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Much effort has been expended on building cellular computational devices for different applications. Despite the significant advances, there are still several addressable restraints to achieve the necessary technological transference. These improvements will ease the development of end-user applications working out of the lab. In this study, we propose a methodology for the construction of printable cellular devices, digital or analogue, for different purposes. These printable devices are designed to work in a 2D surface, in which the circuit information is encoded in the concentration of a biological signal, the so-called carrying signal. This signal diffuses through the 2D surface and thereby interacts with different device components. These components are distributed in a specific spatial arrangement and perform the computation by modulating the level of the carrying signal in response to external inputs, determining the final output. For experimental validation, 2D cellular circuits are printed on a paper surface by using a set of cellular inks. As a proof-of-principle, we have printed and analysed both digital and analogue circuits using the same set of cellular inks but with different spatial topologies. The proposed methodology can open the door to a feasible and reliable industrial production of cellular circuits for multiple applications. Synthetic biology circuits are finding application in a wide range of computational devices, such as contaminant detection. Here, the authors design 2D paper circuits in which the spatial orientation of the cellular components specifies function. [ABSTRACT FROM AUTHOR]

Natural resources policies that promote sustainable management are critical for protecting diverse stakeholders against depletion. Although integrating diverse stakeholders into these policies has been theorized to improve protection, empirical evidence is lacking. Here, we evaluate 108 Sustainability Plans under California's Sustainable Groundwater Management Act to quantify how well stakeholders are integrated into plans and protected from groundwater depletion. We find that the majority of Sustainability Plans do not integrate or protect the majority of their stakeholders. Nevertheless, our results show that when stakeholders are more integrated into a Sustainability Plan, they are more likely to be protected, particularly for those that lack formal access to decision-making processes. Our findings provide strong empirical evidence that integrating diverse stakeholders into sustainability planning is beneficial for stakeholders who are vulnerable to the impacts of natural resource depletion. This paper shows that integrating diverse stakeholders into management plans results in better protection, especially for those most vulnerable to the impacts of natural resource depletion. [ABSTRACT FROM AUTHOR]

The mechanism of how interfacial wettability impacts the CO2 electroreduction pathways to ethylene and ethanol remains unclear. This paper describes the design and realization of controllable equilibrium of kinetic-controlled *CO and *H via modifying alkanethiols with different alkyl chain lengths to reveal its contribution to ethylene and ethanol pathways. Characterization and simulation reveal that the mass transport of CO2 and H2O is related with interfacial wettability, which may result in the variation of kinetic-controlled *CO and *H ratio, which affects ethylene and ethanol pathways. Through modulating the hydrophilic interface to superhydrophobic interface, the reaction limitation shifts from insufficient supply of kinetic-controlled *CO to that of *H. The ethanol to ethylene ratio can be continuously tailored in a wide range from 0.9 to 1.92, with remarkable Faradaic efficiencies toward ethanol and multi-carbon (C2+) products up to 53.7% and 86.1%, respectively. A C2+ Faradaic efficiency of 80.3% can be achieved with a high C2+ partial current density of 321 mA cm−2, which is among the highest selectivity at such current densities. The mechanism of how interfacial wettability impacts the CO2 electcgq Herein, the authors describe the design and realization of controllable equilibrium of kinetic controlled *CO and *H to reveal its contribution to ethylene and ethanol pathways. [ABSTRACT FROM AUTHOR]

Subseasonal forecasting—predicting temperature and precipitation 2 to 6 weeks ahead—is critical for effective water allocation, wildfire management, and drought and flood mitigation. Recent international research efforts have advanced the subseasonal capabilities of operational dynamical models, yet temperature and precipitation prediction skills remain poor, partly due to stubborn errors in representing atmospheric dynamics and physics inside dynamical models. Here, to counter these errors, we introduce an adaptive bias correction (ABC) method that combines state-of-the-art dynamical forecasts with observations using machine learning. We show that, when applied to the leading subseasonal model from the European Centre for Medium-Range Weather Forecasts (ECMWF), ABC improves temperature forecasting skill by 60–90% (over baseline skills of 0.18–0.25) and precipitation forecasting skill by 40–69% (over baseline skills of 0.11–0.15) in the contiguous U.S. We couple these performance improvements with a practical workflow to explain ABC skill gains and identify higher-skill windows of opportunity based on specific climate conditions. This paper proposes a low-cost machine learning correction for physics-based dynamical models that improves subseasonal forecasting of temperature and precipitation two to six weeks ahead. [ABSTRACT FROM AUTHOR]

