Miracle-dz/UniFAR-FTUs huggingface dataset:
3D printing in aerospace and its long-term sustainability

ABSTRACT Astonishingly 3D printing has excited the world of aerospace. This paper takes stock of the popular 3D printing processes in aerospace. Reasons for their popularity over the traditional manufacturing processes are dwelled upon. Materials developed specially for aerospace applications along with their characteristics are discussed. Ongoing activities related to 3D printing at various companies and organisations around the world are looked into. Project works in the area of extra-terrestrial printing are also highlighted. Even though 3D printing processes are operationally simple, they do have limitations in terms of the type, quality, and quantity of the materials they can handle. This paper underlines these points while discussing drawbacks of the printed components. Challenges associated with 3D printing in microgravity are also touched upon. Finally, a glimpse is taken into the future appearance of aerospace industry with 3D printing.
Investigation of the interaction phenomena between foot and insole by means of a numerical approach

The biomechanical response of foot with regard to interaction phenomena occurring at interface with insole inserted in the shoe is investigated by using a numerical approach. At this purpose, a complex three-dimensional model of the foot is defined from biomedical images, while insole conformation is acquired by laser scanner technique, leading to the finite element discretization. Foot tissues are characterized by means of specific constitutive visco-hyperelastic anisotropic formulations, recalling previous research activity. The mechanical behavior of insole material is analyzed by means of experimental compression tests that highlight the geometric and material non-linear response, represented by a hyperelastic formulation, while a hyperfoam formulation is assumed for the sole. Numerical analysis considers the mid-stance phase of the gait and allows to interpret the response of overall foot, insole and sole system. The investigation represents a preliminary step of a parametric analysis addressed to support footwear design considering different configurations of components. As matter of example, the influence of variation in insole material properties is reported.
GIMAP7 as a Potential Predictive Marker for Pan-Cancer Prognosis and Immunotherapy Efficacy

Background GIMAP, a GTP enzyme of immune-related proteins, plays a crucial role in immune mechanisms. Investigating GIMAP7 expression in pan-cancer can provide efficient guidance for predicting clinical prognosis and identifying novel immunotherapy targets. Methods Gene expression in different tumour types and stages was analysed based on The Cancer Genome Atlas and the Genotype-Tissue Expression database. An immunohistochemical assay was used to explore the differences in GIMAP7 protein levels in different tumour types and stages. Further, the cBioPortal was used to obtain the genetic variation characteristics of GIMAP7. Kaplan–Meier analysis and multivariable Cox regression analysis were performed to assess the prognostic value of GIMAP7. The pathways affected by GIMAP7 were studied based on gene set enrichment analysis, and the correlation between GIMAP7 expression and immune infiltration was determined using the TIMER2 database and the CIBERSORT method. ESTIMATE was used to analyse the correlation between GIMAP7 expression and ESTIMATE, immune and stromal scores. In addition, the correlation between GIMAP7 and immunoregulators was analysed. Furthermore, tumour mutational burden and microsatellite instability were evaluated using Spearman correlation assay. Results GIMAP7 expression was significantly low and predicted better survival status in most tumour types. GIMAP7 was positively correlated with the abundance of CD8 + and CD4 + T cells in the tumour microenvironment. However, the high expression of GIMAP7 was negatively correlated with tumour mutations in uveal melanoma and colon adenocarcinoma. A correlation between GIMAP7 and microsatellite instability was found in rectal adenocarcinoma. Additionally, GIMAP7 presented a robust correlation between immune modulators and immunotherapeutic markers. Moreover, high GIMAP7 expression was significantly related to immune-relevant pathways. Conclusion This study suggests the potential role of GIMAP7 as a prognostic and immunotherapeutic marker in pan-cancer, laying the groundwork for prospective functional and mechanistic experiments and their impact in the clinical setting.
Exploitation of Noisy Automatic Data Annotation and Its Application to Hand Posture Classification

: The success of deep learning in recent years relies on the availability of large amounts of accurately annotated training data. In this work, we investigate a technique for utilizing automatically annotated data in classification problems. Using a small number of manually annotated samples, and a large set of data that feature automatically created, noisy labels, our approach trains a Convolutional Neural Network (CNN) in an iterative manner. The automatic annotations are combined with the predictions of the network in order to gradually expand the training set. In order to evaluate the performance of the proposed approach, we apply it to the problem of hand posture recognition from RGB images. We compare the results of training a CNN classifier with and without the use of our technique. Our method yields a significant increase in average classification accuracy, and also decreases the deviation in class accuracies, thus indicating the validity and the usefulness of the proposed approach.
Critically Ill vs. Non-Critically Ill Patients With COVID-19 Pneumonia: Clinical Features, Laboratory Findings, and Prediction

