Performance Efficiency Research Articles

The impact of point symbol similarity on visual search on maps

ABSTRACT The study presented in this paper investigates the impact of the similarity level of cartographic point symbols on visual search performance. Specifically, we explore the application of an algorithm for quantitatively assessing symbols’ shape similarity, and apply the results of such automated analysis in a simple target search task on a map with distractors of varying similarity levels. Our primary hypothesis posits that the more similar the target symbol and distractors, the more challenging the visual search becomes in terms of search efficiency and eye metric performance. In a controlled laboratory experiment utilizing eye-tracking device, 33 participants were recruited, and 60 map stimuli were tested with each participant. The results confirmed the hypothesis assumptions. However, symbols based on a round shape were consistently the easiest to find at every similarity level. Eye movement analysis revealed differences in absolute pupil size between male and female participants, possibly associated with cognitive load, although without affecting search time performance. Interestingly, scan-path speed decreased with an increasing level of similarity between the target symbol and distractors. The practical implication of this study lies in the potential development of tools to assist map designers in selecting symbols with low similarity, thereby ensuring improved map readability.

Empowering Fuel Cell Electric Vehicles Towards Sustainable Transportation: An Analytical Assessment, Emerging Energy Management, Key Issues, and Future Research Opportunities

Fuel cell electric vehicles (FCEVs) have received significant attention in recent times due to various advantageous features, such as high energy efficiency, zero emissions, and extended driving range. However, FCEVs have some drawbacks, including high production costs; limited hydrogen refueling infrastructure; and the complexity of converters, controllers, and method execution. To address these challenges, smart energy management involving appropriate converters, controllers, intelligent algorithms, and optimizations is essential for enhancing the effectiveness of FCEVs towards sustainable transportation. Therefore, this paper presents emerging energy management strategies for FCEVs to improve energy efficiency, system reliability, and overall performance. In this context, a comprehensive analytical assessment is conducted to examine several factors, including research trends, types of publications, citation analysis, keyword occurrences, collaborations, influential authors, and the countries conducting research in this area. Moreover, emerging energy management schemes are investigated, with a focus on intelligent algorithms, optimization techniques, and control strategies, highlighting contributions, key findings, issues, and research gaps. Furthermore, the state-of-the-art research domains of FCEVs are thoroughly discussed in order to explore various research domains, relevant outcomes, and existing challenges. Additionally, this paper addresses open issues and challenges and offers valuable future research opportunities for advancing FCEVs, emphasizing the importance of suitable algorithms, controllers, and optimization techniques to enhance their performance. The outcomes and key findings of this review will be helpful for researchers and automotive engineers in developing advanced methods, control schemes, and optimization strategies for FCEVs towards greener transportation.

New Approach for Tracking, Monitoring, and Diagnosing Faults in a Photovoltaic System in Real Time: An Experimental and Case Study

Photovoltaic installations have emerged as a cornerstone of sustainable energy production, playing a pivotal role in the global transition towards renewable sources of electricity. As the demand for clean energy surges, so too does the need for robust monitoring and diagnostic strategies to ensure the efficient operation of these systems. This study presents a novel methodology for the real-time tracking, monitoring, and diagnosing of faults in photovoltaic systems (PVSs), emphasizing their crucial role in sustainable energy production amid the global shift to renewable energy. The study was conducted in Guelma, Algeria, during the spring and summer seasons. The investigation utilized WatchPower simulation software over a 24-hour performance analysis of the photovoltaic system. On June 19, 2024, with a high temperature of 32°C, the system achieved a peak output power of 600W under optimal conditions, validating its efficiency in energy generation. The study also analyzed the effects of shading on energy output by comparing data from a shaded day on May 14, 2024, at 25°C, to an unshaded day on June 21, 2024, at 32°C, during peak sunlight hours from 9:00 AM to 2:15 PM, the period when sunlight is at its strongest. The results showed a significant drop in output power from 600W to 450W due to shading, underscoring the importance of real-time monitoring to detect performance inefficiencies. This research not only enhances operational reliability and maintenance strategies for PV systems but also demonstrates the effectiveness of integrating real-time data analytics to support decision-making in similar environments.

Tetrafunctionalized Azocalix[4]resorcinarene Dye: A Chromogenic Supramolecule Used for the Selective Liquid-Liquid Extraction and Spectrophotometric Determination of Cu(II)

Background: The detection and extraction of trace metal ions, particularly copper(II), are critical for environmental monitoring and industrial processes. Calixresorcinarene, with its unique cavity structure, offers excellent platforms for designing selective chemosensors and extractants. Functionalization of calixresorcinarene with azo groups can enhance their chromogenic properties, enabling both extraction and detection in a single step. Objective: This study aimed to evaluate its (Azocalix[4]resorcinaren) efficacy as a selective chemosensor for the liquid-liquid extraction and spectrophotometric determination of Cu(II) ions. Methods: Application in Extraction and Detection: The ability of the dye to selectively extract Cu(II) ions from aqueous solutions was investigated via liquid-liquid extraction experiments. The dye-Cu(II) complex formation was monitored by UV-Vis spectrophotometry, with systematic optimization of experimental conditions, including pH, solvent system, and extraction duration. Results: The synthesized azocalix[4]resorcinarene dye exhibited a pronounced selectivity towards Cu(II) ions, forming a stable, colored complex. The complexation induced a distinct bathochromic shift in the absorption spectrum, allowing for precise spectrophotometric detection. Optimal extraction was achieved at a specific pH and solvent combination, with the method demonstrating a low detection limit and high sensitivity. The dye showed minimal interference from other metal ions, confirming its selectivity for Cu(II). Conclusion: The tetrafunctionalized azocalix[4]resorcinarene dye is a highly effective chromogenic agent for the selective extraction and detection of Cu(II) ions. Its robust performance in both extraction efficiency and spectrophotometric detection underscores its potential utility in environmental analysis and industrial applications where trace metal detection is crucial.

Personalized Data Insights: How­­ Machine Learning is Revolutionizing Everyday Business Decisions

The rise of machine learning has fundamentally transformed the way businesses approach decision-making, particularly in the realm of data analytics. In today’s data-driven landscape, organizations are not just gathering information—they’re using advanced algorithms to turn raw data into personalized, actionable insights that drive business strategy. Machine learning enables businesses to go beyond surface-level trends and dig deeper into customer behavior, operational inefficiencies, and market dynamics. This technology is revolutionizing everyday business decisions by delivering real-time, data-driven intelligence that is specifically tailored to each business's unique needs. By leveraging machine learning, businesses can analyze massive datasets, uncover hidden patterns, and make predictions that were previously unimaginable with traditional analytics methods. One of the key benefits of machine learning is its ability to personalize data insights, offering businesses a clearer, more detailed understanding of their customers and operations. It empowers companies to make more accurate decisions in marketing, product development, customer service, and beyond, leading to improved efficiency, customer satisfaction, and overall business performance. This article explores the transformative role of machine learning in generating personalized data insights and its profound impact on everyday business decisions. It highlights how this technology can improve decision-making processes, enhance customer experiences, and drive more effective business strategies through real-time analysis and prediction. Through practical examples, we will demonstrate how businesses across various sectors are already using machine learning to revolutionize their operations and improve outcomes.

Super Low Profile 5G mmWave Highly Isolated MIMO Antenna with 360∘ Pattern Diversity for Smart City IoT and Vehicular Communication

This study introduces a planar folded super low profile 5G mmWave monopole antenna for small IoT devices with superior efficiency and gain. Low-loss Rogers RT-5880 substrate material has been utilized to achieve higher efficiency, gain, and diversity performance in a relatively compact size. The unit element planar monopole antenna has been designed within 6.15×5.13×0.8mm3 with a partial ground. Four unit element antenna was orthogonally arranged on alternating sides of the substrate plane with narrow inter-component spacing in the multiple input multiple output (MIMO) system. The integration of the double negative (DNG) metamaterial array on the antenna led to a cumulative improvement in the overall performance of the antenna, which includes gain, efficiency, and isolation. The minimum isolation of the antenna increased by 11 dB with substrate-loaded metamaterial. The proposed MIMO antenna model with substrate-loaded metamaterial demonstrates 8 GHz instantaneous bandwidth (33.1 GHz - 41.1 GHz), 0.001 envelope correlation coefficient (ECC), diversity gain (DG) greater than 9.99 dB, channel capacity loss (CCL) less than 0.2bits/s/Hz, and a total active reflection coefficient (TARC) of 25 dB, which suggests that the diversity characteristics of the MIMO antenna system is impressive. Moreover, a densely packed 12-element antenna model has been introduced in this study with 360∘ pattern diversity, capable of receiving signals from any direction. Three 4-element MIMO systems have been arranged in an architecture to develop the 12-element MIMO system, and the size of the 12-element MIMO architecture is 15×15×15mm3. Despite having this compact size and dense packing, the 12-element MIMO antenna model shows similar diversity performance as the 4-element antenna model. Analog equivalent circuit models or resistor-inductor-capacitor (RLC) equivalent systems for the antenna models were created. A concept of a 128-element massive MIMO antenna has been presented to support the end devices, creating an application scenario of an IoT-interconnected smart city.

