Miracle-dz/UniFAR-FTUs huggingface dataset:
Employing dependent virtual samples to obtain more manufacturing information in pilot runs

In most highly competitive manufacturing industries, the sample sizes of new products are usually very small in pilot runs because the production schedules are very tight. To obtain the expected quality in mass production runs using limited data is, therefore, always a challenging issue for engineers. Although machine learning algorithms are widely applied to this task, the training sample size is a key weakness when determining the manufacturing parameters. In order to extract more robust information for engineers from the small datasets, this research, based on regression analysis and fuzzy techniques, develops an effective procedure for new production pattern constructions. In addition, a case study of TFT-LCD manufacturing in 2009 is taken as an example to illustrate the presented approach. The experimental results show that it is possible to develop a robust forecasting model which can provide more precise manufacturing predictions with the limited data acquired from pilot runs.
Activity prediction for in-home activity monitoring

In this paper, we propose an extension to a generic model developed for elaborating person's lifestyle patterns and changes in those patterns which could be related to health decline. The baseline model aims at a better understanding of the behaviours engaged in various housing contexts such as conventional housing or Assisted Living (AL) facilities and exploring irregularities within the life rhythm of the resident's everyday life activities. The model, embedded into a customized software program, has been extended to predict the progression of a particular activity on the basis of a circadian period (24- hour period) in order to detect unexpected events such as slowdowns or disruptions which could indicate changes in health status, cognitive or motor impairments. The objective is to help caregivers provide better assessment of functional health status and preventative assistance. A case study, elaborated through software simulations, is presented to show the potential of the software application to determine life patterns in conjunction with activity trend indicators that may indicate health deterioration. (4 pages)
Position and Force Control of Manipulators without Using Force Sensors.

In this paper, position and force control of constrained motion of robotic manipulators is discussed. First, issues related to decoupling position and force-controlled directions are discussed. The fundamentals of estimation of contact forces without using force or torque sensors are then presented, and a control scheme for position and force control of robotic manipulators without force sensing is proposed. The extension of this scheme to redundant manipulators is also discussed. Finally, the proposed control scheme is implemented on a 3-DOF planar redundant manipulator, and some experimental results are presented to illustrate the validity of the control scheme.
Fusion Network Combined With Bidirectional LSTM Network and Multiscale CNN for Useful Life Estimation LSTM Network and Multiscale CNN for Useful Life Estimation

To meet the needs of society and enterprises for high-reliability equipment, Remaining Useful Life (RUL) estimation has received extensive attention. Traditional statistical methods are significantly affected by model and parameter selection. Deep Learning has powerful data processing capabilities and does not require exact physical models and expert prior knowledge. Therefore, Deep Learning has shown a broad application prospect in the field of remaining life prediction. This paper proposes a data-driven method based on Bidirectional Long Short-Term Memory Network (BiLSTM) and Multiscale Convolutional Neural Network (MSCNN) for RUL estimation. Data are generated by sliding time window (TW) to the form of short-term sequences, then the data are filled into two base models in the same batch. Piecewise RUL functions are applied for label setting instead of traditional linear degradation functions. Proposed method is validated on the C-MAPSS dataset provided by NASA. Compared with similar methods, this proposed RUL prediction method has a better prediction capability.
Genetic parameters for the external udder morphology in commercial farms of Istrian sheep from Croatia

Istrian sheep is a local breed with superior milk yield and composition. Excellent udder shape of this breed needs to be preserved in future, since it’s economic value resides within the artisanal cheese production. Genetic parameters for the teat angle (Alpha) and the height of the cisternal part below the teat orifice (Cis) were estimated in order to evaluate the possibility for conservation of udder traits amiable for machine milking. Udder morphometry of the 240 registered Istrian ewes was measured from digital photographs of the posterior view of the udder on 10 commercial farms three times during lactation. Original pedigree records were trimmed to four recent generations. Heritabilities were estimated using single trait animal model. The stage of the lactation, interaction of lactation number, month of lambing and the farm were fixed effects, and the additive genetic value of the individual and permanent environment of the individual were random effects. Estimated heritability was 0.05 for Alpha and 0.71 for Cis mid-lactation measurements. Measurements of Cis during lactation did not result in a significant additive effect. The result shows potential in estimating genetic parameters of important udder traits in small commercial dairy ewe populations, using the digital measurements, given the reduction of non-genetic variability in the sample.
Quasinormal modes and Purcell factors of coupled loss-gain resonators and index-modulated ring resonators near exceptional points

