Miracle-dz/UniFAR-FTUs huggingface dataset:
STanford EArthquake Dataset (STEAD): A Global Data Set of Seismic Signals for AI

Seismology is a data rich and data-driven science. Application of machine learning for gaining new insights from seismic data is a rapidly evolving sub-field of seismology. The availability of a large amount of seismic data and computational resources, together with the development of advanced techniques can foster more robust models and algorithms to process and analyze seismic signals. Known examples or labeled data sets, are the essential requisite for building supervised models. Seismology has labeled data, but the reliability of those labels is highly variable, and the lack of high-quality labeled data sets to serve as ground truth as well as the lack of standard benchmarks are obstacles to more rapid progress. In this paper we present a high-quality, large-scale, and global data set of local earthquake and non-earthquake signals recorded by seismic instruments. The data set in its current state contains two categories: (1) local earthquake waveforms (recorded at “local” distances within 350 km of earthquakes) and (2) seismic noise waveforms that are free of earthquake signals. Together these data comprise ~ 1.2 million time series or more than 19,000 hours of seismic signal recordings. Constructing such a large-scale database with reliable labels is a challenging task. Here, we present the properties of the data set, describe the data collection, quality control procedures, and processing steps we undertook to insure accurate labeling, and discuss potential applications. We hope that the scale and accuracy of STEAD presents new and unparalleled opportunities to researchers in the seismological community and beyond.
Effect Of Cinnamon And Ginger Compared To Doxystin (Antimicrobial Drug) On Serum Lipid Profile In Broiler Chicks

The aim of this study was to assess the effect of the medicinal plants cinnamon (Cinnamomum verum) and ginger (Zingiber officinale), as natural feed additives in comparison to (Doxystin) )“Doxycycline HCl 50 mg and Colistin sulfate” , known antimicrobial growth promoter) on the serum lipid profile of broiler chicks. One hundred and sixty (one day-old) broiler chicks were assigned to four groups of the same mean weight, each with four replicates of ten chicks. The first group was used as control group and fed broilers basal diet, the second group fed the basal diet supplemented with the (Doxystin) as 0.5%, the third and fourth groups fed basal diet mixed with C. verum, and Z. officinale as 2% of the diet respectively. The experimental diets affected all parameters measured follows, total cholesterol and serum (low density lipoprotein) LDL-C concentration was significantly (P<0.05) decreased in groups received spices diet compared to Doxystin and control groups. Whereas, the (high density lipoprotein) HDL-C concentration showed significantly (P<0.05) lower levels in the two spice treated groups compared to the control group only, and the antibiotic treated animals showed similar level to that observed in spice treated groups. Triacylglycerols and the VLDL-C fraction showed clearly reduced values in all treated groups compared to the control group, though the difference was not significant but it was more pronounced in the spice treated groups, as they reported half the level of the control group. It can be concluded that inclusion of C. verum and Z. officinale as feed additives acted as natural hypocholesterolemic agents in broiler chicks in particular and reduced blood lipids in general.
Research on the economic efficiency for technological equipment extending of the kinematic chains on the numerically controlled machines

The article describes the selection of machining holders included in the so called special machining fixture-tools. The equipment presented in the work is readily used by technologists as a solution extending the kinematic chain of the machine tool. It enables multi-axis machining (from many sides) using one item clamping. This paper presents the calculation of productivity and its growth through the use of special equipment. The research was carried out for medium-volume production, which is quite a difficult area in the design of technologies and the study of production costs. In the technical development of tooling and modeling of its usage costs, it was assumed to maintain the required level of dimensional and shape accuracy with a simultaneous significant reduction of operating costs. As part of the work, a model was also developed for determining the break-even point of investment in special machining tools that are used on triaxial milling machining centers.
Scientific Knowledge Communication in Online Q&A Communities: Linguistic Devices as a Tool to Increase the Popularity and Perceived Professionalism of Knowledge Contribution

