Search Results (66)

The northern areas of Pakistan are highly vulnerable to climate change due to anthropogenic activities and deforestation, which directly affect the precipitation pattern and variations in temperature. Due to these climate fluctuations, cloud bursts, extreme events like floods and droughts, and the melting of glaciers occur. This study presents the statistical trend analysis of the Dasu watershed in northern Pakistan using a non–parametric approach. In this study, the hydroclimatic data of 16 meteorological stations from 1980 to 2022 were used and the major focus was on an annual Rabi and Kharif seasonal trend analysis. Research revealed that the rate of precipitation increased from the east to west side of the study area in the annual Kharif season, while in the Rabi season, only four stations showed an increasing trend, and the remaining showed a decreasing trend, of which shendor2 showed a significantly decreasing trend with a rate of −3.91 mm/year. On the contrary, annual temperature was declining in the east side of the study area, while three stations showed significantly increasing temperature trends in the central region of the study area. Kharif season showed a decreasing trend in the major part of the study area, while Rabi season’s temperature revealed a significantly increasing trend in most of the stations and a decreasing trend in some eastern parts of the area. Overall, the majority of the study area revealed non–significant warming trends across the annual Rabi season, while the kharif season showed a decreasing trend. Precipitation trends remained largely non–significant but increasingly variable. The findings of this research can be utilized by research institutions and farmers to modify their cropping patterns and cropping calendar to optimize crop productivities. Full article

Objective: To assess the structure and regional variation in mortality among school-aged children in Kazakhstan from 2015 to 2024, and to determine its association with dietary patterns and socio-economic factors. Materials and Methods: An ecological inter-regional analysis was conducted using official statistical data of the Republic of Kazakhstan. Mortality rates among children aged 6–17 years, the distribution of death causes according to ICD-10, indicators of consumption of major food product groups, and poverty levels were examined. Linear mixed-effects regression with a random intercept for region and fixed effects for year and covariates, and spatial description of regional trends were applied. Results: Variation in school-age mortality across regions and calendar years was evident, with external causes predominating, followed by diseases of the nervous system, neoplasms, and diseases of the circulatory and respiratory systems in the mortality structure. In the multivariable linear mixed-effects model, none of the dietary or socioeconomic predictors showed statistically significant independent associations with mortality (all p > 0.05), and the calendar year was not significant (p = 0.180). Model explanatory power was very low (marginal R² = 0.017; conditional R² = 0.020; ICC = 0.005), and residuals demonstrated significant temporal autocorrelation (p < 0.001). Conclusions: The mortality structure among school-aged children is shaped by a complex interplay of medical, social, and behavioral determinants. Dietary and socioeconomic indicators showed only weak ecological associations with mortality and did not retain independent effects after multivariable adjustment, underscoring the multifactorial nature of regional mortality patterns and the need for multisectoral action, including improved access to nutritious foods, enhanced social well-being, and strengthened health system capacity. Full article

Inconsistencies in Earth’s spinning, changes in calendar systems, etc., necessitate time being represented correspondingly. Date–time handling in programming involves specific challenges, including conflicts between calendars, time zone discrepancies, daylight savings, and leap second adjustments—issues that other data types like numbers and text do not encounter. This article identifies these challenges and investigates existing approaches to date–time representation. Limitations in current systems, including how leap seconds, time zone variations, and inconsistent calendar representations complicate date–time handling, is examined. Inconsistent date–time representations imply significant challenges, especially when considering the interplay of leap seconds and time zone shifts. This study highlights the need for a new approach to date–time data types addressing these problems effectively. The article reviews existing date–time data types and explores their shortcomings, proposing a theoretical framework for a more robust solution. The study suggests that an improved date–time data type could enhance time resolution, support leap seconds, and offer greater flexibility in handling time zone shifts. Such a solution would provide a more reliable alternative to current systems. By addressing issues like leap second handling and time zone shifts, the proposed framework demonstrates the feasibility of a new date–time data type, with potential for broader adoption in future systems. Full article

