System-level Functions Research Articles

Approaching Dynamic Behaviors of Life through Systems Chemistry.

The intricate interplay of metabolic reactions and molecular assembly in living systems enables spatiotemporally organization and gives rise to diverse dynamic behaviors that characterize life. Over the last decades, research efforts have increasingly focused on replicating the remarkable properties and characteristics of living systems, driving the rapid growth of systems chemistry. This young discipline which generally studies interacting molecular networks and emergent system-level properties, behaviors, and functions, offers new concepts and tools to tackle the complexity of life. In this review paper, we have explored seminal research and recent advancements in recreating dynamic behaviors of life with systems chemistry. We believe that the recreation of the dynamic behaviors of life through systems chemistry would set the initial steps to obtain synthetic life de novo.

Numerical eigen-spectrum slicing, accurate orthogonal eigen-basis, and mixed-precision eigenvalue refinement using OpenMP data-dependent tasks and accelerator offload

Performing a variety of numerical computations efficiently and, at the same time, in a portable fashion requires both an overarching design followed by a number of implementation strategies. All of these are exemplified below as we present transitioning the PLASMA numerical library from relying on dependence-driven large tasks to achieving utilization of fine grain tasking and offload to hardware accelerators while keeping its core dependence sets: OpenMP source code pragmas and runtime for most system-level functionality and basic low-level numerical kernels provided directly by hardware vendors or open source projects with vendor contributions. We also present new algorithmic methods and their efficient parallel implementations including fine grained tasking for eigen-spectrum slicing and offload for mixed-precision eigenvalue refinement. We provide performance, scaling, and numerical results showing sizable gains over the available solutions from either the open source and vendor-provided packages.

Packing optimization of practical systems using a dynamic acceleration methodology

System design is a challenging and time-consuming task which often requires close collaboration between several multidisciplinary design teams to account for complex interactions between components and sub-systems. As such, there is a growing demand in industry to create better performing, efficient, and cost-effective development tools to assist in the system design process. Additionally, the ever-increasing complexity of systems today often necessitates a shift away from manual expertise and a movement towards computer-aided design tools. This work narrows the scope of the system design process by focusing on one critical design aspect: the packaging of system components. The algorithm presented in this paper was developed to optimize the packaging of system components with consideration of practical, system-level functionalities and constraints. Using a dynamic acceleration methodology, the algorithm packages components from an initial position to a final packed position inside of a constrained volume. The motion of components from initial to final positions is driven by several acceleration forces imposed on each component. These accelerations are based on physical interactions between components and their surrounding environment. Various system-level performance metrics such as center of mass alignment and rotational inertia reduction are also considered throughout optimization. Results of several numerical case studies are also presented to demonstrate the functionality and capability of the proposed packaging algorithm. These studies include packaging problems with known optimal solutions to verify the efficacy of the algorithm. Finally, the proposed algorithm was used in a more practical study for the packaging of an urban air mobility nacelle to demonstrate the algorithm’s prospective capabilities in solving real-world packaging problems.

Combining cognition and context: entrepreneurial alertness and the microfoundations of entrepreneurial ecosystems

AbstractEntrepreneurial alertness (EA) research has made substantial progress in identifying the psychological and organizational antecedents and consequences of EA. However, the interactions between environmental factors and EA are understudied and it is unclear how alertness influences and is shaped by entrepreneurs’ local ecosystems. In this “perspectives” essay, we contend that EA and entrepreneurial ecosystems research could be enriched by greater cross-fertilization. We respond to calls for more focus on the microfoundations of entrepreneurship by exploring the opportunities in research at the interface of EA and entrepreneurial ecosystems. We develop a multi-level framework to explain how EA is not only influenced by entrepreneurial ecosystems but can collectively influence the system-level functioning and leadership of ecosystems. Our framework clarifies how EA is shaped by the social, cultural, and material attributes of ecosystems and, in turn, how EA influences ecosystem attributes (diversity and coherence) and outcomes (resilience and coordination). We explain why it is critical to treat the environment as more than simply a moderating influence on the effects of EA and why it is fruitful for entrepreneurship research to develop a fuller picture of EA’s contextual determinants and outcomes. We conclude by proposing a research agenda that explores the interplay between EA and entrepreneurial ecosystems.

