High-level Events Research Articles

Statistical Study of Surface-charging Events in Aurora Region Over One Solar Cycle

High-level surface-charging events are frequently observed on spacecrafts in aurora region. Besides the sunlit condition, two major factors causing charging events are the ambient plasma density and the precipitating electron flux. For the first factor, when the ambient plasma density is relatively low due to weak solar UV output during solar minimum, more surface-charging events are reported to occur for polar satellite. Moreover, the increased energetic electron precipitation during high geomagnetic activities in solar cycle decent years, may also contribute to the charging events. To evaluate these two factors comprehensively, we present a statistical survey of over 2000 surface charging events recorded by Defense Meteorological Satellite Program (DMSP) satellites over one solar cycle. The relationship between charging events and Kp index, F10.7 index, and monthly average sunspot number is investigated. It is shown that the occurrence frequency of charging events is larger at descending years of solar cycle. In addition, the conditional probabilities of the charging events increase as Kp increase. There is a threshold for Kp index, under which most of the charging events cannot occur, and this threshold grows as F10.7 increases. Finally, it is demonstrated that the surface charging potential is strongly correlated with the Kp index. Although there is no obvious correlation between the surface charging potential and F10.7 index, we still observe a decreasing trend of the surface potential range as F10.7 increases.

Event reconstruction using temporal pattern of file system modification

Nowadays, several digital forensic tools extract a lot of low-level information from different parts of the system. Constructing high-level information from low-level ones is very challenging. This study reconstructs high-level events by using the traces of applications that are found in the file system metadata. In this regard, an event reconstruction framework is proposed that determines which applications have been run on a compromised system. The proposed framework works in two phases. In the training phase, the signatures of various applications are constructed. The signature of an application is the temporal pattern of file system modification of the application. In the detection phase, at first, the temporal pattern of file system modification of the hard disk (TPFSM-D) of the compromised system is constructed. Then in order to determine whether a particular application has been run on the compromised system, the distance between the signature of the application and the TPFSM-D of the hard disk is calculated by using a proposed distance measure. Finally, a decision engine decides whether the application has been run on the compromised system. The proposed event reconstruction framework has been tested on different scenarios. The empirical results suggest that the framework is effective in reconstructing events.

Automated Stoichiometry Analysis of Single-Molecule Fluorescence Imaging Traces via Deep Learning.

The stoichiometry of protein complexes is precisely regulated in cells and is fundamental to protein function. Singe-molecule fluorescence imaging based photobleaching event counting is a new approach for protein stoichiometry determination under physiological conditions. Due to the interference of the high noise level and photoblinking events, accurately extracting real bleaching steps from single-molecule fluorescence traces is still a challenging task. Here, we develop a novel method of using convolutional and long-short-term memory deep learning neural network (CLDNN) for photobleaching event counting. We design the convolutional layersto accurately extract features of steplike photobleaching drops and long-short-term memory (LSTM) recurrent layers to distinguish between photobleaching and photoblinking events. Compared with traditional algorithms, CLDNN shows higher accuracy with at least 2 orders of magnitude improvement of efficiency, and it does not require user-specified parameters. We have verified our CLDNN method using experimental data from imaging of single dye-labeled molecules in vitro and epidermal growth factor receptors (EGFR) on cells. Our CLDNN method is expected to provide a new strategy to stoichiometry study and time series analysis in chemistry.

Event detection in soccer videos using unsupervised learning of Spatio-temporal features based on pooled spatial pyramid model

Most existing researches for semantic analysis of soccer videos benefit from special approaches to bridge the semantic gap between low-level features and high-level events using a hierarchical structure. In this paper, we propose a novel data-driven model for automatic recognition of important events in soccer broadcast videos based on the analysis of spatio-temporal local features of video frames. Our presented algorithm explores the local visual content of video frames by focusing on spatial and temporal learned features in a low-dimensional transformed sparse space. The proposed algorithm, without using mid-level futures, dynamically extracts the most informative semantic concepts/features and improves the generality of the system. The dictionary learning process plays an important role in sparse coding and sparse representation-based event classification. In this paper, we present a novel dictionary learning method, which calculates several category-specific dictionaries by training the detected shots of various view categories. In order to evaluate the feasibility and effectiveness of the proposed algorithm, an extensive experimental investigation is conducted for the analysis, detection, and classification of soccer events on a large collection of video data. Experimental results indicate that our approach outperforms the state-of-the-art methods and demonstrate the effectiveness of the proposed approach.

