Miracle-dz/UniFAR-FTUs huggingface dataset:
Indoor Visible Light Positioning Based on Improved Whale Optimization Method With Min-Max Algorithm

Recently, the Min-Max algorithm has been widely used in various indoor positioning systems due to its simplicity and robustness. In this article, an improved whale optimization algorithm (IWOA) associated with the Min-Max algorithm is proposed to improve the positioning accuracy concerning the original Min-Max algorithm for visible light positioning (VLP) systems. In the proposed algorithm, the three-dimensional (3-D) region of interest (RoI) affiliated with the target node (TN) is first obtained using the original Min-Max algorithm, and then the IWOA is employed to find the accurate position of the TN in the 3-D RoI with the lowest fitness value. In software simulations, the signal-to-noise ratio (SNR) value of the VLP system is 15 dB and the standard deviation of measured distance noise is 1 m. Our software simulation results show that in the case of two-dimensional (2-D) positioning, the averaged positioning error (APE) of the proposed IWOA-Min-Max algorithm is decreased by 84.6%, 52.1%, 1.9%, and 19.93%, respectively, for the original Min-Max algorithm, the extended Min-Max algorithms, the whale optimization algorithm (WOA), and the least square estimation (LSE). Furthermore, in the case of 3-D positioning, it is also reduced by 81.7%, 75.4%, 8.5%, and 63.9%, respectively, compared with that of the above four existing algorithms. Finally, hardware-in-the-loop simulation (HILS) results are also provided to verify the effectiveness of the proposed IWOA-Min-Max algorithm.
Examples and generalizations in mathematical reasoning – A study with potentially mathematically gifted children

Mathematical arguments are central components of mathematics and play a role in certain types of modelling of potential mathematical giftedness. However, particular characteristics of arguments are interpreted differently in the context of mathematical giftedness. Some models of giftedness see no connection, whereas other models consider the formulation of complete and plausible arguments as a partial aspect of giftedness. Furthermore, longitudinal changes in argumentation characteristics remain open. This leads to the research focus of this article, which is to identify and describe the changes of argumentation products in potentially mathematically gifted children over a longer period. For this purpose, the argumentation products of children from third to sixth grade are collected throughout a longitudinal study and examined with respect to the use of examples and generalizations. The analysis of all products results in six different types of changes in the characteristics of the argumentation products identified over the survey period and case studies are used to illustrate student use of examples and generalizations of these types. This not only reveals the general importance of the use of examples in arguments. For one type, an increase in generalized arguments can be observed over the survey period. The article will conclude with a discussion of the role of argument characteristics in describing potential mathematical giftedness.
Terahertz Plasma Waves in Gated Graphene Heterostructures

Plasma waves in graphene-based heterostructures with massless (neutrino-like) two-dimensional electron gas and with a highly conducting substrate (n+-Si) serving as a gate and an isolating gate layer (SiO2) are studied. Using the developed model, we show that the sufficiently long plasma waves exhibit a linear (sound-like) dispersion with the wave velocity determined by the gate layer thickness and the gate voltage. The plasma wave velocity in graphene heterostructures can significantly exceed the plasma wave velocity in the commonly employed semiconductor gated heterostructures. The gated graphene heterostructures can be used in different novel voltage tunable THz devices which utilize the plasma waves.
Characterization of Dextran Biosynthesized by Glucansucrase from Leuconostoc pseudomesenteroides and Their Potential Biotechnological Applications

