Level Of System Complexity Research Articles

The Distribution of Planet Radius in Kepler Multiplanet Systems Depends on Gap Complexity

The distribution of small planet radius (<4 R ⊕) is an indicator of the underlying processes governing planet formation and evolution. We investigate the correlation between the radius distribution of exoplanets in Kepler multiplanet systems and the system-level complexity in orbital period spacing. Utilizing a sample of 234 planetary systems with three or more candidate planets orbiting FGK main-sequence stars, we measure the gap complexity (C) to characterize the regularity of planetary spacing and compare it with other measures of period spacing and spacing uniformity. We find that systems with higher gap complexity exhibit a distinct radius distribution compared to systems with lower gap complexity. Specifically, we find that the radius valley, which separates super-Earths and sub-Neptunes, is more pronounced in systems with lower gap complexity (C < 0.165). Planets in high-complexity systems (C > 0.35) exhibit a lower frequency of sub-Earths (2.5 times less) and sub-Neptunes (1.3 times less) and a higher frequency of super-Earths (1.4 times more) than planets in low-complexity systems. This may suggest that planetary systems with more irregular spacings are more likely to undergo dynamic interactions that influence planet scattering, composition, and atmospheric retention. The gap complexity metric proves to be a valuable tool in linking the orbital configurations of planets to their physical characteristics.

TECHNOLOGY FOR DETERMINING THE SAFETY OF AMMUNITION IN ACCORDANCE WITH NATO STANDARDS

September 2023 marks 60 years since NATO countries began implementing the principles of ammunition and explosives safety, which are now described in the AASTP and AOR series of publications. This issue is important and significant because during the preparation and operational process, ammunition is exposed to environmental factors that can change the initial parameters and properties of ammunition.&#x0D; Today, NATO member states assess the safety of ammunition on the basis of more than 45 NATO Standards that have been developed and implemented.&#x0D; The main guiding documents for determining the safety of ammunition are: Standard AOP- 15 Guidelines for the Assessment of Non-Nuclear Ammunition Safety and Suitability for Use by NATO Forces; Standard AAS3P-01 Evaluation Tests for the Safety and Suitability of Non-Nuclear Ammunition; Standard AASTP-3 NATO Guide to Safety Principles for the Classification of Hazards Associated with Military Ammunition and Explosives; Standard AOP- 62 Ammunition Operational Surveillance, General Guidance.&#x0D; The article proposes a technology for determining the safety of ammunition based on NATO standards. The proposed technology for determining the safety of ammunition includes two blocks: an assessment process and a program for determining the safety and suitability for use of ammunition: The presented process of safety and serviceability assessment is a specialized problem-solving process; one stage leads to another, taking into account the guarantees that the ammunition is safe and serviceable.&#x0D; The evaluation process also has several distinctive features that make it possible to apply it effectively in practice. It is possible to repeat the evaluation stages as often as necessary to achieve the desired results. The process can be applied at any level of system complexity, from the development of a general design concept to the final detailing of a subsystem or component design. It also involves making management decisions between the ideal and the most practical option. In this way, the assessment process provides results that meet the definition of system safety, ensures that the optimal degree of risk elimination is achieved within acceptable operational efficiency, time and cost. The program of safety and suitability testing developed on the basis of these factors should be introduced to the requirements for assessing safety parameters at the national operational level.&#x0D; The developed technology is a clear guideline, a step-by-step instruction for military specialists to determine the level of safety of ammunition and its suitability for use.

A critical appraisal of professional competency frameworks: What guidance is provided for stroke rehabilitation clinicians managing 'complexity'?

