Cyber-Physical Modeling of Intelligent Traffic Controllers Using Simulator for Urban Mobility (SUMO)

Transportation problems have always been a global concern. The challenges in traffic congestion were easily observed during pre-pandemic times. However, traffic congestion still persists even during the COVID-19 pandemic (2020 and present) where there has been less number of vehicles because of travel restrictions. The emergence of wireless communication technologies and intelligent transportation systems (ITS) pave the way for solving some of the problems found in the transportation industry. Subsequently, traffic control systems are used at various intersections to manage the flow of traffic and reduce car collisions. However, some intersections are better off without these traffic control systems. The proposed study will analyze a T-junction road in five different setups using different types of traffic controllers. The simulation tool used is SUMO. The study found that an adaptive or vehicle-actuated traffic controller is the ideal method for regulating traffic flow in a T-junction with a one-way or two-way main road. It was observed in the simulation that it reduced the potential car collisions in the non-TL junction. However, the average speed and completion time of the road network was affected by the method. © 2022 IEEE.

Devices with Fuzzy Logic Control of Artificial Lung Ventilation

This article presents the development of a ventilator and its control algorithm. The main feature of the developed ventilator is compressed by a pneumatic drive. The control algorithm is based on the adaptive fuzzy inference system (ANFIS), which integrates the principles of fuzzy logic. The paper also presents a simulation model to test the designed control approach. The results of the experiment provide verification of the developed control system. The novelty of the article is, on the one hand, the implementation of the ANFIS controller, pressure control, with a description of the training process. On the other hand, in the article presented a draft ventilator with a detailed description of the hardware and control system. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Sensing Technologies for Crowd Management, Adaptation, and Information Dissemination in Public Transportation Systems: A Review

Management of crowd information in public transportation (PT) systems is crucial, both to foster sustainable mobility, by increasing the user's comfort and satisfaction during normal operation, as well as to cope with emergency situations, such as pandemic crises, as recently experienced with coronavirus disease (COVID-19) limitations. This article presents a taxonomy and review of sensing technologies based on the Internet of Things (IoT) for real-time crowd analysis, which can be adopted in the different segments of the PT system (buses/trams/trains, railway/metro stations, and bus/tram stops). To discuss such technologies in a clear systematic perspective, we introduce a reference architecture for crowd management, which employs modern information and communication technologies (ICTs) in order to: 1) monitor and predict crowding events;2) implement crowd-aware policies for real-time and adaptive operation control in intelligent transportation systems (ITSs);and 3) inform in real time the users of the crowding status of the PT system, by means of electronic displays installed inside vehicles or at bus/tram stops/stations and/or by mobile transport applications. It is envisioned that the innovative crowd management functionalities enabled by ICT/IoT sensing technologies can be incrementally implemented as an add-on to state-of-the-art ITS platforms, which are already in use by major PT companies operating in urban areas. Moreover, it is argued that, in this new framework, additional services can be delivered to the passengers, such as online ticketing, vehicle access control and reservation in severely crowded situations, and evolved crowd-aware route planning. © 2001-2012 IEEE.

Integer cum fractional ordered active-adaptive synchronization to control vasospasm in chaotic blood vessels to reduce risk of COVID-19 infections

Chaotic states of abnormal vasospasms in blood vessels make heart patients more prone to severe infections of COVID-19, eventually leading to high fatalities. To understand the inherent dynamics of such abrupt vasospasms, an N-type blood vessel model (NBVM) subjected to uncertainties is derived in this paper and investigated both in integer order (IO) as well as fractional-order (FO) dynamics. Active-adaptive controllers are designed to synchronize the chaotic turbulence responsible for undesirable fluctuations in diameter and pressure variations of the blood vessel. The FO-NBVM reveals insightful rich dynamics and faster adaptive synchronization compared to its IO model. The practical implications of this work will be useful in analysing chaotic dysfunctionalities of the blood vessel such as vasoconstriction, ischaemia, necrosis, etc. and help in developing control strategies and modular responses for COVID-19 triggered heart diseases. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Managing spatial irrigation using remote-sensing-based evapotranspiration and soil water adaptive control model

