Search

Background: The Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) has long recognized and championed increasing diversity within the surgical workplace. SAGES initiated the Fundamentals of Leadership Development (FLD) Curriculum to address these needs and to provide surgeon leaders with the necessary tools and skills to promote diversity, equity, and inclusion (DEI) in surgical practice. In 2019, the American College of Surgeons issued a request for anti-racism initiatives which lead to the partnering of the two societies. The primary goal of FLD was to create the first surgeon-focused leadership curriculum dedicated to DEI. The rationale/development of this curriculum and its evaluation/feedback methods are detailed in this White Paper. Methods: The FLD curriculum was developed by a multidisciplinary task force that included surgeons, education experts, and diversity consultants. The curriculum development followed the Analysis, Design, Development, Implementation and Evaluation (ADDIE) instructional design model and utilized a problem-based learning approach. Competencies were identified, and specific learning objectives and assessments were developed. The implementation of the curriculum was designed to be completed in short intervals (virtual and in-person). Post-course surveys used the Kirkpatrick's model to evaluate the curriculum and provide valuable feedback. Results: The curriculum consisted of interactive online modules, an online discussion forum, and small group interactive sessions focused in three key areas: (1) increasing pipeline of underrepresented individuals in surgical leadership, (2) healthcare equity, and (3) conflict negotiation. By focusing on positive action items and utilizing a problem-solving approach, the curriculum aimed to provide a framework for surgical leaders to make meaningful changes in their institutions and organizations. Conclusion: The FLD curriculum is a novel leadership curriculum that provided surgeon leaders with the knowledge and tools to improve diversity in three areas: pipeline improvement, healthcare equity, and conflict negotiation. Future directions include using pilot course feedback to enhance curricular effectiveness and delivery. [ABSTRACT FROM AUTHOR]

As the increasing trend of global consumption of waste electrical and electronic equipment (WEEE), the recycling of WEEE has been thought highly of. In order to solve the problems of high economic cost, environmental detrimental caused by improper recycling, ignorance of social benefits of recycling, uncertain recycling amount and single recycling channel in recycling process of WEEE, this paper proposes a sustainable multi-objective WEEE double recycling channel fuzzy programming model based on triple bottom line (TBL) theory. First of all, TBL is used to develop sustainable multi-objective recycling models concerning economy, environment and society. Secondly, the model is fuzzed. This process includes two steps: the first step is to take WEEE recycling quantity as a triangular fuzzy parameter, and use fuzzy chance constraint method to transform fuzzy constraint into an equivalent clear condition; the second step is to fuzzify suboptimal objective in the model by defining objective membership degree, and transform multi-objective optimization problem into a single-objective optimization problem based on the maximum satisfaction. Finally, taking a recycling enterprise in Shantou, China, as an example, genetic algorithm (GA) and particle swarm optimization (PSO) are comparatively used to solve a calculation case related to the model and the contribution of the model is verified by comparing economic cost, environmental impact and social benefits of the dual recycling channels. [ABSTRACT FROM AUTHOR]

The study was to verify for older adults, the applicability of a recently developed measure of everyday problem solving, a performance-based observed assessment (the Chinese Version of Observed Tasks of Daily Living [OTDL-C]), by comparing with two other measures—a self-report questionnaire and a paper–pencil test. Totally 183 older adults (aged 60–84 years) completed demographic questionnaires and three everyday problem-solving assessments. All assessments measured the same instrumental domains of daily life (medication use, phone use, electric appliance use, and financial management). We observed significant differences in inter-individual variation, and found significant differences in the effects of age and education level on OTDL-C performance compared with self-report and paper–pencil measure performance. Additionally, the inter-individual variance was significantly greater for the OTDL-C and paper–pencil test than for the self-report questionnaire. Age remained a significant predictor of OTDL-C performance after controlling the scores of the self-report and the paper–pencil measures. Education level had more consistent effects on the total score and each domain score of the OTDL-C compared to the other two measures. Overall, our finding indicated that the OTDL-C had greater inter-individual variation, might be a more sensitive tool for examining age-related differences in the ability to solve everyday problem, and could be more applicable to older populations with diverse education levels compared to the self-report and the paper–pencil measures. In circumstances where a high sensitive assessment of everyday problem solving is needed to detect individual differences, the OTDL-C might be a more appropriate choice. [ABSTRACT FROM AUTHOR]

With the continuous development of information technology, ensuring information security has become an important issue. As a widely used multimedia tool, images often face the risk of leakage. Therefore, this paper proposes an image encryption algorithm based on 2D-SAHM chaotic system and a new DNA operation rule. The algorithm first constructs a new chaotic system 2D-SAHM, which has better chaotic characteristics and can generate more random chaotic sequences than the traditional low-dimensional chaotic system. And then, on the basis of this system, the algorithm scrambles and diffuses the image. In the scrambling stage, the whole image is converted into a one-dimensional pixel matrix and divided into several sub-images by double Joseph scrambling. In the diffusion stage, to solve the problem that traditional DNA encryption relies too much on chaotic sequences, this paper proposes a new DNA operation rule. This rule determines the operation symbol of DNA by the type of amino acid, which improves the randomness of the algorithm. The final simulation results and performance analysis show that the algorithm has high security and strong anti-interference ability. [ABSTRACT FROM AUTHOR]

Critical chain buffer management (CCBM) has been extensively studied in recent years. This paper investigates a new formulation of CCBM, the multimode chance-constrained CCBM problem. A flow-based mixed-integer linear programming model is described and the chance constraints are tackled using a scenario approach. A reinforcement learning (RL)-based algorithm is proposed to solve the problem. A factorial experiment is conducted and the results of this study indicate that solving the chance-constrained problem produces shorter project durations than the traditional approach that inserts time buffers into a baseline schedule generated by solving the deterministic problem. This paper also demonstrates that our RL method produces competitive schedules compared to established benchmarks. The importance of solving the chance-constrained problem and obtaining a project buffer tailored to the desired probability of completing the project on schedule directly from the solution is highlighted. Because of its potential for generating shorter schedules with the same on-time probabilities as the traditional approach, this research can be a useful aid for decision makers. [ABSTRACT FROM AUTHOR]

