Federated Learning with Pareto Optimality for Resource Efficiency and Fast Model Convergence in Mobile Environments.

Federated learning (FL) is an emerging distributed learning technique through which models can be trained using the data collected by user devices in resource-constrained situations while protecting user privacy. However, FL has three main limitations: First, the parameter server (PS), which aggregates the local models that are trained using local user data, is typically far from users. The large distance may burden the path links between the PS and local nodes, thereby increasing the consumption of the network and computing resources.

The influence of contralateral circulation on computational fluid dynamics of intracranial arteries: simulated versus measured flow velocities.

Background: This study aimed to retrospectively evaluate the influence of contralateral anterior circulation on computational fluid dynamics (CFD) of intracranial arteries, by comparing the CFD values of flow velocities in unilateral anterior circulation with the measured values from phase-contrast magnetic resonance angiography (PC-MRA).

Methods: We analyzed 21 unilateral anterior circulation models without proximal stenosis from 15 patients who performed both time-of-flight MRA (TOF-MRA) and PC-MRA. CFD was performed with the inflow boundary condition of a pulsatile flow of the internal carotid artery (ICA) obtained from PC-MRA.

Trust-Based Intelligent Routing Protocol with Q-Learning for Mission-Critical Wireless Sensor Networks.

Mission-critical wireless sensor networks require a trustworthy and punctual routing protocol to ensure the worst-case end-to-end delay and reliability when transmitting mission-critical data collected by various sensors to gateways. In particular, the trustworthiness of mission-critical data must be guaranteed for decision-making and secure communications. However, it is a challenging issue to meet the requirement of both reliability and QoS in sensor networking environments where cyber-attacks may frequently occur and a lot of mission-critical data is generated.

Trust Based Multipath QoS Routing Protocol for Mission-Critical Data Transmission in Tactical Ad-Hoc Networks.

In tactical ad-hoc networks, the importance of various tactical sensors and mission-critical data is increasing owing to their role in determining a tactical situation and ensuring the viability of soldiers. In particular, the reliability of mission-critical data has to be ensured for accurate situation determination and decision making. However, managing the network and trustworthiness in an environment where malicious nodes exist and a large amount of mission-critical data occur is a challenging issue.

A Stepwise and Hybrid Trust Evaluation Scheme for Tactical Wireless Sensor Networks.

In tactical wireless sensor networks, tactical sensors are increasingly expected to be exploited for information collection in battlefields or dangerous areas on behalf of soldiers. The main function of these networks is to use sensors to measure radiation, nuclear, and biochemical values for the safety of allies and also to monitor and carry out reconnaissance of enemies. These tactical sensors require a network traffic flow that sends various types of measured information to the gateway, which needs high reliability.

Blockchain-Based Lightweight Trust Management in Mobile Ad-Hoc Networks.

As a trending and interesting research topic, in recent years, researchers have been adopting the blockchain in the wireless ad-hoc environment. Owing to its strong characteristics, such as consensus, immutability, finality, and provenance, the blockchain is utilized not only as a secure data storage for critical data but also as a platform that facilitates the trustless exchange of data between independent parties. However, the main challenge of blockchain application in an ad-hoc network is which kind of nodes should be involved in the validation process and how to adopt the heavy computational complexity of block validation appropriately while maintaining the genuine characteristics of a blockchain.

A Study on Mechanical Characteristics of Phosphor Film Containing Methyl Silicone Resin Based on Crosslinking Reaction Analysis.

This study reveals a methodological research for predicting mechanical properties of phosphor films through the chemical crosslinking reaction of methyl silicone resin during fabrication of the phosphor films. Crosslinking point according to the type of methyl silicone resins was verified through the magnitude of the absorption peak of the functional group and the curing reaction heat. Then, we measured mechanical properties of the fabricated phosphor films.

Conducting Super-Hydrophobic Thin Film for Electric Heating Applications.

Frost presents a serious problem for the human environment, resulting in such phenomena as downed power lines, damaged crops and stalled aircraft. In addition, frost and ice accumulation significantly decrease the performance of ships, wind turbines, and HVAC systems with high failure risk. Super-hydrophobic (SH) surface can be an appropriate solution for frost problems, due to its anti-icing properties that can prevent ice nucleation on the surface.

A Design and Simulation of the Opportunistic Computation Offloading with Learning-Based Prediction for Unmanned Aerial Vehicle (UAV) Clustering Networks.

Drones have recently become extremely popular, especially in military and civilian applications. Examples of drone utilization include reconnaissance, surveillance, and packet delivery. As time has passed, drones' tasks have become larger and more complex.

A Continuous Object Boundary Detection and Tracking Scheme for Failure-Prone Sensor Networks.

In wireless sensor networks, detection and tracking of continuous natured objects is more challenging owing to their unique characteristics such as uneven expansion and contraction. A continuous object is usually spread over a large area, and, therefore, a substantial number of sensor nodes are needed to detect the object. Nodes communicate with each other as well as with the sink to exchange control messages and report their detection status.

Computational Fluid Dynamics of Intracranial and Extracranal Arteries using 3-Dimensional Angiography: Technical Considerations with Physician's Point of View.

We investigate the potentials and limitations of computational fluid dynamics (CFD) analysis of patient specific models from 3D angiographies. There are many technical problems in acquisition of proper vascular models, in pre-processing for making 2D surface and 3D volume meshes and also in post-processing steps for display the CFD analysis. We hope that our study could serves as a technical reference to validating other tools and CFD results.

Computational modeling with fluid-structure interaction of the severe m1 stenosis before and after stenting.

Purpose: Image-based computational models with fluid-structure interaction (FSI) can be used to perform plaque mechanical analysis in intracranial artery stenosis. We described a process in FSI study applied to symptomatic severe intracranial (M1) stenosis before and after stenting.

Materials And Methods: Reconstructed 3D angiography in STL format was transferred to Magics for smoothing of vessel surface and trimming of branch vessels and to HyperMesh for generating tetra volume mesh from triangular surface-meshed 3D angiogram.

Computational flow dynamics of the severe m1 stenosis before and after stenting.

Purpose: Computational flow dynamic (CFD) study has not been widely applied in intracranial artery stenosis due to requirement of high resolution in identifying the small intracranial artery. We described a process in CFD study applied to symptomatic severe intracranial (M1) stenosis before and after stenting.

Materials And Methods: Reconstructed 3D angiography in STL format was transferred to Magics (Materialise NV, Leuven, Belgium) for smoothing of vessel surface and trimming of branch vessels and to HyperMesh (Altair Engineering Inc.

High shear stress at the surface of enhancing plaque in the systolic phase is related to the symptom presentation of severe M1 stenosis.

The computational fluid dynamics methods for the limited flow rate and the small dimensions of an intracranial artery stenosis may help demonstrate the stroke mechanism in intracranial atherosclerosis. We have modeled the high wall shear stress (WSS) in a severe M1 stenosis. The high WSS in the systolic phase of the cardiac cycle was well-correlated with a thick fibrous cap atheroma with enhancement, as was determined using high-resolution plaque imaging techniques in a severe stenosis of the middle cerebral artery.