Miracle-dz/UniFAR-FTUs huggingface dataset:
Positive Influence of a Probiotic Mixture on the Intestinal Morphology and Microbiota of Farmed Guinea Fowls (Numida meleagris)

To understand the effectiveness of a probiotic mixture on intestinal morphology, mucus layer composition, and cecal microbiota diversity, 40 10-day-old Guinea fowls (Numida meleagris) were assigned to two groups: the control group (C), receiving drinking water, and the treated group (P), receiving water plus a commercial multi-strain probiotic (Slab51®, 2 × 1011 CFU/L). Birds were slaughtered after 4 months, and the intestines were collected. Samples from the duodenum, ileum, and cecum were processed for morphological and morphometric studies, and conventional glycohistochemistry. Cecal samples were also used to assess the microbiota by 16S metataxonomic approach. Group P showed significant increase in the villus height (p < 0.001 in the duodenum and p < 0.05 in the ileum and cecum), villus width (p < 0.05 in all investigated tracts), depth of crypts (p < 0.001 in the duodenum and cecum; p < 0.05 in the ileum), and goblet cells per villus (p < 0.001 in all investigated tracts) compared with group C. Cecal microbiota of the birds varied considerably and comparing the relative abundance of the main observational taxonomic units (OTUs), a positive enrichment of several beneficial taxa, such as Oscillospira, Eubacterium, Prevotella, and members of the Ruminococcaceae, was observed. The enrichment of those taxa can improve microbiota stability and resilience facing environmental stresses, enhancing its resistance against invading pathogens. Ruminococcaceae, which represent the most important taxon in both groups, and Prevotella have a key role in the gut physiology due to the production of short-chain fatty acids (SCFAs), which are a vital energy source for enterocytes, improve glucose metabolism, and exert an overall anti-inflammatory effect. Probiotic administration enriches the presence of Coprococcus, Oscillospira, and Eubacterium taxa that produce butyrate, which exerts a beneficial effect on growth performance, structure of villi, and pathogen control and has anti-inflammatory properties too. This study indicates that Slab51® supplementation positively affects the morphology and microbiota diversity of the guinea fowl intestine.
Human–Machine Differentiation in Speed and Separation Monitoring for Improved Efficiency in Human–Robot Collaboration

Human–robot collaborative applications have been receiving increasing attention in industrial applications. The efficiency of the applications is often quite low compared to traditional robotic applications without human interaction. Especially for applications that use speed and separation monitoring, there is potential to increase the efficiency with a cost-effective and easy to implement method. In this paper, we proposed to add human–machine differentiation to the speed and separation monitoring in human–robot collaborative applications. The formula for the protective separation distance was extended with a variable for the kind of object that approaches the robot. Different sensors for differentiation of human and non-human objects are presented. Thermal cameras are used to take measurements in a proof of concept. Through differentiation of human and non-human objects, it is possible to decrease the protective separation distance between the robot and the object and therefore increase the overall efficiency of the collaborative application.
Local generalized multigranulation variable precision tolerance rough sets and its attribute reduction

In the era of big data, as for an important granular computing model, rough set model is an important tool for us to deal with data. As a kind of extension of classical rough sets, multigranulation rough sets have two forms, including optimistic and pessimistic cases. However, these two models have their shortcomings, one is too loose, and the other is too strict. To overcome the above shortcomings, based on the concept of local multigranulation tolerance rough sets based in set-valued information systems, the local generalized multigranulation variable precision tolerance rough sets model by introducing characteristic function is established. Then the related properties are studied and proved. In addition, we define the concepts of lower approximate quality, inner and outer importance of attribute according to different granularity structures in set-valued decision information systems because different granularity structures have different effectives on the decision classes. Finally, the local attribute reduction algorithm and the global attribute reduction algorithm of local generalized multigranulation variable precision tolerance rough sets in set-valued decision information systems are given, and the effectiveness of the algorithms is proved by using UCI data sets.
Evaluating the Effect of Sewage Sludge on Bioactive Components of Momordicacharantia

