Fast Performance Research Articles

A review of finite control set model predictive control for linear machines

AbstractLinear machines (LMs) have become excellent candidates for linear motion applications due to their abilities of generating thrust without any transmissions, and have been widely adopted in transportation, industries etc. Among the numerous control methods of LM drive systems, finite control set model predictive control (FCS‐MPC) method has been given special attention due to its clear concept, fast response performance, and ability to handle constrained multivariate non‐linear control problems. It is widely used to improve the performance and efficiency of overall LM drive systems. Firstly, based on the three elements of FCS‐MPC, the advantages and shortcomings of the FCS‐MPC method in applications are fully investigated. The research status in FCS‐MPC has been summarised, such as multistep MPC, parameters robustness improvement, fast optimisation, and multi‐vector MPC. Then, the key issues in FCS‐MPC such as delay compensation, parameters design, and stability analysis are discussed, and their typical research methods and current research status are explained. Finally, the future development trends of FCS‐MPC in LM drive systems are discussed.

Tunable photo-response in the visible to NIR spectrum range of Germanium-based junctionless nanowire transistor

In this paper, the phototransistor behavior is investigated in the germanium-on-insulator (GeOI)-based junctionless nanowire (JL-NW) transistor under various light conditions. High responsivity and photosensitivity are attributed in the fully depleted regime within the visible and near-infrared bands. The impact of light is also investigated in detail on the electronic and transfer characteristics such as energy bandgap, carrier distribution, electrostatic potential, electric field, generation and recombination rates. Further, the channel doping and thickness are tuned to optimize the optical responsivity. The significant tunability of responsivity is observed with increasing channel thickness. The device exhibits fast optical switching performance, which is further enhanced at higher input light power. Overall, at the nanoscale dimension, our proposed phototransistor demonstrates better detectivity with a significantly smaller illumination area. Thus, the GeOI-based JL-NW phototransistors can be used for imaging (visible wavelength range) and bioimaging (near-infrared wavelength range) applications in advanced technology nodes.

Development of Staff Evaluation Software Based on Association Matrix Methods and Data Mining Using the Streamlit Framework

This study discusses evaluating employee performance in microbiology laboratories using an association matrix implemented in web-based software with the Streamlit framework. The purpose of the research is to improve the employee performance evaluation process, which previously used conventional methods. This software is built from a sample receipt recording history data stored in a MySQL database. The initially unstructured data was processed using Python libraries such as NumPy, Matplotlib, Pandas, and Difflib to generate personnel evaluation information such as specialization, task duration, workload, and individual competencies. This software can provide a fast and accurate performance assessment according to the evaluation period. In a test with the System Usability Scale (SUS), the software scored 75.83, which was rated "good.". These results show that the software is easy to use and can improve the efficiency of employee performance evaluation. Follow-up tests with questionnaires given to 18 users showed that this system was preferable to previous conventional methods. This software helps laboratory managers evaluate employee performance effectively and efficiently.

Ultralow-Power Programmable 3D Vertical Phase-Change Memory with Heater-All-Around Configuration.

Recent advancements in phase-change memory (PCM) technology have predominantly stemmed from material-level designs, which have led to fast and durable device performances. However, there remains a pressing need to address the enormous energy consumption through device-level electrothermal solutions. Thus, the concept of a 3D heater-all-around (HAA) PCM fabricated along the vertical nanoscale hole of dielectric/metal/dielectric stacks is proposed. The embedded thin metallic heater completely encircles the phase-change material, so it promotes highly localized Joule heating with minimal loss. Hence, a low RESET current density of 6-8 MAcm-2 and operation energy of 150-200 pJ are achieved even for a sizable hole diameter of 300nm. Beyond the conventional 2D scaling of the bottom electrode contact, it accordingly enhances ≈80% of operational energy efficiency compared to planar PCM with an identical contact area. In addition, reliable memory operations of ≈105 cycles and the 3-bits-per-cell multilevel storage despite ultrathin (<10nm) sidewall deposition of Ge2Sb2Te5 are optimized. The proposed 3D-scaled HAA-PCM architecture holds promise as a universally applicable backbone for emerging phase-change chalcogenides toward high-density, ultralow-power computing units.

