Comprehensive Performance Comparison Research Articles

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.

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.

A novel biogas-driven CCHP system based on chemical reinjection

In the process of vaporizing water within the biogas-driven CCHP system based on chemical recuperation, a significant temperature disparity in heat transfer exists, leading to increased irreversible loss, and the methane conversion rate is low because the gas turbine exhaust gas is difficult to drive efficient methane conversion. This paper proposed a novel biogas-driven CCHP system based on chemical reinjection. Through the reinjection of a portion of the exhaust gas into the reactor, a self-thermal reforming reaction process is engineered, leading to a substantial enhancement in methane conversion rate. The heat matching of the heat exchange unit is optimized by the reheat air, and the power generation and cooling capacity of the system are improved. Notably, the electricity and cooling generation of the new system increased by 55.52 kW (6.20 %) and 542.49 kW (101.49 %), respectively, while the exergy efficiency increased by 8.31 %. The study delves into the influence of key parameters on the system thermal performance. During the reforming process, the exhaust gas split ratio has a significant impact on carbon deposition and methane conversion. When the methane is nearly completely converted, there is no significant change in electricity generation of the system. Excessive turbine inlet temperature is found to be unfavorable for improving the exergy efficiency of the new system from a holistic system perspective. Finally, a comprehensive comparison of the thermal performance between the new system and the reference system is conducted under various ambient temperatures. This study offer a new design scheme for the efficient utilization of biogas.

Comparison of in-plane compression of additively manufactured Ti6Al4V 2D auxetic structures: Lattice design, manufacturing speed, and failure mode

The metal-based 2D auxetic lattice structures hold the potential for multifunctional tasks in aerospace applications. However, the compression response of those manufactured by powder bed fusion process is underexplored. This study proposes a comprehensive comparison of in-plane quasi-static compression performance of 2D auxetic lattice structures, utilizing three designs (anti-tetrachiral (ATC), double arrow-headed (DAH), and tree-like re-entrant (TLR)), manufactured with stiff Ti6Al4V by the electron beam powder bed fusion process (PBF-EB) with various manufacturing speeds. The results revealed unique failure patterns and superior energy absorptions among 2D lattice structures in the literature. TLR design enhanced energy absorption by overcoming failures between DAH columns and exhibited the lowest standard deviations in specific energy absorption (SEA) values (9.75 %–12.62 %). Besides, Kernel average misorientation (KAM) values followed the order of DAH, TLR, and ATC, and inversely correlated with SEA values. ATC structures with the lowest KAM outperformed DAH and TLR by 47.5 % and 6.44 %, respectively. Scan speed variations affected SEA and porosity values differently for each lattice design while exhibiting similar microstructure characteristics. The findings in this study propose a significant contribution to the development of aerospace sandwich structures where harsh environments exist and employment of 2D topologies are required.

Artificial Ecosystem Optimizer-Based System Identification and Its Performance Evaluation

AbstractThis study delves into the realm of system identification, a crucial sub-field in control engineering, aimed at constructing mathematical models of systems based on input/output data. This work particularly proposes the application of artificial ecosystem algorithm (AEO) for solving system identification problems. Inspired by the energy flow of natural ecosystems, AEO has undergone specific modifications leading to derived versions. Additionally, five diverse meta-heuristic algorithms are employed to assess their applicability and performance in system identification using data from an air stream heater experiment kit. A comprehensive performance comparison is made, considering time bounds, maximum generations, early stopping, and function evaluation constraints, presenting their respective performances. Among the evaluated algorithms, the AEO algorithm enhanced with the sine and cosine strategy stands out with a determined R2 value of 0.951. This algorithm consistently outperforms others in Wilcoxon tests, showcasing its significant success. Our study affirms that meta-heuristic algorithms, particularly the proposed AEO algorithm, can be effectively applied to system identification problems, yielding successful calculations of transfer function parameters.

Thermal performance and energy flow analysis of a PV/T coupled ground source heat pump system

Ground source heat pump (GSHP) system in cold regions will lead to a decrease in soil source temperature over the years after a long period of operation, and if no timely measures are taken, the system performance will get worse and worse, and even face the risk of shut down. The PV/T (photovoltaic/thermal) is an integrated energy supply system that can both improve the efficiency of solar energy utilization and weaken the soil temperature imbalance. In this paper, the thermal performance and year-round energy flow of the system are analyzed by the simulation model, and the effects of different soil thermal storage control strategies on the thermal storage performance in the transition season are explored. The results show that the coefficient of performance of the heat pump unit can reach 3.99 and 3.96 in the heating and cooling periods. The soil temperature only increases by 0.09 °C and 0.11 °C compared with the initial temperature after five and ten years of long-term system operation, respectively. In addition, after a comprehensive comparison of the thermal storage performance of different control strategies in the transition season, this study found that the temperature difference control strategy was superior to the time control strategy. Ultimately, the annual energy supply of the simulation system produces an additional 391.8 kWh of electricity in addition to meeting the energy demand for cooling, heating and power supply of the building, which realizes the zero-energy operation of the building.

