Rising frequency of ozone-favorable synoptic weather patterns contributes to 2015-2022 ozone increase in Guangzhou.

Objective weather classification methods have been extensively applied to identify dominant ozone-favorable synoptic weather patterns (SWPs), however, the consistency of different classification methods is rarely examined. In this study, we apply two widely-used objective methods, the self-organizing map (SOM) and K-means clustering analysis, to derive ozone-favorable SWPs at four Chinese megacities in 2015-2022. We find that the two algorithms are largely consistent in recognizing dominant ozone-favorable SWPs for four Chinese megacities. In the case of classifying six SWPs, the derived circulation fields are highly similar with a spatial correlation of 0.99 between the two methods, and the difference in the mean frequency of each SWP is less than 7%. The six dominant ozone-favorable SWPs in Guangzhou are all characterized by anomaly higher radiation and temperature, lower cloud cover, relative humidity, and wind speed, and stronger subsidence compared to climatology mean. We find that during 2015-2022, the occurrence of ozone-favorable SWPs days increases significantly at a rate of 3.2 day/year, faster than the increases in the ozone exceedance days (3.0 day/year). The interannual variability between the occurrence of ozone-favorable SWPs and ozone exceedance days are generally consistent with a temporal correlation coefficient of 0.6. In particular, the significant increase in ozone-favorable SWPs in 2022, especially the Subtropical High type which typically occurs in September, is consistent with a long-lasting ozone pollution episode in Guangzhou during September 2022. Our results thus reveal that enhanced frequency of ozone-favorable SWPs plays an important role in the observed 2015-2022 ozone increase in Guangzhou.

Existing evidence on the effects of climate variability and climate change on ungulates in North America: a systematic map.

BACKGROUND: Climate is an important driver of ungulate life-histories, population dynamics, and migratory behaviors. Climate conditions can directly impact ungulates via changes in the costs of thermoregulation and locomotion, or indirectly, via changes in habitat and forage availability, predation, and species interactions. Many studies have documented the effects of climate variability and climate change on North America's ungulates, recording impacts to population demographics, physiology, foraging behavior, migratory patterns, and more. However, ungulate responses are not uniform and vary by species and geography. Here, we present a systematic map describing the abundance and distribution of evidence on the effects of climate variability and climate change on native ungulates in North America. METHODS: We searched for all evidence documenting or projecting how climate variability and climate change affect the 15 ungulate species native to the U.S., Canada, Mexico, and Greenland. We searched Web of Science, Scopus, and the websites of 62 wildlife management agencies to identify relevant academic and grey literature. We screened English-language documents for inclusion at both the title and abstract and full-text levels. Data from all articles that passed full-text review were extracted and coded in a database. We identified knowledge clusters and gaps related to the species, locations, climate variables, and outcome variables measured in the literature. REVIEW FINDINGS: We identified a total of 674 relevant articles published from 1947 until September 2020. Caribou (Rangifer tarandus), elk (Cervus canadensis), and white-tailed deer (Odocoileus virginianus) were the most frequently studied species. Geographically, more research has been conducted in the western U.S. and western Canada, though a notable concentration of research is also located in the Great Lakes region. Nearly 75% more articles examined the effects of precipitation on ungulates compared to temperature, with variables related to snow being the most commonly measured climate variables. Most studies examined the effects of climate on ungulate population demographics, habitat and forage, and physiology and condition, with far fewer examining the effects on disturbances, migratory behavior, and seasonal range and corridor habitat. CONCLUSIONS: The effects of climate change, and its interactions with stressors such as land-use change, predation, and disease, is of increasing concern to wildlife managers. With its broad scope, this systematic map can help ungulate managers identify relevant climate impacts and prepare for future changes to the populations they manage. Decisions regarding population control measures, supplemental feeding, translocation, and the application of habitat treatments are just some of the management decisions that can be informed by an improved understanding of climate impacts. This systematic map also identified several gaps in the literature that would benefit from additional research, including climate effects on ungulate migratory patterns, on species that are relatively understudied yet known to be sensitive to changes in climate, such as pronghorn (Antilocapra americana) and mountain goats (Oreamnos americanus), and on ungulates in the eastern U.S. and Mexico.

The influence of specific weather types on stroke occurrence: an analysis of 23,000 patients from Augsburg, Germany.

