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The integrity of a graph G is defined as the minimum value of | S | + m (G − S) taken over all S ⊆ V (G) , where m (H) denotes the maximum cardinality of a component of graph H. In this paper, we investigate bounds on the maximum and minimum values of the weighted version of this parameter. We also consider the same question for the related parameter vertex-neighbor-integrity. [ABSTRACT FROM AUTHOR]

This paper extends existing work on teacher weighting in student-centered surveys by looking into aspects of practical implementation of deriving and using weights for teacher-centered analysis in the Trends in International Mathematics and Science Study (TIMSS) and the Progress in International Reading Literacy Study (PIRLS). The formal conditions to compute teacher-centered weights are detailed, including mathematical equations. We provide a proposal on how to define the targeted populations as well as how to collect data that is needed to derive teacher-centered weights, yet currently unavailable. We also tackle the issue of teacher nonresponse by proposing a respective adjustment factor, as well as mentioning the challenge of multiple selection probabilities when teachers teach in multiple schools. The core part of the paper focuses on studying the level of accuracy that can be expected when estimating teacher population characteristics. We use TIMSS 2019 data and simulate likely scenarios regarding the variance in weights. The results show that (i) the different weighting scenarios lead to relatively similar estimates; however, the differences between the scenarios are sufficient to justify the recommendation to use correctly derived teacher weights; (ii) differences between estimated standard errors based on complex sampling and corresponding estimates based on simple random sampling are sufficiently consistent to support use of a procedure to estimate standard errors that accounts for both sample weights and the complex sampling design; (iii) sample sizes and variance in weights significantly limit estimate precision, so that total population estimates with sufficient precision are available in the majority of countries but subpopulation features are generally not sufficiently precise. To provide a critical evaluation of our results, we recommend implementation of the proposed method in one or more countries. This recommended study will permit examination of logistical considerations in implementation of required changes in data acquisition and will provide data to replicate the analysis with teacher-centered weights. [ABSTRACT FROM AUTHOR]

The researchers wanted to highlight the goal of the research by designing and implementing a proposed device to develop the skill of the side throw for the Al-Qadisiyah University football team. The experimental method was used to avoid the research problem. The research sample consisted of (14) players from the advanced category, namely (AlQadisiyah University football team). Their pre-measurements were collected (height, weight, overhead throw-in test, arm strength test). A device for developing the side throw-in skill was prepared and implemented by the researcher. Given the absence of such a device for this skill, the main experiment and the post-test were conducted. And to verify the sample data to facilitate its statistical processing, the results of the pre- and post-arithmetic calculations for the experimental group were compared and processed statistically. After that, the most important conclusions were reached, which is that the device has a positive effect in increasing the research sample’s indicators of muscular strength and throw-in distance. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to study weight-semi-greedy Markushevich bases, and in particular, find conditions under which such bases are weight-almost greedy. In this context, we prove that, for a large class of weights, the two notions are equivalent. We also show that all weight semi-greedy bases are truncation quasi-greedy and weight-superdemocratic. In all of the above cases, we also bring to the context of weights the weak greedy and Chebyshev greedy algorithms—which are frequently studied in the literature on greedy approximation. In the course of our work, a new property arises naturally and its relation with squeeze symmetric and bidemocratic bases is given. In addition, we study some parameters involving the weak thresholding and Chebyshevian greedy algorithms. Finally, we give examples of conditional bases with some of the weighted greedy-type conditions we study. [ABSTRACT FROM AUTHOR]

Let (X , d , μ) is a space of homogeneous type, we establish a new class of fractional-type variable weights A p (·) , q (·) (X) . Then, we get the new weighted strong-type and weak-type characterizations for fractional maximal operators M η on weighted variable Lebesgue spaces over (X , d , μ) . This study generalizes the results by Cruz-Uribe–Fiorenza–Neugebauer (J Math Anal Appl 64(394):744–760, 2012), Bernardis–Dalmasso–Pradolini (Ann Acad Sci Fenn-M 39:23-50, 2014), Cruz-Uribe–Shukla (Stud Math 242(2):109–139, 2018), and Cruz-Uribe–Cummings (Ann Fenn Math 47(1):457–488, 2022). [ABSTRACT FROM AUTHOR]

