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Restrictive public health measures such as isolation and quarantine have been used to reduce the pandemic viruss transmission. With no proper treatment, older adults have been specifically advised to stay home, given their vulnerability to COVID-19. This pandemic has created an increasing need for new and innovative assistive technologies capable of easing the lives of people with special needs. Smart home systems have become widely popular in providing such assistive services to isolated older adults. These systems can provide better services to assist older people if it anticipates what activities inhabitants will perform ahead of time. For example, a smart home can prompt inhabitants to initiate essential activities like taking medicine using activity prediction. This paper proposes a multi- task activity prediction system that jointly predicts labels, lo- cations, and starting times of future activities. The observed sequence of previous activities characterizes future activities. We use body activity information from wearable sensors and motion information from passive environmental sensors to sense activities of daily living of older adults. The activity prediction system consists of recurrent neural networks to capture temporal dependencies. This work also carries out several experiments on collected and existing real datasets to evaluate the systems performance.

Artificial Intelligence of Things (AIoT), as a fusion of AI and Internet of Things (IoT), has become a new trend to realize the intelligentization of industry 4.0 and the data privacy and security is the key to its successful implementation. To enhance data privacy protection, the federated learning has been introduced in AIoT, which allows participants to jointly train AI models without sharing private data. However, in federated learning, malicious participants might provide malicious models by launching the poisoning attack, which will jeopardize the convergence and accuracy of the global model. To solve this problem, we propose a malicious model detection mechanism based on the isolation forest, named D2MIF, for the federated learning empowered AIoT. In D2MIF, an isolation forest is constructed to compute the malicious score for each model uploaded by the corresponding participant, then the models will be filtered if their malicious scores are higher than the threshold, which is dynamically adjusted using reinforcement learning (RL). The validation experiment is conducted on two public datasets Mnist and Fashion_Mnist. And the experiment results show that the proposed D2MIF can effectively detect malicious models and significantly improve the global model accuracy in federated learning empowered AIoT.

Advanced safe battery storage systems with health prognostic performance are vital for electric vehicles. Various faults of lithium-ion batteries are usually undetectable in their early stage due to their concealment and graduality. This paper presents a real-time fault diagnosis and isolation scheme for real-scenario batteries using the normalized discrete wavelet decomposition. The early frequency-domain features of the fault signals are extracted utilizing the high-frequency detail wavelet components, and a multi-level fault prognosis strategy is developed considering complex charging/driving characteristics under real-vehicle operating conditions. The verification results, implemented on loose wire connection batteries and real-scenario thermal runaway batteries, demonstrate that the proposed method can accurately extract and locate the hidden fault signals even under small magnitudes, as well as effectively detecting and isolating battery faults before thermal runaway. Furthermore, significant reliability and stability of the proposed method are verified on more real-vehicle operation data, enabling online monitorable and traceable of battery faults before triggering thermal runaway, safeguarding drivers and passengers in real-world vehicular operation.

In this paper, the problem of temporal isolation among containerized software components running in shared cloud infrastructures is tackled, proposing an approach based on hierarchical real-time CPU scheduling. This allows for reserving a precise share of the available computing power for each container deployed in a multi-core server, so to provide it with a stable performance, independently from the load of other co-located containers. The proposed technique enables the use of reliable modeling techniques for end-to-end service chains that are effective in controlling the application-level performance. An implementation of the technique within the well-known OpenStack cloud orchestration software is presented, focusing on a use-case framed in the context of network function virtualization. The modified OpenStack is capable of leveraging the special real-time scheduling features made available in the underlying Linux operating system through a patch to the in-kernel process scheduler. The effectiveness of the technique is validated by gathering performance data from two applications running in a real test-bed with the mentioned modifications to OpenStack and the Linux kernel. A performance model is developed that tightly models the application behavior under a variety of conditions. Extensive experimentation shows that the proposed mechanism is successful in guaranteeing isolation of individual containerized activities on the platform.

Battery storage system (BSS) integration in fast charging station (FCS) is becoming popular to achieve higher charging rates with peak-demand shaping possibility. However, the additional conversion stage for integrating the BSS increases the system losses, size and cost. The concept of partial power processing converter (PPPC), can mitigate this effect. Compared to conventional used full power processing converter, PPPC reduces the amount of transferred power from the BSS to the electric vehicle by the converter. As a consequence, the power losses generated by the converter are reduced, leading to lower sized converters and higher system efficiencies. This paper proposes a DC/DC multiport converter which allows the integration of battery storage in FCS based on a partial power processing concept, while maintaining the specific requirements in terms of isolation for FCS. The proposed three-port partial power processing converter (3P-PPPC) is derived from the commonly used triple active bridge (TAB) converter. The resulting design trade-offs, the dynamic behavior and limitations of the topology are investigated. Furthermore, the round-trip efficiency of the 3P-PPPC for integrating BSS in FCS is compared with conventional full power processing converter solutions, highlighting the superiority of the proposed topology. A prototype has been built to validate the 3P-PPPC.

In this paper, a novel method is proposed to design a hybrid network (HN) to increase isolation and shape radiation patterns for multiple-input multiple-output (MIMO) antenna systems. The HN is a combination of a decoupling feeding network (DFN) and a defected ground network (DGN), which are populated by several surface-mounted reactive components whose reactances are determined by the N-ary optimization algorithm. Two decoupling examples are presented to validate the design methodology and elaborate on the design procedure. Measurement results show that the HN helps to realize impedance matching with reflection coefficients below -10 dB, isolation improvement from -5.4 dB/-8.9 dB to below -20 dB, and low envelope correlation coefficient below 0.06 for MIMO antennas with the element separation of 0.160/0.240. Moreover, in both examples, the decoupling case with omnidirectional radiation patterns achieves a better throughput performance, compared with another decoupling case with directional radiation. In comparison to prior decoupling networks only focusing on isolation enhancement, the proposed HN achieves high isolation and desired radiation patterns simultaneously with a very accurate design methodology.

