Search Results (24)

Currently, deep reinforcement learning has been widely applied to energy system optimization and scheduling, and the DRL method relies more heavily on historical data. The lack of historical operation data in new integrated energy systems leads to insufficient DRL training samples, which easily triggers the problems of underfitting and insufficient exploration of the decision space and thus reduces the accuracy of the scheduling plan. In addition, conventional data-driven methods are also difficult to accurately predict renewable energy output due to insufficient training data, which further affects the scheduling effect. Therefore, this paper proposes a small-sample scenario optimization scheduling method based on multidimensional data expansion. Firstly, based on spatial correlation, the daily power curves of PV power plants with measured power are screened, and the meteorological similarity is calculated using multicore maximum mean difference (MK-MMD) to generate new energy output historical data of the target distributed PV system through the capacity conversion method; secondly, based on the existing daily load data of different types, the load historical data are generated using the stochastic and simultaneous sampling methods to construct the full historical dataset; subsequently, for the sample imbalance problem in the small-sample scenario, an oversampling method is used to enhance the data for the scarce samples, and the XGBoost PV output prediction model is established; finally, the optimal scheduling model is transformed into a Markovian decision-making process, which is solved by using the Deep Deterministic Policy Gradient (DDPG) algorithm. The effectiveness of the proposed method is verified by arithmetic examples. Full article

Background/Objectives: This research presents a novel analytical method for breast tumor characterization and tissue classification by leveraging intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) combined with hyperspectral imaging techniques and deep learning. Traditionally, dynamic contrast-enhanced MRI (DCE-MRI) is employed for breast tumor diagnosis, but it involves gadolinium-based contrast agents, which carry potential health risks. IVIM imaging extends conventional diffusion-weighted imaging (DWI) by explicitly separating the signal decay into components representing true molecular diffusion ($D$) and microcirculation of capillary blood (pseudo-diffusion or $D*$). This separation allows for a more comprehensive, non-invasive assessment of tissue characteristics without the need for contrast agents, thereby offering a safer alternative for breast cancer diagnosis. The primary purpose of this study was to evaluate different methods for breast tumor characterization using IVIM-DWI data treated as hyperspectral image stacks. Dice similarity coefficients and Jaccard indices were specifically used to evaluate the spatial segmentation accuracy of tumor boundaries, confirmed by experienced physicians on dynamic contrast-enhanced MRI (DCE-MRI), emphasizing detailed tumor characterization rather than binary diagnosis of cancer. Methods: The data source for this study consisted of breast MRI scans obtained from 22 patients diagnosed with mass-type breast cancer, resulting in 22 distinct mass tumor cases analyzed. MR images were acquired using a 3T MRI system (Discovery MR750 3.0 Tesla, GE Healthcare, Chicago, IL, USA) with axial IVIM sequences and a bipolar pulsed gradient spin echo sequence. Multiple b-values ranging from 0 to 2500 s/mm² were utilized, specifically thirteen original b-values (0, 15, 30, 45, 60, 100, 200, 400, 600, 1000, 1500, 2000, and 2500 s/mm²), with the last four b-value images replicated once for a total of 17 bands used in the analysis. The methodology involved several steps: acquisition of multi-b-value IVIM-DWI images, image pre-processing, including correction for motion and intensity inhomogeneity, treating the multi-b-value data as hyperspectral image stacks, applying hyperspectral techniques like band expansion, and evaluating three tumor detection methods: kernel-based constrained energy minimization (KCEM), iterative KCEM (I-KCEM), and deep neural networks (DNNs). The comparisons were assessed by evaluating the similarity of the detection results from each method to ground truth tumor areas, which were manually drawn on DCE-MRI images and confirmed by experienced physicians. Similarity was quantitatively measured using the Dice similarity coefficient and the Jaccard index. Additionally, the performance of the detectors was evaluated using 3D-ROC analysis and its derived criteria (AUC_OD, AUC_TD, AUC_BS, AUC_TD−BS, AUC_ODP, AUC_SNPR). Results: The findings objectively demonstrated that the DNN method achieved superior performance in breast tumor detection compared to KCEM and I-KCEM. Specifically, the DNN yielded a Dice similarity coefficient of 86.56% and a Jaccard index of 76.30%, whereas KCEM achieved 78.49% (Dice) and 64.60% (Jaccard), and I-KCEM achieved 78.55% (Dice) and 61.37% (Jaccard). Evaluation using 3D-ROC analysis also indicated that the DNN was the best detector based on metrics like target detection rate and overall effectiveness. The DNN model further exhibited the capability to identify tumor heterogeneity, differentiating high- and low-cellularity regions. Quantitative parameters, including apparent diffusion coefficient ($ADC$), pure diffusion coefficient ($D$), pseudo-diffusion coefficient ($D*$), and perfusion fraction ($PF$), were calculated and analyzed, providing insights into the diffusion characteristics of different breast tissues. Analysis of signal intensity decay curves generated from these parameters further illustrated distinct diffusion patterns and confirmed that high cellularity tumor regions showed greater water molecule confinement compared to low cellularity regions. Conclusions: This study highlights the potential of combining IVIM-DWI, hyperspectral imaging techniques, and deep learning as a robust, safe, and effective non-invasive diagnostic tool for breast cancer, offering a valuable alternative to contrast-enhanced methods by providing detailed information about tissue microstructure and heterogeneity without the need for contrast agents. Full article