For the first time, the relationships between large-scale weather types and local stroke events in the urban area of Augsburg, Germany are analyzed. Over 23,000 stroke cases (2006 - 2020) were standardized to account for long-term trends and seasonality. Using ERA5 reanalysis data, a composite analysis identified stroke-related atmospheric variables, while seasonal weather types were classified via the neural network algorithm of self-organizing maps. Cyclonic westerlies during the cold season, which transport warm air masses from the Atlantic Ocean to Germany, were a major risk factor for ischemic stroke, while colder easterly conditions reduced stroke incidence. In the warm season, both anticyclonic conditions and westerly/northerly air advection, leading to slightly warmer or distinctly colder temperatures, were linked to increased ischemic stroke risk. Additionally, hemorrhagic strokes in the cold season were triggered by weather conditions contrary to those associated with ischemic strokes and transitory ischemic attacks.

LiDAR-Based Snowfall Level Classification for Safe Autonomous Driving in Terrestrial, Maritime, and Aerial Environments.

Studies on autonomous driving have started to focus on snowy environments, and studies to acquire data and remove noise and pixels caused by snowfall in such environments are in progress. However, research to determine the necessary weather information for the control of unmanned platforms by sensing the degree of snowfall in real time has not yet been conducted. Therefore, in this study, we attempted to determine snowfall information for autonomous driving control in snowy weather conditions. To this end, snowfall data were acquired by LiDAR sensors in various snowy areas in South Korea, Sweden, and Denmark. Snow, which was extracted using a snow removal filter (the LIOR filter that we previously developed), was newly classified and defined based on the extracted number of snow particles, the actual snowfall total, and the weather forecast at the time. Finally, we developed an algorithm that extracts only snow in real time and then provides snowfall information to an autonomous driving system. This algorithm is expected to have a similar effect to that of actual controllers in promoting driving safety in real-time weather conditions.

The Prognostic Effect of Air Pollution in Patients Presenting to the Emergency Department with Carbon Monoxide Poisoning.

OBJECTIVE: Weather conditions such as low air temperatures, low barometric pressure, and low wind speed have been linked to more cases of carbon monoxide (CO) poisoning. However, limited literature exists regarding the impact of air pollution. This study aims to investigate the relationship between outdoor air pollution and CO poisoning in 2 distinct cities in Turkey. METHODS: A prospective study was conducted at 2 tertiary hospitals, recording demographic data, presenting complaints, vital signs, blood gas and laboratory parameters, carboxyhemoglobin (COHb) levels, meteorological parameters, and pollutant parameters. Complications and outcomes were also documented. RESULTS: The study included 83 patients (Group 1 = 44, Group 2 = 39). The air quality index (AQI) in Group 2 (61.7 ± 27.7) (moderate AQI) was statistically significantly higher (dirtier AQI) than that in Group 1 (47.3 ± 26.4) (good AQI) (P = 0.018). The AQI was identified as an independent predictor for forecasting the need for hospitalization (OR = 1.192, 95% CI: 1.036 - 1.372, P = 0.014) and predicting the risk of developing cardiac complications (OR: 1.060, 95% CI: 1.017 - 1.104, P = 0.005). CONCLUSIONS: The AQI, derived from the calculation of 6 primary air pollutants, can effectively predict the likelihood of hospitalization and cardiac involvement in patients presenting to the emergency department with CO poisoning.

Enriched traffic datasets for the city of Madrid: Integrating data from traffic sensors, the road infrastructure, calendar data and weather data.

The proliferation of urban areas and the concurrent increase in vehicular mobility have escalated the urgency for advanced traffic management solutions. This data article introduces two traffic datasets from Madrid, collected between June 2022 and February 2024, to address the challenges of traffic management in urban areas. The first dataset provides detailed traffic flow measurements (vehicles per hour) from urban sensors and road networks, enriched with weather data, calendar data and road infrastructure details from OpenStreetMap. This combination allows for an in-depth analysis of urban mobility. Through preprocessing, data quality is ensured by eliminating inconsistent sensor readings. The second dataset is enhanced for advanced predictive modelling. It includes time-based transformations and a tailored preprocessing pipeline that standardizes numeric data, applies one-hot encoding to categorical features, and uses ordinal encoding for specific features. In constructing the datasets, we initially employed the k-means algorithm to cluster data from multiple sensors, thereby highlighting the most representative ones. This clustering can be adapted or modified according to the user's needs, ensuring flexibility for various analyses and applications. This work underscores the importance of advanced datasets in urban planning and highlights the versatility of these resources for multiple practical applications. We highlight the relevance of the collected data for a variety of essential purposes, including traffic prediction, infrastructure planning, studies on the environmental impact of traffic, event planning, and conducting simulations. These datasets not only provide a solid foundation for academic research but also for designing and implementing more effective and sustainable traffic policies. Furthermore, all related datasets, source code, and documentation have been made publicly available, encouraging further research and practical applications in traffic management and urban planning.

