Electrifying mobility-on-demand (MoD) fleets is an important step towards a more sustainable transportation system. With increasing fleet size, MoD operators will be able to participate in the energy exchange market and will have access to time-varying electricity prices. They can benefit from intelligent scheduling of charging processes considering forecasts of electricity prices and vehicle utilization. Considering a long time horizon of, e.g., a day improves scheduling decisions, but electricity prices change in a short interval of 15 minutes; hence, an optimization-based approach needs to overcome challenges regarding computational time. For this reason, we develop a macroscopic model to study the tradeoffs between electricity, battery wear and level-of-service costs. In scenarios with varying fleet size and different numbers of charging units, we compare the performance of several reactive and scheduling policies in a simulation framework based on a macroscopic model. Overall, the results of the study show that an MoD provider with 2000 vehicles could save several thousands of euros in daily operational costs by changing from a state of charge reactive charging strategy to one adapting to the price fluctuations of the electricity exchange market.

We propose a deep-learning based annotation efficient framework for vessel detection in ultra-widefield (UWF) fundus photography (FP) that does not require de novo labeled UWF FP vessel maps. Our approach utilizes concurrently captured UWF fluorescein angiography (FA) images, for which effective deep learning approaches have recently become available, and iterates between a multi-modal registration step and a weakly-supervised learning step. In the registration step, the UWF FA vessel maps detected with a pre-trained deep neural network (DNN) are registered with the UWF FP via parametric chamfer alignment. The warped vessel maps can be used as the tentative training data but inevitably contain incorrect (noisy) labels due to the differences between FA and FP modalities and the errors in the registration. In the learning step, a robust learning method is proposed to train DNNs with noisy labels. The detected FP vessel maps are used for the registration in the following iteration. The registration and the vessel detection benefit from each other and are progressively improved. Once trained, the UWF FP vessel detection DNN from the proposed approach allows FP vessel detection without requiring concurrently captured UWF FA images. We validate the proposed framework on a new UWF FP dataset, PRIMEFP20, and on existing narrow field FP datasets. Experimental evaluation, using both pixel wise metrics and the CAL metrics designed to provide better agreement with human assessment, shows that the proposed approach provides accurate vessel detection, without requiring manually labeled UWF FP training data.

Communication has never been more accessible than today. With the help of Instant messengers and Email Services, millions of people can transfer information with ease, and this trend has affected organizations as well. There are billions of organizational emails sent or received daily, and their main goal is to facilitate the daily operation of organizations. Behind this vast corpus of human-generated content, there is much implicit information that can be mined and used to improve or optimize the organizations' operations. Business processes are one of those implicit knowledge areas that can be discovered from Email logs of an Organization, as most of the communications are followed inside Emails. The purpose of this research is to propose an approach to discover the process models in the Email log. In this approach, we combine two tools, supervised machine learning and process mining. With the help of supervised machine learning, fastText classifier, we classify the body text of emails to the activity-related. Then the generated log will be mined with process mining techniques to find process models. We illustrate the approach with a case study company from the oil and gas sector.

The test results of the ITER toroidal field conductors demonstrated a decrease of the current sharing temperature (Tcs) with an increase in the number of electromagnetic cycles in general. This is associating with several factors. One of them is the superconducting Nb3Sn filaments cracking and another one is the redistribution of the relative deformation of the Nb3Sn strands under Lorentz forces. Despite these factors, some conductors have shown the absence or significantly less degradation of Tcs during electromagnetic cycling. This article considers another possible reason for a more stable conductors Tcs behavior, namely, the local compression of Nb3Sn wires in the cross section of a conductor. In this article presents the results of a quantitative analysis Nb3Sn superconducting filaments cracking of strands extracted from a conductor that has passed electromagnetic cycling and the model of a conductor compaction, as well as calculation results based on this model are presented also.

World Health Organization declared COVID-19 as a pandemic after the breakout in the city of Wuhan, China. The disease has negatively affected the global economy and daily life. Most of the countries around the world have imposed travel restrictions, locked down, and social distancing measures. In the current situation, Information and Communications Technology is playing a significant role in connecting people. Majority of the education organizations have adopted online platforms, students and staff are working from home. Besides, these businesses, e-healthcare systems, food deliveries, and online grocery shopping have witnessed a very high demand. Malicious attackers have considered COVID-19 as an opportunity to launch attacks for financial gains and to promote their evil intents. Healthcare systems are being attacked with ransomware and resources such as patient’s records confidentiality, and integrity is being compromised. People are falling prey to phishing attacks through COVID-19 related content. In this research, we have identified the top ten cybersecurity threats that had and could take place during the pandemic. We have also discussed the privacy concerns raised amid COVID-19.

