Search Results (98)

Forests are important biodiversity reservoirs and require sustainable management to prevent deforestation and forest degradation. Forest landscape restoration (FLR) has been proposed as a sustainable initiative aimed at restoring ecosystem functions and improving the well-being of surrounding populations. In 2005, the World Wildlife Fund (WWF) initiated a project to protect 200 ha of savanna in Manzonzi landscape, Democratic Republic of Congo, on the outskirts of the Luki Biosphere Reserve. The biodiversity changes related to this ecological restoration project remain unpublished. To address this knowledge gap, floristic inventories of the protected Manzonzi landscape were carried out over a 12-year period and we assessed how changes in the floral composition of this landscape evolved and affected the provision of ecosystem services (ES). We found that protection of the savanna by banning recurring bush fires and fencing off the area promoted the richness and abundance of forest species, such as Xylopia aethiopica (Dunal) A. Rich, Albizia adianthifolia (Schumach.) W. Wight. These forest taxa replaced grassland species, such as Hymenocardia acida Tul. and Maprounea africana Müll. Arg., and served to benefit the local population, who use these forest taxa as food, fuelwood, and medicines. This study revealed that protected savanna improved woody biomass, plant diversity (richness/abundance), and carbon storage, significantly boosting essential ES for communities; yet these positive trends reversed when active monitoring ceased. Protecting savannas improves the environment and benefits communities, but stopping protection efforts can undo these gains, emphasizing the need for ongoing conservation. Full article

The Amazon wetlands are the largest and most diverse freshwater ecosystem globally, characterized by various flooded vegetation and the Amazon River’s estuary. This critical ecosystem is vulnerable to land use changes, dam construction, mining, and climate change. While several studies have utilized remote sensing to map wetlands in this region, significant uncertainty remains, which limits the assessment of impacts and the conservation priorities for Amazon wetlands. This study aims to enhance wetland mapping by integrating existing maps, remote sensing data, expert knowledge, and cloud computing via Earth Engine. We developed a harmonized regional wetland classification system adaptable to individual countries, enabling us to train and build a random forest model to classify wetlands using a robust remote sensing dataset. In 2020, wetlands spanned 151.7 million hectares (Mha) or 22.0% of the study area, plus an additional 7.4 Mha in deforested zones. The four dominant wetland classes accounted for 98.5% of the total area: Forest Floodplain (89.0 Mha; 58.6%), Lowland Herbaceous Floodplain (29.6 Mha; 19.6%), Shrub Floodplain (16.7 Mha; 11.0%), and Open Water (14.1 Mha; 9.3%). The overall mapping accuracy was 82.2%. Of the total wetlands in 2020, 52.6% (i.e., 79.8 Mha) were protected in Indigenous Territories, Conservation Units, and Ramsar Sites. Threats to the mapped wetlands included 7.4 Mha of loss due to fires and deforestation, with an additional 800,000 ha lost from 2021 to 2024 due to agriculture, urban expansion, and gold mining. Notably, 21 Mha of wetlands were directly affected by both reduced precipitation and surface water in 2020. Our mapping efforts will help identify priorities for wetland protection and support informed decision-making by local governments and ancestral communities to implement conservation and management plans. As 47.4% of the mapped wetlands are unprotected and have some level of threats and pressure, there are also opportunities to expand protected areas and implement effective management and conservation practices. Full article

Three species of land crab occur in The Bahamas; these are an important source of protein and income for Bahamian islanders. The crab harvesters represent an important and largely untapped knowledge source. We conducted surveys on the Bahamian islands of Andros, New Providence, and Eleuthera to document crabbing practices and catalogue this local ecological knowledge (LEK) of land crabs. The survey primarily employed close-ended questions targeting land crab harvesters; we also recorded general feedback from open-ended questions. Crab collection was primarily for self-consumption. Catch rates varied among islands, and were the highest on Andros. There was a preference for white land crabs (Cardisoma guanhumi) on Andros, whereas on Eleuthera and New Providence, there was no preference for either white or black crabs (Gecarcinus ruricola). The majority of respondents reported a decline in white and black crab numbers, with land development and overharvesting being consistently cited factors. On Andros, forest fires were also reported to account for the loss of crab habitat, whereas on Eleuthera, invasive raccoons were blamed for the population decline. Respondents identified broadleaf forests as critical refuges and food sources for black crabs. Birds were the major predator, confirming findings for other land crab species. Land crabs were not merely a food resource but represented a complex nexus of ecological knowledge, economic systems, cultural traditions, and community practices within Bahamian society. We demonstrated a substantial overlap between traditional and scientific knowledge systems, providing valuable insights into land crab behaviour, habitat use, and ecology that complements formal scientific research. Full article

The continuous advancement of fire protection technologies has necessitated the development of comprehensive safety standards, leading to an increasingly diversified and specialized regulatory landscape. This has made it difficult for fire protection professionals to quickly and accurately locate the required fire safety standard information. In addition, the lack of effective integration and knowledge organization concerning fire safety standard entities has led to the severe fragmentation of fire safety standard information and the absence of a comprehensive “one map”. To address this challenge, we introduce FT-FLAT, an innovative CNN–Transformer fusion architecture designed specifically for fire safety standard entity extraction. Unlike traditional methods that rely on rules or single-modality deep learning, our approach integrates TextCNN for local feature extraction and combines it with the Flat-Lattice Transformer for global dependency modeling. The key innovations include the following. (1) Relative Position Embedding (RPE) dynamically encodes the positional relationships between spans in fire safety texts, addressing the limitations of absolute positional encoding in hierarchical structures. (2) The Multi-Branch Prediction Head (MBPH) aggregates the outputs of TextCNN and the Transformer using Einstein summation, enhancing the feature learning capabilities and improving the robustness for domain-specific terminology. (3) Experiments conducted on the newly annotated Fire Safety Standard Entity Recognition Dataset (FSSERD) demonstrate state-of-the-art performance (94.24% accuracy, 83.20% precision). This work provides a scalable solution for constructing fire safety knowledge graphs and supports intelligent information retrieval in emergency situations. Full article

Forest fire detection is an essential application in environmental surveillance since wildfires cause devastating damage to ecosystems, human life, and property every year. The effective and accurate detection of fire is necessary to allow for timely response and efficient management of disasters. Traditional techniques for fire detection often experience false alarms and delayed responses in various environmental situations. Therefore, developing robust, intelligent, and real-time detection systems has emerged as a central challenge in remote sensing and computer vision research communities. Despite recent achievements in deep learning, current forest fire detection models still face issues with generalizability, lightweight deployment, and accuracy trade-offs. In order to overcome these limitations, we introduce a novel technique (FireNet-KD) that makes use of knowledge distillation, a method that maps the learning of hard models (teachers) to a light and efficient model (student). We specifically utilize two opposing teacher networks: a Vision Transformer (ViT), which is popular for its global attention and contextual learning ability, and a Convolutional Neural Network (CNN), which is esteemed for its spatial locality and inductive biases. These teacher models instruct the learning of a Swin Transformer-based student model that provides hierarchical feature extraction and computational efficiency through shifted window self-attention, and is thus particularly well suited for scalable forest fire detection. By combining the strengths of ViT and CNN with distillation into the Swin Transformer, the FireNet-KD model outperforms state-of-the-art methods with significant improvements. Experimental results show that the FireNet-KD model obtains a precision of 95.16%, recall of 99.61%, F1-score of 97.34%, and mAP@50 of 97.31%, outperforming the existing models. These results prove the effectiveness of FireNet-KD in improving both detection accuracy and model efficiency for forest fire detection. Full article

Applicable recommendations play a key role in improving training and procedures used in civil protection. Since 1 January 2025, the Law on Civil Protection and Civil Defense has been in force in Poland. It responds to the experience of current threats, including the war in Ukraine, the 2024 floods in Western Poland, the COVID-19 pandemic, and other crises. The Act systemically regulates the problem of building social resilience, which must be developed and applied regarding today’s modern threats. The primary actor in civil protection is the fire brigade system, in which volunteer firefighters are recruited from local communities and act for their benefit. In this context, it is interesting to ask whether and what solutions should be applied in order to improve the effectiveness of the training and exercise system of volunteer fire brigades (TSOs) in the field of civil protection and crisis management. The aim of this investigation was to develop evaluations and applicable recommendations to improve the effectiveness of the training system for volunteer firefighters based on a survey of volunteer firefighters in the Cracow Poviat. Two survey diagnostic techniques were used: expert interviews and questionnaire research. The findings were compared with the results of an analysis of source documents obtained in TSO units. The expert interviews covered all chief fire officers of the municipalities in the Cracow Poviat. The paper begins with an introduction and a systematic literature review. The conclusions consist of the proposal of applicable changes in the scope of basic, specialist, and additional training. Areas of missing training are also identified. The firefighters’ knowledge of crisis management procedures is verified, deficiencies are identified, and applicable changes in the organization of field exercises are proposed. Full article

The elongated tortoise (Indotestudo elongata), a Critically Endangered (CR) species, faces numerous threats across its range. Yet, the ecological and anthropogenic factors affecting its conservation in fragmented habitats remain poorly understood. This study integrated field surveys and community questionnaires to assess the distribution drivers and local perceptions, such as attitudes, knowledge, conservation practices, and perceived threats, in the Jalthal Forest, one of the last remnants of suitable habitat for the elongated tortoise in eastern Nepal. Using ArcMap, we established 138 randomly selected grids (500 m × 500 m) to evaluate the environmental covariates of tortoise occurrence and anthropogenic pressures. Generalized linear models revealed that tortoise occurrence was negatively associated with dense ground cover (β = −3.50, p = 0.017) and human disturbance (β = −8.11, p = 0.019). Surveys of local residents from community forest user groups (n = 236 respondents) indicated strong local support for tortoise conservation (69% willing to protect the species). Despite this, the respondents identified persistent threats, including hunting for bushmeat and traditional medicine (74%), habitat degradation (65%), and forest fires. While 60% of the respondents recognized the threatened species status, significant knowledge gaps regarding that status and ongoing illegal exploitation persisted. These findings underscore the need for targeted habitat management, reduced anthropogenic pressures, and community-led initiatives to align local attitudes with conservation actions. This study provides critical baseline data for conserving the elongated tortoise in human-modified landscapes and emphasizes the necessity of integrated ecological and socio-cultural strategies for its long-term survival. Full article

The focus of this study was the Intercontinental Biosphere Reserve of the Mediterranean (IBRM, part of the biodiversity hotspot of the Mediterranean Basin) and the Orchidaceae family, which is under-studied in the Moroccan part of the IBRM. For this reason, an inventory of Orchidaceae diversity and factors that could influence their in situ conservation was undertaken, employing a series of field surveys conducted in the Northern Moroccan IBRM ecosystems. In total, 42 sites were surveyed in four protected areas of the Moroccan part of the IBRM. In total, 21 Orchidaceae species and subspecies (taxa) belonging to seven genera were identified, including Orchis spitzelii subsp. cazorlensis, as newly recorded in Morocco, as well as several new reports for different sites and/or areas surveyed, thus updating the previous knowledge of Moroccan Orchidaceae. Most of the Orchidaceae taxa were found in limited numbers of individuals (<30) and were restricted in a few sites (1–3) or a single area; thus, they were assessed as poorly conserved due to the scarcity of rainfall coupled with human pressures, such as the abstraction of surface water, forest fires, and the conversion of protected forests to Cannabis farms. The enforcement of existing laws, the adoption of strategies to combat desertification and forest fires, the prohibition of Cannabis farming, and raising awareness among the local population could reduce the pressures on the protected Orchidaceae members and their habitats, thereby contributing to their conservation. Full article

Introduction: Following the rapid advances in minimally invasive surgery, there are a multitude of surgical modalities available for resecting rectal cancers. Robotic resections represent the current pinnacle of surgical approaches. Currently, decisions on the surgical modality depend on local resources and the expertise of the surgical team. Given limited access to robotic surgery, developing tools based on pre-operative data that can predict the difficulty of surgery would streamline the efficient utilisation of resources. This systematic review aims to appraise the existing literature on artificial intelligence (AI)-driven preoperative MRI analysis for surgical difficulty prediction to identify knowledge gaps and promising models warranting further clinical evaluation. Methods: A systematic review and narrative synthesis were undertaken in accordance with PRISMA and SWiM guidelines. Systematic searches were performed on Medline, Embase, and the CENTRAL Trials register. Studies published between 2012 and 2024 were included where AI was applied to preoperative MRI imaging of adult rectal cancer patients undergoing surgeries, of any approach, for the purpose of stratifying surgical difficulty. Data were extracted according to a pre-specified protocol to capture study characteristics and AI design; the objectives and performance outcome metrics were summarised. Results: Systematic database searches returned 568 articles, 40 ultimately included in this review. AI to support preoperative difficulty assessments were identified across eight domains (direct surgical difficulty grading, extramural vascular invasion (EMVI), lymph node metastasis (LNM), lymphovascular invasion (LVI), perineural invasion (PNI), T staging, and the requirement for multiple linear stapler firings. For each, at least one model was identified with very good performance (AUC scores of >0.80), with several showing excellent performance considerably above this threshold. Conclusions: AI tools applied to preoperative rectal MRI to support preoperative difficulty assessment for rectal cancer surgeries are emerging, with the progressing development and strong performance of many promising models. These warrant further clinical evaluation, which can aid personalised surgical approaches and ensure the adequate utilisation of limited resources. Full article