Univariate regression trees have representation problems for non-orthogonal regression functions. Ensembles of univariate regression trees have better representational power. In some cases, weighted ensembles have shown better performance than unweighted ensembles. In this paper, we study the properties of ensembles of regression trees by using regression classification models. We propose a theoretical framework to study the representational power of infinite-sized weighted ensembles, consisting of randomised finite-sized regression trees. We show for some datasets that the weighted ensembles may have better representational power than unweighted ensembles, but the performance is highly dependent on the weighting scheme and the properties of datasets. Our model cannot be used for all the datasets. However, for some datasets, we can accurately predict the experimental results of ensembles of regression trees. [ABSTRACT FROM AUTHOR]

Abstract This paper extends existing work on teacher weighting in student-centered surveys by looking into aspects of practical implementation of deriving and using weights for teacher-centered analysis in the Trends in International Mathematics and Science Study (TIMSS) and the Progress in International Reading Literacy Study (PIRLS). The formal conditions to compute teacher-centered weights are detailed, including mathematical equations. We provide a proposal on how to define the targeted populations as well as how to collect data that is needed to derive teacher-centered weights, yet currently unavailable. We also tackle the issue of teacher nonresponse by proposing a respective adjustment factor, as well as mentioning the challenge of multiple selection probabilities when teachers teach in multiple schools. The core part of the paper focuses on studying the level of accuracy that can be expected when estimating teacher population characteristics. We use TIMSS 2019 data and simulate likely scenarios regarding the variance in weights. The results show that (i) the different weighting scenarios lead to relatively similar estimates; however, the differences between the scenarios are sufficient to justify the recommendation to use correctly derived teacher weights; (ii) differences between estimated standard errors based on complex sampling and corresponding estimates based on simple random sampling are sufficiently consistent to support use of a procedure to estimate standard errors that accounts for both sample weights and the complex sampling design; (iii) sample sizes and variance in weights significantly limit estimate precision, so that total population estimates with sufficient precision are available in the majority of countries but subpopulation features are generally not sufficiently precise. To provide a critical evaluation of our results, we recommend implementation of the proposed method in one or more countries. This recommended study will permit examination of logistical considerations in implementation of required changes in data acquisition and will provide data to replicate the analysis with teacher-centered weights.

In this article, we address sparse bounds for a class of spectral multipliers that include oscillating multipliers on stratified Lie groups. Our results can be applied to obtain weighted bounds for general Riesz means and for solutions of dispersive equations. [ABSTRACT FROM AUTHOR]

Abstract Background We aimed to establish a novel method for automatically constructing three-dimensional (3D) median sagittal plane (MSP) for mandibular deviation patients, which can increase the efficiency of aesthetic evaluating treatment progress. We developed a Euclidean weighted Procrustes analysis (EWPA) algorithm for extracting 3D facial MSP based on the Euclidean distance matrix analysis, automatically assigning weight to facial anatomical landmarks. Methods Forty patients with mandibular deviation were recruited, and the Procrustes analysis (PA) algorithm based on the original mirror alignment and EWPA algorithm developed in this study were used to construct the MSP of each facial model of the patient as experimental groups 1 and 2, respectively. The expert-defined regional iterative closest point algorithm was used to construct the MSP as the reference group. The angle errors of the two experimental groups were compared to those of the reference group to evaluate their clinical suitability. Results The angle errors of the MSP constructed by the two EWPA and PA algorithms for the 40 patients were 1.39 ± 0.85°, 1.39 ± 0.78°, and 1.91 ± 0.80°, respectively. The two EWPA algorithms performed best in patients with moderate facial asymmetry, and in patients with severe facial asymmetry, the angle error was below 2°, which was a significant improvement over the PA algorithm. Conclusions The clinical application of the EWPA algorithm based on 3D facial morphological analysis for constructing a 3D facial MSP for patients with mandibular deviated facial asymmetry deformity showed a significant improvement over the conventional PA algorithm and achieved the effect of a dental clinical expert-level diagnostic strategy.