Forecasting national energy demand is challenging under climate variability and macroeconomic uncertainty. We assess whether hybrid Prophet–(Q)LSTM models that integrate climate and economic predictors improve long-horizon forecasts for The Netherlands. This study covers 2010–2024 and uses data from ENTSO-E (hourly load), KNMI and Copernicus/ERA5 (weather and climate indices), Statistics Netherlands (CBS), and the World Bank (macroeconomic and commodity series). We evaluate Prophet–LSTM and Prophet–QLSTM, each with and without stacking via XGBoost, under rolling-origin cross-validation; feature choice is guided by Bayesian optimisation. Stacking provides the largest and most consistent accuracy gains across horizons. The quantum-inspired variant performs on par with the classical ensemble while using a smaller recurrent core, indicating value as a complementary learner. Substantively, short-run variation is dominated by weather and calendar effects, whereas selected commodity and activity indicators stabilise longer-range baselines; combining both domains improves robustness to regime shifts. In sustainability terms, improved long-horizon accuracy supports renewable integration, resource adequacy, and lower curtailment by strengthening seasonal planning and demand-response scheduling. The pipeline demonstrates the feasibility of integrating quantum-inspired components into national planning workflows, using The Netherlands as a case study, while acknowledging simulator constraints and compute costs. Full article

Accurate prediction of crop phenological stages is essential for effective crop management. Such a prediction provides the timing of phenological stages, thus aiding in scheduling management practices, understanding the potential risks of adverse weather at critical phenological stages, and adjusting sowing dates. Temperature is the dominant climatic factor affecting maize (Zea mays L.) development, with photoperiod serving as a secondary influence. This study used maize field data with recorded flowering and maturity dates to evaluate the stability of phenological stage predictions obtained using the calendar days method, thermal functions, and photothermal functions. These methods were used to calculate the number of days, accumulated temperature, and accumulated photothermal units from sowing to flowering and from flowering to maturity. Results showed that thermal functions produced the most stable predictions, with the lowest average coefficient of variation (CV) being 8.37%. The thermal functions were further categorized as empirical linear, empirical nonlinear, and process-based. Within each category, the functions with the lowest average CVs were growing degree days (GDD_8,34; 9.12%), thermal leaf unit (GTI; 7.74%), and agricultural production system simulator (APSIM; 8.26%), respectively. Among them, GTI had the lowest CV, indicating its superior stability in predicting maize phenological stages. These results provide a basis for selecting thermal models in maize phenology research and can support improved decision-making in crop scheduling and management. Full article

Background and Objective: The intricate connection between daily behaviours and health necessitates robust monitoring, particularly with the advent of Internet of Things (IoT) systems. This study introduces an innovative approach that exploits the synergy of information from various IoT sources to assess the alignment of behavioural routines with health guidelines. The goal is to improve the readability of behaviour models and provide actionable insights for healthcare professionals. Method: We integrate data from ambient sensors, smartphones, and wearable devices to acquire daily behavioural routines by employing process mining (PM) techniques to generate interpretable behaviour models. These routines are grouped according to compliance with health guidelines, and a clustering method is used to identify similarities in behaviours and key characteristics within each cluster. Results: Applied to an elderly care case study, our approach categorised days into three physical activity levels (Insufficient, Sufficient, Desirable) based on daily step thresholds. The integration of multi-source data revealed behavioural variations not detectable through single-source monitoring. We demonstrated that the proposed visualisations in calendar and timeline views aid health experts in understanding patient behaviours, enabling longitudinal monitoring and clearer interpretation of behavioural trends and precise interventions. Notably, the approach facilitates early detection of behaviour changes during contextual events (e.g., COVID-19 lockdown and Ramadan), which are available in our dataset. Conclusions: By enhancing interpretability and linking behaviour to health guidelines, this work signifies a promising path for behavioural analysis and discovering variations to empower smart healthcare, offering insights into patient health, personalised interventions, and healthier routines through continuous monitoring with IoT-driven data analysis. Full article

