Miracle-dz/UniFAR-FTUs huggingface dataset:
SAC: Solar-Aware E-Taxi Fleet Charging Coordination under Dynamic Passenger Mobility

As electric vehicles (EV) gradually replace traditional fuel vehicles and provide transportation services in cities, e.g., electric taxi/bus fleets, solar-powered charging stations with energy storage systems have been deployed in urban areas to provide charging services for EV fleets [1]. The mixture of solar-powered and traditional charging stations brings efficiency challenges to charging stations and reliability challenges to power systems. In this paper, we explore e-taxis’ mobility and charging demand flexibility to co-optimize service quality of e-taxi fleets and system cost of charging infrastructures, such as under-utilization of solar power and reliability issues of the power distribution network due to reverse power flow. Specifically, we propose SAC, an e-taxi coordination framework to dispatch e-taxis for charging or serving passengers under spatiotemporal dynamics of renewable energy and passenger mobility. We formulate the e-taxi fleet coordination problem as a multi-criterion mixed-integer linear programming problem. We evaluate our solution with a comprehensive dataset for e-taxi systems and charging infrastructures including 726 e-taxis, 7,228 regular fuel taxis, 37 working charging stations, and 62,100 collected taxi trips per day. Our data-driven evaluation shows that SAC significantly outperforms existing solutions, reducing the total reverse power flow per day by up to 95.3%, while maintaining e-taxi service quality with very small overhead.
Investigation of Beamforming-NOMA in mm Wave Mobile Communication Networks with Spectrum Sharing

Orthogonal Multiple Access, or OMA, schemes have been widely adopted in microwave mobile networks to reduce interference. In the sub-6GHz spectrum, current LTE and 5G systems exploit the orthogonal communication resources in terms of time, frequency or code in order to improve the Signal-to-Interference ratio, leading to fast and reliable links. However, the low spectral usage of OMA schemes make them unsuitable for use in future generation mobile networks, especially in the mmwave spectrum. The high propagation losses at mmwave frequencies and the use of M-MIMO antennas allow experimentation with new techniques that allow a highly aggressive spectrum utilization. In this paper, a beam-based Non-orthogonal Multiple Access scheme is combined with Spectrum Sharing as a potential solution for successfully meeting the requirements set for the future generation mmwave networks. Single captures of a network were ray-traced under multiple access strategies and network performance was evaluated in terms of SINR and throughput.
Reach-Avoid Differential Games via Finite-Time Heading Tracking

This paper considers a one-defender-one-attacker reach-avoid differential game (DG) in the plane which is split by a straight line into a goal region and a play region. The attacker aims at entering the goal region from the play region without being captured, while the defender tries to capture the attacker in the play region. We focus on the defense problem where the defender is a Dubins car with non-zero capture radius and the attacker is a simple-motion model. First, a controller is proposed for the defender to track a heading reference which is derived from an evasion space (ES)-based defense strategy. Then, the upper bounds for the time derivative of the heading reference are computed. Based on it, we show that the defender can succeed to track the heading reference within a finite time. Finally, both simulation and experiment examples are provided, where a vision-based re-localization method is used for the experiment to deal with the coordinate inconsistency problem.
Adaptive NN-Based Reference-Tracking Control of Uncertain Nonlinear Parabolic PDE Systems

This paper is focused on the reference-tracking control problem of distributed parameter systems modeled by a class of parabolic partial differential equations (PDEs) with uncertain nonlinear dynamics. An adaptive tracking control scheme is developed by utilizing radial basis function neural networks (RBF NNs) to deal with nonlinear system uncertainties. Specifically, the Galerkin method is first employed to derive a reduced-order ordinary differential equation (ODE) model to approximate the original PDE system. Based on this, an adaptive tracking control scheme is developed based on the singular perturbation theory and Lyapunov stability theory. With the control scheme implemented on the original PDE system, the system output can be guaranteed to track a prescribed reference trajectory with desired system stability and tracking accuracy. Simulation study on a representative transport-reaction process is conducted to demonstrate the effectiveness of the proposed approach.
Charging Electric Vehicles with Valet: a Novel Business Model to Promote Transportation Electrification

This paper considers on-demand valet charging for electric vehicles (EVs) to promote transportation electrification. We propose a novel business model where a platform recruits a fleet of couriers to provide valet charging services to EV owners at an affordable price. Couriers are dispatched to deliver the out-of-battery EVs to charging stations, plug in the cars, and drive them back to customers when the EVs are fully charged. To depict the proposed business model, a queuing network is formulated to capture the stochasticity of the matching dynamics, and an economic equilibrium model is proposed to characterize the interactions among couriers, EV owners, and the platform. Based on the model, we investigate the optimal pricing strategies of the platform and quantify the impacts of charging infrastructure planning on the market outcomes. We show that the interests of different stakeholders are not consistent: couriers enjoy a higher surplus under a lower charging station density, while the platform prefers a higher density as it leads to a higher markup. We identify the critical density of charging stations that achieves the highest EV penetration, and we show that it trades off the time traveling to and waiting at the charging stations. We also briefly analyze the regulatory challenges and suggest prospective directions for government interventions.
Design and Analysis of Hash Algorithm Using Autonomous Initial Value Proposed Secure Hash Algorithm64

A secure hash code or message authentication code is a one-way hash algorithm. It is producing a fixed-size hash function to be used to check verification, the integrity of electronic data, password storage. Numerous researchers have proposed hashing algorithms. They have a very high time complexity based on several steps, initial value, and key constants which are publically known. We are focusing here on the many exiting algorithms that are dependent on the initial value and key constant usage to increasing the security strength of the hash function which is publically known. Therefore, we are proposing autonomous initial value proposed secure hash algorithm (AIVPSHA64) in this research paper to produce sixty-four-bit secure hash code without the need of initial value and key constant, it is very useful for a smart card to verify their identity which has limited memory space. Then evaluate the performance of hash function using autonomous initial value proposed secure hash algorithm (AIVPSHA64) and will compare the result, which is found by python-3.9. 0 programming language.
Exploring Tradeoffs between Energy Consumption and Network Performance in Cellular-IoT: a Survey

Recent growth in the Internet of Things (IoT) has been remarkable. Among the solutions to accommodate such a growth is Cellular IoT (C-IoT), comprising a group of technologies extended from legacy cellular infrastructures. One of the key goals of C-IoT technologies is to extend the battery life of UEs (User Equipment) in the network. However, this often comes at the cost of degrading network performance. This work attempts to identify, categorize, and analyze the available literature on this problem. The literature is broadly categorized into three sections: scheduling, data processing, and sleep modes. In each of these sections, the literature is further sub categorized. Finally, a direction for future research is identified and discussed.
Deep Learning-based QoS Prediction with Innate Knowledge of the Radio Access Network

To enable safe and advanced cooperative driving, quality of service (QoS) prediction in the radio access network has triggered recent attention to gracefully adapt vehicle-to-everything (V2X) applications to conform to expected network performance prior to the actual change in QoS. However, the communication and computation overhead combined with additional factors such as privacy may affect training data availability. Innate knowledge of wireless communication in the QoS prediction model could help improve prediction performance in environments with reduced training data availability. This paper presents a heuristic approach to explicitly incorporate prior knowledge to a deep learning-based QoS prediction model in the form of wireless communication-based penalties in the cost function of a deep neural network. System level simulations for the teleoperated driving (TOD) use case are used to evaluate our proposal. Results show that incorporating cell-load, channel and uplink inter-cell interference penalties in a deep neural network (DNN) improve uplink data rate prediction performance in scenarios with reduced amount of training data compared to an off-the-shelf DNN.
National electronic primary health care database in monitoring performance of primary care in Kyrgyzstan

Abstract Aim: The aim of this study was to assess the feasibility of the national electronic primary health care (PHC) database in Kyrgyzstan in producing information on the disease burden of the patient population and on the processes and quality of care of noncommunicable diseases (NCDs) in PHC. Background: Strengthening of the PHC is essential for low- and middle-income countries (LMICs) to tackle the increasing burden of NCDs. Capacity building and quality improvement require timely data on processes and quality of care. Methods: A data extraction was carried out covering four PHC clinics in Bishkek in 2019 to pilot the use of the national data for quality assessment purposes. The data included patient-level information on all appointments in the clinics during the year 2018 and consisted of data of altogether 48 564 patients. Evaluation indicators of the WHO Package of Essential NCD Interventions framework were used to assess the process and outcome indicators of patients with hypertension or diabetes. Findings: The extracted data enabled the identification of different patient populations and analyses of various process and outcome indicators. The legibility of data was good and the structured database enabled easy data extraction and variable formation on patient level. As an example of process and outcome indicators of those with hypertension, the blood pressure was measured at least on two occasions of 90% of women and 89% of men, and blood pressure control was achieved among 61% of women and 53% of men with hypertension. This study showed that a rather basic system gathering nationally patient-level data to an electronic database can serve as an excellent information source for national authorities. Investments should be made to develop electronic health records and national databases also in LMICs.
Hierarchical MPC applied to bacterial resource allocation and metabolite synthesis

