Abstract
Pollen and molds are environmental allergens that are affected by climate change. As pollen and molds exhibit geographical variations, we sought to understand the impact of climate change ...(temperature, carbon dioxide (CO
2
), precipitation, smoke exposure) on common pollen and molds in the San Francisco Bay Area, one of the largest urban areas in the United States. When using time-series regression models between 2002 and 2019, the annual average number of weeks with pollen concentrations higher than zero increased over time. For tree pollens, the average increase in this duration was 0.47 weeks and 0.51 weeks for mold spores. Associations between mold, pollen and meteorological data (e.g., precipitation, temperature, atmospheric CO
2
, and area covered by wildfire smoke) were analyzed using the autoregressive integrated moving average model. We found that peak concentrations of weed and tree pollens were positively associated with temperature (
p
< 0.05 at lag 0–1, 0–4, and 0–12 weeks) and precipitation (
p
< 0.05 at lag 0–4, 0–12, and 0–24 weeks) changes, respectively. We did not find clear associations between pollen concentrations and CO
2
levels or wildfire smoke exposure. This study’s findings suggest that spore and pollen activities are related to changes in observed climate change variables.
Although genetic factors play a role in the etiology of atopic disease, the rapid increases in the prevalence of these diseases over the last few decades suggest that environmental, rather than ...genetic factors are the driving force behind the increasing prevalence. In modern societies, there is increased time spent indoors, use of antibiotics, and consumption of processed foods and decreased contact with farm animals and pets, which limit exposure to environmental allergens, infectious parasitic worms, and microbes. The lack of exposure to these factors is thought to prevent proper education and training of the immune system. Increased industrialization and urbanization have brought about increases in organic and inorganic pollutants. In addition, Caesarian birth, birth order, increased use of soaps and detergents, tobacco smoke exposure and psychosomatic factors are other factors that have been associated with increased rate of allergic diseases. Here, we review current knowledge on the environmental factors that have been shown to affect the development of allergic diseases and the recent developments in the field.
Policy making regarding biomass co-firing is difficult. The article provides a benefit-cost analysis for decision makers to facilitate policy making process to implement efficient biomass co-firing ...policy. The additional cost is the sum of cost of the biomass procurement and biomass transportation. Co-benefits are sales of greenhouse gas emission credits and health benefit from reducing harmful air pollutants, especially particulate matter. The benefit-cost analysis is constructed for semi-arid U.S. region, Utah, where biomass supply is limited. Results show that biomass co-firing is not economically feasible in Utah but would be feasible when co-benefits are considered. Benefit-cost ratio is critically dependent upon biomass and carbon credit prices. The procedure to build the benefit-cost ratio can be applied for any region with other scenarios suggested in this study.
Recommender systems form the backbone of many interactive systems. They incorporate user feedback to personalize the user experience typically via personalized recommendation lists. As users interact ...with a system, an increasing amount of data about a user’s preferences becomes available, which can be leveraged for improving the systems’ performance. Incorporating these new data into the underlying recommendation model is, however, not always straightforward. Many models used by recommender systems are computationally expensive and, therefore, have to perform offline computations to compile the recommendation lists. For interactive applications, it is desirable to be able to update the computed values as soon as new user interaction data is available: updating recommendations in interactive time using new feedback data leads to better accuracy and increases the attraction of the system to the users. Additionally, there is a growing consensus that accuracy alone is not enough and user satisfaction is also dependent on diverse recommendations.
In this work, we tackle this problem of updating personalized recommendation lists for interactive applications in order to provide both accurate and diverse recommendations. To that end, we explore algorithms that exploit random walks as a sampling technique to obtain diverse recommendations without compromising on efficiency and accuracy. Specifically, we present a novel graph vertex ranking recommendation algorithm called RP
3
β
that reranks items based on three-hop random walk transition probabilities. We show empirically that RP
3
β
provides accurate recommendations with high long-tail item frequency at the top of the recommendation list. We also present approximate versions of RP
3
β
and the two most accurate previously published vertex ranking algorithms based on random walk transition probabilities and show that these approximations converge with an increasing number of samples.
To obtain interactively updatable recommendations, we additionally show how our algorithm can be extended for online updates at interactive speeds. The underlying random walk sampling technique makes it possible to perform the updates without having to recompute the values for the entire dataset.
In an empirical evaluation with three real-world datasets, we show that RP
3
β
provides highly accurate and diverse recommendations that can easily be updated with newly gathered information at interactive speeds (≪ 100
ms
).
Nature underpins human well-being in critical ways, especially in health. Nature provides pollination of nutritious crops, purification of drinking water, protection from floods, and climate ...security, among other well-studied health benefits. A crucial, yet challenging, research frontier is clarifying how nature promotes physical activity for its many mental and physical health benefits, particularly in densely populated cities with scarce and dwindling access to nature. Here we frame this frontier by conceptually developing a spatial decision-support tool that shows where, how, and for whom urban nature promotes physical activity, to inform urban greening efforts and broader health assessments. We synthesize what is known, present a model framework, and detail the model steps and data needs that can yield generalizable spatial models and an effective tool for assessing the urban nature-physical activity relationship. Current knowledge supports an initial model that can distinguish broad trends and enrich urban planning, spatial policy, and public health decisions. New, iterative research and application will reveal the importance of different types of urban nature, the different subpopulations who will benefit from it, and nature's potential contribution to creating more equitable, green, livable cities with active inhabitants.
