In the tropical regions represented by Hainan,there are abundant solar and thermal resources,and it is relatively suitable for the construction of photovoltaic greenhouse(PVG).However,the construction of PVG still rel...In the tropical regions represented by Hainan,there are abundant solar and thermal resources,and it is relatively suitable for the construction of photovoltaic greenhouse(PVG).However,the construction of PVG still relies mainly on experience and is incapable of quantifying the balance between the photovoltaic(PV)generation and the light requirements for agricultural production.As a result,actual PVGs are primarily PV-based,without carefully considering the needs of agricultural daylighting.To quantify the influence of the design parameters of PVGs and the layout of PV panels on the internal daylighting of serrated PVGs,and to optimize the daylighting design of the roof,this paper utilizes the Design Builder software to establish gradient models for a multi-span serrated-type PVG in tropical regions.Gradient models were established in terms of aspects,namely span,width of longitudinal/transverse daylighting strip,height,roof angle,and photovoltaic panel coverage rate(PCR).Daylighting in the greenhouse of each gradient model was simulated,and with the annual average daily light integral(A_(DLI))and distribution uniformity(DU)as evaluation indicators,the influence of various design parameters on the daylighting inside the greenhouse was quantified.The result reveals that:(1)PCR is the decisive indicator for daylighting in the PVG,and a function between PCR and the A_(DLI) is derived as A_(DLI)=-15.5 PCR+16.841;(2)Increasing the width of longitudinal daylighting strip significantly improves the A_(DLI) and enhances DU while increasing the span has a noticeable effect on improving A_(DLI) but does not significantly enhance DU;(3)Increasing the eave height without changing PCR does not enhance A_(DLI) but effectively improves DU;increasing the transverse daylighting strip and adjusting the roof angle hardly improves A_(DLI).In summary,it is recommended that the optimal span for PVGs in tropical regions be set within the range of 6.5-8.0m,and the eave height be set within the range of 2.5-3.5m.Preferably,the longitudinal daylighting strip with a width ranging from 0.5-0.8m should be installed.Based on the above relationship function,the PCR can be calculated according to the appropriate light demand for the cultivated crops.The daylighting design theory proposed in this paper can provide a theoretical basis and reference for the healthy development of the PV industry in tropical regions.展开更多
Background Plant tissue culture has emerged as a tool for improving cotton propagation and genetics,but recalcitrance nature of cotton makes it difficult to develop in vitro regeneration.Cotton’s recalcitrance is inf...Background Plant tissue culture has emerged as a tool for improving cotton propagation and genetics,but recalcitrance nature of cotton makes it difficult to develop in vitro regeneration.Cotton’s recalcitrance is influenced by genotype,explant type,and environmental conditions.To overcome these issues,this study uses different machine learning-based predictive models by employing multiple input factors.Cotyledonary node explants of two commercial cotton cultivars(STN-468 and GSN-12)were isolated from 7–8 days old seedlings,preconditioned with 5,10,and 20 mg·L^(-1) kinetin(KIN)for 10 days.Thereafter,explants were postconditioned on full Murashige and Skoog(MS),1/2MS,1/4MS,and full MS+0.05 mg·L^(-1) KIN,cultured in growth room enlightened with red and blue light-emitting diodes(LED)combination.Statistical analysis(analysis of variance,regression analysis)was employed to assess the impact of different treatments on shoot regeneration,with artificial intelligence(AI)models used for confirming the findings.Results GSN-12 exhibited superior shoot regeneration potential compared with STN-468,with an average of 4.99 shoots per explant versus 3.97.Optimal results were achieved with 5 mg·L^(-1) KIN preconditioning,1/4MS postconditioning,and 80%red LED,with maximum of 7.75 shoot count for GSN-12 under these conditions;while STN-468 reached 6.00 shoots under the conditions of 10 mg·L^(-1) KIN preconditioning,MS with 0.05 mg·L^(-1) KIN(postconditioning)and 75.0%red LED.Rooting was successfully achieved with naphthalene acetic acid and activated charcoal.Additionally,three different powerful AI-based models,namely,extreme gradient boost(XGBoost),random forest(RF),and the artificial neural network-based multilayer perceptron(MLP)regression models validated the findings.Conclusion GSN-12 outperformed STN-468 with optimal results from 5 mg·L^(-1) KIN+1/4MS+80%red LED.Application of machine learning-based prediction models to optimize cotton tissue culture protocols for shoot regeneration is helpful to improve cotton regeneration efficiency.展开更多
Femtosecond laser processing is an important machining method for micro-optical components such as Fresnel zone plate(FZP).However,the low processing efficiency of the femtosecond laser restricts its application.Here,...Femtosecond laser processing is an important machining method for micro-optical components such as Fresnel zone plate(FZP).However,the low processing efficiency of the femtosecond laser restricts its application.Here,a femtosecond laser Bessel beam is proposed to process micro-FZP,which is modulated from a Gaussian beam to a Bessel annular beam.The processing time for FZP with an outer diameter of 60μm is reduced from 30 min to 1.5 min on an important semiconductor material gallium arsenide(GaAs),which significantly improves the processing efficiency.In the modulation process,a central ablation hole that has an adverse effect on the diffraction performance is produced,and the adverse effect is eliminated by superimposing the blazed grating hologram.Meanwhile,the FZP machined by spatial light modulator(SLM)has good morphology and higher diffraction efficiency,which provides a strong guarantee for the application of micro-FZP in computed tomography and solar photovoltaic cells.展开更多
Hypersonic Glide Vehicles(HGVs)are advanced aircraft that can achieve extremely high speeds(generally over 5 Mach)and maneuverability within the Earth's atmosphere.HGV trajectory prediction is crucial for effectiv...Hypersonic Glide Vehicles(HGVs)are advanced aircraft that can achieve extremely high speeds(generally over 5 Mach)and maneuverability within the Earth's atmosphere.HGV trajectory prediction is crucial for effective defense planning and interception strategies.In recent years,HGV trajectory prediction methods based on deep learning have the great potential to significantly enhance prediction accuracy and efficiency.However,it's still challenging to strike a balance between improving prediction performance and reducing computation costs of the deep learning trajectory prediction models.To solve this problem,we propose a new deep learning framework(FECA-LSMN)for efficient HGV trajectory prediction.The model first uses a Frequency Enhanced Channel Attention(FECA)module to facilitate the fusion of different HGV trajectory features,and then subsequently employs a Light Sampling-oriented Multi-Layer Perceptron Network(LSMN)based on simple MLP-based structures to extract long/shortterm HGV trajectory features for accurate trajectory prediction.Also,we employ a new data normalization method called reversible instance normalization(RevIN)to enhance the prediction accuracy and training stability of the network.Compared to other popular trajectory prediction models based on LSTM,GRU and Transformer,our FECA-LSMN model achieves leading or comparable performance in terms of RMSE,MAE and MAPE metrics while demonstrating notably faster computation time.The ablation experiments show that the incorporation of the FECA module significantly improves the prediction performance of the network.The RevIN data normalization technique outperforms traditional min-max normalization as well.展开更多
基金2024 Science and Technology Commissioner Service Group's Emergency Science and Technology Research Project for Wind Disaster Relief in Hainan Province(ZDYF2024YJGG002-8)China Huaneng Group Co.,Ltd.Headquarters Technology Project,Optimization of Photovoltaic Vegetable Greenhouse Structure and Research on Planting Agronomy in Tropical Regions(HNKJ22-HF77)。
文摘In the tropical regions represented by Hainan,there are abundant solar and thermal resources,and it is relatively suitable for the construction of photovoltaic greenhouse(PVG).However,the construction of PVG still relies mainly on experience and is incapable of quantifying the balance between the photovoltaic(PV)generation and the light requirements for agricultural production.As a result,actual PVGs are primarily PV-based,without carefully considering the needs of agricultural daylighting.To quantify the influence of the design parameters of PVGs and the layout of PV panels on the internal daylighting of serrated PVGs,and to optimize the daylighting design of the roof,this paper utilizes the Design Builder software to establish gradient models for a multi-span serrated-type PVG in tropical regions.Gradient models were established in terms of aspects,namely span,width of longitudinal/transverse daylighting strip,height,roof angle,and photovoltaic panel coverage rate(PCR).Daylighting in the greenhouse of each gradient model was simulated,and with the annual average daily light integral(A_(DLI))and distribution uniformity(DU)as evaluation indicators,the influence of various design parameters on the daylighting inside the greenhouse was quantified.The result reveals that:(1)PCR is the decisive indicator for daylighting in the PVG,and a function between PCR and the A_(DLI) is derived as A_(DLI)=-15.5 PCR+16.841;(2)Increasing the width of longitudinal daylighting strip significantly improves the A_(DLI) and enhances DU while increasing the span has a noticeable effect on improving A_(DLI) but does not significantly enhance DU;(3)Increasing the eave height without changing PCR does not enhance A_(DLI) but effectively improves DU;increasing the transverse daylighting strip and adjusting the roof angle hardly improves A_(DLI).In summary,it is recommended that the optimal span for PVGs in tropical regions be set within the range of 6.5-8.0m,and the eave height be set within the range of 2.5-3.5m.Preferably,the longitudinal daylighting strip with a width ranging from 0.5-0.8m should be installed.Based on the above relationship function,the PCR can be calculated according to the appropriate light demand for the cultivated crops.The daylighting design theory proposed in this paper can provide a theoretical basis and reference for the healthy development of the PV industry in tropical regions.
