Performance In Terms Of Stability Research Articles

Enhancement of Field Oriented Control for Permanent Magnetic Synchronous Motor using Ant Colony Optimization

Because of its frequent use in diverse systems, the PMSM drive must be controlled. Field-oriented control (FOC) based PMSM drive is modeled in the present work to optimize the torque and speed performance of the PMSM. The FOC is based on a dissociated speed and flux control approach, which controls the speed and flux of the PMSM independently. The standard Proportional Integrator Derivative (PID) controller regulates the speed in FOC, which is noted for its increased resilience in linear systems, however in nonlinear ones, the PID controller responds poorly to changes in the system’s variables. In this case, the best solutions are frequently based on optimization techniques that produce the controller’s gains in every period. Optimizing the PID’s behavior in response to the system’s nonlinear behavior. The novel proposed strategy for enhancing the gains of the PID controller by employing a cost function such as Integral Time Absolute Error (ITAE) is based on PID speed regulation and is optimized using the Ant Colony Optimization algorithm (ACO) for FOC. To confirm the strategy’s aims, the suggested method is implemented on Matlab/Simulink. The simulation results demonstrated the efficiency of the intelligent ACO-FOC control, which delivers good performance in terms of stability, rapidity, and torque fluctuations.

Smartphone-integrated colorimetric sensor for rapid and highly selective detection of spermine in food based on the laccase-mimicking activity of flower-shaped Mn3O4 nanoparticles

Colorimetric methods have the benefits of rapid detection and easy operation in the detection of spermine (SPM). However, the existing colorimetric sensor for detecting SPM still face challenges in terms of performance stability, result reliability and safety of sensing materials. Herein, it was first found that flower-like Mn3O4 nanoparticles (Mnf) exhibit excellent laccase-mimicking activity. Since SPM can effectively inhibit this activity, a colorimetric sensor was developed to detect SPM in food. The addition of positive charged SPM will induce the aggregation of multiple Mnf with negative charge through electrostatic interactions. In this case, the specific surface area of Mnf is reduced, and some surface-active sites are occupied by SPM, leading to a decrease in the laccase-mimicking activity of Mnf. Consequently, the colorimetric sensor appears light pink. Furthermore, a smartphone was utilized to convert the color of the reaction solution into the corresponding G/B value, facilitating on-site and fast analysis of SPM quantity. The proposed colorimetric sensor exhibits outstanding sensitivity and selectivity, with a limit of detection (LOD) as low as 0.094 μg·mL−1. The colorimetric sensor also features stable performance, reliable results and environmentally friendly safety, all of which will provide strong support for the monitoring of SPM in the food industry.

Research on establishing a joint time-scale of pulsar time and atomic time based on a wavelet analysis method

ABSTRACT Pulsar time has different physical mechanisms from atomic time, and these two time-scales are complementary in terms of stability performance. The joint timekeeping of pulsar and atomic time is currently an important application direction. Our research utilizes a wavelet analysis method to perform multiscale analysis of time signals in both the time and frequency domains, and assigns corresponding weights to different frequency bands for synthesis, which solves the problem of balancing long- and short-term stability in unified weighted synthesis. In this paper, a joint time-scale APT (atomic pulsar time) is obtained by a fusion of pulsar ensemble time (EPT) and atomic time TA(NTSC) (AT) based on wavelet analysis. The results show that APT effectively integrates the stability advantages of both EPT and AT, and the short-term stability of APT at 0.4 yr is consistent with that of AT, reaching 5.6 × 10−15 as evaluated by σz variance. The stability of APT remains consistent with EPT after 3.2 yr, and the long-term stability at 13.1 yr is close to 1.3 × 10−16, perfectly retaining the excellent long-term stability characteristics of pulsar time. The root mean square of APT’s clock difference relative to TT(BIPM) is much lower than that of TA(NTSC). Moreover, we propose ways to improve the stability of APT through experimental comparisons. The results indicate that APT has the potential to serve as a reference for steering UTC(NTSC) in the future, improving the long-term timekeeping capability of local atomic time in China, thereby comprehensively improving the stability and reliability of China’s time reference.