We ﬁrst present a quasinormal mode (QNM) theory for coupled loss and gain resonators, working in the vicinity of an exceptional point. Assuming linear media, which can be fully quantiﬁed using the complex pole properties of the QNMs, we show how the QNMs yield a quantitatively good model to a full dipole spontaneous emission response in Maxwell’s equations at a variety of spatial positions and frequencies (linear response). We also develop a highly accurate and intuitive QNM coupled-mode theory, which can be used to rigorously model such systems using only the QNMs of the bare resonators, where the hybrid QNMs of the complete system are automatically obtained. Near a lossy exceptional point, we analytically show how the QNMs yield a Lorentzian-like and a Lorentzian-squared-like response for the spontaneous emission lineshape, consistent with other works. However, using rigorous analytical and numerical solutions for microdisk resonators, we demonstrate that the general lineshapes are far richer than what has been previously predicted. Indeed, the classical picture of spontaneous emission can take on a wide range of positive and negative Purcell factors from the hybrid modes of the coupled loss-gain system. These negative Purcell factors are unphysical and signal a clear breakdown of the classical dipole picture of spontaneous emission in such media, though the negative local density of states is correct. We also show the rich spectral features of the Green function propagators, which can be used to model various physical observables. Second, we present a QNM approach to model index-modulated ring resonators working near an exceptional point and show unusual chiral power ﬂow from linearly polarized emitters, in agreement with recent experiments, which is quantitatively explained without invoking the interpretation of a missing dimension (the Jordan vector) and a decoupling from the cavity eigenmodes.
Multiresolution equivariant graph variational autoencoder

In this paper, we propose Multiresolution Equivariant Graph Variational Autoencoders (MGVAE), the first hierarchical generative model to learn and generate graphs in a multiresolution and equivariant manner. At each resolution level, MGVAE employs higher order message passing to encode the graph while learning to partition it into mutually exclusive clusters and coarsening into a lower resolution that eventually creates a hierarchy of latent distributions. MGVAE then constructs a hierarchical generative model to variationally decode into a hierarchy of coarsened graphs. Importantly, our proposed framework is end-to-end permutation equivariant with respect to node ordering. MGVAE achieves competitive results with several generative tasks including general graph generation, molecular generation, unsupervised molecular representation learning to predict molecular properties, link prediction on citation graphs, and graph-based image generation. Our implementation is available at https://github.com/HyTruongSon/MGVAE.
Exploring the Social Capital Accounts for a Variation in Desistance and Its Relative Impact on Desistance at the Louisiana State Penitentiary

This study explores the social capital accounts for a variation in desistance and its relative impact on desistance at the Louisiana State Penitentiary. The study adopted a survey research design, binary logistic regression, and a primary data source with a sample of 144 respondents to explore the study. The primary data source comes from the Louisiana State Penitentiary based on self-reported face-to-fact survey interviews initially taken May 2007 and followed by face-to-fact interviews officially obtained data over the period of a year and eight months regarding the same sample population. Results suggested that in the Before study, using self-reported data, there were only two social capital variables that were statistically reliable in distinguishing desistance among inmates. These variables were relationship with mother, which had the most predictive power regarding desistance process, followed by the who raised the inmate variable . The strongest of all variables in this study was the punishment adjustment, in particularly the psychological coping one.
Feasibility study of using line tracer robot under disaster situation

Line tracer robots are widely accepted in various industry for the purpose of moving objects. In this study feasibility of using line tracer robots under disaster situation is studied as the line tracing capability may be useful in case of disaster. A small scale line tracer robot is designed, implemented, and tested under disaster situation. Specifically, the MCU based robot hardware including sensor, encoder, motor, and power supply is designed and implemented. Also, the software for receiving sensor signal, for driving motor and for implementing line searching algorithm is designed. The devised robot detects the guide line using 6 infrared optical sensors and can adjust the direction of motion with the front steering servo motor according to the sensor signal. In driving test, the robot showed reliable performance both on the straight and curved paths having obstacles under limited visibility condition as in disaster situation.
Investigation of Immune Microenvironment in Children with Langerhans Cell Histiocytosis

Background: Langerhans cell histiocytosis (LCH) is a rare disease that mainly occur in children. The aim of this investigation is to explore the immune microenvironment of LCH and feasibility of immunotherapy for children with LCH. Methods: Tissue samples were collected from 15 children with LCH and their clinical characteristics were recorded. The expressions of PD-1 ligand 1(PD-L1) and the presence of CD8 T lymphocytes were assessed by immunohistochemistry (IHC). Results: Of the total 15 patients, 8 of them were PD-L1 positive and it accounted for 53.33% and 9 of them were CD8 T lymphocytes positive and it accounted for 60%. There were 8 out of the 15 patients that were both PD-L1 positive and CD8 positive and they accounted for 53.33%. Conclusions: Our results showed that the expression of PD-L1 and presence of CD8 T lymphocytes occurred in the microenvironment of LCH. The findings indicated a possible new treatment option for LCH in children.
A retrospective study of prevalence and antibiotic resistance of bacteria isolated from the cerebrospinal fluid of neurosurgical patients in a Shanghai tertiary hospital

