Miracle-dz/UniFAR-FTUs huggingface dataset:
Cascaded Active Disturbance Rejection Control for Near space Vehicle Autopilot

There exists a steady-state error in the closed-loop system output with some conventional linear active rejection control approaches when the pitch channel control variable of the near space vehicle is the angle of attack. In this study, a cascaded linear active disturbance rejection control approach based on the cascaded control idea is proposed. Both the inner-loop controller and the outer-loop controller are designed by the linear active disturbance rejection control method. The inner-loop is built by angular rate feedback to improve dynamic performance of the closed-loop system, and the outer-loop is built by angle of attack feedback to remove the undesired steady-state error. The approach is employed to a hypersonic near space vehicle autopilot. The numerical simulations show that the proposed approach preserves the performances on time domain and frequency domain as good as those of the conventional active disturbance rejection control approaches, and it has the better performances on disturbance rejection and angle of attack command tracking accuracy than those of some previous active disturbance rejection control approaches.
Effect of Nano-Clay Filler on the Thermal Breakdown Mechanism and Lifespan of Polypropylene Film Under AC Fields

The wide application of nanocomposites in the insulation system has greatly contributed to the performance improvement of power equipment. However, nano fillers are not omnipotent for improving the properties of composite dielectrics. In some situations, nano-modified materials are in fact a compromise of improving some performance features while sacrificing others. In this work, the breakdown characteristics and time-to-failure of polypropylene film with nano-clay fillers have been evaluated under combined thermal stress and AC electric fields. Experiments on plain polypropylene (PP) samples have also been carried out under the same test conditions as control. Test results indicated that the time-to-failure of the samples with nano-clay filler was shorter than those without nano filler, which is different from the previous experience. SEM and EDS analyses were conducted to study how the failure mechanism had taken place in both plain polypropylene and the nano-clay filled polypropylene. The failure phenomenon in these materials can be explained by molecular thermodynamics. The main reason for the premature thermal breakdown of PP nanocomposite is essentially due to the weak coupling between nano-clay filler and polymer matrix. Finally, suggestions are proposed for nano modification methods and lifespan prediction models of composite dielectrics.
Deep Learning Preserving Renal Dialysis Treatment Recommendation

In China, there are many patients with renal failure and the treatment period of renal failure is long, but the number of doctors is not proportional to the number of patients. Renal dialysis is a kind of blood purification technology, which has a certain significance for alleviating the symptoms of patients with renal failure and prolonging the survival period. In this case, it is meaningful to establish an efficient recommendation model to assist the doctor in determining the renal dialysis treatment plan. Based on 22039 renal dialysis treatment records of 180 patients, we proposed a recommendation model of renal dialysis treatment plan based on deep learning, which can recommend a treatment plan based on patient’s physical data to provide an intelligent reference for doctors. The model can first intelligently complete the missing data and recommend treatment plans for new patients and re-visiting patients. For new patients, the model based on a deep neural network which mines large data of existing patients, recommend the optimal treatment plan. For re-visiting patients, the model based on the LSTM which personalized learns their personal diagnosis and treatment data, recommend a personalized treatment plan. The model recommends dialyzer mode, dialyzer type, anticoagulant type, and anticoagulant first dose according to the type of disease, complications, dry weight, current weight, age and gender. The model can effectively improve the doctor’s diagnosis speed, improve the utilization rate of medical resources and has broad application and promotion prospects.
Energy-Efficient Adaptive Directional Queue-Stable Stochastic Scheduling for SWIPT in NOMA