Evapotranspiration (ET) variation is typically influenced by climatic factors, which are considered the primary drivers of agricultural water requirements. Any changes in ET rates directly affect crop water demands. In this study, temporal trends and magnitudes of key climatic variables, and their impacts on reference evapotranspiration (ETo) during 1981–2020, were evaluated across 36 districts of Punjab, Pakistan. Positive serial correlations, ranging from 0.29 to 0.48, were identified and removed using the pre-whitening technique. Increasing trends in maximum temperature (Tmax) and wind speed (WS) across Punjab and its subregions were observed, while relative humidity (RH) exhibited both increasing and decreasing trends. No significant trends were detected for the minimum temperature (Tmin). On a monthly scale, in the Southern Punjab (SP) region, Sen’s slope estimated an increase in ETo, ranging from 0.239 mm/year in November to 0.636 mm/year in May, at a significance level of α = 0.05 (5%). At the provincial scale, significant upward trends in ETo were observed for the annual, Kharif, and autumn seasons, with Z-values of 2.04, 2.16, and 3.13, respectively, at α = 0.05 and 0.01. It was determined that, on an annual scale in Punjab, ETo sensitivity to climatic parameters followed the following order: Tmax > wind speed (WS) > Tmin > RH. The best-fitted models for Tmax, Tmin, WS, and RH were Gaussian, exponential, and spherical. ETo was found to increase spatially from North to South Punjab, with an approximate rise of 70–80 mm/decade. The results provide a scientific basis for understanding hydroclimatic drivers of ETo in semi-arid regions and contribute to improving climate impact assessments on agricultural water use. The observed ETo increases, particularly in South Punjab and lower Central Punjab, highlight the need for region-specific irrigation scheduling and water allocation. These findings can guide cropping calendars, improve irrigation efficiency, and increase canal water supplies to high-ETo areas, supporting adaptive strategies against climate variability in Punjab. Full article

Trihalomethanes (THMs) are a class of disinfectant by-products present in chlorinated tap water. Mainly due to their carcinogenic potential, their concentration in drinking water is now limited by regulations. In Romania, little is known about their distribution in urban drinking water supply systems, their magnitude, or their seasonal variation. Drinking water suppliers periodically adapt and optimise their water treatment methods for economic reasons and in response to regulatory changes and technological developments. The formation of THMs is influenced by the physicochemical parameters of water (pH, temperature, total organic carbon—TOC) and by environmental factors (geographical, climatological). Most of these factors have significant seasonal variations that lead to the formation of THMs in variable concentrations. In this study, we analysed the seasonal trends in surface water quality (considering variations in temperature, pH, and TOC) and correlated them with the concentration of THMs in drinking water over two calendar years. Water samples were collected from the Arges River, in a geographical area comprised of plains. The results show that the formation of THMs is enhanced by increasing temperature over the course of a year, with the highest concentrations being obtained in July 2022 (98.7 µg/L THMs at 30.5 °C) and in August 2023 (81.9 µg/L THMs at 30.4 °C). The main parameters that trigger the formation of THMs are the organic matter content and the disinfectant dose; the pH has a moderate effect, and its effect is correlated with the concentration of organic matter. There were noted strong seasonal changes in the concentration of THMs, with the maximum peak being in the middle and late summer and the minimum peak being in winter. This indicates the possibility that the quality of drinking water may change as a result of climate change. In addition, monitoring and chlorination experiments have established that the concentration of THMs is directly proportional with the TOC. Full article

Variations in ecological environments (including milk collection equipment and milk storage tanks in the pasture) and seasonal changes may contribute to raw milk contamination, thereby affecting food safety. The composition, structure, and relationships between raw milk and microbial communities in these environments are not well understood. In this study, 84 samples from spring and autumn in Luxian County, Yunnan Province, China, were collected for high-throughput sequencing technology. The results showed that the skin on the nipple surface and the environment (including the wiping samples of the automatic milking machine and the inner cover of the milk tank) had the greatest impact on microbial community composition in raw milk, followed by dung. In addition, microbial diversity in autumn samples was significantly higher, likely due to seasonal factors, including increased rainfall and reduced ultraviolet radiation. By analyzing the microbial community of raw milk and its environmental source, this study traced the origin of microorganisms in milk, providing insights for further exploration of the interaction between the pasture environment and raw milk microorganisms. Full article

