Optimal Speed Research Articles

Optimization of mixing speed and time to disperse the composite conductive agent composed of carbon black and graphene in lithium-ion battery slurry

This paper proposed an optimal approach to disperse the composite conductive agent which is composed of carbon black (CB) and graphene (Gr) within lithium-ion battery (LIB) slurry with different mixing speeds and mixing times. The internal structures of LIB slurry are characterized by Electrochemical Impedance Spectroscopy, Scanning Electron Microscopy, and Raman experiment. Initially, a composite conductive solution is prepared by mixing the composite conductive agent with NMP solvent under the conditions of five different mixing speeds n1 (n1 = 1000, 1100, 1200, 1300, 1400 rpm) in the case of mixing time t1 = 10 min. Subsequently, LIB slurry is prepared by blending the composite conductive solution, LiCoO2 and PVDF-NMP solution under the conditions of five different mixing speeds n2 (n2 = 1000 ± 280, 1100 ± 280, 1200 ± 280, 1300 ± 280, 1400 ± 280 rpm) in the case of mixing time t2 = 6 min. By analyzing the internal structure of different LIB slurries, it shows that in the case of n1 = n2 = 1200 rpm, a conductive network structure is well formed within LIB slurry. Additionally, in order to determine the optimal time to prepare the composite conductive solution for LIB slurry, nine different t1 (t1 = 0, 10, 20, 30, 40, 50, 60, 70, 80 min) are selected. By analyzing the internal structure of different LIB slurries, a well-formed conductive network structure and a uniformly distributed composite conductive agent are deduced in LIB slurry when t1 = 50 min. Therefore, it can be concluded that the composite conductive agent composed of CB and Gr is able to be uniformly dispersed in LIB slurry by establishing a well-formed conductive network structure under the optimal mixing speed n1 = n2 = 1200 rpm and the optimal mixing time t1 = 50 min, t2 = 6 min. This kind of the internal structure has the potential to be used to further analyze the dispersion characterizations of LIB slurry under different composite conductive agent and CB/Gr ratios with the aim of improving the final performance of LIB.

A Comparison of Ryu and Pox Controllers: A Parallel Implementation

Software Defined Network (SDN) network controllers have limitations in handling large volumes of data generated by switches, which can slow down their performance. Using parallel programming methods such as threading, multiprocessing, and MPI aims to improve the performance of the controller in handling a large number of switches. By considering factors such as memory usage, CPU consumption, and execution time. The test results show that although RYU outperforms POX in terms of faster execution time and lower CPU utilization rate, POX shows its prowess by exhibiting less memory usage despite higher CPU utilization rate than RYU. The use of the parallel approach proves advantageous as both controllers exhibit enhanced efficiency levels. Ultimately, RYU's impressive speed and superior resource optimization capabilities may prove to be more strategic than POX over time. Taking into account the specific needs and prerequisites of a given system, this research provides insights in selecting the most suitable controller to handle large-scale switches with optimal efficiency.

Optimization of wear parameters for ECAP-processed ZK30 alloy using response surface and machine learning approaches: a comparative study

The present research applies different statistical analysis and machine learning (ML) approaches to predict and optimize the processing parameters on the wear behavior of ZK30 alloy processed through equal channel angular pressing (ECAP) technique. Firstly, The ECAPed ZK30 billets have been examined at as-annealed (AA), 1-pass, and 4-passes of route Bc (4Bc). Then, the wear output responses in terms of volume loss (VL) and coefficient of friction (COF) have been experimentally investigated by varying load pressure (P) and speed (V) using design of experiments (DOE). In the second step, statistical analysis of variance (ANOVA), 3D response surface plots, and ML have been employed to predict the output responses. Subsequently, genetic algorithm (GA), hybrid DOE–GA, and multi-objective genetic algorithm techniques have been used to optimize the input variables. The experimental results of ECAP process reveal a significant reduction in the average grain size by 92.7% as it processed through 4Bc compared to AA counterpart. Furthermore, 4Bc exhibited a significant improvement in the VL by 99.8% compared to AA counterpart. Both regression and ML prediction models establish a significant correlation between the projected and the actual data, indicating that the experimental and predicted values agreed exceptionally well. The minimal VL at different ECAP passes was obtained at the highest condition of the wear test. Also, the minimal COF for all ECAP passes was obtained at maximum wear load. However, the optimal speed in the wear process decreased with the number of billets passes for minimum COF. The validation of predicted ML models and VL regression under different wear conditions have an accuracy range of 70–99.7%, respectively.