Irrigation has traditionally been managed as uniform applications where an entire field receives the same depth of water. Motivation to improve current irrigation practices has led to different approaches utilizing remotely-sensed images to inform variable rate irrigation management. This study conducted in 2019 and 2020 implemented the Spatial EvapoTranspiration Modeling Interface (SETMI), a remote-sensing-based evapotranspiration (ET) and water balance model, for managing variable rate irrigation of a maize and soybean field. This model tracked soil water content through the estimation of daily ET and tracking of various water fluxes entering and leaving a field. SETMI was used in two different irrigation treatments informed using Planet satellite (SETMI-SAT) and unmanned aerial system (UAS, SETMI-UAS) remotely-sensed images. A uniform irrigation approach managed by a professional crop consultant and a non-irrigated approach were used as the baseline in comparing irrigation management approaches. The irrigation treatments were evaluated on dry grain yield, gross irrigation, actual ET, deep percolation, change in soil water content, and water productivity. The uniform irrigation approach managed by the crop consultant applied the highest irrigation in 2019 and 2020 for maize (2019: 155 mm, 2020: 213 mm) and soybean (2019: 124 mm;2020: 183 mm) while the SETMI irrigation treatments applied less irrigation for maize (2019: 131, 132 mm;2020: 154, 140 mm) and soybean (2019: 116, 94 mm;2020: 154, 175 mm). Maize yield was highest for the uniform irrigation approach in 2019 (14.9 Mg ha−1) and 2020 (13.3 Mg ha−1). The highest soybean yield was produced by the SETMI-SAT irrigation management approach in 2019 (5.0 Mg ha−1) and 2020 (4.8 Mg ha−1). Significant differences (p-value < 0.05) in applied irrigation between the uniform and SETMI irrigation management approaches were observed while there were no significant differences in dry grain yield for both maize and soybean in 2019 and 2020. At least one of the SETMI irrigation treatments produced higher crop, irrigation, and ET water productivity values in comparison to those produced by the uniform irrigation treatment for all crop-years. A post-season analysis was completed using the SETMI-UAS and SETMI-SAT treatments to evaluate the accuracy of estimated rootzone soil water depletion provided by SETMI. Rootzone depletion calculated from neutron probe volumetric soil water content measurements were compared to the modeled depletion from the SETMI-UAS and SETMI-SAT treatments. The 2020 modeled and measured depletion comparison produced better agreement resulting in a root mean squared error (RMSE) < 17 mm compared to 2019 (RMSE < 27 mm). The VRI center pivot malfunctioned during the 2019 season which caused unresolved discrepancies between actually applied irrigation and what the system was programmed to apply. The VRI system was fixed before the 2020 season. • Remotely-sensing-based evapotranspiration model can improve irrigation management. • Variable rate irrigation can be effective informed through remote sensing. • Variable rate irrigation can decrease applied irrigation while maintaining crop yields. [ FROM AUTHOR] Copyright of Agricultural Water Management is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Ivory: Learning Network Adaptive Streaming Codes

With the growing interest in web services during the current COVID-19 outbreak, the demand for high-quality low-latency interactive applications has never been more apparent. Yet, packet losses are inevitable over the Internet, since it is based on UDP. In this paper, we propose Ivory, a new real-world system framework designed to support network adaptive error control in real-time communications, such as VoIP, using a recently proposed low-latency streaming code. We design and implement our prototype over UDP that can correct or retransmit lost packets conditional on network conditions and application requirements.To maintain the highest quality, Ivory attempts to correct as many lost packets as possible on-the-fly, yet incurring the smallest footprint in terms of coding overhead over the network. To achieve such an objective, Ivory uses a deep reinforcement learning agent to estimate the best coding parameters in real-time based on observed network states and experience learned. It learns offline the best coding parameters to use based on previously observed loss patterns and takes into account the round-trip time observed to decide on the optimum decoding delay for a low-latency application. Our extensive array of experiments shows that Ivory achieves a better trade-off between recovering packets and using lower redundancy than the state-of-the-art network adaptive streaming codes algorithms. © 2022 IEEE.

Adaptations of Municipal Solid Waste Management Systems in Response to the Coronavirus Pandemic

The coronavirus pandemic has challenged the operation of municipal solid waste management systems (MSWMSs) in the United States and elsewhere. With the growing concern about the potential exposure to the coronavirus, people are spending more time in their homes while changing their waste generation behaviors. This phenomenon has changed not only how people produce waste but also how MSWMSs plan and adapt the operation of their facilities. Since solid waste management has been declared as an essential service in addition to public health, MSWMSs have faced new challenges and thus developed adaptive measures in order to keep their critical operations. This study (i) identifies a broad range of waste management and operational challenges and (ii) summarizes various adaptive measures taken by different MSWMSs. Ephemeral data were collected and analyzed on the longitudinal impact of the pandemic on multiple MSWMSs in severely affected U.S. states, such as Florida, California, and New York, over a nine-month period. Note that best management practices for such waste-related challenges and adaptive measures can vary across different MSWMSs and states. In order to facilitate the development of different MSWMSs’ plan for future pandemic events, this study will characterize the identified impact of the pandemic and its relevant adaptive measures in terms of system structure (i.e., what facilities exist [entity], how they interact with one another [interdependency], and who control which facilities [control]). © 2023, Canadian Society for Civil Engineering.