Bacterial accumulation dynamics in runoff from extreme precipitation.

Extreme precipitation can significantly influence the water quality of surface waters. However, the total amount of bacteria carried by rainfall runoff is poorly understood. Here, thirty rainfall scenarios were simulated by artificial rainfall simulators, with designed rainfall intensity ranging from 19.3 to 250 mm/h. The instantaneous concentration ranges of R2A, nutrient agar (NA) culturable bacteria, and viable bacteria in runoff depended on the types of underlying surfaces. The instantaneous bacterial concentrations in runoff generated by forest lands, grasslands and bare soil were: R2A culturable bacteria = 104.5-6.3, 104.5-6.1, 104.0-5.3 colony-forming units (CFU)/mL, NA culturable bacteria = 104.0-6.0, 103.9-5.8, 103.2-4.9 CFU/mL, and viable bacteria = 106.4-8.0, 107.0-8.9, 106.4-7.6 cells/mL. Based on the measured bacterial instantaneous concentration in runoff, cumulative dynamic models were established, and the maximum amount of culturable bacteria and viable bacteria entering water sources were estimated to be 109.38-11.31 CFU/m2 and 1011.84-13.25 cells/m2, respectively. The model fitting and the bacterial accumulation dynamics were influenced by the rainfall types (p < 0.01). Surface runoff from the underlying surface of forest lands and grasslands had a high microbial risk that persisted even during the "Drought-to-Deluge Transition". Bacterial accumulation models provide valuable insight for predicting microbial risks in catchments during precipitation and can serve as theoretical support for further ensuring the safety of drinking water under the challenge of climate change.

Effects of Low-Pressure Systems on Temperature, Humidity, Egg Production, and Feed Utilization Efficiency in Large-Scale Poultry Houses during Summer.

Low-pressure systems (LPSs) are among the most critical weather systems, producing excessive precipitation that causes air temperatures to drop and rise considerably. Acute temperature changes directly affect poultry feed intake (FI) and laying performance. To explore the effects of LPSs on hens, the parameters of air temperature, relative humidity, egg production, and feed utilization efficiency were evaluated during different LPSs in three houses. Results indicated that about 2.8 ± 0.7 d, 2.4 ± 0.5 d, and 2.4 ± 0.5 d before the LPS landfall in houses 1, 2, and 3, respectively, the indoor air temperature started to decrease, with the average decreases being 1.7 °C ± 0.4 °C, 2.4 °C ± 0.6 °C, and 1.8 °C ± 0.4 °C, respectively. Significant differences were observed between different LPSs for reducing indoor air temperature (p < 0.05) in the three houses. In house 1, the egg production rates (EPRs) were decreased by 6.6% and 1.1% when LPSs 1 and 2 landed. The average egg weight (AEW) and FI during the LPS landfall were significantly higher than those before the LPS landfall (p < 0.01). Under successive LPSs landfall in the three houses, the EPRs initially reduced by 3.9%, 4.0%, and 0.5%, respectively, but the second LPS event increased the EPRs by 1.8%, 5.3%, and 1.0%, respectively. Furthermore, the LPS landfall increased the feed conversion ratio (FCRe) in the three houses, all above 2.00. In conclusion, LPSs can reduce heat stress, lower the EPRs, and lead to higher FI, FCRe, and AEW.

Association between compound extreme weather event types and the spectrum of emergency ambulance calls: A metropolitan study in Shenzhen.

