Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigations of the Influence of Nano-Admixtures on Early Hydration and Selected Properties of Calcium Aluminate Cement Paste

In this work, the hydration of calcium aluminate cement (CAC, Al2O3 ≥ 70%) paste with nano admixtures (0, 0.05%, 0.1% and 0.2%) of nano-silica (NS) and carbon nano-cones (NC) when W/CAC = 0.35 was investigated. The methods of calorimetry, thermal analysis, X-ray diffraction (XRD), IR spectroscopy, and scanning electron microscopy (SEM) were used. In addition, the physical and mechanical properties of hardened cement pastes were determined after 3 days of hardening. NS was found to shorten the induction period of CAC hydration and accelerate the time of the secondary heat release effect, especially in the specimens with the highest NS content. The incorporation of NC (up to 0.2%) slows down the hydration process. After 3 days of hydration, the formation of hydration products, such as C2AH8, CAH10, C3AH6 and AH3 hydrates, was observed in CAC pastes, however, the quantitative compositions were different depending on the kind of nano admixture and its amount. SEM results obtained show differences in the effect of NS and NC on the formation of the structure of cement paste during its hardening. Significant changes in CAC paste microstructure were caused by the addition of NS and NC admixtures. Compressive strength was found to increase with the increase of NS and the optimal NS content was found to be 0.10 wt. %. The modification of the cement paste with an NS admixture results in a higher amount of hydrates, lower total porosity, and a higher amount of the smallest pores in the microstructure of the sample. NS and NC influence the hydration behaviour of CAC in different ways, which causes characteristic changes in the microstructure and properties of hardened samples.
Path-Tree Optimization in Discrete Partially Observable Environments Using Rapidly-Exploring Belief-Space Graphs

Robots often need to solve path planning problems where essential and discrete aspects of the environment are partially observable. This introduces a multi-modality, where the robot must be able to observe and infer the state of its environment. To tackle this problem, we introduce the Path-Tree Optimization (PTO) algorithm which plans a path-tree in belief-space. A path-tree is a tree-like motion with branching points where the robot receives an observation leading to a belief-state update. The robot takes different branches depending on the observation received. The algorithm assumes a deterministic observation model and is composed of three main steps. First, a rapidly-exploring random graph (RRG) on the state space is grown. Second, the RRG is expanded to a belief-space graph by querying the observation model. In a third step, dynamic programming is performed on the belief-space graph to extract a path-tree. The resulting path-tree combines exploration with exploitation i.e. it balances the need for gaining knowledge about the environment with the need for reaching the goal. We demonstrate the algorithm capabilities on navigation and mobile manipulation tasks, and show its advantage over a baseline using a task and motion planning approach (TAMP) both in terms of optimality and runtime.
Efficiency Through GPU-based Co-Evolution of Control and Pose in Evolutionary Robotics

A key challenge in evolutionary robotics is the computational cost of evolutionary runs. The high computational cost forces researchers to rely on power-hungry computer clusters and, even with these, researchers often are faced with long evaluation cycles that make development of evolutionary experiments a time consuming and tedious effort. In this paper we address this challenge on two fronts. We have developed an evolutionary robotic engine where all individuals are evaluated in parallel using a thread-based implementation on a graphical processing unit (GPU). This engine allows us to run an evolutionary robotics experiment in seconds on a modest laptop. The second avenue of exploration is that we have used this engine to study the role of initial robot poses in fitness evaluation. We find that if we co-evolve initial pose and controller competitively, we can reduce the evaluation period of individuals significantly. Combined the evolutionary robotics engine and the co-evolutionary approach are significant demonstrations of how to make evolutionary robotics more computationally efficient.
The Lived Experience of Postpartum Anxiety During COVID-19: A Hermeneutic Phenomenological Study

