Tuning Scheme Research Articles

Interface Electron Transfer Direction-Tuned Urea Electrooxidation Over Multi-Interface Nickel Sulfide Heterojunctions.

Hydrogen can be produced by electrolyzing urea aqueous solution under smaller overpotentials, owing to the lower thermodynamic potential of urea oxidation reaction (UOR) at anode than oxygen evolution reaction (OER). The efficient and selective electrocatalysts for UOR are crucial to achieve this. Herein, two NiS-based NiS/Ni3S2 and NiS/NiS2 heterojunctions with Ni cores embedding in nitrogen-doped carbon nanotubes (NiS/Ni3S2- and NiS/NiS2-Ni@NCNT) are demonstrated as efficient UOR electrocatalysts. The electrocatalytic UOR performance over heterojunctions is efficiently tuned by altering the electron transfer direction on their interfaces. NiS/Ni3S2-Ni@NCNT with interface electrons transferring from Ni3S2 to NiS, delivers a 10 mA cm-2 UOR current density in 1.0 m KOH with 0.5 m urea at 1.37 V, superior to NiS/NiS2-Ni@NCNT with the electron transfer direction from NiS to NiS2. Experimental and theoretical calculation results reveal that NiS/Ni3S2 Mott-Schottky heterojunctions facilitate the rapid in situ formation of NiOOH active species by removing electrons of Ni3S2, and also accelerate the adsorption and conversion of urea molecules and key intermediates of *CON2 at its interfaces. This work demonstrates an interface electron transfer direction tuning strategy on heterojunctions for harvesting high-performance UOR electrocatalysts.

On passivity-based trajectory tracking for robotic manipulators combining PD+ and Slotine-Li control

Abstract In this paper, a tracking controller for robots is analyzed, which allows both the specification of the convergence rate of the tracking errors and the parameterization of a desired contact stiffness and damping. It is shown that the control approach can be interpreted as a generalization of the well-known PD+ and Slotine-Li controllers, combining the benefits of both approaches. Although mentioned in the literature before, no thorough theoretical and practical analysis of the aforementioned passivity-based control concept has been performed so far. In this work, the implications of the gains are discussed w.r.t. convergence and interaction properties, addressing possible tuning strategies. Finally, the performance of the controller is evaluated in terms of tracking and interaction experiments on a KUKA LWR IV+.

Training and Implementation of Subjective Questions Scoring System Based on the Baidu Qianfan Model Platform

Leveraging the Baidu Qianfan model platform, this paper designs and implements a highly efficient and accurate scoring system for subjective questions, focusing primarily on questions in the field of computer network technology. The system enhances the foundational model by utilizing Qianfan’s training tools and integrating advanced techniques, such as supervised fine-tuning. In the data preparation phase, a comprehensive collection of subjective data related to computer network technology is gathered, cleaned, and labeled. During model training and evaluation, optimal hyperparameters and tuning strategies are applied, resulting in a model capable of scoring with high accuracy. Evaluation results demonstrate that the proposed model performs well across multiple dimensions—content, expression, and development scores—yielding results comparable to those of manual scoring.

Tuning Earth System Models Without Integrating to Statistical Equilibrium.

This paper proposes algorithms for estimating parameters in Earth System Models (ESMs), specifically focusing on simulations that have not yet achieved statistical equilibrium and display climate drift. The basic idea is to treat ESM time series as outputs of an autoregressive process, with parameters that depend on those of the ESM. The maximum likelihood estimate of the parameters and the associated uncertainties are derived. This method requires solving a nonlinear system of equations and often results in unsatisfactory parameter estimates, especially in short simulations. This paper explores a strategy for overcoming this limitation by dividing the estimation process into two linear phases. This algorithm is applied to estimate parameters in the convection scheme of the Community Earth System Model version 2 (CESM2). The modified algorithm can produce accurate estimates from perturbation runs as short as 2years, including those exhibiting climate drift. Despite accounting for climate drift, the accuracy of these estimates is comparable to that of algorithms that do not. While these initial results are not optimal, the autoregressive approach presented here remains a promising strategy for model tuning since it explicitly accounts for climate drift in a rigorous statistical framework. The current performance issues are believed to be technical in nature and potentially solvable through further investigation.

