Due to the increasing popularity of Electric Vehicles (EVs) and infrastructural limitations, it is vital to manage traffic and Charging Stations (CSs) crowdedness. In Bakhshayesh and Kebriaei (2022), the problem of choosing the route and CSs of EVs is modeled as a non-cooperative game of selfish EVs with probabilistic decision strategies. In this paper, we have proposed a linear pricing policy that ensures global efficiency of the obtained Nash strategies of EVs in Bakhshayesh and Kebriaei (2022) for the Smart City Coordinator (SCC). We model the problem as a hierarchical game with a SCC as the leader and EVs as the followers. The leader aims to design optimal price functions of CSs and Traffic Coordinator (TC) and impose them on the EVs to maximize the social profits of CSs and TC. In response, the followers play a non-cooperative game with coupling constraints to optimally decide on their route and charging destination. Thus, we have a Stackelberg Game (SG) between SCC and EVs and also a Nash game among the EVs in the lower level. Compared to the conventional Nash-based pricing policies, our proposed functional SG formulation enables the possibility of simultaneous and global-optimum management of traffic and CSs’ crowdedness. Moreover, we have proposed a two-level decentralized algorithm that preserves the privacy of EVs and have considered a decentralized computation for equilibrium seeking of followers based on the Alternating Direction Method of Multipliers (ADMM) method. Finally, we carry out simulation studies on the transportation network of Sioux Falls City to compare and evaluate the proposed method.
Previous studies have investigated the effect of transcranial direct current stimulation (tDCS) on cognitive functions. However, these studies reported inconsistent results due to differences in experiment design, measurements, and stimulation parameters. Nonetheless, there is a lack of meta-analyses and review studies on tDCS and its impact on cognitive functions, including working memory, inhibition, flexibility, and theory of mind. We performed a systematic review and meta-analysis of tDCS studies published from the earliest available data up to October 2021, including studies reporting the effects of tDCS on cognitive functions in human populations. Therefore, these systematic review and meta-analysis aim to comprehensively analyze the effects of anodal and cathodal tDCS on cognitive functions by investigating 69 articles with a total of 5545 participants. Our study reveals significant anodal tDCS effects on various cognitive functions. Specifically, we observed improvements in working memory reaction time (RT), inhibition RT, flexibility RT, theory of mind RT, working memory accuracy, theory of mind accuracy and flexibility accuracy. Furthermore, our findings demonstrate noteworthy cathodal tDCS effects, enhancing working memory accuracy, inhibition accuracy, flexibility RT, flexibility accuracy, theory of mind RT, and theory of mind accuracy. Notably, regarding the influence of stimulation parameters of tDCS on cognitive functions, the results indicated significant differences across various aspects, including the timing of stimulation (online vs. offline studies), population type (clinical vs. healthy studies), stimulation duration (< 15 min vs. > 15 min), electrical current intensities (1–1.5 m.A vs. > 1.5 m.A), stimulation sites (right frontal vs. left frontal studies), age groups (young vs. older studies), and different cognitive tasks in each cognitive functioning aspect. In conclusion, our results demonstrate that tDCS can effectively enhance cognitive task performance, offering valuable insights into the potential benefits of this method for cognitive improvement.
Seismic design codes strive to strike a balance between construction cost and seismic losses. To this end, current codes calibrate design provisions to the accepted practice. However, the literature demonstrates that calibration by minimizing the expected lifecycle cost (LCC) is the rational approach to determine the optimal design. This paper proposes a probabilistic framework for calibrating design provisions that minimizes not only the expected LCC, but also its measure of uncertainty. The framework is then applied to determine the optimal design base shear coefficient of building structures. The proposed methodology is built upon the premise that the expected LCC varies negligibly with the design base shear in a notable range around its minimum. The proposed approach determines another design base shear in this range that leads to the minimum variance of LCC. The result is an optimal and robust design base shear that leads to the minimum expected LCC within an infinitesimal tolerance and at the same time, has a substantially smaller variance. This approach not only observes the minimum expected cost theory, but also makes large realizations of cost markedly less likely and accounts for a level of risk aversion. The proposed framework is showcased by a comprehensive application to reinforced concrete moment frame buildings at 5706 sites across 14 countries in Western Asia. The resulting design base shears increase by an average of 50% compared to those of the conventional minimum expected LCC approach while the expected LCC remains unchanged and its variance is notably reduced, thus preventing severe casualties. The resulting design base shears also lead to an average reduction of 28% in expected losses and 57% in the variance of losses compared to those obtained from ASCE 7 for this region.
