How to predict very large and complex crystal structures

Large and complex systems from the EMC point of view are objects that contain different structures requiring individually composed methods for modeling as well as calculation. Automobiles, aircraft, or PCBs with devices are large and complex systems. The paper describes activities that were done to handle automotive EMC problems with computer simulations. A set of methods was compiled to allow, in a first stage, a fast calculation model generation, and, to apply in a second stage with minimum user interaction, accurate and fast multi step hybrid methods. The paper focuses less on the methods themselves, but more on the completeness of the process chain that is required to do successful simulations of automobiles or other large systems.

Systems engineering is the branch of engineering concerned with the development of large and complex systems, where a system is understood to be an assembly or combination of interrelated elements or parts working together toward a common objective. Past experience has shown that formal systems engineering methodologies have not always been successfully applied to large and complex cybersecurity systems. These complex systems have become commonplace when applying cyberstrategies in cybersecurity operations. The ability to build, operate and maintain such systems is crucial to the effectiveness of cybersecurity operations. Most importantly, a cyberstrategy program must surround these systems on a global scale across multiple inter-related platforms. In this chapter, the authors demonstrate why a systems engineering approach is best suited for large and complex information systems used in cybersecurity, as well as the overall cyberstrategies that must also reside over these systems.

Debio0932 is a promising lead compound in phase I clinical trials targeting the N-terminal ATP-binding pocket of the molecular chaperone heat-shock protein 90 (Hsp90N). The absence of a crystal structure of the Hsp90N-Debio0932 complex, however, has impeded further structural optimization of Debio0932 and understanding of the molecular-interaction mechanism. Here, a high-resolution crystal structure of the Hsp90N-Debio0932 complex was successfully determined (resolution limit 2.20 Å; PDB entry 6lr9) by X-ray diffraction and the molecular-interaction mechanism was analysed in detail, which suggested that Debio0932 suppresses cancer cells by accommodating itself in the ATP-binding pocket of Hsp90N, disabling its molecular-chaperone capability. The results of a thermal shift assay (ΔTm = 8.83 ± 0.90°C) and isothermal titration calorimetry (Kd = 15.50 ± 1.30 nM) indicated strong binding and favourable thermodynamic changes in the binding of Hsp90N and Debio0932. Based on the crystal structure of the complex and on molecular-interaction analysis, 30 new Debio0932 derivatives were designed and nine new derivatives exhibited increased binding to Hsp90N, as determined by molecular-docking evaluation. Additionally, Debio0932 suppressed cell proliferation (IC50 values of 3.26 ± 2.82 µM for A549, 20.33 ± 5.39 µM for H1299 and 3.16 ± 1.04 µM for H1975), induced cell-cycle arrest and promoted apoptosis in three non-small-cell lung cancer (NSCLC) cell lines. These results provide novel perspectives and guidance for the development of new anti-NSCLC drugs based on the lead compound Debio0932.

