Miracle-dz/UniFAR-FTUs huggingface dataset:
6-DoF Pose Estimation from Stereo LiDAR of Actual Machine using Deep Learning

We adopted two LiDARs as input instead of an optical camera in order to achieve highly-accurate pose estimation at short distances for our space debris removal mission. To overcome the inability to obtain such point clouds of debris on orbit, we developed a simulator and a hardware emulator. There has been no research on suppressing the domain shift of simulated data and emulated data and estimating pose with high accuracy and robustness from stereo point clouds. We maximized the representation of voxels and minimized the size by normalizing the point clouds' extent rather than the space. This provided fixed-length data for a small input suitable for a neural network. In addition, we gave the neural network eight vertices indicating the position of the point clouds. This interpolates the positions of point clouds that have become dimensionless by normalization. We performed environmental randomization to suppress domain shifts. Voxels were randomly removed when input to a neural network based on dropout rate. Making the dropout rate variable rather than fixed improved the accuracy of attitude estimation. This paper also proposed a method to effectively process stereo point clouds by neural networks. The estimation accuracy was improved by a method that extract features individually from stereo point clouds upstream and interfere its features downstream to infer the pose. The dropout layer was often used for approximate Bayesian inference and environment randomization. We performed pose estimation on simulation and emulation point clouds with the same pose labels. With 24 patterns of data, the simulation results had estimated errors of $x, y, z, n_{x}, n_{y}, n_{z}$, and $\theta$, which were 0.59%, 0.36%, 0.97%, 1.22%, 1.05%, 0.02%, and 3.47%, respectively. For the emulation results, the estimated errors of $x, y, z, n_{x}, n_{y}, n_{z}$, and $\theta$ were 0.91%, 0.58%, 0.93%, 4.78%, 2.63%, 0.05%, and 3.44%. There were no large errors in the results from simulated or emulated point clouds, confirming that domain shift suppression was successful. We experimented using the uncertainty output by Bayesian inference to eliminate pose estimation results from noisy inputs. We removed the estimation results, which had high uncertainty values. The number accepted was 9 out of 24. As a result, the estimated errors of $x, y, z, n_{x}, n_{y}, n_{z}$, and $\theta$ were 0.52%, 0.52%, 1.11%, 1.10%, 2.02%, 0.03%, and 3.39%, respectively. Although the amount of data was reduced by about half, the accuracy of all parameters was improved.
Simulation of multiple exciton generation in quantum dot solar cell

A new simulation of quantum dot solar cell through multiple exciton generation process is presented in this dissertation. The model of quantum dot solar cell is focused on silicon material and used in computer simulation. Electronic and optical properties of quantum dot materials are studied and formulation components of quantum dot solar cell model are well defined. MATLAB programming is used to do modeling and simulation throughout this dissertation. Multiple exciton generation process is the main simulation factor in order to demonstrate the effectiveness of quantum dot solar cell. The major process in methodology is the implementation of formulations, mathematical equations and quantum theory in MATLAB programming. Comparison between simulation result and previous research are made. Different sample planes in cubic quantum dot are selected in simulations for exciton generation analysis. The outcome gives approximately four or five exciton per quantum dot with single photon energy absorptions of 5.2159Eg with high efficiency of 495.94% due to the strong Coulomb coupling.
Device Design Aware and Interface Thermal Resistance Assisted Self-Heating Analysis in Nanosheet FET

The nanoscaled geometrical confinement of the Nanosheet FET (NSHFET) has severely aggravated the self-heating effect, affecting the device's characteristics, such as lattice temperature, thermal contact resistance, and drive current. In this paper, we investigated the self-heating effect (SHE) in the NSHFET using well-calibrated TCAD models. We analyzed (i) the behavior of spatial device lattice temperature (TD) gradient in a gate-all-around (GAA) NSHFET; (ii) the impact of varying the drain, source, and gate electrode thermal contact resistances $(\mathrm{R}_{\text{th DSG}})$. (iii) a fair comparison of electrical and thermal characteristics of SOI FinFET, bulk FinFET, and NSHFET based on the percentage change in subthreshold slope (SS), drain-induced barrier lowering (DIBL), drain current, etc. Further, we proposed the design guideline to mitigate SHE-induced thermal degradation in Nanosheet FET.
Multifunctional Antenna Design for Wireless Consumer Electronic Devices: A Soft-Computing Approach

