Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing In Vitro Surface Sterilization of Cyathea latebrosa Spore

Cyathea latebrosa is one of the lowland tree fern species found in Peninsular Malaysia. This fern species is highly demanded in ornamental landscaping. The in vitro cultures are an important tool for propagation which may contribute toward the reduction of over-exploitation. To overcome these problems, an effective spore surface disinfection protocol is crucial to allow the germination stage to be carried out. This studied had carried out three types of methods which are the packet method (PM), centrifuged method (CM), and soak method (SM) with difference percent of concentration (0, 0.1, 0.5, 1.0, 10 & 30) Mercury Chloride (HgCl2) and Sodium hypochlorite (NaOCl). In contrast, the method and concentration of disinfection affect germination. Our results showed that the soak method in both types of disinfection is a significant difference due to statistical analysis (MANOVA) which gives a positive effect on the germination of the spore. This method is efficient for sterilizing which spore loss is kept to a minimum and has a higher rate of germination (HgCl2-90% & NaOCl-80%). The optimum concentration of HgCl2 was 0.1%, then followed by 0.5% and 1.0%, while for NaOCl was 30%, 20%, and 10%.
Silicon quantum dot superlattices: Modeling of energy bands, densities of states, and mobilities for silicon tandem solar cell applications

Quantum dot superlattices offer prospects for new generations of semiconductor devices. One possible recently suggested application is in tandem solar cells based entirely on silicon, using confinement in the quantum dot to control the cell band gap. In this paper, we use the effective mass approach to calculate the conduction band structure of a three-dimensional silicon quantum dot superlattice with the dots embedded in a matrix of silicon dioxide, silicon nitride, or silicon carbide. The quantum dot superlattice is modeled as a regularly spaced array of equally sized cubic dots in the respective matrix. Incorporating the effect of silicon anisotropic effective mass is shown to reduce both the degeneracies of the isotropic solutions and the energy separation between states. Electron densities of state and mobilities are derived from the band structure data. Theoretical results for the effect of dot size, interdot distance, and matrix material have been obtained. These results clarify the required design.
Weakly barred early-type ringed galaxies. III. The remarkable outer-ringed S0+ galaxy NGC 7020

The southern S0+ galaxy NGC 7020 presents an unusual morphology: it includes a very regular outer ring which is completely detached and which envelops an inner ring/lens zone with an exotic hexagonal shape. The ring has a high contrast compared with those usually observed in barred galaxies, yet NGC 7020 is not obviously barred. In this paper, the structure of this galaxy is studied by means of UBVRI CCD surface photometry. The photometry reveals a complex system and shows that most of the recent star formation in the galaxy has taken place in the outer ring. Two bright knots are found on the major axis of the hexagonal zone that appear to be true enhancements of old stars rather than young associations. Between these knots and the bulge there are dips in the surface brightness and a clear zone of rectangular isophotes. 56 refs.
Manual Maneuverability: Metrics for Analysing and Benchmarking Kinesthetic Robot Guidance

Kinesthetic teaching of collaborative robots is applied for intuitive and flexible robot programming by demonstration. This enables non-experts to program such robots on the task-level. Multiple strategies exist to teach velocity- or torque-controlled robots and, thus, the maneuverability among commercial robots differs significantly. However, currently there exists no metric that quantifies how “well” the robot can be guided, e.g., how much effort is required to initiate a motion. In this paper, we propose standardized procedures to quantitatively assess robot manual maneuverability. First, we identify different motion phases during kinesthetic teaching. For each phase, we then propose metrics and experimental setups to evaluate them. The experimental protocols are applied to the proprietary teaching schemes of five commercial robots, namely the KUKA LWR iiwa 14, Yuanda Yu+, Franka Emika robot, and Universal Robot's UR5e and UR10e. The experimental comparison highlights distinct differences between the robots and shows that the proposed methods are a meaningful contribution to the performance and ergonomics assessment of collaborative robots.
Some Remarks on the Extended Galilean Transformation.

The Galilean transformation carries one inertial frame into another with a different velocity. The extended Galilean transformation carries an inertial frame into a rigid frame with uniform spatial, but arbitrary translational acceleration x′=x+ξ (t). Besides being useful in discussing the equivalence principle, this transformation provides a physical interpretation for the theorem in nonrelativistic quantum mechanics, that we cannot coherently combine states of different mass. This restriction comes about because we cannot distinguish between coordinate and proper time in nonrelativistic physics.
Using electrical impedance tomography to characterize lung impairment of children with primary ciliary dyskinesia: A pilot cross‐sectional study

In children with primary ciliary dyskinesia (PCD), measures more sensitive than spirometry are needed to characterize underlying pulmonary impairment. Electrical impedance tomography (EIT) is a promising noninvasive method for monitoring the distribution of lung ventilation, and it does not require patient collaboration. We aimed to provide an assessment of the feasibility and clinical usefulness of EIT in characterizing lung impairment in children with PCD, compared to spirometry and multiple breath nitrogen washout (MBWN2) test.
Hugoniot temperatures and melting of tantalum under shock compression determined by optical pyrometry

LiF single crystal was used as transparent window (anvil) to tamp the shock-induced free surface expansion of Ta specimen, and the Ta/LiF interface temperature was measured under shock compression using optical pyrometry technique. The shock temperatures and/or melting temperatures of Ta up to ∼400 GPa were extracted from the observed interface temperatures based on the Tan–Ahrens’ model for one-dimensional heat conduction across metal/window ideal interface in which initial melting and subsequent solidification were considered under shock loading. The obtained data within the experimental uncertainties are consistent with the results from high-pressure sound velocity measurements. The temperature of the partial melting on Ta Hugoniot is estimated to be ∼9700 K at 300 GPa, supported by available results from theoretical calculations.
On the spread of a disease with gamma distributed latent and infectious periods

SUMMARY In this paper we investigate a model with assumptions as for the so-called general epidemic model, except that the latent and infectious periods are independently gamma distributed. For this model, we obtain a number of results concerning the progress of the epidemic. These results allow some assessment of the effects of the latent period and the infectious period on the behaviour of the epidemic model. Further we note that many of the results can be generalized. k large, and the degenerate distribution at /-1, as k -? oo. The latent period has density f10s and the infectious period density fm,. The model go, is in fact the standard epidemic model. For the model glm' we obtain results concerning the behaviour of the path of the process by means of the deterministic approximation and a martingale central limit theorem result. Equations for the distribution of the size of the outbreak are specified and approximations are derived for the probability of a minor outbreak, the distribution of the size of a minor outbreak and the distribution of the size of a major outbreak. Further, we obtain an approxi- mation for the initial rate of increase of the number of infected individuals and hence the mean time for the epidemic to reach its peak. These results give a reasonably detailed description of the behaviour of the model glm' allowing an assessment of the effects of the parameters I and m, and also enabling a comparison of ? pAm with the standard epidemic model 9Yi, to be made.
TDE-Based Adaptive Super-Twisting Multivariable Fast Terminal Slide Mode Control for Cable-Driven Manipulators With Safety Constraint of Error

An adaptive super-twisting multivariable fast terminal sliding mode control scheme based on time delay estimation (TDE) and asymmetric error constraints are proposed to guarantee high-precision trajectory tracking control of cable-driven manipulators under complicated unknown uncertainties. The control system is constrained by joint position errors, which are asymmetric and time-varying. First, the error range of the joints is designed to ensure that the deviation of the joint position from the desired profile is not too large while ensuring the safety performance of the manipulator, and the remaining set-total system dynamics are estimated and compensated using time-delay estimation. Secondly, the design analysis of the angular false expectation using safety constraint functions allows the constraints of different cross sections to be handled in a unified system architecture; the problem of robotic arm motion trajectories with output constraints and uncertainties is addressed through rigorous analysis. The super-twisting adaptive control effectively ensures fast, accurate and robust convergence of the algorithm in satisfying all constraints in operation, and the stability of the closed-loop control system is analyzed using the Lyapunov method. Finally, the effectiveness and superiority of the proposed adaptive hyper-torsional non-singular terminal sliding mode control scheme are verified by simulations and experiments in 2-DOF.
Tunable distributed feedback laser with photonic crystal mirrors

We report the development of a tunable laser diode based on InP using photonic crystal mirrors. The laser consists of two longitudinally coupled ridge waveguide segments that are coupled through a photonic crystal mirror segment. A laterally defined binary superimposed grating provides a distributed feedback in each of the cavities. Quasi-continuous tuning is achieved in a 24.5 nm window with 56 channels as typically required by wavelength division multiplexing applications.
Enemies in Iraq, Human Beings in Norway: ‘Multilocal’ Boundaries between Radicalised Sunni and Shi‘a Muslims

This article employs symbolic boundary theory to investigate how a sample of radicalised Sunni and Shi‘a Muslims in Norway make meaning of their views, behaviour and interactions with denominational others. It draws on 23 interviews with individuals who legitimise violence or are willing to use violence themselves to achieve political change. Research on boundary construction shows that the relative importance of boundaries varies across geographical contexts for different individuals. This article introduces the concept of ‘multilocal’ boundaries, which vary across contexts for the same individuals. The findings suggest that despite the boundaries being firmly agreed upon, their impact is limited by participants’ avoidance of religion and politics, their need to stand together as a minority and the enforced laws in Norway. At the same time, these boundaries can remain largely undiluted in another context. The war in Syria and Iraq seems to strengthen the influence of boundaries.
Exploring the Relationships between Artificial Intelligence Transparency, Sources of Bias, and Types of Rationality

