Cooperative driver pathways discovery by multiplex network embedding.

Cooperative driver pathways discovery helps researchers to study the pathogenesis of cancer. However, most discovery methods mainly focus on genomics data, and neglect the known pathway information and other related multi-omics data; thus they cannot faithfully decipher the carcinogenic process. We propose CDPMiner (Cooperative Driver Pathways Miner) to discover cooperative driver pathways by multiplex network embedding, which can jointly model relational and attribute information of multi-type molecules. CDPMiner first uses the pathway topology to quantify the weight of genes in different pathways, and optimizes the relations between genes and pathways. Then it constructs an attributed multiplex network consisting of micro RNAs, long noncoding RNAs, genes and pathways, embeds the network through deep joint matrix factorization to mine more essential information for pathway-level analysis and reconstructs the pathway interaction network. Finally, CDPMiner leverages the reconstructed network and mutation data to define the driver weight between pathways to discover cooperative driver pathways. Experimental results on Breast invasive carcinoma and Stomach adenocarcinoma datasets show that CDPMiner can effectively fuse multi-omics data to discover more driver pathways, which indeed cooperatively trigger cancers and are valuable for carcinogenesis analysis. Ablation study justifies CDPMiner for a more comprehensive analysis of cancer by fusing multi-omics data.

Study of Current Measurement Method Based on Circular Magnetic Field Sensing Array.

Classic core-based instrument transformers are more prone to magnetic saturation. This affects the measurement accuracy of such transformers and limits their applications in measuring large direct current (DC). Moreover, protection and control systems may exhibit malfunctions due to such measurement errors. This paper presents a more accurate method for current measurement based on a circular magnetic field sensing array. The proposed measurement approach utilizes multiple hall sensors that are evenly distributed on a circle. The average value of all hall sensors is regarded as the final measurement. The calculation model is established in the case of magnetic field interference of the parallel wire, and the simulation results show that the error decreases significantly when the number of hall sensors n is greater than 8. The measurement error is less than 0.06% when the wire spacing is greater than 2.5 times the radius of the sensor array. A simulation study on the off-center primary conductor is conducted, and a kind of hall sensor compensation method is adopted to improve the accuracy. The simulation and test results indicate that the measurement error of the system is less than 0.1%.

Robot path planning based on artificial potential field with deterministic annealing.

In the context of motion planning in robotics, the problem of path planning based on artificial potential fields has been examined using different algorithms to avoid trapping in local minima. With this objective, this paper proposes a novel method based on a deterministic annealing strategy to improve the potential field function by introducing a temperature parameter to increase the robot's obstacle avoidance efficiency. The annealing and tempering strategies prevent the robot from being trapped at the local minima and allow it to continue towards its destination. The initial path is optimised using an annealing algorithm to enhance the overall performance. The time, length and success rate of the planned path measures the quality of the solution. Simulation results and comparative experiments demonstrate that the proposed algorithm can solve path planning in different environments. The proposed algorithm is suitable for complex environments with convex or non-convex polygon obstacles.

Adaptive neural-network-based sliding mode control of switching distributed delay systems with Markov jump parameters.

This paper is devoted to the issue of observer-based adaptive sliding mode control of distributed delay systems with deterministic switching rules and stochastic jumping process, simultaneously, through a neural network approach. Firstly, relying on the designed Lebesgue observer, a sliding mode hyperplane in the integral form is put forward, on which a desired sliding mode dynamic system is derived. Secondly, in consideration of complexity of real transition rates information, a novel adaptive dynamic controller that fits to universal mode information is designed to ensure the existence of sliding motion in finite-time, especially for the case that the mode information is totally unknown. In addition, an observer-based neural compensator is developed to attenuate the effectiveness of unknown system nonlinearity. Thirdly, an average dwell-time approach is utilized to check the mean-square exponential stability of the obtained sliding mode dynamics, particularly, the proposed criteria conditions are successfully unified with the designed controller in the type of mode information. Finally, a practical example is provided to verify the validity of the proposed method.

Aircraft route recovery based on distributed integer programming method.