This Forum Note concerns the question of necessary optimality conditions in optimal control problems subject to state constraints. Some critical remarks about a recently published paper are made. [ABSTRACT FROM AUTHOR]

In order to solve the problem that image information is not effectively utilized because of unclear traffic video images and random jitter between image sequences, this paper has studied how to achieve stability of traffic video images and proposed an improved Mean Shift algorithm about how to conduct object centroid registration in compensation for the deviations in space localization and on this basis, how to select kernel window width to eliminate the errors in scale positioning. This algorithm gets the tracking effect and computation analysis of improved Mean Shift from the perspective of applications; removes or relieves the impact motion has on imaging, improves the quality of the video image information obtained, automatically adjusts the size of window according to the scale changes of moving object in the image, and effectively enhances the stability and real-time of object tracking. The experiment has proven that the algorithm of this paper is practical to some extent. [ABSTRACT FROM AUTHOR]

Ethical morality is one of the significant issues in self-driving cars. The paper provides a newer approach to solve the ethical decision problems in self-driving cars until there is no concrete ethical decision to all problems. This paper gives a two-way approach to solve a problem, with first being the mapping of problem to the solution already known or which has a fixed set of solutions and action priorities defined to a problem previously. Now, if no solution is found or mapping is unsuccessful, then the second stage activates, where the solution from Deep Q-learning model is calculated. It estimates the best Q value and returns that solution or action which maximizes the reward at that instance. The reward function is designed with decreasing priorities and acts accordingly, where the users can change or define their priorities if needed. The case study and results show that the solution that is present in the paper will lead to solving ethical morality problems in self-driving cars up to a great extent. [ABSTRACT FROM AUTHOR]

For pedestrian detection in all weather conditions, multispectral imagery is the preferred solution for multimodal data acquisition. Due to the complementarity of multispectral data, the performance of pedestrian detection has been continuously improved. However, it is precise because of the diversity of data that the complexity of the model is increased. How to obtain a simple and lightweight high-performance network is the primary problem that the industry needs to solve. To solve this problem, this paper firstly proposes a lightweight high-performance network multi-receptive field and attention mechanism for multispectral pedestrian detection (MAPD). MAPD is a new scalable network based on multi-receptive field and attention mechanism. It cleverly combines multi-receptive field module and convolutional block attention module (CBAM) attention module to obtain multi-receptive field and attention module (MA). The module can be easily embedded into other network structures. After that, we analyze the proportion of pedestrian objects in the image to determine the receptive field range of the target, and use this range to design the multi-receptive field module of the network to obtain a network model suitable for detecting different object tasks. The MAPD network proposed in this paper has a very low parameter amount. Ablation studies on KAIST and CVC-14 datasets show that our method is effective and achieves state-of-the-art detection performance. [ABSTRACT FROM AUTHOR]

In industry, the task of defect classification and defect localization is an important part of defect detection system. However, existing studies only focus on one task and it is difficult to ensure the accuracy of both tasks. This paper proposes a defect detection system based on improved Yolo_v4, which greatly improves the detection ability of minor defects. For K_Means algorithm clustering prianchors question with strong subjectivity, the paper proposes the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to determine the number of Anchors. To solve the problem of low detection rate of small targets caused by insufficient reuse rate of low-level features in CSPDarknet53 feature extraction network, this paper proposes an ECA-DenseNet-BC-121 feature extraction network to improve it. And the Dual Channel Feature Enhancement (DCFE) module is proposed to improve the local information loss and gradient propagation obstruction caused by quad chain convolution in PANet networks to improve the robustness of the model. The experimental results on the fabric surface defect detection datasets show that the mAP of the improved Yolo_v4 is 98.97%, which is 7.67% higher than SSD, 3.75% higher than Faster_RCNN, 10.82% higher than Yolo_v4 tiny, and 5.35% higher than Yolo_v4, and the detection speed reaches 39.4 fps. It can meet the real-time monitoring needs of industrial sites. [ABSTRACT FROM AUTHOR]

In recent decades, Estimation of Distribution Algorithms (EDAs) have gained much popularity in the evolutionary computation community for solving optimization problems. Characterized by the use of probabilistic models to represent the solutions and the interactions between the variables of the problem, EDAs can be applied to either discrete, continuous or mixed domain problems. Due to this robustness, these algorithms have been used to solve a diverse set of real-world and academic optimization problems. However, a straightforward application is only limited to a few cases, and for the general case, an efficient application requires intuition from the problem as well as notable understanding in probabilistic modeling. In this paper, we provide a roadmap for solving optimization problems via EDAs. It is not the aim of the paper to provide a thorough review of EDAs, but to present a guide for those practitioners interested in using the potential of EDAs when solving optimization problems. In order to present a roadmap which is as useful as possible, we address the key aspects involved in the design and application of EDAs, in a sequence of stages: (1) the choice of the codification, (2) the choice of the probability model, (3) strategies to incorporate knowledge about the problem to the model, and (4) balancing the diversification-intensification behavior of the EDA. At each stage, first, the contents are presented together with common practices and advice to follow. Then, an illustration is given with an example which shows different alternatives. In addition to the roadmap, the paper presents current open challenges when developing EDAs, and revises paths for future research advances in the context of EDAs. [ABSTRACT FROM AUTHOR]