Deep Learning Approach to Track Reconstruction in the upgraded VELO

The LHCb experiment will undergo a major upgrade for LHC Run III, scheduled to start taking data in 2021. In order to exploit the full physics potential of the upgraded detector, LHCb will employ a high level event filter entirely implemented in software. The event filter has to operate in real time at the 40 MHz LHC bunch crossing rate. The LHCb collaboration is currently exploring a variety of new new computing paradigms in order to cope with the challenges posed by the high data taking rates. One contribution to this effort is the application of Machine Learning (ML) techniques to particle track reconstruction. We explore an ML approach to the track reconstruction in the upgraded LHCb Vertex Locator (VELO). The reconstruction algorithm is evaluated with fully simulated Minimum-Bias data that reflects the input to the real time event filter. We have achieved a reasonable track reconstruction efficiency and low ghost rate with our first approach and are currently exploring several methods to further quality of the track reconstruction.

Even Driven Multimodal Augmented Reality based Command and Control Systems for Mining Industry

The mining industry is confronted with obligations to improve worker safety and provide more effective maintenance of the new-age mining machineries. It is also essential to develop reconnaissance mission to support emergency rescue operations. An approach to ensure the mine workers safety using AR (Augmented Reality) with remote monitoring CCS (Command Control System) is proposed in this paper. This method provides an interactive and augmented real time instantaneous personal safety solution. The benefit of augmented reality is blended with real time mining environmental sensor data using multimodal learning approach to predict the emergency situation instantaneously. Environmental mining sensory information is processed with Complex Event Processing (CEP) engine to derive high level events that trigger the alarm for the emergency situation. The proposed event driven multimodal AR based CCS, outperforms the existing emergency prediction solutions that use sensor fusion or deep learning AR.

High-Level Video Event Modeling, Recognition, and Reasoning via Petri Net

A Petri net based framework is proposed for automatic high level video event description, recognition and reasoning purposes. In comparison with the existing approaches reported in the literature, our work is characterized with a number of novel features: (i) the high level video event modeling and recognition based on Petri net are fully automatic, which are not only capable of covering single video events but also multiple ones without limit; (ii) more variations of event paths can be found and modeled using the proposed algorithms; (iii) the recognition results are more accurate based on automatic built high level event models. Experimental results show that the proposed method outperforms the existing benchmark in terms of recognition precision and recall. Additional advantages can be achieved such that hidden variations of events hardly identified by humans can also be recognized.

Scalable complex event processing using adaptive load balancing

An essential requirement of large-scale event-driven systems is the real-time detection of complex patterns of events from a large number of basic events and derivation of higher-level events using complex event processing (CEP) mechanisms. Centralized CEP mechanisms are however not scalable and thus inappropriate for large-scale domains with many input events and complex patterns, rendering the horizontal scaling of CEP mechanisms a necessity. In this paper, we propose CCEP as a mechanism for clustering of heterogeneous CEP engines to provide horizontal scalability using adaptive load balancing. We experimentally compare the performance of CCEP with the performances of three CEP clustering mechanisms, namely VISIRI, SCTXPF, and RR. The results of experiments show that CCEP increases throughput by 40 percent and thus it is more scalable than the other three chosen mechanisms when the input event rate changes at runtime. Although CCEP increases the network utilization by about 40 percent, it keeps the load of the system two times more balanced and reduces the input event loss three times.

Identification of Clinical Characteristics Associated With High-Level Care Among Patients With Skin and Soft Tissue Infections

Serious adverse outcomes associated with skin and soft tissue infections are uncommon, and current hospitalization rates appear excessive. It would be advantageous to be able to differentiate between patients who require high-level inpatient services and those who receive little benefit from hospitalization. We sought to identify characteristics associated with theneed for high-level inpatient care among emergency department patients presenting with skin and soft tissue infections. We conducted a nonconcurrent review of existing records to identify emergency department (ED) patients treated for skin and soft tissue infections. For each case, we recorded the presence or absence of select criteria and whether the patient needed high-level care, defined as ICU admission, operating room surgical intervention, or death as the primary outcome. We applied recursive partitioning to identify the principal criteria associated with high-level care. We identified 2,923 patients, including 84 experiencing high-level events. Recursive partitioning identified 6 variables associated with high-level outcomes: abnormal computed tomography, magnetic resonance imaging, or ultrasonographic imaging result; systemic inflammatory response syndrome; history of diabetes; previous infection at the same location; older than 65 years; and an infection involving the hand. One or more of these variables were present in all 84 patients requiring high-level care. A limited number of simple clinical characteristics appear to be able to identify skin and soft tissue infection patients who require high-level inpatient services. Further research is needed to determine whether patients who do not exhibit these criteria can be safely discharged from the ED.