A soft-computing-based novel design approach is proposed for the design of multifunctional antennas to be used in wireless consumer electronic devices. With the combined utilization of the particle swarm optimization (PSO) and the merits of the neural network (NN) technique, the developed formulation produces the design of multifrequency fractal antenna structure for specific multiple frequencies and handles the suitable feeding for these multiple frequencies. The role of the developed trained NN is to remove the need for an electromagnetic simulator that is embedded in the optimization loop of the PSO. The involvement of NN in PSO would eliminate the need for a hit-trial approach during parametric optimization for achieving the set goals as per designer’s requirement. However, as the response of the used soft-computing techniques is fast, the developed design formulation takes significantly less time (the order of seconds) in comparison to the design using simulators or other numerical/analytical methods. Different small size Sierpinski gaskets and Koch monopoles are designed and tested using the developed methodology. The effectiveness of the developed approach is cross-checked with simulation and experimental verifications.
Bilateral transcranial direct‐current stimulation promotes migration of subventricular zone‐derived neuroblasts toward ischemic brain

Ischemic insult stimulates proliferation of neural stem cells (NSCs) in the subventricular zone (SVZ) after stroke. However, only a fraction of NSC‐derived neuroblasts from SVZ migrate toward poststroke brain region. We have previously reported that direct‐current stimulation guides NSC migration toward the cathode in vitro. Accordingly, we set up a new method of transcranial direct‐current stimulation (tDCS), in which the cathodal electrode is placed on the ischemic hemisphere and anodal electrode on the contralateral hemisphere of rats subjected to ischemia–reperfusion injury. We show that the application of this bilateral tDCS (BtDCS) promotes the migration of NSC‐derived neuroblasts from SVZ toward the cathode direction into poststroke striatum. Reversing the position of the electrodes blocks the effect of BtDCS on the migration of neuroblasts from SVZ. BtDCS protects against neuronal death and improves the functional recovery of stroke animals. Thus, the migration of NSC‐derived neuroblasts from SVZ toward poststroke brain region contributes to the effect of BtDCS against ischemia‐induced neuronal death, supporting a potential development of noninvasive BtDCS as an endogenous neurogenesis‐based stroke therapy.
An Engineering Guide to Lunar Geotechnical Properties

The renewed interest in returning human and robotic explorers to the lunar surface has identified a need for a renewed understanding of lunar geotechnical properties related to landing, exploration, excavation, and construction activities on the lunar surface. This paper summarizes measurements conducted during US and Russian/Soviet landed missions as well as experiments performed on returned samples to establish fundamental geotechnical properties such as particle size distribution, particle shape, bulk density, shear strength, cohesion and bearing strength. While many of these properties are well known, how they vary with increased lunar soil depth is less understood, and those properties that vary significantly as a function of depth are explored in additional detail. Selected examples discuss mechanical excavation forces, rocket exhaust erosion forces, and the preparation of launch/landing pad surfaces, with the goal of a better understanding of lunar soil geotechnical properties that apply to large-scale exploration of the lunar surface and dictate the design of future exploration systems.
On cusps of hyperbolic once-punctured torus bundles over the circle

The geometry of certain canonical triangulation of once-punctured torus bundles over the circle is applied to the problem of computing their cusp tori. We are also concerned with the problem of finding the limit points of the set formed by such cusp tori, inside the moduli space of the torus. Our discussion generalizes examples which were elaborated by H. Helling (unpublished) and F. Guéritaud.
Antibiotic use in infants within the first year of life is associated with the appearance of antibiotic-resistant genes in their feces

