Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural bases of hand synergies

The human hand has so many degrees of freedom that it may seem impossible to control. A potential solution to this problem is “synergy control” which combines dimensionality reduction with great flexibility. With applicability to a wide range of tasks, this has become a very popular concept. In this review, we describe the evolution of the modern concept using studies of kinematic and force synergies in human hand control, neurophysiology of cortical and spinal neurons, and electromyographic (EMG) activity of hand muscles. We go beyond the often purely descriptive usage of synergy by reviewing the organization of the underlying neuronal circuitry in order to propose mechanistic explanations for various observed synergy phenomena. Finally, we propose a theoretical framework to reconcile important and still debated concepts such as the definitions of “fixed” vs. “flexible” synergies and mechanisms underlying the combination of synergies for hand control.
Preliminary research on power demand model of high energy consumers for smart grid in China

With development of smart grid, study of power demand model of high energy consumers becomes an important topic in China. As a result of preliminary research, initial power demand model of high energy consumer is built up based on data mining. Such a model opens out the relationship of power demand and market factors, which can be applied to demand side management (DSM) for high energy consumers under Chinese market environment. A power demand forecasting method based on consumer classification and high energy consumer demand model is proposed. Such a model can also be applied to strategy making in DSM. Case study is carried out with real-life data from Guiyang city power grid where a large percentage of power demand is that from high energy consumers mainly consisting of phosphor plants and electrolytic aluminum plants. Case study results shows that demand forecasting precision is improved significantly and quantitative DSM strategies for high energy consumers can be made based on the proposed model.
Advances in Transcranial Magnetic Stimulation Technology

Introduction The importance of transcranial magnetic stimulation (TMS) as a research and clinical tool depends on the capabilities of the underlying technology. A TMS device consists of two key components: a pulse source and a stimulation coil. The pulse source is typically housed in a desktop box that is sometimes mounted on a cart. The pulse source is connected to the coil through a high-voltage, high-current cable. The coil may have various shapes and is placed on the subject’s head. The pulse source controls the intensity, shape, and timing of the pulses, whereas the coil geometry and position determine primarily the spatial distribution of the electric field induced in the head (Peterchev et al., 2012). Around these basic components, modern TMS devices integrate various ancillary systems such as coil cooling, positioning, and field modeling, as well as electromyography and brain imaging modalities. This chapter overviews the state of the art of TMS devices, including pulse sources with flexible control of the output waveform parameters and a wide variety of coil designs. The utility of more flexible pulse sources is supported with a review of the known effects of TMS pulse shape on the device efficiency and the brain response. The spatial stimulation characteristics of a large number of commercial and experimental coils are illustrated with electric field simulations, demonstrating a tradeoff between depth and focality. Technologies for accurate TMS targeting are discussed, including electric field models, frameless stereotaxy, and robotic coil holders. Technological aspects of ancillary coil effects such as heating, noise, vibration, and scalp stimulation are also addressed, as is the implementation of sham TMS coils. The last part of the chapter covers technical considerations for the integration of TMS and neuroimaging devices. The chapter concludes with a summary of promising ongoing and future developments in TMS technology.
Enhancing reuse of constraint solutions to improve symbolic execution

Constraint solution reuse is an effective approach to save the time of constraint solving in symbolic execution. Most of the existing reuse approaches are based on syntactic or semantic equivalence of constraints. For example, the Green framework can reuse constraints which have different representations but are semantically equivalent, through canonizing constraints into syntactically equivalent normal forms. KLEE reuses constraints based on subset/superset querying. However, both equivalence-based approach and subset/superset-based approach cannot cover some kinds of reuse where atomic constraints are not equivalent. Our approach, called GreenTrie, is an extension to the Green framework, which supports constraint reuse based on the logical implication relations among constraints. GreenTrie provides a component, called L-Trie, which stores constraints and solutions into tries, indexed by an implication partial order graph of constraints. L-Trie is able to carry out logical reduction and logical subset and superset querying for given constraints, to check for reuse of previously solved constraints. We report the results of an experimental assessment of GreenTrie against the original Green framework and the KLEE approach, which shows that our extension achieves better reuse of constraint solving result and saves significant symbolic execution time.
Fibrin and Collagen Differentially but Synergistically Regulate Sprout Angiogenesis of Human Dermal Microvascular Endothelial Cells in 3-Dimensional Matrix

