Tetanus is an acute non-contagious and infectious disease caused by Clostridium tetani exotoxins that affect many animal species and humans. It is associated with high mortality rate, ranging from 58% to 80% in Equida...Tetanus is an acute non-contagious and infectious disease caused by Clostridium tetani exotoxins that affect many animal species and humans. It is associated with high mortality rate, ranging from 58% to 80% in Equidae. This study investigated the seroprevalence of C. tetani antibodies in donkeys in Kaduna State. A total of 384 donkeys were sampled from the study area, 5 ml of blood was collected aseptically from the jugular vein and sera was harvested and tested for tetanus using ELISA kits. A seroprevalence of C. tetani of 295/384 (76.8%) was recorded. Male donkeys had a higher sero-prevalence (89.9%) than female (64.1%), young donkeys had 78.5% compared to 75.7% for adults;donkeys with wounds had a seroprevalence of 92.1% while those without wounds (42.4%). Donkeys from free range had a higher seroprevalence of 88.0%. Donkeys with BCS of 1 and 2 had 87.8% being the highest value, based on breeds, the Fari and Idabari had the higher seroprevalence (85.7% and 87.2% respectively), It was concluded that the donkeys in the Northern Kaduna had a high seroprevalence to C. tetani and also sex, age, breeds and presence of wounds were the main risk factors to C. tetani infection in donkeys and it was recommended that the use of donkeys in production of tetanus antitoxins and toxoid should be investigated.展开更多
为解决LTE系统中非实时业务调度算法比例公平PF(proportional fair)算法在分组数据业务模型下性能一般的问题,结合分组数据业务特点,在有限缓存队列模型下,提出一种兼顾系统吞吐量和用户公平性的非实时业务调度算法-基于缓存信息的调度B...为解决LTE系统中非实时业务调度算法比例公平PF(proportional fair)算法在分组数据业务模型下性能一般的问题,结合分组数据业务特点,在有限缓存队列模型下,提出一种兼顾系统吞吐量和用户公平性的非实时业务调度算法-基于缓存信息的调度BIBS(buffer information based scheduling)算法.该算法综合考虑了用户信道条件和缓存区内待传送的数据包信息.仿真结果表明,在不同平均速率的业务下,与PF算法相比,本文提出的算法在有效地提升系统吞吐量的同时,用户间公平性和通信中断性能也得到了极大的改善.展开更多
As the scale of federated learning expands,solving the Non-IID data problem of federated learning has become a key challenge of interest.Most existing solutions generally aim to solve the overall performance improveme...As the scale of federated learning expands,solving the Non-IID data problem of federated learning has become a key challenge of interest.Most existing solutions generally aim to solve the overall performance improvement of all clients;however,the overall performance improvement often sacrifices the performance of certain clients,such as clients with less data.Ignoring fairness may greatly reduce the willingness of some clients to participate in federated learning.In order to solve the above problem,the authors propose Ada-FFL,an adaptive fairness federated aggregation learning algorithm,which can dynamically adjust the fairness coefficient according to the update of the local models,ensuring the convergence performance of the global model and the fairness between federated learning clients.By integrating coarse-grained and fine-grained equity solutions,the authors evaluate the deviation of local models by considering both global equity and individual equity,then the weight ratio will be dynamically allocated for each client based on the evaluated deviation value,which can ensure that the update differences of local models are fully considered in each round of training.Finally,by combining a regularisation term to limit the local model update to be closer to the global model,the sensitivity of the model to input perturbations can be reduced,and the generalisation ability of the global model can be improved.Through numerous experiments on several federal data sets,the authors show that our method has more advantages in convergence effect and fairness than the existing baselines.展开更多
文摘Tetanus is an acute non-contagious and infectious disease caused by Clostridium tetani exotoxins that affect many animal species and humans. It is associated with high mortality rate, ranging from 58% to 80% in Equidae. This study investigated the seroprevalence of C. tetani antibodies in donkeys in Kaduna State. A total of 384 donkeys were sampled from the study area, 5 ml of blood was collected aseptically from the jugular vein and sera was harvested and tested for tetanus using ELISA kits. A seroprevalence of C. tetani of 295/384 (76.8%) was recorded. Male donkeys had a higher sero-prevalence (89.9%) than female (64.1%), young donkeys had 78.5% compared to 75.7% for adults;donkeys with wounds had a seroprevalence of 92.1% while those without wounds (42.4%). Donkeys from free range had a higher seroprevalence of 88.0%. Donkeys with BCS of 1 and 2 had 87.8% being the highest value, based on breeds, the Fari and Idabari had the higher seroprevalence (85.7% and 87.2% respectively), It was concluded that the donkeys in the Northern Kaduna had a high seroprevalence to C. tetani and also sex, age, breeds and presence of wounds were the main risk factors to C. tetani infection in donkeys and it was recommended that the use of donkeys in production of tetanus antitoxins and toxoid should be investigated.
文摘为解决LTE系统中非实时业务调度算法比例公平PF(proportional fair)算法在分组数据业务模型下性能一般的问题,结合分组数据业务特点,在有限缓存队列模型下,提出一种兼顾系统吞吐量和用户公平性的非实时业务调度算法-基于缓存信息的调度BIBS(buffer information based scheduling)算法.该算法综合考虑了用户信道条件和缓存区内待传送的数据包信息.仿真结果表明,在不同平均速率的业务下,与PF算法相比,本文提出的算法在有效地提升系统吞吐量的同时,用户间公平性和通信中断性能也得到了极大的改善.
基金National Natural Science Foundation of China,Grant/Award Number:62272114Joint Research Fund of Guangzhou and University,Grant/Award Number:202201020380+3 种基金Guangdong Higher Education Innovation Group,Grant/Award Number:2020KCXTD007Pearl River Scholars Funding Program of Guangdong Universities(2019)National Key R&D Program of China,Grant/Award Number:2022ZD0119602Major Key Project of PCL,Grant/Award Number:PCL2022A03。
文摘As the scale of federated learning expands,solving the Non-IID data problem of federated learning has become a key challenge of interest.Most existing solutions generally aim to solve the overall performance improvement of all clients;however,the overall performance improvement often sacrifices the performance of certain clients,such as clients with less data.Ignoring fairness may greatly reduce the willingness of some clients to participate in federated learning.In order to solve the above problem,the authors propose Ada-FFL,an adaptive fairness federated aggregation learning algorithm,which can dynamically adjust the fairness coefficient according to the update of the local models,ensuring the convergence performance of the global model and the fairness between federated learning clients.By integrating coarse-grained and fine-grained equity solutions,the authors evaluate the deviation of local models by considering both global equity and individual equity,then the weight ratio will be dynamically allocated for each client based on the evaluated deviation value,which can ensure that the update differences of local models are fully considered in each round of training.Finally,by combining a regularisation term to limit the local model update to be closer to the global model,the sensitivity of the model to input perturbations can be reduced,and the generalisation ability of the global model can be improved.Through numerous experiments on several federal data sets,the authors show that our method has more advantages in convergence effect and fairness than the existing baselines.
