Iterative Approach Research Articles

EXPRESS: AI-based Financial Advice: An Ethical Discourse on AI-based Financial Advice and Ethical Reflection Framework

This article presents a first step in identifying the ethical issues of AI-based financial advice. Consumers must navigate an ever more complex array of financial decisions. (Generative) AI-based financial advice may increase access to and acceptance of financial advice and strengthen consumers’ financial well-being. However, significant ethical challenges exist in designing, developing, and deploying AI-based financial advice. To analyze the perils and pitfalls of AI-based financial advice, we develop a definition of what constitutes good AI-based financial advice and provide a first assessment of ethical challenges related to AI-based financial advice. The iterative multi-stakeholder approach, including workshops and semi-structured interviews with consumers and experts, results in an ethical discourse structured around the four fundamental values of the European Commission’s Ethics Guidelines for Trustworthy AI, human autonomy, explicability, fairness, prevention of harm, and trust as the overall objectives. Based on our analyses, we derive a simple yet comprehensive AI Ethics Framework for Financial Advice (AIFA). The AIFA reflection framework guides public policymakers, managers of financial service providers, and technology developers in incorporating ethical discourse in developing and deploying (generative) AI-based financial advice.

Manganese‐Based Composite‐Structure Cathode Materials for Sustainable Batteries

AbstractManganese‐based cathode materials have garnered extensive interest because of their high capacity, superior energy density, and tunable crystal structures. Despite their cost‐effectiveness, challenges like Mn dissolution and gas evolution originating from the irreversible structural degradation pose risks to stability and prolonged electrochemical behaviors, ultimately constraining their practical applications and market prospects. While the material characteristics and redox mechanisms of Mn‐based cathodes are extensively investigated, a systematic iterative approach to material design that balances performance and application demands remains both necessary and urgent. Recent strategies for enhancing cathode performances emphasize the innovative introduction and customization of composite structures in Mn‐based cathode materials to address the challenges above. This review aims to provide a comprehensive understanding of composite‐structure construction methodologies and offers practical guidelines for effectively designing high‐stability Mn‐based composite‐structure cathode materials. This encompasses the classifications of composite scales, the discussions for the extent of composite‐structure construction inside and outside of the cathode grains, and an exploration of the development potential of these materials, especially for grid‐scale applications.

Recounting the untold stories of breast cancer patient experiences: lessons learned from a patient-public involvement and engagement storytelling event.

Breast cancer remains a prevalent disease in women worldwide. Though advancements in breast cancer care have improved patient survival, a breast cancer diagnosis, and subsequent interventions have a lasting impact on patients' lived experiences during the pandemic. We present the collaborative learning process from this patient engagement workshop series as a community-academic partnership. Narrative medicine tools were used to recount patients' lived experiences following diagnosis, where both patients and researchers shared their cancer research activities in each workshop, and the role of the multidisciplinary healthcare team was discussed. We used an iterative approach to cohort building, narrative development, and the use of multiple media formats to capture stories. Over 20 patients with breast cancer shared their stories for the first time since their diagnosis with a wider audience. Here, we present the learning process and considerations from this event. Understanding patients' lived experiences can support researchers and healthcare professionals in developing an empathetic approach to shared healthcare decision making. Moreover, understanding the lived experiences of patients is critical to addressing disparities in healthcare.

Exactly defined molecular weight poly(ethylene glycol) allows for facile identification of PEGylation sites on proteins.

PEGylation (the covalent attachment of one or more poly(ethylene glycol) (PEG) units to a therapeutic) is a well-established technique in the pharmaceutical industry to increase blood-residence time and decrease immunogenicity. A challenging aspect of PEGylation is the dispersity of PEGylation agents, which results in batch-to-batch variations and analytical limitations. Herein, we present an approach to overcome these limitations by manufacturing a defined molecular weight (dispersity-free) PEGylation agent. We synthesise a defined molecular weight (Mw), linear 5 kDa methoxy-PEG (mPEG) active ester in an efficient and scalable manner using an iterative liquid-phase approach based on Nanostar Sieving. We then perform a comparative study on the random PEGylation and subsequent characterisation of the protein bovine serum albumin (BSA), using both the defined Mw, dispersity-free mPEG active ester, and a commercially available disperse 5 kDa mPEG active ester. We demonstrate that the defined Mw PEG both allows for facile monitoring of chemical modification reactions during the synthesis of the PEGylation agents, and facilitates straightforward identification of the PEGylated fragments within a PEGylated protein via a simple peptide mapping approach using UPLC-MS.

Improved convective cloud differential (CCD) tropospheric ozone from S5P-TROPOMI satellite data using local cloud fields

Abstract. We present the CHORA (Cloud Height Ozone Reference Algorithm) for retrieving tropospheric-ozone columns from S5P-TROPOMI (Sentinel-5 Precursor–TROPOspheric Monitoring Instrument). The method uses a local-cloud reference sector (CLC – CHORA Local Cloud) to determine the stratospheric (above-cloud) column, which is subtracted from the total column in clear-sky scenes in the same zonal band to retrieve the tropospheric column. The standard CCD (convective cloud differential) approach uses cloud data from the Pacific region (CPC – CHORA Pacific Cloud) instead. An important assumption for the standard method is the zonal invariance of stratospheric ozone. The local-cloud approach is the first step to diminish this constraint in order to extend the CCD method to mid-latitudes, where stratospheric-ozone variability is larger. An iterative approach has been developed for the automatic selection of an optimal local-cloud reference sector around each retrieval grid box varying latitudinally by ± 1° and longitudinally between ± 5 and ± 50°. The optimised CLCT (CHORA Local Cloud Theil–Sen) algorithm, a follow-up from the CLC, employs a homogeneity criterion for total ozone from the cloud reference sector in order to overcome the inhomogeneities in stratospheric ozone. It directly estimates the above-cloud column ozone for a common reference altitude of 270 hPa using the Theil–Sen regression. The latter allows for the combination of the CCD method with the cloud-slicing algorithm that retrieves upper-tropospheric ozone volume mixing ratios. Monthly averaged tropospheric-column ozone (TCO) using the Pacific cloud reference sector (CPC) and the local-cloud reference sector (CLC, CLCT) has been determined over the tropics and subtropics (26° S–22° N) using TROPOMI for the time period from 2018 to 2022. The accuracy of the various methods was investigated by means of comparisons with spatially collocated NASA/GSFC SHADOZ (Southern Hemisphere Additional Ozonesondes) measurements and the ESA TROPOMI level-2 tropospheric-ozone product. At eight out of nine tropical stations, tropospheric-ozone columns using the CLCT yield better agreement with ozonesondes than the CPC. In the tropical region (20° S–20° N), the CLCT shows a significantly lower overall mean bias and dispersion of 1 ± 7 %, outperforming both the CPC (12 ± 10 %) and CCD-ESA (22 ± 10 %). The CLCT surpasses the ESA operational product, providing more accurate tropospheric-ozone retrievals at eight out of nine stations in the tropics. For the Hilo station, with a larger stratospheric-ozone variability due to its proximity to the subtropics, the bias of +30 % (CPC) is effectively reduced to −5 % (CLCT). Similarly, in the subtropics (Reunion, Irene, Hanoi, and King's Park), the CLCT algorithm provides an overall bias and scatter of −11 ± 9 % with respect to sondes. The CLCT effectively reduces the impact of stratospheric-ozone inhomogeneity, typically at higher latitudes. These results demonstrate the advantage of the local-cloud reference sector in the subtropics. The algorithm is therefore an important basis for subsequent systematic applications in current and future missions of geostationary satellites, like GEMS (Geostationary Environment Monitoring Spectrometer, Korea), ESA Sentinel-4, and NASA TEMPO (Tropospheric Emissions: Monitoring of POllution), predominantly covering the middle latitudes.

An interactive dashboard for analyzing user interaction patterns in the i2b2 clinical data warehouse.

Clinical data warehouses provide harmonized access to healthcare data for medical researchers. Informatics for Integrating Biology and the Bedside (i2b2) is a well-established open-source solution with the major benefit that data representations can be tailored to support specific use cases. These data representations can be defined and improved via an iterative approach together with domain experts and the medical researchers using the platform. To facilitate these discussions, it is important to understand how users interact with the system. The objective of this work was to develop metrics for describing user interactions with clinical data warehouses in general and i2b2 in particular. Moreover, we aimed to develop a dashboard featuring interactive visualizations that inform data engineers and data stewards about potential improvements. We first identified metrics for different data usage dimensions and extracted the relevant metadata about previous user queries from the i2b2 database schema for further analysis. We then implemented associated visualizations in Python and integrated the results into an interactive dashboard using Dash. The identified categories of metrics include frequency of use, session duration, and use of functionality and features. We created a dashboard that extends our local i2b2 data warehouse platform, focusing on the latter category, further broken down into the number of queries, frequently queried concepts, and query complexity. The implementation is available as open-source software. A range of metrics can be derived from metadata logged in the i2b2 database schema to provide data engineers and data stewards with a comprehensive understanding of how users interact with the platform. This can help to identify the strengths and limitations of specific instances of the platform for specific use cases and aid their iterative improvement.

Climate silence: a comparative study of governmental and non-governmental messaging on soil health in Wisconsin, USA

ABSTRACT Soils are the foundation of healthy ecosystems, and promoting soil health is a primary aim of agricultural strategies intended to sequester carbon and combat climate change. In this exploratory study, we conducted a qualitative content analysis to characterize soil health messaging from 10 governmental and 10 non-governmental sources targeting Wisconsin farmers. Data were collected through internet searches, focusing on accessible web content from governmental agencies, public universities, and local offices, as well as prominent industry, nonprofit, and advocacy groups within the agricultural sector. A coding framework, developed through an iterative and inductive approach, identified six primary categories and 37 sub-categories representing key themes in soil health messaging. The study compared messaging across source types by analyzing code term frequency, highlighting differences in content emphasis between governmental and non-governmental sources. Findings indicate that governmental messaging largely advocates for conservation agriculture and associated principles but does not reference climate change nor connections between soil health and broader environmental issues. In contrast, non-governmental messaging offers a wider array of information while drawing connections among soil health, organic agriculture, ecosystem health, and climate change. The omission of climate change by governmental sources may be a significant constraint on soil health discourse and climate action.

Topology optimization and diverse truss designs considering nodal stability and bar buckling

Ensuring the bar stability is crucial in truss design. However, unstable nodes lacking lateral support complicate the calculation of bar buckling lengths. bar buckling constraints make the feasible region of optimization problems concave, further complicating the solution process. Moreover, traditional truss optimization methods typically yield a single optimal result, limiting the design options available to engineers. In this study, nominal disturbing load conditions are applied to the structure to eliminate unstable nodes, thereby ensuring accurate buckling length calculations. Additionally, the advanced allowable stress iteration (AASI) approach is proposed to address truss optimization problems with bar buckling constraints. To generate geometrically diverse and structurally competitive trusses, we develop a bar-length penalty (BLP) method. To validate the effectiveness of these methods, three numerical studies are presented. The results demonstrate that the proposed AASI approach produces optimized structures free from unstable nodes and bar buckling. Compared to structures optimized using the allowable stress iteration (ASI) method, which can only optimize for a single load case, those designed with the new approach maintain bar stability under all load conditions. Compared to the traditional method of increasing the cross-sectional area of unstable bars to ensure stability, much lighter trusses can be generated while maintaining the same load-bearing capacity. By applying the proposed BLP method to penalize specific bars, it is possible to achieve optimized structures with distinct topologies, similar masses, and equivalent load-carrying capacities. The proposed methods provide valuable insights for truss optimization design.

Stopping criteria for nonlinear variational problems: iterative approach

AbstractIn this paper, we present and study a stopping criteria based on a priori error estimate for nonlinear variational problems. We show that for nonlinear variational problems, the a priori error estimate is divided into two sub errors namely: the discretization error and the linearization error. We then used that representation to devise a stopping criteria that help to reduce the computational time. The methodology is applied to three problems and the numerical results demonstrate the superiority of the new approach.

A New Extragradient-Viscosity Method for a Variational Inequality, an Equilibrium Problem, and a Fixed Point Problem

In this research article, we introduce a novel iterative approach that builds upon a two-step extragradient-viscosity method. This method aims to find a common element among the solution set of a variational inequality, an equilibrium problem, and the set of common fixed points from a countable family of demicontractive mappings in a Hilbert space. We offer a robust convergence theorem for the proposed iterative scheme, considering certain well-conditioned parameters. Our findings represent an improvement over similar results already available in the existing literature. Furthermore, we demonstrate the applicability of our main result to W-mappings. Lastly, we present two numerical examples to exhibit the consistency and accuracy of our devised scheme.

Airborne Multi-Channel Forward-Looking Radar Super-Resolution Imaging Using Improved Fast Iterative Interpolated Beamforming Algorithm

Radar forward-looking imaging is critical in many civil and military fields, such as aircraft landing, autonomous driving, and geological exploration. Although the super-resolution forward-looking imaging algorithm based on spectral estimation has the potential to discriminate multiple targets within the same beam, the estimation of the angle and magnitude of the targets are not accurate due to the influence of sidelobe leakage. This paper proposes a multi-channel super-resolution forward-looking imaging algorithm based on the improved Fast Iterative Interpolated Beamforming (FIIB) algorithm to solve the problem. First, the number of targets and the coarse estimates of angle and magnitude are obtained from the iterative adaptive approach (IAA). Then, the accurate estimates of angle and magnitude are achieved by the strategy of iterative interpolation and leakage subtraction in FIIB. Finally, a high-resolution forward-looking image is obtained through non-coherent accumulation. The simulation results of point targets and scenes show that the proposed algorithm can distinguish multiple targets in the same beam, effectively improve the azimuthal resolution of forward-looking imaging, and attain the accurate reconstruction of point targets and the contour reconstruction of extended targets.

Quantitative analysis of wool and cashmere fiber mixtures using NIR spectroscopy

Abstract The quantitative determination of wool and cashmere mixed fiber is an indispensable quality control link in the textile industry, crucial for improving international trade status, ensuring product quality, and safeguarding consumer rights. Therefore, the goal of this study is to develop a reliable method for estimating fiber contents in wool–cashmere blends based on near-infrared (NIR) spectroscopy. A total of 210 mixed samples of 21 different proportions of cashmere and wool are prepared in the experiment, and data are collected in the NIR spectral band of 1,000–2,500 nm. Convolution Savitzky–Golay (S–G) combined with the second-order derivative is then used for spectral preprocessing. The variable iterative space shrinkage approach (VISSA) optimizes the characteristic wavelengths, and 339 wavelength points are selected. The prediction model of the least squares support vector machine (LSSVM) is established by particle swarm optimization (PSO), fast positioning, and analysis of key information related to the target in complex spectral data. Finally, the training set and the prediction set are divided according to the ratio of 8 : 2. Experiments show that in terms of modeling and prediction, the PSO-LSSVM model based on the wavelength selected by VISSA has a prediction determination coefficient R-squared of 0.9821, a prediction root mean square error of 1.1263, and an mean absolute error of 0.6527. The hybrid modeling method of VISSA, PSO, and LSSVM based on NIR spectroscopy (VISSA–PSO–LSSVM) can provide a more accurate and stable method for the non-destructive detection of cashmere and wool blended fiber content.

Machine Learning Approaches in Advancing Perovskite Solar Cells Research

AbstractThe integration of machine learning (ML) with perovskite solar cells (PSCs) signifies a groundbreaking era in photovoltaic (PV) technology. The traditional iterative approaches in PSC research are often time‐consuming and resource‐intensive. In contrast, ML leverages available data and sophisticated algorithms to quickly identify properties and optimize parameters for novel materials and devices. This review explores how ML‐driven approaches are improving various facets of PSCs research, including the rapid screening of novel compositions, enhancing stability, refining device architectures, and deepening the understanding of underlying physics. The paper is structured to gradually familiarize readers with essential terminologies and concepts, ensuring a solid foundation before delving into more intricate topics. A concise workflow and various introductory toolkits for ML are also briefly discussed. Through a detailed analysis of compelling case studies, a basic research framework within ML‐PSC‐integrated research is provided. This comprehensive review can serve as a valuable reference for researchers aiming to understand and leverage ML‐driven approaches in PSCs research, advancing the path for more efficient and sustainable PV technologies.

The many facets of perceived bladder health in women: Absence of symptoms and presence of healthy behaviors across the life course

Purpose:Qualitative studies exploring bladder health are rare compared to research focusing on lower urinary tract symptoms (LUTS). Our aim was to explore adolescent and adult women’s perceptions about what constitutes a healthy bladder. Methods:We conducted a 7-site focus group study of bladder health with adolescent and adult women across six age categories (11–14 to 65+ years). Transcripts were analyzed using transdisciplinary, directed content analysis and an iterative interpretive consensus building approach to identify key constructs, focusing on “healthy bladder” and “unhealthy bladder” codes. Results:Forty-four focus groups with 360 participants were completed. Three thematic categories were identified: (1) concept of a healthy bladder (conceptual abstraction); (2) experience of a healthy bladder (subjective experience); and (3) lifestyle and life course considerations (bladder health in context). Participants struggled to define the unfamiliar concept of healthy bladder and relied on contextualized experiences and behaviors for characterizing bladder health. They described the concept of a healthy bladder as something that you did not need to think about but did require attention to healthy habits to maintain. Other features of a healthy bladder discussed include having bladder control despite urgency and environmental constraints on voiding, normal voiding frequency, and qualities of urine and urination. Participants struggled with lack of information about healthy bladder habits and noted absence of routine bladder health screening. Discussion:Findings identify need to promote public education and information sharing in health care visits about bladder health. Further they can inform bladder health promotion and clinical research on LUTS prevention.

Rapid qualitative and quantitative detection for adulteration of Atractylodis Rhizoma using hyperspectral imaging combined with chemometric methods

In the field of traditional Chinese medicine, Atractylodis Rhizoma (AR) is commonly used for various diseases due to its excellent ability to dry dampness and strengthen the spleen, especially popular in East Asia. The aim of this study is to proposed Hyperspectral Imaging (HSI) in combination with chemometric methods for the rapid qualitative and quantitative detection of AR adulteration with other types of powder. Partial Least Squares Discriminant Analysis (PLS-DA) was used to construct the classification models the best, with the First-order Derivative (F-D) preprocessing method. The accuracy values of the test sets for classification models were above 99%. Furthermore, Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), and BP Neural Network (BPNN) were used to quantitatively analyze the adulteration level. On the whole, the BPNN model has a relatively stable effect. The R-square (R2) values of different models were all greater than 0.97, the Root Mean Square Error (RMSE) values were all less than 0.0300, and the Relative Percentage Difference (RPD) values were over 6.00. After applying three characteristic wavelength selection algorithms, namely Iterative Retained Information Variable (IRIV), Successive Projections Algorithm (SPA), and Variable Iterative Space Shrinkage Approach (VISSA) algorithms, the classification accuracy values remained over 99.00% while the quantification models’ RPD values were over 4.00. These results demonstrate the reliability of using hyperspectral imaging combined with chemometrics methods for the adulteration problems in AR.

Contribution of ultra-processed food and animal-plant protein intake ratio to the environmental impact of Belgian diets

There is growing concern about the various impacts of food consumption, both on human and planetary health. Given the context-specific nature of consumption patterns, evaluating their national-level impacts is crucial for proactive policy development. This research aims to evaluate the environmental impact of current Belgian diets, with particular attention to the contribution of food groups, ultra-processed foods (UPF), and the animal-to-plant protein ratio. The methodology consists of three key stages. Firstly, the Belgian diet was summarised, based on data from the Belgian National Food Consumption Survey 2014/2015. Secondly, the origin of the most frequently consumed foods was traced using trade databases. Finally, a cradle-to-grave life cycle assessment was conducted to determine the impact of Belgian diets on climate change, water use, land use, and fossil resource scarcity. In this third step, an iterative procedure for selecting the food items to be included in the study was performed. The iterative approach resulted in the inclusion of 227 food items in the analysis. The results indicate greenhouse gas (GHG) emissions of 4.4 [4.27–4.54] kg CO2-equivalent per person per day. Red meat (35 %), beverages (16 %), dairy products (16 %) and snacks (10 %) are identified as primary contributors to climate change. Similar results were observed for land use impacts. Water use and fossil resource scarcity exhibited different trends, with beverages being the most impactful food group. Moreover, UPF account for 50 % of the total climate change and land use impacts, with a linear relationship observed between increased UPF consumption and GHG emissions and land use. A similar linear trend is observed between the ratio of animal-to-plant protein intake and both climate change and land use impact categories. A shift from the current protein ratio to a ratio of 40/60, as suggested in the Flemish Green Deal Protein Shift has been shown to result in a reduction in GHG emissions of the diet by 31 %. This study emphasises the need to target the consumption of high-impact foods such as UPF and animal-based products. Future research will investigate the relationship between environmental and health impacts.

Multi-software based dynamic modelling of a water-to-water heat pump interacting with an aquifer thermal energy storage system

Aquifer thermal energy storage systems may support the decarbonization of heating and cooling energy needs of urban areas, not only in heating-dominated countries but also in Southern Europe. In this framework, this work investigates the adoption of a water-to-water heat pump interacting with two aquifers and activating a small-scale district heating and cooling network serving a mixed-use district of eight residential and office buildings in Rome (Italy). The dynamic behaviour of aquifers has been replicated using the GeoSIAM software. Energy conversion systems and users’ thermal and cooling loads have been simulated in TRNSYS 18. The dynamic models have been integrated using an iterative approach based on conditions regarding plant operation and injection temperature in wells. The proposed solution has been then compared from the energy and environmental perspective with a traditional system, including an air-to-water heat pump. In addition, the balance between heating and cooling mode operation has been assessed. The results obtained encourage the adoption of aquifer thermal energy storage systems in Central Italy. Indeed, the primary energy saving, and the carbon dioxide emissions avoided are equal to 18%, whereas the imbalance between cooling and heating loads is limited to -5.2%.

Integrated measure for training transfer in nursing practice: Development and validation study

BackgroundThe persistent knowledge-practice gap in nursing, where competencies gained through continuing professional development fail to integrate into practice, remains a significant challenge. Effective transfer of acquired knowledge and skills to the workplace is essential for bridging this gap. However, there is a lack of comprehensive, validated instruments to assess factors influencing training transfer in nursing practice. ObjectiveThis study aims to develop a questionnaire capable of assessing training transfer as an integrated construct, encompassing all factors influencing this process in nursing practice. DesignThe study followed a developmental, iterative approach guided by the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN). Setting(s)Eight tertiary teaching hospitals in the Netherlands. ParticipantsEducational experts (n = 8) and nurses (n = 1588). MethodsThe questionnaire development consisted of three phases: Phase 1) content development, in which instruments were identified based on a literature study and translation of included instruments into Dutch, Phase 2) pilot phase: content validation and usability testing using Delphi studies with experts and nurses, Phase 3) psychometric validation: construct validation using exploratory and confirmatory factor analysis, composite reliability and discriminant validity. The phases were completed from January 2023 till June 2024. ResultsThe literature review led to the inclusion of two key instruments: the Learning Transfer System Inventory (LTSI) and the Learning Climate Questionnaire (LCQ). Following item reduction based on Delphi rounds, nurses completed the adapted instruments. Psychometric validation demonstrated a good fit for these instruments in nursing practice. The adapted LTSI showed loadings ranging from 0.37 to 0.99, with a Normed Χ2 of 1.996, TLI of 0.908, CFI of 0.920, and RMSEA of 0.050 [CI = 0.047–0.054]. The adapted LCQ exhibited loadings from 0.64 to 0.94, with a Normed Χ2 of 3.385, TLI of 0.963, CFI of 0.974, and RMSEA of 0.069 [CI = 0.061–0.078]. The combined model demonstrated a Normed Χ2 of 2.014, TLI of 0.900, CFI of 0.907, and RMSEA of 0.051 [CI = 0.048–0.054]. ConclusionsThe adapted LTSI and LCQ, both separately and combined, are reliable and valid instruments for use in the Dutch nursing hospital context. Together, they provide a comprehensive assessment of factors influencing training transfer. They can also be used independently to examine specific components such as trainers (LCQ), context (LTSI), personal aspects (LTSI), or training (LTSI/LCQ). Furthermore, these instruments can facilitate discussions between nurses, managers, and educational experts to address barriers to training transfer in nursing practice.

Convolutional sparse coding network for sparse seismic time-frequency representation

Seismic time-frequency (TF) transforms are essential tools in reservoir interpretation and signal processing, particularly for characterizing frequency variations in non-stationary seismic data. Recently, sparse TF transforms, which leverage sparse coding (SC), have gained significant attention in the geosciences due to their ability to achieve high TF resolution. However, the iterative approaches typically employed in sparse TF transforms are computationally intensive, making them impractical for real seismic data analysis. To address this issue, we propose an interpretable convolutional sparse coding (CSC) network to achieve high TF resolution. The proposed model is generated based on the traditional short-time Fourier transform (STFT) transform and a modified UNet, named ULISTANet. In this design, we replace the conventional convolutional layers of the UNet with learnable iterative shrinkage thresholding algorithm (LISTA) blocks, a specialized form of CSC. The LISTA block, which evolves from the traditional iterative shrinkage thresholding algorithm (ISTA), is optimized for extracting sparse features more effectively. Furthermore, we create a synthetic dataset featuring complex frequency-modulated signals to train ULISTANet. Finally, the proposed method's performance is subsequently validated using both synthetic and field data, demonstrating its potential for enhanced seismic data analysis.

A Novel Analytical Approximate Solution for Strongly Nonlinear Third-Order Jerk Equations Using a Modified Iteration Method

Nonlinear jerk equations, characterized by a third-order time derivative, play a crucial role in modeling various physical phenomena across disciplines like mechanics, circuits, and biology. Accurately solving these equations is essential for understanding and predicting the behavior of such systems. However, obtaining analytical solutions for nonlinear jerk equations can be challenging, necessitating the development of robust and accurate approximation methods. This work explores, for the first time, the application of the modified iteration approach to solve third-order jerk equations. By comparing the obtained approximate solutions with both exact and existing analytical solutions for established engineering problems, we demonstrate the superior accuracy and rapid convergence of the proposed method. The significantly reduced error percentages highlight the effectiveness of the modified iteration approach in providing precise solutions for nonlinear jerk equations, paving the way for its application in a wide range of oscillation problems within nonlinear sciences and engineering.