Optimal Number Research Articles

Event-triggered model predictive-preview control strategy for trajectory tracking of autonomous vehicle

This paper proposes an event-triggered model predictive-preview control strategy for autonomous vehicle trajectory tracking. First, the dynamic equation is established based on the preview road curvature and the vehicle’s 2 degree-of-freedom relationship. A model predictive tracking controller is designed by predicting the system dynamics in the future. Second, to reduce the computational burden of the controller, the triggering conditions are designed according to the system’s stability and feasibility. The proposed event-triggered model predictive-preview control strategy not only makes the autonomous vehicles maintain tracking accuracy but also reduces the number of online optimization. Then, asymptotically stable of closed-loop systems are proved by using Lyapunov’s theorem. Finally, simulation experiments show that the proposed strategy has some advantages over traditional model predictive control in terms of improving vehicle tracking accuracy and reducing algorithm complexity.

Acoustic tomographic inversion of 3D temperature fields with mesoscale anomaly in the South China Sea

Acoustic tomographic inversion is based on travel times measured along the transmission paths between all station pairs to reconstruct three-dimensional temperature structures with mesoscale anomalies. In this study, tomographic simulation experiments were designed based on the Hybrid Coordinate Ocean Model (HYCOM) reanalysis data to reconstruct mesoscale phenomena from travel time data obtained from five, seven, and nine stations in the South China Sea over a domain of 100 × 100 km. The travel times for each station pair were calculated in the vertical section using the Bellhop acoustic ray simulation method. Six Empirical orthogonal function (EOF) modes of sound speed along the sound transmission paths in a vertical slice were used to formulate the inversion equations. The horizontal-slice distributions of temperature in the tomography domain were reconstructed using the grid-segmented method for each depth layer. For station-to-station distances greater than 100 km, the performance of inversion was best for the seven-station case rather than for the nine-station case, with the highest horizontal resolution of the three cases. This case study concluded that the seven-station case rather than the nine-station case provided an optimal station number for reconstructing the three-dimensional temperature fields.

High-density cluster core-based [formula omitted]-means clustering with an unknown number of clusters

The k-means algorithm, known for its simplicity and adaptability, faces challenges related to manual cluster number selection and sensitivity to initial centroid placement. This paper introduces an innovative framework aimed at overcoming these challenges. By proposing a data-driven cluster number estimation method and a robust initialization strategy based on high-density cluster cores, our approach revolutionizes k-means, unlocking its full unsupervised potential and ensuring superior performance, even in scenarios involving overlapping clusters. The method employs a novel density-based technique to accurately identify cluster cores, resulting in substantial improvements over existing methods. Rigorous experimentation on synthetic and real-world datasets demonstrates an average performance enhancement of 15% in terms of the Adjusted Rand Index for datasets with overlapping clusters, surpassing the capabilities of state-of-the-art density-based clustering methods and traditional k-means. Moreover, our method autonomously determines the optimal number of clusters, facilitating true unsupervised learning and eliminating the impact of initial centroid placement on clustering outcomes. This leads to stable and consistent results, addressing key limitations of the conventional k-means algorithm. The practical applicability of our approach is exemplified in image segmentation tasks, showcasing its versatility and reliability in real-world scenarios.

Exploring behavioural patterns and their relationships with social annotation outcomes

AbstractBackgroundSocial annotation has emerged as a promising educational technology that fosters collaborative reading and discussion of digital resources among learners. While the positive impact of social annotation on students' learning process and performance is widely acknowledged, students' behavioural patterns in social annotation are underexplored.ObjectivesThis study investigated patterns in students' use of annotation and response behaviours in social annotation activities. We also explored how students' performance in the behavioural, cognitive, emotional, and social dimensions varied based on their behavioural patterns.MethodsWe recruited 93 undergraduates who were enrolled in an elective course at a large North American University. Students were tasked with collaboratively annotating the class readings uploaded to Perusall, a social annotation platform, over 7 weeks. We used metaclustering to determine the optimal number of clusters pertaining to student behaviours. We compared the differences among clusters across multiple performance dimensions.Results and ConclusionsTwo distinct clusters were identified and defined as initiators and responders. We found that responders had significantly longer active reading time and exhibited greater social annotation effort compared to initiators. However, initiators received more peer acknowledgement, as evidenced by higher upvotes. No significant difference was found in cognitive insight between initiators and responders, but responders demonstrated significantly higher cognitive discrepancy. Additionally, there were no significant differences in positive and negative tones between initiators and responders; however, responders displayed higher levels of prosocial behaviours than initiators. This study has significant practical implications regarding promoting students' collaborative learning experience in social annotation.

