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Background: The stability index estimation algorithm was derived and applied to develop and implement a balance ability diagnosis system that can be used in daily life. Methods: The system integrated an approach based on sensory function interaction, called the clinical test of sensory interaction with balance. A capacitance and resistance sensing type force mat was fabricated, and a stability index prediction algorithm was developed and applied using the center of pressure variables. The stability index prediction algorithm derived a center of pressure variable for 103 elderly people by Nintendo Wii Balance Board to predict the stability index of the balance system (Biodex SD), and the accuracy of this approach was confirmed. Results: As a result of testing with the test set, the linear regression model confirmed that the r-value ranged between 0.943 and 0.983. To confirm the similarity between the WBB and the flexible force mat, each measured center of pressure value was inputted and calculated in the developed regression model, and the result of the correlation coefficient validation confirmed an r-value of 0.96. Conclusion: The system developed in this study will be applicable to daily life in the home in the form of a floor mat. [ABSTRACT FROM AUTHOR]

KubeEdge is an open-source platform that orchestrates containerized Internet of Things (IoT) application services in IoT edge computing environments. Based on Kubernetes, it supports heterogeneous IoT device protocols on edge nodes and provides various functions necessary to build edge computing infrastructure, such as network management between cloud and edge nodes. However, the resulting cloud-based systems are subject to several limitations. In this study, we evaluated the performance of KubeEdge in terms of the computational resource distribution and delay between edge nodes. We found that forwarding traffic between edge nodes degrades the throughput of clusters and causes service delay in edge computing environments. Based on these results, we proposed a local scheduling scheme that handles user traffic locally at each edge node. The performance evaluation results revealed that local scheduling outperforms the existing load-balancing algorithm in the edge computing environment. [ABSTRACT FROM AUTHOR]

To improve the detection capability of the nanoscale samples, we proposed a new approach to integrating acousto-microfluidics and Raman spectroscopy. Different design parameters such as size, optical properties, and different samples have been tested to demonstrate the device's capability and optimize the Raman measurement. The practical application of the system was demonstrated by detecting synthetic particles and low-concentration Ebola virus. Experimental results confirmed a 750-fold improvement in the limit of detection. The device opens up new avenues for research, especially in the biomedical field, where rapid and accurate identification of biomolecules is essential. [Display omitted] • Improved detection limit 750-fold by integrating Raman with acoustofluidics. • Demonstrating particle focusing in 400 μ m inner diameter 2D acoustofluidic devices. • Applicable in-line measurement for bionanoparticles, including viral vaccines. [ABSTRACT FROM AUTHOR]

Edge/fog computing is a new computing paradigm where fully distributed fog/edge nodes located near end devices provide computing resources. The article "Distributed Agent-Based Orchestrator Model for Fog Computing" by Agnius Liutkevicius et al. [[1]] presents a distributed agent-based orchestrator model that enables flexible service provisioning in a dynamic fog computing environment, accounting for the resource constraints and security levels of fog nodes. By analyzing, filtering, and processing at local fog/edge nodes instead of transferring tremendous amounts of data to centralized cloud servers, fog/edge computing can significantly reduce processing delay and network traffic. [Extracted from the article]

Container-based virtualization has recently been flourishing due to its ease of deployment and flexibility of resource provisioning. Kubernetes, a well-known open source project, is an orchestration platform for containerized applications. Herein, applications can have multiple replicas to provide high scalability and availability, but some applications for stateful service require a leader to be elected to maintain consistency and coordinate tasks among the replicas. In this article, the Kubernetes architecture is first analyzed by focusing on load balancing, a leader election algorithm, and a leader-based consistency maintenance mechanism. Then we address the following challenges: the leader has heavy loads due to its inherent design, and the Kubernetes leader election algorithm cannot evenly distribute the leader throughout nodes. Finally, experimental results are provided to prove the importance of leader distribution throughout nodes in the cluster for highly scalable load balancing. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has become an essential part of our daily lives. However, with the increasing use of IoT, the number of botnet attacks targeting resource-constrained IoT devices is also on the rise. To mitigate these threats, intrusion detection systems (IDSs) have been developed. However, traditional IDSs based on heavyweight deep/machine learning, fuzzy logic, rough set theories, or data mining techniques, often lack in detection accuracy and energy efficiency. Therefore, there is a crucial need for more lightweight, accurate, and energy-efficient IDSs capable of detecting a broad spectrum of cyber attacks. This paper presents a solution to these challenges by introducing lightweight and accurate IDSs that use a stochastic gradient descent classifier (SGDC) and four feature-selection algorithms based on a ridge regressor. The hyperparameters of the SGDC algorithm and ridge regressor model were fine-tuned to enhance the accuracy of IDSs while reducing computational complexity. Moreover, the fine-tuned feature selectors were used to decrease the dataset's dimensionality and improve the accuracy of IDSs. To evaluate the proposed IDSs, three network traffic datasets (KDD-CUP-1999, BotIoT-2018, and N-BaIoT-2021) were used. The systems achieved an average accuracy of 92.69%, and the number of features was reduced by an average of 79.93%. The results demonstrate that the proposed systems can be utilized for lightweight IDSs on resource-constrained IoT devices. Overall, this paper presents a significant contribution to the field of IDSs for IoT devices by offering an efficient and accurate solution. The proposed lightweight IDSs have the potential to enhance IoT security and privacy, safeguarding sensitive IoT data. • SGDC-based lightweight IDSs were developed for resource-constrained IoT devices. • The optimal hyperparameters of SGDC were determined with a grid-search algorithm. • The fine-tuned SGDC boosted accuracy and reduced IDSs packet processing time greatly. • The ridge regression-based feature selectors determined the most efficient features. • The fine-tuned feature selectors reduced feature space greatly and improved accuracy. [ABSTRACT FROM AUTHOR]

