Miracle-dz/UniFAR-FTUs huggingface dataset:
Transferable Collision Detection Learning for Collaborative Manipulator Using Versatile Modularized Neural Network

As human-robot collaboration increases and robots are applied to everyday life, interest in safety issues is increasing. To be safely used in real life, in addition to collision prevention algorithms, robots need to quickly detect unexpected collisions and take appropriate actions. Recently, deep learning-based collision detection algorithms have been proposed to overcome the limitations of model-based collision detection methods, but there are also limitations on deep learning methods, especially in data collection. The collected data are often insufficient because collecting collision data is laborious and intrinsically imbalanced, meaning that the collision data are much smaller than the free-motion data. Moreover, since collecting collision data is risky and might cause potential damage to the robot, applying the deep learning method to a new target robot on mass production is highly restricted. Therefore, in this article, an inductive bias is imposed on network structure and input variable to be sample-efficiently trained, which is suitable for insufficient imbalanced data. The proposed modularized neural network removes the connection between other joints at the front part of the network, reducing the search space of the learnable parameter. Moreover, the input variable is selected to take both dynamics features and error-related features into account. Consequently, the proposed method is versatile, showing successful generalization performance to random motion, random collision location, and various loads at the end-effector. Furthermore, to circumvent the limitations of applying deep learning methods to mass production, transfer learning is proposed, which does not require any collision data from the target robot. The proposed data-mixture method and collision ratio adjustment method for fine-tuning are validated with two source robots and one target robot. The transferred network is effective to be applied on mass production without losing performance compared to a specific-robot-trained network(a network trained with specific robot data and applied to the same robot).
A novel self-injection relativistic backward wave oscillator

A novel self-injection relativistic backward wave oscillator (RBWO) has been proposed. By introducing a self-injection path into the RBWO, a small portion of the energy in the reflector can be coupled to the upstream of the reflector, and then the formed electric field in the self-injection path region can pre-modulate the passing electron beam, to promote a frequency-locking oscillation of the electron beam. The pre-modulated electron beam can be expected to enhance the beam-wave interaction and suppress parasitic mode oscillation, which is beneficial for maintaining the dominant role of the operating mode. The proposed self-injection RBWO shows great potential for improving the conversion efficiency and pulse duration time. Through particle-in-cell simulation, a microwave with a power of 10.6 GW is obtained, when the beam voltage is 1.08 MeV, and the beam current is 18.6 kA. The conversion efficiency is 53%.
Examining the streets of Kabalagala’s CBD Using Crime Prevention through Environmental Design (CPTED)

ABSTRACT Developing countries should explore other avenues apart from the traditional approaches of crime prevention mainly involving the criminal justice and social approaches of crime prevention that may assist in arresting crime amid resource constraints and predicted increase in population. This paper evaluates the streets of Kabalagala township Located in Kampala City, Uganda using a Defensible Space checklist and assess the perceptions of property/business owners concerning property crimes at the street level. The findings indicated that the streets of Muyenga, Gaba and Kikubamutwe were impacted by the existence of isolated areas and concealment opportunities and presented signs of poor maintenance and management of street elements which may have adversely affected effective natural surveillance and attracted criminal activity.
Comparison of commercial disinfectants and an in loco-produced solution: free residual chlorine decay in human supply waters

Disinfection process is used in the treatment of water for human supply to promote sanitary safety and provide users with drinking water that meets potability standards. Thus, it is necessary to sustain a minimal concentration of free residual chlorine (FRC) throughout the entire distribution system. The present study investigated the decay process of FRC concentration in water destined for human supply. The decay was evaluated in bench-scale testing, using sodium hypochlorite, calcium hypochlorite, sodium dichloroisocyanurate (organic chlorine) as disinfectant agents, and also an alternative disinfectant solution (ADS) produced in loco, with oxidizing and disinfectant properties, which is being used in Brazilian sanitation industry. To evaluate the decay, four models were fitted: first-order, nth-order, limited first-order and parallel first-order, hence determining the corresponding parameters which describe the decay speed of the FRC concentration in water. Achieved results demonstrated that all models were statistically significant and predictive. However, parallel first-order model produced the best fit. Regarding the evaluated disinfectants, it was noted the preeminence of ADS solution when compared to the others, since it imparted a higher FRC over time, a behavior indicated by lower values for reaction rate constant in all models and when compared to other disinfectants used in this study.
One-port active polysilicon resonant microstructures

