Search Results (16)

Developing clean and renewable energy instead of the ones related to hydrocarbon resources has been known as one of the different ways to guarantee reduced greenhouse gas emissions. Geothermal systems and native hydrogen exploration could represent an opportunity to diversify the global energy matrix and lower carbon-related emissions. All of these natural energy sources require a well to be drilled for its access and/or extractions, similar to the petroleum industry. The main focuses of this technical–scientific contribution and research are (i) to evaluate the global energy matrix; (ii) to show the context over the years and future perspectives on geothermal systems and natural hydrogen exploration; and (iii) to present and analyze the importance of developing technologies on drilling process optimization aiming at accessing these natural energy resources. In 2022, the global energy matrix was composed mainly of nonrenewable sources such as oil, natural gas, and coal, where the combustion of fossil fuels produced approximately 37.15 billion tons of CO₂ in the same year. In 2023, USD 1740 billion was invested globally in renewable energy to reduce CO₂ emissions and combat greenhouse gas emissions. In this context, currently, about 353 geothermal power units are in operation worldwide with a capacity of 16,335 MW. In addition, globally, there are 35 geothermal power units under pre-construction (project phase), 93 already being constructed, and recently, 45 announced. Concerning hydrogen, the industry announced 680 large-scale project proposals, valued at USD 240 billion in direct investment by 2030. In Brazil, the energy company Petroleo Brasileiro SA (Petrobras, Rio de Janeiro, Brazil) will invest in the coming years nearly USD 4 million in research involving natural hydrogen generation, and since the exploration and access to natural energy resources (oil and gas, natural hydrogen, and geothermal systems, among others) are achieved through the drilling of wells, this document presents a technical–scientific contextualization of social interest. Full article

Membrane technology has been widely used in industrial CO₂ capturing, gas purification and gas separation, arousing attention due to its advantages of high efficiency, energy saving and environmental protection. In the context of reducing global carbon emissions and combating climate change, it is particularly important to capture and separate greenhouse gasses such as CO₂. Zr-MOF can be used as a multi-dimensional modification on the polymer membrane to prepare self-assembled MOF-based mixed matrix membranes (MMMs), aiming at the problem of weak adhesion or bonding force between the separation layer and the porous carrier. When defective UiO-66 is applied to PVDF membrane as a functional layer, the CO₂ separation performance of the PVDF membrane is significantly improved. TUT-UiO-3-TTN@PVDF has a CO₂ permeation flux of 14,294 GPU and a selectivity of 27 for CO₂/N₂ and 18 for CO₂/CH₄, respectively. The CO₂ permeability and selectivity of the membrane exhibited change after 40 h of continuous operation, significantly improving the gas separation performance and showing exceptional stability for large-scale applications. Full article

Antibiotic resistance, in particular the dissemination of carbapenemase-producing organisms, poses a significant threat to global healthcare. This study introduces the qPCR CarbaScan LyoBead assay, a robust, accurate, and efficient tool for detecting key carbapenemase genes, including blaKPC, blaNDM, blaOXA-48, and blaVIM. The assay utilizes lyophilized beads, a technological advancement that enhances stability, simplifies handling, and eliminates the need for refrigeration. This feature renders it particularly well-suited for point-of-care diagnostics and resource-limited settings. The assay’s capacity to detect carbapenemase genes directly from bacterial colonies without the need for extensive sample preparation has been demonstrated to streamline workflows and enable rapid diagnostic results. The assay demonstrated 100% specificity and sensitivity across a diverse range of bacterial strains, including multiple allelic variants of target genes, facilitating precise identification of resistance mechanisms. Bacterial strains of the species Acinetobacter baumannii, Citrobacter freundii, Escherichia coli, Enterobacter cloacae, Klebsiella pneumoniae and Pseudomonas aeruginosa were utilized as reference material for assay development (n = 9) and validation (n = 28). It is notable that the assay’s long shelf life and minimal operational complexity further enhance its utility for large-scale implementation in healthcare, food safety, and environmental monitoring. The findings emphasize the necessity of continuous surveillance and the implementation of rapid diagnostic methods for the effective detection of resistance genes. Furthermore, the assay’s potential applications in other fields, such as toxin-antitoxin system research and monitoring of resistant bacteria in the community, highlight its versatility. In conclusion, the qPCR CarbaScan LyoBead assay is a valuable tool that can contribute to the urgent need to combat antibiotic resistance and improve global public health outcomes. Full article

