Miracle-dz/UniFAR-FTUs huggingface dataset:
Electromagnetic-Thermal Model for Improved Axial-Flux Eddy Current Couplings With Combine Rectangle-Shaped Magnets

An improved structure for axial-flux eddy current couplings with combined rectangle-shaped magnets is proposed to minimize cogging torque without sacrificing torque density. Then, a novel analytical electromagnetic-thermal model, which takes the change of material electromagnetic and thermal characteristics under temperature, is presented to calculate cogging torque, electromagnetic torque, and eddy current loss and temperature. The model is valid for the whole working area. The magnetic field distribution is calculated by the quasi 3-D electromagnetic field analytical model. Then cogging torque, electromagnetic torque, and eddy current loss can be obtained. Furthermore, an equivalent thermal resistance network model is established. The thermal model takes the previously obtained eddy current losses as the heat source, which is the eddy current loss of copper and copper back iron. With the help of the electromagnetic-thermal model, the characteristics of slip on electromagnetic torque, eddy current loss, and copper and permanent magnet temperature rise is also analyzed under the whole working range. The validity of the electromagnetic–thermal model is confirmed with finite element method and measurement.
O-TF and O-FTF, Optical Fault-Tolerant DCNs

Performance of a data center is a function of three features; bandwidth, latency, and reliability. By adopting optical technology in data center network, bandwidth increment, in addition to reduction of transmission latency and power consumption, is achieved. Unfortunately, fault tolerance of the optical networks has raised less attention so far. So in this paper, we propose a fault-tolerant, scalable, and high-performance optical architecture built upon previously proposed O-TF network, with the goal of redundancy optimization and reducing the minimum number of wavelength channels required for non-blocking functionality of the network. Moreover, reducing network diameter, in O-FTF network compared to O-TF and Wavecube networks, has led to higher network performance at the presence of network failures.
Discovering research topics from library electronic references using latent Dirichlet allocation

Discovering the research topics and trends from a large quantity of library electronic references is essential for scientific research. Current research of this kind mainly depends on human justification. The purpose of this paper is to demonstrate how to identify research topics and evolution in trends from library electronic references efficiently and effectively by employing automatic text analysis algorithms. ,The authors used the latent Dirichlet allocation (LDA), a probabilistic generative topic model to extract the latent topic from the large quantity of research abstracts. Then, the authors conducted a regression analysis on the document-topic distributions generated by LDA to identify hot and cold topics. ,First, this paper discovers 32 significant research topics from the abstracts of 3,737 articles published in the six top accounting journals during the period of 1992-2014. Second, based on the document-topic distributions generated by LDA, the authors identified seven hot topics and six cold topics from the 32 topics. ,The topics discovered by LDA are highly consistent with the topics identified by human experts, indicating the validity and effectiveness of the methodology. Therefore, this paper provides novel knowledge to the accounting literature and demonstrates a methodology and process for topic discovery with lower cost and higher efficiency than the current methods.
DNN Based Speaker Embedding Using Content Information for Text-Dependent Speaker Verification

In this paper, we are interested in exploring Deep Neural Network (DNN) based speaker embedding for Random-digit task using content information. To this end, a technique is applied to automatically select common phonetic units between the enrollment and test data to produce speaker verification scores. Furthermore, a novel approach is proposed to incorporate content information in the DNN directly. It is hypothesized that features extracted using this DNN will be helpful for the task. Experiments on the RSR dataset show that the proposed method outperforms the baseline i-vector system by 43 % relative equal error rate.
The Initial E-Trust Formation to A Content-Based Web Site : The Role of E-service Quality and Disposition to Trust

Previous researches related to the trust concept have been rich in the field of e-commerce and few of them were devoted to the comprehension of consumer behavior facing the trust in the case of informative website. Thus, this research sheds light on the development of initial e-trust to content-based web sites, more exactly healthcare and wellness web sites, and the effect of this trust on consumer behavioral intent. An online survey was conducted in order to validate a causal model that explains the development of initial e-trust to content-based web sites by emphasizing the impact of disposition to trust and e-service quality on e-trust and the impact of e-trust on behavioral intent.
A Case Study on Attribute Recognition of Heated Metal Mark Image Using Deep Convolutional Neural Networks

