Optimizing crop yields while minimizing environmental impact through deep placement of nitrogen fertilizer

Nitrogen(N)serves as an essential nutrient for yield formation across diverse crop types.However,agricultural production encounters numerous challenges,notably high N fertilizer rates coupled with low N use efficiency and serious environmental pollution.Deep placement of nitrogen fertilizer(DPNF)is an agronomic measure that shows promise in addressing these issues.This review aims to offer a comprehensive understanding of DPNF,beginning with a succinct overview of its development and methodologies for implementation.Subsequently,the optimal fertilization depth and influencing factors for different crops are analyzed and discussed.Additionally,it investigates the regulation and mechanism underlying the DPNF on crop development,yield,N use efficiency and greenhouse gas emissions.Finally,the review delineates the limitations and challenges of this technology and provides suggestions for its improvement and application.This review provides valuable insight and reference for the promotion and adoption of DPNF in agricultural practice.

Optimizing distribution of multiple tuned mass dampers for building structures with inverse element exchange method

Multiple tuned mass dampers(MTMDs)reduce dynamic response with multiple specified frequencies of building structures.Many optimization algorithms for placement design exist,though they rarely conform to code-based verification nor produce high quality solutions without high computational effort and high complexity.This study proposes an inverse element exchange method(IEEM)with multi-level programming and compares it to a single tuned mass damper(STMD)and uniform distribution of multiple tuned mass dampers in the frequency and time domains.A ten-story shear building is used for the numerical case study.The results show that the proposed method can offer improvement over the STMD,uniform distribution of multiple tuned mass dampers,and distribution optimized by genetic algorithms(GA)with regard to minimizing the interstory drift ratio(IDR)in both the frequency and time domains and the time consumption for optimization.

The Impact of Optimizing Details in the Operating Room on the Level of Knowledge, Attitude, and Practice of Hospital Infection Prevention and Control by Surgeons, as Well as the Effectiveness of Infection Control

Objective: This paper aims to explore the impact of optimizing details in the operating room on the level of knowledge, attitude, and practice of hospital infection prevention and control by surgeons, as well as the effectiveness of infection control. Methods: From January 2022 to June 2023, a total of 120 patients were screened and randomly divided into a control group (routine care and hospital infection management) and a study group (optimizing details in the operating room). Results: Significant differences were found between the two groups in the data of surgeons’ level of knowledge, attitude, and practice in hospital infection prevention and control, infection rates, and nursing satisfaction, with the study group showing better results (P Conclusion: The use of optimizing details in the operating room among surgeons can effectively improve surgeons’ level of knowledge, attitude, and practice in hospital infection prevention and control, reduce infection occurrence, and is worth promoting.

Optimizing high-coordination shell of Co-based single-atom catalysts for efficient ORR and zinc-air batteries

Atom-level modulation of the coordination environment for single-atom catalysts(SACs)is considered as an effective strategy for elevating the catalytic performance.For the MNxsite,breaking the symmetrical geometry and charge distribution by introducing relatively weak electronegative atoms into the first/second shell is an efficient way,but it remains challenging for elucidating the underlying mechanism of interaction.Herein,a practical strategy was reported to rationally design single cobalt atoms coordinated with both phosphorus and nitrogen atoms in a hierarchically porous carbon derived from metal-organic frameworks.X-ray absorption spectrum reveals that atomically dispersed Co sites are coordinated with four N atoms in the first shell and varying numbers of P atoms in the second shell(denoted as Co-N/P-C).The prepared catalyst exhibits excellent oxygen reduction reaction(ORR)activity as well as zinc-air battery performance.The introduction of P atoms in the Co-SACs weakens the interaction between Co and N,significantly promoting the adsorption process of ^(*)OOH,resulting in the acceleration of reaction kinetics and reduction of thermodynamic barrier,responsible for the increased intrinsic activity.Our discovery provides insights into an ultimate design of single-atom catalysts with adjustable electrocatalytic activities for efficient electrochemical energy conversion.

