Agriculture plays a crucial role in the economy,and there is an increasing global emphasis on automating agri-cultural processes.With the tremendous increase in population,the demand for food and employment has also i...Agriculture plays a crucial role in the economy,and there is an increasing global emphasis on automating agri-cultural processes.With the tremendous increase in population,the demand for food and employment has also increased significantly.Agricultural methods traditionally used to meet these requirements are no longer ade-quate,requiring solutions to issues such as excessive herbicide use and the use of chemical fertilizers.Integration of technologies such as the Internet of Things,wireless communication,machine learning,artificial intelligence(AI),and deep learning shows promise in addressing these challenges.However,there is a lack of comprehensive documentation on the application and potential of AI in improving agricultural input efficiency.To address this gap,a desk research approach was used by utilizing peer-reviewed electronic databases like PubMed,Scopus,Goo-gle Scholar,Web of Science,and Science Direct for relevant articles.Out of 327 initially identified articles,180 were deemed pertinent,focusing primarily on AI’s potential in enhancing yield through better management of nutrients,water,and weeds.Taking into account researchfindings worldwide,we found that AI technologies could assist farmers by providing recommendations on the optimal nutrients to enhance soil quality and deter-mine the best time for irrigation or herbicide application.The present status of AI-driven automation in agricul-ture holds significant promise for optimizing agricultural input utilization and reducing resource waste,particularly in the context of three pillars of crop management,i.e.,nutrient,irrigation,and weed management.展开更多
Lithium,as the lightest and lowest potential metal,is an ideal "battery metal" and the core strategic metal of the new energy industry revolution.Recovering lithium from spent lithium batteries(LIBs)has beco...Lithium,as the lightest and lowest potential metal,is an ideal "battery metal" and the core strategic metal of the new energy industry revolution.Recovering lithium from spent lithium batteries(LIBs)has become one of the significant approaches to obtaining lithium resources.At present,the lithium extraction being generally placed at the last step of the spent LIBs recovery process has puzzles such as high acid consumption,low Li recovery purity and low recovery efficiency.Selective lithium extraction at the first step of the recovery process can effectively solve those puzzles.Since lithium leaching is a non-spontaneous reaction requiring additional energy to achieve,it is found that these methods can be divided into five ways according to the different types of energy driving the reaction occurring:(ⅰ)electric energy driving lithium extraction;(ⅱ) chemical energy driving lithium extraction;(ⅲ) mechanical energy driving lithium extraction;(ⅳ) thermal energy driving lithium extraction;(ⅴ) other energy driving lithium extraction.Through the analysis of the principle,reaction process and results of recovering lithium methods can provide a few directions for scholars’ subsequent research.It is necessary to speed up the exploration of the principle of these methods.It is expected that this study could provide a reference for the research on the selective lithium extraction.展开更多
This study considers the effect of Eichhornia Crassipes Biodiesel(ECB)blends on the performances,combustion,and emission characteristics of a direct injection compression ignition engine operated in a dual-fuel mode(D...This study considers the effect of Eichhornia Crassipes Biodiesel(ECB)blends on the performances,combustion,and emission characteristics of a direct injection compression ignition engine operated in a dual-fuel mode(DFM)and equipped with an Exhaust gas recirculation technique(EGR).In particular,a single-cylinder,four-stroke,water-cooled diesel engine was utilized and four modes of fuel operation were considered:mode I,the engine operated with an ordinary diesel fuel;mode II,the engine operated with the addition of 2.4 L/min of lique-fied petroleum gas(LPG)and 20%EGR;mode III,20%ECB with 2.4 L/min LPG and 20%EGR;mode IV,40%ECB with 2.4 L/min LPG and 20%EGR.The operation conditions were constant engine speed(1500 rpm),var-iation of load(25%,50%,75%,and 100%),full load,with a compression ratio of 18,and a time injection of 23°BTDC(Before top died center).With regard to engine emissions,carbon dioxide(CO_(2)),carbon monoxide(CO),hydrocarbons(UHC),and nitrogen oxide(NOX)were measured using a gas analyzer.The smoke opacity was measured using an OPABOX smoke meter.By comparing the results related to the different modes with mode I at full load,the BTE(Brake thermal efficiency)increased by 20.17%,11.45%,and 12.66%with modes II,III,and IV,respectively.In comparison to the results for mode II,the BTE decreased due to the combustion of ECB blends by 7.26%and 6.24%for mode III and mode IV,respectively,at full load.In comparison to mode II,the Brake specific energy consumption(BSEC)increased with the ECB substitution.With ECB blends,there is a noticeable decrease in the CO,CO_(2),and UHC emissions at a partial load.Furthermore,the 20%ECB has no effect on CO emissions at full load.For modes II and IV,the CO_(2)increased by 33.33%and 19%,respectively,while the UHC emissions were reduced by 14.49%for mode III and 26.08%for mode IV.The smoke of mode III was lower by 7.21%,but for mode IV,it was higher by 12.37%.In addition,with mode III and mode IV,the NOx emissions increased by 30.50%and 18.80%,respectively.展开更多