In upcoming 5G networks, non-orthogonal multiple access (NOMA) is emerging as a way to improve network performance. Along with NOMA, Simultaneous wireless information and power transfer (SWIPT) is also receiving a lot of attention as a way to increase the energy efficiency of devices such as IoT. In this paper, we propose an energy efficient network by combining SWIPT and NOMA. In the model proposed in this paper, there is a BS with an adaptive directional antenna, and the signal is transmitted through NOMA. Nodes get data and energy using SWIPT with Power split (PS). In the adaptive directional antenna model, each directional antenna can be turned off and on as needed, our goal is to minimize the energy transmitted by the BS. However, we need to ensure the stability of the two queues, not just minimizing them. We define two queues based on battery and data, and minimize the transmit power of the BS within the range to ensure the stability of the two queues. To address this problem, this paper proposes an efficient algorithm based on Lyapunov drift optimization. Numerical results show that the proposed algorithm in SWIPT-based NOMA environment minimizes the energy consumption of BS while ensuring the stability of each node’s battery and data queue.
Optimal Deployment Analysis of Airborne Radars Detecting Stealth Target Using UAV Swarms

Radar deployment is of great importance for airborne radar network performance. An efficient deployment required a good detection performance and energy consumption. This paper focuses on the detection of directional stealth target using UAV swarms equipped with active and passive radars. Based on calculated bistatic radar cross section (RCS) of stealth targets and Cassini oval sensing region, the optimum geometrical structures are analyzed with bistatic radars (BRs) using multiple mobile adaptive forward receivers to extend the dynamic detection range and coverage. Multi-objective optimization model is introduced and many critical constraints have been identified. Grid partition method and wolf pack algorithm (WPA) are applied to search the optimal deployments. In particular, we consider the problem of deploying an airborne network of BRs in the surveillance region to maximize the worst-case intrusion detectability as well as the vulnerability of active radar. Our findings provide valuable and practical insights into deployment of UAVs for stealth target tracking and the efficiency of the proposed algorithm is also validated by our simulation results.
Biomedical application of modificated nanodiamonds: targeted drug delivery and enchancement of therapeutic effect due to supramolecular mechanisms

Supramolecular interaction due to the hydrogen bonds formation and their influence on IR - spectra and structure of molecular complexes which is formed due to interaction between carboxylated nanodiamonds and highly toxic drugs on example of doxorubicin and mitoxantrone is analyzed by molecular modeling. As a result of calculations the combined IR spectrum is obtained as imposing of IR spectra for molecular complexes of drugs and nanodiamonds in various interaction positions. Combined IR spectra demonstrates a good agreement with experimental data. Received results demonstrate that there can be strong supramolecular interaction between drugs and modificated detonation nanodiamonds. Formed hydrogen bonds can be considered as one of main mechanisms for targeted drug delivery and for drug retention in cells and, thus, for enhancement of anti-cancer therapeutic efficacy
Generating Information-Diverse Microwave Speckle Patterns Inside a Room at a Single Frequency With a Dynamic Metasurface Aperture

We demonstrate that dynamic metasurface apertures (DMAs) are capable of generating a multitude of highly uncorrelated speckle patterns in a typical residential environment at a single frequency. We use a DMA implemented as an electrically-large cavity excited by a single port and loaded with many individually-addressable tunable metamaterial radiators. We placed such a DMA in one corner of a plywood-walled L-shape room transmitting microwave signals at 19 GHz as we changed the tuning states of the metamaterial radiators. In another corner, in the non-line-of-sight of the DMA, we conducted a scan of the field generated by the DMA. For comparison, we also performed a similar test where the DMA was replaced by a simple dipole antenna with fixed pattern but generating a signal that spanned 19 – 24GHz. Using singular value decomposition of the scanned data, we demonstrate that the DMA can generate a multitude of highly uncorrelated speckle patterns at a single frequency. In contrast, a dipole antenna with a fixed pattern can only generate such a highly uncorrelated set of patterns when operating over a large bandwidth. The experimental results of this paper suggest that DMAs can be used to capture a diversity of information at a single frequency which can be used for single frequency computational imaging systems, NLOS motion detection, gesture recognition systems, and more.
Regions of Interest Segmentation from LiDAR Point Cloud for Multirotor Aerial Vehicles