Effects of dual plasticizers for improved PVC gel-based varifocal tiny lens

Abstract Electroactive polyvinyl chloride (PVC) gel-based varifocal lenses are attractive due to their excellent transparency, simple fabrication, compact structure, and good flexibility. In this work, dibutyl adipate (DBA) and propylene carbonate (PC) were utilized to plasticize PVC for fabricating transparent, soft, and flexible gels by solvent casting method. The PVC gels were characterized by various experimental techniques to investigate their optical, electrical, mechanical, and thermal properties. Further, the PVC gels were examined to construct a compact plano-convex tiny lens structure for varifocal lens application under electrical stimulation. The work revealed that the introduction of a co-plasticizer PC improves the overall dielectric property as well as transparency of the PVC gels. The observed enhancement in dielectric constant of the PC incorporated gels increased up to 6 times of the only DBA plasticized PVC gel (D9) while the transmittance value is ~5% higher than that of D9 gel. Moreover, the optimal ratio of DBA and PC in PVC gel was found to be 8.6: 0.4: 1 (wt %) and the optimized D8.6P0.4 gel induces better actuation properties resulting in improved focal length variation for the developed lens. The proposed adaptive tiny lens could change its focal length from 3 mm to 20.5 mm with increasing input voltage from 0 V to 1000 V. The developed tiny lens exhibited excellent optical characteristics including fast response speed and good stability. Moreover, this study broadens the scope for property tuning strategies for the EAP design and application.

Enhanced power system stabilizer tuning using marine predator algorithm with comparative analysis and real time validation

This study concentrates on the implementation of Marine Predator Algorithm (MPA) scheme for tuning of a power system stabilizer’s (PSS’s) parameters to damp the low-frequency oscillations in a power system. To this, the single machine infinite bus system (SMIB), the Western System Coordinating Council (WSCC) and the New England 10 machine 39-bus power system are utilized for testing and comparing different metaheuristic algorithms using different fitness functions. Optimal PSS parameters of SMIB test system are validated using CU-SLRT Std, a real-time digital simulator. The comparative studies demonstrate that the MPA optimized PSS yields improvements of up to 98.62% in the Particle Swarm Optimization (PSO) at 69.42%, Whale Optimization Algorithm (WOA) at 71.79%, Flower Pollination Algorithm (FPA) at 72.39%, African vulture optimization algorithm (AVOA) at 78.04%, Wild Horse Optimization (WHO) algorithm at 68.57% under various operating scenarios. The superiority of the MPA optimized PSS has been validated using Hardware-in-the-loop implementation for the SMIB test system.

Optimizing Hydrazine Activation on Dual‐Site Co‐Zn Catalysts for Direct Hydrazine‐Hydrogen Peroxide Fuel Cells

ABSTRACTDirect hydrazine‐hydrogen peroxide fuel cells (DHzHPFCs) offer unique advantages for air‐independent applications, but their commercialization is impeded by the lack of high‐performance and low‐cost catalysts. This study reports a novel dual‐site Co‐Zn catalyst designed to enhance the hydrazine oxidation reaction (HzOR) activity. Density functional theory calculations suggested that incorporating Zn into Co catalysts can weaken the binding strength of the crucial N2H3* intermediate, which limits the rate‐determining N2H3* desorption step. The synthesized p‐Co9Zn1 catalyst exhibited a remarkably low reaction potential of −0.15 V versus RHE at 10 mA cm−2, outperforming monometallic Co catalysts. Experimental and computational analyses revealed dual active sites at the Co/ZnO interface, which facilitate N2H3* desorption and subsequent N2H2* formation. A liquid N2H4‐H2O2 fuel cell with p‐Co9Zn1 catalyst achieved a high open circuit voltage of 1.916 V and a maximum power density of 195 mW cm−2, demonstrating the potential application of the dual‐site Co‐Zn catalyst. This rational design strategy of tuning the N2H3* binding energy through bimetallic interactions provides a pathway for developing efficient and economical non‐precious metal electrocatalysts for DHzHPFCs.

