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In recent years, there has been a rapid increase in the application of machine learning methods about predicting the incidence of dengue fever. However, the predictive factors and models employed in different studies vary greatly. Hence, we conducted a systematic review to summarize machine learning methods and predictors in previous studies. We searched PubMed, ScienceDirect, and Web of Science databases for articles published up to July 2023. The selected papers included not only the forecast of dengue incidence but also machine learning methods. A total of 23 papers were included in this study. Predictive factors included meteorological factors (22, 95.7%), historical dengue data (14, 60.9%), environmental factors (4, 17.4%), socioeconomic factors (4, 17.4%), vector surveillance data (2, 8.7%), and internet search data (3, 13.0%). Among meteorological factors, temperature (20, 87.0%), rainfall (20, 87.0%), and relative humidity (14, 60.9%) were the most commonly used. We found that Support Vector Machine (SVM) (6, 26.1%), Long Short-Term Memory (LSTM) (5, 21.7%), Random Forest (RF) (4, 17.4%), Least Absolute Shrinkage and Selection Operator (LASSO) (2, 8.7%), ensemble model (2, 8.7%), and other models (4, 17.4%) were identified as the best models based on evaluation metrics used in each article. These results indicate that meteorological factors are important predictors that cannot be ignored and SVM and LSTM algorithms are the most commonly used models in dengue fever prediction with good predictive performance. This review will contribute to the development of more robust early dengue warning systems and promote the application of machine learning methods in predicting climate-related infectious diseases. [ABSTRACT FROM AUTHOR]

Associations of exertional heat exhaustion (EHE)-related symptoms with lifestyle habits and health factors specific to female athletes, ranked by relative risks from high to low, remain elusive. Addressing this issue would benefit athletes and coaches, ensuring safer practices during summer sports activities. To address this issue, we distributed paper-based questionnaires to seven universities in Japan, and 983 respondents completed our survey. The questionnaires covered various personal characteristics, lifestyle habits, perceived health factors, perceived athletic activity, and EHE-related symptoms. In this retrospective case–control study, we analyzed the relationships of EHE-related symptoms (objective variables) with lifestyle habits, health factors, and athletic activity conditions (explanatory variables) using the partial proportional odds model. The questionnaire responses revealed that perceived dehydration, sickness, loss of appetite, perceived accumulated fatigue, perceived mental stress, lack of ambient wind, and insufficient rest breaks were positively associated with EHE-related symptoms, with relative risks ranging from high to low. Using an air conditioner during sleep and having a sleep duration of ≥ 6 h were associated with a reduced risk of EHE-related symptoms. The study results suggest that female athletes should be allowed to postpone exercise or reduce its intensity and volume based on their perceptions of dehydration, sickness, loss of appetite, accumulated fatigue, and mental stress in summer to prevent heat-related illnesses. [ABSTRACT FROM AUTHOR]

Meteorological variables are essential inputs for agricultural simulation models and the lack of measured data is a big challenge for the application of these models in many agricultural zones. Studies indicated that gridded meteorological datasets can be proper replacements for measured data. This paper aimed to examine a new gridded meteorological dataset namely CRU-JRA for crop modeling intents. The CRU-JRA is a 6-hourly dataset with a spatial resolution of 0.5° × 0.5° that was primarily constructed for modeling purposes. The CERES-Wheat model in the Decision Support System for Agrotechnology Transfer (DSSAT) was used for the simulation of irrigated and rainfed wheat production systems in Iran. Results showed that the CRU-JRA maximum and minimum temperature values had a relatively fine accuracy with a normalized root mean square error (NRMSE) of 14% for the simulated grain yield. The performance of the CRU-JRA solar radiation values for the simulation of grain yield was similar with a NRMSE of 14.4%. The weakest performance was found for the CRU-JRA precipitation values with a NRMSE of 18.9%. Overall, the CRU-JRA dataset performed comparatively acceptable and similar to existing gridded meteorological datasets for crop modeling purposes in the study area, however further calibrations can improve the accuracy of the next versions of this dataset. More research is necessary for the investigation of the CRU-JRA dataset for agricultural modeling purposes across diverse climates. [ABSTRACT FROM AUTHOR]

In this initial study of a research project, this paper seeks to understand the thermal conditions in the cities of Lisbon and Munich, specifically focusing on Urban Heat Island intensity and on thermal comfort using the Universal Thermal Climate Index modeling data at the Local Climate Zone scale. Based on these datasets, Munich has exhibited more unfavourable thermal conditions than Lisbon. In terms of UHII, both cities have shown that low, medium, and high rise compact urban areas and bare rock or paved areas have the highest values, while sparsely built areas have the lowest. These results differ from the UTCI, which indicates that in Lisbon and Munich, these sparsely built areas as well as areas with low plants and vegetation are the most uncomfortable. In Munich, the population was exposed to very strong heat stress, while Lisbon experienced strong heat stress conditions. Conversely, low, medium, and high rise compact urban areas and densely wooded areas in Munich, and scattered trees areas and large low-rise urban areas in Lisbon, have demonstrated the lowest monthly mean and average maximum values. These results will be further explored in future studies in the city of Lisbon and cross-checked with data obtained from roving missions. This will enable a more detailed temporal and local analysis. [ABSTRACT FROM AUTHOR]

To adapt to Earth's rapidly changing climate, detailed modelling of thermal stress is needed. Dangerous stress levels are becoming more frequent, longer, and more severe. While traditional measurements of thermal stress have focused on air temperature and humidity, modern measures including radiation and wind speed are becoming widespread. However, projecting such indices has presented a challenging problem, due to the need for appropriate bias correction of multiple variables that vary on hourly timescales. In this paper, we aim to provide a detailed understanding of changing thermal stress patterns incorporating modern measurements, bias correction techniques, and hourly projections to assess the impact of climate change on thermal stress at human scales. To achieve these aims, we conduct a case study of projected thermal stress in central Hobart, Australia for 2040–2059, compared to the historical period 1990–2005. We present the first hourly metre-scale projections of thermal stress driven by multivariate bias-corrected data. We bias correct four variables from six dynamically downscaled General Circulation Models. These outputs drive the Solar and LongWave Environmental Irradiance Geometry model at metre scale, calculating mean radiant temperature and the Universal Thermal Climate Index. We demonstrate that multivariate bias correction can correct means on multiple time scales while accurately preserving mean seasonal trends. Changes in mean air temperature and UTCI by hour of the day and month of the year reveal diurnal and annual patterns in both temporal trends and model agreement. We present plots of future median stress values in the context of historical percentiles, revealing trends and patterns not evident in mean data. Our modelling illustrates a future Hobart that experiences higher and more consistent numbers of hours of heat stress arriving earlier in the year and extending further throughout the day. [ABSTRACT FROM AUTHOR]

Dengue is one of the world's most rapidly spreading mosquito-borne viral diseases. As it is found mostly in urban and semi-urban areas, urbanization and associated human activities that affect the environment and larval habitats could become risk factors (e.g., lane width, conditions of street ditches) for the spread of dengue. However, there are currently no systematic studies of micro-scale urbanization-based risk factors for the spread of dengue epidemics. We describe the study area, two micro-scale environmental risk factors associated with urbanization, and meteorological data. Since the observations involve spatial and temporal correlations, we also use some statistical methods for the analysis of spatial and spatial-temporal data for the relationship between urbanization and dengue. In this study, we analyzed data from Kaohsiung, a densely populated city in southern Taiwan, and found a positive correlation between environmental risk factors associated with urbanization (ditches positive for mosquito larvae and closely packed streets termed "dengue lanes") and clustering effects in dengue cases. The statistical analysis also revealed that the occurrence of positive ditches was significantly associated with that of dengue lanes in the study area. The relationship between climate variables and positive ditches was also analyzed in this paper, indicating a relationship between dengue and both rainfall and temperature, with temperature having a greater effect. Overall, this work is immediately relevant and applicable for policymakers in government, who will need to reduce these favorable habitats for vector-born disease spreaders and implement regulations for new urban constructions to thus reduce dengue spread in future outbreaks. [ABSTRACT FROM AUTHOR]

