Miracle-dz/UniFAR-FTUs huggingface dataset:
OAST: Obstacle Avoidance System for Teleoperation of UAVs

This article presents a novel flight assistance system, obstacle avoidance system for teleoperation (OAST), whose main role is to make teleoperation of small multirotor unmanned aerial vehicles (UAVs) safer and more efficient in closed spaces. The OAST allows the operator to avoid obstacles while keeping a liberty of movement. The UAV is controlled through a 3-D haptic controller and the OAST amends the user input to increase safety and efficiency of the teleoperation. The design of the OAST is verified in computerized experiments. Moreover, a simulation involving 20 participants is carried out to validate the proposed scheme. This experiment shows that the OAST improves the completion time of the scenarios by 41% on average while reducing the workload of the operator from 57 to 27 points on the NASA Task Load Index test. The number of collisions with the environment is all but eliminated in these scenarios.
Musician occupational and financial stress and mental health burden

Popular (i.e., nonclassical) musicians have higher rates of depression, anxiety, and alcohol misuse than the general population. In this study, occupational and financial stresses were hypothesized to be associated with depression, anxiety, and alcohol misuse in popular musicians. An online cross-sectional survey was sent to a sample of popular musicians who had received mental health services in the preceding 2 years (n = 655). Validated measures were used to represent the key constructs. A total of 317 musicians (48.4%) completed the survey; 45.4% endorsed significant occupational stress and 81% rated their financial stress as high or overwhelming. Over 36% reported moderate to severe depression, 33.5% expressed similar levels of anxiety, and 65.4% screened positive for alcohol misuse. Univariate tests showed a strong association between occupational stress and depression (odds ratio [OR] = 3.62; 95% confidence interval [CI] = [2.6, 5.0]) and anxiety (OR = 3.05; 95% CI = [2.2, 4.2]). Financial stress also was associated with depression (OR = 1.90; 95% CI = [1.6, 2.2]) and anxiety (OR = 1.72; 95% CI = [1.5, 2.0]). Multivariate analyses showed statistical associations between both occupational stress and financial stress with depression and anxiety persisting when adjusted for by the other variables. Alcohol misuse was not associated with occupational or financial stress. Occupational and financial stresses are strongly associated with depression and anxiety in popular musicians seeking mental health treatment. Future studies should include musicians not engaged in mental health treatment to determine the generalizability of these findings. More research is needed to identify confounding variables associated with musician occupational and financial stress and alcohol misuse.
Defining Scents: A Systematic Literature Review of Olfactory-based Computing Systems

The human sense of smell is a primal ability that has the potential to reveal unexplored relationships between user behaviors and technology. Humans use millions of olfactory receptor cells to observe the environment around them. Olfaction studies are gaining popularity with the progression of scent delivering (commercial and prototype) devices. This influx of research features various software and hardware designs. Additionally, previous studies have explored numerous target audiences and evaluation methodologies. This article presents a systematic review of pertinent literature that investigates olfactory-based computing (OBC) systems in the field of Human-Computer Interaction. Last, this article highlights state-of-the-art study/system designs, evaluation methods, and offers insights on ways to address current challenges/contributions relevant to OBC technologies.
Ultrasonic Lamb wave tomography of through hole flaws in isotropic thin plates

A "cross borehole" tomographic reconstruction technique exploited by seismologists is used for quantitative Lamb wave nondestructive evaluation (NDE) in this paper. Combining ray tracing with algebraic reconstruction technique (ART), ultrasonic Lamb wave tomography for through hole flaws is developed. Based on Snell law and Fermat's principle, ray tracing is used to find actual ray paths and calculate travel times of ultrasonic Lamb waves between transmitting and receiving transducers. ART is then carried out to realize the velocity imaging reconstruction for the given set of arrival times. Reconstruction for both simulated and experimental data acquired on aluminum plates with machined through hole flaws is presented with straight-ray and curved-ray. The results demonstrate that the sizes of flaws reconstructed with curved-ray ART appear to be closer to their real size than straight ray ART, and the artifacts of reconstructed images are also reduced when ray-bending is considered.
Difficult decisions: Coercion in asymmetric security cooperation

