Miracle-dz/UniFAR-FTUs huggingface dataset:
A study of the influence of AR on the perception, comprehension and projection levels of situation awareness

In this work, we examine how Augmented Reality (AR) impacts user's situation awareness (SA) on elements secondary to an AR-assisted main task, i.e. not directly concerned by the main task. These secondary elements can still provide relevant information that we do not want the user to miss. A good understanding of user's awareness about them is therefore interesting, especially in a context of a daily use of AR, in which not all elements of user's environment are controlled. In this regard, we measured SA about secondary elements in an industrial workshop where the AR-assisted main task is a pedestrian navigation. We compared SA between three navigation guidance conditions: a paper map, a virtual path, and a virtual path with virtual cues about secondary elements. These secondary elements were either hazardous areas, for example, for mandatory helmets, or items which could be on user's path, for example, misplaced carts, boxes… We adapted an existing SA method evaluation to a real-world environment. With this method, participants were queried about their SA on three levels: perception, comprehension and projection about different items. We found that the use of AR decreased user's SA about secondary elements, and that this degradation mainly occurs at the perception level: with AR, participants are less likely to detect secondary elements. Participants still felt the most secure with AR and virtual cues about secondary elements.
Toward a New Probabilistic Framework to Score and Merge Ground‐Motion Prediction Equations: The Case of the Italian Region

The ground‐motion prediction equation (GMPE) is a basic component for probabilistic seismic‐hazard analysis. There is a wide variety of GMPEs that are usually obtained by means of inversion techniques of datasets containing ground motions recorded at different stations. However, to date there is not yet a commonly accepted procedure to select the best GMPE for a specific case; usually, a set of GMPEs is implemented (more or less arbitrarily) in a logic‐tree structure, in which each GMPE is weighted by experts, mostly according to gut feeling. Here, we discuss a general probabilistic framework to numerically score and weight GMPEs, highlighting features, limitations, and approximations; finally, we put forward a numerical procedure to score GMPEs, taking into account their forecasting performances, and to merge them through an ensemble modeling. Then, we apply the procedure to the Italian territory; in addition to illustrating how the procedure works, we investigate other relevant aspects (such as the importance of the focal mechanism) of the GMPEs to different site conditions. Online Material: Figures showing regression analysis for peak ground acceleration (PGA) values and location map, and earthquake catalog and summary table of parameters corresponding to each ground‐motion prediction equation (GMPE) implemented.
[DC] Humans and Robots Improvise to Design-Fabricate in Virtual Reality

Robotics in the architectural field has been mainly used as a fabrication tool rather than a design tool. Recent research in the design-fabrication field, including my previous Ph.D. work, has demon-strated the potential for a human designer to co-create with a robotic arm for the craft that blends both parties' strengths, for example, the robot's accuracy and human sensitivity. In this position paper, I covered my theoretical buildup for the Collective Human-Robot Construction (CHRC) field, where VR/ AR/MR is located around the central area of related research topics. I go through two previ-ous projects (Block Play and Improv-Structure) that introduced the idea of improvisation into human-robot construction in the physical world. The theoretical and prototype explorations lead to my ongoing research to bring the improvisational human-robot construction setting into the virtual environment for faster, cheaper, and less risky co-design experiences. I describe the current progress and planned next steps, together with several questions to discuss at the Doctoral Consortium at IEEE VR 2023.
Comparison of Genetic Susceptibility to Coronary Heart Disease in the Hungarian Populations: Risk Prediction Models for Coronary Heart Disease

