The neurobench framework for benchmarking neuromorphic computing algorithms and systems.

Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. This article presents NeuroBench, a benchmark framework for neuromorphic algorithms and systems, which is collaboratively designed from an open community of researchers across industry and academia. NeuroBench introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent and hardware-dependent settings. For latest project updates, visit the project website ( neurobench.ai )

NeuroBench:A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems

Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. Prior neuromorphic computing benchmark efforts have not seen widespread adoption due to a lack of inclusive, actionable, and iterative benchmark design and guidelines. To address these shortcomings, we present NeuroBench: a benchmark framework for neuromorphic computing algorithms and systems. NeuroBench is a collaboratively-designed effort from an open community of nearly 100 co-authors across over 50 institutions in industry and academia, aiming to provide a representative structure for standardizing the evaluation of neuromorphic approaches. The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings. In this article, we present initial performance baselines across various model architectures on the algorithm track and outline the system track benchmark tasks and guidelines. NeuroBench is intended to continually expand its benchmarks and features to foster and track the progress made by the research community

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Correction to: Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

The original version of this article unfortunately contained a mistake

A General Analytical Model of Single-Layer Common-Mode Chokes

To accurately model common-mode (CM) chokes is crucial for ensuring the design and optimization of EMI filters. This letter identifies the limitations of the commonly employed analytical model, which results in significant margin errors when applied to CM chokes. It proposes a comprehensive and more precise analytical model that addresses these errors by considering the influence of time-varying electromagnetic fields and the geometric characteristics of CM chokes. Furthermore, this letter provides a detailed explanation of the physical significance behind the proposed model. Both methods are compared, and the suggested approach is validated through simulations and experiments in four distinct scenarios

Assessment of street space quality and subjective well-being mismatch and its impact, using multi-source big data

This study makes initial efforts by delineating the distribution map of the mismatch between street space quality and SWB in central Qingdao through machine learning approaches, then creatively combines ordered logistic regression and restrictive cubic spline to examine the nonlinear influence of urban variables on the mismatch based on multi-source big data. The study primarily found that low-quality spaces are concentrated in the old city area; The SWB scores of the internal space in central Qingdao are generally good and evenly distributed, while the SWB scores of the peripheral space have significant differences; Road network accessibility, green space, living convenience, and housing prices are positively correlated with SWB significantly higher than street space quality, however, land mixed use, night lighting index, and population density are negatively correlated with it. When the green space agglomeration value reaches 2.9 or exceeds 7.8, the living convenience value exceeds 12.2, and the housing price value reaches 26.6 thousand yuan/m2, improving the street space quality is most likely to enhance residents' SWB. These findings link urban spatial quality with SWB and provide support for urban further planning and regeneration to improve public SWB through targeted interventions.We are grateful for the financial support of the National Natural Science Foundation of China (NSFC), Basic Science Center Program, Multiphase Evolution in Hypergravity (No. 51988101)