The paper will focus on the topic of adapting digital humanities methods from architectural history to technical history, considering mapping and image analysis for increasing disaster resilience in Art Nouveau and Modernist buildings in different geographical areas—including lessons from Europe to the USA. The project proposes the transformation of the collection of photographs of early 20th-century architecture gathered by the applicant over about 30 years of travel into a database by answering the research question on how threats from the hazards of earthquakes, floods, and fires can be answered by taking into account the local culture in the European countries covered, for buildings from a period when the architecture styles were already global at that time. For this purpose, digital humanities methods of image annotation (including architectural volumetric analysis) and mapping are employed. From the knowledge gathered and the resulting database, a prototyping ontology and taxonomy is derived. This outcome can be further developed into a set of evaluation criteria, considering the decisions that can be taken to prioritize the retrofit interventions depending on the geographic positions of the buildings. Full article

The small Mediterranean islands, unique geographical places where coastlines and mountains converge due to volcanic genesis, are among the most threatened environments on Earth. Their marginality, which has historically led to their use as places of detention and punishment, coupled with the extreme climate and rugged geomorphology shaped by terracing practices, has resulted in the loss of systematic land management. This loss stems from the abandonment of cropland in favor of alternative activities and migrations, impacting essential ecosystem services such as the water cycle, soil fertility, and the cultural landscape. The need to counteract the land degradation in these vulnerable areas has been acknowledged for some Mediterranean small islands, including the UNESCO heritage site of Stromboli in the Aeolian Islands, Sicily, Italy—an especially captivating location due to its active volcano. The agricultural abandonment on terraces, intensively cultivated with olives groves and vineyards until the mid-20th century, has rendered the area highly fragile and susceptible to risks such as fires and soil erosion, particularly as a consequence of extreme weather events, as proven in 2022, which saw a destructive fire followed by storms. To mitigate the negative effects of hydrogeological disruptions, the implementation of integrated landscape management—managing ecosystems at the landscape level—has been proposed. Specifically, an agroforestry intervention, coupled with the restoration of dry stone walls, the shaping of soil slopes by recovering the traditional ecological knowledge (TEK), and the design of water-collecting devices incorporated with the traditional hydraulic knowledge, may be proposed as a strategic approach to minimize the soil erosion risks, adapt to climate change, and extensively restore the use of traditional agrobiodiversity to support the local economy and tourism. A pilot intervention by local stakeholders based on these principles is described as an emblematic agrobiodiversity-based landscape design project in a vulnerable area, aiming at the preservation of the cultural landscapes of the small Mediterranean islands. Full article

Vision-based forest fire detection systems have significantly advanced through Deep Learning (DL) applications. However, DL-based models typically require large-scale labeled datasets for effective training, where the quality of data annotation is crucial to their performance. To address challenges related to the quality and quantity of labeling, a domain adaptation-based approach called FireDA is proposed for forest fire recognition in scenarios with limited labels. Domain adaptation, a subfield of transfer learning, facilitates the transfer of knowledge from a labeled source domain to an unlabeled target domain. The construction of the source domain FBD is initiated, which includes three common fire scenarios: forest (F), brightness (B), and darkness (D), utilizing publicly available labeled data. Subsequently, a novel algorithm called Neighborhood Aggregation-based 2-Stage Domain Adaptation (NA2SDA) is proposed. This method integrates feature distribution alignment with target domain Proxy Classification Loss (PCL), leveraging a neighborhood aggregation mechanism and a memory bank designed for the unlabeled samples in the target domain. This mechanism calibrates the source classifier and generates more accurate pseudo-labels for the unlabeled sample. Consequently, based on these pseudo-labels, the Local Maximum Mean Discrepancy (LMMD) and the Proxy Classification Loss (PCL) are computed. To validate the efficacy of the proposed method, the publicly available forest fire dataset, FLAME, is employed as the target domain for constructing a transfer learning task. The results demonstrate that our method achieves performance comparable to the supervised Convolutional Neural Network (CNN)-based state-of-the-art (SOTA) method, without requiring access to labels from the FLAME training set. Therefore, our study presents a viable solution for forest fire recognition in scenarios with limited labeling and establishes a high-accuracy benchmark for future research. Full article

