Search Results (6,164)

This study investigated the influence of sea–land breezes on nocturnal spatial and temporal distribution of ozone (O₃) and its potential effects on particulate nitrate formation in Qingdao, a coastal city in northern China. Observation campaigns were conducted to measure surface air pollutants and meteorological factors during a typical sea–land breezes event from 22 to 23 July 2022. A coherent Doppler lidar (CDL) system was employed to continuously detect three-dimensional wind fields. The results revealed that nocturnal ozone levels were enhanced by a conversion of sea–land breezes. Initially, the prevailing northerly land breeze transported high concentrations of O₃ and other air pollutants from downtown to the Yellow Sea. As the sea breeze developed in the afternoon, the sea breeze front advanced northward, resulting in a flow of high O₃ concentrations back into inland areas. This penetration of the sea breeze front led to a notable spike in O₃ concentrations between 16:00 on 22 July and 02:00 on 23 July across downtown areas, with an average increase of over 70 μg/m³ within 10 min. Notably, a time lag in peak O₃ concentration was observed with southern downtown areas peaking before northern rural areas. During this period, combined pollution of O₃ and PM_2.5 was also observed. These findings indicated that the nighttime increase in O₃ concentrations, coupled with enhanced atmospheric oxidation, would likely promote the secondary conversion of gaseous precursors into PM_2.5. Full article

Highways, urban roads, and bridges are the important transportation infrastructures for the economic development of modern society. The evaluation of bridge and road quality is crucial to the maintenance and management of the bridge and road industry. Road roughness is a widely accepted indicator in the evaluation of road quality and safety, which is a major input source for vehicles. The vehicle responses-based method of identifying road roughness is efficient and convenient. However, the dynamic characteristics of the vehicle have an important impact on the interaction between the vehicle and the road. When the vehicle parameters are not yet clear, the coupling of unknown parameters and unknown road roughness results in the need for mutual iteration when the existing methods simultaneously identify vehicle parameters and road roughness. To address this issue, this study proposes an effective method for the combined identification of vehicle parameters and road roughness using vehicle responses. The test vehicle is modeled as a four-degree-of-freedom half-vehicle model. In view of the coupling effect between tire stiffness and road roughness, the unknown vehicle physical parameters, except for tire stiffness, are first included in the extended state vector. Based on the extended Kalman filter for unknown excitation (EKF-UI), unknown vehicle physical parameters and unknown forces on the axle are identified. Subsequently, based on the property that the front and rear axles of the vehicle pass through the same road roughness area at a fixed time lag, the tire stiffness is identified by combining the identified unknown forces on the axle. Finally, the road roughness is obtained using the identified vehicle parameters and unknown forces. Numerical studies with different levels of roughness, different noise levels, and different vehicle speeds have verified the accuracy of this method in identifying vehicle parameters and road roughness. Full article

In the present paper, a flight dynamics model is adopted to represent the trim and stability characteristics of a side-by-side helicopter in hovering conditions. This paper develops a numerical representation of the rotorcraft behavior and proposes a set of guidelines for trimming and linearizing the highly coupled rotor dynamics derived by the modeling approach. The trim algorithm presents two nested loops to compute a solution of the steady-state conditions averaged around one blade’s revolution. On the other hand, a 38-state-space linear representation of the helicopter and rotor dynamics is obtained to study the effects of flap, lead–lag, and inflow on the overall stability. The results are compared with an analytical framework developed to validate the rotorcraft stability and compare different modeling approaches. The analysis showed that non-uniform inflow modeling led to a coupled longitudinal inflow–phugoid mode which made the vehicle prone to dangerous instabilities. The flap and lead–lag dynamics introduced damping in the system and can be considered beneficial for rotor dynamics. Full article

