Performance Tuning Research Articles

This work presents an InGaAsP/InP photodetector enhanced by an electrically tunable split-ring resonator (SRR) metamaterial, enabling voltage-controlled spectral response in the telecommunication band. The SRR array, fabricated in a 50 nm gold layer with tunable nanogaps, modulates its resonance through electrostatic actuation in the range of −10 V to +10 V, achieving precise control over responsivity and quality factor without altering the device footprint. Three-dimensional finite-difference time-domain simulations confirm strong field confinement within the SRR gaps and efficient coupling to the underlying absorber, consistent with experimental responsivity spectra. Measured baseline responsivity at 1.55 µm is 0.71 A/W, obtained under zero-bias conditions in the fabricated device. This value agrees with the simulated baseline response, while particle-swarm-optimized simulations predict a maximum responsivity of 1.25 A/W at the optimal tuning voltage, reflecting the combined effect of sub-wavelength field confinement and SRR–absorber coupling. The extracted quality factor increases from 28.5 to 35.2 with applied bias, while wavelength-shift sensitivity reaches ∼0.43 nm/V. These results demonstrate that integrating an electrostatically tunable metamaterial layer with a high-speed photodetector can provide dynamic spectral selectivity and controlled performance tuning, offering potential for adaptable optical communication and sensing systems.

We report the electroluminescent properties of a well-known Cu(I) complex based on P∧P-type ligands—specifically bis[2-(diphenylphosphino)phenyl] ether (DPEphos)—and N∧N-type ligands, namely, 2,9-dimethyl-1,10-phenanthroline (dmphen), [Cu(dmphen)(DPEphos)]BF4 (Cu1), as the emissive layer, transitioning from a light-emitting electrochemical cell (LEEC) to an organic light-emitting diode (OLED) configuration. No electroluminescence was observed in simple LEEC-type devices. However, the progressive introduction of injection and transport layers, and using 1,3-bis(carbazol-9-yl)benzene (mCP) as a host matrix, enabled efficient emission centered at 530 nm. We achieved effective charge balance and confinement of the recombination zone within the host through systematic optimization of host and transport layer thicknesses. We further investigated the effect of Cu1 doping concentration in mCP (0%–30%) on the device performance and emission color. While 10% doping yielded the highest luminance and efficiency, the emission color was also modulated—from green with bluish hues at low doping to yellowish green at higher levels—demonstrating a straightforward strategy for color tuning using a single Cu(I) complex. Although Cu1 is not the most efficient emitter available, its well-characterized nature and response to device architecture make it an ideal model for understanding structure–function relationships. This study offers valuable insights into layer engineering and compositional tuning, which can facilitate the development of more efficient and color-tunable devices with next-generation Cu(I)-based emitters.

In the age of high-frequency financial transactions, credit unions must process data with minimal latency while ensuring compliance and security. This paper contains an in-depth study of Azure Data Factory (ADF) pipeline performance tuning and optimization techniques within the context of credit union ETL workloads. I have a production case study where an existing ADF pipeline was unable to meet highly stringent Service Level Agreements (SLAs) during peak-load transaction times. With bottleneck and resource utilization analysis, I architected a new solution that leverages the most recent capabilities of ADF—parallel copy, Data Integration Unit (DIU) optimization, metadata-driven orchestration with control tables, checkpointing, and smart retry logic—to greatly improve throughput and reliability. My design utilizes a parameterized, metadata-driven master pipeline to generate parallel child pipelines (Figure 1), with dynamic partitioning and DIU allocation based on data volume. I utilize a control-table retry mechanism (through Lookup/IfCondition/ForEach) to replay only failed partitions [1][2]. I also utilize dynamically setting up integration runtimes along with custom region configurations and Time-to-Live (TTL) for recycling Spark clusters [3], and utilize staged copy for sink bottleneck removal [4]. Experimentation proves 80–90% reduced pipeline latency, a significant reduction in failure rate, and improved SLA attainment, all at the expense of cost-effectiveness. The comparative results are shown in Table 1. A new orchestration pattern and tuning scheme for ADF are presented specifically for financial data pipelines. Security and compliance (encryption, Key Vault, and certifications) are taken into account along with scalability and cost trade-offs.

