Artificial Intelligence for Maximizing Agricultural Input Use Efficiency: Exploring Nutrient, Water and Weed Management Strategies

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.

Review of preferentially selective lithium extraction from spent lithium batteries: Principle and performance

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.

Investigating the Effects of Eichhornia Crassipes Biodiesel and Liquefied Petroleum Gas on the Performance and Emissions of a Dual-Fuel Engine

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.

Energy Efficient Networks Using Ant Colony Optimization with Game Theory Clustering

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.

Characterization of Flame Retardancy and Oil-Water Separation Capacity of Superhydrophobic Silylated Melamine Sponges

A silylated melamine sponge(SMS)was prepared by two simple steps,namely,immersion and dehydration of a melamine sponge coated with methyltrichlorosilane.The silylated structure of SMS was characterized by FT-IR(Fourier-transform infrared)spectroscopy,SEM(Scanning electron microscopy)and in terms of water contact angles.Its oil-water absorption and separation capacities were measured by FT-IR and UV-visible spectrophoto-metry.The experimental results have shown that oligomeric silanol covalently bonds by Si-N onto the surface of melamine sponge skeletons.SMS has shown superhydrophobicity with a water contact angle exceeding 150°±1°,a better separation efficiency with regard to diesel oil(by 99.31%(wt/wt%)in oil-water mixture and even up to 99.99%(wt/wt%)for diesel oil in its saturated aqueous solution.Moreover,SMS inherited the intrinsicflame retardancy of the melamine sponge.In general,SMS has shown superhydrophobicity,high porosity,excellent selectivity,remarkable recyclability,and better absorption capacity for various oils and organic solvents,and a high separation efficiency for oil in saturated aqueous solutions.

消化内科带教中真实案例的翻转课堂的应用分析

目的分析消化内科带教中真实案例的翻转课堂的应用效果。方法选择2023年1—12月临沂凯旋医院消化内科实习生60名作为研究对象,按随机数字表法分成对比组与研究组,每组30名,对比组给予常规带教模式,研究组给予真实案例的翻转课堂教学模式,对比两组的实习考核成绩、综合能力与教学满意度。结果研究组基础知识考核成绩为(93.25±3.66)分,专业技能考核成绩为(95.66±4.28)分,均高于对比组,差异有统计学意义(t=8.558、9.681,P均<0.05);研究组诊断、问诊、查体、健康教育、组织效能、实践技能、实际问题解决能力等综合能力评分均高于对比组,差异有统计学意义(P均<0.05);研究组教学方式、教学效果、教学内容等教学满意度评分均高于对比组,差异有统计学意义(P均<0.05)。结论在消化内科带教中给予真实案例的翻转课堂教学,有利于提高实习生教学成绩,提高其综合能力,实习生对教学工作更为满意,值得推荐。

Performance Analysis of a Chunk-Based Speech Emotion Recognition Model Using RNN

Recently,artificial-intelligence-based automatic customer response sys-tem has been widely used instead of customer service representatives.Therefore,it is important for automatic customer service to promptly recognize emotions in a customer’s voice to provide the appropriate service accordingly.Therefore,we analyzed the performance of the emotion recognition(ER)accuracy as a function of the simulation time using the proposed chunk-based speech ER(CSER)model.The proposed CSER model divides voice signals into 3-s long chunks to effi-ciently recognize characteristically inherent emotions in the customer’s voice.We evaluated the performance of the ER of voice signal chunks by applying four RNN techniques—long short-term memory(LSTM),bidirectional-LSTM,gated recurrent units(GRU),and bidirectional-GRU—to the proposed CSER model individually to assess its ER accuracy and time efficiency.The results reveal that GRU shows the best time efficiency in recognizing emotions from speech signals in terms of accuracy as a function of simulation time.