With the popularity of question-and-answer (Q&A) communities, widespread dissemination of scientific knowledge has become more viable than ever before. However, those contributing high-quality professional scientific knowledge are confronted with the challenge of making their contributions popular, since nonexpert readers may not recognize the importance of their contributions given the massive amount of information available online. In this study, we show that nonexpert readers are capable of evaluating the professionalism of content contributed in such communities as well as experts. However, we discovered that a salient discrepancy exists between the content nonexperts favor and the content they perceive as professional. In line with studies that have suggested that writing techniques play an important role in how expert content is received by laypersons, we investigated how the use of linguistic devices affects both the perceived professionalism and the popularity of contributions in Q&A communities. Based on both secondary data and a scenario-based survey, we identified specific linguistic devices that can increase content popularity without reducing perceived professionalism. Additionally, we revealed linguistic devices that increase popularity at the expense of perceived professionalism in this context. Finally, we conducted a laboratory experiment to more firmly establish the causal effects of the linguistic device use. The triangulated findings have important implications for both research and practice on communicating scientific knowledge in Q&A communities.
Modeling and Prediction of Remaining Useful Lifetime for Maintenance Scheduling Optimization of a Car Fleet

The remaining useful lifetime (RUL) is the time remaining until an asset no longer meets operational requirements. An accurate estimation of the RUL is central to prognostics and health management systems. However, the RUL of an asset is usually very difficult to estimate and to achieve in any industry. This is because the RUL strongly depends on manufacturing, the operating environment, and the observed condition monitoring. Here, we use physics-based approaches and data-driven approaches to predict the RUL of four essential components of a passenger car, namely engine, brake pads, springs, and tires. Our results show good agreement of both approaches. In addition, we develop a hybrid framework to generate a data set of RULs of a car fleet. This framework can be used to establish an optimal maintenance schedule for a car fleet, such as the fleet of a taxi company.
Optimal Beamforming Design for Underwater Acoustic Communication With Multiple Unsteady Sub-Gaussian Interferers

Traditional beamforming (BF) methods can be used to suppress the Gaussian interferers in underwater acoustic communication systems. However, multiple unsteady sub-Gaussian interferers may exist that degrade the performance of BF. In this paper, a new beamformer is proposed for the suppression of multiple unsteady sub-Gaussian interferers (SMUSGI). The proposed BF combines the multiplier and the quasi-Newton methods to obtain the optimal weight vector. Simulation experiments are conducted to validate the performance of the proposed beamformer. In addition, the proposed beamformer is compared with the traditional methods including the MVDR, Subspace, Bartlett, MDDR, LCMD and LCMV beamformers. The obtained results demonstrate that the proposed beamformer can effectively suppress multiple unsteady sub-Gaussian interferers with a higher output signal-to-interference-plus-noise ratio (SINR), and faster convergence compared with the traditional methods.
Clarke & Wright's Savings Algorithm and Genetic Algorithms Based Hybrid Approach for Flying Sidekick Traveling Salesman Problem

Over the past few years, drones also known as unmanned aerial vehicles (UAV), have been adopted as a part of transportation activities in logistic sector. This paper investigates a new version of traveling salesman problem called as flying sidekick traveling salesman problem(FSTSP) in which trucks and drones serve the customers in coordination with the objective of minimizing the total delivery distance of trucks at the depot after completing the deliveries. Clarke & Wright's savings algorithm is a well-known heuristics approach in literature, which gives better solution for classical vehicle routing problem. In this paper, a hybrid approach based on Clarke & Wright's savings algorithm and genetic algorithm is proposed for solving the new version of travelling salesman problem. In the proposed hybrid algorithm, which is the sequential use of genetic algorithm and Clarke & Wright’s savings algorithm, is used for assignment of the truck, drone or both of them to serve the customer. The solution of the genetic algorithm, which is the well-known metaheuristic approach, is enhanced with Clarke & Wright's savings algorithm. The aim of the problem is to minimize the total delivery distance according to the assignment decisions. This is the first hybrid approach in the literature including Clarke & Wright’s savings algorithm and genetic algorithm that applies for FSTSP problem. The hypothetical experiments conducted on various instances and results confirm the efficiency of the approach and give some insights on this drone delivery system.
The STEAMR Instrument: Optical Design, Development & Testing

The STEAMR instrument is a Swedish national contribution to the ESA PREMIER mission, which is a candidate for the upcoming Earth Core Explorer mission. The STEAMR instrument is envisaged as a multi-beam limb sounding satellite, which will utilise 14 simultaneously observing beams in two 12 GHz wide bands from 323 to 357 GHz. To maximize spatial sampling in the elevation direction the observing beams have an elliptical geometry, which defines the incoming beams as being astigmatic. In this paper we present an antenna optics scheme which corrects for this inherent astigmatism, thereby ensuring optimum imaging of the incoming to the circularly symmetric receiver feed horns. Furthermore, the design, synthesis and electromagnetic verification of a prototype focal plane array for the STEAMR instrument is also reported.
Bayesian nonparametric hypothesis testing

