Improve System Performance Research Articles

Edge Computing and Microservices: Extending Scalability Beyond the Cloud

The convergence of edge computing and microservices architecture represents a transformative shift in how organizations process and manage distributed data systems. This article explores the limitations of traditional cloud-centric approaches and presents edge computing as a solution to address these challenges. Through a detailed analysis of real-world implementations across various sectors, including autonomous vehicles, smart cities, and industrial automation, the article examines how edge computing integrated with microservices architecture improves system performance, reduces latency, and optimizes resource utilization. The article investigates deployment challenges and security considerations and provides insights into effective monitoring strategies. Furthermore, the article explores emerging technologies and future research directions that will shape the evolution of edge computing and microservices integration.

Weighted sum method based multi-objective optimal power flow considering various objectives: an application of whale optimization algorithm

Nowadays, multi-objective optimization plays a vital role in solving optimal power flow problems. Multi-objective optimal power flow (MOOPF) is a nonlinear optimization problem aimed at optimizing control variables while balancing multiple objective functions and satisfying both equality and inequality constraints and addresses this by integrating two more objectives into a single objective using a weighting factor. In this paper this weighted sum type multi-objective technique has been used to formulate the objective function. The whale optimization algorithm (WOA) has been used to reduce the cost, emission, losses, and voltage stability by considering various multi objectives like fuel cost along with emission, fuel cost with losses, fuel cost with voltage stability, fuel cost with voltage deviation and finally fuel cost with emission, losses, voltage deviation. In this paper, the IEEE 30 bus structure has been used to analyze the effect of WOA on the improvement of system performance. Obtained results with WOA have been compared with other optimization techniques like ensemble constraint handling technique with differential evolution (ECHT-DE), the superiority of feasible differential evolution (SF-DE), moth swarm algorithm (MSA), and moth-flame optimization (MFO), available in the literature.

A Compact X and Ku Band Quad Port MIMO Antenna with Path Loss aModel for Urban Macro and Micro Environments

The prospective of multiple-input multiple-output (MIMO) technology to increase network reliability and enhanced data throughput has made it essential in modern wireless communication systems. In this study, a new X and Ku band MIMO antenna with improved isolation is designed and analyzed using a shared radiator. To improve system performance, the MIMO antenna system was built to provide greater isolation between antenna elements by inserting inverted H-shape parasitic components within the radiators, resulting in an out-of-phase current of a comparable magnitude to the coupling current. The antenna achieves an isolation level greater than 15 dB across the operational frequency of 8.5-18.5 GHz with overall dimensions of 25 × 22 × 0.5 mm3 on RT duroid 5880 substrate. Furthermore, the developed MIMO antenna is investigated for radiation characteristics, with a maximum gain of 5.8 dB and acceptable diversity features. A path loss study for the designed antenna is performed using the ABG and CI models under the urban macro and micro model scenarios. The proposed shared radiator quad port MIMO antenna is optimized to realize compactness, wider impedance bandwidth, and inter-port isolation. The antenna design outcomes from simulation and experimental data make it a good contender for use in contemporary communication systems.

Enhancing core loss and thermal performance for wireless EV charging with a cross-laminated core structure

This paper explores the implementation of laminated cores in inductive power transfer (IPT) systems for wireless EV charging. Two conventional structures are initially evaluated: the horizontally laminated core (H-cores) and the vertically laminated cores (V-cores). Fe-based nanocrystalline materials are applied for these structures due to their excellent core loss properties. The study introduces material modelling and loss analysis methods that combine toroidal measurements with eddy current losses from norm flux. Finite Element Method (FEM) simulations reveal that both structures experience significant flux density and thermal imbalances, creating operational challenges for IPT systems. To address these issues, a cross-laminated structure is proposed, combining the benefits of both horizontal and vertical laminations to reduce thermal imbalances. Simulation results show improved system performance, confirmed by experimental tests. Using the cross-lamination approach, the operational duration of IPT systems can be extended, and the maximum equilibrium temperature at an output current of 10 A is reduced by over 40°C, from 90.4 °C to 47.2 °C. Additionally, efficiency improves by over 0.4 % at 11.1 kW output power. This innovative core design provides a practical solution for implementing laminated cores in high-power wireless EV charging applications, enhancing efficiency and thermal performance.

