Seasonal Trends Research Articles

Changes in clinical features and seasonal variations of Crohn’s disease at diagnosis: a 10-year observational study in China

Background and aimsThe clinical aspects of Crohn’s disease (CD) at diagnosis determine its therapy and management. The onset of CD follows a seasonal pattern. We aimed to analyze changes in the clinical features and seasonal variations of newly CD patients over the last decade.MethodsCD patients were divided into cohort 1 (2012–2016) and cohort 2 (2017–2021). The clinical characteristics were collected and the trends according to the year and season of diagnosis were analyzed.ResultsA total of 2038 patients were included. Cohort 1 had a considerably greater proportion of diarrhea, fever, hematochezia, weight loss and extraintestinal manifestations. The levels of platelet and C-reactive protein were higher in cohort 2 patients, but the opposite was true for albumin levels (p<0.05). The rate of increased eosinophils, increased gangliocyte and abundant lymphoplasmacytic infiltrate significantly decreased over the years. Patients with granulomas were diagnosed with CD at an earlier age (p = 0.006). Cohort 1 patients used more conventional drugs, while cohort 2 patients apply more biologics (p<0.05). The diagnosis occurred more frequently in summer and less frequently in winter. Patients diagnosed in winter had notably higher BMI, lower frequency of perianal disease and lowest incidence of asthenia and weight loss.ConclusionThe clinical phenotype, laboratory and pathological characteristics of CD has changed over time in China. The diagnosis of CD tends to have a seasonal trend with the highest incidence in summer. CD patients diagnosed in winter appear to have a milder form of the disease.

Environmental Drivers of the Divergence of Harveyi Clade Pathogens with Distinctive Virulence Gene Profiles

Fish and shellfish pathogens of the Harveyi clade of the Vibrio genus cause significant losses to aquaculture yields and profits, with some of them also causing infections in humans. The present study aimed to evaluate the presence of Harveyi clade fish and shellfish pathogens and their possible diversification in response to environmental drivers in southeastern USA waters. The presence and abundance of potential pathogens were evaluated via the detection and quantitation of six Harveyi-clade-specific virulence genes (toxR, luxR, srp, vhha, vhh, and vhp; VGs) in environmental DNA with clade-specific primers. The environmental DNA was obtained from water and sediments collected from three Georgia (USA) cultured clam and wild oyster grounds. In sediments, the VG concentrations were, on average, three orders of magnitude higher than those in water. The most and least frequently detected VGs were vhp and toxR, respectively. In water, the VGs split into two groups based on their seasonal trends. The first group, composed of luxR, vhp, vhha, and vhh, peaked in August and remained at lower concentrations throughout the duration of the study. The second group, composed of toxR and srp, peaked in June and disappeared between July and December. The first group revealed a high adaptation of their carriers to an increase in temperature, tolerance to a wide range of pH, and a positive correlation with salinity up to 25 ppt. The second group of VGs demonstrated a lower adaptation of their carriers to temperature and negative correlations with pH, salinity, potential water density, conductivity, and dissolved solids but a positive correlation with turbidity. No such trends were observed in sediments. These data reveal the role of VGs in the adaptability of the Harveyi clade pathogens to environmental parameters, causing their diversification and possibly their stratification into different ecological niches due to changes in water temperature, acidity, salinity, and turbidity. This diversification and stratification may lead to further speciation and the emergence of new pathogens of this clade. Our data urge further monitoring of the presence and diversification of Harveyi clade pathogens in a global warming scenario.

Flowering seasonality and airborne pollen recent trends in Sierra de las Nieves, the southernmost National Park in continental Spain

Sierra de las Nieves is the southernmost National Park in continental Spain and, in a global warming scenario, it is important to determine the impacts of climatic variations on the vegetation, with special relevance to their reproductive cycles. The flowering seasonality and intensity of the dominant anemophilous species usually reflect the response of the vegetation to climate variations, which can be monitored through the atmospheric pollen concentrations. In this study, airborne pollen was analysed for 6 years (2018–2023). A pollen calendar was elaborated to show the flowering seasonality and intensity of the dominant anemophilous taxa. Spring was the season with the highest airborne pollen diversity and intensity, Quercus being the pollen type with the highest concentrations. We observed that weather conditions influenced the presence of airborne pollen. Precipitation and relative humidity reduce pollen concentrations, while temperature rises increase them during the pre-peak period and reduce them during the post-peak. Wind dynamics play different roles depending on the pollen type considered given the heterogeneous distribution of the pollen emission sources. Significant advances were detected in the start date of the main pollen season of Quercus and Olea, as well as in the peak date of Quercus. On the contrary, other taxa such as Pinus or Poaceae did not show any trend. This brings to the fore that the responses to climatic variations may differ from one taxon to another, a continuous study in natural areas being required for detecting possible changes in the observed trends.

Seasonal trends and light extinction effects of PM2.5 chemical composition from 2021 to 2022 in a typical industrial city of central China

This study investigates the concentrations, chemical compositions, and sources of PM2.5 in Huangshi, China. Daily average PM2.5 levels ranged from 8.43 to 193.08 μg m−3, with an annual mean of 54.13 μg m−3, exceeding China's annual secondary standard of 35 μg m−3. Seasonal mean concentrations peaked in winter and were lowest in summer. Organic carbon (OC) and elemental carbon (EC) had annual means of 4.89 μg m−3 and 0.94 μg m−3, respectively. Water-soluble inorganic ions (WSIIs) accounted for 52.17% of PM2.5, with NO3−, SO42−, and NH4+ being the major components. The NO3−/SO42− ratio averaged 1.65, indicating a transition from coal combustion to vehicle emissions as the primary pollution source. Chemical mass reconstruction revealed that NH4NO3, (NH4)2SO4, and organic matter (OM) accounted for 65.3% of PM2.5 mass. Seasonal variations in light extinction (bext) highlighted the impact of secondary inorganic salts on visibility, with an annual average bext of 346.30 ± 246.98 Mm−1. Airmass clusters and potential source region analysis suggested PM2.5 and its components were primarily originated from local and nearby regions. These findings underscore the effectiveness of local pollution control measures, changing pollution sources, and the necessity for targeted emission controls to improve air quality and visibility in urban areas.

Unveiling Marine Heatwave Dynamics in the Persian /Arabian Gulf and the Gulf of Oman: A Spatio-Temporal Analysis and Future Projections

Amid escalating global marine heat waves, the Arabian Gulf is critical due to its shallow depths, high temperatures, and vulnerability to climate change impacts. This study provides a detailed spatio-temporal analysis of marine heatwave events from 1982 to 2022 across the Arabian Gulf and the adjacent Gulf of Oman. The study delineates regions of heightened vulnerability within these water bodies by comprehensively analyzing seasonal and annual variabilities and trend assessments. Moreover, an exploration of prevailing surface circulation patterns, underpinned by an extensive study of global circulation model outputs, elucidates the oceanographic mechanisms contributing to temperature dynamics. Identifying 25 significant heat wave events, with a focused examination of the six most protracted episodes, is noteworthy among the findings. Strikingly, the analysis reveals that the Gulf of Oman surpasses the Arabian Gulf in heatwave intensity. Looking forward, the investigation extends to future surface water temperature projections up to the close of the current century. The collective results underscore the region's acute susceptibility to the climate change perturbations of climate change, emphasizing the urgency of targeted interventions to mitigate these effects and address concurrent local stressors.