How do minor states protect themselves against coercion from their major power allies? Asymmetric security cooperation is often reduced to tradeoffs between security and autonomy, but coercion is another factor that minor states fear while cooperating. Coercing partner states can take the form of major powers threatening or undertaking regime change, and minor states must weigh the benefits of cooperation against this risk. To this end, I suggest that minor states with anti-major power political oppositions cooperate more extensively with their major power partners than minor states with pro-major power oppositions. A pro-major power opposition provides opportunities for major powers to threaten or replace the incumbent regime; therefore, such minor states limit their cooperation out of fear of coercion. I employ original data on opposition characteristics to present evidence from security relations between the United States and 65 minor states during 1950–1991 to support the hypothesis.
PREGO: A Literature and Data-Mining Resource to Associate Microorganisms, Biological Processes, and Environment Types

To elucidate ecosystem functioning, it is fundamental to recognize what processes occur in which environments (where) and which microorganisms carry them out (who). Here, we present PREGO, a one-stop-shop knowledge base providing such associations. PREGO combines text mining and data integration techniques to mine such what-where-who associations from data and metadata scattered in the scientific literature and in public omics repositories. Microorganisms, biological processes, and environment types are identified and mapped to ontology terms from established community resources. Analyses of comentions in text and co-occurrences in metagenomics data/metadata are performed to extract associations and a level of confidence is assigned to each of them thanks to a scoring scheme. The PREGO knowledge base contains associations for 364,508 microbial taxa, 1090 environmental types, 15,091 biological processes, and 7971 molecular functions with a total of almost 58 million associations. These associations are available through a web portal, an Application Programming Interface (API), and bulk download. By exploring environments and/or processes associated with each other or with microbes, PREGO aims to assist researchers in design and interpretation of experiments and their results. To demonstrate PREGO’s capabilities, a thorough presentation of its web interface is given along with a meta-analysis of experimental results from a lagoon-sediment study of sulfur-cycle related microbes.
Der „Computational Turn“: ein „interdisziplinärer Turn“? Ein systematischer Überblick zur Nutzung der automatisierten Inhaltsanalyse in der Journalismusforschung

Themen journalistischer Berichterstattung durch maschinelles Lernen identifizieren oder Nachrichtendiffusion automatisiert messen: Die Anwendungsmöglichkeiten der automatisierten Inhaltsanalyse in der Journalismusforschung scheinen vielfältig. Aber wie wird die computerbasierte Methode bisher eingesetzt - und welche Konsequenzen hat der „Computational Turn“ der Kommunikationswissenschaft, besonders im Hinblick auf Interdisziplinarität? Dieser Beitrag fasst auf Basis eines systematischen Literaturüberblicks zusammen, wie die automatisierte Inhaltsanalyse im Forschungsfeld der Journalismusforschung genutzt wird. Dabei zeigt sich, dass die zunehmende Nutzung der Methode ein Indikator für methodische Interdisziplinarität in der ohnehin interdisziplinären Kommunikationswissenschaft ist. Gleichzeitig finden sich kaum Hinweise auf eine Zunahme theoretischer Interdisziplinarität, z. B. Rückgriffe auf fachfremde Theorien. Auch im Hinblick auf praktische Interdisziplinarität, z. B. Kooperationen mit anderen Disziplinen, wird unser Fach keineswegs interdisziplinärer. Vielmehr findet eine Verschiebung zugunsten technischer Disziplinen statt. Der „Computational Turn“ der Kommunikationswissenschaft ist daher zumindest bisher nur teils als „interdisziplinärer Turn“ zu verstehen.
Ingame Worked Examples Support as an Alternative to Textual Instructions in Serious Games About Programming

Serious games are considered an effective method to engage students in programming education and have been increasingly used in classrooms. An important part of the learning process with serious games involves the presentation of the new concepts and the provided support to encounter student difficulties. Although the most common approach is the use of instructional text, it comes with some drawbacks. This paper proposes an alternative method for user support in serious games about programming which mitigates current problems and provides improved learning efficiency. An experiment was conducted (N = 291) to test textual instructions learning efficiency compared to in-game worked examples. Two randomly assigned groups used different types of support in a block-based game, developed for the study. Our implementation provided support through a non-player character who executed worked examples inside the game world. The analysis showed a significant statistical difference in score performance between the two supports on both novice and experienced students. The results point to increased learning efficiency by students, when in-game worked examples and problem-solving are combined. We further argue that the Cognitive load theory can provide adequate justification for the outcomes.
Dynamic Characteristics of Canopy and Vegetation Water Content during an Entire Maize Growing Season in Relation to Spectral-Based Indices

