Search Results (34)

This study employs k-means clustering to analyze local organizations responsible for land improvement in Greece, identifying four distinct groups with consistent geographic patterns but divergent financial and operational characteristics. By integrating unsupervised machine learning with spatial analysis, the research offers a novel perspective on irrigation water pricing and cost recovery. The findings reveal that organizations located on islands, despite high water costs due to limited rainfall and geographic isolation, tend to achieve relatively strong financial performance, indicating the presence of adaptive mechanisms that could inform broader policy strategies. In contrast, organizations managing extensive irrigable land or large volumes of water frequently show poor cost recovery, challenging assumptions about economies of scale and revealing inefficiencies in pricing or governance structures. The spatial coherence of the clusters underscores the importance of geography in shaping institutional outcomes, reaffirming that environmental and locational factors can offer greater explanatory power than algorithmic models alone. This highlights the need for water management policies that move beyond uniform national strategies and instead reflect regional climatic, infrastructural, and economic variability. The study suggests several policy directions, including targeted infrastructure investment, locally calibrated water pricing models, and performance benchmarking based on successful organizational practices. Although grounded in the Greek context, the methodology and insights are transferable to other European and Mediterranean regions facing similar water governance challenges. Recognizing the limitations of the current analysis—including gaps in data consistency and the exclusion of socio-environmental indicators—the study advocates for future research incorporating broader variables and international comparative approaches. Ultimately, it supports a hybrid policy framework that combines data-driven analysis with spatial intelligence to promote sustainability, equity, and financial viability in agricultural water management. Full article

High-standard farmland fertility enhancement projects can lead to the sustainable utilization of arable land resources. However, due to difficulties in project implementation and uncertainties in costs, resource allocation efficiency is constrained. To address these challenges, this study first analyzes the impact of geography and engineering characteristics on cost indicators and applies principal component analysis (PCA) to extract key influencing factors. A hybrid prediction model is then constructed by integrating the Northern Goshawk Optimization (NGO) algorithm with a Backpropagation Neural Network (BP). The NGO–BP model is compared with the RF, XGBoost, standard BP, and GA–BP models. Using data from China’s 2025 high-standard farmland fertility enhancement projects, empirical validation shows that the NGO–BP model achieves a maximum RMSE of only CNY 98.472 across soil conditioning, deep plowing, subsoiling, and fertilization projects—approximately 30.74% lower than those of other models. The maximum MAE is just CNY 88.487, a reduction of about 32.97%, and all R² values exceed 0.914, representing an improvement of roughly 5.83%. These results demonstrate that the NGO–BP model offers superior predictive accuracy and generalization ability compared to other approaches. The findings provide a robust theoretical foundation and technical support for agricultural resource management, the construction of projects, and project investment planning. Full article

Spatial transcriptomics is an emerging technology that maps gene expression within tissue architecture. Its expanding use in medicine and veterinary science supports research, precision diagnostics, biomarker discovery, and development of targeted treatment strategies. While spatial transcriptomics applications in human health are well-documented with significant publication diversity and volume, published applications in veterinary medicine remain limited. A comprehensive search of PubMed was conducted, focusing on studies published from 2016 to early 2025 that employed spatial transcriptomics in the context of disease research, diagnosis, or treatment in human or animal health. The review followed the Arksey and O’Malley framework and adhered to Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. A total of 1398 studies met the inclusion criteria. The studies highlighted emerging trends of comparative research with animal model use for human health research. Commonly used spatial transcriptomics platforms included 10× Visium, Slide-seq, Nanostring (GeoMx, CosMX), and multiplexed error-robust fluorescence in situ hybridization (MERFISH). Key gaps in publications include limited veterinary representation, interspecies comparisons, standardized methods, public data use, and therapeutic studies, alongside biases in disease, species, organ, and geography. This review presents the current landscape of spatial transcriptomics publications for human and animal research and medicine, providing comprehensive data and highlighting underrepresented research areas and gaps for future consideration. Full article

The integration of AI tools like ChatGPT in education has sparked debates on their benefits and limitations, particularly in subjects requiring region-specific knowledge. This study examines ChatGPT’s ability to generate accurate and contextually rich responses to assignments in Romanian history and geography, focusing on topics with limited digital representation. Using a document-based analysis, this study compared ChatGPT’s responses to local archival sources, monographs, and topographical maps, assessing coverage, accuracy, and local nuances. Findings indicate significant factual inaccuracies, including misidentified Dacian tribes, incorrect historical sources, and geographic errors such as misplaced landmarks, elevation discrepancies, and incorrect infrastructure details. ChatGPT’s reliance on widely digitized sources led to omissions of localized details, highlighting a fundamental limitation when applied to non-digitized historical and geographic topics. These results suggest that while ChatGPT can be a useful supplementary tool, its outputs require careful verification by educators to prevent misinformation. Future research should explore strategies to improve AI-generated educational content, including better integration of regional archives and AI literacy training for students and teachers. The study underscores the need for hybrid AI-human approaches in education, ensuring that AI-generated text complements rather than replaces verified academic sources. Full article

