Accurate Performance Evaluation Research Articles

The quality evaluation system of ideological and political classroom teaching in universities based on GA‐BP algorithm

SummaryThe advancement of teaching quality is an indispensable section of the reform and growth of universities, and ideological and political education has critical impact on ideological education. The quality of classroom education can provide data support for efficient development, and has crucial influence on achieving scientific, reasonable, and accurate evaluation of ideological and political teaching performance. Thus, a performance assessment system for ideological and political education in universities with genetic algorithm optimized neural network algorithm is put forward. First, based on existing teaching evaluation indicators and combined with actual situations, a targeted teaching quality evaluation system is proposed. Then, based on BP, an adaptive genetic algorithm is proposed for improvement, and the output results are improved using entropy method. The results indicated that the proposed model could reach its optimal state after 81 iterations in this study. In the fitting test, it reached 0.971. In actual testing, the average error was only 2.68, which was much bigger than the other three algorithms. Its accuracy was 2%–3.2% higher than that of the best existing algorithms. These results indicated that the method put forward in this study had better practical significance, lower error, more accurate evaluation results, and offered scientific data support for the education reform work of universities, which can better accelerate the development and construction of universities.

Comprehensive bottom-up methodology for generating high-resolution yearly building load profiles: A case study in temperate oceanic climate

To facilitate the transition of residential buildings towards decarbonized energy sources, various energy systems are currently being investigated within the scientific community. The accurate sizing and performance evaluation of these systems heavily rely on the quality of input profiles. Addressing this necessity, a method for generating diverse, high-resolution, continuous, consistent demand and production profiles for a whole year is proposed. This method is structured in a modular fashion and draws upon a widely recognized demand model from literature. Each module of the method is systematically presented, and the parametrization process is detailed through a case study focusing on single-family houses in temperate climate. This comprehensive description facilitates the replication of the method in different geographical regions. Subsequently, a Monte Carlo simulation is employed, incorporating variations in weather conditions, building properties, and occupant behaviors. This simulation generates an openly accessible dataset comprising thermal, electrical and photovoltaic profiles for 3500 configurations. The generated weather and electricity demand profiles exhibit trends and variations that closely match the measured data. Photovoltaic production profiles were validated against PVGIS data, showing similar monthly variations and diversity. The generated dataset includes houses with energy consumption profiles that correspond to Energy Performance Certificates ranging from A to E.

Measurement insights and error analysis of electronic parameters for ultrasonic transducers

Abstract Piezoelectric ultrasonic transducers with the function of energy conversation, as well as their numerous advantages in high-power density, quick response, flexible design, and service reliability, are involved in wide applications of industrial processing, precision driving, smart sensing, and medical services. The electromechanical equivalent circuit and Kirchhoff’s law indicate that mechatronics parameters are essential for performance evaluation, reliability analysis, and fault diagnosis of ultrasonic transducers. Importantly, the ultrasonic transducer is a time-variant system, data of one single parameter collected from a certain test cannot match with the data of another single parameter acquired from a different test. So, a synchronous and precise online measurement of electronic parameters is encouraged for performance evaluation and optimization design of ultrasonic transducers. With the combination of virtual instrument technology, an asynchronous measurement system of electrical excitation parameters for the ultrasonic transducers of linear driving motors was established in this study. Furthermore, the systematic measurement methods and error theory were illustrated, including the calculation methods and measuring circuits of electric signals. Experimental results proved that the proposed system and methods for measuring the input electronic power of ultrasonic transducer (e.g. effective value method for voltage and current, energy moment method for frequency, and spectrum analysis method for phase difference) are highly precise, quickly responsive, robust, and reliable for ultrasonic transducers. The findings of this study provide valuable references and suggestions for efficient, accurate, and online performance evaluation of ultrasonic transducers, particularly for piezoelectric transducers utilizing ultrasonic high-voltage exciting signals.

