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Algorithmic HR systems are becoming a more prevalent interface between organizations and employees. Yet little research has examined how automated HR processes impact employee motivation. In a three‐wave study (NTime1 = 401; NTime2 = 379; NTime3 = 303), we investigated the motivational effects of HR systems that automatically capture—and make decisions based on—employee performance, and whether these effects depend on employee attributions regarding the organization's intended use of its automated HR metric system. Additionally, we test whether these motivational states affect employee task prioritization and emotional exhaustion. Results show that employees whose organizations use algorithmic HR systems, and who also attribute managerial control as intent to that system, experience higher levels of extrinsic motivation at work. This, in turn, predicts greater prioritization of metricized tasks and de‐prioritization of non‐metricized tasks. Conversely, employees who believe the purpose of algorithmic HR systems is to provide them with constructive feedback are more likely to experience intrinsic motivation, leading to reduced feelings of emotional exhaustion, greater prioritization of metricized tasks, but reduced non‐metricized behavior de‐prioritization. These results illustrate the critical importance of employee sensemaking around algorithmic HR systems as a precursor to the impact of such systems on employee motivation, behavior, and well‐being. [ABSTRACT FROM AUTHOR]

This research investigates the impact of algorithmic management on worker behaviors, focusing on workers' commitment to service quality and referral tendencies. Drawing upon the job demands‐resources model, we argue that high levels of algorithmic management could create hindrance demands that impede service quality and demotivate referral behaviors. We propose that high workload, as a challenge demand, buffers the negative effects of algorithmic management on worker outcomes. We find support for our proposed research model in an experiment with a sample of 1362 platform‐based food‐delivery riders. We also conduct a qualitative study with 21 riders, which provides a more nuanced understanding of how algorithmic management affects workers' attitudes, behaviors, and referral tendencies. [ABSTRACT FROM AUTHOR]

This paper reflects on the paradigmatic assumptions and ideologies that have shaped algorithmic management research. We identify two sets of assumptions: one about the "ontology of algorithms" (which holds that human resource management [HRM] algorithms are non‐human entities with material agency) and one about the "ontology of management" that HRM algorithms afford (which understands algorithmic management as a form of control for maximizing economic/shareholder value). We explain how these core assumptions underpin existing research of HRM algorithms, causing blind spots that hinder new ways of understanding and studying algorithmic management. After identifying and unpacking the assumptions and blind spots, we offer avenues to overcome these blind spots, allowing for future research based on new ideological assumption grounds that will help move algorithmic management scholarship further in significant ways. [ABSTRACT FROM AUTHOR]

There is an increasing body of research on algorithmic management (AM), but the field lacks measurement tools to capture workers' experiences of this phenomenon. Based on existing literature, we developed and validated the algorithmic management questionnaire (AMQ) to measure the perceptions of workers regarding their level of exposure to AM. Across three samples (overall n = 1332 gig workers), we show the content, factorial, discriminant, convergent, and predictive validity of the scale. The final 20‐item scale assesses workers' perceived level of exposure to algorithmic: monitoring, goal setting, scheduling, performance rating, and compensation. These dimensions formed a higher order construct assessing overall exposure to algorithmic management, which was found to be, as expected, negatively related to the work characteristics of job autonomy and job complexity and, indirectly, to work engagement. Supplementary analyses revealed that perceptions of exposure to AM reflect the objective presence of AM dimensions beyond individual variations in exposure. Overall, the results suggest the suitability of the AMQ to assess workers' perceived exposure to algorithmic management, which paves the way for further research on the impacts of these rapidly accelerating systems. [ABSTRACT FROM AUTHOR]

A major problem in employee selection coalesces around convincing decision‐makers (e.g., hiring managers) to use analytically derived models. Existing recommendations in the literature largely focus on convincing executives to adopt analytical models and then exert their top‐down influence on lower‐level hiring decisions. In contrast to these solutions, we explore end user modifiability (i.e., allowing decision‐makers to modify a statistical model before use) as a bottom‐up approach for increasing hiring managers' implementation of analytical recommendations. From a utility standpoint, we consider how incorporating end user modifiability into hiring decisions will result in a less statistically valid, but potentially more valuable, organizational selection process. We explore these ideas in two studies. In Study 1, we experimentally test whether model modification increases decision‐maker reliance on a statistical model, as well as how much decision‐makers need to modify a model in order to use it. In Study 2, we examine the extent that modifiability introduces implicit biases that might adversely affect marginalized groups. Results suggest that modifiability can increase decision‐makers' perceived usefulness of a model and, importantly, that only a small amount of modifiability is needed to elicit this effect. Further, end user modifications were statistically insignificant predictors of hiring rates across race‐based subgroups, though supplementary analyses suggest important cautionary nuance. Given that analytical models are rarely perfectly or wholly implemented, end user modifiability may offer a viable solution for organizations seeking to increase the implementation of algorithmic guidance in selection decisions, even if it deviates modestly from a statistical optimality. [ABSTRACT FROM AUTHOR]

Work in the gig economy is championed by platform organizations as affording individuals the flexibility to decide when, where, and how much they wish to work. The reality is more complex. In app‐based gig work, we propose the concept of "algorithmic HRM control," which acts as an omnipresent and distinctive control system that differs from traditional forms of control in two significant ways: first, the reliance upon, and pervasiveness of, algorithmic technologies in its enactment; and second, the substantial direct and indirect influence of non‐organizational parties in controlling workers. Through a qualitative research design, this article delineates the scope of algorithmic HRM control in allocating and coordinating tasks, managing performance and rewards, and aligning the actions of workers with organizational objectives. Our analysis also unpacks the rigidity and complexities of the control system, as experienced by workers, and the influential role of non‐organizational parties in exerting unique, distinct forms of control. In so doing, we build upon emerging research on the duality of algorithmic HRM by revealing the inherent flaws or challenges from the perspective of the most central party—the gig worker. While output‐oriented control is pervasive, process and normative control elements are also found to exist in some scenarios, creating significant concerns for workers. [ABSTRACT FROM AUTHOR]