Microorganisms have evolved submitted to a continuous optimisation process that has improved their capacity to proliferate in nature, developing highly optimized distribution mechanisms of their resources. Considering the microbial self-replication process as a resource allocation problem is a novel approach that has motivated numerous applications to the artificial production of metabolites of interest. Model-based optimal control studies are essential in understanding these naturally-evolved allocation strategies, but they are usually represented by open-loop control laws. In this context, we introduce a hierarchical shrinking-horizon non-linear MPC scheme that aims to maximise the production of a metabolite of interest. The control loop acts on an external signal that is able to disrupt the natural allocation process. The approach uses an optimal control-based input parametrisation that takes into account the structure of the open-loop natural allocation strategy of the cell, to emulate a closed-loop control law. We provide examples of the open-loop control strategies, and simulations of the hierarchical scheme.
An Online Multi-Robot SLAM System Based on Lidar/UWB Fusion

Cooperation of multiple robots in simultaneous localization and mapping (SLAM) systems has more advantages compared to single robot configurations such as a faster exploration speed of the environment and the ability to perform tasks with high complexity. In this paper, we present an online multi-robot SLAM system which merges range measurements provided by UWB sensors and Lidar data provided by different mobile robots to build a globally-consistent map that contains individual point cloud maps and the trajectory estimations of all the robots. The proposed system does not require overlaps between robot trajectories. However, when the maps of different robots overlap, the system can further refine the relative robot transformations. The performance of the proposed system is evaluated through two experiments. The results of the experiments show that the proposed system can achieve accurate environment mapping and self-positioning by the cooperation of multiple robots in real-time.
Maximin Efficient Sensor Location for Parameter Estimation of Spatiotemporal Systems

Optimal sensor location for parameter estimation of spatiotemporal systems is usually focused on maximizing an optimality criterion defined on the Fisher information matrix (FIM) associated with the estimated parameters. But different optimality criteria may yield different optimal locations. Therefore, strong interest is generated by compromise locations which would produce decent values for a broadest possible class of design criteria. Here a method is proposed to compute sensor locations which maximize the minimal efficiency with respect to the class of orthogonally invariant information criteria. This class is broad enough to include all optimum design criteria encountered in practice. It turns out that the minimal efficiency with respect to this class equals that with respect to a finite set of criteria generalizing the well-known E-optimum design criterion. The proposed method heavily exploits this property and the attendant semi-infinite programming problem formulation. It alternates between finding the eigenvalues and eigenvectors of the current FIM and solving a linear-programming problem. Thus the nondifferentiability inherent in the original problem is circumvented and the resulting fast algorithm is guaranteed to converge in a finite number of steps.
Modulation of stochastic gene expression by nuclear export processes

Inside mammalian cells, single genes are known to be transcribed in stochastic bursts leading to the synthesis of nuclear RNAs that are subsequently exported to the cytoplasm to create mRNAs. We systematically characterize the role of export processes in shaping the extent of random fluctuations (i.e. noise) in the mRNA level of a given gene. Using the method of Partitioning of Poisson arrivals, we derive an exact analytical expression for the noise in mRNA level assuming that the nuclear retention time of each RNA is an independent and identically distributed random variable following an arbitrary distribution. These results confirm recent experimental/theoretical findings that decreasing the nuclear export rate buffers the noise in mRNA level, and counterintuitively, decreasing the noise in the nuclear retention time enhances the noise in the mRNA level. Next, we further generalize the model to consider a dynamic extrinsic disturbance that affects the nuclear-to-cytoplasm export. Our results show that noise in the mRNA level varies non-monotonically with the disturbance timescale. More specifically, high- and low-frequency external disturbances have little impact on the mRNA noise level, while noise is amplified at intermediate frequencies. In summary, our results systematically uncover how the coupling of bursty transcription with nuclear export can both attenuate or amplify noise in mRNA levels depending on the nuclear retention time distribution and the presence of extrinsic fluctuations.
Maximizing the Connectivity of Wireless Network Slicing Enabled Industrial Internet-of-Things

The emergence of 5G brings unprecedented possibilities for deploying the anticipated Industrial Internet of Things (IIoT). To achieve high density connectivity with multiple services in 5G empowered IIoT, we consider the non-orthogonal network slicing in this work. In particular, we jointly utilize network slicing to incorporate two different types of services and exploit non-orthogonal multiple access (NOMA) to maximize the number of total devices that can be accessed to the system. We formulate the connectivity maximization problem as a mixed-integer nonlinear programming (MINLP) by jointly optimizing the transmit power and device-subcarrier association. To tackle the intractable MINLP, we first transform it into a mixed-integer linear programming (MILP) and then reduce the MILP by devising a simple but effective transmit power allocation scheme. Thereafter, we propose a low-complexity best-effort pairing (BEP) algorithm to solve the reduced MILP. By comprehensive simulations, we find that our proposed BEP significantly outperforms the benchmark schemes.
Leader-following Event-triggered Practical Consensus with Disturbance Rejection of Multiple Uncertain Spacecraft Systems over Switching Networks

