Weighting Scheme Research Articles

State-level variation in the prevalence of child psychopathology symptoms in the US: Results from the ABCD study

ObjectiveTo estimate the prevalence of clinically meaningful youth mood, anxiety, behavioral, and attention symptoms across US states. MethodData are drawn from the Adolescent Brain Cognitive Development (ABCD) study baseline wave, which included 11,876 children ages 9-10. Statistical weighting strategies generated projected state-specific prevalence estimates for the 17 states where ABCD collected data based on state socio-demographics. Twenty dimensions of mental health were assessed with the Child Behavior Checklist using recommended cut-scores to assess clinical and sub-threshold symptoms. ResultsPsychopathology symptom prevalence varied by state and outcome. Projected prevalence of internalizing problems ranged from 11.0% [95% CI: 9.8%, 12.2%; Oklahoma] to 7.9% [95% CI: 6.9%, 9.0%; Maryland] across states. Projected prevalence of externalizing problems ranged from 6.9% [95% CI: 6.1%, 7.8%; South Carolina] to 4.5% [95% CI: 3.7%, 5.4%; California]. Regions with high symptoms included sections of the South (e.g., Oklahoma, South Carolina) and Vermont. Conduct problems had the most variability across states (i.e., greatest state-level prevalence 91% higher than the lowest). Attention problems had the least variability across states (greatest state-level prevalence 26% higher than the lowest). ConclusionsClinically meaningful psychopathology symptoms are common in children across the US, with substantial state-level variability in prevalence. Understanding variability in the prevalence of psychopathology symptoms across the US can help to inform resource allocation to increase the availability of youth mental health services.

Spectral weighting functions for localization of complex sound. III. The effect of sensorineural hearing lossa).

Spectral weighting functions for sound localization were measured in participants with bilateral mild sloping to moderately severe, high-frequency sensorineural hearing loss (SNHL) and compared to normal hearing (NH) participants with and without simulated SNHL. Each participant group localized three types of complex tones, comprised of seven frequency components spatially jittered and presented from the horizontal frontal field. A threshold-elevating noise masker was implemented in the free field to simulate SNHL for participants with NH. On average, participants with SNHL and NH (in quiet and simulated SNHL) placed the greatest perceptual weight on components within the interaural time difference "dominance region," found previously to peak around 800 Hz [Folkerts and Stecker, J. Acoust. Soc. Am. 151, 3409-3425 (2022)]. In addition to the peak at 800 Hz, both participant groups (including NH participants in quiet) placed near equal weight on 400 Hz, resulting in a broadened "peak" in the dominance region, most likely due to the reduction of audibility to higher frequency components. However, individual weighting strategies were more variable across participants with SNHL than participants with NH. Localization performance was reduced for participants with SNHL but not for NH participants with simulated hearing loss when compared to NH participants in quiet.

UAV path planning based on improved dung beetle algorithm with multiple strategy integration

This research presents a multi-strategy augmented dung beetle algorithm (CDBO) for UAV path planning in intricate 3D environments. Its main objective is to address the path planning challenge by formulating an integrated cost function model and environment representation that aligns the optimisation task with the navigation prerequisites and safety constraints of the UAV. Firstly, the algorithm initialises the particle population based on the Lévy flight principle to improve the species diversity and adequately search the solution space. Subsequently, an exponentially decreasing inertia weighting strategy is introduced to improve the convergence speed of the algorithm. In addition, the algorithm uses an adaptive [Formula: see text]-distribution with perturbed variation of the updated positions so that the algorithm avoids falling into a local optimum. Finally, to enhance the robustness of the system, the algorithm incorporates an elite retention strategy based on the Pareto principle. In this study, rigorous tests using 12 CEC2022 test functions and Wilcoxon rank sum test are conducted as benchmarks for the performance of the algorithm. The results show that the enhanced dung beetle algorithm outperforms the traditional algorithm, highlighting its efficiency and robustness. In addition, the efficacy of the enhanced dung beetle algorithm was validated in a variety of complex 3D environments, affirming its ability to generate optimal paths. This study further highlights the importance of effective algorithms in UAV path planning, addressing the key issue of optimising flight operations in intricate spatial scenarios.