Cloud computing is convenient to provide adequate resources for tenants. However, since multiple tenants share the underlying hardware resources, malicious tenants can use the shared processor to launch cache-based attacks. Such attacks can help malicious tenants steal private data of other tenants bypassing isolation mechanisms provided by the system, resulting in information leakage. Moreover, Spectre and Meltdown vulnerabilities can even extract memory contents arbitrarily with the help of cache attacks. Therefore, cache-based attacks pose a serious threat to the security of cloud platforms. To defeat such attacks, many detection methods have been proposed. However, most methods induce high false positives because they completely rely on the hardware performance counters (HPCs) and detect attacks with static criteria. To solve this problem, this paper proposes a self-feedback detector named CBA-Detector to detect cache-based attacks in real time. Specifically, CBA-Detector first uses machine learning technologies to create models for identifying suspicious programs with abnormal hardware behaviors, then analyzes suspicious programs from the instruction level to identify real attacks and provide feedback. Based on the feedback, the models can be updated to further improve their detection accuracy. As our experiments show, CBA-Detector can accurately identify cache-based attacks in real time and introduces a little overhead. Besides, the misjudgment rate decreases with the running time.

The uncertain capacitated arc routing problem has many real-world applications in logistics domains. Genetic programming is a promising approach to training routing policies to make real-time decisions and handle uncertain events effectively. In the real world, there are various problem domains and no single routing policy can work effectively in all of them. Instead of training in isolation, we can leverage the relatedness between the problems and transfer knowledge from previously solved source problems to solve the target problem. The existing transfer methods are not effective enough due to the loss of diversity during the knowledge transfer. To increase the diversity of the transferred knowledge, in this paper we propose a novel genetic programming method that removes phenotypic duplicates from the source individuals to initialise the target individuals. Furthermore, assuming that the transferred knowledge used in initialisation already includes all the important knowledge explored for the source problem, it is more effective to explore new regions that have not been explored for the source problem. Therefore, we propose novel genetic operators that prohibit the search from revisiting the source individuals when solving the target problem. To speed up the revisit check, we propose to adapt a powerful hashing method for routing policies that greatly improves the efficiency of the genetic operators. Our experimental results show that the proposed method significantly outperforms the existing genetic programming approaches with knowledge transfer in terms of both initial and final solution quality.

This paper presents an optimization model to help distribution system planners to site and size battery energy storage systems (BESSs) and to site isolation devices. The purpose is twofold: (i) to enhance system reliability and (ii) to generate revenue by taking advantage of energy arbitrage. Our work fills a research gap regarding the optimal coordination of the siting of BESSs and isolation devices. Input data for the model includes utility historical load and fault data. This data is used to generate multiple scenarios pertaining to normal and faulted operating conditions. The proposed optimization model is recast as a mixed-integer linear programming (MILP) problem and solved using a commercial solver. It is successfully applied in a real-world case study.

In this paper, dynamic modeling and associated closed loop control of a Cascaded H-Bridge (CHB) based Solid State Transformer (SST) with Series Resonant Dual Active Bridge (SRDAB) as a high frequency link isolation is proposed. The SRDAB converters are operated at resonant frequency in a open loop fashion. The proposed integrated modeling of the SST incorporates important non-idealities of the system which in turn reveals a simple yet rugged control of the entire SST system. The crux of the proposed control architecture is to regulate the Low Voltages DC (LVDC) bus voltage by sensing only three HV side DC bus voltage of the input H-Bridge cells. The proposed control technique is also extended for the SST system where LVDC bus is remotely placed. The control technique does not require information of the LVDC bus voltage though there may be sufficient resistive drop in the SRDAB section and long cable drop in case of a remotely placed LVDC section. The proposed controller is easy to realize but takes care of all the inherent dynamics of the system. The feasibility of the proposed control technique is validated experimentally on a 1.65 kV/300 V, 8 kW SST system.

To obtain the seismic performance of plate-shell integrated concrete liquid-storage structures (PSICLSS), a scale test model was built for shaking table testing. A combination isolation layer is proposed, and the dynamic responses of non-isolation and combination isolation PSICLSS under unidirectional and bidirectional earthquakes were tested. The results show that the combination isolation layer can change the spectral characteristics of the input acceleration, reducing the acceleration response and displacement of the structure, but it will increase the hydrodynamic pressure and liquid sloshing height. Under earthquake action, the liquid sloshing of combination isolation PSICLSS clearly appears as a nonlinear splash phenomenon. When considering the vertical earthquake, the horizontal acceleration response of PSICLSS and the liquid sloshing height change little, the peak hydrodynamic pressure changes significantly, and the peak position also changes. The maximum displacement of the isolation layer increases, but the residual displacement decreases.

