Stephen Long pioneered a multidisciplinary approach to advance our knowledge of photosynthesis, by integrating research at the molecular, cellular, organismal, and ecological levels along with a pragmatic understanding of the implications for agriculture. This involved development of new mathematical models, patented equipment for analyzing photosynthetic efficiency, and the world’s largest open-air laboratory for understanding crop responses to atmospheric change. Long fundamentally changed our understanding of how crops respond to rising CO2 and ozone. He inspired and led efforts to engineer photosynthesis to increase yield and respond maximally to rising CO2. He discovered that C4 plants, that have highly efficient photosynthesis due to a CO2 concentrating mechanism, could thrive in cold climates, which was a true paradigm shift as previously C4 photosynthesis had been considered limited to tropical and subtropical climates. He provided a theoretical explanation, and his research led to the discovery that Miscanthus x giganteus could achieve the high yields in cool northern climate achieved by other C4 plants in the tropics. This seminal finding led Long to experiment on the closely related and much more widely used maize plant, and he showed how it could be adapted to cooler conditions and achieve a significant yield jump in the Corn Belt. At a time when society critically needed new ways to achieve increases in productivity in ecologically sustainable ways, Long’s research and leadership changed the way we think about choices of plants and cropping systems.

Previous studies on kafirins in sorghum (Sorghum bicolor Moench) have focused mainly on grain and sprouts grown under tropical and subtropical climate conditions, while data on the content and fractional composition of kafirins in sorghum sprouts cultivated in temperate climates are scarce. In particular, the influence of the northern growing conditions, characteristic of Central Europe, on sorghum storage proteins has not yet been described, despite the fact that sorghum is currently cultivated in Poland. This study aimed to determine the total kafirin content and the distribution of α-, β-, and γ-kafirin fractions in sprouts of white and red sorghum grown under temperate climate conditions in Poland. Six-day-old sprouts were freeze-dried and extracted using a Tris-HCl/SDS/β-mercaptoethanol buffer. Kafirin content was quantified using the Bradford assay, SDS-PAGE, and HPLC, with method validation performed for accuracy, precision, and linearity. Total kafirin content ranged from 5.5 to 7.0 g/100 g dry matter (DM), with α-kafirin as the predominant fraction (4.2–5.0 g/100 g DM), followed by β-kafirin (0.5–1.0 g/100 g DM) and γ-kafirin (0.2–0.6 g/100 g DM). Sprouts of red sorghum varieties showed significantly higher total kafirin levels and a greater proportion of the γ-fraction, which may be associated with differences in protein structural properties and could suggest potential bioactivity, as indicated by previous literature. However, no direct functional or bioactivity assays were performed in this study. Statistical analysis revealed significant differences among selected sorghum varieties in total kafirin content and the proportion of the γ fraction (p < 0.05), with α being the dominant fraction in all tested samples. These results provide, for the first time, detailed data on the kafirin composition of sorghum sprouts grown in a temperate climate and address a key gap in the literature concerning the effect of environmental conditions on sorghum storage proteins. The findings support further research on the use of sorghum sprouts as a raw material for functional foods, protein-enriched products, and animal feed under European growing conditions.

Introduction:Preeclampsia (PE) is the second most common cause of maternal death worldwide. Geographic, sociodemographic, racial, and economic factors have all been proposed as contributors to the rate variations of PE. Among them, seasonal factors (temperature and humidity) may influence PE. Climate change has been connected to the global pattern of PE. The study was conducted to find out the hospital prevalence of SPE in different seasons in a tertiary hospital in the Southeastern part of Bangladesh and its influence on feto-maternal outcomes. Methods:This cross-sectional study was performed in the Department of Obstetrics and Gynecology, Chittagong Medical College Hospital (CMCH) from February 2021 to January 2022. The study year was divided into four seasons: Summer (March to May), Monsoon (June to August), Autumn (September to November), and Winter (December to February) according to the seasons of Bangladesh. The prevalence of SPE and meteorological differences in the four seasons and over the English calendar month was compared. Results:In the past year, 19183 obstetrics patients were hospitalized, 14661 births were documented, and 8,908 CS occurred. Pregnancy-related hypertension was 2150, PE was 1597 (8.33% of all hospitalizations), and SPE was 1315 (6.80% hospital prevalence). Eclampsia was 552 Winter has the most SPE (8.01%) and Autumn the least (5.97%). Winter SPE risk was much higher than Autumn (OR + 1.37, 95% CI: 1.18-1.59). Summer had 22.44% delivery rates, whereas Autumn had 53%. SPE-related CS was lowest in Monsoon (9.78%) and highest in Winter (13.54%). SPE-related CS was 1.38 times higher in winter using the Monsoon as the reference season. Monthly SPE prevalence was 5.23%–9.28%. Average monthly temperature was adversely connected with S. PE admission (r= -0.71; P=0.01). Of 1,315 SPE admissions, 27 women died (2.05%). Others were released alive. There were 64 maternal deaths during the research. SPE killed 42.19% of mothers in four seasons. SPE caused 33 stillbirths (2.92%) out of 1132 deliveries. SPE accounted for 12.09% of 273 SB from all sources in four seasons. Conclusion:This study supports the concept of seasonal influence on the admittance of preeclampsia patients. In the tropical climate, the incidence appeared to be higher in the Winter, with peaks at inter-seasonal periods, when the weather is cooler than the rest of the year. So, a lower temperature is linked to severe Preeclampsia. Understanding the relationship of SPE with Bangladesh's different seasons will help identify the triggering factors of PE and eclampsia(EC). J Bangladesh Coll Phys Surg 2026; 44: 40-46

Weather-adaptive rule-based mixed-mode ventilation in a tropical climate: simulation and real-world validation

Solar energy analysis for agrivoltaic system design in tropical climates: A new integrated modeling framework

Former mining activities in the Bétaré-Oya region of eastern Cameroon have generated long-lasting environmental impacts due to the accumulation of unrehabilitated mine residues. This study provides an integrated assessment of the mineralogical, geochemical, spectroscopic, and microtextural characteristics of mine tailings, contaminated soils, and downstream sediments to evaluate their contamination potential. X-ray Diffraction (XRD) analyses show that tailings are dominated by quartz (up to 55%), kaolinite, muscovite, and metallic sulfides including chalcopyrite, arsenopyrite, and galena, while soils and sediments exhibit more heterogeneous silicate-clay assemblages. Fourier-Transform Infrared Spectroscopy (FTIR) identifies strong absorption bands associated with carbonates (1430-875cm-1), sulfates (1120-980cm-1), and clay-related hydroxyl groups. Geochemically, total carbon (C) ranges from 1.0 to 6.9% in tailings and 1.2-6.4% in soils, whereas sulfur (S) reaches up to 6.5% in some tailings and sediments. Calcium carbonate (CaCO3) is highly variable, with maximum values of 16% in tailings, reflecting processing residues. Major oxides indicate strong iron enrichment in tailings (Fe2O3 up to 13.4wt%), coupled with elevated Al2O3 (up to 35.2wt%) and SiO2 variability (25-60wt%). Silver (Ag) displays anomalous enrichment, reaching up to 8g/t in tailings, 5g/t in soils, and 11g/t in sediments, exceeding typical natural background levels (< 0.1g/t). Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (SEM-EDX) reveals porous and fractured microtextures, dissolution fronts, and micron-scale hotspots of Pb, Zn, Ag, and As within altered sulfides and secondary Fe-oxides. Collectively, these mineralogical and geochemical signatures indicate a high potential for contaminant release and downstream transfer, particularly during intense tropical weathering and seasonal flooding. The findings underscore the urgent need for site rehabilitation, improved tailings management, and sustained environmental monitoring to mitigate long-term risks to local ecosystems and agricultural zones.