Customer-Centric quality management: A framework for organizational excellence in SMEs

In an era where customer expectations are continually evolving, Small and Medium Enterprises (SMEs) must adopt innovative quality management practices to achieve organizational excellence. This review presents a customer-centric quality management framework designed specifically for SMEs, emphasizing the pivotal role of customer satisfaction in enhancing operational efficiency, product quality, and overall business performance. The framework is built on the premise that customer feedback and engagement are integral to quality management processes. By prioritizing customer needs and expectations, SMEs can develop quality assurance strategies that not only meet but exceed customer requirements. This approach involves systematic collection and analysis of customer feedback, enabling organizations to identify areas for improvement and innovate their offerings accordingly. Central to the framework are key components such as continuous improvement, employee involvement, and process optimization. Continuous improvement methodologies, like Lean and Six Sigma, are tailored to suit the unique challenges faced by SMEs, fostering a culture of quality that empowers employees at all levels. Engaging employees in quality initiatives cultivates a sense of ownership and accountability, essential for driving organizational change and enhancing service delivery. Furthermore, the framework emphasizes the importance of aligning quality management practices with customer-centric strategies. This alignment ensures that quality initiatives are not only reactive but also proactive, anticipating customer needs and adapting processes to deliver superior value. Incorporating customer relationship management (CRM) tools enhances communication, allowing SMEs to gather insights that inform quality improvements and strengthen customer loyalty. Implementing a customer-centric quality management framework can significantly impact an SME's competitiveness by enhancing product reliability, reducing defects, and improving customer satisfaction. Ultimately, this approach fosters organizational excellence, leading to increased market share and long-term sustainability. In conclusion, this framework equips SMEs with the necessary tools to integrate customer-centricity into their quality management processes, driving continuous improvement and excellence. Keywords: Customer-Centric Quality Management, Smes, Organizational Excellence, Customer Satisfaction, Continuous Improvement, Lean, Six Sigma, Employee Involvement, Process Optimization, CRM Tools, Competitiveness.

Deutelio approach to fusion energy with the Polomac

Deutelio is a private initiative promoting an alternative path to fusion energy. The Tokamak research line pursued worldwide since 1970 met several difficulties, heavily jeopardising the objective of commercial energy production from fusion before 2050, as a support to the global effort towards Net Zero Emissions. As a result, new plasma magnetic confinement concepts, such as Stellarators or overlooked alternatives like the poloidal confinement are being pursued to achieve more efficiency and better performance. The Polomac is a poloidal magnetic configuration where the outboard magnetic lines are deviated aside together with the plasma, to open some accesses to the dipole coils located inside the plasma. These accesses, called magnetic tunnels, are used to support, feed and cool the dipole coil. They avoid the impact with plasma which led to abandon past poloidal experiments, despite their good stability and confinement efficiency. The poloidal confinement can achieve Deuterium-Tritium reactor conditions with a magnetic field 3 times weaker than the Tokamak, in steady state rather than pulsed. With the same high field as in the Tokamak the poloidal confinement can achieve Deuterium-Deuterium reaction, thus avoiding the development of the breeding blanket to produce the Tritium. This paper presents the Polomac system, explains the development strategy of Deutelio through a small prototype focused to tune and assess the magnetic tunnels, and describes the possibility of deuterium-deuterium reaction.

