Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Loop Closures Detection Method for Geomagnetic Signal and Lidar Fusion

In order to effectively solve the problem of loop closures detection matching speed and loop closures false positives in large-scale maps, this paper proposes a loop closures detection method combining geomagnetic sequence search and lidar point cloud matching. By adding the geomagnetic matching algorithm to the loop closures, the candidate loop detection pose-node set is filtered, which reduces the false detection caused by the high local similarity in the lidar Simultaneous Localization and Mapping (SLAM), as well as the loop false positives and mapping distortion caused by the interference of reflection and transmission of laser beam. In this work, the performance of the algorithm is verified by the lidar point cloud and geomagnetic signal dataset collected in the real environment. The experimental results show that compared with the current product-level SLAM system, Cartographer, the proposed algorithm improves the loop detection speed by 31% (in data of 100 matches) and the matching accuracy is improved by 10% at the 20% recall.
Achievable Secrecy Rate in mmWave Multiple-Input Single-Output Ad Hoc Networks

This paper analyzes the achievable secrecy rate in a millimeter wave (mmWave) multi-input single-output (MISO) ad hoc network in the presence of colluding eavesdroppers. First, using the tools of stochastic geometry, the mathematical analysis of the average achievable secrecy rate is performed, taking into consideration the impact of blockage and Nakagami-m fading. Moreover, a multi-array artificial noise transmission technique is applied at the typical transmitter (Tx-AN) to enhance the average secrecy rate. Consequently, the mathematical expression of the average achievable secrecy rate for mmWave MISO ad hoc network with Tx-AN technique is presented. Numerical and simulation results show that, at given values of colluding eavesdroppers’ intensity and total transmit power, using Tx-AN technique achieves 56.4% improved average secrecy rate over that without. Furthermore, by applying the Tx-AN technique, increasing the colluding eavesdroppers’ intensity provides no negative impact on the average secrecy rate. However, without the AN technique, the average secrecy rate faces a fast degradation when the intensity of colluding eavesdroppers increases. The results therefore show that the Tx-AN technique is a useful technique to enhance the secrecy performance of mmWave MISO ad hoc network in the presence of colluding eavesdroppers.
ExhaleSense: Detecting High Fidelity Forced Exhalations to Estimate Lung Obstruction on Smartphones

Spirometry is the gold standard to measure lung functions by estimating the maximum air an individual can forcefully exhale as quickly as possible. It is used not only to diagnose lung diseases such as asthma, chronic obstructive pulmonary disease (COPD) but also to assess the severity of the pulmonary condition. However, spirometry requires a specialized device called spirometer, which is mostly available in clinical facilities and cumbersome to use. Recent works have shown the feasibility of using smartphone microphone to estimate lung functions from forced exhalation effort sounds. However, maintaining the fidelity of lung function estimation on smartphones becomes challenging in unsupervised field environment in presence of other sounds such as coughs, deep inhalation, regular breathing, and speech. In this paper, we present ExhaleSense that detects forced exhalation efforts on smartphones from audio time-series data, distinguishes high fidelity efforts from poor efforts, and estimates lung obstruction. By conducting three studies with 211 pulmonary patients and healthy subjects, we show that ExhaleSense can detect forced exhalation sounds with 96.74% F1-score and estimate lung obstruction with mean absolute error as low as 7.57%. ExhaleSense shifts the gear of smartphone spirometry research from feasibility to ensuring effort quality towards high fidelity lung function estimation in unsupervised field settings.
Seasonal dynamics of small vagile predators on a marine shallow soft bottom