An innovative benefit‐of‐the‐doubt approach for health system effectiveness: a global case study on amenable mortality

AbstractHealth system performance assessment (HSPA) is essential for health planning and to improve population health. One of the HSPA domains is related to effectiveness, which can be represented considering different dimensions. Composite indicators can be used to summarize complex constructs involving several indicators. One example of such efforts is the Healthcare Access and Quality Index from the Global Burden of Diseases Study, in which different causes of mortality amenable to health care are summarized in this index through principal component analysis and exploratory factor analysis. While these approaches use the variance of the indicators, marginal improvement is not considered, that is, the distance to the best practice frontier. In this study we propose an innovative benefit‐of‐the‐doubt approach to combine frontier analysis and composite indicators, using amenable mortality estimates for 188 countries. In particular, we include flexible aggregating weighting schemes and a robust and conditional approach. The dual formulation gives information on the peers and the potential mortality rate reduction targets considering the background conditions. In absolute terms, Andorra and high‐income countries are the most effective regarding healthcare access and quality, while sub‐Saharan African and South Asian countries are the least effective. North African and Middle Eastern countries benefit the most when epidemiological patterns, geographical proximity, and country development status are considered.

A new high-order RKDG method based on the TENO-THINC scheme for shock-capturing

In recent years, Runge-Kutta Discontinuous Galerkin (RKDG) methods have gained substantial attention in solving hyperbolic conservation laws, attributed to their high-order accuracy and adaptability to unstructured meshes. However, standard RKDG methods cannot capture discontinuities without oscillation unless they are supplemented with troubled cell indicators and limiters. Existing indicators, such as the total variation bounded (TVB) minmod indicator and the KXRCF indicator, typically depend on a critical parameter tied to the equation's solution, necessitating tuning for different cases. In terms of limiters, the popular limiters even fail to guarantee the high-order property in the smooth regions. The advent of Weighted Essentially Non-Oscillatory (WENO) schemes prompts the implementation of WENO limiters for RKDG methods, preserving high-order properties. However, WENO-family schemes exhibit significant numerical dissipation, potentially smearing small-scale flow structures. In this work, the Targeted Essentially Non-Oscillatory (TENO) indicator [1] is utilized, which leverages the nonlinear weighting strategy of the TENO scheme to separate high-wavenumber physical fluctuations and genuine discontinuities from smooth regions with a unified set of parameters. For troubled cells, a novel limiter is proposed for structured meshes, which combines a TENO scheme for resolving high-wavenumber physical fluctuations and a novel non-polynomial Tangent of Hyperbola for the INterface Capturing (THINC) scheme for resolving genuine discontinuities with extremely low numerical dissipation. Furthermore, the shifting between the TENO and THINC schemes is based on a new boundary variation diminishing (BVD) strategy, which only relies on compact neighborhoods and is significantly simpler than its predecessors. Meanwhile, a new strategy is proposed to ensure the consistency of the new limiter applied for 1D and 2D cases. A set of 1D and 2D benchmark cases including strong shockwaves and a broad range of flow length scales is simulated with uniform meshes for 1D cases and structured quadrilateral meshes for 2D cases to demonstrate the performance of the new numerical scheme. The indicator does not activate any limiters in the accuracy test cases to ensure the high-order property of the whole numerical scheme. Other cases with discontinuities demonstrate the low-dissipation properties of the new limiter in comparison to the simple WENO limiter.

Optimizing personalized treatments for targeted patient populations across multiple domains.

