ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Resilient operation of DC microgrid against FDI attack: A GRU based framework

DC microgrid is the most susceptible to cyber-attacks as the communication channel is involved for the imple-mentation of the secondary controller. Accordingly, the false data are injected into the transmitted data (i.e., DC bus voltage) and it may lead to deteriorating the system performance. To address these issues, the gated recurrent unit (GRU) based mechanism is presented to eliminate the false data injection (FDI) attack for the resilient operation of the DC microgrid. The presented GRU-based framework is divided into two parts: 1) estimation strategy: an offline-trained GRU based network is employed herein for online evaluation of the actual DC bus voltage, and 2) mitigation strategy: GRU based trained network is exploited herein with an amalgamation of the proportional-integral (PI) controller to counteract the malicious cyber-attack. The presented GRU-based framework has several advantages such as ease of implementation and computationally efficient, unlike state -of-art methods. The sensitivity analysis is investigated herein to validate the effectiveness of the presented GRU-based framework over state-of-art techniques. Simulation results show satisfactory performance under manifold operating scenarios such as bias injection attack and time-varying attack. In addition, the quantitative and qualitative comparative performances are performed herein to demonstrate the efficacy of the presented framework.
Kernel-Matching Pursuits With Arbitrary Loss Functions

The purpose of this research is to develop a classifier capable of state-of-the-art performance in both computational efficiency and generalization ability while allowing the algorithm designer to choose arbitrary loss functions as appropriate for a give problem domain. This is critical in applications involving heavily imbalanced, noisy, or non-Gaussian distributed data. To achieve this goal, a kernel-matching pursuit (KMP) framework is formulated where the objective is margin maximization rather than the standard error minimization. This approach enables excellent performance and computational savings in the presence of large, imbalanced training data sets and facilitates the development of two general algorithms. These algorithms support the use of arbitrary loss functions allowing the algorithm designer to control the degree to which outliers are penalized and the manner in which non-Gaussian distributed data is handled. Example loss functions are provided and algorithm performance is illustrated in two groups of experimental results. The first group demonstrates that the proposed algorithms perform equivalent to several state-of-the-art machine learning algorithms on well-published, balanced data. The second group of results illustrates superior performance by the proposed algorithms on imbalanced, non-Gaussian data achieved by employing loss functions appropriate for the data characteristics and problem domain.
Symbolism of the ibex motif in Negev rock art

The male ibex is the dominant zoomorphic motif in rock art of the Negev desert, Israel. It recurs in thousands of petroglyphs, either alone or in association with several recurring images; commonly with dogs or other predators but also with hunters. These associations occur in all chronological phases of Negev rock art, implying that they had an enduring symbolic significance. Here we address only some aspects of ibex iconography, focusing on its association with dogs, hunters and astral symbols. We discuss the possible meaning of these associations with regard to the ritual hunting of ibex and connection to deities associated with rainfall, seasonal cycles and celestial constellations. (C) 2016 Elsevier Ltd. All rights reserved.
Optical band gap enhancements of chemically synthesized α-Ni(OH)2 nanoparticles by a novel technique: Precipitator molarity variation

Nickel hydroxide nanoparticles (NHNPs) are extremely important semiconducting materials for applications in energy storage and energy harvesting devices. This study uses a novel variation in molarity of the sodium hydroxide (NaOH) precipitator solution to enhance the direct optical band gap in the NHNPs chemically synthesized by using nickel nitrate hexahydrate (Ni(NO3 )2 ·6H2 O) as the precursor. The simple, energy benign chemical precipitation route involved the usage of 1 M (Ni(NO3 )2 ·6H2 O) solutions as the precursor and 0.4 M, 0.6 M, and 0.8 M NaOH solutions as the precipitator solutions. The simple variation in precipitator molarity induces an increase in pH from about 6.9 to 7.5 of the reactant solution. As the molarity of the precursor solution does not change, the change in pH of the reactant solution is equivalent to the change in the pH of the precipitator solution. The NHNPs characterized by X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), dynamic light scattering (DLS), Fourier-transform infrared (FTIR) and ultraviolet-visible (UV-vis) techniques confirm a reduction of the nanocrystallite size from about 6.8 to 4.5 nm with a concomitant enhancement in the direct optical band gap energy from about 2.64 to 2.74 eV. The possible mechanisms that could be operative behind obtaining microstructurally tuned (MT)-NHNPs and band gap engineering (BGE) of the MT-NHNPs are discussed from both theoretical and physical process perspectives. Further, the implications of these novel results for possible future applications are briefly touched upon. The reported results might be useful to assess the material as an active electrode to improve the performance of batteries.
Software Frameworks for SDR