Background and Aim: It was evaluated whether the integration of genetic risk scores (GRS-unweighted, wGRS-weighted) into conventional risk factor (CRF) models for coronary heart disease or acute myocardial infarction (CHD/AMI) could improve the predictive ability of the models. Methods: Subjects and data collected in a previous survey were used to perform regression and ROC curve analyses as well as to examine the role of genetic components. Thirty SNPs were selected, and genotype and phenotype data were available for 558 participants (general: N = 279 and Roma: N = 279). Results: The mean GRS (27.27 ± 3.43 vs. 26.68 ± 3.51, p = 0.046) and wGRS (3.52 ± 0.68 vs. 3.33 ± 0.62, p = 0.001) were significantly higher in the general population. The addition of the wGRS to the CRF model yielded the strongest improvement in discrimination among Roma (from 0.8616 to 0.8674), while the addition of GRS to the CRF model yielded the strongest improvement in discrimination in the general population (from 0.8149 to 0.8160). In addition to that, the Roma individuals were likely to develop CHD/AMI at a younger age than subjects in the general population. Conclusions: The combination of the CRFs and genetic components improved the model’s performance and predicted AMI/CHD better than CRFs alone.
Transglutaminase-Catalyzed Glycosylation Improved Physicochemical and Functional Properties of Lentinus edodes Protein Fraction

Lentinula edodes has high nutritional value and abundant protein. In order to develop and utilize edible mushroom protein, this study was designed to investigate the effects of TGase-catalyzed glycosylation and cross-linking on the physicochemical and functional properties of Lentinus edodes protein fraction. The results showed that within a certain time, glycosylation and TGase-catalyzed glycosylation decreased the total sulfydryl, free sulfydryl, disulfide bond, surface hydrophobicity, β-fold and α-helix, but increased the fluorescence intensity, random coil, β-turn, particle size and thermal stability. The apparent viscosity and the shear stress of the protein with an increase in shear rate were increased, indicating that TGase-catalyzed glycosylation promoted the generation of cross-linked polymers. In addition, the TGase-catalyzed glycosylated proteins showed a compact texture structure similar to the glycosylated proteins at the beginning, indicating that they formed a stable three-dimensional network structure. The flaky structure of proteins became more and more obvious with time. Moreover, the solubility, emulsification, stability and oil-holding capacity of enzymatic glycosylated Lentinus edodes protein fraction were significantly improved because of the proper TGase effects of glycosylation grafting and cross-linking. These results showed that glycosylation and TGase-catalyzed glycosylation could improve the processing characteristics of the Lentinula edodes protein fraction to varying degrees.
An Integrative Approach to Investigate the Mode of Action of (−)-Dendroparishiol in Bacterial Meningitis: Computer-Aided Estimation of Biological Activity and Network Pharmacology

Bacterial meningitis remains one of the most prevalent infectious diseases worldwide. Although advances in medical care have improved mortality and morbidity, neurological complications remain high. Therefore, aside from antibiotics, therapeutic adjuvants targeting neuroinflammation are essential to combat the long-term neuronal sequelae of bacterial meningitis. In the present study, we propose (−)-dendroparishiol as a potential add-on therapy to improve neuroinflammation associated with bacterial meningitis. The biological activity of (−)-dendroparishiol was first predicted by computational analysis and further confirmed in vitro using a cell-based assay with LPS-induced BV-2 microglial cells. Biological pathways involved with (−)-dendroparishiol were identified by applying network pharmacology. Computational predictions of biological activity indicated possible attenuation of several inflammatory processes by (−)-dendroparishiol. In LPS-induced BV-2 microglial cells, (−)-dendroparishiol significantly reduced the expression of inflammatory mediators: iNOS, NO, COX-2, IL-6, and TNF-α. Molecular docking results demonstrated the potential iNOS and COX-2 inhibitory activity of (−)-dendroparishiol. Network pharmacological analysis indicated the plausible role of (−)-dendroparishiol in biological processes involved in oxidative stress and neuroinflammation with enrichment in neuroinflammatory pathways. Overall, this study provides scientific evidence for the potential application of (−)-dendroparishiol in the management of bacterial meningitis-associated neuroinflammation.
Statistically Dependent Blind Signal Separation Under Relaxed Sparsity