Momordicacharantia L. belongs to the family Cucurbitaceae. This plant has widely been reported for use in traditional medical systems for the treatment of diabetes, rheumatism, intestinal gas and malaria. The ethnicity of M. charantiais Asia, Africa and some parts of Europe where it is considered edible. Sewage sludge, a by-product of waste water treatment plant, contains several nutrients. These nutrients are known as beneficial for the better growth and good yield of the plants. The objective of this study is to optimize soil with different ratios of sewage sludge for increased growth of bioactive components in M. charantia. The plant was grown at six places; in soil containing 20%, 40%, 60% and 80% sewage sludge ratio as well as in pure soil and in pure sewage sludge. Fruit harvesting was carried out after 75 days. Methanol extract of M. charantia (Fruit) was subjected to analysis using GC-MS. Comparative quantification of 7,11-hexadecadienal, oleic Acid and stigmasterol were carried out. It has been found that the soil containing 60% sewage sludge yielded maximum quantity of 7,11-hexadecadienal, oleic Acid and stigmasterol. Therefore, optimization of soil with different ratios of sewage sludge is carried out and 60% ratio of sewage sludge in soil is recommended for the promising growth of Momordicacharantia.
Evaluation of Gully Erosion Susceptibility Using a Maximum Entropy Model in the Upper Mkhomazi River Basin in South Africa

Soil erosion is one of the most challenging environmental issues in the world, causing unsustainable soil loss every year. In South Africa, several episodes of gully erosion have been documented and clearly linked to the presence of Quaternary colluvial deposits on the Drakensberg Mountain footslopes. The aim of this study was to identify and assess the triggering factors of gully erosion in the upper Mkhomazi River basin in KwaZulu-Natal, South Africa. We compiled a gully inventory map and applied remote sensing techniques as well as field surveys to validate the gully inventory. The gullies were subdivided into slope gullies and fluvial gullies. We derived susceptibility maps based on the spatial distribution of gully types to assess the most important driving factors. A stochastic modeling approach (MaxEnt) was applied, and the results showed two susceptibility maps within the spatial distribution of the gully erosion probability. To validate the MaxEnt model results, a subset of the existing inventory map was used. Additionally, by using areas with high susceptibilities, we were able to delineate previously unmapped colluvial deposits in the region. This predictive mapping tool can be applied to provide a theoretical basis for highlighting erosion-sensitive substrates to reduce the risk of expanding gully erosion.
Sparse control for continuous‐time systems

In this survey article, we give a comprehensive review of sparse control for continuous‐time systems, called maximum hands‐off control. The maximum hands‐off control is the L0 optimal control, for which we introduce fundamental properties such as necessary conditions, existence, and equivalence to the L1 optimal control. We also show an efficient numerical computation algorithm for the maximum hands‐off control based on the time discretization and ADMM (alternating direction method of multipliers). A numerical example is shown with an available MATLAB program.
Development of MEMS composite sensor with temperature compensation for tire pressure monitoring system

The pressure and temperature inside the tire is mainly monitored by the tire pressure monitoring system (TPMS). In order to improve the integration of the TPMS system, moreover enhance the sensitivity and temperature-insensitivity of pressure measurement, this paper proposes a microelectromechanical (MEMS) chip-level sensor based on stress-sensitive aluminum–silicon hybrid structures with amplified piezoresistive effect and temperature-dependent aluminum–silicon hybrid structures for hardware and software temperature compensations. Two types of aluminum–silicon hybrid structures are located inside and outside the strained membrane to simultaneously realize the measurement of pressure and temperature. The model of this composite sensor chip is firstly designed and verified for its effectiveness by using finite element numerical simulation, and then it is fabricated based on the standard MEMS process. The experiments indicate that the pressure sensitivity of the sensor is between 0.126 mV/(V·kPa) and 0.151 mV/(V·kPa) during the ambient temperature ranges from −20 °C to 100 °C, while the measurement error, sensitivity and temperature coefficient of temperature-dependent hybrid structures are individually ±0.91 °C, −1.225 mV/(V °C) and −0.150% °C−1. The thermal coefficient of offset (TCO) of pressure measurement can be reduced from −3.553%FS °C−1 to −0.375%FS °C−1 based on the differential output of the proposed sensor. In order to obtain the better performance of temperature compensation, Elman neural network based on ant colony algorithm is applied in the data fusion of differential output to further eliminate the temperature drift error. Based on which, the overall measured error is within 3.45 kPa, which is less than ±1.15%FS. The TCO is −0.017%FS °C−1, and the thermal coefficient of span is −0.020%FS °C−1. The research results may provide a useful reference for the development of the high-performance MEMS composite sensor for the TPMS system.
Interactions and computer experiments

