Simulated Performance Research Articles

Modeling Study on the Impacts of Mineral Dust Photocatalytic Heterogeneous Chemistry on the Sulfur Removal Over East Asia

AbstractDust heterogeneous chemistry substantially influences the atmosphere and has profound impacts on our environment and climate. Gas‐particle partitioning on dust not only modifies species' chemical evolutions but also influences their deposition velocity. However, how does dust heterogeneous chemistry impacts acid deposition remains unexplored. In this research, we integrated dust photocatalytic mechanism into GEOS‐Chem to assess its impact on sulfur removal across various regions and time spans in China. We find the photocatalytic mechanism enhances simulation performances of acid deposition. Observational validation demonstrates significant reductions in modeling bias for sulfur dioxide (SO2) dry deposition and sulfate (SO4) total deposition. Additionally, the improved model captures the declining trend of SO4 deposition over 2006–2020. We further identified two key impacts of photocatalytic chemistry: firstly, our findings indicate it enhances sulfur removal more efficiently in near‐desert areas like North China Plain (NCP) than in downwind areas like Yunnan‐Guizhou‐Chongqing region (YGY). Lifetimes of total sulfur reduced from 3.29 to 2.46 days in NCP, and from 2.46 to 1.95 days in YGY. This discrepancy results from faster conversion of SO2 to dust‐phase SO4 and larger proportions of coarse‐mode particles in NCP, resulting in accelerated SO4 deposition velocity. Secondly, our results indicate that because dust photocatalytic chemistry amplifies removal of sulfur through SO4 formation and deposition, decreased dust emission resulted in enhanced sulfur lifetimes over 2006–2014. Sensitivity experiments further show higher dust concentrations accelerate pollutants' removal. These findings underscore the importance of dust heterogeneous chemistry in influencing sulfur deposition, providing scientific insights for mitigating acid deposition in China.

The Influence of Selected Parameters of the Mathematical Model on the Simulation Performance of a Municipal Waste-to-Energy Plant Absorber

The primary research aim of this manuscript was to present a simplified absorber model and analyse the simulation results of the absorber model created to which, by design, only water was added and the outlet flue gas temperature was optimal. The obtained simulation results of the simplified absorber model were appropriately compared with the operational results of absorbers operating in professional WtE installations. This study focused on the simulation duration. The primary tool used in the paper is OpenFOAM (v2112). Two solvers were used for the calculations: ReactingParcelFoam and LTSReactingParcelFoam. They ran numerical tests on simplified absorber models. We evaluated the results according to the simulation time. We also examined the difference between the measured and calculated flue gas outlet temperatures. The results will guide further research on the absorber. They will speed up and improve the modelling of chemical processes. The only challenge was to define the chemical reactions and add a calcium molecule to the water droplet model. This work shows that we can simplify the absorber’s geometric model. It kept a low relative error and cuts the compute time. Using a local time step instead of a global one in numerical calculations significantly reduced their run time. It did this without increasing the relative error. The research can help develop complex three-phase flow models in the absorber in the future.

Frequency-adjusted borders ordinal forest: A novel tree ensemble method for ordinal prediction.

Ordinal responses commonly occur in psychology, e.g., through school grades or rating scales. Where traditionally parametric statistical models like the proportional odds model have been used, machine learning (ML) methods such as random forest (RF) are increasingly employed for ordinal prediction. With new developments in assessment and new data sources yielding increasing quantities of data in the psychological sciences, such ML approaches promise high predictive performance. As RF does not inherently account for ordinality, several extensions have been proposed. A promising approach lies in assigning optimized numeric scores to the ordinal response categories and using regression RF. However, these optimization procedures are computationally expensive and have been shown to yield only situational benefit. In this work, I propose Frequency-Adjusted Borders Ordinal Forest (fabOF), a novel tree ensemble method for ordinal prediction forgoing extensive optimization while offering improved predictive performance in simulation and an illustrative example of student performance. To aid interpretation, I additionally introduce a permutation variable importance measure for fabOF tailored towards ordinal prediction. When applied to the illustrative example, an interest in higher education, mother's education, and study time are identified as important predictors of student performance. The presented methodology is made available through an accompanying R package.