Rod‐Like Microcrystal Scintillators Based on Organic–Inorganic Hybrid Copper Halides for X‐Ray Imaging

AbstractCopper‐based organic–inorganic hybrid halides (OIHHs) have garnered significant interest in X‐ray imaging due to their flexible structural adjustability, efficient light emission, strong X‐ray absorption, etc. Herein, four copper‐based OIHH crystals composed of the same organic ligands (isopropyl triphenylphosphonium cations, IPP+) and inorganic parts (Cu2I42−) are synthesized by varying the ratios of raw materials and types of reaction solvents. Although these crystals share identical structures, they exhibit different luminescent behaviors (blue, green, white, and yellow) attributed to vacancy defects originating from CuI. Notably, the novel copper‐based OIHH (CP2) with green emission shows the best luminescence efficiency (99.54%) and excellent scintillation performance (50675 photons MeV−1). The traditional technology of scintillation screens mixing scintillator powders and polymers often results in light scattering due to powder aggregation and inhomogeneity. To address this challenge, the dispersant polyvinyl pyrrolidone is introduced to prepare uniform rod‐like microcrystals of CP2. The scintillation screen based on CP2 microcrystals achieves a high spatial resolution of 16–18 lp mm−1, surpassing the resolution of most copper‐based OIHH scintillators. Furthermore, it is imaging capabilities are validated using a commercial X‐ray flat panel detector, demonstrating imaging performance comparable to that of commercial scintillators.

Boosting Zn-air battery performance: Fe single-atom anchored on F, N co-doped carbon nanosheets for efficient oxygen reduction

Sluggish oxygen reduction reaction (ORR) kinetics limit the development of metal-air batteries and fuel cells, hindering overall energy conversion efficiency. Therefore, significant research has focused on cost-effective, highly active, and exceptionally stable non-precious metal ORR electrocatalysts. This study presents the synthesis of a nanohybrid material called Fe SAs/FN-CNs. It is made up of single iron atoms embedded in ultrathin porous carbon nanosheets that are co-doped with F and N. The synthesis process involves an easy one-step pyrolysis technique without additional post-treatment. The Fe SAs/FN-CNs material is designed to function as an effective zinc-air battery ORR electrocatalyst. Based on their distinctive components and structure, the optimal Fe SAs/FN-CNs exhibit outstanding catalytic efficiency and long-lasting performance in the alkaline ORR. They have an onset potential (Eonset) of 0.95 V, a half-wave potential (E0.5) of 0.85 V, a kinetic current density (JK) of 20.49 mA cm−2 at 0.8 V, and a diffusion-limited current (Jd) of 6.2 mA cm−2. In addition, a Zn-air battery made using homemade Fe SAs/FN-CNs demonstrated a power density of 197 mW cm−2, a specific capacitance of 813.5 mAh g−1, and exceptional stability. It outperformed the commercial Pt/C by operating continuously for over 147 hours at 10 mA/cm² (discharge-charge). The Fe SAs/FN-CNs nanohybrid electrocatalyst shows great potential as an electrocatalyst for various metal-air batteries.

Investigating the effects of powder feeding rate on microstructure transformation in linear laser beam additive manufactured thick-walled structures

The coarse columnar crystals that grow through multiple layers are commonly observed in laser additive manufactured structures, with fractures tending to occur in regions where there are abrupt changes in grain morphology. This can lead to a degradation of the mechanical properties of the samples. In this study on laser additive manufacturing, thick-walled structures made of 304 stainless steel were created using a linear beam spot with a rectangular powder feeding nozzle. By adjusting the powder feeding rate to influence the thermal cycling characteristics during the laser additive manufacturing process, a hierarchical grain structure that combines coarse and fine equiaxed grains was achieved, enhancing the forming efficiency and overall mechanical performance of the structure. The results from the thermal cycling characteristics and microstructure analysis indicate that increasing the powder feeding rate during the additive manufacturing process can decrease the hierarchical cooling rate, temperature gradient, and heat accumulation effect. This reduction in turn decreases the grain size and facilitates the transformation of columnar crystals into equiaxed crystals. Furthermore, the transformation of low-angle grain boundaries into high-angle grain boundaries in the interlayer region helps to reduce stress concentration, weaken anisotropic tendencies, and mitigate intergranular fracture tendencies, ultimately improving the mechanical properties of the overall structure. In actual engineering applications, the powder flow can be adjusted to control the temperature gradient and cooling rate of the deposition layer, thereby altering the grain morphology and optimizing the mechanical properties of additive manufacturing parts.

