Search Results (141,326)

Accurate photovoltaic (PV) power forecasting is crucial for effective smart grid management, given the intermittent nature of PV generation. To address these challenges, this paper proposes the Temporal Bottleneck-enhanced Bidirectional Temporal Convolutional Network with Multi-Head Attention and Autoregressive (TB-BTCGA) model. It introduces a temporal bottleneck structure and Deep Residual Shrinkage Network (DRSN) into the Temporal Convolutional Network (TCN), improving feature extraction and reducing redundancy. Additionally, the model transforms the traditional TCN into a bidirectional TCN (BiTCN), allowing it to capture both past and future dependencies while expanding the receptive field with fewer layers. The integration of an autoregressive (AR) model optimizes the linear extraction of features, while the inclusion of multi-head attention and the Bidirectional Gated Recurrent Unit (BiGRU) further strengthens the model’s ability to capture both short-term and long-term dependencies in the data. Experiments on complex datasets, including weather forecast data, station meteorological data, and power data, demonstrate that the proposed TB-BTCGA model outperforms several state-of-the-art deep learning models in prediction accuracy. Specifically, in single-step forecasting using data from three PV stations in Hebei, China, the model reduces Mean Absolute Error (MAE) by 38.53% and Root Mean Square Error (RMSE) by 33.12% and increases the coefficient of determination ($R^2$) by 7.01% compared to the baseline TCN model. Additionally, in multi-step forecasting, the model achieves a reduction of 54.26% in the best MAE and 52.64% in the best RMSE across various time horizons. These results underscore the TB-BTCGA model’s effectiveness and its strong potential for real-time photovoltaic power forecasting in smart grids. Full article

The Machine-Managed Bidding (MMB) system is an innovative bidding mode implemented by the Chinese government to mitigate collusive bidding behavior. Prior studies have focused minimally on the bidding mechanism and the possible collusive bidding behavior under this mode. The objectives of this study are to analyze the bidding mechanism and the dynamic evolution of collusive bidding behavior under the MMB system and provide targeted regulation countermeasures. To this end, this study develops an evolutionary game model among collusion initiators, free bidders, and regulators, explores possible scenarios for evolutionarily stable strategies, and performs sensitivity analysis of critical parameters utilizing MATLAB software (Version R2024a) based on empirical data. Results indicate that: (1) The MMB model significantly mitigates vertical collusive bidding behavior but lacks measures for governing transverse collusive bidding; (2) The game model has five evolutionarily stable strategies, with the one where the collusion initiator adopting the “non-collude” strategy, the free bidder adopting the “bid” strategy, and the regulator adopting the “negative regulate” strategy being the optimal evolutionary stable strategy; (3) Decreasing the costs associated with preparing bid documents, enhancing supervision costs, increasing the technical complexity of collusive bidding, and expanding the total number of construction enterprises with high-credit and low-credit ratings can expedite the evolution of the three participants toward the optimal evolutionarily stable strategy. This study supplements current knowledge on the regulation of collusive bidding behavior and enriches the knowledge framework of the MMB model. This study also provides insights for policymakers to guarantee the smooth implementation of the MMB. Full article

This study examines the impact of digital competencies (DCs) and organizational culture (OC) on digital transformation (DT) in small and medium-sized enterprises (SMEs) in metropolitan Lima. Using a non-experimental and cross-sectional design, 307 business owners were surveyed using a previously validated questionnaire. Data were analyzed through partial least squares structural equation modeling (SEM-PLS). The results show that DCs have a direct and significant impact on DT, being the main driver of this process. Additionally, OC acts as a partial mediator between DCs and DT, although its influence is lesser, compared with DCs. The study highlights the importance of DCs in driving digitalization in SMEs, while OC facilitates, although does not solely determine, the success of the digital transformation process. Despite the limitations and the cross-sectional nature of the study, the findings provide valuable insights for SMEs in emerging economies and offer a basis for future research on the factors influencing digital transformation in similar contexts. Full article

This study advances the inference of travel purposes for dockless bike-sharing users by integrating dockless bike-sharing and point of interest (POI) data, thereby enhancing traditional models. The methodology involves cleansing dockless bike-sharing datasets, identifying destination areas via users’ walking radii from their start and end points, and categorizing POI data to establish a correlation between trip purposes and POI types. The innovative GMOD model (gravity model considering origin and destination) is developed by modifying the basic gravity model parameters with the distribution of POI types and travel time. This refined approach significantly improves the accuracy of predicting travel purposes, surpassing standard gravity models. Particularly effective in identifying less frequent but critical purposes such as transfers, medical visits, and educational trips, the GMOD model demonstrates substantial improvements in these areas. The model’s efficacy in sample data tests highlights its potential as a valuable tool for urban transport analysis and in conducting comprehensive trip surveys. Full article