The demand for continuous monitoring of ultraviolet (UV) radiation, which poses significant health risks, has grown significantly with the advent of the internet of things (IoT) for human health. The need for a self-powered system that does not rely on battery charging in environmental conditions has led to the exploration of triboelectric nanogenerators (TENGs) as a promising energy source for sensor systems. In this study, we present a fully printed UV photodetector (UV-PD) that is fabricated through scalable slot-die printing of either single-layer triple-cation mixed halide perovskite (TCMHP) or a heterojunction of TiO2/TCMHP on patterned fluorine-doped tin oxide (FTO). The integrated TENG generates the required energy from the tapping of Kapton to the FTO contact, making the device self-powered. Our self-powered PD exhibits an excellent responsivity and detectivity of 71.4 mA W⁻¹ and 6.92 × 10¹⁰ Jones, respectively, under a 395 nm wavelength, significantly outperforming spin-coated TCMHP-based devices. We further optimized the performance of our integrated TENG-powered heterojunction TiO2/TCMHP UV-PD by fabricating sensors with groove spacings of 2, 3, 5, and 8 mm. The optimized device demonstrated an unprecedented responsivity, detectivity, and EQE% of 151.9 mA W⁻¹, 1.29 × 10¹¹ Jones, and 47.8%, respectively, under UV irradiation. Our work represents a significant step towards large-scale industrial flexible self-powered UV detection devices that can protect human health and safety. This study highlights the potential of scalable and cost-effective slot-die printing techniques for the industrial production of high-performance self-powered UV sensors, with significant implications for IoT-based health monitoring and environmental protection applications.
Graphene-based nanomaterials have found extensive applications across numerous fields owing to their unique properties; one fascinating example of these applications is as a contrast agent (CA) in biomedical imaging. Biomedical imaging is now an integral part of human life. MRI, CT, PET, SPECT, and ultrasound imaging are the most essential and important medical imaging technologies that in any part of the world —at least one, maybe more, or even a combination of them— are used for diagnostic purposes. Due to the ever-increasing development and maturation of these technologies, the effort to obtain CAs to further enhance the resolution of images also is increasing rapidly. Recently, there has been a lot of focus on graphene-based CAs because of the great platform provided by graphene derivatives such as graphene itself, graphene oxide, reduced graphene oxide, graphene quantum dots, and carbon nanotubes. In this review, first, a concise overview of these derivatives within the graphene family has been provided. Then, various innovations and combinations of nanomaterials based on graphene, which have been synthesized to date for use in biomedical imaging as CAs, have been explored and listed for each mentioned imaging technology. In addition, some aspects of their biocompatibility and toxicity are highlighted along with them. Finally, the last section enumerates multimodal CAs based on graphene, which will allow medical practitioners to harness the benefits of integrating many imaging techniques.
This study investigates nanocarriers (NCs) for drug delivery targeting carotid artery atherosclerosis. This targeted drug delivery mechanism is based on ligand–receptor bindings facilitated by coating NCs with P-selectin aptamers, which exhibit high affinities for P-selectin plaque receptors. Recognizing the significant advantages of metal–organic frameworks (MOFs), such as their high drug-loading percentages, we chose them as nanocarriers for this research. Our evaluation considers critical factors: NC surface density (the number of attached nanocarriers per unit of plaque area), toxicity (percentage of NCs missing the target), and efficient drug transfer to plaque tissue. Employing molecular dynamics (MD) for drug loading calculations via van der Waals interactions and computational fluid dynamics (CFD) for toxicity, surface density, and drug transfer assessments, we achieve a comprehensive analysis. A cardiac cycle-based metric guides optimal MOF release conditions, establishing an ideal dosage of 600 NCs per cycle. MOF-801 exhibits outstanding drug delivery performance, particularly in plaque targeting. While a magnetic field enhances NC adhesion, its impact on drug transfer is limited, emphasizing the need for further optimization in magnetic targeting for NC-based therapies. This study provides crucial insights into NC drug delivery performance in carotid artery atherosclerosis, advancing the field of targeted drug delivery for atherosclerosis treatment.
