The U-shaped yard is an important part of the U-shaped automated container terminal (U-ACT), which consists of a set of blocks used for storing containers, I-lanes for automated guided vehicles (AGVs) travel, and U-lanes for external trucks (ETs) travel. Double-side cantilever yard cranes (DCYCs) perform stacking and unstacking operations for containers transported by AGVs and ETs. Managing and coordinating the operations of DCYC, AGVs, and ETs, not only improves the operation efficiency of U-ACTs but also helps to promote the development of green ports. This paper addresses the problem of scheduling DCYCs and path planning for AGVs and ETs in the U-ACT. To achieve this, we establish a bi-objective mixed integer programming model to minimize both the makespan and the energy consumption. The model considers conflicts between two DCYCs within each block, ensures workload balance for these DCYCs, optimizes parking slots for AGVs and ETs, and schedules appropriate entry times for AGVs and ETs into the yard to reduce conflicts. To solve this model, we develop an improved multi-objective particle swarm optimization (IMOPSO) algorithm, where a globally optimal heuristic search mechanism and several conflict avoidance strategies to plan conflict-free spatiotemporal paths are introduced to accelerate the convergence of the algorithm. Numerical experiments demonstrate the superiority of the IMOPSO approach in terms of multiple metrics, confirming the effectiveness of the optimized vehicle entry timing strategy, which improves efficiency by 7.33% and yields energy savings by 11.72%. These findings clearly highlight that our model and solution approach can effectively enhance the operational efficiency of DCYCs, AGVs, and ETs, contributing to the overall improvement of container terminal operations.

Colorectal cancer is mostly caused by colorectal polyps, which can be prevented through polyp diagnosis using colonoscopy. The current computer-aided decision-making methods suffer from a variety of drawbacks, including inaccurate polyp classification, poor real-time performance, and poor interpretability. To address these issues, we propose an explainable multitask Shapley explanation networks (EMSEN) that can perform real-time explainable multitasks such as polyp detection and classification in colonoscopy videos. The EMSEN accepts two multimodal inputs of different light sources, and outputs the polyp location, classification type, and diagnosis results according to the real-time colonoscopy video, where efficient channel attention (ECA) Mechanism-based network and Shapley explanation networks (ShapNet) are designed to improve the feature extraction performance and model interpretability, respectively. Extensive experiment studies are conducted to verify the efficiency and effectiveness of the proposed method by comparing with the experts and state-of-the-art methods. The results demonstrate that the developed method performs the best, which achieves competitive diagnosis performance.

With the development of online real estate trading platforms, multi-modal housing trading data, including structural information, location, and interior image data, are being accumulated. The accurate appraisal of real estate makes sense for government officials, urban policymakers, real estate sellers, and personal purchasers. In this study, we propose an interpretable multi-modal stacking-based ensemble learning (IMSEL) method that deals with various modalities for real estate appraisals. We crawl the structural and image data of real estate in Chengdu city, China from the nation's largest real estate transaction platform with the location information, including public services, within 2 km of the real estate using Baidu map. We then compare the predictive results from IMSEL with those from previous state-of-art methods in the literature in terms of the root mean square error, mean absolute percentage error, mean absolute error, and coefficient of determination (R2). The comparison results show that IMSEL outperformed the other methods. We verified the improvement of introducing a data transformation strategy and deep visual features through a 10-fold cross-validation. We also discuss the managerial implications of our research findings.

