156-110 download - Check Point Certified Security Principles Associate Updated: 2024 | ||||||||
Pass4sure 156-110 exam braindumps with dump questions and practice test. | ||||||||
|
||||||||
Exam Code: 156-110 Check Point Certified Security Principles Associate download January 2024 by Killexams.com team | ||||||||
Check Point Certified Security Principles Associate CheckPoint Principles download | ||||||||
Other CheckPoint exams156-110 Check Point Certified Security Principles AssociateCCSPA Check Point Certified Security Principles Associate CCSPA 156-315-80 Check Point Certified Security Expert - R80 (156-315.80) 156-585 CheckPoint Certified Troubleshooting Expert 156-315.81 Check Point Certified Security Expert R81 156-560 Check Point Certified Cloud Specialist (CCCS) 156-587 Check Point Certified Troubleshooting Expert (CCTE) - R81.20 156-115.80 Check Point Certified Security Master - R80 (CCSM) 156-915.80.10 Certified Security Expert ? R80.10 (CCSE) 156-315.81.20 Certified Security Expert ? R81.20 (CCSE) | ||||||||
Our 156-110 exam prep material gives all of you that you have to pass 156-110 exam. Our 156-110 156-110 dumps contains questions that are precisely same real 156-110 exam questions. They at killexams guarantees your accomplishment in 156-110 exam with their 156-110 braindumps. | ||||||||
156-110 Dumps 156-110 Braindumps 156-110 Real Questions 156-110 Practice Test 156-110 dumps free CheckPoint 156-110 Check Point Certified Security Principles Associate http://killexams.com/pass4sure/exam-detail/156-110 Question: 88 Why should the number of services on a server be limited to required services? A. Every open service represents a potential vulnerability. B. Closed systems require special connectivity services. C. Running extra services makes machines more efficient. D. All services are inherently stable and secure. E. Additional services make machines more secure. Answer: A Question: 89 Which of the following tests provides testing teams some information about hosts or networks? A. Partial-knowledge test B. Full-knowledge test C. Zero-knowledge test Answer: A Question: 90 _______ can mimic the symptoms of a denial-of-service attack, and the resulting loss in productivity can be no less devastating to an organization. A. ICMP traffic B. Peak traffic C. Fragmented packets D. Insufficient bandwidth E. Burst traffic Answer: D Question: 91 Which of the following is the MOST important consideration, when developing security- awareness training materials? A. Training material should be accessible and attractive. B. Delivery mechanisms should allow easy development of additional materials, to complement core material. C. Security-awareness training materials should never contradict an organizational security policy. D. Appropriate language should be used to facilitate localization, should training materials require translation. E. Written documentation should be archived, in case of disaster. Answer: C Question: 92 To comply with the secure design principle of fail-safe defaults, what must a system do if it receives an instruction it does not understand? The system should: A. send the instruction to a peer server, to see if the peer can execute. B. not attempt to execute the instruction. C. close the connection, and refuse all further traffic from the originator. D. not launch its debugging features, and attempt to resolve the instruction. E. search for a close match in the instruction set it understands. Answer: B Question: 93 Which of the following statements about the maintenance and review of information security policies is NOT true? A. The review and maintenance of security policies should be tied to the performance evaluations of accountable individuals. B. Review requirements should be included in the security policies themselves. C. When business requirements change, security policies should be reviewed to confirm that policies reflect the new business requirements. D. Functional users and information custodians are ultimately responsible for the accuracy and relevance of information security policies. E. In the absence of changes to business requirements and processes, information-security policy reviews should be annual. Answer: D Question: 94 What is mandatory sign-on? An authentication method that: A. uses smart cards, hardware tokens, and biometrics to authenticate users; also known as three-factor authentication B. requires the use of one-time passwords, so users authenticate only once, with a given set of credentials C. requires users to re-authenticate at each server and access control D. stores user credentials locally, so that users need only authenticate the first time a local machine is used E. allows users to authenticate once, and then uses tokens or other credentials to manage subsequent authentication attempts Answer: C Question: 95 One individual is selected from each department, to attend a security-awareness course. Each person returns to his department, delivering the course to the remainder of the department. After training is complete, each person acts as a peer coach. Which type of training is this? A. On-line training B. Formal classroom training C. Train-the-mentor