Search Results (17)

As the scale of missions continues to expand, equipment support has emerged as a critical component of military combat effectiveness. Consequently, the supportability of a system of systems (SOS) for equipment has become as essential quality requirement alongside its performance metrics. This study systematically assessed the effectiveness of equipment SOS support through a task-driven methodology. Initially, a model for generating equipment support tasks was developed to translate the operational requirements into a sequence of support tasks. Subsequently, a simulation model was constructed to evaluate the equipment SOS support system, and solutions were derived for the corresponding SOS-level support effectiveness indexes. Finally, the feasibility and characteristics of the proposed models and simulation methodology were validated through a case study involving an emergency operational mission for an air combat group formation. The results indicate that the increased reliability of the equipment system correlates with a reduced failure rate and lower resource consumption for maintenance and support per device, thereby improving support efficiency. The methodology presented in this article provides a framework for evaluating the effectiveness of equipment SOS support while facilitating informed decision-making in information warfare conditions. Full article

In within-visual-range (WVR) air combat, basic fighter maneuvers (BFMs) are widely used. Air combat engagement database (ACED) is a dedicated database for researching WVR air combat. Utilizing the data in ACED, a Transformer-based BFM decision support scheme is developed to enhance the pilot’s BFM decision making in WVR air combat. The proposed Transformer-based model significantly outperforms the baseline long short-term memory (LSTM)-based model in accuracy. To augment the interpretability of this approach, Shapley Additive Explanation (SHAP) analysis is employed, exhibiting the rationality of the Transformer-based model’s decisions. Furthermore, this study establishes a comprehensive framework for evaluating air combat performance, validated through the utilization of data from ACED. The application of the framework in WVR air combat experiments shows that the Transformer-based model increases the winning rate in combat from 30% to 70%, the average percentage of tactical advantage time from 4.81% to 14.73%, and the average situational advantage time share from 17.83% to 25.19%, which substantially improves air combat performance, thereby validating its effectiveness and applicability in WVR air combat scenarios. Full article

Air target intention recognition (ATIR) is critical for unmanned systems in modern air defense operations. Through the analysis of typical air defense combat scenarios, first, the paper defines the intention space and intention parameters of air units based on military experience and domain knowledge. Then, a rule-based agent for unmanned systems for online intention recognition is proposed, with no training, no tagging, and no big data support, which is not only for intention recognition and parameter prediction, but also for formation identification of air targets. The most critical point of the agent is the introduction and application of a thermal distribution grid graph (TDGG) and virtual grid dictionary (VGD), where the former is used to identify the formation information of air targets, and the latter is used to optimize the storage space and simplify the access process for the large-scale and real-time combat information. Finally, to have a performance evaluation and application analysis for the algorithm, we carried out a data instance analysis of ATIR for unmanned systems and an air defense warfare simulation experiment based on a Wargame platform; the comparative experiments with the classical k-means, FCNIRM, and the sector-based forward search method verified the effectiveness and feasibility of the proposed agent, which characterizes it as a promising tool or baseline model for the battlefield situational awareness tasks of unmanned systems. Full article

Straw burning is recognized as a significant contributor to deteriorating air quality, but its specific impacts, particularly on PM_2.5 concentrations, are still not fully understood or quantified. In this study, we conducted a detailed examination of the spatial and temporal patterns of straw burning in Heilongjiang Province, China—a key agricultural area—utilizing high-resolution fire-point data from the Fengyun-3 satellite. We subsequently employed random forest (RF) models alongside Shapley Additive Explanations (SHAPs) to systematically evaluate the impact of various determinants, including straw burning (as indicated by crop fire-point data), meteorological conditions, and aerosol optical depth (AOD), on PM_2.5 levels across spatial and temporal dimensions. Our findings indicated a statistically nonsignificant downward trend in the number of crop fires in Heilongjiang Province from 2015 to 2023, with hotspots mainly concentrated in the western and southern parts of the province. On a monthly scale, straw burning was primarily observed from February to April and October to November—which are critical periods in the agricultural calendar—accounting for 97% of the annual fire counts. The RF models achieved excellent performance in predicting PM_2.5 levels, with R² values of 0.997 for temporal and 0.746 for spatial predictions. The SHAP analysis revealed the number of fire points to be the key determinant of temporal PM_2.5 variations during straw-burning periods, explaining 72% of the variance. However, the significance was markedly reduced in the spatial analysis. This study leveraged machine learning and interpretable modeling techniques to provide a comprehensive understanding of the influence of straw burning on PM_2.5 levels, both temporally and spatially. The detailed analysis offers valuable insights for policymakers to formulate more targeted and effective strategies to combat air pollution. Full article

The conceptual design of unmanned aerial vehicles (UAVs) presents significant multidisciplinary challenges requiring the optimization of aerodynamic and structural performance, stealth, and propulsion efficiency. This work addresses these challenges by integrating deep neural networks with a multiobjective genetic algorithm to optimize UAV configurations. The proposed framework enables a comprehensive evaluation of design alternatives by estimating key performance metrics required for different operational requirements. The design process resulted in a significant improvement in computational time over traditional methods by more than three orders of magnitude. The findings illustrate the framework’s capability to optimize UAV designs for a variety of mission scenarios, including specialized tasks such as intelligence, surveillance, and reconnaissance (ISR), combat air patrol (CAP), and Suppression of Enemy Air Defenses (SEAD). This flexibility and adaptability was demonstrated through a case study, showcasing the method’s effectiveness in tailoring UAV configurations to meet specific operational requirements while balancing trade-offs between aerodynamic efficiency, stealth, and structural weight. Additionally, these results underscore the transformative impact of integrating AI into the early stages of the design process, facilitating rapid prototyping and innovation in aerospace engineering. Consequently, the current work demonstrates the potential of AI-driven optimization to revolutionize UAV design by providing a robust and effective tool for solving complex engineering problems. Full article

In the pursuit of advancing COVID-19 diagnosis through imaging, this paper introduces a novel approach utilizing adaptive Fourier neural operators (AFNO) for the analysis of high-resolution computed tomography (HRCT) chest images. The study population comprised 395 patients with 181,106 labeled high-resolution COVID-19 CT images from the HRCTCov19 dataset, categorized into four classes: ground glass opacity (GGO), crazy paving, air space consolidation, and negative for COVID-19. The methods included image preprocessing, involving resizing and normalization, followed by the application of the AFNO model, which enables efficient token mixing in the Fourier domain independent of input resolution. The model was trained using the Adam optimizer with a learning rate of 1 × 10⁻⁴ and evaluated using metrics such as accuracy, precision, recall, and F1 score. The results demonstrate AFNO’s superior performance in few-shot segmentation tasks over traditional self-attention mechanisms, achieving an overall accuracy of 94%. Specifically, the model showed high precision and recall for the GGO and negative classes, indicating its robustness and effectiveness. This research has significant implications for the development of AI-powered diagnostic tools, particularly in environments with limited access to high-quality imaging data and those where computational efficiency is critical. Our findings suggest that AFNO could serve as a powerful model for analyzing HRCT images, potentially leading to improved diagnosis and understanding of COVID-19, representing a critical step in combating the pandemic. Full article

This study evaluated the suitability of two types of beech wood pellets as renewable, low-emission biofuel sources in order to combat the energy mix and poor air quality in Serbia. Key solid biofuel characteristics, including the heating values (18.5–18.7 MJ/kg), moisture content (5.54–7.16%), and volatile matter (82.4–84.4%) were assessed according to established standards. The elemental composition (mass fractions of 48.26–48.53% carbon, 6% hydrogen, 0.12–0.2% nitrogen, 0.02% sulfur, non-detected chlorine) and ash content (0.46–1.2%) demonstrated that the analyzed beech pellets met the criteria for high-quality classification, aligning with the ENplus A1 and ENplus A2 standards. The emissions of O₂, CO₂, CO, NO_x, SO₂, and TOC were quantified in the flue gas of an automatic residential pellet stove and compared with the existing literature. While combustion of the beech pellets yielded low emissions of SO₂ (6 mg/m³) and NO_x (188 mg/m³), the fluctuating CO (1456–2064 mg/m³) and TOC (26.75–61.46 mg/m³) levels were influenced by the appliance performance. These findings underscore the potential of beech wood pellets as a premium solid biofuel option for Serbian households, offering implications for both end-users and policymakers. Full article

Executing flight operations demand that military personnel continuously perform tasks that utilize low- and high-order cognitive functions. The autonomic nervous system (ANS) is crucial for regulating the supply of oxygen (O2) to the brain, but it is unclear how sustained cognitive loads of different complexities may affect this regulation. Therefore, in the current study, ANS responses to low and high cognitive loads in hypoxic and normoxic conditions were evaluated. The present analysis used data from a previously conducted, two-factor experimental design. Healthy subjects (n = 24) aged 19 to 45 years and located near Fort Novosel, AL, participated in the parent study. Over two, 2-h trials, subjects were exposed to hypoxic (14.0% O₂) and normoxic (21.0% O₂) air while simultaneously performing one, 15-min and one, 10-min simulation incorporating low- and high-cognitive aviation-related tasks, respectively. The tests were alternated across five, 27-min epochs; however, only epochs 2 through 4 were used in the analyses. Heart rate (HR), HR variability (HRV), and arterial O₂ saturation were continuously measured using the Warfighter Monitor^TM (Tiger Tech Solutions, Inc., Miami, FL, USA), a previously validated armband device equipped with electrocardiographic and pulse oximetry capabilities. Analysis of variance (ANOVA) regression models were performed to compare ANS responses between the low- and high-cognitive-load assessments under hypoxic and normoxic conditions. Pairwise comparisons corrected for familywise error were performed using Tukey’s test within and between high and low cognitive loads under each environmental condition. Across epochs 2 through 4, in both the hypoxic condition and the normoxic condition, the high-cognitive-load assessment (MATB-II) elicited heightened ANS activity, reflected by increased HR (+2.4 ± 6.9 bpm) and decreased HRV (−rMSSD: −0.4 ± 2.7 ms and SDNN: −13.6 ± 14.6 ms). Conversely, low cognitive load (ADVT) induced an improvement in ANS activity, with reduced HR (−2.6 ± 6.3 bpm) and increased HRV (rMSSD: +1.8 ± 6.0 ms and SDNN: vs. +0.7 ± 6.3 ms). Similar observations were found for the normoxic condition, albeit to a lower degree. These within-group ANS responses were significantly different between high and low cognitive loads (HR: +5.0 bpm, 95% CI: 2.1, 7.9, p < 0.0001; rMSSD: −2.2 ms, 95% CI: −4.2, −0.2, p = 0.03; SDNN: −14.3 ms, 95% CI: −18.4, −10.1, p < 0.0001) under the hypoxic condition. For normoxia, significant differences in ANS response were only observed for HR (+4.3 bpm, 95% CI: 1.2, 7.4, p = 0.002). Lastly, only high cognitive loads elicited significant differences between hypoxic and normoxic conditions but just for SDNN (−13.3 ms, 95% CI, −17.5, −8.9, p < 0.0001). Our study observations suggest that compared to low cognitive loads, performing high-cognitive-load tasks significantly alters ANS activity, especially under hypoxic conditions. Accounting for this response is critical, as military personnel during flight operations sustain exposure to high cognitive loads of unpredictable duration and frequency. Additionally, this is likely compounded by the increased ANS activity consequent to pre-flight activities and anticipation of combat-related outcomes. Full article

This study introduces an innovative double-skin façade system integrated with porous materials (DSF-PM) designed to combat air pollution by purifying atmospheric particulate matter without energy consumption. By evaluating three installation strategies—vertical, horizontal, and cross placement—and examining porous materials with pore sizes of 0.5 mm, 1 mm, and 2 mm through a validated computational fluid dynamics (CFD) model, we optimized the DSF-PM system for enhanced particulate matter purification. Our findings reveal that positioning the porous material on both airflow sides with a pore size of 1 mm yields the best purification performance. The seasonal performance analysis demonstrates that the DSF-PM system achieves an average annual purification efficiency of 26.24% for particles larger than 5 µm, surpassing 20% efficiency, comparable to primary filters in global standards, with zero energy input. This passive double-skin façade system, leveraging solar-driven natural convection, emerges as a sustainable solution for ambient air purification in urban environments. Full article