Preventing malware propagation in wireless sensor networks: Hybrid optimization algorithm for controlling

Malware transmission is a significant security issue in WSN, however, the influence of the attack and defensive processes on malware propagation is rarely taken into account in traditional malware propagation prevention methods. Advanced methods are in need to stop the propagation of malware of sensor nodes. With the formulation of representing dynamics among states, a new decision-making problem as the optimal control problem via hybrid optimization algorithm. The proposing model is termed as Butterfly Updated Bald Eagle Optimization based Prevention of Malware Propagation in Wireless Sensor Network (BUBEO-PMPWSN). In the proposed controlling system, optimal system parameters are analyzed via the BUBEO for preventing malware propagation in WSN. Particularly, the sensor node states considered are Susceptible, Infectious, Infectious and sleeping, recovered, Recovered and sleeping, and finally Dead. The system parameter tuning will be under the evaluation of fitness calculation under probability of infectious sensor node becoming recovered and the probability of infectious sensor node entering sleeping state. This optimal tuning strategy ensures the preventing of malware propagation. Finally, the performance of proposed BUBEO-PMPWSN model is evaluated and validated successfully by comparing other state-of-the-art models. The BUBEO-PMPWSN achieved 250 recovered nodes for time 500, while the HGS, BOA, HBA, COOT, and HHO scored 123, 115, 236, 172, and 180, respectively, for recovered nodes.

Dual Detection of Norfloxacin and Gatifloxacin by Excitation Tuning Strategy Based on Yellow Carbon Dots.

Fluoroquinolones (FQs) are widely used in hospitals, animal husbandry and aquaculture for the treatment of infectious diseases. However, overuse of FQs poses a potential threat to the environment and human. Detection of antibiotics is of great importance in various fields. In this paper, bright yellow fluorescence carbon dots (y-CDs) with quantum yield of 32% were synthesized via a simple and rapid microwave-assisted method using o-phenylenediamine and salicylic acid as raw materials. The fluorescence properties of y-CDs varied under different excitation wavelengths. It is discovered that the excitation wavelength is a critical factor to develop a dual functional fluorescence probe. Under selected excitation wavelength (355nm), y-CDs can realize dual detection of norfloxacin (NOR) and gatifloxacin (GAT) with good selectivity and high sensitivity in a "turn-on" mode. The detection limits of NOR and GAT are 0.0881 µM and 0.0125 µM, respectively. y-CDs prove practical application in the detection of NOR and GAT in milk and egg samples.

Analysis of Fractional Resonant Controllers for Voltage-Controlled Applications

This paper investigates the application of fractional proportional–resonant controllers within the voltage control loop of grid-forming inverters. The use of such controllers introduces an additional degree of freedom, enabling greater flexibility in manipulating frequency trajectories. This flexibility can be harnessed to improve tracking error and enhance disturbance rejection, particularly in applications requiring precise voltage regulation. The paper conducts a conceptual stability analysis of ideal fractional proportional–resonant controllers using the Nyquist criterion. A tuning procedure based on robustness criteria for the proposed controller is also addressed. This tuning strategy is used to compare different controllers under the same conditions. In addition, a sensitivity analysis is provided, comparing the performance of fractional proportional–resonant controllers with traditional proportional–resonant controllers equipped with harmonic compensation. The controller’s formulation and performance are validated through simulations and tested with a 20 kVA inverter under high non-linear loads. Compared to classical control approaches, the fractional tuning parameter enhances tracking performance, reduces phase delay, and improves disturbance rejection. These improvements are achieved with a controller designed to minimise computational demands in terms of memory usage and execution time.

Dicyclopenta[4,3,2,1-cde:4',3',2',1'-pqr]-peri-tetracene: Synthesis and An Example of Annulene-Within-An-Annulene Aromaticity in Different Redox States.