A conceptual clustering method for large-scale group decision-making with linguistic truth-valued lattice implication algebra

The increasing complexity of decision-making environments has led to a rise in the involvement of decision-makers (DMs) in group decision-making problems. Clustering is widely used in large-scale group decision-making (LSGDM) to categorize DMs into smaller groups. Ensuring reasonable decision-making results requires providing explanations for the generated groups during the clustering process. To address the clustering problem in LSGDM within uncertain linguistic environments, this paper proposes a conceptual clustering method based on the linguistic concept lattice. The method efficiently manages comparable and incomparable linguistic information. To achieve interpretable clustering results for DMs, attribute and expert induction matrices are first introduced. Cluster stability analysis is then employed to automatically determine the optimal number of clusters. Second, linguistic truth-valued aggregation operators are proposed to aggregate the linguistic evaluation information of DMs in each cluster. In addition, a consensus reaching process is conducted within each cluster, and a feedback mechanism is established to iteratively update clusters when consensus cannot be reached. Finally, numerical examples and comparative analyses are presented that verify the effectiveness of the proposed approach in effectively addressing the LSGDM problem within uncertain linguistic environments.

Analysis of Raw Material Inventory Control Using the Economic Order Quantity (EOQ) Method: Study of Simping MSMEs, Purwakarta Regency, Indonesia

Raw material inventory is an important aspect in managing small and medium enterprises (SMEs). Effective inventory control can help SMEs increase efficiency and profitability. This research aims to analyze raw material inventory control at Simping MSMEs in Purwakarta Regency, Indonesia, using the economic order quantity (EOQ) method. Data was collected through a survey of 30 Simping MSMEs. The research results show that the EOQ method can help Simping MSMEs in determining the optimal number of raw material orders and minimizing inventory costs. Implementing the EOQ method can result in average inventory cost savings of 15% compared to the method currently used.

Photothermal-regulated selective desorption of enantiomers from Ag/Ni3S2 nanosheet-covered Ni foam

Separation of racemates is critically important in the chemical, agrochemical and pharmaceutical industries, since enantiomers may differ in biological interactions, pharmacology, and toxicity. However, the methods for highly enantioselective separation are still insufficient. In this study, we prepared Ni3S2 nanosheet arrays with excellent photothermal properties using Ni foam as the precursor. The PT enantioselective desorption approach is based on a Ni3S2 NSA decorated with Ag nanoparticles modified with D-penicillamine as the enantioselective substrate. Finite-difference time-domain simulation was used to probe the optimal number, shape, and distribution of Ag NP to optimize the localized surface plasmon resonance and photothermal effect. The enantiomers of 3,4-dihydroxyphenylalanine were used as models to demonstrate enantioselective separation based on the different number of stable hydrogen bonds between the homochiral substrate and each enantiomer. This enantioselective desorption approach has the advantages of efficiency, low cost, and recyclability, and provides a new solution for separating racemic molecules.

A Planning Purchasing Policies for Raw Material Welding Wire Using Economic Order Quantity (EOQ) Method to Reduce Overstock

This study aims to determine the optimal order quantity of K71T welding wire for PT BMI's boiler production process to reduce storage and inventory costs. The Economic Order Quantity (EOQ) method is applied to calculate the optimal amount of material to be ordered in one period. The results show that the total inventory cost using the EOQ method is Rp. 89,060,000,- compared to the company policy of Rp. 290,840,000,-. The optimal number of orders is 407 boxes with a purchase frequency of 4 times per year and a safety stock of 181 boxes. The findings of this study can be used as a reference for other manufacturing companies that face similar procurement challenges to optimize their inventory management and reduce costs.
 Highlight :
 
 The study determines the optimal order quantity of K71T welding wire, reducing storage and inventory costs.
 The Economic Order Quantity (EOQ) method is applied, resulting in a total inventory cost of Rp. 89,060,000,- compared to the company policy of Rp. 290,840,000,-.
 The findings can be used as a reference for other manufacturing companies facing similar procurement challenges to optimize their inventory management and reduce costs.
 