Identifying interactions and understanding the underlying generating mechanism is essential for interpreting the response of black‐box models. We offer a systematic analysis of interaction types and corresponding sources, merging results of the broad statistical literature with findings developed within the computer experiment literature. Piecewise‐definiteness emerges a self‐standing interaction mechanism, alternative to the presence of interaction terms. We find that the scale of the analysis is essential for interpretation, and that no single method is capable of providing the correct identification of the underlying interaction generating mechanisms; conversely a combined approach involving indicators at difference scales is required. We propose a graphical tool called Mikado plot that exploits the link between interaction indicators at the finite scale and global scales to ease the regional visualization of two‐factor interactions. The findings are illustrated via numerical experiments with three well‐known computer models of different dimensionality and structure.
Microglia and Astrocytes in Alzheimer’s Disease in the Context of the Aberrant Copper Homeostasis Hypothesis

Evidence of copper’s (Cu) involvement in Alzheimer’s disease (AD) is available, but information on Cu involvement in microglia and astrocytes during the course of AD has yet to be structurally discussed. This review deals with this matter in an attempt to provide an updated discussion on the role of reactive glia challenged by excess labile Cu in a wide picture that embraces all the major processes identified as playing a role in toxicity induced by an imbalance of Cu in AD.
Fertility decline and a pay‐as‐you‐go pension system in a two‐sector model

In this study, we investigate how demographic shrinking affects the effectiveness of a pay-as-you-go pension system by using a twosector overlapping generations model. The seminal paper, Fanti and Gori (2012) showed that demographic shrinking does not necessarily cause a decrease in pension benefit. We revisit the existing results by extending the original one-sector model into a two-sector model. We find that there is a positive effect on the pension benefit of the shrinking population as well and derive relevant conditions in the twosector model. We also present numerical simulations. Our results suggest that the positive effect of population decline on the pension benefit is limited. ∗ Address: Faculty of Economics, Niigata University, 8050 Ikarashi 2-no-cho, Nishi-ku, Niigata City, Niigata 950-2181, Japan Tel. and Fax: +81-25-262-6538, E-mail: khamada@econ.niigata-u.ac. jp † Corresponding author Address: Faculty of Political Science and Economics, Waseda University, 1-6-1 Nishi-Waseda, Shinjuku-ku, Tokyo 169-8050, Japan Tel. and Fax: +81-3-3208-1197, Email: akaneko@waseda.jp ‡ Address: Graduate School of Economics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8601, Japan Tel. : +81-52-789-5952, E-mail: yanagi@soec.nagoya-u.ac. jp
DEVELOPMENT of ROBOT SOCCER SYSTEM for 11-A-SIDE MiroSot

This paper presents development of the robot soccer system for Large League MiroSot which plays with 11 robots each side. The main difference with the Middle League MiroSot, playing with 5 robots each side, is that the playground is twice bigger than that of the 5-a-side MiroSot. The new system includes a new overhead vision system with two CCD cameras to grab the image of the whole playground. The CCD camera is connected to each PC which calculates the posture information of the ball and robots from the grabbed image. The two PCs are connected to each other so that PC_2 is to send the posture information to PC_1. Then PC_1 is to calculate the strategy using the posture information from itself and PC_2, and send the command to the robots via a RF transmitter. The CCD camera, SDC-410 (Samsung CCD Camera) for the vision system shows good performance in the experiment. Also a zone defense strategy is tested with the developed simulator.
Ophthalmic Ultrasonography in Sub-Saharan Africa—A Kinshasa Experience

