Search Results (294)

The internal combustion process in a boiler in power plants has a direct impact on boiler efficiency and NOx generation. The objective of this study is to propose an intelligent deep learning modeling and multi-objective optimization approach that considers NOx emission concentration and boiler thermal efficiency simultaneously for boiler combustion in power plants. Firstly, a hybrid deep learning model, namely, convolutional neural network–bidirectional gated recurrent unit (CNN-BiGRU), is employed to predict the concentration of NOx emissions and the boiler thermal efficiency. Then, based on the hybrid deep prediction model, variables such as primary and secondary airflow rates are considered as controllable variables. A single-objective optimization model based on an improved flow direction algorithm (IFDA) and a multi-objective optimization model based on NSGA-II are developed. For multi-objective optimization using NSGA-II, the average NOx emission concentration is reduced by 5.01%, and the average thermal efficiency is increased by 0.32%. The objective functions are to minimize the boiler thermal efficiency and the concentration of NOx emissions. Comparative analysis of the experiments shows that the NSGA-II algorithm can provide a Pareto optimal front based on the requirements, resulting in better results than single-objective optimization. The effectiveness of the NSGA-II algorithm is demonstrated, and the obtained results provide reference values for the low-carbon and environmentally friendly operation of coal-fired boilers in power plants. Full article

Digital Twins (DTs) are transforming manufacturing by bridging the physical and digital worlds, enabling real-time insights, predictive analytics, and enhanced decision making. In Industry 4.0, DTs facilitate automation and data integration, while Industry 5.0 emphasizes human-centric, resilient, and sustainable production. However, implementing DTs in robotic metal additive manufacturing (AM) remains challenging because of the complexity of the wire-based laser metal deposition (LMD) process, the need for real-time monitoring, and the demand for advanced defect detection to ensure high-quality prints. This work proposes a structured DT architecture for a robotic wire-based LMD cell, following a standard framework. Three DT implementations were developed. First, a real-time 3D simulation in RoboDK, integrated with a 2D Node-RED dashboard, enabled motion validation and live process monitoring via MQTT (message queuing telemetry transport) telemetry, minimizing toolpath errors and collisions. Second, an Industrial IoT-based system using KUKA iiQoT (Industrial Internet of Things Quality of Things) facilitated predictive maintenance by analyzing motor loads, joint temperatures, and energy consumption, allowing early anomaly detection and reducing unplanned downtime. Third, the Meltio dashboard provided real-time insights into the laser temperature, wire tension, and deposition accuracy, ensuring adaptive control based on live telemetry. Additionally, a prescriptive analytics layer leveraging historical data in FireStore was integrated to optimize the process performance, enabling data-driven decision making. Full article

The surface fragmentation of karst landscapes leads to a high degree of coupling between fire scar site boundaries and topographic relief. However, the applicability of spatio-temporal data fusion methods for fire scar extraction in such geomorphological areas remains systematically unevaluated. This study developed a spatial–temporal adaptive fusion model integrating Landsat 30-m data with MODIS daily observations to generate continuous high-precision dNBR datasets. Using a typical karst fire region in Guizhou and Yunnan, China, as a case study, we validated the method’s effectiveness for fire trace extraction in fragmented landscapes. The proposed fusion technique addresses cloud cover limitations in humid climates by constructing continuous NBR time series, enabling precise fire boundary delineation and severity quantification. We comparatively implemented multiple fusion approaches (FSDAF, STARFM, and STDFA) and evaluated their performance through both spectral (RMSE, AD, and PSNR) and spatial (Edge, LBP, and SSIM) metrics. Key findings include the following: (1) FSDAF outperformed other methods in spectral consistency and spatial adaptation, particularly for heterogeneous mountainous terrain with fragmented vegetation. (2) Comparative experiments demonstrated that pre-calculating vegetation indices before temporal fusion (Strategy I) produced superior results to post-fusion calculation (Strategy II). Moreover, in our karst landscape study area, our proposed Hybrid Strategy selection framework can dynamically optimize the fusion process of multi-source satellite data, which is significantly better than a single fusion strategy. (3) The dNBR-based extraction achieved 90.00% producer accuracy, 69.23% user accuracy, and a Kappa coefficient of 0.718 when validated against field data. This study advances fire monitoring in karst regions by significantly improving both the spatio-temporal resolution and accuracy of burn scar detection compared to conventional approaches. The framework provides a viable solution for fire impact assessment in topographically complex landscapes under cloudy conditions. Full article