Clarifying the Limitations of Copper Hexacyanoferrate in Rechargeable Aqueous Zinc-Ion Batteries Via in Situ X-Ray Techniques

Copper hexacyanoferrate (CuHCF) is a compound belonging to the Prussian Blue Analogue (PBA) family, known for its open structure with cubic symmetry and large cavities capable of accommodating various cations, such as alkali-metal ions and divalent cations, making it an interesting candidate for ion-insertion batteries.CuHCF has been increasingly attracting attention for innovative approaches in rechargeable aqueous batteries which represent an emerging alternative technology for realizing sustainable and efficient stationary storage of intermittent electricity produced via renewable sources1.CuHCF stands out as an environmentally friendly cathode material in rechargeable aqueous Zn-ion batteries (ZIBs), enabling the realization of cost-effective and safe cells that support elevated charge/discharge rates and thus provide high power delivery2, 3. Relying only on abundant, inexpensive and non-toxic materials, combined with non-flammable and mildly acidic electrolytes, Zn/CuHCF cells are an attractive electrochemical system for addressing the challenges of developing energy storage devices for power grids due to their quick response and contained fabrication costs, although their energy density is intrinsically limited4 (< 100 Wh·kg-1).CuHCF displays one of the highest operating potentials (≈1.7 V vs. Zn2+/Zn) among cathode materials in aqueous ZIBs2, 3 and a modest capacity of ≈60 mAhg-1. Yet, what sets CuHCF apart is its ability to undergo ion insertion/de-insertion with negligible structural variations or volume changes5, making these electrodes apt for extensive cycling under heavy-duty conditions without risking induced mechanical or electrical damages causing early cell failure.However, a series of obstacles are present along the way of Zn/CuHCF cell development. Aside from issues on the Zinc side (e.g. uncontrolled Zn deposition/stripping), notoriously challenging in terms of interface stabilization6 and effective utilization4, significant limitations arise from the CuHCF ageing mechanisms during cycling. Such mechanisms have remained elusive so far, mostly due to the complexity of this compound and a simultaneous interaction of various species in the electrochemical processes. To date, there have been only speculative attempts to explain the capacity fading in CuHCF and, consequently, only a few scenarios have been suggested for it. Possible phase segregation and formation of spurious, non-stoichiometric Zn-rich phases (e.g. ZnxCu1-xHCF) have been argued7-9, while also signs of Cu dissolution were previously detected10, yet thought to be minimal and not affecting the CuHCF performances.Here, we will shed light on the limitations of CuHCF during cycling in ZIBs and explore its ageing mechanism by conducting a thorough study employing X-ray techniques11. Our investigation reveals that CuHCF is less stable than previously assumed, and that its debated Cu dissolution is linked to the ageing process11, forming Cu vacancies that can accept Zn2+. This Cu dissolution also results in a gradual activation of the Fe3+/Fe2+ couple, which has unambiguously been assigned to the appearance of a plateau on charge at ≈1.8 V vs. Zn2+/Zn (Figure 1). This is believed as the main contributor to the subsequent capacity loss. These insights into the CuHCF ageing in ZIBs may help in devising strategies to mitigate this critical aspect and improve battery performances in terms of stability and cycle life. Figure 1. (a) Scheme of an aqueous Zn/CuHCF cell. (b) Galvanostatic charge-discharge profiles of a Zn/CuHCF cell between 1.00 and 2.15 V at 0.6 mAcm-2. Note the emergence of a sloping plateau around 1.8 V and a slight capacity loss after 200 cycles. Acknowledgements M.V. acknowledges the funding by the ÅForsk Foundation, the support by the Swedish Electromobility Centre, the Swedish Energy Agency and StandUp for Energy. References C. D. Wessells, R. A. Huggins and Y. Cui, Nature communications, 2011, 2, 1-5.R. Trócoli and F. La Mantia, ChemSusChem, 2015, 8, 481-485. Z. Jia, B. Wang and Y. Wang, Materials Chemistry and Physics, 2015, 149, 601-606.G. Zampardi and F. La Mantia, Nature communications, 2022, 13, 687. V. Renman, D. O. Ojwang, M. Valvo, C. P. Gómez, T. Gustafsson and G. Svensson, Journal of Power Sources, 2017, 369, 146-153. Z. Cao, P. Zhuang, X. Zhang, M. Ye, J. Shen and P. M. Ajayan, Advanced Energy Materials, 2020, 10, 2001599.G. Kasiri, R. Trócoli, A. B. Hashemi and F. La Mantia, Electrochimica Acta, 2016, 222, 74-83. R. Trócoli, G. Kasiri and F. La Mantia, Journal of Power Sources, 2018, 400, 167-171. G. Kasiri, J. Glenneberg, A. B. Hashemi, R. Kun and F. La Mantia, Energy Storage Materials, 2019, 19, 360-369. J. Lim, G. Kasiri, R. Sahu, K. Schweinar, K. Hengge, D. Raabe, F. La Mantia and C. Scheu, Chemistry–A European Journal, 2020, 26, 4917-4922. M. Görlin, D. O. Ojwang, M.-T. Lee, V. Renman, C.-W. Tai and M. Valvo, ACS Applied Materials & Interfaces, 2021, 13, 59962-59974. Figure 1