A Comparative Analysis of Advanced Routing and Cluster Head Selection Algorithm Using Lagrange Interpolation

This paper presents a unified performance metric for evaluating the chronological wild geese optimization (CWGO) algorithm in wireless sensor networks (WSNs). The metric combines key performance factors—energy consumption, delay, distance, and trust—into a single measure using Lagrange interpolation, providing a more comprehensive assessment of WSN algorithms. We evaluate CWGO against E-CERP, EECHIGWO, DUCISCA, and DE-SEP across static and dynamic sensor node configurations in various wireless technologies, including LoRa, Wi-Fi, Zigbee, and Bluetooth low energy (BLE). The results show that CWGO consistently outperforms the other algorithms, especially in larger node configurations, demonstrating its scalability and robustness in static and dynamic environments. Moreover, the unified metric reveals significant performance gaps with EECHIGWO, which underperforms all wireless technologies. DUCISCA and DE-SEP show moderate and fluctuating results, underscoring their limitations in larger networks. While E-CERP performs competitively, it generally lags behind CWGO. The unified metric offers a holistic view of algorithm performance, conveying clearer comparisons across multiple factors. This study emphasized the importance of a unified evaluation approach for WSN algorithms and positions CWGO as a superior solution for efficient cluster head selection and routing optimization in diverse WSN scenarios. While CWGO demonstrates superior performance in simulation, future research should validate these findings in real-world deployments, accounting for hardware limitations and in a highly dynamic environment. Further optimization of the unified metrics’ computational efficiency could enhance its real-time applicability in larger, energy-resource-constrained WSNs.

Modeling water balance components of conifer species using the Noah-MP model in an eastern Mediterranean ecosystem

Abstract. Few studies have investigated the performance of land surface models for semiarid Mediterranean forests. This study aims to parameterize and test the performance of the Noah-MP land surface model for an eastern Mediterranean ecosystem. To this end, we calibrated the model for root zone soil moisture and transpiration of two conifer species, Pinus brutia, and Cupressus sempervirens, in a plantation forest on the Mediterranean island of Cyprus. The study area has a long-term average annual rainfall of 315 mm. Observations from 4 sap flow and 48 soil moisture sensors, for the period from December 2020 to June 2022, were used for model parameterization. A local sensitivity analysis found that the surface infiltration (REFKDT), hydraulic conductivity (SATDK), and stomatal resistance (RSMIN) parameters had the highest impacts on the water balance components (soil evaporation, tree transpiration, surface runoff, and drainage). The model performed better during the wetter 9-month validation period (379 mm rain) than during the drier 10-month calibration period (175 mm rain). Average soil moisture in the top 60 cm of the soil profile was reasonably well captured for both species (daily Nash–Sutcliffe efficiency > 0.80 for validation). Among the three soil layers, the second layer (20–40 cm) showed better simulation performance during both periods and for both species. The model exhibited limitations with respect to simulating transpiration, particularly during the drier calibration period. The inclusion of a root distribution function in the model, along with the monitoring of soil moisture below the 60 cm soil depth in the field, could improve the accuracy of model simulations in such water-limited ecosystems.

A sampling-based acceleration method for heterogeneous chiplet NoC simulations

To tackle the challenges posed by Moore’s Law, Chiplet technology emerges as a promising solution. Chiplets comprising CPUs and accelerators are connected by Networks-on-Chip (NoC) for large-scale integration and efficient communications. However, the slow simulation speed of NoCs has become a bottleneck, limiting the overall performance of chiplet simulations. Existing solutions only focus on accelerating NoC simulation in homogeneous architecture. In this paper, we introduce a novel TOPSIS-based Heterogeneous Trace Score-sampling method (THTS) for faster NoC simulation in heterogeneous architecture. THTS enables quick and accurate sampling of representative NoC traces. Additionally, we propose a weight exploration model to further enhance sampling accuracy. Compared with the traditional NoC sampling method (NoCLabs), THTS reduces the error of the average packet latency by 22.17% and the total simulation time by 1.6 folds. THTS estimates the NoC performance with an average loss less than 5%, while speeding up the NoC simulation by up to 3 times. In addition, under different weight space sizes, the time required for the weight exploration model to solve the optimal weight vector is within seconds, remarkably speeding up the solution process. Notably, the predicted NoC simulation error under the optimal weight is only 1.42%.

Enhancing the computational performance of granular flow simulations in DEM and CFD-DEM with adaptive sparse contacts (ASC)

Enhancing the computational performance of granular flow simulations in DEM and CFD-DEM with adaptive sparse contacts (ASC)

Balancing simulation performance and computational intensity of CA models for large-scale land-use change simulations

Balancing simulation performance and computational intensity of CA models for large-scale land-use change simulations

Optimization of leaching level and alternating drip irrigation start time improved water saving, yield enhancement, and salt leaching

Shortage of water resources and salinization of soil are two important factors restricting the sustainable development of agriculture in Xinjiang. The current drip irrigation strategy cannot meet the high efficiency water saving and salt leaching requirement, seriously limiting sustainable development. Therefore, in 2020 and 2021, a field experiment was conducted with three leaching levels (120, 240 and 360 mm) and four alternate drip irrigation (ADI) start time (no ADI, seedling, budding and flowering). The field experiment was combined with HYDRUS to investigate the effect of water saving and salt leaching by combining ADI with leaching during the reproductive period. The results showed that the HYDRUS-2D model simulated soil moisture and salinity with high accuracy under different irrigation conditions, with R2 ranging from 0.63 to 0.93, MRE from 2.45 % to 11.66 %, and RMSE from 1.88 % to 4.41 % for soil moisture and 0.29 g kg−1 to 0.97 g kg−1 for soil salinity, respectively. Optimization of irrigation method, leaching level and ADI start time all improved water and salinity stress characteristics. Although initially high soil salinity resulted in the conversion of some severe stress to moderate due to the irrigation, these disadvantages were offset by reductions in severe stress duration and area. Water-salt production model constructed based on water and salt stress characteristics demonstrated good yield simulation performance. According to HYDRUS, water-salt production model and comprehensive evaluation, starting ADI at the 3rd irrigation event with a leaching level of 290 mm was found to be optimal considering yield, water use efficiency, soil desalination and desalination efficiency. Considering the practicability, the recommended optimum ADI leaching level was 262–318 mm, with ADI initiated between the 2nd and 4th irrigation event. The study provided a vital theoretical basis for efficient water-salt management, supporting sustainable development in saline-alkali cotton fields in Xinjiang under limited water resources.

Assessing water resource vulnerability based on remote sensing data-enhanced SWAT+ and High-Resolution precipitation data

Climate change and human activities have significantly impacted the global water cycle, increasingly threatening water security across various sectors. By improving the performance of hydrological models in simulating the water balance, including precipitation and evapotranspiration, we can better support decision-making for regional water resource management and the deployment of climate change adaptation measures. This study aims to enhance the simulation performance of the vegetation growth module in the SWAT + model and reduce its uncertainty by dynamically updating the model’s data files with satellite-derived leaf area index and phenology data. Additionally, this study modified the weather station allocation mechanism in SWAT + to fully utilize the high-resolution meteorological data grids. The simulation results show that the enhanced SWAT + model achieved a Nash-Sutcliffe Efficiency (NSE) between 0.84 and 0.90 and a PBIAS of less than 6 % for daily streamflow simulations at four hydrological stations in the Weihe River Basin. The enhanced SWAT + model’s water balance simulation results for the Weihe River Basin were used to analyze the vulnerability of water resources within the basin. The results indicate that the Qinling region, benefiting from its well-developed forest ecosystems, has the lowest green water vulnerability. The overall blue water vulnerability of the basin reached 1.12, suggesting that during periods of insufficient precipitation or intense evaporation, blue water resources may not meet the demands of agriculture, industry, ecology, and residential use. Among these, agricultural water use exhibits the highest vulnerability. Efficient irrigation technologies can be employed to improve irrigation water use efficiency, and the use of treated reclaimed water for agricultural irrigation can be promoted to reduce the demand for fresh blue water resources.

