Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Force Sensorless Reflecting Control for Bilateral Haptic Teleoperation System

This paper presents a novel force sensorless reflecting controller for a haptic-enabled device driven by a bilateral pneumatic artificial muscle system, which proposed configuration for the first time the bilateral haptic teleoperation. For details, an adaptive force observer scheme considered to be an alternative to direct force measurement is proposed to estimate the interaction force with an unknown environment for the force reflecting control design. Meanwhile, the separately fast finite time nonsingular terminal sliding mode control schemes are developed based on the force estimation in both subsystems to achieve good tracking performance and fast response. Thus, the great transparency performance with both force feedback and position tracking can be achieved simultaneously by using our proposed method. The finite-time stability of the total controlled system is demonstrated by the Lyapunov approach. Moreover, the comparative experiments are carried out on the developed testbench to validate the effectiveness and advantages of our proposed control design in the different working conditions.
Action Recognition From a Single Coded Image

The unprecedented success of deep convolutional neural networks (CNN) on the task of video-based human action recognition assumes the availability of good resolution videos and resources to develop and deploy complex models. Unfortunately, certain budgetary and environmental constraints on the camera system and the recognition model may not be able to accommodate these assumptions and require reducing their complexity. To alleviate these issues, we introduce a deep sensing solution to directly recognize human actions from coded exposure images. Our deep sensing solution consists of a binary CNN-based encoder network that emulates the capturing of a coded exposure image of a dynamic scene using a coded exposure camera, followed by a 2D CNN for recognizing human action in the captured coded exposure image. Furthermore, we propose a novel knowledge distillation framework to jointly train the encoder and the action recognition model and show that the proposed training approach improves the action recognition accuracy by an absolute margin of 6.2%, 2.9%, and 7.9% on Something<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="kumawat-ieq1-3196350.gif"/></alternatives></inline-formula>-v2, Kinetics-400, and UCF-101 datasets, respectively, in comparison to our previous approach. Finally, we built a prototype coded exposure camera using LCoS to validate the feasibility of our deep sensing solution. Our evaluation of the prototype camera show results that are consistent with the simulation results.
OPTIC: A Deep Neural Network Approach for Entity Linking using Word and Knowledge Embeddings

: Entity Linking (EL) for microblog posts is still a challenge because of their usually informal language and limited textual context. Most current EL approaches for microblog posts expand each post context by considering related posts, user interest information, spatial data, and temporal data. Thus, these approaches can be too invasive, compromising user privacy. It hinders data sharing and experimental reproducibility. Moreover, most of these approaches employ graph-based methods instead of state-of-the-art embedding-based ones. This paper proposes a knowledge-intensive EL approach for microblog posts called OPTIC. It relies on a jointly trained word and knowledge embeddings to represent contexts given by the semantics of words and entity candidates for mentions found in the posts. These embedded semantic contexts feed a deep neural network that exploits semantic coherence along with the popularity of the entity candidates for doing their disambiguation. Experiments using the benchmark system GERBIL shows that OPTIC outperforms most of the approaches on the NEEL challenge 2016 dataset.
Health and Economic Impacts of the National Menu Calorie Labeling Law in the United States

Supplemental Digital Content is available in the text. Background: Excess caloric intake is linked to weight gain, obesity, and related diseases, including type 2 diabetes mellitus and cardiovascular disease (CVD). Obesity incidence is rising, with nearly 3 in 4 US adults being overweight or obese. In 2018, the US federal government finalized the implementation of mandatory labeling of calorie content on all menu items across major chain restaurants nationally as a strategy to support informed consumer choice, reduce caloric intake, and potentially encourage restaurant reformulations. Yet, the potential health and economic impacts of this policy remain unclear. Methods and Results: We used a validated microsimulation model (CVD-PREDICT) to estimate reductions in CVD events, diabetes mellitus cases, gains in quality-adjusted life years, costs, and cost-effectiveness of the menu calorie labeling intervention, based on consumer responses alone, and further accounting for potential industry reformulation. The model incorporated nationally representative demographic and dietary data from National Health and Nutrition Examination Surveys 2009 to 2016; policy effects on consumer diets and body mass index-disease effects from published meta-analyses; and policy effects on industry reformulation, policy costs (policy administration, industry compliance, and reformulation), and health-related costs (formal and informal healthcare costs, productivity costs) from established sources or reasonable assumptions. We modeled change in calories to change in weight using an established dynamic weight-change model, assuming 50% of expected calorie reductions would translate to long-term reductions. Findings were evaluated over 5 years and a lifetime from healthcare and societal perspectives, with uncertainty incorporated in both 1-way and probabilistic sensitivity analyses. Between 2018 and 2023, implementation of the restaurant menu calorie labeling law was estimated, based on consumer response alone, to prevent 14 698 new CVD cases (including 1575 CVD deaths) and 21 522 new type 2 diabetes mellitus cases, gaining 8749 quality-adjusted life years. Over a lifetime, corresponding values were 135 781 new CVD cases (including 27 646 CVD deaths), 99 736 type 2 diabetes mellitus cases, and 367 450 quality-adjusted life years. Assuming modest restaurant item reformulation, both health and economic benefits were estimated to be about 2-fold larger than based on consumer response alone. The consumer response alone was estimated to be cost-saving by 2023, with net lifetime savings of $10.42B from a healthcare perspective and $12.71B from a societal perspective. Findings were robust in a range of sensitivity analyses. Conclusions: Our national model suggests that the full implementation of the US calorie menu labeling law will generate significant health gains and healthcare and societal cost-savings. Industry responses to modestly reformulate menu items would provide even larger additional benefits.
A Desktop Electric Machine Emulator Implementation Method Based on Phase Voltage Reconstruction

