growth and epithelial-to-mesenchymal transition, leading to cell mobility and metastasis. The decreased sensitivity of hypoxic tumor cells to ionizing radiation is one of the main factors affecting the effect of conventional radiotherapy. It is well known that conventional radiotherapy mainly reduces the effect of hypoxic radiation resistance by reoxygenation between fractions. With the improvement of radiation treatment planning and delivery, more and more cancer patients have been treated with hypofractionated radiotherapy (HFRT), which have achieved a much higher effect than conventional radiotherapy. Given that HFRT is delivered within one or a few fractions, does tumor hypoxia affect its efficacy? Is there any way to further improve the effect of HFRT? In this review, we focus on the interaction between HFRT and hypoxia, and how to optimize the regimen of HFRT to decrease the effect of hypoxia and improve the efficacy is discussed in detail.
Egyptian Political Satire: Parsing the Rhetoric of Parody in Ῑhāb Ṭāhir's Mursī qalb al-asad

Political satire is employed by writers to voice their own opinions, using irony, sarcasm and allusions to criticize social, economic or political issues, or to indicate mockery of salient figures, especially politicians. In this respect, this paper will analyze a literary work by Ῑhāb Ṭāhir, entitled Mursī qalb al-asad ‘Mursī, the lion’s heart’ (2013), issued two years after the 25 January Revolution. Ṭāhir, through his satirical and political parody, attempts to find an emotional vent, for the ordinary people of Egypt, through which they could get rid, even if temporarily, of their recurrent encumbrances and burdens of life, enabling them to restore their stolen democracy, freedom of expression, and liberty by the rigid regime of Mursī, the former president of Egypt, and his party, the Muslim Brotherhood (MB). Depending greatly on Paul Simpson’s (2003) theoretical model of satirical discourse analysis, this paper attempts to propose a Pentadic Satirical Script Theory of Verbal Humor (PSSTVH). By employing this proposed theory, Ṭāhir’s style of writing, through which his satirical activity is delivered, and his satirical techniques, adopted to convey his message efficiently, will be investigated.
Expert-guided Deep-Attention Saliency Models for Computer Vision

: Attention is a promising deep learning paradigm to develop efficient and almost interpretable computer vision methods. This is a hot topic as shown by an increasing number of related work in the literature. The present work aims at further investigating attention model under the assumption that a Human expert can convey relevant information to enhance the accuracy of trained machine learning model. Hence, we aim at developing novel deep neural architectures including an deep-attention mechanism that is directly trained using visual information generated by human experts involved in visual segmentation task in real-world scenarios (eg. eye-tracking coordinates and/or ocular measurements). The considered applications in this study will possibly include autonomous driving and precision medicine tasks for which we aim at showing how the resulting deep-saliency function guided by expert human can enhance the efficiency of an existing deep-learning-based prediction method (eg. MRI segmentation, image recognition, etc.). To reach this goal, the recruited internship candidate will first implement a state-of-the-art attention-based deep learning based method applied to a given segmentation task. Second, we will modify the preliminary proposed method to separately train the attention block using a distinct training dataset. Finally, we will assess the different proposed deep learning methods in different scenarios to validate our assumption that a saliency function trained by experts is more efficient.
Polygenic Risk Scores for Prediction of Breast Cancer Risk in Women of African Ancestry: a Cross-Ancestry Approach

Polygenic risk scores (PRSs) are useful to predict breast cancer risk, but the prediction accuracy of existing PRSs in women of African ancestry (AA) remain relatively low. We aim to develop optimal PRSs for prediction of overall and estrogen receptor (ER) subtype-specific breast cancer risk in women of African ancestry. The AA dataset comprised 9,235 cases and 10,184 controls from four genome-wide association study (GWAS) consortia and a GWAS study in Ghana. We randomly divided samples into training and validation sets. Genetic variants were selected by forward stepwise logistic regression or lasso penalized regression in the training set and the corresponding PRSs were evaluated in the validation set. To improve accuracy, we also developed joint PRSs that combined 1) the best PRSs built in the AA training dataset, 2) a previously-developed 313-variant PRS in women of European ancestry, and 3) PRSs using variants that were discovered in previous GWASs in women of European and African ancestry and were nominally significant the training set. For overall breast cancer, the odd ratio (OR) per standard deviation of the joint PRS in the validation set was 1.39 (95%CI: 1.31-1.46) with area under receiver operating characteristic curve (AUC) of 0.590. Compared to women with average risk (40th-60th PRS percentile), women in the top decile of the PRS had a 2.03-fold increased risk (95%CI: 1.68-2.44). For PRSs of ER-positive and ER-negative breast cancer, the AUCs were 0.609 and 0.597, respectively. The proposed PRS can improve prediction of breast cancer risk in women of African ancestry.
Simulation of Efficient Vehicle Dispatching and Routing for the Application in Intelligent Transportation System

This paper provides the simulation for a designed vehicle dispatching and routing system for safe and efficient vehicle transportation. The objective of the simulation is to verify the performance of the cooperative dispatching and routing decisions for vehicles within a specific area consisting of streets and several intersections. In addition to computing a specific routing path for the arrival vehicle based on its destination and safety considerations, the proposed scheme provides a detour mechanism under the transportation cost constraint. This paper also points out the existence of hidden conflicting path problems in the dispatching approach and proposes a backtracking approach to solve this problem at the route decision phase so that the travel time can be guaranteed. The simulation results show that the proposed scheme can effectively assign the vehicles with proper routes and without any collisions with good performance.
General overestimation of ERA5 precipitation in flow simulations for High Mountain Asia basins

Precipitation is one of the most important input to hydrological models, although obtaining sufficient precipitation observations and accurate precipitation estimates in High Mountain Asia (HMA) is challenging. ERA5 precipitation is the latest generation of reanalysis dataset that is attracting huge attention from various fields but it has not been evaluated in hydrological simulations in HMA. To remedy this gap, we first statistically evaluated ERA5 precipitation with observations from 584 gauges in HMA, and then investigated its potential in hydrological simulation in 11 HMA basins using the Variable Infiltration Capacity (VIC) hydrological model. The ERA5 precipitation generally captures the seasonal variations of gauge observations, and the broad spatial distributions of precipitation in both magnitude and trends in HMA. The ERA5 exhibits a reasonable flow simulation (RB of 5%–10%) at the Besham hydrological station of the upper Indus (UI) basin when the contribution from glacier runoff is added to the simulated total runoff. But it overestimates the observations in other HMA basins by 33%–106% without considering glacier runoff, mostly due to the overestimates in the ERA5 precipitation inputs. Therefore, a bias correction is definitely needed before ERA5 precipitation is used for hydrological simulations in HMA basins.
Nonlinear Coupling of Reversed Shear Alfvén Eigenmode and Toroidal Alfvén Eigenmode during Current Ramp

Two novel nonlinear mode coupling processes for reversed shear Alfvén eigenmode (RSAE) nonlinear saturation are proposed and investigated. In the first process, the RSAE nonlinearly couples to a co-propagating toroidal Alfvén eigenmode (TAE) with the same toroidal and poloidal mode numbers, and generates a geodesic acoustic mode. In the second process, the RSAE couples to a counter-propagating TAE and generates an ion acoustic wave quasi-mode. The condition for the two processes to occur is favored during current ramp. Both the processes contribute to effectively saturate the Alfvénic instabilities, as well as nonlinearly transfer of energy from energetic fusion alpha particles to fuel ions in burning plasmas.
Reliable registration of LiDAR data and aerial images without orientation parameters

Purpose – The fusion of aerial imagery and LiDAR point clouds are considered as one of the most promising approaches for many fields, such as 3D city reconstruction and tree detection. The purpose of this paper is to achieve reliable registering LiDAR data and aerial images without orientation parameters based on a progressive optimizing process.Design/methodology/approach – First, combination of edges and their corners is extracted and considered as registration primitives; then search conjugate primitives globally with a suitable buffer of each edge; after that, a progressive algorithm is adopted to optimize the registration; finally, error analysis and data fusion are carried out.Findings – After a progressive optimum algorithm, the number and the distribution of the matched pairs are sufficient for generation of reliable and accurate orientation parameters. The results show RMS of residual errors gets close to one DSM cell, which is equal to or even better than that in other literatures.Originality/va.
An APT Trojan Detection Method Based on Memory Forensics Techniques

Advanced Persistent Threat (APT) is currently reported to be one of the most serious threats. It is very important to detect the APT Trojan as early as possible. There are three types of approaches to conduct APT detection: network traffic analysis, change controlling and sandboxing. Unfortunately, all these approaches have limitations in detecting unknown APT Trojans. This paper proposes a novel APT Trojan detection method by utilizing memory forensics techniques. The proposed method first acquires the raw physical memory image from a target running system and then finds the APT’s traces in the memory image based on the ATP’s characteristics and memory forensics techniques. If enough traces are found, we can judge that there must be Trojans in the target system. Experimental results show that the proposed method can effectively detect new APT Trojans.
USING FACTOR ANALYSIS TO CLARIFY OPERATIONAL CONSTRUCTS FOR MEASURING MISSION PERCEPTION

