Diverse Missions Research Articles

Evaluation of diversity, equity, and inclusion information on US neurosurgery residency program websites.

The objective of this study was to determine the prevalence of diversity, equity, and inclusion (DEI) information on neurosurgery residency program websites and examine the association between program size, ranking, and DEI information presence. A cross-sectional review of 115 US neurosurgery residency program websites was conducted from January 1 to March 2, 2024. Websites were evaluated based on six DEI criteria: 1) diversity commitment in mission/leadership statements, 2) separate diversity mission statement, 3) rotations/fellowships for underrepresented minority students, 4) diversity initiatives, 5) diversity page/section, and 6) appointed diversity leadership positions. Associations between program size, ranking, and DEI information were analyzed. Among the 115 programs, only 35 (30.43%) met at least one DEI criterion. Specifically, 18.26% had a separate diversity mission statement and 15.65% included a DEI statement in mission/leadership messages. Programs with more than 13 residents were significantly more likely to meet at least one DEI criterion (OR 2.96, 95% CI 1.16-7.56; p = 0.022) and to include a DEI statement in mission/leadership messages (OR 5.38, 95% CI 1.71-24.72; p = 0.018). Top 25-ranked programs by U.S. News & World Report were more likely to have diversity initiatives (OR 6.23, 95% CI 1.78-23.97; p = 0.007). Programs meeting any DEI criterion had a higher percentage of female (29.67% vs 22.55%, p = 0.013) and Black (7.85% vs 4.49%, p = 0.019) residents. There was no statistically significant difference in the percentage of White residents (p = 0.206) across programs, and programs with a mention of DEI in the mission statement had a lower percentage of Asian residents (24.08% vs 33.29%, p = 0.042). Most US neurosurgery residency programs lack DEI information on their websites. Larger programs and those with higher rankings are more likely to include DEI content, particularly standalone statements. Programs meeting DEI criteria tend to have a higher representation of female and Black residents. These findings highlight the need for greater transparency and commitment to DEI in neurosurgery residency programs, ensuring prospective applicants have access to comprehensive DEI information.

Morphing Quadrotors: Enhancing Versatility and Adaptability in Drone Applications—A Review

The advancement of drone technology has underscored the critical need for adaptability and enhanced functionality in unmanned aerial vehicles (UAVs). Morphing quadrotors, capable of dynamically altering their structure during flight, offer a promising solution to extend and optimize the operational capabilities of conventional drones. This paper presents a comprehensive review of current advancements in morphing quadrotor research, focusing on morphing concept, actuation mechanisms and flight control strategies. We examine various active morphing approaches, including the integration of smart materials and advanced actuators that facilitate real-time structural adjustments to meet diverse mission requirements. Key design considerations—such as structural integrity, weight distribution, and control algorithms—are meticulously analyzed to assess their impact on the performance and reliability of morphing quadrotors. Despite their significant potential, morphing quadrotors face challenges related to increased design complexity, higher energy consumption, and the integration of sophisticated control systems. The discussion on challenges and opportunities highlights the necessity for ongoing advancements in morphing quadrotor technologies, particularly in addressing adaptive control problems associated with highly nonlinear and dynamic morphing aircraft systems, and in the potential integration with smart materials. By synthesizing the latest research and outlining prospective directions, this paper aims to serve as a valuable reference for researchers and practitioners dedicated to advancing the field of morphing quadrotor technologies.

Application of PID Control Technology in Unmanned Aerial Vehicles

Abstract. The rapid advancement of unmanned aerial vehicle (UAV) technology underscores the essential role of PID (proportional-integral-derivative) control in ensuring flight stability, particularly through precise motor speed adjustments. Thus, this paper systematically analyzes the fundamental principles and limitations of traditional PID control, further highlighting its insufficient adaptability to dynamic external conditions, which includes load fluctuations and wind disturbances. In response to these challenges, it explores adaptive control strategies such as self-tuning PID and fuzzy logic PID, points out how these advanced methods can effectively improve flight stability by dynamically adjusting control parameters, and demonstrates their superiority over traditional PID through comparative analysis. In addition, the paper anticipates future developments in UAV control systems, thereby emphasizing the integration of artificial intelligence and machine learning technologies into PID control. And this integration seeks to optimize control strategies and markedly enhance the autonomous decision-making capabilities and adaptability of UAVs. Meanwhile, the paper also predicts the development trend of hybrid control systems, that is, combining PID with other advanced control methods (such as linear quadratic control) to enhance the overall control capabilities of UAVs. In short, it underscores the necessity for continuous innovation and in-depth research in response to the dynamic flight environment and the diverse mission requirements.

