Global high-resolution growth projections dataset for rooftop area consistent with the shared socioeconomic pathways, 2020-2050.

Assessment of current and future growth in the global rooftop area is important for understanding and planning for a robust and sustainable decentralised energy system. These estimates are also important for urban planning studies and designing sustainable cities thereby forwarding the ethos of the Sustainable Development Goals 7 (clean energy), 11 (sustainable cities), 13 (climate action) and 15 (life on land). Here, we develop a machine learning framework that trains on big data containing ~700 million open-source building footprints, global land cover, road, and population datasets to generate globally harmonised estimates of growth in rooftop area for five different future growth narratives covered by Shared Socioeconomic Pathways. The dataset provides estimates for ~3.5 million fishnet tiles of 1/8 degree spatial resolution with data on gross rooftop area for five growth narratives covering years 2020-2050 in decadal time steps. This single harmonised global dataset can be used for climate change, energy transition, biodiversity, urban planning, and disaster risk management studies covering continental to conurbation geospatial levels.

Functional and Neuropsychological Outcome After Surgical Treatment of Moyamoya Disease.

BACKGROUND: Moyamoya disease (MMD) is a rare cerebrovascular disease characterized by progressive stenosis of the supraclinoid internal carotid artery. As a result of chronically decreased brain perfusion, eloquent areas of the brain become hypoperfused, leading to cognitive changes in patients. Repeated infarcts and bleeds produce clinically apparent neurologic deficits. OBJECTIVES: 1) To study the functional and neuropsychological outcome in MMD after revascularization surgery. 2) To find postrevascularization correlation between functional and neuropsychological improvement and radiologic improvement. METHODS: A single-center prospective and analytic study was carried out including 21 patients with MMD during the study period from March 2021 to December 2022. Patients were evaluated and compared before and after revascularization for functional, neuropsychological, and radiologic status. RESULTS: Postoperative functional outcome in terms of modified Rankin Scale score showed improvement in 33.33% of cases (P = 0.0769). An overall improving trend was observed in different neuropsychological domains in both adult and pediatric age groups. However, the trend of neuropsychological improvement was better in adults compared with pediatric patients. Radiologic outcome in the form of the Angiographic Outcome Score (AOS) significantly improved after revascularization (P = 0.0001). There was a trend toward improvement in magnetic resonance imaging (MRI) perfusion in the middle cerebral artery and anterior cerebral artery territories, 4.7% (P = 0.075) and 9.33% (P = 0.058) respectively, compared with preoperative MRI perfusion. CONCLUSIONS: After revascularization, significant improvement occurred in functional and neuropsychological status. This result was also shown radiologically as evidenced by improvement in MRI perfusion and cerebral angiography.

A deep learning architecture for energy service demand estimation in transport sector for Shared Socioeconomic Pathways.

Meeting current global passenger and freight transport energy service demands accounts for 20% of annual anthropogenic CO2 emissions, and mitigating these emissions remains a considerable challenge for climate policy. Pursuant to this, energy service demands play a critical role in the energy systems and integrated assessment models but fail to get the attention they warrant. This study introduces a novel custom deep learning neural network architecture (called TrebuNet) that mimics the physical process of firing a trebuchet to model the nuanced dynamics inherent in energy service demand estimation. Here we show, how TrebuNet is designed, trained, and used to estimate transport energy service demand. We find that the TrebuNet architecture shows superior performance compared with traditional multivariate linear regression and state of the art methods like densely connected neural network, Recurrent Neural Network, and Gradient Boosted machine learning algorithms when evaluated for regional demand projection for all modes of transport demands at short, decadal, and medium-term time horizons. Finally, TrebuNet introduces a framework to project energy service demand for regions having multiple countries spanning different socio-economic development pathways which can be replicated for wider regression-based task for timeseries having non-uniform variance.

If you can't fix the problem change the standards.