Optimizing wind farm layout for enhanced electricity extraction using a new hybrid PSO-ANN method

With the growing need for renewable energy,wind farms are playing an important role in generating clean power from wind resources.The best wind turbine architecture in a wind farm has a major influence on the energy extraction efficiency.This paper describes a unique strategy for optimizing wind turbine locations on a wind farm that combines the capabilities of particle swarm optimization(PSO)and artificial neural networks(ANNs).The PSO method was used to explore the solution space and develop preliminary turbine layouts,and the ANN model was used to fine-tune the placements based on the predicted energy generation.The proposed hybrid technique seeks to increase energy output while considering site-specific wind patterns and topographical limits.The efficacy and superiority of the hybrid PSO-ANN methodology are proved through comprehensive simulations and comparisons with existing approaches,giving exciting prospects for developing more efficient and sustainable wind farms.The integration of ANNs and PSO in our methodology is of paramount importance because it leverages the complementary strengths of both techniques.Furthermore,this novel methodology harnesses historical data through ANNs to identify optimal turbine positions that align with the wind speed and direction and enhance energy extraction efficiency.A notable increase in power generation is observed across various scenarios.The percentage increase in the power generation ranged from approximately 7.7%to 11.1%.Owing to its versatility and adaptability to site-specific conditions,the hybrid model offers promising prospects for advancing the field of wind farm layout optimization and contributing to a greener and more sustainable energy future.

Optimizing Multi-Row Cam Roller Bearing for Long Fatigue Life of Hydraulic Motors

Cam-lobe radial-piston hydraulic motors are widely used as rotation driving units for various marine machinery owing to their ultrahigh output torque(more than 100 kN m).A multi-row cam roller bearing(MCRB)is the key component that directly determines the fatigue life of a cam-lobe radial-piston hydraulic motor.However,compact geometry and complex loads render MCRB susceptible to fatigue failure,highlighting the need for an optimized MCRB to achieve longer fatigue life and higher reliability.Therefore,this study proposes an innovative geometry optimization method for an MCRB to improve its fatigue life.In this method,a quasi-static model was developed to calculate the load distribution,with the fatigue life of the MCRB calculated using both basic dynamic loading and load distribution.Subsequently,a genetic algorithm was used to obtain the optimized geometry parameters,which significantly improved the fatigue life of the MCRB.Finally,a loading test was conducted on a hydraulic motor installed with both the initial and optimized MCRB to validate the effectiveness of the proposed optimization method.This study provides a theoretical guideline for optimizing the design of MCRB,thereby increasing the fatigue life of hydraulic motors.

An Encode-and CRT-Based Scalability Scheme for Optimizing Transmission in Blockchain

Blockchain technology has witnessed a burgeoning integration into diverse realms of economic and societal development.Nevertheless,scalability challenges,characterized by diminished broadcast efficiency,heightened communication overhead,and escalated storage costs,have significantly constrained the broad-scale application of blockchain.This paper introduces a novel Encode-and CRT-based Scalability Scheme(ECSS),meticulously refined to enhance both block broadcasting and storage.Primarily,ECSS categorizes nodes into distinct domains,thereby reducing the network diameter and augmenting transmission efficiency.Secondly,ECSS streamlines block transmission through a compact block protocol and robust RS coding,which not only reduces the size of broadcasted blocks but also ensures transmission reliability.Finally,ECSS utilizes the Chinese remainder theorem,designating the block body as the compression target and mapping it to multiple modules to achieve efficient storage,thereby alleviating the storage burdens on nodes.To evaluate ECSS’s performance,we established an experimental platformand conducted comprehensive assessments.Empirical results demonstrate that ECSS attains superior network scalability and stability,reducing communication overhead by an impressive 72% and total storage costs by a substantial 63.6%.