Decentralized Multi-Agent Search for Moving Targets Using Road Network Gaussian Process Regressions

Unmanned aerial vehicles (UAVs) can collaborate as teams to accomplish diverse mission objectives, such as target search and tracking. This paper introduces a method that leverages accumulated target-density information over the course of a UAV mission to adapt path-planning rewards, guiding UAVs toward areas with a higher likelihood of target presence. The target density is modeled using a Gaussian process, which is iteratively updated as the UAVs search the environment. Unlike conventional search algorithms that prioritize unexplored regions, this approach incentivizes revisiting target-rich areas. The target-density information is shared across UAVs using decentralized consensus filters, enabling cooperative path selection that balances the exploration of uncertain regions with the exploitation of known high-density areas. The framework presented in this paper provides an adaptive cooperative search method that can quickly develop an understanding of the region’s target-dense areas, helping UAVs refine their search. Through Monte Carlo simulations, we demonstrate this method in both a 2D grid region and road networks, showing up to a 26% improvement in target density estimates.

Real-sea validation of a model predictive controller's inherent robustness for medium-scale unmanned trimaran heading

The development of medium-to large-scale and high-performance unmanned surface vehicles (USVs) is a burgeoning trend in intelligent marine systems. The heading controller is crucial for USVs to execute diverse missions, especially given their inherent underactuation characteristics and constraints. Recent efforts have focused on enhancing the robustness of controllers against external disturbances and employed two main strategies: one converges the control error to a bounded residual set through robust modifications, while the other eliminates the error by modelling disturbances. Notably, these enhancements have primarily catered to small USVs, where disturbances significantly impact their manoeuvrability, necessitating such robust control strategies. This focus has somewhat overshadowed the inherent robustness of closed-loop control systems. Compared with small USVs, medium-to large-scale USVs are less affected by external disturbances, despite undertaking more complex missions. Leveraging the intrinsic robustness of controllers presents an opportunity to simplify controller design, thereby reallocating computational resources towards enhancing mission capabilities. Model Predictive Control (MPC) has attracted significant attention recently, and its receding horizon and optimality theoretically provides a new level of inherent robustness, which remains under-explored in real sea. This paper focuses on the inherent robustness of MPC in managing the heading of a medium-scale unmanned trimaran subjected to the thrust angle and angular velocity constraints. A model predictive controller considering the constraints is designed based on the identified Nomoto model and the asymptotic stability is ensured with a terminal cost. Conducted real-sea experiments and comparative analyses with a Proportional-Integral-Derivative (PID) controller, the most widespread and dominant control algorithm in practical USV engineering, underscore the superiority of MPC in maintaining satisfactory closed-loop performance. Furthermore, the MPC controller is also successfully applied to real-sea path-following missions and demonstrated good tracking performance in various sea conditions ranging from level 3 to 5 and wind speeds spanning from level 6 to 8. This validation opens up new avenues for motion control strategies in the evolving landscape of larger-scale USVs.