Performance improvement by an optimum companding-based Non-Uniform quantization scheme in low-bit resolution IMDD-UFMC system

In this paper, an optimum non-uniform quantization method is presented in the intensity-modulated direct detection universal filter multicarrier (IMDD-UFMC) system to achieve the system performance improvement with low-bit resolution digital-to-analog converter (DAC). By this specifically-designed companding quantizer without the probability density function (PDF) estimation, the number and interval size of the quantization interval allocated in the low probability region and the high probability region can be balanced with a modified optimal mapping relationship to obtain the optimal quantized level and then the low quantization noise is realized to greatly reduce the bit resolution of DAC in IMDD-UFMC system. Owing to this scheme is essentially a peak-to-average power ratio (PAPR) suppression technology of signal distortion, PAPR performance can be improved at the same time. Compared to the clustering and nonlinear programming schemes, the system performance in term of bit error rate (BER) can be improved and the difficulty of implementation can be reduced in the 25-Gb/s transmission system through 20-km fiber transmission. Moreover, a simplified method is presented by fitting the corresponding companding curve with error function and logistic function, which further decreases the complexity of the optimal non-uniform quantization. In this way, our scheme can offer a significant advancement in the design of IMDD-UFMC systems and has the potential to enhance the performance of future multi-carrier optical communication systems.

Comprehensive Approaches to Traffic Flow Prediction

Intelligent transportation systems require traffic flow prediction, and anomaly detection is the key to ensuring accuracy. Using traditional statistical models to handle complex traffic scenarios is becoming increasingly challenging as urbanization accelerates. Consequently, to enhance the precision of forecasting traffic flow and detecting anomalies, various deep learning techniques, and emerging methods have been introduced. The purpose of this paper is to examine how traditional and deep learning methods, as well as emerging technologies, can be used to predict traffic flow and detect anomalies, including techniques such as the autoregressive integrated moving average model (ARIMA), K-nearest neighbor (KNN) algorithm and convolutional neural network (CNN). The result shows that emerging technologies can improve system performance in multiple traffic environments by accurately extracting features from complex data. The research of this paper provides a theoretical basis for the traffic management department and helps to realize a safer and more efficient advanced transportation system.

Enhancing grid voltage stability and reliability by harnessing off-grid power sources with multi-objective fuzzy spark firefly optimization algorithm

ABSTRACT In traditional power systems, distribution networks (DNs) often suffer from significant power losses. However, integrating decentralized sources into these networks not only mitigates these losses but also eases the burden on central power sources. As centralized power plants are frequently located at a distance from consumption centers, incorporating off-grid power sources (OPSs) closer to demand hubs enhances overall system efficiency. This paper focuses on optimizing OPS allocation using the fuzzy spark firefly optimization algorithm (FSFOA). By strategically placing various types of OPS units, the aim is to minimize both real and reactive power losses in the distribution system. The study evaluates system performance metrics, showcasing how different types of OPS optimization affect the network in a range of (11 kV to 13 kV) DN base voltages. The FSFOA offers distinct advantages in efficiently navigating the complex optimization landscape, allowing for the precise placement of OPS to achieve significant improvements in system efficiency and performance. For instance, OPS Type-IV at bus number 6 demonstrates substantial reductions in real power loss by 68.92% to 91.464 kW and in reactive power loss by 62.99% to 73.8374 kVAr, highlighting its effectiveness in managing reactive power and improving overall power factor.

Performance improvement of quartz-enhanced photoacoustic spectroscopy gas system using ICEEMDAN-PE-WTD

The research aims to address the issue of noise interference affecting the second harmonic signal in quartz-enhanced photoacoustic spectroscopy (QEPAS) gas detection system. We propose a novel signal denoising method that integrates several advanced techniques: improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), permutation entropy (PE), and wavelet threshold denoising (WTD). The method is implemented as follows: firstly, the ICEEMDAN algorithm is employed to break down the second harmonic signal into multiple intrinsic mode functions (IMFs). Secondly, the PE values for each IMF are computed, and these values guide the selection of suitable components for signal reconstruction. Finally, the WTD technique is applied to the reconstructed signal to further enhance the denoising level. We applied this algorithm in both simulations and experimental analyses, comparing its performance against three commonly used methods: wavelet filter, Savitzky-Golay (S-G) filter, and CEEMDAN-WTD filter. The results indicate that the proposed algorithm significantly suppresses noise in the QEPAS gas detection system, enhancing the signal-to-noise ratio (SNR) from 105.25 to 595.85. Additionally, it achieves a correlation coefficient (R2) of 0.999 and a minimum detection limit (MDL) of 1.01 ppm. The findings highlight how the proposed approach could improve QEPAS gas detection precision, while also offering new insights for research and applications in related fields.