training D. Alternating-facilitator training E. Self-paced training Answer: C Question: 96 Which of the following is a cost-effective solution for securely transmitting data between remote offices? A. Standard e-mail B. Fax machine C. Virtual private network D. Bonded courier E. Telephone Answer: C Question: 97 Which of the following is MOST likely to cause management to view a security-needs proposal as invalid? A. Real-world examples B. Exaggeration C. Ranked threats D. Quantified risks E. Temperate manner Answer: B Question: 98 A(n) ________________ is a one-way mathematical function that maps variable values into smaller values of a fixed length. A. Symmetric key B. Algorithm C. Back door D. Hash function E. Integrity Answer: D Question: 99 INFOSEC professionals are concerned about providing due care and due diligence. With whom should they consult, when protecting information assets? A. Law enforcement in their region B. Senior management, particularly business-unit owners C. IETF enforcement officials D. Other INFOSEC professionals E. Their organizations’ legal experts Answer: E Question: 100 How do virtual corporations maintain confidentiality? A. Encryption B. Checksum C. Data hashes D. Redundant servers E. Security by obscurity Answer: A Question: 101 All of the following are possible configurations for a corporate intranet, EXCEPT: A. Value-added network B. Wide-area network C. Campus-area network D. Metropolitan-area network E. Local-area network Answer: A Question: 102 Which of the following is NOT an auditing function that should be performed regularly? A. Reviewing IDS alerts B. Reviewing performance logs C. Reviewing IDS logs D. Reviewing audit logs E. Reviewing system logs Answer: B Question: 103 The items listed below are examples of ___________________ controls. *Procedures and policies *Employee security-awareness training *Employee background checks *Increasing management security awareness A. Technical B. Administrative C. Role-based D. Mandatory E. Physical Answer: B Question: 104 Digital signatures are typically provided by a ____________________, where a third party verifies a key’s authenticity. A. Network firewall B. Security administrator C. Domain controller D. Certificate Authority E. Hash function Answer: D For More exams visit https://killexams.com/vendors-exam-list Kill your exam at First Attempt....Guaranteed! | ||||||||
AuthorChristine Jonick, Ed.D. ISBN978-1-940771-15-1 Print Version$32.99 Digital VersionFree The University of North Georgia Press and Affordable Learning Georgia bring you Principles of Financial Accounting. Well-written and straightforward, Principles of Financial Accounting is a needed contribution to open source pedagogy in the business education world. Written in order to directly meet the needs of her students, this textbook developed from Dr. Christine Jonick’s years of teaching and commitment to effective pedagogy. Features:
This textbook is an Open Education Resource. It can be reused, remixed, and reedited freely without seeking permission. Christine Jonick, Ed.D., is a professor of Accounting in the Mike Cottrell College of Business at the University of North Georgia. She has been with UNG for more than 25 years and received the Excellence in Online Teaching Award from UNG in 2016. Dr. Jonick serves as the chairperson of the American Accounting Association SE Member Engagement Committee and president and board of directors member of the Georgia Association of Accounting Educators. Establishing Connection... These files are provided to help users test their download speeds from their servers. You can also run a speed test however downloading files may be useful if you want to do so from different tools. Please be aware that downloading these files will count towards your download usage allowances imposed by your broadband provider and the large files may use up a large proportion of this if you only have a small allowance (1GB - 3GB for example). They suggest only testing the large files if you have a connection speed faster than 10 Mbps. Click the file you want to download to start the download process. If the download does not start you may have to right click on the size and select "Save Target As”. These files will automatically use IPv6 if available, but you can select the IPv4 or IPv6 links to force it as required. NOTE: They provide these download files primarily for UK broadband users; although they do not prohibit their use by others, they do not allow scripted/automated download of these files. Our systems routinely block repetitive attempts which they believe are automated or abusive. If you get an 'unauthorised' error message, you can contact us (please include your IP address when contacting us). Very Large File (1GB) IPv4 Port: 80, 81, 8080 70 mins @ 2 Mbps Large File (512MB) IPv4 Port: 80, 81, 8080 35 mins @ 2 Mbps Large File (200MB) IPv4 Port: 80, 81, 8080 14 mins @ 2 Mbps Medium File (100MB) IPv4 Port: 80, 81, 8080 8 mins @ 2 Mbps Medium File (50MB) IPv4 Port: 80, 81, 8080 4 mins @ 2 Mbps Small File (20MB) IPv4 Port: 80, 81, 8080 80 secs @ 2 Mbps Small File (10MB) IPv4 Port: 80, 81, 8080 40 secs @ 2 Mbps Extra Small File (5MB) IPv4 Port: 80, 81, 8080 20 secs @ 2 Mbps MB = Megabyte; GB = Gigabyte; (there are 8 bits in a Byte) These files are made of random data, and although listed as zip files, will appear to be corrupt if you try and open them. MD5SUMS are available for these files. We are The Associated Press. They have a long-standing role setting the industry standard for ethics in journalism. It is their job — more than ever before — to report the news accurately and honestly. Editor’s note: The following article is an op-ed, and the views expressed are the author’s own. Read more opinions on theGrio. From Dec. 26 to Jan. 1, many Black families across the United States will celebrate Kwanzaa, which means “first fruits” in Swahili or the agricultural harvest festivals that are found throughout Africa. Growing up, my family didn’t celebrate Kwanzaa or many holidays for that matter. But over time, and especially in the last few years through my work with Black-led nonprofits, foundations and advocacy organizations, I have come to understand Kwanzaa’s importance as a way to recognize the strength of Black communities despite systemic pressures meant to break us. Stemming from the Black Power Movement and founded in 1966 by educator and activist Maulana Karenga, Kwanzaa is a time to honor Black people and celebrate their contributions, heritage and culture, while acknowledging their shared struggles and their unwavering efforts to overcome them. The holiday encourages people to honor seven principles: unity, self-determination, collective work and responsibility, cooperative economics, purpose, creativity and faith. Nearly 60 years since its creation the meaning and importance of Kwanzaa continue to resonate. Despite achieving significant progress, Black communities continue to combat systems of oppression, institutional racism, and systemic inequities. These inequities may be most apparent in the racial wealth gap. As a result of centuries of systemic oppression in housing, their education systems and labor force, and insufficient access to capital and other business opportunities, Black people have faced significant barriers to building wealth. In 2022, the typical white family had about six times as much wealth as the typical Black family. This is not just a problem for Black communities but the entire economy. Over the last 20 years, the racial wealth gap has cost the U.S. economy about $16 trillion. Yet, despite great challenges, Black people have made significant strides and demonstrated the power of community development efforts in the face of oppression. The example of Black Wall Street comes to mind. In the early 1900s, the all-Black Greenwood community in Tulsa, Oklahoma — like many other Black communities across the South and Midwest during that time — developed their own stores, banks, schools, hotels, newspapers and a hospital. Despite political limitations through Jim Crow laws and the threat of physical violence, Greenwood thrived — with every dollar circulating through the community 50 times before leaving — and became one of the country’s most prosperous communities before it was demolished by a racist mob in the 1921 Tulsa Race massacre. The massacre killed hundreds of people and destroyed years of Black success and wealth-building. Recommended StoriesBlack Wall Street demonstrated the power and effectiveness of Black people working together to grow their community, which reflects the fourth principle of Kwanzaa, Ujamaa (cooperative economics). What’s clear from this example and others is that when Black people have the freedom to use their agency to build power and create opportunity, Black communities and other communities of color can and do thrive. What’s also clear is that the United States owes these communities great recompense to right its historic wrongs against them. The national racial reckoning spurred by the 2020 murder of George Floyd seemed like a step in the right direction, with politicians, corporations, banks and philanthropic organizations committing billions of dollars to advance economic opportunities for Black communities and dismantle systems of oppression. Three years later, much work remains to realize these commitments. One avenue to advance economic opportunity for Black communities is through the support and funding of Community Development Financial Institutions (CDFIs), which are lenders with a mission to provide fair, responsible financing to communities that mainstream finance doesn’t traditionally reach. In short, they are mission-driven institutions that provide financial services to traditionally underinvested communities. Often situated within the communities they serve and made up of people from the community, CDFIs have a clear-eyed view of the resources communities need. Historically, traditional banking systems have deliberately excluded Black communities and deployed racist lending practices that preyed on Black and low-income people. Today, systemic financial discrimination persists and Black communities are denied access to financial institutions or are faced with high-interest lending practices that prevent them from building and accumulating wealth. My