Numerical investigation of heat and mass transfer of SWCNT/MWCNT‐water suspension over a porous stretching sheet using Sisko fluid model

AbstractThis study presents a comprehensive numerical computation of heat‐mass transfer characteristics of single‐walled carbon nanotube (SWCNT)/multi‐walled carbon nanotube (MWCNT)‐water suspension flow over a porous stretching sheet with an inclined magnetic field. The governing equations for fluid flow characteristics are formulated using the Sisko fluid model to capture the Newtonian and non‐Newtonian behavior of the nanotube‐water mixture. The nonlinear coupled partial differential equations are converted into nonlinear dimensionless coupled ordinary differential equations using suitable similarity transformations. These equations are solved using MATLAB by implementing the four‐stage Lobatto IIIa formula. The comprehensive set of computations is performed to explore the influence of pertinent parameters, including Sisko fluid parameters, concentration of nanotubes, stretching sheet velocity, and porous medium characteristics on the flow, heat, and mass transfer profiles. From the graphs and statistical analysis, it is clear that the volume fraction of SWCNT and MWCNTs are strongly correlated. The investigation reveals that increasing the inclination angle affects the fluid velocity. The variation in all flow features is negligible for volume fractions of CNTs between 0% and 10% but a significant effect is observed only beyond 10%. Higher volume fractions of CNTs result in enhanced local heat transfer coefficient. This can be attributed due to the outstanding heat transfer capabilities of CNTs owing to their high thermal conductivity. However, Shear thickening fluids exhibit high heat transfer phenomena when compared to shear‐thinning and Newtonian fluids. This research provides valuable insights into the optimization of CNT‐based nanofluids for efficient heat and mass transfer applications in electronics cooling, heat exchangers, and solar energy systems, offering opportunities to enhance energy efficiency and device performance.

Implementation of concurrency control mechanisms to enhance the performance of multi-threaded applications

Concurrency control is a crucial aspect of multi-threaded application development, directly impacting performance and resource utilization.This research project investigates the impact of various concurrency control mechanisms on the performance of multi-threaded applications, specifically focusing on mutexes, semaphores, and lock-free data structures implemented using queues. Conducted in a Python environment within Google Colab, the study aims to assess performance metrics such as execution time, cpu usage, and memory usage. Each mechanism’s performance was evaluated through repeated trials, and the results were aggregated and averaged to ensure reliability. By systematically evaluating these concurrency control mechanisms, the project seeks to understand how each approach affects the efficiency of applications in a multi-threaded context. The findings reveal that the queue mechanism provides the fastest execution time, the semaphore mechanism, on the other hand, exhibited the lowest CPU usage, and the mutex mechanism offered a balanced performance in both speed and CPU efficiency. Memory usage was similar in mutex and semaphore, but queue differed significantly from the other two. These results show that the choice of concurrency control mechanism should be guided by the specific requirements of the application, considering factors such as workload nature and system architecture.

Assessing the Effect of Strategic Alliances on Organizational Performance of Commercial Banks in Nigeria

This study was carried out to examine the effect of Strategic Alliances on Organizational performance of commercial banks in Nigeria. The study adopted a cross-sectional survey design with primary data generated through self-administered questionnaire. The population for this study was five (5) commercial banks in South-South, Nigeria. The study however focused mainly on their regional offices located in Asaba, Delta State, Nigeria. Six regional managers for each bank were used as the study respondents giving a total of 30 respondents. Census sampling was adopted since the study sample was small. The reliability of the instrument was achieved by the use of the Cronbach Alpha with all the items scoring alpha coefficient above 0.70. The hypotheses were tested using the spearman rankorder correlation co-efficient with the aid ofstatistical package for social sciences (SPSS). The tests were carried out at a 95% confidence interval and a 0.05 level of significance. The study findings revealed that there is a significant relationship between strategic alliances on revenue performance, cost efficiency performance and on the profitability of commercial banks in South-South, Nigeria. The study recommends that commercial banks should invest in modern technologies to effectively integrate all the banks functions and activities to minimize operational costs. The top management team should invest more on financial innovations so that customers will have a wide scope of banking products to choose from leading to customer satisfaction and improve value addition which in turn enhances financial performance.

