J. Kohn, Gregory S. Piorkowski, Nicole E. Seitz Vermeer, Janelle F. Villeneuve
Highlights Performance of denitrifying bioreactors in Alberta was evaluated. Barley straw was more effective in reducing nitrate compared to wood chips. Hydraulic retention time, feedstock, and season are the primary factors affecting nitrate removal. Abstract. This study evaluated the performance of pilot-scale denitrifying bioreactors (LWD: 6 × 0.6 × 1m) filled with different carbon substrates, including barley straw, hemp straw, and woodchips, for removing dissolved nitrogen from simulated subsurface drainage at two representative geographic locations in Alberta. In this study, the bioreactors were tested under varying hydraulic retention times (4, 8, and 12 h) in the spring, summer, and fall of one year. Tracer studies were conducted to evaluate flow and dispersion characteristics. The mean of nitrate removal efficiency ranged from 19% to 87% during the spring, 44% to 95% during the summer, and 21% to 68% during the fall. We found that barley straw was more effective in reducing nitrate (45% to 95%) compared to wood chips (19% to 54%). This study is the first testing of the effect of different biomass types and hydraulic residence times on bioreactor performance in the Canadian prairies (Alberta) and will allow agricultural producers and regulators to assess the suitability of these systems within the region. Keywords: Bioreactor, Denitrification, Water quality, Wood chips, Agricultural residues, Subsurface Drainage.
{"title":"Assessment of Wood Chips and Agricultural Residues as Denitrifying Bioreactor Feedstocks for Use in the Canadian Prairies","authors":"J. Kohn, Gregory S. Piorkowski, Nicole E. Seitz Vermeer, Janelle F. Villeneuve","doi":"10.13031/ja.15412","DOIUrl":"https://doi.org/10.13031/ja.15412","url":null,"abstract":"Highlights Performance of denitrifying bioreactors in Alberta was evaluated. Barley straw was more effective in reducing nitrate compared to wood chips. Hydraulic retention time, feedstock, and season are the primary factors affecting nitrate removal. Abstract. This study evaluated the performance of pilot-scale denitrifying bioreactors (LWD: 6 × 0.6 × 1m) filled with different carbon substrates, including barley straw, hemp straw, and woodchips, for removing dissolved nitrogen from simulated subsurface drainage at two representative geographic locations in Alberta. In this study, the bioreactors were tested under varying hydraulic retention times (4, 8, and 12 h) in the spring, summer, and fall of one year. Tracer studies were conducted to evaluate flow and dispersion characteristics. The mean of nitrate removal efficiency ranged from 19% to 87% during the spring, 44% to 95% during the summer, and 21% to 68% during the fall. We found that barley straw was more effective in reducing nitrate (45% to 95%) compared to wood chips (19% to 54%). This study is the first testing of the effect of different biomass types and hydraulic residence times on bioreactor performance in the Canadian prairies (Alberta) and will allow agricultural producers and regulators to assess the suitability of these systems within the region. Keywords: Bioreactor, Denitrification, Water quality, Wood chips, Agricultural residues, Subsurface Drainage.","PeriodicalId":29714,"journal":{"name":"Journal of the ASABE","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91144126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Christianson, R. Christianson, C. Díaz-García, G. Johnson, B. Maxwell, R. Cooke, N. Wickramarathne, L. Gentry
Highlights The bulk density of woodchips in denitrifying bioreactors in the field is unknown. In situ bulk density estimation methods were developed for use during construction or excavation. Dry bulk densities of aged woodchips at bioreactor bottoms were lower than previous literature values. Moisture and particle size and density explained some, but not all, of the variation in in situ bulk densities. Abstract. Woodchip bulk density in a denitrifying bioreactor governs system hydraulics, but this prime physical attribute has never been estimated in situ. The objectives were twofold: (1) to establish estimates of in situ woodchip bulk density at bioreactors in the field, and (2) evaluate causal factors for and resulting impacts of these estimates. Proof-of-concept bulk density methods were developed at a pilot-scale bioreactor using three ways to estimate volume: surveying the excavated area, pumping the excavation full through a flow meter, and using iPhone Light Detection and Ranging (LiDAR). These methods were then further tested at two new and three old full-size bioreactors. Additional ex situ (off-site) testing with the associated woodchips included analysis of bulk density along a moisture gradient and particle size, particle density, wood composition, and hydraulic property testing. In situ dry bulk densities based on the entire volume of the new bioreactors (206-224 kg/m3) were similar to values from previous lab-scale studies. In situ estimates for woodchips at the bottom of aged bioreactors (22-mo. to 6-y) were unexpectedly low (120-166 kg/m3), given that these woodchips would presumably be the most compacted. These low moisture-content corrected dry bulk densities were influenced by high moisture contents in situ (>70% wet basis). The impacts of particle size and particle density on bulk density were somewhat mixed across the dataset, but in general, smaller woodchips had higher dry bulk densities than larger, and several woodchips sourced from the bottom of bioreactors had low particle densities. Although dry bulk densities in the zone of flow in bioreactors in the field were shown to be relatively low, the resulting permeability coefficients under those packing conditions did not differ from those of the original woodchips. The LiDAR-based volume estimation method was the most practical for large-scale, full-size evaluations and allowed high precision with small features (e.g., vertical reactor edges, drainage fittings). Keywords: Compaction, Cone penetrometer, Drainable porosity, LiDAR, Moisture content, Survey.
