Severe weather events can impact negatively on tourism and put tourists at risk. To reduce vulnerability, tourists should be aware of and be prepared for possible severe weather. Seeking risk information, a type of protective action behaviour, is an important way to reduce vulnerability. This paper presents the results of a study that investigated the role of Locus of Responsibility (LoR) for protection behaviour for severe weather, by linking it with Information Seeking and related intra-personal antecedents. LoR has previously been found to impact protective action decisions, but not within the context of severe weather and tourism. Our survey research among tourists in New Zealand provided evidence for three Loci of Responsibility; " Internal" , " Shared" and " External" Significant differences between these groups were found for Information Seeking antecedents, though not for Information Seeking. Next, significant differences were found for weather information preferences, both source and content. Findings and implications for tourism and safety management in New Zealand are discussed.
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In the literature about web survey methodology, significant e orts have been made to understand the role of time-invariant factors (e.g. gender, education and marital status) in (non-)response mechanisms. Time-invariant factors alone, however, cannot account for most variations in (non-)responses, especially fluctuations of response rates over time. This observation inspires us to investigate the counterpart of time-invariant factors, namely time-varying factors and the potential role they play in web survey (non-)response. Specifically, we study the effects of time, weather and societal trends (derived from Google Trends data) on the daily (non-)response patterns of the 2016 and 2017 Dutch Health Surveys. Using discrete-time survival analysis, we find, among others, that weekends, holidays, pleasant weather, disease outbreaks and terrorism salience are associated with fewer responses. Furthermore, we show that using these variables alone achieves satisfactory prediction accuracy of both daily and cumulative response rates when the trained model is applied to future unseen data. This approach has the further benefit of requiring only non-personal contextual information and thus involving no privacy issues. We discuss the implications of the study for survey research and data collection.
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Machine learning models have proven to be reliable methods in classification tasks. However, little research has been done on classifying dwelling characteristics based on smart meter & weather data before. Gaining insights into dwelling characteristics can be helpful to create/improve the policies for creating new dwellings at NZEB standard. This paper compares the different machine learning algorithms and the methods used to correctly implement the models. These methods include the data pre-processing, model validation and evaluation. Smart meter data was provided by Groene Mient, which was used to train several machine learning algorithms. The models that were generated by the algorithms were compared on their performance. The results showed that Recurrent Neural Network (RNN) 2performed the best with 96% of accuracy. Cross Validation was used to validate the models, where 80% of the data was used for training purposes and 20% was used for testing purposes. Evaluation metrices were used to produce classification reports, which can indicate which of the models work the best for this specific problem. The models were programmed in Python.
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Machine learning models have proven to be reliable methods in classification tasks. However, little research has been conducted on the classification of dwelling characteristics based on smart meter and weather data before. Gaining insights into dwelling characteristics, which comprise of the type of heating system used, the number of inhabitants, and the number of solar panels installed, can be helpful in creating or improving the policies to create new dwellings at nearly zero-energy standard. This paper compares different supervised machine learning algorithms, namely Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Long-short term memory, and methods used to correctly implement these algorithms. These methods include data pre-processing, model validation, and evaluation. Smart meter data, which was used to train several machine learning algorithms, was provided by Groene Mient. The models that were generated by the algorithms were compared on their performance. The results showed that the Long-short term memory performed the best with 96% accuracy. Cross Validation was used to validate the models, where 80% of the data was used for training purposes and 20% was used for testing purposes. Evaluation metrics were used to produce classification reports, which indicates that the Long-short term memory outperforms the compared models on the evaluation metrics for this specific problem.
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PURPOSE: To study the preliminary effects and feasibility of the “Traffic Light Method for somatic screening and lifestyle” (TLM) in patients with severe mental illness. DESIGN AND METHODS: A pilot study using a quasi-experimental mixed method design with additional content analyses of lifestyle plans and logbooks. FINDINGS: Significant improvements were found in body weight and waist circumference. Positive trends were found in patients’ subjective evaluations of the TLM. The implementation of the TLM was considered feasible. PRACTICE IMPLICATIONS: The TLM may contribute to a higher quality of care regarding somatic screening and lifestyle training.
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Ageing brings about physiological changes that affect people’s thermal sensitivity and thermoregulation. The majority of older Australians prefer to age in place and modifications to the home environment are often required to accommodate the occupants as they age and possibly become frail. However, modifications to aid thermal comfort are not always considered. Using a qualitative approach this study aims to understand the thermal qualities of the existing living environment of older South Australians, their strategies for keeping cool in hot weather and warm in cold weather and to identify existing problems related to planning and house design, and the use of heating and cooling. Data were gathered via seven focus group sessions with 49 older people living in three climate zones in South Australia. The sessions yielded four main themes, namely ‘personal factors’, ‘feeling’, ‘knowing’ and ‘doing’. These themes can be used as a basis to develop information and guidelines for older people in dealing with hot and cold weather. Original publication at MDPI: https://doi.org/10.3390/ijerph16060935 © 2018 by the authors. Licensee MDPI.
