Miracle-dz/UniFAR-FTUs huggingface dataset:
Reference Acceleration Model Predictive Control (RA-MPC) for Cable-Driven Robots

In this paper, a computationally efficient model predictive control (MPC) is proposed for the trajectory tracking of cable-driven robots subject to state/input constraints. While MPC has been an effective tool in dealing with various constraints, the primary drawback is the high computational load caused by the non-convexity of the corresponding optimization problem. In order to avoid the non-convexity, the prediction model in the proposed reference acceleration MPC (RA-MPC) is simplified into a linear one by assuming the reference accelerations being taken in the future horizon steps. As a result, RA-MPC only optimizes for the instantaneous joint accelerations and the corresponding actuator commands for the current step, resulting in a convex quadratic program that can be efficiently solved. It is further shown that by properly selecting parameters, RA-MPC can be interpreted as ‘soft-CTC’ and ‘soft-LQR’, where the joint acceleration is allowed to deviate from the corresponding desired value, computed from a PD gain or an LQR gain. The effectiveness of the proposed RA-MPC are demonstrated in both simulation and hardware experiment using cable-driven robots.
Collision of comet Shoemaker–Levy 9 with Jupiter: what did we see

In July 1994, a dramatic event took place—the collision of the Shoemaker–Levy 9 comet with Jupiter. This collision has been accompanied by a great number of various effects in the atmosphere, ionosphere and magnetosphere of Jupiter. The comet impact became one of the most grandiose active experiments Nature has ever performed. Among the most interesting effects are bursts of radiation registered in a wide spectral range during the cometary fragments fall, the generation of giant gaseous plumes caused by impact, the formation of large-scale long-lived vortex structures in the Jovian atmosphere. Unexpected events were the significant brightening of the Jovian radiation belts during the cometary fragments impact, the peculiarities of polar aurorae induced by the impact, the weakening of the Io plasma torus brightness observed in the extreme UV range, etc. In the present review, the results of the comet impact with Jupiter are discussed and a unified physical model is suggested which explains consistently the basis observational data.
Modeling of Cavitation

The paper deals with the study of source terms for Euler and Navier-Stokes equations which describe cavitation phenomena in one- and three-dimensional flow. Cavitation occurs in many technical applications, such as injectors, throttles and pumps. The modeling of cavitation is a challenge due to the complex structure of the cavitation process and the difficult numerical realization. The main problem is to simulate one- or three-dimensional flow in order to predict the material of cavitation erosion or to determine the mass flow which will be limited if cavitation occurs in a throttle.
Detection of Glyphosate in Water with Photonic-Tailored Silver Nanoparticles

This work describes an approach towards the detection of glyphosate levels in liquid compatible with standards for drinking water. Prismatic nanostructures were produced from spherical silver nanoparticles by photoinduced shape conversion, leading to an enhanced interaction between the nanoparticles and the analyte, inferred by both ultraviolet-visible and surface enhanced Raman spectroscopy. The role of the silver nanoparticles’ shape and charge in the interaction with glyphosate was investigated. SERS spectra revealed fingerprint bands that provide an unambiguous identification of this herbicide. The obtained limit of detection was 0.11 mg/L, which is almost 8.2 times smaller than the health-based reference value.
Deep Embedded Clustering Framework for Mixed Data

Deep embedded clustering (DEC) is a representative clustering algorithm that leverages deep-learning frameworks. DEC jointly learns low-dimensional feature representations and optimizes the clustering goals but only works with numerical data. However, in practice, the real-world data to be clustered includes not only numerical features but also categorical features that DEC cannot handle. In addition, if the difference between the soft assignment and target values is large, DEC applications may suffer from convergence problems. In this study, to overcome these limitations, we propose a deep embedded clustering framework that can utilize mixed data to increase the convergence stability using soft-target updates; a concept that is borrowed from an improved deep Q learning algorithm used in reinforcement learning. To evaluate the performance of the framework, we utilized various benchmark datasets composed of mixed data and empirically demonstrated that our approach outperformed existing clustering algorithms in most standard metrics. To the best of our knowledge, we state that our work achieved state-of-the-art performance among its contemporaries in this field.
WFH-VR: Teleoperating a Robot Arm to set a Dining Table across the Globe via Virtual Reality

