Comprehensive Performance Comparison Research Articles

Comparison of Middlewares in Edge-to-Edge and Edge-to-Cloud Communication for Distributed ROS 2 Systems

AbstractThe increased data transmission and number of devices involved in communications among distributed systems make it challenging yet significantly necessary to have an efficient and reliable networking middleware. In robotics and autonomous systems, the wide application of ROS 2 brings the possibility of utilizing various networking middlewares together with DDS in ROS 2 for better communication among edge devices or between edge devices and the cloud. However, there is a lack of comprehensive communication performance comparison of integrating these networking middlewares with ROS 2. In this study, we provide a quantitative analysis for the communication performance of utilized networking middlewares including MQTT and Zenoh alongside DDS in ROS 2 among a multiple host system. For a complete and reliable comparison, we calculate the latency and throughput of these middlewares by sending distinct amounts and types of data through different network setups including Ethernet, Wi-Fi, and 4G. To further extend the evaluation to real-world application scenarios, we assess the drift error (the position changes) over time caused by these networking middlewares with the robot moving in an identical square-shaped path. Our results show that CycloneDDS performs better under Ethernet while Zenoh performs better under Wi-Fi and 4G. In the actual robot test, the robot moving trajectory drift error over time (96 s) via Zenoh is the smallest. It is worth noting we have a discussion of the CPU utilization of these networking middlewares and the perfosrmance impact caused by enabling the security feature in ROS 2 at the end of the paper.

Performance comparison on improved data-driven building energy prediction under data shortage scenarios in four perspectives: Data generation, incremental learning, transfer learning, and physics-informed

Accurate building energy predictions (BEPs) are crucial for maintaining a built environment's sustainability and energy systems. Many data-driven BEPs rely heavily on sufficient data. However, circumstances of practical data shortage restrict the application of these data-driven models to information-poor buildings. This study aims to address this research gap by enhancing BEP performance via the consideration of four perspectives: data generation (DG), incremental learning (IL), transfer learning (TL) and physics-informed (PI). Long short-term memory (LSTM) is selected as the baseline model for comparing performance under extreme (target building dataset: 1-week), heavy (4-weeks) and mild (13-weeks) data shortage scenarios with respect to prediction errors, time costs and the Taylor diagram, among others. The implementation algorithms of four LSTM improved models are conditional variational autoencoder (CVAE), crude incremental learning, domain-adversarial neural network and statistics-informed (SI) for DG, IL, TL and PI, respectively. The effectiveness of the four enhanced models was confirmed using the open-source dataset Building Data Genome Project 2. A comprehensive and fair performance comparison recommends CVAE for extreme and heavy data shortage scenarios with average mean absolute per cent error (MAPE) of 12.79 % and 8.63 % as well as performance improvement ratios (PIRs) of 0.45 and 0.57. SI should be recommended for mild data shortage scenarios with MAPE of 8.01 % and PIR of 0.66.

