107 research outputs found
Robust Adaptive Critic Based Neurocontrollers for Systems with Input Uncertainties
A two-neural network approach to solving optimal control problems is described in this study. This approach called the adaptive critic method consists of two neural networks: one is called the supervisor or critic, and the other is called an action network or controller. The inputs to both these networks are the current states of the system to be controlled. Each network is trained through an output of the other network and the conditions for optimal control. When their outputs are mutually consistent, the controller network output is optimal. The optimality is limited to the underlying model. Hence, we develop a Lyapunov based theory for robust stability of these controllers when there is input uncertainty. We illustrate this approach through a longitudinal autopilot of a nonlinear missile problem
Stochastic Optimal Control with Neural Networks and Application to a Retailer Inventory Problem
Overwhelming computational requirements of classical dynamic programming algorithms render them inapplicable to most practical stochastic problems. To overcome this problem a neural network based Dynamic Programming (DP) approach is described in this study. The cost function which is critical in a dynamic programming formulation is approximated by a neural network according to some designed weight-update rule based on Temporal Difference(TD)learning. A Lyapunov based theory is developed to guarantee an upper error bound between the output of the cost neural network and the true cost. We illustrate this approach through a retailer inventory problem
Robust State Dependent Riccati Equation Based Robot Manipulator Control
We present a new optimal control approach to robust control of robot manipulators in the framework of state dependent Riccati equation (SDRE) technique. To treat this highly nonlinear control system, we formulate it as a nonlinear optimal regulator problem. SDRE technique was used to synthesize an optimal controller to this class of robot control problem. We also synthesize a neural network based extra controller to achieve the robustness in the presence of the parameter uncertainties. A typical two-link robot position control problem was studied to show the effectiveness of SDRE approach and robust extra control design to robotic application
Incorporating Structured Sentences with Time-enhanced BERT for Fully-inductive Temporal Relation Prediction
Temporal relation prediction in incomplete temporal knowledge graphs (TKGs)
is a popular temporal knowledge graph completion (TKGC) problem in both
transductive and inductive settings. Traditional embedding-based TKGC models
(TKGE) rely on structured connections and can only handle a fixed set of
entities, i.e., the transductive setting. In the inductive setting where test
TKGs contain emerging entities, the latest methods are based on symbolic rules
or pre-trained language models (PLMs). However, they suffer from being
inflexible and not time-specific, respectively. In this work, we extend the
fully-inductive setting, where entities in the training and test sets are
totally disjoint, into TKGs and take a further step towards a more flexible and
time-sensitive temporal relation prediction approach SST-BERT, incorporating
Structured Sentences with Time-enhanced BERT. Our model can obtain the entity
history and implicitly learn rules in the semantic space by encoding structured
sentences, solving the problem of inflexibility. We propose to use a time
masking MLM task to pre-train BERT in a corpus rich in temporal tokens
specially generated for TKGs, enhancing the time sensitivity of SST-BERT. To
compute the probability of occurrence of a target quadruple, we aggregate all
its structured sentences from both temporal and semantic perspectives into a
score. Experiments on the transductive datasets and newly generated
fully-inductive benchmarks show that SST-BERT successfully improves over
state-of-the-art baselines
High-Performance Nanofluidic Osmotic Power Generation Enabled by Exterior Surface Charges under the Natural Salt Gradient
High-performance osmotic energy conversion (OEC) requires both high ionic
selectivity and permeability in nanopores. Here, through systematical
explorations of influences from individual charged nanopore surfaces on the
performance of OEC, we find that the charged exterior surface on the
low-concentration side (surfaceL) is essential to achieve high-performance
osmotic power generation, which can significantly improve the ionic selectivity
and permeability simultaneously. Detailed investigation of ionic transport
indicates that electric double layers near charged surfaces provide high-speed
passages for counterions. The charged surfaceL enhances cation diffusion
through enlarging the effective diffusive area, and inhibits anion transport by
electrostatic repulsion. Different areas of charged exterior surfaces have been
considered to mimic membranes with different porosities in practical
applications. Through adjusting the width of the charged ring region on the
surfaceL, electric power in single nanopores increases from 0.3 to 3.4 pW with
a plateau at the width of ~200 nm. The power density increases from 4200 to
4900 W/m2 and then decreases monotonously that reaches the commercial benchmark
at the charged width of ~480 nm. While, energy conversion efficiency can be
promoted from 4% to 26%. Our results provide useful guide in the design of
nanoporous membranes for high-performance osmotic energy harvesting.Comment: 30 pages and 7 figure
Biochar to improve soil fertility. A review
International audienceAbstractSoil mineral depletion is a major issue due mainly to soil erosion and nutrient leaching. The addition of biochar is a solution because biochar has been shown to improve soil fertility, to promote plant growth, to increase crop yield, and to reduce contaminations. We review here biochar potential to improve soil fertility. The main properties of biochar are the following: high surface area with many functional groups, high nutrient content, and slow-release fertilizer. We discuss the influence of feedstock, pyrolysis temperature, pH, application rates, and soil types. We review the mechanisms ruling the adsorption of nutrients by biochar
SATB2 shows different profiles between appendiceal adenocarcinomas ex goblet cell carcinoids and appendiceal/colorectal conventional adenocarcinomas: An immunohistochemical study with comparison to CDX2
Background: Special AT-rich sequence-binding protein 2 (SATB2) is a novel marker for colorectal adenocarcinomas but little is known about its expression in appendiceal adenocarcinomas. We aim to investigate SATB2 in these tumors and colorectal adenocarcinomas with comparison to CDX2.