Calibration method of relative spectral response function of indirect imaging spectrometer

In imaging spectrometers, area array detectors are usually used as photoelectric conversion devices, but the inconsistency of the spectral response among pixels can distort the collected target spectra. To improve the spectral radiometric accuracy of imaging spectrometers, calibrating and correcting the inconsistency of the spectral response among pixels is essential. The signal received by each pixel of area array detector of the indirect imaging spectrometer is usually the superposition of the target multi-spectral radiation signals or full-spectral radiation signals. Therefore, its relative spectral radiometric calibration requires measuring the spectral response of each pixel at different wavelengths on the array detector. Under the ideal conditions, the response values of each pixel in the area array detector are different, so the indirect imaging spectrometer cannot simply calibrate the relative spectral response (RSR) function between pixels by using the method of “monochromator + integrating sphere”. In this work, taking the interferometric imaging spectrometer for example, we analyze the influence of the inconsistency of the RSR among pixels on the target spectral radiation measurement accuracy, and propose a system-level RSR function measurement method for the indirect imaging spectrometer based on the Fourier transform modulation calibration source. In addition, we establish a mathematical model for calibrating the RSR function,and provide guidelines for selecting calibration system parameters such as light source, spectral resolution, and OPD sampling interval. The simulation results show that under the ideal noise-free condition, the 1% spectral response inconsistency among pixels results in a relative error of 1.02% to the recovered spectra. After RSR correction, the relative error of the recovered spectra of different rows decreases to 0.08%. Furthermore, in this work we simulate and analyse the influence of spectral signal-to-noise ratio on the calibration accuracy of the RSR function, and point out that increasing the brightness of the calibration light source, extending exposure time, and combining multi-frame interferograms can enhance RSR function calibration accuracy in practical applications. The research result can provide a theoretical basis for realizing the relative spectral radiometric calibration of indirect imaging spectrometer, which is of great significance in promoting quantitative spectral remote sensing.

Agency, Goal-Directed Behavior, and Part-Whole Relationships in Biological Systems

In this essay we aim to present some considerations regarding a minimal but concrete notion of agency and goal-directed behavior that are useful for characterizing biological systems at different scales. These considerations are a particular perspective, bringing together concepts from dynamical systems, combinatorial problem-solving, and connectionist learning with an emphasis on the relationship between parts and wholes. This perspective affords some ways to think about agents that are concrete and quantifiable, and relevant to some important biological issues. Instead of advocating for a strict definition of minimally agential characteristics, we focus on how (even for a modest notion of agency) the agency of a system can be more than the sum of the agency of its parts. We quantify this in terms of the problem-solving competency of a system with respect to resolution of the frustrations between its parts. This requires goal-directed behavior in the sense of delayed gratification, i.e., taking dynamical trajectories that forego short-term gains (or sustain short-term stress or frustration) in favor of long-term gains. In order for this competency to belong to the system (rather than to its parts or given by its construction or design), it can involve distributed systemic knowledge that is acquired through experience, i.e., changes in the organization of the relationships among its parts (without presupposing a system-level reward function for such changes). This conception of agency helps us think about the ways in which cells, organisms, and perhaps other biological scales, can be agential (i.e., more agential than their parts) in a quantifiable sense, without denying that the behavior of the whole depends on the behaviors of the parts in their current organization.

Patterns of experience, expression, and physiology of stress relate to depressive symptoms and self-injurious thoughts and behaviors in adolescents: a person-centered approach.

