Highly Conductive Ti3C2Tx MXene Hybrid Fibers for Flexible and Elastic Fiber‐Shaped Supercapacitors

Fiber‐shaped supercapacitors (FSCs) are promising energy storage solutions for powering miniaturized or wearable electronics. However, the scalable fabrication of fiber electrodes with high electrical conductivity and excellent energy storage performance for use in FSCs remains a challenge. Here, an easily scalable one‐step wet‐spinning approach is reported to fabricate highly conductive fibers using hybrid formulations of Ti₃C₂T_x MXene nanosheets and poly(3,4‐ethylenedioxythiophene):polystyrene sulfonate. This approach produces fibers with a record conductivity of ≈1489 S cm^?1, which is about five times higher than other reported Ti₃C₂T_x MXene‐based fibers (up to ≈290 S cm^?1). The hybrid fiber at ≈70 wt% MXene shows a high volumetric capacitance (≈614.5 F cm^?3 at 5 mV s^?1) and an excellent rate performance (≈375.2 F cm^?3 at 1000 mV s^?1). When assembled into a free‐standing FSC, the energy and power densities of the device reach ≈7.13 Wh cm^?3 and ≈8249 mW cm^?3, respectively. The excellent strength and flexibility of the hybrid fibers allow them to be wrapped on a silicone elastomer fiber to achieve an elastic FSC with 96% capacitance retention when cyclically stretched to 100% strain. This work demonstrates the potential of MXene‐based fiber electrodes and their scalable production for fiber‐based energy storage applications.     

On Network Designs with Coding Error Detection and Correction Application

The detection of error and its correction is an important area of mathematics that is vastly constructed in all communication systems. Furthermore, combinatorial design theory has several applications like detecting or correcting errors in communication systems. Network (graph) designs (GDs) are introduced as a generalization of the symmetric balanced incomplete block designs (BIBDs) that are utilized directly in the above mentioned application. The networks (graphs) have been represented by vectors whose entries are the labels of the vertices related to the lengths of edges linked to it. Here, a general method is proposed and applied to construct new networks designs. This method of networks representation has simplified the method of constructing the network designs. In this paper, a novel representation of networks is introduced and used as a technique of constructing the group generated network designs of the complete bipartite networks and certain circulants. A technique of constructing the group generated network designs of the circulants is given with group generated graph designs (GDs) of certain circulants. In addition, the GDs are transformed into an incidence matrices, the rows and the columns of these matrices can be both viewed as a binary nonlinear code. A novel coding error detection and correction application is proposed and examined.     

Alzheimer Disease Detection Empowered with Transfer Learning

Alzheimer's disease is a severe neuron disease that damages brain cells which leads to permanent loss of memory also called dementia. Many people die due to this disease every year because this is not curable but early detection of this disease can help restrain the spread. Alzheimer's is most common in elderly people in the age bracket of 65 and above. An automated system is required for early detection of disease that can detect and classify the disease into multiple Alzheimer classes. Deep learning and machine learning techniques are used to solve many medical problems like this. The proposed system Alzheimer Disease detection utilizes transfer learning on Multi-class classification using brain Medical resonance imagining (MRI) working to classify the images in four stages, Mild demented (MD), Moderate demented (MOD), Non-demented (ND), Very mild demented (VMD). Simulation results have shown that the proposed system model gives 91.70% accuracy. It also observed that the proposed system gives more accurate results as compared to previous approaches.     

Convolutional Neural Network Based Intelligent Handwritten Document Recognition

This paper presents a handwritten document recognition system based on the convolutional neural network technique. In today’s world, handwritten document recognition is rapidly attaining the attention of researchers due to its promising behavior as assisting technology for visually impaired users. This technology is also helpful for the automatic data entry system. In the proposed system prepared a dataset of English language handwritten character images. The proposed system has been trained for the large set of sample data and tested on the sample images of user-defined handwritten documents. In this research, multiple experiments get very worthy recognition results. The proposed system will first perform image pre-processing stages to prepare data for training using a convolutional neural network. After this processing, the input document is segmented using line, word and character segmentation. The proposed system get the accuracy during the character segmentation up to 86%. Then these segmented characters are sent to a convolutional neural network for their recognition. The recognition and segmentation technique proposed in this paper is providing the most acceptable accurate results on a given dataset. The proposed work approaches to the accuracy of the result during convolutional neural network training up to 93%, and for validation that accuracy slightly decreases with 90.42%.     