Isolation Forest (or iForest) is a well-known technique for anomaly detection. It is, however, a bulky approach that assumes the luxury of large storage space and is also ineffective with dynamic streaming data so common nowadays in varied application domains. In this work, we present the Preprocessed Isolation Forest (PiForest) approach for anomaly detection that works well in resource constrained environments and is also effective on streaming data. PiForest is largely based on the iForest algorithm and to effectively handle the streaming data includes a pre-processing stage. In the pre-processing stage, Principal Component Analysis (PCA) is first harnessed to significantly reduce the dimension and bulk of the data. Subsequently, the streaming characteristic of the data is handled through a sliding window mechanism that creates sequential blocks of data for systematic processing. PiForest is able to identify anomalies as effectively as iForest and other state-of-the-art anomaly detection techniques but has substantially low storage and prediction complexity. We conduct empirical evaluation of the proposed approach with standard data sets and show that it performs comparably with standard techniques in terms of Area Under the Receiver Operating Characteristic Curve (AUC-ROC) and is able to work with high-dimensional, streaming data. Subsequently, we do a real-world hardware implementation of PiForest and demonstrate that the approach is realistic and practicable in resource-constrained environments.

A generalized version of fractional models is introduced for the COVID-19 pandemic, including the effects of isolation and quarantine. First, the general structure of fractional derivatives and integrals is discussed; then the generalized fractional model is defined from which the stability results are derived. Meanwhile, a set of real clinical observations from China is considered to determine the parameters and compute the basic reproduction number, i.e., R 0 ≈ 6.6361 . Additionally, an efficient numerical technique is applied to simulate the new model and provide the associated numerical results. Based on these simulations, some figures and tables are presented, and the data of reported cases from China are compared with the numerical findings in both classical and fractional frameworks. Our comparative study indicates that a particular case of general fractional formula provides a better fit to the real data compared to the other classical and fractional models. There are also some other key parameters to be examined that show the health of society when they come to eliminate the disease.

This paper investigates the secure control problem for cyber-physical systems when the malicious data is injected into the cyber realm which is directly connecting to the actuators. Based on moving target defense and reinforcement learning, we propose a novel proactive and reactive defense control scheme. First, the system $(A,B)$ is modeled as a switching system consisting of several controllable pairs $(A,\mathcal{B}_{l})$ to facilitate the construction of the moving target defense control scheme. The controllable pairs $(A,\mathcal{B}_{l})$ can be altered to update system dynamics under certain unpredictable switching probabilities for each subsystem, which can prevent the adversaries from effective attacks. Second, both attack detection and isolation schemes are designed to accurately locate and exclude the compromised actuators from a switching sequence. Third, a reinforcement learning algorithm based on the zero-sum game theory is proposed to design the defense control scheme when there exist no controllable subsystems to switch. To demonstrate the effectiveness of the defense control scheme, a three-tank system under unknown cyber attacks is illustrated.

In a photovoltaic (PV) DC integration system, a DC line-to-line short-circuit fault will cause all converters to be quickly locked. This can result in the sustained outage of the system. In this study, to address this problem, a protection method based on incipient fault identification is proposed to quickly identify and isolate the faulty branch, and to prevent the incipient fault from developing into a short-circuit fault. By analyzing the frequency characteristics of the incipient fault current, a disturbance injection start criterion is formed. Then, by using the converter to inject small disturbance signals to form obvious fault characteristics, the location and isolation of the faulty area can be achieved in time. The loop simulation results based on real-time digital simulator (RTDS) hardware demonstrate that the proposed method accurately recognizes the incipient fault without affecting the system power quality, and can simultaneously isolate the incipient fault. These factors provide guarantees for the safe and efficient operation of PV DC integration systems

LunAres Research Station is an analog research station for crewed space mission simulation, located at the abandoned airport in Poland. The facility provides full isolation, allowing for complex research on the psychological and physiological impact of long-term extra-terrestrial human presence. The general objective of LunAres is to create a research platform to support scientific and technological development in human space exploration. A broad range of specialists is involved in the study from fields like extreme medicine, psychology, biotechnology, robotics and engineering, sociology, architecture. The possible observation and control of the indoor environment, as well as telemetry of the crew’s physical and psychological states, provide large quantities of data for complex studies This paper presents an overview of the LunAres Research Station activity since its establishment in 2017. The contribution to scientific research and technology development is included. Based on the presented experience of carried missions and crews’ feedback a roadmap regarding the station design advancement is determined. The decision-making process of development is presented finalized with the demonstration of the potential for future projects and studies regarding human missions and sustainability. The conclusions of upgraded functional plans and spaces were determined through research on existing references and the new strategy for LunAres. Detailed drawings regarding architectural and technical solutions as well as future steps will be introduced.

Modern data centers host diverse applications, which generate a mix of short flows with stringent latency requirement and long flows requiring large sustained throughput. To solve the problem of resource competition between the mixed flows, we propose an adaptive traffic isolation scheme APS. Based on the packet spraying scheme in the multipath transmission, APS dynamically separates long flows from short ones on different paths to provide the low latency for the short flows. Meanwhile, to resolve the out-of-order problem, APS limits the long flows to a few paths with Equal Cost Multi Path (ECMP). Experimental results of NS2 simulation and Mininet implementation show that, APS reduces the average completion time for short flows by up to 60% and increases the throughputs for long flows by about 1.68x over the state-of-the-art multipath transmission schemes.

The proliferation of IoT devices comes with many challenges among which security is one of the most serious issues. In order to address the security issue for low-end IoT devices, ARM recently proposed the Platform Security Architecture (PSA), which provides execution isolation to safely manage and protect the computing resources of low-end IoT devices. However, developers implementing IoT services for PSA-based IoT devices need to follow complex development procedures and understand the PSA hardware, which dramatically increases the development time and cost of PSA-based IoT devices. This paper analyzes vulnerabilities that may arise from general purpose low-end IoT devices to derive the security requirements and essential security services for PSA-based IoT devices, and proposes a secure platform model based on the analysis results. The proposed secure platform model consists of system security services and application security services based on the basic PSA model and essential trusted sub-systems, and it is designed to be flexible and applicable to various types of PSA-based IoT devices. In addition, it provides secure platform services APIs to enable easy and fast development of IoT services. To evaluate the proposed secure platform model, two proof-of-concept implementations are provided by using both the basic PSA model with Secure Element (SE) and a reference device for ARM’s PSA. Finally, a case study shows that the development of IoT services can be done easily and quickly using the proposed security platform model.