Fire-induced structural failure in axially restrained reinforced concrete (RC) beams is a critical concern in structural fire engineering. Comparative analysis with Eurocode and ASTM E119 fire safety guidelines reveals discrepancies between theoretical predictions and real fire-induced failures, emphasizing the need for revised structural fire safety standards. Moreover, limited analytical solutions exist due to the complexity of fire behavior in axially restrained RC beams. This study develops an improved analytical model for axially restrained beams in fire, focusing on three critical points: (i) the peak axial compression force, (ii) the transition to zero axial force (bending limit), and (iii) the final failure point due to reinforcement fracture. A series of fire resistance experiments were conducted to obtain key structural parameters, including fire resistance time (FRT), axial force redistribution, and failure mechanisms. The experimental results were used to validate and refine the proposed model, enhancing its practical applicability. The original model underpredicts fire endurance by 11–14%, whereas the upgraded model is accurate to within ~2–4% of test results. This improved performance is attributed to the model’s consideration of stiffness degradation and early cracking. Overall, the study provides valuable insights for improving the fire-resistant design of restrained RC beams, particularly in critical infrastructure such as logistics centers. Full article

We present a study that addresses the critical need for a prototype Decision Support System for forest fire information and management in Uttarakhand, India. The study’s main objective was to carry out statistical analysis of large fire incident datasets to understand trends of fires in the region and develop essential spatial decision support tools. These tools address the necessary fire management decision-making along with comprehensive datasets that can enable a decision maker to exercise better management. Moreover, this DSS addresses three major components of forest fire decision support: (i) pre-fire (forest information visualization) tools, (ii) during-fire terrain-based spatial decision support tools, and (iii) post-fire restoration tools. The efforts to develop this DSS included satellite lidar dataset-based fuel load estimations, the Keetch–Byram Drought Index, and the integration of spatial tools that ensure better spatial decisions in fire suppression planning. In addition, based on the bibliographic literature, the study also uses ecological and community-based knowledge, including financial aspects, for fire prevention and post-fire restoration planning. The development of this DSS involves an open-source R Shiny framework, enabling any decision maker at the execution or planning level to access these key datasets and simulate the spatial solutions cost-effectively. Hence, this study aimed to internalize key decision support tools and datasets based on extensive statistical analysis for data-driven forest fire planning and management. Full article

This study investigated the sustainability aspects of implementing a small-scale biogas digester project at the EARTH Centre, a horse-riding facility for the disabled, in South Africa. Firstly, an energy audit of the facility was conducted. From this exercise, energy-saving opportunities through anaerobic digestion of horse manure were identified. Biomethane potential tests (BMPs) were then performed using the Automatic Methane potential test system II (AMPTS II) of BioProcess Control (Lund, Sweden). The horse manure BMP result was 106 L/kg.VS with the biogas averaging a methane content of 40%. This BMP was lower than that of common substrates such as cow manure which can range from 150–210 L/kg.VS. The gas production rate was almost constant in the first 13 days indicating a long hydrolysis period for horse manure. The microbial species in the digester did not change much during the incubation period although small changes were visible in the proportions of each species as the reaction progressed from start to finish. The energy audit showed that 47% of the EARTH Centre’s energy requirements, which equated to 14,372 kWh/year, could be secured from biogas or solar instead of obtaining it from the national grid which is powered mainly by unsustainable coal-fired systems. As a starting point, a 10 cubic meter biogas digester was installed to produce 5512 kWh of energy per year in the form of biogas. To boost biogas production and continue running the system smoothly, it was evident that the horse manure-fed digester would require regular spiking with cow manure as a bioaugmentation strategy. The digester also produced bio-fertiliser and several sustainable development goals were fulfilled by this project. Current efforts are focused on process optimization of this technology at the Earth Centre to further improve the sustainability of the whole business. Full article