An electric machine emulator (EME) system plays a fundamental role in design and validation of hardware and software for power electronic converters and electric drives. In this paper, a desktop digital signal processor (DSP)-based EME is developed for power converters function test. The principle of EME is demonstrated with mathematical derivation. Since the instantaneous phase voltage is either power supply voltage or ground, high-rated voltage sensors are not suitable for DSP-based EME to obtain the average voltage in a pulse-width modulation cycle. To accurately acquiring phase voltages, a method which reconstructs the phase voltage considering the amplitude and duty cycle error is proposed. Voltage drops, dead time, propagation delay, parasitic rising and falling time delay are all considered in reconstructed voltages based on voltage-second balance theory. This is accomplished without any assistant physical circuits. Based on the reconstructed phase voltages, reference current is derived in motor model by using second order Runge-Kutta method. Factors affecting phase voltage accuracy are verified by simulations step by step. The simulation and experimental results support the validity of the proposed method.
Taste-wise fruit sorting system using thermal image processing

Visual appearance of fruits such as texture, shape, size and colour are insufficient for recognizing the quality and taste of fruits. Fruit quality screening without harming them is challenging with high precision level. This paper suggests a non-destructive technique which is based on thermal imaging that examines the internal and external properties of fruits. With the analysis of fruit surface’s thermal properties, the ripening conditions can be recognized effectively. By developing an automated system, tedious human inspection tasks for fruit sorting are decreased.
Dual-Band Compact Wilkinson Power Divider Using Common Inductor and Complex Load

A 1.8/2.4-GHz dual-band compact Wilkinson power divider was designed and implemented using an integrated passive device process. The design used a common inductor to match impedance in the even mode. The common inductor replaced two shunt inductors in the circuit so the circuit size was reduced. An additional complex load was then used for matching impedance in the odd mode. The circuit measurements showed reflection at all ports better than −11.9 dB, isolation at the output ports better than −14 dB and transmission coefficient better than −4.3 dB, at both operating frequencies. The circuit occupied only $0.036\lambda _{\mathrm {o}}\times 0.018\lambda _{\mathrm {o}}$ at the higher operating frequency.
Rationale and Design of ORCHID: A Randomized Placebo-controlled Clinical Trial of Hydroxychloroquine for Adults Hospitalized with COVID-19

The ORCHID (Outcomes Related to COVID-19 treated with Hydroxychloroquine among In-patients with symptomatic Disease) trial is a multicenter, blinded, randomized trial of hydroxychloroquine versus placebo for the treatment of adults hospitalized with coronavirus disease (COVID-19). This document provides the rationale and background for the trial and highlights key design features. We discuss five novel challenges to the design and conduct of a large, multicenter, randomized trial during a pandemic, including 1) widespread, off-label use of the study drug before the availability of safety and efficacy data; 2) the need to adapt traditional procedures for documentation of informed consent during an infectious pandemic; 3) developing a flexible and robust Bayesian analysis incorporating significant uncertainty about the disease, outcomes, and treatment; 4) obtaining indistinguishable drug and placebo without delaying enrollment; and 5) rapidly obtaining administrative and regulatory approvals. Our goals in describing how the ORCHID trial progressed from study conception to enrollment of the first patient in 15 days are to inform the development of other high-quality, multicenter trials targeting COVID-19. We describe lessons learned to improve the efficiency of future clinical trials, particularly in the setting of pandemics. The ORCHID trial will provide high-quality, clinically relevant data on the safety and efficacy of hydroxychloroquine for the treatment of COVID-19 among hospitalized adults. Clinical trial registered with www.clinicaltrials. gov (NCT04332991).
The ENDpoiNTs Project: Novel Testing Strategies for Endocrine Disruptors Linked to Developmental Neurotoxicity