Artificial intelligence (AI) is permeating one human endeavor after another. However, there is increasing concern regarding the use of AI: potential biases it contains, as well as mis-judged AI use. This study continues the recent investigations into the biases and issues that are potentially introduced into human decision-making with AI. We experimentally set-up a decision-making classification task and observe human classifiers when they are guided in their decision-making either by AI or other humans. We find that over-reliance or authoritative stigmatization is present when AI is concerned and that with human guidance discursive explanatory decision-making is present. We conclude that while AI is seen as authoritative even in a low stake decision-making setting, it does not suppress choice, but combined with a lack of transparency, AI suppresses visibility into rationality creation by the decision maker. Based on the emergent explorative relationships between types of rationality, AI transparency and authoritativeness, we provide future research avenues based on our findings.
Expert System-Based Multiagent Deep Deterministic Policy Gradient for Swarm Robot Decision Making

In this article, an expert system-based multiagent deep deterministic policy gradient (ESB-MADDPG) is proposed to realize the decision making for swarm robots. Multiagent deep deterministic policy gradient (MADDPG) is a multiagent reinforcement learning algorithm proposed to utilize a centralized critic within the actor–critic learning framework, which can reduce policy gradient variance. However, it is difficult to apply traditional MADDPG to swarm robots directly as it is time consuming during the path planning, rendering it necessary to propose a faster method to gather the trajectories. Besides, the trajectories obtained by the MADDPG are continuous by straight lines, which is not smooth and will be difficult for the swarm robots to track. This article aims to solve these problems by closing the above gaps. First, the ESB-MADDPG method is proposed to improve the training speed. The smooth processing of the trajectory is designed in the ESB-MADDPG. Furthermore, the expert system also provides us with many trained offline trajectories, which avoid the retraining each time we use the swarm robots. Considering the gathered trajectories, the model predictive control (MPC) algorithm is introduced to realize the optimal tracking of the offline trajectories. Simulation results show that combining ESB-MADDPG and MPC can realize swarm robot decision making efficiently.
Exact eigenfunctions for square-wave gratings: Application to diffraction and surface-plasmon calculations

We show that for dielectric or metallic gratings with square-wave geometry the photon eigenfunctions in the grating region can be expressed in analytical form. The knowledge of these eigenfunctions makes the diffraction calculation not only simple and direct, but also devoid of the many limitations encountered in other solution techniques. In particular, diffraction from deep gratings and the calculation of surface-plasmon excitations present no difficulties. Numerical results on diffraction efficiency are in good agreement with our experimental data. The near-field electromagnetic properties of Ag gratings are examined in detail. These results are of particular importance in understanding the optical behavior of molecules near a rough metal surface (for example, in surface-enhanced Raman scattering). It is shown that coincident with the excitation of surface plasmon, there is an enhancement of the local- (surface-) field intensity by a factor of 100 — 500. For molecules on a grating surface, such increase in local fields can imply an amplifica-tion of 10 — 3 X 10' for the Raman scattering signal.
Visualization for Machine Learning

As machine learning has increased in popularity, visualization has taken an important role in analyzing and communicating aspects of machine learning models. Increasingly, visualization techniques are being used across a broad set of domains and in business-critical use cases. Oftentimes, these visualizations convey non-trivial machine learning concepts, utilize complex visual representations, or demand user interaction. In this tutorial, we seek to provide a foundational understanding, to a broad audience, of the ways in which we can use visualization for machine learning tasks. In particular, we detail visual techniques for model assessment, model understanding, and dimensionality reduction. Furthermore, we outline foundations and recent work in emerging visualization topics such as topological data analysis and understanding deep learning model internals. Index Terms–visualization, machine learning
Hot-wire anemometer behaviour in low velocity air flow

The behaviour characteristics of a commercially available hot-wire anemometer have been examined in the presence of low velocities, lwss than 0.1 m/s for the most part, and in both horizontal and vertical air flows. The orientation of the probe in a horizontal flow has been found to have a definite effect on velocity measurements, and a lower limit to the reliable application of hot-wire anemometry has been evaluated for a particular type of probe. Vertical flow measurements have resulted in the discovery of the magnitude of natural convective velocity, and a possible method for determining the dimensions of the temperature jump distance has been developed indirectly from the description of the shape of the calibration curve.
Accurate Vision-based Flight with Fixed-Wing Drones

