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Multi-workflows are commonly deployed on cloud platforms to achieve efficient computational power. Diverse task configuration requirements, the heterogeneous nature and dynamic electricity price of cloud servers impose significant challenges for economically scheduling multi-workflows. In this paper, we propose a Heuristic Electricity-cost-aware Multi-workflow Scheduling algorithm (HEMS) to search for an optimal scheduling plan which determines the optimal scheduling scheme for each task in each workflow, specifying the server to perform the task with determined resources in specific time. The objective is to minimize the total electricity cost of all servers while satisfying the deadline constraints of all workflows. The HEMS algorithm consists of five components: Workflow Scheduling Sequence Generation, Task Scheduling Sequence Initialization for each workflow, Optimal Scheduling Scheme Determination for each task, initial Task Scheduling Sequence Optimization, and Optimal Scheduling Plan Optimization. Experimental results demonstrate that HEMS consistently achieves the optimal scheduling plan with the lower total electricity cost (saving 54.5–69.1% on average) within slightly longer CPU time for various multi-workflows compared to existing three scheduling approaches. [ABSTRACT FROM AUTHOR]

Using the method of fundamental components for the problem of diffraction of a TE-polarized wave on a half-plane with two-sided impedance boundary conditions a heuristic solution is obtained that approximately describes the scattered field. The verification and adjustment of heuristic formulas using a rigorous solution by the Wiener–Hopf method are carried out. A physical interpretation of the rigorous solution based on the obtained heuristic relations is proposed. [ABSTRACT FROM AUTHOR]

A worldview is, very basically, formed by tenets on the nature of the world and on the way of knowing it held by persons, social groups, intellectual currents or ethnic cultures. It is a term widely used in social sciences, but often left aside in daily research work because of being considered a vague term. Lax definitions are the reason, but such symbolic worlds will not disappear even if we do not refer to them, and we need operational and heuristic conceptualizations, both to analyze such symbolic parameters as a study objective and to refer to them as the appropriate understanding contexts of other topics. Here, definitions with a multidimensional structure that imply heuristic potential are specified as a solution; previous proposals are reviewed; the needs for improvement are set out; and a consequent conceptualization is proposed. Then onto-epistemic tenets of the main cultures on Earth and of history are briefly described as such worldviews, a case in Basque culture tested to assess the heuristic potential, and an outstanding 'transversal' implication is advanced: worldviews should not only be considered multidimensional concepts with heuristic potential, but also formed with areas around prototypes by cognitive-linguistic operators across the tenets. [ABSTRACT FROM AUTHOR]

Coverage path planning is a central task in agricultural field operations such as tillage, planting, cultivation, and harvesting. In future visions, manual operation will be replaced by fleets of autonomous agricultural vehicles that perform the tasks autonomously. A step towards this transition is to enable simultaneous and safe cooperation of autonomous vehicles on the field. In this article a novel approach is presented for coverage path planning (CPP) for two autonomous tractors that perform sequentially dependent tasks simultaneously on the same area. The approach is based on the idea of computing the coverage solutions for each task by dividing them into short paths that consist of a swath and a turn. The approach ensures collision avoidance by examining that the simultaneous short paths, operated by different tractors, do not collide geometrically, and then schedules them to be operated simultaneously in real-time. The approach was demonstrated successfully in a real-world test environment with two autonomous tractors. The tractor that performed the first task was equipped with a disc cultivator and the second tractor was equipped with a seed drill. A test area of 0.8 ha was used for the demonstration drive, during which the tractors drove 22 swaths simultaneously. Both tractors completed their respective tasks. • We present the problem of sequentially dependent coverage path planning. • Sequentially dependent coverage tasks are performed simultaneously on the same area. • A novel algorithm for solving this problem for two autonomous tractors. • The algorithm solves and schedules non-conflicting paths in real-time online. • Successful collision-free completion of autonomous operation in a field test. [ABSTRACT FROM AUTHOR]

Over the years, the finance-based and resource-constrained project scheduling problem (FBRCPSP) has been aimed at scheduling activities and offsetting the capital gap without exceeding credit and renewable resource limits in capital-driven construction projects. However, the heuristics proposed for the FBRCPSP in previous studies neglected to focus on identifying efficient priority rules, which are understandable and intuitive for construction practitioners with different roles. Accordingly, this study explores efficient priority rules and evaluates their performance according to modified serial and parallel schedule generation scheme heuristics for the FBRCPSP. First, 11 priority rules that cover the project network, schedule, activity, and resource information are introduced, and three priority rules related to activity information are designed. Second, the two priority rule-based heuristics are applied in an example to generate multiple project schedules according to the different combinations of priority rules and heuristics. Furthermore, the performance of priority rules is tested on the metrics of project duration and profit through numerical experiments. The results show that the priority rules of the latest start time, latest finish time, and old great rank positional weight are the three best priority rules in general. Additionally, the priority rules evenly present more stable and superior results than those obtained by other priority rules for the different levels of weekly fixed overhead cost and the scenarios of contract terms. Based on the intuitive heuristics, the selected efficient priority rules assist contractors in deciding which priority rule should be applied in practice, and project managers can employ an effective priority rule to establish a baseline schedule in the initial project phase and quickly adjust the plan when it becomes infeasible for project execution. This study evaluates the performance of frequently used priority rules on finance-based and resource-constrained project scheduling heuristics in construction project management. The model and priority rule-based heuristics can be embedded into project management software or digitalized scheduling platforms in practice. Moreover, project managers can employ efficient priority rule-based heuristics to generate a desirable project schedule for mastering cash flow and resource-demand plans in the project planning stage. Intuitive priority rules are incorporated into the heuristics to quickly adjust or update schedules and financing alternatives for project management when a baseline schedule is disrupted during the project execution. In addition, practitioners can obtain more adaptive or efficient priority rules following the testing approach proposed in this work if they have rich similar project experience or big data in construction projects. In summary, this study offers construction project schedulers or managers practical scheduling strategies and approaches for addressing finance-based and resource-constrained construction projects. [ABSTRACT FROM AUTHOR]

