A Machine-Independent port of the MPD language run time system to NetBSD

SR (synchronizing resources) is a PASCAL - style language enhanced with constructs for concurrent programming developed at the University of Arizona in the late 1980s. MPD (presented in Gregory Andrews' book about Foundations of Multithreaded, Parallel, and Distributed Programming) is its successor, providing the same language primitives with a different, more C-style, syntax. The run-time system (in theory, identical, but not designed for sharing) of those languages provides the illusion of a multiprocessor machine on a single Unix-like system or a (local area) network of Unix-like machines. Chair V of the Computer Science Department of the University of Bonn is operating a laboratory for a practical course in parallel programming consisting of computing nodes running NetBSD/arm, normally used via PVM, MPI etc. We are considering to offer SR and MPD for this, too. As the original language distributions were only targeted at a few commercial Unix systems, some porting effort is needed. However, some of the porting effort of our earlier SR port should be reusable. The integrated POSIX threads support of NetBSD-2.0 and later allows us to use library primitives provided for NetBSD's phtread system to implement the primitives needed by the SR run-time system, thus implementing 13 target CPUs at once and automatically making use of SMP on VAX, Alpha, PowerPC, Sparc, 32-bit Intel and 64 bit AMD CPUs. We'll present some methods used for the impementation and compare some performance values to the traditional implementation.Comment: 6 page

AUTOMATED TIMETABLE GENERATOR USING PARTICLE SWARM OPTIMIZATION

The timetabling problem at universities is an NP - hard problem un der multiple constraints and limited resources. Thus a technique that can handle constraints is needed to optimize the problem. Thi s pape r focuses on Particle Swarm Optimization (PSO) for findin g optimal solution s t o th e proble m of course Timetabling a t a Punjabi U niversity Patiala . PSO is a promising scheme for solving NP - hard problems due to its fast convergence and fewer parameter settings . There are tw o objective s i n this . First provide a detaile d introductio n t o the topic of timetabling, Particle Swa rm Optimization thei r metho d an d thei r variations . Th e se c ond objective i s t o appl y the m t o th e proble m o f Course Timetabling . The proposed algorithm is tested using the timetabling data from Department of Computer Science, Punjabi University , Patiala

Report on the sixth blind test of organic crystal structure prediction methods.

The sixth blind test of organic crystal structure prediction (CSP) methods has been held, with five target systems: a small nearly rigid molecule, a polymorphic former drug candidate, a chloride salt hydrate, a co-crystal and a bulky flexible molecule. This blind test has seen substantial growth in the number of participants, with the broad range of prediction methods giving a unique insight into the state of the art in the field. Significant progress has been seen in treating flexible molecules, usage of hierarchical approaches to ranking structures, the application of density-functional approximations, and the establishment of new workflows and `best practices' for performing CSP calculations. All of the targets, apart from a single potentially disordered Z' = 2 polymorph of the drug candidate, were predicted by at least one submission. Despite many remaining challenges, it is clear that CSP methods are becoming more applicable to a wider range of real systems, including salts, hydrates and larger flexible molecules. The results also highlight the potential for CSP calculations to complement and augment experimental studies of organic solid forms.The organisers and participants are very grateful to the crystallographers who supplied the candidate structures: Dr. Peter Horton (XXII), Dr. Brian Samas (XXIII), Prof. Bruce Foxman (XXIV), and Prof. Kraig Wheeler (XXV and XXVI). We are also grateful to Dr. Emma Sharp and colleagues at Johnson Matthey (Pharmorphix) for the polymorph screening of XXVI, as well as numerous colleagues at the CCDC for assistance in organising the blind test. Submission 2: We acknowledge Dr. Oliver Korb for numerous useful discussions. Submission 3: The Day group acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work. We acknowledge funding from the EPSRC (grants EP/J01110X/1 and EP/K018132/1) and the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC through grant agreements n. 307358 (ERC-stG- 2012-ANGLE) and n. 321156 (ERC-AG-PE5-ROBOT). Submission 4: I am grateful to Mikhail Kuzminskii for calculations of molecular structures on Gaussian 98 program in the Institute of Organic Chemistry RAS. The Russian Foundation for Basic Research is acknowledged for financial support (14-03-01091). Submission 5: Toine Schreurs provided computer facilities and assistance. I am grateful to Matthew Habgood at AWE company for providing a travel grant. Submission 6: We would like to acknowledge support of this work by GlaxoSmithKline, Merck, and Vertex. Submission 7: The research was financially supported by the VIDI Research Program 700.10.427, which is financed by The Netherlands Organisation for Scientific Research (NWO), and the European Research Council (ERC-2010-StG, grant agreement n. 259510-KISMOL). We acknowledge the support of the Foundation for Fundamental Research on Matter (FOM). Supercomputer facilities were provided by the National Computing Facilities Foundation (NCF). Submission 8: Computer resources were provided by the Center for High Performance Computing at the University of Utah and the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant number ACI-1053575. MBF and GIP acknowledge the support from the University of Buenos Aires and the Argentinian Research Council. Submission 9: We thank Dr. Bouke van Eijck for his valuable advice on our predicted structure of XXV. We thank the promotion office for TUT programs on advanced simulation engineering (ADSIM), the leading program for training brain information architects (BRAIN), and the information and media center (IMC) at Toyohashi University of Technology for the use of the TUT supercomputer systems and application software. We also thank the ACCMS at Kyoto University for the use of their supercomputer. In addition, we wish to thank financial supports from Conflex Corp. and Ministry of Education, Culture, Sports, Science and Technology. Submission 12: We thank Leslie Leiserowitz from the Weizmann Institute of Science and Geoffrey Hutchinson from the University of Pittsburgh for helpful discussions. We thank Adam Scovel at the Argonne Leadership Computing Facility (ALCF) for technical support. Work at Tulane University was funded by the Louisiana Board of Regents Award # LEQSF(2014-17)-RD-A-10 “Toward Crystal Engineering from First Principles”, by the NSF award # EPS-1003897 “The Louisiana Alliance for Simulation-Guided Materials Applications (LA-SiGMA)”, and by the Tulane Committee on Research Summer Fellowship. Work at the Technical University of Munich was supported by the Solar Technologies Go Hybrid initiative of the State of Bavaria, Germany. Computer time was provided by the Argonne Leadership Computing Facility (ALCF), which is supported by the Office of Science of the U.S. Department of Energy under contract DE-AC02-06CH11357. Submission 13: This work would not have been possible without funding from Khalifa University’s College of Engineering. I would like to acknowledge Prof. Robert Bennell and Prof. Bayan Sharif for supporting me in acquiring the resources needed to carry out this research. Dr. Louise Price is thanked for her guidance on the use of DMACRYS and NEIGHCRYS during the course of this research. She is also thanked for useful discussions and numerous e-mail exchanges concerning the blind test. Prof. Sarah Price is acknowledged for her support and guidance over many years and for providing access to DMACRYS and NEIGHCRYS. Submission 15: The work was supported by the United Kingdom’s Engineering and Physical Sciences Research Council (EPSRC) (EP/J003840/1, EP/J014958/1) and was made possible through access to computational resources and support from the High Performance Computing Cluster at Imperial College London. We are grateful to Professor Sarah L. Price for supplying the DMACRYS code for use within CrystalOptimizer, and to her and her research group for support with DMACRYS and feedback on CrystalPredictor and CrystalOptimizer. Submission 16: R. J. N. acknowledges financial support from the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. [EP/J017639/1]. R. J. N. and C. J. P. acknowledge use of the Archer facilities of the U.K.’s national high-performance computing service (for which access was obtained via the UKCP consortium [EP/K014560/1]). C. J. P. also acknowledges a Leadership Fellowship Grant [EP/K013688/1]. B. M. acknowledges Robinson College, Cambridge, and the Cambridge Philosophical Society for a Henslow Research Fellowship. Submission 17: The work at the University of Delaware was supported by the Army Research Office under Grant W911NF-13-1- 0387 and by the National Science Foundation Grant CHE-1152899. The work at the University of Silesia was supported by the Polish National Science Centre Grant No. DEC-2012/05/B/ST4/00086. Submission 18: We would like to thank Constantinos Pantelides, Claire Adjiman and Isaac Sugden of Imperial College for their support of our use of CrystalPredictor and CrystalOptimizer in this and Submission 19. The CSP work of the group is supported by EPSRC, though grant ESPRC EP/K039229/1, and Eli Lilly. The PhD students support: RKH by a joint UCL Max-Planck Society Magdeburg Impact studentship, REW by a UCL Impact studentship; LI by the Cambridge Crystallographic Data Centre and the M3S Centre for Doctoral Training (EPSRC EP/G036675/1). Submission 19: The potential generation work at the University of Delaware was supported by the Army Research Office under Grant W911NF-13-1-0387 and by the National Science Foundation Grant CHE-1152899. Submission 20: The work at New York University was supported, in part, by the U.S. Army Research Laboratory and the U.S. Army Research Office under contract/grant number W911NF-13-1-0387 (MET and LV) and, in part, by the Materials Research Science and Engineering Center (MRSEC) program of the National Science Foundation under Award Number DMR-1420073 (MET and ES). The work at the University of Delaware was supported by the U.S. Army Research Laboratory and the U.S. Army Research Office under contract/grant number W911NF-13-1- 0387 and by the National Science Foundation Grant CHE-1152899. Submission 21: We thank the National Science Foundation (DMR-1231586), the Government of Russian Federation (Grant No. 14.A12.31.0003), the Foreign Talents Introduction and Academic Exchange Program (No. B08040) and the Russian Science Foundation, project no. 14-43-00052, base organization Photochemistry Center of the Russian Academy of Sciences. Calculations were performed on the Rurik supercomputer at Moscow Institute of Physics and Technology. Submission 22: The computational results presented have been achieved in part using the Vienna Scientific Cluster (VSC). Submission 24: The potential generation work at the University of Delaware was supported by the Army Research Office under Grant W911NF-13-1-0387 and by the National Science Foundation Grant CHE-1152899. Submission 25: J.H. and A.T. acknowledge the support from the Deutsche Forschungsgemeinschaft under the program DFG-SPP 1807. H-Y.K., R.A.D., and R.C. acknowledge support from the Department of Energy (DOE) under Grant Nos. DE-SC0008626. This research used resources of the Argonne Leadership Computing Facility at Argonne National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357. This research used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DEAC02-05CH11231. Additional computational resources were provided by the Terascale Infrastructure for Groundbreaking Research in Science and Engineering (TIGRESS) High Performance Computing Center and Visualization Laboratory at Princeton University.This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1107/S2052520616007447

Teaching Electronics and Programming in Norwegian Schools Using the air:bit Sensor Kit

We describe lessons learned from using the air:bit project to introduce more than 150 students in the Norwegian upper secondary school to computer programming, engineering and environmental sciences. In the air:bit project, students build and code a portable air quality sensor kits, and use their air:bit to collect data to investigate patterns in air quality in their local environment. When the project ended students had collected more than 400,000 measurements with their air:bit kits, and could describe local patterns in air quality. Students participate in all parts of the project, from soldering components and programming the sensors, to analyzing the air quality measurements. We conducted a survey after the project and describe our lessons learned from the project. The results show that the project successfully taught the students fundamental concepts in computer programming, electronics, and the scientific method. In addition, all the participating teachers reported that their students had showed good learning outcomes

An analysis for more equitable revenue and expenditure allocations within Lingnan College

From the triennial 1995-98, the funds allocated from University Grants Committee to universities have decreased. In order to use the limited resources effectively, and to manage their revenue and costs efficiently, universities have to set up a better budgeting system. Therefore, the eight universities in Hong Kong are proposing the Revenue Center Management (RCM) instead of the current budgeting system. The purposes of this project focuses on the analyses of the current budgeting system adopted at Lingnan College, and the proposed RCM budgeting system

Using Technology to Enhance Pre-Service Teacher Preparation

Use of the internet to deliver a portion of the content in an introductory science, education, and technology methods course for pre-service teachers provides an opportunity for a much needed introduction to basic computer literacy. A web page was developed for use in conjunction with the math, science, and technology educational methods courses at Brooklyn College. Students are introduced to this page as a group in the computer lab, and work in small groups with more experienced students serving as mentors to other students. The Brooklyn College Science Education Webpage is designed as a simple jump page with links to various resources for science education. It serves as a starting point to expose pre-service teachers to a wide range of resources available to them on the world wide web and in the real world. Students use their internet research skills in open-ended assignments throughout the semester. The web page continues to serve as a resource for students in the next courses in the math and science education sequence. The Brooklyn College Science Education Webpage helps education graduates to begin their teaching better prepared to use technology in the classroom

Understanding Occupational and Skill Demand in New Jersey's Finance Industry

The finance industry in New Jersey employs over 200,000 people. Many more workers benefit from the state's proximity to the finance industry in New York City. Jobs in the industry are evolving rapidly in response to national and global trends, such as deregulation, increasingly complex laws, and new technologies. As jobs change, skill requirements for both entry-level and incumbent workers increase. This report summarizes the skill, knowledge, and educational requirements of key finance occupations and identifies strategies for meeting the workforce challenges facing the industry