Glucansucrase was purified from Leuconostoc pseudomesenteroides. The glucansucrase exhibited maximum activity at pH 5.5 and 30 °C. Ca2+ significantly promoted enzyme activity. An exopolysaccharide (EPS) was synthesized by this glucansucrase in vitro and purified. The molecular weight of the EPS was 3.083 × 106 Da. Fourier transform infrared (FT-IR) and nuclear magnetic resonance (NMR) spectroscopy showed that the main structure of glucan was 97.3% α-(1→6)-linked D-glucopyranose units, and α-(1→3) branched chain accounted for 2.7%. Scanning electron microscopy (SEM) observation of dextran showed that its surface was smooth and flaky. Atomic force microscopy (AFM) of dextran revealed a chain-like microstructure with many irregular protuberances in aqueous solution. The results showed that dextran had good thermal stability, water holding capacity, water solubility and emulsifying ability (EA), as well as good antioxidant activity; thus it has broad prospects for development in the fields of food, biomedicine, and medicine.
A Recognition Method of Ewe Estrus Crawling Behavior Based on Multi-Target Detection Layer Neural Network

Simple Summary The timely and accurate detection of ewe estrus behavior in precision animal husbandry is an important research topic. The timely detection of estrus ewes in mutton sheep breeding can not only protect the welfare of ewes themselves, but also improve the interests of breeding enterprises. With the continuous increase in the scale of mutton sheep breeding and the gradual intensification of breeding methods, the traditional manual detection methods require high labor intensity, and the contact sensor detection methods will cause stress reaction problems in ewes. In recent years, the rapid development of deep learning technology has brought on new possibilities. We propose a method for the recognition ewe estrus based on a multi-target detection layer neural network. The results show that the method can meet the requirements of timely and accurate detection of ewe estrus behavior in large-scale mutton sheep breeding. Abstract There are some problems with estrus detection in ewes in large-scale meat sheep farming: mainly, the manual detection method is labor-intensive and the contact sensor detection method causes stress reactions in ewes. To solve the abovementioned problems, we proposed a multi-objective detection layer neural network-based method for ewe estrus crawling behavior recognition. The approach we proposed has four main parts. Firstly, to address the problem of mismatch between our constructed ewe estrus dataset and the YOLO v3 anchor box size, we propose to obtain a new anchor box size by clustering the ewe estrus dataset using the K-means++ algorithm. Secondly, to address the problem of low model recognition precision caused by small imaging of distant ewes in the dataset, we added a 104 × 104 target detection layer, making the total target detection layer reach four layers, strengthening the model’s ability to learn shallow information and improving the model’s ability to detect small targets. Then, we added residual units to the residual structure of the model, so that the deep feature information of the model is not easily lost and further fused with the shallow feature information to speed up the training of the model. Finally, we maintain the aspect ratio of the images in the data-loading module of the model to reduce the distortion of the image information and increase the precision of the model. The experimental results show that our proposed model has 98.56% recognition precision, while recall was 98.04%, F1 value was 98%, mAP was 99.78%, FPS was 41 f/s, and model size was 276 M, which can meet the accurate and real-time recognition of ewe estrus behavior in large-scale meat sheep farming.
Performance analysis of the VIA-ESPRIT algorithm

The VIA-ESPRIT algorithm is a computationally efficient method for direction of arrival (DOA) estimation. This algorithm uses virtual interpolated arrays (VIA) to apply ESPRIT type techniques to arbitrary array geometries. This paper presents a performance analysis of VIA-ESPRIT. Expressions for the bias and the variance of the estimation errors are derived. Simulation results verify the theoretically predicted performance.
Force/PositionControl of Heterogeneous Teleoperation System Based on Adaptive Fuzzy& CMAC NN

This study is mainly about the designation of a new type of Haptic Device and a new control algorithm on asymmetric teleoperation robots system. Aiming at the problems of tracking and transparency of the asymmetric teleoperation system, An new Adaptive Fuzzy & CMAC NN control algorithm was proposed. The Haptic Device was chosen as master-Robot of the system. Baxter dual-arm robot was chosen as slave-Robot of the system. The simulation experiment of Heterogeneous Teleoperation System Based on Adaptive Fuzzy & CMAC NN was carried out. The experiment results showed that the maximum values of displacement tracking errors in three directions x,y,z are 0.02m, 0.01m, 0.01m respectively. Compared with Adaptive Fuzzy control, the performance of the new control algorithm is improved. The force feedback experiment on real asymmetric teleoperation robot system was carried out. The results showed that the force feedback wave is consistent with the actual situation and showed that the Adaptive Fuzzy & CMAC NN control algorithm proposed is superior to Adaptive Fuzzy control algorithm. Therefore, the new algorithm perfectly satisfied the system. The experiment parameters also demonstrates the Haptic Device satisfies the design requirements of the asymmetric teleoperation robots system and the industry standards.
A Hybrid Generic Framework for Heart Problem Diagnosis Based on a Machine Learning Paradigm

