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Background Migrants settling in a new country experience multiple complexities in navigating health care systems and adapting to a new way of life in the host country. In South Asia, migrating to another country for better life opportunities has been an ongoing trend and migration to Australia has significantly increased in recent years. Lower utilisation of health services and higher risks of chronic diseases among South Asian migrants poses a continuing challenge for the Australian health care system and little is known about why this demographic group does not access health services at the same rate. This study aimed to explore factors influencing access to health care by South Asian migrants in Australia. Methods Using a mixed-method design, we conducted 62 online survey and 14 in-depth interviews with participants from four South Asian countries: Nepal, India, Bhutan, and Sri Lanka. Participants were recruited using a purposive snowball sampling approach following a standard ethical approval process. Survey data were analysed descriptively in SPSS and interview data were recorded, transcribed, and analysed thematically. Results South Asian migrants experienced various complexities while accessing health services in Australia. The findings of this study highlighted a number of negative factors influencing their experiences of accessing health care: long waiting times for public health care, the expense of private health care, and communication problems due to socio-cultural differences. South Asian migrants also expressed their concern for a greater investment of resources into public health care to enable them to access quality and affordable care in these settings. Conclusions Given limited evidence available to help understand factors leading to the lower utilisation of health care and higher risks of chronic diseases among South Asian migrants, this study plays an important role in highlighting social, cultural, financial, and institutional factors that are critical to designing appropriate health-care strategies. This study recommends incorporating a collaborative and culturally competent model of care to increase access to health care and thereby help reduce existing disparities in health outcomes among South Asian migrant populations.

Accurate building inventories and relevant information are essential for sustainable urban governance, thereby contributing to achieve the Sustainable Development Goal 11, which focuses on sustainable cities and communities. Acquiring building information manually in complex-built environment with densely populated buildings is impractical and may not guarantee the high thematic and spatial detail necessary for real world applications.Recent advancements in remote sensing technology, coupled with the availability of high-resolution satellite and drone-based imagery through open access channels and as machine learning (ML) and deep learning (DL) continue to advance, showcasing their capability to recognise complex patterns, new opportunities for interpreting various surface features on the Earth are made possible. A growing body of literature emphasises the use of both traditional ML and DL for building footprint extraction, yet available and accessible literature reveal limited application of DL in urban building characterisation.This study involves exploring and implementing Random Forest (RF) as machine learning model and dense neural network (DNN) as deep learning model in the context of multi-class building characterisation encompassing six classes. A total of 35 geometric and distribution features calculated using VHR imagery and OpenStreetMap data are used to train the model. The experiments show that overall accuracy of RF (79.9%) is higher than that of the DNN (71.9%). Upon closer examination and comparison of diagonal elements, representing the number of correctly classified samples for each class, it is found that the DNN outperforms RF in correctly classifying more instances for four classes. Further the recall rate using DNN is greater for four classes- ‘Building block in closed construction’, ‘Detached building block’, ‘Free standing individual building’, and ‘Garage’, in comparison to that of RF. Implementation of the DNN and comparison with traditional machine learning algorithm- RF provide additional scientific contribution, especially in situation where there is limited use of deep learning algorithms in the building characterisation.

The socio-cultural context of populations has a significant effect on health outcomes across every stage of life. In Australia, South Asian migrants have a comparatively higher incidence of chronic disease and less use of health services. Often overlooked are community views of health, cultural traits and belief systems. This study aimed to explore the factors that influence health perception and practice of South Asian migrants. The study used a mixed method approach with both a survey and interviews. A total of 62 participants between the age of 18 and 64 years were surveyed and 14 participants completed interviews. Data were analysed descriptively and thematically. South Asians share a common perception and practice around health and illness. This paper highlights two key findings. First, these groups take a broad view of health encompassing physical, mental, emotional, social and economic aspects of life. Second, these cultural groups do not seek medical help as their first choice, but have a high level of trust in family for providing health advice and share a belief in the effectiveness of home remedies for managing health conditions. Participants shared their expectation that the Australian health system should consider their socio-cultural construct to make services culturally safe and engaging to enhance service utilisation.