Background: Postoperative central nervous system infections (PCNSIs) are serious complications following neurosurgery. Effective management of the infections depends on the identification of causative pathogens and the antibiotic sensitivities. The aim of this study was to investigate the clinical features, causative organisms and their antimicrobial susceptibility testing results, which could help clinicians to initiate appropriate empirical antibiotic therapy.Methods: This was a retrospective study conducted over a period of 3.5 years (from January 2016 to July 2019). All in-patients admitted neurosurgery and ICU meeting the inclusion criteria of PCNSIs were included in the study. Demographic characteristics, type of neurosurgery, laboratory data, causative organisms and antimicrobial susceptibility testing results were analyzed.Results: A total of 1500 patients underwent operation during this study. Twenty-three patients with 34 isolates out of 79 patients with 157 CSF culture samples were available for data analysis. The estimated incidence and culture-positive rate of PCNSIs were approximately 1.5% and 29.1%, respectively. The predominant organism was coagulase-negative staphylococci, of which most were methicillin-resistant coagulase-negative staphylococci (MRCoNS). All were susceptible to vancomycin, linezolid, and rifampicin. Acinetobacter baumannii was the most frequent causative Gram-negative agent and was resistant to 14 out of 17 antimicrobials tested. The sensitivity rates for amikacin, sulfamethoxazole-trimethoprim and minocycline were only 44, 44, and 11%, respectively.Conclusions: The MRCoNS were the predominant organism for PCNSIs. The management of Acinetobacter baumannii could be a major clinical challenge with few effective antimicrobials in PCNSIs.
Robust free-space optical communication for indoor information environment

The purpose of our study is to establish a robust communication, while keeping security and privacy, between a handheld communicator and the surrounding information environment. From the viewpoint of low power consumption, we have been developing a reflectivity modulating communication module composed of a liquid crystal light modulator and a corner-reflecting mirror sheet. We installed a corner-reflecting sheet instead of light scattering sheet in a handheld videogame machine with a display screen with a reflection-type liquid crystal. Infrared (IR) LED illuminator attached next to the IR camera of a base station illuminates all the room, and the terminal send their data to the base station by switching ON and OFF of the reflected IR beam. Intensity of reflected light differs with the position and the direction of the terminal, and sometimes the intensity of OFF signal at a certain condition is brighter than that of ON signal at another condition. To improve the communication quality, use of machine learning technique is a possibility of the solution. In this paper, we compare various machine learning techniques for the purpose of free space optical communication, and propose a new algorithm that improves the robustness of the data link. Evaluation using an actual free-space communication system is also described.
Long-lasting relationships and success in cooperation processes

The main objective of this paper is to determine when a partnership can be said to have been successful. In order to do this, we review diverse literature about cooperation, seeking to substantiate the diverse criteria utilised to measure results in this type of relationship. Among these criteria, we focus particularly on the duration of partnerships, not only because the interpretation of the end or the continuity of cooperation is subject to significant controversy but also because it lets us analyse relationships in a dynamic way. We therefore seek to determine: (a) what factors influence the duration of cooperation and (b) whether mere continuity of the relationship is a distinctive, positive characteristic of cooperation processes.
LQR Control for a Self-Balancing Unicycle Robot on Inclined Plane

Abstract A self-balancing unicycle robot, which has a wheel for balancing and moving in the longitudinal plane (pitch angle) as well as a vertical flywheel for balancing in the lateral plane (roll angle), is studied in this paper. The non-linear dynamic equations of the unicycle robot on slope are analyzed using the Lagrangian dynamic formulation, then a linear model of the robot is derived at the equilibrium point, and linear quadratic regulators (LQR) are designed to control the robot on slopes with the angle of inclination varying from -11°to 11°. Simulation and physical experiment results validated that the unicycle robot can achieve good performance both on level plane and on slope. As far as we know, this is the first report of an autonomous unicycle robot moving and balancing on slope.
Inset fed Triple Band U-Slot Antenna for GSM900/GSM1900/WLAN Applications

To cover Global System for Mobile Communication(GSM) and WLAN frequency bands, three distinctive Inset fed antennas like rectangular microstrip, dualband dual slot and antennas are designed. Inset fed micostrip antenna is used for GSM1900 with an impedance bandwidth from 1.90GHz to 1.96GHz. Inset fed dual band dual slot antenna is used for GSM1900 and WLAN with impedance bandwidth is considered first band from 1.90GHz to 1.95GHz and second band from 2.38GHz to 2.42GHz. The proposed Inset fed triple band antenna is used for GSM900, GSM1900 and WLAN with appropriate position of slot, is to operate in frequency ranges of first band is from 920MHz to 940MHz, second band is from 1.91GHz to 1.94GHz and third band is from 2.39GHz to 2.43GHz. A correlation among various feed widths, feed lengths and slot widths are exhibited in this paper.
A method of surface clutter cancellation for an underground CW radar