ACCELERATING IMAGE PROCESSING USING PARALLEL COMPUTATIONS IN OPENMP: DEVELOPMENT AND PERFORMANCE ANALYSIS OF A GRAPHICS EDITOR

In today’s digital world, image processing plays a crucial role in various aspects of human life. From creating visual content for social media to analyzing medical images, powerful tools for image manipulation are in constant demand. The growing requirements for image quality and processing speed make the search for efficient methods to accelerate these processes highly relevant. This paper investigates the development and implementation of a graphics editor that utilizes OpenMP parallel computing to accelerate data processing. This technology enables the efficient distribution of computational tasks across multiple processor cores, significantly improving system performance. The developed software offers a set of popular graphic effects, including negative, grayscale, sepia, blur, sharpening, and posterization. Each effect is implemented in both sequential and parallel modes using OpenMP, allowing for the comparison of different computational approaches. To evaluate the effectiveness of the proposed solution, a series of experiments were conducted on images of various sizes. These experiments involved applying each effect to images ranging from small icons to high-quality photographs. A comparative analysis of the efficiency of sequential and parallel computation methods demonstrated the significant advantages of the latter. The results of the study show a substantial acceleration of image processing when using OpenMP technology. This acceleration is particularly noticeable for computationally intensive effects such as blurring or sharpening, and when working with large images. In some cases, it was possible to achieve a significant increase in processing speed, opening up new possibilities for working with large volumes of graphic data. This research has significant practical value for software developers working on optimizing the performance of graphics editors and other image processing applications. It demonstrates how the application of modern parallel computing technologies can significantly improve the efficiency of working with graphic data, paving the way for the creation of more powerful and faster image processing tools.

Enhancing Electronic Nose Performance for Differentiating Civet and Non‐Civet Roasted Bean Coffee Using Polynomial Feature Extraction Methods

ABSTRACTCoffee, a popular beverage worldwide, requires thorough quality assessment to ensure its authenticity and meet consumer demands. Traditional methods in the industry are often subjective, expensive, and time‐consuming. This study used a compact, portable electronic nose (e‐nose) with machine learning models to classify and distinguish between civet and non‐civet roasted beans. The polynomial feature extraction method was used to extract important parameters from the sensor response and improve system performance. Classification models like linear discriminant analysis (LDA), logistic regression (LR), quadratic discriminant analysis (QDA), and support vector machines (SVM) were applied to classify the samples. Among these, the LDA model with polynomial features yielded the highest validation and test accuracies, with values of 0.89 ± 0.04 and 0.93, respectively. This was higher than the statistical feature methods, which obtained validation and test accuracies of 0.80 ± 0.07 and 0.87, respectively. The acquired e‐nose results were correlated with compound concentrations in roasted coffee beans measured by gas chromatography–mass spectrometry (GC–MS). These findings demonstrate the e‐nose system's promising potential to effectively distinguish civet from non‐civet roasted coffee beans based on their aroma profiles using polynomial feature extraction methods.

Low-Power 8T SRAM Compute-in-Memory Macro for Edge AI Processors

The traditional Von Neumann architecture creates bottlenecks due to data movement. The compute-in-memory (CIM) architecture performs computations within memory bit-cell arrays, enhancing computational performance. Edge devices utilizing artificial intelligence (AI) address real-time problems and have established themselves as groundbreaking technology. The 8T structure proposed in this paper has strengths over other existing structures in that it better withstands environmental changes within the SRAM and consumes lower power during memory operation. This structure minimizes reliance on complex ADCs, instead utilizing a simplified voltage differential approach for multiply-and-accumulate (MAC) operations, which enhances both power efficiency and stability. Based on these strengths, it can achieve higher battery efficiency in AI edge devices and improve system performance. The proposed integrated circuit was simulated in a 90 nm CMOS process and operated on a 1 V supply voltage.