Confronted by myriad interconnected societal challenges, this paper asks: what kind of pedagogy does justice to the experience and challenge of living in a complex world? Departing from a critical reading of a preparative-logic to education, this paper emphasises students' entangledness: more-or-less consciously, students are uniquely shaped-by and shapers-of complex societal challenges in a here-and-now sense. Utilising this premise, the paper develops a set of pedagogical perspectives that might inspire and help teachers to design their own responses to particular complex societal challenges in their unique teaching contexts. Drawing on emerging outcomes from a narrative diffractive inquiry with 12 teachers as co-researchers and engaging with complexity thinking, six perspectives are presented and discussed: entanglement-orientedness, entanglement-awareness, hopeful action, inquiry within complex societal challenges, practicing perceptiveness, and practicing integrity. Together, these perspectives offer a heuristic for embracing complexity in education. [ABSTRACT FROM AUTHOR]

Considerable advances have been made recently in applying reinforcement learning (RL) to packing problems. However, most of these methods lack scalability and cannot be applied in dynamic environments. To address this research gap, we propose a hybrid algorithm called GraphPack to solve the strip packing problem. Two graph neural networks are designed to fully incorporate the problem's structure and enhance generalization performance. SkylineNet encodes the geometry of free space as the context feature, while PackNet, supporting the symmetry of rectangles, chooses the next rectangle to pack from the remaining rectangles at each timestep. We conduct fixed-scale, variable rectangle number and variable strip width experiments to test our method. The experimental results show that our method outperforms classical heuristic methods as well as previous RL methods. Notably, our method exhibits strong generalization ability and produces stable results even when the number of rectangles or strip width differs from that during training. [ABSTRACT FROM AUTHOR]

This study aims to investigate the relationship between written and oral reasoning in an undergraduate chemistry laboratory course as part of an argument-based inquiry approach, which is also a generative learning environment, known as the Science Writing Heuristic (SWH). The study employed the data-transformation variant of convergent design of mixed-method research. Data sources included 180 laboratory reports from nine Pre-service Science Teachers (PSTs) and 20 video recordings across two semesters. Using Walton's argument schemes, PSTs' development, utilisation and correlation of written and oral reasoning and an argument cycle of premise-justification-conclusion were examined. A Friedman test and a Spearman-Brown correlation were conducted for statistical analysis. The results revealed that there is a positive correlation between written and oral reasoning. While the quality of PSTs' written reasoning significantly increased from the first time phase to the following time phases, this pattern was not observed in oral reasoning. An argument cycle of premise-justification-conclusion occurred in each phase of oral arguments. However, this cycle did not occur in every facet of the SWH process across all time phases in written arguments. The study suggests that pre-service science teachers should be provided with learning environments that will allow them to make external evaluations and engage in talking, reading and writing activities for learning purposes. [ABSTRACT FROM AUTHOR]

The purpose of this study was twofold. First, to explore middle and high school students' intuitively-based (mis)conceptions in probability, particularly availability and representativeness heuristics. Second, to investigate teachers' awareness of these intuitively-based (mis)conceptions and the effectiveness of their instructional practices to support students' understanding of probability beyond heuristics. The participants were two middle school mathematics teachers, three high school mathematics teachers and their students. Data were collected through a diagnostic test administered to students as a pretest and posttest, interviews with the teachers and two students from each class and classroom observations. The findings indicated the existence of intuitively-based (mis)conceptions regarding availability and representativeness heuristics among middle and high school students. In general, the teachers did not consider the students' intuitions and difficulties in probability in their instructions. Not only did they rarely guide students to analyze and solve the tasks coherently and deliberately, but they also did not discuss students' intuitively-based (mis)conceptions. The results highlighted that if teachers do not attend to student thinking and change their instructional practices accordingly, their knowledge about students' difficulties will not necessarily help students overcome their intuitions and attain a probabilistic way of thinking. [ABSTRACT FROM AUTHOR]

Opportunistic Networks (OppNets) are characterized by intermittently connected nodes with fluctuating performance. Their dynamic topology, caused by node movement, activation, and deactivation, often relies on controlled flooding for routing, leading to significant resource consumption and network congestion. To address this challenge, we propose the Adaptive Clustering-based Routing Protocol (ACRP). This ACRP protocol uses the common member-based adaptive dynamic clustering approach to produce optimal clusters, and the OppNet is converted into a TCP/IP network. This protocol adaptively creates dynamic clusters in order to facilitate the routing by converting the network from a disjointed to a connected network. This strategy creates a persistent connection between nodes, resulting in more effective routing and enhanced network performance. It should be noted that ACRP is scalable and applicable to a variety of applications and scenarios, including smart cities, disaster management, military networks, and distant places with inadequate infrastructure. Simulation findings demonstrate that the ACRP protocol outperforms alternative clustering approaches such as kRop, QoS-OLSR, LBC, and CBVRP. The analysis of the ACRP approach reveals that it can boost packet delivery by 28% and improve average end-to-end, throughput, hop count, and reachability metrics by 42%, 45%, 44%, and 80%, respectively. [ABSTRACT FROM AUTHOR]