Water vapor stable isotope memory effects of common tubing materials

Abstract. Water molecules in vapor can exchange with gaseous water molecules sticking to surfaces of sampling tubing, and exchange rates are unique for each water isotopologue and tubing material. Therefore, water molecules on tubing walls take some time to reach isotopic equilibrium with a new vapor isotopic signal. This creates a memory effect that is observed as attenuation time for signal propagation in continuous stable water vapor isotope measurement systems. Tubing memory effects in δD and δ18O measurements can limit the ability to observe fast changes, and because δD and δ18O memory are not identical, this introduces transient deuterium excess (D-excess, defined as δD-8×δ18O) artifacts in time-varying observations. To our knowledge, a comprehensive performance comparison of commonly used tubing material water exchange properties in laser-based measurement systems has not been published. We compared how a large isotopic step change propagated through five commonly used tubing materials for water isotopic studies – perfluoroalkoxy (PFA), fluorinated ethylene propylene (FEP), polytetrafluoroethylene (PTFE), high-density polyethylene (HDPE), and copper – at two different temperatures and an airflow rate of 0.635 L min−1 through approximately 100 ft (30.5 m) of 1/4 in. (6.4 mm) outer diameter (o.d.) tubing. All commonly used tubing materials performed similarly to each other in terms of attenuation times, reaching δ18O-location-adjusted δD and δ18O 95 % completion in less than 45 s, with slight variations based on temperature. PFA does appear to perform slightly better than the other materials, although memory metric differences are small. A tubing material commonly used in the early 2000s but reported to have memory effects on δD, Dekabon, was also tested at ambient temperature and changing humidities. The Dekabon isotopic equilibrium was not reached until nearly an hour after source transition, much later than H2O mixing ratios equilibrated. Bev-A-Line XX (used in some soil O2 and CO2 gas studies) was also tested at ambient temperature, but it did not approach isotopic equilibrium until after nearly 6 h of testing. Therefore, we cannot recommend the use of Bev-A-Line XX or Dekabon in water vapor isotope applications. Source transition from heavy to light or from light to heavy affected isotopic transition speed only in experiments where H2O ppmv was changing. While a shorter tubing lengths and smaller inner diameters shorten the delay of signal propagation through the tubing, they did not greatly change the attenuation curves under these conditions for the current commonly used tubing materials tested. However, in Dekabon, attenuation curves were greatly extended with increased tubing length. Our results show that the commonly used plastic tubing materials tested were not inferior to copper in terms of isotopic memory under these conditions, and they are easier to work with and are less expensive than copper.

Enhancing trajectory tracking accuracy in three-wheeled mobile robots using backstepping fuzzy sliding mode control

The rise in robotics technology has increased interest in ThreeWheeled Mobile Robots (TWMRs) due to their agility and adaptability across various applications. However, effectively controlling TWMRs presents a significant challenge owing to their inherent nonholonomic constraints, which restrict independent movement in all directions. Factors like sensor noise, nonlinear system dynamics, and uncertain system parameters also add to the complexity of controlling TWMRs. This research endeavors to enhance the precision of trajectory tracking in TWMRs. Specifically, it employs Backstepping Fuzzy Sliding Mode Control (BFSMC) with parameters optimized through Particle Swarm Optimization (PSO), coupled with the Extended Kalman Filter (EKF) for state estimation. The study conducts a comprehensive performance comparison between Backstepping Sliding Mode Control (BSMC) and Backstepping Fuzzy Sliding Mode Control(BFSMC) across various trajectory patterns, revealing substantial improvements in trajectory tracking accuracy with BFSMC. BFSMC demonstrates improvements in performance across various trajectory types when considering the integral time absolute error (IAE). Specifically, it achieves a 51.97% improvement for circular trajectories, an 82.09% improvement for infinity trajectories, and an 84.073% improvement for spiral trajectories. Moreover, BFSMC demonstrates superior robustness in the presence of disturbances, noise, parameter variations, and unmodeled dynamics compared to BSMC. Integrating the Extended Kalman Filter further improves accuracy, particularly in noisy conditions.

Development and validation of a deep learning-based survival prediction model for pediatric glioma patients: A retrospective study using the SEER database and Chinese data