Over the last few months, established data systems in India have been the target of heated dispute, chiefly by members of the Economic Advisory Council to the Prime Minister, ranging from the inflation numbers [1], to the sampling frame for surveys done by the National Sample Survey Organisation (NSS), the National Family Health Survey (NFHS) and the Periodic Labour Force Survey (PLFS)[2], haemoglobin cut-offs for anaemia [3] and childhood growth standards, female labour force participation rate and life expectancy at birth [4]. The attempts to revise economic data systems has invited a raging debate [5, 6], prompting the government to set up a panel to review the NSS's methodology. However, the arguments being made in favor of downward revision of nutritional standards have received much less scrutiny, except for a recent editorial which comments on the general problem of drawing up standards [7]. This is despite the fact that these proposals have already caught the fancy of the government. A policy decision has already been taken to discontinue gathering of data on Hb-levels as part of the quinquennial National Family Health Surveys, which would now be collected as part of a new Dietary and Bio-markers Survey. Neither the rationale for such a move, nor the details of the methodology of the new survey, or the time-frame within which such data would be released have been made available for public deliberation. Similarly, discussions have been initiated on devising "indigenous" growth standards for children [8]. Hence, it becomes imperative to examine the basis of these renewed calls for revision of existing standards.

A compute-in-memory chip based on resistive random-access memory.

Realizing increasingly complex artificial intelligence (AI) functionalities directly on edge devices calls for unprecedented energy efficiency of edge hardware. Compute-in-memory (CIM) based on resistive random-access memory (RRAM)1 promises to meet such demand by storing AI model weights in dense, analogue and non-volatile RRAM devices, and by performing AI computation directly within RRAM, thus eliminating power-hungry data movement between separate compute and memory2-5. Although recent studies have demonstrated in-memory matrix-vector multiplication on fully integrated RRAM-CIM hardware6-17, it remains a goal for a RRAM-CIM chip to simultaneously deliver high energy efficiency, versatility to support diverse models and software-comparable accuracy. Although efficiency, versatility and accuracy are all indispensable for broad adoption of the technology, the inter-related trade-offs among them cannot be addressed by isolated improvements on any single abstraction level of the design. Here, by co-optimizing across all hierarchies of the design from algorithms and architecture to circuits and devices, we present NeuRRAM-a RRAM-based CIM chip that simultaneously delivers versatility in reconfiguring CIM cores for diverse model architectures, energy efficiency that is two-times better than previous state-of-the-art RRAM-CIM chips across various computational bit-precisions, and inference accuracy comparable to software models quantized to four-bit weights across various AI tasks, including accuracy of 99.0 percent on MNIST18 and 85.7 percent on CIFAR-1019 image classification, 84.7-percent accuracy on Google speech command recognition20, and a 70-percent reduction in image-reconstruction error on a Bayesian image-recovery task.

Deep learning approach for inverse design of metasurfaces with a wider shape gamut.

While the large design degrees of freedom (DOFs) give metasurfaces a tremendous versatility, they make the inverse design challenging. Metasurface designers mostly rely on simple shapes and ordered placements, which restricts the achievable performance. We report a deep learning based inverse design flow that enables a fuller exploitation of the meta-atom shape. Using a polygonal shape encoding that covers a broad gamut of lithographically realizable resonators, we demonstrate the inverse design of color filters in an amorphous silicon material platform. The inverse-designed transmission-mode color filter metasurfaces are experimentally realized and exhibit enhancement in the color gamut.

Numerical Modelling and Analytical Comparison of Delamination during Cryogenic Drilling of CFRP.