Real-time applications based on Wireless Sensor Network(WSN)tech-nologies quickly lead to the growth of an intelligent environment.Sensor nodes play an essential role in distributing information from networking and it...Real-time applications based on Wireless Sensor Network(WSN)tech-nologies quickly lead to the growth of an intelligent environment.Sensor nodes play an essential role in distributing information from networking and its transfer to the sinks.The ability of dynamical technologies and related techniques to be aided by data collection and analysis across the Internet of Things(IoT)network is widely recognized.Sensor nodes are low-power devices with low power devices,storage,and quantitative processing capabilities.The existing system uses the Artificial Immune System-Particle Swarm Optimization method to mini-mize the energy and improve the network’s lifespan.In the proposed system,a hybrid Energy Efficient and Reliable Ant Colony Optimization(ACO)based on the Routing protocol(E-RARP)and game theory-based energy-efficient clus-tering algorithm(GEC)were used.E-RARP is a new Energy Efficient,and Reli-able ACO-based Routing Protocol for Wireless Sensor Networks.The suggested protocol provides communications dependability and high-quality channels of communication to improve energy.For wireless sensor networks,a game theo-ry-based energy-efficient clustering technique(GEC)is used,in which each sen-sor node is treated as a player on the team.The sensor node can choose beneficial methods for itself,determined by the length of idle playback time in the active phase,and then decide whether or not to rest.The proposed E-RARP-GEC improves the network’s lifetime and data transmission;it also takes a minimum amount of energy compared with the existing algorithms.展开更多
The Wireless Sensor Network(WSN)is a network of Sensor Nodes(SN)which adopt radio signals for communication amongst themselves.There is an increase in the prominence of WSN adaptability to emerging applications like t...The Wireless Sensor Network(WSN)is a network of Sensor Nodes(SN)which adopt radio signals for communication amongst themselves.There is an increase in the prominence of WSN adaptability to emerging applications like the Internet of Things(IoT)and Cyber-Physical Systems(CPS).Data secur-ity,detection of faults,management of energy,collection and distribution of data,network protocol,network coverage,mobility of nodes,and network heterogene-ity are some of the issues confronted by WSNs.There is not much published information on issues related to node mobility and management of energy at the time of aggregation of data.Towards the goal of boosting the mobility-based WSNs’network performance and energy,data aggregation protocols such as the presently-used Mobility Low-Energy Adaptive Clustering Hierarchy(LEACH-M)and Energy Efficient Heterogeneous Clustered(EEHC)scheme have been exam-ined in this work.A novel Artificial Bee Colony(ABC)algorithm is proposed in this work for effective election of CHs and multipath routing in WSNs so as to enable effective data transfer to the Base Station(BS)with least energy utilization.There is avoidance of the local optima problem at the time of solution space search in this proposed technique.Experimentations have been conducted on a large WSN network that has issues with mobility of nodes.展开更多
【目的】通过分析棉花枯萎病菌的遗传多样性,探究新疆棉花枯萎病菌株的分群及其演化。【方法】2022年在新疆不同植棉区共分离出22株棉花枯萎病菌株,对延伸因子1α(elongation factor-1α,EF-1α)和β微管蛋白基因进行扩增、测序,并从美...【目的】通过分析棉花枯萎病菌的遗传多样性,探究新疆棉花枯萎病菌株的分群及其演化。【方法】2022年在新疆不同植棉区共分离出22株棉花枯萎病菌株,对延伸因子1α(elongation factor-1α,EF-1α)和β微管蛋白基因进行扩增、测序,并从美国国立生物技术信息中心(National Center for Biotechnology Information,NCBI)数据库获取36个棉花枯萎病菌株的相关基因序列信息。基于上述基因序列分别进行系统进化分析和单倍型分析。【结果】基于57条EF-1α基因序列的进化树分析表明,棉花枯萎病菌可分为3大群,第1大群包含来自新疆、河北和澳大利亚的共31个枯萎病菌株,该大群可分成4个亚群;第2大群包含25个枯萎病菌株,构成比较复杂,可分成3个亚群;第3大群仅包含美国菌株LA140。基于28条β微管蛋白基因序列的进化树分析表明,本次分离的新疆棉花枯萎病菌株与棉花枯萎病菌7号和8号生理小种不同。根据EF-1α基因序列构建的单倍型网络将棉花枯萎病菌株分为19个单倍型,新疆21个棉花枯萎病菌株归属于有共同起源的5种单倍型。【结论】本研究分离的新疆棉花枯萎病菌株与已报道的棉花枯萎病菌1~8号生理小种均不相同,但与河北菌株的亲缘关系较近。EF-1α单倍型分析表明,本研究中的所有棉花枯萎病菌均从1号生理小种演化而来。展开更多
