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The paper explains iterative and non-iterative approaches to control optimization with use of the Fourier series-based method. Both variants of the presented algorithm are used to numerically approximate optimal control of a discontinuous pendulum capsule drive. Firstly, the general algorithm and its two realizations (iterative and non-iterative) are presented. It is shown that the iterative variant assures non-decreasing quality of solutions in subsequent repetitions of the procedure and the background of such guarantees is explained. A numerical example follows: control of a self-propelled capsule drive is optimized using both approaches. Results are compared and discussed. It is expected that the presented methods can be useful in optimal control estimation for complex systems, particularly discontinuous ones. [ABSTRACT FROM AUTHOR]

We present a basic version of a deterministic iterative optimization algorithm that requires only one parameter and is often capable of finding a good solution after very few evaluations of the fitness function. We demonstrate its principles using a multimodal one-dimensional problem. For such problems, the algorithm could be applied with just a ruler and a compass, which is how it got its name. We also provide classical examples and compare its performance with six well-known stochastic optimizers. These comparisons highlight the strengths and weaknesses of RCO. Since this version does not address potential stagnation, it is best suited for low-dimensional problems (typically no more than ten), where each evaluation of a position in the search space is computationally expensive. [ABSTRACT FROM AUTHOR]

In the era of Big Data, entity resolution (ER), i.e., the process of identifying which records refer to the same entity in the real world, plays a critical role in data-integration tasks, especially in mission-critical applications where accuracy is mandatory, since we want to avoid integrating different entities or missing matches. However, existing approaches struggle with the challenges posed by rapidly changing data and the presence of dirtiness, which requires an iterative refinement during the time. We present Detective Gadget, a novel system for iterative ER that seamlessly integrates data-cleaning into the ER workflow. Detective Gadgetemploys an alias-based hashing mechanism for fast and scalable matching, check functions to detect and correct mismatches, and a human-in-the-loop framework to refine results through expert feedback. The system iteratively improves data quality and matching accuracy by leveraging evidence from both automated and manual decisions. Extensive experiments across diverse real-world scenarios demonstrate its effectiveness, achieving high accuracy and efficiency while adapting to evolving datasets. [ABSTRACT FROM AUTHOR]

In mathematical analysis theory of inequalities has considerable influence due to its massive utility in various fields of physical sciences. These are investigated via multiple approaches to acquire more precise and rectified forms of already celebrated consequences. Integral inequalities are investigated to compute the error bounds for quadrature schemes. Among all of them, one is Hermite-Hadamard inequality, which has mighty efficacy. Numerous generalizations have been proposed in the literature based on different novel and innovative procedures. In recent years, Bullen inequality has been very commonly studied inequality. The main objective of our progressive study is to establish a new set of Bullen-type inequalities concerning the Jensen-Mecer inequality. For the completion of the current investigation, we derive a new general Bullen-Mecer equality, which is beneficial to achieve our primary consequences. Furthermore, Considering the Bullen-Mecer equation, we employ the convexity property together with famous Hölder's type and Young's inequalities, bounding, and Lipschitz characteristics of functions to conclude new variants of generalized upper bounds of Bullen inequality. Also, we deliver some applications of outcomes to means, special functions, error bounds, and iterative methods to solve non-linear problems. Lastly, we verify our findings through various simulations. The advantage of the current study is that several results concerning Bullen's inequality can be retrieved from our proposed results and various new results can be achieved by choosing the values for γ and δ. By utilizing the similar technique that we have adopted new iterative schemes can be established from integral inequalities. [ABSTRACT FROM AUTHOR]

This work presents a novel approach to handling epistemic uncertainty estimates with motivation from Bayesian linear regression. We propose treating the model-dependent variance in the predictive distribution—commonly associated with epistemic uncertainty—as a model for the underlying data distribution. Using high-dimensional random feature transformations, this approach allows for a computationally efficient, parameter-free representation of arbitrary data distributions. This allows assessing whether a query point lies within the distribution, which can also provide insights into outlier detection and generalization tasks. Furthermore, given an initial input, minimizing the uncertainty using gradient descent offers a new method of querying data points that are close to the initial input and belong to the distribution resembling the training data, much like auto-completion in associative networks. We extend the proposed method to applications such as local Gaussian approximations, input–output regression, and even a mechanism for unlearning of data. This reinterpretation of uncertainty, alongside the geometric insights it provides, offers an innovative and novel framework for addressing classical machine learning challenges.