Summary: Fault‐tolerance and self‐organization are critical properties in modern distributed systems. Self‐stabilization is a class of fault‐tolerant distributed algorithms which has the ability to recover from any kind and any finite number of transient faults and topology changes. In this article, we propose a self‐stabilizing distributed algorithm for the 1‐MIS problem under the unfair central daemon assuming the distance‐3 model. Here, in the distance‐3 model, each process can refer to the values of local variables of processes within three hops. Intuitively speaking, the 1‐MIS problem is a variant of the maximal independent set (MIS) problem with improved local optimizations. The time complexity (convergence time) of our algorithm is O(n)$$ O(n) $$ steps and the space complexity is O(logn)$$ O\left(\log n\right) $$ bits, where n$$ n $$ is the number of processes. Finally, we extend the notion of 1‐MIS to p$$ p $$‐MIS for each nonnegative integer p$$ p $$, and compare the set sizes of p$$ p $$‐MIS (p=0,1,2,...$$ p=0,1,2,\dots $$) and the maximum independent set. [ABSTRACT FROM AUTHOR]

Summary: Unmanned aerial vehicle operations are quickly gaining ground due to rapid global market penetration. While on one hand, novel technologies that bridge communication networks to aviation industry are yet to be explored, on the other hand, their development requires highly scalable systems to enable beyond visual line‐of‐sight missions. This requirement imposes a big bottleneck in terms of computation complexity. This paper presents a method for fast computation of multiple diffraction of radio waves over knife‐edge obstacles based on the Deygout technique and some offline computation steps, including a ground profile analysis. We prove that this algorithm is equivalent to the original Deygout algorithm for all non‐line‐of‐sight points, show heuristics confirming that it is mostly applicable in the line‐of‐sight case. The computational and memory complexity of our algorithm is approximately O(N), compared to O(N) for the original Deygout algorithm. Finally we discuss how to apply the approach to the Epstein‐Peterson technique and the Giovanelli technique, and how to use it to compute clutter‐loss. [ABSTRACT FROM AUTHOR]

Summary: Ensuring data integrity in wireless sensor networks (WSNs) is crucial for accurate monitoring, yet missing data due to sensor faults present a significant challenge. This research introduces an innovative approach that integrates advanced data recovery techniques with leading‐edge methods to address this issue. The system begins by identifying and isolating fault nodes using a specialized algorithm that analyzes network behavior. By applying fuzzy density‐based spatial clustering of applications with noise (FDBSCAN), potential fault nodes are precisely located based on deviations from expected patterns. Subsequently, an intelligent missing data recovery mechanism powered by bidirectional long short‐term memory (Bi‐LSTM) networks takes action. The Bi‐LSTM model is trained on existing sensor data to capture intricate patterns and dependencies, enabling accurate prediction and reconstruction of missing values caused by identified faults. The synergy between Bi‐LSTM for missing data recovery and FDBSCAN for fault node detection comprehensively addresses the missing data problem in WSNs. In missing data recovery, it demonstrates low mean absolute deviation (MAD) ranging from 0.021 to 0.13 and mean squared deviation (MSD) ranging from 0.0025 to 0.05 across various missing data ratios. Data reliability remains consistently high at 96% to 98%, even with up to 80% missing data. For fault node detection, the approach achieves precision of 95.7%, recall of 96.3%, F1‐score of 96.1%, and accuracy of 97.4%, outperforming existing techniques. The computational cost during training is noted at 5.79 h, presenting a limitation compared to other methods. This research highlights the importance of integrating fault node detection into missing data recovery mechanisms, presenting an innovative solution for the advancement of WSNs. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a new approach for optimizing traffic management in multiple access networks (MANETs) by utilizing the stream‐enabled routing (SER) algorithm. The SER algorithm is used to determine which routing path is the most time‐ and resource‐efficient. The proposed approach makes use of multipath routing in a manner that is consistent with the SER method. By combining the states of flows, queues, and links, a graph neural network (GNN)‐based model attempts to break the circular dependencies that are described by these functions. The simulation is setup with joint parameters consisting of residual energy, packet delivery rate (PDR), and end‐to‐end delay. The results of the experiments show that the proposed protocol provides a significant improvement in terms of network efficiency when compared to using some baseline protocols designed for MANETs. [ABSTRACT FROM AUTHOR]

In this article, the consensus problem for disturbed multi‐agent systems (MASs) under independent denial‐of‐service (DoS) attacks is investigated. The concerned attacks can jam different channels independently, resulting in a time‐varying and unknown network topology. In this situation, a dynamic event‐triggered control scheme with a hybrid communication strategy is presented to schedule information interaction over the network, and a distributed prediction‐based control algorithm is proposed to ameliorate the resilience. The system stability is proved in present of DoS attacks by introducing the decay rates of Lyapunov functions associated with different connectivity modes. In comparison with the most existing MASs studies under DoS attacks, the triggering mechanism that possibly subject to the malicious attacks executed at the triggering times is protected by the proposed randomized transmission protocol, and the tolerable attack intensities are quantified. Finally, simulations are presented to substantiate that the proposed strategy is effective. [ABSTRACT FROM AUTHOR]

The goal of this article is to investigate the bipartite tracking control problem using a prescribed‐time convergence method for Euler–Lagrange systems (ELSs) with external disturbances. Agent interactions are represented by a matrix‐weighted signed directed graph. There are not only cooperative but also adversarial interactions. An essential component of the system, the prescribed‐time hierarchical control (PTHC) algorithm, including a more general time‐varying function, is newly developed to guarantee that all agents converge to the same leader state but with opposite signs within a prescribed time. The salient feature of the introduced method lies in the fact that the convergence time of the control objective can be prespecified by the user. The state transformation, the property of the matrix‐weighted Laplacian, and the generalized Lyapunov stability argument are employed to theoretically validate the proposed algorithms. Finally, the effectiveness of the algorithm is confirmed through the execution of numerical examples. [ABSTRACT FROM AUTHOR]