Heated metal mark is an important trace to identify the cause of fire. However, traditional methods mainly focus on the knowledge of physics and chemistry for qualitative analysis and make it still a challenging problem. This paper presents a case study on attribute recognition of the heated metal mark image using computer vision and machine learning technologies. The proposed work is composed of three parts. Material is first generated. According to national standards, actual needs and feasibility, seven attributes are selected for research. Data generation and organization are conducted, and a small size benchmark dataset is constructed. A recognition model is then implemented. Feature representation and classifier construction methods are introduced based on deep convolutional neural networks. Finally, the experimental evaluation is carried out. Multi-aspect testings are performed with various model structures, data augments, training modes, optimization methods and batch sizes. The influence of parameters, recognitio efficiency and execution time are also analyzed. The results show that with a fine-tuned model, the recognition rate of attributes metal type, heating mode, heating temperature, heating duration, cooling mode, placing duration and relative humidity are 0.925, 0.908, 0.835, 0.917, 0.928, 0.805 and 0.92, respectively. The proposed method recognizes the attribute of heated metal mark with preferable effect, and it can be used in practical application.
Integration of a Very High Quality Factor Filter in Empty Substrate-Integrated Waveguide at  $Q$ -Band

Recently, new methodologies for manufacturing empty waveguides completely integrated in a planar substrate have been proposed in order to improve the performance of substrate-integrated devices. One of these alternatives is the so-called empty substrate-integrated waveguide (ESIW). The height of high-frequency substrates, where ESIW devices are integrated, is very small. Then, the capacity of storing energy of ESIW resonators is drastically reduced, and as a result, their quality factor is not as high as it could be with a higher ESIW. In order to overcome this restriction, a novel integrated structure is proposed to embed very high-quality factor filters based on the ESIW with an increased height. As a result, we show, in this letter, for the first time, the successful integration of an increased-height ESIW filter in a printed circuit board of 0.305 mm height at $Q$ -band, achieving a quality factor above 1000, which is a remarkable result for a completely substrate-integrated device operating in this frequency band. When compared with the same filter manufactured with an ESIW with the same height as the substrate, the novel filter shows a measured quality factor 85% higher.
DRIFT : Drone-Rover Integrated Fire Tracker System

Wildfire reconnaissance and mitigation efforts are a primary concern for the United States Department of the Interior and National Forest Service. The critical consequences of climate change are becoming more prevalent with longer fire seasons throughout the Western United States [1]. The fire seasons are characterized by hotter and drier conditions, allowing for a wildfire to easily start and rapidly spread with the potential to burn millions of acres and cause billions of dollars in property loss. The National Forest Service (NFS) predicts that it will spend over half of its budget in the fight against wildfires within the next decade [2]. Wildfires can have devastating impacts on communities, ecosystems and wildlife, but also pose a dangerous threat to the fire fighters responsible for their mitigation and containment. A Mother Rover-Child Drone Firetracker System will assist firefighters by traveling to locations at risk of wildfire and gather environmental data which it then transmits back to the designated ground station. This provides information on a fire’s intensity, severity, and extent while firefighters remain a safe distance away from the threat. Team DRIFT is a group of eleven undergraduate Aerospace Engineering Sciences students at the University of Colorado Boulder currently developing the Mother-Rover for the Firetracker Systemwith the purpose to secure and carry the Child Drone (Unmanned Aerial Vehicle) to a desired location of interest. The development of this MotherRover involves integrating the hardware and software of the already completed Child Drone and Landing Platform. The Mother Rover, approximately 46.3 by 58.9 inches, weighing 450 lbs and driven by a remote operator, is capable of traversing rough terrain, defined by small gravel, fine dirt, and slopes up to 20 degrees. Due to the image recognition system utilized on the landing platform for the autonomous landing of the Child Drone, the landing platform must be level to within 3.5 degrees in order for the child drone to safely take off and land. Therefore, the Mother Rover utilizes an internal leveling jack system to level in the landing platform to within 3.5 degrees if the child drone is to be deployed on a slope. The considerable weight of the Mother Rover presents a unique engineering challenge as it must be capable of traversing over the defined rough terrain while also maintaining the security of the onboard Child Drone. The design solution for this Mother Rover and associated leveling system will be discussed in this paper.
Self-immolative linkers in polymeric delivery systems

There has been significant interest in the methodologies of controlled release for a diverse range of applications spanning drug delivery, biological and chemical sensors, and diagnostics. The advancement in novel substrate-polymer coupling moieties has led to the discovery of self-immolative linkers. This new class of linker has gained popularity in recent years in polymeric release technology as a result of stable bond formation between protecting and leaving groups, which becomes labile upon activation, leading to the rapid disassembly of the parent polymer. This ability has prompted numerous studies into the design and development of self-immolative linkers and the kinetics surrounding their disassembly. This review details the main concepts that underpin self-immolative linker technologies that feature in polymeric or dendritic conjugate systems and outlines the chemistries of amplified self-immolative elimination.
Utilization of weak received signal strength for accurate indoor position estimation