We present a combined experimental and theoretical investigation of dewetting on substrates with parallel microgrooves. A thin, static liquid film has an equilibrium thickness so as to minimize the sum of the surface free energy and the gravitational potential energy. When the thickness of a liquid film is less than the equilibrium thickness, the film seeks the equilibrium through contraction of the wetted area, which is referred to as dewetting. We experimentally observed the dewetting of thin, metastable liquid films on substrates with parallel microgrooves. The experiments revealed that the films retract in the direction along the grooves and leaves liquid residues with various morphologies. We classify the residue morphologies into three modes and elucidate the dependence of the mode selection on the groove geometry and the equilibrium contact angle of the liquid. We also experimentally examined the dynamic motion of the receding contact lines of the dewetting films, and developed a mechanical model for the receding speed. Our results provide a basis for controlling liquid films using microstructures, which is useful for lubricant-impregnated surface production, painting, spray cooling, and surface cleaning. [ABSTRACT FROM AUTHOR]

Although gallium-based liquid metals are attracting growing interest thanks to its potential applications in deformable, flexible electronic devices, challenges in fabrication associated with the high surface tension and oxide skin remain to be overcome. We report a novel fabrication technique for liquid metal circuits using dewetting. An excessively thin liquid film spontaneously shrinks on a substrate to reduce surface free energy. In the case of a thin liquid metal film on a substrate with microgrooves, the oxide on the microgroove wall and the additional viscous resistance delay the shrinkage in the grooves, which separates the liquid volume inside the microgrooves from the external volume. Utilizing this mechanism, we successfully produced 20- \mu \textm -thick conductive lines of eutectic gallium indium (EGaIn) on polydimethylsiloxane. This fabrication technique is simple, fast, and cost-effective and requires no top covering layer. The resultant metal lines show potentially applicable electrical resistance to flexible and stretchable electronic devices. [2017-0082] [ABSTRACT FROM PUBLISHER]

As software defined networking (SDN) extends its applications to carrier-grade networks, the need for high scalability and availability of a SDN controller is becoming increasingly important. Although existing works have shown the feasibility of a distributed controller, the switches in the data plane are required to know some of the internal specifics such as the IP addresses of the individual controller instances. This constraint increases the operational complexity as the number of controller instances increases. In this paper, we propose a distributed controller cluster architecture called IRIS-HiSA. The main goal is to support seamless load balancing and failover with horizontal scalability, as is done in existing works, but one of the distinguishing features of IRIS-HiSA is to provide transparency to the switches in the data plane. Thus, the switches do not need to know the internal details of the controller cluster, and they simply access it in the same way a single controller is accessed. In addition to proving seamless load balancing and a failover, a performance evaluation is conducted to analyze the high scalability in which the throughput of the flow setup is proportionally increased with the number of controller instances. [ABSTRACT FROM AUTHOR]

During several decades, there have been many researches on approximation algorithms for constructing minimum routing cost tree (MRCT) that minimizes the sum of routing cost of all pairs in a tree topology. Existing algorithms have been mainly studied in the field of graph theory, thus it is difficult to apply them to multi-hop wireless ad-hoc networks due to the theoretical and centralized methodology. In addition, wireless ad-hoc network protocols restrict the maximum degree, which is the maximum number of children a parent may have, in order to prevent excessive concentration of traffic. However, this limitation has not been considered by any existing algorithms. In this paper, we define the degree constrained MRCT (DC-MRCT) problem and extract the characteristics of DC-MRCT by analyzing all possible tree topologies for the given number of nodes. Based on these characteristics that DC-MRCT has the minimum sum of tree level and the maximum square sum of subtree sizes, we propose a distributed DC-MRCT Formation (DC-MRCTF) algorithm that can be applicable to any type of wireless ad-hoc network protocols working on tree topology. Performance evaluation shows that DC-MRCTF gives noticeable benefit for up to 80% of individual communication pair compared with the representative tree formation algorithm in ZigBee as well as significantly reduces the sum of routing cost of all pairs regardless of network density. [ABSTRACT FROM AUTHOR]

Accurately estimated highway traffic flow info plays a decisive role in dynamic and real-time road management, planning, and preventing frequent/recurring traffic jams, traffic rule violations, and chain/fatal traffic accidents. Traffic flow information is extracted by processing raw camera images via vehicle detection and tracking algorithms. Object detectors including the Yolo, single-shot detector, and EfficientNet algorithms are used for vehicle detection; however, You only look once version 5 (Yolov5) has a clear advantage in terms of real-time performance. Due to this reason, the pre-trained Yolov5 models were utilized in the vehicle detection part, and in the vehicle tracking module, a novel tracker algorithm was developed using vehicle detection features. The performance of the proposed approach was measured by comparing it to the Kalman filter-based tracker. The evaluation results show that the proposed tracking approach outperformed the Kalman filter-based tracker with 5.82% (Buses), 2.24% (Cars), 36.50% (Trucks), and overall 2.58% better traffic counting accuracy for the 12 nighttime case study videos captured from the highways with different horizontal and vertical angle-of-views. [ABSTRACT FROM AUTHOR]