Kaczmarz algorithm is an iterative projection method for solving system of linear equations that arise in science and engineering problems in various application domains. In addition to classical Kaczmarz, there are randomized and parallel variants. The main challenge of the parallel implementation is the dependency of each Kaczmarz iteration on its predecessor. Because of this dependency, frequent communication is required which results in a substantial overhead. In this study, a new distributed parallel method that reduces the communication overhead is proposed. The proposed method partitions the problem so that the Kaczmarz iterations on different blocks are less dependent. A frequency parameter is introduced to see the effect of communication frequency on the performance. The communication overhead is also decreased by allowing communication between processes only if they have shared non‐zero columns. The experiments are performed using problems from various domains to compare the effects of different partitioning methods on the communication overhead and performance. Finally, parallel speedups of the proposed method on larger problems are presented. [ABSTRACT FROM AUTHOR]

Summary: This paper develops a new image encryption algorithm based on a novel two‐dimensional chaotic map and bit‐level dynamic scrambling. First, multiple one‐dimensional chaotic maps are coupled to construct a novel two dimensions Logistic‐Sine‐coupling chaos map (2D‐LSCCM). The performance analysis shows that the 2D‐LSCCM has more complex chaotic characteristics and wider chaotic range than many extant 2D chaos maps. Second, original image matrix combines with hash algorithm SHA‐256 to generate a hash value. The initial values of 2D‐LSCCM are generated based on the hash value. Third, the original image matrix is divided into multiple sub‐matrices by wavelet transform, followed by scrambling by an improved Knuth shuffle algorithm. Fourth, the scrambled multiple sub‐matrices are stitched into an image matrix of M×N×3$$ M\times N\times 3 $$ and converted into a binary matrix. The chaotic sequence generated by 2D‐LSCCM is introduced as a control sequence to control the bit‐level scrambling of pixel points, which realizes the bit‐level dynamic scrambling. Finally, the diffusion operation is performed by parameter par and chaotic sequence to obtain the final encrypted image. The algorithm security analysis and simulation examples demonstrate the effectiveness of the proposed encryption scheme. [ABSTRACT FROM AUTHOR]

We propose a simple, efficient, and reliable procedure for implicit time stepping, regarding a special case of the viscoplasticity model proposed by Simo and Miehe (1992). The kinematics of this popular model is based on the multiplicative decomposition of the deformation gradient tensor, allowing for a combination of Newtonian viscosity and arbitrary isotropic hyperelasticity. The algorithm is based on approximation of precomputed solutions. Both Lagrangian and Eulerian versions of the algorithm with equivalent properties are available. The proposed numerical scheme is non‐iterative, unconditionally stable, and first order accurate. Moreover, the integration algorithm strictly preserves the inelastic incompressibility constraint, symmetry, positive definiteness, and w‐invariance. The accuracy of stress calculations is verified in a series of numerical tests, including non‐proportional loading and large strain increments. In terms of stress calculation accuracy, the proposed algorithm is equivalent to the implicit Euler method with strict inelastic incompressibility. The algorithm is implemented into MSC.MARC and a demonstration initial‐boundary value problem is solved. [ABSTRACT FROM AUTHOR]

Summary: The future wireless communication networks (beyond 5G) are intended to provide seamless connectivity, higher data rates, and lower latency. The intelligent reflecting surface (IRS) promises extended network coverage. The non‐orthogonal multiple access (NOMA) technique provides higher spectral efficiency and supports a larger number of users. In general, the cell‐edge users far from the base station are affected by severe fading, which may result in an outage. To overcome this issue, we propose an IRS‐aided device‐to‐device (D2D)‐enabled two cells coordinated NOMA system in which a common cell‐edge user acts as D2D receiver. The cell‐edge user receives the signals from near users, IRSs, and both base stations to achieve better diversity. We analyze the outage probability of cell‐edge user, while all the fading channels are assumed Nakagami‐ m and obtain a closed‐form expression at higher signal‐to‐noise ratios. We develop an algorithm to find the ergodic capacity of the cell‐edge user. We validate the mathematical expressions with simulation results and see a substantial improvement in outage performance over the existing systems. [ABSTRACT FROM AUTHOR]

Summary: The event‐triggered joint sensor fault estimation and mobile robot (MR) localization (MRL) problem (MRLP) subject to the potential fluctuations of the estimator gain are investigated. From the external sensor to the estimator, to decrease the consumption of sensor energy and cut down network bandwidth resources, an event‐triggered scheme is considered. For the sake of characterizing the phenomenon induced by the inaccurate calculation of estimator gain, an uncertainty with the bounded second moment is employed. The purpose of this study is theoretically to find a feasible and effective approach to the addressed joint estimation problem such that the estimation error (EE) covariance (EEC) meets the given performance index. First, a minimum upper bound (UB) of the EEC is derived. Subsequently, in terms of the proposed joint estimation approach and the corresponding results obtained, an algorithm to address the resilient joint sensor fault estimation and MRLP is summarized. At the end, the effectiveness of the proposed algorithm is validated through conducting a set of comparisive experiments. [ABSTRACT FROM AUTHOR]

This article investigates the formation composition and keeping problem for multi‐quadrotor systems under time‐varying communication delays. First, a distributed model predictive control (MPC) approach is employed to transform the formation composition and keeping of multi‐quadrotor systems into an online rolling optimization issue, accompanied by the event‐triggered mechanism to reduce solution frequency and communication load. Second, to address the asynchrony and time‐varying communication delays introduced by event‐triggered distributed MPC, a set of constraints is designed to restrict deviations between the current predictive state and previously broadcasted states. Consequently, based on the previously predicted states of neighbors, the cooperation of the multi‐quadrotor systems is achieved under asynchronous communication and time‐varying delays. This approach guarantees robust asymptotic stability and satisfactory formation performance for multi‐quadrotor systems under various delay scenarios. Finally, the numerical and software‐in‐the‐loop simulations validate the effectiveness of the proposed algorithm of multi‐quadrotors under communication delays. [ABSTRACT FROM AUTHOR]

