The escalating frequency and complexity of natural disasters highlight the urgent need for deeper insights into how individuals and communities perceive and respond to risk information. Yet, conventional research methods—such as surveys, laboratory experiments, and field observations—often struggle with limited sample sizes, external validity concerns, and difficulties in controlling for confounding variables. These constraints hinder our ability to develop comprehensive models that capture the dynamic, context-sensitive nature of disaster decision-making. To address these challenges, we present a novel multi-stage simulation framework that integrates Large Language Model (LLM)-driven social–cognitive agents with well-established theoretical perspectives from psychology, sociology, and decision science. This framework enables the simulation of three critical phases—information perception, cognitive processing, and decision-making—providing a granular analysis of how demographic attributes, situational factors, and social influences interact to shape behavior under uncertain and evolving disaster conditions. A case study focusing on pre-disaster preventive measures demonstrates its effectiveness. By aligning agent demographics with real-world survey data across 5864 simulated scenarios, we reveal nuanced behavioral patterns closely mirroring human responses, underscoring the potential to overcome longstanding methodological limitations and offer improved ecological validity and flexibility to explore diverse disaster environments and policy interventions. While acknowledging the current constraints, such as the need for enhanced emotional modeling and multimodal inputs, our framework lays a foundation for more nuanced, empirically grounded analyses of risk perception and response patterns. By seamlessly blending theory, advanced LLM capabilities, and empirical alignment strategies, this research not only advances the state of computational social simulation but also provides valuable guidance for developing more context-sensitive and targeted disaster management strategies.

This study examines the evolving role of emergency management-related nonprofit organizations (EMNPOs) in China's rapidly transforming socioeconomic and disaster governance contexts. Drawing on a novel database of 13,588 EMNPOs registered from 1949 to 2022-a period encompassing profound regulatory reforms-we harness computational methods to clarify their developmental trajectory. By employing BERT-based language model to capture nuanced organizational semantics and by implementing K-means clustering, we identify distinct functional categories spanning disaster relief, risk reduction, and capacity building. Integrating spatial statistical techniques reveals considerable functional heterogeneity and pronounced regional disparities. While EMNPOs cluster densely in the economically dynamic eastern coastal areas-regions often aligned with strong market forces and robust institutional frameworks-they remain relatively sparse across northeastern, central, and western provinces. Our findings underscore the primacy of socioeconomic determinants-particularly strong economic development, government investment, and infrastructure assets-in shaping EMNPOs' spatial distribution. In contrast, disaster variables exert limited influence, indicating that the conditions enabling EMNPO growth stem primarily from urbanization processes and fiscal resource allocation. These insights emphasize the embedded mobilization of EMNPOs within the government-market-society nexus, wherein diversified resource channels, policy support, and strategic alignments catalyze organizational adaptation and resilience. By disentangling functional complexity and regional asymmetries, this research refines our understanding of EMNPO configurations in China's evolving emergency governance landscape. The analysis provides evidence-based guidance to enhance institutional environments, foster crosssectoral synergies, and strengthen public engagement in disaster risk reduction and recovery. More broadly, it informs a global discourse on how NPOs can ultimately promote greater resilience, equity, and accountability in disaster-prone contexts.

The integration of artificial intelligence into development research methodologies offers unprecedented opportunities to address persistent challenges in participatory research, particularly in linguistically diverse regions like South Asia. Drawing on empirical implementation in Sri Lanka's Sinhala-speaking communities, this study presents a methodological framework designed to transform participatory development research in the multilingual context of Sri Lanka's flood-prone Nilwala River Basin. Moving beyond conventional translation and data collection tools, the proposed framework leverages a multi-agent system architecture to redefine how data collection, analysis, and community engagement are conducted in linguistically and culturally complex research settings. This structured, agent-based approach facilitates participatory research that is both scalable and adaptive, ensuring that community perspectives remain central to research outcomes. Field experiences underscore the immense potential of LLM-based systems in addressing long-standing issues in development research across resource-limited regions, delivering both quantitative efficiencies and qualitative improvements in inclusivity. At a broader methodological level, this research advocates for AI-driven participatory research tools that prioritize ethical considerations, cultural sensitivity, and operational efficiency. It highlights strategic pathways for deploying AI systems to reinforce community agency and equitable knowledge generation, offering insights that could inform broader research agendas across the Global South.