Introduction. Heat stress in dairy cattle is a growing problem, especially in tropical climates like in Costa Rica. This phenomenon negatively impacts milk production, animal health, and reproductive efficiency. Therefore, it is crucial to implement mitigation strategies that improve cattle welfare and farm profitability. Objective. To evaluate the effects of an evaporative cooling system (ECS) on the welfare, productivity, and reproductive performance of Jersey cows. Materials and methods. Over a three-month period during the dry season, the herd was divided into two homogeneous blocks: one exposed to the ECS and the other serving as a control. Variables such as temperature, humidity, surface body temperature, milk yield and composition, reproductive performance, and somatic cell count were measured. Additionally, a financial analysis was conducted to assess the economic feasibility of the system. Results. The results showed a significant reduction in the Temperature-Humidity Index (THI) in the area under the ECS, with an average decrease of 2.91 points, and a drop of up to 3.97 °C in surface body temperature of exposed cows. However, no significant improvements were observed in milk production or reproductive parameters. Somatic cell counts also did not increase. From a financial perspective, the investment was not profitable under the current conditions. Conclusion. The evaporative cooling system significantly reduced the surface temperature of cows without negatively affecting udder health, but it did not improve milk production or reproductive indicators under the environmental conditions of the study.

Heat stress in livestock under tropical climates: impacts and mitigation strategies.

Energy efficiency and thermal comfort in public buildings are critical considerations, particularly in tropical climates like Nigeria's, where high solar radiation significantly impacts indoor temperatures and energy consumption. Shading devices, as passive design strategies, offer a sustainable solution by mitigating solar heat gain while enhancing occupant comfort and reducing cooling energy demands. This study evaluates shading devices tailored to Nigeria's unique public building needs, focusing on their environmental, economic, and architectural performance. The research explores three primary categories of shading devices: fixed systems (e.g., overhangs, fins, louvres), adjustable systems (e.g., operable blinds, retractable awnings), and natural systems (e.g., landscaping, green walls). By reviewing existing literature and analysing case studies from various Nigerian public buildings, the study identifies the most effective strategies tailored to local climatic conditions, cultural contexts, and construction practices. The research employed a mixed-method approach, integrating qualitative insights from case studies and quantitative data analysis. Findings reveal that fixed shading devices, when appropriately designed, are cost-effective and durable, making them widely used in institutional settings. Adjustable systems offer superior flexibility and efficiency but are often limited by higher costs and maintenance challenges. Natural shading solutions provide aesthetic and environmental benefits but require careful integration with architectural design to maximise their potential. The study highlights the importance of early-stage integration of shading strategies into building designs to achieve optimal results. It also emphasises the need for policy incentives, awareness campaigns, and training programs to encourage the adoption of sustainable shading technologies in Nigeria’s public buildings. Ultimately, this research contributes to the broader discourse on sustainable architecture, advocating for shading devices as pivotal elements in achieving energy-efficient and climate-resilient public infrastructure.

Conventional deep learning models struggle with balancing feature extraction and long-term temporal representation in Short-Term Load Forecasting (STLF). This study proposes a Convolutional Neural Network-Embedded Deep Residual Network (CNN-Embedded DRN) designed to enhance early-stage feature extraction and generalization capability across diverse climatic conditions. The objectives of this study are to integrate Convolutional Neural Network (CNN)-based local feature extraction into the DRN framework for capturing fine-grained temporal and spatial load patterns, to employ residual learning for mitigating gradient degradation and improving network stability, to evaluate the model's predictive performance against baseline and ablation models across two datasets representing temperate (ISO-NE) and tropical (Malaysia) climates, and to validate its statistical significance and seasonal robustness through bootstrap analysis and multi-seasonal evaluation. The results demonstrate that the proposed CNN-Embedded DRN consistently outperforms all comparative models, achieving the lowest Mean Absolute Percentage Error (MAPE) values of 1.5303% and 5.0566% on the ISO-NE and Malaysia datasets, respectively. The inclusion of residual network (ResNet) and CNN-Embedded ResNet as ablation experiments confirms that CNN-based local feature extraction effectively complements residual learning, while bootstrap analysis verifies that the observed improvements are statistically significant. The proposed model provides a reliable and generalizable framework for STLF, offering improved accuracy, robustness, and adaptability under varying climatic and demand conditions. Future research will focus on extending this framework toward multi-regional and multi-scale forecasting, incorporating attention mechanisms for enhanced long-term dependency modeling, and exploring adaptive hybrid residual architectures for real-time energy management applications.

