Untethered soft robots that locomote using electrothermally-responsive materials like shape memory alloy (SMA) face challenging design constraints for sensing actuator states. At the same time, modeling of actuator behaviors faces steep challenges, even with available sensor data, due to complex electrical-thermal-mechanical interactions and hysteresis. This article proposes a framework for in-situ sensing and dynamics modeling of actuator states, particularly temperature of SMA wires, which is used to predict robot motions. A planar soft limb is developed, actuated by a pair of SMA coils, that includes compact and robust sensors for temperature and angular deflection. Data from these sensors are used to train a neural network based on the long short-term memory (LSTM) architecture to model both unidirectional (single SMA) and bidirectional (both SMAs) motion. Predictions from the model demonstrate that data from the temperature sensor, combined with control inputs, allow for dynamics predictions over extraordinarily long open-loop timescales (10 minutes) with little drift. Prediction errors are on the order of the soft deflection sensor's accuracy. This architecture allows for compact designs of electrothermally-actuated soft robots that include sensing sufficient for motion predictions, helping to bring these robots into practical application.

In its early days, robotic process automation emerged from rudimentary screen scraping, macros and workflow automation software. Once a script-heavy and limited tool that was almost exclusively used to perform mundane tasks for individual users, RPA has evolved into an enterprisewide megatrend that puts automation at the center of digital business initiatives. In this Breaking Analysis, we present our quarterly update of the trends in RPA and share the latest survey data from Enterprise Technology Research. The new momentum in RPA is around enterprisewide automation initiatives. Once exclusively focused on back office automation in areas such as finance, RPA has now become an enterprise transformation catalyst for many larger organizations. Initially focused on cost savings in the finance department and other back-office functions, RPA has moved beyond the purview of the chief financial officer.

The latest research study released by HTF MI evaluating the market risk side analysis, highlighting opportunities and leveraged with strategic and tactical decision-making support. The market Study is segmented by key a region that is accelerating the marketization. The oil and gas (O&G) industry faces many severe challenges. The shortage of easily accessible hydrocarbon reserves forces companies to use remote reserves that are hard to discover, costly, and risky. Moreover, sustainability concerns are shifting demand away from O&G toward cleaner sources, and COVID-19 has further suppressed the demand.

AI is no longer exclusively for digital native companies like Amazon, Netflix, or Uber. Dow Chemical Company recently used machine learning to accelerate its R&D process for Polyurethane formulations by 200,000x -- from 2–3 months to just 30 seconds. A recent index from Deloitte shows how companies across sectors are operationalizing AI to drive business value. Unsurprisingly, Gartner predicts that more than 75% of organizations will shift from piloting AI technologies to operationalizing them by the end of 2024 -- which is where the real challenges begin. AI is most valuable when it is operationalized at scale. For business leaders who wish to maximize business value using AI, scale refers to how deeply and widely AI is integrated into an organization's core product or service and business processes.

What if there's a quick way to screen molecules and predict their reactivity and other properties? Certainly this will make drug and material design much faster because chemists could then focus more on the most promising compounds instead of trying them all. This is what the Merck Molecular Activity Challenge somehow illustrates. Here, the goal is to predict biological activities of different molecules, both on- and off-target, given numerical descriptors generated from their chemical structures. In other words, we have to predict whether a certain molecule will become highly active towards the intended target and "inert" to others (thereby minimal or zero side effects).

An expert on the future of food responds to JoeAnn Hart's "Good Job, Robin." The first time I seriously considered crickets as the food of the future was in late 2015 during a presentation by undergraduates. Their policy proposal outlining how the adoption of insect protein in the Los Angeles Area could help insulate the region from some of the impacts of climate-change included a tasting of a recent-to-market, paleo-friendly, cricket-based protein bar. As I sunk my teeth into the slightly gummy, peanut-buttery bite being passed around the classroom, my mind flashed between the grim food futures presented in science fiction novels and the much smaller collection of hopeful fiction portrayals of delicious future feasts. What is it about our contemporary anxieties that makes it so easy to imagine such dystopic food futures?

