Exploring the impact of end-user engagement on the diffusion and adoption of a climate resilience tool in the Gulf of Mexico

Climate change-related hazards negatively impact ecosystems, economies, and quality of life. Significant resources have been invested in data collection and research with the goal of enhanced understanding and capacity to predict future conditions in order to mitigate or adapt to intensifying hazard risk. The expansive production of climate science has generated a necessary complimentary enterprise dedicated to enhancing decision-makers’ understanding of and access to climate science as it is essential for future societal and ecological well-being. Though the aim of these many tools is to support resilient decision-making in the face of climate change, professionals report an underutilization of climate resilience tools. It has been suggested that stakeholder engagement during climate resilience tool development will improve the rates of use; however, there have been no studies to explore if the findings from tool diffusion and adoption studies in other sectors translate to climate resilience tools. An end-user engagement process for the development of a climate resilience tool was established and implemented. The process itself and the outcomes of the process, in this case an online climate decision-support tool called Gulf TREE (www.GulfTREE.org), were studied. Findings included documenting that end-user engagement during climate resilience tool development, while more costly and time intensive, does lead to increased rates of diffusion and adoption of a climate resilience tool through both direct and indirect means. This work demonstrated that pre-development engagement to scope tool development is critical for maximizing relative benefit of a climate resilience tool. Additionally, all phases of engagement are necessary for both a useable and useful tool because each phase contributes to different attributes of the tool. Further research areas identified include understanding how much and what kind of stakeholder engagement is necessary to support continued diffusion and adoption after a tool is released, the role that mandates in climate resilience has on the adoption and diffusion of climate resilience tools, and how to define if a climate resilience tool has been successful

Fdm layering deposition effects on mechanical response of tpu lattice structures

Nowadays, fused deposition modeling additive technology is becoming more and more popular in parts manufacturing due to its ability to reproduce complex geometries with many different thermoplastic materials, such as the TPU. On the other hand, objects obtained through this technology are mainly used for prototyping activities. For this reason, analyzing the functional behavior of FDM parts is still a topic of great interest. Many studies are conducted to broaden the spectrum of materials used to ensure an ever-increasing use of FDM in various production scenarios. In this study, the effects of several phenomena that influence the mechanical properties of printed lattice structures additively obtained by FDM are evaluated. Three different configurations of lattice structures with designs developed from unit cells were analyzed both experimentally and numerically. As the main result of the study, several parameters of the FDM process and their correlation were identified as possible detrimental factors of the mechanical properties by about 50% of the same parts used as isotropic cell solids. The best parameter configurations in terms of mechanical response were then highlighted by numerical analysis

Reconfigurable logic for hardware IP protection: Opportunities and challenges

Protecting the intellectual property (IP) of integrated circuit (IC) design is becoming a significant concern of fab-less semiconductor design houses. Malicious actors can access the chip design at any stage, reverse engineer the functionality, and create illegal copies. On the one hand, defenders are crafting more and more solutions to hide the critical portions of the circuit. On the other hand, attackers are designing more and more powerful tools to extract useful information from the design and reverse engineer the functionality, especially when they can get access to working chips. In this context, the use of custom reconfigurable fabrics has recently been investigated for hardware IP protection. This paper will discuss recent trends in hardware obfuscation with embedded FPGAs, focusing also on the open challenges that must be necessarily addressed for making this solution viable

influence of material and manufacturing technology on the failure behavior of composite laminate bonded joints

Abstract The purpose of this work is to evaluate the influence of co-lamination vs. co-bonding on the failure behavior, and namely the fracture toughness, of carbon fibre reinforced (CFR) composite laminate joints in order to assess comparatively their performance. Since the strength of the laminate and ply texture are parameters affecting the strength of the joint, the comparison is extended to two different types of CFR pre-preg fibers, a satin T1100 with 2573 Nanoalloy® epoxy resin supplied by Toray and a twill T700 with ER450 toughened epoxy resin supplied by CIT, Toray group, representative of two different fields of application, racing and automotive, respectively

Using Static Analysis for Enhancing HLS Security

Due to the increasing complexity of modern integrated circuits, High-Level Synthesis (HLS) is becoming a key technology in hardware design. HLS uses optimizations to assist during design space exploration. However, some of them can introduce security weaknesses. We propose an approach that leverages static analysis to identify a class of weaknesses in HLS-generated code. We show that some of these weaknesses can be corrected through the automatic generation of HLS directives. We evaluate our approach by comparing the static analysis results with formal verification. Our results show that the static approach has the same accuracy as formal methods while being 3× to 200× faster

Optimizing the Use of Behavioral Locking for High-Level Synthesis

The globalization of the electronics supply chain requires effective methods to thwart reverse engineering and IP theft. Logic locking is a promising solution, but there are many open concerns. First, even when applied at a higher level of abstraction, locking may result in significant overhead without improving the security metric. Second, optimizing a security metric is application-dependent and designers must evaluate and compare alternative solutions. We propose a meta-framework to optimize the use of behavioral locking during the high-level synthesis (HLS) of IP cores. Our method operates on chip’s specification (before HLS) and it is compatible with all HLS tools, complementing industrial EDA flows. Our meta-framework supports different strategies to explore the design space and to select points to be locked automatically. We evaluated our method on the optimization of differential entropy, achieving better results than random or topological locking: 1) we always identify a valid solution that optimizes the security metric, while topological and random locking can generate unfeasible solutions; 2) we minimize the number of bits used for locking up to more than 90% (requiring smaller tamper-proof memories); 3) we make better use of hardware resources since we obtain similar overheads but with higher security metric

Designing ML-resilient locking at register-transfer level

Various logic-locking schemes have been proposed to protect hardware from intellectual property piracy and malicious design modifications. Since traditional locking techniques are applied on the gate-level netlist after logic synthesis, they have no semantic knowledge of the design function. Data-driven, machine-learning (ML) attacks can uncover the design flaws within gate-level locking. Recent proposals on register-transfer level (RTL) locking have access to semantic hardware information. We investigate the resilience of ASSURE, a state-of-the-art RTL locking method, against ML attacks. We used the lessons learned to derive two ML-resilient RTL locking schemes built to reinforce ASSURE locking. We developed ML-driven security metrics to evaluate the schemes against an RTL adaptation of the state-of-the-art, ML-based SnapShot attack

An enhanced platform for cell electroporation: controlled delivery and electrodes functionalization

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