Miracle-dz/UniFAR-FTUs huggingface dataset:
Ontogenetic changes to bone microstructure in an archaeologically derived sample of human ribs

There is considerable variation in the gross morphology and tissue properties among the bones of human infants, children, adolescents, and adults. Using 18 known‐age individuals (nfemale = 8, nmale = 9, nunknown = 1; birth to 21 years old), from a well‐documented cemetery collection, Spitalfields Christ Church, London, UK, this study explores growth‐related changes in cortical and trabecular bone microstructure. Micro‐CT scans of mid‐shaft middle thoracic ribs are used for quantitative analysis. Results are then compared to previously quantified conventional histomorphometry of the same sample. Total area (Tt.Ar), cortical area (Ct.Ar), cortical thickness (Ct.Th), and the major (Maj.Dm) and minor (Min.Dm) diameters of the rib demonstrate positive correlations with age. Pore density (Po.Dn) increases, but age‐related changes to cortical porosity (Ct.Po) appear to be non‐linear. Trabecular thickness (Tb.th) and trabecular separation (Tb.Sp) increase with age, whereas trabecular bone pattern factor (Tb.Pf), structural model index (SMI), and connectivity density (Conn.D) decrease with age. Sex‐based differences were not identified for any of the variables included in this study. Some samples display clear evidence of diagenetic alteration without corresponding changes in radiopacity, which compromises the reliability of bone mineral density (BMD) data in the study of past populations. Cortical porosity data are not correlated with two‐dimensional measures of osteon population density (OPD). This suggests that unfilled resorption spaces contribute more significantly to cortical porosity than do the Haversian canals of secondary osteons. Continued research using complementary imaging techniques and a wide array of histological variables will increase our understanding of age‐ and sex‐specific ontogenetic patterns within and among human populations.
Urinary Excretion of N1-Methylnicotinamide, as a Biomarker of Niacin Status, and Mortality in Renal Transplant Recipients

Renal transplant recipients (RTR) commonly suffer from vitamin B6 deficiency and its functional consequences add to an association with poor long-term outcome. It is unknown whether niacin status is affected in RTR and, if so, whether this affects clinical outcomes, as vitamin B6 is a cofactor in nicotinamide biosynthesis. We compared 24-h urinary excretion of N1-methylnicotinamide (N1-MN) as a biomarker of niacin status in RTR with that in healthy controls, in relation to dietary intake of tryptophan and niacin as well as vitamin B6 status, and investigated whether niacin status is associated with the risk of premature all-cause mortality in RTR. In a prospective cohort of 660 stable RTR with a median follow-up of 5.4 (4.7–6.1) years and 275 healthy kidney donors, 24-h urinary excretion of N1-MN was measured with liquid chromatography-tandem mass spectrometry LC-MS/MS. Dietary intake was assessed by food frequency questionnaires. Prospective associations of N1-MN excretion with mortality were investigated by Cox regression analyses. Median N1-MN excretion was 22.0 (15.8–31.8) μmol/day in RTR, compared to 41.1 (31.6–57.2) μmol/day in healthy kidney donors (p < 0.001). This difference was independent of dietary intake of tryptophan (1059 ± 271 and 1089 ± 308 mg/day; p = 0.19), niacin (17.9 ± 5.2 and 19.2 ± 6.2 mg/day; p < 0.001), plasma vitamin B6 (29.0 (17.5–49.5), and 42.0 (29.8–60.3) nmol/L; p < 0.001), respectively. N1-MN excretion was inversely associated with the risk of all-cause mortality in RTR (HR 0.57; 95% CI 0.45–0.71; p < 0.001), independent of potential confounders. RTR excrete less N1-MN in 24-h urine than healthy controls, and our data suggest that this difference cannot be attributed to lower dietary intake of tryptophan and niacin, nor vitamin B6 status. Importantly, lower 24-h urinary excretion of N1-MN is independently associated with a higher risk of premature all-cause mortality in RTR.
Analysis of Subsurface Hypotheses through Simulation of Rime Radargrams Based on Available Analogous Data

