Continuity and boundary conditions in thermodynamics: From Carnot's efficiency to efficiencies at maximum power

[...] By the beginning of the 20th century, the principles of thermodynamics were summarized into the so-called four laws, which were, as it turns out, definitive negative answers to the doomed quests for perpetual motion machines. As a matter of fact, one result of Sadi Carnot's work was precisely that the heat-to-work conversion process is fundamentally limited; as such, it is considered as a first version of the second law of thermodynamics. Although it was derived from Carnot's unrealistic model, the upper bound on the thermodynamic conversion efficiency, known as the Carnot efficiency, became a paradigm as the next target after the failure of the perpetual motion ideal. In the 1950's, Jacques Yvon published a conference paper containing the necessary ingredients for a new class of models, and even a formula, not so different from that of Carnot's efficiency, which later would become the new efficiency reference. Yvon's first analysis [...] went fairly unnoticed for twenty years, until Frank Curzon and Boye Ahlborn published their pedagogical paper about the effect of finite heat transfer on output power limitation and their derivation of the efficiency at maximum power, now known as the Curzon-Ahlborn (CA) efficiency. The notion of finite rate explicitly introduced time in thermodynamics, and its significance cannot be overlooked as shown by the wealth of works devoted to what is now known as finite-time thermodynamics since the end of the 1970's. [...] The object of the article is thus to cover some of the milestones of thermodynamics, and show through the illustrative case of thermoelectric generators, our model heat engine, that the shift from Carnot's efficiency to efficiencies at maximum power explains itself naturally as one considers continuity and boundary conditions carefully [...]

Mining complex trees for hidden fruit : a graph–based computational solution to detect latent criminal networks : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Technology at Massey University, Albany, New Zealand.

The detection of crime is a complex and difficult endeavour. Public and private organisations – focusing on law enforcement, intelligence, and compliance – commonly apply the rational isolated actor approach premised on observability and materiality. This is manifested largely as conducting entity-level risk management sourcing ‘leads’ from reactive covert human intelligence sources and/or proactive sources by applying simple rules-based models. Focusing on discrete observable and material actors simply ignores that criminal activity exists within a complex system deriving its fundamental structural fabric from the complex interactions between actors - with those most unobservable likely to be both criminally proficient and influential. The graph-based computational solution developed to detect latent criminal networks is a response to the inadequacy of the rational isolated actor approach that ignores the connectedness and complexity of criminality. The core computational solution, written in the R language, consists of novel entity resolution, link discovery, and knowledge discovery technology. Entity resolution enables the fusion of multiple datasets with high accuracy (mean F-measure of 0.986 versus competitors 0.872), generating a graph-based expressive view of the problem. Link discovery is comprised of link prediction and link inference, enabling the high-performance detection (accuracy of ~0.8 versus relevant published models ~0.45) of unobserved relationships such as identity fraud. Knowledge discovery uses the fused graph generated and applies the “GraphExtract” algorithm to create a set of subgraphs representing latent functional criminal groups, and a mesoscopic graph representing how this set of criminal groups are interconnected. Latent knowledge is generated from a range of metrics including the “Super-broker” metric and attitude prediction. The computational solution has been evaluated on a range of datasets that mimic an applied setting, demonstrating a scalable (tested on ~18 million node graphs) and performant (~33 hours runtime on a non-distributed platform) solution that successfully detects relevant latent functional criminal groups in around 90% of cases sampled and enables the contextual understanding of the broader criminal system through the mesoscopic graph and associated metadata. The augmented data assets generated provide a multi-perspective systems view of criminal activity that enable advanced informed decision making across the microscopic mesoscopic macroscopic spectrum

Application of artificial neural networks and colored petri nets on earthquake resilient water distribution systems

Water distribution systems are important lifelines and a critical and complex infrastructure of a country. The performance of this system during unexpected rare events is important as it is one of the lifelines that people directly depend on and other factors indirectly impact the economy of a nation. In this thesis a couple of methods that can be used to predict damage and simulate the restoration process of a water distribution system are presented. Contributing to the effort of applying computational tools to infrastructure systems, Artificial Neural Network (ANN) is used to predict the rate of damage in the pipe network during seismic events. Prediction done in this thesis is based on earthquake intensity, peak ground velocity, and pipe size and material type. Further, restoration process of water distribution network in a seismic event is modeled and restoration curves are simulated using colored Petri nets. This dynamic simulation will aid decision makers to adopt the best strategies during disaster management. Prediction of damages, modeling and simulation in conjunction with other disaster reduction methodologies and strategies is expected to be helpful to be more resilient and better prepared for disasters --Abstract, page iv