Accurately forecasting sales for fast-moving consumer goods (FMCG) remains a significant challenge due to the volatile and multi-faceted nature of sales data. Existing methods often struggle to capture intricate patterns driven by seasonal trends, external factors, and consumer behavior, hindering effective inventory management and strategic decision-making. To overcome these challenges, we propose STL-DCSInformer-ETS, a hybrid model that integrates three complementary components: STL decomposition, an enhanced DCSInformer model, and the ETS model. The model uses monthly sales data from a FMCG company, with key features including sales volume, product prices, promotional activities, and regulatory factors such as holidays, geographical information, consumer behavior, product factors, etc. STL decomposition partitions time-series data into trend, seasonal, and residual components, reducing data complexity and enabling more targeted forecasting. The enhanced DCSInformer employs dilated causal convolution and a multi-scale feature extraction mechanism to capture long-term dependencies and short-term variations effectively. Meanwhile, the ETS model specializes in modeling seasonal patterns, further refining forecasting precision. To further improve predictive performance, the Random Forest-based Recursive Feature Elimination (RF-RFE) method is applied to optimize feature selection. RF-RFE identifies key predictive factors from multiple dimensions, such as time, geography, and economy, which significantly influence forecasting accuracy. Through numerical experiments, the method demonstrates excellent performance by achieving a 35.9% reduction in Mean Squared Error and a 21.4% decrease in Mean Absolute Percentage Error, significantly outperforming traditional methods. Furthermore, the model effectively captures both medium- and long-term sales trends while addressing short-term fluctuations, leading to more accurate forecasting and improved decision-making for fast-moving consumer goods. This research provides new theoretical insights into hybrid forecasting models and practical solutions for optimizing inventory management and strategic planning in the FMCG industry. Full article

Tourism geographers in Anglo-American geographical departments are transitioning into teaching and research tourism in applied schools and departments, such as business, resource and environment, and even cultural and creative industries. Because of pressure related to national research assessment and funding, tourism geographers are not encouraged to continue undertaking serious theoretical research related to their mother discipline, instead transitioning to conducting applied research and publishing in hybrid journals. Although such changes in tourism geographers’ research behavior are frequently discussed internationally, they have not been assessed and reported in academic contexts within Asia, particularly Taiwan. This study aimed to investigate the research behavior of tourism geographers regarding the differences between those who are staffed in geography departments and those who transition to applied departments such as tourism and recreation, resource and environment, and others. Their research behavior is examined in terms of publishing journals, paradigmatic approaches, research types, research methods, and geographical scales based on content analysis of articles published by tourism geographers in geography and tourism and recreation departments. The results revealed that there are significant differences in the research behavior of tourism geographers between different departments. Finally, this paper explains the differences in terms of Taiwan’s changing social and economic conditions, the academic traditions and politics of the geography and applied departments, the research habits and preferences of tourism geographers among departments, and other aspects. The findings can help international tourism geography academic circles understand the complexity and diversity of the tourism geography academic landscape of China. Full article

The topographical turn in literary and cultural studies has shed new light on the deeply symbolic significance of the natural and urban places where stories unfold. This focus on spatiality is particularly evident in the South Asian literature by contemporary women writers, where locations acquire a personality and significantly contribute to the shaping of gender identities. Although most of these narratives portray female protagonists who develop strategies of resistance and sisterhood within traditional domestic spaces, the widely praised transnational novels Brick Lane and The Mistress of Spices show that women can also achieve independence and self-realization in the bustling urban environment. Drawing on cultural geography as well as gender and social studies, this essay argues that the global dimension of the city offers diasporic women the opportunity to forge new empowered selves in the above-mentioned books. First, the article maintains that London and Oakland, CA, where the main characters live, exert a centripetal force on women, thus triggering change and mobility, both in physical and psychical terms. Second, it claims that the two cities are gendered “heterotopias”, i.e., heterogeneous spaces where border-crossing women, like those featured in the two novels at hand, can overcome alienation and develop creativity, resilience, and self-confidence. In conclusion, urban spaces serve as “safe houses” for immigrant women, where they can cure their emotional and physical diseases and become figures of adaptive hybridity. Full article