Optimizing data for electrical resistivity tomography in hardened sites through the ratio method

To reduce the shielding effect of hardened layers on electrical resistivity tomography,a ratio method based on the distortion correction principle and the isolation coefficient is proposed.The effects of the resistivity and thickness of hardened concrete layers on the detection of target objects are explored.Both numerical simulations and indoor tank tests indicate that when the ratio method is employed to correct the original collected data,the maximum allowable error for the isolation coefficient should not exceed 1%.Notably,when the ratio of hardened layer thickness to electrode spacing does not exceed 1,correction through this method significantly enhances the recognition capability of target objects.However,when the hardened layer thickness is greater than the electrode spacing by a factor of 2 or more,the ratio method cannot achieve satisfactory results.The case study of flood control engineering detection in the Zhangxi section of the Huangpen River in Dongzhi County demonstrates that the detection effect after correction by the ratio method is comparable to that for the adjacent unhardened pavement,and the influence of the hardened layer is obviously weakened,resulting in more reliable results.

Optimizing Biodiesel Production from Karanja and Algae Oil with Nano Catalyst:RSMand ANN Approach

This study delves into biodiesel synthesis from non-edible oils and algae oil sources using Response Surface Methodology(RSM)and an Artificial Neural Network(ANN)model to optimize biodiesel yield.Blend of C.vulgaris and Karanja oils is utilized,aiming to reduce free fatty acid content to 1%through single-step transesterification.Optimization reveals peak biodiesel yield conditions:1%catalyst quantity,91.47 min reaction time,56.86℃reaction temperature,and 8.46:1 methanol to oil molar ratio.The ANN model outperforms RSM in yield prediction accuracy.Environmental impact assessment yields an E-factor of 0.0251 at maximum yield,indicating responsible production with minimal waste.Economic analysis reveals significant cost savings:30%-50%reduction in raw material costs by using non-edible oils,10%-15%increase in production efficiency,20%reduction in catalyst costs,and 15%-20%savings in energy consumption.The optimized process reduces waste disposal costs by 10%-15%,enhancing overall economic viability.Overall,the widespread adoption of biodiesel offers economic,environmental,and social benefits to a diverse range of stakeholders,including farmers,producers,consumers,governments,environmental organizations,and the transportation industry.Collaboration among these stakeholders is essential for realizing the full potential of biodiesel as a sustainable energy solution.

Optimizing Connections:Applied Shortest Path Algorithms for MANETs

This study is trying to address the critical need for efficient routing in Mobile Ad Hoc Networks(MANETs)from dynamic topologies that pose great challenges because of the mobility of nodes.Themain objective was to delve into and refine the application of the Dijkstra’s algorithm in this context,a method conventionally esteemed for its efficiency in static networks.Thus,this paper has carried out a comparative theoretical analysis with the Bellman-Ford algorithm,considering adaptation to the dynamic network conditions that are typical for MANETs.This paper has shown through detailed algorithmic analysis that Dijkstra’s algorithm,when adapted for dynamic updates,yields a very workable solution to the problem of real-time routing in MANETs.The results indicate that with these changes,Dijkstra’s algorithm performs much better computationally and 30%better in routing optimization than Bellman-Ford when working with configurations of sparse networks.The theoretical framework adapted,with the adaptation of the Dijkstra’s algorithm for dynamically changing network topologies,is novel in this work and quite different from any traditional application.The adaptation should offer more efficient routing and less computational overhead,most apt in the limited resource environment of MANETs.Thus,from these findings,one may derive a conclusion that the proposed version of Dijkstra’s algorithm is the best and most feasible choice of the routing protocol for MANETs given all pertinent key performance and resource consumption indicators and further that the proposed method offers a marked improvement over traditional methods.This paper,therefore,operationalizes the theoretical model into practical scenarios and also further research with empirical simulations to understand more about its operational effectiveness.