Learning individualized treatment rules (ITRs) for a target patient population with mental disorders is confronted with many challenges. First, the target population may be different from the training population that provided data for learning ITRs. Ignoring differences between the training patient data and the target population can result in sub-optimal treatment strategies for the target population. Second, for mental disorders, a patient's underlying mental state is not observed but can be inferred from measures of high-dimensional combinations of symptomatology. Treatment mechanisms are unknown and can be complex, and thus treatment effect moderation can take complicated forms. To address these challenges, we propose a novel method that connects measurement models, efficient weighting schemes, and flexible neural network architecture through latent variables to tailor treatments for a target population. Patients' underlying mental states are represented by a compact set of latent state variables while preserving interpretability. Weighting schemes are designed based on lower-dimensional latent variables to efficiently balance population differences so that biases in learning the latent structure and treatment effects are mitigated. Extensive simulation studies demonstrated consistent superiority of the proposed method and the weighting approach. Applications to two real-world studies of patients with major depressive disorder have shown a broad utility of the proposed method in improving treatment outcomes in the target population.

Mitigating the negative impact of over-association for conversational query production

Conversational query generation aims at producing search queries from dialogue histories, which are then used to retrieve relevant knowledge from a search engine to help knowledge-based dialogue systems. Trained to maximize the likelihood of gold queries, previous models suffer from the data hunger issue, and they tend to both drop important concepts from dialogue histories and generate irrelevant concepts at inference time. We attribute these issues to the over-association phenomenon where a large number of gold queries are indirectly related to the dialogue topics, because annotators may unconsciously perform reasoning with their background knowledge when generating these gold queries. We carefully analyze the negative effects of this phenomenon on pretrained Seq2seq query producers and then propose effective instance-level weighting strategies for training to mitigate these issues from multiple perspectives. Experiments on two benchmarks, Wizard-of-Internet and DuSinc, show that our strategies effectively alleviate the negative effects and lead to significant performance gains (2%∼5% across automatic metrics and human evaluation). Further analysis shows that our model selects better concepts from dialogue histories and is 10 times more data efficient than the baseline.

Adaptive Weighted Multi-kernel Learning for Blast-Induced Flyrock Distance Prediction

AbstractIn the field of civil and mining engineering, blasting operations are widely and frequently used for rock excavation, However, some undesirable environmental problems induced by blasting operations cannot be ignored. Blast-induced flyrock is one important issue induced by blasting operation, which needs to be well predicted to identify the blasting zone’s safety zone. This study introduces an adaptive weighted multi-kernel learning model (AW-MKL) to provide an accurate prediction of blast-induced flyrock distance in Sungun Copper Mine site. The proposed model uses a combination of multi-kernel learning (MKL) approach and adaptive weighting strategy based on weighted Euclidean distance and modified local outlier factor (MLOF) to maximally improve the predictive ability of kernel ridge regression (KRR). To demonstrate the superiority of the proposed approach, six machine learning models were developed as comparisons, i.e., KRR, RF, GBDT, SVM, M5 Tree, MARS and AdaBoost. The outcomes of the proposed method achieved the highest accuracy in testing phase, with RMSE of 2.05, MAE of 0.98 and VAF of 99.92, which confirmed the strong predictive capability of the proposed AW-MKL in predicting blast-induced flyrock distance.

Enhancing precision in table tennis officiating through an optimized autonomous sensor node system

