Miracle-dz/UniFAR-FTUs huggingface dataset:
CGD-1, a defensin-related peptide derived from marine Chinese medicine Ostreae concha, inhibits the Gram-negative bacteria by membrane attack

There is an increasing interest in discovering new antibacterial agents derived from nature to enhance the treatment of various bacterial infections. Defensins and their derived peptide fragments exhibit significant antibacterial activity without any cytotoxic effects, making them attractive features for potential novel antibacterial therapeutics. Crassostrea gigas , a traditional seafood that has been used worldwide for centuries, has its shells applied in Chinese medicine as Ostreae concha . In this study, bioinformatics analysis was used to obtain a novel antibacterial peptide, CGD-1, derived from marine Chinese medicine Ostreae concha . The physicochemical characterization and circular dichroism analysis results demonstrated that CGD-1 assembled into an α -helical structure in a simulated membrane environment, and it displayed antibacterial action against Gram-negative bacteria. The minimal inhibitory concentrations against both Pseudomonas aeruginosa ATCC27853 and Escherichia coli ATCC25922 were 25 μ M. CGD-1 was able to efficiently permeate the cell membrane. Changes in bacterial cell morphology were evaluated using a field emission scanning electron microscope. The results suggested that CGD-1 exerted its antibacterial activity through permeabilizing and disrupting the bacterial cell membrane. Therefore, CGD-1 may have potential applications in fighting against pathogenic bacteria such as P. aeruginosa and E. coli .
Adaptive Fuzzy Prescribed Performance Output-Feedback Cooperative Control for Uncertain Nonlinear Multiagent Systems

This article considers the prescribed performance output-feedback cooperative control of multiagent systems with nonparametric uncertainties. Scale and performance functions are proposed to construct a barrier function, based on which the adaptive prescribed performance control scheme is developed. Therein, the fuzzy observers are employed to estimate the unavailable states. The proposed scheme has the following characteristics: first, the tracking errors are always limited to a specified boundary. Second, the tracking error converges to a specified accuracy within a given time, and the settling time and tracking accuracy are determined only by the user and no longer depend on the initial conditions. Third, the distributed fuzzy controller design uses only the output information from agent and its neighbor. The effectiveness and practicality of the proposed method are demonstrated by two simulation examples.
Optimal Attack Energy Allocation Against Remote Estimation in Cyber-Physical Systems

This brief focuses on designing an optimal attack energy allocation strategy to disrupt the performance of remote state estimation with unknown gains for cyber-physical systems (CPSs). To deal with the unknown gains, the Cramér-Rao Bound (CRB) is introduced to obtain a lower bound of the secondary moment of the estimate error. Then, by solving an optimization problem that maximizes the average CRB over a finite time horizon with energy constraint, an optimal attack energy allocation strategy is derived. Compared with the existing works, the rigorous assumption that the attacker has full knowledge of the system is removed, and the computational complexity of solving the optimization problem is significantly reduced. Finally, numerical results validate the presented strategy.
FOR GI/G/1 BUSY PERIODS

A generalization of the GI/G/1 queue is considered where the service time of the n th customer and the inter-arrival time between arrivals n and n + 1 may be dependent random variables. New proofs are obtained of finite moment conditions for busy periods and the ladder epochs of a corresponding random walk. The method of proof, which is much different from the usual ones, directly relates busy period moments to virtual and actual delay moments.
Dynamic aspects ofdelay activity

The population density equation (PDE) has generated considerable interest, recently. The PDE allows e.cient simulation ofpopulations with a large number ofneurons. We apply the PDE to an existing model ofdelay activity, which produces stationary 1ring rates f delay assemblies. In order to observe transient phenomena, we apply the PDE to this model. Here we present the preliminary results. We 1nd that a novel algorithm to solve the PDE is instrumental for this particular application. Our results agree with stationary rates that can be found using a di5usion approximation.
Progress in neuromorphic photonics

