Search Results (127)

Hurricanes are powerful tropical storms that can severely damage mangrove forests through uprooting trees, sediment erosion, and saltwater intrusion, disrupting their critical role in coastal protection and biodiversity. After a hurricane, evaluating mangrove damage helps prioritize rehabilitation efforts, as these ecosystems play a key ecological role in coastal regions. Thus, we analyzed the defoliation of mangrove forest canopies and their early recovery, approximately 2.5 years after the landfall of Category 3 Hurricane Roslyn in October 2002 in Marismas Nacionales, Mexico. The following mangrove traits were analyzed: (1) the yearly time series of the Combined Mangrove Recognition Index (CMRI) standard deviation from 2020 to 2025, (2) the CMRI rate of change (slope) following the hurricane’s impact, and (3) the canopy height model (CHM) before and after the hurricane using satellite and UAV-LiDAR data. Hurricane Roslyn caused a substantial decrease in canopy cover, resulting in a loss of 47,202 ha, which represents 82.8% of the total area of 57,037 ha. The CMRI standard deviation indicated early signs of canopy recovery in one-third of the mangrove-damaged areas 2.5 years post-impact. The CMRI slope indicated that areas near the undammed rivers had a maximum recovery rate of 0.05 CMRI units per month, indicating a predicted canopy recovery of ~2.5 years. However, most mangrove areas exhibited CMRI rates between 0.01 and 0.03 CMRI units per month, anticipating a recovery time between 40 months (approximately 3.4 years) and 122 months (roughly 10 years). Unfortunately, most of the already degraded Laguncularia racemosa forests displayed a negative CMRI slope, suggesting a lack of canopy recovery so far. Additionally, the CHM showed a median significant difference of 3.3 m in the canopy height of fringe-type Rhizophora mangle and Laguncularia racemosa forests after the hurricane’s landfall. Full article

The Tigris-Euphrates basin hosts a diverse assemblage of native aquatic plants vital to the region’s ecological and cultural heritage. However, decades of hydrological alterations, pollution, salinity intrusion, habitat destruction, and climate change have caused significant declines in aquatic plant species diversity. This review compiles historical and contemporary information on key native aquatic plant species, assesses their current conservation status, identifies major threats, and provides recommendations for their protection. Sensitive submerged and floating species, including Vallisneria spiralis, Najas marina, and Potamogeton spp., have been particularly affected, with many now being rare or locally extinct. Although restoration efforts in the Mesopotamian Marshes have partially restored some wetlands, aquatic plant conservation remains largely overlooked. We propose targeted recovery plans, integration of aquatic plants into wetland management, enhancement of water quality measures, and increased cross-border hydrological cooperation. Protecting native aquatic flora is essential for maintaining the ecological integrity and resilience of the Tigris-Euphrates basin. Full article

A quantitative literature review of restoration techniques and supporting management strategies used throughout the Caribbean and Western Atlantic from 1998 through 2024 was compiled using references from the Web of Science to highlight those with potential for reef replenishment. From 93 sources listed, 74 publications were relevant and categorized into subtopics based on the most prevalent restoration techniques. Roughly half the studies focused on three general topics: the benefits of restoring Acropora species, studies utilizing micro-fragmentation and fragment nurseries, and outplanting techniques. Other subtopics, each with at least three references, included optimizing substrates and artificial reefs, enhancing larval recruitment, emphasizing the role of herbivory, improving management practices, and addressing the impacts of tourism and community engagement. The information from the references was compiled to determine the overlap among categories and the ways in which techniques and management strategies might be applied simultaneously to enhance restoration outcomes. Additionally, sources were analyzed according to time and location of publication to better visualize the emergence of this area of research and restoration efforts. An increase in publications was observed from 2014 to 2024, associated with the rise in major events impacting coral reefs. The major locations for published research were the Florida reef tract and Puerto Rico, though restoration studies were also reported from the Bahamas and sites around the Caribbean. Criteria to assess the success of techniques included coral survival, recruitment, coral coverage, habitat structure and complexity, and biomass of marine life, including fish and invertebrates that inhabited a restored reef. Most restoration efforts utilized either fragmentation or assisted sexual breeding, followed by cultivation in nurseries or labs. Outplanting success depended on fragment size, attachment style, and site selection, with less-intrusive techniques and intermediate planting densities promoting survival. Tools like GAO maps can guide site selection based on herbivore presence and algal coverage. Monitoring is critical to ensuring coral survival, especially after the first year of outplanting, while community involvement can foster public engagement in reef conservation. Full article

