Search Results (297)

The capacity of exhibitions to transform a city extends over a long period. The expo area is converted into a unique scenario for architecture, diversity, technology, mobility, and culture during the event itself. After the exhibition is over, work continues with the architectural transformations necessary to reconfigure the place into one that responds to the needs of the city and its inhabitants. The collateral actions of urban development through exhibitions involve the regeneration of different areas of the city, such as emblematic areas, and the reconfiguration of its operational systems such as transport, telecommunications, various networks, etc. Universal Expositions have historically served as catalysts for large-scale urban transformation, leaving behind complex spatial, architectural, and infrastructural legacies. However, the long-term integration of former expo sites into the contemporary city remains uneven and insufficiently documented, particularly in the case of Seville, which hosted both the 1929 Ibero-American Exposition and the 1992 Universal Exposition. This research employs a mixed qualitative methodology combining archival investigation, cartographic and photographic analysis, field observation, and research by design. Based on these findings, the paper presents an original architectural and landscape intervention for the degraded area of Isla de la Cartuja, proposing a multifunctional center and botanical garden, a recreational complex that reactivates an abandoned section of the former American Garden. This study contributes to worldwide discussions on mega-event legacies by offering a structured post-expo evaluation framework, identifying lessons for future regeneration processes, and demonstrating how research by design can support the sustainable transformation of such a former event landscape. Full article

Urban agriculture’s potential for food production and other social benefits is widely documented. However, the diversity of organisational structures and contextual factors that shape and drive the practice leads to a range of productivity levels. Yet, most studies estimate productivity using average production data, which compromises the reliability of the estimates. The objective of the study presented here is to develop a GIS-based spatial analytical framework that takes into account varying levels of productivity for four urban food garden types: Home, Community, Educational, and Commercial. We apply this analytical framework in Bogotá, Colombia, a city at the forefront of policies promoting urban agriculture, where we collected data from a sample of urban food gardens (i.e., produce yield, resource use, and social benefits). To increase the precision and reliability of the estimates, we perform a spatial Multi-Criteria Decision Analysis through several ArcGIS pro 3.1 functions. This allows the identification of suitable areas for each urban agriculture type, based on key spatial and social characteristics (location, proximity to roads and to rivers, private or public land, urban density, and socio-economic demographic conditions). Results suggest that 25% of Bogotá’s surface area (including vacant urban land and roofs) presents potential physical and social conditions for food growing, within which Home Gardens occupy the largest share of suitable land. This shows that land availability is not a key limiting factor to a possible expansion of urban agriculture, particularly at a household level. Resource consumption and educational benefits are also estimated, hence providing a comprehensive picture of the impact of urban food production at a city scale. Full article

With the emergence of smart agriculture, precise behavior recognition in tea gardens has become increasingly important for operational monitoring and intelligent management. However, existing behavior detection systems face limitations when deployed in real-world agricultural environments, particularly due to dense vegetation, variable lighting, and diverse human–machine interactions. This paper proposes a novel deep learning framework for picking behavior recognition tailored to complex tea plantation environments. We first construct a large-scale, annotated dataset comprising 12,195 images across 7 behavior categories, collected from both field and web sources, capturing a diverse range of geographic, temporal, and environmental conditions. To address occlusion and multi-scale detection challenges, we enhance YOLOv5 by integrating an Exponential Moving Average (EMA) attention mechanism, Complete Intersection over Union (CIoU) loss, and Atrous Spatial Pyramid Pooling (ASPP), achieving a 73.6% mAP@0.5, representing an 11.6% relative improvement over the baseline model, which indicates a notable enhancement in detection accuracy under complex tea garden conditions. Furthermore, we propose an SE-Faster R-CNN model by embedding Squeeze-and-Excitation (SE) channel attention modules and anchor box optimization strategies, which significantly boosts performance in complex scenarios. A lightweight visual interface for real-time image and video-based detection is also developed to enhance the practical deployability of the system. Experimental results demonstrate the effectiveness, robustness, and real-time potential of the proposed system in recognizing tea garden behaviors under real field conditions. Full article