Air target intention recognition (ATIR) is critical for unmanned systems in modern air defense operations. Through the analysis of typical air defense combat scenarios, first, the paper defines the intention space and intention parameters of air units based on military experience and domain knowledge. Then, a rule-based agent for unmanned systems for online intention recognition is proposed, with no training, no tagging, and no big data support, which is not only for intention recognition and parameter prediction, but also for formation identification of air targets. The most critical point of the agent is the introduction and application of a thermal distribution grid graph (TDGG) and virtual grid dictionary (VGD), where the former is used to identify the formation information of air targets, and the latter is used to optimize the storage space and simplify the access process for the large-scale and real-time combat information. Finally, to have a performance evaluation and application analysis for the algorithm, we carried out a data instance analysis of ATIR for unmanned systems and an air defense warfare simulation experiment based on a Wargame platform; the comparative experiments with the classical k-means, FCNIRM, and the sector-based forward search method verified the effectiveness and feasibility of the proposed agent, which characterizes it as a promising tool or baseline model for the battlefield situational awareness tasks of unmanned systems. Full article

Skin wounds often form scar tissue during healing. Early intervention with tissue-engineered materials and cell therapies may promote scar-free healing. Exosomes and extracellular vesicles (EV) secreted by mesenchymal stromal cells (MSC) are believed to have high regenerative capacity. EV bioactivity is preserved after lyophilization and storage to enable use in remote and typically resource-constrained environments. We developed a bioprinted bandage containing reconstituted EVs that can be fabricated at the point-of-need. An alginate/carboxymethyl cellulose (CMC) biomaterial ink was prepared, and printability and mechanical properties were assessed with rheology and compression testing. Three-dimensional printed constructs were evaluated for Young’s modulus relative to infill density and crosslinking to yield material with stiffness suitable for use as a wound dressing. We purified EVs from human MSC-conditioned media and characterized them with nanoparticle tracking analysis and mass spectroscopy, which gave a peak size of 118 nm and identification of known EV proteins. Fluorescently labeled EVs were mixed to form bio-ink and bioprinted to characterize EV release. EV bandages were bioprinted on both a commercial laboratory bioprinter and a custom ruggedized 3D printer with bioprinting capabilities, and lyophilized EVs, biomaterial ink, and thermoplastic filament were deployed to an austere Arctic environment and bioprinted. This work demonstrates that EVs can be bioprinted with an alginate/CMC hydrogel and released over time when in contact with a skin-like substitute. The technology is suitable for operational medical applications, notably in resource-limited locations, including large-scale natural disasters, humanitarian crises, and combat zones. Full article

Fires are one of the main sources of disturbance in fire-sensitive ecosystems such as the Amazon. Any attempt to characterize their impacts and establish actions aimed at combating these events presupposes the correct identification of the affected areas. However, accurate mapping of burned areas in humid tropical forest regions remains a challenging task. In this paper, we evaluate the performance of four operational BA products (MCD64A1, Fire_cci, GABAM and MapBiomas Fogo) on a regional scale in the southwestern Amazon and propose a new approach to BA mapping using fraction images extracted from data cubes of the Brazilian orbital sensors CBERS-4/WFI and CBERS-4A/WFI. The methodology for detecting burned areas consisted of applying the Linear Spectral Mixture Model to the images from the CBERS-4/WFI and CBERS-4A/WFI data cubes to generate shadow fraction images, which were then segmented and classified using the ISOSEG non-supervised algorithm. Regression and similarity analyses based on regular grid cells were carried out to compare the BA mappings. The results showed large discrepancies between the mappings in terms of total area burned, land use and land cover affected (forest and non-forest) and spatial location of the burned area. The global products MCD64A1, GABAM and Fire_cci tended to underestimate the area burned in the region, with Fire_cci underestimating BA by 88%, while the regional product MapBiomas Fogo was the closest to the reference, underestimating by only 7%. The burned area estimated by the method proposed in this work (337.5 km²) was 12% higher than the reference and showed a small difference in relation to the MapBiomas Fogo product (18% more BA). These differences can be explained by the different datasets and methods used to detect burned areas. The adoption of global products in regional studies can be critical in underestimating the total area burned in sensitive regions. Our study highlights the need to develop approaches aimed at improving the accuracy of current global products, and the development of regional burned area products may be more suitable for this purpose. Our proposed approach based on WFI data cubes has shown high potential for generating more accurate regional burned area maps, which can refine BA estimates in the Amazon. Full article