Recently, there has been a rapid increase in the volume of medical image repositories because of the maximized utilization of digitized image data in hospitals. As a result, there is a complexity in querying and handling such large databases, which has led to the development of a novel Content-Based Medical Image Retrieval (CBMIR) technique. In this work, the CBMIR technique is developed using three types of visual features (i.e., color, texture and shape) along with 12 distance measures, and is optimized with the Crossover-Gravitational Search Algorithm (CRO-GSA). For ease, we named this technique Content-Based Medical Image Retrieval using Visual features with CRO-GSA (CBMIRVC). The initial step of our CBMIRVC technique is to extract three types of visual features from the images. Then, each type of feature is employed with a suitable distance measure, which is used for the computation of image similarities between the medical images in the database and a provided query image. The optimization of our proposed CBMIRVC technique is done by the CRO-GSA algorithm. This algorithm optimizes the CBMIRVC technique by identifying the optimal combinations of visual features and similarity measures. Additionally, optimal weights are computed in accordance with the three types of features for the three similarities. Experimental validations were done using retrieved cancer-related and endoscopic color images from databases holding numerous image categories. The experimental outcome shows that our proposed CBMIRVC technique outperforms other conventional image retrieval techniques by effectively retrieving similar images from large databases. [ABSTRACT FROM AUTHOR]

Auxiliary information sources, a subset of target recognition data sources, play a significant role in target recognition. The reliability and importance of these sources can vary, thereby affecting the effectiveness of the data provided. Consequently, it is essential to integrate these auxiliary information sources prior to their utilization for identification. The Dempster-Shafer (DS) evidence theory, a well-established data-fusion method, offers distinct advantages in handling and combining uncertain information. In cases where conflicting evidence sources and minimal disparities in fundamental probability allocation are present, the implementation of DS evidence theory may demonstrate deficiencies. To address these concerns, this study refined DS evidence theory by introducing the notion of invalid evidence sources and determining the similarity weight of evidence sources through the Pearson correlation coefficient, reflecting the credibility of the evidence. The significance of evidence is characterized by entropy weights, taking into account the uncertainty of the evidence source. The proposed asymptotic adjustment compression function adjusts the basic probability allocation of evidence sources using comprehensive weights, leading to symmetric compression and control of the influence of evidence sources in data fusion. The simulation results and their application in ship target recognition demonstrate that the proposed method successfully incorporates basic probability allocation calculations for ship targets in various environments. In addition, the method effectively integrates data from multiple auxiliary information sources to produce accurate fusion results within an acceptable margin of error, thus validating its efficacy. The superiority of the proposed method is proved by comparing it with other methods that use the calculated weights to weight the basic probability allocation of the evidence sources. [ABSTRACT FROM AUTHOR]

In this work we propose a low rank approximation of areal, particularly three dimensional, data utilizing additional weights. This way we enable effective compression if additional information indicates that parts of the data are of higher interest than others. The guiding example are high fidelity finite element simulations of an abdominal aortic aneurysm, i.e. a deformed blood vessel. The additional weights encapsulate the areas of high stress, which we assume indicates the rupture risk of the aorta. The stress values on the grid are modeled as a Gaussian Markov random field and we define our approximation as a basis of vectors that solve a series of optimization problems. Each of these problems describes the minimization of an expected weighted quadratic loss. We provide an effective numerical heuristic to compute the basis under general conditions, which relies on the sparsity of the precision matrix to ensure acceptable computing time even for large grids. We explicitly explore two such bases on the surface of a high fidelity finite element grid and show their efficiency for compression. Finally, we utilize the approach as part of a larger model to predict the van Mises stress in areas of interest using low and high fidelity simulations. [ABSTRACT FROM AUTHOR]