Kill Two Birds with One Stone: Cracking Lithography Technology for High-Performance Flexible Metallic Network Transparent Conductors and Metallic Micronano Sheets.

Flexible electronics have sparked a wide range of exciting applications, such as flexible display technologies, lighting, sensing, etc. Flexible conductors are essential components of flexible electronics and seriously affect efficiency and overall performance. Here, a facile and kill-two-birds-with-one-stone strategy of cracking lithography technology has been proposed to simultaneously fabricate two high-performance flexible conductors, flexible metallic network transparent conductors (f-NTCs) and metallic micronano sheets (MMNSs). The PET substrate flexible transparent conductors (FTCs) based on this strategy yield 88.1% transparency within the visible spectrum and a sheet resistance of 9 Ω/sq. In addition, the FTCs show exceptional mechanical stability, with the sheet resistance remaining virtually unchanged even after 6000 s of bending tests. Subsequently, the remaining MMNSs are recycled to manufacture a Ag paste, showing a very low conductive percolation threshold (∼13%) and excellent flexibility with 140% breaking elongation. After 1000 s of stretching tests, it showed excellent mechanical stability. Furthermore, flexible electroluminescent devices based on the FTCs and sensors fabricated with MMNSs both show excellent performance, demonstrating their potential wide applications in flexible electronics.

Spontaneous combustion coal gangue-based composite cement: Compressive performance and environmental benefits under grinding kinetics control

This study aims to tackle the environmental pressure of spontaneous combustion coal gangue by using its active silica-alumina content to create eco-friendly composite cement through a low-energy process. The Divas-Aliavden grinding kinetics equation was established, and powder characteristics were analyzed using particle size distribution, the Rosin-Rammler-Bennett model, and fractal dimension theory. The compressive performance of the coal gangue-based composite cement was tested. A comprehensive evaluation model linked grinding time, powder characteristics, compressive performance, and energy consumption, while also analyzing hydration products and environmental impact. Results showed that initial grinding stages had larger particle sizes and higher energy utilization efficiency. As grinding progressed, energy consumption increased. A grinding time of 35 min achieved optimal balance between energy efficiency and compressive performance, improving particle distribution uniformity and complexity. The compressive performance of the composite cement reached 95 % of ordinary Portland cement after 28 days of curing. The evaluation model indicated that specific surface area, d50, and fractal dimension are key factors influencing energy efficiency, determining energy consumption and particle distribution during grinding. Environmental assessment revealed that substituting 30 % of cement with coal gangue could reduce CO2 emissions by 250 kg CO2/t, achieving a reduction rate of 29.5 %. This study supports the large-scale application of spontaneous combustion coal gangue, contributing to efficient resource utilization and environmental protection.

YOLOv8-CDD: an improved concrete defect detection method combined CNN with transformer

Abstract Concrete defect detection is a critical task to ensure the safe and stable operation of concrete infrastructure. In order to effectively solve the problems of low efficiency, high cost and poor performance of existing methods, a high-precision concrete defect detection method YOLOv8-CDD (Concrete Defect Detection) combining convolutional neural network and transformer is proposed in this paper. Firstly, based on the features with a large span of concrete defect features, a bot-transformer module that can effectively extract the global information of defect features is proposed to improve the network’s ability to extract global features. Secondly, in order to further strengthen the interaction between defect feature channels and spatial information, a convolutional triplet attention module is introduced into the feature enhancement network to effectively integrate the information of different dimensions of defect features and improve the model detection accuracy. Additionally, in order to enhance the learning of samples with different degrees of difficulty, the introduction of Focaler-CIoU instead of the original boundary regression loss function can optimize the model training process. Finally, the dataset was collected and organized in concrete scenarios from bridge towers, dams, and tunnel corridors, and our method achieved 0.898 average precision, 0.893 average recall, 0.031 average FPR, 0.895 average F1 score, 0.929 mAP50, and 0.731 mAP50:95 on the dataset. The experimental results show that the proposed method achieves the best performance in concrete defect detection.