This study aims to give an insight into the development trends and patterns of social organizations (SOs) in China from the perspective of network science integrating geography and public policy information embedded in the network structure. Firstly, we constructed a first-of-its-kind database which encompasses almost all social organizations established in China throughout the past decade. Secondly, we proposed four basic structures to represent the homogeneous and heterogeneous networks between social organizations and related social entities, such as government administrations and community members. Then, we pioneered the application of graph models to the field of organizations and embedded the Organizational Geosocial Network (OGN) into a low-dimensional representation of the social entities and relations while preserving their semantic meaning. Finally, we applied advanced graph deep learning methods, such as graph attention networks (GAT) and graph convolutional networks (GCN), to perform exploratory classification tasks by training models with county-level OGNs dataset and make predictions of which geographic region the county-level OGN belongs to. The experiment proves that different regions possess a variety of development patterns and economic structures where local social organizations are embedded, thus forming differential OGN structures, which can be sensed by graph machine learning algorithms and make relatively accurate predictions. To the best of our knowledge, this is the first application of graph deep learning to the construction and representation learning of geosocial network models of social organizations, which has certain reference significance for research in related fields.

In large-scale sow production, real-time detection and recognition of sows is a key step towards the application of precision livestock farming techniques. In the pig house, the overlap of railings, floors, and sows usually challenge the accuracy of sow target detection. In this paper, a non-contact machine vision method was used for sow targets perception in complex scenarios, and the number position of sows in the pen could be detected. Two multi-target sow detection and recognition models based on the deep learning algorithms of Mask-RCNN and UNet-Attention were developed, and the model parameters were tuned. A field experiment was carried out. The data-set obtained from the experiment was used for algorithm training and validation. It was found that the Mask-RCNN model showed a higher recognition rate than that of the UNet-Attention model, with a final recognition rate of 96.8% and complete object detection outlines. In the process of image segmentation, the area distribution of sows in the pens was analyzed. The position of the sow’s head in the pen and the pixel area value of the sow segmentation were analyzed. The feeding, drinking, and lying behaviors of the sow have been identified on the basis of image recognition. The results showed that the average daily lying time, standing time, feeding and drinking time of sows were 12.67 h(MSE 1.08), 11.33 h(MSE 1.08), 3.25 h(MSE 0.27) and 0.391 h(MSE 0.10), respectively. The proposed method in this paper could solve the problem of target perception of sows in complex scenes and would be a powerful tool for the recognition of sows.

Regulated deficit irrigation (RDI) is considered among the best water-saving techniques forsupplementing Regulated water to fully achieve the water needs of the plant while maximizing water productivity with little or no substantial decrease in final produce compared to the conventional forms of irrigating crops.The aim of this paper is to review existing RDI approaches used in citrus production as well as plant-water stress indicators.Most of the approaches employed in citrus RDI scheduling require weather data for evapotranspiration calculations which is very technical, laborious and time consuming.Nonetheless, the time domain reflectometer (TDR) offers a simple way of scheduling RDI based on the soil-water status at any given time.This approach will help address the challenges in setting up on-farm synoptic stations to measure weather data to compute evapotranspiration or from using data from weather stations which might be different from the farm conditions.The pros and cons of all the approaches have been discussed and recommended that the TDR can be adopted as an alternative to schedule irrigation in citrus orchards to ensure that plants are supplied with adequate volume of water for maximum water use efficiency.

