Consistency checking of STNs with decisions: Managing temporal and access-control constraints in a seamless way

A Simple Temporal Network (STN) consists of time points modeling temporal events and constraints modeling the minimal and maximal temporal distance between them. A Simple Temporal Network with Decisions (STND) extends an STN to model temporal plans with decisions. STNDs label time points and constraints by conjunctions of literals saying for which scenarios (i.e., complete truth value assignments to the propositions) they are relevant. In this paper, we deal with the use of STNDs for modeling and synthesizing execution strategies. We propose an incremental hybrid SAT-based consistency checking algorithm for STNDs that is faster than the one previously proposed and allows for the synthesis of all consistent scenarios and related early execution schedules (offline temporal planning). We carry out an experimental evaluation with Kappa, a tool that we developed for STNDs. We also show that any STND can be easily translated into a disjunctive temporal network and vice versa

Towards Sustainable Development of Nanomanufacturing

Sustainability is a buzz word these days not just among regulatory agencies but even with corporations, as evident by the release of annual sustainability report by a large number of firms. Companies are starting to portray profit making along with corporate environmental responsibility. Nanotechnology and nanomanufacturing which holds a lot of promise for development in a multitude of fields in science and engineering is the new kid on the block and carries a lot of apprehension due to public concern about their potential unwanted side effects that may result in the case of an untoward incident or lack of oversight. This thesis covers the following aspects of nanomanufacturing in light of sustainable development Identifying regulatory needs, Life cycle thinking in evaluating products and use of green methods for nanomanufacturing, Methods for selection of manufacturing processes that cause least harm to the environment, Use of industrial engineering tools for evaluating manufacturing processes at a process step level to identify areas of environmental performance improvement, and Provide guidance to nanomanufacturing facilities in the form of expert opinion to help implement workplace controls

A review on nanocellulosic fibres as new material for sustainable packaging: process an applications

The demand for exploring advanced and eco-friendly sustainable packaging materials with superior physical, mechanical and barrier properties is increasing. The materials that are currently used in packaging for food, beverage, medical and pharmaceutical products, as well as in industrial applications, are non-degradable, and thus, these materials are raising environmental pollution concerns. Numerous studies have been conducted on the utilization of bio-based materials in the pursuit of developing sustainable packaging materials. Although significant improvements have been achieved, a balance among environmental concerns, economic considerations and product packaging performance is still lacking. This is likely due to bio-based materials being used in product packaging applications without a proper design. The present review article intends to summarize the information regarding the potential applications of cellulosic nanofiber for the packaging. The importance of the design process, its principles and the challenges of design process for sustainable packaging are also summarized in this review. Overall it can be concluded that scientists, designers and engineers all are necessarily required to contribute towards research in order to commercially exploit cellulose nanofiber for sustainable packaging

Engineering a Better Future

This open access book examines how the social sciences can be integrated into the praxis of engineering and science, presenting unique perspectives on the interplay between engineering and social science. Motivated by the report by the Commission on Humanities and Social Sciences of the American Association of Arts and Sciences, which emphasizes the importance of social sciences and Humanities in technical fields, the essays and papers collected in this book were presented at the NSF-funded workshop ‘Engineering a Better Future: Interplay between Engineering, Social Sciences and Innovation’, which brought together a singular collection of people, topics and disciplines. The book is split into three parts: A. Meeting at the Middle: Challenges to educating at the boundaries covers experiments in combining engineering education and the social sciences; B. Engineers Shaping Human Affairs: Investigating the interaction between social sciences and engineering, including the cult of innovation, politics of engineering, engineering design and future of societies; and C. Engineering the Engineers: Investigates thinking about design with papers on the art and science of science and engineering practice

Intrusion Detection: Embedded Software Machine Learning and Hardware Rules Based Co-Designs

