A Global Assessment of Climate Change Adaptation in Marine Protected Area Management Plans

Marine protected area (MPA) efficacy is increasingly challenged by climate change. Experts have identified clear climate change adaptation principles that MPA practitioners can incorporate into MPA management; however, adoption of these principles in MPA management remains largely unquantified. We conducted a text analysis of 647 English-language MPA management plans to assess the frequency with which they included climate change-related terms and terms pertaining to ecological, physical, and sociological components of an MPA system that may be impacted by climate change. Next, we manually searched 223 management plans to quantify the plans’ climate change robustness, which we defined as the degree of incorporation of common climate change adaptation principles. We found that climate change is inadequately considered in MPA management plans. Of all plans published since 2010, only 57% contained at least one of the climate change-related terms, “climate change,” “global warming,” “extreme events,” “natural variability,” or “climate variability.” The mean climate change robustness index of climate-considering management plans was 10.9 or 39% of a total possible score of 28. The United States was the only region that had plans with climate robustness indices of 20 or greater. By contrast, Canada lags behind other temperate jurisdictions in incorporating climate change adaptation analysis, planning, and monitoring into MPA management, with a mean climate change robustness index of 6.8. Climate change robustness scores have generally improved over time within the most common MPA designations in Oceania, the United Kingdom, and the United States, though the opposite is true in Canada. Our results highlight the urgent need for practitioners to incorporate climate change adaptation into MPA management in accordance with well-researched frameworks.

Testing the Robustness of Commercial Lane Departure Warning Systems

Assessment methods are needed to rate the performances of advanced driver assistance systems in a range of real-world conditions, enabling the possibility of mandating minimum performance requirements beyond standardized, regulatory pass-or-fail tests, and ultimately ensuring a real and objectively measurable safety benefit. To bridge the gap between regulatory and real-world performance, this work presents a novel robustness assessment methodology and defines a robustness index determined from regulatory tests to analyze the real-world performance of lane departure warning (LDW) systems. In this context, a robust system means that it is insensitive to changes in driving conditions or environmental conditions. Distance to line (DTL) and time to line crossing (TTLC) were calculated for a light truck and a passenger car, and a black box model of the LDW systems was developed to predict their performance over different lane markings, drifting directions, and vehicle lateral and longitudinal speeds. During the test, neither of the vehicles triggered warning in around 10% of the trials despite the perfect condition of the markings painted on the proving ground. The type of lane marking significantly influenced DTL for both vehicles. For the light truck, the drifting direction, marking type, and their interaction were found to be statistically significant, which resulted in a lower robustness index than that of the passenger car. For both vehicles, TTLC was inversely proportional to the lateral speed, which greatly influences crash avoidance.

Robustness Optimization of Product Assembly Architecture for Personalization

Personalization has received extensive attention as a new manufacturing paradigm to address increased market demand for personalized products. An open product architecture that assembling common, customized, and personalized modules is regarded as a key enabler for product personalization, which can deliver one-of-a-kind products for individual customers at near mass production efficiency. Offering the best product architecture should consider the variations in design variables and parameters that influence the performance of a product architecture. This is especially true when designing open architecture for personalized products as many uncertain design quantities need to be considered in early product design stage. A robustness optimization method is proposed to simultaneously optimize product variety, module variant selection, and configuration of personalized module variants for a personalized assembly architecture. First, a profit model is developed to measure the performance of a product architecture, which incorporates individual customer preferences and manufacturing cost. A three-step process is proposed to model heterogeneous customer preferences: conjoint analysis of the preferences of a sample of customers from target market, market segmentation by a multi-variate normal mixture method, and simulation of personal preferences for a broader market by Monte-Carlo simulation. Thus, the simulated individual customer preferences are used to predict the sales and profit of product offerings. Second, the variation of profit associated with a product family architecture due to the uncertainty in customer preference and manufacturing cost estimates is formulated by a sensitivity analysis. A robustness index is defined by combining the objectives of maximizing profit and minimizing its variation. Lastly, a robustness optimization model is established to optimize product architecture by maximizing its robustness index. The proposed method is demonstrated with a personalized bicycle architecture design example.