The experience of pregnancy and postpartum anxiety disorders results in adverse birth outcomes and the disrupted development of infants and children. Since the COVID-19 Pandemic, the Centers for Disease Control and Prevention (CDC) has designated pregnant and postpartum women as more vulnerable to COVID-19 (CDC, 2021), and perinatal mood and anxiety disorders rates have increased. However, research regarding the lived experience of women with postpartum anxiety (PPA) during a global pandemic remains lacking. Using van Manen’s hermeneutic phenomenological research method, we interviewed eight women self-identifying as having had PPA during the COVID-19 pandemic. Analysis revealed five themes describing the lived experience of PPA during COVID-19: Wired, Trapped, Lost in Time, No Safety Net, and Doubting Myself. The lived experience of PPA was both mirrored and masked by the lived experience of a global pandemic, exacerbating PPA due to the unknown and constricting nature of the pandemic. These findings suggest the need for future research to include subjective human experiences as pivotal components in creating support practices and a deeper understanding of PPA in the context of unprecedented life events.
A Prior Level Fusion Approach for the Semantic Segmentation of 3D Point Clouds Using Deep Learning

Three-dimensional digital models play a pivotal role in city planning, monitoring, and sustainable management of smart and Digital Twin Cities (DTCs). In this context, semantic segmentation of airborne 3D point clouds is crucial for modeling, simulating, and understanding large-scale urban environments. Previous research studies have demonstrated that the performance of 3D semantic segmentation can be improved by fusing 3D point clouds and other data sources. In this paper, a new prior-level fusion approach is proposed for semantic segmentation of large-scale urban areas using optical images and point clouds. The proposed approach uses image classification obtained by the Maximum Likelihood Classifier as the prior knowledge for 3D semantic segmentation. Afterwards, the raster values from classified images are assigned to Lidar point clouds at the data preparation step. Finally, an advanced Deep Learning model (RandLaNet) is adopted to perform the 3D semantic segmentation. The results show that the proposed approach provides good results in terms of both evaluation metrics and visual examination with a higher Intersection over Union (96%) on the created dataset, compared with (92%) for the non-fusion approach.
Protein Intake and Sarcopenia in Older Adults: A Systematic Review and Meta-Analysis

Background: The present systematic review and meta-analysis investigated the cross-sectional and longitudinal associations between protein intake and sarcopenia in older adults. Methods: Observational studies that investigated the association between protein intake and sarcopenia as the primary or secondary outcome in people aged 60 years and older were included. Studies published in languages other than English, Italian, Portuguese, and Spanish were excluded. Studies were retrieved from MEDLINE, SCOPUS, EMBASE, CINAHL, AgeLine, and Food Science Source databases through January 31, 2022. A pooled effect size was calculated based on standard mean differences. Results: Five cross-sectional studies, one longitudinal study, and one case-control study that investigated 3353 community-dwelling older adults with a mean age of approximately 73 years were included. The meta-analysis of four studies indicated that older adults with sarcopenia consumed significantly less protein than their peers with no sarcopenia. Conclusions: Results of the present study suggest that an inadequate protein intake might be associated with sarcopenia in older adults.
Design and Experiment of a Prescribed-Time Trajectory Tracking Controller for a 7-DOF Robot

We present an analytical design and experimental verification of trajectory tracking control of a 7-DOF robot manipulator, which achieves convergence of all tracking errors to the origin within a finite terminal time, also referred as the “settling time. ” A key feature of this control strategy is that the settling time is explicitly assigned by the control designer to a value desired, or “prescribed” by the user, and that the settling time is independent of the initial conditions and of the reference signal. In order to achieve this beneficial property with the controller, a scaling of the state by a function of time that grows unbounded towards the terminal time is employed. Through Lyapunov analysis, we first demonstrate that the proposed controller achieves regulation of all tracking errors within the prescribed time as well as the uniform boundedness of the joint torques, even in the presence of a matched, non-vanishing disturbance. Then, through both simulation and experiment, we demonstrate that the proposed controller is capable of converging to the desired trajectory within the prescribed time, despite large distance between the initial conditions and the reference trajectory, i.e., in spite of large initial tracking errors, and in spite of a sinusoidal disturbance being applied in each joint.
On Generalized Quasilikelihood Inference in Longitudinal Mixed Model for Count Data