Carbon Emission Trend Prediction for Regional Cities in Jiangsu Province Based on the Random Forest Model

This study accounted for and analyzed the carbon emissions of 13 cities in Jiangsu Province from 1999 to 2021. We compared the simulation effects of four models—STIRPAT, random forest, extreme gradient boosting, and support vector regression—on carbon emissions and performed model optimization. The random forest model demonstrated the best simulation performance. Using this model, we predicted the carbon emission paths for the 13 cities in Jiangsu Province under various scenarios from 2022 to 2040. The results show that Xuzhou has already achieved its peak carbon target. Under the high-speed development scenario, half of the cities can achieve their peak carbon target, while the remaining cities face significant challenges in reaching their peak carbon target. To further understand the factors influencing carbon emissions, we used the machine learning interpretation method SHAP and the features importance ranking method. Our analysis indicates that electricity consumption, population size, and energy intensity have a greater influence on overall carbon emissions, with electricity consumption being the most influential variable, although the importance of the factors varies considerably across different regions. Results suggest the need to tailor carbon reduction measures to the differences between cities and develop more accurate forecasting models.

Influence of Planting Date on Potato Yield in Gircha Highland, Arba Minch, Ethiopia

Potatoes are the world's fourth most important food crop, with climate, variety, seed quality, planting timing, nutrition, irrigation, pests, illnesses, and weeds all influencing its growth and productivity. Planting timing is critical for maximizing yields since it regulates temperature and light. Planting date effects on potato development and yield were investigated in an experiment at Gircha Farmland, Arba Minch, from February 8, 2020 to August 19, 2020. The impact of planting dates on potato production response was investigated using the Decision Support System for Agro-technology Transfer (DSSAT) model software application program. The experiment used Gudene cultivar potato root crops in Gircha highland plot areas, with gross plot area, rows per plot, length, and spacing. Harvest area was 2.3 m<sup>2</sup>, with two rows, and soil depth was 15 cm. No additional irrigation was supplied, and 150 kg/ha N and 80 kg/ha P fertilizer was applied. Eight planting dates—February 8, February 18, February 28, March 9, March 19, March 29, April 8, and April 18—were selected for this investigation. The relationship between observed and simulated potato yield performance, with RMSE (7.256), IOA (0.842), and R<sup>2</sup> (0.962), and validation values (19.658), IOA (0.827), and R<sup>2</sup> (0.974), indicating good agreement. The latest planting date of April 18 marked the pinnacle of potato output. Furthermore, on April 18, 2020, the planting date that was closest to March 30, 2020, the maximum tuber fresh weight of 48.73 mg/ha was recorded, while on February 8, 2020, the minimum tuber fresh weight of 16.08 mg/ha was acquired. Similar to this, the planting date of April 18<sup>th </sup>yielded the maximum potato production (9746 kg/ha), while February 8<sup>th</sup> yielded the lowest yield (3215 kg/ha). Gircha's higher rainfall, lowest solar radiation, and warmer temperatures may have contributed to the crop's increased production. Potato planting date significantly affects growth and yield, with April 18, 2020 being the best date for highest yield. Delays in planting led to increased crop growth and production, influenced by rainfall, solar radiation, and mean temperature.

