Compositional Constraints Research Articles

Robust optimization of unit commitment with wind power considering composite flexibility constraints

The availability of power system flexible resources depends on the network flexibility, which plays a vital role in operation security of power systems with large-scale renewable energy. In this paper, a robust optimization method of unit commitment with wind power considering the composite flexibility constraints is proposed, which can ensure the availability of node flexible resources and then improve composite flexibility of power system. Firstly, the composite flexibility constraints of unit commitment problem are established. The composite flexibility reflects the mechanism of the interaction between node flexibility and network flexibility, which can consider the availability of node flexible resources and enable more accurately assessment of the system operation flexibility. Secondly, the concept of network flexibility balance degree is proposed to guarantee the overall network flexibility balance, and the network flexibility balance degree and key line constraints are developed to guarantee the system network balance level and the network flexibility of key lines, while improving the convergence speed of solving algorithm. Finally, the effectiveness of the proposed method is investigated through the case study on modified IEEE 24-bus system.

New Compositional and Structural Constraints on the Smithsonian Microanalytical Reference Materials: Amphiboles from Kakanui and Arenal

The amphiboles from Kakanui and Arenal are two natural minerals that have been used worldwide as microanalytical reference materials, but their compositions and crystal structures are still poorly constrained. In this paper, we report new data on H2O and trace element mass fractions and single‐crystal structural refinement of these two amphiboles. H2O mass fractions of the Kakanui and Arenal amphiboles determined via Karl‐Fischer titration are 0.92 ± 0.18 (2s) and 1.56 ± 0.22% m/m (2s), respectively; these values estimated based on crystal‐structure refinement are 0.86 and 1.46% m/m, respectively. Trace element mass fractions measured via LA‐ICP‐MS in two laboratories are in good agreement, and spots from five fragments for both Kakanui and Arenal amphiboles are generally consistent within reproducibility precision (2s). Our measurements indicate a better homogeneity for the amphiboles from Kakanui than that from Arenal. According to the latest scheme for amphibole classification and nomenclature (Hawthorne et al. 2012), the sample from Arenal is a (partially dehydrogenated) pargasite, and that from Kakanui is a kaersutite. The significant amount of oxo‐component and that CTi4+ content is strongly ordered at the M(1) site for both amphiboles indicate crystallisation under high fO2 conditions.

The origin of platinum group minerals in oceanic crust

Abstract Highly siderophile elements (HSEs), including Re and Os, are used extensively as geochemical tracers and geochronometers to investigate the formation and evolution of Earth’s crust and mantle. Mantle rocks are commonly serpentinized, but the effect of serpentinization on the distribution of HSEs is controversial because HSEs are commonly hosted by rare, micrometer- to sub-micrometer-scale grains of platinum group minerals (PGMs) of ambiguous origin that are challenging to identify, characterize, and interpret. In this study, atom probe tomography (APT) is used to characterize two spatially close PGM grains hosted by a partially serpentinized harzburgite from Macquarie Island, Australia. The APT data reveal an extraordinary level of detail that provides insights into the origin of a complex Cu–Pt alloy grain (average composition ~Cu4Pt). The grain hosts Fe-, Ni-, and Pt-rich sub-grains associated with Rh, variably overlapping networks of Pd- and Cd-enrichment, and OH-rich volumes identified as fluid inclusions. Osmium and Ru are hosted by an idioblastic laurite (RuS2) grain. Compositional, textural, and phase-diagram constraints are consistent with a modified pre-serpentinization origin for the PGMs, and a comparison between observed and calculated grain distributions indicate that while Os isotope ratios were probably unaffected by serpentinization, whole-rock and grain-scale HSE and isotopic ratios may have been decoupled during serpentinization.

Compositionally Constrained Sites Drive Long-Branch Attraction.

