An Empirical study on Predicting Blood Pressure using Classification and Regression Trees

Blood pressure diseases have become one of the major threats to human health. Continuous measurement of bloodpressure has proven to be a prerequisite for effective incident prevention. In contrast with the traditional prediction models with lowmeasurement accuracy or long training time, non-invasive blood pressure measurement is a promising use for continuousmeasurement. Thus in this paper, classification and regression trees (CART) are proposed and applied to tackle the problem. Firstly,according to the characteristics of different information, different CART models are constructed. Secondly, in order to avoid theover-fitting problem of these models, the cross-validation method is used for selecting the optimum parameters so as to achieve thebest generalization of these models. Based on the biological data collected from CM400 monitor, this approach has achieved betterperformance than the common existing models such as linear regression, ridge regression, the support vector machine and neuralnetwork in terms of accuracy rate, root mean square error, deviation rate, Theil IC, and the required training time is also comparativelyless. With increasing data, the accuracy rate of predicting systolic blood pressure and diastolic blood pressure by CART exceeds 90%,and the training time is less than 0.5s

Development of a hardware-In-the-Loop (HIL) testbed for cyber-physical security in smart buildings

As smart buildings move towards open communication technologies, providing access to the Building Automation System (BAS) through the intranet, or even remotely through the Internet, has become a common practice. However, BAS was historically developed as a closed environment and designed with limited cyber-security considerations. Thus, smart buildings are vulnerable to cyber-attacks with the increased accessibility. This study introduces the development and capability of a Hardware-in-the-Loop (HIL) testbed for testing and evaluating the cyber-physical security of typical BASs in smart buildings. The testbed consists of three subsystems: (1) a real-time HIL emulator simulating the behavior of a virtual building as well as the Heating, Ventilation, and Air Conditioning (HVAC) equipment via a dynamic simulation in Modelica; (2) a set of real HVAC controllers monitoring the virtual building operation and providing local control signals to control HVAC equipment in the HIL emulator; and (3) a BAS server along with a web-based service for users to fully access the schedule, setpoints, trends, alarms, and other control functions of the HVAC controllers remotely through the BACnet network. The server generates rule-based setpoints to local HVAC controllers. Based on these three subsystems, the HIL testbed supports attack/fault-free and attack/fault-injection experiments at various levels of the building system. The resulting test data can be used to inform the building community and support the cyber-physical security technology transfer to the building industry.Comment: Presented at the 2023 ASHRAE Winter Conferenc

PowerNet: Transferable Dynamic IR Drop Estimation via Maximum Convolutional Neural Network

IR drop is a fundamental constraint required by almost all chip designs. However, its evaluation usually takes a long time that hinders mitigation techniques for fixing its violations. In this work, we develop a fast dynamic IR drop estimation technique, named PowerNet, based on a convolutional neural network (CNN). It can handle both vector-based and vectorless IR analyses. Moreover, the proposed CNN model is general and transferable to different designs. This is in contrast to most existing machine learning (ML) approaches, where a model is applicable only to a specific design. Experimental results show that PowerNet outperforms the latest ML method by 9% in accuracy for the challenging case of vectorless IR drop and achieves a 30 times speedup compared to an accurate IR drop commercial tool. Further, a mitigation tool guided by PowerNet reduces IR drop hotspots by 26% and 31% on two industrial designs, respectively, with very limited modification on their power grids

Monitoring of postoperative neutrophil-to-lymphocyte ratio, D-dimer, and CA153 in: Diagnostic value for recurrent and metastatic breast cancer