Cost-effective coastal flood adaptation requires a realistic valuation of losses, costs and benefits considering the uncertainty of future flood projections and limited resources for adaptation. Here we present an approach to quantify the flood protection benefits of beaches accounting for the dynamic interaction of storm erosion, long-term shoreline evolution and flooding. We apply the method in Narrabeen-Collaroy (Australia) considering uncertainty in different shared socioeconomic pathways, sea-level rise projections, and beach conditions. By 2100, results show that failing to consider erosion can underestimate flood damage by a factor of 2 and maintaining present-day beach width can avoid 785 million AUD worth assets from flood damage. By 2050, the flood protection and recreational benefits of holding the current mean shoreline could be more than 150 times the cost of nourishment. Our results give insight on the benefits of beaches for adaptation and can help accelerate financial instruments for restoration. This paper presents a method for quantifying the benefits of beaches in reducing storm and long-term coastal flood risk. This method can contribute to cost-effective decision-making on climate change adaptation in many of the world's coasts. [ABSTRACT FROM AUTHOR]

Synthesis of buckybowls have stayed highly challenging due to the large structural strain caused by curved π surface. In this paper, we report the synthesis and properties of two trichalcogenasupersumanenes which three chalcogen (sulfur or selenium) atoms and three methylene groups bridge at the bay regions of hexa-peri-hexabenzocoronene. These trichalcogenasupersumanenes are synthesized quickly in three steps using an Aldol cyclotrimerization, a Scholl oxidative cyclization, and a Stille type reaction. X-ray crystallography analysis reveals that they encompass bowl diameters of 11.06 Å and 11.35 Å and bowl depths of 2.29 Å and 2.16 Å for the trithiasupersumanene and triselenosupersumanene, respectively. Furthermore, trithiasupersumanene derivative with methyl chains can form host-guest complexes with C60 or C70, which are driven by concave-convex π ⋯ π interactions and multiple C–H ⋯ π interactions between bowl and fullerenes. Synthesis of buckybowls has remained challenging due to the inherent high strain induced by the curvature. Herein, the authors report the synthesis of two bowl-shaped polycyclic aromatics with three chalcogen atoms and three methylene groups embedded at the bay regions of hexa-peri-hexabenzocoronene and demonstrate guest host complexation with trithiasupersumanene. [ABSTRACT FROM AUTHOR]

Compared to most ATP-site kinase inhibitors, small molecules that target an allosteric pocket have the potential for improved selectivity due to the often observed lower structural similarity at these distal sites. Despite their promise, relatively few examples of structurally confirmed, high-affinity allosteric kinase inhibitors exist. Cyclin-dependent kinase 2 (CDK2) is a target for many therapeutic indications, including non-hormonal contraception. However, an inhibitor against this kinase with exquisite selectivity has not reached the market because of the structural similarity between CDKs. In this paper, we describe the development and mechanism of action of type III inhibitors that bind CDK2 with nanomolar affinity. Notably, these anthranilic acid inhibitors exhibit a strong negative cooperative relationship with cyclin binding, which remains an underexplored mechanism for CDK2 inhibition. Furthermore, the binding profile of these compounds in both biophysical and cellular assays demonstrate the promise of this series for further development into a therapeutic selective for CDK2 over highly similar kinases like CDK1. The potential of these inhibitors as contraceptive agents is seen by incubation with spermatocyte chromosome spreads from mouse testicular explants, where they recapitulate Cdk2-/- and Spdya-/- phenotypes. Despite the therapeutic interest in targeting CDK2, developing a selective CDK2 inhibitor has been challenging. Here, the authors describe a potent and selective CDK2 inhibitor that binds an allosteric pocket, preventing activating protein partners from binding and showing potential as a contraceptive. [ABSTRACT FROM AUTHOR]