PurposeThe purpose of the study is to address concerns regarding the subjectivity and imprecision of decision-making in table tennis refereeing by developing and enhancing a sensor node system. This system is designed to accurately detect the points on the table tennis table where balls collide. The study introduces the twined-reinforcement chimp optimization (TRCO) framework, which combines two novel approaches to optimize the distribution of sensor nodes. The main goal is to reduce the number of sensor units required while maintaining high accuracy in determining the locations of ball collisions, with error margins significantly below the critical 3.5 mm cutoff. Through complex optimization procedures, the study aims to improve the efficiency and reliability of decision-making in table tennis refereeing by leveraging sensor technology.Design/methodology/approachThe study employs a design methodology focused on developing a sensor array system to enhance decision-making in table tennis refereeing. It introduces the twined-reinforcement chimp optimization (TRCO) framework, combining dual adaptive weighting strategies and a stochastic approach for optimization. By meticulously engineering the sensor array and utilizing complex optimization procedures, the study aims to improve the accuracy of detecting ball collisions on the table tennis table. The methodology aims to reduce the number of sensor units required while maintaining high precision, ultimately enhancing the reliability of decision-making in the sport.FindingsThe optimization research study yielded promising outcomes, showcasing a substantial reduction in the number of sensor units required from the initial count of 60 to a more practical 49. The sensor array system demonstrated excellent accuracy in identifying the locations of ball collisions, with error margins significantly below the critical 3.5 mm cutoff. Through the implementation of the twined-reinforcement chimp optimization (TRCO) framework, which integrates dual adaptive weighting strategies and a stochastic approach, the study achieved its goal of enhancing the efficiency and reliability of decision-making in table tennis refereeing.Originality/valueThis study introduces novel contributions to the field of table tennis refereeing by pioneering the development and optimization of a sensor array system. The innovative twined-reinforcement chimp optimization (TRCO) framework, integrating dual adaptive weighting strategies and a stochastic approach, sets a new standard for sensor node distribution in sports technology. By substantially reducing the number of sensor units required while maintaining high accuracy in detecting ball collisions, this research offers practical solutions to address the inherent subjectivity and imprecision in decision-making processes. The study’s originality lies in its meticulous design methodology and complex optimization procedures, offering significant value to the field of sports technology and officiating.

Recommendation-Based Trust Evaluation Model for the Internet of Underwater Things

The Internet of Underwater Things (IoUT) represents an emerging and innovative field with the potential to revolutionize underwater exploration and monitoring. Despite its promise, IoUT faces significant challenges related to reliability and security, which hinder its development and deployment. A particularly critical issue is the establishment of trustworthy communication networks, necessitating the adaptation and enhancement of existing models from terrestrial and marine systems to address the specific requirements of IoUT. This work explores the problem of dishonest recommendations within trust modelling systems, a critical issue that undermines the integrity of trust modelling in IoUT networks. The unique environmental and operational constraints of IoUT exacerbate the severity of this issue, making current detection methods insufficient. To address this issue, a recommendation evaluation method that leverages both filtering and weighting strategies is proposed to enhance the detection of dishonest recommendations. The model introduces a filtering technique that combines outlier detection with deviation analysis to make initial decisions based on both majority outcomes and personal experiences. Additionally, a belief function is developed to weight received recommendations based on multiple criteria, including freshness, similarity, trustworthiness, and the decay of trust over time. This multifaceted weighting strategy ensures that recommendations are evaluated from different perspectives to capture deceptive acts that exploit the complex nature of IoUT to the advantage of dishonest recommenders. To validate the proposed model, extensive comparative analyses with existing trust evaluation methods are conducted. Through a series of simulations, the efficacy of the model in capturing dishonest recommendation attacks and improving the accuracy rate of detecting more sophisticated attack scenarios is demonstrated. These results highlight the potential of the model to significantly enhance the trustworthiness of IoUT establishments.

On the use of hindcast skill for merging NMME seasonal forecasts across the western U.S.

Abstract Multi-model ensemble forecasts have gained widespread use over the past decade. A yet unresolved issue is whether forecast skill benefits from the use of prior skill from each model in providing a weighted combination. Here we use the available seasonal ensemble forecasts of six models from the North American Multi-Model Ensemble (NMME) to study various aspects of prior skill-based weighting schemes and explore ways to merge multi-model forecasts. First, we post-process each NMME model through quantile mapping and a simple spread error adjustment. Then, using an equal weighted combination as the baseline forecast, we test merging the models together through skill-based weights by varying the prior skill metric and varying how the metrics are aggregated across the different subbasins and time of year. Results confirm prior work that the combined forecasts do outperform individual models. When evaluating prior skill, equal weighting generally performed as well or slightly better than all weighting schemes tried. The skill of the weighting scheme was not found to be strongly dependent on prior metric but did improve when aggregating all forecasted months and subbasins together to provide one overall weight to each model. Also, we found that including an offset to the prior metric that nudged the weights closer to equal weighting improves skill especially at longer leads where individual model skill is low. Results also show that the weighting schemes performed better than regression-based techniques including multiple linear regression and random forest.