Objectives The objective of this study was to investigate the clinical features and laboratory findings of patients with and without critical COVID-19 pneumonia and identify predictors for the critical form of the disease. Methods Demographic, clinical, and laboratory data of 63 COVID-19 pneumonia patients were retrospectively reviewed. Laboratory parameters were also collected within 3–5 days, 7–9 days, and 11–14 days of hospitalization. Outcomes were followed up until March 12, 2020. Results Twenty-two patients developed critically ill pneumonia; one of them died. Upon admission, older patients with critical illness were more likely to report cough and dyspnoea with higher respiration rates and had a greater possibility of abnormal laboratory parameters than patients without critical illness. When compared with the non-critically ill patients, patients with serious illness had a lower discharge rate and longer hospital stays, with a trend towards higher mortality. The interleukin-6 level in patients upon hospital admission was important in predicting disease severity and was associated with the length of hospitalization. Conclusions Many differences in clinical features and laboratory findings were observed between patients exhibiting non-critically ill and critically ill COVID-19 pneumonia. Non-critically ill COVID-19 pneumonia also needs aggressive treatments. Interleukin-6 was a superior predictor of disease severity.
A Novel Current Injection Model of GCSC for Control and Damping of Power System Oscillations

The paper proposes the theory and the modeling technique of the new invention of the flexible AC transmission system (FACTS) device, i.e., Gate controlled series capacitor (GCSC) for power flow control of power transmission lines and damping of low-frequency oscillations. In this study, a current injection model of the GCSC is used for studying the effect of the GCSC on the low-frequency oscillations, which is incorporated in the transmission system model. To be sure of optimal adjustment of the applied damping controller, the particle swarm optimization algorithm is employed as an efficient heuristic optimizer to And out the near global optimum set of the damping controller parameters. The proposed model is applied to a damping controller design of a multi-machine power system. Detailed simulations are carried out with MATLAB/SIMULINK environment and the effect of the GCSC-based damping controller and the current injection model over the system stability is studied.
Combined Thermal Runaway Investigation of Coin Cells with an Accelerating Rate Calorimeter and a Tian-Calvet Calorimeter

Commercial coin cells with LiNi0.6Mn0. 2Co0.2O2 positive electrode material were investigated using an accelerating rate calorimeter and a Tian-Calvet calorimeter. After cycling and charging to the selected states of charge (SOCs), the cells were studied under thermal abuse conditions using the heat-wait-seek (HWS) method with the heating step of 5 K and a threshold for self-heating detection of 0.02 K/min. The onset temperature and the rate of the temperature rise, i.e., the self-heating rate for thermal runaway events, were determined. The morphology of the positive electrode, negative electrode and the separator of fresh and tested cells were compared and investigated with scanning electron microscopy (SEM). Furthermore, the microstructure and the chemical compositions of the individual components were investigated by X-ray diffraction (XRD) and inductively coupled plasma with optical emission spectrometry (ICP-OES), respectively. In the Tian-Calvet calorimeter, the coin cells with the selected SOCs and the individual components (positive electrode, negative electrode and separator) were heated up with a constant heating rate of 0.1 °C/min (ramp heating mode). Simultaneously, the heat flow signals were recorded to analyze the heat generation. The combination of the three different methods—the HWS method using the ES-ARC, ramp heating mode on both cells and the individual components using the Tian-Calvet calorimeter—together with a post-mortem analysis, give us a complete picture of the processes leading to thermal runaway.
Glycan processing in the Golgi as optimal information coding that constrains cisternal number and enzyme specificity

Many proteins that undergo sequential enzymatic modification in the Golgi cisternae are displayed at the plasma membrane as cell identity markers. The modified proteins, called glycans, represent a molecular code. The fidelity of this glycan code is measured by how accurately the glycan synthesis machinery realises the desired target glycan distribution for a particular cell type and niche. In this paper, we quantitatively analyse the tradeoffs between the number of cisternae and the number and specificity of enzymes, in order to synthesize a prescribed target glycan distribution of a certain complexity. We find that to synthesize complex distributions, such as those observed in real cells, one needs to have multiple cisternae and precise enzyme partitioning in the Golgi. Additionally, for fixed number of enzymes and cisternae, there is an optimal level of specificity of enzymes that achieves the target distribution with high fidelity. Our results show how the complexity of the target glycan distribution places functional constraints on the Golgi cisternal number and enzyme specificity.
Combined Experimental and Numerical Method for Loss Separation in Permanent-Magnet Brushless Machines