Mass and energy rate (ER) data have been collected for a wide variety of (complex) systems from the biological, cultural, and cosmological realms. They range from the cytochrome oxidase protein (10-22 kg and 6x10-19 W) to the observable universe (1.5x1053 kg and 1048 W) and, thus, span 75 mass and 66 ER orders of magnitude. Many of these systems are relevant for the big history (BH) narrative, i.e., the development of complexity over “big time” from the Big Bang up to the human society on Earth of today. The purpose of this paper is not per se to describe their history though, but to explore a master plot of ER vs. mass. Notably, the development of systems over big time has followed a rather tortuous path criss-crossing over this ER vs. mass master plot. The true mass of the system as a whole is used (for example trees including the non-living wood, living organisms including their intrinsic water, and social systems including the built constructs), because these inactive parts are essential for the performance of the system and facilitate its ER. A double logarithmic master plot of all ER vs. mass data shows clusters of data points. To some extent, this provides quantitative support for the distinction between the (sub-)realms, which is based on a qualitative description of their material structure and energy processing. In the master plot, small systems with low mass and ER converge into larger systems with larger mass and ER, which is typically accompanied by a decrease of the energy rate density (ERD = ER/mass). Correlation of ER with mass for various groups of systems demonstrates both sub- and supra-linear scaling with the power law β constant varying between 0.5 and 4.0, showing that the mechanisms of self-organisation are quite different for the corresponding system groups. The combination of convergence and scaling with β always larger than zero explains why the ER & mass data points fall in a diagonal band with a width of 17 orders of magnitude.
 ER and mass have changed over wide ranges during the evolution of groups of systems, suggesting that evolution can be viewed as a process of systems exploring a larger ER vs. mass area until they run into ER and/or mass limitations. Indeed, there is a diagonal ER vs. mass limit for stable systems in all realms, corresponding to an ERD value of around 105 W/kg. Systems with ER & mass combinations above this limit, such as bombs, super-novae and cosmological transients, are unstable and “explosive”. This raises the interesting question of whether such an ERD maximum puts a limit on the development of complexity over big time. It seems that the low, right side of the master plot is empty. However, it is argued here that it is full of systems with low ER, such as dormant, living organisms, technological systems with their power adjusted or even switched off, as well as cooling, cosmological objects. Such systems are typically considered of less interest in a BH context, but they are viewed here as simple, complex systems which are out of equilibrium with matter, energy and information stored in their structure. While ERD appears to increase with the ‘advancement’ of systems over big time [5,51,52], there are quite some confounding factors regarding the efficacy of ERD as a metric for complexity in BH. For example, ERD decreases during the lifetime of a human and the human society (the mass of human-made constructs has grown faster than the global energy consumption), as well as during the evolution of living organisms and stars, whereas complexity is considered to increase. High ERD values of system parts may be illustrative for the complexity of the larger system, but are not representative for ERD of the system itself. Machines with an increased efficiency of energy conversion have a lower ERD, but could be considered more complex. The smallest and largest ERD values observed for the various realms appear to correlate with activity level and reciprocally with size, which do not per se reflect complexity. It is hoped that the raw data collected and the major trends observed in this paper will offer new insights into various aspects of the evolution of the universe over big time, and serve as an important resource for other related studies.

Testing consumes significant human and machine resources, especially for large, complex systems such as database servers. While a variety of testing approaches have been proposed to improve the efficiency of the testing process, it is difficult to evaluate these approaches. Mutation testing has been proposed as a way to assess the adequacy of a test suite, assigning a score that can be used to compare testing approaches. While promising, serious obstacles appear to prevent mutation testing with large software systems. Recent advances in mutation testing have scaled to medium-sized programs of around 100,000 lines of code but to our knowledge there are no reported studies working with large systems with millions of lines of code and other features of database systems that complicate testing. In this paper we explore using mutation testing on a database server to evaluate its suitability for comparing test suites or testing approaches.

This study investigates the potential of graph neural networks (GNNs) for estimating system-level integrated information and major complex in integrated information theory (IIT) 3.0. Owing to the hierarchical complexity of IIT 3.0, calculating the integrated information and identifying the major complex are computationally prohibitive for large systems. To overcome this difficulty, we propose a GNN model with transformer convolutions characterized by multi-head attention mechanisms for estimating the major complex and its integrated information. For evaluation, we begin by obtaining exact solutions for integrated information and major complexes in systems with 5, 6, and 7 nodes, and conduct two experiments: (1) a non-extrapolative setting in which the model is trained and tested on a mixture of systems with 5, 6, and 7 nodes, and (2) an extrapolative setting in which systems with 5 and 6 nodes are used for training and systems with 7 nodes are used for testing. We then examine the scaling behavior for tree-like, fully connected, and loop-containing graph topologies in larger systems. Although accurate estimation is difficult, our approximate estimates for larger systems generally preserve the qualitative patterns of integrated information and major complex size that are observed in small systems. Finally, based on this observation, we qualitatively analyze a split-brain–like system of 100 nodes. The system consists of two weakly coupled subsystems of 50 nodes each, representing a structurally meaningful, brain-inspired configuration. When the connectivity between the subsystems is low, “local integration” emerges, and a single subsystem forms a major complex. As the connectivity increases, local integration rapidly disappears, and the integrated information gradually rises toward “global integration,” in which a large portion of the entire system forms a major complex. Our analysis suggests that the proposed GNN-based framework provides a practical approach to qualitative analysis of integrated information and major complexes in large systems.