Radar for Icy Moon Exploration (RIME) is designed to characterize the subsurface geology of the Jovian icy moons. The RIME radargrams will show the combined response of a number of geophysical and geological characteristics of the ice-shells of these moons. Thus, radar sounder simulations are needed to understand the relationship between these target variables and the RIME response. In this paper, we use a computationally simple simulation approach that is based on reprocessing the radargrams available from the geological analogs of RIME targets. Moreover, we present a case study for a particular RIME target using this simulation technique for the generation of a database of RIME radar-grams, and a technique for analyzing this database. From the preliminary analysis of the simulated radargrams, we could derive important information regarding the underlying target variables. This confirms the usefulness of the presented approach to support the geological interpretation of the RIME radargrams.
Landslide Detection and Monitoring for Moutainous Areas of Southwest China Using Time Series Insar

Jinsha River Valley area is located in the complex fault zone of southwest China. As the region has been strongly affected by landslide disaster in the past few years, it is very imperative to detect the regions with large deformation around this area. InSAR technique is widely used in obtaining the terrain displacement, especially for this inaccessible areas. Thus this work utilizes time series InSAR (TSInSAR) method to investigate the landslide risk for the Ruba-Lagang section of Jinsha River by using Sentinel-1 images. Several landslide-hidden areas are identified and the deformation time series for them are analyzed, which will be of great importance to the disaster prevention and local infrastructure construction.
High-Gain Observer-Based Sliding Mode Force Control for the Single-Rod Electrohydraulic Servo Actuator

A high-gain observer based sliding mode force control system for the single-rod servo actuator is presented in this paper. In order to track the desired force, full states for feedback are needed. Since only the output force is measured, a high-gain observer with easy implementation and calibration is designed for the estimation of the unavailable states. Then the sliding mode controller is proposed and a continuously varying function instead of the traditional sign function is used to constitute the switching term of the control input which takes the distance of the system states from the sliding surface into account. So the chattering phenomena are eliminated. The stability of the closed-loop force control system is analyzed by the singular perturbation method and simulations show the effectiveness of the proposed force control method for the single-rod electrohydraulic servo actuator.
Flavobacterium microcysteis sp. nov., isolated from a culture of Microcystis aeruginosa.

A Gram-stain-negative, yellow-pigmented, strictly aerobic bacterium, designated strain MaA-Y11T, was isolated from a culture of Microcystis aeruginosa in the Republic of Korea. Cells were non-motile rods showing catalase- and oxidase-positive reactions. Growth of strain MaA-Y11T was observed at 25-37 °C (optimum, 30 °C) and pH 6.0-9.0 (optimum, 7.0) and in the presence of 0-1.0 % (w/v) NaCl (optimum, 0 %). Strain MaA-Y11T did not produce flexirubin-type pigments. Strain MaA-Y11T contained iso-C15 : 0, iso-C17 : 0 3-OH and summed feature 3 (C16 : 1ω7c and/or C16 : 1ω6c) as major cellular fatty acids and menaquinone-6 as the sole isoprenoid quinone. Phosphatidylethanolamine, three unidentified aminolipids and four unidentified lipids were detected as the major polar lipids. The G+C content of the genomic DNA was 37.0 mol%. Phylogenetic analysis, based on 16S rRNA gene sequences, showed that strain MaA-Y11T formed a phyletic lineage with Flavobacterium lindanitolerans IP-10T within the genus Flavobacterium. Strain MaA-Y11T was most closely related to Flavobacterium lindanitolerans IP-10T, with a 98.85 % 16S rRNA gene sequence similarity, and shared less than 93.87 % sequence similarities with other type strains. Average nucleotide identity and in silico DNA-DNA hybridization values between strain MaA-Y11T and the type strain of F. lindanitolerans were 87.0 and 32.3 %, respectively. Here, we conclude that strain MaA-Y11T represents a new species of the genus Flavobacterium, for which the name Flavobacterium microcysteis sp. nov. is proposed. The type strain is MaA-Y11T (=KACC 21225T=JCM 33501T).
Global Vectors Representation of Protein Sequences and Its Application for Predicting Self-Interacting Proteins with Multi-Grained Cascade Forest Model