Software-Defined Networking (SDN) stands as a pivotal paradigm in network implementation, exerting a profound influence on the trajectory of technological advancement. The critical role of security within SDN cannot be overstated, with distributed denial of service (DDoS) emerging as a particularly disruptive threat, capable of causing large-scale disruptions. DDoS operates by generating malicious traffic that mimics normal network activity, leading to service disruptions. It becomes imperative to deploy mechanisms capable of distinguishing between benign and malicious traffic, serving as the initial line of defense against DDoS challenges. In addressing this concern, we propose the utilization of traffic classification as a foundational strategy for combatting DDoS. By categorizing traffic into malicious and normal streams, we establish a crucial first step in the development of effective DDoS mitigation strategies. The deleterious effects of DDoS extend to the point of potentially overwhelming networked servers, resulting in service failures and SDN server downtimes. To investigate and address this issue, our research employs a dataset encompassing both benign and malicious traffic within the SDN environment. A set of 23 features is harnessed for classification purposes, forming the basis for a comprehensive analysis and the development of robust defense mechanisms against DDoS in SDN. Initially, we compare GenClass with three common classification methods, namely the Bayes, K-Nearest Neighbours (KNN), and Random Forest methods. The proposed solution improves the average class error, demonstrating 6.58% error as opposed to the Bayes method error of 32.59%, KNN error of 18.45%, and Random Forest error of 30.70%. Moreover, we utilize classification procedures based on three methods based on grammatical evolution, which are applied to the aforementioned data. In particular, in terms of average class error, GenClass exhibits 6.58%, while NNC and FC2GEN exhibit average class errors of 12.51% and 15.86%, respectively. Full article

The development of artificial intelligence technology provides a new model for substation inspection in the power industry, and effective defect diagnosis can avoid the impact of substation equipment defects on the power grid and improve the reliability and stability of power grid operation. Aiming to combat the problem of poor recognition of small targets due to large differences in equipment morphology in complex substation scenarios, a visual fault detection algorithm of substation equipment based on improved YOLOv5 is proposed. Firstly, a deformable convolution module is introduced into the backbone network to achieve adaptive learning of scale and receptive field size. Secondly, in the neck of the network, a simple and effective BiFPN structure is used instead of PANet. The multi-level feature combination of the network is adjusted by a floating adaptive weighted fusion strategy. Lastly, an additional small object detection layer is added to detect shallower feature maps. Experimental results demonstrate that the improved algorithm effectively enhances the performance of power equipment and defect recognition. The overall recall rate has increased by 7.7%, precision rate has increased by nearly 6.3%, and mAP@0.5 has improved by 4.6%. The improved model exhibits superior performance. Full article

The widespread tree mortality caused by the European spruce bark beetle (Ips typographus L.) is a significant concern for Norway spruce-dominated (Picea abies H. Karst) forests in Europe and there is evidence of increases in the affected areas due to climate warming. Effective forest monitoring methods are urgently needed for providing timely data on tree health status for conducting forest management operations that aim to prepare and mitigate the damage caused by the beetle. Unoccupied aircraft systems (UASs) in combination with machine learning image analysis have emerged as a powerful tool for the fast-response monitoring of forest health. This research aims to assess the effectiveness of deep neural networks (DNNs) in identifying bark beetle infestations at the individual tree level from UAS images. The study compares the efficacy of RGB, multispectral (MS), and hyperspectral (HS) imaging, and evaluates various neural network structures for each image type. The findings reveal that MS and HS images perform better than RGB images. A 2D-3D-CNN model trained on HS images proves to be the best for detecting infested trees, with an F1-score of 0.759, while for dead and healthy trees, the F1-scores are 0.880 and 0.928, respectively. The study also demonstrates that the tested classifier networks outperform the state-of-the-art You Only Look Once (YOLO) classifier module, and that an effective analyzer can be implemented by integrating YOLO and the DNN classifier model. The current research provides a foundation for the further exploration of MS and HS imaging in detecting bark beetle disturbances in time, which can play a crucial role in forest management efforts to combat large-scale outbreaks. The study highlights the potential of remote sensing and machine learning in monitoring forest health and mitigating the impacts of biotic stresses. It also offers valuable insights into the effectiveness of DNNs in detecting bark beetle infestations using UAS-based remote sensing technology. Full article

