Nasa's Opportunity rover is officially dead, the space agency has said, after it disappeared in a dust storm on Mars. Clearly emotional Nasa staff, standing in front of a life-sized replica of the rover, said they had not heard back from the rover and that the mission would come to an end. Scientists described the difficult process of saying goodbye to the rover, which they had nicknamed Oppy and described as being like a beloved member of the family. "I am standing here with a sense of deep appreciation and gratitude," said Nasa associate administrator Thomas Zurbuchen, before he announced that the Opportunity mission is now considered complete. The robot set a huge number of records as it travelled across the Martian surface, eventually travelling some 28 miles and lasting far longer than any other Mars lander.

Nasa has sent its last message to the Opportunity Mars rover. But it doesn't expect a reply. The robot has been silent for the past eight months, disappearing amid an intense dust storm on the red planet. As the thick dust whipped up and around the rover – and across the entirety of Mars – the sunlight that powers it was blocked and its batteries ran out. Nasa has now issued a last series of recovery commands.

Nasa is about to announce the future of the Opportunity rover, a pioneering robot that has spent years exploring the Martian surface. The space agency hasn't heard from its rover for eight months, when it disappeared and has not been heard from since. Now Nasa will send its last messages to the rover. But more than 1,000 have already been sent, with no reply from the rover, which is now facing a cold winter that will almost certainly kill it off if it doesn't manage to wake up. It's just as hard to say goodbye to Opportunity, as it was to its fellow rover Spirit, project manager John Callas told The Associated Press.

Meteorological forecasting provides reliable prediction about the future weather within a given interval of time. Meteorological forecasting can be viewed as a form of hybrid diagnostic reasoning and can be mapped onto an integrated conceptual framework. The automation of the forecasting process would be helpful in a number of contexts, in particular: when the amount of data is too wide to be dealt with manually; to support forecasters education; when forecasting about underpopulated geographic areas is not interesting for everyday life (and then is out from human forecasters' tasks) but is central for tourism sponsorship. We present logic MeteoLOG, a framework that models the main steps of the reasoner the forecaster adopts to provide a bulletin. MeteoLOG rests on several traditions, mainly on fuzzy, temporal and probabilistic logics. On this basis, we also introduce the algorithm Tournament, that transforms a set of MeteoLOG rules into a defeasible theory, that can be implemented into an automatic reasoner. We finally propose an example that models a real world forecasting scenario.

Computer simulators are nowadays widely used to understand complex physical systems in many areas such as aerospace, renewable energy, climate modeling, and manufacturing. One fundamental issue in the study of computer simulators is known as experimental design, that is, how to select the input settings where the computer simulator is run and the corresponding response is collected. Extra care should be taken in the selection process because computer simulators can be computationally expensive to run. The selection shall acknowledge and achieve the goal of the analysis. This article focuses on the goal of producing more accurate prediction which is important for risk assessment and decision making. We propose two new methods of design approaches that sequentially select input settings to achieve this goal. The approaches make novel applications of simultaneous and sequential contour estimations. Numerical examples are employed to demonstrate the effectiveness of the proposed approaches.

Cross-validation of predictive models is the de-facto standard for model selection and evaluation. In proper use, it provides an unbiased estimate of a model's predictive performance. However, data sets often undergo a preliminary data-dependent transformation, such as feature rescaling or dimensionality reduction, prior to cross-validation. It is widely believed that such a preprocessing stage, if done in an unsupervised manner that does not consider the class labels or response values, has no effect on the validity of cross-validation. In this paper, we show that this belief is not true. Preliminary preprocessing can introduce either a positive or negative bias into the estimates of model performance. Thus, it may lead to sub-optimal choices of model parameters and invalid inference. In light of this, the scientific community should re-examine the use of preliminary preprocessing prior to cross-validation across the various application domains. By default, all data transformations, including unsupervised preprocessing stages, should be learned only from the training samples, and then merely applied to the validation and testing samples.

