Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Navigation Solution for Vision-Based Autonomous Rendezvous

This paper proposes Thales Alenia Space vision-based navigation solution for close proximity operations in autonomous space rendezvous with non-cooperative targets. The proposed solution covers all the phases of the navigation. First, a neural network robustly extracts the target silhouette from complex background. Then, the binary silhouette is used to retrieve the initial relative pose using a detection algorithm. We propose an innovative approach to retrieve the object's pose using a precomputed set of invariants and geometric moments. The observation is extended over a set of consecutive frames in order to allow the rejection of outlying measurements and to obtain a robust pose initialization. Once an initial estimate of the pose is acquired, a recursive tracking algorithm based on the extraction and matching of the observed silhouette contours with the 3D geometric model of the target is initialized. The detection algorithm is run in parallel to the tracker in order to correct the tracking in case of diverging measurements. The measurements are then integrated into a dynamic filter, increasing the robustness of target pose estimation, allowing the estimation of target translational velocity and rotation rate, and implementing a computationally efficient delay management technique that allows merging delayed and infrequent measurements. The overall Navigation solution has a low computational load, which makes it compatible with space-qualified microprocessors. The solution is tested and validated in different close proximity scenarios using synthetic images generated with Thales Alenia Space rendering engine SpiCam.
VSGD: a Bi-modal Dataset for Gait Analysis

Gait refers to the displacement of the center of gravity during motion. Gait analysis has been a focus of much research in recent years. However, the literature on gait analysis either deals with visual gait data or with inertial gait data collected using wearable sensors. In this work, we propose a new dataset collected on our campus of 45 subjects (32 males and 13 females) of ages from 18 to 23 walking a straight path while wearing 4 inertial measurement units and being filmed using two smartphones fixed at two different directions. Both the visual and inertial data are recorded at the same time, and an asynchronous signal was performed by every subject to be able to align both modalities together. We validate our data on two classification models for gender and person recognition and show that the two models perform well on our data.
On Cycles, Attackers and Supporters - A Contribution to The Investigation of Dynamics in Abstract Argumentation

Abstract argumentation as defined by Dung in his seminal 1995 paper is by now a major research area in knowledge representation and reasoning. Dynamics of abstract argumentation frameworks (AFs) as well as syntactical consequences of semantical facts of them are the central issues of this paper. The first main part is engaged with the systematical study of the influence of attackers and supporters regarding the acceptability status of whole sets and/or single arguments. In particular, we investigate the impact of addition or removal of arguments, a line of research that has been around for more than a decade. Apart from entirely new results, we revisit, generalize and sum up similar results from the literature. To gain a comprehensive formal and intuitive understanding of the behavior of AFs we put special effort in comparing different kind of semantics. We concentrate on classical admissibility-based semantics and also give pointers to semantics based on naivity and weak admissibility, a recently introduced mediating approach. In the second main part we show how to infer syntactical information from semantical one. For instance, it is well-known that if a finite AF possesses no stable extension, then it has to contain an odd-cycle. In this paper, we even present a characterization of this issue. Moreover, we show that the change of the number of extensions if adding or removing an argument allows to conclude the existence of certain even or odd cycles in the considered AF without having further information.
Heteroscedasticity in Errors-in-Variables Regression

We suggest new plotting methods for residual analysis in errors-in-variables regression. The standard residuals analyses are based on the methods of Miller and Fuller and are appropriate when the errors in the regression and the measurement error are symmetrically distributed. By “appropriate,” we mean that in large samples the plots will not falsely identify a nonexistent pattern of heteroscedasticity or nonlinearity. The standard methods are not appropriate in this sense for skewed error distributions. Our methods require replication of the error-prone predictors, but they are appropriate for both symmetric and skewed error distributions. Besides residual plots, we also construct hypothesis tests for heteroscedasticity. In terms of power for detecting heteroscedasticity, we show that the standard plot is more efficient when the residuals are normally distributed, although it does not achieve its nominal level for skewed error distributions. Simulations are used to illustrate the results. We also consider the case that measurement error in the response is correlated with the measurement error in the predictors, suggesting new residual plots in this setting. The article also contains a short summary of plotting techniques for detecting heteroscedasticity in regression.
PROGNOSTICATING CLINICAL INCIDENTS VIA RECURRENT NEURAL NETWORKS BY USING CLINICAL DOCTOR AI

: Doctor AI imitates human doctor’s forecasting potential and gives diagnostic results that are clinically significant. Prognosticating Clinical Incidents is a timeseries based RNN model. It is implemented and employed to longitudinal time stamped electronic health record data from a twenty thousand patients over a decade. Encounter medical logs of patients data such as diagnosis codes, medication codes and procedure codes are input data to RNN to predict the diagnosis and medication types for a future visit of patients in a hospital. Doctor AI evaluates the history of patient’s to prepare one label for each diagnosis predictions and medication types i.e.,multi-label forecasting/prediction. Leveraging huge historical patient details in electronic health records (EHR), a collective generic and comprehensive predictive model that covers perceived health state and medication uses for EHR, is new approach in disease progress identification.
On the Non-isothermal Crystallization of PCL by FTIR