Marine heatwaves (MHWs) are prolonged extreme warm water events in the ocean, exerting devastating impacts on marine ecosystems. A comprehensive knowledge of physical processes controlling MHW life cycles is pivotal to improve MHW forecast capacity, yet it is still lacking. Here, we use a historical simulation from a global eddy-resolving climate model with improved representation of MHWs, and show that heat flux convergence by oceanic mesoscale eddies acts as a dominant driver of MHW life cycles over most parts of the global ocean. In particular, the mesoscale eddies make an important contribution to growth and decay of MHWs, whose characteristic spatial scale is comparable or even larger than that of mesoscale eddies. The effect of mesoscale eddies is spatially heterogeneous, becoming more dominant in the western boundary currents and their extensions, the Southern Ocean, as well as the eastern boundary upwelling systems. This study reveals the crucial role of mesoscale eddies in controlling the global MHW life cycles and highlights that using eddy-resolving ocean models is essential, albeit not necessarily fully sufficient, for accurate MHW forecasts. New paper by Bian and colleagues shows that oceanic mesoscale eddies act as a dominant driver of #marine #heatwave life cycles over most parts of the global #ocean. [ABSTRACT FROM AUTHOR]

Institutions in highly regulated domains such as finance and healthcare often have restrictive rules around data sharing. Federated learning is a distributed learning framework that enables multi-institutional collaborations on decentralized data with improved protection for each collaborator's data privacy. In this paper, we propose a communication-efficient scheme for decentralized federated learning called ProxyFL, or proxy-based federated learning. Each participant in ProxyFL maintains two models, a private model, and a publicly shared proxy model designed to protect the participant's privacy. Proxy models allow efficient information exchange among participants without the need of a centralized server. The proposed method eliminates a significant limitation of canonical federated learning by allowing model heterogeneity; each participant can have a private model with any architecture. Furthermore, our protocol for communication by proxy leads to stronger privacy guarantees using differential privacy analysis. Experiments on popular image datasets, and a cancer diagnostic problem using high-quality gigapixel histology whole slide images, show that ProxyFL can outperform existing alternatives with much less communication overhead and stronger privacy. Federated learning enables multi-institutional collaborations on decentralized data with improved privacy protection. Here, authors propose a new scheme for decentralized federated learning with much less communication overhead and stronger privacy. [ABSTRACT FROM AUTHOR]

As clinical testing declines, wastewater monitoring can provide crucial surveillance on the emergence of SARS-CoV-2 variant of concerns (VoCs) in communities. In this paper we present QuaID, a novel bioinformatics tool for VoC detection based on quasi-unique mutations. The benefits of QuaID are three-fold: (i) provides up to 3-week earlier VoC detection, (ii) accurate VoC detection (>95% precision on simulated benchmarks), and (iii) leverages all mutational signatures (including insertions & deletions). Sapoval et al. introduce QuaID, a bioinformatics tool for SARS-CoV-2 variant detection based on quasi-unique mutations. QuaID leverages all mutations, including insertions and deletions, and provides precise detection of variants early in their spread. [ABSTRACT FROM AUTHOR]

Measuring the value chain carbon footprints of listed companies is essential for cumulative climate actions and climate-efficient capital allocation. We trace the carbon emissions embodied in the value chains of Chinese listed companies and find that there is an increasing trend in terms of the carbon footprints of listed companies over the period 2010–2019. In 2019, the direct emissions from these companies reached 1.9 billion tonnes, accounting for 18.3% of national emissions. The indirect emissions were well over twice as large as the direct emissions from 2010 to 2019. Energy, construction and finance companies tend to have a greater volume of value chain carbon footprints, yet the distribution of their carbon footprints varies significantly. Finally, we apply the results to evaluate the financed emissions of leading asset managers’ equity portfolio investment in China’s stock market.A paper led by Prof. Zhang evaluates the value chain carbon footprints of Chinese listed companies. The results could encourage collaborative climate actions along value chains and help investors understand the environmental impacts of their investment. [ABSTRACT FROM AUTHOR]

Sintering theory predicts no long-range mass transport or distortion for uniformly heated particles during particle coalescence. However, in sintering-based manufacturing processes, permanent part distortion is often observed. The driving forces and mechanisms leading to this phenomenon are not understood, and efforts to reduce distortion are largely limited to a trial-and-error approach. In this paper, we demonstrate that distortion during sintering results from mass-transport driven by nonhomogeneous temperature distribution. We then show that hitherto unknown mass transport mechanisms, working in the direction opposite to temperature gradient are the likely cause of distortion. The experimental setup, designed for this purpose, enables the quantification of distortion during sintering. Two possible mass transport mechanisms are defined, and the continuum model applicable to both is formulated. The model accurately predicts the transient and permanent distortion observed during experiments, including their size dependence. Methods to control distortion that can give rise to 4D printing are discussed. Shape distortion is a limitation for sintering-based manufacturing techniques, yet the fundamental causes are not fully understood. Here, the authors study and characterize such behavior and develop a continuum model to describe the effect and provide fundamental understanding to control distortion. [ABSTRACT FROM AUTHOR]

