Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanics of Advanced Composite Structures

Polymer-modified mortar Semi-destructive tests Mechanical properties Finite element method Adhesion Nowadays, polymers in cement-based mortars are frequently used to improve mechanical properties and increase the adhesion between the repair mortar and concrete substrate. In the present study, the mechanical properties of the polymer-modified mortars in the various ages were evaluated using the semi-destructive and in-situ “friction-transfer” and “pull-off” tests. For this purpose, the repair mortar was prepared with various styrene butadiene rubber (SBR) latex-cement ratios (10, 15, and 20%) and tested at 7, 42, and 90 days of age. The correlation between the results from semi-destructive tests and the compressive and flexural strengths of mortars were determined. The calibration diagrams were presented to determine the mechanical properties of the mortars. Also, the effect of the polymer was investigated on the shrinkage and bond strength between mortars and concrete substrate. Finally, the stress and cracks obtained from the “friction-transfer” and “pull-off” tests were presented using the finite element analysis software (ABAQUS). There was excellent congruence between the results from the above tests and the finite element analysis. There was a high correlation between the results of the “friction-transfer” and “pull-off” tests. Therefore, the simple and inexpensive “friction-transfer” device can be used instead of the expensive “pull-off” device. Besides, the significant correlation between the mechanical properties of the polymer-modified mortars and the above tests shows the suitability of the semi-destructive methods in investigating the mechanical properties of
Natural Radioactivity and dose Assessment in Sand and Sediment Samples from Kuakata Beach, Bangladesh

The activity concentrations of radionuclides and their respective annual effective dose rates produced by 226Ra, 232Th and 40K were measured by using high purity germanium (HPGe) detector of relative efficiency 20%. The activity concentrations of 226 Ra, 232 Th and 40 K in the sand samples were found to vary from 22.83 ± 4.11 to 100.21 ± 2.39 BqKg -1 , 68.76 ± 2.86 BqKg -1 to 297.37 ± 4.32 BqKg -1 and 75.87 ± 15.75 to 161.81 ± 19.90 BqKg -1 with mean values 48.76, 126.11 and 292.38 Bqkg -1 , respectively. For sediment samples the corresponding radionuclides ranged between 12.11 ± 1.99 and 31.64 ± 1.64 BqKg -1 , 18.94 ± 1.90 BqKg -1 and 71.11 ± 2.88 BqKg -1 and 182.73 ± 19.81 BqKg -1 and 345.77 ± 21.07 BqKg -1 , with mean values 19.67, 32.13 and 243.38 Bqkg -1 , respectively. The absorbed dose rate was estimated to the range from 30.50 to 242.93 nGyh -1 with an average value of 74.87 nGyh -1 . The estimated outdoor annual effective dose varied between 0.22 and 1.79 mSv/year with a mean value of 0.55 mSv/year, which is higher than the world average for outdoor annual effective dose (0.07 mSv/year). The external hazard indices for the samples varied from 0.17 to 1.43 with the average value of 0.44, which is less than the unity. The average value of radium equivalent activity was estimated and found to be 161.40 BqKg -1 which is less than the acceptable limit of world average value of 370 BqKg -1 . Journal of Bangladesh Academy of Sciences, Vol. 40, No. 1, 45-55, 2016
Adding Virtual Objects to Realtime Face Images; A Case Study in Augmented Reality

Augmented reality applications related with faces such as make-up, hair design, wearing glasses are mostly prepared for entertainment purposes. Facilitating the preparation of augmented reality applications and more accurate analysis of real-world data in applications will enable these applications to be used more widely in different sectors such as R&D, education and marketing. In generally, the steps in image-based augmented reality applications can be listed as follows; detection of the targeted object, finding two reference points for each targeted object in 2D images, determining the boundaries of virtual object in its image and inserting the virtual object in real time. In this study, the problems that may be encountered in preparations of these augmented reality applications expected to be used more in the future are examined through a case study. Firstly, haar cascade classifiers, used to find different face areas, are compared and as a result of the comparison, it is decided to use eye haar cascade. Afterwards, rule-based approaches have been used to eliminate the wrong ones among the found eyes and to match the eyes of the same face. Then the position, size and angle of the virtual object to be added are calculated and it is added to the face using affine transformations. The problems encountered in augmented reality and algorithms used for problem solving are explained through the virtual hat application, but these simply prepared algorithms, can be used for different objects such as hair and glasses by changing the target points.
Early Recognition of Rainfall to Plan and Manage the Cultivation: A Forecasting Model Using Artificial Neural Networks for Dompe Divisional Secretariat in Gampaha District, Sri Lanka