In this paper, we present PEG (Parallel ECC library on GPU), which is efficient implementation of Elliptic Curve Scalar Multiplication over G F ( 2 m ) on Graphic Processing Units. While existing ECC implementations over GPU focused on limited parameterizations such as (fixed scalar and different curves) or (different scalars and same base point), PEG covers all parameter options ((a) fixed scalar and variable points, (b) random scalars and fixed input point, and (c) random scalars and variable points) which are used for ECC-based protocols such as ECDH, ECDSA and ECIES. With GPU optimization concerns and through analyzing parameter types used for ECC-based protocols, we investigate promising algorithms at both finite field arithmetic and scalar multiplication level for performance optimization according to each parameterization. PEG covers ECC implementations over G F ( 2 163 ) , G F ( 2 233 ) and G F ( 2 283 ) for 80-bit, 112-bit and 128-bit security on GTX285 and GTX480. PEG can achieve remarkable performance compared with MIRACL, one of the most famous ECC library, running on Intel i7 CPU (2.67 GHz). [ABSTRACT FROM AUTHOR]

The ZigBee tree routing is widely used in many resource-limited devices and applications, since it does not require any routing table and route discovery overhead to send a packet to the destination. However, the ZigBee tree routing has the fundamental limitation that a packet follows the tree topology; thus, it cannot provide the optimal routing path. In this paper, we propose the shortcut tree routing (STR) protocol that provides the near optimal routing path as well as maintains the advantages of the ZigBee tree routing such as no route discovery overhead and low memory consumption. The main idea of the shortcut tree routing is to calculate remaining hops from an arbitrary source to the destination using the hierarchical addressing scheme in ZigBee, and each source or intermediate node forwards a packet to the neighbor node with the smallest remaining hops in its neighbor table. The shortcut tree routing is fully distributed and compatible with ZigBee standard in that it only utilizes addressing scheme and neighbor table without any changes of the specification. The mathematical analysis proves that the 1-hop neighbor information improves overall network performances by providing an efficient routing path and distributing the traffic load concentrated on the tree links. In the performance evaluation, we show that the shortcut tree routing achieves the comparable performance to AODV with limited overhead of neighbor table maintenance as well as overwhelms the ZigBee tree routing in all the network conditions such as network density, network configurations, traffic type, and the network traffic. [ABSTRACT FROM AUTHOR]

An increasing number of recent experimental works have demonstrated that the supposedly secure channels in the Internet are prone to privacy breaking under many respects, due to packet traffic features leaking information on the user activity and traffic content. We aim at understanding if and how complex it is to obfuscate the information leaked by packet traffic features, namely packet lengths, directions, and times: we call this technique traffic masking. We define a security model that points out what the ideal target of masking is, and then define the optimized traffic masking algorithm that removes any leaking (full masking). Further, we investigate the tradeoff between traffic privacy protection and masking cost, namely required amount of overhead and realization complexity/feasibility. Numerical results are based on measured Internet traffic traces. Major findings are that: 1) optimized full masking achieves similar overhead values with padding only and in case fragmentation is allowed, and 2) if practical realizability is accounted for, optimized statistical masking attains only moderately better overhead than simple fixed pattern masking does, while still leaking correlation information that can be exploited by the adversary. [ABSTRACT FROM AUTHOR]

Abstract: The full scale Soil Cement Mixing (SCM) wall was constructed to investigate viability of the wall. Long term behavior of the wall resulted in viability of the wall. However, design guidelines on SCM wall are limited due to its complex geometry. In this paper, the three dimensional finite element method is employed to study the influence of various design decisions for SCM wall. The numerical model is first calibrated with an instrumented case history. Then a parametric study is constructed. The results give information on the influence of the following factors on the wall behavior: size of column, column overlapping, column embedment, existence of relieving platform, and column strength. The implications in design are discussed. [Copyright &y& Elsevier]

Abstract: In this study, the photosensitive carbon nanotube (CNT) paste has been prepared by a 3-roll milling process of the mixture having CNT, organic binder, terpineol, and additives such as Ag powder, frit glass, and photosensitive polymer. The composition of the mixture was varied to control the viscosity. The field emission display (FED) panel has been fabricated with a photolithography process. TGA experiment shows that most of the organic components of the paste are decomposed at 400°C. The current density of 330.7μA/cm2 was obtained with applying electric field of 7.5V/μm. When the surface of the CNTs was treated by adhesive tape, the field enhancement factor could be improved due to the enlargement of emitting area. [Copyright &y& Elsevier]

Energy storage have attracted attention to compensate for the mismatches of electricity supply and demand caused by renewable energies. A regenerative hydrogen fuel cell (RHFC) system composed of an electrolyzer, hydrogen storage, and fuel cell is a promising large capacity energy storage technology. A liquid organic hydrogen carrier (LOHC) is considered for hydrogen storage technology because of its advantages of good safety, easy handling, and high storage density. In this original research, a comparative energetic study on LOHC and other hydrogen storage technologies was conducted using net energy analysis to quantitatively compare various hydrogen storage technologies. First, the basic RHFC system was modeled by a 0-dimensional simulation, and the system efficiency was analyzed. Then, a net energy analysis was conducted by evaluating energy stored on investment (ESOI e) values. The RHFC system with LOHC showed a higher ESOI e of 53 for weekly (<100 h) and 18 for monthly (<1000 h) energy storage than other energy storage technologies due to the low embodied energy cost of LOHC materials. Based on the analysis, the RHFC system with LOHC was concluded to be an attractive option for weekly and monthly energy storage of the both wind and solar power. • Energy demand for hydrogen storage in LOHC was evaluated. • Various hydrogen storage methods were compared in terms of system efficiency. • Net energy analysis was conducted for RHFC with LOHC. • LOHC can be an energetically promising option for weekly and monthly energy storage. [ABSTRACT FROM AUTHOR]