Active mission assignment for improving the effectiveness of opportunistic maintenance in a fleet of mission-critical systems

In a fleet of mission-critical systems, maintaining high availability for each individual system is vital to ensure consistent fleet readiness to meet mission demands. This study introduces a novel mission assignment strategy aimed at actively aligning maintenance schedules within each system to maximise opportunistic maintenance potential, thus enhancing system availability. However, solely focusing on system-level assignments may result in simultaneous maintenance needs across the fleet, leading to resource conflicts. The proposed strategy addresses this issue by coordinating mission assignments to consider both the alignment of schedules within a system and the even distribution of schedules among systems to prevent fleet-level conflicts. Two operational conditions, intra-system and inter-system balance, are defined to evaluate the criticality of each system at both system and fleet levels. Based on these conditions, scheduling rules are developed to determine assignment priority across the fleet in a two-phase process. Simulation studies demonstrate the effectiveness of this approach, showing a 2% improvement in system availability, equivalent to a two-month reduction in downtime across the fleet over a one-year period. The results underscore the advantages of the proposed strategy in optimising maintenance operations in complex fleet environments with diverse mission requirements.

Arts and The Environment: Intersections and Dynamics of Community Outreach

Objective: The objective of this study aims to reflect on an organized set of synergies that contribute to an effective artistic-environmental dialogue: the aim of this interdisciplinary dialogue is to promote a balanced, conscious and informed plan of environmental education, mediated by art. In this specific case, the promotion of artistic and cultural events becomes a subsidiary of agents' ecological awareness and consequently acts as a force capable of mobilizing and raising awareness among current and future generations of the central causes and issues of contemporary society, precisely, those that relate to the “common home”. Theoretical Framework: We have put together a tripartite theoretical framework in which ethical, aesthetic and artistic issues are related from a dialogical perspective between art and the environment and their respective impacts in terms of intervention in the community and with the community. Method: Narrative literature review, seeking to describe or discuss the current state of the research topic and, in particular, to draw conclusions for the results and discussion of the effects and impacts of the practices resulting from this initiative that covers a significant and broad generational framework (from early childhood to old age), applying the same model and method. In this case, the MILMESA Method and the DEP Model. Results and Discussion: The main results emerge as the conclusion of a concrete analysis of the reality observed and the theoretical matrices that underpinned the model and method used. These results are important for future reflection, in terms of what is meant by future adjustments to the results obtained in this research context. Implications of the research: In the future, there are certain considerations that can be reflected on and improved, based on the structure and conceptual basis emanating from the reference authors (and works) that deal with the subject under debate and intergenerational reflection. Originality/Value: This research promotes, in a unique way, a reflection on the making of technical-scientific, participatory and interdisciplinary events, which are in great demand at an educational and scientific level, in which art and art education emerge as a dialogue between areas and as an added value in the framework of the methodologies used in this type of initiative. The aim is to align research and artistic work (and related works) and, at the same time, to reflect on the practices and projects carried out in this field with scientific and training institutions in general, including institutions with more plural and diverse missions in terms of formal, non-formal and informal education.

Corporate Social Responsibility and Social Needs in Health Care Sectors-A Critical Analysis of Social Innovation in the Health Sector in Taiwan.

Social innovation is often used as a mechanism to jump-start public-private partnerships to leverage resources to achieve social impact; the analysis of sustainability and the impact of corporate social responsibility (CSR) cannot be emphasized enough. Due to advances in the information and communication technology industry in Taiwan, this paper aims to explore whether these advancements drive CSR as a form of social innovation to meet health needs in Taiwan. This paper uses a case study to look at CSR programs in the health sector in Taiwan. Corporations with diverse missions and different CSR approaches that are available on the internet are selected. The analysis of the case study takes a qualitative, exploratory approach to shed light on current initiatives. The majority of CSR programs in Taiwan are private sector activities that emerged during the COVID-19 pandemic; current CSR activities in Taiwan are driven by awards, public relations, and external interests. Corporations in Taiwan have the potential to address the health care gaps of urban-rural health utilization among Taiwanese indigenous communities. It is recommended for corporations to (1) develop partnerships with public health experts or to (2) employ CSR personnel with health care backgrounds who can navigate the intersection between health, business, and policies to develop CSR strategies. Further evaluation of the projects mentioned in this paper to assess the direct and indirect impact on health outcomes could provide a more comprehensive understanding of the field of CSR in the health sector in Taiwan.

Empowering diversity: striving for inclusivity by leveraging the American Medical Informatics Association's "For Your Informatics" Podcast.

Starting in 2018, the 'Women in American Medical Informatics Association (AMIA) Podcast' was women-focused, in 2021 the podcast was rebranded and relaunched as the "For Your Informatics Podcast" (FYI) to expand the scope of the podcast to include other historically underrepresented groups. That expansion of the scope, together with a rebranding and marketing campaign, led to a larger audience and engagement of the AMIA community. The goals of this case report are to characterize our rebranding and expanding decisions, and to assess how they impacted our listenership and engagement to achieve the Podcast goals of increasing diversity among the Podcast team, guests, audience, and improve audience engagement. This descriptive case study is focused on the FYI Podcast team's processes to develop a revised mission, vision, and values, increase the diversity of guests, augment listenership through social media, and track the reach through the number of followers, downloads, and impressions. As of December 2023, 35 FYI Podcast episodes are available with 685 social media followers, over 20 000 downloads, and nearly 145 000 impressions. In addition to introductions to informatics and loyal listeners within AMIA, the FYI Podcast episodes have been used by students as teaching material in a graduate biomedical informatics curriculum, and as introductory material for student clubs and programs. The Podcast relaunching led to 98% of guests from underrepresented groups and growth in listenership by 329% since May 2021. The FYI Podcast supports AMIA's diversity mission, and gives voices to underrepresented groups, engages the clinical informatics community in critical conversations on justice, equity, diversity and inclusion, and supports education.

How the INCOSE Model‐Based Capability Matrix Has Steered Model‐Based Systems Engineering Transformation at NASA

AbstractThe National Aeronautics and Space Administration (NASA) is embarking on new, complex, and diverse missions to accomplish its scientific and exploration objectives, and it views digital transformation as a key enabler for those missions. The NASA Model‐Based Systems Engineering (MBSE) Leadership Team (MLT) is leading the charge in the digital transformation of the systems engineering domain at NASA, and it is using the INCOSE Model‐Based Capability Matrix (MBCM) as a roadmap. This paper discusses the modifications and tailoring of the INCOSE MBCM (Hale & Hoheb, 2020) for use at NASA, the process the team has taken on multiple rounds of assessment, findings to date, and work products that have been generated as a result of the assessment. The paper will also discuss findings and potential changes that should be made to the original product.

Risk analysis and assessment of underwater glider mission failure using Dynamic Bayesian Network method

The Autonomous Underwater Glider (AUG), with its extended operational endurance, encounters various uncertain risks during diverse mission tasks. Therefore, conducting dynamic risk analysis is crucial for implementing subsequent decisions and improving equipment safety. This paper combines Fault Tree Analysis (FTA) and Dynamic Bayesian Network (DBN) to develop a customized dynamic risk assessment method for AUG task failure under multiple risk factors. Firstly, preliminary risk factors are identified by FTA using existing experimental data. Next, transformation rules are constructed to map FTA into a basic DBN model. Then, parameters such as conditional probabilities are determined through logic gates and parameter learning, and the proposed model is validated based on axioms. Finally, key risk factors are identified based on actual failure data. The research results indicate that the key risk factors leading to AUG task failure are progressive abnormalities such as biofouling and ocean current disturbance. The method proposed in this paper can achieve dynamic risk assessment, providing strong references for operators in making decisions for subsequent tasks.

Sensing and Control Integration for Thrust Vectoring in Heavy UAVs: Real-World Implementation and Performance Analysis

Unmanned Aerial Vehicles (UAVs) have garnered significant attention among researchers due to their versatility in diverse missions and resilience in challenging conditions. However, electric UAVs often suffer from limited flight autonomy, necessitating the exploration of alternative power sources such as thermal engines. On the other hand, managing thermal engines introduces complexities and internal uncertainties into the system. In this paper, an Adaptive Robust attitude controller (ARAC) is proposed to address these challenges by drawing inspiration from helicopter solutions while minimizing mechanical intricacies. Specifically, the designed algorithm employs Thrust Vector Control (TVC) for an industrial heavy Multi-Ducted Fan (MDF), known for its superior static stability compared to conventional ducted fans. Subsequently, an integrated flap vanes system is positioned at the exhaust of the ducts for precise attitude control, effectively removing unwanted yaw moments associated with traditional propellers. This research builds on prior authors’ works to establish a proper mathematical and aerodynamic model. Also, using former simulation results to conduct real flight experiments aimed at enhancing TVC functionality. The findings highlight the effectiveness of this approach for heavy UAV applications. It is worth noting that the practical value of this research lies in its potential to significantly extend flight autonomy supplied by thermal engines and improve the resilience of UAVs in challenging real-world missions. This is particularly achievable provided that the design of flap vanes aligns closely with the dimensions of the duct system, offering a promising solution to a critical engineering challenge in the field of UAV technology.

The Resilience of Electrical Support in UAV Swarms in Special Missions

Unmanned aerial vehicle (UAV) swarms serve as a dynamic platform for diverse missions, including communication relays, search and rescue operations, and environmental monitoring. The success of these operations crucially depends on the resilience of their electrical support systems, especially in terms of battery management. This paper examines the reliability of electrical support for UAV swarms engaged in missions that require prioritization into high and low categories. The paper proposes a dynamic resource allocation strategy that permits the flexible reassignment of drones across different-priority tasks, ensuring continuous operation while optimizing resource use. By leveraging the Markov chain theory, an analytical model for the evaluation of the resilience of the battery management system under different operational scenarios was developed. The paper quantitatively assesses the impact of different operational strategies and battery management approaches on the overall system resilience and mission efficacy. This approach aims to ensure uninterrupted service delivery for critical tasks while optimizing the overall utilization of available electrical resources. Through modeling and analytical evaluations, the paper quantifies the impact of various parameters and operating strategies on overall system resilience and mission availability, considering the utilization strategies of batteries and their reliability and maintenance metrics. The developed models and strategies can inform the development of robust battery management protocols, resource allocation algorithms, and mission planning frameworks, ultimately enhancing the operational availability and effectiveness of UAV swarms in critical special missions.

Multi-attempt missions with multiple rescue options

In spite of intensive works on modeling and optimizing diverse mission aborting systems from the reliability community, none of the existing models have considered multiple rescue options. This paper pioneers the study of a multi-attempt mission system with different types of rescue procedures characterized by dissimilar system performance, shock rates, and costs. A new optimization problem is formulated, which determines the aborting rule and rescue option choice for each attempt to minimize the expected mission losses (EML) encompassing the mission failure penalty, system loss cost and rescue cost. A new numerical algorithm is then put forward to assess the EML of the considered multi-attempt mission system under any given mission abort and rescue policy (MARP). The genetic algorithm is further implemented to solve the MARP optimization problem. The proposed model is demonstrated through an unmanned aerial vehicle (UAV) performing a payload delivery mission with four possible rescue options. The UAV case study also investigates the accuracy and efficiency of the proposed numerical EML evaluation algorithm and reveals the impacts of several key parameters (mission failure penalty, system loss cost, the allowed number of attempts, shock resistance deterioration factor) on the EML and the optimal MARP solutions.

Demonstration of dual-thrust capability in hybrid rockets using multi-layered tubular fuel grains

This study demonstrates the thrust modulation capabilities of a hybrid rocket motor employing a Multi-Layered Tubular (MLT) fuel grain. The MLT configuration involves arranging the fuel grains with distinct regression rates as layers. As combustion progresses, the regression rate changes based on the burning of each fuel layer, leading to variable thrust while maintaining a constant oxidizer flow rate. Variable-thrust fuel grains are suitable for diverse mission types, including employment in sounding rockets and cruise missiles, particularly those designed for anti-ship operations and missions wherein a boost-sustain thrust profile is required. The MLT fuel grains were designed for a boost-sustain phase thrust profile generally observed in missiles. Wax was used as a booster fuel, and wax with 20%EVA was used as a sustainer fuel. Two MLT configurations with different durations for the boost phase were demonstrated. During the tests, the boost-sustain phase was achieved successfully. However, a transition phase was observed while shifting from the boost to the sustain phase. The delay of the transition phase was observed to increase when the duration of the boost phase increased. The combined effect of axial variation of fuel regression rate and local mass flux is the reason for the transition phase. The Thrust Turn-Down Ratio (TDR) for Multi-Layered Tubular (MLT) fuel grains was calculated. At a web thickness (for booster fuel) of 4 mm, the TDR was approximately 1.23:1, and at a 5.5 mm web thickness, it was 1.28:1. Further, 20%Mg was added to the wax to increase the regression rate of booster fuel. The Wax/20%Mg fuel showed 41% and 14% improvement in regression rate and thrust, respectively. The TDR was improved marginally compared to pure wax-based MLT grain.

