Desirable Performance Research Articles

Event-triggered model predictive tracking control of aero-engine with varying prediction horizon

Aero-engine is a complex thermal-mechanical system with strong nonlinearity, uncertainty, and time variation. Thus, it is crucial to design an effective controller for such a complex system to obtain the desired performances of the aero-engine. In recent years, model predictive control (MPC) has shown great potential in dealing with control problems with complex constraints of multi-variable systems, which has been applied to aero-engine control, achieving good results. Furthermore, the MPC strategy using an event-driven mechanism is good at balancing system resources and ensuring system control performances. In this paper, the problem of event-triggered MPC for aero-engine systems with bounded disturbances is studied. Firstly, an event-triggered strategy with a dynamic forced-trigger mechanism is proposed. Then, an MPC algorithm based on an event-triggered mechanism is designed. Finally, an application to the JT9D aero-engine model provided by T-MATS verifies the effectiveness of the designed algorithm. It is shown that the calculation load is significantly reduced, which proves the superiority of this method.

Designing a novel porous Ti3C2Tx MXene/MOF-based 3D-printed architecture as an efficient and easy recoverable adsorbent for organic dye removal from aqueous solution

ABSTRACT Recently, the 3D-printing approach has received considerable attention in the field of wastewater treatment. This is due to the ability to fabricate adsorbents with fine architectures that are easily recoverable through this technique. Here, the Ti3C2Tx MXene nanosheets were hybridised with zirconium-based metal-organic framework (UiO-66) through a simple in-situ solvothermal synthesis to develop an efficient adsorbent for organic dye removal. To avoid the secondary pollution of the powdery composite as well as to facilitate the recycling of adsorbent, the 3D-printed MXene/UiO-66 architectures with different lattice geometries of grid, star, and honeycomb were produced. The prepared adsorbent underwent thorough XRD, FTIR, SEM-EDS, TEM, BET, and UV-Vis absorption characterisation. Structural characterisations revealed the successful synthesis of MXene/UiO-66 composite with specific surface area of 347.28 m2/g and a large pore volume of 0.26 cm3/g, which was uniformly immobilised over and within the 3D-printed architectures with hierarchical porous structures. The 3D-printed MXene/UiO-66 architecture with honeycomb geometry exhibited desired dye removal performance for both the MO (88.95%) and DR31 (76.98%) dyes. The adsorption kinetic was analysed with different kinetic models and it was found that the adsorption followed the pseudo-first-order model. The 3D-printed MXene/UiO-66 architectures also displayed good reusability and stability after four cycles. This work suggests the 3D-printed MXene/UiO-66 architecture as a potential candidate for the removal of organic pollutants from wastewater and environmental protection.

SEH: Size Estimate Hedging Scheduling of Queues

For a single server system, Shortest Remaining Processing Time (SRPT) is an optimal size-based policy. In this article, we discuss scheduling a single-server system when exact information about the jobs’ processing times is not available. When the SRPT policy uses estimated processing times, the underestimation of large jobs can significantly degrade performance. We propose an index-based policy with a single parameter, Size Estimate Hedging (SEH), that only uses estimated processing times for scheduling decisions. A job’s priority is increased dynamically according to an SRPT rule until it is determined that it is underestimated, at which time the priority is frozen. Numerical results suggest that SEH has desirable performance for estimation error variance that is consistent with what is seen in practice.

An Unbiased Quantum Random Number Generator Based on Boson Sampling

AbstractIt is proven that Boson sampling is a much promising model of optical quantum computation, which is applied to designing quantum computer successfully, such as “Jiuzhang”. However, the meaningful randomness of Boson sampling results has not been utilized or exploited. In this research, Boson sampling is applied to design a quantum random number generator (QRNG) by fully exploiting the randomness of Boson sampling results, and its prototype system is constructed with the programmable silicon photonic processor, which can generate unbiased random sequences and overcome the shortcomings of the existing discrete QRNGs such as source‐restricted, high demand for the photon number resolution capability of detector and slow self‐detection generator speed. Boson sampling is implemented as a random entropy source, and random bit strings with satisfactory randomness and uniformity can be obtained after post‐processing the sampling results. It is the first approach for applying the randomness of Boson sampling results to develop a practical prototype system for actual tasks, and the experiment results demonstrate that the designed Boson sampling‐based QRNG prototype system passes 15 tests of the NIST SP 800‐22 statistical test component, which proves that Boson sampling has great potential for practical applications with desirable performance besides quantum advantage.