In this thesis, we propose novel Bayesian Nonparametric hypothesis testing procedures for correlated data. First, we develop and study a proposal for comparing the distributions of paired samples. Next, we propose and analyze a hypothesis testing procedure for longitudinal data analysis. Both proposals are based on a flexible model for the joint distribution of the observations. The flexibility is given by a mixture of Dirichlet processes. Besides, for setting up the hypothesis testing procedures, we use a hierarchical representation with a spike-slab prior specification for the base measure of the Dirichlet process and a prior specification on the space of models. For the paired sample test, we use an appropriate parametrization for the kernel of the mixture to facilitate the comparisons and posterior inference. Consequently, the joint model allows us to derive the marginal distributions and test whether they differ or not. The procedure exploits the correlation between samples, relaxes the parametric assumptions, and detects possible differences throughout the entire distributions. For the longitudinal data, we propose to use a mixture of Dependent Dirichlet Processes to capture the correlation between the repeated measurements. The weights of the mixture are built via a stick-breaking prior, that comes from a Markovian process evolving in time. The effect of the predictors is modeled by the underlying atoms. The proposal can provide an estimation of the density through the time for different levels of the predictors, and at the same time can identify the effect of the predictors, without assuming restrictive distributional assumptions. We show the performance throughout the document of our proposals in illustrations with simulated and real data sets. Finally, we provide concluding remarks and discuss open problems.
Measure on prediction of an ordinal response for multi-way contingency tables

For two-way contingency tables with a nominal explanatory and an ordinal response variables, Yamamoto et al. (2011) considered a proportional reduction in error measure, which describes the relative decrease in the probability of making an error in predicting the value of the response variable when the value of the explanatory variable is known, as opposed to when it is not known. The present paper extends the measure to a multi-way contingency table with multiple explanatory variables and an ordinal response variable. An example is shown.
Intelligent Attitude Control of Aircraft Based on LSTM

The flight attitude control is the core part of the maneuvering process in air combats. Traditional flight attitude control methods have high computational complexity, low flexibility and poor ability to learn sequential feature. This paper proposes a flight attitude control model based on long short term memory network, which utilizes its special gates structure to memorize historical information, and acquire the variation law of the attitude control variable from the time sequential data including the battlefield situation and flight parameters automatically. Moreover, the basic framework and training methods of the model are also introduced, and the influence caused by various LSTM network parameters is deeply discussed. The experiment results show that the proposed model has better prediction accuracy and convergence performance than the traditional recurrent neural network.
A Comparison of Software Quality Characteristics and Software Sustainability Characteristics

Software sustainability has generated much interest in the software engineering field in recent times, and has been widely investigated across different fields and from different standpoints. The relationship between software quality and software sustainability is still an open question. In this study, a literature survey and comparison was conducted using three-phases, having as a starting point the comparison of basic models for software quality. A follow-up study, conducted at a more comprehensive level to cover both basic models and the most cited tailored models. , Software sustainability literature is investigated to find the most frequent characteristics. Finally, data gathered from these studies and a comparison shows a similarity in the top level of these characteristics between software sustainability and software quality, and the emphasis on sustainability, maintainability and portability. The study suggests that ISO 25010 can be utilised by software sustainability. As a future work, the findings will be investigated empirically to support designing software sustainability framework identifying the most important criteria in the technical dimension.
Human Iris Recognition using Elman Neural Networks

The human iris is one of the best biometric features in the human body for pattern recognition. In this paper, the iris recognition system is described that consist of two main parts: image processing and its recognition. Image processing follows the capture of image, then followed by image segmentation, then localization of iris region by determining the inner and outer boundaries of the iris region and by removing the eyelashes and eyelids. To overcome the discrepancies in the iris region, a rectangular shape with constant dimensions is proposed, which is represented by a set of data following the normalization procedure. Human Iris Recognition using Elman Neural Networks. ١٠٩ Using these data set as input data of the Elman Artificial Neural Networks in order to classify the iris patterns. The adaptive learning strategy could be applied to verify each iris image of a particular person. This is done by using MATLAB®2008a.
Frequency domain modeling and multidisciplinary design optimization of floating offshore wind turbines