colleagues and I at Fenton, one of the nation’s leading national full-service, public-interest communications agencies, are proud to work with organizations at the forefront of the movement to move capital to communities of color. With Locus, formerly Virginia Community Capital Social Enterprises, they are partners in its work to ensure that everyone, no matter their background, location or economic status, can live in healthy, thriving communities. They recently partnered with Locus to develop and unveil their new brand and their salient tagline, “community-focused capital.” Its project, the Community Investment guarantee Pool (CIGP), an innovative, racial equity-driven credit enhancement tool to support small businesses, the creation of affordable housing, and climate financing in communities of low wealth, recently received nearly $20 million from funders MacKenzie Scott and the Kresge Foundation, increasing its capacity to accelerate community investment and support equity in financing for green energy. Since its founding, Locus has generated over $2 billion in total impact in local communities. Likewise, they partner with Hope Credit Union, a CDFI headquartered in Jackson, Mississippi, in its efforts to strengthen communities, Excellerate lives and invest in the financial growth of historically distressed communities in the Deep South. Hope recently received $92.6 million in secondary capital from the U.S. Treasury Department’s Emergency Capital Investment Program (ECIP). These funds will enable the nation’s leading Black- and women-owned financial institution to serve more than 150,000 people over the next 10 years. To date, Hope has generated or leveraged more than $3.6 billion in financing that has benefitted over 2 million people in Alabama, Arkansas, Louisiana, Mississippi, and Tennessee. We also work with the African American Alliance of CDFI CEOs and leading philanthropies that aim to buttress the efforts of CDFIs, including the Annie E. Casey Foundation, the Kresge Foundation, W.K. Kellogg Foundation, and many more. Finally, they work with organizations leading efforts to grow power in these communities and push policies to strengthen them, such as Black Voters Matter and Color Of Change. Our work with these organizations has shown us that communities thrive when they have equitable access to capital and systems of oppression are eradicated. Black communities and other marginalized ones have been able to overcome significant barriers through their own fortitude and principles like Ujamaa. Imagine what Black Wall Street and its sister communities across the country might be today if their economic and political systems truly upheld the moral imperative to advance equity, racial justice and economic opportunity. During this seven-day celebration, they reaffirm the history of Kwanzaa, the need for community self-determination and revolutionary social change so that all people can live healthy, prosperous lives. DontĂ© Donald is a vice president at Fenton, the largest full-service, public-interest communications agency in the country. At Fenton, he works on the firm’s Diversity, Equity, Inclusion and Justice practice, where he focuses on racial equity and economic justice issues in partnership with Black-led nonprofits, CDFIs, foundations, and advocacy organizations. Never miss a beat: Get their daily stories straight to your inbox with theGrio’s newsletter. Maddox, J. Crystals from first principles. Nature 335, 201–201 (1988). Parker, S. C. Prediction of mineral crystal structures. Solid State Ionics 8, 179–186 (1983). Catlow, C. R. A., Thomas, J. M., Parker, S. C. & Jefferson, D. A. Simulating silicate structures and the structural chemistry of pyroxenoids. Nature 295, 658–662 (1982). Ghosht, A., Sarkarf, A. K. & Basus, A. N. The breathing shell model calculation of the relative stability of structure of alkali halide crystals. J. Phys. C 8, 1332–1338 (1975). Donnay, G., Donnay, J. D. H. & Takeda, H. Trioctahedral one-layer micas. II. Prediction of the structure from composition and cell dimensions. Acta Cryst. 17, 1374–1381 (1964). Catlow, C. R. A. & Price, G. D. Computer modelling of solid-state inorganic materials. Nature 347, 243–248 (1990). Catlow, C. R. A. et al. Computer modelling of inorganic materials. Annu. Rep. Prog. Chem. A 101, 513–547 (2005). Lewis, G. V. & Catlow, C. R. A. Potential models for ionic oxides. J. Phys. C 18, 1149–1161 (1985). Shannon, M. D., Casci, J. L., Cox, P. A. & Andrews, S. J. Structure of the two-dimensional medium-pore high-silica zeolite NU-87. Nature 353, 417–420 (1991). Kirkpatrick, S., Gellat, J. C. D. & Vecchi, M. P. Optimization by simulated annealing. Science 220, 671–680 (1983). Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A. & Teller, E. Equation of state calculations by fast computing machines. J. Chem. Phys. 21, 1087–1092 (1953). Pannetier, J., Bassas-Alsina, J., Rodriguez-Carvajal, J. & Caignaert, V. Prediction of crystal structures from crystal chemistry rules by simulated annealing. Nature 346, 343–345 (1990). Schön, J. C. & Jansen, M. First step towards planning of syntheses in solid-state chemistry: Determination of promising structure candidates by global optimization. Angew. Chem. Int Ed. Engl. 35, 1287–1304 (1996). Schön, J. C. & Jansen, M. Determination, prediction, and understanding of structures, using the energy landscapes of chemical systems. Z. Kristallogr. 216, 307–325 (2001). Mellot-Draznieks, C., Newsam, J. M., Gorman, A. M., Freeman, C. M. & FĂ©rey, G. De novo prediction of inorganic structures developed through automated assembly of secondary building units (AASBU method). Angew. Chem. Int. Ed. 39, 2270–2275 (2000). Mellot-Draznieks, C. et al. Computational design and prediction of interesting not-yet-synthesized structures of inorganic materials by using building unit concepts. Chem. Eur. J. 8, 4102–4113 (2002). Mellot-Draznieks, C., Girard, S. & FĂ©rey, G. R. Novel inorganic frameworks constructed from double-four-ring (D4R) units: Computational design, structures, and lattice energies of silicate, aluminophosphate, and gallophosphate candidates. J. Am. Chem. Soc. 124, 15326–15335 (2002). Mellot-Draznieks, C., Dutour, J. & FĂ©rey, G. R. Hybrid organic–inorganic frameworks: Routes for computational design and structure prediction. Angew. Chem. Int. Ed. 43, 6290–6296 (2004). Wales, D. J. & Scheraga, H. A. Review: Chemistry. Global optimization of clusters, crystals, and biomolecules. Science 285, 1368–1372 (1999). Wales, D. J. & Doyle, J. P. K. Global optimization by basin-hopping and the lowest energy structures of Lennard-Jones clusters containing up to 110 atoms. J. Phys. Chem. A 101, 5111–5116 (1997). Hamad, S., Catlow, C. R. A., Woodley, S. M., Lago, S. & MejĂas, J. A. Structure and stability of small TiO2 nanoparticles. J. Phys. Chem. B 109, 15741–15748 (2005). Coley, D. A. An Introduction to Genetic Algorithms for Scientists and Engineers (World Scientific, 1999). Lloyd, L. D., Johnston, R. L. & Salhi, S. Strategies for increasing the efficiency of a genetic algorithm for the structural optimization of nanoalloy clusters. J. Comp. Chem. 26, 1069–1078 (2005). Hartke, B. in Applications of Evolutionary Computation in Chemistry, 33–53 (Springer, 2004). Johnston, R. L. Evolving better nanoparticles: Genetic algorithms for optimising cluster geometries. Dalton Trans. 22, 4193–4207 (2003). Woodley, S. M. Engineering microporous architectures: Combining an evolutionary algorithm with predefined exclusion zones. Phys. Chem. Chem. Phys. 9, 1070–1077 (2006). Abraham, N. L. & Probert, M. I. J. A periodic genetic algorithm with real-space representation for crystal structure and polymorph prediction. Phys. Rev. B 73, 224104 (2006). Woodley, S. M. in Applications of Evolutionary Computation in Chemistry, 95–132 (Springer, 2004). Harris, K. D. M., Johnston, R. L. & Habershon, S. in Applications of Evolutionary Computation in Chemistry, 55–94 (Springer, 2004). Turner, G. W., Tedesco, E., Harris, K. D. M., Johnston, R. L. & Kariuki, B. M. Implementation of Lamarckian concepts in a genetic algorithm for structure solution from powder diffraction data. Chem. Phys. Lett. 321, 183–190 (2000). Roberts, C., Johnston, R. L. & Wilson, N. T. A genetic algorithm for the structural optimization of Morse clusters. Theor. Chem. Acc. 104, 123–130 (2000). Oganov, A. R. & Glass, C. W. Crystal structure prediction using ab initio evolutionary techniques: Principles and applications. J. Chem. Phys. 124, 244704 (2006). Woodley, S. M. & Catlow, C. R. A. Structure prediction of titania phases: Implementation of Darwinian versus Lamarckian concepts in an evolutionary algorithm. Comp. Mater. Sci. (in the press). Pickard, C. J. & Needs, R. J. When is H2O not water? J. Chem. Phys. 127, 244503 (2007). Wells, A. F. The geometrical basis of crystal chemistry. 1–4. Acta Crystallogr. 7, 535–554; 842–853 (1954). Smith, J. V. Enumeration of 4-connected 3-dimensional nets and classification of framework silicates. 1. Perpendicular linkage from simple hexagonal net. Am. Mineral. 62, 703–709 (1977). Smith, J. V. Enumeration of 4-connected 3-dimensional nets and classification of framework silicates. 2. Perpendicular and near-perpendicular linkages from 4.82, 3.122 and 4.6.12 nets. Am. Mineral. 63, 960–969 (1978). Smith, J. V. Enumeration of 4-connected 3-dimensional nets and classification of framework silicates. 3. Combination of helix, and zigzag, crankshaft and saw chains with simple 2d-nets. Am. Mineral. 64, 551–562 (1979). O'Keefe, M. & Hyde, B. G. Crystal Structures I. Patterns and Symmetry (Mineral. Soc. Am., 1996). Treacy, M. M. J., Randall, K. H., Rao, S., Perry, J. A. & Chadi, D. J. Enumeration of periodic tetrahedral frameworks. Z. Kristallogr. 212, 768–791 (1997). Treacy, M. M. J., Rivin, I., Balkovsky, E., Randall, K. H. & Foster, M. D. Enumeration of periodic tetrahedral frameworks. II. Polynodal graphs. Micropor. Mesopor. Mater. 74, 121–132 (2004). Foster, M. D. et al. Chemically feasible hypothetical crystalline networks. Nature Mater. 