Non-intrusive load monitoring (NILM) provides a cost-effective solution for smart services across numerous appliances by inferring appliance-level information from mains electrical measurements. With the rapid growth in appliance diversity, continual learning that adapts to new appliances while retaining knowledge of previously learned appliances is of great interest. However, existing methods can handle only a few new appliance types and suffer from high computational complexity and data leakage risks. Therefore, an analytic continual learning-based (ACL) NILM method is proposed. The method employs a lightweight model that is constructed with dual output branches using depthwise separable convolution for load identification and novelty detection. Meanwhile, a supervised contrastive learning strategy is applied to enhance the distinctiveness among appliance types in the feature extraction module. When the novelty detection branch determines that new data need to be learned, the parameters of the dual branches are updated by recursively calculating the analytical solution using only the current data. Experiments on four public datasets demonstrate superior performance on pre-collected appliances with lower computational effort. It also significantly outperforms existing methods during the continual learning process, as the number of appliance types increases to 56. The practicality of the proposed method is validated through a real-world application on an STM32F407-based smart socket. Full article

This study analyzes the ore potential of the tectono-stratigraphic zones in the Shyngys-Tarbagatai folded system using metallogenic diagrams. These diagrams condense extensive geological and metallogenic data, illustrating stratified and intrusive formations, formation types, depositional environments, and ore loads in chronological sequence. The analysis highlights variations in ore mineralization intensity across the zones, identifying both highly and less ore-bearing areas. Most zones show polymetallic mineralization with 2 to 12 types of minerals; gold and copper are present in all zones. Temporal analysis identified key productive levels in the Late Ordovician, Early Silurian, and Early Devonian, corresponding to active stages of island arcs, forearc and backarc basins, and the Devonian volcanic–plutonic belt. The structures of the Shyngys-Tarbagatai folded system are classified as island-arc structures of active continental margins. Comparing the ore potential of its tectono-stratigraphic zones with similar modern structures shows that, except for the Maikain zone, all others have significantly lower ore potential. The obtained data is most likely a result of the region’s poor exploration coverage. As such, future efforts should prioritize further investigation of the identified mineralization zones. This is evident from the dominance of small, medium, and large deposits, and ore occurrences in all tectono-stratigraphic zones when assessing their ore potential. Preliminary analysis of the ore potential in the tectono-stratigraphic zones of the Shyngys-Tarbagatai folded system, based on metallogenic diagrams, clearly supports the need for regional and exploration studies. These should focus on poorly explored stratigraphic levels, ore-bearing geological formations, and geodynamic settings that are favorable for deposit formation. This will provide a more accurate assessment of the potential in these zones. Full article

The integration of Internet of Things audio sensors with Artificial Intelligence techniques is revolutionizing predictive maintenance systems in machining operations, playing a pivotal role in advancing the sustainability goals of Industry 5.0. The synergy between these technologies enhances operational efficiency, reduces downtime, and minimizes waste, aligning with energy conservation and resource optimization goals. The use of audio sensors provides a cost-effective, non-intrusive solution for machining operations. In this work, a bibliometric analysis of the progress achieved in this field is performed, identifying which challenges have been extensively addressed and which remain unexplored. By assessing the existing research, this study aims to highlight gaps that necessitate further investigation, guiding future research efforts toward the most critical and promising directions for enhancing predictive maintenance in machining processes. Through a comprehensive analysis of publication trends, collaboration networks, and research gaps, this study intends to provide valuable insights for academia and industry stakeholders, to motivate their efforts in this field. Understanding these trends is essential for fostering innovation and ensuring that the development of predictive models continues to evolve to maximize both production efficiency and sustainability. Full article

