Electronic skins (e-skins) are flexible electronic devices that emulate properties of human skin, such as high stretchability and toughness, perception of stimuli, and self-healing. These devices can serve as an alternative to natural human skin or as a human-machine interface ([ 1 ][1]–[ 3 ][2]). For on-skin applications, an e-skin should be multimodal (sense more than one external stimulus), have a high density of sensors, and have low interference with natural skin sensation. On pages 961 and 966 of this issue, You et al. ([ 4 ][3]) and Lee et al. ([ 5 ][4]), respectively, report advances of skin-like electronic devices. You et al. present a stretchable multimodal ionic-electronic (IE) conductor–based “IEM-skin” that can measure both strain and temperature inputs without signal interference. Lee et al. describe an ultrathin capacitive pressure sensor based on conductive and dielectric nanomesh structures that can be attached to a human fingertip for grip pressure and force measurement without affecting natural skin sensation. The human skin contains a large number of mechanoreceptors and thermoreceptors (nerve endings that sense deformation and temperature, respectively) that provide distinct perception of the spatial distributions of strain and temperature on our skin induced by touch stimulations ([ 6 ][5]). To replicate these sensory functions of the natural skin, different types of sensors that act as artificial receptors are integrated onto an e-skin for multimodal sensation ([ 7 ][6]). However, an e-skin containing a high-density array of sensory “pixels” of different types for sensing different physical quantities tends to have a complex structure and is challenging to manufacture. A preferred strategy for realizing multimodal sensation on an e-skin is to use the same sensory unit for detecting different physical quantities without signal interference, an approach called decoupled multimodal sensing. Traditional stretchable sensors are sensitive to both strain and temperature and cannot be used as artificial multimodal receptors without signal interference. Targeting interference-free strain and temperature sensing by a single sensory unit, You et al. creatively used the ion relaxation dynamics of an ion conductor (an elastomer mixed with an ionic liquid) to decouple the strain and temperature measurement and developed the IEM-skin composed of an array of artificial multimodal ionic receptors. They fabricated the IEM-skin by sandwiching a thin layer of ion conductor with two layers of orthogonally patterned stretchable electrode strips (see the figure, top ). A pixelated matrix of millimeter-sized artificial receptors formed between the top and bottom electrodes. The electrical properties of each receptor are affected by the externally applied strain and temperature stimuli and can be measured through impedance measurement. You et al. used a strain-independent intrinsic electrical parameter of the ion conductor, the charge relaxation time, which reflects the ionic charge dynamics of the ion conductor and is equal to the ratio of material's dielectric constant and ion conductivity ([ 8 ][7], [ 9 ][8]). The charge relaxation time is the signal readout for temperature and is not affected by the deformation of the IEM-skin. For strain measurement, the bulk capacitance of the ion conductor is measured. The effect of temperature on the capacitance is eliminated through normalization against a reference capacitance at the temperature measured by the receptor. Thus, an external strain input only changes geometric parameters of the ion conductor, whereas a temperature input primarily modulates the intrinsic electrical properties (dielectric constant and ion conductivity) of the ion conductor. Another enabling factor of the IEM-skin design is its emulation of the epidermis and dermis bilayer of the human skin by suspending the receptor matrix layer over a low-friction interface layer filled with talcum powder. This design allows three-dimensional wrinkle-like deformations of the IEM-skin under different contact modes (such as shear, pinch, tweak, and torsion) and permits the IEM-skin to distinguish these contact modes through the measured temperature and strain profiles. Data confirm that the IEM-skin can perform decoupled, real-time measurement of strain and temperature with high accuracy. The IEM-skin can serve as a human-machine interface that accepts tactile inputs of different contact modes and can be integrated into prosthetic and robotic devices to provide tactile and thermal feedback with high spatial resolution. The concept of using intrinsic electrical parameters, such as conductivity and dielectric constant of sensing materials, for strain-independent temperature sensing can be generalized to developing other types of stretchable multimodal sensors for humidity, chemicals, and biomolecules. One limitation is that the method for recognizing different tactile input modes through the measured temperature and strain profiles only works for interactions with hot or cold objects at temperatures different from that of the IEM-skin. Alternative solutions may include the use of learning-based recognition models purely based on strain-distribution data or modulation of the temperature of the IEM-skin (by adding a heating layer) based on the environment. Skin-like electronic sensors also hold great potential for construction of hand-wearing devices such as instrumented gloves for quantifying tactile signals like force and pressure during finger or in-hand manipulation ([ 10 ][9]). Such data could facilitate the decoding of human hand sensation and its roles in object manipulation and enable better designs of robotic and prosthetic hands with biomimetic sensory feedback ([ 11 ][10]). Targeting imperceptible wearing and tactile sensing on fingertips, Lee et al. developed an ultrathin capacitive