This study aims to evaluate and optimize the influences of operational factors, including the engine’s rotational speed, methane mass, diesel mass, and the duration of injected diesel fuel on the methane/diesel reactivity-controlled compression ignition (RCCI) light-duty engine’s performance and emissions by executing the Nondominated Sorting Genetic Algorithm-II (NSGAII). The optimizations aimed to minimize peak firing pressure simultaneously, decrease indicated specific fuel consumption, and reduce tailpipe emissions. It is found that the excess air ratios of (2.22 to 2.37) are the range of feasible results of the RCCI engine, and the power should be less than 0.89 from the maximum design load of the diesel engine when it works without it after treatment. The methane/diesel RCCI engine achieves an indicative thermal efficiency of 51%. The Pareto results from the NSGA algorithm occur on multiple fronts, and there is a tradeoff between power and nitrogen oxide (NOx) in addition to unburned hydrocarbons (UHCs) and carbon monoxide (CO) with NOx emissions. Moreover, EURO IV emissions regulations can occur when using a start of injection (SOI) of −35 CA, a diesel mass of 1.82 mg, a methane mass of 9.74 mg, a diesel injection duration of 2.63 CA, and a rotational speed of 2540 rpm. This accomplished a reduction in indicative specific fuel consumption by 27.8%, higher indicative efficiency by 21.9%, and emissions reductions compared to a conventional diesel engine. Full article

The immune system is alerted for virally infected cells in the body by the antigen presentation pathway, which is in turn mediated by the major histocompatibility complex (MHC) class I and II molecules. Cancer cells overcome immune evasion as a major hallmark by downregulation of the antigen presentation pathway. Therefore, the present study aimed to explore the effect of genetic variants in genes involved in MHC class I and II pathways in patients treated with first-line chemotherapy in combination with targeted antibodies in metastatic colorectal cancer (mCRC) patients. Genomic DNA from the blood samples of 775 patients enrolled in three independent, randomized, first-line trials, namely TRIBE (FOLFIRI-bevacizumab, N = 215), FIRE-3 (FOLFIRI-bevacizumab, N = 107; FOLFIRI-cetuximab, N = 129), and MAVERICC (FOLFIRI-bevacizumab, N = 163; FOLFOX6-bevacizumab, N = 161), was genotyped through OncoArray, a custom array manufactured by Illumina including approximately 530K SNP markers. The impact on the outcome of 40 selected SNPs in 22 genes of MHC class I and II pathways was analyzed. We identified several SNPs in multiple genes associated with targeted treatment benefits across different treatment arms in our study population (p < 0.05). Treatment–SNP interaction analyses confirmed a significant treatment interaction with the targeted agents (bevacizumab vs. cetuximab) and the chemotherapy backbone (FOLFIRI vs. FOLFOX) in certain selected SNPs. Our results highlight a potential role for MHC SNPs as prognostic and predictive biomarkers for first-line treatment in mCRC, with differential effects based on the biologic agent and chemotherapy backbone. These biomarkers, when further validated, may contribute to personalized treatment strategies for mCRC patients. Full article

Large wildfires have increased in the Mediterranean region due to socio-economic and land-use changes. The most immediate and concerning consequence of the wildfires is the loss of vegetation. However, there are few studies on the relationship between wildfire and vegetation recovery, especially on the complex relationship between species composition, burn severity and geo-environmental context. This study focuses on the analysis of post-fire vegetation regrowth (RV) in Mediterranean forests. Therefore, two objectives were set: (i) to analyse the influence of pre-fire conditions, burn severity and topographic variables on growth rates for each stage of recovery and (ii) to identify the drivers of post-fire vegetation recovery. The results show that NDVI increases rapidly in the first two years after the wildfire and more slowly in the following years. Except for the first year, RV shows a positive relationship with burn severity. In the first year, the importance of topographical features, especially curvature and flow accumulation, stands out. In the fourth year, when NDVI values are highest, RV is mainly explained by the presence of pre-fire vegetation, followed by burn severity and altitude. These results can be an important step towards more effective local management strategies leading to a resilient and sustainable territory. Full article