Fixed-wing drones must navigate to the desired location accurately for maneuvers such as picking up objects and perching. However, current GNSS receivers limit their navigation accuracy to several meters in outdoor environments, making such maneuvers impossible. RTK GNSS can improve flight accuracy, but it requires ground stations at the target location and additional communication modules on the drone. Here, we describe a fixed-wing platform with onboard computation that uses positional information from a GNSS receiver and vision from an onboard camera. The drone relies on a GNSS signal for flying towards a point of interest and switches to vision-based information to accurately reach the target. We conducted outdoor experiments to compare the flight accuracy of three navigation methods: GNSS, RTK GNSS, and the proposed GNSS-vision method. We also systematically assessed the robustness of vision-based control to compensate for GNSS errors and quantify the accuracy of the proposed method. Our results show that the accuracy of the proposed GNSS-vision system is on par with RTK GNSS. GNSS-vision reduces the average error of GNSS by over an order of magnitude, from 3.033 m to 0.283 m, and reduces the variance across repeated flights from 2.095 m to 0.309 m. We open-source the software-hardware architecture used in this paper to enable the research community to build on these results and expand the capabilities of fixed-wing drones.
Fault Diagnosis for Resistive Random-Access Memory and Monolithic Inter-tier Vias in Monolithic 3D Integration

Resistive random-access memory (RRAM) constitutes a promising technology for next-generation memory architectures due to its simple structure, high on/off ratio, and processing-in-memory ability. Its compatibility with emerging monolithic 3D (M3D) integration enables extremely high density using monolithic inter-tier vias (MIVs). However, both RRAM and M3D are susceptible to high defect rates due to immature manufacturing processes and process variations. Fault diagnosis for M3D-integrated RRAM and MIVs is therefore necessary to facilitate yield learning. In this work, we present a detailed characterization of RRAM faulty behaviors in the presence of process variations and manufacturing defects. We develop a diagnosis procedure by identifying appropriate reference resistance based on RRAM characteristics to efficiently distinguish fault origins. Results show that the proposed solution is compatible with existing test algorithms to significantly improve diagnostic resolution without affecting fault coverage.
Effects of Temperature and Polyethylene Plastic Packaging on Physicochemical Changes and Antioxidant Properties of Tomato During Storage

This study determined the effects of different storage temperatures and packaging on the physicochemical changes and antioxidant properties of tomatoes during storage in two tomato species (Lycopersicon esculentum Mill. tomato and Solanum lycopersicum var. Cerasiforme cherry tomato). Samples underwent storage process with different temperatures of 4 °C and room temperature (25 °C); with or without polyethylene plastic packaging. The physicochemical changes studied include weight, color, firmness, and total soluble solids (TSS), while the antioxidant properties studied include lycopene content, ascorbic acid content, total phenolic content (TPC), and free radical scavenging activity (2,2-Diphenyl-1-picrylhydrazyl, DPPH), measured at three-time points (day 1, 8, 15). Based on the two-way ANOVA, both temperature and packaging factors play an important role in the physicochemical changes and antioxidant properties of both tomato species. For tomatoes, the temperature had a significant (p<0.05) effect on all measurements, except for redness value (a*) and ascorbic acid content (p>0.05). While packaging had a significant (p<0.05) effect on all measurements, excluding the ascorbic acid and TPC (p>0.05). For cherry tomatoes, the temperature had a significant (p<0.05) effect on all measurements, not including ascorbic acid content (p>0.05). Whereas packaging had a significant (p<0.05) effect on all measurements, except for TPC (p>0.05). For both samples studied, temperature and packaging factors had significant interactions (p<0.05) on all measurements, except for ascorbic acid and TPC (p>0.05). In conclusion, storage at a low temperature of 4 °C with the packaging was found to be able to maintain the physicochemical and antioxidant properties in both tomato species.
Journal of Quantitative Analysis in Sports The Quarterback Prediction Problem : Forecasting the Performance of College Quarterbacks Selected in the NFL Draft

National Football League (NFL) teams spend substantial time and money trying to predict which college quarterbacks eligible to be drafted into the NFL will have successful professional careers. But despite this investment of resources, it is common for quarterbacks to perform much better or worse than anticipated. Prior work on this “quarterback prediction problem” has concluded that NFL teams are poor at determining which quarterbacks are likely to be successful based on information available prior to the draft. However, these analyses have generally focused only on quarterbacks who played in the NFL, ignoring those who were drafted but did not appear in a professional game. Using data on all quarterbacks drafted since 1997, we considered the problem of predicting NFL success as defined by two metrics (games played and Net Points), based on when a quarterback was drafted and his performances in college and at the NFL Combine. Our analyses suggest that college and combine statistics have little value for predicting whether a quarterback will be successful in the NFL. Contrary to previous work, we conclude that NFL teams aggregate pre-draft information—including qualitative observations—quite effectively, and their inability to consistently identify college quarterbacks who will excel in the professional ranks is a consequence of random variability in future performance due to factors which are unlikely to be observable.