This paper describes the state of the art in software frameworks for executing Software Defined Radio (SDR) components. These frameworks are catalyzing drastic changes in signal processing by enabling software engineers and signal processing engineers to work in tandem on core challenges, such as effectively processing large amounts of data in real-time on limited hardware resources. In addition to a historical perspective of this area, we showcase the REDHAWK framework as an example of a modern SDR framework which provides many facilities for distributed SDR deployment.
Anthropogenic influences on coastal environmental changes in the Mekong Delta: a study from Ben Tre Province, Southern Vietnam

Low-lying coastal environments are highly dynamic and sensitive to natural as well as anthropogenic perturbations. Climate change, sea level rise, storms and tsunamis are the natural phenomena that affect the deltaic coasts in Southeast Asia in general and Vietnam in particular. The effects of these phenomena can be exacerbated by human activities such as mangrove deforestation, aquaculture and infrastructure development. Conversely, the low-lying coastal areas are important in the economic development of Southeast Asian countries. In the Vietnamese Mekong Delta, coastal areas have been affected by a number of factors, such as climate change, sea level rise, aquaculture, pollution and tourism-related activities in recent decades. The present study investigated shoreline changes, expansion of aquaculture ponds, soil salinity changes and salinity intrusion in the river systems along the coastal areas of Ben Tre Province in the Vietnamese Mekong Delta between 1998 and 2020 using satellite imagery and field data. Variations in erosion and accretion were found to be not unique along the coast of Ben Tre. There was a rapid expansion of aquaculture ponds between 1998 and 2015 and a slight decline since then. Soil salinity has been increased between 1998 and 2020; it is seen from recent satellite data that soil is becoming more saline in the inland areas of Ben Tre. Saltwater intrusion into the rivers of Ben Tre is considered associated with El Niño-La Niña conditions. It is suggested that reforestation of abandoned shrimp ponds in Ben Tre by mangrove vegetation can be effective as a bioshield against coastal hazards, such as sea level rise and shoreline erosion.
On the Improved High-Frequency Linearity of Drain Extended MOS Devices

Based upon two-tone measurements, 5dB improvement in the linearity behavior of drain extended MOS (DeMOS) device is reported by novel drain engineering. The presented modification significantly improves the device saturation characteristic, ON resistance and transconductance without affecting the breakdown behavior. Formation of IMD sweet-spot by device design is shown and verified using DeMOS devices fabricated in state-of-the-art 28nm CMOS technology. Detailed analysis towards the achieved improvement is also given.
ART(2)ool: a model-driven framework to generate target code for robot handling tasks

Nowadays, robotic manipulation tasks are present in modern production industries, making robotics a decisive discipline in the industrial sector. Additionally, in a short period of time, handle robots will be also become essential in daily life. There is an increase in demand for applications for handle robots with software requirements such as reusability, flexibility, and adaptability. Unfortunately, the current lack of standardization of hardware and software platforms hinders the fulfillment of these requirements. Hence, it is necessary to define a methodology that provides guidelines to design, implement, and support at runtime of such types of applications. This work explores the advantages of Model Driven Engineering (MDE) in the design and development of tasks performed by handle robots. Concretely, the authors present the ART(2)ool (Arm based Robotic Tasks modeling Tool), a MDE framework, which is very useful for application domain experts, because it guides them along the design of the application functionality, abstracting from the emerging techniques. Besides, the proposed framework supports an automatic code generation by Mode to Text transformation techniques for component-based and ROS communication middleware, achieving the requirements mentioned previously.
Highly Stable 4.6 V LiCoO2 Cathodes for Rechargeable Li Batteries by Rubidium-Based Surface Modifications

Among extensively studied Li-ion cathode materials, LiCoO2 (LCO) remains dominant for portable electronic applications. Although its theoretical capacity (274 mAh g-1 ) cannot be achieved in Li cells, high capacity (≤240 mAh g-1 ) can be obtained by raising the charging voltage up to 4.6 V. Unfortunately, charging Li-LCO cells to high potentials induces surface and structural instabilities that result in rapid degradation of cells containing LCO cathodes. Yet, significant stabilization is achieved by surface coatings that promote formation of robust passivation films and prevent parasitic interactions between the electrolyte solutions and the cathodes particles. In the search for effective coatings, the authors propose RbAlF4 modified LCO particles. The coated LCO cathodes demonstrate enhanced capacity (>220 mAh g-1 ) and impressive retention of >80/77% after 500/300 cycles at 30/45 °C. A plausible mechanism that leads to the superior stability is proposed. Finally the authors demonstrate that the main reason for the degradation of 4.6 V cells is the instability of the anode side rather than the failure of the coated cathodes.
Louse flies holding on mammals' hair: Comparative functional morphology of specialized attachment devices of ectoparasites (Diptera: Hippoboscoidea)

