Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost.One of them is a sensor network with embe...Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost.One of them is a sensor network with embedded sensors working as the primary nodes,termed Wireless Sensor Networks(WSNs),in which numerous sensors are connected to at least one Base Station(BS).These sensors gather information from the environment and transmit it to a BS or gathering location.WSNs have several challenges,including throughput,energy usage,and network lifetime concerns.Different strategies have been applied to get over these restrictions.Clustering may,therefore,be thought of as the best way to solve such issues.Consequently,it is crucial to analyze effective Cluster Head(CH)selection to maximize efficiency throughput,extend the network lifetime,and minimize energy consumption.This paper proposed an Accelerated Particle Swarm Optimization(APSO)algorithm based on the Low Energy Adaptive Clustering Hierarchy(LEACH),Neighboring Based Energy Efficient Routing(NBEER),Cooperative Energy Efficient Routing(CEER),and Cooperative Relay Neighboring Based Energy Efficient Routing(CR-NBEER)techniques.With the help of APSO in the implementation of the WSN,the main methodology of this article has taken place.The simulation findings in this study demonstrated that the suggested approach uses less energy,with respective energy consumption ranges of 0.1441 to 0.013 for 5 CH,1.003 to 0.0521 for 10 CH,and 0.1734 to 0.0911 for 15 CH.The sending packets ratio was also raised for all three CH selection scenarios,increasing from 659 to 1730.The number of dead nodes likewise dropped for the given combination,falling between 71 and 66.The network lifetime was deemed to have risen based on the results found.A hybrid with a few valuable parameters can further improve the suggested APSO-based protocol.Similar to underwater,WSN can make use of the proposed protocol.The overall results have been evaluated and compared with the existing approaches of sensor networks.展开更多
Fusarium head blight(FHB)is one of the most detrimental wheat diseases which greatly decreases the yield and grain quality,especially in the middle and lower reaches of the Yangtze River of China.Fhb1 and Fhb2 are two...Fusarium head blight(FHB)is one of the most detrimental wheat diseases which greatly decreases the yield and grain quality,especially in the middle and lower reaches of the Yangtze River of China.Fhb1 and Fhb2 are two major resistance loci against Fusarium graminearum.Yangmai 15(YM15)is one of the most popular varieties in the middle and lower reaches of the Yangtze River,and it has good weak gluten characters but poor resistance to FHB.Here we used Fhb1 and Fhb2 to improve the FHB resistance of YM15 by a molecular marker-assisted selection(MAS)backcrossing strategy.The selection of agronomic traits was performed for each generation.We successfully selected seven introgressed lines which carry homozygous Fhb1 and Fhb2 with significantly higher FHB resistance than the recurrent parent YM15.Three of the introgressed lines had agronomic and quality characters that were similar to YM15.This study demonstrates that the pyramiding of Fhb1 and Fhb2 could significantly improve the FHB resistance in wheat using the MAS approach.展开更多
To guarantee the security of Internet of Things(IoT)devices,the blockchain tech⁃nology is often applied to clustered IoT networks.However,cluster heads(CHs)need to un⁃dertake additional control tasks.For battery-power...To guarantee the security of Internet of Things(IoT)devices,the blockchain tech⁃nology is often applied to clustered IoT networks.However,cluster heads(CHs)need to un⁃dertake additional control tasks.For battery-powered IoT devices,the conventional CH se⁃lection algorithm is limited.Based on the above problem,an unmanned aerial vehicle(UAV)network assisted clustered IoT system is proposed,and a corresponding UAV CH se⁃lection algorithm is designed.In this scheme,UAVs are selected as CHs to serve IoT clus⁃ters.The proposed CH selection algorithm considers the maximal transmit power,residual energy and distance information of UAVs,which can greatly extend the working life of IoT clusters.Through Monte Carlo simulation,the key performance indexes of the system,in⁃cluding energy consumption,average secrecy rate and the maximal number of data packets received by the base station(BS),are evaluated.The simulation results show that the pro⁃posed algorithm has great advantages compared with the existing CH selection algorithms.展开更多
In Wireless Sensor Networks(WSNs),Clustering process is widely utilized for increasing the lifespan with sustained energy stability during data transmission.Several clustering protocols were devised for extending netw...In Wireless Sensor Networks(WSNs),Clustering process is widely utilized for increasing the lifespan with sustained energy stability during data transmission.Several clustering protocols were devised for extending network lifetime,but most of them failed in handling the problem of fixed clustering,static rounds,and inadequate Cluster Head(CH)selection criteria which consumes more energy.In this paper,Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm(SRITL-AGOA)-based Clustering Scheme for energy stabilization and extending network lifespan.This SRITL-AGOA selected CH depending on the weightage of factors such as node mobility degree,neighbour's density distance to sink,single-hop or multihop communication and Residual Energy(RE)that directly influences the energy consumption of sensor nodes.In specific,Grasshopper Optimization Algorithm(GOA)is improved through tangent-based nonlinear strategy for enhancing the ability of global optimization.On the other hand,stochastic ranking and violation constraint handling strategies are embedded into Teaching-Learning-based Optimization Algorithm(TLOA)for improving its exploitation tendencies.Then,SR and VCH improved TLOA is embedded into the exploitation phase of AGOA for selecting better CH by maintaining better balance amid exploration and exploitation.Simulation results confirmed that the proposed SRITL-AGOA improved throughput by 21.86%,network stability by 18.94%,load balancing by 16.14%with minimized energy depletion by19.21%,compared to the competitive CH selection approaches.展开更多
Wireless sensor networks(WSNs)are characterized by their ability to monitor physical or chemical phenomena in a static or dynamic location by collecting data,and transmit it in a collaborative manner to one or more pr...Wireless sensor networks(WSNs)are characterized by their ability to monitor physical or chemical phenomena in a static or dynamic location by collecting data,and transmit it in a collaborative manner to one or more processing centers wirelessly using a routing protocol.Energy dissipation is one of the most challenging issues due to the limited power supply at the sensor node.All routing protocols are large consumers of energy,as they represent the main source of energy cost through data exchange operation.Clusterbased hierarchical routing algorithms are known for their good performance in energy conservation during active data exchange in WSNs.The most common of this type of protocol is the Low-Energy Adaptive Clustering Hierarchy(LEACH),which suffers from the problem of the pseudo-random selection of cluster head resulting in large power dissipation.This critical issue can be addressed by using an optimization algorithm to improve the LEACH cluster heads selection process,thus increasing the network lifespan.This paper proposes the LEACH-CHIO,a centralized cluster-based energyaware protocol based on the Coronavirus Herd Immunity Optimizer(CHIO)algorithm.CHIO is a newly emerging human-based optimization algorithm that is expected to achieve significant improvement in the LEACH cluster heads selection process.LEACH-CHIO is implemented and its performance is verified by simulating different wireless sensor network scenarios,which consist of a variable number of nodes ranging from 20 to 100.To evaluate the algorithm performances,three evaluation indicators have been examined,namely,power consumption,number of live nodes,and number of incoming packets.The simulation results demonstrated the superiority of the proposed protocol over basic LEACH protocol for the three indicators.展开更多
Fusarium head blight(FHB) or scab caused by Fusarium graminearum is a major threat to wheat production in China as well as in the world. To combat this disease, multiple efforts have been carried out internationally. ...Fusarium head blight(FHB) or scab caused by Fusarium graminearum is a major threat to wheat production in China as well as in the world. To combat this disease, multiple efforts have been carried out internationally. In this article, we review our long-time effort in identifying the resistance genes and dissecting the resistance mechanisms by both forward and reverse genetics approaches in the last two decades. We present recent progress in resistance QTL identification, candidate functional gene discovery, marker-assisted improvement of FHB resistant varieties, and findings in investigating association of signal molecules, such as Ca^(++),SA, JA, and ET, with FHB response, with the assistance from rapidly growing genomics platforms. The information will be helpful for designing novel and efficient approaches to curb FHB.展开更多
Wireless Sensor Network(WSN)forms an essential part of IoT.It is embedded in the target environment to observe the physical parameters based on the type of application.Sensor nodes inWSN are constrained by different f...Wireless Sensor Network(WSN)forms an essential part of IoT.It is embedded in the target environment to observe the physical parameters based on the type of application.Sensor nodes inWSN are constrained by different features such as memory,bandwidth,energy,and its processing capabilities.In WSN,data transmission process consumes the maximum amount of energy than sensing and processing of the sensors.So,diverse clustering and data aggregation techniques are designed to achieve excellent energy efficiency in WSN.In this view,the current research article presents a novel Type II Fuzzy Logic-based Cluster Head selection with Low Complexity Data Aggregation(T2FLCH-LCDA)technique for WSN.The presented model involves a two-stage process such as clustering and data aggregation.Initially,three input parameters such as residual energy,distance to Base Station(BS),and node centrality are used in T2FLCH technique for CH selection and cluster construction.Besides,the LCDA technique which follows Dictionary Based Encoding(DBE)process is used to perform the data aggregation at CHs.Finally,the aggregated data is transmitted to the BS where it achieves energy efficiency.The experimental validation of the T2FLCH-LCDAtechnique was executed under three different scenarios based on the position of BS.The experimental results revealed that the T2FLCH-LCDA technique achieved maximum energy efficiency,lifetime,Compression Ratio(CR),and power saving than the compared methods.展开更多
Wireless Sensor Networks (WSNs) have been applied in many different areas. Energy efficient algorithms and protocols have become one of the most challenging issues for WSN. Many researchers focused on developing energ...Wireless Sensor Networks (WSNs) have been applied in many different areas. Energy efficient algorithms and protocols have become one of the most challenging issues for WSN. Many researchers focused on developing energy efficient clustering algorithms for WSN, but less research has been concerned in the mobile User Equipment (UE) acting as a Cluster Head (CH) for data transmission between cellular networks and WSNs. In this paper, we propose a cellular-assisted UE CH selection algorithm for the WSN, which considers several parameters to choose the optimal UE gateway CH. We analyze the energy cost of data transmission from a sensor node to the next node or gateway and calculate the whole system energy cost for a WSN. Simulation results show that better system performance, in terms of system energy cost and WSNs life time, can be achieved by using interactive optimization with cellular networks.展开更多
Water-environment monitoring network (WMN) is a wireless sensor network based real-time system, which collects, transmits, analyzes and processes water-environment parameters in large area. Both cluster selection mech...Water-environment monitoring network (WMN) is a wireless sensor network based real-time system, which collects, transmits, analyzes and processes water-environment parameters in large area. Both cluster selection mechanisms and energy saving strategies play an important role on designing network routing protocols for the WMN. Since those existing routing algorithms can not be used directly in the WMN, we thus propose an improved version of LEACH, a LEACH-Head Expected Frequency Appraisal (LEACH-HEFA) algorithm, for the WMN in this paper. Simulation results show that the LEACH-HEFA can balance the energy consumption of nodes, rationalize the clustering process and prolong the network lifetime significantly in the WMN. It indicates that the LEACH-HEFA is suitable to the WMN.展开更多
The study was conducted in the Awbarre district of the Fafen zone of the Somali regional state of Ethiopia. The objective of the study was to assess the breeding practices and reproductive performance of Black-head So...The study was conducted in the Awbarre district of the Fafen zone of the Somali regional state of Ethiopia. The objective of the study was to assess the breeding practices and reproductive performance of Black-head Somali sheep under a traditional management system. Purposive and simple random sampling techniques were used to select targeted kebeles and households, respectively. A total of 120 households were selected from four kebeles, each of 30 households, based on the production system and sheep population. Semi-structured questionnaires, group discussions, key informants interviews and field observations were used to generate the required data. The primary purpose of keeping sheep was for income generation, followed by saving as a future asset. The majority (89.2%) of the respondents separated male and female animals during herding. The selection criteria for breeding rams were appearance, growth, pedigree, and color while for breeding ewes were appearance, adaptability, pedigree, color, and lamb growth. The overall weaning age of Black-head Somali sheep in the study area was 3.7 months for both males & females. The castration of male sheep was common for the purpose of fattening, fattening with breeding control and breeding control as well. The castration is mainly performed during the summer and autumn and the methods of castration were both traditional and modern methods, the traditional castration method being the most important one in pastoral areas. The age of sexual maturity was 7.64 months for rams and 8.97 months for ewe’s male and female lambs in the pastoral area and 8.42 & 8.38 for rams & ewes in agro-pastoral and overall lambing interval was 11 months. On average, the ewe of Black-head Somali sheep in pastoral & agro-pastoral could produce 9.49 & 9.57 lambs, respectively in their lifetime. As the pastoralists and agro-pastoralists indicated the source of the breeding ram was their own, so the exchange of breeding ram is recommended to minimize the risk of inbreeding and further studies of on-farm performance investigation would be necessary to be carried out so as to understand the uniqueness of the breed better.展开更多
Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of...Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians.Moreover,some potentially useful quantitative information in medical images,especially that which is not visible to the naked eye,is often ignored during clinical practice.In contrast,radiomics performs high-throughput feature extraction from medical images,which enables quantitative analysis of medical images and prediction of various clinical endpoints.Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis,demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine.However,radiomics remains in a developmental phase as numerous technical challenges have yet to be solved,especially in feature engineering and statistical modeling.In this review,we introduce the current utility of radiomics by summarizing research on its application in the diagnosis,prognosis,and prediction of treatment responses in patients with cancer.We focus on machine learning approaches,for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling.Furthermore,we introduce the stability,reproducibility,and interpretability of features,and the generalizability and interpretability of models.Finally,we offer possible solutions to current challenges in radiomics research.展开更多
文摘Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost.One of them is a sensor network with embedded sensors working as the primary nodes,termed Wireless Sensor Networks(WSNs),in which numerous sensors are connected to at least one Base Station(BS).These sensors gather information from the environment and transmit it to a BS or gathering location.WSNs have several challenges,including throughput,energy usage,and network lifetime concerns.Different strategies have been applied to get over these restrictions.Clustering may,therefore,be thought of as the best way to solve such issues.Consequently,it is crucial to analyze effective Cluster Head(CH)selection to maximize efficiency throughput,extend the network lifetime,and minimize energy consumption.This paper proposed an Accelerated Particle Swarm Optimization(APSO)algorithm based on the Low Energy Adaptive Clustering Hierarchy(LEACH),Neighboring Based Energy Efficient Routing(NBEER),Cooperative Energy Efficient Routing(CEER),and Cooperative Relay Neighboring Based Energy Efficient Routing(CR-NBEER)techniques.With the help of APSO in the implementation of the WSN,the main methodology of this article has taken place.The simulation findings in this study demonstrated that the suggested approach uses less energy,with respective energy consumption ranges of 0.1441 to 0.013 for 5 CH,1.003 to 0.0521 for 10 CH,and 0.1734 to 0.0911 for 15 CH.The sending packets ratio was also raised for all three CH selection scenarios,increasing from 659 to 1730.The number of dead nodes likewise dropped for the given combination,falling between 71 and 66.The network lifetime was deemed to have risen based on the results found.A hybrid with a few valuable parameters can further improve the suggested APSO-based protocol.Similar to underwater,WSN can make use of the proposed protocol.The overall results have been evaluated and compared with the existing approaches of sensor networks.
基金supported by the National Natural Science Foundation of China(31901544 and 2071999)the National Key Research and Development Program of China(2017YFD0100801)。
文摘Fusarium head blight(FHB)is one of the most detrimental wheat diseases which greatly decreases the yield and grain quality,especially in the middle and lower reaches of the Yangtze River of China.Fhb1 and Fhb2 are two major resistance loci against Fusarium graminearum.Yangmai 15(YM15)is one of the most popular varieties in the middle and lower reaches of the Yangtze River,and it has good weak gluten characters but poor resistance to FHB.Here we used Fhb1 and Fhb2 to improve the FHB resistance of YM15 by a molecular marker-assisted selection(MAS)backcrossing strategy.The selection of agronomic traits was performed for each generation.We successfully selected seven introgressed lines which carry homozygous Fhb1 and Fhb2 with significantly higher FHB resistance than the recurrent parent YM15.Three of the introgressed lines had agronomic and quality characters that were similar to YM15.This study demonstrates that the pyramiding of Fhb1 and Fhb2 could significantly improve the FHB resistance in wheat using the MAS approach.
文摘To guarantee the security of Internet of Things(IoT)devices,the blockchain tech⁃nology is often applied to clustered IoT networks.However,cluster heads(CHs)need to un⁃dertake additional control tasks.For battery-powered IoT devices,the conventional CH se⁃lection algorithm is limited.Based on the above problem,an unmanned aerial vehicle(UAV)network assisted clustered IoT system is proposed,and a corresponding UAV CH se⁃lection algorithm is designed.In this scheme,UAVs are selected as CHs to serve IoT clus⁃ters.The proposed CH selection algorithm considers the maximal transmit power,residual energy and distance information of UAVs,which can greatly extend the working life of IoT clusters.Through Monte Carlo simulation,the key performance indexes of the system,in⁃cluding energy consumption,average secrecy rate and the maximal number of data packets received by the base station(BS),are evaluated.The simulation results show that the pro⁃posed algorithm has great advantages compared with the existing CH selection algorithms.
文摘In Wireless Sensor Networks(WSNs),Clustering process is widely utilized for increasing the lifespan with sustained energy stability during data transmission.Several clustering protocols were devised for extending network lifetime,but most of them failed in handling the problem of fixed clustering,static rounds,and inadequate Cluster Head(CH)selection criteria which consumes more energy.In this paper,Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm(SRITL-AGOA)-based Clustering Scheme for energy stabilization and extending network lifespan.This SRITL-AGOA selected CH depending on the weightage of factors such as node mobility degree,neighbour's density distance to sink,single-hop or multihop communication and Residual Energy(RE)that directly influences the energy consumption of sensor nodes.In specific,Grasshopper Optimization Algorithm(GOA)is improved through tangent-based nonlinear strategy for enhancing the ability of global optimization.On the other hand,stochastic ranking and violation constraint handling strategies are embedded into Teaching-Learning-based Optimization Algorithm(TLOA)for improving its exploitation tendencies.Then,SR and VCH improved TLOA is embedded into the exploitation phase of AGOA for selecting better CH by maintaining better balance amid exploration and exploitation.Simulation results confirmed that the proposed SRITL-AGOA improved throughput by 21.86%,network stability by 18.94%,load balancing by 16.14%with minimized energy depletion by19.21%,compared to the competitive CH selection approaches.
文摘Wireless sensor networks(WSNs)are characterized by their ability to monitor physical or chemical phenomena in a static or dynamic location by collecting data,and transmit it in a collaborative manner to one or more processing centers wirelessly using a routing protocol.Energy dissipation is one of the most challenging issues due to the limited power supply at the sensor node.All routing protocols are large consumers of energy,as they represent the main source of energy cost through data exchange operation.Clusterbased hierarchical routing algorithms are known for their good performance in energy conservation during active data exchange in WSNs.The most common of this type of protocol is the Low-Energy Adaptive Clustering Hierarchy(LEACH),which suffers from the problem of the pseudo-random selection of cluster head resulting in large power dissipation.This critical issue can be addressed by using an optimization algorithm to improve the LEACH cluster heads selection process,thus increasing the network lifespan.This paper proposes the LEACH-CHIO,a centralized cluster-based energyaware protocol based on the Coronavirus Herd Immunity Optimizer(CHIO)algorithm.CHIO is a newly emerging human-based optimization algorithm that is expected to achieve significant improvement in the LEACH cluster heads selection process.LEACH-CHIO is implemented and its performance is verified by simulating different wireless sensor network scenarios,which consist of a variable number of nodes ranging from 20 to 100.To evaluate the algorithm performances,three evaluation indicators have been examined,namely,power consumption,number of live nodes,and number of incoming packets.The simulation results demonstrated the superiority of the proposed protocol over basic LEACH protocol for the three indicators.
基金supported by National Key Research and Development Program (2016YFD0101802, 2016YFD0101004)National Basic Research Program of China (2004CB117205, 2012CB125902)+5 种基金National Key Technology R&D Program of China (2002AA224161)National Science and Technology Major Project (2009ZX08009-049B, 2012ZX08009003)Jiangsu Collaborative Innovation Initiative for Modern Crop Production,'111' project B08025Natural Science Foundation of Jiangsu Province (BK20131316)Innovation Team Program for Jiangsu Universities (2014)the long-term funding from the National Science Foundation of China (30025030,30430440,30721140555,31030054,30671295)
文摘Fusarium head blight(FHB) or scab caused by Fusarium graminearum is a major threat to wheat production in China as well as in the world. To combat this disease, multiple efforts have been carried out internationally. In this article, we review our long-time effort in identifying the resistance genes and dissecting the resistance mechanisms by both forward and reverse genetics approaches in the last two decades. We present recent progress in resistance QTL identification, candidate functional gene discovery, marker-assisted improvement of FHB resistant varieties, and findings in investigating association of signal molecules, such as Ca^(++),SA, JA, and ET, with FHB response, with the assistance from rapidly growing genomics platforms. The information will be helpful for designing novel and efficient approaches to curb FHB.
文摘Wireless Sensor Network(WSN)forms an essential part of IoT.It is embedded in the target environment to observe the physical parameters based on the type of application.Sensor nodes inWSN are constrained by different features such as memory,bandwidth,energy,and its processing capabilities.In WSN,data transmission process consumes the maximum amount of energy than sensing and processing of the sensors.So,diverse clustering and data aggregation techniques are designed to achieve excellent energy efficiency in WSN.In this view,the current research article presents a novel Type II Fuzzy Logic-based Cluster Head selection with Low Complexity Data Aggregation(T2FLCH-LCDA)technique for WSN.The presented model involves a two-stage process such as clustering and data aggregation.Initially,three input parameters such as residual energy,distance to Base Station(BS),and node centrality are used in T2FLCH technique for CH selection and cluster construction.Besides,the LCDA technique which follows Dictionary Based Encoding(DBE)process is used to perform the data aggregation at CHs.Finally,the aggregated data is transmitted to the BS where it achieves energy efficiency.The experimental validation of the T2FLCH-LCDAtechnique was executed under three different scenarios based on the position of BS.The experimental results revealed that the T2FLCH-LCDA technique achieved maximum energy efficiency,lifetime,Compression Ratio(CR),and power saving than the compared methods.
基金Supported by the National Science and Technology Major Projects of China (No.2011ZX03005-003-02)Shanghai Natural Science Foundation (No.11ZR-1435100)Shanghai Science and Technology Innovation Program(No.11DZ0512500, 12511503300, 12DZ2250200)
文摘Wireless Sensor Networks (WSNs) have been applied in many different areas. Energy efficient algorithms and protocols have become one of the most challenging issues for WSN. Many researchers focused on developing energy efficient clustering algorithms for WSN, but less research has been concerned in the mobile User Equipment (UE) acting as a Cluster Head (CH) for data transmission between cellular networks and WSNs. In this paper, we propose a cellular-assisted UE CH selection algorithm for the WSN, which considers several parameters to choose the optimal UE gateway CH. We analyze the energy cost of data transmission from a sensor node to the next node or gateway and calculate the whole system energy cost for a WSN. Simulation results show that better system performance, in terms of system energy cost and WSNs life time, can be achieved by using interactive optimization with cellular networks.
文摘Water-environment monitoring network (WMN) is a wireless sensor network based real-time system, which collects, transmits, analyzes and processes water-environment parameters in large area. Both cluster selection mechanisms and energy saving strategies play an important role on designing network routing protocols for the WMN. Since those existing routing algorithms can not be used directly in the WMN, we thus propose an improved version of LEACH, a LEACH-Head Expected Frequency Appraisal (LEACH-HEFA) algorithm, for the WMN in this paper. Simulation results show that the LEACH-HEFA can balance the energy consumption of nodes, rationalize the clustering process and prolong the network lifetime significantly in the WMN. It indicates that the LEACH-HEFA is suitable to the WMN.
文摘The study was conducted in the Awbarre district of the Fafen zone of the Somali regional state of Ethiopia. The objective of the study was to assess the breeding practices and reproductive performance of Black-head Somali sheep under a traditional management system. Purposive and simple random sampling techniques were used to select targeted kebeles and households, respectively. A total of 120 households were selected from four kebeles, each of 30 households, based on the production system and sheep population. Semi-structured questionnaires, group discussions, key informants interviews and field observations were used to generate the required data. The primary purpose of keeping sheep was for income generation, followed by saving as a future asset. The majority (89.2%) of the respondents separated male and female animals during herding. The selection criteria for breeding rams were appearance, growth, pedigree, and color while for breeding ewes were appearance, adaptability, pedigree, color, and lamb growth. The overall weaning age of Black-head Somali sheep in the study area was 3.7 months for both males & females. The castration of male sheep was common for the purpose of fattening, fattening with breeding control and breeding control as well. The castration is mainly performed during the summer and autumn and the methods of castration were both traditional and modern methods, the traditional castration method being the most important one in pastoral areas. The age of sexual maturity was 7.64 months for rams and 8.97 months for ewe’s male and female lambs in the pastoral area and 8.42 & 8.38 for rams & ewes in agro-pastoral and overall lambing interval was 11 months. On average, the ewe of Black-head Somali sheep in pastoral & agro-pastoral could produce 9.49 & 9.57 lambs, respectively in their lifetime. As the pastoralists and agro-pastoralists indicated the source of the breeding ram was their own, so the exchange of breeding ram is recommended to minimize the risk of inbreeding and further studies of on-farm performance investigation would be necessary to be carried out so as to understand the uniqueness of the breed better.
基金supported in part by the National Natural Science Foundation of China(82072019)the Shenzhen Basic Research Program(JCYJ20210324130209023)+5 种基金the Shenzhen-Hong Kong-Macao S&T Program(Category C)(SGDX20201103095002019)the Mainland-Hong Kong Joint Funding Scheme(MHKJFS)(MHP/005/20),the Project of Strategic Importance Fund(P0035421)the Projects of RISA(P0043001)from the Hong Kong Polytechnic University,the Natural Science Foundation of Jiangsu Province(BK20201441)the Provincial and Ministry Co-constructed Project of Henan Province Medical Science and Technology Research(SBGJ202103038,SBGJ202102056)the Henan Province Key R&D and Promotion Project(Science and Technology Research)(222102310015)the Natural Science Foundation of Henan Province(222300420575),and the Henan Province Science and Technology Research(222102310322).
文摘Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians.Moreover,some potentially useful quantitative information in medical images,especially that which is not visible to the naked eye,is often ignored during clinical practice.In contrast,radiomics performs high-throughput feature extraction from medical images,which enables quantitative analysis of medical images and prediction of various clinical endpoints.Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis,demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine.However,radiomics remains in a developmental phase as numerous technical challenges have yet to be solved,especially in feature engineering and statistical modeling.In this review,we introduce the current utility of radiomics by summarizing research on its application in the diagnosis,prognosis,and prediction of treatment responses in patients with cancer.We focus on machine learning approaches,for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling.Furthermore,we introduce the stability,reproducibility,and interpretability of features,and the generalizability and interpretability of models.Finally,we offer possible solutions to current challenges in radiomics research.
