In this paper, we compare the pandemic management performance of 22 countries that belong to the middle-high income class based on criteria including the pandemic data, population characteristics, ...and health system capacity. The management of the COVID-19 pandemic requires considering many and often conflicting aspects at the same time which necessitates an MCDM approach. We use a standard deviation (SDV) based range of value (ROV) method which coincides with the black-box nature of the disease. The weights obtained from the SDV method reveal that the number of COVID-19 deaths, current health expenditure, and deaths due to cardiovascular diseases are the most important criteria. The ROV method indicates that most Asian countries are ranked in higher positions due to their strong healthcare systems and quick implementation of social distancing rules. The lowest performances belong to Bulgaria, Montenegro, and Bosnia and Herzegovina. They have experienced an elevated number of deaths due to having an elderly population and inefficient usage of healthcare resources. We also show that extreme poverty is an important determinant of country performance. In countries where poverty is higher, as the case with Indonesia, implementing the social distancing rules becomes almost impossible which affects the overall country performance significantly.
•Influence of environmental benefits (EB) of self-driving vehicles (SDVs) was examined.•Positive EB information increased risk acceptance of and willingness to ride (WTR) in SDVs.•Trust mediated the ...relation between positive EB information and risk acceptance and WTR.•Positive EB information increased the socially acceptable risks of SDVs in magnitude.•People may be willing to accept more risks for obtaining the EBs of SDVs.
Mass adoption of self-driving vehicles (SDVs) is predicted to have a profound effect on the environment. Here, we present three studies (N = 1258) that examine the impact of the environmental benefits (EB) of SDVs on individuals’ acceptance of their risks, and their willingness to ride (WTR) in them. Two types of SDVs were presented: SDVs with a clear mention of positive EB information (“EB-enhanced SDVs”) and SDVs without the mention of positive EB information (“normal SDVs”). Study 1 and Study 2 found that participants expressed higher risk acceptance and WTR regarding EB-enhanced SDVs. Further, Study 2 reported that higher trust in EB-enhanced SDVs, rather than lower negative affect associated with EB-enhanced SDVs, accounted for the participants’ higher risk acceptance and WTR. Study 3 observed that the participants’ acceptable risk of EB-enhanced SDVs was greater than that of normal SDVs in magnitude, although not significant. If SDVs can achieve the purported EB, the public may be willing to tolerate their risks more. Highlighting the environmental advantages of SDVs and increasing public trust in them are likely to be useful strategies for increasing societal acceptance of SDVs.
New challenges such as automation, connection, electrification, and sharing (ACES) have brought disruptive changes to vehicles, transportation, and mobility services, which urgently requires an ideal ...solution for sustainable transportation. This paper introduces the Internet as a paradigm and, for the first time, proposes the Transportation Internet (TI), inspired by the similarity between the Internet and transportation. Referring to the construction ideas of the Internet, this paper establishes the framework of TI, proposes the transportation router based on the transportation switching and routing models, and preliminarily forms a large-scale automatic transportation solution. Following the latest technologies of the Internet, this paper further presents the software-defined transportation (SDT) by separating the control plane and transport plane of the transportation router, which can enhance transportation routing and provide Internet-like capabilities such as centralized intelligent control, terminals plug-and-play, and open application ecology. The evaluation of the prototype system shows promising results. The software-defined signals (SDS) can save 36% energy compared to signal machines, and the software-defined vehicles (SDV) automatic driving can save 24% energy compared to manual driving. Overall, TI brings innovations to sustainable transportation, and provides a framework for a new generation of Intelligent Transportation Systems (ITS).
In this article, design of a high-order-mode (TE 20 -mode) staggered double vane (SDV) traveling wave tube amplifier (TWTA) with two pencil electron beams is presented. Since, the dimensions are ...larger when compared to fundamental mode TWTAs, the high-power capacity can be achieved. Meanwhile, several ways to suppress the fundamental mode competition and improve the stability of our high-order-mode TWTA are studied thoroughly, such as limiting the gain of each stage and using metal or wave-absorbing material columns. A three-stage TE 20 -mode SDV TWTA with aluminum nitride column at the center of vanes is simulated to get an over 145-W peak power with a corresponding gain of 32.5 dB. The frequency spectrum is pure at 220 GHz and the output signal is a stable TE 20 -mode signal. The bandwidth reaches about 38 GHz. The results show that high-order-mode operation can develop the high-power capacity vacuum electron devices at terahertz band.