Accurate phylogenies are fundamental to our understanding of the pattern and process of evolution. Yet, phylogenies at deep evolutionary timescales, with correspondingly long branches, have been fraught with controversy resulting from conflicting estimates from models with varying complexity and goodness of fit. Analyses of historical as well as current empirical datasets, such as alignments including Microsporidia, Nematoda, or Platyhelminthes, have demonstrated that inadequate modeling of across-site compositional heterogeneity, which is the result of biochemical constraints that lead to varying patterns of accepted amino acids along sequences, can lead to erroneous topologies that are strongly supported. Unfortunately, models that adequately account for across-site compositional heterogeneity remain computationally challenging or intractable for an increasing fraction of contemporary datasets. Here, we introduce "compositional constraint analysis," a method to investigate the effect of site-specific constraints on amino acid composition on phylogenetic inference. We show that more constrained sites with lower diversity and less constrained sites with higher diversity exhibit ostensibly conflicting signals under models ignoring across-site compositional heterogeneity that lead to long-branch attraction artifacts and demonstrate that more complex models accounting for across-site compositional heterogeneity can ameliorate this bias. We present CAT-posterior mean site frequencies (PMSF), a pipeline for diagnosing and resolving phylogenetic bias resulting from inadequate modeling of across-site compositional heterogeneity based on the CAT model. CAT-PMSF is robust against long-branch attraction in all alignments we have examined. We suggest using CAT-PMSF when convergence of the CAT model cannot be assured. We find evidence that compositionally constrained sites are driving long-branch attraction in two metazoan datasets and recover evidence for Porifera as the sister group to all other animals. [Animal phylogeny; cross-site heterogeneity; long-branch attraction; phylogenomics.].

Research on Multi-Decision Sinter Composition Optimization Based on OLS Algorithm

The adjustment of sintering raw materials has a decisive influence on the composition of blast furnace slag and the properties of sinter. In order to smelt high-quality molten iron in the blast furnace, the composition of the sinter must be properly adjusted so that the composition of the blast furnace slag and the metallurgical properties of the sinter are optimal for the quality of the iron and are conducive to the smooth operation of the blast furnace. In view of the huge difference in the quality and price of sintering raw materials, this paper proposes an automatic sintering ore blending model to quickly configure sintering raw materials according to the requirements of the production line. This method is based on the calculation process of blast furnace charge, combined with the constraints of process composition and cost performance, to establish a multi-decision sintering ore blending model based on the OLS(Ordinary least squares) algorithm to automatically screen from available raw materials and give the sinter that meets the requirements of the furnace. The plan finally makes TFe, CaO, MgO, SiO2, TiO2, Al2O3, P, Mn, Na2O, K2O, Zn, and other components meet the requirements of the production line, and meet the cost performance requirements of the enterprise for sinter. The model can complete the screening and proportioning of 43 kinds of raw materials within 10 s, and its performance can meet the requirements of the production of variable materials. Combined with an example, a comparative analysis experiment is carried out on the accuracy and practicability of the designed sintering and ore blending model. The experimental results show that the accuracy and efficiency of the method proposed in this paper are higher than those of the current ore blending scheme designed by enterprise engineers. This method can provide an effective reference for the stable operation of the sintering production line.

Textural and compositional constraints on the origin, thermal history, and REE mobility in the Lakeh Siah iron oxide-apatite deposit—NE Bafq, Iran

Textural and compositional constraints on the origin, thermal history, and REE mobility in the Lakeh Siah iron oxide-apatite deposit—NE Bafq, Iran

Discontinuous Dynamics of a 2-DOF Friction Oscillator with Rigid and Elastic Composite Constraints

Discontinuous Dynamics of a 2-DOF Friction Oscillator with Rigid and Elastic Composite Constraints

H2 and CH4 outgassing rates in the Samail ophiolite, Oman: Implications for low-temperature, continental serpentinization rates

Reduced (H2- and CH4-rich) and hyperalkaline fluids are products of subsurface reactions accompanying serpentinization of ultramafic rocks. H2 and CH4 produced during serpentinization can fuel microorganisms and support habitable subsurface environments. CH4 is also a potent greenhouse gas and can offset negative greenhouse emissions arising from active CO2 removal accompanying carbon mineralization in ultramafic rocks. However, the rate at which reduced volatiles are delivered to the surface and the rate of reactions that generate these volatiles at low-temperature conditions are poorly known. In this work, we measured H2 and CH4 outgassing rates in several hyperalkaline spring sites in the Samail ophiolite, Oman. H2 and CH4 outgassing in these sites are variable and range up to 70,000 and 7,000 mol yr−1, respectively. CH4 outgassing in spring sites are unlikely to offset negative carbon emissions estimated from active carbon mineralization reactions in the Samail ophiolite. However, diffused CH4 outgassing from peridotite outcrops remain unconstrained. Compositional and isotopic constraints show that volatiles are likely derived from active serpentinization, fluid inclusion decrepitation, or a combination of both. Calculated active serpentinization rates of up to 8 × 10−14 sec−1 account for measured outgassing rates and these are consistent with slow rates expected at low temperatures. In calculations of serpentinization rates, this work uses reaction-path models to account incorporation of both ferrous and ferric iron in the resulting alteration assemblages, which yields ∼0.3 mol H2 kg−1 of ultramafic rock altered, lower than simulations based on iron oxidation to magnetite only. Contribution from decrepitation of H2- and CH4-bearing fluid inclusions is possible but would require much more mass of ultramafic rocks to account for observed outgassing. Further studies can help quantify extents of each source on active outgassing in Oman. Overall, this work shows that low-temperature serpentinization on geologically short timescales can account for observed flux of reduced volatiles in hyperalkaline environments.

Poisoned Babies, Shot Fathers, and Ruined Experiments: Experimental Evidence in Favor of the Compositionality Constraint of Actual Causation

AbstractLivengood and Sytsma (2020) challenge the compositionality constraint of actual causation (CCAC), according to which each intermediary of a causal chain is an effect of its predecessor and a cause of its successor link. In several studies, they find support for their hypothesis that the CCAC is not in accordance with the ordinary causal attributions of laypeople. We argue that there are three interrelated problems in their studies’ design that we call the causality-responsibility confusion (CRC), the intermediary-ontology confusion (IOC), and the cause-end questioning (CEQ). Avoiding the CRC, the IOC, and the CEQ leads to strong empirical support for the CCAC.

Adaptive control of hypersonic vehicles using intelligent allocation

This paper proposes an intelligent allocation-based adaptive controller for the longitudinal motion of air-breathing hypersonic vehicles (AHVs). A control-allocation (CA) module is developed to deal with composite actuator servo constraints that have usually been neglected in existing AHV control works. This CA process is realized with the help of the recent deep deterministic policy gradient algorithm without involving any online optimization. In the followed adaptive controller design, several auxiliary signals are constructed to compensate for the possible CA error, while adaptive super-twist differentiators are employed to fast estimate the lumped effect of uncertain aerodynamic coefficients and unknown external disturbances. As a result, the adaptive control algorithm only needs three parameter updating laws, whose dimension is much lower than traditional adaptive control strategies for AHVs. Simulations are provided to verify the proposed intelligent allocation-based adaptive controller.

Analysis of codon usage patterns in Bupleurum falcatum chloroplast genome

ObjectiveIn order to distinguish the traditional Chinese medicine Bupleurum falcatum and its adulterants effectively and develop a better understanding of the factors affecting synonymous codon usage, codon usage patterns of chloroplast genome, we determine the complete chloroplast (cp) genome of B. falcatum and clarify the main factors that influence codon usage patterns of 78 genes in B. falcatum chloroplast genome. MethodsThe total genomic DNA of fresh leaves from a single individual of B. falcatum was extracted with EASYspin plus Total DNA Isolation Kit and 2 μg genome DNA was sequenced using Illumina Hiseq 2500 Sequencing Platform. The cp genome of B. falcatum was reconstructed with MITObim v1.8 and annotated in the program CPGAVAS2 with default parameters. Python script and Codon W were used to calculate the codon usage bias parameters. ResultsThe full length of B. falcatum cp genome was 155 851 bp, 132 different genes were annotated in this cp genome containing 80 protein-coding genes, 30 tRNA genes, and four rRNA genes. The codon usage models tended to use A/T-ending codons. The neutrality plot, ENC plot, PR2-Bias plot and correspondence analysis showed that both compositional constraint under selection and mutation could affect the codon usage models in B. falcatum cp genome. Furthermore, three optimal codons were identified and most of these three optimal codons ended with G/U. ConclusionThe cp genome of B. falcatum has been characterized and the codon usage bias in B. falcatum cp genome is influenced by natural selection, mutation pressure and nucleotide composition. The results will provide much more barcode information for species discrimination and lay a foundation for future research on codon optimization of exogenous genes, genetic engineering and molecular evolution in B. falcatum.