文摘Agriculture plays a crucial role in the economy,and there is an increasing global emphasis on automating agri-cultural processes.With the tremendous increase in population,the demand for food and employment has also increased significantly.Agricultural methods traditionally used to meet these requirements are no longer ade-quate,requiring solutions to issues such as excessive herbicide use and the use of chemical fertilizers.Integration of technologies such as the Internet of Things,wireless communication,machine learning,artificial intelligence(AI),and deep learning shows promise in addressing these challenges.However,there is a lack of comprehensive documentation on the application and potential of AI in improving agricultural input efficiency.To address this gap,a desk research approach was used by utilizing peer-reviewed electronic databases like PubMed,Scopus,Goo-gle Scholar,Web of Science,and Science Direct for relevant articles.Out of 327 initially identified articles,180 were deemed pertinent,focusing primarily on AI’s potential in enhancing yield through better management of nutrients,water,and weeds.Taking into account researchfindings worldwide,we found that AI technologies could assist farmers by providing recommendations on the optimal nutrients to enhance soil quality and deter-mine the best time for irrigation or herbicide application.The present status of AI-driven automation in agricul-ture holds significant promise for optimizing agricultural input utilization and reducing resource waste,particularly in the context of three pillars of crop management,i.e.,nutrient,irrigation,and weed management.
基金financially supported by the National Key Research and Development Program of China(2019YFC1907900)the Key Project of Research and Development Plan of Jiangxi Province(20201BBE51007)the National Science Fund for Distinguished Young Scholars(52125002)。
文摘Lithium,as the lightest and lowest potential metal,is an ideal "battery metal" and the core strategic metal of the new energy industry revolution.Recovering lithium from spent lithium batteries(LIBs)has become one of the significant approaches to obtaining lithium resources.At present,the lithium extraction being generally placed at the last step of the spent LIBs recovery process has puzzles such as high acid consumption,low Li recovery purity and low recovery efficiency.Selective lithium extraction at the first step of the recovery process can effectively solve those puzzles.Since lithium leaching is a non-spontaneous reaction requiring additional energy to achieve,it is found that these methods can be divided into five ways according to the different types of energy driving the reaction occurring:(ⅰ)electric energy driving lithium extraction;(ⅱ) chemical energy driving lithium extraction;(ⅲ) mechanical energy driving lithium extraction;(ⅳ) thermal energy driving lithium extraction;(ⅴ) other energy driving lithium extraction.Through the analysis of the principle,reaction process and results of recovering lithium methods can provide a few directions for scholars’ subsequent research.It is necessary to speed up the exploration of the principle of these methods.It is expected that this study could provide a reference for the research on the selective lithium extraction.
文摘This study considers the effect of Eichhornia Crassipes Biodiesel(ECB)blends on the performances,combustion,and emission characteristics of a direct injection compression ignition engine operated in a dual-fuel mode(DFM)and equipped with an Exhaust gas recirculation technique(EGR).In particular,a single-cylinder,four-stroke,water-cooled diesel engine was utilized and four modes of fuel operation were considered:mode I,the engine operated with an ordinary diesel fuel;mode II,the engine operated with the addition of 2.4 L/min of lique-fied petroleum gas(LPG)and 20%EGR;mode III,20%ECB with 2.4 L/min LPG and 20%EGR;mode IV,40%ECB with 2.4 L/min LPG and 20%EGR.The operation conditions were constant engine speed(1500 rpm),var-iation of load(25%,50%,75%,and 100%),full load,with a compression ratio of 18,and a time injection of 23°BTDC(Before top died center).With regard to engine emissions,carbon dioxide(CO_(2)),carbon monoxide(CO),hydrocarbons(UHC),and nitrogen oxide(NOX)were measured using a gas analyzer.The smoke opacity was measured using an OPABOX smoke meter.By comparing the results related to the different modes with mode I at full load,the BTE(Brake thermal efficiency)increased by 20.17%,11.45%,and 12.66%with modes II,III,and IV,respectively.In comparison to the results for mode II,the BTE decreased due to the combustion of ECB blends by 7.26%and 6.24%for mode III and mode IV,respectively,at full load.In comparison to mode II,the Brake specific energy consumption(BSEC)increased with the ECB substitution.With ECB blends,there is a noticeable decrease in the CO,CO_(2),and UHC emissions at a partial load.Furthermore,the 20%ECB has no effect on CO emissions at full load.For modes II and IV,the CO_(2)increased by 33.33%and 19%,respectively,while the UHC emissions were reduced by 14.49%for mode III and 26.08%for mode IV.The smoke of mode III was lower by 7.21%,but for mode IV,it was higher by 12.37%.In addition,with mode III and mode IV,the NOx emissions increased by 30.50%and 18.80%,respectively.
