Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancement of the Brick constitutive model to incorporate viscous soil behaviour

The Brick model is an advanced elasto-plastic constitutive model for soils and has become a recognised tool for modelling ground behaviour, following its implementation into the finite element program Safe. The model can reproduce many essential elements of soil behaviour, which is viewed from a strain-based perspective. An analogue for the model is that of a man walking around a room, pulling a number of bricks with strings of different lengths behind him. The room represents strain space, the man’s movement is the applied strain and each brick movement is the response of a proportion of the soil. The present Brick model, although advanced, does not account for viscous behaviour, including creep and strain rate dependent stiffness, which can be very significant for clay soils. The principal aim of the work was to incorporate such behaviour into the Brick model. Two main approaches were identified manipulating either the velocity of the bricks or the string lengths. Both approaches were implemented into the Brick model allowing a series of tests to be conducted into their predictive capabilities. Isotach strain rate behaviour was investigated with both models by simulating both constant and step rate of strain tests. Simulations of past experimental work into the combined effects of creep and recent stress history on clay soil stiffness were also conducted. It was demonstrated, in accordance with the experiments, that creep can erase the effects on the initial stiffness of recent stress history involving relatively short stress paths approaching the current state, though this was not true for longer approach paths. The experimental results were more correctly simulated using the model with manipulated, strain rate dependent string lengths, the SRD Brick model. The SRD Brick model was then implemented into Safe to allow the analysis of two case histories, thereby quantifying the influence that viscous effects can have. The first involved the analysis of surface displacements above the westbound tunnel forming part of the Jubilee Line extension beneath St James’s Park, London. The second analysis was concerned with the prediction of heave displacements of a deep basement in Horseferry Road, London. The SRD Brick model was able to significantly improve on the predictions given by the original Brick model in both cases.
Removal of Cd++ from Contaminated Water Using Bio-surfactant Modified Ground Grass as a Bio-sorbent

Bio-sorbent activated carbon adsorption method has been applied in removing the Cd++ ion from cadmium contaminated water. Dry ground grass has been chosen as a carbon source while bio-sorbent rhamnolipid has been used to activate of the carbon. Different bio-sorbent-grass doses were prepared and characterized by the Scanning Electron Microscopy, Atomic Absorption Spectroscopy and Fourier Transform Infrared Spectroscopy.Results indicates that both porosity and surface roughness are very essential and required for the adsorption of Cd++ ion. In addition, cadmium ion adsorption was significantly influenced by the change in the pH value of the media. The highest removal value of 85.77 % of the Cd++ was recorded at pH 7 at 40mg of rhamnolipid. Increase in the concentration of rhamnolipid also increases the removal of the cadmium ion. Fourier Transform Infrared spectroscopy (FTIR) spectrum of the rhamnolipid modified grass before the adsorption indicates the presence of characteristic functional groups of O-H, C-H, N-H and P-O that are required to facilitate the adsorption of the cadmium ions. While the spectra of rhamnolipid modified grass after the adsorption shows a new peak and slightly shift of mostly peaks that reveals the adsorption of the cadmium ions. Results shows that modified grass could be potentially used in the heavy metal remediation and provides an efficient, economical and biodegradable bio-sorbent for the removal of the toxic and heavy metals from the environmental and industrial contaminated wastewater.
The impact of energy performance regulations on systems of building control

The awareness of climate change influenced by CO2 emissions, but also the dependency of fossil fuel and the risks of increasing prices all lead to the need of better energy performances of houses. Therefore the energy performance regulations for houses will be put on increasing levels during the next decade. Several European countries are formulating policies aiming at net zero energy or carbon neutral houses in the years 2015 2020. Technical solutions do exist and are already brought into practise; this concept is called the passive house. In some European countries more and more examples of passive house projects are realised. However there is quite some evidence that it will be a big challenge to achieve these performances at a large scale in the construction practice. Research under recently build houses in the Netherlands demonstrates that in many cases even the current levels of required performances are not met, due to mistakes in the design and in the construction processes. In this paper we argue for the need of innovation in the systems of building control and quality assurance to support the energy. In stead of a system of control that basically aims for avoiding large safety failures, a system is needed that guarantees a high level of certainty of performances. In this paper we describe the possible changes of building processes due to the introduction of the passive house concept, and the urgency of reliable quality assurance to adequately reaching the energy ambitions and to assure other quality issues at the same time. We illustrate this with passive house certification schemes from some European countries
Finite-Order Beliefs and Welfare-Enhancing Instruments in the Centipede Game