This work presents a design coupling analysis (DCA) framework to investigate the interactions among control and plant design variables in floating offshore wind turbine (FOWT) and to support the formulation of tractable control co-design (CCD) optimization strategies. DCA provides quantitative information that reveals the relationships and dependencies among design variables and to objective function, enabling improved design variable selection, identification of dominant variables that drive system interactions, and informed selection of optimization solution strategies. However, applying DCA to complex systems is challenging because the models used to describe their dynamics are computationally expensive, and constructing DCA information requires exhaustive model evaluations and optimizations. Here, a surrogate model of the FOWT system is employed to make the repeated model evaluations required for DCA computationally feasible. Using this framework, the bidirectional couplings between control and plant design variables, as well as the couplings among plant design variables, are estimated. The results reveal strong interactions among various design variables, and identify the most influential plant design variables affecting system performance. These insights guide the development of two DCA-based optimization strategies for large CCD problems: a sequential decomposition approach that preserves dominant design variable couplings while reducing problem size at each stage, and a reduced dimensional optimization approach that focuses on collectively the most influential variables. The results demonstrate that these strategies significantly reduce computational complexity while achieving solutions comparable to those obtained through full simultaneous optimization, demonstrating the value of DCA for understanding and solving complex design problems.

In many critical machine learning applications, such as autonomous driving and medical image diagnosis, the detection of out-of-distribution (OOD) samples is as crucial as accurately classifying in-distribution (ID) inputs. Recently, outlier exposure (OE)-based methods have shown promising results in detecting OOD inputs via model fine-tuning with auxiliary outlier data. However, most of the previous OE-based approaches emphasize more on synthesizing extra outlier samples or introducing regularization to diversify OOD sample space, which is rather unquantifiable in practice. In this work, we propose a novel and straightforward method called Margin-bounded Confidence Scores (MaCS) to address the nontrivial OOD detection problem by enlarging the disparity between ID and OOD scores, which in turn makes the decision boundary more compact facilitating effective segregation with a simple threshold. Specifically, we augment the learning objective of an OE regularized classifier with a supplementary constraint, which penalizes high confidence scores for OOD inputs compared to that of ID and significantly enhances the OOD detection performance while maintaining the ID classification accuracy. Extensive experiments on various benchmark datasets for image classification tasks demonstrate the effectiveness of the proposed method by significantly outperforming state-of-the-art methods on various benchmarking metrics. The code is publicly available at https://github.com/lakpa-tamang9/margin_ood/tree/kais

In many critical machine learning applications, such as autonomous driving and medical image diagnosis, the detection of out-of-distribution (OOD) samples is as crucial as accurately classifying in-distribution (ID) inputs. Recently, outlier exposure (OE)-based methods have shown promising results in detecting OOD inputs via model fine-tuning with auxiliary outlier data. However, most of the previous OE-based approaches emphasize more on synthesizing extra outlier samples or introducing regularization to diversify OOD sample space, which is rather unquantifiable in practice. In this work, we propose a novel and straightforward method called Margin-bounded Confidence Scores (MaCS) to address the nontrivial OOD detection problem by enlarging the disparity between ID and OOD scores, which in turn makes the decision boundary more compact facilitating effective segregation with a simple threshold. Specifically, we augment the learning objective of an OE regularized classifier with a supplementary constraint, which penalizes high confidence scores for OOD inputs compared to that of ID and significantly enhances the OOD detection performance while maintaining the ID classification accuracy. Extensive experiments on various benchmark datasets for image classification tasks demonstrate the effectiveness of the proposed method by significantly outperforming state-of-the-art methods on various benchmarking metrics. The code is publicly available at https://github.com/lakpa-tamang9/margin_ood/tree/kais