Variable Neighborhood Search for Multi-Port Berth Allocation with Vessel Speed Optimization

This paper delves into the multi-port berth allocation problem (MBAP), enriching the traditional berth allocation problem (BAP) with vessel speed optimization (VSO). In the MBAP, it is assumed that there is cooperation between the port and the shipping companies, and the operation of the vessels and the ports is planned to maximize the overall benefits. Exploring this potential collaboration between ports and shipping entities has the potential to mitigate, or even resolve, the challenges plaguing maritime transportation, e.g., port congestion and suboptimal vessel schedules, to ultimately enhance the efficiency of maritime trade. In this paper, a new mixed-integer linear programming (MILP) model for the MBAP is formulated, which attempts to minimize the total cost incurred during operations, with various constraints such as vessel sailing, the vessel space–time relationship in ports, and the planning period. Meanwhile, an innovative variable neighborhood search (VNS) algorithm is presented, in which the initial solution generation method and neighborhood structures are proposed according to the MBAP characteristics. Furthermore, two sets of MBAP instances are generated to test the proposed MILP and VNS, of which the first set is based on real-world port data and the second on existing studies. The numerical experiments verify that the VNS can efficiently and reliably solve instances of all scales, with each neighborhood structure contributing uniquely to the iterative process. In addition, by analyzing the impact of varying oil prices on the MBAP, the study offers valuable management insights. Finally, a case study based on real data from a port group in the Yangtze River Basin is presented to further demonstrate the necessity of considering vessel service time window and planning period in the MBAP as well as the important role of the VSO in scheduling.

Numerical investigation and optimization of the melting performance of latent heat thermal energy storage unit strengthened by graded metal foam and mechanical rotation

In this paper, a combined passive graded metal foam and active mechanical rotation strategy is proposed to simultaneously solve the problem of slow melting rate and non-uniform phase change problem of the latent heat thermal energy storage (LHTES) technology. The enthalpy-porosity method and fixed grid structure are employed to numerically investigate the effects of porosity range, the number of graded layers, and the rotational angular velocity. Moreover, the response surface method (RSM) is further selected to optimize the structural parameters and obtain the function of melting performance and various factors. The result shows that there is an optimal porosity range and the total melting time decreases first and then increases with the increase of porosity range. When the porosity range is 12 % and the graded layer is three, it can shorten the melting time by 35.54 % and increase the thermal energy storage rate (TESR) by 51.57 %. In addition, the melting performance is gradually improved as the number of graded layers increases. The strengthening effect of graded metal foam with just four layers is close to that of the maximum layers considered in this paper. The trend of the influence of rotation speed on the melting performance of the LHTES unit is consistent with the porosity range, indicating the existence of the optimal rotational speed. The RSM optimization structure, with a 14.969 % porosity range, 5-layer graded number, and 2.267 rpm rotational speed, shortens the melting time by 42.42 % and increases the TESR by 62.75 % compared with the stationary case with uniform metal foam. This work verifies the effectiveness of active rotation coupling with passive graded porous structures, which could provide a new strengthening approach to enhance LHTES.

Vision-Based Reinforcement Learning Approach to Optimize Bucket Elevator Process for Solid Waste Utilization

An energy-intensive industry such as cement manufacturing requires a constant supply of high amounts of traditional fossil fuels, such as coal or gas, for the calcination process. A way to overcome this fuel need is the usage of solid waste or Alternative Fuel Resources (AFRs), such as wood or paper. An advantage of using such waste is that their combustion byproduct, “ash”, can be used as a raw material alternative in the cement manufacturing process. However, for structural reasons, only bucket elevator technology is feasible to convey the fuel vertically for feeding the calciner in most cement plants. During the fuel feeding process, the inhomogeneous characteristics of AFRs lead to discharge parabolas of these materials varying over the infeed sample. Hence, a need arises to observe these trajectories and estimate a method for their optimal discharge. Thus, the purpose of this study is to develop an intelligent high-performance bucket elevator system. As such, a vision-based reinforcement learning algorithm is proposed in this study to monitor and control the speed of the elevator depending on the material properties observed at the inlet. These inlet materials properties include the type of material used in the simulation, and the particle size distribution within the infeed sample. A relationship is established between the inlet material properties and the speed of the bucket elevator. The best possible scenario is then deduced using a reward function. Here, the reward function is formulated via the deep learning image segmentation algorithm, a novel approach. After observing the test simulation conducted with a random-parameters setup, it was noted that the optimum speed for a given infeed sample was predicted correctly. As such, it can be concluded that the goal of developing an intelligent bucket elevator system was achieved.