We propose GraphOS, a system that allows a client that owns a graph database to outsource it to an untrusted server for storage and querying. It relies on doubly-oblivious primitives and trusted hardware to achieve a very strong privacy and efficiency notion which we call oblivious graph processing : the server learns nothing besides the number of graph vertexes and edges, and for each query its type and response size. At a technical level, GraphOS stores the graph on a doubly-oblivious data structure , so that all vertex/edge accesses are indistinguishable. For this purpose, we propose Omix++, a novel doubly-oblivious map that outperforms the previous state of the art by up to 34×, and may be of independent interest. Moreover, to avoid any leakage from CPU instruction-fetching during query evaluation, we propose algorithms for four fundamental graph queries (BFS/DFS traversal, minimum spanning tree, and single-source shortest paths) that have a fixed execution trace , i.e., the sequence of executed operations is independent of the input. By combining these techniques, we eliminate all information that a hardware adversary observing the memory access pattern within the protected enclave can infer. We benchmarked GraphOS against the best existing solution, based on oblivious relational DBMS (translating graph queries to relational operators). GraphOS is not only significantly more performant (by up to two orders of magnitude for our tested graphs) but it eliminates leakage related to the graph topology that is practically inherent when a relational DBMS is used unless all operations are "padded" to the worst case.
The main objective of this research is to explore the impact of deploying social robots in team-based intellectual cooperation, specifically during brainstorming sessions. A total of 72 participants (36 females and 36 males) were involved, with groups of four participants (2 females and 2 males) engaging in brainstorming sessions. In nine sessions, three Nao robots were present; while in the other nine sessions, three human fellows participated instead of robots. The creativity of participants was assessed by measuring the average number of unique ideas generated using Bouchard and Hare’s coding rules. The sessions with robots showed a significant increase in participant creativity. After the sessions, the participants completed a questionnaire, which revealed higher satisfaction, reduced production blocking, decreased free-riding, and increased synergy in sessions where robots were present. These findings were further supported by the video analysis. Future research can explore the long-term effects of interacting with social robots, including those equipped with artificial intelligence.
Dyslexia is a neurodevelopmental disorder that has the highest prevalence among different types of learning disorders. Dyslexic children usually have difficulty in reading and as a result, they face different educational problems at school. Currently, social robots are widely used as educational assistants and tutors, mainly for children with special needs. The Taban robot is a modern educational social robot, which was developed specifically for dyslexic children. In this paper, an android application was designed and developed to facilitate child-robot interaction just by means of a tablet. Using this smart tablet game, the children could collaborate with the Taban social robot in solving the pedagogic problems and practicing educational concepts, while the robot provides them not only beneficial verbal and physical reactions by its hands, but also effective visual feedback on its touch screen. For the first step in this research, the acceptability of the designed tablet game was investigated for twenty-one participants that fifth of them had dyslexia. The hopeful results of the SAM questionnaires filled out by the children, demonstrate high acceptability of the tablet game. Furthermore, by implementing automatic assessment based on the designed standard criteria, the platform could meaningfully distinguish the two groups of children (dyslexic and typically developed (TD)) according to their achieved scores in the game. Thus, the high potential value of the designed robot-aided tablet game was illustrated to be used as an assistive tool for dyslexic children.
Emerging technologies such as social robotics and virtual reality have found a wide application in the field of education and tutoring particularly for children with special needs. Taban is a novel social robot that has been designed and programmed specifically for educational interaction with dyslexic children, who have various problems in reading despite their normal intelligence. In this paper, the acceptability and eligibility of a virtual reality serious game with the presence of the Taban social robot avatar was studied among nineteen children six of whom were dyslexic. In this game, children perform attractive practical exercises while interacting with the Taban avatar in a virtual environment to strengthen their reading skills; then the game automatically evaluates their performance and the avatar gives them appropriate feedback. The sense of immersion in the 3D virtual space and the presence of the Taban robot avatar motivates the children to do the assignments. The results of the psychological assessment using the SAM questionnaire are promising and illustrate that the game was highly accepted by both groups of children. Moreover, according to statistical analysis, the performance of children with dyslexia in the exercises was significantly weaker than their typically developing peers. Thus, this V2R lexicon game has the potential for screening dyslexia.
In this paper, we provide three applications for f-divergences: (i) we introduce Sanov’s upper bound on the tail probability of the sum of independent random variables based on super-modular f-divergence and show that our generalized Sanov’s bound strictly improves over ordinary one, (ii) we consider the lossy compression problem which studies the set of achievable rates for a given distortion and code length. We extend the rate-distortion function using mutual f-information and provide new and strictly better bounds on achievable rates in the finite blocklength regime using super-modular f-divergences, and (iii) we provide a connection between the generalization error of algorithms with bounded input/output mutual f-information and a generalized rate-distortion problem. This connection allows us to bound the generalization error of learning algorithms using lower bounds on the f-rate-distortion function. Our bound is based on a new lower bound on the rate-distortion function that (for some examples) strictly improves over previously best-known bounds.