Imagine a field of wheat that extends to the horizon, being grown for flour that will be made into bread to feed cities' worth of people. Imagine that all authority for tilling, planting, fertilizing, monitoring and harvesting this field has been delegated to artificial intelligence: algorithms that control drip-irrigation systems, self-driving tractors and combine harvesters, clever enough to respond to the weather and the exact needs of the crop. Then imagine a hacker messes things up. A new risk analysis, published today in the journal Nature Machine Intelligence, warns that the future use of artificial intelligence in agriculture comes with substantial potential risks for farms, farmers and food security that are poorly understood and under-appreciated. "The idea of intelligent machines running farms is not science fiction. Large companies are already pioneering the next generation of autonomous ag-bots and decision support systems that will replace humans in the field," said Dr. Asaf Tzachor in the University of Cambridge's Center for the Study of Existential Risk (CSER), first author of the paper.

Department of Labor)-backed mine safety mission – achieved a historic unmanned underground mine inspection at one of the US' largest underground room and pillar limestone operations in this comprehensive IM report. Using ten ADR Explora XL unmanned robots, a Rajant wireless Kinetic Mesh below-ground communication network, and PBE hardware and technology, a horizontal mobile infrastructure distance of 1.7 km was achieved. This allowed the unmanned robots to record high-definition video and LiDAR to create a virtual 3D mine model to assess the condition of the mine, for the deepest remote underground mine inspection in history. The inspection made it possible for MSHA to conclude within a very short time that it was safe to re-enter the operation and begin remediation efforts, which included allowing mine personnel back into the mine to re-establish power and communications, after which mining was able to recommence quickly at the site. The project, in many ways, is the ultimate example of necessity breeding innovation.

According to the institute, its forecasting solution will help farmers deal with crop diseases in a timely manner and curb overuse of pesticides, which is rampant due to the lack of accurate information about the extent of crop infection. IIIT Naya Raipur's forecasting solution uses drones to monitor crops and capture live images if it detects any issues in them. The images are then sent from the drone in real time to the institute's servers, where an image classification model based on convolutional neural networks (CNN) is used to identify the disease and insects that are affecting it. CNNs are AI algorithms commonly used for image and video recognition. They can process an image, assign importance to its various attributes, and differentiate one image from another.

Inhomogeneities in the aortic wall can lead to localized stress accumulations, possibly initiating dissection. In many cases, a dissection results from pathological changes such as fragmentation or loss of elastic fibers. But it has been shown that even the healthy aortic wall has an inherent heterogeneous microstructure. Some parts of the aorta are particularly susceptible to the development of inhomogeneities due to pathological changes, however, the distribution in the aortic wall and the spatial extent, such as size, shape, and type, are difficult to predict. Motivated by this observation, we describe the heterogeneous distribution of elastic fiber degradation in the dissected aortic wall using a stochastic constitutive model. For this purpose, random field realizations, which model the stochastic distribution of degraded elastic fibers, are generated over a non-equidistant grid. The random field then serves as input for a uni-axial extension test of the pathological aortic wall, solved with the finite-element (FE) method. To include the microstructure of the dissected aortic wall, a constitutive model developed in a previous study is applied, which also includes an approach to model the degradation of inter-lamellar elastic fibers. Then to assess the uncertainty in the output stress distribution due to this stochastic constitutive model, a convolutional neural network, specifically a Bayesian encoder-decoder, was used as a surrogate model that maps the random input fields to the output stress distribution obtained from the FE analysis. The results show that the neural network is able to predict the stress distribution of the FE analysis while significantly reducing the computational time. In addition, it provides the probability for exceeding critical stresses within the aortic wall, which could allow for the prediction of delamination or fatal rupture.