PEDOT:PSS-coated platinum electrodes for neural stimulation

Safe and long-term electrical stimulation of neurons requires charge injection without damaging the electrode and tissue. A common strategy to diminish adverse effects includes the modification of electrodes with materials that increases the charge injection capacity. Due to its high capacitance, the conducting polymer PEDOT:PSS is a promising coating material; however, the neural stimulation performance in terms of stability and safety remains largely unexplored. Here, PEDOT:PSS-coated platinum (Pt-PEDOT:PSS) microelectrodes are examined for neural stimulation and compared to bare platinum (Pt) electrodes. Microelectrodes in a bipolar configuration are used to deliver current-controlled, biphasic pulses with charge densities ranging from 64 to 255 μC cm−2. Stimulation for 2 h deteriorates bare Pt electrodes through corrosion, whereas the PEDOT:PSS coating prevents dissolution of Pt and shows no degradation. Acute stimulation of primary cortical cells cultured as neurospheres shows similar dependency on charge density for Pt and Pt-PEDOT:PSS electrodes with a threshold of 127 μC cm−2 and increased calcium response for higher charge densities. Continuous stimulation for 2 h results in higher levels of cell survival for Pt-PEDOT:PSS electrodes. Reduced cell survival on Pt electrodes is most profound for neurospheres in proximity of the electrodes. Extending the stimulation duration to 6 h increases cell death for both types of electrodes; however, neurospheres on Pt-PEDOT:PSS devices still show significant viability whereas stimulation is fatal for nearly all cells close to the Pt electrodes. This work demonstrates the protective properties of PEDOT:PSS that can be used as a promising approach to extend electrode lifetime and reduce cell damage for safe and long-term neural stimulation.

An Efficient Maritime Route Planning Method Based on an Improved A* with an Adaptive Heuristic Function and Parallel Computing Structure

Maritime route planning under minimal-risk conditions plays an important part in the development and utilization of marine resources. High-resolution weather forecasting data places higher demands on the algorithms’ ability to optimize and compute, and existing algorithms are significantly deficient in these aspects. Therefore, we propose a parallel computing-based planning method, segment parallel A* (SPA*), which splits the path into small segments and runs A* separately on CPU cores through a control algorithm. In segment planning, we propose an adaptive heuristic function on A*. It automatically balances the order of magnitude difference between the risk assessment value and the estimated distance, thus significantly reducing the A* expansion useless grid to improve the performance and running speed of the algorithm. Finally, the complete route is obtained by splicing the above segments. In the static planning experiments, the time of SPA* is reduced by about 5~12,425 times compared with 6 traditional and swarm intelligence-based algorithms, i.e., Dijkstra, A*, bidirectional A* (BA*), ant colony optimization (ACO), Harris hawks optimization (HHO), and sparrow search algorithm (SSA). And the abilities to control the risk caused by wind and waves and the comprehensive risk are improved by 7.68%~25.14% and 8.44%~14.38%, respectively; in the dynamic planning experiments, the above results are 4.8~1262.9 times, 3.87%~9.47% and 7.21%~10.36%, respectively. By setting the recommended range of the number of segments for each case, SPA* shows stable performance in terms of the calculation and risk control. SPA* demonstrates a unique structure for using parallel computing in route planning, which is representative and general in both reducing time and improving efficiency.