Theoretical and experimental characteristics of a two-terminal, or one-port, resonant microstructure are discussed. An equivalent circuit model that is useful for design and analysis of these devices is presented. This model is verified by experimental measurements, with a worst-case error between model and experimental parameters of 30%. A process for integrating polysilicon resonant microstructures with on-chip NMOS (N-metal oxide semiconductor) circuitry is also described. A novel feature of this process is the use of rapid thermal annealing (RTA) for strain-relief of the non-implanted phosphorus-doped polysilicon. The RTA-strain-relieved polysilicon has a Young's modulus of 0.9. 10/sup 12/ dynes/cm/sup 2/ and residual strain of 0.002% as measured by resonant frequency techniques. This low value of strain indicated that RTA is a useful strain-relief technique. <<ETX>>
A Novel High-Power Amplifier Using a Generalized Coupled-Line Transformer with Inherent DC-Block Function

The purpose of this paper is to propose a novel generalized single-band transformer for two arbitrary complex load and source impedances and a novel high-power amplifler using this new transformer. By adding two reactive parts at across terminals, the coupled line with ∞exible electrical length has practical even- and odd-mode characteristic impedances. Thus, the total circuit layout can be realized on common printed circuit board without any restriction. The synthesis theory of this proposed transformer is complete and analytical. Furthermore, unlike conventional quarter- wavelength transformers, this structure exhibits four main features such as efiective matching for extremely low-resistive load impedance, efiective matching for extremely high-resistive load impedance, tunable characteristic for equivalent electrical length and inherent DC-block function. For theoretical veriflcation, several impedance transformers, which include some flxed operating-frequency cases and a tunable case, for smaller than 7Ohm or larger than 1500Ohm, are presented. As a typical experimental example, this analyzed transformer with inherent DC-block function has been applied in a 4-Watt power amplifler as output matching structure.
Advanced Control and Management Functionalities for Multi-MicroGrids

This paper addresses the extension of the microgrid concept, following a massive integration of these active cells in power distribution networks, by adopting a coordinated management strategy together with distributed generation units directly connected to the medium voltage distribution network. In order to achieve this, a technical and commercial management scheme must be developed for coordinated control of a distribution system with multi-microgrids, which should take into account the specific technical capabilities and characteristics of each type of generating source. In particular, tools for coordinated voltage support and frequency control, as well as for state estimation have been developed for this type of network. Concerning voltage support, a new methodology exploiting an optimization tool based on a meta-heuristic approach was developed. For state estimation, two approaches were considered: multi-microgrid state estimation and fuzzy state estimation. Regarding frequency control, the hierarchical structure of the multi-microgrid is exploited to deal with the transition to islanded operation and load following in islanded operation. All these tools have proved to be efficient in managing the multi-microgrid system in normal interconnected mode and, in case of the frequency control, in islanded operation. Copyright © 2010 John Wiley & Sons, Ltd.
A Classical Approach to Handcrafted Feature Extraction Techniques for Bangla Handwritten Digit Recognition

Bangla Handwritten Digit recognition is a significant step forward in the development of Bangla OCR. However, intricate shape, structural likeness and distinctive composition style of Bangla digits makes it relatively challenging to distinguish. Thus, in this paper, we benchmarked four rigorous classifiers to recognize Bangla Handwritten Digit: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Gradient-Boosted Decision Trees (GBDT) based on three handcrafted feature extraction techniques: Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP), and Gabor filter on four publicly available Bangla handwriting digits datasets: NumtaDB, CMARTdb, Ekush and BDRW. Here, handcrafted feature extraction methods are used to extract features from the dataset image, which are then utilized to train machine learning classifiers to identify Bangla handwritten digits. We further fine-tuned the hyperparameters of the classification algorithms in order to acquire the finest Bangla handwritten digits recognition performance from these algorithms, and among all the models we employed, the HOG features combined with SVM model (HOG+SVM) attained the best performance metrics across all datasets. The recognition accuracy of the HOG+SVM method on the NumtaDB, CMARTdb, Ekush and BDRW datasets reached 93.32%, 98.08%, 95.68% and 89.68%, respectively as well as we compared the model performance with recent state-of-art methods.
Parameter optimization of a hydraulic engine mount based on a genetic neural network

Abstract Hydraulic engine mounts (HEMs) are important vehicle components to isolate the vehicle structure from engine vibration. A parameter optimization methodology for an HEM based on a genetic neural network (NN) model is proposed in this study. Samples of HEMs with different structures and rubber materials are manufactured and their dynamic characteristics are tested on an MTS 831 elastomer test system. Then the test results are used as samples to train the NN model which defines the non-linear global mapping relationship between the HEM's structural parameters and its dynamic characteristics. The fitness values of the population in the genetic algorithm are calculated by the trained NN model, and the optimal solution was acquired with the mutation of population. Finally, experiments are made to validate the reliability of the optimal solution. The proposed optimization method can specify the structures and materials of HEMs to meet the design requirements automatically.
Moral Judgement along the Academic Training