Offshore floating wind turbine technology is growing rapidly and has the potential to become one of the main sources of affordable renewable energy. However, this technology is still immature owing in part to complications from the integrated design of wind turbines and floating platforms, aero-hydro-servo-elastic responses, grid integrations, and offshore wind resource assessments. This research focuses on developing methodologies to investigate the technical and economic feasibility of a wide range of floating offshore wind turbine support structures. To achieve this goal, interdisciplinary interactions among hydrodynamics, aerodynamics, structure and control subject to constraints on stresses/loads, displacements/rotations, and costs need to be considered. Therefore, a multidisciplinary design optimization approach for minimum levelized cost of energy executed using parameterization schemes for floating support structures as well as a frequency domain dynamic model for the entire coupled system. This approach was based on a tractable framework and models (i.e. not too computationally expensive) to explore the design space, but retaining required fidelity/accuracy. In this dissertation, a new frequency domain approach for a coupled wind turbine, floating platform, and mooring system was developed using a unique combination of the validated numerical tools FAST and WAMIT. Irregular wave and turbulent wind loads were incorporated using wave and wind power spectral densities, JONSWAP and Kaimal. The system submodels are coupled to yield a simple frequency domain model of the system with a flexible moored support structure. Although the model framework has the capability of incorporating tower and blade structural DOF, these components were considered as rigid bodies for further simplicity here. A collective blade pitch controller was also defined for the frequency domain dynamic model to increase the platform restoring moments. To validate the proposed framework, predicted wind turbine, floating platform and mooring system responses to the turbulent wind and irregular wave loads were compared with the FAST time domain model.
Monocular 3D mesh prediction for cars in autonomous driving scenarios

In the context of autonomous driving cars, a necessity for precise 3D reconstructions of the road and environment is every day more explicit. The present work aims to expose an approach for reconstructing vehicles from a single (RGB) image of a road-like scenario. It is based on previous 3D mesh prediction algorithms that mix Convolutional Neural Network architectures for feature extraction with Graph Convolutional Networks. We build our own dataset and train our model to predict 3D meshes, providing the network with a single RGB image of a car and a 3D bounding box. We reveal how this method recovers precisely most of the geometric details of a car as well as shows successful levels of accuracy.
Traffic Regulator Detection and Identification from Crowdsourced Data - A Systematic Literature Review

Mapping with surveying equipment is a time-consuming and cost-intensive procedure that makes the frequent map updating unaffordable. In the last few years, much research has focused on eliminating such problems by counting on crowdsourced data, such as GPS traces. An important source of information in maps, especially under the consideration of forthcoming self-driving vehicles, is the traffic regulators. This information is largely lacking in maps like OpenstreetMap (OSM) and this article is motivated by this fact. The topic of this systematic literature review (SLR) is the detection and recognition of traffic regulators such as traffic lights (signals), stop-, yield-, priority-signs, right of way priority rules and turning restrictions at intersections, by leveraging non imagery crowdsourced data. More particularly, the aim of this study is (1) to identify the range of detected and recognised regulatory types by crowdsensing means, (2) to indicate the different classification techniques that can be used for these two tasks, (3) to assess the performance of different methods, as well as (4) to identify important aspects of the applicability of these methods. The two largest databases of peer-reviewed literature were used to locate relevant research studies and after different screening steps eleven articles were selected for review. Two major findings were concluded—(a) most regulator types can be identified with over 80% accuracy, even using heuristic-driven approaches and (b) under the current progress on the field, no study can be reproduced for comparative purposes nor can solely rely on open data sources due to lack of publicly available datasets and ground truth maps. Future research directions are highlighted as possible extensions of the reviewed studies.
Clinical and economical aspects of chemotherapy in patients with breast cancer

23%, compared to 9% in the TAC arm with primary G ‐ CSF prophylaxis. We conclude that both regimens are equally effective in achieving pCR, althought this was reached at a lower cumulative dose per chemotherapeutic agent for patients treated with AC ‐ T compared to those treated with TAC chemotherapy.
Analisis dan Implementasi DL4MD: Deep Learning Framework for Intelligent Malware Detection

In recent years, malware has issued a very serious threat and continues to make malware detection a major concern. Prevention and prevention of malware can be done through detection of signatures and protection of these malware. Malicious software that can be used for the signature matching process using dynamic software, making it difficult for the system to defend itself against malware. By being able to restore malware, there is some information obtained, one of which is the API call feature. Several related studies have been conducted to deal with malware attacks, and have good accuracy. However, the evaluation is broader than the evaluation of superficial architecture. The deep learning method used, Autoencoder is a method that is very rarely used. Autoencoder is an architecture in deep learning that is used to reduce data. Therefore, in this final project, Convolutional Neural Network Autoencoder (CNN-AE) is used as a malware detection learning method. The detection accuracy that was achieved by the CNN-AE model was 97.14%.