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Printed circuit board (PCB) orthogonal packing problems derive from the massive circuit board production process in the electronic industry. In the early stage, it mainly relied on human experiences to make decisions on how to make the most of each sheet while meeting customers’ orders. Up till now, researchers in enterprises and academic circles are still trying to explore effective mathematical models and feasible optimization methods. In most cases, what PCB companies generally face is the orthogonal layout decision, which considers positioning each rectangle PCB piece (PCB-P) on a rectangle sheet board (panel) in an ideal way so that each panel has the minimal remains with the most PCB-Ps. In this paper, multi-round gradual packing methods (MGPMs) are proposed based on the idea of combinatorial optimization and dynamic programming. MGPMs include the depth priority-based method (MGPM-DP) and breadth priority-based branch-and-prune method (MGPM-BC). The former has the advantage of finding better solutions, while the latter has the advantage of consuming short computational time. Through extensive computational tests on real data from a PCB production enterprise in China, both MGPM-DP and MGPM-BC have achieved noticeable and satisfactory results compared with the simplex method, dynamic programming method, and a widely used industrial software (Yuanbo) in China.

ASLR and other defense measures have been deployed in modern computers against memory data leakage and control flow hijacking, which are two common vulnerability exploitation methods, but attackers can always bypass these security defense mechanisms to attack the computer. For the problem of the randomization strategy in ASLR technology is partial-oriented memory space. This paper presents a heap randomization strategy for the entire heap memory space. By changing the access order of memory pool, the strategy reduces the time consumption of memory region search, a random memory block allocation algorithm is proposed to increase the unpredictability of heap memory addresses and improve the security of computer systems. The randomization algorithm increases the variation interval of memory block gap by adding randomization parameters, then through the attack experiment and time performance experiment, the appropriate randomization parameters are determined to ensure the efficiency of the algorithm.

The graph optimization has become the mainstream technology to solve the problems of SLAM (simultaneous localization and mapping). The pose graph in the graph based SLAM is consisted with a series of nodes and edges that connect the adjacent or related poses. With the widespread use of mobile robots, the scale of pose graph has rapidly increased. Therefore, optimizing a large-scale pose graph is the bottleneck of application of graph based SLAM. In this paper, we propose an optimization method basing on the decomposition of pose graph, of which we have noticed the sparsity. With the extraction of the Single-chain and the Parallel-chain, the pose graph is decomposed into many small subgraphs. Compared with directly processing the original graph, the speed of calculation is accelerated by separately optimizing the subgraph, which is because the computational complexity is increasing exponentially with the increase of the graph’s scale. This method we proposed is very suitable for the current multi-threaded framework adopted in the mainstream SLAM, which separately calculate the subgraph decomposed by our method, rather than the original optimization requiring a large block of time in once may cause CPU obstruction. At the end of the paper, our algorithm is validated with the open source dataset of the mobile robot, of which the result illustrates our algorithm can reduce the one-time resource consumption and the time consumption of the calculation with the same map-constructing accuracy.

We propose a new approach to calculating the first passage time densities for Brownian motion crossing piecewise linear boundaries which can be discontinuous. Using this approach we obtain explicit formulas for the first passage densities and show that they are continuously differentiable except at the break points of the boundaries. Furthermore, these formulas can be used to approximate the first passage time distributions for general nonlinear boundaries. The numerical computation can be easily done by using the Monte Carlo integration, which is straightforward to implement. Some numerical examples are presented for illustration. This approach can be further extended to compute two-sided boundary crossing distributions.

Melatonin, as an indoleamine molecule, is involved in multiple physiological processes in plants. In this study, the effects of exogenous melatonin on plant growth, reactive oxygen metabolism and ion homeostasis in alkaline-treated tomato seedlings were investigated. Alkaline stress caused much lower level in maximum photochemistry efficiency of PSII (Fv/Fm), chlorophyll content, the activity of antioxidant enzymes and biomass accumulation of tomato plants, accompanied by the reactive oxygen species (ROS) burst, lipid peroxidation and membrane damage. The exogenous melatonin could increase tomato plants tolerance to sodic alkaline stress, and the effect depended on the concentration. In the experiment, treatment with 0.5 μM melatonin showed the strongest powerful effect on promoting tomato plant growth under alkaline stress and reducing oxidative stress via inducing activities of antioxidant enzymes and accumulations of ascorbic acid (AsA) and glutathione (GSH), which could be partly responsible for protecting chlorophyll from degradation and keeping higher Fv/Fm. Furthermore, we found that melatonin treatment decreased Na + content and increased K + content in tomato leaves under alkaline stress, which showed the ion homeostasis maintenance by melatonin also played important role in enhancing tomato tolerance to alkaline stress