An RF signal similar to the undesired signals is synthesized and used to cancel the undesired portion of the received signal from the antenna before it reaches the receiver. With a progressing degree of cancellation, the transmitter power is accordingly increased to expand the dynamic range of the radar. A canceller subsystem is proposed to eliminate undesired signals such as surface reflection and mutual coupling between antennas. >
Green Peptide–nanomaterials; A Friendly Healing Touch for Skin Wound Regeneration

The complex phenomenon by which the body responds to any injury of skin or tissue is known as wound healing. A number of phases like exudative, proliferative, and extracellular matrix remodeling are orchestrated events to be occurred involving blood cells, parenchymal cells, and different soluble mediators. Different internal, as well as external factors, regulate the speed and quality of healing. The delay in wound healing process causes the chronic wound or scar formation. At the present moment, the upscale research for identification of agents causing accelerated healing is important. Moreover, the biocompatibility of the accelerators needs to be investigated. Recent biomedical researches for wound care target to provide antimicrobial protection as well as matrix scaffolding for quick repairing of the skin tissue. In recent studies with natural peptides have shown that they are important components in developing the nano-medicines for their usefulness and therapeutic efficiency. New therapeutic formulations can be developed using these natural peptides utilizing different nanoparticle delivery system. This review deals with the developmental study on efficient wound care system where the possible use of natural peptides in combination with nanomaterials has been explored. A trial has also been made on the findings made over the past few years on the use of peptides as tissue regenerating agents through effective wound healing pathway.
TELEOPERATED MANIPULATOR CONTROL

This work is a bibliographical review about different models used to design teleoperated manipulator controllers, there is a quick description of some advantages and disadvantages to implement conventional or intelligent techniques (Neural Networks and Fuzzy Logic) to develop control systems applied to these devices. In addition, here the teleoperation systems are descriptively presented; finally there is a specialized section that shows some control strategies in order to deal with signal delays, inherent to manipulator teleoperation. In the last section some important applications for these systems are described.
Fuzzy FMEA with a guided rules reduction system for prioritization of failures

Purpose – To propose a generic method to simplify the fuzzy logic‐based failure mode and effect analysis (FMEA) methodology by reducing the number of rules that needs to be provided by FMEA users for the fuzzy risk priority number (RPN) modeling process.Design/methodology/approach – The fuzzy RPN approach typically requires a large number of rules, and it is a tedious task to obtain a full set of rules. The larger the number of rules provided by the users, the better the prediction accuracy of the fuzzy RPN model. As the number of rules required increases, ease of use of the model decreases since the users have to provide a lot of information/rules for the modeling process. A guided rules reduction system (GRRS) is thus proposed to regulate the number of rules required during the fuzzy RPN modeling process. The effectiveness of the proposed GRRS is investigated using three real‐world case studies in a semiconductor manufacturing process.Findings – In this paper, we argued that not all the rules are actual.
A Comment on 'Efficient Pollution Regulation: Getting the Prices Right' by Muller and Mendelsohn

In their recent paper , Efficient Pollution Regulation: Getting the Prices Right (henceforth, EPR), Muller and Mendelsohn describe a broader, more appealing concept of efficiency that incorporates information on damages caused by emissions from specific sources: “The science and economics related to pollution control”, they write, “have advanced to the point where regulations can now move from cost-effectiveness to efficiency. ” We argue that despite the appeal of the EPR solution, its conclusion that source-specific marginal damage estimates are ready for use in regulations is simply incompatible with the empirical evidence presented in EPR. In particular, we explore the implications of the EPR finding of negative marginal damages from NOx emissions for many heavily populated counties. The associated nonconvexities, we show, imply that the source-specific trading ratios that EPR advocates lead to unattractive outcomes not likely to be efficient. We also discuss how the EPR assumption that the regulators know damages with certainty oversimplifies key aspects of efficient air pollution regulation.
UWB bandpass filter with sharp rejection and narrow notched band

A novel ultra-wideband (UWB) bandpass filter with sharp rejection and a narrow notched band is presented. This filter is designed using a multiple-mode resonator (MMR) which consists of a ring resonator and a stepped-impedance stub. The MMR has six resonance frequencies and three transmission zeros (TZs). All three TZs are close to the resonance frequencies, thus creating sharp roll-off and narrow notched bands. Different passband bandwidths and notched bands can be obtained by tuning the stepped-impedance admittance ratios. The simulated and measured results demonstrate that this simple bandpass filter has 97.3 and 30.6 dB/GHz attenuation slope at the lower and upper transition bands, respectively. The 3 dB notched band is from 5.6 to 5.8 GHz with 10.3 dB insertion loss at 5.7 GHz.