Flexible Generation of Fast and Accurate Software Performance Simulators From Compact Processor Descriptions

Flexible Generation of Fast and Accurate Software Performance Simulators From Compact Processor Descriptions

Rapid Determination of Trace Ethylene Glycol and Diethylene Glycol in Propylene Glycol-Contained Syrups by Ultrahigh-Performance Supercritical Fluid Chromatography-Mass Spectrometry after Precolumn Derivatization

Ethylene glycol (EG) and diethylene glycol (DEG) are two contaminants known to cause a variety of human health problems, when ingested in certain amounts, they can cause adverse effects such as nephrotoxicity, neurotoxicity and even death. They are widely found in propylene glycol, which is commonly used as a base for pharmaceutical syrups. The aim of this study was to develop an analytical method for the evaluation of EG and DEG in commercially available pediatric syrups. In this study, a fast, simple and reliable ultrahigh performance supercritical fluid chromatography-electrospray ionization-mass spectrometry (UHPSFC-ESI-MS) method was developed. The work involved the evaluation of three derivatization reagents for UHPSFC-ESI-MS. Benzoyl chloride was finally selected as the optimal derivatization reagent. Compared with an approach without derivatization, the present method achieved the separation and detection of EG and DEG efficiently, sensitively and rapidly, and analysis of EG and DEG in syrup formulations was realized within 7 min. The linear determination coefficients of EG and DEG in the concentration range of 0.25–25.0 μg/mL were greater than 0.999. The limits of detection for EG and DEG were 0.02 μg/mL and 0.07 μg/mL, respectively, and the limits of quantification were 0.09 μg/mL and 0.25 μg/mL, respectively. The recovery rates of DEG and EG ranged from 85.5%–108.1% and 86.7%–117.2%, respectively. The absolute values of the matrix effects in the three types of syrups considered were all less than 10%. Meanwhile, a gas chromatography-hydrogen flame ionization detection method was established for cross-testing of the analytical results. In 10 batches of syrup formulations, DEG was not detected by either method. The presence of EG was detected by UHPSFC-ESI-MS in only three batches, none of which exceeded 90.23 parts per million (ppm), with a mean absolute error of 5.95 ppm between the two sets of results. The developed UHPSFC-ESI-MS method was rapid, simple, efficient, sensitive and accurate for the determination of EG and DEG in genuine syrup formulations.

Mitigating Long Range Jahn‐Teller Ordering to Stabilize Mn Redox Reaction in Biphasic Layered Sodium Oxide

AbstractTo develop the next‐generation commercial oxide cathodes for sodium‐ion batteries, it is crucial to reduce the expensive Ni element content, and further regulate redox reaction of cheap transition metal elements such as Mn to elevate specific capacity. Nevertheless, the activation of Mn redox reaction (MRR) remains a challenge, and notably, MRR induces pronounced Jahn‐Teller effect, resulting in severe structural distortion and fast performance decay. Herein, activated by Na vacancies and weakened hybridization of O (2p)‐TM (3d‐t2g) orbital, a biphasic low‐Ni Mn‐based oxide P2/O3‐Na0.8Ni0.23Fe0.34Mn0.43O2 (P2/O3) exhibits reversible MRR, which performs the transition between Mn4+ and Mn3+ during charging and discharging. Due to the interlaced arrangement of P2‐type and O3‐type crystal domains in P2/O3, the long range Jahn‐Teller ordering is restricted to mitigate the cooperative distortion of MnO6 octahedron induced by MRR and the Jahn‐Teller effect is suppressed, ensuring sustained stable involvement of MRR in charge compensation. In addition, owing to the introduction of P2‐type phase, there is a significant reduction of the migration barrier for sodium ions and no obvious capacity decline after air exposure, leading to a marked enhancement in dynamic performance and air stability of P2/O3, respectively. Consequently, P2/O3 exhibits excellent electrochemical and processing performance.