This paper describes a mixed methods strategy of inquiry for developing and testing factors that represent constructs of institutional mission. The primary objective of this research was to develop an instrument that measures a college student’s perception of mission, in order to provide some evidence of mission effectiveness to address accreditation standards. A second objective, once an administration of the instrument was conducted and an adequate data set obtained, was to investigate the instrument’s internal reliability. Third, if the instrument was indeed found reliable, the next objective was to uncover the presence within of any constructs or subscales of items. Last, if operational constructs were present within the instrument, the final objective was to observe relationships among them using correlation analysis. The study was designed to address the following research questions: • Are the Research Mission Questions valid and reliable measures of student perception of institutional mission? • Are there factors present in the Research Mission Questions? • If there are factors, what is the relationship among them?
A Review of Mobile Robot Path Planning Based on Deep Reinforcement Learning Algorithm

Path planning refers to that the mobile robot can obtain the surrounding environment information and its own state information through the sensor carried by itself, which can avoid obstacles and move towards the target point. Deep reinforcement learning consists of two parts: reinforcement learning and deep learning, mainly used to deal with perception and decision-making problems, has become an important research branch in the field of artificial intelligence. This paper first introduces the basic knowledge of deep learning and reinforcement learning. Then, the research status of deep reinforcement learning algorithm based on value function and strategy gradient in path planning is described, and the application research of deep reinforcement learning in computer game, video game and autonomous navigation is described. Finally, I made a brief summary and outlook on the algorithms and applications of deep reinforcement learning.
A Critical Review On Mixing Parameters For High Content Reclaimed Asphalt Mixtures

High content reclaimed asphalt (RA) mixtures have been identified as one of the options to reduce the environmental and economic impacts of pavements construction. However, the process of designing and producing high content RA mixtures is challenging and the asphalt industry have serious concerns towards quality and long-term performance these mixtures. In laboratory, several parameters affect mixture characteristic, and if not controlled, may results into inaccurate estimation of performance. This state- of-the-art study aims to identify critical parameters for high content RA mixture production and highlight the effects of these parameters on mixture performance. The mixing parameters adopted in several laboratory studies have been highlighted and compared. The best practices to mix recycled asphalt in laboratory are reviewed in order to optimize the laboratory mixing. Based on review, important considerations for evaluating laboratory performance have been discussed.
Top Spray Fluidized Bed Granulated Paddy Urea Fertilizer

Top spray granulation process is a common technique used widely in pharmaceutical, food and special chemical modification for fertilizer manufacturing. Nevertheless, there is still a lack of studies regarding to the description of controlled parameters with dynamic correlation in targeting to produce urea granules. Thus, this research was carried out to introduce the crucial applied process parameters using top spray technique for paddy urea fertilizer production.The acquisition process parameter readings were verified by obtained yield of urea granules (UG) which featured as an optimum particle diameter size from 2 mm to 6 mm with reasonable hardness (crush strength) in range 2.0 kg/granule to 4.0 kg/granule, these criteria were required as a slow - release mechanism during soil adsorption interaction in paddy field to reduce amount of fertilizer consumption. Three significant parameters have been selected namely as air inlet temperature, the viscosity of binder solution and rate of top spraying from starch liquid binder to generate greater UG size from wet granulation interaction with smooth coalescence and consolidation growth . The data classification was screened by One-Factor-at-a-Time (OFAT) 101 method and supported by 2 levels and 3 factors (23 ) of full factorial design for clear description to vindicate the critical parameter required during urea granulation using fluidized bed granulator corresponds to low energy consumption and economical process. The obtained parameter readings and findings of UG features were useful to be applied further for detail investigation on next stage regarding to agglomeration profile and mechanism using CCD camera and PDA monitoring devices.
CoDEC: Connected Data For Road Infrastructure Asset Management