Occurrence, residency, and habitat characterization of leopard seals in Chile

Leopard seals have traditionally been considered Antarctic predators with a Southern Ocean distribution. Historically, sightings north of the Antarctic Polar Front were considered extralimital. However, recent studies suggest a significant presence of leopard seals in subantarctic regions. Here, we assess the spatial occurrence, residency status, and temporal trends of leopard seals in Chile using historical records, stranding reports, standardized monitoring data, photo-identification (photo ID) catalogs, and sightings from four research expeditions. We also characterize glaciers where sightings are concentrated, identifying glaciological and geomorphic attributes that prolong iceberg residency time, which is linked to high leopard seal concentrations. Based on these attributes, we evaluated other potential suitable glacial habitats in Patagonia. We obtained 438 sighting records of leopard seals from 1927 to 2023. Over the last 15 years, we documented a 4-18% annual increase in stranding events reported to national authorities. Most sightings (75%) were concentrated in two hotspots: National Park San Rafael Lagoon, located in Northern Patagonia, and Parry Fjord in Tierra del Fuego. Using photo ID catalogs, we identified 19 resident leopard seals, including 16 multi-year residents observed between 2010-2023 (10 in San Rafael, 6 in Tierra del Fuego) and 3 potential residents (observed multiple months in the same year in Tierra del Fuego). San Rafael monitoring data showed no inter-annual trend, but seasonal trends were observed. We also provide evidence of breeding in Chile, with records of at least 14 pups born and at least two females giving birth in multiple years. Our habitat characterization suggests that calving flux, fjord sinuosity, and fjord width variation are crucial for prolonging iceberg residency in hotspot areas. Based on these attributes, we identified 13 additional fjords in Patagonia as “very likely” suitable for leopard seals. Our study confirms that Patagonia is part of the species’ breeding distribution, shifting the paradigm that leopard seals are merely visitors north of the Antarctic Polar Front. Given the limited number of suitable glaciers in Chile and the potential impacts of climate change, our assessment highlights glacial retreat as a major threat for the ecosystem of this pagophilic marine apex predator in South America.

Trend Analysis of Rainfall and Temperature in the Kharun Watershed: Implications for Climate and Water Management

Rainfall and temperature are critical meteorological variables for understanding climate change and its potential impacts on water resources. Rainfall and Temperature are the most important for climatic parameters. The result of this study will help to take necessary steps and measures for agricultural production. This study focuses on analyzing rainfall and temperature trends in the Kharun watershed, Chhattisgarh, using the Mann-Kendall (MK) test and Sen's slope (SS) estimator. The analysis covers seasonal (summer, winter, and monsoon) and annual trends across key stations. Results for precipitation trends reveal mixed outcomes. At Dhamtari, a significant increasing trend in summer rainfall (z = 3.21, p < 0.01, Sen's slope = 0.77) was observed, though the annual trend remains insignificant. In Patharidih, summer rainfall also shows a significant upward trend (z = 2.04, p = 0.04), while Raipur exhibits a significant decline in both annual and summer precipitation (z = -1.04, p = 0.03, Sen's slope = -0.27). Temperature trends indicate no significant changes in maximum temperature across most stations, though slight positive trends are observed in summer and monsoon seasons. However, minimum temperature trends show a significant decline during winter at Dhamtari and Raipur (z = -2.12, Sen's slope = -0.04). These findings highlight localized seasonal variability in rainfall and temperature, underlining potential challenges for water resource management and climate adaptation in the region.

Long-term analysis of reference evapotranspiration variations in the lower Bhavani basin

Evapotranspiration is a crucial component of the hydrological cycle and is profoundly influenced by climate change. Assessing regional evapotranspiration changes is essential for effective water resource management. The study investigated the dynamics of reference evapotranspiration in the Lower Bhavani Basin, a region with diverse landscapes increasingly vulnerable to climatic variability. Reference evapotranspiration data from the Food and Agriculture Organization using the AgERA5 dataset was analyzed to assess annual and seasonal trends from 2000 to 2022. The result showed that the mean yearly reference evapotranspiration in the basin is 1672.87 mm, with a significant decline of 3.53 mm per year. Seasonal analysis revealed a consistent decreasing trend across all seasons, with the southwest monsoon season showing the most significant reduction in evapotranspiration rates. The notable shift in the annual evapotranspiration rate was observed in 2016, highlighting the impact of climate change. Further, temperature, solar radiation and relative humidity were identified as the dominant factors influencing evapotranspiration rates in the basin. The relative moisture index indicated prevalent dry conditions, making the region susceptible to water stress. The findings provide critical insights for regional water resources management and accentuate the need for sustainable water management strategies to mitigate the impacts of climate change.