IMPACT OF THE OBJECTIVE ATTRIBUTE WEIGHTING ON FIVE POPULAR MULTI-CRITERIA DECISION-MAKING METHODS: AN EMPIRICAL STUDY

In a Multi Criteria Decision Making (MCDM) problem, it is rarely possible to optimize all objectives simultaneously, since they can be contradictory, ambiguous or may involve other types of inconsistencies or uncertainties. Therefore, when trying to choose from a number of available alternatives, a decision maker is expected to assign weights to attributes whose values are utilized to evaluate the alternative under consideration for ranking. Attributes can be qualitative or quantitative, and their weights can be assigned by the decision maker in a somewhat subjective manner or algorithmically. In this paper, the impact of attribute weighting approaches on the ranking results across a number of widely used MCDM methods are discussed. That is, it examines how different weighting methods affect the results on the same multi-criteria decision-making methods when making a rating. In doing so, consider five MCDM methods, namely, Evamix, Aras, Topsis, Vikor, Waspas, under three different objective attribute weight assignment procedures, namely, Critic, Entropy, and Standart Deviation (SD). Results indicate that, in some cases, the employed attribute weight-assignment mechanism influences the rating results more heavily than the MCDM method itself. In other words, different MCDM methods tend to yield similar results under the same weight assignment method whereas, the same method produces more distinguishable results under different weighting schemes.

An Energy-Efficient Weighting Strategy for Various TRPC-UWB Communication Systems

An Energy-Efficient Weighting Strategy for Various TRPC-UWB Communication Systems

Multi-Strategy Improved Harris Hawk Optimization Algorithm and Its Application in Path Planning.

Path planning is a key problem in the autonomous navigation of mobile robots and a research hotspot in the field of robotics. Harris Hawk Optimization (HHO) faces challenges such as low solution accuracy and a slow convergence speed, and it easy falls into local optimization in path planning applications. For this reason, this paper proposes a Multi-strategy Improved Harris Hawk Optimization (MIHHO) algorithm. First, the double adaptive weight strategy is used to enhance the search capability of the algorithm to significantly improve the convergence accuracy and speed of path planning; second, the Dimension Learning-based Hunting (DLH) search strategy is introduced to effectively balance exploration and exploitation while maintaining the diversity of the population; and then, Position update strategy based on Dung Beetle Optimizer algorithm is proposed to reduce the algorithm's possibility of falling into local optimal solutions during path planning. The experimental results of the comparison of the test functions show that the MIHHO algorithm is ranked first in terms of performance, with significant improvements in optimization seeking ability, convergence speed, and stability. Finally, MIHHO is applied to robot path planning, and the test results show that in four environments with different complexities and scales, the average path lengths of MIHHO are improved by 1.99%, 14.45%, 4.52%, and 9.19% compared to HHO, respectively. These results indicate that MIHHO has significant performance advantages in path planning tasks and helps to improve the path planning efficiency and accuracy of mobile robots.

Decimeter-Level Accuracy for Smartphone Real-Time Kinematic Positioning Implementing a Robust Kalman Filter Approach and Inertial Navigation System Infusion in Complex Urban Environments.