Since pedestrians are impacted by solar radiation differently, urban designers must evaluate solar radiation exposure of pedestrian paths adopting an inclusive approach. This paper proposes a maximum threshold of direct solar radiation exposure for pedestrians based on activity, user profile and environmental conditions, defined as the difference between the energy consumption before feeling exhausted and the energy cost of walking. Two users of diverse walking abilities, a young adult and an elderly person with mobility impairment, were characterised by metabolic activity, walking speed and maximum energy capacity. Based on the theoretical framework, the energy budget of young adults to cope with thermal stress was set as three times higher than for the elderly. This framework was used to quantify the contribution of direct solar radiation to energy balance and then classify walkability during clear-sky summer hours; the term 'walkable' referred to environmental conditions allowing users to walk without feeling exhausted. The methodology was tested on an open area and an urban canyon in Milan; applicability by urban designers was key in developing a simplified way to evaluate shading needs. This approach could be applied to evaluate solar radiation exposure of pedestrian paths adopting diverse user experiences as an evaluation criterion. [ABSTRACT FROM AUTHOR]

The intensity and frequency of climate extremes such as heat waves, droughts and extreme rainfall events are projected to rise. This will increase the severity of their impacts across socio-ecological systems. Economic sectors such as nature-based tourism become more vulnerable because of their reliance on climate and natural capital as key resources. While attempts have been made to understand how climate change may impact tourists and the industry itself, little is known about the same on tourism-dependent communities. This paper determines the extent to which tourism-dependent communities are vulnerable to climate change in the Okavango Delta, Botswana, to enhance their wider livelihood the development of strategies for improving adaptive capacity, resilience, and reduced exposure sensitivities. A household survey of 172 households was conducted in three purposively selected villages of Mababe, Sankuyo and Khwai, actively involved in community-based tourism for their socio-economic development. Information sourced related to livelihood options, peoples’ resilience, local risks, and hazards. The data was analysed using descriptive statistics and thematic analysis. The results indicate that respondents have observed climatic changes over the years such as increased temperatures, decreased rainfall, and increased frequencies of extreme events. The respondents attributed changes in natural capital to these observed climatic conditions in the form of desiccation, dwindling populations of some wildlife species, decreased fish stocks and reduced vegetation cover. This renders the tourism-dependent communities vulnerable as their livelihood is threatened. The paper thus concludes that climate change adaptation is an urgent priority for local communities who are already exposed to existing climatic and non-climatic stresses. [ABSTRACT FROM AUTHOR]

Crop yield prediction gains growing importance for all stakeholders in agriculture. Since the growth and development of crops are fully connected with many weather factors, it is inevitable to incorporate meteorological information into yield prediction mechanism. The changes in climate-yield relationship are more pronounced at a local level than across relatively large regions. Hence, district or sub-region-level modeling may be an appropriate approach. To obtain a location- and crop-specific model, different models with different functional forms have to be explored. This systematic review aims to discuss research papers related to statistical and machine-learning models commonly used to predict crop yield using weather factors. It was found that Artificial Neural Network (ANN) and Multiple Linear Regression were the most applied models. Support Vector Regression (SVR) model has a high success ratio as it performed well in most of the cases. The optimization options in ANN and SVR models allow us to tune models to specific patterns of association between weather conditions of a location and crop yield. ANN model can be trained using different activation functions with optimized learning rate and number of hidden layer neurons. Similarly, the SVR model can be trained with different kernel functions and various combinations of hyperparameters. Penalized regression models namely, LASSO and Elastic Net are better alternatives to simple linear regression. The nonlinear machine learning models namely, SVR and ANN were found to perform better in most of the cases which indicates there exists a nonlinear complex association between crop yield and weather factors. [ABSTRACT FROM AUTHOR]