Unmanned Combat Aerial Vehicle (UCAV) path planning is a challenging optimization problem that seeks the optimal or near-optimal flight path for military operations. The problem is further complicated by the need to operate in a complex battlefield environment with minimal military risk and fewer constraints. To address these challenges, highly sophisticated control methods are required, and Swarm Intelligence (SI) algorithms have proven to be one of the most effective approaches. In this context, a study has been conducted to improve the existing Spider Monkey Optimization (SMO) algorithm by integrating a new explorative local search algorithm called Beta-Hill Climbing Optimizer (BHC) into the three main phases of SMO. The result is a novel SMO variant called SMOBHC, which offers improved performance in terms of intensification, exploration, avoiding local minima, and convergence speed. Specifically, BHC is integrated into the main SMO algorithmic structure for three purposes: to improve the new Spider Monkey solution generated in the SMO Local Leader Phase (LLP), to enhance the new Spider Monkey solution produced in the SMO Global Leader Phase (GLP), and to update the positions of all Local Leader members of each local group under a specific condition in the SMO Local Leader Decision (LLD) phase. To demonstrate the effectiveness of the proposed algorithm, SMOBHC is applied to UCAV path planning in 2D space on three different complex battlefields with ten, thirty, and twenty randomly distributed threats under various conditions. Experimental results show that SMOBHC outperforms the original SMO algorithm and a large set of twenty-six powerful and recent evolutionary algorithms. The proposed method shows better results in terms of the best, worst, mean, and standard deviation outcomes obtained from twenty independent runs on small-scale (D = 30), medium-scale (D = 60), and large-scale (D = 90) battlefields. Statistically, SMOBHC performs better on the three battlefields, except in the case of SMO, where there is no significant difference between them. Overall, the proposed SMO variant significantly improves the obstacle avoidance capability of the SMO algorithm and enhances the stability of the final results. The study provides an effective approach to UCAV path planning that can be useful in military operations with complex battlefield environments. Full article

The Sahelian Great Green Wall (SGGW) is an influential project to combat desertification and promote sustainable land management on a large scale, involving 11 countries in the Sahel region of Africa. The UNCCD’s 2020 progress report showed a mixed picture concerning the meeting of the initial targets. At the One Planet Summit in 2021, announcements were made to consolidate the implementation of the SGGW, most notably with the creation of the Great Green Wall Accelerator. In this context, our paper sets out to review the scientific work conducted with regard to the SGGW. We have thus carried out a bibliometric analysis of the literature on SGGW. Although the initiative involves 11 countries and covers a large spectrum of scientific disciplines, our results show the predominance of ecological studies in the SGGW literature and a concentration of studies in certain geographies of interest, such as northern Senegal. Moreover, based on a secondary analysis of publications on land restoration and sustainable ecosystem management in Sahelian countries, we show that the literature relevant to SGGW topics is richer and fills in the information gaps we have identified at thematic and geographical levels. By showing that SGGW studies are overly focused on certain topics and geographical areas, our paper argues for a better interdisciplinary mobilization of researchers working on GGW-related topics. The scientific and operational success of the project depends on stronger networking between the different research teams and themes, both in Africa and internationally. Full article

This research investigates the feasibility of using mobile hybrid microgrids to increase energy resilience in DoD Installations. The primary question examined is whether a standardized mobile microgrid, constrained within an International Standards Organization (ISO) Triple Container (TriCon) and not to exceed 10,000 lbs (approximately 4535 kg), can provide the necessary power for small critical sites with an average 10 kW load on DoD installations with similar resilience to a customized single load microgrid or emergency backup generator. Key assumptions for this research are that power outages may be accompanied by a fuel constrained environment (e.g., natural disaster that restricts fuel transport), an existing installation microgrid is in place, and the risk of outages does not warrant the development of redundant customized single load microgrids for each critical load. The feasibility of a mobile hybrid microgrid is investigated by constructing an architectural design that attempts to find a satisfactory combination of commercial off-the-shelf components for battery energy storage, photovoltaic power, and generator power within the constraints of an 8 ft × 6 ft 5 in × 8 ft (approximately 2.4 m × 2 m × 2.4 m) shipping container. The proposed design is modeled and simulated over a two-week period using Global Horizontal Index solar irradiance data, and a randomized average 10 kW load. Results of the model are used to analyze the feasibility of the system to meet the load while reducing dependency on fuel resources. Trade-offs between a customized single load microgrid and standardized mobile microgrid are discussed. The result of this research indicates that a standardized mobile microgrid holds significant promise for DoD and other potential users (public safety, private industry, etc.) in having a rapidly deployable solution to bring critical loads back online during an emergency situation that reduces generator usage. Full article