With global efforts intensifying towards achieving carbon neutrality, accurately monitoring and managing energy consumption in industrial sectors has become critical. Non-Intrusive Load Monitoring (NILM) technology presents a cost-effective solution for industrial energy management by decomposing aggregate power data into individual device-level information without extensive hardware requirements. However, existing NILM methods primarily tailored for residential applications struggle to capture complex inter-device correlations and production-dependent load dynamics prevalent in industrial environments, such as cement plants. This paper proposes a novel sequence-to-sequence-based non-intrusive load disaggregation method that integrates Convolutional Neural Networks (CNN) and Transformer architectures, specifically addressing the challenges of multi-device load disaggregation in industrial settings. An innovative time–application attention mechanism was integrated to effectively model long-term temporal dependencies and the collaborative operational relationships between industrial devices. Additionally, global constraints—including consistency, smoothness, and sparsity—were introduced into the loss function to ensure power conservation, reduce noise, and achieve precise zero-power predictions for inactive equipment. The proposed method was validated on real-world power consumption data collected from a cement production facility. Experimental results indicate that the proposed method significantly outperforms traditional NILM approaches with average improvements of 4.98%, 3.70%, and 4.38% in terms of accuracy, recall, and F1-score, respectively. These findings underscore its superior robustness in noisy conditions and under device fault conditions, further affirming its applicability and potential for deployment in industrial settings. Full article

Magmatic nickel–copper–platinum group element (PGE) deposits hosted in mafic–ultramafic intrusions within volcanic arc systems are highly attractive targets for mineral exploration, yet their genesis remains poorly understood. This study investigates metagabbroic intrusions in the Paleoproterozoic Lynn Lake greenstone belt of the Trans-Hudson Orogen to identify the key factors, in the original gabbros, that control the formation of magmatic Ni-Cu-PGE deposits in volcanic arc systems. By examining the field relationships, geochemical and sulfur and oxygen stable isotope compositions, mineralogy, and structural fabrics, this study aims to explain why some intrusions host mineralization (e.g., Lynn Lake and Fraser Lake intrusions), whereas others remain barren (e.g., Ralph Lake, Cartwright Lake, and Snake Lake intrusions). Although both the fertile and barren gabbroic, likewise original, intrusions exhibit metaluminous, tholeiitic to calc-alkaline affinity with volcanic arc geochemical signatures, they differ significantly in shape, ranging from vertical and tube-like to tabular forms, reflecting distinct geological settings and magma dynamics. The gabbroic rocks of fertile intrusions exhibit erratic trace element profiles, lower (Nb/Th)_N and higher (Cu/Zr)_N ratios, as well as a larger range of δ³⁴S values than those in barren intrusions. Key factors influencing Ni-Cu-PGE mineralization include the degree of partial melting of the mantle, early sulfide segregation, and crustal contamination, particularly from volcanogenic massive sulfide deposits. These processes likely triggered sulfide saturation in the mafic magmas. Geochemical proxies, such as PGE concentrations and sulfur and oxygen stable isotopes, provide critical insights into these controlling factors. The results of this study enhance our understanding of the metallogenic processes responsible for the formation of magmatic Ni-Cu-PGE deposits in the gabbroic intrusions emplaced in an extensional setting due to slab rollback, during the geological evolution of the Lynn Lake greenstone belt, offering valuable guidance for mineral exploration efforts. Full article

The increase in new cyber threats is the result of the rapid growth of using the Internet, thus raising questions about the effectiveness of traditional Intrusion Detection Systems (IDSs). Machine learning (ML) technology is used to enhance cybersecurity in general and especially for reactive approaches, such as traditional IDSs. In several instances, it is seen that a single assailant may direct their efforts towards different servers belonging to an organization. This behavior is often perceived by IDSs as infrequent attacks, thus diminishing the effectiveness of detection. In this context, this paper aims to create a machine learning-based IDS model able to detect malicious traffic received by different organizational network interfaces. A centralized proxy server is designed to receive all the incoming traffic at the organization’s servers, scan the traffic by using the proposed IDS, and then redirect the traffic to the requested server. The proposed IDS was evaluated by using three datasets: CIC-MalMem-2022, CIC-IDS-2018, and CIC-IDS-2017. The XGBoost model showed exceptional performance in rapid detection, achieving 99.96%, 99.73%, and 99.84% accuracy rates within short time intervals. The Stacking model achieved the highest level of accuracy among the evaluated models. The developed IDS demonstrated superior accuracy and detection time outcomes compared with previous research in the field. Full article