Abstract As society’s appetite for information continues to grow, so does our need to process this information with increasing speed and versatility. Many believe that the one-size-fits-all solution of digital electronics is becoming a limiting factor in certain areas such as data links, cognitive radio, and ultrafast control. Analog photonic devices have found relatively simple signal processing niches where electronics can no longer provide sufficient speed and reconfigurability. Recently, the landscape for commercially manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. By bridging the mathematical prowess of artificial neural networks to the underlying physics of optoelectronic devices, neuromorphic photonics could breach new domains of information processing demanding significant complexity, low cost, and unmatched speed. In this article, we review the progress in neuromorphic photonics, focusing on photonic integrated devices. The challenges and design rules for optoelectronic instantiation of artificial neurons are presented. The proposed photonic architecture revolves around the processing network node composed of two parts: a nonlinear element and a network interface. We then survey excitable lasers in the recent literature as candidates for the nonlinear node and microring-resonator weight banks as the network interface. Finally, we compare metrics between neuromorphic electronics and neuromorphic photonics and discuss potential applications.
ZERO-ONE-IN-THREE 3SAT is in P

$ONE-IN-THREE \ 3SAT$ is the problem of deciding whether a given boolean formula $\phi$ in $3CNF$ has a truth assignment such that each clause in $\phi$ has exactly one true literal. This problem is $NP-complete$. We define a similar language: $ZERO-ONE-IN-THREE \ 3SAT$ is the problem of deciding whether a given boolean formula $\phi$ in $3CNF$ has a truth assignment such that each clause in $\phi$ has at most one true literal. Indeed, $ONE-IN-THREE \ 3SAT \subseteq ZERO-ONE-IN-THREE \ 3SAT$. In this work, we prove $ZERO-ONE-IN-THREE \ 3SAT \in P$.
Real-time pedestrian detection using radar-camera fusion and clustering

The design and development of autonomous vehicles ensure to move safely on roads while focusing on pedestrian detection systems has brought convention so that pedestrians can be detected quickly and precisely. Moreover, the researchers have mentioned that pedestrian skin detection has proven to be a tough challenge since the color of the skin can vary in appearance due to various factors such as weather conditions, sun lighting, occlusion, race, etc. Our proposed methodology, the radar-camera fusion technique, is used to predict the obstacle in any scenario. A convolution neural network extracts pedestrian features from RGB images and radar data. Also, we have introduced data preparation and feature extraction. We feature mapping to get more detection accuracy and clustering to find the similarities between features that will attain darker skin pedestrian details.
A Monitoring System for Elderly People Using WiFi Sensing with Channel State Information

Healthcare professionals, industry, and academics have all recently expressed a strong interest towards WiFi sensing. These techniques could be used to identify critical occurrences that sensitive persons may suffer, such as falls, sleep abnor-malities, wandering behavior, respiratory illnesses, and irregular heart activity. In this paper, we propose a low-cost, non-intrusive method to remotely monitor el-derly people without deploying devices on their bodies in a given space, using channel state information (CSI) from passive WiFi detection. Specifically, we monitored variables such as sitting and standing activity, and experimental valida-tion in practical situations with variable occupants, various environmental set-tings, and interference from other WiFi devices demonstrates robustness and scalability. Our results suggest that the proposed method can be put into practical-real use to detect activity and alert emergency personnel immediately, providing rapid medical assistance, saving lives, minimizing damage, and reducing anxiety for elderly people residing alone.
Modelagem Matemática Caixa Cinza de Módulo Fotovoltaico