 Keywords: welding wire, optimal order quantity, inventory management, procurement challenges, manufacturing companies

Molecular characteristics of eight lignites based on the big data obtained from Orbitrap mass spectrometry

Eight lignites from different regions were selected for thermal dissolution (TD) sequentially using cyclohexane, methanol and ethanol. The TD extracts of the eight coal samples were characterized using atmospheric pressure chemical ionization source (APCI)-Orbitrap mass spectrometry (MS). Chemometric methods were employed to deeply explore the molecular structural and composition information of the organic compounds detected by Orbitrap MS. The longest distance method in systematic clustering of chemometrics was employed to ascertain the optimal number of groups, which was found to be 7 by clustering 24 soluble portions (SPs) using Euclidean distance. Taking one group as an example, the similarities and differences of SPs within the group were derived based on the compositional class of O1N1, N1, O2N1 and O2, and heteroatom distribution patterns of the SPs were proposed and compared. The integration of high-resolution MS data and chemometric methods not only exposes the existence of heteroatomic compounds in lignite but also furnishes evidence for the origin and evolution of lignite, as well as a methodology for the high-value utilization of lignites from different sources.

The Automatic Algorithm of Optimizing the Position of Structured Light Sensors

Optical 3D detection technology has a wide range of applications in industrial detection, agricultural production, and so on. Its advantages are non-contact, efficiency, and high precision. However, the specular reflection problem affects model coverage and measurement accuracy. An optimization algorithm for calculating the number and pose of sensors has been proposed to address this issue. First, the specular reflection problem is viewed as a multi-sensor position search problem. Then, an optimization algorithm is used to find the optimal number and bit positions of sensors to avoid specular reflection. The experiment shows that the optimization results of this algorithm can cover the area to be measured with the least number of sensor combinations while avoiding the influence of specular reflection.

Determination of optimal experimental conditions for accurate 3D reconstruction of the magnetization vector via XMCD-PEEM.

This work presents a detailed analysis of the performance of X-ray magnetic circular dichroism photoemission electron microscopy (XMCD-PEEM) as a tool for vector reconstruction of magnetization. For this, 360° domain wall ring structures which form in a synthetic antiferromagnet are chosen as the model to conduct the quantitative analysis. An assessment is made of how the quality of the results is affected depending on the number of projections that are involved in the reconstruction process, as well as their angular distribution. For this a self-consistent error metric is developed which allows an estimation of the optimum azimuthal rotation angular range and number of projections. This work thus proposes XMCD-PEEM as a powerful tool for vector imaging of complex 3D magnetic structures.

Small Channels Assembled by Multilayer ZIF-8 in Nanocomposite Membranes for Filtration of Ofloxacin in Water.

Metal-organic framework (MOF)-based hybrid membranes still face many unsolved difficulties in the field of liquid separation, with a reliable production technique standing out, in particular, for the water-stable MOF membranes. In this study, zeolitic imidazolate framework-8 (ZIF-8) with acceptable water stability, favorable polymer affinity, and high selectivity was meticulously grafted on commercial poly(vinylidene fluoride) (PVDF) via substrate carboxylation-assisted etching and then overlaid onto PVDF to fabricate a novel hybrid membrane by a layer-by-layer self-assembly method. The optimal membrane manufacturing conditions, including assembly time (10 min), Hmim/Zn2+ molar ratio (8:1), and optimal layer number (three layers), were thoroughly investigated for cutting-off ofloxacin in water filtration. Under low pressure, a nanofiltration scale permeability of about 199.2 L m-2 h-1 MPa-1 and 97.9% rejection of ofloxacin were obtained in bench-scale tests based on the synergistic effect of the Donnan effect and steric hindrance. More significantly, the resulting hybrid membrane demonstrated excellent hydrophilicity, high antifouling, and mechanical and repeatability performances, suggesting promising application possibilities in real-world wastewater filtering settings.

Numerical study of the laser isotope separation of optically pumped 102Pd

The feasibility of laser isotope separation of 102Pd through pulsed laser optical pumping followed by isotope selective photoionization has been studied through density matrix formalism. The effect of various parameters such as bandwidth of the excitation lasers, intensity of the lasers and Doppler broadening of the atomic ensemble on the efficiency of optical pumping and isotope selective photoionization have been evaluated. The optimum number density in the laser-atom interaction region has been derived from the studies of the effect of charge exchange collisions on the degree of enrichment. It has been shown that it is possible to enrich 102Pd up to ~ 23.7% at a production rate of 1.1 mg /h. The achievable degree of enrichment through this photoionization scheme is higher than the previously reported laser isotope separation process. The radionuclidic purity of the irradiated enriched mixture has been found to be suitable for medical applications.