Rapid Parameter-Optimizing Strategy for Plug-and-Play Devices in DC Distribution Systems under the Background of Digital Transformation

By integrating advanced digital technologies such as cloud computing and the Internet of Things in sensor measurement,information communication,and other fields,the digital DC distribution network can efficiently and reliably access DistributedGenerator(DG)and Energy Storage Systems(ESS),exhibiting significant advantages in terms of controllability and meeting requirements of Plug-and-Play(PnP)operations.However,during device plug-in and-out processes,improper systemparametersmay lead to small-signal stability issues.Therefore,before executing PnP operations,conducting stability analysis and adjusting parameters swiftly is crucial.This study introduces a four-stage strategy for parameter optimization to enhance systemstability efficiently.In the first stage,state-of-the-art technologies in measurement and communication are utilized to correct model parameters.Then,a novel indicator is adopted to identify the key parameters that influence stability in the second stage.Moreover,in the third stage,a local-parameter-tuning strategy,which leverages rapid parameter boundary calculations as a more efficient alternative to plotting root loci,is used to tune the selected parameters.Considering that the local-parameter-tuning strategy may fail due to some operating parameters being limited in adjustment,a multiparameter-tuning strategy based on the particle swarm optimization(PSO)is proposed to comprehensively adjust the dominant parameters to improve the stability margin of the system.Lastly,system stability is reassessed in the fourth stage.The proposed parameter-optimization strategy’s effectiveness has been validated through eigenvalue analysis and nonlinear time-domain simulations.

Optimizing Sustainability:Exergoenvironmental Analysis of a Multi-Effect Distillation with Thermal Vapor Compression System for Seawater Desalination

Seawater desalination stands as an increasingly indispensable solution to address global water scarcity issues.This study conducts a thorough exergoenvironmental analysis of a multi-effect distillation with thermal vapor compression(MED-TVC)system,a highly promising desalination technology.The MED-TVC system presents an energy-efficient approach to desalination by harnessing waste heat sources and incorporating thermal vapor compression.The primary objective of this research is to assess the system’s thermodynamic efficiency and environmental impact,considering both energy and exergy aspects.The investigation delves into the intricacies of energy and exergy losses within the MED-TVC process,providing a holistic understanding of its performance.By scrutinizing the distribution and sources of exergy destruction,the study identifies specific areas for enhancement in the system’s design and operation,thereby elevating its overall sustainability.Moreover,the exergoenvironmental analysis quantifies the environmental impact,offering vital insights into the sustainability of seawater desalination technologies.The results underscore the significance of every component in the MED-TVC system for its exergoenvironmental performance.Notably,the thermal vapor compressor emerges as pivotal due to its direct impact on energy efficiency,exergy losses,and the environmental footprint of the process.Consequently,optimizing this particular component becomes imperative for achieving a more sustainable and efficient desalination system.

Barriers and Motivators of Young Dutch Elite Athletes for Optimizing Their Nutritional Intake

Many young elite athletes do not meet their daily energy and nutrient requirements. However, little research has been done on why these athletes do not meet their daily needs. The aim was to research the barriers and motivators of young Dutch elite athletes to optimize their nutritional intake. Quantitative and qualitative research was conducted among 8 handball and 4 volleyball players at the Dutch National Sports Center (17.2 ± 0.8 years). First, the nutritional intake was tracked through food diaries and analyzed in Nutritics. Thereupon, five semi-structured interviews based on the COM-B model were carried out. The interviews were transcribed and coded. The athletes had a reduced intake of energy, carbohydrates, vitamins A, C, E, D, calcium, potassium, zinc, and iron compared to their requirements. Seven themes for optimizing their nutritional intake emerged in the interviews: needs assessment, practical translation, portion size, lack of time, involvement, individuality, and food distribution. Barriers that the athletes experienced were that they did not know what their total daily nutritional needs were and how this translates into practice. In addition, the portion size at dinner was too small. They also had little time to eat a full meal due to time pressure from training and school. On the other hand, motivators were receiving meal options to translate their needs into practice with a distribution of moments when they need to eat. Covering these topics in nutritional workshops where athletes actively participate with more individual focus, could contribute to the optimization of their nutritional intake.

