Miracle-dz/UniFAR-FTUs huggingface dataset:
Attention-based LSTM-FCN for earthquake detection and location

Earthquake detection and localization are challenging since the seismic signal usually is noisy and the microearthquakes are hidden in the seismic noise. Traditional detection and localization methods often rely on manually picked phases or computationally intensive algorithms. Inspired by the successful application of a deep learning model, ConvNetQuake, in detecting and locating the seismic events, we train an attention-based Long Short-Term Memory Fully Convolutional Network (LSTM-FCN) model to improve the detection and location accuracy on the same dataset. We use a parallel structure of FCN and LSTM to extract different features separately and merge them as a vector for better classification. In particular, FCN is used to extract high-level features and, similarly, LSTM is employed to model the temporal dependencies. Besides, an attention mechanism is added to the LSTM to select a significant input segment along with a squeeze-and-excitation block in FCN to enhance useful feature maps for classification. We show that the trained model has a classification accuracy of 89.1 per cent, which represents 14.5 per cent improvement compared to the ConvNetQuake model. Moreover, the ConvNetQuake model only considered classifying seismic events roughly into one of the six geographic regions. But our model can locate the seismic events with a higher resolution by classifying them into as a greater number of regions as to 15 while a relatively high accuracy is maintained. We also demonstrate that the incorporated attention mechanism can effectively improve the classification performance by automatically and selectively enhancing the significant feature maps and inputs.
Design and Development of an Omnidirectional Mobile Robot

Most mobile robots use differential-drive concept, where they are equipped with two actuators that permit only single-direction rotation at a time. This concept limits the robots navigation because its orientation must always change according to the direction of movement. This paper presents the development of an omnidirectional mobile robot that uses three actuators, aligned in 120 degrees separation and each attached to an omniwheel. By manipulating actuator speed, the robot can navigate to any direction without changing its orientation. Using NIsbRIO9632xt as the main controller, navigation algorithm is implemented in LabVIEW, integrated with PID controller to fine-tune robot movements.
Generator operations of saturated induction machines

This paper shows that, due to the saturation of the main magnetic circuits, the generator behaviour of induction machines may not be evaluated theoretically with accuracy by means of traditional mathematical models, especially when operations at variable frequencies are dealt with. For this reason the paper presents a mathematical model of saturated machines. Voltage and current space vectors are used. The model has been carried out on the basis of two air-gap machine inductances (one differential and the other rational) previously introduced by another author (Kovacs, 1984). It has been validated at steady state by comparing the theoretically evaluated and experimentally measured armature currents of a mass-produced machine. The influence of saturation on armature current amplitudes is evidenced by comparing numerical results obtained by the suggested model with those of a traditional model. The model is also used for the performance analysis of dynamic and steady-state generator behaviour of cylindrical induction machines and for carrying out conditions that obtain self-excitation and stable operations of machines supplying passive electrical networks.
Technical Features of Asynchronous and Synchronous Community Platforms and their Effects on Community Cohesion: A Comparative Study of Forum-based and Chat-based Online Mental Health Communities

Across online support communities, community cohesion varies by platform and can impact the self-disclosure of members and their exchanges of social support. Through a comparative study of forum and chat-based mental health communities, this research examines how technical features of both platforms influence community cohesion by affecting communication patterns among members, including evenness of communication, speed of communication, and number of participants. This study collected four weeks of data from 20 forum-based and 20 chat-based mental health communities in mainland China. Multilevel mediation analyses show that chat-based communities were more cohesive with higher member retention, network connectedness, and language conformity. Chat-based platforms facilitated faster communication and greater evenness of receiving messages among participants which in turn fostered community cohesion. Members of online mental health communities can benefit from more cohesive communities, where they are more likely to reveal themselves to each other and exchange social support. This article examines how technical features of forums and chat groups influence community cohesion by affecting patterns of social interactions among participants. Forums support asynchronous and threaded conversations while chat groups support synchronous and sequential ones. These technical features seem to affect how fast the communication is, whether the communication is evenly distributed, and how many members actively post messages that may contribute to community cohesion. Data from 20 forums and 20 chat groups on mental health in mainland China show that chat groups were more cohesive than forums. Members of chat groups in comparison to forums responded to each other more quickly and received each other’s responses more equally, which partially accounted for greater community cohesion.
Engineering and economic analysis of the production of sieve shaker for teaching particle size to students with visual impairment

The purpose of this study was to evaluate the feasibility study from the engineering and economic perspectives in the production of sieve shaker for teaching particle size to students with visual impairment. The sieve shaker is important as a tool for improving students' understanding of the definition of particles and their sizes. Different from other sieve shakers for industrial purposes, the present sieve shaker is inexpensive, simple, user-friendly, and portable. Since it is made for educational goals only and can be used for developing countries, the total cost is inexpensive. Engineering analysis was performed based on a mass balance analysis, whereas the economic evaluation was carried out using several economic parameters under the ideal and non-ideal conditions in 20 years of the project. The engineering evaluation confirmed the potentiality of the project even it is in small-scale industries because all processing steps could be carried out using commercially available equipment. Economic evaluation results showed positive values for all economic parameters with a few exceptions. This research was complemented by theories to support the definition and the importance of sieve shaker for supporting the education of students with special needs.
Evolução do melhoramento genético de bovinos de corte no Brasil.

