Vaccine development is one of the most promising fields, and multi-epitope vaccine, which does not need laborious culture processes, is an attractive alternative to classical vaccines with the advantage of safety, and efficiency. The rapid development of algorithms and the accumulation of immune data have facilitated the advancement of computer-aided vaccine design. Here we systemically reviewed the in silico data and algorithms resource, for different steps of computational vaccine design, including immunogen selection, epitope prediction, vaccine construction, optimization, and evaluation. The performance of different available tools on epitope prediction and immunogenicity evaluation was tested and compared on benchmark datasets. Finally, we discuss the future research direction for the construction of a multiepitope vaccine.

Typically, deep learning-based fault diagnosis models fail to fully utilize the potential information in large amounts of normal state data and encounter difficulties when learning from limited fault samples. To address these challenges, this study proposes an auxiliary contrastive learning framework designed for multi-sensor data. The framework incorporates auxiliary classifiers after each sensor-specific branch to enhance feature representation, and enables model pretraining using only normal condition data. In addition, a phased fine-tuning strategy is developed, which combines full-model fine-tuning with lightweight adapter tuning to improve the adaptability of the fine-tuning process. A novel multi-sensor data augmentation technique is also introduced to enrich the contrastive learning tasks by generating structurally diverse negative samples. By enabling the effective utilization of normal condition data in model training, the proposed framework offers a new perspective for fault diagnosis applications. Experimental results on three benchmark datasets demonstrate that the proposed method significantly improves the generalization capability of the pre-trained model. Furthermore, the phased fine-tuning strategy exhibits high adaptability to the target tasks. Compared to other data fusion methods, the proposed auxiliary contrastive learning framework achieves notable performance advantages.

The Profitable Tour Problem with Simultaneous Pickup and Delivery (PTPSPD) is a practical and challenging variant of the Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD), in which not all customers need to be served. This reflects real-world scenarios where fleet size is often limited and incurs significant costs. This study addresses the PTPSPD under realistic constraints and proposes an effective iterated local search algorithm that integrates complementary components to balance exploration and exploitation. A cluster-first, route-second heuristic is employed to generate high-quality initial solutions. The search process is intensified via a randomized variable neighborhood descent strategy. To enhance global exploration, a combination of random perturbation mechanisms and adaptive threshold acceptance criteria is employed. A dynamic tabu list further promotes diversification and prevents premature convergence. The proposed method is evaluated on a benchmark set of 117 PTPSPD instances involving 50 to 199 customers. Results highlight the effectiveness and robustness of the approach, establishing new best-known lower bounds for 64 instances. Additionally, the algorithm is tested on classical VRPSPD instances from the literature, further confirming its ability to consistently provide high-quality solutions within reasonable computational time.

This paper introduces a new designed modular tactile sensing dexterous hand for domestic service robots. This fully-actuated hand consists of 1 palm and 3 fingers, with embedded tactile sensors, motors and control boards. The palm and each finger have 2 degrees of freedom (DOFs). The modular design makes it easy to attach and detach the hand, even by inexperienced users. The tactile sensor unit with new structure can help to decrease sensor number and keep a good sensing ability. A series of experiments to test the sensor unit and evaluated the hand performance with an object set was performed in this paper. The results show that the sensor unit can provide precise sensing result and perceive continuous vibration data, and the hand has excellent grasp ability. In addition to its good performance, the hand features a cost of $500 USD with a scale of one hundred sets. This hand is affordable for researchers and for domestic service robots in the consumer market. In future research, this hand will be used to promote the robotic manipulation research based on visual and tactile data.

This paper addresses the integrated multiobjective production planning and decomposing (P&D) problem for mineral processing. A novel multiobjective 0-1 mixed integer nonlinear programming model is presented for the simultaneous P&D problem (O-model). In order to reduce the computational cost for solving O-model, a rolling horizon-based two-level decomposition approach is proposed to separate O-model into an upper level model (H-model) and a lower level model (L-model). An interactive partition (IP) and multiobjective gradient (MO-G)-based hybrid evolutionary multiobjective (EMO) algorithm named as IG-NSGA-II/IG-SPEA2, which takes the popular NSGA-II/SPEA2 as the basic EA, is proposed to solve both H-model and L-model, where an IP technique is designed to generate the efficient feasible combinational nodes, an ideal solution technique is provided for fathoming the infeasible nodes, an improved multiobjective gradient-based operator is developed to accelerate the evolution process in each selected node, and a cut with all continuous variables is constructed to exclude the previous feasible combination if it is not desired for the decision makers (DMs). The experimental results demonstrate that the presented two-level decomposition strategy can effectively integrate both levels. Moreover, the proposed hybrid method can effectively reduce the combinatorial space so as to concentrate the computing resource on the subspace of most interest, and can generate better feasible solutions than the pure EA in the full-space under computation time limits.

The production planning optimization for mineral processing is important for non-renewable raw mineral resource utilization. This paper presents a nonlinear multiobjective programming model for a mineral processing production planning (MPPP) for optimizing five production indices, including its iron concentrate output, the concentrate grade, the concentration ratio, the metal recovery, and the production cost. A gradient-based hybrid operator is proposed in two evolutionary algorithms named the gradient-based NSGA-II (G-NSGA-II) and the gradient-based SPEA2 (G-SPEA2) for MPPP optimization. The gradient-based operator of the proposed hybrid operator is normalized as a strictly convex cone combination of negative gradient direction of each objective, and is provided to move each selected point along some descent direction of the objective functions to the Pareto front, so as to reduce the invalid trial times of crossover and mutation. Two theorems are established to reveal a descent direction for the improvement of all objective functions. Experiments on standard test problems, namely ZDT 1-3, CONSTR, SRN, and TNK, have demonstrated that the proposed algorithms can improve the chance of minimizing all objectives compared to pure evolutionary algorithms in solving the multiobjective optimization problems with differentiable objective functions under short running time limitation. Computational experiments in MPPP application case have indicated that the proposed algorithms can achieve better production indices than those of NSGA-II, T-NSGA-FD, T-NSGA-SP, and SPEA2 in the case of small number of generations. Also, those experimental results show that the proposed hybrid operators have better performance than that of pure gradient-based operators in attaining either a broad distribution or maintaining much diversity of obtained non-dominated solutions.

Knowledge is considered as an enterprise's invisible assets. Surviving in today's highly competitive and ever expanding global economy increasingly raised requires for extended enterprises to stimulate the integration of knowledge management (KM) function into ERP systems for knowledge asset management. Due to the fact that both types of assets need to be properly managed, the integration of KM and ERP becomes a strategic initiative for providing competitive advantages to enterprises. This paper discusses how to deploy KM and ERP concurrently in the framework of enterprise information systems, with a discussion of the interaction of KM and ERP systems in systems perspectives.