The emergence of CubeSats as a viable means for realizing satellite networks at low costs has given rise to ubiquitous cyber-physical systems spanning air, ground, and space, in what is being recognized as the Internet of Space Things (IoST). IoST is expected to serve a wide variety of applications ranging from monitoring and reconnaissance to in-space backhauling, i.e., the network architecture must serve a plethora of application scenarios with differing service-level agreement (SLA) requirements over the same physical infrastructure in an end-to-end manner. At the same time, since the different use cases might belong to a variety of different stakeholders, IoST must support multi-tenancy and functional isolation of services. Consequently, a network slicing framework is vital to the success of IoST. To this end, an automatic network slicing framework for space-ground integrated networks is presented in this paper. The proposed framework has been designed to address the dual objectives of route computation and resource allocation with minimal SLA violations. Different from the existing state-of-the-art, the framework presented herein is purpose-built for ultra-dense CubeSat networks, and is fully automated. In other words, the framework is purely SLA-based, and does not require prior information concerning the resource requirements associated with a slice. Other key innovations introduced through this framework include a robust SLA model for slice customization, a novel topology construction mechanism, and a unique segment routing-based online admission control solution. Furthermore, the flexibility and efficacy of the proposed framework have been evaluated through a comprehensive use case driven evaluation scenario.

This paper designs a class of low-profile compact MIMO antennas on a single radiating patch using simple and systematic characteristic modes (CMs) method. Rather than using conventional method of radiation pattern and polarization diversities, this work demonstrates that by taking advantage of a proper superposition of some even/odd modes, it is able to generate similar radiation pattern and polarization no matter which port is excited. It is also found that port isolation and impedance matching are dependent on not only operating modes but also input couplings. The proposed CMs method consists of three main steps. First, adjust desired even and odd modes to be equal in resonant frequency (ResF). Second, suppress unwanted modes without affecting desired ones. Lastly, determine feeding positions to obtain proper input couplings. In this way, satisfactory port isolation and impedance matching could be achieved simultaneously and quickly, getting rid of complicated decoupling structures and time-consuming optimization. To exemplify how the proposed CMs method works, two MIMO antennas are successfully designed by just using simple slots and shorting pins. A prototype for the proposed four-port MIMO antenna is fabricated and measured for validation.

Fast and accurate methods of fault detection and isolation are a pre-emptive measure in multiterminal dc (MTdc) systems. Zonal isolation of faults is necessary to prevent any misopertation of the dc breakers that can lead to a shutdown of the network. Existing techniques require fast-communication or data synchronization methods have their own disadvantages. This article proposes a method for efficient fault zone isolation without the need of a communication link that prevents any misoperation of the dc breakers in a radial MTdc. This method provides individual local measurement-based control to the hybrid dc circuit breakers (dcCB). Faults created outside the zone of protection for a breaker create a change in the rate of change of current or voltage leading to misoperation. To avoid detection using a fixed threshold, an adaptive threshold based approach is suggested that updates the threshold based on the present operating status. Sensitivity analysis by varying the current limiting inductance and fault location is performed. A three terminal radial model of an MTdc is used for zonal isolation using power system computer aided design/electromagnetic transients including dc.

This work proposes a design methodology that saves the power dissipation under voltage over-scaling (VOS) operation. The key idea of the proposed design methodology is to combine critical path isolation (CPI) and bit-width scaling (BWS) under the constraint of computational quality, e.g., Peak Signal-to-Noise Ratio (PSNR) in the image processing domain. Conventional CPI inherently cannot reduce the delay of intrinsic critical paths (CPs), which may significantly restrict the power saving effect. On the other hand, the proposed methodology tries to reduce both intrinsic and non-intrinsic CPs. Therefore, our design dramatically reduces the supply voltage and power dissipation while satisfying the quality constraint. Moreover, for reducing co-design exploration space, the proposed methodology utilizes the exclusiveness of the paths targeted by CPI and BWS, where CPI aims at reducing the minimum supply voltage of non-intrinsic CP, and BWS focuses on intrinsic CPs in arithmetic units. From this key exclusiveness, the proposed design splits the simultaneous optimization problem into three sub-problems; (1) the determination of bit-width reduction, (2) the timing optimization for non-intrinsic CPs, and (3) investigating the minimum supply voltage of the BWS and CPI-applied circuit under quality constraint, for reducing power dissipation. Thanks to the problem splitting, the proposed methodology can efficiently find quality-constrained minimum-power design. Evaluation results show that CPI and BWS are highly compatible, and they significantly enhance the efficacy of VOS. In a case study of a GPGPU processor, the proposed design saves the power dissipation by 42.7% with an image processing workload and by 51.2% with a neural network inference workload.

Feeder automation is important to improve the reliability of power supply for distribution network. Existing voltage‐time feeder automation scheme exists the problems of long fault processing time and the upstream switches of the fault point need to tolerate fault current twice and the upstream sections need to experience two short time power outage. In this paper, a quick fault location and isolation method for distribution network based on adaptive reclosing is proposed. Three key technologies of the strategy are introduced, including a phase segregated switch of the distribution network based on the permanent magnet operating structure, the phase selector based on transient current characteristics, and the temporary fault and permanent fault identification method based on the power frequency voltage characteristics. The proposed strategy can avoid the second impact on upstream switches and power‐off of the upstream section in case of the permanent fault and can effectively shorten the fault processing time.