Design and thermodynamic performance analysis of a novel adiabatic compressed air energy storage system based on liquid piston re-pressurization

Compressed air energy storage (CAES) is a crucial technology for integrating renewable energy into the grid and supporting the “dual carbon” goals. To further utilize compressed heat and reduce throttling losses, this paper proposes a novel A-CAES energy storage system with liquid piston re-pressurization (LP-A-CAES). During the energy release process, the air in the air storage tank enters the liquid piston directly without passing through the throttle valve, then undergoes further pressurization and expansion. This approach avoids throttling losses while increasing the expansion ratio of the expander, facilitating the efficient use of compressed heat. By establishing thermodynamic models for both the proposed re-pressurized A-CAES and the traditional A-CAES systems, we compare their performance variations with changes in key parameters. Results indicate that under design conditions, the system's energy efficiency is 70.74 %, a 5.07 % improvement over traditional throttle-based A-CAES systems. The total exergy loss of the system is 1.42 × 1010 J, with the compressor and expander units accounting for the largest exergy losses, at 32 % and 31.6 %, respectively. The liquid piston exhibits excellent isothermal performance, maintaining air and water temperature rises within 20 K and 2.5 K, respectively. Reducing the maximum pressure difference in the air storage tank enhances system performance, although the performance improvement of the re-pressurized A-CAES system compared to the traditional A-CAES system diminishes. Increasing the height of the thermal energy storage and reducing the diameter of the thermal storage material both contribute to higher system energy efficiency. When the height of the thermal energy storage increases from 4 m to 8 m, the system's efficiency rises from 70.04 % to 70.74 %. Similarly, reducing the diameter of the thermal storage material from 0.05 m to 0.01 m increases the system's efficiency from 68.85 % to 71.47 %.

Influences of Community Coalescence on the Assembly of Bacterial Communities of the Small-Scale Complex Aquatic System from the Perspective of Bacterial Transmission, Core Taxa, and Co-occurrence Patterns

Recirculating aquaculture and aquaponics are considered sustainable aquaculture models playing important roles in animal-derived protein supply. In these aquaculture systems, microorganisms are crucial for the system stability. The community coalescence by mixing substances and microorganisms from various microhabitats under hydraulic forces is important for shaping the bacterial communities in these small-scale complex systems. However, the influences of community coalescence on bacterial communities remain rarely revealed in these systems. In this study, aquaponics (APS) and recirculating aquaculture (RAS) systems were set up to explore the bacterial community coalescence across different microhabitats, including water, fish feces, biofilter biofilms, and plant rhizosphere environment. Our results showed that diversity and compositions varied across different microhabitats in both systems. However, bacterial transmissions across these microhabitats differed between systems. The core microbiome of the RAS and APS were formed under community coalescence with the highest contribution of bacterial taxa derived from the fish feces. Nevertheless, the plant rhizosphere bacterial community also contributed to the core microbiome of the APS. Furthermore, the core taxa showed a higher average degree than the other nodes in the bacterial community networks in all microhabitats except for the plant rhizosphere environment, implying the important roles of core taxa in maintaining these bacterial community networks. Our results provide new insights into the assembly of bacterial communities under community coalescence in the artificial aquatic ecosystems comprising complex microhabitats, which is vital for developing microbial solutions for regulating the microbial communities to improve system performance in the future.

Optimal allocation of electric vehicle charging stations and distributed generation in radial distribution networks