The prevalence of wildfires presents significant challenges for fire detection systems, particularly in differentiating fire from complex backgrounds and maintaining detection reliability under diverse environmental conditions. It is crucial to address these challenges for developing sustainable and effective fire detection systems. In this paper: (i) we introduce a channel-wise attention-based architecture, achieving 95% accuracy and demonstrating an improved focus on flame-specific features critical for distinguishing fire in complex backgrounds. Through ablation studies, we demonstrate that our channel-wise attention mechanism provides a significant 3–5% improvement in accuracy over the baseline and state-of-the-art fire detection models; (ii) evaluate the impact of augmentation on fire detection, demonstrating improved performance across varied environmental conditions; (iii) comprehensive evaluation across color spaces including RGB, Grayscale, HSV, and YCbCr to analyze detection reliability; and (iv) assessment of model vulnerabilities where Fast Gradient Sign Method (FGSM) perturbations significantly impact performance, reducing accuracy to 41%. Using Local Interpretable Model-Agnostic Explanations (LIME) visualization techniques, we provide insights into model decision-making processes across both standard and adversarial conditions, highlighting important considerations for fire detection applications. Full article

Forest fires are the result of poor land management and climate change. Depending on the type of the affected eco-system, they can cause significant biodiversity losses. This study was conducted in the Amazonas department in Peru. Binary data obtained from the MODIS satellite on the occurrence of fires between 2010 and 2022 were used to build the risk models. To avoid multicollinearity, 12 variables that trigger fires were selected (Pearson ≤ 0.90) and grouped into four factors: (i) topographic, (ii) social, (iii) climatic, and (iv) biological. The program Rstudio and three types of machine learning were applied: MaxENT, Support Vector Machine (SVM), and Random Forest (RF). The results show that the RF model has the highest accuracy (AUC = 0.91), followed by MaxENT (AUC = 0.87) and SVM (AUC = 0.84). In the fire risk map elaborated with the RF model, 38.8% of the Amazonas region possesses a very low risk of fire occurrence, and 21.8% represents very high-risk level zones. This research will allow decision-makers to improve forest management in the Amazon region and to prioritize prospective management strategies such as the installation of water reservoirs in areas with a very high-risk level zone. In addition, it can support awareness-raising actions among inhabitants in the areas at greatest risk so that they will be prepared to mitigate and control risk and generate solutions in the event of forest fires occurring under different scenarios. Full article

With respect to sustainability, the material must maintain the quality and properties of concrete and be safe for human health, the environment, and long-time use. In recent times, the emission of CO₂ from cement production processes has lessened with the passage of time due to its effect on the environment. In order to lessen the emissions and reduce environmental waste, available by-products with pozzolanic properties are applied. With respect to Portland limestone cement (CEMI II-BL), i.e., cement with lower carbon dioxide emissions and better workability than CEM I, the two main materials applied in the study as substitutes are brick dust (BD) and waste glass powder (WGP) bottles. Waste glass powder and brick dust, in quantities varying from 5% to 10%, 15%, and 20%, with a water/cement ratio of 0.35 and a 1.5% superplasticizer, were utilized to observe the effectiveness of BD and WGP on the flowability, compressive strength, flexural strength, water absorption, density, drying shrinkage, and fire resistance of the specimen mortar. The output shows that a WGP of 20% increased flowability compared to the control, whereas the inclusion of brick dust decreased it. At the age of 28, glass powder of 20% increased the compressive strength, while 20% brick dust exhibited a reduction; 15% WGP with 5% BD displayed the lowest absorption of water; and the density of all the samples proved to be much lower than the traditional mix, with 20% BD being the lowest (hereby labeled as light mortar). The 10% WGP with 10% BD displayed better resistance to fire, and the drying shrinkage of the sample was relatively low after several days of air curing. The impact on the environment and cost were considered without accounting for the transportation and manufacturing energy. As to the outcome of this experiment, we concluded that the use of both brick dust and glass powder with CEM II for producing mortar has proven very promising in a variety of different respects, including the mechanical and fresh features of mortar, with the combination of 5% WGP and 15% BD exhibiting the most potential in all of the acquired parameters. Full article