We report a robust strategy for tuning the electronic structure and chemical stability of π-conjugated polycyclic hydrocarbons (PHs). By fusing two cyclopentadienyl rings in the peri-tetracene bay regions, we introduce antiaromatic character into the π-system, leading to unique photophysical and electronic properties. A stable mesityl-substituted dicyclopenta-peri-tetracene derivative was synthesized through stepwise formylation/intramolecular cyclization at the bay regions of the dihydro peri-tetracene precursor, followed by oxidative dehydrogenation. This compound features an open-shell singlet diradical ground state, global antiaromaticity, exceptional stability under ambient conditions, and amphoteric redox behavior with a small energy gap. Its dication also possesses an open-shell singlet ground state, and its structure was identified by X-ray crystallographic analysis, revealing a unique [14]annulene-within-[22]annulene global aromatic structure. In contrast, the dianion exhibits a closed-shell singlet ground state. This work provides valuable insights for designing and synthesizing novel open-shell PHs with tunable electronic structures and remarkable stability.

Performance Tuning in Cloud Environments: Techniques for Enhancing Application Efficiency

This research aims at examining the strategies of performance tuning in cloud computing with emphasis on the optimization of applications, minimized response time, and optimal, affordable resource utilization. The research therefore includes a systematic literature review together with quantitative findings through empirical testing of the proposed model on Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), in addition to qualitative insights of experts. Auto-scaling, load balancing caching, database optimizing, integration with edge computing and predictive workloads using Artificial Intelligence are the aspects that are also studied as key performance tuning latter. Soon, the investigation, which was made based on the results from applying the four techniques on different applications and two clouds, demonstrates the strength of each technique in achieving different goals. While auto-scale and load balance feature is very helpful in control of workload fluctuations, the caching and database optimization helps in the efficient retrieval of the data. Edge computing reduces latency in response to real-time applications, and the application of artificial intelligence in workload forecast smoothes resource utilization in environments with a rapidly changing workload. Accordingly, the research has shown need for careful choosing of suitable performance-oriented interventions to enhance the application’s interactions, decrease CPU utilization, and cut costs in a cloud environment. Lastly, this work offers practical knowledge about the methods of cloud performance tuning to support better application deployment in the cloud environments.

Active disturbance rejection control of pressurizer pressure during startup process in a pressurized water reactor nuclear power plant

To cope with the participation of power plant peak shaving and realize the diversification of nuclear energy applications including cogeneration, desalination, etc. The application of automatic procedure startup and shutdown system (APS) is an important research direction to improve the safety, economy and flexibility of pressurized water reactor (PWR) nuclear power plant (NPP). But it is difficult to realize automatic control of pressurizer pressure in the PWR NPP startup condition with drastic changes and disturbances. To solve this problem, an active disturbance rejection control (ADRC) algorithm was proposed for the pressure control of the pressurizer. The controller parameters were tuned based on a two-step tuning scheme. Its control performance and disturbance rejection ability were studied and compared with proportional-integral-derivative (PID) controller under different operation conditions. The simulation results showed that the designed controller with fixed parameters can outperform the PID controller with specific optimized parameters on setpoint tracking and disturbance rejection. This study is of great significance for realizing the automatic control of pressurizer pressure in the startup process, and can provide reference for the design of the automatic control system during PWR startup. The controller was applied in typical startup process and achieved satisfactory control performance.

Understanding and Tuning the Effects of H2O on Catalytic CO and CO2 Hydrogenation.

Catalytic COx (CO and CO2) hydrogenation to valued chemicals is one of the promising approaches to address challenges in energy, environment, and climate change. H2O is an inevitable side product in these reactions, where its existence and effect are often ignored. In fact, H2O significantly influences the catalytic active centers, reaction mechanism, and catalytic performance, preventing us from a definitive and deep understanding on the structure-performance relationship of the authentic catalysts. It is necessary, although challenging, to clarify its effect and provide practical strategies to tune the concentration and distribution of H2O to optimize its influence. In this review, we focus on how H2O in COx hydrogenation induces the structural evolution of catalysts and assists in the catalytic processes, as well as efforts to understand the underlying mechanism. We summarize and discuss some representative tuning strategies for realizing the rapid removal or local enrichment of H2O around the catalysts, along with brief techno-economic analysis and life cycle assessment. These fundamental understandings and strategies are further extended to the reactions of CO and CO2 reduction under an external field (light, electricity, and plasma). We also present suggestions and prospects for deciphering and controlling the effect of H2O in practical applications.