Angiogenesis is a highly regulated event involving complex, dynamic interactions between microvascular endothelial cells and extracellular matrix (ECM) proteins. Alteration of ECM composition and architecture is a hallmark feature of wound clot and tumor stroma. We previously reported that during angiogenesis, endothelial cell responses to growth factors are modulated by the compositional and mechanical properties of a surrounding three-dimensional (3D) extracellular matrix (ECM) that is dominated by either cross-linked fibrin or type I collagen. However, the role of 3D ECM in the regulation of angiogenesis associated with wound healing and tumor growth is not well defined. This study investigates the correlation of sprout angiogenesis and ECM microenvironment using in vivo and in vitro 3D angiogenesis models. It demonstrates that fibrin and type I collagen 3D matrices differentially but synergistically regulate sprout angiogenesis. Thus blocking both integrin alpha v beta 3 and integrin alpha 2 beta 1 might be a novel strategy to synergistically block sprout angiogenesis in solid tumors.
New clock generation techniques for synchronous sampling of 16-QAM RF signals

Direct conversion digital receivers require precise sample timing in order to synchronize them with the carrier waves of received RF signals. This means a carrier regeneration technique for modulated RF signals is required. Unfortunately, carrier regeneration is considered to be very difficult to apply in the case of 64-QAM signals. To address this problem, we have developed a sampling clock generator composed of Field-programmable Gate Array (FPGA) devices that makes use of three new key clock generation techniques. Experiments conducted on the generator demonstrate the validity of these techniques for synchronous sampling of 16-QAM RF signals.
Incidence of acquisition of methicillin-resistant Staphylococcus aureus, Clostridium difficile, and other health-care-associated pathogens by dogs that participate in animal-assisted interventions.

OBJECTIVE To determine whether dogs that visited human health-care facilities were at greater risk of acquiring certain health-care-associated pathogens, compared with dogs performing animal-assisted interventions in other settings, and to identify specific behaviors of dogs associated with an increased risk of acquiring these pathogens. DESIGN Prospective cohort and nested case-control studies. ANIMALS 96 dogs that visited human health-care facilities and 98 dogs involved in other animal-assisted interventions. PROCEDURES Fecal samples and nasal swab specimens were collected from dogs at the time of recruitment and every 2 months for 1 year and were tested for methicillin-resistant Staphylococcus aureus (MRSA), Clostridium difficile, and other selected bacteria. Information was also obtained on facilities visited during animal-assisted interventions, dog diet, dog illnesses, and antimicrobial use within the home. At the end of the study, dog handlers were asked about the behavior of their dogs during visits to health-care facilities. RESULTS Rates of acquisition of MRSA and C difficile were 4.7 and 2.4 times as high, respectively, among dogs that visited human health-care facilities, compared with rates among dogs involved in other animal-assisted interventions. Among dogs that visited human health-care facilities, those that licked patients or accepted treats during visits were more likely to be positive for MRSA and C difficile than were dogs that did not lick patients or accept treats. CONCLUSIONS AND CLINICAL RELEVANCE Results suggested that dogs that visited human health-care facilities were at risk of acquiring MRSA and C difficile, particularly when they licked patients or accepted treats during visits.
Square-Root Sigma-Point Information Consensus Filters for Distributed Nonlinear Estimation