The goal of this research is to use a WORKSWELL WIRIS AGRO R INFRARED CAMERA (WWARIC) to assess the crop water stress index (CWSIW) on tomato growth in two soil types. This normalized index (CWSI) can map water stress to prevent drought, mapping yield, and irrigation scheduling. The canopy temperature, air temperature, and vapor pressure deficit were measured and used to calculate the empirical value of the CWSI based on the Idso approach (CWSIIdso). The vegetation water content (VWC) was also measured at each growth stage of tomato growth. The research was conducted as a 2 × 4 factorial experiment arranged in a Completely Randomized Block Design. The treatments imposed were two soil types: sandy loam and silt loam, with four water stress treatment levels at 70–100% FC, 60–70% FC, 50–60% FC, and 40–50% FC on the growth of tomatoes to assess the water stress. The results revealed that CWSIIdso and CWSIW proved a strong correlation in estimating the crop water status at R2 above 0.60 at each growth stage in both soil types. The fruit expansion stage showed the highest correlation at R2 = 0.8363 in sandy loam and R2 = 0.7611 in silt loam. VWC and CWSIW showed a negative relationship with a strong correlation at all the growth stages with R2 values above 0.8 at p < 0.05 in both soil types. Similarly, the CWSIW and yield also showed a negative relationship and a strong correlation with R2 values above 0.95, which indicated that increasing the CWSIW had a negative effect on the yield. However, the total marketable yield ranged from 2.02 to 6.8 kg plant−1 in sandy loam soil and 1.75 to 5.4 kg plant−1 in silty loam soil from a low to high CWSIW. The highest mean marketable yield was obtained in sandy loam soil at 70–100% FC (0.0 < CWSIW ≤ 0.25), while the least-marketable yield was obtained in silty loam soil 40–50% FC (0.75 < CWSIW ≤ 1.0); hence, it is ideal for maintaining the crop water status between 0.0 < CWSIW ≤ 0.25 for the optimum yield. These experimental results proved that the WWARIC effectively assesses the crop water stress index (CWSIW) in tomatoes for mapping the yield and irrigation scheduling.

Drought and water scarcity due to global warming, climate change, and social development have been the most death-defying threat to global agriculture production for the optimization of water and food security. Reflectance indices obtained by an Analytical Spectral Device (ASD) Spec 4 hyperspectral spectrometer from tomato growth in two soil texture types exposed to four water stress levels (70–100% FC, 60–70% FC, 50–60% FC, and 40–50% FC) was deployed to schedule irrigation and management of crops’ water stress. The treatments were replicated four times in a randomized complete block design (RCBD) in a 2 × 4 factorial experiment. Water stress treatments were monitored with Time Domain Reflectometer (TDR) every 12 h before and after irrigation to maintain soil water content at the desired (FC%). Soil electrical conductivity (Ec) was measured daily throughout the growth cycle of tomatoes in both soil types. Ec was revealing a strong correlation with water stress at R2 above 0.95 p < 0.001. Yield was measured at the end of the end of the growing season. The results revealed that yield had a high correlation with water stress at R2 = 0.9758 and 0.9816 p < 0.01 for sandy loam and silty loam soils, respectively. Leaf temperature (LT °C), relative leaf water content (RLWC), leaf chlorophyll content (LCC), Leaf area index (LAI), were measured at each growth stage at the same time spectral reflectance data were measured throughout the growth period. Spectral reflectance indices used were grouped into three: (1) greenness vegetative indices; (2) water overtone vegetation indices; (3) Photochemical Reflectance Index centered at 570 nm (PRI570), and normalized PRI (PRInorm). These reflectance indices were strongly correlated with all four water stress indicators and yield. The results revealed that NDVI, RDVI, WI, NDWI, NDWI1640, PRI570, and PRInorm were the most sensitive indices for estimating crop water stress at each growth stage in both sandy loam and silty loam soils at R2 above 0.35. This study recounts the depth of 858 to 1640 nm band absorption to water stress estimation, comparing it to other band depths to give an insight into the usefulness of ground-based hyperspectral reflectance indices for assessing crop water stress at different growth stages in different soil types.

