Jiangsu Provincial Meteorological Bureau

Purpose This study aims to explore how innovation ecosystem characteristics (actor heterogeneity, relational embeddedness and organizational interdependence) impact technological innovation performance in megaprojects. It focuses on the roles of value co-creation and network innovation capability, aiming to uncover the mechanisms driving innovation in complex project environments. Design/methodology/approach A quantitative approach is employed, using survey data from megaproject stakeholders. Structural equation modeling analyzes the relationships between ecosystem characteristics, value co-creation, network innovation capability and innovation performance. Findings Value co-creation significantly enhances technological innovation performance, while network innovation capability partially mediates this relationship. Actor heterogeneity has no direct effect, but relational embeddedness and organizational interdependence indirectly influence performance through value co-creation and network innovation capability. Value co-creation emerges as the stronger driver of innovation outcomes. Originality/value This research integrates innovation ecosystem and network theories to reveal the critical role of value co-creation in megaproject innovation. It introduces network innovation capability as a mediator, emphasizing the importance of relational and organizational dynamics. The study offers practical insights for fostering collaboration and innovation in megaprojects, advancing both theoretical and practical understanding.

ABSTRACT Tornadoes are characterized by short duration and small spatial scales, while conventional observational data exhibits inadequate spatiotemporal resolution for detailed analysis, presenting a significant challenge to mechanistic studies. In this study, the June 23, 2016, EF4 Funing tornado was simulated using the WRF model driven by ERA5 reanalysis data, by employing hectometer‐scale grid spacing and optimized numerical schemes. Based on the simulation results, the structural characteristics of the occurrence and development of the tornado were analyzed. The three‐dimensional dynamics of the tornado vortex and the intensification of its rotation were further investigated through diagnostic equations. Results show that the Funing tornado occurred under the typical circulation situation of the Meiyu period, and the hook‐shaped echoes and other features of the individual tornado were successfully reproduced on the 111 m grid. The cyclonic circulation driven by gust fronts and cold surges played a crucial role, revealing that the main energy for the formation and development of tornadoes originated from the lower troposphere. In addition, during the genesis and evolution of tornadoes, there were two tornado‐like vortices (TLV), accompanied by an increase in the vertical acceleration of low‐level small‐scale airflows and an enhancement of near‐surface vortices. The mature TLV exhibited the typical characteristics of sinking in the middle and rising in the periphery. The stretching term of the vorticity equation played a dominant role in the intensity variation of the TLV near the ground, while the tilting term affected the vertical structure formation of the tornado vortex at upper levels. Together, they drove the tornado from its inception to maturity. These results have significant implications for better understanding and analysis of tornadoes.

Greenhouse cultivation enables high yields through multi-cropping under controlled environments. Research on the association between crop growth and environmental meteorological factors is crucial for achieving precise dynamic regulation of environmental factors and efficient crop growth management within greenhouse. To address the stability problems of inversion model of crop phenotype-accumulated temperature during different sowing dates, this study analyzed the relationship between color skewed-distribution parameters of cabbage canopy and environmental accumulated temperature during different sowing dates. Three types of canopy color parameters (depth, distribution, and mixed parameters) were used as independent variables to construct inversion models of canopy color-accumulated temperature, and the model’s stability was tested across various growth cycles. The results showed that the skewed-distribution parameters of canopy images were significantly correlated with the environmental accumulated temperature, and the correlation coefficient was generally above 0.8. Among the models, the one using distribution parameters as the main independent variable demonstrated the highest fitting accuracy. For the same sowing date, the fitting accuracy was 87.11% and 91.16%, while for different sowing dates, it remained approximately 85%. These findings provide a useful theoretical basis and practical reference for stable and accurate inversion of accumulated temperature based on crop canopy color phenotype during different growth cycles, offering new insights for intelligent, high-quality and efficient management of facility agriculture.

In-toeing gait is a common developmental condition in children and may lead to gait instability, frequent falls, and discomfort. However, evidence supporting conservative interventions, such as proprioceptive stimulation foot pads, remains limited. To evaluate the effects of proprioceptive stimulation foot pads on gait parameters in children with in-toeing gait. This retrospective study included 119 children aged 5–12 years who were diagnosed with in-toeing gait in Children’s Hospital of Nanjing Medical University between January 2020 and April 2023. Based on clinical records, children who received proprioceptive stimulation foot pads were assigned to the treatment group, while those managed by observation alone comprised the control group. Gait parameters measured at baseline and follow-up were extracted for analysis. Compared with the control group, the treatment group demonstrated a significant improvement in foot progression angle. In contrast, walking speed, step length, stride length, arch index, and plantar pressure showed changes in both groups, with no consistent between-group differences. Proprioceptive stimulation foot pads are associated with improved foot progression angle in children with in-toeing gait, while effects on other gait parameters appear limited. These findings provide preliminary support for the use of this conservative intervention.