In longitudinal studies for count data, a small number of repeated count responses along with a set of covariates are collected from a large number of independent individuals. In this set up, it is traditionally assumed that the repeated responses are serially correlated. In this paper, we consider an extended model where it is assumed that the repeated responses are also in∞uenced by an individual random efiect causing the repeated responses to be overdispersed as well as serially correlated. Through a simulation study, we examine the performance of the generalized quasilikelihood (GQL) estimation approach in estimating the regression efiects of the time dependent covariates on the responses, the overdispersion parameter, as well as the serial correlation parameter. Various misspeciflcation efiects such as efiects of ignoring the serial correlations, overdispersion and non-stationarity in serial correlations, are also studied. The estimation methodology is illustrated by analysing a real data on health care utilization. AMS (2000) subject classiflcation. Primary 62H12; Secondary 62F12, 62P10.
Reconstructing Superquadrics from Intensity and Color Images

The task of reconstructing 3D scenes based on visual data represents a longstanding problem in computer vision. Common reconstruction approaches rely on the use of multiple volumetric primitives to describe complex objects. Superquadrics (a class of volumetric primitives) have shown great promise due to their ability to describe various shapes with only a few parameters. Recent research has shown that deep learning methods can be used to accurately reconstruct random superquadrics from both 3D point cloud data and simple depth images. In this paper, we extended these reconstruction methods to intensity and color images. Specifically, we used a dedicated convolutional neural network (CNN) model to reconstruct a single superquadric from the given input image. We analyzed the results in a qualitative and quantitative manner, by visualizing reconstructed superquadrics as well as observing error and accuracy distributions of predictions. We showed that a CNN model designed around a simple ResNet backbone can be used to accurately reconstruct superquadrics from images containing one object, but only if one of the spatial parameters is fixed or if it can be determined from other image characteristics, e.g., shadows. Furthermore, we experimented with images of increasing complexity, for example, by adding textures, and observed that the results degraded only slightly. In addition, we show that our model outperforms the current state-of-the-art method on the studied task. Our final result is a highly accurate superquadric reconstruction model, which can also reconstruct superquadrics from real images of simple objects, without additional training.
Eating ourselves to death: How food is a drug and what food abuse costs

The objective of this paper is to examine definitions of the terms “drug,” “drug use, “drug abuse,” and “addiction” to determine if the most commonly consumed foods in America are similar or consistent with drug use, abuse, and addiction. The methodology consists of reviewing published studies in the “food addiction” literature to determine if any consensus on the issue is achieved. Further, the author analyzes research on harms (including illness, death, medical costs and productivity losses) of illicit drugs, licit drugs, and foods. The author finds evidence that some food consumption is consistent with drug use and drug abuse, and that food addiction is real. Further, the harms caused by the unhealthy foods we eat dwarf those caused by crime and illegal drugs combined. Based on the data, the author suggests we reprioritize what we believe to be dangerous and “criminal” as well as rethink the “war on drugs. ”
A Review of Woven Tracheal Stents: Materials, Structures, and Application

The repair and reconstruction of tracheal defects is a challenging clinical problem. Due to the wide choice of materials and structures, weaving technology has shown unique advantages in simulating the multilayer structure of the trachea and providing reliable performance. Currently, most woven stent-based stents focus only on the effect of materials on stent performance while ignoring the direct effect of woven process parameters on stent performance, and the advantages of weaving technology in tissue regeneration have not been fully exploited. Therefore, this review will introduce the effects of stent materials and fabric construction on the performance of tracheal stents, focusing on the effects of weaving process parameters on stent performance. We will summarize the problems faced by woven stents and possible directions of development in the hope of broadening the technical field of artificial trachea preparation.
Towards Better Dependency Management: A First Look at Dependency Smells in Python Projects