In China, a substantial portion of highway asphalt pavements are no longer capable of accommodating increasing traffic volumes and necessitate renovation and expansion. Prior to commencing such activities, it is crucial to evaluate the performance of the existing asphalt pavements. This study developed a novel normal cloud framework integrating a comprehensive weighted indicator system for existing asphalt pavement. Five key performance indicators including riding quality index (RQI), rutting area ratio (Rr), cracking area ratio (Cr), patching area ratio (Pr), and pavement structural strength index (PSSI) were selected to holistically represent the pavement condition in highway renovation and expansion projects. Subsequently, a method was proposed to determine the weights of these indicators by integrating the analytic hierarchy process (AHP) and entropy. A normal cloud model was constructed to address data characteristics and representation of indicator fuzziness/randomness through digital cloud modeling. The model was applied to 12 sections of the Jingjintang Expressway (Tianjin section). The results revealed only one section where the normal cloud model differed from the pavement maintenance quality assessment (PQI) model. The 3D ground-penetrating radar detection results of this different section indicated that the normal cloud model more closely aligned with the road structure condition. Compared to absolute pass/fail criteria of the traditional PQI model, the cloud model offered enhanced sensitivity to define graded condition assessments essential for reconstruction planning and decision analysis. Therefore, the normal cloud model is more suitable for assessing the performance of existing asphalt pavements in highway reconstruction and extension projects compared to the PQI model. [ABSTRACT FROM AUTHOR]

Objective To explore the optimal prediction model for hantavirus hemorrhagic fever with renal syndrome (HFRS) in Hubei province, and to provide a basis for establishing a monitoring and early warning model for HFRS. Methods Using monthly surveillance data of HFRS incidence in Hubei province from 2005 to 2021, eight single time series models based on exponential smoothing (ETS), seasonal autoregressive integrated moving average (SARIMA) with and without regression variables, a state space model with Box 一 Cox transformation, ARMA errors, trend, and seasonal components (TBATS), a time series neural network model (NNETAR) with and without regression variables, a linear regression time series model (TSLM), and a cubic spline prediction model (SPLINEF) were used to build 162 models through 1 -4 model combinations. The mean absolute percentage error (MAPE) was used as an evaluation index to evaluate the fitting and prediction performance of the models. The comprehensive fitting and prediction performance were evaluated by calculating the mean MAPE of fitting and prediction. Results The TSLM model and its combined models had a comprehensive MAPE of more than 100%. Among the other 98 models, the optimal fitting models for single, two, three, and four - model combinations were SPLINEF (IL 98%), SARIMA - SPLINEF (15. 14%), SARIMA - NNETAR - REG - SPLINEF (16.06%), and SARIMA - TBAT - NNETAR 一 REG 一 SPLINEF (17. 75%), respectively. The optimal prediction models for single, two, three, and four - model combinations were SARIMA - REG (34.48%), SARIMA - REG - TBATS (22.77%), SARIMA - TBATS - SPLINEF (23.84%), and SARIMA - SARIMA - REG - TBATS - SPLINEF (22.31%), respectively. The optimal fitting and prediction models for single, two, three, and four 一 model combinations were SPLINEF (24.75%), SARIMA 一 SPLINEF (22. 55%), SARIMA - TBATS - SPLINEF (20. 92%), and SARIMA - SARIMA - REG - TBATS - SPLINEF (20.75%), respectively. Conclusion Based on the number of models, fitting and prediction accuracy, SARIMA 一 TBATS - SPLINEF is considered the optimal prediction model and can be used for monitoring and early warning of HF RS in Hubei. [ABSTRACT FROM AUTHOR]

Background: We aimed to establish a novel method for automatically constructing three-dimensional (3D) median sagittal plane (MSP) for mandibular deviation patients, which can increase the efficiency of aesthetic evaluating treatment progress. We developed a Euclidean weighted Procrustes analysis (EWPA) algorithm for extracting 3D facial MSP based on the Euclidean distance matrix analysis, automatically assigning weight to facial anatomical landmarks. Methods: Forty patients with mandibular deviation were recruited, and the Procrustes analysis (PA) algorithm based on the original mirror alignment and EWPA algorithm developed in this study were used to construct the MSP of each facial model of the patient as experimental groups 1 and 2, respectively. The expert-defined regional iterative closest point algorithm was used to construct the MSP as the reference group. The angle errors of the two experimental groups were compared to those of the reference group to evaluate their clinical suitability. Results: The angle errors of the MSP constructed by the two EWPA and PA algorithms for the 40 patients were 1.39 ± 0.85°, 1.39 ± 0.78°, and 1.91 ± 0.80°, respectively. The two EWPA algorithms performed best in patients with moderate facial asymmetry, and in patients with severe facial asymmetry, the angle error was below 2°, which was a significant improvement over the PA algorithm. Conclusions: The clinical application of the EWPA algorithm based on 3D facial morphological analysis for constructing a 3D facial MSP for patients with mandibular deviated facial asymmetry deformity showed a significant improvement over the conventional PA algorithm and achieved the effect of a dental clinical expert-level diagnostic strategy. [ABSTRACT FROM AUTHOR]