Affectedness, empowerment and norm contestation – children and young people as social agents in international politics

ABSTRACTIn my contribution to this collection, I aim to expose how the growing transnationalisation of groups of affected persons – in this case children and young people – has brought to the fore normative contradictions and tensions built into the 1989 UN Convention on the Rights of the Child. More specifically, I will show how the universal notion of children’s rights and a strong global consensus on the ‘scourge’ of child labour has been challenged through the empowerment of affected persons – in this case child workers. Building on critical constructivist thinking on norms, my core argument is that the increasing access of affected persons’ organisations (APOs) to international organisations and high-level events brings with it an increase in norm contestation. Rather than creating new normative contestations, I will show in my analysis, the inclusion of the most affected brings to light normative inconsistencies and ambiguities that have been potentially ingrained in international treaties but hitherto successfully suppressed by powerful norm advocates. The articulation of subversive perspectives on child labour by working children and young people, I will conclude, results in normative tensions and collisions and a reconsideration of seemingly universal values previously taken for granted.

Influence of Quaternary sea level changes in the littoral of Chubut, Argentina

Geomorphologic and stratigraphic studies accomplished in the littoral area between Cabo Raso and Punta Fabián, eastern Chubut Province, allowed the identification of different features and geomorphologic processes related to the modern dinamic, as well as a set of coastal units related to the different transgressive events occurred from the middle Pleistocene to the Holocene. The distribution and development of the littoral deposits displays a completely different situation from that of the area of Bahía Camarones, Bahía Bustamante, Cabo Raso and Bahía Vera (south and north of the study area), favored by a very hard substrate (Marifil Complex) at higher topographic positions. Abrasion platforms, cliffs, islands, capes and tómbolos form a broken coast in which the most relevant accumulation morphologies are gravel beaches. This situation prevailed as well in each of the high sea level event, forming beach crests in some places that corresponded to bays, wider than the modern ones. According to the altitud of these geoforms, respect to similar ones located to the south which have a significant number of numerical ages, the highest sea levels correspond to 500 k B.P., quite probably the Marine Isotopic Stage 11. This analysis includes a mean elevation of the continent of 0.12 ± 0.04 mm/yr. Although eight beach crests were clearly identified, not necessarily each of them responds to independent transgressive events. In fact, some of them may represent different positions of the same transgression during the regressive phase.

Impact of Omega-3 Supplementation on High Sensitive C-Reactive Protein Level and 30-Day Major Adverse Cardiac Events After the Implementation of Coronary Stent in Patients with Chronic Kidney Disease: A Randomized Clinical Study.

Purpose: Studies have revealed that patients with chronic kidney disease (CKD) are more susceptible to adverse effects of percutaneous coronary intervention (PCI). In addition, the role of elevated high sensitive C-reactive protein (hs-CRP) in the prediction of adverse cardiac outcomes after coronary stent implantation has already been shown. Therefore, in this study, we aimed to evaluate the effect of omega-3 supplementation on hs-CRP and 30-day major adverse cardiac events (MACE) in patients with CKD undergoing elective PCI.Methods: In this randomized trial, 80 CKD patients who were candidates for elective PCI, were randomly assigned to two groups; the first group received a single dose of omega-3 (2500 mg, 12 h before PCI) as well as the standard drug regimen of PCI and the second group received placebo plus the standard therapy (325 mg loading dose of aspirin, 600 mg loading dose of clopidogrel, and weight-adjusted intravenous heparin). Hs-CRP levels were measured at baseline and 24 h after the intervention as a primary endpoint. The secondary endpoint was the incidence of MACE over a 30-day period after PCI.Results: Omega-3 did not significantly decrease post-PCI serum level of hs-CRP; however, the overall 30-day MACE was significantly lower in the omega-3 group compared to the control group (p=0.05).Conclusion: Our results revealed the positive effect of the omega-3 supplement on decreasing 30-day MACE; hence, omega-3 may be considered as an effective adjunctive therapy to the standard drug regimen used before PCI. The evaluation of the effect of omega-3 on long-term MACE is recommended for future studies.