Harnessing Novel Reduced Graphene Oxide-Based Aerogel for Efficient Organic Contaminant and Heavy Metal Removal in Aqueous Environments.

Ensuring clean water sources is pivotal for sustainable development and the well-being of communities worldwide. This study represents a pioneering effort in water purification, exploring an innovative approach utilizing modified reduced graphene oxide (rGO) aerogels. These advanced materials promise to revolutionize environmental remediation efforts, specifically removing organic contaminants from aqueous solutions. The study investigates the exceptional adsorption properties of rGO-aerogel, enhanced with cysteamine, to understand its efficacy in addressing water pollution challenges. The characterization methods utilized encompass various analytical techniques, including FE-SEM, BET, FTIR, TGA, DSC, XPS, NMR, and elemental analysis. These analyses provide valuable insights into the material's structural modifications and surface chemistry. The research comprehensively explores the intricacies of adsorption kinetics, equilibrium, and isothermal study to unravel the underlying mechanisms governing contaminant removal. MO and Ni2+ exhibited adsorption of 542.6 and 150.6 mg g-1, respectively, at 25 °C. Ni2+ has unveiled the highest removal at pH 5, and MO has shown high removal in a wide pH range (pH 4-7). Both contaminants have shown fast adsorption kinetic performance on an rGO-aerogel surface. This study aims to identify the synergistic effect of cysteamine and rGO in aerogel formation to remove heavy metals and organic contaminants. These findings mark a significant stride in advancing sustainable water-treatment methods and pioneering in synthesizing innovative materials with versatile applications in environmental contexts, offering a potential solution to the global water pollution crisis.

The ALICE Fast Interaction Trigger performance and upgrade

The ALICE Fast Interaction Trigger performance and upgrade

Modular YOLOv8 optimization for real-time UAV maritime rescue object detection.

The task of UAV-based maritime rescue object detection faces two significant challenges: accuracy and real-time performance. The YOLO series models, known for their streamlined and fast performance, offer promising solutions for this task. However, existing YOLO-based UAV maritime rescue object detection methods tend to prioritize high accuracy, often at the expense of real-time performance and ease of implementation and expansion. This study proposes a modular plug-and-play optimization approach based on the YOLOv8 framework, aiming to enhance real-time performance while maintaining high accuracy for UAV maritime rescue object detection. The proposed optimization modules are flexible, easy to implement, and extendable. In experiments on the large-scale publicly available SeaDronesSee dataset, our method achieved a 13.53% improvement in accuracy over YOLOv8x while reducing computational cost by 85.63%. Additionally, it surpassed the detection speed of the SeaDronesSee official code's two-stage detector by over 20 times, while maintaining comparable accuracy. Furthermore, our analysis of the experimental results highlights differences in detection difficulty among various objects and potential biases within the dataset.

Analysis of Azathioprine Metabolites in Autoimmune Hepatitis Patient Blood-Method Development and Validation.

Autoimmune hepatitis (AIH) is a chronic inflammatory liver disease treated by steroids and immunomodulator thiopurine drugs such as azathioprine (AZA). AZA is metabolized in the human body into bioactive forms such as 6-thioguanine (6-TG) and 6-methyl-mercaptopurine (6-MMP). Monitoring the levels of bioactive AZA metabolites is very important for proper treatment of patients. In this study, our aim was to develop and validate a fast and sensitive ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) method for the analysis of 6-TG and 6-MMP from blood samples of patients with AIH to monitor the level of these bioactive metabolites. The detection and quantification of the analytes was carried out by Selected Reaction Monitoring (SRM)-based targeted mass spectrometry. The method was validated according to the EMA guidelines. Blood samples from patients with AIH treated with AZA were analysed with the developed method. The method was successfully validated with appropriate accuracy and precision for the target biomolecules and their concentration in the samples from patients with AIH was determined. The developed and validated UHPLC-MS method enables the fast and precise analysis of AZA metabolites.