This paper focuses on the convergence rate and numerical characteristics of the nonlinear information consensus filter for object tracking using a distributed sensor network. To avoid the Jacobian calculation, improve the numerical characteristic and achieve more accurate estimation results for nonlinear distributed estimation, we introduce square-root extensions of derivative-free information weighted consensus filters (IWCFs), which employ square-root versions of unscented transform, Stirling’s interpolation and cubature rules to linearize nonlinear models, respectively. In addition, to improve the convergence rate, we introduce the square-root dynamic hybrid consensus filters (DHCFs), which use an estimated factor to weight the information contributions and shows a faster convergence rate when the number of consensus iterations is limited. Finally, compared to the state of the art, the simulation shows that the proposed methods can improve the estimation results in the scenario of distributed camera networks.
Transcranial Doppler Ultrasound for Screening Cerebral Small Vessel Disease: A Community Study

Background and Purpose— We explored the association between pulsatility index (PI) as derived from transcranial Doppler ultrasound with various measures of small vessel disease in the community. Methods— We performed transcranial Doppler and magnetic resonance imaging in 205 consecutive community-dwelling elderly subjects who were participants of the Shanghai Aging Study. We investigated the association between middle cerebral artery (MCA) PI with measures of white matter lesions (WML), lacunes, and microbleeds. Results— Multiple logistic regression found that MCA PI was associated with severe WML (odds ratio, 1.33 per 0.1 increase in PI; 95% confidence interval, 1.04–1.70; P=0.02). At optimal MCA PI cut-off, the area under curve, positive predictive value, and negative predictive value were 0.70 (95% confidence interval, 0.60–0.80), 34.9%, and 85.6%, respectively, for detection of severe WML. No association was found between MCA PI and measures of lacunes or microbleeds. Conclusions— PI correlates with WML severity. With a high negative predictive value, the chance of having severe WML with a normal PI is low. Transcranial Doppler may guide selective magnetic resonance imaging scanning for the detection of WML in the community.
A framework for scalable analysis and design of system-wide graceful degradation in distributed embedded systems

We present a framework that will enable scalable analysis and design of graceful degradation in distributed embedded systems. We define graceful degradation in terms of utility. A system that gracefully degrades suffers a proportional loss of system utility as individual software and hardware components fail. However, explicitly designing a system to gracefully degrade; i.e. handle all possible combinations of component failures, becomes impractical for systems with more than a few components. We avoid this exponential complexity of component combinations by exploiting the structure of the system architecture to partition components into subsystems. We view each subsystem as a configuration of components that changes when components are removed or added. Thus, a subsystem's utility changes when components fail or are repaired. We then view the system as a composition of subsystems that each contribute to overall system utility. We demonstrate the scalability of our framework by applying it to an example automobile navigation system. Using this framework, we improve the system dependability by identifying architectural properties that enhance a system's ability to gracefully degrade.
Capabilities to Achieve Business Intelligence Agility - Research Model and Tentative Results

The class of business intelligence (BI) systems is used as a basis for decision making in most big organizations. Extensive initiatives have been launched to accomplish adequate and timely decision support as an important factor to achieve and sustain competitive advantage. Within turbulent market environments it is challenging to keep up a distinguishable long-term strategy while quickly reacting to changing circumstances. This area of conflicts holds particularly true for BI as it is originally used to retrospectively reflect an organization’s performance and built upon stability and efficiency. Therefore, we investigate how dynamic BI capabilities, i.e. adoption of assets, market understanding and intimacy as well as business operations, impact the agility of BI. We approach our goal from a dynamic capability perspective. Starting from a literature review of dynamic capabilities of information systems (IS) and BI, we propose hypotheses to connect dynamic BI capabilities and BI agility. Derived hypotheses based on existing literature will be tested in our prospective research agenda. A small pre-study showed promising results. In-memory (IM) technology seems to be a technology enabler for agile BI. However, adoption of BI assets and the focus on market orientation and business operations may even intensify the positive effect.
A multicopy plasmid of the extremely thermophilic archaeon Sulfolobus effects its transfer to recipients by mating