In the realm of distributed computing frameworks, such as Apache Spark and MapReduce Hadoop, the efficacy of these frameworks varies across diverse applications and algorithms contingent upon distinctive evaluation metrics and critical parameters. This research paper diligently scrutinizes the extant body of research that compares these two frameworks concerning said evaluation metrics and parameters. Subsequently, it conducts empirical investigations to authenticate the performance of these frameworks in the context of an iterative Gradient Boosting Tree Regression (GBTR) algorithm. Remarkably, the comparative analyses in previous studies encompass a spectrum of iterative machine learning regression and classification techniques, batch processing, SQL, and Graph processing algorithms. Furthermore, numerous investigations have explored the application of machine learning algorithms encompassing logistic regression, Page Rank, K-Means, KNN, and the HiBench suite. This paper presents the comparison between the two distributed computing platforms on iterative GBTR for classification task on the HIGGS dataset from the physics domain and for the regression task on the Covid-19 dataset from the healthcare domain. The empirical findings corroborate that Apache Spark exhibits superior execution speed in iterative tasks when the available physical memory significantly exceeds the dataset size. Conversely, Hadoop outperforms Spark when dealing with substantial datasets or constrained physical memory resources. [ABSTRACT FROM AUTHOR]

Pan-genome studies are important for understanding plant evolution and guiding the breeding of crops by containing all genomic diversity of a certain species. Three short-read-based strategies for plant pan-genome construction include iterative individual, iteration pooling, and map-to-pan. Their performance is very different under various conditions, while comprehensive evaluations have yet to be conducted nowadays. Here, we evaluate the performance of these three pan-genome construction strategies for plants under different sequencing depths and sample sizes. Also, we indicate the influence of length and repeat content percentage of novel sequences on three pan-genome construction strategies. Besides, we compare the computational resource consumption among the three strategies. Our findings indicate that map-to-pan has the greatest recall but the lowest precision. In contrast, both two iterative strategies have superior precision but lower recall. Factors of sample numbers, novel sequence length, and the percentage of novel sequences' repeat content adversely affect the performance of all three strategies. Increased sequencing depth improves map-to-pan's performance, while not affecting the other two iterative strategies. For computational resource consumption, map-to-pan demands considerably more than the other two iterative strategies. Overall, the iterative strategy, especially the iterative pooling strategy, is optimal when the sequencing depth is less than 20X. Map-to-pan is preferable when the sequencing depth exceeds 20X despite its higher computational resource consumption. [ABSTRACT FROM AUTHOR]

