Artificial intelligence enabled systems have been an inevitable part of everyday life. However, efficient software engineering principles and processes need to be considered and extended when developing AI-enabled systems. The objective of this study is to identify and classify software engineering challenges that are faced by different companies when developing software-intensive systems that incorporate machine learning components. Using case study approach, we explored the development of machine learning systems from six different companies across various domains and identified main software engineering challenges. The challenges are mapped into a proposed taxonomy that depicts the evolution of use of ML components in software-intensive system in industrial settings. Our study provides insights to software engineering community and research to guide discussions and future research into applied machine learning.
Data is the new currency and key to success. However, collecting high-quality data from multiple distributed sources requires much effort. In addition, there are several other challenges involved while transporting data from its source to the destination. Data pipelines are implemented in order to increase the overall efficiency of data-flow from the source to the destination since it is automated and reduces the human involvement which is required otherwise. Despite existing research on ETL (Extract-Transform-Load) and ELT (Extract-Load-Transform) pipelines, the research on this topic is limited. ETL/ELT pipelines are abstract representations of the end-to-end data pipelines. To utilize the full potential of the data pipeline, we should understand the activities in it and how they are connected in an end-to-end data pipeline. This study gives an overview of how to design a conceptual model of data pipeline which can be further used as a language of communication between different data teams. Furthermore, it can be used for automation of monitoring, fault detection, mitigation and alarming at different steps of data pipeline.
Acetazolamide, a carbonic anhydrase inhibitor, is primarily used in the treatment of glaucoma, due to its role in decreasing intraocular pressure by lowering the production of aqueous humor. Additionally, by lowering cerebrospinal fluid (CSF) production, it is also used in the treatment of raised intracranial pressure. Drug-induced myokymia has rarely been reported, with known triggers being clozapine, gabapentin and flunarizine, and topiramate. Acetazolamide-induced myokymia itself has only been reported once before, to the best of our knowledge, and the exact mechanism behind this occurrence remains unknown. We, therefore, report a rare case of periorbital myokymia induced by the use of acetazolamide in a patient diagnosed with idiopathic intracranial hypertension. The nature of her symptoms was significant, as they caused her considerable distress, and subsided almost immediately upon discontinuation of the drug.
Quadriplegia or dysesthesia in all four limbs may be the initial symptom of bilateral medial medullary infarction (MMI), a very rare cerebrovascular accident with a dismal prognosis. Clinical diagnosis of bilateral MMI is still challenging and can be confirmed by diffusion-weighted (DW) magnetic resonance imaging (MRI) in the early stage. Here, we report the case of a 60-year-old male who presented to the emergency department complaining of numbness in all four limbs. DW-MRI was used to identify brain lesions 24 hours after the symptom onset. The infarct, on axial MRI sections, showed the characteristic 'airpod sign'/heartshaped appearance due to the morphology of the area involved in the medulla.
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