Robustness analysis of technological units for drinking water clarification: Normal and emergency operating conditions

The primary goal of a water supply system is the protection of human health by providing microbiologically and chemically safe drinking water. Significant changes in water quality require sufficiently robust systems for water preparation, performances of which are unaffected by present variations and changing operational conditions. Water turbidity is an important parameter for the water filtration control and efficiency of disinfection. The efficiency of turbidity removal in the drinking water treatment plant ?Vodovod? in Banjaluka under normal and emergency operating conditions was examined in this paper. At normal conditions the maximal detected value was 25 NTU while at emergency operating conditions it was above 240 NTU. Robustness evaluation of the water clarification system was performed separately for periods of normal and emergency operating conditions (during and after emptying the accumulation). The robustness index was calculated based on a more stringent target turbidity value (0.5 NTU) than that specified by the current legislation, which represents a new criterion in the risk analysis in the existing practice. Data processing results indicate high operational stability of technological units under normal conditions. The filtered water quality was below the target value during most of the time of filter operation in all cycles. The recorded turbidity value was ? 0.3 NTU for 92.9 % of filtered water samples. Analysis of the water turbidity data has shown that 17% of all taken measurements under emergency operating conditions (336 samples) had higher turbidity than the target value (0.5 NTU). Large variations in raw water turbidity over short periods of times during the emergency operating conditions, present a problem for prompt response in the drinking water plant. Calculated robustness index values point to inadequate efficiency of the water clarification process in a certain number of filter operating cycles. We have found a significant impact of the plant operating conditions on the filtered water turbidity under emergency conditions, such as suboptimal coagulation and flocculation conditions as well as the nature of suspended and colloid particles inducing turbidity and insufficient particle interactions with the coagulant. Along with the negative influence on water turbidity, excessive coagulant dosage leads to increased concentrations of residual aluminum in filtered water. Optimization of emergency working conditions could be performed based on adequate monitoring of water sources, which would further decrease potential risks of pathogen appearance in drinking water.

Phytotoxic Effect of Brachiaria decumbens Extract on the Development of Eucalyptus urograndis Seedlings

The objective of this work was to evaluate the phytotoxic effect of Brachiaria decumbens Stapf. on the morphometry of Eucalyptus urograndis plants, genetically modified hybrid of Eucalyptus urophylla S. T. Blake × Eucalyptus grandis W. Hill ex Maiden. Pots containing the eucalyptus plants were conditioned in protected environments in a completely randomized experimental design. The treatments were composed of aqueous extract of Brachiaria decumbens grass at different concentrations: 0% (control, distilled water only), 20%, 40%, 60%, 80% and 100% (g/L). On day 30 of the experiment, the following morphological parameters of the plants were evaluated: aerial part height, diameter of stem, aerial dry mass, root dry mass, total dry mass, and robustness index. The data were submitted to analysis of variance at the 5% level of significance, and a regression equation was constructed for the quantitative treatments. The results indicated that the Brachiaria decumbens grass extract did not have an inhibitory effect on aerial part height and robustness index. However, the extract at concentrations higher than 20% had negative effects on root dry mass, aerial dry mass, and total dry mass. For the diameter of the stem, only concentrations above 60% had an inhibitory effect.

Model-Based Grasping of Unknown Objects from a Random Pile

Grasping an unknown object in a pile is no easy task for a robot—it is often difficult to distinguish different objects; objects occlude one another; object proximity limits the number of feasible grasps available; and so forth. In this paper, we propose a simple approach to grasping unknown objects one by one from a random pile. The proposed method is divided into three main actions—over-segmentation of the images, a decision algorithm and ranking according to a grasp robustness index. Thus, the robot is able to distinguish the objects from the pile, choose the best candidate for grasping among these objects, and pick the most robust grasp for this candidate. With this approach, we can clear out a random pile of unknown objects, as shown in the experiments reported herein.