Graphics processing units (GPUs) facilitate massive parallelism and high-capacity storage, and thus are suitable for the iterative reconstruction of ultrahigh-resolution micro computed tomography (CT) scans by on-the-fly system matrix (OTFSM) calculation using ordered subsets expectation maximization (OSEM). We propose a finite state automaton (FSA) method that facilitates iterative reconstruction using a heterogeneous multi-GPU platform through parallelizing the matrix calculations derived from a ray tracing system of ordered subsets. The FSAs perform flow control for parallel threading of the heterogeneous GPUs, which minimizes the latency of launching ordered-subsets tasks, reduces the data transfer between the main system memory and local GPU memory, and solves the memory-bound of a single GPU. In the experiments, we compared the operation efficiency of OS-MLTR for three reconstruction environments. The heterogeneous multiple GPUs with job queues for high throughput calculation speed is up to five times faster than the single GPU environment, and that speed up is nine times faster than the heterogeneous multiple GPUs with the FIFO queues of the device scheduling control. Eventually, we proposed an event-triggered FSA method for iterative reconstruction using multiple heterogeneous GPUs that solves the memory-bound issue of a single GPU at ultrahigh resolutions, and the routines of the proposed method were successfully executed on each GPU simultaneously. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Dil Araştırmaları is the property of Journal of Dil Arastirmalari and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Fusing of terrestrial laser scanning (TLS) and airborne laser scanning (ALS) point cloud data has been recognized as an effective approach in forest studies. In this regard, co-registration of point clouds is considered one of the crucial steps in the integration process. Co-registering point clouds in forest environments faces various challenges, including unstable features, extensive occlusions, different viewpoints, and differences in point cloud densities. To address these intricate challenges, this study introduces an automated and robust method for co-registering TLS and ALS point clouds based on the correspondence of individual tree locations in forest environments. Initially, the positions of individual trees in both TLS and ALS data are extracted. Then, a filtering approach is applied to eliminate positions with low potential for corresponding matches in the TLS and ALS dataset. Since larger trees in the TLS data have a higher potential for corresponding matches in the ALS data, an iterative process is applied to identify correspondences between trees in both datasets. After estimating transformation parameters, the co-registration process is executed. The proposed method is applied on six datasets with varying forest complexities. The results demonstrate a high success rate up to 100% if the starting position of the TLS plots are located within ∼4 hectares (∼2000 trees). Additionally, the potential of the proposed method for co-registering TLS data with ALS data across different search areas and varying number of trees is evaluated in detail. The outcomes indicate that successful co-registration of TLS plot with 50 m diameter to ALS data is successful in the best case within a search radius of approximately 113 hectares (∼60,000 tree locations) and in the worst case for around 20 hectares (∼10,000 tree locations) depending on the forest complexity.

In the era of Big Data, entity resolution (ER), i.e., the process of identifying which records refer to the same entity in the real world, plays a critical role in data-integration tasks, especially in mission-critical applications where accuracy is mandatory, since we want to avoid integrating different entities or missing matches. However, existing approaches struggle with the challenges posed by rapidly changing data and the presence of dirtiness, which requires an iterative refinement during the time. We present Detective Gadget, a novel system for iterative ER that seamlessly integrates data-cleaning into the ER workflow. Detective Gadgetemploys an alias-based hashing mechanism for fast and scalable matching, check functions to detect and correct mismatches, and a human-in-the-loop framework to refine results through expert feedback. The system iteratively improves data quality and matching accuracy by leveraging evidence from both automated and manual decisions. Extensive experiments across diverse real-world scenarios demonstrate its effectiveness, achieving high accuracy and efficiency while adapting to evolving datasets.

Multimedia networks utilize low-power scalar nodes to modify wakeup cycles of high-performance multimedia nodes, which assists in optimizing the power-toperformance ratios. A wide variety of machine learning models are proposed by researchers to perform this task, and most of them are either highly complex, or showcase low-levels of efficiency when applied to large-scale networks. To overcome these issues, this text proposes design of a Q-learning based iterative sleep-scheduling and fuses these schedules with an efficient hybrid bioinspired multipath routing model for largescale multimedia network sets. The proposed model initially uses an iterative Q-Learning technique that analyzes energy consumption patterns of nodes, and incrementally modifies their sleep schedules. These sleep schedules are used by scalar nodes to efficiently wakeup multimedia nodes during adhoc communication requests. These communication requests are processed by a combination of Grey Wolf Optimizer (GWO) & Genetic Algorithm (GA) models, which assist in the identification of optimal paths. These paths are estimated via combined analysis of temporal throughput & packet delivery performance, with node-to-node distance & residual energy metrics. The GWO Model uses instantaneous node & network parameters, while the GA Model analyzes temporal metrics in order to identify optimal routing paths. Both these path sets are fused together via the Q-Learning mechanism, which assists in Iterative Adhoc Path Correction (IAPC), thereby improving the energy efficiency, while reducing communication delay via multipath analysis. Due to a fusion of these models, the proposed Q-Learning based Iterative sleep-scheduling & hybrid Bioinspired Multipath Routing model for Multimedia Networks (QIBMRMN) is able to reduce communication delay by 2.6%, reduce energy consumed during these communications by 14.0%, while improving throughput by 19.6% & packet delivery performance by 8.3% when compared with standard multimedia routing techniques.

The increasing development of today's technology specifically in the field of telecommunications triggers new problems associated with the increasing number of network connectivity users so that this makes the increased load of traffic on the Base Station (BS). To address traffic problems, a D2D technology is needed as a solution to enhance connectivity on 5G networks. In the use of D2D is still not optimal for dealing with the rapidly increasing load of traffic due to the large number of users. This research suggests the addition of devices in D2D communication, namely Relay Node. (RN). The results of the study showed that by using the relay aided communication scheme can improve the sumrate performance parameter is 1,970 × 107 bps and the spectral efficiency is 19,704 bpz/Hz, but less effective in the power efficiency parameter is 7,672 × 103 bps/mW due to the addition of relay devices that increase power consumption. Using iterative algorithms on relay aided communication schemes has been shown to improve performance parameters values more optimally than using full duplex and half duplex communications schemes. Therefore, the relay aided scheme is the most accurate communication scheme in dealing with transmission systems on D2D because by using the aided relay scheme, sumrate and spectral efficiency are improved by 55%.

The co-planning problem of transmission and energy storage system (ESS) requires a large amount of historical and forecasted input data to account for the volatility of renewable energy and loads. However, the large input data usually make the planning problem difficult to solve, so time series aggregation is often used to reduce the computational complexity. Nevertheless, it is difficult to guarantee the reliability of operation on the whole input data. Therefore, this paper proposes an iterative method to select extreme scenarios, and designs two indicators to select extreme scenarios, considering the system power balance and peak shaving capacity situation. Based on these two indicators, the periods of maximum load shedding and the periods of maximum renewable energy curtailment will be selected as extreme scenarios in the results of the operational optimization problem. We iteratively add extreme scenarios to the set of scenarios of the planning problem until the reliability of system operation can be adequately met. At the same time, in order to ensure the effectiveness of extreme scenarios, the operation statuses of thermal units in those periods are also taken into account. Our method is tested on an IEEE RTS-24 system with some modification. The results show that our method can guarantee the reliability of the whole system and is superior to the method that simply selects extreme scenarios. Meanwhile, we also perform a sensitivity analysis of the price of energy storage.

Pan-genome studies are important for understanding plant evolution and guiding the breeding of crops by containing all genomic diversity of a certain species. Three short-read-based strategies for plant pan-genome construction include iterative individual, iteration pooling, and map-to-pan. Their performance is very different under various conditions, while comprehensive evaluations have yet to be conducted nowadays. Here, we evaluate the performance of these three pan-genome construction strategies for plants under different sequencing depths and sample sizes. Also, we indicate the influence of length and repeat content percentage of novel sequences on three pan-genome construction strategies. Besides, we compare the computational resource consumption among the three strategies. Our findings indicate that map-to-pan has the greatest recall but the lowest precision. In contrast, both two iterative strategies have superior precision but lower recall. Factors of sample numbers, novel sequence length, and the percentage of novel sequences’ repeat content adversely affect the performance of all three strategies. Increased sequencing depth improves map-to-pan’s performance, while not affecting the other two iterative strategies. For computational resource consumption, map-to-pan demands considerably more than the other two iterative strategies. Overall, the iterative strategy, especially the iterative pooling strategy, is optimal when the sequencing depth is less than 20X. Map-to-pan is preferable when the sequencing depth exceeds 20X despite its higher computational resource consumption.

In this work, we propose a numerical algorithm for calculating the magnetic field in an open magnetic trap, which is an axisymmetric chamber filled with plasma. The plasma is held in the trap by a special configuration of the magnetic field generated by current coils located at the ends of the chamber. The problem consists in developing an efficient algorithm for calculating the configuration of the magnetic field, which is determined by a given distribution of the external azimuthal current in the coils. The task is solved in two steps. First, the magnetic field distribution is found from the known arrangement of coils, and then this distribution is scaled so that the magnitude of the field in the center of the chamber and the mirror ratio are equal to the given values. The proposed algorithm can be easily generalized to solve the Poisson equation with Neumann boundary conditions on two opposing boundaries of the computational domain. This allows us to apply the developed method to calculate the potential in nonstationary problems. [ABSTRACT FROM AUTHOR]

In this paper, we consider a first-order iterative differential inclusion. We study the existence of solutions and some topological proprieties of the attainable set, where the right hand side is an upper semi-continuous multifunction with convex values. Then, we treat the autonomous problem under assumptions that do not require the convexity of the values and that weaken the assumption on the upper semi-continuity. [ABSTRACT FROM AUTHOR]

Purpose: A wide variety of encryption-based, key-exchange-based, privacy-based, and confidentiality-based models are proposed by researchers, which assist in enhancing security performance under real-time attacks. But most of these models are either unsalable due to their QoS (Quality of Service) performance under large loads, or showcase lower attack detection efficiency for heterogeneous attacks. Methods: To overcome these issues, this text proposes the design of a novel iterative learning contextual-sidechaining model for improving the security of priority-aware cloud resources. The proposed model initially uses a single-chained blockchain, which is split into multiple sidechains via Grey Wolf Optimization (GWO). These sidechains are reconfigured via a Bacterial Foraging Optimizer (BFO), which assists in deciding encryption and hashing techniques for individual chains. The BFO Model uses a resource-level priority metric, which assists in deciding the level of security and QoS for individual sidechains. The model is further extended by a Particle Swarm Optimizer (PSO) which assists in iteratively optimizing the GWO & BFO Models for QoS & Security enhancements. Results: Due to these enhancements, the proposed model is able to improve multiple QoS metrics even under heterogeneous attacks. The proposed model was tested under Sybil, distributed denial of service, masquerading, and spoofing attacks with multiple communication configurations. Conclusion: It was observed that the model was able to reduce the delay needed for communication by 8.5 %, improve the energy efficiency by 3.9 %, and increase the throughput by 4.5 %, and improve the request processing performance by 2.5 % under large-scale requests. [ABSTRACT FROM AUTHOR]

Abstract Background Because children spend much of their time in schools, schools can play an important role in asthma education for the one in 12 affected children in the United States. School-based asthma education programs are commonly repeated annually, however few studies have evaluated the impact of repeated participation in asthma education in school-based programs. Methods This observational study evaluated the impact of Fight Asthma Now© (FAN), a school-based asthma education program for children in Illinois schools. Participants completed a survey at the start and end of the program, including demographics, prior asthma education, and 11 asthma knowledge questions (maximum knowledge score = 11). Results Among 4,951 youth participating in the school-based asthma education program, mean age was 10.75 years. Approximately half were male and Black. Over half reported no prior asthma education (54.6%). At baseline, repeat attendees had significantly higher knowledge versus first-time attendees (mean: 7.45 versus 5.92; p

In this paper, we propose a class of successive over relaxation-based alternately linearized implicit iteration method for computing the minimal nonnegative solution of nonsymmetric algebraic Riccati equations. Under certain conditions, we prove the convergence of the iterative method. Finally, numerical examples are given to show the iterative method is efficient.

ObjectivesCommunity popular opinion leaders have played a critical role in HIV prevention interventions. However, it is often difficult to identify these 'HIV influencers' who are qualified and willing to promote HIV campaigns, especially online, because social media influencers change frequently. We sought to use an iterative deep learning framework to automatically discover HIV-related online social influencers.Design and methodOut of 1.15 million Twitter users' data from March 2018 to March 2020, we extracted tweets from 1099 Twitter users who had mentioned the keywords 'HIV' or 'AIDS'. Two Twitter users determined to be 'online HIV influencers' based on their conversation topics and engagement were hand-picked by domain experts and used as a seed training dataset. We modelled social influence and discovered new potential influencers based on these seeds using a graph neural network model. We tested the model's precision and recall compared with other baseline model approaches. We validated the results through manual verification.ResultsThe model identified 23 new (manually verified) HIV-related influencers, including health and research organizations and local HIV advocates across the United States. Our proposed model achieved the highest accuracy/recall, with an average improvement of 38.5% over the other baseline models.ConclusionResults suggest that iterative deep learning models can be used to automatically identify new and changing key HIV-related influencers online. We discuss the implications and potential of HIV researchers/departments applying this approach across online big data (e.g. hundreds of millions of social media posts per day) to help promote HIV prevention campaigns to affected communities.

This paper explores an iterative approach to solve linear thermo-poroelasticity problems, with its application as a high-fidelity discretization utilizing finite elements during the training of projection-based reduced order models. One of the main challenges in addressing coupled multi-physics problems is the complexity and computational expenses involved. In this study, we introduce a decoupled iterative solution approach, integrated with reduced order modeling, aimed at augmenting the efficiency of the computational algorithm. The iterative technique we employ builds upon the established fixed-stress splitting scheme that has been extensively investigated for Biot’s poroelasticity. By leveraging solutions derived from this coupled iterative scheme, the reduced order model employs an additional Galerkin projection onto a reduced basis space formed by a small number of modes obtained through proper orthogonal decomposition. The effectiveness of the proposed algorithm is demonstrated through numerical experiments, showcasing its computational prowess.

An innovating organization faces the challenge of how to prioritize distinct goals of novelty and value, both of which underlie innovation. Popular practitioner frameworks like Agile management suggest that organizations can adopt an iterative approach of frequent meetings to prioritize between these goals, a practice we refer to as iterative coordination. Despite iterative coordination's widespread use in innovation management, its effects on novelty and value in innovation remain unknown. With the information technology firm Google, we embed a field experiment within a hackathon software development competition to identify the effect of iterative coordination on innovation. We find that iterative coordination causes firms to implicitly prioritize value in innovation: Although iteratively coordinating firms develop more valuable products, these products are simultaneously less novel. Furthermore, by tracking software code, we find that iteratively coordinating firms favor integration at the cost of knowledge-creating specialization. A follow-on laboratory study documents that increasing the frequency and opportunities to reprioritize goals in iterative coordination meetings reinforces value and integration, while reducing novelty and specialization. This article offers three key contributions: highlighting how processes to prioritize among multiple performance goals may implicitly favor certain outcomes; introducing a new empirical methodology of software code version tracking for measuring the innovation process; and leveraging the emergent phenomenon of hackathons to study new methods of organizing. History: This paper has been accepted for the Organization Science Special Issue on Experiments in Organizational Theory. Funding: The authors gratefully acknowledge financial support from Google LLC, the Harvard Business School Division of Research and Faculty Development, and the National Science Foundation [Grant 1122374]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2021.1499. [ABSTRACT FROM AUTHOR]

This paper discuss necessary and sufficient conditions for the existence of Hermite positive definite solutions of the quadratic matrix equation X²+BX+XB*+Q=0. on the quaternion field and its iterative solution method. Some necessary and sufficient conditions for the existence of Hermite positive definite solutions of this equation are proved mainly for the characteristics of coefficient matrices by introducing appropriate parameters to establish matrix inequalities and by using the theory of fixed points on convex sets. On this basis, three convergent iteration formats are constructed for different conditions and solution existence intervals, and the selection of the initial matrix is given according to each iteration property. The algorithm is solved in the Matlab environment by using the complexization operator of the quaternion matrix. At the same time a perturbation analysis is carried out on the solution of the equation, and two perturbation error bounds are obtained. Three numerical examples are used to test the effectiveness and feasibility of the given method. [ABSTRACT FROM AUTHOR]

Prosthetic polymeric heart valves (PHVs) have the potential to overcome the inherent material and design limitations of traditional valves in the treatment of valvular heart disease; however, their durability remains limited. Optimal design of the valve structure is necessary to improve their durability. This study aimed to enhance the fatigue resistance of PHVs by improving the stress distribution. Iterative subregional thickening of the leaflets was used, and the mechanical stress distribution and hemodynamics of these polymeric tri‐leaflet valves were characterized using a fluid–structure interaction approach. Subregional thickening led to a reduction in stress concentration on the leaflet, with the effective orifice area still meeting ISO 5840‐3 and the regurgitant volume achieving a similar value to those in previous studies. The maximum stress in the final iteration was reduced by 28% compared with that of the prototype. The proposed method shows potential for analyzing the stress distribution and hemodynamic performance of subregional thickened valves and can further improve the durability of PHVs. [ABSTRACT FROM AUTHOR]