High-order quasi-reflectionless bandpass filters with improved passband flatness and good impedance matching both in-band and out-of-band are proposed in this work. The proposed design consists of conventional coupled-lines bandpass sections loaded with the presented absorptive stubs at the input and output. Analysis shows that the absorptive stub is equivalent to a 2-pole bandstop filter. Compared to the prior art, the higher-order nature of the presented absorptive stub enables a flatter passband and better out-of-band rejection. The overall filter stopband attenuation can be readily improved by increasing the number of coupled-lines sections without altering the passband responses. Furthermore, cross-coupling between the two absorptive stubs can be used to improve the out-of-band rejection by introducing two transmission zeros without affecting the absorption characteristics. The proposed design concepts are experimentally validated by the design and fabrication of a set of 2.4-GHz 1-, 2-, and 3-pole microstrip quasi-reflectionless bandpass filters. Measured frequency responses of these filters closely match those of the simulation.

This paper serves as an overview for sequential learning algorithms for single hidden layer neural nets. Cite as: M. H. Arshad, M. A. Abido. An Overview of Sequential Learning Algorithms for Single Hidden Layer Networks: Current Issues & Future Trends. Abstract: In this paper, a brief survey of the commonly used sequential-learning algorithms used with single hidden layer feed-forward neural networks is presented. A glimpse at the different kinds that are available in the literature up until now, how they have developed throughout the years, and their relative execution is summarized. Most important things to take note of during the designing phase of neural networks are its complexity, computational efficiency, maximum training time, and ability to generalize the under-study problem. The comparison of different sequential learning algorithms in regard to these merits for single hidden layer neural networks is drawn.

This work proposes a workload model for modern graphics APIs, named GAMORRA, to estimate each frame's workload and possibly predict rendering times on a target hardware. Also, a suite of benchmarks is described that evaluates the performance of the target hardware and the overhead of different graphical operations to be used in the proposed model for workload estimation.

No one predicted the corona virus outbreak, and naturally, nobody was prepared. The corona virus, otherwise, adoringly known as COVID-19, has put the entire world in a difficult position because of the apparent life-threatening danger it poses and the rate of spread and infection worldwide. Besides taking away so many lives, it has caused a variety of problems such as unemployment, social distancing, disruption of businesses and daily life. Circumstances are worse for those who live from hand to mouth. Within the three months of its presence, COVID-19 has forced humanity to find and implement alternative ways to sustain businesses and life. In this research paper, we have unearthed 7 lessons learned from the COVID-19 pandemic. These lessons include aspects of business, education, online presence, network communication, cyber security, healthcare, and the purpose of life. This research delves deeper into the response to the unannounced pandemic in hand. It aims to provide the right direction to address a potential pandemic in the future should it happen.

Image segmentation is an important pre-processing step towards higher level tasks such as object recognition, computer vision or image compression. Most of the existing segmentation algorithms deal with grayscale images only. But in the modern world, color images are extensively used in many situations. A new approach for color image segmentation is presented in this paper. There are many ways to deal with image segmentation problem and in these techniques; a particular class of algorithms traces their origin from region-based methods. These algorithms group homogeneous pixels, which are connected to primitive regions. They are easy to implement and are promising. Therefore, here one of the most efficient region-based segmentation algorithms is explained. The color image is quantized adaptively, using a wavelet transform. Then the region growing process is adopted. As preprocess, before actual region merging, small regions are eliminated by merging them with neighbor regions depending upon color similarity. After this, homogeneous regions are merged to get segmented output.

Contrast limited adaptive histogram equalization is applied to obtain local contrast enhancement.retinal blood vessels are segmented by minimum spanning superpixel tree detector.

Contract-for-Difference financial instruments are available to renewable electricity generators in day-ahead electricity markets to allow them to hedge against revenue risk. Traditional CfDs while designed to hedge revenue risk, introduce other new risks such as counterparty credit, margining and third-party risks. We therefore propose a novel financial instrument - an Ethereum blockchain-based dual escrow smart contract, to serve as the mediator in a CfD agreement between a renewable electricity generator and supplier. This financial instrument addresses hedging related risks that result from traditional CfD agreements in day-ahead electricity markets. In this paper, we design the logic of the financial instrument, translate this logic to smart contract codes and demonstrate its expected performance. Overall, the proposed financial instrument has the benefits of reducing hedging related risks inherent in traditional CfDs. Likewise, it enables secure, efficient, cost-effective, consistent, reliable, transparent and frictionless transactions between contracting parties in a CfD agreement.

A multi-functional photonic circuit implemented on silicon on insulator (SOI) platform is reported. Eight phase modulators are connected to provide four Mach-Zehnder interferometer structure in parallel. Multimode interference couplers are used as the splitter/combiner of the MZI structures to provide static DC bias. This architecture generates two principle spatially separated 1st order harmonics from a single light source, which may be collected from separate ports without using any optical demultiplexing filters which permits remote heterodyning. A carrier suppression of >20 dB and spurious sideband suppression > 12 dB relative to the principle harmonics is achieved with bias voltage tuning only. In addition to the frequency conversion, the circuit can also perform as a sub-carrier generator, IQ modulator and frequency multiplier.