Hippoboscidae and Nycteribiidae of the dipteran superfamily Hippoboscoidea are obligate ectoparasites, which feed on the blood of different mammals. Due to their limited flight capability, the attachment system on all tarsi is of great importance for a secure grasp onto their host and thus for their survival. In this study, the functional morphology of the attachment system of two hippoboscid species and two nycteribiid species was compared in their specificity to the host substrate. Based on data from scanning electron microscopy and confocal laser scanning microscopy, it was shown that the attachment systems of both Hippoboscidae and Nycteribiidae (Hippoboscoidea) differ greatly from that of other calyptrate flies and are uniform within the respective families. All studied species have an attachment system with two monodentate claws and two pulvilli. The claws and pulvilli of the Hippoboscidae are asymmetric, which is an adaptation to the fur of even-toed ungulates (Artiodactyla). The fur of these mammals possesses both, thinner woolen and thicker coat hair; thus, the asymmetry of the attachment system of the hippoboscid species enables a secure attachment to all surfaces of their hosts. The claws and pulvilli of the nyceribiid species do not show an asymmetry, since the fur of their bat (Chiroptera) hosts consists of hairs with the same thickness. The claws are important for the attachment to mammals' fur, because they enable a secure grip by mechanical interlocking of the hairs through the claws. Additionally, well-developed pulvilli are able to attach on thicker hairs of Artiodactyla or on smooth substrates such as the skin.
Sample-Adaptive GANs: Linking Global and Local Mappings for Cross-Modality MR Image Synthesis

Generative adversarial network (GAN) has been widely explored for cross-modality medical image synthesis. The existing GAN models usually adversarially learn a global sample space mapping from the source-modality to the target-modality and then indiscriminately apply this mapping to all samples in the whole space for prediction. However, due to the scarcity of training samples in contrast to the complicated nature of medical image synthesis, learning a single global sample space mapping that is "optimal" to all samples is very challenging, if not intractable. To address this issue, this paper proposes sample-adaptive GAN models, which not only cater for the global sample space mapping between the source- and the target-modalities but also explore the local space around each given sample to extract its unique characteristic. Specifically, the proposed sample-adaptive GANs decompose the entire learning model into two cooperative paths. The baseline path learns a common GAN model by fitting all the training samples as usual for the global sample space mapping. The new sample-adaptive path additionally models each sample by learning its relationship with its neighboring training samples and using the target-modality features of these training samples as auxiliary information for synthesis. Enhanced by this sample-adaptive path, the proposed sample-adaptive GANs are able to flexibly adjust themselves to different samples, and therefore optimize the synthesis performance. Our models have been verified on three cross-modality MR image synthesis tasks from two public datasets, and they significantly outperform the state-of-the-art methods in comparison. Moreover, the experiment also indicates that our sample-adaptive strategy could be utilized to improve various backbone GAN models. It complements the existing GANs models and can be readily integrated when needed.
Multi-Objective Memetic Search for Robust Motion and Distortion Correction in Diffusion MRI

Effective image-based artifact correction is an essential step in the analysis of diffusion MR images. Many current approaches are based on retrospective registration, which becomes challenging in the realm of high b-values and low signal-to-noise ratio, rendering the corresponding correction schemes more and more ineffective. We propose a novel registration scheme based on memetic search optimization that allows for simultaneous exploitation of different signal intensity relationships between the images, leading to more robust registration results. We demonstrate the increased robustness and efficacy of our method on simulated as well as in vivo datasets. In contrast to the state-of-art methods, the median target registration error (TRE) stayed below the voxel size even for high b-values (3000 s . mm(-2) and higher) and low SNR conditions. We also demonstrate the increased precision in diffusion-derived quantities by evaluating Neurite Orientation Dispersion and Density Imaging (NODDI) derived measures on a in vivo dataset with severe motion artifacts. These promising results will potentially inspire further studies on metaheuristic optimization in diffusion MRI artifact correction and image registration in general.
Multi-Slice Fusion for Sparse-View and Limited-Angle 4D CT Reconstruction

Inverse problems spanning four or more dimensions such as space, time andother independent parameters have become increasingly important. State-of-the-art 4D reconstruction methods use model based iterative reconstruction (MBIR), but depend critically on the quality of the prior modeling. Recently, plug-and-play (PnP) methods have been shown to he an effective way to incorporate advanced prior models using state-of-the-art denoising algorithms. However, state-of-the-art denoisers such as BM4D and deep convolutional neural networks (CNNs) are primarily available for 2D or 3D images and extending them to higher dimensions is difficult due to algorithmic complexity and the increased difficulty of effective training. In this paper, we present multi-slice fusion, a novel algorithm for 4D reconstruction, based on the fusion of multiple low-dimensional denoisers. Our approach uses multi-agent consensus equilibrium (MACE), an extension of plug-and-play, as a framework for integrating the multiple lower-dimensional models. We apply our method to 4D cone-beam X-ray CT reconstruction for non destructive evaluation (NDE) of samples that are dynamically moving during acquisition. We implement multi-slice fusion on distributed, heterogeneous clusters in order to reconstruct large 4D volumes in reasonable time and demonstrate the inherent parallelizable nature of the algorithm. We present simulated and real experimental results on sparse-view and limited-angle CT data to demonstrate that multi-slice fusion can substantially improve the quality of reconstructions relative to traditional methods, while also being practical to implement and train.
Automatic fire pixel detection using image processing: a comparative analysis of rule-based and machine learning-based methods