The widespread spread of electric vehicles requires the establishment of charging stations (EVCSs), and this is considered a large load on the network. This gives priority to distributing the stations in a way that reduces the load on the network, and in parallel, re-planning the network and supplying it with the necessary energy to maintain energy efficiency and power quality. Total energy loss minimization, voltage deviation minimization, and voltage stability index improvement are the considered power quality indices in the multi-objective function. Vehicle to grid (V2G) feature, distributed generation units (DG), and capacitor banks are used for improving system performance, by injecting the required active and reactive power. In addition, the initial and running costs of V2G, DG units, and capacitor banks are considered in the multi-objective function. The optimal sizing and allocation of charging stations, V2G, DG units, and capacitor banks are performed using a proposed Self-Adaptive Multi-Population Elitist JAYA (SAMPE-JAYA) algorithm and checked using the genetic algorithm (GA). The proposed algorithm is tested using various scenarios, two standard IEEE test systems. To emphasize the effectiveness and applicability of the proposed algorithm, it is applied on a real-world distribution system. To accommodate the optimal allocation of EVCS, which constitute 80.7 % and 78.9 % of the base active load for the IEEE 33 and 69 bus systems, respectively. Integrating DG amounting to 17.35 % and 18.25 % of the base load is necessary. Additionally, capacitor banks, contributing 36.84 % and 31.166 % of the reactive power load, are also required for effective voltage support and system reliability in each respective case.

Adaptive load balancing strategies in service composition for improved system performance

Service composition enables the integration of multiple services to create new functionalities, optimizing resource utilization and supporting diverse applications in critical domains such as safety-critical systems, telecommunications, and business operations. This paper addresses the challenges in comparing load-balancing algorithms within service composition environments and proposes a novel dynamic load-balancing algorithm designed specifically for these systems. The proposed algorithm aims to improve response times, enhance system efficiency, and optimize overall performance. Through a simulated service composition environment, the algorithm was validated, demonstrating its effectiveness in managing the computational load of a BMI calculator web service. This dynamic algorithm provides real-time monitoring of critical system parameters and supports system optimization. In future work, the algorithm will be refined and tested across a broader range of scenarios to further evaluate its scalability and adaptability. By bridging theoretical insights with practical applications, this research contributes to the advancement of dynamic load balancing in service composition, offering practical implications for high-tech system performance.

A novel sharding algorithm based on TOPSIS method for blockchain systems

PurposePerformance optimization algorithms based on node attributes are of great importance for sharding blockchain systems. Currently, existing studies on blockchain sharding algorithms consider only random selection sharding strategies. However, the random selection strategy does not perfectly utilize the performance of a node to break the bottleneck of blockchain performance.Design/methodology/approachThis paper proposes a blockchain sharding algorithm called TOPSIS Optimization Sharding System (TOSS), which is based on entropy weight method, relative Euclidean distance and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). It defines a multi-attribute matrix to assess node performance and applies TOPSIS for scoring nodes. Then, an algorithm based on the TOPSIS method is proposed to calculate the performance score of each data node. In addition, an entropy weighting method is introduced to obtain the weights of each attribute to balance the impact of dimensional differences of attributes on the attribute weights. Nodes are ranked by composite scores to guide partitioning.FindingsThe effectiveness of the proposed algorithm in this paper is verified by comparing it with various comparative algorithms. The experimental results show that the TOSS algorithm outperforms the comparison algorithms in terms of performance improvement for the blockchain system, and the throughput metrics are improved by about 20% in comparison.Originality/valueThis study introduces a novel approach to blockchain sharding by incorporating the entropy weight method and relative Euclidean distance TOPSIS into the sharding process. This approach allows for a more effective utilization of node performance attributes, leading to significant improvements in system throughput and overall performance, addressing the limitations of the random selection strategy commonly used in existing algorithms.

Enhanced transmitter designs for indoor MIMO-VLC systems

Multiple-input multiple-output (MIMO) technology, a fundamental element of 6G, has been widely implemented in visible light communication (VLC) systems. However, actual MIMO-VLC systems face significant challenges due to severe channel correlation. To tackle this issue, in this paper, we propose two enhanced transmitter designs for performance improvement of indoor MIMO-VLC systems, including single transmitter partial coverage (STPC) and enhanced STPC. For the STPC scheme, a single LED transmitter only needs to cover part of the receiving plane, instead of covering the whole receiving plane as in the conventional single transmitter full coverage (STFC) scheme. For the enhanced STPC scheme, each light-emitting diode (LED) is replaced with an LED subarray so as to further improve the system performance. Our simulation results reveal that the system performance is influenced by the LED array spacing, LED subarray spacing, and the LED semi-angle at half power. We identify the optimal combinations of these parameters to maximize the average achievable spectrum efficiency of the system. Notably, the STPC and Enhanced STPC schemes demonstrate increases in average achievable spectrum efficiency of 478.14% and 589.49%, respectively, compared to the benchmark STFC scheme.