Road infrastructure asset management is rapidly transforming into a digital environment where data accessibility, effective integration and collaboration and accessibility from different sources and assets are key. However, current asset management processes are not yet fully integrated or linked, and there are incompatibilities between various systems and platforms that limit the ability to integrate asset management with BIM. The CoDEC project has sought to understand the current status of information management for assets, including inventory, condition and new data sources such as sensors and scanning systems, to identify the challenges and needs for linking and integrating different data sets to support effective asset management. As a result, CoDEC has developed a data dictionary framework to help link/integrate static and dynamic data for the “key” infrastructure assets (road pavements, bridges, tunnels). This will enable BIM and Asset Management Systems (AMS) to exchange data and help optimise and integrate data management across systems and throughout the different asset lifecycle phases, from build to operation. This work will be followed up with three pilot projects to demonstrate the feasibility of integrating asset data from various sources through linked data/semantic web technology to build the connection between AMS and BIM platforms.
The implications of information sharing on bullwhip effects in a toy supply chain

This paper compares actual bullwhip effects provided by retailers who shared downstream demand information and retailers who did not share in a three-level (divergent) toy supply chain. The toy supply chain faces high forecast errors and bullwhip effects due to unpredictable and seasonal demand patterns. The results indicate a reduction of the bullwhip effect and an improvement of the fill rate due to the increase of retailers who shared downstream demand information. This paper also addresses two main causes of the bullwhip effect, which are the supply variability and limited use of the shared demand information. To reduce the bullwhip effect, two solutions using the shared information are tested. They are a proposed premature replenishment rule and a simple forecast updating mechanism. Both solutions are proven capable in reducing bullwhip effects of the toy supply chain even without coordination between the toy manufacturer and the retailers.
Additive manufacturing for fused deposition modeling of carbon fiber–polylactic acid composites: the effects of process parameters on tensile and flexural properties

In this paper the effect of process parameters on the tensile and flexural properties of carbon fiber–polylactic acid (CF-PLA) composites is analyzed. We used a commercially available fused deposition modeling (FDM) three-dimensional (3D) printer and CF-PLA. When there are a variety of processing parameters, especially when no linear processing parameters are defined, the exact factors required in the design of experiments (DOEs) is hard to research. Furthermore, a large number of samples are needed to discover the optimum processing parameters. The key effects of four processing parameters for the FDM process, i.e. layer height, infill density, printing speed and infill pattern, were examined in this work using the Taguchi DOE. The mechanical characteristics of the fabricated FDM components expressed the power of the processing parameters. We used the Taguchi L9 range of nine runs with three specimens each, so 54 different processes were used to make a total of 54 specimens. In comparison with the 3D computer-aided design model, the measurements of the manufactured specimens were tested according to the standards ASTM D638 and ASTM D790. Variance analysis (ANOVA) was generated using Design Expert tools in order to assess the importance of the variables and their interactions for tensile and flexural strength. After performing ANOVA we obtained the exact parameters for which the mechanical properties are highest.
UAV Dual-Band Antenna

The Portuguese Air Force, through the Research and Technology Program on Unmanned Aerial Vehicles, develops technology with the purpose of improving Unmanned Aerial Vehicles capabilities. In this context, the need arises to optimize their transmission system, fulfilling the requirements proposed by the Air Force. This article presents the theoretical principles considered when dimensioning a dual band microstrip antenna. The dual band operation with a rectangular patch can be achieved by the etching of slots either on the ground plane of the antenna (Defect Ground Structures) or on the metallic patch surface. Furthermore, the simulation results obtained are presented. These were carried out for the substrate RT Duroid 5870. The final goal is to dimension a dual-band (1.33 GHz and 5 GHz) patch antenna for the transmission of a video channel. The best simulation result was obtained for an inset-fed slot-loaded patch antenna. The simulation work on the antenna development was done using COMSOL Multiphysics.
Overview of epidemiology and management of rickets among children in Saudi Arabia

Rickets is a common bone disease worldwide that is associated with disturbances in calcium and phosphate homeostasis and can lead to short stature and joint deformities. The study aimed to summarize the updated evidence as regards: Epidemiology, risk factors, etiology, pathophysiology, clinical manifestation, diagnosis and management. Rickets can be diagnosed based on history and physical examination, radiological features, and biochemical tests. The acquired rickets (nutritional) is the most common cause of rickets among children in Saudi Arabia which is due to vitamin D deficiency. Risk factors include, exclusively breast fed, immigrant adults in industrialized countries, decreased exposure to sunlight, use of sunscreen, old age of the housebound, morbid obesity and certain medications. Treatment strategies of rickets depend on the underlying etiology. There are several regimens utilized to treat rickets, all of them comprise some form of vitamin D administration and presence of adequate calcium and phosphate levels.