Current resilient consensus algorithms deal with multi-agent networks with lower-order nodal dynamics where each agent excludes certain extreme values from its neighbors. This does not scale well in general networks. In this paper, a new type of resilient consensus algorithm based on distributed attack isolation (DAI-RC) is proposed for general higher-order networks. In the DAI-RC algorithm, the evolution of each normal agent can avoid influence from those neighbors which are isolated as victims of attack. To characterize a feasible communication topology to isolate attacked agents, a notion of graph isolability that captures attack isolability is proposed, based on which a sufficient condition to isolate the attacked agents is presented. Simulations are finally provided to illustrate the effectiveness of the proposed algorithm.

In this paper, a low-profile wideband quad-element MIMO antenna with high isolation is designed, which is utilized to propose a 20-elements MIMO system. Each element of the MIMO antenna covers a wideband occupying a small area of 15×15 mm2. The orthogonal symmetric arrangement of a single antenna forms an extremely compact quad-element MIMO antenna having shared ground, and its occupied volume is 0.84λg×0.84λg ×0.019λg(λg at 3.95GHz) with a minimum(0.155λg) edge to edge distance between elements. The proposed antenna has a -10 dB (Sij e i=j) impedance bandwidth of 77.52% (3.95-8.95 GHz) and a minimum isolation (Sij e i ≠ j) value of 17 dB. The measured and simulated results are found in good agreement that confirms the proposed antenna is appropriate for extending a higher-order MIMO system. Further, a low-profile quad-element MIMO antennas are arranged orthogonally at the four-corners and a quad-element MIMO antenna at the center of form a 20-elements planar MIMO antenna. It can be utilized as a localization system where different user’s devices are connected to the primary host.

From the viewpoint of the development of molecular response systems, stimulus-induced switching of multi-conformational/multi-configurational overcrowded ethylenes are interesting, whose properties could be manipulated by understanding the detailed isomerization paths. Anthraquinodi-methane (AQD) ring-flip is usually a very fast process, and thus less studied experimentally. Herein, we studied AQDs with dibenzo- and tribenzocycloheptatrienylidene units, which have large enough steric hindrance to retard the AQD ring-flip to allow determination of the Delta G(not equal) value experimentally. Their thermal isomerization was also scrutinized using the artificial force induced reaction method to elucidate the intermediates. Based on the structural unsymmetry in a newly prepared AQD, one of the isomers that undergoes a reversible conformational change via AQD ring-flip was isolated and analyzed by X-ray for the first time.

DNAs exist in nature and could provide solutions to computing, storage and communications as existing ones approach their physical limits. Plenty of research work has been conducted on DNA-based computing, data storage and molecular communications, but largely in isolation. There is a lack of a unified place where the triples are put together to be discussed. This paper aims to filling in this gap by providing an overview of each triplet. Starting from an overall description of DNA features and their reading and writing in practical terms, the paper goes on to describe each of the three from three aspects: requirements and differences from current electronic-dominating technologies, their working principles and practical considerations. Recent advancement in each area is summarized and discussed. Furthermore, the paper intends to call for researches that go beyond the boundary of each and encourages interconnection and joint research among the three. It proposes a molecular ICT system architecture with all its three components underpinned by DNAs. The paper also identifies and discusses some new future directions such as joint coding for storage and communications, directional DNA-based molecular communications, interfaces between molecular DNA systems and electronic systems. It is hoped that this paper can spark more joint research across computing, storage and communications in this exciting field of DNA-based molecular ICT systems.

Container-based virtualization has gradually become a main solution in today's cloud computing environments. Detecting and analyzing anomaly in containers present a major challenge for cloud vendors and users. This paper proposes an online container anomaly detection system by monitoring and analyzing multidimensional resource metrics of the containers based on the optimized isolation forest algorithm. To improve the detection accuracy, it assigns each resource metric a weight and changes the random feature selection in the isolation forest algorithm to the weighted feature selection according to the resource bias of the container. In addition, it can identify abnormal resource metrics and automatically adjust the monitoring period to reduce the monitoring delay and system overhead. Moreover, it can locate the cause of the anomalies via analyzing and exploring the container log. The experimental results demonstrate the performance and efficiency of the system on detecting the typical anomalies in containers in both simulated and real cloud environments.

Seismic isolation systems exposed to far-fault earthquakes can reduce floor accelerations and story drift ratios to acceptable levels. However, they exhibit different structural performances in each earthquake due to different excitation frequency contents. By optimizing the isolation system parameters, their performance may be maintained at the best level under different far-fault earthquakes. In this study, the optimization of the parameters of the nonlinear isolation system of a 5-story benchmark building is performed by Teaching-Learning Based Optimization (TLBO) algorithm to minimize peak floor accelerations under historical far-fault earthquakes with and without exceeding a specified base displacement limit. According to the results of the analyses, it can be said that TLBO algorithm is a robust algorithm with low standard deviations for determining optimum nonlinear isolation system parameters.

The optimal frequency control of autonomous microgrids (MGs), i.e., to achieve fast frequency recovery and dynamic power adjustment of the distributed generators in proportion to predefined participation factors, can be achieved in a fully distributed way based on the subgradient consensus protocol. However, such a distributively controlled MG is susceptible to different types of cyber attacks infiltrated from different locations. In this article, a robust and resilient distributed optimal frequency control scheme is proposed to address the threat of cyber attacks. It is facilitated by introducing an auxiliary networked system interconnecting with the original cooperative control system. On condition that the cyber attacks are within certain ranges, the robust design can maintain the functionalities by significantly attenuating the impact. Otherwise, the cyber attacks can be easily detected, and resilient reactions can be taken to mitigate their influences via isolation. Simulation results in a modified IEEE 34-bus MG validate the effectiveness of the proposed approach.