This paper presents a unified analytical and simulation framework for optimizing the performance of M/M/1 queueing systems that incorporate differentiated working vacations, server breakdowns, and customer balking behavior. Other features of the solution include dynamical transitions between full-service mode, two levels of working vacation (with reduced service rates) phases, and random breakdown-repair cycles. Customers arrive via a Poisson process and decide to join or balk based on the server's current state. Embedded Markov chains, probability generating functions, and Matlab based discrete event simulation are applied to analyze key performance metrics, including average waiting time, queue length, and server utilization. A particle swarm optimization (PSO) algorithm is used to identify parameter configurations that minimize congestion and delay. Application scenarios in 5G/6G networks and service platforms demonstrate how adaptive vacation scheduling and resilience strategies improve energy efficiency and throughput. The results offer valuable information for performance tuning in resource-constrained telecommunication systems.

In the context of the global energy transition, the intermittent challenge of renewable energy urgently requires support from efficient energy storage technologies. Phase change materials (PCMs) have emerged as a key solution due to their high-density latent heat storage/release capabilities. However, solid-liquid phase change materials face bottlenecks such as leakage and low thermal conductivity. Carbon nanofibers (CNFs), with their excellent thermal conductivity, porous structure, and mechanical stability, have become an ideal carrier for optimizing composite phase change materials (CPCMs). CNFs can form a continuous thermal conduction network through uniform dispersion and utilize capillary forces to suppress leakage; when combined with materials like graphene to form hybrid systems, they can create multi-dimensional thermal conduction pathways, synergistically enhancing thermal conductivity and alleviating supercooling degree issues; by self-assembling into two-dimensional films or three-dimensional aerogels and other multi-dimensional structures, they can significantly enhance thermal conductivity and encapsulation stability. By leveraging the multi-form regulation strategy of CNFs, CPCMs achieve high energy storage density, rapid thermal response, and excellent cycle reliability, providing an effective pathway for the efficient storage and utilization of renewable energy.

This study compared the effectiveness of gallium nitride (GaN) with a single carbon-doped (C-doped) buffer layer and a composite carbon/iron-doped (C/Fe-doped) buffer layer within an AlGaN/GaN high-electron-mobility transistor (HEMT). In traditional power devices, Fe-doping has a large memory effect, causing Fe ions to diffuse outwards, which is undesirable in high-power-device applications. In the present study, a C-doped GaN layer was added above the Fe-doped GaN layer to form a composite buffer against Fe ion diffusion. Direct current (DC) characteristics, pulse measurement, low-frequency noise, and variable temperature analysis were performed on both devices. The single C-doped buffer layer in the AlGaN/GaN HEMT had fewer defects in capturing and releasing carriers, and better dynamic characteristics, whereas the composite C/Fe-doped buffers, by suppressing Fe migration toward the channel, showed higher vertical breakdown voltage and lower sheet resistance, and still demonstrated potential for further performance tuning to achieve enhanced semi-insulating behavior. With optimized doping concentrations and layer thicknesses, the dual-layer configuration offers a promising path toward improved trade-offs between leakage suppression, trap control, and dynamic performance for next-generation GaN-based power devices.

Achieving efficient damping performance tuning in NiTi alloy via laser powder bed fusion

Organic-inorganic hybrid perovskites (OIHPs) offer a promising pathway for the development of low-cost and efficient solar hydrogen production systems. Despite remarkable advancements, poor chemical stability of the OIHPs in aqueous environments limits their practical applications. Herein, we design a photoelectrochemical (PEC) device consisting of layer-by-layer assembled P(VDF-TrFE)/CH3NH3PbBr3 (MAPbBr3) hybrid films that simultaneously achieve efficient and stable solar water splitting. The multilayered PEC device shows long-term chemical stability for ∼7200 s in an aqueous electrolyte due to hydrophobic P(VDF-TrFE) encapsulation. In addition, leveraging the ferroelectric coupling effect, we achieved an extraordinary photocurrent tunability, from 30 μA/cm-2 to 1.09 mA/cm-2 (∼3500% modulation at 0.4 V vs Ag/AgCl), simply by switching the polarization direction in the ferroelectric layers. Comprehensive characterizations reveal that such PEC performance tuning originates from ion migration induced changes in the band alignment, which regulates the charge transfer efficiency at the photoelectrode/electrolyte interface. Our work demonstrates that coordinating functional semiconductors with ferroelectric polymers in a hybrid multilayer framework presents a versatile strategy for engineering high-performance composites and advances the design of next-generation solar hydrogen production systems.

