Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling the Ranked Antenatal Care Visits Using Optimized Partial Least Square Regression

The frequency and timing of antenatal care visits are observed to be the significant factors of infant and maternal morbidity and mortality. The present research is conducted to determine the risk factors of reduced antenatal care visits using an optimized partial least square regression model. A data set collected during 2017-2018 by Pakistan Demographic and Health Surveys is used for modeling purposes. The partial least square regression model coupled with rank correlation measures are introduced for improved performance to address ranked response. The proposed models included PLSρs, PLSτA, PLSτB, PLSτC, PLS D, PLSτGK, PLS G, and PLS U. Three filter-based factor selection methods are executed, and leave-one-out cross-validation by linear discriminant analysis is measured on predicted scores of all models. Finally, the Monte Carlo simulation method with 10 iterations of repeated sampling for optimization of validation performance is applied to select the optimum model. The standard and proposed models are executed over simulated and real data sets for efficiency comparison. The PLSρs is found to be the most appropriate proposed method to model the observed ranked data set of antenatal care visits based on validation performance. The optimal model selected 29 influential factors of inadequate use of antenatal care. The important factors of reduced antenatal care visits included women's educational status, wealth index, total children ever born, husband's education level, domestic violence, and history of cesarean section. The findings recommended that partial least square regression algorithms coupled with rank correlation coefficients provide more efficient estimates of ranked data in the presence of multicollinearity.
Caractérisation in situ de soudure sans plomb

The work comes from a project about the characteriz ations of a material in microelectronic structures. The objective of the project is to improve the reliabil ity of the microelectronic products. In this work, we try to understand the creep behavior in the solder. Consid ering the scale of microelectronic structure, to ge t the parameters of the creep constitutive equation, for example Norton or Garofalo model, from the traditio nal creep experiments is unsuitable, so nanoindentation campaigning is used. Mots clefs : soudure sans plomb (lead free solder), fluage, prop riete mecanique, nanoindentation
An ANN-Based Data Fusion Algorithm for INS/CNS Integrated Navigation System

For INS/CNS integrated navigation system, the performance of data fusion algorithms based on the Kalman filter is seriously degraded when the subsystems have discrepant sampling-frequency. Therefore, a fusion algorithm based on locally weighted linear regression (LWLR), long short-term memory (LSTM), and cubature Kalman filter (CKF) is proposed. First, we construct a deeper coupled INS/CNS system model based on the low-frequency error of the star sensor. Then, during the CNS data sampling interval, LWLR is employed to fit the CNS data, and CKF is used to fuse the fitted CNS data with INS data. And the estimated error calculated using CKF is optimized by LSTM. Finally, the experimental results show that the proposed algorithm suppresses the divergence problem caused by different sampling-frequency and improves the attitude estimation accuracy of the navigation system.
Quality Assessment of Cookies Made from Composite Flours Containing Malted Barley Flour and Wheat Flour

Wheat-based short-dough cookies are considered low nutritional value foods because their recipes are high in fat and sugar. The aim of this study was to investigate the effects of replacing part of the wheat flour (WF) with different types of malted barley flour (MBF), while reducing sucrose in the recipe, in order to produce cookies with increased nutritional value, enhanced functional properties, and acceptable technological and sensory characteristics. Three types of brewer’s MBF (Pilsen, Amber, and Black) were used to replace WF in amounts of 20, 40, and 60%, while simultaneously reducing the addition of sucrose. Sucrose was added at levels of 66.6, 33.3, and 0% of the original standard recipe. MBF mitigated the effects of the reduced sucrose addition, likely due to its own high sugar content derived from barley malt. Snapping force determined with a texture analyzer decreased proportionally to sucrose reduction and MBF addition, indicating a softer texture of the cookies. MBF significantly increased the total phenolic content (TPC) and antioxidant activity (AOA) of the cookies. The results of the sensory analysis showed that cookies with Pilsen MBF and Amber MBF had a pleasantly sweet and rich flavor, while the addition of Black MBF produced an exaggerated bitter flavor and a nutty roasted aroma. The results suggest that different types of brewer’s MBF can be successfully used to produce functional cookies with reduced sucrose addition.
Event-Triggered Consensus on Matrix-weighted Networks

In this work, we consider the consensus problem of the multi-agent system over matrix-weighted networks using an event-triggered mechanism. An event-triggered coordination strategy is proposed to steer this generalized category of multi-agent systems towards the bipartite consensus state. The triggering condition is periodically checked and distributed in the sense that the event detector only uses neighbors’ information and local computation at discrete sampling instants. Moreover, it is shown that the triggering condition is closely related to the spectrum of matrix-valued weights corresponding to the edges in the matrix-weighted network. In the meantime, the Zeno phenomenon can be avoided automatically under the proposed event trigger approach.
An open-source natural language processing toolkit to support software development: addressing automatic bug detection, code summarisation and code search