This paper presents a comparative analysis of state-of-the art image processing-based fire color detection rules and methods in the context of geometrical characteristics measurement of wildland fires. Two new rules and two new detection methods using an intelligent combination of the rules are presented, and their performances are compared with their counterparts. The benchmark is performed on approximately two hundred million fire pixels and seven hundred million non-fire pixels extracted from five hundred wildland images under diverse imaging conditions. The fire pixels are categorized according to fire color and existence of smoke; meanwhile, non-fire pixels are categorized according to the average intensity of the corresponding image. This characterization allows to analyze the performance of each rule by category. It is shown that the performances of the existing rules and methods from the literature are category dependent, and none of them is able to perform equally well on all categories. Meanwhile, a new proposed method based on machine learning techniques and using all the rules as features outperforms existing state-of-the-art techniques in the literature by performing almost equally well on different categories. Thus, this method, promises very interesting developments for the future of metrologic tools for fire detection in unstructured environments.
A quick pipeline for the isolation of 3D cell culture-derived extracellular vesicles

Recent advances in cell biology research regarding extracellular vesicles have highlighted an increasing demand to obtain 3D cell culture-derived EVs, because they are considered to more accurately represent EVs obtained in vivo. However, there is still a grave need for efficient and tunable methodologies to isolate EVs from 3D cell cultures. Using nanofibrillar cellulose (NFC) scaffold as a 3D cell culture matrix, we developed a pipeline of two different approaches for EV isolation from cancer spheroids. A batch method was created for delivering high EV yield at the end of the culture period, and a harvesting method was created to enable time-dependent collection of EVs to combine EV profiling with spheroid development. Both these methods were easy to set up, quick to perform, and they provided a high EV yield. When compared to scaffold-free 3D spheroid cultures on ultra-low affinity plates, the NFC method resulted in similar EV production/cell, but the NFC method was scalable and easier to perform resulting in high EV yields. In summary, we introduce here an NFC-based, innovative pipeline for acquiring EVs from 3D cancer spheroids, which can be tailored to support the needs of variable EV research objectives.
Naturally occurring pre-S mutations promote occult HBV infection by affecting pre-S2/S promoter activity

Occult hepatitis B virus (HBV) infection (OBI) has non-negligible clinical significance, but the mechanism of its occurrence remains unclear. Growing evidence suggests that mutations in the pre-S region of HBV genome may be associated with the occurrence of OBI. However, the role of pre-S mutations in OBI and its molecular mechanism was not fully understand. Here, the pre-S sequences from 307 OBI blood donors and 293 hepatitis B surface protein (HBsAg)-positive blood donors were obtained, and we observed a higher frequency of naturally occurring pre-S mutations in OBI donors infected with genotype B/C HBV than in HBsAg-positive donors, suggesting their potential positive role in OBI. In both genotype B and C, several pre-S mutants resulted in markedly reduced HBsAg production in vitro. In particular, the T68I, S78N and N98T mutants of genotype B were proven to significantly decrease the HBsAg synthesis by affecting the pre-S2/S promoter activity, and thereby promoting the occurrence of OBI.
On the Security and Privacy Challenges of Virtual Assistants

Since the purchase of Siri by Apple, and its release with the iPhone 4S in 2011, virtual assistants (VAs) have grown in number and popularity. The sophisticated natural language processing and speech recognition employed by VAs enables users to interact with them conversationally, almost as they would with another human. To service user voice requests, VAs transmit large amounts of data to their vendors; these data are processed and stored in the Cloud. The potential data security and privacy issues involved in this process provided the motivation to examine the current state of the art in VA research. In this study, we identify peer-reviewed literature that focuses on security and privacy concerns surrounding these assistants, including current trends in addressing how voice assistants are vulnerable to malicious attacks and worries that the VA is recording without the user's knowledge or consent. The findings show that not only are these worries manifold, but there is a gap in the current state of the art, and no current literature reviews on the topic exist. This review sheds light on future research directions, such as providing solutions to perform voice authentication without an external device, and the compliance of VAs with privacy regulations.