Increasing vehicle operating speeds place greater thermal and mechanical demands on automotive tires, making the assessment of tire behavior under varying speed conditions essential for safety and durability. This study investigates the effects of speed, load, and inflation pressure on the temperature distribution and durability of the Bridgestone ECOPIA EP150 tire using numerical simulation in Ansys Workbench. The results indicate that the shoulder region exhibits the highest temperature, which rises with increasing vehicle speed. Inflation pressure and vertical load significantly influence the contact area and stress distribution. An inflation pressure of 34 psi is identified as optimal, limiting localized heat generation and maintaining tire durability under realistic operating conditions. The findings provide practical guidance for tire selection and usage, particularly in tropical climates, and support improved safety and operational efficiency. Furthermore, the simulation-based approach demonstrates the effectiveness of numerical analysis as a predictive tool for evaluating tire performance under complex operating conditions, reducing reliance on extensive experimental testing.

The construction industry is a major contributor to global carbon emissions, largely due to the use of conventional materials with high thermal conductivity. This highlights the need for environmentally sustainable insulation materials aligned with circular economy principles. As the world’s fourth-largest banana producer, Indonesia generates vast amounts of banana peel waste that can be repurposed as an eco-friendly thermal insulation material.This study evaluates the thermal performance of banana peel-based biocomposite (BP80-PS20) as an insulation material for tropical buildings. The research employed a systematic literature review and numerical simulations using DesignBuilder software on a simplified model of a tropical house. Key parameters analyzed include thermal conductivity, U-value, and R-value, benchmarked against Indonesian (SNI 6389:2011) and international (ASHRAE 90.1-2019) standards. Simulation results indicate that BP80–PS20 significantly improves thermal resistance, reducing wall U-values from 2.97 to 0.68 W/m²K and partition U-values from 1.64 to 0.30 W/m²K, corresponding to an increase of over 300% in R-value. The insulated building maintained indoor temperatures between 24.5°C and 27.2°C, which fully falls within the SNI and ASHRAE comfort zones. These findings confirm that banana peel waste can serve as a sustainable thermal insulation material, enhancing building energy efficiency and promoting green construction practices in tropical climates.

Solar-integrated desiccant-based air conditioning systems offer an environmentally sound alternative to conventional vapor compression systems by leveraging solar thermal energy for regeneration. This study quantitatively evaluates the potential electrical energy savings of a liquid desiccant system compared to a standard vapor compression system via simulation, focusing on an office building archetype across six major Colombian cities representing diverse climates. The results consistently demonstrate that the desiccant-based system exhibits superior electrical performance in all analyzed locations, achieving substantial electrical power savings ranging from 52.2 to 60.9%. While a key technical challenge is the large solar collector area required (ranging from 158.3 to 205 m2), a sensitivity analysis provides a critical optimization strategy: increasing the concentration of the CaCl2 solution significantly reduces the collector area by lowering the required regeneration heat. Consequently, operating the system near saturation (≈50% weight) is highly advantageous for practical implementation. This research confirms the high energy efficiency potential of solar-assisted desiccant cooling in tropical climates and offers a pathway for reducing the associated installation footprint.

An Approach for the Integration of Sustainable Strategies from Hausa Traditional Architecture on Contemporary Building Designs in Urban Built Environments in Tropical Climate: A Discourse