Modelling environmental life cycle performance of alternative marine power configurations with an integrated experimental assessment approach: A case study of an inland passenger barge

There is pressure on the global shipping industry to move towards greener propulsion and fuel technologies to reduce greenhouse gas emissions. Hydrogen and electricity are both recognised as pathways to achieve a net-zero. However, in the evaluation of the environmental performance of these alternative marine power configurations, conventional life cycle assessment (LCA) methods have limitations reflecting the varied nature of ship design and operational modes. The integration of LCA with experimental assessment could remedy the shortcoming of conventional approaches to data generation. The system energy demand data in this study was generated based on specific ship design and directly fed into life cycle assessment. To demonstrate the effectiveness and potential the approach was applied to a case study of inland waterway vessel. Suitable hybrid PV/electricity/diesel and hydrogen powered fuel cell systems for the case vessel were modelled; and hydrodynamic testing and dynamic system simulation was undertaken to provide ship performance data under various operational/environmental profiles. Lifecycle assessment (LCA) indicated hydrogen and electrical propulsion technologies have the potential for 85.7 % and 56.2 % emissions reduction against an MGO base case, respectively. The results highlight that implementation of both technologies is highly dependent on energy production pathways. Hydrogen systems reliant on fossil feedstocks risk an increase in emissions of up to 6.3 % against the MGO base case. Sensitivity analysis indicated an electrical system with electricity production from 79.5 % renewables could achieve savings of 82.2 % in GHG emissions compared to the MGO base case. Crucially, the results demonstrate a further development of the LCA approach which can enable a more accurate environmental performance evaluation of alternative marine power configurations considering specific ship design and operational characteristics. Ultimately this addition makes the results more meaningful for commercial operations and decision making in the selection of alternative marine power systems to support the transition to net-zero.

An automatic completion method for design domain knowledge graph using surrogate model, for rapid performance evaluation

The data and information generated during the design process, such as part parameters, affect the efficiency and quality of product design. Knowledge graph (KG) is often used to express and reuse the above knowledge. However, due to the decentralised and complex nature of the KG, it lacks the quantitative knowledge required for part selection, e.g. the relationship between part type and performance. To complete the knowledge and meet the requirements for rapid and accurate performance evaluation, this paper proposes an automatic KGC method based on a surrogate model. Firstly, the schema layer is established based on the design knowledge ontology. For the sampling process, the query statement is applied to extract knowledge such as design parameters, which define the range of sample points. Secondly, the design of experiments (DOE) method can obtain the data set of the target performance through simulation. Surrogate models are built based on multiple machine learning algorithms. Meanwhile, the hyperparameter optimisation method can further improve the prediction accuracy of the model. Finally, the performance of the design scheme is predicted, and the results are used to complete the KG through knowledge extraction. The method is validated by the selection and evaluation of bogie parts.

Compressive failure analysis of composite honeycomb sandwich panels with impact damage and stepped-scarf repairs

Composite honeycomb sandwich panels (CHSPs) have been widely used in the aerospace industry owing to their lightweight and superior mechanical properties. However, these CHSPs are susceptible to impact damage, leading to a significant reduction in compressive strength and potentially jeopardize aircraft safety. It is crucial to investigate repair methods for CHSPs with impact damage. This paper aims to evaluate the repair performance of CHSPs with impact damage through an analysis of their compressive failure behaviors. To accomplish this, both experimental tests and advanced numerical models are employed. The numerical models for the intact, damaged and stepped-scarf repaired CHSPs are established using the progressive failure analysis model, cohesive zone model and sandwich plate theory. A good agreement is observed between the experimental results and numerical predictions of compressive failure behaviors. Moreover, the validated numerical models are successfully utilized for determining optimum repair parameters by parametric analysis of the repaired CHSPs. The establishment of these numerical models offers an accurate and cost-effective evaluation of repair performance for CHSPs with impact damage, highlighting the novelty and main contribution of this paper.

Direct Determination of Absolute Radical Quantum Yields in Hydroxyl and Sulfate Radical-Based Treatment Processes.

