Search Results (262)

Biosphere Reserves (BRs), designated by UNESCO, are uniquely positioned to serve as model territories for sustainable development, as they aim to harmonize biodiversity conservation with the socio-economic vitality and cultural identity of local communities. This work examines the commitment of the Spanish Network of Biosphere Reserves to the United Nations 2030 Agenda and the Sustainable Development Goals (SDGs). Using a survey-based research design, this study assesses the extent to which the reserves have integrated the SDGs into their strategic frameworks and operational practices. It also identifies and analyses successful initiatives and best practices implemented across Spain that exemplify this integration. The findings highlight the need for enhanced awareness and understanding of the 2030 Agenda among stakeholders, alongside stronger mechanisms for participation, cooperation, and governance. The conclusion emphasises the importance of equipping all reserves with strategic planning tools and robust systems for monitoring, evaluation, and accountability. Moreover, the analysis of exemplary cases reveals the transformative potential of sustainability-oriented projects—not only in advancing environmental goals but also in revitalizing local economies and reinforcing cultural heritage. These insights contribute to a broader understanding of how BRs can act as dynamic laboratories for sustainable development and heritage preservation. Full article

Machine learning (ML) has established itself as an innovative tool in sustainable forest management, essential for tackling critical challenges such as deforestation, biodiversity loss, and climate change. Through the analysis of large volumes of data from satellites, drones, and sensors, machine learning facilitates everything from precise forest health assessments and real-time deforestation detection to wildfire prevention and habitat mapping. Other significant advancements include species identification via computer vision and predictive modeling to optimize reforestation and carbon sequestration. Projects like SILVANUS serve as practical examples of this approach’s success in combating wildfires and restoring ecosystems. However, for these technologies to reach their full potential, obstacles like data quality, ethical issues, and a lack of collaboration between different fields must be overcome. The solution lies in integrating the power of machine learning with ecological expertise and local community engagement. This partnership is the path forward to preserve biodiversity, combat climate change, and ensure a sustainable future for our forests. Full article

This paper proposes a Distributed Adaptive Regularization Algorithm (DARN) for solving composite non-convex and non-smooth optimization problems in multi-agent systems. The algorithm employs a three-phase iterative framework to achieve efficient collaborative optimization: (1) a local regularized optimization step, which utilizes proximal mappings to enforce strong convexity of weakly convex objectives and ensure subproblem well-posedness; (2) a consensus update based on doubly stochastic matrices, guaranteeing asymptotic convergence of agent states to a global consensus point; and (3) an innovative adaptive regularization mechanism that dynamically adjusts regularization strength using local function value variations to balance stability and convergence speed. Theoretical analysis demonstrates that the algorithm maintains strict monotonic descent under non-convex and non-smooth conditions by constructing a mixed time-scale Lyapunov function, achieving a sublinear convergence rate. Notably, we prove that the projection-based update rule for regularization parameters preserves lower-bound constraints, while spectral decay properties of consensus errors and perturbations from local updates are globally governed by the Lyapunov function. Numerical experiments validate the algorithm’s superiority in sparse principal component analysis and robust matrix completion tasks, showing a 6.6% improvement in convergence speed and a 51.7% reduction in consensus error compared to fixed-regularization methods. This work provides theoretical guarantees and an efficient framework for distributed non-convex optimization in heterogeneous networks. Full article

This study introduces Edible Streets as a distinct and scalable model of community-led urban food growing, specifically investigating the drivers and barriers to the initiative. Unlike traditional urban food-growing initiatives, Edible Streets explores the integration of edible plants into street verges and footpaths with direct community involvement of the people who live/work in a street. This study contributes new knowledge by evaluating Edible Streets through the COM-B model of behavioural change, through policy and governance in addition to behaviour change, and by developing practical frameworks to facilitate its implementation. Focusing on Oxford, the research engaged residents through 17 in-person interviews and 18 online surveys, alongside a stakeholder workshop with 21 policymakers, community leaders, and NGO representatives. Findings revealed strong motivation for Edible Streets, driven by values of sustainability, community resilience, and improved well-being. However, capability barriers, including knowledge gaps in gardening, land-use policies, and food preservation, as well as opportunity constraints related to land access, water availability, and environmental challenges, hindered participation. To address these, a How-to Guide was developed, and a pilot Edible Street project was launched. Future steps include establishing a licensing application model to facilitate urban food growing and conducting a Post-Use Evaluation and Impact Study. Nationally, this model could support Right to Grow policies, while globally, it aligns with climate resilience and food security goals. Locally grown food enhances biodiversity, reduces carbon footprints, and strengthens social cohesion. By tackling key barriers and scaling solutions, this study provides actionable insights for policymakers and practitioners to create resilient, equitable urban food systems. Full article

In the real world, most data are unlabeled, which drives the development of semi-supervised learning (SSL). Among SSL methods, least squares regression (LSR) has attracted attention for its simplicity and efficiency. However, existing semi-supervised LSR approaches suffer from challenges such as the insufficient use of unlabeled data, low pseudo-label accuracy, and inefficient label propagation. To address these issues, this paper proposes dual label propagation-driven least squares regression with feature selection, named DLPLSR, which is a pseudo-label-free SSL framework. DLPLSR employs a fuzzy-graph-based clustering strategy to capture global relationships among all samples, and manifold regularization preserves local geometric consistency, so that it implements the dual label propagation mechanism for comprehensive utilization of unlabeled data. Meanwhile, a dual-feature selection mechanism is established by integrating orthogonal projection for maximizing feature information with an ℓ_2,1-norm regularization for eliminating redundancy, thereby jointly enhancing the discriminative power. Benefiting from these two designs, DLPLSR boosts learning performance without pseudo-labeling. Finally, the objective function admits an efficient closed-form solution solvable via an alternating optimization strategy. Extensive experiments on multiple benchmark datasets show the superiority of DLPLSR compared to state-of-the-art LSR-based SSL methods. Full article

This study explores a strategic framework for the sustainable development of Northern and Central Euboea (Evia), Greece, through the preservation and promotion of cultural and environmental assets. This research aims to redirect tourism flows from overdeveloped coastal zones to underutilized inland areas by leveraging local heritage and natural resources. The methodology was developed within the context of the AEI research project and combines bibliographic research, stakeholder consultation, GIS analysis, and socioeconomic assessment. Based on this framework, a series of thematic cultural routes and agritourism initiatives were designed to enhance regional attractiveness and resilience. The study proposes the utilization of ICT tools such as GIS-based mapping, a digital development platform, and an online tourism portal to document, manage, and promote key assets. The socioeconomic impact of the proposed interventions was evaluated using an input–output model, revealing that each EUR 1 million invested in the region is expected to generate EUR 650,000 in local GDP and create 14 new jobs. The results underscore the potential of alternative tourism to stimulate inclusive and sustainable growth, particularly in post-disaster rural regions. This integrated approach can serve as a model for other territories facing similar environmental, economic, and demographic challenges. Full article

Hydraulic concrete is a critical infrastructure material, with its surface condition playing a vital role in quality assessments for water conservancy and hydropower projects. However, images taken in complex hydraulic environments often suffer from degraded quality due to low lighting, shadows, and noise, making it difficult to distinguish defects from the background and thereby hindering accurate defect detection and damage evaluation. In this study, following systematic analyses of hydraulic concrete color space characteristics, we propose a Dual-Branch Luminance–Chrominance Attention Network (DBLCANet-HCIE) specifically designed for low-light hydraulic concrete image enhancement. Inspired by human visual perception, the network simultaneously improves global contrast and preserves fine-grained defect textures, which are essential for structural analysis. The proposed architecture consists of a Luminance Adjustment Branch (LAB) and a Chroma Restoration Branch (CRB). The LAB incorporates a Luminance-Aware Hybrid Attention Block (LAHAB) to capture both the global luminance distribution and local texture details, enabling adaptive illumination correction through comprehensive scene understanding. The CRB integrates a Channel Denoiser Block (CDB) for channel-specific noise suppression and a Frequency-Domain Detail Enhancement Block (FDDEB) to refine chrominance information and enhance subtle defect textures. A feature fusion block is designed to fuse and learn the features of the outputs from the two branches, resulting in images with enhanced luminance, reduced noise, and preserved surface anomalies. To validate the proposed approach, we construct a dedicated low-light hydraulic concrete image dataset (LLHCID). Extensive experiments conducted on both LOLv1 and LLHCID benchmarks demonstrate that the proposed method significantly enhances the visual interpretability of hydraulic concrete surfaces while effectively addressing low-light degradation challenges. Full article

Stripe noise along the scanning direction significantly degrades the quality of high-resolution infrared line-scan images and impairs downstream tasks such as target detection and radiometric analysis. This paper presents a lightweight, single-frame, reference-free non-uniformity correction (NUC) method tailored for such images. The proposed approach enhances the directionality of stripe noise by projecting the 2D image into a 1D row-mean signal, followed by adaptive guided filtering driven by local median absolute deviation (MAD) to ensure spatial adaptivity and structure preservation. A spectral-entropy-constrained frequency-domain masking strategy is further introduced to suppress periodic and non-periodic interference. Extensive experiments on simulated and real datasets demonstrate that the method consistently outperforms six state-of-the-art algorithms across multiple metrics while maintaining the fastest runtime. The proposed method is highly suitable for real-time deployment in airborne, satellite-based, and embedded infrared imaging systems. It provides a robust and interpretable framework for future infrared enhancement tasks. Full article

Masonry industrial buildings, common in the 19th and 20th centuries, represent a significant architectural typology. These structures are crucial to the study of industrial archeology, which focuses on preserving and revitalizing historical industrial heritage. Often left neglected and deteriorating, they hold great potential for adaptive reuse, transforming into vibrant cultural, commercial, or residential spaces through well-planned restoration and consolidation efforts. This paper explores a case study of such industrial architecture: a decommissioned factory near Naples. The complex consists of multiple structures with vertical supports made of yellow tuff stone and roofs framed by wooden trusses. To improve the building’s seismic resilience, a comprehensive analysis was conducted, encompassing its historical, geometric, and structural characteristics. Using advanced computer software, the factory was modelled with a macro-element approach, allowing for a detailed assessment of its seismic vulnerability. This approach facilitated both a global analysis of the building’s overall behaviour and the identification of potential local collapse mechanisms. Non-linear analyses revealed a critical lack of seismic safety, particularly in the Y direction, with significant out-of-plane collapse risk due to weak connections among walls. Based on these findings, a restoration and consolidation plan was developed to enhance the structural integrity of the building and to ensure its long-term safety and functionality. This plan incorporated metal tie rods, masonry strengthening through injections, and roof reconstruction. The proposed interventions not only address immediate seismic risks but also contribute to the broader goal of preserving this industrial architectural heritage. This study introduces a novel multidisciplinary methodology—integrating seismic analysis, traditional retrofit techniques, and sustainable reuse—specifically tailored to the rarely addressed typology of masonry industrial structures. By transforming the factory into a functional urban space, the project presents a replicable model for preserving industrial heritage within contemporary cityscapes. Full article

Seismic waveform feature extraction is a critical task in seismic exploration, as it directly impacts reservoir prediction and geological interpretation. However, large-scale seismic data and nonlinear relationships between seismic signals and reservoir properties are challenging for traditional machine learning methods. To address these limitations, this paper proposes a novel framework combining Convolutional Neural Network (CNN) and Uniform Manifold Approximation and Projection (UMAP) for seismic waveform feature extraction and analysis. The UMAP-CNN framework leverages the strengths of manifold learning and deep learning, enabling multi-scale feature extraction and dimensionality reduction while preserving both local and global data structures. The evaluation experiments, which considered runtime, receiver operating characteristic (ROC) curves, embedding distribution maps, and other quantitative assessments, illustrated that the UMAP-CNN outperformed t-distributed stochastic neighbor embedding (t-SNE), locally linear embedding (LLE) and isometric feature mapping (Isomap). A case study in the Ordos Basin further demonstrated that UMAP-CNN offers a high degree of accuracy in predicting coal seam thickness. Furthermore, our framework exhibited superior computational efficiency and robustness in handling large-scale datasets. Full article

The landscape of the Warmian municipality of Dobre Miasto has significant natural and cultural value. However, the municipality’s tourism potential remains untapped. The absence of comprehensive local zoning plans covering the entire municipality or most of its territory has disrupted the landscape, leading to the emergence of visually discordant elements. Due to rapid land-use changes in the Region of Warmia, the protection and preservation of its rich natural and cultural heritage are increasingly challenging. The aim of this study was to assess the impact of natural and cultural resources, as well as tourism infrastructure, on the development potential of rural tourism in Dobre Miasto municipality in Poland’s historical region of Warmia. Attempts were made to identify spatial disparities in tourism attractiveness and to determine the ways in which the local environmental and the cultural landscape may support sustainable tourism planning. The results provide valuable insights for implementing appropriate land-use strategies and setting new directions for future development. Tourism infrastructure has to be modernized, expanded, and adapted to new projects, while ensuring that planning and tourism management align with the principles of sustainable development. The growth of tourism creates new opportunities for stimulating rural areas, but it requires careful planning and the implementation of policies that effectively regulate tourist flows while maintaining ecological and cultural integrity. Full article