In this study, a new ellipse‐fitting algorithm is proposed to achieve the reconstruction of bubble shapes in bubbly flow captured by a high‐speed camera in the gas–liquid two‐phase column reactor. Bubble flow patterns and geometric parameters in the experimental images are recognized and identified successfully, represented by means of the topological parameters. Three logical steps are carried out in detail. First, the area threshold and the circularity factors are established to identify the bubbles whether belonging to a single bubble or not. The overlapping bubbles in images can be separated from single bubbles based on a watershed segmentation algorithm. Second, a single bubble image and an overlapping bubble image are combined into one image. After that, statistical analysis for the size distributions and ellipse area bubbles is performed for further analysis and discussion. The advantage of this algorithm is that it can make use of a set of major and minor axes of an ellipse to capture the ellipse parameters more effectively. Simulation results are well agreed with experimental measurements. Moreover, it can be used to detect many ellipse‐like bubbles that are dispersed in high‐speed camera images, indicating that it is a better strategy for the recognition and identification of bubbly turbulent flow accurately. [ABSTRACT FROM AUTHOR]

Existing tunnel detection methods include crack and water‐leakage segmentation networks. However, if the automated detection algorithm cannot process all defect cases, manual detection is required to eliminate potential risks. The existing intelligent detection methods lack a universal method that can accurately segment all types of defects, particularly when multiple defects are superimposed. To address this issue, a defect segmentation model is proposed based on Vision Transformer (ViT), which is completely different from the network structure of a convolutional neural network. The model proposes an adapter and a decoding head to improve the training effect of the transformer encoder, allowing it to be fitted to small‐scale datasets. In post‐processing, a method is proposed to quantify the threat level for the defects, with the aim of outputting qualitative results that simulate human observation. The model showed impressive results on a real‐world dataset containing 11,781 defect images collected from a real subway tunnel. The visualizing results proved that this method is effective and has uniform criteria for single, multiple, and comprehensive defects. Moreover, the tests proved that the proposed model has a significant advantage in the case of multiple‐defect superposition, and it achieved 93.77%, 88.36%, and 92.93% for mean accuracy (Acc), mean intersection over union, and mean F1‐score, respectively. With similar training parameters, the Acc of the proposed method is improved by more than 10% over the DeepLabv3+, Mask R‐convolutional neural network, and UPerNet‐R50 models and by more than 5% over the Swin Transformer and ViT‐Adapter. This study implemented a general method that can process all defect cases and output the threat evaluation results, thereby making more intelligent tunnel detection. [ABSTRACT FROM AUTHOR]

Digital libraries aim to provide value to users by housing content that is accessible and searchable. Often such access is afforded through external web search engines. In this article, we measure how easily digital library content can be retrieved (i.e., how retrievable) through a well‐known search engine (Google) using its analytics platforms. Using two measures of document retrievability, we contrast our results with simulation‐based studies that employed synthetic query sets. We determine that estimating the retrievability of content given a Digital Library index is not a strong predictor of how retrievable the content is in practice (via external search engines). Retrievability established the notion that search algorithms can be biased. In our work, we find that while there such bias is present, much of the variation in retrievability appears to be strongly influenced by the queries submitted to the library, a side of retrievability less examined in past work. [ABSTRACT FROM AUTHOR]

Background and Aims: The brain age gap (BAG), calculated as the difference between a machine learning model‐based predicted brain age and chronological age, has been increasingly investigated in psychiatric disorders. Tobacco and alcohol use are associated with increased BAG; however, no studies have compared global and regional BAG across substances other than alcohol and tobacco. This study aimed to compare global and regional estimates of brain age in individuals with substance use disorders and healthy controls. Design: This was a cross‐sectional study. Setting: This is an Enhancing Neuro Imaging through Meta‐Analysis Consortium (ENIGMA) Addiction Working Group study including data from 38 global sites. Participants: This study included 2606 participants, of whom 1725 were cases with a substance use disorder and 881 healthy controls. Measurements: This study used the Kaufmann brain age prediction algorithms to generate global and regional brain age estimates using T1 weighted magnetic resonance imaging (MRI) scans. We used linear mixed effects models to compare global and regional (FreeSurfer lobestrict output) BAG (i.e. predicted minus chronological age) between individuals with one of five primary substance use disorders as well as healthy controls. Findings Alcohol use disorder (β = −5.49, t = −5.51, p < 0.001) was associated with higher global BAG, whereas amphetamine‐type stimulant use disorder (β = 3.44, t = 2.42, p = 0.02) was associated with lower global BAG in the separate substance‐specific models. Conclusions: People with alcohol use disorder appear to have a higher brain‐age gap than people without alcohol use disorder, which is consistent with other evidence of the negative impact of alcohol on the brain. [ABSTRACT FROM AUTHOR]

Automatic first‐break picking is a basic step in seismic data processing, so much so that the quality of the picking largely determines the effect of subsequent processing. To a certain extent, artificial intelligence technology has solved the shortcomings of traditional first‐break picking algorithms, such as poor applicability and low efficiency. However, some problems still remain for seismic data, with a low signal‐to‐noise ratio and large first‐break change leading to inaccurate picking and poor generalization of the network. In order to improve the accuracy of the automatic first‐break picking results of the above seismic data, we propose a multi‐view automatic first‐break picking method driven by multi‐network. First, we analysed the single‐trace boundary characteristics and the two‐dimensional boundary characteristics of the first break. Based on these two characteristics of the first break, we used the Long Short‐Term Memory and the ResNet attention gate UNet (resudual attention gate UNet) networks to extract the characteristics of the first arrival and its location from the seismic data, respectively. Then, we introduced the idea of multi‐network learning in the first‐break picking work and designed a feature fusion network. Finally, the multi‐view first‐break features extracted by the Long Short‐Term Memory and resudual attention gate UNet networks are fused, which effectively improves the picking accuracy. The results obtained after applying the method to field seismic data show that the accuracy of the first break detected by a feature fusion network is higher than that given by the above two networks alone and has good applicability and resistance to noise. [ABSTRACT FROM AUTHOR]

