Combining Search Methods Research Articles

High-precision object detection network for automate pear picking

To address the urgent need for agricultural intelligence in the face of increasing agricultural output and a shortage of personnel, this paper proposes a high precision object detection network for automated pear picking tasks. The current object detection method using deep learning does not fully consider the redundant background information of the pear detection scene and the mutual occlusion characteristics of multiple pears, so that the detection accuracy is low and cannot meet the needs of complex automated pear picking detection tasks. The proposed, High-level deformation-perception Network with multi-object search NMS(HDMNet), is based on YOLOv8 and utilizes a high-level Semantic focused attention mechanism module to eliminate irrelevant background information and a deformation-perception feature pyramid network to improve accuracy of long-distance and small scale fruit. A multi-object search non-maximum suppression is also proposed to choose the anchor frame in a combined search method suitable for multiple pears. The experimental results show that the HDMNet parameter amount is as low as 12.9 M, the GFLOPs is 41.1, the mAP is 75.7%, the mAP50 reaches 93.6%, the mAP75 reaches 70.2%, and the FPS reaches 73.0. Compared with other SOTA object detection methods, it has the transcend of real-time detection, low parameter amount, low calculation amount, high precision, and accurate positioning.

Code and Data Repository Product Redesign and Innovation Based on Online Reviews: A Multistage Combined Search Method

Code and Data Repository Product Redesign and Innovation Based on Online Reviews: A Multistage Combined Search Method

Product Redesign and Innovation Based on Online Reviews: A Multistage Combined Search Method

Online reviews published on the e-commerce platform provide a new source of information for designers to develop new products. Past research on new product development (NPD) using user-generated textual data commonly focused solely on extracting and identifying product features to be improved. However, the competitive analysis of product features and more specific improvement strategies have not been explored deeply. This study fully uses the rich semantic attributes of online review texts and proposes a novel online review–driven modeling framework. This new approach can extract fine-grained product features; calculate their importance, performance, and competitiveness; and build a competitiveness network for each feature. As a result, decision making is assisted, and specific product improvement strategies are developed for NPD beyond existing modeling approaches in this domain. Specifically, online reviews are first classified into redesign- and innovation-related themes using a multiple embedding model, and the redesign and innovation product features can be extracted accordingly using a mutual information multilevel feature extraction method. Moreover, the importance and performance of features are calculated, and the competitiveness and competitiveness network of features are obtained through a personalized unidirectional bipartite graph algorithm. Finally, the importance performance competitiveness analysis plot is constructed, and the product improvement strategy is developed via a multistage combined search algorithm. Case studies and comparative experiments show the effectiveness of the proposed method and provide novel business insights for stakeholders, such as product providers, managers, and designers. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This work was supported by the National Natural Science Foundation of China [Project 72071151] and the Natural Science Foundation of Hubei Province, China [Grant 2023CFB712]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0333 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0333 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Geometric and electronic structures of medium-sized boron clusters doped with plutonium

Doping metal heteroatoms is an effective strategy to regulate the geometric and electronic structure of boron based nanoclusters. However, the exploration of the ground state structures of metal-boron-based nanoclusters is still a challenge duo to the complexity of the bonding interactions between heterogeneous atoms and boron cluster and the number of isomers on the potential energy surface increases exponentially with cluster size. Here, we use the CALYPSO cluster structural search method in combination with density functional theory calculations to study the geometries and electronic properties of anionic boron clusters doped with plutonium (PuB, n = 10–20). Our results show that the medium-sized PuB cluster exhibits excellent stability with highest occupied molecular orbital–lowest unoccupied molecular orbital energy gap of 2.30 eV. The remarkable stability of the anionic PuB cluster is due to the robust interactions between the Pu metal and the B14 skeleton, along with the strong covalent interactions between the B atoms. These findings enrich the geometric structure database of metal doped clusters and provide valuable insights for the future synthesis of boron based nanomaterials.

Interception of chain shape Ce nitrides at ambient conditions

A high-pressure study of the CeN5 compounds is performed by the crystal structure search method in combination with the first principle calculations. Four new phases C2-CeN5, P-1-CeN5, P2/c-CeN5, and P21/m-CeN5 were predicted at 0, 20, 50, and 100 GPa, respectively. The analysis of stability shows that C2-CeN5, P-1-CeN5, and P21/m-CeN5 can be quenched to ambient conditions. The stabilization mechanism of Ce nitrides is clarified by the analysis of electronic structures. The phase transformation of CeN5 with the pressure is revealed by the enthalpy difference analysis. P2/c-CeN5 and P21/m-CeN5 with polymeric N-chains not only exhibit high energy densities (8.67 and 9.37 kJ/cm3), but also outstanding detonation velocities (11.43 and 12.10 km/s) and detonation pressures (90.39 and 102.82 GPa).

Variables e instrumentos para evaluar la salud mental en nadadores de competición: una revisión narrativa.

Mental health issues are becoming increasingly prevalent among elite athletes, and during the Tokyo 2020 Olympic Games, this issue became a pressing concern. In particular, several athletes complained about their mental health in relation to the highly demanding demands of their sport. High-performance swimming exposes athletes to a variety of stressors due to the physical, technical and mental demands of the sport. The study has carried out a narrative review of the main variables related to mental health, their dimensions and evaluations in competitive swimmers. Clearly planned and ordered potential studies were identified using combined search methods. The search was carried out in different bibliographic databases (Dialnet, Web of Science and Scopus) between 1990 and 2023. Google Scholar was used to manually search the reference lists of the retrieved studies to identify potentially eligible studies that were not included in the electronic searches. The studies were examined from three different perspectives. Firstly, the methodology, sample, swimming categories and their relationship with mental health were analysed. Secondly, the variables related to mental health symptoms and disorders. Finally, the main mental health analysis tools and the conclusions of the studies. The concept of mental health in competitive swimmers needs to be clarified in the scientific literature, as different variables have been analysed and multiple instruments have been used with small samples of swimmers, without any intervention and prevention protocol. In addition, the bodies and institutions involved should work in an interdisciplinary and collaborative manner, establishing specific programmes to ensure effective prevention and care. Studies are needed to fill this gap and it is necessary to work under the same consensus and in collaboration with specified technical teams. In order to monitor, evaluate and provide services through detection tests and support to swimmers in their training and competitions.

Enhancement of Robot Navigation Systems via Human Pointing Instruction

The determination of destinations and paths is a critical aspect of robot navigation. In recent years, research has been conducted on methods to provide robots with realistic destinations for use in practical situations. One such general method is a spatial search based on limited information such as the name of the target object or a photograph of the destination. However, if the rough location of the destination is known from the viewpoint of an instructor, and can be communicated to the robot, then it is possible to reduce the search load on the robot. Therefore, in this study, we propose a new task that integrates the pointed instructions of an operator using and augmented reality device into the robot's search route. However, the instructor may not be able to point directly to the route if it is within a blind spot. Thus, we propose a combined search method; where the pointing path is represented as a set of vectors rather than a sequence of points and used to generate a potential field which is integrated with an existing traditional robot navigation method. In this study, we evaluate the proposed method in an experiment using the 3D simulator Habitat, and show that the proposed method outperforms existing methods under the pointing path information.

A combined search method based on a deep learning combined surrogate model for groundwater DNAPL contamination source identification

Ensemble Kalman filter (EnKF) and optimization methods are two mainstream methods in groundwater contamination source identification, but most researchers usually only use or improve on one method. However, each method has strengths and weaknesses, and using one method alone may cause inaccuracy of the identification results for complex situations. Therefore, it is necessary to explore the combination of methods. In this paper, for the optimization method, to further enhance the solution precision of the optimization model for groundwater dense non-aqueous phase liquid (DNAPL) contamination source identification (GDCSI), we first constructed an improved butterfly optimization algorithm by introducing the dynamic switching probability mechanism in the previous butterfly optimization algorithm. Next, the EnKF method and optimization method (based on the improved butterfly optimization algorithm) were used respectively for GDCSI, to assess the strengths and weaknesses of the methods. Then, according to the strengths and weaknesses of the EnKF and optimization methods, the two methods were merged to build a more robust and practical combined search method (CSM) for GDCSI, to further enhance the identification accuracy and effectiveness. In addition, we innovatively applied the EnKF method to combine two single deep learning surrogate models: the deep belief neural network surrogate model and the deep residual neural network surrogate model. And a deep learning combined surrogate model was established, which further enhanced the approximation precision and applicability to the groundwater DNAPL contamination multiphase flow numerical simulation model. Finally, the results of CSM were compared with those of two single methods to analyze the former's effectiveness. The results showed that the CSM significantly improved the identification accuracy and effectiveness of GDCSI compared with any single method.