The early, valid prediction of heart problems would minimize life threats and save lives, while lack of prediction and false diagnosis can be fatal. Addressing a single dataset alone to build a machine learning model for the identification of heart problems is not practical because each country and hospital has its own data schema, structure, and quality. On this basis, a generic framework has been built for heart problem diagnosis. This framework is a hybrid framework that employs multiple machine learning and deep learning techniques and votes for the best outcome based on a novel voting technique with the intention to remove bias from the model. The framework contains two consequent layers. The first layer contains simultaneous machine learning models running over a given dataset. The second layer consolidates the outputs of the first layer and classifies them as a second classification layer based on novel voting techniques. Prior to the classification process, the framework selects the top features using a proposed feature selection framework. It starts by filtering the columns using multiple feature selection methods and considers the top common features selected. Results from the proposed framework, with 95.6% accuracy, show its superiority over the single machine learning model, classical stacking technique, and traditional voting technique. The main contribution of this work is to demonstrate how the prediction probabilities of multiple models can be exploited for the purpose of creating another layer for final output; this step neutralizes any model bias. Another experimental contribution is proving the complete pipeline’s ability to be retrained and used for other datasets collected using different measurements and with different distributions.
Characterization and Biodegradation of Phenol by Pseudomonas aeruginosa and Klebsiella variicola Strains Isolated from Sewage Sludge and Their Effect on Soybean Seeds Germination

Phenols are very soluble in water; as a result, they can pollute a massive volume of fresh water, wastewater, groundwater, oceans, and soil, negatively affecting plant germination and animal and human health. For the detoxification and bioremediation of phenol in wastewater, phenol biodegradation using novel bacteria isolated from sewage sludge was investigated. Twenty samples from sewage sludge (SS) were collected, and bacteria in SS contents were cultured in the mineral salt agar (MSA) containing phenol (500 mg/L). Twenty colonies (S1 up to S20) were recovered from all the tested SS samples. The characteristics of three bacterial properties, 16S rDNA sequencing, similarities, GenBank accession number, and phylogenetic analysis showed that strains S3, S10, and S18 were Pseudomonas aeruginosa, Klebsiella pneumoniae, and Klebsiella variicola, respectively. P. aeruginosa, K. pneumoniae, and K. variicola were able to degrade 1000 mg/L phenol in the mineral salt medium. The bacterial strains from sewage sludge were efficient in removing 71.70 and 74.67% of phenol at 1000 mg/L within three days and could tolerate high phenol concentrations (2000 mg/L). The findings showed that P. aeruginosa, K. pneumoniae, and K. variicola could potentially treat phenolic water. All soybean and faba bean seeds were germinated after being treated with 250, 500, 750, and 1000 mg/L phenol in a mineral salt medium inoculated with these strains. The highest maximum phenol removal and detoxification rates were P. aeruginosa and K. variicola. These strains may help decompose and detoxify phenol from industrial wastewater with high phenol levels and bioremediating phenol-contaminated soils.
Observation-Driven Model for Zero-Inflated Daily Counts of Emergency Room Visit Data