Satellite communication systems experience significant signal degradation during rain events, a phenomenon that can be leveraged for meteorological applications. This study introduces a novel hybrid machine learning framework combining unsupervised clustering with cluster-specific supervised deep learning models to transform satellite signal attenuation into a predictive tool for rainfall prediction. Unlike conventional single-model approaches treating all atmospheric conditions uniformly, our methodology employs K-Means Clustering with the Elbow Method to identify four distinct atmospheric regimes based on Signal-to-Noise Ratio (SNR) patterns from a 12-m Ku-band satellite ground station at King Mongkut’s Institute of Technology Ladkrabang (KMITL), Bangkok, Thailand, combined with absolute pressure and hourly rainfall measurements. The dataset comprises 98,483 observations collected with 30-s temporal resolutions, providing comprehensive coverage of diverse tropical atmospheric conditions. The experimental platform integrates three subsystems: a receiver chain featuring a Low-Noise Block (LNB) converter and Software-Defined Radio (SDR) platform for real-time data acquisition; a control system with two-axis motorized pointing incorporating dual-encoder feedback; and a preprocessing workflow implementing data cleaning, K-Means Clustering (k = 4), Synthetic Minority Over-Sampling Technique (SMOTE) for balanced representation, and standardization. Specialized Long Short-Term Memory (LSTM) networks trained for each identified cluster enable capture of regime-specific temporal dynamics. Experimental validation demonstrates substantial performance improvements, with cluster-specific LSTM models achieving R2 values exceeding 0.92 across all atmospheric regimes. Comparative analysis confirms LSTM superiority over RNN and GRU. Classification performance evaluation reveals exceptional detection capabilities with Probability of Detection ranging from 0.75 to 0.99 and False Alarm Ratios below 0.23. This work presents a scalable approach to weather radar systems for tropical regions with limited ground-based infrastructure, particularly during rapid meteorological transitions characteristic of tropical climates.

Peatlands play a critical role in global and regional climate regulation by functioning as long-term carbon sinks, regulating hydrology, and modulating land–atmosphere energy exchange. Intact peat ecosystems store large amounts of organic carbon and stabilize local climate through high water retention and evapotranspiration, whereas peatland degradation disrupts these functions and can transform peatlands into significant sources of greenhouse gas emissions and climate extremes such as drought and fire. Indonesia contains approximately 13.6–40.5 Gt of carbon, around 40% of which is stored on the island of Sumatra. However, tropical peatlands in this region are highly vulnerable to climate anomalies and land-use change. This study investigates the impacts of major climate anomalies—specifically El Niño and positive Indian Ocean Dipole (pIOD) events in 1997/1998, 2015/2016, and 2019—on peatland cover change across South Sumatra, Jambi, Riau, and the Riau Islands. Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager/Thermal Infrared Sensor imagery were analyzed using a Random Forest machine learning classification approach. Climate anomaly periods were identified using El Niño-Southern Oscillation (ENSO) and IOD indices from the National Oceanic and Atmospheric Administration. To enhance classification accuracy and detect vegetation and hydrological stress, spectral indices including the Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index (MSAVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI) were integrated. The results show classification accuracies of 89–92%, with kappa values of 0.85–0.90. The 2015/2016 El Niño caused the most severe peatland degradation (&gt;51%), followed by the 1997/1998 El Niño (23–38%), while impacts from the 2019 pIOD were comparatively limited. These findings emphasize the importance of peatlands in climate regulation and highlight the need for climate-informed monitoring and management strategies to mitigate peatland degradation and associated climate risks.

HighlightsWhat are the main findings?Rapid 6 h hydrothermal synthesis of VO2(M)pH ≈ 8.5 enables phase stabilizationW-doping lowers Tc by ~17 °C per wt.%Crystallites below 35 nm without annealingPowders suitable for dispersion processingWhat are the implications of the main findings?Enables low-cost thermochromic coatingsScalable route for smart-window materialsFacilitates integration into polymer filmsThermochromic smart windows are a promising technology to reduce energy consumption in buildings, particularly in tropical regions where cooling demands are high. Vanadium dioxide (VO2) is the most studied thermochromic material due to its reversible semiconductor-to-metal transition near 68 °C. Conventional synthesis routes require long reaction times and post-annealing steps. In this work, we report a rapid hydrothermal synthesis of monoclinic VO2(M) and tungsten-doped VO2(M) powders obtained within only 6 h at 270 °C, using vanadyl sulfate as precursor and controlled precipitation at pH ≈ 8.5. Differential scanning calorimetry confirmed the reversible transition at 59 °C for the undoped VO2, with a hysteresis of 18 °C, while tungsten doping reduced the transition temperature by ~17 °C per wt.% of W. X-ray diffraction verified the monoclinic phase with minor traces of VO2(B), a non-thermochromic polymorph of VO2, and microstructural analysis revealed crystallite sizes below 35 nm. Electron microscopy and dynamic light scattering confirmed particle sizes suitable for dispersion in polymeric matrices. This approach significantly reduces synthesis time compared to typical hydrothermal methods requiring 20–48 h and avoids further annealing. The resulting powders provide a low-cost and scalable route for fabricating thermochromic coatings with transition temperatures closer to ambient conditions, making them relevant for smart-window applications in tropical climates, where lower transition temperatures are generally regarded as beneficial.