A document by Maryam Khalid . Click on the document to view its contents.

Background: At present, the gesture recognition using sEMG signals requires vast amounts of training data or limits to a few hand movements. This paper presents a novel dynamic energy model that can decode continuous hand actions with force information, by training small amounts of sEMG data. Method: As activating the forearm muscles, the corresponding fingers are moving or tend to move (namely exerting force). The moving fingers store kinetic energy, and the fingers with moving trends store potential energy. The kinetic and potential energy of fingers is dynamically allocated due to the adaptive-coupling mechanism of five-fingers in actual motion. At this certain moment, the sum of the two energies is constant. We regarded energy mode with the same direction of acceleration of each finger, but likely different movements, as the same one, and divided hand movements into ten energy modes. Independent component analysis and machine learning methods were used to model associations between sEMG signals and energy mode, to determine the hand action, including speed and force adaptively. This theory imitates the self-adapting mechanism in the actual task; thus, ten healthy subjects were recruited, and three experiments mimicking activities of daily living were designed to evaluate the interface: (1) decoding untrained configurations, (2) decoding the amount of single-finger energy, and (3) real-time control. Results:(1) Participants completed the untrained hand movements (100 /100, p < 0.0001). (2) The test of pricking balloon with a needle tip was designed with significantly better than chance (779 /1000, p < 0.0001).(3) The test of punching a hole in the plasticine on the balloon was with over 95% success rate (97.67±5.04 %, p <0.01). Conclusion: The model can achieve continuous hand actions with force information, by training small amounts of sEMG data, which reduces trained complexity.

The high rate of change of frequency (RoCoF) issue incurred by the integration of renewable energy sources (RESs) into a modern power system significantly threatens the grid security, and thus needs to be carefully examined in the operational planning. However, severe fluctuation of regional frequency responses concerned by system operators could be concealed by the conventional assessment based on aggregated system frequency response. Moreover, the occurrence probability of a high RoCoF issue is actually a very vital factor during the system planner’s decision making. Therefore, this paper proposes a fast-algorithmic evaluation method to determine the probabilistic distribution of regional RoCoF for the operational planning of a RES penetrated power system. Firstly, an analytical sensitivity (AS) that quantifies the relationship between the regional RoCoF and the stochastic output of the RES is derived based on the generator and network information. Then a linear sensitivity-based analytical method (LSM) is established to calculate regional RoCoF and the corresponding probabilistic distribution, which takes much less computational time when comparing with the scenario-based simulation (SBS) and involves much less complicated calculation procedure when comparing with the cumulant-based method (CBM). The effectiveness and efficiency of the proposed method are verified in a modified 16-machine 5-area IEEE benchmark system by numerical SBS and analytical CBM.

Novel Coronavirus (2019-nCoV) or Human Coronavirus (HCoV) could be a new coronavirus that belongs to Betacoronaviruses type Human Coronaviruses, just like the Middle-East Respiratory Syndrome (MERS) coronavirus and Severe Acute Respiratory Syndrome (SARS) coronavirus. Wuhan, the capital city of Hubei province, China recorded the initial cases of this virus in December 2019. Until 2 May 2020, 10:00 CET, near 23,8287 deaths were reported out of 3.34 million confirmed cases recorded across the world. By the tip of January 2020, China confirmed that the COVID-19 transmitted from human to human. This study aims to check a completely unique medicament called “Echinacea purpurea” against the crystal structure of 2019-nCoV Main Protease. This study presents an ideal model for Echinacea purpurea to be tested in silico against 2019-nCoV Main Protease.

Most of the data gathering devices used for monitoring driver’s behavior require large storage, strong cellular signals, and unlimited internet. Touching mobile devices, during driving, is prohibited by many law enforcement agencies. There are situations, especially in developing countries, where people get stuck on roads with a low battery, low device-memory, and no mobile network. The drivers in such situations are not able to report against reckless drivers effectively.This paper proposes the framework of the “citizen reporting program” (CRP) aided with mobile apps to reduce reckless driving in such resource-constrained situations (RCS). A mobile app was designed, developed, and tested as a tool for this purpose. It could convert speech to text without a cellular network, capture the nearest geolocation, and send data to a server on the network or internet availability. We tested its reliability for converting speech to text and got a “word error rate” (WER) of less than 5%. We tested its functional usability and got a score of more than 71% on the system usability scale (SUS). The survey showed a favorable response of 70 plus % in reducing reckless driving via CRP in RCS if aided with mobile apps.