This paper describes a method to derive stable fingerprint of the WLAN RSS (Received Signal Strength) for accurate indoor location estimation. In order to find stable values of received signals, we utilized the mode of each received signal from different access points. According to the pilot survey, the elimination of weak signals have significant effect on the accuracy improvement. The experimental result showed the sub-meter accuracy at the evaluation points. The obtained accuracy is considered as sufficient for our application of this location estimation system for the rescue of evacuees at disasters.
Identifying Transferable Information Across Domains for Cross-domain Sentiment Classification

Getting manually labeled data in each domain is always an expensive and a time consuming task. Cross-domain sentiment analysis has emerged as a demanding concept where a labeled source domain facilitates a sentiment classifier for an unlabeled target domain. However, polarity orientation (positive or negative) and the significance of a word to express an opinion often differ from one domain to another domain. Owing to these differences, cross-domain sentiment classification is still a challenging task. In this paper, we propose that words that do not change their polarity and significance represent the transferable (usable) information across domains for cross-domain sentiment classification. We present a novel approach based on χ2 test and cosine-similarity between context vector of words to identify polarity preserving significant words across domains. Furthermore, we show that a weighted ensemble of the classifiers enhances the cross-domain classification performance.
Evaluation of an approach–avoidance training intervention for children and adolescents with obesity: A randomized placebo‐controlled prospective trial

This study evaluated the efficacy of approach-avoidance training as an additional treatment for children and adolescents with obesity seeking inpatient treatment. Two hundred thirty-two participants (8-16 years, 53.9% girls) were randomly assigned either to multisession approach-avoidance (IG) or to placebo training (CG). As outcomes, cognitive biases post intervention, body mass index, eating behaviour, food intake, self-regulation, and weight-related quality of life were assessed, also at 6- and 12-month follow-up. Modification of approach-avoidance bias was observed, but lacked in transfer over sessions and in generalization to attention and association bias. After 6 months, the IG reported less "problematic" food consumption, higher self-regulation, and higher quality of life; effects did not persist until the 12-month follow-up; no significant interaction effects were observed regarding weight course. Despite there was no direct effect on weight course, approach-avoidance training seems to be associated with promising effects on important pillars for weight loss. Further research concerning clinical effectiveness is warranted.
OH/IR stars and their superwinds as observed by the Herschel Space Observatory ⋆

Aims. In order to study the history of mass loss in extreme OH/IR stars, we observed a number of these objects using CO as a tracer of the density and temperature structure of their circumstellar envelopes. Methods. Combining CO observations from the Herschel Space Observatory with those from the ground, we trace mass loss rates as a function of radius in five extreme OH/IR stars. Using radiative transfer modelling, we modelled the dusty envelope as well as the CO emission. The high-rotational transitions of CO indicate that they originate in a dense superwind region close to the star while the lower transitions tend to come from a more tenuous outer wind which is a result of the mass loss since the early AGB phase. Results. The models of the circumstellar envelopes around these stars suggest that they have entered a superwind phase in the past 200–500 years. The low 18 O/ 17 O( ∼0.1 compared to the solar abundance ratio of ∼5) and 12 C/ 13 C (3–30 cf. the solar value of 89) ratios derived from our study support the idea that these objects have undergone hot-bottom burning and hence that they are massive
Resource-Based Dynamic Rewards for Factored MDPs

Factored MDPs provide an efficient way to reduce the complexity of large, real-world domains by exploiting structure within the state space. This avoids the need for the state space to be fully enumerated, which is impractical in large domains. However, defining a reward function for state transitions is difficult in a factored MDP since transitions are not known prior to execution. In this paper, we provide a novel method for deriving rewards from information within the states in order to determine intermediate rewards for state transitions. We do this by treating some specific state variables as resources, allowing costs and rewards to be inferred from changes to the resources and ensuring the agent is resource-aware while also being goal-oriented. To facilitate this, we propose a novel variant of Dynamic Bayesian Networks specifically for modelling action transitions and capable of dealing with relative changes to realvalued state variables (such as resources) in a compact fashion. We also propose a number of reward functions which model resource types commonly found in real-world situations. We go on to show that our proposed framework offers an improvement over existing techniques involving reward functions for factored MDPs as it improves both the efficiency and decision quality of online planners when operating on these models.
Predicting Human Trustfulness from Facebook Language

Trustfulness — one’s general tendency to have confidence in unknown people or situations — predicts many important real-world outcomes such as mental health and likelihood to cooperate with others such as clinicians. While data-driven measures of interpersonal trust have previously been introduced, here, we develop the first language-based assessment of the personality trait of trustfulness by fitting one’s language to an accepted questionnaire-based trust score. Further, using trustfulness as a type of case study, we explore the role of questionnaire size as well as word count in developing language-based predictive models of users’ psychological traits. We find that leveraging a longer questionnaire can yield greater test set accuracy, while, for training, we find it beneficial to include users who took smaller questionnaires which offers more observations for training. Similarly, after noting a decrease in individual prediction error as word count increased, we found a word count-weighted training scheme was helpful when there were very few users in the first place.
A smart phone based multi-floor indoor positioning system for occupancy detection