We propose a novel filter for segmenting the regions of interest from LiDAR 3D point cloud for multirotor aerial vehicles. It is specially targeted for real-time applications and works on sparse LiDAR point clouds without preliminary mapping. We use this filter as a crucial component of fast obstacle avoidance system for agriculture drone operating at low altitude. As the first step, each point cloud is transformed into a depth image and then identify places near to the vehicle (local maxima) by locating areas with high pixel densities. Afterwards, we merge the original depth image with identified locations after maximizing intensities of pixels in which local maxima were obtained. Next step is to calculate the range angle image that represents angles between two consecutive laser beams based on the improved depth image. Once the corresponding range angle image is constructed, smoothing is applied to reduce the noise. Finally, we find out connected components within the improved depth image while incorporating smoothed range angle image. This allows separating the regions of interest. The filter has been tested on various simulated environments as well as an actual drone and provides real-time performance. We make our source code, dataset2 and real world experiment3 available online.
Effectiveness of an occupational therapy program after AECOPD

Introduction: Occupational therapy can improve occupational outcomes in adults with chronic diseases such as chronic obstructive pulmonary disease (COPD). Objective: To evaluate the effectiveness of an occupational therapy intervention after discharge from hospital due to an acute exacerbation (AE) of COPD Methods: Randomized controlled trial. Severe AECOPD patients were included in this study. Patients were asked to identify the activities that were difficult to perform with the Canadian Occupational Performance Measure (COPM). The intervention consisted on three sessions of individualized treatment where the occupational therapist focused the treatment in the identified problems. Patients were assessed at discharge from hospital and at one month of discharge. Additionally to COPM, London Chest Activity of Daily Living (LCADL) scale was used to assess dyspnea during ADL in patients with COPD. Results: 26 severe COPD patients were included in the study, the clinical profile of the patients at baseline is shown in [table 1. ][1] View this table: Clinical profile of patients at baseline. Mean change values in COPM performance subscale was of 2.66 ±1.15 p≤0.001 and satisfaction was of 1.66 ±1.66 p=0.005 . LCADL self-care subscale showed a mean change of 1 ± 2 p=0.111. Conclusions: The results revealed that an occupational therapy program after hospitalization due to AECOPD can improves significantly the occupational performance of the patients. [1]: #T1
Improving Speech Intelligibility using Deep Learning

Speech Intelligibility is of prime importance in places of mass gathering such as railway stations and airports as it is imperative for the speech to be understandable for the benefit and safety of the people. Often, the desired speech is corrupted by external noise which leads to discrepancies in the way it is perceived by the target audience. Therefore, the purpose of a speech enhancement algorithm is to identify and eliminate the noise. Recent advancements in speech processing and machine learning have resulted in various methods to do this. This research aims to improve speech intelligibility using Deep Neural Network (DNN) based speech enhancement system. Optimum Soft Mask (OSM) is used as a training target for the DNN system. The performance of the system is evaluated using Short Time Objective Intelligibility (STOI) metric. It is found that the proposed system provided good enhancement of noisy speech without compromising on intelligibility. In addition, it is found that usage of optimum soft mask instead of an ideal binary mask (IBM) provided additional improvement in speech intelligibility.
Photovoltaic module parameters extraction using best-so-far ABC algorithm

In the present work, a nature inspired algorithm, which is the best-so-far Artificial Bee Colony algorithm, has been used to make the extraction of the electrical parameters of a Photovoltaic (PV) module. This algorithm emulates the behavior of bees in nature, where they search their food sources, to identify the one diode model (ODM) parameters. The effectiveness of our strategy has been checked by using two types of electrical characteristics (I-V and P-V) obtained from the simulation of LG395N2W PV module at two operating conditions. Finally, a comparative study has been elaborated with other heuristic algorithm, Particle Swarm Optimization (PSO) algorithm. Results show clearly that the best-so-far ABC noticeably outperforms PSO in the parameters accuracy, fitness value and convergence rate.
Multi-chip dynamic white light emitting diode with high level photobiological safety and good color fidelity

