Miracle-dz/UniFAR-FTUs huggingface dataset:
Human protein subcellular localization prediction based on error correcting output coding strategy while combining immunohistochemistry image and amino acid sequence

Human protein subcellular localization prediction is essential for understanding function of proteins. Only when the proteins arrive at the right place at the right time can they perform their functions. In the past decades, various methods based on machine learning have been proposed to predict the subcellular localization of proteins. However, a common drawback is that most of these methods only focus on single signal of proteins, such as amino acid sequences or protein images. In this paper, a novel prediction model based on error correcting output coding (ECOC) strategy was proposed, which named ECOC-locator. It has two advantages, one is that ECOC-locator takes full advantage of amino acid sequence information, for example, rich context information of amino acid sequences, and takes interpretability of protein images into account. The other is that ECOC-locator balances the class contributions of the two signals to the prediction model by employing ECOC. The ensemble classifier integrated 8 classifiers, five of which were trained by immunohistochemistry (IHC) images and the rest trained by amino acid sequence data. The experimental results show that the accuracy of ECOC-locator can reach 40.34%, which fully demonstrates the effectiveness.
Performance Determinants in LoRa Networks: A Literature Review

The LoRa radio technology is one of the most prominent choices in the Internet of Things Low-Power Wide Area Networks (LPWANs) industry due to its versatile and robust technical characteristics along with its ability to achieve long communication ranges combined with low energy consumption and reduced cost. One of the main issues in LoRa networks is how many end-devices can be reporting efficiently while meeting the requirements set by the application they support. This is known as the capacity metric and it is affected by many network parameters and various factors. A literature overview is presented in this work, studying works on LoRa-based networks, outlining their behavior and categorizing them based on their technological breakthroughs. Throughout this survey, a number of performance determinants that stand out are highlighted. These factors span five main categories that encompass physical layer characteristics, deployment and hardware features, end device transmission settings, LoRa MAC protocols, and application requirements. Discussion follows the presentation of each of the factors pinpointing the relevant research, and describing the impact of each one of them on the achieved network efficiency focusing especially on the capacity metric. Open issues and research directions are also highlighted for each of the five identified categories.
Online Health Monitoring of DC-Link Capacitors in Modular Multilevel Converters for FACTS and HVDC Applications

This article presents an online health monitoring scheme for dc capacitors in modular multilevel converters (MMCs). The health monitoring algorithm is based on detecting changes in the dc capacitance value over time. The proposed algorithm only utilizes measurements that are typically available in flexible alternating current transmission systems and high-voltage direct current applications. Hence, in the proposed estimation method, no additional sensors are used. The estimation scheme considers the presence of noise in voltage and current measurements, and utilizes a recursive least square estimator in conjunction with a special low-pass filter to minimize the estimation errors. Simulation results of a hardware replica, as well as experimental results on a low-power MMC prototype show that the proposed scheme can identify the dc-link capacitance value with a maximum error of 1%.
Adaptive Local Ternary Pattern on Parameter Optimized-Faster Region Convolutional Neural Network for Pulmonary Emphysema Diagnosis

Emphysema is a lung disease that occurs due to abnormal alveoli expansion. This chronic disease causes difficulty in breathing which can lead to lung cancer. The progressive destruction of emphysema can be assessed by Computed Tomography (CT) scans and pulmonary function tests. The severity of the disease may extend to a stage where one can risk their life emphasizing the early detection of emphysema. Primary diagnosis can be done using spirometry and CT for early detection of the disease reducing the mortality rates. Difficulties associated with different diagnostic procedures and inter and intra-observer variations have made blooming researches on more computer-aided techniques. This paper intends to develop a computer-aided technique using the improved deep learning strategy. The initial process is image pre-processing, which is performed by histogram equalization and median filtering. Further, the Fuzzy C Means (FCM) clustering is used for segmentation. After segmentation, a new Adaptive Local Ternary Pattern (ALTP) is used for extracting the pattern descriptor, which is further utilized for classification. As a new contribution, the Parameter Optimized-Faster Region Convolutional Neural Network (PO-FRCNN) is developed for performing the diagnosis. The enhancement of pattern formation and deep classification is accomplished by the Improved Red Deer Algorithm (IRDA), which helps to tune the significant parameters that have a positive influence on the accurateness. The benchmark and real-time dataset are used for performing the experimentation. The results show that the proposed method yields the best result and can effectively diagnose emphysema when compared to state-of-the-art techniques.
A Cooperative Jamming Scheme for Physical Layer Security enhancement in Multihop IoT Networks with Colluding Eavesdroppers