Direct current motors are widely used in industry applications, and it has become necessary to carry out studies and experiments for their optimization. In this manuscript, a comparison between heuristic and metaheuristic algorithms is presented, specifically, the Steiglitz–McBride, Jaya, Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO) algorithms. They were used to estimate the parameters of a dynamic model that approximates the actual responses of current and angular velocity of a DC motor. The inverse of the Euclidean distance between the current and velocity errors was defined as the fitness function for the metaheuristic algorithms. For a more comprehensive comparison between algorithms, other indicators such as mean squared error (MSE), standard deviation, computation time, and key points of the current and velocity responses were used. Simulations were performed with MATLAB/Simulink 2010 using the estimated parameters and compared to the experiments. The results showed that Steiglitz–McBride and GWO are better parametric estimators, performing better than Jaya and GA in real signals and nominal parameters. Indicators say that GWO is more accurate for parametric estimation, with an average MSE of 0.43%, but it requires a high computational cost. On the contrary, Steiglitz–McBride performed with an average MSE of 3.32% but required a much lower computational cost. The GWO presented an error of 1% in the dynamic response using the corresponding indicators. If a more accurate parametric estimation is required, it is recommended to use GWO; however, the heuristic algorithm performed better overall. The performance of the algorithms presented in this paper may change if different error functions are used. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of dynamic target search in a curve-shaped area by an unmanned aerial vehicle (UAV) with an optical camera. Our objective is to generate an optimal path for UAVs to obtain the maximum detection reward by a camera in the shortest possible time, while satisfying the constraints of maneuverability and obstacle avoidance. First, based on prior qualitative information, the original target probability map for the curve-shaped area is modeled by Parzen windows with 1-dimensional Gaussian kernels, and then several high-value curve segments are extracted by density-based spatial clustering of applications with noise (DBSCAN). Then, given an example that a target floats down river at a speed conforming to beta distribution, the downstream boundary of each curve segment in the future time is expanded and predicted by the mean speed. The rolling self-organizing map (RSOM) neural network is utilized to determine the coverage sequence of curve segments dynamically. On this basis, the whole path of UAVs is a successive combination of the coverage paths and the transferring paths, which are planned by the Dubins method with modified guidance vector field (MGVF) for obstacle avoidance and communication connectivity. Finally, the good performance of our method is verified on a real river map through simulation. Compared with the full sweeping method, our method can improve the efficiency by approximately 31.5%. The feasibility is also verified through a real experiment, where our method can improve the efficiency by approximately 16.3%. [ABSTRACT FROM AUTHOR]

Deformable linear objects (DLOs), such as rods, cables, and ropes, play important roles in daily life. However, manipulation of DLOs is challenging as large geometrically nonlinear deformations may occur during the manipulation process. This problem is made even more difficult as the different deformation modes (e.g., stretching, bending, and twisting) may result in elastic instabilities during manipulation. In this paper, we formulate a physics-guided data-driven method to solve a challenging manipulation task—accurately deploying a DLO (an elastic rod) onto a rigid substrate along various prescribed patterns. Our framework combines machine learning, scaling analysis, and physical simulations to develop a physics-based neural controller for deployment. We explore the complex interplay between the gravitational and elastic energies of the manipulated DLO and obtain a control method for DLO deployment that is robust against friction and material properties. Out of the numerous geometrical and material properties of the rod and substrate, we show that only three non-dimensional parameters are needed to describe the deployment process with physical analysis. Therefore, the essence of the controlling law for the manipulation task can be constructed with a low-dimensional model, drastically increasing the computation speed. The effectiveness of our optimal control scheme is shown through a comprehensive robotic case study comparing against a heuristic control method for deploying rods for a wide variety of patterns. In addition to this, we also showcase the practicality of our control scheme by having a robot accomplish challenging high-level tasks such as mimicking human handwriting, cable placement, and tying knots. [ABSTRACT FROM AUTHOR]

We consider the problem of designing lightweight, load-bearing planar frame structures for additive manufacturing, which can be formulated as a nonlinear, non-convex mathematical programming problems. Even using state-of-the-art commercial solvers, exact methods are only capable of solving small unrealistic instances (with very few variables). In this paper, we develop a heuristic method which is fast and capable of solving the design problem for larger-scale, weight-optimized, planar frame structures for additive manufacturing. The approach explicitly considers manufacturability constraints stemming from the use of additive manufacturing technology and leverages the problem structure imposed by these constraints. The proposed heuristic method is based on iteratively resolving a relaxed master problem on a reduced ground structure. The approach differs from the existing methods in two important aspects. First, we consider planar frame optimization master problem directly (instead of simpler but less relevant truss optimization). Secondly, we employ both element and node addition, which allows us to enforce additive manufacturability constraints without using binary variables (and hence, avoiding the need for computationally expensive integer programming). [ABSTRACT FROM AUTHOR]

The usability of virtual reality (VR) training applications is crucial for their success, but examining the usability in the early development stages remains challenging. A realistic and plausible solution would be revisiting and reconciling Heuristics Evaluation (HE) methods among the most widely used usability inspection methods in the human-computer interaction (HCI) domain. While research on studying and using HE methods is growing within the VR domain, few studies have considered the novel VR environment challenges new requirements for fitting HE methods to the context and applying them effectively. To this end, we conducted a user study with 14 evaluators using the standard HE methods to complete two HE sessions for a VR training application. We identified five critical challenges that evaluators encountered in the HE process by observing and interviewing them. Based on our findings, we discuss the importance of considering an easy-to-use heuristic set, how we can facilitate the HE procedures in the VR context, and the opportunities for developing HE-supporting tools. [ABSTRACT FROM AUTHOR]