Skin lesion diagnosis is a key step for skin cancer screening, which requires high accuracy and interpretability. Though many computer-aided methods, especially deep learning methods, have made remarkable achievements in skin lesion diagnosis, their generalization and interpretability are still a challenge. To solve this issue, we propose an interpretability-based multimodal convolutional neural network (IM-CNN), which is a multiclass classification model with skin lesion images and metadata of patients as input for skin lesion diagnosis. The structure of IM-CNN consists of three main paths to deal with metadata, features extracted from segmented skin lesion with domain knowledge, and skin lesion images, respectively. We add interpretable visual modules to provide explanations for both images and metadata. In addition to area under the ROC curve (AUC), sensitivity, and specificity, we introduce a new indicator, an AUC curve with a sensitivity larger than 80% (AUC_SEN_80) for performance evaluation. Extensive experimental studies are conducted on the popular HAM10000 dataset, and the results indicate that the proposed model has overwhelming advantages compared with popular deep learning models, such as DenseNet, ResNet, and other state-of-the-art models for melanoma diagnosis. The proposed multimodal model also achieves on average 72% and 21% improvement in terms of sensitivity and AUC_SEN_80, respectively, compared with the single-modal model. The visual explanations can also help gain trust from dermatologists and realize man-machine collaborations, effectively reducing the limitation of black-box models in supporting medical decision making.

The COVID-19 pandemic has severely affected urban transport patterns, including the way residents travel. It is of great significance to predict the demand of urban ride-hailing for residents’ healthy travel, rational platform operation, and traffic control during the epidemic period. In this paper, we propose a deep learning model, called MOS-BiAtten, based on multi-head spatial attention mechanism and bidirectional attention mechanism for ride-hailing demand prediction. The model follows the encoder-decoder framework with a multi-output strategy for multi-steps prediction. The pre-predicted result and the historical demand data are extracted as two aspects of bidirectional attention flow, so as to further explore the complicated spatiotemporal correlations between the historical, present and future information. The proposed model is evaluated on the real-world dataset during COVID-19 in Beijing, and the experimental results demonstrate that MOS-BiAtten achieves a better performance compared with the state-of-art methods. Meanwhile, another dataset is used to verify the generalization performance of the model.

Recently, Shabtay considered a scheduling problem on a single serial-batching machine with rejection to minimize the dual criteria of total completion time and total rejection cost, where the number of jobs to be included in each batch is not restricted. He studied four variants of the problem: the first is to minimize the sum of the two criteria; the second and third are to minimize one criterion, subject to the other criterion not exceeding a given value; and the last is to find the Pareto-optimal solutions for the bicriterion problem. Shabtay provided an${O}$($\textit{n}^{\mathbf {5}}$) algorithm for the first variant and an${O}$(${n} ^{\mathbf {6}}$/$\boldsymbol {\varepsilon }^{\mathbf {2}}$) fully polynomial-time approximation scheme (FPTAS) for the fourth variant. In this paper, we provide an alternative${O}$(${n} ^{\mathbf {4}}$) algorithm to solve the first variant and an${O}$(${n} ^{\mathbf {5}}$/$\boldsymbol {\varepsilon }$) FPTAS for the fourth variant, which are more efficient than those developed by Shabtay from a theoretical perspective. However, when the size of each batch is bounded by a given number${b}~\boldsymbol {>}1$, the corresponding time complexities of our algorithms for the first and fourth variants reduce to${O}$(bn$^{\mathbf {3}}$) and${O}$(bn$^{\mathbf {4}}$/$\boldsymbol {\varepsilon }$), respectively.

There lies a geometric relationship (homography) between two frames in the video sequences captured by pan-tilt (PT) cameras due to their constrained movement, and the geometric relationship is valuable for reducing the spatial redundancy in video coding. In this paper, we propose a novel homography-based search (HBS) algorithm for block motion estimation in coding the sequences captured by PT cameras, which well utilizes the homography between two frames. In addition, adaptive thresholds are adopted in our method to classify different kinds of blocks. Compared with other traditional fast algorithms, the proposed HBS algorithm is proved more efficient for the sequences captured by PT cameras.

An adaptive threshold for early termination is proposed in this paper to enhance the cross octagonal search algorithm (COSA). In block motion estimation, search patterns have a very important impact on searching speed and distortion performance. COSA achieves almost the same visual quality with full-search algorithm while it takes much fewer search points than hybrid unsymmetrical-cross multi-hexagon-grid search (UMHexagonS) indeed. An adaptive threshold for early termination is introduced to COSA which avoids meaningless calculation after the searching point is good enough. The simulation results show that the proposed method reduces the motion estimation time by from 10.90% to 42.75% than UMHexagonS according to different types of sequences with negligible coding loss.