Carbon-Fibre-Reinforced Polymers (CFRPs) have seen a steady rise in modern industrial applications due to their high strength-to-weight ratio and corrosion resistance. However, their potential is being hindered by delamination which is induced on them during machining operations. This has led to the adoption of new and innovative techniques like cryogenic-assisted machining which could potentially help reduce delamination. This study is aimed at investigating the effect of cryogenic conditions on achieving better hole quality with reduced delamination. In this paper, the numerical analysis of the drilling of CFRP composites is presented. Drilling tests were performed experimentally for validation purposes. The effects of cooling conditions and their subsequent effect on the thrust force and delamination were evaluated using ABAQUS/CAE. The numerical models and experimental results both demonstrated a significant reduction in the delamination factor in CFRP under cryogenic drilling conditions.

High resolution global spatiotemporal assessment of rooftop solar photovoltaics potential for renewable electricity generation.

Rooftop solar photovoltaics currently account for 40% of the global solar photovoltaics installed capacity and one-fourth of the total renewable capacity additions in 2018. Yet, only limited information is available on its global potential and associated costs at a high spatiotemporal resolution. Here, we present a high-resolution global assessment of rooftop solar photovoltaics potential using big data, machine learning and geospatial analysis. We analyse 130 million km2 of global land surface area to demarcate 0.2 million km2 of rooftop area, which together represent 27 PWh yr-1 of electricity generation potential for costs between 40-280 $ MWh-1. Out of this, 10 PWh yr-1 can be realised below 100 $ MWh-1. The global potential is predominantly spread between Asia (47%), North America (20%) and Europe (13%). The cost of attaining the potential is lowest in India (66 $ MWh-1) and China (68 $ MWh-1), with USA (238 $ MWh-1) and UK (251 $ MWh-1) representing some of the costliest countries.

Development and Validation of a Mobile Clinical Decision Support Tool for the Diagnosis of Drug Allergy in Adults: The Drug Allergy App.

BACKGROUND: Penicillin allergy overdiagnosis has been associated with inappropriate antibiotic prescribing, increased antimicrobial resistance, worse clinical outcomes, and increased health care costs. OBJECTIVE: To develop and validate a questionnaire-based algorithm built in a mobile application to support clinicians in collecting accurate history of previous reactions and diagnosing drug allergy appropriately. METHODS: A survey was completed by 164 medical and nonmedical prescribers to understand barriers to best practice. Based on the survey recommendations, we created a 10-item questionnaire-based algorithm to allow classification of drug allergy history in line with the National Institute for Health and Care Excellence guidelines on drug allergy. The algorithm was incorporated into a mobile application and retrospectively validated using anonymized clinical databases at regional immunology and dermatology centers in Manchester, United Kingdom. RESULTS: A total of 55.2% of prescribers (95% confidence interval, 47% to 63.4%) thought it impossible to draw a firm conclusion based on history alone and 59.4% (95% CI, 51.4% to 67.5%) believed that regardless of the details of the penicillin allergy history, they would avoid all ß-lactams. A drug allergy mobile application was developed and retrospectively validated, which revealed a low risk for misclassification of outcomes compared with reference standard drug allergy investigations in the allergy and dermatology clinics. CONCLUSIONS: Perceived lack of time and preparedness to collect an accurate drug allergy history appear to be important barriers to appropriate antimicrobial prescribing. The Drug Allergy App may represent a useful clinical decision support tool to diagnose drug allergy correctly and support appropriate antibiotic prescribing.

Health programmes and services addressing the prevention and management of infectious diseases in people who inject drugs in Canada: a systematic integrative review.