Habitat loss, pollution, and climate change have a global impact on bird diversity, particularly in central Mexico, where human disturbances and unplanned urbanization can lead to the decline of this faunal group. In this study, the effects of season (rainy, warm–dry, or cool–dry) and environmental variables (size, perimeter, vegetation cover, built cover, distance to nearby greenspaces and distance to the closet natural vegetation patch) on the avian diversity at different sites located in a peri-urban landscape in the Metropolitan Area of Mexico City were determined. The study was conducted using the linear transect method to assess the diversity and composition of bird communities from November 2019 to March 2022, recording 290 total bird species. Zumpango Lagoon was the study site with the highest diversity (N = 209, H′  =  3.22) and evenness index (J′  =  0.76). Linear mixed models were used to determine the effects of season and environmental variables of the study sites on the avian diversity. The effect of distance to the nearest greenspace was significantly more positive during the rainy season than the two dry seasons. An ANOSIM test also showed that the avian community associated with water bodies differed significantly from the other communities (R = 0.16, p < 0.001). Despite some anthropogenic activities and human intrusion, sites with water bodies retain a high diversity of birds. This finding indicates the need for immediate conservation efforts to protect many resident breeding species and wintering migratory birds in the study area. Full article

As climate change transforms our environment and human intrusion into natural ecosystems escalates, there is a growing demand for disease spread models to forecast and plan for the next zoonotic disease outbreak. Accurate parametrization of these models requires data from diverse sources, including the scientific literature. Despite the abundance of scientific publications, the manual extraction of these data via systematic literature reviews remains a significant bottleneck, requiring extensive time and resources, and is susceptible to human error. This study examines the application of a large language model (LLM) as an assessor for screening prioritisation in climate-sensitive zoonotic disease research. By framing the selection criteria of articles as a question–answer task and utilising zero-shot chain-of-thought prompting, the proposed method achieves a saving of at least 70% work effort compared to manual screening at a recall level of 95% (NWSS@95%). This was validated across four datasets containing four distinct zoonotic diseases and a critical climate variable (rainfall). The approach additionally produces explainable AI rationales for each ranked article. The effectiveness of the approach across multiple diseases demonstrates the potential for broad application in systematic literature reviews. The substantial reduction in screening effort, along with the provision of explainable AI rationales, marks an important step toward automated parameter extraction from the scientific literature. Full article

This study focuses on the Fourque massif, one of the thirty Variscan plutons outcropping along the Axial zone of the Pyrenees. It hosts a significant tungsten deposit that was actively mined until 1986. However, since the closure of the mine, no detailed geochemical or geochronological studies have been conducted until recent investigations in 2019, leaving a significant gap in our understanding of this intrusion. This lack of research, along with the ongoing debate and uncertainties regarding the timing and magmatic processes of Variscan plutonism in the Pyrenees, underscores the importance of further investigations. To address these gaps, we present new zircon U–Pb geochronology, whole-rock and zircon geochemistry (X-ray fluorescence and LA-ICP-MS), and Ti-in-zircon thermometry. Our study compares nine new whole-rock geochemistry samples with the limited previous dataset from 1987, refining the petrogenetic interpretation of the intrusion. These efforts are framed within the ongoing debate surrounding the different Variscan intrusions in the Pyrenees, including the discussions on their emplacement age, magmatic context, type, and origin. Geochronological data indicate an age ranging from 304.6 ± 2.3 to 308.4 ± 2.6 Ma, with crystallization temperatures ranging from 700 to 800 °C. The granodiorite is characterized by differentiated petrogenetic facies, related to successive batches of magma rising from a deeper source. The granodiorite exhibits high ASI ratios (>1.3), classifying it as strongly peraluminous. While I-type granites are typically metaluminous to weakly peraluminous, such elevated ASI values suggest a significant influence of crustal assimilation during magmatic evolution. The geochemical signature of the intrusion is enriched in large ion lithophile elements (LILE) and light rare earth elements (LREEs) while showing depletion in heavy rare earth elements (HREEs), consistent with a high-K calc-alkaline, magnesian, syn-orogenic setting. Whole-rock and zircon trace element data suggest that the magma source involved partial melting of the continental crust, with evidence of interaction with a subduction-modified mantle component. By applying methods previously unapplied to this pluton, this study provides new data on its geochemistry and geochronology, revealing significant differences from previous interpretations. These findings offer deeper insights into the emplacement and evolution of the Fourque granodiorite, refining its role within the broader context of Variscan orogenesis in the Pyrenean Axial Zone and similar plutonic systems worldwide. Full article