Effects of cryogenic cooling on the spectral luminescence of BACs in bismuth-doped aluminosilicate fiber

The results of liquid nitrogen cooling (LNT, 77 K) on the spectral properties of bismuth-doped aluminosilicate fibers (BDFs) have been presented under varying pump wavelengths. It is revealed that the spectral luminescence of bismuth active centers (BACs) is mildly affected upon excitation at 405, 532, and 980 nm at LNT, whereas notable luminescence enhancement (∼1.3 times) is observed in the range of 1100-1350 nm (BACs-Al) under 830 nm pumping. The experimental data on the luminescence shape and kinetics of BACs-Al have been obtained at both room temperature (RT) and LNT with varied pumping powers. It is revealed that the enhanced luminescence is caused by the steep rise of emission around 1300 nm (denoted as BAC-Al (II)), which is more efficiently stimulated at a lower temperature. The key laser parameters of BACs have also been evaluated at both RT and LNT. The obtained results may shed some light on the NIR-emitting nature of BACs, and provide a promising strategy for tuning the spectral NIR luminescence scheme of BDFs.

An efficient Q-learning integrated multi-objective hyper-heuristic approach for hybrid flow shop scheduling problems with lot streaming

Efficient scheduling in flow shop environments with lot streaming remains a critical challenge in various industrial settings, necessitating innovative approaches to optimize production processes. This study investigates a hybrid flow shop scheduling problem dominant in real-world printed circuit board assembly shops. A novel multi-objective hyper-heuristic combining Q-learning, i.e., two-stage improved spider monkey optimization (TS-ISMO), is tailored to address the complexities of the flow shop scheduling problems. The proposed method aims to simultaneously optimize conflicting objectives such as minimizing makespan, total energy consumption, and total tardiness time while incorporating lot streaming considerations. For multi-objective hyper-heuristic techniques, the algorithm dynamically selects and adapts a diverse set of low-level heuristics to explore the solution space comprehensively and strike a balance among competing objectives. The proposed TS-ISMO algorithm incorporates several significant features aimed at enhancing its performance. These features encompass hybrid heuristics for solution initialization, a contribution value method for comprehensive convergence and diversity assessment, diverse evolutionary state judgments to promote the algorithm’s balance between exploration and exploitation capabilities, and a Q-learning strategy for self-adaptive parameter tuning. The integration of Q-learning facilitates intelligent parameter control, enabling the algorithm to autonomously adjust its behavior based on past experiences and evolution dynamics. This adaptive mechanism enhances convergence speed and solution quality by effectively guiding the search process toward promising regions of the solution space. Extensive computational experiments are conducted on benchmark instances of hybrid flow shop scheduling problems with lot streaming to evaluate the performance of the proposed algorithm. Comparative analyses against state-of-the-art approaches demonstrate its superior solution quality and computational efficiency.

To Bag is to Prune

Abstract It is notoriously difficult to build a bad Random Forest (RF). Concurrently, RF blatantly overfits in-sample without apparent consequences out-of-sample. Arguments like the bias-variance trade-off or double descent cannot rationalize this paradox. I propose a new explanation: bootstrap aggregation and model perturbation as implemented by RF automatically prune a latent “true” tree. More generally, I document that randomized ensembles of greedily optimized learners implicitly perform optimal early stopping out-of-sample. So, letting RF overfit the training data is a dominant tuning strategy against nature’s undisclosed choice of noise level. Additionally, novel ensembles of Boosting and MARS are also eligible. I empirically demonstrate the property, with simulated and real data, by reporting that these new completely overfitting ensembles perform similarly to their tuned counterparts – or better.