Adaptive Learning Environment Design in the System of Future Maritime Specialits' Training

In an environment of education reformation aimed at transition of higher education to competence based and individual approaches, we face the need to construct individual learning path for every future maritime professional. In this respect technology of adaptive learning based on modern ICT becomes of high importance. At the same time COVID-19 pandemic has changed system of education at all its levels, but the issue of quality and efficiency is still to be considered and studied by scientists and practitioners. Under these conditions the issue of adaptive information environment creation becomes relevant for training modern and competitive specialists. This environment should be based on implementation of adaptive technologies for education and training of maritime students, therefore, article provides investigation of pedagogical problem of future navigators' professional culture building in training system of adaptive information environment of maritime educational establishment. Feasibility of adaptive learning technologies implementation is grounded as a tool for future navigators' professional culture building in the process of their fundamental education and training. Example of higher mathematics adaptive learning implementation for future navigators at Kherson State Maritime Academy is considered. Higher mathematics adaptive learning was introduced through: adaptive feeding of educational content of the course;problems solving support based on examples and pre-created typical algorythms;adaptive testing;analysis of test tasks answers;system teacher support;constant support conditions for individual tasks completion;adaptive course navigation, etc. As the result of experiment there was found out that higher mathematics adaptive learning for future navigators presupposes: individual learning path designing;possibility to timely provide advising and objective control as well as evaluation;enhancement of learning activity and motivation of through improved degree of autonomy;promotion of students' research skills development;creation of cooperation, partnership and maritime brotherhood atmosphere. © 2022 CEUR-WS. All rights reserved.

Mining the Associations between V(D)J Gene Segments and COVID-19 Disease Characteristics

The emerging COVID-19 variants lead to a new wave of infections, spreading more rapidly with more severe illnesses. The adaptive immune system plays an essential role in the control and clearance of viral infection and influences clinical outcomes. However, the understanding of the adaptive immune responses to COVID-19 is not sufficient, which impedes the development progress of treatments and vaccines. To address this issue, we proposed a machine-learning-based method (termed as VDJ-Seg-Miner) to mine the underlying associations between the V(D)J gene segments of the T cell receptor in personalized immune repertoires and COVID-19 disease characteristics for immune system analysis. Our VDJ-Seg-Miner can interpretively reveal multiple associations between the V(D)J gene segments and COVID-19 disease characteristics and assign confidence scores to indicate its confidence in each revealed association. Furthermore, experimental results based on the real-world dataset suggested that the identified associations were highly consistent with those reported in previous work. © 2021 IEEE.

S-Light: Smart LED Lamppost using PWM-based Adaptive Light Controller

Conventional streetlight’s constant need for high power and the ill effects it has spawned on the environmental ecosystem has led researchers to adopt the idea of smart lights in order to minimize energy consumption and maximize power efficiency. This paper proposes S-LIGHT, which is a PWM-based LED adaptive light controlling system that can be deployed at public parks and other outdoor recreational venues, which applies intelligent illumination control of an LED lights. The design is based on Pulse Width Modulation technique which optimizes the overall power consumption and simultaneously supporting a multi-functional and user-friendly post. Smart street lighting aims to make cities feel safer at night, make lights more efficient, and substantially reduce costs of maintenance and energy by integrating sensors and alternative technologies to automate the light. S-LIGHT uses an Arduino UNO board along with a Passive Infrared (PIR) sensor to swiftly increase the brightness of the high-power LED light during the night in the presence of human motion, and a Light Dependent Resistor (LDR) sensor to turn on/off the light by adapting itself to the time of night/day. S-LIGHT also provides a multi-functional post that supports an emergency button feature that easily initiates an Emergency call to the police, a surveillance camera that streams live footage of the area, and an LCD screen that displays to the public awareness messages about the COVID-19 pandemic. © 2021 IEEE.

