Insufficient Convergence Research Articles

Insuficiencia de convergencia y trastorno por déficit de atención e hiperactividad: revisión de literatura

Children are subjected to a high demand for visual tasks from an early age, associated with their school activities, that can cause alterations in the oculomotor system generating symptoms that, sometimes, affect your school performance and emotional state, which leads to a closer study of binocular vision disorders such as convergence insufficiency (CI) and child behavioral disorders, They are characterized by being persistent, repetitive and inappropriate, such as attention deficit hyperactivity disorder (ADHD). The objective of this review was to identify and describe the definition, prevalence, clinical signs, the symptomatology and treatment for convergence insufficiency and how it is associated with attention deficit hyperactivity disorder through a literature review of different databases, and in which articles were identified in which the visual function of children with ADHD diagnosis was intended to be known, the presence of symptoms associated with this disorder in patients diagnosed with insufficient convergence and even clinical trials comparing changes in The results of this study were based on the results of a study conducted by the European Commission in March 1998. The results of this review lead to the conclusion that an association between ADHD and HF in children is evident, but not a causal relationship, studies on representative samples are therefore necessary.

The Family Climate Questionnaire: A general measure of autonomy support from family members.

The current study examines the psychometric characteristics of the Family Climate Questionnaire (FCQ), which was intended to measure the degree of autonomy-support among family members for a respondent with health concerns. The sample included military veterans (N = 350), a portion of whom had congestive heart failure (N = 86) or diabetes (N = 77), and a portion who were referred from primary care for behavioral health concerns (N = 187). Overall, 92.6% of the participants were male, and 56.7% were Black or African American and 40.6% were Caucasian. The findings highly supported the factor structure, internal consistency, and construct validity of the Autonomy Support subscale. In addition, there was high support for factorial invariance across subsamples of veterans with chronic medical problems compared to those referred from primary care. The findings for an additional subscale developed for this study, Coercion, were less supportive, with insufficient convergence in factor structure and relatively poorer internal consistency. The FCQ Autonomy Support measure appears to have potential as a useful measure of a family environment that supports autonomy for health among individuals with both medical and mental health conditions, and it is a flexible measure that can be used with a range of family member types. The FCQ Coercion measure received less consistent empirical support and will require additional development and testing. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The distribution characteristics of strabismus surgery types in a tertiary hospital in the Central Plains region during the COVID-19 epidemic

ObjectiveThis study aimed to analyze the distribution of different types of strabismus surgery in a tertiary hospital in Central China during the three-year period of the COVID-19 pandemic.MethodsA retrospective analysis was conducted on the clinical data of strabismus patients who underwent surgery and were admitted to the Department of Strabismus and Pediatric Ophthalmology at the First Affiliated Hospital of Zhengzhou University between January 2020 and December 2022.ResultsA total of 3939 strabismus surgery patients were collected, including 1357 in 2020, 1451 in 2021, and 1131 in 2022. The number of surgeries decreased significantly in February 2020, August 2021, and November and December 2022. Patients aged 0–6 years accounted for 37% of the total number of strabismus surgery patientsr. The majority (60%) of all strabismus surgery patients were diagnosed with exotropia, with intermittent exotropia accounting for the highest proportion (53%). There was no statistically significant difference in the proportion of intermittent exotropia and constant exotropia during the three-year period (χ2 = 2.642, P = 0.267 and χ2 = 3.012, P = 0.221, respectively). Among patients with intermittent exotropia, insufficient convergence type was the most common form of strabismus (accounting for over 70%). Non-accommodative esotropia accounted for more than 50% of all internal strabismus cases.ConclusionDuring the period from 2020 to 2022, the total number of strabismus surgeries in our hospital did not show significant fluctuations, but there was a noticeable decrease in the number of surgeries during months affected by the pandemic. Exotropia accounted for the highest proportion among strabismus surgery patients. Intermittent exotropia was the most common type among patients undergoing surgery for exotropia, and the most prevalent subtype was the insufficient convergence type. The age distribution of patients varied in different months, with a concentration of surgeries for strabismus patients in the 7–12 years old age group during the months of July and August each year.