Creating grayscale images from a color reality has been an inherent human practice since ancient times, but it became a technological challenge with the advent of the first black-and-white televisions and digital image processing. Decolorization is a process that projects visual information from a three-dimensional feature space to a one-dimensional space, thus reducing the dimensionality of the image while minimizing the loss of information. To achieve this, various strategies have been developed, including the application of color channel weights and the analysis of local and global image contrast, but there is no universal solution. In this paper, we propose a bio-inspired approach that combines findings from neuroscience on the architecture of the visual system and color coding with evidence from studies in the psychology of art. The goal is to simplify the decolorization process and facilitate its control through color-related concepts that are easily understandable to humans. This new method organizes colors in a scale that links activity on the retina with a system of opponent and complementary channels, thus allowing the adjustment of the perception of warmth and coolness in the image. The results show an improvement in chromatic contrast, especially in the warmth and coolness categories, as well as an enhanced ability to preserve subtle contrasts, outperforming other approaches in the Ishihara test used in color blindness detection. In addition, the method offers a computational advantage by reducing the process through direct pixel-level operation. Full article

This study develops a value-added processing technique for grade-out cape gooseberry (Physalis peruviana Linn.) by applying mild hydrostatic osmotic pretreatment combined with rotary tray drying. Fruits classified as grade-out, often discarded due to aesthetic flaws, were subjected to osmotic treatment at 0.5 bar for 12 h using a sucrose solution enhanced with citric acid and glycerin. Pretreatment significantly elevated water loss (52.61%) and solid gain (18.12%), reducing moisture content prior to drying. Rotary tray drying was conducted at temperatures of 50, 60, and 70 °C. Drying at 60 °C achieved the ideal balance between efficiency and product quality. Samples pretreated and dried at 60 °C exhibited a 35% reduction in drying time while preserving superior color (ΔE = 13.54 ± 1.81), vitamin C (71.76 ± 2.57 mg/100 g dry matter, DM), total phenolic content (202.9 ± 10.91 mg GAE/100 g DM), and antioxidant activity (ABTS = 95.87 ± 3.41 µmol TE/g DM; DPPH = 89.97 ± 1.27 µmol TE/g DM). A production trial was conducted using 1500 kg of raw material from the Mae Hae Royal Project Development Center in Chiang Mai, Thailand. This process yielded 220 kg of high-quality dried fruit at an overall cost of USD 6.93 per kg. Local farmers successfully applied this technique, demonstrating its potential to enhance livelihoods, avoid postharvest losses, and valorize low-quality produce in line with Sustainable Development Goal 12. This supports the Royal Project Foundation’s vision for sustainable agriculture. Full article

Metropolitan Planning Organizations (MPOs) are required to prepare a Transportation Improvement Plan (TIP) that outlines a fiscal strategy over a four-year period in order to qualify for federal funding. However, the growing population and limited financial resources available often pose significant challenges for transportation agencies in aligning their needs with available budgets. This article examines the project selection criteria used by 20 MPOs in the Southeastern United States to identify the best practices for prioritizing projects in TIPs. Using document analysis, this study categorizes the most commonly used criteria into nine broad groups: safety and security; environmental impacts; mobility, accessibility, and connectivity; preservation; environmental justice; equity; economic factors; alignment with other plans; and local support. Many of these categories are further divided into subcategories and metrics. Despite variations in criteria, weighting, scoring, and methodologies across these MPOs, the study identifies several shared factors that support effective decision-making in regional transportation planning. These findings can help transportation planners and policymakers refine their project prioritization strategies, promote consistency, and lead to improved decision-making frameworks for future TIP development. Full article

The Hellenic Cadastre, which is expected to be fully operational by the end of 2025, represents a major modernization step in Greece’s technical and legal documentation of property rights as the successor to the country’s land registry system. It will also constitute a land administration system, since it will encompass not only property rights but also restrictions and regulations in the context of RRR. A significant technical but also legal challenge inherent to this system pertains to the resolution of deviations between surfaces calculated prior to 1990 based on older geodetic reference systems and recalculated today using the current Greek Geodetic Reference System, GGRS87. Deviations that arise from geodetic transformations between older and modern projected reference systems are compounded by limitations inherent in historical surveying techniques and in the geodetic infrastructure that was available at the time. To address this issue, we introduce the Local Area Distortion Factor (LADF), a novel metric factor designed to adjust and harmonize property areas across different geodetic systems. This real-world case study offers a practical illustration of the application of LADF, demonstrating its capacity to enhance the precision of cadastral records while preserving interpretability for both experts and non-experts. LADF can also be used to improve land adjustment processes during the implementation of urban plans, property valuation, taxation, and notary acts that are in different reference systems. Full article