OBJECTIVES: People who inject drugs (PWID) experience a high burden of injection drug use-related infectious disease and challenges in accessing adequate care. This study sought to identify programmes and services in Canada addressing the prevention and management of infectious disease in PWID. DESIGN: This study employed a systematic integrative review methodology. Electronic databases (PubMed, CINAHL and Web of Science Core Collection) and relevant websites were searched for literature published between 2008 and 2019 (last search date was 6 June 2019). Eligible articles and documents were required to address injection or intravenous drug use and health programmes or services relating to the prevention or management of infectious diseases in Canada. RESULTS: This study identified 1607 unique articles and 97 were included in this study. The health programmes and services identified included testing and management of HIV and hepatitis C virus (n=27), supervised injection facilities (n=19), medication treatment for opioid use disorder (n=12), integrated infectious disease and addiction programmes (n=10), needle exchange programmes (n=9), harm reduction strategies broadly (n=6), mobile care initiatives (n=5), peer-delivered services (n=3), management of IDU-related bacterial infections (n=2) and others (n=4). Key implications for policy, practice and future research were identified based on the results of the included studies, which include addressing individual and systemic factors that impede care, furthering evaluation of programmes and the need to provide comprehensive care to PWID, involving medical care, social support and harm reduction. CONCLUSIONS: These results demonstrate the need for expanded services across a variety of settings and populations. Our study emphasises the importance of addressing social and structural factors that impede infectious disease care for PWID. Further research is needed to improve evaluation of health programmes and services and contextual factors surrounding accessing services or returning to care. PROSPERO REGISTRATION NUMBER: CRD42020142947.

Effects of charge fluctuation and charge regulation on the phase transitions in stoichiometric VO2.

Detailed electrical and photoemission studies were carried out to probe the chemical nature of the insulating ground state of VO2, whose properties have been an issue for accurate prediction by common theoretical probes. The effects of a systematic modulation of oxygen over-stoichiometry of VO2 from 1.86 to 2.44 on the band structure and insulator-metal transitions are presented for the first time. Results offer a different perspective on the temperature- and doping-induced IMT process. They suggest that charge fluctuation in the metallic phase of intrinsic VO2 results in the formation of e- and h+ pairs that lead to delocalized polaronic V3+ and V5+ cation states. The metal-to-insulator transition is linked to the cooperative effects of changes in the V-O bond length, localization of V3+ electrons at V5+ sites, which results in the formation of V4+-V4+ dimers, and removal of [Formula: see text] screening electrons. It is shown that the nature of phase transitions is linked to the lattice V3+/V5+ concentrations of stoichiometric VO2 and that electronic transitions are regulated by the interplay between charge fluctuation, charge redistribution, and structural transition.

Using synchronized oscillators to compute the maximum independent set.

Not all computing problems are created equal. The inherent complexity of processing certain classes of problems using digital computers has inspired the exploration of alternate computing paradigms. Coupled oscillators exhibiting rich spatio-temporal dynamics have been proposed for solving hard optimization problems. However, the physical implementation of such systems has been constrained to small prototypes. Consequently, the computational properties of this paradigm remain inadequately explored. Here, we demonstrate an integrated circuit of thirty oscillators with highly reconfigurable coupling to compute optimal/near-optimal solutions to the archetypally hard Maximum Independent Set problem with over 90% accuracy. This platform uniquely enables us to characterize the dynamical and computational properties of this hardware approach. We show that the Maximum Independent Set is more challenging to compute in sparser graphs than in denser ones. Finally, using simulations we evaluate the scalability of the proposed approach. Our work marks an important step towards enabling application-specific analog computing platforms to solve computationally hard problems.

Health programmes and services addressing the prevention and management of infectious diseases in persons who inject drugs in Canada: a systematic integrative review protocol.