This paper investigates the effectiveness of two instruments designed to defer termination in the centipede game: an insurance against termination by the opponent, and an option to offer the opponent a bonus for not terminating the game. The rational prediction in both cases is passing until close to the end. Empirically, however, only the bonus option is used by the subjects. The results indicate that subjects readily understand the strategic effect of the bonus, which, once offered, renders passing until close to the end the strictly dominant strategy for both players. Yet, they fail to realise the slightly more involved strategic signal entailed in the insurance, namely that passing until close to the end is a strictly dominant strategy for an insured player. In order to further investigate this effect, we propose a simple behavioural model based on level-k thinking and show that it is largely consistent with the data.
Are importing and exporting complements or substitutes in an emerging economy? The case of Colombia

The aim of this paper is to investigate the impact of two trading strategies (exporting and importing) on total factor productivity (TFP) and the potential complementarity/substitutability effects of these strategies. In order to assess these effects, robust estimates of TFP are obtained using a general method of moments approach that explicitly determines the ability of a firm's trading experience to affect productivity. Data from the Annual Manufacturing Survey spanning from 2007 to 2016 is used for Colombian manufacturing firms. Our estimation results suggest that, regardless of the technological intensity of the
Generating Classification Rules for Large DataSets

Classification is an important topic in the field of artificial intelligence. Several approaches have been proposed for classification, based on a given set of training instances. One well-known is the decision tree approach, by which a tree is built and classification rules are extracted from the resulting tree by tracking from the root node down to the leave nodes. The extracted rules can then be used for classification. However, overfitting may occur and generalization can be low for large datasets. In this paper, we propose a data mining approach to find the classification rules. First of all, frequent termsets are generated. Then classification rules are derived from the frequent termsets. Through the threshold settings, a desired set of classification rules can be obtained and overfitting can be alleviated and controlled. Examples are shown to illustrate the effectiveness of the proposed approach. The interestingness measures of the developed rules are computed and compared.
Hot Topic Detection on Twitter Data Streams with Incremental Clustering Using Named Entities and Central Centroids

Nowadays, hot topic detection is an important knowledge discovery task on social data streams to determine hot topics that are being discussed the most on a social platform. One of the main challenges of this task is the processing of a very large social dataset in a sequence order efficiently and effectively. In this paper, our work proposes a novel incremental clustering-based solution with named entities and central centroids for hot topic detection on Twitter data streams. Using named entities, each tweet and its cluster can be semantically represented. They are then efficiently used for searching in noise removal and incremental clustering. After that, clusters of higher quality can be formed and lay the basis for deriving true hot topics. Besides, central centroids are defined instead of normal centroids to speed up our incremental clustering process. As a result, our solution is more efficient and effective than the other approaches in the existing works as shown in an empirical evaluation on Twitter Events2012 dataset. Indeed, our entity and central centroid-based incremental clustering method outperforms the others with Recall of about 0.92, Normalized Mutual Information of about 0.85, and execution time of about 30 minutes on processing 500,000 tweets with more than 500 topics. Such better results confirm the appropriateness of our new method design features for hot topic detection on Twitter data streams.
A Test of a Loran-C Multi-chain Positioning Method Using ASF Estimates

In this paper, we described a multi-chain ToA positioning method to estimate positions using all received Loran-C signals from multiple chains without constraining to a single chain and an ASF estimation method based on a receiver clock offset estimation. To validate these methods, we carried out a test. We firstly installed one receiver at fixed location in land and two receivers on the vessel. And we collected raw ToA measurements data from three receivers while the vessel moved along the path. Next, we estimated the ASF with the data provided by the land fixed receiver and measured the temporal change of ASF to generate the differential correction. The data provided by one of the receivers installed on the vessel was used to generate the ASF map. Loran-C multi-chain positioning was performed using the remaining receiver data. In this process, we mitigated the error by ASF using differential correction and ASF map. As a result, the horizontal positioning error was 2. 12m and 2DRMS was 24. 12m
Hate Speech Detection Using Latent Semantic Analysis (LSA) Method Based On Image

Hate speech are a words, actions which is prohibited because it leads to acts that trigger anarchism and violence attidudes toward other individuals or groups. Ethics in the internet are needed considering that internet is a matter that important use for today's society. However, more side are miss using the internet to spread such kind a hate speech, such as ethnicity, religion and race. The development of a system for detecting hate speech through images is quite rare for now a days. Therefore, this study study is classified to detect whether there is an element of hatred in the image that will be selected. In this final project, the author hopes to make how to classify the element of hate speech in an image performed by the machine learning, which later that machine learning can recognize any kind of hate speech on the image through the existing text. With using Latent Semantic Analysis (LSA) method, we get the result of this research is precision 67%, recall 76.84%, and accuracy 57.9%. After creation of this research, it is hoped the computer can know and classify the existence of hate speech in the image.
A Qualitative Analysis of User Reviews on Mental Health Apps: Who Used it? for What? and Why?