A plasmid of 45 kb, designated pNOB8, was found in high copy number in a new heterotrophic Sulfolobus isolate, NOB8H2, from Japan. Dissemination of the plasmid occurred in six cultures of nine different Sulfolobus strains when small amounts of the donor were added. These mixed cultures exhibited a high average copy number of the plasmid, between 20 and 40 per chromosome, and showed a marked growth retardation. Horizontal transfer of pNOB8 was proved by isolating transcipients from mating mixtures via single colonies. In these isolates, the copy number of the plasmid appeared to be subject to a control mechanism. Cell-free filtrates of donor cultures did not transmit the plasmid, and plating of the donor on lawns of recipients did not result in plaque formation, suggesting that the transfer was not mediated by a virus. Rapid formation of cell-to-cell contacts between differently stained donor and recipient partners was demonstrated after the two strains were mixed. Electron microscopic analysis of mating mixtures revealed many cell aggregates made up of 2 to 30 cells and intercellular cytoplasmic bridges connecting two or more cells. Cells that had been transformed with purified plasmid DNA as well as transcipients isolated from mating mixtures were shown to serve as donors for further transmission of pNOB8. The plasmid undergoes extensive genetic variations, since deletions and insertions were frequently observed in plasmid preparations from the donor strain and from mating mixtures.
Pre-crack fatigue life assessment of relevant aircraft materials using fractal analysis of eddy current test data

Successful determination of residual fatigue life requires a comprehensive understanding of the fatigue related material deformation mechanism. Neither macroscopic continuum mechanics nor micromechanic observations provide sufficient data to explain subsequent deformation structures occurring during the fatigue life of a metallic structure. Instead mesomechanic deformation on different scaling levels can be studied by applying fractal analysis of various means of nondestructive inspection measurements. The resulting fractal dimension data can be correlated to the actual material damage states, providing an estimation of the remaining residual fatigue life before macroscopic fracture develops. Recent efforts were aimed to apply the fractal concept to aerospace relevant materials AA7075-T6 and Ti-6Al-4V. Proven and newly developed fractal analysis methods were applied to eddy current (EC) measurements of fatigued specimens, with the potential to transition this approach to an aircraft for an in-situ nondest.
3D electrode localization on wireless sensor networks for wearable BCI

This paper presents a solution for electrode localization on wearable BCI radio-enabled electrodes. Electrode positioning is a common issue in any electrical physiological recording. Although wireless node localization is a very active research topic, a precise method with few centimeters of range and a resolution in the order of millimeters is still to be found, since far-field measurements are very prone to error. The calculation of 3D coordinates for each electrode is based on anchorless range-based localization algorithms such as Multidimensional Scaling and Self-Positioning Algorithm. The implemented solution relies on the association of a small antenna to measure the magnetic field and a microcontroller to each electrode, which will be part of the wireless sensor network module. The implemented solution is suitable for EEG applications, namely the wearable BCI, with expected range of 20 cm and resolution of 5 mm.
Limiting amplitudes of fully nonlinear interfacial tides and solitons

Abstract. A new two-fluid layer model consisting of forced rotation-modified Boussinesq equations is derived for studying tidally generated fully nonlinear, weakly nonhydrostatic dispersive interfacial waves. This set is a generalization of the Choi–Camassa equations, extended here with forcing terms and Coriolis effects. The forcing is represented by a horizontally oscillating sill, mimicking a barotropic tidal flow over topography. Solitons are generated by a disintegration of the interfacial tide. Because of strong nonlinearity, solitons may attain a limiting table-shaped form, in accordance with soliton theory. In addition, we use a quasi-linear version of the model (i.e. including barotropic advection but linear in the baroclinic fields) to investigate the role of the initial stages of the internal tide prior to its nonlinear disintegration. Numerical solutions reveal that the internal tide then reaches a limiting amplitude under increasing barotropic forcing. In the fully nonlinear regime, numerical experiments suggest that this limiting amplitude in the underlying internal tide extends to the nonlinear case in that internal solitons formed by a disintegration of the internal tide may not reach their table-shaped form with increased forcing, but appear limited well below that state.
Dynamic Threshold Energy Detection Technique for Cognitive Radio