The non-isothermal crystallization of poly(e-caprolactone)(PCL) samples with different molecular weight was studied by FT-infrared spectrometry. The non-isothermal crystallization parameters of PCL were obtained by quantitative analysis on the characteristic bands. The crystallization rate and the initiation temperature of crystallization increased with increase in the molecular weight of PCL. The non-isothermal crystallization parameters obtained by FTIR were consistent with those obtained by DSC.
Antioxidant Capacity and Effect of Coconut Water on Alcl3 Amnesic-Induced Drosophila Melanogaster

Coconut water (CW) has gained wide attraction as a known functional food that offers additional benefits to its basic nutritional value. CW is employed in the management of oxidative stressassociated diseases for its natural antioxidant features. This study sought to investigate CW antioxidant capacity and effect upon its dietary inclusion in an Alcl3-induced amnesic Drosophila melanogaster. CW was extracted from the coconut fruit cotyledon and supplemented in the flies’ diet for five days. The CW extract antioxidant activity was examined in vitro through the total phenol, total flavonoid content, ferric reducing power, ironchelating ability, ABTS* and DPPH* scavenging ability assays. Likewise, the anti-lipid peroxidation potential of CW extract was also measured in vivo using D. melanogaster. The CW extract has a total phenol content of 1.48 ± 0.43 (mg/g GAE) and a total flavonoid content of 0.53 ± 0.02 (mg/g QE) which could be attributed to its scavenging ability against ABTS* and DPPH* in vitro with an increase in extract concentration. Similarly, a positive trend was detected in the ferric reducing antioxidant power and iron-chelating ability tests. Furthermore, dietary inclusion of CW in groups fed with 0.1% and 1% CW lowers Malondialdehyde (MDA) concentration significantly (p < 0.01 and p < 0.0001 respectively) in Alcl3 induced flies. This correlates with CW’s ability to considerably (p < 0.05) reduced MDA in vitro in a concentrationdependent manner (0.01 – 0.03 mg/mL). These findings provide substantial information that
Study Of Quality Of Service Framework In 5G Network And Proposed Smart Scheduling Policy Based On Slicing And Network Virtualization

: In this paper, we present the main key indicators for an enhanced quality of service for the 5G network. In addition, we present the main characteristics of network virtualization in enabling a service-based network. The quality-of-service model in 5G is presented then we give our proposal which consists of an enhancement of a previous proposal. We give a smart downlink scheduler that profits from network slicing in order to enhance the overall QoS experienced by the 5G network. An adaptive metric rule for the three main services:
Fault Diagnosis of Batch Chemical Processes Using a Dynamic Time Warping (DTW)-Based Artificial Immune System

Fault diagnosis is important for ensuring chemical processes stability and safety. The strong nonlinearity and complexity of batch chemical processes make such diagnosis more difficult than that for continuous processes. In this paper, a new fault diagnosis methodology is proposed for batch chemical processes, based on an artificial immune system (AIS) and dynamic time warping (DTW) algorithm. The system generates diverse antibodies using known normal and fault samples and calculates the difference between the test data and the antibodies by the DTW algorithm. If the difference for an antibody is lower than a threshold, then the test data are deemed to be of the same type of this antibody’s fault. Its application to a simulated penicillin fermentation process demonstrates that the proposed AIS can meet the requirements for online dynamic fault diagnosis of batch processes and can diagnose new faults through self-learning. Compared with dynamic locus analysis and artificial neural networks, the proposed me.
CAN-20-0354-ET 3157..3169

State-Transition Analysis of Time-Sequential Gene Expression Identifies Critical Points That Predict Development of Acute Myeloid Leukemia A C Russell C. Rockne, Sergio Branciamore, JingQi, David E. Frankhouser, DenisO'Meally,Wei-Kai Hua, Guerry Cook, Emily Carnahan, Lianjun Zhang, Ayelet Marom, Herman Wu, Davide Maestrini, Xiwei Wu, Yate-Ching Yuan, Zheng Liu, Leo D. Wang, Stephen Forman, Nadia Carlesso, Ya-Huei Kuo, and Guido Marcucci
How to facilitate different types of innovation in an organization through entrepreneurial orientation and knowledge management