The Wii balance board (WBB) is a simplified force plate system used to evaluate the balancing ability of the elderly via a sensory interaction task to confirm a significant standing balance index. The accuracy of this system has been verified in previous studies. In this study, an instrumented and modified clinical test of sensory interaction on balance (i-mCTSIB) was performed on 84 elderly subjects, and the variables for center of pressure (CoP) were calculated using WBB for each task condition. The results indicate that the visual condition has a significant effect on the sway proprioceptive sensory variables with a foam condition as their complexity increases. In addition, the correlation between the variable and Berg Balance Scale was not confirmed since CTSIB is a sensory interaction on balance ability. Therefore, WBB can be used to evaluate balancing ability based on sensory interactions consisting of the surface condition. [ABSTRACT FROM AUTHOR]

Fog/edge computing is a new computing paradigm where fully distributed fog/edge nodes located nearby end devices provide computing resources. 33671542 3 Hossain M.D., Sultana T., Hossain M.A., Hossain M.I., Huynh L.N.T., Park J., Huh E.-N. Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks. The prevalence of smart devices and cloud computing has led to an explosion in the amount of data generated by IoT devices. [Extracted from the article]

Container-based virtualization is becoming a de facto way to build and deploy applications because of its simplicity and convenience. Kubernetes is a well-known open-source project that provides an orchestration platform for containerized applications. An application in Kubernetes can contain multiple replicas to achieve high scalability and availability. Stateless applications have no requirement for persistent storage; however, stateful applications require persistent storage for each replica. Therefore, stateful applications usually require a strong consistency of data among replicas. To achieve this, the application often relies on a leader, which is responsible for maintaining consistency and coordinating tasks among replicas. This leads to a problem that the leader often has heavy loads due to its inherent design. In a Kubernetes cluster, having the leaders of multiple applications concentrated in a specific node may become a bottleneck within the system. In this paper, we propose a leader election algorithm that overcomes the bottleneck problem by evenly distributing the leaders throughout nodes in the cluster. We also conduct experiments to prove the correctness and effectiveness of our leader election algorithm compared with a default algorithm in Kubernetes. [ABSTRACT FROM AUTHOR]

Time synchronization is an important issue in ad-hoc networks for reliable information exchange. The algorithms for time synchronization in ad-hoc networks are largely categorized into two types. One is based on a selection of a reference node, and the other is based on a consensus among neighbor nodes. These two types of methods are targeting static environments. However, synchronization errors among nodes increase sharply when nodes move or when incorrect synchronization information is exchanged due to the failure of some nodes. In this paper, we propose a synchronization technique for mobile ad-hoc networks, which considers both the mobility of nodes and the abnormal behaviors of malicious or failed nodes. Specifically, synchronization information extracted from a median of the time information of the neighbor nodes is quickly disseminated. This information effectively excludes the outliers, which adversely affect the synchronization of the networks. In addition, Kalman filtering is applied to reduce the synchronization error occurring in the transmission and reception of time information. The simulation results confirm that the proposed scheme has a fast synchronization convergence speed and low synchronization error compared to conventional algorithms. [ABSTRACT FROM AUTHOR]

Wireless sensor networks are penetrating our daily lives, and they are starting to be deployed even in an industrial environment. The research on such industrial wireless sensor networks (IWSNs) considers more stringent requirements of robustness, reliability, and timeliness in each network layer. This Special Issue presents the recent research result on industrial wireless sensor networks. Each paper in the special issue has unique contributions in the advancements of industrial wireless sensor network research and we expect each paper to promote the relevant research and the deployment of IWSNs. [ABSTRACT FROM AUTHOR]

In this paper, a novel distributed scheduling scheme for an ad-hoc network is proposed. Specifically, the throughput and the delay of packets with different importance are flexibly adjusted by quantifying the importance as weight factors. In this scheme, each node is equipped with two queues, one for packets with high importance and the other for packets with low importance. The proposed scheduling scheme consists of two procedures: intra-node slot reallocation and inter-node reallocation. In the intra-node slot reallocation, self-fairness is adopted as a key metric, which is a composite of the quantified weight factors and traffic loads. This intra-node slot reallocation improves the throughput and the delay performance. Subsequently, through an inter-node reallocation algorithm adopted from LocalVoting (slot exchange among queues having the same importance), the fairness of traffics with the same importance is enhanced. Thorough simulations were conducted under various traffic load and weight factor settings. The simulation results show that the proposed algorithm can adjust packet delivery performance according to a predefined weight factor. Moreover, compared with conventional algorithms, the proposed algorithm achieves better performance in throughput and delay. The low average delay while attaining the high throughput ensures the excellent performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Smart home is one of the most promising applications of the Internet of Things. Although there have been studies about this technology in recent years, the adoption rate of smart homes is still low. One of the largest barriers is technological fragmentation within the smart home ecosystem. Currently, there are many protocols used in a connected home, increasing the confusion of consumers when choosing a product for their house. One possible solution for this fragmentation is to make a gateway to handle the diverse protocols as a central hub in the home. However, this solution brings about another issue for manufacturers: compatibility. Because of the various smart devices on the market, supporting all possible devices in one gateway is also an enormous challenge. In this paper, we propose a software architecture for a gateway in a smart home system to solve the compatibility problem. By creating a mechanism to dynamically download and update a device profile from a server, the gateway can easily handle new devices. Moreover, the proposed gateway also supports unified control over heterogeneous networks. We implemented a prototype to prove the feasibility of the proposed gateway architecture and evaluated its performance from the viewpoint of message execution time over heterogeneous networks, as well as the latency for device profile downloads and updates, and the overhead needed for handling unknown commands. [ABSTRACT FROM AUTHOR]