3, 234–238 (2004). Dress, A. W. M., Huson, D. H. & Molnar, E. The classification of face-transitive periodic 3-dimensional tilings. Acta Crystallogr. A 49, 806–817 (1993). Delgado, O., Huson, D. & Zamorzaeva, E. The classification of 2-isohedral tilings of the plane. Geometriae Dedicata 42, 43–117 (1992). Friedrichs, O. D., Dress, A. W. M., Huson, D. H., Klinowski, J. & Mackay, A. L. Systematic enumeration of crystalline networks. Nature 400, 644–647 (1999). O'Keeffe, M. Three-periodic nets and tilings: regular and related infinite polyhedra. Acta Crystallogr. A 64, 425–429 (2008). Winkler, B., Pickard, C. J., Milman, V. & Thimm, G. Systematic prediction of crystal structures. Chem. Phys. Lett. 337, 36–42 (2001). Le Bail, A. Inorganic structure prediction with GRINSP. J. Appl. Cryst. 38, 389–395 (2005). Tajima, N., Tsuzuki, S., Tanabe, K., Aoki, K. & Hirano, T. First principles prediction of crystal structures of CO2 . Electron. J. Theor. Chem. 2, 139–148 (1997). Arikawa, T., Tajima, N., Tsuzuki, S., Tanabe, K. & Hirano, T. A possible crystal-structure of 1,2-dimethoxyethane—prediction based on a lattice variable molecular-dynamics. Theochem: J. Mol. Struct. 339, 115–124 (1995). Hirano, T., Tsuzuki, S., Tanabe, K. & Tajima, N. Totally ab initio prediction of the structures of CO2 molecular crystal. Chem. Lett. 12, 1073–1074 (1995). Chaka, A. M., Zaniewski, R., Youngs, W., Tessier, C. & Klopman, G. Predicting the crystal structure of organic molecular materials. Acta Crystallogr. B 52, 165–183 (1996). Ammon, H. L., Du, Z. Y., Holden, J. R. & Paquette, L. A. Acta Crystallogr. B 50, 216–220 (1994). Van Eijck, B. P. & Kroon, J. Upack program package for crystal structure prediction: force fields and crystal structure generation for small carbohydrate molecules. J. Comput. Chem. 20, 799–812 (1999). Holden, J. R., Du, Z. Y. & Ammon, H. L. Prediction of possible crystal-structures for C-containing, H-containing, N-containing, O-containing and F-containing organic-compounds. J. Comput. Chem. 14, 422–437 (1993). Aakeroy, C. B., Nieuwenhuyzen, M. & Price, S. L. Three polymorphs of 2-amino-5-nitropyrimidine: Experimental structures and theoretical predictions. J. Am. Chem. Soc. 120, 8986–8993 (1998). Beyer, T. & Price, S. L. Dimer or catemer? Low-energy crystal packings for small carboxylic acids. J. Phys. Chem. B 104, 2647–2655 (2000). Price, S. L. & Wibley, K. S. Predictions of crystal packings for uracil, 6-azauracil, and allopurinol: The interplay between hydrogen bonding and close packing. J. Phys. Chem. A 101, 2198–2206 (1997). Beyer, T., Day, G. M. & Price, S. L. The prediction, morphology, and mechanical properties of the polymorphs of paracetamol. J. Am. Chem. Soc. 123, 5086–5094 (2001). Gdanitz, R. J. Prediction of molecular-crystal structures by Monte-Carlo simulated annealing without reference to diffraction data. Chem. Phys. Lett. 190, 391–396 (1992). Price, S. L. From crystal structure prediction to polymorph prediction: interpreting the crystal energy landscape. Phys. Chem. Chem. Phys. 10, 1996–2009 (2008). Dunitz, J. D. & Gavezzotti, A. Molecular recognition in organic crystals: Directed intermolecular bonds or nonlocalized bonding? Angew. Chem. Int. Ed. 44, 1766–1787 (2005). Desiraju, G. R. Crystal engineering: A holistic view. Angew. Chem. Int. Ed. 46, 8342–8356 (2007). Raiteri, P., MartoĹák, R. & Parrinello, M. Exploring polymorphism: The case of benzene. Angew. Chem. Int. Ed. 44, 3769–3773 (2005). Curtarolo, S., Morgan, D., Persson, K., Rodgers, J. & Ceder, G. Predicting crystal structures with data mining of quantum calculations. Phys. Rev. Lett. 91, 135503 (2003). Fischer, C. C., Tibbetts, K. J., Morgan, D. & Ceder, G. Predicting crystal structure by merging data mining with quantum mechanics. Nature Mater. 5, 641–646 (2006). Hofmann, D. W. M. & Apostolakis, J. Crystal structure prediction by data mining. J. Mol. Struct. 647, 17–39 (2003). Schön, J. C., ÄŚanÄŤarević, Ĺ˝. P., Hannermann, A. & Jansen, M. Free enthalpy landscape of SrO. J. Chem. Phys. 128, 194712 (2008). MartoĹák, R., Laio, A. & Parrinello, M. Predicting crystal structures: The Parrinello–Rahman method revisited. Phys. Rev. Lett. 90, 75503 (2003). Schön, J. C., Pentin, I. V. & Jansen, M. Ab initio computation of the low-temperature phase diagrams of the alkali metal iodide-bromides: MBrxI1â’x (0 ≤ x ≤ 1), where M = Li, Na, K, Rb, or Cs. J. Phys. Chem. B 111, 3943–3952 (2007). Ceriani, C. et al. Molecular dynamics simulation of reconstructive phase transitions on an anhydrous zeolite. Phys. Rev. B 70, 113403 (2004). Brown, I. D. Computer Modelling in Inorganic Crystallography Ch. 2 (ed. Catlow, C. R. A.) (Academic, 1994). Lacorre, P., Pannetier, J., Hoppe, R., Averdunk, F. & Ferey, G. Crystal and magnetic-structures of LiCoF4—the 1st compound with a dirutile structure. J. Solid State Chem. 79, 1–11 (1989). Freeman, C. M. & Catlow, C. R. A. Structure predictions in inorganic solids. J. Chem. Soc. Chem. Commun. 89–91 (1992). Freeman, C. M., Newman, J. M., Levine, S. M. & Catlow, C. R. A. Inorganic crystal-structure prediction using simplified potentials and experimental unit cells—application to the polymorphs of titanium-dioxide. J. Mater. Chem. 3, 531–535 (1993). Woodley, S. M., Battle, P. D., Gale, J. D. & Catlow, C. R. A. The prediction of inorganic crystal structures using a genetic algorithm and energy minimisation. Phys. Chem. Chem. Phys. 1, 2535–2542 (1999). Reinaudi, L., Carbonio, R. E. & Leiva, E. P. M. Inclusion of symmetry for the enhanced determination of crystalline structures from powder diffraction data using simulated annealing. J. Chem. Soc. Chem. Commun. 255–256 (1998). Reinaudi, L., Leiva, E. P. M. & Carbonia, R. E. Simulated annealing prediction of the crystal structure of ternary inorganic compounds using symmetry restrictions. J. Chem. Soc., Dalton Trans. 23, 4258–4262 (2000). Bush, T. S., Catlow, C. R. A. & Battle, P. D. Evolutionary programming techniques for predicting inorganic crystal-structures. J. Mater. Chem. 5, 1269–1272 (1995). Doll, K., Schön, J. C. & Jansen, M. Global exploration of the energy landscape of solids on the ab initio level. Phys. Chem. Chem. Phys. 9, 6128–6133 (2007). Schön, J. C. & Jansen, M. Determination of candidate structures for Lennard-Jones-crystals through cell optimization. Ber. Bunsenges Phys. Chem. 98, 1541–1544 (1994). Jansen, M. & Schön, J. C. Structure candidates for the alkali metal nitrides. Z. Anorg. Allg. Chem. 624, 533–540 (1998). Putz, H., Schön, J. C. & Jansen, M. Investigation of the energy landscape of Mg2OF2 . Comput. Mater. Sci. 11, 309–322 (1998). Wevers, M. A. C., Schön, J. C. & Jansen, M. Determination of structure candidates of simple crystalline AB2 systems. J. Solid State Chem. 136, 233–246 (1998). Schön, J. C., Wevers, M. A. C. & Jansen, M. Prediction of high pressure phases in the systems Li3N, Na3N, (Li,Na)3N, Li2S and Na2S. J. Mater. Chem. 11, 69–77 (2001). Ciobanu, C. V., Chuang, F. C. & Lytle, D. E. On the structure of the Si(103) surface. Appl. Phys. Lett. 91, 171909 (2007). Briggs, R. M. & Ciobanu, C. V. Evolutionary approach for finding the atomic structure of steps on stable crystal surfaces. Phys. Rev. B 75, 195415 (2007). Kasuya, A. et al. Ultra-stable nanoparticles of CdSe revealed from mass spectrometry. Nature Mater. 3, 99–102 (2004). Hamad, S., Cristol, S. & Catlow, C. R. A. Simulation of the embryonic stage of ZnS formation from aqueous solution. J. Am. Chem. Soc. 127, 2580–2590 (2005). Wakisaka, A. Nucleation in alkali metal chloride solution observed at the cluster level. Faraday Discuss. 136, 299–308 (2007). Burnin, A. & Belbruno, J. J. ZnnSm+ cluster production by laser ablation. Chem. Phys. Lett. 362, 341–348 (2002). Whetten, R. L. Alkali-halide nanocrystals. Acc. Chem. Res. 26, 49–56 (1993). Hamad, S., Catlow, C. R. A., Spano, E., Matxain, J. M. & Ugalde, J. M. Structure and properties of ZnS nanoclusters. J. Phys. Chem. B 109, 2703–2709 (2005). Al-Sunaidi, A. A., Sokol, A. A., Catlow, C. R. A. & Woodley, S. M. Structures of zinc oxide nanoclusters: As found by evolutionary algorithm techniques. J. Phys. Chem. C (in the press). Hamad, S. & Catlow, C. R A. Computational study of the relative stabilities of ZnS clusters, for sizes between 1 and 4 nm. J. Cryst. Growth 294, 2–8 (2006). Michaelian, K. Evolving few-ion clusters of Na and Cl. Am. J. Phys. 66, 231–240 (1998). Wootton, A. & Harrowell, P. Inorganic nanotubes stabilized by ion size asymmetry: Energy calculations for AgI clusters. J. Phys. Chem. B 108, 8412–8418 (2004). Roberts, C. & Johnston, R. L. Investigation of the structures of MgO clusters using a genetic algorithm. Phys. Chem. Chem. Phys. 3, 5024–5034 (2001). Woodley, S. M., Sokol, A. A. & Catlow, C. R. A. Structure prediction of inorganic nanoclusters with a predefined architecture using a genetic algorithm. Z. Anorg. Allg. Chem. 630, 2343–2353 (2004). Flikkema, E. & Bromley, S. T. Dedicated global optimization search for ground state silica nanoclusters: (SiO2)N (N = 6–12). J. Phys. Chem. B 108, 9638–9645 (2004). Shevlin, S. A. et al. Structure, optical properties and defects in nitride (III–V) nanoscale cage clusters. Phys. Chem. Chem. Phys. 10, 1944–1959 (2008). Michaelian, K., RendĂłn, N. & GarzĂłn, I. L. Structure and energetics of Ni, Ag, and Au nanoclusters. Phys. Rev. B 60, 20003–2010 (1999). Ferrando, R., Fortunelli, A. & Johnston, R. L. Searching for the optimum structures of alloy nanoclusters. Phys. Chem. Chem. Phys. 10, 640–649 (2008). Paz-Borbon, L. O., Johnston, R. L., Barcaro, G. & Fortunelli, A. Structural motifs, mixing, and segregation effects in 38-atom binary clusters. J. Chem. Phys. 128, 134517 (2008). Deem, M. W. & Newsam, J. M. Determination of 4-connected framework crystal-structures by simulated annealing. Nature 342, 260–262 (1989). Deem, N. W. & Newsam, J. M. Framework crystal-structure solution by simulated annealing: test application to known zeolite structures. J. Am. Chem. Soc. 114, 7189–7198 (1992). Falcioni, M. & Deem, M. W. A biased Monte Carlo scheme for zeolite structure solution. J. Chem. Phys. 110, 1754–1766 (1999). Akporiaye, D. E. et al. UiO-7: A new aluminophosphate phase solved by simulated annealing and high-resolution powder diffraction. J. Phys. Chem. 100, 16641–16646 (1996). Boisen, M. B., Gibbs, G. V. & Bukowinski, M. S. T. Framework silica structures generated using simulated annealing with a potential-energy function-based on an H6Si2O7 molecule. Phys. Chem. Miner. 21, 269–284 (1994). Teter, D. M., Gibbs, G. V., Boisen, M. B., Allan, D. C. & Teter, M. P. First-principles study of several hypothetical silica framework structures. Phys. Rev. B 52, 8064–8073 (1995). Boisen, M. B., Gibbs, G. V., O'Keeffe, M. & Bartelmehs, K. L. A generation of framework structures for the tectosilicates using a molecular-based potential energy function and simulated annealing strategies. Micropor. Mesopor. Mater. 29, 219–266 (1999). Woodley, S. M., Catlow, C. R. A., Battle, P. D. & Gale, J. D. The prediction of close packed and porous inorganic crystal structures. Acta Cryst. A 58, C196 (2002). Woodley, S. M. Prediction of inorganic crystal framework structures. Part II: using a genetic algorithm and a direct approach to exclusion zones. Phys. Chem. Chem. Phys. 6, 1823–1829 (2004). Woodley, S. M., Battle, P. D., Gale, J. D. & Catlow, C. R. A. Prediction of inorganic crystal framework structures. Part I: Using a genetic algorithm and an indirect approach to exclusion zones. Phys. Chem. Chem. Phys. 6, 1815–1822 (2004). Zwijnenburg, M. A., Cora, F. & Bell, R. G. Dramatic differences between the energy landscapes of SiO2 and SiS2 zeotype materials. J. Am. Chem. Soc. 129, 12588–12589 (2007). Carrasco, J., Illas, F. & Bromley, S. T. Ultralow-density nanocage-based metal-oxide polymorphs. Phys. Rev. Lett. 99, 235502 (2007). Lewis, D. W., Catlow, C. R. A., Thomas, J. M., Willock, D. J. & Hutchings, G. J. De novo design of structure-directing agents for the synthesis of microporous solids. Nature 382, 604–606 (1996). Sankar, G. et al. Structure of templated microcrystalline DAF-5 (Co0.28Al0.72PO4C10H20N2) determined by synchrotron-based diffraction methods. Chem. Commun. 1, 117–118 (1998). Hulme, A. T., Price, S. L. & Tocher, D. A. A new polymorph of 5-fluorouracil found following computational crystal structure predictions. J. Am. Chem. Soc. 127, 1116–1117 (2005). Hamad, S., Moon, C., Catlow, C. R. A., Hulme, A. T. & Price, S. L. Kinetic insights into the role of the solvent in the polymorphism of 5-fluorouracil from molecular dynamics simulations. J. Phys. Chem. B 110, 3323–3329 (2006). Lommerse, J. P. M. et al. A test of crystal structure prediction of small organic molecules. Acta Crystallogr. B 56, 697–714 (2000). Neumann, M. A., Leusen, F. J. J. & Kendrick, J. A major advance in crystal structure prediction. Angew. Chem. Int. Ed. 47, 2427–2430 (2008). Moult, J. A decade of CASP: progress, bottlenecks and prognosis in protein structure prediction. Curr. Opin. Struct. Biol. 15, 285–289 (2005). Petrey, D. & Honig, B. Protein structure prediction: Inroads to biology. Mol. Cell 20, 811–819 (2005). Floudas, C. A., Fung, H. K., McAllister, S. R., Monnigmann, M. & Rajgaria, R. Advances in protein structure prediction and de novo protein design: A review. Chem. Eng. Sci. 61, 966–988 (2006). Zhang, Y. Progress and challenges in protein structure prediction. Curr. Opin. Struct. Biol. 18, 342–348 (2008). Jansen, M. A concept for synthesis planning in solid-state chemistry. Angew. Chem. Int. Ed. 41, 3746–3766 (2002). Jansen, M. in Turning Points in Solid-State, Materials and Surface Science (eds Harris, K. M. & Edwards, P.) 22–50 (Royal Society of Chemistry, 2008). Cancarevic, Z. P., Schön, J. C. & Jansen, M. Stability of alkali metal halide polymorphs as a function of pressure. Chem. Asian J. 3, 561–572 (2008). Liebold-Ribeiro, Y., Fischer, D. & Jansen, M. Experimental substantiation of the 'Energy landscape concept' for solids: Synthesis of a new modification of LiBr. Angew. Chem. Int. Ed. 47, 4428–4431 (2008). CHRISTMAS suggests cakes, and these the wish on my part to describe a method of cutting them that I have recently devised to my own amusement and satisfaction. The problem to be solved was, “given a round tea-cake of some 5 inches across, and two persons of moderate appetite to eat it, in what way should it be cut so as to leave a minimum of exposed surface to become dry?” The ordinary method of cutting out a wedge is very faulty in this respect. The results to be aimed at are so to cut the cake that the remaining portions shall fit together. Consequently the chords (or the arcs) of the circumferences of these portions must be equal. The direction of the first two vertical planes of section is unimportant; they may be parallel, as in the first figure, or they may enclose a wedge. The cuts shown on the figures represent those made with the intention of letting the cake last for three days, each successive operation having removed about one-third of the area of the original disc. A common india-rubber band embraces the whole and keeps its segments together. | ||||||||
156-110 guide | 156-110 basics | 156-110 Topics | 156-110 information search | 156-110 exam contents | 156-110 information search | 156-110 syllabus | 156-110 availability | 156-110 learning | 156-110 information search | | ||||||||
Killexams exam Simulator Killexams Questions and Answers Killexams Exams List Search Exams |
Customer Reviews help to evaluate the exam performance in real test. Here all the reviews, reputation, success stories and ripoff reports provided.
We hereby announce with the collaboration of world's leader in Certification Exam Dumps and Real Exam Questions with Practice Tests that, we offer Real Exam Questions of thousands of Certification Exams Free PDF with up to date VCE exam simulator Software.