Several algorithms for nuclear power plant (NPP) break event detection, isolation, localization, and size estimation are proposed. A break event can be promptly detected and isolated after its occurrence by simultaneously monitoring changes in the sensing readings and by employing an interquartile range-based isolation scheme. By considering the multi-sensor data block of a break to be rank-one, it can be located as the position whose lead field vector is most orthogonal to the noise subspace of that data block using the Multiple Signal Classification (MUSIC) algorithm. Owing to the flexibility of deep neural networks in selecting the best regression model for the available data, we can estimate the break size using multiple-sensor recordings of the break regardless of the sensor types. The efficacy of the proposed algorithms was evaluated using the data generated by Maanshan NPP simulator. The experimental results demonstrated that the MUSIC method could distinguish two near breaks. However, if the two breaks were close and of small sizes, the MUSIC method might wrongly locate them. The break sizes estimated by the proposed deep learning model were close to their actual values, but relative errors of more than 8% were seen while estimating small breaks’ sizes.

The problem of finding anomalies and outliers in datasets is one of the most important challenges of modern data analysis. Among the commonly dedicated tools to solve this task one can find Isolation Forest (IF) that is an efficient, conceptually simple, and fast method. In this study, we propose the Probabilistic Generalization of Isolation Forest (PGIF) that is an intuitively appealing and efficient enhancement of the original approach. The proposed generalization is based on nonlinear dependence of segment-cumulated probability from the length of segment. Introduction of the generalization allows to achieve more effective splits that are rather performed between the clusters, i.e. regions where datapoints constitute dense formations and not through them. In a comprehensive series of experiments, we show that the proposed method allows us to detect anomalies hidden between clusters more effectively. Moreover, it is demonstrated that our approach favorably affects the quality of anomaly detection in both artificial and real datasets. In terms of time complexity our method is close to the original one since the generalization is related only to the building of the trees while the scoring procedure (which takes the main time) is kept unchanged.

In order to investigate the application of 3D base-seismic isolation system in nuclear power plants (NPPs), comprehensive analysis of constitution and design theory for 3-dimensional combined isolation bearing (3D-CIB) was presented and derived. Four different vertical stiffness of 3D-CIB was designed to isolate the nuclear island (NI) building. This paper aimed at investigating the isolation effectiveness of 3D-CIB through modal analysis and dynamic time-history analysis. Numerical results in terms of dynamic response of 3D-CIB, relative displacement response, acceleration and floor response spectra (FRS) of the superstructure were compared to validate the reliability of 3D-CIB in mitigating seismic response. The results showed that 3D-CIB can significantly attenuate the horizontal acceleration response, and a fair amount of the vertical acceleration response reduction of the upper structure was still observed. 3D-CIB plays a significant role in reducing the horizontal and vertical FRS, the vertical FRS basically do not vary with the floor height. The smaller the vertical stiffness of 3D-CIB is, the better the vertical isolation effectiveness is, whereas, it will increase the displacement and the rocking effect of superstructure. Although the advantage of 3D-CIB is that the vertical stiffness can be flexibly adjusted, it should be designed by properly accounting for the balance between the isolation effectiveness and displacement control including rocking effect. The results of this study can provide the technical basis and guidance for the application of 3D-CIB to engineering structure.

A low-cost differentially-fed dual-mode multiple-input- multiple-output (MIMO) antenna is proposed for enhanced isolation and filtering response based on the substrate-integrated suspended line (SISL) platform. Stacked patches are placed above the driven patches to realize dual-mode performance and filtering response in the upper band. The stacked patches are widened and notched to enhance the isolation and the filtering response in the lower band. Eight metal stripes are set at both sides of the feed lines to enhance the frequency selectivity further. Compared with the conventional MIMO antenna based on patch elements, the isolation is enhanced by 7.6 dB, and the out-of-band suppression with 17.6 dB is achieved. To verify the design, a prototype is fabricated and measured. In a operating band of 4.8-4.96 GHz, maximum gain of 8.3 dBi, and isolation of 31.3 dB are realized. The filtering response is achieved by three broadside radiation nulls at 4.36 GHz, 4.62 GHz, and 5.08 GHz.

Dynamic capability (DC) is an ability that has helped big corporations navigate successfully through the ever-changing and competitive business environment. However, SMEs in the southeast of Nigeria seem not to have fully maximized the opportunities embedded in these practices, hence, the need to examine the dynamic capabilities SMEs in Southeast Nigeria need, to stay relevant and ahead of competitors. This study, therefore, looked at the concept of DC and those capabilities that could help SMEs stay afloat in the ever-changing business environment. The capabilities looked at include sensing, absorptive and adaptive capabilities. Others are innovative networking and integrative capabilities. The study further examined the micro-foundations of DC and the composition of SMEs in South East, Nigeria. To embellish the work, Dynamic Capability Theory (DCT) was used to anchor the work. Some studies were reviewed empirically. The study concluded that DC is one of the surest ways to ensure that SMEs compete favourably in the ever dynamic marketplace. Hence, it was recommended among other things that SMEs need not to be operating in isolation of what is happening in the global market, as what happens outside has an implication on what happens inside the organization, and as such, need to be alert through the application of DC principles. Keywords: Dynamic Capability, Sustainability, SMEs, South-East, Absorptive, Adaptive and Innovative Capability.