ObjectiveDevelop a time-dependent deep learning model to accurately predict the prognosis of pediatric glioma patients, which can assist clinicians in making precise treatment decisions and reducing patient risk. Study designThe study involved pediatric glioma patients from the Surveillance, Epidemiology, and End Results (SEER) Registry (2000–2018) and Tangdu Hospital in China (2010–2018) within specific time frames. For training, we selected two neural network-based algorithms (DeepSurv, neural multi-task logistic regression [N-MTLR]) and one ensemble learning-based algorithm (random survival forest [RSF]). Additionally, a multivariable Cox proportional hazard (CoxPH) model was developed for comparison purposes. The SEER dataset was randomly divided into 80 % for training and 20 % for testing, while the Tangdu Hospital dataset served as an external validation cohort. Super-parameters were fine-tuned through 1000 repeated random searches and 5-fold cross-validation on the training cohort. Model performance was assessed using the concordance index (C-index), Brier score, and Integrated Brier Score (IBS). Furthermore, the accuracy of predicting survival at 1, 3, and 5 years was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and the area under the ROC curves (AUC). The generalization ability of the model was assessed using the C-index of the Tangdu Hospital data, ROC curves for 1, 3, and 5 years, and AUC values. Lastly, decision curve analysis (DCA) curves for 1, 3, and 5-year time frames are provided to assess the net benefits across different models. ResultsA total of 9532 patients with pediatric glioma were included in this study, comprising 9274 patients from the SEER database and 258 patients from Tangdu Hospital in China. The average age at diagnosis was 9.4 ± 6.2 years, and the average survival time was 96 ± 66 months. Through comprehensive performance comparison, the DeepSurv model demonstrated the highest effectiveness, with a C-index of 0.881 on the training cohort. Furthermore, it exhibited excellent accuracy in predicting the 1-year, 3-year, and 5-year survival rates (AUC: 0.903–0.939). Notably, the DeepSurv model also achieved remarkable performance and accuracy on the Chinese dataset (C-index: 0.782, AUC: 0.761–0.852). Comprehensive analysis of DeepSurv, N-MTLR, and RSF revealed that tumor stage, radiotherapy, histological type, tumor size, chemotherapy, age, and surgical method are all significant factors influencing the prognosis of pediatric glioma. Finally, an online version of the pediatric glioma survival predictor based on the DeepSurv model has been established and can be accessed through https://pediatricglioma-tangdu.streamlit.app. ConclusionsThe DeepSurv model exhibits exceptional efficacy in predicting the survival of pediatric glioma patients, demonstrating strong performance in discrimination, calibration, stability, and generalization. By utilizing the online version of the pediatric glioma survival predictor, which is based on the DeepSurv model, clinicians can accurately predict patient survival and offer personalized treatment options.

Bank Balance: A Comparative Study of SBI & HDFC

The research project titled "Bank Balance: A Comparative Study of SBI & HDFC" aims to provide a comprehensive comparison of the financial performance of two of India's leading banks: the State Bank of India (SBI), a prominent public sector bank, and Housing Development Finance Corporation(HDFC) Bank, a major private sector player. The objective is to evaluate their financial health through key metrics such as profitability, asset quality, and operational efficiency, thus informing stakeholders about their competitive positions. Utilizing a quantitative methodology, the study analyzes financial statements and key performance indicators from the fiscal years 2019 to 2024. Data was sourced from official bank reports, regulatory filings, and financial databases, allowing for a robust comparative analysis.

A NoisyNet deep reinforcement learning method for frequency regulation in power systems

AbstractThis study thoroughly investigates the NoisyNet Deep Deterministic Policy Gradient (DDPG) for frequency regulation. Compared with the conventional DDPG method, the suggested method can provide several benefits. First, the parameter noise will explore different strategies more thoroughly and can potentially discover better policies that it might miss if only action noise were used, which helps the actor achieve an optimal control strategy, resulting in enhanced dynamic response. Second, by employing the delayed policy update policy work with the proposed framework, the training process exhibits faster convergence, enabling rapid adaptation to changing disturbances. To substantiate its efficacy, the scheme is subjected to simulation tests on both an IEEE three‐area power system, an IEEE 39 bus power system, and an IEEE 68 bus system. A comprehensive performance comparison was performed against other DDPG‐based methods to validate and evaluate the performance of the proposed LFC scheme.