As the human population has grown, our demands on the ocean have increased rapidly. These demands have similarly increased the pressure we place on these systems, and we now cause considerable damage globally. If we want to maintain healthy ocean ecosystems into the future, we must learn to use ocean resources in a sustainable way and facilitate recovery in regions that have declined. Determining how to make these goals a reality, however, is no small challenge. Ingeman et al. review the challenge presented by attempting both to recover and to use ecosystems simultaneously and discuss several approaches for facilitating this essential dual goal. Ocean defaunation and loss of marine ecosystem services present an urgent need to recover degraded ocean ecosystems. Growing scientific awareness, strong regulations, and effective management have begun to fulfill the promise of recovery. Unfortunately, many efforts remain unsuccessful, in part because marine ecosystems and human societies are changing. Rapid shifts in environmental conditions are undermining previously effective recovery strategies. Moreover, divergent perceptions of recovery exist. Efforts toward reversing marine degradation must address the dynamic social-ecological landscape in which recoveries occur, or forever chase a moving target. Recovery efforts of tomorrow will require institutional and tactical flexibility to keep pace with a changing ocean, and an inclusive concept of recovery. Further, vital population-level efforts will be most successful when complemented by a broader ecosystem and social-ecological perspective. In this Review, we provide a synthesis of ocean-recovery goals as moving targets and highlight promising steps forward. While acknowledging the priority of basic conservation imperatives, successful recoveries can encompass a range of outcomes in the space between minimum ecological viability and maximum carrying capacity. Ongoing advances are improving our ability to predict the effects of environmental change on ocean productivity and to calibrate recovery targets to changing conditions. As a complement to predict-and-prescribe methods, research can also point the way toward robust approaches in the face of irreducible uncertainty.

Artificial intelligence, energy diversification and the transformation of the workforce will be amongst the major talking points at SPE Offshore Europe 2019. Senior international industry figures will co-chair the keynote sessions which also includes late life and decommissioning, underwater innovation, transformative technologies to lower the carbon footprint, digital security, integrated technologies, digitalisation, standardisation and finance. The event will take place from 3-6 September at the new £333million The Event Complex Aberdeen (TECA), under the theme: 'Breakthrough to Excellence – Our license to operate'. Michael Borrell, SPE Offshore Europe 2019 Conference Chair & Senior Vice President, North Sea and Russia at Total said: "Our committee is full of international oil and gas industry leaders and they have developed an excellent programme which gets to the heart of the main opportunities and challenges facing the region. "Offshore Europe 2019 is a great opportunity for us to challenge ourselves in the North Sea basin.

Abstract-- We applied predefined kernels also known as filters or masks developed for image processing to convolution neural network. Instead of letting neural networks find its own kernels, we used 41 different general-purpose kernels of blurring, edge detecting, sharpening, discrete cosine transformation, etc. for the first layer of the convolution neural networks. This architecture, thus named as general filter convolutional neural network (GFNN), can reduce training time by 30% with a better accuracy compared to the regular convolutional neural network (CNN). GFNN also can be trained to achieve 90% accuracy with only 500 samples. Furthermore, even though these kernels are not specialized for the MNIST dataset, we achieved 99.56% accuracy without ensemble nor any other special algorithms.

With the help of artificial intelligence, BP says it needs 40% fewer workers to keep its natural gas flowing in Wyoming. A visitor to one of BP's natural gas fields in Wyoming a few years ago might have noticed an odd sight: smartphones in plastic bags tied to pumps with zip ties. This was an early test of a multistate initiative by the oil giant to link a network of Wi-Fi sensors to an artificial intelligence system--one that now operates the Wamsutter field in Wyoming with far less human oversight than before. Artificial intelligence has come to the oil patch, accelerating a technical change that is transforming the conditions for the oil and gas industry's 150,000 U.S. workers. Giant energy companies like Shell and BP are investing billions to bring artificial intelligence to new refineries, oilfields and deepwater drilling platforms.