Em advento do crescente aumento mundial de consumo de energia elétrica no século XX, a busca por fontes renováveis de energia que não apresentam impactos ambientais significativos tornou-se essencial para suprir tal demanda. Dentre essas fontes renováveis, a energia solar fotovoltaica tem ganhado destaque no cenário energético mundial. Embora a sua participação seja pequena em ńıvel global, a capacidade instalada cresceu em média 56% no peŕıodo de 2009 a 2014, segundo o relatório fotovoltaico anual de 2015 do Instituto de Fraunhofer [2]. Devido a essa grande expansão, a caracterização dos fenômenos associados aos módulos é essencial para um adequado projeto, visando redução de custos e o aprimoramento da eficiência energética e operacional. Para isso, a modelagem matemática é uma ferramenta imprescind́ıvel para a representação do seu comportamento dinâmico. Diferentes técnicas de modelagem podem ser aplicadas, classificadas como caixa branca, caixa preta e caixa cinza. A modelagem caixa branca descreve o sistema baseado somente nas leis f́ısicas que governam seu comportamento. Por outro lado, a modelagem caixa preta utiliza apenas dados coletados para identificar a relação entrada-sáıda. Por fim, a modelagem caixa cinza é um método intermediário, em que suas técnicas envolvem um conjunto de informações f́ısicas e emṕıricas no intuito de descrever o sistema [1]. Analisando o comportamento experimental de um módulo fotovoltaico, este apresenta caracteŕıstica não-linear em sua curva de tensão versus corrente. Esse comportamento deve-se ao fato da constituição f́ısica da célula fotovoltaica, a qual pode ser representada pelos modelos de diodo único e diodo duplo. O modelo do diodo único é o mais utilizado na literatura, devido a sua relação entre precisão e simplicidade [3]. Esse modelo vem sendo aprimorado visando a descrição do módulo fotovoltaico para diferentes condições de temperatura e radiação. Os parâmetros f́ısicos intŕınsecos do siĺıcio não são fornecidos pelo fabricante. Tendo em vista a necessidade de identificação desses parâmetros, este trabalho propõe a aplicação da técnica de modelagem caixa cinza a um módulo fotovoltaico, com base na variação de radiação a uma temperatura fixa de 25 ◦C. andybalbino@hotmail.com deividizaions@hotmail.com Proceeding Series of the Brazilian Society of Applied and Computational Mathematics, Vol. 5, N. 1, 2017.
Building a robust clinical diagnosis support system for childhood cancer using data mining methods

Progress in understanding core pathways and processes of cancer requires thorough analysis of many coding and noncoding regions of the genome. Data mining and knowledge discovery have been applied to datasets across many industries, including bioinformatics. However, data mining faces a major challenge in its application to bioinformatics: the diversity and dimensionality of biomedical data. The term ‘big data’ was applied to the clinical domain by Yoo et al. (2014), specifically referring to single nucleotide polymorphism (SNP) and gene expression data. This research thesis focuses on three different types of data: gene-annotations, gene expression and single nucleotide polymorphisms. Genetic association studies have led to the discovery of single genetic variants associated with common diseases. However, complex diseases are not caused by a single gene acting alone but are the result of complex linear and non-linear interactions among different types of microarray data. In this scenario, a single gene can have a small effect on disease but cannot be the major cause of the disease. For this reason there is a critical need to implement new approaches which take into account linear and non-linear gene-gene and patient-patient interactions that can eventually help in diagnosis and prognosis of complex diseases. Several computational methods have been developed to deal with gene annotations, gene expressions and SNP data of complex diseases. However, analysis of every gene expression and SNP profile, and finding gene-to-gene relationships, is computationally infeasible because of the high-dimensionality of data. In addition, many computational methods have problems with scaling to large datasets, and with overfitting. Therefore, there is growing interest in applying data mining and machine learning approaches to understand different types of microarray data. Cancer is the disease that kills the most children in Australia (Torre et al., 2015). Within this thesis, the focus is on childhood Acute Lymphoblastic Leukaemia. Acute Lymphoblastic Leukaemia is the most common childhood malignancy with 24% of all new cancers occurring in children within Australia (Coates et al., 2001). According to the American Cancer Society (2016), a total of 6,590 cases of ALL have been diagnosed across all age groups in USA and the expected deaths are 1,430 in 2016. The project uses different data mining and visualisation methods applied on different types of biological data: gene annotations, gene expression and SNPs. This thesis focuses on three main issues in genomic and transcriptomic data studies: (i) Proposing, implementing and evaluating a novel framework to find functional relationships between genes from gene-annotation data. (ii) Identifying an optimal dimensionality reduction method to classify between relapsed and non-relapsed ALL patients using gene expression. (iii) Proposing, implementing and evaluating a novel feature selection approach to identify related metabolic pathways in ALL This thesis proposes, implements and…
CONSERT constrains the internal structure of 67P at a few metres size scale