An aim of Internet-of-Things (IoT) networks is to enable smart cities by connecting billions of devices for various applications. Such a massive connectivity can face many vital challenges, one of which is security. Accordingly, we propose a Cooperative Jamming (CJ) scheme for multihop IoT networks, where each transmitted symbol is protected by two hybrid-duplex jamming nodes from randomly located colluding eavesdroppers. Our proposed scheme provides a significant performance enhancement compared to a conventional Single Jamming (SJ) approach, at no additional power cost. In particular, to achieve a secrecy outage probability of 10−1, and assuming the number of hops is 8, the proposed CJ scheme outperforms the SJ case by 3.5 dB of jamming Signal-to-Noise Ratio (SNR). Integral closed-form expressions are derived for the secrecy outage probability and verified via Monte Carlo simulations.
Parallel Block Particle Filtering

Particle filtering (PF) is a powerful method to estimate the posterior distribution in nonlinear/ non Gaussian state space models. To overcome the curse of dimensionality of PF, the block PF (BPF) partitions the state space and runs correction and resampling steps separately on each subspace. Using a blocking step can significantly reduce the variance of the filtering distribution estimate, but it breaks correlation across subspaces.In this paper, we introduce a parallelisation scheme in the block PF. The scheme consists in dispatching the set of particles into M parallel BPFs. We show that the usual benefit of parallelisation in terms of bias-variance trade-off remains valid and, most importantly, that assigning different partitions to the parallel filters leads to far better performance than naive parallelisation using only one partition.
Study of the Imbibition Behavior of Hydrophilic Tight Sandstone Reservoirs Based on Nuclear Magnetic Resonance

Hydraulic fracturing is one of the key technologies to enhance the oil recovery from tight sandstone reservoirs. The study on the behavior of imbibition for tight sandstone reservoir is of great significance to increase oil production after fracturing. In this paper, nuclear magnetic resonance (NMR) and high-pressure mercury injection were implemented on the samples taken from the Yanchang Formation of the Ordos Basin. The NMR T2 curves of the core at the state of 100% water saturated were converted to pore-throat radius distribution curves. On this basis, the experiments of imbibition and displacement were conducted and the changes of fluid distribution, imbibition quantity, and imbibition rate with different scale pores and different imbibition times were analyzed. Moreover, the results of imbibition and displacement were compared to assess the distribution of the recoverable and remaining oil under two exploitation modes and the contribution of imbibition made to displacement in pores at different scal.
Comprehensive BEOL Performance Assessment: Interconnects Optimized for Signal Routing and Power Delivery in Advanced CMOS Technology Nodes (Invited)

We analyze the impact of interconnect resistance on power delivery network performance using circuit-level simulation for beyond 7 nm technology nodes. We assess the impact of alternate conductors on wide line delay and find between 10-20% performance penalty relative to Cu. In addition, we assess the performance impact of a backside power rail (BPR) and find up to a 25% reduction in delay at a fixed power tap spacing, or equivalently a 1.5x increase in power tap spacing at a fixed voltage (IR) drop. We find that IR drop along power lines can vary from ~10 mV to ~100 mV depending on power tap frequency, inverter drive strength and activity factor.
Fault Tolerant Multi-Robot Cooperative Localization Based on Covariance Union

This paper studies the multi-robot cooperative localization (CL) problem, a challenging scenario in which robots may receive spurious sensor data, potentially causing inconsistent state estimates. To address this problem, this paper presents a fully decentralized CL algorithm based on covariance union (CU), referred to as DCL-CU. The proposed approach is fault-tolerant and supports generic measurement models. Extensive Monte Carlo simulations and a group of real-world experiments were conducted to verify the performance of the proposed DCL-CU approach. The results show that the DCL-CU approach can efficiently deal with spurious sensor data.
Dispersal from the Natal Colony of the Grey Heron Ardea cinerea Nesting in Poland

Abstract. Natal dispersal is the movement from the natal site to the colony of the first reproduction, while breeding dispersal is the movement from one breeding colony to another within or between breeding seasons. These processes are major determinants of genetic structure and a key component of population dynamics. In this study we investigated whether age of recovered bird (immatures, adults) and/or period (three multi-year periods characterized by various climate change dynamics) of recovery from the breeding season affect distance and direction of dispersal from the natal colony in Grey Herons Ardea cinerea nesting in Central Europe, in Poland. We found that the distance of dispersal from the natal area for Grey Herons ranged from 0 to 392 km with mean ± SD 84.1 ± 99.1 km (N = 72). Independently of periods, individuals recovered as immatures had longer dispersal distance than those recovered as adults. Our study revealed that proportion of recoveries representing strong natal fidelity (within the distance of ≤ 38 km around the natal colony, N = 37, 51.4%) differed significantly between the studied periods with the highest proportion (71%) in 1981–2014 and lower proportions in periods 1932–1939 (35%) and 1940–1980 (27%). We also found that the distance of dispersal from the natal colony was affected significantly by the period, with the distance in 1980–2014 being significantly shorter compared to 1940–1980 and with a tendency to be shorter than in 1932–1939. Direction of dispersal from the natal colony was affected significantly neither by period nor age of recovered birds. On average Grey Herons dispersed with a marked SW directional component. Our results indicate that Central European population of Grey Herons have shortened average distance of dispersal from the natal colony since the 1930s. This observation is concordant with changes reported for other European waterbirds.
Segmentation of Hippocampus based on 3DUnet-CBAM Model