Phase-locked loops (PLLs) are nonlinear automatic control circuits widely used in telecommunications, computer architecture, gyroscopes, and other applications. One of the key problems of nonlinear analysis of PLL systems has been stated by Floyd M. Gardner as being “to define exactly any unique lock-in frequency.” The lock-in range concept describes the ability of PLLs to reacquire a locked state without cycle slipping and its calculation requires nonlinear analysis. The present work analyzes a second-order type 2 phase-locked loop with a sinusoidal phase detector characteristic. Using the qualitative theory of dynamical systems and classical methods of control theory, we provide stability analysis and suggest analytical lower and upper estimates of the lock-in range based on the exact lock-in range formula for a second-order type 2 PLL with a triangular phase detector characteristic, obtained earlier. Applying phase plane analysis, an asymptotic formula for the lock-in range which refines the existing formula is obtained. The analytical formulas are compared with computer simulation and engineering estimates of the lock-in range. The comparison shows that engineering estimates can lead to cycle slipping in the corresponding PLL model and cannot provide a reliable solution for the Gardner problem, whereas the lower estimate presented in this article guarantees frequency reacquisition without cycle slipping for all parameters, which provides a solution to the Gardner problem.
Motivated by the applicability of homomorphically encrypted discrete-time controllers possessing integer state matrices to work in an infinite time horizon, this paper deals with design of such controllers to guarantee that the step response of the control system is monotonic. On the basis of locating the poles of designed controllers at positive Pisot numbers and some of their algebraic conjugates and benefiting from the idea of using non-minimal state space models, an analytical approach is proposed to obtain discrete-time controllers with integer state matrices which result in extrema-free closed-loop step responses in control of a class of discrete-time plants whose zeros are between zero and one.
The ever-increasing rate of static power consumption in nanoscale technologies, and consequently, the breakdown of Dennard scaling acts as a power wall for further device scaling. With intensified power density, designers are forced to selectively power off portions of chip area, known as dark silicon. With significant power consumption of routing resources in the field-programmable gate array (FPGA) and their low utilization rate, power gating of unused routing resources can be used to reduce the overall device power consumption. While power gating has taken great attention, previous studies neglect major factors that affect the effectiveness of power gating, for example, routing architecture, topology, and technology. In this article, we propose a power-efficient routing architecture (PERA) for SRAM-based FPGAs, which is designed pursuant to the utilization pattern of routing resources with different topologies. PERA is applicable to different granularity from a multiplexer to a switch-matrix (SM) level. We examine the efficiency of the proposed architecture with different topologies, structures, and parameters of routing resources. We further propose a routing algorithm to reduce the scattered use of resources and hence to take advantage of opportunities of power gating in routing resources. Our experiments using a versatile place and route (VPR) toolset on the FPGA architecture similar to commercial chips over an extensive set of circuits from Microelectronics Center of North Carolina (MCNC), International Workshop on Logic Synthesis (IWLS), Verilog to routing (VTR), and Titan benchmarks indicate that PERA reduces the static power consumption by 43.3%. This improvement is obtained at the expense of 7.4% area overhead. PERA along with the optimized routing algorithm offers a total routing leakage power reduction of up to 64.9% when compared to nonpower-gating architectures and 6.9% when compared with the conventional routing algorithm across all benchmark circuits and architectures with various wire segment lengths. This is while the optimized routing algorithm degrades performance by only less than 3%.
Payment channel networks (PCNs) are a promising technology to improve the scalability of cryptocurrencies. PCNs, however, face the challenge that the frequent usage of certain routes may deplete channels in one direction, and hence prevent further transactions. In order to reap the full potential of PCNs, recharging and rebalancing mechanisms are required to provision channels, as well as an admission control logic to decide which transactions to reject in case capacity is insufficient. This paper presents a formal model of this optimisation problem. In particular, we consider an online algorithms perspective, where transactions arrive over time in an unpredictable manner. Our main contributions are competitive online algorithms which come with provable guarantees over time. We empirically evaluate our algorithms on randomly generated transactions to compare the average performance of our algorithms to our theoretical bounds. We also show how this model and approach differs from related problems in classic communication networks.
A spinning disk reactor (SDR) was applied and tested successfully for precipitated calcium carbonate particles synthesis in liquid–liquid system which is poorly understood in literature. The proposed SDR reactor consists of a spinning disk rotating at 4000–16,000 rpm. The proposed SDR resulted in a high local supersaturation ratio due to the intense energy dissipation produced by a high-speed spinning disk. The higher rotational speed of SDR produces calcium carbonate nanoparticles with smaller mean particle sizes and higher aragonite content. At the rotating speed of 15,000 rpm, precipitated calcium carbonate nanoparticles with a size of around 975 nm were produced. In addition, aragonite content increased from 10 to 95 wt% by increasing disk speed from 4000 to 15,000 rpm.
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