Search Results (344)

This contributed paper presents results from efforts by the Association of Zoos and Aquariums (AZA) to investigate the impact of integrating conservation into AZA members’ organizational cultures. Part of this work included AZA setting goals related to organizational and professional culture, strategic communication, developing communities of practice, and promoting tools and resource-sharing. Prior to implementing the majority of these steps, a baseline assessment was administered to directors plus a random sample of AZA organizations in 2020—assessing how well conservation is integrated into the institutional culture and measures of perceived conservation impact. The same sample of organizations was re-surveyed in 2023 with the intent of a three-year cycle of surveying to monitor change and identify additional ways that AZA could support and strengthen a culture of conservation within the profession. These findings will help the zoological and broader conservation community assess opportunities to integrate conservation into organizational cultures across a broad association for the purpose of achieving the mission and impact. Full article

The development of efficient and sustainable water recycling systems is essential for long-term human missions and the establishment of space habitats on the Moon, Mars, and beyond. Humidity condensate (HC) is a low-strength wastewater that is currently recycled on the International Space Station (ISS). The main contaminants in HC are primarily low-molecular-weight organics and ammonia. This has caused operational issues due to microbial growth in the Water Process Assembly (WPA) storage tank as well as failure of downstream systems. In addition, treatment of this wastewater primarily uses adsorptive and exchange media, which must be continually resupplied and represent a significant life-cycle cost. This study demonstrates the integration of a membrane-aerated biological reactor (MABR) for pretreatment and storage of HC, followed by brackish water reverse osmosis (BWRO). Two system configurations were tested: (1) periodic MABR fluid was sent to batch RO operating at 90% water recovery with the RO concentrate sent to a separate waste tank; and (2) periodic MABR fluid was sent to batch RO operating at 90% recovery with the RO concentrate returned to the MABR (accumulating salinity in the MABR). With an external recycle tank (configuration 2), the system produced 2160 L (i.e., 1080 crew-days) of near potable water (dissolved organic carbon (DOC) < 10 mg/L, total nitrogen (TN) < 12 mg/L, total dissolved solids (TDS) < 30 mg/L) with a single membrane (weight of 260 g). When the MABR was used as the RO recycle tank (configuration 1), 1100 L of permeate could be produced on a single membrane; RO permeate quality was slightly better but generally similar to the first configuration even though no brine was wasted during the run. The results suggest that this hybrid system has the potential to significantly enhance the self-sufficiency of space habitats, supporting sustainable extraterrestrial human habitation, as well as reducing current operational problems on the ISS. These systems may also apply to extreme locations such as remote/isolated terrestrial locations, especially in arid and semi-arid regions. Full article

The rapid growth of the private space industry has intensified the demand for lightweight, efficient, and cost-effective photovoltaic technologies. Metal halide perovskite solar cells (PSCs) offer high power conversion efficiency (PCE), mechanical flexibility, and low-temperature solution processability, making them strong candidates for next-generation space power systems. However, exposure to extreme thermal cycling, high-energy radiation, vacuum, and ultraviolet light in space leads to severe degradation. This study addresses these challenges by introducing three key design strategies: self-healing perovskite compositions that recover from radiation-induced damage, gradient buffer layers that mitigate mechanical stress caused by thermal expansion mismatch, and advanced encapsulation that serves as a multifunctional barrier against space environmental stressors. These approaches enhance device resilience and operational stability in space. The design strategies discussed in this review are expected to support long-term power generation for low-cost satellites, high-altitude platforms, and deep-space missions. Additionally, insights gained from this research are applicable to terrestrial environments with high radiation or temperature extremes. Perovskite solar cells represent a transformative solution for space photovoltaics, offering a pathway toward scalable, flexible, and radiation-tolerant energy systems. Full article

With the enhancement of thermodynamic cycle parameters and heat dissipation constraints in aero-engines, effective thermal management has become a critical challenge to ensure safe and stable engine operation. This study developed a transient temperature evaluation model applicable to the entire flight envelope, considering fluid–solid coupling heat transfer on both the main flow path and fuel systems. Firstly, the impact of heat transfer on the acceleration and deceleration performance of a low-bypass-ratio turbofan engine was analyzed. The results indicate that, compared to the conventional adiabatic model, the improved model predicts metal components absorb 4.5% of the total combustor energy during cold-state acceleration, leading to a maximum reduction of 1.42 kN in net thrust and an increase in specific fuel consumption by 1.18 g/(kN·s). Subsequently, a systematic evaluation of engine thermal management performance throughout the complete flight mission was conducted, revealing the limitations of the existing thermal management design and proposing targeted optimization strategies, including employing Cooled Cooling Air technology to improve high-pressure turbine blade cooling efficiency, dynamically adjusting low-pressure turbine bleed air to minimize unnecessary losses, optimizing fuel heat sink utilization for enhanced cooling performance, and replacing mechanical pumps with motor pumps for precise fuel supply control. Full article

Terrestrial water storage (TWS) is a critical component of the hydrological cycle and plays a key role in regional water resource management. The launch of the Gravity Recovery and Climate Experiment (GRACE) satellite mission in 2002 has provided precise measurements of TWS, enabling systematic investigations into its spatial pattern and driving mechanisms. However, a comprehensive evaluation of the spatial drivers of TWS variations across China is still lacking. In this study, we employed a robust machine learning model to capture the spatial patterns of TWS in China and further applied the Shapley Additive Explanations (SHAP) method to disentangle the individualized effects of hydroclimatic variables. Our findings reveal that precipitation is the dominant driver in northern and southern China, while soil moisture and snow water equivalent are key contributors on the Tibetan Plateau. In northwestern China, air pressure and groundwater runoff are the main influencing factors, whereas temperature shows a pronounced negative effect. Importantly, most variables demonstrate non-monotonic influences: in particular, we found that the importance of precipitation diminishes beyond a certain threshold, and surface pressure shifts sharply toward a negative impact. The explainable machine learning framework demonstrated strong adaptability in identifying complex drivers of TWS, offering a powerful methodological advancement for exploring TWS dynamics and providing valuable insights for water resource management in China. Full article

High-test peroxide (HTP) monopropellant thrusters are being considered for spacecraft lander missions due to their simplicity and reduced toxicity compared to traditional propellants. Pulse-Width Modulation (PWM) throttling is a key technique for precise thrust control in such systems. However, PWM throttling can lead to pressure surges and oscillations in the propellant feed system, potentially compromising system reliability. This study investigates the influence of PWM parameters, specifically duty cycle and frequency, on pressure surges and oscillations in a 50-newton-class HTP monopropellant thruster. The objective is to identify stable operating conditions that mitigate these effects, thereby enhancing the reliability of PWM throttling for lander applications. An experimental setup was developed, including a 50-newton-class thruster with a ${\mathrm{MnO}}_2{\mathrm{/La/Al}}_2{\mathrm{O}}_3$ catalyst and a solenoid valve for PWM control. Cold flow tests using water characterized the valve response and water hammer effects, while hot fire tests with 90 wt.% HTP were used to evaluate thruster performance under steady-state and PWM conditions. Analytical methods, including Joukowsky’s equation and power spectral density analysis, were used to interpret the data and understand the underlying mechanisms. The results showed that while surge pressures generally aligned with steady-state values, specific PWM conditions led to amplified surges, particularly at low duty cycles. Additionally, high duty cycles induced chugging instability. The natural frequencies of the feed system were found to play a crucial role in these phenomena. Stable operating conditions were identified by avoiding duty cycles that cause constructive interference of pressure waves. This research demonstrates that by carefully selecting PWM parameters based on the feed system’s dynamic characteristics, pressure surges and oscillations can be minimized, ensuring reliable operation of HTP monopropellant thrusters in PWM throttling mode. These findings contribute to the development of more efficient and safer propulsion systems for spacecraft landers. Full article