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Dam safety monitoring is critical due to its social, environmental, and economic implications. Although conventional statistical approaches have been used for surveillance, advancements in technology, particularly in Artificial Intelligence (AI) and Machine Learning (ML), offer promising avenues for enhancing predictive capabilities. We investigate the application of ML algorithms, including Boosted Regression Trees (BRT), Random Forest (RF), and Neural Networks (NN), focussing on their combination by Stacking to improve prediction accuracy on concrete dam deformation using radial displacement data from three dams. The methodology involves training first-level models (experts) using those algorithms, and a second-level meta-learner that combines their predictions using BRT, a Linear Model (LM) and the Greedy Weighted Algorithm (GWA). A comparative analysis demonstrates the superiority of Stacking over traditional methods. The GWA emerged as the most suitable meta-learner, enhancing the optimal expert in all cases, with improvement rates reaching up to 16.12% over the optimal expert. Our study addresses critical questions regarding the GWA's expert weighting and its impact on prediction precision. The results indicate that the combination of accurate experts using the GWA improves model reliability by reducing error dispersion. However, variations in optimal weights over time necessitate robust error estimation using cross-validation by blocks. Furthermore, the assignment of weights to experts closely correlates with their precision: the more accurate a model is, the more weight that is assigned to it. The GWA improves on the optimal expert in most cases, including at extreme values of error, with improvement rates up to 41.74%. Our findings suggest that the proposed methodology significantly advances AI applications in infrastructure monitoring, with implications for dam safety. [ABSTRACT FROM AUTHOR]

Abstract The use of large-scale assessments (LSAs) in education has grown in the past decade though analysis of LSAs using multilevel models (MLMs) using R has been limited. A reason for its limited use may be due to the complexity of incorporating both plausible values and weighted analyses in the multilevel analyses of LSA data. We provide additional functions in R that extend the functionality of the WeMix (Bailey et al., 2023) package to allow for the automatic pooling of plausible values. In addition, functions for model comparisons using plausible values and the ability to export output to different formats (e.g., Word, html) are also provided.

We deal with the boundedness properties of higher order commutators related to some generalizations of the multilinear fractional integral operator of order m, Iαm, from a product of weighted Lebesgue spaces into adequate weighted Lipschitz spaces, extending some previous estimates for the linear case. Our study includes two different types of commutators and suffi- cient conditions on the weights in order to guarantee the continuity properties described above. We also exhibit the optimal range of the parameters involved. The optimality is understood in the sense that the parameters defining the corresponding spaces belong to a certain region, being the weights trivial outside of it. We further show examples of weights for the class which cover the mentioned area. [ABSTRACT FROM AUTHOR]

In this article, we obtain Hessian estimates for Kolmogorov–Fokker–Planck operators in non-divergence form in several Banach function spaces. Our approach relies on a representation formula and newly developed sparse domination techniques in harmonic analysis. Our result when restricted to weighted Lebesgue spaces yields sharp quantitative Hessian estimates for the Kolmogorov–Fokker–Planck operators. [ABSTRACT FROM AUTHOR]

The use of large-scale assessments (LSAs) in education has grown in the past decade though analysis of LSAs using multilevel models (MLMs) using R has been limited. A reason for its limited use may be due to the complexity of incorporating both plausible values and weighted analyses in the multilevel analyses of LSA data. We provide additional functions in R that extend the functionality of the WeMix (Bailey et al., 2023) package to allow for the automatic pooling of plausible values. In addition, functions for model comparisons using plausible values and the ability to export output to different formats (e.g., Word, html) are also provided. [ABSTRACT FROM AUTHOR]