Cement is widely used as the primary binder in concrete; however, growing environmental concerns and the rapid expansion of the construction industry have highlighted the need for more sustainable alternatives. Geopolymers have emerged as promising eco-friendly binders due to their lower carbon footprint and potential to utilize industrial byproducts. Geopolymer mortar, like other cementitious substances, exhibits brittleness and tensile weakness. Basalt fibers serve as fracture-bridging reinforcements, enhancing flexural and tensile strength by redistributing loads and postponing crack growth. Basalt fibers enhance the energy absorption capacity of the mortar, rendering it less susceptible to abrupt collapse. Basalt fibers have thermal stability up to about 800–1000 °C, rendering them appropriate for geopolymer mortars designed for fire-resistant or high-temperature applications. They assist in preserving structural integrity during heat exposure. Fibers mitigate early-age microcracks resulting from shrinkage, drying, or heat gradients. This results in a more compact and resilient microstructure. Using basalt fibers improves surface abrasion and impact resistance, which is advantageous for industrial flooring or infrastructure applications. Basalt fibers originate from natural volcanic rock, are non-toxic, and possess a minimal ecological imprint, consistent with the sustainability objectives of geopolymer applications. This study investigates the mechanical and thermal performance of a geopolymer mortar composed of metakaolin and red mud as binders, with basalt powder and limestone powder replacing traditional sand. The primary objective was to evaluate the effect of basalt fiber incorporation at varying contents (0.4%, 0.8%, and 1.2% by weight) on the durability and strength of the mortar. Eight different mortar mixes were activated using sodium hydroxide (NaOH) and sodium silicate (Na₂SiO₃) solutions. Mechanical properties, including compressive strength, flexural strength, and ultrasonic pulse velocity (UPV), were tested 7 and 28 days before and after exposure to elevated temperatures (200, 400, 600, and 800 °C). The results indicated that basalt fiber significantly enhanced the performance of the geopolymer mortar, particularly at a content of 1.2%. Specimens with 1.2% fiber showed up to 20% improvement in compressive strength and 40% in flexural strength after thermal exposure, attributed to the fiber’s role in microcrack bridging and structural densification. Subsequent research should concentrate on refining fiber type, dose, and dispersion techniques to improve mechanical performance and durability. Examinations of microstructural behavior, long-term durability under environmental settings, and performance following high-temperature exposure are crucial. Furthermore, investigations into hybrid fiber systems, extensive structural applications, and life-cycle evaluations will inform the practical and sustainable implementation in the buildings. Full article