The objective of this study was to determine the authorship, provenance, and technology of the mudejar enamelled tiles from the Olite Castle (northern Spain, 14th century). According to previous knowledge, Olite’s enamelled tiles had been manufactured in Manises (Valencia, Spain). The analysis of ceramic pastes revealed the existence of two different chemical compositions, suggesting the use of two different clay sources, probably one from the Tudela area, and another from the Tafalla–Olite area. Those probably made in the Tudela area stood out with a higher diopside (CaMgSi₂O₆) content. Those probably made in the Tafalla–Olite area stood out for their calcium-bearing minerals, such as calcite (CaCO₃) or gehlenite (Ca₂Al(AlSi)O₇). On this basis, production in Manises has been ruled out. However, it is highly probable that the artisans of Manises would have led the production from Tudela. The study of the firing temperatures and composition of the enamels indicated that the production methods and materials used in Tafalla–Olite (800–850 °C) and Tudela (higher than 900 °C) were different, reflecting the influence of local and Manises artisans, respectively. In Olite tiles, enamel was applied following recipes from the 14th and 15th centuries. Full article

Timely forecast of atmospheric pollutants fallout due to accidental fires can provide decision-makers with useful information for effective emergency response, for planning environmental monitoring and for conveying essential alerts to the population to minimize health risks. The SAPERI project (Accelerated simulation of accidental releases in the atmosphere on heterogeneous platforms—from its Italian initials) implements a modeling chain to quickly supply evidence about the dispersion of pollutants accidentally released in the atmosphere, even in the early stages of the emergency when full knowledge of the incident details is missing. The SAPERI modeling chain relies on SPRAY-WEB, a Lagrangian particle dispersion model openly shared for research purposes, parallelized on a GPU to take advantage of local or cloud computing resources and interfaced with open meteorological forecasts made available by the Meteo Italian SupercompuTing PoRtAL (MISTRAL) consortium over Italy. The operational model provides a quantitative and qualitative estimate of the impact of the emergency event by means of a maximum ground level concentration and a footprint map. In this work, the SAPERI modeling chain is tested in a real case event that occurred in Beinasco (Torino, Italy) in December 2021, mimicking its use with limited or missing local input data as occurs when an alert message is first issued. An evaluation of the meteorology forecast is carried out by comparing the wind and temperature fields obtained from MISTRAL with observations from weather stations. The concentrations obtained from the dispersion model are then compared with the observations at three air quality monitoring stations impacted by the event. Full article

Ongoing neural oscillations reflect cycles of excitation and inhibition in local neural populations, with individual neurons being more or less likely to fire depending upon the oscillatory phase. As a result, the oscillations could determine whether or not a sound is perceived and/or whether its neural representation enters into later processing stages. While empirical support for this idea has come from sound detection studies, large gaps in knowledge still exist regarding memory for sound events. In the current study, it was investigated how sensory entrainment impacts the fidelity of working memory representations for pitch. In two separate experiments, an 8 Hz amplitude modulated (AM) entraining stimulus was presented prior to a multitone complex having an f₀ between 270 and 715 Hz. This “target” sound could be presented at phases from 0 to 2π radians in relation to the previous AM. After a retention interval of 4 s (Experiment 1; n = 26) or 2 s (Experiment 2; n = 28), listeners were tasked to reproduce the target sound’s pitch by moving their finger along the horizontal axis of a response pad. It was hypothesized that if entrainment modulates auditory working memory fidelity, reproductions of a target’s pitch would be more accurate and precise when targets were presented in phase with the entrainment. Cosine fits of the average data for both experiments showed a significant entrainment “echo” in the accuracy of pitch matches. There was no apparent echo in the matching precision. Fitting of the individual data accuracy showed that the optimal phase was consistent across individuals, aligning near the next AM peak had the AM continued. The results show that sensory entrainment modulates auditory working memory in addition to stimulus detection, consistent with the proposal that ongoing neural oscillatory activity modulates higher-order auditory processes. Full article