Prevalence and genetic evolution analysis of porcine epidemic diarrhea virus and porcine circovirus type 2 in Sichuan Province, China, from 2023 to 2024

IntroductionPorcine circovirus type 2 (PCV2) and Porcine epidemic diarrhea virus (PEDV) are highly prevalent in Sichuan, significantly affecting the swine industry’s development. PCV2, known for its immunosuppressive effects, can compromise pigs’ immune systems, while PEDV typically causes diarrhea in piglets, leading to high mortality rates. Despite their impact, recent studies on the epidemiology and genetic diversity of PCV2 and PEDV within Sichuan Province remain limited.MethodsThis study examines clinical samples from 352 diarrheal piglets across 63 pig farms in 17 regions of Sichuan Province, revealing positivity rates of 42.33% (149/352) for PCV2 and 50.28% (177/352) for PEDV, with a co-infection rate of 27.56% (97/352). Notably, the highest positivity rates were observed in Ziyang for PCV2 at 61.90% (13/21), and in Meishan for PEDV at 73.81% (31/42), both regions also reported the highest co-infection rates of 47.62%.Results and discussionSeasonal analysis indicated that PEDV infections peaked during winter, whereas PCV2 showed no significant seasonal trends. Phylogenetic analysis identified 14 PCV2 strains, categorizing 2 as PCV2b (14.29%), 10 as PCV2d (71.43%), and 2 as PCV2e (14.29%). Among the 16 PEDV strains, 2 were classified as G1a (12.5%) and 14 as G2a (87.5%), with PCV2d and PEDV G2a identified as the predominant strains in the region. The study also highlights a high mutation rate at the antigenic sites of both viruses, potentially affecting vaccine efficacy. These findings underscore the need for ongoing surveillance and vaccine development tailored to the prevalent strains to improve control measures within the province.

Trends in Vegetation Seasonality in the Iberian Peninsula: Spatiotemporal Analysis Using AVHRR-NDVI Data (1982–2023)

Vegetation seasonality is a critical indicator of ecological responses to global climate change, especially in the Iberian Peninsula, where the intersection of human activity and climate variability amplifies these effects. Understanding these changes is vital for adopting ecogeographical sustainability and developing effective climate adaptation strategies. This study examines trends in vegetation seasonality in the Iberian Peninsula from 1982 to 2023, based on weekly AVHRR NDVI data (2184 images). By integrating Seasonal Trend Analysis (STA) with Robust Trend Analysis (RTA)—including the Theil–Sen (TS) slope estimator, the Contextual Mann–Kendall (CMK) test (α = 0.05), and false discovery rate (FDR) control—we identified significant phenological shifts and widespread vegetation greening. The results reveal a regional response to global patterns of climate change, with 94.2% of the study area exhibiting significant trends, particularly in the Mediterranean ecoregion, where earlier growing seasons are becoming increasingly common. These shifts highlight the urgent need for sustainable land and resource management in the face of accelerating global change. Our findings provide critical insights into the ecological dynamics of the Iberian Peninsula, offering a robust foundation for formulating policies that promote environmental sustainability and enhance resilience to climate change.

Full-scale wind turbine performance assessment using the turbine performance integral (TPI) method: a study of aerodynamic degradation and operational influences

Abstract. This study investigates how blade aerodynamic modifications, including leading edge roughness (LER), influence wind turbine performance over their operational lifespan. It introduces a methodology developed to examine the intricate relationship between blade erosion, blade enhancements, operations and maintenance (O&M) events, control programmable logic controller (PLC) parameter updates, and their cumulative impact on turbine efficiency. Analysing data from 12 multi-megawatt offshore turbines over a 12-year period, the investigation hinges on the integration of supervisory control and data acquisition (SCADA) data, O&M records, and air density corrections. A key contribution is the development of the turbine performance integral (TPI) method, which, for the investigated turbines, leverages generator speed and power output data to track performance trajectories. Seasonal trend decomposition using locally estimated scatterplot smoothing (STL) further isolates long-term trends and seasonal variations in performance. Despite data availability and quality limitations, the study reveals significant findings concerning the impact of manufacturer software updates on turbine control strategies, resulting in improved performance; the variable effects of blade repairs and enhancements; and the complex interaction between O&M events and performance. This work applies a methodical approach and statistical rigour, offering a path forward for effectively monitoring wind turbine efficiency and advancing renewable energy.