Based on the components of glasses, four algorithms, namely K-Nearest Neighbor, Random Forest, Support Vector Machine, and eXtreme Gradient Boosting, were used to construct an optimal machine learning model to predict the thermal and optical properties of oxyfluoride glass, namely glass transition temperature, density, Abbe number, liquidus temperature, thermal expansion coefficient, and refractive index. We perform SHAP analysis on the constructed machine learning model to explain the effects of different components on the properties. Based on the trained machine learning models, we developed several ternary system prediction maps that can effectively predict the properties of glasses composed of different proportions of components. This study provides a method to design new oxyfluoride glasses only knowing the components of glasses, which is instructive for the development of new types of oxyfluoride glasses as well as for computer-aided reverse design. Full article

Buildings account for 39% of global carbon emissions, making the construction sector a pivotal contributor to climate change. In ecologically fragile plateau regions, the tension between urban development and environmental sustainability poses a significant challenge. This study examines the spatiotemporal characteristics and influencing mechanisms of building carbon emissions (BCEs) in plateau cities using an empirical analysis of 13-year panel data (2010–2022) from two municipalities and six prefectures in Qinghai Province, China. By employing the eXtreme Gradient Boosting (XGBoost) model, we comprehensively assess drivers across four dimensions: socioeconomic structure, demographic and urban environmental factors, urban expansion patterns, and climatic topographic attributes. Key findings include: (1) The XGBoost model exhibits robust predictive performance (R² > 0.9, MSE < 0.1, RMSE < 0.3), validating its effectiveness for plateau urban systems. (2) Socioeconomic structure and urban expansion characteristics significantly positively influence building carbon emissions, with GDP, per capita GDP, and built-up areas being particularly influential. (3) The interaction between climate and terrain increases carbon emissions in urban buildings. (4) While socioeconomic structure is a common factor affecting BCEs across different types of plateau urban buildings, other factors, such as urban population density, the housing construction area, and the urban shape index, exhibit variability. These insights inform policy recommendations for cross-regional carbon flow balancing and adaptive low-carbon planning strategies tailored to plateau ecosystems. Full article

The construction industry accounts for approximately 28% of global CO₂ emissions, and emission management at the building demolition stage is important for achieving carbon neutrality goals. Systematic studies on the demolition stage, however, are still lacking. In this study, research on the development of optimal machine learning (ML) models was conducted to predict CO₂ emissions at the demolition stage. CO₂ emissions were predicted by applying various ML algorithms (e.g., gradient boosting machine [GBM], decision tree, and random forest), based on the information on building features and the equipment used for demolition, as well as energy consumption data. GBM was selected as a model with optimal prediction performance. It exhibited very high accuracy with R² values of 0.997, 0.983, and 0.984 for the training, test, and validation sets, respectively. The GBM model also showed excellent results in generalization performance, and it effectively learned the data patterns without overfitting in residual analysis and mean absolute error (MAE) evaluation. It was also found that features such as the floor area, equipment, wall type, and structure significantly affect CO₂ emissions at the building demolition stage and that equipment and the floor area are key factors. The model developed in this study can be used to support decision-making at the initial design stage, evaluate sustainability, and establish carbon reduction strategies. It enables efficient data collection and processing and provides scalability for various analytical approaches compared to the existing life cycle assessment (LCA) approach. In the future, it is deemed necessary to develop ML tools that enable comprehensive assessment of the building life cycle through system boundary expansion. Full article