Agriculture remains the backbone and major source of livelihood for men and women in most parts of sub-Saharan Africa. However, the gender-differentiated roles in agricultural transformation as a coping strategy to climate change in this context still beg for empirical substantiation. Using the Santchou Landscape of Cameroon as a case, this study sought to (a) examine the effects of climate change on agricultural practices, (b) characterize gender-differentiation in agro-based climate adaptation interventions, and (c) explore the gender-based challenges to agro-based climate adaptation planning. A representative sample of 159 households was conducted in five communities in the study area, complemented by key informant interviews (N = 5). The data collected were analyzed descriptively. The findings of this study revealed the following conclusions: Firstly, climate change significantly affects agricultural practices in the Santchou Landscape as mirrored in faming season fluctuation as well as the alteration of the farming calendar. Secondly, men and women play differentiated roles in agro-based climate adaptation, especially through farming practices such as the introduction of drought-resistant crops, the the practice of intercropping and agroforestry. Thirdly, gender-based challenges to agro-based climate adaptation include unequal access to land between men and women and unequal access to farm inputs, agricultural training, and technology. This study provides empirical evidence to substantiate the theoretical position on gender-differentiated roles in agro-based climate adaptation. Further studies are required to establish the incidence of gender variations in agro-based climate adaptation on livelihoods. Full article

National Forest Inventories (NFIs) offer a comprehensive and consistent dataset for forest analysis, enabling the refinement of growth and yield models by integrating regional environmental factors. This study investigates the influence of climate on the growth of three dominant tree species in Romania: Norway spruce (Picea abies L. Karst), European beech (Fagus sylvatica L.), and Sessile oak (Quercus petraea (Matt.) Liebl). Increment core analysis revealed a general increase in diameter growth since 1960, partially correlated with temperature trends. Repeated measures analysis confirmed significant variations in radial growth across ecoregions. The analysis further explored the impact of climatic variables on diameter at breast height (DBH) and basal area (BA) growth and yield. Among nine climatic attributes and their combinations, total precipitation and average growing season temperature significantly affected DBH and BA growth. However, yield was largely insensitive to precipitation, with only Sessile oak yield showing a temperature dependence. Beyond ecoregion and climate, the growth and yield of DBH and BA exhibited positive correlations with the calendar year, age, and previous growth/yield values. Notably, DBH and BA growth demonstrated a dependence on the preceding four to five years, whereas yield was significantly influenced only by the previous year. The observed influence of both the calendar year and previous years suggests a prolonged environmental memory in tree growth and yield responses. Full article

The marine ecological carrying capacity (MECC) of marine ranching serves as a crucial indicator for assessing the conservation effect of fishery resources and forms a significant basis for scientific management of coastal fisheries. The environmental impacts on the MECC of marine ranching in the northern South China Sea were analyzed quantitatively by employing Generalized Additive Models (GAMs), which have been successfully applied to the study of the relationship between fishery resources and environmental factors, and factor analysis, using satellite and survey observations. Results showed that 95.40% of the total variation in MECC was explained by these factors. Based on the GAMs, the most important factor was Year (calendar years), with a contribution of 66.20%, followed by Chlorophyll a concentration (Chl-a), Sea Surface Temperature (SST), Dissolved Inorganic Nitrogen (DIN) and Water Current, with contributions of 20.60%, 4.40%, 3.60%, and 0.60%, respectively. The findings of this study inspire us to establish a long-term marine ranching resource and environment monitoring platform, and an early warning and forecasting expert decision-making system, providing scientific references for planning and management of coastal marine ranching. Full article