Path Planning of Obstacle-Crossing Robot Based on Golden Sine Grey Wolf Optimizer

This paper proposes a golden sine grey wolf optimizer (GSGWO) that can be adapted to the obstacle-crossing function to solve the path planning problem of obstacle-crossable robot. GSGWO has been improved from the gray wolf optimizer (GWO), which provide slow convergence speed and easy to fall into local optimum, especially without obstacle-crossing function. Firstly, aiming at the defects of GWO, the chaotic map is introduced to enrich the initial population and improve the convergence factor curve. Then, the convergence strategy of the golden sine optimizer is introduced to improve the shortcomings of GWO, such as insufficient convergence speed in the later stage and the ease with which it falls into the local optimum. Finally, by adjusting the working environment model, path generation method and fitness function, the path-planning problem of the obstacle-crossing robot is adapted. In order to verify the feasibility of the algorithm, four standard test functions and three different scale environment models are selected for simulation experiments. The results show that in the performance test of the algorithm, the GSGWO has higher convergence speed and accuracy than the GWO under different test functions. In the path-planning experiment, the length, number and size of inflection points and stability of the path planned by the GSGWO are better than those of the GWO. The feasibility of the GSGWO is verified.

Mechanical Performance Forecast for BP Neural Network Materials Optimized by Genetic Algorithm

The mechanical properties of steel materials play a crucial role in their design, selection, and application. In order to better predict the mechanical properties through chemical composition and process parameters, this paper uses genetic algorithm to optimize the BP neural network to establish a mechanical property prediction model for steel materials. The model can predict three mechanical properties, including yield strength, tensile strength, and elongation, through chemical composition and process parameters. After optimization by genetic algorithm, the problems of insufficient convergence effect, random initialization of weights and thresholds in the BP neural network model are improved, and the prediction error is significantly reduced. The experimental results show that the GA-BP algorithm model has excellent performance in predicting the mechanical properties of steel materials.

Improving Wild Horse Optimizer: Integrating Multistrategy for Robust Performance across Multiple Engineering Problems and Evaluation Benchmarks

In recent years, optimization problems have received extensive attention from researchers, and metaheuristic algorithms have been proposed and applied to solve complex optimization problems. The wild horse optimizer (WHO) is a new metaheuristic algorithm based on the social behavior of wild horses. Compared with the popular metaheuristic algorithms, it has excellent performance in solving engineering problems. However, it still suffers from the problem of insufficient convergence accuracy and low exploration ability. This article presents an improved wild horse optimizer (I-WHO) with early warning and competition mechanisms to enhance the performance of the algorithm, which incorporates three strategies. First, the random operator is introduced to improve the adaptive parameters and the search accuracy of the algorithm. Second, an early warning strategy is proposed to improve the position update formula and increase the population diversity during grazing. Third, a competition selection mechanism is added, and the search agent position formula is updated to enhance the search accuracy of the multimodal search at the exploitation stage of the algorithm. In this article, 25 benchmark functions (Dim = 30, 60, 90, and 500) are tested, and the complexity of the I-WHO algorithm is analyzed. Meanwhile, it is compared with six popular metaheuristic algorithms, and it is verified by the Wilcoxon signed-rank test and four real-world engineering problems. The experimental results show that I-WHO has significantly improved search accuracy, showing preferable superiority and stability.

