Political and regulatory uncertainty is strongly negatively associated with merger and acquisition activity at the macro and firm levels. The strongest effects are for uncertainty regarding taxes, ...government spending, monetary and fiscal policies, and regulation. Consistent with a real options channel, the effect is exacerbated for less reversible deals and for firms whose product demand or stock returns exhibit greater sensitivity to policy uncertainty, but attenuated for deals that cannot be delayed due to competition and for deals that hedge firm-level risk. Contractual mechanisms (deal premiums, termination fees, MAC clauses) unanimously point to policy uncertainty increasing the target’s negotiating power.
Optimal performance is extremely important for hypersonic flight control. Different from most existing methodologies, which only consider basic control performance including stability, robustness, ...and transient performance, this article deals with the design of nearly optimal tracking controllers for hypersonic flight vehicles (HFVs). First, main controllers are developed for the velocity subsystem and the altitude subsystem of HFVs via concise fuzzy approximations. Then, optimal controllers are nearly implemented utilizing single-network adaptive critic design. Moreover, the stability of closed-loop systems and the convergence of optimal controllers are theoretically proved. Finally, compared simulation results are given to verify the superiority. The special contribution is the application of a low-complex control structure owing to the critic-only network and advanced learning laws developed for fuzzy approximations, which is expected to guarantee satisfied real-time performance.
Magnetically tunable micropillar arrays with uniform, continuous and extreme tilt angles for real‐time manipulation are reported. We experimentally show uniform tilt angles ranging from 0° to 57°, ...and develop a model to accurately capture the behavior. Furthermore, we demonstrate that the flexible uniform responsive microstructures (μFUR) can dynamically manipulate liquid spreading directionality, control fluid drag, and tune optical transmittance over a large range.
We established an international consortium to review and discuss relevant clinical evidence in order to develop expert consensus statements related to cancer management during the severe acute ...respiratory syndrome coronavirus 2-related disease (COVID-19) pandemic. The steering committee prepared 10 working packages addressing significant clinical questions from diagnosis to surgery. During a virtual consensus meeting of 62 global experts and one patient advocate, led by the European Society for Medical Oncology, statements were discussed, amended and voted upon. When consensus could not be reached, the panel revised statements until a consensus was reached. Overall, the expert panel agreed on 28 consensus statements that can be used to overcome many of the clinical and technical areas of uncertainty ranging from diagnosis to therapeutic planning and treatment during the COVID-19 pandemic.
This work proposes a fully convolutional neural network (CNN) for real-time speech enhancement in the time domain. The proposed CNN is an encoder-decoder based architecture with an additional ...temporal convolutional module (TCM) inserted between the encoder and the decoder. We call this architecture a Temporal Convolutional Neural Network (TCNN). The encoder in the TCNN creates a low dimensional representation of a noisy input frame. The TCM uses causal and dilated convolutional layers to utilize the encoder output of the current and previous frames. The decoder uses the TCM output to reconstruct the enhanced frame. The proposed model is trained in a speaker- and noise-independent way. Experimental results demonstrate that the proposed model gives consistently better enhancement results than a state-of-the-art real-time convolutional recurrent model. Moreover, since the model is fully convolutional, it has much fewer trainable parameters than earlier models.
Handheld real-time PCR device Ahrberg, Christian D; Ilic, Bojan Robert; Manz, Andreas ...
Lab on a chip,
02/2016, Letnik:
16, Številka:
3
Journal Article
Recenzirano
Odprti dostop
Here we report one of the smallest real-time polymerase chain reaction (PCR) systems to date with an approximate size of 100 mm × 60 mm × 33 mm. The system is an autonomous unit requiring an external ...12 V power supply. Four simultaneous reactions are performed in the form of virtual reaction chambers (VRCs) where a ≈200 nL sample is covered with mineral oil and placed on a glass cover slip. Fast, 40 cycle amplification of an amplicon from the H7N9 gene was used to demonstrate the PCR performance. The standard curve slope was -3.02 ± 0.16 cycles at threshold per decade (mean ± standard deviation) corresponding to an amplification efficiency of 0.91 ± 0.05 per cycle (mean ± standard deviation). The PCR device was capable of detecting a single deoxyribonucleic acid (DNA) copy. These results further suggest that our handheld PCR device may have broad, technologically-relevant applications extending to rapid detection of infectious diseases in small clinics.