文摘Background Plant tissue culture has emerged as a tool for improving cotton propagation and genetics,but recalcitrance nature of cotton makes it difficult to develop in vitro regeneration.Cotton’s recalcitrance is influenced by genotype,explant type,and environmental conditions.To overcome these issues,this study uses different machine learning-based predictive models by employing multiple input factors.Cotyledonary node explants of two commercial cotton cultivars(STN-468 and GSN-12)were isolated from 7–8 days old seedlings,preconditioned with 5,10,and 20 mg·L^(-1) kinetin(KIN)for 10 days.Thereafter,explants were postconditioned on full Murashige and Skoog(MS),1/2MS,1/4MS,and full MS+0.05 mg·L^(-1) KIN,cultured in growth room enlightened with red and blue light-emitting diodes(LED)combination.Statistical analysis(analysis of variance,regression analysis)was employed to assess the impact of different treatments on shoot regeneration,with artificial intelligence(AI)models used for confirming the findings.Results GSN-12 exhibited superior shoot regeneration potential compared with STN-468,with an average of 4.99 shoots per explant versus 3.97.Optimal results were achieved with 5 mg·L^(-1) KIN preconditioning,1/4MS postconditioning,and 80%red LED,with maximum of 7.75 shoot count for GSN-12 under these conditions;while STN-468 reached 6.00 shoots under the conditions of 10 mg·L^(-1) KIN preconditioning,MS with 0.05 mg·L^(-1) KIN(postconditioning)and 75.0%red LED.Rooting was successfully achieved with naphthalene acetic acid and activated charcoal.Additionally,three different powerful AI-based models,namely,extreme gradient boost(XGBoost),random forest(RF),and the artificial neural network-based multilayer perceptron(MLP)regression models validated the findings.Conclusion GSN-12 outperformed STN-468 with optimal results from 5 mg·L^(-1) KIN+1/4MS+80%red LED.Application of machine learning-based prediction models to optimize cotton tissue culture protocols for shoot regeneration is helpful to improve cotton regeneration efficiency.
基金Projects(51875584,51875585,51975590)supported by the National Natural Science Foundation of China。
文摘Femtosecond laser processing is an important machining method for micro-optical components such as Fresnel zone plate(FZP).However,the low processing efficiency of the femtosecond laser restricts its application.Here,a femtosecond laser Bessel beam is proposed to process micro-FZP,which is modulated from a Gaussian beam to a Bessel annular beam.The processing time for FZP with an outer diameter of 60μm is reduced from 30 min to 1.5 min on an important semiconductor material gallium arsenide(GaAs),which significantly improves the processing efficiency.In the modulation process,a central ablation hole that has an adverse effect on the diffraction performance is produced,and the adverse effect is eliminated by superimposing the blazed grating hologram.Meanwhile,the FZP machined by spatial light modulator(SLM)has good morphology and higher diffraction efficiency,which provides a strong guarantee for the application of micro-FZP in computed tomography and solar photovoltaic cells.
文摘Hypersonic Glide Vehicles(HGVs)are advanced aircraft that can achieve extremely high speeds(generally over 5 Mach)and maneuverability within the Earth's atmosphere.HGV trajectory prediction is crucial for effective defense planning and interception strategies.In recent years,HGV trajectory prediction methods based on deep learning have the great potential to significantly enhance prediction accuracy and efficiency.However,it's still challenging to strike a balance between improving prediction performance and reducing computation costs of the deep learning trajectory prediction models.To solve this problem,we propose a new deep learning framework(FECA-LSMN)for efficient HGV trajectory prediction.The model first uses a Frequency Enhanced Channel Attention(FECA)module to facilitate the fusion of different HGV trajectory features,and then subsequently employs a Light Sampling-oriented Multi-Layer Perceptron Network(LSMN)based on simple MLP-based structures to extract long/shortterm HGV trajectory features for accurate trajectory prediction.Also,we employ a new data normalization method called reversible instance normalization(RevIN)to enhance the prediction accuracy and training stability of the network.Compared to other popular trajectory prediction models based on LSTM,GRU and Transformer,our FECA-LSMN model achieves leading or comparable performance in terms of RMSE,MAE and MAPE metrics while demonstrating notably faster computation time.The ablation experiments show that the incorporation of the FECA module significantly improves the prediction performance of the network.The RevIN data normalization technique outperforms traditional min-max normalization as well.