Role-oriented binary grey wolf optimizer using foraging-following and Lévy flight for feature selection

Feature selection can effectively define the feature subset, remove redundant, irrelevant, and noisy features. In order to adapt the feature selection problem, this paper adopts role-oriented modeling paradigm to make up for the defects of binary grey wolf optimizer such as premature, declining diversity, and insufficient convergence, then comes up with novel V-shaped linear transfer functions. Ultimately, a role-oriented binary grey wolf optimizer is proposed. The top grey wolves combine the foraging-following to enhance convergence, while the ordinary grey wolves integrate Lévy flight to avoid premature and improve diversity. The outstanding ability of role-oriented binary grey wolf optimizer in balance of exploitation and exploration is verified by benchmarks and the effectiveness in solving feature selection is demonstrated through feature selection tasks. The results of benchmark and feature selection task have comprehensively demonstrated the competitiveness of role-oriented modeling paradigm in improving metaheuristic algorithms.

Invoking team trust to facilitate performance management in the context of virtual teams

Purpose: Recent years have seen an upsurge in virtual working arrangements. However, many managers find it difficult to manage and motivate employees in the absence of face-to-face contact. Traditional, structured performance management approaches therefore need to give way to more holistic and technology-enabled approaches that are better suited to virtual work. This study set out to investigate how managers optimise the performance of virtual teams, with specific reference to the role of trust, both within and across teams. While there is growing interest in the role of trust as a driver of virtual team performance, there is insufficient convergence between the respective literatures on virtual teams, team trust and performance management, leaving a research gap.Design/methodology/approach: Semi-structured interviews were conducted with 18 middle and top managers of virtual teams who worked in large companies in South Africa. The collected data were then subject to thematic analysis.Findings/results: These included: Virtual work can lead to a work–life imbalance; a lack of human contact can strain interpersonal relationships and erode trust; and optimal performance management in a virtual context depends on a trusting environment, clear and realistic goals, ‘agile management practices’ (including coaching and frequent feedback) and appropriate technologies.Practical implications: The study provides new insights into the challenges faced by middle managers in creating trusting and performance-geared relationships with virtual team members.Originality/value: The study expands on the existing team dynamics literature while also providing a convenient conceptual framework to guide future studies on the drivers of virtual team trust and optimal performance management.

A quadratic approximation for volume minimization topology optimization

This paper proposes a variable density-based topology optimization method, aiming to minimize the volume fraction subject to multiple constraints. The methods presented in literature have proved successful in a variety of applications, although they are all first-order monotonous schemes, exhibit insufficient convergence properties. Following the SINH scheme, the elemental volume is penalized instead of the stiffness matrix. Through the intervention of reciprocal variables, the topology optimization reformulation introduces the positive-definite and separable Hessian matrix. The initial optimization formulation is decomposed into a sequence of quadratic programming. To resolve the problem efficiently, the quadratic programming can be precast into a dual form, and Lagrange multipliers are updated directly. The corresponding MATLAB code is a built-in quadratic programming subroutine explored to solve the dual problem. Finally, four numerical tests encompassing 2D and 3D structures are presented to illustrate the feasibility and practicality by incorporating the results with existing approaches.

Modified Artificial Gorilla Troop Optimization Algorithm for Solving Constrained Engineering Optimization Problems

The artificial Gorilla Troop Optimization (GTO) algorithm (GTO) is a metaheuristic optimization algorithm that simulates the social life of gorillas. This paper proposes three innovative strategies considering the GTO algorithm’s insufficient convergence accuracy and low convergence speed. First, a shrinkage control factor fusion strategy is proposed to expand the search space and reduce search blindness by strengthening the communication between silverback gorillas and other gorillas to improve global optimization performance. Second, a sine cosine interaction fusion strategy based on closeness is proposed to stabilize the performance of silverback gorillas and other gorilla individuals and improve the convergence ability and speed of the algorithm. Finally, a gorilla individual difference identification strategy is proposed to reduce the difference between gorilla and silverback gorillas to improve the quality of the optimal solution. In order to verify the optimization effect of the modified artificial gorilla troop optimization (MGTO) algorithm, we used 23 classic benchmark functions, 30 CEC2014 benchmark functions, and 10 CEC2020 benchmark functions to test the performance of the proposed MGTO algorithm. In this study, we used a total of 63 functions for algorithm comparison. At the same time, we carried out the exploitation and exploration balance experiment of 30 CEC2014 and 10 CEC2020 functions for the MGTO algorithm. In addition, the MGTO algorithm was also applied to test seven practical engineering problems, and it achieved good results.