INTRODUCTION: Injection drug use (IDU) and intravenous drug use (IVDU) are of concern to the people using drugs, their families and health systems. One of the complications of IDU/IVDU is the risk of infection. Clinical experience has shown that persons who inject drugs (PWID) are hospitalised and re-hospitalised frequently. In Canada there are sparse data about the reasons for which PWID are admitted to hospital and their health trajectories, especially for infectious diseases. There are special concerns regarding PWID with infections who leave the hospital against medical advice and those who leave with a peripherally inserted central catheter line in place for administration of long-term antibiotics or other therapies. Improving our understanding of current programmes and services addressing the prevention and management of infectious diseases and their complications in PWID could lead to focused interventions to enhance care in this population. METHODS AND ANALYSIS: An integrative systematic review allows for inclusion of a variety of methodologies to understand a health issue from different viewpoints. PubMed, CINAHL, Web of Science Databases and websites of the Public Health Agency of Canada, Canadian Institute for Substance Use Research, and Canadian Centre on Substance Use and Addiction will be searched using terms for infectious diseases, drug use and geography (Canada) and limited to the last 10 years (2009-2019). The Quality Appraisal Tool in Studies with Diverse Designs will be used to appraise the quality of identified studies and documents. Quantitative, qualitative or mixed methods data synthesis will be used as needed. ETHICS AND DISSEMINATION: This study is a secondary analysis of publicly available documents; therefore, no ethics approval is required. This information will inform a research agenda to further investigate interventions that aim to address these issues. PROSPERO REGISTRATION NUMBER: CRD42020142947.

Supervised Learning in All FeFET-Based Spiking Neural Network: Opportunities and Challenges.

The two possible pathways toward artificial intelligence (AI)-(i) neuroscience-oriented neuromorphic computing [like spiking neural network (SNN)] and (ii) computer science driven machine learning (like deep learning) differ widely in their fundamental formalism and coding schemes (Pei et al., 2019). Deviating from traditional deep learning approach of relying on neuronal models with static nonlinearities, SNNs attempt to capture brain-like features like computation using spikes. This holds the promise of improving the energy efficiency of the computing platforms. In order to achieve a much higher areal and energy efficiency compared to today's hardware implementation of SNN, we need to go beyond the traditional route of relying on CMOS-based digital or mixed-signal neuronal circuits and segregation of computation and memory under the von Neumann architecture. Recently, ferroelectric field-effect transistors (FeFETs) are being explored as a promising alternative for building neuromorphic hardware by utilizing their non-volatile nature and rich polarization switching dynamics. In this work, we propose an all FeFET-based SNN hardware that allows low-power spike-based information processing and co-localized memory and computing (a.k.a. in-memory computing). We experimentally demonstrate the essential neuronal and synaptic dynamics in a 28 nm high-K metal gate FeFET technology. Furthermore, drawing inspiration from the traditional machine learning approach of optimizing a cost function to adjust the synaptic weights, we implement a surrogate gradient (SG) learning algorithm on our SNN platform that allows us to perform supervised learning on MNIST dataset. As such, we provide a pathway toward building energy-efficient neuromorphic hardware that can support traditional machine learning algorithms. Finally, we undertake synergistic device-algorithm co-design by accounting for the impacts of device-level variation (stochasticity) and limited bit precision of on-chip synaptic weights (available analog states) on the classification accuracy.

The Impact of Idiopathic Generalized Epilepsy on Executive Functions.

Background: Idiopathic generalized epilepsy is defined as seizures with a possible hereditary predisposition without an underlying cause or structural pathology. Assessment of executive dysfunction in idiopathic generalized epilepsies based on standard Indian battery is not available in the literature. Aims and Objectives: To assess specific executive functions affected in patients with idiopathic epilepsy and their association with various variables. Materials and Methods: Type of observational cross-sectional study, where clinical profile of all idiopathic epilepsy patients attending the neurology OPD was studied and their executive higher mental functions were assessed using the NIMHANS battery. Results: A total of 75 idiopathic generalized epilepsy patients were included in the study. Executive functions that were commonly found abnormal in our study were word fluency (P ≤ .001), category fluency (P < .001), verbal n-back (P < .001), Tower of London (p < 0.01), and Stroop test (P < 0.01). Executive functions showed a significant correlation with age at symptom onset, duration of epilepsy, and in those with uncontrolled seizures. Conclusion: Patients of idiopathic generalized epilepsy according to the present study were found to have significant executive dysfunction in multiple domains. This necessitates the screening for executive dysfunctions, which if detected should prompt the clinician to initiate cognitive retraining.