HiCorr: an influenza surveillance model using over-the-counter sales from community pharmacies

Abstract Background In large-scale community transmission, such as seasonal influenza, monitoring geographic trends and estimating the transmission intensity are critical to support public health decision-making. Previous studies have successfully used sales data from over-the-counter (OTC) products in community pharmacies to detect and monitor different epidemiological outbreaks, developing surveillance systems that are able to anticipate the load on primary care services and hospitals by up to 3 weeks. Methods The subset of OTC products sold in Portuguese community pharmacies was selected by correlating sales data to primary care attendance in previous flu seasons. This data pertained to daily coded episodes of influenza-like-illness (International Classification of Primary Care, 2nd Edition, code R80) in the Portuguese health cluster Oeste Sul. By fitting a moving epidemic method to historical sales data and then applying it to daily collected sales data, the developed index was used to anticipate the relative load to the primary care system as well as the start and the peak of epidemic activity for the current flu season. Results were compared to the public health data available on respiratory infections in Portugal. Results The model pinpointed the onset of the 2023-2024 flu epidemic in week 47 and its peak in week 52. The index curve showed a high correlation with the data published by the Portuguese national health service, with no time lag. However, the availability of consolidated public data was delayed, on average, by approximately two weeks. Discussion and conclusions Data from community pharmacies significantly enhance the early detection of seasonal flu trends, while reflecting population distribution. This underscores their invaluable contribution to public health assessments and the potential for improving the timeliness and accuracy of seasonal flu surveillance. Key messages • Community pharmacy data significantly enhance the early detection of seasonal flu trends. • Real world sales data maybe useful to support epidemic surveillance in public health.

Air pollution and cerebrovascular diseases - retrospective analysis of 10,486 cases of acute ischemic stroke requiring intravenous thrombolysis. A report from the EP-PARTICLES study

Abstract Introduction Air pollution (AP) is becoming an increasingly important non-classical CVD risk factor. Recent years have brought us the introduction of new types of AP, such as ‘Polish smog’, rich in particulate matter (PM) and benzo(alpha)pyrene (B(a)P) - thus far overlooked in scientific research. Acute ischemic stroke (AIS) is an increasing healthcare challenge, with the ageing population and associated presence of atrial fibrillation worldwide. Intravenous thrombolysis (IVT), alongside thrombectomy, is the most effective AIS treatment method. Purpose To analyze the impact of AP on AIS requiring IVT incidence. To our knowledge this is the first study of its kind worldwide. Methods The study covered 500 municipalities inhabited by 5.7 million people in Poland in 2011-2020. AIS requiring IVT incidence data [hospitalizations with ICD-10 diagnosis (I63.X) and concomitant ICD-9 procedure (99.101/99.102/99.103)] and AP concentrations (PM2.5 and B(a)P) were obtained from the National Health Fund and Inspectorate for Environmental Protection, respectively. IVT was administered within 4.5 hours from the onset of AIS symptoms, ensuring accuracy in the timing of exposure. Using the GEM-AQ model we calculated AP levels for all 500 municipalities. To analyze the effects of AP we used quasi-Poisson generalized additive models with covariates such as weather conditions, day of the week, bank holidays, and seasonal trends. Results are presented as relative risks (RRs) and 95% confidence intervals (95% CI) per interquartile range increase in AP on the day of exposure (LAG 0) and the following days (LAG 1, etc). Results In the study period, we recorded 96,122 AIS with a slight predominance of females (52.6%) and median age 76 years old (y.o) [IQR 66-83]; 10,486 (10.9%) were treated with IVT (median age 74 y.o. IQR 65-82), females 51.3%. Mortality rates were lower in patients treated with IVT than overall AIS patients (13.3% vs 15.4%, p<0.001). Exposure to B(a)P was associated with the highest increase in AIS requiring IVT incidence on LAG 0 and this effect persisted for up to 3 days after exposure [Figure 1]. In the overall population, an increase in PM2.5 concentration was associated with an increased risk of AIS requiring IVT on LAG 0, LAG 1, and LAG 3 [Figure 1]. AP increased the risk of AIS requiring IVT in both young and older individuals, as well as in women and men. We recorded no differences between the effects of analyzed air pollutants and AIS requiring IVT depending on age and sex [Figure 2]. Conclusions AP acts as a trigger for AIS requiring IVT with a similar impact regardless of sex and age. Patients undergoing IVT are characterized by better survival. Public health changes should consider efforts to decrease levels of air pollutants. Highly polluted areas should have centers with rapid access to AIS treatments and our study could help to identify high risk areas that require adaptations in local infrastructure.Figure 1.Figure 2.