A comprehensive performance comparison between compressed air energy storage and compressed carbon dioxide energy storage

Currently, working fluids for adiabatic compressed energy storage primarily rely on carbon dioxide and air. However, it remains an unresolved issue to which of these two systems performs better. Therefore, this paper compares the advantages and disadvantages of both systems in terms of thermodynamic and economic performances under the given boundary conditions. To accurately obtain the performance of energy storage systems, quasi-dynamic models are established for key components. On this foundation, corresponding thermodynamic-economic models are developed. The results indicate that at thermal storage temperatures of 120 ℃, 140 ℃, and 160 ℃, 100 MW×5h compressed carbon dioxide energy storage systems have higher round-trip efficiencies than compressed air energy storage systems. However, the compressed carbon dioxide energy storage also faces the difficulties of higher cost and longer payback period. Specifically, at the thermal storage temperature of 140 ℃, round-trip efficiencies of compressed air energy storage and compressed carbon dioxide energy storage are 59.48 % and 65.16 % respectively, with costs of $11.54 × 107 and $13.45 × 107, and payback periods of 11.86 years and 12.57 years respectively. Compared to compressed air energy storage system, compressed carbon dioxide energy storage system has 9.55 % higher round-trip efficiency, 16.55 % higher cost, and 6 % longer payback period. At other thermal storage temperatures, similar phenomenons can be observed for these two systems. After comprehensively considering the obtained thermodynamic and economic performances, the overall performance of compressed air energy storage is superior to that of compressed carbon dioxide energy storage. In addition, in practical engineering, key components of compressed air energy storage are more mature than those of compressed carbon dioxide energy storage, and air has higher safety than carbon dioxide. In the future work, the comparison for performances between different types of compressed carbon dioxide energy storage and compressed air energy storage should be taken into account, and dynamic models of the systems should be developed. Additionally, the industry chain of compressed carbon dioxide energy storage should be accelerated to reduce equipment costs, enabling it to compete with compressed air energy storage.

Enhancing Work Zone Safety: Evaluating Static Merge Strategies Through Microscopic Traffic Simulation

Background The utilization of merging control has been proposed as a strategy to maximize the capacity of roads in work zone areas. The static Early Merge (EM) and static Late Merge (LM) controls are extensively implemented among these strategies. Although many studies have investigated the efficacy of these controls through the analysis of field data or microscopic traffic simulations, such comparisons are frequently conducted under different work zone conditions, which can result in inconsistent, contradictory conclusions. Materials and Methods A simulation study was carried out using the VISSIM microscopic traffic simulator within a self-developed work zone featuring a 2-to-1 lane closure setup to comprehensively assess and contrast the traffic efficiency of the EM and LM controls. Furthermore, through a comprehensive analysis and comparison of network performance within the designed work zone across various scenarios, including queue length, vehicle delays, and travel time, the significant VISSIM parameters influencing the system's performance were identified. Results The analysis revealed that the parameters CC1 (headway time) and CC2 (longitudinal following threshold) from the car-following model exert more significant influence in the simulated work zone throughput than the Safety Distance Reduction Factor (SDRF) parameter from the lane-changing model. Discussion According to the simulation findings, implementing the EM control is preferable when drivers display aggressive behavior and maintain relatively short safety distances (i.e., low CC1 values). Conversely, opting for the LM control is more advisable in work zone areas where drivers demonstrate cautious driving tendencies and maintain longer safety distances (i.e., high CC1 values). Conclusion The efficacy of static EM and LM was analyzed in a 2-to-1 lane closure work zone on a freeway using the microscopic traffic simulator VISSIM. Simulation results were compared to identify the most relevant VISSIM parameters that influence work zone throughput. Our results indicate that the parameters CC1 and CC2 from the car-following model have a more substantial impact than the SDRF parameter from the lane-changing model. In particular, our comparison results suggest that the work zone throughput decreases in both the EM and LM scenarios as the values of CC1 and CC2 increase. Additionally, SDRF has a relatively negligible effect on the network performance of both merge strategies.