Summary: The identification of switched systems amounts to a mixed integer nonlinear optimization problem, where the continuous variables are associated to the model parameterizations of the different modes, and the discrete ones are related to the switching signal (each data sample is assigned to a mode, and switching occurs when the mode assignment changes over time). In the batch form of the identification problem, the combinatorial complexity increases exponentially with the size of the training set, which makes the precise identification of the switching signal the most challenging task in the identification problem. To tackle this complexity we propose a distributed optimization approach, based on the solution of multiple instances of a much simpler problem, where switching can occur only at specific time instants (different for each subproblem), and an information sharing mechanism that preserves likely switching times to improve the local solutions. We employ an adapted version of a previously developed randomized algorithm to solve the individual subproblems. Another important feature of the proposed method is an a posteriori heuristic correction method, that is applied to further refine the switching locations based on the estimated local models before the information sharing phase. The performance of the proposed algorithm is analyzed and compared with other methods on synthetic datasets. [ABSTRACT FROM AUTHOR]

Quantum computing is currently a research hotspot in both academia and industry. The inherent parallelism of quantum computers and the resulting powerful computing power will bring new solutions to many problems that are difficult for classical computers. However, due to the limitations of technical conditions, it is difficult to achieve full direct coupling of all qubits on a quantum chip. When compiling a quantum circuit onto a physical chip, it is necessary to ensure those two-qubit gates act on pairs of directly coupled qubits by inserting SWAP gates. It will cause great additional cost when a large number of SWAP gates are inserted, leading to the execution time of quantum circuits longer. In this paper, we designed a strategy based on the business of each individual qubit to insert SWAP gates, named Busy-Qubits-Avoid Strategy. On the one hand, we try to hide the time overhead incurred by the inserted SWAP gates by exploiting the uneven distribution of quantum gates over qubits. On the other hand, we also expect the inserted SWAP gates to make as little negative impact on subsequent two-qubit gates as possible. We designed a heuristic function which takes into account both of these points. Compared with Sabre and tket, we achieved a better effect. In addition, as the number of two-qubit gates increases, better optimization results will be achieved. This implies higher execution efficiency and lower decoherence error rate. [ABSTRACT FROM AUTHOR]

This paper argues that emotions can play an epistemic role as justifiers of evaluative beliefs. It also presents the heuristics theory of emotion as an empirically informed explanation of how emotions can play such a role and why they in practice usefully complement non-affective evaluative judgments. As such, the heuristics theory represents a form of moderate rationalism: it acknowledges that emotions can be epistemically valuable, even privileged in some sense, but denies that they would be uniquely privileged. I argue that judgments and emotional responses pick out different but correlated kinds of evaluative properties and therefore emotional responses and non-affective evaluative judgments play mutually complementary rather than mutually exclusive roles. It follows that emotional responses can be legitimately drawn upon to support evaluative beliefs, but they lack supreme epistemic authority. [ABSTRACT FROM AUTHOR]

Intuitive predictions about the future can expect upcoming events to conform with a pattern - representativeness heuristic - or to simply repeat the most recent and accessible outcome - availability heuristic. The present work proposes that while representativeness predictions require a relatively abstract comparison process, availability-based predictions reflect a concrete and local use of accessible information. Grounded on the construal level theory, this research pits one heuristic against the other in predictions about random events (rolling dice; coin tosses). Three studies suggest that low construal levels increase predictions consistent with the most accessible and recent outcomes while high levels of construal facilitate representativeness-consistent predictions. The findings highlight how construal level may determine reliance on one heuristic or the other. [ABSTRACT FROM AUTHOR]

The fluctuation of human infections by the Puumala orthohantavirus (PUUV) in Germany has been linked to weather and phenology parameters that drive the population growth of its host species. We quantified the annual PUUV-outbreaks at the district level by binarizing the reported infections in the period 2006–2021. With these labels we trained a model based on a support vector machine classifier for predicting local outbreaks and incidence well in advance. The feature selection for the optimal model was performed by a heuristic method and identified five monthly weather variables from the previous two years plus the beech flowering intensity of the previous year. The predictive power of the optimal model was assessed by a leave-one-out cross-validation in 16 years that led to an 82.8% accuracy for the outbreak and a 0.457 coefficient of determination for the incidence. Prediction risk maps for the entire endemic area in Germany will be annually available on a freely-accessible permanent online platform of the German Environment Agency. The model correctly identified 2022 as a year with low outbreak risk, whereas its prediction for large-scale high outbreak risk in 2023 was not confirmed. [ABSTRACT FROM AUTHOR]

The location planning of relief distribution centres (DCs) is crucial in humanitarian logistics as it directly influences the disaster response and service to the affected victims. In light of the critical role of facility location in humanitarian logistics planning, the study proposes a two-stage relief distribution location problem. The first stage of the model determines the minimum number of relief DCs, and the second stage find the optimal location of these DCs to minimize the total cost. To address a more realistic situation, restrictions are imposed on the coverage area and capacity of each DCs. In addition, for optimally solving this complex NP-hard problem, a novel two-phase algorithm with exploration and exploitation phase is developed in the paper. The first phase of the algorithm i.e., exploration phase identifies a near-optimal solution while the second phase i.e. exploitation phase enhances the solution quality through a close circular proximity investigation. Furthermore, the comparative analysis of the proposed algorithm with other well-known algorithms such as genetic algorithm, pattern search, fmincon, multistart and hybrid heuristics is also reported and computationally tested from small to large data sets. The results reveal that the proposed two-phase algorithm is more efficient and effective when compared to the conventional metaheuristic methods. [ABSTRACT FROM AUTHOR]