As remote sensing technology has advanced, the use of satellites and similar technologies has become increasingly prevalent in daily life. Now, it plays a crucial role in hydrology, agriculture, and geography. Nevertheless, because of the distinct qualities of remote sensing, including expansive scenes and small, densely packed targets, there are many challenges in detecting remote sensing objects. Those challenges lead to insufficient accuracy in remote sensing object detection. Consequently, developing a new model is essential to enhance the identification capabilities for objects in remote sensing imagery. To solve these constraints, we have designed the OD-YOLO approach that uses multi-scale feature fusion to improve the performance of the YOLOv8n model in small target detection. Firstly, traditional convolutions have poor recognition capabilities for certain geometric shapes. Therefore, in this paper, we introduce the Detection Refinement Module (DRmodule) into the backbone architecture. This module utilizes Deformable Convolutional Networks and the Hybrid Attention Transformer to strengthen the model’s capability for feature extraction from geometric shapes and blurred objects effectively. Meanwhile, based on the Feature Pyramid Network of YOLO, at the head of the model framework, this paper enhances the detection capability by introducing a Dynamic Head to strengthen the fusion of different scales features in the feature pyramid. Additionally, to address the issue of detecting small objects in remote sensing images, this paper specifically designs the OIoU loss function to finely describe the difference between the detection box and the true box, further enhancing model performance. Experiments on the VisDrone dataset show that OD-YOLO surpasses the compared models by at least 5.2% in mAP50 and 4.4% in mAP75, and experiments on the Foggy Cityscapes dataset demonstrated that OD-YOLO improved mAP by 6.5%, demonstrating outstanding results in tasks related to remote sensing images and adverse weather object detection. This work not only advances the research in remote sensing image analysis, but also provides effective technical support for the practical deployment of future remote sensing applications. Full article

Urban stormwater drainage systems, which include many personholes to collect and discharge precipitation within a city, are extensively constructed to prevent streets and buildings from flooding. This research intends to build a machine learning model to predict whether a personhole will overflow soon, which is crucial to alleviate the damage caused by floods. To address the challenges posed by many diverse personholes, we proposed segmenting the personholes into several groups and have designed two methods employing different personhole features. The first, the geography-based method, uses the geographical locations of the personholes for the grouping. The second, the hydrology-based method, uses the characteristics that are directly related to the overflowing situation, such as the depth of the personhole, and the average and the maximum water level of the personholes. We also investigated several machine learning techniques, such as the multilayer perceptron (MLP) model and a fine-tuning architecture. The study area was located in the new Taipei city and the experimental results have shown the impressive predictive ability of the proposed approaches. Particularly, by applying the hydrology-based grouping method, and using a hybrid model combining the machine learning model prediction results with heuristic rules, we can obtain the best prediction result, and the accuracy is over 99%. We have also noticed the influence of the activation function used in the neural network and the number of frozen layers in the fine-tuning architecture. Particularly, using the tanh function with one frozen layer is good in some cases. However, since it is not general enough, we suggest the readers perform empirical studies before choosing the best setting in their own environment. Full article

The geography, histories, and ethnic composition of the Latin American continent pose a great challenge when attempting to identify and describe the region’s constitutive religious traditions and experiences. This task is further complexified by the hybridity, fluidity, and porosity of the region’s cultural groups. However, there is an aspect of Latin American religiosity that shares a significant family resemblance across the continent: the small community settings in which religiosity often emerges and consolidates as a worldview, commonly known as Ecclesial base communities. Informed by liberation theology, these communities are a uniquely generative experiment in social, political, and religious life. Scholarly accounts of liberation theology fail to identify key aspects of how Ecclesial base communities generate ways of being, knowing, and making meaning. While many of these accounts depict liberation theology as a socio-political discourse of theological origin, they do not unearth the multidirectional interaction between political practice and theological thought at the heart of these communities. In this paper, I aimed to fill this gap in the literature by reframing liberation theology as a set of social imaginaries, making use of Paul Ricoeur’s theories of memory and cultural imagination to provide the philosophical ground to understand the lived theology of Ecclesial base communities. In doing so, I maintain that liberation theology is not only a theoretical discourse that emerges from these communities, but also the inarticulate background of their ways of thinking, communicating, and living, one that provides an existential orientation through which Latin Americans can provide coherence to their collective action and recognize their own capacity to change their reality of oppression. Full article

Geographic analysis in rural areas has been dominated in recent decades by socio-economic and socio-political perspectives. The new materialism can constitute a way of progress in rural geographical studies that involve the material and the human in the form of hybrid experiences. Materiality can be expressed in three ways: (1) the surface matter that corresponds to the traditional elements of the material rural space, (2) the materiality that would be associated with a joint and undifferentiated vision of human artefacts in a village, mainly the set of rural houses and (3) the experimental materiality that has an individual dimension, the result of the multiple processes of destruction, revival and reconstruction that simultaneously happen in one place. The experimental materiality is of the most interest for geographic analysis since it is where the materiality of the rural house and the individual are hybridized in the process of recovery of the traditional rural house. In the process of recovery of materialities, binary oppositions built in the rigid framework of modernity can be dissolved and established fluid postmodern alternatives co-produced between people and materiality. The new materialism in rural geography can be an alternative path that allows analyzing heterogeneous realities in a hybrid way and contributing in a practical context to the development of new rural policies. Full article