The robust design of magneto-rheological elastomer in the presence of seismic uncertainty is the theme of this work. This issue is addressed by optimizing the phenomenological parameters to ensure robustness. Once the fundamental characteristics are tuned, minor adjustment can be carried out using current flow to meet the instantaneous demand for control force. For this purpose, subset simulation is proposed in this study. The rationale behind the selection of subset simulation is its ability to estimate probability using very few samples. The proposed robust design methodology is demonstrated using simulated samples of El Centro ground motions. However, the method is general and can be adopted for any other uncertainty model, specific to a particular structure or location. The proposed robust design of magneto-rheological elastomer is verified using a three-storied frame that clearly shows the advantage of tuning the phenomenological parameters of the isolator. Finally, the performance of the optimal isolator is also demonstrated using different recorded earthquakes and the performance of an isolator is discussed in the light of different structural performance index.

The mouse hematopoietic system has served as a paradigm for analysis of developmental fate decisions in tissue homeostasis and regeneration. However, multiple immunophenotypic definitions of, and sometimes divergent nomenclatures used to classify, murine multipotent progenitors (MPPs) have emerged in the field over time. This has created significant confusion and inconsistency in the hematology field. To facilitate easier comparison of murine MPP phenotypes between research laboratories, a working group of four International Society for Experimental Hematology (ISEH) members with extensive experience studying the functional activities associated with different MPP phenotypic definitions reviewed the current state of the field with the goal of developing a position statement toward a simplified and unified immunophenotypic definition of MPP populations. In November of 2020, this position statement was presented as a webinar to the ISEH community for discussion and feedback. Hence, the Simplified MPP Identification Scheme presented here is the result of curation of existing literature, consultation with leaders in the field, and crowdsourcing from the wider experimental hematology community. Adoption of a unified definition and nomenclature, while still leaving room for individual investigator customization, will benefit scientists at all levels trying to compare these populations between experimental settings.

This paper presents a novel and simple method for the decoupling of a 2×2 MIMO antenna array. Two distinct radiation modes, with different patterns and polarizations, are created by novel patch element design. This new configuration enables the excitation of additional coupling paths between antennas, and ultimately the neutralization of mutual coupling. Since the two radiating modes are generated by the same patch element, the proposed MIMO antenna also features simple structure and compact size. For verification, a 2×2 MIMO antenna with center-to-center spacing of 0.475 λ0 is prototyped and characterized. Simulation and experimental results reveal that the proposed design can offer enhanced port isolation, antenna efficiency, and pattern diversity.

International audience; Network slicing is a relatively recent paradigm that has become a subject of intensive research. Most of the existing approaches follow the ETSI NFV MANO model with some extensions related to multi-domain slicing, slice selection, etc. The framework is a part of the standalone variant of the 5G network standardized by 3GPP. However, the current implementation of network slicing in 5G has several limitations, especially regarding management and orchestration: The isolation of slice management is not appropriately addressed, the slice tenant management capabilities are limited, and the management and orchestration centralization raises serious scalability and reliability issues. Moreover, the slice-level operations are not well-separated from other processes due to a lack of proper separation of concerns. As a result, the overall network slicing architecture has complex interactions with many components of the 5G network. In this paper, we describe a new framework that uses an approach to sustain self-managed and self-orchestrated slices. To that end, we propose a modular architecture in which slices have embedded (in-slice) management and orchestration support, and multi-domain slices rely on multiple slice-agnostic orchestrators. The framework enables the composition of new slices as a combination of single domain-slices in a relatively easy, technology-independent manner.

Background. The artificial environment of confined space causes a decrease in the functional reserve of the central nervous system and can affect human health and the success of space missions. In solving this problem, the urgent task is to study adaptation mechanisms that adapt the functioning of the visual sensory system to the conditions of the extreme environment. Purpose to obtain new objective data on the alterations in the functional activity of the visual system during prolonged stay of a person in extreme environmental conditions. Methods. Before and after a 4-month isolation experiment simulating a flight to the moon, an electrophysiological study was conducted of six practically healthy crew members with registration of a set of electroretinograms (ERG) and pattern-reversal visual evoked cortical potentials (VEP) according to the ISCEV standards. In dynamics, corrected monocular visual acuity (MVA) was assessed on board. Results. After the end of the experiment, on average for the group, there were no statistically significant changes in the MVA and functional activity of the retina and visual cortex compared with the initial data. However, individual changes on the part of the flicker ERG and reduction of VEP to small patterns stimulating the parvocellular channel of the visual system were revealed in three testers. These changes were associated with higher visually intense work and physical activity of these crew members, and with an individual reaction to sleep deprivation of pilots with increased responsibility. Conclusion. Four-month isolation with imitation of a space mission did not cause significant changes in the functional activity of the retina and visual pathways in healthy crew members. Individual differences of VEP-responses of the parvocellular visual system were revealed, which can reflect a high level of psychophysiological adaptation and stress resistance in physically active crew members.

Anomaly or outlier detection is consider as one of the vital application of data mining, which deals with anomalies or outliers. Anomalies are considered as data points that are dramatically different from the rest of the data points. In this survey, we comprehensively present anomaly detection algorithms in an organized manner. We begin this survey with the definition of anomaly, then provide essential elements of anomaly detection, such as different types of anomaly, different application domains, and evaluation measures. Such anomaly detection algorithms are categorized in seven categories based on their working mechanisms, which includes total of 52 algorithms. The categories are anomaly detection algorithms based on statistics, density, distance, clustering, isolation, ensemble and subspace. For each category, we provide the time complexity of each algorithm and their general advantages and disadvantages. In the end, we compared all discussed anomaly detection algorithms in detail.