A variety of spectral vegetation indices (SVIs) have been constructed to monitor crop water stress. However, their abilities to reflect dynamic canopy water content (CWC) and vegetation water content (VWC) during the growing season have not been concurrently examined, and the underlying mechanisms remain unclear, especially in relation to soil drying. In this study, a field experiment was conducted and designed with various irrigation regimes applied during two consecutive growing seasons of maize. The results showed that CWC, VWC, and the SVIs exhibited obvious trends of first increasing and then decreasing within a growing season. In addition, VWC was allometrically related to CWC across the two growing seasons. A linear relationship between the five SVIs and CWC occurred within a certain CWC range (0.01–0.41 kg m−2), while the relationship between these SVIs and VWC was nonlinear. Furthermore, the five SVIs indicated critical values for VWC, and these values were 1.12 and 1.15 kg m−2 for the water index (WI) and normalized difference water index (NDWI), respectively; however, the normalized difference infrared index (NDII), normalized difference vegetation index (NDVI), and optimal soil-adjusted vegetation index (OSAVI) had the same critical value of 0.55 kg m−2. Therefore, in comparison to the NDII, NDVI, and OSAVI, the WI and NDWI better reflected the crop water content based on their sensitives to CWC and VWC. Moreover, CWC was the most important direct biotic driver of the dynamics of SVIs, while leaf area index (LAI) was the most important indirect biotic driver. VWC was a critical indirect regulator of WI, NDWI, NDII, and OSAVI dynamics, whereas vegetation dry mass (VDM) was the critical indirect regulator of NDVI dynamics. These findings may provide additional information for estimating agricultural drought and insights on the impact mechanism of soil water deficits on SVIs.
A Low-Phase Error Cascode CMOS Variable Gain Amplifier With 180° Phase Control for Phase Array Systems

A comprehensive analysis is derived to understand the mechanism of phase errors in cascode CMOS variable gain amplifiers (VGAs). According to the analysis, source–drain parasitic capacitors (<inline-formula> <tex-math notation="LaTeX">$C_{\mathrm {ds}}$ </tex-math></inline-formula>) of common gate (CG) transistors in off-region are the main causes for phase errors. An independent cascode current canceling (ICCC) VGA is proposed to relax phase errors by minimizing the effect of <inline-formula> <tex-math notation="LaTeX">$C_{\mathrm {ds}}$ </tex-math></inline-formula> within a broadband. The proposed VGA achieves a 16-dB gain control with a 1.6° phase error and a 0.2-dB gain error from 15 to 25 GHz by 40-nm CMOS technology. Owing to the cascode structure, the peak gain of the VGA is 12 dB with only one stage including input–output pads consuming 16-mW power from 1.1-V voltage. Besides, the VGA can also serve as a 180° phase shifter (PS) due to ICCC structure with a 1.5° phase error and a 0.2-dB gain error from 15 to 25 GHz. The measured −3-dB bandwidth is 5 GHz, and output 1-dB compression point (OP1dB) is over 2.5 dBm in the maximum gain state. The core area is 0.065 mm<sup>2</sup> excluding input–output pads.
Zygomatic implant-supported prosthetic rehabilitation of a patient with Brown et al. Class II c maxillary defect: A clinical report

The genesis of acquired maxillary defects poses a significant challenge when it comes to rehabilitating a patient prosthetically. These defects lead to functional and esthetic impairment, affecting the quality of life of an individual. This clinical report describes a satisfactory zygomatic implant-supported overdenture rehabilitation of a patient who underwent subtotal bilateral maxillectomy after an industrial accident. The result shows zygomatic implant-supported overdenture as a viable, predictable, and economical treatment option for a patient with an extensive maxillary defect.
Simulation Research on Automatic Compensation Technology of ADN Based on Neutral Point Active Compensation