Permanent-magnet synchronous machines are a high-efficiency motion solution. As the efficiency bar is raised, it is no longer adequate to consider only the dominant core, dc conduction, and friction losses. The challenge for motor developers experiencing additional losses is determining which of the previously neglected losses is significant for their motor. This research presents a best-fit least-squares approach to loss separation that can be applied to experimental data to highlight the source of additional losses. This is a powerful tool for practical motor designers looking to meet challenging specifications. The proposed technique has been applied to the experimental data from three different prototype motors. Although a simple core model of core loss is used, in each case the losses were classified with a residual of less than 2%. Each loss type (defined by proportionality to speed and current) showed good agreement with the corresponding loss type calculated from finite-element analysis (FEA).
Unidentified Floating Object detection in maritime environment using dictionary learning

Maritime domain is one of the most challenging scenarios for object detection due to the complexity of the observed scene. In this article, we present a new approach to detect unidentified floating objects in the maritime environment. The proposed approach is capable of detecting floating objects without any prior knowledge of their visual appearance, shape or location. The input image from the video stream is denoised using a visual dictionary learned from a K-SVD algorithm. The denoised image is made of self-similar content. Later, we extract the residual image, which is the difference between the original image and the denoised (self-similar) image. Thus, the residual image contains noise and salient structures (objects). These salient structures can be extracted using an a contrario model. We demonstrate the capabilities of our algorithm by testing it on videos exhibiting varying maritime scenarios.
Positive Performance Feedback and Innovation Search: New Ideas for Sustainable Business Development

Although the literature suggests that firms tend to adopt “conservative” behavior in the face of positive performance feedback, there are also studies that take the opposite view on the impact of positive performance feedback. Based on the behavior theory of the firm and regulatory focus theory, this study explored the impact of positive performance feedback on innovation search behavior and the boundary effect of CEO regulatory focus to gain insight into the mechanisms of innovation search behavior and to promote innovation for sustainable development. Based on data from 230 biopharmaceutical companies in China, the analysis found that: (1) positive performance feedback had a significant positive effect on depth search behavior and a significant negative effect on breadth search behavior; (2) CEOs’ promotion focus had a negative moderating effect on the relationship between positive performance feedback and depth search behavior and a positive moderating effect on the relationship between positive performance feedback and breadth search behavior; and (3) CEOs’ prevention focus positively moderated the relationship between positive performance feedback in relation to depth search behavior. This study extends the behavior theory of the firm and reveals the mechanism of the differential impact of positive performance feedback on innovation search behavior, which has implications for the study of which innovation search practices should be conducted by high-performing firms to promote sustainable development.
Racial Disparities in Social Workers’ Licensing Rates

Objectives This study aimed at examining how racial disparities in social workers’ licensing rates are explained by the major sources of the disparities: racial differences in educational eligibilities, the field of practice, and demographic vulnerabilities of social workers. Methods Drawing a nationally representative sample of 3990 social workers from the Current Population Survey, this study tested how the probabilities of their licensing were explained by the major sources of racial disparities. Results For master’s level social workers, African American–White differences in the field of practice and the type of employers explained away the disparity in required licensing rates. The Hispanic–White and other race–White disparities were largely due to non-White social workers’ limited educational eligibilities and the states of residence that might have imposed varying occupational regulations. Conclusions As licensing disparities reflect socioeconomic injustice that confronts many non-White social workers, they call for more purposeful research and interventions within the profession.
Does revenue respond asymmetrically to the occupancy rate? Evidence from the Swedish hospitality industry

ABSTRACT This paper elucidates, does revenue per available room respond asymmetrically to the occupancy rate? The two-step nonlinear autoregressive distributed lag model is used on a Swedish dataset with monthly observations spreading over March 2008 to February 2020 to address this research question. Results of empirical analysis offer corroborative evidence that revenue per available room is asymmetrically related to the occupancy rate. Our results show that revenue per available room responds marginally more strongly to a decrease in occupancy rate than to the rise in occupancy rate. The paper ends with plausible implications for effective revenue management.
Clustering of Slovak Sentence Melody - Methods and Results

find typical “sentence (clause) melody contour” for each group of sentences characterised by the sentence type and by the number of words. Sentence types recognised by the TTS system are listed in Table 3. Proposed method obtains original sentence melody contours using software Praat [11]. Then weighted-MA method [4] defined in formula (1) is used to remove short-term melody variations and to obtained sentence melody contour values. The smoothed values are computed in equidistant time events along the whole sentence (clause) waveform. The number of melody contour values is set proportionally to the number of words (bars) in the sentence. The number is equal for all sentences in the same sentence group, which makes comparison of different length waveforms possible. Sentence melody contours are further centred to 0 Hz mean frequency value. Contour dynamics remains unchanged. Similar melodies are grouped into separate clusters (the best clustering method for this purpose is found). Cluster with melodies best suited for analysed sentence type is selected and typical melody of this cluster is recommended for our TTS system. Modern speech synthesis systems implement prosody features to achieve more naturally sounding voices, trying to produce sounds similar to the human speech. This article deals with obtaining one of prosody characteristics (sentence melody contour) from human speech recordings. Similarities of melody contours are studied using cluster analysis. Several clustering methods are evaluated for this purpose, estimation of proper number of clusters is described and typical melodies for different types of Slovak language sentences (declarative, interrogative, exclamatory, clauses with final “,” etc.) are found and recommended for implementation in our text-to-speech system.
The microenvironment—a general hypothesis on the homeostatic function of extracellular vesicles

Extracellular vesicles (EVs), exosomes and microvesicles, is a burgeoning field of biological and biomedical research that may change our understanding of cell communication in plants and animals while holding great promise for the diagnosis of disease and the development of therapeutics. However, the challenge remains to develop a general hypothesis about the role of EVs in physiological homeostasis and pathobiology across kingdoms. While they can act systemically, EVs are often seen to operate locally within a microenvironment. This microenvironment is built as a collection of microunits comprised of cells that interact with each other via EV exchange, EV signaling, EV seeding, and EV disposal. We propose that microunits are part of a larger matrix at the tissue level that collectively communicates with the surrounding environment, including other end‐organ systems. Herein, we offer a working model that encompasses the various facets of EV function in the context of the cell biology and physiology of multicellular organisms.
Generalized Quadrature Space-Frequency Index Modulation System With Low-Complexity Detection

In this article, we propose a generalized quadrature space-frequency index modulation (GQSFIM) system that maps the information bits to the subcarrier activation pattern (SAP), the two antenna activation patterns (AAPs), and the modulated symbols. Specifically, two independent AAPs are utilized for the modulated symbols in a GQSFIM subblock to get higher spectral efficiency. With the help of the union bounding technique, the closed-form average bit error probability (ABEP) expression of the GQSFIM system with the maximum-likelihood (ML) detection is derived. Additionally, two low-complexity step-by-step signal detection algorithms, local ML based minimum mean-squared error (MMSE) and local ordered block based MMSE, are provided to reduce the high computational complexity of the ML detection, where the SAP, the two AAPs, and the modulated symbols are detected separately. Numerical results verify the correctness and accuracy of the theoretical ABEP of the GQSFIM system, especially in the high signal-to-noise ratio region. Moreover, the proposed GQSFIM system achieves better spectral efficiency and ABEP performance compared with the typical space-frequency index modulation systems.
Functional EL-HN Fragment as a Potent Candidate Vaccine for the Prevention of Botulinum Neurotoxin Serotype E

Clostridium botulinum produces botulinum neurotoxin (BoNT), which is the most toxic known protein and the causative agent of human botulism. BoNTs have similar structures and functions, comprising three functional domains: catalytic domain (L), translocation domain (HN), and receptor-binding domain (Hc). In the present study, BoNT/E was selected as a model toxin to further explore the immunological significance of each domain. The EL-HN fragment (L and HN domains of BoNT/E) retained the enzymatic activity without in vivo neurotoxicity. Extensive investigations showed EL-HN functional fragment had the highest protective efficacy and contained some functional neutralizing epitopes. Further experiments demonstrated the EL-HN provided a superior protective effect compared with the EHc or EHc and EL-HN combination. Thus, the EL-HN played an important role in immune protection against BoNT/E and could provide an excellent platform for the design of botulinum vaccines and neutralizing antibodies. The EL-HN has the potential to replace EHc or toxoid as the optimal immunogen for the botulinum vaccine.
Generalizable Online 3D Pedestrian Tracking with Multiple Cameras

3D pedestrian tracking using multiple cameras is still a challenging task with many applications such as surveillance, behavioral analysis, statistical analysis, and more. Many of the existing tracking solutions involve training the algorithms on the target environment, which requires extensive time and effort. We propose an online 3D pedestrian tracking method for multi-camera environments based on a generalizable detection solution that does not require training with data of the target scene. We establish temporal relationships between people detected in different frames by using a combination of graph matching algorithm and Kalman filter. Our proposed method obtained a MOTA and MOTP of 77.1% and 96.4%, respectively on the test split of the public WILDTRACK dataset. Such results correspond to an improvement of approximately 3.4% and 22.2%, respectively, compared to the best existing online technique. Our experiments also demonstrate the advantages of using appearance information to improve the tracking performance.