Evolving regularised random vector functional link by seagull optimisation algorithm for yarn‐dyed fabric colour difference classification

AbstractTo address the issue of low precision in classifying the colour differences of yarn‐dyed fabrics and the high cost of manual detection, a colour difference classification method relying on an improved seagull optimisation algorithm (SOA) optimised regularised random vector functional link (RRVFL) model is proposed for dyed fabrics. First, to address the issue of the slow convergence speed of the SOA, the current study optimises the initial SOA group with the marine predators algorithm (MPA) so that it can effectively improve the convergence ability and global optimisation ability of the SOA. Subsequently, the enhanced SOA is applied to fine‐tune the parameters of the RRVFL. Compared with the methods that only optimise weights and bias, the proposed algorithm obtained by optimizing the initial group of SOA through the Marine Predators Algorithm (MSOA)‐RRVFL model in this paper also increases the optimisation of the number of nodes in the hidden layer and regularisation parameters, which also effectively avoids the issue of the low classification accuracy of the RRVFL model due to random related parameters. Finally, by comparing the RRVFL model with other optimisation algorithms, the experimental outcomes demonstrate that the convergence ability of the improved SOA has been improved, and that the average accuracy of colour difference classification by the MSOA‐RRVFL model is as high as 99.79%, and that the classification error fluctuation can be stabilised below 0.2%. In general, the MSOA‐RRVFL model displays an excellent performance in terms of stability and significance.

Capillary enhanced phase change in a microfabricated self-oscillating fluidic heat engine (SOFHE)

This paper reports the design, fabrication, and characterization of a miniaturized version of a self-oscillating fluidic heat engine (SOFHE) for thermal energy harvesting. This new design includes capillary corners of a square cross-section, as well as an etched capillary path on the bottom wall that improves the performance in terms of stability and mechanical power owing to the enhanced phase change. The engine consists of a vapor bubble trapped in a microchannel by an oscillating liquid plug (acting as a piston) set in motion by periodic evaporation and condensation in the vapor bubble. The underlying physics of the oscillations is similar to those of a single-branch pulsating heat pipe. The channel is microfabricated by anodically bonding a grooved glass wafer (top and sidewalls) to a silicon wafer (bottom wall). To further increase the phase change, two more channels are fabricated with an etched capillary path on the bottom wall at two different widths of 25 and 50 µm and a depth of 100 µm. This is the first miniaturized SOFHE that generates a reliable amplitude in the millimeter range. By measuring the change in the volume of the vapor bubble and the frequency, we calculated the change in pressure using the momentum balance on the liquid plug, and then calculated the work, mechanical power, and power density. We observed that the addition of the etched capillary path at a width of 50 µm increased the amplitude (from 1.6 to 4 mm) leading to a fivefold increase in the generated power (from 8 to 40 µW). This study opens a new path towards designing different wicking structures to maximize the amplitude and power density of the SOFHE, making it a promising thermal energy harvester to power wireless sensors.

A Comprehensive Benchmark of Transcriptomic Biomarkers for Immune Checkpoint Blockades.

Immune checkpoint blockades (ICBs) have revolutionized cancer therapy by inducing durable clinical responses, but only a small percentage of patients can benefit from ICB treatments. Many studies have established various biomarkers to predict ICB responses. However, different biomarkers were found with diverse performances in practice, and a timely and unbiased assessment has yet to be conducted due to the complexity of ICB-related studies and trials. In this study, we manually curated 29 published datasets with matched transcriptome and clinical data from more than 1400 patients, and uniformly preprocessed these datasets for further analyses. In addition, we collected 39 sets of transcriptomic biomarkers, and based on the nature of the corresponding computational methods, we categorized them into the gene-set-like group (with the self-contained design and the competitive design, respectively) and the deconvolution-like group. Next, we investigated the correlations and patterns of these biomarkers and utilized a standardized workflow to systematically evaluate their performance in predicting ICB responses and survival statuses across different datasets, cancer types, antibodies, biopsy times, and combinatory treatments. In our benchmark, most biomarkers showed poor performance in terms of stability and robustness across different datasets. Two scores (TIDE and CYT) had a competitive performance for ICB response prediction, and two others (PASS-ON and EIGS_ssGSEA) showed the best association with clinical outcome. Finally, we developed ICB-Portal to host the datasets, biomarkers, and benchmark results and to implement the computational methods for researchers to test their custom biomarkers. Our work provided valuable resources and a one-stop solution to facilitate ICB-related research.