FSP4HSP: Frequent sequential patterns for the improved classification of heat shock proteins, their families, and sub-types

Heat shock proteins (HSPs) from different families and sub-types play a vital role in the folding and unfolding of proteins, in maintaining cellular health, and in preventing serious disorders. Previous computational methods for HSP classification have yielded promising performance. However, most of the existing methods rely heavily on amino acid composition features and still face challenges related to interpretability and accuracy. To overcome these issues, we introduce a novel frequent sequential pattern (FSP)-based analysis and classification method for the classification of HSPs, their families, and sub-types. The proposed method is called FSP4HSP, which stands for “FSP for HSP”. It identifies FSPs of amino acids (FSPAAs) and utilizes them for analysis and classification. Besides FSPAAs, sequential rules among amino acids are also discovered. Both binary and multi-class classification scenarios are considered, with the utilization of eight integer-based and four string-based classifiers. The incorporation of FSPAAs in the classification/prediction task enhances the interpretability of FSP4HSP and a comprehensive performance comparison using various evaluation measures demonstrates that it surpasses existing methods for the classification/recognition of HSPs.

A study of forecasting the Nephila clavipes silk fiber's ultimate tensile strength using machine learning strategies

Recent advancements in biomaterial research conduct artificial intelligence for predicting diverse material properties. However, research predicting the mechanical properties of biomaterial based on amino acid sequences have been notably absent. This research pioneers the use of classification models to predict ultimate tensile strength from silk fiber amino acid sequences, employing logistic regression, support vector machines with various kernels, and a deep neural network (DNN). Remarkably, the model demonstrates a high accuracy of 0.83 during the generalization test. The study introduces an innovative approach to predicting biomaterial mechanical properties beyond traditional experimental methods. Recognizing the limitations of conventional linear prediction models, the research emphasizes the future trajectory toward DNNs that can adeptly capture non-linear relationships with high precision. Moreover, through comprehensive performance comparisons among diverse prediction models, the study offers insights into the effectiveness of specific models for predicting the mechanical properties of certain materials. In conclusion, this study serves as a pioneering contribution, laying the groundwork for future endeavors and advocating for the seamless integration of AI methodologies into materials research.

Extensive experimental comparison among multilabel methods focused on ranking performance

Multilabel classification is a growing paradigm in the fields of data mining and machine learning. In multilabel learning, each input sample can belong to more than one binary class (termed “label”) in a multilabel framework, with contrasts with the standard single-label learning scenario. The challenge of producing a better classifier lies in the advantageous use of the correlations among different labels. In recent years, many multilabel models have been proposed, making it difficult to decide which methods to use. In this paper, we present the most comprehensive ranking performance comparison carried out thus far among numerous methods. We conduct a comprehensive analysis by comparing several configurations of 56 distinct methods, resulting in a total of 173 trained models. In addition, we utilize an extensive collection of problems involving 65 datasets. Furthermore, we analyze the effectiveness of the tested techniques by evaluating their performance with six different ranking performance metrics. Our findings indicate that while certain strategies consistently rank highly among the top-performing models, the most effective methods are strongly correlated with the specific metrics used to assess their performance. Finally, we examine many behavioral aspects of the different approaches.

A Comprehensive Performance Comparison Study of Various Statistical Models that Accommodate Challenges of the Gut Microbiome Data

A Comprehensive Performance Comparison Study of Various Statistical Models that Accommodate Challenges of the Gut Microbiome Data

A comprehensive comparison of performances of anode-supported, cathode-supported, and electrolyte-supported solid oxide fuel cells during warming-up process

Although the influence of supporting layer features on the steady performance of solid oxide fuel cells (SOFCs) has been discussed in previous studies, the literature shows a lack of similar studies for unsteady performance. The present study numerically assesses the effects of the type and thickness of the supporting layer on the warming-up process of an SOFC. The warming-up process is desired to be rapid; however, the rapidness can impose harmful temperature gradients. The warming-up process is also energy-demanding. Therefore, all the mentioned factors are considered in the present study, and an optimal set of supporting layer features is proposed using a multi-criteria decision analysis approach. The results indicate that the warming-up durations of anode-supported and cathode-supported cells are close to each other but considerably lower than that of the electrolyte-supported cell. Furthermore, the warming-up duration, which is directly proportional to the supporting layer thickness, is more sensitive to the electrolyte thickness than the anode and cathode thicknesses. On the other hand, the temperature gradients inside the SOFC, which are inversely proportional to the supporting layer thickness, are more affected by the electrolyte thickness than the anode and cathode thicknesses. In terms of energy usage, the electrode-supported cells are seen to be superior to the electrolyte-supported cells. Considering all the concerns simultaneously, the anode-supported cell with a supporting layer thickness of 1.5 mm is proposed as the best trade-off solution.