BACKGROUND: Compound extreme weather events, a combination of weather and climate drivers that lead to potentially high-impact events, are becoming more frequent with climate change. The number of emergency ambulance calls (EACs) is expected to increase during compound extreme weather events. However, the extent of these increases and the trends over time have not been fully assessed. METHODS: We obtained 242,165 EAC records for Shenzhen from January 1, 2020, to June 30, 2023. A compound extreme weather event was defined as the occurrence of at least two extreme weather events on the same day. A distributed lag non-linear model was used to explore the exposure-response and lag-response relationships between various compound extreme weather events and all-cause and specific-cause EACs. FINDING: Compound Cold & Strong Monsoon events had more significant impacts on EACs for all causes and endocrine diseases, with the cumulative relative risk (CRR) of 1.401 (95% confidence interval (CI):1.290-1.522) and 1.641 (95% CI:1.279-2.105). Compound Heat Wave & Lightning events had more obvious impacts on digestive disease and endocrine disease EACs, with the CRRs of 1.185 (95% CI:1.041-1.348) and 1.278 (95% CI:0.954-1.711), respectively. Compound Rainstorm & Lightning & Heat Wave events also led to increased RRs of EACs for all causes (CRR: 1.168, 95% CI:1.012-1.348), cardiovascular diseases (CRR: 1.221, 95% CI:0.917-1.624), digestive diseases (CRR: 1.395, 95% CI:1.130-1.721), and endocrine diseases (CRR: 1.972, 95% CI:1.235-3.149). There was no increased RR in the compound Rainstorm & Lightning events for all types of EACs. INTERPRETATION: Our study explored the relationship between EACs and compound extreme weather events, suggesting that compound extreme weather events are associated with the acute onset of cardiovascular diseases, digestive diseases, and endocrine diseases, increasing the burden on emergency ambulance resources for both all causes and specific diseases mentioned above.

Seasonal Abundance and Diversity of Culicoides Biting Midges in Livestock Sheds in Kanchanaburi Province, Thailand.

Culicoides biting midges were collected using UV light traps from nine livestock farms in Kanchanaburi Province, Thailand. Collections were made one night per month from June 2020 to May 2021 to determine the seasonal changes and spatial distribution of the Culicoides assemblage. The influence of four environmental factors (temperature, rainfall, humidity, and wind speed), types of animals in each shed (cattle, pigs, and avians), and neighboring animals (those in the other sheds and their proximity) were assessed. A subsample of 130,670 out of a total of 224,153 specimens collected was identified and counted. The collections were predominantly female (76.9%), though males were also commonly collected (23.1%). The dominant species included C. peregrinus (97,098 individuals; 43.0%), C. oxystoma (55,579; 24.6%), C. arakawae (45,996; 20.4%), and C. imicola (15,703; 7.0%), while other species accounted for the remaining 9777 individuals (4.4%). Trap captures were strongly influenced by seasons and animal sheds. Cattle exhibited the greatest effect on the community, likely due to their large biomass. Humidity and temperature had a positive effect on trap captures, whereas wind speed exhibited a negative effect. Cattle positively influenced all major species, except for C. arakawae, which showed a positive association with avians. A "neighboring effect" was also observed. Additionally, the lowest dispersal ability of C. arakawae was suggested.

Core Competencies of the Public Health Workforce in Climate Change and Extreme Weather Events Preparedness, Response, and Recovery: A Scoping Review.

Climate change poses a significant threat to public health and safety, necessitating an urgent, coordinated response. Public health officials must be well-trained to effectively prepare for, respond to, and recover from extreme weather events. Despite emerging frameworks, a gap remains in their systematic application, risking future unpreparedness. This review aimed to identify the necessary competencies for public health professionals to manage climate change and the best methods to teach these skills. An academic librarian helped develop a keyword chain for a PubMed search, which included original articles and reviews concerning our research questions published in English or French between 1 January 2013 and 31 January 2024. Out of 255 potential articles, 31 were included in this scoping review. The results aligned with our objectives, revealing three main themes: core competencies, training and pedagogy strategies, and assessment approaches for public health professionals' preparedness, responses, and recovery in the context of climate change and extreme weather events. This scoping review enabled us to provide a set of clear recommendations for future research and practice in training the public health workforce for managing extreme weather events and climate change.

Enhancing work zone crash severity analysis: The role of synthetic minority oversampling technique in balancing minority categories.