Features of search for complex solutions of non-self-adjoint boundary value problems of electrodynamics

The features of finding of complex solutions of the dispersion equations of guiding electrodynamic structures waves described by non-self-adjoint operators have been considered. These solutions are found using the combined numerical method for complex roots finding combining the Muller method and the phase variation method. A brief explanation of the combined search method essence has been given, and its advantages have been described. As examples, structures with simple geometry are selected in order to avoid difficult mathematical expressions and focus on the process of complex solutions finding. The guiding electrodynamic structures both without losses and with absorbing metal films have been considered in the approximation of the surface current method and based on its real thickness. A new approach to evaluate the physical correctness and accuracy of the results of solving dispersion problems set in an open form for guiding structures described by non-self-adjoint operators has been shown – by the tendency to zero of the power flow of proper complex waves on average over a period through the cross section of a non-dissipative guide structure. The formulation and the solution features of the adjoint boundary value problem for a round open dielectric waveguide have been considered. The existence possibility of the adjoint waves in this waveguide has been shown.

A systematic review of the \u201cpromising zone\u201d design

IntroductionSample size calculations require assumptions regarding treatment response and variability. Incorrect assumptions can result in under- or overpowered trials, posing ethical concerns. Sample size re-estimation (SSR) methods investigate the validity of these assumptions and increase the sample size if necessary. The “promising zone” (Mehta and Pocock, Stat Med 30:3267–3284, 2011) concept is appealing to researchers for its design simplicity. However, it is still relatively new in the application and has been a source of controversy.ObjectivesThis research aims to synthesise current approaches and practical implementation of the promising zone design.MethodsThis systematic review comprehensively identifies the reporting of methodological research and of clinical trials using promising zone. Databases were searched according to a pre-specified search strategy, and pearl growing techniques implemented.ResultsThe combined search methods resulted in 270 unique records identified; 171 were included in the review, of which 30 were trials. The median time to the interim analysis was 60% of the original target sample size (IQR 41–73%). Of the 15 completed trials, 7 increased their sample size. Only 21 studies reported the maximum sample size that would be considered, for which the median increase was 50% (IQR 35–100%).ConclusionsPromising zone is being implemented in a range of trials worldwide, albeit in low numbers. Identifying trials using promising zone was difficult due to the lack of reporting of SSR methodology. Even when SSR methodology was reported, some had key interim analysis details missing, and only eight papers provided promising zone ranges.

Development of combined search methods for efficiency indicator extreme in variation statement of forecasting tasks for determine characteristics of composite materials

Modified search methods for global extreme of multiparameter functions are studied, considering the structural features of efficiency criteria used in problems of forecasting residual life and durability of structures made of composite polymeric materials. Comparative and qualitative studies have been carried out and methods have been developed to select the most promising areas of search for the absolute minimum of the studied performance indicators in refined variation formulation of inverse problems of forecasting residual life of composites.

New modulated structures of solid bromine at high pressure

Crystal structures of bromine under pressure have been studied through the advanced structure search method in combination with first-principles calculations. At 10 and 90 GPa, we successfully reproduced the experimentally observed molecular phase I and monatomic body-centred orthorhombic phase II, respectively. At 120 and 200 GPa, the lowest-enthalpy structures predicted are body centered tetragonal and face-centered cubic phase, respectively, which are also consistent with previously theoretical study. Interestingly, in the pressure range of 50–80 GPa, our structural searches predicted four structurally similar and modulated structures, oF20, oC24, oF28 and oC12, whose magnitude of the modulation vectors k are 2/5, 1/6, 2/7 and 1/3, respectively. Enthalpy calculations show that oF20, oC24, oF28 and oC12 are thermaldynamically stable in the pressure range of 52–53, 53–58, 58–65 and 65–79 GPa, respectively. Through comparison of the structural characterization and X-ray diffraction patterns of the predicted oF28, oC12 in bromine and the known incommensurate modulated phase V in iodine, we suggest that bromine might also adopt an incommensurate modulated structure. In addition, the prediction of two new phases (oF20 and oC24) between molecular phase I and incommensurate phases are consistent with Raman scattering and X-ray absorption spectroscopy (XAS) experimental results. For the first time, we presented the structure information between molecular phase I and monatomic phase II in bromine. The calculated elastic stiffness coefficients indicate that the four modulated phases are mechanically stable. The calculated electronic density of states for these four predicted phases show that they are all metallic.