Bi-objective ship speed optimization based on machine learning method and discrete optimization idea

With the introduction of increasingly stringent carbon dioxide (CO2) emission regulations in the shipping industry, shipping companies face significant pressure to reduce emissions while ensuring profitable ship operations. A practical bi-objective speed optimization (BSO) algorithm for container ships is developed based on the variable weight idea, the discrete optimization idea, and the ideal point method to meet the urgent needs of shipping companies to improve operating profits and reduce emissions. By constructing two BSO models of ship main engine fuel consumption-ship sailing time and ship operating costs (SOC)-ship CO2 emissions (SCE), the effectiveness of the BSO algorithm is confirmed through extensive optimization experiments. The results show that compared to the historical sailing results, the BSO reduces the SOC by 4.43 % without causing a significant increase in the SCE, and reduces the SCE by 0.25 %. Moreover, the results demonstrate the effectiveness and feasibility of the BSO in reducing the cost of ship operations while reducing the impact on the environment. The results provide valuable references for shipping companies in the trade-off analysis between economic and environmental benefits and optimization decision-making.

Energy-saving operation in urban rail transit: A deep reinforcement learning approach with speed optimization

The energy consumption of urban rail transit plays a significant role in the operating costs of trains. It is particularly crucial to decrease the energy consumption of the traction power supply in subway systems, as it accounts for approximately half of the total energy consumption of the subway operating organization. To overcome the limitations of traditional real-time speed profile generation methods and the limited exploration capabilities of popular reinforcement learning algorithms in the speed domain, this paper presents the Energy-Saving Maximum Entropy Deep Reinforcement Learning (ES-MEDRL) algorithm. The ES-MEDRL algorithm incorporates Lagrange multipliers and maximum policy entropy as penalties to formulate a novel objective function. This function aims to intensify exploration in the speed domain, minimize train traction energy consumption, and ensure a balance between ride comfort, punctuality, and safety within the subway system. This leads to the optimization of speed profile strategies. To further reduce energy consumption, this paper proposes a secondary optimization strategy for the energy-saving speed profile. This approach involves trading acceptable travel time for improved energy efficiency. To validate the performance of the proposed model and algorithm, numerical experiments are conducted using the Yizhuang Line of the Beijing Metro. The findings demonstrate a minimum 20 % increase in energy efficiency with the ES-MEDRL algorithm compared to manual driving. This algorithm can guide energy-efficient train operations at the planning level.

Identifying Patterns of Primary Care In-Person and Telemedicine Use in the Veterans Health Administration: A Latent Class Analysis.

The Veterans Health Administration increased synchronous telemedicine (video and telephone visits) in primary care in response to the COVID-19 pandemic. Our objective was to determine veteran use patterns of in-person and telemedicine primary care when all modalities were available. A retrospective cohort analysis. We performed a latent class analysis of primary care visits over a 1-year period to identify veteran subgroup (i.e., class) membership based on amount of primary care use and modality used. Then, we used multinomial logistic regression with a categorical outcome to identify patient characteristics associated with class identification. A random national sample consistingof 564,580 primary care empaneled veterans in June 2021. Latent class membership. We identified three latent classes: those with few primary care visits that were predominantly telephone-based (45%), intermediate number of visits of all modalities (50%), and many visits of all modalities (5%). In an adjusted model, characteristics associated with the "few" visits class, compared to the intermediate class, were older age, male sex, White race, furtherdriving distance to primary care, higher Gagne, optimal internet speed, and unmarried status (OR 1.002, 1.52, 1.13, 1.004, 1.04, 1.05, 1.06, respectively; p < .05). Characteristics associated with membership in the "many" visits class, compared to the intermediate class, were Hispanic race, higher JEN Frailty Index and Gagne (OR 1.12, 1.11, 1.02, respectively; p < .05), and higher comorbidity by Care Assessment Need score quartile (Q2 1.73, Q3 2.80, Q4 4.12; p < 0.05). Veterans accessing primary care in-person or via telemedicine do so primarily in three ways: (1) few visits, predominantly telephone; (2) intermediate visits, all modalities, (3) many visits, all modalities. We found no groups of veterans receiving a majority of primary care through video.

