On the Performance of Energy Harvesting Dual-Hop Free-Space Optical Communication Systems with Secrecy Analysis.

In this study, we present a dual-hop decode-and-forward relaying-based free-space optical (FSO) communication system. We consider utilizing simultaneous lightwave information and power transfer (SLIPT) with a time-splitting technique at the relay, where the direct current component of the received optical signal is harvested as a transmit power for the relay. It is assumed that the FSO links experience a Malaga turbulence channel with pointing errors.

The effects of some chemical compounds on the sound absorbing ability of tree bark.

Tree bark is an important natural polymer for sound absorption. The main components in the bark of different tree species are polymers with high molecular weight such as cellulose, hemicellulose, and lignin. The aim of this study is to determine the noise reduction coefficient (NRC), lignin, alcohol-benzene solubility (ABS), carbon (C), and nitrogen (N) contents in samples taken from the bark of different tree species-black locust (Robinia pseudoacacia), narrow-leaved ash (Fraxinus angustifolia), stone pine (Pinus pinea), silver lime (Tilia tomentosa), sweet chestnut (Castanea sativa), sessile oak (Quercus petraea), and maritime pine (Pinus pinaster) and to investigate the relationship between these chemical properties and sound absorption measurements.

Evaluation of the effectiveness of panoramic radiography in impacted mandibular third molars on deep learning models developed with findings obtained with cone beam computed tomography.

Objective: The aim of this study is to determine the contact relationship and position of impacted mandibular third molar teeth (IMM) with the mandibular canal (MC) in panoramic radiography (PR) images using deep learning (DL) models trained with the help of cone beam computed tomography (CBCT) and DL to compare the performances of the architectures.

Methods: In this study, a total of 546 IMMs from 290 patients with CBCT and PR images were included. The performances of SqueezeNet, GoogLeNet, and Inception-v3 architectures in solving four problems on two different regions of interest (RoI) were evaluated.

MRSNet: Multi-Resolution Scale Feature Fusion-Based Universal Density Counting Network.

This study focuses on the problem of dense object counting. In dense scenes, variations in object scales and uneven distributions greatly hinder counting accuracy. The current methods, whether CNNs with fixed convolutional kernel sizes or Transformers with fixed attention sizes, struggle to handle such variability effectively.

LUNGBANK: a novel biorepository strategy tailored for comprehensive multiomics analysis and P-medicine applications in lung cancer.

Background/aim: LUNGBANK was established as part of Project LUNGMARK, pioneering a biorepository dedicated exclusively to lung cancer research. It employs cutting-edge technologies to streamline the handling of biospecimens, ensuring the acquisition of high-quality samples. This infrastructure is fortified with robust data management capabilities, enabling seamless integration of diverse datasets.

A Four Element Stringray-Shaped MIMO Antenna System for UWB Applications.

This paper presents a CoPlanar-Waveguide (CPW)-fed stingray-shaped Ultra-WideBand (UWB) Multiple-Input-Multiple-Output (MIMO) antenna system designed for microwave imaging applications. Featuring a diagonal square with four inner lines and a vertical line at the center from toe to tip with a CPW feed line, the unit antenna element looks like a stingray fish skeleton and is, therefore, named as a stingray-shaped antenna. It offers a bandwidth spanning from 3.

Smart Contract Vulnerability Detection Based on Deep Learning and Multimodal Decision Fusion.

With the rapid development and widespread application of blockchain technology in recent years, smart contracts running on blockchains often face security vulnerability problems, resulting in significant economic losses. Unlike traditional programs, smart contracts cannot be modified once deployed, and vulnerabilities cannot be remedied. Therefore, the vulnerability detection of smart contracts has become a research focus.

CUI-Net: a correcting uneven illumination net for low-light image enhancement.

Uneven lighting conditions often occur during real-life photography, such as images taken at night that may have both low-light dark areas and high-light overexposed areas. Traditional algorithms for enhancing low-light areas also increase the brightness of overexposed areas, affecting the overall visual effect of the image. Therefore, it is important to achieve differentiated enhancement of low-light and high-light areas.

Modeling and simulation of an anthropomorphic hand prosthesis with an object interaction.

Background And Objective: Kinematic and dynamic modeling of any physical process is critical especially for the design phase of associated control units and the prediction of their performance levels. In this paper, a dynamical model and a development frame including interactions with the objects for a tendon-driven underactuated hand are developed and examined. Using the proposed dynamic model, free movement and the object interaction of the underactuated hand can be simulated with a single, integrated simulation step in the simulation frame.