As the agricultural productivity is climate sensitive, forecasting climatic factors is important to maximize the harvest and to planning and management of the cultivation. Even though all the climatic factors have influence on cultivation, rainfall is one of the major influential factor to Sri Lankan agriculture. Rainfall forecasting is vital in agriculture and it is a challenging task due to the uncertainty of natural phenomena. Artificial neural network (ANN) approach is being applied for forecasting real time rainfall using climatic factors affecting to rainfall. The aim of this study was to identify a neural network model which is capable of forecasting rainfall of Dompe Divisional Secretariat in Gampaha District with a high accuracy. Feed forward neural network model (FFNN) was applied with Levenberg-Marquardt back propagation algorithm and the network parameters were adjusted to minimize the forecasting error. The suggested FFNN model consisted of 12 input variables with two hidden layers with 13 and 10 hidden neurons in first and second layers, respectively. Log sigmoid transfer function used in hidden layer 1 and 2 while pure linear transfer function was used in the output layer. The model forecasts rainfall with mean squared error of 0.0895 and normalized mean squared error of 0.1975. The coefficient of determination (R2) of the testing set was 0.8. These results demonstrated the suitability of other climatic factors: temperature, wind speed, air pressure, humidity, percentage of clouds and their lags in forecasting rainfall using ANN technique in forecasting rainfall with high accuracy.
Drone Formation Control via Belief-Correlated Imitation Learning

The proliferation of unmanned aerial vehicles (UAVs) has ﬂourished various intelligent services, in which the eﬀective coordination plays a signiﬁcant role in enhancing swarm execution eﬃciency. However, due to the unreliable communication in the air as well as the heterogeneity in operation mode, it is challenging to achieve highly coordinated actions, particularly in the fully distributed environment with incomplete observations. In this paper, we leverage the generative adversarial imitation learning (GAIL) technique to coordinate the drones’ actions by directly imitating the peer’s demonstrations. In order to characterize the true environment state under local incomplete observations, we transform historical observation-action trajectories into belief representations, which are trained in conjunction with the imitation policies. We also gain regularized belief representations by correlating the prediction of future states, the trace of historical contexts, and the action-assisted guidance information, which contribute to more accurate imitation policies. We evaluate the proposed algorithm on the drones’ formation control scenario. Evaluation results show the superiorities on imitation accuracy, teamwork execution time and energy cost.
Discovering Relationships between OSDAs and Zeolites through Data Mining and Generative Neural Networks

Organic structure directing agents (OSDAs) play a crucial role in the synthesis of micro- and mesoporous materials especially in the case of zeolites. Despite the wide use of OSDAs, their interaction with zeolite frameworks is poorly understood, with researchers relying on synthesis heuristics or computationally expensive techniques to predict whether an organic molecule can act as an OSDA for a certain zeolite. In this paper, we undertake a data-driven approach to unearth generalized OSDA–zeolite relationships using a comprehensive database comprising of 5,663 synthesis routes for porous materials. To generate this comprehensive database, we use natural language processing and text mining techniques to extract OSDAs, zeolite phases, and gel chemistry from the scientific literature published between 1966 and 2020. Through structural featurization of the OSDAs using weighted holistic invariant molecular (WHIM) descriptors, we relate OSDAs described in the literature to different types of cage-based, small-pore zeolites. Lastly, we adapt a generative neural network capable of suggesting new molecules as potential OSDAs for a given zeolite structure and gel chemistry. We apply this model to CHA and SFW zeolites generating several alternative OSDA candidates to those currently used in practice. These molecules are further vetted with molecular mechanics simulations to show the model generates physically meaningful predictions. Our model can automatically explore the OSDA space, reducing the amount of simulation or experimentation needed to find new OSDA candidates.
Evaluation of 3D printability of cementitious materials according to thixotropy behavior

