The specific surface area of a filler influences its dispersion and interaction with the rubber and consequently the properties of thermal insulation in aerospace components. However, its determination generally requires complex instrumental methods. Therefore, the development of simpler methodologies with equivalent or higher precision remains of scientific interest. This study investigates the application of Fourier transform infrared spectroscopy using near-infrared (NIR) reflectance and the more conventional diffuse reflectance for silica analysis, a filler widely used in polymeric formulations. The analyzed samples presented surface area values between 170 and 800 m2·g-1. Results showed a methodological error within the instrumental limit (2%), lower than that reported for conventional methods (4-7%). The NIR reflectance methodology based on the relative band (A5260/A4540) provided the most accurate results and can be considered a practical alternative to conventional gas adsorption techniques, enabling shorter analysis times, a feature that is particularly relevant for aerospace processes subject to demanding project schedules.
This paper aims to develop a quantitative model for evaluating the signal detection capability of a laser proximity fuze mounted on small air-defense missiles under strong background noise conditions. The proposed approach integrates three key components: background noise power estimation to characterize environmental effects, signal-to-noise ratio (SNR) computation to relate noise with system parameters, and probability of detection (Pd) evaluation as the primary performance metric. The formulations are established based on quantum photonics and statistical optics, while Monte Carlo simulation is employed to estimate SNR, and the likelihood-ratio test is applied to determine Pd. Numerical results indicate that, among the considered wavelengths (950 nm, 1,300 nm, and 1,550 nm), the 1,550 nm band provides the highest SNR. At a range of 20 m with a transmitted power of 0.01 W, the corresponding Pd reaches 0.76; achieving Pindings demonstrate that the proposed model can effectively support the selection of operating wavelength and transmitted power, thereby ensuring reliable signal detection performance of laser proximity fuzes in noisy environments.
The aim of the study was to analyze effective models and algorithms of decision support that contribute to enhancing aviation safety. The study employed decision-making task formalization, probabilistic modeling, machine learning, multi-criteria analysis, and data analysis methods for processing heterogeneous information, including technical parameters, video streams, and behavioral characteristics. The research identified key features of decision support models and algorithms that improve aviation safety through real-time monitoring and adaptive response. Machine learning methods achieved up to 92% accuracy with a response time of 80 milliseconds, while multi-criteria analysis methods, including the analytic hierarchy process, reached 88% accuracy. The integration of probabilistic models with adaptive algorithms enabled consideration of operational environment variability and timely risk assessment. In 2024, 45% of aviation incidents were associated with crew error, 24% with technical malfunctions, and 13% with weather conditions. Intelligent decision support systems could potentially prevent 75% of incidents related to procedural violations. Neural networks were most suitable for behavioral analysis, decision trees for access control, and Bayesian networks for assessing technical failures. The findings contribute to the development of intelligent data analysis methods and decision support algorithms for aviation safety.
This study presents the design and preliminary laboratory validation of an acoustic levitator intended for future integration into the payload bay of an experimental rocket. The work aims to explore the feasibility of acoustic levitation as a contactless particle-handling system for experimental rocketry. The research combined a theoretical review, prototype design, numerical acoustic simulation, RocketPy-based flight-condition simulation, and laboratory testing of a small-scale acoustic levitation system. The prototype was evaluated under controlled static laboratory conditions using a 40 kHz ultrasonic configuration. The prototype achieved particle suspension under laboratory conditions. The system was configured with 36 ultrasonic transducers arranged in two opposed arrays, a 12 V DC power supply, and a transparent acrylic chamber for particle observation. AKBAL-II flight simulations defined preliminary environmental conditions, including peak acceleration, atmospheric pressure variation, estimated vibration range, parachute events, and total flight time. The results support the preliminary feasibility of the proposed acoustic levitation system at the prototype level. However, in-flight operation has not yet been experimentally validated and remains future work. Further testing is required to evaluate vibration resistance, pressure effects, thermal behavior, energy consumption, repeatability, and stability under representative flight conditions.