This paper aims to introduce the innovative work carried out in the Horizon 2020 DECODER project – acronym for “DEveloper COmpanion for Documented and annotatEd code Reference” – (Grant Agreement no. 824231) by linking the fields of natural language processing (NLP) and software engineering. The project as a whole addresses the development of a framework, namely the Persistent Knowledge Monitor (PKM), that acts as a central infrastructure to store, access, and trace all the data, information and knowledge related to a given software or ecosystem. This meta-model defines the knowledge base that can be queried and analysed by all the tools integrated and developed in DECODER. Besides, the DECODER project offers a friendly user interface where each of the predefined three roles (i.e., developers, maintainers and reviewers) can access and query the PKM with their personal accounts. The paper focuses on the NLP tools developed and integrated in the PKM, namely the deep learning models developed to perform variable misuse, code summarisation and semantic parsing. These were developed under a common work package – “Activities for the developer” – intended to precisely target developers, who can perform tasks such as detection of bugs, automatic generation of documentation for source code and generation of code snippets from natural languages instructions, among the multiple functionalities that DECODER offers. These tools assist and help the developers in the daily work, by increasing their productivity and avoiding loss of time in tedious tasks such as manual bug detection. Training and validation were conducted for four use cases in Java, C and C++ programming languages in order to evaluate the performance, suitability, usability, etc. of the developed tools.
Moving Objects Tracking Based on Geometric Model-Free Approach With Particle Filter Using Automotive LiDAR

In this paper, we propose a Geometric Model-Free Approach with a Particle Filter (GMFA-PF) through the use of automotive LiDAR for real-time tracking of moving objects within an urban driving environment. GMFA-PF proved to be lightweight, capable of finishing the process within the sensing period of the LiDAR on a single CPU. The proposed GMFA-PF tracks and estimates moving objects without any assumptions on the geometry of the target. This approach enables efficient tracking of multiple object classes, with robustness to a sparse point cloud. Point cloud on moving objects is classified via the predicted Static Obstacle Map (STOM). A likelihood field is generated through the classified point cloud and is used in particle filtering to estimate the moving object’s pose, shape, and speed. Quantitative and qualitative comparisons - with Geometric Model-Based Tracking (MBT), Deep Neural Network (DNN), and GMFA - are performed for GMFA-PF using urban driving and scenario driving data gathered on an autonomous vehicle fitted with close-to-market sensors. The proposed approach shows robust tracking and accurate estimation performance in both sparse and dense point clouds; GMFA-PF achieves improved tracking performance in dense traffic and reduces yaw estimation delay compared to the others. Autonomous vehicles with GMFA-PF demonstrated auto-nomous driving on urban roads.
AVR-AKG: using virtual reality for domain knowledge generation from first-person demonstration

Manipulation tasks in specific environment, such as truss assembly in the space and "Hape" building blocks assembly in the toy room, unfold intentionally under corresponding domains, which contains a lot of domain knowledge . Being able to process contextual knowledge in these activities under corresponding domains over time can help us understand manipulation intentions . However, most researchers use methods of machine learning to make robots understand manipulation scenarios , which is a black model consuming a lot of computing resources. Moreover, the demonstration of assembly tasks in the real world is time-consuming and labor-intensive, and it is not suitable to initialize the assembly scene. To overcome these limitations, we introduced AVR-AKG: an implementing framework for domain knowledge generation that can generate dynamic knowledge graph in real time from assembly demonstrations. A combination of an Assembly Virtual Reality subsystem and an Assembly Knowledge Generation subsystem, in correspondence with assembly tasks and knowledge generation, is used to represent robot manipulation knowledge with Entity-Relation- Entity (E-R-E) and Entity-Attribute-Value (E-A-V) tuples. Using this framework, we propose a case study in which the demonstrator completes an assembly task using "Hape" building blocks, generating knowledge related to the operational context with knowledge graphs during first-person demonstration.
Buckwheat and Amaranth as Raw Materials for Brewing, a Review

Globally, beer is considered the most-consumed low-alcohol beverage, it ranks third, after water and tea, in the top sales of these drinks. New types of beer are the result of the influence of several factors, including innovations in science and technology, changing requirements for food consumption of the population, competition between producers, promotion of food for health, flavor, and quality, the limited nature of traditional food resource raw materials, and the interest of producers in reducing production costs. Manufacturers are looking for new solutions for obtaining products that meet the requirements of consumers, authentic products of superior quality, with distinctive taste and aroma. This review proposes the use of two pseudocereals as raw materials in the manufacture of beer: buckwheat and amaranth, focusing on the characteristics that recommend them in this regard. Due to their functional and nutraceutical properties, these pseudocereals can improve the quality of beer—a finished product. Additionally, all types of beer obtained from these pseudocereals are recommended for diets with particular nutritional requirements, especially gluten-free diets. Researchers and producers will continue to improve and optimize the sensory and technological properties of the new types of beer obtained from these pseudocereals.
Cooperative Differential Graphical Game Theoretic for Tracking Control of Nonlinear Multi-Agent Systems With Unknown Dynamics