Energy Dissipation and Toughening of Covalent Networks via a Sacrificial Conformation Approach.

Covalent polymer networks find wide utility in diverse engineering applications owing to their desirable stiffness and resilience. However, the rigid covalent chemical structure between crosslinking points imposes limitations on enhancing their toughness. Although the incorporation of sacrificial chemical bonds has shown promise in improving toughness through energy dissipation, composite networks struggle to maintain both rapid recovery and stiffness. Consequently, a significant challenge persists in achieving a covalent network that combines high strength, stiffness, toughness, and fast recovery performance. To address this challenge, we propose a novel sacrificial structure termed "sacrificial conformation." In this approach, β-cyclodextrin is covalently embedded into the network skeleton as the sacrificial conformation element. Compared to traditional covalent networks (LCN), well-designed cyclodextrin-embedded covalent network (CCN) exhibit a 100-fold increase in Young's modulus and a 60-fold increase in toughness. Importantly, CCN maintains excellent elasticity, ensuring swift recovery after deformation. This sacrificial conformational strategy enables efficient energy dissipation without necessitating the rupture of chemical bonds, thereby overcoming the limitations of traditional approaches. This advancement holds great promise for the design and fabrication of advanced elastomers and hydrogels with superior mechanical properties and dynamic behavior.

Energy Dissipation and Toughening of Covalent Networks via a Sacrificial Conformation Approach

Covalent polymer networks find wide utility in diverse engineering applications owing to their desirable stiffness and resilience. However, the rigid covalent chemical structure between crosslinking points imposes limitations on enhancing their toughness. Although the incorporation of sacrificial chemical bonds has shown promise in improving toughness through energy dissipation, composite networks struggle to maintain both rapid recovery and stiffness. Consequently, a significant challenge persists in achieving a covalent network that combines high strength, stiffness, toughness, and fast recovery performance. To address this challenge, we propose a novel sacrificial structure termed "sacrificial conformation." In this approach, β‐cyclodextrin is covalently embedded into the network skeleton as the sacrificial conformation element. Compared to traditional covalent networks (LCN), well‐designed cyclodextrin‐embedded covalent network (CCN) exhibit a 100‐fold increase in Young's modulus and a 60‐fold increase in toughness. Importantly, CCN maintains excellent elasticity, ensuring swift recovery after deformation. This sacrificial conformational strategy enables efficient energy dissipation without necessitating the rupture of chemical bonds, thereby overcoming the limitations of traditional approaches. This advancement holds great promise for the design and fabrication of advanced elastomers and hydrogels with superior mechanical properties and dynamic behavior.

Preparation of natural adhesives with high-strength and low temperature fast-curing properties

The development of natural adhesives to substitute petroleum-based products is crucial for sustainable progress. However, the bond strength of most existing natural adhesives still falls short of that of commercially available adhesives and the curing process often demands prolonged high-temperature treatment. Herein, we prepared pure natural adhesives using gelatin (GE), tung oil (TO), and dopamine (DA) as the main raw materials. The shear strength of the adhesive to wood reached up to 6.35 MPa, a significant improvement over most natural wood adhesives reported. Additionally, the tensile strength of the adhesive to wood reached 10.50 MPa, which is comparable to commercially available neoprene-phenolic wood adhesives (9.99 MPa) and cyanoacrylate instant adhesives (9.48 MPa). In addition, the natural adhesive prepared by us exhibits fast curing performance at room temperature and can be fully cured in only 12 min, which is significantly better than the commercially available adhesive. The adhesives prepared in this study also possessed excellent reversible curing properties, antimicrobial properties, low toxicity, and degradability. These characteristics offer new insights for the advancement and dissemination of natural adhesives.