Overcoming power efficiency limitation of white fluorescence light-emitting diodes via multilevel-hydrogen-bond matrix

High power efficiency and low efficiency roll-off at practical luminance are two requirements for new-generation energy-saving lighting technologies,which are still bottlenecks of thermally activated delayed fluorescence(TADF)white organic light-emitting diodes(WOLED),despite the advantages of TADF materials and devices in low cost and high sustainability.Herein,we developed a spiro phosphine oxide host named SSOXSPO,which can form multiple and multidirectional intermolecular hydrogen bonds(IHB).The resulted multilevel IHB network integrates long-range ordered and short-range disordered alignments for suppressing triplet-polaron quenching(TPQ)and triplet-triplet annihilation(TTA).Electronic characteristics of SSOXSPO matrix are further regulated,leading to the optimal exciton allocation through balancing energy and charge transfer.As consequence,using SSOXSPO as host,the single-emissive-layer TADF WOLEDs realized the record performance,including ultralow operation voltage as∼4.0 V,power efficiency beyond fluorescent tube(70.1 lm W−1)and negligible external quantum efficiency roll-off(3%)at 1000 nits for indoor lighting.This work demonstrates that multiple interplays supported by host matrixes in TADF WOLEDs can facilitate the synergistic effects of TADF emitters on 100%exciton utilization.

Chaotic Sandpiper Optimization Based Virtual Machine Scheduling for Cyber-Physical Systems

Recently,with the growth of cyber physical systems(CPS),several applications have begun to deploy in the CPS for connecting the cyber space with the physical scale effectively.Besides,the cloud computing(CC)enabled CPS offers huge processing and storage resources for CPS thatfinds helpful for a range of application areas.At the same time,with the massive development of applica-tions that exist in the CPS environment,the energy utilization of the cloud enabled CPS has gained significant interest.For improving the energy effective-ness of the CC platform,virtualization technologies have been employed for resource management and the applications are executed via virtual machines(VMs).Since effective scheduling of resources acts as an important role in the design of cloud enabled CPS,this paper focuses on the design of chaotic sandpi-per optimization based VM scheduling(CSPO-VMS)technique for energy effi-cient CPS.The CSPO-VMS technique is utilized for searching for the optimum VM migration solution and it helps to choose an effective scheduling strategy.The CSPO algorithm integrates the concepts of traditional SPO algorithm with the chaos theory,which substitutes the main parameter and combines it with the chaos.In order to improve the process of determining the global optimum solutions and convergence rate of the SPO algorithm,the chaotic concept is included in the SPO algorithm.The CSPO-VMS technique also derives afitness function to choose optimal scheduling strategy in the CPS environment.In order to demonstrate the enhanced performance of the CSPO-VMS technique,a wide range of simulations were carried out and the results are examined under varying aspects.The simulation results ensured the improved performance of the CSPO-VMS technique over the recent methods interms of different measures.

High Linear Voltage Gain in QZNC Through Synchronizing Switching Circuits

The solar powered systems require high step-up converter for efficient energy transfer.For this,quasi-impedance network converter has been introduced.The quasi-impedance network converter(QZNC)is of two types:type-1 and type-2 configuration.Both the type-1 and type-2 QZNC configurations have drooping voltage gain profile due to presence of high switching noise.To overcome this,a new quasi-impedance network converter synchronizing the switching circuit with low frequency noise has been proposed.In this paper,the proposed QZNC con-figuration utilizes the current controlling diode to prevent the output voltage drop.Thus,the suggested topology provides linear high voltage gain profile,low load voltage ripple,and reduced impedance network stress and device stress.There-fore,the efficiency of proposed QZNC has been improved.The topology descrip-tion,working principle,parameter design and comparison with traditional converters are illustrated.Andfinally,both simulation and practical results are presented to confirm the converter characteristics and performance.From the results,it has been found that the performance of the suggested topology is better as it achieves a higher efficiency of 81%and hence,it is suitable for high power applications.