The evidence of effectiveness of Cognitive Behavioral Therapy (CBT) based mental health applications is shown to be equivalent with face-to-face CBT. Although there is a high number of users, the adherence of users to the intervention is low. Meanwhile, user non-adherence could happen on purpose. However, the reasons for this circumstance are not well examined, particularly from a user's perspective. The question of which features engage them to use as well as to continue using these interventions are not well understood. From the designer's perspective, to answer this question, it is essential to understand the context of app uses, particularly, three precedent questions: who are intended users, what they use it for, and why they use it need to be examined. This study answers these three questions by analyzing user reviews of several sample mental health applications which the core design is primarily based on a CBT. The qualitative analysis indicates that most of reviews are from people with common MH problems. Some people simply use the app to relax, track their mood, practice mindfulness, self-care, or build healthy habits. The majority of users have used the apps as complementary to the professional-based treatment. Users use the app because it makes them more positive, happy, self-conscious, calm, fun, focused, relaxed, motivated, mindful, self-controlled or better at sleep. Importantly, they highly appreciate the ability to figure out what going on their health, to help them keep on progress of health, to see correlation between causes and effects of their health problems, to conduct self-evaluation and self-reflection, to learn good things, to build good habits, and to provoke, reframe, and organize their thoughts. These findings will help the designers think of appropriate approaches to design the app which helps users better at achieving their targets.
Content-Oriented Surveillance System Based on ICN in Disaster Scenarios

This paper deals with an efficient image object detection method for use in a disaster prevention network that uses information-centric networking (ICN). In ICN for disaster prevention, a large number of surveillance cameras are arranged, and disaster image contents corresponding to the user's requests are distributed directly from the node attached to the surveillance camera. At this time, the name of the content requested by the user does not necessarily match the name of the image acquired by the surveillance camera. In this paper, the content requested by the user is processed and named using natural language processing (NLP). In addition, the image content from the surveillance camera is named using artificial intelligence technology. In this way, a method for improving the hit ratio between users and cameras was established. Furthermore, the volume of the interest packets decreases based on the information which area often occurs disaster. As a result, the network efficiency of ICN can be improved.
Monocular Semidirect Visual Odometry for Large-Scale Outdoor Localization

To achieve large-scale outdoor real-time localization, a semidirect visual odometry method for a monocular camera is proposed. In this method, the performance of the features from the accelerated segment test (FAST) algorithm is improved to detect corners with good tracking and distribution properties. A multiscale Lucas–Kanade (LK) approach is presented to build the correspondence between features and map points robustly and efficiently. Thereafter, a semidirect visual odometry system is put forward to achieve long-distance and large-scale localization for the ground vehicle. The main contribution of this method is that it integrates the robustness and efficiency advantages of feature-based methods and the accuracy virtue of direct methods into one visual odometry system. As a result, the proposed method can be applied to localize long-distance and large-scale outdoor scenes, without loop closing and global BA. Several experiments illustrate the performances of the improved FAST algorithm, the multiscale LK approach, and our semidirect visual odometry system. The experimental results demonstrate that our semidirect visual odometry system can be operated on the KITTI benchmark accurately and efficiently. The average computational time for a single frame is below 60 ms on a general notebook computer with a CPU.
Measurements of electromagnetic parameters of soil

A nondestructive method for measuring the dielectric permittivity of soil has been implemented. The sensor is realized as an open-ended semirigid coaxial cable. The measurement procedure is based on measuring the reflection coefficient of the sensor. A computer program for performing the analysis of measured data has been developed. The dielectric constant and conductivity of soil from a test site in Dalmatia, Croatia, has been measured. The results show the dependence of the electromagnetic properties of soil on frequency and on moisture content. The measured results indicate the accuracy of the implemented measurement method.
Evaluation of Chevron Markings on Freeway-to-Freeway Connector Ramps in Texas