High-density genetic linkage maps are necessary for precisely mapping quantitative trait loci (QTLs) controlling grain shape and size in wheat. By applying the Infinium iSelect 9K SNP assay, we have constructed a high-density genetic linkage map with 269 F 8 recombinant inbred lines (RILs) developed between a Chinese cornerstone wheat breeding parental line Yanda1817 and a high-yielding line Beinong6. The map contains 2431 SNPs and 128 SSR & EST-SSR markers in a total coverage of 3213.2 cM with an average interval of 1.26 cM per marker. Eighty-eight QTLs for thousand-grain weight (TGW), grain length (GL), grain width (GW) and grain thickness (GT) were detected in nine ecological environments (Beijing, Shijiazhuang and Kaifeng) during five years between 2010-2014 by inclusive composite interval mapping (ICIM) (LOD ≥ 2.5). Among which, 17 QTLs for TGW were mapped on chromosomes 1A, 1B, 2A, 2B, 3A, 3B, 3D, 4A, 4D, 5A, 5B and 6B with phenotypic variations ranging from 2.62% to 12.08%. Four stable QTLs for TGW could be detected in five and seven environments, respectively. Thirty-two QTLs for GL were mapped on chromosomes 1B, 1D, 2A, 2B, 2D, 3B, 3D, 4A, 4B, 4D, 5A, 5B, 6B, 7A and 7B, with phenotypic variations ranging from 2.62% to 44.39%. QGl.cau-2A.2 can be detected in all the environments with the largest phenotypic variations, indicating that it is a major and stable QTL. For GW, 12 QTLs were identified with phenotypic variations range from 3.69% to 12.30%. We found 27 QTLs for GT with phenotypic variations ranged from 2.55% to 36.42%. In particular, QTL QGt.cau-5A.1 with phenotypic variations of 6.82-23.59% was detected in all the nine environments. Moreover, pleiotropic effects were detected for several QTL loci responsible for grain shape and size that could serve as target regions for fine mapping and marker assisted selection in wheat breeding programs.

[Objective]This research aims to provide theoretical basis for improvement of the planting techniques and technical reserve for melon drip irrigation production by means of investigation and research on diseases,utilization eflGciency of fertilizer and water,output and quality of melon stereo drip irrigation planting.[Method]The project was carried out by conducting investigation and analysis of melon diseases development,irrigating and fertilizing state,melon yield and quality,compared with melon conventional planting.[Result]Melon stereo drip irrigation planting can reduce the field air relative humidity,significandy restrain and lighten the development and harm of downy mildew,but can not lighten the harm of powdery mildew.As for utilization efficiency of fertilizer and water,melon stereo drip irrigation planting is superior to conventional planting and reduces irrigation amount by 140%.The increase of yield is not significant for melon variety with middle or small fruit,but very significant for those varieties with big fruit.[Conclusion]Melon stereo drip irrigation planting can reach the purpose of lightening diseases,water - saving,fertilizer -saving and increasing yield;it is a planting mode which is appropriate to be planted in Kashigar.

Heat stress has a detrimental effect on the health and production performance of dairy cow. To alleviate the heat stress, sprinkle cooling system is widely used in the dairy barns in Eastern and Southern China, where is characterized by a hot and humid climate in summer. Due to a lack of systemic design and study, the cooling effect usually could not reach the targeted goals and the animals suffer a severe stress. In order to improve the system performance, this study was conducted to test the effect of spraying interval (SI) and water temperature (WT) on alleviating the heat stress of dairy cows. Based on the parity, day in milk, and milk yield, 48 Chinese Holstein dairy cows were assigned into four groups with different treatments: Thermal insulation a (TIa), Thermal insulation b (TIb), Non thermal insulation a (NTIa) and Non thermal insulation b (NTIb). In period 1, a 4-min SI was used for TIa and NTIa, and a 6-min SI was used for (TIb) and (NTIb). After a 3-day transition, the 4-min SI was exchanged with the 6-min one. The results showed that spraying water temperature of TI groups was 2.8 ºC lower. The rectal temperature of TIa group cows was about 0.2 ºC lower than that of cows in the other three treatments, and its vulva temperature was lower as well. Respiration rates of the cows in TIb and NTIb groups were 79.5 and 92.8 per min, respectively, and the TIb group was significantly lower. Comparing NTI group, the Blood serum potassium of the TIa group was much higher (PTI = 0.003). Blood serum calcium contents of dairy cows in the TI groups were decreased (PTI = 0.036). Cortisol in cows of TIa group was higher than that of TIb and NTIb groups (P = 0.039). TI group had a lower growth hormone (GH) than NTI group (P = 0.049). This research shows the cooling effect of the spraying system could be improved by adding thermal insulation on the water tank and decreasing the SI, which does not greatly alter concentrations of metabolic hormones in lactating dairy cows.