An Assessment of the Vertical Movement of Water in a Flooded Paddy Rice Field Experiment Using Hydrus-1D

A quantitative estimation of the major components of the field water balance provides management decisions on how the scheme ought to be operated to ensure better distribution of irrigation water and increased delivery performance. Therefore, in this study, the water balance component in transplanted and broadcasted rice fields with conventional irrigation (flooding irrigation) in the Tanjung Karang Rice Irrigation Scheme (TAKRIS), Sawah Sempadan were observed and then modeled using Hydrus-1D numerical model during two consecutive rice growing seasons. During the off-season, irrigation water accounted for 59.6% of the total water input (irrigation + rainfall), but about 76.2% of total water input during the main season. During the main season, rainfall water only contributed to 23.8% of total water input and 40.4% during the off-season. Drainage water accounted for 37.3% of the total water input during the off-season and 43.7% during the main season, respectively, which was the main path of water losses from conventional rice fields, which indicates that maintaining a high water level and huge rainfall events during both seasons increased drainage water. Simulated ET during the off-season and the main season accounted for 38.1% and 49.5% of the total water input, respectively. Observed and simulated water percolation revealed about 17.1% to 19.2% of total water input during both seasons, respectively. Additionally, the water productivities analyzed from total water input and irrigation water were 0.43 and 0.72 kg m−3 during the off-season and 0.60 and 0.78 kg m−3 during the main season, respectively. The water productivity index evaluated from observed and modeled evapotranspiration was 1.03 and 1.13 kg m−3 during the off-season and 0.98 and 0.94 kg m−3 during the main season, respectively. The overall results revealed that Hydrus-1D simulations were a reasonable and effective tool for simulating vertical water flow in both broadcasted and transplanted rice experimental fields.

Statistical characteristics of wind erosion events in the erosion area of Northern China

A wind erosion event is the basic minimum unit of an aeolian process. To understand the characteristics of such events, we analyzed wind speed data from 1980 to 2015 from 151 meteorological stations in the area of northern China subject to wind erosion. We classified wind erosion events into 15 levels based on the maximum wind speed during each event, and analyzed wind speed distribution of these different levels of events. The wind speed in each event level showed a significant linear distribution, providing the basis for improving the calculation of wind erosion: Qj = Tj ∙ ∑i=1i=j[R(ui) ∙ F(ui)] (details in the article). This would help to improve the calculation accuracy based on wind level compared to values based on average wind speed. We found that low-level wind erosion events were frequent but shorter, whereas high-level events were less frequent, but lasted longer and contributed more to wind erosion. >87% of the total wind erosion events lasted no >10 h. Only 9% of the individual wind erosion events lasted 11 to 20 h. Fewer than 4% of individual events lasted >20 h. Thus, it is important to consider the roles of both weak wind erosion events (<11.0 m s−1) and strong wind erosion events (≥11.0 m s−1). The spatial distribution of wind erosion events varied greatly among the sub-areas in the wind erosion area of northern China, demonstrating environmental differences should be considered when studying the effects of wind erosion in the large study area.

CoAP-based collaborative sensor networks in the Semantic Web of Things

The Semantic Web of Things (SWoT) integrates knowledge representation and reasoning techniques originally devised for the Semantic Web into Internet of Things architectures, in order to provide more advanced service/resource management and discovery. This paper proposes a novel SWoT framework, enabling collaborative discovery of sensors and actuators in pervasive contexts. It is based on a backward-compatible extension of the Constrained Application Protocol (CoAP), supporting advanced semantic matchmaking via non-standard inference services. The proposed mobile agent is able to discover devices and share smartphone embedded sensors in a peer-to-peer fashion. Efficient data stream mining is also integrated to analyze raw data gathered from the environment and detect high-level events, annotating them with machine-understandable metadata. Finally, a case study about cooperative environmental risk monitoring and prevention in Hybrid Sensor and Vehicular Networks is presented. Experimental performance results on a real testbed assess the approach.