Objective: As an effective lesion heterogeneity depiction, texture information extracted from computed tomography has become increasingly important in polyp classification. However, variation and redundancy among multiple texture descriptors render a challenging task of integrating them into a general characterization. Considering these two problems, this work proposes an adaptive learning model to integrate multi-scale texture features. Methods: To mitigate feature variation, the whole feature set is geometrically split into several independent subsets that are ranked by a learning evaluation measure after preliminary classifications. To reduce feature redundancy, a bottom-up hierarchical learning framework is proposed to ensure monotonic increase of classification performance while integrating these ranked sets selectively. Two types of classifiers, traditional (random forest + support vector machine)- and convolutional neural network (CNN)-based, are employed to perform the polyp classification under the proposed framework with extended Haralick measures and gray-level co-occurrence matrix (GLCM) as inputs, respectively. Experimental results are based on a retrospective dataset of 63 polyp masses (defined as greater than 3 cm in largest diameter), including 32 adenocarcinomas and 31 benign adenomas, from adult patients undergoing first-time computed tomography colonography and who had corresponding histopathology of the detected masses. Results: We evaluate the performance of the proposed models by the area under the curve (AUC) of the receiver operating characteristic curve. The proposed models show encouraging performances of an AUC score of 0.925 with the traditional classification method and an AUC score of 0.902 with CNN. The proposed adaptive learning framework significantly outperforms nine well-established classification methods, including six traditional methods and three deep learning ones with a large margin. Conclusions: The proposed adaptive learning model can combat the challenges of feature variation through a multiscale grouping of feature inputs, and the feature redundancy through a hierarchal sorting of these feature groups. The improved classification performance against comparative models demonstrated the feasibility and utility of this adaptive learning procedure for feature integration. Full article

Recurring and increasing large-scale wildfires across the globe (e.g., Southern Europe, California, Australia), as a result of worsening climate conditions with record temperatures, drought, and strong winds, present a challenge to mankind. Early fire detection is crucial for a quick reaction and effective firefighting operations, minimizing the risk to human lives as well as the destruction of assets, infrastructures, forests, and wildlife. Usually, ground firefighting relies on human intervention and dangerous exposition to high temperatures and radiation levels, proving the need for mechanisms and techniques to remotely or autonomously detect and combat fire. This paper proposes an autonomous firefighting system built with a motorized water turret, narrow beam far infrared (FIR) sensors, and a micro-controller running novel algorithms and techniques. Experimental field results validated the technical approach, indicating that when a small fire front is within the field of view of the FIR sensor and within the range of the water jet, it is possible to provide an early alarm and even autonomously extinguish or delay the approaching fire front, increasing the chance for evacuation. Full article

Carbon capture, utilisation and storage (CCUS) via mineral carbonation is an effective method for long-term storage of carbon dioxide and combating climate change. Implemented at a large-scale, it provides a viable solution to harvesting and storing the modern crisis of GHGs emissions. To date, technological and economic barriers have inhibited broad-scale utilisation of mineral carbonation at industrial scales. This paper outlines the mineral carbonation process; discusses drivers and barriers of mineral carbonation deployment in Australian mining; and, finally, proposes a unique approach to commercially viable CCUS within the Australian mining industry by integrating mine waste management with mine site rehabilitation, and leveraging relationships with local coal-fired power station. This paper discusses using alkaline mine and coal-fired power station waste (fly ash, red mud, and ultramafic mine tailings, i.e., nickel, diamond, PGE (platinum group elements), and legacy asbestos mine tailings) as the feedstock for CCUS to produce environmentally benign materials, which can be used in mine reclamation. Geographical proximity of mining operations, mining waste storage facilities and coal-fired power stations in Australia are identified; and possible synergies between them are discussed. This paper demonstrates that large-scale alkaline waste production and mine site reclamation can become integrated to mechanise CCUS. Furthermore, financial liabilities associated with such waste management and site reclamation could overcome many of the current economic setbacks of retrofitting CCUS in the mining industry. An improved approach to commercially viable climate change mitigation strategies available to the mining industry is reviewed in this paper. Full article