Multiple Iterations Research Articles

Research on Dual-Grating Spacing Calibration Method Based on Multiple Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Combined with Hilbert Transform

The paper proposes a method for the calibration of spacing in dual-grating based on Multiple Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with Hilbert Transform (HT), referred to as Multiple ICEEMDAN-HT. This method addresses the potential impact of nonlinear factors on phase extraction accuracy, consequently on ranging precision in the homodyne interference of the dual-grating. Building upon the ICEEMDAN algorithm, the signal undergoes iterative decomposition and reconstruction using the sample entropy criterion. The intrinsic mode functions (IMFs) obtained from multiple iterations are then reconstructed to obtain the complete signal. Through a simulation and comparison with other signal decomposition methods, the repeatability and completeness of signal reconstruction by Multiple ICEEMDAN are verified. Finally, an actual dual-grating ranging system is utilized to calibrate the spacing of the planar grating. Experimental results demonstrate that the calibration relative error of the Multiple ICEEMDAN-HT phase unwrapping method can be reduced to as low as 0.07%, effectively enhancing the signal robustness and spacing calibration precision.

Phase I quality control framework for monitoring organ-at-risk dose

Objective. The aim of this work was to develop a Phase I control chart framework for the recently proposed multivariate risk-adjusted Hotelling’s T2 chart. Although this control chart alone can identify most patients receiving extreme organ-at-risk (OAR) dose, it is restricted by underlying distributional assumptions, making it sensitive to extreme observations in the sample, as is typically found in radiotherapy plan quality data such as dose-volume histogram (DVH) points. This can lead to slightly poor-quality plans that should have been identified as out-of-control (OC) to be signaled in-control (IC). Approach. We develop a robust iterative control chart framework to identify all OC patients with abnormally high OAR dose and improve them via re-optimization to achieve an IC sample prior to establishing the Phase I control chart, which can be used to monitor future treatment plans. Main Results. Eighty head-and-neck patients were used in this study. After the first iteration, P14, P67, and P68 were detected as OC for high brainstem dose, warranting re-optimization aimed to reduce brainstem dose without worsening other planning criteria. The DVH and control chart were updated after re-optimization. On the second iteration, P14, P67, and P68 were IC, but P40 was identified as OC. After re-optimizing P40’s plan and updating the DVH and control chart, P40 was IC, but P14* (P14’s re-optimized plan) and P62 were flagged as OC. P14* could not be re-optimized without worsening target coverage, so only P62 was re-optimized. Ultimately, a fully IC sample was achieved. Multiple iterations were needed to identify and improve all OC patients, and to establish a more robust control limit to monitor future treatment plans. Significance. The iterative procedure resulted in a fully IC sample of patients. With this sample, a more robust Phase I control chart that can monitor OAR doses of new plans was established.

The Behaviour Change Technique Ontology: Transforming the Behaviour Change Technique Taxonomy v1

Background The Behaviour Change Technique Taxonomy v1 (BCTTv1) specifies the potentially active content of behaviour change interventions. Evaluation of BCTTv1 showed the need to extend it into a formal ontology, improve its labels and definitions, add BCTs and subdivide existing BCTs. We aimed to develop a Behaviour Change Technique Ontology (BCTO) that would meet these needs. Methods The BCTO was developed by: (1) collating and synthesising feedback from multiple sources; (2) extracting information from published studies and classification systems; (3) multiple iterations of reviewing and refining entities, and their labels, definitions and relationships; (4) refining the ontology via expert stakeholder review of its comprehensiveness and clarity; (5) testing whether researchers could reliably apply the ontology to identify BCTs in intervention reports; and (6) making it available online and creating a computer-readable version. Results Initially there were 282 proposed changes to BCTTv1. Following first-round review, 19 BCTs were split into two or more BCTs, 27 new BCTs were added and 26 BCTs were moved into a different group, giving 161 BCTs hierarchically organised into 12 logically defined higher-level groups in up to five hierarchical levels. Following expert stakeholder review, the refined ontology had 247 BCTs hierarchically organised into 20 higher-level groups. Independent annotations of intervention evaluation reports by researchers familiar and unfamiliar with the ontology resulted in good levels of inter-rater reliability (0.82 and 0.79, respectively). Following revision informed by this exercise, 34 BCTs were added, resulting in the first published version of the BCTO containing 281 BCTs organised into 20 higher-level groups over five hierarchical levels. Discussion The BCTO provides a standard terminology and comprehensive classification system for the content of behaviour change interventions that can be reliably used to describe interventions. The development and maintenance of an ontology is an iterative and ongoing process; no ontology is ever ‘finished’. The BCTO will continue to evolve and grow (e.g. new BCTs or improved definitions) as a result of user feedback and new available evidence.