Ventilation arrangement evaluation and proactive goaf inertisation for spontaneous combustion and gas explosion management during longwall panel sealing-off process.

When a longwall face approaches the finish-off line, 1month is normally required to relocate the longwall equipment and seal the longwall panel, during which time the goaf gas atmosphere changes and the risk of spontaneous combustion and gas explosion considerably increases. To minimise the occurrence of these hazards, an improved insight into gas flow dynamics within the longwall panel is essential during the panel sealing-off process. Based on mining conditions of an Australian underground coal mine, three-dimensional computational models were developed and calibrated with onsite gas monitoring data, allowing for evaluating ventilation arrangements and understanding methane dispersion in the longwall workings during the six-stage panel sealing-off process with confidence. The simulation results indicate that nitrogen should be injected on the travel road side at a distance of 120m behind the longwall face at a rate of 0.75 m3/s and the rear of the travel road should be tightly sealed at the final sealing-off stage, resulting in oxygen levels lowering than 5% in the longwall workings and producing desired panel sealing-off performance. In addition, gas sensors should be employed and positioned at the appropriate locations to reliably monitor goaf atmosphere change. This study sheds improved insights into evaluating ventilation arrangements and understanding gas flow dynamics during the panel sealing-off process and provides critical knowledge of effective proactive goaf inertisation strategies, thus minimising the risk of spontaneous heating and gas explosion and reducing environmental pollution induced by these hazards.

Half‐Metallic PN2 Monolayer as High‐performance Anode Material for Metal Ion (Li, Na and K) Batteries: A First‐Principles Study

AbstractSearching for an appropriate anode material with desirable electrochemical performance is crucial in the development of metal ion batteries. In this work, based on the extensive first‐principles computations, we have systemically assessed the potential of an emerging PN2 monolayer as an anode material for lithium (LIBs), sodium (NIBs) and potassium (KIBs) ion batteries. The PN2 monolayer possesses excellent thermal, dynamical and mechanical stability, and it is half‐metallic with two energy bands crossing over the Fermi level in the spin‐down channel. Moreover, the diffusion barriers of Li, Na and K atoms on the PN2 are as low as 0.11, 0.09 and 0.05 eV, showing a high ionic mobility. The storage capacities of PN2 anode are predicted to be 2725.53, 1413.24, and 908.51 mAh/g for LIBs, NIBs and KIBs. Importantly, the PN2 anode exhibits strong affinity towards metal atoms and negligible structural distortion during the whole intercalation process, which is of importance to improve the cyclability and prevent the metallic dendrite formation. All the encouraging findings demonstrate that the PN2 anode holds great promise in the high‐performance metal ion battery application.

Additive manufacturing TPMS lattice structures: Experimental study on airflow resistivity

Triply periodic minimal surface structures (TPMS) are continuously adopted in the architectural engineering and aircraft sectors in pursuit of noise mitigation. The airflow resistivity significantly influences the acoustic performance of lattice structures. The present work is a step toward designing appropriate structures of TPMS to obtain desired acoustics performance using the standard of ISO 9053–1:2018(E). In this work, the four TPMS lattices Gyroid, Diamond, Lidinoid, and Split P have been designed with three different parameters (porosity, sample length, and diameter) to predict the airflow resistivity and pressure drop of the structures. Various experiment sets were conducted on customized airflow resistivity experimental apparatus for cylindrical lattices with multiple parameters to identify the most effective lattice structures. Based on the findings, it was observed that lattice structures with less porosity, sample length, and sample diameter depict the highest level of airflow resistivity and pressure drop. However, for all four lattices, close differences in airflow resistivity values were noticed.

One-step synthesis of AIE-active fluorescent cyclomatrix polyphosphazene spheres as drug delivery carriers

The functional materials with aggregation-induced emission (AIE) feature have been extensively explored for biomedical applications, however, the synthesis of AIE-active functional materials with biodegradable potential and controlled drug delivery behavior have been seldom demonstrated. In this work, we reported for the first time that AIE-active fluorescent cyclomatrix polyphosphazene spheres (named as QM-HCCP-PEG) can be facilely fabricated via a one-step nucleophilic substitution to conjugate hexacyclotriphosphazene (HCCP), amino modified polyethylene glycol (PEG-NH2) and an AIEgen (QM-OH). The resultant QM-HCCP-PEG shows amphiphilicity and can be capable of self-assembling into spheres with high water dispersibility and desirable fluorescence performance. During self-assembly, antitumor drug bortezomib (BTZ) can be encapsulated in the core of QM-HCCP-PEG with a relative high drug loading capacity up to 12.2%. In vitro drug release profile showed that the micelles could successfully release BTZ in physiological solution. Moreover, the QM-HCCP-PEG displayed excellent biological imaging ability for its outstanding AIE feature and BTZ-loaded nanospheres exhibited significant cytotoxicity against MDA-MB-231 cells. Thus, this work provides new perspective for fabrication of AIE-active functional materials via a convenient and highly efficient experimental procedure and the resultant AIE-active functional materials are of great potential as fluorescent probes and drug delivery vehicles.

Elevational clines predict genetically determined variation in tropical forest seedling performance in Borneo: implications for seed sourcing to support reforestation

While much research has focused on genetic variation in plants in relation to abiotic clines in temperate and boreal forests, few studies have examined similar relationships in tropical forests. Genetic variation in desirable performance traits of trees, such as drought tolerance, fast‐growth, and carbon sequestration rates, is widely used to improve reforestation efforts in nontropical systems. However, evolutionary processes such as local adaptation are poorly understood in tropical forests making it difficult to locate desired phenotypes. To test for genetic variation in growth rate in relationship to climatic clines, we conducted a common garden study over 18 months in a nursery using four dipterocarp tree species, represented by 9–12 half‐sib families, sourced across an elevational gradient ranging from lowland to hill forests (circa 130–470 m above sea‐level) in Malaysian Borneo. We found genetic variation in growth for all four species with fast‐growing half‐sib families growing 42–88% faster than poorly performing half‐sib families. Furthermore, in three species we found that elevation of seedling origin predicted seedling performance; in Shorea fallax and S. johorensis, half‐sib families originating from low elevations performed the best. In S. argentifolia half‐sib families' seedlings from low elevations grew slowly. Because elevation is a good proxy for climate, the finding of elevational clines predicting genetic variation in growth provides evidence of evolution affecting the function of tropical tree species. Our research highlights opportunities to better understand evolutionary processes in tropical forests and to use such information to improve seed source selection in reforestation.

Corporate integrity culture on environmental, social, and governance (ESG) performance

AbstractRecent research has viewed desirable environmental performance as the proxy of corporate integrity culture. Based on the stakeholder theory, we extend this literature by examining whether the corporate integrity culture is associated with environmental, social, and governance (ESG) performance among China A‐listed firms. We first build the integrity culture dictionary by the word embedding model. We also absorb the social influence into the integrity dictionary and identify corporate integrity culture through textual analysis. The results show that corporate integrity culture is positively associated with ESG performance. This research further discloses that this relationship is from both an indirect of integrity on green innovation and a direct effect of integrity in signaling internal stakeholders' behavioral integrity and verifying integrity culture to external stakeholders. Overall, the empirical evidence further enriches our understanding of the relationship between ESG performance and corporate integrity culture in China.