Advancements in MIMO Antennas for Enhanced Performance in 5G Smartphones Communication

In this review paper provides a comprehensive overview of recent advancements in multiple input multiple output (MIMO) antenna designs for 5G smartphones. There are various antenna configurations that are discussed along with benefit and limitations. The paper also examines key challenges such as antenna mutual coupling, isolation and the size constraints inherent in smartphones. Furthermore, the impact of MIMO antenna design on 5G smartphone performance characteristics such as gain, radiation efficiency and spatial multiplexing capability is also analyzed. Finally, future research directions and potential innovations in MIMO antenna technology for 5G smartphones are identified to address emerging requirements and opportunities in next-generation wireless communication systems. The comprehensive comparison of different MIMO antenna performance is also given.

The rebound effect of material and energy efficiency for the EU and its major trading partners

This study presents a comprehensive analysis and comparison of material and energy efficiency performances and their rebound effects, providing insights into their implications for sustainability and climate change policies. Using Stochastic Frontier Analysis (SFA), we examine material and energy efficiency scores and rebound effects in the European Union (EU) member countries and their major trading partners from 1995 to 2019. Our findings reveal several key insights. Firstly, developed EU countries and affluent trading partners exhibit higher material and energy efficiency scores, while less developed members and partners rank lower. The study highlights the potential implications of the EU's Carbon Border Adjustment Mechanism (CBAM) for low-efficiency trading partners, which may face substantial penalties and be incentivized to take significant steps to enhance material and energy efficiency. Secondly, a positive correlation is observed between efficiency improvements and rebound effects, suggesting that policymakers should take the rebound issue seriously. Thirdly, while energy efficiency scores are relatively higher, material efficiency performances are lower and improving over the years. Finally, the rebound effects of energy efficiency exceed those of material efficiency, implying that energy reduction potentials are largely offset. Therefore, prioritizing material consumption for environmental sustainability becomes crucial due to the significant potential for improvement with lower rebound effects.

Forward Head Posture Classification Using Deep Learning Models on Facial Recognition Data

Forward Head Posture (FHP) refers to a condition where the head protrudes forward, significantly contributing to neck pain and being associated with decreased productivity and psychological distress. This study investigates the nuanced classification of FHP and proposes a universally applicable methodology for its identification and analysis using deep learning. Leveraging the Korean Facial Image (K-FACE) dataset, rigorous image preprocessing with the Yolo-v8 model was conducted to facilitate accurate measurement of the CranioVertebral Angle (CVA) from various perspectives. The study meticulously evaluated the classification effectiveness of three advanced deep learning models: EfficientNet-B7, NFNet-F7, and ResNet-152. Among these, EfficientNet-B7 demonstrated superior performance with an accuracy of 0.69 and a recall score of 0.69 compared to other models. Additionally, comparisons based on camera angles within the EfficientNet-B7 model highlighted its excellence, particularly at the ±75° angle. The importance of image regions in EfficientNet-B7 was confirmed through Grad-CAM analysis, emphasizing the critical role of the neck region in accurately classifying FHP images. This comprehensive performance comparison and the proposed detailed classification methodology underscore the potential for generalization in FHP classification. Furthermore, by leveraging a unique dataset and employing state-of-the-art classification techniques, this research offers a novel perspective on the discourse surrounding FHP. Future research could integrate expanded facial image datasets and apply transfer learning techniques to further enhance the precision of FHP classification, thereby improving diagnostic accuracy and offering targeted interventions for individuals experiencing neck pain associated with FHP.