Serverless computing has revolutionized modern software architectures by offering scalability, agility, and cost-efficiency. AWS Lambda, in particular, enables developers to execute code without provisioning or managing servers, while MongoDB Atlas offers a fully managed NoSQL database service in the cloud. However, realizing high throughput from such architectures requires deliberate tuning. This paper presents a comprehensive analysis of performance optimization strategies specifically tailored for AWS Lambda functions interfacing with MongoDB Cloud. By identifying typical performance bottlenecks such as cold starts, connection limitations, and VPC overheads, we demonstrate practical solutions including provisioned concurrency, persistent connections via Lambda layers, and usage of VPC endpoints. The methodology focuses on balancing execution time, latency, and cost-effectiveness, ensuring the infrastructure supports both burst and steady-state loads. Our real-world case study from the fuel retail industry validates the success of these tuning strategies through metrics such as request latency, transaction per second (TPS), and connection stability. Furthermore, we investigate the synergy between event-driven triggers like Amazon SQS and data-intensive operations in MongoDB to achieve sustained throughput at scale. The findings from this research can guide engineers and architects in building robust, responsive, and scalable serverless applications using AWS and MongoDB Cloud, ultimately aligning business outcomes with technical performance.

The development of efficient and selective catalytic methods for synthesizing well-defined polycarbonates and their copolymers represents a significant advancement toward sustainable polymer production. In this study, we report a series of innovative single-molecule hydrogen-bonding catalysts/initiators for the ring-opening polymerization (ROP) of cyclic carbonates, enabling rapid and precise synthesis of polycarbonates and their copolymers with polylactide. These catalysts uniquely facilitate simultaneous activation of both monomer and chain-end within a single molecular architecture, demonstrating superior activity compared to conventional multicomponent hydrogen-bonding initiating systems. Density functional theory (DFT) calculations reveal that alkyl substitution plays a critical role in enhancing catalytic activity for ROP by reducing the energy barrier relative to aryl-substituted analogues. The modular design of these catalysts allows for facile structural optimization and performance tuning. Notably, Cat. 1 exhibits high catalytic efficiency at 25°C, producing polycarbonates with well-defined structures and high molecular weights (Mn up to 164.8kDa, Ð of 1.37). We further demonstrate versatile copolymerization strategies: one-step copolymerization yields gradient polycarbonate-g-polylactide copolymers, whereas sequential monomer addition in one-pot reactions produces well-defined block polycarbonate-b-polylactide copolymers within minutes. These block copolymers exhibit high molecular weights (Mn up to 189.4kDa, Ð of 1.37), precisely tunable thermal properties, and exceptional mechanical performance, highlighting their potential for advanced material applications.

Abstract The development of efficient and selective catalytic methods for synthesizing well‐defined polycarbonates and their copolymers represents a significant advancement toward sustainable polymer production. In this study, we report a series of innovative single‐molecule hydrogen‐bonding catalysts/initiators for the ring‐opening polymerization (ROP) of cyclic carbonates, enabling rapid and precise synthesis of polycarbonates and their copolymers with polylactide. These catalysts uniquely facilitate simultaneous activation of both monomer and chain‐end within a single molecular architecture, demonstrating superior activity compared to conventional multicomponent hydrogen‐bonding initiating systems. Density functional theory (DFT) calculations reveal that alkyl substitution plays a critical role in enhancing catalytic activity for ROP by reducing the energy barrier relative to aryl‐substituted analogues. The modular design of these catalysts allows for facile structural optimization and performance tuning. Notably, Cat. 1 exhibits high catalytic efficiency at 25 °C, producing polycarbonates with well‐defined structures and high molecular weights (Mn up to 164.8 kDa, Ð of 1.37). We further demonstrate versatile copolymerization strategies: one‐step copolymerization yields gradient polycarbonate‐g‐polylactide copolymers, whereas sequential monomer addition in one‐pot reactions produces well‐defined block polycarbonate‐b‐polylactide copolymers within minutes. These block copolymers exhibit high molecular weights (Mn up to 189.4 kDa, Ð of 1.37), precisely tunable thermal properties, and exceptional mechanical performance, highlighting their potential for advanced material applications.