Preliminary evidence shows that discordance in stress experience, expression, and physiology (EEP) in adolescents is linked to depression, suicidal ideation (SI), non-suicidal self-injury (NSSI), and brain functioning. This study employs person-centered analysis to probe the relationship between stress responses, psychopathology, and neural patterns in female adolescents who are oversampled for engagement in NSSI. Adolescent females (N = 109, ages 12-17) underwent a social stress test from which self-report measures of stress experience, observer ratings of stress expression, and physiological metrics of stress (via salivary cortisol) were obtained. Multi-trajectory modeling was employed to identify concordant and discordant stress EEP groups. Depressive symptoms, SI and attempt, NSSI engagement, frontal and limbic activation to emotional stimuli, and resting state fronto-limbic connectivity were examined in the EEP groups derived from the multi-trajectory models. Four groups were identified, three of which demonstrated relatively concordant EEP and one which demonstrated discordant EEP (High Experience-High Expression-Low Physiology). Further, replicating past research, the High Experience-High Expression-Low Physiology discordant group exhibited higher depressive symptoms, SI, suicide attempt, and NSSI episodes (only for sensitivity analyses based on past year) relative to other EEP groups. No significant group differences in brain functioning emerged. Results indicate that within-person, multi-level patterns in stress responding capture risk for dysfunction including depression and self-injurious thoughts and behaviors. Further interrogating of system-level stress functioning may better inform assessment and intervention efforts.

A Biologically Inspired Model of Collective Bio-nanomachine Rotation via Chemical and Physical Interactions.

Creating a large-scale functional system from a group of bio-nanomachines is a key objective for engineering innovative applications. This paper aims to create a large-scale system of bio-nanomachines and proposes a collective rotational motion model to describe their behaviour. The proposed model is based on the notion that spinning objects are stable against perturbations, meaning that a rotating cluster of bio-nanomachines may be stable and suitable for large-scale bio-nanomachine systems to be engineered for applications in dynamic and noisy environments. In developing the model, we draw inspiration from biological pattern formation, where biological entities interact chemically and physically to form a functional structure. Accordingly, we develop a collective rotational motion model of bio-nanomachines based on their chemical and physical interactions. Through modeling and simulations, we gain insight into the design and engineering of rotating clusters of bio-nanomachines. This paper demonstrates the importance of physical interactions in creating system-level functionality from a group of bio-nanomachines, giving rise to new challenges and opportunities in molecular communications research.

Wireless, Fully Implantable and Expandable Electronic System for Bidirectional Electrical Neuromodulation of the Urinary Bladder.

Current standard clinical options for patients with detrusor underactivity (DUA) or underactive bladder─the inability to release urine naturally─include the use of medications, voiding techniques, and intermittent catheterization, for which the patient inserts a tube directly into the urethra to eliminate urine. Although those are life-saving techniques, there are still unfavorable side effects, including urinary tract infection (UTI), urethritis, irritation, and discomfort. Here, we report a wireless, fully implantable, and expandable electronic complex that enables elaborate management of abnormal bladder function via seamless integrations with the urinary bladder. Such electronics can not only record multiple physiological parameters simultaneously but also provide direct electrical stimulation based on a feedback control system. Uniform distribution of multiple stimulation electrodes via mesh-type geometry realizes low-impedance characteristics, which improves voiding/urination efficiency at the desired times. In vivo evaluations using live, free-moving animal models demonstrate system-level functionality.

Topology-Based Resilience Metrics for Seismic Performance Evaluation and Recovery Analysis of Water Distribution Systems

Water distribution systems (WDSs) need to be resilient against seismic hazards to ensure rapid recovery of the community following an earthquake. Topology-based resilience metrics are often used to determine the system-level performance of WDSs. However, existing topology-based resilience metrics are unable to estimate seismic performance of WDSs accurately because they do not account for the vulnerability of pipelines in the metrics. This study tailored an existing topological metric and developed a new edge-betweenness-based topological metric for evaluating the seismic resilience of a complex water distribution network. System-level performance of WDSs is compared using four performance measures including minimum cut set (MCS)–based system reliability, topological resilience metric (TRM), modified TRM, and the newly developed edge-betweenness-based TRM. These metrics were applied for four WDSs (i.e., Anytown, New York Tunnel, Jilin, and Bellingham WDSs) with unique characteristics to validate their effectiveness in estimating the seismic performance of WDSs against seismic hazards. The outcomes of these applications show that the proposed TRM can be used to determine pipelines’ seismic performance and functionality after an earthquake with an acceptable accuracy compared with existing approaches. While the topology-based resilience analysis provides information about system-level functionality, it is also vital to determine an optimal recovery sequence for damaged WDSs to maximize the functionality during the recovery process. Therefore, an easy-to-use recovery strategy is proposed to determine the optimal recovery sequence based on a repair index. The optimal recovery strategy was tested for the recovery procedure for the damaged Anytown WDS due to an earthquake, and outcomes show the system functionality is restored quickest using the proposed optimal recovery strategy.