An improved multi-strategy beluga whale optimization for global optimization problems.

This paper presents an improved beluga whale optimization (IBWO) algorithm, which is mainly used to solve global optimization problems and engineering problems. This improvement is proposed to solve the imbalance between exploration and exploitation and to solve the problem of insufficient convergence accuracy and speed of beluga whale optimization (BWO). In IBWO, we use a new group action strategy (GAS), which replaces the exploration phase in BWO. It was inspired by the group hunting behavior of beluga whales in nature. The GAS keeps individual belugas whales together, allowing them to hide together from the threat posed by their natural enemy, the tiger shark. It also enables the exchange of location information between individual belugas whales to enhance the balance between local and global lookups. On this basis, the dynamic pinhole imaging strategy (DPIS) and quadratic interpolation strategy (QIS) are added to improve the global optimization ability and search rate of IBWO and maintain diversity. In a comparison experiment, the performance of the optimization algorithm (IBWO) was tested by using CEC2017 and CEC2020 benchmark functions of different dimensions. Performance was analyzed by observing experimental data, convergence curves, and box graphs, and the results were tested using the Wilcoxon rank sum test. The results show that IBWO has good optimization performance and robustness. Finally, the applicability of IBWO to practical engineering problems is verified by five engineering problems.

Separation Structure Speech Separation Model Fusing Conformer and NBC

The quality of speech separation affects the entire speech technology ecosystem. Aiming at the problems of low utilization of local feature information, insufficient convergence speed, too many calculation parameters and too long calculation time in blind source separation in view of Transformer dual-path cyclic neural network, a blind source separation model based on fusion of Conformer and NBC (Narrow-band Conformer NBC) is proposed. First, for the problems of low utilization of speech local feature information and insufficient convergence speed, the Transformer block is replaced by the Conformer in the block of the dual-path recurrent network. It can improve the utilization and convergence speed of local features. Secondly, the NBC block is used to replace the Transformer block in the inter-block loop. The NBC block simplifies the calculation of vector similarity and vector aggregation, reduces lots of parameters and calculation cost, and reduces the computational complexity of the model. In the experiments on WSJ0-2mix[7] dataset and WHAM[13] dataset, contrast with other models, the convergence speech is faster and the blind source separation effect is better.

An Improved New Caledonian Crow Learning Algorithm for Global Function Optimization.

The New Caledonian crow learning algorithm (NCCLA) is a novel metaheuristic algorithm inspired by the learning behavior of New Caledonian crows learning to make tools to obtain food. However, it suffers from the problems of easily falling into local optima and insufficient convergence accuracy and convergence precision. To further improve the convergence performance of NCCLA, an improved New Caledonian crow learning algorithm (INCCLA) is proposed in this paper. By determining the parent individuals based on the cosine similarity, the juveniles are guided to search toward different ranges to maintain the population diversity; a novel hybrid mechanism of complete and incomplete learning is proposed to balance the exploration and exploitation capabilities of the algorithm; the update strategy of juveniles and parent individuals is improved to enhance the convergence speed and precision of the algorithm. The test results of the CEC2013 and CEC2020 test suites show that, compared with the original NCCLA algorithm and four of the best metaheuristics to date, INCCLA has significant advantages in terms of convergence speed, convergence precision, and stability.