Feedback and Control Course Labs for Distance Learning

Classic Feedback and Control is an undergraduate course that introduces students to concepts and methods for modeling, analysis, and design of single-input-single-output feedback control systems in the Electrical and/or Computer Engineering majors. In addition to using lectures to explain theories and assigning homework assignments for students to practice their modeling and analyses skills, instructors would usually supplement the course by a series of hardware-based experiments and software-based simulation labs so that students can apply the acquired knowledge to physical systems and real-world control problems. Similar to many other institutions, our ECE program offers a Feedback and Control course to junior students in the Electrical Engineering and Electromechanical Engineering majors. This course is a 3-hour lecture, 2-hour lab, as a 4-credit course. Topics discussed include modeling in both the time and the frequency domains, time response, model reduction, stability, steady-state error, root locus, design via root locus, frequency response, and design via frequency response. Due to the COVID-19 pandemic, both students and faculty in our institution were forced to work and study from home in summer 2020. In order to engage students in distance learning, application-oriented and active-learning opportunities were created. A series of exclusively software-based labs and projects were designed to help students gain a better understanding of how the knowledge are useful in real-world situations. Particularly, nine simulation labs and two simulation projects were used in the class of summer 2020. In order to evaluate the effectiveness of the designed simulation labs and projects in helping students to grasp and then apply the control concepts and ideas, surveys were conducted in the summer 2020 class to collect students' opinions and feedbacks. Among the 27 participating students, 81.4% of students “agree” or “strongly agree” that simulation laboratory exercises increased their interest in the subject, 85.1% of students “agree” or “strongly agree” that simulation laboratory exercises helped them better to learn course content, and 77.7% of the students thought simulation laboratory exercises were excellent or very good. We also compared the percentage of students who performed at the A, A-, B+, B, and B- levels with past records (while teaching was in-person), which turned out to be comparable and similar. This indicates the effectiveness of these simulation-based labs & projects, and their contribution in helping to maintain the course standard. © American Society for Engineering Education, 2021

Design and Evaluation of Undergraduate Feedback-control System Course in Distance Learning

This paper is a work-in-progress (WIP) paper. COVID19 pandemic profoundly changed the way educators teach and the way students learn. Our institution, the New York City College of Technology, abruptly switched to distance learning mode in Spring 2020 and continues to offer all courses online in Fall 2020. This paper presents the redesign and evaluation of an undergraduate Feedback Control System course to adapt to distance learning. Feedback Control System course is the last required course for the Bachelor of Technology (BTech) program in Computer Engineering Technology (CET), which has a 3-hour lecture lesson and a 3-hour lab session every week. Due to our BTech students' diverse mathematical backgrounds, students think this course is demanding even in the traditional face-to-face teaching mode. Teaching such a mathematically involved class in the distance learning mode poses significant challenges to both the instructors and the students. This paper documents our re-structure and redesign process of both the lecture and lab components to facilitate students' remote learning experience, satisfy the ABET accreditation criteria and maintain our pre-set learning standard. The online characteristic gives the instructors the freedom and a framework to teach classes in various delivery modes via synchronous lectures (like virtual meetings) and asynchronous online supplementary resources (for example, Blackboard). The arrangements we made to adjust to the distance learning mode include: a) decomposition of the course context into three modules and clear specification of the corresponding learning objectives of each module;b) combination of different technologies to create friendly and inclusive learning environment;c) frequent assessment of students' performance via online quizzes/tests;and d) carefully-designed laboratory assignments via MATLAB simulations that are able to demonstrate the entire feedback control process. A comparison of students' performance under the traditional face-to-face learning mode and the new distance learning mode is conducted. Based on assessment results, we will evaluate the effectiveness of our current teaching methodology/plan developed for distance learning and possibly identify potential areas for further improvement. © American Society for Engineering Education, 2021

An adaptive control framework based on Reinforcement learning to balance energy, comfort and hygiene in heat pump water heating systems

A major challenge in the operation of water heating systems lies in the highly stochastic nature of occupant behavior in hot water use, which varies over different buildings and can change over the time. However, the current operational strategies of water heating systems are detached from occupant behavior, and follow a conservative and energy intensive approach to ensure the availability of hot water any time it is demanded. This paper proposes a Reinforcement learning-based control framework which can learn and adapt to the occupant behavior of each specific building and make a balance between energy use, occupant comfort and water hygiene. The proposed framework is compared to the conventional approach using the real-world measurements of hot water use behavior in a single family residential building. Although the monitoring campaign has been executed during home lockdown due to COVID-19, when the occupants exhibited a very different schedule and water use related behavior, the proposed framework has learned the occupant behavior over a relatively short period of 8 weeks and provided 24.5% energy use reduction over the conventional approach, while preserving occupant comfort and water hygiene. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.