Traditional computer-aided design (CAD) tools based on keyboard and mouse interactions present challenges to efficiency and quality in terms of free creation, iteration efficiency, and operational experience. This paper proposes a multimodal interactive guiding grammar framework for virtual-reality modeling scenarios. In studies on the characteristics of multimodal combinations, the present heuristic method leads to a high cognitive load and experimental cost, and the heuristic is difficult. Therefore, we propose a two-stage heuristic method. Subsequently, we use this method to decompose the interaction task into two stages: modalities and interactive operations. A clearer and more specific reference for multimodal selection is provided for the interaction design phase, based on the two dimensions of modality and task. This can reduce the heuristic cost and difficulty of users in completing tasks in multimodal interaction scenarios. Finally, the proposed virtual modeling platform which is used for experimental verification, and the results shows that, compared with traditional modeling, the interactive modeling method built according to the results of modal interaction in this study can better satisfy users in terms of emotional experience. In the index evaluation, high scores are achieved, with an average score of 5 points (1 and 7 are the lowest and highest scores of the evaluation index, respectively), and the large-area distribution of high scores also shows a good user experience. To a certain extent, this method improves the interaction mode of the previous 3D modeling. [ABSTRACT FROM AUTHOR]

This article discusses a robust network interdiction problem considering uncertainties in arc capacities and resource consumption. The problem involves two players: an adversary seeking to maximize the flow of a commodity through the network and an interdictor whose objective is to minimize this flow. The interdictor plays first and selects network arcs to interdict, subject to a resource constraint. The problem is formulated as a bilevel problem, and an upper bound single level mix-integer linear formulation is derived. The upper bound formulation is solved using three heuristics tailored for this problem and the network structure, based on Lagrangian relaxation and Benders' decomposition. On average, each heuristic provides a reduction in run time of at least 85% compared to a state-of-the-art solver. Enhanced Benders' decomposition achieves a solution with an optimality gap of less than 5% for all tested instances. Sensitivity analyses are conducted for the level of uncertainty in network parameters and the uncertainty budget. Robust decisions are also compared to decisions not accounting for uncertainty to evaluate the value of robustness, showing a reduction in simulation maximum flows by as much as 89.5%. [ABSTRACT FROM AUTHOR]

International institutions strive to achieve their own objectives while operating within a complex network of interdependencies. These interdependencies create an extensive web of relationships that serve as potential pathways for broader institutional impacts. The actions taken by individual institutions, their mutual influences, and the pattern of connectivity collectively shape the overall performance of institutional complexes. Understanding the performance of these complexes is crucial, yet we currently lack a methodology to assess it. To address this gap, we have developed a conceptual framework that integrates three distinct areas of study on three different scales: institutional effectiveness, institutional interlinkages, and institutional networks. This framework enables us to consider what we call the latent net effectiveness, or collective problem-solving potential, of a group of interconnected institutions. To put this framework into practice, we have devised a heuristic model, drawing from the extensive literature on international environmental institutions. As an illustrative example, we have applied this model to a network of 378 multilateral environmental agreements with 810 known issue linkages. Our work underscores the relevance of considering the system-level properties of institutional complexes and emphasizes the need for timely network-based governance and policy interventions to enhance the overall effectiveness of institutional complexes. This article is part of the theme issue 'A complexity science approach to law and governance'. [ABSTRACT FROM AUTHOR]

We describe a novel method for modeling the global, steady solar wind using photospheric magnetic fields as a driving boundary condition. Prior wind models in this class include both rapid heuristic methods that use potential field extrapolation and variants thereof, trading rigor for computation speed, and detailed 3D magnetohydrodynamic (MHD) models that attempt to simulate the entire solar corona with a degree of physical rigor, but require large amounts of computation. The Field Line Universal relaXer, an open-source numerical code that implements the "fluxon" semi-Lagrangian approach to MHD modeling, provides an intermediate approach between these two general classes. In particular, the fluxon approach to MHD describes the magnetic field through discrete analogs of magnetic field lines, relaxing these structures to a stationary state of force balance. In this work we introduce a 1D solar wind solution along each field line, providing an ensemble of solutions that are interpolated back onto a uniform grid at an outer boundary surface. This provides advantages in physical rigor over heuristic semianalytic techniques, and in computational efficiency over full 3D MHD techniques. Here we describe the underlying methodology and the FLUXPipe modeling pipeline process. [ABSTRACT FROM AUTHOR]

This paper aims to design a guide for engineering problem solving with a mathematical approach, based on the use of creative, heuristic and reasoning strategies. The research is divided in two phases: the literature review and the conception of a guide for engineering problems with an open or closed solution. This guide combines the engineering method with algorithmic and heuristic-creative components in three steps: a) understanding, approaching and characterising the problem; b) planning, tackling and deciding on the best alternative; and c) solving, reviewing and evaluating the problem. The authors conclude that this guide will allow future engineers to reach a solution to a problem considering the implications in the financial, technical, environmental and other domains. [ABSTRACT FROM AUTHOR]

The article proposes a cripistemology of eco-anxiety that critiques a white, cis-heteronormative, ableist form of ignorance that characterizes mainstream concerns about eco-anxious youth, including what the article suggests are the ecofascist tendencies of this ignorance. Rather than deny the debilitating effects that anxiety can have, the focus here is on an individualized and depoliticized mainstream approach to eco-anxiety that operates as a form of epistemic injustice even as it expresses care and concern for the well-being of those who are eco-anxious. Furthermore, the article critiques the ableist assumption that because it can be debilitating, eco-anxiety cannot also be lived in ways that generate important insights for learning to live otherwise in a context of climate change—or, put differently, the ableist assumption that disabled body-minds are only epistemic objects and never epistemic subjects. The suggestion is that a cripistemology of eco-anxiety attends to the possibility of this queer-crip wisdom. [ABSTRACT FROM AUTHOR]

While crip time serves as a useful heuristic for understanding how disabled people challenge normative expectations regarding pace, what the article terms demented time conveys the meaningful inversions of past and present modeled by people with dementia and misunderstood by those without it. The article interweaves the documentary film Dick Johnson is Dead and personal stories of dementia to study the ways they present "forgotten" memory in new and surprising forms. If Johnson's film insists that disabled people are not yet dead, the grandmother of the article's author—in forgetting who is dead—keeps them among the living. While mourning typically frames the dead as lost, the person with dementia recapitulates this loss as presence. A cripistemology of memory, then, asks that we embrace demented time, that we unknow categorical distinctions between life and death, and that we privilege the ways memories of the dead interrupt the presumed certainty of the living. [ABSTRACT FROM AUTHOR]

A groundbreaking study employs interval arithmetic to address the challenging multi-objective interval traveling salesperson problem. Customizing methods like a nearest neighbor, branch and bound, two-way heuristics, and dynamic programming effectively resolve this complex problem. Preserving interval values without the need for classical form conversion is a significant advantage. Researchers validated this approach through extensive experiments, consistently demonstrating superior outcomes compared to existing methods. These algorithmic approaches were optimized for Python 3.11 64-bit to enhance processing speed and efficiency. [ABSTRACT FROM AUTHOR]

Human-like automatic deductive reasoning has always been one of the most challenging open problems in the interdisciplinary field of mathematics and artificial intelligence. This paper is the third in a series of our works. We built a neural-symbolic system, named FGeo-DRL, to automatically perform human-like geometric deductive reasoning. The neural part is an AI agent based on deep reinforcement learning, capable of autonomously learning problem-solving methods from the feedback of a formalized environment, without the need for human supervision. It leverages a pre-trained natural language model to establish a policy network for theorem selection and employ Monte Carlo Tree Search for heuristic exploration. The symbolic part is a reinforcement learning environment based on geometry formalization theory and FormalGeo, which models geometric problem solving (GPS) as a Markov Decision Process (MDP). In the formal symbolic system, the symmetry of plane geometric transformations ensures the uniqueness of geometric problems when converted into states. Finally, the known conditions and objectives of the problem form the state space, while the set of theorems forms the action space. Leveraging FGeo-DRL, we have achieved readable and verifiable automated solutions to geometric problems. Experiments conducted on the formalgeo7k dataset have achieved a problem-solving success rate of 86.40%. [ABSTRACT FROM AUTHOR]

We propose a heuristic method to solve polynomial matrix equations of the type ∑ k = 1 m a k X k = B , where a k are scalar coefficients and X and B are square matrices of order n. The method is based on the decomposition of the B matrix as a linear combination of the identity matrix and an idempotent, involutive, or nilpotent matrix. We prove that this decomposition is always possible when n = 2 . Moreover, in some cases we can compute solutions when we have an infinite number of them (singular solutions). This method has been coded in MATLAB and has been compared to other methods found in the existing literature, such as the diagonalization and the interpolation methods. It turns out that the proposed method is considerably faster than the latter methods. Furthermore, the proposed method can calculate solutions when diagonalization and interpolation methods fail or calculate singular solutions when these methods are not capable of doing so. [ABSTRACT FROM AUTHOR]

Spatial clusters contain biases and artifacts, whether they are defined via statistical algorithms or via expert judgment. Graph‐based partitioning of spatial data and associated heuristics gained popularity due to their scalability but can define suboptimal regions due to algorithmic biases such as chaining. Despite the broad literature on deterministic regionalization methods, approaches that quantify regionalization probability are sparse. In this article, we propose a local method to quantify regionalization probabilities for regions defined via graph‐based cuts and expert‐defined regions. We conceptualize spatial regions as consisting of two types of spatial elements: core and swing. We define three distinct types of regionalization biases that occur in graph‐based methods and showcase the use of the proposed method to capture these types of biases. Additionally, we propose an efficient solution to the probabilistic graph‐based regionalization problem via performing optimal tree cuts along random spanning trees within an evidence accumulation framework. We perform statistical tests on synthetic data to assess resulting probability maps for varying distinctness of underlying regions and regionalization parameters. Lastly, we showcase the application of our method to define probabilistic ecoregions using climatic and remotely sensed vegetation indicators and apply our method to assign probabilities to the expert‐defined Bailey's ecoregions. [ABSTRACT FROM AUTHOR]