A convection‐permitting numerical study of diurnal cycles of pre‐summer rainfall over southern China

AbstractIn this study, the convection‐permitting model (CPM) with 3‐km horizontal resolution has been used to investigate the simulated ability on diurnal cycles of pre‐summer rainfall over southern China with various subregions and rainfall systems. Compared with the observations, the model captures the peak time of nocturnal rainfall in the western inland related to the eastward‐propagating rainfall system over the eastern slope of the Yungui Plateau. The simulated eastward‐propagating rainfall system moves a short distance and has a short lifetime due to the weaker westerly‐steering flow and southerly winds in the late night bringing less warm and moist air with the weaker upward flow of the mountain–plain solenoid circulation that is unfavorable for convection instability. Over the eastern inland, the model shows good performance on simulating the quasi‐stationary rainfall system in the morning because of the high resolution of topography. However, the model cannot simulate the climatology of the secondary morning rainfall peak due to overestimation of the rainfall from noon to evening. On the other hand, the weaker simulated southwesterly winds especially on the north side of the Nanling Mountains with decreased upward movement and moisture convergence and the more stable atmospheric condition lead to the disadvantage of convective rainfall. The CPM reproduces the afternoon rainfall peak but with a lead of 2–3 hr and still cannot simulate the morning peak well over the coastal region. The main source of mode bias is the overestimation of the onshore winds in the boundary layer over the land with the reduced temperature difference between land and sea resulting in insufficient convergence in the coastal region.

Deep Recommendation With Adversarial Training

Recommender systems provide an effective solution to the information overload and have become a research hotspot in both industry and academia. Some existing work on implicit feedback has made great progress. However, there are still the following shortcomings: 1) existing pairwise ranking methods mostly sample unobserved items uniformly to obtain negative samples, which brings a biased solution and insufficient convergence; 2) the recommendation result comes from a complicated process, which makes it less interpretable. Therefore, we put forward a Deep Recommendation with Adversarial Training (DRAT) model, which provides users with personalized recommendations by utilizing an encoder-decoder structure and adversarial training. It consists of two components: one is feature learning module in which the encoder captures the text features of items from user-generated reviews, and the decoder reconstructs the text with the captured features; the other is rating prediction module which utilizes adversarial training to generate personalized negative samples tackling the drawbacks of uniform negative sampling. Extensive experiments on five real-world datasets show that our proposed model significantly outperforms the baseline methods.

A new noise network and gradient parallelisation‐based asynchronous advantage actor‐critic algorithm

Asynchronous advantage actor-critic (A3C) algorithm is a commonly used policy optimization algorithm in reinforcement learning, in which asynchronous is parallel interactive sampling and training, and advantage is a sampling multi-step reward estimation method for computing weights. In order to address the problem of low efficiency and insufficient convergence caused by the traditional heuristic exploration of A3C algorithm in reinforcement learning, an improved A3C algorithm is proposed in this paper. In this algorithm, a noise network function, which updates the noise tensor in an explicit way is constructed to train the agent. Generalised advantage estimation (GAE) is also adopted to describe the dominance function. Finally, a new mean gradient parallelisation method is designed to update the parameters in both the primary and secondary networks by summing and averaging the gradients passed from all the sub-processes to the main process. Simulation experiments were conducted in a gym environment using the PyTorch Agent Net (PTAN) advanced reinforcement learning library, and the results show that the method enables the agent to complete the learning training faster and its convergence during the training process is better. The improved A3C algorithm has a better performance than the original algorithm, which can provide new ideas for subsequent research on reinforcement learning algorithms.

Approach to the correction and treatment of various types of refractions in children üith insufficient convergence

Approach to the correction and treatment of various types of refractions in children üith insufficient convergence

A Source of WRF Simulation Error for the Early‐Summer Warm‐Sector Heavy Rainfall Over South China Coast: Land‐Sea Thermal Contrast in the Boundary Layer