Road work zones are becoming increasingly common due to the aging infrastructure and the need for capacity enhancement. They present significant safety risks due to narrow lanes, uneven traffic flow, lower speed, and reduced visibility. It is particularly important to understand the role of human behavioral factors in WZ crash injury severity due to difficulty navigating such areas. Furthermore, the crash injury data available is mostly imbalanced, primarily due to the lower incidence of high-cost fatal and severe injuries, and can benefit from the use of emerging analysis techniques. This research study examines a unique dataset comprising 7,855 WZ crashes in Tennessee from 2018 to 2022 as a case study to provide useful insight into the behavioral factors associated with injury severity and how they change after adjusting for the underrepresented fatal and serious injuries within the dataset. The study applies frequentist methods and a machine learning technique enhanced with the Synthetic Minority Oversampling Technique (SMOTE), addressing the data imbalance (relatively fewer fatal and serious injuries) for useful inferences and predictions. The study results indicate that aggressive driving, overspeeding, and drunk driving significantly elevate injury severity. Additionally, after balancing the minority categories of crash injury severity levels, the importance of contributing factors changes. The study offers engineers and data analysts a framework for analyzing imbalanced data, a prevalent issue in crash injury severity analysis. By exploring key behavioral factors responsible for injury severity in WZ crashes, the study provides useful insight and valuable information to traffic safety engineers, transportation agencies, and policymakers to implement enhanced safety measures in WZ design and management, ultimately aiming to mitigate injury severity and to improve overall safety for road users.

Strategic analysis of Iran's climate resilient health system.

INTRODUCTION: Climate change is a long-term systematic climate variability caused by human activities that alters the composition of the global atmosphere. Health systems should be adaptive and resilient to climate change. Hence, this research aimed to strategically analyze the resilience of Iran's health system to climate change. METHOD: This study utilized a multiple methods approach. First, in-depth semi-structured interviews were conducted with 32 key climate change and health experts to identify the strengths, weaknesses, opportunities and threats of the Iranian health system's resilience to climate change. Purposeful and snowball sampling techniques were used to ensure maximum diversity among the participants. Then, a questionnaire was developed based on the findings of the first stage and was completed by 33 climate change and health experts. Finally, the strategic position of Iran's health system's resilience to climate change was determined using the internal - external factors matrix. FINDINGS: A total of 84 internal factors and 101 external factors were identified that affect the resilience of Iran's health system against climate change. The internal factors were categorized into seven dimensions (i.e., governance and leadership; health financing; health workforce; facilities, equipment and medicines; health information system; health services delivery, and key results). The external factors were categorized into six dimensions (i.e., political, economic, social, technological, environmental, and legal factors). The average score of internal and external factors were 2.47 and 2.12, out of 4 respectively. Iran's health system was found to be in the strategic position of V in terms of resilience to climate change. Therefore, precautionary strategies such as strengthening the climate resilience of healthcare facilities, promoting healthcare facilities' adaptation to climate change, public-private partnership, strengthening the health service delivery system, quality management and cost management, should be implemented to strengthen the resilience of Iran's health system to climate change. DISCUSSION: Iran's health system is facing significant weaknesses and challenges that have hindered its resilience to climate change. Iran's health system can better prepare and respond to the health impacts of climate change, and safeguarding the health and well-being of its population by addressing these challenges and implementing adaptive and resilience strategies.

The Impact of Heat Exposure on the Health and Performance of Soccer Players: A Narrative Review and Bibliometric Analysis.

The impact of heat exposure on the health and performance of soccer players is a widely discussed topic. The purpose of this study is to provide a comprehensive overview of the international literature that has addressed this issue. To achieve this objective, we initially conducted a bibliometric analysis and a literature review of the main topics that emerged through bibliometric techniques. For the bibliometric analysis, we employed VOSviewer software (version 1.6.20.0) and used documents found in the Scopus database. The analysis ultimately included 133 documents published in 66 sources. Key journals and authors were identified, highlighting significant contributions to the field. Science mapping revealed collaboration networks and research focus areas such as physical health, safety, soccer performance, dehydration and hydration, physiological mechanisms and monitoring, nutrition, fluid intake, and cooling techniques. Based on the key areas highlighted in the identified clusters, which emerged from the co-occurrence analysis of the author keywords, the following three topics were developed in the literature review: (a) the physiology and health of football players; (b) performance impacts; and (c) strategies to prevent negative consequences. The review showed that high heat exposure can reduce the physical and cognitive performance of athletes and prove detrimental to their health. To mitigate the negative consequences, appropriate hydration strategies, heat acclimatization, and cooling techniques have been proposed. Our findings provide the international scientific community with comprehensive knowledge of the existing literature, laying the foundation for future research while simultaneously offering coaches and athletes the necessary theoretical knowledge to help improve safety and performance.

