This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Dynamic disease pathways are a combination of complex dynamical processes among bio-molecules in a cell that leads to diseases. Network modeling of disease pathways considers disease-related bio-molecules (e.g. DNA, RNA, transcription factors, enzymes, proteins, and metabolites) and their interaction (e.g. DNA methylation, histone modification, alternative splicing, and protein modification) to study disease progression and predict therapeutic responses. These bio-molecules and their interactions are the basic elements in the study of the misregulation in the disease-related gene expression that lead to abnormal cellular responses. Gene regulatory networks, cell signaling networks, and metabolic networks are the three major types of intracellular networks for the study of the cellular responses elicited from extracellular signals. The disease-related cellular responses can be prevented or regulated by designing control strategies to manipulate these extracellular signals. The paper reviews the regulatory mechanisms, the dynamic models, and the control strategies for each intracellular network. The applications, limitations and the prospective for modeling and control are also discussed.

Evolutionary sequential transfer optimization (ESTO), which attempts to enhance the evolutionary search of a target task using the knowledge captured from several previously-solved source tasks, has been receiving increasing research attention in recent years. Despite the tremendous approaches developed, it is worth noting that existing benchmark problems for ESTO are not well designed, as they are often simply extended from other benchmarks in which the relationships between the source and target tasks are not well analyzed. Consequently, the comparisons conducted on these problems are not systematic and can only provide numerical results without a deeper analysis of how an ESTO algorithm performs on problems with different properties. Taking this clue, this two-part paper revisits a large body of solution-based ESTO algorithms on a group of newly developed test problems, to help researchers and practitioners gain a deeper understanding of how to better exploit optimization experience towards enhanced optimization performance. Part A of the series designs a problem generator based on several newly defined concepts to generate benchmark problems with diverse properties, which are competitive in resembling real-world problems. Part B of the series empirically revisits various algorithms by answering five key research questions related to knowledge transfer. The results demonstrated that the performance of many ESTO algorithms is highly problem-dependent, which suggest the necessity of more research efforts on transferability measurement and enhancement in ESTO algorithm design. The source code of the benchmark suite developed in part A is available at https://github.com/XmingHsueh/Revisiting-S-ESTOs-PartA.

Cloud providers offer virtual machines (VM) located in physical machines (PM) using the “pay-as-you-go” model to satisfy emerging demand for online computational services. If the instantaneous utilized capacity requested by VMs exceeds a certain threshold of the total capacity a PM can offer, a hotspot happens and may cause unacceptable VM performance degradation. Hotspots can be resolved by relocating some VMs to other PMs using live migration. However, the problem of selecting which VM(s) to release is challenging because the utilized capacity demanded by VMs changes continuously over time. In this work, we propose a Predicted Mixed Integer Linear Programming (MILP) Robust Solver (PMRS), which predicts the utilized capacity range of each VM and applies the Γ-robustness theory to ensure that PM is hotspot-safe with desired probability. The latter allows us to formulate the hotspot resolution as a Γ-robust knapsack problem (Γ-RKP) that can be solved by a novel MILP model. Extensive experiments based on real-trace data and large-scale synthetic data demonstrate the effectiveness of the PMRS. More encouragingly, the application of the PMRS in the real-production environment benefits Huawei Cloud by resolving all existing and 94%+ potential future hotspots with minimal migration overhead.Cloud providers offer virtual machines (VM) located in physical machines (PM) using the “pay-as-you-go” model to satisfy emerging demand for online computational services. If the instantaneous utilized capacity requested by VMs exceeds a certain threshold of the total capacity a PM can offer, a hotspot happens and may cause unacceptable VM performance degradation. Hotspots can be resolved by relocating some VMs to other PMs using live migration. However, the problem of selecting which VM(s) to release is challenging because the utilized capacity demanded by VMs changes continuously over time. In this work, we propose a Predicted Mixed Integer Linear Programming (MILP) Robust Solver (PMRS), which predicts the utilized capacity range of each VM and applies the Γ-robustness theory to ensure that PM is hotspot-safe with desired probability. The latter allows us to formulate the hotspot resolution as a Γ- robust knapsack problem (Γ-RKP) that can be solved by a novel MILP model. Extensive experiments based on real-trace data and large-scale synthetic data demonstrate the effectiveness of the PMRS. More encouragingly, the application of the PMRS in the real-production environment benefits Huawei Cloud by resolving all existing and 94%+ potential future hotspots with minimal migration overhead.