How to promote health in indoor living and working spaces: quali-quantitative analysis to evaluate the indoor air quality through users’ perception and low-cost sensors

Abstract It is well-known that humans spend most of their time in indoor spaces: what it is sometimes underestimated; however, is how much the space in which someone is can affect their abilities, feelings and even their mental and physical health. A team developed a methodology to allow the collection of useful data and giving rise to management strategies for guaranteeing healthy spaces, in terms of Indoor Environmental Quality (IEQ). In particular, the methodology is based on a quali-quantitative analysis of Indoor Air Quality (IAQ) supported by a questionnaire on users’ perception and well-being, based on a scoping review, and low-cost sensors for IAQ monitoring air contaminants (carbon dioxide, temperature, relative humidity, volatile organic compounds, etc.). In particular, for the quantitative analysis, the team adopted a method that considers the location of the sensors in different typology of settings with various solar exposures, ventilation systems, human occupancy, type of activity, etc. with the scope of testing the trends in different seasons. As well as the qualitative data collect several information about the users’ perceived comfort and the symptoms (headache, fatigue, etc.) during their stay. The data analysis highlights how the different users’ perceptions affect the conditions of the environmental units, in particular in crowded rooms (4-6 people). Adequate lifestyles, such as door and window-openings, regular air-changes, etc. can highly affect the performances and the productivity, as well as the quality of the spaces. In that sense the use of low-cost sensors with light signals can be helpful as strategy for informing the user of the state of the air and any actions to be implemented. In conclusion, starting from the methodology, the team gives rise to a decalogue of strategies and good habits that permits -through the support of low-cost sensors - to inform the users on how to guarantee healthy living and working spaces.

Leveraging Machine Learning to Optimize Pricing and Restocking Strategies for Fresh Vegetables

In the competitive and diverse retail market, restocking and pricing perishable vegetables is challenging. Using big data and machine learning to analyze sales data, this research can build predictive models to refine these strategies and predict trends[1][2]. This research analyzed sales data from 2020 to 2023, identifying key trends in sales volumes, seasonality, and consumer preferences. It incorporated these findings into a multi-objective programming model that also considered wholesale and loss rates. This research used statistical tests like the Kruskal-Wallis and Mann-Whitney U to detect significant sales differences across vegetable categories, while the Spearman correlation showed strong seasonal and culinary impacts on consumer buying behaviors. To optimize freshness and maximize sales and profits, this research utilized genetic algorithms and regression analyses, including an effective XGBoost model. This model had a Mean Absolute Percentage Error of 16.97 and an R² of 0.94, aiding in predicting daily sales and improving pricing strategies and restocking schedules. These methods helped enhance decision-making, ensuring vegetable freshness and maximizing profitability, with projected profits reaching ¥41,582.87. This study not only highlights the effectiveness of integrating big data and machine learning in the retail sector but also sets a benchmark for operational efficiency in managing perishable goods.

A data-driven framework for the definition of heat in CV research for better comprehension of climate changes on human health

Abstract Background Recent literature has identified numerous combinations of meteorological indicators, methods and comparison thresholds to define heat in order to analyse its impact on adverse cardiovascular (CV) events. In fact, despite increasing attention to the issue due to ongoing climate change, there is no standardised definition of heat nor guidelines for its establishment, and in studies often little attention is given to the selected definition, despite its impact on the results. Purpose To fill this gap, our aim was to propose a data-driven framework for selecting the temperature threshold for defining heat in the context of studies on adverse CV outcomes, combining the most common approaches identified in the literature. Methods The following five-step framework is proposed: 1) Selection of temperature lags based on the contour plot obtained by the distributed lag non-linear model (DLNM); 2) Estimation of the relative risk (RR) of having a CV emergency in heat versus non-heat days by Poisson regression considering the 90th-99th percentile (%ile) of the temperature distribution for each lag; 3) Selection of the final lag based on the curvatures of the 10 included %iles; 4) Calculation of the % change in RR from %ile i to %ile i+1; 5) Selection of the heat threshold, defined as the %ile from which all the next %iles are associated with a higher % change, or, if not existing, the %ile preceding the one with the highest % change. We tested this framework, for each year, on retrospective CV emergencies in May-September 2017-2022 in Milan, Italy, using the apparent temperature data, accounting for air temperature, relative humidity and wind speed. Results The contour plot of the DLNM, controlled for air pollution, seasonality and long-term trend, showed that lags 0 and 1 were characterised by an increased RR for a CV emergency. The RR estimated for each %ile for each considered lag (i.e., heat on day i, heat on day i or on day i-1, and heat on day i and on day i-1), produced a similar curvature, so first lag option was arbitrarily selected. For Milan, the 95th %ile was the point from which all the subsequent %iles were associated to progressively higher % changes, resulting in a RR of having a CV emergency compared to non-heat days equal to 1.113 (95% CI: 1.087-1.139). The mean daily apparent temperature values associated to such %ile, varying from 27.7 °C to 29.9 °C depending on the year, should then be considered as the heat threshold in the examined context. Conclusion We proposed a flexible data-driven framework for the definition of heat that could easily be extended to similar scenarios. Although the framework still requires some arbitrary choices to be made, and the need of validation on other real-world data, it could provide a valuable guideline for relevant research, and could contribute to the standardization of heat definition for CV health research aiming at a better comprehension of climate changes on human health.