This paper will start by focusing on the morphosemantics of a Latin verb lūgeō 'mourn', which represents an emotion felt by people mentally excruciated by their loved one's death. Traditionally, it has been believed that the Proto-Indo-European verbal root *leu̯g- 'break' underlies lūgeō, but recently this etymology has been challenged. However, I will support the traditional 'break' hypothesis through a novel semantic comparison to doleō 'feel pain', a verb also expressing a type of sensation humans often experience. Since its underlying root *delh1- means 'hew, split', similar to 'break', the semantic development of doleō would provide a neat parallel for lūgeō. Having salvaged the connection with *leu̯g-, I will advocate a stative formation (with *-eh1-i̯é-) for lūgeō instead of the more commonly presumed iterative reconstruction (with *-éi̯e-). The analysis conducted for lūgeō turns out to be useful for doleō, too; I will propose that the latter verb's wider semantic range is best explained as the result of the convergence of two formations, a stative form meaning 'feel pain' (with *-eh1-i̯é-) and an iterative form meaning 'habitually give pain to' (with *-éi̯e-, as previously assumed for this verb). [ABSTRACT FROM AUTHOR]

Background: To increase uptake of implementation science (IS) methods by researchers and implementers, many have called for ways to make it more accessible and intuitive. The purpose of this paper is to describe the iPRISM webtool (Iterative, Practical, Robust Implementation and Sustainability Model) and how this interactive tool operationalizes PRISM to assess and guide a program's (a) alignment with context, (b) progress on pragmatic outcomes, (c) potential adaptations, and (d) future sustainability across the stages of the implementation lifecycle. Methods: We used an iterative human-centered design process to develop the iPRISM webtool. Results: We conducted user-testing with 28 potential individual and team-based users who were English and Spanish speaking from diverse settings in various stages of implementing different types of programs. Users provided input on all aspects of the webtool including its purpose, content, assessment items, visual feedback displays, navigation, and potential application. Participants generally expressed interest in using the webtool and high likelihood of recommending it to others. The iPRISM webtool guides English and Spanish-speaking users through the process of iteratively applying PRISM across the lifecycle of a program to facilitate systematic assessment and alignment with context. The webtool summarizes assessment responses in graphical and tabular displays and then guides users to develop feasible and impactful adaptations and corresponding action plans. Equity considerations are integrated throughout. Conclusions: The iPRISM webtool can intuitively guide individuals and teams from diverse settings through the process of using IS methods to iteratively assess and adapt different types of programs to align with the context across the implementation lifecycle. Future research and application will continue to develop and evaluate this IS resource. [ABSTRACT FROM AUTHOR]

We consider the system of linear equations arising from the finite element discretization of the distributed optimal control problem with time‐periodic parabolic equations. A block alternating splitting iteration (BASI) method is presented for solving the obtained system. We prove that the BASI method is unconditionally convergent. We derive the BASI preconditioner and present an estimation formula for the parameter of the BASI preconditioner. Numerical results are presented to verify the efficiency of both the BASI method and the BASI preconditioner. Comparison with some existing methods are also given. [ABSTRACT FROM AUTHOR]

One way to estimate mortality in countries with incomplete data is to utilize intercensal methods, which do not require model life tables and provide accurate results even in the presence of age distortions and death underregistration. In this article, I revisit three of these techniques (census based, death distribution, and an iterative procedure) and introduce ilt, a command to calculate singledecrement life tables and the net flow of migrants by age. The required inputs are two age-specific population distributions and the average number of deaths between them. The empirical example draws on data from Vietnam, but the methods are extendable to any context and period. [ABSTRACT FROM AUTHOR]

In this article, we describe an iterative algorithm for accurate superposition of contours with non-uniform sampling step. The processing contours are characterized by the same shape, but the sampling step is non-uniform, with no matching between points of the superposed contours. This makes impossible the use of methods for estimating superposition parameters by matching points. The algorithm proposed herein allows estimating the offsets and rotation angle separately. The idea of the algorithm is to perform the iterative correction of parameters. An estimate of the offsets is used to estimate the rotation angle and, vice versa, an estimate of the rotation angle is used to estimate the offsets. The proposed algorithm is characterized by a higher speed of processing than a brute force algorithm and a lower estimation error than algorithms that analyze contour macroparameters.

