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Dense crossbar arrays of non-volatile memory (NVM) can potentially enable massively parallel and highly energy-efficient neuromorphic computing systems. The key requirements for the NVM elements are continuous (analog-like) conductance tuning capability and switching symmetry with acceptable noise levels. However, most NVM devices show non-linear and asymmetric switching behaviors. Such non-linear behaviors render separation of signal and noise extremely difficult with conventional characterization techniques. In this study, we establish a practical methodology based on Gaussian process regression to address this issue. The methodology is agnostic to switching mechanisms and applicable to various NVM devices. We show tradeoff between switching symmetry and signal-to-noise ratio for HfO 2 -based resistive random access memory. Then, we characterize 1000 phase-change memory devices based on Ge 2 Sb 2 Te 5 and separate total variability into device-to-device variability and inherent randomness from individual devices. These results highlight the usefulness of our methodology to realize ideal NVM devices for neuromorphic computing. The application of resistive and phase-change memories in neuromorphic computation will require efficient methods to quantify device-to-device and switching variability. Here, the authors assess the impact of a broad range of device switching mechanisms using machine learning regression techniques.

Innovation studies continue to struggle with an apparent disconnect between innovation’s supposedly universal dynamics and a sense that policy frameworks and associated instruments of innovation are often ineffectual or even harmful when transported across regions or countries. Using a cross-country comparative analysis of three implementations of the ‘MIT model’ of innovation in the UK, Portugal and Singapore, we show how key features in the design, implementation and performance of the model cannot be explained as mere variations on an identical solution to the same underlying problem. We draw on the concept of sociotechnical imaginaries to show how implementations of the ‘same’ innovation model–and with it the notion of ‘innovation’ itself–are co-produced with locally specific diagnoses of a societal deficiency and equally specific understandings of acceptable remedies. Our analysis thus flips the conventional notion of ‘best-practice transfer’ on its head: Instead of asking ‘how well’ an innovation model has been implemented, we analyze the differences among the three importations to reveal the idiosyncratic ways in which each country imagines the purpose of innovation. We replace the notion of innovation as a ‘panacea’ – a universal fix for all social woes – with that of innovationas-diagnosis in which a particular ‘cure’ is ‘prescribed’ for a ‘diagnosed’ societal ‘pathology, ’which may in turn trigger ‘reactions’ within the receiving body. This approach offers new possibilities for theorizing how and where culture matters in innovation policy. It suggests that the ‘successes’ and ‘failures’ of innovation models are not a matter of how well societies are able to implement a sound, universal model, but more about how effectively they articulate their imaginaries of innovation and tailor their strategies accordingly.

Load flexibility (LF) is a crucial resource to enable sustainable and affordable building electrification and decarbonization. User acceptance of LF technologies is essential to their widespread utilization, however user perceptions and experiences with them are largely unknown. This paper presents findings about user experience with automated LF across a variety of technologies for residential and commercial buildings. Narrative analysis of data from 54 interviews with users (householders, building occupants, and system operators) was used to identify physical and psychological impacts of LF on users and four aspects of user interactions with LF technologies: enabling, controlling parameters, overriding, and learning. LF operations manipulating space heating and cooling setpoints often caused minor thermal discomfort but were generally acceptable to users who could override. LF technologies that manipulated energy and thermal storage processes were less disruptive, but often still not imperceptible (e.g., due to equipment sound). Challenges for LF technologies involving advanced controls software for commercial energy equipment included operator anxiety related to lack of transparency and control. Findings also suggest there is a need to develop user-centered interfaces to support key user interactions, including settings for enabling LF, setting parameters and accessing information and feedback to understand the concept of LF and confirm benefits of participation.