Urban edible greening initiatives, such as urban orchards and community fruit gardens, can deliver ecological and social benefits, but their long-term success depends on community acceptance. This study examines the establishment phase of a newly planted orchard in a housing estate in a mid-sized Czech city and operationalizes esthetic fit over time, i.e., the extent to which early-phase design is perceived as orderly, suitable, and promising using targeted items on design legibility, species–site suitability, and perceived promise. Data were collected through standardized face-to-face interviews with 150 residents, using a stratified sampling strategy. The survey elicited anticipated burdens and benefits, current and future evaluations of the orchard, and attitudes toward its care. Attitudes were measured with an adapted Urban Green Attachment Scale (UGAS). Descriptive and inferential analyses, including logistic regression and non-parametric tests, were conducted. Findings reveal that residents credited the orchard with design legibility, beauty, and ecological promise, while pragmatic concerns focused on maintenance tasks (leaf litter, watering) and questions of fruit access. Window views of the orchard and general satisfaction with the residential environment significantly increased the odds of higher attachment, while gender differences suggested varied engagement pathways. Importantly, attachment was strongly associated with stewardship intentions; residents with higher UGAS scores were more likely to defend the orchard, taste the fruit, participate in maintenance, and even support its preservation through higher property taxes. The results underscore that attachment is measurable before full ecological performance emerges, arising from a combination of design legibility and daily visibility. Practically, visible routines of care can pace expectations and sustain legitimacy. Conceptually, the study demonstrates that early-phase esthetic fit spans installation with stewardship, providing a foundation for long-term resilience and co-stewardship of edible urban greening. Full article

In the context of urban cultural heritage tourism, this study examines how visitors’ expectations and perceptions contribute to satisfaction and loyalty. We develop and validate an integrative model using structural equation modeling (SEM) on data collected from a large-scale visitor survey of Lingnan classical gardens in China, supplemented by quantitative simulation analysis. The results reveal that Cultural Expectation (CE) and Spatial Expectation (SE) serve as key antecedents to Perceived Cultural Immersion (PCI) and Perceived Spatial Aesthetics (PSA), respectively. PCI and PSA both positively influence visitors’ Perceived Value (PV), which in turn drives overall Satisfaction (SAT) and ultimately enhances Loyalty (LOY). Notably, PCI and PSA act as mediators in the model, channeling the influence of initial expectations into value perception and satisfaction. By highlighting the roles of cultural immersion and spatial aesthetics, this research extends the perceived value–satisfaction–loyalty paradigm in cultural heritage tourism. Theoretically, it bridges urban cultural heritage and environmental psychology with place-attachment theories. Practically, the findings offer guidance for cultural landscape management and visitor experience enhancement, suggesting that narrative storytelling, interactive cultural engagement, and authentic spatial design can elevate perceived value, satisfaction, and loyalty. This article will provide valuable reference material for urban planners. Full article

Aiming at the low qualification rate and high damage caused by the lack of identification, localization, and posture estimation of tea buds in the mechanical harvesting process of famous tea, a framework of lightweight detection + PCA-skeleton fusion posture estimation was proposed. Based on the YOLOv8n model, the StarNet backbone network was introduced to enable lightweight detection, and the ASF-YOLO multi-scale attention module was embedded to improve the feature fusion ability. Based on the detection frame, the GrabCut-Watershed fusion segmentation was employed to obtain the bud mask. Combined with PCA and skeleton extraction algorithms, the main direction deviations of bent buds and clasped leaves were solved by Bézier curve fitting, and the morphology–posture dual-factor scoring model was thereby constructed to realize the picking ranking. Compared with the original YOLOv8n model, the results showed that the detection accuracy and mAP50 of the Improved model decreased to 85.6% and 90.5%, respectively, and the recall rate increased to 81.7%. Meanwhile, the calculation load of the improved model was reduced by 23.6%, reaching 6.8 GFLOPs, indicating a significant improvement in lightweight. The morphology–posture dual-factor scoring model achieved a score of 0.88 for a single bud in vertical direction (θ ≈ 90°), a score of approximately 0.66–0.71 for buds with partially unfolded leaves and slightly bent buds, and a score of 0.48–0.53 for severely bent and overlapped buds. The results of this study have the potential to guide the picking robotic arms to preferentially pick tea buds with high adaptability and provide a reliable visual solution for low-loss and high-efficiency mechanized harvesting of famous tea in complex tea gardens. Full article

To address the challenges of variable target scale, complex background, blurred image, and serious occlusion in the yield detection of Yunnan large-leaf tea tree, this study proposes a deep learning network DE-YOLOv13-S that integrates the visual mechanism of primates. DynamicConv was used to optimize the dynamic adjustment process of the effective receptive field and channel the gain of the primate visual system. Efficient Mixed-pooling Channel Attention was introduced to simulate the observation strategy of ‘global gain control and selective integration parallel’ of the primate visual system. Scale-based Dynamic Loss was used to simulate the foveation mechanism of primates, which significantly improved the positioning accuracy and robustness of Yunnan large-leaf tea tree yield detection. The results show that the Box Loss, Cls Loss, and DFL Loss of the DE-YOLOv13-S network decreased by 18.75%, 3.70%, and 2.54% on the training set, and by 18.48%, 14.29%, and 7.46% on the test set, respectively. Compared with YOLOv13, its parameters and gradients are only increased by 2.06 M, while the computational complexity is reduced by 0.2 G FLOPs, precision, recall, and mAP are increased by 3.78%, 2.04% and 3.35%, respectively. The improved DE-YOLOv13-S network not only provides an efficient and stable yield detection solution for the intelligent management level and high-quality development of tea gardens, but also provides a solid technical support for the deep integration of bionic vision and agricultural remote sensing. Full article

In the context of smart agriculture, the tea industry is undergoing a transformative shift toward intelligent development. As the birthplace of tea, China holds a significant position in the global tea industry, with Anxi County in Quanzhou City, Fujian Province—renowned as the origin of Tie Guan Yin—standing as the world’s largest oolong tea production area. Its intelligent transformation of the tea industry is typical and representative. However, current research on the digital literacy of farmers is not yet mature, and there is a lack of systematic research on this specific group of tea farmers, which to some extent restricts the transformation of the tea industry towards intelligent development. The level of digital literacy among tea farmers is crucial for the intelligent development and transformation of the tea industry. Improving the digital literacy of tea farmers is the key to promoting the intelligent development of the tea industry. Therefore, studying the digital literacy of tea farmers has significant practical significance. This article takes Anxi County as the research area and focuses on moderate-scale tea farmers as the research object. Based on the United Nations Global Framework for Digital Literacy and taking into account the actual situation of tea farmers, an evaluation index system and analysis framework for tea farmers’ digital literacy have been constructed from seven dimensions: equipment and software operation skills, digital information literacy, digital communication and collaboration literacy, digital content creation literacy, digital security literacy, problem-solving literacy, and professional digital literacy. Using literature review, questionnaire survey, interview, and quantitative analysis methods, a questionnaire containing the above-mentioned dimensions was designed. After collecting data, the rationality of the questionnaire structure was verified using SPSS software. The digital literacy level of 440 medium-sized tea farmers from 11 major tea-producing townships in Anxi County was measured, analyzed, and Two-Tailed correlation tests were conducted. The results indicate that there are currently six aspects of digital literacy among tea farmers that are at a moderate level, and professional digital literacy is the weakest among the seven digital literacy. The overall digital literacy level of tea farmers needs to be strengthened. Large-scale tea farmers have the conditions to apply smart agricultural equipment and technology, which can achieve intelligent and refined management of tea gardens and intelligent upgrading of the entire industry chain. Based on the research results of the seven digital literacy of tea farmers, this article proposes improvement measures corresponding to the seven digital literacy of tea farmers from the perspectives of “government, industry associations, and training institutions”, providing reference for Anxi County and other tea-producing areas in the world. Full article

Background/Objectives: The benefits of nature-based interventions to support well-being and mental health are increasingly well-documented in the literature; however, study of an occupational therapy gardening program for adolescents with exposure to adverse childhood experiences (ACEs) is limited. Methods: This study evaluates the feasibility of a novel school gardening program for youth with a history of ACEs including the following: (1) recruitment; (2) data collection procedures and outcome measures; (3) acceptability and suitability of the intervention; and (4) evaluation of the response to a gardening intervention as measured by a visual analog scale of emotional state, a heartbeat counting task designed to capture changes in interoceptive awareness, and qualitative data from the teacher and researchers. This feasibility study was designed as an 8- to 10-week program (10 sessions minimum) to accommodate the school’s academic curriculum and support the participants’ academic progression. Three adolescents were recruited, ages 12–17, two of whom completed a shorter version of the program and one who dropped out. Results: Results indicated the gardening intervention recruitment and data collection procedures were feasible. Intervention was acceptable to participants. Outcome measures that produce both quantitative and qualitative changes are needed. Interoceptive measures show promise but require further refinement. Response to intervention seemed to be influenced by the participant’s psychosocial history but suggests possible changes in prosocial behavior. External factors such as absenteeism influenced aspects of participation, including frequency and duration of intervention. Conclusions: These findings suggest gardening interventions in occupational therapy are feasible and suitable for adolescents with a history of adversity. Potential exists for enhanced social connectedness, which supports mental health and well-being. Suggestions are offered for implementation and outcome measurements appropriate for this population. Full article

Urban growth in hot, arid regions intensifies the urban heat island effect, making green spaces vital for climate mitigation. This research investigates the impact of public gardens on the surrounding urban thermal environment and on the mitigation of the urban heat island (UHI) in a hot arid region. This study selects an important public garden in Biskra, the “5 July 1962” Garden, as a case study of significance at the urban scale. To achieve research objectives, onsite measurement using a digital measurement device (5-in-1 Environmental Meter “Extech EN300”) and satellite remote sensing data from LANDSAT8 are employed, capturing summer measurements of key parameters and indices: Land Surface Temperature (LST), Air Temperature (AT), Relative Humidity (RH), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Difference Moisture Index (NDMI). The analysis and correlation of these indices with the LST values allow us to evaluate the zoning and distance impacts of the garden studied. Land surface temperature rises gradually from the garden outward, peaking in the North-East with the strongest heat island effect and remaining lower in the cooler, vegetation-rich South-West. The results reveal that air temperature is the primary driver of land surface temperature (72% impact), while relative humidity (17.3%), vegetation index (7.8%), moisture index (2.9%), and water index (1.7%) contribute to cooling, with vegetation and moisture reducing surface temperatures through shading, transpiration, and latent heat exchange. Full article

The Building Houses on Rented Land Model (BHRLM) was a pivotal land development model that drove Shanghai’s urbanization in the early modern era. This research examines the spatial generative mechanism of the Prepaid Building Houses on Rented Land Model (PBHRLM), prevalent during 1938–1941. It reveals how the wartime economic environment enabled interest alliances constituted with developers, landowners, and tenants to stimulate urban spatial growth. Firstly, we aim to analyze the features of architectural types linked to the PBHRLM using data-driven methods. Secondly, we aim to apply financial capital theory to investigate the innovations of financing methods. Finally, we draw on speculation theory to establish connections between the features of architectural types and the innovations of financing methods. The results include the following: (1) The PBHRLM’s dominant architectural types—new-styled lane houses, semi-shikumen lane houses, and garden houses—shared low-rise, high-density spatial features. (2) The PBHRLM’s innovations of financing methods lie in its convergence of financing and profitability, reflecting developers’ speculative intent. The research concludes that the PBHRLM operated as a spatial actuarial practice. Through risk games, the developers utilized the model to liberate land development from the control of financial capital and achieved multi-stakeholder synergy, generating small-scale, dispersed land development patterns. At the same time, surging housing demand thus perpetuated architectural types catering to the middle class with low-rise, low-tech tectonics and independent dwelling styles that continued to densely populate Shanghai concessions. Full article

Accurate detection of tea shoots in natural environments is crucial for facilitating intelligent tea picking, field management, and automated harvesting. However, the detection performance of existing methods in complex scenes remains limited due to factors such as the small size, high density, severe overlap, and the similarity in color between tea shoots and the background. Consequently, this paper proposes an improved target detection algorithm, RFA-YOLOv8, based on YOLOv8, which aims to enhance the detection accuracy and robustness of tea shoots in natural environments. First, a self-constructed dataset containing images of tea shoots under various lighting conditions is created for model training and evaluation. Second, the multi-scale feature extraction capability of the model is enhanced by introducing RFCAConv along with the optimized SPPFCSPC module, while the spatial perception ability is improved by integrating the RFAConv module. Finally, the EIoU loss function is employed instead of CIoU to optimize the accuracy of the bounding box positioning. The experimental results demonstrate that the improved model achieves 84.1% and 58.7% in mAP@0.5 and mAP@0.5:0.95, respectively, which represent increases of 3.6% and 5.5% over the original YOLOv8. Robustness is evaluated under strong, moderate, and dim lighting conditions, yielding improvements of 6.3% and 7.1%. In dim lighting, mAP@0.5 and mAP@0.5:0.95 improve by 6.3% and 7.1%, respectively. The findings of this research provide an effective solution for the high-precision detection of tea shoots in complex lighting environments and offer theoretical and technical support for the development of smart tea gardens and automated picking. Full article

The Work-Unit Compound (WUC) is a common neighborhood type that became prevalent during China’s socialist era, typically offering communal spaces that serve as vital spatial carriers for communal life. Following the shift in public housing provision from the work-unit (state-owned enterprise) through the social welfare system to a market-oriented system, the decline of work-unit systems and the privatization of housing led to the distribution of responsibility for communal spaces in WUC neighborhoods becoming more intricate. Issues related to these spaces, such as underutilization and poor management, were exacerbated as the built environment deteriorated. By the 2010s, these challenges had become central targets of various participatory regeneration initiatives. However, current discourse on participatory regeneration predominantly focuses on social outcomes, paying limited attention to post-regeneration governance modes. In response, this study develops a framework to examine the continuity and heterogeneity of communal space governance during and after regeneration projects in WUC neighborhoods. It offers a nuanced investigation of context-specific facilitating mechanisms, with the goal of supporting more effective and sustainable communal space governance in the future. Using a case study approach, the research draws on in-depth interviews that were systematically analyzed. The findings indicate that daily communal space governance in the sampled projects continues to rely on internal problem-solving methods inherited from the work-unit system (e.g., the collective sense of honor and a persistent reliance on state actors). Additionally, governance is driven by economic initiatives repurposing underutilized spaces—for instance, vegetable cultivation in enclosed areas is employed for self-sufficiency (Case A), and small-scale business that benefits the neighborhood is performed (Case B). This study also identifies a blend of formal and informal institutional arrangements linked to participatory regeneration, including the coproduction of space management between residents and grassroots government (Case A), and the government-funded purchase of community services (Case B). Moreover, extra-local networks—such as gaining mutual support from Community Garden Networks (Case A) and Community Economic Cooperatives (Case B)—play a significant role. In conclusion, this study highlights the role of facilitating mechanisms associated with participatory regeneration in shaping daily communal space governance and explores the potential of participatory strategies within contemporary neighborhood governance, particularly under recent Chinese policies targeting dilapidated urban neighborhoods. Practically, this study offers recommendations for planners and practitioners regarding incorporating facilitating mechanisms into participatory regeneration to enhance community engagement in communal space governance, especially in other post-socialist cities experiencing similar challenges. Full article

Fusarium wilt (Foc) TR4 was first reported in Northern Vietnam in 2018. Since then, it has rapidly spread across most northern provinces along the Red River basin banana production landscapes, impacting Cavendish (Musa AAA genome) production. The other main banana cultivars which are widely grown in this production zone are Pisang Awak (Musa ABB genome) and Pisang Mas (Musa AA genome). Field surveys were conducted in 2022/2023 across this banana production region to assess pathogen spread from Cavendish monocropping systems into adjacent smaller-scale mixed cultivar systems. Across 130 sites, a total of 210 banana pseudostem tissue samples were collected from symptomatic Cavendish, Pisang Awak and Pisang Mas plants. Foc TR4 incursions into mixed small-to-mid-sized Cavendish–Pisang Awak plantations were confirmed, and the pathogen was also recorded in Pisang Awak plantations and backyard gardens that did not contain any Cavendish mats. A screenhouse-based Foc TR4 screening trial including seven commonly cultivated Musa varieties in Northern Vietnam indicated that Pisang Awak and Pisang Mas are susceptible to the pathogen. While Pisang Awak, an important local variety, is known to be susceptible to both Foc Race 1 and TR4, recent field observations suggest a limited susceptibility of Pisang Awak to Foc TR4 in mixed cultivar plantation settings. Local farmers similarly reported observing reduced susceptibility, with several having already replanted TR4-affected Cavendish fields with Pisang Awak as part of their disease management strategy. No infections were observed on field-grown Pisang Mas plants in TR4-affected mixed banana cultivar production landscapes. These results and insights provide solutions for the revival of TR4-affected Cavendish production fields or landscapes, through the cultivation of less susceptible local cultivars. In addition, the introduction, validation and scaling of Formosana (i.e., GCTCV-218, a Cavendish somaclone with moderate resistance to Foc TR4) should be envisaged. Full article

Leaf phenology is a crucial functional trait in temperate forest trees that integrates environmental signals and reflects species’ adaptive capacity to stress. This study examined how moderate drought and elevated phosphorus availability, alone and in combination, affect the spring and autumn phenology of juvenile Fagus sylvatica and Quercus petraea saplings from two climatically distinct Croatian provenances. In a common garden experiment, saplings were subjected to four treatments involving drought and phosphorus addition. Phenological stages were scored using standardized ordinal scales across two growing seasons. Results revealed that phosphorus consistently advanced autumn leaf senescence in both species, independent of drought, while drought effects were species- and provenance-specific. Spring phenology was more sensitive to drought: beech from the drier provenance advanced budburst, suggesting an escape strategy, whereas oak delayed leaf-out under the same conditions. Notably, combined drought and phosphorus treatments often neutralized individual effects, indicating physiological compensation. Provenance-level differences highlighted contrasting strategies—phenotypic plasticity versus stress tolerance—under multi-stressor conditions. These findings underscore the dominant role of phosphorus in regulating phenology and the complex, non-additive nature of drought–nutrient interactions, emphasizing the need for integrative approaches in predicting phenological responses under climate change. Full article