Pramet Kaewmesri

Tropical dry evergreen forests retain a closed canopy during long dry seasons, yet their carbon exchange remains poorly measured. We analyzed 20 months (April 2024–December 2025) of eddy-covariance observations at Sakaerat, Thailand, including a single-point CO₂ storage term, gap filling, and flux partitioning. We assessed whether tower soil measurements and optical, thermal, and Sentinel-1 radar predictors improved daily net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (R_eco) estimates beyond tower meteorology. Estimated monthly NEE was negative throughout the record, including the dry season. Cumulative NEE over 1 January–17 December 2025 was −1043 g C m⁻² (GPP 2620; R_eco 1635 g C m⁻²). Uptake was stronger in the wetter, cooler second wet season, consistent with directly measured daytime fluxes. In the later held-out period, soil water content and temperature improved NEE and GPP estimates, while cross-calibrated Sentinel-1 backscatter improved GPP estimates. Optical and thermal products did not improve NEE and degraded GPP. These gains mainly tracked the interannual shift in flux level rather than daily variation. Better constraints on nighttime respiration are needed to confirm the magnitude of the carbon sink.

Efficient water management is crucial for enhancing crop productivity and ensuring food security, especially with growing global water scarcity. To support data-driven agricultural decision-making, this research develops a spatial decision support system using satellite-based Big Earth Observation Data. The system leverages MODIS time-series products to automatically retrieve and analyze Evapotranspiration (ET) and Gross Primary Production (GPP), which are used to calculate Water Use Efficiency (WUE)—a key metric for assessing crop water productivity.The core innovation is a scalable architecture that integrates multi-temporal satellite data with a Web GIS platform. The system uses PostgreSQL with PostGIS for efficient spatial database management and optimized SQL queries. Its backend, powered by Node.js, delivers RESTful APIs, while GeoServer publishes spatial layers as Web Map Services (WMS). These layers are visualized on an interactive, React-based interface using Leaflet.js, allowing users to dynamically analyze historical water use patterns by region and time via a standard browser.This system's ability to process and visualize large-scale spatiotemporal data empowers agricultural stakeholders to identify inefficiencies, adapt irrigation practices, and optimize resource allocation. The platform demonstrates how advanced geospatial information and Big Data processing contribute to sustainable agriculture and climate resilience, ultimately helping to improve crop yields and secure long-term food stability. Future enhancements may integrate ground-based or farmer-contributed data to further strengthen its decision-making capacity.

Rework is currently a necessity for businesses and commercial organizations across the world. It is only beneficial in tackling climate change if the process emits less greenhouse gases than would otherwise be emitted. This study designs an optimal production scheduling model to reduce both carbon emissions during the processes of production, transport and storage, and setup cost by leveraging on green technology efforts in an imperfect production process where a fraction of items is erroneous so that the firm may run out of inventory. The producer implements a rework strategy to rectify the flawed products, anda flexible rework rate is offered since the rework might be executed on various schemes. The flexible rework allows the producer to choose therework rate, which can differ from the production rate, as well as the rework process itself, which can be asynchronous or synchronous.The two forms of green investments: quadratic and exponential are considered in the study. The main point of the study is to derive a solution procedure of the various problem settings associated with the rework rate, rework process and green investment. The findings suggest that developing the optimal production schedule (lot-sizes, backorders, setup cost and green investment amount) can lower the manufacturing sector’s excessive ecological carbon emissions. The findings also support the idea that making green investments is the most cost-effective way to cut carbon emissions and setup cost simultaneously.

Many nations have created ecological policies and regulations to prevent industries from emitting excessive amounts of carbon emissions into the environment. While significant progress has been achieved in the direction of sustainable growth, many nations still rely on nonrenewable energy sources. This study explores the viability of investing in green technology to achieve the optimal decisions (lot sizes, lead time, and green investment amount) in a two-echelon supply chain system by considering human error with two carbon emission strategies: carbon taxes and limited carbon emissions. It entails the inspection of every shipped lot by the buyer to identify defective products that could have resulted from the vendor’s production process. We show a constrained non-linear program and design a calculus-optimization technique to solve it. The methodology used in this research is the quantitative method, which is based on the principles of operations research, and the models are built on mathematically oriented inventory theory. The results imply that an outsized ecological carbon footprint can be reduced without compromising customer service by designing optimal inventory strategies. The findings also confirm that green investment is the greatest economical method for reducing carbon emissions and system costs.

Owing to the various physical aspects of nanofluids as thermally enhanced working fluids and the significance of swirling flows in rheological devices as well as in the spin coating and lubrication applications, the current comprehensive examination aimed to explore the important features of spinning flows of chemically reactive Newtonian nanofluids over a uniformly revolving disk in the existence of a radially applied magnetic field along with an exponentially decaying space-dependent heat source, in the case where the disk surface is heated convectively and unaffected by the vertical nanoparticles’ mass flux. Based on feasible boundary layer approximations and Buongiorno’s nanofluid formulation, the leading coupled differential equations are stated properly in the sense of Arrhenius’s and Von Kármán’s approaches. By employing an advanced generalized differential quadrature algorithm, the obtained boundary layer equations are handled numerically with a higher order of accuracy to generate adequate graphical and tabular illustrations for the different values of the influencing flow parameters. As findings, the graphical results confirm that the nanofluid motion decelerates meaningfully thanks to the resistive magnetic influence. A significant thermal amelioration can be achieved by strengthening the magnetic impact, the generation of heat, the thermal convective process, and the thermophoresis mechanism. Moreover, it is found that the thermo-migration of nanoparticles can be reinforced more via the intensification in the convective process, the thermo-migration of nanoparticles, and the activation energy.