F-MPJ: scalable Java message-passing communications on parallel systems

This paper presents F-MPJ (Fast MPJ), a scalable and efficient Message-Passing in Java (MPJ) communication middleware for parallel computing. The increasing interest in Java as the programming language of the multi-core era demands scalable performance on hybrid architectures (with both shared and distributed memory spaces). However, current Java communication middleware lacks efficient communication support. F-MPJ boosts this situation by: (1) providing efficient non-blocking communication, which allows communication overlapping and thus scalable performance; (2) taking advantage of shared memory systems and high-performance networks through the use of our high-performance Java sockets implementation (named JFS, Java Fast Sockets); (3) avoiding the use of communication buffers; and (4) optimizing MPJ collective primitives. Thus, F-MPJ significantly improves the scalability of current MPJ implementations. A performance evaluation on an InfiniBand multi-core cluster has shown that F-MPJ communication primitives outperform representative MPJ libraries up to 60 times. Furthermore, the use of F-MPJ in communication-intensive MPJ codes has increased their performance up to seven times.     

Multiobjective hierarchical control architecture for greenhouse crop growth

The problem of determining the trajectories to control greenhouse crop growth has traditionally been solved by using constrained optimization or applying artificial intelligence techniques. The economic profit has been used as the main criterion in most research on optimization to obtain adequate climatic control setpoints for the crop growth. This paper addresses the problem of greenhouse crop growth through a hierarchical control architecture governed by a high-level multiobjective optimization approach, where the solution to this problem is to find reference trajectories for diurnal and nocturnal temperatures (climate-related setpoints) and electrical conductivity (fertirrigation-related setpoints). The objectives are to maximize profit, fruit quality, and water-use efficiency, these being currently fostered by international rules. Illustrative results selected from those obtained in an industrial greenhouse during the last eight years are shown and described.     

A Haar wavelet quasilinearization approach for numerical simulation of Burgers’ equation

In this paper, an efficient numerical scheme based on uniform Haar wavelets and the quasilinearization process is proposed for the numerical simulation of time dependent nonlinear Burgers’ equation. The equation has great importance in many physical problems such as fluid dynamics, turbulence, sound waves in a viscous medium etc. The Haar wavelet basis permits to enlarge the class of functions used so far in the collocation framework. More accurate solutions are obtained by wavelet decomposition in the form of a multi-resolution analysis of the function which represents a solution of boundary value problems. The accuracy of the proposed method is demonstrated by three test problems. The numerical results are compared with existing numerical solutions found in the literature. The use of the uniform Haar wavelet is found to be accurate, simple, fast, flexible, convenient and has small computation costs.     

Constrained unscented recursive estimator for nonlinear dynamic systems

Nonlinear constrained state estimation is an important task in performance monitoring, online optimization and control. There has been recent interest in developing estimators based on the idea of unscented transformation for constrained nonlinear systems. One of these approaches is the unscented recursive nonlinear dynamic data reconciliation (URNDDR) method. The URNDDR approach follows the traditional predictor-corrector framework. Constraints are handled in the prediction step through a projection algorithm and in the correction step through an optimization formulation. It has been shown that URNDDR produces very accurate estimates at the cost of computational expense. However, there are two issues that need to be addressed in the URNDDR framework: (i) URNDDR approach was primarily developed to handle bound constraints and needs to be enhanced to handle general nonlinear equality and inequality constraints, and (ii) computational concerns in the application of the URNDDR approach needs to be addressed. In this paper, a new estimation technique named constrained unscented recursive estimator (CURE) is proposed, which eliminates these disadvantages of URNDDR, while providing estimates with almost the same accuracy.     

Support and precursor effects on the preparation of new heterogenized Pt/Sn catalysts for the selective hydroformylation of 1-pentene

New heterogenized Pt/Sn catalysts selective for the hydroformylation of 1-pentene have been synthesized. The complex cis-[PtCl₂(PPh₃)₂] and the SnCl₂.2H₂O or SnC₂O₄ precursors have been anchored on silica-, magnesia- and alumina-carriers. X-ray photoelectron spectroscopy was used to determine the surface composition and the nature of the anchored species. The hydroformylation activity was found to depend on the type of support and tin precursor used. Only the silica supported catalysts were active in the hydroformylation reaction. Samples prepared from SnCl₂-2H₂O were 200-fold more active than those prepared from SnC₂O₄. Selectivity ton-hexanal of the silica-supported catalyst prepared from SnCl₂-2H₂O was as high as 94.4% at 39.2% conversion of 1-pentene.     

Network Discovery and Verification

Due to its fast, dynamic, and distributed growth process, it is hard to obtain an accurate map of the Internet. In many cases, such a map-representing the structure of the Internet as a graph with nodes and links-is a prerequisite when investigating properties of the Internet. A common way to obtain such maps is to make certain local measurements at a small subset of the nodes, and then to combine these in order to "discover" (an approximation of) the actual graph. Each of these measurements is potentially quite costly. It is thus a natural objective to minimize the number of measurements which still discover the whole graph. We formalize this problem as a combinatorial optimization problem and consider it for two different models characterized by different types of measurements. We give several upper and lower bounds on the competitive ratio (for the online network discovery problem) and the approximation ratio (for the offline network verification problem) in both models. Furthermore, for one of the two models, we compare four simple greedy strategies in an experimental analysis     

Orthographic Structure Versus Morphological Structure: Principles of Lexical Organization in a Given Language.

Most models of visual word recognition in alphabetic orthographies assume that words are lexically organized according to orthographic similarity. Support for this is provided by form-priming experiments that demonstrate robust facilitation when primes and targets share similar sequences of letters. The authors examined form-orthographic priming effects in Hebrew, Arabic, and English. Hebrew and Arabic have an alphabetic writing system but a Semitic morphological structure. Hebrew morphemic units are composed of noncontiguous phonemic (and letter) sequences in a given word. Results demonstrate that form-priming effects in Hebrew or Arabic are unreliable, whereas morphological priming effects with minimal letter overlap are robust. Hebrew bilingual subjects, by contrast, showed robust form-priming effects with English material, suggesting that Semitic words are lexically organized by morphological rather than orthographic principles. The authors conclude that morphology can constrain lexical organization even in alphabetic orthographies and that visual processing of words is first determined by morphological characteristics. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

考虑环保因素的最小费用电源规划研究

介绍电力部门降低污染物排放的必要性、可行性以及需要采取的一些措施，并介绍由泰国亚洲理工大学（AIT）开发的考虑环保因素的综合资源规划软件(IRPA)及其规划思路、所需数据和特点。对我国某省2003年～2017年的电力发展进行了规划研究。     

New Algorithms for Resource Reclaiming from Precedence Constrained Tasks in Multiprocessor Real-Time Systems

The scheduling of tasks in multiprocessor real-time systems has attracted many researchers in the recent past. Tasks in these systems have deadlines to be met, and most of the real-time scheduling algorithms use worst case computation times to schedule these tasks. Many resources will be left unused if the tasks are dispatched purely based on the schedule produced by these scheduling algorithms, since most of the tasks will take less time to execute than their respective worst case computation times. Resource reclaiming refers to the problem of reclaiming the resources left unused by a real-time task when it takes less time to execute than its worst case computation time. In this paper, we propose two algorithms to reclaim these resources from real-time tasks that are constrained by precedence relations and resource requirements, in shared memory multiprocessor systems. We introduce a notion called a restriction vector for each task which captures its resource and precedence constraints with other tasks. This will help not only in the efficient implementation of the algorithms, but also in obtaining an improvement in performance over the reclaiming algorithms proposed in earlier work [[2]]. We compare our resource reclaiming algorithms with the earlier algorithms and, by experimental studies, show that they reclaim more resources, thereby increasing the guarantee ratio (the ratio of the number of tasks guaranteed to meet their deadlines to the number of tasks that have arrived), which is the basic requirement of any resource reclaiming algorithm. From our simulation studies, we demonstrate that complex reclaiming algorithms with high reclaiming overheads do not lead to an improvement in the guarantee ratio.     

Semantic conflict resolution ontology (SCROL): an ontology for detecting and resolving data and schema-level semantic conflicts

Establishing semantic interoperability among heterogeneous information sources has been a critical issue in the database community for the past two decades. Despite the critical importance, current approaches to semantic interoperability of heterogeneous databases have not been sufficiently effective. We propose a common ontology called semantic conflict resolution ontology (SCROL) that addresses the inherent difficulties in the conventional approaches, i.e., federated schema and domain ontology approaches. SCROL provides a systematic method for automatically detecting and resolving various semantic conflicts in heterogeneous databases. SCROL provides a dynamic mechanism of comparing and manipulating contextual knowledge of each information source, which is useful in achieving semantic interoperability among heterogeneous databases. We show how SCROL is used for detecting and resolving semantic conflicts between semantically equivalent schema and data elements. In addition, we present evaluation results to show that SCROL can be successfully used to automate the process of identifying and resolving semantic conflicts.