Performance Of Systems Research Articles

Bridging minds and machines: Unmasking the limits in text-based automatic personality recognition for enhanced psychology-AI synergy.

Text-based automatic personality recognition (APR) operates at the intersection of artificial intelligence (AI) and psychology to determine the personality of an individual from their text sample. This covert form of personality assessment is key for a variety of online applications that contribute to individual convenience and well-being such as that of chatbots and personal assistants. Despite the availability of good quality data utilizing state-of-the-art AI methods, the reported performance of these recognition systems remains below expectations in comparable areas. Consequently, this work investigates and identifies the source of this performance limit and attributes it to the flawed assumptions of text-based APR. These insights are obtained via a large-scale comprehensive benchmark and analysis of text data from five corpora with diverse characteristics and complementary personality models (Big Five and Dark Triad) applied to an assortment of AI methods ranging from hand-crafted linguistic features to data-driven transformers. Finally, the work concludes by identifying the open problems that can help navigate the limitations in text-based automatic personality recognition to a great extent.

The role of regulatory agencies within health systems during emergencies in BrazilPapel de los organismos regulatorios en los sistemas de salud en situaciones de emergencia en Brasil

The performance of health systems is related to ensuring the right to health, an important component of human rights that includes the right to health protection. Regulatory action by the state aims to reduce risks to health and thus help improve health system performance, particularly with respect to the principles of comprehensiveness and equity. The aim of this article is to discuss resilience from the perspective of national regulatory agencies - especially when they are faced with public health emergencies (PHEs) - and their contribution to health system resilience, taking as a model the work of the Brazilian Health Regulatory Agency (ANVISA). In the context of PHEs, health regulators have been improving their practices and working on several fronts. Regulatory agencies have used specific regulatory instruments to speed up access to effective and safe products for responding to pandemics, to ensure the quality of care, and to communicate with the public. ANVISA specifically has brought new areas of action under its purview, such as overseeing the development of mechanical ventilators. It is crucial to recognize the interdependence between improving response capacity and improving performance in dealing with PHEs. Examples include the fast-track approval of COVID-19 vaccines, which allowed more rapid vaccination; cooperation among regulatory agencies; and, in the case of Brazil, work by ANVISA to strengthen the role of the country's Unified Health System (SUS) and the right to health in the face of disinformation and denialism.

Enhancing IoNT performance with fog computing: A hybrid architecture for real-time data processing

<p>The rapid evolution of the Internet of Nano Things (IoNT) and Fog Computing presents new opportunities for creating advanced smart systems that are both efficient and responsive. Integrating IoNT with Fog Computing offers a powerful paradigm that can address the limitations of cloud-centric architectures, particularly in terms of latency, bandwidth, and real-time processing. The paper explores the synergistic combination of IoNT and Fog Computing, focusing on the development of a hybrid architecture that leverages the proximity and computational capabilities of fog nodes to process data generated by nanoscale devices. Key challenges such as resource management, data processing efficiency, and security concerns are addressed in this study. The proposed architecture not only enhances the performance of smart systems by reducing latency and optimizing resource utilization but also ensures robust security and privacy for the vast amounts of generated data. A comprehensive dataset was generated to assess the integration of the IoNT with Fog Computing, focusing on environmental parameters such as Temperature, Humidity, and Wind Speed, as collected from five IoNT sensors. Python was employed to generate and augment this dataset, ensuring the accurate representation of varied environmental conditions. The data transmission between IoNT sensors and FogNode_1 successfully demonstrated the framework’s ability to capture and process real-time environmental information. Aggregated data from the fog and cloud layers confirmed the system’s efficiency in reducing latency and maintaining data integrity. Furthermore, the implementation of advanced communication protocols and effective resource management highlights the robustness of the integration, contributing to the real-time monitoring and decision-making processes in environmental applications. As well as, this study compares Fog Computing and Cloud Computing, concluding that Fog Computing offers significant advantages in areas like latency, bandwidth utilization, resource efficiency, security, privacy, real-time processing, and edge intelligence. These benefits make Fog Computing particularly suitable for applications requiring low latency, local data processing, enhanced security, and the ability to leverage edge intelligence. While Cloud Computing may have advantages in certain areas, Fog Computing’s overall performance and versatility make it a compelling choice for those seeking to optimize their computing infrastructure. This research aims to pave the way for more resilient and intelligent smart systems that can operate effectively in various domains, including healthcare, environmental monitoring, and industrial automation.</p>

Thermodynamics of Electrochemical Marine Inorganic Carbon Removal.