Erratum to article [1]: J. Brummer and V. Naibo, Weighted fractional Leibniz-type rules for bilinear multiplier operators, Potential Anal. 51(1), 71–99 (1). [ABSTRACT FROM AUTHOR]

Abstract In this article, we analyze the boundedness for the fractional bilinear Hardy operators on variable exponent weighted Morrey–Herz spaces M K ˙ q , p ( ⋅ ) α ( ⋅ ) , λ ( w ) ${M\dot{K}^{\alpha (\cdot ),\lambda}_{q,p(\cdot)}(w)}$ . Similar estimates are obtained for their commutators when the symbol functions belong to BMO space with variable exponents.

In this article, we analyze the boundedness for the fractional bilinear Hardy operators on variable exponent weighted Morrey–Herz spaces M K ˙ q , p (⋅) α (⋅) , λ (w) . Similar estimates are obtained for their commutators when the symbol functions belong to BMO space with variable exponents. [ABSTRACT FROM AUTHOR]

In order to determine the safety status and key influencing factors of TBM tunnel construction, combined with the characteristics of TBM tunnel construction, the safety risk evaluation index system of TBM tunnel construction was built and the combination weighting method based on two-tuple linguistic was proposed. The fuzzy normal distribution under the condition of multi-level intersection was introduced as the membership function distribution of fuzzy evaluation grade and the comprehensive evaluation model of safety risk of TBM tunnel construction was built. It conducted analysis by taking the Western Water Diversion Route of Anyang City in the South-to-North Water Diversion Project as an example. The results show that the construction safety risk level is in the middle risk level, and the safety accident risk is a lower risk; technical risk, equipment risk, management risk and natural risk are medium risks; geological risk is a higher risk and should be emphasized for control. [ABSTRACT FROM AUTHOR]

Background: A wide variety of static, non-progressive motor impairments, such as cerebral palsy, can develop from birth or infancy as a result of damage to the central nervous system's neuromotor components. Regulating motor activity frequently requires communication between the cerebral cortex, thalamus, basal ganglia, brain stem, Cerebellum, spinal cord, and linking sensory-motor pathways. Task-oriented training is utilized as a rehabilitation technique to hone motor skills and as a programme to enhance muscular function or strength. Objective of this study is to compare the effectiveness of Conventional therapy and task-oriented strength training in children with cerebral palsy. Purpose: To compare the effectiveness of conventional therapy and task-oriented strength training in children with cerebral palsy. Materials and Methods: A total of 48 subjects were included into the study based on the selection criteria and informed consent was obtained from the parents of the children. Group A (n=24) will be treated with Conventional shoulder exercises while Group B (n=24) will be treated with task-oriented strength training with weights. Outcome measures include Shoulder ROM and GMFCS Scale. Results should be tabulated and statistically analyzed. Both training programs will be given for 4 weeks, weekly 5 days from November 2022 to April 2023. Result: Statistical analysis made with quantitative data revealed statistically significant differences between group A and group B. The test shows that subjects who received task oriented strength training have better effects in improving the motor function of children with Cerebral Palsy. [ABSTRACT FROM AUTHOR]

In this paper, we study oscillatory properties of a fourth-order differential equation and spectral properties of a corresponding differential operator. These properties are established by first proving a new second-order Hardy-type inequality, where the weights are the coefficients of the equation and the operator. This new inequality, in its turn, is established for functions satisfying certain boundary conditions that depend on the boundary behavior of one of its weights at infinity and at zero. [ABSTRACT FROM AUTHOR]