Optimizing Grey Wolf Optimization: A Novel Agents’ Positions Updating Technique for Enhanced Efficiency and Performance

Grey Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm that has gained popularity for solving optimization problems. In GWO, the success of the algorithm heavily relies on the efficient updating of the agents’ positions relative to the leader wolves. In this paper, we provide a brief overview of the Grey Wolf Optimization technique and its significance in solving complex optimization problems. Building upon the foundation of GWO, we introduce a novel technique for updating agents’ positions, which aims to enhance the algorithm’s effectiveness and efficiency. To evaluate the performance of our proposed approach, we conduct comprehensive experiments and compare the results with the original Grey Wolf Optimization technique. Our comparative analysis demonstrates that the proposed technique achieves superior optimization outcomes. These findings underscore the potential of our approach in addressing optimization challenges effectively and efficiently, making it a valuable contribution to the field of optimization algorithms.

Optimizing Memory Access Efficiency in CUDA Kernel via Data Layout Technique

Over the past decade, Graphics Processing Units (GPUs) have revolutionized high-performance computing, playing pivotal roles in advancing fields like IoT, autonomous vehicles, and exascale computing. Despite these advancements, efficiently programming GPUs remains a daunting challenge, often relying on trial-and-error optimization methods. This paper introduces an optimization technique for CUDA programs through a novel Data Layout strategy, aimed at restructuring memory data arrangement to significantly enhance data access locality. Focusing on the dynamic programming algorithm for chained matrix multiplication—a critical operation across various domains including artificial intelligence (AI), high-performance computing (HPC), and the Internet of Things (IoT)—this technique facilitates more localized access. We specifically illustrate the importance of efficient matrix multiplication in these areas, underscoring the technique’s broader applicability and its potential to address some of the most pressing computational challenges in GPU-accelerated applications. Our findings reveal a remarkable reduction in memory consumption and a substantial 50% decrease in execution time for CUDA programs utilizing this technique, thereby setting a new benchmark for optimization in GPU computing.

Investigation on the Optimizing Range of CombustionChamber Configuration Parameters of DSCS

Aim To obtain an optimizing range of the main configuration parameters of double swirls combustion system (DSCS) Methods To analyze the influence of DS combustion cham-ber configuration parameters on fuel spray and mixing by means of the fuel jet developmentperiphery charts obtained by the high speed photography with a modeling test device deve-loped by authors,and to examine it by the tests on a single cylinder diesel engine.Resultsand Conclusion The mixing process can be divided into four phases.The optimizing range of the ration of the inner chamber diameter to the cylinder bore,d2/D,is 0.4-0.7; and the outerchamber diameter,d1 the height of the circular ridge to the piston top face,h1,the radius of outer/inner chamber circle,R1,R2 ,the max depth of outer/inner chamber bowl,H1,H2,etc. are also important

Minimizing Weight by Optimizing Different Truss Parts Using Finite Element Analysis

This paper presents a study of minimizing weight by optimizing different truss parts using finite element analysis and comparing Warren trusses with other trusses. The aim of the optimization is to find a light design. Existing structural steel trusses were initially optimized for minimum weight and constrained with allowable stresses and deflections. Applicable Eurocode 3 design conditions are presented, which provide the constraints for the problem. Steel truss is a preferred solution in large-span roof structures due to its good attributes, such as being lightweight and durable. Existing structural steel trusses were initially optimized for minimum weight and constrained with allowable stresses and deflections. Constant spans of the trusses have been considered, and each truss has been subjected to the same types of load cases. The top chord member load has been kept constant in each truss at 2 kN/m. Two sets of load conditions are taken as the self-weight of the truss and the snow load, but the structure is calculated by the load combination. The structural steel trusses were optimized using the design optimization tool as a first-order optimization method in RFEM, and it was extended to compare the most suitable truss geometry for the minimum weight. Finally, it is concluded that the Warren truss has a higher stiffness-to-weight ratio than other trusses after optimization. The goal of this study was to analyze all trusses and ensure that the structural stress is less than the allowable stress and that the deflection is less than the allowable deflection. The span and height are constant in all cases because they have no impact on the weight increase;only the position of the rods and cross-section size affect the building’s ability to withstand loads and weight increases. In this paper, a finite element analysis (FEA)-based optimization technique is proposed for the optimization of a light design that is constrained by allowable stresses and deflections. For this purpose, there have been studies on sizing optimization to minimize the mass of different steel truss roof system types both in the past and today. For this purpose, weight design and analysis of the optimum weight are carried out on ten different structural systems.