Defect-Mediated Coexistence of Bipolar Switching and Negative Differential Resistance in Carbon Nanotube–PVA Memristors: Mechanistic Insights and Performance Tuning

This article presents a new multi-objective model that optimizes Kafka configuration to minimize end-to-end latency while quantifying independent parameter influence, interaction effects and sensitivity to local parameter changes. The proposed model addresses a challenging problem of selecting the configuration to prevent overloading while maintaining high availability and low latency of Kafka cluster. The study proposes an algorithm to implement this model using an adaptive optimization strategy that combines gradient-based and derivative-free search methods. This strategy enables a balance between convergence speed and global search capabilities, which is critical when dealing with the nonlinear parameter space characteristic of large-scale Kafka deployments. Experimental evaluation demonstrates 99% accuracy of the model verified against a trained XGBRegressor model and tested across multiple optimization strategies. The experimental results show that alternative configurations can be selected to meet secondary objectives-such as operational constraints - without significantly impacting latency. In this context, the designed multi-objective model serves as a valuable tool to guide the configuration selection process by quantifying and incorporating such secondary objectives into the optimization landscape. The proposed multi-objective function could be adopted in real time applications as a tool for Kafka performance tuning.

Silicon carbide offers distinct advantages in the field of power electronic devices. However, manufacturing processes remain a significant barrier to its widespread adoption. Polycrystalline SiC is less expensive and easier to produce than single crystal. But stabilizing and controlling its performance are critical challenges that must be addressed urgently. Due to its material properties and excellent performance in applications, 3C-SiC is gaining increasing attention in research. This article presents the electrical and material properties of a series of polycrystalline 3C-SiC samples and investigates their interrelationship. The samples were examined using TEM, which confirmed their polycrystalline structure. Combined with XRD and Raman spectroscopy, the grain orientations within the samples were analyzed, and the presence of stress was verified. EBSD was employed to statistically examine the grain structure and size across samples. For samples with similar doping levels, grain size is the most influential factor in determining electrical characteristics. Further EBSD measurements reveal the relationship between resistivity and grain size as log(ρ) = −1.93 + 8.67/d. These findings provide a foundation for the quantitative control and application of polycrystalline 3C-SiC. This work offers theoretical evidence for optimizing the performance tuning of 3C-SiC ceramics and enhancing their effectiveness in electronic applications.

This work introduces a novel, rapid, label-free, affinity-enabled electrochemical sensor for the detection of interleukin-6 (IL-6), a critical proinflammatory cytokine associated with severe conditions like sepsis and COVID-19. Unlike conventional approaches, this platform leverages an innovative biofunctional nanocomposite of Ti3C2Tx MXene, tetraethylene pentaamine-functionalized reduced graphene oxide (TEPA-rGO), and Nafion, functionalized with anti-IL-6 antibodies, integrated into a carbon-based screen-printed three-electrode chip. The system achieves unprecedented sensitivity in IL-6 quantification, with a single-digit pg/mL detection limit and a broad range of 3-1000 pg/mL using ∼5 μL of serum. The sensor design is uniquely enhanced through the introduction of a genetic algorithm-based thin-layer diffusion model, which optimizes critical, previously unknown electrochemical transport parameters, including diffusion coefficient, rate constant, charge transfer coefficient, and electrochemically active surface area. This approach represents a significant advancement in biosensor modeling and performance tuning. The sensor demonstrates exceptional selectivity (signal-to-noise ratio ∼ 6.9) against relevant interferents (e.g., sepsis-related antigens, small molecules, electroactive compounds), retains operational stability for a month, and offers a sample-to-answer time of ∼15 min (i.e., up to 12 times faster than traditional ELISA), while maintaining comparable sensitivity. Detailed morphological, topographical, and chemical analyses validate the structural and functional integrity of the TEPA-rGO/MXene/Nafion nanocomposite. By combining cutting-edge nanomaterials with advanced computational modeling, this IL-6 sensor sets a new benchmark for rapid, precise cytokine detection, offering transformative potential for early disease diagnosis and prognosis.