The high voltage system of the novel MPGD-based photon detectors of COMPASS RICH-1 and its development towards a scalable High Voltage Power Supply System for MPGDs

The COMPASS RICH-1 detector has undergone a major upgrade in 2016 with the installation of four novel MPGD-based photon detectors. They consist of large-size hybrid MPGDs with multi-layer architecture composed of two layers of Thick-GEMs and bulk resistive MicroMegas. A dedicated high voltage power supply system, based on CAEN HV modules, has been built and put in operation: it controls more than 100 HV channels. The system is required to protect the detectors against errors by the operator, monitor voltages and currents at a 1 Hz rate and automatically react to detector misbehavior. It includes also a HV compensation system against environmental pressure and temperature variation to grant the detector stability. The operation of a MPGD based single photon detector poses challenging requirements to the high voltage power supply systems employed in terms of high-resolution diagnostic features and dynamic voltage control. Systems satisfying all the needed features are not commercially available; for this reason a novel single channel high voltage system matching the MPGD needs has been designed and realized. In this article the COMPASS RICH-1 MPGD HV system implementation is described as well as its performance in terms of stability of the novel MPGD-based photon detectors during the physics data taking at COMPASS. The design, implementation and performance of a novel HV power supply system based on DC to DC converters and controlled by a FPGA device is presented. The capabilities of the first prototype of the new single HV channel power supply are illustrated when operated with a MPGD based single photon detector during a test beam exercise. The preliminary result of the multi channel system are briefly discussed.

Radiation Damage by Heavy Ions in Silicon and Silicon Carbide Detectors.

While silicon has been a steadfast semiconductor material for the past 50 years, it is now facing competition from other materials, especially for detector design. In that respect, due to its high resistance to radiation damage, silicon carbide is one of the most promising materials. In this work, we discuss the radiation damage studies of a new, large area, p-n junction silicon carbide device developed by the SiCILIA collaboration. We have studied the general performances of several devices, as a function of fluence, irradiated in different experimental conditions with different beams. A standard p-n junction silicon detector was also irradiated for comparison. The new detectors manifest excellent performance in terms of stability of the main parameters, linearity, defect distribution, charge collection efficiency, energy resolution, leakage current, etc. Experimental results evidence a radiation resistance of SiC devices more than two order of magnitude higher than Si devices. The new construction technology applied to silicon carbide material has made it possible to create very robust devices with excellent performance. These devices will soon be available for all those scientific projects where a high resistance to radiation damage is required.

Design of a high performance CNFET 10T SRAM cell at 5nm technology node

This article proposes a CNFET 10T SRAM cell based on Stanford Virtual Source model at 5nm technology node, through optimization design and simulation analysis to select optimum gate widths of transistors to ensure best performance in terms of stability, speed and power consumption. We compare the proposed 10T CNFET SRAM with the optimized 6T CNFET SRAM in [9]. It was found that the timing and power characteristics of the proposed 10T SRAM cell is better than that of the 6T structure, the static power consumption is greatly reduced while the RSNM is improved by 93.5%, read and write EDP are improved by 68.5% and 96%, respectively.

Fixed-Time Controller for Altitude/Yaw Control of Mini-Drones: Real-Time Implementation with Uncertainties

Gradually, it has become easier to use aerial transportation systems in practical applications. However, due to the fixed-length wire, recent studies on load-suspended transportation systems have revealed some practical constraints, especially when using quadrotor unmanned aerial vehicles (UAVs). By actively adjusting the distance between the quadrotor and the payload, it becomes possible to carry out a variety of challenging tasks, including traversing confined spaces, collecting samples from offshore locations, and even landing a payload on a movable platform. Thus, mass variable aerial transportation systems should be equipped with trajectory tracking control mechanisms to accomplish these tasks. Due to the above-mentioned reasons, the present paper addresses the problem of the altitude/yaw tracking control of a mini-quadrotor subject to mass uncertainties. The main objective of this paper is to design a fixed-time stable controller for the perturbed altitude/yaw motions, based on recent results using the fixed-time stability approach. For comparison reasons, other quadrotor motion controllers such as dual proportional integral derivative (PID) loops were considered. To show its effectiveness, the proposed fixed-time controller was validated on a real mini-quadrotor under different scenarios and has shown good performance in terms of stability and trajectory tracking.