In recent years, marine carbon removal technologies have gained attention as a means of reducing greenhouse gas concentrations. One family of these technologies is electrochemical systems, which employ Faradaic reactions to drive alkalinity-swings and enable dissolved inorganic carbon (DIC) removal as gaseous CO2 or as solid minerals. In this work, we develop a thermodynamic framework to estimate upper bounds on performance for Faradaic DIC removal systems. To assess the fundamental mass balances of these systems, we first define unit operations in the DIC/total alkalinity (TA) space. By coupling a seawater speciation model to an electrochemical framework, we provide a generalized comparison of gas evolution and mineralization DIC removal routes, focusing on asymmetric charge/discharge systems. We then show how this framework can be extended to other processes, such as those employing dilution schemes. Finally, we provide a minimum energetic assessment of mCDR pathways relative to direct air capture. Overall, this thermodynamic framework aims to guide system and process design and to drive material discovery and engineering for future electrochemical marine DIC removal systems.

Solvent-Driven Na Storage in SnS2 Anodes: Atomistic Simulation-Guided Strategies for Reversible Reactions, Solid Electrolyte Interphase, and Morphological Transformation.

Crystalline SnS2 accommodates Na ions through intercalation-conversion-alloying (ICA) reactions, exhibiting a natural potential for high energy storage, while its layered structure facilitates rapid charging. However, these intrinsic advantages are not fully realized in practical battery applications. Herein, utilizing an innovative integration of machine-learning-based thermodynamics, artificial-neural-network-assisted molecular dynamics, and density functional theory, specific solvents are demonstrated to effectively tailor the reaction pathways. This strategy not only steers phase transition pathways but also significantly reduces the formation of the solid electrolyte interphase (SEI), which is a common issue in recent battery research. These characteristics of solvents enable reversible ICA reactions and also aid the transformation of microsized SnS2 particles into 3D porous nanostructures with minimal SEI formation. The performance of our Na-SnS2 half-cells achieve 1100 mAh g-1 (97% of the theoretical capacity) at 0.5 C, placing them among the top performers for Na storage. By moving beyond the traditional view of electrolyte solvents as a simple medium for ion transport, this work highlights the critical impact of solvent selection on enabling reversible reactions and morphological transformation of SnS2 anodes with minimal SEI formation and setting benchmarks for anode performance in energy storage systems based on ICA reactions.

In-Situ Nanoindentation Surface Topography of Lead-Free Piezoelectric Thin Films

Surface roughness significantly affects the performance of microelectromechanical systems (MEMS) and piezoelectric films. This study investigates the impact of surface roughness on the mechanical properties of thin piezoelectric films using nanoindentation and scanning probe microscopy (SPM). Four piezoelectric films with different thicknesses (220, 350, and 450 nm) and substrate configurations (LNO/SiO2/Si or LNO/Si) were analyzed. A discriminant analysis revealed that the fractal dimension is more effective than the arithmetic mean height (Sa) for distinguishing surfaces, with only 2% misclassification versus 25% for Sa. A multiscale analysis identified the Smr2 parameter with low-pass filtering at 140 nm as highly effective for surface discrimination, achieving only 0.1% misclassification. The analysis of the roughness parameter Sa at various scales showed that band-pass filtering at 500 nm yielded a 0.7% misclassification rate, indicating its relevance for fractal roughness characterization. Most relevant roughness parameters for mechanical property correlation were found: Smr2 with low-pass filtering at 500 nm correlated best with hardness (R2 = 0.82), and Vvc with low-pass filtering at 2 nm correlated best with reduced elastic modulus (R2 = 0.84). These results demonstrate that surface roughness features like valley volume and voids significantly impact the apparent mechanical properties of piezoelectric films.

Thermal and Flow Characteristics of Alumina Nanofluids in Microfluidic Systems: A Low-Concentration Study

Microfluidic technologies and nanofluids represent a synergistic combination with significant potential for enhancing heat transfer and thermal management applications. This study investigates the thermal and flow characteristics of a 0.001 wt.% alumina (Al₂O₃)-water nanofluid within a custom-designed serpentine microfluidic channel. The nanofluid was prepared and characterized for its thermal conductivity, viscosity, specific heat, and density. Experimental microfluidic studies, supplemented by numerical simulations, were conducted to evaluate the fluid's behavior under controlled conditions. Results indicated a slight increase in thermal conductivity for the Al₂O₃ nanofluid compared to pure water, with increments ranging from 0.16% at 20°C to 0.30% at 80°C, attributed to enhanced Brownian motion of the nanoparticles. Viscosity measurements revealed marginal increases, suggesting minimal impact on fluid flow dynamics. The microfluidic experiments demonstrated a consistent pressure gradient and laminar flow regime, essential for precise control and efficient thermal management. Temperature contours showed effective heat dissipation, with a steady thermal gradient from the inlet to the outlet. The study concludes that low-concentration Al₂O₃ nanofluids can enhance thermal performance in microfluidic systems without significantly affecting flow characteristics, making them suitable for applications requiring efficient heat dissipation, such as electronic cooling and chemical reactions. These findings provide a foundation for future research into higher nanoparticle concentrations and different base fluids, aimed at optimizing the thermal and flow properties of nanofluids in microfluidic environments. The integration of nanofluids with microfluidic technologies holds promise for advancing the performance and reliability of next-generation thermal management systems.

Deep learning based bio-metric authentication system using a high temporal/frequency resolution transform

IntroductionIdentity verification plays a crucial role in modern society, with applications spanning from online services to security systems. As the need for robust automatic authentication systems increases, various methodologies—software, hardware, and biometric—have been developed. Among these, biometric modalities have gained significant attention due to their high accuracy and resistance to falsification. This paper focuses on utilizing electrocardiogram (ECG) signals for identity verification, capitalizing on their unique, individualized characteristics.MethodsIn this study, we propose a novel identity verification framework based on ECG signals. Notable datasets, such as the NSRDB and MITDB, are employed to evaluate the performance of the system. These datasets, however, contain inherent noise, which necessitates preprocessing. The proposed framework involves two main steps: (1) signal cleansing to remove noise and (2) transforming the signals into the frequency domain for feature extraction. This is achieved by applying the Wigner-Ville distribution, which converts ECG signals into image data. Each image captures unique cardiac signal information of the individual, ensuring distinction in a noise-free environment. For recognition, deep learning techniques, particularly convolutional neural networks (CNNs), are applied. The GoogleNet architecture is selected for its effectiveness in processing complex image data, and is used for both training and testing the system.ResultsThe identity verification model achieved impressive results across two benchmark datasets. For the NSRDB dataset, the model achieved an accuracy of 99.3% and an Equal Error Rate (EER) of 0.8%. Similarly, for the MITDB dataset, the model demonstrated an accuracy of 99.004% and an EER of 0.8%. These results indicate that the proposed framework offers superior performance in comparison to alternative biometric authentication methods.DiscussionThe outcomes of this study highlight the effectiveness of using ECG signals for identity verification, particularly in terms of accuracy and robustness against noise. The proposed framework, leveraging the Wigner-Ville distribution and GoogleNet architecture, demonstrates the potential of deep learning techniques in biometric authentication. The results from the NSRDB and MITDB datasets reflect the high reliability of the model, with exceptionally low error rates. This approach could be extended to other biometric modalities or combined with additional layers of security to enhance its practical applications. Furthermore, future research could explore additional preprocessing techniques or alternative deep learning architectures to further improve the performance of ECG-based identity verification systems.

Hybrid bagging and boosting with SHAP based feature selection for enhanced predictive modeling in intrusion detection systems

The novelty and growing sophistication of cyber threats mean that high accuracy and interpretable machine learning models are needed more than ever before for Intrusion Detection and Prevention Systems. This study aims to solve this challenge by applying Explainable AI techniques, including Shapley Additive explanations feature selection, to improve model performance, robustness, and transparency. The method systematically employs different classifiers and proposes a new hybrid method called Hybrid Bagging-Boosting and Boosting on Residuals. Then, performance is taken in four steps: the multistep evaluation of hybrid ensemble learning methods for binary classification and fine-tuning of performance; feature selection using Shapley Additive explanations values retraining the hybrid model for better performance and reducing overfitting; the generalization of the proposed model for multiclass classification; and the evaluation using standard information metrics such as accuracy, precision, recall, and F1-score. Key results indicate that the proposed methods outperform state-of-the-art algorithms, achieving a peak accuracy of 98.47% and an F1 score of 96.19%. These improvements stem from advanced feature selection and resampling techniques, enhancing model accuracy and balancing precision and recall. Integrating Shapley Additive explanations-based feature selection with hybrid ensemble methods significantly boosts the predictive and explanatory power of Intrusion Detection and Prevention Systems, addressing common pitfalls in traditional cybersecurity models. This study paves the way for further research on statistical innovations to enhance Intrusion Detection and Prevention Systems performance.

Optimization of Imaging Reconnaissance Systems Using Super-Resolution: Efficiency Analysis in Interference Conditions

Image reconnaissance systems are critical in modern applications, where the ability to accurately detect and identify objects is crucial. However, distortions in real-world operational conditions, such as motion blur, noise, and compression artifacts, often degrade image quality, affecting the performance of detection systems. This study analyzed the impact of super-resolution (SR) technology, in particular, the Real-ESRGAN model, on the performance of a detection model under disturbed conditions. The methodology involved training and evaluating the Faster R-CNN detection model with original and modified data sets. The results showed that SR significantly improved detection precision and mAP in most interference scenarios. These findings underscore SR’s potential to improve imaging systems while identifying key areas for future development and further research.

Enhanced catalytic degradation of methylene blue using self-propelled Janus micromotors: An insight into decomposing characteristics of passive and active hematite particles studded with Pt nanoparticles.

Recent advancements in catalytic micromotors have shown significant potential for environmental applications, yet challenges such as particle agglomeration persist. In this study, we compare the degradation of methylene blue using hematite particles fully coated with platinum and those partially decorated with platinum. The selective decoration, confirmed through techniques like EDX, FESEM, TEM, and XPS, plays a crucial role in the micromotors' behavior. The decomposition of hydrogen peroxide (H2O2) by Pt nanoparticles on one side of the hematite particles generates thrust, propelling the micromotors and enhancing their interaction with pollutant molecules. This active mobility helps counteract agglomeration, preventing the formation of irregular 3D clusters and improving catalytic efficiency. Our findings show that partially decorated particles achieve up to 85% dye removal within 90min, outperforming fully decorated particles, which reach only 33% efficiency due to aggregation and sedimentation. These results underscore the importance of optimized surface decoration for improving the performance and stability of catalytic systems in pollutant degradation.

Effect of base slab mass and number of rubber layers on base isolation system performance in enhancing building dynamic response

Purpose Traditional designs might not withstand earthquake forces adequately, potentially leading to structural damage. This paper aims to examine the impact of varying the base slab mass and the number of rubber layers in isolators on the performance of isolation systems to improve the seismic performance of buildings. Design/methodology/approach The isolated structure is modeled as a two-degree of freedom system, and its corresponding equation of motion is established. Displacement expressions for the base slab and roof are derived using the mode superposition method. Findings A base isolation system improves the buildings’ behavior, particularly when higher rubber layer numbers and a heavier base slab are assumed. At an excitation frequency of 8 rad/s, an increase in the rubber layer numbers from 9 to 11 led to a 30% reduction in relative displacement. At an excitation frequency of 6 rad/s, a 40% reduction in relative displacement was observed passing from a base slab mass (mb) of 7.5 tons with damping ratios of 12% and 10% in the first and second modes to a mass (mb) of 30 tons with lower damping ratios of 9.5% and 4.2%. Originality/value The high damping ratio in the isolation mode significantly increases the damping ratio of the fixed-base building. However, higher damping ratios in the first and second modes do not necessarily lead to better seismic performance. Besides, an increase in the base slab mass and the number of rubber layers enhances the dynamic response of the isolated structure by shifting its fundamental frequency away from the excitation frequency.

Enhanced Modeling and Control of Organic Rankine Cycle Systems via AM‐LSTM Networks Based Nonlinear MPC

ABSTRACTThe organic Rankine cycle (ORC) serves as an effective means of converting low‐grade heat sources into power, playing a pivotal role in environmentally friendly production and energy recovery. However, the inherent complexity, strong and unidentified nonlinearity, and control constraints pose significant challenges to designing an optimal controller for ORC systems. To address these issues, this research introduces a novel modeling and control framework for ORC systems. Leveraging an attention mechanism‐based long short‐term memory (AM‐LSTM) network, the dynamic characteristics of ORC systems, which are subject to non‐Gaussian disturbances, are accurately modeled. A performance metric based on survival information potential (SIP) is developed to optimize the network parameters. Furthermore, a multi‐objective optimization approach that integrates nonlinear model predictive control (NMPC) with the multiverse optimizer (MVO) algorithm is implemented to ensure effective control under varying operating conditions and constraints. Through extensive simulations, the proposed framework demonstrates superior accuracy, robustness, and control performance for ORC systems.