Study on the influence of a magnetorheological finishing path on the mid-frequency errors of optical element surfaces

Magnetorheological finishing (MRF) is a deterministic optical processing technique based on CCOS that achieves high removal efficiency and processing accuracy while reducing subsurface damage. This technique still suffers from multiple iterations of processing due to variations in removal efficiency and the inability to fully correct mid-frequency errors below the cut-off frequency of the removal function. For the above problems, this paper attempted to establish the error model of removal function efficiency change for predicting the change of MRF efficiency. Based on the analysis of the distribution of surface shape residuals under different machining paths, a process combining spiral scanning and raster scanning is proposed, which can realize the correction of surface shape and restrain the deterioration of mid-frequency errors. The experimental results show that when the low-frequency errors of fused silica element surface converge rapidly, by optimizing the machining removal coefficient and using the spiral scanning and raster scanning combined method, the PSD analysis results show that the mid-frequency errors of the combined process is lower than the initial value, which expands the process route for the MRF of high-precision optical elements.

Utilize the Prediction Results from the Neural Network Gate Recurrent Unit (GRU) Model to Optimize Reactive Power Usage in High-Rise Buildings

The growing urbanization and the construction sector, efficient use of electric energy becomes important, especially the use of reactive power. If excessive use causes decreased efficiency and increased operational costs. Decreased efficiency contributes to increasing exhaust gas volumes and greenhouse emissions. Efficient energy can achieved if planning and predictions are correct. This research applies the GRU neural network method with grid search initialization as a novelty predictive model for energy-use high-rise buildings in form fast training without multiple iterations because optimal hyperparameters are obtained. Experimental show the MAE and RMSE performance metrics of the GRU better than LSTM in predicting energy consumption data peak loads, off-peak loads and reactive power. The accuracy of GRU predictions can optimize the use of energy to contribute to saving the environment from exhaust emissions and the greenhouse effect in urban systems. Experimental results demonstrate the superiority of GRU over LSTM, proof of the much lower MAE and RMSE values. This metric shows the accuracy of GRU in generalizing data both during peak and off-peak hours, as well as in reactive power usage. By Utilizing GRU's capabilities, building management can manage reactive power usage effectively, allocate reactive power resources appropriately, and mitigate peak load times and the power factor within the threshold, thus avoiding additional costs and electrical system efficiency and contributing to reducing the carbon footprint and gas emissions greenhouse. Research on GRU is widely open in the high-rise building sector, including its integration with sensors to automatically control energy use.

Generative AI tools can enhance climate literacy but must be checked for biases and inaccuracies

In the face of climate change, climate literacy is becoming increasingly important. With wide access to generative AI tools, such as OpenAI’s ChatGPT, we explore the potential of AI platforms for ordinary citizens asking climate literacy questions. Here, we focus on a global scale and collect responses from ChatGPT (GPT-3.5 and GPT-4) on climate change-related hazard prompts over multiple iterations by utilizing the OpenAI’s API and comparing the results with credible hazard risk indices. We find a general sense of agreement in comparisons and consistency in ChatGPT over the iterations. GPT-4 displayed fewer errors than GPT-3.5. Generative AI tools may be used in climate literacy, a timely topic of importance, but must be scrutinized for potential biases and inaccuracies moving forward and considered in a social context. Future work should identify and disseminate best practices for optimal use across various generative AI tools.