Evolution of beef cattle breeding in Brazil Besides providing a historical trajectory of beef cattle breeding in Brazil, this work pointed out the scientific and technological advances, as well as the important role played by the private sector in designing breeding programs. In addition, it presented potential areas and its interactions, which will require joint effort of universities and research institutions to assure the progress needed in order to maintain the position achieved by Brazil as an important player in world beef supply chain. In this context, it is important to make investments in basic aspects, guaranteeing the genetic progress vital to keep cattle-raising not only as key element in the meat supply chain, but also to become an essential component of the Brazilian agribusiness.
The Common Values of Social Media Marketing and Luxury Brands. The Millennials and Generation Z Perspective

As consumers spend more time on social media, brands can take advantage of this opportunity to better serve and communicate with their followers. Still, given the characteristics of luxury, luxury brands may be reluctant to use social media. To determine the extent to which Millennials and Generation Z consumers perceive compatibility between social media marketing and luxury brands, a purposive sampling technique was employed. We collected data from 303 Romanian visitors of the Facebook page entitled Do You Like Luxury? This page, with postings from luxury producers, was specially created and sponsored in order to raise interest in completing the questionnaires. For data analysis, we used statistical tests, including confirmatory factorial analysis, regression, and independent-samples t-test. Our research shows that, for Millennials and Generation Z, luxury brands are compatible with social media apps as marketing channels. Even in the case of inadequacy, managers can still use the facilities of digital technologies to depict exclusivity, emotions, and sensory experiences specific to luxury. Unlike other studies, which only focused on a few dimensions, we proposed a multidimensional approach of the perceived value of luxury and social media marketing. Moreover, this is one of the few studies to focus on Eastern European consumers.
Fine-Grained Pests Recognition Based on Truncated Probability Fusion Network via Internet of Things in Forestry and Agricultural Scenes

Accurate identification of insect pests is the key to improve crop yield and ensure quality and safety. However, under the influence of environmental conditions, the same kind of pests show obvious differences in intraclass representation, while the different kinds of pests show slight similarities. The traditional methods have been difficult to deal with fine-grained identification of pests, and their practical deployment is low. In order to solve this problem, this paper uses a variety of equipment terminals in the agricultural Internet of Things to obtain a large number of pest images and proposes a fine-grained identification model of pests based on probability fusion network FPNT. This model designs a fine-grained feature extractor based on an optimized CSPNet backbone network, mining different levels of local feature expression that can distinguish subtle differences. After the integration of the NetVLAD aggregation layer, the gated probability fusion layer gives full play to the advantages of information complementarity and confidence coupling of multi-model fusion. The comparison test shows that the PFNT model has an average recognition accuracy of 93.18% for all kinds of pests, and its performance is better than other deep-learning methods, with the average processing time drop to 61 ms, which can meet the needs of fine-grained image recognition of pests in the Internet of Things in agricultural and forestry practice, and provide technical application reference for intelligent early warning and prevention of pests.
Detection of Emergent Behaviour in Distributed Software Systems using Data Analysis Techniques

Distributed Software Systems (DSS) and their sub-category, Multi-Agent Systems (MAS), are composed of several collaborative components working towards a common goal. Requirement engineering involves the consideration of competing needs and concerns for the proper presentation of the specification of software systems. The analysis of the Scenario-Based Specifications (SBS) has many important advantages to minimize the generation of unexpected behaviours in DSS. These behaviours are known as Emergent Behaviours (EB), and can potentially lead to irreversible, costly damages. In this thesis, we focus on analyzing the software behaviours from the SBS to detect EBs. The verification of the software behaviours in the early stages of software development can detect and prevent unwanted behaviours. In this thesis, different methodologies that aim to detect EBs are discussed. This thesis aims to provide a new automated, homogenous methodology to detect EBs based on their common cause of occurrence. Subsequently, different algorithms were proposed to detect the types of EBs, and examples were presented to explain the algorithms. With the adoption of data analysis techniques, we contribute to preventing these behaviours and ensuring system quality. To evaluate the proposed methodology based on the analysis of the SBS, we used two different approaches: (1) the conventional way by comparing the proposed methodology against related works’ methodologies, (2) the dynamic analysis of system traces, which requires the simulation of the SBS to aggregate the system behaviour in runtime. Results show a higher efficiency of the proposed methodology in detecting EBs compared to similar work, which was also proven by the statistical modelling of the case studies’ traces. Additionally, we verified the efficiency of the proposed methodology using sequential pattern mining techniques. This thesis contributes to the research of requirement engineering specifically and software engineering generally by providing an automated methodology to analyze the SBS as a black box. It assists the reusability of components and design sustainability. Moreover, the early identification of the cause of EBs empowers the designer and the software development team to handle these EBs and aids in decreasing the system cost.
One high performance technology of infrared scene projection