Conceptual Design and Energy Efficiency Evaluation for a Novel Torque Vectoring Differential Applied to Front-Wheel-Drive Electric Vehicles

This paper presents a novel differential with lateral torque vectoring activated by an auxiliary motor, called motor-modulated lateral torque vectoring differential, or briefly, MLTD. Its architecture and kinematic characteristics are described, and the optimal cornering performance due to torque vectoring is evaluated using a steady-state vehicle dynamic model. Then, a conceptual design case of MLTD installed in a front-wheel-drive electric vehicle is conducted to assess its feasibility in terms of cornering, driving performances, and energy efficiency performance. The calculations show that an optimal distribution ratio between left and right torques for maximum lateral acceleration can be obtained for a specific cornering condition, further used for DM sizing in the preliminary stage. The simulations for energy consumption at constant-speed turning reveal that energy efficiencies of MLTD are lower than those of conventional differentials with evenly distributed torques. However, this deficiency may be paid off by moderately cutting down the rolling resistance of tire while the superior cornering performances brought by torque vectoring is still preserved. Accordingly, the newly proposed MLTD possesses greater flexibility to improve energy efficiency and driving range of electric vehicles without trading off against its desirable cornering performance and safe handling, and is thus worthy of further development.

DCapsNet: Deep capsule network for human activity and gait recognition with smartphone sensors

Recently, deep neural networks are used to recognize human activity/gait through mobile sensors which have attracted a great attention. Although the existing deep neural networks that perform automatic feature extraction have achieved desirable performance, their classification accuracy needs to be improved. In this paper, a deep neural network that combines a set of convolutional layers and capsule network is proposed. The proposed architecture named DCapsNet is suited to automatically extract the activity or gait features through built in sensors and classify them. The convolutional layers of the DCapsNet are more suitable for processing temporal sequences and provide scalar outputs but not the equivariance. The capsule network (CapsNet) is then trained by a dynamic routing algorithm to capture the equivariance having a magnitude and orientation, which increases the efficiency of the model classification. The performance of the proposed model is evaluated on four public datasets: two HAR datasets (UCI-HAR and WISDM) and two gait datasets (WhuGAIT). The recognition accuracy of the proposed model for the UCI-HAR and WISDM datasets are 97.92 % and 99.30 %, respectively, and for the WhuGAIT Dataset #1 and Dataset #2 are 94.75 % and 97.16 %, respectively. Experimental results show that the proposed model achieves the highest recognition accuracy over the reported results of the state-of-the-art models.

Simple preparation of Si@C composite and its improved lithium storage performance

In spite of improved performance, a simple preparation approach for Si/C anodes is still urgently needed. Herein, a very simple approach is demonstrated to prepare Si/C composite (Si@C), just consisting of simple mixing and carbonization processes. Moreover, this Si@C anode shows outstanding performance with 1301.9 and 583.3 mAh/g after 100 and 500 cycles at 100 and 1000 mA g−1, respectively, which is superior to many reported Si/C composites. Thus simple preparation approach and desirable performances offer Si@C anode promising in application of lithium-ion batteries. Also this preliminary research offers meaningful insight into simplifying techniques for preparing advanced Si/C anodes.

Effect of nano-ZrO2 protective layer on the microstructure and surface property strengthening mechanism of underwater wet laser Fe-based cladding layer

Underwater wet laser cladding technology has become one of the research hotspots in the marine engineering, but the existence of the water environment inevitably leads to process instability and defects. In this study, the nano-ZrO2 protective layer was used to increase the viscosity of the molten pool surface to resist the impact force generated by the high-speed water jet, and the underwater wet laser Fe-based cladding layer with good forming quality and desirable performance was successfully prepared. The nano-ZrO2 protective layer improved the formability of the cladding layer and guaranteed the depth of the melt pool, while reducing the depth of the heat affected zone. The addition of nano-ZrO2 particles reduced the average grain size of the cladding layer from 4.90 μm to 3.66 μm. In addition,the corrosion resistance of the cladding layer was greatly improved with the application of the nano-ZrO2 protective layer, with the evident passivation region of 0.74 V. The wear rate of the cladding layer with the nano-ZrO2 protective layer (0.64×10-15m3N-1m-1) was significantly lower than that of the substrate (9.01×10-15m3N-1m-1), and the excellent wear resistance of the cladding layer with nano-ZrO2 protective layer benefited from the good carrying capacities and high microhardness of the microstructure with ZrO2 particles. This study reveals the strengthening mechanism of nano-ZrO2 protective layer on the Fe-based cladding layer, which can provide theoretical guidance for the development of the underwater laser cladding.