Seeing on the fly: Physiological and behavioral evidence show that space-to-space representation and processing enable fast and efficient performance by the visual system.

When we view the world, our eyes saccade quickly between points of interest. Even when fixating a target our eyes are not completely at rest but execute small fixational eye movements (FEMs). That vision is not blurred despite this ever-present jitter has seemingly motivated an increasingly popular theory denying the reliance of the visual system on pure spatial processing in favor of a space-to-time mechanism generated by the eye drifting across the image. Accordingly, FEMs are not detrimental but rather essential to good visibility. However, the space-to-time theory is incompatible with physiological data showing that all information is conveyed by the short neural volleys generated when the eyes land on a target, and with our faithful perception of briefly displayed objects, during which time FEMs have no effect. Another difficulty in rejecting the idea of image representation by the locations and nature of responding cells in favor of a timecode, is that somewhere, somehow, this code must be decoded into a parallel spatial one when reaching perception. Thus, in addition to the implausibility of generating meaningful responses during retinal drift, the space-to-time hypothesis calls for replacing efficient point-to-point parallel transmission with a cumbersome, delayed, space-to-time-to-space process. A novel physiological framework is presented here wherein the ability of the visual system to quickly process information is mediated by the short, powerful neural volleys generated by the landing saccades. These volleys are necessary and sufficient for normal perception without FEMs contribution. This mechanism enables our excellent perception of brief stimuli and explains that visibility is not blurred by FEMs because they do not generate useful information.

Performance Evaluation of NovaplexTM Multiplex Real-Time PCR Assay for Detection of Streptococcus agalactiae Serotypes.

Streptococcus agalactiae, or group B streptococcus (GBS), is a Gram-positive pathogen with an extended track record of colonization in the gastrointestinal and genitourinary tracts. GBS can induce disease in individuals across all age demographics, yet it predominantly triggers infections in neonates and the elderly. Identification of the serotype is vital for effective management of the disease as it provides critical information for clinicians on the cause of the disease. In this study, we evaluated the rapid, simple, and easy-to-adopt multiplex real-time PCR technique, NovaplexTM (NovaPCR). A total of 131 clinical isolates of different serotypes were tested using NovaPCR. Observations revealed that 129 isolates showed the same observations as LA and conventional mPCR. NovaPCR accurately identified serotypes IV and V, which were first classified as serotype Ia in the LA test and mPCR, and the difference between the traditional (LA test and mPCR) and NovaPCR methods is only 1.52%. Accurate serotype identification is helpful for monitoring the epidemics and achieving optimal clinical outcomes, and NovaPCR showed a reliable, fast, easy-to-interpret, and cost-efficient performance in GBS serotyping.

Accelerating Li-Ion Diffusion in LiFePO4 by Polyanion Lattice Engineering.

Despite the widespread commercialization of LiFePO4 as cathodes in lithium-ion batteries, the rigid 1D Li-ion diffusion channel along the [010] direction strongly limits its fast charge and discharge performance. Herein, lattice engineering is developed by the planar triangle BO3 3- substitution on tetrahedron PO4 3- to induce flexibility in the Li-ion diffusion channels, which are broadened simultaneously. The planar structure of BO3 3- may further provide additional paths between the channels. With these synergetic contributions, LiFe(PO4)0.98(BO3)0.02 shows the best performance, which delivers the high-rate capacity (66.8 mAhg-1 at 50 C) and long cycle stability (ultra-low capacity loss of 0.003% every cycle at 10 C) at 25°C. Furthermore, excellent rate performance (34.0 mAhg-1 at 40 C) and capacity retention (no capacity loss after 2500 cycles at 10 C) at -20°C are realized.

Boosting Fast Charging of Lithium-Metal Batteries via Weak Interactions Between Non-Solvating Solvents and Anions in High-Safety Eutectic Electrolytes.