Pagoda[5]arene (P5), which is composed of five 2,6-dimethoxylanthracene (2,6-DMA) subunits bridged by methylene groups at 1,5 positions, was conveniently synthesized in 43% by trifluoroacetic acid ...

<div class="section abstract"><div class="htmlview paragraph">The objective of this paper is two-fold. Firstly, provide guidance to best implement end to end traceability from program requirements to physical implementation, and Secondly provide techniques to review and understand large scale complex systems. Even with a Digital Engineering Environment (DEE) being an enabler towards applying Systems Engineering practices to develop large scale complex systems, many organizations are unclear on the methodology for modeling their architectures and enabling stakeholders to easily review, understand and assess those architectures. An architecture can be a conceptual, logical or physical architecture, depending on the system’s lifecycle state. For the context of this paper, the modeling environment is any System’s Modeling Language (SysML) based tool along with modeling tools for electrical, mechanical and software development and product life cycle management tool. The intended audience is any engineering organization defining end-to-end architecture within a DEE, and all stakeholders tasked with reviewing conceptual, logical and physical architecture. The outcome of this paper is to provide engineering organizations with guidance on underlying principles for modeling and understanding or assessing architecture that describe large complex systems.</div></div>

Vital Signs: Deficiencies in Environmental Control Identified in Outbreaks of Legionnaires’ Disease—North America, 2000–2014

Multiparadigm programming languages have been envisioned as a vehicle for constructing large and complex heterogeneous systems, such as a stock market exchange or a telecommunications network. General-purpose multiparadigm languages, as opposed to hybrid multiparadigm languages, embody several prevalent programming paradigms without being motivated by a single problem. One such language is Leda, which embodies the foundational paradigms of imperative, functional, logic, and object-oriented programming. We explore aspects of solving complex problems using Leda, in order to illustrate the benefits of using a multiparadigm language in expressing solutions to complex systems. We claim that general-purpose multiparadigm programming languages like Leda greatly expedite solutions to a variety of complex problems.

Advanced military and large scale commercial systems are getting bigger and more complex as we approach the 21st century. A new systems engineering paradigm which employs simulation and modeling capabilities may provide significant opportunities to reduce cost, schedule and risk in the development of large complex systems. The central feature of a simulation based systems engineering paradigm is an advanced systems engineering environment which supports synthesis, capture, analysis, simulation and optimization of complex system designs. The focus of this paper is a description of a high level architecture for such an environment. The essential components of an advanced distributed complex systems engineering (ADCSE) environment are partitioned into five categories: generic computing infrastructure; ADCSE specific schema; the capability to support execution of distributed simulations; application specific tools; and design libraries of reusable components.

A path-based simulation approach for multistate flow network reliability estimation without using boundary points

We treat the problem of selecting the next degree of freedom for update in an extremal optimization algorithm designed to find the ground state of a system with a complex energy landscape. We show that there exists a best distribution for selecting the next degree of freedom in order to optimize any linear function of the state probabilities, e.g., the expected number of visits to the ground state. We dub the class of algorithms using this best distribution in conjunction with extremal optimization fitness threshold accepting. In addition, we construct an extended random walk and use it to show that fitness threshold accepting is optimal also for several other measures of algorithm performance, such as maximizing the expected probability of seeing the ground state and minimizing the expected value of the lowest energy seen.

SU9516: biochemical analysis of cdk inhibition and crystal structure in complex with cdk2

The synthesis and crystal structure of the complex [Ni(B7C2H9Me2)(PEt3)2] is described. The nickel is joined to a B2C system in a 1,2,3-η bonding mode.