The frequent occurrence of forest fires in mountainous regions has posed severe threats to both the ecological environment and human activities. This study proposed a multi-target firefighting task planning method of forest fires by multiple UAVs (Unmanned Aerial Vehicles) integrating task allocation and path planning. The forest fire environment factors such high temperatures, dense smoke, and signal shielding zones were considered as the threats. The multi-UAVs task allocation and path planning model was established with the minimum of flight time, flight angle, altitude variance, and environmental threats. In this process, the study considers only the use of fire-extinguishing balls as the fire suppressant for the UAVs. The improved multi-population grey wolf optimization (MP–GWO) algorithm and non-Dominated sorting genetic algorithm II (NSGA-II) were designed to solve the path planning and task allocation models, respectively. Both algorithms were validated compared with traditional algorithms through simulation experiments, and the sensitivity analysis of different scenarios were conducted. Results from benchmark tests and case studies indicate that the improved MP–GWO algorithm outperforms the grey wolf optimizer (GWO), pelican optimizer (POA), Harris hawks optimizer (HHO), coyote optimizer (CPO), and particle swarm optimizer (PSO) in solving more complex optimization problems, providing better average results, greater stability, and effectively reducing flight time and path cost. At the same scenario and benchmark tests, the improved NSGA-II demonstrates advantages in both solution quality and coverage compared to the original algorithm. Sensitivity analysis revealed that with the increase in UAV speed, the flight time in the completion of firefighting mission decreases, but the average number of remaining fire-extinguishing balls per UAV initially decreases and then rises with a minimum of 1.9 at 35 km/h. The increase in UAV load capacity results in a higher average of remaining fire-extinguishing balls per UAV. For example, a 20% increase in UAV load capacity can reduce the number of UAVs from 11 to 9 to complete firefighting tasks. Additionally, as the number of fire points increases, both the required number of UAVs and the total remaining fire-extinguishing balls increase. Therefore, the results in the current study can offer an effective solution for multiple UAVs firefighting task planning in forest fire scenarios. Full article

This paper aims to study the provenance of archaeological Saadien ceramics (16th century, Marrakech) based on the chemical, mineralogical and lead isotope composition of clays used as raw materials in the manufacture of ceramics in Morocco and collected in the six major potter sites of Marrakech (Ourika I and II, Saada I and II and Mzouda) and Fez (Benjlikh). The clay chemical, mineralogical and isotopic signatures of these raw materials are compared to the compositions of decorated ceramics from El Badi Palace and Saadien Tombs, the most visited archaeological sites in Marrakech, described as World Heritage by UNESCO. The chemical composition was determined using X-ray fluorescence analysis, while the structural changes of the mineral phases during firing were studied using X-ray diffraction over a temperature range between 500–1000 °C. Pb isotopes, on the other hand, were measured using the Nu Plasma MC-ICP-MS technique. Results show that Saadien ceramics were made using calcareous clay from the Fez region. These clays were imported by the artisans from 400 km away to be used in the manufacturing of ceramics in the Saadien buildings of Marrakech. The firing temperature of these materials ranges between 600 and 700 °C for El Badi Palace, and from 800 to 900 °C for the Saadien Tombs ceramics using traditional ovens. This study reveals the mystery behind the source of Saadien ceramics and provides artisans with information about the origin of the raw materials used in Marrakech’s 16th-century buildings, which should be considered for any future restoration of these materials. Full article

Wildfires significantly alter watershed functions, particularly the mobilization of organic carbon (OC). This study investigated OC mobility and the physicochemical characteristics of wildfire-impacted soils and ashes from the northern California and Nevada fires (Dixie, Beckworth, Caldor). Organic carbon in wildfire-derived ashes (9.2–57.3 mg/g) generally exceeded levels in the background soils (4.3–24.4 mg/g), except at the Dixie fire sites. The mobile OC fraction varied from 0.0093 to 0.029 in ashes and 0.010 to 0.065 in soils, though no consistent trend was observed between the ashes and soils. Notably, the ash samples displayed lower OC mobility compared with the soils beneath them. A negative correlation was found between the mobile OC fraction and bulk OC content. Wildfire increased the total amount of mobile OC substantially by 5.2–574% compared to the background soils. Electron paramagnetic resonance (EPR) spectra confirmed the presence of environmentally persistent free radicals (EPFRs), which correlated with observed redox reactivity. Additionally, X-ray absorption near edge structure (XANES) and X-ray fluorescence (XRF) imaging revealed that Fe(II) oxidation in soils beneath the ashes may have enhanced the OC mobility, likely driven by pyrogenic carbon and free radicals. These findings enhance our understanding of post-wildfire OC mobilization and the impact of ash–soil physicochemical properties on watershed health. Full article