Ceramic-based back covers are commercially utilized as a high-end option for the physical protection of mobile phones. Herein, an unprecedented composite structure consisting of a cordierite (2MgO.2Al 2 O 3 5SiO 2) glass–ceramic strengthened with yttria-stabilized zirconia (YSZ) fillers is introduced as a protective back cover, specifically for next-generation communication mobile phones additionally requiring a low dielectric constant at gigahertz frequencies. Crystallization sequence was rather complicated with the involvements of multiple crystalline phases, α- and μ-cordierite, tetragonal and monoclinic ZrO 2 , and ZrSiO 4 , depending on the glass and composite compositions. This unique approach effectively induced the best mechanical performance for the 10 wt% YSZ/glass–ceramic structure, despite the low sintering temperature of 1050 °C, with the highest Vickers hardness of ∼9.2 GPa and flexural strength of ∼196.8 MPa attained for the optimal composite. Although effective dielectric constant of the composites depended on the relative contents of the evolved crystalline phases, overall dielectric constant was kept low due to the utilization of cordierite glass as the main component. [ABSTRACT FROM AUTHOR]

COVID-19 has resulted in a significant global impact on health, the economy, education, and daily life. The disease can range from mild to severe, with individuals over 65 or those with underlying medical conditions being more susceptible to severe illness. Early testing and isolation are vital due to the virus’s variable incubation period. Chest radiographs (CXR) have gained importance as a diagnostic tool due to their efficiency and reduced radiation exposure compared to CT scans. However, the sensitivity of CXR in detecting COVID-19 may be lower. This paper introduces a deep learning framework for accurate COVID-19 classification and severity prediction using CXR images. U-Net is used for lung segmentation, achieving a precision of 0.9924. Classification is performed using a Convulation-capsule network, with high true positive rates of 86% for COVID-19, 93% for pneumonia, and 85% for normal cases. Severity assessment employs ResNet50, VGG-16, and DenseNet201, with DenseNet201 showing superior accuracy. Empirical results, validated with 95% confidence intervals, confirm the framework’s reliability and robustness. This integration of advanced deep learning techniques with radiological imaging enhances early detection and severity assessment, improving patient management and resource allocation in clinical settings. [ABSTRACT FROM AUTHOR]

Kubernetes (K8s) is expected to be a key container orchestration tool for edge computing infrastructures owing to its various features for supporting container deployment and dynamic resource management. For example, its horizontal pod autoscaling feature provides service availability and scalability by increasing the number of replicas. kube-proxy provides traffic load-balancing between replicas by distributing client requests equally to all pods (replicas) of an application in a K8s cluster. However, this approach can result in long delays when requests are forwarded to remote workers, especially in edge computing environments where worker nodes are geographically dispersed. Moreover, if the receiving worker is overloaded, the request-processing delay can increase significantly. To overcome these limitations, this paper proposes an enhanced load balancer called resource adaptive proxy (RAP). RAP periodically monitors the resource status of each pod and the network status among worker nodes to aid in load-balancing decisions. Furthermore, it preferentially handles requests locally to the maximum extent possible. If the local worker node is overloaded, RAP forwards its requests to the best node in the cluster while considering resource availability. Our experimental results demonstrated that RAP could significantly improve throughput and reduce request latency compared with the default load-balancing mechanism of K8s. [ABSTRACT FROM AUTHOR]

Holographic Optical Elements (HOEs) employ refractive index (RI) modulation to manipulate light, establishing their widespread application in augmented and virtual reality technologies. Despite significant strides in HOEs' fabrication, a comprehensive 3D examination and measurement of these elements remain largely unexplored. This study targets this void by conducting an in‐depth exploration of the 3D RI distributions of volume holographic gratings (VHGs), a quintessential representative of HOEs. Direct tomographic measurements of the 3D RI distributions within millimeter‐scale VHGs are performed at a spatial resolution of 156 nm × 156 nm × 1.2 µm and an RI precision of 2×10−4. This level of precision facilitates a systematic high‐resolution characterization of VHGs. The findings present a substantial evolution from traditional measurement techniques and promise to considerably advance the evaluation of intricate HOE structures and refine the corresponding fabrication processes. [ABSTRACT FROM AUTHOR]

