One of the major challenges in cybersecurity is the provision of an automated and effective cyber-threats detection technique. In this paper, we present an AI technique for cyber-threats detection, ...based on artificial neural networks. The proposed technique converts multitude of collected security events to individual event profiles and use a deep learning-based detection method for enhanced cyber-threat detection. For this work, we developed an AI-SIEM system based on a combination of event profiling for data preprocessing and different artificial neural network methods, including FCNN, CNN, and LSTM. The system focuses on discriminating between true positive and false positive alerts, thus helping security analysts to rapidly respond to cyber threats. All experiments in this study are performed by authors using two benchmark datasets (NSLKDD and CICIDS2017) and two datasets collected in the real world. To evaluate the performance comparison with existing methods, we conducted experiments using the five conventional machine-learning methods (SVM, k-NN, RF, NB, and DT). Consequently, the experimental results of this study ensure that our proposed methods are capable of being employed as learning-based models for network intrusion-detection, and show that although it is employed in the real world, the performance outperforms the conventional machine-learning methods.
Background Previous studies have reported a high incidence of acute kidney injury (AKI) after thoracic aortic surgery in heterogeneous patient cohorts, including various aortic diseases and the use ...of deep hypothermic circulatory arrest. Moderate hypothermia with cerebral perfusion makes deep hypothermia nonessential, but can make end organs susceptible to ischemia during circulatory arrest. We investigated the incidence and risk factors of AKI after thoracic aortic surgery with and without moderate hypothermic circulatory arrest for acute dissection. Methods We reviewed the medical records of 98 patients undergoing graft replacement of the thoracic aorta for acute dissection between 2008 and 2011 at a university hospital. Acute kidney injury was defined by RIFLE criteria, which is based on serum creatinine or glomerular filtration rate. Results The mean age was 55 ± 15 years. The surgical procedures, 96% of which were emergencies, involved the ascending aorta (67%), aortic arch (41%), descending aorta (41%), and aortic valve (5%). Moderate hypothermic circulatory arrest was performed in 75%. The overall incidence of AKI was 54%, and 11% of 98 patients required renal replacement therapy. Thirty-day mortality increased with AKI severity ( p = 0.002). Independent risk factors for AKI were long cardiopulmonary bypass duration (>180 minutes; odds ratio, 7.50; p = 0.008) and preoperative serum creatinine level (odds ratio, 8.43; p = 0.016). Conclusions Acute kidney injury was common after thoracic aortic surgery for acute dissection with or without moderate hypothermic circulatory arrest and worsened 30-day mortality. Prolonged cardiopulmonary bypass and increased preoperative serum creatinine were independent risk factors for AKI, but moderate hypothermic circulatory arrest was not.
The development of unmanned aerial vehicles (UAVs) is expected to become one of the most commercialized research areas in the world over the next decade. Globally, unmanned aircraft have been ...increasingly used for safety surveillance in the construction industry and civil engineering fields. This paper presents an aerial image-based approach using UAVs to inspect cracks and deformations in buildings. A state-of-the-art safety evaluation method termed SMART SKY EYE (Smart building safety assessment system using UAV) is introduced; this system utilizes an unmanned airplane equipped with a thermal camera and programmed with various surveying efficiency improvement methods, such as thermography, machine-learning algorithms, and 3D point cloud modeling. Using this method, crack maps, crack depths, and the deformations of structures can be obtained. Error rates are compared between the proposed and conventional methods.
Abstract
The resistive switching behavior in Ta
2
O
5
based memristors is largely controlled by the formation and annihilation of conductive filaments (CFs) that are generated by the migration of ...oxygen vacancies (OVs). To gain a fundamental insight on the switching characteristics, we have systematically investigated the electrical transport properties of two different Ta
2
O
5
polymorphs (
$$\epsilon$$
ϵ
-Ta
2
O
5
and λ-Ta
2
O
5
), using density functional theory calculations, and associated vacancy induced electrical conductivity using Boltzmann transport theory. The projected band structure and DOS in a few types of OVs, (two-fold (O
2f
V), three-fold (O
3f
V), interlayer (O
IL
V), and distorted octahedral coordinated vacancies (O
ε
V)) reveal that the presence of O
IL
V would cause Ta
2
O
5
to transition from a semiconductor to a metal, leading to improved electrical conductivity, whereas the other OV types only create localized mid-gap defect states within the bandgap. On studying the combined effect of OVs and Si-doping, a reduction of the formation energy and creation of defect states near the conduction band edge, is observed in Si-doped Ta
2
O
5
, and lower energy is found for the OVs near Si atoms, which would be advantageous to the uniformity of CFs produced by OVs. These findings can serve as guidance for further experimental work aimed at enhancing the uniformity and switching properties of resistance switching for Ta
2
O
5
-based memristors.
Abstract
Embedding metal-halide perovskite particles within an insulating host matrix has proven to be an effective strategy for revealing the outstanding luminescence properties of perovskites as an ...emerging class of light emitters. Particularly, unexpected bright green emission observed in a nominally pure zero-dimensional cesium–lead–bromide perovskite (Cs
4
PbBr
6
) has triggered intensive research in better understanding the serendipitous incorporation of emissive guest species within the Cs
4
PbBr
6
host. However, a limited controllability over such heterostructural configurations in conventional solution-based synthesis methods has limited the degree of freedom in designing synthesis routes for accessing different structural and compositional configurations of these host–guest species. In this study, we provide means of enhancing the luminescence properties in the nominal Cs
4
PbBr
6
powder through a guided heterostructural configuration engineering enabled by solid-state mechanochemical synthesis. Realized by an in-depth study on time-dependent evaluation of optical and structural properties during the synthesis of Cs
4
PbBr
6
, our target-designed synthesis protocol to promote the endotaxial formation of Cs
4
PbBr
6
/CsPbBr
3
heterostructures provides key insights for understanding and designing kinetics-guided syntheses of highly luminescent perovskite emitters for light-emitting applications.