The aim of this study was to analyze the use of the diagnostic B-scan ultrasound. Should it be made accessible to all surgical centers in Sub-Saharan Africa in order to (i) avoid unnecessary cataract surgery and (ii) evaluate extraocular pathology? This study was conducted in Kinshasa from 2006 to 2019. Three hundred and twenty-three patients were included and separated into two groups. Group 1 included 262 patients with dense cataract. Group 2 consisted of 61 patients with pathologies of the ocular adnexa, and all were examined with a B-scan ultrasound. In group 1, there were 437 systematically screened eyes. Three hundred and ninety-eight eyes (91.08%) showed no abnormalities, 13 (2.97%) retinal detachments were identified, and 15 (3.43%) demonstrated a detached posterior hyaloid membrane. In the second group, 61 patients were examined (group 2). In 20 of them, surgery was performed for biopsy, tumor excision, mucoceles drainage, and palliative treatment. The need for routine B-scan examinations in dense cataract patients seems to be limited and can most likely be replaced by a thorough application of locally available examination techniques. B-scan application is recommended to manage orbital patients in the most cost-effective way.
Fine-grained Topic Detection and Tracking on Twitter

: With its large volume of data and free access to information, Twitter revolutionised Topic Detection and Tracking (TDT). Thanks to Twitter, TDT could build timelines of real-world events in real-time. However, over the years TDT struggled to adapt to Twitter’s noise. While TDT’s solutions stiﬂed noise, they also kept the area from building granular timelines of events, and today, TDT still relies on large datasets from popular events. In this paper, we detail Event TimeLine Detection (ELD) as a solution: a real-time system that combines TDT’s two broad approaches, document-pivot and feature-pivot methods. In ELD, an on-line document-pivot technique clusters a stream of tweets, and a novel feature-pivot algorithm ﬁlters clusters and identiﬁes topical keywords. This mixture allows ELD to overcome the technical limitations of traditional TDT algorithms to build ﬁne-grained timelines of both popular and unpopular events. Nevertheless, our results emphasize the importance of robust topic tracking and the ability to ﬁlter subjective content.
Fourier-transform-infrared-spectroscopy based metabolomic spectral biomarker selection towards optimal diagnostic differentiation of diabetes with and without retinopathy

Abstract The purpose of this study is to identify Fourier transform infrared (FTIR) spectroscopy-based serum metabolomic spectral biomarkers using chemometrics for the diagnosis of Diabetic Retinopathy. FTIR spectroscopy was performed on 85 human serum samples [30 type 2 diabetes patients each without retinopathy and with retinopathy along with 25 normal healthy individuals as control]. Difference between mean spectra (DBMS), forward feature selection (FFS), and Mann–Whitney’s U tests were applied for spectral biomarker selection. Classification of disease conditions was achieved using analysis of different combinations of spectral features with linear, quadratic, and cubic Support Vector Machine at 10-fold cross validation. Twelve spectral signatures extracted by FFS could differentiate diabetes and diabetic retinopathy with 90% sensitivity, 92.7% specificity, and 90.5% overall accuracy. Two peaks (1042, 1114.18 cm−1) were associated with carbohydrate and polysaccharide content and five peaks (1114.18, 1165, 1211.18, 1402.70, 1451.14, 1657 cm−1) represented aberrations in total lipid content. Four peaks (1114.18, 1117, 1147, 1165 cm−1) were associated with protein phosphorylation and three peaks (1527, 1544.71, 1591.10 cm−1) with Amide II group. Again, lipidic signatures were strongly corroborated with glycosylated hemoglobin levels in diabetic retinopathy and diabetic subjects. Spectral signatures also revealed an elevated level of β-sheet containing proteins in serum in diabetic retinopathy condition. The method was validated through spectral biomarker selection by the DBMS technique. Thus, this method has the capability of diagnostic cost minimization for detection of diabetic retinopathy by label-free spectral biomarker identification.
Convolutional-Recurrent Neural Networks With Multiple Attention Mechanisms for Speech Emotion Recognition