This paper investigates the cooperative tracking control problem of networked nonlinear multi-agent systems (MASs) with completely unknown dynamics. By formulating the optimal tracking control problem into a cooperative differential graphical game, we can employ the off-policy integral reinforcement learning (IRL) scheme to find optimal tracking controllers online along with the system trajectories without requiring the knowledge of the system dynamics. In contrast to the existing literature where the Nash equilibrium is utilized to characterize the performance of the designed controllers for the cooperative control of MASs, we introduce a new solution concept regarded as Pareto optimality strategies which devote to minimize performance cost and risk of all agents simultaneously. A simulation example is presented to verify the effectiveness of the proposed approach.
Lactate Dehydrogenase Enzyme Level in Women with Severe Preeclampsia: A Cross-sectional Study

Aim: To determine the frequency of raised lactate dehydrogenase enzyme in women with severe preeclampsia. Study Design: Cross-sectional study. Place and Duration: Obstetrics and Gynecology department at Liaquat University Hospital Hyderabad from June 2018 to December 2018. Methodology: There were 141 primiparous women with severe preeclampsia, included in this study. After taking history regarding hypertension and obstetrical examination, patients were subjected to a relevant investigation that is, 5ml of blood sent to an institutional laboratory for the measurement of lactate dehydrogenase enzyme, if it is >400 U/L then it was labeled as raised. Results: The frequency of raised lactate dehydrogenase enzyme (400 U/L) was 13.48% (19/141) women with severe preeclampsia. Conclusion: About 14% of women having Pre-eclampsia had raised lactate dehydrogenase enzyme. Severe preeclampsia is associated with severe multisystemic involvement. Raised LDH levels are indicative of cellular damage and severe hypertension in pregnancy.
Preclinical Models of Brain Metastases in Breast Cancer

Breast cancer remains a leading cause of mortality among women worldwide. Brain metastases confer extremely poor prognosis due to a lack of understanding of their specific biology, unique physiologic and anatomic features of the brain, and limited treatment strategies. A major roadblock in advancing the treatment of breast cancer brain metastases (BCBM) is the scarcity of representative experimental preclinical models. Current models are predominantly based on the use of animal xenograft models with immortalized breast cancer cell lines that poorly capture the disease’s heterogeneity. Recent years have witnessed the development of patient-derived in vitro and in vivo breast cancer culturing systems that more closely recapitulate the biology from individual patients. These advances led to the development of modern patient-tissue-based experimental models for BCBM. The success of preclinical models is also based on the imaging technologies used to detect metastases. Advances in animal brain imaging, including cellular MRI and multimodality imaging, allow sensitive and specific detection of brain metastases and monitoring treatment responses. These imaging technologies, together with novel translational breast cancer models based on patient-derived cancer tissues, represent a unique opportunity to advance our understanding of brain metastases biology and develop novel treatment approaches. This review discusses the state-of-the-art knowledge in preclinical models of this disease.
Microwave vortex-beam generator based on corrugated metal–insulator–metal ground supported spoof surface plasmon polaritons

The vortex beam is an electromagnetic wave that carries orbital angular momentum (OAM). It has an infinite variety of orthogonal modes and provides a new method for increasing wireless communication channels and improving the resolution of radar imaging, therefore it has attracted more attention in recent years. In this paper, a vortex-beam generator is proposed, whose overall structure is made up of a series of circular radiation patches placed on a dielectric substrate and a ground plane, and the ground is composed of corrugated metal–insulator–metal (CMIM). The circular patches are radiating units that can also generate the phase shifts needed for the vortex beam, they are placed in different layers with the spoof surface plasmon polaritons (SSPPs) waveguide to enhance the coupled electric field. The CMIM structure functions both as a waveguide of SSPPs and as the ground plane. Reasonable design, numerical simulation, and experimental measurement show that the generator can generate left and right circularly polarized vortex beams with different OAM modes at different frequencies without changing the structure. And, the generator has many advantages, such as single feed, unidirectional, multi-mode, and small size, and it can be integrated into the system and integrated circuits more easily.
Screw-besed Adaptive Fuzzy Sliding-mode Control for Rigid Manipulator Subject to Uncertain Nonlinear Dynamics

