Search Results (3,948)

Organic pig farming in Europe is endorsed as a promising route to more sustainable livestock production, but its ultimate contribution to the United Nations (UN) Sustainable Development Goals (SDGs) is a contested matter. This study takes a critical perspective on the potential of organic pig farming to contribute to SDGs that may include SDG 2 (Zero Hunger), SDG 3 (Good Health and Well-being), SDG 8 (Decent Work and Economic Growth), SDG 12 (Responsible Consumption and Production), SDG 13 (Climate Action), and SDG 15 (Life on Land). Organic farming systems delivered better animal welfare outcomes and positive benefits for biodiversity, soil health, and rural employment. Continued improvements in sourcing feed, greenhouse gas emissions per unit of product, animal health, and market could improve their contributions to agricultural sustainability. This study concludes that organic pig farming does not represent a guarantee of sustainable livestock production, but it could represent credible sources of sustainable livestock innovation if sufficient policy, practice, cost accounting, and sustainable metrics are organized together to support organic systems. Organic pig farming focused on innovation and policy support can make it a role model for the transition of European livestock sector towards the 2030 Agenda. Full article

In many land cover classification tasks, the limited precision of individual sensors hinders the accurate separation of certain classes, largely due to the complexity of the Earth’s surface morphology. To mitigate these issues, decision fusion methodologies are employed, allowing data from multiple sensors to be synthesized into robust and more conclusive classification outcomes. This study employs fully polarimetric Synthetic Aperture Radar (PolSAR) imagery and leverages the strengths of three decomposition methods, namely Pauli’s, Krogager’s, and Cloude’s, by extracting their respective components for improved detection. From each decomposition method, three scattering components are derived, enabling the extraction of informative features that describe the scattering behavior associated with various land cover types. The extracted scattering features, treated as independent sensors, were used to train three neural network classifiers. The resulting outputs were then considered as local decisions for each land cover type and subsequently fused through a decision fusion rule to generate more complete and accurate classification results. Experimental results demonstrate that the proposed Multi-Class Bahadur–Lazarsfeld Expansion (MC-BLE) fusion significantly enhances classification performance, achieving an overall accuracy (OA) of 95.78% and a Kappa coefficient of 0.94. Compared to individual classification methods, the fusion notably improved per-class accuracy, particularly for complex land cover boundaries. The core innovation of this work is the transformation of the Bahadur–Lazarsfeld Expansion (BLE), originally designed for binary decision fusion into a multi-class framework capable of addressing multiple land cover types, resulting in a more effective and reliable decision fusion strategy. Full article

Although Paegam Sŏngch’ong 栢庵性聰 (1631–1700) received orthodox transmission in Sŏn Buddhism in the Puhyu lineage 浮休係 (deriving from Puhyu Sŏnsu 浮休善修, 1543–1615), he is remembered as an important advocate of Huayan 華嚴 doctrinal learning in the mid-Chosŏn period. He collected Buddhist works from the Chinese Jia-xing Canon 嘉興藏 that had washed ashore on Imja Island 荏子島 in Chŏlla Province and published them in more than 190 volumes. In 1686, the first work produced in this endeavor was Precious Writings on the Pure Land (Chŏngt’o posŏ 淨土寶書), in one volume. It is a compilation, in fourteen sections (including the preface), of excerpts and summaries of Pure Land writings and stories published in the supplementary canon section 續藏 of the Jiaxing Canon. The core and longest section of the work is chapter thirteen: “Efficacy of the Fruit of the Pure Land” (Chŏngt’o kwahŏm 淨土果驗). This chapter comprises devotional narratives on cases of rebirth in the Pure Land classified according to the social or birth status of the main figures: monks, kings and ministers, nobles and commoners, nuns, women, evildoers, animals, and so forth. The primary purpose of these narratives is to underscore to virtue of chanting the name of the Buddha Amitābha (yŏmbul, Ch. nianfo 念佛) as a means of rebirth in Sukhāvatī. This work is significant because it demonstrates the value and function of Chinese Pure Land literature in the popularization of Pure Land practice in the mid and late Chosŏn period. Full article