Background Antibiotic resistance, an increasing challenge, is not only a national threat but also a global threat. Carriage of resistance genes is not limited to adults alone, various microbiota niches present in the body system of children have been found to harbor bacteria carrying resistant genes, especially, their gut microbiota. This study aims to identify selected antibiotic-resistant genes from the fecal samples of infants and the association of antibiotics use with the occurrence of resistant genes in the infant’s gut. Methods A total number of 172 metagenomic DNA samples previously extracted from stool samples of 28 Nigerian babies longitudinally within their first year of life were screened for the presence of ESBL genes (blaSHV, blaTEM, and blaCTX-M), PMQR genes (qnrA, qnrB, qnrS, qepA), ribosomal protection protein tetracycline resistance gene, (RPP) β-lactamase (blaZ), macrolide (ermA, ermB, mefA/E), aminoglycoside modifying enzymes gentR (aac(6′)/aph(2″)) and dfrA genes by PCR. Nineteen (19) of the 28 babies used antibiotics during the study. The association between antibiotic use by the babies within the first year of life and occurrence of resistant genes were analyzed by Spearman rank correlation. Results One hundred and twenty-two (122) samples (71%) out of the 172 isolates had antibiotic-resistance genes. PMQR genes were absent in all the samples. Three isolates had blaTEM gene, nine isolates had blaSHV gene, six isolates had blaCTX-M gene and 19 isolates had dfrA gene, 31 samples had tet gene, 29 samples had mef gene, 27 samples had ermB gene, four samples had ermA gene, 13 samples had blaZ gene and 16 samples had aac gene. The babies whose samples had resistant genes used antibiotics in the same months the samples were collected. Interestingly, the 11 babies whose samples had the dfrA gene all used antibiotics in the same months their samples were collected but none of them used trimethoprim/sulfamethoxazole antibiotic. The overall correlation matrix of the babies showed a strong association between antibiotic use (AU) and antibiotic use presence of resistance genes (AUPRG) with a coefficient of 0.89. Antibiotic-resistant genes are present in the gut of infants and their occurrence is strongly connected with antibiotic use by infants.
Estimation du nombre de composantes d'un signal à l'aide du critère GDE amélioré

Among the numerous criteria which are available in literature to estimate the number of sources or of frequential components, we have chosen to examine the GDE criterion. This criterion is based on the estimation of the Gerschgorin disks’ radii and can be applied to the estimation mentioned above, provided the covariance matrix has been changed beforehand with a unitary transformation, so as to separate the Gerschgorin disks in two distinct sets : one associated to the signal sources, the other related to the noise. In this paper, we aim at modifying that criterion into a new one called SGDE by using the sum of the disks’ radii. Besides, we suggest applying a deflation method to the covariance matrix before using the new criterion, in order to make it less sensitive to sources of different power. The results show that the SGDE criterion can be applied in many situations, particularly in the case of coloured noise, of signals containing few samples or of sources with different power.
Language of Bargaining

Leveraging an established exercise in negotiation education, we build a novel dataset for studying how the use of language shapes bilateral bargaining. Our dataset extends existing work in two ways: 1) we recruit participants via behavioral labs instead of crowdsourcing platforms and allow participants to negotiate through audio, enabling more naturalistic interactions; 2) we add a control setting where participants negotiate only through alternating, written numeric offers. Despite the two contrasting forms of communication, we find that the average agreed prices of the two treatments are identical. But when subjects can talk, fewer offers are exchanged, negotiations finish faster, the likelihood of reaching agreement rises, and the variance of prices at which subjects agree drops substantially. We further propose a taxonomy of speech acts in negotiation and enrich the dataset with annotated speech acts. We set up prediction tasks to predict negotiation success and find that being reactive to the arguments of the other party is advantageous over driving the negotiation.
Comparative Anatomical and Transcriptomics Reveal the Larger Cell Size as a Major Contributor to Larger Fruit Size in Apricot