The seasonal on- and offshore migrations of the epibenthic community on a marine shallow soft bottom in Gullmar Fjord (Sweden) was recorded for a period of three years. Quantitative sampling was carried out in shallow water (≤ 1 m depth) using drop-nets. A new sampling device was also introduced which allowed quantitative sampling in deeper water. The brown shrimp Crangon crangon L. , juvenile plaice Pleuronectes platessa L. and the gobiid fish species Pomatoschistus minutus (Pallas) and P. microps (Kroyer) constituted the dominant inhabitants of shallow water, contributing >90% of the total number. The annual production was 2.2–2.9 g m−2 yr−1 dry wt with the brown shrimp as the dominant contributor. The abundance in shallow water fluctuated strongly between the seasons, while a relatively small seasonal variation was recorded in deeper water (2.5–10 m depth). The shallow/deep water abundance ratio for the epibenthic community heavily increased during the summer months, for the brown shrimp the ratio exceeded 200 in July. However, the heavy concentration in shallow water did not correspond to the reduced density of animals in deeper water. The boardering deeper water seemed to serve only as a transition area for animals arriving from or migrating to still deeper areas. Heavy mortality in the shallow nursery due to predation by the small epibenthic carnivores may also explain the low abundance in deeper areas during the autumn migration.
Hybrid Precoding Design for Two Carriers Aggregated in 5G Massive MIMO System

Hybrid digital and analog beamforming are today widely research topics in massive MIMO systems due to low cost and low power consumption as compared to conventional fully digital beamforming method. The beamforming matrix consists of analog radio frequency (RF) precoder and it is implemented by using phase shifters and baseband digital precoder. Carrier aggregation (CA) is a technology that allows bandwidth expansion to improve data capacity by combining two or more carriers in the same or different frequency bands. The digital baseband precoder is the main challenge and can be adapted to the channel of each carrier in the frequency domain while the same analog RF precoder must be applied to all carriers simultaneously in the time domain. The main goal of our research is to combine CA technology with hybrid digital and analog beamforming architecture to provide an improved system performance with less penalty. This paper proposes a hybrid precoding algorithm to support two carriers aggregated that are based on this scenario. We present numerical results that prove theoretical expressions and give practical explanation of the proposed method demonstrating its superiority when compared to the traditional hybrid beamforming design.
Evaluation of Data Storage Patterns in Microservices Archicture

A microservice is an architectural style that structures an application as group of loosely-coupled services that are independently deployable and centered around the business capabilities. Since microservices are a shift from the conventional monolithic application which normally uses a relational database backend, it becomes essential that attention be given to data storage structure for Microservices. In this research, we would examine five possible data storage patterns for microservices. The evaluation would include setting up prototypes and evaluating the performance of both RDBMS and document-store enabled data stores for microservice architecture. To carry out the evaluations, the five existing patterns for data persistence in microservice architecture were modeled: shared database, per-service pattern(3 variations) and the CQRS/EV pattern. Then a simplified hypothetical Electronic Medical Record(EMR) was designed and implemented using of the five existing patterns under consideration. The results of the evaluation in terms of performance and resource utilization are presented in this work.
Impact of Noise on Machine Learning-based Condition Monitoring Applications: a Case Study

In the paper, application of Machine Learning (ML) techniques for the continuous monitoring of the health status of mild mission-critical industrial equipment is considered. A meaningful real-life case-study is presented in order to show how acquisition conditions may severely impact on the performance of the system. In particular, it is shown that a wrong estimate of noise effects in the deployed system may induce a wrong choice of the best features feeding the ML monitoring algorithm, hence affecting accuracy of the target devices. The discussed results may provide an useful guidance to the practitioner in the field during the design phase of ML-based devices depending of the equipment specifications and environmental conditions.
The Ability of Immunoglobulin Yolk Recognized the Antigen in the Tissue of Ascaridia galli