AbstractQuantitative prediction of the early‐summer warm‐sector heavy rainfall over South China coast (SCC) is still a challenge. This study conducts convection‐permitting Weather Research and Forecast model simulations for a representative nocturnal‐to‐morning heavy rainfall event related to low‐level jets during 29–30 May 2020, and explores sources of simulated rainfall bias using multi‐source observations, reanalysis data and sensitivity experiments. Results show that the model initiated about 12 hr before the coastal convection initiation only produces sporadic convection and misses the heavy rainfall at SCC, although it captures the major synoptic features as in previous studies. The underestimated convection development is closely related to insufficient convergence of boundary layer (BL) wind due to overestimated speed of the onshore BL wind. Increasing the land surface friction effects in the simulations through either multiplying the surface roughness length by 10 or including sub‐grid topography drag effects could help little suppress the simulated biases. Using hourly data assimilation to improve the simulation of marine/land BL temperature from the afternoon to nighttime produces a desired change in the nocturnal BL wind across the coastline and significantly improves rainfall simulation with the maximum and average rainfall over SCC increased by about 4 and 6.5 times, respectively. These findings indicate that, to improve the prediction of the nocturnal heavy rainfall in the warm‐sector over SCC during early summer, it is necessary to more accurately predict the BL thermal structure during the daytime, in addition to the previously emphasized nocturnally enhanced marine BL flow.

Adaptive Guided Spatial Compressive Cuckoo Search for Optimization Problems

Cuckoo Search (CS) is one of the heuristic algorithms that has gradually drawn public attention because of its simple parameters and easily understood principle. However, it still has some disadvantages, such as its insufficient accuracy and slow convergence speed. In this paper, an Adaptive Guided Spatial Compressive CS (AGSCCS) has been proposed to handle the weaknesses of CS. Firstly, we adopt a chaotic mapping method to generate the initial population in order to make it more uniform. Secondly, a scheme for updating the personalized adaptive guided local location areas has been proposed to enhance the local search exploitation and convergence speed. It uses the parent’s optimal and worst group solutions to guide the next iteration. Finally, a novel spatial compression (SC) method is applied to the algorithm to accelerate the speed of iteration. It compresses the convergence space at an appropriate time, which is aimed at improving the shrinkage speed of the algorithm. AGSCCS has been examined on a suite from CEC2014 and compared with the traditional CS, as well as its four latest variants. Then the parameter identification and optimization of the photovoltaic (PV) model are applied to examine the capacity of AGSCCS. This is conducted to verify the effectiveness of AGSCCS for industrial problem application.

A Novel UAV Path Planning Algorithm Based on Double-Dynamic Biogeography-Based Learning Particle Swarm Optimization

Particle swarm optimization (PSO), one of the classical path planning algorithms, has been considered for unmanned aerial vehicle (UAV) path planning more frequently in recent years. A large amount of studies on UAV path planning based on modified PSO have been reported. However, most UAV path planning algorithms still optimize only one kind terrain problem which is mountain terrain. At the same time, many modified PSO algorithms also have some problems, such as insufficient convergence and unsatisfactory efficiency. In this paper, six kinds of terrain functions of UAV path planning are proposed to simulate real-world application. The terrain functions contain city, village without houses, village with houses, mountainous area without houses, mountainous area with houses, and mountainous area with a huge building. Inspired by CLPSO and BLPSO, we proposed a new double-dynamic biogeography-based learning particle swarm optimization (DDBLPSO) algorithm to solve these problems. The double-dynamic biogeography-based learning strategy replacing the traditional learning mechanism from the personal and global best particles is used to select the learning particles. In this strategy, each particle will learn from the better one of two selected particles which are not worse than itself. However, one random component of particle will replaced by corresponding component of other particle if all components of the particle only learn from itself. In this way, particles sufficiently learn from better objects and maintain the ability of jumping out of local optimality. The superiority of our algorithm is verified with four relevant algorithms, a PSO variant, and a BBO variant on the benchmark suite of CEC2015. Real-world application demonstrates that the algorithm we proposed outperforms four relevant algorithms, a PSO variant, and a BBO variant both in small-scale problems and large-scale problems. This paper shows a good application of our novel algorithm.