This paper proposes a novel integrated robust control framework tailored for folding-wing aircraft during large-angle morphing maneuvers. The morphing process induces severe nonlinearities, strong coupling between wing kinematics and vehicle dynamics, and significant time-varying shifts in the center of gravity and aerodynamic characteristics, posing substantial challenges to flight stability. To address these issues, a two-time-scale hybrid control architecture is developed: a robust sliding mode controller (SMC) governs the fast inner-loop attitude dynamics to counteract model uncertainties and disturbances, while a well-tuned proportional-integral-derivative (PID) controller manages the slower outer-loop trajectory variables, such as altitude and velocity. A high-fidelity multi-body dynamic model is derived from first principles using Newtonian mechanics, fully accounting for morphing-induced inertial and aerodynamic variations. Extensive closed-loop simulations validate the approach, achieving stable transitions with altitude errors within ± 1.5 m, velocity errors below ± 1.8 m·s-1, and settling times under 50 seconds – even under ± 20% perturbations in aerodynamic coefficients. To the best of the author’ knowledge, this is the first implementation of a hybrid SMC–PID strategy specifically designed for large-scale rigid-body wing reconfiguration, offering a practical and theoretically grounded pathway toward deployable morphing aircraft systems.
The study aimed to analyze how artificial intelligence (AI) systems could be used to make air defense for critical infrastructure more effective. An analytical review method was used to systematically analyze how AI is used in air defense systems based on open sources from the United States, Israel, Ukraine, Germany, and South Korea, with an emphasis on the automation of processes for detecting, classifying, and prioritizing air threats for the period from 2020 to 2024. The study established that convolutional neural networks provide 92.8% accuracy in air target recognition, with an F1 score of 0.91. Random forest and eXtreme Gradient Boosting models achieved classification accuracies of 90.4% and 93.8%, respectively, at the final threat classification stage. The use of clustering algorithms (k-means, density based spatial clustering of applications with noise, DBSCAN) reduced the average time to determine coordinates to 1.4 seconds and the full response cycle to 2.1 seconds. The integration of AI into the centralized control system increased the probability of successful interception of air threats by 12-15%. The study provides analytical and simulation-based support for the potential use of AI systems to improve the effectiveness of air defense for critical infrastructure and identifies promising directions for the development of adaptive and automated defense technologies.
To map and synthesize research on locating urban vertiports for vertical takeoff and landing aircraft, including electrically powered variants. A systematic mapping review used a predefined protocol with research questions, selection criteria, and structured data extraction. In 2025, searches covered five themes: urban air mobility, location-allocation, optimization, network and routing, and vertical takeoff and landing aircraft across major scientific databases. From 1,056 records, duplicates were removed, and screening yielded 31 studies. Most studies propose mathematical optimization models, predominantly integer linear programming formulations solved with commercial optimization tools. Some incorporate vertiport capacity and socio-environmental criteria such as noise, land use, and equitable access, while others address only operational factors. Applications on real networks with observed demand data are common; specialized simulation is less frequent. Evaluations mainly report travel time, demand coverage, and costs, and often include sensitivity analyses and limited validation procedures. Research is progressing in modeling vertiport networks, but validation remains weakly standardized, and uncertainty treatment is still limited. Comparable evaluation protocols and stronger socio-environmental robustness are needed to better support planning decisions in advanced air mobility.
This study presents an initial step toward an objective methodology for characterizing night vision goggle (NVG) systems using the modulation transfer function. Although NVGs remain essential in operational environments, their performance has traditionally been assessed through subjective judgments made by human observers. To overcome this limitation, the proposed approach replaces the human observer – specifically the human eye and its visual acuity – with a conventional camera operating in the visible region of the electromagnetic spectrum. The methodology is fully reproducible using a standard computer monitor and an ordinary camera. A simple optical assembly was developed to couple the camera to the NVG under evaluation. The results demonstrate that the methodology can detect performance degradation introduced by the NVG, including contrast loss, increased noise, and reduced sampling efficiency. The approach also supports the selection of appropriate color-target combinations and monitor-imaging conditions for repeatable characterization. The method detected sampling-efficiency reductions from 100% (reference) to 45%, 40%, and 23% under full-moon, quarter-moon, and starlight conditions, respectively. Rather than replacing human observers, this approach should be regarded as a complementary analytical procedure.
The formulation of public policies to promote innovation should be supported by empirical evidence, ensuring greater reliability in their design and implementation. The role of the State in supporting an innovation ecosystem has been widely recognized as essential for economic and social development. In this context, the present study examines whether military investments in Brazil are associated with patent production. Using annual data from 2001 to 2019, a dynamic distributed lag specification is estimated to assess whether defense expenditures are related to changes in technological innovation over time. The results indicate a positive association between government defense spending and the number of patents filed, particularly with a 1-year lag. Given the limited sample size (19 annual observations), the findings should be interpreted as exploratory evidence of a lagged relationship. Overall, the results are consistent with a positive temporal association between defense expenditure and patent activity, without implying causal inference.
Establishing a permanent and sustainable human presence on the Moon has long been one of humanity’s most ambitious goals. The Moon is continuously exposed to ionizing radiation from the Sun and deep space, rendering it an extreme environment that poses significant radiological risks to future human exploration missions. Accurate radiation dosimetry is essential to ensure astronaut safety. This study aimed to assess the current state of research on radiation dosimetry for potential lunar environments and bases. A bibliometric analysis was conducted based on the results of various keyword searches in Scopus to identify key trends and research gaps related to lunar radiation dosimetry. The analysis revealed a growing body of research focused on radiation protection, the biological effects of ionizing radiation, and simulation models to assess radiation exposure on the Moon. Furthermore, the analysis highlighted an increasing involvement of various countries worldwide in these research areas, with the United States leading in the number of publications. This study provides a global overview of the current state of research in lunar radiation dosimetry, emphasizing areas where further investigation is required to support safe human exploration.
This review analyzes the integration, functionality, and regulatory environment of unmanned aerial vehicles. In this review, the role of drone technologies with advanced payloads, Red-Green-Blue, and infrared sensors to revolutionize the process of surveying, inspection, and safety monitoring by providing extremely high accuracy in real-time spatial data delivery is considered. The technologies are essential in increasing productivity, minimizing expenses, and decision-making in project management. Other than the technical capability, the paper discusses the international regulatory frameworks and governance models developed by agencies, including the Federal Aviation Administration, the European Union Aviation Safety Agency, the Directorate General of Civil Aviation, and the International Civil Aviation Organization. Even though such systems guarantee the security of the airspace and responsibility of the operators, the analysis indicates that the operations of UAVs between countries remain cumbersome as the loopholes continue to persist. The economic analysis shows that there is a high return on investment in the form of less time spent in the survey, lower labor expenses, and better safety results. Further research should be oriented to artificial intelligence-related data analytics, autonomous UAV swarm control, and automatic connection to building information modelling to allow real-time monitoring of the project and predicting its maintenance requirements.
Waves can impair the gliding stability of amphibious aircraft on the water. This study employed numerical modeling to investigate the hydrodynamics of aircraft floats. Hydrodynamic forces were evaluated for different float cross-sections using the volume of fluid (VOF) method. The influence of wave properties on gliding stability was also investigated, along with forces during accelerated taxiing and the role of the acceleration coefficient. By comparing pressure and vortex patterns, the effect mechanism of wave height and wavelength on the stability was clarified. The findings indicate that the stability is highly sensitive to the position of the wave crest impact relative to the center of gravity. An impact near the center of gravity can cause instability with a wave height of 0.5 m and a wavelength of 5 m. Moreover, the accelerated taxiing exerts the most substantial influence on aircraft stability, potentially triggering roll and pitch motions. These findings offer key theoretical support for the design and optimization of amphibious aircraft floats.
The purpose of this study was to identify key stages in the evolution of air defense concepts and evaluate effective approaches to neutralizing modern airborne threats, particularly drones. The research involved analyzing open scientific sources, defense agency reports, and systematizing cases of successful counteractions to drones, with a focus on countries like Israel, Turkey, the United States of America, and Ukraine. The study found that countermeasure effectiveness depends on integrating electronic warfare, cyber tools, physical interception, and artificial intelligence (AI)-based detection algorithms. The use of sensor platforms, electromagnetic countermeasures, and software components reduced response times and improved target disabling probability without kinetic effectors. However, existing air defense systems were largely unprepared for swarm attacks from miniature drones, with conventional weapons lacking energy autonomy for prolonged counteraction. The research highlighted the need for multi-layered defense architectures with a cognitive response cycle under 5 seconds and advanced AI integration. Additionally, international cooperation and information exchange were crucial for developing sustainable early warning systems. The study's findings can inform the modernization of national air defense programs and regulatory frameworks addressing unmanned technology challenges.
This study aimed to develop and evaluate a human-interpretable artificial intelligence (AI) model for short-term operational risk forecasting in air traffic control (ATC). To do so, it compared logistic and Poisson regression models with random forest and gradient boosting classifiers using a transparently generated synthetic dataset designed to reflect realistic workload, weather, staffing, and sector complexity conditions. Model performance was assessed through cross-validation, calibration analysis, sensitivity to class imbalance, and decision-curve analysis. Among the tested approaches, gradient boosting achieved the best predictive performance, with an area under the curve of 0.93, and provided the most reliable probability estimates, outperforming the regression-based baseline models. Explainability analysis using Shapley additive explanations showed that the most influential predictors were controller workload, weather severity, sector complexity, and staffing ratio, which is consistent with established human factors theory. Decision-curve analysis also indicated measurable operational benefit at realistic alert thresholds, supporting potential applications in dynamic staffing and flow management. These findings suggest that responsible AI can strengthen safety management systems by providing accurate, transparent, and reproducible risk forecasts while supporting regulatory expectations for documentation, calibration, and interpretability.
Low-orbit satellite communication systems are an important element of geographically distributed heterogeneous networks (HetNets) and enable global fixed and personal mobile communications across the entire surface of the Earth. The article is devoted to the study of the average delay of information exchange in "down" communication line between repeater satellite with phased array antenna with discrete beam hopping (DBH) on board and user terminals. A mathematical model as a queuing system (QS) has been developed to calculate the average delay time of information transmission in "down" communication line. Analytical expressions have been obtained that relate the value of the average delay time to the main system parameters: channel bandwidth, number of scanning beams and of time slots in the scan frame, etc. for two beam scanning algorithms: a static and a dynamic one. The dependencies illustrating the advantages of using the technology of discretely scanning beams are given, including estimate of the gain in the presence of several beams. Estimates of the number of beams of phased array antenna depending on the limitations on its size, antenna pattern and the size of the scanning sector are given, and an estimate of the throughput of the communication line is obtained depending on the number of beams.
Volatile organic compounds (VOCs) are important in defining aroma and acting as distinctive chemical signatures for food identification. In this context, culinary mushrooms have become increasingly popular in daily diets, particularly in Indonesia, which offers a rich diversity. Investigations on volatile phytochemical profiles of Indonesian culinary mushrooms has been limited. Therefore, this research aimed to thoroughly characterize the VOC profiles of Indonesian culinary mushrooms for the purposes of aroma discrimination and identification. The extraction and analysis of mushroom VOCs were carried out using solid phase microextraction gas chromatography-mass spectrometry (SPME-GC/MS). Principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA) successfully classified 13 Indonesian culinary mushrooms into 5 groups based on similar volatile chemical profiles, independent of genetic backgrounds. The results showed that markers contributing to the characteristic aroma of mushrooms, including 1-octen-3-ol, 3-octanol, octanal, and 3-octanone, were identified in nearly all samples. A. mesenterica, A. delicata, and L. squarrosolus were distinctly reported, with octanal, acetic acid, and 3-octanol serving as the strongest aroma markers, respectively.