Prior beliefs are central to Bayesian accounts of cognition, but many of these accounts do not directly measure priors. More specifically, initial states of belief heavily influence how new information is assumed to be utilized when updating a particular model. Despite this, prior and posterior beliefs are either inferred from sequential participant actions or elicited through impoverished means. We had participants to play a version of the game 'Plinko', to first elicit individual participant priors in a theoretically agnostic manner. Subsequent learning and updating of participant beliefs was then directly measured. We show that participants hold various priors that cluster around prototypical probability distributions that in turn influence learning. In follow‐up studies, we show that participant priors are stable over time and that the ability to update beliefs is influenced by a simple environmental manipulation (i.e., a short break). These data reveal the importance of directly measuring participant beliefs rather than assuming or inferring them as has been widely done in the literature to date. The Plinko game provides a flexible and fecund means for examining statistical learning and mental model updating. [ABSTRACT FROM AUTHOR]

Background: Strawberry, being an important economic crop, requires a large amount of human labor for harvesting operations. Efficient and non‐destructive harvesting by strawberry harvesting robots requires the precise location of the picking points. Current algorithms for locating picking points encounter significant issues with location errors and minimal effective information in complex situations. Results: To improve the accuracy of the location of picking points, this study proposes a visual location method based on composite models. This method employs object detection and instance segmentation models to detect fruits and segment peduncles sequentially, thereby enabling the identification of picking points and inclination on the peduncle. Different object detection algorithms and instance segmentation models were validated to explore the optimal model combination, and the Convolutional Block Attention Module (CBAM) was integrated into YOLOv8s‐seg to construct YOLOv8s‐seg‐CBAM. Test results show that the composite model built with YOLOv8s and YOLOv8s‐seg‐CBAM achieved a peduncle detection accuracy of 86.2%, with an inference time of 30.6 ms per image. Conclusion: The picking point visual location method based on YOLOv8s and YOLOv8s‐seg‐CBAM composite models can better balance accuracy and efficiency and can provide more accurate guidance for automated harvesting. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

A sufficient number of participants should be included to adequately address the research interest in the surveys with sensitive questions. In this paper, sample size formulas/iterative algorithms are developed from the perspective of controlling the confidence interval width of the prevalence of a sensitive attribute under four non‐randomized response models: the crosswise model, parallel model, Poisson item count technique model and negative binomial item count technique model. In contrast to the conventional approach for sample size determination, our sample size formulas/algorithms explicitly incorporate an assurance probability of controlling the width of a confidence interval within the pre‐specified range. The performance of the proposed methods is evaluated with respect to the empirical coverage probability, empirical assurance probability and confidence width. Simulation results show that all formulas/algorithms are effective and hence are recommended for practical applications. A real example is used to illustrate the proposed methods. [ABSTRACT FROM AUTHOR]

The task of a semantic community query is to obtain a subgraph S based on a given query vertex q (or vertex set) and other query parameters in an attributed graph G such that S belongs to G, contains q and satisfies a predefined community cohesiveness model. In most cases, existing community query models based on the network structure for traditional attributed networks usually lack community semantics. However, the features of vertex attributes, especially the attributes of the query vertices, which are closely related to the community semantics, are rarely considered in an attributed graph. Existing community query algorithms based on both structure cohesiveness and attribute cohesiveness usually do not take the attributes of the query vertex as an important factor of the community cohesiveness model, which leads to weak semantics of the communities. This paper proposes a semantic community query method named SCQ in a large‐scale attributed graph. First, the k‐core structure model is adopted as the structure cohesiveness of our community query model to obtain a subgraph of the original graph. Second, we define attribute cohesiveness based on the average distance between the query vertices and other vertices in terms of attributes in the community to prune the subgraph and obtain the semantic community. In order to improve the community query efficiency in large‐scale attributed graphs, SCQ applies two heuristic pruning strategies. The experimental results show that our method outperforms the existing community query methods in multiple evaluation metrics and is ideal for querying semantic communities in large‐scale attributed graphs. [ABSTRACT FROM AUTHOR]

As is well known, one of the main challenges in dealing with the time‐fractional diffusion‐wave equation is that the solutions have weak regularity at the initial values. This necessitates the use of nonuniform grids for error analysis. However, the theoretical analysis of nonuniform grids is relatively complex and lacks efficient and stable numerical methods. The main research problem addressed in this article is the time‐fractional diffusion‐wave equation on nonuniform grids. We use the order reduction method and discrete complementary convolution kernel to discretize the Caputo derivative of the equation and obtain the L1‐type finite element method on nonuniform grids. The properties related to discrete complementary convolutional kernels are often used for algorithm convergence analysis, which simplifies the process of finite element theory analysis and is efficient and innovative. This article demonstrates the solvability, stability, and convergence of the L1‐type finite element schemes on nonuniform grids. Finally, the consistency between the proposed finite element scheme and the convergence order results obtained from theoretical analysis was verified through experiments. [ABSTRACT FROM AUTHOR]

In this work, an efficient and stable exponential time difference method is presented for solving boundary layer problems. By combining exponential time difference schemes with spatial direct discontinuous Galerkin discretization based on exponential boundary layer approximations, the proposed algorithm not only may admit large time step sizes but also could provide good spatial approximations even if on rather coarse spatial grids in the boundary layer. Some energy stabilities of the numerical scheme are rigorously derived. Numerical examples illustrate the accuracy, stability and efficiency of the algorithm. [ABSTRACT FROM AUTHOR]

For the nonseparable noncanonical Hamiltonian systems, we propose efficient K‐symplectic‐like methods which are semiexplicit and energy‐preserving. By introducing two copies of the phase space and constructing an augmented Hamiltonian, we can separate the noncanonical Hamiltonian system into two explicitly integrable parts. Subsequently, explicit K‐symplectic methods can be constructed by using the splitting and composing method. To enforce constraints on the two copies of the phase space, we provide two transformations with energy conservation property. This enables us to obtain semiexplicit K‐symplectic‐like methods that preserve energy. Two algorithms are provided to implement the semiexplicit K‐symplectic‐like methods with energy conservation and their convergence has been proved. Numerical results on two noncanonical Hamiltonian systems demonstrate that the energy errors of our proposed methods remain bounded within machine precision over long time without exhibiting energy drift. Furthermore, the proposed methods exhibit superior computational efficiency compared to the canonicalized symplectic methods of the same order. [ABSTRACT FROM AUTHOR]