Agrivoltaic systems, which integrate solar energy generation with agricultural production, offer a promising solution to optimize land use efficiency. This work presents a case study for the assessment of an agrivoltaic pilot project in a small-scale solar farm operated by SOLENIUM in San Diego (Cesar, Colombia), located in the Colombian tropical dry forest. The project evaluated environmental conditions, selected melon and watermelon as shade-tolerant crops, and assessed technical challenges, including mechanization constraints. Preliminary results indicated that agrivoltaic systems can maintain agricultural productivity while generating renewable energy, with photosynthetically active radiation measurements averaging 1342 μmol/m²/s in cultivation areas. This case study demonstrates the viability of agrivoltaic systems as a scalable model for sustainable rural development in the Colombian tropical dry forest. Full article

Amidst the increasing aerial traffic and road traffic congestion, Urban Air Mobility (UAM) has emerged as a new mode of aerial transport offering less travel time and ease of portability. A critical factor in reducing travel time is the emerging electric Vertical Take-Off and Landing (eVTOL) vehicles, which require infrastructure such as vertiports to operate smoothly. However, the dynamics of vertiport operations, particularly the integration of battery charging facilities, remain relatively unexplored. This work aims to bridge this gap by delving into vertiport management by utilizing separate taxing and parking levels. The study also focuses on the time eVTOLs spend at the vertiport to anticipate potential delays. This factor helps optimise arrival and departure times via a scheduling strategy that accounts for hourly demand fluctuations. The simulation results, conducted with hourly demand, underscore the significant impact of battery charging on operational time while also highlighting the role of parking spots in augmenting capacity and facilitating more efficient scheduling. Full article

Vietnam’s wooden forest products industry is an important export sector, contributing to industrial growth and employment. However, it is facing increasing pressures related to challenges such as forest and export sustainability. Despite its potential, Vietnam’s export performance remains uneven across destination markets, related to the presence of significant unrealized trade potential. This study examines the determinants of export efficiency in Vietnam’s wooden forest products sector by moving beyond traditional gravity variables to incorporate institutional and cultural dimensions. Using a panel of 70 trading partners between 2004 and 2023, covering more than 93% of Vietnam’s total wood exports, this study employs an instrumental-variable single-stage stochastic frontier gravity model (IV-SFGM) to estimate trade potential. The results show that economic size, favorable exchange rates, and shared borders significantly enhance export performance. Furthermore, geographical distance and land enclosure remain persistent structural barriers, particularly relevant for bulky and logistics-intensive wood products. Institutional and cultural distance constitute substantial non-tariff barriers, significantly reducing export efficiency across markets. Conversely, regional trade agreements, trade freedom, and foreign direct investment play a critical role in mitigating inefficiencies and facilitating market penetration. Export efficiency in Vietnam’s wooden forest products sector indicates considerable improvement, rising from approximately 25% in the mid-2000s to over 55% in recent years, indicating notable progress in the market and highlighting considerable untapped potential. So, integrating institutional and cultural factors into a frontier-based gravity framework, this study offers novel empirical evidence from an emerging, biodiversity-rich economy with evolving governance institutions. The findings provide important policy implications for aligning export growth with institutional reform and trade liberalization, thereby contributing to the achievement of SDGs such as Decent Work and Economic Growth. Full article