Innovative geopolymer-based cold asphalt emulsion mixture as eco-friendly material

In recent years, there has been a growing interest in cold asphalt emulsion mixture (CAEM) due to its numerous advantages, including reduced CO2 emissions, energy savings, and improved safety during construction and application. However, CAEM has often been considered inferior to hot mix asphalt (HMA) in terms of performance. To address this issue and achieve desirable performance characteristics, researchers have been exploring the modification of CAEM using high-cost additives like ordinary Portland cement. In this study, the focus was on investigating the effects of utilizing waste alkaline Ca(OH)2 solution, ground granulated blast-furnace slag (GGBFS), and calcium carbide residue (CCR) as modifiers to enhance the properties of CAEM. The aim was to develop an innovative geopolymer geopolymer-based cold asphalt emulsion mixture (GCAE). The results of the study revealed that the use of waste alkaline Ca(OH)2 solution led to an increase in early hydration, which was confirmed through scanning electron microscopy. Furthermore, the experimental findings demonstrated that waste alkaline Ca(OH)2 solution significantly contributed to the rapid development of early-age strength in GCAE. As a result, GCAE showed great potential for utilization in pavement applications, particularly for roads subjected to harsh service conditions involving moisture and temperature. By exploring these alternative modifiers, the study highlights a promising avenue for enhancing the performance of CAEM and potentially reducing the reliance on expensive additives like ordinary Portland cement. The development of GCAE has the potential to offer improved performance and durability in pavement applications, thus contributing to sustainable and efficient road infrastructure.

Knee extension tracking and fatigue regulation results using a robust MPC approach in a hybrid exoskeleton

A nonlinear model predictive controller (NMPC) is a promising approach for cooperative control of functional electrical stimulation (FES) and an exoskeleton to assist limb function in people with spinal cord injury (SCI). Potentially, the cooperative control can mitigate the effects of FES-induced muscle fatigue, a significant technical challenge impeding the clinical translation of FES. However, previous NMPC-based cooperative designs have been limited to limb regulation and may not include fatigue regulation in the control objective. In this paper, we design a new NMPC law for limb tracking and fatigue regulation and present experimental results from participants with no disability and a participant with SCI. For robust tracking, this paper augments the NMPC with a Lyapunov-based feedback controller. A terminal controller and terminal constraint regions are specifically derived to show the robust NMPC scheme’s recursive feasibility and to satisfy the input constraints. The experimental results of continuous knee tracking show dynamic allocation of exoskeleton assistance despite decreasing control effectiveness of FES due to muscle fatigue. The robust tube-based MPC reduced the knee angle’s root mean square tracking error by 34% compared to a nominal MPC. It elicited cooperative behavior when it automatically modulated (increased or decreased) FES and motor inputs for different desired fatigue levels while maintaining desired knee angle tracking performance. The statistical t-test showed that the FES input was significantly reduced in the post-fatigue trial (with the desired fatigue level of 1), and the muscle was significantly less fatigued compared to the pre-fatigue experiment (with the desired fatigue level of 0.5).