The absolute radical quantum yield () is a critical parameter to evaluate the efficiency of radical-based processes in engineered water treatment. However, measuring is fraught with challenges, as current quantification methods lack selectivity, specificity, and anti-interference capabilities, resulting in significant error propagation. Herein, we report a direct and reliable time-resolved technique to determine at pH 7.0 for commonly used radical precursors in advanced oxidation processes. For H2O2 and peroxydisulfate (PDS), the values of •OH and at 266 nm were measured to be 1.10 ± 0.01 and 1.46 ± 0.05, respectively. For peroxymonosulfate (PMS), we developed a new approach to determine with terephthalic acid as a trap-and-trigger probe in the nonsteady state system. For the first time, the value was measured to be 0.56 by the direct method, which is stoichiometrically equal to (0.57 ± 0.02). Additionally, radical formation mechanisms were elucidated by density functional theory (DFT) calculations. The theoretical results showed that the highest occupied molecular orbitals of the radical precursors are O-O antibonding orbitals, facilitating the destabilization of the peroxy bond for radical formation. Electronic structures of these precursors were compared, aiming to rationalize the tendency of the values we observed. Overall, this time-resolved technique with specific probes can be used as a reliable tool to determine , serving as a scientific basis for the accurate performance evaluation of diverse radical-based treatment processes.

The Relevance of Surface Resistances on the Conductive Thermal Resistance of Lightweight Steel-Framed Walls: A Numerical Simulation Study

The accurate evaluation of the thermal performance of building envelope components (e.g., facade walls) is crucial for the reliable evaluation of their energy efficiency. There are several methods available to quantify their thermal resistance, such as analytical formulations (e.g., ISO 6946 simplified calculation method), numerical simulations (e.g., using finite element method), experimental measurements under lab-controlled conditions or in situ. Regarding measurements, when using the heat flow meter (HFM) method, very often, the measured value is based on surface conditions (e.g., temperature and heat flux), achieving in this way the so-called surface-to-surface or conductive thermal resistance (Rcond). When the building components are made of homogeneous layers, their Rcond values are constant, regardless of their internal and external surface boundary conditions. However, whenever this element is composed of inhomogeneous layers, such as in lightweight steel-framed (LSF) walls, their Rcond values are no longer constant, depending on their thermal surface resistance. In the literature, such systematic research into how these Rcond values vary is not available. In this study, the values of four LSF walls were computed, with different levels of thermal conductivity inhomogeneity, making use of four finite elements’ numerical simulation tools. Six external thermal surface resistances (Rse) were modelled, ranging from 0.00 up to 0.20 m2·K/W. The average temperature of the partition LSF walls is 15 °C, while for the facade LSF walls it is 10 °C. It was found that the accuracy values of all evaluated numerical software are very high and similar, the Rcond values being nearly constant for walls with homogeneous layers, as expected. However, the variation in the Rcond value depends on the level of inhomogeneity in the LSF wall layers, increasing up to 8%, i.e., +0.123 m2·K/W, for the evaluated Rse values.

A novel approach to evaluate dynamic performance for photovoltaic system using software platform

With the growing demand for renewable energy, solar photovoltaic (PV) systems have gained popularity as a reliable source of clean electricity. However, the performance of these systems can be limited by factors such as suboptimal maximum power point tracking (MPPT) algorithms. In order to improve the power generation efficiency of PV systems, it is important to evaluate the performance of dynamic MPPT algorithms that can adapt to varying operating conditions. Traditionally, such evaluations have been time consuming and expensive, often requiring extensive testing and measurement equipment. In this paper, we propose a novel approach to evaluate dynamic MPPT performance very quickly and simply using PSIM software. This approach enables accurate and efficient evaluation of MPPT performance under a wide range of operating conditions, while minimizing the cost and time involved in traditional testing methods. When applying the proposed method to a 3.7 kW inverter using the traditional perturbation and observation (P and O) method, we found that the highest average efficiency was 98.92% at an MPPT control period of 0.1s and a voltage perturbation of 1 V. This evaluation technique provides valuable insights into the design and optimization of more efficient MPPT control algorithms, leading to improved power generation efficiency and increased adoption of solar PV systems.

Numerical investigation of electricity generation potential by water circulating through three horizontal wells at Fengshun geothermal field in China

Fengshun geothermal field is the first medium and low temperature geothermal area in China, which is used for electricity generation. At present only vertical wells are employed to extract heat energy. After about 40 years of exploitation, the production temperature and rate are all declined. In this work, based on the hydrogeological data of the study area, the deep reservoir temperature and corresponding circulation depth of underground hot water are predicted with a silica thermometer. To fully develop the deep heat reservoir, we proposed a new heat exploitation scheme through three horizontal wells, computed the electricity generation potential and efficiency, and analyzed the main factors affecting heat production. The results indicate that under the reference conditions, the three horizontal well system attaches an electric power of 2.70–1.5 MW, a reservoir impedance of 0.13–0.27 MPa/(kg/s), a pump power of 0.29–1.16 MW, and energy efficiency of 7.34–1.30 during the reservoir lifetime of 43.6 years. Thus, this three-horizontal-well system has significant development potential in Fengshun deep geothermal reservoir. Sensitivity analysis indicates that water production rate, injection temperature and initial reservoir temperature have a significant impact on heat production performance. Reducing the water production rate and injection temperature within a certain range can improve energy efficiency. For the precise design of power generation systems and the accurate evaluation of production performance, field logging data is necessary.

Comparison between the human physiological sweat and pre-wetting method on the heat and mass transfer of thermal protective clothing under radiant exposure

In this study, a thorough investigation of human sweating behavior was conducted to ensure an accurate evaluation of the thermal protection performance of garments, addressing the lack of existing standardized methods. Three fabrics were compared at different sweating rates under radiant exposure, focusing on exploring heat and mass transfer mechanisms using the traditional pre-wetting method and the innovative continuous sweating method. It was found that the pre-wetting method was effective in situations where fabrics experienced transient moisture absorption, whereas the continuous sweating method excelled in scenarios requiring continuous moisture supply before saturation and was used to estimate liquid moisture transfer. In particular, for materials with low hygroscopicity, the pre-wetting method resulted in 86.24 kJ/m2 more absorbed thermal energy at the skin surface compared with the continuous sweating method, with a sweat rate of 10 μL/min/cm2, highlighting the tendency of the pre-wetting method to overestimate the thermal protective performance. Conversely, for fabrics with high hygroscopicity and testing with high sweat rate, a 32.81% reduction in absorbed thermal energy on the skin surface was observed when the continuous sweating method was used in place of the pre-wetting method, significantly reducing operational risks. This study provides valuable guidance in selecting appropriate sweat simulation methods to evaluate the thermal protection performance based on specific environmental conditions, and provides critical insights for further studies on heat and moisture transfer.

Implementation of FFT-Based Convolutional Neural Networks on Medical Imagery for Studying Performance Boost

This study focuses on the implementation of CNNs based on the Fast Fourier Transform (FFT) in medical images to study the performance boost compared to traditional neural networks. Using a Kaggle dataset of brain tumour imagery, we examine the effectiveness of FFT-based CNNs for the detection and classification of brain tumours. By leveraging the advantages of FFT, we aim to improve the efficiency and accuracy of CNNs in analyzing medical images. Our results demonstrate that FFT-based CNNs outperform traditional CNNs in terms of accuracy and computational efficiency. This performance boost is attributed to the inherent properties of FFT, which enable faster convolution operations and reduced complexity. In order to ensure a fair comparison between traditional CNNs and Fourier Transform-based convolutions, the forward pass for both methods was implemented from scratch using NumPy and SciPy libraries. By doing so, any optimization benefits gained by either technique through specific libraries or computation platforms are eliminated. This approach allows for a more accurate evaluation of the relative performance of each method in terms of inference time and accuracy, while also providing insights into potential areas for improvement and optimization.

Multidimensional Evaluation Methods for Deep Learning Models in Target Detection for SAR Images

As artificial intelligence technology advances, the application of object detection technology in the field of SAR (synthetic aperture radar) imagery is becoming increasingly widespread. However, it also faces challenges such as resource limitations in spaceborne environments and significant uncertainty in the intensity of interference in application scenarios. These factors make the performance evaluation of object detection key to ensuring the smooth execution of tasks. In the face of such complex and harsh application scenarios, methods that rely on single-dimensional evaluation to assess models have had their limitations highlighted. Therefore, this paper proposes a multi-dimensional evaluation method for deep learning models used in SAR image object detection. This method evaluates models in a multi-dimensional manner, covering the training, testing, and application stages of the model, and constructs a multi-dimensional evaluation index system. The training stage includes assessing training efficiency and the impact of training samples; the testing stage includes model performance evaluation, application-based evaluation, and task-based evaluation; and the application stage includes model operation evaluation and model deployment evaluation. The evaluations of these three stages constitute the key links in the performance evaluation of deep learning models. Furthermore, this paper proposes a multi-indicator comprehensive evaluation method based on entropy weight correlation scaling, which calculates the weights of each evaluation indicator through test data, thereby providing a balanced and comprehensive evaluation mechanism for model performance. In the experiments, we designed specific interferences for SAR images in the testing stage and tested three models from the YOLO series. Finally, we constructed a multi-dimensional performance profile diagram for deep learning object detection models, providing a new visualization method to comprehensively characterize model performance in complex application scenarios. This can provide more accurate and comprehensive model performance evaluation for remote sensing data processing, thereby guiding model selection and optimization. The evaluation method proposed in this study adopts a multi-dimensional perspective, comprehensively assessing the three core stages of a model’s lifecycle: training, testing, and application. This framework demonstrates significant versatility and adaptability, enabling it to transcend the boundaries of remote sensing technology and provide support for a wide range of model evaluation and optimization tasks.

Electrodeposited Large-Area Nickel-Alloy Electrocatalysts for Alkaline Hydrogen Evolution under Industrially Relevant Conditions

Developing efficient water splitting electrocatalysts plays a critical role in lowering the energy cost for hydrogen production, which requires accurate evaluation of the catalytic performance especially under industrially relevant conditions. In this work, we design a two-chamber electrolyzer testing system simulating practical water electrolyzer system to enable catalytic performance evaluation under industrially relevant conditions. A one-step electrodeposition method is used to synthesize binary and ternary Ni alloy HER catalysts and the catalytic electrodes are screened and optimized with the two-chamber electrolyzer testing system. A voltage of 1.64V without iR compensation at a current density of 200mA/cm2 is obtained with continuous operation for 70h. Different HER activity trend is observed from conventional three-electrode experiments with low-concentration alkaline electrolytes at room temperature and the two-chamber electrolyzer measurements, further supporting the importance of catalytic performance evaluation under industrially relevant conditions. The electrodeposition synthesis method is further used to prepare large-area catalytic electrodes over 400 cm2 with good uniformity.

Research on Measurement Technology of Coefficient of Thermal Expansion of Honeycomb Structure

Honeycomb structure is widely used as the main sandwich layer of load-bearing structure in aviation, aerospace and other fields because of its light weight and high strength. Under some service conditions, the deformation of the honeycomb structure affected by high and low temperature will affect the overall mechanical performance of the structure. Therefore, accurate evaluation of the thermal expansion performance of the honeycomb structure becomes the key to structural design. However, the existing measurement methods of thermal expansion coefficient mainly have problems such as the hollow structure cannot be effectively contacted, the area of the grid optical target is limited and difficult to detect. In view of the above conditions, in this paper, a method for measuring the thermal expansion coefficient of honeycomb structure is proposed, and the measurement system is built, and the thermal expansion performance of honeycomb structure is effectively obtained by testing the honeycomb structure. At the same time, the measurement accuracy of the system is verified by using standard test pieces. The experimental results show that the method has high measurement accuracy.

Accurate measurement of the bond stress between rebar and concrete in reinforced concrete using FBG sensing technology

Recent earthquakes in several developing countries have shown that reinforced concrete (RC) buildings with improper structural detailing experience severe damage under seismic motions. Using low-quality construction materials such as brick aggregates, resulting in low-strength concrete, significantly impacts the bond between rebar and concrete. Accurate evaluation of the bond performance of such low-strength concrete is one of the key issues for seismic safety assessment of RC buildings, especially in Bangladesh; thus, the bond performance is usually evaluated through laboratory tests. However, conventional measurements of bond stress based on rebar strains measured by electrical resistance strain gauges are likely to negatively impact the bond behavior/performance because of the reduced total contact area between rebar and concrete as well as the changing rebar surface boundary conditions. Under the above social and academic backgrounds, in this study, a new measurement technique that applies fiber Bragg grating (FBG) sensors embedded in optical fiber to rebar strain measurements is developed, and its effectiveness is investigated to realize more accurate measurements of the bond stress between rebar and concrete. Two 70% scaled RC beam-column joint specimens in which the beam rebar was anchored in a straight manner were constructed with identical detailing, except for the beam rebar strain measuring methods. The specimens were then subjected to cyclic lateral loading until failure. By comparing the experimental data acquired by the above two different devices (the FBG sensors and conventional strain gauges), it was found that the experimental bond strength on the beam rebar based on the strain data measured by the FBG sensors was much higher than that from the data measured using conventional strain gauges. Which negatively impacted the test data on the beam-column joint’s capacity in the specimen applied the conventional measuring method, indicating the necessity of the presented method not only for accurate evaluation of the bond stress between rebar and concrete but also for seismic safety assessments of RC buildings.

The effect of a preceptorship programme on newly hired experienced professional nurses’ self-efficacy in nursing clinical competency in Saudi Arabia

AimThis study aimed to determine if participation in a preceptorship programme affected newly hired experienced professional nurses’ self-efficacy in clinical practice and whether their perceived competence was related to respondents’ demographic profiles at a university hospital in Saudi Arabia. BackgroundFormal preceptorship programmes are initiated with the aim of improving the characteristics related to new workplace adaptations, such as reducing stress and anxiety, improving effective communication, enhancing clinical performance and competencies, and learning new clinical skills. These programmes’ effectiveness in improving nurses’ self-efficacy in clinical practice is seldom measured, especially among newly hired experienced professional nurses. MethodsA quasi-experimental, quantitative pre- and post-test one-group study design with convenient sampling was used. ResultsSelf-efficacy post-test scores were significantly higher than pre-test scores (p<0.001). The programme positively influenced the respondents’ self-efficacy scores. As perceived by the respondents, the preceptorship programme had a positive effect and improved their clinical performance in the nursing process. There was no relationship between perceived competence and respondents’ demographic profiles. ConclusionsReporting on self-efficacy in clinical practice may not be the most effective, reliable or accurate measure or evaluation of clinical performance since self-scoring may be affected by existing cultural implications and a strong sense of self-efficacy. The self-scoring results may be followed by qualitative information, like interviews, to determine respondents’ knowledge of the nursing process, direct observation of performance in the clinical area and care outcomes, and confirm if the respondents’ clinical performance matches the obtained results.

Research on Piano Curriculum Education and Its Performance Ecosystem Based on Network Flow Optimization

Abstract This paper investigates music education, where an efficient and accurate performance evaluation system in the piano teaching and performance ecosystem is increasingly becoming an essential tool for improving teaching quality and performance level. The objective evaluation of students’ performance skills can be achieved by carefully analyzing piano performances using the network flow optimization technique. This technique optimizes the performance evaluation system’s audio recognition ability by analyzing the piano audio signal and solving the multi-constraint nonlinear optimization problem in a limited time domain. This paper establishes a network flow optimization model, applies the multi-constraint nonlinear optimization technique, and combines the non-negative matrix decomposition and dynamic time regularization algorithm to analyze the piano performance for experiments. After optimization processing, hundreds of piano audio samples were collected, and the audio recognition accuracy was improved by 20%. By optimizing and processing the audio signals from the network stream, the evaluation system could detect polyphony more accurately and track the musical score effectively, improving accuracy and efficiency. Using the non-negative matrix decomposition algorithm, the accuracy of detecting polyphony can reach 85%, while the dynamic temporal regularization algorithm can match the position of the musical score with 95% accuracy. The accuracy of piano performance evaluation is optimized by this network flow optimization method, providing new technical means for music education, and promoting the quality of teaching and performance.

Within-season vegetation indices and yield stability as a predictor of spatial patterns of Maize (Zea mays L) yields

Accurate evaluation of crop performance and yield prediction at a sub-field scale is essential for achieving high yields while minimizing environmental impacts. Two important approaches for improving agronomic management and predicting future crop yields are the spatial stability of historic crop yields and in-season remote sensing imagery. However, the relative accuracies of these approaches have not been well characterized. In this study, we aim to first, assess the accuracies of yield stability and in-season remote sensing for predicting yield patterns at a sub-field resolution across multiple fields, second, investigate the optimal satellite image date for yield prediction, and third, relate bi-weekly changes in GCVI through the season to yield levels. We hypothesize that historical yield stability zones provide high accuracies in identifying yield patterns compared to within-season remote sensing images.To conduct this evaluation, we utilized biweekly Planet images with visible and near-infrared bands from June through September (2018–2020), along with observed historical yield maps from 115 maize fields located in Indiana, Iowa, Michigan, and Minnesota, USA. We compared the yield stability zones (YSZ) with the in-season remote sensing data, specifically focusing on the green chlorophyll vegetative index (GCVI). Our analysis revealed that yield stability maps provided more accurate estimates of yield within both high stable (HS) and low stable (LS) yield zones within fields compared to any single-image in-season remote sensing model.For the in-season remote sensing predictions, we used linear models for a single image date, as well as multi-linear and random forest models incorporating multiple image dates. Results indicated that the optimal image date for yield prediction varied between and within fields, highlighting the instability of this approach. However, the multi-image models, incorporating multiple image dates, showed improved prediction accuracy, achieving R2 values of 0.66 and 0.86 by September 1st for the multi-linear and random forest models, respectively. Our analysis revealed that most low or high GCVI values of a pixel were consistent across the season (77%), with the greatest instability observed at the beginning and end of the growing season. Interestingly, the historical yield stability zones provided better predictions of yield compared to the bi-weekly dynamics of GCVI. The historically high-yielding areas started with low GCVI early in the season but caught up, while the low-yielding areas with high initial GCVI faltered.In conclusion, the historical yield stability zones in the US Midwest demonstrated robust predictive capacity for in-field heterogeneity in stable zones. Multi-image models showed promise for assessing unstable zones during the season, but it is crucial to link these two approaches to fully capture both stable and unstable zones of crop yield. This study provides opportunities to achieve better precision management and yield prediction by integrating historical crop yields and remote sensing techniques.

Evaluation of stochastic flow lines with provisioning of auxiliary material

Flow lines are often used to perform assembly operations in multi-stage processes. During these assembly operations, components that are relatively small, compared to the work pieces travelling down the flow line, are mounted to the work pieces at a given stage. Those components, or more generally, any kind of auxiliary material, are provisioned to the corresponding production stage in a repetitive but not necessarily deterministic manner using a certain delivery frequency, each time filling the local storage up to a predetermined order-up-to level. Just like random processing times, machine failures, and repairs, the randomness of the provisioning process can impact the long-term throughput of such a flow line. In this paper, we develop a fast and accurate analytical performance evaluation method to estimate the long-term throughput of a Markovian flow line of this type for the practically important case of limited buffer capacities between the production stages. We first give an exact characterization of a two-machine line of that type and show how to determine system state probabilities and aggregate performance measures. Furthermore, we show how to use this two-machine model as the building block of an approximate decomposition approach for longer flow lines. As opposed to previous decomposition approaches, even the state space of the two-machine lines can become so large that an exact solution of the Markov chains can become impractical. We hence show how to set up, train, and use an artificial neural network to replace the Markov chain solver embedded in the decomposition approach, which then leads to an accurate and extremely fast flow line evaluation tool. The proposed methodology is evaluated by a comparison with simulation results and used to characterize the structural patterns describing the behaviour of flow lines of this type. The method can be used to systematically consider the combined impact of the delivery frequency and the local order-up-to levels for the auxiliary material when designing a flow line of this type.