Extreme wildfires are becoming increasingly frequent and severe across many regions worldwide, driven by climate change, land-use transitions, and long-standing fire-suppression legacies. In this context, Nature-based Solutions (NbS)—defined as actions that work with ecological processes to address societal challenges while providing biodiversity and socio-economic benefits—offer a promising yet underdeveloped pathway for enhancing wildfire resilience. This Special Issue brings together eleven contributions spanning empirical ecology, landscape configuration, simulation modelling, spatial optimisation, ecosystem service analysis, governance assessment, and community-based innovation. Collectively, these studies demonstrate that restoring ecological fire regimes, promoting multifunctional landscapes, and integrating advanced decision support tools can substantially reduce wildfire hazard while sustaining ecosystem functions. They also reveal significant governance barriers, including fragmented policies, limited investment in prevention, and challenges in incorporating social demands into territorial planning. By synthesising these insights, this editorial identifies several strategic priorities for advancing NbS in fire-prone landscapes: mainstreaming prevention within governance frameworks, strengthening the science–practice interface, investing in long-term socio-ecological monitoring, managing trade-offs transparently, and empowering local communities. Together, the findings highlight that effective NbS emerge from the alignment of ecological, technological, institutional, and social dimensions, offering a coherent pathway toward more resilient, biodiverse, and fire-adaptive landscapes. Full article

In terms of his reception in East Asia and the legacy of his commentaries and compendia in translation, Vasubandhu 世親 (ca. 320–400 centuries CE) is among the most important figures in the textual history of Indian Buddhism. Although perhaps best known by modern scholars through his works concerning abstruse intellectual ideas presented from the Yogācāra or mind-only and Abhidharma perspectives, his legacy is arguably best represented as an authoritative voice concerning the Pure Land of Amitāyus buddha. Both Nāgārjuna 龍樹 (ca. 150–250 CE) and Vasubandhu are considered to be patriarchs (soshi 祖師) for Jōdo Shin 浄土真宗 Buddhists, following Shinran’s 親鸞 (1173–1263) teachings. In this paper I investigate the textual history of these two Indian masters who are considered to be patriarchs by Pure Land and Shin Buddhists in Japan. No one believes these individuals transmitted some sort of true mind or essential teaching from one to another as in the Chan or Zen 禪宗 tradition; they are recognized because of fundamental texts with key ideas that are ascribed to them. These key texts were never singled out in any Chinese or Indian set of special texts, nor were they highlighted in various catalogs to the Buddhist “canon.” This research demonstrates how the sacred teachings ascribed to Vasubandhu, and to a certain extent Nāgārjuna as well, by Pure Land and Shin Buddhists reveal how and why Pure Land practices were expected to be seen as mainstream Mahāyāna Buddhism and nothing at all like a reformation for a later age. Full article

The photovoltaic (PV) bands on the mid-wave and long-wave infrared (MWIR and LWIR) cold focal plane assemblies of Terra and Aqua MODIS have suffered from gradually increasing electronic crosstalk contamination as both instruments have continued to operate in their extended missions, respectively. This contamination has considerable impact, particularly for the PV LWIR bands, which includes image striping and radiometric bias in the Level-1B (L1B)-calibrated radiance products as well as higher level (and mostly atmospheric but also land and oceanic) products (e.g., cloud phase particle, cloud mask, land and sea surface temperatures). The crosstalk was characterized early in the mission, and test corrections were developed then. Ultimately, the groundwork for a robust electronic crosstalk correction algorithm was developed in 2016 and implemented in MODIS Collection 6.1 (C6.1) back in 2017 for the Terra MODIS PV LWIR bands. It was later introduced in Aqua MODIS C6.1 for the same group of bands in April 2022. Additional improvements were made in MODIS Collection 7 (C7) to better characterize the electronic crosstalk in the PV LWIR bands, and the electronic crosstalk correction algorithm was also extended to select detectors in the MODIS MWIR bands. This work will describe the electronic crosstalk correction algorithm and its application on the MODIS L1B product, the differences in application between C6.1 and C7, as well as additional improvements made to enhance the contamination correction and improve image quality for the Aqua MODIS PV LWIR bands. The electronic crosstalk correction coefficient time series for the MODIS PV bands will be discussed, and some cases will be presented to illustrate how image quality improves on the L1B and Level 2 products after the correction is applied. Lastly, experiences gained regarding the PV bands electronic crosstalk and the strategy used to correct it will be discussed to provide future data users and scientists with an insight as to how to improve on the legacy record that the Terra and Aqua MODIS sensors will leave behind after both spacecrafts are decommissioned. Full article