This paper presents an easy-to-deploy, virtual reality-based teleoperation system for controlling a robot arm. The proposed system is based on a consumer-grade virtual reality device (Oculus Quest 2) with a low-cost robot arm (a LoCoBot) to allow easy replication and set up. The proposed Work-from-Home Virtual Reality (WFH-VR) system allows the user to feel an intimate connection with the real remote robot arm. Virtual representations of the robot and objects to be manipulated in the real-world are presented in VR by streaming data pertaining to orientation and poses. The user studies suggest that 1) the proposed telerobotic system is effective under conditions both with and without network latency, whereas a method that simply streams video does not. This design enables the system implemented at an arbitrary distance from the actual work site. 2) The proposed system allows novices to perform manipulation tasks requiring higher dexterity than traditional keyboard controls can support, such as setting tableware. All results, hardware settings, and questionnaire feedback can be obtained at https://arg-nctu.github. io/projects/vr-robot-arm.html.
NUDT1 Could Be a Prognostic Biomarker and Correlated with Immune Infiltration in Clear Cell Renal Cell Carcinoma

Background Clear cell renal cell carcinoma (ccRCC) is a malignant tumor with high morbidity and mortality. As a member of the Nudix hydrolase superfamily, Nudix (nucleoside diphosphate-linked moiety X)-type motif 1 (NUDT1) is closely related to the occurrence and development of cancer. Our study aims to explore the role of NUDT1 in ccRCC and its relationship with immune infiltration. Methods The NUDT1 expression matrix and corresponding clinical information were obtained from The Cancer Genome Atlas (TCGA) database. The expression difference of NUDT1 in ccRCC and its relationship with the clinical characteristics were investigated using R software. Kaplan–Meier (K–M) analysis, univariate Cox regression, multivariate Cox regression, receiver operating characteristic (ROC) curve, and nomogram were utilized to evaluate the survival and prognosis of patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were utilized to explore the function of differential genes in low- or high-expression group of NUDT1. TCGA dataset and Tumor IMmune Estimation Resource (TIMER) database were utilized to explore the relationship between NUDT1 and immune infiltration. Finally, TCGA dataset was utilized for gene set enrichment analysis (GSEA). Results NUDT1 was not only overexpressed in ccRCC but also significantly correlated with clinicopathological features (P < 0.05). K–M survival analysis showed that upregulated NUDT1 was closely related to the decrease of overall survival (OS) and progression-free survival (PFS) in ccRCC patients. Multivariate Cox regression revealed that NUDT1 was a independent prognostic indicator (HR = 1.437, 95% CI: 1.065–1.939, P=0.018). The ROC curve showed that NUDT1 had a certain accuracy in predicting the outcome of ccRCC patiens. Furthermore, a total of 150 coexpressed genes and 1,886 differentially expressed genes (DEGs) were identified. GO/KEGG and GSEA results suggested that NUDT1 and its DEGs were involved in the immune-related pathways. NUDT1 expression was positively correlated with infiltrating levels of regulatory T cells (Tregs), CD8+ T cells, follicular helper T cells, and M0 macrophages. In addition, NUDT1 was positively related to immune checkpoints, such as PD-1, LAG3, CTLA4, and CD70, in ccRCC. Conclusion NUDT1 plays a key role in the prognosis and immune cell infiltration of ccRCC patients, indicating its potential use as a prognostic biomarker and therapeutic target.
Tapered fiber Mach–Zehnder interferometer for simultaneous measurement of refractive index and temperature