A novel Co-estimation framework of state-of-charge, state-of-power and capacity for lithium-ion batteries using multi-parameters fusion method

A battery management system can intelligently manage and maintain battery systems by effectively estimating and predicting battery internal states. Owing to battery nonlinear characteristics related to various influence factors, the estimation of battery internal states should consider the available capacity and ohmic internal resistance. This paper proposes a co-estimation framework for state of charge (SOC), state of power (SOP) and battery available capacity. Firstly, the first-order equivalent model method is used to identify the battery parameters by recursive least squares algorithm with variable forgetting factor, and the SOC-OCV curve of the battery is obtained by combining the ampere-time integration method. Secondly, three Kalman filters are utilized to estimate battery SOCs and the maximum available capacity and internal resistance are estimated by a forgetting factor recursive least square algorithm. Then peak current and power are estimated under the composite constraints of the estimated capacity and internal resistance. Finally, the experimental data are collected at temperatures 25 °C and 40 °C to verify and analyze the proposed method. The results of battery state estimation indicate that the proposed framework can accurate estimation battery internal states and also provide an effective reference for the driving of powered vehicles.

Interpretation of well logs and core data via Bayesian inversion

Estimating in situ petrophysical and compositional properties of rocks (e.g., porosity, mineralogy, and fluid saturation) from well logs and core measurements is critical for the evaluation of subsurface fluid resources. Traditional multimineral analysis of well logs is susceptible to abnormal borehole and geometric conditions, such as shoulder-bed effects and tool/borehole-related biases. Moreover, traditional multimineral analysis rarely includes uncertainty quantification, much less assessments of the impact of measurement noise, borehole conditions, and/or inaccurate rock-physics models (RPMs) on estimated properties. Probabilistic formation evaluation with credible-interval estimations can improve the outcome of mineral and fluid evaluations from well logs by explicitly incorporating the effects of measurement biases and a priori compositional and petrophysical constraints in the estimation. A probabilistic workflow is developed to estimate petrophysical/compositional properties from well logs and available core data. The workflow consists of two sequential steps: borehole measurements (e.g., density, resistivity, and gamma ray) are first converted into a layer-by-layer earth model, which consists of piecewise constant property values, with associated uncertainty estimations. The estimated earth model is tool/borehole independent, allowing for a common baseline to effectively compare properties among neighboring wells. Next, the estimated earth-model physical properties are used to derive petrophysical properties (e.g., volumetric concentration of fluid and solid rock constituents) via petrophysical joint inversion. In each step, Bayesian inversion is implemented with Markov chain Monte Carlo sampling in contrast to deterministic algorithms used by traditional multimineral methods. The developed method has the following advantages over traditional approaches: (1) it propagates uncertainty from well logs to petrophysical properties by assimilating measurement noise, tool/borehole-related biases, and RPM errors and (2) it enables the implementation of a priori knowledge per rock class to constrain the petrophysical estimation, effectively integrating field-specific core/regional geologic data. Examples of thinly laminated and organic shale formations successfully verify the reliability, efficiency, and accuracy of our estimation method.

Autonomous discovery of emergent morphologies in directed self-assembly of block copolymer blends.

The directed self-assembly (DSA) of block copolymers (BCPs) is a powerful approach to fabricate complex nanostructure arrays, but finding morphologies that emerge with changes in polymer architecture, composition, or assembly constraints remains daunting because of the increased dimensionality of the DSA design space. Here, we demonstrate machine-guided discovery of emergent morphologies from a cylinder/lamellae BCP blend directed by a chemical grating template, conducted without direct human intervention on a synchrotron x-ray scattering beamline. This approach maps the morphology-template phase space in a fraction of the time required by manual characterization and highlights regions deserving more detailed investigation. These studies reveal localized, template-directed partitioning of coexisting lamella- and cylinder-like subdomains at the template period length scale, manifesting as previously unknown morphologies such as aligned alternating subdomains, bilayers, or a "ladder" morphology. This work underscores the pivotal role that autonomous characterization can play in advancing the paradigm of DSA.