Seasonal trends in drug reaction with eosinophilia and systemic symptoms

Seasonal trends in drug reaction with eosinophilia and systemic symptoms

Forecasting oil futures markets using machine learning and seasonal trend decomposition

ABSTRACT Can machine learning improve prediction for seasonal commodity prices? We explore the effectiveness of a combined method that integrates seasonal trend decomposition using LOESS (STL) with machine learning (ML), referred to as STL-ML. We apply Extreme Gradient Boosting and Random Forest to forecast oil futures price dynamics over a sample period from 2004 to 2023. Our STL-ML results indicate no significant improvement for crude oil futures but enhanced accuracy for heating oil futures, highlighting STL’s benefit for datasets with strong seasonality. We demonstrate the potential for machine learning performance enhancement with STL, emphasising the variability of predictive model effectiveness due to data characteristics and providing insights for refining investment strategies based on seasonality and trends.

Forecasting Wholesale Electricity Market Prices Considering Bidding Conditions Using Price Sensitivity

This article proposes a sophisticated forecasting model to predict significant price volatility on the Japan Electric Power Exchange (JEPX) due to the growing influence of solar photovoltaics (PV) in the energy mix. As solar power generation continues to grow, it significantly impacts the daily and seasonal price trends in the market. This method employs a neural network that integrates daily prices, net electricity demand (total demand minus PV output), and underlying time‐series data to determine the objective variable, defined as the deviation from the average price over the past week. Incorporating price sensitivity data from JEPX increases the accuracy of the model. These data reflect how prices respond to market‐specified bid adjustments, including a variety of bidding, providing a nuanced understanding of market responses. The results demonstrate that adding multiple price sensitivities significantly improves the model's ability to detect price spikes and provides a robust tool for transmission and distribution system operators to manage risk and optimize their market strategies in an increasingly renewable energy‐dominated landscape. This approach not only addresses daily and seasonal price fluctuations but also aligns with broader sustainability goals as the share of solar PV generation continues to grow.

A Systematic Review of Features Forecasting Patient Arrival Numbers.

Adequate nurse staffing is crucial for quality healthcare, necessitating accurate predictions of patient arrival rates. These forecasts can be determined using supervised machine learning methods. Optimization of machine learning methods is largely about minimizing the prediction error. Existing models primarily utilize data such as historical patient visits, seasonal trends, holidays, and calendars. However, it is unclear what other features reduce the prediction error. Our systematic literature review identifies studies that use supervised machine learning to predict patient arrival numbers using nontemporal features, which are features not based on time or dates. We scrutinized 26 284 studies, eventually focusing on 27 relevant ones. These studies highlight three main feature groups: weather data, internet search and usage data, and data on (social) interaction of groups. Internet data and social interaction data appear particularly promising, with some studies reporting reduced errors by up to 33%. Although weather data are frequently used, its utility is less clear. Other potential data sources, including smartphone and social media data, remain largely unexplored. One reason for this might be potential data privacy challenges. In summary, although patient arrival prediction has become more important in recent years, there are still many questions and opportunities for future research on the features used in this area.