This paper aims to investigate the dynamic nexus between economic complexity index (ECI), technological development (TIN), human capital (HC) and environmental quality in India for transition towards a sustainable environment. This study is based on secondary data covering the period from 1985 to 2018. For empirical analysis, this study applied “Stochastic Impacts by Regression on Population, Affluence, and Technology” (STIRPAT) model framework under the estimation of autoregressive distributed lag (ARDL) model and vector error correction model (VECM) model. The empirical findings of model 1 show ECI, TIN, HC and urbanization (URB) as the helping hands to mitigate the problem of environmental degradation by shrinking the level of EF, whereas for model 2, ECI and TIN failed to influence the CO 2 emissions, but HC served as a stimulant for environmental quality enhancement by declining the level of CO 2 emissions. In contrast, GDP growth and URB strengthen the CO 2 emissions levels. Moreover, in VECM framework, estimated findings reveal that the covariables Granger-cause EF and CO 2 emissions, inferring that causality flows asynchronously from its covariables to EF and CO 2 . Impulse response function (IRF) revealed that the responses in EF and CO 2 emissions ascribed to changes in its covariables. The outcome of the study has some implications for environmental policy strategists to prepare sustainable environment policies and other responsible authorities for sustainable development goal (SDGs), academician and scholars. All the stakeholders involved in environmental economics and policymakers can evaluate this study to design proper policy framework with respect to the environment. There are few studies that explore the dynamic nexus between ECI, TIN and HC with environmental quality in the control environment of URB and GDP growth using the STIRPAT model for India.

This study compares the hydromorphological quality of two sub-Himalayan Rivers, the Mahananda–Balason system (MBS) and the Chel–Neora system (CNS), to analyse the role of human intervention in channel hydromorphology. An indicator-based assessment, developed by Mitra et al. (Adv Space Res 71:1397–1417, 2022), is adopted here to obtain the hydromorphological quality. A hypothesis test using a two-tailed t-test and principal component analysis (PCA) is performed to identify the reaches with similar hydromorphological conditions along with aspects that needed restoration. Results show that the hydromorphological condition is highly influenced by the intensity of human alterations, and their interrelations are strongly proportionate. While the hydromorphological quality of unconfined MBS is highly degraded, that of unconfined CNS and the confined reaches of both systems have remained almost at their least disturbed condition (LAC). Intensive human interventions on MBS have impacted the groundwater table, riverine sediments, and fish habitat. Now, to maintain sustainability regarding riverine resources of CNS, close and regular monitoring must be initiated, and this methodology can serve that purpose. Being dependent on monitoring the ongoing interventions and the channel's physical forms, assessment protocols like this can serve the purpose of rapid hydromorphological assessment for the river systems of data-scarce regions.

The presentation of solitary rectal ulcer syndrome is very similar to a wide variety of conditions including inflammatory bowel diseases, ischemic colitis and rectal carcinoma. Histopathological examination comes as an important tool for its diagnosis. Hence, high index of suspicion is required for early diagnosis of this rare condition.

Vaccination saves lives and can be an effective strategy for preventing the spread of the COVID-19, but negative attitudes towards vaccines lead to vaccine hesitancy. This study aimed to explore the factors influencing the uptake of the COVID-19 vaccine in the Nepali community in the United Kingdom (UK). This qualitative study included in-depth interviews with 20 people from Nepal living in the UK. Interviews were conducted by a native-Nepali speaker and all interviews were audio-recorded, transcribed, and translated into English before being analysed thematically. Our study found that attitudes towards COVID-19 are generally positive. Nine overlapping themes around barriers to COVID-19 vaccination were identified: (a) rumours and mis/disinformation; (b) prefer home remedies and yoga; (c) religion restriction; (d) concern towards vaccine eligibility; (e) difficulty with online vaccine booking system; (f) doubts of vaccine effectiveness after changing the second dose timeline; (g) lack of confidence in the vaccine; (h) past bad experience with the influenza vaccine; and (i) worried about side-effects. Understanding barriers to the uptake of the COVID-19 vaccine can help in the design of better targeted interventions. Public health messages including favourable policy should be tailored to address those barriers and make this vaccination programme more viable and acceptable to the ethnic minority communities in the UK.