ObjectiveThis stydy aims to assess the value of monitoring of postoperative neutrophil-to-lymphocyte ratio (NLR), D-dimer, and carbohydrate antigen 153 (CA153) for diagnosis of breast cancer (BC) recurrence and metastasis.Materials/MethodsA cohort of 252 BC patients who underwent surgery at the First Affiliated Hospital of Anhui Medical University between August 2008 and August 2018 were enrolled in this retrospective study. All patients were examined during outpatient follow-ups every 3 months for 5 years postoperation and every 6 months thereafter. Recurrence or metastasis was recorded for 131 patients but not for the remaining 121. Retrospective analysis of hematological parameters and clinicopathological characteristics allowed comparison between the two groups and evaluation of these parameters for the recurrent and metastatic patients.ResultsLymph node metastasis, higher tumor node metastasis (TNM) staging, and higher histological grade correlated with BC recurrence and metastasis (p < 0.05). Statistical differences were found in absolute neutrophil count (ANC), absolute lymphocyte count (ALC), CEA, CA153, D-dimer, NLR, platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR) between the recurrent and metastatic and control groups (p < 0.05). Logistic regression analysis showed that CA153, D-dimer, NLR, and TNM staging were risk factors for BC recurrence and metastasis (p < 0.05). Combined values for the NLR, D-dimer, and CA153 had good diagnostic values, giving the highest area under the curve (AUC) of 0.913. High NLR, D-dimer, and CA153 values were significantly associated with recurrence and metastasis at multiple sites, lymph node metastasis, and higher TNM staging (p < 0.05). Patients with high CA153 were more likely to have bone metastases (p < 0.05), and those with high D-dimer were prone to lung metastasis (p < 0.05). With the increasing length of the postoperative period, the possibility of liver metastases gradually decreased, while that of chest wall recurrence gradually increased (p < 0.05).ConclusionMonitoring postoperative NLR, D-dimer, and CA153 is a convenient, practical method for diagnosing BC recurrence and metastasis. These metrics have good predictive value in terms of sites of recurrence and metastasis and the likelihood of multiple metastases

Health data driven on continuous blood pressure prediction based on gradient boosting decision tree algorithm

Diseases related to issues with blood pressure are becoming a major threat to human health. With the development of telemedicine monitoring applications, a growing number of corresponding devices are being marketed, such as the use of remote monitoring for the purposes of increasing the autonomy of the elderly and thus encouraging a healthier and longer health span. Using machine learning algorithms to measure blood pressure at a continuous rate is a feasible way to provide models and analysis for telemedicine monitoring data and predicting blood pressure. For this paper, we applied the gradient boosting decision tree (GBDT) while predicting blood pressure rates based on the human physiological data collected by the EIMO device. EIMO equipment-specific signal acquisition includes ECG and PPG. In order to avoid over-fitting, the optimal parameters are selected via the cross-validation method. Consequently, our method has displayed a higher accuracy rate and better performance in calculating the mean absolute error evaluation index than methods, such as the traditional least squares method, ridge regression, lasso regression, ElasticNet, SVR, and KNN algorithm. When predicting the blood pressure of a single individual, calculating the systolic pressure displays an accuracy rate of above 70% and above 64% for calculating the diastolic pressure with GBDT, with the prediction time being less than 0.1 s. In conclusion, applying the GBDT is the best method for predicting the blood pressure of multiple individuals: with the inclusion of data such as age, body fat, ratio, and height, algorithm accuracy improves, which in turn indicates that the inclusion of new features aids prediction performance

A novel method for determining the neutral axis position of the asymmetric cross section and its application in the simplified progressive collapse method for damaged ships

Ultimate strength is an important design consideration for the safety of intact or damaged ship structures. The simplified progressive collapse method is a commonly used iterative method to obtain the accurate ultimate strength of ships. Since the accuracy of the neutral axis position directly affects the accuracy of the ultimate strength, the force equilibrium criterion and the force vector equilibrium criterion are adopted to search for the height and angle of the neutral axis, especially for damaged ships. However, the search for the neutral axis position based on the two criteria requires iterative computation which decreases the calculation efficiency. In this paper, the relationship between the results of the iterative process and the neutral axis position is studied, and it is found that the relationship is approximately linear. Then a new iterative method based on the linear equation is proposed to obtain the neutral axis position and is adopted to improve the simplified progressive collapse method. Finally, the new method is used to calculate the neutral axis position of a damaged VLCC. The comparison of the ultimate strength results shows that the improved simplified progressive collapse method based on the linear equation has improved efficiency and good accuracy

A critical review of cyber-physical security for building automation systems

Modern Building Automation Systems (BASs), as the brain that enables the smartness of a smart building, often require increased connectivity both among system components as well as with outside entities, such as optimized automation via outsourced cloud analytics and increased building-grid integrations. However, increased connectivity and accessibility come with increased cyber security threats. BASs were historically developed as closed environments with limited cyber-security considerations. As a result, BASs in many buildings are vulnerable to cyber-attacks that may cause adverse consequences, such as occupant discomfort, excessive energy usage, and unexpected equipment downtime. Therefore, there is a strong need to advance the state-of-the-art in cyber-physical security for BASs and provide practical solutions for attack mitigation in buildings. However, an inclusive and systematic review of BAS vulnerabilities, potential cyber-attacks with impact assessment, detection & defense approaches, and cyber-secure resilient control strategies is currently lacking in the literature. This review paper fills the gap by providing a comprehensive up-to-date review of cyber-physical security for BASs at three levels in commercial buildings: management level, automation level, and field level. The general BASs vulnerabilities and protocol-specific vulnerabilities for the four dominant BAS protocols are reviewed, followed by a discussion on four attack targets and seven potential attack scenarios. The impact of cyber-attacks on BASs is summarized as signal corruption, signal delaying, and signal blocking. The typical cyber-attack detection and defense approaches are identified at the three levels. Cyber-secure resilient control strategies for BASs under attack are categorized into passive and active resilient control schemes. Open challenges and future opportunities are finally discussed.Comment: 38 pages, 7 figures, 6 tables, submitted to Annual Reviews in Contro

FIST: A Feature-Importance Sampling and Tree-Based Method for Automatic Design Flow Parameter Tuning

Design flow parameters are of utmost importance to chip design quality and require a painfully long time to evaluate their effects. In reality, flow parameter tuning is usually performed manually based on designers' experience in an ad hoc manner. In this work, we introduce a machine learning-based automatic parameter tuning methodology that aims to find the best design quality with a limited number of trials. Instead of merely plugging in machine learning engines, we develop clustering and approximate sampling techniques for improving tuning efficiency. The feature extraction in this method can reuse knowledge from prior designs. Furthermore, we leverage a state-of-the-art XGBoost model and propose a novel dynamic tree technique to overcome overfitting. Experimental results on benchmark circuits show that our approach achieves 25% improvement in design quality or 37% reduction in sampling cost compared to random forest method, which is the kernel of a highly cited previous work. Our approach is further validated on two industrial designs. By sampling less than 0.02% of possible parameter sets, it reduces area by 1.83% and 1.43% compared to the best solutions hand-tuned by experienced designers

A deep learning model for prediction of post hepatectomy liver failure after hemihepatectomy using preoperative contrast-enhanced computed tomography: a retrospective study

ObjectivePost-hepatectomy liver failure (PHLF) remains clinical challenges after major hepatectomy. The aim of this study was to establish and validate a deep learning model to predict PHLF after hemihepatectomy using preoperative contrast-enhancedcomputed tomography with three phases (Non-contrast, arterial phase and venous phase).Methods265 patients undergoing hemihepatectomy in Sir Run Run Shaw Hospital were enrolled in this study. The primary endpoint was PHLF, according to the International Study Group of Liver Surgery’s definition. In this study, to evaluate the proposed method, 5-fold cross-validation technique was used. The dataset was split into 5 folds of equal size, and each fold was used as a test set once, while the other folds were temporarily combined to form a training set. Performance metrics on the test set were then calculated and stored. At the end of the 5-fold cross-validation run, the accuracy, precision, sensitivity and specificity for predicting PHLF with the deep learning model and the area under receiver operating characteristic curve (AUC) were calculated.ResultsOf the 265 patients, 170 patients with left liver resection and 95 patients with right liver resection. The diagnosis had 6 types: hepatocellular carcinoma, intrahepatic cholangiocarcinoma, liver metastases, benign tumor, hepatolithiasis, and other liver diseases. Laparoscopic liver resection was performed in 187 patients. The accuracy of prediction was 84.15%. The AUC was 0.7927. In 170 left hemihepatectomy cases, the accuracy was 89.41% (152/170), and the AUC was 82.72%. The accuracy was 77.47% (141/182) with liver mass, 78.33% (47/60) with liver cirrhosis and 80.46% (70/87) with viral hepatitis.ConclusionThe deep learning model showed excellent performance in prediction of PHLF and could be useful for identifying high-risk patients to modify the treatment planning

Direct and indirect effects of climate on richness drive the latitudinal diversity gradient in forest trees

Data accessibility statement: Full census data are available upon reasonable request from the ForestGEO data portal, http://ctfs.si.edu/datarequest/ We thank Margie Mayfield, three anonymous reviewers and Jacob Weiner for constructive comments on the manuscript. This study was financially supported by the National Key R&D Program of China (2017YFC0506100), the National Natural Science Foundation of China (31622014 and 31570426), and the Fundamental Research Funds for the Central Universities (17lgzd24) to CC. XW was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB3103). DS was supported by the Czech Science Foundation (grant no. 16-26369S). Yves Rosseel provided us valuable suggestions on using the lavaan package conducting SEM analyses. Funding and citation information for each forest plot is available in the Supplementary Information Text 1.Peer reviewedPostprin