Abstract PurposeThis work was set out to investigate the feasibility of dose reduction in SPECT myocardial perfusion imaging (MPI) without sacrificing diagnostic accuracy. A deep learning approach was proposed to synthesize full-dose images from the corresponding low-dose images at different dose reduction levels in the projection space.MethodsClinical SPECT-MPI images of 345 patients acquired on a dedicated cardiac SPECT camera in list-mode format were retrospectively employed to predict standard-dose from low-dose images at half-, quarter-, and one-eighth-dose levels. To simulate realistic low-dose projections, 50%, 25%, and 12.5% of the events were randomly selected from the list-mode data through applying binomial subsampling. A generative adversarial network was implemented to predict non-gated standard-dose SPECT images in the projection space at the different dose reduction levels. Well-established metrics, including peak signal-to-noise ratio (PSNR), root mean square error (RMSE), and structural similarity index metrics (SSIM) in addition to Pearson correlation coefficient analysis and clinical parameters derived from Cedars-Sinai software were used to quantitatively assess the predicted standard-dose images. For clinical evaluation, the quality of the predicted standard-dose images was evaluated by a nuclear medicine specialist using a seven-point (− 3 to + 3) grading scheme.ResultsThe highest PSNR (42.49 ± 2.37) and SSIM (0.99 ± 0.01) and the lowest RMSE (1.99 ± 0.63) were achieved at a half-dose level. Pearson correlation coefficients were 0.997 ± 0.001, 0.994 ± 0.003, and 0.987 ± 0.004 for the predicted standard-dose images at half-, quarter-, and one-eighth-dose levels, respectively. Using the standard-dose images as reference, the Bland–Altman plots sketched for the Cedars-Sinai selected parameters exhibited remarkably less bias and variance in the predicted standard-dose images compared with the low-dose images at all reduced dose levels. Overall, considering the clinical assessment performed by a nuclear medicine specialist, 100%, 80%, and 11% of the predicted standard-dose images were clinically acceptable at half-, quarter-, and one-eighth-dose levels, respectively.ConclusionThe noise was effectively suppressed by the proposed network, and the predicted standard-dose images were comparable to reference standard-dose images at half- and quarter-dose levels. However, recovery of the underlying signals/information in low-dose images beyond a quarter of the standard dose would not be feasible (due to very poor signal-to-noise ratio) which will adversely affect the clinical interpretation of the resulting images.

Investigating practical methods for real-time monitoring of modulation formats (MF) and optical signal-to-noise ratio (OSNR) in coherent optical communication systems is critical for advancing future dynamic and heterogeneous optical networks. In this work, we propose a residual network with an attention mechanism(SA-ResNet) to perform joint monitoring of MF and OSNR for mainstream quadrature phase shift keying (QPSK) and M-ary quadrature amplitude modulation (MQAM) signals, including 8QAM, 16QAM, 32QAM, 64QAM, and 128QAM. After applying Taylor pruning to the model, its floating-point operations (FLOPs) were reduced from 40.5 M to 9.5 M, and its parameter memory was decreased from 2.6 M to 0.5 M. Notably, following fine-tuning, the model still achieved 100% MF recognition accuracy and an average absolute error of 0.34 dB for OSNR estimation under a sample length of 16,000 and fiber length of 160 km. When the model is evaluated using 5-fold cross-validation, the average MF recognition accuracy is 99.988%, and the mean of average absolute errors for OSNR estimation is 0.32 dB. These results indicate that the proposed model has acceptable monitoring performance and requires relatively low computational resources, which makes it attractive for lightweight application scenarios of optical fiber monitoring systems.

Incessant increases in vehicles and massive road networks lead to traffic-related problems and noise pollution. Road tunnels are considered a more feasible and effective solution for solving traffic problems. Compared to other traffic noise abatement strategies, road tunnels also offer enormous benefits to urban mass transit systems. However, the road tunnels that are non-complying with the design and safety standards negatively impact commuters’ health of being exposed to the high noise level inside the tunnel, particularly for road tunnels above 500 m in length. The study aims to evaluate the applicability of the ASJ RTN-Model 2013 by validating predicted data with the measurement data at the tunnel portal. The study also investigates the acoustic characteristics of noise inside the tunnel by analysing octave frequencies to inspect the correlation of noise spectrum for noise-induced hearing loss (NIHL) and discussed the possible health effect on the pedestrian and vehicle riders passing through the tunnel. The result shows that people are exposed to a high noise level inside the tunnel. The equivalent sound pressure levels at different locations inside the tunnel along the length observed between 78.9 to 86.5 dB(A), which exceeded the CPCB, recommended permissible limits for road traffic noise. The locations L1, L5, L6 and L7 found higher sound pressure levels at 4 kHz and relates to NIHL. The observed average difference between the measurement and predicted LAeq value at the tunnel portal was 2.8 dB(A) which is highly acceptable and confirms the ASJ RTN-2013 prediction model applicability for predicting tunnel portal noise in the Indian road conditions. The study recommends complete restriction of honking inside the tunnel. Considering the commuter’s safety perspective, the road tunnels above 500 m must have separate walk sides for pedestrians with a barrier.

Classical time-resolved optical spectroscopy experiments are performed using sequences of ultrashort light pulses, with photon fluxes incident on the sample which are many orders of magnitude higher than real-world conditions corresponding to sunlight illumination. Spectroscopy and microscopy schemes that use quantum states of light have been widely described theoretically with fewer experimental demonstrations that typically require very long measurements that can extend for hours or more. Here, we show that time-resolved spectroscopy with quantum light can be performed without compromising measurement time or wavelength tunability, recording a fluorescence lifetime trace in biological samples in less than a second with acceptable signal-to-noise ratio. Starting from spontaneous parametric down-conversion driven by a continuous-wave laser, we exploit the temporal correlation between randomly generated signal/idler pairs to obtain temporal resolution, and their spectral correlation to select the excitation frequency. We also add spectral resolution in detection, using a ‘photon-efficient’ Fourier transform approach which employs a common-path interferometer. We demonstrate the potential of this approach by resolving, at the single-photon level, excitation energy transfer cascades from LH2 to LH1 in the photosynthetic membrane and disentangling the lifetimes of two dyes in a mixture. Our results provide a new approach to ultrafast optical spectroscopy, where experiments are performed under illumination intensity conditions comparable to real-world sunlight illumination. Correlated photons from parametric down-coversion driven by a CW laser are used for time- and frequency-resolved spectroscopy, enabling sub-second acquisition times on biological samples and single-photon excitation of photosynthetic processes.