This study is a basic research for evaluating the buildability of cementitious materials for three-dimensional (3D) printing. In the cement paste step, the thixotropy behavior according to the resting time, which represents the time interval between each layer, was analyzed. In addition, the relationship between the thixotropy behavior and 3D concrete printing buildability was derived by proposing a measurement method that simulates the 3D concrete printing buildup process. The analysis of the tendency of the thixotropy behavior according to the resting time revealed that the area of the hysteresis loop (AHyst) showed a tendency to increase and then converge as the resting time increased, which means hysteresis loop approach critical resting time for sufficient buildability. In the thixotropy behavior analysis that simulates the 3D concrete printing buildup process, the buildup ratio, which is the recovery rate of the shear stress, showed a tendency to increase and then converge as the resting time increased, which are similar results like hysteresis loop. It was concluded that AHyst and the buildup ratio can be used as parameters for determining the resting time, and they have close relationships with 3D concrete printing buildability.
Decision Model for a Virtual Agent that can Touch and be Touched

Touch is an essential sense to the social development and well-being of individuals, acting as a communicative channel for emotions and empathy. Our overall objective is to enhance the ability of embod-ied conversational agents (ECAs) to bond with humans. To reach such an objective, we have endowed an ECA with the capacity to touch and be touched in a social interaction with a human in an immersive environment. By drawing inspiration from literature on human-human social touch and related works on human-agent tactile interactions, we have developed a framework for a touching ECA able to perceive when and how a user touches it and decide when and how to respond accordingly. This paper focuses on going beyond a simple bi-directional touch interaction through a decision model that actually adapts its behaviour to the content of the interaction, the level of rapport and the human’s touch avoidance sensibility. This enables an actual interactive loop between the agent and the human.
APOFERRITIN-MEDIATED DOXORUBICIN INTERNALIZATION THROUGH TRANSFERRIN RECEPTOR 1

This work is aimed at the possibilities of targeted drug delivery into the tumour tissue. This approach can greatly reduce the otherwise serious side effects of conventional treatment – systemic toxicity. For this purpose, ubiquitous protein cage apoferritin was employed as a carrier of cytotoxic drugs. Its molecule size of 10–12 nm allows it to employ the effect of increased permeability and retention as well as to avoid renal clearance. The cellular uptake of this carrier is known to be mediated via the transferrin receptor 1 (TfR1), which is overexpressed on metabolically highly active cells, such as cancer cells. Therefore, apoferritin’s ability to deliver drug molecules to site-of-action was tested using cell lines with high, medium and low expression of TfR1. The optimal conditions for studying the expression of TfR1 using western blotting were as follows: lysate of 50 000 cells applied in non-reducing non-denaturing buffer and the concentration of the primary antibody of 1.0 μg/ml. The properties of encapsulated doxorubicin were not affected by apoferritin, thus preserving its toxicity for cells with high level of TfR1expression (30% growth inhibition of these cells after 24 h of treatment). The suitable usage of apoferritin as a nanocarrier for chemotherapeutic delivery was confirmed in this work.
Soft X-ray Diagnostic System Upgrades and Data Quality Monitoring Features for Tokamak Usage