Bubbly flow is a flow regime common in many industrial applications involving heat and mass transfer, such as reactors, cooling systems, and separation units. Accurate knowledge of bubble velocity, shape, and volume is crucial as these parameters directly influence the efficiency of phase interaction and the mixing process performance. Over the past few decades, numerous techniques have been developed to measure the velocity, shape, and volume of bubbles. Most efforts have focused on non-intrusive methods to minimize disturbance to the flow. However, a technique capable of simultaneously measuring these bubble characteristics across a dense spatial domain remains elusive. In this research, an image-based technique that enables simultaneous measurement of bubble velocity, shape, and volume in bubbly flows over a densely sampled linear domain is presented. A high-speed camera captures the variation in light intensity as bubbles pass in front of a collimated laser sheet, providing real-time, high-resolution data. The accuracy of the proposed methodology is evaluated and the uncertainties associated with the velocity and volume measurements are quantified. Given the promising results and the simplicity of the hardware and setup, this study represents an important step toward developing a technique for online monitoring of industrial processes involving bubbly flows. Full article

In the rapid development of the Internet of Things (IoT) and large-scale distributed networks, Intrusion Detection Systems (IDS) face significant challenges in handling complex spatiotemporal features and addressing data imbalance issues. This article systematically reviews recent advancements in applying deep learning techniques in IDS, focusing on the core challenges of spatiotemporal feature extraction and data imbalance. First, this article analyzes the spatiotemporal dependencies of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in network traffic feature extraction and examines the main methods these models use to solve this problem. Next, the impact of data imbalance on IDS performance is explored, and the effectiveness of various data augmentation and handling techniques, including Generative Adversarial Networks (GANs) and resampling methods, in improving the detection of minority class attacks is assessed. Finally, the paper highlights the current research gaps and proposes future research directions to optimize deep learning models further to enhance the detection capabilities and robustness of IDS in complex network environments. This review provides researchers with a comprehensive perspective, helping them identify the challenges in the current field and laying a foundation for future research efforts. Full article

Understanding baseline O₃ is important as it defines the fraction of O₃ coming from global sources and not subject to local control. We report the occurrence and sources of high baseline ozone days, defined as a day where the daily maximum 8 h average (MDA8) exceeds 70 ppb, as observed at the Mount Bachelor Observatory (MBO, 2.8 km asl) in Central Oregon from 2004 to 2022. We used various indicators and enhancement ratios to categorize each high-O₃ day: carbon monoxide (CO), aerosol scattering, the water vapor mixing ratio (WV), the aerosol scattering-to-CO ratio, backward trajectories, and the NOAA Hazard Mapping System Fire and Smoke maps. Using these, we identified four causes of high-O₃ days at the MBO: Upper Troposphere/Lower Stratosphere intrusions (UTLS), Asian long-range transport (ALRT), a mixed UTLS/ALRT category, and events enhanced by wildfire emissions. Wildfire sources were further divided into two categories: smoke transported in the boundary layer to the MBO and smoke transported in the free troposphere from more distant fires. Over the 19-year period, 167 high-ozone days were identified, with an increasing fraction due to contributions from wildfire emissions and a decreasing fraction of ALRT events. We further evaluated trends in the O₃ and CO data distributions by season. For O₃, we found an overall increase in the mean and median values of 2.2 and 1.5 ppb, respectively, from the earliest part of the record (2004–2013) compared to the later part (2014–2022), but no significant linear trends in any season. For CO, we found a significant positive trend in the summer 95th percentiles, associated with increasing fires in the Western U.S., and a strong negative trend in the springtime values at all percentiles (1.6% yr⁻¹ for 50th percentile). This decline was likely associated with decreasing emissions from East Asia. Overall, our findings are consistent with the positive trend in wildfires in the Western United States and the efforts in Asia to decrease emissions. This work demonstrates the changing influence of these two source categories on global background O₃ and CO. Full article