Background: Geriatric depression, which has primarily been studied in high-income nations, is anticipated to become more prevalent as the world's old population grows. In low- and middle-income ...nations like Nepal, similar studies are rare.
Objectives: This aimed to determine the prevalence of geriatric depression and its associated factors in 60 years and above age group of both sexes.
Setting and Design: A community based cross-sectional study was conducted in the Kalika rural municipality of Rasuwa district, Nepal.
Methods and materials: Face to face interview technique and Geriatric Depression Scale (GDS-15) was used to collect information from 305 respondents aged above 60 years which was the required sample size of the study. Simple random technique was used for the selection of respondents.
Statistical analysis used: Chi-square test at 5% level of significance was used to identify association between socio-demographic, individual, contextual factors with geriatric depression.
Results: A total of 305 elderly people were participated in this study. The mean age was 70.91(±9.165) years. Overall prevalence of depression was 31.1%. Study also found that geriatric depression was significantly associated with living with children, family type, working status, family income, chronic illness, physical capabilities, involving in social activities, worried of being elderly, feeling of stress about life, family support, communication with family member and vital role in decision making (p<0.05).
Conclusion: Geriatric depression was prevalent in kalika rural municipality. Based on these identified variables, current health programs should focus on addressing these challenges.
Key words: Geriatric depression, community based cross-sectional study, Geriatric Depression Scale, prevalence.
News recommender systems provide a technological architecture that helps shaping public discourse. Following a normative approach to news recommender system design, we test utility and external ...effects of a diversity-aware news recommender algorithm. In an experimental study using a custom-built news app, we show that diversity-optimized recommendations (1) perform similar to methods optimizing for user preferences regarding user utility, (2) that diverse news recommendations are related to a higher tolerance for opposing views, especially for politically conservative users, and (3) that diverse news recommender systems may nudge users towards preferring news with differing or even opposing views. We conclude that diverse news recommendations can have a depolarizing capacity for democratic societies.
Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications. Crossover interactions -- bi-directional effects ...between entities and relations --- help select related information when predicting a new triple, but haven't been formally discussed before. In this paper, we propose CrossE, a novel knowledge graph embedding which explicitly simulates crossover interactions. It not only learns one general embedding for each entity and relation as most previous methods do, but also generates multiple triple specific embeddings for both of them, named interaction embeddings. We evaluate embeddings on typical link prediction tasks and find that CrossE achieves state-of-the-art results on complex and more challenging datasets. Furthermore, we evaluate embeddings from a new perspective -- giving explanations for predicted triples, which is important for real applications. In this work, an explanation for a triple is regarded as a reliable closed-path between the head and the tail entity. Compared to other baselines, we show experimentally that CrossE, benefiting from interaction embeddings, is more capable of generating reliable explanations to support its predictions.
Personalized ranking systems — also known as recommender systems — use different big data methods, including collaborative filtering, graph random-walks, matrix factorization, and latent-factor ...models. With their wide use in various social-network, e-commerce, and content platforms, online platforms and developers are in need of better ways to choose the systems that are most suitable for their use-cases. At the same time, the research literature on recommender systems describes a multitude of performance measures to evaluate the performance of different algorithms. For the end-user however, the large number of available measures do not provide much help in deciding which algorithm to deploy. Some of the measures are correlated, while others deal with different aspects of recommendation performance like accuracy and diversity. To address this problem, we propose a novel benchmarking framework that mixes different evaluation measures in order to rank the recommender systems on each benchmark dataset, separately. Additionally, our approach discovers sets of correlated measures as well as sets of evaluation measures that are least correlated. We investigate the robustness of the proposed methodology using published results from an experimental study involving multiple big datasets and evaluation measures. Our work provides a general framework that can handle an arbitrary number of evaluation measures and help end-users rank the systems available to them.
Reasoning is essential for the development of large knowledge graphs, especially for completion, which aims to infer new triples based on existing ones. Both rules and embeddings can be used for ...knowledge graph reasoning and they have their own advantages and difficulties. Rule-based reasoning is accurate and explainable but rule learning with searching over the graph always suffers from efficiency due to huge search space. Embedding-based reasoning is more scalable and efficient as the reasoning is conducted via computation between embeddings, but it has difficulty learning good representations for sparse entities because a good embedding relies heavily on data richness. Based on this observation, in this paper we explore how embedding and rule learning can be combined together and complement each other's difficulties with their advantages. We propose a novel framework IterE iteratively learning embeddings and rules, in which rules are learned from embeddings with proper pruning strategy and embeddings are learned from existing triples and new triples inferred by rules. Evaluations on embedding qualities of IterE show that rules help improve the quality of sparse entity embeddings and their link prediction results. We also evaluate the efficiency of rule learning and quality of rules from IterE compared with AMIE+, showing that IterE is capable of generating high quality rules more efficiently. Experiments show that iteratively learning embeddings and rules benefit each other during learning and prediction.
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