Speech emotion recognition (SER) aims to endow machines with the intelligence in perceiving latent affective components from speech. However, the existing works on deep-learning-based SER make it difficult to jointly consider time–frequency and sequential information in speech due to their structures, which may lead to deficiencies in exploring reasonable local emotional representations. In this regard, we propose a convolutional-recurrent neural network with multiple attention mechanisms (CRNN-MAs) for SER in this article, including the paralleled convolutional neural network (CNN) and long short-term memory (LSTM) modules, using extracted Mel-spectrums and frame-level features, respectively, in order to acquire time–frequency and sequential information simultaneously. Furthermore, we set three strategies for the proposed CRNN-MA: 1) a multiple self-attention layer in the CNN module on frame-level weights; 2) a multidimensional attention layer as the input features of the LSTM; and 3) a fusion layer summarizing the features of the two modules. Experimental results on three conventional SER corpora demonstrate the effectiveness of the proposed approach through using the convolutional-recurrent and multiple-attention modules, compared with other related models and existing state-of-the-art approaches.
LiDAR Positioning Algorithm Based on ICP and Artificial Landmarks Assistance

As one of the automated guided vehicle (AGV) positioning methods, the LiDAR positioning method, based on artificial landmarks, has been widely used in warehousing logistics industries in recent years. However, the traditional LiDAR positioning method based on artificial landmarks mainly depends on the three-point positioning method, the performance of which is limited due to landmarks’ layout and detection requirements. This paper proposes a LiDAR positioning algorithm based on iterative closest point (ICP) and artificial landmarks assistance. It provides improvements based on the traditional ICP algorithm. The result of positioning provided by the landmarks is used as the initial iteration ICP value. The combination of the ICP algorithm and landmarks enables the positioning algorithm to maintain a certain positioning precision when landmark detection is disturbed. By comparing the proposed algorithm with the positioning scheme developed by SICK in Germany, we prove that the combination of the ICP algorithm and landmarks can effectively improve the robustness under the premise of ensuring precision.
Cyclic olefin copolymer‐based copper clad laminate and the application for low loss broadband baluns

In this article, a novel copper clad laminate based on cyclic olefin copolymer (COC) is proposed and manufactured by using lamination process. The loss characteristic of manufactured laminate is investigated by comparing the measured results of two microstrips on proposed COC‐based laminate and traditional Rogers RT/duroid 5880, respectively. Moreover, the SIW/HMSIW baluns operated at X‐band are designed on the proposed laminate, and the measured results verify the inherent out‐of‐phase performance and good amplitude characteristics in broadband frequency range. This work shows a novel type of high‐frequency circuit boards on COC material, which is significantly to be one of the advanced candidates for microwave/millimeter wave circuit applications.
Signal Control Period Division Method Based on Locally Linear Embedding and Particle Swarm Optimization Combined with K-means Clustering

In order to optimize the existing signal control period division method and improve signal control effect, a new period division method based on Locally Linear Embedding and Particle Swarm Optimization combined with K-means clustering (LLE-PSO-K) algorithm is proposed in this paper. Firstly, traffic flow characteristics of signal-controlled intersections are fully considered, and a multi-dimensional flow matrix is constructed based on the phase traffic flow. In order to reduce the computational complexity of the model and improve the operating efficiency of the method, manifold learning Locally Linear Embedding (LLE) algorithm is brought in to reduce the dimension of the multidimensional phase flow matrix. Then, the dimensionality reduction matrix is used as input data, and signal control period is divided by using Particle Swarm Optimization combined with K-means clustering (PSO-K) algorithm. Finally, an actual intersection in a city is selected to verify the performance of the proposed method. For comparative analysis, control periods are divided based on the phase traffic flow data with 15min, 30min and 1h interval respectively. Results show that for different time intervals, the division of the proposed method is better than other methods, of which the invalid control periods are less. Besides, the optimal clustering number can be obtained, which proves the effectiveness of the new proposed method.