While there exists a rich array of matrix column subset selection problem (CSSP) algorithms for use with interpolative and CUR‐type decompositions, their use can often become prohibitive as the size of the input matrix increases. In an effort to address these issues, in earlier work we developed a general framework that pairs a column‐partitioning routine with a column‐selection algorithm. Two of the four algorithms presented in that work paired the Centroidal Voronoi Orthogonal Decomposition (CVOD; Centroidal Voronoi Tessellation Based Proper Orthogonal Decomposition Analysis, 2003, 137–150) and an adaptive variant (adaptCVOD) with the Discrete Empirical Interpolation Method (DEIM; SIAM J. Sci. Computer. 38 (2016), no. 3, A1454–A1482). In this work, we extend this framework and pair the CVOD‐type algorithms with any CSSP algorithm that returns linearly independent columns. Our results include detailed error bounds for the solutions provided by these paired algorithms, as well as expressions that explicitly characterize how the quality of the selected column partition affects the resulting CSSP solution. In addition to examples involving matrix approximation, we test several of our partition‐based constructions on tasks commonly encountered in model order reduction (MOR). [ABSTRACT FROM AUTHOR]

We discuss the identification of a time‐dependent potential in a time‐fractional diffusion model from a boundary measurement taken at a single point. Theoretically, we establish a conditional Lipschitz stability for this inverse problem. Numerically, we develop an easily implementable iterative algorithm to recover the unknown coefficient, and also derive rigorous error bounds for the discrete reconstruction. These results are attained by leveraging the (discrete) solution theory of direct problems, and applying error estimates that are optimal with respect to problem data regularity. Numerical simulations are provided to demonstrate the theoretical results. [ABSTRACT FROM AUTHOR]

We present an efficient numerical method to approximate the flux variable for the Darcy flow model. An important feature of our new method is that the approximate solution for the flux variable is obtained without approximating the pressure at all. To accomplish this, we introduce a user‐defined parameter delta, which is typically chosen to be small so that it minimizes the negative effect resulting from the absence of the pressure, such as inaccuracy in both the flux approximation and the mass conservation. The resulting algebraic system is of significantly smaller degrees of freedom, compared to the one from the mixed finite element methods or least‐squares methods. We also interpret the proposed method as a single step iterate of the augmented Lagrangian Uzawa applied to solve the mixed finite element in a special setting. Lastly, the pressure recovery from the flux variable is discussed and an optimal‐order error estimate for the method is obtained. Several examples are provided to verify the proposed theory and algorithm, some of which are from more realistic models such as SPE10. [ABSTRACT FROM AUTHOR]

Phase field models have been employed extensively in the study of microstructure evolution in materials. Elasticity plays an important role in solid‐state phase transformation processes, and it is usually introduced into phase field models in terms of the elastic strain energy by applying Khachaturyan–Shatalov microelasticity theory. Conventionally, this energy is derived in the reciprocal space and results in full‐space Fourier transformation in practice, which becomes bottle‐neck in large‐scale and massively‐parallel applications. In this article, we propose an error‐controlled approximation algorithm for scalable and efficient calculation of the elastic strain energy in phase field models. We first derive a real‐space convolutional representation of the elastic strain energy by representing the equilibrium displacements in the Khachaturyan–Shatalov microelasticity theory using Green's function. Then we propose an error‐controlled truncation criterion to approximate the corresponding terms in the phase field model. Finally, a carefully designed parallel algorithm is presented to carry out large‐scale simulations. The accuracy and efficiency of the proposed algorithm are demonstrated by real‐world large‐scale phase field simulations. [ABSTRACT FROM AUTHOR]

In this article, we develop stable and efficient numerical schemes for a binary fluid‐surfactant phase‐field model which consists of two Cahn–Hilliard type equations with respect to the free energy containing a Ginzburg–Landau double‐well potential, a logarithmic Flory–Huggins potential and a nonlinear coupling entropy. The numerical schemes, which are decoupled and linear, are established by the central difference spatial approximation in combination with the first‐ and second‐order exponential time differencing methods based on the convex splitting of the free energy. For the sake of the linearity of the schemes, the nonlinear terms, especially the logarithmic term, are approximated explicitly, which requires the bound preservation of the numerical solution to make the algorithm robust. We conduct the convergence analysis and prove the bound‐preserving property in details for both first‐ and second‐order schemes, where the high‐order consistency analysis is applied to the first‐order case. In addition, the energy stability is also obtained by the nature of the convex splitting. Numerical experiments are performed to verify the accuracy and stability of the schemes and simulate the dynamics of phase separation and surfactant adsorption. [ABSTRACT FROM AUTHOR]

This study explores how digital platforms generate economic and non‐economic value for a specific group of users: mumpreneurs. We collected qualitative data from 26 mumpreneurs in the United Kingdom who have caring responsibilities for young children and are running a business on the community‐based platform Instagram. We found that through using Instagram and its algorithms, mumpreneurs can create various types of value in this context. Drawing on previous research into value creation, we make several contributions to the information systems literature. First, we unpack and explain alternative forms of value generated by digital platforms. Our findings show that through community‐based platforms such as Instagram, mumpreneurs can create various types of economic and non‐economic value—engagement, cognitive, economic, and self‐preservation value—that is consistent with their business, social, and personal needs. Second, we propose a process model of value creation; and we identify two mechanisms that lead to value creation through Instagram's algorithms: recommended connectivity and adaptability. Third, we identify a temporal dimension of value creation through Instagram. This article contributes to the theory in the growing body of literature on value creation linked to digital platforms and explains several implications for theory and practice. [ABSTRACT FROM AUTHOR]