We address an inventory-routing problem that arises in a liquid oxygen-producing company. Decisions must be made for the efficient transport of the product from sources to destinations by means of a heterogeneous fleet of trucks. This combinatorial problem has been stated as a constrained minimization one, whose objective function is the quotient of the operating cost divided by the total amount of delivered product. The operating cost comes from the distances traveled, the drivers' salary, and the drivers' overnight accommodation. The constraints include time windows for drivers and destinations, inventory safety levels, lower bounds for the quantity of product delivered to destinations, and maximum driving times. To approximate the optimal solution of this challenging problem, we developed a heuristic algorithm that first finds a feasible solution, and then iteratively improves it by combining the Metropolis criterion with local search. Our results are competitive with the best proposals in the literature. [ABSTRACT FROM AUTHOR]

Purpose: The global rise in obesity can be closely linked to excessive calorie consumption and misperceptions regarding food intake. Thus, the purpose of this paper is to review the existing literature to have a better understanding how heuristic cues – mental shortcuts used for decision-making – impact calorie underestimation and consequently lead to unhealthy eating habits. Design/methodology/approach: A search was conducted across multiple databases with priority given to studies in developed countries that provided insights into the cognitive processes behind food choices, the application of specific heuristics, and the association with eating behaviours. Articles were also selected based on their methodological quality. Findings: The main findings are that the dichotomous categorization of foods as healthy or unhealthy can result in underestimating the calorie content in those foods perceived as healthy. Although nutrition claims, health claims and campaigns help in the fight against obesity, there is also the risk that consumers' reliance on heuristic-based decision-making could aggravate the problem because a misinterpretation or misrepresentation could lead to calorie underestimation and overeating. Practical implications: To establish effective behavioural interventions for obesity prevalence -, it is critical for interventions and policies to understand how consumers perceive calorie content and how they interpret claims on food marketing or packaging. Recognizing and addressing these heuristic-driven biases and understanding the factors influencing food choices are crucial for encouraging healthier eating habits. Originality/value: To the best of the authors' knowledge, this paper is the only review to date that consolidates research on the topic, drawing from multiple disciplines. [ABSTRACT FROM AUTHOR]

Given a grid of cells, each of which is assigned a numerical value quantifying its suitability for a certain use, one problem in geographic information science concerns the selection of a region, i.e. a connected set of cells, with a specified size that maximizes the sum of all their values. This task can be cast as a combinatorial optimization problem called the maximum value region problem, and exact and heuristic methods exist for its solution. While those solutions are guaranteed to be feasible (if not optimal), they may not be desirable for practical use if they contain too narrow segments (down to the width of a single cell). In this paper, we present a new variation of the maximum value region problem—the maximum value wide region problem—that requires a region to be at least as wide as a specified width. We offer a heuristic method for its solution which models a region as a set of neighborhoods and test its performance through computational experiments. Results demonstrate that the method generates good feasible solutions in terms of connectedness, size, width, and value, but requires more computing time than methods for maximum value regions without minimum width requirements. [ABSTRACT FROM AUTHOR]

A mathematical model for production scheduling of open pit mines maximises the net present value and satisfies the reserves, pit slope angle, and production capacity constraints. A solution to this model aims to deliver an extraction sequence and the movement of materials across various stages within the operation over a defined planning horizon. However, given that the mineral reserves delineated into thousands of mining blocks form the geological input, the model falls in the category of large-scale optimisation problems, i.e. it is computationally intractable, and exact methods cannot solve realistic scenarios of the problem. Therefore, this paper contributes an alternative to the conventional mathematical model along with a corresponding heuristic method to solve this proposed model. An implementation of the proposed method at various realistic instances reveals better performance in terms of net present value, computation time and optimality gap as compared to the traditional methods. [ABSTRACT FROM AUTHOR]

Restaurants can engage and attract social media users by showcasing their food or services through influencer marketing campaigns. However, the influencers and restaurant companies need to understand the underlying mechanism for presenting online reviews to attract customers to restaurants better. This study applies the theory of judgment heuristics and develops a conceptual model to test the effects of review availability (present vs. absent) and heuristic cues (scarcity, vividness, and frequency) on followers' perceptions and behavioral intention. Two experiments were conducted. Results showed that the scarcity cue moderates the mediation effect of informativeness between review availability and visit intention. The study also found that instead of being cognitive "misers", consumers use multiple cues to assist their purchase decisions. The findings provide restaurant companies and food influencers with meaningful insights that they can employ to craft effective social media content. [ABSTRACT FROM AUTHOR]

Given a graph G = (V , E) with n = | V | vertices, the graph coloring problem is defined as Find a color vector C = (c (v)) , where c (v) ∈ { 1 , 2 , ... , n } denotes the color of vertex v ∈ V , such that no monochromatic edge exists in G and the span , the total number of distinct colors in C , is minimized. Since the problem is NP-complete, a greedy coloring is commonly used for solving it. Greedy coloring visits the vertices of G following an order S , and while visiting a vertex v , it puts the minimum color absent in all neighbors of v. We show that the orders producing span ≤ k , where k ≤ n is a positive integer, can be partitioned into disjoint subsets of equivalent orders. Next, we propose a selective search (SS) algorithm, which takes ρ as an input parameter, selects ≥ ρ orders each from a different set of equivalent orders with high probability, applies greedy coloring on them, and returns the color vector with minimum span. We analytically show that SS performs better than greedy coloring with high probability by evaluating the same number of orders. We propose an incremental search heuristic (ISH), which ρ 1 times execute SS with parameter ρ 2 and returns the color vector with minimum span. A parallel version of ISH called PISH is also proposed, executing ρ 1 SS calls in parallel. We show that ISH hits an optimum coloring for a graph G = (V , E) with χ (G) (Δ (G) + 1) ≤ n e in expected O (| V | + | E |) time and space complexities. We have evaluated ISH and PISH on 136 challenging benchmarks and shown that they significantly outperform 10 existing state-of-the-art algorithms. Finally, we validated our theoretical findings by evaluating ISH and greedy coloring on random graphs. [ABSTRACT FROM AUTHOR]