In this paper, Protist algorithm (PA) and Otocyon megalotis optimization algorithm (OOA) are applied to solve the power loss lessening problem. Protist Algorithm (PA) is modelled based on the Protist's natural activities. Protist exists in moist places. The leading nutritious phase is Plasmodium, the energetic and vibrant phase of Protist. In this segment, the organic substance in Protist search for food in surroundings and conceals enzymes for digestion. Natural actions of Otocyon megalotis are emulated to design the OOA approach. In the projected OOA searching of regions in exploration, for foodstuff the Otocyon megalotis mark the prey in the space is indicated as a global exploration. Real power loss reduction and Voltage stability enhancement are the key objectives of the paper. To solve the problem, Protist algorithm (PA) and Otocyon megalotis optimization algorithm. In the course of the migration procedure, the anterior end outspreads and interconnected arterial system that authorize cytoplasm to stream inside. Then, mutation and cross-over probability are employed to augment the performance of the Protist algorithm (PA). With this integration engendering of the population is done. Mutation classes the population exploration agents (PN) in uphill order conferring to the agents appropriateness (fitness) cost. Consequently, the technique splits the organized agents into three fragments rendering to their fitness value. In which PN/3 denotes to the population possess pre-eminent (aptness) fitness values, subsequently with second pre-eminent and poorest aptness (fitness) values. Then, in this paper, Otocyon megalotis optimization algorithm (OOA) is applied for solving the Power loss lessening problem. In the subsequent segment, navigate during the haunt to seal prey previous to the hit was replicated as a local search. In exploration, the data obtained is shared to all the associates of the family unit for continued existence and growth. Examination of the nearby terrain is modelled with reference to the fitness of all entities. Most excellent entity has investigated the majority fascinating terrain and it will be shared with family unit of Otocyon megalotis. Primarily, Otocyon megalotis show that it not involved in hunting. Conversely, as soon as moving near to prey Otocyon megalotis will perform the attack in quick mode. This approach imitated and designed in the local search segment. Authenticity of the Protist algorithm (PA) and Otocyon megalotis optimization algorithm (OOA) is corroborated in 23 benchmark functions, IEEE 30, 57, 300 and 354 test systems. Power Loss reduction achieved with voltage stability enhancement. Real power loss reduction attained. Both the Protist algorithm (PA) and Otocyon megalotis optimization algorithm (OOA) performed well in solving the Power loss reduction problem. [ABSTRACT FROM AUTHOR]

In this expository review paper, we show that co-kriging, a widely used geostatistical multivariate optimal linear estimator, has a diverse range of extensions that we have collected and illustrated to show the potential of this spatial interpolator. In the context of spatial stochastic processes, this paper covers scenarios including increasing the spatial resolution of a spatial variable (downscaling), solving inverse problems, estimating directional derivatives, and spatial interpolation taking boundary conditions into account. All these spatial interpolators are optimal linear estimators in the sense of being unbiased and minimising the variance of the estimation error. [ABSTRACT FROM AUTHOR]

The PID controller parameters directly affect the dynamic characteristics of the hybrid magnetic bearing (HMB) system. However, currently, control parameter tuning mainly depends on experience and lacks the support of design theory. In this paper, the control parameter tuning method was studied to solve this problem. First, the expressions of equivalent stiffness and equivalent damping of the system were established, and the effects of each control parameter on the support characteristics of the system were analyzed separately. Then, based on the dynamic response results of the system, the tuning principles of control parameters were obtained: the proportional gain and derivative gain value ensure that the system works with "natural stiffness" and "natural damping". The integral gain and time constant value ensure a positive damping ratio near the nature frequency. Finally, the method of this paper was applied to complete the PID controller design. The simulation and experiment results show that the rotor can maintain stable operation under different frequency excitations. The results are in good agreement with the theoretical analysis results. The conclusion of this paper provides helpful guidance for tuning control parameters and speeding up the development process of magnetic bearing systems. [ABSTRACT FROM AUTHOR]

There are many methods for solving the topology optimization problem of single-material. It is a great challenge how to utilize existing methods or make appropriate improvements to solve multi-material topology optimization problems. In this paper, a multiple solid isotropic material with penalization model (SIMP) of variable density method is proposed to solve the problem of multi-material topology optimization. All candidate materials, including void material, are arranged in descending order of elastic modulus. The material conversion scheme of multiple SIMP is based on the elastic modulus of the candidate material after interpolation. The iterative criterion of multi-material topology optimization is derived from the Kuhn-Tucker condition using the guide-weight method. The innovation of this paper is to transform the multi-material topology optimization problem into multiple SIMP of the variable density method. Three examples show that it is effective and moderate to use the proposed method to solve the problem of multi-material topology optimization. [ABSTRACT FROM AUTHOR]

Modern real-world control problems call for continuous control domains and robust, sample efficient and explainable control frameworks. We are presenting a framework for recursively composing control skills to solve compositional and progressively complex tasks. The framework promotes reuse of skills, and as a result quick adaptability to new tasks. The decision tree can be observed, providing insight into the agents' behavior. Furthermore, the skills can be transferred, modified or trained independently, which can simplify reward shaping and increase training speeds considerably. This paper is concerned with efficient composition of control algorithms using reinforcement learning and soft attention. Compositional and temporal abstraction is the key to improving learning and planning in reinforcement learning. Our Thompson sampling inspired soft-attention model is demonstrated to efficiently solve the composition problem. [ABSTRACT FROM AUTHOR]

Harris Hawks optimization (HHO) algorithm was a powerful metaheuristic algorithm for solving complex problems. However, HHO could easily fall within the local minimum. In this paper, we proposed an improved Harris Hawks optimization (IHHO) algorithm for solving different engineering tasks. The proposed algorithm focused on random location-based habitats during the exploration phase and on strategies 1, 3, and 4 during the exploitation phase. The proposed modified Harris hawks in the wild would change their perch strategy and chasing pattern according to updates in both the exploration and exploitation phases. To avoid being stuck in a local solution, random values were generated using logarithms and exponentials to explore new regions more quickly and locations. To evaluate the performance of the proposed algorithm, IHHO was compared to other five recent algorithms [grey wolf optimization, BAT algorithm, teaching–learning-based optimization, moth-flame optimization, and whale optimization algorithm] as well as three other modifications of HHO (BHHO, LogHHO, and MHHO). These optimizers had been applied to different benchmarks, namely standard benchmarks, CEC2017, CEC2019, CEC2020, and other 52 standard benchmark functions. Moreover, six classical real-world engineering problems were tested against the IHHO to prove the efficiency of the proposed algorithm. The numerical results showed the superiority of the proposed algorithm IHHO against other algorithms, which was proved visually using different convergence curves. Friedman's mean rank statistical test was also inducted to calculate the rank of IHHO against other algorithms. The results of the Friedman test indicated that the proposed algorithm was ranked first as compared to the other algorithms as well as three other modifications of HHO. [ABSTRACT FROM AUTHOR]