Genetic-Chicken Swarm Algorithm for Minimizing Energy in Wireless Sensor Network

Wireless Sensor Network(WSN)technology is the real-time applica-tion that is growing rapidly as the result of smart environments.Battery power is one of the most significant resources in WSN.For enhancing a power factor,the clustering techniques are used.During the forward of data in WSN,more power is consumed.In the existing system,it works with Load Balanced Cluster-ing Method(LBCM)and provides the lifespan of the network with scalability and reliability.In the existing system,it does not deal with end-to-end delay and deliv-ery of packets.For overcoming these issues in WSN,the proposed Genetic Algo-rithm based on Chicken Swarm Optimization(GA-CSO)with Load Balanced Clustering Method(LBCM)is used.Genetic Algorithm generates chromosomes in an arbitrary method then the chromosomes values are calculated using Fitness Function.Chicken Swarm Optimization(CSO)helps to solve the complex opti-mization problems.Also,it consists of chickens,hens,and rooster.It divides the chicken into clusters.Load Balanced Clustering Method(LBCM)maintains the energy during communication among the sensor nodes and also it balances the load in the gateways.The proposed GA-CSO with LBCM improves the life-span of the network.Moreover,it minimizes the energy consumption and also bal-ances the load over the network.The proposed method outperforms by using the following metrics such as energy efficiency,ratio of packet delivery,throughput of the network,lifetime of the sensor nodes.Therefore,the evaluation result shows the energy efficiency that has achieved 83.56%and the delivery ratio of the packet has reached 99.12%.Also,it has attained linear standard deviation and reduced the end-to-end delay as 97.32 ms.

Hybrid Optimisation with Black Hole Algorithm for Improving Network Lifespan

Wireless sensor networks(WSNs)are projected to have a wide range of applications in the future.The fundamental problem with WSN is that it has afinite lifespan.Clustering a network is a common strategy for increasing the life-time of WSNs and,as a result,allowing for faster data transmission.The cluster-ing algorithm’s goal is to select the best cluster head(CH).In the existing system,Hybrid grey wolf sunflower optimization algorithm(HGWSFO)and optimal clus-ter head selection method is used.It does not provide better competence and out-put in the network.Therefore,the proposed Hybrid Grey Wolf Ant Colony Optimisation(HGWACO)algorithm is used for reducing the energy utilization and enhances the lifespan of the network.Black hole method is used for selecting the cluster heads(CHs).The ant colony optimization(ACO)technique is used tofind the route among origin CH and destination.The open cache of nodes,trans-mission power,and proximity are used to improve the CH selection.The grey wolf optimisation(GWO)technique is the most recent and well-known optimiser module which deals with grey wolves’hunting activity(GWs).These GWs have the ability to track down and encircle food.The GWO method was inspired by this hunting habit.The proposed HGWACO improves the duration of the net-work,minimizes the power consumption,also it works with the large-scale net-works.The HGWACO method achieves 25.64%of residual energy,25.64%of alive nodes,40.65%of dead nodes also it enhances the lifetime of the network.

Modeling and Control of Parallel Hybrid Electric Vehicle Using Sea-Lion Optimization

This paper develops a parallel hybrid electric vehicle(PHEV)propor-tional integral controller with driving cycle.To improve fuel efficiency and reduce hazardous emissions in hybrid electric vehicles(HEVs)combine an electric motor(EM),a battery and an internal combustion engine(ICE).The electric motor assists the engine when accelerating,driving longer highways or climbing hills.This enables the use of a smaller,more efficient engine.It also makes use of the concept of regenerative braking to maximize energy efficiency.In a Hybrid Electric Vehicle(HEV),energy dissipated while braking is utilized to charge the battery.The proportional integral controller was used in this paper to analyze engine,motor performance and the New European Driving Cycle(NEDC)was used in the vehicle driving test using Matlab/Simulink.The proportional integral controllers were designed to track the desired vehicle speed and manage the vehi-cle’s energyflow.The Sea Lion Optimization(SLnO)methods were created to reduce fuel consumption in a parallel hybrid electric vehicle and the results were obtained for the New European Driving Cycle.

水利水电工程动态联盟分包商选择机制研究

选择优秀的分包商对总承包商来说是实现总包合同目标的关键,然而目前国内并没有统一的总包商选择分包商的流程和方法。根据水利水电工程的特点,从施工总承包商的视角,建立了分包商选择和评价三阶段动态模型;根据不同阶段评价目的,设计了相应的选择评价指标体系,建立了DEA对抗交叉评价模型,并对模型的应用作了详细说明。结合工程实例对模型进行了验证。结果证明,DEA分包商选择与评价模型,克服了目前国内总承包商选择分包商主观随意性强的弱点,其评价结果更有效,更符合客观实际,为总承包商选择分包商提供了定量的、科学的决策方法。