Probabilistic Seismic Multi-hazard Risk and Restoration Modeling for Resilience-informed Decision Making in Railway Networks

ABSTRACT Transportation systems, such as railways, are considered critical infrastructure. It is essential to identify potential hazards that can affect the functionality of these systems and quantify metrics that can be used for resilience-informed decision-making. This paper develops an integrated probabilistic model for seismic multi-hazard risk and restoration assessment of railway systems by accounting for the effects of seismic waves propagation, liquefaction and landslide as main phenomena affecting the integrity of distributed networked infrastructure; this is done via a GIS-based user interface. An illustrative case study is then presented to assess the seismic risk and restoration of the Tehran-Sari railway in Iran. The implementation results demonstrate the capability of the presented methodology to quantify physical metrics (including combined damage state of network components, component- and system-level functionality and restoration) and economic loss. These metrics can assist officials with implementing retrofit plans to reduce loss and improve the resilience of railway system segments.

Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom.

In higher plants, the complexity of a system and the components within and among species are rapidly dissected by omics technologies. Multi-omics datasets are integrated to infer and enable a comprehensive understanding of the life processes of organisms of interest. Further, growing open-source datasets coupled with the emergence of high-performance computing and development of computational tools for biological sciences have assisted in silico functional prediction of unknown genes, proteins and metabolites, otherwise known as uncharacterized. The systems biology approach includes data collection and filtration, system modelling, experimentation and the establishment of new hypotheses for experimental validation. Informatics technologies add meaningful sense to the output generated by complex bioinformatics algorithms, which are now freely available in a user-friendly graphical user interface. These resources accentuate gene function prediction at a relatively minimal cost and effort. Herein, we present a comprehensive view of relevant approaches available for system-level gene function prediction in the plant kingdom. Together, the most recent applications and sought-after principles for gene mining are discussed to benefit the plant research community. A realistic tabulation of plant genomic resources is included for a less laborious and accurate candidate gene discovery in basic plant research and improvement strategies.

A Flexible Energy Gateway for Hybrid Nanogrids

This article presents the topology and control of a three-port energy gateway (EG) for hybrid ac/dc nanogrids. The simple hardware architecture allows connecting renewable energy generators, energy storage (ES) devices, such as ultracapacitors, and the utility grid through three different interface converters, which, altogether, define the three-port EG of the nanogrid. The proposed EG represents a tradeoff between circuit complexity and controls flexibility, allowing: 1) operation of the ES port over a wide voltage range; 2) control of the local dc-bus voltage at a predefined set-point; 3) multidirectional power flow; and 4) support of the local ac-bus voltage with the possibility to transition into islanded operation. A hierarchical control strategy is presented that enables flexible power exchange between the ac and dc buses. At the top of the control hierarchy, a human–machine interface is dedicated to operation mode selection and parameter preset; then, a supervisory control layer is present for system-level monitoring and control functions; the lower layer of the hierarchy is constituted by converter control functions for power flow regulation, achieved leveraging on voltage and current controllers. The flexibility and effectiveness of the proposed EG architecture, control, and implementation are demonstrated in this article in a variety of operation modes by means of experimental results.

