Studies on mainstreaming climate-smart agriculture(CSA)practices can increase smallholder farmers’capacity and awareness to improve food security and establish sustainable livelihoods through resilient agricultural s...Studies on mainstreaming climate-smart agriculture(CSA)practices can increase smallholder farmers’capacity and awareness to improve food security and establish sustainable livelihoods through resilient agricultural systems,while achieving adaptation and mitigation benefits.Hence,valuable insights can be obtained from smallholder farmers in responding to present and forthcoming challenges of climate change impacts.However,there is little research work on trade-off and synergy assessments.Taking Geshy watershed in Southwest Ethiopia as a case study area,both quantitative and qualitative data analysis were undertaken in this study.The data were collected from 15 key informant interviews,6 focus group discussions,and 384 households to answer the following questions:(1)what are the top 5 preferred CSA practices for smallholder farmers in Geshy watershed when coping with the impacts of climate change?(2)What is the performance of the preferred CSA practices?And(3)which trade-offs and synergies are experienced upon the implementation of CSA practices?The study came up with the most preferred CSA practices such as the use of improved crop varieties,small-scale irrigation,improved animal husbandry,the use of efficient inorganic fertilizers,and crop rotation with legumes.The selected CSA practices showed that the productivity goal exhibit the best synergy,while the mitigation goal has trade-offs.The study also indicated that the use of improved crop varieties causes high synergies in all 3 goals of CSA practices;small-scale irrigation provides a medium synergy on productivity goal but high synergy for adaptation and mitigation goals;improved animal husbandry shows a high synergy with the adaptation goal,a relatively lower synergy with the productivity goal,and a trade-off with the mitigation goal;the use of efficient inorganic fertilizers shows maximum synergy for the productivity and adaptation goals;and crop rotation with legumes exhibits high synergy with the productivity and mitigation goals but a relatively lower synergy with the adaptation goal.These results can provide evidence to various stakeholder farmers in the value chain that the impacts of climate change can be addressed by the adoption of CSA practices.In general,CSA practices are considered indispensable.Smallholder farmers prefer CSA practices that help to increase crop productivity and household resilience to climate change impacts.The results generate a vital foundation for recommendations to smallholder farming decision-makers.It also sensitizes actions for innovative and sustainable methods that are able to upscale the preferred CSA practices in the agricultural system in Geshy watershed of Southwest Ethiopia and other regions.展开更多
This paper described the therapeutic efficacy of immunosuppressive (IS) treatment agents (ATG and CSA) and androgens in the treatment of childhood aplastic anemia (AA). The results showed that the overall curative rat...This paper described the therapeutic efficacy of immunosuppressive (IS) treatment agents (ATG and CSA) and androgens in the treatment of childhood aplastic anemia (AA). The results showed that the overall curative rate was 52.4% in the ATG therapy group (21 cases) and 58.3% in the CSA therapy group (12 cases) respectively. The effective rate of all patients (SAA and CAA) was 58.1% in the IS group (18/31) and 40.4% in the androgens group (42/104), P】0.05. But, in the childhood patients with SAA, the clinical effective rate was 68.4% in the IS group and 7.9% in the androgens group, P【0.01. The laboratory tests revealed that the majority of the AA patients displalyed abnormal immunological states: inversed CD4/ CD8 ratio and increased IL-2 activity. These abnormal immunological states could be normalized in several patients when clinical response was abtained following IS therapy with ATG and CSA.展开更多
The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis andplanning intervention. This research work addresses the major issues pertaining to the field of medical image...The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis andplanning intervention. This research work addresses the major issues pertaining to the field of medical imageprocessing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposesan improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. Thebetter resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In thisprocess, the visual challenges of the K-means are addressed with the integration of four nature-inspired swarmintelligent techniques. The techniques experimented in this paper are K-means with Artificial Bee Colony (ABC),K-means with Cuckoo Search Algorithm (CSA), K-means with Particle Swarm Optimization (PSO), and Kmeanswith Firefly Algorithm (FFA). The testing and evaluation are performed on Early Lung Cancer ActionProgram (ELCAP) database. The simulation analysis is performed using lung cancer images set against metrics:precision, sensitivity, specificity, f-measure, accuracy,Matthews Correlation Coefficient (MCC), Jaccard, and Dice.The detailed evaluation shows that the K-means with Cuckoo Search Algorithm (CSA) significantly improved thequality of lung cancer segmentation in comparison to the other optimization approaches utilized for lung cancerimages. The results exhibit that the proposed approach (K-means with CSA) achieves precision, sensitivity, and Fmeasureof 0.942, 0.964, and 0.953, respectively, and an average accuracy of 93%. The experimental results prove thatK-meanswithABC,K-meanswith PSO,K-meanswith FFA, andK-meanswithCSAhave achieved an improvementof 10.8%, 13.38%, 13.93%, and 15.7%, respectively, for accuracy measure in comparison to K-means segmentationfor lung cancer images. Further, it is highlighted that the proposed K-means with CSA have achieved a significantimprovement in accuracy, hence can be utilized by researchers for improved segmentation processes of medicalimage datasets for identifying the targeted region of interest.展开更多