Nano-TiO₂ combined with cement slurry can be utilized to degrade nitrogen oxides (NO_x) in vehicle exhaust, making it an excellent photocatalytic material for air purification. In practice, environmental factors can significantly affect the photocatalytic performance. In this study, a vehicle exhaust test system was developed, and the test methods and evaluation criteria for the degradation test are provided. This study investigated the photocatalytic degradation of NO₂ using nano-TiO₂ cement slurry through laboratory tests. The effects of temperature, relative humidity, ultraviolet (UV) radiation flux, cement slurry thickness, surface dust adherence, and the number of water rinsing cycles were examined. Additionally, nano-TiO₂ cement slurries were applied to an expressway toll station. The results showed that the efficiency of photocatalytic degradation was significantly influenced by temperature and UV radiation flux, while the thickness of the cement slurry had minimal impact. The photocatalytic degradation efficiency was negatively correlated to the relative humidity, when the relative humidity of the cement slurry specimens was high. This is because the excess water (H₂O) competes with NO₂ for adsorption. The photocatalytic performance of the samples was significantly reduced by surface dust and rain erosion, as both led to a decrease in the amount of nano-TiO₂ participating in the reaction. Furthermore, the photocatalytic material has wide-ranging potential applications. The findings of this study would support the promotion of environmentally friendly roads as a strategy to combat air pollution. Full article

In today’s world, the importance of preserving the environment has become increasingly evident. As a result, more sustainable solutions and techniques are being developed to combat environmental destruction. Higher education institutions are now including environmental themes in their technological courses to promote sustainable behavior and indirectly enhance environmental literacy among the population. This study aims to evaluate the level of literacy to biotechnological solutions for environmental sustainability in four areas, namely Air Pollution, Aquatic Pollution, Global Warming, and Energy Resources. A questionnaire was developed and distributed to a sample consisting of 471 individuals of both genders, age range between 15 and 78 years old, to collect data characterizing the sample and assess their literacy in environmental issues. The questionnaire was distributed in Portugal, and the participants were asked to indicate their level of agreement with several statements related to the aforementioned environmental themes. The findings suggest that literacy regarding biotechnological solutions for environmental sustainability is influenced by age group and academic qualifications. The age group above 65 years old is the one with the lowest levels of literacy, exhibiting frequencies of response I don’t know exceeding 50% in 10 out of the 22 issues present in the questionnaire. The findings also suggest that the levels of literacy are higher in the thematic areas of Global Warming and Aquatic Pollution and lower in the thematic areas of Air Pollution and Energy Resources, with lower levels of literacy in the issues that have not been widely disseminated by the media. Additionally, a model based on Artificial Neural Networks was presented to predict literacy to biotechnological solutions for environmental sustainability. The proposed model performs well, achieving accuracy rates of 90.8% for the training set and 86.6% for the test set. Full article

Innovative carbon reduction and sustainability solutions are needed to combat climate change. One promising approach towards cleaner air involves the utilization of lithium-ion batteries (LIB) and electric power vehicles, showcasing their potential as innovative tools for cleaner air. However, we must focus on the entire battery life cycle, starting with production. By prioritizing the efficiency and sustainability of lithium-ion battery manufacturing, we can take an essential step toward mitigating climate change and creating a healthier planet for future generations. A comprehensive case study of the leading LIB manufacturers demonstrates the usefulness of the suggested hybrid methodology. Initially, we utilized the Malmquist model to evaluate these firms’ total efficiency while dissecting their development into technical and technological efficiency change components. We employed the Epsilon-Based Measure (EBM) model to determine each organization’s efficiency and inefficiency scores. The findings show that the EBM approach successfully bridged the gap in the LIB industry landscape. Combined with the Malmquist model, the resulting framework offers a powerful and equitable evaluation paradigm that is easily applicable to any domain. Furthermore, it accurately identifies the top-performing organizations in specific aspects across the research period of 2018–2021. The EBM model demonstrates that most organizations have attained their top level, except for A10, which has superior technology adoption but poor management. A1, A2, A4, A6, A8, A9, and A10 were unable to meet their targets because of the COVID-19 pandemic, despite productivity improvements. A12 leads the three highest-scoring enterprises in efficiency and total productivity changes, while A3 and A5 should focus on innovative production techniques and improved management. The managerial implications provide vital direction for green energy practitioners, enhancing their operational effectiveness. Concurrently, consumers can identify the best LIB manufacturers, allowing them to invest in long-term green energy solutions confidently. Full article