pressure sensor consisting of multilayers of conductive and dielectric nanomesh structures. This sensor design is derived from the design of conductive nanomesh electrodes proposed by Miyamoto et al. ([ 12 ][11]), which can be directly laminated on human skin during fabrication. The electrode is fabricated by first electrospinning a water-soluble polymer, polyvinyl alcohol (PVA) into a multilayered mesh-like network of 300- to 500-nm-wide nanofibers. A 100-nm-thick gold layer is then deposited onto the PVA nanomesh sheet, and the gold-coated nanomesh sheet is transferred onto the skin surface. The sacrificial PVA nanofibers are washed off by water, but a residual layer of the dissolved PVA greatly facilitates the attachment of the resultant gold nanomesh layer onto the textured skin surface with excellent adhesion and conformal contact. The skin-integrated nanomesh electrode is stretchable and highly breathable and has exceptionally low bending stiffness, and so it creates no mechanical constraint or dermatological irritation to the skin. To fabricate a nanomesh pressure sensor (see the figure, bottom), Lee et al. first laminated a nanomesh electrode on the skin surface and then sequentially attached a dielectric nanomesh layer made of electrospun polyurethane and parylene nanofibers and another nanomesh electrode layer to form a parallel-plate capacitor structure. Then, a nanomesh passivation layer of polyurethane nanofibers was attached to the top electrode layer with dissolved PVA nanofibers as the filler and adhesive. The total thickness of the nanomesh pressure sensor is ∼13 μm. When fingers wearing such a pressure sensor grip an object, the grip force applied to the pressure sensor deforms the middle dielectric nanomesh layer and leads to a change in the capacitance measured between the top and bottom electrodes as the sensor readout. ![Figure][12] Improved electronic skins Two goals in artificial touch sensors are to sense more than one stimulus with one receptor and to create wearable sensors that maintain natural skin sensation. GRAPHIC: C. BICKEL/ SCIENCE Through object-gripping experiments performed by human participants, Lee et al. investigated the effect of the finger-integrated pressure sensor on the natural fingertip sensation and found no decrease of the sensory feedback caused by the attachment of the pressure sensor. They hypothesized that the ultrathin and compliant structure of the nanomesh pressure sensor renders the device imperceptible on the fingertip. In addition, the intimate and conformal adhesion of the sensor's bottom nanomesh electrode layer to the skin surface may also contribute to the negligible interference of the finger skin sensation by the sensor attachment. This sensor also shows excellent mechanical durability under cyclic compression, shearing, and surface friction, which is attributed to the high mechanical robustness of the multilayered nanomesh structure of the pressure sensor. This work highlights another new application of the previously reported skin-integrated nanomesh electronics ([ 12 ][11]) to wearable physical sensing with unprecedented performance. Future work may involve the further examination of fundamental mechanisms for the on-skin imperceptibility of the nanomesh pressure sensor, the systematic study of the skin-integrated pressure sensor performance for grasping objects of different materials and properties (such as insulating versus conductive, hard versus soft, and smooth versus textured), and the scalable fabrication of pixelated nanomesh pressure sensors in a large area with high density. The nanomesh pressure sensor could record tactile signals of human-hand manipulation that could provide superior sensing performance and zero data artifacts over existing instrumented gloves and e-skins. Multimodal sensation and nonobstructive skin integration are two important features that are desirable in e-skin designs. The studies reported by You et al. and Lee et al. , respectively, provide new solutions to better realize these attractive features with simplified device structures and enhanced sensing performance without impeding natural sensation. These results will inspire new sensor designs and lead to applications of e-skins as wearable health care monitoring, sensory prosthetic and robotic devices, and high-performance human-machine interfaces. 1. [↵][13]1. J. C. Yang et al ., Adv. Mater. 31, 1904765 (2019). [OpenUrl][14] 2. 1. T. R. Ray et al ., Chem. Rev. 119, 5461 (2019). [OpenUrl][15] 3. [↵][16]1. T. Someya, 2. M. Amagai , Nat. Biotechnol. 37, 382 (2019). [OpenUrl][17][CrossRef][18][PubMed][19] 4. [↵][20]1. I. You et al ., Science 370, 961 (2020). [OpenUrl][21][CrossRef][22] 5. [↵][23]1. S. Lee et al ., Science 370, 966 (2020). [OpenUrl][24][CrossRef][25] 6. [↵][26]1. A. Zimmerman, 2. L. Bai, 3. D. D. Ginty , Science 346, 950 (2014). [OpenUrl][27][Abstract/FREE Full Text][28] 7. [↵][29]1. 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Scientists have found that robotic vacuum cleaners could allow snoopers to remotely listen in to household conversations, despite not being fitted with microphones. US experts found they can perform a remote eavesdropping attack on a Xiaomi Roborock robot cleaner by remotely accessing its Lidar readings – which helps these cleaners to avoid bumping into furniture. Lidar is a method for measuring distances by illuminating the target with laser beams and measuring their reflection with a sensor. But Lidar can also capture sound signals by obtaining reflections off of objects in the home, like a rubbish bin, that vibrate due to nearby sound sources, such as a person talking. A hacker could repurpose a vacuum's Lidar sensor to sense acoustic signals in the environment, remotely harvest the Lidar data from the cloud and process the raw signal with deep learning techniques to extract audio information.