Self-interacting proteins (SIPs) is of paramount importance in current molecular biology. There have been developed a number of traditional biological experiment methods for predicting SIPs in the past few years. However, these methods are costly, time-consuming and inefficient, and often limit their usage for predicting SIPs. Therefore, the development of computational method emerges at the times require. In this paper, we for the first time proposed a novel deep learning model which combined natural language processing (NLP) method for potential SIPs prediction from the protein sequence information. More specifically, the protein sequence is de novo assembled by k-mers. Then, we obtained the global vectors representation for each protein sequences by using natural language processing (NLP) technique. Finally, based on the knowledge of known self-interacting and non-interacting proteins, a multi-grained cascade forest model is trained to predict SIPs. Comprehensive experiments were performed on yeast and human datasets, which obtained an accuracy rate of 91.45% and 93.12%, respectively. From our evaluations, the experimental results show that the use of amino acid semantics information is very helpful for addressing the problem of sequences containing both self-interacting and non-interacting pairs of proteins. This work would have potential applications for various biological classification problems.
An Extended D-Tomosar System for the Retrieval of Three-Dimensional Surface Deformation

Traditional differential synthetic aperture radar tomography (D-TomoSAR) is difficult to fully reflect the actual deformation of ground, because it can only capture onedimensional (1-D) deformation along the line of sight (LOS) of the SAR. An extended D-TomoSAR model for retrieving the three-dimensional (3-D) deformation parameters of ground is proposed in this paper. The 3-D deformation parameters in the directions of azimuth, range, and elevation are firstly introduced into the traditional D-TomoSAR signal model. Then the sparse reconstruction technique is used to reconstruct the scatterers’ elevation and deformation parameters. Simulations and experimental results with semireal data verify the effectiveness of the proposed signal model and algorithm.
Object-Oriented Open Pit Extraction Based on Convolutional Neural Network, A Case Study in Yuzhou, China

Mineral resources are an important material basis for economic and social development. The development and utilization of mineral resources is also an inevitable requirement for modernization construction. However, it is always inevitable to have a negative impact on the natural environment in the process of mineral extraction. The phenomenon of landscape destruction, including open pit, scrap slag heap, tailings reservoir and so on, is common. Especially the damage to the environment caused by open pit is particularly serious. In order to achieve fast and accurate detection of open pits, this paper uses the method of combining convolution neural network with object-oriented thought to extract the open pit in the mining area of Yuzhou, Henan province. Accuracy evaluation of classification results based on actual field survey data and land use data. The final total accuracy is 91.18%, kappa coefficient is 0.89, which shows the usability and advantages of this method in the application of open pit extraction in mining area.
Energy-Efficiency-Aimed Radio Resource Scheduling for D2D Communications Underlaying Cellular Network

In this paper, we study the resource management strategies for device-to-device (D2D) communications underlying the cellular networks. To cater for green communications, our design goal is to maximize ergodic energy efficiency (EE) of all D2D links with the fact that it may be tricky for the base station (BS) to receive all the real-time channel state information (CSI) while guaranteeing the stability and the power requirements for D2D links. We first formulate the optimization problem which is difficult to resolve directly for its non-convex nature. Then a novel joint power control and channel allocation iterative algorithm is proposed, which consist of two sub-problem: the power control (PC) sub-problem which can be solved by employing convex optimization theory for both cellular user equipment (CUE) and D2D user equipment (DUE) and the channel allocation (CA) subproblem. Furthermore, simulation results show that our proposed algorithm is superior to the existing algorithms and particularly shed light into the impact on EE matric by revealing the nonlinear power relationship between CUE and DUE.
Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