This paper studies the resource allocation problem when multiple jammers follow the aircraft formation to support ground penetration. A joint optimization allocation method of multi-jammer beam-power based on the improved artificial bee colony (IABC) algorithm is proposed. The air-to-ground “many-to-many” assault of the multi-jammer cooperative suppression jamming model is given. The constant false alarm probability detection model of the networked radar is used to evaluate the suppression effect, and a coordinated control model of multi-jammer jamming beams and emitting power is established. The optimal allocation scheme under different combat scenarios is solved by using the IABC algorithm. The search efficiency of the ABC algorithm is improved by cross mutation operation and the replacement of the worst nectar source, and the search performance of the algorithm is enhanced by the random key encoding. Due to the infeasible solution generated by the special random key encoding method, the feasible adjustment strategy is adopted. By changing the jamming parameters, the effect on the detection probability of the radar network is analyzed. Compared to the GWO, SCA, BBO and ABC algorithms, the jamming resource allocation scheme obtained by the proposed IABC algorithm makes the radar detection probability lower. The IABC algorithm has better global search capability and robustness. Full article

Combat sports athletes competing in the same discipline exhibit notable and substantial differences in body weight, body composition (BC) and adiposity. No studies have considered the influence of adiposity levels in the agreement between different BC assessment methods. The aim of this study was to analyze the influence of adiposity in the agreement between different methods used to estimate relative body fat (%BF) in Olympic combat sport athletes. A total of 38 male athletes were evaluated using air displacement plethysmography and dual-energy X-ray absorptiometry (DXA) as laboratory methods, and bioelectrical impedance analysis (BIA), near-infrared interactance (NIR) and anthropometry as field methods. All methods were compared to DXA. Agreement analyses were performed by means of individual intraclass correlation coefficients (ICCs) for each method compared to DXA, Bland–Altman plots and paired Student t-tests. The ICCs for the different methods compared to DXA were analyzed, considering tertiles of %BF, tertiles of body weight and type of sport. For the whole group, individual ICCs oscillated between 0.806 for BIA and 0.942 for anthropometry. BIA showed a statistically significant underestimation of %BF when compared to DXA. The agreement between every method and DXA was not affected by %BF, but it was highest in athletes at the highest %BF tertile (>13%). The ICC between NIR and DXA was poor in 72–82 kg athletes. Our results indicate that field methods are useful for routine %BF analysis, and that anthropometry is particularly appropriate, as it showed the highest accuracy irrespective of the athletes’ adiposity. Full article

This paper presents an analysis of the effects of the COVID-19 pandemic on the air quality of the Metropolitan Region of São Paulo (MRSP). The effects of social distancing are still recent in the society; however, it was possible to observe patterns of environmental changes in places that had adhered transportation measures to combat the spread of the coronavirus. Thus, from the analysis of the traffic volumes made on some of the main access highways to the MRSP, as well as the monitoring of the levels of fine particulate matter (PM_2.5), carbon monoxide (CO) and nitrogen dioxide (NO₂), directly linked to atmospheric emissions from motor vehicles–which make up about 95% of air polluting agents in the region in different locations–we showed relationships between the improvement in air quality and the decrease in vehicles that access the MRSP. To improve the data analysis, therefore, the isolation index parameter was evaluated to provide daily information on the percentage of citizens in each municipality of the state that was effectively practicing social distancing. The intersection of these groups of data determined that the COVID-19 pandemic reduced the volume of vehicles on the highways by up to 50% of what it was in 2019, with the subsequent recovery of the traffic volume, even surpassing the values from the baseline year. Thus, the isolation index showed a decline of up to 20% between its implementation in March 2020 and December 2020. These data and the way they varied during 2020 allowed to observe an improvement of up to 50% in analyzed periods of the pollutants PM_2.5, CO and NO₂ in the MRSP. The main contribution of this study, alongside the synergistic use of data from different sources, was to perform traffic flow analysis separately for light and heavy duty vehicles (LDVs and HDVs). The relationships between traffic volume patterns and COVID-19 pollution were analyzed based on time series. Full article