文摘Studies on mainstreaming climate-smart agriculture(CSA)practices can increase smallholder farmers’capacity and awareness to improve food security and establish sustainable livelihoods through resilient agricultural systems,while achieving adaptation and mitigation benefits.Hence,valuable insights can be obtained from smallholder farmers in responding to present and forthcoming challenges of climate change impacts.However,there is little research work on trade-off and synergy assessments.Taking Geshy watershed in Southwest Ethiopia as a case study area,both quantitative and qualitative data analysis were undertaken in this study.The data were collected from 15 key informant interviews,6 focus group discussions,and 384 households to answer the following questions:(1)what are the top 5 preferred CSA practices for smallholder farmers in Geshy watershed when coping with the impacts of climate change?(2)What is the performance of the preferred CSA practices?And(3)which trade-offs and synergies are experienced upon the implementation of CSA practices?The study came up with the most preferred CSA practices such as the use of improved crop varieties,small-scale irrigation,improved animal husbandry,the use of efficient inorganic fertilizers,and crop rotation with legumes.The selected CSA practices showed that the productivity goal exhibit the best synergy,while the mitigation goal has trade-offs.The study also indicated that the use of improved crop varieties causes high synergies in all 3 goals of CSA practices;small-scale irrigation provides a medium synergy on productivity goal but high synergy for adaptation and mitigation goals;improved animal husbandry shows a high synergy with the adaptation goal,a relatively lower synergy with the productivity goal,and a trade-off with the mitigation goal;the use of efficient inorganic fertilizers shows maximum synergy for the productivity and adaptation goals;and crop rotation with legumes exhibits high synergy with the productivity and mitigation goals but a relatively lower synergy with the adaptation goal.These results can provide evidence to various stakeholder farmers in the value chain that the impacts of climate change can be addressed by the adoption of CSA practices.In general,CSA practices are considered indispensable.Smallholder farmers prefer CSA practices that help to increase crop productivity and household resilience to climate change impacts.The results generate a vital foundation for recommendations to smallholder farming decision-makers.It also sensitizes actions for innovative and sustainable methods that are able to upscale the preferred CSA practices in the agricultural system in Geshy watershed of Southwest Ethiopia and other regions.
基金中央高校基本科研业务费专项资金资助(Supported by the Fundamental Research Funds for the Central Universities)“基于预售策略的CSA(Community Supported Agriculture)模式的融资策略研究”(2020JX051)。
文摘This paper described the therapeutic efficacy of immunosuppressive (IS) treatment agents (ATG and CSA) and androgens in the treatment of childhood aplastic anemia (AA). The results showed that the overall curative rate was 52.4% in the ATG therapy group (21 cases) and 58.3% in the CSA therapy group (12 cases) respectively. The effective rate of all patients (SAA and CAA) was 58.1% in the IS group (18/31) and 40.4% in the androgens group (42/104), P】0.05. But, in the childhood patients with SAA, the clinical effective rate was 68.4% in the IS group and 7.9% in the androgens group, P【0.01. The laboratory tests revealed that the majority of the AA patients displalyed abnormal immunological states: inversed CD4/ CD8 ratio and increased IL-2 activity. These abnormal immunological states could be normalized in several patients when clinical response was abtained following IS therapy with ATG and CSA.
基金the Researchers Supporting Project(RSP2023R395),King Saud University,Riyadh,Saudi Arabia.
文摘The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis andplanning intervention. This research work addresses the major issues pertaining to the field of medical imageprocessing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposesan improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. Thebetter resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In thisprocess, the visual challenges of the K-means are addressed with the integration of four nature-inspired swarmintelligent techniques. The techniques experimented in this paper are K-means with Artificial Bee Colony (ABC),K-means with Cuckoo Search Algorithm (CSA), K-means with Particle Swarm Optimization (PSO), and Kmeanswith Firefly Algorithm (FFA). The testing and evaluation are performed on Early Lung Cancer ActionProgram (ELCAP) database. The simulation analysis is performed using lung cancer images set against metrics:precision, sensitivity, specificity, f-measure, accuracy,Matthews Correlation Coefficient (MCC), Jaccard, and Dice.The detailed evaluation shows that the K-means with Cuckoo Search Algorithm (CSA) significantly improved thequality of lung cancer segmentation in comparison to the other optimization approaches utilized for lung cancerimages. The results exhibit that the proposed approach (K-means with CSA) achieves precision, sensitivity, and Fmeasureof 0.942, 0.964, and 0.953, respectively, and an average accuracy of 93%. The experimental results prove thatK-meanswithABC,K-meanswith PSO,K-meanswith FFA, andK-meanswithCSAhave achieved an improvementof 10.8%, 13.38%, 13.93%, and 15.7%, respectively, for accuracy measure in comparison to K-means segmentationfor lung cancer images. Further, it is highlighted that the proposed K-means with CSA have achieved a significantimprovement in accuracy, hence can be utilized by researchers for improved segmentation processes of medicalimage datasets for identifying the targeted region of interest.