Security of innovative technologies in future generation networks such as (Cyber Physical Systems (CPS) and Wi-Fi has become a critical universal issue for individuals, economy, enterprises, organizations and governments. The rate of cyber-attacks has increased dramatically, and the tactics used by the attackers are continuing to evolve and have become ingenious during the attacks. Intrusion Detection is one of the solutions against these attacks. One approach in designing an intrusion detection system (IDS) is software-based machine learning. Such approach can predict and detect threats before they result in major security incidents. Moreover, despite the considerable research in machine learning based designs, there is still a relatively small body of literature that is concerned with imbalanced class distributions from the intrusion detection system perspective. In addition, it is necessary to have an effective performance metric that can compare multiple multi-class as well as binary-class systems with respect to class distribution. Furthermore, the expectant detection techniques must have the ability to identify real attacks from random defects, ingrained defects in the design, misconfigurations of the system devices, system faults, human errors, and software implementation errors. Moreover, a lightweight IDS that is small, real-time, flexible and reconfigurable enough to be used as permanent elements of the system's security infrastructure is essential. The main goal of the current study is to design an effective and accurate intrusion detection framework with minimum features that are more discriminative and representative. Three publicly available datasets representing variant networking environments are adopted which also reflect realistic imbalanced class distributions as well as updated attack patterns. The presented intrusion detection framework is composed of three main modules: feature selection and dimensionality reduction, handling imbalanced class distributions, and classification. The feature selection mechanism utilizes searching algorithms and correlation based subset evaluation techniques, whereas the feature dimensionality reduction part utilizes principal component analysis and auto-encoder as an instance of deep learning. Various classifiers, including eight single-learning classifiers, four ensemble classifiers, one stacked classifier, and five imbalanced class handling approaches are evaluated to identify the most efficient and accurate one(s) for the proposed intrusion detection framework. A hardware-based approach to detect malicious behaviors of sensors and actuators embedded in medical devices, in which the safety of the patient is critical and of utmost importance, is additionally proposed. The idea is based on a methodology that transforms a device's behavior rules into a state machine to build a Behavior Specification Rules Monitoring (BSRM) tool for four medical devices. Simulation and synthesis results demonstrate that the BSRM tool can effectively identify the expected normal behavior of the device and detect any deviation from its normal behavior. The performance of the BSRM approach has also been compared with a machine learning based approach for the same problem. The FPGA module of the BSRM can be embedded in medical devices as an IDS and can be further integrated with the machine learning based approach. The reconfigurable nature of the FPGA chip adds an extra advantage to the designed model in which the behavior rules can be easily updated and tailored according to the requirements of the device, patient, treatment algorithm, and/or pervasive healthcare application

Biocomposite Inks for 3D Printing

Three-dimensional (3D) printing has evolved massively during the last years. The 3D printing technologies offer various advantages, including: i) tailor-made design, ii) rapid prototyping, and iii) manufacturing of complex structures. Importantly, 3D printing is currently finding its potential in tissue engineering, wound dressings, tissue models for drug testing, prosthesis, and biosensors, to name a few. One important factor is the optimized composition of inks that can facilitate the deposition of cells, fabrication of vascularized tissue and the structuring of complex constructs that are similar to functional organs. Biocomposite inks can include synthetic and natural polymers, such as poly (ε-caprolactone), polylactic acid, collagen, hyaluronic acid, alginate, nanocellulose, and may be complemented with cross-linkers to stabilize the constructs and with bioactive molecules to add functionality. Inks that contain living cells are referred to as bioinks and the process as 3D bioprinting. Some of the key aspects of the formulation of bioinks are, e.g., the tailoring of mechanical properties, biocompatibility and the rheological behavior of the ink which may affect the cell viability, proliferation, and cell differentiation.The current Special Issue emphasizes the bio-technological engineering of novel biocomposite inks for various 3D printing technologies, also considering important aspects in the production and use of bioinks

MINERVA 2020

This issue of Minerva includes an article on 2020 Honors Read Rising Out of Hatred; a piece by Professors Mimi Killinger and Katie Quirk on teaching during a pandemic; and a story on the UMaine UVote initiative led by Rob Glover and Jenny Desmond. Other highlights include reflections by current students; an article on the Honors Outdoor Program Series (HOPS); and profiles recognizing several alumni accomplishments

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation