Search Results (1,233)

With the rapid development of the low-altitude economy, the application of drones in both military and civilian fields has become increasingly widespread. The safety and accuracy of their positioning and navigation have become critical factors in ensuring the successful execution of missions. Currently, GNSS spoofing attack techniques are becoming increasingly sophisticated, posing a serious threat to the reliability of drone positioning. This paper proposes a GNSS spoofing detection and autonomous positioning method for drones operating in mission mode, which is based on visual sensors and does not rely on additional hardware devices. First, during the deception detection phase, the ResNet50-SE twin network is used to extract and match real-time aerial images from the drone’s camera with satellite image features obtained via GNSS positioning, thereby identifying positioning anomalies. Second, once deception is detected, during the positioning recovery phase, the system uses the SuperGlue network to match real-time aerial images with satellite image features within a specific area, enabling the drone’s absolute positioning. Finally, experimental validation using open-source datasets demonstrates that the method achieves a GNSS spoofing detection accuracy of 89.5%, with 89.7% of drone absolute positioning errors controlled within 13.9 m. This study provides a comprehensive solution for the safe operation and stable mission execution of drones in complex electromagnetic environments. Full article

China has identified the high-quality development of its green manufacturing transition as the top priority for upgrading their industrial structure system which will lead to the sustainable development of an innovation ecosystem. To assess their progress in this area, this study selects the panel data of 31 provinces in China from 2011 to 2021 and constructs an evaluation index system for the green transformation of the manufacturing industry from four dimensions: environment, resources, economy, and industrial structure. This not only comprehensively and systematically reflects the dynamic changes in the green transformation of the manufacturing industry but also addresses the limitations of currently used indices. The entropy value method is used to calculate the comprehensive score of the green transformation of the manufacturing industry, while the key factors influencing the convergence of the green transformation of the manufacturing industry are further explored. The results show that first, the overall level of the green transformation of the manufacturing industry has significantly improved as evidenced by an approximate 32% increase. Second, regional differences are significant with the eastern region experiencing significantly higher levels of transformation compared to the central and western regions, along with a decreasing trend from the east to the central and western regions. From a policy perspective, the findings suggest that tailored production methods for each region should be adopted with a greater emphasis on knowledge exchanges to promote green transition in less developed regions. In addition, further regulations are required which, in part, focus on increasing the degree of openness to the outside world to promote the level of green manufacturing transition. Full article

This article investigates the macroeconomic and labor market conditions that shape the adoption of artificial intelligence (AI) technologies among large firms in Europe. Based on panel data econometrics and supervised machine learning techniques, we estimate how public health spending, access to credit, export activity, gross capital formation, inflation, openness to trade, and labor market structure influence the share of firms that adopt at least one AI technology. The research covers all 28 EU members between 2018 and 2023. We employ a set of robustness checks using a combination of fixed-effects, random-effects, and dynamic panel data specifications supported by Clustering and supervised learning techniques. We find that AI adoption is linked to higher GDP per capita, healthcare spending, inflation, and openness to trade but lower levels of credit, exports, and capital formation. Labor markets with higher proportions of salaried work, service occupations, and self-employment are linked to AI diffusion, while unemployment and vulnerable work are detractors. Cluster analysis identifies groups of EU members with similar adoption patterns that are usually underpinned by stronger economic and institutional fundamentals. The results collectively suggest that AI diffusion is shaped not only by technological preparedness and capabilities to invest but by inclusive macroeconomic conditions and equitable labor institutions. Targeted policy measures can accelerate the equitable adoption of AI technologies within the European industrial economy. Full article

Economic growth, especially in emerging economies, has altered the composition of international tourism. It is therefore essential to possess the skills necessary to understand the influence of culture on human behaviour, thereby enabling an appropriate response to the traveller. This research aims to develop a tool for identifying openness, flexibility, awareness, and intercultural preparedness. It focuses on the metacognitive and cognitive aspects of cultural intelligence that shape the development of empathy in customer service staff in hotels in Quintana Roo. The variables were validated and incorporated into a quantitative study using multivariate analysis and inferential statistics. A sample of 77 questionnaires was analysed using simple random sampling under a proportional design. Multiple Correspondence Analysis (MCA) was employed as a discriminatory technique to identify the most significant independent variables. These were subsequently entered as regressors into ordinal logistic regression (OLR), along with age and work experience, in order to estimate the probabilities associated with each level of the dependent variable. The results indicated that age had minimal influence on the metacognitive and cognitive variables, whereas years of experience among tourism staff exerted a significant effect. Full article