Fruit size is one of the essential quality traits and influences the economic value of apricots. To explore the underlying mechanisms of the formation of differences in fruit size in apricots, we performed a comparative analysis of anatomical and transcriptomics dynamics during fruit growth and development in two apricot cultivars with contrasting fruit sizes (large-fruit Prunus armeniaca ‘Sungold’ and small-fruit P. sibirica ‘F43’). Our analysis identified that the difference in fruit size was mainly caused by the difference in cell size between the two apricot cultivars. Compared with ‘F43’, the transcriptional programs exhibited significant differences in ‘Sungold’, mainly in the cell expansion period. After analysis, key differentially expressed genes (DEGs) most likely to influence cell size were screened out, including genes involved in auxin signal transduction and cell wall loosening mechanisms. Furthermore, weighted gene co-expression network analysis (WGCNA) revealed that PRE6/bHLH was identified as a hub gene, which interacted with 1 TIR1, 3 AUX/IAAs, 4 SAURs, 3 EXPs, and 1 CEL. Hence, a total of 13 key candidate genes were identified as positive regulators of fruit size in apricots. The results provide new insights into the molecular basis of fruit size control and lay a foundation for future breeding and cultivation of larger fruits in apricot.
DERIVING INHERENT OPTICAL PROPERTIES FROM MERIS IMAGERY AND IN SITU MEASUREMENT USING QUASI-ANALYTICAL ALGORITHM

The paper describes inherent optical properties (IOP) of the Berau coastal waters derived from in situ measurements and Medium Resolution Imaging Spectrometer (MERIS) satellite data. Field measurements of optical water, total suspended matter (TSM), and chlorophyll-a (Chl-a) concentrations were carried out during the dry season of 2007. During this periode, only four MERIS data were coincided with in situ measurements on 31 August 2007. The MERIS top-of-atmosphere radiances were atmospherically corrected using the MODTRAN radiative transfer model. The in situ optical measurement have been processed into apparent optical properties (AOP) and sub surface irradiance. The remote sensing reflectance of in situ measurement as well as MERIS data were inverted into the IOP using quasi-analytical algorithm (QAA). The result indicated that coefficient of determination (R 2) of backscattering coefficients of suspended particles (bbp) increased with increasing wavelength, however the R2 of absorption spectra of phytoplankton (aph) decreased with increasing wavelength.
Smart Textile Materials for Monitoring ECG Signals

Smart textiles play a significant role in wearable devices for monitoring the physiological parameters of the human body, such as heart rate, temperature, pressure, etc. The electrocardiograph (ECG) is the most used heart diagnostic tool that records the heart's electrical signals (heart rhythm). The current ECG electrodes used in the hospital for ECG signals are wet electrode types that use Ag/AgCl gel electrodes. Here the Ag/AgCl gel-type electrode sticks on the human skin for an extended period until the ECG reading is wholly taken. The gel used is disposable, sticky, and does not feel comfortable for the user's long-term monitoring. This research has been made to develop ECG electrodes with textile materials. Here the 12-lead ECG was reduced to a 3-electrode system. Two different types of conductive textile materials were used to create electrodes. The nylon yarn coated with the Silver nanoparticle and two-ply yarn of nylon coated with Stainless Steel nanoparticle is taken for the development of conducting material. The conductive yarn is stitched into the cotton fabric using the lock stitch method. The readings were taken by placing the commercial and the textile electrodes parallel on the right arm, left arm, and right leg readings are recorded. The material's morphological structure and surface roughness are analyzed using SEM, and XRD is used to analyze the material's crystallinity. The yarn is tested for its anti-microbial property to investigate the material's biocompatibility. For silver yarn case, the voltage range is -0.4V to 1.2 V, whereas, for stainless steel, the voltage range is from -0.4V to 0.7V. That implies silver yarn is relatively more sensitive than stainless steel yarn for detecting ECG signals
3-D Printing of Calibrated Germanium-68 PET Phantoms Without Inactive Walls