The seismic records in the Cambrian southwest depression of the Tarim Basin exhibit discrepancies when compared to the actual geological setting, which is caused by the presence of multiples. Despite the application of the Radon transform, multiple interferences persist beneath the Cambrian salt in the pre-stack data, with significant variations in energy and frequency across the horizontal direction. In addition, other multiple suppression methods are also difficult to handle this problem. To address this issue, we have developed an iterative virtual event internal multiple suppression method for post-stack data. This novel algorithm extends the traditional virtual event internal multiple suppression approach, eliminating the need for data regularization and avoiding the problem of the traditional virtual events method requiring sequential extraction of primaries from relevant layers, which greatly improves computational efficiency and simplifies the implementation steps of the traditional method. Numerical experiments demonstrate the efficacy of our method in suppressing internal multiples in both synthetic and field data while preserving primary signals. When applied to real seismic data profiles, the iterative method yields structural characteristics that align more closely with sedimentary laws and reduces disparities in energy and frequency of multiples along the horizontal axis. Consequently, our method provides a robust foundation for subsequent hydrocarbon source rock prediction. [ABSTRACT FROM AUTHOR]

Background: Because children spend much of their time in schools, schools can play an important role in asthma education for the one in 12 affected children in the United States. School-based asthma education programs are commonly repeated annually, however few studies have evaluated the impact of repeated participation in asthma education in school-based programs. Methods: This observational study evaluated the impact of Fight Asthma Now© (FAN), a school-based asthma education program for children in Illinois schools. Participants completed a survey at the start and end of the program, including demographics, prior asthma education, and 11 asthma knowledge questions (maximum knowledge score = 11). Results: Among 4,951 youth participating in the school-based asthma education program, mean age was 10.75 years. Approximately half were male and Black. Over half reported no prior asthma education (54.6%). At baseline, repeat attendees had significantly higher knowledge versus first-time attendees (mean: 7.45 versus 5.92; p < 0.001). After the program, both first-time and repeat attendees had significant knowledge improvements (first-time: mean = 5.92◊9.32; p < 0.001; repeat: mean = 7.45◊9.62; p < 0.001). Conclusions: School-based asthma education is effective for increasing asthma knowledge. Notably, repeated asthma education in school leads to incremental benefits for knowledge. Future studies are needed to understand the effects of repeated asthma education on morbidity. [ABSTRACT FROM AUTHOR]

Due to the precision of the first break, time-variant wavelet, and strata dip angle, the popular iterative vertical seismic profiling (VSP) wavefield separation method may not yield high-precision wavefield separation results. The single-stage multitask U-Network (SUN) VSP wavefield separation method can avoid the impact of the first break, time-variant wavelet, and the strata dip angle, but it faces challenge in complex VSP wavefield due to its network performance. In this letter, based on the iterative VSP wavefield separation method (ISM), the U-Network and multitask deep learning, we propose a two-stage multitask U-Network VSP wavefield separation method (TSM). The TSM comprises the two-stage multitask U-Network (TUN), the loss function, and the synthetic VSP training data automatic generation (STG). The TUN aims to simultaneously output high-precision downgoing and upgoing wavefield, as well as the residual wavefield, while the STG aims to automatically generate numerous and various VSP training data. Applications of both synthetic and actual VSP data demonstrate that the TSM can be widely used for high-precision VSP wavefield separation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Social Sciences Research / Sosyal Bilimler Arastirmalari Dergisi is the property of ODU Journal of Social Sciences Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we study the existence and uniqueness of solution for a perturbed first-order iterative differential inclusion governed by a maximal monotone operator. This result allows us to extend to Bolza-type relaxation property of an optimal control problem associated with such equations where the controls are Young measures. [ABSTRACT FROM AUTHOR]