When the single-phase grounding fault occurs in the distribution network, the neutral point via the arc suppression coil grounding mode can effectively reduce the fault current and realize the self-extinguishing arc. However, as a large number of distributed generator and power electronic devices are connected to the distribution network, the structure of the network becomes more and more complex. In order to effectively reduce the fault current, the 10 kV active distribution network (ADN) was taken as the research object for simulation in this paper, and the support vector machines (SVM) algorithm were used for training by extracting the fault components, the neutral compensated current components and so on as eigenvalues. Used the controller to judge the system compensation state and complete the automatic compensation. The results show that under ideal conditions, the judgment time of the algorithm is 0.32 ms. And the algorithm can quickly respond to the fault component, improve the response speed and reduce the damage to electrical equipments.
Validation of 13 Hot and Potentially Terrestrial TESS Planets

The James Webb Space Telescope will be able to probe the atmospheres and surface properties of hot, terrestrial planets via emission spectroscopy. We identify 18 potentially terrestrial planet candidates detected by the Transiting Exoplanet Survey Satellite (TESS) that would make ideal targets for these observations. These planet candidates cover a broad range of planet radii (R p ∼ 0.6–2.0R ⊕) and orbit stars of various magnitudes (K s = 5.78–10.78, V = 8.4–15.69) and effective temperatures (T eff ∼ 3000–6000 K). We use ground-based observations collected through the TESS Follow-up Observing Program (TFOP) and two vetting tools—DAVE and TRICERATOPS—to assess the reliabilities of these candidates as planets. We validate 13 planets: TOI-206 b, TOI-500 b, TOI-544 b, TOI-833 b, TOI-1075 b, TOI-1411 b, TOI-1442 b, TOI-1693 b, TOI-1860 b, TOI-2260 b, TOI-2411 b, TOI-2427 b, and TOI-2445 b. Seven of these planets (TOI-206 b, TOI-500 b, TOI-1075 b, TOI-1442 b, TOI-2260 b, TOI-2411 b, and TOI-2445 b) are ultra-short-period planets. TOI-1860 is the youngest (133 ± 26 Myr) solar twin with a known planet to date. TOI-2260 is a young (321 ± 96 Myr) G dwarf that is among the most metal-rich ([Fe/H] = 0.22 ± 0.06 dex) stars to host an ultra-short-period planet. With an estimated equilibrium temperature of ∼2600 K, TOI-2260 b is also the fourth hottest known planet with R p < 2 R ⊕.
Modeling and Analysis of Spike-Train Based on Poisson-GML

Mathematical modeling is of great significance to the study of brain function. In this paper, an analysis method based on Poisson’s general linear model is adopted to model the brain impulse sequence. First, the ideas of Hodegkin-Hulex model, STA and Poisson’s general linear model are introduced. Secondly, the implementation of the general linear model is introduced. Finally, Poisson GLM Fit Spike-train is simulated and compared with Linear Gaussian-GLM. The AIC tradeoff estimation model fits well. The superiority of Poisson GLM Fit Spike-Train is demonstrated
A Strabismus Surgery Parameter Design Model with WGAN-GP Data Enhancement Method

The purpose of this paper is a machine learning model that could predict the strabismus surgery parameter through the data of patients as accurately as possible. A strabismus surgery parameter design model’s input is a Medical records and return is a surgical value. The Machine learning algorithms is difficult to get a desired result in this process because of the small amount and uneven distribution strabismus surgery data. This paper enhanced the data set through a WGAN-GP model to improve the performance of the LightGBM algorithm. The performance of model is increased from 69.32% to 84.52%.
Grid Data Inventory Model Based on Improved Redundancy Elimination and Label Extraction

In view of the difficulty of data inventory caused by the large amount of accumulated data of the current grid business data, multiple data sources, and complex data, this paper proposes a data inventory scheme for structured data and unstructured data. Firstly, this paper uses the YOLOv3 deep learning network to label unstructured data to convert unstructured data into structured data; Secondly, the Semantic Hypergraph Clustering (SHC) method is used to realize the identification of the same entity of the power grid data and the elimination of redundancy. This method introduces semantic information in the block; Finally, in the comparative experiments of AR and RR, 96.88% and 95.03% respectively verify the effectiveness of the scheme.
Kinematics and Simulation Analysis of Longitudinal-Torsional Ultrasonic Assisted Grinding of Zirconia Ceramics