Infrared scenes generation technologies are used to simulate the infrared radiation characteristics of target and background in the laboratory. They provide synthetic infrared imagery for thermal imager test and evaluation application in the infrared imaging systems. At present, many Infrared scenes generation technologies have been widely used, and they make a lot of achievements. In this paper, we design and manufacture one high performance IR scene generation technology, and the whole thin film type transducer is the key, which is fabricated based on micro electro mechanical systems (MEMS). The specific MEMS technological process parameters are obtained from a large number of experiments. The properties of infrared scene generation chip are investigated experimentally. It achieves high resolution, high frame, and reliable performance, which can meet the requirements of most simulation system. The radiation coefficient of the thin film transducer is measured to be 0.86. The frame rate is 160 Hz. The emission spectrum is from 2μm to 12μm in infrared band. Illuminated by the visible light with different intensities the equivalent black body temperature of transducer could be varied in the range of 290K to 440K. The spatial resolution is more than 256×256.The geometric distortion and the uniformity of the generated infrared scene is 5 percent. The infrared scene generator based on the infrared scene generation chip include three parts, which are visual image projector, visual to thermal transducer and the infrared scene projector. The experimental results show that this thin film type infrared scene generation chip meets the requirements of most of hardware-in-the-loop scene simulation systems for IR sensors testing.
Utilizing higher moments to detect time-varying target in radar echo with non-stationary background

Detecting time-varying target in non-stationary background is difficult and attractive problem. Time-varying movement exists widely in radar and communication systems. The non-linear processing with higher moments is discussed in the situation. Firstly the signal model of time-varying target with fix acceleration is analyzed. Then the radar echoes from synthetic aperture radar (SAR) are processed with higher moments. It not only restrains Gaussian noise automatically, but also suppresses non-stationary noise. Time-varying targets are different from the non-stationary background clutters. Moreover, the influences of the shadows about time-varying targets are reduced in the algorithm of higher moments. The proposal mentioned above utilizes higher moments to avoid analyzing the complex electromagnetic wave propagation and scatter theory. The differences of detection probability are compared between the chip with time-varying target and the clutter chip. The performances among different number order moments are compared by processing lots of actual SAR data added non-stationary noise. Lastly, the suitable number order moments are suggested by comparing the results from processing the actual radar echoes.
Machine Learning Training for HOM reduction and Emittance Preservation in a TESLA-type Cryomodule at FAST

Low emittance electron beams are of high importance at facilities like the Linac Coherent Light Source II (LCLS-II) at SLAC. Emittance dilution effects due to off-axis beam transport for a TESLA-type cryomodule (CM) have been shown at the Fermilab Accelerator Science and Technology (FAST) facility. The results showed the correlation between the electron beam-induced cavity high-order modes (HOMs) and bunch-by-bunch centroid slewing and oscillation down-stream of the CM. Mitigation of emittance dilution can be achieved by reducing the HOM signals and the standard deviation in the bunch-by-bunch beam positions downstream of the CM. Here we present a Machine Learning (ML) based optimization and model construction for HOM signal level reduction using Neural Networks (NN). To gather training data we performed experiments using 50 bunch electron beams with charges up to 600 pC/b. We measured HOM signals of all cavities and beam position with a set of BPMs downstream of the CM. The beam trajectory was changed using V/H125 corrector set located upstream of the CM. The preliminary results presented here will inform the LCLS-II injector commissioning and will serve as a prototype for HOM reduction and emittance preservation.
Fast Track API Manufacturing from Shake Flask to Production Scale Using a 1000-L Single-Use Facility