Dual-level design for cost-effective sizing and power management of hybrid energy storage in photovoltaic systems

Integration of hybrid energy storage systems (HESS) into photovoltaic (PV) applications has been a hot topic due to their versatility. However, the proper allocation and power management schemes of HESS are challenges under diverse mission profiles. In this paper, a cost-effectiveness-oriented two-level scheme is proposed as a guideline for the PV-HESS system (i.e., PV, Li-ion battery and supercapacitor), to size the system configuration and extend battery lifespan while considering the power ramp-rate constraint. On the first level, a sizing methodology is proposed to balance the self-sufficiency and the energy throughput between the PV system and the grid to achieve the most cost-effectiveness. On the second level, an improved adaptive ramp-rate control strategy is implemented that dynamically distributes the power between the battery and supercapacitor to reduce the battery cycles. The case study presents the whole two-level design process in detail, and verifies the effectiveness of the proposed strategy, where the results show that the battery cycles are reduced by up to 13% over one year without affecting the self-sufficiency of the PV system.

Methodologies and Handling Techniques of Large-Scale Information in Decision Support Systems for Complex Missions

Designing integrating systems for support, real-time monitoring, and executing of complex missions is challenging, since they often fail due to high levels of complexity and overwhelming volume of input data. Past attempts have resorted to “ad hoc” solutions, which face issues of being non-updatable, non-upgradable, and not applicable to similar missions, necessitating a complete redesign and reconstruction of the system. In the national defense and security sector, the impact of this reconstruction requirement leads to significant costs and delays. This study presents advanced methodologies for organizing large-scale datasets and handling complex operational procedures systematically, enhancing the capabilities of Decision Support Systems (DSSs). By introducing Complex Mission Support Systems (CMSSs), a novel SS sub-component, improved accuracy and effectiveness are achieved. The CMSS includes mission conceptualization, analysis, real-time monitoring, control dynamics, execution strategies, and simulations. These methods significantly aid engineers in developing DSSs that are highly user-friendly and operational, thanks to human-reasoning-centered design, increasing performance and efficiency. In summary, the systematic development of data cores that support complex processes creates an adaptable and adjustable framework in a wide range of diverse missions. This approach significantly enhances the overall sustainability and robustness of an integrated system.

Letter from the Editor

Dear Reader, We are excited to share The Columbia University Journal of Global Health Fall 2023 Issue with you. As a journal we strive to uplift diverse perspectives that address the most pressing challenges to health equity faced by communities across the globe. This issue highlights the work of four academics, whose research explores themes of silence and confinement within healthcare systems. Ranging from the impacts of COVID-19 on incarcerated people to the overlooked struggles of obstetric fistulas among Ethiopian women, the articles critically examine the health environments of different marginalized groups. We hope you find their manuscripts to be insightful and thought-provoking. We have continued to host events that promote health and wellbeing in our community, including a food and nutrition event that brought together the expertise of Dr. Jessica Bihuniak, Assistant Professor of Clinical Nutrition in the Department of Nutrition and Food Studies at New York University, and the cooking skills of Columbia Gormand to discuss affordable, nutritious meals for college students. In our journal’s aim of highlighting student voices, we have been increasing our efforts in staff writing and publication. Our staff members have shared their perspectives on public health through our “What is Global Health?” blog and podcast, discussing important topics ranging from community health workers to asbestos in homes. Our most recent podcast features a discussion of careers in scientific research with Dr. Rachel Narehood Austin, Diana T. and P. Roy Vagelos Professor of Chemistry at Barnard College. On our blog, we were able to publish our first bilingual staff article “Obesity in Latine Communities in the U.S: What is at play? / Obesidad en Comunidades Latinas en Estados Unidos: ¿Qué está en juego?”. As we consider how to make our journal more accessible, we hope to continue to share staff perspectives across multiple subject areas and in multiple languages, furthering our journal’s mission of diversity and inclusivity. The past year has been an exciting one for our journal: we published ten new articles that accumulated almost 22,000 downloads and were accessed by over 10,000 site users from 143 countries. We are honored to publish work that has reached such a wide, diverse community, and we hope to continue to expand this impact with the publication of our Fall 2023 Issue. We also recently welcomed two new faculty advisors, Dr. Julianna A. Bol, PhD and Dr. Ana Navas-Acien, MD, PhD, MPH, who will assist our journal in this growth while maintaining the academic rigor that we have always been proud of. Our journal would not be possible without the dedication of our incredible team, which has grown in the past year. We are truly grateful for the support of our faculty advisor Esther Jackson, the resilience of our journal team, the insight of our peer reviewers, and the submissions from our authors. As always, thank you to our readers and listeners for engaging with our work and forming a vital part of The Columbia University Journal of Global Health community. Sincerely, Kairaluchi Oraedu & Ann Phan Co-Editors-in-Chief, The Columbia University Journal of Global Health

Application of TLS and UAV data integration to special object modelling

Contemporary measurement techniques facilitate the rapid and highly precise development of three-dimensional models of any spatial object. Terrestrial laser scanning (TLS) stands as one of the most precise methodologies. Nevertheless, instances arise wherein restrictions imposed by the terrain configuration or infrastructure design impede the acquisition of comprehensive information regarding its geometry. In such scenarios, the optimal resolution lies in the integration of data sourced from diverse measurement instruments. In the context of working with large objects, the optimal approach to capturing comprehensive data, particularly pertaining to the upper parts, involves utilising an unmanned aerial vehicle (UAV). The high resolution of images acquired at a low altitude enables the generation of a point cloud with remarkable accuracy, delivering a satisfactory outcome. When it comes to the modelling of special objects, such as brine graduation towers, the selection of suitable software that facilitates the creation of realistic three-dimensional models is of paramount significance. The study utilised the integration of data acquired from a low altitude using the DJI Air 2S Fly More Combo unmanned aerial vehicle. Diverse mission types were employed, and the data was subsequently recorded using a terrestrial Leica ScanStation P40 laser scanner. The research was conducted on a brine graduation tower situated above the Nowa Huta reservoir in Kraków. The tower’s dimensions necessitated the incorporation of TLS and UAV data. This study analyses three 3D models of the brine graduation tower in Nowa Huta. The models were generated using various computer programmes, namely MeshLab, Agisoft Metashape, and Cyclone 3DR, each of which demonstrated specific capabilities and suitability for modelling a special object like a brine graduation tower. The accuracy of the constructed three-dimensional model of the tower was determined by comparing sections that were measured in the field on the structure between photographic points marked by discs and the equivalent points on the model. Eighteen sections were measured, yielding a mean error of 0.039 m.

Crack Propagation Tests for Load Sequences Developed Using Different Flight Parameters of a Trainer Aircraft

Abstract Military aircraft are subjected to highly variable and unpredictable loads due to diverse mission profiles, armament configurations, and individual piloting styles. This variability complicates the definition of precise load spectra, particularly in cases where data loss occurs due to Flight Data Recorder (FDR) malfunctions or data mishandling. This paper investigates the use of different flight parameters, such as load factor (nz ), barometric height (Hb ), and horizontal velocity (Vp ), to define load sequences for the PZL-130 “Orlik” TC-II military trainer aircraft. These sequences were then used to evaluate crack propagation using Compact Tension (CT) specimens. The results show that the incorporation of additional flight parameters improves the accuracy of crack propagation predictions when compared to direct strain measurements. This study highlights the potential of using available flight data to develop reliable load spectra for fatigue life estimation in military aircraft, even when direct load measurements are not financially feasible.