Optimizing friction stir processing parameters for aluminium alloy 2024 reinforced with SiC particles: A taguchi approach of investigation

The present investigation focuses on the microstructural behavior and mechanical properties of Aluminium alloy 2024 reinforced with SiC nanoparticles by applying the Friction Stir Processing (FSP) technique. Taguchi L9 orthogonal array was used to find the optimal process parameters. The experiment used the expected best process parameters, which confirmed the predicted highest value for the mechanical characteristic. Traverse speed, axial load, and rotating speed are the main factors affecting aluminum metal matrix composite. Hence, this experiment examined the effects of five different tool spinning speeds, specifically 800, 900, 1000, 1100, and 1200 rpm. Weld samples are fabricated wherein the stir zone contains SiC nanoparticles. The results of the study indicate that the optimal welding rotational speed of 1000 rpm has a significant impact on the microstructure, wear rate, and microhardness. Specifically, it was observed that the stirred zone exhibited enhanced wear resistance compared to other zones. The study's findings revealed that after implementing friction stir processing (FSP), a well-defined shear zone was observed on the advancing side of the stir zone. Additionally, it is found that the uniform dispersion and strong bonding of SiC particles with an aluminium matrix further enhance wear resistance.

Advancing forensic-based investigation incorporating slime mould search for gene selection of high-dimensional genetic data

Modern medicine has produced large genetic datasets of high dimensions through advanced gene sequencing technology, and processing these data is of great significance for clinical decision-making. Gene selection (GS) is an important data preprocessing technique that aims to select a subset of feature information to improve performance and reduce data dimensionality. This study proposes an improved wrapper GS method based on forensic-based investigation (FBI). The method introduces the search mechanism of the slime mould algorithm in the FBI to improve the original FBI; the newly proposed algorithm is named SMA_FBI; then GS is performed by converting the continuous optimizer to a binary version of the optimizer through a transfer function. In order to verify the superiority of SMA_FBI, experiments are first executed on the 30-function test set of CEC2017 and compared with 10 original algorithms and 10 state-of-the-art algorithms. The experimental results show that SMA_FBI is better than other algorithms in terms of finding the optimal solution, convergence speed, and robustness. In addition, BSMA_FBI (binary version of SMA_FBI) is compared with 8 binary algorithms on 18 high-dimensional genetic data from the UCI repository. The results indicate that BSMA_FBI is able to obtain high classification accuracy with fewer features selected in GS applications. Therefore, SMA_FBI is considered an optimization tool with great potential for dealing with global optimization problems, and its binary version, BSMA_FBI, can be used for GS tasks.

Critical parameters to standardize the size and concentration determination of nanomaterials by nanoparticle tracking analysis

The size and concentration are critical for the diagnostic and therapeutic applications of nanomaterials but the accurate measurement remains challenging. Nanoparticle tracking analysis (NTA) is widely used for size and concentration determination. However, highly repeatable standard operating procedures (SOPs) are absent. We adopted the “search-evaluate-test” strategy to standardize the measurement by searching the critical parameters. The particles per frame are linearly proportional to the sample concentration and the measured results are more accurate and repeatable when the concentration is 108-109 particles/ml. The optimal detection threshold is around 5. The optimal camera level is such that it allows clear observation of particles without diffractive rings and overexposure. The optimal speed is ≤ 50 in AU and ∼ 10 μl/min in flow rate. We then evaluated the protocol using polydisperse polystyrene particles and we found that NTA could discriminate particles in bimodal mixtures with high size resolution but the performance on multimodal mixtures is not as good as that of resistive pulse sensing (RPS). We further analyzed the polystyrene particles, SiO2 particles, and biological samples by NTA following the SOPs. The size and concentration measured by NTA differentially varies to those determined by RPS and transmission electron microscopy.

Integrated design optimization method for pavement structure and materials based on further development of finite element and particle swarm optimization algorithm

The maintenance design for existing asphalt pavements, characterized by their typical structure and material composition, falls short of meeting the diverse requirements of pavements. There is an urgent need for a more rational design approach that can achieve accurate pavement design while also reducing construction costs. This paper focuses on selected conventional road surface structures and conducts an analysis of how pavement structure and materials impact fatigue life and construction expenses. It employs Python to perform three-dimensional finite element road modeling, analysis, and data extraction at specific locations, using normalized fatigue equation to calculate pavement fatigue life. The optimization variables selected are the modulus E and thickness h, with pavement maintenance cost serving as the optimization target function. The particle swarm optimization algorithm is employed for optimizing these variables. A comparative analysis is carried out for five improved optimization algorithms, optimizing combinations of inertia weight, learning factors, particle speed, number of particles, and iteration times. The results reveal that the enhanced optimization algorithms significantly improve iteration trends, featuring velocity limitations and natural selection mechanisms. Between 0 and 20 iterations, all algorithms search for the optimal solution over a wider interval with greater fluctuations. Between 20 and 30 iterations, each algorithm gradually converges towards its solution, stabilizing in the later stages. Based on optimization speed and actual engineering cost benchmarks, the NDPSO algorithm is selected, resulting in a 52.8% reduction in actual engineering costs. Adjustments to the particle count and iteration number of the NDPSO algorithm are made based on computational cost and optimization accuracy, thus choosing 20 particles and 50 iterations as the recommended parameter combination. By combining automated finite element modeling, analysis, and particle swarm optimization, this paper offers a novel and effective automated design reference for extending the lifespan of asphalt pavement structures.