Most traditional blind source separation (BSS) methods are based on the basic assumption that source signals are mutually independent. However, many signals in the real world are often interrelated to each other. In this letter, a novel BSS method is proposed for dependent signals which only relies on the relaxed sparsity of sources. Under this constraint, the column vector of high-dimensional observed signals would be distributed in a specific subspace. According to the representation relation in time-frequency (TF) domain, each column vector of observed signal is uniquely clustered into the subspace, to which it belongs. Furthermore, the estimation of source signals is calculated by the inverse matrix or the pseudo-inverse matrix of the mixing matrix. It is also demonstrated mathematically and experimentally that under relaxed sparsity conditions, proposed subspace clustering methods is promising and hopeful to solve the dependent BSS (DBSS) problems.
Spin-orbit-interaction-induced generation of optical vortices in multihelicoidal fibers

We have studied the effect of the spin-orbit interaction on generation and conversion of optical vortices in multihelicoidal fibers, that is, the fibers possessing a multihelical refractive index profile. On the basis of a fully analytical approach we have obtained the spectra of coupled modes and their structure. Specifically, we have established selection rules, under which the spin-orbit interaction mediates the conversion of optical vortices into vortices with the topological charge changed by $\ifmmode\pm\else\textpm\fi{}(\ensuremath{\ell}\ifmmode\pm\else\textpm\fi{}2)$, $\ensuremath{\ell}$ being the number of helical branches in refractive index distribution. Also, we have shown that the spin-orbit interaction can lead to generation of radially and azimuthally polarized TE and TM modes from optical vortices. We have also demonstrated that if such generation is mediated by a scalar-type perturbation of the fiber's form, it is possible only for weakly deformed fibers. For strongly deformed fibers such perturbation can result only in generation of vortices with zero total angular momentum. Additionally, we have studied the possibility of polarization control over the orbital angular momentum of the generated state in such a system.
Multimorbidity and adverse longitudinal outcomes among patients attending chronic outpatient medical care in Bahir Dar, Northwest Ethiopia

Background Multimorbidity is becoming more prevalent in low-and middle-income countries (LMICs). However, the evidence base on the burden and its longitudinal outcomes are limited. This study aimed to determine the longitudinal outcomes of patients with multimorbidity among a sample of individuals attending chronic outpatient non communicable diseases (NCDs) care in Bahir Dar, northwest Ethiopia. Methods A facility-based longitudinal study was conducted among 1,123 participants aged 40+ attending care for single NCD (n = 491) or multimorbidity (n = 633). Data were collected both at baseline and after 1 year through standardized interviews and record reviews. Data were analyzed using Stata V.16. Descriptive statistics and longitudinal panel data analyzes were run to describe independent variables and identify factors predicting outcomes. Statistical significance was considered at p-value <0.05. Results The magnitude of multimorbidity has increased from 54.8% at baseline to 56.8% at 1 year. Four percent (n = 44) of patients were diagnosed with one or more NCDs and those having multimorbidity at baseline were more likely than those without multimorbidity to develop new NCDs. In addition, 106 (9.4%) and 22 (2%) individuals, respectively were hospitalized and died during the follow up period. In this study, about one-third of the participants had higher quality of life (QoL), and those having higher high activation status were more likely to be in the higher versus the combined moderate and lower QoL [AOR1 = 2.35, 95%CI: (1.93, 2.87)] and in the combined higher and moderate versus lower level of QoL [AOR2 = 1.53, 95%CI: (1.25, 1.88)]. Conclusion Developing new NCDs is a frequent occurrence and the prevalence of multimorbidity is high. Living with multimorbidity was associated with poor progress, hospitalization and mortality. Patients having a higher activation level were more likely than those with low activation to have better QoL. If health systems are to meet the needs of the people with chronic conditions and multimorbidity, it is essential to understand diseases trajectories and of impact of multimorbidity on QoL, and determinants and individual capacities, and to increase their activation levels for better health improve outcomes through education and activation.
Chronic Kidney Disease: Interaction of Adiponectin Gene Polymorphisms and Diabetes