Antigen-antibody reaction is an important tool for the analysis of localization of target molecules, including antigenic protein within worm tissues. The purpose of the present research was to demonstrate the ability of immunoglobulin yolk (IgY) anti-excretory/secretory recognized the antigen in the tissue of Ascaridia galli by mean of immunohistochemistry method. The excretory/secretory protein was procured from A. galli and concentrated by mean of vivaspin 30,000 MWCO. IgY was produced by egg yolks of immunized chickens with excretory/secretory, and purified using fast protein liquid chromatography (FPLC) method. A. galli adult worms were cut in transversal and longitudinal section of the center and anterior region. Slides were incubated with both primary IgY for overnight at 4 o C and secondary antibody rabbit anti-chicken IgY HRP-conjugate for one hour at room temperature. The slides were stained with 3-amino, 9-ethylcarbazole (AEC) chromogen, counterstained with Lillie Mayer Haematoxylin, and mounted in glyserin aqueous mount. Antigen-antibody reaction was investigated under a microscope. The result showed that antigen was appeared in the tissues such as cuticle, epicuticle, buccal cavity, and eggs inside the uterine of A. galli . This research concluded that IgY stimulated by the excretory/secretory was able to recognized the antigen scattered in the tissues of A. galli so the IgY could be applied for immunodiagnostic.
Identification of the causal agent of Armillaria root rot of Pinus species in South Africa

Abstract Armillaria root rot was reported on economically important Pinus and Eucalyptus species grown in plantations in South Africa since the early 1900s. Armillaria species have been well studied in North America and Europe, but have received minimal attention in South Africa. Most reports of Armillaria root rot in South Africa suggest that A. mellea is the causal agent. The name A. heimii has also been used in more recent reports, although these have not been based on mycological studies. The taxonomic disposition of Armillaria in South Africa, therefore, remains unknown. The aim of this study was to identify and characterize Armillaria isolates from forest plantations in South Africa based on morphology, PCR-RFLP profiles and sequence data. Analysis of these characters revealed that the isolates originating from the plantations in South Africa are distinct from A. mellea and A. heimii. We believe that they represent A. fuscipes.
Embryo culture of early ripening seeded grape (Vitis vinifera) genotypes.

Immature grape embryos from early ripening genotypes of Vitis vinifera were successfully cultured in vitro on Difco orchid agar or a modified White's agar medium. Germination was increased in vitro for five genotypes from 0%, 7%, 11%, 12%, and 16% in vivo to 15%, 24%, 23%, 34%, and 24%, respectively. Subculturing embryos onto liquid culture from seeds that failed to germinate on agar also was possible. Differences in germination rates, as affected by pollen, were significant. This method will allow accelerated development of early ripening cultivars by allowing breeders to use such genotypes as females, as well as males.
Widespread implementation of controlled upwelling in the North Pacific Subtropical Gyre would counteract diazotrophic N2 fixation

Karl & Letelier (2008; Mar Ecol Prog Ser 364:257-268) examined the stimulation of dia- zotrophic N2 fixation by controlled upwelling of deep-water nutrients as a potential carbon seques- tration strategy in low-nutrient, low-chlorophyll regions such as the North Pacific Subtropical Gyre. They did not examine the effect of buoyancy on vertical stratification. Since seawater upwelled from depth is colder and saltier, and therefore denser than surface water, it would have to be diluted suf- ficiently to prevent its convective sinking. This dilution would lead to a decrease in vertical stratifica- tion, which could counteract N2 fixation, as diazotrophs bloom only under well-stratified conditions. A back-of-the-envelope calculation of the effect of large-scale controlled upwelling on upper ocean stratification shows that controlled upwelling is unlikely to scale up and serve as a climate stabiliza- tion wedge as defined by Pacala & Socolow (2004; Science 305:968-972), i.e. it would not sequester 1 Gt C yr -1 over 30 yr.
Drift-Free and Self-Aligned IMU-Based Human Gait Tracking System With Augmented Precision and Robustness

IMU-based human joint motion acquisition system is attractive for real-time control and monitoring in the emerging wearable technology due to its portability. However, in practical applications, it heavily suffers from long-term drift, magnetic interference and inconsistency of rotational reference frames, which causes precision degradation. In this letter, a novel on-line IMU-based human gait estimation framework was proposed to obtain the joint rotational angles directly under the kinematic constraints between multiple body segments, whereas traditional methods need to estimate the orientation of each individual segment. This framework consists of an on-line algorithm to align IMU frames with human joints and motion estimation algorithms for hip and knee without the aid of magnetometer. Both a 2-DoF robot and human gait tests were performed to validate the proposed method as compared with the predictions from commercial IMUs, joint encoders and an optical tracking system. The outcome demonstrated its advantages of adaptive alignment, drift rejection and low computational cost, which alleviates the practical barriers faced by human motion data collection in the wearable devices.
Biofilms: Recent Developments on an Old Battle