Moral reasoning and consequent decision making are central in the everyday life of all people, independent of their profession. It is undoubtedly crucial in the so-called “helping professions”, when the professional through his/her decisions can support or not support others. Our study aimed to investigate whether academic training can play an essential role in influencing moral reasoning. We used three different conditions: 20 moral personal, 20 moral impersonal, and 20 nonmoral dilemmas to assessed differences in moral judgement between students of Economics, Medicine, and Psychology at their first year and at the end of university training. We observed a difference between school and year of course: psychology students showing more time when asked to read and answer the proposed questions. Moreover, medical students showed a significant increase in sensitiveness to moral issues as a function of academic ageing, whereas such a moral sense regressed from the first to the fifth year of academic training in other students. Gender was also relevant, with women showing an increased response and reading times compared to than men when asked to cope with moral decisions. This study shows that the main factor driving moral decision making is the faculty to which one is enrolled, significantly modulated by sex and academic seniority.
Application of Single Neuron PID in the Control of Loader Working Device

In order to improve the maneuverability and the automation of loader working device, this paper establishes it’s Electro-Hydraulic Servo Position System mathematical model based on the working principle of its Hydraulic System, and designs a position controller for the loader working device using Single Neuron PID Control Algorithm. The tests of simulation were carried out on the platform of Matlab/Simulink. The simulation results show that Single Neuron PID Control Algorithm can improve the system control effect, process its real-time and static-dynamic characteristics with a simple controller.
Microcontroller-based power-angle instrument for a power systems laboratory

This paper presents the design and construction of a microcontroller-based instrument to measure the power angle, the rotor speed, and the terminal voltage frequency of synchronous machines. The instrument is suitable for both steady-state and transient measurements. It can be used as a stand alone instrument or connected to a personal computer. The instrument uses a 256-slit optical encoder and a signal transformer to detect the rotor position and the terminal voltage, respectively. Most of previous power-angle instruments require a laborious manual procedure to calibrate the power-angle zero reference each time the instrument is disconnected or turned off or the generator field is deenergized. This manual procedure affects the accuracy of the instrument. The instrument presented in this paper is self-calibrating, i.e., it is automatically self-calibrated by software. The power angle can be measured with a resolution of 0.0216 electrical degrees. To use the instrument with a personal computer, a special graphical user interface was designed.
Personalized Context-Aware Attentive Services in the Mobile Environment

In critical, dynamic mobile environments, users' attention should be guided to critical information and objects for optimal performance. We propose an adaptive, multimodal system to provide this guidance by proactive notification based on user, task and context models. In the police domain, these models will be empirically validated in prototypes using a proven cognitive engineering framework. We will show that such personalized attentive support will result in more relevant interaction and higher task efficiency.
A highly sensitive cell-based luciferase assay for high-throughput automated screening of SARS-CoV-2 nsp5/3CLpro inhibitors

Effective drugs against SARS-CoV-2 are urgently needed to treat severe cases of infection and for prophylactic use. The main viral protease (nsp5 or 3CLpro) represents an attractive and possibly broad-spectrum target for drug development as it is essential to the virus life cycle and highly conserved among betacoronaviruses. Sensitive and efficient high-throughput screening methods are key for drug discovery. Here we report the development of a gain-of-signal, highly sensitive cell-based luciferase assay to monitor SARS-CoV-2 nsp5 activity and show that it is suitable for high-throughput screening of compounds in a 384-well format. A benefit of miniaturisation and automation is that screening can be performed in parallel on a wild-type and a catalytically inactive nsp5, which improves the selectivity of the assay. We performed molecular docking-based screening on a set of 14,468 compounds from an in-house chemical database, selected 359 candidate nsp5 inhibitors and tested them experimentally. We identified four molecules, including the broad-spectrum antiviral merimepodib/VX-497, which show anti-nsp5 activity and inhibit SARS-CoV-2 replication in A549-ACE2 cells with IC50 values in the 4-21 µM range. The here described assay will allow the screening of large-scale compound libraries for SARS-CoV-2 nsp5 inhibitors. Moreover, we provide evidence that this assay can be adapted to other coronaviruses and viruses which rely on a viral protease.
A simulation-based methodology to compare the performance of highway space inventory control and ramp metering control

