Rapid 21st century urbanization combined with anthropogenic climate warming are significantly increasing heat-related health threats in cities worldwide. In Los Angeles (LA), increasing trends in extreme heat are expected to intensify and exacerbate the urban heat island effect, leading to greater health risks for vulnerable populations. Partnerships between city policymakers and scientists are becoming more important as the need to provide data-driven recommendations for sustainability and mitigation efforts becomes critical. Here we present a model to produce heat vulnerability index (HVI) maps driven by surface temperature data from National Aeronautics and Space Administration’s (NASA) new Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) thermal infrared sensor. ECOSTRESS was launched in June 2018 with the capability to image fine-scale urban temperatures at a 70 m resolution throughout different times of the day and night. The HVI model further includes information on socio-demographic data, green vegetation abundance, and historical heatwave temperatures from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Aqua spacecraft since 2002. During a period of high heat in July 2018, we identified the five most vulnerable communities at a sub-city block scale in the LA region. The persistence of high HVI throughout the day and night in these areas indicates a clear and urgent need for implementing cooling technologies and green infrastructure to curb future warming.

New ECOSTRESS and MODIS Land Surface Temperature Data Reveal Fine-Scale Heat Vulnerability in Cities: A Case Study for Los Angeles County, California

This position paper discusses the potential for using generative AIs like ChatGPT in software engineering education. Currently, discussions center around potential threats emerging from student's use of ChatGPT. For instance, generative AI will limit the usefulness of graded homework dramatically. However, there exist potential opportunities as well. For example, ChatGPT's ability to understand and generate human language allows providing personalized feedback to students, and can thus accompany current software engineering education approaches. This paper highlights the potential for enhancing software engineering education. The availability of generative AI will improve the individualization of education approaches. In addition, we discuss the need to adapt software engineering curricula to the changed profiles of software engineers. Moreover, we point out why it is important to provide guidance for using generative AI and, thus, integrate it in courses rather than accepting the unsupervised use by students, which can negatively impact the students' learning.

https://dl.acm.org/doi/pdf/10.1145/3587102.3588815

How ChatGPT Will Change Software Engineering Education

The Domain Name System (DNS) is thought of as having the simple-sounding task of resolving domains into IP addresses. With its stub resolvers, different layers of recursive resolvers, authoritative nameservers, a multitude of query types, and DNSSEC, the DNS ecosystem is actually quite complex.In this paper, we introduce DNS Observatory: a new stream analytics platform that provides a bird's-eye view on the DNS. As the data source, we leverage a large stream of passive DNS observations produced by hundreds of globally distributed probes, acquiring a peak of 200 k DNS queries per second between recursive resolvers and authoritative nameservers. For each observed DNS transaction, we extract traffic features, aggregate them, and track the top-k DNS objects, e.g., the top authoritative nameserver IP addresses or the top domains.We analyze 1.6 trillion DNS transactions over a four month period. This allows us to characterize DNS deployments and traffic patterns, evaluate its associated infrastructure and performance, as well as gain insight into the modern additions to the DNS and related Internet protocols. We find an alarming concentration of DNS traffic: roughly half of the observed traffic is handled by only 1 k authoritative nameservers and by 10 AS operators. By evaluating the median delay of DNS queries, we find that the top 10 k nameservers have indeed a shorter response time than less popular nameservers, which is correlated with less router hops.We also study how DNS TTL adjustments can impact query volumes, anticipate upcoming changes to DNS infrastructure, and how negative caching TTLs affect the Happy Eyeballs algorithm. We find some popular domains with a a share of up to 90 % of empty DNS responses due to short negative caching TTLs. We propose actionable measures to improve uncovered DNS shortcomings.

DNS Observatory: The Big Picture of the DNS

The outbreak of the COVID-19 epidemic has focused enormous attention on the genetics of viral infection and related disease. Since the beginning of the pandemic, we focused on the collection and integration of SARS-CoV-2 databases, which contain information on the structure of the virus and on its ability to spread, mutate, and evolve; data are made available from several open-source databases. In the past, we gathered experience on human genomics data by building models and integrated databases of genomic datasets (representing, e.g., mutations, gene expression profiles, epigenetic signals). We also coordinated the development of a data dictionary describing the clinical phenotype of the COVID19 disease, in the context of a very large consortium. The main objective of this paper is to describe the content of the data dictionary and the process of data collection and organization. We also argue that—in the context of the COVID-19 disease—interoperability between the three domains of viral genomics, clinical phenotype, and human host genomics is essential for empowering important analysis processes and results. We call for actions that could be performed to link these data.

/pdf/interoperability-of-covid-19-clinical-phenotype-data-with-39fl90pa.pdf

Interoperability of COVID-19 Clinical Phenotype Data with Host and Viral Genetics Data

Background: Approximately thirty thousand people in Scotland are diagnosed with cancer annually, of whom a third live less than one year. The timing, nature and value of hospital-based healthcare for patients with advanced cancer are not well understood. The aim of this study was to describe patterns of hospital-based healthcare use and associated costs in the last year of life for patients with a cancer diagnosis. Methods: We undertook a Scottish population-wide administrative data linkage study of hospital-based healthcare use for individuals with a cancer diagnosis aged 60 years and over on their date of death, who died between 2012 and 2017. Hospital admissions, length of stay (LOS), number and nature of outpatient and day case appointments were analysed for all cancer types. Generalised linear models were used to adjust costs for age, gender, socioeconomic deprivation status, rural-urban (RU) status and comorbidity. Results: The study included 85,732 decedents with a cancer diagnosis, for whom 64,553 (75.3%) cancer was the primary cause of death. Mean age at death was 80.01 (SD 8.15) years. The mean number of inpatient stays in the last year of life was 5.88 (SD 5.68), with a mean LOS of 7 days. Mean total inpatient, outpatient and day case costs per patient were 10261 pound, 1275 pound and 977 pound respectively. Admission rates rose sharply in the last month of life. One year adjusted and unadjusted costs decreased with increasing age. A higher comorbidity burden was associated with higher costs and major cost differences between cancer types were also observed. Conclusions: People in Scotland in their last year of life with cancer are high users of secondary care. Hospitalisation accounts for a high proportion of costs, particularly in the last month of life. Further research is needed to examine triggers for unplanned hospitalisation and to identify modifiable reasons for variation in hospital use among different cancer cohorts. Keywords: healthcare use, end of life care, secondary care, costs, cancer

/pdf/variation-in-hospital-cost-trajectories-at-the-end-of-life-354yvyg6.pdf

Variation in hospital cost trajectories at the end of life by age, multimorbidity and cancer type

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