Search Results (244)

Nitrites and nitrates are admitted into the body through the consumption of various foods, primarily cured meat products. These nitrites and nitrates are precursors of reactive nitrogen species NO and NO₂, which, in excess, promote nitrosative stress. Attempts have been made to combat oxidative and nitrosative stress through C₆₀ fullerenols in animal models. Furthermore, experimental and theoretical studies have shown that the use of carbon nanomaterials such as defective or doped graphene and C₆₀ metallofullerenes facilitates the capture of NO_x pollutants contained in the air. This leads us to propose that the inclusion of metals in C₆₀ fullerenols may have the potential to capture these reactive nitrogen species and be considered in nitrosative stress tests in animal models. Alternatively, viewed from another perspective, a certain grade of hydroxylation of metallofullerenes could enhance the capture of atmospheric nitrogen pollutants. Therefore, Li, Ca and Y metals were included in C₆₀ fullerenols at different coating grades. Using density functional theory (DFT), we analyzed the antiradical character of these metallofullerenols, and the adsorption energies of the free radicals (NO_x) were calculated to evaluate the ability of these metallofullerenols to adsorb these nitrogen species. Although both fullerenols and endohedral metallofullerenes have individually shown promise as radical scavengers, a systematic understanding of how the encapsulated metal and the grade of hydroxylation jointly govern the capture of nitrogen species is still lacking. In particular, it remains unclear whether increasing the number of hydroxyl groups monotonically enhances the capture capacity or whether optimal combinations of metal identity and surface functionalization exist. Addressing this gap is crucial, since excessive hydroxylation may alter the electronic structure, stability, and mechanism of interaction with NO_x radicals, potentially compromising capture capacity. Therefore, a rational evaluation that simultaneously considers electronic donor–acceptor properties, local reactivity, and adsorption thermodynamics is required to identify metallofullerenols with possible potential to sense or scavenge NO_x. Full article

Unmanned aerial vehicles have become indispensable components of modern defense systems by conducting Intelligence, Surveillance, and Reconnaissance (ISR) missions to collect critical operational information through onboard sensing technologies, supporting secure communication and information sharing among distributed military assets, and enhancing battlefield situational awareness through real-time sensing and data fusion. In addition, UAVs are increasingly capable of executing a wide range of defense missions, including target search and tracking, electronic warfare, search-and-rescue, and combat support. The integration of AI into UAV-based systems has the potential to enhance these operational capabilities by enabling intelligent perception, autonomous decision-making, adaptive mission planning, autonomous navigation, resilient communications, and cooperative multi-UAV coordination, thereby enabling the autonomous and collaborative execution of complex defense missions. This paper presents an up-to-date review of AI-enabled UAV-based defense systems, focusing on major operational domains including autonomous air combat and cooperative UAV operations, path planning and autonomous navigation, target tracking/detection/classification, cybersecurity, electronic warfare protection, and resilient UAV operation. In addition to surveying the recent literature, this paper provides an integrated system architecture, a functional classification framework, and an analysis of the AI paradigms enabling next-generation UAV-based defense systems. Furthermore, this review synthesizes the key technological trends, lessons learned, and cross-domain research challenges identified across the reviewed studies, providing a unified perspective on the current state of the field. Finally, this paper highlights promising future research directions for resilient, scalable, secure, and intelligent next-generation AI-enabled UAV-based defense systems. Full article

The modern multi-domain battlespace increasingly demands cost-effective, persistent platforms for Forward Air Control (Airborne) (FAC(A)) and Close Air Support (CAS) missions. While utilizing Commercial Derivative Aircraft (CDA) presents a viable alternative to high-maintenance military assets, the specific aerodynamic, ergonomic, and cognitive workload limitations encountered during the transition across the civil–military “airworthiness seam” remain a significantly underexplored gap in contemporary aerospace literature. To address this gap, a comprehensive empirical flight test campaign was executed on the LX7-135 turboprop to evaluate its tactical mission suitability. Employing the Cooper–Harper Handling Qualities Rating and Bedford Workload scales, and guided by military specifications (MIL-HDBK-516C, MIL-F-8785C), the study systematically assessed the aircraft’s pure performance, unaugmented flight dynamics, and human–machine interface during simulated combat scenarios, including dynamic 9-Line briefings and kinetic “Box Pattern” delivery profiles. Flight test data indicated 12 “SATISFACTORY” parameters, highlighting climb rates, extended endurance, and heavily damped short-period and Dutch roll modes optimal for target tracking. Conversely, the evaluation identified 4 “UNSATISFACTORY” and 10 “TOLERABLE” deficiencies—chiefly a single-door egress bottleneck, restricted stick clearance, degraded longitudinal static stability, and the absence of secure tactical communications. Although these constraints elevated pilot cognitive workload during multi-axis tasks, the LX7-135 demonstrates potential suitability for further development for the FAC(A) role, subject to the successful implementation and subsequent flight-test verification of the identified critical engineering modifications and specialized training syllabi. Full article