Retrosynthesis analysis is an important task in organic chemistry with numerous industrial applications. Previously, machine learning approaches employing natural language processing techniques achieved promising results in this task by first representing reactant molecules as strings and subsequently predicting reactant molecules using text generation or machine translation models. Chemists cannot readily derive useful insights from traditional approaches that rely largely on atom-level decoding in the string representations, because human experts tend to interpret reactions by analyzing substructures that comprise a molecule. It is well-established that some substructures are stable and remain unchanged in reactions. In this paper, we developed a substructure-level decoding model, where commonly preserved portions of product molecules were automatically extracted with a fully data-driven approach. Our model achieves improvement over previously reported models, and we demonstrate that its performance can be boosted further by enhancing the accuracy of these substructures. Analyzing substructures extracted from our machine learning model can provide human experts with additional insights to assist decision-making in retrosynthesis analysis. Retrosynthesis is a critical task for organic chemistry with numerous industrial applications. Here, the authors build a machine learning model to learn the concept of substructures from a large reaction dataset to achieve chemist-like intuitions. [ABSTRACT FROM AUTHOR]

Chemical bonds, including covalent and ionic bonds, endow semiconductors with stable electronic configurations but also impose constraints on their synthesis and lattice-mismatched heteroepitaxy. Here, the unique multi-scale van der Waals (vdWs) interactions are explored in one-dimensional tellurium (Te) systems to overcome these restrictions, enabled by the vdWs bonds between Te atomic chains and the spontaneous misfit relaxation at quasi-vdWs interfaces. Wafer-scale Te vdWs nanomeshes composed of self-welding Te nanowires are laterally vapor grown on arbitrary surfaces at a low temperature of 100 °C, bringing greater integration freedoms for enhanced device functionality and broad applicability. The prepared Te vdWs nanomeshes can be patterned at the microscale and exhibit high field-effect hole mobility of 145 cm2/Vs, ultrafast photoresponse below 3 μs in paper-based infrared photodetectors, as well as controllable electronic structure in mixed-dimensional heterojunctions. All these device metrics of Te vdWs nanomesh electronics are promising to meet emerging technological demands. The limited scalability of 1D semiconductors has restricted their large-area optoelectronic applications so far. Here, the authors report the low-temperature synthesis of wafer-scale van der Waals nanomeshes composed of self-welding Te nanowires on various substrates, showing improved transport and photoelectric properties. [ABSTRACT FROM AUTHOR]

Metastable noble metal nanocrystals may exhibit distinctive catalytic properties to address the sluggish kinetics of many important processes, including the hydrogen evolution reaction under alkaline conditions for water-electrolysis hydrogen production. However, the exploration of metastable noble metal nanocrystals is still in its infancy and suffers from a lack of sufficient synthesis and electronic engineering strategies to fully stimulate their potential in catalysis. In this paper, we report a synthesis of metastable hexagonal Pt nanostructures by coherent growth on 3d transition metal nanocrystals such as Ni without involving galvanic replacement reaction, which expands the frontier of the phase-replication synthesis. Unlike noble metal substrates, the 3d transition metal substrate owns more crystal phases and lower cost and endows the hexagonal Pt skin with substantial compressive strains and programmable charge density, making the electronic properties particularly preferred for the alkaline hydrogen evolution reaction. The energy barriers are greatly reduced, pushing the activity to 133 mA cmgeo–2 and 17.4 mA μgPt–1 at –70 mV with 1.5 µg of Pt in 1 M KOH. Our strategy paves the way for metastable noble metal catalysts with tailored electronic properties for highly efficient and cost-effective energy conversion. Synthesis and modulation of metastable noble metal nanocrystal catalysts is interesting yet challenging. Here the authors report synthesis of metastable hexagonal Pt skins on Ni nanocrystals with modulated electronic structures and enhanced hydrogen evolution activities. [ABSTRACT FROM AUTHOR]