Low-Rank Semidefinite Programming for the MAX2SAT Problem

This paper proposes a new algorithm for solving MAX2SAT problems based on combining search methods with semidefinite programming approaches. Semidefinite programming techniques are well-known as a theoretical tool for approximating maximum satisfiability problems, but their application has traditionally been very limited by their speed and randomized nature. Our approach overcomes this difficult by using a recent approach to low-rank semidefinite programming, specialized to work in an incremental fashion suitable for use in an exact search algorithm. The method can be used both within complete or incomplete solver, and we demonstrate on a variety of problems from recent competitions. Our experiments show that the approach is faster (sometimes by orders of magnitude) than existing state-of-the-art complete and incomplete solvers, representing a substantial advance in search methods specialized for MAX2SAT problems.

Exploration of high-pressure structural transition and electronic properties of BaFe2S3

The iron-based compound BaFe2S3 has received considerable attention in recent years due to its unique electronic properties with important applications. But, there has been relatively little attention devoted to the structure evolution of BaFe2S3 under high pressure. Here, we report a detailed theoretical study of the structural, electronic and sound velocity properties of BaFe2S3 under high pressure by CALYPSO structure search method in combination with the first-principles calculations. We have uncovered three novel structures of BaFe2S3 under high pressure with space group P2221, Imm2, and C2/m symmetries. Theoretical calculations reveal that the structures of BaFe2S3 under high pressure satisfy the phase transition sequence of Cmcm → P2221 → Imm2 → C2/m, and the corresponding transition pressures are 31.6, 47.4 and 57.0 GPa, respectively. Our results enrich the high pressure phases of BaFe2S3 and will stimulate future experiments to synthesize these new phases.

Exploration of the structural and optical properties of a red-emitting phosphor K2TiF6:Mn4+**Project supported by the National Natural Science Foundation of China (Grant No. 11574220), Fundamental Research Funds for the Central Universities, China (Grant No. SWU118055), and the Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase), China.

The exploration of the appropriate red phosphor with good luminescence properties is an important issue in the development of current white light-emitting diode (WLED) devices. Transition metal Mn-doped compounds are fascinating luminescent materials. Herein, we performed a systematic theoretical study of the microstructure and optical properties of K2TiF6:Mn4+ using the CALYPSO structure search method in combination with first-principles calculations. We uncovered a novel structure of K2TiF6:Mn4+ with space group P-3m1 symmetry, where the impurity Mn4+ ions are accurately located at the center of the MnF6 octahedra. Based on our developed complete energy matrix diagonalization (CEMD) method, we calculated transition lines for 2Eg → 4A2, 4A2 → 4T2, and 4A2 → 4T2 at 642, nm, 471 nm, and 352 nm, respectively, which are in good agreement with the available experimental data. More remarkably, we also found another transition (4A2 → 2T2) that lies at 380 nm, which should be a promising candidate for laser action.

First-principle study of pressure-induced phase transitions and electronic properties of electride Y2C

Trigonal yttrium hypocarbide (Y2C), crystallizing in a layered hR3 structure, is an intriguing quasi-two-dimensional electride metal with potential application for the next generation of electronics. By using an efficient structure search method in combination with first-principles calculations, we have extensively explored the phase transitions and electronic properties of Y2C in a wide pressure range of 0–200GPa. Three structural transformations were predicted, as hR3 → oP12 → tI12 → mC12. Calculated pressures of phase transition are 20, 118, and 126GPa, respectively. The high-pressure oP12 phase exhibits a three-dimensional extended C-Y network built up from face- and edge-sharing CY8 hendecahedrons, whereas both the tI12 and mC12 phases are featured by the presence of C2 units. No anionic electrons confined to interstitial spaces have been found in the three predicted high-pressure phases, indicating that they are not electrides. Moreover, Y2C is dynamically stable and also energetically stable relative to the decomposition into its elemental solids.