At present there is significant research interest in simulating building environment with artificial agents and predicting energy usage and other building performance related factors that helps to promote understanding of more sustainable buildings. To understand these energy demands it is important to understand how the building spaces are being used by individuals i.e. the occupancy pattern of individuals. There are lots of other sensors and methodology being used to understand building occupancy such as PIR sensors, logging information of Wi-Fi APs or ambient sensors such as light or CO2 composition. Indoor positioning can also play an important role in understanding building occupancy pattern. Due to the growing interest and progress being made in this field it is only a matter of time before we start to see extensive application of indoor positioning in our daily lives. This research proposes an indoor positioning system that makes use of the smart phone and its built-in integrated sensors; Wi-Fi, Bluetooth, accelerometer and gyroscope. Since smart phones are easy to carry; participants can carry on with their usual daily work without any distraction but at the same time provide a reliable pedestrian positioning solution for detecting occupancy. The positioning system uses the traditional Wi-Fi and Bluetooth fingerprinting together with pedestrian dead reckoning to develop a cheap but effective multi-floor indoor positioning solution. The paper discusses the novel application of indoor positioning technology to solve a real world problem of understanding building occupancy. It discusses the positioning methodology adopted when trying to use existing positioning algorithm and fusing multiple sensor data. It also describes the novel approach taken to identify step like motion in absence of a foot mounted inertial system. Finally the paper discusses results from limited scale trials showing trajectory of motion throughout the Nottingham Geospatial Building covering multiple floors.
Access to Community Living Infrastructure and Its Impact on the Establishment of Community-Based Day Care Centres for Seniors in Rural China

Community-based day care centres play an important role in service delivery for Chinese seniors. Little research has examined how community living infrastructure has influenced the establishment of these day care centres in rural communities. The purposes of this study were: (1) explore regional differences in community living infrastructure; and (2) to examine the impact of such infrastructure on the establishment of day care centres for Chinese seniors in rural communities. The data were derived from “The Fourth Sample Survey on the Living Conditions of Elderly People in Urban and Rural China (2015)”. The establishment of at least one day care centre was the outcome of interest, which was dichotomized at the community level into the establishment of at least one day care centre or the absence of any day care centres. Logistic regression analysis was employed to examine the impact of various community living infrastructural characteristics on the establishment of day care centres. The results showed that of the 4522 rural communities surveyed in 2015, only 10.1% had established at least one day care centre. Community living infrastructural characteristics that were significantly associated with the establishment of day care centres were the availability of cement/asphalt roads, natural gas, tap drinking water, sewage systems, and centralized garbage disposal. Our findings suggest that the significant association between community-level characteristics, especially community living infrastructure, and the establishment of rural day care centre for seniors may inform policy decision making.
A 16-Channel CMOS Chopper-Stabilized Analog Front-End ECoG Acquisition Circuit for a Closed-Loop Epileptic Seizure Control System

In this paper, a 16-channel analog front-end (AFE) electrocorticography signal acquisition circuit for a closed-loop seizure control system is presented. It is composed of 16 input protection circuits, 16 auto-reset chopper-stabilized capacitive-coupled instrumentation amplifiers (AR-CSCCIA) with bandpass filters, 16 programmable transconductance gain amplifiers, a multiplexer, a transimpedance amplifier, and a 128-kS/s 10-bit delta-modulated successive-approximation-register analog-to-digital converter (SAR ADC). In closed-loop seizure control system applications, the stimulator shares the same electrode with the AFE amplifier for effective suppression of epileptic seizures. To prevent from overstress in MOS devices caused by high stimulation voltage, an input protection circuit with a high-voltage-tolerant switch is proposed for the AFE amplifier. Moreover, low input-referred noise is achieved by using the chopper modulation technique in the AR-CSCCIA. To reduce the undesired effects of chopper modulation, an improved offset reduction loop is proposed to reduce the output offset generated by input chopper mismatches. The digital ripple reduction loop is also used to reduce the chopper ripple. The fabricated AFE amplifier has 49.1-/59.4-/67.9-dB programmable gain and 2.02-μVrms input referred noise in a bandwidth of 0.59–117 Hz. The measured power consumption of the AFE amplifier is 3.26 μW per channel, and the noise efficiency factor is 3.36. The in vivo animal test has been successfully performed to verify the functions. It is shown that the proposed AFE acquisition circuit is suitable for implantable closed-loop seizure control systems.