Time series data with excessive zeros frequently occur in medical and health studies. To analyze time series count data without excessive zeros, observation-driven Poisson regression models are commonly used in the literature. As handling excessive zeros in count data is not straightforward, observation-driven models are rarely used to analyze time series count data with excessive zeros. In this paper an observation-driven zero-inflated Poisson (ZIP) model for time series count data is proposed. This approach can accommodate an autoregressive serial dependence structure which commonly appears in time series. The estimation of the model parameters by using the quasi-likelihood estimating equation approach is discussed. To estimate the correlation parameters of the dependence structure, a moment approach is used. The proposed methodology is illustrated by applying it to a data set of daily emergency room visits due to bronchitis.
Strict positivity of the density for a poisson driven S.D.E

We consider a one-dimensional stochastic differential equation driven by a compensated Poisson measure. We assume that this equation admits a unique solution We prove that under a strong non-degeneracy condition, for each t ≥ 0, the law of is bounded below by a measure that admits a strictly positive continuous density with respect to the Lebesgue measure on . To this aim, we develop Bismut's approach of the Malliavin calculus for Poisson functionals
Efficacy Analysis of Trastuzumab Combined With FLOT as Neoadjuvant Treatment of Human Epidermal Growth Factor Receptor 2 Positive Advanced Gastric Cancer

This research aims to assess the efficacy of neoadjuvant trastuzumab with 5-fluorouracil, leucovorin, oxaliplatin and docetaxel for the treatment of human epidermal growth factor receptor 2 positive metastatic gastric cancer. To conduct the research, we recruited 80 patients having human epidermal growth factor receptor 2 positive advanced gastric cancers between January 2020 and December 2021 admitted to our institution and randomly allocated them to the control and observation groups, each with 40 cases. Patients in the 5-fluorouracil, leucovorin, oxaliplatin and docetaxel group served as controls, while those in the observation group underwent a combination of trastuzumab and 5-fluorouracil, leucovorin, oxaliplatin and docetaxel. The short-term curative outcome of patients after neoadjuvant therapy was evaluated; the levels of carcinoembryonic antigen, serum glycoprotein 19-9 and glycoprotein (carcinoembryonic antigen 72-4) were measured by chemiluminescence immunoassay. Side effects experienced by patients in both groups were recorded when they occurred during therapy. The disease control rate and objective response rate of the observation group were found to be statistically and clinically higher than those of the control group (p<0.05), per the findings of the short-term effectiveness assessment. After treatment, there was a notable reduction in serum carcinoembryonic antigen, carcinoembryonic antigen 19-9 and carcinoembryonic antigen 72-4 in both groups (both p<0.05) and the levels of carcinoembryonic antigen 72-4, carcinoembryonic antigen 19-9 and carcinoembryonic antigen in the observation group were considerably lower than the control group (p<0.05). There was no evident difference between the two patient groups in adverse reactions. Trastuzumab combined with 5-fluorouracil, leucovorin, oxaliplatin and docetaxel has high efficacy and safety in neoadjuvant therapy for human epidermal growth factor receptor 2-positive advanced gastric cancer patients and it can be popularized and applied as a new model of clinical treatment.
Optical clearing method for monitoring cutaneous microcirculation response to vasoactive drugs with high sensitivity

Laser speckle contrast imaging technique has been playing an important role in monitoring cutaneous microcirculation, but the strong scattering of skin restricts the imaging depth and contrast, and also makes it impossible to assess the cutaneous microcirculation response dynamically with high sensitivity. The tissue optical clearing is helpful for opening a visible window on mouse dorsal skin. In this work, the cutaneous microcirculation response to vasoactive noradrenaline is monitored with the laser speckle contrast imaging system before and after skin optical clearing. The results show that the optical clearing method can significantly enhance the contrast of laser speckle contrast imaging, and small blood vessels whose diameter less than 20μm can be distinguished with high resolution. The dynamic changes in cutaneous microvascular diameter and blood flow velocity caused by drug can be monitored sensitively. In contrast, it is difficult to detect the cutaneous microcirculation response that occurred in the blood vessels more than 100μm in the intact skin, and the signal-to-noise ratio is too low to monitor the dynamic changes caused by the same drug. Thus, skin optical clearing method can enhance the ability of laser speckle contrast imaging in accessing cutaneous microcirculation response, including the imaging contrast, resolution and sensitivity.
On the Nature of the Outer Rings around SN 1987A