An approach to achieve simultaneous measurement of refractive index and temperature is proposed by using a Mach–Zehnder interferometer realized on tapered single-mode optical fiber. The attenuation peak wavelength of the interference with specific order in the transmission spectrum shifts with changes in the environmental refractive index and temperature. By utilizing S-band and C/L-band light sources, simultaneous discrimination of refractive index and temperature with the tapered fiber Mach–Zehnder interferometer is demonstrated with the corresponding sensitivities of −23.188 nm/RIU (refractive index unit) and 0.071 nm/ °C, and −26.087 nm/RIU (blueshift) and 0.077 nm/°C (redshift) for the interference orders of 169 and 144, respectively.
Using bodily displays to facilitating approach action outcomes within the context of a personally relevant task

Considerable attention has been paid to the effect of bodily (expansive and contractive) displays on affective, behavioral, and hormonal outcomes. However, the majority of past studies are limited by a lack of control groups with neutral displays and low personal relevance of the experimental tasks employed. The present study aimed to investigate the effect of adopting different bodily displays, including neutral displays, within the context of a personally relevant task.
Modern Rehabilitation Technologies of Patients with Motor Disorders at an Early Rehabilitation of Stroke (Review)

Cerebral stroke is one of the leading disability causes among adult population worldwide. The number of post-stroke patients, who need rehabilitation including motor recovery, keeps growing annually. Standard motor rehabilitation techniques have a limited effect on recovering extremity motor defunctionalization. In this regard, in recent years, new technologies of post-stroke rehabilitation are being suggested. The present review summarizes the existing literature data on current techniques applied in patients with motor disorders at an early rehabilitation period of cerebral stroke. The current modern technologies are divided into the methods based on “interhemispheric inhibition” theory (repetitive transcranial magnetic stimulation, transcranial direct current stimulation), and on “mirror neurons” theory (virtual reality systems and brain–computer interfaces). The authors present the neurophysiological causes and feasible protocols of using the techniques in clinical practice, the clinical research findings due to the initial severity level of motor disorders and stroke age, as well as the factors contributing to the motor rehabilitation efficiency when using these methods.
Learning with Yourself: a Tangible Twin Robot System to Promote STEM Education

This paper presents a customized programmable robotic system, TanTwin (Tangible Twin), designed to promote STEM education for K-12 children. Firstly, TanTwin is implemented based on a wheel-robot with standard LEGO bricks. With several deep neural networks, a child can convert a captured portrait of himself/herself into standard LEGO bricks, therefore he/she can build a tangible twin robot of him-selflherself automatically. Besides, to adapt to the customized appearance, the corresponding visual element and content of the robotic system were also changed by a rule-based adaption algorithm. To demonstrate the effectiveness of TanTwin and to investigate whether tangible twin robots could contribute to children's learning, we conducted a controlled experimental study to compare learning with a TanTwin and with a standard robot system through measuring students' cognitive learning outcomes. The pre-/post- knowledge test results indicated that learning with a tangible twin robot leads to significantly better learning outcomes. Given the results, we validate our system and customization technology can promote STEM education.
Crack detection of plate structures using differential quadrature method

In this study, the problem of crack identification in plates is investigated using the differential quadrature method. The crack, which is assumed to be open, is modeled by the extended rotational spring. The crack, with finite length, divides the plate into six segments. Then, the differential quadrature is applied to the governing differential equations of motion of each segment and the corresponding boundary and continuity conditions. An eigenvalue analysis will be performed on the resulting system of algebraic equations to obtain the natural frequencies of the cracked plate. Here, the crack detection practice is considered as an optimization problem, and the location, size, and depth of the crack are regarded as the design variables. The weighted sum of the squared errors between the measured and computed natural frequencies is used as the objective function. The Bees algorithm, a swarm-based evolutionary optimization technique, is used to solve the optimization problem. To insure the integrity and robustness of the presented method, extensive experimental case studies are carried out on the cantilever plates having a finite-length open crack parallel to the clamped edge. The results show that the crack parameters can be predicted well by the presented methodology.
Robot Contact Reflexes: Adaptive Maneuvers in the Contact Reflex Space