Memory-Efficient Synaptic Connectivity for Spike-Timing- Dependent Plasticity.

Spike-Timing-Dependent Plasticity (STDP) is a bio-inspired local incremental weight update rule commonly used for online learning in spike-based neuromorphic systems. In STDP, the intensity of long-term potentiation and depression in synaptic efficacy (weight) between neurons is expressed as a function of the relative timing between pre- and post-synaptic action potentials (spikes), while the polarity of change is dependent on the order (causality) of the spikes. Online STDP weight updates for causal and acausal relative spike times are activated at the onset of post- and pre-synaptic spike events, respectively, implying access to synaptic connectivity both in forward (pre-to-post) and reverse (post-to-pre) directions. Here we study the impact of different arrangements of synaptic connectivity tables on weight storage and STDP updates for large-scale neuromorphic systems. We analyze the memory efficiency for varying degrees of density in synaptic connectivity, ranging from crossbar arrays for full connectivity to pointer-based lookup for sparse connectivity. The study includes comparison of storage and access costs and efficiencies for each memory arrangement, along with a trade-off analysis of the benefits of each data structure depending on application requirements and budget. Finally, we present an alternative formulation of STDP via a delayed causal update mechanism that permits efficient weight access, requiring no more than forward connectivity lookup. We show functional equivalence of the delayed causal updates to the original STDP formulation, with substantial savings in storage and access costs and efficiencies for networks with sparse synaptic connectivity as typically encountered in large-scale models in computational neuroscience.

Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems.

OBJECTIVE: Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model. APPROACH: This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity. MAIN RESULTS: Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology. SIGNIFICANCE: Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a computational tool for investigating fundamental questions related to neural dynamics, the sophistication of current neuromorphic systems now allows direct interfaces with large neuronal networks and circuits, resulting in potentially interesting clinical applications for neuroengineering systems, neuroprosthetics and neurorehabilitation.

Hierarchical Address Event Routing for Reconfigurable Large-Scale Neuromorphic Systems.

We present a hierarchical address-event routing (HiAER) architecture for scalable communication of neural and synaptic spike events between neuromorphic processors, implemented with five Xilinx Spartan-6 field-programmable gate arrays and four custom analog neuromophic integrated circuits serving 262k neurons and 262M synapses. The architecture extends the single-bus address-event representation protocol to a hierarchy of multiple nested buses, routing events across increasing scales of spatial distance. The HiAER protocol provides individually programmable axonal delay in addition to strength for each synapse, lending itself toward biologically plausible neural network architectures, and scales across a range of hierarchies suitable for multichip and multiboard systems in reconfigurable large-scale neuromorphic systems. We show approximately linear scaling of net global synaptic event throughput with number of routing nodes in the network, at 3.6×107 synaptic events per second per 16k-neuron node in the hierarchy.

Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines.

Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism for inducing the stochasticity observed in cortex. Here, we introduce Synaptic Sampling Machines (S2Ms), a class of neural network models that uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised learning. Similar to the original formulation of Boltzmann machines, these models can be viewed as a stochastic counterpart of Hopfield networks, but where stochasticity is induced by a random mask over the connections. Synaptic stochasticity plays the dual role of an efficient mechanism for sampling, and a regularizer during learning akin to DropConnect. A local synaptic plasticity rule implementing an event-driven form of contrastive divergence enables the learning of generative models in an on-line fashion. S2Ms perform equally well using discrete-timed artificial units (as in Hopfield networks) or continuous-timed leaky integrate and fire neurons. The learned representations are remarkably sparse and robust to reductions in bit precision and synapse pruning: removal of more than 75% of the weakest connections followed by cursory re-learning causes a negligible performance loss on benchmark classification tasks. The spiking neuron-based S2Ms outperform existing spike-based unsupervised learners, while potentially offering substantial advantages in terms of power and complexity, and are thus promising models for on-line learning in brain-inspired hardware.