The Highway Space Inventory Control System (HSICS) is a novel travel demand management concept in which all road users have to make reservations in advance to enter a highway. A modeling framework for such a system is proposed and illustrated for a hypothetical highway section. The proposed system allocates highway space temporally and spatially over time so as to optimize highway utilization by maximizing passenger miles traveled. In this paper, a methodology is developed to perform a quantitative comparison of the operational performance of HSICS and two isolated ramp metering techniques: the ALINEA algorithm and fixed pre-timed metering. The methodology involves a combination of microscopic traffic simulation and heuristic optimization. The proposed framework is generic and can be applied to compare other traffic control and demand management techniques. The proposed methodology is tested on a simulated highway section with traffic and geometric characteristics similar to a real congested site. Simulation results show that HSICS produces approximately 35 and 45% more passenger miles than ALINEA and fixed pre-timed metering, respectively. Vehicle miles traveled values for HSICS are comparable to those of ALINEA − 4% lower.
The Impact of Marital Quality as Older Couples Adjust to Dementia Onset.

OBJECTIVES A burgeoning literature links being married to better cognitive health, but less attention has been paid to how couples view their marital relationships. Couples do not always concur in their assessments, and such discrepancies affect both partners' health. We present a dyadic study on whether and how overall and discrepant views of marital quality predicted (a) dementia onset and (b) changes in older adults' depressive symptoms with spousal dementia. METHOD A pooled sample of couples aged 50+ (dyad N = 3,936) from the Health and Retirement Study rated positive and negative marital quality at baseline (2006/2008). Each participant reported whether they had been told of having dementia and their depressive symptoms once every other year (2006-2016). RESULTS Cox proportional hazards regression revealed that older adults who rated their marriages either more positively or more negatively than their spouses were more likely to develop dementia. We applied multiphase growth curve modeling to older adults whose spouses developed dementia, finding that those in marriages that were more negative overall reported more depressive symptoms but exhibited a smaller increase in these symptoms in response to spousal dementia. DISCUSSION This study adds to the literature by showing how discrepant marital assessments shape cognitive aging and offers new insights into identifying older adults with greater dementia risk. Findings also revealed the impact of overall negative marital quality on older adults' psychological adjustment to spousal dementia, which could inform interventions intended to help couples better cope with early-stage dementia from a relational perspective.
Prognostic Significance of Pre-treatment Serum Inflammatory Biomarkers on Survival in Patients with Carcinoma Cervix Treated by Radical Radiotherapy or Chemo-radiation

Background: Inflammation has an important role in the initiation and progression of carcinoma cervix. The measurement of inflammatory biomarkers is a cost-effective method of identifying patients at high risk of recurrence after treatment. This study was done to identify the influence of pre-treatment inflammatory biomarkers on survival in patients treated with radical chemo-radiation or radiation. Methods: Patients with biopsy proven carcinoma cervix treated with Radiotherapy or Chemo-radiation from January 1st, 2016 to September 30th, 2017 were included. Pre-treatment complete blood counts, differential counts, serum albumin and C-reactive protein (CRP) were obtained. Neutrophil lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR) and CRP Albumin ratio (CAR) were calculated. The best cut off values for NLR, PLR, serum albumin, CRP and CAR were found out from receiver operating characteristic (ROC) curves. OS (Overall Survival) and DFS (Disease Free Survival) were estimated using Kaplan -Meier method. The prognostic value of inflammatory biomarkers on survival was assessed by cox regression model. Results: Sixty-three patients were included. The median follow up was 42.5 months. The best cut off values for NLR, PLR, albumin, CRP and CAR from the ROC curve were 2.36, 122.725, 3.95, 0.65 and 0.8 respectively. The three-year OS and DFS probability were 68.3% and 63.5% respectively. Patients with CAR >0.8 had 5.7 times more risk of death and 6.01 times risk of relapse or progression compared to patients with CAR ≤0.8. Conclusion: Patients with a CRP-Albumin ratio of more than 0.8 are at significant risk of relapse and death after chemoradiation or radiation.
Networked control approaches for a nuclear power plant pressurizer subsystem

Abstract This paper extends former results about the networked reference tracking control of a pressurizer in a pressurized water nuclear power plant. An L2-gain based method is used in a deterministic framework to compute the maximum allowable transmit intervals (MATIs) for the actually implemented dynamic inversion based controller that still guarantee the required performance. Furthermore, a stochastic linear quadratic regulator with a state estimator is also designed that can take packet losses into consideration. It is shown through the comparison that both methods are applicable for networked control but the LQ controller combined with a Kalman filter has better output variance properties.