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At a time when the population is ageing and most people choose to live in their own home for as long as possible, it is important to consider various aspects of supportive and comfortable environments for housing. This study, conducted in South Australia, aims to provide information about the links between the type of housing in which older people live, the weather and occupants’ heating and cooling behaviours as well as their health and well-being. The study used a Computer-Assisted Telephone Interviewing (CATI) system to survey 250 people aged 65 years and over who lived in their own home. The respondents were recruited from three regions representing the three climate zones in South Australia: semi-arid, warm temperate and temperate. The results show that while the majority of respondents reported being in good health, many lived in dwellings with minimal shading and no wall insulation and appeared to rely on the use of heaters and coolers to achieve thermally comfortable conditions. Concerns over the cost of heating and cooling were shared among the majority of respondents and particularly among people with low incomes. Findings from this study highlight the importance of providing information to older people, carers, designers and policy makers about the interrelationships between weather, housing design, heating and cooling behaviours, thermal comfort, energy use and health and well-being, in order to support older people to age in place independently and healthily. https://doi.org/10.1016/j.buildenv.2019.03.023 LinkedIn: https://www.linkedin.com/in/jvhoof1980/
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Older people are often over-represented in morbidity and mortality statistics associated with hot and cold weather, despite remaining mostly indoors. The study “Improving thermal environment of housing for older Australians” focused on assessing the relationships between the indoor environment, building characteristics, thermal comfort and perceived health/wellbeing of older South Australians over a study period that included the warmest summer on record. Our findings showed that indoor temperatures in some of the houses reached above 35 °C. With concerns about energy costs, occupants often use adaptive behaviours to achieve thermal comfort instead of using cooling (or heating), although feeling less satisfied with the thermal environment and perceiving health/wellbeing to worsen at above 28 °C (and below 15 °C). Symptoms experienced during hot weather included tiredness, shortness of breath, sleeplessness and dizziness, with coughs and colds, painful joints, shortness of breath and influenza experienced during cold weather. To express the influence of temperature and humidity on perceived health/wellbeing, a Temperature Humidity Health Index (THHI) was developed for this cohort. A health/wellbeing perception of “very good” is achieved between an 18.4 °C and 24.3 °C indoor operative temperature and a 55% relative humidity. The evidence from this research is used to inform guidelines about maintaining home environments to be conducive to the health/wellbeing of older people. Original publication at MDPI: https://doi.org/10.3390/atmos13010096 © 2022 by the authors. Licensee MDPI.
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Schepen in moeilijkheden op zee leveren vaak besluitvormingsproblemen op tussen de scheepseigenaar/kapitein en de kuststaat. Kuststaten en met name de lokale overheden willen een probleem schip graag zo ver mogelijk weg sturen van hun gebied terwijl de eigenaar/kapitein zijn schip graag zo snel mogelijk naar de kust, een beschutte locatie of haven wil brengen. Het onderzoek geeft onderbouwing voor de besluitvorming rond schepen in moeilijkheden, zowel voor de zeescheepvaart als de betrokken besluitvormers van oeverstaten. Het product van het project is: een, op uitgewerkte scenario’s per scheepstype en lading gebaseerde besluitvormingsprocedure voor zeeschepen in moeilijkheden
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Abstract: Climate change is related with weather extremes, which may cause damages to infrastructure used by freight transport services. Heavy rainfall may lead to flooding and damage to railway lines, roads and inland waterways. Extreme drought may lead to extremely low water levels, which prevent safe navigation by inland barges. Wet and dry periods may alternate, leaving little time to repair damages. In some Western and Middle-European countries, barges have a large share in freight transport. If a main waterway is out of service, then alternatives are called for. Volume- and price-wise, trucking is not a viable alternative. Could railways be that alternative? The paper was written after the unusually long dry summer period in Europe in 2022. It deals with the question: If the Rhine, a major European waterway becomes locally inaccessible, could railways (temporarily) play a larger role in freight transport? It is a continuation of our earlier research. It contains a case study, the data of which was fed into a simulation model. The model deals with technical details like service specification route length, energy consumption and emissions. The study points to interesting rail services to keep Europe’s freight on the move. Their realization may be complex especially in terms of logistics and infrastructure, but is there an alternative?
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