Background: Pleural effusion is one of the complications of human non-small cell lung cancer (NSCLC). High mobility group box-1 protein (HMGB1) correlates highly with invasion and metastasis in multiple tumors. The aim of this study was to explore the clinical value of HMGB1 in NSCLC patients, and to investigate the role of HMGB1 in the development of pleural effusion. In addition, we also investigated the regulatory role of HMGB1 in the sensitivity of NSCLC cells to cisplatin. Methods: 46 NSCLC malignant pleural effusion (MPE) and 31 benign pleural effusion samples were quantitatively analyzed with Enzyme-Linked Immunosorbent Assay (ELISA) for cytokines, such as IL-1beta, IL-6, IL-8 and HMGB1. The HMGB1 expression in NSCLC tissues was examined with RT-qPCR and western blotting methods. Then the influence by HMGB1 on the chemosensitivity of lung cancer A549 cells was examined with MTT assay and colony forming assay for the A549 cells post the treatment with cisplatin or (and) HMGB1. Results: The results demonstrated that HMGB1 was up-regulated in the pleural effusion of NSCLC patients, along with the up-regulated levels of proinflammatory cytokines such as IL-6 and IL-8. And the up-regulation of HMGB1 was confirmed at both the mRNA and protein levels in the NSCLC tissues. Recombinant HMGB1 reduced the sensitivity of A549 cells to cisplatin in vitro. Conclusions: In conclusion, HMGB1 was up-regulated in the pleural effusion and tumor tissues of NSCLC patients. HMGB1 reduced the sensitivity of NSCLC A549 cells to cisplatin in vitro.

In order to adapt to the accelerative open tendency of collaborations between enterprises, this paper proposes the cross-management-domain social-level macro-governing mode for regulating and controlling the social-level visible macro-behaviors of the social individuals participating in collaborations. Then this mode is achieved by creating Social-level Macro-Governing methodology for cross-management-domain Service Collaboration processes, called SMGSC, which consists of two levels of standalone and complementary technologies: the social -- level collaboration process norm system represented as social dependence norm sets and the rational agents whose macro-behaviors in collaboration processes conform to norms. Since the rational agents forming dynamically collaboration relationships can make their macro-behaviors governed by social dependence norms, SMGSC not only can remove effectively the uncontrollability hindrance confronted with by open social activities, but also enables cross-management-domain collaborations to be implemented by uniting the centralized controls of social individuals for respective social activities. Therefore, this paper provides a brand-new system construction mode to promote the convenient development and large-scale deployment of service collaborations.

The present study was designed to determine the interactive effect of exogenous melatonin and nitric oxide (NO) on sodic alkaline stress mitigation in tomato seedlings. It was observed that exogenous melatonin treatment elevated NO levels in alkaline-stressed tomato roots. However, exogenous NO had little effects on melatonin levels. Importantly, melatonin-induced NO generation was accompanied by increased tolerance to alkaline stress. Chemical scavenging of NO reduced melatonin-induced alkaline stress tolerance and defense genes' expression. However, inhibition of melatonin biosynthesis had a little effect on NO-induced alkaline stress tolerance. These results strongly suggest that NO, acting as a downstream signal, is involved in the melatonin-induced tomato tolerance to alkaline stress. This process creates a new signaling pathway for improving stress tolerance in plant.

In order to adapt to the accelerative open tendency of collaborations between enterprises, this paper proposes a Social-level Norm-based methodology for Macro-Governing service collaboration processes, called SNMG, to regulate and control the social-level visible macro-behaviors of the social individuals participating in collaborations. SNMG not only can remove effectively the uncontrollability hindrance confronted with by open social activities, but also enables across-management-domain collaborations to be implemented by uniting the centralized controls of social individuals for respective social activities. Therefore, this paper provides a brand-new system construction mode to promote the development and large-scale deployment of service collaborations.

Cloud computing is a business model accessed by services and it integrates a variety of resources via specified methods through the internet, so it has excellent prospect. Agent technology has unmatched intelligence compared with other techniques, and have been widely used in various fields. To build Agent Cloud platform and provide it as a service is already a trend, but from IaaS (Infrastructure as a Service) to AaaS (Agent as a Service), it requires a lot of heavy and difficult work. Nowadays Agent software system's development and deployment is still using the traditional technology solutions that is already unable to quickly adapt to various characteristics of cloud computing. Therefore this paper proposes ADT (Agent Development Toolkit) to overcome the technical problems and further achieve a smooth transition from IaaS to AaaS, in this way Agent cloud platform could be constructed fast.

According to personalized service in teaching system, this paper proposes curriculum resources personalized recommendation algorithm based on semantic web technology. First, we get a collection of curriculum resources of interests in terms of the user evaluation and user browsing behavior, and then based on the relationship between the concept in the domain ontology, calculate semantic similarity between core concepts of different user evaluation, and finally according to the similarity decide the similarity of user preferences interests, find the nearest neighbors with similar interests, in order to achieve learning resources personalized recommendations. Combination of ontology model of “compile theory “course knowledge, we design and implement personalized learning recommendation system.