In this paper, the stress fields in nonlinear sandwich and laminated composite shells subjected to displacement-dependent loads are analyzed. To solve the problem, the geometrically exact (GeX) hybrid-mixed four-node solid-shell element is proposed. The term GeX means that the middle surface is described by analytical functions, i.e., the parametrization of the surface is known. The laminated solid-shell element formulation is based on the choice of an arbitrary number of sampling surfaces (SaS) parallel to the middle surface and located at the Chebyshev polynomial nodes within the layers, in order to introduce the displacements of these surfaces as unknown functions. The outer surfaces and interfaces are also included into a set of SaS. Due to analytical integration utilized to evaluate the tangent stiffness matrix, the developed GeX solid-shell element under non-conservative loading shows excellent performance in the case of coarse meshes and makes it possible to use only one load step in most of the considered benchmark problems. It is shown that the difference between the second Piola–Kirchhoff and Cauchy stress tensors in some non-conservative problems for sandwich and laminated shells undergoing arbitrarily large rotations can be significant. [ABSTRACT FROM AUTHOR]

Edge computing provides task offloading services to extend the computational capacity of mobile users and reduce task latency. Prior studies mainly focus on tasks with strict deadlines. However, in the real world, some tasks may not always have to be finished before hard deadlines, e.g., multimedia tasks. Tasks with soft deadlines can miss their primary deadlines, but not by too much, and still be timely. This has not been properly considered by existing offloading approaches. In this paper, we propose CONFECT, a novel offloading approach that handles tasks with mixtures of hard deadlines and soft deadlines. Specifically, we first formulate the problem as an integer linear programming and prove its hardness. Then, we propose two online algorithms with proven competitive ratios to solve the problem collectively, including an algorithm that assigns tasks to edge servers to maximize the task completion ratio and an algorithm that adjusts the task execution order to maximize the task completion revenue. Moreover, to balance the fairness among tasks and system revenue elastically, we extend CONFECT by using a tunable fairness knob. Finally, extensive experiments show that CONFECT outperforms five baseline algorithms in terms of task completion ratio and completion revenue. [ABSTRACT FROM AUTHOR]

This paper studies a random caching design in multi-tier cache-enabled Internet of Things (IoT) networks. A cross-tier base station (BS) cooperation scheme is proposed, where the user equipment (UE) is jointly served by the BSs that cache the requested file with the maximal long-term averaged received power form each tier. The goal is to design a caching strategy to improve the network performance by taking successful transmission probability (STP) as the metric. We first derive the expression of the STP in integral form for the M-tier networks. Then, as special cases, an optimal caching strategy for the single-tier networks and a sub-optimal caching strategy (SCS) for the two-tier networks are obtained by solving two optimization problems where the caching probability matrix is optimized to maximize the STP, respectively. The former problem is solved by the Lagrangian method, based on which and combining with the gradient projection method, an algorithm is designed for the latter problem. Simulation results show that the SCS outperforms some existing strategies in terms of STP. [ABSTRACT FROM AUTHOR]

With the development of heterogeneous network structure, dynamic user requests as well as complex service types and applications scenarios, current networks may not accommodate the increasingly stringent requirements. As a result, the research of the beyond fifth generation (B5G) or the sixth generation (6G) networks has been put on the agenda. In B5G/6G networks, achieving the automatic, flexible, and cost-effective orchestration and management of network resources is a significant but challenging issue. Network function virtualization (NFV), as a promising paradigm to address this issue, has received considerable attention from both industry and academia. NFV leverages the virtualization technology to decouple network functions from dedicated hardware appliances to software middleboxes or called virtual network functions (VNFs) that run on the commodity servers. The demand for a network service becomes a request for running a set of VNFs deployed on the substrate network. The requested network service is orchestrated in the form of a VNF-forwarding graph (VNF-FG). The problem of embedding the VNF-FG into the substrate network is known as VNF-FG embedding (VNF-FGE). The efficiency and the management cost of a network are highly dependent on the optimization of VNF-FGE. This paper mainly presents a survey on solving the VNF-FGE problem. To this end, we present a general formulation and several objectives of the VNF-FGE problem. In the meanwhile, we summarize its different application scenarios from four perspectives and divide the approaches into four main categories based on the optimization methods. The main challenges and potential future directions due to the appearance of B5G/6G are also discussed. [ABSTRACT FROM AUTHOR]

With the rapid development of multimedia technology, considerable achievement has been achieved in image retrieval technology. On the one hand, users' demand for cross-modal retrieval is increasing rapidly. On the other hand, deep hashing algorithms as one of the most prominent high-dimensional reduction methods have received extensive attention. Under such background, the cross-modal retrieval method based on deep hashing came into being. Although the cross-modal hashing learning method has moved a long way in recent years, it still has space of promotion. First, existing cross-modal based hashing algorithms can only map the feature vectors into binary codes with a fixed length. The length of hash codes cannot be modified according to the practical situation. Second, the saturation level of the probability function used in existing methods is too high to concentrate relevant samples very well, which results in low retrieval efficiency. To solve the above problem, a novel variable-length hash code based on adaptive weight was proposed in this paper. The length of hash codes could be adjusted according to the importance of each bit. And a novel probability function based on Cauchy distribution was proposed to generate compact binary codes and make hashing retrieval more efficient. The experiment shows that the proposed cross-modal image retrieval algorithm based on deep hashing outperforms existing related algorithms on the accuracy of retrieval. [ABSTRACT FROM AUTHOR]

Applying parallel-in-time algorithms to multiscale Hamiltonian systems to obtain stable long-time simulations is very challenging. In this paper, we present novel data-driven methods aimed at improving the standard parareal algorithm developed by Lions et al. in 2001, for multiscale Hamiltonian systems. The first method involves constructing a correction operator to improve a given inaccurate coarse solver through solving a Procrustes problem using data collected online along parareal trajectories. The second method involves constructing an efficient, high-fidelity solver by a neural network trained with offline generated data. For the second method, we address the issues of effective data generation and proper loss function design based on the Hamiltonian function. We show proof-of-concept by applying the proposed methods to a Fermi-Pasta-Ulam (FPU) problem. The numerical results demonstrate that the Procrustes parareal method is able to produce solutions that are more stable in energy compared to the standard parareal. The neural network solver can achieve comparable or better runtime performance compared to numerical solvers of similar accuracy. When combined with the standard parareal algorithm, the improved neural network solutions are slightly more stable in energy than the improved numerical coarse solutions. [ABSTRACT FROM AUTHOR]

Evaluation in Design education is subjective and generally depends upon the pedagogues' personal perspective. Conducting subjective evaluation on a large scale is associated with multiple challenges; therefore, digitized evaluation is integral to maintain consistency in the evaluation process. This systematic literature review utilized SCOPUS, Web of Science, JSTOR, ScienceDirect (Elsevier), EBSCOhost, and Google Scholar data repositories to retrieve and analyse available literature on digitizing creativity evaluation in Design education. This review intends to provide the researcher community with multiple aspects of digitized creativity evaluation from 2008 to 2021 in Design education. This paper highlights digitized creativity evaluation in the context of Design education, factors of digitized creativity evaluation, research purposes, methods, results, findings, and limitations of this review. Significant findings indicate that most literature studies suggested factors of creativity evaluation, but hardly any studies have highlighted indicators associated with digitizing creativity evaluation. In addition, many articles focused on generalised digitization approaches; however, only a few studies highlighted integrating digitization with creativity evaluation. Moreover, few studies enlightened the difference in factors and techniques associated with evaluation in different Design educational settings, such as classrooms, design studios, mass examinations, etc. Future research may investigate factors and problem-solving techniques of digitized creativity evaluation in Design education from the aspect of multiple educational settings and self-adapting intelligent models that might migrate from one setting to another on demand. [ABSTRACT FROM AUTHOR]

The rapidly advancing fields of machine learning and mathematical modeling, greatly enhanced by the recent growth in artificial intelligence, are the focus of this special issue. This issue compiles extensively revised and improved versions of the top papers from the workshop on Mathematical Modeling and Problem Solving at PDPTA'23, the 29th International Conference on Parallel and Distributed Processing Techniques and Applications. Covering fundamental research in matrix operations and heuristic searches to real-world applications in computer vision and drug discovery, the issue underscores the crucial role of supercomputing and parallel and distributed computing infrastructure in research. Featuring nine key studies, this issue pushes forward computational technologies in mathematical modeling, refines techniques for analyzing images and time-series data, and introduces new methods in pharmaceutical and materials science, making significant contributions to these areas. [ABSTRACT FROM AUTHOR]

Using attribute graphs for node embedding to detect community structure has become a popular research topic. However, most of the existing algorithms mainly focus on the network structure and node features, which ignore the higher-order relationships between nodes. In addition, only adopting the original graph structure will suffer from sparsity problems, and will also result in suboptimal node clustering performance. In this paper, we propose a hypergraph network embedding (HGNE) for community detection to solve the above problems. Firstly, we construct potential connections based on the shared feature information of the nodes. By fusing the original topology with feature-based potential connections, both the explicit and implicit relationships are encoded into the node representations, thus alleviating the sparsity problem. Secondly, for integrating the higher-order relationship, we adopt hypergraph convolution to encode the higher-order correlations. To constrain the quality of the node embedding, the spectral hypergraph embedding loss is utilized. Furthermore, we design a dual-contrast mechanism, which draws similar nodes closer by comparing the representations of different views. This mechanism can efficiently prevent multi-node classes from distorting less-node classes. Finally, the dual-contrast mechanism is jointly optimized with self-training clustering to obtain more robust node representations, thus improving the clustering results. Extensive experiments on five datasets indicate the superiority and effectiveness of HGNE. [ABSTRACT FROM AUTHOR]

In the previous paper, we introduced a commuting family of Baxter Q-operators for the quantum Ruijsenaars hyperbolic system. In the present work, we show that the wave functions of the quantum system found by M. Hallnäs and S. Ruijsenaars also diagonalize Baxter operators. Using this property, we prove the conjectured duality relation for the wave function. As a corollary, we show that the wave function solves bispectral problems for pairs of dual Macdonald and Baxter operators. Besides, we prove the conjectured symmetry of the wave function with respect to spectral variables and obtain new integral representation for it. [ABSTRACT FROM AUTHOR]

In this paper, we study the variational inequality over the solution set of the split common solution problem with multiple output sets for monotone operator equations in real Hilbert spaces. We propose a new approach for solving this problem without depending on the norm of the transfer mappings. Some of its applications for solving the variational inequality over the solution set of the split common fixed point problem with multiple output sets and related problems are also mentioned. Finally, we give numerical examples to illustrate the performance of the proposed algorithm and compare it with several existing previous algorithms. [ABSTRACT FROM AUTHOR]

To solve the problem of oscillation instability in permanent magnetic synchronous generator (PMSG)-based wind power connected systems during low-voltage ride through (LVRT) process, a parameter adjustment strategy based on interaction energy path optimization is proposed in this paper. Firstly, a modular state-space model of PMSG under fault transient conditions is constructed, and the system is divided into five subsystems. Then, the dynamic energy function of subsystems reflecting the oscillation stability of the system is derived. Based on that, the dynamic energy flow path is described considering the introduction of LVRT control. On this basis, the interaction energy between LVRT control links and subsystems is analyzed, and the coupling mechanism of voltage support and damping characteristics in the LVRT process is explained. Further, aiming at the optimal change rate of the total interaction energy in the LVRT process, the adjustment strategy of LVRT control parameters is constructed to meet voltage and damping requirements. Finally, a PMSG-connected system model is built on the MATLAB/Simulink platform to verify the effectiveness of the adjustment strategy. The results show that the proposed method can effectively improve the damping level under the fault transient condition, as well as supporting system voltage. [ABSTRACT FROM AUTHOR]

Influence maximization (IM) problem for messages propagation is an important topic in mobile social networks. The success of the spreading process depends on the mechanism for selection of the influential user. Beside selection of influential users, the computation and running time should be considered in this mechanism to ensure the accurecy and efficient. In this paper, considering that the overhead of exact computation varies nonlinearly with fluctuations in data size, random algorithm with smoother complexity change was designed to solve the IM problem in combination with greedy algorithm. Firstly, we proposed a method named two-hop neighbor network influence estimator to evaluate the influence of all nodes in the two-hop neighbor network. Then, we developed a novel greedy algorithm, the random walk probability cost-effective with lazy-forward (RWP-CELF) algorithm by modifying cost-effective with lazy-forward (CELF) with random algorithm, which uses 25–50 orders of magnitude less time than the state-of-the-art algorithms. We compared the influence spread effect of RWP-CELF on real datasets with a theoretically proven algorithm that is guaranteed to be approximately optimal. Experiments show that the spread effect of RWP-CELF is comparable to this algorithm, and the running time is much lower than this algorithm. [ABSTRACT FROM AUTHOR]

To solve the problems of slow convergence and insufficient accuracy of traditional ant colony algorithms in solving large-scale problems, this paper proposes a multi-ant colony optimization algorithm based on game strategy and hierarchical temporal memory model (GHMACO). Firstly, the heterogeneous multi-ant colony model is constructed, and each colony collaborates to improve the performance of the algorithm. Secondly, in order to enhance the communication among the heterogeneous colonies, a non-cooperative game strategy is introduced. The heterogeneous ant colonies are divided into the propagating colony and the absorbing colonies, where the propagating colony propagates the optimal payoffs of the game, and the absorbing colonies choose optimal strategies for absorption to balance the convergence and diversity of the algorithm. Further, the hierarchical temporal memory model is adopted to perform hierarchical optimization strategies based on path memories which includes: local exploration strategy, pheromone redistribution strategy and path replacement strategy, thus improving the accuracy of the algorithm and helping the colonies to jump out of the local optimum. Experiments on the traveling salesman problem show that the improved algorithm is effective in improving the convergence and accuracy of the traditional ant colony algorithms, especially in large-scale problems. [ABSTRACT FROM AUTHOR]

The gazelle optimization algorithm (GOA) is an iterative optimization method inspired by the agile movements of gazelles, employing adaptive step sizes and velocity adjustments for rapid convergence in continuous search spaces. However, GOA tends to lack diversity, leading to issues like local minima trapping and premature convergence. This paper addresses these limitations by introducing dynamic opposition-based learning (OBL) and incorporating a balanced sine-control-logistic chaotic mapping system, resulting in the improved GOA (IGOA). Dynamic OBL accelerates the search process, improving learning and selecting superior candidate solutions, while chaotic mapping in chaotic local search widens ranges for exploration and local optima escape. Evaluating IGOA against seven algorithms, including GOA and others, across 31 general test functions, the results consistently showcase IGOA's superior efficiency in achieving solutions closest to optima, early convergence, and hit rate when compared to alternative algorithms. [ABSTRACT FROM AUTHOR]

Affordance grounding aims to locate objects' "action possibilities" regions, an essential step toward embodied intelligence. Due to the diversity of interactive affordance, i.e., the uniqueness of different individual habits leads to diverse interactions, which makes it difficult to establish an explicit link between object parts and affordance labels. Human has the ability that transforms various exocentric interactions into invariant egocentric affordance to counter the impact of interactive diversity. To empower an agent with such ability, this paper proposes a task of affordance grounding from the exocentric view, i.e., given exocentric human-object interaction and egocentric object images, learning the affordance knowledge of the object and transferring it to the egocentric image using only the affordance label as supervision. However, there is some "interaction bias" between personas, mainly regarding different regions and views. To this end, we devise a cross-view affordance knowledge transfer framework that extracts affordance-specific features from exocentric interactions and transfers them to the egocentric view to solve the above problems. Furthermore, the perception of affordance regions is enhanced by preserving affordance co-relations. In addition, an affordance grounding dataset named AGD20K is constructed by collecting and labeling over 20K images from 36 affordance categories. Experimental results demonstrate that our method outperforms the representative models regarding objective metrics and visual quality. The code is available via: github.com/lhc1224/Cross-View-AG. [ABSTRACT FROM AUTHOR]

Existing methods for filter pruning mostly rely on specific data-driven paradigms but lack the interpretability. Besides, these approaches usually assign layer-wise compression ratios automatically only under given FLOPs by neural architecture search algorithms or just manually, which are short of efficiency. In this paper, we propose a novel interpretable task-inspired adaptive filter pruning method for neural networks to solve the above problems. First, we treat filters as semantic detectors and develop the task-inspired importance criteria by evaluating correlations between input tasks and feature maps, and observing the information flow through filters between adjacent layers. Second, we refer to the human neurobiological mechanism for the better interpretability, where the retained first layer filters act as individual information receivers. Third, inspired by the phenomenon that each filter has a deterministic impact on FLOPs and network parameters, we provide an efficient adaptive compression ratio allocation strategy based on differentiable pruning approximation under multiple budget constraints, as well as considering the performance objective. The proposed method is validated with extensive experiments on the state-of-the-art neural networks, which significantly outperforms all the existing filter pruning methods and achieves the best trade-off between neural network compression and task performance. With ResNet-50 on ImageNet, our approach reduces 75.49% parameters and 70.90% FLOPs, only suffering from 2.31% performance degradation. [ABSTRACT FROM AUTHOR]

This paper introduces a novel method for solving the resection problem in two and three dimensions based on conformal geometric algebra (CGA). Advantage is taken because of the characteristics of CGA, which enables the representation of points, lines, planes, and volumes in a unified mathematical framework and offers a more intuitive and geometric understanding of the problem, in contrast to existing purely algebraic methods. Several numerical examples are presented to demonstrate the efficacy of the proposed method and to compare its validity with established techniques in the field. Numerical simulations indicate that our vector geometric algebra implementation is faster than the best-known algorithms to date, suggesting that the proposed GA-based methods can provide a more efficient and comprehensible solution to the two- and three-dimensional resection problem, paving the way for further applications and advances in geodesy research. Furthermore, the method's emphasis on graphical and geometric representation makes it particularly suitable for educational purposes, allowing the reader to grasp the concepts and principles of resection more effectively. The proposed method has potential applications in a wide range of other fields, including surveying, robotics, computer vision, or navigation. [ABSTRACT FROM AUTHOR]

Function generation synthesis of mechanisms can be considered as the design of correlation of crank angles and dead dead-center position design. These two problems have been clearly defined and solved separately. But some problems may require both correlation of crank angles and dead dead-center design at different configurations. Such problems are called mixed function generation problems. In this paper, an overview of these mixed function generation problems for the planar four-bar mechanism are given and the problems are solved analytically or semi-analytically. Except three of them, all presented mixed function generation problem formulations are novel. The solutions of all problems including three positions for the four-bar mechanism and the solution of a problem including four positions for a four-bar mechanism are addressed. All problems are first reduced to a univariate equation and a fast solution is found. Thus, link lengths can be found quickly by changing the problem definition problems, and several design iterations can be performed in a short time. Numerical solutions of all problems have been demonstrated using Excel. [ABSTRACT FROM AUTHOR]

Polyhedral models of metabolic networks are computationally tractable and can predict some cellular functions. A longstanding challenge is incorporating metabolites without losing tractability. In this paper, we do so using a new second-order cone representation of the Michaelis–Menten kinetics. The resulting model consists of linear stoichiometric constraints alongside second-order cone constraints that couple the reaction fluxes to metabolite concentrations. We formulate several new problems around this model: conic flux balance analysis, which augments flux balance analysis with metabolite concentrations; dynamic conic flux balance analysis; and finding minimal cut sets of networks with both reactions and metabolites. Solving these problems yields information about both fluxes and metabolite concentrations. They are second-order cone or mixed-integer second-order cone programs, which, while not as tractable as their linear counterparts, can nonetheless be solved at practical scales using existing software. [ABSTRACT FROM AUTHOR]

The sparse triangular solver (SpTRSV) is mostly used for scientific and engineering applications. The structured-grid triangular solver of regular dependencies (STRSV) is a special kind of SpTRSV. Some general SpTRSVs that disregards the regularity of the matrix are unsuitable for solving this problem. This paper proposes an efficient parallel algorithm for STRSV on the SW26010 (a kind of China independently designed many-core processors), namely swStructTRSV. The algorithm makes full use of the fine-grained and low latency communication characteristics of the SW26010 to reduce the waiting time for synchronization, maximizes the regularity of access to improve memory access bandwidth, and achieves overlap between memory access and computation simultaneously. Moreover, the idea of the algorithm can be extended to incomplete LU factorization (ILU factorization) because of consistent dependencies. The experimental results on a core group(8 * 8 network composed of 64 cores) of SW26010 show that swStructTRSV can achieve an average speedup of over 30 in the sequential version. swStructTRSV on SW26010 achieves solving speedups of 2.2 and 6.3 over the fast STRSV (fSpTRSV) previously implemented on SW26010 and MKL on Intel Xeon Gold 6132, respectively. swStructTRSV significantly outperforms cuSparse on NVIDIA TITAN RTX in terms of overall execution time. [ABSTRACT FROM AUTHOR]

Non-blind image deblurring has attracted a lot of attention in the field of low-level vision. However, the existing non-blind deblurring methods cannot effectively deal with a saturated blurry image. The key point is that the degradation model of saturated blurry images does not satisfy the linear convolution model of a conventional blurry image. To solve the problem, in this paper, we proposed a novel deep non-blind deblurring method, dubbed saturated image non-blind deblurring network(SDBNet). The SDBNet contains two trainable sub-network, i.e., confident estimate network (CEN) and detail enhance network (DEN). Specifically, the SDBNet uses CEN to estimate the confidence map for the saturated blurry image, which is used to recognize saturated pixels in the blurry image, and then uses the confidence map, and blur kernel to restore the blurry image. Finally, we use DEN to enhance the edges and textures of the restored image. We first pre-train CEN and DEN. In order to effectively pre-train CEN, we propose a new robust function, which is used to generate label data for CEN. The experimental results show that compared with several existing non-blind deblurring methods, SDBNet can effectively restore saturated blurry images and better restore the texture, edge, and other structural information of blurry images. [ABSTRACT FROM AUTHOR]

The Manta Ray Foraging Optimization Algorithm (MRFO) is a metaheuristic algorithm for solving real-world problems. However, MRFO suffers from slow convergence precision and is easily trapped in a local optimal. Hence, to overcome these deficiencies, this paper proposes an Improved MRFO algorithm (IMRFO) that employs Tent chaotic mapping, the bidirectional search strategy, and the Levy flight strategy. Among these strategies, Tent chaotic mapping distributes the manta ray more uniformly and improves the quality of the initial solution, while the bidirectional search strategy expands the search area. The Levy flight strategy strengthens the algorithm's ability to escape from local optimal. To verify IMRFO's performance, the algorithm is compared with 10 other algorithms on 23 benchmark functions, the CEC2017 and CEC2022 benchmark suites, and five engineering problems, with statistical analysis illustrating the superiority and significance of the difference between IMRFO and other algorithms. The results indicate that the IMRFO outperforms the competitor optimization algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we develop computing machinery within the framework of the String Multiset Rewriting calculus (SMSR), as defined by Barbuti et al. [4], to solve the SAT problem in linear time regarding the number of variables of a given conjunctive normal form. This shows that SMSR can be considered a computational model capable of significantly reducing the time requirement of classical decision problems. [ABSTRACT FROM AUTHOR]

The authors considered the problem of optimal control of loading points and the corresponding reaction functions described by a loaded parabolic equation. They obtained optimality conditions for control actions. The authors used the objective functional gradient formulas contained in these conditions in the algorithm for numerically solving the problem of controlling the movement of loading points and corresponding reaction functions based on first-order optimization methods. The paper provides the results of numerical experiments. [ABSTRACT FROM AUTHOR]