—The validation of the measurements quality after on-site diagnostic system installation is necessary in order to provide reliable data and output results. This topic is often neglected or not discussed in detail regarding measurement systems. In the paper recently installed system for soft X-ray measurements is described in introduction. The system is based on multichannel GEM detector and the data is collected and sent in special format to PC unit for further postprocessing. The unique feature of the system is the ability to compute final data based on raw data only. The raw data is selected upon algorithms by FPGA units. The FPGAs are connected to the analog front-end of the system and able to register all of the signals and collect the useful data. The interface used for data streaming is PCIe Gen2 x4 for each FPGA, therefore high throughput of the system is ensured. The paper then discusses the properties of the installation environment of the system and basic functionality mode. New features are described, both in theoretical and practical approach. New modes correspond to the data quality monitoring features implemented for the system, that provide extra information to the postprocessing stage and final algorithms. In the article is described also additional mode to perform hardware simulation of signals in a tokamak-like environment using FPGAs. The summary describes the implemented features of the data quality monitoring features and additional modes of the system.
The Effect of Physical Activity on Diabetes Mellitus Patients with Hypertension

Physical activity plays an important role in controlling blood glucose levels and reducing the risk of cardiovascular disorders in patients with diabetes mellitus (DM). DM patients should follow the principle of FITT (frequency, intensity, type and time) while doing a physical activity, which has a direct effect on their health condition. However, the identification of physical activity in DM patients with hypertension is still limited. Therefore, this study aimed to describe the effect of physical activity on DM patients with hypertension. Accordingly, this cross-sectional study included patients with hypertension, aged 18–65 years, able to communicate verbally, and visiting a public healthcare centre in Semarang between February and March 2020. The Baecke Physical Activity Questionnaire (BPAQ) was used to identify the physical activities done by the participants. Quantitative data analysis revealed the distribution of frequency, percentage, mean, and standard deviation. A total of 150 respondents participated in the study, mostly in late adulthood (54.7%), women (68%), and having DM with hypertension for one–five years. The results showed that 40.7% of the respondents had a low level of physical activity, of which 46% did not play any sport and 92.7% had low occupational physical activity. The average leisure time index was 2.31 ± 0.6, which means that most of them did a few physical activities during leisure time. It is concluded that since many DM patients with hypertensions were categorized as having low level of physical activity, this may affect their blood glucose level and blood pressure.
Groundnut (Arachis hypogaea) pod and haulm production in the tropical legume project States, Nigeria

The production of groundnut pod and haulm is one way of addressing the challenge of scarcity of livestock feed as well as improving the incomes of small-scale farmers. This study examines the revenue associated with groundnut pod and haulm production, using data obtained from 253 participating farmers. The data was analyzed using descriptive and inferential statistics. Results showed that average haulm yield ranged from 551 to 1,364 kg ha-1 while the average pod yield varied from 1,208 to 1,580 kg ha-1. Revenue obtained from sales of haulm and grain was sensitive to price movements and differed significantly across locations and from season to season. The average revenue from haulm was lowest at US $75.8 ha-1 in October to December and highest at US $215.7 ha-1 in July to September. Similarly, the average revenue from the sales of pod was lowest at US $447.6 ha-1 in October to December and highest at US $616.3 ha-1 in July to September. The study concludes that groundnut varieties that combine high haulm and high pod yields are desirable for farmers’ livelihood and should be made readily available. Key words: Groundnut, pod, haulm, yield, revenue.
LSTM Recurrent Neural Networks for Cybersecurity Named Entity Recognition

— The automated and timely conversion of cybersecurity information from unstructured online sources, such as blogs and articles to more formal representations has become a necessity for many applications in the domain nowadays. Named Entity Recognition (NER) is one of the early phases towards this goal. It involves the detection of the relevant domain entities, such as product, version, attack name, etc. in technical documents. Although generally considered a simple task in the information extraction field, it is quite challenging in some domains like cybersecurity because of the complex structure of its entities. The state of the art methods require time-consuming and labor intensive feature engineering that describes the properties of the entities, their context, domain knowledge, and linguistic characteristics. The model demonstrated in this paper is domain independent and does not rely on any features specific to the entities in the cybersecurity domain, hence does not require expert knowledge to perform feature engineering. The method used relies on a type of recurrent neural networks called Long Short-Term Memory (LSTM) and the Conditional Random Fields (CRFs) method. The results we obtained showed that this method outperforms the state of the art methods given an annotated corpus of a decent size.
The TU Wien Turbulent Water Channel: measurements and statistics