Liquid organic hydrogen carrier (LOHC) is a chemical hydrogen storage method that stores hydrogen in the form of liquid organics. Dibenzyltoluene (DBT) is a promising LOHC material due to its high storage density, low ignitability, and low cost. In this study, Pt/Al 2 O 3 and Pt/CeO 2 catalysts are synthesized using a combustion nanocatalyst synthesis method called the glycine nitrate process (GNP) to obtain high catalytic activity for the dehydrogenation of perhydro-dibenzyltoluene (H18-DBT). Pt/CeO 2 exhibits much faster dehydrogenation than Pt/Al 2 O 3 , 80.5%/2.5 h versus 3.5%/2.5 h. To investigate the causes of the difference in the dehydrogenation rates, microstructural characterization by N 2 physisorption, CO chemisorption and transmission electron microscopy analysis are conducted, and the catalytic activities are evaluated at various liquid hourly space velocities (LHSVs). The differences in dehydrogenation can be attributed to the mass transport of liquid H18-DBT into the catalyst pores being slow due to the small pores in Pt/Al 2 O 3 , which is a rarely addressed issue for other LOHC materials. This is because many LOHC materials are dehydrogenated at the gas phase, which has higher diffusivity than that of the liquid phase. Pt/CeO 2 synthesized by the GNP is also compared with a commercial Pt/Al 2 O 3 catalyst. The commercial Pt/Al 2 O 3 catalyst shows a dehydrogenation of 17.8%/2.5 h, which is much slower than that of Pt/CeO 2 synthesized by the GNP, at 80.5%/2.5 h. • Pt/Al 2 O 3 and Pt/CeO 2 catalysts were synthesized by the glycine nitrate process (GNP). • The dehydrogenation reaction of H18-DBT with Pt/Al 2 O 3 and Pt/CeO 2 was evaluated. • Pt/Al 2 O 3 showed slow reaction of 3.5%/2.5h due to poor mass transfer by small pores. • Pt/CeO 2 showed faster reaction of 80.5%/2.5h than commercial catalyst of 17.8%/2.5h. [ABSTRACT FROM AUTHOR]

Recently, hydrogen energy technologies attract attention as power systems. To develop hydrogen energy systems, hydrogen storage methods with high storage density and good safety are required. Liquid organic hydrogen carrier (LOHC) is one of the novel hydrogen storage technologies. LOHC has advantages of high storage density, good safety, and easy handling. In this study, a polymer electrolyte membrane fuel cell (PEMFC) stack is operated with hydrogen released from LOHC to evaluate the feasibility of the connected operation of the PEMFC stack and LOHC dehydrogenation reactor. Dibenzyltoluene (H0-DBT) is used as a LOHC material, and the dehydrogenation of perhydro dibenzyltoluene (H18-DBT) is conducted at 240–300 °C. Released hydrogen is purified by adsorbent of activated carbon to remove impurities. However, 100–1400 ppm of methane is observed after the purification, and the PEMFC stack power is reduced from 39.4 W to 39.0 W during the operation by hydrogen dilution and physical adsorption of methane. Then, to evaluate the irreversible damage, pure hydrogen was supplied to the PEMFC stack. The stack power is recovered to 39.4 W. It is concluded that the connected operation of the LOHC dehydrogenation reactor and PEMFC stack is feasible, and the activated carbon adsorbent can be a cost-effective purification method for LOHC. • Connected characteristics of a LOHC reactor and PEMFC was evaluated. • Hydrogen was released from LOHC by catalytic reaction at the in-house reactor. • Hydrogen released from LOHC was purified by activated carbon adsorbent. • Stable PEMFC stack operation was achieved with hydrogen from LOHC. • Methane was produced from LOHC, but there was no irreversible damage at PEMFC. [ABSTRACT FROM AUTHOR]

Atherosclerotic plaques with high likelihood of rupture often show local temperature increase with respect to the surrounding arterial wall temperature. In this work, atherosclerotic plaque temperature was numerically determined during the different levels of blood flow reduction produced by the introduction of catheters at the vessel lumen. The temperature was calculated by solving the energy equation and the Navier-Stokes equations in 2D idealized arterial models. Arterial wall temperature depends on three basic factors: metabolic activity of the inflammatory cells embedded in the plaque, heat convection due to luminal blood flow, and heat conduction through the arterial wall and plaque. The calculations performed serve to simulate transient blood flow reduction produced by the presence of thermography catheters used to measure arterial wall temperature. The calculations estimate the spatial and temporal alterations in the cooling effect of blood flow and plaque temperature during the measurement process. The mathematical model developed provides a tool for analyzing the contribution of factors known to affect heat transfer at the plaque surface. Blood flow reduction leads to a nonuniform temperature increase ranging from 0.1°C to 0.25°Celsius in the plaque/lumen interface of the arterial geometries considered in this study. The temperature variation as well as the Nusselt number calculated along the plaque surface strongly depended on the arterial geometry and distribution of inflammatory cells. The calculations indicate that the minimum required time to obtain a steady temperature profile after arterial occlusion is 6s. It was seen that in arteries with geometries involving bends, the temperature profiles appear asymmetrical and lean toward the downstream edge of the plaque. [ABSTRACT FROM AUTHOR]

This letter reports optical-lever detection with spray-coated liquid metal reflectors for transparent hydrogel atomic force microscope (AFM) cantilevers. Eutectic gallium–indium (EGaIn) filled in an air brush was locally sprayed over poly(ethylene) glycol-diacrylate (PEG-DA) AFM cantilevers with a shadow mask. Oxygen plasma treatment for bare hydrogel cantilevers prior to the EGaIn spray was shown to improve the wetting characteristics between EGaIn and PEG-DA, thus enabled relatively thin coating of $\sim 800$ nm within $\sim 100$ ms. PEG-DA AFM cantilevers coated with EGaIn were underwent mechanical characterization, and were employed for AFM imaging in both air and deionized (DI) water. Except the reflectivity degraded down to 40% of the initial value after 1-h dwell in DI water, their overall performance was comparable with those of PEG-DA AFM cantilevers with solid metal reflectors. [2016-009] [ABSTRACT FROM AUTHOR]

