Energy management in microgrids is typically formulated as an offline optimization problem for day-ahead scheduling by previous studies. Most of these offline approaches assume perfect forecasting of ...the renewables, the demands, and the market, which is difficult to achieve in practice. Existing online algorithms, on the other hand, oversimplify the microgrid model by only considering the aggregate supply-demand balance while omitting the underlying power distribution network and the associated power flow and system operational constraints. Consequently, such approaches may result in control decisions that violate the real-world constraints. This paper focuses on developing an online energy management strategy (EMS) for real-time operation of microgrids that takes into account the power flow and system operational constraints on a distribution network. We model the online energy management as a stochastic optimal power flow problem and propose an online EMS based on Lyapunov optimization. The proposed online EMS is subsequently applied to a real-microgrid system. The simulation results demonstrate that the performance of the proposed EMS exceeds a greedy algorithm and is close to an optimal offline algorithm. Lastly, the effect of the underlying network structure on energy management is observed and analyzed.
Objective
To identify the feasibility of using a deep convolutional neural network (DCNN) for the detection and localization of hip fractures on plain frontal pelvic radiographs (PXRs).
Summary of ...background data
Hip fracture is a leading worldwide health problem for the elderly. A missed diagnosis of hip fracture on radiography leads to a dismal prognosis. The application of a DCNN to PXRs can potentially improve the accuracy and efficiency of hip fracture diagnosis.
Methods
A DCNN was pretrained using 25,505 limb radiographs between January 2012 and December 2017. It was retrained using 3605 PXRs between August 2008 and December 2016. The accuracy, sensitivity, false-negative rate, and area under the receiver operating characteristic curve (AUC) were evaluated on 100 independent PXRs acquired during 2017. The authors also used the visualization algorithm gradient-weighted class activation mapping (Grad-CAM) to confirm the validity of the model.
Results
The algorithm achieved an accuracy of 91%, a sensitivity of 98%, a false-negative rate of 2%, and an AUC of 0.98 for identifying hip fractures. The visualization algorithm showed an accuracy of 95.9% for lesion identification.
Conclusions
A DCNN not only detected hip fractures on PXRs with a low false-negative rate but also had high accuracy for localizing fracture lesions. The DCNN might be an efficient and economical model to help clinicians make a diagnosis without interrupting the current clinical pathway.
Key Points
•
Automated detection of hip fractures on frontal pelvic radiographs may facilitate emergent screening and evaluation efforts for primary physicians.
• Good visualization of the fracture site by Grad-CAM enables the rapid integration of this tool into the current medical system.
• The feasibility and efficiency of utilizing a deep neural network have been confirmed for the screening of hip fractures
.
Energy management in microgrids is typically formulated as a nonlinear optimization problem. Solving it in a centralized manner does not only require high computational capabilities at the microgrid ...central controller (MGCC), but may also infringe customer privacy. Existing distributed approaches, on the other hand, assume that all generations and loads are connected to one bus, and ignore the underlying power distribution network and the associated power flow and system operational constraints. Consequently, the schedules produced by those algorithms may violate those constraints and thus are not feasible in practice. Therefore, the focus of this paper is on the design of a distributed energy management strategy (EMS) for the optimal operation of microgrids with consideration of the distribution network and the associated constraints. Specifically, we formulate microgrid energy management as an optimal power flow problem, and propose a distributed EMS where the MGCC and the local controllers jointly compute an optimal schedule. We also provide an implementation of the proposed distributed EMS based on IEC 61850. As one demonstration, we apply the proposed distributed EMS to a real microgrid in Guangdong Province, China, consisting of photovoltaics, wind turbines, diesel generators, and a battery energy storage system. The simulation results demonstrate the effectiveness and fast convergence of the proposed distributed EMS.
Introduction. Postoperative delayed hyponatremia is a complication associated with transsphenoidal pituitary surgery. Due to a wide spectrum of symptoms, the reported incidence and predictors of ...postoperative delayed hyponatremia vary among studies, and this deserves to be reviewed systematically. Methods. PubMed, EMBASE, and CENTRAL databases were searched until September 1, 2020. Studies were included when (1) the event number of delayed hyponatremia after transsphenoidal pituitary surgery was reported, or (2) the associated factors of such complication were evaluated. Results. A total of 27 studies were included for meta-analysis. The pooled incidence of overall and symptomatic delayed hyponatremia was 10.5% (95% confidence interval (CI) = 7.4–14.7%) and 5.0% (95% CI = 3.6–6.9%), respectively. No overt variations of the pooled estimates were observed upon subgroups stratified by endoscopic and microscopic procedure, publication year, and patients’ age. In addition, 44.3% (95% CI = 29.6–60.2%) of unplanned hospital readmissions within 30 days were caused by delayed hyponatremia. Among the predictors evaluated, older age was the only significant factor associated with increased delayed hyponatremia (odds ratio = 1.16, 95% CI = 1.04–1.29, P = 0.006). Conclusion. This meta-analysis and systematic review evaluated the incidence of postoperative delayed hyponatremia and found it as a major cause of unplanned readmissions after transsphenoidal pituitary surgery. Older patients are more prone to such complications and should be carefully followed. The retrospective nature and heterogeneity among the included studies and the small number of studies used for risk factor evaluation might weaken the corresponding results. Future prospective clinical studies are required to compensate for these limitations.
