Search Results (233)

Intelligent algorithms and decision models are key tools for improving the efficiency and adaptability of multi-objective optimization and allocation, and for achieving sustainable utilization of water resources. This study takes Hancheng City as a case study to develop a water resource optimization allocation model based on economic, social, and ecological benefits, analyzing and predicting the supply and demand of conventional and unconventional water resources in the study area. The model is solved using the NSGA algorithm, and solutions are screened from the Pareto front using the TOPSIS-CCDM two-level decision model, with the RSR method used for comparative verification. The results show that the schemes II-2022-21 (water shortage of 17,802.35 m³/d, economic benefits of 21,019,556.17 yuan, pollutant emissions of 745. 92 tons), II-2027-ACS (shortage of 14,098.76 m³/d, economic benefits of 29,401,252.75 yuan, emissions of 712. 07 tons), and II-2032-ACS (shortage of 12,709.33 m³/d, economic benefits of 36,660,367.83 yuan, emissions of 700.96 tons) are in line with the water resource allocation planning for Hancheng City before 2035. These schemes not only meet the regional planning requirements but also maximize economic benefits while minimizing water shortages and pollutant emissions. The study finds that NSGA-II has an advantage in selecting more coordinated schemes, while NSGA-III focuses more on the selectivity of specific targets. Although the TOPSIS-CCDM model performs well in comprehensive evaluation, it also exposes limitations such as sensitivity to data fluctuations and high computational complexity. By developing and applying advanced optimization and decision models, this study provides a scientific water resource allocation scheme for Hancheng City, supporting the sustainable management of regional water resources, and offering a reference for future research in addressing data uncertainties and improving computational efficiency. Full article

The growing challenges of freshwater scarcity and the high generation of agro-industrial waste, particularly from fruit and vegetable (F&V) processing, pose significant threats to the sustainability of global food systems. F&V waste, which represents a major portion of the 1.3 billion tons of annual food waste, is characterized by a high moisture content (80–95%), making it a largely overlooked but promising source of water recovery. This review critically assesses the techno-economic and environmental feasibility of extracting water from moisture-rich agro-industrial waste streams. Potential technologies such as solar distillation and membrane separation are evaluated to determine their capacity to treat complex organic effluents and recover high-quality water. The potential end uses of reclaimed water in all sectors are explored, focusing on agricultural irrigation, fertigation, industrial reuse and environmental restoration. This study addresses a key research gap and proposes the reclassification of agro-industrial waste as a viable water resource aligned with circular bioeconomy principles and Sustainable Development Goals (SDGs) 6 and 12. Full article

Millions of tons of slag and clinker can be found in the dumps of enterprises across the Republic of Kazakhstan. The goal of this project is to create a technology that conserves energy in waste treatment. The novelty of the work is the discovery of a new phenomenon, which shows that in the melt layer, there are two reactions opposite in direction and intensity: slow reactions of the decomposition of complex components into simple molecules and rapid responses of the formation of complex components from simple molecules. The dominance of one of the two reactions affects the process’s fuel consumption. Using this phenomenon, a melting reactor was created, which will reduce specific fuel consumption by 3–4 times compared to a Waelz kiln. It is shown that using a new method of CO₂ decarbonization by zinc, it is possible to ensure the production of zinc sublimates and cast stone products and the full neutralization of CO₂. The lowest market potential only for Achisai dump clinker would be around USD 125,600,000 if the cost of commercial clinker sublimates is USD 800/t. The expected net profit would be USD 4,466,039/y. Full article