Steam traps are essential for industrial systems, ensuring steam quality and energy efficiency by removing condensate and preventing steam leakage. However, their failure results in energy loss, operational disruptions, and increased greenhouse gas emissions. This paper proposes a novel predictive maintenance system for steam traps that integrates statistical time series features and transformer encoder–decoder models for fault diagnosis and visualization. The proposed system combines IoT sensor data, operational parameters, open data (e.g., weather information and public holiday calendars), machine learning, and two-dimensional diagnostic projection to improve reliability and interpretability. Experiments were conducted in two industrial plants: an aluminum processing plant and a food manufacturing plant, and the system achieved superior defect detection accuracy and diagnostic reliability compared to existing methods. The transformer-based model outperformed traditional methods, including random forest, gradient boosting, and variational autoencoder, in classification and clustering. The system also demonstrated an average 6.92% reduction in thermal energy across both sites, highlighting its potential to improve energy efficiency and reduce carbon emissions. This research highlights the transformative impact of AI-based predictive maintenance technologies in industrial operations and provides a framework for sustainable manufacturing practices. Full article

Land–atmosphere interactions (LSIs) involve intricate complex processes that drive critical exchanges of energy and matter that influence ecosystem and climate dynamics, with variations in ecosystem responses and feedback effects depending on their specific environmental characteristics. To this end, this study represents the first extensive validation of the Sim2DSphere model, to establish its coherence and use in the study of LSIs across a range of biomes and climatic zones. For this purpose, water and energy fluxes from a total of 10 eddy covariance sites and a total of 12 calendar days were analyzed. Earth observation (EO) data were integrated with ground observations at the different sites to execute the Sim2DSphere model. The diurnal dynamics of energy fluxes were compared against corresponding ground measurements. The results showed that the highest accuracy was observed for the grassland sites (R² from >0.85; RMSE < 68.50 Wm⁻²), whereas the lowest accuracy was found in forest sites (R² from >0.80; RMSE < 75.0 Wm⁻²). All in all, these initial results obtained herein are very promising and demonstrate the models’ promising potential in the study of LSIs at variant spatiotemporal resolutions. Full article

In recent years, extreme climate events have shown to be occurring more frequently. As a highly populated area in central China, the Jialing River Basin (JRB) should be more deeply explored for its patterns and associations with climatic factors. In this study, based on the daily precipitation and atmospheric temperature datasets from 29 meteorological stations in JRB and its vicinity from 1960 to 2020, 10 extreme indices (6 extreme precipitation indices and 4 extreme temperature indices) were calculated. The spatial and temporal variations of extreme precipitation and atmospheric temperature were analyzed using Mann–Kendall analysis, to explore the correlation between the atmospheric circulation patterns and extreme indices from linear and nonlinear perspectives via Pearson correlation analysis and wavelet coherence analysis (WTC), respectively. Results revealed that among the six selected extreme precipitation indices, the Continuous Dry Days (CDD) and Continuous Wetness Days (CWD) showed a decreasing trend, and the extreme precipitation tended to be shorter in calendar time, while the other four extreme precipitation indices showed an increasing trend, and the intensity of precipitation and rainfall in the JRB were frequent. As for the four extreme temperature indices, except for TN10p, which showed a significant decreasing trend, the other three indices showed a significant increasing trend, and the number of low-temperature days in JRB decreased significantly, the duration of high temperature increased, and the basin was warming continuously. Spatially, the spatial variation of extreme precipitation indices is more obvious, with decreasing stations mostly located in the western and northern regions, and increasing stations mostly located in the southern and northeastern regions, which makes the precipitation more regionalized. Linearly, most of the stations in the extreme atmospheric temperature index, except TN10p, show an increasing trend and the significance is more obvious. Except for the Southern Oscillation Index (SOI), other atmospheric circulation patterns have linear correlations with the extreme indices, and the Arctic Oscillation (AO) has the strongest significance with the CDD. Nonlinearly, NINO3.4, Pacific Decadal Oscillation (PDO), and SOI are not the main circulation patterns dominating the changes of TN90p, and average daily precipitation intensity (SDII), maximum daily precipitation amount (RX1day), and maximum precipitation in 5 days (Rx5day) were most clearly associated with atmospheric circulation patterns. This also confirms that atmospheric circulation patterns and climate tend not to have a single linear relationship, but are governed by more complex response mechanisms. This study aims to help the relevant decision-making authorities to cope with the more frequent extreme climate events in JRB, and also provides a reference for predicting flood, drought and waterlogging risks. Full article