{"title":"Denitrifying Bioreactor In Situ Woodchip Bulk Density","authors":"L. Christianson, R. Christianson, C. Díaz-García, G. Johnson, B. Maxwell, R. Cooke, N. Wickramarathne, L. Gentry","doi":"10.13031/ja.15364","DOIUrl":"https://doi.org/10.13031/ja.15364","url":null,"abstract":"Highlights The bulk density of woodchips in denitrifying bioreactors in the field is unknown. In situ bulk density estimation methods were developed for use during construction or excavation. Dry bulk densities of aged woodchips at bioreactor bottoms were lower than previous literature values. Moisture and particle size and density explained some, but not all, of the variation in in situ bulk densities. Abstract. Woodchip bulk density in a denitrifying bioreactor governs system hydraulics, but this prime physical attribute has never been estimated in situ. The objectives were twofold: (1) to establish estimates of in situ woodchip bulk density at bioreactors in the field, and (2) evaluate causal factors for and resulting impacts of these estimates. Proof-of-concept bulk density methods were developed at a pilot-scale bioreactor using three ways to estimate volume: surveying the excavated area, pumping the excavation full through a flow meter, and using iPhone Light Detection and Ranging (LiDAR). These methods were then further tested at two new and three old full-size bioreactors. Additional ex situ (off-site) testing with the associated woodchips included analysis of bulk density along a moisture gradient and particle size, particle density, wood composition, and hydraulic property testing. In situ dry bulk densities based on the entire volume of the new bioreactors (206-224 kg/m3) were similar to values from previous lab-scale studies. In situ estimates for woodchips at the bottom of aged bioreactors (22-mo. to 6-y) were unexpectedly low (120-166 kg/m3), given that these woodchips would presumably be the most compacted. These low moisture-content corrected dry bulk densities were influenced by high moisture contents in situ (>70% wet basis). The impacts of particle size and particle density on bulk density were somewhat mixed across the dataset, but in general, smaller woodchips had higher dry bulk densities than larger, and several woodchips sourced from the bottom of bioreactors had low particle densities. Although dry bulk densities in the zone of flow in bioreactors in the field were shown to be relatively low, the resulting permeability coefficients under those packing conditions did not differ from those of the original woodchips. The LiDAR-based volume estimation method was the most practical for large-scale, full-size evaluations and allowed high precision with small features (e.g., vertical reactor edges, drainage fittings). Keywords: Compaction, Cone penetrometer, Drainable porosity, LiDAR, Moisture content, Survey.","PeriodicalId":29714,"journal":{"name":"Journal of the ASABE","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91166711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Highlights An effective seed treatment method is provided. Three generations of field growth trials were conducted. We Investigated the effects of low-temperature plasma treatment on the biological characters and yield components. Abstract. An atmospheric pressure, low-temperature dielectric barrier discharge (DBD) plasma seed treatment device was developed for plasma seed treatment. The device worked continuously on alfalfa seeds and evenly distributed the seeds in a plasma discharge range. The processing time, voltage amplitude, and frequency were adjustable. The device was used to study the effect of DBD plasma treatment at different voltages and times on alfalfa seed germination using untreated alfalfa seeds as the control (CK). The results showed that the DBD plasma treatment of alfalfa seeds promoted seed germination and seedling growth, and the optimal discharge conditions were a discharge voltage of 11 kV and a discharge time of 40 s. Compared with CK, the germination potential and germination rate increased by 12.49% and 18.08%, respectively. After treatment using the optimal discharge time, the germination potential, germination rate, dry weight, and seedling height increased by 9.9%, 16.1%, 15%, and 32.9%, respectively, compared with CK. The Scanning Electron Microscope images of the seed epidermis treated with 11 kV and 40 s plasma showed that the surface of alfalfa seeds was etched. Different doses of discharge radiation had different effects on physiological processes in seeds, and their sensitivity to plasma discharge was different. In a certain range, the germination rate, germination potential, fresh weight, dry weight, root length, and seedling height of alfalfa seeds improved to different degrees under different discharge voltages and times. Plasma has a good application prospect for improving the growth of alfalfa seeds. Keywords: Alfalfa, Dielectric barrier discharge plasma, Germination, Seed treatment device, Seedling growth.
{"title":"Design of Non-Thermal Plasma Alfalfa Seed Vigor Enhancement Device and Study of Treatment Effect","authors":"Yunting Hui, Yangyang Liao, Sibiao Li, Changyong Shao, Decheng Wang, Yong You","doi":"10.13031/ja.15309","DOIUrl":"https://doi.org/10.13031/ja.15309","url":null,"abstract":"Highlights An effective seed treatment method is provided. Three generations of field growth trials were conducted. We Investigated the effects of low-temperature plasma treatment on the biological characters and yield components. Abstract. An atmospheric pressure, low-temperature dielectric barrier discharge (DBD) plasma seed treatment device was developed for plasma seed treatment. The device worked continuously on alfalfa seeds and evenly distributed the seeds in a plasma discharge range. The processing time, voltage amplitude, and frequency were adjustable. The device was used to study the effect of DBD plasma treatment at different voltages and times on alfalfa seed germination using untreated alfalfa seeds as the control (CK). The results showed that the DBD plasma treatment of alfalfa seeds promoted seed germination and seedling growth, and the optimal discharge conditions were a discharge voltage of 11 kV and a discharge time of 40 s. Compared with CK, the germination potential and germination rate increased by 12.49% and 18.08%, respectively. After treatment using the optimal discharge time, the germination potential, germination rate, dry weight, and seedling height increased by 9.9%, 16.1%, 15%, and 32.9%, respectively, compared with CK. The Scanning Electron Microscope images of the seed epidermis treated with 11 kV and 40 s plasma showed that the surface of alfalfa seeds was etched. Different doses of discharge radiation had different effects on physiological processes in seeds, and their sensitivity to plasma discharge was different. In a certain range, the germination rate, germination potential, fresh weight, dry weight, root length, and seedling height of alfalfa seeds improved to different degrees under different discharge voltages and times. Plasma has a good application prospect for improving the growth of alfalfa seeds. Keywords: Alfalfa, Dielectric barrier discharge plasma, Germination, Seed treatment device, Seedling growth.","PeriodicalId":29714,"journal":{"name":"Journal of the ASABE","volume":"120 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77419558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Highlights Addressing the complex political, economic, and societal challenges inherent in sustainable agriculture and food production requires interdisciplinary thinking and approaches. Relevant pedagogical models and extracurricular experiences can be provided by the Grand Challenges Scholars Program, now spread to nearly 100 universities globally. The complexities of agriculture and food production today can be addressed by future engineering leaders based on this program. Abstract. The education of engineers and other professionals to address the global grand challenge of sustainable food production will require much more than excellent technical skills. New mindsets, human-centered design principles, and collaborative leadership skills will be required to develop leaders who will be successful in addressing the complex political, economic, and societal challenges inherent in sustainable agriculture and food production today. This will require supplementing—not replacing—the technical core of engineering education with new pedagogical models and extracurricular experiences. One such model that has proven effective in this area and has spread to nearly 100 universities globally is the Grand Challenges Scholars Program. This article explains how the complexities of agriculture and food production today can be addressed by future engineering leaders based on this program. Keywords: Food chain, Sustainable agriculture.
{"title":"Perspective: Preparing Leaders to Engineer Sustainability and Resilience Across the Food Chain Through the Grand Challenges Scholars Program","authors":"Richard K. Miller, Y. Yortsos","doi":"10.13031/ja.14915","DOIUrl":"https://doi.org/10.13031/ja.14915","url":null,"abstract":"Highlights Addressing the complex political, economic, and societal challenges inherent in sustainable agriculture and food production requires interdisciplinary thinking and approaches. Relevant pedagogical models and extracurricular experiences can be provided by the Grand Challenges Scholars Program, now spread to nearly 100 universities globally. The complexities of agriculture and food production today can be addressed by future engineering leaders based on this program. Abstract. The education of engineers and other professionals to address the global grand challenge of sustainable food production will require much more than excellent technical skills. New mindsets, human-centered design principles, and collaborative leadership skills will be required to develop leaders who will be successful in addressing the complex political, economic, and societal challenges inherent in sustainable agriculture and food production today. This will require supplementing—not replacing—the technical core of engineering education with new pedagogical models and extracurricular experiences. One such model that has proven effective in this area and has spread to nearly 100 universities globally is the Grand Challenges Scholars Program. This article explains how the complexities of agriculture and food production today can be addressed by future engineering leaders based on this program. Keywords: Food chain, Sustainable agriculture.","PeriodicalId":29714,"journal":{"name":"Journal of the ASABE","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87966278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Javier Campos, Heping Zhu, Hongyoung Jeon, Ramón Salcedo, Erdal Ozkan, Carla Roman, Emilio Gil
Highlights Air-pinch PWM valve was investigated as an alternative to electric PWM valves to manipulate hollow-cone nozzles. Air-pinch and electric PWM valves performed comparable accuracy in flow rate modulations. Droplet sizes from hollow-cone nozzles with both PWM valves were comparable across DUCs ranging from 20% to 100%. Air-pinch PWM valve had great potential of use due to its capacity to isolate the internal parts of the valve from chemicals. Abstract. Electric pulse width modulation (PWM) solenoid valves are commonly used to regulate nozzle flow rates to achieve precision variable-rate spray applications. However, some pesticide formulations, such as wettable powders and adhesive additives, can potentially cause a malfunction such that the valve cannot completely shut off during flow rate modulation if spray lines are not cleaned thoroughly after spray applications. An air-pinch PWM valve was evaluated as a potential alternative to conventional PWM valves to modulate the flow rates of hollow-cone nozzles used on air-assisted orchard sprayers. With the air-pinch valve, spray mixtures only passed through a flexible tube to avoid chemicals directly contacting the moving components inside the valve chamber. The flow rate modulation was performed by pinching and releasing the tube back and forth with air-pilot PWM actions. Evaluations included the flow rate modulation capability along with droplet size distributions from three disc-core hollow-cone nozzles coupled with the PWM pinch valve and compared with a conventional electric PWM valve. Both air-pinch and electric PWM valves performed comparably in the flow rate modulation accuracy and droplet size distribution for hollow-cone nozzles operated at 414 and 827 kPa pressures across the duty cycles (DUCs) ranging from 10% to 100%, except for the air-pinch valve that could not activate at 10% DUC. The flow rates of nozzles modulated with both PWM valves at all DUCs were 5.3% greater on average than the target flow rates, while the flow rates were similar at 90% and 100% DUCs. Droplet size classifications based on ASABE Standard S-572.3 were generally consistent across DUCs ranging from 20% to 100% for the same nozzle and pressure with the air-pinch PWM valve and from 10% to 100% with the conventional electric PWM valve. The consistency of droplet sizes across DUCs and accuracy of flow rate modulations demonstrated the potential advantage of using the air-pinch PWM solenoid valve as an alternative for precision variable-rate sprayers to accurately apply different chemicals. Keywords: Droplet size, Flow rate control, Pesticide, Pinch valve, Precision farming, Pulse width modulation.
{"title":"Air-Pinch PWM Valve to Regulate Flow Rate of Hollow-Cone Nozzles for Variable-Rate Sprayers","authors":"Javier Campos, Heping Zhu, Hongyoung Jeon, Ramón Salcedo, Erdal Ozkan, Carla Roman, Emilio Gil","doi":"10.13031/ja.15601","DOIUrl":"https://doi.org/10.13031/ja.15601","url":null,"abstract":"Highlights Air-pinch PWM valve was investigated as an alternative to electric PWM valves to manipulate hollow-cone nozzles. Air-pinch and electric PWM valves performed comparable accuracy in flow rate modulations. Droplet sizes from hollow-cone nozzles with both PWM valves were comparable across DUCs ranging from 20% to 100%. Air-pinch PWM valve had great potential of use due to its capacity to isolate the internal parts of the valve from chemicals. Abstract. Electric pulse width modulation (PWM) solenoid valves are commonly used to regulate nozzle flow rates to achieve precision variable-rate spray applications. However, some pesticide formulations, such as wettable powders and adhesive additives, can potentially cause a malfunction such that the valve cannot completely shut off during flow rate modulation if spray lines are not cleaned thoroughly after spray applications. An air-pinch PWM valve was evaluated as a potential alternative to conventional PWM valves to modulate the flow rates of hollow-cone nozzles used on air-assisted orchard sprayers. With the air-pinch valve, spray mixtures only passed through a flexible tube to avoid chemicals directly contacting the moving components inside the valve chamber. The flow rate modulation was performed by pinching and releasing the tube back and forth with air-pilot PWM actions. Evaluations included the flow rate modulation capability along with droplet size distributions from three disc-core hollow-cone nozzles coupled with the PWM pinch valve and compared with a conventional electric PWM valve. Both air-pinch and electric PWM valves performed comparably in the flow rate modulation accuracy and droplet size distribution for hollow-cone nozzles operated at 414 and 827 kPa pressures across the duty cycles (DUCs) ranging from 10% to 100%, except for the air-pinch valve that could not activate at 10% DUC. The flow rates of nozzles modulated with both PWM valves at all DUCs were 5.3% greater on average than the target flow rates, while the flow rates were similar at 90% and 100% DUCs. Droplet size classifications based on ASABE Standard S-572.3 were generally consistent across DUCs ranging from 20% to 100% for the same nozzle and pressure with the air-pinch PWM valve and from 10% to 100% with the conventional electric PWM valve. The consistency of droplet sizes across DUCs and accuracy of flow rate modulations demonstrated the potential advantage of using the air-pinch PWM solenoid valve as an alternative for precision variable-rate sprayers to accurately apply different chemicals. Keywords: Droplet size, Flow rate control, Pesticide, Pinch valve, Precision farming, Pulse width modulation.","PeriodicalId":29714,"journal":{"name":"Journal of the ASABE","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135214085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Highlights Comprehensive evaluation of the measurement accuracy of an inexpensive solid-state LiDAR for object detection. Development of an algorithm to acquire point clouds of objects with various shapes under both static and dynamic conditions. Utilization of pseudo-color images to assess the surfaces of regular-shaped cartons and irregular artificial plants. Proposal for integrating the solid-state LiDAR into variable-rate spray applications for greenhouses. Abstract. An effective variable-rate spraying system for greenhouses requires accurate canopy structure parameters of plants to ensure proper pesticide dosage adjustment. While conventional laser systems integrated into spray systems can provide precise point cloud data of plants, they still present a high expense. This study examines the performance of a recently introduced, cost-effective, and high-resolution solid-state LiDAR (Intel RealSense L515) in relation to its potential for greenhouse spray applications. Additionally, a specialized point cloud acquisition algorithm was developed for this solid-state LiDAR to obtain the geometrical parameters of objects. To assess the LiDAR sensor's suitability for greenhouse spray applications, the performance of the LiDAR sensor and the algorithm was evaluated using five different sized regular-shaped cartons and three artificial plants with complex geometry. Various factors were analyzed, such as the horizontal distances between objects and the LiDAR sensor (0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 m), the tilt angle of the LiDAR sensor relative to the ground (45°, 60°, and 75°), the height of the LiDAR sensor from the ground (ranging from 0.3 to 0.8 m with 0.5 m distance intervals), and the forward speed of the LiDAR sensor (0.1, 0.3, 0.6, and 0.9 m s-1). The findings revealed that the optimal detection distance for this LiDAR sensor is 1.0 m. Increasing or decreasing the detection distance of the object relative to the LiDAR sensor diminished the measurement accuracy. The accuracy of the derived geometrical variables was affected by the height and tilt angle of the LiDAR sensor. Nevertheless, the geometrical parameters obtained from the solid-state LiDAR showed a favorable correspondence with the results of manual measurements. The highest root mean square error (RMSE) and coefficient of variation (CV) for the overall test were 14.3 mm and 14.3% in the X (length) direction, 14.3 mm and 14.3% in the Y (width) direction, and 10.8 mm and 10.8% in the Z (height) direction, respectively. The contour Edge Similarity Score for objects measured using the solid-state LiDAR and images obtained with an RGB camera exceeded 0.90. These findings suggest that the proposed solid-state LiDAR and the specifically designed algorithm could be effectively adapted to acquire the geometrical parameters of objects and to develop precise variable-rate spraying systems for greenhouse applications. Keywords: Canopy structure measurements, Point cloud, Precision agriculture, Pr
{"title":"Static and Dynamic Performance Evaluation of a Solid-State LiDAR for 3D Object Detection in Greenhouse Spray Applications","authors":"Zhihong Zhang, Jianing Long, Qinghui Lai, Qingmeng Zhu, Hao He, Ramón Salcedo, Tingting Yan","doi":"10.13031/ja.15285","DOIUrl":"https://doi.org/10.13031/ja.15285","url":null,"abstract":"Highlights Comprehensive evaluation of the measurement accuracy of an inexpensive solid-state LiDAR for object detection. Development of an algorithm to acquire point clouds of objects with various shapes under both static and dynamic conditions. Utilization of pseudo-color images to assess the surfaces of regular-shaped cartons and irregular artificial plants. Proposal for integrating the solid-state LiDAR into variable-rate spray applications for greenhouses. Abstract. An effective variable-rate spraying system for greenhouses requires accurate canopy structure parameters of plants to ensure proper pesticide dosage adjustment. While conventional laser systems integrated into spray systems can provide precise point cloud data of plants, they still present a high expense. This study examines the performance of a recently introduced, cost-effective, and high-resolution solid-state LiDAR (Intel RealSense L515) in relation to its potential for greenhouse spray applications. Additionally, a specialized point cloud acquisition algorithm was developed for this solid-state LiDAR to obtain the geometrical parameters of objects. To assess the LiDAR sensor's suitability for greenhouse spray applications, the performance of the LiDAR sensor and the algorithm was evaluated using five different sized regular-shaped cartons and three artificial plants with complex geometry. Various factors were analyzed, such as the horizontal distances between objects and the LiDAR sensor (0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 m), the tilt angle of the LiDAR sensor relative to the ground (45°, 60°, and 75°), the height of the LiDAR sensor from the ground (ranging from 0.3 to 0.8 m with 0.5 m distance intervals), and the forward speed of the LiDAR sensor (0.1, 0.3, 0.6, and 0.9 m s-1). The findings revealed that the optimal detection distance for this LiDAR sensor is 1.0 m. Increasing or decreasing the detection distance of the object relative to the LiDAR sensor diminished the measurement accuracy. The accuracy of the derived geometrical variables was affected by the height and tilt angle of the LiDAR sensor. Nevertheless, the geometrical parameters obtained from the solid-state LiDAR showed a favorable correspondence with the results of manual measurements. The highest root mean square error (RMSE) and coefficient of variation (CV) for the overall test were 14.3 mm and 14.3% in the X (length) direction, 14.3 mm and 14.3% in the Y (width) direction, and 10.8 mm and 10.8% in the Z (height) direction, respectively. The contour Edge Similarity Score for objects measured using the solid-state LiDAR and images obtained with an RGB camera exceeded 0.90. These findings suggest that the proposed solid-state LiDAR and the specifically designed algorithm could be effectively adapted to acquire the geometrical parameters of objects and to develop precise variable-rate spraying systems for greenhouse applications. Keywords: Canopy structure measurements, Point cloud, Precision agriculture, Pr","PeriodicalId":29714,"journal":{"name":"Journal of the ASABE","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135319864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mary Katherine Watson, Elizabeth Flanagan, Caye M. Drapcho
Highlights Non-carbonate components of BG11 media impact TIC calculation on average 4.00 mg/L at high pH. BG11 media non-carbonate alkalinity (NCA) varies with pH: NCA (meq/L) = 0.0393×e 0.2075×pH + (2.086×10 -9 )e 1.860×pH . Monod kinetic constants with CO 2 , HCO 3 - , and CO 3 2- as inorganic carbon sources are improved from a previous report. Kinetic constants continue to be the only known reports considering multiple inorganic carbon sources. Algal stoichiometric reactions are developed that account for variation in cell content and carbon source. Abstract. Due to increasing atmospheric CO2, algal growth systems at high pH are of interest to support enhanced diffusion and carbon capture. Given the interactions between algal growth, pH, and alkalinity, data from Watson and Drapcho (2016) were re-examined to determine the impact of the non-carbonate constituents in BG11 media on estimates of Monod kinetic parameters, biomass yield, and cell stoichiometry. Based on a computational method, non-carbonate alkalinity (NCA) in BG11 media varies with pH according to: NCA (meq/L) = 0.0393×e0.2075×pH + (2.086×10-9)e1.860×pH (R2 = 0.999) over the pH range of 10.3 – 11.5. Updated maximum specific growth rates were determined to be 0.060, 0.057, and 0.051 hr-1 for CO2, HCO3, and CO3, respectively. Generalizable stoichiometric algal growth equations that consider variable nutrient ratios and multiple inorganic carbon species were developed. Improved kinetic and stoichiometric parameters will serve as the foundation for a dynamic mathematical model to support the design of high pH algal carbon capture systems. Keywords: Algae, Alkalinity, Carbon Abatement, Carbon Capture, Kinetics, Stoichiometry, Total Inorganic Carbon.
{"title":"Inorganic Carbon-Limited Freshwater Algal Growth at High Ph: Revisited with Focus on Alkalinity","authors":"Mary Katherine Watson, Elizabeth Flanagan, Caye M. Drapcho","doi":"10.13031/ja.15411","DOIUrl":"https://doi.org/10.13031/ja.15411","url":null,"abstract":"Highlights Non-carbonate components of BG11 media impact TIC calculation on average 4.00 mg/L at high pH. BG11 media non-carbonate alkalinity (NCA) varies with pH: NCA (meq/L) = 0.0393×e 0.2075×pH + (2.086×10 -9 )e 1.860×pH . Monod kinetic constants with CO 2 , HCO 3 - , and CO 3 2- as inorganic carbon sources are improved from a previous report. Kinetic constants continue to be the only known reports considering multiple inorganic carbon sources. Algal stoichiometric reactions are developed that account for variation in cell content and carbon source. Abstract. Due to increasing atmospheric CO2, algal growth systems at high pH are of interest to support enhanced diffusion and carbon capture. Given the interactions between algal growth, pH, and alkalinity, data from Watson and Drapcho (2016) were re-examined to determine the impact of the non-carbonate constituents in BG11 media on estimates of Monod kinetic parameters, biomass yield, and cell stoichiometry. Based on a computational method, non-carbonate alkalinity (NCA) in BG11 media varies with pH according to: NCA (meq/L) = 0.0393×e0.2075×pH + (2.086×10-9)e1.860×pH (R2 = 0.999) over the pH range of 10.3 – 11.5. Updated maximum specific growth rates were determined to be 0.060, 0.057, and 0.051 hr-1 for CO2, HCO3, and CO3, respectively. Generalizable stoichiometric algal growth equations that consider variable nutrient ratios and multiple inorganic carbon species were developed. Improved kinetic and stoichiometric parameters will serve as the foundation for a dynamic mathematical model to support the design of high pH algal carbon capture systems. Keywords: Algae, Alkalinity, Carbon Abatement, Carbon Capture, Kinetics, Stoichiometry, Total Inorganic Carbon.","PeriodicalId":29714,"journal":{"name":"Journal of the ASABE","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135710166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Qin, Jeehwa Hong, Hyunjeong Cho, J. V. Van Kessel, I. Baek, K. Chao, M. Kim
Highlights A multimodal optical sensing system was developed for food safety applications. The prototype system can conduct dual-band Raman spectroscopy at 785 and 1064 nm. The system can automatically measure samples in Petri dishes or well plates. The system with AI software is promising for identifying species of foodborne bacteria. Abstract. A novel multimodal optical sensing system was developed for automated and intelligent food safety inspection. The system uses two pairs of compact point lasers and dispersive spectrometers at 785 and 1064 nm to realize dual-band Raman spectroscopy and imaging, which is suitable to measure samples generating low- and high-fluorescence interference signals, respectively. Automated spectral acquisition can be performed using a direct-drive XY moving stage for solid, powder, and liquid samples placed in customized well plates or randomly scattered in standard Petri dishes (e.g., bacterial colonies). Three LED lights (white backlight, UV ring light, and white ring light) and two miniature color cameras are used for machine vision measurements of samples in the Petri dishes using different combinations of illuminations and imaging modalities (e.g., transmission, fluorescence, and color). Real-time image processing and motion control techniques are used to implement automated sample counting, positioning, sampling, and synchronization functions. System software was developed using LabVIEW with integrated artificial intelligence functions able to identify and label interesting targets instantly. The system capability was demonstrated by an example application for rapid identification of five common foodborne bacteria, including Bacillus cereus, E. coli, Listeria monocytogenes, Staphylococcus aureus, and Salmonella spp.. Using a machine learning model based on a linear support vector machine, a classification accuracy of 98.6% was achieved using Raman spectra automatically collected from 222 bacterial colonies of the five species grown on nutrient nonselective agar in 90 mm Petri dishes. The entire system was built on a 30×45 cm2 breadboard, enabling it compact and portable and its use for field and on-site biological and chemical food safety inspection in regulatory and industrial applications. Keywords: Artificial intelligence, Automated sampling, Bacteria, Food safety, Machine learning, Machine vision, Raman, Sensing.
{"title":"A Multimodal Optical Sensing System for Automated and Intelligent Food Safety Inspection","authors":"J. Qin, Jeehwa Hong, Hyunjeong Cho, J. V. Van Kessel, I. Baek, K. Chao, M. Kim","doi":"10.13031/ja.15526","DOIUrl":"https://doi.org/10.13031/ja.15526","url":null,"abstract":"Highlights A multimodal optical sensing system was developed for food safety applications. The prototype system can conduct dual-band Raman spectroscopy at 785 and 1064 nm. The system can automatically measure samples in Petri dishes or well plates. The system with AI software is promising for identifying species of foodborne bacteria. Abstract. A novel multimodal optical sensing system was developed for automated and intelligent food safety inspection. The system uses two pairs of compact point lasers and dispersive spectrometers at 785 and 1064 nm to realize dual-band Raman spectroscopy and imaging, which is suitable to measure samples generating low- and high-fluorescence interference signals, respectively. Automated spectral acquisition can be performed using a direct-drive XY moving stage for solid, powder, and liquid samples placed in customized well plates or randomly scattered in standard Petri dishes (e.g., bacterial colonies). Three LED lights (white backlight, UV ring light, and white ring light) and two miniature color cameras are used for machine vision measurements of samples in the Petri dishes using different combinations of illuminations and imaging modalities (e.g., transmission, fluorescence, and color). Real-time image processing and motion control techniques are used to implement automated sample counting, positioning, sampling, and synchronization functions. System software was developed using LabVIEW with integrated artificial intelligence functions able to identify and label interesting targets instantly. The system capability was demonstrated by an example application for rapid identification of five common foodborne bacteria, including Bacillus cereus, E. coli, Listeria monocytogenes, Staphylococcus aureus, and Salmonella spp.. Using a machine learning model based on a linear support vector machine, a classification accuracy of 98.6% was achieved using Raman spectra automatically collected from 222 bacterial colonies of the five species grown on nutrient nonselective agar in 90 mm Petri dishes. The entire system was built on a 30×45 cm2 breadboard, enabling it compact and portable and its use for field and on-site biological and chemical food safety inspection in regulatory and industrial applications. Keywords: Artificial intelligence, Automated sampling, Bacteria, Food safety, Machine learning, Machine vision, Raman, Sensing.","PeriodicalId":29714,"journal":{"name":"Journal of the ASABE","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74394392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Highlights RZWQM2-P was tested and validated for clay loam soil using daily discharge and load data. The model performed satisfactorily in predicting hydrology and TP load, but DRP prediction was unsatisfactory. Inability of the model to simulate P loss in subsurface drainage discharge after fertilization event was one of the reasons for the unsatisfactory model performance. Abstract. Phosphorus (P) loss and transport through subsurface drainage systems is a primary focus for addressing harmful algal blooms in freshwater systems. The recent development of the phosphorus (P) routine of the Root Zone Water Quality Model (RZWQM2-P) has the potential to enhance our understanding of the fate and transport of P from subsurface-drained fields to surface water. However, there is a need to test the model under different fertilization, soil, climate, and cropping conditions. The objective of this study was to test the model's performance with daily drainage discharge, dissolved reactive phosphorus (DRP), and total phosphorus (TP) load collected from a subsurface-drained field with clay loam soil. We calibrated RZWQM2-P using two years of measured data. Subsequently, we validated RZWQM2-P using a year and nine months of measured data. We used the Nash-Sutcliffe model efficiency (NSE) and percentage bias (PBIAS) statistics for the RZWQM2-P model evaluation. The results showed that the model performance was “good” (daily NSE = 0.66 and PBIAS = -7.16) in predicting hydrology for the calibration period. For the validation period, the hydrology prediction of the model was “very good” (daily NSE = 0.76), but it had a “satisfactory” underestimation bias (PBIAS = 23.57). The model’s performance was “unsatisfactory” in simulating DRP for both calibration (daily NSE = 0.31 and PBIAS = -61.50) and validation (daily NSE = 0.32 and PBIAS = 43.68) periods. The P model showed “satisfactory” performance in predicting TP load for both calibration (daily NSE = 0.46 and PBIAS = -32.41) and validation (daily NSE = 0.39 and PBIAS = 42.90) periods, although both periods showed “unsatisfactory” percent bias. The underperformance may have been due to the model’s inability to partition fertilizer P into different P pools under high water tables or ponding conditions when using daily data. In conclusion, the RZWQM2-P model performed well for drainage discharge with daily data, but further investigation is needed to improve the P component of the model. Keywords: Field-scale modeling, Nutrient load, Phosphorus modeling, Subsurface drainage, Tile drainage, Water Quality.
{"title":"Calibration and Validation of RZWQM2-P Model to Simulate Phosphorus Loss in a Clay Loam Soil in Michigan","authors":"Md Sami Bin Shokrana, E. Ghane, Z. Qi","doi":"10.13031/ja.15283","DOIUrl":"https://doi.org/10.13031/ja.15283","url":null,"abstract":"Highlights RZWQM2-P was tested and validated for clay loam soil using daily discharge and load data. The model performed satisfactorily in predicting hydrology and TP load, but DRP prediction was unsatisfactory. Inability of the model to simulate P loss in subsurface drainage discharge after fertilization event was one of the reasons for the unsatisfactory model performance. Abstract. Phosphorus (P) loss and transport through subsurface drainage systems is a primary focus for addressing harmful algal blooms in freshwater systems. The recent development of the phosphorus (P) routine of the Root Zone Water Quality Model (RZWQM2-P) has the potential to enhance our understanding of the fate and transport of P from subsurface-drained fields to surface water. However, there is a need to test the model under different fertilization, soil, climate, and cropping conditions. The objective of this study was to test the model's performance with daily drainage discharge, dissolved reactive phosphorus (DRP), and total phosphorus (TP) load collected from a subsurface-drained field with clay loam soil. We calibrated RZWQM2-P using two years of measured data. Subsequently, we validated RZWQM2-P using a year and nine months of measured data. We used the Nash-Sutcliffe model efficiency (NSE) and percentage bias (PBIAS) statistics for the RZWQM2-P model evaluation. The results showed that the model performance was “good” (daily NSE = 0.66 and PBIAS = -7.16) in predicting hydrology for the calibration period. For the validation period, the hydrology prediction of the model was “very good” (daily NSE = 0.76), but it had a “satisfactory” underestimation bias (PBIAS = 23.57). The model’s performance was “unsatisfactory” in simulating DRP for both calibration (daily NSE = 0.31 and PBIAS = -61.50) and validation (daily NSE = 0.32 and PBIAS = 43.68) periods. The P model showed “satisfactory” performance in predicting TP load for both calibration (daily NSE = 0.46 and PBIAS = -32.41) and validation (daily NSE = 0.39 and PBIAS = 42.90) periods, although both periods showed “unsatisfactory” percent bias. The underperformance may have been due to the model’s inability to partition fertilizer P into different P pools under high water tables or ponding conditions when using daily data. In conclusion, the RZWQM2-P model performed well for drainage discharge with daily data, but further investigation is needed to improve the P component of the model. Keywords: Field-scale modeling, Nutrient load, Phosphorus modeling, Subsurface drainage, Tile drainage, Water Quality.","PeriodicalId":29714,"journal":{"name":"Journal of the ASABE","volume":"176 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73169360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Toby A. Adjuik, S. Nokes, M. Montross, M. Sama, O. Wendroth
Highlights In this study, six machine learning (ML) models were developed using a large database of soils to predict saturated hydraulic conductivity of these soils using easily measured soil characteristics. Tree-based regression models outperformed all other ML models tested. Neural networks were not suitable for predicting saturated hydraulic conductivity. Clay content, followed by bulk density, explained the highest amount of variation in the data of the predictors examined. Abstract. One of the most important soil hydraulic properties for modeling water transport in the vadose zone is saturated hydraulic conductivity. However, it is challenging to measure it in the field. Pedotransfer Functions (PTFs) are mathematical models that can predict saturated hydraulic conductivity (Ks) from easily measured soil characteristics. Though the development of PTFs for predicting Ks is not new, the tools and methods used to predict Ks are continuously evolving. Model performance depends on choosing soil features that explain the largest amount of Ks variance with the fewest input variables. In addition, the lack of interpretability in most “black box” machine learning models makes it difficult to extract practical knowledge as the machine learning process obfuscates the relationship between inputs and outputs in the PTF models. The objective of this study was to develop a set of new PTFs for predicting Ks using machine learning algorithms and a large database of over 8000 soil samples (the Florida Soil Characterization Database) while incorporating statistical methods to inform predictor selection for the model inputs. Of the machine learning (ML) models tested, random forest regression (RF) and gradient-boosted regression (GB) gave the best performances, with R2 = 0.71 and RMSE = 0.47 cm h-1 on the test data for both. Using the permutation feature importance technique, the GB and RF regression models showed similar results, where clay content described the most variation in the data, followed by bulk density. The implication of this study is that, when predicting Ks using the Florida Soil Characterization Database, priority should be given to obtaining quality data on clay content and bulk density as they are the most influential predictors for estimating Ks. Keywords: Deep learning, Gradient boosted regression, Pedotransfer functions, Random forest regression, Soil database, Soil properties.
在这项研究中，利用一个大型土壤数据库开发了六个机器学习(ML)模型，利用易于测量的土壤特征来预测这些土壤的饱和水力传导性。基于树的回归模型优于所有其他测试的ML模型。神经网络不适合预测饱和水导率。粘土含量，其次是体积密度，解释了所检查的预测数据中最大的变化。摘要其中一个最重要的土壤水力性质的模拟水在渗透带是饱和水力传导性。然而，在现场测量它是具有挑战性的。土壤传递函数(PTFs)是一种数学模型，可以根据容易测量的土壤特性预测饱和水力传导率(Ks)。虽然用于预测k的ptf的发展并不新鲜，但用于预测k的工具和方法仍在不断发展。模型的性能取决于选择用最少的输入变量解释最大数量的k方差的土壤特征。此外，大多数“黑箱”机器学习模型缺乏可解释性，这使得提取实用知识变得困难，因为机器学习过程模糊了PTF模型中输入和输出之间的关系。本研究的目的是开发一套新的ptf，用于使用机器学习算法和超过8000个土壤样本的大型数据库(佛罗里达土壤特征数据库)来预测k，同时结合统计方法来为模型输入的预测器选择提供信息。在测试的机器学习(ML)模型中，随机森林回归(RF)和梯度增强回归(GB)的性能最好，两者的测试数据的R2 = 0.71, RMSE = 0.47 cm h-1。使用排列特征重要性技术，GB和RF回归模型显示了相似的结果，其中粘土含量描述了数据中最大的变化，其次是体积密度。本研究的含义是，当使用佛罗里达土壤特征数据库预测k时，应优先考虑获得粘土含量和容重的高质量数据，因为它们是估计k的最具影响力的预测因子。关键词:深度学习，梯度增强回归，土壤传递函数，随机森林回归，土壤数据库，土壤性质
{"title":"Predictor Selection and Machine Learning Regression Methods to Predict Saturated Hydraulic Conductivity From a Large Public Soil Database","authors":"Toby A. Adjuik, S. Nokes, M. Montross, M. Sama, O. Wendroth","doi":"10.13031/ja.15068","DOIUrl":"https://doi.org/10.13031/ja.15068","url":null,"abstract":"Highlights In this study, six machine learning (ML) models were developed using a large database of soils to predict saturated hydraulic conductivity of these soils using easily measured soil characteristics. Tree-based regression models outperformed all other ML models tested. Neural networks were not suitable for predicting saturated hydraulic conductivity. Clay content, followed by bulk density, explained the highest amount of variation in the data of the predictors examined. Abstract. One of the most important soil hydraulic properties for modeling water transport in the vadose zone is saturated hydraulic conductivity. However, it is challenging to measure it in the field. Pedotransfer Functions (PTFs) are mathematical models that can predict saturated hydraulic conductivity (Ks) from easily measured soil characteristics. Though the development of PTFs for predicting Ks is not new, the tools and methods used to predict Ks are continuously evolving. Model performance depends on choosing soil features that explain the largest amount of Ks variance with the fewest input variables. In addition, the lack of interpretability in most “black box” machine learning models makes it difficult to extract practical knowledge as the machine learning process obfuscates the relationship between inputs and outputs in the PTF models. The objective of this study was to develop a set of new PTFs for predicting Ks using machine learning algorithms and a large database of over 8000 soil samples (the Florida Soil Characterization Database) while incorporating statistical methods to inform predictor selection for the model inputs. Of the machine learning (ML) models tested, random forest regression (RF) and gradient-boosted regression (GB) gave the best performances, with R2 = 0.71 and RMSE = 0.47 cm h-1 on the test data for both. Using the permutation feature importance technique, the GB and RF regression models showed similar results, where clay content described the most variation in the data, followed by bulk density. The implication of this study is that, when predicting Ks using the Florida Soil Characterization Database, priority should be given to obtaining quality data on clay content and bulk density as they are the most influential predictors for estimating Ks. Keywords: Deep learning, Gradient boosted regression, Pedotransfer functions, Random forest regression, Soil database, Soil properties.","PeriodicalId":29714,"journal":{"name":"Journal of the ASABE","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79780815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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