48 research outputs found
QoS-Aware Middleware for Web Services Composition
The paradigmatic shift from a Web of manual interactions to a Web of programmatic interactions driven by Web services is creating unprecedented opportunities for the formation of online Business-to-Business (B2B) collaborations. In particular, the creation of value-added services by composition of existing ones is gaining a significant momentum. Since many available Web services provide overlapping or identical functionality, albeit with different Quality of Service (QoS), a choice needs to be made to determine which services are to participate in a given composite service. This paper presents a middleware platform which addresses the issue of selecting Web services for the purpose of their composition in a way that maximizes user satisfaction expressed as utility functions over QoS attributes, while satisfying the constraints set by the user and by the structure of the composite service. Two selection approaches are described and compared: one based on local (task-level) selection of services and the other based on global allocation of tasks to services using integer programming
Physical-aware Cross-modal Adversarial Network for Wearable Sensor-based Human Action Recognition
Wearable sensor-based Human Action Recognition (HAR) has made significant
strides in recent times. However, the accuracy performance of wearable
sensor-based HAR is currently still lagging behind that of visual
modalities-based systems, such as RGB video and depth data. Although diverse
input modalities can provide complementary cues and improve the accuracy
performance of HAR, wearable devices can only capture limited kinds of
non-visual time series input, such as accelerometers and gyroscopes. This
limitation hinders the deployment of multimodal simultaneously using visual and
non-visual modality data in parallel on current wearable devices. To address
this issue, we propose a novel Physical-aware Cross-modal Adversarial (PCA)
framework that utilizes only time-series accelerometer data from four inertial
sensors for the wearable sensor-based HAR problem. Specifically, we propose an
effective IMU2SKELETON network to produce corresponding synthetic skeleton
joints from accelerometer data. Subsequently, we imposed additional constraints
on the synthetic skeleton data from a physical perspective, as accelerometer
data can be regarded as the second derivative of the skeleton sequence
coordinates. After that, the original accelerometer as well as the constrained
skeleton sequence were fused together to make the final classification. In this
way, when individuals wear wearable devices, the devices can not only capture
accelerometer data, but can also generate synthetic skeleton sequences for
real-time wearable sensor-based HAR applications that need to be conducted
anytime and anywhere. To demonstrate the effectiveness of our proposed PCA
framework, we conduct extensive experiments on Berkeley-MHAD, UTD-MHAD, and
MMAct datasets. The results confirm that the proposed PCA approach has
competitive performance compared to the previous methods on the mono
sensor-based HAR classification problem.Comment: First IMU2SKELETON GANs approach for wearable HAR problem. arXiv
admin note: text overlap with arXiv:2208.0809
Smartwatch-Based IoT Fall Detection Application
This paper proposes using only the streaming accelerometer data from a commodity-based smartwatch (IoT) device to detect falls. The smartwatch is paired with a smartphone as a means for performing the computation necessary for the prediction of falls in realtime without incurring latency in communicating with a cloud server while also preserving data privacy. The majority of current fall detection applications require specially designed hardware and software which make them expensive and inaccessible to the general public. Moreover, a fall detection application that uses a wrist worn smartwatch for data collection has the added benefit that it can be perceived as a piece of jewelry and thus non-intrusive. We experimented with both Support Vector Machine and Naive Bayes machine learning algorithms for the creation of the fall model. We demonstrated that by adjusting the sampling frequency of the streaming data, computing acceleration features over a sliding window, and using a Naive Bayes machine learning model, we can obtain the true positive rate of fall detection in real-world setting with 93.33% accuracy. Our result demonstrated that using a commodity-based smartwatch sensor can yield fall detection results that are competitive with those of custom made expensive sensors
Experimentation and Analysis of Ensemble Deep Learning in IoT Applications
This paper presents an experimental study of Ensemble Deep Learning (DL) techniques for the analysis of time series data on IoT devices. We have shown in our earlier work that DL demonstrates superior performance compared to traditional machine learning techniques on fall detection applications due to the fact that important features in time series data can be learned and need not be determined manually by the domain expert. However, DL networks generally require large datasets for training. In the health care domain, such as the real-time smartwatch-based fall detection, there are no publicly available large annotated datasets that can be used for training, due to the nature of the problem (i.e. a fall is not a common event). Moreover, fall data is also inherently noisy since motions generated by the wrist-worn smartwatch can be mistaken for a fall. This paper explores combing DL (Recurrent Neural Network) with ensemble techniques (Stacking and AdaBoosting) using a fall detection application as a case study. We conducted a series of experiments using two different datasets of simulated falls for training various ensemble models. Our results show that an ensemble of deep learning models combined by the stacking ensemble technique, outperforms a single deep learning model trained on the same data samples, and thus, may be better suited for small-size datasets
Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study
Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research
Fludarabine, cytarabine, granulocyte colony-stimulating factor, and idarubicin with gemtuzumab ozogamicin improves event-free survival in younger patients with newly diagnosed aml and overall survival in patients with npm1 and flt3 mutations
Purpose
To determine the optimal induction chemotherapy regimen for younger adults with newly diagnosed AML without known adverse risk cytogenetics.
Patients and Methods
One thousand thirty-three patients were randomly assigned to intensified (fludarabine, cytarabine, granulocyte colony-stimulating factor, and idarubicin [FLAG-Ida]) or standard (daunorubicin and Ara-C [DA]) induction chemotherapy, with one or two doses of gemtuzumab ozogamicin (GO). The primary end point was overall survival (OS).
Results
There was no difference in remission rate after two courses between FLAG-Ida + GO and DA + GO (complete remission [CR] + CR with incomplete hematologic recovery 93% v 91%) or in day 60 mortality (4.3% v 4.6%). There was no difference in OS (66% v 63%; P = .41); however, the risk of relapse was lower with FLAG-Ida + GO (24% v 41%; P < .001) and 3-year event-free survival was higher (57% v 45%; P < .001). In patients with an NPM1 mutation (30%), 3-year OS was significantly higher with FLAG-Ida + GO (82% v 64%; P = .005). NPM1 measurable residual disease (MRD) clearance was also greater, with 88% versus 77% becoming MRD-negative in peripheral blood after cycle 2 (P = .02). Three-year OS was also higher in patients with a FLT3 mutation (64% v 54%; P = .047). Fewer transplants were performed in patients receiving FLAG-Ida + GO (238 v 278; P = .02). There was no difference in outcome according to the number of GO doses, although NPM1 MRD clearance was higher with two doses in the DA arm. Patients with core binding factor AML treated with DA and one dose of GO had a 3-year OS of 96% with no survival benefit from FLAG-Ida + GO.
Conclusion
Overall, FLAG-Ida + GO significantly reduced relapse without improving OS. However, exploratory analyses show that patients with NPM1 and FLT3 mutations had substantial improvements in OS. By contrast, in patients with core binding factor AML, outcomes were excellent with DA + GO with no FLAG-Ida benefit
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Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study
Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat