Iterative Process Research Articles

A qualitative account of mothers of pediatric heart transplant recipients' experience at a pilot mindfulness-based retreat: insights for implementation.

To explore participants' shared experience attending a pilot mindfulness-based retreat (MBR) intervention supporting the psychological well-being of mothers of pediatric heart transplant (HTx) recipients. A qualitative description approach was used within a larger hybrid mixed-methods pilot study evaluating the implementation-effectiveness of a 2-day MBR tailored specifically for a pediatric HTx population. A purposive sample of mothers was recruited from a leading Canadian pediatric HTx center, and each represented a primary, female-identifying caregiver, sharing residence with a pediatric HTx recipient of at least 4 months post-transplant. Two focus groups were conducted after and 16 individual interviews 3 months after the MBR to elicit participants' in-depth experience attending the MBR. Reflexive thematic data analysis led to theme development through an iterative process with consensus among team members. Sixteen mothers of pediatric HTx recipients (mean patient age 9.8 years; mean time post-transplant 8.2 years) participated in the MBR held in Ontario, Canada. Qualitative findings illuminated mothers' shared experience attending the MBR, reflecting three themes that describe key intervention elements beyond those affiliated with mindfulness practice alone: "respite," "readiness," and "relationships." Findings highlight acceptability and appropriateness as distinct yet interrelated implementation outcomes. Elements of "respite," "readiness," and "relationships" are key components of the MBR experience that offer valuable intervention and clinical considerations related to mothers' psychological well-being. Aligning intervention acceptability and appropriateness through tailored strategies may aid implementation. Future research considering maternal readiness relative to support intervention participation is warranted.

POLYCORE: Polygon-based contour refinement for improved Intravascular Ultrasound Segmentation

Segmentation of the coronary vessel wall in intravascular ultrasound is a fundamental step in guiding coronary intervention. However, it is an challenging task, even for highly skilled cardiologists, due to image artefacts and shadowed regions caused by calcified plaque, guide wires and vessel side branches. Recently, dense-based neural networks have been applied to this task, however, they often fail to predict anatomically plausible contours in these low-signal areas. We propose a novel methodology called Polygon-based Contour Refiner (POLYCORE) that addresses topological error in dense-based segmentation networks using a relational inductive bias through higher-order connections between vertices to learn anatomically rational contours. Our approach remedies the over-smoothing phenomena common in polygon networks by introducing a new vector field refinement module which enables pixel-level detail to be added in an iterative process. POLYCORE is enhanced with augmented polygon aggregation which we show is more effective than typical dense-based test-time augmentation strategies. We achieve state-of-the-art results on two diverse datasets, observing particular improvements when segmenting the lumen structure and in topologically-challenging regions containing shadow artefacts. Our source code is available here: http://orcid.org/https://github.com/kitbransby/POLYCORE.

From deconstruction to reconstruction: A search for natural kinds in developmental psychopathology.

A "natural kind" is a specific classification that identifies some structure of truth and reality, a delimited entity. Psychiatric disorders are not natural kinds. As one moves from physics and chemistry to biology and medicine, natural kinds degrade, and the boundaries of differentiating phenomena become less clear. Within psychiatry, the categorization of psychopathology has further ontological challenges, especially across development. We suggest that to identify and isolate clinical subgroups, it is critical to integrate external validators in an iterative process, with the goal of linking classification to treatments with maximal clinical benefit. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Training physicians in motivational communication: An integrated knowledge transfer study protocol.

While behavior change counseling (BCC) targeting health risk behaviors has shown efficacy for improving patient health outcomes, barriers to knowledge translation have resulted in poor uptake among health care providers (HCPs). This article outlines the development of a new BCC training framework for HCPs, from inception to readiness for efficacy testing. It provides an example of integrated knowledge translation (iKT) used in alignment with the obesity-related behavioral intervention trials model. (a) A modified Delphi process identified essential BCC skills for HCPs; (b) a survey assessed HCP attitudes and training needs; (c) an online competency assessment tool was developed using iKT mixed methods; (d) a training program was developed and refined using a logic model; and (e) the program was optimized using iterative rounds of participant feedback. A future proof-of-concept trial (f) will determine the program's readiness for full efficacy testing. A Delphi panel (n = 46) identified 11 core BCC competencies for HCPs, defining "motivational communication." The HCP survey (n = 80) showed willingness to devote 4 hr to introductory training in BCC. The Motivational Communication Competency Assessment Test (MC-CAT: an online, interactive evaluation tool) and a motivational communication training program (MOTIVATOR: accredited for continuing education by the Royal College of Physicians and Surgeons of Canada) were collaboratively developed with knowledge users. The optimization process (n = 11) provided key feedback, with minor changes being made to the program. In developing a new BCC framework, obstacles to BCC implementation were addressed through an iterative iKT process. This should improve eventual intervention uptake. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A GAN-based cyclic iterative unsupervised change detection network

ABSTRACT Remote sensing image change detection has significant applications across various fields. In recent years, the powerful feature extraction capability of deep learning technology has introduced innovative approaches to change detection methodologies. However, the accuracy of convolutional neural network algorithms hinges heavily on labelled data, necessitating substantial manpower and resources to generate labelled samples. Therefore, this paper proposes an unsupervised change detection model called CIUCD. This model operates on the fundamental premise of unsupervised change detection, employing a generator and a segmenter for iterative training. During each iteration, the generator and segmenter update and optimize their parameters, with the segmenter leveraging the generated change images as labels for training purposes. This iterative labelling process circumvents the need for manually labelled data, thereby mitigating the difficulty of obtaining change detection labels for actual remote sensing images. To validate the efficacy of the proposed model, we conducted experiments using actual remotely sensed images from the 2023 Turkey earthquake and the 2022 Afghanistan earthquake events. The results show that our method enhances the F1, Pr, Re and OA by 5.98%, 2.05%, 11.82% and 8.50% (Turkey earthquake), and by 9.54%, 5.87%, 4.52% and 11.34% (Afghanistan earthquake), respectively, when compared to the unsupervised change detection algorithm KPCA-Mnet. Experiments on real remote sensing images of 1000 × 1000 pixels showed that the CIUCD algorithm not only accurately detect larger change areas but is also capable of identifying changes in subtle objects such as solar panels. Additionally, the CIUCD algorithm is resilient to natural environmental factors like the angle of illumination, enabling precise predictions of changes in the actual remote sensing images and yielding clearer detection results. The performance of the CIUCD algorithm remains outstanding even when applied to larger images with a resolution of 3000 × 3000 pixels.

Mitigating Racial Bias in Health Care Algorithms: Improving Fairness in Access to Supportive Housing.

Algorithms for guiding health care decisions have come under increasing scrutiny for being unfair to certain racial and ethnic groups. The authors describe their multistep process, using data from 3,465 individuals, to reduce racial and ethnic bias in an algorithm developed to identify state Medicaid beneficiaries experiencing homelessness and chronic health needs who were eligible for coordinated health care and housing supports. Through an iterative process of adjusting inputs, reviewing outputs with diverse stakeholders, and performing quality assurance, the authors developed an algorithm that achieved racial and ethnic parity in the selection of eligible Medicaid beneficiaries.

LCA standards for environmental product assessments in the bioeconomy with a focus on biogenic carbon: A systematic review

Abstract Purpose Climate change mitigation is one of the priorities of the European Bioeconomy Strategy (BES). In this regard, renewable raw materials are a crucial resource, as they help to substitute fossil resources and reduce greenhouse gas (GHG) emissions by storing biogenic carbon (C). To control the environmental impact of the European BES, the life cycle assessment (LCA) is the preferred method. Standards provide guidance for LCA. This review aims to present the state of the art of LCA-related standards, focusing on the assessment of biogenic C and its global warming potential (GWP) to evaluate them for decision-making with regard to the transition to a bioeconomy (BE) and climate change mitigation. Methods A systematic review is conducted using the database Nautos to reveal the relevant standards guiding LCA in the BE. In an iterative process, criteria for a comparative analysis are defined. The eligible standards are then qualitatively analysed and compared based on criteria previously derived in the iterative process. Results and discussion In the context of LCA and bio-based products, 13 documents are identified. Differences are revealed in the accounting of biogenic C flows, the calculation of the duration of biogenic C storage and the biogenic C content as well as the consideration of substitution effects. Furthermore, the standards do not provide any information on the assessment of biogenic C in the circular economy so far. Conclusion The importance of biogenic C aspects in LCA has been reflected in the standards over the last 20 years. However, further research is needed to harmonise the different aspects and integrate new relevant ones related to biogenic C and BE to support decision-making for the transition to a BE. This becomes more important as new legislation refers to LCA standards as the preferred assessment method.

Co-designing postpartum contraceptive services with and for immigrant women in Sweden: lessons learned from the IMPROVE-it project

Background and aimImmigrant women in many high-income countries including Sweden, report unmet need of sexual and reproductive health and rights, and face worse pregnancy outcomes and higher risk of unintended pregnancies. Postpartum contraceptive services are often inadequate to meet their needs. Co-design has shown to reduce health inequities, yet little is known about using this method for postpartum contraceptive service development and even less in collaborating with immigrant populations. The aim of this paper is to describe the co-design process and the strategies that were developed to help develop tailored and acceptable postpartum contraceptive services for immigrant women in Sweden.MethodsThe paper describes a co-design process that took place during 2022–2023, including the cyclical digital consultations with Arabic and Somali speaking immigrant women, midwives and researchers, as well as the outputs from the process. The theoretical framework for the co-design process was the ‘Double Diamond’ Design Process Model. Data analysis included qualitative content analysis.ResultsThe co-design process led to the joint development of intervention materials and strategies to improve postpartum contraceptive services. Specifically, the process revealed ideas on how to improve contraceptive counseling within three pre-identified areas of change: improve physical access to contraceptive services; improved communication strategies using visual aids and information charts; and empowerment strategies that focus on reflective practice without assumptions about what a group of women might expect. We found that participants contributed actively to the process with ideas and suggestions, and that the co-design process facilitated positive reflections on ongoing counseling practices.ConclusionThe co-design process resulted in the successful and participative development of innovative tools and activities to improve contraceptive counseling services. This approach is original because it involves both immigrant women, often left behind, and midwives delivering the services. Whilst this interplay allowed for careful refinement of services and tools by using an iterative process, it also facilitated reflective midwifery practice.

The Development of the Service-Learning Outcomes Measurement Scale—Short Version (S-LOMS-SV) and Its Preliminary Validation

This article describes the development and validation of a short version of the Service-Learning Outcomes Measurement Scale, the S-LOMS-SV. The original Service-Learning Outcomes Measurement Scale (S-LOMS) is a previously validated 56-item instrument measuring developmental outcomes in 11 domains under four overarching categories, especially appropriate for use in Hong Kong and related contexts. In developing SLOMS-SV, 14 experienced service-learning researchers and practitioners reviewed the full-length S-LOMS for content validity and identified items with potential for inclusion in a shortened version. Through an iterative process, 21 items maintaining the original scale’s factor structure were selected for the S-LOMS-SV. Validity and internal consistency analyses using preexisting student data sets demonstrated that the S-LOMS-SV has robust psychometric properties that match those of the full-length S-LOMS. Theoretical and practical implications and next steps are discussed. The 21-item S-LOMS-SV is included in the Appendix.

Towards a 3D-Printed Millifluidic Device for Investigating Cellular Processes

Microfluidic devices (µFDs) have been explored extensively in drug screening and studying cellular processes such as migration and metastasis. However, the fabrication and implementation of microfluidic devices pose cost and logistical challenges that limit wider-spread adoption. Despite these challenges, light-based 3D printing offers a potential alternative to device fabrication. This study reports on the development of millifluidic devices (MiFDs) for disease modeling and elucidates the methods and implications of the design, production, and testing of 3D-printed MiFDs. It further details how such millifluidic devices can be cost-efficiently and effortlessly produced. The MiFD was developed through an iterative process with analytical tests (flow tests, leak tests, cytotoxicity assays, and microscopic analyses), driving design evolution and determination of the suitability of the devices for disease modeling and cancer research. The design evolution also considered flow within tissues and replicates interstitial flow between the main flow path and the modules designed to house and support organ-mimicking cancer cell spheroids. Although the primary stereolithographic (SLA) resin used in this study showed cytotoxic potential despite its biocompatibility certifications, the MiFDs possessed essential attributes for cell culturing. In summary, SLA 3D printing enables the production of MiFDs as a cost-effective, rapid prototyping alternative to standard µFD fabrication for investigating disease-related processes.

A Cross-Scale Electrothermal Co-Simulation Approach for Power MOSFETs at Device–Package–Heatsink–Board Levels

This paper proposes a cross-scale simulation approach for evaluating the steady-state electrothermal performance of power MOSFETs at the device–package–heatsink–board (DPHB) level. A co-simulation framework is designed by employing the iterative process of power loss and chip temperature to bridge the device and package–heatsink–board (PHB) level simulators. As a result, the cross-scale electrothermal coupling effect within multilevel settings is considered. Correspondingly, variation values in chip temperature and temperature-dependent drain current can be obtained at various voltage biases, level settings, and DPHB structural parameters, incorporating cross-level physical insights. The simulation results are compared with existing methods, and their features and limitations are discussed. Additionally, this paper also derives an empirical equation from the co-simulations to characterize the relationship between the drain current and the chip temperature under different operations exactly. A commercial MOSFET with TO-220F packaging is implemented in experiments to extract the chip temperature and drain current in electrothermal equilibrium. The method comparisons and fair agreement among simulations, equations, and measurements presents the proposed approach as generalized and powerful for describing variations in chip temperature and drain current considering from micrometer devices to millimeter packages–heatsinks–PCB boards, thus providing effective support for DPHB-level co-design.

A conceptual framework for integrating digital transformation in healthcare marketing to boost patient engagement and compliance

This paper presents a conceptual framework for integrating digital transformation in healthcare marketing to enhance patient engagement and compliance with treatment regimens. As healthcare systems evolve, the adoption of digital technologies has become imperative for fostering meaningful interactions between patients and providers. The proposed framework emphasizes leveraging digital marketing techniques, data analytics, and personalized communication strategies to strengthen patient-provider relationships and improve health outcomes. At the core of the framework is the use of digital marketing techniques, including social media outreach, content marketing, and mobile applications, to reach patients effectively. These tools facilitate the dissemination of vital health information and resources, enabling patients to make informed decisions about their care. By utilizing data analytics, healthcare organizations can gain insights into patient behaviors, preferences, and demographics, allowing for the customization of marketing efforts to meet the specific needs of diverse patient populations. Personalized communication strategies are pivotal in this framework, as they empower healthcare providers to deliver tailored messages that resonate with individual patients. This personalization fosters a sense of connection and trust, encouraging patients to engage actively in their treatment plans. By utilizing patient data, providers can send reminders, educational content, and motivational messages that enhance treatment adherence and compliance. Moreover, the framework addresses the importance of feedback loops, where healthcare organizations continuously gather and analyze patient responses to marketing efforts. This iterative process allows for the optimization of strategies, ensuring they remain effective and relevant over time. By fostering a culture of engagement, organizations can cultivate long-term relationships with patients, ultimately leading to improved health outcomes. In conclusion, this conceptual framework serves as a strategic guide for healthcare organizations aiming to harness digital transformation in their marketing efforts. By integrating digital marketing techniques, data analytics, and personalized communication strategies, organizations can enhance patient engagement, improve treatment adherence, and positively impact overall health outcomes.

Advancing Nonlinear Problem Solutions: Newton-Like Iterative Techniques for Varied Convergence Orders using Homotopy Perturbation

Abstract Iterative methods are crucial in numerical analysis for approximating solutions to nonlinear problems encountered in various fields, such as biomathematics, thermodynamics, chemical engineering, and fluid mechanics. This paper introduces innovative iterative methods of convergence orders three and six to eight, developed using the homotopy perturbation technique (HPT). We address the limitations of traditional derivative-based methods by incorporating divided differences, resulting in derivative-free schemes with optimal convergence properties. We present four newly designed algorithms (HPM1, HPM2, HPM3, and HPM4) and provide a comprehensive convergence analysis. Numerical simulations and basins of attraction demonstrate the superior performance of the proposed methods compared to existing methods of the same order. The proposed methods exhibit faster convergence and larger basins of attraction, making them highly effective for solving nonlinear equations. Our new numerical methods rely on approximations and iterative processes, with each small step bringing us closer to the exact solution. Exploring these numerical methods encourages us to apply mathematics to solve challenging problems in physics, engineering, finance, and more.

Multi-Dimensional Fuzzy Clustering-Based Trajectory Initialization Algorithm for Infrared Weak Target Trajectories in Robust Clutter Environments

When conducting maneuver target tracking, trajectory initialization plays a crucial role in enhancing the accuracy of tracking algorithms. During maneuver target tracking, the accuracy of the tracking algorithm can be significantly improved through trajectory initialization. However, the traditional trajectory initialization algorithms face issues such as susceptibility to noise interference, lack of universality, and poor robustness in environments with high clutter levels. To address these issues, this study proposes a trajectory initialization algorithm based on multidimensional fuzzy clustering (MDF-clustering). The algorithm utilizes multidimensional feature information of the target, such as speed and irradiance, to determine point trajectory affiliation by assigning weights based on the clustering center of each feature type. Subsequently, it updates the clustering center and weight assignment using the new target features, ultimately deriving the correct trajectory through iterative processes. Experimental results demonstrate that the proposed method achieves an average stable initialization frame number of 3.12 frames, an average correct trajectory initialization rate of 99.59%, an average false trajectory occupancy rate of 0.04%, and an average missed batch rate of 0.06%. These results indicate improvements of at least 0.87 frames, 27.11%, 60.28%, and 6.48%, respectively, in terms of initialization rate, false trajectory rate, and missed batch rate, when compared to traditional methods. The algorithm enhances the accuracy and robustness of trajectory initialization in challenging environments characterized by solid clutter and target maneuvers, offering significant practical value for target tracking in complex scenarios.

All-loop group-theory constraints for four-point amplitudes of SU(N), SO(N), and Sp(N) gauge theories

In the decomposition of gauge-theory amplitudes into kinematic and color factors, the color factors (at a given loop order L) span a proper subspace of the extended trace space (which consists of single and multiple traces of generators of the gauge group, graded by powers of N). Using an iterative process, we systematically construct the L-loop color space of four-point amplitudes of fields in the adjoint representation of SU(N), SO(N), or Sp(N). We define the null space as the orthogonal complement of the color space. For SU(N), we confirm the existence of four independent null vectors (for L ≥ 2) and for SO(N) and Sp(N), we establish the existence of seventeen independent null vectors (for L ≥ 5). Each null vector corresponds to a group-theory constraint on the color-ordered amplitudes of the gauge theory.

Customer journey mapping framework for SMES: Enhancing customer satisfaction and business growth

Customer journey mapping (CJM) is an essential tool for small and medium-sized enterprises (SMEs) to understand and optimize their interactions with customers, thereby driving satisfaction and business growth. This review outlines a customer journey mapping framework designed specifically for SMEs, aiming to enhance customer experiences through a structured approach to identifying key touchpoints, pain points, and opportunities for improvement across the customer lifecycle. The proposed framework involves systematically mapping out every stage of the customer journey, from initial awareness to post-purchase interactions, highlighting moments that significantly impact customer satisfaction. By leveraging customer data, feedback, and analytics, SMEs can gain valuable insights into customer behavior, motivations, and preferences. This framework emphasizes the importance of personalization, using data to tailor the customer experience and foster deeper connections. Additionally, the framework integrates feedback loops, allowing SMEs to continuously gather and analyze real-time customer data. This iterative process helps businesses to proactively identify emerging trends, address customer concerns, and adapt to evolving market demands. By aligning customer needs with business objectives, SMEs can create more targeted marketing campaigns, improve product or service offerings, and enhance customer retention strategies. The implementation of this customer journey mapping framework can lead to several key benefits for SMEs, including improved customer satisfaction, increased brand loyalty, and greater operational efficiency. It also enables SMEs to compete more effectively in their markets by delivering personalized and seamless customer experiences. The framework supports scalability, allowing SMEs to refine their customer engagement strategies as their businesses grow. In conclusion, the customer journey mapping framework serves as a critical tool for SMEs looking to enhance customer satisfaction and drive sustainable business growth. By focusing on data-driven insights and continuous improvement, SMEs can deliver more value to customers, strengthen brand loyalty, and achieve long-term success.

Phase synthesis method in multi-aperture optical systems based on iterative image processing algorithms

Background. Modern technologies in the field of optics and photonics place high demands on the quality and output power of the radiation source. The fulfillment of these requirements can currently be achieved by creating multi-aperture laser systems with coherent beam addition. The main problem of creating such systems is the development of phase synthesis methods in a multichannel optical system. Aim. In this paper, we consider a phase synchronization method without a reference beam for a coherent addition system with active feedback, which is based on the Gerchberg–Saxton algorithm. This algorithm makes it possible to reconstruct complex field amplitudes in the aperture and focal planes from intensity distributions in these fields. The application of this algorithm for multichannel systems is analyzed and its features, such as the occurrence of stagnation and ambiguity of the solution, are revealed. Methods. Solutions are proposed to eliminate the problem of convergence of the algorithm to side solutions and getting the iterative procedure into the local extremum using global optimization algorithms, methods of reducing the dimension of the problem and the introduction of antisymmetric amplitude modulation. Results. The paper demonstrates the results of phase field reconstruction for a large number of optical sources. For a seven-aperture system, a physical simulation was performed to restore phase information, confirming the results of numerical modeling. Conclusion. The proposed approach is a reasonable alternative to currently existing methods and can be used in the problem of coherent addition in multichannel optical systems.

Comparing Virtual and Real-Life Rapid Prototyping Methods for User Testing Smart City Interfaces: A Case Study

In the development of complex embedded interactive systems, a tension arises between, on the one hand, ever shorter and highly iterative design processes, and, on the other hand, the need for user testing with early prototypes to validate systems from a user-centred design perspective. This study focuses on the integration of Virtual Reality (VR) into prototyping embedded interactive systems, examining its potential to bridge the gap between rapid prototyping and user-centered design validation. Adopting a comparative research approach, we analyze a case study: the development of a cultural smart city experience. It juxtaposes in situ, low-fidelity prototype testing with VR-based testing, evaluating their realism, interactivity, functionality, presence and task difficulty. This mixed-method research design incorporates both qualitative and quantitative methodologies, engaging 27 design students in a comparative study, conducting participatory research and 8 expert interviews. These findings reveal divergent roles in field testing and VR in the new product development process, highlighting VR’s strengths in visualizing procedures and facilitating discussion. This study identifies the limitations of VR in mimicking realistic interactions and incorporating social context yet underscores its superiority over paper prototypes in its realism and interactivity. Where field testing can hold broader contextual insights, the VR prototype gives more concrete and applied insights. The main advantage of VR testing is its visualisation of procedures and its final materialisation according to the participants interviewed. According to the experts interviewed, VR can be used as a useful tool within the development process especially for visualisation and testing user flows of complex interfaces.

Development of Worksheets for Immunomodulator Shared Decision-Making to Facilitate Patient-Clinician Communication: A Quality Improvement Project Employing Design Thinking Principles.

Shared decision-making (SDM) is a principle of humanistic, patient-centered health care within the field of rheumatology. However, clear communication between patients and their clinicians regarding the benefits and risks of immunomodulators may be challenging in a clinical setting. The design-thinking process is a human-centered approach to quality improvement that can help to identify insights to uphold high-quality communication. The development process adhered to the Stanford design thinking process framework, encompassing 5 stages: (1) empathize, (2) define, (3) ideate, (4) prototype, and (5) test. During the empathy stage, quality improvement members spent 4 hours immersed in the clinical setting observing how patients and clinicians engage in SDM conversations. These observations were augmented by unstructured debriefing sessions to better understand the needs and drivers of high-quality SDM. Following this, a rapid ideation workshop was convened to generate creative solutions. These led to rapid prototyping and testing, yielding a final product. The iterative design process identified 4 critical needs: (1) ensuring comprehensibility of materials, (2) upholding accuracy of information, (3) balancing standardization with individualization, and (4) promoting retention of knowledge. During the rapid ideation workshop, the concept of a Worksheet for Immunomodulator Shared Decision-Making (WISDM) was introduced and selected for further elaboration. This led to the creation of 5 prototypes for methotrexate, which were subsequently tested. These were reconciled and modified to make a final product. The WISDM template contains 7 elements that support SDM. Forty-five WISDMs were created for 23 immunomodulators. Further investigation will focus on how WISDMs exactly impact SDM.

Solving an internal problem for finite regular two-dimensional lattice spiral elements, excitable plate electromagnetic wave

Background. The work is aimed at developing and researching rigorous methods for solving internal problem of electrodynamics for multi-element structures (metastructures) consisting from the final number of elements, as well as to study the physical processes occurring in them. A special case of such structures are two-dimensional lattices with a fixed interelement distance, consisting of identical elements having the same spatial orientation (regular lattices). Aim. In this work, based on an iterative approach, the internal solution is solved. problems of electrodynamics for a finite regular two-dimensional lattice of spiral elements. In order to obtain a priori information about the electrodynamic characteristics of elements lattice and justification for the choice of projection function systems are analyzed spectral characteristics of the integral operator of the internal problem for a single spiral element. Then the currents on the structure elements are calculated, their spectral characteristics are determined. The results of spectral analysis allow increase the efficiency of solving an internal problem. Methods. The research is based on a strict electrodynamic approach, within the framework of which, for the specified structure in the thin-wire approximation, an integral representation of the electromagnetic field is formed, which, when considered on the surface of conductors together with boundary conditions, is reduced to a system of Fredholm integral equations of the second kind, written relative to unknown current distributions on conductors (internal task). The solution of the internal problem within the framework of the method of moments is reduced to solving a SLAE with a block matrix. Results. A mathematical model of a finite two-dimensional lattice of spiral elements is proposed radiating structure. For the specified structure, in the case of its excitation by a flat electromagnetic wave, based on the iterative approach, the internal problem of electrodynamics was solved. The following were carried out in a wide frequency range: analysis of the convergence of the iterative process, spectral analysis of the integral operator of the internal problem for a single spiral element, as well as spectral analysis of external field and current functions functions on lattice elements. Conclusion. The feasibility of determining the spectral characteristics of integral operators is shown internal task for the elements forming the metastructure. A relationship has been identified between the frequency dependence eigenvalues of the integral operator of the internal problem of single elements, forming a metastructure, with resonance phenomena arising in the metastructure, the influence of resonances on the convergence of the iterative process was confirmed. The feasibility of considering averaged amplitude current spectra is shown. It was revealed that the averaged spectrum of current functions is close to degenerate, especially near resonant frequencies. This allows for use as projection functions a compact set of eigenfunctions that have significant amplitudes in the vicinity of the frequency under study, which significantly simplifies the solution of the internal problem.