Network performance optimization has always been one of the important research subjects in mobile wireless sensor networks. With the expansion of the application field of MWSNs and the complexity of the working environment, traditional network performance optimization algorithms have become difficult to meet people’s requirements due to their own limitations. The traditional swarm intelligence algorithms have some shortcomings in solving complex practical multi-objective optimization problems. In recent years, scholars have proposed many novel swarm intelligence optimization algorithms, which have strong applicability and achieved good experimental results in solving complex practical problems. These algorithms, like their natural systems of inspiration, show the desirable properties of being adaptive, scalable, and robust. Therefore, the swarm intelligent algorithms (PSO, ACO, ASFA, ABC, SFLA) are widely used in the performance optimization of mobile wireless sensor networks due to its cluster intelligence and biological preference characteristics. In this paper, the main contributions is to comprehensively analyze and summarize the current swarm intelligence optimization algorithm and key technologies of mobile wireless sensor networks, as well as the application of swarm intelligence algorithm in MWSNs. Then, the concept, classification and architecture of Internet of things and MWSNs are described in detail. Meanwhile, the latest research results of the swarm intelligence algorithms in performance optimization of MWSNs are systematically described. The problems and solutions in the performance optimization process of MWSNs are summarized, and the performance of the algorithms in the performance optimization of MWSNs is compared and analyzed. Finally, combined with the current research status in this field, the issues that need to be paid attention to in the research of swarm intelligence algorithm optimization for MWSNs are put forward, and the development trend and prospect of this research direction in the future are prospected.
The Potential Role of Dysfunctions in Neuron-Microglia Communication in the Pathogenesis of Brain Disorders

The bidirectional communication between neurons and microglia is fundamental for the proper functioning of the central nervous system (CNS). Chemokines and clusters of differentiation (CD) along with their receptors represent ligand-receptor signalling that is uniquely important for neuron – microglia communication. Among these molecules, CX3CL1 (fractalkine) and CD200 (OX-2 membrane glycoprotein) come to the fore because of their cell-type-specific localization. They are principally expressed by neurons when their receptors, CX3CR1 and CD200R, respectively, are predominantly present on the microglia, resulting in the specific axis which maintains the CNS homeostasis. Disruptions to this balance are suggested as contributors or even the basis for many neurological diseases. In this review, we discuss the roles of CX3CL1, CD200 and their receptors in both physiological and pathological processes within the CNS. We want to underline the critical involvement of these molecules in controlling neuron – microglia communication, noting that dysfunctions in their interactions constitute a key factor in severe neurological diseases, such as schizophrenia, depression and neurodegeneration-based conditions.
Spatial Enhanced-SSD For Multiclass Object Detection in Remote Sensing Images

Accurate multiclass object detection in remote sensing images is a challenging task, especially for small objects. Since the scales of objects in remote sensing images have a great variance, almost all of the advanced detection methods have shortcomings. Consequently, improving the accuracy of multiclass objects detection has always been the direction of researchers’ efforts. In this paper, a spatial enhanced-Single Shot MultiBox Detector (SE-SSD) is proposed. First, to enhance the spatial information, we enlarge the input image channels with embedding oriented-gradients feature maps. Second, the multiple output layers in the backbone network are changed to reduce one pooling operation. Finally, we design a context module to enhance the receptive field for feature layer description in SE-SSD framework. Experimental results on DOTA dataset demonstrate that Spatial Enhanced-SSD method reaches a much higher mean average precision (mAP) than Faster R-CNN, SSD and other classic detection network.
A Multi-Satellite Regional Imaging Mission Planning Method Based on Moom for Emergency Surveying and Mapping

Aiming at the imaging problem of regional target in emergency surveying and mapping, a multi-objective optimization model(MOOM) is proposed, which takes the imaging lateral swing angles of satellite as decision variables and takes the maximum coverage rate of regional target and the minimum number of holes as objective functions. Aiming at the two key problems of evaluation function calculation and multi-objective model solving, Vatti algorithm and NSGAII algorithm are used to solve them respectively. Finally, STK simulation data are used to verify the feasibility of the optimization method.
Multi-Hazard Analysis of Etna 2018 Eruption by Sar Imaging

