Background: The only conclusive way to diagnose Alzheimer’s is to carry out brain autopsy of the patient’s brain tissue and ascertain whether the subject had Alzheimer’s or any other form of dementia. However, due to nonfeasibility of such methods, to diagnose and conclude the conditions, medical practitioners use tests that examine patient’s mental ability. Objective: Accurate diagnosis at an early stage is the need of hour for initiation of therapy. The cause for most Alzheimer’s cases still remains unknown except where genetic distinctions have been observed. Thus, a standard drug regimen ensues in every Alzheimer’s patient, irrespective of the cause, which may not always be beneficial in halting or reversing the disease progression. To provide better life to such patients by suppressing existing symptoms, early diagnosis, curative therapy, site specific delivery of drugs and application of hyphenated methods like artificial intelligence need to be brought into main field of Alzheimer’s therapeutics. Methods: In this review, we have compiled existing hypotheses to explain the cause of the disease, and highlighted gene therapy, immunotherapy, peptidomimetics, metal chelators, probiotics and quantum dots as advancements in the existing strategies to manage Alzheimer’s. Conclusion: Biomarkers, brain-imaging, and theranostics along with artificial intelligence is understood to be the future of management of Alzheimer’s.
The construction sector is responsible for the 40% of consumed resources, 40% of CO2 emissions, and approximately 40% of construction and demolition waste. For the assessment of the building, there exists a standardized method, life cycle assessment (LCA), however, the process requires time, cost, and most importantly expertise. In this paper, a method is proposed and analyzed for the life cycle assessment of the building for the embodied carbon in the three stages, construction, operation, and demolition. Moreover, the result of the analysis is considered as the base result, and de-carbonization strategies identified through literature study for the three stages of construction, operation, and demolition are assessed with the same method to know how much each strategy will be effective in minimizing the embodied carbon. For the base case, a high-rise residential building in an urban region of India is analyzed, based on existing conditions through the building information modeling (BIM) method. The carbon emission of the selected building comes out to be 414 kg CO2e/m2/year, and assessing different decarbonization strategies, considering the first analysis as the baseline, it can be minimized to 135 kg CO2e/m2/year.
The environment demands a reduction in greenhouse gas (GHG) emissions, as building and construction are responsible for more than 40% of the energy consumed worldwide and 30% of the world’s GHG emissions. Many countries have aligned themselves with the Paris agreement, following its target of achieving net zero carbon emissions, although some governments are focused on the operational energy efficiency part of the equation instead of the whole equation. This study emphasizes the significance of incorporating the minimization of embodied emissions into all parts of the building, with a focus on the measurement of embodied carbon, concepts of its management and strategies proposed and enacted for mitigation. As estimate is an important part of any debate, the measurement approach covers the uncertainty analysis from diverse points of view through a novel approach; management covers the early design tools, and the significance of the lifecycle stages; mitigation covers the reduction strategies of embodied carbon, although reduction in embodied carbon is a subjective topic and depends on region. The analysis covers the ideal approaches for mitigation irrespective of the region.
In the era of programmatic advertising, the advertisers have huge amount of first party data to leverage on enabling them to do highly granular re-targeting. Programmatic re-targeting is the ability to use data to show an ad to a user who has demonstrated an interest in your product offerings before. Re-targeting ads are a powerful conversion optimization tool and are typically known to outperform conventional targeting in terms of performance. As per 99 Firms, 41% of marketing allocation in 2018 to paid display spend was on re targeting and for most of the websites, only 2% of web-traffic converts on the first time visit. In this paper, a conversion is referred as a purchase made and a converting user is one who made the purchase on the website. The question that arises is - “should we be re-targeting all the users who have landed on the site?”. In ad campaigns which has low budgets or in campaigns where the conversion rate is really low even though a huge volume of users visit the site, it may not make complete sense to simply re-target all those users, instead we would want to re-target those who are clearly showing an intent to make a purchase either through their on-site browsing behaviors or their past conversion patterns. Through this paper we present the use of first party privacy preserving data to do predictive programmatic re-targeting of users who are going to make a conversion in the next few days given their past site-browsing and conversion behavior using a structured data science and advanced ML based framework. Additionally, this project allows to tie the model results to real time programmatic activation by the creation of user segments depending on whether the user is going to make a conversion for the first time, or is converting again. The final outputs are these user segments, which are going to be used by in house ad-traders who would be able to bid deferentially for a specified period of time against each of the segments on a demand side platform. We have successfully tested this model on 2 advertising clients and were able to capture 80-85% of the actual converts happening over the next few days of them landing.
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