It has been recently argued that numerous enigmatic observations remain challenging to explain within the framework of conventional physics. These anomalies include unexpected correlations between temperature variations in the stratosphere, the total electron content of the Earth’s atmosphere, and earthquake activity on one hand and the positions of planets on the other. Decades of collected data provide statistically significant evidence for these observed correlations. These works suggest that these correlations arise from strongly interacting “streaming invisible matter” which gets gravitationally focused by the solar system bodies including the Earth’s inner mass distribution. Here, we propose that some of these, as well as other anomalies, may be explained by rare yet energetic events involving the so-called axion quark nuggets (AQNs) impacting the Earth. In other words, we identify the “streaming invisible matter” conjectured in that works with AQNs, offering a concrete microscopic mechanism to elucidate the observed correlations. It is important to note that the AQN model was originally developed to address the observed similarity between the dark matter and visible matter densities in the Universe, i.e., ${\Omega }_{\mathrm{DM}}\sim {\Omega }_{\mathrm{visible}}$, and not to explain the anomalies discussed here. Nonetheless, we support our proposal by demonstrating that the intensity and spectral characteristics of AQN-induced events are consistent with the aforementioned puzzling observations. Full article

Background/Objectives: For healthcare institutions developing a robotic programme, delivering value for patients, clinicians, and payers is key. However, the impact on the surgeon, training pathways, and logistics are often overlooked. We conducted a study on the impact of robotic surgery on surgeons, access to robotic surgical training, and factors associated with developing a successful robotic programme. Method: In our international mixed-methods study, a customised web-based survey was circulated to gynaecological oncologists. The Wilcoxon rank-sum test and Fisher’s exact test, tested the hypothesis of the differences in continuous and categorical variables. Multiple linear regression was used to model the effect of variables on outcomes adjusting for gender, age, and postgraduate experience. Outcomes included situational awareness, surgeon fatigue/stress, and the surgical learning curve. Qualitative data were collected via in-depth semi-structured interviews using an inductive theoretical framework to explore access to surgical training and logistical considerations in the development of a successful robotic programme. Results: In total, 94%, 45%, and 48% of survey respondents (n = 152) stated that robotic surgery was less physically tiring/mentally tiring/stressful in comparison to laparoscopic surgery. Our data suggest gender differences in the robotics learning curve with men six times more likely to state robotic surgery had negatively impacted their situational awareness in the operating theatre (OR = 6.35, p ≤ 0.001) and 2.5 times more likely to state it had negatively impacted their surgical ability due to lack of haptic feedback in comparison to women (OR = 2.62, p = 0.046). Women were more risk-averse in case selection, but there were no self-reported differences in the intra-operative complication rates between male and female surgeons (OR = 1, p = 0.1). In total, 22/25 robotically trained surgeons interviewed did not follow a structured curriculum of learning. Low and middle income country centres had less access to robotic surgery. The success of robotic programmes was measured by the number of cases performed per annum, with 74% of survey respondents stating that introducing robotics increased the proportion of surgeries performed by minimal access surgery. There was a distinct lack of knowledge on the environmental impact of robotic surgery. Conclusions: Whilst robotic surgery is considered a landmark innovation in surgery, it must be responsibly implemented through effective training and waste minimisation, which must be a key metric in measuring the success of robotic programmes. Full article

Respiratory diseases pose a significant threat to global public health, as exemplified by the COVID-19 pandemic. Molecular communication (MC), as a new method in communication systems, provides a framework for the modeling of diseases. Current studies, however, largely restrict MC models to transmission scenarios involving a single source and single receiver, leaving scenarios with multiple receivers insufficiently explored. This study investigates respiratory virus transmission through air, applying a single-input-multiple-output (SIMO) MC model to analyze the in vitro transmission process. In this context, a COVID-19-positive individual can transmit the virus to multiple recipients, modeled as a SIMO MC system where the affected person is the transmitter, susceptible individuals are receivers, and the intervening air serves as the communication channel. A theoretical model is developed to elucidate the virus transmission process, yielding foundational analytical expressions for the absorption probability. Numerical data validate the model and reveal factors influencing the cumulative reception probability. The results indicate that both the distance and angle between the transmitter and receiver significantly impact the absorption probability, which decreases with increasing distance and angle. Optimal absorption occurs when the receiver is directly in front of the emitter. These findings introduce a new perspective on viral transmission mechanisms and provide a scientific basis for future prevention and control measures. Full article

Composite materials play a crucial role in various industries, including aerospace, automotive, and shipbuilding. These materials differ from traditional metals due to their high specific strength and low weight, which reduce energy consumption in these industries. The damage behavior of such materials, especially when subjected to stress discontinuities such as central holes, differs significantly from materials without holes. This study examines this difference and predicts the damage behavior of carbon fiber composites with multiple holes using a progressive damage model through finite element analysis (FEM). Two holes were positioned along the central axis of symmetry in the longitudinal and transverse directions relative to the load. The presence of additional holes acts as a stress-relief factor, reducing stress by up to 17% when the holes are arranged in the longitudinal direction. A cohesive zone model with two parameters, including constant and linear shapes, was applied to develop a simple analytical model for calculating the nominal strength of multi-hole composite laminates, based on the unnotched plate properties of the material. The results closely match experimental findings. The data also provide design tables that can assist with material selection. Full article

Active learning has been a research area conducted across various domains for a long time, from traditional machine learning to the latest deep learning research. Particularly, obtaining high-quality labeled datasets for supervised learning requires human annotation, and an effective active learning strategy can greatly reduce annotation costs. In this study, we propose a new insight, CFP-AL (Combining model Features and Prediction for Active Learning), from the perspective of feature space by analyzing and diagnosing methods that have shown good performance in NLP (Natural Language Processing) sentence classification. According to our analysis, while previous active learning strategies that focus on finding data near the decision boundary to facilitate classifier tuning are effective, there are very few data points near the decision boundary. Therefore, a more detailed active learning strategy is needed beyond simply finding data near the decision boundary or data with high uncertainty. Based on this analysis, we propose CFP-AL, which considers the model’s feature space, and it demonstrated the best performance across six tasks and also outperformed others in three Out-Of-Domain (OOD) tasks. While suggesting that data sampling through CFP-AL is the most differential classification standard, it showed novelty in suggesting a method to overcome the anisotropy phenomenon of supervised models. Additionally, through various comparative experiments with basic methods, we analyzed which data are most beneficial or harmful for model training. Through our research, researchers will be able to expand into the area of considering features in active learning, which has been difficult so far. Full article

To address the issues of limited adaptability and low spatial utilization in traditional rigid actuators, a biomimetic actuator with water-induced helical deformation functionality was designed. This actuator is capable of adaptive gripping and retrieval of objects in a narrow lumen. A numerical model was established to analyze its helical deformation mechanism, and the helical deformation characteristics of the actuator were calculated under different structural parameters. Based on four-dimensional (4D) printing technology, which integrates three-dimensional printed structures with responsive materials, experimental samples of biomimetic actuators were fabricated by combining thermoplastic polyurethane fiber scaffolds with water-absorbing polyurethane rubbers. By comparing the simulation results with the experimental data, the numerical model was corrected, providing theoretical guidance for the structural optimization design of the actuator. The experiment shows that the biomimetic actuator can act as a gripper to capture a small target in a lumen less than 5 mm in diameter. This research provides a theoretical and technical foundation for the development of specialized actuators aimed at narrow spaces. Full article

Designing waveforms with a Constant Modulus Constraint (CMC) to achieve
desirable Slow-Time Ambiguity Function (STAF) characteristics is significantly important in radar technology. The problem is NP-hard, due to its non-convex quartic objective function and CMC constraint. Existing methods typically involve model-based approaches with relaxation and data-driven Deep Neural Networks (DNNs) methods, which face the challenge of dataimitation. We observe that the Complex Circle Manifold (CCM) naturally satisfies the CMC. By projecting onto the CCM, the problem is transformed into an unconstrained minimization problem that can be tackled using the CCM gradient
descent model. Furthermore, we observe that the gradient descent model over the CCM can be unfolded as a Deep Learning (DL) network. Therefore, byeveraging the powerfulearning ability of DL and the CCM gradient descent model, we propose a Model-Adaptive Learned Network (MAL-Net) method without relaxation. Initially, we reformulate the problem as an Unconstrained Quartic Problem (UQP) on the CCM. Then, the MAL-Net is developed to earn the step sizes of allayers adaptively. This is accomplished by unrolling the CCM gradient descent model as the networkayer. Our simulation results demonstrate that the proposed MAL-Net achieves superior STAF performance compared to existing methods. Full article

Since the mid-1990s, X-ray phase contrast imaging (XPCI) has attracted increasing interest in the industrial and bioimaging fields due to its high sensitivity to weakly absorbing materials and has gained widespread acceptance. XPCI can simultaneously provide three imaging modalities with complementary information, offering enriched details and data. This study proposes an image fusion method that simultaneously retrieves the three complementary channels of XPCI. It integrates block features, non-subsampled contourlet transform (NSCT), and a spiking cortical model (SCM), comprising three steps: (I) Image denoising, (II) Block-based feature-level NSCT-SCM fusion, and (III) Image quality enhancement. Compared with other methods in the XPCI image fusion field, the fusion results of the proposed algorithm demonstrated significant advantages, particularly with an impressive increase in the standard deviation by over 50% compared to traditional NSCT-SCM. The results revealed that the proposed algorithm exhibits high contrast, clear contours, and a short operation time. Experimental outcomes also demonstrated that the block-based feature extraction procedure performs better in retaining edge strength and texture information, with released computational resource consumption, thus, offering new possibilities for the industrial application of XPCI technology. Full article

An accurate assessment of grassland carbon stocks is essential for understanding their role in China’s terrestrial carbon cycle. At regional scales, combining remote sensing technology with carbon density has become a common approach. However, substantial variability among remote sensing inversion models, particularly in theoretical foundations, variable selection, and algorithmic implementation, introduces significant uncertainty into estimating grassland carbon density. This study focuses on the grassland ecosystems in Gansu Province, China, employing both an overall approach (without distinguishing between grassland types) and a stratified approach, classifying the grassland into seven distinct types: alpine meadow steppe, temperate steppe, lowland meadow, alpine meadow, mountain meadow, shrubby grassland, and temperate desert. Using remote sensing, topography, climate, and 490 measured sample data points, this study employs five representative inversion models from three model categories: parametric (single-factor model and stepwise multivariate linear regression), spatial (geographically weighted regression (GWR) and multi-scale geographically weighted regression (MGWR)), and non-parametric (random forest (RF)). Inversion models were constructed for four components of the grassland ecosystem: aboveground (AGBC) and belowground biomass carbon density (BGBC), dead organic matter carbon density (DOMC), and soil organic carbon density (SOC). The applicability of each model was then systematically compared and analyzed. The main conclusions are as follows: (1) The overall estimation results demonstrate that the GWR model is the optimal choice for inverting AGBC, DOMC, and SOC, with coefficients of determination (R²) of 0.67, 0.60, and 0.92, respectively. In contrast, the MGWR model is best suited for BGBC, with an R² value of 0.73. (2) The stratified estimation results suggest that the optimal inversion models for AGBC and BGBC are predominantly the MGWR and RF models selected through the recursive feature elimination algorithm. For DOMC, the optimal model is a spatial model, while SOC is most accurately estimated using the GWR and RF models selected via the Boruta algorithm. (3) When comparing the inversion results of the optimal overall and stratified approaches, the stratified estimations of AGBC, BGBC, and DOMC (R² = 0.80, 0.78, and 0.73, respectively) outperformed those of the overall approach. In contrast, the SOC estimates followed an opposite trend, with the overall approach yielding a higher R² value of 0.92. (4) Generally, variable selection significantly enhanced model accuracy, with spatial and non-parametric models demonstrating superior precision and stability in estimating the four carbon density components of grassland. These findings provide methodological guidance for converting sample point carbon density data into regional-scale estimates of grassland carbon storage. Full article

The realization of intelligent transformation is an important path for the industry to move towards low-carbon development. Based on panel data from 30 provinces in China, this study utilizes the intermediate effect model and spatial econometric model to analyze the influence of industrial intelligence on carbon emissions. The research reveals that industrial intelligence helps with carbon reduction, and the result is still valid after undergoing various tests. Industrial intelligence relies on green technological innovation, industrial structure upgrading, and energy intensity to realize carbon reduction. There is a spatial spillover role of industrial intelligence on carbon emissions, which has a positive influence on carbon reduction in local and adjoining regions. The influence of industrial intelligence on carbon emissions exhibits heterogeneity in the regional dimension, time dimension, and industrial intelligence level dimension. The research provides empirical evidence and implications for using artificial intelligence to achieve carbon reduction. Full article

The low-carbon city pilot (LCCP) policy represents a pioneering approach to fostering sustainable development. It offers a scientific framework to reconcile the relationship between economic growth, resource utilization, and environmental protection. This study measures urban ecological efficiency (UEE) through the non-radial directional distance function (NDDF) model using the panel data of 284 cities in China, from 2007 to 2021, and analyzes the impact of the LCCP policy on UEE, adopting a multi-period difference-in-differences (DID) model. The results of the baseline regression indicate that the pilot cities exhibit an average ecological efficiency that is approximately 3.0% higher than that observed in non-pilot cities, which pass both the parallel trend test and the robustness test. Mechanism analysis reveals that industrial upgrading and energy consumption reduction are the primary pathways through which the LCCP policy enhances UEE. In addition, the policy effects are particularly significant in improving UEE in non-resource-based cities, large cities, and cities in the eastern region. Finally, the spatial spillover effects demonstrated by the LCCP policy can effectively inform neighboring cities of strategies to enhance their UEE. The research findings provide invaluable insight and direction for China’s efforts in the development of low-carbon cities and ecological sustainability. Full article