Study and optimization of the influence of the water tank temperature on the performance of a solar assisted multi-source heat pump drying system

Solar assisted air source heat pump drying (SASHPD) system has been widely studied due to its excellent energy saving and high dried quality of product. To improve system performance, the control logic of a solar assisted multi-source heat pump drying (SMSHPD) system was investigated, and the simulation model and the experimental platform were established in this paper. The seasonal operating characteristics of the air source heat pump drying (ASHPD), the SASHPD and the SMSHPD modes were studied. The energy consumption, coefficient of performance (COP) and specific moisture extraction rate (SMER) of the three drying systems were calculated and analyzed. Additionally, the water tank temperature were also measured to determine the seasonal control logic of the drying system. It was found that compared to the ASHPD system, in summer, the energy consumption of the SASHPD system was reduced by 33.06 %, and COP and SMER was increased by 49.4 % and 49.38 %, respectively. Moreover, because of the high ambient temperature and water tank temperature, the experimental results indicated that only the SASHPD control logic should be implemented in summer. The maximum error of the simulation results was 9.1 % and the accuracy of the simulation model was confirmed. In autumn, the lower water tank temperature after drying illustrated that the solar energy can be more fully utilized, which explained why the performance of the SMSHPD mode was increased by 6.5 % compared to the SASHPD mode. Consequently, it is necessary to implement the SMSHPD control logic in autumn. Moreover, the operating temperature of the water source heat pump drying mode in autumn was optimized and the optimal temperature range was 28–54 °C. The average COP in one week after optimization was 6.21 % higher than that of the original operating temperature. Based on the validated simulation model, the optimal operating temperatures in spring and winter were also calculated and the control logic of the SMSHPD system operating in the four seasons were all obtained. This paper has important guiding significance for the reasonable choice of the control logic of a solar combined air source heat pump drying system.

Event-Driven Architecture: Harnessing Kafka and Spring Boot for Scalable, Real-Time Applications

This comprehensive article explores the transformative impact of Event-Driven Architecture (EDA) in modern financial services, focusing on its implementation using Apache Kafka and Spring Boot. The article examines how financial institutions have revolutionized their transaction processing capabilities through EDA adoption, achieving significant improvements in system performance, reliability, and customer experience. Through a detailed examination of real-world implementations, the analysis demonstrates how EDA has enabled banks to handle unprecedented transaction volumes while substantially reducing system latency and improving resource utilization. The integration of Kafka and Spring Boot has proven particularly effective, with major financial institutions leveraging these technologies to achieve superior performance metrics and system availability. The article further explores critical patterns, including event sourcing, CQRS, and saga patterns, alongside cloud-based best practices for monitoring, performance optimization, and security implementations. By examining various implementation strategies and their outcomes, this article provides a comprehensive framework for building scalable, resilient financial systems that meet the demanding requirements of modern banking operations while ensuring regulatory compliance and maintaining high security and reliability standards.

Artificial Intelligence and/or Machine Learning Algorithms in Microalgae Bioprocesses.

This review examines the increasing application of artificial intelligence (AI) and/or machine learning (ML) in microalgae processes, focusing on their ability to improve production efficiency, yield, and process control. AI/ML technologies are used in various aspects of microalgae processes, such as real-time monitoring, species identification, the optimization of growth conditions, harvesting, and the purification of bioproducts. Commonly employed ML algorithms, including the support vector machine (SVM), genetic algorithm (GA), decision tree (DT), random forest (RF), artificial neural network (ANN), and deep learning (DL), each have unique strengths but also present challenges, such as computational demands, overfitting, and transparency. Despite these hurdles, AI/ML technologies have shown significant improvements in system performance, scalability, and resource efficiency, as well as in cutting costs, minimizing downtime, and reducing environmental impact. However, broader implementations face obstacles, including data availability, model complexity, scalability issues, cybersecurity threats, and regulatory challenges. To address these issues, solutions, such as the use of simulation-based data, modular system designs, and adaptive learning models, have been proposed. This review contributes to the literature by offering a thorough analysis of the practical applications, obstacles, and benefits of AI/ML in microalgae processes, offering critical insights into this fast-evolving field.