Correct simulation of overwinter condition is important for the growth of winter crops and for initial growth of spring crops. The objective of this study was to investigate overwinter soil water and temperature dynamics with the simultaneous heat and water (SHAW) model and with its linkage to the root zone water quality model (RZWQM), a hybrid model of RZWQM and SHAW (RZ-SHAW) in a Siberian wildrye grassland under two irrigation treatments (non-irrigation and pre-winter irrigation) in two seasons (2005–2006 and 2006–2007). Experimental results showed that pre-winter irrigation considerably increased soil water content for the top 60-cm soil profile in the following spring, but had little effect on soil temperature. Both SHAW and RZ-SHAW simulated these irrigation effects equally well, which demonstrated a correct linkage between RZWQM and SHAW. Across the treatments and years, the average root mean square deviation (RMSD) for simulated total soil water content (liquid plus frozen) was 0.031 m3 m−3 for both RZ-SHAW and SHAW models, and that for liquid water content alone was 0.028 m3 m−3 for both models. Both models provided better simulation of total and liquid soil water contents under non-irrigation condition than under pre-winter irrigation conditions. On average, RZ-SHAW simulated soil temperature slightly better with an average RMSD of 1.4°C compared to that of 1.8°C by SHAW. Both RZ-SHAW and SHAW simulated the soil freezing process well, but were less accurate in simulating the soil thawing processes, where further improvements are desirable. These simulation results show that the SHAW model is correctly implemented in RZWQM (RZ-SHAW), which adds the capability of RZWQM in simulating overwinter soil conditions that are critical for winter crops.

Siberian wildrye grass ( Elymus sibiricus L.) is the primary forage species in alpine cold areas of North China. Scheduled irrigation is an important way to increase the forage mass because of water shortage and the inconsistency between rainy season and the most intense water use period. Irrigation experiments were conducted during 2006 to 2008 to study the feasibility of applying single irrigation before winter (WI) or at the elongating stage (EI), irrigating at these two stages (WEI, in 2006–2007), and irrigating before winter with mulching straw before next reviving (WMI, only in 2008) to increase forage mass (FM) and water use efficiency (WUE). The results showed that irrigation at the elongating stage played the most important role in mass and WUE increase. The FMs under EI achieved 6381, 6883, and 5763 kg ha −1 , increased by 66, 218, and 99%, respectively, and WUE was 1.9, 2.3, and 1.8 kg m −3 , increased by 56, 134, and 81%, respectively, compared with no irrigation treatment (NI) in 3 yr. The FMs under WI were increased by 40, 53, and 44%, and the WUE was increased by 30, 31, and 34%, respectively. No significant FM and WUE increment under WEI were found compared with EI during 2006 to 2007. The FM and WUE under WMI were higher than values under EI in 2008. Therefore, EI can achieve relatively high production and WUE of Siberian wildrye grass. If water resources at the elongating stage were deficient, the WMI treatment might be a good choice.

Siberian wildrye grass ( Elymus sibiricus L.) is widely planted in the agropastoral ecotone of North China (APENC). Scheduled irrigation is an important approach to increase the forage yield in this semiarid region. Based on field experiments conducted in Bashang Plateau in APENC during 2002 to 2004, we studied the feasibility of applying single irrigation (SI) to increase forage yield by bringing the soil water storage in the root zone (0–60 cm) to field capacity at the elongating stage. The results showed that Siberian wildrye grass consumed water most rapidly during the elongating stage. With 48 to 62 mm of water applied during elongating stage, forage yields reached 6000 kg ha −1 , a 110% increase compared with no irrigation (NI). With full irrigation (FI) in the growing season, forage yield was only increased by 10% compared with that under SI. The average water use efficiency (WUE) under SI was 1.9 kg m −3 , a 76% increase compared with NI or a 10% increase compared with FI. The average irrigation water use efficiency (IWUE) under SI was 10.8 kg m −3 which was almost three times that under FI. Therefore, a single irrigation can be a simple agricultural practice for local farmers, and has great potential to contribute to sustainability of semiarid APENC. In addition, forage yield and WUE showed a quadratic trend with total evapotranspiration (ET). The maximum forage yield in this semiarid area was about 6900 kg ha −1 at 390 mm ET, whereas the maximum WUE was 1.9 kg m −3 at 360 mm ET.