In order to further promote the application and development of unmanned aviation in the manned field, and reduce the difficulty that airlines cannot avoid due to unexpected factors such as bad weather, aircraft failure, and so on, the problem of restoring aircraft routes has been studied. To reduce the economic losses caused by flight interruption, this paper divides the repair problem of aircraft operation plans into two sub problems, namely, the generation of flight routes and the reallocation of aircraft. Firstly, the existing fixed-point iteration method proposed by Dang is used to solve the feasible route generation model based on integer programming. To calculate quickly and efficiently, a segmentation method that divides the solution space into mutually independent segments is proposed as the premise of distributed computing. The feasible route is then allocated to the available aircraft to repair the flight plan. The experimental results of two examples of aircraft fault grounding and airport closure show that the method proposed in this paper is effective for aircraft route restoration.

Sliding-mode variable structure control for complex automatic systems: a survey.

Automatic systems (ASs) can automatically control the work of controlled objects without unattended participation. They have been extensively used in industry, agriculture, automobiles, robots and other fields in recent years. However, the performance of the controller cannot meet the work requirements under complex environmental conditions. Therefore, improving the control performance is one of the difficult problems that automated systems should solve. Sliding-mode variable structure control has the advantages of fast response, insensitivity to uncertainty and interference and easy implementation; thus, it has been extensively used in the field of complex control systems. This article analyses and explains the research status of motors, microgrids, switched systems, aviation guidance, robots, mechanical systems, automobiles and unmanned aerial vehicles (UAVs) and prospects for the application of sliding-mode variable structure control in complex ASs.

Observer-based H∞ control design for singular switching semi-Markovian jump systems with random sensor delays.

This paper studies the problem of H∞ control design for a class of singular switching semi-Markovian jump systems, in which the transition rates of jumping process are uncertain and the output measurements suffer from random sensor delays. Firstly, a Luenberger observer is designed to estimate the system state components, based on which an error dynamic is obtained. Then, stochastic stability analysis for the overall closed-loop system is given based on the upper and lower bounds of transition rates. Further, the state feedback controller gain matrices are given by solving a set of sufficient conditions in terms of strict linear matrix inequalities (LMIs), which also guarantee the closed-loop system to be stochastically stabilizable and has a prescribed H∞ performance index γ. Finally, a numerical example is provided to verify the effectiveness of the obtained result.

Data-driven trajectory tracking control for autonomous underwater vehicle based on iterative extended state observer.

In this study, we explore the precise trajectory tracking control problem of autonomous underwater vehicle (AUV) under the disturbance of the underwater environment. First, a model-free adaptive control (MFAC) is designed based on data-driven ideology and a full-form dynamic linearization (FFDL) method is utilized to online estimate time-varying parameter pseudo gradient (PG) to establish an equivalent data model of AUV motion. Second, the iterative extended state observer (IESO) scheme is designed to combine with FFDL-MFAC. Because the proposed novel controller is able to learn from repeated iterations, the proposed novel controller can estimate and compensate the model approximation error produced by external environmental unknown disturbance. Third, three-dimensional motion is decoupled into horizontal and vertical and a multi closed-loop control structure is designed that exhibits faster convergence rate and reduces sensitivity to parameter jumps than single closed-loop system. Finally, two simulation scenarios are designed featuring non external disturbance and Gaussian noise of signal-to-noise ratio of 90 dB. The simulation results reveal the superiority of FFDL. Furthermore, we adpot the technical parameters data of T-SEA I AUV to conduct numerical simulation, aunderwater trajectory as the tracking scenario and set waves to 0.5 m and current to 0.2 m/s to simulate Lv.2 ocean conditions of "International Ocean State Standard". The simulation results demonstrate the effectiveness and robustness of the proposed tracking control algorithm.

Event-triggered observer-based H∞ sliding mode control of nonlinear systems.

The problem of sliding mode control for a class of Takagi-Sugeno fuzzy model-based nonlinear one-sided Lipschitz systems is investigated in the paper. Due to the state components are not available, a state observer is designed based on an event-triggering mechanism. Meanwhile, the output measurements transmitted through the communication channels suffer from signal delays. Based on the estimated state, an integral sliding surface is proposed. Then, the sliding mode dynamics is obtained by virtue of equivalent control principle. Further, by constructing appropriate sliding mode controller, the finite-time reachability of predefined sliding surface is surely guaranteed. Moreover, the stability with an H∞ performance analysis of sliding mode dynamics is undertaken via Lyapunov function theory and the criteria are established in terms of LMI. Finally, numerical examples are provided to demonstrate the effectiveness of the proposed method.