Stroke is a major cause of mortality and disability, highlighting the importance of prevention. Clinical trials play an important role in evaluating interventions that can maximize stroke prevention. Traditional composite endpoints (TCE) used in clinical trials have limitations, as they pool together events of varying clinical importance. Weighted composite endpoints (WCE) have emerged as a solution to address these limitations and provide more accurate assessments of outcomes. In this study, we investigate the use of WCE in a previously reported negative clinical trial for stroke prevention. We analyzed data from the Vitamin Intervention for Stroke Prevention (VISP) trial, which compared high dose and low dose multivitamin therapy. We utilized weighted methods to analyze time-to-event outcomes with censoring. The primary outcomes of interest were time to nonfatal stroke, nonfatal coronary events, and death. We calculated modified Kaplan-Meier (KM) curves for each intervention group. We also performed a modified log-rank test to assess significant differences based on the weighted KM curves. The analysis included 3668 VISP trial participants, and most remained event-free throughout the study period. The TCE KM curve showed no significant difference in outcomes between high dose and low dose groups. Similarly, the WCE KM curves, with different weights assigned to each outcome, did not reveal significant differences in outcomes between the studied groups. This post-hoc analysis confirms the negative trial results of VISP and demonstrates the feasibility of using WCE in assessing nutrition-based interventions for stroke prevention. • Most cardiovascular trials use composite endpoints to determine efficacy of an outcome. • This traditional form of composite endpoint analysis is limited by pooling events of varying clinical importance. • Weighted composite outcome endpoints have emerged as a solution to provide more accurate assessments of outcomes. [ABSTRACT FROM AUTHOR]

We recall that $w\in C_{p}^+$ if there exist $\varepsilon >0$ and $C>0$ such that for any $a with $c-b and any measurable set $E\subset (a,b)$ , the following holds \[ \int_{E}w\leq C\left(\frac{|E|}{(c-b)}\right)^{\varepsilon}\int_{\mathbb{R}}\left(M^+\chi_{(a,c)}\right)^{p}w This condition was introduced by Riveros and de la Torre [33] as a one-sided counterpart of the $C_{p}$ condition studied first by Muckenhoupt and Sawyer [30, 34]. In this paper we show that given $1 if $w\in C_{q}^+$ then \[ \|M^+f\|_{L^{p}(w)}\lesssim\|M^{\sharp,+}f\|_{L^{p}(w)} \] and conversely if such an inequality holds, then $w\in C_{p}^+$. This result is the one-sided counterpart of Yabuta's main result in [37]. Combining this estimate with known pointwise estimates for $M^{\sharp,+}$ in the literature we recover and extend the result for maximal one-sided singular integrals due to Riveros and de la Torre [33] obtaining counterparts a number of operators. [ABSTRACT FROM AUTHOR]

One of the difficult problems in agriculture is predicting the crop production. At the international, regional, and crop level, it is crucial to make decisions on this. In Most of the cases, agricultural, land, climatic, atmospheric, and other characteristics are used to forecast crop production. ML is a crucial decision-support model for estimating agricultural yields, enabling choices about which crops to cultivate and what to do while they are in the growing season. Numerous ML and DL algorithms have been applied to support studies on agricultural yield prediction. In this paper, a new crop yield prediction model is proposed which includes preprocessing, feature extraction and yield prediction phase. In preprocessing, data cleaning will takes place. Higher order statistical feature, information gain and improved entropy based features are extracted in feature extraction phase. The prediction is done by the hybrid model that combines Bi-GRU model and Maxout classifiers. To enhance the performance of this hybrid classifier, a new Self Adaptive Archimedes Optimization Algorithm (SAAOA) is introduced for training the weight parameters optimally. Finally the overall performance is evaluated and the better result is determined. [ABSTRACT FROM AUTHOR]