In dynamic and competitive environment, the importance of innovation is accepted as a necessary ingredients for firms to create value and sustain competitive advantage. However, very little empirical research has specifically addressed to what extent different kinds of innovation rely on specific knowledge management processes and entrepreneurial orientation. The objective of this study is to identify the different types of innovation that are predominant in companies, and how to facilitate different types of innovation activities. A questionnaire survey was conducted and 169 valid replies were received. This research analyzes the relationship among knowledge management processes, as well as entrepreneurial orientation and different types of innovation. The results from an empirical survey study reveal that organizations facilitate different types of innovation (i.e., administrative versus technical innovation, and product versus process innovation) through entrepreneurial orientation and knowledge management process (i.e., knowledge acquisition, knowledge sharing and knowledge application). The results also show that the partial mediating role of knowledge management processes in the relationship between entrepreneurial orientation and different types of innovation.
Pharmacokinetics of hydroxysafflor yellow A in rats

Aim:To study the pharmacokinetics of hydroxysafflor yellow A in rat.Methods:Hydroxysafflor yellow A in blood plasma was determined after ig or iv injection in rats by HPLC,and the pharmacokinetic parameters were calculated by 3P97.Excreta(biliary,urinary and fecal) were collected within 24 h after administration.Results:Pharmacokinetics parameters of hydroxysafflor yellow A in rats after iv injection were fit to the two-compartment open model.Hydroxysafflor yellow A was distributed and eliminated quickly.Cumulative biliary and urinary excretions within 24 h at a dose of 0.1 mg/kg after iv administration was taken 1.32% and 88.6% of the dose.Hydroxysafflor yellow A was not detected in fecal after iv injection.Cumulative biliary,urinary and fecal excretions within 24 h at a dose of 12.5 mg/kg after ig accounted for 0.062%,2.9% and 48% of the dose,respectively.Conclusion:Hydroxysafflor yellow A was poorly absorbed from the gastrointestine tract,and near half of it was excreted into biliary after absorption from the gastrointestine.Hydroxysafflor yellow A in plasma was mainly excreted into urinary through the kidney.
Fuelling Employee Innovative Behaviour Through Servant Leadership: A Moderated Mediated Framework

Dr. Sajid Rahman Khattaka, Dr. Muhammad Fayazb, Dr. Shams ur Rahmanc, Dr. Zafar Saleemd, Dr. Muhammad Fayaze, aAssistant Professor IBMS, The University of Agriculture, Peshawar, bLecturer IBMS, The University of Agriculture, Peshawar, cAssistant Professor IBMS, The University of Agriculture, Peshawar, dAssistant Professor Mir Chakar Khan Rind University, Sibbi, eAssociate Professor Agriculture University Peshawar, Email: asajidktk99@yahoo.com, bfayazarbab96@yahoo.com, csayedshams@aup.edu. pk, ddrsaleem1947@gmail.com, emfayaz@aup.edu. pk
Interweaving Teaching Strategies in Interprofessional Education: A Pilot Study with Graduate Occupational Therapy and Physician Assistant Students

The importance of interprofessional education (IPE) has been emphasized by the World Health Organization and is currently required by both the occupational therapy (OT) and physician assistant (PA) education accreditation agencies in the United States. Moreover, a growing body of evidence suggests that IPE fosters learners’ understanding of the roles different disciplines play in healthcare delivery and prepares future health professionals for patient-centered care. Authors designed and implemented an IPE event aimed to foster the following core Interprofessional Education Collaborative (IPEC) competencies among graduate OT and PA students: Values/Ethics for Interprofessional Practice, Roles and Responsibilities, Interprofessional Communication, and Teams and Teamwork. The specific content of this event addressed pharmacological and non-pharmacological treatment for older adult clients with complex clinical presentation (with emphasis on OTand PAspecific clinical interventions) and incorporated a discussion of a clinical case study. Team-Based Learning approach, Jigsaw technique and role-play were used as teaching strategies during this activity. Students’ perceptions of IPE were measured by administering the Students Perceptions of Interprofessional Clinical Education Revised (SPICE-R) survey before and after the IPE event. This project is unique, because it incorporates a novel combination of teaching strategies to augment the IPE experience.
Evolutionary Prediction of Nonstationary Event Popularity Dynamics of Weibo Social Network Using Time-Series Characteristics

A growing number of web users around the world have started to post their opinions on social media platforms and offer them for share. Building a highly scalable evolution prediction model by means of evolution trend volatility plays a significant role in the operations of enterprise marketing, public opinion supervision, personalized recommendation, and so forth. However, the historical patterns cannot cover the systematical time-series dynamic and volatility features in the prediction problems of a social network. This paper aims to investigate the popularity prediction problem from a time-series perspective utilizing dynamic linear models. First, the stationary and nonstationary time series of Weibo hot events are detected and transformed into time-dependent variables. Second, a systematic general popularity prediction model N- SEP 2 M is proposed to recognize and predict the nonstationary event propagation of a hot event on the Weibo social network. Third, the explanatory compensation variable social intensity (SI) is introduced to optimize the model N- SEP 2 M. Experiments on three Weibo hot events with different subject classifications show that our prediction approach is effective for the propagation of hot events with burst traffic.
Social Capital and Protests in the United States