Diversity of physical encounters in urban environments is known to spur economic productivity while also fostering social capital. However, mobility restrictions during the pandemic have forced people to reduce urban encounters, raising questions about the social implications of behavioral changes. In this paper, we study how individual income diversity of urban encounters changed during the pandemic, using a large-scale, privacy-enhanced mobility dataset of more than one million anonymized mobile phone users in Boston, Dallas, Los Angeles, and Seattle, across three years spanning before and during the pandemic. We find that the diversity of urban encounters has substantially decreased (by 15% to 30%) during the pandemic and has persisted through late 2021, even though aggregated mobility metrics have recovered to pre-pandemic levels. Counterfactual analyses show that behavioral changes including lower willingness to explore new places further decreased the diversity of encounters in the long term. Our findings provide implications for managing the trade-off between the stringency of COVID-19 policies and the diversity of urban encounters as we move beyond the pandemic. Mobile phone data reveals a significant decrease in the income diversity of urban encounters during the COVID-19 pandemic in the USA, even though overall mobility returned to pre-pandemic levels by late 2021. This was mainly due to persistent behavioral changes including less willingness to explore new places. [ABSTRACT FROM AUTHOR]

Magnetoelectric coupling, as a fundamental physical nature and with the potential to add functionality to devices while also reducing energy consumption, has been challenging to be probed in freestanding membranes or two-dimensional materials due to their instability and fragility. In this paper, we report a magnetoelectric coupling probed by optical second harmonic generation with external magnetic field, and show the manipulation of the ferroelectric and antiferromagnetic orders by the magnetic and thermal fields in BiFeO3 films epitaxially grown on the substrates and in the freestanding ones. Here we define an optical magnetoelectric-coupling constant, denoting the ability of controlling light-induced nonlinear polarization by the magnetic field, and found the magnetoelectric-coupling was suppressed by strain releasing but remain robust against thermal fluctuation for freestanding BiFeO3. Here, the authors probe a magneto-electric coupling in freestanding BiFeO3 by optical second harmonic generation with varying magnetic field and temperature. They find the coupling to be suppressed by strain releasing whereas it remains robust against thermal fluctuations. [ABSTRACT FROM AUTHOR]

Machine learning (ML) models have long overlooked innateness: how strong pressures for survival lead to the encoding of complex behaviors in the nascent wiring of a brain. Here, we derive a neurodevelopmental encoding of artificial neural networks that considers the weight matrix of a neural network to be emergent from well-studied rules of neuronal compatibility. Rather than updating the network's weights directly, we improve task fitness by updating the neurons' wiring rules, thereby mirroring evolutionary selection on brain development. We find that our model (1) provides sufficient representational power for high accuracy on ML benchmarks while also compressing parameter count, and (2) can act as a regularizer, selecting simple circuits that provide stable and adaptive performance on metalearning tasks. In summary, by introducing neurodevelopmental considerations into ML frameworks, we not only model the emergence of innate behaviors, but also define a discovery process for structures that promote complex computations. Neuroscience has long inspired AI, however the neuroevolutionary search that produces sophisticated behaviors has not been systematized. This paper defines neurodevelopmental ML as a discovery process for structures that promote complex computations. [ABSTRACT FROM AUTHOR]

For simulation to be an effective tool for the development and testing of autonomous vehicles, the simulator must be able to produce realistic safety-critical scenarios with distribution-level accuracy. However, due to the high dimensionality of real-world driving environments and the rarity of long-tail safety-critical events, how to achieve statistical realism in simulation is a long-standing problem. In this paper, we develop NeuralNDE, a deep learning-based framework to learn multi-agent interaction behavior from vehicle trajectory data, and propose a conflict critic model and a safety mapping network to refine the generation process of safety-critical events, following real-world occurring frequencies and patterns. The results show that NeuralNDE can achieve both accurate safety-critical driving statistics (e.g., crash rate/type/severity and near-miss statistics, etc.) and normal driving statistics (e.g., vehicle speed/distance/yielding behavior distributions, etc.), as demonstrated in the simulation of urban driving environments. To the best of our knowledge, this is the first time that a simulation model can reproduce the real-world driving environment with statistical realism, particularly for safety-critical situations. Simulation of naturalistic driving environment for autonomous vehicle development is challenging due to its complexity and high dimensionality. The authors develop a deep learning-based framework to model driving behavior including safety-critical events for improved training of autonomous vehicles. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