We aimed to print a solid-state germanium-68 phantom for positron emission tomography (PET) with excellent control of the activity concentration in the phantom material, with safe handling characteristics, and without inactive walls. To this end, we developed a novel method for incorporating germanium-68 into hydrophobic stereo-lithography monomers. The spontaneous phase transfer process provides complexation and induces phase transfer of aqueous ionic germanium-68 into the hydrophobic monomers, ensures homogenous isotope distribution in the rigid polymer matrix, and prevents leaching in aqueous environments. With this method, we additively manufactured radioactive spheres following the International Electrotechnical Commission design with the addition of a small sphere of 7.7-mm diameter. Extended leaching testing demonstrated near perfect source tightness. The same phantom can be repeatedly used in the quality management of multicenter clinical trials, without preparative errors. Long-lived solid-state phantoms produced with this method will provide consistent and reproducible validation of total body PET/CT systems. PET images with these phantoms acquired within a decaying gallium-68 background at different signal-to-background ratios showed homogenous activity distribution and no fill-related artefacts. The varying background allowed the assessment of trial-specific acquisition protocols that match a given clinical question much better than today’s predefined standard protocol.
Urinary NT-proBNP: A Useful Biomarker for the Diagnosis of Respiratory Distress in the Neonatal Population

Objective: To determine the diagnostic accuracy of urinary NT-proBNP levels in the detection and classification of the severity of respiratory distress in neonates after birth. Methods: We compared the urinary NT- proBNP levels between the respiratory distress (RD) group and the control group on the 1st, 3rd, and 5th day of life (DOL). Results: The RD group (55 neonates) showed higher levels of NT-proBNP compared to the control group (63 neonates) on DOL1 (585.4 pg/ml vs 396.1 pg/ml (p=0.014)), DOL3 (805.1 pg/ml vs 271.9 pg/ml (p<0.001)) and DOL5 (409.7 pg/ml vs 94.4 pg/ml (p<0.001)). Especially, on DOL5, the area under the ROC curve was 0.884 and the NT-proBNP cut-off value (221.8 pg/ml) showed a sensitivity of 71% and specificity of 79%. The RD group was subclassified into neonates with mild (21 neonates), moderate (19 neonates), and severe (15 neonates) disease. NT-proBNP cut-off point of 668 pg/ml for DOL5 can safely differentiate neonates with severe disease from those with mild and moderate disease (combined subgroups) since the sensitivity was 80% and specificity was 77.5% for DOL5. Conclusion: Urinary NT-proBNP levels are a useful biomarker in detecting clinical signs of respiratory distress in neonates that are born within the first week of life; they can also detect neonates that are vulnerable to severe forms of the disease.
Self-adaptive graph convolution networks with application to industrial soft sensor modeling

In recent years, deep learning has been widely studied in soft sensor modeling. However, the prediction of the deep learning model is difficult to explain, and it is hard to incorporate prior experience into the model. These shortcomings of deep learning prevent its application in real industrial processes. In this article, we propose a self-adaptive graph convolution networks (SAGCN) for industrial soft sensor modeling. This model uses the graph convolution network to introduce prior knowledge and construct the displayed nonlinear relationship among variables. And the graph convolution network can aggregate information to extract features from data. Because it is difficult and highly subjective to rely on prior knowledge and mechanisms to obtain the graph structure, this article proposes a graph structure self-learning method to realize the joint learning of the nonlinear relationship among auxiliary variables and the regression relationship between auxiliary variables and quality variables. The proposed method is verified through the CO2 absorption column process from a real ammonia synthesis process. Based on the results, SAGCN demonstrates high accuracy and a certain capacity to discover knowledge.
Rapid and generalised space vector modulation algorithm for cascaded multilevel converter based on zero-order voltage constraint

To simplify space vector modulation (SVM) algorithm, reduce zero-order voltage, and find the shortest switching path of the switching state vectors, a rapid and generalised SVM algorithm for cascaded multilevel converter is proposed. Using coordinate transformation which leads to all basic vectors locating in the integer grids in new coordinates, the algorithm simplifies the locating of basic vectors synthesising reference vectors and also their dwell time calculation. Switching state vectors calculation based on minimum zero-order voltage criteria reduces the workload of SVM. To find a most suitable switching path, the switching state vectors are mapped in three-dimensional (3D) space coordinates. Switching frequency is minimal along adjacent two sides of a unit cube's surface in 3D space coordinates, which improves performance of the converter. Finally, a three-cell seven-level converter experimental prototype is built and tested. Simulation and experimental results are consistent with the theoretical prediction, which verifies the correctness and effectiveness of the proposed algorithm.