This paper studies the leader-following practical consensus with disturbance rejection problem of a group of rigid spacecraft systems with uncertain inertia matrices, which extends our previous work by further considering disturbance rejection. Like our previous results, we use the adaptive distributed observer technique to handle switching communication constraints. To handle the disturbance, we further adopt the internal model approach to convert our problem into a practical stabilization problem of an augmented system. Specific effort is made on designing an event-triggered control law with an event-triggered mechanism that avoids the Zeno phenomenon.
Fixed-Time Seeking and Tracking of Time-Varying Extrema

Motivated by recent (semi-global practical) fixed-time convergence results in time-invariant model-free optimization problems, in this paper we introduce new tracking bounds and guidelines for the design of extremum seeking controllers in model-free optimization problems with dynamic cost functions. Using semi-global practical input-to-state stability characterizations, we show that the proposed non-smooth ES dynamics are able to significantly reduce the tracking error compared to the traditional smooth algorithms studied in the literature. Moreover, under a suitable tuning of the gains of the algorithm, the nominal average dynamics of the controller are able to achieve global fixed-time tracking for a general class of dynamic cost functions. For tuning parameters that do not completely eliminate the tracking error in the nominal average dynamics, but which preserve the continuity of the vector field, we show that "almost complete" error rejection is achieved whenever the gain of the algorithm exceeds a particular threshold. Numerical results are presented to illustrate the performance of the algorithms.
Sequential Convex Programming Revisited

This paper revisits the well-known family of sequential convex programming methods. We adopt the difference of convex programming technique to relax a wide variety of nonconvex optimization problems into convex programs. We extend this approach to a sequential convex programming algorithm that can generate a convergent sequence of feasible points whose objective values monotonically improve. As an improvement upon the existing sequential methods, we prove that under certain assumptions, the proposed algorithm reaches feasibility within a finite number of rounds, as opposed to asymptotic feasibility. The effectiveness of the proposed approach is corroborated through experiments on the problem of robust linear regression.
Linear Quadratic Mean Field Stackelberg Games: Master Equations and Time Consistent Feedback Strategies

We consider linear quadratic (LQ) Stackelberg games with a major player (leader) and N minor players (followers) and derive two master equations in a mean field limit model. We show the resulting decentralized strategies are time consistent by adapting a procedure introduced by Ekeland and Lazrak (2006).
Cathodic protection of pipelines from stray currents

DC stray current can cause severe corrosion on buried pipelines. For this reason a reliable protection of pipelines from stray currents should be provided. In the present paper a scheme of the cathodic protection is considered. Different types of corrosion protection (drainage and cathodic) are mentioned and presented as a block diagram giving the relationship between them. The two types of automatic control (analog and digitally) are also noted there. First one (electromagnetic and electromechanical) can be realized in two ways – either by relay or by transformer. The paper finish with conclusion remarks about the importance of the considered cathodic protection.
Signaling Overhead-Constrained Throughput Optimization for 5G Packet-Based Random Access with mMTC

To reduce the signaling overhead for sporadic small packet transmission in massive Machine Type Communications (mMTC), Packet-Based Random Access (PBRA) scheme is introduced in 5G system, where devices can transmit data packets in the random access procedure without connection establishment. Yet, even with PBRA, the signaling overhead may surge if the system parameters are configured improperly. This paper aims to address this issue by studying how to tune the Access Class Barring (ACB) factor to maximize the throughput while maintaining the signaling-to-throughput ratio below a certain level. Explicit expressions of maximum throughput and the corresponding optimal ACB factor in saturated and unsaturated cases are derived. It reveals that with a demanding requirement on signaling-to-throughput ratio, the throughput performance has to be sacrificed even with optimal tuning of ACB factor. To boost the throughput performance, the system should either loose the signaling constraint or enlarge the packet length. The analysis is verified by simulations and sheds important light on practical 5G network design for supporting mMTC with PBRA.
Error Bounds for Locally Optimal Distributed Filters with Random Communication Graphs

We consider the problem of analyzing the performance of distributed filters for continuous-time linear stochastic systems under certain information constraints. We associate an undirected and connected graph with the measurements of the system, where the nodes have access to partial measurements in continuous time. Each node executes a locally optimally filter based on the available measurements. In addition, a node communicates its estimate to a neighbor at some randomly drawn discrete time instants, and these activation times of the graph edges are governed by independent Poisson counters. When a node gets some information from its neighbor, it resets its state using a convex combination of the available information. Consequently, each node implements a filtering algorithm in the form of a stochastic hybrid system. We derive bounds on expected value of error covariance for each node, and show that they converge to a common value for each node if the mean sampling rates for communication between nodes are large enough.