Impact of Evidence-Based Standardized Training on Competencies in Critical Care Nurses: A Quasi-Experimental Study Series Protocol on the Behalf of International Developing Standardized Learning Curve Team

Abstract Background Critical care nurses are vital in the care of patients with life-threatening illnesses in intensive care units. To optimize their effectiveness, it is crucial to enhance their proficiency in professional care techniques. Implementing evidence-based training standards can improve nurses' knowledge, skills, and attitudes, ultimately enhancing healthcare services for patients. Aim/purpose The aim of this study protocol is to outline a series of quasi-experimental, non-randomized, single-group studies that assess the impact of standardized training courses on the development of competencies in diverse care techniques for critical care nurses. Methodology A comprehensive checklist will be created to evaluate nurses' professional qualifications in the specific care techniques under investigation. This checklist will be used during three expert panel sessions for each study. Nurses will then undergo theoretical and practical training to enhance their competence in implementing these techniques. To assess the training effectiveness, each nurse's competency level will be evaluated before, during, and after the training using a checklist to track their learning curve. Results This study will use the learning curve model to determine the optimal number of repetitions required for novice intensive care unit nurses to attain a satisfactory level of competence in each targeted care technique after training. Conclusion These findings could standardize nursing education practices and optimize skill practice frequency, leading to improved quality of education in the field. Trial registration This study is also registered at U.S. National Library of Medicine under "Clinicaltrials.gov" identifier NCT02830971, https://www.clinicaltrials.gov/ct2/show/NCT02830971, first date registered 13/07/2016 and last updated on 15/04/2021.

Annual Daily Irradiance Analysis of Clusters in Mexico by Machine Learning Algorithms

The assessment of solar resources involves the utilization of physical or satellite models for the determination of solar radiation on the Earth’s surface. However, a critical aspect of model validation necessitates comparisons against ground-truth measurements obtained from surface radiometers. Given the inherent challenges associated with establishing and maintaining solar radiation measurement networks—characterized by their expense, logistical complexities, limited station availability and the imperative consideration of climatic criteria for siting—countries endowed with substantial climatic diversity face difficulties in station placement. In this investigation, the measurements of annual solar irradiation, from meteorological stations of the National Weather Service in Mexico, were compared in different regions clustered by similarities in altitude, TL Linke, albedo and cloudiness index derived from satellite images; the main objective is to find the best ratio of annual solar irradiation in a set of clusters. Employing machine learning algorithms, this research endeavors to identify the most suitable model for predicting the ratio of annual solar irradiation and to determine the optimal number of clusters. The findings underscore the efficacy of the L-method as a robust technique for regionalization. Notably, the cloudiness index emerges as a pivotal feature, with the Random Forest algorithm yielding superior performance with a R2 score of 0.94, clustering Mexico into 17 regions.

Genetic analysis of Saker Falcon (Falcocherrug) subspecies.

Two subspecies of Saker Falcon are commonly accepted - Western (Falcocherrugcherrug) and Eastern (Falcocherrugmilvipes), which are differentiated by their distribution range and phenotype. In Bulgaria, Western Saker Falcons are breeding ex situ in the Wildlife Rehabilitation and Breeding Centre, part of Green Balkans - Stara Zagora NGO, with the aim of restoring the nesting population of the species in the country and both Western and Eastern - in the Breeding Centre for Birds of Prey in Burgas for the purpose of sale for the needs of falconry in the country and abroad. In 2021, a total of 115 birds from the two breeding centres were sampled. The samples were analysed in Bielefeld University (Germany) at nine microsatellite loci. Structure analyses were performed to establish the optimal explanatory number of groups. We compared the putative genetic groups with the known/expected origin of falcons. A separation in two groups best explained the allelic variation between samples. Out of 68 Saker Falcons with putatively Eastern origin, 66 were ascribed to genetic group 2 and two falcons had unclear, mixed or hybrid genetic fingerprints. Out of 42 Sakers with putatively Western origin, 33 were ascribed to genetic group 1, seven to genetic group 2 and two individuals appeared to have a mixed signature of genetic groups 1 and 2 with dominating alleles of group 2. Five known hybrids were scored as mixed signature with dominating genetic cluster 2. This suggests that the two (Eastern and Western) populations of Saker Falcon origin suggested by the subspecies' definitions are also adequate to be considered in breeding programmes. Genetic cluster 1 might represent the ancestral alleles shared with other falcons, while specific novel alleles allow the discrimination of secured Eastern Sakers (group 2), while these populations may be occasionally invaded by individuals from the west.