Semantic Mapping in Video Retrieval

In the modern world, networked sensor technology makes it possible to capture the world around us in real-time. In the security domain cameras are an important source of information. Cameras in public places, bodycams, drones and recordings with smart phones are used for real time monitoring of the environment to prevent crime (monitoring case); and/or for investigation and retrieval of crimes, for example in evidence forensics (forensic case). In both cases it is required to quickly obtain the right information, without having to manually search through the data. Currently, many algorithms are available to index a video with some pre-trained concepts, such as people, objects and actions. These algorithms require a representative and large enough set of examples (training data) to recognize the concept. This training data is, however, not always present. In this thesis, we aim to assist an analyst in their work on video stream data by providing a search capability that handles ad-hoc textual queries, i.e. queries that include concepts or events that are not pre-trained. We use the security domain as inspiration for our work, but the analyst can be working in any application domain that uses video stream data, or even indexed data. Additionally, we do only consider the technical aspects of the search capability and not on the legal, ethical or privacy issues related to video stream data. We focus on the retrieval of high-level events, such as birthday parties. We assume that these events can be composed of smaller pre-trained concepts, such as a group of people, a cake and decorations and relations between those concepts, to capture the essence of that unseen event (decompositionality assumption). Additionally, we hold the open world assumption, i.e. the system does not have complete world knowledge. Although current state of the art systems are able to detect an increasingly large number of concepts, this number still falls far behind the near infinite number of possible (textual) queries that a system needs to be able to handle. In our aim to assist the analyst, we focus on the improvement of the visual search effectiveness (e.g. performance) by a semantic query-to-concept mapping: the mapping from the user query to the set of pre-trained concepts. We use the TRECVID Multimedia Event Detection benchmark, as it contains high-level events inspired by the security domain. In this thesis, we show that the main improvements can be achieved by using a combination of i) queryto- concept mapping based on semantic word embeddings (+12%), ii) exploiting user feedback (+26%) and iii) fusion of different modalities (data sources) (+17%). First, we propose an incremental word2vec (i-w2v) method [1], which uses word2vec trained on GoogleNews items as a semantic embedding model and incrementally adds concepts to the set of selected concepts for a query in order to deal with query drift. This method improve performance in terms of MAP compared to the state of the art word2vec method and knowledge based techniques. In combination with a state of the art video event retrieval pipeline, we achieve top performance on the TRECVID MED benchmark regarding the zero-example task (MED14Test results). This improvement is, however, dependent on the availability of the concepts in the Concept Bank: without concepts related to or occurring in the event, we cannot detect the event. We, thus, need a properly composed Concept Bank to properly index videos. Second, we propose an Adaptive Relevance Feedback interpretation method named ARF [2] that not only achieves high retrieval performance, but is also theoretically founded through the Rocchio algorithm from the text retrieval field. This algorithm is adjusted to the event retrieval domain in a way that the weights for the concepts are changed based on the positive and negative annotations on videos. The ARF method has higher visual search effectiveness compared to k-NN based methods on video level annotations and methods based on concept level annotations. Third, we propose blind late fusion methods that are based on state of the art methods [3], such as average fusion or fusion based on probabilities. Especially the combination of a Joint Ratio (ratio of probabilities) and Extreme Ratio (ratio of minimum and maximum) method (JRER) achieves high performance in cases with reliable detectors, i.e. enough training examples. This method is not only applicable to the video retrieval field, but also in sensor fusion in general. Although future work can be done in the direction of implicit query-to-concept mapping through deep learning methods, smartly combining the concepts and the usage of spatial and temporal information, we have shown that our proposed methods can improve the visual search effectiveness by a semantic query-to-concept mapping which brings us a step closer to a search capability that handles ad-hoc textual queries for analysts.

200\xa0ka of glacial events in NW Svalbard: an emergence cycle facies model and regional correlations

Late Quaternary sedimentary units at Kongsfjordhallet, NW Svalbard, represent five cycles of glaciations and subsequent deglaciations during high relative sea levels. The high sea level events are interpreted as glacioisostatically induced and imply preceding regional glaciations, which we constrain in time by luminescence and radiocarbon ages to just prior to ~ 195, ~ 130, ~ 85, ~ 60, and ~ 15 ka. Combined with the stratigraphical record from nearby Leinstranda we identify six, possibly seven, major glacial advances during the last 200 ka in the Kongsfjorden region. Two of these occurred during the Saalian and at least four during the Weichselian. The results are based on detailed sedimentological, stratigraphical and chronological investigations of the uppermost 15 m of the 40-m-high Kongsfjordhallet coastal sections. The succession is dominated by sediments of marine and littoral origin, representing partial shallowing-upward sequences due to isostatic rebound. Only one subglacial till was recognised. Interestingly, alluvial and periglacial deposits, not commonly recognised in this type of setting, occur in the sequence. These include weathered coarse alluvium, sandy channel fills as well as cryoturbated sediments and solifluction deposits, which are positive evidence of a non-glacial environment. The sequence of sediments that represents an emergence cycle has been formalised in a facies model.