Zirconia ceramics is a typical hard and brittle material, which is very difficult to process. In recent years, with the rapid development of the 3C industry, zirconia ceramic materials have achieved a wide range of applications. Longitudinal-torsional ultrasonic assisted grinding (LTUAG) is efficient for processing hard and brittle materials. However, the mechanism of LTUAG of zirconia ceramic is not studied sufficiently. In this study, the spatial trajectory of the grinding grain in three different grinding methods is established and the critical cutting speed of LTUAG is calculated. Simulation model of LTUAG of zirconia ceramic is established. The results show that the longitudinal-torsional ultrasonic vibration increases the spatial trajectory length of the grinding grain, thereby reducing the thickness of the maximum removal layer and reducing the cutting force. When the cutting speed is less than the critical cutting speed, LTUAG can significantly reduce the Mises stress, the subsurface damage and the cutting force.
SNR Classification Based Multi-Estimator IRM Speech Enhancement Algorithm

Deep neural network(DNN)-based ideal ratio mask(IRM) estimation methods are often adopted in speech enhancement tasks. In the previous work, IRM estimation was usually realized by a single DNN-based IRM estimator without considering the SNR levels, which had a limited performance in real applications. Therefore, a two stage speech enhancement method is proposed in this paper. Firstly, a DNN-based SNR classifier is employed to classify the speech frames into three classes according to different SNR thresholds. Secondly, three corresponding DNN based IRM estimators related to the three SNR classes are trained respectively, from which the amplitude spectrum is corrected. Finally, speech enhancement is realized by doing IDFT to the corrected speech spectrum combined with the phase information of noisy speech. Experiment results show that the algorithm proposed in this paper has better performances in the evaluation of short time objective intelligibility(STOI), perceptual evaluation of speech quality(PESQ) and segmental signal-to-noise ratio improvement(SSNRI) scores.
Design of Wind Turbine Parameter Monitoring and Early Warning Software System Based on Deep Learning Modeling

This paper adopts deep learning self-encoding algorithm, using JAVA, C++ and python language mixed programming computing technology to realize wind turbine parameter monitoring and early warning and software system design and development. This paper describes the database design, process management mode, fault early warning model, alarm value processing and human-computer interaction interface of the system, explains the software operation process and workflow, as well as the early warning mechanism and algorithm of the system, and realizes the early fault early warning function of wind turbine operation parameters.
Synthesis TiO2 using sonochemical method and responses the CO2 gas of the nanoparticle TiO2 layers on the QCM sensor surfaces

TiO2 synthesis using sonochemical methods and their coating on the surface of the Quartz Crystal Microbalance (QCM) sensor utilizing the spin coating method has been carried out. The synthesis started by mixing 7 ml of titanium (IV) isopropoxide (TTIP) (97%) in 70 ml of ethanol (96%). The mixture of TTIP solution was stirred for 30 minutes. After ward, the sonication was carried out for 4 hours at a frequency of 40 kHz and heated for 16 hours at 80 °C. To produce TiO2 powder, the precursor was calcined at 500°C for 3 hours. The TiO2 nanoparticle was analyzed using XRD, and the particle size was observed using SEM/Eds. Based on the XRD results, the crystal structure of Anatases was obtained at the 2θ (deg) of 25.503°(101), 29.56° (103), 36.20° (004), 37.977° (112), 48. 25.9° (200), 54.100° (105), 55.272° (211), 62.89°. The particle size from the SEM results showed the average particle size in the range of ±7μm-±54 nm with the majority in ±96 nm. The obtained TiO2 was dissolved with ethanol then the solvent was deposited on the QCM surface using the spin coating method. A homogeneous and porous surface structure is obtained from the SEM observation. The coated QCM was tested for its ability to adsorb CO2 gas. QCM was exposed to CO2 gas for 100 seconds with a constant exposure discharge. A positive response was obtained for the adsorbability of the coated QCM to adsorb CO2 gas with time variations. TiO2’s response against CO2 gas adsorption indicates that TiO2 can be further investigated as a CO2 gas sensor. The measuring response of the TiO2 layer on the QCM sensor was conducted to adsorb CO2 gas.