Biogas production from agricultural waste reduces methane emissions and addresses climate change challenges by converting livestock and organic waste into energy. This study analyzed biogas production in agricultural enterprises under the European Green Deal, the advantages of biogas as an energy source, and the use of digestate in agriculture. The raw material for biogas production from agro-industrial wastes in Ukraine has been investigated, showing that the country’s biogas production potential amounts to 34.59 billion m³, including 0.65 billion m³ from processing plant wastes. The main types of biomass that can be used for biogas production in Ukraine are crop residues (71.4%), manure (26.6%), and food industry waste (2.0%). The implementation of biogas production projects will reduce greenhouse gas emissions by 3.98 billion tons of CO₂ and increase profits through electricity sales. This study examines the barriers and prospects for the development of electricity generation from biogas in Ukraine in the context of the integration of Ukraine’s energy system into the EU energy space. Directions for developing the biogas industry, focusing on electricity production within the framework of European decarbonization initiatives, will enhance the energy security of Ukraine and the EU. Estimating the energy production from agricultural waste allows for determining biogas output from organic waste. A regional biogas cluster model was developed based on the agro-industrial complex, which combines the production of biogas, electricity, water, and biofertilizers with increased efficiency and regional sustainable development. Full article

Traditional intrusion detection systems (IDSs) rely on static rules and one-dimensional features, and they have difficulty dealing with zero-day attacks and highly concealed threats; furthermore, mainstream deep learning models cannot capture the correlation between multiple views of attacks due to their single perspective. This paper proposes a knowledge graph-enhanced multi-view deep learning framework, considering the strategy of integrating network traffic, host behavior, and semantic relationships; and evaluates the impact of the secondary fusion strategy on feature fusion to identify the optimal multi-view model configuration. The primary objective is to verify the superiority of multi-view feature fusion technology and determine whether incorporating knowledge graphs (KGs) can further enhance model performance. First, we introduce the knowledge graph (KG) as one of the feature views and neural networks as additional views, forming a multi-view feature fusion strategy that emphasizes the integration of spatial and relational features. The KG represents relational features combined with spatial features extracted by neural networks, enabling a more comprehensive representation of attack patterns through the synergy of both feature types. Secondly, based on this foundation, we propose a two-level fusion strategy. During the representation learning of spatial features, primary fusion is performed of each view, followed by secondary fusion with relational features from KG, thereby deepening and broadening feature integration. These strategies for understanding and deploying the multi-view concept improve the model’s expressive power and detection performance and also demonstrate strong generalization and robustness across three datasets, including TON_IoT and UNSW-NB15, marking a contribution of this study. After experimental evaluation, the F1 scores of multi-view models outperformed single-view models across all three datasets. Specifically, the F1 score of the multi-view approach (Model 6) improved by 10.57% on the TON_IoT Network+Win10 dataset compared with the best single-view model. In contrast, improvements of 5.53% and 3.21% were observed on the TON_IoT network and UNSW-NB15 datasets. In terms of feature fusion strategies, the secondary fusion strategy (Model 6) outperformed primary fusion (Model 5). Furthermore, incorporating KG-based relational features as a separate view improved model performance, a finding validated by ablation studies. Experimental results show that the deep fusion strategy of multi-dimensional data overcomes the limitations of traditional single-view models, enables collaborative multi-dimensional analysis of network attack behaviors, and significantly enhances detection capabilities in complex attack scenarios. This approach establishes a scalable multimodal analysis framework for intelligent cybersecurity, advancing intrusion detection beyond traditional rule-based methods toward semantic understanding. Full article

This study integrates deep learning and geospatial analysis to enhance soil loss estimation in the Moulouya Watershed, a region prone to erosion due to diverse topography and climatic conditions. Traditional models like the Universal Soil Loss Equation (USLE) and its revised version (RUSLE) often fall short in capturing complex environmental interactions, leading to inaccurate soil loss predictions. This research introduces a novel approach using Convolutional Neural Networks (CNNs) combined with Geographic Information Systems (GISs) to improve the precision and spatial resolution of soil loss risk assessments. High-resolution satellite imagery, soil maps, and climatic data were processed to extract critical factors, such as slope, land cover, and rainfall erosivity, which were then fed into the CNN model. The findings revealed that the CNN model outperformed traditional methods, achieving a low Root Mean Square Error (RMSE) of 2.3 and an R-squared value of 0.92, significantly surpassing the USLE and RUSLE models. The resulting high-resolution soil loss maps identified high-risk erosion areas, particularly in the central and eastern regions of the watershed, with soil loss rates exceeding 40 tons/ha/year. These findings demonstrate the superior predictive capabilities of deep learning, offering valuable insights for targeted soil conservation strategies and highlighting the potential of advanced computational techniques to revolutionize environmental modeling. Full article

