The astrophysics of visible-light orbital phase curves in the space age

The field of visible-light continuous time series photometry is now at its golden age, manifested by the continuum of past (CoRoT, Kepler), present (K2), and future (TESS, PLATO) space-based surveys delivering high precision data with a long baseline for a large number of stars. The availability of the high quality data has enabled astrophysical studies not possible before, including for example detailed asteroseismic investigations and the study of the exoplanet census including small planets. This has also allowed to study the minute photometric variability following the orbital motion in stellar binaries and star-planet systems which is the subject of this review. We focus on systems with a main sequence primary and a low-mass secondary, from a small star to a massive planet. The orbital modulations are induced by a combination of gravitational and atmospheric processes, including the beaming effect, tidal ellipsoidal distortion, reflected light, and thermal emission. Therefore, the phase curve shape contains information about the companion's mass and atmospheric characteristics, making phase curves a useful astrophysical tool. For example, phase curves can be used to detect and measure the mass of short-period low-mass companions orbiting hot fast-rotating stars, out of reach of other detection methods. Another interesting application of phase curves is using the orbital phase modulations to look for non-transiting systems, which comprise the majority of stellar binary and star-planet systems. We discuss the science done with phase curves, the first results obtained so far, and the current difficulties and open questions related to this young and evolving subfield.Comment: Invited Review accepted to PAS

Development of a Web-based woody biomass energy expert system

Woody biomass is evolving as a potential bioenergy feedstock at an industrial scale to provide the required supply for industries relying on these resources at necessary levels and feasible costs. In order to effectively utilize woody biomass for energy, it is essential to know in advance the availability of biomass, the equivalent energy provided, and the associated procurement costs. Expert systems, using computer based programming and containing knowledge bases reflecting the knowledge of human experts in the field, are being used in industrial facilities for real time problem analysis and knowledge enhancement. This study draws on this approach and attempts to fill gaps in energy information by designing an expert system capable of predicting the amount of biomass residue, energy equivalent provided, and the cost of procurement for biomass availability across the state of West Virginia. The system employs the latest web based database technique in providing real time and continuous feedback to its intended users. Procurement distance, biomass handling systems, and associated costs needed to collect required energy amounts are primary factors in the analysis tool. Biomass availability, procurement distance, and delivered costs were analyzed under different biomass feedstocks, equipment combinations, and operational conditions. The developed knowledge base system can be used to promote sustainable and efficient utilization of woody biomass in West Virginia

Urban Air Pollution Monitoring Using Wireless Sensor Networks: A Comprehensive Review

Air pollution is evolving as a severe environmental concern due to its enormous impact on the well being of the people, universal environment and also on the global economy. Conventional air pollution systems are not able to provide air pollution data of high spatiotemporal resolution due to non-scalability and limited data availability. With the advances in the areas of Micro Electro Mechanical Sensor (MEMS) and Wireless Sensor Network (WSN), the researchers had implemented various state-of-the-art air pollution monitoring systems with better and efficient results. A comprehensive review of continuous air pollution surveillance of both indoor and outdoor pollution by employing WSN was presented. In the proposed paper attempts to provide the details related to the existing methods for measuring major air pollutants like CO2, CO, O3, SO2, VOC and Particulate Matter (PM). It presents the various methods, algorithms and dedicated network designs in air pollution monitoring which are useful for generating new solutions to improve the performance through WSN. A comprehensive and detailed review of the existing methods of Air Quality Monitoring systems using WSN was done along with their comparison

Construction and Verification of Performance and Reliability Models

Over the last two decades formal methods have been extended towards performance and reliability evaluation. This paper tries to provide a rather intuitive explanation of the basic concepts and features in this area. Instead of striving for mathematical rigour, the intention is to give an illustrative introduction to the basics of stochastic models, to stochastic modelling using process algebra, and to model checking as a technique to analyse stochastic models

Developing Decentralized Data Storage Network Using Blockchain Technology to Prevent Data Alteration