The prototype reduction (PR) methods, as an important data pre-processing task, can improve instance-based classifiers by removing noise and/or redundant samples. Recently, a series of PR methods with different heuristic strategies have been developed. Among them, clustering-based PR methods have shown competitive performance. Yet, they still suffer from the following issues: (a) most methods heavily rely on parameters; (b) most fail to remove suspicious noisy samples from the training set; (c) almost all fail to handle manifold data with nonspherical distributions effectively; (d) some have a relatively high time complexity. To advance the state of the art of clustering-based PR methods by overcoming the above issues, a novel heuristics PR method based on supervised local density peaks clustering (PRLDPC) is proposed. The main ideas of PRLDPC are concluded as follows: (a) a supervised local density peaks clustering (SLDPC) is first proposed to divide the training set into homogeneous and heterogeneous sub-clusters; (b) SLDPC-based edition method is second proposed to identify and remove noisy samples from heterogeneous sub-clusters; (c) an SLDPC-based condensing method is third proposed to obtain reduced samples from homogeneous sub-clusters and pruned heterogeneous sub-clusters. Intensive experiments have proven that (a) PRLDPC can outperform six state-of-the-art PR methods on extensive UCI and Kaggle data sets in weighing the reduction rate and classification accuracy of three instance-based classifiers; (b) PRLDPC is relatively fast and has a relatively low time complexity O (n log n). [ABSTRACT FROM AUTHOR]

The index tracking problem consists of constructing a portfolio that has the closest performance of a given index as possible, with fewer assets in its composition. When considering a large number of assets, including all of them on the solution may incur high transaction fees, harming the accumulated returns in the long term. The constraint that limits the portfolio size imposes a combinatorial nature on the problem and the computation of the optimal solution becomes infeasible as the universe of assets grows. To get around this issue, pure and hybrid metaheuristics have been proposed in the literature to achieve good solutions in practical time. Different from pure metaheuristics, hybrid metaheuristics do not need any constraint handling or solution repairing approaches since they often use general-purpose solvers to adjust portfolio weights. This work presents a comparison between pure and hybrid genetic algorithms, which is one of the most popular heuristics applied in the portfolio selection field. We considered linear and nonlinear index tracking models in the experiments, where the GA that obtained the best performance in a single period optimization strategy, was selected to backtest in a dynamic index tracking approach. The results showed that hybrid GAs can compute good or even better solutions than the CPLEX solver and pure GAs, in a shorter time. [ABSTRACT FROM AUTHOR]

Arithmetic Optimization Algorithm (AOA) is a heuristic method developed in recent years. The original version was developed for continuous optimization problems. Its success in binary optimization problems has not yet been sufficiently tested. In this paper, the binary form of AOA (BinAOA) has been proposed. In addition, the candidate solution production scene of BinAOA is developed with the xor logic gate and the BinAOAX method was proposed. Both methods have been tested for success on well-known uncapacitated facility location problems (UFLPs) in the literature. The UFL problem is a binary optimization problem whose optimum results are known. In this study, the success of BinAOA and BinAOAX on UFLP was demonstrated for the first time. The results of BinAOA and BinAOAX methods were compared and discussed according to best, worst, mean, standard deviation, and gap values. The results of BinAOA and BinAOAX on UFLP are compared with binary heuristic methods used in the literature (TSA, JayaX, ISS, BinSSA, etc.). As a second application, the performances of BinAOA and BinAOAX algorithms are also tested on classical benchmark functions. The binary forms of AOA, AOAX, Jaya, Tree Seed Algorithm (TSA), and Gray Wolf Optimization (GWO) algorithms were compared in different candidate generation scenarios. The results showed that the binary form of AOA is successful and can be preferred as an alternative binary heuristic method. [ABSTRACT FROM AUTHOR]

Retrosynthetic planning, which aims to identify synthetic pathways for target molecules from starting materials, is a fundamental problem in synthetic chemistry. Computer-aided retrosynthesis has made significant progress, in which heuristic search algorithms, including Monte Carlo Tree Search (MCTS) and A* search, have played a crucial role. However, unreliable guiding heuristics often cause search failure due to insufficient exploration. Conversely, excessive exploration also prevents the search from reaching the optimal solution. In this paper, MCTS exploration enhanced A* (MEEA*) search is proposed to incorporate the exploratory behavior of MCTS into A* by providing a look-ahead search. Path consistency is adopted as a regularization to improve the generalization performance of heuristics. Extensive experimental results on 10 molecule datasets demonstrate the effectiveness of MEEA*. Especially, on the widely used United States Patent and Trademark Office (USPTO) benchmark, MEEA* achieves a 100.0% success rate. Moreover, for natural products, MEEA* successfully identifies bio-retrosynthetic pathways for 97.68% test compounds. Computer-aided retrosynthetic planning algorithms such as Monte Carlo Tree Search (MCTS) and A* search can expedite the identification of synthetic pathways, however, achieving a high success rate remains challenging. Here, the authors develop an enhanced search algorithm by incorporating the exploration capability of MCTS into A* search, achieving synthesis success rates of up to 100%. [ABSTRACT FROM AUTHOR]