Geographic Information Systems provide the means to explore the spatial distribution of insect species across various land-use types to understand their relationship with shared or overlapping spatiotemporal resources. Blow fly species richness and total fly abundance were correlated among six land-use types (residential, commercial, waste, woods, roads, and agricultural crop types) and distance to streams. To generate multivariate models of species richness and total fly abundance, blow fly trapping sites were chosen across the land-use gradient of Windsor–Essex County (Ontario, Canada) using a stratified random sampling approach. Sampling occurred in mid-June (spring), late August (summer), and late October (fall). Spring species richness correlated highest to residential (−), woods (−), distance to streams (+), and tomato fields (+) in models across all three land-use buffer scale distances (0.5, 1, 2 km), with waste (+/−), roads (−), wheat/corn (−), and commercial (−) correlating at only two of the three scales. Spring total fly abundance correlated with all but one land-use variable across all buffer scale distances, but the distance to streams (+), followed by orchards/vineyards (+) exhibited the greatest importance to these models. Summer blow fly species richness correlated with roads (−) and commercial (+) across all buffer distances, whereas at two of three buffer distances wheat/corn (−), residential (+), distance to streams (+), waste (−), and orchards/vineyards (+) were also important. Summer total fly abundance correlated to models with distance to streams (+), orchards/vineyards (+), and sugar beets/other vegetables (+) at the 2 km scale. Species richness and total abundance models at the 0.5 km buffer distance exhibited the highest correlation, lowest root mean square error, and similar prediction error to those derived at larger buffer distances. This study provides baseline methods and models for future validation and expansion of species-specific knowledge regarding adult blow fly relationships with spatiotemporal resources across land-use types and landscape features. Full article

The green space system in metropolitan areas is crucial for maintaining environmental health and stability by regulating and supporting ecosystem service values (ESVs). The Suzhou–Wuxi–Changzhou metropolitan area is located in the core of the Yangtze River Delta, and its green space exemplifies this importance, despite facing challenges from rapid urbanization in past decades. Studying the categories of ESVs and their driving factors can facilitate the comprehension of ESVs’ dynamics, thereby promoting regional sustainable development. In this article, we used the inVEST module to calculate six ESV indicators (soil retention, annual water yield, habitat quality, carbon storage, nitrogen, and phosphorus absorption) of the Suzhou–Wuxi–Changzhou metropolitan area’s green space system from 2015 to 2020 and combined it with the entropy weight method (EWM) to allocate weights for these indicators and evaluate the total value of the ESVs. To address the weakness of the inVEST model in calculating the total value of multiple ESVs, the Xgboost algorithm was combined with PCA methods to screen its main driving factors from numerous measures. Finally, the GWR method was used to reveal the spatial and temporal change in the main driving factors’ impacts on ESVs in the study area over five years. The result shows (1) the spatial distribution of the total value of regulating and supporting ESVs in the Suzhou–Wuxi–Changzhou metropolitan area has become more uneven in 2020 compared with 2015; (2) the most important driving factors include landscape diversity, topographic gradient, economic activity intensity, humidity, and surface temperature; and (3) based on the analysis of GWR results, the study area has an overall increase in regional soil erosion due to the expansion of impervious areas. And some mountainous areas have habitat fragmentation because of incorrect economic activity. This study provides a new perspective for evaluating the sum of multiple types of ESVs and exploring their driving factors, as well as revealing the ecosystem problems of the Suzhou–Wuxi–Changzhou metropolitan area in recent years. It also provides a reference for policymakers to maintain local ecological stability and security. Full article

Global Navigation Satellite Systems (GNSS) offer comprehensive position, navigation, and timing (PNT) estimates worldwide. Given the growing demand for reliable location awareness in both indoor and outdoor contexts, the advent of fifth-generation mobile communication technology (5G) has enabled expansive coverage and precise positioning services. However, the power received by the signal of interest (SOI) at terminals is notably low. This can lead to significant jamming, whether intentional or unintentional, which can adversely affect positioning receivers. The diagnosis of jamming types, such as classification, assists receivers in spectrum sensing and choosing effective mitigation strategies. Traditional jamming diagnosis methodologies predominantly depend on the expertise of classification experts, often demonstrating a lack of adaptability for diverse tasks. Recently, researchers have begun utilizing convolutional neural networks to re-conceptualize a jamming diagnosis as an image classification issue, thereby augmenting recognition performance. However, in real-world scenarios, the assumptions of independent and homogeneous distributions are frequently violated. This discrepancy between the source and target distributions frequently leads to subpar model performance on the test set or an inability to procure usable evaluation samples during training. In this paper, we introduce LJCD-Net, a deep adversarial migration-based cross-domain jamming generalization diagnostic network. LJCD-Net capitalizes on a fully labeled source domain and multiple unlabeled auxiliary domains to generate shared feature representations with generalization capabilities. Initially, our paper proposes an uncertainty-guided auxiliary domain labeling weighting strategy, which estimates the multi-domain sample uncertainty to re-weight the classification loss and specify the gradient optimization direction. Subsequently, from a probabilistic distribution standpoint, the spatial constraint imposed on the cross-domain global jamming time-frequency feature distribution facilitates the optimization of collaborative objectives. These objectives include minimizing both the source domain classification loss and auxiliary domain classification loss, as well as optimizing the inter-domain marginal probability and conditional probability distribution. Experimental results demonstrate that LJCD-Net enhances the recognition accuracy and confidence compared to five other diagnostic methods. Full article

A double optimal solution (DOS) of a least-squares problem $\mathbf{A}\mathbf{x}=\mathbf{b},\mathbf{A}\in {\mathbb{R}}^{q\times n}$ with $q\ne n$ is derived in an $m+1$-dimensional varying affine Krylov subspace (VAKS); two minimization techniques exactly determine the $m+1$ expansion coefficients of the solution $\mathbf{x}$ in the VAKS. The minimal-norm solution can be obtained automatically regardless of whether the linear system is consistent or inconsistent. A new double optimal algorithm (DOA) is created; it is sufficiently time saving by inverting an $m\times m$ positive definite matrix at each iteration step, where $m\ll min(n,q)$. The properties of the DOA are investigated and the estimation of residual error is provided. The residual norms are proven to be strictly decreasing in the iterations; hence, the DOA is absolutely convergent. Numerical tests reveal the efficiency of the DOA for solving least-squares problems. The DOA is applicable to least-squares problems regardless of whether $q<n$ or $q>n$. The Moore–Penrose inverse matrix is also addressed by adopting the DOA; the accuracy and efficiency of the proposed method are proven. The $m+1$-dimensional VAKS is different from the traditional m-dimensional affine Krylov subspace used in the conjugate gradient (CG)-type iterative algorithms CGNR (or CGLS) and CGRE (or Craig method) for solving least-squares problems with $q>n$. We propose a variant of the Karush–Kuhn–Tucker equation, and then we apply the partial pivoting Gaussian elimination method to solve the variant, which is better than the original Karush–Kuhn–Tucker equation, the CGNR and the CGNE for solving over-determined linear systems. Our main contribution is developing a double-optimization-based iterative algorithm in a varying affine Krylov subspace for effectively and accurately solving least-squares problems, even for a dense and ill-conditioned matrix $\mathbf{A}$ with $q\ll n$ or $q\gg n$. Full article

Internet security is a major concern these days due to the increasing demand for information technology (IT)-based platforms and cloud computing. With its expansion, the Internet has been facing various types of attacks. Viruses, denial of service (DoS) attacks, distributed DoS (DDoS) attacks, code injection attacks, and spoofing are the most common types of attacks in the modern era. Due to the expansion of IT, the volume and severity of network attacks have been increasing lately. DoS and DDoS are the most frequently reported network traffic attacks. Traditional solutions such as intrusion detection systems and firewalls cannot detect complex DDoS and DoS attacks. With the integration of artificial intelligence-based machine learning and deep learning methods, several novel approaches have been presented for DoS and DDoS detection. In particular, deep learning models have played a crucial role in detecting DDoS attacks due to their exceptional performance. This study adopts deep learning models including recurrent neural network (RNN), long short-term memory (LSTM), and gradient recurrent unit (GRU) to detect DDoS attacks on the most recent dataset, CICDDoS2019, and a comparative analysis is conducted with the CICIDS2017 dataset. The comparative analysis contributes to the development of a competent and accurate method for detecting DDoS attacks with reduced execution time and complexity. The experimental results demonstrate that models perform equally well on the CICDDoS2019 dataset with an accuracy score of 0.99, but there is a difference in execution time, with GRU showing less execution time than those of RNN and LSTM. Full article

The clear boundary between urban and rural areas is gradually disappearing, and urban and rural areas are two poles of a gradient with many continuous human settlements in between, which is a concept known as the rural–urban continuum. Little is known about the distribution and change trajectories of the various types in the rural–urban continuum across the globe. Therefore, using global land-cover data (FROM-GLC Plus) and global population data (Worldpop) based on the decision-making tree method, this study proposed a method and classification system for global rural–urban continuum mapping and produced the mapping results on a global scale in the Google Earth Engine platform. With the expansion of built-up areas and the increase in population, the global human settlements follow the pattern that develops from wildland to villages (isolated—sparse—dense), and then to towns (sparse—dense), and finally to urban areas (edge—center). From a regional perspective, there are some obvious differences: Africa is dominated by sparse villages; Asia has the highest proportion of densely clustered towns; the proportion of dense villages in Europe is high. Rural–urban continuum mapping and analysis provide a database and new insights into urbanization and differences between urban and rural areas around the world. Full article

In this survey, we review some old and new results initiated with the study of expansive mappings. From a variational perspective, we study the convergence analysis of expansive and almost-expansive curves and sequences governed by an evolution equation of the monotone or non-monotone type. Finally, we propose two well-defined algorithms to remedy the shortcomings concerning the ill-posedness of expansive-type evolution systems. Full article

The contradiction between urban expansion and ecological protection in fragile vegetation areas has become increasingly prominent with regional development. Revealing the relationship between urbanization and ecosystem services will help to provide solutions to this problem. In order to clarify the impact of urbanization on typical mountain areas with fragile vegetation on the Qinghai Tibet Plateau, we built an ecosystem service value (ESV) evaluation index system. We also evaluated the ESV and its spatial response to the urbanization of Shannan Prefecture in Tibet from 1990 to 2015 based on different terrain gradients (TGs) using vegetation biophysical data obtained from remote sensing platforms. The results show that ESV in Shannan increased first and then declined as the TG increased, reaching a maximum value at the third TG. ESV showed a decreased trend during the study period, with a significant decline at the second and third TGs, which were the main distribution areas of vegetation in Shannan. Through spatial correlation analysis, we observed that urbanization and ESV showed a significant spatial aggregation effect. Among them, the high–low type accounted for the largest proportion in the grid with the agglomeration effect, mainly concentrated at the lower TG in the southern of Shannan, where ESV decreases with the increasing urbanization. We highlight the need for targeted, sustainable development policies to rationally organize the urbanization process in the different-gradient plateau regions with fragile vegetation. These results can provide a reference for applying ESV to vegetation restoration and ecological protection in ecologically fragile mountain areas. Full article

The in-situ combustion–explosion fracturing technology in shale reservoirs can promote continuous fracture expansion with a radial detonation wave first converging into a shock wave and then decaying into an elastic wave. The transformation scale of the shale reservoir is determined by the range of wave propagation during combustion–explosion. As wave propagation paths are usually tortuous and fractal, the previous integer wave models are not competent to describe the wave propagation and estimate the impact range of the combustion–explosion fracturing process. This study develops two fractional wave propagation models and seeks analytical solutions. Firstly, a novel fractional wave model of rotation angle is proposed to describe the process of detonation waves converting into shock waves in a bifurcated structure. The radial displacement gradient of the detonation wave is represented by the internal expansion and rotation deformation of the shale. Secondly, another fractional wave propagation model of radial displacement is proposed to show the process of a shock wave decaying into an elastic wave. Thirdly, the proposed models are analytically solved through the fractional variable separation method and variational iteration method, respectively. Analytical solutions for rotation angle and radial displacement with fractal time and space are obtained. Finally, the impacts of the branching parameter of the detonation wave converge bifurcation system, aggregation order of detonation compression wave, and different types of explosives on the rotation angle of the shock wave are investigated. The propagation mechanism of the primary wave (P-wave) with time and space is analyzed. The analytical solutions can well describe the wave propagation process in fractured shales. The proposed fractional wave propagation models can promote the research of wave propagation in the combustion–explosion fracturing process of shale reservoirs. Full article