A novel thiazole-based derivative (C1–C5) incorporating amide linkages was designed and synthesized by integrating the bioactive 3,4,5-trimethoxyphenyl and 2-aminothiazole scaffolds. The structural features of the compounds were confirmed by 1H-, 13C-NMR, and mass spectrometry. Computational docking studies against the CBS of tubulin revealed favorable binding affinities for C3 and C4, surpassing those of the reference compound CA-4. The MTT assays were used to test how well they could fight cancer by using the MCF-7 breast cancer cell line, with tamoxifen as the standard drug. Among the synthesized molecules, C1 and C3 exhibited the most potent cytotoxicity, reducing cell viability to 70–40%. The SAR analysis indicated that acyl substituents, particularly NO2 and CF3 groups, enhance cytotoxicity. Moreover, PASS prediction analysis indicated low to moderate toxicity risks, supporting the preliminary safety assessment of these compounds. Collectively, the results indicate that the synthesized thiazole derivatives serve as promising molecular scaffolds for the design of potent tubulin polymerization inhibitors with potential anticancer applications.
Pork is often used as a low-cost adulterant in place of beef or chicken, making its identification particularly important in processed meat products such as meatballs. This study investigates the use of Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (ATR-FTIR) to detect pork contamination in meatball products. This technique offers a rapid and cost-effective means of analysis. However, the spectral differences among meat types are subtle and not easily discernible by visual inspection alone. To improve differentiation, Principal Component Analysis (PCA) was employed to highlight variations in spectral data. Fresh pork, beef, and chicken samples were sourced from a local supermarket, and meatballs were prepared in a lab. Spectral data were recorded across the 400–4000 cm−1 range. PCA results showed that the first and second principal components clearly separated uncontaminated samples from those adulterated with pork. Complementary PLS-DA analysis (5-fold cross-validation) quantified the spectral separation: beef vs. pork (accuracy = 94.7%, BER = 5.7%), chicken vs. pork (accuracy = 100.0%, BER = 0.0%), beef-meatball vs. pork-meatball (accuracy = 100.0%, BER = 0.0%), and chicken-meatball vs. pork-meatball (accuracy = 100.0%, BER = 0.0%). This approach demonstrates the potential of ATR-FTIR combined with PCA as a robust analytical method for meat authentication, offering a practical solution for quality control and food safety in the meat processing industry.
Universally, the usage of unlawful drugs is one of the serious matters that reduces the prosperity and health of the users and communities. Methamphetamine (MAPA) is one of the illicit drugs that is fabricated in clandestine laboratories. It is considered one of the most harmful drugs spreading among youths around the globe. Therefore, the development of sophisticated sensing technology for its rapid and accurate detection is required. Sensors consist mainly of a recognition element, a transduction element and a signal processor for detecting MAPA and recording its chemical concentration. Different chemical sensors, such as optical, magnetic, thermal and electrochemical sensors have been utilized. They differ in the working mechanism and the type of measured signal. The aim of this review is to provide a summary of the up-to-date advancements in optical and electrochemical sensors that have been used for MAPA detection in different samples, particularly from 2012 to 2025.
The renewable energy transition demands efficient energy storage, where lithium-ion batteries (LiBs) are crucial for battery energy storage systems (BESS). This study reports the hydrothermal synthesis and characterization of nitrogen-doped graphene (NDG) from puspa wood biomass for anode applications. Puspa wood contains approximately 43.98% carbon with a low ash content of 1.256%, indicating its suitability as a carbon precursor. Through carbonization and graphitization, amorphous carbon was successfully transformed into an ordered graphite structure, as confirmed by the disappearance of –OH groups (3400–3600 cm−1) in Fourier-transform infrared (FTIR) spectra and by X-ray diffraction (XRD) peak shifts from 23.50° to 26.50°. Subsequent oxidation produced graphene oxide (GO) characterized by carbonyl (C=O) groups at 1704 cm−1, while nitrogen doping introduced C–N (1200 cm−1) bonds, resulting in the formation of NDG. Nitrogen doping is known to enhance electrochemical properties. These findings highlight puspa wood as a promising precursor for NDG synthesis and provide a foundational material characterization that supports its further electrochemical investigation. This study demonstrates the promise of puspa wood biomass as a sustainable carbon source for advanced anode materials, contributing to eco-friendly battery technology.