Low-cost flexible microwave circuits with compact size and light weight are highly desirable for flexible wireless communication and other miniaturized microwave systems. However, the prevalent studies on flexible microwave electronics have only focused on individual flexible microwave elements such as transistors, inductors, capacitors, and transmission lines. Thinning down supporting substrate of rigid chip-based monolithic microwave integrated circuits has been the only approach toward flexible microwave integrated circuits. Here, we report a flexible microwave integrated circuit strategy integrating membrane AlGaN/GaN high electron mobility transistor with passive impedance matching networks on cellulose nanofibril paper. The strategy enables a heterogeneously integrated and, to our knowledge, the first flexible microwave amplifier that can output 10 mW power beyond 5 GHz and can also be easily disposed of due to the use of cellulose nanofibril paper as the circuit substrate. The demonstration represents a critical step forward in realizing flexible wireless communication devices. Though flexible microwave integrated circuits (MICs) are desirable for the construction of functional microwave amplifier circuits, realizing low cost III-V-based MMICs remains a challenge. Here, the authors report a heterogeneous integration strategy for the fabrication of flexible low-cost MICs. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

We investigate the degree to which impact in science and technology is associated with surprising breakthroughs, and how those breakthroughs arise. Identifying breakthroughs across science and technology requires models that distinguish surprising from expected advances at scale. Drawing on tens of millions of research papers and patents across the life sciences, physical sciences and patented inventions, and using a hypergraph model that predicts realized combinations of research contents (article keywords) and contexts (cited journals), here we show that surprise in terms of unexpected combinations of contents and contexts predicts outsized impact (within the top 10% of citations). These surprising advances emerge across, rather than within researchers or teams—most commonly when scientists from one field publish problem-solving results to an audience from a distant field. Our approach characterizes the frontier of science and technology as a complex hypergraph drawn from high-dimensional embeddings of research contents and contexts, and offers a measure of path-breaking surprise in science and technology. Here, using hypergraph modeling the authors show that surprising research (in terms of unexpected combinations of research contents and contexts) is associated with impact and arises from scientific outsiders solving problems in distant disciplines. [ABSTRACT FROM AUTHOR]

It has been shown that it is theoretically possible for there to exist quantum and classical processes in which the operations performed by separate parties do not occur in a well-defined causal order. A central question is whether and how such processes can be realised in practice. In order to provide a rigorous framework for the notion that certain such processes have a realisation in standard quantum theory, the concept of time-delocalised quantum subsystem has been introduced. In this paper, we show that realisations on time-delocalised subsystems exist for all unitary extensions of tripartite processes. This class contains processes that violate causal inequalities, i.e., that can generate correlations that witness the incompatibility with definite causal order in a device-independent manner, and whose realisability has been a central open problem. We consider a known example of such a tripartite classical process that has a unitary extension, and study its realisation on time-delocalised subsystems. We then discuss this finding with regard to the assumptions that underlie causal inequalities, and argue that they are indeed a meaningful concept to show the absence of a definite causal order between the variables of interest. Quantum theory can describe scenarios with an indefinite causal order, but whether such processes could be witnessed in real scenarios by violating causal inequalities is still subject to debate. Here, the authors give an affirmative answer, showing that noncausal processes admit a description using the framework of time-delocalised subsystems. [ABSTRACT FROM AUTHOR]

Earthquakes occur because faults weaken with increasing slip and slip rate. Thermal pressurization (TP) of trapped pore fluids is deemed to be a widespread coseismic fault weakening mechanism. Yet, due to technical challenges, experimental evidence of TP is limited. Here, by exploiting a novel experimental configuration, we simulate seismic slip pulses (slip rate 2.0 m/s) on dolerite-built faults under pore fluid pressures up to 25 MPa. We measure transient sharp weakening, down to almost zero friction and concurrent with a spike in pore fluid pressure, which interrupts the exponential-decay slip weakening. The interpretation of mechanical and microstructural data plus numerical modeling suggests that wear and local melting processes in experimental faults generate ultra-fine materials to seal the pressurized pore water, causing transient TP spikes. Our work suggests that, with wear-induced sealing, TP may also occur in relatively permeable faults and could be quite common in nature. This paper presents experimental evidence of thermal pressurization (TP) weakening of seismic faults, and suggests TP processes could be significantly promoted by wear-induced sealing during earthquakes even for relatively permeable faults. [ABSTRACT FROM AUTHOR]