This paper proposes two distinct approaches, namely Piece-by-Piece MNA and General MNA, for construction of Modified Nodal Analysis (MNA) matrices in order to be used in Model Order Reduction (MOR) techniques. In this work, we successfully applied the proposed approaches to construct MNA matrices of a multi-port network with several sub-circuits, then, reduced order of network using obtained MNA matrices in each approach. As a numerical example, the Y-parameters of full-order and reduced-order networks for a 4-port optical network, operating in frequency range 175-215 THz, are obtained through the proposed approaches, validated through simulating in Advanced Design System (ADS) software, and compared in terms of order of system (or matrix dimensions), execution time, Root Mean Square Error (RMSE), reduction controllability, reduction efficiency, and passivity point of view.

Demand response (DR) is regarded as a solution to the issue of high electricity prices in the wholesale market, as the flexibility of the demand can be harnessed to lower the demand level for price reductions. As an across-the-board DR in a system is impractical due to the enrollment budget for instance, it is necessary to select a small group of nodes for DR implementing. Current studies resort to intuitive yet naive approaches for DR targeting, as price is implicitly associated with demand, though optimality cannot be ensured. In this paper, we derive such a relationship in the security-constrained economic dispatch via the multi-parametric programming theory, based on which the DR targeting problem is rigorously formulated as a mixed-integer quadratic programming problem aiming at reducing the averaged price to a reference level by efficiently reducing targeted nodes’ demand. A solution strategy is proposed to accelerate the computation. Numerical studies demonstrate compared with the benchmarking strategy, the proposed approach can reduce the price to the reference point with less efforts in demand reduction. Besides, we empirically show that the proposed approach is immune to inaccurate system parameters, and can be generalized to variants of DR targeting tasks.

Objective: Microelectrodes allow for the recording of neural activities with a high spatial resolution. However, their small sizes result in their high impedance, therefore, leading to a high thermal noise and a poor signal- to-noise ratio. In the context of drug-resistant epilepsy, efficient Fast Ripples (FRs; 250-600 Hz) detection can help in pre-surgical investigations and delineation of epileptic zones. Consequently, good quality recordings with a high spatial resolution promise to improve the surgical outcome. This work aims at proposing a reliable model-based microelectrode design that is optimized for FRs recordings and detection. Methods: A 3D microscale computational model simulates the FRs generated in the CA1 subfield of the hippocampus. It couples with the electrode-tissue interface characteristics that account for the microelectrode’s biophysical properties and their impact on the recorded signal. To validate this model, analyses of the electrode’s geometrical (diameter, position, and direction) and physical (materials, coating) characteristics were con- ducted in order to propose an optimal microelectrode de- sign tuned for FRs recording. In addition, Results: Experimental signals were recorded from mice in the hippocampal CA1 subfield from different electrodes for comparison. ( between stainless steel, gold and gold coated with poly(3,4-ethylene dioxythiophene) /Poly(styrene sulfonate) (PEDOT/PSS)). Conclusion: Both in silico and in vivo quantified results show a clear improvement in FRs observability using PEDOT/PSS coated microelectrode.

This paper presents the electromagnetic characterization of new carbon fiber reinforced polymer (CFRP) composite applied in aircraft for tackling the lightning direct effect and enhancing the electromagnetic shielding effectiveness. The CFRP composite design is deliberated for ameliorating the electrical conductivity of composite. With the aid of equivalent medium modeling, the inherent electromagnetic properties of CFRP composite have been investigated. The simulated results illustrate that the lightning strike protection (LSP) capability and the electromagnetic shielding behavior of the newly developed aircraft composite have been improved as compared to the original CFRP composite, and comparable to that with copper mesh as a protective layer on top. In addition to simulation, an artificial lightning experiment has been carried out in accordance with SAE ARP5416 by mimicking the natural lightning strike attachment and also the measurement of the shielding effectiveness of the proposed CFRP composite has been conducted by using electromagnetic waveguide transmission line method. The experiment data has validated the simulation results and demonstrated the feasibility of the proposed CFRP composite design.