An image auto-focusing algorithm for industrial image measurement

Auto-focusing task, which automatically obtains the best image focus, plays an important role to improve the image definition for the industrial image measurement application. Image-based auto-focusing is one of the widely used methods for this task because of its fast response, convenience, and intelligence. In general, the image-based auto-focusing algorithm often consists of two important steps which are the image definition evaluation and the search strategy. In this paper, we have developed an image auto-focusing algorithm for industrial image measurement. First, we propose a new image definition evaluation method based on the fuzzy entropy, which can reduce the negative effects of noise and variations of light intensity and lens magnification. Second, a combined search method is proposed to combine the multi-scale global search and fine-level curve fitting method, which can avoid the disturbance of the local peaks and obtain the best image focus. The proposed image auto-focusing algorithm has the advantages of high focusing accuracy, high repeatability and stability under the variations of lens magnification, and light intensity index, which make it applicable for the industrial image measurement. Experimental results and comparisons on the practical industrial image measurement system have been presented to show the effectiveness and superiority of the proposed algorithm.

Two dimensional Dirac carbon allotropes from graphene

Using a structural search method in combination with first-principles calculations, we found lots of low energy 2D carbon allotropes and examined all possible Dirac points around their Fermi levels. Three amazing 2D Dirac carbon allotropes have been discovered, named as S-graphene, D-graphene and E-graphene. By analyzing the topology correlations among S-, T, net W graphene and graphene, we found that a general rule is valuable for constructing 2D carbon allotropes that are keen to possess Dirac cones in their electronic structures. Based on this rule, we have successfully designed many new 2D carbon allotropes possessing Dirac cones. Their energy order can be well described by an Ising-like model, and some allotropes are energetically more stable than those recently reported. The related electronic structures of these Dirac allotropes are anisotropy distinguished from those of graphene. Moreover, the fact that D- and E-graphene present Dirac cones suggests that sp hybridization or sp(3) hybridization could not suppress the emerging of Dirac features. Our results demonstrate that the Dirac cone and carrier linear dispersion is a very common feature in 2D carbon allotropes and can exist beyond the limitations of fundamental structure features of graphene.

Comparison of the multivariate and bivariate analysis of corporate competitiveness factors synergy

Corporate competitiveness is influenced by a number of factors. Their impact is not partial, but synergistic. It is necessary to respect the phenomenon of synergy consistently when examining which of these potential competitiveness attributes can really function as these factors. Consequently, feature selection and classification methods of statistical pattern recognition have been used for the multivariate statistical analysis of and search for competitiveness factors. The calculations conducted herein show that the Sequential Forward Floating Search method in combination with k-Nearest Neighbours classification is capable of capturing the synergistic effect of the whole set of factors, providing much better results than simple bivariate analysis methods that test only the partial effects of individual factors.

Enhancing Peptide Identification Confidence by Combining Search Methods

Confident peptide identification is one of the most important components in mass-spectrometry-based proteomics. We propose a method to properly combine the results from different database search methods to enhance the accuracy of peptide identifications. The database search methods included in our analysis are SEQUEST (v27 rev12), ProbID (v1.0), InsPecT (v20060505), Mascot (v2.1), X! Tandem (v2007.07.01.2), OMSSA (v2.0) and RAId_DbS. Using two data sets, one collected in profile mode and one collected in centroid mode, we tested the search performance of all 21 combinations of two search methods as well as all 35 possible combinations of three search methods. The results obtained from our study suggest that properly combining search methods does improve retrieval accuracy. In addition to performance results, we also describe the theoretical framework which in principle allows one to combine many independent scoring methods including de novo sequencing and spectral library searches. The correlations among different methods are also investigated in terms of common true positives, common false positives, and a global analysis. We find that the average correlation strength, between any pairwise combination of the seven methods studied, is usually smaller than the associated standard error. This indicates only weak correlation may be present among different methods and validates our approach in combining the search results. The usefulness of our approach is further confirmed by showing that the average cumulative number of false positive peptides agrees reasonably well with the combined E-value. The data related to this study are freely available upon request.