Retirement and Household Expenditure in Turbulent Times

We examine the impact of own and spousal retirement on household expenditure during a period of financial deterioration. We use detailed household data covering the period 2009-2016 in Greece, during which the country experienced a severe financial crisis that affected retirees in ways that were not anticipated. Similar to Moreau and Stancanelli (2015) our empirical strategy allows for the household expenditure to depend on both own and spousal retirement status. We employ an instrumental variable identification strategy by exploiting variation coming from the early retirement age threshold. Our Two-Stage Least Squares estimates show that, even after controlling for income, total expenditure drops significantly when the husband retires and as he becomes older. The reduction is stronger in 2010, when the first wave of austerity plans, including measures affecting pensioners were announced, and after 2014 when horizontal pension cuts were implemented. Expenditure does not change significantly when the wife retires neither the older she gets. A drop-in expenditure for clothing, transport, housing and communication drives the overall reduction in expenditure. Overall, our results can have significant policy implications in the design of structural pension reforms in a period of financial hardship

Impacts of introducing and lifting non-pharmaceutical interventions on COVID-19 daily growth rate and compliance in the US

We evaluate the impacts of implementing and lifting non-pharmaceutical interventions (NPIs) in US counties on the daily growth rate of COVID-19 cases and compliance, measured through the percentage of devices staying home, and evaluate whether introducing and lifting NPIs protecting selective populations is an effective strategy. We use difference-in-differences methods, leveraging on daily county-level data and exploit the staggered introduction and lifting of policies across counties over time. We also assess heterogenous impacts due to counties’ population characteristics, namely ethnicity and household income. Results show that introducing NPIs led to a reduction in cases through the percentage of devices staying home. When counties lifted NPIs, they benefited from reduced mobility outside of the home during the lockdown, but only for a short period. In the long-term, counties experienced diminished health and mobility gains accrued from previously implemented policies. Notably, we find heterogenous impacts due to population characteristics implying that measures can mitigate the disproportionate burden of COVID-19 on marginalized populations and find that selectively targeting populations may not be effective

Development and Integration of Metocean Data Interoperability for Intelligent Operations and Automation Using Machine Learning: A Review

The current oil industry is moving towards digitalization, which is a good opportunity that will bring value to all its stakeholders. The digitalization of oil and gas discovery, which are production-based industries, is driven by enabling technologies which include machine learning (ML) and big data analytics. However, the existing Metocean system generates data manually using sensors such as the wave buoy, anemometer, and acoustic doppler current profiler (ADCP). Additionally, these data which appear in ASCII format to the Metocean system are also manual and silos. This slows down provisioning, while the monitoring element of the Metocean data path is partial. In this paper, we demonstrate the capabilities of ML for the development of Metocean data integration interoperability based on intelligent operations and automation. A comprehensive review of several research studies, which explore the needs of ML in oil and gas industries by investigating the in-depth integration of Metocean data interoperability for intelligent operations and automation using an ML-based approach, is presented. A new model integrated with the existing Metocean data system using ML algorithms to monitor and interoperate with maximum performance is proposed. The study reveals that ML is one of the crucial and key enabling tools that the oil and gas industries are now focused on for implementing digital transformation, which allows the industry to automate, enhance production, and have less human capacity. Lastly, user recommendations for potential future investigations are offered

A facile energy-efficient approach to prepare super oil-sorbent thin films

Oil spills on water surface and shoreline have caused significant water pollution, and one of the ways to deal with them is to use oil sorbents. An effective sorbent provides high oil uptake and retention values, high selectivity, super-fast uptake kinetics, and sufficient mechanical strength to ensure practical application under different conditions. In this regard, synthetic sorbents made up of graphene, carbon nanotubes, and polymers in the form of aerogels, thin films, pads, and non-woven fibers have been widely explored. However, none of them addresses all the attributes of an ideal oil sorbent. Aerogels provide extremely high uptake values, but they are so light that it is difficult for the end user to handle them. On the other hand, thin films and non-woven fibers can quickly absorb oil but suffer from low uptake capacity with low retention values. Similarly, commercial oil sorbent pads have sufficient mechanical strength, but low uptake capacity compared to aerogels. Herein, we present a super oil sorbent with a porous structure using a facile energy-efficient approach. The as-prepared sorbent comprises a porous thin film with micropores and macro-cavities, resulting in super-fast uptake kinetics and a high oil uptake value of 85 g/g. Moreover, tensile test results confirm sorbent’s effectiveness in spill response. Lastly, our unique design does not involve expensive hydrophobic functionalization and thus utilizes lower embodied energy and generates lower carbon footprints

Design and Development of Heat Recovery System in Water Cooler

More than 15% of total energy consumption of the world is for the cooling and airconditioning application. Beign the huge contributor in consumption of electricity, these systems have the scope of developments in terms of optimum utilization of the resources. The cooling systems are improved over the years by means of design, use of different cooling mediums and th performance. The conventional water coolers are less energy efficient and the wastage of water is also the issue to be addressed. Authors have presented the improved design in terms of energy efficiency and the waste water utilization in this paper. The design fo the system components in Solid Works software is presented in the paper along with the parameter calcutions. Water and electricity are important aspects to be saved. Optimal utlistion of the electricity and water results in saving the environment, cost, and environmental hazards

AHI1, a pivotal neurodevelopmental gene, and C6orf217 are associated with susceptibility to schizophrenia

Schizophrenia, a severe neuropsychiatric disorder, is believed to involve multiple genetic factors. A significant body of evidence supports a pivotal role for abnormalities of brain development in the disorder. Linkage signals for schizophrenia map to human chromosome 6q. To obtain a finer localization, we genotyped 180 single nucleotide polymorphisms (SNPs) in a young, inbred Arab-Israeli family sample with a limited number of founders. The SNPs were mostly within a approximately 7 Mb region around the strong linkage peak at 136.2 Mb that we had previously mapped. The most significant genetic association with schizophrenia for single SNPs and haplotypes was within a 500 kb genomic region of high linkage disequilibrium (LD) at 135.85 Mb. In a different, outbred, nuclear family sample that was not appropriate for linkage analysis, under-transmitted haplotypes incorporating the same SNPs (but not the individual SNPs) were significantly associated with schizophrenia. The implicated genomic region harbors the Abelson Helper Integration Site 1 (AHI1) gene, which showed the strongest association signal, and an adjacent, primate-specific gene, C6orf217. Mutations in human AHI1 underlie the autosomal recessive Joubert Syndrome with brain malformation and mental retardation. Previous comparative genomic analysis has suggested accelerated evolution of AHI1 in the human lineage. C6orf217 has multiple splice isoforms and is expressed in brain but does not seem to encode a functional protein. The two genes appear in opposite orientations and their regulatory upstream regions overlap, which might affect their expression. Both, AHI1 and C6orf217 appear to be highly relevant candidate genes for schizophrenia