Digital histopathological images, high‐resolution images of stained tissue samples, are a vital tool for clinicians to diagnose and stage cancers. The visual analysis of patient state based on these ...images are an important part of oncology workflow. Although pathology workflows have historically been conducted in laboratories under a microscope, the increasing digitization of histopathological images has led to their analysis on computers in the clinic. The last decade has seen the emergence of machine learning, and deep learning in particular, a powerful set of tools for the analysis of histopathological images. Machine learning models trained on large datasets of digitized histopathology slides have resulted in automated models for prediction and stratification of patient risk. In this review, we provide context for the rise of such models in computational histopathology, highlight the clinical tasks they have found success in automating, discuss the various machine learning techniques that have been applied to this domain, and underscore open problems and opportunities.
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Distributed devices in smart grid systems are decentralized and connected to the power grid through different types of equipment transmit, which will produce numerous energy losses when power flows ...from one bus to another. One of the most efficient approaches to reduce energy losses is to integrate distributed generations (DGs), mostly renewable energy sources. However, the uncertainty of DG may cause instability issues. Additionally, due to the similar consumption habits of customers, the peak load period of power consumption may cause congestion in the power grid and affect the energy delivery. Energy management with DG regulation is considered to be one of the most efficient solutions for solving these instability issues. In this paper, we consider a power system with both distributed generators and customers, and propose a distributed locational marginal pricing (DLMP)-based unified energy management system (uEMS) model, which, unlike previous works, considers both increasing profit benefits for DGs and increasing stability of the distributed power system (DPS). The model contains two parts: 1) a game theory-based loss reduction allocation (LRA); and 2) a load feedback control (LFC) with price elasticity. In the former component, we develop an iterative loss reduction method using DLMP to remunerate DGs for their participation in energy loss reduction. By using iterative LRA to calculate energy loss reduction, the model accurately rewards DG contribution and offers a fair competitive market. Furthermore, the overall profit of all DGs is maximized by utilizing game theory to calculate an optimal LRA scheme for calculating the distributed loss of every DG in each time slot. In the latter component of the model, we propose an LFC submodel with price elasticity, where a DLMP feedback signal is calculated by customer demand to regulate peak-load value. In uEMS, LFC first determines the DLMP signal of a customer bus by a time-shift load optimization (LO) algorithm based on the changes of customer demand, which is fed back to the DLMP of the customer bus at the next slot-time, allowing for peak-load regulation via price elasticity. Results based on the IEEE 37-bus feeder system show that the proposed uEMS model can increase DG benefits and improve system stability.
Global health which denotes equitable access to healthcare, particularly in remote-rural-developing regions, is characterized by unique challenges of affordability , accessibility , and availability ...for which one of the most promising technological interventions that is emerging is the Internet of Things (IoT)-based remote health monitoring. We present an IoT-based smart edge system for remote health monitoring, in which wearable vital sensors transmit data into two novel software engines, namely rapid active summarization for effective prognosis (RASPRO) and criticality measure index (CMI) alerts, both of which we have implemented in the IoT smart edge. RASPRO transforms voluminous sensor data into clinically meaningful summaries called personalized health motifs (PHMs). The CMI alerts engine computes an aggregate criticality score. Our IoT smart edge employs a risk-stratified protocol consisting of rapid guaranteed push of alerts and PHMs directly to the physicians, and best effort pull of detailed data-on-demand through the cloud. We have carried out both clinical validation and performance evaluation of our smart edge system. The clinical validation on 183 patients demonstrated that the IoT smart edge is highly effective in remote monitoring, advance warning and detection of cardiac conditions, as quantified by three measures, precision (0.87), recall (0.83), and F1-score (0.85). Furthermore, performance evaluation showed significant reductions in the bandwidth (98%) and energy (90%), thereby making it suitable for emerging narrow-band IoT networks. In the deployment of our system in the cardiology institute of our University hospital, we observed that our IoT smart edge helped to increase the availability of physicians by 59%. Hence, our IoT smart edge system is a significant step toward addressing the requirements for global health.