This study aims to improve our understanding of how ageing affects the performance of asphalt mixtures used in pavements, focusing on two commonly applied binders: conventional Petroleum Asphalt Cement (PAC 50/70) and Polymer Modified Asphalt (PMA 55/75). Considering the tropical climate in Brazil, where temperature and moisture both play a crucial role in pavement durability, four ageing conditions were simulated: no ageing, short-term thermal ageing, long-term thermal ageing, and long-term thermal ageing combined with moisture exposure. The results indicate that ageing increases binder stiffness and improves resistance to permanent deformation, especially in polymer-modified mixtures. However, exposure to moisture reduces this resistance, primarily affecting mixtures with conventional binders as a result of adhesive failure. Statistical analysis confirms that there are significant differences in susceptibility to ageing between the two binder types. Overall, the polymer modification enhances the mixture is resilience against combined thermal and moisture ageing. These findings highlight the importance of incorporating realistic ageing scenarios and including moisture effects in laboratory evaluations to better predict the performance of pavements in tropical regions.

Abstract Climate change is expected to have wide-ranging effects on human health, yet the extent to which environmental factors drive health outcomes is poorly understood, particularly in tropical locations. Here, we leverage a large dataset of approximately 30 million individual-level hospitalizations from Mexico, linked with locally resolved climate data, to understand the seasonality of morbidity and the role of climate in driving these patterns. We first apply a Fourier transform to identify disease categories that exhibit significant seasonal signals. Next, we apply fixed effect regression models to identify climate drivers of these seasonal patterns for both broad disease categories specified by the International Classification of Diseases (ICD) and a comprehensive range of specific disease subcategories defined by the World Health Organization (WHO). We found that half of the ICD disease category hospitalizations had a significant seasonal signal. Among these, 89% exhibited a significant positive association with temperature, 33% exhibited a significant positive association with precipitation, and 11% exhibited a significant negative association with precipitation. Overall, we found that temperature is a significant driver of 37% of disease subcategories defined by the WHO. The disease areas most influenced by climate are infectious, cardiovascular, respiratory, injury, and maternal conditions. These findings highlight how precipitation and temperature drive seasonal hospitalization patterns for communicable diseases, non-communicable diseases, and injuries in tropical and temperate climates.

Climate variability significantly impacts the agricultural sector, necessitating accurate forecasting methods to support agricultural planning. This study aims to develop a rainfall forecasting model using the Gaussian Process Regression (GPR) method with Brownian motion prior. Monthly climate data from the Yogyakarta Geophysics Station for the period January 2015 to December 2024 were utilized, comprising predictor variables (air humidity and wind speed) and response variable (rainfall). The posterior GPR model was developed for parameter estimation using the marginal log-likelihood approach, with missing data handled through seasonal mean imputation that preserves temporal patterns. The results demonstrate that the GPR model achieves reasonable forecasting performance with a Mean Absolute Percentage Error (MAPE) of 36.84% and strong correlation (r = 0.94) between predicted and actual values. The highest predicted rainfall occurred in March 2024 (20.148 mm) and the lowest in June 2024 (0.022 mm), consistent with the seasonal patterns of Indonesia's tropical climate.