ABSTRACT Accurate crop structure information is fundamental for precision agriculture, particularly in regions with highly fragmented farmland. This study focuses on Jiangsu Province, China, and proposes a multi-source remote sensing approach to address the classification challenges posed by complex agricultural landscapes. Optical Sentinel-2, synthetic aperture radar (SAR) Sentinel-1, and high-resolution Gaofen-2 (GF-2) imagery were integrated to exploit their spatial and temporal complementarities. A modified multi-scale attention U-Net (MSA-UNet) was developed to extract cropland parcels from GF-2 imagery, which served as the basic classification units. Time-series spectral and polarimetric features derived from Sentinel-1/2 data were used to train a convolutional neural network – bidirectional long short-term memory (CNN – BiLSTM) model for parcel-level summer crop classification. The proposed MSA-UNet achieved an intersection over union (IoU) of 73.89% and an F1-score of 84.66%, outperforming the baseline U-Net by 5.87 and 4.07% points, respectively. Incorporating both optical and SAR features improved the precision to 91.28%, representing gains of 2.37 and 13.87% points compared to models using single-source inputs. The CNN – BiLSTM architecture effectively captured both intra-seasonal dynamics and long-term dependencies in the time series. These results demonstrate that the proposed method significantly improves the accuracy of crop mapping in fragmented agricultural regions and offers a practical solution for large-scale agricultural monitoring and decision support.

To tackle the aging problem of metal oxide arresters (MOA) in online monitoring, a novel genetic algorithm (GA)-based method is proposed for monitoring MOA degradation. Using the operating voltage and measured leakage current of MOA, the GA's optimization capability is employed to determine the parameters k and c in the MOA equivalent model, which vary with aging. This approach ultimately enables the monitoring of MOA degradation. Additionally, simulations using Matlab are conducted to analyze the effects of voltage harmonics, frequency fluctuations, and voltage fluctuations in the power grid on the algorithm. The study demonstrates that the new algorithm can adapt the calculated leakage current to match the measured leakage current of MOA (with a standard deviation of 1.498%) and accurately calculate the parameters k and c . Furthermore, Matlab simulation results show that the maximum errors of k and c are only 0.08% and 0.05% respectively, even under voltage harmonics, voltage fluctuations, and frequency fluctuations. This demonstrates the strong anti-interference robustness of the proposed algorithm. By relying on only two simple parameters, the method also simplifies the monitoring process, improving both computational efficiency and practical applicability for online MOA aging assessment.

On September 19, 2023, a rare autumn tornado outbreak, consisting of three successive tornadoes (including one EF3), occurred in northern Jiangsu, China, causing significant damage. This study presents a comprehensive analysis of this event using multisource data, including Doppler radar, ERA5 reanalysis, and surface observations. We find that pretornadic mesoscale vortices were triggered and intensified during the eastward movement of the Jianghuai cyclone, along low‐level shear lines on the warm side of a quasi‐stationary mesoscale front. The synoptic environment was characterized by an anomalously strong and persistent subtropical high, which provided record‐breaking moisture (precipitable water >66 mm) and instability (CAPE >2200 J kg −1 ), coupled with extreme low‐level wind shear (>18 m s −1 in 0–1 km). These conditions created a classic yet extreme setting for tornadic supercells. At the storm scale, the tornadoes formed during the merging and intensification of multicell thunderstorms. Radar analysis revealed that the two strongest tornadoes (EF2 in Nancai and EF3 in Funing) exhibited clear supercell characteristics, including hook echoes and tornado vortex signatures (TVSs). Tornadogenesis in both cases coincided with the peak values of low‐level differential velocity (LLDV exceeding 40 m s −1 ). The Funing EF3 tornado was further confirmed by a prominent tornado debris signature (TDS) in dual‐polarization radar data. A key advance presented here is the identification of a hybrid tornadogenesis mechanism. While the storms displayed classic supercell dynamics (tilting and stretching of vertical vorticity), the process was significantly enhanced by the amplification of pre‐existing horizontal vorticity along successive, evolving meso‐β‐scale convergence centers. This mechanism, which shares characteristics with both supercellular and landspout processes, explains the sequential development of this “tornado cluster.” Our results underscore that in high‐shear, high‐instability environments, detailed analysis of mesoscale boundaries are crucial for anticipating tornadogenesis. This study enhances the understanding of autumn tornado outbreaks in eastern China and provides insights for improving monitoring and warning strategies for such rare but high‐impact events.

Abstract In this study, we evaluate two advanced post-processing techniques—Ensemble Model Output Statistics (EMOS) and the point-based European Centre for ECMWF statistical ensemble method (ecPoint)—for calibrating ensemble precipitation forecasts. A comprehensive assessment of performance of these ensemble post-processing methods is conducted using the CMA Global Ensemble Forecasting System (CMA-GEPS) forecast over eastern China. The results demonstrate that both methods significantly reduce systematic biases and improve the reliability and dispersion of ensemble forecasts. Notably, improvement on forecast accuracy is observed even under convective weather conditions, and early warnings of extreme precipitation events. Overall, while both methods show comparable performance, they exhibit distinct behaviors across different regions. The ecPoint method slightly outperforms EMOS in terms of Continuous Ranked Probability Score (CRPS) and provides improved resolution and early warning capabilities at various precipitation thresholds.