Proper design of the solvation structure is crucial for the development of lithium metal batteries (LMBs). In this paper, the use of 1,2-Dimethoxyethane (DME) as a non-solvating cosolvent in amide-based eutectic electrolytes is proposed to address challenges related to high viscosity, high polarization, and low conductivity, thus enhancing the compatibility with lithium metal anodes. Through physical characterization combined with simulation calculations the existence of a weak interaction between DME and anions is confirmed, which promotes the dissociation of lithium salts and increases the Li+ transference number and diffusion coefficient, thus improving the fast charging performance of eutectic electrolytes. In addition, stable SEI layer enriched with inorganic components is formed during the cycling process, resulting in uniform and dense lithium deposition. The fast charging performance of the cell can be effectively improved by utilizing the interaction between anions and solvents. The LiFePO4 (LFP)||Li cell has a capacity retention of 97% after 1200 cycles at 5 C and also performs well at high temperature of 50°C. Overall, the use of a non-solvating cosolvent in eutectic electrolytes presents a promising and innovative approach for enhancing electrolyte performance in LMBs.

Bio-derived gel polymer electrolytes from zein and honey blends integrated with ammonium nitrate for electrical double layer capacitors

Although electrical double layer capacitors (EDLCs) are known to exhibit impressive power density, rapid charging/discharging, and long cycle life, their energy density remains a hurdle for broader use. Gel polymer electrolytes (GPEs) offer an interesting approach to improve the energy density of EDLCs while maintaining their other desirable characteristics. This study investigates the development of novel GPEs based on zein, honey, and ammonium nitrate (NH4NO3) for EDLC applications. The GPEs are prepared by incorporating zein, honey, and NH4NO3 in a dimethyl sulfoxide (DMSO) solvent system through a solution casting method. The degree of crystallinity of zein successfully reduced from 33.0 % to 15.71 % with the addition of 20 wt% NH4NO3. The interaction between zein, honey and NH4NO3 is confirmed by observing the shifting of the hydroxyl band. The ambient ionic conductivity of zein-honey increases from (4.86 ± 0.36) × 10−4 S cm−1 to (4.19 ± 0.40) × 10−3 S cm−1 with the incorporation of 20 wt% NH4NO3 (ZH20), exhibiting the significantly lowest Tg value (77.53 °C). Linear sweep voltammetry (LSV) demonstrates high electrochemical stability up to 2.6 V of ZH20. Additionally, the transference number (tion) of 0.989 indicates a dominant role of ionic charge carriers in this work. Finally, the ZH20-based GPE is used as both an electrolyte and separator to fabricate EDLC. Its electrochemical behavior has demonstrated stable results in terms of specific capacitance, energy, and power density, with fast rate performance observed through cyclic voltammetry and galvanostatic charge-discharge techniques.

Surrogate model for reservoir performance prediction with time-varying well control based on depth generative network

This paper proposes a novel intelligent method for defining and solving the reservoir performance prediction problem within a manifold space, fully considering geological uncertainty and the characteristics of reservoirs performance under time-varying well control conditions, creating a surrogate model for reservoir performance prediction based on Conditional Evolutionary Generative Adversarial Networks (CE-GAN). The CE-GAN leverages conditional evolution in the feature space to direct the evolution of the generative network in previously uncontrollable directions, and transforms the problem of reservoir performance prediction into an image evolution problem based on permeability distribution, initial reservoir performance and time-varying well control, thereby enabling fast and accurate reservoir performance prediction under time-varying well control conditions. The experimental results in basic (egg model) and actual water-flooding reservoirs show that the model predictions align well with numerical simulations. In the basic reservoir model validation, the median relative residuals for pressure and oil saturation are 0.5% and 9.0%, respectively. In the actual reservoir model validation, the median relative residuals for both pressure and oil saturation are 4.0%. Regarding time efficiency, the surrogate model after training achieves approximately 160-fold and 280-fold increases in computational speed for the basic and actual reservoir models, respectively, compared with traditional numerical simulations. The reservoir performance prediction surrogate model based on the CE-GAN can effectively enhance the efficiency of production optimization.