Differential evolution–based integrated model for predicting concrete slumps

Concrete slump, a crucial indicator of fluidity, directly affects the pumpability and construction efficiency of concrete. Conventionally, concrete slumps are assessed through multiple test iterations conducted by skilled professionals to obtain accurate results. In this study, a predictive model was established to predict concrete slumps directly based on concrete mix proportions, thereby mitigating labor and time costs. An open-source dataset was used in this study. The selected water-cement ratio ranged from 0.29 to 0.66. Cement, slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate were employed as input parameters. Furthermore, the performances of a variety of algorithms in predicting concrete slumps were analyzed and compared. The algorithms included SVM, KNN, Extra-Trees, Gradient Boosting, Decision Tree, Elastic Net, Lasso, Ridge, Random Forest, Bagging, AdaBoost, and XGBoost. Through a comprehensive comparison of the prediction performance of these models, AdaBoost, Bagging, Extra-Trees, and Random Forest were adopted as base models. Moreover, the SVM algorithm optimized using Differential Evolution was employed as a secondary model to construct an enhanced integrated prediction model. The final prediction model boasts an MSE of 2.099984, MAE of 1.225597, RMSE of 1.449132, and R2 of 0.970418. Compared to conventional prediction models, the proposed model has the potential to substantially improve prediction performance.

Reverse design and optimization of digital terahertz bandpass filters

In this paper, an ingenious reverse design method is applied to the design and optimization of terahertz bandpass filters in order to achieve standardized design of high-performance terahertz functional devices. An equivalent model of subwavelength metasurface mapped to digital space is established. Based on ideal objective functions and constraints, intelligent algorithms begin a bold journey to explore the vast potential structure in the solution space. Through iterative refinement, the algorithm reveals optimal structural patterns, unlocking areas of unparalleled performance. The direct binary search (DBS) algorithm and the binary particle swarm optimization (BPSO) algorithm are compared in optimization process. When using the DBS algorithm to optimize the design area, it takes a long time to poll the logic states of all pixel units point by point, and it is easy to get stuck in the local optimal value. However, BPSO algorithm has stronger global search capabilities, faster convergence speed, and higher accuracy. Through a comprehensive comparison of the device performance optimized by the two algorithms, the solution optimized by BPSO algorithm has better out-of-band suppression performance and a narrower full width at half peak, but slightly lower transmittance at the center frequency. The bandpass filter has a center frequency of 0.51 THz, a bandwidth of 41.5 GHz, and an insertion loss of -0.1071 dB. When considering computational efficiency, DBS algorithm lags behind, the simulation time is 11550 s, while BPSO algorithm only needs 9750 s. Compared with the traditional forward design, the reverse design method can achieve the narrower band, lower insertion loss, better out-of-band suppression and polarization stability. The fine structural changes of the optimal results have a significant influence on spectral performance, demonstrating the superiority and uniqueness of reverse design. This technology contributes to the design and optimization of high-performance and novel functional devices.

Design and Analysis of a New Dual-Stator Hybrid Magnet Flux Modulation Machine

This paper proposes a new dual-stator hybrid-magnet flux modulation machine (DS-FMHMM) for direct-drive applications, which employs NdFeB magnet excitation and Ferrite magnet excitation on the rotor and outer stator sides, respectively. With this design, the proposed DS-FMHMM can not only fully use the bidirectional flux modulation effect, but also effectively alleviate the magnetic saturation issue. The machine configuration is described, together with the operating principle. Then, the design parameters of DS-FMHMM are globally optimized for obtaining high torque quality, and the influence of magnet dimensions on torque is analyzed. To evaluate the merits of the proposed DS-FMHMM, the electromagnetic performances of machines under different magnet excitation sources are analyzed, and a comprehensive electromagnetic performance comparison of DS-FMHMM and two existing dual-stator flux modulation machines (DSFMMs) is developed.