Video analytics will drive a wide range of applications with great potential to impact society. A geographically distributed architecture of public clouds and edges that extend down to the cameras is ...the only feasible approach to meeting the strict real-time requirements of large-scale live video analytics.
Demand response (DR) is a recent effort to improve the efficiency of the electricity market and the stability of the power system. A successful implementation relies on both appropriate policy design ...and enabling technology. This paper presents a multiagent system to evaluate optimal residential DR implementation in a distribution network, in which the main stakeholders are modeled by heterogeneous home agents (HAs) and a retailer agent (RA). The HA is able to predict and control electricity load demand. A real-time price prediction model is developed for the HA and the RA. The optimal control of electricity consumption is formulated into a convex programming problem to minimize electricity payment and waiting time under real-time pricing. Simulation results show that the peak-to-average power ratio and electricity payments are significantly reduced using the proposed algorithms. The HA, with the proposed optimal control algorithms, can be embedded into a home energy management system to make intelligent decisions on behalf of homeowners responding to DR policies. The proposed agent system can be utilized to evaluate various strategies and emerging technologies that enable the implementation of DR.
•A lightweight model using a one-dimensional CNN for real-time SER system is proposed.•A multi-learning trick (MLT) is proposed for utilizing UFLBs, and stacked GRUs setup.•Proposed model have ...peculiar ability to parallel learn spatial and temporal features.•A 1D dilated CNN architecture is explored, in order to enhance the usage of features.•We evaluated our model on benchmark corpora and improve the current baseline methods.
Speech is the most dominant source of communication among humans, and it is an efficient way for human–computer interaction (HCI) to exchange information. Nowadays, speech emotion recognition (SER) is an active research area that plays a crucial role in real-time applications. In this era, the SER system has lacked real-time speech processing. To address this problem, we propose an end-to-end real-time SER model that is based on a one-dimensional dilated convolutional neural network (DCNN). Our model used a multi-learning strategy to parallel extract spatial salient emotional features and learn long term contextual dependencies from the speech signals. We used residual blocks with a skip connection (RBSC) module, in order to find a correlation, the emotional cues, and the sequence learning (Seq_L) module, to learn the long term contextual dependencies in the input features. Furthermore, we used a fusion layer to concatenate these learned features for the final emotion recognition task. Our model structure is quite simple, and it is capable of automatically learning salient discriminative features from the speech signals. We evaluated our model using benchmark IEMOCAP and EMO-DB datasets and obtained a high recognition accuracy, which were 73% and 90%, respectively. The experimental results indicated the significance and the efficiency of our proposed model have shown excessive assistance with the implementation of a real-time SER system. Hence, our model is capable of processing original speech signals for the emotion recognition that utilizes lightweight dilated CNN architecture that implements the multi-learning trick (MLT) approach.
Wearable sensing technologies have received considerable interests due to the promising use for real‐time monitoring of health conditions. The sensing part is typically made into a thin film that ...guarantees high flexibility with different sensing materials as functional units at different locations. However, a thin‐film sensor easily breaks during use because it cannot adapt to the soft or irregular body surfaces, and, moreover, it is not breathable or comfortable for the wearable application. Herein, a new and general strategy of making electrochemical fabric from sensing fiber units is reported. These units efficiently detect a variety of physiological signals such as glucose, Na+, K+, Ca2+, and pH. The electrochemical fabric is highly flexible and maintains structural integrity and detection ability under repeated deformations, including bending and twisting. They demonstrate the capacity to monitor health conditions of human body in real time with high efficacy.
An integrated electrochemical fabric is developed as a promising wearable platform for real‐time health monitoring by weaving different kinds of sensing fibers as the building blocks. It maintains real‐time, high sensing performance for various physiological signals such as glucose, Na+, K+, Ca2+, and pH under repeated deformations, including bending and twisting.
PDF