Citizen science: Development of a low-cost magnetometer system for a coordinated space weather monitoring.

As part of Ham Radio Science Citizen Investigation (HamSCI) Personal Space Weather Station (PSWS) project, a low-cost, commercial off-the-shelf magnetometer has been developed to provide quantitative and qualitative measurements of the geospace environment from the ground for both scientific and operational purposes at a cost that will allow for crowd-sourced data contributions. The PSWS magnetometers employ a magneto-inductive sensor technology to record three-axis magnetic field variations with a field resolution of ∼ 3 nT at a 1 Hz sample rate. The measurement range of the sensor is ± 1 . 1 × 1 0 6 nT) and is valid over a temperature range of -40 °C to +85 °C. Data from the PSWS network will combine these magnetometer measurements with high frequency (HF, 3-30 MHz) radio observations to monitor large-scale current systems and ionospheric disturbances due to drivers from both space and the atmosphere. A densely-spaced magnetometer array, once established, will demonstrate their space weather monitoring capability to an unprecedented spatial extent. Magnetic field data obtained by the magnetometers installed at various locations in the US are presented and compared with the existing magnetometers nearby, demonstrating that the performance is very adequate for scientific investigations.

Climatology of severe hail potential in Europe based on a convection-permitting simulation.

We present a new approach to identify severe hailstorms in a convection-permitting climate model, and build a climatology of severe hail potential in Europe using an ingredients-based approach based on a 20-year long hindcast simulation. Severe hail in Europe occurs mostly in southern regions (up to 40 times a year per 10,000 km2 around Northern Italy), and from May to August. It peaks from afternoon to evening hours on land, whilst sea areas are prone to hail at any time of the day. The Mediterranean Sea experiences severe hailstorms mostly in autumn: the central Mediterranean has the highest frequency among all regions studied, and may be considered as an unknown alley for hailstorms in Europe. Results derived from the high-resolution model are in very good agreement with existing hail climatologies constructed from observations, including the fine scale spatial variation. We conclude that our approach provides a reliable proxy for studying future changes in severe hail in convection-permitting simulations.

Comparison of hunting site strategies of the common buzzard Buteo buteo in open landscapes and along expressways.

Background: The expansion of human activities in their many forms increases the frequency, diversity, and scale of human-wildlife interactions. One such negative form is the expansion of road infrastructure, causing road kill and traffic-related noise as well as habitat loss and fragmentation. Even so, habitats around road infrastructure are attractive foraging areas that attract certain bird species. We assessed the impact of road infrastructure on the foraging strategies of the common buzzard Buteo buteo. Methods: Birds were observed during two winter seasons in two land-use types, along an expressway and an open agricultural landscape. Individual birds were tracked for a 10-min sequence as a separate sample was analysed. The material, covering 1,220 min along the expressway, and 1,100 min in the agricultural landscape, was collected. Results: Time spent by buzzards on medium-height sites was higher along the expressway than in farmland. Buzzards changed their hunting sites following the mean wind speed. Also, they more often changed their sites along the expressway than in farmland. The land-use types, snow cover, and the mean wind speed mediated the number of attacks on prey. These results illustrate the high plasticity of the buzzards' behaviour, which can adapt their hunting strategies to both foraging locations (expressway and farmland) and weather conditions. Roadsides along expressways are attractive foraging areas for this diurnal raptor, so reducing the risk of vehicle collisions with this and other birds of prey may require targeted planning efforts.

Seasonal variations of insect abundance: Correlating growth stage-specific metrics with weather patterns in Rangpur Region, Bangladesh.

