Mapping lessons from ants to free flight: An ant-based weather aviodance algorithm in free flight airspace

The continuing growth of air traffic worldwide motivates the need for new approaches to air traffic management that are more flexible both in terms of traffic volume and weather. Free Flight is one such approach seriously considered by the aviation community. However the benefits of Free Flight are severely curtailed in the convective weather season when weather is highly active, leading aircrafts to deviate from their optimal trajectories. This paper investigates the use of ant colony optimization in generating optimal weather avoidance trajectories in Free Flight airspace. The problem is motivated by the need to take full advantage of the airspace capacity in a Free Flight environment, while maintaining safe separation between aircrafts and hazardous weather. The experiments described herein were run on a high fidelity Free Flight air traffic simulation system which allows for a variety of constraints on the computed routes and accurate measurement of environments dynamics. This permits us to estimate the desired behavior of an aircraft, including avoidance of changing hazardous weather patterns, turn and curvature constraints, and the horizontal separation standard and required time of arrival at a pre determined point, and to analyze the performance of our algorithm in various weather scenarios. The proposed Ant Colony Optimization based weather avoidance algorithm was able to find optimum weather free routes every time if they exist. In case of highly complex scenarios the algorithm comes out with the route which requires the aircraft to fly through the weather cells with least disturbances. All the solutions generated were within flight parameters and upon integration with the flight management system of the aircraft in a Free Flight air traffic simulator, successfully negotiated the bad weather

Left-right Discrepancy for Adversarial Attack on Stereo Networks

Stereo matching neural networks often involve a Siamese structure to extract intermediate features from left and right images. The similarity between these intermediate left-right features significantly impacts the accuracy of disparity estimation. In this paper, we introduce a novel adversarial attack approach that generates perturbation noise specifically designed to maximize the discrepancy between left and right image features. Extensive experiments demonstrate the superior capability of our method to induce larger prediction errors in stereo neural networks, e.g. outperforming existing state-of-the-art attack methods by 219% MAE on the KITTI dataset and 85% MAE on the Scene Flow dataset. Additionally, we extend our approach to include a proxy network black-box attack method, eliminating the need for access to stereo neural network. This method leverages an arbitrary network from a different vision task as a proxy to generate adversarial noise, effectively causing the stereo network to produce erroneous predictions. Our findings highlight a notable sensitivity of stereo networks to discrepancies in shallow layer features, offering valuable insights that could guide future research in enhancing the robustness of stereo vision systems

Reviews and Responses for Sampling-Based Aircraft Path Planning with Soft Actor-Critic

See detailed reviews and responses in the PDF file. DOI for the original paper: https://doi.org/10.59490/joas.2025.787

Effect of replacement therapy on clinical symptoms in patients with vitamin D deficiency

Background: 25(OH) D is an important component for human health. Vitamin D deficiency is a worldwide problem. Previous epidemiological studies have shown the association of this deficiency with development of other chronic disease that also increase the morbidity. So early detection of deficiency helps to plan intervention and treatment to avoid morbidity. Objective: To evaluate the association of vitamin D deficiency in individuals suffering with chronic diseases and the effect of replacement therapy on the clinical symptoms of vitamin D deficiency. Method: This study analyzed a total of 115 patients visiting the Diabetes and Endocrine Research Centre. Factors such as age, gender, duration of sun exposure and body parts exposed to sunshine were studied. The data was recorded on a questionnaire performa. Patients with 25(OH) D levels below 30ng/ml were considered to have insufficient levels. 48 patients who agreed for treatment were given standard loading and maintenance dose of vitamin D. 31 Patients reported back after 3 months of maintenance dose treatment. Results: The mean age of patients was 47.82±13.86 years. Duration of sunshine exposure was significantly low with p-value of 0.005. In our study, 112/115(97.3%) patients were found to have 25(OH) D level below 30ng/ml and 41/115 (21.5%) were severely deficient. 48(41%) patients agreed for replacement therapy. However, only 31 reported back with vitamin D level. In comparison to pretreatment records, there was significant improvement in vitamin D levels after 3 months of treatment. There was improvement with symptoms such as lethargy which improved in 11/17, whereas depression and body aches improved in 12/19 and 16/26 patients respectively. Conclusion: It is important to recognize the deficiency of vitamin D level in patient suffering from chronic diseases in order to avoid other co morbidities. So, this study can help to make policy in future about which population needs to be screened and what preventive precautions can be taken

Evolutionary-Computation Based Risk Assessment of Aircraft Landing Sequencing Algorithms

Abstract. Usually, Evolutionary Computation (EC) is used for optimisation and machine learning tasks. Recently, a novel use of EC has been proposedMultiobjective Evolutionary Based Risk Assessment (MEBRA). MEBRA characterises the problem space associated with good and inferior performance of computational algorithms. Problem instances are represented ("scenario Representation") and evolved ("scenario Generation") in order to evaluate algorithms ("scenario Evaluation"). The objective functions aim at maximising or minimising the success rate of an algorithm. In the "scenario Mining" step, MEBRA identifies the patterns common in problem instances when an algorithm performs best in order to understand when to use it, and in instances when it performs worst in order to understand when not to use it. So far, MEBRA has only been applied to a limited number of problems. Here we demonstrate its viability to efficiently detect hot spots in an algorithm's problem space. In particular, we apply the basic MEBRA rationale in the area of Air Traffic Management (ATM). We examine two widely used algorithms for Aircraft Landing Sequencing: First Come First Served (FCFS) and Constrained Position Shifting (CPS). Through the use of three different problem ("scenario") representations, we identify those patterns in ATM problems that signal instances when CPS performs better than FCFS, and those when it performs worse. We show that scenario representation affects the quality of MEBRA outputs. In particular, we find that the variable-length chromosome representation of aircraft scheduling sequence scenarios converges fast and finds all relevant risk patterns associated with the use of FCFS and CPS

A Dynamic Debris Hazard Corridor for Space and Air Traffic Management

The airspace is a national asset shared by a multitude of users, including aircraft, drones, and spacecraft. Regulating airspace usage among various stakeholders has traditionally been achieved through the segregation of different operations. This approach was effective when the airspace was primarily allocated for air traffic, and spacecraft and other users were relatively rare. However, with the growing volume of both air traffic and space activities, there is now an urgent need to develop new dynamic and adaptative strategies for ensuring the safe and efficient sharing of airspace among diverse stakeholders. Space launch activities have recently experienced tremendous growth with the development of commercial space launch. In this context, the current airspace management strategies, including adaptive risk envelope, space transition corridor, and temporary flight restriction, have demonstrated their effectiveness in various space launch operations. Nevertheless, these methodologies do have some limitations, such as their inability to assess risks at different altitudes, resulting in inflexible regulations. They can also tend to be overly conservative, considering only a limited set of factors. In addition, as space missions continue to grow and air traffic demand increases, airspace closures are capable of ensuring safety, but can lead to extensive rerouting, delays, reduced airport accessibility, and constrained nearby airspace utilization. In this study, we develop the Dynamic Debris Hazard Corridor (DDHC) as a pioneering concept, offering the potential to bridge the gap between traditional and emerging needs. The primary objective of this study is to compare the traditional, conserved approach of airspace closure with a proposed dynamic method that involves the sequential release of convex hull segments in the Dynamic Debris Hazard Corridor (DDHC). Unlike conventional approaches, the DDHC is ideally suited for managing dense air traffic. The findings indicate that the dynamic management of the DDHC can potentially reduce disruptions in air traffic without compromising safety

Integrated Air and Space Traffic Management: An Agent-Based Simulation for Analysis of Space-Launch Impact on Air Traffic

The recent surge in space launch activities, driven by the emergence of commercial space launches, has compelled the aviation and space launch sectors to collaborate for the safe and efficient integration of space launch activities. This paper introduces an agent-based modeling (ABM) and simulation framework designed to assess the impact of spacecraft launches on air traffic within an integrated air and space traffic management system. The proposed framework incorporates various agents involved in the execution phase of space launches and considers the interactions and coordination between air traffic management and space traffic management. The paper firstly provides a comprehensive overview of the current state of space launch operations and their effects. Then, a general agent-based model is developed for space launch execution phase in order to gain an understanding of various entities involved in a space launch activity as well as the interactions among these entities. Using Monte-Carlo simulations based on the ABM, the paper assesses the impact on air traffic operations in the event of a space launch failure. In each simulation, various factors are taken into account, including launch site position, launch slot, failure probability during the execution phase, debris dispersion, and time delay in Air Traffic Management (ATM)/Space Traffic Management (STM) coordination. To demonstrate the practical application of the proposed framework in an operational context, the paper presents a case study of a sea-based space launch in the Singapore FIR. The paper makes a valuable contribution to the field of air and space traffic management by addressing the need for innovative strategies to ensure the safe sharing ofairspace among different stakeholders

A Study of Electrocardiographic Changes in patients with Newly Diagnosed Primary Hypothyroidism: A Cross-Sectional Study

Background: The thyroid hormones have an important role in the cardiovascular system; even minimal change in its level can cause significant alteration in the cardiac activity which can cause considerable electrocardiographic changes. We conducted this study to assess the electrocardiographic (ECG) changes in patients who were newly diagnosed with primary hypothyroidism. Methods: This study is a descriptive cross-sectional study conducted among 71 newly diagnosed subclinical and overt primary hypothyroidism patients visiting the out-patient department (OPD) of Universal College of Medical Sciences, Nepal from December 2018 to June 2020 after taking ethical clearance from the institutional review committee (UCMS/IRC/212/18). ECG was obtained for each patient at the time of diagnosis of primary hypothyroidism. The data were analyzed with SPSS Version 16. Results: The most common ECG changes were sinus bradycardia seen in 32.4%, followed by T wave inversion in 21.1%, low voltage QRS complex in 15.5%, and prolonged PR interval in 14.1%. ECG changes were seen in 62% of cases of newly diagnosed primary hypothyroidism. Among all patients, subclinical hypothyroidism accounted for 7%, while overt hypothyroidism accounted for 55% of the ECG findings. Conclusion: Our study found ECG changes like sinus bradycardia, T wave inversion, low QRS voltage, and prolonged PR interval in newly diagnosed primary hypothyroidism. We suggest that every newly diagnosed hypothyroid patient should be evaluated for ECG changes