Experimental validation of non-dual adaptive controller based DSTATCOM for power quality enhancement

Abstract In this manuscript, a new control approach is counselled for DSTATCOM to resolve power quality (PQ) related issues. The Non-dual Adaptive Controller (NDAC) is employed with DSTATCOM to enhance the performance in terms of stability. The DSTATCOM is the constituent of voltage source inverter shunt DC-capacitor. A hysteresis block is employed for switching pulse triggering. The counselled scheme is a new method of shunt compensation and offers superior performance, for the lower frequency band in comparision with traditional strategies. Now-a-days we are dealing with huge nonlinear loads which draws non sinusoidal currents and also they generate large amount of harmonics. There are various advantages of using this new modified adaptive controllers over the tradition controllers. We are going to compare the performance of this new counselled controllers performance with the existing one in this literature. This new controller is initially simulated over MATLAB 2016 and then the efficacy of the counselled control approach (NDAC) is investigated using Laboratory prototype. Hardware setup is then authenticated through prototype with the help of dSPACE 1104. The execution reflects wide variation in the performances of both the controllers and also shows the supremacy of Non-dual adaptive controllers in terms of various aspects like harmonic current reduction, reduction in Total harmonic distortions during load dynamics.

Utilization of Electrodeionization for Lithium Removal.

In this work, usage of a hybrid polymeric ion exchange resin and a polymeric ion exchange membrane in the same unit to remove Li+ from aqueous solutions was reported. The effects of the applied potential difference to the electrodes, the flow rate of the Li-containing solution, the presence of coexisting ions (Na+, K+, Ca2+, Ba2+, and Mg2+), and the influence of the electrolyte concentration in the anode and cathode chambers on Li+ removal were investigated. At 20 V, 99% of Li+ was removed from the Li-containing solution. In addition, a decrease in the flow rate of the Li-containing solution from 2 to 1 L/h resulted in a decrease in the removal rate from 99 to 94%. Similar results were obtained when the concentration of Na2SO4 was decreased from 0.01 to 0.005 M. The selectivity test showed that the simultaneous presence of monovalent ions such as Na+ and K+ did not change the removal rate of Li+. However, the presence of divalent ions, Ca2+, Mg2+, and Ba2+, reduced the removal rate of Li+. Under optimal conditions, the mass transport coefficient of Li+ was found as 5.39 × 10-4 m/s, and the specific energy consumption was found as 106.2 W h/g LiCl. Electrodeionization provided stable performance in terms of the removal rate and transport of Li+ from the central compartment to the cathode compartment.

A deep learning model using chest X-ray for identifying TB and NTM-LD patients: a cross-sectional study

BackgroundTimely differentiating between pulmonary tuberculosis (TB) and nontuberculous mycobacterial lung disease (NTM-LD), which are radiographically similar, is important because infectiousness and treatment differ. This study aimed to evaluate whether artificial intelligence could distinguish between TB or NTM-LD patients by chest X-rays (CXRs) from suspects of mycobacterial lung disease.MethodsA total of 1500 CXRs, including 500 each from patients with pulmonary TB, NTM-LD, and patients with clinical suspicion but negative mycobacterial culture (Imitator) from two hospitals, were retrospectively collected and evaluated in this study. We developed a deep neural network (DNN) and evaluated model performance using the area under the receiver operating characteristic curves (AUC) in both internal and external test sets. Furthermore, we conducted a reader study and tested our model under three scenarios of different mycobacteria prevalence.ResultsAmong the internal and external test sets, the AUCs of our DNN model were 0.83 ± 0.005 and 0.76 ± 0.006 for pulmonary TB, 0.86 ± 0.006 and 0.64 ± 0.017 for NTM-LD, and 0.77 ± 0.007 and 0.74 ± 0.005 for Imitator. The DNN model showed higher performance on the internal test set in classification accuracy (66.5 ± 2.5%) than senior (50.8 ± 3.0%, p < 0.001) and junior pulmonologists (47.5 ± 2.8%, p < 0.001). Among different prevalence scenarios, the DNN model has stable performance in terms of AUC to detect TB and mycobacterial lung disease.ConclusionDNN model had satisfactory performance and a higher accuracy than pulmonologists on classifying patients with presumptive mycobacterial lung diseases. DNN model could be a complementary first-line screening tool.