Experimental Characterization of Laser Trepanned Microholes in Superalloy GH4220 with Water-Based Assistance.

An experiment using water-assisted millisecond laser trepanning on superalloy GH4220 was carried out, and the effects of pulse energy on the hole entrance morphology, diameter, roundness, cross-section morphology, taper angle, sidewall roughness, and recast layer in air and with water-based assistance were compared and analyzed. The results show that, compared with the air condition, the water-based assistance improved the material removal rate and hole quality, increased the diameter of the hole entrance and exit, increased the hole roundness, decreased the hole taper angle, decreased the hole sidewall roughness, and reduced the recast layer thickness. In addition, under the combined action of water and steam inside the hole, the sidewall surface morphology quality was improved. Compared with the air condition, the spatter around the hole entrance was reduced, but the oxidation phenomenon formed by the thermal effect surrounding the hole entrance with water-based assistance was more obvious. The research provided technical support for the industrial application of millisecond laser drilling.

Predictive control for indoor environment based on thermal adaptation.

Previous studies show that the indoor environment quality (IEQ) of buildings directly affects human health and comfort. This study aims to predict the change of indoor parameters at the next moment under the influences of the current indoor climate and outdoor climate and control the IEQ parameters based on the human thermal adaption in advance. We combine the simulation and the mathematical method to establish the office building model with air-conditioning and lighting systems and construct the bilinear model of the IEQ parameters and control variables. Unknown parameters are identified using the experimental method. Model predictive control (MPC) based on human thermal comfort is discussed by considering human thermal adaptation, and the neutral temperature is calculated through the dynamic relationship between outdoor and indoor temperatures. Results show that the temperature setpoint is adjusted in accordance with human adaptability, and the air-conditioning, fan, and lighting systems are controlled via MPC. The usage time of air-conditioning and light is reduced, and thus, energy is saved.

A Deterministic Annealing Neural Network Algorithm for the Minimum Concave Cost Transportation Problem.

In this article, a deterministic annealing neural network algorithm is proposed to solve the minimum concave cost transportation problem. Specifically, the algorithm is derived from two neural network models and Lagrange-barrier functions. The Lagrange function is used to handle linear equality constraints, and the barrier function is used to force the solution to move to the global or near-global optimal solution. In both neural network models, two descent directions are constructed, and an iterative procedure for the optimization of the neural network is proposed. As a result, two corresponding Lyapunov functions are naturally obtained from these two descent directions. Furthermore, the proposed neural network models are proved to be completely stable and converge to the stable equilibrium state, therefore, the proposed algorithm converges. At last, the computer simulations on several test problems are made, and the results indicate that the proposed algorithm always generates global or near-global optimal solutions.

An approximation algorithm for graph partitioning via deterministic annealing neural network.

Graph partitioning, a classical NP-hard combinatorial optimization problem, is widely applied to industrial or management problems. In this study, an approximated solution of the graph partitioning problem is obtained by using a deterministic annealing neural network algorithm. The algorithm is a continuation method that attempts to obtain a high-quality solution by following a path of minimum points of a barrier problem as the barrier parameter is reduced from a sufficiently large positive number to 0. With the barrier parameter assumed to be any positive number, one minimum solution of the barrier problem can be found by the algorithm in a feasible descent direction. With a globally convergent iterative procedure, the feasible descent direction could be obtained by renewing Lagrange multipliers red. A distinctive feature of it is that the upper and lower bounds on the variables will be automatically satisfied on the condition that the step length is a value from 0 to 1. Four well-known algorithms are compared with the proposed one on 100 test samples. Simulation results show effectiveness of the proposed algorithm.

Disparities in Geographic Access to Hospital Outpatient Pulmonary Rehabilitation Programs in the United States.