Adiponectin is an adipokine multipeptide hormone with insulin-sensitizing; anti-atherosclerotic; and anti-inflammatory properties. Chronic kidney disease (CKD) may be associated with low adiponectin. The adiponectin gene ADIPOQ is thought to be the only major gene responsible for plasma adiponectin levels; which are associated with diabetes and diabetic nephropathy. The purpose of this study was to investigate the association between ADIPOQ polymorphism and CKD. In addition; the combined effects of ADIPOQ polymorphism and diabetes and levels of total urinary arsenic and blood cadmium on CKD were also explored. This study included 215 CKD patients and 423 age–sex matched controls. The ADIPOQ polymorphisms were determined using the Agena Bioscience Mass ARRAY System. The levels of blood cadmium and urinary arsenic species were measured. The ADIPOQ rs182052 GA/AA genotype had a marginally lower odds ratio (OR) for CKD than the GG genotype. The OR (95% confidence interval; CI) was 16.33 (5.72–46.66) of CKD in subjects carrying the ADIPOQ rs182052 GG genotype and diabetes compared to non-diabetes subjects carrying the ADIPOQ rs182052 GA/AA genotype; the interaction term had p = 0.015; and the synergy index was 6.64 (1.81–24.36) after multivariate adjustment. A significant interaction of diabetes and ADIPOQ rs1501299 risk genotype increased the OR of CKD after multivariate adjustment with a synergy index of 0.31 (0.11–0.86) and a multiplicative interaction with p = 0.001. These results suggest that ADIPOQ rs182052 and rs1501299 risk genotypes may significantly modify the association between diabetes and CKD but not the association between total urinary arsenic and blood cadmium and CKD.
The Evolution Sequence in Narrow-Line and Broad-Line SEYFERT 1 Galaxies

Abstract Collecting a sample of 50 narrow-line Seyfert 1 galaxies (NLS1s) and 44 broad-line Seyfert 1 galaxies (BLS1s), we show that the theoretical maximum starburst line derived by Kewley et al. can separate well BLS1s from NSL1s on the BPT diagrams, and the [N II]λ 6584/Hα ratio increases along the list from NLS1s to BLS1s. We suggest that: (1) they have a sequence from low metallicities to high metallicities between NLS1s and BLS1s; (2) with regard to the “star-froming sequence” of Kewley et al. who noted that it forms a tight sequence from low metallicities to high metallicities, the sequence may be connected to the NLS1-BLS1 sequence of “active galactic nucleus (AGN)” sequences.
ANN-Based Classification of Rain Acoustic Sensor Data Using Modified Mel Frequency Cepstral Coefficients

Tropical cyclones are a common occurrence in the Philippines since it is located in the Western North Pacific region. It is essential to monitor and measure rainfall as it has implications for reducing disaster risk, agricultural planning, and transportation planning. In this study, machine learning artificial neural networks are employed in the classification of acoustic data collected from a rain acoustic sensor (RAS) developed. Mel-frequency cepstrum coefficients are extracted and modified into inputs for an artificial neural network (ANN) that classifies rainfall into Light, Moderate, Heavy, Intense and Torrential according to the Philippine Atmospheric, Geophysical, and Astronomical Services Administration (PAGASA) Rainfall Classification System. Using scaled conjugate gradient backpropagation, the neural network was trained, and its results were compared with the standard tipping bucket rain gauge data. A 94.6% accuracy rate was achieved using five-fold cross-validation for classifying PAGASA’s rainfall data in the experimental study.
Attenuation map reconstruction in uncollimated cone beam geometry using coupled PDEs

SPECT images suffer, among other physical causes of degradation, from photonic attenuation. The most accurate method for correcting photonic attenuation is to incorporate a model of the linear attenuation phenomenon into the emission reconstruction process. This technique requires computation of the nonuniform attenuation distribution (the attenuation map), which is achieved by reconstructing from transmission data. The presence of noise in these data makes it necessary to use a regularized algorithm. The algebraic reconstruction method presented here involves a second order regularization, which theoretically produces a higher quality attenuation map. Through the use of a conjugate gradient technique, the algorithm alternately solves two coupled PDE's associated respectively with the attenuation map and the image of its edges (the boundaries map). This method was applied to attenuation map reconstructions in cone beam geometry. The acquisitions were performed without use of a collimator since the direction of photon incidence is radial. The evaluation was performed on a numerical thorax simulation and on a very simple phantom composed of materials which have linear attenuation coefficients similar to those encountered in the thoracic region. As a result, the attenuation coefficients of the phantom are slightly underestimated. We attribute this discrepancy to scatter and partial volume effect. However, the results from these studies are encouraging since the geometry of the objects is accurately recovered.
Semi-Autonomous In Vivo Computation in Internet of Bio-Nano Things

