With the rapid development of e-commerce, logistics industry becomes a crucial component in the e-commercial ecological chain. Impelled by both economical and environmental benefit, logistics ...companies demand automated tools more urgently than ever. In this paper, a dynamic logistic dispatching system is proposed. The underlying model of the dispatching system is the dynamic vehicle routing problem which allows new orders being received as the working day progress. With this feature, the system becomes more practical than the systems with traditional static vehicle routing models, but is also more challenging as the vehicles must be scheduled in a dynamic way. The core of the system is a specially designed set-based particle swarm optimization algorithm. According to the characteristic of the problem, a new encoding scheme is defined by set and possibility, and a local refinement method is designed to accelerate the convergence speed of the algorithm. In addition, two more techniques: 1) region partition and 2) archive strategy are incorporated in the dispatching system to reduce the complexity of the problem and to facilitate the optimization process, helping the dispatcher control the vehicles in real time. The proposed system is tested on various benchmarks with different scales. Experimental results show that the proposed dispatching system is effective.
A 3-D integration technology using bumpless stacking based on a new bottom-up Cu electroplating method without backside Cu chemical-mechanical planarization removal is presented in this paper. The ...approach successfully achieves via plating without thick Cu overburden by probing unique bottom electrodes for different I/O ports of TSV without additional steps in the conventional processes. The concepts and fabrication processes are described in detail. The results obtained from through-silicon via (TSV) daisy chains show excellent electrical characteristics and good reliability in leakage current measurement. The proposed approach therefore has potential for low-cost via-last 3-D integration.
Density clustering has shown advantages over other types of clustering methods for processing arbitrarily shaped datasets. In recent years, extensive research efforts has been made on the ...improvements of DBSCAN or the algorithms incorporating the concept of density peaks. However, these previous studies remain the problems of being sensitive to the parameter settings, and some of them will stuck in weak results when encountering the situations of varying-density distributions. To overcome these issues, we propose an evolution framework named EvoS&R that evolves multiple seeds and the corresponding radii for varying-density data clustering. Compared with the traditional methods, EvoS&R handles the parameter tuning and multi-density fitting problems in an integrated and straightforward manner. Note that, however, the underlying task in EvoS&R is a mixed-variable optimization problem that is challenging in nature. We specifically design a hybrid encoding differential evolution algorithm with novel encoding, mutation, etc., to solve the optimization problem efficiently. Extensive experiments on density-based datasets shows that our algorithm outperforms the other state-of-the-arts in most cases, which validates the effectiveness of the proposed method.
Adjuvant gefitinib therapy prolonged disease-free survival in patients with resected early-stage EGFR-mutation positive NSCLC in the ADJUVANT study (CTONG 1104). However, treatment failure patterns ...after gefitinib therapy are less well characterized.
Overall, 222 stage N1–N2, EGFR-mutant NSCLC patients received gefitinib or vinorelbine plus cisplatin (VP) treatment. Tumor recurrences or metastases occurring during follow-up were defined as treatment failure; sites and data of first treatment failure were recorded. A post hoc analysis of treatment failure patterns which was estimated by Kaplan-Meier and hazard rate curves in modified intention-to-treat patients was conducted.
There were 114 recurrences and 10 deaths before recurrence across 124 progression events. Spatial distribution analysis showed that the first metastasis site was most frequently the central nervous system in the gefitinib group (29 of 106 27.4%), extracranial metastases were most frequent in the VP group (32 of 87 36.8%). Temporal distribution analysis showed lower tumor recurrence with gefitinib than with VP 0 to 21 months post-surgery. However, recurrence with gefitinib showed a constant rate of increase 12 months post-surgery. The first peak of extracranial metastasis appeared during 9 to 15 months with VP and 24 to 30 months with gefitinib. The highest peak for central nervous system metastases post-surgery occurred after 12 to 18 months with VP and 24 to 36 months with gefitinib.
Adjuvant gefitinib showed advantages over VP chemotherapy in treatment failure patterns especially in extracranial metastasis. Adjuvant tyrosine kinas inhibitors may be considered as a treatment option in resected stage N1–N2 EGFR-mutant NSCLC but longer duration should be explored.
Virtual network embedding (VNE) is a key technique for flexible sharing of physical resources in the modern Internet. As the VNE problem is nondeterministic polynomial hard, natural inspired ...population-based algorithms have been increasingly considered as promising approaches. But as the VNE problem has two tightly related tasks: node mapping and link mapping, it remains challenging for metaheuristics to deal with both tasks coordinately and effectively. This paper proposes a constructive particle swarm optimizer for virtual network embedding (CPSO-VNE). CPSO-VNE uses discrete vectors and matrices with probabilities to encode positions and velocities on VNE search space, respectively. What makes CPSO-VNE different from previous works is the step-by-step solution construction scheme introduced in CPSO-VNE. In this scheme, each node is mapped along with the mapping of its adjacent virtual links. In this way, node and link mappings are coordinated in one stage. In addition, with this step-by-step construction scheme, path information of networks can be introduced as heuristic information to guide the search, which can further improve performance. The proposed method is tested on the scenarios with a single VN request and with a set of online VN requests. The simulation results show that the proposed approach is promising.
Collective decision-making problems consisting of individual decisions are commonly seen in social applications. In this article, the vehicle energy station distribution problem (VESDP) is ...considered, which is modeled as a network-based collective decision-making problem fulfilling consumers' requirements by arranging the distribution of energy stations rationally. This problem involves the game among the government and energy station investors. The government intends to maximize the satisfaction of both gas and electric vehicle (EV) customers through policy guidance, while investors aim to maximize their own profits. To solve this problem, we propose an individual evolutionary game model guided by global evolutionary optimization with the following three features. From the individual perspective, we use a network-based evolutionary game with a confidence mechanism to describe the behavior of investors. From the global perspective, we design a genetic algorithm to find out the global-optimized program, which considers the satisfaction of all customers. To heal the divergence between these two perspectives, we design a policy formulation method for the government to motivate selfish investors to adopt strategies in accordance with the overall interests of all customers by using subsidies and taxation. Experiments are performed on both square grid and real-world networks. Experimental results demonstrate the effectiveness of the proposed model.
Path planning is one of the most important problems in the development of autonomous underwater vehicles (AUVs). In some common AUV missions, e.g., wreckage search for rescue, an AUV is often ...required to traverse multiple targets in a complex environment with dense obstacles. In such case, the AUV path planning problem becomes even more challenging. In order to address the problem, this paper develops a two-layer algorithm, namely ACO-A*, by combining the ant colony optimization (ACO) with the A* search. Once a mission with a set of arbitrary targets is assigned, ACO is responsible to determine the traveling order of targets. But, prior to ACO, a cost graph indicating the necessary traveling costs among targets must be quickly established to facilitate traveling order evaluation. For this purpose, a coarse-grained modeling with a representative-based estimation (RBE) strategy is proposed. Following the order obtained by ACO, targets will be traversed one by one and the pairwise path planning to reach each target can be performed during vehicle driving. To deal with the dense obstacles, A* is adopted to plan paths based on a fine-grained modeling and an admissible heuristic function is designed for A* to guarantee its optimality. Experiments on both synthetic and realistic scenarios have been designed to validate the efficiency of the proposed ACO-A*, as well as the effectiveness of RBE and the necessity of A*.
Mechanism-driven models based on transmission dynamics and statistic models driven by public health data are two main methods for simulating and predicting emerging infectious diseases. In this ...paper, we intend to combine these two methods to develop a more comprehensive model for the simulation and prediction of emerging infectious diseases. First, we combine a standard epidemic dynamic, the susceptible–exposed–infected–recovered (SEIR) model with population migration. This model can provide a biological spread process for emerging infectious diseases. Second, to determine suitable parameters for the model, we propose a data-driven approach, in which the public health data and population migration data are assembled. Moreover, an objective function is defined to minimize the error based on these data. Third, based on the proposed model, we further develop a swarm-optimizer-assisted simulation and prediction method, which contains two modules. In the first module, we use a level-based learning swarm optimizer to optimize the parameters required in the epidemic mechanism. In the second module, the optimized parameters are used to predicate the spread of emerging infectious diseases. Finally, various experiments are conducted to validate the effectiveness of the proposed model and method.
Nine undescribed phloroglucinol derivatives (dryatraols A-I) with five different backbones and three known dimeric acylphloroglucinols were isolated from the rhizome of Dryopteris atrata (Wall. Ex ...Kunze) Ching (Dryopteridaceae). Dryatraol A contains an unprecedented carbon skeleton—a butyrylphloroglucinol and a rulepidanol-type sesquiterpene are linked via a furan ring to form a 6/5/6/6 ring system. Dryatraols B and C are the first examples of monomeric phloroglucinols coupled with the aristolane-type sesquiterpene through the C–C bond. Dryatraol D features a rare spiro benzofuran-2′,5″-furan backbone. Dryatraols E-I are five undescribed adducts with a butyrylphloroglucinol or filicinic acid incorporated into the germacrene-type sesquiterpene via a pyran ring. These undescribed structures were determined by comprehensively analysing the spectroscopic data, X-ray diffraction results, and electronic circular dichroism calculations. The result of in vitro antiviral activity evaluation indicated that dryatraol C displayed the strongest antiviral effect against both respiratory syncytial virus and influenza A virus (H1N1), with IC50 values of 11.9 μM and 5.5 μM, respectively. Dryatraols F–H exhibited considerable inhibitory activity against herpes simplex virus type 1 (HSV-1), with IC50 values ranging from 2.6 to 6.3 μM. Analysis of the inhibitory mechanism using a time-of-addition assay revealed that dryatraol G may inhibit the replication of HSV-1 by interfering with the late stage of the viral life cycle.
Nine undescribed phloroglucinol derivatives were isolated from Dryopteris atrata, and some of them displayed significant anti-HSV-1 and anti-H1N1 activities. Time-of-addition test indicated that dryatraol G might inhibit the late stage of HSV-1 replication. Display omitted
•Nine undescribed phloroglucinol derivatives were isolated from Dryopteris atrata.•Dryatraol A represents the first example of an acylphloroglucinol-rulepidanol adduct.•Dryatraol D possesses a rare spiro benzofuran-2′,5″-furan backbone.•Most isolates were evaluated for antiviral activities against three common viruses.•A few isolates showed comparable antiviral effect to the positive controls.
Taking the advantage of estimation of distribution algorithms (EDAs) in preserving high diversity, this paper proposes a multimodal EDA. Integrated with clustering strategies for crowding and ...speciation, two versions of this algorithm are developed, which operate at the niche level. Then these two algorithms are equipped with three distinctive techniques: 1) a dynamic cluster sizing strategy; 2) an alternative utilization of Gaussian and Cauchy distributions to generate offspring; and 3) an adaptive local search. The dynamic cluster sizing affords a potential balance between exploration and exploitation and reduces the sensitivity to the cluster size in the niching methods. Taking advantages of Gaussian and Cauchy distributions, we generate the offspring at the niche level through alternatively using these two distributions. Such utilization can also potentially offer a balance between exploration and exploitation. Further, solution accuracy is enhanced through a new local search scheme probabilistically conducted around seeds of niches with probabilities determined self-adaptively according to fitness values of these seeds. Extensive experiments conducted on 20 benchmark multimodal problems confirm that both algorithms can achieve competitive performance compared with several state-of-the-art multimodal algorithms, which is supported by nonparametric tests. Especially, the proposed algorithms are very promising for complex problems with many local optima.
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