There is growing concern over the increased risk of heat stress induced by the changing climate, with increased global temperatures expected to exacerbate conditions. Heat stress in school sports is a critically understudied research area in South Africa (SA). As demonstrated by several studies conducted in the global north, the stress elicited by meteorological conditions on students participating in physical education lessons and after-school sports activities could have serious consequences on their health. This paper represents the first research in the region to investigate the risk of heat stress to school students during physical activity. Meteorological data were collected at a school in Johannesburg between March and July, which encompasses the seasons of late summer, autumn, and winter. These were inputted into four heat stress indices: Humidex, Heat Index, Wet Bulb Globe Temperature, and the Universal Thermal Climate Index, together with the measured Wet Bulb Temperature to determine the level of risk of heat stress during physical education lessons and after-school sports activities between March and July. Of the 2700 index scores calculated over this period, 56% indicate some level of heat stress, with almost 6% indicating a high and very high level of risk. Heat stress is calculated to be most likely to occur between 11h00-15h00, and the danger of pre-season training, which takes place in the summer months, is demonstrated by these results. Additionally, this research finds that heat stress is far more likely on artificial surfaces than on natural grass. The findings of this research intimate that school stakeholders should consider the implications of heat stress when considering timetabling and policy-making in the interests of safeguarding their students. [ABSTRACT FROM AUTHOR]

Mud therapy is a safe, cost-effective approach for Knee Osteoarthritis (OA), promoting healing through warmth and minerals, providing pain relief, and improving function. The main objective of the current review is to assess the effectiveness of local mud application for pain management in patients with Knee OA. The published papers were obtained from PubMed, Embase, Scopus, Cochrane Library, ClinicalTrails.gov, Web of Science, and references from relevant systematic reviews from 1990 January to February 2023. Trials meeting the predefined criteria were included in this meta-analysis. The main focus of this study was to assess pain as a primary outcome measure. R software version 4.3.1 and Cochrane Risk of Bias Tool 1 were used to derive meta-analysis and risk of bias in the included studies. A meta-analysis was performed using the fixed-effects model to obtain a summary of treatment effects, which calculated the effect size (standardized mean difference; SMD) along with a 95% Confidence Interval (CI).In the current meta-analysis, a total of ten studies with 560 Knee OA patients were included. Findings demonstrated significant effects of local mud application on pain (SMD: -0.36, 95% CI: -0.56, -0.16), and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scale (SMD: -0.49, 95% CI: -0.68, -0.29) for individuals suffering from Knee OA. The current study identified compelling evidence to support the recommendation of mud application as an effective intervention for individuals with Knee OA. While local mud application may serve as a supplementary therapy alongside conventional medical approaches for improved Knee OA management, additional high-quality randomized controlled trials are required to validate these findings. [ABSTRACT FROM AUTHOR]

This paper provides an overview of the HEAT (Healthy Environments for AthleTes) project, which aims to understand the impact of environmental conditions on athlete health and performance during major sporting events such as long-distance running, cycling, and triathlons. In collaboration with the SAFER (Strategies to reduce Adverse medical events For the ExerciseR) initiative, the HEAT project carried out a field campaign at the 2022 Comrades Marathon in the KwaZulu-Natal province of South Africa. The measurement campaign deployed seven weather stations, seven PM2.5 monitors and one spore trap along the 90 km route to capture spatially representative measurements of complex micro-climates, allergenic aerospora, and particulate matter exposure. The results indicate that runners were exposed to moderate risk heat stress conditions. Novel findings from this initial campaign shows elevated and potentially harmful PM2.5 levels at spectator areas, possibly coinciding with small fire events around the race day festivities. Our findings show values PM2.5 levels over the WHO 24-h guidelines at all stations, while 2000 µg/m3 at two stations. However, the lack of an acute exposure standard means direct health impacts cannot be quantified in the context of a sport event. The HEAT project highlights important aspects of race day monitoring; regional scale climatology has an impact on the race day conditions, the microclimatic conditions (pollution and meteorology) are not necessarily captured by proximity instruments and direct environmental measurements are required to accurately capture conditions along the route. [ABSTRACT FROM AUTHOR]