sensing is the main feature of cognitive radio technology. Spectrum sensing gives an idea of detecting the presence of the primary users in a licensed spectrum. The dynamic threshold energy detection is proposed in this paper for the spectrum sensing of Cognitive Radio. The proposed dynamic energy scheme depends on the current state of the primary user. Depending on this, dynamic thresholds are evaluated considering the effect of noise uncertainty. The thresholds evaluated are used to increase the value of Pd and decrease the value of �� ����. The performance of dynamic energy detection technique is optimized against the values of parameters. It is observed that the proposed dynamic energy detection (DED) scheme is better than the classical energy detection (CED) scheme. Keywordssensing, Energy detection, Probability of detection
Viral Infection at High Magnification: 3D Electron Microscopy Methods to Analyze the Architecture of Infected Cells

As obligate intracellular parasites, viruses need to hijack their cellular hosts and reprogram their machineries in order to replicate their genomes and produce new virions. For the direct visualization of the different steps of a viral life cycle (attachment, entry, replication, assembly and egress) electron microscopy (EM) methods are extremely helpful. While conventional EM has given important information about virus-host cell interactions, the development of three-dimensional EM (3D-EM) approaches provides unprecedented insights into how viruses remodel the intracellular architecture of the host cell. During the last years several 3D-EM methods have been developed. Here we will provide a description of the main approaches and examples of innovative applications.
CMOS Alcohol Sensor Employing ZnO Nanowire Sensing Films

This paper reports on the utilization of zinc oxide nanowires (ZnO NWs) on a silicon on insulator (SOI) CMOS micro‐hotplate for use as an alcohol sensor. The device was designed in Cadence and fabricated in a 1.0 μm SOI CMOS process at XFAB (Germany). The basic resistive gas sensor comprises of a metal micro‐heater (made of aluminum) embedded in an ultra‐thin membrane. Gold plated aluminum electrodes, formed of the top metal, are used for contacting with the sensing material. This design allows high operating temperatures with low power consumption. The membrane was formed by using deep reactive ion etching. ZnO NWs were grown on SOI CMOS substrates by a simple and low‐cost hydrothermal method. A few nanometer of ZnO seed layer was first sputtered on the chips, using a metal mask, and then the chips were dipped in a zinc nitrate hexahydrate and hexamethylenetramine solution at 90° C to grow ZnO NWs. The chemical sensitivity of the on‐chip NWs were studied in the presence of ethanol (C2H5OH) vapour (with 1.
Detection of transposition of the great arteries in fetuses reduces neonatal morbidity and mortality.

BACKGROUND Transposition of the great arteries (TGA) is a life-threatening malformation in neonates, but it is amenable to complete repair. Prenatal detection, diagnosis, and early management may modify neonatal mortality and mortality. METHODS AND RESULTS Preoperative and postoperative morbidity and mortality were compared in 68 neonates with prenatal diagnosis and in 250 neonates with a postnatal diagnosis of TGA over a period of 10 years. The delay between birth and admission was 2+/-2.8 hours in the prenatal group and 73+/-210 hours in the neonatal group (P<0.01). Clinical condition at arrival, including metabolic acidosis and multiorgan failure, was worse in the neonatal group (P<0.01). Once in the pediatric cardiology unit, the management was identical in the 2 groups (atrioseptostomy, PGE1 infusion, operation date). Preoperative mortality was 15 of 250 (6%; 95% CI, 3% to 9%) in the neonatal group and 0 of 68 in the prenatal group (P<0.05). Postoperative morbidity was not different (25 of 235 versus 6 of 68), but hospital stay was longer in the neonatal group (30+/-17 versus 24+/-11 days, P<0.01). In addition, postoperative mortality was significantly higher in the neonatal group (20 of 235 versus 0 of 68, P<0.01); however, the known risk factors for operative mortality were identical in the 2 groups. CONCLUSIONS Prenatal diagnosis reduces mortality and morbidity in TGA. Prenatal detection of this cardiac defect must be increased to improve early neonatal management. In utero transfer of fetuses with prenatal diagnosis of TGA in an appropriate unit is mandatory.