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Enhancing resource use efficiency in agricultural field management and breeding high-performance crop varieties are crucial approaches for securing crop yield and mitigating negative environmental impact of crop production. Crop stress sensing and plant phenotyping systems are integral to variable-rate (VR) field management and high-throughput plant phenotyping (HTPP), with both sharing similarities in hardware and data processing techniques. Crop stress sensing systems for VR field management have been studied for decades, aiming to establish more sustainable management practices. Concurrently, significant advancements in HTPP system development have provided a technological foundation for reducing conventional phenotyping costs. In this paper, we present a systematic review of crop stress sensing systems employed in VR field management, followed by an introduction to the sensors and data pipelines commonly used in field HTPP systems. State-of-the-art sensing and decision-making methodologies for irrigation scheduling, nitrogen application, and pesticide spraying are categorized based on the degree of modern sensor and model integration. We highlight the data processing pipelines of three ground-based field HTPP systems developed at the University of Nebraska-Lincoln. Furthermore, we discuss current challenges and propose potential solutions for field HTPP research. Recent progress in artificial intelligence, robotic platforms, and innovative instruments is expected to significantly enhance system performance, encouraging broader adoption by breeders. Direct quantification of major plant physiological processes may represent one of next research frontiers in field HTPP, offering valuable phenotypic data for crop breeding under increasingly unpredictable weather conditions. This review can offer a distinct perspective, benefiting both research communities in a novel manner.

Background: This study aimed to evaluate six novel lymphocyte‐based inflammatory markers (neutrophil‐lymphocyte ratio, monocyte‐lymphocyte ratio, platelet‐lymphocyte ratio [PLR], systemic immune inflammation index [SII], systemic inflammatory response index, and systemic immune inflammation response index [SIIRI]) in patients with newly diagnosed coronary artery disease [CAD]. Methods: A total of 959 patients newly diagnosed with CAD and underwent diagnostic coronary angiography were enrolled in this study and followed for major adverse cardiovascular events (MACEs), including cardiovascular death, nonfatal myocardial infarction, and nonfatal stroke. The best cutoff value was used to compare the six indicators. Cox risk regression analysis evaluated the relationship between novel lymphocyte‐based inflammatory markers and MACEs in newly diagnosed CAD patients. Results: During a mean follow‐up period of 33.3 ± 9.9 months, 229 (23.9%) MACEs were identified. Multivariate Cox regression analysis showed that only SIIRI (hazard ratio [HR]: 5.853; 95% confidence interval [CI]: 4.092–8.371; p <.001) and PLR (HR: 1.725; 95% CI: 1.214–2.452; p =.002) were independent predictors of MACEs. Nevertheless, following the adjustment for covariates, only the SIIRI was found to be a significant predictor MACEs and its corresponding specific endpoint occurrences. The predictive ability of the model was improved when six different inflammatory markers were added to the basic model established by traditional risk factors, namely, the C‐index increased, and the SIIRI increased most significantly (AUC: 0.778; 95% CI: 0.743–0.812; p <.001). However, among the six novel inflammatory markers, only SIIRI had improved net reclassification improvement (NRI) and integrated discrimination improvement (IDI) (NRI: 0.187; 95% CI: 0.115–0.259, p <.001. IDI: 0.135; 95% CI: 0.111–0.159, p <.001), which was superior to the basic model established by traditional risk factors. Conclusions: SIIRI is independent predictor of MACEs in newly diagnosed CAD patients. SIIRI was superior to other measures in predicting MACEs. The combination of SIIRI and traditional risk factors can more accurately predict MACEs. [ABSTRACT FROM AUTHOR]

Chest pain, a common initial symptom in hypertrophic cardiomyopathy (HCM) patients, is closely linked to myocardial ischemia, despite the absence of significant coronary artery stenosis. This study explored microvascular dysfunction in HCM patients by employing angiography-derived microcirculatory resistance (AMR) as a novel tool for comprehensive assessment. This retrospective analysis included HCM patients with chest pain as the primary symptom and control patients without cardiac hypertrophy during the same period. The AMR was computed through angiography, providing a wire-free and adenosine-free index for evaluating microcirculatory function. Propensity score matching ensured balanced demographics between groups. This study also investigated the correlation between the AMR and clinical outcomes by utilizing echocardiography and follow-up data. After matching, 76 HCM patients and 152 controls were analyzed. While there was no significant difference in the incidence of epicardial coronary stenosis, the AMR of three epicardial coronary arteries was markedly greater in HCM patients. The criterion of an AMR ≥ 250 mmHg*s/m was that 65.7% of HCM patients experienced coronary microvascular dysfunction (CMD). Independent risk factors for CMD included increased left ventricular (LV) wall thickness (OR = 1.209, 95% CI 1.013–1.443, p = 0.036). Furthermore, an AMR_LAD ≥ 250 mmHg*s/m had an increased cumulative risk of the endpoint (log-rank p = 0.023) and was an independent risk factor for the endpoint (HR = 11.64, 95% CI 1.13–120.03, p = 0.039), providing valuable prognostic insights. [ABSTRACT FROM AUTHOR]

With the CMOS technology downscaling to the deep nanoscale, the aging effects of devices degrade circuit performance and even lead to functional failure. The stress analysis is critical to evaluate the influence of aging effects on digital circuits. Some related analytical work has recently focused on reliability-aware circuit analysis. Nevertheless, the aging dependence among different devices is not considered, which will induce errors of degradation evaluation in the digital circuit. In order to improve the accuracy of reliability-aware static timing analysis, an improved analytical method is proposed by employing logical resolving. Experimental results show that the proposed method has a better evaluation accuracy of aging path delay than traditional strategies. For aging timing evaluation on aging paths, excessive pessimism can be reduced by employing the proposed method. And, a 378× speedup is achieved while having a 0.56% relative error compared with precise SPICE simulation. Moreover, the circuit performance sacrifice of an aging-aware synthesis flow with the proposed method can be decreased. Due to the high efficiency and high accuracy, the proposed method can meet the speed demands of large-scale digital circuit reliability analysis while achieving transistor simulation accuracy. [ABSTRACT FROM AUTHOR]