In this paper, we present a performance-oriented approach for calculating magnetostatic fields produced by circular coils. Those include the magnetic vector potential, magnetic flux density and magnetic field gradient, together addressed as fields. Many inductive elements used in a wide range of applications can be modeled as circular coils with a rectangular cross-section, and this is reflected in the number of papers on the subject. Instead of extensively utilising analytic methods, we focused on achieving maximum performance by efficiently implementing the Gauss-Legendre quadrature and applying it to expressions which are simple with respect to computational intensity. To properly leverage this, we implemented our approach in the C++ programming language. The CUDA framework enabled us to utilise the full power of Nvidia graphics cards, especially when multiple coils are present. Our approach can also be used from Python and MATLAB with a small performance penalty. Performance of field compute methods is primarily expressed in the number of field values calculated every second. Processor performance was tested on multiple systems and exceeded one million points per second on all of them. Graphics card performance is particularly noteworthy, with over 5 million magnetic flux density values computed every second for a system of 100 coils, placing effective performance at more than 500 million points per second on a contemporary laptop graphics card. The meticulous implementation, available on GitHub, and unique performance are the highlights of our work.

Due to the increased use and popularity of social media platforms in the most recent technological period, sentiment categorization has emerged as an important research area among those platforms. When it comes to Twitter, the main problem of previous research is, they all did a sentiment classification on the document level (Tweet level). It cannot classify the sentiment for any particular aspect. When it comes to the review of any multifunctional product and service, gathering an overall positive or negative mood may not be helpful to the firms as it is more crucial to ascertain precisely what their customers are happy or upset about, to bring the updates and changes on that particular product and service. In addition to this, what if someone wants to know the sentiments about recently generated data or tweets? What if someone wants to know the sentiment for data between a particular date range? What if users want to get sentiment of the tweets regarding current ongoing events and happenings? Along with this, very few of them performed aspect-based sentiment analysis on other platforms and they are using the same data set for training purposes as well as analytics purposes. So here we come up with the idea of Aspect based sentiment analysis on twitter, in which we train our model with a publicly available dataset, and then the user will give a particular hashtag and aspects. Our system will get tweets related to that specific hashtag from publicly available daily search twitter API and our model will take those tweets as input for analytics. Then machine learning operations will be performed on those tweets to find sentiment analysis for that hashtag’s tweet and its aspects with the best accuracy. In that way, we can get responses from people on any event, feedback or national issue, or matter of people’s support. The experimental findings also showed that our method beats current state-of-the-art approaches.

Cough is an important defense mechanism of the respiratory system and is also a symptom of lung diseases, such as asthma. Acoustic cough detection collected by portable recording devices is a convenient way to track potential condition worsening for patients who have asthma. However, the data used in building current cough detection models are often clean, containing a limited set of sound categories, and thus perform poorly when they are exposed to a variety of real-world sounds which could be picked up by portable recording devices. The sounds that are not learned by the model are referred to as Out-of-Distribution (OOD) data. In this work, we propose two robust cough detection methods combined with an OOD detection module, that removes OOD data without sacrificing the cough detection performance of the original system. These methods include adding a learning confidence parameter and maximizing entropy loss. Our experiments show that 1) the OOD system can produce dependable In-Distribution (ID) and OOD results at a sampling rate above 750 Hz; 2) the OOD sample detection tends to perform better for larger audio window sizes; 3) the model’s overall accuracy and precision get better as the proportion of OOD samples increase in the acoustic signals; 4) a higher percentage of OOD data is needed to realize performance gains at lower sampling rates. The incorporation of OOD detection techniques improves cough detection performance by a significant margin and provides a valuable solution to real-world acoustic cough detection problems.

With the advancements of high-resolution cameras, highly sensitive photodetectors, and energy-efficient light emitting-diodes, optical wireless communication (OWC) has emerged as key-enabling technology for 6G and beyond. Similarly, digital twin (DT) technology has been recently proposed to meet the diverse requirements of emerging applications and provide efficient optimization of the overall system resources by enabling selfsustaining and proactive online learning. In DT, various technology disciplines, including big data, artificial intelligence, intelligent computing, communication, and security-aware technologies, are integrated to create a virtual replica that reflects the physical system. Motivated by this, the presented article discusses DT and its role in the reliable and ubiquitous implementation of OWC networks. We first provide an overview of this emerging research area by shedding light on the key enabling technologies and potential applications in the context of smart-autonomous OWC networks. Subsequently, we provide recent advancements and design aspects related to DT-assisted OWC systems. Finally, future research directions and the impact of different system factors on the overall network performance are discussed. To the best of the authors’ knowledge, this is the first in-depth review in the literature on the integration of DT with OWC networks.

