The inherent tensions within sustainable supply chains: a case study from Bangladesh

The complexities surrounding the supply chain logistics for perishable commodities within Bangladesh are extensive. Poor infrastructure, fragmented transportation and corruption compound the operational complexities within this emerging market. This case study analyses many of the day-to-day operational challenges and tensions inherent within Micro-Small-Medium Enterprises (MSMEs) forming the backbone of the Bangladesh socio-economic structure. The drive for transition toward greater levels of sustainability and corporate responsibility is problematic, affecting many levels within an extended and fragmented supply chain. The selected case study highlights the “lived in” geographical, environmental, economic and cultural factors that impact the ability of emerging market enterprises to remain profitable within emergency scenarios whilst transitioning toward a more sustainable model. This study, whilst detailing many of the tensions and critical issues facing MSMEs, highlights the benefits of direct Government intervention, criticality of a leaner and more efficient supply chain and reassessment of financial incentives to drive the transition to a more efficient and sustainable economy

Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson's disease and schizophrenia

Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided