Managing fertilization in integrated crop-livestock systems(ICLS)during periods of low nutrient export,known as system fertilization,can optimize nutrient use by enhancing the soil's biochemical and physical-hydri...Managing fertilization in integrated crop-livestock systems(ICLS)during periods of low nutrient export,known as system fertilization,can optimize nutrient use by enhancing the soil's biochemical and physical-hydric properties.However,interdisciplinary studies on processes that improve input utilization in ICLS remain scarce.This study aimed to assess the relationships between the effciencies of different nutrient management strategies in ICLS and pure crop systems(PCS)and the biochemical and physical-hydric quality of soil.Two fertilization strategies(system fertilization and crop fertilization)and two cropping systems(ICLS and PCS)were evaluated in a randomized block design with three replicates.In the PCS,soybean was grown followed by ryegrass as a cover crop.In the ICLS,sheep grazed on the ryegrass.In the crop fertilization,phosphorus and potassium were applied to the soybean planting,and nitrogen was applied in the ryegrass establishment.Nitrogen,phosphorus,and potassium were applied during ryegrass establishment in the system fertilization.Soil quality indexes were calculated using fourteen physical-hydric and biochemical soil indicators,and primary production and nutrient utilization effciency were evaluated.System fertilization in ICLS enhanced the soil functions of water storage and availability for plants,structural stability,and resistance to degradation.System fertilization in ICLS improved the soil quality by 14%over PCS and 13%over crop fertilization in ICLS.Notably,this optimized system yielded the highest primary production.These findings underscore the pivotal role of system fertilization in ICLS to boost food production and enhance soil ecosystem services without increasing the consumption of external fertilizers.They advocate for a strategic shift towards system-level fertilization in integrated systems,and demonstrate for the frst time in ICLS,the delicate balance between nutrient management,soil health,and sustainable productivity.展开更多
Background:Stretching has wide appeal,but there seems to exist some mismatch between its purported applications and what the evidence shows.There is compelling evidence for some stretching applications,but for others,...Background:Stretching has wide appeal,but there seems to exist some mismatch between its purported applications and what the evidence shows.There is compelling evidence for some stretching applications,but for others,the evidence seems heterogeneous or unsupportive.The discrepancies even affect some systematic reviews,possibly due to heterogeneous eligibility criteria and search strategies.This consensus paper seeks to unify the divergent findings on stretching and its implications for both athletic performance and clinical practices by delivering evidence-based recommendations.Methods:A panel of 20 experts with a blend of practical experience and scholarly knowledge was assembled.The panel meticulously reviewed existing systematic reviews,defined key terminologies(e.g.,consensus definitions for different stretching modes),and crafted guidelines using a Delphi consensus approach(minimum required agreement:80%).The analysis focused on 8 topics,including stretching's acute and chronic(long-term)effects on range of motion,strength performance,muscle hypertrophy,stiffness,injury prevention,muscle recovery,posture correction,and cardiovascular health.Results:There was consensus that chronic and acute stretching(a)improves range of motion(although alternatives exist)and(b)reduces muscle stiffness(which may not always be desirable);the panel also agreed that chronic stretching(c)may promote vascular health,but more research is warranted.In contrast,consensus was found that stretch training does not(a)contribute substantively to muscle growth,(b)serve as an allencompassing injury prevention strategy,(c)improve posture,or(d)acutely enhance post-exercise recovery.Conclusion:These recommendations provide guidance for athletes and practitioners,highlighting research gaps that should be addressed to more comprehensively understand the full scope of stretching effects.展开更多
Handling missing data accurately is critical in clinical research, where data quality directly impacts decision-making and patient outcomes. While deep learning (DL) techniques for data imputation have gained attentio...Handling missing data accurately is critical in clinical research, where data quality directly impacts decision-making and patient outcomes. While deep learning (DL) techniques for data imputation have gained attention, challenges remain, especially when dealing with diverse data types. In this study, we introduce a novel data imputation method based on a modified convolutional neural network, specifically, a Deep Residual-Convolutional Neural Network (DRes-CNN) architecture designed to handle missing values across various datasets. Our approach demonstrates substantial improvements over existing imputation techniques by leveraging residual connections and optimized convolutional layers to capture complex data patterns. We evaluated the model on publicly available datasets, including Medical Information Mart for Intensive Care (MIMIC-III and MIMIC-IV), which contain critical care patient data, and the Beijing Multi-Site Air Quality dataset, which measures environmental air quality. The proposed DRes-CNN method achieved a root mean square error (RMSE) of 0.00006, highlighting its high accuracy and robustness. We also compared with Low Light-Convolutional Neural Network (LL-CNN) and U-Net methods, which had RMSE values of 0.00075 and 0.00073, respectively. This represented an improvement of approximately 92% over LL-CNN and 91% over U-Net. The results showed that this DRes-CNN-based imputation method outperforms current state-of-the-art models. These results established DRes-CNN as a reliable solution for addressing missing data.展开更多
This paper proposes a method to evaluate the reliability of power system with different capacities of wind power while considering carbon tax. The proposed method is a hybrid approach which combines Frequency and Dura...This paper proposes a method to evaluate the reliability of power system with different capacities of wind power while considering carbon tax. The proposed method is a hybrid approach which combines Frequency and Duration (F&D) method and Monte Carlo Simulation (MCS) method. MCS method is used to achieve a model to simulate the random status of power system. Also, the proposed method is applied on the IEEE 14-bus test system to investigate the effects of integrating different capacities of wind energy to the reliability of power system with considering carbon tax.展开更多
The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed memorysystems.However, MPI implementations can contain defects that impact the reliability and performance of par...The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed memorysystems.However, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.展开更多
Airplanes are a social necessity for movement of humans,goods,and other.They are generally safe modes of transportation;however,incidents and accidents occasionally occur.To prevent aviation accidents,it is necessary ...Airplanes are a social necessity for movement of humans,goods,and other.They are generally safe modes of transportation;however,incidents and accidents occasionally occur.To prevent aviation accidents,it is necessary to develop a machine-learning model to detect and predict commercial flights using automatic dependent surveillance–broadcast data.This study combined data-quality detection,anomaly detection,and abnormality-classification-model development.The research methodology involved the following stages:problem statement,data selection and labeling,prediction-model development,deployment,and testing.The data labeling process was based on the rules framed by the international civil aviation organization for commercial,jet-engine flights and validated by expert commercial pilots.The results showed that the best prediction model,the quadratic-discriminant-analysis,was 93%accurate,indicating a“good fit”.Moreover,the model’s area-under-the-curve results for abnormal and normal detection were 0.97 and 0.96,respectively,thus confirming its“good fit”.展开更多
Objective:To characterize the infection patterns and growth characteristics of the Zika virus(ZIKV)strain JMB-185 from Indonesia in various mammalian cell lines.Methods:ZIKV was grown in human(A549,HEK293,HepG2,Huh7,J...Objective:To characterize the infection patterns and growth characteristics of the Zika virus(ZIKV)strain JMB-185 from Indonesia in various mammalian cell lines.Methods:ZIKV was grown in human(A549,HEK293,HepG2,Huh7,Jurkat,and THP-1)and non-human mammalian(RAW264.7,Vero,and Vero76)cell lines.Viral replication kinetics were measured using plaque assay,while intra-and extracellular viral RNA concentrations were assessed using RT-PCR.Flow cytometry was used to quantify the infected cells and cell viability was measured using an MTT assay.The ability of ZIKV to infect cell lines was visualized using a fluorescence immunostaining assay.Results:This ZIKV strain preferentially infected the lung,kidney,and liver cell lines A549,HEK293,Huh7,Vero,and Vero76,but not the immune cells Jurkat,RAW264.7,and THP-1.By contrast,the ZIKV showed no sign of infection in HepG2 cells,while maintaining viral titer over 3 days post-infection,with no infection recorded in immunostaining,no increase in viral RNA,and no indication of cell deterioration.Conclusions:The Indonesian ZIKV strain has a similar infection profile as other strains,except for its poor infectivity on HepG2 cells.Information on the growth characteristics of Indonesia ZIKV will help expand our understanding of the biology of ZIKV which will be useful for various applications including antiviral discovery.展开更多
基金funded by the Funda??o Agrisus through project code‘PA3010/20’the Coordination for the Improvement of Higher Education Personnel,Brasil,under Finance Code 001。
文摘Managing fertilization in integrated crop-livestock systems(ICLS)during periods of low nutrient export,known as system fertilization,can optimize nutrient use by enhancing the soil's biochemical and physical-hydric properties.However,interdisciplinary studies on processes that improve input utilization in ICLS remain scarce.This study aimed to assess the relationships between the effciencies of different nutrient management strategies in ICLS and pure crop systems(PCS)and the biochemical and physical-hydric quality of soil.Two fertilization strategies(system fertilization and crop fertilization)and two cropping systems(ICLS and PCS)were evaluated in a randomized block design with three replicates.In the PCS,soybean was grown followed by ryegrass as a cover crop.In the ICLS,sheep grazed on the ryegrass.In the crop fertilization,phosphorus and potassium were applied to the soybean planting,and nitrogen was applied in the ryegrass establishment.Nitrogen,phosphorus,and potassium were applied during ryegrass establishment in the system fertilization.Soil quality indexes were calculated using fourteen physical-hydric and biochemical soil indicators,and primary production and nutrient utilization effciency were evaluated.System fertilization in ICLS enhanced the soil functions of water storage and availability for plants,structural stability,and resistance to degradation.System fertilization in ICLS improved the soil quality by 14%over PCS and 13%over crop fertilization in ICLS.Notably,this optimized system yielded the highest primary production.These findings underscore the pivotal role of system fertilization in ICLS to boost food production and enhance soil ecosystem services without increasing the consumption of external fertilizers.They advocate for a strategic shift towards system-level fertilization in integrated systems,and demonstrate for the frst time in ICLS,the delicate balance between nutrient management,soil health,and sustainable productivity.
文摘Background:Stretching has wide appeal,but there seems to exist some mismatch between its purported applications and what the evidence shows.There is compelling evidence for some stretching applications,but for others,the evidence seems heterogeneous or unsupportive.The discrepancies even affect some systematic reviews,possibly due to heterogeneous eligibility criteria and search strategies.This consensus paper seeks to unify the divergent findings on stretching and its implications for both athletic performance and clinical practices by delivering evidence-based recommendations.Methods:A panel of 20 experts with a blend of practical experience and scholarly knowledge was assembled.The panel meticulously reviewed existing systematic reviews,defined key terminologies(e.g.,consensus definitions for different stretching modes),and crafted guidelines using a Delphi consensus approach(minimum required agreement:80%).The analysis focused on 8 topics,including stretching's acute and chronic(long-term)effects on range of motion,strength performance,muscle hypertrophy,stiffness,injury prevention,muscle recovery,posture correction,and cardiovascular health.Results:There was consensus that chronic and acute stretching(a)improves range of motion(although alternatives exist)and(b)reduces muscle stiffness(which may not always be desirable);the panel also agreed that chronic stretching(c)may promote vascular health,but more research is warranted.In contrast,consensus was found that stretch training does not(a)contribute substantively to muscle growth,(b)serve as an allencompassing injury prevention strategy,(c)improve posture,or(d)acutely enhance post-exercise recovery.Conclusion:These recommendations provide guidance for athletes and practitioners,highlighting research gaps that should be addressed to more comprehensively understand the full scope of stretching effects.
基金supported by the Intelligent System Research Group(ISysRG)supported by Universitas Sriwijaya funded by the Competitive Research 2024.
文摘Handling missing data accurately is critical in clinical research, where data quality directly impacts decision-making and patient outcomes. While deep learning (DL) techniques for data imputation have gained attention, challenges remain, especially when dealing with diverse data types. In this study, we introduce a novel data imputation method based on a modified convolutional neural network, specifically, a Deep Residual-Convolutional Neural Network (DRes-CNN) architecture designed to handle missing values across various datasets. Our approach demonstrates substantial improvements over existing imputation techniques by leveraging residual connections and optimized convolutional layers to capture complex data patterns. We evaluated the model on publicly available datasets, including Medical Information Mart for Intensive Care (MIMIC-III and MIMIC-IV), which contain critical care patient data, and the Beijing Multi-Site Air Quality dataset, which measures environmental air quality. The proposed DRes-CNN method achieved a root mean square error (RMSE) of 0.00006, highlighting its high accuracy and robustness. We also compared with Low Light-Convolutional Neural Network (LL-CNN) and U-Net methods, which had RMSE values of 0.00075 and 0.00073, respectively. This represented an improvement of approximately 92% over LL-CNN and 91% over U-Net. The results showed that this DRes-CNN-based imputation method outperforms current state-of-the-art models. These results established DRes-CNN as a reliable solution for addressing missing data.
文摘This paper proposes a method to evaluate the reliability of power system with different capacities of wind power while considering carbon tax. The proposed method is a hybrid approach which combines Frequency and Duration (F&D) method and Monte Carlo Simulation (MCS) method. MCS method is used to achieve a model to simulate the random status of power system. Also, the proposed method is applied on the IEEE 14-bus test system to investigate the effects of integrating different capacities of wind energy to the reliability of power system with considering carbon tax.
基金the Deanship of Scientific Research at King Abdulaziz University,Jeddah,Saudi Arabia under the Grant No.RG-12-611-43.
文摘The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed memorysystems.However, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.
文摘Airplanes are a social necessity for movement of humans,goods,and other.They are generally safe modes of transportation;however,incidents and accidents occasionally occur.To prevent aviation accidents,it is necessary to develop a machine-learning model to detect and predict commercial flights using automatic dependent surveillance–broadcast data.This study combined data-quality detection,anomaly detection,and abnormality-classification-model development.The research methodology involved the following stages:problem statement,data selection and labeling,prediction-model development,deployment,and testing.The data labeling process was based on the rules framed by the international civil aviation organization for commercial,jet-engine flights and validated by expert commercial pilots.The results showed that the best prediction model,the quadratic-discriminant-analysis,was 93%accurate,indicating a“good fit”.Moreover,the model’s area-under-the-curve results for abnormal and normal detection were 0.97 and 0.96,respectively,thus confirming its“good fit”.
基金supported by a research grant from the Ministry of Education,Culture,Research and Technology(KEMENDIKBUD RISTEK)number NKB-022/UN2.RST/HKP.05.00/2021 awarded to AB.
文摘Objective:To characterize the infection patterns and growth characteristics of the Zika virus(ZIKV)strain JMB-185 from Indonesia in various mammalian cell lines.Methods:ZIKV was grown in human(A549,HEK293,HepG2,Huh7,Jurkat,and THP-1)and non-human mammalian(RAW264.7,Vero,and Vero76)cell lines.Viral replication kinetics were measured using plaque assay,while intra-and extracellular viral RNA concentrations were assessed using RT-PCR.Flow cytometry was used to quantify the infected cells and cell viability was measured using an MTT assay.The ability of ZIKV to infect cell lines was visualized using a fluorescence immunostaining assay.Results:This ZIKV strain preferentially infected the lung,kidney,and liver cell lines A549,HEK293,Huh7,Vero,and Vero76,but not the immune cells Jurkat,RAW264.7,and THP-1.By contrast,the ZIKV showed no sign of infection in HepG2 cells,while maintaining viral titer over 3 days post-infection,with no infection recorded in immunostaining,no increase in viral RNA,and no indication of cell deterioration.Conclusions:The Indonesian ZIKV strain has a similar infection profile as other strains,except for its poor infectivity on HepG2 cells.Information on the growth characteristics of Indonesia ZIKV will help expand our understanding of the biology of ZIKV which will be useful for various applications including antiviral discovery.