In this review, we analyzed the history and the past and present trends in photocatalysis research, trying to outline possible scenarios for the future in the medium term. The in-depth analysis of the literature reported here—from a mere bibliometric point of view—allowed us to divide the history of photocatalysis into four different periods characterized by different maturity of the topic and different bibliometric features. The turn of the 20th century saw an explosion in scientific production, which is still continuing now and has reached more than 15,000 papers published each year. Research interest is still growing significantly, and the analysis of different keywords suggests that such growth is substantial and not merely due to “publish or perish” behavior. The analysis of the most-investigated topics in the field of photocatalysis highlighted that, during its history, the focus of the research moved from inorganic oxides to carbon and hybrid materials. Concomitantly, the investigation of the “geography” of photocatalysis allowed us to underline its evolution over the years, with the repositioning of its center of mass from the Atlantic Ocean (USA and Europe) to Asia (China and India). Photocatalysis is active as never before but still awaiting major breakthroughs, which would allow a much broader technological and commercial output. Without such breakthroughs in this decade, the growth in scientific interest could level out or even decrease. Full article

This paper will explore ways in which dynamics of visibility/invisibility of American Muslim women activists are transformed in secular places like USA, while these women struggle surviving on the borderlands. Borderland and boundary are perceived as lived spaces that are culturally hybrid and are seen as a theatre for radical action. In this paper I contend that Muslim women activists in the USA operate from geographies of borderland and while inhabiting this hybrid third space they generate discourses of dissent that challenge stereotypes about them. Hailing from diverse backgrounds and countries, with different cultural roots yet same belief system and faith, American Muslim women activists adapt varied resistance strategies to challenge the Muslim patriarchy and the western hegemony that has persisted to portray Muslim women as an oppressed group of people in need of saving. Tracing Muslim women activists’ emotional and experiential geographies I will look at ways in which dynamics of solidarity between them have moved beyond dichotomous divisions of global-local, global North-global South, and empire-colony. With the discussion of lives and activism of Amina Wadud, Linda Sarsour and Asra Nomani, this paper will contextualize these activists within the spaces of resistance which they inhabit, while navigating their challenges in the context of geopolitical tensions and conflicts which are their lived realities in the USA. Full article

Hybridization between native and nonnative fish species is a major conservation issue, especially in ecosystems with high levels of endemism, such as Iberian streams. To date, hybridization with the invasive bleak Alburnus alburnus has been reported for the Iberian chub Squalius alburnoides and S. pyrenaicus and in scattered locations only. However, the bleak is spreading in the region, potentially increasing the risks of hybridization with other Squalius species. To gather a more comprehensive picture on the current geography of hybridization, we compiled records on hybrids between bleak and chub in Portugal and conducted genetical assessments of hybrids between bleak and S. carolitertii. We found that hybridization with bleak is widespread throughout Portuguese river basins and involves at least S. alburnoides, S. pyrenaicus and S. carolitertii. Hybridization with bleak may not only cause waste of reproductive effort and damage the genetic integrity of these endemic species but also promote shifts in the reproductive dynamics of the S. alburnoides hybrid complex, which includes individuals with various ploidy levels and combinations of parental genomes, reproducing sexually and asexually. We recommend that future studies characterize the fitness of bleak hybrids and their ecological and genetic interactions with native fish, in order to design effective conservation measures. Full article

The purpose of this research is to demystify literature on aerotropolis using systematic review. Literature on aerial life and aeronautical studies suggests that airports are frequently cited outside urban centres. However, recent events surrounding the growth of aerotropolis contradicts existing realities. In fact, the pull and push factors constitute the life cycle of aerotropolis in urban enclaves. In generating data for this study, Dimensions, an artificial intelligence databank, was adopted, and a hybrid method which combines both VOSviewer and Citespace software was the preferred analytical tool for analysis. Key findings were imperative in establishing certain parameters regarding aerial life, including but not limited to knowledge about the technologies adopted, quality of stakeholders, in addition to existing relationships of urban space, urbanisation, and geography. Furthermore, two recurrent themes were identified, such as the development in ICT, and smart technologies, which corresponds with the multiple potentials that exist for developing sustainable airports, such as eco-innovation, greenovation, and social innovation. This study contributes to the concept of transit-bound tourism, a concept we coined to depict the role tourism can play in transit philosophy and economics. Full article