In the last decade we have witnessed rising protests in the United States associated with issues that form part of society's social fabric that can either facilitate or break down collective behaviour. Rising social inequalities can cause people to no longer share the same values and force individuals to work against each other. This breakdown in social capital can be a key driver for protests as the marginalised attempt to voice their grievances. Using social capital data from the Social Capital Project and protest data from the GDELT Project for U.S counties, we nd that higher social capital decreases di erent types of protests, moreso demonstrations and violent protests. At a disaggregated level, we nd that community engagement and collective e cacy (i.e. level of social organisation) are better predictors of protests in relation to quality of household health and level of con dence in institutions. These results remain consistent when controlling for economic and social inequalities, such as income, unemployment and race. The ndings highlight the importance of social capital in the development process, particularly in mitigating the incentives to engage in violence.
Development of 3D environmental laser scanner using pinhole projection

Three-dimensional (3D) information of capturing and reconstructing an object existing in its environment is a big challenge. In this work, we discuss the 3D laser scanning techniques, which can obtain a high density of data points by an accurate and fast method. This work considers the previous developments in this area to propose a developed cost-effective system based on pinhole projection concept and commercial hardware components taking into account the current achieved accuracy. A laser line auto-scanning system was designed to perform close-range 3D reconstructions for home/office objects with high accuracy and resolution. The system changes the laser plane direction with a microcontroller to perform automatic scanning and obtain continuous laser strips for objects’ 3D reconstruction. The system parameters were calibrated with Matlab’s built-in camera calibration toolbox to find camera focal length and optical center constraints. The pinhole projection equation was defined to optimize the prototype rotating axis equation. The developed 3D environmental laser scanner with pinhole projection proved the system’s effectiveness on close-range stationary objects with high resolution and accuracy with a measurement error in the range (0.05–0.25) mm. The 3D point cloud processing of the Matlab computer vision toolbox has been employed to show the 3D object reconstruction and to perform the camera calibration, which improves efficiency and highly simplifies the calibration method. The calibration error is the main error source in the measurements, and the errors of the actual measurement are found to be influenced by several environmental parameters. The presented platform can be equipped with a system of lower power consumption, and compact smaller size
ARTÍCULOS PRINCIPALES ARTIGOS PRINCIPAIS FEATURE ARTICLES HUNTING IMPACTS ASSOCIATED WITH BRAZIL NUT HARVESTING IN THE BOLIVIAN AMAZON

We assessed the hunting practiced by workers on a 63,900 ha Brazil nut gathering property on the Madre de Dios River, northern Bolivia (11o 54' 05'' S; 67o 57' 31'' W). During 45 days of the dry season, we monitored 18 nut storage depots ranging from the central depot (the “Barraca”) to remote sites. We recorded species, weight, and hunting technique for all harvested mammals, birds and reptiles. Number of individuals and total biomass harvested per person and per day were estimated for different taxa and compared between depots near to and far from the central Barraca. The recorded harvest included 445 individual animals (total biomass 2,428 kg) belonging to 61 species. Of these, mammals contributed 26 species, 55.3% of individuals and 88.8% of biomass. Birds (29 species) contributed 40% of individuals and 6.7% of biomass, whereas reptiles (6 species) contributed 4.7% individuals and 4.5% of biomass. Per capita harvest rates for the14 most closely monitored depots were 0.072 animals/person/day (s.d.=0.036) and 0.367 kg/person/ day (s.d.=0.241). Harvest rates for primates were lesser near the Barraca than in more remote depots. Successful hunting trips averaged 6.98 hr (range 1-44) and yielded on average 1.53 animals per trip or 0.22 animals per hour. Comparisons with other studies suggest that some species are suffering from a high hunting pressure and that improved practices of forest management should be applied.
Evolution of Firm Dynamics: With Minimal Assumptions∗

This paper investigates the evolution of firm distributions for entrant manufacturing firms in Canada using nonparametric methods. These nonparametric methods allow a flexible method based on functional principal components or dynamic densities to characterize how these densities evolve over time. This method is applied to a novel administrative firm-level database from Statistics Canada to investigate the evolution of the 1985 and 1989 cohorts of new entrants. We find that firm leverage (debt-to-asset ratio) distributions have persistent deviations from the initial distributions and bootstrap test statistics suggest that the distributions are different across all time periods. Firm size and labour productivity have transitory deviations and some of the distributions are the same across all time periods. Univariate finite mixture and stochastic dominance tests are used to conduct pairwise comparisons as robustness measures. We find that these static pairwise comparisons confirm the dynamic evolution of these densities. This method illustrates the efficacy of functional principal components to analyze firm distributions.