The kinematics and dynamics with multiple degrees of freedom(multi-DOF) based on screw theory are established in this paper. The iterative forward and inverse algorithms of the twist and wrench between adjacent joints are completed by using the Lie bracket. At the same time, the exponential product mapping of the screw motion of rigid bodies and the Jacobian matrix of robots are expressed by Lie group and Lie algebra. Moreover, the recursive algorithm and the closed form of the dynamic equation are derived. The multi-DOF manipulator body and its working environment will cause the system to have a certain kind of interference and uncertainty of model parameters. However, The manufacturing error of the manipulator, as well as the disturbance of the external environment, will lead to the uncertainty of the ideal model, which leads to the loss of control accuracy. To this end, an adaptive sliding mode tracking control is designed for improving the control accuracy, in which a fuzzy approximation is employed for estimating the nonlinear uncertainty in the plant. On this basis, a fuzzy-based adaptive sliding mode algorithm is developed to ensure the closed-loop stability and finite-time convergence of the controlled manipulator. The control performance in position and velocity have been demonstrated through tracking errors in the simulation platform which takes a 6-R open-chain manipulator as a simulation model.
Somatization Mediates the Structural Relationship of Alexithymia with Anxiety and Depression

Objective: The study of factors affecting anxiety and depression as the most common emotional disorders has always been at the forefront of psychological research. Among different factors, alexithymia and somatization have considerable importance due to their emotional nature with makes them more integrated with anxiety and depression. Several studies have demonstrated a link between these four concepts, but as far as we know, the quality of the relationship has not been addressed yet. The present paper aims to investigate the mediating role of somatization in the structural relationship of alexithymia with anxiety and depression. Method : A total of 334 college students were recruited through cluster sampling and were asked to complete the Toronto Alexithymia Scale (TAS), Beck Depression Inventory – Second Edition (BDI-II), Beck Anxiety Inventory (BAI), and Somatization Subscale from the Symptom Checklist-90-Revised Questionnaire. Data were analyzed using correlational as well as structural equation modeling. Results: Based on the correlation analysis, there was significant relationship between alexithymia, somatization, anxiety, and depression. According to the results of regression weights, there is a moderate relationship between alexithymia and somatization (regression weight = 0.44). The relationship between somatization and depression is at moderate level (regression weight = 0.42) and the relationship of somatization with anxiety is at strong level (regression weight = 0.85). the goodness of fit indices for the hypothetical model showed significant coefficients at P < 0.05 (CFI = 0.98, RMSEA = 0.059). Conclusion: Findings indicated the important and influential role of somatization in explaining the relationship of alexithymia with anxiety and depression. Therefore, it seems that emotional components such as difficulty in identifying and expressing emotions as well as regulating mood states are important in the psychopathology of emotional disorders.
High Dimensional Factor Models with an Application to Mutual Fund Characteristics

This paper considers extensions of 2-dimensional factor models to higher-dimension data that can be represented as tensors. I describe decompositions of tensors that generalize the standard matrix singular value decomposition and principal component analysis to higher dimensions. I estimate the model using a 3-dimensional data set consisting of 25 characteristics of 1,342 mutual funds observed over 34 quarters. The tensor factor models reduce the data dimensionality by 97% while capturing 93% of the variation of the data. I relate higher-dimensional tensor models to standard 2-dimensional model and show that the components of the model have clear economic interpretations.
Platelets Purification Is a Crucial Step for Transcriptomic Analysis

Platelets are small anucleate cells derived from the fragmentation of megakaryocytes and are involved in different biological processes especially hemostasis, thrombosis, and immune response. Despite their lack of nucleus, platelets contain a reservoir of megakaryocyte-derived RNAs and all the machinery useful for mRNA translation. Interestingly, platelet transcriptome was analyzed in health and diseases and led to the identification of disease-specific molecular signatures. Platelet contamination by leukocytes and erythrocytes during platelet purification is a major problem in transcriptomic analysis and the presence of few contaminants in platelet preparation could strongly alter transcriptome results. Since contaminant impacts on platelet transcriptome remains theoretical, we aimed to determine whether low leukocyte and erythrocyte contamination could cause great or only minor changes in platelet transcriptome. Using microarray technique, we compared the transcriptome of platelets from the same donor, purified by common centrifugation method or using magnetic microbeads to eliminate contaminating cells. We found that platelet transcriptome was greatly altered by contaminants, as the relative amount of 8274 transcripts was different between compared samples. We observed an increase of transcripts related to leukocytes and erythrocytes in platelet purified without microbeads, while platelet specific transcripts were falsely reduced. In conclusion, serious precautions should be taken during platelet purification process for transcriptomic analysis, in order to avoid platelets contamination and result alteration.