In order to transform a robot into an intelligent machine it needs to be enabled to react to unforeseen events (most importantly collisions) during task execution and have a plan on how to continue the task afterwards. This requires a flexible operational framework that allows to define adaptive reactions and interactions with the motion generation and task planning stage. Within this work we first reason about the choices the robot has for reactions to unforeseen events such as collisions with respect to safety of humans in the workspace, the robot itself and the environment as well as the successful task execution. We further present a flexible reflex engine together with a concept of integration into the motion generation and control work flow. The reflex engine and it's reflex maneuvers are a combination of state machines and decision trees that take into account the state of the robot and the world. It is capable of choosing safe reactions and can differentiate between different levels of contact severity and according reaction sets. Several reflex maneuvers are evaluated towards safety performance criteria in real robot experiments using an ISO/TS 15066 conform measurement device. Some of the tested reflexes are furthermore integrated into an implementation of the proposed approach for a simple real world example task where the robot needs to pickup a container and dispose it's content into a bin.
An Empirical Approach to the Implementation of Lean Manufacturing as a Strategy to Mitigate Industrial Waste in South Africa

This study investigates the influence of lean manufacturing as a strategy to eliminate waste if not minimizing it by considering the different types of waste to eliminate, the 5S Lean method, Kaizen, Just in Time, and Value-added management. The study provides a theoretical application of lean manufacturing on how it can be introduced and implemented in the business environment. A quantitative approach with the use of questionnaires has been used to gather information from 80 organizations that voluntarily took part in the study. Results have shown that many of the respondents are aware of lean and use it, nevertheless, different internal barriers such as reluctance to change, management support, and staff training make lean’s application difficult even though they all agreed that it had a positive impact on their production process. The sample size is a major limitation to the study, using a much bigger one would provide more reliable result to confirm these findings and enrich the existing literature.
New-generation hollow-core photonic crystal fibers and their outstanding possibilities

In recent years, hollow-core fiber technology has witnessed tremendous advancement, which consolidated this class of optical fibers as an enabling technology for future developments in photonics. In this paper, we develop on the progress in the hollow-core fiber field and highlight their outstanding properties and current state-of-the-art. Moreover, we provide an overview of the promising avenues for hollow-core fiber applications and discuss their bright future landscape.
Effects of a practical teacher-training program on STEAM activity planning

We sought to analyse the effects of a practical teacher-training program on teachers’ abilities in planning STEAM activities. For that, a case study is presented in a mixed-method research approach. 14 Brazilian teachers took part in a course wherein they received mentoring, instruments, and feedback to develop STEAM plans. In this research, data were collected from two surveys, from previously validated open-ended questions, and from the STEAM plans as final products. Quantitative data was analysed through nonparametric statistical tests, and qualitative data underwent content analysis according to the Ground Theory commandments. As result, teachers reported a positive view of the STEAM impact and a high predisposition towards it. Although they considered merging STEAM areas challenging, especially Engineering and Technology, the training helped them surpass it. All plans had at least two areas, and half of them had all five STEAM areas. Around 70% of the teachers used an active learning teaching method which was new to them. There was a significant augment in teachers' self-efficacy in planning STEAM activities.
BSA - Bi-Stiffness Actuation for optimally exploiting intrinsic compliance and inertial coupling effects in elastic joint robots

