Crowdcloud: A Crowdsourced System for Cloud Infrastructure

The widespread adoption of truly portable, smart devices and Do-It-Yourself computing platforms by the general public has enabled the rise of new network and system paradigms. This abundance of wellconnected, well-equipped, affordable devices, when combined with crowdsourcing methods, enables the development of systems with the aid of the crowd. In this work, we introduce the paradigm of Crowdsourced Systems, systems whose constituent infrastructure, or a significant part of it, is pooled from the general public by following crowdsourcing methodologies. We discuss the particular distinctive characteristics they carry and also provide their “canonical” architecture. We exemplify the paradigm by also introducing Crowdcloud, a crowdsourced cloud infrastructure where crowd members can act both as cloud service providers and cloud service clients. We discuss its characteristic properties and also provide its functional architecture. The concepts introduced in this work underpin recent advances in the areas of mobile edge/fog computing and co-designed/cocreated systems

Adaptive data acquisition strategies for energy-efficient, smartphone-based, continuous processing of sensor streams

Ministry of Education, Singapore under its Academic Research Funding Tier

Unilateral Cervical Lymphadenopathy due to Cladosporium oxysporum: A Case Report and Review of the Literature

Phaeohyphomycosis is a fungal infection caused by Dermatiacae group of fungi, by Cladosporium spp. The term phaeohyphomycosis was introduced by Ajello et al. in 1974 to designate infections by brown pigmented filamentous fungi. Cladosporium oxysporum is a very rare etiological agent in humans. Phaeohyphomycosis of the cervical lymph node in an immunocompetent individual is a very rare clinical entity. To the best of our knowledge we report the first case of phaeohyphomycosis caused by Cladosporium oxysporum in the absence of other systemic manifestations in a 16-year-old male

The requirements to enhance the design of context-aware mobile patient monitoring systems using wireless sensors

Designing and developing Context-aware Mobile Patient Monitoring Systems (CMPMS) using wireless sensors are emerging in the biomedical informatics domain.However, previous studies related to this topic are fragmented.In fact, the literature has no standard types and sources of context information.These types and sources are required to design such systems.In addition, there is no standard context reasoning approach to facilitate the development of these systems.To address these absences, this paper is a survey of the CMPMS in the biomedical informatics to identify potential types and sources of context information as well as the context reasoning approaches that are required to be addressed in designing and developing such systems. The results are expected to help researchers to enhance the design and facilitate the development of CMPMS

Evaluation of prognostic risk models for postoperative pulmonary complications in adult patients undergoing major abdominal surgery: a systematic review and international external validation cohort study

Background Stratifying risk of postoperative pulmonary complications after major abdominal surgery allows clinicians to modify risk through targeted interventions and enhanced monitoring. In this study, we aimed to identify and validate prognostic models against a new consensus definition of postoperative pulmonary complications. Methods We did a systematic review and international external validation cohort study. The systematic review was done in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched MEDLINE and Embase on March 1, 2020, for articles published in English that reported on risk prediction models for postoperative pulmonary complications following abdominal surgery. External validation of existing models was done within a prospective international cohort study of adult patients (≥18 years) undergoing major abdominal surgery. Data were collected between Jan 1, 2019, and April 30, 2019, in the UK, Ireland, and Australia. Discriminative ability and prognostic accuracy summary statistics were compared between models for the 30-day postoperative pulmonary complication rate as defined by the Standardised Endpoints in Perioperative Medicine Core Outcome Measures in Perioperative and Anaesthetic Care (StEP-COMPAC). Model performance was compared using the area under the receiver operating characteristic curve (AUROCC). Findings In total, we identified 2903 records from our literature search; of which, 2514 (86·6%) unique records were screened, 121 (4·8%) of 2514 full texts were assessed for eligibility, and 29 unique prognostic models were identified. Nine (31·0%) of 29 models had score development reported only, 19 (65·5%) had undergone internal validation, and only four (13·8%) had been externally validated. Data to validate six eligible models were collected in the international external validation cohort study. Data from 11 591 patients were available, with an overall postoperative pulmonary complication rate of 7·8% (n=903). None of the six models showed good discrimination (defined as AUROCC ≥0·70) for identifying postoperative pulmonary complications, with the Assess Respiratory Risk in Surgical Patients in Catalonia score showing the best discrimination (AUROCC 0·700 [95% CI 0·683–0·717]). Interpretation In the pre-COVID-19 pandemic data, variability in the risk of pulmonary complications (StEP-COMPAC definition) following major abdominal surgery was poorly described by existing prognostication tools. To improve surgical safety during the COVID-19 pandemic recovery and beyond, novel risk stratification tools are required. Funding British Journal of Surgery Society