In this paper, we consider a class of three-composite nonconvex nonsmooth optimization problems, where one of nonsmooth functions is further composed with linear operator. Based on the variable smoothing method, as well as first-order methods with suitable majorization techniques, we propose a proximal variable smoothing gradient (ProxVSG) method for solving this kind of problem. The ProxVSG can be implemented efficiently, thanks to the fact that at each iteration, one just separately computes the proximal mapping of each nonsmooth function, rather than that of the sum of these nonsmooth functions. Furthermore, within our broad and flexible analysis framework, we propose a new proximal variable smoothing incremental aggregated gradient (ProxVSIAG) generalizations of the ProxVSG. In ProxVSIAG, an incremental aggregated estimate of the gradient is used, instead of the full gradient. Under suitable assumptions, we prove a complexity of O (ϵ - 3) to achieve an ϵ -approximate solution. Preliminary numerical experiments show the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we introduce one-parameter families of multistep numerical methods for solving stiff initial value problems of ordinary differential equations. These methods are adaptive versions of second derivative backward differentiation formulas and their extensions. The stability properties of the proposed schemes are better than those of the main methods which make them suitable for solving stiff problems. Numerical experiments on some problems arising from chemical reactions verify the theoretical results. [ABSTRACT FROM AUTHOR]

A nonnegative integer p is realizable by a graph-theoretical invariant I if there exists a graph G such that I (G) = p . The inverse problem for I consists of finding all nonnegative integers p realizable by I. In this paper, we consider and solve the inverse problem for the Mostar index, a recently introduced graph-theoretical invariant which attracted a lot of attention in recent years in both the mathematical and the chemical community. We show that a nonnegative integer is realizable by the Mostar index if and only if it is not equal to one. Besides presenting the complete solution to the problem, we also present some empirical observations and outline several open problems and possible directions for further research. [ABSTRACT FROM AUTHOR]

Fabric defect detection is an important part of the textile industry to ensure product quality. To solve the problems such as the difficulty of detecting small defects and the coexistence of multi-scale defects in fabric defect detection, a fabric defect detection method based on the feature purification fusion structure is proposed in this paper. Specifically, we improve the feature extraction network to enhance the network's ability to focus on small defective features and effectively reduce the model parameters. The existing methods use direct fusion between multi-level feature maps, which will lead to feature confusion. Therefore, we propose the feature purification fusion structure (FPF), which includes the semantic information supplementation strategy (SIS) and the detail information supplementation strategy (DIS). SIS extracts valid information from the deep feature map and supplements it to the shallow feature map, weakening feature information irrelevant to the shallow feature map detection task. DIS adaptively supplements the feature information required by the deep feature map detection task from the shallow feature map. FPF improves the ability of the network to detect small defects and effectively mitigates the aliasing effect generated during feature fusion. The experimental results show that compared to the baseline model YOLOv5s algorithm, our model achieved a 6.8% improvement in detection accuracy, with an average inference frame rate of 37.6 FPS, demonstrating better detection performance in fabric defect detection. Furthermore, extending this model to aluminum profile defect datasets also demonstrates strong performance. [ABSTRACT FROM AUTHOR]

In this paper, we solve two problems concerning the ideally conjunctive join-semilattices. First, we show that L / R 1 (Id L) | L is ideally conjunctive for all join-semilattices L. Then we characterize those ideally conjunctive join-semilattices L such that coz a is compact for all a ∈ L. Moreover, we give the definition of conjunctive posets and prove that the category of ideally conjunctive join-semilattices and join homomorphisms is reflective in the category of conjunctive posets and weakly ideal-continuous maps. As a corollary, we obtain the free ideally conjunctive join-semilattices over conjunctive posets. [ABSTRACT FROM AUTHOR]

This paper considers a reconfigurable intelligent surface (RIS) aided multi-user multiple-input single-output (MU-MISO) mmWave downlink communication system. The hybrid beamforming (HBF) and the programmable reflecting elements (PREs) are respectively applied at the base station (BS) and the RIS, where the HBF consists of the digital beamforming (DBF) and the analog beamforming (ABF). To maximize the geometric mean of the users' rates (GM-rate) for providing feasible links to all users in the aspect of the same time slot and same bandwidth, which results in the superiority of a rational rate distribution for the users without imposing a minimum quality of service (QoS) constraint. The joint PREs, ABF, and DBF optimization problems are formulated, the problem is generally non-convex due to the log-determinant as well as unit modulus constraints for both PREs and ABF. Efficient alternating descent iteration algorithms are developed to solve the intractable problem. Finally, simulation results are included to verify the efficiency of the proposed approaches, results also show that the proposed algorithms can improve the fairness among users and accommodate discrete phase shifts for both PREs and ABF. [ABSTRACT FROM AUTHOR]

Drones play an important role in modern cities, they bring convenience but also bring corresponding risks. Falling drones can cause casualties or damage to urban facilities and serious property damage, which increases the third-party risk problem. An effective way to reduce these third-party risks is to avoid high-risk areas in path planning, but most current path planning methods are optimized to minimize flight distance, and third-party risk costs are rarely considered. In this paper, a comprehensive risk-cost UAV path planning method is proposed, which evaluates urban risk by establishing a third-party risk model that incorporates obstacle risk, death risk, and property loss risk. This paper proposes a Min-cost A* algorithm based on city risk assessment, and smooths the generated low-risk path through the improved Floyd algorithm. The results show that the path planning method can effectively reduce the risk in the flight path, improve the reliability of the UAV flight path in the urban environment, and solve the problem of planning the third-party risk path of the UAV. [ABSTRACT FROM AUTHOR]

Due to the increasing complexity of decision problems, many managers employ multiple experts to reach a good decision in a group decision making. Now, if there is ambiguity in the evaluation of experts, the use of fuzzy numbers can be useful for each expert. In these situations, the use of hesitant fuzzy numbers (HFNs) which consists of several fuzzy numbers with special conditions can be suggested. HFNs are as an extension of the fuzzy numbers to take a better determining the membership functions of the parameters by several experts. Because of simple and fast calculations, in this paper, we use triangular HFNs in the pairwise comparison matrix of analytic hierarchy process by opinions of a group of decision makers in a hesitant fuzzy environment. We define consistency of the hesitant fuzzy pairwise comparison matrix and use the arithmetic operations on the HFNs and a new method of comparing HFNs to get the hesitant fuzzy performance score. By using score function to hesitant fuzzy score we can get a final score for alternatives. Finally, a practical example is provided to show the the effectiveness of this study. The obtained results from this paper show that new method can get a better answer by keeping the experts' opinions in the process of solving the problem. [ABSTRACT FROM AUTHOR]