Managing cross-project dependencies is tricky in modern software development. A primary way to manage dependencies is using dependency configuration files, which brings convenience to the entire software ecosystem, including developers, maintainers, and users. However, developers may introduce dependency smells if dependency configuration files are not well written and maintained. Dependency smells are recurring violations of dependency management in dependency configuration files and can potentially lead to severe consequences. This paper provides an in-depth look at three dependency smells, namely, Missing Dependency, Bloated Dependency, and Version Constraint Inconsistency in Python projects. First, we implement a tool called Python Cross-project Dependency- PyCD to accurately extract dependency information from configuration files. The evaluation result on 212 Python projects shows that PyCD outperforms state-of-the-art tools. Then, we make an empirical study for three dependency smells in 132 Python projects to investigate the pervasiveness, causes, and evolution. The results show that: 1) dependency smells are prevalent in Python projects and exist inconsistently in different projects; 2) dependency smells are introduced into Python projects for different reasons, mainly due to the problems of synchronous update and collaborative development; and 3) dependency smells can be removed with different patterns according to different dependency smells. Furthermore, we report and get responses for 40 harmful dependency smell instances, 34 of which have been responded that these dependency smells do exist in the projects, and 10 instances are fixed or under process. The feedback from developers indicates that dependency smells can have a negative impact on project maintenance. Our study highlights that these dependency smells deserve the attention of developers.
Supporting the Royal Australian Navy's Campaign Plan for Robotics and Autonomous Systems: Human-Machine Teaming and the Future Workforce

The authors draw on a review of relevant open-source academic and grey literature, with a focus on identifying possible lessons for the RAN. The analysis concentrated on the overall impacts of RAS-AI on the Defence workforce and skills, with a particular focus on the implications of human-machine teaming (HMT) for the Defence workforce. The findings underscore the fundamentally different and novel way of working required to effectively adopt HMT. Integration of HMT into the workforce will require flexible management of complex personnel networks and continuous adaptation of existing structures and concepts. This report is a continuation of work conducted in support of the RAN’s RAS-AI Strategy 2040, released in 2020. RAND Australia was asked to provide policy analysis and advice to support development of an actionable RAS-AI Campaign Plan that could assist RAS-AI implementation efforts. The research team has examined three specific areas to support development of an actionable plan: military innovation, a missions and technology assessment for maritime RAS-AI, and HMT. This work should inform the RAN, other Australian Defence services, and Defence more broadly about the implications of HMT for the RAN’s future workforce.
Specific Targeting of Antiapoptotic Bcl-2 Proteins as a Radiosensitizing Approach in Solid Tumors

Avoidance of therapy-induced apoptosis is a hallmark of acquired resistance towards radiotherapy. Thus, breaking resistance still challenges modern cancer therapy. The Bcl-2 protein family is known for its regulatory role in apoptosis signaling, making Bcl-2, Mcl-1 and Bcl-xL promising targets. This study evaluates the effects of highly specific inhibitors for Bcl-xL (WEHI-539), Bcl-2 (ABT-199) and Mcl-1 (S63845) as radiosensitizers. Covering a broad spectrum of solid tumors, Non-Small-Cell Lung Cancer (NSCLC), Head and Neck Squamous Cell Carcinoma (HNSCC) and synovial sarcoma cell lines were exposed to fractionated radiation as standard therapy with or without Bcl-2 protein inhibition. Protein expression was detected by Western blot and cell death was assessed by flow cytometry measuring apoptosis. In contrast to NSCLC, a high level of Bcl-xL and its upregulation during radiotherapy indicated radioresistance in HNSCC and synovial sarcoma. Radioresistant cell lines across all entities benefited synergistically from combined therapy with Bcl-xL inhibition and fractionated radiation. In NSCLC cell lines, Mcl-1 inhibition significantly augmented radiotherapy independent of the expression level. Our data suggest that among antiapoptotic Bcl-2 proteins, targeting Bcl-xL may break resistance to radiation in HNSCC, synovial sarcoma and NSCLC in vitro. In NSCLC, Mcl-1 might be a promising target that needs further investigation.
Weak Inversion Model of an Inverting CMOS Schmitt Trigger