Event-triggered adaptive control of hypersonic vehicles subject to actuator nonlinearities

This paper investigates the event-triggered adaptive control problem for air-breathing hypersonic vehicles (AHVs) subject to physical actuator nonlinearities. In contrast with abundant AHV control strategies belonging to the traditional sample-data framework, the presented adaptive control works with an event-triggered mechanism, which naturally reduces the executing frequency of scramjet fuel-to-air equivalency ratio (FER) and aerodynamic control surface (ACS). During the event-triggered controller design, practical actuator characteristics including magnitude-rate composite constraints and servo transient dynamics are synthetically considered. In the velocity loop, the built-in FER saturation, which is a fusion of physical FER magnitude and rate constraints, is handled by reshaping the velocity reference with the help of an adaptive reference governor. Meanwhile in the altitude loop, a neural control allocator directly generates feasible ACS commands via a neural network that is well trained by means of the optimal control allocation. As the result, ACS magnitude-rate composite constraints and servo transient dynamics can be accommodated without implementing any time-consuming optimization searching process. Theoretical analysis indicates the guaranteed closed-loop stability and tracking performance while excluding the undesired Zeno behavior. Simulations validate the effectiveness of the proposed event-triggered adaptive control.

Deep Metric Learning with Adaptively Composite Dynamic Constraints.

In this paper, we propose a deep metric learning with adaptively composite dynamic constraints (DML-DC) method for image retrieval and clustering. Most existing deep metric learning methods impose pre-defined constraints on the training samples, which might not be optimal at all stages of training. To address this, we propose a learnable constraint generator to adaptively produce dynamic constraints to train the metric towards good generalization. We formulate the objective of deep metric learning under a proxy Collection, pair Sampling, tuple Construction, and tuple Weighting (CSCW) paradigm. For proxy collection, we progressively update a set of proxies using a cross-attention mechanism to integrate information from the current batch of samples. For pair sampling, we employ a graph neural network to model the structural relations between sample-proxy pairs to produce the preservation probabilities for each pair. Having constructed a set of tuples based on the sampled pairs, we further re-weight each training tuple to adaptively adjust its effect on the metric. We formulate the learning of the constraint generator as a meta-learning problem, where we employ an episode-based training scheme and update the generator at each iteration to adapt to the current model status. We construct each episode by sampling two subsets of disjoint labels to simulate the procedure of training and testing and use the performance of the one-gradient-updated metric on the validation subset as the meta-objective of the assessor. We conducted extensive experiments on five widely used benchmarks under two evaluation protocols to demonstrate the effectiveness of the proposed framework.

Cloud Service Composition using Firefly Optimization Algorithm and Fuzzy Logic

Cloud computing involves the dynamic provision of virtualized and scalable resources over the Internet as services. Different types of services with the same functionality but different non-functionality features may be delivered in a cloud environment in response to customer requests, which may need to be combined to satisfy the customer's complex requirements. Recent research has focused on combining unique and loosely-coupled services into a preferred system. An optimized composite service consists of formerly existing single and simple services combined to provide an optimal composite service, thereby improving the quality of service (QoS). In recent years, cloud computing has driven the rapid proliferation of multi-provision cloud service compositions, in which cloud service providers can provide multiple services simultaneously. Service composition fulfils a variety of user needs in a variety of scenarios. The composite request (service request) in a multi-cloud environment requires atomic services (service candidates) located in multiple clouds. Service composition combines atomic services from multiple clouds into a single service. Since cloud services are rapidly growing and their Quality of Service (QoS) is widely varying, finding the necessary services and composing them with quality assurances is an increasingly challenging technical task. This paper presents a method that uses the firefly optimization algorithm (FOA) and fuzzy logic to balance multiple QoS factors and satisfy service composition constraints. Experimental results prove that the proposed method outperforms previous ones in terms of response time, availability, and energy consumption.

A Prospective Study of Conventionally Fractionated Dose Constraints for Reirradiation of Primary Brain Tumors in Adults

Dose constraints for reirradiation of recurrent primary brain tumors are not well-established. This study was conducted to prospectively evaluate composite dose constraints for conventionally fractionated brain reirradiation. A single-institution, prospective study of adults with previously irradiated, recurrent brain tumors was performed. For 95% of patients, electronic dosimetry records from the first course of radiation (RT1) were obtained and deformed onto the simulation computed tomography for the second course of radiation (RT2). Conventionally fractionated treatment plans for RT2 were developed that met protocol-assigned dose constraints for RT2 alone and the composite dose of RT1+RT2. Prospective composite dose constraints were based on histology, interval since RT1, and concurrent bevacizumab. Patients were followed with magnetic resonance imaging including spectroscopy and perfusion studies. Primary endpoint was the rate of symptomatic brain necrosis at 6 months after RT2. Patients were enrolled from March 2017 to May 2018; 20 were evaluable. Eighteen had glioma, 1 had atypical choroid plexus papilloma, and 1 had hemangiopericytoma. Nineteen patients were treated with volumetric modulated arc therapy, and one was treated with protons. Median RT1 dose was 57 Gy (range, 50-60 Gy). Median RT1-RT2 interval was 49 months (range, 9-141 months). Median RT2 dose was 42.4 Gy (range, 36-60 Gy). Median planning target volume was 186 cc (range, 8-468 cc). Nineteen of 20 patients (95%) were free of grade 3+ central nervous system necrosis. One patient had grade 3+ necrosis 2 months after RT2; the patient recovered fully and lived another 18 months until dying of disease progression. Median overall survival from RT2 start for all patients was 13.3 months (95% credible interval, 6.3-20.7); for patients with glioblastoma, 11.5 months (95% credible interval, 6.1-20.1). Brain reirradiation can be safely performed with conventionally fractionated regimens tailored to previous dose distributions. The prospective composite dose constraints described here are a starting point for future studies of conventionally fractionated reirradiation.

Mineral compositional constraints on the petrogenesis of gabbroic and monzodioritic rocks in Rangpur District, NW Bangladesh

This research deals with the petrography and mineral chemistry of gabbroic and monzodioritic rocks to characterize the minerals, including their genesis and tectonic evolution. The rocks are dominated by gabbro in Barapaharpur and monzodiorite in Voktipur, and they contain plagioclase, hornblende, biotite, and magnetite, with few epidote and apatite, as well as clinopyroxene and orthopyroxene in gabbroic member. Chemical analyses of minerals were carried out by an electron microprobe analyzer. The chemistry of plagioclases is compositionally oligoclase-andesine and andesine, amphiboles are dominantly magnesiohornblende, biotites are Mg-biotites (phlogopite), and pyroxenes are diopside and enstatite types. The calculated P-T conditions of the gabbroic rocks yielded about 850–957 °C and 5.8–9.3 kbar, which are the highest geothermobarometric values in the region. However, the monzodioritic rocks yielded about 696–723 °C and 5.6–6.2 kbar. The estimated emplacement depths from the calculated highest pressure values equalize at ∼ 20–33 km and at ∼ 19–22 km, respectively. Biotite chemistry provides quantitative fO2 within QFM and HM buffers ranging from −10.6 to −12.0 in gabbroic rocks and −11.6 to −13.5 in quartz monzodiorite, respectively. These data are also echoed by amphibole and pyroxene mineral chemistry, which collectively demonstrate that the source materials were in a relatively higher oxidation state within arc magmatism. The mineral data also offer calc-alkaline I-type suites formed within subduction-related environments. However, gabbroic rocks show a dominantly island arc signature substantiating the suprasubduction zone, and also have the affinity of the island arc tholeiitic and calc-alkaline basaltic equivalents within orogenic environments and marginally boninitic affinities.

Adaptive Neural-Network-Based Fault-Tolerant Control for a Flexible String With Composite Disturbance Observer and Input Constraints

We propose an adaptive neural-network-based fault-tolerant control scheme for a flexible string considering the input constraint, actuator gain fault, and external disturbances. First, we utilize a radial basis function neural network to compensate for the actuator gain fault. In addition, an observer is used to handle composite disturbances, including unknown approximation errors and boundary disturbances. Then, an auxiliary system eliminates the effect of the input constraint. By integrating the composite disturbance observer and auxiliary system, adaptive fault-tolerant boundary control is achieved for an uncertain flexible string. Under rigorous Lyapunov stability analysis, the vibration scope of the flexible string is guaranteed to remain within a small compact set. Numerical simulations verify the high control performance of the proposed control scheme.