This study introduces an innovative Emotional Artificial Neural Network (EANN) model to predict ship fuel consumption with high accuracy, addressing the challenges posed by complex environmental conditions and operational variability. This research examines the impact of climate change on maritime operations and fuel efficiency by analyzing climatic variables such as wave period, wind speed, and sea-level rise. The model’s performance is assessed using two ship types (bulk carrier and container ship with max 60,000 dead weight tonnage (DWT)) under various climate scenarios. A comparative analysis demonstrates that the EANN model significantly outperforms the conventional Feedforward Neural Network (FFNN) in predictive accuracy. For bulk carriers, the EANN achieved a Root Mean Squared Error (RMSE) of 5.71 tons/day during testing, compared to 9.91 tons/day for the FFNN model. Similarly, for container ships, the EANN model achieved an RMSE of 5.97 tons/day, significantly better than the FFNN model’s 10.18 tons/day. A sensitivity analysis identified vessel speed as the most critical factor, contributing 33% to the variance in fuel consumption, followed by engine power and current speed. Climate-change simulations showed that fuel consumption increases by an average of 22% for bulk carriers and 19% for container ships, highlighting the importance of operational optimizations. This study emphasizes the efficacy of the EANN model in predicting fuel consumption and optimizing ship performance. The proposed model provides a framework for improving energy efficiency and supporting compliance with International Maritime Organization Standards (IMO) environmental standards. Meanwhile, the Carbon Intensity Indicator (CII) evaluation results emphasize the urgent need for measures to reduce carbon emissions to meet the IMO’s 2030 standards. Full article

In this paper, a novel technical route, namely combining the low-pressure exhaust gas recirculation (LP-EGR) and variable compression ratio (VCR), is proposed to address the inferior fuel economy for marine dual-fuel engines of low-pressure gas injection in diesel mode. To validate the applicability of the proposed technical route, firstly, a zero-dimensional/one-dimensional (0-D/1-D) engine simulation model with a predictive combustion model DI-Pulse is established using GT-Power. Then, parametric investigations on two LP-EGR schemes, which is implemented with either a back-pressure valve (LP-EGR-BV) or a blower (LP-EGR-BL), are performed to qualitatively identify the combined impacts of exhaust gas recirculation (EGR) and compression ratio (CR) on the combustion process, turbocharging system, and nitrogen oxides (NOx)-brake specific fuel consumption (BSFC) trade-offs. Finally, an optimization strategy is formulated, and an optimization program based on response surface methodology (RSM)–particle swarm optimization (PSO) is designed with the aim of improving fuel economy while meeting Tier III and various constraint conditions. The results of the parametric investigations reveal that the two LP-EGR schemes exhibit opposite impacts on the turbocharging system. Compared with the LP-EGR-BV, the LP-EGR-BL can achieve a higher in-cylinder pressure level. NOx-BSFC trade-offs are observed for both LP-EGR schemes, and the VCR is confirmed to be a viable approach for mitigating the penalty on BSFC caused by EGR. The optimization results reveal that for LP-EGR-BV, compared with the baseline engine, the optimized BSFC decreases by 10.16%, 11.95%, 10.32%, and 9.68% at 25%, 50%, 75%, and 100% maximum continuous rating (MCR), respectively, whereas, for the LP-EGR-BL scheme, the optimized BSFC decreases by 10.11%, 11.93%, 9.93%, and 9.58%, respectively. Furthermore, the corresponding NOx emissions level improves from meeting Tier II regulations (14.4 g/kW·h) to meeting Tier III regulations (3.4 g/kW·h). It is roughly estimated that compared to the original engine, both LP-EGR schemes achieve an approximate reduction of 240 tons in annual fuel consumption and save annual fuel costs by over USD 100,000. Although similar fuel economy is obtained for both LP-EGR schemes, LP-EGR-BV is superior to LP-EGR-BL in terms of structure complexity, initial cost, maintenance cost, installation space requirement, and power consumption. The findings of this study provide meaningful theoretical supports for the implementation of the proposed technical route in real-world engines. Full article

This paper presents an advanced framework for real-time monitoring and analysis of network traffic and endpoint security in large-scale enterprises by addressing the increasing complexity and frequency of cyber-attacks. Our Network Security Traffic Analysis Platform employs a comprehensive technology stack including the Elastic Stack, ZEEK, Osquery, Kafka, and GeoLocation data. By integrating supervised machine learning models trained on the UNSW-NB15 dataset, we evaluate Random Forest (RF), Decision Trees (DT), and Support Vector Machines (SVM), with the Random Forest classifier achieving a notable accuracy of 99.32%. Leveraging Artificial Intelligence and Natural Language Processing, we apply the BERT model with a Byte-level Byte-pair tokenizer to enhance network-based attack detection in IoT systems. Experiments on UNSW-NB15, TON-IoT, and Edge-IIoT datasets demonstrate our platform’s superiority over traditional methods in multi-class classification tasks, achieving near-perfect accuracy on the Edge-IIoT dataset. Furthermore, Network Security Traffic Analysis Platform’s ability to produce actionable insights through charts, tables, histograms, and other visualizations underscores its capability in static analysis of traffic data. This dual approach of real-time and static analysis provides a robust foundation for developing scalable, efficient, and automated security solutions, essential for managing the evolving threats in modern networks. Full article

This paper presents a systematic literature review based on the PRISMA model on machine learning-based Distributed Denial of Service (DDoS) attacks in Internet of Things (IoT) networks. The primary objective of the review is to compare research trends on deployment options, datasets, and machine learning techniques used in the domain between 2019 and 2024. The results highlight the dominance of certain datasets (BoT-IoT and TON_IoT) in combination with Decision Tree (DT) and Random Forest (RF) models, achieving high median accuracy rates (>99%). This paper discusses various datasets that are used to train and evaluate machine learning (ML) models for detecting Distributed Denial of Service (DDoS) attacks in Internet of Things (IoT) networks and how they impact model performance. Furthermore, the findings suggest that due to hardware limitations, there is a preference for lightweight ML solutions and preprocessed datasets. Current trends indicate that larger or industry-specific datasets will continue to gain popularity alongside more complex ML models, such as deep learning. This emphasizes the need for robust and scalable deployment options, with Software-Defined Networks (SDNs) offering flexibility, edge computing being extensively explored in cloud environments, and blockchain-integrated networks emerging as a promising approach for enhancing security. Full article

Lignin, the most abundant aromatic polymer in nature, plays a critical role in lignocellulosic biomasses by providing structural support. However, its presence complicates the industrial exploitation of these materials for biofuels, paper production and other high-value compounds. Annually, the industrial extraction of lignin reaches an estimated 225 million tons, yet only a fraction is recovered for reuse, with most incinerated as low-value fuel. The growing interest in lignin potential has sparked research into sustainable recovery methods from lignocellulosic agro-industrial wastes. This review examines the chemical, physical and physicochemical processes for isolating lignin, focusing on innovative, sustainable technologies that align with the principles of a circular economy. Key challenges include lignin structural complexity and heterogeneity, which hinder its efficient extraction and application. Nonetheless, its properties such as high thermal stability, biodegradability and abundant carbon content place lignin as a promising material for diverse industrial applications, including chemical synthesis and energy generation. A structured analysis of advancements in lignin extraction, characterization and valorization offers insights into transforming this undervalued by-product into a vital resource, reducing reliance on non-renewable materials while addressing environmental sustainability. Full article

Nitrogen oxides (NOx) are known to be irritant gases, which present considerable risks to human health. TROPOMI NO₂ vertical column density (VCD) is commonly employed to estimate NOx emissions through the integration of complex models. However, satellite data often suffer from incompleteness, hindering the ability to achieve long-term and comprehensive estimates. In this study, we propose a reconstruction method to achieve comprehensive coverage of NO₂ VCD in China by leveraging the relationship between satellite data and meteorological variables. In addition, the CNN-BiLSTM-ATT model was developed to estimate China’s monthly NOx emissions from 2021 to 2023 in combination with other ancillary data, such as ERA5 meteorological data, topographic data, and nighttime light data, achieving a correlation coefficient (R) of 0.83 and a root mean squared error (RMSE) of 9.05 tons (T). The factors influencing NO₂ VCD were assessed using SHAP values, and the spatiotemporal characteristics and density distribution of NOx emissions were analyzed. Additionally, annual emission trends were evaluated. This study offers valuable insights for air quality management and policymaking, contributing to efforts focused on mitigating the adverse health and environmental impacts of NOx emissions. Full article

Slope stability and landslide analysis in open-pit mines present significant engineering challenges due to the complexity of predisposing factors. The Sijiaying Iron Mine has an annual production capacity of 21 million tons, with a mining depth reaching 330 m. Numerous small-scale landslides have occurred in the shallow areas. This study identifies four key factors contributing to landslides: topography, engineering geology, ecological environment, and mining engineering. These factors encompass both microscopic and macroscopic geological aspects and temporal surface displacement rates. Data are extracted using ArcGIS Pro 3.0.2 based on slope units, with categorical data encoded via LabelEncoder. Multivariate polynomial expansion is applied for data coupling, and SMOTENC–TomekLinks is used for resampling landslide samples. A landslide sensitivity model is developed using the LightGBM algorithm, and SHAP is applied to interpret the model and assess the impact of each factor on landslide likelihood. The primary sliding factors at Sijiaying mine include distance from rivers, slope height, profile curvature, rock structure, and distance from faults. Safety thresholds for each factor are determined. This method also provides insights for global and individual slope risk assessment, generating high-risk factor maps to aid in managing and preventing slope instability in open-pit mines. Full article

The integration of photovoltaic (PV) systems in buildings is crucial for reducing reliance on conventional energy sources while promoting sustainability. This study evaluates and compares three energy generation systems: rooftop PV, building-integrated photovoltaics (BIPV), and a hybrid combination of both. The analysis covers energy production, economic feasibility through the levelized cost of electricity (LCOE), and environmental impact by assessing unreleased carbon dioxide (UCD). A residential building in Kerman, Iran, serves as the case study. The results indicate that rooftop PV exhibits the lowest LCOE at USD 0.023/kWh, while BIPV has a higher LCOE of USD 0.077/kWh due to installation complexities. The hybrid system, combining both technologies, achieves a balance with an LCOE of USD 0.05/kWh while maximizing energy generation at 16.2 MWh annually. Additionally, the hybrid system reduces CO₂ emissions by 9.7 tons per year, surpassing the standalone rooftop PV (5.0 tons) and BIPV (4.7 tons). The findings highlight the synergistic benefits of integrating both PV systems, ensuring higher self-sufficiency and enhanced environmental impact. This research underscores the necessity of comprehensive urban energy planning to optimize renewable energy utilization and accelerate the transition toward zero-energy buildings. Full article

Energy efficiency represents a strategic priority in both Italian and European legislation to mitigate the energy consumption of buildings, which are significant contributors to greenhouse gas emissions. Currently, about 75% of the EU building stock is considered to be energy inefficient and requires substantial retrofitting. This study examines the energy redevelopment of a large building complex, which currently has an energy class E label. The aim is to achieve a significant improvement in energy efficiency and reduce fossil fuels usage, in line with sustainability standards. The intervention includes replacing the existing air-conditioning and heating systems with high-efficiency air-to-water heat pumps, powered by electricity generated, in part, by an integrated photovoltaic system. Through the analysis of available technological solutions and the application of a Building Information Modeling (BIM) methodology, the research proposes strategies to optimize the energy efficiency of buildings while minimizing the environmental impact and ensuring compliance with current regulations. The results highlight the effectiveness of such approaches in supporting the energy transition, with the implemented measures reducing the non-renewable energy demand from 191,684 kWh/m²/year to 76,053 kWh/m²/year. This led to a decrease in CO₂ emissions of 604 tons/year, representing a 78% reduction compared to initial levels, a clear contribution toward achieving European sustainability goals. Full article