The internal properties of cometary nuclei are the best clues to the size distribution and properties of the early planetesimals that formed the planets during the early solar system nebula processes. The CONSERT radar was designed to probe the interior of comet 67P/Churyumov-Gerasimenko with a wavelength of about 3 meters. It was successfully operated during the First Science Sequence of the Rosetta mission when the CONSERT’s wave propagated through part of the nucleus’ small lobe. The shape of the received signals provides some hints about the internal structure of the comet's small lobe. The limited broadening observed on the received pulses is compared to simulations performed on a series of non-homogeneous nucleus models to put constrains on the potential structures at the wavelength's scale. The study allows to exclude structures showing a permittivity contrast larger than 0.25 inside the sounded part of the nucleus at a few-metre size scale.
Permeability of wormholes created by CO2-acidized water flow through stressed carbonate rocks

Sequestration of CO2 relies on the storage capabilities of the deep geologic setting throughout the lifetime of the storage activity. Preferred storage horizons are largely composed of sandstone, which is considered to be chemically inert to the injected CO2. Carbonate rocks and carbonate zones existing as seams or lenses in sandstone formations are, however, prone to chemical alteration during reactive flows of CO2-acidized water that can be created by the mixing of the injected CO2 with either fresh or saline water present in a storage horizon. Reactive flows can erode the fabric of carbonate rocks leading to the creation of high permeability pathways that are referred to as wormholes. The paper first examines the generation of wormholes in cylindrical samples of calcium carbonate-rich Indiana Limestone that are subjected to geostatic stress states representative of deep sequestration sites. The leakage potential of the wormhole is examined by appeal to computational fluid dynamics simulations of Stokes.
Power Supply for Medical Implants by Wiegand Pulse Generated from Magnetic Wire

Implantable medical devices are utilized in the human body for maintaining good health. As these devices are becoming increasingly functionalized, supplying power to them has become very important. Instead of batteries, technologies for wireless power supplies are being developed, such as inductive coupling or piezoelectric elements. However, we propose the use of magnetic wires for this purpose. Inside these wires, a fast magnetization reversal, called a “large Barkhausen jump,” occurs due to an applied magnetic field. This reversal induces a large pulse voltage in pick- up coils, which is called a “Wiegand pulse. ” By applying this pulse as an electric source, a higher electric power is expected compared with the conventional method using a sinusoidal excitation field. A wire core coil was prepared, and open-circuit voltage was measured. In addition, DC voltage and electric power were measured by connecting the wire core coil to a rectifier. The experimental results confirmed the superiority of using a Wiegand pulse at an applied magnetic field intensity of 60 Oe and a frequency of lower than 10 kHz.
DDAC-SpAM: A Distributed Algorithm for Fitting High-dimensional Sparse Additive Models with Feature Division and Decorrelation*

Distributed statistical learning has become a popular technique for large-scale data analysis. Most existing work in this area focuses on dividing the observations, but we propose a new algorithm, DDAC-SpAM, which divides the features under a high-dimensional sparse additive model. Our approach involves three steps: divide, decorrelate, and conquer. The decorrelation operation enables each local estimator to recover the sparsity pattern for each additive component without imposing strict constraints on the correlation structure among variables. The effectiveness and efficiency of the proposed algorithm are demonstrated through theoretical analysis and empirical results on both synthetic and real data. The theoretical results include both the consistent sparsity pattern recovery as well as statistical inference for each additive functional component. Our approach provides a practical solution for fitting sparse additive models, with promising applications in a wide range of domains.
Effect of hydrogen on the device performance and stability characteristics of amorphous InGaZnO thin-film transistors with a SiO2/SiNx/SiO2 buffer