Proton MRS is used clinically to collect localized, quantitative metabolic data from living tissues. However, the presence of baselines in the spectra complicates accurate MRS data quantification. The occurrence of baselines is not specific to short‐echo‐time MRS data. In short‐echo‐time MRS, the baseline consists typically of a dominating macromolecular (MM) part, and can, depending on B0 shimming, poor voxel placement, and/or localization sequences, also contain broad water and lipid resonance components, indicated by broad components (BCs). In long‐echo‐time MRS, the MM part is usually much smaller, but BCs may still be present. The sum of MM and BCs is denoted by the baseline. Many algorithms have been proposed over the years to tackle these artefacts. A first approach is to identify the baseline itself in a preprocessing step, and a second approach is to model the baseline in the quantification of the MRS data themselves. This paper gives an overview of baseline handling algorithms and also proposes a new algorithm for baseline correction. A subset of suitable baseline removal algorithms were tested on in vivo MRSI data (semi‐LASER at TE = 40 ms) and compared with the new algorithm. The baselines in all datasets were removed using the different methods and subsequently fitted using spectrIm‐QMRS with a TDFDFit fitting model that contained only a metabolite basis set and lacked a baseline model. The same spectra were also fitted using a spectrIm‐QMRS model that explicitly models the metabolites and the baseline of the spectrum. The quantification results of the latter quantification were regarded as ground truth. The fit quality number (FQN) was used to assess baseline removal effectiveness, and correlations between metabolite peak areas and ground truth models were also examined. The results show a competitive performance of our new proposed algorithm, underscoring its automatic approach and efficiency. Nevertheless, none of the tested baseline correction methods achieved FQNs as good as the ground truth model. All separately applied baseline correction methods introduce a bias in the observed metabolite peak areas. We conclude that all baseline correction methods tested, when applied as a separate preprocessing step, yield poorer FQNs and biased quantification results. While they may enhance visual display, they are not advisable for use before spectral fitting. [ABSTRACT FROM AUTHOR]

We present new algorithms for exact string matching of long patterns. Our algorithms read q$$ q $$‐grams at constant distances and are variations of the simplified BNDM algorithm. We demonstrate the competitiveness of our solutions through practical experiments. Many of our algorithms were faster than previous methods for English and DNA patterns between 400 and 50,000 in length. Our algorithms were still better when the preprocessing time was taken into account or when the patterns were taken from a different text of the same type. [ABSTRACT FROM AUTHOR]

Eaton, Kortum, and Kramarz (2011) (EKK) discovered empirical patterns from French manufacturing firms that a baseline firm heterogeneity model could not explain. The authors proposed and estimated a model that closely matches the patterns observed in French data. This paper successfully replicates their findings using author‐provided data, re‐implementing their algorithms in Python and leveraging graphics processing unit computing to significantly boost computational speed. Applying the model to Chinese manufacturing data, despite differences in context, showed that the model explains most observed patterns well. [ABSTRACT FROM AUTHOR]

Due to the widespread applications of 2‐dimensional spherical designs, there has been an increasing requirement of modeling on the 핊2 manifold in recent years. Due to the non‐Euclidean nature of the sphere, it has some challenges to find a method to represent 2D regions on 핊2 manifold. In this paper, a skeleton‐based representation method of regions on 핊2, disk B‐spline(DBSC) on 핊2 is proposed. Firstly, we give the definition and basic algorithms of DBSC on 핊2. Then we provide the calculation method of DBSC on 핊2, which includes calculating the boundary points, internal points and their corresponding derivatives. Based on that, we give some modeling methods of DBSC on 핊2, including approximation, deformation. In the end, some stunning application examples of DBSC on 핊2 are shown. This work lays a theoretical foundation for further applications of DBSC on 핊2. [ABSTRACT FROM AUTHOR]

We propose a novel method for generating symmetric piecewise developable approximations for shapes in approximately global reflectional or rotational symmetry. Given a shape and its symmetry constraint, the algorithm contains two crucial steps: (i) a symmetric deformation to achieve a nearly developable model and (ii) a symmetric segmentation aided by the deformed shape. The key to the deformation step is the use of the symmetric implicit neural representations of the shape and the deformation field. A new mesh extraction from the implicit function is introduced to construct a strictly symmetric mesh for the subsequent segmentation. The symmetry constraint is carefully integrated into the partition to achieve the symmetric piecewise developable approximation. We demonstrate the effectiveness of our algorithm over various meshes. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions For patients with metal implants, computed tomography (CT) imaging results suffer from metal artifacts, which seriously affect image evaluation and even lead to misdiagnosis. Because spectral CT technology has the advantage of quantitative imaging, basis material decomposition, and so on, the current metal artifact reduction methods are utilizing spectral information to reduce metal artifacts with good results. However, they usually require projection data from multiple spectra or energy‐windows, which is difficult to realize in conventional CT.To satisfy the status quo, the aim of this work is to propose a metal artifact reduction (MAR) method based on single spectral CT (MARSS). By incorporating prior information, the average density of some base materials, and a constrained image reconstruction model is established. It forces the solution spaces of the materials to be discrete and finite, making the model easier to solve.The MARSS method uses the idea of discrete tomography to establish a constrained reconstruction model. By incorporating priori knowledge, the constraint forces the solution spaces of some materials to be discrete, which can effectively downsize the solution space and reduce the ill‐posedness of this problem. Then, an iteration algorithm is developed to solve this model. This algorithm iterates alternately between reconstruction and discretization. It ensures that the solution spaces are discrete while the polychromatic projection of the reconstructed image converges to that of the scanned object.The MRASS method significantly reduces artifacts and restores structures near metal to a large extent. Unlike the comparison MAR methods, it effectively prevents the introduction of new artifacts and distortion of the structure.The MARSS method can achieve MAR based on single spectral CT. Subjective and quantitative evaluation of the results show that the method significantly improves image quality compared to competing methods. [ABSTRACT FROM AUTHOR]

The point of common coupling (PCC) voltage feedforward control strategy can effectively reduce the startup inrush current and improve the anti‐interference ability of the LCL‐type converters, which is widely used in the renewable energy converters control field. With the high penetration of the new energy, the increase of grid impedance leads to the weakening of the grid. Under weak grid conditions, the PCC voltage feedforward control strategy will generate an additional feedback loop of grid‐side current, which has a great impact on the stability of the system. In this paper, the damping characteristic of the PCC voltage feedforward control strategy and the effect on the open loop characteristic of the system have been deeply analyzed. An improved PCC voltage feedforward control strategy based on a proportional plus band‐pass filter is proposed, which can effectively improve the phase margin and achieve active damping under weak grid conditions, therefore, the robustness of the converters can be effectively improved. A three‐phase LCL‐type AC/DC converter platform is built and comparative experiments are carried out to verify the effectiveness of the proposed algorithm. The experimental results are consistent with the theoretical analysis. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