Novel modular multilevel matrix converter topology for efficient high-voltage AC-AC power conversion

Introduction. This paper delves into the practical application of multilevel technology, particularly focusing on the capacitor-clamped converter as a promising solution for medium-to-high voltage power conversion, with specific emphasis on direct AC-AC switching conditions. Problem. The limitations of conventional single-cell matrix converters (MC) in efficiency and performance for medium-to-high voltage power conversion applications are well-recognized. Goal. The primary objective is to investigate the performance of the 3 phase modular multilevel matrix converter (3MC) with three flying capacitors (FCs) modeling. This investigation utilizes the Venturini method for gate pulse generation, aiming to compare the performance of the 3MC with standard converter designs. Methodology. To achieve the research goal, the Venturini method is adopted for generating gate pulses for the 3MC, representing a departure from conventional approaches. Detailed simulations employing MATLAB/Simulink are conducted to comprehensively evaluate the performance of the 3MC in comparison to conventional converter designs. Results. The simulation outcomes reveal a significant reduction of 73 % in total harmonic distortion (THD) achieved by the 3MC. This reduction in THD indicates improved robustness and suitability for medium-to-high voltage power conversion systems necessitating direct AC-AC conversion. These results highlight the efficacy of the 3MC in enhancing power conversion efficiency and overall performance. Originality. This paper contributes novel insights into the practical implementation of multilevel technology, particularly within the realm of capacitor-clamped converters. Furthermore, the utilization of the Venturini method for gate pulse generation in the 3MC represents an original approach to enhancing converter performance. Practical value. The research findings present significant advancements in multilevel transformer technology, offering valuable guidance for optimizing transformer design in various industrial and renewable energy applications. These contributions serve to enhance the development of reliable and efficient power systems, addressing critical needs in the energy sector. References 54, tables 3, figures 4.

TriMLP : A Foundational MLP-Like Architecture for Sequential Recommendation

In this work, we present TriMLP as a foundational MLP-like architecture for the sequential recommendation, simultaneously achieving computational efficiency and promising performance. First, we empirically study the incompatibility between existing purely MLP-based models and sequential recommendation, that the inherent fully-connective structure endows historical user–item interactions (referred as tokens) with unrestricted communications and overlooks the essential chronological order in sequences. Then, we propose the MLP-based Triangular Mixer to establish ordered contact among tokens and excavate the primary sequential modeling capability under the standard auto-regressive training fashion. It contains (1) a global mixing layer that drops the lower-triangle neurons in MLP to block the anti-chronological connections from future tokens and (2) a local mixing layer that further disables specific upper-triangle neurons to split the sequence as multiple independent sessions. The mixer serially alternates these two layers to support fine-grained preferences modeling, where the global one focuses on the long-range dependency in the whole sequence, and the local one calls for the short-term patterns in sessions. Experimental results on 12 datasets of different scales from 4 benchmarks elucidate that TriMLP consistently attains favorable accuracy/efficiency tradeoff over all validated datasets, where the average performance boost against several state-of-the-art baselines achieves up to 14.88%, and the maximum reduction of inference time reaches 23.73%. The intriguing properties render TriMLP a strong contender to the well-established RNN-, CNN-, and Transformer-based sequential recommenders. Code is available at https://github.com/jiangyiheng1/TriMLP .

Analysis of the relationship among technical efficiency, competitive strategy, and export performance in the cocoa agroindustry in Indonesia

The goals of the current study are to analyze the technical efficiency of the Decision Making Unit (DMU) of the cocoa agroindustry and the relationship among technical efficiency, export performance, and competitive strategy in the cocoa agroindustry in Indonesia. Respondents in this study are production and marketing managers at 42 active DMUs. The results show that the average DMU technical efficiency value is 0.963 and 0.985 based on the models of Constant Returns to Scale (CRS) and Variable Returns to Scale (VRS), with the distribution of the full technical efficiency category of 80.95%, the high category of 16.67%, the medium category of 2.38%, and the low category of 0%. This study finds a model for increasing the export performance of the cocoa agroindustry, in which the technical efficiency variable has a significant positive effect on the export performance variable with competitive strategy variables as an intervening variable.