Knowledge-informed Variational Bayesian Gaussian mixture regression model for predicting mixed oil length

Precise prediction of mixed oil length is a critical task in the multi-product pipeline. Current predictive models may not achieve desirable performance as they neglected the multimode characteristics of the data, which has been alleviated by using the finite mixture model (FMM) to identify the different modes and develop localized predictive models while it faces the challenge of assigning a suitable number of components. Variational Bayesian Gaussian mixture regression model (VBGMR) may remedy these deficiencies but the linear assumption between variables can result in unsatisfactory performance, and it may also not handle the model selection well since all input variables are forced to participate in the mode identification process, even if some are not important. To address such issues, this article proposes a new modeling approach named as the Knowledge-informed Variational Bayesian Gaussian mixture regression model (KIVBGMR) by incorporating the conventional VBGMR with the guidance of the domain knowledge. Subsequently, the learning procedure for the KIVBGMR based on the Variational Inference method is developed. The performance of the KIVBGMR modeling method is evaluated based on two case studies including a numerical example and a real-life industrial dataset, which demonstrates the performance superiority of the proposed method.

Diagnosis of Esophageal Squamous Cell Carcinoma by High-Performance Serum Metabolic Fingerprints: A Retrospective Study.

Esophageal squamous cell carcinoma (ESCC) is a highly prevalent and aggressive malignancy, and timely diagnosis of ESCC contributes to an increased cancer survival rate. However, current detection methods for ESCC mainly rely on endoscopic examination, limited by a relatively low participation rate. Herein, ferric-particle-enhanced laser desorption/ionization mass spectrometry (FPELDI MS) is utilized to record the serum metabolic fingerprints (SMFs) from a retrospective cohort (523 non-ESCC participants and 462 ESCC patients) to build diagnostic models toward ESCC. The PFELDI MS achieved high speed (≈30 s per sample), desirable reproducibility (coefficients of variation < 15%), and high throughput (985 samples with ≈124 200 data points for each spectrum). Desirable diagnostic performance with area-under-the-curves (AUCs) of 0.925-0.966 is obtained through machine learning of SMFs. Further, a metabolic biomarker panel is constructed, exhibiting superior diagnostic sensitivity (72.2-79.4%, p < 0.05) as compared with clinical protein biomarker tests (4.3-22.9%). Notably, the biomarker panel afforded an AUC of 0.844 (95% confidence interval [CI]: 0.806-0.880) toward early ESCC diagnosis. This work highlighted the potential of metabolic analysis for accurate screening and early detection of ESCC and offered insights into the metabolic characterization of diseases including but not limited to ESCC.

Efficient Auto‐scaling for Host Load Prediction through VM migration in Cloud

SummaryThe expeditious deployment of Cloud applications and services on wide‐ranging Cloud Data Centres (CDC) gives rise to the utilization of many resources. Moreover, by the increase in resource utilization, virtualization also greatly impacts achieving desired performance. The major challenges in virtualization are detecting over‐utilized or under‐utilized hosts at the right time and the proper scaling of Virtual Machines (VM) on the accurate host. Auto‐scaling in Cloud Computing allows the service providers to scale up or down the resources automatically and provides on‐demand computing power and storage capacities. Effective utilization and autonomous scaling of resources eventually reduce the load, energy consumption, and operating costs. In this paper, an efficient auto‐scaling approach for predicting host load through VM migration has been proposed. The ensemble method using different time‐series forecasting models has been proposed to forecast the approaching workload on the host. Based on this predicted load, different algorithms have been devised to detect over‐utilized and under‐utilized hosts and VMs can be migrated. The designed approach has been validated by experimentation on a real‐time Google cluster dataset. The proposed technique significantly improves average CPU utilization and reduces over‐utilization and under‐utilization. It also minimizes response time, service level agreement violations, and the slighter number of migrations and scaling overhead.

Event-triggered finite-time [formula omitted] control of switched systems via composite anti-bump switching control input

This paper discusses the event-triggered composite anti-bump H∞ control problem for the networked switched systems in the finite-time stability sense. An event-triggered scheme (ETS) is constructed to reduce resource usage while maintaining desired control performance. Then, a novel switching bumpless control method based on ETS is given to resist the impact of bump transition between different modes. Sufficient criteria for finite-time boundedness of the closed-loop systems with bumpless transfer characteristics and H∞ performance are derived by resorting to a mode-dependent average dwell time (MADT), and a trigger-based bumpless transfer controller is designed. Finally, a simulation example is provided to demonstrate the efficacy and feasibility of the proposed control strategy.