UAV path planning in complex urban environments faces significant challenges due to dense obstacles, narrow corridors, and stringent safety requirements. To address these issues, this paper proposes LLSOA, a modified Seagull Optimization Algorithm that integrates Latin Hypercube Sampling (LHS) for population initialization and Lévy Flight for global search. The key innovation lies in the problem-driven design: LHS ensures uniform coverage in dense urban maps, while Lévy Flight helps escape local optima. Compared with four state-of-the-art swarm intelligence algorithms (DBO, GWO, PIO, and PSO) across four urban scenarios, LLSOA achieves the best comprehensive fitness. Considering multiple constraints including path length, curvature, collision avoidance, and obstacle-avoidance logic, the trajectories generated by LLSOA show competitive overall performance, with no unsafe points recorded in the test scenarios and the best fitness values among the compared algorithms, albeit with a slight trade-off in path length. High-fidelity AirSim simulations with GPS/IMU noise further demonstrate that the planned trajectories remain within engineering acceptable limits. Compared with the noise-free baseline, the maximum trajectory deviation increases by 2.3% and the average deviation increases by 2.8% under high GPS/IMU noise. The main contributions are: (1) a problem-driven LLSOA that combines LHS and Lévy Flight, specifically tailored to dense urban environments; (2) theoretical analysis and simulation verification demonstrating its feasibility for multi-constraint path planning under the tested conditions; (3) high-fidelity (UE+AirSim) validation showing that the generated trajectories retain stability even under realistic sensor noise. Full article

Healthcare-associated infections represent a global public health challenge, primarily driven by biofilm-forming multidrug-resistant pathogens on medical device surfaces, leading to high morbidity, mortality, and healthcare costs. As antibiotic strategies fail due to antimicrobial resistance, this study investigates a primary-prevention nanotechnology strategy based on the photochemical functionalization of clinically relevant substrates (polyurethane venous catheters, cotton gauze, and glass-fiber HEPA filter membranes) with immobilized silver nanoparticles. While the photochemical deposition ensures a controlled silver release experimentally quantified here by ICP-MS (≈0.44 ppm·day⁻¹) within safety thresholds, the core value of this functionalization lies in its broad-spectrum and long-term efficacy. Agar diffusion assays performed against a panel of 10 multidrug-resistant clinical isolates, including pathogens such as Pseudomonas aeruginosa, Klebsiella pneumoniae, Staphylococcus epidermidis, Candida parapsilosis, and Aspergillus sydowii, demonstrated antimicrobial activity over 15 days across all substrates. Biofilm quantification by Crystal Violet Assay revealed inhibition exceeding 97% for bacterial strains and 96% for fungal species at day 15. By preventing both bacterial and fungal colonization on diverse materials, these results validate silver-functionalized surfaces as an effective and bio sustainable approach to combat healthcare-associated infections and antimicrobial resistance. This strategy holds significant promise for translation into clinical settings, hospital air filtration systems, and wound care applications, aligning with Antimicrobial Stewardship and One Health principles. Full article

The impact of air pollution on human health has been widely studied in recent decades. Recent findings show that even low levels of air pollution can be harmful to our health, causing disease and early death. However, these studies are very limited in the central region of Türkiye. Therefore, this study focused on the association between the daily variations in air pollutants (PM₁₀, PM_2.5, SO₂, and NO₂) and hospital admissions due to respiratory, cardiovascular, and total (non-accidental) causes in the Sivas province. Daily average concentrations of air pollutants were obtained from two air quality (AQ) monitoring stations, and daily meteorological (air temperature and relative humidity) data were obtained from one meteorological station in Sivas province to determine the effects of air pollution on hospital admissions. It was found to be a significant relationship between air pollution and respiratory hospital admissions in the province. The results of the study showed the relative magnitudes of the risks of cardiovascular diseases and hospital admissions related to air pollutants were as follows: The highest association of each pollutant with cardiovascular diseases was observed for PM₁₀ at lag 4 (ER = 1.74%; 95% CI = 0.95–3.19%), PM_2.5 at lag 2 (ER = 5.12%; 95% CI = 1.39–19.0%), NO₂ at lag 8 (ER = 4.89%; 95% CI = 0.08–288.8%) and SO₂ at lag 5 (ER = 1.21%; 95% CI = 1.10–1.32%). It was seen that short-term exposure to air pollution in Sivas between 2016 and 2019 was positively associated with increasing respiratory hospital admissions. As the first air pollution study to use the generalized linear model (GLM) method in hospital admissions in Sivas, these findings may have implications for local environmental policies and help to combat air pollution. Full article

Addressing the high-dimensional, strongly constrained multi-objective optimization problem of air–ground collaborative jamming scheduling in complex terrain, existing methods are often limited by incomplete modeling and low optimization efficiency in discrete feasible regions. This paper proposes a Terrain-Aware Multi-Scale Discrete Operator (TA-MSDO). A joint optimization model integrating discrete terrain characteristics and practical combat constraints is first constructed. Then, by leveraging the topological adjacency of terrain units, TA-MSDO employs a block-level crossover and a multi-scale mutation mechanism, replacing traditional continuous genetic operations to enable efficient and directional exploration of the discrete feasible region. Integrating TA-MSDO into the NSGA-III framework yields the enhanced ENSGA3 algorithm. Experimental results in a typical hilly terrain scenario demonstrate that ENSGA3 achieves a statistically significant performance improvement over the decomposition-based MOEA/D algorithm in terms of maximum achievable suppression effectiveness and hypervolume. As a comprehensive metric integrating convergence and Pareto frontier coverage, hypervolume further verifies the superior comprehensive optimization capability of the proposed algorithm. Meanwhile, compared with other classic mainstream multi-objective optimization algorithms including NSGA-II, standard NSGA-III and SPEA2, the proposed algorithm exhibits clear positive advantages in the upper bound of suppression effectiveness for elite solutions and operational stability across random initializations, with a favorable trend in Pareto frontier coverage for multi-objective collaborative optimization. This work provides an effective solution for jamming resource scheduling in complex battlefield environments. Full article

To meet the need for global coverage, space–air–ground integrated networks (SAGINs) are crucial, but the openness of wireless links makes communications vulnerable to eavesdropping. This paper investigates the physical layer security (PLS) of uplink transmissions in a cooperative dual-hop SAGIN. The system comprises a ground source with a directional antenna, an unmanned aerial vehicle (UAV) relay cluster, and a low Earth orbit (LEO) satellite. Utilizing stochastic geometry, we model the spatial randomness of terrestrial eavesdroppers and the multi-layered dual-orbital LEO destination. To combat mixed radio-frequency (RF) and free-space optical (FSO) fading, multiple relay selection and maximum ratio combining (MRC) are integrated into the UAV cluster. We analytically derive the piecewise probability density function for the FSO link distance, obtaining exact closed-form expressions for the end-to-end secrecy outage probability (SOP). Monte Carlo simulations strictly validate the derivations. The results demonstrate that while increasing available relays and antennas enhances PLS via spatial diversity, a security bottleneck restricts the RF-FSO architecture under high-transmit power regimes, generating asymptotic secrecy floors. These findings provide explicit theoretical guidelines for the secure design and parameter optimization of future SAGINs. Full article

Deployed armed forces and the public engaged in outdoor activities are at high risk for mosquito bites and the diseases they transmit. Current mosquito bite-resistant garments prevent blood-feeding with slow-release insecticide formulations. Many people today want to avoid contact with pesticides, especially in their clothing. Insecticide treated clothing also is costly and requires regulatory agency approvals. Using mosquito bite-resistant mathematical textile models and a WholeGarment^® knitting technique, a seamless garment was constructed with military-compliant, no-melt, no-drip flame retardant yarns using an AiryPique knit architecture. The garment was 99.5% bite proof in walk-in cage bioassays with 200 Aedes aegypti host-seeking mosquitoes where the human subjects did not move for 20 min. A standard flame test and a PyroMan^TM flammability study validated the garment’s fire protection, a requirement for military uniforms. The thermal physiological comfort tests (air permeability, wetting time/radius, thermal resistance, evaporative resistance, and sweating thermal manikin test) were similar to current army combat uniforms and appropriate for use in everyday clothing. Bite prevention occurred by physically blocking the insect mouth parts from obtaining a blood meal. The knitting technique is well-suited for mass production of bite-resistant clothing through automation, significantly reducing labor, time, and cost by optimizing “fit on demand” for different body types compared to traditional manufacturing methods. This innovation provides a non-insecticidal, safe, scalable, and efficient solution for protecting individuals against mosquito bites. Full article

In this paper, an explainable decision-making and guidance integration method is developed based on dynamic Bayesian network and the optimized control method. The proposed method can be applied for the autonomous decision-making and guidance in the game of attacking and defending of unmanned combat aerial vehicles in close air combat. Firstly, the target maneuver recognition and target trajectory prediction are carried out according to the target information detected by the sensor. Then, a dynamic Bayesian network model for close combat decision is established by combining space occupancy situation and equipment performance information with target maneuver identification results. The decision model realizes the intelligent selection of the optimization index function of the maneuver. The optimal control constrained gradient method is adopted to realize the optimal calculation of the unmanned combat aerial vehicle occupancy guidance quantity by considering the constraint of unmanned combat aerial vehicle flight performance. The simulation results of several typical close air combat show that the proposed method can realize rationalized autonomous decision-making and space occupancy guidance of unmanned combat aerial vehicles, overcome the solidification of mobile action mode by traditional methods, and has better real-time performance and optimization performance. Full article

The objective of this study is to explore the applicability of irregular area cartograms for the visualization of sustainable development indicator components, utilizing earth observation (EO) data. The analysis focuses on selected Sustainable Development Goals (SDG 11 ‘Make cities and human settlements inclusive, safe, resilient and sustainable’ and SDG 13 ‘Take urgent action to combat climate change and its impacts’) and specific targets and indicators related to green urban areas and air quality (targets: 13.2, 11.6, and 11.7; indicators: 11.6.2., 11.7.1., 13.2.2.). A comprehensive review of the relevant literature indicates that irregular area cartograms are employed only sporadically in the context of SDG monitoring, particularly at lower levels of territorial division (i.e., communes and counties). To address this gap, a series of thematic maps, including choropleth maps and irregular area cartograms, was developed. These visualizations are based on EO-derived datasets and supplemented with statistical information obtained from the Local Data Bank of the Statistics Poland. The analysis demonstrates that irregular area cartograms provide an effective means of visualizing spatial disparities in variables such as urban green space availability and air pollution at the commune and county levels. These visualizations enhance the interpretability of complex indicator structures and support more nuanced assessments of progress toward selected Sustainable Development Goals, especially in spatially detailed analytical frameworks. Preliminary usability testing among potential users revealed that irregular area cartograms are perceived as an interesting visualization technique that enhances data interpretation. Full article

The increasing amount of carbon dioxide (CO₂) in the atmosphere is the main factor contributing to climate change. Recent studies have determined that simply reducing emissions is insufficient to restore the Earth’s atmospheric system—negative emissions are therefore necessary. Current carbon (i.e., CO₂) capture technologies use thermal or pressure swings. These approaches suffer from low energy efficiency, high cost, and geographic constraints. Electro-swing chemistry-based carbon capture has emerged as a promising potential solution to these challenges. However, strong CO₂-binding sorbents, not susceptible to oxygen interference, remain elusive. In this study, three electron-deficient quinones were designed and tested as CO₂ capture molecular sorbents. Cyclic voltammetry (CV) on these novel quinones reveals that 2,3-dicyano-1,4-benzoquinone (DBQ) has a second reduction potential positive of that of oxygen reduction. Moreover, this sorbent binds to CO₂ with a free energy ΔG_bind of −5.39 kcal/mol when activated by electrochemical reduction. These results suggest that DBQ may be a sorbent candidate that can capture >70% of CO₂ in the current atmosphere using electro-swing chemistry without the interference of oxygen in the air. This novel sorbent can be further developed for large-scale carbon capture and combating global warming and its associated impacts. Full article