Land abandonment and recurrent wildfires are major drivers of landscape transformation in Mediterranean Europe, creating favorable conditions for the spread of non-native invasive woody species. Among these, Australian wattles (genus Acacia) are particularly widespread and problematic in Portugal. This work analyzed the spatiotemporal dynamics of Acacia spp. in two municipalities of central Portugal (Sertã and Pedrógão-Grande) by combining multitemporal photointerpretation of aerial imagery (2004–2021), generalized additive models (GAMs), and local perception surveys. Results reveal a 417% increase in occupied area over the last two decades. Modeling outcomes indicate a temporal shift in invasion drivers: from an establishment phase (2004–2010), mainly constrained by altitude and proximity to primary introduction sites, to a disturbance-driven expansion phase (2010–2021), influenced by fire recurrence, slope, and land-use context. Spatial clustering persisted throughout, underscoring the role of founder populations. Surveys confirmed high public awareness of Acacia invasiveness and identified abandonment and wildfire as the main perceived triggers of spread. By integrating ecological and social dimensions, this study provides a socioecological perspective on Acacia spp. expansion in Mediterranean rural landscapes and highlights the urgent need for integrated, landscape-scale management strategies. Full article

This article explores how Herta Müller and Paul Bailey transform the apparatus of state bordering, i.e., passports, permits and catechisms, into metaphors for an interior struggle between flight and belonging. In The Passport, The Land of Green Plums and Bailey’s Kitty & Virgil, emigration is portrayed not as departure alone but as a prolonged contest between the body that moves and the spirit that lingers. Those who cross borders geographically remain anchored, often painfully, in the mental and moral landscapes of the home they leave behind. The paper examines how documents, bodies, and languages become shifting frontier zones where identity is repeatedly issued and withdrawn, shaped by the pressures of memory, exile, and biopolitical control. Müller’s vision, written from within Romania’s history, and Bailey’s, refracted through an English consciousness yet partly set in Romania, converge in a poetics of witness that treats exile as both wound and testimony. Ultimately, these works suggest that identity survives in the liminal space between motion and remembrance where thought halts at its own threshold, memory traces its faint watermark, and the self bears its unspoken credential. Full article

Mapping coastal landforms is critical for the sustainable management of ecosystems influenced by both natural dynamics and human activity. This study investigates the application of Transformer-based semantic segmentation models for pixel-level classification of key surface types such as water, sandy shores, rocky areas, vegetation, and built structures. We utilize a diverse, multi-resolution dataset that includes NAIP (1 m), Quadrangle (6 m), Sentinel-2 (10 m), and Landsat-8 (15 m) imagery from U.S. coastlines, along with high-resolution aerial images of the Greek coastline provided by the Hellenic Land Registry. Due to the lack of labeled Greek data, models were pre-trained on U.S. datasets and fine-tuned using a manually annotated subset of Greek images. We evaluate the performance of three advanced Transformer architectures, with Mask2Former achieving the most robust results, further improved 11 through a coastal-class weighted focal loss to enhance boundary precision. The findings demonstrate that Transformer-based models offer an effective, scalable, and cost-efficient solution for automated coastal monitoring. This work highlights the potential of AI-driven remote sensing to replace or complement traditional in-situ surveys, and lays the foundation for future research in multimodal data integration and regional adaptation for environmental analysis. Full article