Recent developments in deep learning-based automatic weeding systems have shown promise for unmanned weed eradication. However, accurately distinguishing between crops and weeds in varying field ...conditions remains a challenge for these systems, as performance deteriorates when applied to new or different fields due to insignificant changes in low-level statistics and a significant gap between training and test data distributions. In this study, we propose an approach based on unsupervised domain adaptation to improve crop-weed recognition in new, unseen fields. Our system addresses this issue by learning to ignore insignificant changes in low-level statistics that cause a decline in performance when applied to new data. The proposed network includes a segmentation module that produces segmentation maps using labeled (training field) data while also minimizing entropy using unlabeled (test field) data simultaneously, and a discriminator module that maximizes the confusion between extracted features from the training and test farm samples. This module uses adversarial optimization to make the segmentation network invariant to changes in the field environment. We evaluated the proposed approach on four different unseen (test) fields and found consistent improvements in performance. These results suggest that the proposed approach can effectively handle changes in new field environments during real field inference.
Cytoplasmic chloroplast (cp) genomes and nuclear ribosomal DNA (nR) are the primary sequences used to understand plant diversity and evolution. We introduce a high-throughput method to simultaneously ...obtain complete cp and nR sequences using Illumina platform whole-genome sequence. We applied the method to 30 rice specimens belonging to nine Oryza species. Concurrent phylogenomic analysis using cp and nR of several of specimens of the same Oryza AA genome species provides insight into the evolution and domestication of cultivated rice, clarifying three ambiguous but important issues in the evolution of wild Oryza species. First, cp-based trees clearly classify each lineage but can be biased by inter-subspecies cross-hybridization events during speciation. Second, O. glumaepatula, a South American wild rice, includes two cytoplasm types, one of which is derived from a recent interspecies hybridization with O. longistminata. Third, the Australian O. rufipogan-type rice is a perennial form of O. meridionalis.
Fine segmentation labelling tasks are time consuming and typically require a great deal of manual labor. This paper presents a novel method for efficiently creating pixel-level fine segmentation ...labelling that significantly reduces the amount of necessary human labor. The proposed method utilizes easily produced multiple and complementary coarse labels to build a complete fine label via supervised learning. The primary label among the coarse labels is the manual label, which is produced with simple contours or bounding boxes that roughly encompass an object. All others coarse labels are complementary and are generated automatically using existing algorithms. Fine labels can be rapidly created during the supervised learning of such coarse labels. In the experimental study, the proposed technique achieved a fine label IOU (intersection of union) of 92% in segmenting our newly constructed bean field dataset. The proposed method also achieved 95% and 92% mean IOU when tested on publicly available agricultural CVPPP and CWFID datasets, respectively. Our proposed method of segmentation also achieved a mean IOU of 81% when it was tested on our newly constructed paprika disease dataset, which includes multiple categories.
The installation of battery energy storage systems (BESSs) with various shapes and capacities is increasing due to the continuously rising demand for renewable energy. To prepare for potential ...accidents, a study was conducted to select the optimal location for installing an input BESS in terms of frequency stability when the index assumes the backup input of the BESS. This study builds on the premise that installing a BESS on a bus in an area where active power absorption and transmission are the most active can significantly contribute to increasing the frequency recovery of the power system. Based on this premise, the magnitude of the active power flow and the proportional characteristics of the phase difference between buses were mathematically confirmed. This study also calculated the effective power sensitivity index of a bus with 13 FR-ESSs installed in a domestic system and reviewed the frequency output by establishing a table for each failure scenario. The results indicated that the effect of frequency rise can be estimated at the level of tidal current calculation. Thus, the study suggested a direction for subsequent studies to improve the sensitivity index.
Although arginase primarily participates in the last reaction of the urea cycle, we have previously demonstrated that arginase II is an important cytosolic calcium regulator through spermine ...production in a p32-dependent manner. Here, we demonstrated that rhaponticin (RPT) is a novel medicinal-plant arginase inhibitor and investigated its mechanism of action on Ca
2+
-dependent endothelial nitric oxide synthase (eNOS) activation. RPT was uncompetitively inhibited for both arginases I and II prepared from mouse liver and kidney. It also inhibited arginase activity in both aorta and human umbilical vein endothelial cells (HUVECs). Using both microscope and FACS analyses, RPT treatments induced increases in cytosolic Ca
2+
levels using Fluo-4 AM as a calcium indicator. Increased cytosolic Ca
2+
elicited the phosphorylations of both CaMKII and eNOS Ser1177 in a time-dependent manner. RPT incubations also increased intracellular L-arginine (L-Arg) levels and activated the CaMKII/AMPK/Akt/eNOS signaling cascade in HUVECs. Treatment of L-Arg and ABH, arginase inhibitor, increased intracellular Ca
2+
concentrations and activated CaMKII-dependent eNOS activation in ECs of WT mice, but, the effects were not observed in ECs of inositol triphosphate receptor type 1 knockout (IP3R1
−/−
) mice. In the aortic endothelium of WT mice, RPT also augmented nitric oxide (NO) production and attenuated reactive oxygen species (ROS) generation. In a vascular tension assay using RPT-treated aortic tissue, cumulative vasorelaxant responses to acetylcholine (Ach) were enhanced, and phenylephrine (PE)-dependent vasoconstrictive responses were retarded, although sodium nitroprusside and KCl responses were not different. In this study, we present a novel mechanism for RPT, as an arginase inhibitor, to increase cytosolic Ca
2+
concentration in a L-Arg-dependent manner and enhance endothelial function through eNOS activation.
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