The assessment of the hippocampus is indispensable for the supplementary diagnosis of Alzheimer's disease. In response to the small size of the hippocampus and the difficulty of accurate segmentation, this paper proposes a 3DUnet-CBAM based hippocampal segmentation model. A 3DUnet convolutional network is used to avoid information loss between two-dimensional slices, and a CBAM attention mechanism is incorporated in the last layer of downsampling. The deep and superficial features of the hippocampus are effectively combined. The segmentation accuracy of the hippocampus is finally improved. Experimental results show that the model achieves 89.01%, 89.04% and 88.97% for dice, precision and recall under the validation set, respectively, which can better assist doctors in diagnosing Alzheimer's disease.
Multi-Output Kernel Adaptive Filtering with Reduced Complexity

In this paper, two new multi-output kernel adaptive filtering algorithms are developed that exploit the temporal and spatial correlations among the input-output multivariate time series. They are multi-output versions of the popular kernel least mean squares (KLMS) algorithm with two different sparsification criteria. The first one, denoted as MO-QKLMS, uses the coherence criterion in order to limit the dictionary size. The second one, denoted as MO-RFF-KLMS, uses random Fourier features (RFF) to approximate the kernel functions by linear inner products. Simulation results with synthetic and real data are presented to assess convergence speed, steady-state performance and complexities of the proposed algorithms.
Machine-learning-based quality-level-estimation system for inspecting steel microstructures

Abstract Quality control of special steel is accomplished through visual inspection of its microstructure based on microscopic images. This study proposes an ‘automatic-quality-level-estimation system’ based on machine learning. Visual inspection of this type is sensory-based, so training data may include variations in judgments and training errors due to individual differences between inspectors, which makes it easy for a drop in generalization performance to occur due to overfitting. To deal with this issue, we here propose the preprocessing of inspection images and a data augmentation technique. Preprocessing reduces variation in images by extracting features that are highly related to the level of quality from inspection images. Data augmentation, meanwhile, suppresses the problem of overfitting when training with a small number of images by taking into account information on variation in judgment values obtained from on-site experience. While the correct-answer rate for judging the quality level by an inspector was about 90%, the proposed method achieved a correct-answer rate of 92.5%, which indicates that the method shows promise for practical applications.
Indium-antimony-halide single crystals for high-efficiency white-light emission and anti-counterfeiting

Indium-antimony bimetallic halide single crystal exhibits single-source white-light emission and anti-counterfeiting potential. Although single-source white emissive perovskite has emerged as a class of encouraging light-emitting material, the synthesis of lead-free halide perovskite materials with high luminous efficiency is still challenging. Here, we report a series of zero-dimensional indium-antimony (In/Sb) alloyed halide single crystals, BAPPIn2–2xSb2xCl10 (BAPP = C10H28N4, x = 0 to 1), with tunable emission. In BAPPIn1.996Sb0. 004Cl10, bright yellow emission with near 100% photoluminescence quantum yield (PLQY) is yielded when it was excited at 320 nm, which turns into bright white-light emission with a PLQY of 44.0% when excited at 365 nm. Combined spectroscopy and theoretical studies reveal that the BAPP4+-associated blue emission and inorganic polyhedron–afforded orange emission function as a perfect pair of complementary colors affording white light in BAPPIn1.996Sb0. 004Cl10. Moreover, the interesting afterglow behavior, together with excitation-dependent emission property, makes BAPPIn2–2xSb2xCl10 as high-performance anti-counterfeiting/information storage materials.
Scalable Financial Index Tracking with Graph Neural Networks

As a prevailing passive investment strategy in the financial world, index tracking aims at replicating or surpassing the performance of a financial index. The core part of an index tracking strategy is to design a sparse index tracking portfolio (ITP) from a basket of candidate financial assets. In this paper, a scalable two-stage approach is developed for ITP design under the minimax criterion, which consists of an asset selection stage (i.e., to select a subset of the assets from the index constituent stocks) and a capital allocation stage (i.e., to allocate the capital among the selected assets). The asset selection problem is tackled via a well-calibrated graph neural network (GNN), followed by a light-weight linear programming problem for capital allocation resolved via a standard solver. The idea proposed in this paper is novel for the area of ITP design in that it is especially scalable for tracking large-scale and dynamic-updating financial indices. Numerical simulations validate the scalability and high-efficiency of the proposed GNN-based approach with comparisons to the standard solver-based approach.
Electromagnetic Side-Channel Analysis for IoT Forensics: Challenges, Framework, and Datasets