Given current health system trends, clinicians increasingly care for patients with complex care needs. There is a recognized lack of evidence to support clinician decision-making in these situations, as complex or multimorbid patients have been historically excluded from the types of research that inform clinical practice guidelines. However, expert clinicians at sites of excellence (e.g., Stroke Distinction sites) provide measurably excellent care. We sought to review profession-specific competency frameworks to locate information that may be supporting the development of clinician expertise when managing the care of patients with complex care needs. We conducted a review of the professional competency frameworks for core members of the inpatient stroke rehabilitation team, to determine the degree of guidance and/or preparation for the management of patients with complex care needs. We developed and applied an assessment rubric to locate references to patient complexity, multimorbidity and complexity theory. Across the professional competency frameworks, there are some references to complexity at patient- and team-levels; there are fewer references to system-level complexity. We noted a lack of clear guidance for clinicians regarding the management of patients with complex care needs. Further research is needed to explore how clinicians develop expertise in the management of patients with complex care needs, as we noted minimal guidance in the professional competency frameworks. However, we suggest that integrating complexity-related language into professional competency frameworks could better prime novice clinicians for new learning in the workplace and ease their transition into working in a complex context.

How Much Does Software Complexity Matter for Maintenance Productivity? The Link Between Team Instability and Diversity

Software complexity decreases maintenance productivity, as do team attributes of instability and knowledge diversity. We know little about the extent to which the two team attributes interact with software complexity and shape productivity across systems of varying complexity. We address this gap by investigating whether and to what degree software complexity moderates the effects of team instability and knowledge diversity on maintenance productivity over the life of a system. We posit, given the exponential growth of code and task dependencies inherent in complex software systems, that system-level complexity has a significant nonlinear amplifying effect on the adverse effects of the two team attributes. To validate the presence of such an effect, we conduct a robust split-sample econometric analysis using three years of maintenance data from 426 mission-critical systems of a Fortune 100 company. The sampled systems vary in size (50KLOC to 2000KLOC, where 20% exceed 500KLOC), with a considerable portion of the sample manifesting “high” to “very high” software complexity. The analysis corroborates the known adverse effects of team instability, team knowledge diversity, and software complexity on maintenance productivity. More importantly, it shows—as theorized—that the adverse effects of the team attributes on maintenance productivity are significantly amplified only when software complexity grows high. We conclude with practical and research implications about how to manage software teams maintaining complex software over the life of a system.

A Data-Driven Self-Optimization Solution for Inter-Frequency Mobility Parameters in Emerging Networks

Densification and multi-band operation means inter-frequency handovers can become a bottleneck for mobile user experience in emerging cellular networks. The challenge is aggravated by the fact that there does not exist a method to optimize key inter-frequency handover parameters namely A5 time-to-trigger, A5-threshold1 and A5-threshold2. This paper presents a first study to analyze and optimize the three A5 parameters for jointly maximizing three key performance indicators that reflect mobile user experience: handover success rate (HOSR), reference signal received power (RSRP), and signal-to-interference-plus-noise-ratio (SINR). As analytical modeling cannot capture the system-level complexity, we exploit a data-driven approach. To minimize the training data generation time, we exploit shapley additive explanations (SHAP) sensitivity analysis. The insights from SHAP analysis allow the selective collection of the training data thereby enabling the easier implementation of the proposed solution in a real network. We show that joint RSRP, SINR and HOSR optimization problem is non-convex and solve it using genetic algorithm (GA). We then propose an intelligent mutation scheme for GA, which makes the solution 5x times faster than the legacy GA and 21x faster than the brute force search. This paper thus presents first solution to implement computationally efficient closed-loop self-optimization of inter-frequency mobility parameters.

CancerOmicsNet: a multi-omics network-based approach to anti-cancer drug profiling.

Development of novel anti-cancer treatments requires not only a comprehensive knowledge of cancer processes and drug mechanisms of action, but also the ability to accurately predict the response of various cancer cell lines to therapeutics. Numerous computational methods have been developed to address this issue, including algorithms employing supervised machine learning. Nonetheless, high prediction accuracies reported for many of these techniques may result from a significant overlap among training, validation, and testing sets, making existing predictors inapplicable to new data. To address these issues, we developed CancerOmicsNet, a graph neural network with sophisticated attention propagation mechanisms to predict the therapeutic effects of kinase inhibitors across various tumors. Emphasizing on the system-level complexity of cancer, CancerOmicsNet integrates multiple heterogeneous data, such as biological networks, genomics, inhibitor profiling, and gene-disease associations, into a unified graph structure. The performance of CancerOmicsNet, properly cross-validated at the tissue level, is 0.83 in terms of the area under the receiver operating characteristics, which is notably higher than those measured for other approaches. CancerOmicsNet generalizes well to unseen data, i.e., it can predict therapeutic effects across a variety of cancer cell lines and inhibitors. CancerOmicsNet is freely available to the academic community at https://github.com/pulimeng/CancerOmicsNet.

An integrated network representation of multiple cancer-specific data for graph-based machine learning

Genomic profiles of cancer cells provide valuable information on genetic alterations in cancer. Several recent studies employed these data to predict the response of cancer cell lines to drug treatment. Nonetheless, due to the multifactorial phenotypes and intricate mechanisms of cancer, the accurate prediction of the effect of pharmacotherapy on a specific cell line based on the genetic information alone is problematic. Emphasizing on the system-level complexity of cancer, we devised a procedure to integrate multiple heterogeneous data, including biological networks, genomics, inhibitor profiling, and gene-disease associations, into a unified graph structure. In order to construct compact, yet information-rich cancer-specific networks, we developed a novel graph reduction algorithm. Driven by not only the topological information, but also the biological knowledge, the graph reduction increases the feature-only entropy while preserving the valuable graph-feature information. Subsequent comparative benchmarking simulations employing a tissue level cross-validation protocol demonstrate that the accuracy of a graph-based predictor of the drug efficacy is 0.68, which is notably higher than those measured for more traditional, matrix-based techniques on the same data. Overall, the non-Euclidean representation of the cancer-specific data improves the performance of machine learning to predict the response of cancer to pharmacotherapy. The generated data are freely available to the academic community at https://osf.io/dzx7b/.

Reliable Ge2Sb2Te5 based phase-change electronic synapses using carbon doping and programmed pulses

Hardware electronic synapse and neuro-inspired computing system based on phase change random access memory (PCRAM) have attracted an extensive investigation. However, due to the intrinsic asymmetric reversible phase transition, the defective weight update of PCRAM synapses in aspects of tuning range, linearity and continuity has long required a system-level complexity of circuits and algorithms. The cell-level improvements to a great extent may slim the system thus achieving efficient computing. We report in this work the great enhancement of Ge2Sb2Te5 (GST) based PCRAM synapses by combining materials engineering and pulse programming. It is found that carbon doping in GST retards the rate of phase changing thus increasing the controllability of the conductance, while non-linear programmable pulse excitations can eventually lead to a reliable synaptic potentiation and depression. A set of improved programmable pulse schemes for spike-timing dependent plasticity was then demonstrated, suggesting its potential superiority in flexible programming and reliable data collection. Our methods and results are of great significance for implementing PCRAM electronic synapses and high-performance neuro-inspired computing.

A Zipfian Approach to Words in Contexts: The Cases of Modern English and Chinese

ABSTRACT The system-level complexity of language has been thoroughly investigated in terms of Zipf’s law, whose quantitative features have proved to reflect text/language typology. This study extends the scope of Zipf’s law from the macroscopic scale of language to specific words in contexts, with the aim of examining its potential as an indicator of word typology. The focus is confined to the high-frequency words in English and Chinese as found in the FLOB and LCMC corpora. It has been found that the log–log rank-frequency distributions of contextual words of the words in question generally abide by the linear function y = ax+b. Moreover, it has been shown that an adjusted version of parameter a can help to distinguish the words in question’s classes. The contextual information as reflected by this Zipf-based index might be more important to the emergence of word classes of Chinese, which has no real inflection as a word-class indicator. From a Zipfian approach, the findings have preliminarily approved Saussure’s systems thinking regarding linguistic signs. Meanwhile, they may also contribute to such fields as usage-based linguistics.

Minimizing the Effects of On-Chip Hotspots Using Multi-Objective Optimization of Flow Distribution in Water-Cooled Parallel Microchannel Heatsinks

Abstract The industry shift to multicore microprocessor architecture will likely cause higher temperature nonuniformity on chip surfaces, exacerbating the problem of chip reliability and lifespan. While advanced cooling technologies like two phase embedded cooling exist, the technological risks of such solutions make conventional cooling technologies more desirable. One such solution is remote cooling with heatsinks with sequential conduction resistance from chip to module. The objective of this work is to numerically demonstrate a novel concept to remotely cool chips with hotspots and maximize chip temperature uniformity using an optimized flow distribution under constrained geometric parameters for the heatsink. The optimally distributed flow conditions presented here are intended to maximize the heat transfer from a nonuniform chip power map by actively directing flow to a hotspot region. The hotspot-targeted parallel microchannel liquid cooling design is evaluated against a baseline uniform flow conventional liquid cooling design for the industry pressure drop limit of approximately 20 kPa. For an average steady-state heat flux of 145 W/cm2 on core areas (hotspots) and 18 W/cm2 on the remaining chip area (background), the chip temperature uniformity is improved by 10%. Moreover, the heatsink design has improved chip temperature uniformity without a need for any additional system level complexity, which also reduces reliability risks.

A clustering approach to detect faults with multi-component degradations in aircraft fuel systems

Accurate fault diagnosis and prognosis can significantly increase the safety and reliability of engineering systems and also reduce the maintenance costs. There is very limited relative research reported on the fault diagnosis of a complex system with multi-component degradation. The Complex Systems (CS) problem, which features multiple components simultaneously and nonlinearly interacting with each other and corresponding environment on multiple levels, has become an essential challenge in system engineering. In CS, even a single component degradation could cause misidentification of the fault severity level and lead to serious consequences. This paper introduces a new test rig to simulate multi-component degradations of the aircraft fuel system. A data analysis approach based on machine learning classification of both the time and frequency domain features is then proposed to detect and identify the fault severity level of CS with multi-component degradation. Results show that a) the fault can be sensitively detected with an accuracy > 99%; b) the severity of fault can be identified with an accuracy of 100%.

Linking Emergence to the Complex Product System

The increasing complexity of product calls for manufacturing integration, while in turn high integration brings the problems of system level complexity. This paper proposes that complex product system (CoPS) should be managed as a dynamical system. The dynamical characteristics of CoPS are discussed from the perspective of emergence. A conceptual model is established to analyze the cause, process and result of the CoPS emergence. The mechanism of inner state emergence in CoPS is interpreted by formal languages to provide a point view of state space. It is concluded that the behavior of CoPS, especially the complexity, exhibits the ‘entity is greater than the sum of the parts’ phenomena when satisfying given necessary conditions. A novel methodology is then established to evaluate this emergence-based complexity. The feasibility and application of the novel complexity measurement is verified by an example of turbine housing production process. Further discussions are made on how to manage the potential emerging complexity based on the proposed measurement.

Human adaptation to latency in teleoperated multi-robot human-agent search and rescue teams

Abstract Teleoperation of unmanned vehicles in high stress environments has been a subject of research in many domains, which focus primarily on system and operator performance. Unmanned ground vehicles for rescue, also known as search and rescue robots, serve as extensions of responders in a disaster, providing real-time video and other relevant information about the situation. However, physically separating responder and robot introduces latency between the human input provided to the unmanned vehicle to execute an operation and the subsequent response provided by the system. This latency (lag or time delay) is determined by the distance and the bandwidth of the connection between the operator and the unmanned vehicle. Automating these systems may mitigate the effect of latency to an extent; however, this has its own consequences, such as leaving the responder out of the loop, which subsequently leads to detrimental effects on situational awareness. This research investigates the relationship between latency and the performance of the human operator of a teleoperated robot at different levels of system complexity and the effect of different levels of automation on this relationship. Eighty participants operated one or two unmanned teleoperated robots to complete two search and rescue tasks. The study utilized a 2 × 2 × 2 mixed-subjects experimental design with the automation level and latency level being the between-subjects factors and the system complexity (controlling one or two robots) being the within-subjects factor. The dependent variables were operator performance, perceived workload, and the subjective rating of trust with automation. A latency of 500 ms showed a significant decrease in performance in time to complete the task and a significant increase in the perceived physical workload. Both the automation level and latency level moderated the system complexity effect on the subjective rating of trust in the robotic system. The level of trust decreased over time in the one-robot condition as opposed to no change in the two-robot condition. The error rate decreased over time at different rates based on the number of robots or the latency level. Based on the results of the study, several design implications are suggested for improving performance including adding features to the automation that will allow the operator to use common strategies and providing necessary information using multiple sensory channels. Future research directions are also proposed.

Analysis of Space Period Blocks Codes for Independent Channel Coefficient Using MIMI-CDMA Techniques

The future of communication system is changing towards high capacity and faster data transmission with error less environment. Wireless and effcient Communication Systems follows the Improvement in the Bit error rate (BER) over fading channel with decrease in the system level complexity. Multiple Input multiple output (MIMO) generally improves spectral effciency and link reliability. And CDMA is a simple technique that has many advantages, spread spectrum gives best results in CDMA technique, for the improvement in signal quality, the combination of both techniques rise to give a simple solution. For better Bit error rate (BER) there is permutation spreading technique runs on space time block code (STBC) matrices. Here the crucial part is spreading strategy that follows frequency of data bits and each transmit antenna uses different spreading sequences gives fewer errors in symbols. The assignment of spreading sequence allotment follows some permutation designs that should have required squared Euclidean distance from message to another message for better spatial diversity, orthogonality and code symmetry. Permutation spreading development can be achieved by changing the pattern of space time block code environment. The results indicate that, there is an improvement in Bit Error rate.

An Agent Based Distributed Shop Floor Control System for a Job Shop Environment

Abstract In addition to the inherent dynamism associated with any shop floor, a job shop environment exhibits certain additional features that promotes the use of decentralized over centralized decision support systems on the benchmark of responsiveness. The current work first highlighted the system level and operational complexities associated with a job shop environment and very objectively established the need for a decentralized decision support system for such set ups. Unlike various attempts of developing agent systems for shop floor, where major focus has been on imparting intelligence to scheduling agents, the current work took into account the need for integrating multiple shop floor functions, namely scheduling and maintenance, and demonstrated the development of a decision mechanism considering more than one shop floor function. The detailed design constitutes a global scheduling agent for entire floor and a set of local scheduling and maintenance agents for each machine on the floor. The novelty of the work lies in the thoroughness with which individual agent design has been attempted. The work reported in this paper added to the multiagent literature, specifically in the context of manufacturing, by answering certain questions like “how should intelligence be built into a shop floor agent?’ and ‘how could agents from different domains be integrated?” The work also highlights the fact that the advantage of agent features can be leveraged in true sense and systems based on them could be successful only if means of integrating heterogeneous functions are taken into account.

An argument for infrastructure system stewardship: A new zealand transportation case study in benefit management

© 2017 WIT Press. Benefits are intrinsic to infrastructure and the public sector, yet these remain a problem for many infrastructure sectors (including transportation), organizations and countries. Much of the focus upon benefit management targets project delivery; by contrast, this study considers system-level benefit management using a transportation case study from New Zealand. In so doing, it investigates why the matter of benefits might be so problematic, and in what way this might affect the integration of projects into the extant system. The research shows system-level coordination and integration are being lost within the 'tactical strategy' of programmes and initiatives. In turn, this is creating a 'red queen'-like busyness without a real understanding of whether anything has been achieved relative to the intended or necessary outcomes being sought. System stewardship has therefore been advanced as an appropriate response to systemlevel complexity and a potential enabler of strategic agility and adaptive capacity.

Variance-reduced simulation of lattice discrete-time Markov chains with applications in reaction networks

We propose an algorithm to accelerate Monte Carlo simulation for a broad class of stochastic processes. Specifically, the class of countable-state, discrete-time Markov chains driven by additive Poisson noise, or lattice discrete-time Markov chains. In particular, this class includes simulation of reaction networks via the tau-leaping algorithm. To produce the speedup, we simulate pairs of fair-draw trajectories that are negatively correlated. Thus, when averaged, these paths produce an unbiased Monte Carlo estimator that has reduced variance and, therefore, reduced error. Numerical results for three example systems included in this work demonstrate two to four orders of magnitude reduction of mean-square error. The numerical examples were chosen to illustrate different application areas and levels of system complexity. The areas are: gene expression (affine state-dependent rates), aerosol particle coagulation with emission and human immunodeficiency virus infection (both with nonlinear state-dependent rates). Our algorithm views the system dynamics as a “black-box”, i.e., we only require control of pseudorandom number generator inputs. As a result, typical codes can be retrofitted with our algorithm using only minor changes. We prove several analytical results. Among these, we characterize the relationship of covariances between paths in the general nonlinear state-dependent intensity rates case, and we prove variance reduction of mean estimators in the special case of affine intensity rates.

Modeling and application of mixed model assembly system complexity introduced by auto-body personalization

Assembly system complexity, especially welding system complexity introduced by auto-body product personalization is regarded as a major contributor of uncertainty in the system planning and designing. The welding system complexity is defined based on information entropy theory, the station-level integrated complexity model, and system-level complexity flow model are established to obtain the complexity source of welding system. Complexity source sensitivity indices are proposed to indentify key station and key equipment that contribute most to the complexity. Based on the application of auto-body side welding line case, the result indicates that the proposed complexity model and key complexity source identifying and diagnosing process can be used as the decision support tool of auto-body welding system.

A novel method of energy efficient hotspot-targeted embedded liquid cooling for electronics: An experimental study

The shift to multicore microprocessor architecture is likely to result in higher coolant flow requirements and thus exacerbate the problem of increasing data center energy consumption, also with respect to hotspot elimination. We present and experimentally prove a novel concept, for embedded, hotspot-targeted and energy efficient cooling of heterogeneous chip power landscapes. The rationally distributed, embedded microstructures presented here are able to adapt the heat transfer capability to a steady but non-uniform chip power map by passively throttling the flow in low heat flux areas. For the industrially acceptable limit on pressure drop of approximately 0.4bar, the hotspot-targeted embedded liquid cooling (HT-ELC) designs are evaluated against a conservatively chosen conventional embedded liquid cooling (C-ELC) design and existing heat sinks in the literature. For an average steady-state heat flux of 150W/cm2 in core areas (hotspots) and 20W/cm2 over the remaining chip area (background), the chip temperature variation is reduced from 10°C under the conventional cooling to 4°C under the current hotspot targeted heat sink – a reduction of 57%. For heat fluxes of 300 and 24W/cm2, the temperature variation is reduced by 30%. We show that the HT-ELC designs consume less than 0.3% of total chip power as pumping power to achieve this thermal performance, which the C-ELC design cannot match under all feasible levels of pumping power. Moreover, the HT-ELC designs achieve at least 70% improvement over the existing hotspot targeted heat sinks in terms of normalized chip temperature non-uniformity, without the need for any additional system level complexity, reducing reliability risks.

Predicting ecosystem emergent properties at multiple scales.

Predicting ecosystem emergent properties at multiple scales.