This article presents the design and analysis of a staggered double-vane (SDV) slow-wave structure (SWS) for <inline-formula> <tex-math notation="LaTeX">W </tex-math></inline-formula>-band amplifier, ...with 20-dB gain and a very high bandwidth (~25%). The use of dual Bragg reflector at either end of the interaction structure increases the impedance matching and the radio frequency (RF) coupling efficiency at the input and output ports, thereby reducing the RF leakage at the electron gun and collector ends, from 15% to 25% to less than 0.6%. The attenuator section is simple to fabricate and optimally designed in order to provide an effective isolation (>20 dB) between the input RF signal in the input section and the RF signal reflected from the output section. The dispersion analysis, the transmission analysis of each section, and the beam-wave interactions were simulated using the Dassault system's computer simulation technology (CST) eigenmode solver, time-domain solver, and the particle-in-cell (PIC) solver, respectively. The proposed design of the SDV SWS conclusively provides an enormous bandwidth of ~25 GHz with 20-dB gain for <inline-formula> <tex-math notation="LaTeX">W </tex-math></inline-formula>-band amplifier, when compared to its solid-state counterparts and earlier reported work as per the author's knowledge.
Dendrometers recording stem diameter variations (SDV) at high-resolution are useful to assess trees' water relation since water reserves are stored in the elastic tissue of the bark. These tissues ...typically shrink during the day as they release water when evaporative demand is high and swell during the night as they are replenished when evaporative demand is low, generating the typical SDV profile known as the diel SDV cycle. However, similar SDV cycles have been observed on dead trees due to the hygroscopic shrinking and swelling of the dead bark tissues. In order to remove this hygroscopic effect of the bark, dendrometers are applied as close as possible to the living bark tissues by removing the outer dead layer, however with questionable success. In this study, we used SDV time series from 40 point dendrometers applied on dead-bark-removed mature trees to assess and quantify the remaining hygroscopic effect on individual trees. To do so, we checked SDV behavior in the cold season and explored the relation between the diel SDV cycle and changes in relative humidity (RH). Our results showed that (a) the hygroscopic effect in SDV can be well-detected based on the amplitude of the diel SDV cycle (diel SDV
ampl
) and the correlation between SDV and RH during both the cold and the warm season; (b) the level of the hygroscopic effect varies strongly among individuals; (c) diel SDV
ampl
is proportional to both changes in RH and transpiration so that the hygroscopic effect on the diel SDV cycle can be quantified using a linear model where (diel SDV
ampl
) is a function of RH changes and transpiration. These results allow the use of the model to correct the amplitude of the diel SDV cycles and suggest that this method can be applied to other ecological relevant water-related SDV variables such as tree water deficit.
The emergence of software-defined vehicles (SDVs), combined with autonomous driving technologies, has enabled a new era of vehicle computing (VC), where vehicles serve as a mobile computing platform. ...However, the interdisciplinary complexities of automotive systems and diverse technological requirements make developing applications for autonomous vehicles challenging. To simplify the development of applications running on SDVs, we propose a comprehensive suite of vehicle programming interfaces (VPIs). In this study, we rigorously explore the nuanced requirements for application development within the realm of VC, centering our analysis on the architectural intricacies of the Open Vehicular Data Analytics Platform (OpenVDAP). We then detail our creation of a comprehensive suite of standardized VPIs, spanning five critical categories: Hardware, Data, Computation, Service, and Management, to address these evolving programming requirements. To validate the design of VPIs, we conduct experiments using the indoor autonomous vehicle, Zebra, and develop the OpenVDAP prototype system. By comparing it with the industry-influential AUTOSAR interface, our VPIs demonstrate significant enhancements in programming efficiency, marking an important advancement in the field of SDV application development. We also show a case study and evaluate its performance. Our work highlights that VPIs significantly enhance the efficiency of developing applications on VC. They meet both current and future technological demands and propel the software-defined automotive industry toward a more interconnected and intelligent future.
Self-Driving Vehicles (SDVs) are increasingly popular, with companies like Google, Uber, and Tesla investing significantly in self-driving technology. These vehicles could transform commuting, ...offering safer, and efficient transport. A key SDV aspect is motion planning, generating secure, and efficient routes. This ensures safe navigation and prevents collisions with obstacles, pedestrians, and other vehicles. Deep Learning (DL) could aid SDV motion planning. AI tools and algorithms, like Artificial Neural Networks (ANNs), Machine Learning (ML) and DL can learn from data to create effective driving strategies, enhancing SDV adaptability to changing conditions for improved safety and efficiency. This survey gives a DL-based motion planning overview for SDVs, covering behaviour planning, trajectory planning, and End to End Learning (E2EL). It assesses various DL-based behaviour and trajectory planning methods, comparing and summarizing them. It also reviews diverse E2EL techniques including Imitation Learning (IL) and Reinforcement Learning (RL) gaining traction lately. Additionally, this review emphasizes the significance of two crucial enablers: datasets and simulation deployment frameworks for SDVs. The survey compares strategies using multiple metrics and highlights DL-based SDV implementation challenges, including simulation and real-world use cases. This article also suggests future research directions to address E2EL and DL-based motion planning limitations. The presented article is an excellent reference for scholars, engineers, and decision-makers who have an interest in DL-based SDV motion planning.
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