华南地区11种高等水生维管植物净化城镇污水效益评价

采用定标、单项评分和综合评价的方法，从净化能力、抗逆性、管理、资源化程度和景观５方面，对华南地区筛选出的１１种高等维管植物净化城镇污水效益进行了评价。结果表明：黑藻、假马齿苋和水筛对污水的净化能力与风眼莲相当，而综合效能优于风眼莲。评价结果为城镇污水的处理提供了新的资料。

沿海三大城市群产业协作效果评价与比较

本文在对都市圈产业分工与协作理论分析的基础上,运用专业化指数、多样化指数以及灰色关联度方法,对京津冀、长三角、珠三角产业协作效果进行评价。结果表明,从产业专业化和多样化看,三大城市群产业协作均呈现出良好的发展态势,珠三角产业协作效果最好,其次是长三角,而京津冀最差;从产业协同发展成熟度看,三大城市群与城市群内不同城市的产业协同发展成熟度各有所长;从各城市群与非农产业的协同关联度看,珠三角和长三角的协同关联度要好于京津冀,而且第三产业对三大城市群产业协作的作用越来越大;从产业协作的综合效率来看,珠三角产业协作的综合效率最高,京津冀次之,长三角最低。为了促进三大城市群的产业协作,须降低行政性交易成本,提升城市群内部各城市间交通通达性,遵循市场规律在更大范围内配置资源。

京津冀协同发展背景下河北省增长极选择与培育问题研究

对京津冀协同发展这一战略目标而言,河北省增长极的选择与培育问题都具有重要的现实意义。虽然河北省在这方面具备一些有利条件,但也存在诸如增长极作用不突出等问题。为此,应明确河北省增长极培养的战略目标,增加科技创新力度,注重优秀人才培养。

城市综合交通枢纽信息化管理模式评价研究

城市综合交通枢纽信息化管理模式的优劣是衡量其运行效率的主要指标。通过对城市综合交通枢纽信息化管理模式在建设阶段和运营阶段的主要影响因素进行分析,明确各因素的重要性程度。采用SWOT法、IFE矩阵、EFE矩阵等方法,对城市综合交通枢纽管理模式进行评价,并以虹桥枢纽为例进行案例分析。研究结论将为城市综合交通枢纽信息化管理模式的持续改进提供了科学支撑和理论依据。

知识产权管理绩效评价:一种基于专家意见遴选的模糊综合评判模型

本文从权利主体的角度出发，探讨可用于企业、科研机构的综合型知识产权管理能力的绩效评价体系，用以辅助政府机构对企业（科研机构）的知识产权管理能力进行综合量化评价，也可作为企业（科研机构）对自身知识产权管理能力进行量化评价改进的手段。评价指标体系区分基础项与加分项，加分项为成果产出绩效，鼓励高水平知识产权开发和落到实处的知识产权转化。为了避免专家意见分离引起的权重不合理现象，在AHP法的基础上引入欧几里德距离对专家意见进行遴选，并依据最大隶属度原则和置信度原理，结合模糊综合评判模型进行综合评价。通过问卷调研和访谈，构建了指标权重并依托某组织进行了评价模型的实例应用，指出了该组织在激励机制与控制，保护体系方面的不足。

基于江西省三级医院数据的三阶段DEA模型江西公立医院资源使用效率研究

目的测评2017年江西省三级医院资源使用情况。方法从江西省卫生健康委员会系统收集2017年江西省55家公立医院投入与产出的数据,对指标进行筛选后,利用三阶段DEA模型对公立医院的进行效率测评,并分析第一阶段和第三阶段的效率情况。结果公立医院整体综合效率、纯技术效率和规模效率值分别是0.595、0.685和0.867;从不同医院类别来看,综合医院、中医医院、专科医院、妇幼保健院的纯技术效率分别是0.644、0.694、0.629、0.915,规模效率为0.926、0.783、0.757、0.908,综合效率为0.597、0.537、0.477和0.837。结论利用DEA三阶段模型测算医院资源使用效率,更可靠、更真实和更加准确,对提高医院资源使用效率具有重要性意义。