Exploring deep echo state networks for image classification: a multi-reservoir approach

Echo state networks (ESNs) belong to the class of recurrent neural networks and have demonstrated robust performance in time series prediction tasks. In this study, we investigate the capability of different ESN architectures to capture spatial relationships in images without transforming them into temporal sequences. We begin with three pre-existing ESN-based architectures and enhance their design by incorporating multiple output layers, customising them for a classification task. Our investigation involves an examination of the behaviour of these modified networks, coupled with a comprehensive performance comparison against the baseline vanilla ESN architecture. Our experiments on the MNIST data set reveal that a network with multiple independent reservoirs working in parallel outperforms other ESN-based architectures for this task, achieving a classification accuracy of 98.43%. This improvement on the classical ESN architecture is accompanied by reduced training times. While the accuracy of ESN-based architectures lags behind that of convolutional neural network-based architectures, the significantly lower training times of ESNs with multiple reservoirs operating in parallel make them a compelling choice for learning spatial relationships in scenarios prioritising energy efficiency and rapid training. This multi-reservoir ESN architecture overcomes standard ESN limitations regarding memory requirements and training times for large networks, providing more accurate predictions than other ESN-based models. These findings contribute to a deeper understanding of the potential of ESNs as a tool for image classification.

Using soil tuff-modified polymetallic lead–zinc tailings sand to facilitate sustainable development of subgrade engineering

The use of soil tuff, an industrial solid waste, for the modification of lead–zinc tailings as subgrade fillers presents a sustainable solution for concurrently addressing the negative impacts of tailings accumulation and resource depletion resulting from road construction. To demonstrate this, we conducted a thorough series of macroscopic and microscopic experiments, including the physical performance, durability, and microstructural behaviors of soil tuff-modified lead–zinc tailings sand (STM-LZTS) and cement-modified LZTS with varying modifier dosages, compaction degrees, and curing ages. Furthermore, we conducted a comprehensive comparison of the sustainability performance of these two modified LZTSs in terms of cost, CO2 emissions, and energy consumption. The results indicate that the incorporated 6% soil tuff filled the voids with the hydrated calcium silicate gel generated during the hydration process, which improved the structural stability. The CBR value was further enhanced to over 4% upon modification. With an increase in compaction degree to 96%, the CBR value escalated to over 8% (＞4%). The STM-LZTS specimens exhibited compressive strengths ranging from 0.818 to 0.958 (＞0.3 MPa). The variations in shear strength ranged between 200 and 550 kPa. Meanwhile, the modification of LZTS with soil tuff considerably delayed the deterioration of specimens under dry–wet, freeze–thaw, and salt-solution immersion conditions. Furthermore, compared with the cement-modified LZTS, the modified material formed a denser binder and a more robust nonparticle gel due to its lower calcium–silica ratio and higher aluminum–silica ratio, exhibiting superior performances in terms of compressive strength and resistance to salt corrosion. In addition, the use of soil tuff substantially reduced the cost, carbon emissions, and energy consumption, making it a promising and ecofriendly alternative for waste management and achieving carbon neutrality goals in subgrade engineering.

Active quasi circulator: Comprehensive review and performance comparison

AbstractDesigning circulator as an antenna interface device becomes a daunting task, particularly active‐quasi circulator. This article focuses on demonstrating the basic operation principle, design methods, technical parameters, and performance metrics of active quasi‐circulator. In addition, the study provides an analogy of the circuits and structures proposed by the researchers to enhance certain performance metrics. Active signal cancellation and passive signal cancellation are identified as the major design approaches. Tunable, wideband, and wideband‐tunable are the major types of circulators found in existing literature. Moreover, this article provides a performance comparison of the active‐quasi circulators available in existing literature. Several active quasi‐circulators were able to operate in high frequencies such as 60 GHz with acceptable isolation levels. On the other hand, several designs have over 30 dB isolation, which is a highly desired parameter. At last, the future design challenges associated with active‐quasi circulators have been discussed to provide insight into future research.