Since the early 2000s, China has carried out extensive "grain-for-green" and grazing exclusion practices to combat desertification in the desertification-prone region (DPR). However, the environmental and socioeconomic impacts of these practices remain unclear. We quantify and compare the changes in fractional vegetation cover (FVC) with economic and population data in the DPR before and after the implementation of these environmental programmes. Here we show that climatic change and CO2 fertilization are relatively strong drivers of vegetation rehabilitation from 2001-2020 in the DPR, and the declines in the direct incomes of farmers and herders caused by ecological practices exceed the subsidies provided by governments. To minimize economic hardship, enhance food security, and improve the returns on policy investments in the DPR, China needs to adapt its environmental programmes to address the potential impacts of future climate change and create positive synergies to combat desertification and improve the economy in this region. This paper shows that desertification combating practices decline incomes of farmers and herders, and China needs to adapt its ecological programmes to address the impacts of climate change and create positive synergies to combat desertification. [ABSTRACT FROM AUTHOR]

Soft, wireless physiological sensors that gently adhere to the skin are capable of continuous clinical-grade health monitoring in hospital and/or home settings, of particular value to critically ill infants and other vulnerable patients, but they present risks for injury upon thermal failure. This paper introduces an active materials approach that automatically minimizes such risks, to complement traditional schemes that rely on integrated sensors and electronic control circuits. The strategy exploits thin, flexible bladders that contain small volumes of liquid with boiling points a few degrees above body temperature. When the heat exceeds the safe range, vaporization rapidly forms highly effective, thermally insulating structures and delaminates the device from the skin, thereby eliminating any danger to the skin. Experimental and computational thermomechanical studies and demonstrations in a skin-interfaced mechano-acoustic sensor illustrate the effectiveness of this simple thermal safety system and suggest its applicability to nearly any class of skin-integrated device technology. Minimizing patient risk to thermal failure in wearable electronics typically requires complex circuit control, sensors, or passive materials. Here, Yoo et al. present an active materials system with a self-inflating bladder that delaminates upon excessive heat, mitigating the risk of thermal injury. [ABSTRACT FROM AUTHOR]

Transition metal-catalyzed enantioselective free radical substitution reactions have recently attracted attention as convenient and important building tools in synthetic chemistry, although construction of stereogenic carbon centers at the propargylic position of propargylic alcohols by reactions with free radicals remains unchallenged. Here we present a strategy to control enantioselective propargylic substitution reactions with alkyl radicals under photoredox conditions by applying dual photoredox and diruthenium catalytic system, where the photoredox catalyst generates alkyl radicals from 4-alkyl-1,4-dihydropyridines, and the diruthenium core with a chiral ligand traps propargylic alcohols and alkyl radicals to guide enantioselective alkylation at the propargylic position, leading to high yields of propargylic alkylated products containing a quaternary stereogenic carbon center at the propargylic position with a high enantioselectivity. The result described in this paper provides the successful example of transition metal-catalyzed enantioselective propargylic substitution reactions with free alkyl radicals. Construction of stereogenic carbon centers of propargylic alcohols with free radicals could mark a simple route to molecular complexity from easily obtained starting materials. Here the authors present a strategy to control enantioselective propargylic substitution reactions with alkyl radicals under photoredox conditions. [ABSTRACT FROM AUTHOR]

Reconfigurable electromagnetic devices, specifically reconfigurable antennas, have shown to be integral to the future of communication systems. However, mechanically robust designs that can survive real-world, harsh environment applications and high-power conditions remain rare to this day. In this paper, the general framework for a field of both discrete and continuously mechanically reconfigurable devices is established by combining compliant mechanisms with electromagnetics. To exemplify this new concept, a reconfigurable compliant mechanism antenna is demonstrated which exhibits continuously tunable performance across a broad band of frequencies. Moreover, three additional examples are also introduced that further showcase the versatility and advanced capabilities of compliant mechanism enabled electromagnetic devices. Unlike previous approaches, this is achieved with minimal part counts, additive manufacturing techniques, and high reliability, which mechanical compliant mechanism devices are known for. The results presented exemplify how compliant mechanisms have the capacity to transform the broader field of reconfigurable electromagnetic devices. Reconfigurability and robustness are two key properties for electromagnetic devices. Here, the use of compliant mechanisms—which are known for their inherent durability, structural stability, and high-accuracy manipulation capabilities in mechanical applications—is introduced for reconfigurable electromagnetic systems. [ABSTRACT FROM AUTHOR]