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.

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.

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.

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.

数字基础设施建设如何影响民营企业投资效率——营商环境的中介效应

为探究数字基础设施建设对中国民营企业投资效率的影响,并考察营商环境在其中的中介作用。基于2013-2022年中国30个省级行政区和上市民营企业的面板数据,采用双向固定效应模型与中介效应模型进行经验分析。研究结果显示,数字基础设施建设显著提升了上市民营企业的投资效率,并有效缓解了企业投资不足与投资过度的问题。营商环境的中介效应检验结果表明,数字基础设施建设优化了营商环境,通过聚集人才和吸引投入促进了民营企业投资效率的提升。异质性分析显示,改善的数字基础设施在经济发达的东部地区对解决民营企业的投资过度和不足问题更为显著,而在经济发展相对落后的中西部地区,其改善主要集中于解决企业的投资不足问题。综上所述,针对民营企业投资疲软的现实困境,应当加快数字基础设施投资与建设,释放数字红利,并进一步发挥数字基础设施建设对营商环境的改善作用,以助力民营企业实现投资效率的提升与高质量发展。

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.

能源工业投资结构变迁、能源消费结构低碳化和全要素能源效率的互动关系

运用夹角余弦法测算了2004—2019年30个省份的能源工业投资结构变迁指数和能源消费结构低碳化指数,并基于非期望产出的SBM模型测算了全要素能源效率,然后建立面板向量自回归模型考察上述三个内生变量的短期及长期的动态互动效应。研究结果表明,三个内生变量的自我效应都是当期达到峰值后随时间衰退,且另外两个内生变量对能源工业投资结构变迁作用显著为负,但能源消费结构低碳化能帮助预测能源工业投资结构变迁。同时,能源消费结构低碳化受全要素能源效率的正向推动,并和能源工业投资结构变迁共同促进了全要素能源效率的提升,二者对全要素能源效率有一定的预测能力。因此,要注意提升能源工业投资结构变迁效益和全要素能源效率,以延长能源消费结构低碳化周期并增强低碳化强度。

数字化转型、高管认知能力与企业投资效率研究

数字化改造已是企业转型升级的最重要路径,现有研究加深了社会和企业家对数字化转型的认知,但是尚未有学者关注到企业数字化转型与投资效率的关系。文章以2012年以来我国制造业上市公司作为研究对象,分析了不同类型数字化转型对企业投资效率的影响,并考察了企业专业化发展的中介效应和高管认知能力的调节效应。分析发现:数字化转型对企业投资效率存在“倒U”型影响,现阶段大多数企业数字化转型水平较低,尚未达到投资效率最大值对应的数字化转型深度,说明“倒U”型的右侧负效应主要来自少数极端值。专业化发展在数字化转型与投资效率之间承担了显著的中介角色,数字化转型促进了企业专业化发展,专业化发展与投资效率存在“倒U”型关系。高管认知能力对数字化转型与投资效率关系存在调节效应,但主要发生在二者正相关阶段,高管认知能力越高的企业,数字化转型对投资效率的正影响越强。此外,异质性分析表明数字化转型对投资效率的影响仅存在主动实施数字化转型的企业中,被动实施数字化转型无法改善企业投资效率。

审计关系错配与企业投资效率

文章基于2016-2020年A股上市公司数据,分析审计关系错配对企业投资效率的影响及作用机制,探究客户——审计师关系暗含的企业内部治理机制和外部监督力量之间的博弈。研究结果显示:审计关系错配区分向上错配与向下错配两种,其中,向上错配关系与企业投资效率呈正相关关系,向下错配关系与企业投资效率呈负相关关系,而良好的治理环境有助于弱化错配关系对投资效率的影响。机制检验发现,向上错配主要是通过监督治理渠道降低企业过度投资,从而实现投资效率的提升;而向下错配通过信号传递渠道导致企业投资效率降低。研究丰富了审计关系错配经济后果文献,有助于厘清外部监管与企业行为间的互动关系,对促进审计市场良性关系发展具有一定现实意义。

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.

制度差异、营商环境与对外直接投资

基于2006—2020年我国对97个国家和地区进行直接投资的数据,考察制度差异对我国对外直接投资的影响以及营商环境的调节作用,并将制度差异与营商环境的细分因素相结合来讨论投资企业在面临制度差异的情况下,营商环境各子因素的作用。研究结果表明,制度差异与我国对外直接投资呈负相关关系,而东道国较好的营商环境能够有效缓解其负向作用,这种影响对东道国非“一带一路”沿线国家及未与我国签订双边投资协定的国家更为显著。此外,在对东道国营商环境的具体分析中,文章发现保护少数者投资、跨境贸易和破产处理便利度能够有效提升企业投资意愿,而信贷便利度则会因东道国较大的竞争力导致企业投资意愿下降。因此,企业应全面地考察东道国的营商情况,做到合理决策、精准投资。

Emission Reduction Investment, Technology Choice and Business Environmental Performance: Evidence from China’s Foreign Investment Liberalization Reform

Pollution has become an unavoidable concern as China’s high-quality development is underway.How to reduce pollution is an imperative issue for China to address.Pollution emissions are closely related to factor inputs,production processes and pollution control measures.Are there other forces to cut emissions besides regulatory control?Taking sulfur dioxide as an example,this paper probes into the potential mechanism through which technical efficiency drives pollution reduction in the context of opening to foreign investment.The results reveal that the openness to foreign investment remarkably lowers pollution emissions of fi rms,with SOEs,large fi rms and exporters seeing more pronounced pollution reduction eff ect after opening to foreign investment,while fi rms in pollution-intensive industries and less regulated areas are weaker in pollution reduction.A look into fi rm behavior suggests that the openness to foreign investment reduces pollutant emissions by improving technical efficiency rather than by raising investment in pollution control.The pollution reduction eff ect resulting from the openness is refl ected in the improvement of intra-fi rm emission reduction capacity instead of inter-fi rm resource reallocation eff ect,according to an analysis at the aggregate level.This paper concludes that technical efficiency gains are an important tool to advance pollution reduction,and that China must be more flexible in leveraging the pollution reduction effect of other policies regarding technical effi ciency to drive its high-quality development that is green.

中国城市市政公用设施投资与建设的区域差异及效率评价

利用2001~2010年相关统计数据,探讨中国城市市政公用设施投资建设的区域差异,并采用数据包络分析方法进行投资效率评价。研究表明：①全国各省区累计投资差异较大,东部省区各分项设施建设投资均远高于中西部省区,但中西部省区投资的平均增速高于东部省区;②研究时段内绝大部分省区分项设施建设水平都有较大幅度提高,2010年全国范围设施建设水平大致呈现由东部地区向中西部地区递减的格局。③设施投资效率区域差异大,在空间分布方面不具有明显的东中西梯度特征。研究认为,应首先着力提高建设效率,并进一步综合考虑投资能力与设施需求,及时调整投资力度,促进设施建设与社会经济发展需求相协调。

我国农业资金投入结构对农业技术效率的影响分析——基于2003-2009年间全国10个省区微观调研面板数据

采用不考虑技术进步和考虑技术进步因素的C-D生产函数,运用随机前沿分析方法 (SFA),使用2003-2009年间全国10个省区835个样本农户跟踪调研数据,考察农业技术效率以及农业资金投入结构对农业技术效率的影响程度。结果表明我国农业平均技术效率仅为0.70,具有很大的提升空间,其中各省区农业技术效率差别显著,辽宁最高(0.86),而甘肃最低(0.59);整体上财政支农资金和农户自有资金对我国农业技术效率具有促进作用,而农业信贷资金和农村集体资金对我国农业技术效率的促进作用不明显,但是从各省区来看,农业资金投入结构对农业技术效率影响各不相同。