Centralization and Long-Term System Performance: Evidence From State Takeovers of School Districts

Does centralization affect the performance of public institutions in the long run? Can states more successfully improve struggling bureaucracies than local governments? This paper explores these questions in the context of educational governance with a focus on state takeovers of school districts in the U.S.—a shift away from the traditional school board governance arrangement toward more centralized decision-making at the state level. In recent decades, takeovers have become a more common policy response to perceived low performance of public school systems. This paper extends an earlier study on the topic to examine the longer-run effects of this form of political centralization on system performance. Using a nationwide sample and up-to-date event study methods, the paper finds no evidence that takeovers of districts between 2010 and 2018 generated improvements in student reading and math performance, up to nearly a decade after takeover occurred. Takeovers lasting a greater number of years are not associated with differential impacts. Findings are not driven by compositional changes in student populations or bias due to variation in treatment timing. This form of political centralization from local to state levels therefore does not appear to be a consistent tool for improving the performance of public institutions.

Optimizing System Performance with Adaptive Algorithms for Signal Processing in Wireless Communication Networks

In modern wireless communication networks, the need for high-speed, reliable, and efficient data transmission is paramount. Signal processing algorithms play a critical role in optimizing network performance, especially under dynamic conditions such as varying network loads and environmental interference. This paper presents an in-depth study of adaptive signal processing algorithms designed to improve the performance of wireless communication systems. We examine various techniques, including adaptive filtering, channel estimation, and interference cancellation, and assess their effectiveness in optimizing signal quality and network throughput. Through simulation and real-world case studies, the paper demonstrates how these algorithms can be applied to mitigate signal distortion and reduce data transmission errors in wireless networks. The results show that adaptive algorithms significantly enhance the overall system performance, particularly in environments with fluctuating signal conditions. The paper concludes with recommendations for further development in adaptive algorithms for future wireless communication standards, particularly 5G and beyond.

Enhanced model predictive control using artificial neural networks for grid-connected two-level inverters

Integrating artificial intelligence (AI) to improve the performance of photovoltaic systems is compelling for two main reasons. First, AI technologies are making significant progress and are widely applied across various fields, such as medicine and agriculture. Second, the world is experiencing an increasing demand for energy while simultaneously facing the challenge of reducing reliance on traditional energy sources that exacerbate carbon emissions and climate change. This emphasizes the critical importance of transitioning to sustainable and clean energy solutions. In this context, photovoltaic panels have emerged as one of the most promising and widely adopted renewable energy sources. As the global reliance on solar energy grows, enhancing the efficiency and reliability of solar energy systems has become imperative. However, this transition is accompanied by significant challenges, particularly in integrating these systems with existing power grids. The complexities involved in achieving seamless integration, maintaining consistent performance, and preventing system failures necessitate the adoption of advanced technologies. These challenges are further exacerbated by the need to manage fluctuations in energy production, optimize energy storage, and ensure the long-term sustainability of solar power. This paper presents a novel approach for controlling ANN-MPC solar inverters, leveraging artificial neural networks (ANN) to address the challenges related to the efficiency of photovoltaic systems and their integration with the grid, ultimately reducing dependence on conventional energy sources. Initially, the ANN-based model is trained using large datasets derived from the system inputs and outputs, taking into account all critical operating conditions, such as sudden changes in current. Following the training process, the model acquires extensive data, enabling it to adapt to all transient conditions the system may encounter. The model continuously evolves to improve the efficiency of grid-connected photovoltaic systems. Additionally, experimental validation and performance testing of the proposed model are conducted to verify the effectiveness and flexibility of the ANN-based approach. The experimental results confirm the proposed approach's ability to enhance the performance of grid-connected electrical systems, reduce computational costs, and achieve model-free control.

Insights on the tribological characteristics of titanium alloys in demanding environments

Abstract Titanium alloys are widely used in demanding applications due to their exceptional strength-to-weight ratio, high-temperature resilience, and excellent corrosion resistance. Understanding their tribological behavior is critical, as the performance and durability of several mechanical systems, particularly in gas turbine engines, are often constrained by friction and wear in complex contacting and mobile assemblies. This study investigates the tribological behavior of two widely used titanium alloys, Ti-6-4 and Ti-6-2-4-2, focusing on their interfacial phenomena under varied operational conditions. Tribological testing was conducted using a reciprocating tribometer at different temperatures and loading conditions. Ex-situ analyses revealed that wear mechanisms were heavily influenced by the properties of the oxide layer formed during sliding. Under higher loads, the oxide layer on the alloy surface fractured, resulting in the generation of flake-like debris, which contributed to third-body abrasion. Additionally, the study examined the transfer film formation on the alumina counterface under various conditions, correlating friction and wear behavior with interfacial processes, particularly the oxide formation on the worn surfaces. This study enhances the understanding of the tribological behavior of titanium alloys, paving the way for improved performance in demanding applications through advanced surface modification techniques.

Seismic performance assessment of base isolation systems in five hospitals during the Mw7.8 and Mw7.6 2023 earthquakes in Southeast Turkey

The February 6, 2023, Kahramanmaraş earthquakes (Mw7.8 and Mw7.6) in Southeast Turkey ruptured a 300 km section of the East Anatolian Fault, resulting in severe ground motions. These two strong earthquakes occurred 9 hours apart and were followed by large-magnitude aftershocks. Strong ground motions from the Kahramanmaraş earthquakes were recorded by more than 280 strong motion stations. Nine base-isolated hospital buildings located at distances less than 50 km from the ruptured fault were subjected to strong ground shaking. This paper examines five of the nine hospitals for which seismic isolation designs were available to the authors. The ground motions acting on these hospital buildings were recorded by strong motion stations situated in close proximity. Consequently, the February 6 earthquakes have presented a unique opportunity to assess the seismic performance of base isolation systems during severe earthquakes. All five hospitals were equipped with double spherical sliding (friction pendulum) seismic isolation devices. Subsequently, the authors conducted site visits to the hospitals following the February 6 earthquakes and collected data on maximum and residual response displacements at their isolation levels, as well as on the seismic response of structural and nonstructural components. The maximum response displacements measured at the isolation levels are compared with the maximum displacements calculated by nonlinear response history analysis under the ground motions recorded at the strong motion stations in close proximity to the hospitals.

Advanced Neural Network-Based Load Frequency Regulation in Two-Area Power Systems

In this paper, enhancing dynamic performance in power systems through load frequency control (LFC) is explored across diverse operating scenarios. A new Neural Network Model Predictive Controller (NN-MPC) specifically tailored for two-zone load frequency power systems is presented. ” Make your paper more scientific. The NN-MPC marries the predictive accuracy of neural networks with the robust capabilities of model predictive control, employing the nonlinear Levenberg-Marquardt method for optimization. Utilizing local area error deviation as feedback, the proposed controller's efficacy is tested against a spectrum of operational conditions and systemic variations. Comparative simulations with a Fuzzy Logic Controller (FLC) reveal the proposed NN-MPC's superior performance, underscoring its potential as a formidable solution in power system regulation.

Scaling up Trickle Bed Reactor for Gas Fermentation Technology: The Effect of Temperature and Reactor Characteristics on Mass Transfer

The increasing demand for efficient and sustainable industrial processes has accelerated research into green alternatives. Gas fermentation in a trickle bed reactor is a promising technology; however, optimal scaling up is still challenging. A mass transfer model is crucial for identifying bottlenecks and suggesting design improvements to optimize the scale-up of TBR for gas fermentation. This study explores the effects of temperature, reactor dimensions, and packing material size on the volumetric mass transfer coefficient (kLa) in a commercial-scale trickle bed reactor (TBR). Using dynamic mass transfer modeling, the research results highlight that thermophilic conditions (60 °C) significantly enhance kLa and mass transfer rates for H2, CO, and CO2, despite reduced gas solubility at higher temperatures. Additionally, packing material of smaller particles improves kLa by increasing the surface for gas–liquid interaction, while reactor dimensions, particularly volume and diameter, are shown to critically influence kLa. This study provides valuable insights into optimizing TBR design and scale-up, emphasizing the importance of thermophilic conditions, proper packing material selection, and reactor geometry for efficient gas–liquid mass transfer in syngas (a mixture of H2, CO, and CO2) biological conversion. Overall, the findings offer practical guidelines for enhancing the performance of industrial-scale TBR systems.