Automatic identification of factor profiles can be achieved by improved machine learning model

The identification of factor profiles is a pivotal step in the source apportionment model. Currently, this process heavily relies on human experience, resulting in high subjectivity in the results and requiring a time-consuming procedure. In this study, a pseudo label extra trees classifier model was proposed to facilitate the automated identification of factor profiles. The source profiles serve as domain knowledge to train the model, as they accurately reflect the distinctive characteristics of emission sources. The findings indicate that the recognition rate of seven factors is 94.3%, significantly outperforming four factors (25%), five factors (30%), six factors (60%). Significantly, the model demonstrates its proficiency in determining the optimal number of factors. And the factor profiles identified using this approach demonstrate complete concurrence with manual recognition. For offline datasets, the model is also proficient at identifying factor profiles and exhibits excellent generalization. This approach facilitates the identification of emission sources in intricate environments and advances the model's capacity to automatically discern source categories by utilizing domain knowledge characteristics.

Chemical composition and source apportionment of PM2.5 in Seoul during 2018–2020

Air pollution, particularly high ambient particulate matter (PM) concentrations, poses a serious public health concern in South Korea. This study analyzed 139 daily PM2.5 samples collected from an urban site in Seoul between March 2018 and January 2020. This study aimed to quantify the chemical composition of PM2.5 and identify the source characteristics. The average PM2.5 mass concentration for the entire study period was 25.3 μg m−3. The chemical composition was dominated by ionic particles (13.2 μg m−3, 52%), carbonaceous particles (7.0 μg m−3, 28%), and trace elements (0.5 μg m−3, 2%). The Positive Matrix Factorization (PMF) model was employed to identify PM2.5 sources using quality-controlled sampling data after eliminating factors that could adversely affect the modeling. Seven sources were resolved through PMF, applying a trial-and-error method to determine the optimal number of sources. The PMF modeling results indicated that secondary sulfate (28%) was the largest contributor, followed by internal factors (23%), secondary nitrate (19%), soil (14%), biomass burning (9%), and aged sea salt (7%). As an additional case study, the analysis of backward trajectories for the winter of 2018, which had the highest PM2.5 concentrations, revealed that the air current carrying PM2.5 had passed from the northwest region of Seoul. Particulary, source identification for this winter case resulted in the highest secondary nitrate across all study periods. This more specific interpretation of the pollutant characteristics of PM2.5 in Seoul could aid in developing effective control strategies and policies for air pollution mitigation.

Twisted pair transmission line coil – a flexible, self-decoupled and robust element for 7 T MRI

ObjectiveThis study evaluates the performance of a twisted pair transmission line coil as a transceive element for 7 T MRI in terms of physical flexibility, robustness to shape deformations, and interelement decoupling. MethodsEach coil element was created by shaping a twisted pair of wires into a circle. One wire was interrupted at the top, while the other was interrupted at the bottom, and connected to the matching circuit. Electromagnetic simulations were conducted to determine the optimal number of twists per length (in terms of B₁+ field efficiency, SAR efficiency, sensitivity to elongation, and interelement decoupling properties) and for investigating the fundamental operational principle of the coil through fields streamline visualisation. A comparison between the twisted pair coil and a conventional loop coil in terms of B₁+ fields, maxSAR₁₀g, and stability of S₁₁ when the coil was deformed was performed. Experimentally measured interelement coupling between individual elements of multichannel arrays was also investigated. ResultsIncreasing the number of twists per length resulted in a more physically robust coil. Poynting vector streamline visualisation showed that the twisted pair coil concentrated most of the energy in the near field. The twisted pair coil exhibited comparable B₁+ fields and improved maxSAR₁₀g to the conventional coil but demonstrated exceptional stability with respect to coil deformation and a strong self-decoupling nature when placed in an array configuration. DiscussionThe findings highlight the robustness of the twisted pair coil, showcasing its stability under shape variations. This coil holds great potential as a flexible RF coil for various imaging applications using multiple-element arrays, benefiting from its inherent decoupling.

Classification of Student Grade Data Using the K-Means Clustering Method

The fourth industrial revolution has brought significant changes in various sectors, and education has been greatly affected by technological advances. Automation, particularly in data processing, has simplified educational processes, particularly in managing student grade data. However, the increasing volume of data poses challenges in efficient processing. This research explores the application of K-Means clustering, a data mining technique, to cluster student grade data. This research uses the Elbow Method to determine the optimal number of clusters. The dataset, sourced from the Information Systems Study Program at the Telkom Institute of Technology Purwokerto, includes attributes such as Credits Taken, GPA, Number of Ds, Number of Es, and Credits Not Taken. The results identified three groups of students: "High Achievers," "Average Performance," and "Needs Improvement." Recommendations include academic challenges for high performers, better learning methods for average performers, and remedial programs for those who need improvement. This research demonstrates the efficacy of K-Means clustering in improving educational strategies and support systems based on student characteristics.