In edge computing service, edge devices collect data from a number of embedded devices, like sensors, CCTVs (Closed-circuit Television), and so on, and communicate with application servers. Since a large portion of communication in edge computing services are conducted in wireless, the transmitted data needs to be properly encrypted. Furthermore, the application servers (resp. edge devices) are responsible for encrypting or decrypting a large amount of data from edge devices (resp. terminal devices), the cryptographic operation needs to be optimized on both server side and edge device side. Actually, the confidentiality and integrity of data are essential for secure communication. In this paper, we present two versions of security software which can be used on edge device side and server side for secure communication between them in edge computing environment. Our softwares are basically web-based application because of its universality where the softwares can be executed on any web browsers. Our softwares make use of ESTATE (Energy efficient and Single-state Tweakable block cipher based MAC-Then-Encrypt)algorithm, which is a promising candidate of NIST LWC (National Institute of Standards and Technology LightWeight Cryptography) competition and it provides not only data confidentiality but also data authentication. It also implements the ESTATE algorithm using Web Assembly for efficient use on edge devices, and optimizes the performance of the algorithm using the properties of the underlying block cipher. Several methods are applied to efficiently operate the ESTATE algorithm. We use conditional statements to XOR the extended tweak values during the operation of the ESTATE algorithm. To eliminate this unnecessary process, we use a method of expanding and storing the tweak value through pre-computation. The measured results of the ESTATE algorithm implemented with Web Assembly and the reference C/C++ ESTATE algorithm are compared. ESTATE implemented as Web Assembly is measured in web browsers Chrome, FireFox, and Microsoft Edge. For efficiency on server side, we make use of OpenCL which is parallel computing framework in order to process a number of data simultaneously. In addition, when implementing with OpenCL, using conditional statements causes performance degradation. We eliminated the conditional statement using the loop unrolling method to eliminate the performance degradation. In addition, OpenCL operates by moving the data to be encrypted to the local memory because the local memory has a high operation speed. TweAES-128 and TweAES-128-6, which have the same structure as AES algorithm, can apply the previously existing studied T-table method. In addition, the input value 16-byte is processed in parallel and calculated. In addition, since it may be vulnerable to cache-timing attack, it is safely operated by applying the previously existing studied T-table shuffling method. Our softwares cover the necessary security service from edge devices to servers in edge computing services and they can be easily used for various types of edge computing devices because they are all web-based applications. [ABSTRACT FROM AUTHOR]

Kubernetes, an open-source container orchestration platform, enables high availability and scalability through diverse autoscaling mechanisms such as Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler and Cluster Autoscaler. Amongst them, HPA helps provide seamless service by dynamically scaling up and down the number of resource units, called pods, without having to restart the whole system. Kubernetes monitors default Resource Metrics including CPU and memory usage of host machines and their pods. On the other hand, Custom Metrics, provided by external software such as Prometheus, are customizable to monitor a wide collection of metrics. In this paper, we investigate HPA through diverse experiments to provide critical knowledge on its operational behaviors. We also discuss the essential difference between Kubernetes Resource Metrics (KRM) and Prometheus Custom Metrics (PCM) and how they affect HPA's performance. Lastly, we provide deeper insights and lessons on how to optimize the performance of HPA for researchers, developers, and system administrators working with Kubernetes in the future. [ABSTRACT FROM AUTHOR]

We have demonstrated the creation of polarization-dependent nanogratings with a period of about 250 nm on the surface of sapphire by scanning the femtosecond laser beam with appropriate irradiation conditions. Laser fluence range for nanograting self-formation was very narrow in the slow scan mode. The grating depth variation was observed with atomic force microscope image analysis. To see the composition distribution in the nanostructure, we carried out the Auger signal analysis. In addition, we found a new periodic structure with a period of a few μm and discussed the complex formation mechanism. [ABSTRACT FROM AUTHOR]

The time synchronization protocol is indispensable in various applications of wireless sensor networks, such as scheduling, monitoring, and tracking. Numerous protocols and algorithms have been proposed in recent decades, and many of them provide micro-scale resolutions. However, designing and implementing a time synchronization protocol in a practical wireless network is very challenging compared to implementation in a wired network; this is because its performance can be deteriorated significantly by many factors, including hardware quality, message delay jitter, ambient environment, and network topology. In this study, we measure the performance of the Flooding Time Synchronization Protocol (FTSP) and Gradient Time Synchronization Protocol (GTSP) in terms of practical network conditions, such as message delay jitter, synchronization period, network topology, and packet loss. This study provides insights into the operation and optimization of time synchronization protocols. In addition, the performance evaluation identifies that FTSP is highly affected by message delay jitter due to error accumulation over multi-hops. We demonstrate that the proposed extended version of the FTSP (E-FTSP) alleviates the effect of message delay jitter and enhances the overall performance of FTSP in terms of error, time, and other factors. [ABSTRACT FROM AUTHOR]