New smartphones provide real-time access to GNSS pseudorange, Doppler, or carrier-phase measurement data at 1 Hz. Simultaneously, they can receive corrections broadcast by GNSS reference stations to perform real-time kinematic (RTK) positioning. This study aims at the real-time positioning capabilities of smartphones using raw GNSS measurements as a conventional method and proposes an improvement to the positioning through the integration of Inertial Navigation System (INS) measurements. A U-Blox GNSS receiver, model ZED-F9R, was used as a benchmark for comparison. We propose an enhanced ambiguity resolution algorithm that integrates the traditional LAMBDA method with an adaptive thresholding mechanism based on real-time quality metrics. The RTK/INS fusion method integrates RTK and INS measurements using an extended Kalman filter (EKF), where the state vector x includes the position, velocity, orientation, and their respective biases. The innovation here is the inclusion of a real-time weighting scheme that adjusts the contribution of the RTK and INS measurements based on their current estimated accuracy. Also, we use the tightly coupled (TC) RTK/INS fusion framework. By leveraging INS data, the system can maintain accurate positioning even when the GNSS data are unreliable, allowing for the detection and exclusion of abnormal GNSS measurements. However, in complex urban areas such as Qazvin City in Iran, the fusion method achieved positioning accuracies of approximately 0.380 m and 0.415 m for the Xiaomi Mi 8 and Samsung Galaxy S21 Ultra smartphones, respectively. The subsequent detailed analysis across different urban streets emphasized the significance of choosing the right positioning method based on the environmental conditions. In most cases, RTK positioning outperformed Single-Point Positioning (SPP), offering decimeter-level precision, while the fusion method bridged the gap between the two, showcasing improved stability accuracy. The comparative performance between the Samsung Galaxy S21 Ultra and Xiaomi Mi 8 revealed minor differences, likely attributed to variations in the hardware design and software algorithms. The fusion method emerged as a valuable alternative when the RTK signals were unavailable or impractical. This demonstrates the potential of integrating RTK and INS measurements for enhanced real-time smartphone positioning, particularly in challenging urban environments.

Short-term PM2.5 forecasting using a unique ensemble technique for proactive environmental management initiatives

Particulate matter with a diameter of 2.5 microns or less (PM2.5) is a significant type of air pollution that affects human health due to its ability to persist in the atmosphere and penetrate the respiratory system. Accurate forecasting of particulate matter is crucial for the healthcare sector of any country. To achieve this, in the current work, a new time series ensemble approach is proposed based on various linear (autoregressive, simple exponential smoothing, autoregressive moving average, and theta) and nonlinear (nonparametric autoregressive and neural network autoregressive) models. Three ensemble models are also developed, each employing distinct weighting strategies: equal distribution of weight among all single models (ESME), weight assignment based on training average accuracy errors (ESMT), and weight assignment based on validation mean accuracy measures (ESMV). This technique was applied to daily PM2.5 concentration data from 1 January 2019, to 31 May 2023, in Pakistan’s main cities, including Lahore, Karachi, Peshawar, and Islamabad, to forecast short-term PM2.5 concentrations. When compared to other models, the best ensemble model (ESMV) demonstrated mean errors ranging from 3.60% to 25.79% in Islamabad, 0.81%–13.52% in Lahore, 1.08%–7.06% in Karachi, and 1.09%–12.11% in Peshawar. These results indicate that the proposed ensemble approach is more efficient and accurate for short-term PM2.5 forecasting than existing models. Furthermore, using the best ensemble model, a forecast was made for the next 15 days (June 1 to 15 June 2023). The forecast showed that in Lahore, the highest PM2.5 value (236.00 μg/m3) was observed on 8 June 2023. Other days also displayed higher and poor air quality throughout the 15 days. Conversely, Karachi experienced moderate PM2.5 concentration levels between 50 μg/m3 and 80 μg/m3. In Peshawar, the PM2.5 concentration levels were consistently unhealthy, with the highest peak (153.00 μg/m3) observed on 9 June 2023. This forecasting experience can assist environmental monitoring organizations in implementing cost-effective planning to minimize air pollution.