Two-stage three-way enhanced multi-criteria classification optimization for risk-averse product design programming

Product design is essential in the product lifecycle. In product design, decision errors from designers are ineluctable due to the lack of sufficient knowledge and experience on design parameters. However, the penalties (e.g., expected risk loss costs) of design decisions have been overlooked as always. The result is that being risk-averse naturally becomes a major issue to be advocated. Meanwhile, the preferences of designers, representing rational cognition of product design, are crucial in product design. In response to the above two critical issues, a two-stage three-way enhanced multi-criteria classification optimization for risk-averse product design programming is proposed. In the first stage, the three-way decisions (3WD) theory is introduced to handle the relation between the penalties and design decisions, and provide an uncertain decision representing an intermediate attitude of designers on design parameters. Additionally, designers are endowed to preferentially consider design parameters that are profitable for the success of product design. The purpose of the first stage is to formulate multiple alternative design schemes. In the second stage, under the framework of the ELimination Et Choix Traduisant la REalité (ELECTRE) TRI, a multi-criteria classification optimization is constructed to cater to designers’ preferences. Subsequently, we can identify the optimal design scheme from these multiple alternative design schemes. The effectiveness and merits of the proposed approach are demonstrated with two illustrative examples and a substrate design case. As shown from the case studies, the proposed approach has a stable performance in terms of identifying the optimal design scheme in a stochastic environment.

Spatially confined protein assembly in hierarchical mesoporous metal-organic framework

Immobilization of biomolecules into porous materials could lead to significantly enhanced performance in terms of stability towards harsh reaction conditions and easier separation for their reuse. Metal-Organic Frameworks (MOFs), offering unique structural features, have emerged as a promising platform for immobilizing large biomolecules. Although many indirect methods have been used to investigate the immobilized biomolecules for diverse applications, understanding their spatial arrangement in the pores of MOFs is still preliminary due to the difficulties in directly monitoring their conformations. To gain insights into the spatial arrangement of biomolecules within the nanopores. We used in situ small-angle neutron scattering (SANS) to probe deuterated green fluorescent protein (d-GFP) entrapped in a mesoporous MOF. Our work revealed that GFP molecules are spatially arranged in adjacent nanosized cavities of MOF-919 to form “assembly” through adsorbate-adsorbate interactions across pore apertures. Our findings, therefore, lay a crucial foundation for the identification of proteins structural basics under confinement environment of MOFs.

Copper-substituted polyoxometalate-soldered interpenetrating polymeric networks membranes for water remediation

Attributing water remediation membranes with antifouling properties continues to be a significant research focus. Herein we have fabricated a Copper-substituted polyoxometalate (Cu-POM) mixed matrix sequential interpenetrating polymeric network membrane via integrating engineering aspects of IPNs and in-situ electrostatic attachment. The incorporation of Cu-POMs endowed the IPN membranes with exceptional and unique surface morphology. The inclusions enhanced the surface roughness, pore structure, and hydrophilic nature of the membranes. Superior antifouling characteristics and charge-carrying capacity were typical characteristics of the designed membranes. The membranes were characterized thoroughly via multiple spectroscopic and microscopic techniques apart from Zeta potential, water contact angle, TGA, and XRD. Over several and repeated operational cycles, the membranes were found to exhibit stable performance in terms of salt rejection (ca. 98 %), dye removal (>97 % for both cationic and anionic dyes), and antibiotic removal performance (>96 %). Despite their reuse, the membranes maintained their inherent antifouling and chlorine-tolerant behavior. The synthesized electrostatically tagged in-situ decorated Cu-POM-based IPN (Cu-POM @IPN) membranes were pliable and mechanically robust. They could potentially be emerging prospective candidates for efficient and expeditious water remediation.

Task offloading in Multiple-Services Mobile Edge Computing: A deep reinforcement learning algorithm

Multiple-Services Mobile Edge Computing enables task-relate services cached in edge server to be dynamically updated, and thus provides great opportunities to offload tasks to edge server for execution. However, the requirements and popularity of services, the computing requirement and the amount of data transferred from users to edge server are dynamic with time. How to adaptively adjust the subset of total service types in the resource-limited edge server and determine the task offloading destination and resource allocation decisions to improve the overall system performance is a challenge problem. To solve this challenge, we firstly convert it into a Markov decision process, then propose a soft actor–critic deep reinforcement learning-based algorithm, called DSOR, to jointly determine not only the discrete decisions of service caching and task offloading but also the continuous allocation of bandwidth and computing resource. To improve the accuracy of our algorithm, we employ an efficient trick of converting the discrete action selection into a continuous space to deal with the key design challenge that arises from continuous-discrete hybrid action space. Additionally, to improve resource utilization, a novel reward function is integrated to our algorithm to speed up the convergence of training while making full use of system resources. Extensive numerical results show that compared with other baseline algorithms, our algorithm can effectively reduce the long-term average completion delay of tasks while accessing excellent performance in terms of stability.