Restaurants and open markets generate considerable quantities of organic waste. Converting these residues into poultry feed ingredients offers a sustainable disposal route. This study aimed to evaluate the nutritional and sensory viability of a novel feed complement formulated from Bonito fish meal (Sarda chiliensis chiliensis) and Única potato peel flour (Solanum tuberosum L. cv. Única). This study was conducted in three phases: (i) production and nutritional characterization of the two by-product flours; (ii) formulation of a 48:52 (w/w) blend, incorporated into broiler diets at 15%, 30%, and 45% replacement levels over a 7-week trial divided into starter (3 weeks), grower (3 weeks), and finisher (1 week) phases; and (iii) assessment of growth performance (weight gain, final weight, and feed conversion ratio), followed by a sensory evaluation of the resulting meat using a Check-All-That-Apply (CATA) analysis. The Bonito fish meal exhibited 50.78% protein, while the Única potato peel flour was rich in carbohydrates (74.08%). The final body weights of broiler chickens ranged from 1872.1 to 1886.4 g across treatments, and the average feed conversion ratio across all groups was 0.65. Replacing up to 45% of commercial feed with the formulated complement did not significantly affect growth performance (p > 0.05). Sensory analysis revealed that meat from chickens receiving 15% and 45% substitution levels was preferred in terms of aroma and taste, whereas the control group was rated higher in appearance. These findings suggest that the formulated feed complement may represent a viable poultry-feed alternative with potential sensory and economic benefits, supporting future circular-economy strategies. Full article

The growth and sustainability of today’s global economy heavily relies on smooth maritime operations. The increasing security concerns to marine environments pose complex security challenges, such as smuggling, illegal fishing, human trafficking, and environmental threats, for traditional surveillance methods due to their limitations. Artificial intelligence (AI), particularly deep learning, has offered strong capabilities for automating object detection, anomaly identification, and situational awareness in maritime environments. In this paper, we have reviewed the state-of-the-art deep learning models mainly proposed in recent literature (2020–2025), including convolutional neural networks, recurrent neural networks, Transformers, and multimodal fusion architectures. We have highlighted their success in processing diverse data sources such as satellite imagery, AIS, SAR, radar, and sensor inputs from UxVs. Additionally, multimodal data fusion techniques enhance robustness by integrating complementary data, yielding more detection accuracy. There still exist challenges in detecting small or occluded objects, handling cluttered scenes, and interpreting unusual vessel behaviours, especially under adverse sea conditions. Additionally, explainability and real-time deployment of AI models in operational settings are open research areas. Overall, the review of existing maritime literature suggests that deep learning is rapidly transforming maritime domain awareness and response, with significant potential to improve global maritime security and operational efficiency. We have also provided key datasets for deep learning models in the maritime security domain. Full article

This study focuses on the impact of China’s digital economy on sustainable modernization from 2011 to 2021, using provincial panel data for empirical analysis. By applying threshold and mediation models, we find that the digital economy promotes modernization through industrial upgrading (with a mediating effect of 38%) and trade openness (coefficient = 0.234). The research reveals “U-shaped” nonlinear threshold effects at specific levels of digital development (2.218), market efficiency (9.212), and technological progress (12.224). Eastern provinces benefit significantly (coefficient ranging from 0.12 to 0.15 ***), while western regions initially experience some inhibition (coefficient = −0.08 *). Industrial digitalization (coefficient = 0.13 ***) and innovation ecosystems (coefficient = 0.09 ***) play crucial roles in driving eco-efficiency and equity, in line with Sustainable Development Goals 9 and 13. Meanwhile, the impacts of infrastructure (coefficient = 0.07) and industrialization (coefficient = 0.085) are delayed. Economic modernization improves (coefficient = 0.37 ***), yet social modernization declines (coefficient = −0.12 *). This study not only enriches economic theory but also extends the environmental Kuznets curve to the digital economy domain. We propose tiered policy recommendations, including the construction of green digital infrastructure, carbon pricing, and rural digital transformation, which are applicable to China and offer valuable references for emerging economies aiming to achieve inclusive low-carbon growth in the digital era. Future research could further explore the differentiated mechanisms of various digital technologies in the modernization process across different regions and how to optimize policy combinations to better balance digital innovation with sustainable development goals. Full article

The rapid rise of the silver economy in the new era has become a new driving force for socio-economic development. From the perspective of Marxist political economy theory, this paper analyzes the intrinsic logic of the silver economy’s development through three dimensions: surplus value, labor market, and capital. The study finds that the silver economy in the new era faces challenges such as insufficient supply of high-quality elderly care services, simultaneous shortages in both total talent quantity and structural imbalances, and contradictions between capital’s profit-seeking nature and social welfare. By introducing the multiple streams model, the paper elucidates the coupling process of these three streams and the timing of policy window openings. It proposes targeted strategies, including strengthening technological innovation, deepening labor market reforms, and optimizing capital allocation, to promote the robust development of China’s silver economy and inject strong momentum into sustainable and high-quality economic growth. Full article

The decarbonization of electricity generation by 2030 and the realization of a net-zero economy by 2050 are central to the United States’ climate strategy. However, large-scale renewable integration introduces operational challenges, including extreme ramping, unsafe dispatch, and price volatility. This review investigates how demand–response (DR) aggregators and distributed loads can support these climate goals while addressing critical operational challenges. We hypothesize that current DR aggregator frameworks fall short in the areas of distributed load operational flexibility, scalability with the number of distributed loads (prosumers), prosumer privacy preservation, DR aggregator and prosumer competition, and uncertainty management, limiting their potential to enable large-scale prosumer participation. Using a systematic review methodology, we evaluate existing DR aggregator and prosumer frameworks through the proposed FCUPS criteria—flexibility, competition, uncertainty quantification, privacy, and scalability. The main results highlight significant gaps in current frameworks: limited support for decentralized operations; inadequate privacy protections for prosumers; and insufficient capabilities for managing competition, uncertainty, and flexibility at scale. We conclude by identifying open research directions, including the need for game-theoretic and machine learning approaches that ensure privacy, scalability, and robust market participation. Addressing these gaps is essential to shape future research agendas and to enable DR aggregators to contribute meaningfully to US climate targets. Full article

To explore the integration efficiency of ecology, culture and tourism in China, this study uses a Super-Efficiency SBM model with undesirable outputs to measure integration efficiency, employs kernel density estimation (KDE) to analyze dynamic spatial distribution characteristics, applies the standard deviational ellipse (SDE) to examine the migration trend of the spatial agglomeration center of gravity, and uses Tobit regression to identify spatiotemporal influencing factors. The findings show that: the national integration efficiency presents a trend that first decreases and then increases, with North and South China having relatively high integration efficiency. The national integration efficiency has gone through three stages: narrowing differences, coexistence of slow efficiency, and gradient effects, and increasing efficiency with weakened multipolarization. The degree of spatial agglomeration has gradually increased, and the center of gravity has shifted eastward as a whole. The internal gaps in East and South China have expanded, while the internal balance in North China has improved; the internal differences in other regions have narrowed. The influencing factors of integration efficiency have shifted from traditional economy-led to innovation and institutional collaboration. Economic development level and market openness have a positive impact on the overall integration efficiency, while transportation conditions show a restraining effect. Full article

Cultural heritage in insular regions faces increasing challenges due to overtourism, seasonal economies, and insufficient protection frameworks. This study investigates the adaptive reuse of Sarakina Mansion, a deteriorated 18th-century estate on the island of Zakynthos, as a model for integrating cultural heritage preservation with sustainable tourism. The research addresses the gap in localized strategies for heritage-led development in the context of islands with overtourism. Through a qualitative case study methodology—including site analysis, archival research, and stakeholder interviews—this paper explores how abandoned cultural assets can be reactivated to foster community engagement and diversify tourism models. Two distinct SWOT analyses were conducted as follows: one at the territorial level (Zakynthos Island) and another focused on the island’s cultural heritage. The findings highlight key obstacles such as environmental degradation and policy fragmentation, but they also reveal opportunities for adaptive reuse grounded in local identity and sustainable practices. The proposed reuse scenario for Sarakina promotes partial structural stabilization and community-driven cultural programming, aiming to create a hybrid open-air cultural hub. This study contributes a replicable framework for reimagining neglected heritage assets in overtourism-affected areas, aligned with the UN Sustainable Development Goals (SDGs). Full article

The production of spirits generates significant amounts of waste in the form of fusel oils-previously treated mainly as an environmental problem. This paper presents an innovative installation designed to recover valuable components from this difficult waste. The key achievement is the effective separation and recovery of pyrazine derivatives-natural aromatic compounds with high utility value in the food, cosmetics and pharmaceutical industries. The designed system allows for the recovery of as much as 98% of pyrazines and isoamyl alcohol and isobutanol fractions with a purity above 96%, which is a significant advance compared to previous disposal methods. The installation was designed to be consistent with the idea of a circular economy, maximizing the use of by-products and minimizing losses. The results of the work indicate that fusel oils, previously perceived as waste, can become a source of valuable secondary raw materials, and the presented solution opens up new possibilities for the sustainable development of the alcohol industry. Full article

Housing prices have recently risen sharply in many countries, primarily linked to the global credit cycle. Although various factors play a role, the ability of developing countries to navigate this cycle and maintain autonomous monetary policies is crucial. This paper introduces a dynamic macroeconomic model featuring a housing production sector within an imperfect banking framework. It captures key housing and economic dynamics in advanced and emerging economies. The analysis shows domestic liquidity policies, such as bank capital requirements, reserve ratios, and currency devaluation, can stabilize investment and production. However, their effectiveness depends on foreign interest rates and liquidity. Stabilizing housing prices and risk-free bonds is more effective in high-interest environments, while foreign liquidity shocks have asymmetric impacts. They can boost or lower the effectiveness of domestic policy, depending on the country’s level of financial development. These findings have several policy implications. For example, foreign capital controls would be adequate in the short term but not in the long term. Instead, governments would try to promote the development of local financial markets. Controlling debt should be a target for macroprudential policy as well as promoting saving instruments other than real estate, especially during low interest rates. Full article

Pebble Bed Reactors (PBRs) represent a new generation of nuclear reactors. However, modeling TRi-structural ISOtropic (TRISO) fuel particles employed in PBRs presents a unique challenge in comparison to most conventional reactor designs. Rapid generation of different possible fuel particle configurations for Monte-Carlo simulations provides improved insights into the effects of particle distribution irregularities on the neutron economy. Defective pebbles could cause changes in the neutron flux in a nuclear reactor due to increased or decreased moderating effects. Different configurations of particle fuel also impact isotope production within the nuclear reactor. This study simulates several TRISO configurations representing limited capabilities of randomization algorithms, manufacturing defects configurations and/or special pebble design. All predictions are compared to an equivalent homogenized model used as baseline. The results show that the TRISO configuration has a non-negligible impact on the parameters under consideration. To explain these results, the ratio of the thermal flux of each model to the thermal flux of the homogeneous model is calculated. A clear pattern is observed in the data: as irregularities in the moderator medium emerge due to the distribution of TRISO particles, the neutron spectrum softens, leading to higher values of k_∞ and better fuel utilization. This dependence of the spectrum on the TRISO configuration is used to explain the pattern observed in the depletion calculation. The results open the possibility of optimizing the TRISO configuration in manufactured pebbles for fuel utilization and safeguards. Future work should focus on full core simulations to determine the extent of these findings. Full article

The pursuit of sustainable alternatives to portland cement has become a global imperative within the construction sector, driven by the need to reduce carbon dioxide emissions and energy consumption. Among the promising alternatives, agricultural ashes have garnered attention for their potential as alternative supplementary cementitious materials (ASCMs), owing to their inherent pozzolanic properties when appropriately processed. However, the availability and utilization of these ashes have predominantly been concentrated in tropical and subtropical regions, where such biomass is more abundant. This review offers a comprehensive bibliometric analysis to identify and assess agricultural ashes (specifically switchgrass, barley, sunflower, and oat husks) that are cultivated in temperate and cold climates and exhibit potential for SCM application. The analysis aims to bridge the knowledge gap by systematically mapping the existing research landscape and highlighting underexplored resources suitable for cold-region implementation. Key processing parameters, including incineration temperature, retention duration, and post-combustion grinding techniques, are critically examined for their influence on the resulting ash’s physicochemical characteristics and pozzolanic reactivity. In addition, the effect on fresh, hardened, and durability properties was evaluated. Findings reveal that several crops grown in colder regions may produce ashes rich in reactive silica, thereby qualifying them as viable ASCM candidates and bioenergy sources. Notably, the ashes derived from switchgrass, barley, oats, and sunflowers demonstrate significant reactive silica content, reinforcing their potential in sustainable construction practices. Hence, this study underscores the multifaceted benefits of contributing to the decarbonization of the cement industry and circular economy, while addressing environmental challenges associated with biomass waste disposal and uncontrolled open-air combustion. Full article