Human Suspicious Activity Recognition (HSAR) is a critical and active research area in computer vision that relies on artificial intelligence reasoning. Significant advances have been made in this field recently due to important applications such as video surveillance. In video surveillance, humans are monitored through video cameras when doing suspicious activities such as kidnapping, fighting, snatching, and a few more. Although numerous techniques have been introduced in the literature for routine human actions (HAR), very few studies are available for HSAR. This study proposes a deep convolutional neural network (CNN) and optimal featuresbased framework for HSAR in video frames. The framework consists of various stages, including preprocessing video frames, fine-tuning deep models (Darknet 19 and Nasnet mobile) using transfer learning, serial-based feature fusion, feature selection via equilibrium feature optimizer, and neural network classifiers for classification. Fine-tuning two models using some hit and trial methods is the first challenge of this work that was later employed for feature extraction. Next, features are fused in a serial approach, and then an improved optimization method is proposed to select the best features. The proposed technique was evaluated on two action datasets, Hybrid-KTH01 and Hybrid-KTH02, and achieved an accuracy of 99.8% and 99.7%, respectively. The proposed method exhibited higher precision compared to existing state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In this paper we study the commutators of integral operator T in variable Lebesgue spaces L p (·) (R n) , with p (·) ∈ K 0 (R n) ∩ N ∞ (R n) , where T is the operator with kernel K (x , y) = k 1 (x - A 1 y) ⋯ k m (x - A m y) , A 1 , ⋯ , A m are invertible matrices and each k i satisfies certain fractional size condition S n - α i , Ψ i , and certain fractional Hörmander condition H n - α i , Ψ i , with α 1 + ⋯ + α m = n - α , 0 ≤ α < n and Ψ i are Young functions. We obtain the maximal operator M α , L r log L λ , with 1 ≤ r < α n and λ ≥ 0 , and the commutators of T are bounded from L p (·) (R n) into L q (·) (R n) , for 1 q (·) = 1 p (·) - α n and certain p (·) . Also, in the case α = 0 we obtain that the commutator of T satisfies a L log L -type endpoint estimate. [ABSTRACT FROM AUTHOR]

Auxiliary information sources, a subset of target recognition data sources, play a significant role in target recognition. The reliability and importance of these sources can vary, thereby affecting the effectiveness of the data provided. Consequently, it is essential to integrate these auxiliary information sources prior to their utilization for identification. The Dempster-Shafer (DS) evidence theory, a well-established data-fusion method, offers distinct advantages in handling and combining uncertain information. In cases where conflicting evidence sources and minimal disparities in fundamental probability allocation are present, the implementation of DS evidence theory may demonstrate deficiencies. To address these concerns, this study refined DS evidence theory by introducing the notion of invalid evidence sources and determining the similarity weight of evidence sources through the Pearson correlation coefficient, reflecting the credibility of the evidence. The significance of evidence is characterized by entropy weights, taking into account the uncertainty of the evidence source. The proposed asymptotic adjustment compression function adjusts the basic probability allocation of evidence sources using comprehensive weights, leading to symmetric compression and control of the influence of evidence sources in data fusion. The simulation results and their application in ship target recognition demonstrate that the proposed method successfully incorporates basic probability allocation calculations for ship targets in various environments. In addition, the method effectively integrates data from multiple auxiliary information sources to produce accurate fusion results within an acceptable margin of error, thus validating its efficacy. The superiority of the proposed method is proved by comparing it with other methods that use the calculated weights to weight the basic probability allocation of the evidence sources.

In China, a substantial portion of highway asphalt pavements are no longer capable of accommodating increasing traffic volumes and necessitate renovation and expansion. Prior to commencing such activities, it is crucial to evaluate the performance of the existing asphalt pavements. This study developed a novel normal cloud framework integrating a comprehensive weighted indicator system for existing asphalt pavement. Five key performance indicators including riding quality index (RQI), rutting area ratio (Rr), cracking area ratio (Cr), patching area ratio (Pr), and pavement structural strength index (PSSI) were selected to holistically represent the pavement condition in highway renovation and expansion projects. Subsequently, a method was proposed to determine the weights of these indicators by integrating the analytic hierarchy process (AHP) and entropy. A normal cloud model was constructed to address data characteristics and representation of indicator fuzziness/randomness through digital cloud modeling. The model was applied to 12 sections of the Jingjintang Expressway (Tianjin section). The results revealed only one section where the normal cloud model differed from the pavement maintenance quality assessment (PQI) model. The 3D ground-penetrating radar detection results of this different section indicated that the normal cloud model more closely aligned with the road structure condition. Compared to absolute pass/fail criteria of the traditional PQI model, the cloud model offered enhanced sensitivity to define graded condition assessments essential for reconstruction planning and decision analysis. Therefore, the normal cloud model is more suitable for assessing the performance of existing asphalt pavements in highway reconstruction and extension projects compared to the PQI model.