Tropical forests provide essential ecosystem services, playing a critical role in climate regulation, biodiversity conservation, and regional hydrological cycles while also supporting livelihoods. However, they are increasingly threatened by deforestation and land-use change. Accurate land cover (LC) mapping is vital to monitor these changes, but mapping tropical forests is challenging due to complex spatial patterns, spectral similarities, and frequent cloud cover. This study aims to improve LC classification accuracy in such a heterogeneous tropical forest region in Southeast Asia, namely Kulen, Cambodia, which is characterized by natural forests, regrowth forests, and agricultural lands including cashew plantations and croplands, using Sentinel-2 imagery, recursive feature elimination (RFE), and Random Forest. We generated 65 variables of spectral bands, indices, bi-seasonal differences, and topographic data from Sentinel-2 Level-2A and Shuttle Radar Topography Mission datasets. These variables were extracted from 1000 random points per 12 LC classes from reference polygons based on observed GPS points, Uncrewed Aerial Vehicle imagery, and high-resolution satellite data. The random forest models were optimized through correlation-based filtering and recursive feature elimination with hyperparameter tuning to improve classification accuracy, validated via confusion matrices and comparisons with global and national-scale products. Our results highlight the significant role of topographic variables such as elevation and slope, along with red-edge spectral bands and spectral indices related to tillage, leaf water content, greenness, chlorophyll, and tasseled cap transformation for tropical land cover mapping. The integration of bi-seasonal datasets improved classification accuracy, particularly for challenging classes like semi-evergreen and deciduous forests. Furthermore, correlation-based filtering and recursive feature elimination reduced the variable set from 65 to 19, improving model efficiency without sacrificing accuracy. Combining these variable selection methods with hyperparameter tuning optimized the classification, providing a more reliable LC product that outperforms existing LC products and proves valuable for deforestation monitoring, forest management, biodiversity conservation, and land use studies. Full article

The Qinghai–Tibet Plateau (QTP), a critical hydrological regulator for Asia through its extensive glacier systems, high-altitude lakes, and intricate network of rivers, exhibits amplified sensitivity to climate-driven alterations in precipitation regimes and ice mass balance. While the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) missions have revolutionized monitoring of terrestrial water storage anomalies (TWSAs) across this hydrologically sensitive region, spatial resolution limitations (3°, equivalent to ~300 km) constrain process-scale analysis, compounded by mission temporal discontinuity (data gaps). In this study, we present a novel downscaling framework integrating temporal gap compensation and spatial refinement to a 0.25° resolution through Gated Recurrent Unit (GRU) neural networks, an architecture optimized for univariate time series modeling. Through the assimilation of multi-source hydrological parameters (glacier mass flux, cryosphere–precipitation interactions, and land surface processes), the GRU-based result resolves nonlinear storage dynamics while bridging inter-mission observational gaps. Grid-level implementation preserves mass conservation principles across heterogeneous topographies, successfully reconstructing seasonal-to-interannual TWSA variability and also its long-term trends. Comparative validation against GRACE mascon solutions and process-based hydrological models demonstrates enhanced capacity in resolving sub-basin heterogeneity. This GRU-derived high-resolution TWSA is especially valuable for dissecting local variability in areas such as the Brahmaputra Basin, where complex water cycling can affect downstream water security. Our study provides transferable methodologies for mountainous hydrogeodesy analysis under evolving climate regimes. Future enhancements through physics-informed deep learning and next-generation climatology–hydrology–gravimetry synergy (e.g., observations and models) could further constrain uncertainties in extreme elevation zones, advancing the predictive understanding of Asia’s water tower sustainability. Full article

Integrating machine learning into additive manufacturing offers transformative opportunities to optimize material properties and design high-performance, fatigue-resistant structures for critical applications in aerospace, biomedical, and structural engineering. This study explores mechanistic machine learning techniques to tailor microstructural features, leveraging data from ultrasonic fatigue tests where very high cycle fatigue properties were assessed up to $1\times {10}^{10}$ cycles. Machine learning models predicted critical fatigue thresholds, optimized process parameters, and reduced design iteration cycles by over 50%, leading to faster production of safer, more durable components. By refining grain orientation and phase uniformity, fatigue crack propagation resistance improved by 20–30%, significantly enhancing fatigue life and reliability for mission-critical aerospace components, such as turbine blades and structural airframe parts, in an industry where failure is not an option. Additionally, the machine learning-driven design of metamaterials enabled structures with a 15% weight reduction and improved yield strength, demonstrating the feasibility of bioinspired geometries for lightweight applications in space exploration, medical implants, and high-performance automotive components. In the area of titanium and aluminum alloys, machine learning identified key process parameters such as temperature gradients and cooling rates, which govern microstructural evolution and enable fatigue-resistant designs tailored for high-stress environments in aircraft, biomedical prosthetics, and high-speed transportation. Combining theoretical insights and experimental validations, this research highlights the potential of machine learning to refine microstructural properties and establish intelligent, adaptive manufacturing systems, ensuring enhanced reliability, performance, and efficiency in cutting-edge engineering applications. Full article

Background: A grid search, at the cost of training and testing a large number of models, is an effective way to optimize the prediction performance of deep learning models. A challenging task concerning grid search is time management. Without a good time management scheme, a grid search can easily be set off as a “mission” that will not finish in our lifetime. In this study, we introduce a heuristic three-stage mechanism for managing the running time of low-budget grid searches with deep learning, sweet-spot grid search (SSGS) and randomized grid search (RGS) strategies for improving model prediction performance, in an application of predicting the 5-year, 10-year, and 15-year risk of breast cancer metastasis. Methods: We develop deep feedforward neural network (DFNN) models and optimize the prediction performance of these models through grid searches. We conduct eight cycles of grid searches in three stages, focusing on learning a reasonable range of values for each of the adjustable hyperparameters in Stage 1, learning the sweet-spot values of the set of hyperparameters and estimating the unit grid search time in Stage 2, and conducting multiple cycles of timed grid searches to refine model prediction performance with SSGS and RGS in Stage 3. We conduct various SHAP analyses to explain the prediction, including a unique type of SHAP analyses to interpret the contributions of the DFNN-model hyperparameters. Results: The grid searches we conducted improved the risk prediction of 5-year, 10-year, and 15-year breast cancer metastasis by 18.6%, 16.3%, and 17.3%, respectively, over the average performance of all corresponding models we trained using the RGS strategy. Conclusions: Grid search can greatly improve model prediction. Our result analyses not only demonstrate best model performance but also characterize grid searches from various aspects such as their capabilities of discovering decent models and the unit grid search time. The three-stage mechanism worked effectively. It not only made our low-budget grid searches feasible and manageable but also helped improve the model prediction performance of the DFNN models. Our SHAP analyses not only identified clinical risk factors important for the prediction of future risk of breast cancer metastasis, but also DFNN-model hyperparameters important to the prediction of performance scores. Full article

In this paper, we investigate the heliospheric modulation of cosmic rays in interplanetary space, focusing on their propagation times and energy losses over the solar cycle. To perform the calculations, we employed a data-driven model based on the stochastic method. Our model was calibrated using time-resolved and energy-resolved data from several missions including AMS-02, PAMELA, EPHIN/SOHO, BESS, and data from Voyager-1. This approach allows us to calculate probability density functions for the propagation time and energy losses of cosmic protons and antiprotons in the heliosphere. Furthermore, we explore the temporal evolution of these probabilities spanning from 1993 to 2018, covering a full 22-year cycle of magnetic polarity, which includes two solar minima and two magnetic reversals. Our calculations were carried out for cosmic protons and antiprotons, enabling us to investigate the role of charge-sign dependent effects in cosmic ray transport. These findings provide valuable insights into the physical processes of cosmic-ray propagation in the heliosphere and contribute to a deeper understanding of the solar modulation phenomenon. Full article

The expected life and reliability of components is a critical aspect for railway applications where the expected life and maintenance intervals of rolling stock are quite demanding issues both in terms of equivalent mileage and duration. For these reasons, when the mileage of the mission is within 100 km, adopted accumulators are based on lithium titanate chemistry, which, despite a relatively low density, ensures a very long operational life both in terms of cycle and time aging. In this work, the authors introduce a benchmark test case, an Italian line between Reggio Calabria and Catanzaro, in which the required autonomy, more than 170 km, involves the usage of high-energy batteries such as LiNMC or LiFePO₄ derived from corresponding automotive applications. In this work, the authors propose a simulation model based on IEC 62864-1:2016 to investigate how the combined effect of cycle and time aging should influence in different ways the design of the system and how relatively small interventions such as the partial electrification of a small intermediate section of the line should improve the overall stability and reliability of the performed engineering analysis. Full article

The COllaborative Carbon Column Observing Network has become a reliable source of high-quality ground-based remote sensing network data that provide column-averaged dry-air mole fractions of carbon dioxide (XCO₂), methane (XCH₄), and carbon monoxide (XCO). The fiducial reference measurements of these gases from the COCCON complement the TCCON and NDACC-IRWG data. This study shows the application of COCCON data for the validation of existing greenhouse gas satellite products. This study includes the validation of XCH₄ and XCO products from the European Copernicus Sentinel-5 Precursor (S5P) mission, XCO₂ products from the American Orbiting Carbon Observatory-2 (OCO-2) mission, and XCO₂ and XCH₄ products from the Japanese Greenhouse gases Observing SATellite (GOSAT). A total of 27 datasets contributed to this study; some of these were collected in the framework of campaign activities and covered only a short time period. In addition, several permanent stations provided long-term observations. The random uncertainties in the validation results, specifically for S5P with a lot of coincidences pairs, are found to be similar to the comparison with the TCCON. The comparison results of OCO-2 land nadir and land glint observation modes to the COCCON on a global scale, despite limited coincidences, are very promising. The stations can, therefore, expand on the coverage of the already existing ground-based reference remote sensing sites from the TCCON and the NDACC network. The COCCON data can be used for future satellite and model validation studies and carbon cycle studies. Full article

Due to their distinct economic efficiency and adaptability advantages, Unmanned Aerial Vehicles (UAVs) can serve as mobile data collectors, collecting data from Internet of Things Devices (IoTDs). As a promising emerging technology, the Intelligent Reflecting Surface (IRS) holds the potential to overcome architectural barriers and improve communication quality in urban environments. This study investigates the development of an IoT data collection system tailored for urban environments, leveraging the synergistic operation of multiple UAVs and IRSs. In light of the limited coverage capacity of an individual IRS, we deploy several IRSs, with multiple UAVs stationed at various base stations (BSs) to collect data from IoTDs. We propose a grouping genetic algorithm-independent double deep-Q network-alternating optimization (GGA-IDDQN-AO) approach, aiming to minimize the average mission completion time for a mission cycle. This approach optimizes both the deployment and mission allocation strategies of UAVs using the grouping genetic algorithm. Additionally, by integrating deep reinforcement learning with the alternating optimization algorithm, the flight trajectories of UAVs and IRSs’ phase shifts are fine-tuned. The effectiveness of the GGA-IDDQN-AO approach is validated through comprehensive simulations, which demonstrate that the integration of IRSs leads to a notable performance enhancement in the IoT data collection system. Full article

Internal waves are crucial for understanding oceanographic parameters such as spatiotemporal distribution and energy transfer. They significantly impact ocean circulation, marine ecosystems, and offshore operations. However, studying internal waves is challenging due to their dynamic nature and the need for effective observation methods. This study investigated nonlinear internal solitary waves (ISWs) in the South China Sea using SSHa data from the SWOT satellite mission (Cycles 2 to 20). The distribution patterns and seasonal variations in ISWs were analyzed, revealing that ISWs are more frequently observed in summer while being rarely detected in winter. By combining SSHa observations with a Mode-1 vertical structure model, the isopycnal displacement, velocity fields, and energy characteristics of ISWs were reconstructed. The results show a maximum isopycnal displacement of 160 m at 400 m depth and peak kinetic energy near the surface (~2000 J/m³) and potential energy at a depth of around 300 m (~9000 J/m³). These findings highlight the vertical variability of ISWs and demonstrate the capability of SWOT data in capturing their fine-scale evolution, providing new opportunities for oceanic research and enhancing our understanding of internal waves’ impact on marine environments and ocean circulation. Full article