This report presents an evaluation of the use of converging chevron pavement markings in an attempt to reduce speeds on freeway-to-freeway connector ramps. In this project, a converging chevron marking was designed and implemented on a freeway-to-freeway connector ramp in El Paso, Texas. Speed measurements were conducted at the project site in three study periods: before the implementation of chevron markings, early-after the implementation, and late after the implementation of chevron markings. Speed measurements were taken upstream of the curve, at the start of the curve, and in the middle of the curve of the connector ramp. A detailed before-after analysis of the speed data was conducted by vehicle classification, light conditions, and location along the curve. From the analysis results, it was found that chevron markings appeared to reduce speeds where the markings were in place, though the reduction in the average speeds varied based on vehicle class and curve location. Maximum reduction was observed at the upstream location of the curve with about a 4 mile per hour (mph) reduction observed for heavy trucks.
Real-Time Rendering of Translucent Material with Directional Subsurface Scattering

We provide a fast rendering technique to render translucent materials, like fruit, marble and skin. The method we developed has low memory requirements, does not require UV mapping and little or not pre-processing. We will employ new BSSRDF analytical model that considers the directionality of the incoming light into account. Using this approach, we obtain real-time results of 30 FPS for complex models of the magnitude normally employed in the computer game industry
Linear Predictive Model for Discriminative Feature Representation of Object Classification

In order to improve the performance for recognition in computer vision tasks, a high-level feature representation plays a crucial role to transform a raw input data (low-level) into a new informative representation for learning algorithms. Sparse coding is one of the widely used methods to generate a high-level feature representation for classification. In particular, an input data can be represented as a sparse linear combination of a set of training overcomplete dictionary. However, the main problem in traditional sparse coding is that it is fairly slow to compute the corresponding coding coefficients due to an l0/l1 optimization. In this work, an efficient linear model with low computational effort is proposed to create the discriminative coding coefficients. The comparison of classification performance between the proposed method and the existing discriminative sparse coding is experimented on image databases for face and scene recognitions under the same learning condition. The results indicate that our proposed method both achieves promising classification accuracies and outperforms in computation time.
Cordless and portable computing in a wireless office environment

The ability of a network to support mobile terminals lies very much in the network architecture and the protocols used. The impact of mobile computers on the existing LAN architecture and the network protocols used are studied. A robust network architecture based on the DECT system is proposed, together with appropriate interworking procedures, which enable the network and protocols such as TCP/IP to handle mobile computing.
Modeling of Nanoplate Parasitic Extension Resistance and its Associated Dependency on Spacer Materials

This paper presents a new model for parasitic extension resistance considering the effect of the spacer dielectric constant on the nanoplate-field-effect transistor (NP-FET) structure. Unlike the previous model that considers the accumulation carriers under the gate overlap, the newly proposed model is developed based on the extension surface potential, which is dependent on the spacer dielectric constant. In addition, surface mobility is redefined by considering the change of the surface electric field with respect to the changes of spacer material. The accuracy of the model was validated by changing physical parameters such as nanoplate width, thickness, source and drain bulk doping concentration, and spacer materials, and it was found that the errors were within 5% of the 3-D technology computer-aided design device simulation results.
Extracting deck trend to plan descent trajectory for safe landing of UAVs

This paper outlines a feasible scheme to extract deck trend when a rotary-wing unmanned aerial vehicle (RUAV)approaches an oscillating deck. An extended Kalman filter (EKF) is de- veloped to fuse measurements from multiple sensors for effective estimation of the unknown deck heave motion. Also, a recursive Prony Analysis (PA) procedure is proposed to implement online curve-fitting of the estimated heave mo- tion. The proposed PA constructs an appropriate model with parameters identified using the forgetting factor recursive least square (FFRLS)method. The deck trend is then extracted by separating dominant modes. Performance of the proposed procedure is evaluated using real ship motion data, and simulation results justify the suitability of the proposed method into safe landing of RUAVs operating in a maritime environment.
MOSFET simulation - TCAD tools/packages

Industry-standard TCAD software packages are widely used by semiconductor companies to optimise processes and devices in integrated circuits fabrication. After process and device simulation one can go ahead with circuit simulation using Spice. This paper focuses on the matching of these program packages in the area of parameter and characteristics extraction. We compared the transfer characteristics of n-type MOSFET, biased in the linear region, obtained from Synopsys package including TSUPREM4 and MEDICI with transfer characteristics from PSpice package.