Highly efficient liquid droplet manipulation via human-motion-induced direct charge injection

The current electrowetting mechanisms show a low efficiency, although manipulating liquid droplets is essential to biological and chemical fields. Herein, we propose a highly efficient droplet manipulating method using direct charge injection (DCI) via human motion induced triboelectricity. A triboelectric nanogenerator (TENG) is used to provide both the charges and strong electric fields to drive the movement of liquid droplets. Using this method, the charge quantity and average velocity of the droplet (10 μL) reach 0.25 nC and 255 mm s−1, respectively, over 6 times higher than those of traditional methods (0.03 nC and 43.2 mm s−1). Alternative charge injection was demonstrated to enable both reciprocating and jumping motions of the droplet. Finally, we also successfully devised and demonstrated a platform with versatile system-level functions including droplets transportation, positioning, merging, and cleaning. This work advances the current droplet manipulation field via introducing a compelling approach using human-motion-induced direct charge injection.

Predicting the cumulative variation of 3-D mechanical assemblies using an ‘Idea Algebra’ framework

Improving productivity and performance of products require professional tolerance analysis and variation control and, in this context, to make sure the final product will achieve the aimed function, analysing the cumulative effects of component tolerances in an assembly is necessary. Such tolerance analysis generally involves complex and tedious calculations which take time and are susceptible to human error; therefore, for automating this process, computer-aided tolerance (CAT) systems have been used extensively. They are, however, complicated and also require relatively detailed definitions of an assembly (CAD models) before they can be used. In this paper, system topology with highly abstracted geometrical information is translated into an analytical vector model based on a semantic tool in the manner of an ‘idea algebra’. Tolerances may be defined at any level of the system (ranging from the component level to the system-level function), to more readily realise the design intent. This lightweight 3D-capable representation is possible already at an early concept stage, prior to CAD, and persists and is updated throughout the design process. Using a comparative numerical simulation-based study, it was demonstrated that the presented method provides quicker computation with equally accurate output as CAT, while requiring substantially lower complexity of input.

Seismic resilience of internet data center building with different disaster mitigation techniques

Internet data center buildings have great importance for maintaining the post-earthquake functionality of telecommunication networks. It is essential to maintain the functionality of internet data center buildings during earthquakes or recover immediately after earthquakes, which is referred to as seismic resilience. In this study, a seismic resilience assessment framework based on the Chinese code GB/T 38591-2020 is introduced first. The seismic damage and post-earthquake repair of both structural components and non-structural components are considered in the resilience assessment framework. A method for post-earthquake functionality loss quantification is proposed based on damage state and functionality loss of component. The subsystem level and system level functionality loss can be obtained by an integration principle. The seismic resilience of a typical internet data center building was evaluated to demonstrate the effectiveness of the proposed method. To enhance the seismic resilience level, different disaster mitigation techniques including the energy dissipation technology using buckling restrained braces and the base-isolation technology using lead-rubber bearings are adopted. The seismic resilience is quantified and the corresponding seismic resilience curves under different earthquake intensities are developed based on evaluation results.

Five state factors control progressive stages of freshwater salinization syndrome.

Factors driving freshwater salinization syndrome (FSS) influence the severity of impacts and chances for recovery. We hypothesize that spread of FSS across ecosystems is a function of interactions among five state factors: human activities, geology, flowpaths, climate, and time. (1) Human activities drive pulsed or chronic inputs of salt ions and mobilization of chemical contaminants. (2) Geology drives rates of erosion, weathering, ion exchange, and acidification-alkalinization. (3) Flowpaths drive salinization and contaminant mobilization along hydrologic cycles. (4) Climate drives rising water temperatures, salt stress, and evaporative concentration of ions and saltwater intrusion. (5) Time influences consequences, thresholds, and potentials for ecosystem recovery. We hypothesize that state factors advance FSS in distinct stages, which eventually contribute to failures in systems-level functions (supporting drinking water, crops, biodiversity, infrastructure, etc.). We present future research directions for protecting freshwaters at risk based on five state factors and stages from diagnosis to prognosis to cure.