Modeling abscission of cacti branches

During evolution, various functional principles have evolved that allow plants to create predetermined breaking points for the spatially defined abscission of organs. In the plant family of cacti, some species, such as Cylindropuntia bigelovii, have fragile branch–branch junctions that serve vegetative reproduction, while in other species, such as Opuntia ficus-indica, they are very stable. The fracture behavior of these junctions has been thoroughly characterized anatomically and mechanically, the data being the prerequisite for the performance of cactus-inspired phase field simulations. We have found that models composed of homogeneous materials or material systems with low elastic modulus contrast (analogous to Cylindropuntia bigelovii) exhibit a fracture mode where cracks initiation occurs at the epidermis of the junction notch. In comparison, heterogeneous material systems with high elastic modulus contrast (similar to Opuntia ficus-indica) show fracture nucleation along the inner vascular bundles, with an increase in the maximum fracture energy by a factor of 2.2. In the high contrast heterogeneous models, the V-notch and stiffening of the dermal tissue (“periderm”) have a negligible effect on their fracture behavior. In addition, the fracture morphologies of these models resemble the rough junction fracture sites found experimentally. The knowledge gained about the geometric influences and the importance of the contrasts in the mechanical properties of the individual materials in the overall system can be transferred as functional principles to bioinspired engineering composites in order to program their fracture behavior.

Historical memory in remotely sensed soil moisture can enhance flash flood modeling for headwater catchments in Germany

The wetness precondition of a catchment affects available soil water storage capacity and infiltration rate, thus influences flash flood generation. Remotely sensed (RS) soil moisture (SM) can provide valuable information on catchment wetness, but typically only represents the top 5 cm of the land surface. However, hydrological models for flash flood simulation need to consider deeper layers to calculate the total soil water storage. Therefore, a key challenge is to link RS SM to total soil water storage and assimilate RS SM into flash flood models to correctly describe initial catchment wetness. In this study, we developed an approach to combine present and antecedent RS SM to infer present soil water storage based on four regression models. The inferred soil water storage from SMAP (soil moisture active passive) SM was assimilated into the operational LARSIM (Large Area Runoff Simulation Model) hydrological model. We tested this new approach with 12 events in the headwater catchments Körsch, Adenauer Bach and Fischbach in Germany. Results show that random forest regression performs the best among the four regression models. The BIC (Bayesian Information Criterion) score suggests that regressions considering antecedent RS SM can well infer soil water storage, resulting in R2 of 0.85, 0.94 and 0.93 for the Körsch, Adenauer Bach and Fischbach catchments, respectively. Compared to the open loop (without data assimilation) simulations, our approach enhanced the general performance of event simulations with average KGE increases of 0.09, 0.24 and 0.33 for the Körsch, Adenauer Bach and Fischbach, respectively; and the mean error in the 12 simulated event peaks is reduced 15 %. Moreover, the simulation uncertainty is reduced, too. The transferability of the proposed approach to other RS products is also discussed. Although assimilating RS SM can enhance flash flood modeling, it is primarily affected by the uncertainty in precipitation. In the future, the proposed approach should be tested with more catchments and events to verify its general validity.

Physics-Based Modeling to Elucidate Design Principles for Redox-Mediated Electrochemical Systems