Methods: Immunohistochemical stains for SATB2 and CDX2 were performed in 49 appendiceal adenocarcinomas (23 conventional, 26 adenocarcinoma ex goblet cell carcinoids (AdexGCCs)) and 57 colorectal adenocarcinomas. Their expression was correlated with tumor differentiation and growth patterns.
Results: SATB2 staining was positive in 26/26 (100%) appendiceal AdexGCCs and 15/23 (65%) appendiceal conventional adenocarcinomas (P = 0.001). Their mean percentage of SATB2-positive cells was 93% and 34%, respectively (P \u3c 0.0001). CDX2 staining was seen in 26/26 (100%) AdexGCCs and 22/23 (96%) appendiceal conventional adenocarcinomas (P = 0.4694). SATB2 and CDX2 showed similar staining in AdexGCCs but CDX2 labeled more tumor cells than SATB2 in conventional adenocarcinomas (mean 84% vs. 34%, P \u3c 0.0001). SATB2 and CDX2 staining was seen in 82% (47/57) and 96% (55/57) colorectal adenocarcinomas, respectively (P = 0.01). The mean percentage of cells positive for SATB2 and CDX2 was 48% and 91%, respectively (P \u3c 0.00001). Decreased SATB2 immunoreactivity was associated with non-glandular differentiation particularly signet ring cells in colorectal (P = 0.001) and appendiceal conventional adenocarcinomas (P = 0.04) but not in appendiceal AdexGCCs.
Conclusions: SATB2 is a highly sensitive marker for appendiceal AdexGCCs with similar sensitivity as CDX2. In colorectal and appendiceal conventional adenocarcinomas, SATB2 is not as sensitive as CDX2 and its immunoreactivity is dependent on tumor differentiation
Sirtuin 1 and Autophagy Attenuate Cisplatin-Induced Hair Cell Death in the Mouse Cochlea and Zebrafish Lateral Line
Cisplatin-induced ototoxicity is one of the major adverse effects in cisplatin chemotherapy, and hearing protective approaches are unavailable in clinical practice. Recent work unveiled a critical role of autophagy in cell survival in various types of hearing loss. Since the excessive activation of autophagy can contribute to apoptotic cell death, whether the activation of autophagy increases or decreases the rate of cell death in CDDP ototoxicity is still being debated. In this study, we showed that CDDP induced activation of autophagy in the auditory cell HEI-OC1 at the early stage. We then used rapamycin, an autophagy activator, to increase the autophagy activity, and found that the cell death significantly decreased after CDDP injury. In contrast, treatment with the autophagy inhibitor 3-methyladenine (3-MA) significantly increased cell death. In accordance with in vitro results, rapamycin alleviated CDDP-induced death of hair cells in zebrafish lateral line and cochlear hair cells in mice. Notably, we found that CDDP-induced increase of Sirtuin 1 (SIRT1) in the HEI-OC1 cells modulated the autophagy function. The specific SIRT1 activator SRT1720 could successfully protect against CDDP-induced cell loss in HEI-OC1 cells, zebrafish lateral line, and mice cochlea. These findings suggest that SIRT1 and autophagy activation can be suggested as potential therapeutic strategies for the treatment of CDDP-induced ototoxicity
PD-L1 and PD-1 expression are correlated with distinctive clinicopathological features in papillary thyroid carcinoma
GATA3 is a sensitive marker for primary genital extramammary paget disease: an immunohistochemical study of 72 cases with comparison to gross cystic disease fluid protein 15
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