Microbial cells are able to adhere to surfaces and through an exo-polymeric matrix they establish microbial communities known as biofilms. This form of immobilised biomass can be responsible for heat and mass transfer limitations in industrial processes and be a source of contamination and proliferation of infections in water supply systems and medical devices. Several processes to prevent and destroy biofilms in surfaces and tissues have been patented and the new developments are reviewed. Most of the patents propose the use of UV radiation, high temperatures and addition of oxidant compounds to clean surfaces, which may be protected by antimicrobial coatings containing metal ions, non-pathogenic bacteria, time- release agents and biocides. Several biocidal compositions, comprising mixtures of disinfectants and biocides, are also presented. Mechanical, chemical and enzymatic procedures are discussed and particular emphasis is given to the cleaning and protection of medical devices and water supply systems.
Predicting Personality Using Answers to Open-Ended Interview Questions

One’s personality is widely accepted as an indicator of job performance, job satisfaction and tenure intention. The ability to measure an applicant’s personality in the selection process helps recruiters, hiring managers and the applicant make better hiring decisions. Our work shows that textual content of answers to standard interview questions related to past behaviour and situational judgement can be used to reliably infer personality traits. We used data from over 46,000 job applicants who completed an online chat interview that also included a personality questionnaire based on the six-factor HEXACO personality model to self-rate their personality. Using natural language processing (NLP) and machine learning methods we built a regression model to infer HEXACO trait values from textual content. We compared the performance of five different text representation methods and found that term frequency-inverse document frequency (TF-IDF) with Latent Dirichlet Allocation (LDA) topics performed the best with an average correlation of r = 0.39. As a comparison, a large study of Facebook messages based inference of Big 5 personality found an average correlation of r = 0.35 and IBM’s Personality Insights service built using twitter text data reports an average correlation of r = 0.31. We further validated our model with a group of 117 volunteers who used an agreement scale of yes/no/maybe to rate the individual trait descriptors generated based on the model outcomes. On average, 87.83% of the participants agreed with the personality description given for each of the six traits. The ability of algorithms to objectively infer a candidate’s personality using only the textual content of interview answers presents significant opportunities to remove the subjective biases involved in human interviewer judgement of candidate personality.
Adaptive Sampling for the Optimal Signal Reconstruction of Vehicle Telematics

Telematics collects the key operating parameters of a vehicle, e.g., vehicle speed and GPS locations, etc. that will be wirelessly transmitted to a cloud platform for making big data analysis and providing big-data-enabled services, e.g., fleet management. However, the high cost of the data transmission and storage makes it hard to collect the vehicle operating data in a high sampling frequency; as a result, limiting the performance of data analysis and the quality of the related services. To balance the cost of the data collection and the quality of data-enabled services, this paper proposes a sampling strategy that can adaptively determine the sampling locations under the constraint of a pre-specified fixed sample size or sampling rate with the aim of best reconstructing the original sensing data (original signals). The effectiveness of the proposed method is demonstrated using a real telematics data application of estimating the vehicle kinetic energy consumption.
Voronoi-Based Cooperative Node Deployment Algorithm in Mobile Sensor Networks