Electrochemical systems are increasingly being developed and deployed as sustainable technologies that support reductions in greenhouse gas emissions across the electric power, transportation, and industrial sectors. However, the need to overcome challenges related to cost, performance, and scalability continues to motivate exploration into alternative material sets and reactor designs that offer new pathways to widespread adoption. Redox-mediated processes, which decouple electrode reactions that activate a soluble mediator from “off-electrode” reactions where the activated mediator exchanges electrons with a solid material, are of growing interest.1,2 Recent literature has illustrated the potential of this approach for increasing the charge-storage capacity of redox flow batteries,2 performing impractical electrochemical processes,3 enhancing selectivity and efficiency of electrochemical separations and recycling,1,4,5 and unlocking spatial and temporal flexibility in electrochemical operations.6 Previously, we have developed and qualitatively validated a model capable of capturing key dynamics observed in redox-mediated flow batteries.7 Grounded in first principles and contextualized by experiments, this model has the potential to rapidly assess tradeoffs and opportunities for redox-mediated systems for applications in energy storage and conversion as well as materials recovery and recycling. Here, we describe how our model can be used to gain generalizable insights into the design-space for redox-mediated electrochemical processes. By incorporating models for pressure drop in porous media with simulated electrochemical performance, we assess the trade-offs associated with capacity utilization, power output, and parasitic pumping losses. Furthermore, we demonstrate how the model can support constrained optimization to guide the design of a system of known physical parameters. Ultimately, we seek to translate these findings into universal design principles for how electrochemical protocol and solid incorporation can be tuned to develop high-performing redox-mediated systems. Acknowledgements N.J.M gratefully acknowledges the NSF Graduate Research Fellowship Program under Grant Number 2141064. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. References Xue et al., Green Chem. 2020, 22 (19): 6288-6309. Yan and Wang, Mat. 2018, 30 (47). Vardner et al., ChemElectroChem 2022, 9 (24). Cotty et al., ACS Sustain Chem Eng. 2023, 11(9):3975-3986. Park et al., ACS Sustain Chem Eng. 2021, 9 (24): 8214-8221. Wang et al, Joule 2021, 5 (1): 149-165. Matteucci et al., ECS Meeting Abstracts 2023, MA2023-01 (3), 765.

PELATIHAN BUILDING INFORMATION MODELLING BERBASIS TEKLA STRUCTURES PADA SISWA SEKOLAH MENENGAH KEJURUAN KELAS XI DI KABUPATEN MOJOKERTO

Vocational High School (SMK) graduates contributed the most to the percentage of TPT (Open Unemployment Rate) as of August 2022 of 5.86% or 8.42 million people. The importance of increasing the involvement of partners in the world of work in designing and managing learning in vocational schools and developing the potential of students' abilities and expertise that are in line with opportunities to work in the world of work and/or entrepreneurship can reduce the TPT score. The results of direct observations at schools and confirmed based on the provisions of BSKAP 033/H/KR/2022, the learning achievements of students have not achieved their competencies in either phase E or F, namely regarding the ability to model 3D pictorial projections using BIM technology. The purpose of this PKM in the field of BIM applications is to improve students' skills in applying Building Information Modeling in achieving 3D modeling abilities and competencies. The empowerment method applied is in the form of training and mentoring of BIM applications in 3D modeling of construction buildings to improve the skills of partners related to drawing 3D building structures with BIM technology integrated with software. From the results of the initial and final assessment analysis, it was found that there was an increase in partner skills related to drawing 3D building structures with BIM technology integrated with software of ≥ 70% based on the performance of the 3D modeling simulation that was built after participating in the training as well as an increase in skills in creating informative construction design visualizations with software of ≥ 70%.

A novel multi‐target TBD scheme for GNSS‐based passive bistatic radar

AbstractGlobal Navigation Satellite System (GNSS)‐based passive bistatic radar (PBR) systems hold promise for use as low‐altitude surveillance mechanisms in critical urban and suburban zones, attributed to their low power consumption, good concealment, and worldwide reach. However, the increasing demand for airspace regulation presents challenges for multi‐target tracking. Additionally, the limited power budget of GNSS signals results in low target SNR, restricting the detection range. Hence, a novel multi‐target track‐before‐detect (TBD) scheme is proposed. This strategy employs a dual‐channel coarse focusing (DCCF)‐based cluster centroid extraction algorithm to identify potential target information in the range Doppler (RD) domain. Subsequently, a modified cardinalised probability hypothesis density (MCPHD) filter is utilised, enhanced with a birth target intensity estimation module assisted by Doppler and a trajectory management module, to accurately track multiple targets under conditions of low SNR. Simulation results and performance analysis using the Optimal Sub‐pattern Assignment (OSPA) metric confirm the effectiveness of our approach. Furthermore, in a real‐world experiment using the GPS L5 signal as an illuminator, the authors successfully processed experimental data to track a civil aircraft over 10 frames, demonstrating the practical applicability of the proposed method in GNSS‐based PBR systems.

