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To address the low efficiency in priority signal control within intelligent transportation systems, this study introduces a novel eight-phase priority signal control method, CBQL-TSP, leveraging a hybrid decision-making framework that integrates cooperative game theory and reinforcement learning. This approach conceptualizes the allocation of bus signal priorities as a multi-objective decision-making problem across an eight-phase signal sequence, differentiating between priority and non-priority phases. It employs a cooperative game model to facilitate this differentiation. The developed hybrid decision-making algorithm, CBQL, effectively tackles the multi-objective decision-making challenges inherent in the eight-phase signal sequence. By computing the Shapley value function, it quantifies the marginal contributions of each participant, which in turn inform the construction of a state transition probability equation based on Shapley value ratios. Compared to conventional control methods, the CBQL-TSP method not only upholds the fairness principles of cooperative game theory but also harnesses the adaptive learning capabilities of Q-Learning. This enables dynamic adjustments to signal timing in response to real-time traffic conditions, significantly enhancing the flexibility and efficiency of priority signal control.

Vision-dependent underwater vehicles are widely used in seabed resource exploration. The visual perception system of underwater vehicles relies heavily on high-quality images for its regular operation. However, underwater images taken underwater often have color distortion, blurriness, and poor contrast. To address these degradation issues, we develop an adaptive weighted multiscale retinex (AWMR) method for enhancing underwater images. To utilize the local detail features, we first divide the image into multiple sub-blocks and calculate the detail sparsity index for each one. Then, we combine the global detail sparsity index with the local detail sparsity indices to determine the optimal scale parameter and corresponding weights for each sub-block. We apply retinex processing to each sub-block using these parameters and then subject the processed sub-blocks to detail enhancement, color correction, and saturation correction. Finally, we use a gradient domain fusion method based on structure tensors to fuse the corrected and enhanced sub-blocks and obtain the final output image. Our approach improves underwater images through comparisons with current state-of-the-art (SOTA) techniques on several open-source datasets, both quality, and performance. © 2023 Elsevier Ltd

In this paper, we present a novel Amplitude-Modulated Stochastic Perturbation and Vortex Convolutional Network, AMSP-UOD, designed for underwater object detection. AMSP-UOD specifically addresses the impact of non-ideal imaging factors on detection accuracy in complex underwater environments. To mitigate the influence of noise on object detection performance, we propose AMSP Vortex Convolution (AMSP-VConv) to disrupt the noise distribution, enhance feature extraction capabilities, effectively reduce parameters, and improve network robustness. We design the Feature Association Decoupling Cross Stage Partial (FAD-CSP) module, which strengthens the association of long and short range features, improving the network performance in complex underwater environments. Additionally, our sophisticated post-processing method, based on Non-Maximum Suppression (NMS) with aspect-ratio similarity thresholds, optimizes detection in dense scenes, such as waterweed and schools of fish, improving object detection accuracy. Extensive experiments on the URPC and RUOD datasets demonstrate that our method outperforms existing state-of-the-art methods in terms of accuracy and noise immunity. AMSP-UOD proposes an innovative solution with the potential for real-world applications. Our code is available at https://github.com/zhoujingchun03/AMSP-UOD.

Visually restoring underwater scenes primarily involves mitigating interference from underwater media. Existing methods ignore the inherent scale-related characteristics in underwater scenes. Therefore, we present the synergistic multi-scale detail refinement via intrinsic supervision (SMDR-IS) for enhancing underwater scene details, which contain multi-stages. The low-degradation stage from the original images furnishes the original stage with multi-scale details, achieved through feature propagation using the Adaptive Selective Intrinsic Supervised Feature (ASISF) module. By using intrinsic supervision, the ASISF module can precisely control and guide feature transmission across multi-degradation stages, enhancing multi-scale detail refinement and minimizing the interference from irrelevant information in the low-degradation stage. In multi-degradation encoder-decoder framework of SMDR-IS, we introduce the Bifocal Intrinsic-Context Attention Module (BICA). Based on the intrinsic supervision principles, BICA efficiently exploits multi-scale scene information in images. BICA directs higher-resolution spaces by tapping into the insights of lower-resolution ones, underscoring the pivotal role of spatial contextual relationships in underwater image restoration. Throughout training, the inclusion of a multi-degradation loss function can enhance the network, allowing it to adeptly extract information across diverse scales. When benchmarked against state-of-the-art methods, SMDR-IS consistently showcases superior performance. The code is publicly available at: https://github.com/zhoujingchun03/SMDR-IS.

Welcome to District Line, an exciting new destination in the making. This multi-family,mixed-use project is being planned for three underutilized parcels in SeatPleasant,Maryland, just across DC’s eastern border.The site is comprised of the lots from 5922 to5950 Martin Luther King Jr Highway.Currently the location of a CVS(indicated on the map to the right)and assorted strip retail surrounded by surface parking, we have created plans for higher density apartment homes over more desirable shopping and dining options. The buildings will have discreet off-street parking withlandscapedroofs, offering novel outdoor space for thecommunity.Based on our market research and financial models, we have planned the delivery of District Line In two phases, one for each building; the two separated by a thoughtful programmed promenade. Phase 1 will include 140 apartments (25 studio units and 115 1-bedroom units) on a 20-foot tall podium containing 12,500 square feet of retail and 166 parking spots.Phase 2 will offer 105 apartments (50 1-bedroom and 552-bedroom units) on a 20-foot tall podium containing 25,510 square feet of retail space and 148 parking spots.We are pleased to share with you our vision in greater detail.

Recommender systems have received great commercial success. Recommendation has been used widely in areas such as e-commerce, online music FM, online news portal, etc. However, several problems related to input data structure pose serious challenge to recommender system performance. Two of these problems are Matthew effect and sparsity problem. Matthew effect heavily skews recommender system output towards popular items. Data sparsity problem directly affects the coverage of recommendation result. Collaborative filtering is a simple benchmark ubiquitously adopted in the industry as the baseline for recommender system design. Understanding the underlying mechanism of collaborative filtering is crucial for further optimization. In this paper, we do a thorough quantitative analysis on Matthew effect and sparsity problem in the particular context setting of collaborative filtering. We compare the underlying mechanism of user-based and item-based collaborative filtering and give insight to industrial recommender system builders.

Households may imperfectly implement energy saving measures. This study identifies two factors resulting in imperfect use of energy-saving technology by households. Households often continue to use old technologies alongside new ones, and the energy-saving technologies have shorter actual lifetimes than their designed lifetimes. These two factors are considered when computing marginal energy conservation cost and marginal CO₂ abatement cost using data collected from a survey of rural households in three provinces in China. The results show that there are cost reduction for most space heating technologies, and their marginal abatement cost under full implementation ranges from −60 to 15 USD/t-CO₂, while the marginal abatement cost of cooking technologies ranges from 12 to 85 USD/t-CO₂. The marginal abatement costs of the majority of technologies increased after accounting for the two implementation factors. The marginal abatement cost in the imperfect implementation scenario is higher, with a range of −1 to 15 USD/t-CO₂ for space heating, and 18 to 165 USD/t-CO₂ for cooking. Assuming implementation factors are constant until 2035, annually achievable CO₂ abatement by 2035 is estimated to be 57, 11, and 10 Mt-CO₂/y in Hebei, Guizhou, and Guangxi Provinces.

The functional Magnetic Resonance Imaging (fMRI) is one of the most important tools for exploring the operation of the brain as it allows the spatially localized characteristics of brain activity to be observed. However, fMRI studies generate huge volumes of data and the signals of interest have low signal to noise ratio making its analysis a very challenging problem. There is a growing need for new methods that can efficiently and objectively extract the useful information from fMRI data and translate it into intelligible knowledge. In this paper, we introduce a swarm-based rough set approach to fMRI data analysis. Our approach is based on exploiting the power of particle swarm optimization to discover the feature combinations in an efficient manner by observing the change in positive region as the particles proceed through the search space. The approach supports multi-knowledge extraction. We evaluate the performance of the algorithm using benchmark and fMRI datasets. The results demonstrate its potential value for cognition research.

The functional Magnetic Resonance Imaging (fMRI) is one of the most important tools for exploring the operation of the brain as it allows the spatially localized characteristics of brain activity to be observed. However, fMRI studies generate huge volumes of data and the signals of interest have low signal to noise ratio making its analysis a very challenging problem. There is a growing need for new methods that can efficiently and objectively extract the useful information from fMRI data and translate it into intelligible knowledge. In this paper, we introduce a swarm-based rough set approach to fMRI data analysis. Our approach is based on exploiting the power of particle swarm optimization to discover the feature combinations in an efficient manner by observing the change in positive region as the particles proceed through the search space. The approach supports multi-knowledge extraction. We evaluate the performance of the algorithm using benchmark and fMRI datasets. The results demonstrate its potential value for cognition research.

Pituitary adenylate cyclase-activating polypeptide (PACAP), a member of the secretin/glucagon/vasoactive intestinal peptide family expressed throughout the nervous system, binds to the PACAP-specific G-protein-coupled receptor family members to promote both neuronal differentiation and survival. Although the PACAP receptor is known to activate its effector protein, adenylate cyclase (AC), and thus enhance cAMP generation, the molecular mechanism utilized by the receptor to activate AC is lacking. Here, we show that PACAP induces neurite outgrowth in PC12 cells by induction of translocation of the PACAP type 1 receptor (PAC1R) into caveolin-enriched Triton X-100-insoluble microdomains, leading to stronger PAC1R-AC interaction and elevated cAMP production. Moreover, we demonstrate that translocation of PAC1R is blocked by various treatments that selectively disrupt caveolae. As a result, intracellular cAMP level is decreased and consequently the PACAP-induced neurite outgrowth retarded. In contrast, addition of exogenous ganglioside GM1 to the cells shows the opposite effects. These results therefore identify the PACAP-induced translocation of its G-protein-coupled receptor into caveolae, where both AC and the regulating G-proteins reside, as the key molecular event in activating AC and inducing cAMP-mediated differentiation of PC12 cells.