In the face of escalating global data exchange, the pronounced vulnerability oftraditional centralized storage networks to manipulation and attacks poses a pressing challenge. Digital service providers, entrusted with vast datasets, grapple with the formidable task of ensuring the security, integrity, and continuous availability of their stored information. This paper tackles these multifaceted issues by proposing a decentralized data storage network empowered by blockchain technology. This approach systematically mitigates the inherent susceptibilities of centralized systems, thereby providing heightened resilience against unauthorized alterations and malicious attacks that compromise digital information integrity. Moreover, the decentralized model holds significant promise for securing public data. By leveraging the transparency and immutability of blockchain ledgers, this approach not only safeguards against unauthorized access but also actively fosters transparency and accountability in data management. This makes it particularly well-suited for ensuring the security and integrity of public data, addressing concerns related to trust and reliability in the ever-evolving landscape of information exchange

Remote health monitoring systems for elderly people: a survey

This paper addresses the growing demand for healthcare systems, particularly among the elderly population. The need for these systems arises from the desire to enable patients and seniors to live independently in their homes without relying heavily on their families or caretakers. To achieve substantial improvements in healthcare, it is essential to ensure the continuous development and availability of information technologies tailored explicitly for patients and elderly individuals. The primary objective of this study is to comprehensively review the latest remote health monitoring systems, with a specific focus on those designed for older adults. To facilitate a comprehensive understanding, we categorize these remote monitoring systems and provide an overview of their general architectures. Additionally, we emphasize the standards utilized in their development and highlight the challenges encountered throughout the developmental processes. Moreover, this paper identifies several potential areas for future research, which promise further advancements in remote health monitoring systems. Addressing these research gaps can drive progress and innovation, ultimately enhancing the quality of healthcare services available to elderly individuals. This, in turn, empowers them to lead more independent and fulfilling lives while enjoying the comforts and familiarity of their own homes. By acknowledging the importance of healthcare systems for the elderly and recognizing the role of information technologies, we can address the evolving needs of this population. Through ongoing research and development, we can continue to enhance remote health monitoring systems, ensuring they remain effective, efficient, and responsive to the unique requirements of elderly individuals

RELEASE: A High-level Paradigm for Reliable Large-scale Server Software

Erlang is a functional language with a much-emulated model for building reliable distributed systems. This paper outlines the RELEASE project, and describes the progress in the rst six months. The project aim is to scale the Erlang's radical concurrency-oriented programming paradigm to build reliable general-purpose software, such as server-based systems, on massively parallel machines. Currently Erlang has inherently scalable computation and reliability models, but in practice scalability is constrained by aspects of the language and virtual machine. We are working at three levels to address these challenges: evolving the Erlang virtual machine so that it can work effectively on large scale multicore systems; evolving the language to Scalable Distributed (SD) Erlang; developing a scalable Erlang infrastructure to integrate multiple, heterogeneous clusters. We are also developing state of the art tools that allow programmers to understand the behaviour of massively parallel SD Erlang programs. We will demonstrate the e ectiveness of the RELEASE approach using demonstrators and two large case studies on a Blue Gene

Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World

This report documents the program and the outcomes of GI-Dagstuhl Seminar 16394 "Software Performance Engineering in the DevOps World". The seminar addressed the problem of performance-aware DevOps. Both, DevOps and performance engineering have been growing trends over the past one to two years, in no small part due to the rise in importance of identifying performance anomalies in the operations (Ops) of cloud and big data systems and feeding these back to the development (Dev). However, so far, the research community has treated software engineering, performance engineering, and cloud computing mostly as individual research areas. We aimed to identify cross-community collaboration, and to set the path for long-lasting collaborations towards performance-aware DevOps. The main goal of the seminar was to bring together young researchers (PhD students in a later stage of their PhD, as well as PostDocs or Junior Professors) in the areas of (i) software engineering, (ii) performance engineering, and (iii) cloud computing and big data to present their current research projects, to exchange experience and expertise, to discuss research challenges, and to develop ideas for future collaborations