Real-Time Probabilistic Data Fusion for Large-Scale IoT Applications

Internet of Things (IoT) data analytics is underpinning numerous applications, however, the task is still challenging predominantly due to heterogeneous IoT data streams, unreliable networks, and ever increasing size of the data. In this context, we propose a two-layer architecture for analyzing IoT data. The first layer provides a generic interface using a service oriented gateway to ingest data from multiple interfaces and IoT systems, store it in a scalable manner and analyze it in real-time to extract high-level events; whereas second layer is responsible for probabilistic fusion of these high-level events. In the second layer, we extend state-of-the-art event processing using Bayesian networks in order to take uncertainty into account while detecting complex events. We implement our proposed solution using open source components optimized for large-scale applications. We demonstrate our solution on real-world use-case in the domain of intelligent transportation system where we analyzed traffic, weather, and social media data streams from Madrid city in order to predict probability of congestion in real-time. The performance of the system is evaluated qualitatively using a web-interface where traffic administrators can provide the feedback about the quality of predictions and quantitatively using F-measure with an accuracy of over 80%.

Semantic Reasoning in Zero Example Video Event Retrieval

Searching in digital video data for high-level events, such as a parade or a car accident, is challenging when the query is textual and lacks visual example images or videos. Current research in deep neural networks is highly beneficial for the retrieval of high-level events using visual examples, but without examples it is still hard to (1) determine which concepts are useful to pre-train ( Vocabulary challenge ) and (2) which pre-trained concept detectors are relevant for a certain unseen high-level event ( Concept Selection challenge ). In our article, we present our Semantic Event Retrieval System which (1) shows the importance of high-level concepts in a vocabulary for the retrieval of complex and generic high-level events and (2) uses a novel concept selection method ( i-w2v ) based on semantic embeddings. Our experiments on the international TRECVID Multimedia Event Detection benchmark show that a diverse vocabulary including high-level concepts improves performance on the retrieval of high-level events in videos and that our novel method outperforms a knowledge-based concept selection method.

Prognostic value of exercise capacity among men undergoing pharmacologic treatment for erectile dysfunction: The FIT Project.

Vascular erectile dysfunction (ED) has been identified as a potentially useful risk factor for predicting future cardiovascular events, particularly in younger men. Because these men typically score more favorably on traditional cardiovascular disease risk assessment tools, there exists a gap in knowledge for how to most appropriately identify those men who would benefit from more aggressive treatments. To date, no studies have examined the impact of fitness on cardiovascular outcomes in men with ED. This study sought to examine the prognostic impact of maximal exercise capacity on cardiovascular-related outcomes in men ages 40 to 60 years being treated for ED. We hypothesized that there would be an independent association between higher baseline fitness level and lower cardiovascular events. We analyzed 1152 men with pharmacy claims file-confirmed active pharmacologic treatment for ED from the Henry Ford Exercise Testing (FIT) Project (1991-2009). All patients were free of coronary heart disease and heart failure, and underwent clinician-referred exercise stress testing, with fitness measured in metabolic equivalents of task (METs). Multivariable Cox proportional hazard models adjusted for traditional cardiovascular risk factors were used to study the association between fitness and all-cause mortality, major adverse cardiovascular events (MACE) (defined as myocardial infarction or revascularization), and incident type 2 diabetes mellitus. The mean age of the population was 53 years, with 39% African Americans. In multivariable analysis, each 1 MET of fitness was associated with a 16% lower risk of death (hazard ratio [HR]: 0.84, 95% confidence interval [CI]: 0.76-0.94, P = 0.002), and a nonsignificant reduction in MACE (HR: 0.89, 95% CI: 0.79-1.003, P = 0.048), and incident diabetes (HR: 0.92, 95% CI: 0.85-1.01, P = 0.129). Higher baseline fitness is associated with improved cardiovascular prognosis in a population of middle-aged men treated for ED.