Air pollution has become a serious environmental problem causing severe consequences in our ecology, climate, health, and urban development. Effective and efficient monitoring and mitigation of air pollution require a comprehensive understanding of the air pollution process through a reliable database carrying important information about the spatiotemporal variations of air pollutant concentrations at various spatial and temporal scales. Traditional analysis suffers from the severe insufficiency of data collected by only a few stations. In this study, we propose a rigorous framework for the integration of air pollutant concentration data coming from the ground-based stations, which are spatially sparse but temporally dense, and mobile sensors, which are spatially dense but temporally sparse. Based on the integrated database which is relatively dense in space and time, we then estimate air pollutant concentrations for given location and time by applying a two-step local regression model to the data. This study advances the frontier of basic research in air pollution monitoring via the integration of station and mobile sensors and sets up the stage for further research on other spatiotemporal problems involving multi-source and multi-scale information. [ABSTRACT FROM AUTHOR]

Software-defined networking is moving from its initial deployment in small-scale data center networks to large-scale carrier-grade networks. In such environments, high availability and scalability are two of the most prominent issues, and thus extensive work is ongoing. In this article, we first review the state of the art on high availability and scalability issues in SDN and investigate relevant open source activities. In particular, two well-known open source projects, OpenDaylight (ODL) and Open Network Operating System (ONOS), are analyzed in terms of high availability (i.e., network state database replication/synchronization and controller failover mechanisms) and scalability (i.e., network state database partition/ distribution and controller assignment mechanisms) issues. We also present experimental results on the flow rule installation/read throughput and the failover time upon a controller failure in ONOS and ODL, and identify open research challenges. [ABSTRACT FROM PUBLISHER]

Fog computing is a potential solution to overcome the shortcomings of cloud-based processing of IoT tasks. These drawbacks can include high latency, location awareness, and security—attributed to the distance between IoT devices and cloud-hosted servers. Although fog computing has evolved as a solution to address these challenges, it is known for having limited resources that need to be effectively utilized, or its advantages could be lost. Computational offloading and resource management are critical to be able to benefit from fog computing systems. We introduce a dynamic, online, offloading scheme that involves the execution of delay-sensitive tasks. This paper proposes an architecture of a fog node able to adjust its offloading threshold dynamically (i.e., the criteria by which a fog node decides whether tasks should be offloaded rather than executed locally) using two algorithms: dynamic task scheduling (DTS) and dynamic energy control (DEC). These algorithms seek to minimize overall delay, maximize throughput, and minimize energy consumption at the fog layer. Compared to other benchmarks, our approach could reduce latency by up to 95%, improve throughput by 71%, and reduce energy consumption by up to 67% in fog nodes. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a leading trend with numerous opportunities accompanied by advantages as well as disadvantages. Parallel with IoT development, significant privacy and personal data protection challenges are also growing. In this regard, the General Data Protection Regulation (GDPR) is often considered the world's strongest set of data protection rules and has proven to be a catalyst for many countries around the world. The concepts and interaction of the data controller, the joint controllers, and the data processor play a key role in the implementation of the GDPR. Therefore, clarifying the blurred IoT actors' relationships to determine corresponding responsibilities is necessary. Given the IoT transformation reflected in shifting computing power from cloud to the edge, in this research we have considered how these computing paradigms are affecting IoT actors. In this regard, we have introduced identification of IoT actors according to a new five-computing layer IoT model based on the cloud, fog, edge, mist, and dew computing. Our conclusion is that identifying IoT actors in the light of the corresponding IoT data manager roles could be useful in determining the responsibilities of IoT actors for their compliance with data protection and privacy rules. [ABSTRACT FROM AUTHOR]

Processing IoT applications directly in the cloud may not be the most efficient solution for each IoT scenario, especially for time-sensitive applications. A promising alternative is to use fog and edge computing, which address the issue of managing the large data bandwidth needed by end devices. These paradigms impose to process the large amounts of generated data close to the data sources rather than in the cloud. One of the considerations of cloud-based IoT environments is resource management, which typically revolves around resource allocation, workload balance, resource provisioning, task scheduling, and QoS to achieve performance improvements. In this paper, we review resource management techniques that can be applied for cloud, fog, and edge computing. The goal of this review is to provide an evaluation framework of metrics for resource management algorithms aiming at the cloud/fog and edge environments. To this end, we first address research challenges on resource management techniques in that domain. Consequently, we classify current research contributions to support in conducting an evaluation framework. One of the main contributions is an overview and analysis of research papers addressing resource management techniques. Concluding, this review highlights opportunities of using resource management techniques within the cloud/fog/edge paradigm. This practice is still at early development and barriers need to be overcome. [ABSTRACT FROM AUTHOR]

Edge computing (EC) has recently emerged as a promising paradigm that supports resource-hungry Internet of Things (IoT) applications with low latency services at the network edge. However, the limited capacity of computing resources at the edge server poses great challenges for scheduling application tasks. In this paper, a task scheduling problem is studied in the EC scenario, and multiple tasks are scheduled to virtual machines (VMs) configured at the edge server by maximizing the long-term task satisfaction degree (LTSD). The problem is formulated as a Markov decision process (MDP) for which the state, action, state transition, and reward are designed. We leverage deep reinforcement learning (DRL) to solve both time scheduling (i.e., the task execution order) and resource allocation (i.e., which VM the task is assigned to), considering the diversity of the tasks and the heterogeneity of available resources. A policy-based REINFORCE algorithm is proposed for the task scheduling problem, and a fully-connected neural network (FCN) is utilized to extract the features. Simulation results show that the proposed DRL-based task scheduling algorithm outperforms the existing methods in the literature in terms of the average task satisfaction degree and success ratio. [ABSTRACT FROM AUTHOR]