The bandgap characteristics of periodic structures show a great application prospect in seismic isolation and vibration mitigation. A T-shaped partially embedded periodic pile barrier is designed and studied based on the Bloch’s theorem. The proposed structure is fabricated and tested in field experiment to validate the simulation and the isolation performance. Good agreements are observed between the measured and the corresponding simulated results which present effective isolation for surface waves. The influences of the embedded length in the soil and the arrangement of the pile are investigated. The results show that the embedded length is the key parameter affecting the vibration attenuation. The smaller the embedded length, the lower the frequency of attenuation zone, and thanks to the embedded length; the proposed barrier exhibits better performances than the fully embedded and non-embedded barriers in maximum bandgap width, tunability, and feasibility in engineering. Gradient distribution of the embedded length leads to a wider frequency range of attenuation.

During the COVID-19 pandemic, face-to-face assessments were limited. Fortunately, older adults have access to web-technology (60%). Thus, we aimed to explore if assessing physical performance remotely is as reliable and valid as in person. At the end of the first lockdown, 15 older adults agreed to perform two similar evaluations in remote and face-to-face conditions. Functional capacities [5-repetitions Sit-to-Stand (STS); unipodal balance, 4-m walking speed (normal (NWS); fast (FWS)), 3-m Timed-Up and Go (normal (nTUG); fast (fTUG))] and muscle power and endurance were assessed. Fast walking speed was moderately reliable. Unipodal balance, NWS and nTUG were highly reliable (ICC>0.7). fTUG, STS, muscle endurance and power were extremely reliable (ICC>0.9). For absolute reliability, SEM varied from 15.54 to 5.14%. Finally, the MDC varied from 43.07 to 14.21%. Assessing functional capacities and muscle function remotely is as reliable and valid as a face-to-face assessment and should be considered as a clinical practice.

The design of genetic circuits typically relies on characterization of constituent modules in isolation to predict the behavior of modules’ composition. However, it has been shown that the behavior of a genetic module changes when other modules are in the cell due to competition for shared resources. In order to engineer multi-module circuits that behave as intended, it is thus necessary to predict changes in the behavior of a genetic module when other modules load cellular resources. Here, we introduce two characteristics of circuit modules: the demand for cellular resources and the sensitivity to resource loading. When both are known for every genetic module in a circuit library, they can be used to predict any module’s behavior upon addition of any other module to the cell. We develop an experimental approach to measure both characteristics for any circuit module using a resource sensor module. Using the measured resource demand and sensitivity for each module in a library, the outputs of the modules can be accurately predicted when they are inserted in the cell in arbitrary combinations. These resource competition characteristics may be used to inform the design of genetic circuits that perform as predicted despite resource competition.

To cope with the DC fault isolation issue of a hybrid high-voltage direct-current (HVDC) system, a promising option is to coordinate superconducting fault current limiter (SFCL) and DC circuit breaker (DCCB). In this paper, a multi-objective Pareto optimization method is proposed for the combinatorial use of a resistive-type SFCL and a DCCB in a hybrid HVDC. Firstly, theoretical modeling and coordinated action process of the SFCL and DCCB are expounded. Then, a multi-objective function that collaboratively minimizes the SFCL resistance and the breaking capability of the DCCB is established, and a non-dominated sorting genetic algorithm-II (NSGA-II) is utilized to acquire the satisfying Pareto optimal solution set. The optimizing calculation is done in a 320 kV hybrid HVDC for validating the approach efficacy. Besides, transient simulations are implemented by PSCAD/EMTDC, and comprehensive performance analyses are executed to evaluate the rationality of the combinatorial optimal schemes. The findings indicate that the DC fault current is reduced and interrupted with the minimal rating of the resistive-type SFCL and the DCCB, and also, the HVDC system's safe and stable operation will be ensured. Therefore, the proposed approach is adequately verified for its availability and feasibility.

In this article, a current balancing coupled inductor (CBCL) based asymmetrically configured quad-active-bridge (QAB) converter is proposed. The QAB converter provides high-frequency isolation for a dc–dc stage of a solid state transformer (SST), as well as provides a modular solution to the SST medium-voltage (MV) dc link. The proposed CBCL ensures current balancing among/three active H-bridges without using any dedicated controller and without expensive sensors when transferring equal power from MV to low-voltage side. The proposed CBCL is the integration of three inductors of the active H-bridges required for power transfer and zero-voltage-switching and does not require an extra magnetic volume. Experimental results are presented to validate the performance.

Hybrid renewable energy system (HRES) is a nexus of several renewable energy systems that concertedly overcomes the limitations of individual systems operating in isolation. This concept of HRES is extended in this study by considering the network of several such HRESs which further improves the flexibility in overcoming the individual limitations. However, operating such a network of HRESs involves complex decision-making and requires efficient optimization. In this study, a novel hierarchical two-tier optimization methodology for a network of HRESs is proposed when they trade power with each other and with the macro grid. In the proposed methodology, the operational optimization of each HRES is carried out by minimizing the operating cost of various sub-units in the first layer. In the second layer, three cooperative optimization strategies inspired from concepts of double-auction e-marketing are applied when HRESs are assumed to operate under the grand coalition. These three cooperative strategies try to achieve the following objectives (i) minimization of power loss during local trade, (ii) maximization of total market gain, and (iii) simultaneous power loss minimization and total market gain maximization, respectively. Simulation results depict that the cooperative optimization approach yields a reduction in power loss up to 7 % and improvement in market utility up to 29% relative to a scenario when they operate non-cooperatively.