ABSTRACT In this article, we propose an extension of trace ratio based Manifold learning methods to deal with multidimensional data sets. Based on recent progress on the tensor‐tensor product, we present a generalization of the trace ratio criterion by using the properties of the t‐product. This will conduct us to introduce some new concepts such as Laplacian tensor and we will study formally the trace ratio problem by discussing the conditions for the existence of solutions and optimality. Next, we will present a tensor Newton QR decomposition algorithm for solving the trace ratio problem. Manifold learning methods such as Laplacian eigenmaps, linear discriminant analysis and locally linear embedding will be formulated in a tensor representation and optimized by the proposed algorithm. Finally, we will evaluate the performance of the different studied dimension reduction methods on several synthetic and real world data sets. [ABSTRACT FROM AUTHOR]

ABSTRACT In this article, we propose an adaptive Kalman filtering with adaptive window length based on Q‐learning for dynamic systems with unknown model information. The iteration step length of the Q‐function is quantitatively adjusted through the influence function. The adaptive Kalman filtering algorithm is used to set an appropriate weight matrix for the Q‐function to estimate unknown model parameters. One numerical example and a practice‐oriented case are given to illustrate the effectiveness of the proposed method. It is shown that this filtering can provide state estimates of best accuracy among all the compared methods when the model mismatch and noise statistical characteristics change. [ABSTRACT FROM AUTHOR]

ABSTRACT The target‐attacker‐defenders game (TADs) is an important topic in multi‐agent systems, concerning group cooperation and decision‐making. To effectively counter fast attackers, this study introduces a passive defensive approach, where a group of defenders position themselves around a formation point, rather than intercept the attacker actively. An evaluation model is developed to determine the optimal formation point, and a comprehensive analysis of the model is conducted, demonstrating that the formation point changes at a slower rate than the attacker, allowing the defenders to effectively track the attacker's movements. To enhance the global search capabilities, a backbone exchange mechanism is integrated into the original pigeon‐inspired optimization (PIO) algorithm. Finally, a series of comparative experiments are conducted, providing a comprehensive assessment of the defense strategy and the improved algorithm. The simulation results indicate the defenders can block the attacker with a slower speed following the proposed strategy, while the UAVs using the comparison method fail to follow the attacker, and the attacker escape the encirclement of defenders. The results prove the effectiveness of the proposed passive method in the article. [ABSTRACT FROM AUTHOR]

After the launch of Google Scholar older papers experienced an increase in their citations, a finding consistent with a reduction in search costs and introduction of ranking algorithms. I employ this observation to examine how recombination of science takes place in the era of online search platforms. The findings show that as papers become more discoverable, their knowledge is diffused beyond their own broad field. Results are mixed when examining knowledge diffusion within the same field. The results contribute to the ongoing debate of narrowing of science. While there might a general reduction in recombination of knowledge across distant fields over the last decades, online search platforms are not the culprits. [ABSTRACT FROM AUTHOR]

ABSTRACT The autonomous economic control for the islanded AC/DC interconnected microgrid has various modes due to the restriction of the feasible regions of inner and inter‐microgrid resources. To properly handle the cooperative relationship between resources with different regulation characteristics and realize the frequency, voltage stability, and economic operation of the AC/DC interconnected microgrid, a distributed control strategy considering the regulation boundary of the consensus variable is proposed. Firstly, the influence of the variable feasible regions on the consensus control objective is analyzed, and the fundamental principle of the consensus control algorithm considering the variable regulation boundary is expounded. Secondly, the trilevel control strategy consisting of the local equipment control and inner and inter‐microgrid cooperative control is constructed. The local equipment control adopts droop control, and the cooperative control adopts consensus control to realize the elimination of the frequency and voltage deviation and the economic dispatch of active power within and between microgrids. Finally, the AC/DC interconnected microgrid model is built based on PSCAD/EMTDC, and the simulation results verify the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper presents an efficient environment map sampling algorithm designed to render high‐quality, low‐noise images with only a few light samples, making it ideal for real‐time applications. We observe that bright pixels in the environment map produce high‐frequency shading effects, such as sharp shadows and shading, while the rest influence the overall tone of the scene. Building on this insight, our approach differs from existing techniques by categorizing the pixels in an environment map into emissive and non‐emissive regions and developing specialized algorithms tailored to the distinct properties of each region. By decomposing the environment lighting, we ensure that light sources are deposited on bright pixels, leading to more accurate shadows and specular highlights. Additionally, this strategy allows us to exploit the smoothness in the low‐frequency component by rendering a smaller image with more lights, thereby enhancing shading accuracy. Extensive experiments demonstrate that our method significantly reduces shadow artefacts and image noise compared to previous techniques, while also achieving lower numerical errors across a range of illumination types, particularly under limited sample conditions. [ABSTRACT FROM AUTHOR]

In this paper, we proposed the distributed quantization and sparsity aware zero attracting least mean square (DQA‐ZA‐LMS) and its reweighted version (DQA‐RZA‐LMS) algorithms that can perform sparse spectrum sensing with the lowest power possible. The usage of the quantization aware diffusion adaptive networks has recently been proposed and they can be used in many possible mobile communicative applications. The sparsity aware feature of the proposed algorithm can help the network to track and estimate sparse random vectors that are shown to be the case with the spectrum of the new generation wireless communication systems such as 4G, 5G, 6G, and beyond. The spectrum sensing is considered in this paper to be performed by small cell eNode Bs (SC‐eNBs) for the 4th generation long term evolution (LTE) and the next generation eNB (ng‐eNB) networks for the 5th and 6th generation mobile communication systems that are scattered in an area collecting distributed quantized data from the environment and working collaboratively to estimate the sparse spectrum vectors. Our findings show that in comparison with the nonquantized version of the distributed ZA‐LMS (DZA‐LMS) and distributed regularized ZA‐LMS (DRZA‐LMS) algorithms, our proposed schemes perform considerably well using the quantized data and also reduce power consumption. [ABSTRACT FROM AUTHOR]