Compliance in actuation has been exploited to generate highly dynamic maneuvers such as throwing that take advantage of the potential energy stored in joint springs. However, the energy storage and release could not be well-timed yet. On the contrary, for multi-link systems, the natural system dynamics might even work against the actual goal. With the introduction of variable stiffness actuators, this problem has been partially addressed. With a suitable optimal control strategy, the approximate decoupling of the motor from the link can be achieved to maximize the energy transfer into the distal link prior to launch. However, such continuous stiffness variation is complex and typically leads to oscillatory swing-up motions instead of clear launch sequences. To circumvent this issue, we investigate decoupling for speed maximization with a dedicated novel actuator concept denoted Bi-Stiffness Actuation. With this, it is possible to fully decouple the link from the joint mechanism by a switch-and-hold clutch and simultaneously keep the elastic energy stored. We show that with this novel paradigm, it is not only possible to reach the same optimal performance as with power-equivalent variable stiffness actuation, but even directly control the energy transfer timing. This is a major step forward compared to previous optimal control approaches, which rely on optimizing the full time-series control input.
Wafer Defect Pattern Classification with Explainable-Decision Tree Technique

Local defect patterns (LDP) in wafer maps are usually induced by problems in the manufacturing process. Therefore, defect pattern recognition is useful for root cause analysis, which is very important for yield optimization. Machine learning (ML) based methods can achieve good LDP recognition rate. In general, these techniques are designed to identify spatial patterns, which may not directly linked to the root cause. Besides, ML methods are more time-consuming because of the training process. In this paper, we propose to apply a rule-based method for LDP classification such that the recognized patterns are explainable. Experimental results show that the proposed method achieves roughly the same level of accuracy as other ML methods while the exaction time is much faster.
Projection of Hourly Extreme Precipitation Using the WRF Model Over Eastern China

The Weather Research and Forecasting (WRF) model was applied to simulate the current and future climates over eastern China at a convection‐permitting resolution (∼4 km). A continuous 10‐year (1998–2007) simulation was performed for the historical climate, and three pseudo‐global warming (PGW) experiments driven by climate perturbations during 2070–2099 were conducted under the RCP2.6, RCP4.5, and RCP8.5 scenarios. After validation against reanalysis and observational data, the historical simulation was found capable of reasonably reproducing the characteristics of mean precipitation, extreme hourly precipitation, and large‐scale circulation. The PGW experiments projected an increase in precipitation intensity over most regions, especially North China under the RCP8.5 scenario (up to 30%). However, the precipitation frequency was projected to decrease, especially over the Yangtze River Basin in summer. A wetting (drying) trend was observed over North China (South China) in summer (winter), due to the increase (decrease) in precipitation intensity (frequency). The intensity and frequency of extreme events were projected to increase over most areas of eastern China, and the increase was the most dramatic under the RCP8.5 scenario, indicating a greater flood risk under stronger global warming. The relationship between extreme precipitation and temperature (EP–T) displayed a hook‐shape structure in both the present and future climates. The extreme precipitation intensity increased with temperature at a super Clausius–Clapeyron (C–C) scaling rate (>7%/K) until reaching a peak at temperatures of around 25–29°C, and then decreased at higher temperatures. The decreases were mainly attributed to moisture limitation at high temperatures. In the future, the EP–T relationship is expected to change mostly following the C–C law, with greater precipitation peaks under scenarios with higher carbon emissions, indicating that warmer climates contribute to further enhancement of extreme precipitation.
FULL-ORBIT H-ALPHA EMISSION IN RW TAU

H-alpha observations of the Algol system RW Tauri were obtained over a four year period (1988 October - 1992 September) and ~1 A resolution, with coverage over the full orbit. Strong single-peaked emission near orbital quadrature is easily seen in the raw spectra. Subtracting the stellar compontents' light reveals a second, weaker, often double-peaked emission component at almost all phases, at most epochs. An absorption component is seen just before and after primary eclipse. Visibility and velocities for the strong emission place it between the stellar components, near the primary star. Depending on the epoch, this stronger emission has one of two distinct patterns in a velocity versus phase plot. The weaker emission feature's visibility and velocity indicate that it usually arises from a slowly rotating structure near the primary star, with a radial extent of at least 0.33 R1. A third feature (which we interpret as a transient ring) was present in 3 of 12 runs.