Blue light hazard has been the main concern of photobiological safety of light emitting diode (LED) and blue light hazard efficiency of radiation (BLHER) is used to evaluate the extent of blue light hazard. In this work, we used four monochromatic LEDs with the color of purple, azure, green, and red to mix light without blue LED, in order to reduce the BLHER of hybrid white light. Also, the mixed white light kept high level of color fidelity when the correlated color temperature (CCT) ranges from 2700 to 7000 K. Color rendering index (CRI), color fidelity score (Rf), and color gamut score (Rg) are used to evaluate the color fidelity. We simulated the spectrum and parameters of hybrid white light based on different peak wavelengths, full width at half maxima (FWHMs), and relative ratios of four LEDs. Finally, we fabricated a four-channel LED to obtain tunable white light with high level of photobiological safety and good color fidelity, which BLHER is 0.185 at 7110 K, CRI, Rf and Rg are larger than 85.0, 78.7 and 87.4, respectively.
Collaborative Applying the Ultra-low-k Dielectric and the High-k Dielectric Materials for Performance Enhancement in Coupled Multilayer Graphene Nanoribbon Interconnects

Based on transmission line modeling, a new technology of collaborative applying the ultra-low-k dielectric and the high-k dielectric materials in coupled multilayer graphene nanoribbon (MLGNR) interconnects (i.e., case 4) to reduce propagation delay and to expand 3-dB bandwidth of the conventional pristine (undoped) MLGNR interconnects is proposed in this paper. By using the decoupling technique and ABCD parameter matrix approach, the transfer function of the equivalent circuit model for coupled MLGNR interconnects is derived to obtain step response, propagation delay, transfer gain and 3-dB bandwidth under in-phase and out-of-phase crosstalk modes at global level of 7.5 nm technology node, which is based on the defined four cases. The results show that the maximum reduction of propagation delay between the proposed new technology (case 4) and the conventional MLGNR interconnects (case 1) can reach 15.911 ns at out-of-phase crosstalk mode for an interconnect length of 4000 $\mu \text{m}$ . The corresponding 3-dB bandwidth for them can be expanded over 2.978 times in the same length as the former. It is demonstrated that the proposed case 4 can obviously reduce the propagation delay and enhance the transfer gain and 3-dB bandwidth compared with the conventional case 1. Moreover, it is found that the coupled MLGNR interconnect under in-phase mode outperform that under out-of-phase mode in terms of propagation delay, transfer gain and 3-dB bandwidth at the same condition. In addition, it is manifested that the victim line of two-line coupled MLGNR interconnects have lesser propagation delay, higher transfer gain and larger 3-dB bandwidth, as compared to three-line coupled MLGNR interconnects at the same case. The proposed new technology in this paper would be beneficial to improve the performance of MLGNR interconnects and to provide the guidelines for the design of next generation on-chip MLGNR interconnect system.
Impact of Grains on Thermal Transport of Polycrystalline Graphene Nanoribbon

We have analyzed the thermal transport characteristics in polycrystalline graphene nanoribbon (GNR) employing equilibrium molecular dynamics (EMD) simulation with Adaptive Intermolecular Reactive Empirical Bond Order (AIREBO) potential. Thermal conductivity decreases with the introduction of grain boundary; in the presence of 2 grains the thermal conductivity of 10nm x 3nm polycrystalline GNR is computed to be 271.85 W/mK at room temperature. We have studied the effect of number of grains on thermal transport profile of poly GNR which decreases as more grains are introduced. Moreover, temperature dependence has been investigated in the presence of 2 and 4 grains and a decaying trend is observed. In the case of 4 grains the reduction in thermal transport is greater at low temperature. The impact of grain orientation has been studied for three different samples. Furthermore, we have also generated phonon density of states (PDOS) to support our findings.
Synthesis of Microwave Concave Semitransparent Screen for Reduction of Radiation in the Shadow Domain