Fast frequency response services, designed to quickly balance the electrical grid within seconds, have a critical importance for managing sudden anomalies in low-inertia power systems. Battery systems often serve as versatile prosumers on the demand side to facilitate fast frequency response services. However, the nature of the fast frequency response services leads to a highly fluctuating power profile for batteries, which can shorten their lifetime. In contrast, distributed air-source heat pumps in residential areas have a substantial untapped potential to support fast frequency response services. This paper seeks to integrate them into the existing services through a controller upgrade. We analyze the influence of air-source heat pumps' inherent complex thermal dynamics on fast frequency response services, revealing control challenges posed by unpredictable operating condition changes. Such a challenge is tackled with a decentralized control strategy based on H∞ and inverse-droop control, guaranteeing practical and stable operations within the permitted operating condition range. Finally, the proposed fast frequency response service scheme is tested through multiphysics simulations on a small-size low-inertia residential microgrid. The obtained results strongly supported the proposed new service.

Kuramoto model based analysis of Josephson junction array in series and comparison with small numbers of junctions as compared to higher number as proposed in the Kuramoto model.

In this work, we consider a real-time IoT monitoring system in which an energy harvesting sensor with a finite-size battery measures a physical process and transmits the status updates to an aggregator. The aggregator, equipped with caching capabilities, can serve the external requests of a destination network with either a stored update or a fresh update from the sensor. We assume that the destination network act as a gossiping network in which the update packets are forwarded among the nodes in a randomized setting. We utilize the Markov Decision Process framework to model and optimize the network’s average Version Age of Information (AoI) and obtain the optimal policy at the aggregator. The structure of the optimal policy is analytically demonstrated and numerically verified. Numerical results highlight the effect of the system parameters on the average Version AoI. The simulations reveal the superior performance of the optimal policy compared to a set of baseline policies.

This is the era of internet and there are a lot of Social Media platforms out there that facilitate the sharing a vast variety of user generated content. LinkedIn is a business and employment oriented social platform whereas Twitter is used to share recent news trends and updates. Pinterest is about discovering new content and ideas while Facebook is more about catching up with friends and family. These networks and virtual communities cast a tremendous amount of influence on their users. Here we have a similar content-oriented platform called Social. It is designed and built for the users to connect and share digital content (like text, images and/or gifs) related to community, social, healthcare and welfare services. MERN stands for MongoDB, ExpressJS, ReactJS and NodeJS (the four key technologies that make the stack) and is used to built this fully responsive web application in conjugation with other APIs and tools.

In this paper, a new method is used to solve the above problems: the current flight state of UAV is considered in UAV flight prediction, and the UAV track is predicted by establishing a model according to different flight states of UAV. This method improves the accuracy and foresight of prediction, and can be used in UAV safety monitoring technology.

Elastic full waveform inversion can construct highresolution P-wave and S-wave velocity models in complex geological settings. However, several factors make the application of elastic FWI challenging. Elastic FWI is prone to the problem of cycle skipping phenomenon when low-frequency in the data are unavailable and the starting model is inaccurate. Multiparameter FWI also suffers from crosstalk issue due to coupling between different model parameters. We extend our physics guided deep convolutional autoencoder network to the problem of multiparameter elastic full waveform inversion. Our training is completely unsupervised. Our autoencoder which is composed of convolutional neural networks (CNNs) maps the multicomponent shot gathers to the target velocity models. The output from the network is given as input to partial differential equations which generate synthetic data. We compare the observed data against the synthetic data and then compute the misfit. We calculate the gradient of the misfit with respect to the model parameters and then use it to update the neural network weights. We note that the neural network generates velocity models that explains the observed data. We demonstrate that the network can introduces regularization in the inversion and overcome issues related to cycle skipping and parameter crosstalks. A toy model, the marmousi model and left part of the BP salt model are used to demonstrate the effectiveness of the proposed approach. Finally, we provide an explanation of the efficacy of the proposed approach by examining the nature of the loss landscape of neural networks based full waveform inversion.

We present a novel mathematical framework for computing the number of maintenance cycles in a system with critical and non-critical components, where “critical” (CR) means that the component’s failure is fatal for the system’s operation and renders any more repairs inapplicable, whereas “non-critical” (NC) means that the component  can undergo corrective maintenance (replacement or minimal repair) whenever it fails, provided that the CR component is still in operation. Whenever the NC component fails, the CR component can optionally be preventively replaced. We extend traditional renewal theory (whether classical or generalized) for various maintenance scenarios for a system composed of one CR and one NC component in order to compute the average number of renewals of NC under the restriction (“bound”) necessitated by CR. We also develop approximations in closed form for the proposed “bounded” renewal functions. We validate our formulas by simulations on a variety of component lifetime distributions, including actual lifetime distributions of wind turbine components.