MNN: Mixed nearest-neighbors for self-supervised learning

In contrastive self-supervised learning, positive samples are typically drawn from the same image but in different augmented views, resulting in a limited source of positive samples. An effective way to alleviate this problem is to incorporate the relationship between samples, which involves including the top-K nearest neighbors of positive samples. However, the issue of false neighbors (i.e., neighbors that do not belong to the same category as the positive sample) is a significant yet often overlooked challenge. In this paper, we present a simple self-supervised learning framework called Mixed Nearest-Neighbors (MNN) for Self-Supervised Learning. The primary objective of the MNN is to enhance the robustness and accuracy of the model by strategically incorporating synthesized neighboring samples. Specifically, our proposed method utilizes image mixture to mitigate the effects of noise introduced by false neighbors, while adopting an intuitive weighting strategy that effectively integrates these synthesized neighbors into the model. The linear evaluation of MNN on CIFAR-10, CIFAR,100, STL-10, and Tiny ImageNet shows an improvement of 0.41% to 1.96% over the previous state-of-the-art (SOTA) methods and 1.82% to 8.8% over the Mean Shift (MSF) method. Furthermore, the proposed components can be seamlessly integrated into other self-supervised learning algorithms to enhance their performance. Code is available at https://github.com/pc-cp/MNN.

Optimizing Prehospital Stroke Diagnosis: Integrating Machine Learning with the FAST Scoring System

Strokes are a leading cause of disability and death worldwide, and timely diagnosis is critical for effective treatment. Prehospital stroke diagnosis by emergency medical services (EMS) is essential for ensuring prompt care. The FAST (Face, Arms, Speech, and Time) scoring system is a common tool used to identify stroke symptoms quickly. However, the traditional FAST system has limitations, including reliance on subjective assessments and potential misdiagnoses due to symptom variability. This study aims to optimize the FAST scoring system and compare its performance with various machine learning models to enhance stroke prediction accuracy. A dataset from an ambulance service, including prehospital data such as age, sex, and FAST test results, was used. Data preprocessing involved handling missing values, encoding categorical variables, and applying the Synthetic Minority Over-sampling Technique (SMOTE) to balance the dataset. A grid search was conducted to test different weighting schemes for the FAST system. Machine learning models, including Logistic Regression, Random Forest, Support Vector Machine (SVM), XGBoost, and Decision Tree, were trained and evaluated using cross-validation on the training set and tested on a separate test set. The traditional FAST system showed an accuracy of 62.95%. Optimizing the FAST system through grid search marginally increased accuracy to 64.36%. However, machine learning models, particularly Random Forest, significantly outperformed the FAST system, achieving a test set accuracy of 88.54%. XGBoost also demonstrated strong performance with an accuracy of 77.07%, while Logistic Regression, SVM, and Decision Tree showed lower accuracies. These findings suggest that integrating machine learning models into EMS protocols could substantially improve the accuracy of prehospital stroke diagnosis, leading to quicker and more accurate identification and treatment of stroke patients. Although the optimized FAST system offers slight improvements, machine learning models present a promising avenue for enhancing stroke prediction and improving patient outcomes in emergency settings while acknowledging practicality issues.

Research on the influence of applying counterweight on the deflection and stability of cantilever beam of machine tool

Abstract The deflection and stability of the cantilever beam of the machine tool are important factors that affect the machining accuracy of the machine tool. Because of the space size, it needs to be weighted within a limited size. By designing different weighting schemes, the static analysis and modal analysis are carried out by finite element analysis software, and the influence law of the deflection and stress of the beam is obtained, so the appropriate weighting scheme is selected. Through the analysis of different schemes, it is known that the deflection of the beam is the smallest under the action of the arm of 900mm and the force of 1000N, and the machine tool structure is more compact, which is the key component of the subsequent machine tool.

BM-FL: A Balanced Weight Strategy for Multi-Stage Federated Learning Against Multi-Client Data Skewing

BM-FL: A Balanced Weight Strategy for Multi-Stage Federated Learning Against Multi-Client Data Skewing