文摘Real-time applications based on Wireless Sensor Network(WSN)tech-nologies quickly lead to the growth of an intelligent environment.Sensor nodes play an essential role in distributing information from networking and its transfer to the sinks.The ability of dynamical technologies and related techniques to be aided by data collection and analysis across the Internet of Things(IoT)network is widely recognized.Sensor nodes are low-power devices with low power devices,storage,and quantitative processing capabilities.The existing system uses the Artificial Immune System-Particle Swarm Optimization method to mini-mize the energy and improve the network’s lifespan.In the proposed system,a hybrid Energy Efficient and Reliable Ant Colony Optimization(ACO)based on the Routing protocol(E-RARP)and game theory-based energy-efficient clus-tering algorithm(GEC)were used.E-RARP is a new Energy Efficient,and Reli-able ACO-based Routing Protocol for Wireless Sensor Networks.The suggested protocol provides communications dependability and high-quality channels of communication to improve energy.For wireless sensor networks,a game theo-ry-based energy-efficient clustering technique(GEC)is used,in which each sen-sor node is treated as a player on the team.The sensor node can choose beneficial methods for itself,determined by the length of idle playback time in the active phase,and then decide whether or not to rest.The proposed E-RARP-GEC improves the network’s lifetime and data transmission;it also takes a minimum amount of energy compared with the existing algorithms.
文摘The Wireless Sensor Network(WSN)is a network of Sensor Nodes(SN)which adopt radio signals for communication amongst themselves.There is an increase in the prominence of WSN adaptability to emerging applications like the Internet of Things(IoT)and Cyber-Physical Systems(CPS).Data secur-ity,detection of faults,management of energy,collection and distribution of data,network protocol,network coverage,mobility of nodes,and network heterogene-ity are some of the issues confronted by WSNs.There is not much published information on issues related to node mobility and management of energy at the time of aggregation of data.Towards the goal of boosting the mobility-based WSNs’network performance and energy,data aggregation protocols such as the presently-used Mobility Low-Energy Adaptive Clustering Hierarchy(LEACH-M)and Energy Efficient Heterogeneous Clustered(EEHC)scheme have been exam-ined in this work.A novel Artificial Bee Colony(ABC)algorithm is proposed in this work for effective election of CHs and multipath routing in WSNs so as to enable effective data transfer to the Base Station(BS)with least energy utilization.There is avoidance of the local optima problem at the time of solution space search in this proposed technique.Experimentations have been conducted on a large WSN network that has issues with mobility of nodes.
文摘【目的】通过分析棉花枯萎病菌的遗传多样性,探究新疆棉花枯萎病菌株的分群及其演化。【方法】2022年在新疆不同植棉区共分离出22株棉花枯萎病菌株,对延伸因子1α(elongation factor-1α,EF-1α)和β微管蛋白基因进行扩增、测序,并从美国国立生物技术信息中心(National Center for Biotechnology Information,NCBI)数据库获取36个棉花枯萎病菌株的相关基因序列信息。基于上述基因序列分别进行系统进化分析和单倍型分析。【结果】基于57条EF-1α基因序列的进化树分析表明,棉花枯萎病菌可分为3大群,第1大群包含来自新疆、河北和澳大利亚的共31个枯萎病菌株,该大群可分成4个亚群;第2大群包含25个枯萎病菌株,构成比较复杂,可分成3个亚群;第3大群仅包含美国菌株LA140。基于28条β微管蛋白基因序列的进化树分析表明,本次分离的新疆棉花枯萎病菌株与棉花枯萎病菌7号和8号生理小种不同。根据EF-1α基因序列构建的单倍型网络将棉花枯萎病菌株分为19个单倍型,新疆21个棉花枯萎病菌株归属于有共同起源的5种单倍型。【结论】本研究分离的新疆棉花枯萎病菌株与已报道的棉花枯萎病菌1~8号生理小种均不相同,但与河北菌株的亲缘关系较近。EF-1α单倍型分析表明,本研究中的所有棉花枯萎病菌均从1号生理小种演化而来。