We present recent Hubble Space Telescope (HST) Faint Object Spectrograph and ground-based spectroscopic observations of SN 1987A and its surroundings and discuss them in conjunction with HST Faint Object Camera and Wide Field Planetary Camera 2 narrowband imaging. We have determined the following properties of the outer rings around SN 1987A: (1) the ionization and the temperature in the northern outer ring of SN 1987A are similar to those measured in the inner ring; (2) the [O II] electron density in the outer rings, both the northern and the southern, is ~800 cm-3, less than a third of that in the inner ring; (3) the N/O and N/C abundance ratios measured in the northern ring are ~3 times lower than in the inner ring, whereas the N/H abundance ratio is about half the value measured in the inner ring. Moreover, narrowband imaging indicates that the prevailing conditions in both the northern and the southern outer rings are quite similar to each other and to those measured in the portion of the northern outer ring that projects on the ejecta. These findings indicate that the gas in the outer rings consists of material that was ejected by the supernova progenitor at an earlier stage of evolution than the inner ring, when CNO processing was less advanced. Comparison with evolutionary models suggests that the material that constitutes the outer rings was ejected from the supernova progenitor ~104 yr before the bulk of the inner-ring material was ejected.
Plain set and stirred yogurt with different additives: implementation of food safety system as quality checkpoints

Hazard Analysis Critical Control Point (HACCP) is a risk management protocol developed to ensure food safety through a precautionary approach that is believed to offer assurances in producing safe food for customers. Yogurt is made in a number of phases, commencing with the collection of raw milk and ending with consumer consumption. While this is happening, major economic and health issues might arise from exposing the manufacturing line to biological, chemical, and/or physical contaminations. As a result, the decision tree approach was used to determine the CCPs during the production of yogurt. Additionally, biological dangers are incorporated as a by-product of the system’s implementation performance. In particular, the plain set and nut puree-honey-fortified stirred yogurt manufacturing techniques are highlighted for the first time in this study. The potential manufacturing risks are described for the first time, together with information on how HACCP plans may guard against major risks that could result in the production of yogurt that is not in compliance with established standards.
Target detection and tracking in airborne video imagery using statistical snake and mean shift

Target detection and tracking in real-time videos are very important and yet difficult for many applications. Numerous detection and tracking techniques have been proposed, typically by imposing some constraints on the motion and image to simplify the problem depending on the application and environment. This paper focuses on target detection and tracking in airborne videos, in which not much simplification can be made. We have recently proposed a combined/switching detection and tracking method which is based on the combination of a spatio-temporal segmentation and statistical snake model. This paper improves the statistical snake model by incorporating both edge and region information and enhancing the snake contour deformation. A more complex motion model is used to improve the accuracy of object detection and size classification. Mean-shift is integrated into the proposed combined method to track small point objects and deal with the problem of object disappearance-reappearance. Testing results using real UAV videos are provided.
Discrimination of Temporal Gaps

This research investigates the human observer's ability to judge differences in duration without the benefit of such cues as differences in energy and energy spectrum. Three observers compared two silent durations, T and T+ΔT, in a two‐alternative forced‐choice procedure. The value of T ranged from 0.63 to 640 msec. For each T, seven values of ΔT were chosen to cover the range of the psychometric function. The duration and amplitude of noise markers bounding the temporal gaps were varied across three experimental conditions. For silent durations less than 160 msec, the results showed that discrimination depended on the parameters of the marker. For each marker condition, the function relating the Weber fraction and T was non‐monotonic, reaching a local minimum at about 2.5 msec and a local maximum at 10 msec. These results were used to evaluate current theories about the central processing of duration.