Turbulence measurements in a swirling confined jet flowfield using a triple hot-wire probe

An axisymmetric swirling confined jet flowfield, similar to that encountered in gas turbine combustors was investigated using a triple hot-wire probe. The raw data from the three sensors were digitized using ADC's and stored on a Tektronix 4051 computer. The data were further reduced on the computer to obtain time-series for the three instantaneous velocity components in the flowfield. The time-mean velocities and the turbulence quantities were deduced. Qualification experiments were performed and where possible results compared with independent measurements. The major qualification experiments involved measurements performed in a non-swirling flow compared with conventional X-wire measurements. In the swirling flowfield, advantages of the triple wire technique over the previously used multi-position single hot-wire method are noted. The measurements obtained provide a data base with which the predictions of turbulence models in a recirculating swirling flowfield can be evaluated

Sustainability Assessment of a Residential Building using a Life Cycle Assessment Approach

Building and construction industry is responsible for resource scarcity, global warming impacts, land use changes and the loss of bio-diversity, which have direct and indirect socio-economic implications. Sustainable building design is thus inevitable through the selection of highly durable and less energy intensive-materials that could reduce environmental degradation in an economically viable and socially acceptable manner. This paper presents the life cycle sustainability assessment (LCSA) framework to assess the environmental, social and economic objectives of residential buildings. Two buildings of different material compositions have been used to test this framework. Firstly, the service life of this building has been calculated as durability of building materials play a key role in enhancing resource conservation for the future generations. A factor method has been used to carry out the service life of each component of the building envelope. The minimum estimated service life of building systems is considered as the overall service life of building components. Secondly, a life cycle assessment framework utilising environmental life cycle assessment, life cycle costing and social life cycle assessment have been utilised to determine environmental, economic and social indicators of the studied buildings. All these triple bottom line indicators in this framework have been calculated on an annual basis in order to capture the advantage of increased service life of buildings. This framework will be applied to assess the sustainability performance of alternative buildings for comparative analysis and to find out the most sustainable building option

Impact of service life on the environmental performance of buildings

The environmental performance assessment of the building and construction sector has been in discussion due to the increasing demand of facilities and its impact on the environment. The life cycle studies carried out over the last decade have mostly used an approximate life span of a building without considering the building component replacement requirements and their service life. This limitation results in unreliable outcomes and a huge volume of materials going to landfill. This study was performed to develop a relationship between the service life of a building and building components, and their impact on environmental performance. Twelve building combinations were modelled by considering two types of roof frames, two types of wall and three types of footings. A reference building of a 50-year service life was used in comparisons. Firstly, the service life of the building and building components and the replacement intervals of building components during active service life were estimated. The environmental life cycle assessment (ELCA) was carried out for all the buildings and results are presented on a yearly basis in order to study the impact of service life. The region-specific impact categories of cumulative energy demand, greenhouse gas emissions, water consumption and land use are used to assess the environmental performance of buildings. The analysis shows that the environmental performance of buildings is affected by the service life of a building and the replacement intervals of building components

A novel augmented deep transfer learning for classification of COVID-19 and other thoracic diseases from X-rays

Deep learning has provided numerous breakthroughs in natural imaging tasks. However, its successful application to medical images is severely handicapped with the limited amount of annotated training data. Transfer learning is commonly adopted for the medical imaging tasks. However, a large covariant shift between the source domain of natural images and target domain of medical images results in poor transfer learning. Moreover, scarcity of annotated data for the medical imaging tasks causes further problems for effective transfer learning. To address these problems, we develop an augmented ensemble transfer learning technique that leads to significant performance gain over the conventional transfer learning. Our technique uses an ensemble of deep learning models, where the architecture of each network is modified with extra layers to account for dimensionality change between the images of source and target data domains. Moreover, the model is hierarchically tuned to the target domain with augmented training data. Along with the network ensemble, we also utilize an ensemble of dictionaries that are based on features extracted from the augmented models. The dictionary ensemble provides an additional performance boost to our method. We first establish the effectiveness of our technique with the challenging ChestXray-14 radiography data set. Our experimental results show more than 50% reduction in the error rate with our method as compared to the baseline transfer learning technique. We then apply our technique to a recent COVID-19 data set for binary and multi-class classification tasks. Our technique achieves 99.49% accuracy for the binary classification, and 99.24% for multi-class classification

Pre-text Representation Transfer for Deep Learning with Limited Imbalanced Data : Application to CT-based COVID-19 Detection

Annotating medical images for disease detection is often tedious and expensive. Moreover, the available training samples for a given task are generally scarce and imbalanced. These conditions are not conducive for learning effective deep neural models. Hence, it is common to 'transfer' neural networks trained on natural images to the medical image domain. However, this paradigm lacks in performance due to the large domain gap between the natural and medical image data. To address that, we propose a novel concept of Pre-text Representation Transfer (PRT). In contrast to the conventional transfer learning, which fine-tunes a source model after replacing its classification layers, PRT retains the original classification layers and updates the representation layers through an unsupervised pre-text task. The task is performed with (original, not synthetic) medical images, without utilizing any annotations. This enables representation transfer with a large amount of training data. This high-fidelity representation transfer allows us to use the resulting model as a more effective feature extractor. Moreover, we can also subsequently perform the traditional transfer learning with this model. We devise a collaborative representation based classification layer for the case when we leverage the model as a feature extractor. We fuse the output of this layer with the predictions of a model induced with the traditional transfer learning performed over our pre-text transferred model. The utility of our technique for limited and imbalanced data classification problem is demonstrated with an extensive five-fold evaluation for three large-scale models, tested for five different class-imbalance ratios for CT based COVID-19 detection. Our results show a consistent gain over the conventional transfer learning with the proposed method.Comment: Best paper at IVCN

Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions

Medical Image Analysis is currently experiencing a paradigm shift due to Deep Learning. This technology has recently attracted so much interest of the Medical Imaging community that it led to a specialized conference in `Medical Imaging with Deep Learning' in the year 2018. This article surveys the recent developments in this direction, and provides a critical review of the related major aspects. We organize the reviewed literature according to the underlying Pattern Recognition tasks, and further sub-categorize it following a taxonomy based on human anatomy. This article does not assume prior knowledge of Deep Learning and makes a significant contribution in explaining the core Deep Learning concepts to the non-experts in the Medical community. Unique to this study is the Computer Vision/Machine Learning perspective taken on the advances of Deep Learning in Medical Imaging. This enables us to single out `lack of appropriately annotated large-scale datasets' as the core challenge (among other challenges) in this research direction. We draw on the insights from the sister research fields of Computer Vision, Pattern Recognition and Machine Learning etc.; where the techniques of dealing with such challenges have already matured, to provide promising directions for the Medical Imaging community to fully harness Deep Learning in the future

Conjoint utilization of structured and unstructured information for planning interleaving deliberation in supply chains

Effective business planning requires seamless access and intelligent analysis of information in its totality to allow the business planner to gain enhanced critical business insights for decision support. Current business planning tools provide insights from structured business data (i.e. sales forecasts, customers and products data, inventory details) only and fail to take into account unstructured complementary information residing in contracts, reports, user\u27s comments, emails etc. In this article, a planning support system is designed and developed that empower business planners to develop and revise business plans utilizing both structured data and unstructured information conjointly. This planning system activity model comprises of two steps. Firstly, a business planner develops a candidate plan using planning template. Secondly, the candidate plan is put forward to collaborating partners for its revision interleaving deliberation. Planning interleaving deliberation activity in the proposed framework enables collaborating planners to challenge both a decision and the thinking that underpins the decision in the candidate plan. The planning system is modeled using situation calculus and is validated through a prototype development

SAM-SoS: A stochastic software architecture modeling and verification approach for complex System-of-Systems

A System-of-Systems (SoS) is a complex, dynamic system whose Constituent Systems (CSs) are not known precisely at design time, and the environment in which they operate is uncertain. SoS behavior is unpredictable due to underlying architectural characteristics such as autonomy and independence. Although the stochastic composition of CSs is vital to achieving SoS missions, their unknown behaviors and impact on system properties are unavoidable. Moreover, unknown conditions and volatility have significant effects on crucial Quality Attributes (QAs) such as performance, reliability and security. Hence, the structure and behavior of a SoS must be modeled and validated quantitatively to foresee any potential impact on the properties critical for achieving the missions. Current modeling approaches lack the essential syntax and semantics required to model and verify SoS behaviors at design time and cannot offer alternative design choices for better design decisions. Therefore, the majority of existing techniques fail to provide qualitative and quantitative verification of SoS architecture models. Consequently, we have proposed an approach to model and verify Non-Deterministic (ND) SoS in advance by extending the current algebraic notations for the formal models as a hybrid stochastic formalism to specify and reason architectural elements with the required semantics. A formal stochastic model is developed using a hybrid approach for architectural descriptions of SoS with behavioral constraints. Through a model-driven approach, stochastic models are then translated into PRISM using formal verification rules. The effectiveness of the approach has been tested with an end-to-end case study design of an emergency response SoS for dealing with a fire situation. Architectural analysis is conducted on the stochastic model, using various qualitative and quantitative measures for SoS missions. Experimental results reveal critical aspects of SoS architecture model that facilitate better achievement of missions and QAs with improved design, using the proposed approach

Harassment and mental health in surgical training: A pilot survey of surgical trainees in Pakistan

Objective: To assess harassment among surgical trainees and its effects on mental health, and to explore its association with gender.Methods: The nationwide cross-sectional pilot study was conducted by the Association of Women Surgeons of Pakistan from July to September 2019, and included surgical trainees of either gender working in both public and private hospitals. Data was collected using an anonymous online survey form to assess harassment and self-perceived burnout and depression. Data was analysed using SPSS 22.Results: Of the 147 respondents, 49(33.3%) were males; 98(66.6%) were females; and 118(80.3%) were residents. Workplace harassment was reported by 80(54.4%) trainees. Among the males it was reported by 24(49%) and among the females by 56(57%) (p=0.349). Of those having faced harassment, 9(11.3%) reported it to the administration. Severe self-perceived burnout was reported by 102(69.4%) respondents, and severe self-perceived depression by 69(46.9%). Respondents experiencing bullying were more likely to report severe self-perceived burnout than those not experiencing bullying (p=0.02). Multivariable logistic regression showed female gender to be significantly associated with sexual harassment (odds ratio: 4.261 [95% confidence interval: 1.067-17.019]) and severe self-perceived depression (odds ratio: 5.052 [95% confidence interval: 1.187-21.503]). Need for a support group was identified by 134(91.2%) trainees.Conclusions: An overwhelming need was found for trainee surgeon support groups and other interventions targeted at improving the workplace environment for surgical trainees in Pakistan