Coding with the machines: machine-assisted coding of rare event data.

While machine coding of data has dramatically advanced in recent years, the literature raises significant concerns about validation of LLM classification showing, for example, that reliability varies greatly by prompt and temperature tuning, across subject areas and tasks-especially in "zero-shot" applications. This paper contributes to the discussion of validation in several different ways. To test the relative performance of supervised and semi-supervised algorithms when coding political data, we compare three models' performances to each other over multiple iterations for each model and to trained expert coding of data. We also examine changes in performance resulting from prompt engineering and pre-processing of source data. To ameliorate concerns regarding LLM's pre-training on test data, we assess performance by updating an existing dataset beyond what is publicly available. Overall, we find that only GPT-4 approaches trained expert coders when coding contexts familiar to human coders and codes more consistently across contexts. We conclude by discussing some benefits and drawbacks of machine coding moving forward.

Volume Transfer: A New Design Concept for Fabric‐Based Pneumatic Exosuits

The fabric‐based pneumatic exosuit is now a hot research topic because it is lighter and softer than traditional exoskeletons. Existing research focuses more on the mechanical properties of the exosuit (e.g., torque and speed), but less on its wearability (e.g., appearance and comfort). This work presents a new design concept for fabric‐based pneumatic exosuits: volume transfer, which means transferring the volume of pneumatic actuators beyond the garment's profile to the inside. This allows for a concealed appearance and a larger stress area while maintaining adequate torques. In order to verify this concept, a fabric‐based pneumatic exosuit is developed for knee extension assistance. Its profile is only 26 mm and its stress area wraps around almost half of the leg. A mathematical model and simulation is used to determine the parameters of the exosuit, avoiding multiple iterations of the prototype. Experiment results show that the exosuit can generate a torque of 7.6 Nm at a pressure of 90 kPa and produce a significant reduction in the electromyography activity of the knee extensor muscles. It is believed that volume transfer can be utilized prevalently in future fabric‐based pneumatic exosuit designs to achieve a significant improvement in wearability.

Fast distributed co-optimization of electricity and natural gas systems hedging against wind fluctuation and uncertainty

The synergistic operation of integrated electricity and natural gas systems (IEGS) in the presence of wind power necessitates a distributed optimization framework that ensures information privacy. However, incorporating wind power penetration leads to distributed optimization problems including uncertainty and unstable distributed algorithm’s convergence. With the increasing proportion of wind power in IEGS, fluctuating wind power penetration highly affects the convergence of distributed optimization solutions, resulting in uncontrollable optimization time of the distributed algorithm. Accordingly, this paper investigates the impact of wind fluctuation and uncertainty on distributed IEGS optimization and proposes a novel fast distributed co-optimization framework. Specifically, an adaptive alternating direction method of multipliers (ADMM) is developed to accommodate wind fluctuation. Based on designed rules, the penalty parameter is updated in every step to maximize the gradient of the optimization objective. The experiments are conducted using real wind power generation data sourced from a wind farm in Australia and a classical IEGS framework composed of the IEEE 24-bus electricity system and a 12-node natural gas system. Compared to original ADMM and residual balancing ADMM, the proposed framework achieves an average reduction of 0–91.4% in the number of iterative steps for multiple solution iterations across various scenarios, with a corresponding decrease in the standard deviation by 13.8%–93.0%.

An Efficient Image Cryptosystem Utilizing Difference Matrix and Genetic Algorithm.

Aiming at addressing the security and efficiency challenges during image transmission, an efficient image cryptosystem utilizing difference matrix and genetic algorithm is proposed in this paper. A difference matrix is a typical combinatorial structure that exhibits properties of discretization and approximate uniformity. It can serve as a pseudo-random sequence, offering various scrambling techniques while occupying a small storage space. The genetic algorithm generates multiple ciphertext images with strong randomness through local crossover and mutation operations, then obtains high-quality ciphertext images through multiple iterations using the optimal preservation strategy. The whole encryption process is divided into three stages: first, the difference matrix is generated; second, it is utilized for initial encryption to ensure that the resulting ciphertext image has relatively good initial randomness; finally, multiple rounds of local genetic operations are used to optimize the output. The proposed cryptosystem is demonstrated to be effective and robust through simulation experiments and statistical analyses, highlighting its superiority over other existing algorithms.

A clustering-based adaptive undersampling ensemble method for highly unbalanced data classification

The class imbalance issue is prevalent in various practical classification tasks. A high unbalanced rate will significantly decrease the classification performance of unbalanced learning. However, existing methods for highly unbalanced data classification still face two key difficulties: (1) fairly learning key information, and (2) maintaining consistency. To address these difficulties, we propose a novel majority clustering-based adaptive undersampling enhanced ensemble classification method, which integrates undersampling and ensemble techniques. In the adaptive undersampling process, we first consider the spatial distribution of majority samples to ensure distribution consistency. We then consider an adaptive sampling rate and introduce a feedback mechanism to obtain more representative majority samples from each cluster. In the classifier ensemble process, multiple ensemble iterations are introduced to achieve fair attention to key information in different classes. Finally, six kinds of experiments are conducted on 17 real highly unbalanced datasets from multiple fields. Experimental results demonstrate that the proposed method outperforms existing methods in terms of effectiveness, robustness, and adaptability.

Equivocal diagnostics: Making a 'good' point-of-care test for elimination in global health.

What is a diagnostic test for? We might assume the answer to this question is straightforward. A good test would help identify what disease someone suffers from, assist health providers to determine the correct course of treatment and/or enable public health authorities to know and intervene in health at the level of the population. In this article, we show that what a specific diagnostic test is for, the value it holds for different actors, and what makes it good, or not, is often far from settled. We tell the story of the development and design of a rapid antibody test for onchocerciasis, or river blindness, tracking multiple iterations of the device through three configurational moments in the framing of onchocerciasis disease and reshaping of the global health innovation ecosystem. Efforts to build that ecosystem for diagnostics are often premised on the notion that public health needs for diagnostics are pre-given and stable; the challenge is seen to be how to incentivize investment and find a customer base for diagnostics in under-resourced settings. By contrast, we show that for any disease, diagnostic needs are both multiple and constantly in flux, and are unlikely to be met by a single, stand-alone product. In the case of the onchocerciasis Ov-16 rapid test, the failure to recognize and address the multiplicity and instability of diagnostic needs in the innovation process resulted in the development of a rapid point of care test that might be manufactured, procured and used, but is unloved by public health experts and commercial manufacturers alike. The equivocal value of the onchocerciasis rapid test, we suggest, reveals the inadequacy of the current global health innovation ecosystem for developing diagnostic 'goods'.

Multi-dimensional evaluation of BIPV installations: Development of a tool to assess the performance as building component and electricity generator

The demand for building-integrated photovoltaics is constantly increasing, which is reflected in an increasing number of innovative BIPV products on the market and attractively implemented BIPV installations. To support architects, system developers, and other stakeholders involved in the planning of new BIPV projects, the experts of IEA-PVPS Task 15 have developed a multi-dimensional assessment tool based on the experiences and learnings of already installed BIPV-projects. The creation of a comprehensive, interdisciplinary evaluation methodology makes it possible to compare the multi-dimensional performance of existing BIPV systems, to derive strengths and weaknesses and to use this knowledge to plan application-optimized new BIPV systems. In a systematic step-by-step development process, four performance categories were defined: energy-related, economic, environmental and optical/visual. After defining the relevant performance indicators per category, a numerical rating system was established. In addition, a classification scheme for all relevant BIPV installation types was defined. Initial analyses were performed by a group of experts on several case studies of BIPV-roofs, facades and external devices in order to test and validate the methodology and approaches. Multiple iterations were required to optimize the methodology, but issues such as (i) lack of data availability for full project assessment or (ii) objectivity in visual assessments exist and will remain demanding. The research work also identified challenges and limitations in the context of pioneering and pilot building-integrated photovoltaics (BIPV) projects, which often used newly developed, immature or early-stage technologies at the time of their construction. The evaluation results of such lighthouse projects can vary significantly from those of BIPV installations using standardized products and well-established processes.

Target location method of intelligent deicing robot based on nonlinear auto disturbance rejection neural network

A visual navigation scheme for intermittent walking deicing robots was designed to address the issues of multiple iterations, long computation time, and poor real-time performance in the nonlinear optimization of the original SLAM system. A computer image acquisition unit, a computer image processing module, and a visual navigation parameter extraction algorithm were also designed. Implemented a visual navigation system for deicing robots based on image processing. It can meet the navigation requirements of the deicing robot. Simulation and experiments have shown that the method proposed in this paper can quickly and accurately identify abnormal point clouds. The visual servo control scheme constructed in this paper is aimed at robot task operations with unknown system calibration and target depth information. By calibrating the robot vision system, the conversion between camera pixel coordinates and robot base coordinates is achieved, with a transmission frame rate of 53.65 per second; The maximum error in positioning accuracy in space is 10.6 mm. The feature trajectory of visual space is smooth and stable within the camera's field of view, and the end movement of Cartesian space robots is stable without rebound, resulting in high grasping and positioning accuracy.

Off-axis telescope misalignment correction based on defocus spot moment features

Optical mirror misalignments, which are caused by assembly mistakes and changes in the surrounding environment (such as gravity, temperature, and atmosphere), degrade the system’s imaging performance. Therefore, active misalignment correction is essential for ensuring the image quality of the off-axis telescope. In this paper, a novel misalignment correction method without wavefront sensors is proposed. The point spread functions (PSFs) of the system are analytically related to the optical mirror misalignments. On this basis, a fully connected neural network (FCNN) is used to establish the mapping relationship between the misalignments and the discrete orthogonal unbiased finite impulse response (UFIR) moment features, which can effectively characterize changes of intensity and geometric of the spot image. The simulation and experimental results in this paper justify the effectiveness and practicality of the proposed method. This approach offers a low-cost and straightforward technical method for achieving high imaging quality throughout the alignment and observation phases. This approach can prevent the accumulation of errors caused by wavefront detection and the high delay of multiple iterations.

Synergizing meat Science and AI: Enhancing long-chain saturated fatty acids prediction

In the field of the food industry, establishing a global predictive model for the content of long-chain saturated fatty acids (LC-SFAs) in red meat is of profound significance. However, this work requires the accumulation of a large number of diverse samples for model calibration. To address these formidable challenges, the Generative Inference Adversarial Autoencoder (GI-AAE) was adopted to enhance the model training process. Through multiple iterations, GI-AAE successfully generated simulated samples of higher quality than traditional generative adversarial networks and autoencoders. After incorporating these generated samples into the training dataset, multiple sets of models showed significant improvements. These results clearly demonstrated that our approach can effectively overcome the challenges posed by generative models in the regression domain, greatly enhancing the model's generalization ability. This advancement holds significant potential for applications in the food industry.

Wind and PV Power Consumption Strategy Based on Demand Response: A Model for Assessing User Response Potential Considering Differentiated Incentives

In China, the inversion between peak periods of wind and photovoltaic (PV) power (WPVP) generation and peak periods of electricity demand leads to a mismatch between electricity demand and supply, resulting in a significant loss of WPVP. In this context, this article proposes an improved demand response (DR) strategy to enhance the consumption of WPVP. Firstly, we use feature selection methods to screen variables related to response quantity and, based on the results, establish a response potential prediction model using random forest algorithm. Then, we design a subsidy price update formula and the subsidy price constraint conditions that consider user response characteristics and predict the response potential of users under differentiated subsidy price. Subsequently, after multiple iterations of the price update formula, the final subsidy and response potential of the user can be determined. Finally, we establish a user ranking sequence based on response potential. The case analysis shows that differentiated price strategy and response potential prediction model can address the shortcomings of existing DR strategies, enabling users to declare response quantity more reasonably and the grid to formulate subsidy price more fairly. Through an improved DR strategy, the consumption rate of WPVP has increased by 12%.

Pick a PACC: Comparing domain-specific and general cognitive composites in Alzheimer disease research.

We aimed to illustrate how complex cognitive data can be used to create domain-specific and general cognitive composites relevant to Alzheimer disease research. Using equipercentile equating, we combined data from the Charles F. and Joanne Knight Alzheimer Disease Research Center that spanned multiple iterations of the Uniform Data Set. Exploratory factor analyses revealed four domain-specific composites representing episodic memory, semantic memory, working memory, and attention/processing speed. The previously defined preclinical Alzheimer disease cognitive composite (PACC) and a novel alternative, the Knight-PACC, were also computed alongside a global composite comprising all available tests. These three composites allowed us to compare the usefulness of domain and general composites in the context of predicting common Alzheimer disease biomarkers. General composites slightly outperformed domain-specific metrics in predicting imaging-derived amyloid, tau, and neurodegeneration burden. Power analyses revealed that the global, Knight-PACC, and attention and processing speed composites would require the smallest sample sizes to detect cognitive change in a clinical trial, while the Alzheimer Disease Cooperative Study-PACC required two to three times as many participants. Analyses of cognition with the Knight-PACC and our domain-specific composites offer researchers flexibility by providing validated outcome assessments that can equate across test versions to answer a wide range of questions regarding cognitive decline in normal aging and neurodegenerative disease. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Unreliability of clustering results in sensory studies and a strategy to address the issue

Researchers commonly use hierarchical clustering (HC) or k-means (KM) for grouping products, attributes, or consumers. However, the results produced by these approaches can differ widely depending on the specific methods used or the initial “seed” aka “starting cluster centroid” chosen in clustering. Although recommendations for various clustering techniques have been made, the realities are that objects in groups can, and do, change their clusters. That can impact interpretation of the data. Researchers usually does not run the clustering algorithms multiple times to determine stability, nor do they often run multiple methods of clustering although that has been recommended previously. This study applied hierarchical agglomerative clustering (HAC), KM and fuzzy clustering (FC) to a large descriptive sensory data set and compared attribute clusters from the methods, including multiple iterations of same methods. Sensory attributes (objects) shuffled among clusters in varying ways, which could provide different interpretations of the data. That frequency was captured in the KM output and used to form the “best possible” clusters via manual clustering (MC). The HAC and FC results were studied and compared with KM results. Attribute correlation coefficients also were compared with clustering information. Using results from one clustering approach may not be reliable, and results should be confirmed using other clustering approaches. A strategy that combines multiple clustering approaches, including a MC process is suggested to determine consistent clusters in sensory data sets.

Double‐difference constrained reflection tomography in two‐dimensional elastic media

AbstractThe inverted velocity model obtained from the reflection tomography based on the angle domain common‐image gathers has a certain fuzziness. The inverted velocity model's stratigraphic interface is always not clear enough in areas with complex stratigraphic structure. In order to improve the accuracy and resolution of the inverted velocity model, a double‐difference constraint condition is added on the basis of minimizing the absolute travel‐time residual at the subsurface imaging points. This constraint makes the inverted velocity model local structure information more refined by minimizing the differential travel‐time residual at adjacent imaging points (i.e. closely spaced points within the same layer) and makes the variation of velocity model information within a certain range more accurate. The method in this paper is based on angle domain common‐image gathers, the tomography inversion equation is established by using the ray tracing method, and the conversion relationship between the traveltime residual and the residual curvature of the angle domain common‐image gathers. Then, by adding differential constraint and double‐differential constraint conditions and using the least squares QR decomposition method to solve the set of equations, the inverted velocity model can be obtained through multiple iterations, which provides a high‐precision velocity field for the migration and improves the accuracy of seismic imaging. Numerical experiments on both one typical model and a field data example demonstrate the effectiveness of the proposed double‐difference constrained elastic reflection tomography in generating high‐precision velocity models.