The statistical properties of a turbulent duct flow are investigated experimentally. Experiments are performed in the TU Wien Turbulent Water Channel at a shear Reynolds number of 370. A high-speed imaging system is used to record the flow and the velocity fields are obtained via threedimensional particle tracking velocimetry (3D-PTV). Experimental results are first used to compute the first four moments of the velocity distribution and then compared against direct numerical simulations (DNS). It was found that in the duct, the statistics of all velocity components exhibit good agreement with those obtained from DNS. Slight overestimation of the main Reynolds stresses, particularly close to the wall, has been observed. Skewness and flatness of the stream-wise velocity were underestimated in the viscous sub-layer. Moreover, a new method is proposed to correct the misalignment of the measurement volume with respect to the actual flow direction. This method successfully reduced the average span-wise velocity. Finally, we analysed the effect of the width-to-height aspect ratio (AR) on the flow statistics. We analysed DNS of duct flows with 1 ≤ AR ≤ 8 and we observed that, at a shear Reynolds number of 150, a duct with AR ≤ 8 is not sufficient to reproduce the statistical properties observed in a turbulent channel flow (AR = ∞). These findings are significant because they provide evidence of the turbulence characteristics in the TU Wien Turbulent Water Channel.
Fermentability of Concentrated Sulfuric acid Hydrolyzates from Aspenwood and Pinewood

The fermentability of hydrolyzates derived from two-stage concentrated sulfuric acid hydrolysis of Trembling aspen (Populus tremula) and Scots pine (Pinus sylvestris) were investigated. Three types of hydrolyzates were produced at mild, moderate and high decrystallization severity conditions. Portions of each of the original hydrolyzates were concentrated by vacuum evaporation to increase the sugar fraction to simulate industrial applications. Both sets of hydrolyzates were fermented anaerobically using Saccharomyces cerevisiae ATCC 96581. After 23 hours of fermentation, complete glucose consumption was observed for all the original hydrolyzates, with no signs of inhibition. The ethanol yields from these hydrolyzates ranged from 68% to 90% of theoretical value. Fermentation of concentrated aspen hydrolyzates produced at mild or moderate decrystallization severity showed a significant lag phase, associated with relatively high furfural content in the samples (approximately 2 g/L). No lag phase was apparent for aspen produced at high decrystallization severity or pine hydrolyzates. However, furfural had no adverse effect on the maximum ethanol yield. No inhibitory effect of HMF, acetic acid, formic acid or levulinic acid was detected in the concentrated hydrolyzates due to the relatively low concentrations of these compounds. The ethanol yields from concentrated hydrolyzates were above 97% of theoretical with exception of pine hydrolyzate produced at high severity which had a fairy good yield of 87%. The quantitative analysis of inhibitors and the fermentability investigation showed that both the original and concentrated hydrolyzates from the concentrated sulfuric acid process were readily fermentable, and furfural was singled out as the most important inhibitor in these hydrolyzates.
Impact of Recommender Systems on the Dynamics of Users' Choices

The major focus of recommender systems (RSs) research has so far been on improving the precision and quality of the recommendations. However, it is also important to understand whether the recommended items are actually chosen and how they influence users’ choice making. Few studies have attempted to analyse the impact of RSs on users’ choice making. In this PhD research, we aim at better understanding the impact of RSs on the evolution of the choices made by a collection of users. We propose simulation procedures where users are simulated to make choices over a period of time when they are exposed to alternative RSs. We measure several properties of the users’ choices distribution, which capture the RS effect. Our goal is to understand the evolution of the choices of a collection of users as time goes; next choices are influenced by previous choices used by the RS to generate recommendations. Ad-ditionally, we propose online experiments to study the effect of RSs on real users’ choices. We propose to design web-based platforms where alternative RSs recommend items to the users and study RSs impact by analysing the evolution of the choices.