Ubiquitous exposure to endocrine-disrupting chemicals (EDCs) has caused serious concerns about the ability of these chemicals to affect neurodevelopment, among others. Since endocrine disruption (ED)-induced developmental neurotoxicity (DNT) is hardly covered by the chemical testing tools that are currently in regulatory use, the Horizon 2020 research and innovation action ENDpoiNTs has been launched to fill the scientific and methodological gaps related to the assessment of this type of chemical toxicity. The ENDpoiNTs project will generate new knowledge about ED-induced DNT and aims to develop and improve in vitro, in vivo, and in silico models pertaining to ED-linked DNT outcomes for chemical testing. This will be achieved by establishing correlative and causal links between known and novel neurodevelopmental endpoints and endocrine pathways through integration of molecular, cellular, and organismal data from in vitro and in vivo models. Based on this knowledge, the project aims to provide adverse outcome pathways (AOPs) for ED-induced DNT and to develop and integrate new testing tools with high relevance for human health into European and international regulatory frameworks.
Disease-Pertinent Knowledge Extraction in Online Health Communities Using GRU Based on a Double Attention Mechanism

Relationship extraction among diseases, symptoms and tests has always been a concerning research issue in the biomedical field. Disease-pertinent relationship extraction for user-generated content in the online health community represents a research trend. By training the word embedding vectors for the medical-health field, conducting entity recognition and relationship annotation, and using deep learning technology, we construct a relation extraction model for extracting the relationships among diseases, symptoms and tests. Our relationship extraction model of the bidirectional gate recurrent unit (BiGRU) network based on character-level and sentence-level attention mechanisms achieved the best results on question-answer data in the online health community. Our research results can not only help physician diagnoses but also help patients perform health management, which has important industrial application value.
Predicting variation in sperm precedence

Sperm competition theory predicts that males are adapted for success in sperm competition by the production of large numbers of sperm. This is supported by both inter- and intraspecific studies showing that males mating under high sperm competition risk increase investment in sperm production. Such an increase in sperm production is an advantage if sperm mix randomly or if sperm displacement occurs. When two males mate with the same female, the measurement of the proportion of eggs fertilized by the second male to mate (termed P2) has been used to help elucidate sperm competition mechanisms. P2 is usually quoted as a mean value, with little attention being paid to its variance, although P2 estimates are notoriously variable. By predicting an expected variance for P2, additional information on sperm competition mechanisms may be obtained. Here we present a technique for analysing the variance in P2 when a given mechanism of P2 is assumed. We apply this technique to P2 data collected from Plodia interpunctella (Lepidoptera, Pyralidae), assuming a 'fair raffle' mechanism of sperm competition. We compare observed distributions of P2 with theoretical distributions generated assuming random mixing of two ejaculates drawn randomly from a population of known mean and variance in sperm numbers. Ejaculates of known size were obtained by counting the number of sperm ejaculated by males mating for the first (large ejaculate) or second (small ejaculate) time. Females either received two small or one small and one large ejaculate, and the distribution of P2 (estimated using the sterile male technique) was compared with our theoretical predictions. The observed variance in P2 was greater than our model prediction, thus we conclude that sperm from P. interpunctella do not mix randomly before fertilization.
Capillary Fiber Bragg Grating Fabricated by Femtosecond Laser for Sensing Applications

We report the demonstration of fiber Bragg grating (FBG) fabricated in silica capillary fiber by use of femtosecond laser. The FBG is inscribed in the silica cladding surrounding the air core by a transversal scanning method through a phase mask. Multimode resonances which result from the overlap of the grating structure and the cladding modes transmitting in the capillary fiber, are observed in the reflection spectra. Sensing capability the FBG is exemplified by infiltrating the hollow core with liquids of different refractive indices (RIs), and shift in resonant wavelengths is observed. The temperature characteristic is investigated with a heating treatment from room temperature to 1000°C. Linear wavelength response is exhibited, with temperature sensitivity of 8 pm/°C. The FBG can be finding applications as optofluidic element for biochemical sensing.
3.75ppm/°C, -91dB PSRR, 27nW, 0.9V PVT Invariant Voltage Reference for Implantable Biomedical Applications