Efficient GPU Cloth Simulation with Non-distance Barriers and Subspace Reuse

This paper pushes the performance of cloth simulation, making the simulation interactive even for high-resolution garment models while keeping every triangle untangled. The penetration-free guarantee is inspired by the interior point method, which converts the inequality constraints to barrier potentials. We propose a major overhaul of this modality within the projective dynamics framework by leveraging an adaptive weighting mechanism inspired by barrier formulation. This approach does not depend on the distance between mesh primitives, but on the virtual life span of a collision event and thus keeps all the vertices within feasible region. Such a non-distance barrier model allows a new way to integrate collision resolution into the simulation pipeline. Another contributor to the performance boost comes from the subspace reuse strategy. This is based on the observation that low-frequency strain propagation is near orthogonal to the deformation induced by collisions or self-collisions, often of high frequency. Subspace reuse then takes care of low-frequency residuals, while high-frequency residuals can also be effectively smoothed by GPU-based iterative solvers. We show that our method outperforms existing fast cloth simulators by at least one order while producing high-quality animations of high-resolution models.

High-performance ammonia sensor at room temperature based on 2D conductive MOF Cu3(HITP)2

Sensitive detection of ammonia in the environment is crucial due to its potential danger to human ecology and health. In gas detection technology, resistive sensors utilizing golden cross finger electrodes combined with gas-sensitive materials are commonly employed. In this study, we demonstrated a room-temperature sensor for ambient ammonia detection. The sensor is composed of two-dimensional layer-stacked metal-organic framework (MOF) Cu3(HITP)2 nanomaterials drop-coated onto gold-forked finger electrodes. Density-functional theory simulation (DFT) and sensor gas-sensitive performance testing were conducted for characterization. The sensor exhibited high sensitivity, selectivity, low detection limit, excellent reproducibility, and stability. This can be attributed to the abundant Cu active sites exposed in the hexagonal ring and layer-stacked framework structure of Cu3(HITP)2 nanomaterials. Ammonia adsorption leads to electron transfer into the Cu3(HITP)2 framework, resulting in decreased sensor resistance. Real-time monitoring of sensor resistance changes enabled quantitative analysis. Results showed a 91.4 % response of the Cu3(HITP)2 sensor to 100 ppm NH3, with response and recovery times of 26 s and 20 s, respectively. The sensor's limit of detection (LOD) was approximately 15 ppb. The sensor exhibited a relatively high response to NH3 at 25 °C, as demonstrated by dynamic gradient test curves. These findings suggest that constituting a room-temperature ammonia sensor by uniformly drop-coating Cu3(HITP)2 onto a gold-forked finger electrode is a feasible strategy.

Innovative modeling on the effects of low-temperature stress on rice yields.

The increasing frequency and intensity of low-temperature events in temperate and cold rice production regions threaten rice yields under climate change. While process-based crop models can project climate impacts on rice yield, their accuracy under low-temperature conditions has not been well-evaluated. Our six-year chamber experiments revealed that low temperatures reduce spikelet fertility from panicle initiation to flowering, grain number per spike during panicle development, and grain weight during grain filling. We examined the algorithms of spikelet fertility response to temperature used in crop models. Results showed that simulation performance is poor for crop yields if the same function was used at different growth stages outside the booting stage. Indeed, we replaced a parameter spikelet fertility algorithm of the ORYZA model and developed the function of estimating grain number per spike and grain weight. After that, the improved equation algorithm was applied to 10 rice growth models. New functions considered the harmful effects of low temperatures on rice yield at different stages. In addition, the threshold temperatures of the cold tolerance were set for different rice varieties. The improved algorithm enhances the model's ability to simulate rice yields under climate change, providing a more reliable tool for adapting rice production to future climatic challenges.