Layers, folds, and semi-neuronal information processing

What role does phenotypic complexity play in the systems-level function of an embodied agent? The organismal phenotype is a topologically complex structure that interacts with a genotype, developmental physics, and an informational environment. Using this observation as inspiration, we utilize a type of embodied agent that exhibits layered representational capacity: meta-brain models. Meta-brains are used to demonstrate how phenotypes process information and exhibit self-regulation from development to maturity. We focus on two candidate structures that potentially explain this capacity: folding and layering. As layering and folding can be observed in a host of biological contexts, they form the basis for our representational investigations. First, an innate starting point (genomic encoding) is described. The generative output of this encoding is a differentiation tree, which results in a layered phenotypic representation. Then we specify a formal meta-brain model of the gut, which exhibits folding and layering in development in addition to different degrees of representation of processed information. This organ topology is retained in maturity, with the potential for additional folding and representational drift in response to inflammation. Next, we consider topological remapping using the developmental Braitenberg Vehicle (dBV) as a toy model. During topological remapping, it is shown that folding of a layered neural network can introduce a number of distortions to the original model, some with functional implications. The paper concludes with a discussion on how the meta-brains method can assist us in the investigation of enactivism, holism, and cognitive processing in the context of biological simulation.

On the operating speed and energy efficiency of GaN-based monolithic complementary logic circuits for integrated power conversion systems

Gallium nitride (GaN)-based power conversion systems exhibit striking competitiveness in realizing compact and high-efficiency power management modules. Recently emerging GaN-based p-channel field-effect transistors (FETs) and monolithic integration techniques enable the implementation of GaN-based complementary logic (CL) circuits and thereby offer an additional pathway to improving the system-level energy efficiency and functionality. In this article, holistic analyses are conducted to evaluate the potential benefits of introducing GaN CL circuits into the integrated power systems, based on the material limit of GaN and state-of-the-art experimental results. It is revealed that the propagation delay of a single-stage CL gate based on the commercial p-GaN gate power HEMT (high-electron-mobility transistor) platform could be as short as sub-nanosecond, which sufficiently satisfies the requirement of power conversion systems typically with operating frequencies less than 10 MHz. With the currently adopted n-FET-based logic gates (e.g., directly coupled FET logic) replaced by CL gates, the power consumption of peripheral logic circuits could be substantially suppressed by more than 103 times, mainly due to the elimination of the pronounced static power loss. Consequently, the energy efficiency of the entire system could be substantially improved.

Synaptic dysfunction of Aldh1a1 neurons in the ventral tegmental area causes impulsive behaviors

BackgroundAldh1a1 neurons are a subtype of gamma-aminobutyric acid (GABA) inhibitory neurons that use Aldh1a1 rather than glutamate decarboxylase (GAD) as an enzyme for synthesizing GABA transmitters. However, the behaviors and circuits of this newly identified subtype of inhibitory interneurons remain unknown.MethodsWe generated a mutant mouse line in which cyclization recombination enzyme (CRE) was expressed under the control of the Aldh1a1 promotor (Aldh1a1-CRE mice). Using this mutant strain of mice together with the heterozygous male Alzheimer’s disease (AD) related model mice (APPswe/PSEN1dE9, or AD mice) and a genetically modified retrograde and anterograde synaptic tracing strategy, we have studied a specific synaptic circuit of Aldh1a1 neurons with system-level function and disease progression in AD mice.ResultsWe demonstrate that Aldh1a1 neurons encode delay of gratification that measures self-control skills in decision making by projecting inhibitory synapses directly onto excitatory glutamate neurons in the intermediate lateral septum (EGNIS) and receiving synaptic inputs from layer 5b pyramidal neurons in the medial prefrontal cortex (L5PN). L5PN → Aldh1a1 synaptic transmission undergoes long-term potentiation (LTP). Pathway specific inhibition by either genetic silencing presynaptic terminals or antagonizing postsynaptic receptors impairs delay of gratification, resulting in the impulsive behaviors. Further studies show that reconstitution of Aldh1a1-deficient neurons with the expression of exogenous Aldh1a1 (eAldh1a1) restores Aldh1a1 → EGNIS synaptic transmission and rescues the impulsive behaviors in AD mice.ConclusionsThese results not only identify a specific function and circuit of Aldh1a1 neurons but also provide a cellular point of entry to an important but understudied synaptic mechanism for the induction of impulsive behaviors at an early stage of AD.