This brief presents a CMOS-only ultra low power voltage reference of 0.925V which is designed and simulated in TSMC 180nm technology. Current mixing between Self-Biased Self Cascode MOSFET (SBSCM) and Self Cascode MOSFET (SCM) pair is introduced along with stacking of proportional to absolute temperature (PTAT) and complementary to absolute temperature (CTAT) voltage generators to get second order curvature compensation. Temperature coefficient (TC) of 3.75ppm$/^{\circ}\mathrm{C}$ over a wide temperature range of $- 55^{\circ}\mathrm{C}$ to $140^{\circ}\mathrm{C}$ is achieved. To feed on the power reduction requirement of low power biomedical applications, the entire circuit is designed in sub-threshold region. Therefore, the total power consumption is 27nW @ 1.45V at room temperature $(27^{\circ}\mathrm{C})$. Furthermore, a supply independent current bias aids in realizing high PSRR of -91dB @ 10Hz / -50dB @ 100MHz and line regulation of 0.0779%/V over a supply range 1.45V to 3.6V. Besides, an employed trimming technique attenuates the process spread from ± 3% to ± 0.3%. Moreover, the proposed design occupies an active area of 0.0054mm2 only.
An empirically derived recommendation for the classification of body dysmorphic disorder: Findings from structural equation modeling

Body dysmorphic disorder (BDD), together with its subtype muscle dysmorphia (MD), has been relocated from the Somatoform Disorders category in the DSM-IV to the newly created Obsessive-Compulsive and Related Disorders category in the DSM-5. Both categorizations have been criticized, and an empirically derived classification of BDD is lacking. A community sample of N = 736 participants completed an online survey assessing different psychopathologies. Using a structural equation modeling approach, six theoretically derived models, which differed in their allocation of BDD symptoms to various factors (i.e. general psychopathology, somatoform, obsessive-compulsive and related disorders, affective, body image, and BDD model) were tested in the full sample and in a restricted sample (n = 465) which indicated primary concerns other than shape and weight. Furthermore, measurement invariance across gender was examined. Of the six models, only the body image model showed a good fit (CFI = 0.972, RMSEA = 0.049, SRMR = 0.027, TLI = 0.959), and yielded better AIC and BIC indices than the competing models. Analyses in the restricted sample replicated these findings. Analyses of measurement invariance of the body image model showed partial metric invariance across gender. The findings suggest that a body image model provides the best fit for the classification of BDD and MD. This is in line with previous studies showing strong similarities between eating disorders and BDD, including MD. Measurement invariance across gender indicates a comparable presentation and comorbid structure of BDD in males and females, which also corresponds to the equal prevalence rates of BDD across gender.
FIRST REPORT OF GYMNODINIUM PULCHELLUM (DINOPHYCEAE) IN NORTH AMERICA AND ASSOCIATED FISH KILLS IN THE INDIAN RIVER, FLORIDA

Fish and invertebrate kills were reported from September to October 1996 in the Indian River, Florida, coincident with blooms of the dinoflagellate Gymnodinium pulchellum Larsen 1994. This is the first report of a bloom of this species in the Americas. Fish and invertebrate species affected were common snook (Centropomus undecimalis), striped mullet (Mugil cephalus), hardhead catfish (Arius felis), red drum (Sciaenops ocellatus), sheepshead (Archosargus probatocephalus), black drum (Pogonias cromis), blue crab (Callinectes sapidus), and shrimp (Penaeus spp.). However, Gymnodinium pulchellum has previously caused fish kills in Japan and Australia. Examination of archived phytoplankton samples from a fish kill reported in the same area of the Indian River in August 1990 confirmed the presence of high concentrations of G. pulchellum. Fish kills associated with Alexandrium monilatum and potentially Pfiesteria‐like species in the Indian River also are discussed. Scanning electron microscopy provided additional morphological detail on this distinct but little‐known dinoflagellate.
Toward Integrated Virtual Emotion System with AI Applicability for Secure CPS-Enabled Smart Cities: AI-Based Research Challenges and Security Issues

Cyber-physical systems (CPS) basically pursue a new form of integrated interaction with humans through computation and physical capabilities covering complex, intelligent, autonomous systems. Also, artificial intelligence (AI) is considered as a promising technology that will be applicable to numerous combined next generation applications including CPS, security, and communication in smart cities. However, cyber security based on AI technologies is still in its infancy and, in particular, the differential challenges or issues should be addressed for various AI-enabled applications and systems. In this article, we introduce a new integrated virtual emotion system with AI applicability, called as I-VEmoSYS, toward secure CPS-enabled smart cities. The integrated virtual emotion system covers several subsystems such as virtual emotion barrier, virtual emotion map, and virtual emotion block. We describe their system settings, concepts, components, and operations, and also deal with AI applicability to those subsystems. Furthermore, we discuss future challenges and security issues that must be met to achieve secure advanced smart cities using the AI-enabled virtual emotion system.