Magnetically assembled bioresorbable nanoswimmers (NSs) can be used to highlight small tumors, thereby increasing the diagnostic capability of existing medical imaging techniques. Built upon our earlier work, this article proposes a novel in vivo computational framework for early cancer detection. Engineered NSs experience a change in their physical properties under the influence of tumor-induced biological gradients. The biologically sensed data by such bio-nano things (NSs) can either trigger an autonomous target-directed motion or be assisted through external manipulation for steering the swarm toward the target. Previously developed externally manipulable in vivo computation requires constant monitoring of NSs, introducing positioning and steering errors along with a limit on the swarm size. A parallel approach called autonomous in vivo computation helps to resolve the above drawbacks, but the tumor homing is slow contributing to a higher percentage of predetection loss of NSs. We propose the spot sampling strategy for an autonomous swarm which considers the whole swarm as a single entity for the purpose of its tracking and steering. We show through computational experiments: 1) that the proposed semi-autonomous in vivo framework can achieve faster tumor sensitization in complex environments having static and mobile obstacles and 2) that the spot sampling provides sufficiently precise data to steer the swarm toward the target, saving around 90% of the monitoring resource. Our proposed framework also helps to achieve a large swarm size (number of NSs) which in return can achieve a higher deposition of NSs on malignant tumors.
Towards an Edge Cloud Based Coordination Platform for Multi-User AR Applications Built on Open-Source SLAMs

Multi-user and collaborative AR applications pose several chal-lenges. The expected user experience requires accurate pose infor-mation for each device and precise synchronization of the respective coordinate systems in real-time. Unlike mobile phones or AR glasses running on battery with constrained resource capacity, cloud and edge platforms can provide the computing power for the core functions under the hood. In this paper, we propose a novel edge cloud based platform for multi-user AR applications realizing an essential coordination service among the users. The latency criti-cal, computation intensive Simultaneous Localization And Mapping (SLAM) function is offloaded from the device to the edge cloud infrastructure.
Mycobacterium tuberculosis Rv0229c Shows Ribonuclease Activity and Reveals Its Corresponding Role as Toxin VapC51

The VapBC system, which belongs to the type II toxin–antitoxin (TA) system, is the most abundant and widely studied system in Mycobacterium tuberculosis. The VapB antitoxin suppresses the activity of the VapC toxin through a stable protein–protein complex. However, under environmental stress, the balance between toxin and antitoxin is disrupted, leading to the release of free toxin and bacteriostatic state. This study introduces the Rv0229c, a putative VapC51 toxin, and aims to provide a better understanding of its discovered function. The structure of the Rv0229c shows a typical PIN-domain protein, exhibiting an β1-α1-α2-β2-α3-α4-β3-α5-α6-β4-α7-β5 topology. The structure-based sequence alignment showed four electronegative residues in the active site of Rv0229c, which is composed of Asp8, Glu42, Asp95, and Asp113. By comparing the active site with existing VapC proteins, we have demonstrated the justification for naming it VapC51 at the molecular level. In an in vitro ribonuclease activity assay, Rv0229c showed ribonuclease activity dependent on the concentration of metal ions such as Mg2+ and Mn2+. In addition, magnesium was found to have a greater effect on VapC51 activity than manganese. Through these structural and experimental studies, we provide evidence for the functional role of Rv0229c as a VapC51 toxin. Overall, this study aims to enhance our understanding of the VapBC system in M. tuberculosis.