Promisingly, the technique of hippocampus sparing during WBRT (HS-WBRT) might preserve NCFs. In this research, we examined oncological outcomes, with emphasis on neurologic/non-neurologic causes of ...death, CNS progression, and leptomeningeal disease (LMD) recurrence in cancer patients who underwent HS-WBRT.
One hundred and fourteen cancer patients with newly diagnosed brain oligometastases underwent HS-WBRT were consecutively enrolled. The cumulative incidence of cancer-specific deaths (neurologic or non-neurologic), LMD recurrence, and the composite endpoint of CNS progression (CNS-CE) as the first event were computed with a competing-risks approach to characterize the oncological outcomes after HS-WBRT.
Patients with intact brain metastases had a significantly increased likelihood of dying from non-neurologic causes of death associated with early manifestation of progressive systemic disease (hazard ratio for non-neurologic death, 1.78; 95% CI, 1.08-2.95;
= 0.025; competing-risks Fine-Gray regression), which reciprocally rendered them unlikely to encounter LMD recurrence or any pattern of CNS progression (HR for CNS-CE as the first event, 0.13; 95% CI, 0.02-0.97;
= 0.047; competing-risks Fine-Gray regression). By contrast, patients with resection cavities post-craniotomy had reciprocally increased likelihood of CNS progression which might be associated with neurologic death eventually.
Patterns of oncological endpoints including neurologic/non-neurologic death and cumulative incidence of CNS progression manifesting as LMD recurrence are clearly clarified and contrasted between patients with intact BMs and those with resection cavities, indicating they are clinically distinct subgroups.
ClinicalTrials.gov, Identifier: NCT02504788, NCT03223675.
This study investigated energy saving effects of published papers related to energy management system (EMS), building energy management system (BEMS), industrial, company and factory energy ...management system (I/C/F/EMS); and EMS for heating, ventilation, air conditioning (HVAC) and refrigerating equipment, artificial lighting systems, motors and others (EMS for equipment). From 1976 to 2014, management performance reported by 305 EMS cases (105 BEMS cases, 103 I/C/F EMS cases and 97 cases of EMS for equipment) is analyzed to evaluate varied energy saving effects. Statistical results show that saving effects of BEMS increased from 11.39% to 16.22% yearly. Inversely, saving effects of I/C/F EMS decreased from 18.89% to 10.35%. Regarding to EMS for equipment, there is no obvious trend but only the averaged saving effect can be reported. EMS for artificial lighting systems has the highest saving effect up to 39.5% in average. For HVAC and other equipment, energy saving effects are around 14.07% and 16.66% respectively. These energy saving performances are correlated with developed EMS functions. The key EMS functions could be identified from their developing progress for effective energy savings. Based on the quantitative analysis, the future trends of EMS are discussed.
Wireless sensor networks (WSNs) have evolved over the last few decades due to the availability of low-cost, short-range and easy deployed sensors. WSN systems focus on sensing and transmittingthe ...real-time sense information of a specific monitoring environment for the back-end system to do further processing and analysis ....
The birth of mass production started in the early 1900s. The manufacturing industries were transformed from mechanization to digitalization with the help of Information and Communication Technology ...(ICT). Now, the advancement of ICT and the Internet of Things has enabled smart manufacturing or Industry 4.0. Industry 4.0 refers to the various technologies that are transforming the way we work in manufacturing industries such as Internet of Things, cloud, big data, AI, robotics, blockchain, autonomous vehicles, enterprise software, etc. Additionally, the Industry 4.0 concept refers to new production patterns involving new technologies, manufacturing factors, and workforce organization. It changes the production process and creates a highly efficient production system that reduces production costs and improves product quality. The concept of Industry 4.0 is relatively new; there is high uncertainty, lack of knowledge and limited publication about the performance measurement and quality management with respect to Industry 4.0. Conversely, manufacturing companies are still struggling to understand the variety of Industry 4.0 technologies. Industrial standards are used to measure performance and manage the quality of the product and services. In order to fill this gap, our study focuses on how the manufacturing industries use different industrial standards to measure performance and manage the quality of the product and services. This paper reviews the current methods, industrial standards, key performance indicators (KPIs) used for performance measurement systems in data-driven Industry 4.0, and the case studies to understand how smart manufacturing companies are taking advantage of Industry 4.0. Furthermore, this article discusses the digitalization of quality called Quality 4.0, research challenges and opportunities in data-driven Industry 4.0 are discussed.
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