An Analysis of Deterministic Chaos as an Entropy Source for Random Number Generators.

This paper presents an analytical study on the use of deterministic chaos as an entropy source for the generation of random numbers. The chaotic signal generated by a phase-locked loop (PLL) device is investigated using numerical simulations. Depending on the system parameters, the chaos originating from the PLL device can be either bounded or unbounded in the phase direction.

Dynamic Flow Characteristics and Design Principles of Laminar Flow Microbial Fuel Cells.

Laminar flow microbial fuel cells (MFCs) are used to understand the role of microorganisms, and their interactions with electrodes in microbial bioelectrochemical systems. In this study, we reported the flow characteristics of laminar flow in a typical MFC configuration in a non-dimensional form, which can serve as a guideline in the design of such microfluidic systems. Computational fluid dynamics simulations were performed to examine the effects of channel geometries, surface characteristics, and fluid velocity on the mixing dynamics in microchannels with a rectangular cross-section.

Clinical phenotype of hereditary spastic paraplegia due to KIF1C gene mutations across life span.

Hereditary spastic paraplegias (HSPs) are a group of genetic disorders resulting in pyramidal tract impairment, predominantly in lower limbs. KIF1C gene has recently been identified as one of the genetic causes of HSP and associated with pure or complicated HSP. We present three patients with complicated HSP from two unrelated families, who had early onset progressive cerebellar signs and developed pyramidal tract signs during follow-up.

Securing Cooperative Spectrum Sensing Against Collusive SSDF Attack using XOR Distance Analysis in Cognitive Radio Networks.

Cooperative spectrum sensing (CSS) is considered as a powerful approach to improve the utilization of scarce spectrum resources. However, if CSS assumes that all secondary users (SU) are honest, it may offer opportunities for attackers to conduct a spectrum sensing data falsification (SSDF) attack. To suppress such a threat, recent efforts have been made to develop trust mechanisms.

Device-Free Passive Identity Identification via WiFi Signals.

Device-free passive identity identification attracts much attention in recent years, and it is a representative application in sensorless sensing. It can be used in many applications such as intrusion detection and smart building. Previous studies show the sensing potential of WiFi signals in a device-free passive manner.

Performance Evaluation Model for Application Layer Firewalls.

Application layer firewalls protect the trusted area network against information security risks. However, firewall performance may affect user experience. Therefore, performance analysis plays a significant role in the evaluation of application layer firewalls.

FMFilter: A fast model based variant filtering tool.

The availability of whole exome and genome sequencing has completely changed the structure of genetic disease studies. It is now possible to solve the disease causing mechanisms within shorter time and budgets. For this reason, mining out the valuable information from the huge amount of data produced by next generation techniques becomes a challenging task.

Privacy preserving processing of genomic data: A survey.

Recently, the rapid advance in genome sequencing technology has led to production of huge amount of sensitive genomic data. However, a serious privacy challenge is confronted with increasing number of genetic tests as genomic data is the ultimate source of identity for humans. Lately, privacy threats and possible solutions regarding the undesired access to genomic data are discussed, however it is challenging to apply proposed solutions to real life problems due to the complex nature of security definitions.

Bayesian pathway analysis of cancer microarray data.

High Throughput Biological Data (HTBD) requires detailed analysis methods and from a life science perspective, these analysis results make most sense when interpreted within the context of biological pathways. Bayesian Networks (BNs) capture both linear and nonlinear interactions and handle stochastic events in a probabilistic framework accounting for noise making them viable candidates for HTBD analysis. We have recently proposed an approach, called Bayesian Pathway Analysis (BPA), for analyzing HTBD using BNs in which known biological pathways are modeled as BNs and pathways that best explain the given HTBD are found.

Bayesian network prior: network analysis of biological data using external knowledge.

Motivation: Reverse engineering GI networks from experimental data is a challenging task due to the complex nature of the networks and the noise inherent in the data. One way to overcome these hurdles would be incorporating the vast amounts of external biological knowledge when building interaction networks. We propose a framework where GI networks are learned from experimental data using Bayesian networks (BNs) and the incorporation of external knowledge is also done via a BN that we call Bayesian Network Prior (BNP).