Electromagnetic (EM) side-channel radiation from Internet of Things (IoT) devices are shown to be effective at acquiring forensic insights during digital investigations. These EM radiation patterns can be analysed with the help of machine learning algorithms to detect internal behaviours of IoT devices, which can be relevant to an investigation. However, the real-world application of EM side-channel analysis for digital forensic purposes is obstructed by the lack of suitable tools and the technical expertise among law-enforcement communities. Although certain frameworks, such as EMvidence, exist to cater this requirement, the sheer diversity of the IoT ecosystem makes it difficult to support a sufficiently large collection of devices that are commonly encountered in forensic investigations. The work presented in this paper makes multiple contributions towards addressing this problem. Initially, a detailed discussion on the challenges of applying EM side-channel analysis in practical digital forensic purposes is provided, where the practical difficulties are illustrated. Then, it was shown that the existing EM side-channel analysis frameworks, such as EMvidence, can be used to overcome the diversity of IoT devices in forensics by equipping them with extensible plug-ins targeting the internal system-on-chips (SoC) of each device type. These plug-ins are expected to incorporate trained machine learning models, which are capable of recognising patterns of specific IoT device SoCs. However, the development of such plug-ins requires sufficiently diverse EM datasets from IoT devices. Facilitating this requirement, this work presents a comprehensive EM side-channel dataset representing a diverse collection of popular IoT devices and smartphones. The presented dataset is used to demonstrate the potential usage of machine learning models to recognise device behaviour.
Analysis of a 3-Conductor Transmission Line with Nonlinear Resistive Loads

This paper analyzes the electromagnetic compatibility characteristics of lossless transmission lines terminated by nonlinear loads suggested by C. Paul. The nonlinearities of the resistive loads are of polynomial type. We examine the mutual interaction between the two lines without neglecting the impact of the receptor line, which enables a more general mathematical model contrasted to the C. Paul model. We articulate a mixed problem for a system corresponding to 3-conductor transmission line. After that the hyperbolic system is transformed to a diagonal form and it is reduced to an initial value problem on the boundary. We obtained a system of two functional equations and two neutral equations for four unknown functions.
Fuzzy Adaptive Sliding Mode Impedance Control of Fracture Reduction Robot

With the development of robotics, it will be possible for fracture reduction robots to replace doctors in performing surgery operations. The surgery accuracy and safety by the reduction robot is important. In order to compensate various uncertainties and nonlinear problems in the reduction robot impedance control system, this paper proposes a fuzzy adaptive sliding mode impedance control of the reduction robot, which includes force residual observer (FRO) and fuzzy adaptive sliding mode controller (FASMC). The FRO is used to estimate the external force between the reduction robot and the fractured musculoskeletal tissue in real-time, no force sensor is installed at the end-effector of reduction robot. The FASMC mainly includes three parts: linearizing the system by inverse dynamics and PD control, improving the system’s robustness by the sliding mode control and fuzzy adaptive switching gain to reduce chattering, eliminating the nonlinear disturbance term of the system, and the normalized factor linear combination fuzzy system is used to compensate. The co-simulation of the fracture reduction robot shows that the FASMC impedance control method has a better steady-state position and force tracking accuracy, the steady-state position accuracy is −0.733 mm, and the steady-state force accuracy is −2.12 N. This paper provides an effective method for impedance control of the fracture reduction robot.
Overvoltage and Oscillation Suppression Circuit With Switching Losses Optimization and Clamping Energy Feedback for SiC MOSFET

The snubber circuit is a cost-effective solution to reduce the severe turn-off overvoltage and oscillation caused by the fast switching characteristics of SiC MOSFET. However, the turn-on switching losses will significantly increase as the snubber decouples the power loop parasitic inductance during the switching process. In this article, the effect of the power loop parasitic inductance on the switch performance of SiC MOSFETs and the quasi-zero voltage switching (QZVS) turn-on condition are studied. On this basis, an SiC MOSFET overvoltage and oscillation suppression circuit (OVSC) with not only the switching losses optimization feature but also the clamping energy feedback characteristic is proposed. The overvoltage and oscillation can be effectively suppressed by clamping capacitors, and those capacitors do not participate in the switching process until the overvoltage occurs, which is of benefit to the switching losses reduction compared with the other normal snubber circuits. Besides, the turn-off overvoltage and oscillation energy stored in the clamping capacitors can be feedback to dc and load side through a passive branch composed of an energy feedback inductor and a diode. The experimental studies and comprehensive comparison studies are carried out to verify the validity of the proposed OVSC. The experimental results show that OVSC has excellent overvoltage and oscillation suppression performance and can significantly reduce the switching losses.