The disclosure of insect occurrences correlating with meteorological data is pivotal in devising effective management strategies for rice fields. The study collected data on harmful and beneficial insects, along with meteorological variables, for three consecutive rice-growing seasons in 2021 and 2022. Month-wise insect distribution were delineated, with prominent species such as green leafhopper, brown planthopper, white-backed planthopper, rice whorl maggot, damselfly, parasitic wasps and green mirid bug observed consistently. Notably, most of the harmful insects were predominantly present during March-July and September-October. The spatial distribution, further segmented based on the rice-growing seasons, revealing the Aus season's preeminence in hosting harmful insects followed by Aman and Boro. The prevalence of most of the harmful insects were in tillering stage. Beneficial insects, displayed their dominance during specific months, growth stage, emphasizing their potential role in controlling harmful species and their prevalence were increased by increased of harmful insects. Furthermore, the study elucidated the correlation between climatic factors and insect abundance, emphasizing the role of temperature, rainfall and relative humidity. Temperature metrics and relative humidity manifested significant associations with several insects both harmful and beneficial species differently. While rainfall notably correlated with rice bug, short-horned grasshoppers, yellow stem borer, and non-significant association with other insects. The study underscored mutual dependencies between predator and prey species, emphasizing the ecological balance within the agricultural ecosystem. This comprehensive analysis indicated the need for integrated pest management strategies, considering both harmful and beneficial insect dynamics, to promote sustainable practices for optimizing rice production in changing climatic scenarios.

Climate Change Impacts on and Response Strategies for Kiwifruit Production: A Comprehensive Review.

Climate change, a pressing global concern, poses significant challenges to agricultural systems worldwide. Among the myriad impacts of climate change, the cultivation of kiwifruit trees (Actinidia spp.) faces multifaceted challenges. In this review, we delve into the intricate effects of climate change on kiwifruit production, which span phenological shifts, distributional changes, physiological responses, and ecological interactions. Understanding these complexities is crucial for devising effective adaptation and mitigation strategies to safeguard kiwifruit production amidst climate variability. This review scrutinizes the influence of rising global temperatures, altered precipitation patterns, and a heightened frequency of extreme weather events on the regions where kiwifruits are cultivated. Additionally, it delves into the ramifications of changing climatic conditions on kiwifruit tree physiology, phenology, and susceptibility to pests and diseases. The economic and social repercussions of climate change on kiwifruit production, including yield losses, livelihood impacts, and market dynamics, are thoroughly examined. In response to these challenges, this review proposes tailored adaptation and mitigation strategies for kiwifruit cultivation. This includes breeding climate-resilient kiwifruit cultivars of the Actinidia species that could withstand drought and high temperatures. Additional measures would involve implementing sustainable farming practices like irrigation, mulching, rain shelters, and shade management, as well as conserving soil and water resources. Through an examination of the literature, this review showcases the existing innovative approaches for climate change adaptation in kiwifruit farming. It concludes with recommendations for future research directions aimed at promoting the sustainability and resilience of fruit production, particularly in the context of kiwifruit cultivation, amid a changing climate.

Does the Change of Weather Influence Disease Activity in Rheumatoid Arthritis Patients: Patients' Self-Assessment via WebApp.

Objectives: The aim was to evaluate the influence of weather parameters on disease activity assessed by Routine Assessment of Patient Index Data (RAPID) scores via a Web-based smartphone application (WebApp). Methods: Correlation of changes of temperature (change of temperature, °C) and air pressure (change of air pressure, hPa) two days prior to and weekly self-assessment of disease activity by RAPID-3 scores over three months. To define background noise and quadrants of weather changes, we defined a central quadrant ± 2 hPa and ± 2° C, called E1. Based on this inner square, four quadrants were defined: A1 = sector left side above with increasing temperature and air pressure (improving weather); B1 = sector right side above; C1 = decreasing temperature and air pressure sector right side down (worsening weather); and D1 = sector left side down. Alterations of RAPID-3 scores analyzed changes in disease activity compared to RAPID-3 scores detected one week in advance. Results: Eighty patients were included in the analysis (median RA duration, 4.5 years; age, 57 years; 59% female). Median disease activity was 2.8 as assessed by DAS 28. In total, 210 time points were analyzed for quadrant A1, 164 for quadrant B1, 160 for quadrant C1, 196 for quadrant D1, and 145 for the inner square E1 were found during follow-up. The middle square E1 was balanced between increasing or decreasing values for RAPID scores. The odds for increasing RAPID scores were 1.33 (95% confidence interval CI: 1.0-1.78) for patients with ameliorating weather conditions which improve or alleviate unfavorable or adverse conditions (A1) compared to 0.98 (CI: 0.67-1.45) for worsening weather (C1) as defined by temperature and air pressure. Conclusions: On average, more patients developed a slight increase of disease activity if they were in the quadrant with increasing temperature and air pressure (improving weather). Thus, no correlation between the worsening of the weather and changing RAPID-3 scores was found.