We investigate the influence of the multi-layered buffer consisting of SiO2/SiNx/SiO2 on amorphous InGaZnO (a-IGZO) thin-film transistors (TFTs). The multi-layered buffer inhibits permeation of water from flexible plastic substrates and prevents degradation of overlying organic layers. The a-IGZO TFTs with a multi-layered buffer suffer less positive bias temperature stress instability compared to the device with a single SiO2 buffer layer after annealing at 250 °C. Hydrogen from the SiNx layer diffuses into the active layer and reduces electron trapping at loosely bound oxygen defects near the SiO2/a-IGZO interface. Quantitative analysis shows that a hydrogen density of 1.85 × 1021 cm−3 is beneficial to reliability. However, the multi-layered buffer device annealed at 350 °C resulted in conductive characteristics due to the excess carrier concentration from the higher hydrogen density of 2.12 × 1021 cm−3.
Insecure fisheries: How illegal, unreported, and unregulated fishing affects piracy

We examine greed and grievance mechanisms that connect illegal, unreported, and unregulated (IUU) fishing and piracy. We use several cases (e.g. Somalia, Nigeria) to illustrate these mechanisms and empirically examine the relationship between IUU fishing and state-year piracy events from 1990 to 2015. We find that countries experiencing significant levels of IUU fishing face much greater risks for piracy. We also evaluate several mediating conditions of our theory with interaction terms (state capacity, state fragility, and legal fishing incentives) and find that the relationship between IUU fishing and piracy is strongest for moderately developed states with greater state fragility and higher fish catch values.
Enhancing quality of service in IoT through deep learning techniques

When evaluating an Internet of Things (IoT) platform, it is crucial to consider the quality of service (QoS) as a key criterion. With critical devices relying on IoT technology for both personal and business use, ensuring its security is paramount. However, the vast amount of data generated by IoT devices makes it challenging to manage QoS using conventional techniques, particularly when attempting to extract valuable characteristics from the data. To address this issue, we propose a dynamic-progressive deep reinforcement learning (DPDRL) technique to enhance QoS in IoT. Our approach involves collecting and preprocessing data samples before storing them in the IoT cloud and monitoring user access. We evaluate our framework using metrics such as packet loss, throughput, processing delay, and overall system data rate. Our results show that our developed framework achieved a maximum throughput of 94%, indicating its effectiveness in improving QoS. We believe that our deep learning optimization approach can be further utilized in the future to enhance QoS in IoT platforms.
A retinal image sharpness metric based on histogram of edge width

Retinal image sharpness assessment is one of the critical requirements of automatic quality evaluation in telemedicine screening for diabetic retinopathy. In this paper, a new sharpness metric measuring the spread of edges is presented to quantify fundus image clarity. After edge detection on the region of interest of retinal image, the width of each edge is calculated and the histogram of region of interest generated. Based on the histogram, a distance-based factor is introduced to gain the weighted edge width, which is defined as the sharpness metric for the fundus image. The method was tested on Messidor dataset and a proprietary dataset. The results show that the proposed metric performs well over different image distortion levels and resolutions and is of low computational complexity. The weighted edge width value of gradable retinal image, which is irrelevant to resolution, is always within the range of 3–7 pixels.
Robust adaptive backstepping control for hierarchical multi-agent systems with signed weights and system uncertainties

: This study is concerned with the robust adaptive backstepping control for hierarchical multi-agent systems with signed weights and non-linear system uncertainties. The considered network model is divided into several levels, and the corresponding Laplacian matrix in each level may be non-positive definiteness due to the existence of signed weights, which can lead to the instability of the whole system. To solve this problem, two definitions on recovery Laplacian and controlled Laplacian are firstly introduced, and by combining with a novel graph theoretical result, an effective robust backstepping control scheme is then developed to ensure the stability of hierarchical multi-agent systems. In particular, this study is a generalisation of the prior works with non-negative weights. Finally, an example on hierarchical multi-vehicle systems is given to demonstrate the validity of the proposed method.