ACO-DTSP Algorithm: Optimizing UAV Swarm Routes with Workload Constraints

In this research, we present a variant of the Ant Colony Optimization (ACO) algorithm tailored for optimizing routing within a limited-capacity environment for a swarm of drones. Our primary objective is to minimize task completion time while ensuring that individual node capacity constraints are never breached. Our algorithm leverages both pheromone trails and heuristic information to guide drone movements intelligently, and it dynamically updates the pheromone trail based on solution quality. This research underscores the importance of ideal routing algorithms in drone swarm systems and showcases ACO's effectiveness in optimizing such challenges. We conducted a comprehensive performance comparison against the existing methods like Christofides algorithm, Simulated Annealing, K-Opt and Lin-Kernighan Heuristic and the Brute-force method. Results indicate that our algorithm significantly improves time efficiency and is feasible for real-time environments. Furthermore, we highlight the potential for substantial enhancements in the system by deploying suitable hardware, such as quadcopters with specific capabilities.

Exploring the Performance of Container Runtimes within Kubernetes Clusters

The advent of cloud computing, with its Pay-As-You-Go model, has significantly simplified IT maintenance and revolutionized the industry. In the era of Microservices, containerized deployment and Kubernetes orchestration have permeated almost every working domain, drastically reducing the time to market for software releases. Kubernetes utilizes container runtimes to manage Containers, with the Container Runtime Interface (CRI) serving as a communication medium with low-level container runtimes such as runc and kata container. With the deprecation of Dockershim, developers are left to choose between CRI-O and Containerd, two CRI implementations. This study configures a Kubernetes cluster with both Containerd and CRI-O separately and analyzes performance parameters such as throughput, response time, CPU, memory, and network utilization. Additionally, we examine the impact of using runc and kata container runtimes together within the cluster. The study, conducted using a performance script created by JMeter, reveals that different container runtimes cater to distinct business use-cases and can complement each other when used together in a cluster environment. High compute applications are best run using runc, while high-security requirements are fulfilled by kata. The study provides a comprehensive performance comparison between Containerd and CRI-O, shedding light on the depth and versatility of container runtimes.

Thermal-Economic Analysis of an Organic Rankine Cycle System with Direct Evaporative Condenser

The organic Rankine cycle (ORC) system for power generation has proven to be an effective technology for low-temperature waste heat utilization. Accurate prediction and comprehensive comparison of system performance under different conditions are necessary for the development and application of suitable ORC configurations. This paper proposed an organic Rankine cycle (ORC) system using a direct evaporative condenser to realize performance enhancement and analyzed its dynamic performance based on the actual climatic condition, which is beneficial for the performance optimization of this system. This study begins with an introduction to the thermal economics model of the proposed system and evaluates the performance of the system based on the 3E (energy, exergy, economy) analysis method. Secondly, four candidate working fluids were compared and analyzed, leading to the selection of R142b as the best working fluid for the proposed system. Finally, the dynamic performance of the proposed system using the working fluid of R142b was analyzed based on the hourly environment temperature. The result showed that the net thermos-electric conversion efficiency of the system was negatively correlated with the ambient wet-bulb temperature. The annual average exergy efficiency of the system is about 65.79%, and the average exergy loss of the heat absorption unit, evaporative condenser, pump, and expander account for 61.07%, 6.92%, 2.99%, and 29.01% of the exergy loss of the system respectively. In the case 8760 h of operation per year, the payback period of the proposed ORC system using direct evaporative condenser is about 2.14 years.

Control strategies and performance analysis for a novel nuclear heating system with heat storage

A nuclear heating system integrated with a heat storage tank (HST-NHS) is a promising technical solution for countries with large-scale heating demand. HST-NHS can greatly curtail carbon dioxide emissions and contribute to sustainable development. Appropriate control strategies are not only crucial to realize the real-time matching between HST-NHS heat supply and heat demand, but also ensure the stable and safe operation of HST-NHS. Until now, control strategies for HST-NHS have received limited attention. A refined dynamic simulation model for HST-NHS is established based on APROS software. The verification of the built model in steady state and transient state is completed. Two basic control strategies for the primary heating network (PHN) of HST-NHS, constant supply water temperature and variable flow rate (CT-VF) and variable supply water temperature and constant flow rate (VT-CF) are presented in detail. Based on various typical working conditions of a central heating region in Liaoyuan City, the control effects of two control strategies are investigated. Additionally, a comprehensive performance comparison between the two strategies is explored. Simulation results indicated that under the two control strategies, the indoor temperature can be effectively controlled at a high comfort level. During the same typical day, the total energy consumption cost under the VT-CF control strategy is 0.5 × 105 yuan lower than that under the CT-VF control strategy.