Search Results (414)

The presence of nest boxes not only increases the reproductive success of many passerine birds in transformed forest habitats, but they also constitute important artificial microhabitats for many groups of invertebrates. One of such groups which have been often found in this microhabitat is saprophagous mites from the suborder Uropodina (Acari: Mesostigmata). The current study was conducted in October 2023 and 2024 in Bory Tucholskie National Park (BTNP) (northern Poland), where material from 137 tit (Paridae) and nuthatch (Sitta europaea) nest boxes was collected. The aim of this study was to analyse the stability of the communities of Uropodina in nest boxes in the park in two seasons and to determine whether the mite community structure within these nest boxes is similar in each year. The second aim was to analyse the abundance of Uropodina in relation to the composition of the nest box bedding material. This study revealed that the community in the scrutinised nest boxes was formed in both seasons by two species of nidicolous Uropodina species, i.e., Leiodinychus orbicularis (C.L. Koch, 1839) and Chiropturopoda nidiphila (Wiśniewski and Hirschmann, 1993), and that the species composition and the community structure were also very similar in both years. This study revealed that Ch. nidiphila dominated in the nest boxes with moss and grass, whereas L. orbicularis was most abundant in the boxes where the bedding was a mixture of mammalian hair and grass. However, no statistically significant differences in the abundance of these two mite species in both cases were revealed. Full article

As the development of offshore wind energy transforms from coastal to deep-sea regions, designing a cost effective mooring system while ensuring the safety of floating offshore wind turbine (FOWT) remains a critical challenge, especially considering extreme wave environments. This study employs a model coupling computational fluid dynamics (CFD) and finite element method (FEM) to investigate the responses of a parked FOWT platform with its mooring system under rogue wave conditions. Specifically, the mooring dynamics are solved using a local discontinuous Galerkin (LDG) method, which is believed to provide high accuracy. Firstly, rogue wave generation and the coupled CFD-FEM are validated through comparisons with existing experimental and numerical data. Secondly, FOWT platform motions and mooring tensions caused by a rogue wave are obtained through simulations, which are compared with the ones caused by a similar peak-clipped rogue wave. Lastly, analysis of four different mooring design schemes is conducted to evaluate their performance on reducing the mooring tensions. The results indicate that the rogue wave leads to significantly enlarged FOWT platform motions and mooring tensions, while doubling the number of mooring lines with specific line angles provides the most balanced performance considering cost-effectiveness and structural safety under identical rogue wave conditions. Full article

This study evaluates the feasibility and visibility of electric vehicles (EVs) in Egypt, addressing critical research gaps and proposing actionable strategies to drive adoption. Employing a systematic review of academic, governmental, and industry sources, the paper identifies underexplored areas such as rural–urban adoption disparities, lifecycle assessments of EV batteries, and sociocultural barriers, including gender dynamics and entrenched consumer preferences. Its primary contribution is an interdisciplinary framework that integrates technical aspects, such as grid resilience and climate-related battery degradation, with socioeconomic dimensions, providing a holistic overview of EV feasibility in Egypt tailored to Egypt’s context. Key findings reveal infrastructure limitations, inconsistent policy frameworks, and behavioral skepticism as major hurdles, and highlight the untapped potential of renewable energy integration, particularly through synergies between solar PV generation (e.g., Benban Solar Park) and EV charging infrastructure. Recommendations prioritize policy reforms (e.g., tax incentives, streamlined tariffs), solar-powered charging infrastructure expansion, public awareness campaigns, and local EV manufacturing to stimulate economic growth. The study underscores the urgency of stakeholder collaboration to transform EVs into a mainstream solution, positioning Egypt as a regional leader in sustainable mobility and equitable development. Full article

Japan’s 8.5 million vacant homes (Akiyas) represent a paradox of scarcity amid surplus: while rural depopulation leaves properties abandoned, housing shortages and bureaucratic inefficiencies hinder their reuse. This study proposes AKI2ALL, an AI-blockchain framework designed to automate the circular repurposing of Akiyas into ten high-value community assets—guesthouses, co-working spaces, pop-up retail and logistics hubs, urban farming hubs, disaster relief housing, parking lots, elderly daycare centers, exhibition spaces, places for food and beverages, and company offices—through smart contracts and data-driven workflows. By integrating circular economy principles with decentralized technology, AKI2ALL streamlines property transitions, tax validation, and administrative processes, reducing operational costs while preserving embodied carbon in existing structures. Municipalities list properties, owners select uses, and AI optimizes assignments based on real-time demand. This work bridges gaps in digital construction governance, proving that automating trust and accountability can transform systemic inefficiencies into opportunities for community-led, low-carbon regeneration, highlighting its potential as a scalable model for global vacant property reuse. Full article

The scientific investment decision of Park-level Integrated Energy System (PIES) projects is of great significance to energy enterprises for improving the efficient utilization of funds, promoting green and low-carbon transformation, and achieving the goal of carbon neutrality. This paper proposed a two-stage investment framework that integrates a multi-objective 0–1 programming model with a multi-criteria decision-making (MCDM) technique to determine the optimal PIES project investment portfolios under the constraint of quota investment. First, a multi-objective (MO) 0–1 programming model was constructed for typical PIES projects in Stage-I, which considers economic and environmental benefits to obtain Pareto frontier solutions, i.e., PIES project portfolios. Second, an evaluation index system from multiple dimensions was established, and a hybrid MCDM technique was adopted to comprehensively evaluate the Pareto frontier solutions in Stage-II. Finally, the proposed model was applied to an empirical case, and the simulation results show that the decision framework can achieve the best overall benefit of PIES project portfolios with maximal economic benefit and minimum carbon emissions. In addition, the robustness analysis was performed by changing the indicator weights to verify the stability of the proposed framework. This research work could provide a theoretical tool for investment decisions regarding PIES projects for energy enterprises. Full article

Accurate land-use classification is critical for urban planning and environmental monitoring, yet effectively integrating heterogeneous data sources such as hyperspectral imagery and laser radar (LiDAR) remains challenging. To address this, we propose MixtureRS, a compact multimodal network that effectively integrates hyperspectral imagery and LiDAR data for land-use classification. Our approach employs a 3-D plus heterogeneous convolutional stack to extract rich spectral–spatial features, which are then tokenized and fused via a cross-modality transformer. To enhance model capacity without incurring significant computational overhead, we replace conventional dense feed-forward blocks with a sparse Mixture-of-Experts (MoE) layer that selectively activates the most relevant experts for each token. Evaluated on a 15-class urban benchmark, MixtureRS achieves an overall accuracy of 88.6%, an average accuracy of 90.2%, and a Kappa coefficient of 0.877, outperforming the best homogeneous transformer by over 12 percentage points. Notably, the largest improvements are observed in water, railway, and parking categories, highlighting the advantages of incorporating height information and conditional computation. These results demonstrate that conditional, expert-guided fusion is a promising and efficient strategy for advancing multimodal remote sensing models. Full article

This study employs Hannah Arendt’s (1958) the human condition as a philosophical framework to examine walking not merely as a physical activity but as a meaningful form of environmental consciousness. Homo faber, which denotes tool making, corresponds to the nature-based railway regeneration exemplified by the Gyeongui Line Forest Park in Seoul City, South Korea. By applying walking as a method, bifurcated themes are explored: a pedestrian-provision focus on walkability and an environmentally oriented focus consisting of nature and culture, supporting the notion that environmental elements are co-experienced through the embodied activity of walking. Thematic findings are supported by generalized additive models, grounded in a between-method triangulation attempt. The results confirm the interdependencies among the park’s environment, pedestrian satisfaction, and environmental consciousness. Specifically, the environment surrounding the park, which traverses natural and cultural elements, is strongly associated with both pedestrian satisfaction and environmental sensitivity. The research reifies walking as a fundamental human condition, encompassing labor, work, and action, while arguing for heuristic reciprocity between homo faber and nature, as well as framing walking as a sustainably meaningful urban intervention. This study contributes to maturing the theoretical understanding of walking as a vital human condition and suggests practical insights for pedestrian-centered spatial transformation. Full article

In order to promote the green and low-carbon transformation of the economy and society, as the economic center of the western region of China, Chengdu actively promotes the national green and low-carbon policies. Some specific measures are proposed to develop Chengdu’s low-carbon economy, such as increasing the ownership of new energy vehicles, promoting the development of park cities and increasing the proportion of clean energy and non-fossil energy, etc. So, in order to accurately evaluate Chengdu’s low-carbon economy-development achievements, firstly, this paper uses literature research to construct an evaluation indicator system for the low-carbon economy development of Chengdu city from five dimensions: economy, energy, technology, environment, and transportation. Then, an improved Analytic Hierarchy Process (AHP) method based on judgment matrices is proposed to determine subjective weights of indicators, while Entropy Weight Method (EWM) and Variation Coefficient (VC) method are used to determine objective weights of the evaluation indicators. Finally, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is used for the multi-indicator comprehensive evaluation of Chengdu’s low-carbon economy development. The evaluation results show that the comprehensive performance of Chengdu’s low-carbon economy has continued to improve from 2018 to 2023, simultaneously, the main influencing factors and weak links are analyzed, and targeted suggestions and strategies for improvement are put forward to promote the low-carbon economy development of Chengdu city. Full article

Contemporary urban planning faces the ongoing challenge of developing Green Infrastructure capable of providing vital ecosystem services. Within this framework, the Barcelona metropolitan area has advanced a network of parks that, while serving local neighborhoods, also aim for metropolitan relevance. This study offers a forward-looking analysis of selected parks in the southern Llobregat River basin—an area shaped by historic villages and working-class settlements—to evaluate their contribution to both Green Infrastructure and the region’s polycentric structure. Building on previous landmark studies and multidisciplinary perspectives, the research examines eight parks through four spatial and scalar lenses, assessing their territorial role and accessibility, ecological connectivity, urban integration and permeability, and landscape design with both qualitative and quantitative data. Using a comparative framework alongside research-by-design methods tested in urban design studios, the research links analytical insights to design-based strategies. The outcome is a set of actionable guidelines aimed at enhancing local park performance, with broader implications for over 50 ‘Metropolitan Parks’ spread in more than 30 municipalities. These insights contribute to shaping a more integrated, livable, and resilient metropolitan region. Full article

Sustainable urban development takes place in cities that encourage residents to adopt sustainable consumption behaviors. Cities are transforming towards achieving sustainable urban consumption, meeting the needs of communities without compromising the wealth of future generations. A key element of urban development is sustainable urban mobility, which helps improve residents’ quality of life and protect the environment. The development of sustainable mobility is possible thanks to, among others, investment in infrastructure that improves travel. One element of this infrastructure that plays an important role in sustainable mobility is parking lots. They have a significant impact on the quality of life in the city, and searching for available parking spaces is a serious problem in modern urban mobility. This article includes an analysis of parking data obtained from the Intelligent Paid Parking System in the context of sustainable urban consumption. Three streets in the city of Rzeszów were analyzed. For the period under study, the factors determined included parking space utilization indicators, whose average value for the streets analyzed was in the range of 57–59%, and a turnover indicator, whose average value was in the range of 4.8–6.0. These indicators assessed the degree to which city residents are involved in ideas related to sustainable development, as well as their habits in relation to sustainable consumption. Full article

Accurate, detailed, and up-to-date tree species distribution information is essential for effective forest management and environmental research. However, existing tree species maps face limitations in resolution and update cycle, making it difficult to meet modern demands. To overcome these limitations, this study proposes a novel framework that utilizes existing medium-resolution national tree species maps as ‘weak labels’ and fuses multi-temporal Sentinel-2 and PlanetScope satellite imagery data. Specifically, a super-resolution (SR) technique, using PlanetScope imagery as a reference, was first applied to Sentinel-2 data to enhance its resolution to 2.5 m. Then, these enhanced Sentinel-2 bands were combined with PlanetScope bands to construct the final multi-spectral, multi-temporal input data. Deep learning (DL) model training data was constructed by strategically sampling information-rich pixels from the national tree species map. Applying the proposed methodology to Sobaeksan and Jirisan National Parks in South Korea, the performance of various machine learning (ML) and deep learning (DL) models was compared, including traditional ML (linear regression, random forest) and DL architectures (multilayer perceptron (MLP), spectral encoder block (SEB)—linear, and SEB-transformer). The MLP model demonstrated optimal performance, achieving over 85% overall accuracy (OA) and more than 81% accuracy in classifying spectrally similar and difficult-to-distinguish species, specifically Quercus mongolica (QM) and Quercus variabilis (QV). Furthermore, while spectral and temporal information were confirmed to contribute significantly to tree species classification, the contribution of spatial (texture) information was experimentally found to be limited at the 2.5 m resolution level. This study presents a practical method for creating high-resolution tree species maps scalable to the national level by fusing existing tree species maps with Sentinel-2 and PlanetScope imagery without requiring costly separate field surveys. Its significance lies in establishing a foundation that can contribute to various fields such as forest resource management, biodiversity conservation, and climate change research. Full article

This article analyses the impact of urban sprawl on the Valencia Plain aquifer system from 1990 to 2018, focusing on land use and land cover (LULC) changes and their environmental implications. The study applies the Weighted Environmental Index (WEI), a composite indicator based on a functional landscape perspective, to quantify changes in the environmental value over time. The WEI combines CORINE Land Cover and World Settlement Footprint data to enhance spatial resolution and urban land detection. The results show a significant territorial transformation, with urban surfaces expanding by 70% and rainfed agricultural areas declining by over 59%. Consequently, the WEI decreased from 44.80 in 1990 to 40.68 in 2018, representing a 9.2% reduction in the environmental value. These changes threaten the sustainability of key ecosystems such as the Albufera Natural Park and indicate a reduced capacity to deliver ecosystem services, including aquifer recharging, biodiversity conservation, and climate regulation. The findings underscore the need for integrated land-use planning, the protection of peri-urban agricultural areas, and the implementation of nature-based solutions to counteract the environmental impacts of urban growth in Mediterranean metropolitan contexts. Full article

This article traces the development of video activism in Turkey over the past two decades, focusing on its transformation during and after the 2013 Gezi Park protests. Situating video activism as a form of radical media, it examines how activists have used digital recording technologies to document protests, amplify marginalized voices, and contest dominant narratives. Drawing on interviews with members of eight activist collectives, the article maps the evolving practices, ethics, and internal dynamics of video activism, paying close attention to tensions between visibility and security and between participation and professionalism. The Gezi protests marked a turning point, expanding video activism into a decentralized, diverse, and participatory field. In the aftermath, while collective structures weakened due to increasing repression and shrinking protest spaces, the use of video as a tool for testimony, expression, and archiving has continued to evolve. New actors, forms, and platforms have emerged, transforming video activism from a specialized practice into a broader, more diffuse form of engagement. This article argues that video activism remains a significant mode of mediated resistance, reflecting the challenges and the evolving potential of visual intervention in an ever-changing political landscape. Full article

The rise of knowledge-based economies has positioned higher education institutions as key actors in human capital development, requiring them to engage more actively with labor markets through strategic partnerships. Within this context, university-affiliated science and technology parks (STPs) have evolved into integrated learning environments that support experiential learning and mentorship practices. This study aims to explore the lived experiences of undergraduate students who participated in these processes within an STP in İstanbul, Türkiye. Using a qualitative phenomenological approach, data were collected through semi-structured interviews with 15 students selected via purposive maximum variation sampling. Thematic analysis, supported by MAXQDA 2024, was used to examine the data. Two main themes were identified: (i) talent development through experiential learning and (ii) talent development through mentorship. The findings indicate that students reconstructed theoretical knowledge through real-world applications, developed a clearer professional identity, and gained strategic career awareness. Mentorship provided both technical and psychosocial support, fostering self-confidence, emotional security, and role modeling. This study concludes that STPs play a strategic role in aligning academic learning with employability and institutional talent development goals. These results contribute to broader educational and workforce development discussions and are closely aligned with Sustainable Development Goals 4 (Quality Education) and 8 (Decent Work and Economic Growth), highlighting STPs as transformative platforms in higher education. Moreover, this study offers practical implications for aligning higher education with employment systems through structured experiential learning and mentorship practices. Full article

Video surveillance is widely used in different areas like roads, malls, education, industries, retail, parks, bus stands, and restaurants, each presenting distinct anomaly patterns that demand specialized detection strategies. Adapting anomaly detection models to new camera viewpoints or environmental variations within the same scenario remains a significant challenge. Extending these models to entirely different surveillance environments or scenarios often requires extensive retraining, which can be both resource-intensive and time-consuming. To overcome these limitations, model frameworks, i.e., the video anomaly detector model, have been proposed, leveraging the meta-learning framework for faster adaptation using swin transformer for feature extraction to new concepts. In response, the dataset named MSAD (multi-scenario anomaly detection) having 14 different scenarios from multiple camera views, is the high resolution anomaly detection dataset that includes diverse motion patterns and challenging variations such as varying lighting and weather conditions, offering a robust foundation for training advanced anomaly detection models. Experiments validate the effectiveness of the proposed framework, which integrates model-agnostic meta-learning (MAML) with a ten-shot, one-query adaptation strategy. Leveraging the swin transformer as a spatial feature extractor, the model captures rich hierarchical representations from surveillance videos. This combination enables rapid generalization to novel viewpoints within the same scenario and maintains competitive performance when deployed in entirely new environments. These results highlight the strength of MAML in few-shot learning settings and demonstrate its potential for scalable anomaly detection across diverse surveillance scenarios. Full article