Providing adequate coverage for the Region of Interest (RoI) is an essential step to achieve the smooth functioning of Wireless Sensor Networks (WSNs) in many applications such as national security, surveillance, military, health care, and environmental monitoring. One of the techniques to provide the required coverage is to employ a node deployment algorithm to accurately locate all sensor nodes in the RoI to obtain the maximum coverage. Distributed node deployment algorithms in Mobile Sensor Networks (MSNs) are of more interest as all nodes have to cooperatively move to a position to provide overall optimal coverage. The final coverage offered by most of the algorithms reported in the literature is highly dependent on the initial coverage after the initial deployment. In this paper, the Voronoi-based Cooperative Node Deployment (VCOND) algorithm is proposed to efficiently provide the maximum coverage regardless of the initial locations of sensor nodes. Despite other algorithms which terminate the movement of each node solely based on the status of the current node and neglect the neighbourhood circumstances, the knowledge of the node one-hop vicinity is reflected on VCOND decision using two criteria: density of neighbourhood and sensing coverage efficiency. This effectively alleviates the dependency of the algorithm to the initial deployment of sensors while providing the maximum coverage. Our extensive simulation study shows not only a higher percentage of area coverage by VCOND but with less dependency on the initial deployment of sensors.
A Super-Wideband and High Isolation MIMO Antenna System Using a Windmill-Shaped Decoupling Structure

In this paper, a novel super-wideband multiple-input multiple-output (SWB-MIMO) antenna system with high isolation has been proposed, each antenna element consisting of a spade-shaped radiation patch, a defected ground structure (DGS) as well as a windmill-shaped decoupling structure. The elliptical slot etched on the radiation patch and the rectangular DGS are jointly utilized to make better the radiation performance and impedance bandwidth. Both of four-element and eight-element SWB-MIMO antenna systems are simulated and analyzed, respectively. A prototype of the four-element SWB-MIMO antenna system with total dimensions of 58 mm <inline-formula> <tex-math notation="LaTeX">$\times58$ </tex-math></inline-formula> mm <inline-formula> <tex-math notation="LaTeX">$\times 1$ </tex-math></inline-formula> mm was fabricated to verify the concept of design using an FR4 material substrate. Measurement and simulation results indicate that the proposed SWB-MIMO antenna system can work in the frequency band ranging from 2.9 to 40 GHz with a mutual coupling of lower than–17 dB. Also, excellent performance has been achieved with a low envelop correlation coefficient (ECC) of lower than 0.04, high multiplexing efficiency (<inline-formula> <tex-math notation="LaTeX">$\eta _{\mathrm {mux}}$ </tex-math></inline-formula>) of more than–3.0 dB, stable gain of up to 13.5 dBi as well as quasi-omnidirectional radiation properties. This design provides an important guideline for obtaining a super-wide MIMO antenna.
Antifungal efficiency of Miswak & Cardamom Extract on Some Virulence Factors of Candida albicans as Oral Pathogen

In vitro , inhibitory effects of Miswak & Cardamom extracts on some virulence factors belong to C.albicans as oral pathogen was investigated in this study. Results showed that both plants extract displayed a variable degree of antifungal activity against tested microorganisms but Cardamom extract showed more strongly effect than Miswak extract in which the inhibitory values was 2 mm as inhibitory zone, 15% adherent rate, 10% germination rate at The concentration 100% Cardamom extract compared with 1 mm as inhibitory zone, 35% adherent rate, 29% germination rate when treated with Miswak extract at The same concentration . The above results recommended that Miswak & Cardamom will be a great help for use as oral hygiene tool & fungicidal therapeutically agent for oral infections caused by Candida albicans .
Mixture Gaussian Time Series Modeling of Long-Term Market Returns

Abstract Stochastic modeling of investment returns is an important topic for actuaries who deal with variable annuity and segregated fund investment guarantees. The traditional lognormal stock return model is simple, but it is generally less appealing for longer-term problems. In recent years, the use of regime-switching lognormal (RSLN) processes for modeling maturity guarantees has been gaining popularity. In this paper we introduce the class of mixture Gaussian time series processes for modeling long-term stock market returns. It offers an alternative class of models to actuaries who may be experimenting with the RSLN process. We use monthly data from the Toronto Stock Exchange 300 and the Standard and Poor-s 500 indices to illustrate the mixture time series modeling procedures, and we compare the fits of the mixture models to the lognormal and RSLN models. Finally, we give a numerical example comparing risk measures for a simple segregated fund contract under different stochastic return models.