The use of SAR data for imaging hazardous events is nowadays a well-consolidated approach. Thanks to the synoptic capabilities of satellite data, their high spatial resolution and high revisit time, fast and accurate analysis can be performed at both regional and local scale. This work presents the results of SAR Interferometry applied to a multi-hazard event induced by the eruption of Etna volcano, occurred in late December 2018. We analyse both the syn-eruptive deformation of volcano edifice and the effects of an induced Mw 4.9 earthquake and a small-scale landslide by exploiting ESA Sentinel-1 data. The data highlight a main horizontal displacement of the Etna summit of more than 40 cm and 65 cm along west and east directions, respectively. The earthquake caused about 15 cm displacement toward east and 20 cm toward west. Finally, approximately 5 cm displacement along satellite Line of Sight was observed associated to the landslide.
The Potential of Multi-Sensor Remote Sensing Mineral Exploration: Examples from Southern Africa

Traditional exploration techniques usually rely on extensive geological field work complemented by geophysical ground surveying. However, this approach can be limited by field accessibility, financial status, area size and climate and can be confronted with public rebuff. We recommend the use of multi-scale hyperspectral remote sensing to mitigate the limitations of traditional exploration. Multi-scale remote sensing is particularly beneficial, especially in inaccessible and remote areas with little infrastructure, because it allows for a systematic, dense and generally non-invasive surveying.Additionally, Unmanned Aerial Systems (UAS) coupled with various sensors provide an opportunity to conduct noninvasive exploration in socially sensitive areas and in relatively inaccessible locations. The development of operational technologies and the evaluation of appropriate processing techniques in different mineral deposit environments contribute to a rapidly evolving field at the cutting edge of exploration technologies. Ultimately, this study provides the opportunity to advance the discovery of critical raw material deposits.
InWalker: smart white cane for the blind

Abstract Background/Purpose: Visual impairment is a disability more commonly caused by diseases that lead to several disadvantages to the daily activities amongst those blind. For almost a century since the white cane was first introduced, the cane has remained as the most reliable tool for those affected by blindness. Methods: By using a combination of the capabilities of Internet of Things (IoT) and existing devices, such as mobile phones, an InWalker system is proposed to expand the functionality of the typical white cane, so as to introduce several new features that enhance the safety and confidence amongst people who are blind. As such, this paper explores the existing works and projects to comprehend the motivation and the standard practices for each proposed feature. Each of the strength and drawback has been assessed thoroughly to refine the scope of this project. Results: The proposed project, InWalker, is an intelligent system that has an embedded board system with various sensors to enhance the usability of white cane. The inputs from the sensor are processed on a microcontroller, which then pass the data to a smartphone via Bluetooth for additional features, such as global positioning system (GPS) tracking and SMS services. Conclusions: Based on the initial user testing, the proposed system has successfully fulfilled most of the users’ need. Implication for Rehabilitation Visual impairment is a disability more commonly caused by diseases that lead to several disadvantages amongst those blind. The white cane has been seen as the most reliable tool for the visual impaired. This tool could be further improved with the integration of additional sensors that works with today’s mobile devices. The proposed system, InWalker, is able to improve the overall quality of life among people who are blind through several features: obstacle detection, GPS tracking and a light illumination in dark environments for increased safety.
Implementation and Influence of C-SVPWM and BCSVPWM Techniques using DSP for Voltage Oriented Control of Three Phase Active Front End Rectifier

The Voltage Oriented Control method with Conventional Space Vector PWM (C-SVPWM) and Bus-clamp SVPWM (BC-SVPWM) for a Three Phase Active Front End rectifier which is used in 4-Quadrant drive has been designed, simulated and implemented. The system simulation has been done using Matlab/Simulink software. The CSVPWM and 60○ Bus-clamped SVPWM techniques are implemented and compared experimentally using TI DSP TMS320F28335 as the controller platform. This paper investigates the effect of both PWM techniques on THD and power losses of three phase AFE. The measure of THD performance and switching loss are evaluated based on supply current ripple analysis and the variation of angle between line current and line voltage respectively. The switching loss mainly depends on the modulating wave shape and fundamental power factor in bus clamped PWM. Finally, the effectiveness of the proposed PWM technique is validate by theoretical and experimental results conducted on back to back converter loaded with induction motor. The result will be discussed and compared.