In this article the subthreshold characteristics of an inverting single input CMOS Schmitt trigger circuit are analyzed. Analytical expressions for the low-to-high and high-to-low hysteresis transition voltages are determined. The analytical model provides physical insight into the circuit behavior. The derived expressions are linearly dependent on the supply voltage and the temperature, and logarithmically dependent on the dimensions of the transistors. Simulation results validated the proposed model, with a maximum error between the analytical and simulated transition points smaller than 14mV. An ASIC in AMS 0. 35μm CMOS process has been fabricated to experimentally validate the derived expressions. The maximum error between the analytical and measured transition points is below 36mV.
Monitorización y periodización del rendimiento desde la fisioterapia deportiva ¿Hacia dónde vamos?

RESUMEN Introducción El concepto de monitorización y periodización es un visión que se ha desarrollado en la última década, donde se busca que se pueda tener un control de la carga interna y externa en los deportistas de distintas disciplinas deportivas producidas por el proceso de interacción con variables de prescripción de ejercicio que causan modificaciones a nivel fisiológico, bioquímico, biomecanico, muscular, neuromuscular ocasionando la tensión en los subsistemas del movimiento corporal humano generando adaptaciones especificas ante una carga determinada la cual puede ser medida mediante el uso de tecnología especifica o test indirectos Metodología Revisión de la literatura con la combinación de palabras clave como Monitoring, Performance, sports training load, Periodization en bases de datos como Pubmed, Ebsco, Medline, Scopus, Science Direct Resultados se pudo identificar 65 artículos que referencian la existencia de herramientas tecnológicas para realizar un proceso de monitorización y periodización desde la rehabilitación , prevención, control de carga, recuperación y readaptación deportiva que permiten la generación de datos estadísticos y crear perfiles desde cada área de actuación del fisioterapeuta deportivo. Conclusión la fisioterapia es una profesión encargada de muchos procesos deportivos que deben ser monitorizados y generen datos que permitan estandarizar procesos, crear perfiles de seguimiento específicos para facilitar la toma de decisiones desde el equipo biomédico e investigaciones en el deporte de alto rendimiento fusionando la practica con la evidencia científica.
Genome-Wide Study of Drug Resistant Mycobacterium tuberculosis and Its Intra-Host Evolution during Treatment

The emergence of drug resistant Mycobacterium tuberculosis (MTB) strains has become a global public health problem, while, at the same time, there has been development of new antimicrobial agents. The main goals of this study were to determine new variants associated with drug resistance in MTB and to observe which polymorphisms emerge in MTB genomes after anti-tuberculosis treatment. We performed whole-genome sequencing of 152 MTB isolates including 70 isolates as 32 series of pre- and post-treatment MTB. Based on genotypes and phenotypic drug susceptibility, we conducted phylogenetic convergence-based genome-wide association study (GWAS) with streptomycin-, isoniazid-, rifampicin-, ethambutol-, fluoroquinolones-, and aminoglycosides-resistant MTB against susceptible ones. GWAS revealed statistically significant associations of SNPs within Rv2820c, cyp123 and indels in Rv1269c, Rv1907c, Rv1883c, Rv2407, Rv3785 genes with resistant MTB phenotypes. Comparisons of serial isolates showed that treatment induced different patterns of intra-host evolution. We found indels within Rv1435c and ppsA that were not lineage-specific. In addition, Beijing-specific polymorphisms within Rv0036c, Rv0678, Rv3433c, and dop genes were detected in post-treatment isolates. The appearance of Rv3785 frameshift insertion in 2 post-treatment strains compared to pre-treatment was also observed. We propose that the insertion within Rv3785, which was a GWAS hit, might affect cell wall biosynthesis and probably mediates a compensatory mechanism in response to treatment. These results may shed light on the mechanisms of MTB adaptation to chemotherapy and drug resistance formation.