Controllable Regulation of CO2 Adsorption Behavior via Precise Charge Donation Modulation for Highly Selective CO2 Electroreduction to Formic Acid.

The synthesis of value-added products via CO2 electroreduction (CO2ER) is of great significance, but the development of efficient and versatile strategies for the controllable selectivity tuning is extremely challenging. Herein, the tuning of CO2ER selectivity through the modulation of CO2 adsorption behavior is proposed. Using the constructed zeolitic MOF (SNNU-339), CO2 adsorption behavior is controllably changed from *CO2 to CO2* via the precise ligand-to-metal charge donation (LTMCD) regulation. It is confirmed that the high electronegativity of the coordinate ligand directly restricts the LTMCD, reduces the charge density on the metal sites, lowers the Gibbs free energy for CO2* adsorption, and leads to the transformation of CO2 adsorption mode from *CO2 to CO2*. Owing to the modulated CO2 adsorption behavior and regulated kinetics, SNNU-339 exhibits superior HCOOH selectivity (≈330% promotion, 85.6% Faradaic efficiency) and high CO2ER activity. The wide applicability of the proposed approach sheds light on the efficient CO2ER. This study provides a competitive strategy for rational catalyst design and underscores the significance of adsorption behavior tuning in electrocatalysis.

Spin-dependent activity of cobalt contained hydroxy double perovskite for efficient antibiotic removal via peroxymonosulfate activation

Cobalt-containing catalysts exhibit good performance in the advancement of efficient catalytic oxidation reactions. However, achieving an optimal balance between reaction kinetics and reactant adsorption that is governed by the spin states of Co ions remains a significant challenge. This study aims to develop a convenient compositional tuning strategy to achieve such balance in stable CoxZn1-xSn(OH)6 perovskite hydroxides. The effect of varying spin state on peroxymonosulfate activation and tetracycline hydrochloride removal is systematically investigated. The magnetic properties analysis reveals that the content of high-spin Co2+ ions in octahedral site increases with Co content (x) and reaches 59.5 % in Co0.5Zn0.5Sn(OH)6. Experimental results and DFT calculations reveal that the spin state transition of Co2+ ions enhances the interaction between Co and O atoms. This enhanced interaction not only improves the reactivity due to the formation of highly covalent Co-OH bonds that serve as catalytically activated charge transfer channels, but also optimizes the suitable adsorption energy of PMS molecules at −3.52 eV for Co0.5Zn0.5Sn(OH)6. Consequently, Co0.5Zn0.5Sn(OH)6 shows superior catalytic activity, achieving nearly 100 % antibiotic removal efficiency within just 10 min, surpassing previously reported catalysts. These findings underscore the potential of compositional tuning spin states of transition metal ions in advancing oxidation processes and designing innovative catalysts.

An LQR‐Lagrange Algorithm Generated by Interdisciplinary Integration With Optimal Control and Optimization

ABSTRACTInterdisciplinary integration is a superior method to improve the optimization algorithm. In this paper, control theory and optimization are combined, and the optimization algorithm is regarded as a control process. Based on the premise of optimal control, the state equation corresponding to Lagrange Algorithm is established with the Karush–Kuhn–Tucker (KKT) conditions as the objective. As an optimal control method, linear quadratic regulator (LQR) is utilized to control the calculation process, and an innovative LQR‐Lagrange Algorithm is proposed. The Lyapunov stability criterion is applied to analyze the convergence, and it is proved that the proposed LQR‐Lagrange Algorithm is bound to converge as long as the parameter matrices and are positive definite. The analysis indicates that the influence of parameters in LQR‐Lagrange Algorithm on the calculation speed is monotonic, and the elements in and has no effect on the convergence. Therefore, the proposed algorithm has a monotonic and user‐friendly parameter tuning strategy. It perfectly tackles the game between parameter tuning strategy and calculation speed, and cracks the difficulties and dilemmas of conventional algorithms in this issue, realizing a win‐win situation.