In the wartime scenario of large-scale air combat, the battlefield situation is dynamically changing, and damaged aircraft often face issues such as limited maneuverability and fuel shortages. Relying on fixed corridors for return flights is no longer feasible. To quickly address wartime demands, it is essential to determine alternative routes for damaged aircraft in real-time, which must not only avoid multi-source threats but also adapt to their performance constraints. This paper improves the traditional artificial potential field algorithm by adopting a two-tier strategy that combines the greedy algorithm with the artificial potential field algorithm. First, to address limitations of the traditional APF method, relative distance is introduced to solve target unreachability, adjustment force is introduced to break local minimum traps, a dynamic potential field reconstruction strategy is introduced to traverse dense obstacles, and a memory-based resultant force is introduced to suppress oscillations. Subsequently, the greedy algorithm optimizes the initial path planned by the APF to shorten the distance. However, when sudden or moving threats are encountered and their influence range is entered, the fast-computing APF algorithm is used for real-time avoidance. The simulation results confirm that the improved algorithm effectively resolves the above issues, with fast planning, smooth, high-quality paths, and guaranteed safe return of aircraft. Full article

To address the challenge of decision making in close-range air combat for fixed-wing unmanned air vehicle (UAV) swarms, this paper proposes a distributed Hierarchical Cooperative Soft Actor-Critic with maximum entropy (HC-SAC) framework. A top-level target decision-making and bottom-level maneuvering framework is designed to resolve convergence issues in traditional multi-agent reinforcement learning, typically for long mission durations and complex state spaces. Friendly tactics are incorporated into top-level decisions to enhance coordination, with both offensive and defensive sub-policies balanced for swarm confrontations. These policies are trained using the Soft Actor-Critic (SAC) deep reinforcement learning algorithm with specific reward functions, and the effectiveness of this method is verified through 5v5 swarm game confrontation simulations. Full article

Air pollution is one of the major global environmental challenges, particularly in urban and industrial areas, where multiple sources emit pollutants that compromise air quality and threaten human health. This study aims to analyze the effects of air pollution on the health of vulnerable urban populations, emphasizing monitoring techniques for key pollutants and comparing national and international air quality standards through a literature review. It also discusses the implications of these pollutants considering the 2030 Agenda, highlighting environmental education as a strategy for pollution mitigation, public awareness, and strengthening air quality policies. A qualitative and descriptive methodology was adopted, based on national and international research publications between 2005 and 2023, using databases such as PubMed, Scopus, Web of Science, LILACS, and Google Scholar, with descriptors including “Air Pollution,” “Industrialization,” “Public Health,” and “Environmental Education.” The results indicate that industrial and transportation activities are the main sources of air pollution, contributing to an increase in cases of asthma, lung cancer, and cardiovascular diseases, as well as negatively impacting ecosystems and the economy. Even when pollutant levels comply with legal standards, vulnerable populations experience higher morbidity and mortality rates, highlighting the need for more stringent protection policies. Comparisons between the standards of the World Health Organization (WHO), the European Union (EU), the United States Environmental Protection Agency (EPA), and the National Council for the Environment (CONAMA) reveal significant disparities in exposure limits. The WHO and the EU, aligned with Sustainable Development Goals (SDGs) 3 and 13, advocate for stricter limits, while EPA and CONAMA regulations remain less stringent. This gap emphasizes the importance of internationally harmonized, evidence-based, and equitable air quality policies. Combating air pollution requires an integrated approach that combines stricter regulations, continuous monitoring, emissions control strategies, and environmental education. Promoting environmental awareness among children and young people can encourage behavioral changes and civic engagement. Environmental education, along with political and social responsibility, remains a fundamental path to mitigating health impacts and promoting sustainable development, in line with the 2030 Agenda. Full article