Phishing emails continue to evade conventional detection systems due to their increasingly sophisticated, multi-faceted social engineering tactics. To address the limitations of single-modality or rule-based approaches, we propose SAHF-PD, a novel phishing detection framework that integrates multi-modal feature extraction with semantic-aware hierarchical fusion, based on large language models (LLMs). Our method leverages modality-specialized large models, each guided by domain-specific prompts and constrained to a standardized output schema, to extract structured feature representations from four complementary sources associated with each phishing email: email body text; open-source intelligence (OSINT) derived from the key embedded URL; screenshot of the landing page; and the corresponding HTML/JavaScript source code. This design mitigates the unstructured and stochastic nature of raw generative outputs, yielding consistent, interpretable, and machine-readable features. These features are then integrated through our Semantic-Aware Hierarchical Fusion (SAHF) mechanism, which organizes them into core, auxiliary, and weakly associated layers according to their semantic relevance to phishing intent. This layered architecture enables dynamic weighting and redundancy reduction based on semantic relevance, which in turn highlights the most discriminative signals across modalities and enhances model interpretability. We also introduce PhishMMF, a publicly released multimodal feature dataset for phishing detection, comprising 11,672 human-verified samples with meticulously extracted structured features from all four modalities. Experiments with eight diverse classifiers demonstrate that the SAHF-PD framework enables exceptional performance. For instance, XGBoost equipped with SAHF attains an AUC of 0.99927 and an F1-score of 0.98728, outperforming the same model using the original feature representation. Moreover, SAHF compresses the original 228-dimensional feature space into a compact 56-dimensional representation (a 75.4% reduction), reducing the average training time across all eight classifiers by 43.7% while maintaining comparable detection accuracy. Ablation studies confirm the unique contribution of each modality. Our work establishes a transparent, efficient, and high-performance foundation for next-generation anti-phishing systems. Full article

Remote sensing building change detection (RSBCD) is critical for land surface monitoring and understanding interactions between human activities and the ecological environment. However, existing deep learning-based RSBCD methods often result in mis-detected pixels concentrated around object boundaries, mainly due to ambiguous object shapes and complex spatial distributions. To address this problem, we propose a new Multiscale Edge-Aware change detection Network (MEANet) that accurately locates edge pixels of changed objects and enhances the separability between changed and unchanged pixels. Specifically, a high-resolution feature fusion network is adopted to preserve spatial details while integrating deep semantic information, and a multi-scale supervised contrastive loss (MSCL) is designed to jointly optimize pixel-level discrimination and embedding space separability. To further improve the handling of difficult samples, hard negative sampling is adopted in the contrastive learning process. We conduct comparative experiments on three benchmark datasets. Both Visual and quantitative results demonstrate that our new MEANet significantly reduces misclassified pixels at object boundaries and achieve superior detection accuracy compared to existing methods. Especially on the GZ-CD dataset, MEANet improves F1-Score and mIoU by more than 2% compared with ChangeFormer, demonstrating strong robustness in complex scenarios. It is worth noting that the performance of MEANet may still be affected by extremely complex edge textures or highly blurred boundaries. Future work will focus on further improving robustness under such challenges and extending the method to broader RSBCD scenarios. Full article

Shipborne UAV-assisted dock is an important way to recover unmanned systems for remote water surface low-altitude detection. The lack of resisting deck disturbances capability for UAV autonomous landing in dynamic dock stations has led to the inability of traditional hovering recovery methods for single UAV guidance and flight attitude control systems to meet the growing demand for landing assistance. In this work, we present a shipborne manipulator arm designed to grasp drones that use low-altitude visual servo technology for landing on the water surface. The shipborne manipulator arm is fabricated as a key component of a seaplane drone dock comprising a ship-type embedded drone storage, a packaged helistop for power transfer and UAV recovery, and a multi-degree-of-freedom arm integrated with multi-source information sensors for the treatment of air-to-water-related airplane crashes. Dynamic model tests have demonstrated that the end-effector of the shipborne manipulator arm stabilizes and performs optimally for water surface disturbances. A down-to-top grasp docking paradigm for a UAV-assisted perching on a shipborne helistop that enables the charging components of the station system to be equipped automatically to ensure that the drone performs its mission in the best condition is also presented. The surface grasp experiments have verified the efficacy of this grasp paradigm when compared to the traditional autonomous landing method. Full article