The provision of healthcare to those in homelessness often fails to consider what is important to those receiving care. Using an ethnographic approach grounded in a social constructivist research paradigm, this study aimed to explore the perspectives of homeless service users in Dublin, Ireland on their priority health and healthcare needs. Active participant observations and informal interviews were conducted with 74 adult clients attending a low-threshold primary care and addiction service between October, 2022 and April, 2023. Participants were selected using purposive, critical case sampling; clinic medical or operations staff identified adult homeless clients of sound mental capacity with whom they had an established rapport. Field note data were collected, anonymised, and analysed using inductive thematic analysis in accordance with the Declaration of Helsinki and the researchers’ institutional Research Ethics Committee. All participants gave informed verbal consent. Clients’ priority concerns relate to their mental health and personal safety, strengthening ties with children and families, finding a sense of purpose, and alleviating physical pain. These challenges are acute both prior to and during experiences of homelessness, coupled with disproportionately high levels of grief, fear, isolation, and fatigue. In terms health services, clients value accessing primary care and harm reduction in a social environment where positive exchanges with friends and providers take place. Conversely, barriers to accessing mental health and addiction services persist including the internalised belief that one is beyond help, lack of access to information on available services and their entry requirements, and lingering stigma within a health system that treats addiction as separate to health. Health initiatives recommended by homeless service users to improve their lives and conditions include the provision of more/more acceptable single occupancy housing, refuge spaces, mental health and parenting supports, residential addiction treatment, and meaningful social activities. Study limitations include the obtention of data from a single site and from only English speakers. The authors received no specific funding for this work.

A novel circularly polarized (CP) substrate integrated magneto‐electric dipole (MED) antenna has been designed for appropriate wireless communication. The antenna comprises two printed radiating arc‐shaped patches and a feeding strip on top, two rows of embedded metallic vias, and a ground plane. A coax probe is used to excite the patches and via rows simultaneously with the help of a printed feeding strip. Finally, the antenna design has been prototyped and its performance experimentally was verified in terms of impedance bandwidth, axial‐ratio (AR), gain, efficiency, and radiation patterns. The measured impedance bandwidth (under −10 dB) and AR bandwidth (under 3 dB) are 6.15–7.01 (13%) GHz and 6.24–6.40 (2.53%) GHz respectively. Typically, the measured gain value within 3‐dB AR bandwidth at 6.3 GHz is 4.5 dBic with average measured in‐band antenna efficiency of 85.2%. Moreover, the proposed antenna shows an acceptable agreement with predicted counterparts, including unidirectional radiation patterns.

Digital image watermarking, the process of marking a host image with a watermark, is generally used to authenticate the data. In the medical field, it is of utmost importance to verify the authenticity of the data using Medical Image Watermarking (MIW), especially in e-healthcare applications. Recently, MIW with image fusion, the merging of multimodal images to improve image quality, is being widely utilized to make diagnosis more accessible and precise with the verified data. This paper offers a durable and secure fusion-based hybrid MIW approach. The method initially used Fast Filtering (FF) to merge two medical images from different modalities to form the cover image. A first-level Redundant Discrete Wavelet Transform (RDWT) is employed on this host image to locate the component with the highest entropy. Then a single-level Discrete Wavelet Transform (DWT) is applied to it. It performed a Multi-resolution Singular Value Decomposition (MSVD) on the wavelet decomposed component and the embedding watermark. Finally, a Hyperchaotic System-Fibonacci Q Matrix (HFQM) encryption system was utilized, which increases the watermarked image’s security. Here, using various medical images, the performance of the proposed technique is evaluated. Without any attacks, the approach achieved a maximum Peak Signal to Noise Ratio (PSNR) of 90.31 dB and a Structural Similarity Index Matrix (SSIM) of value 1. Various watermarking assaults were employed to test the proposed method’s resilience. The suggested technique achieved a perfect value of 1 for the Normalised Correlation (NC) for almost all attacks with acceptable imperceptibility, which substantially improves over current procedures. The suggested technique’s average embedding and extraction times are 0.3958 and 0.4721 seconds, respectively, which are pretty short compared to existing approaches.