A systematic and integrated use of single-use technologies was combined with a robust monoclonal antibody platform, which led to a substantial reduction of manufacturing costs, reduced timelines and increased flexibility in clinical manufacturing. A direct scale-up of a high titer monoclonal antibody-expressing CHO DG44-based cell culture platform was performed from shake flasks to a 1000 L production scale in a completely single-use manufacturing facility. The scale-up was done on the basis of calculating the specific volumetric power input which allowed a direct transfer from small culture volumes to the production scale. The timelines for process development were reduced to 3 months from the Research Cell Bank to the drug substance with highly optimized cells and appropriate culture conditions.
A Bloom filter variant for Blockchain

Blockchain is a single linked list of blocks consisting of transactions identified by their hash value. Querying Blockchain, primarily searching blocks/transactions, can be treated as membership queries and efficiently evaluated by a Bloom filter (BF). The existing BFs for Blockchain, however, do not exploit the data characteristics of Blockchain and the features of modern processors, which otherwise have the potential to greatly improve their performance. This article proposes a novel Bloom filter variant for Blockchain, called Blockchain Bloom Filter (BBF), with three alternatives to improve BF. First, we reorganize BF structure to make use of Single Instruction Multiple Data provided by modern processors, which parallelize the bit‐map and bit‐test processes of BBF and accelerate element insertion and membership querying. On that basis, hash computation of BBF is parallelized, which reduce the hashing time to 1/k that of the BF in theory, where k is the number of hash functions. Second, hash functions derived from block/transaction hash are developed to reduce the complexity of hash computation and accelerate the element insertion and membership query processes of BBF. Finally, we further refine BBF structure to limit the mapping range of an element within a cache‐line in the on‐chip cache in order to increase cache efficiency. With the improvements, both theoretical analysis and extensive simulations show that the performance of BBF membership queries greatly exceeds the three state‐of‐the‐art Bloom filter variants, which enables efficient data queries and application specific analysis over Blockchain.
Reducing local traffic emissions at urban intersection using ITS countermeasures

In many countries traffic emissions have significantly increased during the last two decades because of the increased number of vehicles. As such, traffic emissions have become the main source of air pollution in urban areas, where breaches of the EU limit values frequently occur. To reduce these emissions, local traffic measures can be implemented complementary to regional and national measures. In this study, the impact of various traffic measures at a single intersection is investigated using a traffic model and an emission model. The measures included are traffic demand control, banning heavy duty vehicles (HDVs) and speed restriction. It was found that reducing traffic demand by 20% led to about 23% reduction in terms of CO2, NOx and PM 10 emissions. Banning HDVs led to a significant reduction of NO x and PM10 emissions. Although speed restriction reduced both CO2 and NOx emissions by 16.1 and 13.4%, PM 10 emissions increased by 19%, mainly from HDVs. © The Institution of Engineering and Technology 2013.
The influence of social and emotional context on the gaze leading orienting effect

ABSTRACT We spontaneously orient our attention towards people whose gaze we have led (the “gaze leading” effect). Here, we investigated whether this orienting effect is sensitive to the social and emotional content of the stimuli within the interactions. Experiment 1 replicated the gaze leading effect but found no reliable influence of facial dominance or object valence. Experiment 2, where only object valence was manipulated, replicated Experiment 1. Thus, the gaze leading effect appears reliable but insensitive to the properties of the shared referent object. Experiment 3 varied only facial dominance; a marginally significant interaction indicated that attention was deployed towards high-dominant faces more than low-dominant gaze followers. Experiment 4 varied the social information relating to the social status that participants hold regarding the faces with which they interacted, but statistical support for an influence of biographical information on gaze leading was weak. Overall, the gaze leading effect appears generally reliable, and may vary when information about the individuals following our gaze is manipulated, though it is not yet fully clear which socio-evaluative features are most relevant. Future investigations may therefore require more powerful or sensitive designs to better evaluate the role of socioemotional factors and processes on this social orienting effect.
Employment of Reduplicated Words in E-C Translation of Children’s Literature: A Case Study of Ren Rongrong’s Translation of The Wind in the Willows

This thesis is to explore the application of reduplicated words in Chinese translation of children’s literature. It selects Ren Rongrong’s translation of The Wind in the Willows to conduct research. First, data collection and analysis of reduplicated words in the text are carried out. Then, from the three effects of reduplicated words, rhythmical effect, imaging effect, and emotional effect, the corresponding sentences in Ren Rongrong’s translation of The Wind in the Willows are selected and analyzed. Finally, it is concluded that reduplicated words are widely used in the English-Chinese translation of children’s literature. In the process of English-Chinese translation, verbs, adjectives, adverbs, nouns, quantifiers, numerals and onomatopoeia can be translated into reduplicated words when necessary. On the one hand, it can add language charm and make the translation more vivid, and on the other hand, it is easy for children to accept.