Science, innovation, and public services: editorial introduction

The quality of public services is critically influenced by innovation and, ultimately, by advances in basic research, which however embeds the feature of a global public good. Two broad issues emerge. The first concerns the evaluation of the socio-economic impact of science. What are the benefits and spillovers that R&D investments, research infrastructures and big science can bring to society? The second concerns which type of institutions and policies are most suitable for supporting R&D activities. These topics discussed in this article represent the core of the special issue \u201cInnovation and Public Services: from the lab to enterprises and citizens\u201d

Efficiency and Automation in Threat Analysis of Software Systems

Context: Security is a growing concern in many organizations. Industries developing software systems plan for security early-on to minimize expensive code refactorings after deployment. In the design phase, teams of experts routinely analyze the system architecture and design to find potential security threats and flaws. After the system is implemented, the source code is often inspected to determine its compliance with the intended functionalities. Objective: The goal of this thesis is to improve on the performance of security design analysis techniques (in the design and implementation phases) and support practitioners with automation and tool support.Method: We conducted empirical studies for building an in-depth understanding of existing threat analysis techniques (Systematic Literature Review, controlled experiments). We also conducted empirical case studies with industrial participants to validate our attempt at improving the performance of one technique. Further, we validated our proposal for automating the inspection of security design flaws by organizing workshops with participants (under controlled conditions) and subsequent performance analysis. Finally, we relied on a series of experimental evaluations for assessing the quality of the proposed approach for automating security compliance checks. Findings: We found that the eSTRIDE approach can help focus the analysis and produce twice as many high-priority threats in the same time frame. We also found that reasoning about security in an automated fashion requires extending the existing notations with more precise security information. In a formal setting, minimal model extensions for doing so include security contracts for system nodes handling sensitive information. The formally-based analysis can to some extent provide completeness guarantees. For a graph-based detection of flaws, minimal required model extensions include data types and security solutions. In such a setting, the automated analysis can help in reducing the number of overlooked security flaws. Finally, we suggested to define a correspondence mapping between the design model elements and implemented constructs. We found that such a mapping is a key enabler for automatically checking the security compliance of the implemented system with the intended design. The key for achieving this is two-fold. First, a heuristics-based search is paramount to limit the manual effort that is required to define the mapping. Second, it is important to analyze implemented data flows and compare them to the data flows stipulated by the design

Evidence-enabled verification for the Linux kernel

Formal verification of large software has been an elusive target, riddled with problems of low accuracy and high computational complexity. With growing dependence on software in embedded and cyber-physical systems where vulnerabilities and malware can lead to disasters, an efficient and accurate verification has become a crucial need. The verification should be rigorous, computationally efficient, and automated enough to keep the human effort within reasonable limits, but it does not have to be completely automated. The automation should actually enable and simplify human cross-checking which is especially important when the stakes are high. Unfortunately, formal verification methods work mostly as automated black boxes with very little support for cross-checking. This thesis is about a different way to approach the software verification problem. It is about creating a powerful fusion of automation and human intelligence by incorporating algorithmic innovations to address the major challenges to advance the state of the art for accurate and scalable software verification where complete automation has remained intractable. The key is a mathematically rigorous notion of verification-critical evidence that the machine abstracts from software to empower human to reason with. The algorithmic innovation is to discover the patterns the developers have applied to manage complexity and leverage them. A pattern-based verification is crucial because the problem is intractable otherwise. We call the overall approach Evidence-Enabled Verification (EEV). This thesis presents the EEV with two challenging applications: (1) EEV for Lock/Unlock Pairing to verify the correct pairing of mutex lock and spin lock with their corresponding unlocks on all feasible execution paths, and (2) EEV for Allocation/Deallocation Pairing to verify the correct pairing of memory allocation with its corresponding deallocations on all feasible execution paths. We applied the EEV approach to verify recent versions of the Linux kernel. The results include a comparison with the state-of-the-art Linux Driver Verification (LDV) tool, effectiveness of the proposed visual models as verification-critical evidence, representative examples of verification, the discovered bugs, and limitations of the proposed approach