Pelvic fracture reduction planning is clinically critical yet technically demanding due to the complex anatomical structure of pelvis and the topological discontinuities introduced by fractures. Existing computer-assisted planning approaches dominantly rely on shape-based models, overlooking the rich CT intensity information that is essential for accurate and patient-specific planning. To address this limitation, we propose SIRDiff, a novel framework that incorporates anatomical shape and CT intensity information to generate biomechanically plausible reference models for pelvic fracture reduction planning. SIRDiff comprises three key components: 1) the structure-aware diffusion model to reconstruct the global anatomical structure, 2) the topology-adaptive structural conditioning strategy that maps fracture landmarks into a healthy anatomical graph domain for robust structure guidance, and 3) the detail-preserved autoencoder to ensure the fine-grained image reconstruction from latent representations. Additionally, SIRDiff adopts a multi-task learning approach to jointly predict the reference CT image and corresponding bone segmentation map, which enhances its potential for clinical application and ensures better anatomical consistency. Despite being trained exclusively on synthetic fracture data, SIRDiff shows the strong generalizability to real clinical cases and consistently outperforms existing methods across multiple clinically relevant evaluation metrics, demonstrating its potential as a robust and deployable solution for pelvic fracture reduction planning.

During the past two decades or so, many researchers devoted themselves to avoiding the deadlocks of automated manufacturing systems (AMSs). Many deadlock control policies have been developed under the assumption that AMSs do not contain unreliable resources and assembly operations. This work focuses on the deadlock control of failure-prone AMS with assembly operations and a single unreliable resource. At first, an automata model is developed to characterize the studied AMS. Then, four properties that a robust deadlock control policy for the studied AMS should satisfy are proposed and rigorously formulated for the first time. Finally, a new robust deadlock control policy based on the modified Banker’s algorithms is proposed. Compared with existing control policies, only the proposed one can well satisfy the newly proposed properties, thus representing a significant contribution to the field of robust control of failure-prone manufacturing systems.

Manufacturers are pursuing energy-efficient production in response to the fluctuating energy price, growing global competition, rigorous international laws, and severe environmental crisis. This article proposes to boost the energy efficiency of production systems by controlling the production. It extends the existing energy-saving control research by presenting integrated modeling, analyzing, and controlling approaches. The work starts from the modeling of the production systems and establishes an analytical model to systematically quantify the production loss resulted from energy-saving control and the various disruptions. A dynamic control algorithm is proposed to reduce energy consumption and maintain desirable productivity. Simulation studies are utilized to demonstrate the application of the proposed method and verify its effectiveness. Note to Practitioners—The previous research indicates that it is possible to strategically turn off stations during production for energy saving. However, production systems are complex dynamic systems consisting of interconnected stations and supporting subsystems. Similar to station random failures, turning off stations for energy saving can severely jeopardize the production, and deviate the production from the desired target. Therefore, a quantitative method is established to calculate the production loss resulted from the energy-saving control and disruptions. The method is important to understand the real control cost. Based on the analysis, a dynamic control algorithm is formulated to balance the achieved control benefit and the production loss. It provides plant managers a useful tool to make energy-saving control decisions with a thorough understanding of production system dynamics. The presented research is established for serial batch production systems. The examples of batch stations include the refrigerator foaming equipment in refrigerator assembly lines and the vacuum oven in battery assembly lines. The model cannot be directly applied in serial-parallel production systems.

The real-time traffic control of railway networks imposes safety constraints and authorizes movements of trains. This work deals with it by focusing on conflict and deadlock avoidance problems. A place-timed Petri net model is developed for a railway network. Based on it, a conflict avoidance method is presented to ensure that safety principles in railway networks are respected. Also, a polynomial deadlock avoidance policy based on the Banker’s algorithm is proposed. It authorizes the movement of trains between block sections and stations. The proposed conflict avoidance method and deadlock avoidance policy together ensure a railway network to be conflict-free and deadlock-free. Experimental results show that they are effective and efficient.

This work investigates a scheduling problem of deadlock-prone flexible manufacturing systems modeled by place-timed Petri nets. It proposes an anytime branch and bound (AB&B) algorithm for it to minimize system makespan based on the branch tree of a net model and a highly permissive deadlock controller. The proposed algorithm searches a sequence of transitions in the branch tree that evolves the model from the initial marking to the final one. In order to prune the branch tree and increase search speed, this work develops two pruning rules, a lower bound of makespan, and a novel branching strategy. Their usage ensures AB&B’s high search efficiency. Experimental results demonstrate that the proposed algorithm surpasses the state-of-the-art ones.Note to Practitioners—In practice, scheduling is one of the most important issues for production managers to address. Existing approaches to deadlock-prone flexible manufacturing system (FMS) scheduling have multiparameters. Their settings can impact the scheduling results greatly. Since their turning is a challenging task, it is difficult to find the best parameter settings. This article proposes an anytime branch and bound (AB&B) algorithm for minimizing the makespan of deadlock-prone FMSs. It has only one parameter, i.e., maximal CPU time, which needs no turning. When it is highly tight, AB&B can output a feasible and decent schedule. If it is larger and larger, AB&B can output a better and better schedule. Comparison studies show that AB&B outclasses existing ones significantly. It is suitable for real-time scheduling cases in which satisfied schedules must be offered in a very short time.

Aggregation method has been widely utilized to evaluate the performance measures of production lines. However, traditional aggregation method (TAM) has low prediction accuracy in production lines with multiple bottlenecks, such as “inverted bowl” lines and “oscillatory” lines. Therefore, the root causes of the low prediction accuracy are first investigated. Extensive numerical studies indicate that A-units are one of the major causes, where a A-unit is defined as the subsystem between two consecutive bottlenecks. Then, an improved aggregation method (IAM) is proposed to improve the prediction accuracy of TAM. IAM is established by extending the traditional two-machine aggregation building blocks to general multimachine aggregation building blocks in A-units. Specifically, for a small-scale A-unit, an aggregation building block is established for all the machines and buffers in the A-unit. For a large-scale A-unit, a heuristic rule is proposed to divide the A-unit into several small-scale production line segments, where an aggregation building block is established for each segment. Numerical studies indicate that IAM can effectively improve the estimation accuracy of the aggregation method while maintaining a reasonable computational efficiency.

Identifying the interactions between proteins and Long non-coding RNAs (lncRNAs) can provide valuable clues for understanding the mechanisms and physiological functions of lncRNAs. In this work, we propose a computational method, PLIPCOM, which can accurately detect protein-lncRNA interactions by integrating two groups of network features. Low dimensional diffusion characteristics and HeteSim features are combined to build the protein-lncRNA interaction predictor using the Gradient Tree Boosting (GTB) algorithm. In a cross-validation experiment on the benchmark data set, our PLIPCOM method substantially outperformed previous state-of-the-art approaches in predicting the interactions between proteins and lncRNAs.

The state-of-the-art partial-duplicate image search systems reply heavily on the match of local features like SIFT. Independently matching local features across two images ignores the overall geometry structure and therefore may incur many false matches. To reduce such matches, several geometry verification methods have been proposed. This paper introduces a new geometry verification method named as Strong Geometry Consistency (SGC), which uses the orientation, scale and location information of the local feature points to accurately and quickly remove the false matches. We also propose a simple scale weighting (SW) strategy, which gives feature points with larger scales greater weights, based on the intuition that a larger-scale feature point tends to be more robust for image search as it occupies a larger area of an image. Extensive experiments performed on three popular datasets show that SGC significantly outperforms state-of-the-art geometry verification methods, and SW can further boost the performance with marginal additional computation.

This paper presents a fast and accurate pedestrian detection method. To find a balance between speed and accuracy, we propose a Multi-Pose Learning Boosted Integrable Features Pool (MPL-Boosted IFP) approach. Our method achieves high recall-rate while taking the speed-advantage of cascade-of-rejectors approach. We build different types of feature sets, in which features are extremely fast to compute by using integral image. These features are used for building a large number of candidate weak classifiers by using linear SVM. Finally, MPL-Boost method selects the best weak classifiers suited for detection and construct the rejector-based cascade detector. The experiment results show our method achieve better detection precision than HOG and HOG-LBP classifier, meanwhile, speed up these methods near 30 times.

We address the problem of pedestrian detection in still images. Current pedestrian detection systems are hard to improve both speed and accuracy simultaneously. In order to achieve a balance between speed and accuracy, we propose a novel MPL-Boosted cascade of weak FIK-SVM classifiers. Our method achieves high recall while taking the speed-advantage of cascade-of-rejectors approach. Each feature in our algorithm corresponds to a 66-D HOG-LBP feature vector that describe a block. The weak classifiers we use are the separating hyper-plane computed by using a FIK-SVM. We use MPL-Boost to select features from a large set of possible blocks. The integral image and convoluted trilinear interpolation are used for rapid calculation of block feature. For a 320×240 image, the system can process 16 frames per second with sparse scan, while defeat the accuracy level of existing methods.

We address the problem of pedestrian detection in still images. Many current pedestrian detection systems limit their performance by ignoring the underlying 3D geometric context in the image. We can estimate the geometric context by learning appearance-based method. We propose a novel context feature and local integrable features. These features are used for building many candidate weak classifiers by using linear SVM. Finally, MPL-Boost method selects the best weak classifiers suited for detection and construct the rejector-based cascade detector. We provide a thorough quantitative evaluation of our method on TUD-Brussels dataset and demonstrate that it outperforms the state-of-the-art pedestrian detector in recall rate, meanwhile, shows faster speed than other context incorporation method.