Devastated by industrial crisis, America's former "steel city" has reinvented itself as an innovation hub. But today its main challenge is to keep its "One Pittsburgh" promise by ensuring that everybody in its diverse population shares the benefits of new growth. Pittsburgh is back from the brink. A flagship of triumphant industrialisation in the early 20th century, the city has since seen its steel mills decline and then shut down. As the economy lurched from one crisis to another, Pennsylvania's rusting "steel city" became an emblem of decline, like other urban "dead stars" in the rustbelt of America's Middle West. But Pittsburgh never gave up.

Understanding the set of elementary steps and kinetics in each reaction is extremely valuable to make informed decisions about creating the next generation of catalytic materials. With physical and mechanistic complexity of industrial catalysts, it is critical to obtain kinetic information through experimental methods. As such, this work details a methodology based on the combination of transient rate/concentration dependencies and machine learning to measure the number of active sites, the individual rate constants, and gain insight into the mechanism under a complex set of elementary steps. This new methodology was applied to simulated transient responses to verify its ability to obtain correct estimates of the micro-kinetic coefficients. Furthermore, experimental CO oxidation data was analyzed to reveal the Langmuir-Hinshelwood mechanism driving the reaction. As oxygen accumulated on the catalyst, a transition in the mechanism was clearly defined in the machine learning analysis due to the large amount of kinetic information available from transient reaction techniques. This methodology is proposed as a new data driven approach to characterize how materials control complex reaction mechanisms relying exclusively on experimental data.

Machine learning and other artificial intelligence (AI) algorithms are becoming more commonplace in modern-day society. They are starting to become a very valuable tool for chemical research--at both the fundamental research and industrial-scale optimisation levels. This is primarily due to the rise in computational chemistry methods which use simulations and advanced numerical algorithms to predict best how molecules will behave (and how they will look structurally in the case of complex systems). While research is going into a lot of different chemicals, the various allotropes of elemental phosphorus (i.e. Still, it is relatively hard to simulate using conventional computational methods compared to other elements and molecules.

Time series modelling is essential for solving tasks such as predictive maintenance, quality control and optimisation. Deep learning is widely used for solving such problems. When managing complex manufacturing process with neural networks, engineers need to know why machine learning model made specific decision and what are possible outcomes of following model recommendation. In this paper we develop framework for capturing and explaining temporal dependencies in time series data using deep neural networks and test it on various synthetic and real world datasets.

While Artificial Intelligence (AI) is a much touted technology in mining, it would seem that the sector is yet to fully embrace this advance technology. Why is this and how can we insure that AI can be beneficial to mining in Africa. According to Prof. Frederick Cawood, Director of Wits Mining Institute at the University of the Witwatersrand, it will take a policy change to ensure that it can benefit mining in Africa. Cawood was a panellist on a recent Mining Review Africa webinar titled Mining 2025: A 5-year vision for AI in mining. Cawood was joined on the panel by Eric Croeser, MD for Africa at Accenture Industry X and Jean-Jacques Verhaeghe, programme manager for real-time information management systems at Mandela Mining Precinct.

I love talking about business models because in the end, it's usually the best business model, not the best technology, that wins the day. And digital transformation has the potential to reinvent business models by leveraging superior customer, product and operational insights to disrupt industry value chains and disintermediate customer relationships (see Figure 1). As the title of the book "Moneyball" states ("Moneyball: The Art of Winning an Unfair Game"), some of these reinvented business models will be based on "winning an unfair game". We have historical lessons about how Robber Barons[1] of the late 1800's created and won "an unfair game" that gave them monopoly power over suppliers, customers and competitors. To create this unfair game, Robber Barons leveraged a concept called "vertical integration" to dominate industry value chains and construct indissoluble customer and supplier dependencies. Let's review the lessons of these Robber Barons to understand how digital transformation might enable modern companies to win the digital unfair game.

At least a quarter of the warming that the Earth is experiencing today is due to anthropogenic methane emissions. There are multiple satellites in orbit and planned for launch in the next few years which can detect and quantify these emissions; however, to attribute methane emissions to their sources on the ground, a comprehensive database of the locations and characteristics of emission sources worldwide is essential. In this work, we develop deep learning algorithms that leverage freely available high-resolution aerial imagery to automatically detect oil and gas infrastructure, one of the largest contributors to global methane emissions. We use the best algorithm, which we call OGNet, together with expert review to identify the locations of oil refineries and petroleum terminals in the U.S. We show that OGNet detects many facilities which are not present in four standard public datasets of oil and gas infrastructure. All detected facilities are associated with characteristics known to contribute to methane emissions, including the infrastructure type and the number of storage tanks.

Progressive digitalization is changing the game of many industrial sectors. Focus-ing on product quality the main profitability driver of this so-called Industry 4.0 will be the horizontal integration of information over the complete supply chain. Therefore, the European RFCS project 'Quality4.0' aims in developing an adap-tive platform, which releases decisions on product quality and provides tailored information of high reliability that can be individually exchanged with customers. In this context Machine Learning will be used to detect outliers in the quality data. This paper discusses the intermediate project results and the concepts developed so far for this horizontal integration of quality information.