In the exascale computing era, tuning High-Performance Computing (HPC) applications has become a significant computational challenge. Although Bayesian optimization (BO) has emerged as a promising tool for HPC performance tuning, the BO workflow is inherently sequential (i.e., one function evaluation at a time) and cannot leverage the huge amount of parallel resources present in modern supercomputers, resulting in a considerable underutilization of their computational capabilities. This paper explores the trade-off between search quality and parallelism in BO, investigating a diverse set of methods. Building upon both previous approaches from the literature and novel methodologies introduced in this work, our study provides a deep analysis to accelerate BO performance tuning. By examining a set of synthetic functions and practical HPC applications, our exploration analyzes the interaction among various BO methods for parallelization, the quantity of parallel resources, the runtime distribution of target HPC applications, and the costs associated with different search orchestration mechanisms that have been overlooked in previous studies. Compared to sequential BO, our novel methodology achieves comparable quality while demonstrating robust scalability in search time as the amount of parallel resources increases; it also outperforms a state-of-the-art tuner, which supports parallelization, achieving up to 3.67x faster search time. We provide high-value insights for practitioners seeking to leverage the power of parallel computing for efficient HPC application tuning. Additionally, to further assist researchers in accelerating the performance tuning of their HPC applications, we provide an extension of an existing open-source tuning framework that incorporates our methods.

This article explores comprehensive strategies for optimizing database performance in enterprise environments facing exponential data growth and increasingly complex architectures. As organizations transition to hybrid and cloud database systems, traditional hardware-focused approaches prove insufficient, necessitating sophisticated software optimization techniques. The research examines four critical pillars of database performance: query optimization, concurrency management, structural optimization, and continuous monitoring. Drawing from extensive case studies across financial services, healthcare, and other regulated industries, the article demonstrates how systematic performance tuning delivers significant benefits, including reduced response times, lower operational costs, improved user experience, and enhanced compliance capabilities. By analyzing execution plans, implementing connection pooling, leveraging strategic indexing, and establishing comprehensive monitoring frameworks, organizations can achieve substantial performance improvements without additional hardware investments. The article highlights that performance optimization is not a one-time effort but an iterative process requiring continuous refinement as data volumes grow and usage patterns evolve, making it a strategic business imperative rather than merely a technical exercise.

The integration of Artificial Intelligence (AI) into software development and deployment processes is revolutionizing the way applications are built, tested, and delivered. This research paper explores the concept of AI-powered automation within the AutoDevOps framework, which merges DevOps practices with intelligent systems to streamline the entire Software Development Life Cycle (SDLC). AutoDevOps leverages machine learning, predictive analytics, and intelligent orchestration to automate critical tasks such as code integration, testing, monitoring, and deployment, reducing manual effort and accelerating release cycles. This study investigates how AI enhances AutoDevOps by enabling adaptive decision-making, real-time anomaly detection, and resource optimization in cloud-native environments. Tools that utilize AI for continuous integration/continuous deployment (CI/CD), security analysis, and performance tuning are examined to understand their impact on productivity, code quality, and operational efficiency. Real-world case studies and survey data from industry professionals are analyzed to assess the effectiveness and adoption of AI-driven AutoDevOps solutions. In addition to highlighting the benefits, the paper also addresses key challenges, including model reliability, data privacy, integration complexity, and the evolving role of human oversight. The findings suggest that AI-augmented AutoDevOps represents a significant leap forward in modern software engineering practices, offering organizations a scalable, intelligent, and resilient approach to software delivery. This research contributes to a deeper understanding of how AI is shaping the future of automated development and deployment ecosystems.

PEDOT:PSS-based electronic materials: Preparation, performance tuning, processing, applications, and future prospect

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