Optimizing a Transportation System Using Metaheuristics Approaches (EGD/GA/ACO): A Forest Vehicle Routing Case Study

The large-scale optimization problem requires some optimization techniques, and the Metaheuristics approach is highly useful for solving difficult optimization problems in practice. The purpose of the research is to optimize the transportation system with the help of this approach. We selected forest vehicle routing data as the case study to minimize the total cost and the distance of the forest transportation system. Matlab software helps us find the best solution for this case by applying three algorithms of Metaheuristics: Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Extended Great Deluge (EGD). The results show that GA, compared to ACO and EGD, provides the best solution for the cost and the length of our case study. EGD is the second preferred approach, and ACO offers the last solution.

Optimizing Healthcare Big Data Processing with Containerized PySpark and Parallel Computing: A Study on ETL Pipeline Efficiency

In this study, we delve into the realm of efficient Big Data Engineering and Extract, Transform, Load (ETL) processes within the healthcare sector, leveraging the robust foundation provided by the MIMIC-III Clinical Database. Our investigation entails a comprehensive exploration of various methodologies aimed at enhancing the efficiency of ETL processes, with a primary emphasis on optimizing time and resource utilization. Through meticulous experimentation utilizing a representative dataset, we shed light on the advantages associated with the incorporation of PySpark and Docker containerized applications. Our research illuminates significant advancements in time efficiency, process streamlining, and resource optimization attained through the utilization of PySpark for distributed computing within Big Data Engineering workflows. Additionally, we underscore the strategic integration of Docker containers, delineating their pivotal role in augmenting scalability and reproducibility within the ETL pipeline. This paper encapsulates the pivotal insights gleaned from our experimental journey, accentuating the practical implications and benefits entailed in the adoption of PySpark and Docker. By streamlining Big Data Engineering and ETL processes in the context of clinical big data, our study contributes to the ongoing discourse on optimizing data processing efficiency in healthcare applications. The source code is available on request.

Impact of Optimizing Emergency Nursing Processes on Resuscitation Success in Patients with Acute Chest Pain

Objective:To analyze the effect of optimizing the emergency nursing process in the resuscitation of patients with acute chest pain and the impact on the resuscitation success rate.Methods:66 patients with acute chest pain received by the emergency department of our hospital from January 2022 to December 2023 were selected as the study subjects and divided into two groups according to the differences in the emergency nursing process,i.e.,33 patients receiving routine emergency care were included in the control group,and 33 patients receiving the optimization of emergency nursing process intervention were included in the observation group.Patients’resuscitation effect and satisfaction with nursing care in the two groups were compared.Results:The observation group’s consultation assessment time,reception time,admission to the start of resuscitation time,and resuscitation time were shorter than that of the control group,the resuscitation success rate was higher than that of the control group,and the incidence of adverse events was lower than that of the control group,with statistically significant differences(P<0.05);and the observation group’s satisfaction with nursing care was higher than that of the control group,with statistically significant differences(P<0.05).Conclusion:Optimization of emergency nursing process intervention in the resuscitation of acute chest pain patients can greatly shorten the rescue time and improve the success rate of resuscitation,with higher patient satisfaction.