Semitransparent concave screens are viewed as means of reducing radiation in nonworking angular area of an antenna (shadow domain). An analytical procedure for synthesis of screen impedance in the geometrical optics (GO) approximation has been developed. Beyond the limits of the GO approximation, the numerical synthesis procedure has been applied. It has been shown that it is the radii of the screen that defines the achievable cutoff of the fields. To achieve a cutoff on the order of −20 dB in the angular sector of + - 10 degrees with respect to the desired boundary of the shadow domain, the radii of the concave screen is shown to be 2 wavelengths. An example of a compact antenna system for high precision satellite positioning is also presented.
New approaches in esophageal carcinomas

Abstract New approaches in the treatment of esophageal cancer comprise endoscopy with refinements of esophagoscopic intraluminal resection by endoscopic submucosal dissection. Radical open surgery is more and more replaced by minimally invasive esophagectomy (MIO), especially in the hybrid technique with laparoscopic gastrolysis and transthoracic esophageal resection and gastric pull-up. Total MIO also in the robotic technique has not yet shown that it produces superior results than the hybrid technique. Fluorescent dye can improve the intraoperative visualization of the vascularization of the gastric conduit. The individualization of neoadjuvant therapy is the magic word in clinical research of multimodal treatment of esophageal cancer. This means response prediction based on molecular markers or clinical response evaluation. The documentation of the diversity of postoperative complications is now standardized by an international consensus. The value of enhanced recovery after surgery is not yet approved compared to conventional management.
Learning to Recognize Chest-Xray Images Faster and More Efficiently Based on Multi-Kernel Depthwise Convolution

The development of convolutional neural networks has promoted the progress of computer-aided diagnostic systems. Details in medical image, such as the texture and tissue structure, are crucial features for diagnosis. Therefore, large input images combined with deep convolution neural networks are adopted to boost the performance in recent research of chest X-ray diagnosis. Meanwhile, due to the variable sizes of thoracic diseases, many researchers have worked to introduce additional module to capture multi-scale feature of images in CNN. However, these efforts hardly consider the computational costs of large inputs and introduced additional modules. This paper aims to automatically diagnose diseases on chest X-rays images quickly and effectively. We propose the multi-kernel depthwise convolution(MD-Conv) which contains depthwise convolution kernels with different filter sizes in one depthwise convolution layer. MD-Conv has high calculation efficiency and few parameters. Because its ability to learn multi-scale feature based on the multi-size kernels, it is appropriate for medical images diagnosis tasks in which abnormalities varied in sizes. In addition, larger depthwise convolution kernels are adopted in MD-Conv to obtain a larger receptive field efficiently, which can ensure sufficient receptive field for high resolution inputs. MD-Conv can be easily applied in modern lightweight networks to replace the normal depthwise convolution layer. We conduct experiments on the Chest X-ray 14 Dataset, which is the largest available chest x-ray dataset, and obtain competitive results. We also evaluate the MD-Conv on the new released dataset for pediatric pneumonia diagnosis. We obtain a better performance of 98.3% AUC than original paper (96.8%) for recognize pneumonia versus normal. Meanwhile we compare the FLOPs and Params of different models to show their efficiency for chest X-rays recognition.
A Dual-polarized Wideband Tightly Coupled Reflectarray Antenna Proposed for 5G Communication

In this paper, a dual-polarized wideband reflectarray antenna using tightly coupled element for 5G communication is proposed. Tightly coupled elliptical dipoles are employed as array elements and a log periodic dipole antenna is used as a feed antenna. The concept of equivalent distance delay is employed to design the unit cell element to minimize phase error. The dual-polarized reflectarray antenna is configured in the shape of an egg crate. A 21*21 reflectarray is designed and analyzed. Simulation results show that the antenna can work from 1.7GHz to 5GHz without main beam distortion. The gain of the antenna varies from 10.5 dBi to 21.4 dBi in both polarizations.