This study aims to examine the relationship between real estate development and environmental impact in Albania during the period of 1995–2022. This is among the first scientific studies of this nature in Albania and the region. Using an Autoregressive Distribution Lag (ARDL) model and EViews 12 software, we analyze how carbon emissions relate to economic indicators such as the issuance of building permits, Gross Domestic Product (GDP), and energy consumption patterns. Our results show a positive relationship between construction activities and increased carbon emissions, signaling a development model that currently diverges from sustainable practices. This research is important as it not only fills a critical gap by quantifying the environmental footprint of the real estate sector in Albania but also provides strong signals to support policy makers in guiding sustainable development initiatives. This study recommends that future strategies integrate and harmonize economic growth with environmental care by targeting sustainability in construction techniques and renewable energy. Full article

Background: Malaria remains a climate-driven public health issue in Burkina Faso, yet the interactions between climatic factors and malaria interventions across different zones are not well understood. This study estimates time delays in the effects of climatic factors on malaria incidence, develops forecasting models, and assesses their short-term forecasting performance across three distinct climatic zones: the Sahelian zone (hot/arid), the Sudano-Sahelian zone (moderate temperatures/rainfall); and the Sudanian zone (cooler/wet). Methods: Monthly confirmed malaria cases of children under five during the period 2015–2021 were analyzed using Bayesian generalized autoregressive moving average negative binomial models. The predictors included land surface temperature (LST), rainfall, the coverage of insecticide-treated net (ITN) use, and the coverage of artemisinin-based combination therapies (ACTs). Bayesian variable selection was used to identify the time delays between climatic suitability and malaria incidence. Wavelet analysis was conducted to understand better how fluctuations in climatic factors across different time scales and climatic zones affect malaria transmission dynamics. Results: Malaria incidence averaged 9.92 cases per 1000 persons per month from 2015 to 2021, with peak incidences in July and October in the cooler/wet zone and October in the other zones. Periodicities at 6-month and 12-month intervals were identified in malaria incidence and LST and at 12 months for rainfall from 2015 to 2021 in all climatic zones. Varying lag times in the effects of climatic factors were identified across the zones. The highest predictive power was observed at lead times of 3 months in the cooler/wet zone, followed by 2 months in the hot/arid and moderate zones. Forecasting accuracy, measured by the mean absolute percentage error (MAPE), varied across the zones: 28% in the cooler/wet zone, 53% in the moderate zone, and 45% in the hot/arid zone. ITNs were not statistically important in the hot/arid zone, while ACTs were not in the cooler/wet and moderate zones. Conclusions: The interaction between climatic factors and interventions varied across zones, with the best forecasting performance in the cooler/wet zone. Zone-specific intervention planning and model development adjustments are essential for more efficient early-warning systems. Full article

The prediction of the chemical oxygen demand (COD) and total nitrogen (TN) in integrated anaerobic–anoxic–oxic (A2O) and anoxic–oxic (AO) processes (i.e., A2O+AO process) was achieved using a dynamic ensemble model that reflects the dynamics of wastewater treatment plants (WWTPs). This model effectively captures the variability in the influent characteristics and fluctuations within each reactor of the A2O+AO process. By employing a time-lag approach based on the hydraulic retention time (HRT), artificial intelligence (AI) selects suitable input (i.e., pH, temperature, total dissolved solid (TDS), NH₃-N, and NO₃-N) and output (COD and TN) data pairs for training, minimizing the error between predicted and observed values. Data collected over two years from the actual A2O+AO process were utilized. The ensemble model adopted machine learning-based XGBoost for COD and TN predictions. The dynamic ensemble model outperformed the static ensemble model, with the mean absolute percentage error (MAPE) for the COD ranging from 9.5% to 15.2%, compared to the static ensemble model’s range of 11.4% to 16.9%. For the TN, the dynamic model’s errors ranged from 9.4% to 15.5%, while the static model showed lower errors in specific reactors, particularly in the anoxic and oxic stages due to their stable characteristics. These results indicate that the dynamic ensemble model is suitable for predicting water quality in WWTPs, especially as variability may increase due to external environmental factors in the future. Full article

The aim of this paper is to create a digitalization index for banking sectors using a set of indicators based on World Bank data for the period of 2010–2021, which will allow us to rank the sectors of Central and Eastern European countries (CEECs). The digitalization index is built based on how ready banks are for digitalization, the potential customers available for digital banking, and the level of digital infrastructure, with each of these aspects representing one pillar. Based on the calculation of the digitalization index, we emphasize that Romania is the leader, followed by Latvia and Lithuania, while Hungary and Estonia are at the opposite pole. Furthermore, we applied the fully modified ordinary least squares (FMOLS) method to measure the impact of digitalization on banking performance. This study reveals that Romania, Latvia, and Lithuania lead in digital banking transformation due to significant investments in infrastructure and customer engagement, while Hungary and Poland lag in terms of digital readiness. The results indicate that digitalization has a significant positive effect on banking performance (ROE), although countries experiencing market saturation had the potential to see a decline post-2018, necessitating further innovation to sustain growth. In the digitalization context, the results are relevant for policymakers, showing that investing more in digitalization is important and that there is a need to help people have greater access to banking services due to a lack of willingness and financial education, factors which prevent them from embracing digital changes. The results show that improving banking digitalization positively influences banking performances. This study provides an innovative and complex index for assessing banking digitalization in Central and Eastern Europe, with valuable implications for policymakers. We highlight the need to align digitalization policies with the specific level of digital development of each country in order to optimize the integration of digital technologies and enhance economic competitiveness. Full article

Despite the MFI insurgency, agricultural financing remains critically low, even though microcredit is widely accepted as both a substitute and compliment to formal credit. Zimbabwe is an agro-based economy and very little is known about the determinants of microcredit demand and microcredit size in smallholder resettled sugarcane farmers. Research is concentrated in short-term agriculture activities. Thus, this study aims to fill the unattended gap in lagged returns agriculture activities such as sugarcane production which takes at least a year to mature, hence, the greater need for agriculture financing alternatives such as microfinance. The study examined the determinants of both microcredit demand and loan size (magnitude of microcredit participation) by smallholder resettled A2 sugarcane farmers in Chiredzi. Primary data from 370 smallholder resettled sugarcane farmers (214 borrower participants and 156 non-borrower participants) were used. Probit and Tobit regression models were used for data analysis in STATA. Operational costs, interest rate, grace period, and land size significantly affect both the demand for microcredit and microcredit size, while education, household farming assets, extension services, and payback period significantly affect microfinance demand, and risk attitude/perception additionally determine microcredit size. Special microfinance schemes best suitable for the agriculture sector and crop/plant-specific agriculture financing schemes, currency, and macroeconomic stability are the major policy recommendations. Full article

This paper presents a novel approach to enhancing the security and reliability of drone communications through the integration of Quantum Random Number Generators (QRNG) in Frequency Hopping Spread Spectrum (FHSS) systems. We propose a multi-drone framework that leverages QRNG technology to generate truly random frequency hopping sequences, significantly improving resistance against jamming and interception attempts. Our method introduces a concurrent access protocol for multiple drones to share a QRNG device efficiently, incorporating robust error handling and a shared memory system for random number distribution. The implementation includes secure communication protocols, ensuring data integrity and confidentiality through encryption and Hash-based Message Authentication Code (HMAC) verification. We demonstrate the system’s effectiveness through comprehensive simulations and statistical analyses, including spectral density, frequency distribution, and autocorrelation studies of the generated frequency sequences. The results show a significant enhancement in the unpredictability and uniformity of frequency distributions compared to traditional pseudo-random number generator-based approaches. Specifically, the frequency distributions of the drones exhibited a relatively uniform spread across the available spectrum, with minimal discernible patterns in the frequency sequences, indicating high unpredictability. Autocorrelation analyses revealed a sharp peak at zero lag and linear decrease to zero values for other lags, confirming a general absence of periodicity or predictability in the sequences, which enhances resistance to predictive attacks. Spectral analysis confirmed a relatively flat power spectral density across frequencies, characteristic of truly random sequences, thereby minimizing vulnerabilities to spectral-based jamming. Statistical tests, including Chi-squared and Kolmogorov-Smirnov, further confirm the unpredictability of the frequency sequences generated by QRNG, supporting enhanced security measures against predictive attacks. While some short-term correlations were observed, suggesting areas for improvement in QRNG technology, the overall findings confirm the potential of QRNG-based FHSS systems in significantly improving the security and reliability of drone communications. This work contributes to the growing field of quantum-enhanced wireless communications, offering substantial advancements in security and reliability for drone operations. The proposed system has potential applications in military, emergency response, and secure commercial drone operations, where enhanced communication security is paramount. Full article

With the increasing global demand for renewable energy and heightened environmental awareness, electric vehicles (EVs) are rapidly becoming a popular clean and efficient mode of transportation. However, the widespread adoption of EVs has presented several challenges, such as the lagging development of charging infrastructure, the impact on the power grid, and the dynamic changes in user charging behavior. To address these issues, this paper first proposes a vehicle-to-grid (V2G) optimization framework that responds to regional dynamic pricing. It also considers power balancing in charging and discharging stations when a large number of EVs are involved in scheduling, with the aim of maximizing the benefits for EV owners. Next, by leveraging the interaction between environmental states and the dynamic behavior of EVs, we design an optimization algorithm that combines the recurrent proximal policy optimization (RPPO) algorithm and long short-term memory (LSTM) networks. This approach enhances system convergence and improves grid stability while maximizing benefits for EV owners. Finally, a simulation platform is used to validate the practical application of the RPPO algorithm in optimizing V2G and grid-to-vehicle (G2V) charging strategies, providing significant theoretical foundations and technical support for the development of smart grids and sustainable transportation systems. Full article

Green growth aims to foster economic development while ensuring environmental sustainability by optimizing resource use and reducing pollution. Despite growing attention, the nexus between forest trade, bioenergy, and green growth remains underexplored. Therefore, the main aim of this study is to investigate the impact of forest trade and bioenergy on green growth. To that end, we apply cross-sectional autoregressive distributed lag (CS-ARDL) using 33 global economies. The findings of the CS-ARDL show that forest trade helps enhance green growth both in the short- and long run. However, bioenergy significantly boosts green growth only in the long run, while the short-run estimate of bioenergy is insignificant. The estimates of the regional analysis signify that forest trade and bioenergy enhance green growth in both developed and developing economies only in the long run. Policymakers in both developed and emerging economies should focus on boosting forestry trade and promoting bioenergy production to stimulate green growth. Full article

To address the issue of efficiently reusing the massive amount of unstructured knowledge generated during the handling of track circuit equipment faults and to automate the construction of knowledge graphs in the railway maintenance domain, it is crucial to leverage knowledge extraction techniques to efficiently extract relational triplets from fault maintenance text data. Given the current lag in joint extraction technology within the railway domain and the inefficiency in resource utilization, this paper proposes a joint extraction model for track circuit entities and relations, integrating Global Pointer and tensor learning. Taking into account the associative characteristics of semantic relations, the nesting of domain-specific terms in the railway sector, and semantic diversity, this research views the relation extraction task as a tensor learning process and the entity recognition task as a span-based Global Pointer search process. First, a multi-layer dilate gated convolutional neural network with residual connections is used to extract key features and fuse the weighted information from the 12 different semantic layers of the RoBERTa-wwm-ext model, fully exploiting the performance of each encoding layer. Next, the Tucker decomposition method is utilized to capture the semantic correlations between relations, and an Efficient Global Pointer is employed to globally predict the start and end positions of subject and object entities, incorporating relative position information through rotary position embedding (RoPE). Finally, comparative experiments with existing mainstream joint extraction models were conducted, and the proposed model’s excellent performance was validated on the English public datasets NYT and WebNLG, the Chinese public dataset DuIE, and a private track circuit dataset. The F1 scores on the NYT, WebNLG, and DuIE public datasets reached 92.1%, 92.7%, and 78.2%, respectively. Full article

Supraglacial melt-lakes form and evolve along the western edge of the Greenland Ice Sheet and have proven to play a significant role in ice sheet surface hydrology and mass balance. Prior methods to quantify melt-lake volume have relied upon Landsat-8 optical imagery, available at 30 m spatial resolution but with temporal resolution limited by satellite overpass times and cloud cover. We propose two novel methods to quantify the volume of meltwater stored in these lakes, including a high-resolution surface DEM (ArcticDEM) and an ablation model using daily averaged automated weather station data. We compare our methods to the depth-reflectance method for five supraglacial melt-lakes during the 2021 summer melt season. We find agreement between the depth-reflectance and DEM lake infilling methods, within +/−15% for most cases, but our ablation model underproduces by 0.5–2 orders of magnitude the volumetric melt needed to match our other methods, and with a significant lag in meltwater onset for routing into the lake basin. Further information regarding energy balance parameters, including insolation and liquid precipitation amounts, is needed for adequate ablation modelling. Despite the differences in melt-lake volume estimates, our approach in combining remote sensing and meteorological methods provides a framework for analysis of seasonal melt-lake evolution at significantly higher spatial and temporal scales, to understand the drivers of meltwater production and its influence on the spatial distribution and extent of meltwater volume stored on the ice sheet surface. Full article

The literature written in Chinese mainly and in English with a small amount is reviewed to obtain the overall status of flywheel energy storage technologies in China. The theoretical exploration of flywheel energy storage (FES) started in the 1980s in China. The experimental FES system and its components, such as the flywheel, motor/generator, bearing, and power electronic devices, were researched around thirty years ago. About twenty organizations devote themselves to the R&D of FES technology, which is developing from theoretical and laboratory research to the stage of engineering demonstration and commercial application. After the research and accumulation in the past 30 years, the initial FES products were developed by some companies around 10 years ago. Today, the overall technical level of China’s flywheel energy storage is no longer lagging behind that of Western advanced countries that started FES R&D in the 1970s. The reported maximum tip speed of the new 2D woven fabric composite flywheel arrived at 900 m/s in the spin test. A steel alloy flywheel with an energy storage capacity of 125 kWh and a composite flywheel with an energy storage capacity of 10 kWh have been successfully developed. Permanent magnet (PM) motors with power of 250–1000 kW were designed, manufactured, and tested in many FES assemblies. The lower loss is carried out through innovative stator and rotor configuration, optimizing magnetic flux and winding arrangement for harmonic magnetic field suppression. Permanent magnetic bearings with high load ability up to 50–100 kN were developed both for a 1000 kW/16.7 kWh flywheel used for the drilling practice application in hybrid power of an oil well drilling rig and for 630 kW/125 kWh flywheels used in the 22 MW flywheel array applied to the flywheel and thermal power joint frequency modulation demonstration project. It is expected that the FES demonstration application power stations with a total cumulative capacity of 300 MW will be built in the next five years. Full article

Boundary layer ingestion propulsion systems have attracted much attention due to their significant potential to reduce the fuel consumption of future commercial aircraft. However, the aeroelastic stability of the fan blade is affected by the continuous non-uniform incoming flow induced by the ingestion of the boundary layer. When the fan blades rotate in the junction area between the distorted area and the clean area, blade pressure fluctuations occur. This phenomenon triggers a dynamic response process in the blade. Previous numerical investigations explored the influence of the distorted inflow on the blade vibration amplitude, and found that there are two sources of low-order excitation to the blades: the distorted inflow and the dynamic response of the blade. The results show that the low-order excitation existing in the distorted inflow varies sinusoidally with the distortion extent. However, as a new source of excitation, the key influence mechanism of dynamic response is still unclear. To explore this issue, calculations and analyses were conducted for different distorted inflow intensities. The results show that the blade vibration amplitude increases with the rise in distortion intensity. The total pressure at the leading and trailing edge of the rotor blade was extracted for analysis. It was found that when the blade enters or leaves the distorted area, there is a consistent lag in the change in total pressure at the trailing edge compared to the leading edge. This lag leads to an abrupt variation in the total pressure ratio, which constitutes the dynamic response process of the rotor blade. This periodic change generates a second-order excitation that causes the blade to vibrate. Full article