Comparison of the PSO Algorithm and JAYA Algorithm for Economic Load Dispatch

The electrical power industry has undergone the significant changes, leading to a competitive market and the need for optimal economic dispatch solutions. As energy resources becoming scarce, prices rise, environmental concerns intensify, and electricity consumption increases, classical optimization techniques struggle to handle these complex issues. This project compares particle swarm optimization (PSO) and JAYA optimization method for economic load dispatch (ELD) problems in the electrical power industry. PSO has gained recognition as an effective solution for ELD problems in the last decade. The aim of this study is to show that Jaya optimization is the best optimization methodology for ELD problems. By comparing the performance, convergence speed, and accuracy of PSO and JAYA optimization, this paper aims to provide insights into the superiority of JAYA optimization. Key Words: Economic Load dispatch, Optimization method, PSO algorithm, JAYA algorithm, Incremental fuel cost

Time-domain fatigue damage assessment for wind turbine tower bolts under yaw optimization control at offshore wind farm

Yaw optimization control is recognized as the most effective active wake control strategy for enhancing the overall power generation of wind farms. However, the potential adverse effects of yaw optimization control on the fatigue life of wind turbines remain unclear. This study examined the effect of yaw optimization control on the fatigue life of offshore wind turbines using tower bolts. Initially, the yaw misalignment of the wind turbines was optimized using the open-source FLORIS package to maximize wind farm power generation. Subsequently, the time-varying load of the wind turbines was obtained through the OpenFAST software, using the optimal yaw misalignment, wind speed, and turbulence intensity at the hub height as inputs. The fatigue life of the wind turbines was then calculated by integrating the Schmidt–Neuper engineering stress algorithm, rain flow counting method, and Miner-Palmgren fatigue damage accumulation theory. Finally, a case study of the Horns Rev I large-scale offshore wind farm, comprising 80 wind turbines, revealed that yaw optimization control could significantly enhance the annual power generation of the wind farm by approximately 1.818%. However, optimization comes at the cost of reducing the fatigue life of wind turbine tower bolts by approximately 3–9 years.

Multi-robot navigation based on velocity obstacle prediction in dynamic crowded environments

PurposeThe purpose of this paper is to propose a new velocity prediction navigation algorithm to develop a conflict-free path for robots in dynamic crowded environments. The algorithm BP-prediction and reciprocal velocity obstacle (PRVO) combines the BP neural network for velocity PRVO to accomplish dynamic collision avoidance.Design/methodology/approachThis presented method exhibits innovation by anticipating ahead velocities using BP neural networks to reconstruct the velocity obstacle region; determining the optimized velocity corresponding to the robot’s scalable radius range from the error generated by the non-holonomic robot tracking the desired trajectory; and considering acceleration constraints, determining the set of multi-step reachable velocities of non-holonomic robot in the space of velocity variations.FindingsThe method is validated using three commonly used metrics of collision rate, travel time and average distance in a comparison between simulation experiments including multiple differential drive robots and physical experiments using the Turtkebot3 robot. The experimental results show that our method outperforms other RVO extension methods on the three metrics.Originality/valueIn this paper, the authors propose navigation algorithms capable of adaptively selecting the optimal speed for a multi-robot system to avoid robot collisions during dynamic crowded interactions.