Lithium-ion batteries show poor performance for high power applications involving ultrafast charging/discharging rates. Here we report a functionally strain-graded carbon−aluminum−silicon anode ...architecture that overcomes this drawback. It consists of an array of nanostructures each comprising an amorphous carbon nanorod with an intermediate layer of aluminum that is finally capped by a silicon nanoscoop on the very top. The gradation in strain arises from graded levels of volumetric expansion in these three materials on alloying with lithium. The introduction of aluminum as an intermediate layer enables the gradual transition of strain from carbon to silicon, thereby minimizing the mismatch at interfaces between differentially strained materials and enabling stable operation of the electrode under high-rate charge/discharge conditions. At an accelerated current density of ∼51.2 A/g (i.e., charge/discharge rate of ∼40C), the strain-graded carbon−aluminum−silicon nanoscoop anode provides average capacities of ∼412 mAh/g with a power output of ∼100 kW/kgelectrode continuously over 100 charge/discharge cycles.
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The increased complexity of modern engineered systems has introduced novel challenges for assessing their safety early in the life cycle. For example, due to the iterative nature of the design and ...safety life cycle, there is constant data transformation and feedback of information between the system design models, safety analyses, and safety verification. Data transformation and feedback are often manually performed by engineers, which is time-consuming and error prone and can introduce inconsistencies in safety assessments. Although several model-based systems engineering approaches have been developed for safety analysis and safety verification, current approaches do not address the inconsistencies introduced in the safety assessment process. This study describes the Integrated System Design and Safety (ISDS) framework, which is a model-based safety assessment framework that aims to eliminate such inconsistencies. The framework combines a model-based safety analysis approach with a model-based safety verification. This paper extends previous work, which focused on the model-based safety analysis approach, to describe the model-based safety verification approach adopted in the ISDS framework. Safety verification is performed using a simulation-based fault injection approach and enabled by a fault injection engine, which injects failures into the system design and characterizes system behaviors to identify safety violations impacting the system. The results from the case study, in which the framework is used to assess the safety of a forward collision warning system, highlight that the algorithms and automated feedback loops of the framework can reduce inconsistencies in the safety assessment process while also identifying safety violations impacting the system.
Safety analysis is often performed independent of the system design life cycle, leading to inconsistency between the system design and the safety artifact. Additionally, the process of generating ...safety artifacts is manual, time-consuming, and error-prone. As a result, safety analysis often requires re- work, is expensive, and increases system development time. Several model-based systems engineering (MBSE) approaches have been developed to automatically generate certain safety artifacts. However, these approaches only cover part of the system design and safety life cycle. To truly leverage the benefits of MBSE, system design must be undertaken together with safety analysis for the entire life cycle, and multiple safety artifacts must be generated from the same model. Moreover, MBSE approaches that require a model transformation between the system design and the safety model suffer from the inability to automatically reflect changes made to a safety artifact in the system and the safety model. This paper presents a framework to integrate the entire system design and safety life cycle using an MBSE approach. Both the system design and the safety data are captured in a single SysML model, from which safety artifacts such as failure modes and effects analysis (FMEA) tables and fault trees are automatically generated. This framework ensures consistency between the system design and the safety analysis by requiring no model transformation, thus reducing the resources required for safety analysis. The proposed Integrated System Design and Safety (ISDS) framework comprises three phases that together cover the entire system design and safety life cycle. In this paper, the application of Phase 1 of the framework to a real-world case study is demonstrated.
Rechargeable lithium ion batteries are integral to today's information‐rich, mobile society. Currently they are one of the most popular types of battery used in portable electronics because of their ...high energy density and flexible design. Despite their increasing use at the present time, there is great continued commercial interest in developing new and improved electrode materials for lithium ion batteries that would lead to dramatically higher energy capacity and longer cycle life. Silicon is one of the most promising anode materials because it has the highest known theoretical charge capacity and is the second most abundant element on earth. However, silicon anodes have limited applications because of the huge volume change associated with the insertion and extraction of lithium. This causes cracking and pulverization of the anode, which leads to a loss of electrical contact and eventual fading of capacity. Nanostructured silicon anodes, as compared to the previously tested silicon film anodes, can help overcome the above issues. As arrays of silicon nanowires or nanorods, which help accommodate the volume changes, or as nanoscale compliant layers, which increase the stress resilience of silicon films, nanoengineered silicon anodes show potential to enable a new generation of lithium ion batteries with significantly higher reversible charge capacity and longer cycle life.
Silicon anodes offer a larger theoretical charge capacity compared to graphite; however, the huge volume change associated with the insertion and extraction of lithium from silicon causes cracking and pulverization of the anode. Recent research suggests that nanostructured silicon anode architectures (see image) may overcome these limitations and deliver higher reversible charge capacity and longer cycle life.
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