Multi-objective optimization of a grid-connected PV system for a public building: Faculty of Sciences and Technology at Pau

Clean Technologies and Environmental Policy - In this paper, an optimal sizing of a grid-connected PV system to accommodate the load demands of a public building (i.e., Faculty of Sciences and...     

Hemostatic efficacy of a fibrin sealant dressing in an animal model of kidney injury

Arterial spin tagging techniques have been used to image tissue perfusion in MR without contrast injection or ionizing radiation. Currently, spin tagging studies are performed primarily using single-slice imaging sequences, which are time consuming. This note reports a multislice echo-planar arterial spin tagging technique (Simultaneous Multislice Acquisition with aRterial-flow Tagging, or "SMART"). Multiband RF encoding (Hadamard) is used to provide simultaneous multislice acquisition capability for spin tagging techniques (such as echo planar imaging signal targeting with alternating radio frequency and flow-sensitive alternative inversion recovery). The method is illustrated with a two-slice pulse sequence that was implemented using the FAIR technique to generate two perfusion weighted images simultaneously. Compared with single-slice sequences, this two-slice sequence provided similar image quality, signal-to-noise ratio, and twice the spatial coverage compared with the single-slice technique within the same scan time.     

Incremental one-class classifier based on convex–concave hull

Pattern Analysis and Applications - One subject that has been considered less is a binary classification on data streams with concept drifting in which only information of one class (target class)...     

A decoupled source current reconstruction method for noisy and reactive near‐field to far‐field transformation

In this article, a decoupled source current reconstruction method (SRM) for noisy and reactive near‐field (NF) to far‐field (FF) transformation is introduced. It is shown that the traditional SRM for NF/FF transformation shows instability in the regions that the amounts of noise or reactive radiations are noticeable. Therefore, in these regions, equivalent currents should be determined from a Tikhonov SRM equation. However, this equation increases the computational cost of the SRM. To simplify the Tikhonov SRM equation, a Tikhonov radial field retrieval algorithm is also proposed. In this algorithm, the Tikhonov integral equation is decoupled by considering and retrieving the radial components of the electric field. Results of far‐field calculation with both the proposed Tikhonov SRM equation and Tikhonov radial field retrieval algorithm with three different antennas are presented and compared with those of the full‐wave simulation and measurements. The results show more accurate field transformation with the proposed algorithms.     

Improving PAPR performance of filtered OFDM for 5G communications using PTS

The filtered orthogonal frequency division multiplexing (F-OFDM) system has been recommended as a waveform candidate for fifth-generation (5G) communications. The suppression of out-of-band emission (OOBE) and asynchronous transmission are the distinctive features of the filtering-based waveform frameworks. Meanwhile, the high peak-to-average power ratio (PAPR) is still a challenge for the new waveform candidates. Partial transmit sequence (PTS) is an effective technique for mitigating the trend of high PAPR in multicarrier systems. In this study, the PTS technique is employed to reduce the high PAPR value of an F-OFDM system. Then, this system is compared with the OFDM system. In addition, the other related parameters such as frequency localization, bit error rate (BER), and computational complexity are evaluated and analyzed for both systems with and without PTS. The simulation results indicate that the F-OFDM based on PTS achieves higher levels of PAPR, BER, and OOBE performances compared with OFDM. Moreover, the BER performance of F-OFDM is uninfluenced by the use of the PTS technique.     

Correction to: Automatic COVID-19 detection mechanisms and approaches from medical images: a systematic review

Multimedia Tools and Applications -