BACKGROUND: For individuals with COPD, pulmonary rehabilitation (PR) improves outcomes in terms of exercise capacity, severity of dyspnea, and health-related quality of life. However, many US patients with COPD do not use PR services. There has been limited research on geographic access to needed health-care services for individuals who live in rural communities in the United States. This study: (1) examines the geographic distribution of hospital-based outpatient PR programs in the US; and (2) compares the organizational characteristics of hospitals that offer PR programs and those that do not. METHODS: A multistep process supported the determination of whether a hospital provided PR services and included: program directory data from the American Association of Cardiovascular and Pulmonary Rehabilitation (AACVPR) website and websites for AACVPR state affiliates and other COPD-relevant organizations; searches of hospital websites; e-mails with state contacts and other state organizations; and phone calls with hospital staff and state contacts. The study population included all Medicare-certified short-term acute care general medical and surgical hospitals. Data were collected and analyzed from January to November 2018. Medicare Provider of Service and American Hospital association data were used to compare the characteristics of hospitals with and without PR programs, using descriptive and bivariate statistics. RESULTS: 1,776 US counties do not have a hospital outpatient PR program located in a short-term acute care general medical or surgical hospital in the county, including 697 counties that do not have a hospital. The availability of a hospital outpatient PR program varies significantly by county type, hospital type and Census region. Hospitals located in a noncore county, designated as a Critical Access Hospital, or located in the South and the West were less likely to have an outpatient PR program. CONCLUSIONS: Significant geographic disparities exist in access to hospital outpatient PR. Potential strategies for addressing these disparities include: increasing clinician and patient awareness of the potential benefits of PR; offering staff training and incentives to supervise and provide PR services; improving Medicare reimbursement rates for PR services; replicating PR programs that have success serving rural areas; expanding cardiac rehabilitation programs to include PR; and assessing the use of telehealth technologies to provide PR in isolated areas.

Selective adsorption and decomposition of pollutants using RGO-TiO2 with optimized surface functional groups.

Reduced graphene oxide (RGO) samples with optimized types of surface functional groups were hybridized with TiO2 to achieve the selective adsorption and removal of various pollutants. A high ratio of hydroxyl groups was found to be remarkably advantageous for the adsorbtion and decomposition of rhodamine-B (and similar pollutants), while a high ratio of carboxyl groups was found to promote the ability to adsorb and decompose phenol. Moreover, the presence of carboxyl groups on the RGO edge provides a pre-condition to form a close chemical connection with TiO2, which has been proven by the obtained electron paramagnetic resonance (EPR) curve, infrared spectroscopy (IR) and electron lifetime. The resulting composite photocatalysts display excellent photocatalytic activities under both UV- and visible-light illumination, indicating that the well-designed surface micro-circumstances of the RGO are quite significant.

[Influence of pre-ALIP number and its distance from trabeculae on AML relapse].

This study was purposed to detect the abnormal quantity and localization of pre-ALIP in bone marrow of acute myelocytic leukemia patients (AML) during the complete remission (CR) and investigate their correlation with AML relapse. The bone marrow biopsy and prognosis of 62 patients with CR were retrospectively analyzed. The bone marrow was divided into the pre-relapse group and the no-relapse group according to prognosis of patients. In order to clarify the correlation of abnormal quantity and localization of pre-ALIP with AML relapse, the number of single and double-cluster precursor cells and the sum of both were calculated, and their distance from bone trabeculae was surveyed with the computer image segment method. The results showed that the number of pre-ALIP in pre-relapse group (11 ± 11.71/mm(2)) and no-relapse group (8.33 ± 9.17/mm(2)) were more than that in normal control group (5.29 ± 4.00) (P < 0.01). The number of pre-ALIP more than 11/mm(2) was observed in 17 among all AML patients, and out of them 12 patients with pre-ALIP number >11/mm(2) (70.6) were found in the pre-relapse group, which was higher than that in no-relapse group (P < 0.05). While the distance between pre-ALIP and trabeculae [(341.31 ± 266.16) µm] in pre-relapse group showed the tendency of migrating to the intermediate zone of bone trabeculae, compared with that in no-relapse group [(242.41 ± 174.65) µm, P < 0.01]. Moreover, about 77.8 of 18 patients showed the distance of pre-ALIP from trabeculae was more than 341 µm in the pre-relapse group, and significantly higher than that in no-relapse group (P < 0.01). It is concluded that the average number of "pre-ALIP" more than 11/mm(2) or the average distance from trabeculae longer than 341 µm in bone marrow sections during CR may be the indicators for early relapse of AML.