A piezoelectric energy harvester with parallel connection using beams of different lengths to improve output performance

The purpose of this paper is to design an energy harvester to improve output performance. The theoretical analysis of the piezoelectric energy harvester has been performed. Reducing the length of one cantilever beam, thereby changing the relative impact position, causing the amplitude of the two cantilever beams to be different, and making the waveform of two beams different. Some experiments have been tested to verify the feasibility of the device and compare the differences with Plan A. Based on the experiment, it can be concluded that the output voltage is higher at both high and low speeds. When the rotation speed is 255 r min−1, Plan B arrives at the optimum speed, and the maximum output voltage is 166.2 V, which significantly increases from 97.2 V of Plan A. The maximum output power is 0.966 W under the load resistance of 10 kΩ. The maximum voltage is 157.7 V under the load resistance of 120 kΩ. Nevertheless, the maximum voltage and maximum power of Plan A are 92.62 V and 0.52 W. Besides, the prototype has fewer materials and nearly 1.5 times the energy conversion rate compares to Plan A. It can light up 42 LEDs easily and can adapt to environmental vibration frequency changes, so it has an intensely adaptable and outstanding performance in practical applications.

DC to DC Boost Converter Optimized Speed Control for BLDC Motor

BLDC motors have the capacity to replace induction motors, but the major setback is the implementation of its drive system which should be efficient as well as low cost and can be implemented easily. Here we try to implement a improved speed control system for a BLDC motor using sensors and easily available controllers which can be utilized for operating under various conditions directly for many applications which will help in the replacement of induction motors with BLDC motor systems. The response of the system has been studied for various speed and load conditions using MATLAB simulation tools.

Enhancing speed recovery rapidity in bipedal walking with limited foot area using DCM predictions

The research on bipedal robots with limited foot area is gaining increasing attention. To tackle the challenge of dealing with unknown disturbances in the environment, the adjustment of footstep placement plays a vital role in maintaining stable motion during bipedal walking. This paper introduces an innovative approach based on the relationship between the Divergent Component of Motion (DCM) and footstep. It utilizes a DCM prediction model to optimize the optimal speed for recovering the foothold position. The goal is to enable quick and relevant footstep selection for bipedal robots, thereby facilitating the swift recovery of robot speed. The paper provides insights into the process of designing the desired DCM for achieving an optimal average walking speed without relying on predefined footstep sequences. By establishing a state equation between the DCM and footstep placement, this approach enables the prediction of multiple footstep positions within a fixed walking cycle, thereby facilitating the desired average motion speed. Extensive numerical simulations are conducted to compare the proposed method with various conventional footstep adjustment algorithms. The results emphasize our method’s ability to converge more rapidly to the target speed, even with minor step adjustments. To validate the feasibility and robustness of the algorithm, we conduct experiments on the bipedal robot BHR-B2. These experiments further confirm the algorithm’s effectiveness. Given its promising performance, this algorithm holds potential for applications in legged robots with point feet.

Boolean interpretation, matching, and ranking of natural language queries in product selection systems

E-commerce is a massive sector in the US economy, generating $767.7 billion in revenue in 2021. E-commerce sites maximize their revenue by helping customers find, examine, and purchase products. To help users easily find the most relevant products in the database for their individual needs, e-commerce sites are equipped with a product retrieval system. Many of these modern retrieval systems parse user-specified constraints or keywords embedded in a simple natural language query, which is generally easier and faster for the customer to specify their needs than navigating a product specification form, and does not require the seller to design or develop such a form. These natural language product retrieval systems, however, suffer from low relevance in retrieved products, especially for complex constraints specified on products. The reduced accuracy is in part due to under-utilizing the rich semantics of natural language, specifically queries that include Boolean operators, and lacking of the ranking on partially-matched relevant results that could be of interest to the customers. This undesirable effect costs e-commerce vendors to lose sales on their merchandise. In solving this problem, we propose a novel product retrieval system, called QuePR\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ extit{QuePR}}$$\\end{document}, that parses arbitrarily simple and complex natural language queries with(out) Boolean operators, utilizes combinatorial numeric and content-based matching to extract relevant products from a database, and ranks retrieved resultant products by relevance before presenting them to the end-user. The advantages of QuePR\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ extit{QuePR}}$$\\end{document} are its ability to process explicit and implicit Boolean operators in queries, handle natural language queries using similarity measures on partially-matched records, and perform best guess or match on ambiguous or incomplete queries. QuePR\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ extit{QuePR}}$$\\end{document} is unique, easy to use, and scalable to all product categories. To verify the accuracy of QuePR\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ extit{QuePR}}$$\\end{document} in retrieving relevant products on different product domains, we have conducted different performance analyses and compared QuePR\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ extit{QuePR}}$$\\end{document} with other ranking and retrieval systems. The empirical results verify that QuePR\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ extit{QuePR}}$$\\end{document} outperforms others while maintaining an optimal runtime speed.